From 10b007d1e888a3e33fe23fec47d6aece85d86cd2 Mon Sep 17 00:00:00 2001
From: Omar Shouman <o.shouman@tum.de>
Date: Wed, 29 Jan 2025 22:18:05 +0200
Subject: [PATCH 1/4] Feature/add precursor charge prediction modules (#55)

* chargestate models and init updates

* dev notebooks for chargestate

* cleanup - docstrings- initial tests

* run script for dominant charge

* fix: added hf_hub dependency by default

* Adding Noahs changes & trained model to Florians / Omars CSD prediction repo (#54)


---------

Co-authored-by: omsh <omar.shouman@gmail.com>

---------

Co-authored-by: radox96 <boessl.florian@t-online.de>
Co-authored-by: Ayla <131137738+ayla-s@users.noreply.github.com>
---
 .../Example_PCP_huggingface_walkthrough.ipynb |  678 +++
 .../baselines.ipynb                           |  563 +++
 .../data_split_tests.ipynb                    |  867 ++++
 .../model_dev.ipynb                           | 3758 +++++++++++++++++
 .../run_prosit_chargestate_distribution.py    |   64 +
 .../run_prosit_chargestate_dominant.py        |   55 +
 setup.py                                      |    1 +
 src/dlomix/constants.py                       |    6 +
 src/dlomix/eval/__init__.py                   |    3 +-
 src/dlomix/eval/chargestate.py                |   61 +
 src/dlomix/models/__init__.py                 |    4 +
 src/dlomix/models/chargestate.py              |  263 ++
 tests/test_models.py                          |   26 +-
 13 files changed, 6347 insertions(+), 2 deletions(-)
 create mode 100644 notebooks/precursor_charge_state_dev/Example_PCP_huggingface_walkthrough.ipynb
 create mode 100644 notebooks/precursor_charge_state_dev/baselines.ipynb
 create mode 100644 notebooks/precursor_charge_state_dev/data_split_tests.ipynb
 create mode 100644 notebooks/precursor_charge_state_dev/model_dev.ipynb
 create mode 100644 run_scripts/run_prosit_chargestate_distribution.py
 create mode 100644 run_scripts/run_prosit_chargestate_dominant.py
 create mode 100644 src/dlomix/eval/chargestate.py
 create mode 100644 src/dlomix/models/chargestate.py

diff --git a/notebooks/precursor_charge_state_dev/Example_PCP_huggingface_walkthrough.ipynb b/notebooks/precursor_charge_state_dev/Example_PCP_huggingface_walkthrough.ipynb
new file mode 100644
index 00000000..9980cf3d
--- /dev/null
+++ b/notebooks/precursor_charge_state_dev/Example_PCP_huggingface_walkthrough.ipynb
@@ -0,0 +1,678 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e875394b-3f3d-4179-b2ad-004ac33901c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "# hugginface datasets\n",
+    "from datasets import load_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "36a38e74",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'dlomix'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mdlomix\u001b[39;00m\n\u001b[1;32m      2\u001b[0m dlomix\u001b[38;5;241m.\u001b[39m__version__\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'dlomix'"
+     ]
+    }
+   ],
+   "source": [
+    "# I DONT GET IT...\n",
+    "import dlomix\n",
+    "dlomix.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa8a5cd4",
+   "metadata": {},
+   "source": [
+    "## Load data from Huggingface for further work"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aef81bd4-cbb7-45c8-ac3b-e9b2f8b597f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = load_dataset(\"Wilhelmlab/prospect-ptms-charge\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "983f43fa-5084-49d3-98f7-a7cfc167ed91",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['modified_sequence', 'raw_file', 'scan_number', 'package', 'most_abundant_charge_state', 'observed_charge_states', 'charge_state_dist'],\n",
+       "        num_rows: 1142537\n",
+       "    })\n",
+       "    val: Dataset({\n",
+       "        features: ['modified_sequence', 'raw_file', 'scan_number', 'package', 'most_abundant_charge_state', 'observed_charge_states', 'charge_state_dist'],\n",
+       "        num_rows: 328072\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['modified_sequence', 'raw_file', 'scan_number', 'package', 'most_abundant_charge_state', 'observed_charge_states', 'charge_state_dist'],\n",
+       "        num_rows: 161588\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce2eee0b",
+   "metadata": {},
+   "source": [
+    "### Convert test set to pandas dataframe (only if manipulation is needed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a06f3a7b-b327-4b1a-897c-f5da1e39f91d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = dataset[\"test\"].to_pandas()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f98c8ec-9791-42c4-a7fa-0927412bbdfb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>modified_sequence</th>\n",
+       "      <th>raw_file</th>\n",
+       "      <th>scan_number</th>\n",
+       "      <th>package</th>\n",
+       "      <th>most_abundant_charge_state</th>\n",
+       "      <th>observed_charge_states</th>\n",
+       "      <th>charge_state_dist</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[UNIMOD:737]-YIHNILSI-[]</td>\n",
+       "      <td>[02527_BA6-TMT_TUM_HLA_6_01_01-TMT1-1h-R1, 025...</td>\n",
+       "      <td>[53707, 59458, 59518]</td>\n",
+       "      <td>[TMT_TUM_HLA]</td>\n",
+       "      <td>[0, 1, 0, 0, 0, 0]</td>\n",
+       "      <td>[0, 1, 0, 0, 0, 0]</td>\n",
+       "      <td>[0.0, 1.0, 0.0, 0.0, 0.0, 0.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[]-FHTPSNSYTAQR-[]</td>\n",
+       "      <td>[02208a_GC3-TUM_second_addon_27_01_01-2xIT_2xH...</td>\n",
+       "      <td>[13616, 13659, 13660, 13759, 13760, 13761, 138...</td>\n",
+       "      <td>[TUM_second_addon]</td>\n",
+       "      <td>[0, 0, 1, 0, 0, 0]</td>\n",
+       "      <td>[0, 1, 1, 0, 0, 0]</td>\n",
+       "      <td>[0.0, 0.18388216654438674, 0.8161178334556133,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[]-IPGPEGQEVLC[UNIMOD:4]KQIK-[]</td>\n",
+       "      <td>[01748a_BH4-TUM_second_pool_66_01_01-2xIT_2xHC...</td>\n",
+       "      <td>[21369, 21370, 21371, 21397, 21398, 21399, 214...</td>\n",
+       "      <td>[TUM_second_pool]</td>\n",
+       "      <td>[0, 0, 1, 0, 0, 0]</td>\n",
+       "      <td>[0, 1, 1, 0, 0, 0]</td>\n",
+       "      <td>[0.0, 0.03440733446425485, 0.9655926655357452,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[]-TGQK[UNIMOD:1]VALKK-[]</td>\n",
+       "      <td>[02195a_GA4-TUM_mod_acetylated_4_01_01-DDA-1h-...</td>\n",
+       "      <td>[9811, 9913, 9914, 10066, 10067]</td>\n",
+       "      <td>[TUM_mod_acetylated]</td>\n",
+       "      <td>[0, 1, 0, 0, 0, 0]</td>\n",
+       "      <td>[0, 1, 0, 0, 0, 0]</td>\n",
+       "      <td>[0.0, 1.0, 0.0, 0.0, 0.0, 0.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[]-ATLC[UNIMOD:4]C[UNIMOD:4]ARPEPR-[]</td>\n",
+       "      <td>[01717a_BF3-TUM_second_pool_34_01_01-2xIT_2xHC...</td>\n",
+       "      <td>[8995, 8996, 8997, 9041, 9042, 9043, 9300, 935...</td>\n",
+       "      <td>[TUM_second_pool]</td>\n",
+       "      <td>[0, 0, 1, 0, 0, 0]</td>\n",
+       "      <td>[0, 1, 1, 0, 0, 0]</td>\n",
+       "      <td>[0.0, 0.007387597915967662, 0.9926124020840323...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       modified_sequence  \\\n",
+       "0               [UNIMOD:737]-YIHNILSI-[]   \n",
+       "1                     []-FHTPSNSYTAQR-[]   \n",
+       "2        []-IPGPEGQEVLC[UNIMOD:4]KQIK-[]   \n",
+       "3              []-TGQK[UNIMOD:1]VALKK-[]   \n",
+       "4  []-ATLC[UNIMOD:4]C[UNIMOD:4]ARPEPR-[]   \n",
+       "\n",
+       "                                            raw_file  \\\n",
+       "0  [02527_BA6-TMT_TUM_HLA_6_01_01-TMT1-1h-R1, 025...   \n",
+       "1  [02208a_GC3-TUM_second_addon_27_01_01-2xIT_2xH...   \n",
+       "2  [01748a_BH4-TUM_second_pool_66_01_01-2xIT_2xHC...   \n",
+       "3  [02195a_GA4-TUM_mod_acetylated_4_01_01-DDA-1h-...   \n",
+       "4  [01717a_BF3-TUM_second_pool_34_01_01-2xIT_2xHC...   \n",
+       "\n",
+       "                                         scan_number               package  \\\n",
+       "0                              [53707, 59458, 59518]         [TMT_TUM_HLA]   \n",
+       "1  [13616, 13659, 13660, 13759, 13760, 13761, 138...    [TUM_second_addon]   \n",
+       "2  [21369, 21370, 21371, 21397, 21398, 21399, 214...     [TUM_second_pool]   \n",
+       "3                   [9811, 9913, 9914, 10066, 10067]  [TUM_mod_acetylated]   \n",
+       "4  [8995, 8996, 8997, 9041, 9042, 9043, 9300, 935...     [TUM_second_pool]   \n",
+       "\n",
+       "  most_abundant_charge_state observed_charge_states  \\\n",
+       "0         [0, 1, 0, 0, 0, 0]     [0, 1, 0, 0, 0, 0]   \n",
+       "1         [0, 0, 1, 0, 0, 0]     [0, 1, 1, 0, 0, 0]   \n",
+       "2         [0, 0, 1, 0, 0, 0]     [0, 1, 1, 0, 0, 0]   \n",
+       "3         [0, 1, 0, 0, 0, 0]     [0, 1, 0, 0, 0, 0]   \n",
+       "4         [0, 0, 1, 0, 0, 0]     [0, 1, 1, 0, 0, 0]   \n",
+       "\n",
+       "                                   charge_state_dist  \n",
+       "0                     [0.0, 1.0, 0.0, 0.0, 0.0, 0.0]  \n",
+       "1  [0.0, 0.18388216654438674, 0.8161178334556133,...  \n",
+       "2  [0.0, 0.03440733446425485, 0.9655926655357452,...  \n",
+       "3                     [0.0, 1.0, 0.0, 0.0, 0.0, 0.0]  \n",
+       "4  [0.0, 0.007387597915967662, 0.9926124020840323...  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cb77685",
+   "metadata": {},
+   "source": [
+    "## Create dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a43a6dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'dlomix'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdlomix\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcharge_state\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ChargeStateDataset\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdlomix\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconstants\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PTMS_ALPHABET\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'dlomix'"
+     ]
+    }
+   ],
+   "source": [
+    "from dlomix.data.charge_state import ChargeStateDataset\n",
+    "from dlomix.constants import PTMS_ALPHABET"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91d69194",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/root/miniforge3/envs/pcp_env/lib/python3.11/site-packages/dlomix/data/dataset.py:280: UserWarning: The provided data is assumed to be hosted on the Hugging Face Hub since data_format is set to \"hub\". Validation and test data sources will be ignored.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "348c7d4dcf3e4c97a83e70fe21fbec5e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Mapping SequenceParsingProcessor:   0%|          | 0/914029 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d7e77afce2146bdbc218add1eedbfed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Mapping SequenceParsingProcessor:   0%|          | 0/228508 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ee72f9d0fe947e9ae06241e2462400a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Mapping SequenceEncodingProcessor:   0%|          | 0/914029 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f28141af6be8411e94cfb4d9b96360bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Mapping SequenceEncodingProcessor:   0%|          | 0/228508 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "facd105b5d674c3c97204e5f1789ca9a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Mapping SequencePaddingProcessor:   0%|          | 0/914029 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d0dfa5b26de840e28e1241754050c104",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Mapping SequencePaddingProcessor:   0%|          | 0/228508 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad1f676e51384b6dafc5aa939469ad80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Mapping SequencePaddingProcessor:   0%|          | 0/161588 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "860c951b450442ee89a8aba914bb499a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Filter:   0%|          | 0/914029 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1355db68e8b469eba4950f3e7c6c4bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Filter:   0%|          | 0/228508 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = ChargeStateDataset(\n",
+    "    data_source=\"Wilhelmlab/prospect-ptms-charge\", \n",
+    "    data_format=\"hub\", \n",
+    "    sequence_column=\"modified_sequence\", \n",
+    "    label_column=\"charge_state_dist\", \n",
+    "    pad=True, \n",
+    "    padding_value=0, \n",
+    "    alphabet=PTMS_ALPHABET, \n",
+    "    encoding_scheme=\"naive-mods\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f05ed4e7",
+   "metadata": {},
+   "source": [
+    "## Create model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b87d9f14",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-08-13 17:49:31.894083: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
+      "2024-08-13 17:49:31.971368: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "2024-08-13 17:49:31.971390: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+      "2024-08-13 17:49:31.989429: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2024-08-13 17:49:32.016176: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.optimizers import Adam"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04ef3817-6f98-4e1d-86e3-479c8c8252ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-08-13 17:49:33.496867: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
+      "Your kernel may have been built without NUMA support.\n",
+      "2024-08-13 17:49:33.604179: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
+      "Your kernel may have been built without NUMA support.\n",
+      "2024-08-13 17:49:33.604211: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
+      "Your kernel may have been built without NUMA support.\n",
+      "2024-08-13 17:49:33.609482: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
+      "Your kernel may have been built without NUMA support.\n",
+      "2024-08-13 17:49:33.609517: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
+      "Your kernel may have been built without NUMA support.\n",
+      "2024-08-13 17:49:33.609532: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
+      "Your kernel may have been built without NUMA support.\n",
+      "2024-08-13 17:49:33.714640: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
+      "Your kernel may have been built without NUMA support.\n",
+      "2024-08-13 17:49:33.714854: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
+      "Your kernel may have been built without NUMA support.\n",
+      "2024-08-13 17:49:33.714861: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2022] Could not identify NUMA node of platform GPU id 0, defaulting to 0.  Your kernel may not have been built with NUMA support.\n",
+      "2024-08-13 17:49:33.714883: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
+      "Your kernel may have been built without NUMA support.\n",
+      "2024-08-13 17:49:33.715028: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 21770 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3090, pci bus id: 0000:01:00.0, compute capability: 8.6\n"
+     ]
+    }
+   ],
+   "source": [
+    "optimizer = Adam(learning_rate=0.0001)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d0d6047",
+   "metadata": {},
+   "source": [
+    "## Import model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8b93c59b",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'dlomix.models.chargestate'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[17], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdlomix\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchargestate\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ChargeStateDistributionPredictor\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'dlomix.models.chargestate'"
+     ]
+    }
+   ],
+   "source": [
+    "from dlomix.models.chargestate import ChargeStateDistributionPredictor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9662b240",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'ChargeStateDistributionPredictor' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m charge_distribution_model \u001b[38;5;241m=\u001b[39m \u001b[43mChargeStateDistributionPredictor\u001b[49m(seq_length\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m30\u001b[39m, vocab_dict\u001b[38;5;241m=\u001b[39mPTMS_ALPHABET)\n\u001b[1;32m      2\u001b[0m charge_distribution_model\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39moptimizer, loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmean_absolute_error\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'ChargeStateDistributionPredictor' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "charge_distribution_model = ChargeStateDistributionPredictor(seq_length=30, vocab_dict=PTMS_ALPHABET)\n",
+    "charge_distribution_model.compile(optimizer=optimizer, loss='mean_absolute_error')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b9149df",
+   "metadata": {},
+   "source": [
+    "## Train model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a50afb28",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5\n",
+      "4446/4446 [==============================] - 179s 40ms/step - loss: 0.0521 - val_loss: 0.0340\n",
+      "Epoch 2/5\n",
+      "4446/4446 [==============================] - 179s 40ms/step - loss: 0.0349 - val_loss: 0.0288\n",
+      "Epoch 3/5\n",
+      "4446/4446 [==============================] - 179s 40ms/step - loss: 0.0314 - val_loss: 0.0265\n",
+      "Epoch 4/5\n",
+      "4446/4446 [==============================] - 178s 40ms/step - loss: 0.0297 - val_loss: 0.0253\n",
+      "Epoch 5/5\n",
+      "4446/4446 [==============================] - 178s 40ms/step - loss: 0.0288 - val_loss: 0.0250\n"
+     ]
+    }
+   ],
+   "source": [
+    "charge_distribution_model_history = charge_distribution_model.fit(\n",
+    "    data.tensor_train_data, \n",
+    "    validation_data=data.tensor_val_data,\n",
+    "    epochs=5,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8eef5a2f",
+   "metadata": {},
+   "source": [
+    "### Train History"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0c64b6f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d79c27ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_learning_curves(history, title='Learning Curves'):\n",
+    "    history_dict = history.history\n",
+    "    loss = history_dict['loss']\n",
+    "    val_loss = history_dict.get('val_loss', [])\n",
+    "    \n",
+    "    epochs = range(1, len(loss) + 1)\n",
+    "    \n",
+    "    plt.figure(figsize=(8, 5))\n",
+    "    plt.plot(epochs, loss, 'b-', label='Training Loss')\n",
+    "    if val_loss:\n",
+    "        plt.plot(epochs, val_loss, 'r-', label='Validation Loss')\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel('Epochs')\n",
+    "    plt.ylabel('Loss')\n",
+    "    plt.legend()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b86f321",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_learning_curves(charge_distribution_model_history, title='Charge State Distribution Model')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/precursor_charge_state_dev/baselines.ipynb b/notebooks/precursor_charge_state_dev/baselines.ipynb
new file mode 100644
index 00000000..1edd8064
--- /dev/null
+++ b/notebooks/precursor_charge_state_dev/baselines.ipynb
@@ -0,0 +1,563 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
+    "from sklearn.dummy import DummyClassifier\n",
+    "\n",
+    "# Constants\n",
+    "from dlomix.constants import ALPHABET_UNMOD"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Baselines"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preprocessing\n",
+    "\n",
+    "The data preprocessing involves loading sequences, removing modifications, encoding sequences using a predefined alphabet, and padding them to a uniform length.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load and preprocess data\n",
+    "def preprocess_data(file_path):\n",
+    "    \"\"\"Load data, remove specific sequences, encode, and pad sequences to uniform length.\"\"\"\n",
+    "    data = pd.read_parquet(file_path)\n",
+    "    # Removing sequences containing 'UNIMOD'\n",
+    "    data = data[~data.modified_sequence.str.contains('UNIMOD')]\n",
+    "    # Encoding sequences\n",
+    "    data['encoded_sequences'] = data.modified_sequence.apply(list).apply(\n",
+    "        lambda x: np.array([ALPHABET_UNMOD[aa] for aa in x], dtype=int))\n",
+    "    max_len = data.encoded_sequences.apply(len).max()\n",
+    "    # Padding sequences\n",
+    "    data.encoded_sequences = data.encoded_sequences.apply(\n",
+    "        lambda x: np.pad(x, (0, max_len - len(x)), 'constant'))\n",
+    "    return data, max_len"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "processed_data, max_sequence_length = preprocess_data(\"/mnt/c/Users/Florian/Desktop/Uni/MSc/FoPr/preprocessed_pcp_data.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Baseline Models\n",
+    "\n",
+    "### Random Predictions\n",
+    "\n",
+    "Random predictions are generated first uniformly and then biased based on the observed distribution of charge states in the dataset.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate random and biased random predictions\n",
+    "def generate_random_predictions(data, min_charge, max_charge):\n",
+    "    \"\"\"Generate random and distribution-biased random predictions for charge states.\"\"\"\n",
+    "    np.random.seed(42)\n",
+    "    data['random_prediction'] = np.random.randint(min_charge, max_charge + 1, size=len(data))\n",
+    "    charge_distribution = data['charge_by_max_intensity'].value_counts(normalize=True)\n",
+    "    charges = charge_distribution.index.to_list()\n",
+    "    probabilities = charge_distribution.values\n",
+    "    data['biased_random_prediction'] = np.random.choice(charges, size=len(data), p=probabilities)\n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "processed_data = generate_random_predictions(processed_data, 1, 6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Evaluation of Predictions\n",
+    "\n",
+    "Evaluate the accuracy of the random and biased random predictors using standard classification metrics.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Evaluate prediction accuracy\n",
+    "def evaluate_predictions(data, prediction_column, true_column):\n",
+    "    \"\"\"Calculate and print accuracy and classification report for given prediction data.\"\"\"\n",
+    "    accuracy = accuracy_score(data[true_column], data[prediction_column])\n",
+    "    print(f\"Accuracy of the {prediction_column}: {accuracy:.2f}\")\n",
+    "    print(classification_report(data[true_column], data[prediction_column]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy of the random_prediction: 0.17\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           1       0.05      0.17      0.08     28035\n",
+      "           2       0.56      0.17      0.26    295679\n",
+      "           3       0.32      0.17      0.22    168677\n",
+      "           4       0.06      0.17      0.09     32807\n",
+      "           5       0.01      0.17      0.01      3601\n",
+      "           6       0.00      0.13      0.00       266\n",
+      "\n",
+      "    accuracy                           0.17    529065\n",
+      "   macro avg       0.17      0.16      0.11    529065\n",
+      "weighted avg       0.42      0.17      0.22    529065\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "evaluate_predictions(processed_data, 'random_prediction', 'charge_by_max_intensity')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy of the biased_random_prediction: 0.42\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           1       0.05      0.05      0.05     28035\n",
+      "           2       0.56      0.56      0.56    295679\n",
+      "           3       0.32      0.32      0.32    168677\n",
+      "           4       0.06      0.06      0.06     32807\n",
+      "           5       0.01      0.01      0.01      3601\n",
+      "           6       0.00      0.00      0.00       266\n",
+      "\n",
+      "    accuracy                           0.42    529065\n",
+      "   macro avg       0.17      0.17      0.17    529065\n",
+      "weighted avg       0.42      0.42      0.42    529065\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "evaluate_predictions(processed_data, 'biased_random_prediction', 'charge_by_max_intensity')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dummy Classifier Baselines\n",
+    "\n",
+    "Using scikit-learn's `DummyClassifier` to establish baseline models including most frequent, stratified, constant, and uniform strategies.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Baseline predictions using dummy classifiers\n",
+    "def dummy_classifier_baselines(data, features, target, strategies):\n",
+    "    \"\"\"Evaluate different dummy classifier strategies.\"\"\"\n",
+    "    for strategy in strategies:\n",
+    "        # Access the target column correctly from the dataframe\n",
+    "        target_series = data[target]\n",
+    "        if strategy == 'constant':\n",
+    "            dummy = DummyClassifier(strategy=strategy, constant=target_series.mode()[0])\n",
+    "        else:\n",
+    "            dummy = DummyClassifier(strategy=strategy)\n",
+    "\n",
+    "        # Assuming features is a column of lists, if not, adjust accordingly\n",
+    "        dummy.fit(data[features].tolist(), target_series)\n",
+    "        predictions = dummy.predict(data[features].tolist())\n",
+    "        print(f\"Accuracy of the {strategy} strategy: {accuracy_score(target_series, predictions):.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy of the most_frequent strategy: 0.56\n",
+      "Accuracy of the stratified strategy: 0.42\n",
+      "Accuracy of the constant strategy: 0.56\n",
+      "Accuracy of the uniform strategy: 0.17\n"
+     ]
+    }
+   ],
+   "source": [
+    "strategies = ['most_frequent', 'stratified', 'constant', 'uniform']\n",
+    "dummy_classifier_baselines(processed_data, 'encoded_sequences', 'charge_by_max_intensity', strategies)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plotting the charge distribution histogram\n",
+    "def plot_charge_distribution(data, column):\n",
+    "    \"\"\"Plot a histogram of charge distributions.\"\"\"\n",
+    "    import matplotlib.pyplot as plt\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    data[column].hist(bins=np.arange(1.5, data[column].max() + 2.5, 1), color='grey', alpha=0.7)\n",
+    "    plt.xlabel('Charge State')\n",
+    "    plt.ylabel('Frequency')\n",
+    "    plt.title('Charge Distribution Histogram')\n",
+    "    plt.xticks(np.arange(1, data[column].max() + 1))\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_charge_distribution(processed_data, 'charge_by_max_intensity')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Advanced Baseline Models\n",
+    "\n",
+    "This section explores more sophisticated baseline methods like majority baselines and evaluates them against the dataset to determine how a simple model performs relative to random guesses.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "majority charge is: 2\n",
+      "Accuracy of the majority baseline: 0.56\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           2       0.56      1.00      0.72    295679\n",
+      "\n",
+      "   micro avg       0.56      1.00      0.72    295679\n",
+      "   macro avg       0.56      1.00      0.72    295679\n",
+      "weighted avg       0.56      1.00      0.72    295679\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "def majority_baseline(df, k=1):\n",
+    "    \"\"\"\n",
+    "    Calculate and print the performance metrics for the majority baseline model.\n",
+    "    This model predicts the most frequent charge state for all entries.\n",
+    "    \"\"\"\n",
+    "    if 'charge_by_max_intensity' not in df.columns:\n",
+    "        raise ValueError(\"Define a column with one integer value for the precursor charge\")\n",
+    "\n",
+    "    charge_counts = df['charge_by_max_intensity'].value_counts()\n",
+    "    top_k_charges = charge_counts.nlargest(k).index.tolist()\n",
+    "    top_charge = top_k_charges[0]\n",
+    "    baseline_predictions = [top_charge] * len(df)\n",
+    "    actual_charges = df['charge_by_max_intensity'].tolist()\n",
+    "    accuracy = accuracy_score(actual_charges, baseline_predictions)\n",
+    "    report = classification_report(actual_charges, baseline_predictions, labels=[top_charge], zero_division=0)\n",
+    "\n",
+    "    print(f\"majority charge is: {top_charge}\")\n",
+    "    print(f\"Accuracy of the majority baseline: {accuracy:.2f}\")\n",
+    "    print(report)\n",
+    "\n",
+    "baseline_metrics = majority_baseline(processed_data, k=1)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task-specific Dummy Classifiers\n",
+    "\n",
+    "In this section, dummy classifiers are tailored for specific tasks to benchmark against more sophisticated models later.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Task 1: Evaluate charge prediction accuracy using different dummy strategies\n",
+    "def evaluate_dummy_strategies(data, feature_column, target_column, strategies):\n",
+    "    \"\"\"\n",
+    "    Evaluate various dummy strategies and report their accuracy and classification report.\n",
+    "    \"\"\"\n",
+    "    for strategy in strategies:\n",
+    "        if strategy == 'constant':\n",
+    "            dummy_clf = DummyClassifier(strategy=strategy, constant=data[target_column].mode()[0])\n",
+    "        else:\n",
+    "            dummy_clf = DummyClassifier(strategy=strategy)\n",
+    "        dummy_clf.fit(data[feature_column].tolist(), data[target_column])\n",
+    "        predictions = dummy_clf.predict(data[feature_column].tolist())\n",
+    "        accuracy = accuracy_score(data[target_column], predictions)\n",
+    "        print(f\"Accuracy of the {strategy} baseline: {accuracy:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy of the most_frequent baseline: 0.56\n",
+      "Accuracy of the stratified baseline: 0.42\n",
+      "Accuracy of the constant baseline: 0.56\n",
+      "Accuracy of the uniform baseline: 0.17\n"
+     ]
+    }
+   ],
+   "source": [
+    "evaluate_dummy_strategies(processed_data, 'encoded_sequences', 'charge_by_max_intensity', strategies)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def evaluate_multi_label_classification(data, feature_column, label_column):\n",
+    "    \"\"\"\n",
+    "    Evaluate the performance of a dummy classifier on multi-label data.\n",
+    "    \"\"\"\n",
+    "    from sklearn.dummy import DummyClassifier\n",
+    "    from sklearn.metrics import accuracy_score, hamming_loss\n",
+    "    from sklearn.multioutput import MultiOutputClassifier\n",
+    "    import numpy as np  # Make sure numpy is imported if used for operations\n",
+    "\n",
+    "    data['more_than_one_charge_state'] = data[label_column].apply(lambda x: np.sum(x) > 1)\n",
+    "    count_more_than_one = data['more_than_one_charge_state'].sum()\n",
+    "    print(\"Sequences with more than one charge state:\", count_more_than_one, \n",
+    "          \"->\", count_more_than_one / len(data) * 100, \"%\")\n",
+    "\n",
+    "    X = np.array(data[feature_column].tolist())\n",
+    "    y = np.array(data[label_column].tolist())\n",
+    "    dummy = DummyClassifier(strategy=\"most_frequent\")\n",
+    "    multi_target_dummy = MultiOutputClassifier(dummy, n_jobs=-1)\n",
+    "    multi_target_dummy.fit(X, y)\n",
+    "    y_pred = multi_target_dummy.predict(X)\n",
+    "    accuracy = accuracy_score(y, y_pred)\n",
+    "    hamming = hamming_loss(y, y_pred)\n",
+    "    print(f\"Accuracy of the multi-label dummy classifier: {accuracy:.2f}\")\n",
+    "    print(f\"Hamming loss of the multi-label dummy classifier: {hamming:.2f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sequences with more than one charge state: 209133 -> 39.52879135833971 %\n",
+      "Accuracy of the multi-label dummy classifier: 0.44\n",
+      "Hamming loss of the multi-label dummy classifier: 0.14\n"
+     ]
+    }
+   ],
+   "source": [
+    "evaluate_multi_label_classification(processed_data, 'encoded_sequences', 'charge_state_vector')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Regression Baselines for Continuous Outcomes\n",
+    "\n",
+    "This section introduces a regression baseline using a `DummyRegressor` to predict continuous charge distributions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def evaluate_regression_baseline(data, feature_column, target_column):\n",
+    "    \"\"\"\n",
+    "    Evaluate the performance of a regression baseline model.\n",
+    "    \"\"\"\n",
+    "    from sklearn.dummy import DummyRegressor\n",
+    "    from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
+    "\n",
+    "    X = np.stack(data[feature_column].values)\n",
+    "    y = data[target_column].values\n",
+    "    dummy_regressor = DummyRegressor(strategy=\"mean\")\n",
+    "    dummy_regressor.fit(X, y)\n",
+    "    y_pred = dummy_regressor.predict(X)\n",
+    "    mse = mean_squared_error(y, y_pred)\n",
+    "    mae = mean_absolute_error(y, y_pred)\n",
+    "    print(f\"MSE of the mean baseline: {mse:.2f}\")\n",
+    "    print(f\"MAE of the mean baseline: {mae:.2f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE of the mean baseline: 0.08\n",
+      "MAE of the mean baseline: 0.25\n"
+     ]
+    }
+   ],
+   "source": [
+    "processed_data['charge_distribution'] = np.random.rand(len(processed_data))  # Creating a dummy charge_distribution column\n",
+    "evaluate_regression_baseline(processed_data, 'encoded_sequences', 'charge_distribution')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualization of Prediction Results\n",
+    "\n",
+    "The final sections visualize the prediction accuracy using histograms and confusion matrices to provide visual insights into the model performance.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_confusion_matrix(data, true_column, predicted_column, title):\n",
+    "    \"\"\"\n",
+    "    Plot a confusion matrix to visualize the accuracy of predictions.\n",
+    "    \"\"\"\n",
+    "    import matplotlib.pyplot as plt\n",
+    "    import seaborn as sns\n",
+    "    conf_matrix = confusion_matrix(data[true_column], data[predicted_column])\n",
+    "    plt.figure(figsize=(8, 6))\n",
+    "    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='viridis', xticklabels=np.unique(data[true_column]), yticklabels=np.unique(data[true_column]))\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel('Predicted Charge')\n",
+    "    plt.ylabel('True Charge')\n",
+    "    plt.show()\n",
+    "\n",
+    "plot_confusion_matrix(processed_data, 'charge_by_max_intensity', 'random_prediction', 'Confusion Matrix for Random Predictor')\n",
+    "plot_confusion_matrix(processed_data, 'charge_by_max_intensity', 'biased_random_prediction', 'Confusion Matrix for Biased Random Predictor')\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/precursor_charge_state_dev/data_split_tests.ipynb b/notebooks/precursor_charge_state_dev/data_split_tests.ipynb
new file mode 100644
index 00000000..07230957
--- /dev/null
+++ b/notebooks/precursor_charge_state_dev/data_split_tests.ipynb
@@ -0,0 +1,867 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Initial Setup and Data Loading\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 757820 entries, 0 to 758525\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column                             Non-Null Count   Dtype  \n",
+      "---  ------                             --------------   -----  \n",
+      " 0   modified_sequence                  757820 non-null  object \n",
+      " 1   precursor_charge                   757820 non-null  object \n",
+      " 2   precursor_intensity                757820 non-null  object \n",
+      " 3   charge_by_max_intensity            757820 non-null  int64  \n",
+      " 4   max_intensity                      757820 non-null  float64\n",
+      " 5   charge_by_avg_intensity            757820 non-null  int64  \n",
+      " 6   avg_intensity                      757820 non-null  float64\n",
+      " 7   one_hot_most_abundant_charge       757820 non-null  object \n",
+      " 8   charge_state_vector                757820 non-null  object \n",
+      " 9   normalized_intensity_distribution  757820 non-null  object \n",
+      "dtypes: float64(2), int64(2), object(6)\n",
+      "memory usage: 63.6+ MB\n",
+      "Total number of data points: 757820\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load the preprocessed data\n",
+    "processed_data = pd.read_parquet(\"/mnt/c/Users/Florian/Desktop/Uni/MSc/FoPr/preprocessed_pcp_data.parquet\")\n",
+    "processed_data.head()\n",
+    "processed_data.info()\n",
+    "print(f\"Total number of data points: {len(processed_data)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preparation for Task 1\n",
+    "\n",
+    "Here we split the data into training, validation, and test sets, ensuring that the splits are stratified by the `charge_by_max_intensity` to maintain distribution consistency across datasets.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Train Set Relative Distribution:\n",
+      "charge_by_max_intensity\n",
+      "2    52.579548\n",
+      "3    34.693155\n",
+      "4     7.359047\n",
+      "1     4.505028\n",
+      "5     0.800322\n",
+      "6     0.062900\n",
+      "Name: proportion, dtype: float64\n",
+      "\n",
+      "Validation Set Relative Distribution:\n",
+      "charge_by_max_intensity\n",
+      "2    52.579108\n",
+      "3    34.692935\n",
+      "4     7.359267\n",
+      "1     4.505028\n",
+      "5     0.800322\n",
+      "6     0.063340\n",
+      "Name: proportion, dtype: float64\n",
+      "\n",
+      "Test Set Relative Distribution:\n",
+      "charge_by_max_intensity\n",
+      "2    52.579108\n",
+      "3    34.693595\n",
+      "4     7.359267\n",
+      "1     4.505028\n",
+      "5     0.800322\n",
+      "6     0.062680\n",
+      "Name: proportion, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Copying data to avoid modification of the original dataframe\n",
+    "df = processed_data.copy()\n",
+    "\n",
+    "# Stratified split to maintain distribution of charge_by_max_intensity\n",
+    "train_df, test_df = train_test_split(df, test_size=0.2, stratify=df['charge_by_max_intensity'])\n",
+    "train_df, val_df = train_test_split(train_df, test_size=0.25, stratify=train_df['charge_by_max_intensity'])  # Further split to create validation set\n",
+    "\n",
+    "# Function to calculate and display relative distribution of charge states\n",
+    "def calculate_relative_distribution(dataframe):\n",
+    "    distribution = dataframe['charge_by_max_intensity'].value_counts(normalize=True)\n",
+    "    return distribution * 100\n",
+    "\n",
+    "# Displaying relative distribution of charge states in each set\n",
+    "print(\"\\nTrain Set Relative Distribution:\")\n",
+    "print(calculate_relative_distribution(train_df))\n",
+    "print(\"\\nValidation Set Relative Distribution:\")\n",
+    "print(calculate_relative_distribution(val_df))\n",
+    "print(\"\\nTest Set Relative Distribution:\")\n",
+    "print(calculate_relative_distribution(test_df))\n",
+    "\n",
+    "# Plot distribution for training, validation, and test sets\n",
+    "def plot_distribution(dataframe, title):\n",
+    "    plt.figure(figsize=(12, 6))\n",
+    "    dataframe['charge_by_max_intensity'].value_counts().plot(kind='bar')\n",
+    "    plt.title(title)\n",
+    "    plt.show()\n",
+    "\n",
+    "plot_distribution(train_df, 'Training Set Distribution')\n",
+    "plot_distribution(val_df, 'Validation Set Distribution')\n",
+    "plot_distribution(test_df, 'Test Set Distribution')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import ast\n",
+    "from collections import Counter\n",
+    "\n",
+    "# Helper function to convert string representations to list\n",
+    "def literal_eval_list(col):\n",
+    "    return [int(x) for x in ast.literal_eval(col)]\n",
+    "\n",
+    "# Function to count unique combinations of charge states\n",
+    "def count_charge_states(df, col):\n",
+    "    tuples = df[col].apply(lambda x: tuple(x))\n",
+    "    return Counter(tuples)\n",
+    "\n",
+    "# Function to plot distribution with labels\n",
+    "def plot_distribution_with_labels(counter, title):\n",
+    "    labels, counts = zip(*sorted(counter.items()))\n",
+    "    label_strings = [str(label) for label in labels]\n",
+    "    plt.figure(figsize=(12, 6))\n",
+    "    plt.bar(range(len(counts)), counts, tick_label=label_strings)\n",
+    "    plt.xlabel(\"Charge State Vector (binary)\")\n",
+    "    plt.ylabel(\"Count\")\n",
+    "    plt.title(title)\n",
+    "    plt.xticks(rotation=90)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "# Counting and plotting distributions\n",
+    "train_counter = count_charge_states(train_df, 'charge_state_vector')\n",
+    "val_counter = count_charge_states(val_df, 'charge_state_vector')\n",
+    "test_counter = count_charge_states(test_df, 'charge_state_vector')\n",
+    "\n",
+    "plot_distribution_with_labels(train_counter, \"Training Set Distribution (Task 2)\")\n",
+    "plot_distribution_with_labels(val_counter, \"Validation Set Distribution (Task 2)\")\n",
+    "plot_distribution_with_labels(test_counter, \"Test Set Distribution (Task 2)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Missing classes in validation set:\n",
+      "(0, 0, 1, 0, 0, 1)\n",
+      "(1, 0, 1, 0, 0, 0)\n",
+      "(0, 1, 1, 1, 0, 1)\n",
+      "(0, 1, 1, 1, 1, 1)\n",
+      "(0, 1, 0, 1, 1, 1)\n",
+      "Missing classes in test set:\n",
+      "(0, 1, 1, 1, 0, 1)\n",
+      "(1, 1, 1, 1, 0, 0)\n",
+      "(0, 1, 0, 1, 1, 1)\n",
+      "(0, 0, 1, 0, 1, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# show which classes are missing in the validation set\n",
+    "missing_classes = set(train_counter.keys()) - set(val_counter.keys())\n",
+    "print(\"Missing classes in validation set:\")\n",
+    "for missing_class in missing_classes:\n",
+    "    print(missing_class)\n",
+    "# show which classes are missing in the test set\n",
+    "missing_classes = set(train_counter.keys()) - set(test_counter.keys())\n",
+    "print(\"Missing classes in test set:\")\n",
+    "for missing_class in missing_classes:\n",
+    "    print(missing_class)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analysis of Single vs Multiple Charge States\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Set - Single: 281592 (61.93%), Multiple: 173100 (38.07%)\n",
+      "Validation Set - Single: 93774 (61.87%), Multiple: 57790 (38.13%)\n",
+      "Test Set - Single: 93697 (61.82%), Multiple: 57867 (38.18%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "def is_single_charge_state(vector):\n",
+    "    \"\"\"\n",
+    "    Check if the vector represents a single charge state.\n",
+    "    Returns True if only one charge state is present, otherwise False.\n",
+    "    \"\"\"\n",
+    "    return sum(vector) == 1\n",
+    "\n",
+    "def count_and_relative_distribution(df, col):\n",
+    "    \"\"\"\n",
+    "    Calculate the count and relative distribution of single vs multiple charge states.\n",
+    "    Returns counts and relative percentages of single and multiple charge states.\n",
+    "    \"\"\"\n",
+    "    single_charge_state = df[col].apply(is_single_charge_state)\n",
+    "    single_count = single_charge_state.sum()\n",
+    "    multiple_count = len(df) - single_count\n",
+    "    \n",
+    "    total_count = len(df)\n",
+    "    single_relative = single_count / total_count\n",
+    "    multiple_relative = multiple_count / total_count\n",
+    "    \n",
+    "    return single_count, multiple_count, single_relative, multiple_relative\n",
+    "\n",
+    "# Analyze charge state distributions in the datasets\n",
+    "train_single, train_multiple, train_single_rel, train_multiple_rel = count_and_relative_distribution(train_df, 'charge_state_vector')\n",
+    "val_single, val_multiple, val_single_rel, val_multiple_rel = count_and_relative_distribution(val_df, 'charge_state_vector')\n",
+    "test_single, test_multiple, test_single_rel, test_multiple_rel = count_and_relative_distribution(test_df, 'charge_state_vector')\n",
+    "\n",
+    "# Plotting relative distributions for a visual comparison\n",
+    "def plot_relative_distributions(single_rel, multiple_rel, title, ax):\n",
+    "    \"\"\"\n",
+    "    Plot relative distributions of single vs multiple charge states.\n",
+    "    \"\"\"\n",
+    "    labels = ['Single Charge State', 'Multiple Charge States']\n",
+    "    relative_counts = [single_rel, multiple_rel]\n",
+    "    ax.bar(labels, relative_counts, color=['C0', 'C1'])\n",
+    "    ax.set_ylabel(\"Relative Distribution (%)\")\n",
+    "    ax.set_title(title)\n",
+    "    ax.set_ylim(0, 1)\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 3, figsize=(18, 6))\n",
+    "plot_relative_distributions(train_single_rel, train_multiple_rel, \"Training Set\", axs[0])\n",
+    "plot_relative_distributions(val_single_rel, val_multiple_rel, \"Validation Set\", axs[1])\n",
+    "plot_relative_distributions(test_single_rel, test_multiple_rel, \"Test Set\", axs[2])\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Output relative distributions in text format for clear documentation\n",
+    "print(f\"Training Set - Single: {train_single} ({train_single_rel:.2%}), Multiple: {train_multiple} ({train_multiple_rel:.2%})\")\n",
+    "print(f\"Validation Set - Single: {val_single} ({val_single_rel:.2%}), Multiple: {val_multiple} ({val_multiple_rel:.2%})\")\n",
+    "print(f\"Test Set - Single: {test_single} ({test_single_rel:.2%}), Multiple: {test_multiple} ({test_multiple_rel:.2%})\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Advanced Visualization\n",
+    "\n",
+    "This section provides heatmaps and other visual tools to further analyze and visualize the data characteristics, focusing on charge states and intensity distributions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2400x1600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Heatmap function for charge state vectors and intensity distributions\n",
+    "def plot_heatmaps(dataframe, title):\n",
+    "    charge_state_df = pd.DataFrame(dataframe['charge_state_vector'].tolist(), index=dataframe.index, columns=[f'{i}' for i in range(1, 7)])\n",
+    "    intensity_dist_df = pd.DataFrame(dataframe['normalized_intensity_distribution'].tolist(), index=dataframe.index, columns=[f'{i}' for i in range(1, 7)])\n",
+    "\n",
+    "    plt.figure(figsize=(24, 16))\n",
+    "    plt.subplot(1, 2, 1)\n",
+    "    sns.heatmap(charge_state_df, cmap='viridis', cbar=True)\n",
+    "    plt.title(f'{title} - Charge State Vector')\n",
+    "    plt.xlabel('Charge State')\n",
+    "\n",
+    "    plt.subplot(1, 2, 2)\n",
+    "    sns.heatmap(intensity_dist_df, cmap='viridis', cbar=True)\n",
+    "    plt.title(f'{title} - Normalized Intensity Distribution')\n",
+    "    plt.xlabel('Charge State')\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "plot_heatmaps(processed_data, \"Full Dataset\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ReVLBsdcBEAAIp0+fFtuuX78u6OvrC76+vmJb7969lX5ulI1ZWG1vvg///vuvoKenJ3h5eQl5eXlivyVLlggABJlMJrap+nkgIioMv46QiIiIiEiDMjMzAQBGRkbFOs/Dw0NudVf9+vVhbGyMv//+W2wzMDAQ//nly5d4+PAhateuDVNTU/z2228KY/br1w86Ojri69OnT+Phw4fo168fdHX/20S3R48eqFixoty5CQkJcHJygqOjIx48eCD+tGnTBgBw+PDhIu8nKioK69evR8OGDbF//36MHz8ejRo1wueff67w9XmFef1+nz17hgcPHqBFixYQBAFnzpx56/kJCQkwMTGBp6en3D00atQIhoaGb72HihUr4ssvv8SOHTvEncQEQcCGDRvQuHFj1KlTB4IgYPPmzejYsSMEQZC7jre3NzIyMsT3ZvPmzahUqRKGDBmicK3CtnQvcOfOHZw9exYhISFyO4vVr18fnp6e2LNnj8I5AwYMeOszIiIiIiIiIipLbG1t0bNnT6xYsQJ37txR2qfgd+iRI0fKtY8aNQoAsHv3brl2GxsbeHt7Kx2rV69ecjvuNG3aFIIgoE+fPnL9mjZtips3byI3N1dsez33yMjIwIMHD+Dm5oa///5b/Pq4dzFlyhTs2rULMTExcHZ2BgAcPHgQ6enpCAoKkssudHR00LRpUzEjKcgfevfuDRMTE3FMT09Pcax3pUr2VZgtW7YgPz8f/v7+cvVbWlrC3t5eIeMxNDREcHCw+FpPTw9NmjSRu5apqSn++ecf/PLLL+91XwX8/f2hr6+PdevWiW379+/HgwcP5Gp5V4aGhgCAJ0+eKD1e8H7t378fWVlZ73yd4uRJzZs3R6NGjcTXNWrUQKdOnbB//37k5eW9cw1vk5iYiJycHAwfPhza2v9Nj+jXrx+MjY0V/gyr8nkgIioMJ2EREREREWmQsbExgMIDkcLUqFFDoa1ixYp4/Pix+Pr58+eYOHEirK2tIZFIUKlSJVSuXBnp6elKwzkbGxu519evXwcA1K5dW65dV1dX4avsLl++jAsXLqBy5cpyP3Xq1AEA/Pvvv2+9p6CgICQnJ+Px48c4cOAAunfvjjNnzqBjx4548eLFW8+/ceOGOOnI0NAQlStXhpubGwCoFEZevnwZGRkZsLCwULiPp0+fqnQPPXr0wLNnz7B9+3YAwPHjx5GWloYePXoAAO7fv4/09HSsWLFC4RqhoaEA/ntWV69ehYODg9wEOFUVvHevf+VBAScnJzx48ECcKFbgzfefiIiIiIiI6FMwYcIE5ObmYubMmUqPX79+Hdra2gr5iKWlJUxNTcXfwQsU9fv1m3lOwUQYa2trhfb8/Hy5POPYsWPw8PBAhQoVYGpqisqVK2PcuHEAVMs9lNm3bx+ioqIQEREBPz8/sf3y5csAgDZt2ijkFwcOHBCzi4J7t7e3VxhbWSZRHKpkX4W5fPkyBEGAvb29Qv0XL15UyHiqV6+usODtzWuFh4fD0NAQTZo0gb29PQYNGoRjx4694929mtTVsWNHrF+/Xmxbt24dqlWrJi5qfB9Pnz4FUPjCTxsbG4wcORKrVq1CpUqV4O3tjaVLlxb7s1ScPEnZ56ROnTrIysrC/fv3i3Xd4igsJ9PT04Otra3Cn2FVPg9ERIUpfppPRERERERqY2xsDCsrK5w/f75Y572+Y9XrBEEQ/3nIkCGIjo7G8OHD0bx5c5iYmEBLSwuBgYHIz89XOPf1FZXFlZ+fj3r16mH+/PlKj78ZJhbF2NgYnp6e8PT0RLly5RAbG4uTJ0+KE6qUycvLg6enJx49eoTw8HA4OjqiQoUKuHXrFkJCQpTer7J7sLCwkFuB+LrKlSu/dYwOHTrAxMQE69evR/fu3bF+/Xro6OggMDBQvAYABAcHo3fv3krHqF+//luv8yG8z/tPREREREREVFrZ2toiODgYK1aswLfffltov7ftSl2gqN+vC8tz3pbzXL16FW3btoWjoyPmz58Pa2tr6OnpYc+ePViwYIFKucebrl27hh49esDT0xNTp06VO1Yw3po1a2Bpaalw7rssGCsuVbKvwuTn50NLSwt79+5VOk7BLlHFuZaTkxNSU1Oxa9cu7Nu3D5s3b8ayZcswceJEREVFvbUmZXr16oWEhAQcP34c9erVw44dOzBw4EC53Zre1fnz52FhYSEuAFVm3rx5CAkJwfbt23HgwAEMHToUM2bMwIkTJ1C9enWVrqPuPKmwP2cfcqesN73PZ4+IiJOwiIiIiIg0rEOHDlixYgVSUlLQvHlztY27adMm9O7dG/PmzRPbXrx4gfT0dJXOr1mzJgDgypUraN26tdiem5uLtLQ0uclCdnZ2+P3339G2bVuVQ0lVNG7cGLGxseJXAhQ29h9//IFLly4hNjYWvXr1EtsPHjyo0LewMezs7JCYmAhXV9d3DpAkEgm6du2K1atX4969e0hISECbNm3EwLJy5cowMjJCXl4ePDw8ihzLzs4OJ0+exMuXL+W+qkCVeyl471JTUxWO/fXXX6hUqRIqVKhQnFsjIiIiIiIiKrMmTJiAtWvXYtasWQrHatasifz8fFy+fBlOTk5i+71795Ceni7+Dv4h7dy5E9nZ2dixY4fcDlFvfq2eqp4/f44uXbrA1NQUcXFxCpN+Cr4G0MLCosj8ouDeC3bOep2yTELdisp4BEGAjY2NuEu7OlSoUAEBAQEICAhATk4OunTpgmnTpiEiIgL6+vrFqhEAfHx8ULlyZaxbtw5NmzZFVlYWevbs+d51pqSk4OrVqyp9rWG9evVQr149TJgwAcePH4erqyt++OEHcWKeOnM+ZZ+TS5cuoXz58uLix4oVKyrNLt/crao4tb2ek9na2ortOTk5uHbt2lszOiKi4uDXERIRERERadjYsWNRoUIF9O3bF/fu3VM4fvXqVSxatKjY4+ro6Cis0Fq8eLHKK8caN24Mc3NzrFy5Erm5uWL7unXrFLbf9vf3x61bt7By5UqFcZ4/f67w1Xevy8rKQkpKitJje/fuBfDfduEFE4feDGMKVqi9fr+CICh9boWN4e/vj7y8PEyZMkXhnNzcXJUnr/Xo0QMvX75E//79cf/+ffGrCAvq9PPzw+bNm5Xufvb61ut+fn548OABlixZotCv4D7Lly+v9F6qVq2Kzz77DLGxsXLHzp8/jwMHDuDLL79U6V6IiIiIiIiIPgV2dnYIDg7Gjz/+iLt378odK/gdeuHChXLtBbuBt2/f/oPXpyz3yMjIQHR09DuNN2DAAFy6dAlbt25FxYoVFY57e3vD2NgY06dPx8uXLxWOF+QXr+cPr3+N3cGDB/Hnn3++U23FUVjG06VLF+jo6CAqKkohGxMEAQ8fPiz2td48R09PD87OzhAEQekzer3GwjIlXV1dBAUFYePGjYiJiUG9evXee4f069evIyQkBHp6ehgzZkyh/TIzM+XyPuDVhCxtbW1kZ2erVH9xpaSk4LfffhNf37x5E9u3b4eXl5f4Gbezs0NGRgbOnTsn9rtz5w62bt2qMJ6qtXl4eEBPTw/ff/+93OdBKpUiIyPjo/wZJqJPB3fCIiIiIiLSMDs7O6xfvx4BAQFwcnJCr169ULduXeTk5OD48eNISEhASEhIscft0KED1qxZAxMTEzg7OyMlJQWJiYkwNzdX6Xw9PT1ERkZiyJAhaNOmDfz9/ZGWloaYmBjY2dnJrTbr2bMnNm7ciAEDBuDw4cNwdXVFXl4e/vrrL2zcuBH79+9H48aNlV4nKysLLVq0QLNmzeDj4wNra2ukp6dj27ZtSE5ORufOndGwYUPxWZmamuKHH36AkZERKlSogKZNm8LR0RF2dnYYPXo0bt26BWNjY2zevFlhshgANGrUCAAwdOhQeHt7i18X6Obmhv79+2PGjBk4e/YsvLy8UK5cOVy+fBkJCQlYtGgRunbt+tbn5ubmhurVq2P79u0wMDBAly5d5I7PnDkThw8fRtOmTdGvXz84Ozvj0aNH+O2335CYmIhHjx4BeLUl/erVqzFy5EicOnUKX3zxBZ49e4bExEQMHDgQnTp1goGBAZydnREfH486derAzMwMdevWRd26dTFnzhy0a9cOzZs3R1hYGJ4/f47FixfDxMQEkZGRKn0GiIiIiIiIiD4V48ePx5o1a5CamgoXFxexvUGDBujduzdWrFiB9PR0uLm54dSpU4iNjUXnzp3ldg//ULy8vKCnp4eOHTuif//+ePr0KVauXAkLCwtx93BV7d69G6tXr4afnx/OnTsnN9nF0NAQnTt3hrGxMZYvX46ePXvi888/R2BgICpXrowbN25g9+7dcHV1FReNzZgxA+3bt0fLli3Rp08fPHr0CIsXL4aLiwuePn2q1ufwpoKMZ/z48QgMDES5cuXQsWNH2NnZYerUqYiIiEBaWho6d+4MIyMjXLt2DVu3bsXXX3+N0aNHF+taXl5esLS0hKurK6pUqYKLFy9iyZIlaN++PYyMjIqscfny5Zg6dSpq164NCwsLtGnTRjzeq1cvfP/99zh8+LDSndiK8ttvv2Ht2rXIz89Heno6fvnlF2zevBlaWlpYs2ZNkRO6Dh06hMGDB6Nbt26oU6cOcnNzsWbNGnEB4ev1JyYmYv78+bCysoKNjQ2aNm1arDoL1K1bF97e3hg6dCgkEgmWLVsGAHJf5xgYGIjw8HD4+vpi6NChyMrKwvLly1GnTh25CVzFqa1y5cqIiIhAVFQUfHx88NVXXyE1NRXLli3D//73P5V2DCMiUplAREREREQlwqVLl4R+/foJtWrVEvT09AQjIyPB1dVVWLx4sfDixQuxHwBh0KBBCufXrFlT6N27t/j68ePHQmhoqFCpUiXB0NBQ8Pb2Fv766y+FftHR0QIA4ZdfflFa1/fffy/UrFlTkEgkQpMmTYRjx44JjRo1Enx8fOT65eTkCLNmzRJcXFwEiUQiVKxYUWjUqJEQFRUlZGRkFHrfL1++FFauXCl07txZvE758uWFhg0bCnPmzBGys7Pl+m/fvl1wdnYWdHV1BQBCdHS0IAiC8OeffwoeHh6CoaGhUKlSJaFfv37C77//LtdHEAQhNzdXGDJkiFC5cmVBS0tLePPXohUrVgiNGjUSDAwMBCMjI6FevXrC2LFjhdu3bxd6D28aM2aMAEDw9/dXevzevXvCoEGDBGtra6FcuXKCpaWl0LZtW2HFihVy/bKysoTx48cLNjY2Yr+uXbsKV69eFfscP35caNSokaCnpycAECZNmiQeS0xMFFxdXQUDAwPB2NhY6Nixo/Dnn3/KXWPSpEkCAOH+/fsq3x8RERERERFRaVVUDtK7d28BgODi4iLX/vLlSyEqKkr8/dza2lqIiIiQy2sE4VU20759e4VxDx8+LAAQEhISVKpF2e/qO3bsEOrXry/o6+sLtWrVEmbNmiXIZDIBgHDt2jWxn5ubm+Dm5ia+vnbtmlw2UnBNZT81a9ZUqNvb21swMTER9PX1BTs7OyEkJEQ4ffq0XL/NmzcLTk5OgkQiEZydnYUtW7YIvXv3VhhPGTc3N4XnrWr2JQiCMGXKFKFatWqCtra2wrPYvHmz0LJlS6FChQpChQoVBEdHR2HQoEFCampqkdcXBEGh/h9//FFo1aqVYG5uLkgkEsHOzk4YM2aMXOZV8Gxfr+Hu3btC+/btBSMjIwGA3HtTwMXFRdDW1hb++eefQp6SvIL3tOBHV1dXMDMzE5o2bSpEREQI169fVzin4DN4+PBhQRAE4e+//xb69Okj2NnZCfr6+oKZmZnQunVrITExUe68v/76S2jVqpVgYGAgABCff1F5UsGx1xW8p2vXrhXs7e0FiUQiNGzYUKzndQcOHBDq1q0r6OnpCQ4ODsLatWuVjllYbcreB0EQhCVLlgiOjo5CuXLlhCpVqgjffPON8PjxY7k+qn4eiIgKoyUIb+zBSEREREREVIT8/HxUrlwZXbp0Ufr1g0RERERERERERKSahg0bwszMDD/99JOmSyEiovekrekCiIiIiIio5Hrx4gXeXLexevVqPHr0CO7u7popioiIiIiIiIiIqAw4ffo0zp49i169emm6FCIiUgNOwiIi+ohSUlKgo6OD9u3bF9onLi4OOjo6GDRoUJFjzZgxAzo6OpgzZ06R/fr37w8dHR0kJCQU2c/b2xs6Ojr45Zdfiuzn6OgIiUSCu3fvFtrn+fPnMDMzQ6VKlZCdnV1ov3/++Qd6enqoW7dukddU5bkBfHavEwQBK1euRPPmzWFsbAxDQ0O4uLhg2LBhuHLlitgvMjISn332WZF1X7lyBX369EGNGjUgkUhQrVo1tG3bFuvWrUNubq7YT0tLC9u2bSt0nOLUpKWlBS0tLejq6qJWrVoYMWIEnj59WmSdRPRhnDhxAp9//jmmT5+OH3/8Ef3790ffvn1Rt25ddOvWTdPlERERERF9UpgtyWO2pIjZErMlIiodzp8/j9jYWPTp0wdVq1ZFQECApksiIiI14CQsIqKPSCqVYsiQIfj5559x+/btQvuMHTsWcXFxePHiRaFjyWQyjB07FjKZrNA+WVlZ2LBhw1v73bhxA8ePH8fgwYOL7Hf06FE8f/4cXbt2RWxsbKH9Nm/eDBcXFzg6OhYZnMTExMDf3x+ZmZk4efJkof1UeW4F/fjsXgVS3bt3x9ChQ/Hll1/iwIED+PPPPyGVSqGvr4+pU6cWOu6bTp06hc8//xwXL17E0qVLcf78eSQlJaFv375Yvnw5Lly4oNI4xa3JxcUFd+7cQVpaGmbNmoUVK1Zg1KhRKtdNROpTq1YtWFtb4/vvv8eQIUOwfft29OrVCz/99BP09PQ0XR4RERER0SeF2ZI8ZkuKmC29wmyJiEq6TZs2ITQ0FC9fvkRcXBz09fU1XRIREamDQEREH8WTJ08EQ0ND4a+//hICAgKEadOmKfT5+++/BQMDAyE9PV1o2rSpsG7dOqVjJSUlCdWqVRNycnIEKysr4dixY0r7xcTECM2aNRPS09OF8uXLCzdu3FDaLzIyUggMDBQuXrwomJiYCFlZWUr7hYSECN9++62wd+9eoU6dOoXeq7u7u/DDDz8Iy5cvFzw9PZX2yc/PF2xtbYV9+/YJ4eHhQr9+/ZT2U+W5CQKf3evi4uIEAML27dsLPb/ApEmThAYNGhTaz8nJSWjUqJGQl5f31rEACFu3blXa731r6tevn2Bpaan0XCIiIiIqW44cOSJ06NBBqFq1apF/x3zd4cOHhYYNGwp6enqCnZ2dEB0d/cHrJCL62JgtyWO2pByzJWZLRERERKS5fIk7YRERfSQbN26Eo6MjHBwcEBwcDJlMBkEQ5PpER0ejffv2MDExQXBwMKRSqdKxpFIpgoKCUK5cOQQFBRXZLzg4GCYmJmjXrh1iYmIU+giCgOjoaAQHB8PR0RG1a9fGpk2bFPo9efIECQkJCA4OhqenJzIyMpCcnKzQ7+rVq0hJSYG/vz/8/f2RnJyM69evK/Q7fPgwsrKy4OHhgeDgYGzYsAHPnj17p+cG8Nm9/uzi4uLg4OCAr776Sum9aWlpKW1/09mzZ3Hx4kWMHj0a2trK/8qg6ljvW5OBgQFycnJUuhYRERERlW7Pnj1DgwYNsHTpUpX6X7t2De3bt0fr1q1x9uxZDB8+HH379sX+/fs/cKVERB8XsyV5zJaYLRVnHGZLRERERJ8WTeVLnIRFRPSRFAQvAODj44OMjAwcOXJEPJ6fn4+YmBixT2BgII4ePYpr167JjZOZmYlNmzaJ/YKDg7Fx40Y8ffpUrt/ly5dx4sQJ8XvEg4ODER0drRAyJSYmIisrC97e3mI/ZeHRhg0bYG9vDxcXF+jo6CAwMFBpP5lMhnbt2qFixYowMzODt7c3oqOjlT6PwMBA6OjooG7durC1tUVCQkKxnxufneKzu3TpEhwcHOTOGT58OAwNDWFoaIjq1asrjKnMpUuXAEBurH///Vccx9DQEMuWLVN5rHet6ddff8X69evRpk2bQvtkZ2cjMzNT7ic7O1ul2oiIiIjowyvO39fatWuHqVOnwtfXV6Wxf/jhB9jY2GDevHlwcnLC4MGD0bVrVyxYsECdt0BEpHHMlhSfB7OlD/PsPsVsCWC+RERERFTSlYZ8SbdYvYmI6J2kpqbi1KlT2Lp1KwBAV1cXAQEBkEqlcHd3BwAcPHgQz549w5dffgkAqFSpEjw9PSGTyTBlyhRxrLi4ONjZ2aFBgwYAgM8++ww1a9ZEfHw8wsLCxH4ymQze3t6oVKkSAODLL79EWFgYDh06hLZt28r1CwgIgK7uq/8kBAUFYcyYMbh69Srs7Ozk+hUETMCrUMjNzQ2LFy+GkZERACAvLw+xsbFYtGiRXL/Ro0dj4sSJ4oq39PR0bNmyBUePHpXrJ5VKERISUqznxmen/Nm9afz48Rg8eDC2bNmC6dOnF9rvbczNzXH27FkAgLu7+3utICyqpj/++AOGhobIy8tDTk4O2rdvjyVLlhQ61owZMxAVFSXXZgMn2Gm5vHN9pGj/7d81XUKZ4m3VQNMlEBGVegfzFf9H68eUf7eORq9fmsz4obvC39cmTZqEyMjI9x47JSUFHh4ecm3e3t4YPnz4e49NRFRSMFtitsRsqXg1FTdbApgvfQzMltSP+RIR0ftjvlR6lIZ8iZOwiIg+AqlUitzcXFhZWYltgiBAIpFgyZIlMDExgVQqxaNHj2BgYCD2yc/Px7lz5xAVFSUGJVKpFBcuXBDDmYJ+MplMDHsKQpe7d+/K9cvLy4NMJhPDnkePHmHr1q14+fIlli9frtBv2rRpAIA///wTJ06cwKlTpxAeHi7Xb8OGDejXrx8AYP/+/bh165a4yu/1fj/99BM8PT0BAOvXr8eLFy/QtGlTueeRn5+PS5cuoU6dOio/t4J+fHb/PTt7e3ukpqbKjVO5cmVUrlwZFhYWUJW9vT2AV4Flw4YNAQA6OjqoXbs2AMg9H1XGKk5NDg4O2LFjB3R1dWFlZQU9Pb0ix4+IiMDIkSPl2so9/hwSCYMdIiIiopJA2d/XJBKJWsa+e/cuqlSpItdWpUoVZGZm4vnz53K/JxARlVbMlpgtMVv6sNkSoPzvK11tRkBbS0flOqloN3KfaLoEIiIiKsVKQ77ESVhERB9Ybm4uVq9ejXnz5sHLy0vuWOfOnREXF4du3bph+/bt2LBhA1xc/ltZlZeXh5YtW+LAgQPw8fHBH3/8gdOnTyMpKQlmZmZiv0ePHsHd3R1//fUXHB0dsWfPHjx58gRnzpyBjs5/IcH58+cRGhqK9PR0mJqaYt26dahevTq2bdsmV9eBAwcwb948TJ48GTo6OpBKpWjVqpXCd+ZGR0dDKpWKYU/BVubjx4+X6zdt2jRIpVIx7JFKpRg1apTC6rqBAwdCJpNh5syZKj23AQMG4OHDh3x2bzy7oKAgdO/eHdu3b0enTp3wrho2bAhHR0fMnTsX/v7+YuD4Lopbk56enhjIqUIikSj8JSs/i9+6TERERFRSKPv7GhERqYbZErMlZksfPlsClP995YF/3WKNQUXzP19J0yWUOSa4rOkSiIiIPprSkC9xEhYR0Qe2a9cuPH78GGFhYeLqugJ+fn6QSqV48eIFzM3N4e/vDy0tLbk+X375JaRSKXx8fCCVStGkSRO0atVK4Tr/+9//IJVKMWfOHEilUrRv317cGr2As7MzRowYgXXr1mHQoEGQSqXo2rUr6taVDxOsra0RERGBffv2wcvLC2vWrMHkyZMV+vXt2xfz58/HhQsXYGFhgZ07d2LHjh0K/Xr16gVfX188evQIN27cwG+//YZ169bB0dFRrl9QUBAmT56MqVOnqvTcBgwYgDVr1vDZvfHsAgMDsWXLFgQGBiIiIgLe3t6oUqUKrl+/jvj4eLkAEACeP38ubgNfwMjICHZ2doiOjoanpydcXV0REREBJycnvHz5Ej///DPu37+vMNa1a9cUxrK3ty92TerArbjVj1vGExERUUlkaWmJe/fuybXdu3cPxsbG3AWLiMoEZkvMlpgtaSZbAgDzH49/kHE/VfsnMVtSN28wAyUiIlIHdeVLnIRFRPSBSaVSeHh4KIQ9wKvAZ/bs2fj111/xzTffKAQ9BX169uyJf//9F2vXrpXbdvzNfvPmzcPo0aOxe/durF+/XqGPtrY2fH19IZVK0axZM/z+++9YuXKlQj8TExO0bdtWDPEePnwIX19fhX5OTk5wcnKCVCpFtWrVUKFCBXFL9de1bdsWBgYGWLt2LS5fvgxnZ2eFoAcAfH19MXjwYOzZs0el53bu3DnIZDL4+vry2b327L766ivEx8dj5cqViI6OxuzZs/Hy5UtUr14dbdu2xfz58+XOvXTpkrgl/OvXTUxMRLNmzfDrr79i+vTpGDRoEO7evYsKFSqgQYMGWLBgAfr06SN33ptbgAJAcnIyWrZsWayaiD4FnNSmfpx8SUT0aWrevDn27Nkj13bw4EE0b95cQxUREakXsyVmS8yWmC2VFV86KU5ipPcTe3PP2ztRsfS2dtV0CUREpAHqype0BEEQ1FkYERERUUmQf7eOpksgKhInDBERvb+D+QkavT7/vqE6bctLKvd9+vQprly5AuDV1xjNnz8frVu3hpmZGWrUqIGIiAjcunULq1evBvBqx4y6deti0KBB6NOnDw4dOoShQ4di9+7d8Pb2/iD3Q0RERJ+GgJQBmi6hTLm5wF7TJZQ5FTad0HQJRESlHvOl0qM05EvcCYuIiIjKJE5wUT/u3KRefJ5UGvDfpURFy0e+pksoNbSL0ff06dNo3bq1+LpgR4zevXsjJiYGd+7cwY0bN8TjNjY22L17N0aMGIFFixahevXqWLVqFSdgERER0Xt75PpQ0yWUKcaG2ZouocyJuXlM0yWUOT25ExYRfWTMl1RXGvIl7oRFREREZZKndjdNl1DmcNKQenFyCxHR+9P0SsXcu7U1ev3SRNfyiqZLICIiIio25kvqxWxJ/ZgvERG9P+ZLpUdpyJe4ExYRERERkQYweFQ/Bo9EREREREREJVf75h00XUKZE3Vtm6ZLKHMm2TTSdAlERFSKcRIWEREREZEGcMIQEREREREREX1Kdqfs0nQJZY63FScMERERlSSchEVERERlEncZIiIiIiIiIiKi96Glo6PpEsqUl0KupksgIiIi+qA4CYuIiIjKJO4yRCUdJwqqH//cExERERERkToJeXmaLqFM6VCNuzapG/Ml9WO+RERE74OTsIiIiKhMYgChfgwg1IvPk4io9MsT8jVdQqnBAIqIiIhKI+ZL6sUsRP34TImISj/mS6orDflSaaiRiIhKoZSUFLRs2RI+Pj7YvXu32J6WlgYbGxvxtaGhIWrUqAF3d3cMHz4c9vb24rGYmBgMHz4c6enpb72eo6Mjrl27huvXr8PS0lLh+K5duzBnzhz89ttvyMvLg4uLCwYNGoSQkBAAwK+//orGjRsjJSUFzZo1Uzi/bdu2MDExwZYtWxASEoLY2FiFPt7e3ti3bx8A4Pfff8d3332HEydOIDMzE5aWlmjatCkWL14MCwsLhedQoEePHli7dq34OjY2FkuWLMGFCxego6ODzz//HGPGjEGHDh3EPklJSWjdurXS53Lnzh1YWloiMjIS27Ztw9mzZ5X2c3d3x5EjRxAXF4fAwECxfeHChVi4cCHS0tIAvHpPQkNDFc5fuXIl+vbt+9b3LCQkBOnp6di2bZvS47Vq1cL169cBAAYGBrCzs8OwYcPQt29fpf2LwgCCSjoGuerHP/dERERERGUL8yXmS8owXyL6D/Ml9eOfeyIieh+chEVERB+EVCrFkCFDIJVKcfv2bVhZWckdT0xMhIuLC7KysvDHH39g0aJFaNCgAXbu3Im2bdsW61pHjx7F8+fP0bVrV8TGxiI8PFzu+OLFizF8+HCEh4dj+fLl0NPTw/bt2zFgwACcP38ec+fORaNGjdCgQQPIZDKFkCwtLQ2HDx/Gzp07xTYfHx9ER0fL9ZNIJACA+/fvo23btujQoQP2798PU1NTpKWlYceOHXj27JnS51DAwMBA/OfRo0djyZIlmDp1Kjp37oyXL19i7dq16NSpExYtWoTBgwfLjZWamgpjY2O5NgsLC1UfI/T19TFhwgT4+fmhXLlyhfYzNjZGamqqXJuJiYnK13mbyZMno1+/fsjKykJCQgL69euHatWqoV27dmq7Br0bhjrqxUCHiIiIiIioaMyXmC+9K+ZLJROzJfVjvkRERFSycBIWERGp3dOnTxEfH4/Tp0/j7t27iImJwbhx4+T6mJubiysKbW1t0bFjR7Rt2xZhYWG4evUqdHR0VL6eVCpF9+7d4ebmhmHDhsmFZDdv3sSoUaMwfPhwTJ8+XWwfNWoU9PT0MHToUHTr1g1NmzZFWFgYJkyYgIULF6J8+fJi35iYGFStWhU+Pj5im0QiUboiEgCOHTuGjIwMrFq1Crq6r/5Ta2Njo3Q14evP4XUnTpzAvHnz8P3332PIkCFi+7Rp0/DixQuMHDkSnTp1grW1tXjMwsICpqamKjwx5YKCgrBjxw6sXLkSAwcOLLSflpZWofeuDkZGRuL44eHhmD17Ng4ePFjskIyhDhEREREREVHpxXyJ+dL7UFe+tOvWrx+ivE+Wj3UTTZdQBuVpugAiIiJ6DSdhERGR2m3cuBGOjo5wcHBAcHAwhg8fjoiICGhpaRV6jra2NoYNGwZfX1/8+uuvaNJEtV/Inzx5goSEBJw8eRKOjo7IyMhAcnIyvvjiCwDApk2b8PLlS4wePVrh3P79+2PcuHGIi4tD06ZN0aNHD4wZMwabNm1Cr169AACCICA2NhYhISEqB3eWlpbIzc3F1q1b0bVr1yLvuzBxcXEwNDRE//79FY6NGjUK8+fPx+bNmzF8+PBij10YY2NjjB8/HpMnT0bv3r1RoUIFtY39LvLz87F161Y8fvwYenp6RfbNzs5Gdna2XJtv1brQ1lI9bKW348Q2IiIiIiIi+liYLzFfUof3zpesPmO+pEb7b/+m6RLKHO6ERUREVLJwEhYREamdVCpFcHAwgFfbqmdkZODIkSNwd3cv8jxHR0cAr7ZnVzUk27BhA+zt7cUt1wMDAyGVSsWQ7NKlSzAxMUHVqlUVztXT04OtrS0uXboEADAzM4Ovry9kMpkYkh0+fBhpaWkIDQ2VO3fXrl0wNDSUaxs3bhzGjRuHZs2aYdy4cejevTsGDBiAJk2aoE2bNujVqxeqVKkid06LFi2gra0tvk5OTkbDhg1x6dIl2NnZKQ2HrKysYGxsLNZdoHr16nKva9asiQsXLhT+8JQYOHAgFi1ahPnz5+O7775T2icjI0Pu3g0NDXH37t1iXaco4eHhmDBhArKzs5GbmwszMzP07du3yHNmzJiBqKgouTYbOMEOLoWcQe+CoY56cVKb+vEzSkRERERUdjBfYr70PpgvlUz8vV39mC+pHz+nRET0PjgJi4iI1Co1NRWnTp3C1q1bAQC6uroICAiAVCp9a0gmCAIAFGtln0wmEwM5AAgODoabmxsWL14MIyMjlcZ4PYjq06cPvL29cfXqVdjZ2UEmk8HNzQ21a9eWO6d169ZYvny5XJuZmZn4z9OmTcPIkSNx6NAhnDx5Ej/88AOmT5+On3/+GfXq1RP7xcfHw8nJSXz9+vbvBc9DlbqBVwHb6/dcrly5Is9XRiKRYPLkyRgyZAi++eYbpX2MjIzw22//rVp7PeRThzFjxiAkJAR37tzBmDFjMHDgQIXn/6aIiAiMHDlSrs3XJEStdRFDHXVjoENEVPrlo+i/rxEREdG7Yb70CvOld8d8qWRitqR+zJeIiEo/5ktlCydhERGRWkmlUuTm5sLKykpsEwQBEokES5YsKfLcixcvAgBsbGxUutaff/6JEydO4NSpUwgPDxfb8/LysGHDBvTr1w/29vbIyMjA7du35WoCgJycHFy9ehXe3t5iW9u2bVGjRg3ExMRgzJgx2LJlC3788UeFa1eoUOGtwY25uTm6deuGbt26Yfr06WjYsCHmzp2L2NhYsY+1tbXScezt7XH06FHk5OQohGG3b99GZmYm6tSpI9duY2MDU1PTImtSRXBwMObOnYupU6eiVq1aCse1tbXfeu/vo1KlSqhduzZq166NhIQE1KtXD40bN4azs3Oh50gkEkgkEvk6uVU8lXAMHtWPwSMRERERUdnAfOk/zJfeDfOlkikf+ZouoezRUu8ERgIg8HNKRETvjpOwiIhIbXJzc7F69WrMmzcPXl5ecsc6d+6MuLg4+Pj4KD03Pz8f33//PWxsbNCwYUOVrieVStGqVSssXbpUrj06OhpSqRT9+vVD165dER4ejnnz5mHevHly/X744QdkZWWJW8MDrwKg0NBQSKVSVKtWDXp6eujatatK9RRFT08PdnZ2ePbsmUr9g4KCsHjxYvz4448YMmSI3LG5c+dCX18fAQEB712XMtra2pgxYwa6dOlS6GrFj8Xa2hoBAQGIiIjA9u3bi3UuJ7hQSccJQ0RERERERIqYLxWO+dK7eZ98Sdfc/ANV9Wm6kavaZ5eKgROGiIiIShROwiIiIrXZtWsXHj9+jLCwMJiYmMgd8/Pzg1QqFUOyhw8f4u7du8jKysL58+excOFCnDp1Crt374aOzn8rzPLy8nD27Fm5sSQSCWrXro01a9Zg8uTJqFu3rtzxvn37Yv78+bhw4QJcXFwwe/ZsjB49Gvr6+ujZsyfKlSuH7du3Y9y4cZg6darC+aGhoZg8eTLGjRuHoKAgGBgYKNxrdnY27t69K9emq6uLSpUqYdeuXdiwYQMCAwNRp04dCIKAnTt3Ys+ePYiOjlbpWTZv3hzDhg3DmDFjkJOTg86dO+Ply5dYu3Ytvv/+e8TExMD8jRDo33//xYsXL+TazM3NxW3jnz9/rvAsjYyMYGdnp3D99u3bo2nTpvjxxx9RpUoVlWouUNh7VrAtfkZGhsJxc3Nzua3yXzds2DDUrVsXp0+fRuPGjVWugxNc1I8T24iIiIiIiOhDY77EfAkoOflS7sOHxaqbilZLV7Wv9yQiIiIqrTgJi4iI1EYqlcLDw0MhIANehWSzZ89GZmYmAMDDwwMAUL58edSsWROtW7fGihUrFLYhf/r0qcLKRTs7O8yaNQsPHz6Er6+vwrWcnJzg5OQEqVSK+fPnY8SIEbC1tcW8efOwaNEicbVgXFwcAgMDFc6vUaMGPDw8cODAAfTp00fpve7btw9Vq1aVa3NwcMBff/0FZ2dnlC9fHqNGjcLNmzchkUhgb2+PVatWoWfPnoU9PgULFy5E/fr1sWzZMkyYMAEvXryAnp4eDh06hFatWin0d3BwUGhLSUlBs2bNAACXLl1SeJZt27ZFYmKi0uvPmjULLVq0ULneAoW9Z1euXAEAJCUlKRwPCwvDqlWrlI7n7OwMLy8vTJw4EXv27Cl2PUQlFSe1qR8nXxIRERERlX7Ml5gvAcyXyqq03CeaLqHM4W5t6sfJl0RE9D60BEEQNF0EERHRx/To0SO0bdsWxsbG2Lt3L8qXL6/pklSSlpYGNzc3NG/eHOvWrZNb0UmK8u/W0XQJZQ4nuBARUUlzMD9Bo9d/fsdGo9cvTQyqXtN0CURERGrFfOnTwHxJvZgtERFRScR8qfQoDfkSd8IiIqJPjpmZGRITE7F06VKkpKSgbdu2mi5JJbVq1UJSUhJiY2Nx9uxZNGrUSNMllWgMdaik405YVBrw36VERctHvqZLICIiIg1hvvRp4O9EVNJpN3TWdAllTvcNBzVdQpmy1qGapksgKvGYL5Ut3AmLiIiIyiRP7W6aLqHM4aQhKukYjhN9ejS9UvHZnZoavX5pUqHqdU2XQERERFRszJfUrBl/b1e3DrIkTZdQ5uxyrqjpEojoI2O+VHqUhnyJO2EREREREWkAJwwRERERERER0adk/5bVmi6hzGG+REREVLJwEhYRERGVSdy1iYiIiIiIiIiI3kfCPyc0XUKZ0s6utaZLKIOea7oAIiIieg0nYREREVGZxFVg6seJbURERERERET0KelWvZmmSyhT9t/mpDZ1YwZKRERUsnASFhERERGphKGOenFSm/rxM0pERERERERUcvH3dvVjvqR+/JwSEdH74CQsIqIyICUlBS1btoSPjw92794ttqelpcHGxkahf48ePbB27dpCjxeM2axZM8TExCA0NFThuEQiwYsXLwAA9+/fx8SJE7F7927cu3cPFStWRIMGDTBx4kS4uroCAH7//Xd89913OHHiBDIzM2FpaYmmTZti8eLFsLCwUKjFzMwMjRo1wqxZs9CwYcMi7zUkJASxsbGFPp+aNWsiLS0N7u7uOHLkiFi/ra0tBg8ejIEDBwIAYmJiMHz4cKSnp4uvld37ypUr0bdvX5WOvzne66/fpMpzrFWrFq5fvw4AKF++PBwcHBAREYFu3bqJ42RmZmLOnDnYsmUL/v77b5QvXx62trbo1q0b+vXrh4oVK6JevXpwdXXFDz/8oFDHmjVr0LdvX9y6dQvnz59H69avtgnX0tKCkZERbG1t4enpiREjRqBq1aoK58fFxSE4OBgDBgzA0qVL5Y4lJSWhdevWePz4MUxNTRXOjYyMxLZt23D27FmxLTk5GR07dkRISAgWLFgALS0tpc+PPjyGOurFQIeIqPTLEwRNl0BERKQ2zJeYLzFfog+N2ZL6MV8iIir9mC+VLdqaLoCIiN6fVCrFkCFD8PPPP+P27dsKxxMTE3Hnzh3x583g4s3jd+7cQaNGjcTjxsbGCscLghoA8PPzw5kzZxAbG4tLly5hx44dcHd3x8OHDwG8Cn/atm0LMzMz7N+/HxcvXkR0dDSsrKzw7NkzpbXs378fT58+Rbt27eRCJWX3umjRIrnaACA6Olp8/csvv4jn9+vXD3fu3MGff/4Jf39/DBo0CHFxcYU+W2X33qNHD5WPF8fbnmOByZMn486dOzhz5gz+97//ISAgAMePHwcAPHr0CM2aNUN0dDRGjx6NkydP4rfffsO0adNw5swZrF+/HgAQFhaGDRs24Pnz5wp1REdH46uvvkKlSpXEttTUVNy+fRu//PILwsPDkZiYiLp16+KPP/5QOF8qlWLs2LGIi4sTg9R3tXv3bnh7e2PkyJFYuHAhAzIiIiIiIiKiD4T5EvMl5ktERERERO+HO2EREZVyT58+RXx8PE6fPo27d+8iJiYG48aNk+tjbm4OS0vLQsd423EtLa1Cj6enpyM5ORlJSUlwc3MD8GplYJMmTcQ+x44dQ0ZGBlatWgVd3Vf/6bGxsRFXwCmrxdLSEnPnzoWrqytOnjwJb2/vQu/VxMQEJiYmcuOYmpoqrbl8+fJie2RkJNavX48dO3YgKCio2PeuynFVqfIcCxgZGYnPaOnSpVi7di127tyJFi1aYNy4cbhx4wYuXboEKysr8ZyaNWvCy8sLwv/Ppg8ODkZ4eDg2b96M4OBgsd+1a9eQlJSEPXv2yF3TwsJCfKZ16tRBp06d0LBhQ3zzzTc4evSo3PnHjx/H5s2bcfjwYWzZsgXdu3d/p2eyfv16hIaGYt68eRg8eHCxz+fKOiIiIiIiIiLVMF9ivsR8STktHZ13ui4p91LI1XQJRERERB8Ud8IiIirlNm7cCEdHRzg4OCA4OBgymUwMQj4GQ0NDGBoaYtu2bcjOzlbax9LSErm5udi6dWuxajMwMAAA5OTkAPgw92pgYCCOr0mqPEdldHV1Ua5cOeTk5CA/Px/x8fEIDg6WC8heV7DSr1KlSujUqRNkMpnc8ZiYGFSvXh1eXl5FXtfAwAADBgzAsWPH8O+//4rt0dHRaN++PUxMTBAcHAypVKryvbxu6dKlCA0NhUwme6eAjIiIiIiIiIhUx3yJ+RLzJSIiIiKi98edsIiISjmpVCquNPPx8UFGRgaOHDkCd3d3sU+LFi2grf3fvNvk5GQ0bNiw0OPAqxWQBTIyMmBoaCh3/IsvvsDevXuhq6uLmJgY9OvXDz/88AM+//xzuLm5ITAwEPXr1wcANGvWDOPGjUP37t0xYMAANGnSBG3atEGvXr1QpUoVpfeVnp6OKVOmwNDQUFytp8q9qiovLw9xcXE4d+4cvv7660L7vXnvhoaGuHv3rsrHVaXKc3xTTk4O5s2bh4yMDLRp0wb3799Heno6HBwc5Po1atQIqampAICOHTuK2+OHhYWhXbt2uHbtGmxsbCAIAmJjY9G7d2+Fz4Myjo6OAIC0tDRYWFggPz8fMTExWLx4MQAgMDAQo0aNEsdX1cWLFzF48GBIpVKVt97Pzs5WCBd9q9aFthZXK6oTdxdTLz5P9fO2aqDpEoiIiIiISiXmS8yXmC8pz5fycnOYL6nRnTzFr66k96NjVlHTJZQ5eY8ea7oEIiIqxTgJi4ioFEtNTcWpU6ewdetWAK+CloCAAEilUrngKD4+Hk5OTuJra2truXHePP4mIyMj/Pbbb3JtBasIAcDPzw/t27dHcnIyTpw4gb1792L27NlYtWoVQkJCAADTpk3DyJEjcejQIZw8eRI//PADpk+fjp9//hn16tUTxyoI7J49ewZbW1vEx8ejSpUqKt/r2yxbtgyrVq1CTk4OdHR0MGLECHzzzTcq3/ub4dHbjheHKs8RAMLDwzFhwgS8ePEChoaGmDlzJtq3b4979+4pHXfr1q3IyclBeHg4nj//L+jw9PRE9erVER0djcmTJ+Onn37CjRs3EBoaqlK9BatEC1Y/Hjx4EM+ePcOXX34J4NVqSE9PT8hkMkyZMkXl51C9enWYmppizpw5aNeuHapWrfrWc2bMmIGoqCi5Nhs4wQ4uKl+XiIiIiIiI6FPEfIn5EvOlV5gvfXg9h4zSdAllTu29FzRdQpnzT1NNV0BERKUZJ2EREZViUqkUubm5cluDC4IAiUSCJUuWiG3W1taoXbt2oeO87bi2tnaRxwFAX18fnp6e8PT0xHfffYe+ffti0qRJcuGOubk5unXrhm7dumH69Olo2LAh5s6di9jYWLFPfHw8nJ2dYW5uDlNTU5Xv1cTEpMj6CvTo0QPjx4+HgYEBqlat+tZQ6233rsqzKQ5VnuOYMWMQEhICQ0NDVKlSRQypKleuDFNTU3FVYoEaNWoAeBXopaeny9UeEhKC2NhYREZGIjo6Gq1bt4atra1KtV68eBEAUKtWLQCv3qNHjx7JBaj5+fk4d+4coqKiVA4QjYyMkJiYCE9PT7Ru3RqHDx9+a1AWERGBkSNHyrX5moSodD0iTeGuTUREpV8+Pt7XNBEREX0ozJeYLzFfeoX50od3ZPkKTZdQ5jBfIiIq/ZgvlS2chEVEVErl5uZi9erVmDdvHry8vOSOde7cGXFxcfDx8dFQdYCzszO2bdtW6HE9PT3Y2dnh2bNncu3W1taws7OTa1PlXgcMGKBSXSYmJmoNtT40Zc+xUqVKSu9BW1sb/v7+WLt2LSZOnCgXKBYmNDQUU6dOxZYtW7B161asWrVKpbqeP3+OFStWoFWrVqhcuTIePnyI7du3Y8OGDXBx+W91YF5eHlq2bIkDBw4U6/NYsWJFJCYmwsvLC+7u7jh8+HCR9yORSCCRSOTauFU8lXT8OkL1Y/BIRERERFQ8zJdeYb70H+ZLzJc+pC/bdtN0CWXO4Cu7NV1CmbOktr2mSyAiolKMk7CIiEqpXbt24fHjxwgLC1NYpefn5wepVKpyKPHw4UPcvXtXrs3U1BT6+voAXq0IfPM4AFhYWODx48fo1q0b+vTpg/r168PIyAinT5/G7Nmz0alTJ7HWDRs2IDAwEHXq1IEgCNi5cyf27NmD6OhotdyrqiGZpuXl5eHs2bNybRKJBBYWFm99jqqYPn06kpKS0KRJE0yePBmNGzdGhQoVcO7cOaSkpKBu3bpy/W1sbNCmTRt8/fXXkEgk6NKli9Jx//33X7x48QJPnjzBr7/+itmzZ+PBgwfYsmULAGDNmjUwNzeHv7+/uHKywJdffqnwefzjjz9gZGQkvtbS0kKDBvKTJ0xNTXHw4EF4e3vD3d0dSUlJKgV/RERERERERKQa5kvy98p86RXmS/Sh/PtFJU2XUObMuNxO0yWUOUa4oukSiIioFOMkLCKiUkoqlcLDw0PpNul+fn6YPXs2MjMzVRrLw8NDoS0uLg6BgYEAgMzMTKXbdd+5cwcVK1ZE06ZNsWDBAly9ehUvX76EtbU1+vXrh3HjxgF4tdqufPnyGDVqFG7evAmJRAJ7e3usWrUKPXv2VMu9njt3DvXr11fpfjXp6dOnaNiwoVybnZ0dLly48NbnqApzc3OcOnUKs2bNwpw5c3Dt2jVoa2vD3t4eAQEBGD58uMI5YWFh+OmnnzBw4EAxGH2Tg4MDtLS0YGhoCFtbW3h5eWHkyJGwtLQEAMhkMvj6+ioEZMCr96hnz5548OCB2NaqVSu5Pjo6OsjNzVU418TERFzl6ObmhqSkJFSrVk3l50HqxV2G1Is7Yakfn6n68c89ERERUdnGfEn+XpkvvcJ8iT4U8xXHNV1CmZPxvLmmSyhztA8/13QJZUp+61uaLoGI6KPSEgSBXzBJREREZY6nNrc3p5KNE4aoNOAkLCrpDuYnaPT66betNXr90sTU6qamSyAiIiIqNuZLVNLp1qqp6RLKnHGHtmm6hDJlsm3Dt3ci0jDmS6VHaciXuBMWEREREamEk4aopOOEISL62PLAdW1ERERERKrSrW2r6RLKnJH7d2i6hDKHk4aI6GNjvlS2cBIWERERlUmcMEQlHScMEREREREREZVsVxY103QJZYpWnuJXTdL7mW1XV9MlEBER0Ws4CYuIiIjKJE5wIfr0cPKl+vHfpURERERE9CmrPeyEpksgoo+M+ZJ6MVsiok8NJ2ERERERkUoYQFBJx1CHiIiIiIiIqOR62L+Fpksoc3Q73dd0CWWOt5WmKyAiotKMk7CIiIiIiDSAE4aIiIiIiIiI6FNyetJyTZdQ5jBfIiIiKlk4CYuIiIiIVMJQR724s5j68TNKREREREREVHLx93b1Y76kfvycEhHR++AkLCIqVVJSUtCyZUv4+Phg9+7dYntaWhpsbGxw5swZfPbZZ3LnuLu747PPPsPChQvF10eOHAEASCQS2NraYvDgwRg4cKB4Tk5ODhYtWoS4uDikpqZCV1cXtWrVQseOHTFw4EBYWb3ajzYkJATp6enYtm1boTWfOXMGM2fOxM8//4xHjx7B0tIS9erVQ//+/dGhQwdoaWmJ9RcwMzNDo0aNMGvWLDRs2BD16tWDq6srfvjhB4Xx16xZg759++LWrVs4f/48WrduLR6zsLBAy5YtMWfOHNja2ortcXFxCA4OxoABA7B06dJCa58xYwYmTJiAmTNnYsyYMQCAzZs3w9/fHzdu3EC1atUUzrG3t0fHjh0xf/58lZ41ADg6OuLatWu4fv06LC0tldby/PlzVKtWDdra2rh16xYkEonSfv/88w9sbW1Rp04dnD9/vtB7K+yzBOCt70eBCxcuICoqCocPH0ZmZiZq1qyJwMBAfPvttyhfvrzYr1atWrh+/ToAwMDAAHZ2dhg2bBj69u2rUNfb3pvMzEzMmTMHW7Zswd9//43y5cvD1tYW3bp1Q79+/VCxYkWVx0tKSnrvz8ubY7zuzp074vupat1vfmZq1KiB0NBQfPvtt9DS0lJ6Hfp4GOqoFwMdIqLSLx+CpksgIqJiYrbEbInZErMl0hxmS+rHfImIqPRjvlS2cBIWEZUqUqkUQ4YMgVQqxe3bt8XAqrj69euHyZMnIysrC6tXr8agQYNQsWJFBAUFITs7G15eXjh37hyioqLg6uqKypUr49q1a4iLi8PixYsxY8YMla6zfft2+Pv7w8PDA7Gxsahduzays7Nx/PhxTJgwAV988QVMTU3F/omJiXBxccE///yDoUOHol27dvjrr78QFhaGyMhILFiwAAYGBnLXiI6OxldffYVKlSqJbampqTAyMsLly5fx9ddfo2PHjjh37hx0dHTE5zh27Fj8+OOPmDdvHvT19ZXWL5PJMHbsWMhkMjEo++qrr2Bubo7Y2FiMGzdOrv/PP/+MK1euICwsTKVnDQBHjx7F8+fP0bVrV8TGxiI8PFxpLZs3b4aLiwsEQcC2bdsQEBCgtF9MTAz8/f3x888/4+TJk2jatKnSfqp8lgp7P0xNTXHixAl4eHjAw8MDu3fvRpUqVXDq1CmMGjUKP/30Ew4fPgw9PT1xrMmTJ6Nfv37IyspCQkIC+vXrh2rVqqFdu3YKdRX23jx69AgtW7ZEZmYmpkyZgkaNGsHExASpqamIjo7G+vXrMWjQIJXHK6COz0tqaiqMjY3l2iwsLN6p7oLPTHZ2Ng4dOoSvv/4apqam+Oabb5S+l4VhqENERERERERvYrbEbInZErMlIiIiIqIPhZOwiKjUePr0KeLj43H69GncvXsXMTExCkGNqsqXLy+uooqMjMT69euxY8cOBAUFYcGCBTh69ChOnz4ttzKtRo0acHNzgyCoNhv52bNnCAsLQ/v27bFlyxa5Y05OTggLC1MYy9zcHJaWlrC0tMTcuXPh6uqKkydPIjg4GOHh4di8eTOCg4PF/teuXUNSUhL27NkjN46FhQVMTU1RtWpVTJw4ET169MCVK1fg4OCAa9eu4fjx49i8eTMOHz6MLVu2oHv37gr1HzlyBM+fP8fkyZOxevVqHD9+HC1atEC5cuXQs2dPpc9fJpOhadOmcHFxUelZA69CmO7du8PNzQ3Dhg0rNCiTSqUIDg6GIAiQSqVKgzJBEBAdHY1ly5ahevXqkEqlSoMyVT9Lhb0fXl5eCAsLg5OTE7Zs2QJtbW0AQM2aNVGnTh00bNgQCxYskLsXIyMj8TmEh4dj9uzZOHjwoFxQ9rb3Zty4cbhx4wYuXbokF+zVrFkTXl5eCp8nVd9rdXxeCsZQprh1v/6ZCQ0NxZIlS3Dw4MFiB2VcBaZ+nNhGRERERESlGbMlZkvMlpgtERERERF9SNqaLoCISFUbN26Eo6MjHBwcEBwcDJlMpnJo9TYGBgbIyckB8Gp7bE9PT7mQ7HWqblt94MABPHz4EGPHji20T1FjFaxKzMnJQaVKldCpUyfIZDK5PjExMahevTq8vLxUGgd4tbqxffv2MDExQXBwMKRSqdLzpFIpgoKCUK5cOQQFBcn1CwsLw+XLl/Hzzz+LbU+fPsWmTZvkVioWVk9BLU+ePEFCQgKCg4Ph6emJjIwMJCcnK5xz9epVpKSkwN/fH/7+/khOTha3YH/d4cOHkZWVBQ8PDwQHB2PDhg149uyZQr93+Sy9/hzPnj2LP//8EyNHjhRDsgINGjSAh4cH4uLilI6Tn5+PzZs34/Hjx3KrGYGi35v8/HzEx8cjODi40FW6b36eVH2vC7vPdx3jzfstbt0FBEFAcnIy/vrrL4VnRURERERERFRczJaYLTFbYrZERERERPQhcRIWEZUaBavVAMDHxwcZGRk4cuSIXJ8WLVrA0NBQ7kdZ8FIgLy8Pa9euxblz59CmTRsAwKVLl+Dg4CDXz9fXVxyvRYsWKtV76dIlAJAb65dffpGrbdeuXUrPTU9Px5QpU2BoaIgmTZoAeBVOJSUl4dq1awBehQixsbHo3bu3QlhT4M6dO5g7dy6qVasGBwcH5OfnIyYmRnyOgYGBOHr0qDhmgczMTGzatEnsFxwcjI0bN+Lp06cAAGdnZzRr1kwuuNu4cSMEQUBgYKDSWpQ96w0bNsDe3h4uLi7Q0dFBYGCg0iBGJpOhXbt2qFixIszMzODt7Y3o6GiFflKpFIGBgdDR0UHdunVha2uLhIQEpf3e9ll63ZvvR8F76+TkpLS/k5OT2KdAeHg4DA0NIZFI0LVrV1SsWBF9+/YVj7/tvbl//z7S09MVPpuNGjUSP08FK0BVGU+Zd/28AED16tXlPtsFK1aLWzcALFu2THxWrVq1Qn5+PoYOHVpo3QCQnZ2NzMxMuZ98Ia/Ic4iIiIiIiOjTwmyJ2RKzJWZLRWG+RERERETvi19HSESlQmpqKk6dOoWtW7cCAHR1dREQEACpVAp3d3exX3x8vEJ40aNHD4Xxli1bhlWrViEnJwc6OjoYMWJEkdtRL1u2DM+ePcP3338vt0KvuOrXr4+zZ88CAOzt7ZGbmyt3vEWLFtDW1sazZ89ga2uL+Ph4VKlSBQDg6emJ6tWrIzo6GpMnT8ZPP/2EGzduIDQ0VOE61atXhyAIyMrKQoMGDbB582bo6elh//79ePbsGb788ksAQKVKleDp6QmZTIYpU6aI58fFxcHOzg4NGrz6OrfPPvsMNWvWRHx8vLgasU+fPhgxYgQWL14MIyMjyGQydOvWDUZGRgrPrrBnLZPJ5LbADw4Ohpubmzgm8Cpgi42NxaJFi+T6jR49GhMnThRDwvT0dGzZsgVHjx6V6yeVShESEiK2qfpZetv7AaDIFY5vrq4bM2YMQkJCcOfOHYwZMwYDBw5E7dq1xeMHDx5U6b1509atW5GTk4Pw8HA8f/78ncZ7388LACQnJ8u99+XKlSu05qLqBl79mR0/fjweP36MSZMmoUWLFm8NqGfMmIGoqCi5Nhs4wQ4uhZxB74Jf8ahe/HpH9eNnlIiIiIgKw2yJ2RKzJeWYLf2H+dKHx9/b1Y/5kvrxc0pERO+Dk7CIqFSQSqXIzc2V23JaEARIJBIsWbJEbLO2tpYLH4D/tsB+XcEv4gYGBqhatarcaj97e3ukpqbK9a9atSoAwMzMTOWa7e3tAbwKZpo1awYAkEgkCvW9Lj4+Hs7OzjA3N4epqancMW1tbYSEhCA2NhaRkZGIjo5G69atYWtrqzBOcnIyjI2NYWFhIRdeSKVSPHr0SO6Z5Ofn49y5c4iKihKfg1QqxYULF6CrqyvXTyaTiUFZYGAgRowYgY0bN6JVq1Y4duwYZsyYoVBLYc/6zz//xIkTJ3Dq1CmEh4eL/fPy8rBhwwb069cPALB//37cunULAQEBcuPm5eXhp59+gqenJwBg/fr1ePHiBZo2bSr2EQQB+fn5uHTpEurUqSPeW1GfJRMTk7e+HwXv7cWLF5V+tcDFixfF6xWoVKkSateujdq1ayMhIQH16tVD48aN4ezsrNJ7U7lyZZiamip8NmvUqAEAMDIyQnp6utiu6nsNvP/nBQBsbGwUPrMAil03AJiYmIh/TjZu3IjatWujWbNm8PDwUBi/QEREBEaOHCnX5msSUmh/IiIiInXIU9NXWBER0YfHbInZErMlZktFZUsA8yUiIiLSDOZLZQsnYRFRiZebm4vVq1dj3rx58PLykjvWuXNnxMXFwcfHp1hjvv6L+JuCgoIwYcIEnDlzRmkIoiovLy+YmZlh1qxZ4sq4t7G2toadnV2hx0NDQzF16lRs2bIFW7duxapVq5T2UxZaPHz4ENu3b8eGDRvE7byBV4FTy5YtceDAAfj4+OCPP/7A6dOnkZSUJBcMPnr0CO7u7vjrr7/g6OgIIyMjdOvWDTKZDFevXkWdOnXwxRdfKNRS2LOWSqVo1aoVli5dKtceHR0NqVQqBmUF28CPHz9ert+0adMglUrFoEwqlWLUqFFyKxMBYODAgZDJZJg5c6ZKn6UBAwaIbYW9Hw0bNoSjoyMWLFiAwMBAucDo999/R2JiolyA+yZra2sEBAQgIiIC27dvV/m98ff3x9q1azFx4kS5oO9Nqo5X4H0+L2+jra2tct3KGBoaYtiwYRg9ejTOnDkDLS0tpf0kEgkkEon8tbV0inUtejuurFMvrqojIiIiIvo4mC39h9nSf5gtMVt6E/OlD4/ZkvoxXyIiIipZOAmLiEq8Xbt24fHjxwgLC5NbSQYAfn5+kEqlxQ7KijJixAjs3r0bbdu2xaRJk/DFF1+gYsWKuHTpEvbu3QsdHflfvDMyMsRt4AuYm5vD2toaq1atQkBAANq3b4+hQ4fC3t4eT58+xb59+wBAYay3sbGxQZs2bfD1119DIpGgS5cuKp+7Zs0amJubw9/fXyFs+PLLL8XnKJVK0aRJE7Rq1UphjP/973+QSqWYM2cOACAsLAxffPEFLl68KLfi8G1evnyJNWvWYPLkyahbt67csb59+2L+/Pm4cOECLCwssHPnTuzYsUOhX69eveDr64tHjx7hxo0b+O2337Bu3To4OjrK9QsKCsLkyZMxdepUlT5LrwdlhdHS0sKqVavg5eUFPz8/REREwNLSEidPnsSoUaPg7e2N/v37FznGsGHDULduXZw+fRpHjx5V6b2ZPn06kpKS0KRJE0yePBmNGzdGhQoVcO7cOaSkpIjPSNX3uijFHePff//Fixcv5PqZm5ujXLlyKtddmP79+2PKlCnYvHkzunbtWmRfIiIiIiIiojcxW/oPs6X/MFtitkREREREpG6chEVEJZ5UKoWHh4dCsAG8Cjdmz56NzMxMtV1PX18fP/30ExYuXIjo6GhEREQgPz8fNjY2aNeuHUaMGCHXPykpSWFVY1hYGFatWgVfX18cP34cs2bNQq9evfDo0SOYmJigcePG2LBhAzp06FDs+sLCwvDTTz9h4MCB0NfXV/k8mUwGX19fpau9/Pz80LNnT/z7779Yu3ZtoaGXn58f5s2bh+nTp6NcuXJo2bIlHBwccOXKFfTq1UvlWnbs2IGHDx/C19dX4ZiTkxOcnJwglUpRrVo1VKhQAW3btlXo17ZtWxgYGGDt2rW4fPkynJ2dFUIyAPD19cXgwYOxZ88elT5L586dg7Gx8VvvwdXVFSdOnEBUVBTatWuHR48eAQAGDx6MBQsWvDUEdXZ2hpeXFyZOnIh//vnnre/NgwcPUKlSJZw6dQqzZs3CnDlzcO3aNWhra8Pe3h4BAQEYPnw4ANXe6wcPHhRZX3HHcHBwUOiXkpKCZs2awdzcXKW6C2NmZoZevXohMjISXbp0kVsdSlSacfWn+nH1JxEREREpw2xJHrOlV5gtMVuij+/Luu6aLqHMWXljp6ZLKHP61Wip6RKIiKgU0xIEfsEkERHR+8rPz0dYWBj279+PI0eOwN7eXtMlffI8tbtpuoQyh5OG1IsThoiI3t/B/ASNXv/ureJ9Hc6nzLLabU2XQEREVKIxWyqZmC+pF7Ml9WO+RET0/pgvlR6lIV/iTlhERERqoK2tDalUisWLFyM5OZlBGZVJDHXUi8EjlQb8c09ERERE9HEwW6JPAX/HVD+tYn4tLb3dzhunNF1CmdKhWiNNl0BE9FFxEhYREZGaaGtrY9iwYZoug+iD4aQhKukY5hLRx5av6QKIiIioTGG2RGWdtp6epksoc7ZcO6rpEsqcDtWaaLoEIvrEMF8qWzgJi4iIiIiIiIiIiIiIiIg+qL1pv2i6hDLH24oThoiIiEoSTsIiIiIiIpVwlyH14s5iRERERERERPQpYbakfsyX1I+fUyIieh+chEVEREREKmGoo14MdIiIiIiIiIjoU8JsSf2YLxEREZUs2pougIiIiIiIiIiIiIiIiIiIiIiIqDTjTlhEREQqSElJQcuWLeHj44Pdu3eL7WlpabCxscGZM2fw2WefKZwXExOD4cOHIz09Xem4ISEhSE9Px7Zt28S2TZs2ITg4GNOmTUNSUhJevnyJffv2KZybnJyMVq1aISkpCe7u7khJSUGzZs0U+rVt2xYmJibYsmULQkJCEBsbCwDQ1dWFmZkZ6tevj6CgIISEhEBb+7/52bVq1cLw4cMxfPjwQp/L5s2bsXTpUpw5cwYvXrxAjRo14OrqiiFDhqBhw4biMwgNDYW3t7fcfaSnp6NixYo4fPgw3N3d5cbt378/Vq1ahQ0bNqBbt26FXp+oNOPqT/Xj6k8iIiIiIirJmC8px3zp0+HRo4+mSyhzXH8/qekSypwTDfi/z4mI6N3xvyJEREQqkEqlGDJkCKRSKW7fvg0rK6sPcp1Vq1Zh0KBB+OGHHxAaGgo7Ozv4+fnhn3/+QfXq1eX6RkdHo3HjxnBzc0ODBg0gk8kUQrK0tDQcPnwYO3fuFNt8fHwQHR2NvLw83Lt3D/v27cOwYcOwadMm7NixA7q6qv31IDw8HPPmzcPQoUMRFRWFmjVr4v79+9i7dy8iIiLkAjFdXV0kJibi8OHDaN26dZHjZmVlYcOGDRg7dixkMhlDMiqzOGGIiKj0y4Og6RKIiIioFGG+pIj50qclcZ1M0yWUOcyXiIhKP+ZLZQu/jpCIiOgtnj59ivj4eHzzzTdo3749YmJiPsh1Zs+ejSFDhmDDhg0IDQ0FAHTo0AGVK1dWuObTp0+RkJCAsLAwAEBYWBji4+ORlZUl1y8mJgZVq1aFj4+P2CaRSGBpaYlq1arh888/x7hx47B9+3bs3btX5Xs7ceIEZs+ejfnz52P+/Pn44osvUKNGDTRq1AgTJkzA3r175fpXqFABffr0wbfffvvWsRMSEuDs7Ixvv/0WP//8M27evKlSTUREREREREREJRXzJUXMl4iIiIiorOFOWERERG+xceNGODo6wsHBAcHBwRg+fDgiIiKgpaWltmuEh4dj2bJl2LVrF9q2bSu26+rqolevXoiJicH48ePFayYkJCAvLw9BQUEAgB49emDMmDHYtGkTevXqBQAQBAGxsbEICQmBjo5Okddv06YNGjRogC1btqBv375vrTcuLg6GhoYYOHCg0uPKnk1kZCRq166NTZs2oWvXroWOLZVKERwcDBMTE7Rr1w4xMTH47rvv3lrTm/hVb0RERERERERUUjBfUlQa8qVeqZy8pU7t2gVpuoQy6E9NF0BERESv4SQsIiKitygIbYBXW61nZGTgyJEjcHd3V8v4e/fuxfbt2/HTTz+hTZs2Csf79OmDOXPmyF0zOjoafn5+MDExAQCYmZnB19cXMplMDMkOHz6MtLQ0cdXj2zg6OuLcuXMq9b106RJsbW3ltpafP38+Jk6cKL6+deuWWB8AWFlZYdiwYRg/fjw6d+6sdNzLly/jxIkT2LJlCwAgODgYI0eOxIQJE4oMJbOzs5GdnS3X5lu1LrS1ig4HqXg4sY2IiIiIiIjo3TBfUlQa8qWYOlbMl9Ro/+04TZdQ5vDrCImIiEoWTsIiIiIqQmpqKk6dOoWtW7cCeLVyMCAgAFKpVG0hWf369fHgwQNMmjQJTZo0gaGhodxxR0dHtGjRAjKZDO7u7rhy5QqSk5MxefJkuX59+vSBt7c3rl69Cjs7O8hkMri5uaF27doq1SEIwnutvuzTpw+++uornDx5EsHBwRAExe+wDg8Px48//giZTAZ/f3+F4zKZDN7e3qhUqRIA4Msvv0RYWBgOHTokt4LzTTNmzEBUVJRc28RRZpg02vyd74eIiIiIiIiISB2YL6mupOVLtlousNOp+873Q/JeCrmaLoGIiIjog9LWdAFEREQlmVQqRW5uLqysrKCrqwtdXV0sX74cmzdvRkZGhlquUa1aNSQlJeHWrVvw8fHBkydPFPqEhYVh8+bNePLkCaKjo2FnZwc3Nze5Pm3btkWNGjUQExODzMxMbNmyBWFhYSrXcfHiRdjY2KjU197eHn///TdevnwptpmamqJ27dqoVq1aoeeZmpoiIiICUVFRyMrKkjuWl5eH2NhY7N69W3zW5cuXx6NHjyCTyYqsJyIiAhkZGXI/3w6pqNK9EBERERERERF9SMyXlCsN+VItOELIF/ijph8dLW3+qPmHiIiIShbuhEVERFSI3NxcrF69GvPmzYOXl5fcsc6dOyMuLg4+Pj5quVbNmjVx5MgRtG7dGj4+Pti3bx+MjIzE4/7+/hg2bBjWr1+P1atX45tvvlFYVaitrY3Q0FBIpVJUq1YNenp66Nq1q0rXP3ToEP744w+MGDFCpf5BQUFYvHgxli1bhmHDhql+owCGDBmC77//HosWLZJr37NnD548eYIzZ85AR+e/bd7Pnz+P0NBQpKenw9TUVOmYEokEEolErs3TtGGx6iL62Pj1jurHLfiJ6GPLU9yYgYiIiEgO86XClYZ8SRtagJBfrNqocO2smNepG/Ml9WO+REQfG/OlsoWTsIiIiAqxa9cuPH78GGFhYTAxMZE75ufnB6lUKoZkqampCue7uLgAeLUC7+zZs3LHJBIJnJyc5Nqsra2RlJSE1q1bw9vbG/v27YOxsTEAwNDQEAEBAYiIiEBmZiZCQkKU1hwaGorJkydj3LhxCAoKgoGBgUKf7Oxs3L17F3l5ebh37x727duHGTNmoEOHDujVq5dc31u3binUXrNmTTRv3hyjRo3CqFGjcP36dXTp0gXW1ta4c+cOpFIptLS0oK2tfCWWvr4+oqKiMGjQILl2qVSK9u3bo0ED+V9ynZ2dMWLECKxbt07hnKIwgKCSjoEOERERERFR2cd8qXTnS9pK7p3eXfzlw5ouoczxtmqm6RKIiIjoNZyERUREVAipVAoPDw+FgAx4FZLNnj0bmZmZAIDAwECFPjdv3gQAPH36FA0byq/ysrOzw5UrVxTOqV69ulxQtn//fjEoCwsLg1QqxZdffgkrKyulNdeoUQMeHh44cOAA+vTpo7TPvn37ULVqVejq6qJixYpo0KABvv/+e/Tu3Vsh2Jo7dy7mzp0r17ZmzRoEBwdj7ty5aNKkCZYvXw6ZTIasrCxUqVIFrVq1QkpKili3Mr1798a8efPw559/AgDu3buH3bt3Y/369Qp9tbW14evrC6lUWqyQjBNc1I8T24iIiIiIiIiKh/lS6c6X8p8/V7kvvZ2xNie1ERERUdmmJQgCNzcjIiKiMsdTu5umSyhzOAlLvThRkIjo/R3MT9Do9dP+qarR65cmtarf0XQJRERERMXGfEm9mC2pH/MlIqL3x3yp9CgN+ZLyfVyJiIiIiIiIiIiIiIiIiIiIiIhIJZyERURERERERERERERERERERERE9B50NV0AEREREREREVFplK/pAoiIiIiIiIiIqFRjvlS2cCcsIiIiIiIiIiIiIiIiIiIiIiKi98BJWERERERERERERERERERERERERO+BX0dIRERERCrxtmqg6RLKlP23f9d0CWUOP6NEREREREREJRd/b1c/5kvqx88pERG9D07CIiIiKiVSUlLQsmVL+Pj4YPfu3WJ7WloabGxsxNdmZmZo1KgRZs2ahYYNG4rtV65cwfTp05GYmIh79+6hUqVKcHR0RJ8+fRAQEABd3Vd/LdDS0sLWrVvRuXNnhRqSkpLQunVrPH78GKampgAAd3d3HDlypNC63dzckJSUhFq1auH69esAgPLly8PBwQERERHo1q0bACAyMhJRUVEAAG1tbVhZWaFdu3aYOXMmzMzM3u2hkVox1CEiIiIiIiIq3ZgvkSb9O7SFpksocxqcctZ0CWWOJf7UdAlERFSKcRIWERFRKSGVSjFkyBBIpVLcvn0bVlZWcscTExPh4uKCf/75B0OHDkW7du3w119/wdTUFKdOnYKHhwdcXFywdOlSODo6AgBOnz6NpUuXom7dumjQ4N1W+GzZsgU5OTkAgJs3b6JJkyZiLQCgp6cn9p08eTL69euHzMxMzJs3DwEBAahWrRpatHgVwLi4uCAxMRF5eXm4ePEi+vTpg4yMDMTHx79TbURERERERERE9B/mS6RJlkfSNV1CmRM8aL+mSyhzVsNa0yUQEVEpxklYREREpcDTp08RHx+P06dP4+7du4iJicG4cePk+pibm8PS0hKWlpaYO3cuXF1dcfLkSXh5eSEkJAR16tTBsWPHoK2tLZ5jb2+PoKAgCILwzrW9vorwxYsXcrW8ycjISKxx6dKlWLt2LXbu3CmGZLq6uuJ51apVQ7du3RAdHf1OdXHXJvXjVtxERETy8qCl6RKIiIiIVMZ8qfiYL6mXt9Xb+1DxrHbghCEiotKO+VLZov32LkRERKRpGzduhKOjIxwcHBAcHAyZTFZksGVgYAAAyMnJwdmzZ3Hx4kWMHj1aLiB7nZbWx/8Lnq6uLsqVKyeucnxTWloa9u/fL7fSkYiIiIiIiIiI3g3zJSIiIiKiD4s7YREREZUCUqkUwcHBAAAfHx9kZGTgyJEjcHd3V+ibnp6OKVOmwNDQEE2aNEFSUhIAwMHBQezz77//wtbWVnw9e/ZsDBw48IPew+tycnIwb948ZGRkoE2bNmL7H3/8AUNDQ+Tl5YmrHufPn//W8bKzs5GdnS3X5lu1LrS1dNRb+CeOqz/VizuLERERERER0cfEfKlozJc+PGZL6sd8iYiIqGThTlhEREQlXGpqKk6dOoWgoCAAr1b4BQQEQCqVyvVr0aIFDA0NUbFiRfz++++Ij49HlSpVlI5pbm6Os2fP4uzZszA1NS10taC6hYeHw9DQEOXLl8esWbMwc+ZMtG/fXjzu4OCAs2fP4pdffkF4eDi8vb0xZMiQt447Y8YMmJiYyP1cw18f8laIiIiIiIiIiEoN5kvMl4iIiIjow+NOWERERCWcVCpFbm4urKysxDZBECCRSLBkyRKxLT4+Hs7OzjA3N4epqanYbm9vD+BV2NawYUMAgI6ODmrXrg3gVej2sYwZMwYhISEwNDRElSpVFLap19PTE+sqCNCioqIwZcqUIseNiIjAyJEj5dp8TULUWjuRunH1p/px9ScREREREZFyzJeYL5UE7Rt5a7qEMmf+9a2aLqHMGVmzuaZLICKiUoyTsIiIiEqw3NxcrF69GvPmzYOXl5fcsc6dOyMuLg4+Pj4AAGtra9jZ2SmM0bBhQzg6OmLu3Lnw9/eHtrbmNsKsVKmSGIKpYsKECWjTpg2++eYbuZDwTRKJBBKJRK6NW8VTSccJQ0REpV++oOkKiIiIiN6O+RLzpZJi96/7NV1CmeNtxQlDRESlHfOlsoWTsIiIiEqwXbt24fHjxwgLC4OJiYncMT8/P0ilUjEkK4yWlhaio6Ph6ekJV1dXREREwMnJCS9fvsTPP/+M+/fvQ0dHPlC6du0azp49K9dWsOIRAP744w8YGRnJXaNBA/VPKGnevDnq16+P6dOny63KJCoLuBOW+nFiGxERERERkSLmS8yXSor2zTtouoQyJ+raNk2XUOZMsmmk6RKIiKgU4yQsIiKiEkwqlcLDw0MhIANehWSzZ89GZmbmW8dp1qwZfv31V0yfPh2DBg3C3bt3UaFCBTRo0AALFixAnz595Pq/ufU6ACQnJ4v/3KpVK7ljOjo6yM3NVfW2imXEiBEICQlBeHg4rK2tP8g1SDWc4KJenISlfnym6sc/90REREREpR/zJeZLJUXu9ZuaLqHMibRvpukSypwdt1I0XUKZ8lW1xpougYjoo9ISBIGbmxEREVGZ46ndTdMllDmc4KJenNxCRPT+DuYnaPT6qTcL/zobkudgfVvTJRAREREVG/Ml9WK2pH7Ml4iI3h/zpdKjNORL3AmLiIiIiFTCUEe9GDyqHz+jRERERERERCUXf29XP+ZL6sfPKRERvQ9OwiIiIiIilTDUUS8GOkRERERERET0KWG2pH7Ml4iIiEoWTsIiIiIiIiIiInoHedDSdAlERERERERERFSKMV8qWzgJi4iIiIhIA7j6U/24+pOIiIiIiIio5Gpn01TTJZQ5m/85oukSyhy/6vycEhHRu+MkLCIiIiIiIiIiIiIiIiL6oLQrm2u6hDInNVdb0yUQERHRazgJi4iIiMok7jJEJR13bSIiIiIiIiKiT8nuU3s0XUKZ4231P02XQERERK/hJCwiIipVUlJS0LJlS/j4+GD37t1ie1paGmxsbMTXFStWRL169TB16lR88cUXYntkZCSioqIAADo6OqhevTp8fX0xZcoUGBoaiv369++PVatWYcOGDejWrRsAYN68eZg6dSru3LkDfX19ubqysrJgaWmJqVOnYujQoahVqxaGDx+O4cOHK9xDQa2VK1fG1atXYWRkJB777LPP0LlzZ0RGRgIA3N3d8dlnn2HhwoVKn4eW1n/fE12+fHlYWVnB1dUVQ4YMQaNGjcRjSUlJaN26NZydnXHu3Dno6OiIx0xNTbFw4UKEhISIbWfOnMHMmTPx888/49GjR7C0tES9evXQv39/dOjQAVpaWuJ9nDlzBp999pl4bmxsLJYsWYILFy5AR0cHn3/+OcaMGYMOHToo1PP48WOYmpqKrwvuycjICLa2tvD09MSIESNQtWpVpfdfFE5wUT9ObFMvPk/14597IiIiIiJSBfMlecyX6GNp3aevpksocz779YymSyhz/myUq+kSiIioFOMkLCIiKlWkUimGDBkCqVSK27dvw8rKSu54YmIiXFxc8ODBA0ybNg0dOnTApUuXUKVKFbGPi4sLEhMTkZubi2PHjqFPnz7IysrCjz/+COBV4LVhwwaMHTsWMplMDMl69uyJiIgIbNmyBd27d5e77qZNm5CTk4Pg4GCV7+XJkyeYO3euGNq9q+joaPj4+ODFixe4dOkSVqxYgaZNm0Imk6FXr15yff/++2+sXr0aoaGhhY63fft2+Pv7w8PDA7Gxsahduzays7Nx/PhxTJgwAV988QVMTU2Vnjt69GgsWbIEU6dORefOnfHy5UusXbsWnTp1wqJFizB48OAi7yU1NRXGxsbIzMzEb7/9htmzZ0MqlSIpKQn16tUr9rMhIiIiIiIiInoT8yVFzJfoY1jwwxJNl1DmjHf11XQJZdBtTRdARESlGCdhERFRqfH06VPEx8fj9OnTuHv3LmJiYjBu3Di5Pubm5rC0tISlpSXGjRuHDRs24OTJk/jqq6/EPrq6urC0tAQABAQE4KeffsKOHTvEkCwhIQHOzs749ttvYWVlhZs3b8La2hoWFhbo2LEjZDKZQkgmk8nQuXNnmJmZqXw/Q4YMwfz58zFo0CBYWFi862OBqampeD+1atWCl5cXevfujcGDB6Njx46oWLGi3DUnTZqE7t27QyKRKIz17NkzhIWFoX379tiyZYvcMScnJ4SFhUEQBKV1nDhxAvPmzcP333+PIUOGiO3Tpk3DixcvMHLkSHTq1AnW1taF3ouFhYV4P3Xq1EGnTp3QsGFDfPPNNzh69GixngupH3cZUi/uhEVEVPrlQevtnYiIiKhEYb6kHPMl+hjCazXVdAllzv7bezVdQpnDDJSIPjbmS2WLtqYLICIiUtXGjRvh6OgIBwcHBAcHQyaTFRrYPH/+HKtXrwYA6OnpFTmugYEBcnJyxNdSqRTBwcEwMTFBu3btEBMTIx4LCwvDoUOHcP36dbHt77//xs8//4ywsLBi3U9QUBBq166NyZMnF+s8VYwYMQJPnjzBwYMH5dqHDx+O3NxcLF68WOl5Bw4cwMOHDzF27NhCx359i/rXxcXFwdDQEP3791c4NmrUKLx8+RKbN28uxl28em8GDBiAY8eO4d9//y3WuUREREREREREb2K+pDrmS0RERERExcNJWEREVGoUhFcA4OPjg4yMDBw5ckSuT4sWLWBoaIgKFSpg7ty5aNSoEdq2bVvomL/++ivWr1+PNm3aAAAuX76MEydOICAgAAAQHByM6OhoMYzz9vaGlZUVoqOjxTFiYmJgbW1d5HWU0dLSwsyZM7FixQpcvXq1WOe+jaOjIwAgLS1Nrr18+fKYNGkSZsyYgYyMDIXzLl26BABwcHAQ23755RcYGhqKP7t27VJ6zUuXLsHOzk5pKGllZQVjY2NxfHXcy+uys7ORmZkp95Mv5BX7WkRERERERERUtjFfUh3zJeZLRERERFQ8/DpCIiIqFVJTU3Hq1Cls3boVwKst3wMCAiCVSuHu7i72i4+Ph6OjI86fP4+xY8ciJiYG5cqVkxvrjz/+gKGhIfLy8pCTk4P27dtjyZIlAF5t++7t7Y1KlSoBAL788ktxdWLbtm2ho6OD3r17IyYmBpMmTYIgCIiNjUVoaCi0tYs/t9nb2xstW7bEd999h/Xr17/j01FUEOopW1UYFhaGefPmYdasWZg+ffpbx6pfvz7Onj0LALC3t0dubu5br1uYt60aLWrMwlZIAsCMGTMQFRUl12YDJ9jBpdjXo8Lx6/OIiIiIiIioNGO+VDzMl5gvqduuW79quoQyx8e6iaZLKIM4+ZKIiN4dJ2EREVGpIJVKkZubCysrK7FNEARIJBIx4AIAa2tr2Nvbi2GOr68vzp8/D4lEIvZxcHDAjh07oKurCysrKzG4ycvLQ2xsLO7evQtd3f/+E5mXlweZTCauROzTpw9mzJiBQ4cOIT8/Hzdv3kRoaOg739vMmTPRvHlzjBkz5p3HeNPFixcBADY2NgrHdHV1MW3aNISEhGDw4MFyx+zt7QG8CiWbNWsGAJBIJKhdu/Zbr2lvb4+jR48iJydHIQy7ffs2MjMzUadOnXe+l1q1ahXaJyIiAiNHjpRr8zUJKfa1iD4mb6sGmi6BiIiIiIjok8J8qXiYLzFfUrdyWvzfkuom5HHCEBERUUnCryMkIqISLzc3F6tXr8a8efNw9uxZ8ef333+HlZUV4uLilJ7XtWtX6OrqYtmyZXLtenp6qF27NmrVqiUX5uzZswdPnjzBmTNn5K4TFxeHLVu2ID09HQBgZ2cHNzc3yGQyREdHw8PDAzVr1nzn+2vSpAm6dOmCb7/99p3HeNPChQthbGwMDw8Ppce7desGFxcXhdV9Xl5eMDMzw6xZs4p9zaCgIDx9+hQ//vijwrG5c+dCX19f3IZfVc+fP8eKFSvQqlUrVK5cudB+EokExsbGcj/aWjrFvgciIiIiIiIiKpuYLxUf8yXmS0RERERUPJxyTkREJd6uXbvw+PFjhIWFwcTERO6Yn58fpFIpfHx8FM7T0tLC0KFDERkZif79+6N8+fJFXkcqlaJ9+/Zo0EB+dxpnZ2eMGDEC69atw6BBgwC82nK9X79+AICYmBil4926dUvcZr1AYWHatGnT4OLiIrdCssD9+/cVxqlatSqqVKkCAEhPT8fdu3eRnZ2NS5cu4ccff8S2bduwevVqmJqaFnq/M2fOhLe3t1yboaEhVq1ahYCAALRv3x5Dhw6Fvb09nj59in379gEAdHSUh0/NmzfHsGHDMGbMGOTk5KBz5854+fIl1q5di++//x4xMTEwNzcvtB4A+Pfff/HixQs8efIEv/76K2bPno0HDx5gy5YtRZ5HHwd3blIvfr2j+vEzSkQfW75Q+NfZEBERUcnCfIn5EvMlzePv7erHfEn9+Dkloo+N+VLZwklYRERU4kmlUnh4eCgEZMCrkGz27NnIzMxUem7v3r0xfvx4LFmyBGPHji30Gvfu3cPu3buxfv16hWPa2trw9fWFVCoVQzI/Pz8MHjwYOjo66Ny5s9Ix586di7lz58q1rVmzBi1btlToW6dOHfTp0wcrVqxQOLZ+/XqFuqZMmYIJEyYAgLhVvb6+PqpVq4aWLVvi1KlT+Pzzzwu9XwBo06YN2rRpgwMHDsi1+/r64vjx45g1axZ69eqFR48ewcTEBI0bN8aGDRvQoUOHQsdcuHAh6tevj2XLlmHChAl48eIF9PT0cOjQIbRq1arIeoBXW/lraWnB0NAQtra28PLywsiRI2FpafnWc4mIGDyqH4NHIiIiIiormC8xX2K+RGVRq0Ffa7qEMscm5S9Nl1Cm3G2eoekSiIg+Ki1BEARNF0FERERlU1paGtzc3NC8eXOsW7eu0FWOH4KndrePdq1PBSe4qBcntxARvb+D+Qkavf7vN6w1ev3SpEGNm5ougYiIiEop5ktlB7Ml9WO+RET0/pgvlR6lIV/iTlhERET0wdSqVQtJSUmIjY3F2bNn0ahRI02XRO+BoQ7Rp4cBuXrx36NERERERMXHfKns4O9ERJ8eZkvqx3+XEpVs3AmLiIiIyqT8u3U0XQJRkfjLMhHR++NKxdKjNKxUJCIiInoTd8JSL07GUD/mS0RE74/5UulRGvIl7oRFREREZRIDCPVjUEZERERERERERERERESkHCdhERERERERERG9gzxoaboEIiIiIiIiIiIqxZgvlS2chEVEREREKuHuYurFncXUj59RIiIiIiIiopKLv7erH/Ml9ePnlIiI3gcnYRERERGRShjqEBERERERERHRu7q8tKmmSyhz6qxprukSyhwbpGi6BCIiKsU4CYuIiOgdpKSkoGXLlvDx8cHu3bsREhKC2NjYQvtbWlrCzMwMrq6uWLFihdyxsWPHIiEhAefOncPmzZsRGhqqcP7KlSvRt29fxMTEiMe1tbVhbGyMOnXqoH379hg2bBhMTEzEc+7fv4+JEydi9+7duHfvHipWrIgGDRpg4sSJcHV1BQDUqlUL169fV7jejBkz4OnpicaNGyMlJQXNmjVT6NO2bVuYmJjg5cuXePnyJfbt26fQJzk5Ga1atcLvv/8OY2Nj2NjYiMfMzMzQqFEjzJo1Cw0bNgQAuLu748iRIwAAiUSCGjVqIDQ0FN9++y20tLgdK5UtXFVHRERERET06WK2xGzpU/S374q3d6JiYb5ERERUsnASFhER0TuQSqUYMmQIpFIpbt++jUWLFmHmzJni8apVqyI6Oho+Pj4AAB0dHdy4cQPNmzeHn58fvL29AQAnTpzAggULkJiYCCMjIwCAsbExUlNT5a73egBWcFwQBKSnp+P48eOYMWMGoqOjcezYMVhZWQEA/Pz8kJOTg9jYWNja2uLevXv46aef8PDhQ7mxJ0+ejH79+sm1GRkZoUKFCmjQoAFkMplCUJaWlobDhw9j586dePnyJfz8/PDPP/+gevXqcv2io6PRuHFj1K9fH2lpaQCAxMREuLi44J9//sHQoUPRrl07/PXXXzA1NQUA9OvXD5MnT0Z2djYOHTqEr7/+Gqampvjmm29Ufn+ISgPuLKZ+DB6JiIiIiKi0YLbEbOlT9DT/haZLKHO0JfqaLqHMyc/m55SIiN4dJ2EREREV09OnTxEfH4/Tp0/j7t27iImJwbhx4+TCLAAwNTWFpaWl+Lpy5coYP348wsLCcP78eejr6yM0NBRDhgyBm5ub2E9LS0vuvDe9frxq1apwcnJCx44d4eLigrFjx2Lt2rVIT09HcnIykpKSxLFr1qyJJk2aKIxnZGRU6PXCwsIwYcIELFy4EOXLlxfbY2JiULVqVfj4+EAQBFSuXBkxMTGYMGGC3HNKSEjAnDlz5MY0NzeHpaUlLC0tMXfuXLi6uuLkyZNieFi+fHmxntDQUCxZsgQHDx5kUFYCcIKLenESFhERERER0aeJ2RKzpU+VX3V+HaG67b99UtMllDnMQImI6H1wEhYREVExbdy4EY6OjnBwcEBwcDCGDx+OiIgIlbY0Hz9+PHbu3ImhQ4fCwsICWlpamD59+nvXZGFhgR49ekAmkyEvLw+GhoYwNDTEtm3b0KxZM0gkkncat0ePHhgzZgw2bdqEXr16AQAEQUBsbCxCQkKgo6MDAOjVqxdiYmIwfvx48TkkJCQgLy8PQUFBhY5vYGAAAMjJyVE4JggCjh49ir/++gv29vbvVD+pFycNqRcDHSKi0i8P2pougYiIiEohZkvMlj5VzJbUj/kSEVHpx3ypbOG7SUREVExSqRTBwcEAAB8fH2RkZODIkSMqnaurq4vVq1cjISEBixcvxurVq6GvL79ldEZGhhh0GRoaFrly8XWOjo548uQJHj58CF1dXcTExCA2NhampqZwdXXFuHHjcO7cOYXzwsPD5a5naGiI5ORkAICZmRl8fX0hk8nE/ocPH0ZaWhpCQ0PFtj59+uDq1atyzyE6Ohp+fn4KqzgLpKenY8qUKTA0NJRbRbls2TIYGhpCIpGgVatWyM/Px9ChQ4u89+zsbGRmZsr95At5Kj03IiIiIiIiIqKPidlSycuWAOZLRERERPT+uBMWERFRMaSmpuLUqVPYunUrgFfBV0BAAKRSKdzd3VUaw9nZGX5+fkhPT0fjxo0VjhsZGeG3334TX2trqzZnWhAEABBXC/r5+aF9+/ZITk7GiRMnsHfvXsyePRurVq1CSEiIeN6YMWPkXgNAtWrVxH/u06cPvL29cfXqVdjZ2UEmk8HNzQ21a9cW+zg6OqJFixaQyWRwd3fHlStXkJycjMmTJyvU2aJFC2hra+PZs2ewtbVFfHw8qlSpIh7v0aMHxo8fj8ePH2PSpElo0aIFWrRoUeS9z5gxA1FRUXJtE0eZYdJo86IfGhERERERERHRR8RsqWRmS4DyfMkGTrCDy1vPJSIiIiICOAmLiIioWP6PvTuPqzHt/wD+OXWIijZLipZBUijM0GAeLVLZRmhTkxZhLCO7CAllHctgGJ1T+Y0lSzWWmayFZzDGYDAa2whj1GOGsoc6vz+8OtPttJw4nDo+79+r12vOdV/3dX/v+5x+M32e67qORCLBixcvYGZmJm+TyWTQ0dHBypUry12Z9yqxWAyxuOx/DWtpaQlCKGVlZ2ejfv36MDH5d+JRnTp14O7uDnd3d8yYMQNDhw7FrFmzBMFYgwYNKryem5sbLCwskJSUhEmTJiE1NRVr165V6BceHo4xY8Zg1apVSExMRPPmzdG9e3eFfikpKbCzs4OJiQkMDQ0VjhsYGMjr2bJlC1q0aAEnJyf06NGj3BqjoqIwfvx4QZu3QQg8vtQu9xyqOm4Zr1p8nqrHLfiJiIiIiKi6Y7ZUPbMloPx8iVSnd6de6i5B48TlpKu7BI0zzeojdZdAREQ1GCdhERERKenFixdYv349lixZgp49ewqO9e/fH5s2bcKIESPUUtv//vc/bNy4Ef37969wdaOdnR3S09OrNLaWlhZCQ0MhkUhgbm6O2rVrY9CgQQr9fH19MXbsWGzcuBHr16/H559/Ll85WVqzZs3QvHlzpa6tr6+PsWPHYuLEiTh9+nSZ4wGAjo4OdHR0hHWLOAGLqjdOGCIiIiIiInq/MFuqvtkSwHzpXdh94nt1l6BxPMw4YYiIiKg64SQsIiIiJe3atQv37t1DeHi4wqrEgQMHQiKRvJOgTCaTITc3FzKZDPn5+Th27Bji4uJgYGCA+fPnAwD++ecf+Pj4ICwsDO3atUO9evVw8uRJLFy4EJ9++qlgvAcPHiA3N1fQpquri/r168tfh4aGIjY2FtOmTUNAQADq1q2rUJe+vj78/PwQFRWF+/fvK2xD/7qGDx+OOXPmYPv27WUGdEQ1FXfCUj1ObCMiIiIiouqM2RKzpfddLzvFnc3ozUhv7lJ3CRonrFk3dZdAREQ1GCdhERERKUkikaBHjx5lbgs/cOBALFy4EGfPnkW7du3eah33799HkyZNIBKJUL9+fbRq1QpDhgzB2LFj5eGWvr4+OnfujKVLl+Lq1at4/vw5mjVrhoiICEybNk0w3syZMzFz5kxB2/Dhw7FmzRr5awsLC/To0QN79+5FWFhYubWFh4dDIpGgV69egm3134SxsTGCg4MRExODAQMGVLgak94uTnBRLU7CIiKq+Ypl5e+kQERERPQqZkvMlt53Rfn56i5B45hr11N3CURE9IaYL2kWkUwmk6m7CCIiIiJVc9fyUXcJGoeThlSLk9qIiN7cvuKtar3+T9et1Xr9mqSz5TV1l0BERERUZcyXVIvZkuoxXyIienPMl2qOmpAvcbo/ERERERERERERERERERERERHRG+AkLCIiIiIiIiIiIiIiIiIiIiIiojfASVhERERERERERERERERERERERERvgJOwiIiIiIiIiIiIiIiIiIiIiIiI3oBY3QUQERERUc3gYeag7hI0yp6/flV3CRqHn1EieteKIFJ3CURERERENQb/blc95kuqx88pEb1rzJc0CydhERHROyUSvfwPCQMDA+Tn56u3mBrCysoK169fBwDcu3cPhoaG6i2I3lsMdYiIiIiIiKg6YL5UdcyXqDq4uuRjdZegcZpvdVJ3CRqnBY6ruwQiIqrBOAmLiIhUKjc3F/PmzcPu3btx69YtNGrUCI6OjoiMjISbmxsAIDExEb169ZKfExMTg/T0dJw5c0YwVk5ODqytrXH69Gk4OjoiKysLLi4usLOzw9mzZ6GtrS3va2hoiGXLliEkJATAy2ApMjISkZGR8tfXr1/Hpk2b4O/vL7iOvb09Lly4gMTERPn5AHD06FHMnTsXx44dw5MnT9CyZUuEhoZi7NixgmuXBH8AoKurCzMzM3Tt2hVjxoxBx44dlX528+fPR1RUFMaOHYtly5bJ23/++WccOXIEAwcOlLclJSUhMjKyzKBRJBIhLS0N/fv3F7QPHz4cCQkJ2Lx5M3x8fAAAS5Yswdy5c3H79m3UqVNH0P/x48cwNTXF3Llz8cUXXyg809Jefa8qUlYdJWJiYjB79mwAgJaWFszMzODl5YX58+fD2Ni4wnFfxQlDVN1xVR0REREREVHZmC8xX6ou+RKp1pWANeouQeMwXyIiIqpeOAmLiIhUJicnB127doWhoSEWLVqEtm3b4vnz59izZw9GjRqF33//HcDLQKtRo0avfZ0//vgD69evR2hoaJXOa9asGRITEwUh2fHjx5Gbmws9PT1B37S0NPj6+iI0NBSZmZkwNDTE/v37MXnyZBw7dgxbtmwRhGOJiYnw9PTE06dPcenSJXzzzTfo3LkzpFIpgoODK63t559/xtq1a9GuXTuFYw0bNnzjgOjx48fYvHkzJk+eDKlUKg+nPvvsM0RFRSE1NRWDBw8WnLNt2zY8e/YMQUFBb3RtZeoozd7eHvv370dRURGys7MRFhaGgoICpKSkVOlaDCBUjxPbiIiIiIiI6G1jvsR86XXrKE1V+RIRERERUVVoqbsAIiLSHCNHjoRIJMKJEycwcOBA2NjYwN7eHuPHj8fx46rbwnfMmDGYNWsWCgsLq3ReYGAgDh06hJs3b8rbpFIpAgMDIRb/Oy/50aNHiIiIQL9+/fDNN9/A0dERVlZWGDp0KJKTk7Ft2zZs2bJFMLahoSFMTU1hZWWFnj17Ytu2bQgMDMTo0aNx7969Cut6+PAhAgMDsW7dOhgZGVXpnpS1detW2NnZYerUqTh8+LD8GTRq1Ah9+/aFVCpVOEcqlaJ///4qXSFYXh2licVimJqawtzcHD169ICPjw/27dunshqIiIiIiIiIqPpivsR86XXrKI35EhERERGpA3fCIiIilbh79y4yMjIwb948hVV/wMsQSVUiIyPx7bff4quvvsLEiROVPq9x48bw8PBAcnIyoqOj8fjxY6SkpODQoUNYv369vN/evXvxzz//lDl23759YWNjg02bNsHPz6/C640bNw7r16/Hvn374OvrC+DlVu6vbks/atQo9O7dGz169MDcuXOVvp+qkEgkCAoKgoGBAby8vJCUlIQZM2YAAMLDw9GnTx9cv34dlpaWAF6uBj18+DD27NnzzuooS05ODvbs2YPatWurtA6i6oA7i6ked8AjonetSMa1bURERKrEfEkR86Wq1VEW5kvVRy+77uouQeNIb+5SdwkaJ6xZN3WXQETvGeZLmoWTsIiISCWuXLkCmUwGW1vbt34tXV1dzJo1C9OmTUNERAQMDAyUPjcsLAwTJkzA9OnTsW3bNjRv3hyOjo6CPpcuXQIAtG7duswxbG1t5X0qUvIscnJy5G2tWrUS1Lt582acOnUKP//8s9L3UKKgoAD6+vqV9rt8+TKOHz+O1NRUAEBQUBDGjx+P6OhoiEQieHh4wMzMDImJiYiJiQEAJCUloVmzZnBzc6tyXa9bR4lz585BX18fRUVFePr0KQDgyy+/rHDswsJChZWrxbIiaIm0VVY/kapxwhAREREREZEQ86Wy+wHMl5StowTzperp+wuH1F2CxvEw44QhIiKi6oRT6oiISCVkMtk7vV54eDhMTEywYMGCKp3Xu3dvPHz4EIcPH4ZUKkVYWFi5fd/0nkrOLx0A/f777/D29gYA3Lx5E2PHjsWGDRtQp06dKo9fr149nDlzRuHnVVKpFB4eHmjQoAEAoFevXigoKMDBgwcBANra2hgyZAiSkpIgk8lQXFyM5ORkhIaGQktLdf+pUFkdJVq1aoUzZ87g559/xpQpU+Dh4YExY8ZUOHZ8fDwMDAwEP9fwu8pqJyIiIiIiIqK3j/lS+eczX1KujhLMl4iIiIhIHbgTFhERqUTLli0hEonw++9VDybq16+PgoIChfb8/HwAKHMlolgsxrx58xASEoLRo0crfS2xWIzPPvsMs2bNwk8//YS0tDSFPjY2NgCA7OxsdOnSReF4dnY27OzsKr1WdnY2AMDa2rrM47/88gv+97//oUOHDvK2oqIiHD58GCtXrkRhYSG0tctfaaelpYUWLVpUWENRURGSk5ORm5sLsVgsaJdKpfKViGFhYYiPj8fBgwdRXFyMmzdvIjQ0tNJ7VJaydQBA7dq15fc1f/589O7dG7Nnz8acOXPKHT8qKgrjx48XtHkbhKisfnqJOzepFr+OkGoC/t4TERER0bvEfEkR86Wq1wEwX6qu+Dem6omtLNVdgsaZdjBd3SVolNgP2qu7BCKid4qTsIiISCWMjY3h4eGBVatW4YsvvoCenp7geH5+PgwNDcs8t1WrVvjzzz+Rl5eHxo0by9tPnTqFOnXqwMLCoszzfHx8sGjRIsyePbtKtYaFhWHx4sXw8/ODkZGRwvGePXvC2NgYS5YsUQjJduzYgcuXL1cY2JRYtmwZ6tevjx49epR53M3NDefOnRO0hYaGwtbWFlOmTKkwIFPW999/jwcPHuD06dOC8c6fP4/Q0FD5+9K8eXN0794dUqkUMpkMPXr0gKWl6v6AV7aOskRHR8PV1RWff/45zMzMyuyjo6MDHR0dQRu3ilc9ThpSLQaPREREREREQsyXFDFfqnodZWG+VD0wW1I9j7I/zvQGOGmIiIjeBCdhERGRyqxatQpdu3ZFp06dEBsbi3bt2uHFixfYt28fvv76a/nKvVd5eHigVatWCAgIwNy5c2FqaopTp04hOjoaY8eOrTAsmj9/Pjw8PKpUZ+vWrfH3339DV1e3zON6enpYu3Yt/P39MWzYMIwePRr169fHgQMHMGnSJAwaNAi+vr6Cc/Lz85Gbm4vCwkJcunQJa9euRXp6OtavXy8If2xtbREfHw9vb2/Uq1cPbdq0Ubi2iYmJQvvrkkgk6N27NxwchJM97OzsMG7cOGzYsAGjRo0C8HIL/oiICABAUlJSmePdunVLYUv60mHaxYsXFc6xt7evUh2v+vjjj9GuXTvExcVh5cqVFd4vEREREREREdVszJeYLzFfIiIiIqKaipOwiIhIZT744AOcOnUK8+bNw4QJE3D79m00bNgQHTt2xNdff13ueWKxGHv37sW0adMQEBCAO3fuwNraGmPHjlXYAvxVrq6ucHV1xd69e6tUq4mJSYXHBw0ahMzMTMybNw+ffPIJnj59ipYtW2L69OmIjIyESCQS9C/ZWr1OnTowNzdHt27dcOLECcFW8MDLEKmsrfHfhry8POzevRsbN25UOKalpQVvb29IJBJ5ODVw4ECMHj0a2tra6N+/f5ljLl68GIsXLxa0/d///R+6desGAPD391c4Jycnp0p1lGXcuHEICQnBlClT0KxZs3L70dvFnZtUi6s/qSbg7z1RxYqhpe4SiIiINA7zJeZLzJc0F//GVD1xwwbqLkHjrPjlO3WXoFFGWnRVdwlE1R7zJc0ikslkMnUXQURE7w+RSIS0tLRyQxgqW1ZWFlxcXHDv3r1yt1UnIXctH3WXQFQhTsJSPYa5RO+ffcVb1Xr9Qzk2ar1+TdLd6pK6SyAiIg3CfOn1MF+qOuZLVN0xX1I95ktE7x/mSzVHTciXuBMWERG9cwEBATAxMcGff/6p7lJqBHt7e/zxxx/qLoOIoQ4RERERERFVG8yXqob5ElUH2gYG6i5B4+QWPVR3CURERFQKJ2EREdE7dfnyZQCAtra2miupOb7//ns8f/4cAFC/fn01V0NEqsJVdURERERERK+H+VLVMV+i6uD77MPqLkHjeJjxq96IiIiqE07CIiKid6pFixbqLqHGsbS0VHcJRERERERERETVBvOlqmO+RERERET09nESFhEREREphTs3qRa/3pFqAv7eExERERERkarwb0zVe+LdWd0laBzryb+ruwSNkvtxgbpLICJ6pzgJi4iIiIiUwklDVN0xzCWid60IInWXQERERERUY7ief6TuEjTOgc+fqbsEjcNJQ0T0rjFf0iychEVERO+dkJAQJCcnAwDS0tLQv39/9RakIZydnXHo0CEAwOnTp+Ho6KjegoiIiIiIiIiI3gJmS28HsyXNd7i3rbpL0DixWQnqLkHjzLLuqO4SiIioBuMkLCIiqpLSIZNYLIaxsTHatWuHgIAAhISEQEtLS9Dfw8MD+/fvx/Hjx/HRRx8BAMaMGYP9+/cjOztbYfwbN27A2toaUqkUQ4cOxf/93//B399foV94eDhOnz6NU6dOISYmBrNnz4aHhwcyMjIE/RYtWoTJkyeje/fuyMrKkrd7enoiMTERRkZGAICcnBxYW1uXGfA4OzvD0dERy5YtAwBYWVnh+vXrOHbsGJycnOT9IiMjcebMGfl1YmJikJ6ejjNnzshfV7XOu3fvIjY2Fmlpabh9+zYaNGgAT09PxMTEwMLC4rXfl9IuXryIESNG4MKFCygoKICZmRkGDx6MWbNmoVatWgCApKQkhIaGCs7T0dHB06dP5a9TU1Nx9epVdOrUSd6WlZUFFxcX+etGjRqhW7duWLRoET744AP584yMjERkZCQAQCaTYdKkSfjmm2+wY8cOODs7K/QhIiIiIiIiopqJ2RKzpRLMlt4/l0c2VXcJGmfw9yPVXYLGaYmf1F0CERHVYJyERUREVVYSMhUVFSEvLw8ZGRkYO3Ystm3bhh07dkAsfvmvlxs3buDo0aMYPXo0pFKpPCgLDw/HypUrcfToUXTp0kUwdlJSEho1aoTAwECkpaVBKpUqBGWPHj3Cli1bMH/+fHlbkyZNkJmZiT///BNNm/77x7xUKhUESiV0dHRgamr62s+gTp06mDJlinx1nrKqUufdu3fh5OSE2rVrY82aNbC3t0dOTg6io6Px0Ucf4dixY/KwCVD+fXlVrVq1EBwcjA4dOsDQ0BC//vorIiIiUFxcjLi4OHm/+vXr4+LFi/LXIpFwe1RjY2Pcv3+/zGtcvHgR9erVw+XLlzFs2DD07dsXZ8+ehba2tqBfUVERIiIisGvXLmRmZqJjR646qk74VW+qxa93JCIiIiKi9xWzJWZLALOl95H1lGPqLkHjMF9SPY9RzECJiOj1cRIWERFVWemQydzcHB06dICTkxPc3NyQlJSEoUOHAgASExPRp08ffP7553BycsKXX36JunXrwtHRER06dIBUKhUEZTKZDElJSRgyZAjEYjHCw8PRv39/3LhxQxAibd26FS9evEBgYKC8rVGjRujYsSOSk5Mxffp0AMDRo0fx999/w8fHBxcuXFDpMxg2bBjWrFmD77//Hr169VL6vKrUOX36dPz111+4cuWK/HlbWFhgz549aNmyJUaNGoUffvhB3l/Z9+VVH3zwgSBws7S0RFZWFo4cOSLoJxKJXjtcbNSoEQwNDdGkSRPMnDkTgYGBuHLlClq1aiXvU1hYiICAAJw8eRJHjhwRHCMiUgaDR9Xj5EsiIiIiehuYLTFbqipmS0Rlu/z8obpL0DjiRg3VXYJGefG/O+ougYjoneIkLCIiUglXV1c4ODggNTUVQ4cOhUwmQ2JiIlatWgVbW1u0aNEC27Ztw2effQbg5YrFqVOnYvny5dDT0wPwcnvxa9euISwsDADQq1cvNG7cGElJSZg5c6b8WomJiRgwYAAMDQ0FNYSFhWHy5MnyAEoqlQrCNFWytrbGiBEjEBUVBU9Pzwq3ZH+VMnUWFxdj8+bNCAwMVAin6tati5EjRyI6Ohp3796FsbFxudd69X0BXm4vn5OTI9iavrQrV64gIyMDAwYMELQ/fPgQlpaWKC4uRocOHRAXFwd7e3ul77t0/QDw7Nkzwdi9e/fGn3/+iR9//BHNmjWr8rj09nGCC1V3nDBERERERFRzMVtitqQsZks11//d/FHdJWicEDtPdZegcYoecNIQERG9Pk7CIiIilbG1tcXZs2cBAPv378fjx4/h4eEBAAgKCoJEIpEHZYMHD8aECROwdetWhISEAHgZgHXr1g02NjYAAG1tbQwZMgRJSUmYMWMGRCIRrl69iiNHjmDfvn0K1+/Tpw9GjBiBw4cPo2PHjtiyZQv++9//QiqVvpX7jY6ORmJiIjZs2CC/L2UoU+edO3eQn5+P1q1blzlG69atIZPJcOXKFXTq1KnC65V+X4CX29YXFxcr9OvSpQtOnTqFwsJCDBs2DLGxsfJjrVq1glQqRbt27VBQUIDFixejS5cu+O233wRb31fm9u3bWLx4MczNzQWrEefMmYN69eohOzsbDRtWfaVRYWEhCgsLBW07rp2Gjo7yASZVjhNciIiIhIpk/G8NIiIiVWK2pBxmS6rPlgDmS++Ch1lXdZeggR6ouwAiInpDzJc0CydhERGRyshkMohEIgAvV+D5+flBLH75r5qAgABMmjQJV69eRfPmzWFoaIgBAwZAKpUiJCQE9+/fx/bt27Fq1SrBmGFhYZg/fz4yMzPh6uqKxMREWFlZwdXVVeH6tWrVQlBQEBITE/HHH3/AxsYG7dq1e2v327BhQ0ycOBEzZ86En5+f0udVpU6ZTPbGdZZ+XwAgPj6+zH4pKSl48OABfv31V0yaNAmLFy/G5MmTAQAff/wxPv74Y3nfLl26oHXr1li7di3mzJlTaQ1NmzaFTCbD48eP4eDggO3bt6N27dry4z179sT+/fsRFxeHpUuXVvke4+PjMXv2bEGbNVqjuajqqympfNwJi6o7ThQkIiIiIqrZmC0ph9mS6rOlkvtivvR2uZ5/pO4SNM6Bz7upuwSNIzpyWt0lEBFRDcZJWEREpDLZ2dmwtrbG3bt3kZaWhufPn+Prr7+WHy8qKoJUKsW8efMAvNw23s3NDVeuXEFmZia0tbXh4+MjGLNly5b45JNPkJiYCGdnZ6xfvx4RERGC4Ke0sLAwdO7cGefPn5dvPa+M+vXrAwAKCgoUjuXn58PAwKDM88aPH4/Vq1dj9erVSl9LmTobNmwIQ0NDZGdnl3l+dnY2RCIRWrRoUem1St6XypRs025nZ4eioiIMGzYMEyZMgLa2tkLfWrVqoX379rhy5Uql4wLAkSNHUL9+fTRq1Aj16tVTOO7m5oYxY8bg008/RXFxMZYvX67UuCWioqIwfvx4QZu3QUiVxqDKcYKLanFSGxERERERkRCzJeUxWxJ602wJYL70Lhxso6fuEjTO3r+S1F2CxmEGSkREb4KTsIiISCUOHjyIc+fOYdy4cdiwYQOaNm2K9PR0QZ+9e/diyZIliI2Nhba2NlxcXGBtbY3ExERkZmbC398fenqKf4iHh4fj888/R79+/XDr1i35FvNlsbe3h729Pc6ePYvBgwcrXb+xsTEaNGiAX375Bd27d5e3379/H1euXJFvY/8qfX19zJgxAzExMejXr5/S16usTi0tLfj6+mLDhg2IjY2Fqamp/NiTJ0+wevVqeHh4wNjYuMLrlH5fqqK4uBjPnz9HcXFxmUFZUVERzp07h169eik1nrW1NQwNDSvs07NnT+zcuRP9+vWDTCbDihUrlK5XR0cHOjo6gjYtkWLd9GY4aYiIiIiIiIjeFmZLzJYq8razJYD50rsgbmqu7hI0zi/PflZ3CURERFQKJ2EREVGVFRYWIjc3F0VFRcjLy0NGRgbi4+PRp08fBAcHo2PHjhg0aBDatGkjOK9Zs2aIiopCRkYGevfuDZFIhLCwMHz55Ze4d+9euVuF+/j44IsvvsDw4cPRs2dP+aq68hw8eBDPnz+vNJh51fjx4xEXF4fGjRvDyckJ//zzD+bMmYOGDRtiwIAB5Z43bNgwLF26FBs3bkTnzp2Vvl5ldcbFxeHAgQNwd3fHwoUL0aZNG1y7dg3R0dF4/vy5wvb6lb0vJaKionDr1i2sX78eALBhwwbUqlULbdu2hY6ODk6ePImoqCj4+fmhVq1aAIDY2Fg4OTmhRYsWyM/Px6JFi3D9+nUMHTpU6ftVRo8ePbBr1y707dsXxcXFWLlypUrHJyIiIiIiIiL1Y7YkxGxJdZgtVW+XFzdQdwkaJ2T1WHWXoHHMcFTdJRARUQ3GSVhERFRlGRkZaNKkCcRiMYyMjODg4IAVK1ZgyJAhOH36NH799VesW7dO4TwDAwO4ublBIpGgd+/eAICQkBDMmjUL9vb25YZMurq68Pf3xzfffKPUNvBlrXhUxuTJk6Gvr48FCxbg6tWrMDY2RteuXZGZmYm6deuWe16tWrUwZ86cKq2OVKZOExMTHD9+HLGxsRg+fDhyc3NhbGwMLy8vfPvtt7CwsBD0r+h90dLSkve7ffs2bty4IX8tFouxYMECXLp0CTKZDJaWlhg9erRgheO9e/cQERGB3NxcGBkZoWPHjjh69Cjs7OyqdM/KcHV1xe7du9GnTx/IZLLXDsu4axNVd9zanIiIiIiI3lfMloSYLamWqrIlABCVsYsXvb7fPpGquwSN08e/o7pLICIiolJEMplMpu4iiIiI3qWQkBDk5+crbGlPby4nJwfW1tY4ffo0HB0d1VqLu5aPWq+viTixTbU4CYuI6M3tK96q1uvvuab6/9FQU3lYX1B3CURERCrDbOntqU7ZEsB8SdWYLake8yUiojfHfKnmqAn5EnfCIiKi99KuXbugr6+PzZs3o0+fPuouRyN4eXnh8OHD6i6DqMZg8Kh6DB6JiIiIiOhdYbakesyWNB//blc95kuqx88pERG9Ce6ERURE753//e9/uH//PgCgSZMmr73FPAndunULT548AQBYWFigdu3aaq2HKxWpumNIpnoMyYjeP1ypWHPUhJWKREREymK29HZUt2wJYL5E1R/zJdVjvkT0/mG+VHPUhHyJO2EREdF7p1GjRmjUqJG6y9A45ubm6i6B3jKGOqrFQIeIiIiIiKhmYrb0djBb0nzMllSP+RIREVH1oqXuAoiIiIiIiIiIiIiIiIiIiIiIiGoyTsIiIiIiIiIiIiIiIiIiIiIiIiJ6A/w6QiIiIiIiIiKi11DEtW1ERERERERERPQGmC9pFk7CIiKiai0kJATJyckAALFYDGNjY7Rr1w4BAQEICQmBltbL/zCxsrLC9evXFc6Pj4+Hu7s7PvzwQxw7dgxOTk4Kfdzc3GBgYIDU1FTB9Uq7fPkyWrRogZCQEOTn5yM9PV1+bNu2bQgKCkJUVBTmzp2LzMxMdOvWTX780aNHaNu2LQYMGIDFixfD2dkZjo6OWLZsWYX3fuzYMXTr1g2enp7YvXu34FhOTg6sra1x+vRpODo6AgAePHiAfv36IS8vD3v37kXTpk0BAE+ePIG5uTm0tLRw69Yt6Ojo4NKlS3B0dERCQgIGDx4sH7e4uBjdunWDmZkZtm3bBgDYtGkTgoKCMGLECKxatUqhznXr1mHlypW4evUqxGIxrK2t4evri6ioKABATEwMZs+ejeHDh2PNmjXy886cOYP27dvj2rVrsLKyKvOeSij7zOjt8jBzUHcJGmXPX7+quwSNw88oERERERG9itkSsyWA2VJ1wb/bVY/5kurxc0pERG+Ck7CIiKja8/T0RGJiIoqKipCXl4eMjAyMHTsW27Ztw44dOyAWv/zXWWxsLCIiIgTn1qtXD3p6enBwcIBUKlUIynJycpCZmYmdO3cqXK+0hg0blllbQkICRo0ahTVr1iA0NBQFBQUICQnBr7/+Cj09PQDA5MmTUbduXcydO7dK9y2RSDBmzBhIJBL89ddfMDMzK7fvnTt34OXlBS0tLRw5cgQmJibyY9u3b4e9vT1kMhnS09Ph5+cHGxsbzJ8/H2PGjIGLiwuaNGkCAFiyZAn++OMP7NixQ1DH5MmTsXbtWixZsgR16tSRH5NKpYiMjMSKFSvQvXt3FBYW4uzZszh//rygvjp16kAikWDChAlo2bJllZ7D62IAQdUdAx0iIiIiIqJ3g9kSsyWqHpjXqR7zJSIiouqFk7CIiKja09HRgampKQDA3NwcHTp0gJOTE9zc3JCUlIShQ4cCeBmKlfR7VXh4OKKjo7Fs2TLo6urK25OSktCkSRN4enqWeb2KLFy4ELNmzcLmzZvh7e0NAIiLi0NGRgamTJmClStXIjMzEwkJCTh69KggYKrMw4cPkZKSgpMnTyI3NxdJSUmYNm1amX1v3rwJd3d3mJub47vvvoO+vr7guEQiQVBQEGQyGSQSCfz8/AAAY8aMQXp6OiIiIrBr1y78/vvvmDlzJlJSUtCgQQMAwLVr13D06FFs374dmZmZSE1NFaxu3LFjB3x9fREeHi5vs7e3V6ixVatWaNSoEaZPn44tW7Yo/RzeBAMI1WNQRkRERERERDURsyVmS0RERERE7wInYRERUY3k6uoKBwcHpKamyoOyigQGBmLSpEnYtm0bgoODAQAymQzJyckICQmBtrZ2la4/ZcoUrF69Grt27YKbm5u8vU6dOli/fj26dOkCd3d3REZGYtq0aejYsWOVxt+yZQtsbW3RqlUrBAUFITIyElFRURCJRIJ+Fy9exKRJk/Dhhx9i06ZN0NHRERy/evUqjh07htTUVMhkMowbNw7Xr1+HpaUlRCIREhMT0a5dO6xbtw4SiQT+/v7o16+f/PzExET07t0bBgYGCAoKgkQiEQRlpqamOHTokHzMisyfPx8fffQRTp48iQ8//LBKz4OqB05sUy1OaqOagL/3RERERKSpmC29xGyJ3iX+jal6d4d2UXcJGqeOT666S9Aouh5/qLsEIqJ3ipOwiIioxrK1tcXZs2flr6dMmYLo6GhBnx9++AGffPIJjI2N4e3tDalUKg/KMjMzkZOTg9DQUME5u3btEqz48/LywtatWwVjfvfddzhw4ABcXV0V6vrwww8RFRWFAQMGoH379pg+fXqV761khSHwcgv7goICHDp0CM7OzoJ+wcHB6Nq1K7Zu3Vpm2CeVSuHl5QUjIyMAgIeHBxITExETEwMAsLS0xLJlyzB06FA0bdoUe/fulZ9bXFyMpKQkfPXVVwAAf39/TJgwAdeuXYO1tTUAYNasWRgwYACsrKxgY2ODjz/+GL169cKgQYOgpaUlqKVDhw7w9fXFlClTcODAgXLvvUuXLgrnPnnyBI6OjuWeU1hYiMLCQkFbsawIWqKqBaBUMU4aUi0Gj0REREREROrFbInZUmnMl94+Zkuq51H+t4zS60pQdwFERFSTcRIWERHVWDKZTLB6b9KkSQgJCRH0MTc3l/9zWFgYPDw8cPXqVTRv3hxSqRTdu3dHixYtBOe4uLjg66+/lr/W09MTHG/Xrh3+/vtvzJo1C506dVLYoh0AZsyYgdjYWEydOhVicdX+dXvx4kWcOHECaWlpAACxWAw/Pz9IJBKFoKxfv35IT09HamoqfHx8BMeKioqQnJyM5cuXy9uCgoIwceJEzJw5Ux5GhYaGYsaMGRgzZgzq168v77tv3z48evQIvXr1AgA0aNAA7u7ukEqlmDNnDgCgSZMmOHbsGM6fP4/Dhw/j6NGjGDJkCBISEpCRkaEQeM2dOxetW7fG3r170ahRozLvPyUlBa1btxa0BQYGVvjM4uPjMXv2bEHbzAnGmDXRpMLzqGo4aYiIiEioSKZVeSciIiKqtpgtMVsqjfnS28dsiYiISBHzJc3CSVhERFRjZWdny1fNAS+DnFdDr9Lc3NxgYWGBpKQkTJo0CampqVi7dq1CPz09vQrHMTc3x7Zt2+Di4gJPT0/88MMPqFevnqBPSThW1ZAMeLlS8cWLFzAz+3cZk0wmg46ODlauXAkDAwN5+/Tp09GuXTsMHjwYMpkMvr6+8mN79uzBrVu34OfnJxi/qKgIBw4cgLu7u6DeV2uVSCS4e/cu6tatK28rLi7G2bNnMXv2bEEI1qZNG7Rp0wYjR47EiBEj8Mknn+DQoUNwcXERjNm8eXNERERg6tSpkEgkZd5/s2bNFJ5/6RrKEhUVhfHjxwvavA1C4PElVyoSvU+4olb1GJATERERkSZjtsRsqTTmS0QEMF9SNWZLRPS+4SQsIiKqkQ4ePIhz585h3LhxSp+jpaWF0NBQSCQSmJubo3bt2hg0aNBrXd/S0lIeBHl6eiIjI0MhLHsdL168wPr167FkyRL07NlTcKx///7YtGkTRowYIWifMWMGtLS0EBgYCJlMJg/GJBIJ/P39FbasnzdvHiQSiSAoe9U///yD7777Dps3b4a9vb28vaioCN26dcPevXvh6elZ5rl2dnYAgEePHpV5fObMmWjevDk2b95c7vWrSkdHBzo6OoI2bhWvegwgqLpjqENERERERMpitvQvZksvMV96+5gtqR6zENXjMyUiojfBSVhERFTtFRYWIjc3F0VFRcjLy0NGRgbi4+PRp08fBAcHy/s9ePAAubm5gnN1dXUF26CHhoYiNjYW06ZNQ0BAgFKr4MrTrFkzZGVlwcXFBR4eHsjIyBBcqyJ37tzBmTNnBG0l26/fu3cP4eHhglWJADBw4EBIJBKFoAx4uWpRW1sbgYGBKC4uRo8ePbBz507s2LEDbdq0EfQNDg6Gt7c37t69C2Nj4zLr+7//+z+YmJjA19dXsC0/APTq1QsSiQSenp74/PPPYWZmBldXVzRt2hS3b9/G3Llz0bBhQ3z88cdljt24cWOMHz8eixYtquwxEWk0BjpERERERETvBrOll5gtERERERG9XfxySSIiqvYyMjLQpEkTWFlZwdPTE5mZmVixYgW+++47aGv/uxpt5syZaNKkieBn8uTJgrEsLCzQo0cP3Lt3D2FhYW9cW9OmTZGVlYW///4bHh4euH//vlLnbdy4Ee3btxf8rFu3DhKJBD169FAIyYCXQdnJkydx9uzZMsecOnUq4uLi8Nlnn+Gbb76Bnp4e3NzcFPq5ubmhbt26+Pbbb8utTyqVwtvbWyEkK6ljx44d+Pvvv9GjRw8cP34cPj4+sLGxwcCBA1GnTh0cOHAAJiYm5Y4/ceJE6Ovrl3uciIiIiIiIiEhVmC29xGyJiIiIiOjtEslkMpm6iyAiIiJSNXctH3WXQFQhbsFPNQF3bKPqbl/xVrVef+cf7dR6/Zqk7wdl/4+9RERERNUZ8yWq7h4EOKm7BI3T+PNr6i5Bozz+T566SyCqFPOlmqMm5Ev8OkIiIiIiUgonDVF1xwlDRPSuFXODcSIiIiIipYltW6q7BI0zfU6yukvQOCtatFJ3CUT0nmG+pFk4CYuIiIiIlMIJLlTdcaKg6vH3noiIiIiIiFTlxe+X1V2CxuGEIdVjvqRazJaI6H3DSVhEREREpBQGEFTdMdQhIiIiIiIiqr6i/2C2pGrze3yq7hI0joeZuisgIqKajJOwiIiIiIiIiIiIiIiIiOitio6MUHcJGqfV5t/UXYLGud5J3RUQEVFNxklYRERERERERERERERERPRWJXy1VN0laJzIj/qruwQN9EjdBRARUQ3GSVhERERERGrAr84jIiIiIiIiovdJy1r66i5B47z43x11l0BERESlcBIWEZEaODs749ChQwCA06dPw9HRUb0F1QAhISFITk4GAKSlpaF///7qLYjoPcRJQ1Td7fnrV3WXoHH4e09UsSKZSN0lEBHRe4z5UtUxXyJSL/6NSTUB8yXV4u89UeWYL2kWTsIiohqndFgiFothbGyMdu3aISAgACEhIdDS0gIAWFlZITIyEpGRkYLzY2JikJ6ejjNnzgAAHj9+jDlz5mDLli24desW6tWrBzs7O4wfPx6ffvopgH9DrU2bNsHf318+1rJly7Bs2TLk5OTI2549e4Zly5Zhw4YNuHz5MnR1ddGqVSsMHToUQUFBqFWrFgAgIiICsbGxaNCgAQAgKysLLi4uuHfvHgwNDQU1v3ovIpEIOjo6uHjxIiwtLeX9+vfvD0NDQyQlJcmfVX5+PtLT0wXPbvjw4VizZo3gGqNGjcLq1asxZMgQ+fkAcPPmTcyaNQsZGRn4+++/0aRJE/Tv3x8zZ86EiYmJvF/p4K927dpo0KABOnTogNDQUAwYMKC8t1PBjz/+iO7du6NNmzby9wgAli9fjvnz56NJkyaC/mlpaViwYAGys7NRXFwMCwsLuLu7Y9myZQCApKQkREZGIj8/v8zr3blzBzNnzsTu3buRl5cHIyMjODg4YObMmejatSuAl8//+vXrgvPMzc1x5swZtGnTBl988QWmTZsmOO7r64sbN27gxx9/hLa2No4dO4Zu3brB09MTu3fvFvTNycmBtbV1uYFpUVERFi1ahKSkJFy/fh1169ZFy5YtERERgaFDhwL4972Nj4/H1KlT5eemp6fD29sbMplM8LtTFktLS/ln+cqVK4iLi8P+/fuRl5eHBg0awNbWFmFhYfDz84NYLPxPiOHDhyMhIQGbN2+Gj4+P4FhMTAxmz54NANDW1oahoSHs7OwwYMAAfP7559DR0ZH3Lf050tHRgYWFBUJDQzF16lSIRPyPUHVjAKFaDCBUj8+UiIiIiJTFfIn5UmnMl5gv0bvBbEn1mIWoHp8pERG9CS11F0BE9Do8PT1x+/Zt5OTk4IcffoCLiwvGjh2LPn364MWLF1Uaa8SIEUhNTcVXX32F33//HRkZGRg0aBD++ecfQb86deogOjoaz58/L3esZ8+ewcPDA/Pnz8ewYcNw9OhRnDhxAqNGjcJXX32F3377Td5XV1cXpqamCmGDskQiEWbOnFnl85o1a4bNmzfjyZMn8ranT59i48aNsLCwEPT9448/8OGHH+Ly5cvYtGkTrly5gjVr1uDAgQP4+OOPcffuXUH/iIgI3L59G1evXsX27dthZ2cHf39/DBs2TKna8vPzERwcDDc3N4VjBgYGMDU1FbQdOHAAfn5+GDhwIE6cOIFffvkF8+bNq/A9etXAgQNx+vRpJCcn49KlS9ixYwecnZ0V3v/Y2Fjcvn1b/nP69Gk0aNAA33zzDWbPno1z587J+27duhW7du1CcnIytLW1AQASiQRjxozB4cOH8ddffyldHwDMnj0bS5cuxZw5c3DhwgVkZmZi2LBhCsFfnTp1sGDBAty7d6/McZYvXy64BwBITEyUv/75558BACdOnECHDh2QnZ2NVatW4fz588jKysLQoUPx9ddfCz7HwMugefPmzZg8eTKkUmmZ17a3t8ft27dx48YNZGZmwsfHB/Hx8ejSpQsePHgg6FvyObp48SKioqIwc+ZMhVCXiIiIiIiI6E0xX2K+BDBfYr5ERERERKQ63AmLiGokHR0deWBibm6ODh06wMnJCW5ubkhKSpKv3lLGjh07sHz5cvTq1QvAy1VpHTt2VOgXEBCAHTt2YN26dRg5cmSZYy1btgyHDx/GyZMn0b59e3n7Bx98AB8fHzx79qwqt1mh0aNH48svv8SkSZPQpk0bpc/r0KEDrl69itTUVAQGBgIAUlNTYWFhAWtra0HfUaNGoXbt2ti7dy/q1q0LALCwsED79u3RvHlzTJ8+HV9//bW8f0nwBwBNmzaFk5OTfHWbr68vevToUWFtI0aMwODBg6GtrS1fXVmRnTt3omvXrpg0aZK8zcbGRumt5PPz83HkyBFkZWWhe/fuAF6u1uvUqZNC33r16imEdADQr18/DB48GEOGDMFPP/2E/Px8jBo1CvPnz0erVq0AAA8fPkRKSgpOnjyJ3NxcJCUlKaxsrMiOHTswcuRIwQpABwfF1Tg9evTAlStXEB8fj4ULFyocNzAwgIGBgaDN0NBQcF8lKxptbGzw448/ylf+AkDLli0REBAAmUwmGGPr1q2ws7PD1KlTYWZmhps3b6JZs2aCPmKxWH4dMzMztG3bFu7u7nBwcMCCBQswd+5ced/Sn6PQ0FCsXLkS+/btw+eff17psyKqSbj6U/W4UpGIiIiIqoL5EvMlgPnSq5gv0dv0wd4wdZegcerM1Km8E1VJs9ij6i6BiIhqME7CIiKN4erqCgcHB6SmplYpJDM1NcX333+PAQMGoF69euX2q1+/PqZPn47Y2FgMGTIEenp6Cn02bNiAHj16CAKyErVq1ZJvFa8KXbt2xaVLlzB16lTs2rWrSueGhYUhMTFRHpJJpVKEhoYiKytL3ufu3bvYs2cP5s2bJw/ISpiamiIwMBApKSlYvXp1hdt4DxkyBBMmTEBqaqo8JHN2doaVlZVgW/rExET88ccf+PbbbwWBSUVMTU2xceNGnD9/vkpBYQl9fX3o6+sjPT0dTk5Ogm3Lq2L58uVo27Yt5syZg+zsbLRp0wZjxoyRH9+yZQtsbW3RqlUrBAUFITIyElFRUUpvf25qaoqDBw9i5MiRaNiwYbn9tLW1ERcXh8GDB+OLL75A06ZNq3wvZ86cQXZ2NjZt2iQIyEp7tW6JRIKgoCAYGBjAy8sLSUlJmDFjRqXXsrW1hZeXF1JTU8t8z2UyGf773//i999/R8uWLSscq7CwEIWFhYK2YlkRtETaldZBpC6cMEREREREVP0wX1Ie86WXmC8pYr5E5fmjZ9m7nNHrY75ERERUvXASFhFpFFtbW5w9e1b+esqUKYiOjhb0efbsGezs7OSvv/nmGwQGBsLExAQODg7o1q0bBg0ahK5duyqMP3LkSCxfvhxffvllmSHA5cuX4ezsrLobqkR8fDzatWuHI0eO4JNPPlH6vKCgIERFReH69esAgB9//BGbN28WhGSXL1+GTCZD69atyxyjdevWuHfvHu7cuYNGjRqVey0tLS3Y2NggJydH3mZhYYEmTZoIrjV16lQcOXKkStvnjxkzBkeOHEHbtm1haWkJJycn9OzZE4GBgUoFXmKxGElJSYiIiMCaNWvQoUMHdO/eHf7+/mjXrp2g76ufpbi4OHzxxRcAXgaoiYmJ6NmzJ/T09HD27FlBkFQSIgEvv+qgoKAAhw4dUvqz8uWXX2LQoEEwNTWFvb09unTpgk8//RReXl4Kfb29veHo6IhZs2ZBIpEoNX5ply5dAgD5KksA+N///ocPPvhA/nrhwoXy1bqXL1/G8ePHkZqaCuDlZ2v8+PGIjo5WKgS0tbXF3r17BW2rV69GQkICnj17hufPn6NOnTryZ12e+Ph4zJ49W9BmjdZoDvtKayDlMdRRLe6ERTUBf++JKlaEsv9HRSIiqtmYLymH+dJLzJcUMV+i8vBvTNUTWzarvBNVyYysdHWXoFFmWSvuDEpEQsyXNAsnYRGRRpHJZII/zCdNmoSQkBBBnxUrVuDw4cPy1//5z3/wxx9/4Pjx4zh69CgOHDiA5cuXY/bs2QpBmI6ODmJjYzFmzJgyt65+dRvtt83Ozg7BwcGYOnUqfvzxR6XPa9iwIXr37o2kpCTIZDL07t0bDRo0KLOvKu7p1fdl/fr18n8uKirC4MGDMXv2bNjY2FRpXD09PezevRtXr15FZmYmjh8/jgkTJmD58uU4duwYdHV1Kx1j4MCB6N27N44cOYLjx4/jhx9+wMKFC5GQkCD47Lz6WXr1ebm6usLJyQmOjo6wtLSUt1+8eBEnTpxAWloagJfBnJ+fHyQSidIhmZ2dHc6fP49ffvkFP/74Iw4fPoy+ffsiJCQECQkJCv0XLFgAV1dXTJw4UanxK2NiYoIzZ84AeLnKtPTXHkilUnh4eMifR69evRAeHo6DBw/Czc2t0rFf/WwAQGBgIKZPn4579+5h1qxZ6NKlC7p06VLhOFFRURg/frygzdsgRIm7o6rgpCHVYvBIRERERFQ9MV9SDvOlfzFfqhzzJQKYLb0NHmbqrkDzcNIQERG9CU6pIyKNkp2dDWtra/nrBg0aoEWLFoIfY2NjhfNq1aqFTz75BFOmTMHevXsRGxuLOXPmCMKAEkFBQbC0tCxze2sbGxv8/vvvr1V7/fr1AQAFBQUKx/Lz82FgYFDmebNnz8apU6eQnp5epeuFhYUhKSkJycnJCAsLUzjeokULiEQiZGdnl3l+dnY2jIyMKty+HHgZgl2+fFnwvpT24MEDnDx5EqNHj4ZYLIZYLEZsbCx+/fVXiMViHDx4sNJ7ad68OYYOHYqEhAScOnUKFy5cQEpKSqXnlahTpw7c3d0xY8YMHD16FCEhIZg1a5agz6ufJUNDQ4VxSuovTSKR4MWLFzAzM5Mf//rrr7F9+/Yy3+vyaGlp4aOPPkJkZCRSU1ORlJQEiUSCa9euKfT9z3/+Aw8PD0RFRSk9fomSbdkvXrwob9PW1pbfd+n7KyoqQnJyMnbv3i2/N11dXdy9exdSqXJbi7/6OwsABgYGaNGiBT766CNs2bIFK1euxP79+yscR0dHB/Xr1xf8cKt4IiIiIiIieh3Ml5THfOlfzJf+xXyJiIiIiN5X3AmLiDTGwYMHce7cOYwbN+6Nx7Kzs8OLFy/w9OlT1K5dW3BMS0sL8fHxGDBggMJqxcGDB2PatGk4ffo02rdvLzj2/PlzPHv2DHp6emVes2XLltDS0sIvv/wiWOn2xx9/oKCgoNxVfM2aNcPo0aMxbdo0NG/eXOl79PT0xLNnzyASieDh4aFw3MTEBO7u7li9ejXGjRuHunXryo/l5uZiw4YNCA4OrnRL8OTkZNy7dw8DBw4s83j9+vVx7tw5Qdvq1atx8OBBbNu2rdxwrTxWVlbQ1dXFo0ePqnReaXZ2dlUOHcvy4sULrF+/HkuWLEHPnj0Fx/r3749NmzZhxIgRr10jgHLvc/78+XB0dBRs+66M9u3bw9bWFosXL4avry+0tMqfr/3999/jwYMHOH36NLS1/w2kzp8/j9DQUOTn55cZJpb4/fffkZGRUWGYp6+vj7Fjx2LixIk4ffq0UlvQE9UUXP2petxdjIiIiIjeFPMl5ksA86USzJdI1TwtPlR3CRpnx61j6i5B4/Qz5+eUiIheHydhEVGNVFhYiNzcXBQVFSEvLw8ZGRmIj49Hnz59EBwcXKWxnJ2dERAQgA8//BAmJia4cOECpk2bBhcXF/nqwVf17t0bnTt3xtq1a9G4cWN5e2RkJHbv3g03NzfMmTMH3bp1Q7169XDy5EksWLAAEokEjo6OZY5Zr149DB06FBMmTIBYLEbbtm1x8+ZNTJkyBU5OThVulx0VFYV169bh2rVr8PPzU+q+tbW15asQSwccpa1cuRJdunSBh4cH5s6dC2tra/z222+YNGkSzM3NMW/ePEH/x48fIzc3Fy9evMCff/6JtLQ0LF26FJ9//jlcXFzk/YKDg2Fubo74+HhoaWmhTZs2gnEaNWqEOnXqKLS/KiYmBo8fP0avXr1gaWmJ/Px8rFixAs+fP4e7u7u8X1FRkXy78xI6Ojpo1KgRfHx8EBYWhnbt2snfq4ULF+LTTz+t9BlWZteuXbh37x7Cw8MVVpoOHDgQEolEEJKVXh1Ywt7eHgEBAejatSu6dOkCU1NTXLt2DVFRUbCxsYGtrW2Z127bti0CAwOxYsWKKtUsEomQmJgId3d3dO3aFVFRUWjdujWeP3+Ow4cP486dO/LPi0QiQe/eveHgIJz0YGdnh3HjxmHDhg0YNWoUgJeBYW5uLoqLi/HPP/8gKysLc+fOhaOjIyZNmlRhTcOHD8ecOXOwfft2DBo0qEr3Q6rFCS6qxUlYqsdnqnr8vSciIiJNxnxJiPkS86VXMV8iVZO9eK7uEjROLe7WpnoifpGUSsmK1V0BEdE7xUlYRFQjZWRkoEmTJhCLxTAyMoKDgwNWrFiBIUOGVLiyqiweHh5ITk7GtGnT8PjxY5iZmaFPnz6YOXNmhectWLBAIbjS0dHBvn37sHTpUqxduxYTJ06Erq4uWrdujS+++KLS0Gf58uWYP38+pkyZguvXr8PU1BTu7u6YN29ehSu0jI2NMWXKFEybNk35GwfKDQFLtGzZEidPnsSsWbPg6+uLu3fvwtTUFP3798esWbMUtt5ft24d1q1bh9q1a8PExAQdO3ZESkoKvL29Bf1u3LhR5fepLN27d8eqVasQHByMvLw8GBkZoX379ti7d69ghd7Dhw8VVo42b94cv/32Gzp37oylS5fi6tWreP78OZo1a4aIiIgqP8uySCQS9OjRo8yt/gcOHIiFCxfi7Nmz8vfB399fod/Nmzfh4eGBTZs2IT4+HgUFBTA1NYWrqytiYmIUtqcvLTY2tkrb5pdwcnLCL7/8gri4OIwaNQq5ubnQ09ODg4MDli5dirCwMOTl5WH37t3YuHGjwvlaWlrw9vaGRCKRh2S//fYbmjRpAm1tbRgYGMDOzg5RUVH4/PPPoaOjU2E9xsbGCA4ORkxMDAYMGKCSzw4RERERERER8yUh5kvMl8rCfImoehvxZzd1l6Bxnva1V3cJGqXOjhPqLoGI6J0SyWQymbqLICJ63zg7O8PR0RHLli1Tdyk1jkgkQlpaGvr376/uUqiac9fyUXcJGoe7DKkWdxgiInpz+4q3qvX6KVc+Uuv1axK/Fj+ruwQiIo3DfOn1MV8iZTFfUi1mS6rHfImI6M0xX6o5akK+xKn+RERqsnr1aujr6+PcuXPqLqVGGDFiBPT19dVdBhERERERERFRtcF8qWqYLxERERER0dvEryMkIlKDDRs24MmTJwAACwsLNVdTM8TGxmLixIkAgCZNmqi5GqoJuLKOiIiIiIiINBnzpapjvkRVJuJeBqpUjGJ1l0BERET0VnESFhGRGpibm6u7hBqnUaNGaNSokbrLoBqEW3FTdceJgqrH33siIiIiep8wX6o65ktUZTJOGlIlL7P26i5B4zBfUj3mS0RE9CY4CYuIiIiIlMJQh4iIiIiIiIiIXteOWyfVXYLG8bT4WN0laKDn6i6AiIhqME7CIiIiIiKlcBWYanFSGxERERERERG9T/qZf6juEjTOnr84sU3VmIESEdGb4CQsIiKiaiYkJATJyckAALFYDGNjY7Rr1w4BAQEICQmBlpYWAMDKygqRkZGIjIxUGCMnJwfW1tZljn/s2DE4OTnJXz958gTm5ubQ0tLCrVu3oKOjU+Z5f/75Jz744APY2Njg/PnzCsdFIhHS0tLQv3//Cu8vPj4e0dHRmD9/PiZNmiQ4lpSUhMjISOTn58vbsrOz0bNnTzg5OWHDhg2oXbt2hePT28NJQ6rFQIeIqOYrgpa6SyAiIiIqE/Ml5kvVEbMl1WO+RERU8zFf0iychEVERFQNeXp6IjExEUVFRcjLy0NGRgbGjh2Lbdu2YceOHRCLlftX+P79+2Fvby9oMzExEbzevn077O3tIZPJkJ6eDj8/vzLHSkpKgq+vLw4fPoyffvoJnTt3fq17k0qlmDx5MqRSqUJI9qqff/4ZXl5e8Pb2xtq1a+UBIakHQx3VYvCoevyMEhERERER/Yv50kvMl6oP/t2uesyXVI+fUyIiehP8L00iIqJqSEdHB6ampjA3N0eHDh0wbdo0fPfdd/jhhx+QlJSk9DgmJiYwNTUV/NSqVUvQRyKRICgoCEFBQZBIJGWOI5PJkJiYiM8++wyDBw8ut19lDh06hCdPniA2Nhb379/H0aNHy+178OBBuLq6Ijw8HOvWrWNARkRERERERERUBcyXmC8RERER0bvFnbCIiIhqCFdXVzg4OCA1NRVDhw5VyZhXr17FsWPHkJqaCplMhnHjxuH69euwtLQU9MvMzMTjx4/Ro0cPmJubo0uXLli6dCn09PSqdD2JRIKAgADUqlULAQEBkEgk6NKli0K/tLQ0DB48GDExMZgyZcob3SMRvT+4+lP1uPqTiIiIiEizMF8i0iwfzfhc3SVonLp7b6u7BI1St+c1dZdARPROcRIWERFRDWJra4uzZ88q3b9Lly4KK/wePnwo/2epVAovLy8YGRkBADw8PJCYmIiYmBjBORKJBP7+/tDW1kabNm3wwQcfYOvWrQgJCVG6lvv372Pbtm04duwYACAoKAiffPIJli9fDn19fUF9Pj4+mDZtmtIBWWFhIQoLCwVtxbIiaIm0la6PiIiIiIiIiOh9wHypbMyXqCYyvPJU3SVonO6mv6u7BI1yBDrqLoGI6J3iJCwiIqIaRCaTQSQSKd0/JSUFrVu3LvNYUVERkpOTsXz5cnlbUFAQJk6ciJkzZ8rDtfz8fKSmpuK///2voJ9EIqlSSLZp0yY0b94cDg4vd1VxdHSEpaUlUlJSEB4eLu9Xt25ddOvWDevWrUNAQEC59ZcWHx+P2bNnC9qs0RrNYa90fVQ57jKkWtxhiIiIiIiIiNSB+VLZmC+9fcyWVM/DTN0VaJ4j7ThpiIiIXh8nYREREdUg2dnZsLa2Vrp/s2bN0KJFizKP7dmzB7du3YKfn5+gvaioCAcOHIC7uzsAYOPGjXj69Ck6d+4s7yOTyVBcXIxLly7BxsZGqVokEgl+++03iMX//udHcXExpFKpICTT1tZGeno6BgwYABcXF2RmZlYalEVFRWH8+PGCNm+DEKXqIiIiInpdRTLl/8dLIiIiouqC+VLZmC8RERGROjBf0iychEVERFRDHDx4EOfOncO4ceNUMl7JFvDTp08XtM+bNw8SiUQekkkkEkyYMEFhVeLIkSMhlUoxf/78Sq917tw5nDx5EllZWTA2Npa33717F87Ozvj9999ha2srb9fR0UFqaioGDRoEFxcXHDx4EHZ2duWOr6OjAx0d4QolbhWvety5SbW4+lP1+BklIiIiIiKqGPMl5kvqxL/bVY/5kurxc0pERG+Ck7CIiIiqocLCQuTm5qKoqAh5eXnIyMhAfHw8+vTpg+DgYHm/W7du4cyZM4JzLS0t5f/8zz//IDc3V3Dc0NAQDx48wM6dO7Fjxw60adNGcDw4OBje3t64e/cubty4gVOnTmHDhg2CEAsAAgICEBsbi7lz58pXH167dk2hnpYtW0IikaBTp074z3/+o3CvH330ESQSCRYtWiRo19HRwfbt2+Hj4yMPyuztuf27OjHUUS0GOkRERERERPQ2MV9ivlTdMFtSPeZLRERE1QsnYREREVVDGRkZaNKkCcRiMYyMjODg4IAVK1ZgyJAh0NLSkvdbvHgxFi9eLDj3//7v/9CtWzcAQI8ePRTG3rRpE27dugU9PT24ubkpHHdzc0PdunXx7bff4vLly7Czs1MIyADA29sbo0ePxvfff49+/foBgMKW7QBw6NAhfPvtt5gyZUqZ9zpw4EAsWbIEcXFxCsdq166Nbdu2wdfXVx6UvRrq0bvDUEe1GDyqHj+jRERERERE/2K+9BLzpeqDf7erHvMl1ePnlIiI3oRIJpPJ1F0EERERkaq5a/mouwSiCjEkUz2GZETvn33FW9V6/f+77KTW69ckn7U8ru4SiIiIiKqM+RJVd8yXVI/5EtH7h/lSzVET8iXuhEVERERESmGoQ0REREREREREr0tsZVl5J6qSH5+eVncJREREVAonYRERERGRUrgKTLU4qU31+ExVj7/3RBUrhlblnYiIiIiICADwIue6ukvQOB10nqu7BI2jVbu2ukvQKMXPnqm7BKJqj/mSZuEkLCIiIiJSCie4qBYntxARERERERHR+4TZkup5mHVSdwkaiJOGiIjo9XFKHRERERERERERERERERERERER0RvgTlhERERERGrA1Z+qx93FiIiIiIiIiIiIiIhIXTgJi4iIiIiUwgkuqsVJWERERERERET0PmG2pHrMl1SPn1MiInoTnIRFRKThRCIRAMDAwAD5+fnqLUZD5OTkwNraGgDg4OCAM2fOqLcgoneEoQ4REREREdH7ifmS6jFfovfRjlsn1V2CxvG0+FjdJWig5+ougIiIajBOwiIiqiZCQkKQnJwMABCLxTA2Nka7du0QEBCAkJAQaGlpAQCsrKxw/fp1bNq0Cf7+/oIx7O3tceHCBSQmJiIkJETenpiYiF69eslfx8TEID09XSHcKQl/Tp8+DUdHR2RlZcHFxQV2dnY4e/YstLW15X0NDQ2xbNky+XWsrKwQGRmJyMjI167z6NGjmDt3Lo4dO4YnT56gZcuWCA0NxdixYwXXLgn+AEBXVxdmZmbo2rUrxowZg44dO1b4nL/++mt8/fXXyMnJkdcyc+ZMeHl5yfs4Ozvj0KFDgvOGDx+ONWvWAACaNWuG27dvY/Hixdi/f7+8T+n3sLTLly+jRYsWSr/HAPDrr79ixowZOH78OO7fvw9TU1N07twZX331FRo1agQASEtLw4IFC5CdnY3i4mJYWFjA3d0dy5YtAwAkJSUhMjJSEI5mZ2ejZ8+ecHJywoYNG1C7dm0AwLFjx9CtWzd4enpi9+7dgvpLPhcNGzbE1atXUa9ePfkxR0dH9O/fHzExMeU+OwB4/vw5xGKx/FkmJCRg8+bN8PHxEfQr67N55MgR9O3bFyEhIVi6dKng/ad3i6vAVIuT2oiIar4imVblnYiIiN4h5kvMl5gvMV+qzvqZf6juEjTOnr84sU3VmIES0bvGfEmzcBIWEVE14unpicTERBQVFSEvLw8ZGRkYO3Ystm3bhh07dshDhmbNmiExMVEQPh0/fhy5ubnQ09NTGNfQ0FAerLyOP/74A+vXr0doaGiVzqtKnWlpafD19UVoaCgyMzNhaGiI/fv3Y/LkyTh27Bi2bNkiCEcSExPh6emJp0+f4tKlS/jmm2/QuXNnSKVSBAcHl1tT06ZNMX/+fLRs2RIymQzJycn49NNPcfr0adjb28v7RUREIDY2Vv5aV1dX/s/a2towNTWFvr6+wvgl72FpDRs2VDhe0Xt8584duLm5oU+fPtizZw8MDQ2Rk5ODHTt24NGjRwCAAwcOwM/PD/PmzUO/fv0gEolw4cIF7Nu3r9x7//nnn+Hl5QVvb2+sXbtWEMpJJBKMGTMGEokEf/31F8zMzBTOf/DgARYvXozZs2eXe42ynh0A+Wf38ePH2Lx5MyZPngypVKoQkr1q9+7d8PHxwdSpUzFz5swK+xIRcWKb6jF4JCIiIqp5mC8xX2K+JMR8iTRZ74+8Ku9EVbIgJ03dJWiUKVad1V0CEdE7xUlYRETViI6ODkxNTQEA5ubm6NChA5ycnODm5oakpCQMHToUABAYGIilS5fi5s2baNasGQBAKpUiMDAQ69evV3ldY8aMwaxZszB48GDo6OgofZ6ydT569AgRERHo168fvvnmG3n70KFD0bhxY/Tr1w9btmyBn5+f/JihoaH8WVlZWaFnz54YMmQIRo8ejb59+8LIyKjMmvr27St4PW/ePHz99dc4fvy4ICTT1dWVj18Vpd/Dyo6X9x7/+OOPKCgoQEJCgjxcsra2houLi3ycnTt3omvXrpg0aZK8zcbGBv379y/zugcPHsSnn36KkSNHYsGCBYJjDx8+REpKCk6ePInc3FwkJSVh2rRpCmOMGTMGX375JUaNGlVh6FrRs9u6dSvs7OwwdepUmJmZCT4br9q4cSNCQ0OxZMkSjB49utzrlYeTMai64+QWIiIiIiJ6G5gvMV9ivvSvN82XRKV2T6M3t/PGCXWXoHH6mKu7As3DSUNERPQmOAmLiKiac3V1hYODA1JTU+UhWePGjeHh4YHk5GRER0fj8ePHSElJwaFDh95KSBYZGYlvv/0WX331FSZOnKj0ecrWuXfvXvzzzz9ljt23b1/Y2Nhg06ZNgpCsLOPGjcP69euxb98++Pr6Ani5tfyr29KXKCoqwtatW/Ho0SN8/PHHgmMbNmzAt99+C1NTU/Tt2xczZswQrFZUpVffY1NTU7x48QJpaWkYNGhQmdujm5qaYuPGjTh//jzatGlT4fhpaWkYPHgwYmJiMGXKFIXjW7Zsga2tLVq1aoWgoCBERkYiKipK4boBAQHYt28fYmNjsXLlyte6V4lEgqCgIBgYGMDLywtJSUmYMWOGQr9Vq1Zh/Pjx8lD1dXCCC1V3nCioevy9JyIiIiIqG/Ml5kvMl14vX5IVFb3WeVS2PuYVf9UnVR3zJdVjvkRERG+Ck7CIiGoAW1tbnD17VtAWFhaGCRMmYPr06di2bRuaN28OR0fHt3J9XV1dzJo1C9OmTUNERAQMDAyUPleZOi9dugQAaN26dZlj2NrayvtUxNbWFgCQk5Mjb2vVqpVCvefOncPHH3+Mp0+fQl9fH2lpabCzs5MfHzx4MCwtLWFmZoazZ89iypQpuHjxIlJTUyutYdeuXYJt5L28vLB161alai95j52cnDBt2jQMHjwYI0aMQKdOneDq6org4GA0btwYwMtVg0eOHEHbtm1haWkJJycn9OzZE4GBgYLVpA8fPoSPjw+mTZtWZkAG/BtcAS+3sy8oKMChQ4fg7Ows6CcSiTB//nz07dsX48aNQ/Pmzcscb/Xq1UhISJC/Hj58OJYsWYLLly/j+PHj8ucYFBSE8ePHIzo6WhDIZWdnY/To0ZBIJEoHZIWFhSgsLBS0FcuKoCXiakWi9wmDR9Vj8EhERESkOZgvMV9ivlQx5ktUE/Xy9K+8E1VJ6KU96i5BoyTaWKi7BCKid4qTsIiIagCZTKawaqx3794YPnw4Dh8+DKlUirCwsLdaQ3h4OJYsWYIFCxYgLi5O6fOqUqdMJnujGkvOL/2sfv/9d4V+rVq1wpkzZ1BQUIBt27ZhyJAhOHTokDwoGzZsmLxv27Zt0aRJE7i5ueHq1avlBkMlXFxc8PXXX8tf6+npKV176brnzZuH8ePH4+DBg/jpp5+wZs0axMXF4fDhw2jbti309PSwe/duXL16FZmZmTh+/DgmTJiA5cuX49ixY/JVlXXr1kW3bt2wbt06BAQEKASRFy9exIkTJ5CW9vJ77sViMfz8/CCRSBRCMgDw8PBAt27dMGPGDGzcuLHMewkMDMT06dPlrw0NDQG8/KoADw8PNGjQAADQq1cvhIeH4+DBg3Bzc5P3b9q0KQwNDbFo0SJ4eXmhSZMmlT6/+Ph4zJ49W9BmjdZoDvtyzqDXwQkuqsXJLURERERE9C4xX1IO8yXmS6UxX1ItZkuq52Gm7go0DycNERHRm9BSdwFERFS57OxsWFtbC9rEYjE+++wzzJo1Cz/99FOVttSuX78+CgoKFNrz8/MBoMyViGKxGPPmzcPy5cvx119/KX0tZeq0sbEB8PI+y5KdnS3vU5GS8199Vq+qXbs2WrRogY4dOyI+Ph4ODg5Yvnx5uf07d375HfBXrlyptAY9PT20aNFC/qNMwFNS+6t1m5iYwMfHB4sXL0Z2djbMzMywePFiQZ/mzZtj6NChSEhIwKlTp3DhwgWkpKTIj2trayM9PR0dOnSAi4uLwjOWSCR48eIFzMzMIBaLIRaL8fXXX2P79u1lfkYAYP78+UhJScHp06fLPG5gYCB4Bg0aNEBRURGSk5Oxe/du+XV0dXVx9+5dSKVSwfn16tXD/v37oaenBxcXF9y+fbvS5xcVFYWCggLBjzVsKz2PiIiI6E0UQ8QfJX+IiEj9mC8xX2K+VDHmS0RERKQO6s5satJPTcCdsIiIqrmDBw/i3LlzGDdunMKxsLAwLF68GH5+fjAyMlJ6zFatWuHPP/9EXl6efPtxADh16hTq1KkDC4uyV3r4+Phg0aJFCivCKlNZnT179oSxsTGWLFmCLl26CI7t2LEDly9fxpw5cyq9zrJly1C/fn306NGjSvUVFxcrbDVe2pkzZwBA6cCrqip6j0vUrl0bzZs3x6NHj8rtY2VlBV1dXYU+Ojo6SE1NxaBBg+Di4oKDBw/Czs4OL168wPr167FkyRL07NlTcE7//v2xadMmjBgxQuE6nTp1woABAzB16lSl7/H777/HgwcPcPr0aWhr/7uF+/nz5xEaGor8/Hz5ikYAMDIywv79+9GzZ084OzsjMzMTZmblL+vS0dERbJMPgFvFvwXcuUm1uPqTagL+3hMRERFpBuZLzJcA5kvMl9SPf2OqXsDvlU8wpKpJGVy1//9PFSs+9Zu6SyAieqc4CYuIqBopLCxEbm4uioqKkJeXh4yMDMTHx6NPnz4IDg5W6N+6dWv8/fff8q3BleXh4YFWrVohICAAc+fOhampKU6dOoXo6GiMHTtWEGK8av78+fDw8KjS9SqrU09PD2vXroW/vz+GDRuG0aNHo379+jhw4AAmTZqEQYMGwdfXV3BOfn4+cnNzUVhYiEuXLmHt2rVIT0/H+vXrBWGLra0t4uPj4e3tDeDlijYvLy9YWFjgwYMH2LhxI7KysrBnz8vveb969So2btyIXr16wcTEBGfPnsW4cePwn//8B+3atavSfZdFmfd4165d2Lx5M/z9/WFjYwOZTIadO3fi+++/R2JiIgAgJiYGjx8/Rq9evWBpaYn8/HysWLECz58/h7u7u8J1dXR0sH37dvj4+MiDssuXL+PevXsIDw9XWJ06cOBASCSSMkMy4OV29vb29hCLlftPCYlEgt69e8PBQRi02NnZYdy4cdiwYQNGjRolOGZoaIh9+/bBw8MDzs7OyMrKqjAoexUnuFB1x+CRiIiIiIjeBuZLzJcA5ksl3jRfElXwOaaq23njhLpL0Dh9zDuquwQNxElDRET0+jgJi4ioGsnIyECTJk0gFothZGQEBwcHrFixAkOGDIGWVtnfIGtiYlLl64jFYuzduxfTpk1DQEAA7ty5A2tra4wdOxbjx4+v8FxXV1e4urpi7969VbpmZXUOGjQImZmZmDdvHj755BM8ffoULVu2xPTp0xEZGQmRSLjFZGhoKACgTp06MDc3R7du3XDixAl06NBB0O/ixYuCbc//97//ITg4GLdv34aBgQHatWuHPXv2yIOl2rVrY//+/Vi2bBkePXqEZs2aYeDAgYiOjq7S/ZZHmffYzs4Ourq6mDBhAm7evAkdHR20bNkSCQkJ+OyzzwAA3bt3x6pVqxAcHIy8vDwYGRmhffv22Lt3L1q1alXmtWvXro1t27bB19cXLi4usLe3R48ePcr8eoCBAwdi4cKFOHv2LOrXr69w3MbGBmFhYfjmm28qvee8vDzs3r0bGzduVDimpaUFb29vSCQShZAMeLn1/N69e+Hp6Ynu3bsjKysL5ubmlV4T4ASXt4ET21SLz1P1+HtPRERERMR8ifkS86VXvUm+JCsqUqofKad/u56Vd6IqWXvjO3WXoHGGW3RTdwlERFSDiWQymUzdRRAR0dsjEomQlpaG/v37q7sUjRMTE4P09HT5dvJUvbhr+ai7BI3DSUNU3XESFtH7Z1/xVrVeP+HSJ2q9fk0y1OaIuksgIqI3wHzp7WG+VL0xX1ItcaOG6i5B4yz7OV3dJWic0ZZd1V0CEb1jzJdqjpqQL3EnLCKi90BAQABMTEzw559/qrsUjXDjxg3Y2dnh2bNnsLOzU3c5RERERERERERvHfMl1WK+RO+jZ5vqqLsEjRM0c6K6S9A4hjim7hKIiKgG4yQsIiINd/nyZQCAtra2mivRHGZmZvLViTo6OuothohqLO7aRERU8xXJyv5KJyIiIk3DfEn1mC/R+2hf613qLkHjeCQzXyIiqumYL2kWTsIiItJwLVq0UHcJGkcsFvO5EtEb49c7qh4nthERERERvR3MQVSP+RK9j3p8FqbuEjTOx2dOqLsEjXPCkROOiYjo9XESFhERERERERERERERERG9VQ/Naqu7BI1z8p6FukvQQLfUXQAREdVgnIRFREREGom7DFF1x12biIiIiIiIiOh9cnzBGnWXoHGYLxEREVUvnIRFRERERERERERERERE9CqRlror0CjFKFZ3CURERERvFf/rkYhIw4SEhEAkEkEkEqFWrVpo3Lgx3N3dIZVKUVws/CP36NGj6NWrF4yMjFCnTh20bdsWX375JYqKigAAeXl5qFWrFjZv3lzmtcLDw9GhQwf56ydPnsDY2BgNGjRAYWGhQn8rKyuIRCIcP35c0B4ZGQlnZ2cAwJgxY9C6desyr3fjxg1oa2tjx44dACC/T5FIhPr16+Ojjz7Cd999JzgnKSlJ0K/kJyEhQXDc09NTcF5+fj5EIhGysrLeynNYtmyZ/LVMJsPEiRNRv359ZGVlAQB+/fVX9OvXD40aNUKdOnVgZWUFPz8//O9//wMA5OTkQCQS4cyZM2XWVFLHrFmzYGNjAx0dHTRo0AA+Pj747bffBP1iYmLg6OgoaDty5AgMDQ0RGRkJmUxWZp/79+9jxowZsLe3R926dWFiYoKPPvoICxcuxL179+T9nJ2d5c+9Tp06sLOzw+rVq+XHk5KSYGhoWO7r1+Vh5sAfFf8QERGRUBG0+KPkDxER1SzMl5gvla7jfc6XICvmjwp/tPh/Kv8/IiKq+dSd2dSkn5qAO2EREWkgT09PJCYmoqioCHl5ecjIyMDYsWOxbds27NixA2KxGGlpafD19UVoaCgyMzNhaGiI/fv3Y/LkyTh27Bi2bNmCxo0bo3fv3pBKpfD39xdc49GjR9iyZQvmz58vb9u+fTvs7e0hk8mQnp4OPz8/hdrq1KmDKVOm4NChQ2XWHh4ejpUrV+Lo0aPo0qWL4FhSUhIaNWqEXr16ydsSExPh6emJ+/fvY/Xq1Rg0aBBOnTqFtm3byvvUr18fFy9eFIxlYGAg/2exWIz9+/cjMzMTLi4uCjW9jedQoqioCBEREdi1axcyMzPRsWNH3LlzB25ubujTpw/27NkDQ0ND5OTkYMeOHXj06FG5Y5VWWFiIHj164MaNG1iyZAk6d+6MvLw8xMfHo3Pnzti/fz+cnJzKPHf37t3w8fHB1KlTMXPmzDL73L17F926dcP9+/cxZ84cdOzYEQYGBrh48SISExOxceNGjBo1St4/IiICsbGxePz4MdavX49Ro0bByMgIAQEBSt0PkSbiV2aqHicLEhERERGpDvMl5kvMl0jVBl51V3cJGueh/wfqLkHj6G8+XnknIiKicnASFhGRBtLR0YGpqSkAwNzcHB06dICTkxPc3NyQlJSEgIAAREREoF+/fvjmm2/k5w0dOhSNGzdGv379sGXLFvj5+SE8PBz9+/fHjRs3YGFhIe+7detWvHjxAoGBgfI2iUSCoKAgyGQySCSSMsOhYcOGYc2aNfj+++8FYVcJR0dHdOjQAVKpVBCSyWQyJCUlYciQIRCL//3Xl6GhIUxNTWFqaoo5c+Zg+fLlyMzMFIRkIpFI/jzKoqenB19fX0ydOhU//fRTmX1U/RyAl0FWQEAATp48iSNHjqBVq1YAgB9//BEFBQVISEiQ36u1tXWZAV55li1bhmPHjuH06dNwcHg5KcHS0hLbt29H586dER4ejvPnz0MkEgnO27hxI0JDQ7FkyRKMHj263PGnTZuGGzdu4NKlSzAzM5O3W1paomfPnpDJZIL+urq68vcgJiYGGzduxI4dOxiS0XuNE4aIiIiIiKg6Y77EfIn5Eqna9ub71F2CxvHYzHyJiIioOuEkLCKi94SrqyscHByQmpoKExMT/PPPP5g4caJCv759+8LGxgabNm2Cn58fevXqhcaNGyMpKUmwai0xMREDBgyQb+t99epVHDt2DKmpqZDJZBg3bhyuX78OS0tLwfjW1tYYMWIEoqKi4OnpCS0txa0jw8PDMXXqVCxfvhx6enoAgKysLFy7dg1hYWFl3t+LFy8gkUgAALVr167y84mJiUGLFi2wbds2DBo0SOG4qp/Dw4cP0bt3b/z555/48ccf0axZM/kxU1NTvHjxAmlpaRg0aJBCkKWMjRs3wt3dXR6QldDS0sK4ceMQGBiIX3/9VbAF/KpVqzB+/HhIpVJB6Peq4uJipKSkICgoSBCQlVZZzXXr1sWzZ8+UvyEiDcSdsFSPE9uIiIiIiN4u5ksVY77EfIkq5jEgWN0laJw+F7LUXYLG2WVnpO4SiIioBuMkLCKi94itrS3Onj2LS5cuAQBat25dbr+SPtra2hgyZAiSkpIwY8YMiEQiXL16FUeOHMG+ff+uXJJKpfDy8oKR0cs/UDw8PJCYmIiYmBiF8aOjo5GYmIgNGzbgs88+Uzg+ePBgTJgwAVu3bkVISAiAl2FUt27dYGNjI+gbEBAAbW1tPHnyBMXFxbCysoKvr6+gT0FBAfT19eWv9fX1kZubK+hjZmaGsWPHYvr06ejfv79CTap+DnPmzEG9evWQnZ2Nhg0bCo45OTlh2rRpGDx4MEaMGIFOnTrB1dUVwcHBaNy4sUJtZbl06VK5KxtL3vdLly7JQ7Ls7GyMHj0aEomkwoAMAO7cuYP8/Hz5ysoSHTt2lG/L37dvX2zatEnh3KKiImzatAlnz57FsGHDlLoXZRQWFqKwsFDQViwrgpZIW2XXICIiIiIiIiLmSyWYLzFfoqoT/69A3SVoHMc6N9RdgsbZBU7CIiKi18dJWERE7xGZTCZYQfbqlt6llV7tFxYWhvnz5yMzMxOurq5ITEyElZUVXF1dAbwMPpKTk7F8+XL5OUFBQZg4cSJmzpypsBqxYcOG8mNlbaVuaGiIAQMGQCqVIiQkBPfv38f27duxatUqhb5Lly5Fjx498Mcff2DcuHFYsWIFjI2NBX3q1auHU6dOyV+XtToSAKZMmYK1a9dCKpUqBG2qfg49e/bE/v37ERcXh6VLlypca968eRg/fjwOHjyIn376CWvWrEFcXBwOHz4s2Aq/IhW9v4DwPW7atCkMDQ2xaNEieHl5oUmTJkpdo7S0tDQ8e/YMU6ZMwZMnTwTHVq9ejYSEBDx79gza2toYN24cPv/88ypfozzx8fGYPXu2oM0ardEc9iq7BhERERERERExXyrBfOkl5ktUFTEHtqm7BI0T072/ukvQQH+quwAiIqrBOAmLiOg9kp2dDWtra7Rs2VL+ukuXLmX2K72NeMuWLfHJJ58gMTERzs7OWL9+PSIiIuSB2549e3Dr1i2FwKuoqAgHDhyAu7u7wjXGjx+P1atXY/Xq1WXWGh4eDjc3N1y5cgWZmZnQ1taGj4+PQj9TU1O0aNECLVq0QGJiInr16oULFy6gUaNG8j5aWlpo0aJFpc/H0NAQUVFRmD17Nvr06aNwXJXPwc3NDWPGjMGnn36K4uJiQbBWwsTEBD4+PvDx8UFcXBzat2+PxYsXIzk5udJ7admyJbKzs8s8VtJeetVnvXr1sH//fri7u8PFxQWZmZnlBmUNGzaEoaGhfFViCQsLC/lY+fn5gmOBgYGYPn066tatiyZNmpQbVL6uqKgojB8/XtDmbRCi0msQqRq/Oo+IqOYrllX9a32IiIhqOuZLFWO+xHyJyveRTi11l6BxXtzghCEiopqO+ZJm4SQsIqL3xMGDB3Hu3DmMGzcOHh4eMDY2xpIlSxRCsh07duDy5ctYtmyZoD08PByff/45+vXrh1u3bsm3cQcAiUQCf39/TJ8+XXDOvHnzIJFIygzJ9PX1MWPGDMTExKBfv34Kx11cXGBtbY3ExERkZmbC398fenp6Fd5jp06d0LFjR8ybN6/M0EkZY8aMwYoVK8o9X5XPoWfPnti5cyf69esHmUyGFStWlFtX7dq10bx5czx69Eip+wgICMD06dPx66+/wsHh34kexcXFWLp0KT788EPY2dkJzjEyMsL+/fvRs2dPODs7IzMzE2ZmZgpja2lpwdfXF99++y1mzpxZZp9XGRgYKBVUvi4dHR3o6OgI2vbdPv/WrkdERERERET0PmK+pBzmS5qTL9UyNC6nN72O3KKH6i6BiIiI6K3iJCwiIg1UWFiI3NxcFBUVIS8vDxkZGYiPj0efPn0QHBwMbW1trF27Fv7+/hg2bBhGjx6N+vXr48CBA5g0aRIiIiLQq1cvwZg+Pj744osvMHz4cPTs2RPNmjUDANy5cwc7d+7Ejh070KZNG8E5wcHB8Pb2xt27dxW2cAeAYcOGYenSpdi4cSM6d+4sOCYSiRAWFoYvv/wS9+7dK3NL9bJERkbC29sbkydPhrm5eVUeGwCgTp06mD17NkaNGlXmcVU/hx49emDXrl3o27cviouLsXLlSuzatQubN2+Gv78/bGxsIJPJsHPnTnz//fdITEwUnP/qakEAsLe3x7hx4/Ddd9+hb9++WLJkCTp37oy8vDzExcXh8uXLOHr0aJn3Z2hoiH379sHDwwPOzs7IysoqMwSLi4tDVlYWOnXqhNjYWHz44YfQ09PD2bNncezYMYVnUFVFRUU4c+aMoE1HRwetW7dWegzuMkTV3Z6/flV3CRqHv/dERERERKrDfIn5EvMloKig4I1qIKEhzbqquwSNw3xJ9ZgvERHRm+AkLCIiDZSRkYEmTZpALBbDyMgIDg4OWLFiBYYMGSLfpnvQoEHIzMzEvHnz8Mknn+D+/fsAgAULFmDy5MkKY+rq6sLf3x/ffPMNwsLC5O3r16+Hnp4e3NzcFM5xc3ND3bp18e233+KLL75QOF6rVi3MmTMHgwcPLvM+QkJCMGvWLNjb2yuEaOXx9PSEtbU15s2bV+5W9JUZMmQIlixZggsXLigcexvPwdXVFbt370afPn0gk8kwfvx46OrqYsKECbh58yZ0dHTQsmVLJCQk4LPPPhOc6+/vrzDezZs30bRpUxw4cADx8fGIiorC9evX8eLFC7Ro0QLnz59H06ZNy71/AwMD7N27F56enujevTuysrIU+piYmODEiRNYsGABFi1ahGvXrkFLSwstW7aEn58fIiMjyx1fGQ8fPkT79u0Fbc2bN8eVK1feaFyi6oSBjuoxeFQ9fk6JiIiI3l/Ml5gvMV8iqv74d7vqMV9SLX5Gieh9I5LJZDJ1F0FEROr39OlTfPrpp7h58yYOHTqEhg0bqrskUrEffvgB3t7eWLx4MUaPHq3uct46dy0fdZegcRhAUHXHUIfo/bOveKtar7/yd1e1Xr8mGW17UN0lEBHRO8B8SfMxX6I3wWxJ9ZiFEBG9OeZLNUdNyJe4ExYREQF4uU36d999h2XLluHw4cMYOHCguksiFfPy8sIPP/yAI0eO4O+//0aDBg3UXRLRe40hGRERERERaRrmS5qP+RIRERERUfk4CYuIiOTq1KmDqVOnqrsMeotcXFzg4uKi7jKIiIiINEIRtNRdAhERUbXDfEnzMV8iIiIiUh3mS5qFk7CIiIiIiNSAW/CrHncXIyIiIiIiIiIiIiIideEkLCIiIiIiIiIiIiIiIiJ6qzzM26u7BI3zw1+/qLsEjeNlxs8pERG9Pk7CIiIiIiJSA+7aRERERERERETvkz23Tqu7BI3jwQlDRERE1Qq/XJKIqAYICQmBSCSCSCRCenq6usvRGM7OzvLneubMGXWXQ0RERERERET01jBfejuYLxERERERUQnuhEVENVpISAiSk5MBAGKxGMbGxmjXrh0CAgIQEhKCw4cPw8XFpcIxMjMz4ezsjGfPnmH58uXYtGkTLl68CLFYDCsrK/Tt2xcjR46EmZmZwjVL8/DwQEZGBgDAysoK169fBwDo6uqiVatWiIqKgo+PDwAgJiYGs2fPFpxTYtGiRZg8eTK6d++OrKwsebunpycSExNhZGQEAMjJyYG1tTVOnz4NR0dHwRjOzs5wdHTEsmXLBPUcO3YMTk5O8n6RkZE4c+aM/DoxMTFIT0+XB0avU+fdu3cRGxuLtLQ03L59Gw0aNICnpydiYmJgYWEh71fZe6elVf484YsXL2LEiBG4cOECCgoKYGZmhsGDB2PWrFmoVasWACApKQmhoaGC83R0dPD06VP569TUVFy9ehWdOnUS9JPJZEhISIBUKsVvv/2G4uJiWFpaokePHhgzZgxatGgheD4AoKWlBTMzM3h5eWH+/PkwNjYWjHn69GnMnz8fhw8fxt27d2Fqaoq2bdti+PDh6NOnT4X3CwCzZs1CSEiIwnv+4MED9OvXD3l5efj666/h7Oys8D6XcHNzg4GBAVJTUxESEoL8/Hx56Prq61dZWVkhMjISkZGRgvayPjOlX7/q1c8mACxfvhyTJ09GcnIy/P394ezsjEOHDimcO3z4cKxZs6aix0RUo+z561d1l6BxuLsYEREREb0O5kvMl5gvMV+id2Pkrc7qLkHjFPZpp+4SNI7OrhPqLoGIiGowTsIiohqvJDwqKipCXl4eMjIyMHbsWGzbtg3p6em4ffu2vO/YsWNx//59JCYmytuMjY1RWFiInj174uzZs5g9eza6du2Khg0b4tq1a9i0aRO++uorxMfHK1yzNB0dHcHr2NhYRERE4P79+1iyZAn8/Pxgbm6OLl26AACaNGmCzMxM/Pnnn2jatKn8PKlUKgiTSo9vamr62s+pTp06mDJlSpnhQ0WqUufdu3fh5OSE2rVrY82aNbC3t0dOTg6io6Px0Ucf4dixY/jggw/k/St673bs2AGxuOx/TdWqVQvBwcHo0KEDDA0N8euvvyIiIgLFxcWIi4uT96tfvz4uXrwofy0SiQTjGBsb4/79+4I2mUyGwYMHIz09HdOmTcPSpUthZmaGv/76C2lpaZg7dy6SkpLk/e3t7bF//34UFRUhOzsbYWFhKCgoQEpKirzPd999B19fX/To0QPJyclo0aIFCgsLcfToUURHR+OTTz4RfE5TUlIwc+ZMQe36+vr4+++/BbXeuXMHXl5e0NLSwpEjR2BiYgIHBwdIpVKFkCwnJweZmZnYuXNnmc9UXWbNmoXFixfju+++g6enp7w9IiICsbGxgr66urrvujwiIiKiChXLuME4EZGmYL6kHOZLzJeYL9GbmND4gLpL0Dhf/KSv7hI0zgt1F0BE7x3mS5qFk7CIqMYrHR6Zm5ujQ4cOcHJygpubG9avX4+hQ4fK+9atWxeFhYUKYdP8+fPx3//+FydPnkT79v9+h7qFhQW6d+8OmUxW7jXLU69ePZiamsLU1BSrVq3Ct99+i507d8pDskaNGqFjx45ITk7G9OnTAQBHjx7F33//DR8fH1y4cOH1H0oZhg0bhjVr1uD7779Hr169lD6vKnVOnz4df/31F65cuSJ/PhYWFtizZw9atmyJUaNG4YcffpD3r+i9S0pKErx3pX3wwQeCsM3S0hJZWVk4cuSIoJ9IJKpysJiSkoLNmzfju+++Q79+/eTtFhYWcHJyUvgsiMViwT34+PgIAtRHjx4hPDwcvXv3RmpqquDc1q1bIzw8HDKZDIaGhvJ2AwODMmsvHZLdvHkT7u7uMDc3x3fffQd9/Zd/bIeHhyM6OhrLli0ThEpJSUlo0qSJIIhSJ5lMhi+++ALffvst9u3bJ/+9KKGrq/tGoTARERERERFRVTBfUg7zJeUwX3o3mC/VPE21dSrvRFVSdK9A3SUQERFRKZyERUQaydXVFQ4ODkhNTS03aClt06ZNcHd3FwRkpb26wq2qxGIxatWqhWfPngnaw8LCMHnyZHn4JJVKERgY+EbXKo+1tTVGjBiBqKgoeHp6Vro9eVXrLC4uxubNmxEYGKgQbtStWxcjR45EdHQ07t69q7CVemllvXchISHIyckRbEtf2pUrV5CRkYEBAwYI2h8+fAhLS0sUFxejQ4cOiIuLg729fYX3umnTJrRq1UoQkJVW0WchJycHe/bsQe3ateVte/fuxT///IPJkyeXe15VP18XL17EpEmT8OGHH2LTpk2CVbKBgYGYNGkStm3bhuDgYAAvA6nk5GSEhIRAW1u7Std6G168eIGgoCAcPHgQhw4dQrt23DKbiIiIiIiIqh/mS4qYLzFfYr5Eb6Kvf4S6S9A4bmf+q+4SNM7BNnrqLoGIiGowTsIiIo1la2uLs2fPKtX30qVLcHZ2FrR5e3tj3759AIB27drh6NGj8mO7du2SrwwrMW3aNEybNk1h7GfPnmHJkiUoKCiAq6ur4FifPn0wYsQIHD58GB07dsSWLVvw3//+F1KpVKm6qyo6OhqJiYnYsGEDPvvsM6XPU6bOO3fuID8/H61bty5zjNatW0Mmk+HKlSvo1KlThdd79b1r0qQJiouLFfp16dIFp06dQmFhIYYNGybYXrxVq1aQSqVo164dCgoKsHjxYnTp0gW//fabYNv7V126dAmtWrUStEVGRiIhIQEAYGhoiD///FN+7Ny5c9DX10dRURGePn0KAPjyyy8F45XUU+Lnn3+Gi4uL/PXmzZvRp0+fCp9JacHBwejatSu2bt2qEHoZGxvD29sbUqlUHpJlZmYiJycHoaGhSl+jLFOmTEF0dLSg7dmzZ7Czs6vSOOvWrQMA/Prrr7C1tS2zz+rVq+XPvMTatWvLDZELCwtRWFgoaCuWFUFLpP5QkKg8HmYO6i6BiIiIiIgqwXxJEfMl5ktvgvnS+21vSpK6S9A4zJeIiIiqF365JBFpLJlM9kYrDFevXo0zZ84gLCwMjx8/FhxzcXHBmTNnBD8jRowQ9JkyZQr09fWhq6uLBQsWYP78+ejdu7egT61atRAUFITExERs3boVNjY2b3XVVsOGDTFx4kTMnDlTYdVkRapS56vbqb+OV9+7+Ph4rF+/XqFfSkoKTp06hY0bN2L37t1YvHix/NjHH3+M4OBgODo6onv37khNTUXDhg2xdu3aKtczffp0nDlzBjNnzsTDhw8Fx1q1aoUzZ87g559/xpQpU+Dh4YExY8ZUOF67du3kn5tHjx7hxYuqfct8v379cOTIEYXt50uEhYXh8OHDuHr1KoCXK0u7d++OFi1aVOk6r5o0aVKln3tldOvWDfr6+pgxY0a59x4YGKhwrfJWjwIvPyMGBgaCn2v4vcq1EREREREREZXGfEkR8yXmS2+C+RIRERERaTLuhEVEGis7OxvW1tZK9W3ZsiUuXrwoaGvSpAkAlLm1uZ6eXqWBw6RJkxASEgJ9fX00bty43MAuLCwMnTt3xvnz5xEWFqZUvQBQv359AEBBgeJ3vufn58PAwKDM88aPH4/Vq1dj9erVSl9LmTobNmwIQ0NDZGdnl3l+dnY2RCKRUkGNsu9ds2bNAAB2dnYoKirCsGHDMGHChDK3RK9Vqxbat2+PK1euVDhmWZ+Fhg0bomHDhmjUqJFC/9q1a8vvqSQInT17NubMmSMfD3i5xbuTkxMAQEdH540Cq+nTp6Ndu3YYPHgwZDIZfH19Bcfd3NxgYWGBpKQkTJo0Campqa8VDr6qQYMGCnVXtPV/edq2bYslS5agR48e8PPzQ0pKCsRi4X+SGBgYVOkZRUVFYfz48YI2b4OQKtdGREREREREVBrzJeZLpTFfYr5ERERERFQR7oRFRBrp4MGDOHfuHAYOHKhU/4CAAOzbtw+nT59WWQ0lgYKpqWmFKybt7e1hb2+P8+fPY/DgwUqPb2xsjAYNGuCXX34RtN+/fx9XrlyBjY1NmeeVrBCbN28eHjx4oPT1KqtTS0sLvr6+2LhxI3JzcwXHnjx5gtWrV8PDw6PSUKWq712J4uJiPH/+vMxt5QGgqKgI586dk4ef5QkICMDFixfx3XffVen6JaKjo7F48WL89ddfAICePXvC2NgYCxYseK3xyjNjxgzExMQgMDAQKSkpgmNaWloIDQ1FcnIyNm7ciNq1a2PQoEEqvf6bcnR0xIEDB3D48GH4+vri+fPnbzSejo4O6tevL/jhVvFERET0thVBxB8lf4iIaiLmS8yXXsV8ifkSERERkaqpO7OpST81AXfCIqIar7CwELm5uSgqKkJeXh4yMjIQHx+PPn36IDg4WKkxxo0bh927d8PNzQ2zZs3CJ598AiMjI1y6dAk//PCDwsq3kmuWJhaL0aBBg9e6h4MHD+L58+cwNDSs0nnjx49HXFwcGjduDCcnJ/zzzz+YM2cOGjZsiAEDBpR73rBhw7B06VJs3LgRnTt3VlmdcXFxOHDgANzd3bFw4UK0adMG165dQ3R0NJ4/f45Vq1YJ+iv73kVFReHWrVvyLeM3bNiAWrVqoW3bttDR0cHJkycRFRUFPz8/1KpVCwAQGxsLJycntGjRAvn5+Vi0aBGuX7+OoUOHVniP/v7+SE1Nhb+/P6KiouDh4YHGjRvj+vXrSElJKXMVZGkff/wx2rVrh7i4OKxcuRL6+vpISEiAn58fevfujS+++AItW7bEw4cPkZGRAQCVjlme6dOnQ1tbG4GBgSguLkZAQID8WGhoKGJjYzFt2jQEBASgbt26lY5XUFCAM2fOCNpMTEzkK0KV9eTJE4Vx6tWrh+bNmwvaHBwccPDgQbi5ucHX1xdbtmyRv3+PHz9W+B3T0dGBkZFRlWohIiIiIiIiUgbzJeZLzJeYLxERERERvSlOwiKiGi8jIwNNmjSBWCyGkZERHBwcsGLFCgwZMgRaWspt+FenTh0cOHAAy5YtQ2JiIqKiolBcXAxra2t4eXlh3LhxZV6ztFatWuH3339/rXvQ09N7rfMmT54MfX19LFiwAFevXoWxsTG6du2KzMzMCkORWrVqYc6cOVVaGalMnSYmJjh+/DhiY2MxfPhw5ObmwtjYGF5eXvj2229hYWEh6K/se3f79m3cuHFD/losFmPBggW4dOkSZDIZLC0tMXr0aMH7dO/ePURERCA3NxdGRkbo2LEjjh49Cjs7uwrvQSQSISUlBevWrUNiYiIWLlyI58+fo2nTpnBzc8OXX35Z6XMaN24cQkJCMGXKFDRr1gze3t44evQoFixYgODgYNy9excGBgb48MMPsXnzZvTp06fSMcszdepUaGlp4bPPPoNMJpO/pxYWFujRowf27t2r9NcQZGVloX379oK28PBwJCQkVKmmS5cuKYzj5uaG/fv3K/Rt27atPCjz8fHBli1bAADr1q3DunXrBH09PDzkwSKRJtjz16/qLkHjeJg5qLsEIiIiIqqhmC8xX2K+xHyJ3j7PZh3UXYLG2XXrhLpL0Dh9zDuquwQiIqrBRDKZTKbuIoiIuGIMLwABAABJREFUqGIhISHIz89Henq6ukvRODk5ObC2tsbp06fh6Oio7nJIhdy1fNRdgsbhpCHV4oQhIqI3t694q1qvvzjbQ63Xr0kmtt6j7hKIiN57zJfeHuZLmov5kmoxW1I95ktERG+O+VLNURPyJe6ERURUQ+zatQv6+vpvvLKN/uXl5YXDhw+ruwwiek8xeKSagGEuERERkWZhvqR6zJeIlMe/MVXv8tfKfx0sKUec/3pf70pls446qu4SiIjeKU7CIiKqARYuXIjo6GgAUNimnl5fQkICnjx5AgAKW9kTEb1tDB6JiIiIiOhdYr70djBfIlIeF6SpnoeZuisgIiKi0jgJi4ioBmjUqBEaNWqk7jI0jrm5ubpLIKL3GINH1ePENiJ614plWuougYiISGnMl94O5ktEytv60EDdJWgc7ba26i5B4xSd+13dJRDRe4b5kmbhJCwiIiIiUgonuKgWJ2GpHp+p6vH3noiIiIiIiFQlwcZK3SVoHG3jPHWXoHHW3fivukvQKBEW3dRdAhHRO8VJWERERKSROBmDqjtObiEiIiIiIiKq3rT19dVdgkb5v9/3qrsEjTO4aRd1l6BxOGmIiIjeBCdhERERkUbiBBfV48Q2IiIiIiIiInqfFD18qO4SNIqJlp66SyAiIiJ6q/jlkkRE9F4LCQmBSCSCSCRCrVq10LhxY7i7u0MqlaK4uBgA4O/vD09PT8F5GRkZEIlEiImJEbTHxMTAwsJC/jotLQ1OTk4wMDBAvXr1YG9vj8jISACAs7Oz/Npl/Tg7OwMArKysFI41bdpUfg0rKyssW7as3HusqAYAKCoqwvz582Fra4u6devC2NgYnTt3RkJCgsJYT548gbGxMRo0aIDCwkKF429Sa05OjuA8Y2NjdO/eHUeOHCn33oiIiIiIiIiI1I35EvMlIiIiIiKAk7CIiIjg6emJ27dvIycnBz/88ANcXFwwduxY9OnTBy9evICLiwt+/PFHvHjxQn5OZmYmmjVrhqysLMFYmZmZcHFxAQAcOHAAfn5+GDhwIE6cOIFffvkF8+bNw/PnzwEAqampuH37Nm7fvo0TJ04AAPbv3y9vS01NlY8bGxsrb799+zZOnz6t1L1VVgMAzJ49G0uXLsWcOXNw4cIFZGZmYtiwYcjPz1cYb/v27bC3t4etrS3S09PLvObr1lqi5BkcPnwYZmZm6NOnD/Ly8qo0BhERERERERHRu8R8ifkSERERERG/jpCIiN57Ojo6MDU1BQCYm5ujQ4cOcHJygpubG5KSkuDi4oKHDx/i5MmTcHJyAgBkZWVh6tSpmDBhAp4+fYo6derg6dOn+OmnnxAaGgoA2LlzJ7p27YpJkybJr2VjY4P+/fsDAIyNjeXtT58+BQCYmJjIaymtXr16ZbZXprIaAGDHjh0YOXIkfHx85G0ODmV/lZ9EIkFQUBBkMhkkEgn8/PxUVmuJkmdgamqKadOmYfPmzfjpp5/Qr1+/1x6TqDri1zuqHr+GlIjetSKI1F0CERERVRPMl5gvUeVcwoaquwSN4/hL1SYoUuUudHxReSciIhVivqRZOAmLiIioDK6urnBwcEBqaiqGDh0KMzMzZGZmwsnJCQ8ePMCpU6ewa9cufPXVVzh27BhcXFxw9OhRFBYWylcqmpqaYuPGjTh//jzatGmjlvtQpgZTU1McPHgQI0eORMOGDcsd6+rVqzh27BhSU1Mhk8kwbtw4XL9+HZaWlm+l9idPnmD9+vUAgNq1a1f5fE5woeqOE4aIiIiIiIg0G/MlRTUtXxKbmKi6pPfa2m+WqrsEjTPcopu6SyAiIqJSOAmLiIioHLa2tjh79iwAwMXFBVlZWYiKisKRI0dgY2ODhg0b4j//+Q+ysrLkx62treWh0ZgxY3DkyBG0bdsWlpaWcHJyQs+ePREYGAgdHZ0q1TJlyhRER0fLX8fFxeGLL76o9Dxlavjyyy8xaNAgmJqawt7eHl26dMGnn34KLy8vwVhSqRReXl4wMjICAHh4eCAxMRExMTEqqbVEly5doKWlhcePH0Mmk6Fjx45wc3Or8JzCwkIUFhYK2moVFkNHh9+8TERERERERETqw3ypZudLz/7+H7RE2kpflypmJa6n7hKIiIiI3ipOwiIiIiqHTCaDSPRyC1BnZ2dERkbi+fPnyMrKgrOzMwCge/fuWLt2LQDIw7ISenp62L17N65evYrMzEwcP34cEyZMwPLly3Hs2DHo6uoqXcukSZMQEhIif92gQQOlzlOmBjs7O5w/fx6//PILfvzxRxw+fBh9+/ZFSEgIEhISAABFRUVITk7G8uXL5WMHBQVh4sSJmDlzJrS0/p3s9Lq1lkhJSYGtrS3Onz+PyZMnIykpCbVq1arwnPj4eMyePVvQZo3WaC6yr9K1qWLcXYyIiIiIiIioapgvaUC+BOZLRERERKQcbg9BRERUjuzsbFhbWwN4uVLx0aNH+Pnnn5GZmYnu3bsDeBmS/fTTT7h79y5++uknuLq6KozTvHlzDB06FAkJCTh16hQuXLiAlJSUKtXSoEEDtGjRQv5jaGhYpfMrq0FLSwsfffQRIiMjkZqaiqSkJEgkEly7dg0AsGfPHty6dQt+fn4Qi8UQi8Xw9/fH9evXceDAAZXW2qxZM7Rs2RLe3t6Ii4uDt7e3wirEV0VFRaGgoEDwYw3bKl2X/p+9O4+rKf//AP66t7RpU6GiFErJ1tgiVJTs+1Io0di37C2WhBpLsm/jVvalIYwZmUGREWbGMsPYv4wZhGGSNan7+8OvM47byuXW9Xo+Hh5f93M+53Pe59zbzPT6fj6fS0RERERERETKxnyJ+RIRERERfT64ExYREVE+Dh8+jN9//x3jx48H8CZksrKywt69e3H27FkhJKtSpQqqVKmC6OhovHr1SrRSMT82NjbQ09PDs2fPPvo9fEgNtWvXBgChj0wmg4+PD8LCwkT95s6dC5lMBi8vr49Sa69evTBjxgysXLlSeC/yo62trbAFP7eKVz5vy/qqLkGtcGcxKgv4c09ERERE9P6YLzFfIjH+jql8Eg1+RpXt21unVF2CWulUpaGqSyAi+qQ4CYuIiD57WVlZSE9PR05ODu7du4ekpCRERUWhU6dO8Pf3F/p5eHhg5cqVqFmzJipXriy0u7m5YdmyZbC3t4elpaXQHh4ejufPn6NDhw6oVq0aMjIysHTpUmRnZys9VLp9+zbOnj0raqtWrRqWLFlSZA29evWCq6srmjdvDnNzc9y4cQMhISGwt7eHg4MDHjx4gG+//RZ79+5FnTp1RNfw9/dH9+7d8ejRI5iYmHxQrfmRSCQYO3YswsPDMWzYsBJtsU9Enx+GuUT0qeXKucE4ERERvcF8ifkSkSok/XVa1SWoHW9LThoiok+L+ZJ64SQsIiL67CUlJcHCwgKampqoUKEC6tevj6VLl2LgwIGQSv/7Dx8PDw9s2LAB7u7uovPd3NwQFxeHfv36KbSvWLEC/v7+uHfvHipUqABnZ2f88MMPqFWrllLvYeHChVi4cKGobePGjcWqwdvbG1u3bkVUVBQeP34Mc3NztG7dGuHh4dDU1MSGDRtQvnx5tGnTRuG6bdq0ga6uLjZt2oSxY8d+UK0tWrTIt//AgQMRFhaG5cuXY8qUKcW6Bn0c3LlJuThhiIiIiIiISH0wX2K+REVjtqR8zJeIiIhKF4lcLperuggiIiIiZfOS9lZ1CWqHQZlyMSQjIvpwP+YmqPT6cy90Uun1y5Iwp32qLoGIiIioxJgvKRezJeVjvkRE9OGYL5UdZSFf4k5YREREREQqwOBR+Rg8EhEREREREZVeHWq7qboEtRP7V+n/P6PLmsFW+e+oR0REVBychEVERERExcIJLsrFSVhERERERERE9DnJychQdQlqp4qGgapLICIiordwEhYRERERFQsnDSkXJ7UREZV9OXKpqksgIiIiIiozmC0pH/MlIqKyj/mSeuG7SURERERERERERERERERERERE9AE4CYuIiIiIiIiIiIiIiIiIiIiIiOgD8OsIiYiIiKhYuL25cnELfuXjZ5SIiIiIiIio9OLv7crHfEn5+DklIqIPwUlYRET0WQsICMD69esV2r29vZGUlAQbGxv8+eefAAAdHR1UrlwZTZo0wfDhw9G6dWuh/82bN2Frayu8NjExQcOGDTFv3jw4OzsDAG7cuIGwsDCkpKTg0aNHMDMzE/o4ODgI5yYnJyM6OhonT57EkydPUKVKFTRq1AijRo1Cq1atFGp1cHDAjRs38Oeff8Lc3FzheFHj7dy5E3369MGtW7dQpUoVhfPt7OzQuXNnLFq0CO7u7jhy5AgAQFtbG9WrV8fo0aMxcuRIAEB8fDwGDRoEAJBIJKhcuTJatWqFBQsWIDc3V/SM8hMXFwcbGxt4eHjg33//hbGxcaH96dNiqKNcDHSIiIiIiIjUA/Ml5ktUPMyWlI/5EhERUenCryMkIqLPXrt27XD37l3Rn61btwrHIyIicPfuXVy+fBkbNmyAsbExPD09MXfuXIWxDh48iLt37+LAgQN4+vQp2rdvj4yMDGRnZ8PLywuPHz/Grl27cPnyZWzfvh1169ZFRkaGcP7KlSvRpk0bmJqaYvv27bh8+TISExPRvHlzjB8/XuF6x44dw4sXL9CrV698w77ijNelSxeYmprme/7Ro0dx7do1BAYGCm1DhgzB3bt38ccff6BPnz4YNWqU6HkZGhri7t27uH37Nnbu3InLly+jd+/esLKyEj3jiRMnwsnJSdTWt2/f4r1pRERERERERESlCPMl5ktERERERNwJi4iIPnva2tr5rvDLY2BgIBy3trZGq1atYGFhgRkzZqBXr16oVauW0NfU1BTm5uYwNzfHwoUL4erqipMnT6Jy5cq4fv06Dh06hGrVqgEAqlWrBldXV+HcW7duISgoCEFBQVi0aJGohnr16mHs2LEKtclkMvTr1w9ubm4YN24cpk6dWuLxypUrBz8/P8THxyM0NFTULzY2Fk2bNoWTk5PQpqenJzyP8PBwbNmyBXv37oWvry+ANysU845bWFggMDAQY8eOxbNnz0TPWV9fH5qamoU+eyJ1xtWfysfVn0T0qeVCouoSiIiIqJRgvsR8iYrmOn6YqktQO5bHrqu6BLWT2eKBqksgos8M8yX1wklYRERE72HcuHGYPXs29uzZgylTpuTbR1dXFwDw6tUrVKxYEVKpFN988w2CgoKgoaGh0H/nzp3Izs4ucDyJRPwfYU+ePEFCQgJOnjwJBwcHPH78GKmpqWjZsmWJxwsMDMSiRYtw9OhRYUv6p0+f4ptvvkFMTEyhz0JXVxevXr3K99j9+/eRmJgIDQ2NfO+Z6HPGCUNERERERESfN+ZL4vtkvqT+fopZo+oS1A7zJSIiotKFk7CIiOizt2/fPujr64vaQkNDFVbtvc3ExASVKlXCzZs38z2ekZGB2bNnQ19fH02aNEHlypWxdOlSTJkyBbNmzUKjRo3g4eGB/v37o3r16gCAK1euwNDQULRyb+fOnRg4cKDwOi0tDXXr1gUAbNu2DXZ2dsIqQh8fH8hkMiEkK8l4tWvXhouLC2JjY4WQbMeOHZDL5fDx8cn3HnNycrB161b89ttvGDp0qND++PFj6OvrQy6X4/nz5wCAsWPHonz58gU+zw+VlZWFrKwsUVuuPAdSCYM5ZWKoo1zcCYvKAv7cExEREREVD/Ml5ktUNP6OqXxPfVxUXYLaMR/1P1WXoFaetryv6hKIiD4pTsIiIqLPnoeHB1atWiVqMzExKfI8uVyusHqwefPmkEqlePbsGapXr47t27ejcuXKAIBRo0bB398fKSkpOHHiBBISEhAZGYm9e/fCy8sLgOJqRG9vb5w9exa3b9+Gu7s7cnJyhGOxsbEYMGCA8HrAgAFwc3PDsmXLYGBgUOLxBg8ejPHjxwvnx8bGonfv3sJYeVauXIl169bh1atX0NDQwPjx4zFixAjhuIGBAU6fPo3s7Gzs378fmzdvxty5c4t8nh8iKioKs2bNErXZwhE14FTAGfQ+OGmISjuGuUREREREpCrMl95gvkSF0axcSdUlqB3ZvEVFd6ISGVetuapLICKiMoyTsIiI6LNXvnx51KxZs0TnPHz4EA8ePICtra2offv27ahduzZMTU1hbGyscJ6BgQE6d+6Mzp07Y86cOfD29sacOXPg5eUFOzs7PH78GOnp6cLqQn19fdSsWROamuJ/Zf/xxx84ceIETp06halTpwrtOTk52LZtG4YMGVKi8YA3Kx3Hjx+PHTt2oFWrVvjpp58QFRWl0K9///4ICwuDrq4uLCwsIJVKRcelUqnwPB0dHXH9+nWMGDECGzduLMaTfT8hISGYMGGCqK27UcBHux6RMnDCEBERERERkfpgvvQG8yUqzHdnflR1CWrH25IThoiIiEoTTsIiIiJ6D0uWLIFUKkW3bt1E7VZWVqhRo0axxpBIJHBwcMDx48cBAL169UJwcDDmzZuHmJiYQs+VyWRo1aoVVqxYIWqPi4uDTCbDkCFDSjQe8CbA6927N2JjY3H9+nXY29sLW8+/zcjIqEShYnBwMGrUqIHx48fjiy++KPZ5JaGtrQ1tbW1RG7eKp9KOO4spHye2ERERERFRWcJ8ifnS56bukhFFd6ISebn1uapLUDvVfc+qugQiIirDOAmLiIg+e1lZWUhPTxe1aWpqwszMDADw5MkTpKenIzs7Gzdu3MCmTZuwbt06REVFFTssOnv2LGbOnAk/Pz/Url0bWlpaOHLkCGJjY4WVhtbW1oiOjsa4cePw6NEjBAQEwNbWFo8ePcKmTZsAABoaGsjOzsbGjRsRERGBOnXqiK7z5ZdfYtGiRbhw4QKcnJyKNd7bAgMD0bJlS1y8eFG0AvJDWFlZoXv37pgxYwb27dtX7PN+//130Vb1EokE9esXf4IFJ7hQaccJQ0REZV+OXFp0JyIiIvosMF/6jzrlS6Rcv49bVXQnKhHmS0REZR/zJfXCSVhERPTZS0pKgoWFhaitVq1auHTpEgBgxowZmDFjBrS0tGBubg4XFxccOnQIHh4exb5G1apVYWNjg1mzZuHmzZuQSCTC6/Hjxwv9xowZA0dHRyxatAi9evVCZmYmTE1N0axZMyQlJaFu3brYuXMnHj58iO7duytcx9HREY6OjpDJZFi0aFGxxntbixYtUKtWLVy7dg3+/v4leYyFGj9+PJo1a4ZTp06hSZMmxTqnVatWotcaGhp4/fp1sa/JAEL5OLFNufg8lY8/90REREREpCrMl/6jTvkSKdfD3GeqLkHtaOjrq7oEtZPz9KmqSyAiojJMIpfL5aougoiIiEjZvKS9VV2C2uGkIeXihCEiog/3Y26CSq8//XfF/9OS8je7bqKqSyAiIiIqMeZLysVsSfmYLxERfTjmS2VHWciXuBMWEREREZEKMHhUPgaPRERERERERKXXyoyqqi5B7Uia1lN1CWpHfvI3VZdARERlGCdhERERERGpACcMERERFW7FihVYsGAB0tPTUb9+fSxbtqzQrx5avHgxVq1ahVu3bsHMzAy9evVCVFQUdHR0PmHVRERERFSQkcZ/q7oEtbOHE4aIiIgKpIpsSaqMwomIiIiIiIiIiJRl+/btmDBhAmbOnInTp0+jfv368Pb2xv379/Ptv2XLFgQHB2PmzJm4ePEiZDIZtm/fjtDQ0E9cORERERERERERqZqqsiXuhEVERERERERE9B5y5RJVl1BmZGVlISsrS9Smra0NbW3tfPsvWrQIQ4YMwaBBgwAAq1evxnfffYfY2FgEBwcr9D9+/DhcXV3Rr18/AICNjQ18fX1x8uRJJd8JEREREREREZHyMF8qvpLkS6rKlrgTFhERERERERERfVRRUVEwMjIS/YmKisq376tXr/Drr7/C09NTaJNKpfD09ERaWlq+5zRv3hy//vorTp06BQD43//+h++//x4dOnRQ/s0QEREREREREdEnV9x8SZXZEnfCIiIiIiIiIiKijyokJAQTJkwQtRW0C9Y///yDnJwcVK5cWdReuXJlXLp0Kd9z+vXrh3/++QctWrSAXC7H69evMXz4cH4dIRERERERERGRmihuvqTKbIk7YRERERERERER0Uelra0NQ0ND0Z+CJmG9j5SUFERGRmLlypU4ffo0du3ahe+++w6zZ89W2jWIiIiIiIiIiEh1Pma+pKxsiTthERERvaeAgACsX79eod3b2xtJSUkAgDNnzuCrr77C0aNH8ejRI5ibm6Nu3boYNmwYOnXqBIlEgps3b8LW1lZhnP79+2PIkCHw9PREcnIyWrRoIRx79uwZ6tatix49emDhwoVwd3fHkSNHsHXrVvj4+Aj9Fi9ejMWLF+PmzZsAgPj4eOG7j9+mra2NFy9ewMvLCxoaGjhw4IDo+MqVKxEaGorz58/j2rVr8PDwgLGxMe7evQsdHR2h388//4wmTZoAAORyudCek5ODpUuXIjY2FlevXoWuri5cXFwwbdo0uLq6Cv3erk8ikaBy5cpo1aoVFixYAGtr64LfDKIy6MCdc6ouQe14W9ZXdQlERKQEZmZm0NDQwL1790Tt9+7dg7m5eb7nTJ8+HX5+fvjyyy8BAHXr1sWzZ88wdOhQhIWFQSrlOkQiIip9mC0xW/rctOsyQNUlqJ1eFw+pugS1841jJVWXQEREH0iV2RInYREREX2Adu3aIS4uTtSWN+N6z5496NOnDzw9PbF+/XrUrFkTWVlZOH78OKZNm4aWLVvC2NhYOO/gwYNwcnISXuvq6sLIyAhjxoxBQEAAzp07h/LlywMApkyZAl1dXcyZM0for6Ojg2nTpqFnz54oV65cgTUbGhri8uXLojaJRAKJRIK4uDjUrVsXa9aswbBhwwAAN27cwJQpU7Bq1SpUrVoV165dAwAYGBggMTERvr6+wjgymQzW1ta4deuW0CaXy+Hj44ODBw9iwYIFaNOmDTIzM7FixQq4u7sjISEB3bp1U6hPLpfjxo0bGDlyJHr37o2TJ08W+l7Qx8cJLsrFSVjKx2eqfPy5JyJV0NLSQsOGDXHo0CHhvxNzc3Nx6NAhjB49Ot9znj9/rhCGaWhoABD/H7hERESlDbMlZkufE/kvv6u6BLWTMKStqktQO23PH1N1CWrlxzr6qi6BiD5DqsyWOAmLiIjoA2hra+c7Y/rZs2cIDAxEx44dsWvXLtExR0dHBAYGKvwL29TUNN+xIiMjkZSUhKlTp2L58uVITk7GunXrcPz4cdFKQV9fX+zduxdff/01Ro4cWWDNEomkwFneVlZWWLJkCUaPHo22bdvCxsYGgYGBaNu2Lfz8/ER9Bw4ciNjYWCEoe/HiBbZt24axY8eKtubcsWMHvvnmG+zduxedO3cW2teuXYuHDx/iyy+/hJeXlxACvl2fhYUFAgMDMXbsWGRmZsLQ0LDA+6KPjxNclIuTW4iIyr4ccHelj2XChAkYOHAgGjVqhCZNmmDx4sV49uyZsLOFv78/qlSpgqioKABA586dsWjRIjg7O6Np06a4du0apk+fjs6dOwuBGRERUWnEbInZ0ueE2ZLyeVuqugL1w0lDRPSpMV/6OFSVLXESFhER0Ufwww8/4OHDh5gyZUqBfSQSSbHG0tHRwYYNG9C8eXN4eXkhKCgIoaGhaNiwoaifoaEhwsLCEBERgYEDBwrBU0kNHDgQiYmJGDx4MHr06IHz58/jwoULCv38/PywYMEC3Lp1C9bW1ti5cydsbGzwxRdfiPpt2bIF9vb2opAsz8SJE7Fr1y78+OOPohWLee7fv4/ExERoaGgU+h84WVlZyMrKErXlynMglfD/cKPSi8Gj8nFiGxGR+ujbty8ePHiAGTNmID09HQ0aNEBSUhIqV64MALh165ZodeK0adMgkUgwbdo03L59GxUrVkTnzp0xd+5cVd0CERHRB2G29J9PkS0BzJc+hTb+gaouQe00PfuzqktQO782KN4/W4mIqHRTVbbESVhEREQfYN++fdDXF6+MCQ0NFUKdWrVqCe0///wzPDw8hNfbtm1Dp06dhNfNmzcX/cs+NTUVzs7OAIBGjRohJCQEPXr0gLOzM8LCwvKtZ+TIkViyZAkWLVqE6dOn59vn8ePHCjW3bNkS+/fvF16vXbsWTk5OOHr0KHbu3ImKFSsqjFOpUiW0b98e8fHxmDFjBmJjYzF48GCFfleuXIGjo2O+teS1X7lyRaE+uVyO58+fAwDGjh1baPAXFRWFWbNmidps4YgacCrgDHofnOCiXJyEpXx8psrHn3siUqXRo0cXuEV8SkqK6LWmpiZmzpyJmTNnfoLKiIiIlIfZUunIlgDmS5+C5sFfVF2C2vl57BdFd6ISaf37cVWXoFZS6uqqugQi+oypIlviJCwiIqIP4OHhgVWrVonaTExM8PXXXyv0rVevHs6ePQsAsLOzw+vXr0XHt2/fLgqUrKysRMenT5+OiIgIBAcHQ1Mz/3+Fa2trIyIiAmPGjMGIESPy7WNgYIDTp0+L2nR1xb8IVapUCcOGDcPu3bvzXUWYZ/DgwRg3bhwGDBiAtLQ0JCQkIDU1VaFfUd+VrKWlpVBfdnY29u/fj82bNxc5yzwkJAQTJkwQtXU3Cij0HCJSP5wwREREREREZQ2zpdKRLQHMl6hs+nFbvKpLUDvMl4iI6ENwEhYREdEHKF++PGrWrKnQbmdnBwC4fPkyXFxcALwJsfLrm8fKyqrQ43nhWEEhWZ4BAwZg4cKFmDNnDmxsbBSOS6XSQq/z9vWKulb79u0xdOhQBAYGonPnzjA1NVXoY2dnh4sXL+Z7fl67vb19vvU5Ojri+vXrGDFiBDZu3FhgHdra2tDW1ha1cat4IiIiIiIiIirtmC2VjmwJYL5ERERERB+Ok7CIiIg+grZt28LExATz5s1DYmLiJ722VCpFVFQUevToUeCKRWXR1NSEv78/5s+fL9py/m2+vr7o168fvv32W3Tu3Fl0LDo6GpaWlvDy8irwGsHBwahRowbGjx+PL74o/vba/FoyKu24qo6IiIiIiIgKwmzpP6rKlgAAEmnRfajY9t/+VdUlqB1vS2dVl0BERERv4SQsIiKiD5CVlYX09HRRm6amJszMzLBu3Tr07dsXHTt2xNixY2FnZ4enT58iKSkJAKCh8fFW0nXs2BFNmzbFmjVrULlyZdExuVyuUDPwZpt4qbTkwdLs2bMxefLkfFcqAoCPjw927NiBgQMHYsGCBWjTpg0yMzOxYsUK7Nu3D0lJSShXrlyB41tZWaF79+6YMWMG9u3bV+y6OMFF+TixTbn4PJWPP/dE9KnlyiWqLoGIiIjKOGZLpTdbAgDIc0vWnwrVwbqJqktQO3tvp6m6BLXTpUojVZdARJ8Z5kvqhZOwiIiIPkBSUhIsLCxEbbVq1cKlS5fQvXt3HD9+HPPmzYO/vz8ePXoEIyMjNGrUCNu2bUOnTp0+am3z5s1D8+bNFdozMzMVagaAu3fvwtzcvMTX0dLSgpmZWYHHJRIJEhISsHjxYsTExGDkyJF49eoVTExMcObMGdSuXbvIa4wfPx7NmjXDqVOn0KQJwxpSD5wwRERERERERMyWmC19TpJu/aLqEtSOtyUnDBEREZUmErlcLld1EURERPR5OX36NDw9PREYGIgFCxZ8lGt4SXt/lHE/Z9y5iUo7Tmwj+vz8mJug0utPOcf/3iiu+fVV+14RERGRevkU2RLAfEnZpHp6qi5B7Wy5ckjVJagdn6rNVF0CEX1izJfKjrKQL3EnLCIiIvrkvvjiCxw6dAh79uzB9evXUaNGDVWXRPTJccIQERERERER0fthtlQ27b/Gr85TNm9LThgiIiIqTTgJi4iIiFTC2dkZzs7Oqi6DSGW4s5jycWIbERERERHR54PZUtnzNPelqktQO1JtHVWXoHZys/g5JSKi98dJWERERERERERERERERET0UXUJHK3qEtSOc9ppVZegds5/oeoKiIioLOMkLCIiIiIqFu4ypFzcCYuIqOzLhVTVJRARERERlRnlDvys6hLUTnQc8yVl8wYzUCL6tJgvqRdOwiIiUhPh4eGYNWsWACAmJgZBQUGqLagMiI+Px6BBgwAA48aNw+LFi1VbEFEpx0lDRERERERE6o35UskxXyIqvteejVRdgtoJvS9XdQlERET0Fk7CIiKVCQgIwPr16xXavb29kZSUBBsbGwQFBeUb9ty8eRO2trbCa319fVhbW8Pd3R1BQUGws7NTOCctLQ0tWrRAu3bt8N133+U73pkzZ9CgQQOFc98OUyQSCSpXroxWrVphwYIFsLa2FvW9du0aIiMjcfDgQdy7dw9mZmZwcHDA4MGD0bdvX2hq/veP3n379mHBggU4ffo0cnJy4OTkhFGjRiEgIEChhp07d2LZsmU4c+YMcnJyUL16dfTq1QujR4+GiYkJAMDJyQkHDx6EoaGhcJ5EIkFiYiK6desmGi8gIAAZGRnYvXs3AMDd3R1HjhzB1q1b4ePjI/RbvHgxFi9ejJs3bwrPIigoCBkZGaJn4+DggIsXL4qukZCQgD59+qBatWrC+QDw4sULfPXVV9i6dSv+/PNPGBgYwMPDA+Hh4XBychL6vR38aWhowNjYGLVr10aPHj0wYsQIaGtrKzyn/Dx8+BD169fH7du38e+//8LY2BgA0LdvX7Rr1w49evRQOOf48eOYM2cO0tLS8OLFC9jZ2WHQoEEYN24cNDQ0FPoPGzYM69atw7Zt29C7d2+F4xcuXMCsWbOQnJyMzMxMVKtWDT4+PggODoaenp5C/6ioKEybNg1fffUVJk+eLDr27ntQGAcHB9y4cQN//vknzM3NFY4nJycjOjoaJ0+exJMnT1ClShU0atQIo0aNQqtWrUo0Xt5n6F3Dhg3D6tWrART8eQSAlJQUeHh4CK/NzMzQuHFjzJs3D3Xr1i3yXonKGu4sRkRERET04ZgvMV9ivsR86W3Ml0q3Qxtkqi5B7TBfIiIiKl04CYuIVKpdu3aIi4sTtRU3+ACAgwcPwsnJCc+fP8fvv/+OJUuWoH79+vj222/Rpk0bUV+ZTIYxY8ZAJpPhzp07sLS0LFGthoaGuHz5MuRyOW7cuIGRI0eid+/eOHnypNDn1KlT8PT0hJOTE1asWAEHBwcAwC+//IIVK1agTp06qF//zS9Fy5YtQ1BQEKZOnYpVq1ZBS0sLe/bswfDhw3H+/HksXLhQGDcsLAzz5s3D+PHjERkZCUtLS1y9ehWrV6/Gxo0bMW7cOACApqZmvkFIceno6GDatGno2bMnypUrV+zzypcvj/v37yMtLQ3NmjUT2mUymUKImJWVBU9PT9y6dQvR0dFo2rQp7t27h6ioKDRt2hQHDx6Ei4uL0D8v+MvNzcXDhw+RkpKCOXPmYOPGjUhJSYGBgUGR9QUGBqJevXq4ffu2qF1XVxe6urrQ0tIStScmJqJPnz4YNGgQkpOTYWxsjIMHD2LKlClIS0vDjh07IJFIhP7Pnz/Htm3bMGXKFMTGxiqEZCdOnICnpyc8PT3x3XffoXLlyjh16hQmTpyIQ4cOITk5WaGG2NhYYbx3Q7LiOnbsGF68eIFevXph/fr1mDp1quj4ypUrMXr0aPj5+WH79u2oUaMGHj9+jOTkZIwfPx6//vpricYDgCFDhiAiIkLUll8IWJjLly/D0NAQd+7cweTJk9GxY0dcu3ZN4RkRlXXcWUz5GDwSERERfZ6YLzFfYr7EfOldzJdKJy/fQaouQe24/Zam6hLUTmq94v83BBER0bs4CYuIVEpbW/uDQh1TU1Ph/OrVq6Nz585o06YNAgMDcf36dWFF2dOnT7F9+3b88ssvSE9PR3x8PEJDQ0t0LYlEIlzLwsICgYGBGDt2LDIzM2FoaAi5XI6AgADY29vjp59+glT63/f32tnZwdfXF3L5m62B//rrL0ycOBFBQUGIjIwU+k2cOBFaWloYO3YsevfujaZNm+LUqVOIjIzE4sWLhTAMAGxsbODl5VWs1WrF5evri7179+Lrr7/GyJEji32epqYm+vXrh9jYWCEk+/vvv5GSkoLx48dj69atQt/FixcjLS0NZ86cEQLDatWqYefOnWjatCkCAwNx/vx5IYR6O/iztLRE3bp14eXlhfr162PevHmYM2dOobWtWrUKGRkZmDFjBvbv31/kvTx79gxDhgxBly5dsHbtWqH9yy+/ROXKldGlSxfs2LEDffv2FY4lJCSgdu3aCA4OhqWlJf766y9YWVkBAORyOQIDA+Ho6Ihdu3YJn4tq1arB3t4ezs7OiImJEQVOR44cwYsXLxAREYENGzbg+PHjaN68eZG1v0smk6Ffv35wc3PDuHHjRNe4deuWsBJ40aJFovPq1auHsWPHlmi8PHp6eh/0Mw0AlSpVgrGxMczNzREUFIQuXbrg0qVLqFev3geNSx+OE1yUi5OwiIiIiIiUg/kS8yWA+RLzJTHmS6WT9MhpVZegdqaZXVJ1CWrHG8xAiYjo/XESFhGpFalUinHjxqF79+749ddf0aRJEwDAjh074ODggFq1amHAgAEICgpCSEiIaLVZSdy/fx+JiYnQ0NAQgrizZ8/i4sWL2Lp1qygge1ve9b755htkZ2dj0qRJCn2GDRuG0NBQbN26FU2bNsXmzZuhr69fYGiVt/W5MhgaGiIsLAwREREYOHAgypcvX+xzBw8eDHd3dyxZsgR6enqIj49Hu3btULlyZVG/LVu2CCHX26RSKcaPH4/+/fvj3Llz+W7bn8fBwQHt27fHrl27hJAsPDwc8fHxom3p//jjD0RERODkyZP43//+V6z7+OGHH/Dw4cN835vOnTvD3t4eW7duFYVkMpkMAwYMgJGREdq3b4/4+HhMnz4dwJvPxR9//IEtW7YofC7q168PT09PbN26VRQ4yWQy+Pr6oly5cvD19YVMJitxSPbkyRMkJCTg5MmTcHBwwOPHj5GamoqWLVsCePP1A9nZ2ZgyZUq+57/7s1HUeB/D48ePsW3bNgDgKsVSgpOGiIiIiIjoc8B86cMwX2K+lIf5Er3rekyzojtRidTc4VJ0JyqRGjih6hKIiKgM4yQsIlKpffv2QV9fX9QWGhpa4lWEb8vbov3mzZtCSJYXYgBvtqh//Pgxjhw5And392KP+/jxY+jr60Mul+P58+cAgLFjxwpB0pUrVwAAtWrVEs65f/8+qlevLryeP38+Ro4ciStXrsDIyAgWFhYK19HS0kL16tWF8a5evYrq1auXaPv2DzFy5EgsWbIEixYtEoKe4nB2dkb16tXxzTffwM/PD/Hx8Vi0aJFCOHXlyhV4eHjkO4ajo6PQp7CQDHjzPv/www/CazMzM9SoUUN4nZWVBV9fXyxYsADW1tbFDsnynnteLfldN68P8Ob9OXHiBHbt2gUAGDBgACZMmIBp06ZBIpEUOZ6joyOOHTsmvM7MzMQ333yDtLQ0YbyWLVtiyZIlCj8rhdm2bRvs7Ozg5OQEAPDx8YFMJhNCrStXrsDQ0FC0qnDnzp0YOHCg8DotLQ1169Yt1nh5Vq5ciXXr1ona1qxZg/79+xe79qpVqwJ4s2oUALp06SL8XBckKysLWVlZorZceQ6kEo1iX5eIyj5OFFQ+7oBHVLgc+fv9n+5ERKRczJeYL72N+RLzJYD5UmllF3xG1SWone3XU1RdgtrpHcSJbUT0aTFfUi+chEVEKuXh4YFVq1aJ2kxMTD5ozLwt2fNWWl2+fBmnTp1CYmIigDfbj/ft2xcymaxEIZmBgQFOnz6N7Oxs7N+/H5s3b8bcuXMLPcfU1BRnz54FALi7u+PVq1fFvl7eyqy8+/lUtLW1ERERgTFjxmDEiBElOnfw4MGIi4uDtbU1nj17hg4dOmD58uUK/ZRxT3K5XLSabvTo0Rg9erTwOiQkBI6OjkI4+j7jF+TtVXOxsbHw9vaGmZkZAKBDhw4IDAzE4cOH0aZNmxKPt3XrVtSoUUNYydmgQQNUq1YN27dvR2BgYLHrj42NFd37gAED4ObmhmXLlsHAwACA4mpEb29vnD17Frdv34a7uztycnJKNB4A9O/fH2FhYaJx312tWpTU1FTo6enhxIkTiIyMxOrVq4s8JyoqCrNmzRK12cIRNeBUomsTUdnGCUNEREREnyfmSwVjvlQ45kuFY77EfEnZJDraqi5B7TyX5xTdiYiIiD4ZTsIiIpUqX748atasqdQxL168CACwtbUF8GaV4uvXr2FpaSn0kcvl0NbWxvLly2FkZFSscaVSqVCro6Mjrl+/jhEjRmDjxo0AADs7OwBvQjlnZ2cAgIaGhnCOpuZ//8i1s7PD48ePcefOHVFdAPDq1Stcv34d3t7eAAB7e3scO3YM2dnZ77Va0cDAAI8fP1Zoz8jIKPDeBwwYgIULF2LOnDmwsbEp9rX69++PKVOmIDw8HH5+fqJ7zmNvby+8R+/Ka7e3ty/yWhcvXhTe4/wcPnwYv//+O7755hsA/4VUZmZmCAsLUwhU8uS9jxcvXsx3i/aLFy8KqyhzcnKwfv16pKeni+41JycHsbGxaNOmjWi8vM/Fu+O9fb8ymQwXLlwQjZebm4vY2Nhih2R//PEHTpw4gVOnTom2oc/JycG2bdswZMgQ4TOYnp4urFbU19dHzZo1Fd634oyXx8jI6IN/pm1tbWFsbIxatWrh/v376Nu3L44ePVroOSEhIZgwYYKorbtRwAfVQfSxccIQEREREZFyMF9ivvQ25kvMlwDmS6XV9xcLfw+o5LwtXVVdAhEREb2Fk7CISK3k5uZi6dKlsLW1hbOzM16/fo0NGzYgOjoabdu2FfXt1q0btm7diuHDh7/XtYKDg1GjRg2MHz8eX3zxBZydneHg4ICFCxeiT58+kEqlBZ7bq1cvTJ06FdHR0YiOjhYdW716NZ4/fw5/f38AQL9+/bB06VKsXLkS48aNUxgrIyMDxsbGBV6rVq1a+PXXX0XbgOfk5ODcuXP48ssv8z1HKpUiKioKPXr0KNFqRRMTE3Tp0gU7duwocHWZj48PwsLCcO7cOWE1HvDmvYuJiUHt2rVF7fm5dOkSkpKSEBISUmCfnTt34sWLF8Lrn3/+GYMHD0ZqaqpoW/l3eXt7w8TEBNHR0Qoh2d69e3H16lUsXrwYAPD999/jyZMnOHPmDDQ0/tuW/Pz58xg0aBAyMjKEz0VMTAx8fHxEn4tz587h4MGDwmrO33//Hb/88gtSUlJEK3YfPXoEd3d3XLp0qcht04E3QVurVq2wYsUKUXtcXBxkMhmGDBmCXr16ITg4GPPmzUNMTMwHj/exjBo1ClFRUUhMTET37t0L7KetrQ1tbfFKOm4VT6UdvzpP+TixjYiIiIiUgfmSIuZLbzBfYr5EH6Zjkw6qLkHtRN7creoS1E6oTWNVl0BERGUYJ2ERkUplZWUhPT1d1KapqSlsvX379m1hu/U81apVE/7+8OFDpKen4/nz5zh//jwWL16MU6dO4bvvvoOGhgZ2796Nf//9F4GBgQqr8nr27AmZTCYKyS5fvqxQo5NT/ttNW1lZoXv37pgxYwb27dsHiUSCuLg4eHl5wdXVVdiuPDs7G0ePHsWDBw+EIMXa2hrz58/HpEmToKOjAz8/P5QrVw579uxBaGgo5syZgzp16gAAmjZtiilTpmDixIm4ffs2unfvDktLS1y7dg2rV69GixYt8g3P8kyYMAGBgYFwcHCAl5cXnj17hmXLluHff/8tMCQDgI4dO6Jp06ZYs2ZNibb7jo+Px8qVK2Fqaprv8fHjx2PPnj3o3LkzoqOj0bRpU9y7dw+RkZG4ePEiDh48KNrG/PXr10hPT0dubi4ePnyIlJQUzJkzBw0aNMDkyZOFfsuXL0diYiIOHToEAApB2D///APgzSrTwkLF8uXLY82aNfDx8cHQoUMxevRoGBoa4tChQ5g8eTKGDBmCDh3ehAUymQwdO3ZUCPVq166N8ePHY/PmzRg1ahTWrVuHtm3bomfPnggJCYG5uTlOnjyJiRMnwtvbG8OGDRPGa9KkCVq1aqVQV+PGjSGTybBgwQIAb4LOd382tLW1UbNmTWzcuBERERHCZyjPl19+iUWLFuHChQtwcnJCdHQ0xo0bh0ePHiEgIAC2trZ49OgRNm3aBODNStvs7OxijwcAz58/V/iZ1tbWRoUKFYTXN27cUKg9b0Xnu/T09DBkyBDMnDkT3bp1U9jinqgs44QhIiIiIiLlYL7EfIn5EvMlgPlSWfDdqe9VXYLa8bbkhCEiIqLShJOwiEilkpKSYGFhIWqrVasWLl26BABYuHAhFi5cKDq+ceNGtGjRAgDg6ekJ4M0v0tWqVYOHhwfWrl0rbFctk8ng6emZ77boPXv2xPz58/Hbb7/B0NAQwJtVdO/666+/Cqx//PjxaNasGU6dOoUmTZrAxcUFv/76KyIjIzFq1Cikp6ejfPnyqF+/PmJiYjB48GDRudWrV0d0dDSWLFmCZ8+eAQC2bt2qUMe8efPQsGFDrFixAqtXr0Zubi5q1KiBXr16iVYg5sfX1xdyuRyLFi1CcHAw9PT00LBhQxw9erTI8GvevHn5bpleGF1dXejq6hZ4XEdHB4cPH0ZkZCRCQ0Px559/wsDAAB4eHjhx4oRCEHPhwgVYWFhAQ0MDRkZGqF27NkJCQjBixAjRyrR//vkH169fL1GtBenVqxeSk5Mxd+5ctGzZEpmZmQDePI8pU6YAAO7du4fvvvsOW7ZsUThfKpWie/fukMlkGDVqFFxdXXHixAnMmjUL7du3x6NHjwAAo0ePRkxMDDQ0NPDq1Sts2rRJtB3723r27Ino6GhERkYCAJ4+faqw/XyNGjUwb948PHz4MN9VfY6OjnB0dIRMJsOiRYswZswYODo6YtGiRejVqxcyMzNhamqKZs2aISkpCXXr1sXOnTuLPR4AfP311/j6669F/by9vZGUlCS8fndbdwBITU3N977zntOiRYuQkJCAPn36FNiPiIiI6FPLlfP/wCMiKg2YLzFfYr7EfAlgvkRERERlE/Ml9SKR532JORERqdSjR4/Qpk0bGBoaYv/+/dDT0yvR+eHh4di9e7fCCjAqmru7Oxo0aCBsA/+uly9fomvXrvjrr79w5MgRVKxY8YOul5ubi8DAQBw4cABHjhwpcJUefRgvaW9Vl6B2+PV5ysWdsIiIPtyPuQkqvf64M74qvX5ZssR5q6pLICL6LDBfUh3mS+qJ+ZJyMVtSPuZLREQfjvlS2VEW8iXuhEVEVEqYmJjg4MGDWLFiBdLS0tCmTZsSj/H7779DX18f8+fPx8iRIz9Clepl8+bNGDZsGF68eIEGDRoU2E9HRwd79uzB4sWLcfToUfTs2fODriuVSiGTybBs2TKkpqYyJKMyg6GOcjF4VD5+RomIiIjoc8d86dNjvkRUfPy9XfmYLykfP6dERPQhuBMWEZGaePTokbANecWKFfPdIp/Enjx5gnv37gEAjI2NYWZmpuKKSJm4UlH5GOooFwMdIqIPx5WKZUdZWKlIRETMl94H8yX1xnxJuZgtKR/zJSKiD8d8qewoC/kSd8IiIlITJiYmMDExUXUZZYqBgQEMDAxUXQYRERERERERUanAfKnkmC8REREREVEeqaoLICIiIiIiIiIiIiIiIiIiIiIiKsu4ExYRERERERER0XvIlXNtGxERERERERERvT/mS+qFk7CIiIiIqFi8LeurugS1cuDOOVWXQFQk/twTERERERGRsvB3TOVrcjZH1SWonbSJTVRdglrROPSrqksgIvqkOAmLiIiIiIqFk4aIiIiIiIiIiOh9STQ0VF2C2hluekzVJaidX08aqboEtcJpgkT0ueEkLCIiKlUCAgKwfv16hXZvb28kJSXBxsYGf/75J9LS0uDi4iIcDwoKwtmzZ5GSkgIAeP78OWbPno0dO3bg9u3bMDAwQO3atTFhwgR07doVAODu7o4GDRpg8eLF+dYikUiEv+vp6cHS0hKurq4YM2YMGjZsCAB4+vQpKlSogI0bN8LHx0fo7+Pjg+3bt+PGjRuwsbER2m1sbODn54fZs2cjPDwcs2bNUrhurVq1cOnSJaHGI0eOAAC0tLRgZmaGL774AoMGDUKPHj2Ec1xcXNCgQQOsXr1aaFu9ejVGjBiBuLg4BAQEiJ7x9evXkZqaCgDIycnB0qVLERsbi6tXr0JXVxcuLi6YNm0aXF1dAQCzZ8/GypUrceHCBZiYmAhjnTt3Dk2aNMHOnTvRqVMn0TPL4+rqimPH/gsDHBwccOPGDfz5558wNzcX9X37frW1tWFtbY1BgwYhODg437GJyjKu/iQiIiIiIvo4mC8xX2K+VDol/XVa1SWoHW/LFqouQQ09VXUBRERUhnESFhERlTrt2rVDXFycqE1bW1v4u46ODqZOnSqEKfkZPnw4Tp48iWXLlqF27dp4+PAhjh8/jocPH5aolri4OLRr1w4vX77ElStXsHbtWjRt2hSxsbHw9/eHvr4+GjVqhJSUFFFIlpKSAisrK6SkpAgBVV4w1Lp1a6Gfk5MTDh48KLqmpqb4X89DhgxBREQEXr9+jb///huJiYnw8fFBQEAA1q5dCwDw8PBAYmKi6Lzk5GSFGvJqGzhwIABALpfDx8cHBw8exIIFC9CmTRtkZmZixYoVcHd3R0JCArp164aQkBB8++23GDVqFLZu3QoAyM7OxsCBAzFgwAB06tRJ4Znl0dLSEv5+7NgxvHjxAr169cL69esxdepUhWeed79ZWVk4fPgwhg4dCmNjY4wYMaLgN4qoDOLOYsrHiW1ERERERJSH+RLzJeZLpU96Die3KJuGEXdtUracx49VXQIREZVhnIRFRESljra2tsIKtrcNHToUq1evxvfff48OHTrk22fv3r1YsmSJcNzGxkZYXVgSxsbGQi02NjZo27YtBg4ciNGjR6Nz586oUKECPDw8sGvXLuGcixcv4uXLlxg3bpwooEpJSYG2tjaaNWsm9NXU1Cz0XoE3qyTz+lStWhUuLi5wcHDA4MGD0adPH3h6esLDwwNfffUV0tPThb5HjhzBjBkzMH/+fGGsvKDOw8MDALBjxw5888032Lt3Lzp37iz0W7t2LR4+fIgvv/wSXl5eKF++PDZs2ABnZ2d888036NWrF+bOnYuMjAzExMQU+MzeJZPJ0K9fP7i5uWHcuHH5hmRv3++gQYOwfPly/PjjjwzJiIiIiIiIiKjYmC+JMV9ivlQarHrUVNUlqJ1XX9RUdQlqRyP5V1WXQEREZRgnYRERUZlja2uL4cOHIyQkBO3atYNUKlXoY25uju+//x49evSAgYGBUq8/fvx4bNiwAT/++CP69OkDDw8PREVF4e7du7CwsEBycjJatGiB1q1bY82aNcJ5ycnJaNasGXR0dD64hoEDB2LixInYtWsXPD094erqinLlyiE5ORm+vr74448/8OLFCwQGBmLq1Km4ceMGbG1tkZycDB0dHSGo27JlC+zt7UUBWZ688X/88Ud069YNDg4OiIqKwogRI2BgYICoqCgkJSXB0NCwWDU/efIECQkJOHnyJBwcHPD48WOkpqaiZcuW+faXy+U4duwYLl26BDs7u0LHzsrKQlZWlqgtV54DqUSjWLURERERvY8c8OtsiIiIyirmS8yX3sV86eP7xctC1SWonVU/L1V1CWpndDVXVZdARJ8Z5kvqhZOwiIio1Nm3bx/09fVFbaGhoQgNDRVeT5s2DXFxcdi8eTP8/PwUxli7di369+8PU1NT1K9fHy1atECvXr3g6vrhv0A5ODgAAG7evAkAcHV1hZaWFlJSUuDr64uUlBS4ubmhYcOG+Oeff4SA6siRIwgMDBSN9fvvvyvc64ABA7B69epCa5BKpbC3txdqKF++PJo0aSKqoUWLFtDW1kbz5s2RkpICW1tbpKSkoFmzZsL2+1euXIGjo2O+18hrv3LlitA2btw47NmzBx06dMCYMWOEFY9v8/X1hYbGf+HUpk2b0K1bN2zbtg12dnZwcnICAPj4+EAmkymEZCtXrsS6devw6tUrZGdnQ0dHB2PHji30eURFRWHWrFmiNls4ogacCj2PSoZf9aZc/DpC5eMzVT7+3BMRERFRWcV8ifkS86XS5/X9B6ouQe2s/MdN1SWonVftnFVdglrRSvpZ1SUQEX1SnIRFRESljoeHB1atWiVqMzExEb2uWLEiJk2ahBkzZqBv374KY7Rq1Qr/+9//cOLECRw/fhyHDh3CkiVLMGvWLEyfPv2D6pPL5QAAieTNzHQ9PT00btxYCKiOHDmCyZMnQ1NTUwio5HI5bt26pRAq1apVC3v37hW1FXfln1wuF2oAAHd3dyQkJAB4szW9u7s7AMDNzQ0pKSkYNGgQUlJSMGTIkHzvpyBaWlrC3yUSCcLCwpCSkoJp06bl2z8mJgaenp7CawuLNyvcYmNjMWDAAKF9wIABcHNzw7Jly0SrSfv374+wsDD8+++/mDlzJpo3b47mzZsXWmNISAgmTJggautuFFDoOVRynOCiXJzcQkRERERE9PEwX2K+xHyp9GG2pHzMl5RPC5w0RERE74+TsIiIqNQpX748atYs+rvsJ0yYgJUrV2LlypX5Hi9XrhxatmyJli1bYurUqZgzZw4iIiIwdepUUfBTUhcvXgTwZtv6PB4eHti+fTsuXLiAFy9e4IsvvgDwJqBKTk5Gbm4u9PT00LRpU9FYWlpaxbrXd+Xk5ODq1ato3LixqIa5c+fi9u3bSElJwaRJk4Qa1qxZg+vXr+Ovv/5C69athXPs7OyE+ynoPu3t7UXtmpqaov99l7m5ucI9/fHHHzhx4gROnTqFqVOniu5j27ZtouDOyMhIOH/Hjh2oWbMmXFxcRMHbu7S1tYXVl3m4VTwRERERERHR54v5UtGYL4kxXyIiIiKiD6X4JedERERlhL6+PqZPn465c+fiyZMnRfavXbs2Xr9+jZcvX37QdRcvXgxDQ0NRaOPh4YGrV69iy5YtaNGihbBdeqtWrXDkyBGkpKQI28orw/r16/Hvv/+iZ8+eQlvz5s2hpaWFlStX4uXLl2jYsCEAoHHjxnjw4AFiY2OFbeXz+Pr64urVq/j2228VrhEdHQ1LS0t4eXl9cL0ymQytWrXCuXPncPbsWeHPhAkTIJPJCjxPX18f48aNw6RJk4pcUUlEREREREREVFLMl5gvEREREREpC3fCIiKiUicrKwvp6emiNk1NTZiZmSn0HTp0KGJiYrBlyxbRKkB3d3f4+vqiUaNGMDU1xR9//IHQ0FB4eHiItmN/8OABzp49KxrTwsIClStXBgBkZGQgPT0dWVlZuHLlCtasWYPdu3djw4YNMDY2Fs5p3rw5tLW1sWzZMoSFhQntTZo0wf3797Fnzx6EhIQo1P/69WuFe5VIJML1AeD58+dIT0/H69ev8ffffyMxMRExMTEYMWKEaPt5XV1duLi4YNmyZXB1dRWCOi0tLVF7uXLlhHN8fHywY8cODBw4EAsWLECbNm2QmZmJFStWYN++fUhKShL1fx/Z2dnYuHEjIiIiUKdOHdGxL7/8EosWLcKFCxfg5OSU7/nDhg3D7NmzsXPnTvTq1euDaiEqTbgFv/JxC34iIiIiIsrDfIn50tuYL5UObfsGqLoEtdPm/DFVl6B2Dtcpr+oSiIioDOMkLCIiKnWSkpJgYWEhaqtVqxYuXbqk0LdcuXKYPXs2+vXrJ2r39vbG+vXrERoaiufPn8PS0hKdOnXCjBkzRP22bNmCLVu2iNpmz56NadOmAQAGDRoEANDR0UGVKlXQokULnDp1StgOPo+Ojg5cXFxw5MgRuLu7C+3a2tpwcXFBSkqKKNDKc+HCBYV71dbWFq2m/Prrr/H1119DS0sLpqamaNiwIbZv347u3bsrjOfh4YGjR4+KagD+27b+3RokEgkSEhKwePFixMTEYOTIkXj16hVMTExw5swZ1K5dW+EaJbV37148fPgw33odHR3h6OgImUyGRYsW5Xu+iYkJ/P39ER4ejh49ekAq5UaepB44YYiIqOzLlUtUXQIREREVgPkS86W3MV8qHX7YHq/qEtQO8yUiorKP+ZJ6kci59yoRERG95fTp0/D09ERgYCAWLFig6nLem5e0t6pLUDvcuUm5GJIREX24H3MTVHr94b/6qfT6ZcnqhhtVXQIRERF9QsyXKD/MlpSP+RIR0YdjvlR2lIV8iTthERERkcgXX3yBQ4cOYc+ePbh+/Tpq1Kih6pKI1BKDR+Vj8EhERERERFQ6MF+i/HRo56PqEtTOoCsHVF2C2omzt1Z1CUREVIZxEhYREREpcHZ2hrOzs6rLIFJrnDBERERERERE6oz5Er3r+6Rtqi5B7TBfIiIiKl34pddEREREREREREREREREREREREQfgDthEREREVGxcGWdcvHrCJWPn1EiIiIiIiKi0ou/tysf8yXl4+eUiIg+BHfCIiIiKkJ8fDwkEgkkEgmCgoJUXY7aCA8PF57r4sWLVV0OERERUYnlyqX8U8w/REREnzvmSx8H8yUiIiIq61Sd2ZSlP2VB2aiSqBQKCAgQfrl7+0+7du0AAOfOnUOXLl1QqVIl6OjowMbGBn379sX9+/eFMRITE+Hi4gIjIyMYGBjAyclJ9At4Tk4OvvrqKzg4OEBXVxcmJiZo2rQp1q1bp1DPixcvYGJiAjMzM2RlZSkct7GxEWosX748vvjiCyQkJCgcy+9PQEAAAEAikWD37t35Po+UlBShv1QqhZGREZydnTFlyhTcvXs333O2bt0KDQ0NjBo1Kt/jmZmZmD59OpycnKCrqwtTU1M0btwY8+fPx7///gsAqFu3LoYPH57v+Rs3boS2tjb++ecfUX0SiQSVK1dGz5498b///S/fZ/T2n6+++kr0y3xBfwDgwYMHGDFiBKytraGtrQ1zc3N4e3vjp59+Eq5T1Gfj5s2bkEgkOHv2rMI9ubu7iz4jN27cQL9+/WBpaQkdHR1UrVoVXbt2xaVLl4Q+R44cQevWrWFiYgI9PT3Y2dlh4MCBePXqlei9y8jIEM75+uuvUb9+fejr68PY2BjOzs6Iiooq9Dm9+3kBgH379sHNzQ0GBgbQ09ND48aNER8fL6qtXLlyOHbsmOg+nz17hurVq2PSpEnCfeeNr6OjA3t7e0RFRUEulys8o/Xr16Nx48bQ09ODgYEB3NzcsG/fPlGfvHuuUKECXr58KTr2888/i97TPIaGhrh79y5mz54ttNnY2OQb7oSHh6NBgwbC67x/Xnz11Veifrt37xZd59334n3qzMnJQUxMDOrWrQsdHR1UqFAB7du3F30GAXHwp6GhgQoVKqBp06aIiIjA48ePFe7pXV26dIG1tTV0dHRgYWEBPz8/3LlzRzie9zl+98+JEyeEPpMmTcLdu3dRtWpV0djv/rMmOzsbvr6+qFKlCs6fP19kbURERERERGUF8yUx5kvMl5gv/Yf5EvMlIiIiIip7+HWERB+gXbt2iIuLE7Vpa2vjwYMHaNOmDTp16oQDBw7A2NgYN2/exN69e/Hs2TMAwKFDh9C3b1/MnTsXXbp0gUQiwR9//IEff/xRGGvWrFlYs2YNli9fjkaNGiEzMxO//PKLEBC9befOnXBycoJcLsfu3bvRt29fhT4REREYMmQIMjMzER0djb59+6JKlSr4+eefkZOTAwA4fvw4evbsicuXL8PQ0BAAoKurW+xnkndeZmYmTp8+jfnz50MmkyElJQV169YV9ZXJZJgyZQrWrFmD6Oho6OjoCMcePXqEFi1aIDMzE7Nnz0bDhg1hZGSEy5cvIy4uDlu2bMGoUaMQGBiI8PBwxMTEKNQZFxeHLl26wMzMTFSfgYEBrl69iqFDh6Jz58747bffoKGhIXpGbzMwMIBcLheFcY0bN8bQoUMV+vbs2ROvXr3C+vXrUb16ddy7dw+HDh3Cw4cPAaBYn43iys7OhpeXF2rVqoVdu3bBwsICf//9N/bv3y+ELH/88QfatWuHMWPGYOnSpdDV1cXVq1exc+dO4T1/V2xsLIKCgrB06VK4ubkhKysLv/32mxBOFPfzsmzZMgQFBWHq1KlYtWoVtLS0sGfPHgwfPhznz5/HwoUL4ebmhjFjxiAgIADnzp1D+fLlAQBTpkyBrq4u5syZI9Q1ZMgQREREICsrC4cPH8bQoUNhbGyMESNGCH0mTZqE5cuXY86cOejWrRuys7OxadMmdO3aFUuWLMHo0aMV3tvExET4+voKbTKZDNbW1rh165aor0Qigbm5eYneo7fp6Ohg3rx5GDZsGCpUqFCic4tbp1wuh4+PDw4ePIgFCxagTZs2yMzMxIoVK+Du7o6EhAR069ZN6G9oaIjLly9DLpcjIyMDx48fR1RUFOLi4vDTTz/B0tKywJo8PDwQGhoKCwsL3L59G5MmTUKvXr1w/PhxUb+DBw/CyclJeG1qair8XV9fH/r6+sLPX36eP3+Onj174urVqzh27BhsbW2L9czo4+D25srFrc2JiIiICGC+lB/mS8yXmC/lj/nSG8yXyi5mS8rHfImIiKh04SQsog+QtxLtXbt378bjx4+xbt06aGq++TGztbWFh4eH0Ofbb7+Fq6srJk+eLLTZ29uLfoHdu3cvRo4cid69ewtt9evn/x/UMpkMAwYMgFwuh0wmyzckMzAwgLm5OczNzbFixQps2rQJ3377rbACDQBMTEwAAJUqVYKxsXHxHsRb8s4zNzeHvb09unbtCmdnZ4wYMUK0Gu3GjRs4fvw4du7cieTkZOzatQv9+vUTjoeGhuLWrVu4cuWK6Bf1atWqoW3btsIKtQEDBmDq1KnYuXMnBgwYIBo/JSUF33//fb71WVhYYMaMGejfvz+uXbuGWrVqiZ5RfvT19YW/a2hoKPTNyMhAamoqUlJS4ObmJtTbpEkToc9PP/1U5GejuC5cuIDr16/j0KFDqFatmnA9V1dXoc8PP/wAc3NzzJ8/X2irUaOGsKI2P3v37kWfPn0QGBgotL0dclSsWFH4e0Gfl7/++gsTJ05EUFAQIiMjhfaJEydCS0sLY8eORe/evdG0aVNERkYiKSkJU6dOxfLly5GcnIx169bh+PHjouBUT09PeN6DBg3C8uXL8eOPPwoh2YkTJxAdHY2lS5dizJgxwnlz587Fy5cvMWHCBHTt2hVWVlbCsYEDByI2NlYIn168eIFt27Zh7NixohWJyuDp6Ylr164hKipK9H4UR3Hr3LFjB7755hvs3bsXnTt3FtrXrl2Lhw8f4ssvv4SXl5cQRr4d/FlYWMDR0RGdO3eGk5MTpkyZgk2bNhVY0/jx44W/V6tWDcHBwUIwWa5cOeGYqanpe4eLGRkZ6NixI54+fYpjx459UEhJRERERERUWjFfUsR8ifkSwHwpP8yXSo75EhERERF9Svw6QqKPwNzcHK9fv0ZiYmK+21nn9blw4UKhWx+bm5vj8OHDePDgQaHXu379OtLS0tCnTx/06dMHqamp+PPPPws9R1NTE+XKlRO2DP9YdHV1MXz4cPz000+irfLj4uLQsWNHGBkZYcCAAZDJZMKx3NxcbN++HQMGDChwpVTeFtlmZmbo2rUrYmNjRcfj4+NRtWpVtG3bttDaACjtGeStutq9e3e+W/YDxftsFFfFihUhlUrxzTffFLjq0NzcHHfv3sXRo0eLPa65uTlOnDhR5GeoMN988w2ys7OF7d7fNmzYMOjr62Pr1q0A3qzg27BhA9auXYs9e/Zg8ODBCA0NRcOGDfMdWy6XIzU1FZcuXYKWlpbQvnXrVujr62PYsGEK50ycOBHZ2dnYuXOnqN3Pzw+pqanCar+dO3fCxsYGX3zxxXvfe0E0NDQQGRmJZcuW4e+//y7RucWtc8uWLbC3txcFZHkmTpyIhw8filZD56dSpUro378/9u7dK3yu8raWL8ijR4+wefNmNG/eXBSQARC+GqFFixbYu3dvse4XANLT04Ww+ciRIwzIiIiIiIjos8N86T/Ml8SYLzFfYr5UPMyXiIiIiOhT405YRB9g3759otVrwJsVdnl/+vXrh+HDh6NJkyZo3bo1/P39UblyZQDAmDFjkJqairp166JatWpwcXFB27Zt0b9/f2hrawMAFi1ahF69esHc3BxOTk5o3rw5unbtivbt24uuGRsbi/bt2wtbUHt7eyMuLg7h4eH51v3q1StER0fj8ePHaN26tZKfiiIHBwcAwM2bN1GpUiXk5uYiPj4ey5YtAwD4+Phg4sSJuHHjBmxtbfHgwQNkZGQIqwfzNGzYEJcvXwYAdO7cWQhZAgMD0b59e+F8uVyO9evXY+DAgZBK859revfuXSxcuBBVqlQRXWfq1KmYNm2aqO/+/fvRsmXLIu9TU1MT8fHxGDJkCFavXo0vvvgCbm5u8PHxQb169QAALi4uRX428jRv3lyh/hcvXqBBgwYAgCpVqmDp0qWYMmUKZs2ahUaNGsHDwwP9+/dH9erVAQC9e/fGgQMH4ObmBnNzc7i4uKBNmzbw9/cXtnd/18yZM9GjRw/Y2NjA3t4ezZo1Q4cOHdCrV68Cn+e7rly5AiMjI1hYWCgc09LSQvXq1XHlyhWhrVGjRggJCUGPHj3g7OyMsLAwhfNWrlyJdevW4dWrV8jOzoaOjg7Gjh0rumaNGjVEwVkeS0tLGBoaiq4JvAmE2rdvj/j4eMyYMQOxsbEYPHhwse7xfXTv3h0NGjTAzJkzRcFwUYpb55UrV+Do6JjvGHnt7z6D/Dg4OODJkyd4+PAhKlWqBCMjI4WfRwDC6tLnz5/DxcUF+/btE47p6+sjOjoarq6ukEql2LlzJ7p164bdu3ejS5cuRdYwbtw4VK9eHT/++CP09PSK7J+VlaUQTufKcyCVFLwVPZUctzdXLm7BT2UBf+6JiIg+PuZLxcN8ifnS25gvMV9ivlQ28XdM5ZM0rafqEtROl7hkVZegVvbUNi26ExGRGuEkLKIP4OHhgVWrVona8rbPnjt3LiZMmIDDhw/j5MmTWL16NSIjI3H06FHUrVsX5cuXx3fffYfr168jOTkZJ06cwMSJE7FkyRKkpaVBT08PtWvXxvnz5/Hrr7/ip59+wtGjR9G5c2cEBARg3bp1AICcnBysX78eS5YsEWoYMGAAJk2ahBkzZohCjbwA6OXLl9DX18dXX32Fjh07fvTnlLciL2+l048//ohnz56hQ4cOAN6sNvTy8kJsbGyhW3QnJibi1atXmDp1Kl68eCG0e3l5oWrVqoiLi0NERAQOHTqEW7duYdCgQQpjVK1aFXK5HM+fP0f9+vWxc+dOUagyefJkBAQEiM6pUqVKse+1Z8+e6NixI1JTU3HixAns378f8+fPx7p164Rxi/ps5Nm+fbtC4NG/f3/R61GjRsHf3x8pKSk4ceIEEhISEBkZib1798LLywsaGhqIi4vDnDlzhOtFRkZi3rx5OHXqVL4hloWFBdLS0nD+/HkcPXoUx48fx8CBA7Fu3TokJSUVOygryrth1vTp0xEREYHg4GBhK/137z0sLAz//vsvZs6ciebNm6N58+aiPkWt/swvQBs8eDDGjRuHAQMGIC0tDQkJCUhNTX2POyqeefPmoXXr1vmu4ixMcev80BWwb4+R9zPbvXt3dO/eXaHf5MmTERgYiD///BOzZs2Cv78/9u3bB4lEAjMzM0yYMEHo27hxY9y5cwcLFiwoVkjWqVMn7N69G2vWrBFtTV+QqKgozJo1S9Q2Y6IJZk7iL3hUejF4JCIq+3JR8G4ORETFxXypeJgvMV/KD/Ml5kuFUUa+VKFNW5h6FvzVm1Qy2SavVV2C2rEbfkrVJagdThoiok+N+ZJ64SQsog9Qvnx51KxZs8Djpqam6N27N3r37o3IyEg4Oztj4cKFWL9+vdCnRo0aqFGjBr788kuEhYXB3t4e27dvFwIeqVSKxo0bo3HjxggKCsKmTZvg5+eHsLAw2Nra4sCBA7h9+zb69u0runZOTg4OHToELy8voS0vANLX10flypUL3f5ZmS5evAgAsLGxAQDIZDI8evRI2K4deLNF/G+//YZZs2ahYsWKMDY2FlYl5rG2tgYAGBgYICMjQ2iXSqUICAjA+vXrER4ejri4OHh4eAir9d6WmpoKQ0NDVKpUCQYGBgrHzczMCn1Pi0NHRwdeXl7w8vLC9OnT8eWXX2LmzJmi8K04nw0rKyuFWt5+ZnkMDAzQuXNndO7cGXPmzIG3tzfmzJkjeu+rVKkCPz8/+Pn5Yfbs2bC3t8fq1asVQoW31alTB3Xq1MHIkSMxfPhwtGzZEkeOHIGHh0eRz8DOzg6PHz/GnTt3FLb8f/XqFa5fvw5vb29Re14wll9ABgBGRkbC89ixYwdq1qwJFxcXeHp6Ctc8duwYXr16pRCG3blzB5mZmbC3t1cYt3379hg6dCgCAwPRuXNnmJoW/xcsQ0NDPH78WKE9IyMDRkZG+Z7TqlUreHt7IyQkRCGQLUxx6rS3txd+3t6V157fM8ivr6GhYZHPwszMDGZmZrC3t4ejoyOsrKxw4sQJNGvWLN/+TZs2LXK7+jx+fn7o0qULBg8eDLlcLgrc8hMSEqLQp7tRALwXcaWiMnHnJuXi81Q+TmwjIiKisoj5UvEwX2K+9DbmS8yXikNZ+ZL08PFiXY+KJtEsV3QnKpE9t39RdQlqp0uVRqougYiIyjBOwiL6RLS0tFCjRg08e/aswD42NjbQ09MrtE/t2rUBQOgjk8ng4+OjsL323LlzIZPJREGJMgKgknrx4gXWrl2LVq1aoWLFinj48CH27NmDbdu2wcnJSeiXk5ODFi1a4IcffkC7du3Qp08fbNq0CTNmzFAIWfIzaNAgzJkzB7t27UJiYqKwkvNdtra2MDY2VtbtFUvt2rWxe/fuAo8X57NRXBKJBA4ODjh+vOBgoEKFCrCwsCjR9d793BWlV69emDp1KqKjoxEdHS06tnr1ajx//hz+/v7Fvv679PX1MW7cOEyaNAlnzpyBRCKBr68vli1bhjVr1mDMmDGi/gsXLoSOjo5CmAy8CeX8/f0xf/587N+/v0R11KpVC7/++qtC++nTp/PdXj3PV199hQYNGhTa533q9PHxQb9+/fDtt9+ic+fOomPR0dEwNTUV/TMhP/fv38eWLVvQrVu3Eq1Kzc3NBQCFLdvfdvbs2XxXxxYk7ysfBg0ahNzc3EJXd2prawtftZGHW8UrHye4KBcnYRERERFRSTFfYr5UEOZLJcd8ifnSu5gvfXzy19mqLkHtaEs4sY2IiKg04SQsog+QlZWF9PR0UZumpiZOnDiBbdu2wcfHB/b29pDL5fj222/x/fffIy4uDgAQHh6O58+fo0OHDqhWrRoyMjKwdOlSZGdnC7/E9urVC66urmjevDnMzc1x48YNhISEwN7eHg4ODnjw4AG+/fZb7N27F3Xq1BHV4e/vj+7du+PRo0fCFvbKcOPGDZw9e1bUZmdnJ/z9/v37ePnyJZ48eYJff/0V8+fPxz///INdu3YBADZu3AhTU1P06dNHYaVkhw4dIJPJ0K5dO0RGRiIlJQVNmjRBREQEGjVqhPLly+O3335DWlqawv3a2tqidevWGDp0KLS1tdGjR4/3ur8nT54ovKd6enowNDQs8tyHDx+id+/eGDx4MOrVqwcDAwP88ssvmD9/Prp27QoA2LdvX5GfjeI6e/YsZs6cCT8/P9SuXRtaWlo4cuQIYmNjMXXqVADAmjVrcPbsWXTv3h01atTAy5cvsWHDBly4cAHLli3Ld9wRI0bA0tISrVu3RtWqVXH37l3MmTMHFStWLHAF2rusra0xf/58TJo0CTo6OvDz80O5cuWwZ88ehIaGYs6cOQrvYUkNGzYMs2fPxs6dO9GrVy80a9YM48aNw+TJk/Hq1St069YN2dnZ2LRpE5YuXYr4+PgCV97Nnj0bkydPLtEqRQAYP348WrZsiblz56JHjx7IycnB1q1bkZaWhpUrVxZ4Xt26ddG/f38sXbq0RNcrqk4fHx8kJCRg4MCBWLBgAdq0aYPMzEysWLECe/fuRUJCAsqXLy/0l8vlSE9Ph1wuR0ZGBtLS0hAZGQkjIyN89dVXQr/ExESEhITg0qVLAICTJ0/i559/RosWLVChQgVcv34d06dPR40aNYTPyPr166GlpQVnZ2cAwK5duxAbG1tggF0QPz8/SKVSDBw4EHK5HJMnTy7R+aRcnDSkXJzURkREREQA86U8zJfeYL70H+ZLzJeYL6kfZkvKx3yJiIiodOEkLKIPkJSUpLDqplatWvj++++hp6eHiRMn4q+//oK2tjbs7Oywbt06+Pn5AQDc3NywYsUK+Pv74969e6hQoQKcnZ3xww8/CKuXvL29sXXrVkRFReHx48cwNzdH69atER4eDk1NTWzYsAHly5dHmzZtFGpr06YNdHV1sWnTJowdO1Zp95zfls2pqami+5dIJNDX10f16tXRtm1bTJgwAebm5gCA2NhYdO/ePd+t6nv27Ak/Pz/8888/MDMzw6lTpzBv3jwsWLAAN27cgFQqhZ2dHfr27YugoCCF8wMDA3Ho0CGMHDkSOjo673V/M2bMwIwZM0Rtw4YNw+rVq4s8V19fH02bNkVMTAyuX7+O7OxsWFlZYciQIQgNDQXwZsVfUZ+N4qpatSpsbGwwa9Ys3Lx5ExKJRHg9fvx4AECTJk1w7NgxDB8+HHfu3IG+vj6cnJywe/duuLm55Tuup6cnYmNjsWrVKjx8+BBmZmZo1qwZDh06VKIQafz48ahevTqio6OxZMkSYZXj1q1b4ePjU6J7zY+JiQn8/f0RHh6OHj16QCqVYvHixahXrx5WrlyJadOm4eXLl9DS0sLhw4fRqlWrAsfS0tKCmZlZiWto3rw59u/fj4iICERHR0MqlaJu3bo4dOhQkSFgREQEtm/fXqLrFVWnRCLBjh07sHjxYsTExAg/C82aNUNKSgpcXV1F/TMzM2FhYQGJRAJDQ0PUqlULAwcOxLhx40TB8OPHj0Vf36Cnp4ddu3Zh5syZePbsGSwsLNCuXTtMmzZNtFpw9uzZ+PPPP6GpqQkHBwds374dvXr1KtE9A0D//v0hlUrh5+eH3NxcIQQmKusYPCofg0ciIiIqi5gvvcF86Q3mS2LMl5gvMV9SL+3b+6q6BLXjf/mAqktQOxtqWam6BCIiKsMkcrlcruoiiIhI/T169Aht2rSBoaEh9u/fDz09vY9+zZs3b8LNzQ3NmjXD5s2boaHxftuHx8fHIygoCBkZGcotkAC8+aqMoKCgfMPvD+El7a3U8YiThqj04yQsos/Pj7kJKr3+l78EqPT6Zcm6RvGqLoGIiNQA8yUqCPOlsuGxX/F2wqPiqzD4lqpLUDu5HrdVXQIRfWLMl8qOspAvcScsIiL6JExMTHDw4EGsWLECaWlp+a6wVTYbGxukpKRg/fr1OHv2LBo2bPjeYz1+/Bj6+voYNWoU5s2bp8QqP1+RkZGIjIzE8+fPVV0KkUpwwhARUdmXI1fcgYWIiIg+HuZL9C7mS2XLiXlF7whIJcN8iYio7GO+pF64ExYREVERnjx5gnv37gEAjI2N32treVL06NEjPHr0CABQsWJFGBkZKXV8rlSk0o47iykfg0eiz4+qVyoO+nmQSq9flsQ1jlN1CURERCrFfOnjYL5EnzvmS8rHfIno88N8qewoC/kSd8IiIiIqgoGBAQwMDFRdhtoxMTGBiYmJqssgIiIiIiIiIvromC99HMyXiIiIiKg0kaq6ACIiIiIiIiIiIiIiIiIiIiIiorKMO2ERERERUbFwe3Pl4tbmRERERERERPQ5YbakfMyXiIiIShdOwiIiIiIiUgEGj8rH4JGIiIiIiIio9PLsP1jVJagd13MnVV2C2jlRn//3ORERvT/+W4SIiKgQAQEBWL9+vUK7t7c3jI2NkZGRgaSkJKE9KSkJ7du3x8yZMxEeHi60h4eHIzY2Frdu3RLadu7ciRUrVuDMmTN4+fIlrK2t4erqijFjxsDZ2RkAEB8fj0GDBgEAJBIJLC0t4eXlhXnz5qFSpUpCe2JiIrp16ya8fperqyuOHTsmHNfW1sbly5dRrVo1oU+3bt1gbGyM+Ph44d4zMjKwe/fufF+/y8bGBkFBQQgKCgIAyOVyTJ48GWvXrsXevXvh7u4OGxsb/PnnnwrnRkVFITg4GDdv3oStrS3OnDmDBg0aAACePHmCLl264N69e/jhhx9QtWrVfK9PRERE9KnlyqWqLoGIiIjKAOZLzJfojWFrdqq6BLUT17OdqktQQ5dUXQARfWaYL6kXTsIiIiIqQrt27RAXFydq09bWxo4dOzBp0iS8fv0amppv/pWanJwMKysrpKSkiPonJyfDw8NDeD116lRER0dj7NixmDVrFqpVq4YHDx5g//79CAkJEQVvhoaGuHz5MnJzc3Hu3DkMGjQId+7cwYEDBwqsOS4uDu3a/fcLuJaWlui4RCLBjBkz8g0AlSEnJwdDhgzBvn37kJycjIYNGwrHIiIiMGTIEFF/AwODfMd58OAB2rdvD6lUitTUVJiamn6Ueql4uMuQcnEnLOXjM1U+/twTERERESkH86WSY76kftbZ26i6BLXz1MdY1SWoHfOVlVRdglp52vK+qksgIvqkOAmLiIioCNra2jA3N1do9/DwwNOnT/HLL7/AxcUFAJCSkoLg4GBMnDgRL1++hI6ODl6+fImTJ08KKw5PnDiB+fPnY8mSJRg7dqwwnrW1NRo2bAi5XC66jkQiEa5vaWmJsWPHYvr06Xjx4gV0dXXzrdnY2DjfmvOMHj0aixYtwuTJk1GnTp2SPZAiZGVlwdfXF7/88gtSU1NRq1Yt0XEDA4NCa8vz119/wcvLC1WqVMGePXugr6+v1Dqp5DjBhUo7ThgiIiIiIqLSivlSyTBfUk+aFc1UXYLa2TA/WtUlqJ2R1q6qLoGIiMowTsIiIiJ6T/b29rC0tERycjJcXFzw5MkTnD59Gvv27cOyZcuQlpYGDw8PHD9+HFlZWcJKxa1bt0JfXx8jR47Md9z8tnt/m66uLnJzc/H69ev3rt3V1RVXrlxBcHAw9u3b997jvOvp06fo2LEj/v77b/z000+wsrJ6r3EuX76MyZMno1GjRti6dSu0tbWVViMRERERERERUWnBfEkR8yX1dX2svapLUDueB4JUXYLascfPqi6BiIjKME7CIiIiKsK+ffsUVsmFhoYiNDQUHh4eSElJQUhICFJTU2Fvb4+KFSuiVatWSElJEY7b2tqiWrVqAIArV66gevXqwhbzALBo0SLMmDFDeH379m0YGRkp1HL16lWsXr0ajRo1KnCLdQDw9fWFhoaG8HrTpk3o1q2bqE9UVBTq1auH1NRUtGzZskTPpCCzZ8+GgYEBLl68iIoVK+bbZ+rUqZg2bZqobf/+/aIa/P394erqioSEBNF9FCQrKwtZWVmitlx5DqSSos8lIiIiIiIiIvrYmC8VH/Ml9WXwxT+qLkHtlN/H3cWIiIhKE07CIiIiKoKHhwdWrVolajMxMQEAuLu7IygoCNnZ2UhJSYG7uzsAwM3NDWvWrAEAISwrzODBg9GlSxecPHkSAwYMEG0Z//jxY+jr6yM3NxcvX75EixYtsG7dukLHi4mJgaenp/DawsJCoU/t2rXh7++P4OBg/PTTT4WOV1xt27bFwYMHERkZiZiYmHz7TJ48GQEBAaK2KlWqiF536dIFu3fvxq5du9C7d+8irxsVFYVZs2aJ2mzhiBpwKtkNEH1C/Oo8IqKyL1de+A4TRERERHmYLxUf8yX19bNzgqpLUDveHZkvERGVdcyX1AsnYRERERWhfPnyqFmzZr7HPDw88OzZM/z8889ITk7G5MmTAbwJyQYPHoxHjx7h5MmTGDZsmHCOnZ0djh07huzsbJQrVw4AYGxsDGNjY/z9998K1zAwMMDp06chlUphYWEBXV3dIms2NzcvsOa3zZo1C/b29ti9e3eRfYujTZs2GDNmDLp27Yrc3FwsWbJEoY+ZmVmRtYWFhaFevXro168f5HI5+vTpU2j/kJAQTJgwQdTW3SigxPUTEREREREREX0MzJeKj/kSEREREZVVUlUXQEREVJbVqFEDVlZW2Lt3L86ePQs3NzcAb1beValSBdHR0Xj16pVopaKvry+ePn2KlStXFusaUqkUNWvWRPXq1YsVkJWElZUVRo8ejdDQUOTk5ChlzLZt2+Lbb7/F119/jbFjx773ONOnT0d4eDj69++P7du3F9pXW1sbhoaGoj/cKp6IiIiIiIiIygLmS4qYLxERERFRWcSdsIiIiIqQlZWF9PR0UZumpibMzMwAvFmtuHLlStSsWROVK1cW+ri5uWHZsmWwt7eHpaWl0N6sWTNMnDgREydOxJ9//okePXrAysoKd+/ehUwmg0QigVT66eZJh4SE4Ouvv8aNGzfQt2/fQvs+fvwYZ8+eFbWZmprCyspK1Obp6Yl9+/ahc+fOyM3NxfLly4VjT548UXieenp6MDQ0VLheWFgYNDQ00L9/f+Tm5sLX17eEd0fKxK/PU64Dd86pugS1w88oERERERGVVsyX/sN86fPF39uVj/mS8vFzSkREH4KTsIiIiIqQlJQECwsLUVutWrVw6dIlAG9Csg0bNsDd3V3Ux83NDXFxcejXr5/CmAsXLkSTJk2watUqxMbG4vnz56hcuTJatWqFtLS0fAOjj8XExARTp05FaGhokX1TUlLg7OwsagsMDMS6desU+rZu3RrfffcdOnXqBLlcLgRlM2bMwIwZM0R9hw0bhtWrV+d7zeDgYEilUvj5+UEul+f7POnTYKhDRERERERE9H6YL/2H+dLnK2NgM1WXoHa8LloV3YlKRIq/VF0CERGVYRK5XC5XdRFEREREyuYl7a3qEtQOJ2EpF1fVERF9uB9zE1R6fb+TX6r0+mXJxqaK/6cqERERUWnHfEm5mC0pH/MlIqIPx3yp7CgL+RJ3wiIiIiIiIiIieg+5kKi6BCIiIiIiIiIiKsOYL6kXTsIiIiIiIlIBrv6ksoAraomIiIiIiEhZ2tfk1xEqW/eLt1RdgtrZ26eFqktQKzm/X1J1CUREnxQnYRERERERqQAntxARERERERHR52T/tTRVl6B2mC99DJw0RERE74+TsIiIiIiIVIA7YSkfg0ciIiIiIiIiIiIiIlIVTsIiIiIiomLhBBfl4iQsIiIiIiIiIvqcMFtSPuZLysfPKRERfQhOwiIiIiK1xABC+RhAKBefJxEREREREVHpxnxJuZiFKB+fKRERUenCSVhERPRJBQQEYP369QCAxMREdOvWTbUFlQHh4eGYNWsWACAmJgZBQUGqLaiMYABBpR2DXOXjzz0RfWq5comqSyAiIvosMV8qOeZL74e/Z1Jpx3xJ+fhzT0SfGvMl9cJJWEREJAqu3nb16lXMmTMHGRkZ2L17t+hYSkoKPDw88O+//8LY2BgAIJfL8fXXX0Mmk+HChQvQ1NREzZo1MWDAAAwdOhR6enoAgHbt2iEuLg4VKlQAANy8eRO2trY4c+YMGjRoILqOu7s7GjRogMWLFwMAbGxs8OeffyItLQ0uLi5Cv6CgIJw9exYpKSkA3gRLu3fvxtmzZ4XXs2bNgre3N5KSkkTXWLBgAaZMmQI3NzfhfAB49OgRIiIikJiYiLt378LMzAzt2rVDeHg4rK2t831+mpqaMDExQb169eDr64uAgABIpdIi3wMAuHbtGpydnaGhoYGMjAyhfdKkSRg+fDgaN26c73nDhg3DunXrsG3bNvTu3RsAIJEU/h9sM2fORHh4OABg586dWLFiBc6cOYOXL1/C2toarq6uGDNmDDIzM+Hp6Ynk5GS0aNFCOP/Zs2eoW7cuevTogYULFwIALly4gFmzZiE5ORmZmZmoVq0afHx8EBwcLLz3wJv3MCgoKN+wL7/PwpMnT9ClSxfcu3cPP/zwA6pWrVrUo6SPhKGOcjHQISIiIiIidcJ8ifkS8yUqCrMl5WO+REREVLpwEhYREQH4L7h6W8WKFUs0hp+fH3bt2oVp06Zh+fLlqFixIs6dO4fFixfDxsZGWJWora0Nc3Pz965VR0cHU6dOxZEjR0p0noWFBZKTk/H333+LgpbY2FhR6AW8CchcXFygpaWF1atXw8nJCTdv3sS0adPQuHFjpKWloXr16kL/vOeXk5ODe/fuISkpCePGjcM333yDvXv3QlOz8H/lZmdnw9fXFy1btsTx48dFx/T19aGvrw8NDQ2F854/f45t27ZhypQpiI2NFUKyu3fvCn22b9+OGTNm4PLly6IxAWDq1KmIjo7G2LFjMWvWLFSrVg0PHjzA/v37ERISgqSkJIwZMwYBAQE4d+4cypcvDwCYMmUKdHV1MWfOHADAiRMn4OnpCU9PT3z33XeoXLkyTp06hYkTJ+LQoUNITk6GlpZWoc8gPw8ePED79u0hlUqRmpoKU1PTEo9BVFoxeFQ+Bo9ERERERKrFfIn5EvMlKkzHuq1VXYLaWXNrj6pLUDvDrFsU3YmIiKgAnIRFREQAPjy42rFjBzZv3ozdu3eja9euQruNjQ26dOmCzMxMZZQJABg6dChWr16N77//Hh06dCj2eZUqVULDhg2xfv16hIWFAQCOHz+Of/75B71798Yff/wh9A0LC8OdO3dw7do14blYW1vjwIEDsLOzw6hRo7B//36h/9vPr0qVKvjiiy/g4uKCNm3aID4+Hl9++WWhtU2bNg0ODg5o06aNQkhWmISEBNSuXRvBwcGwtLTEX3/9BSsrK9F7aWRkBIlEovD+njhxAvPnz8eSJUswduxYod3a2hoNGzaEXC4HAERGRiIpKQlTp07F8uXLkZycjHXr1uH48ePQ0dGBXC5HYGAgHB0dsWvXLmFlZrVq1WBvbw9nZ2fExMRg6tSpxb4vAPjrr7/g5eWFKlWqYM+ePUKwV1yc4KJ8nOBCREREREREhWG+xHwpD/Mlyg+zJeXjhCEiIqLShZOwiIhIKTZv3oxatWqJArI8EokERkZGSruWra0thg8fjpCQELRr167Y27EDwODBgzFlyhQhJIuNjUX//v1FfXJzc7Ft2zb0799fIVjS1dXFyJEjMW3aNDx69AgmJiYFXqt169aoX78+du3aJYRkAQEBuHnzpmhb+sOHDyMhIQFnz57Frl27in0vACCTyTBgwAAYGRmhffv2iI+Px/Tp04t17tatW6Gvr4+RI0fmezxvy3kdHR1s2LABzZs3h5eXF4KCghAaGoqGDRsCAM6ePYs//vgDW7ZsUXgv6tevD09PT2zdurVEIdnly5cxefJkNGrUCFu3boW2tnaxz83DUEf5GDxSacefeyIiIiKiso35kiLmS8yX1MmLbk1VXYLaqRF8UdUlqJ07Lsqb8EtERJ8fTsIiIiIAwL59+0Qrwdq3b4+EhIR8jwFATk6O6PXVq1dRq1atj1/o/5s2bRri4uKwefNm+Pn5Ffu8Tp06Yfjw4Th69CgaNmyIHTt24NixY4iNjRX6PHjwABkZGXB0dMx3DEdHR8jlcly7dg1NmjQp9HoODg747bffhNcWFhbIzc0VXj98+BABAQHYtGkTDA0Ni30fwJtnfuLECSFYGzBgACZMmIBp06YJAVdhrly5gurVq4u2sl+0aBFmzJghvL59+zaMjIzQqFEjhISEoEePHnB2dhZCxrxxABT6vI4dO1aie/P394erqysSEhLy3Sb/XVlZWcjKyhK15cpzIJUUfS4RERERERERKQfzJeZLAPMlKtjj6nyWynbi72qqLkHtWON3VZdARERlGCdhERERAMDDwwOrVq0SXpcvX77AYwBw8uRJDBgwQHidt7X4p1KxYkVMmjQJM2bMQN++fYt9Xrly5TBgwADExcXhf//7H+zt7VGvXr18+yrjnuRyuSiwioqKEh0fMmQI+vXrh1atWpV47NjYWHh7e8PMzAwA0KFDBwQGBuLw4cNo06bNe9U7ePBgdOnSRXh/334G06dPR0REBIKDg0XBWp7CnpeWllaJ6ujSpQt2796NXbt2oXfv3kX2j4qKwqxZs0RttnBEDTiV6LpEnxJX0xIRlX258uLvmEFERPQ5YL6kiPkS8yX6z7lJq4ruRCXCfImIqOxjvqReOAmLiIgAvAnFatasWexjf//9t+i1vb09Ll269F7Xzluh9/jxY4VjGRkZBW41P2HCBKxcuRIrV64s0fUGDx6Mpk2b4vz58xg8eLDC8YoVK8LY2BgXL+a/lfPFixchkUgKfF7v9rW1tS3w+OHDh7F3714sXLgQwJugKTc3F5qamli7dm2+9QFvVoquX78e6enposAqJycHsbGxxQrJ7OzscOzYMWRnZ6NcuXIAAGNjYxgbGyu8vwCE67wbkNnZ2Qn36uzsrHDexYsXYW9vX2Q9bwsLC0O9evXQr18/yOVy9OnTp9D+ISEhmDBhgqitu1FAia5J9Knx6x2Vj8EjEREREZFqMV/6D/Ml5kukqGPrXqouQe2MvbZP1SWonaU1P92OjEREpH44CYuIiJSiX79+8PHxwZ49e9C1a1fRMblcjszMzALDLhMTE5iZmeHXX3+Fm5ub0J6ZmYlr164VGLDo6+tj+vTpCA8PR5cuXYpdq5OTE5ycnPDbb7+hX79+CselUin69OmDzZs3IyIiAubm5sKxFy9eYOXKlfD29oaJiUmh1zl8+DB+//13jB8/vsA+aWlpoq339+zZg3nz5uH48eOoUqVKged9//33ePLkCc6cOSPaTv38+fMYNGgQMjIyYGxsXGh9vr6+WLZsGVauXIlx48YV2rcwzs7OcHBwQExMDHx8fCCV/jdj/9y5czh48CCWL19e4nGnT58OqVSK/v37Qy6XF7oiVVtbG9ra2qI2bhVPpR0nDBEREREREYkxX1LEfIn5kjr57vA3qi5B7TBfIiIiKl04CYuIiJSiT58+SExMhK+vL6ZNm4a2bduiYsWK+P333xETE4MxY8agW7duBZ4/YcIEREZGonLlynBxccHDhw8xe/ZsVKxYET169CjwvKFDhyImJgZbtmxB06ZNi13v4cOHkZ2dXWCQFBkZiUOHDsHLywvz589HnTp1cOPGDUybNg3Z2dlYsWKFqH9WVhbS09ORk5ODe/fuISkpCVFRUejUqRP8/f2FfiEhIbh9+zY2bNgAAHB0dBSN88svv0AqlaJOnTqF1i+TydCxY0fUry/+Jbt27doYP348Nm/ejFGjRhU6RrNmzTBx4kRMnDgRf/75J3r06AErKyvcvXsXMpkMEolEFHgVRCKRYN26dWjbti169uyJkJAQmJub4+TJk5g4cSK8vb0xbNgw0Tm3b9/G2bNnRW3VqlVTGDssLAwaGhro378/cnNz4evrW2Q9RERERERERFQ2MV9ivsR8iYiIiIjKMk7CIiIipZBIJNiyZQvWrl2L2NhYzJ07F5qamrCzs4O/vz+8vb0LPX/KlCnQ19fHvHnzcP36dZiYmMDV1RXJycnQ1dUt8Lxy5cph9uzZ+a44LEz58uULPW5qaooTJ04gIiICw4YNQ3p6OkxMTNC+fXts2rQJ1tbWov5JSUmwsLCApqYmKlSogPr162Pp0qUYOHCgKGi6e/cubt26VaJa33Xv3j1899132LJli8IxqVSK7t27QyaTFRmSAcDChQvRpEkTrFq1CrGxsXj+/DkqV66MVq1aIS0tTdjKvyiurq44ceIEZs2ahfbt2+PRo0cAgNGjRyMmJka0mjLvunlb5OfZuHEjWrRooTB2cHAwpFIp/Pz8IJfLS/xek/JwZR2VdvyKRyrt+M9RIiIiosIxX2K+xHxJvfF3IioLJE3rqboEtdIlLlnVJaidPbVNVV0CERVCIpfL5aougoiIPh8BAQHIyMjA7t27VV1KmWNjY4OgoCAEBQWpupQi5ebmIjAwEAcOHMCRI0dgZ2f36WtIz/9rBohKCwaPREQf7sfcBJVev2/acJVevyzZ3my1qksgIiI1wnzp/TFfKpnX6TU/+TXVWfsqDVVdgvqR56q6AiKiMo/5UtlRFvIl7oRFRESf3L59+6Cvr49t27ahU6dOqi6n1IuMjERkZCSeP3+u6lKKTSqVQiaTYdmyZUhNTVVJSMYJLsrHXYaIiIjEcuUSVZdARET02WK+VDLMl95Pe0vnT35NdXbgzhlVl6B2mIESEZV9zJfUC3fCIiKiT+r+/fvIzMwEAFhYWBS5bTsBjx49ErZfr1ixIoyMjFRcUdnAnbCotGNIRkT04VS9UrH38REqvX5ZktB8lapLICIiNcJ8qeSYL70fL42+qi5Brey//auqS1A7nChIRPThmC+VHWUhX+JOWERE9ElVqlQJlSpVUnUZZYqJiQlMTExUXUaZwwkuysedsJSLz1P5+HNPRERERPR5YL5UcsyX3hO/6k2pOrX1UXUJamfo1f2qLkHtrLWrruoSiIioDOMkLCIiIiIiFeCEISIiIiIiIiL6nHz/ww5Vl6B2mC8RERGVLlJVF0BERERERERERERERERERERERFSWcRIWERERERERERERERERERERERHRB+AkLCIiUrmAgABIJBKFP9euXRMd09LSQs2aNREREYHXr18DAFJSUoTjUqkURkZGcHZ2xpQpU3D37l2Faz169AhBQUGoVq0atLS0YGlpicGDB+PWrVtCn86dO6Ndu3b51pqamgqJRILffvsNFhYW+Oqrr0THg4ODIZFIkJKSImp3d3eHn58fACA+Ph7Gxsb5ji+RSLB79+4CX7/t7Xt/9096ejoAIDw8HA0aNMj3/Ly6goKCCjyeJy0tDRoaGujYsaPCsZs3b4qubWpqirZt2+LMmTOi6+Qd19HRgb29PaKioiCXywscx8TEBG5ubkhNTS2yPiIiIiJVyIWEf4r5h4iI6GNjvvQf5kvMl4iIiKjsUHVmU5b+lAWaqi6AiIgIANq1a4e4uDhRW8WKFUXHsrKy8P3332PUqFEoV64cQkJChL6XL1+GoaEhMjMzcfr0acyfPx8ymQwpKSmoW7cugDcBmYuLC7S0tLB69Wo4OTnh5s2bmDZtGho3boy0tDRUr14dgYGB6NmzJ/7++29UrVpVVFNcXBwaNWqEevXqwd3dHSkpKQgODhaOJycnw8rKCikpKXB3dwcAvHz5EidOnMDAgQM/xqMT7v1tlSpVUuo1ZDIZxowZA5lMhjt37sDS0lKhz8GDB+Hk5IS///4bY8eORfv27XHp0iUhEBwyZAgiIiKQlZWFw4cPY+jQoTA2NsaIESPyHeeff/7B3Llz0alTJ1y5cgWVK1dW6j1RyXlb1ld1CWrlwJ1zqi5B7fAzSkRERET0eWO+9P6YL9GnwN/blY/5kvLxc0pERB+Ck7CIiKhU0NbWhrm5eZHHRowYgcTEROzdu1cUklWqVAnGxsYwNzeHvb09unbtCmdnZ4wYMQLHjh0DAISFheHOnTu4du2aMJ61tTUOHDgAOzs7jBo1Cvv370enTp1QsWJFxMfHY9q0acI1nj59ioSEBCxYsAAA4OHhgYkTJ+L169fQ1NTEkydPcObMGcTExCAhIUE4Ly0tDVlZWfDw8FDuQ3vn3j+Wp0+fYvv27fjll1+Qnp6O+Ph4hIaGKvQzNTWFubk5zM3NsXDhQri6uuLkyZPw9vYGAOjp6QnPfdCgQVi+fDl+/PFHhZDs7XFCQ0Oxbds2nDx5El26dPlo90jFw1BHuRjoEBERERERKRfzpffHfIk+BWZLysd8iYiIqHThJCwiIipzdHV18fDhwyL7DB8+HOPHj8f9+/dhZmaGbdu2oX///gphnK6uLkaOHIlp06bh0aNHMDExgb+/P+Lj4xEWFgaJ5M32lgkJCcjJyYGvry+ANyHZ06dP8fPPP6NZs2ZITU2Fvb09evbsiUmTJuHly5fQ0dFBcnIybGxsYGNj81Gex8e2Y8cOODg4oFatWhgwYACCgoIQEhIiPJf86OrqAgBevXqlcEwul+PYsWO4dOkS7OzsChzjxYsX2LBhAwBAS0vrA++CqPRh8Kh8DB6JiIiIiKi4mC99WsyXCAA6tOmt6hLUzuhr36m6BLWzvGbB/0whIiIqCidhERFRqbBv3z7o6+sLr9u3by9a7Qe8CVcOHTqEAwcOYMyYMUWO6eDgAAC4efMm5HI5MjIy4OjomG9fR0dHyOVyXLt2DU2aNMHgwYOxYMECHDlyRNj2PS4uDj179oSRkREAwM7ODlWqVEFKSgqaNWuGlJQUuLm5wdzcHNbW1khLS4OHhwdSUlIUVik+fvxYdL8f4t0t7atVq4YLFy4oZWzgzVbxAwYMAPBm6/7Hjx+Lnsu7MjIyMHv2bOjr66NJkyZC+8qVK7Fu3Tq8evUK2dnZ0NHRwdixYxXOb968OaRSKZ4/fw65XI6GDRuiTZs2hdaYlZWFrKwsUVuuPAdSiUYJ75bo0+GEISIiIiIiIuVivvT+mC8xX/oUvj+UUHQnKhHmS0RERKULJ2EREVGp4OHhgVWrVgmvy5cvL/w9L0DLzs5Gbm4u+vXrh/Dw8CLHlMvlACBaUZfXVpC8FXEODg5o3rw5YmNj4e7ujmvXriE1NRURERGi/u7u7khJSUFISAhSUlIwefJkAICbmxtSUlLg4uKCkydPYsiQIaLzDAwMcPr0aYXrF7ZyryCpqakwMDAQXpcrV67EYxTk8uXLOHXqFBITEwEAmpqa6Nu3L2QymUJIlhduPXv2DNWrV8f27dtRuXJl4Xj//v0RFhaGf//9FzNnzkTz5s3RvHlzhWtu374dDg4OOH/+PKZMmYL4+Pgi7ykqKgqzZs0Stc2YaIKZk0zf884pPwx1iIiIiIiIqDRjvvQG8yXmS6UVsyUiIiJSd5yERUREpUL58uVRs2bNfI/lBWhaWlqwtLSEpmbx/vV18eJFAICNjQ1MTU1hbGwstOXXV1NTE7a2tkJbYGAgxowZgxUrViAuLg41atSAm5ubQm3jxo3Dw4cPcebMGeG4m5sb1qxZg1atWuHVq1do3bq16DypVFrg/ZaUra0tjI2NlTLWu2QyGV6/fg1LS0uhTS6XQ1tbG8uXLxdWbQJvwq3atWsLz/pdRkZGwj3v2LEDNWvWhIuLCzw9PUX9rKysYGdnBzs7O7x+/Rrdu3fH+fPnoa2tXWCdISEhmDBhgqit3L9fvM8tUyH49XnKxeCRiKjsy5UX/PU5RERE9OkxX3p/zJfyz5e6GwXAexF3wlIWZkvKx3yJiKjsY76kXjgJi4iISr3CArSCvHjxAmvXrkWrVq1QsWJFAECfPn2wefNmREREwNzcXNR35cqV6N69uyj06dOnD8aNG4ctW7Zgw4YNGDFihGjVI/AmJHv27BkWLVoEOzs7VKpUCQDQqlUrBAYGYv/+/cK28mXN69evsWHDBkRHR6Nt27aiY926dcPWrVsxfPhwoc3Kygo1atQo1tj6+voYN24cJk2ahDNnzig81zy9evXCjBkzsHLlSowfP77A8bS1tRVCNC9j52LVQqQqDB6Vj8EjEREREREVhPmSapT1fIlfRahc/L1d+ZgvKR8/p0RE9CE4CYuIiNTC/fv38fLlSzx58gS//vor5s+fj3/++Qe7du0S+sydOxeHDh2Cl5cX5s+fjzp16uDGjRuYNm0apFIplixZIhpTX18fffv2RUhICDIzMxEQEKBw3erVq8Pa2hrLli1D//79hXYrKytYWlpi7dq18PX1/aB7u3HjBs6ePStqe3tb+bx7f5upqamwxfqLFy8UzjcwMBACrQcPHigct7CwQFpaGv79918EBgaKwkMA6NmzJ2QymSgkK6lhw4Zh9uzZ2LlzJ3r16pVvH4lEgrFjxyI8PBzDhg2Dnp5escdnAKF8DCCUi8+TiIiIiIiodGG+xHzpXcyXlItZiPLxmRIREZUunIRFRERqoVatWpBIJNDX10f16tXRtm1bTJgwQbQi0czMDCdOnEBERASGDRuGO3fuICcnB82bN8fZs2dhYmKiMG5gYCBkMhk6dOgg2jL9bR4eHli/fj3c3d1F7W5uboiPj4eHh8cH3du726ADQGpqqvD3WrVqKRxPS0uDi4sLAODKlStwdhbvCtWmTRscPHgQALBlyxZs2bJFdHz27Nk4efIkPD09FQIy4E1INn/+fPz2228wNDQs+U0BMDExgb+/P8LDw9GjR48C+w0cOBBhYWFYvnw5pkyZUuzxGUAoH4NH5eJnlIiIiIiIqHRhviTGfIm/uysbsyXl42eUiIiodJHI5XK5qosgIiJSFZlMhpEjR2L79u3o1q2bqsshJfKS9lZ1CWqHQZlyMSQjIvpwP+YmqPT63X8apdLrlyWJritUXQIREdFHw3xJfTFfUi5mS8rHfImI6MMxXyo7ykK+JFV1AURERKoUGBiIbdu24eLFi3jx4oWqyyEiIiIiIiIiojKG+RIREREREQH8OkIiIiJ0795d1SUQlQlcWadcXP2pfPyMEtGnliuXqLoEIiIiKiWYLxEVjb+3Kx/zJeXj55SIPjXmS+qFk7CIiIiIqFgY6igXAx0iIiIiIiIi+pwwW1I+5ktERESlCydhERERERGpAINH5WPwSERERERERFR6dWzSQdUlqJ3Im7tVXYLaCbVprOoSiIioDOMkLCIiIiIiFeCEISIiIiIiIiL6nHx36ntVl6B2vC05YYiIiKg04SQsIiIiIiIV4E5YyseJbURERERERESlV4uxw1Rdgtqx+umqqktQO49cH6q6BCIiKsM4CYuISA0EBARg/fr1Cu3t2rWDXC5HUlKSwrHU1FS0atUK586dg6GhIWxtbRX69O/fH5s2bcLNmzdha2uLM2fOoEGDBsLrPPr6+rC2toa7uzuCgoJgZ2cnHIuPj8egQYMUxtbW1sbLly9F9UdFRSE4OFjos3v3bnTv3h1yuRw7d+5Enz59cOvWLVSpUkVhPDs7O3Tu3BmLFi0CAFy4cAGzZs1CcnIyMjMzUa1aNfj4+CA4OBh6enrCeTY2NggKCkJQUBAAQC6XY/LkyVi7di327t0Ld3d3AMDx48cxZ84cpKWl4cWLF7Czs8OgQYMwbtw4aGhoFHifb7tx4wbi4+Oxe/dunD17Nt8+7u7uaNCgARYvXozg4GDs3r0bly5dEo5funQJjo6OGDhwIOLj40XPediwYcjIyICuri4kEgkSExPRrVu3fK/z9ddfY/ny5bh+/To0NTVha2uLPn36ICQkBADw/PlzzJ49Gzt27MDt27dhYGCA2rVrY8KECejataswTnGfc3GeYZ53a8/Ozoa/vz+OHj2KAwcOoE6dOoU+Z6KyghOGiIjKvly5RNUlEBERKQ3zJeZLzJfoYzu2dI2qS1A7zJeIiMo+5kvqhZOwiIjURLt27RAXFydq++mnn9CnTx/8/fffqFq1quhYXFwcGjVqhHr16uHmzZsAgIMHD8LJyUnoo6urW+g18/o/f/4cv//+O5YsWYL69evj22+/RZs2bYR+hoaGuHz5suhciUT8HxQ6OjqYN28ehg0bhgoVKihcq0uXLjA1NcX69esRGhoqOnb06FFcu3YNgYGBAIATJ07A09MTnp6e+O6771C5cmWcOnUKEydOxKFDh5CcnAwtLS2Fa+Tk5GDIkCHYt28fkpOT0bBhQwBAYmIi+vTpg0GDBiE5ORnGxsY4ePAgpkyZgrS0NOzYsQN9+/ZFu3bthLF69OiBOnXqICIiQmirWLFioc/zXR4eHpg3bx7S09Nhbm4OAEhOToaVlRVSUlJEfZOTk+Hi4lLkewYAsbGxCAoKwtKlS+Hm5oasrCz89ttvOH/+vNBn+PDhOHnyJJYtW4batWvj4cOHOH78OB4+/G8VUEmec3Ge4bufCeBNWNezZ09cvXoVx44dyzfMLQh3GSIiIiIiIiIqGeZLzJeYL4lVSjMudl/6P/buPK7m7P8D+OveIu2pJERatFrHMpHoUrKF7CmKGCNbliQZipTGWMc2uJX5GjEZu7GrMBXGYDCWGdsMwyyRJYS6vz/8utPHbXe5db2ej4fHuOdzPufz/ny6odecc27p3MZ9ouoS1I4OTqi6BCIiIiqEk7CIiNSElpaWPEgp0Lt3b9SqVQuJiYmYOXOmvP3JkydITk7GggULBP1NTEwUxihJ4f7W1tbw9vZG586dERQUhGvXrslXn4lEolLH9fDwwG+//YbY2Fh8/vnnCserVauGoUOHIjExUSEki4+Px8cffwxnZ2fIZDIEBQXB0dERW7duhVgsBgBYWlrCzs4OLVq0wOLFixEWFiYYIzc3F76+vvjxxx9x7Ngx2NvbAwBycnIwatQo9OrVC2vWrJH3HzlyJGrXro1evXrJQ7LCAVX16tWho6NTruf5pvbt26NatWpITU3F4MGDAQCpqakYO3Ys5s2bh5s3b6Jhw4by9tJWShbYuXMnBg4cKA8VAQjC0YI+S5cuRffu3QG8XtFZEBoCKNdzLs8zLCw7Oxs9evTAkydPcPz48XI/S64CUz5ObFMuPk/l4/c9EREREdHbYb7EfIn5ktDfbbPL1Z9Kpq93UdUlqJ3/3U5XdQlqZ4hFO1WXQEREVRgnYRERqTFNTU0MGzYMiYmJiIiIkK8ES05ORl5eHnx9fZV6PbFYjIkTJ8LHxwenT59GmzZtynyuhoYGYmJiMGTIEEyYMEFhZSUABAUFYdGiRTh69Cg6dOgA4HXgt2XLFixevBgAcPbsWfzyyy/YuHGjPLgp0KxZM3h4eCApKUkQkj158gQ9evTA7du38cMPP6B+/fryYwcOHEBWVhamTp2qUI+3tzfs7OyQlJSkEPAog66uLlq3bo2UlBRBSBYaGorU1FSkpKRg+PDhuH79On7//XdIJJIyjWtubo60tDTcunULlpaWxfb5/vvv0bdvX+jr6yscL89zrsgzvHfvHjp27Ag9PT2kpaXByMioTPdWGCe4UGXHCUNERERERFQVMF9ivlSUDyVfIuX6/uoPqi5B7XjV5YQhIiKiyoSTsIiI1MTu3buhp6cnf92tWzckJydjxIgRWLBgAdLS0uDu7g7g9Vbx/fr1g6GhoWCMdu3aCQKPY8eOoUWLFuWqw8HBAQBw8+ZNeUj28OFDQW0A4Obmhr179wrafHx80Lx5c8yePRtSqVRhbCcnJ7i4uCA+Pl4ekn377beQyWTyEOnq1asAAEdHxyLrc3R0xPHjxwVtc+fOhb6+Pi5duqSwpXtp4zk4OMj7vAsSiQTJyckAgF9++QXPnz9HixYt0KFDB/nqxNTUVNSoUQMuLi5lGnP27Nno27cvGjZsCDs7O7Rt2xbdu3dH//795V//NWvWwM/PDyYmJmjWrBnat2+P/v37w9XVFUD5nnNFnuHEiRNhbW2NgwcPQkdHp9R7ys3NRW5urqDNp05jiEUapZ5LZceJbcrF50lVAScLEhEREX1YmC8xX2K+JMyX8mV5zJeUqEdzD1WXoHZcf76j6hLUTqZfU1WXoFbyzl9WdQlERO8VJ2EREakJiUSCVatWyV/r6uoCeB1AtGvXDvHx8XB3d8dvv/2GY8eOYc6cOQpjbN68WRBkFF6xV1YymQwA5KsiAUBfXx8//fSToF/hrdULi4uLQ6dOnYpc1QYAI0aMwKRJk/Dll19CX18f8fHxGDBggMJquoI6ilK9enXB6y5duuDQoUOIiYmRr3gs7r7KMp4yubu7Y968ebh79y5SU1PRvn17aGhooGPHjli9ejWA16sX27VrBy0trTKNWadOHWRkZODChQs4evQo0tPTERAQgHXr1mHfvn0Qi8Xo0KEDrl+/jszMTKSnp+Pw4cNYunQpoqKi8Nlnn8nHKs9zKU/fnj17Yvv27fjqq68wadKkUu8pNjYWUVFRgjYrOMIGzsWcQUTqiBOGiIiIiIjeDvMl5kvMl5gvvUtbz+wtvROVS2/LtqouQe3IXnHSEBERVRwnYRERqQldXV3Y2toWeSwoKAjjx4/HihUrkJCQABsbG3Ts2FGhX/369Ysdo6wuXboEALCyspK3icXiMo/boUMHeHl5ITw8HIGBgQrHBw8ejEmTJuHbb79Fhw4d8MMPPyA2NlZ+vFGjRvI6ilpleenSJdjZ2QnaOnfujPHjx6N3797Iz8/H0qVLixyvXTvFrZ0vXbqE5s2bl+neKsLV1RXVq1dHSkoKUlJS5F+31q1b499//8X169eRmpqK0aNHl3vsxo0bo3HjxggODsann34KNzc3pKWlybedr1atGtzc3ODm5oawsDBER0djzpw5CAsLK9dzrsgzHDp0KHr16oURI0ZAJpNh8uTJJd5LeHi4Qh8fw8AyPQciVeGEISKiqi9fJiq9ExERURXCfOk15ktlx3yJykNLVE3VJagd2auXqi6BiIjeEvMl9SIuvQsREVV1AwcOhFgsxsaNG/H1119jxIgRgpWEypKfn49ly5bBysqq3NvMFzZ//nzs2rULGRkZCsf09fUxYMAAxMfHIyEhAXZ2dnBzc5Mfb9GiBRwcHLB48WLk5+cLzj137hwOHTpUZPjWpUsX7Nq1C2vXrsWECRPk7V5eXjA2NsbChQsVztm5cyd+/fXXIsdTFm1tbXz88cdITU0VbPlfrVo1uLi4QCqV4o8//pAHWxXl5OQEAMjJySmxz6tXr+Rb1pf1OVf0GQYEBCAxMRHTpk3DF198UWL9WlpaMDAwEPziVvFEREREREREysN86TXmS8VjvkREREREHzruhEVE9AHQ09PDoEGDEB4ejkePHikt1MnKysK9e/fw9OlTXLhwAUuWLMHJkyexZ88eaGj8F1DIZDLcu3dP4XwzMzOIxYrzgZs0aQI/Pz8sW7asyOsGBQXBzc0Nly5dQlhYmOCYSCTCunXr0KVLF/Tr1w/h4eEwNzfHiRMnMGXKFHh5eRW7qs/DwwO7d++Gt7c38vPzsXz5cujq6uKrr77C4MGD8cknn2DcuHEwMDDA4cOHERoailGjRqF79+7leWx49uwZzp49K2jT19eHjY1Nkf0lEol8G/uPPvpI3t6xY0d88cUX0NXVRevWrRXOu3HjhsJ1GjVqhKlTp6Ju3bro1KkTLCwscPfuXURHR6NWrVpo2/b19tXu7u7w9fVFq1atYGJigl9++QUzZsyARCKBgYEBAJT5Ob/NMxw6dCjEYjECAgIgk8kQGhpa+gMmqiL2/3lO1SWoHe4uRkRERET07jBfYr5UgPkSVVQ3GxdVl6B2km+nqLoEtTPAgu9TIiKqOE7CIiL6QAQFBUEqlaJ79+6oW7euUsb08PAAAOjo6MDS0hISiQRr1qxR2Br+0aNHqFOnjsL5d+/ehbm5eZFjz5kzB5s3by7yWPv27WFvb4/ffvsNw4YNUzju6uqKzMxMREVFoVu3brh//z4AYNy4cVi8eLEgwHtTp06dsGfPHvTs2RMymQzLly9H//79kZKSgnnz5sHNzQ2PHj0CAMTFxWHatGnFjlWcq1evKqzk7Ny5Mw4dOlRkf4lEgjlz5qBr167Q1Pzvr+6OHTti9uzZ8PLyQrVqilt5F7XF+rFjx+Dh4YH4+HisWrUKWVlZMDU1Rdu2bXH48GGYmJgAeL26cP369ZgxYwaePn2KunXromfPnpg1a5Z8rPI857d5hn5+fhCLxRg6dCjy8/MVglGiqooThoiIiIiIqKphvsR8CWC+RBW391qmqktQO151OWGIiIioMhHJZDKZqosgIiJ6l/Lz8xEUFIT9+/cjLS0NjRo1eqvxnj9/jt69e+OPP/5AWloaatWqpaRKq7byPOf38Qw9xQOUPuaHjjs3KRcnYRERvb2D+ckqvX6PoxNK70QAgD0dit6FhIiIqKpgvvR+MF9Sb8yWlI/5EhHR22O+VHVUhXxJcY9eIiIiNSMWiyGVShEWFoZjx4699Xg1atTAjh07MGzYMBw9elQJFaqH8jxnPkMiIiIiIiIiqkqYL70fzJeIiIiIqCrjTlhERESklrhSUfm4WpEqO67+JPrwqHqlYrejE1V6/apkb4elqi6BiIiIqNyYLylXbs82qi5B7TjN/lnVJaida62fq7oEInrPmC9VHVUhX9IsvQsRERFR1cMJQ1TZccIQERERERERUeX2+5Ymqi5BrVRP5/+WVDZOGCIiIqpc+K8dIiIiUkuc4KJ8nNimXHyeVBXwz1IiIiIiIvqQNeh/XtUlEJXo+sYWqi5B7Wj9rK3qEtRKvfnpqi6BiOi94iQsIiIiIiIV4OQWIiIiIiIiIvqQcEGa8nnVVXUFREREVBgnYREREZVBw4YNcevWLQDAgwcPYGRkpNqCqgB3d3ekpaUBAM6cOYPmzZurtiB6a5w0pFwMHqkq4Pc9EREREZHyMF8qP+ZL6oU/YypfXueWqi5B7bRdeFLVJaiVk801VF0CEdF7xUlYRESVyL179xAbG4s9e/bg9u3bMDQ0hK2tLfz9/REQEAAdHR1BWFNYbGwspk+fjps3b8LKykrerqenhwYNGsDd3R0hISFo1KiR/FhiYiKGDx+uMJaWlhaeP3/9WfKBgYFYv369Qh8vLy/s27cPAMpcU0FQUpYa09LS4OHhgZSUFLRv317eNycnB02aNMFHH32E7777rsTnmZCQgIYNG0IikZQYbD169AhxcXH47rvvcPPmTRgZGaFx48YIDg6Gj48PRCIRAGDOnDkYNWoUDA0N5c8vJCQE2dnZCmOKRCJs27YNffr0kd9vrVq1cO3aNejr68v7NW/eHH369EFkZCSA18FS8+bNsWTJEvnrtLQ0+bMsrEePHvj+++8xe/Zs+fkAcPHiRURFRSElJQWPHj2CpaUlBg8ejOnTp0NHR0fer/DXrUaNGqhduzbatGmDTz/9FJ06dSrx2Ra2adMm+Pr6onfv3ti+fbu8fevWrbh27RratGkjb0tNTYVEIlEYIyIiAtHR0QrHTU1N0bp1a8TFxaFJkyZlroneDU4aosqOYS4REREREfMl5kvMl5gvVV6y9s1VXYLa6bo0TdUlqJ2DjfVUXQIREVVhnIRFRFRJXL9+Ha6urjAyMkJMTAyaNGkCLS0tnD9/HmvWrEG9evXQq1cvAP+FNYUVDl4A4NChQ3B2dsbTp09x/vx5LF26FM2aNcOuXbvQuXNneT8DAwNcuXJFcG5BKFSga9euSEhIELRpaWkJXpelpjeVVuP48eMRGBiIc+fOQVdXFwAwbdo0aGtrY/369Vi+fLl8rC+++AL79u3DoUOH5G2GhoY4ceJEiTVkZ2ejffv2ePjwIaKjo9G6dWtoamoiLS0N06ZNQ6dOneThmr6+PszNzUscrySPHz/GF198gaioqHKdV79+fSQmJgpCsjt37uDw4cOoU6eOoG9mZiY8PDzg4eGBPXv2oHbt2jh58iSmTJmCw4cPIyUlBdWrV5f3L/i6vXjxAjdv3sSGDRvg4eGBuXPnIiIiotTabt68ialTp8LNzU3hmLGxMR49elTkeVeuXIGBgYH8tZ6eXpHH//zzT4SGhqJHjx747bffBLUTERERqVq+TFR6JyIioveI+RLzpeIwX2K+VBlonLqs6hLUzqdGF1Vdgto5iI9VXQIRfWCYL6kXTsIiIqokgoODoampiR9//FEeCAGAtbU1evfuDZlMJm8rS1hjYmIi72NtbQ1vb2907twZQUFBuHbtGjQ0Xm8BKxKJSh1LS0ur1D4VCZBKqzEmJgb79u1DWFgYli9fjpSUFKxbtw7p6enQ1dUVPCc9PT1oamqWu4YZM2bg5s2buHr1KurWrStvt7Ozg6+vL2rUqFGu8Uoyfvx4LFq0CGPHjoWZmVmZz+vZsye+/fZb/PDDD3B1dQUArF+/Hl26dMHvv/8u7yeTyRAUFARHR0ds3boVYrEYAGBpaQk7Ozu0aNECixcvRlhYmPycwl+3Bg0aoEOHDqhTpw5mzZqF/v37w97evti68vLy4Ofnh6ioKBw7dqzIFZvFMTMzK3HL/YLj5ubmCAkJQa9evXD58mU0bdq0zNcgquy4axMRERERESkb8yXmS8VhvsR8qTLYe6PkCY1Ufl51OWGIiIioMhGrugAiIgKysrJw4MABjB07VhD8FPbm6sHyEovFmDhxIm7duoXTp0+/1Vjvyps11qhRA19//TXWrFmDHTt2YMSIEZgxYwZatlTO59zn5+dj06ZN8PPzEwRkBQqCN2Xx9fWFra0t5syZU67zqlevDj8/P8Fq0cTERIwYMULQ7+zZs/jll18wefJkeUBWoFmzZvDw8EBSUlKp15s4cSJkMhl27Nghb2vYsKFgS3rg9SpHMzMzBAUFlet+yuPhw4fYtGkTAHCVIhEREREREVEJmC+9xnypaMyXmC8RERER0bvHnbCIiCqB3377DTKZTGFVmKmpKZ4/fw4AGDt2LOLi4gAAYWFhmDlzpqDv3r17i9yyuzAHBwcAr7f4btOmDYDXIcSbW3W7ublh79698te7d+9W6DNjxgzMmDFD/rqiNZVWY6tWrRAeHo6+ffuiRYsWZdrCvKz+/fdfPHjwQH7Nd00kEmH+/Pnw9vbGpEmTYGNjU+ZzR4wYATc3NyxduhSnT5/Gw4cP0bNnT0FwdfXqVQCAo6NjkWM4Ojri+PHjpV7L2NgYZmZmuHnzprzNxsYGpqam8tfHjx+HVCrF2bNny3wPhVlYWAhe37p1CyYmJgrHc3JyAAC9evUq8euUm5uL3NxcQdvOG2egpcX55srEnZuIiIiIiIgqL+ZLxdfIfOk15kvMl1SN2RIRERGpO07CIiKqxE6ePIn8/Hz4+fkJAoDQ0FAEBgYK+tarV6/U8Qq2nC+86lFfXx8//fSToJ+2trbgtUQiwapVqwRtxsbGgtcVraksNX722WeYM2cOpk+frtSVg4W34H9fvLy80L59e3z22WfYuHFjmc9r1qwZGjVqhC1btiAlJQVDhw4t9lko475kMpnga3D48GH57x8/foyhQ4di7dq1guCsPI4dOwZ9fX3565o1ayoc19HRQWZmJmJiYrB69eoSx4uNjUVUVJSgzQqOsBE5V6g+Ktr+P8+pugS1wuCRiIiIiIjeB+ZLrzFfYr7EfEn1mC0pH/MlIiKiyoWTsIiIKgFbW1uIRCJcuXJF0G5tbQ1AMbQyNTWFra1tua9z6dIlAICVlZW8TSwWlzqWrq5uqX0qWlNZaiwIg5QZkAFArVq1YGRkhMuXL1fofAMDA+Tk5CA/P1+wPXt2djYAwNDQsMjz5s+fj7Zt2yI0NLRc1xsxYgRWrFiBX375BSdPnlQ4bmdnB+D1M2zRooXC8UuXLsn7lCQrKwv//POP4GtQ2LVr13Dz5k14e3vL2/Lz8wG8/hpduXKl1FWYVlZWMDIyKvW4vb09/v77bwwaNAhHjx4ttn94eDgmT54saKv24CNoaTHYUSaGOkRERERERJUX86WSa2S+9BrzJeZLqsRsiYiIiNQdJ2EREVUCJiYm8PT0xPLlyzF+/Hjo6uoq/Rr5+flYtmwZrKysigxQKoP3XaNYLMbgwYPxv//9D7Nnz0bdunUFx588eYIaNWoUG87Z29vj1atXOHv2LD766CN5e8HKz+ICqTZt2qBv376YPn16ueodMmQIpk6dimbNmsHJyUnhePPmzeHg4IDFixdj8ODBguDu3LlzOHToEGJjY0u9ztKlSyEWi9GnT58ijzs4OOD8+fOCtpkzZ+Lx48dYunQp6tevX677Ks3YsWMRGxuLbdu2wcfHp8g+Wlpa0NLSErR5GlXO93lVxtWKysXgkYio6suXiUrvRERE9J4wX3qN+VLJmC8xX1IlZkvKx3yJiKjqY76kXjgJi4iokli5ciVcXV3RqlUrREZGomnTphCLxTh16hQuX76Mli1byvs+fvwY9+7dE5yvo6MDAwMD+eusrCzcu3cPT58+xYULF7BkyRKcPHkSe/bsgYaGhryfTCZTGAsAzMzM5CFLbm6uQh9NTU3BVuFlqelNZa1RGc6fPy/YnlwkEqFZs2aYN28eUlNT8fHHH2PevHlo1aoVqlWrhmPHjiE2NhanTp0qdkWds7MzunTpghEjRmDhwoWwtrbGlStXEBISgkGDBpW4Xf68efPg7OxcrtWXNWvWxN27d1GtWrUij4tEIkilUnh6eqJfv34IDw+Hubk5Tpw4gSlTpqBt27YICQkRnFPwdXv58iVu3LiBDRs2YN26dYiNjRWsPO3cuTN8fHwwbtw41KhRA40bNxaMU/CM3mxXBh0dHYwaNQqzZ89Gnz59BNvYExEREREREdF/mC8xXyoN8yXmS0RERET07nASFhFRJWFjY4MzZ84gJiYG4eHhuH37NrS0tODk5ISpU6ciODhY3nfWrFmYNWuW4PzRo0dj9erV8tceHh4AXgcMlpaWkEgkWLNmjcKW7o8ePUKdOnUU6rl79y7Mzc0BAPv27VPoY29vL9hmvSw1vamsNSpDhw4dBK81NDTw6tUrGBsbIzMzE/Pnz0d0dDRu3bqFmjVrokmTJliwYEGxW74X2Lx5M2bPno3Ro0fjzz//hIWFBXx8fPDZZ5+VeJ6dnR1GjBiBNWvWlOs+StpiHQDatWuHzMxMREVFoVu3bnj8+DEaNGiAgIAAhIeHK6zmK/i6Va9eHebm5nBxccHhw4chkUgE/a5du4Z///23XLUq07hx47Bo0SIkJydj4MCBKquDiIiIiIiIqDJjvsR8qSyYLzFfIiIiIqJ3QySTyWSqLoKIiKiya9iwIUJCQhRW+lHJbt68CSsrK5w5cwbNmzd/r9f2FA94r9f7EHDLeOXidvFERG/vYH6ySq/vmTpJpdevSg66L1Z1CURERCrHfKlimC+pD2ZLysd8iYjo7TFfqjqqQr4kLr0LERERAUBYWBj09PTw8OFDVZdSJXTr1g3Ozs6qLoOIiIiIiIiIqNJgvlQ+zJeIiIiIqCrhxxESERGVQVpaGl6+fAkA0NfXV3E1VcO6devw7NkzAECDBg1UXA0RERERERERkWoxXyo/5ktEREREVJVwEhYREVEZWFpaqrqEKqdevXoqvT63N1c+bm9OREQklC8TqboEIiIiqkKYL5Uf8yX1wmyJiIhIEfMl9cJJWERERKSWGOoQEb09/g8H5eLfTUREREREVQv/DU9E9HaYLSkf/24iqtw4CYuIiIiISAUYQCgfAwjl4zMlIiIiIiIiqryYLykfsxDl4vMkog8NJ2ERERERUZkw1FEuBhBERERERERE9CFhtqR8zJeIiIgqF07CIiIiqqTu3buH2NhY7NmzB7dv34ahoSFsbW3h7++PgIAA6OjooGHDhggJCUFISIjg3MjISGzfvh1nz54t8vWbbty4gYiICKSmpuL+/fswNTVFy5YtERcXBwcHB0HfpKQk+Pv749NPP8WKFSsEx1JTUyGRSPDgwQMYGRkpvC6Jl5cXDh06hMzMTLRu3VpwLDAwENnZ2di+fXtpj42oymDwqHwMHomIiIiIiISYL/2H+ZLqNUoNVHUJakdruraqS1A79eanq7oEIiKqwjgJi4iIqBK6fv06XF1dYWRkhJiYGDRp0gRaWlo4f/481qxZg3r16qFXr15KudbLly/h6ekJe3t7bN26FXXq1MHt27exd+9eZGdnK/SXSqWYNm0avvrqKyxcuBA1atR46xp+//13pKenY9y4cYiPj1cIyYjUEScMERERERER0bvEfIn5UmXzq3uiqktQO15DmC8RERFVJpyERUREVAkFBwdDU1MTP/74I3R1deXt1tbW6N27N2QymdKudfHiRVy7dg2HDx+GpaUlAMDS0hKurq4KfW/cuIH09HR89913SElJwdatWzFkyJC3riEhIQE9e/bEmDFj4OLigkWLFkFb++1WcXGXISIiInrX8mUiVZdAREREVCzmS2+fLxERERG9a8yX1AsnYREREVUyWVlZOHDgAGJiYgQBWWEikfL+QVarVi2IxWJs2bIFISEh0NDQKLZvQkICevToAUNDQ/j7+0Mqlb51SCaTyZCQkIAVK1bAwcEBtra22LJlC4YOHfpW43KXIeXjxDYiIiIiIiKiqoH5knLyJSIiIiKi8uAkLCIiokrmt99+g0wmg729vaDd1NQUz58/BwCMHTsWcXFxAICwsDDMnDlT0PfFixdwcnIq0/Xq1auHZcuWYdq0aYiKikKrVq0gkUjg5+cHa2treb/8/HwkJibiyy+/BAAMHjwYU6ZMwY0bN2BlZVXh+z106BCePn0KLy8vAJCHb+UJyXJzc5Gbmytoy5flQSwqPvAjUjVOalM+Tr4kIiIiIiJ6jfkS86XKqFXkGFWXoHaq7/1b1SWoHf1uv6m6BCIiqsI4CYuIiKiKOHnyJPLz8+Hn5ycIhEJDQxEYGCjou2zZMhw9erTMY48dOxbDhg1DamoqMjMzkZycjJiYGOzcuROenp4AgIMHDyInJwfdu3cH8Dq08/T0RHx8PObOnVvh+4qPj8egQYOgqfn6nyW+vr4IDQ3FtWvXYGNjU6YxYmNjERUVJWizgiNs4FzhuoiIiIiIiIiI1A3zpeIxX3r3zDZfVHUJaif+s+9VXYLaCYDix6gSERGVFSdhERERVTK2trYQiUS4cuWKoL1g1aC2trag3dTUFLa2toI2Y2Pjcl9XX18f3t7e8Pb2RnR0NLy8vBAdHS0PyaRSKe7fvy+4fn5+Pn7++WdERUVBLBaX+5r379/Htm3b8PLlS6xatUrenpeXh/j4eMybN69M44SHh2Py5MmCNh/DwHLXQ/Q+cdcmIiIiIiIieleYLzFfqoy+v1T2SX1UNl51OWGIiIioMin/v2aJiIjonTIxMYGnpyeWL1+OnJwcldQgEong4OAgv35WVhZ27NiBTZs24ezZs/JfZ86cwYMHD3DgwIEKXeebb76BhYUFzp07Jxh34cKFSExMRF5eXpnG0dLSgoGBgeAXt4onIiIiIiIiog8V8yXmS0RERET0/nEnLCIiokpo5cqVcHV1RatWrRAZGYmmTZtCLBbj1KlTuHz5Mlq2bFnuMZ89e4azZ88K2vT19fH48WPMnj0bQ4cOhZOTE6pXr460tDTEx8cjLCwMAPC///0PJiYmGDhwIEQikWCM7t27QyqVomvXrsVe+/z589DX15e/FolEaNasGaRSKfr374/GjRsL+tevXx/h4eHYt28fevToUe57pXeDOzcp1/4/z6m6BKJS8fueqGQymaj0TkREREQqwnyJ+VJlw58xlU/naG1Vl6B2/lplpeoS1Ip+UqaqSyCq9JgvqRdOwiIiIqqEbGxscObMGcTExCA8PBy3b9+GlpYWnJycMHXqVAQHB5d7zKtXr6JFixaCts6dO2PTpk1o2LAhoqKicPPmTYhEIvnrSZMmAQDi4+Ph4+OjEJABQL9+/TB06FD8+++/xV67Q4cOgtcaGho4ceIEzp07h7Vr1yr0NzQ0ROfOnSGVShmSVSKcNKRcDB6JiIiIiIjoXWK+xHypsmG2pHzMl5RPH3+pugQiIqrCRDKZTKbqIoiIiIiUzVM8QNUlEJWIwaPyMXgk+vAczE9W6fXdD09V6fWrktTOX6i6BCIiIqJyY75ElR3zJeVjvkT04WG+VHVUhXxJrOoCiIiIiIiIiIiIiIiIiIiIiIiIqjJ+HCERERERlQlX1hERERERERERUUX99vVHqi5B7TivbKfqEtSOBdJVXQIREVVhnIRFRERERGXCrbiVi5PalI/PVPn4fU9UsnyIVF0CEREREVGVYTvsJ1WXoHZeebRSdQlqp+VZmapLUCunm/PnZqLSMF9SL5yERURERERlwgkuysXJLURERERERET0IWG2pHxedVVdgfrhpCEiInobnIRFREREaomhDhERERERERERERERERG9L5yERUT0gUhMTMTw4cMBABMnTsSSJUtUW1AVkJqaColEAgDo3bs3tm/frtqCqFy4y5DycWIbERERERHRh435UvkxXyIiIiIi+nBwEhYRffDu3buH2NhY7NmzB7dv34ahoSFsbW3h7++PgIAA6OjooGHDhggJCUFISIj8vMuXL8PR0REZGRlwcXGRt7u4uODs2bPIzs5GjRo1AADPnz+HkZERVqxYgaCgIAQGBmL9+vUKtXh5eWHfvn3y1+np6YiOjkZGRgaePXuGRo0aYfjw4Zg4cSI0NDQE56akpGDBggU4ceIEnj17hoYNG6Jbt26YPHky6tWrBwAwMDDAlStXoKurKz+vqHsDgMjISGzfvh1nz54FAHnNsbGxmD59urzf9u3b4ePjA5ns9eekFwRLDx48gJGRkfy1kZER7t69K38mAHDq1Cm0adMGAOTnA0BeXh6WLVuG+Ph4/Prrr9DW1oaLiwtmzpwJV1dXeb/CwZ9YLIaBgQHs7OzQo0cPTJw4EYaGhgrPuCi5ubn4+OOPce7cOZw5cwbNmzcHALRr1w53797FxIkTkZubKzjn4sWLiIqKQkpKCh49egRLS0sMHjwY06dPh46OTqnPFwBu3rwJKysrwTULS0xMREhICLKzs+Vtly5dQpcuXeDi4oJvvvkGGhoaWLBgARITE3Hr1i1oa2ujUaNGGDVqFEaOHAmRqOStk2fPno3IyEhs27YNcXFxuHTpEvLz89GgQQN4enpiyZIlWLhwIaKjoxW+fgDw9OlTmJubIzo6GhMmTEDDhg1x69YtQZ969erh9u3b8tdeXl44dOgQMjMz0bp1a0Hfwt8bmpqasLCwwIABAzBnzhyFaxNVdZzUpnycfElEREREqsJ8ifkS8yXmS1S67o4dVF2C2ln/x/eqLkHtBNR3Lb0TERFRMTgJi4g+aNevX4erqyuMjIwQExODJk2aQEtLC+fPn8eaNWtQr1499OrVq8hzHRwcYG5ujtTUVHlI9vjxY/z000+oXbs2MjMz4e7uDgDIyMhAbm4uOnXqJD+/a9euSEhIEIyppaUl//22bdswcOBADB8+HCkpKTAyMsKhQ4cwbdo0ZGRk4Ntvv5UHIF999RWCg4MREBCA7777Dg0bNsTvv/+Or7/+GgsXLsSiRYsAACKRCObm5hV+XjVq1EBcXBxGjx6NmjVrlutcfX19bNu2Db6+vvI2qVSKBg0a4Pfff5e3yWQyDB48GIcOHcKCBQvQuXNnPHr0CCtWrIC7uzuSk5PRp08fef+C4E8mkyE7Oxvp6emIjY1FQkICfvjhB9StW7fU2qZNm4a6devi3DnhhIjq1avD3Nwc2tragpAsMzMTHh4e8PDwwJ49e1C7dm2cPHkSU6ZMweHDh5GSkoLq1auX6/mUxalTp9CtWzf4+Pjgq6++glgsxqxZs/DVV19h+fLlaNWqFR49eoQff/wRDx48AADcvXtXfv7mzZsxa9YsXLlyRd6mp6eHw4cPY9CgQZg3bx569eoFkUiEX375BQcPHgQADB06FOHh4di6dSuGDBkiqGnLli148eIF/P395W1z5szBqFGj5K8LB7q///470tPTMW7cOMTHxyuEZMB/3xsvX77E6dOnERAQAJFIhLi4uHI9L05wUT5OcCEiIiIiIlLEfKl8mC+9xnzpP8yXPhzMlpSPE4aIiIgqF07CIqIPWnBwMDQ1NfHjjz8KVu9ZW1ujd+/egtVzRZFIJEhNTZWv3Dt+/Djs7OzQoUMHpKamykOy1NRUWFpawsrKSn6ulpZWsYFVTk4ORo0ahV69emHNmjXy9pEjR6J27dro1asXvv32WwwaNAi3b9/GhAkTMGHCBCxevFjet2HDhujQoYNgldvb8vDwwG+//YbY2Fh8/vnn5To3ICAA8fHx8pDs2bNn2LRpEyZMmIC5c+fK+3377bfYsmULdu7cCW9vb3n7mjVrkJWVhZEjR8LT01P+9Soc/NWpUweOjo7w9vaGs7Mzpk2bhg0bNpRY1969e3HgwAF899132Lt3b6n3IZPJEBQUBEdHR2zduhVisRgAYGlpCTs7O7Ro0QKLFy9GWFhYuZ5PaY4cOYLevXsjODhYEBbt3LkTwcHBGDBggLytWbP/wozC7zFDQ8Mig9Jdu3bB1dUVoaGh8jY7Ozt5GGlmZgZvb2/Ex8crhGTx8fHo06cPjI2N5W36+vrFvrcTEhLQs2dPjBkzBi4uLli0aBG0tbUFfQp/b9SvXx8eHh44ePBguUMyhjrKx+CRKjt+3xPR+5YvK3lXCCIi+jAwXyof5kvMl5gvfbj+Cmmn6hLUzqsOD1Vdgtqp1/eiqksgog8M8yX1wklYRPTBysrKwoEDBxATEyMIyAorbattiUSCSZMm4dWrV9DU1ERKSgrc3d3h5uaGlStXIjIyEsDrrdwlEkmZaztw4ACysrIwdepUhWPe3t6ws7NDUlISBg0ahOTkZLx48QLTpk0rciwjI6MyX7c0GhoaiImJwZAhQzBhwgRYWFiU+dyhQ4diwYIF+P3339GgQQP5isqPPvpI0G/jxo2ws7MTBGQFpkyZgq1bt+LgwYOC1YpvMjMzg5+fH+Lj45GXlwcNDQ351vKFg8+//voLo0aNwvbt2wVbvJfk7Nmz+OWXX7Bx40Z5QFagWbNm8PDwQFJSklJDsm3btmHIkCGIjIxUGNfc3BxHjhxBcHAwatWqVaHxzc3NsXHjRly4cAGNGzcusk9QUBB69uyJW7duwdLSEsDrlb5Hjx7F/v37y3QdmUyGhIQErFixAg4ODrC1tcWWLVswdOjQYs+5cOEC0tPT5dck1WLwqFyc1EZEREREVPUxXyo/5kvMl5gvfbhqL0lXdQlqZ/805kvK5gVmoEREVHGchEVEH6zffvsNMpkM9vb2gnZTU1M8f/4cADB27NgSV0dJJBLk5OTg1KlTaNu2LVJTUxEaGor27dsjICAAz58/h0wmw8mTJzFy5EjBubt374aenp6gbcaMGZgxYwauXr0KAHB0dCzyug4ODvI+v/76KwwMDFCnTp3yPYAK8vHxQfPmzTF79mxIpdIyn2dmZoZu3bohMTERs2bNQnx8PEaMGKHQ7+rVq8Xed0F7wb2XxMHBAY8fP0ZWVhbMzMxgaGgo+FrLZDIEBgbi008/RatWrXDz5s0y3UdpXxtHR0ccP368TGOVxZMnTzBgwADMmDGjyOBt0aJF6N+/P8zNzeHs7Ix27dqhd+/e6NatW5mvMX78eBw7dgxNmjSBpaUlXFxc0KVLF/j5+ck/wsDLywt169ZFQkKCPPxNTExE/fr10blzZ8F4YWFhmDlzpvx1TEwMJkyYgEOHDuHp06fw8vICAPj7+0MqlSqEZAXfG69evUJubi7EYjGWL19e4j3k5uYKtvQHgHxZHsQijWLOoIrgpCEiIiIiIiIh5ksVw3yJ+RLzpQ/Tb0tdVF2C2rFNaqvqEtSODTJUXQIREVVhnIRFRPSGkydPIj8/H35+fgo/dL/J1tYWFhYWSE1NhbOzM86cOYOOHTvCzMwMDRo0QEZGBmQyGXJzcxVWKkokEqxatUrQVnjLbQAlbldfvXp1eZ/SVlQqW1xcHDp16lTkSsqSjBgxAhMnToS/vz8yMjKQnJyMY8eOKfQrbZv+sigYo+DZ+Pj4wMfHR378yy+/xOPHjxEeHv5W4xel4GujDNra2mjfvj3Wrl0LX19fhXDOyckJFy5cwOnTp/HDDz/g6NGj8Pb2RmBgINatW1ema+jq6mLPnj24du0aUlJSkJmZiSlTpmDp0qXIyMiAjo4ONDQ0EBAQgMTERMyePRsymQzr16/H8OHDFVZshoaGIjAwUP7a1NQUwOut5QcNGgRNzdf//PD19UVoaCiuXbsGGxsbef+C742cnBwsXrwYmpqa6NevX4n3EBsbi6ioKEGbFRxhA+cyPQMiIiIiIiIiZWK+VDrmS8yXmC99eBp9k6PqEtROn/Upqi5B7WybYqrqEoiIqArjJCwi+mDZ2tpCJBLhypUrgnZra2sAr8OJsnB3d0dKSgqaNm2KRo0awczMDADQsWNHpKSkQCaTwdbWFvXr1xecp6urC1tb2yLHbNSoEQDg0qVLaNeuncLxS5cuoXnz5gAAOzs7PHz4EHfv3q3QakUDAwM8fKj4ufHZ2dkwNDQs8pwOHTrAy8sL4eHhgjCkNN26dcMnn3yCoKAgeHt7w8TERKGPnZ0dLl26VOT5Be12dnalXuvSpUswMDAo8hoAcOTIEWRkZMhX4hVo1aoV/Pz8sH79+iLPK/y1adGiRZHXLUt9ZaWhoYHt27ejb9++kEgkSElJUQjKxGIxWrdujdatWyMkJAQbNmzA0KFDERERASsrqzJfy8bGBjY2Nhg5ciQiIiJgZ2eHzZs3Y/jw4QBeh5yxsbE4cuQI8vPz8ccff8iPFWZqaqrw3r5//z62bduGly9fCsLhvLw8xMfHY968efK2wt8b8fHxaNasGaRSKYKCgoqtPTw8HJMnTxa0+RgGlvneiYiIiIiIiCqC+dJrzJeYLxVgvkQl+auNvqpLUDtf/dpe1SWoHTNcVnUJRERUhXESFhF9sExMTODp6Ynly5dj/Pjx0NXVrdA4EokEEyZMgJOTE9zd3eXtHTp0wNq1ayGTyRRWKZbGy8sLxsbGWLhwoUJItnPnTvz6669YsmQJAKB///6YPn06Pv/8cyxevFhhrOzsbBgZGRV7LXt7e5w+fVqh/aefflLYSr+w+fPno3nz5iX2eZOmpiaGDRuGzz//HHv37i2yz+DBgzFkyBDs2rUL3t7egmMLFy6Uf91K8vfff2Pjxo3o06ePwiq6AsuWLUN0dLT89Z9//gkvLy9s3rwZH3/8cbFjt2jRAg4ODli8eDEGDx4sGP/cuXM4dOhQqVubl5eWlha2bt2K/v37QyKR4MiRI3Byciq2f8GxnJyKryxr2LAhdHR0BGPY2NigY8eOiI+Ph0wmg4eHBywtLcs03jfffAMLCwts375d0H7gwAEsXLgQc+bMgYaG4tbuYrEYM2bMwOTJkzFkyJBiw2stLS2FwJNbxVNl51W3mapLICIiIiKit8R86TXmS68xXxJivkRvOjNjVemdqFyYLxEREVUunIRFRB+0lStXwtXVFa1atUJkZCSaNm0KsViMU6dO4fLly2jZsqW87507d3D27FnB+ZaWlpBIJMjJyUF8fDzWrl0rP9axY0eMHDkSABAcHKxw7dzcXNy7d0/QpqmpCVNTU+jq6uKrr77C4MGD8cknn2DcuHEwMDDA4cOHERoailGjRqF79+4AgPr162Px4sUYN24cHj16hGHDhqFhw4a4ffs2vv76a+jp6WHhwoXFPoNJkybBzc0N8+bNQ9++fZGXl4ekpCRkZGRg5cqVxZ7XpEkT+Pn5YdmyZcU/4CLMnTsXoaGhxa4gHDx4MJKTkxEQEIAFCxagc+fOePToEVasWIGdO3ciOTlZEGjKZDLcu3cPMpkM2dnZyMjIQExMDAwNDTF//nx5v23btiE8PByXL79exdKgQQPBdfX09AC8DoIsLCyKrV8kEmHdunXo0qUL+vXrh/DwcJibm+PEiROYMmUKvLy8MHr0aME5xb13Cry5WhYAnJ2F25xraWnhu+++w4ABA+RBmbOzM/r37w9XV1e0a9cO5ubmuHHjBsLDw2FnZwcHB4di76OwyMhIPH36FN27d4elpSWys7OxbNkyvHz5UiGQDAoKwqhRowAAiYmJZRofAKRSKfr374/GjRsL2uvXr4/w8HDs27cPPXr0KPLcAQMGIDQ0FCtWrCj3RxQQERERvUsy2fv92CYiIqqcmC8xXyrAfIn5EhEREVF5MV9SL0Uv3yAi+kDY2NjgzJkz8PDwQHh4OJo1a4ZWrVrhyy+/xNSpUzF37lx53y+++AItWrQQ/NqzZw+srKxgaWmJx48fo2PHjvL+DRo0QN26dfHixQvBCsYC+/btQ506dQS/2rf/b+vg/v37IyUlBb///jvc3NxgZWWFkSNHYvr06VizZo1grODgYBw4cAB37tyBj48PHBwcMHLkSBgYGJQaKrRr1w579+7F3r174erqCnd3d6Snp+Pw4cMKgcab5syZg/z8/BL7vKl69eowNTWFSFT0PyhEIhG+/fZbzJgxA4sXL4a9vT3c3Nxw69YtpKamok+fPoL+jx49Qp06dVCvXj20bdsWX331FQICAnDmzBnB9vkPHz4sMoyqCFdXV2RmZkJDQwPdunWDpaUlBg4ciN69e2PXrl0KK+6Ke+8UGDx4sMLxv/76S+G61atXx5YtW9CuXTtIJBJcuHABXl5e8lWddnZ2CAgIgIODAw4cOABNzbLNte7YsSOuX7+OYcOGwcHBAd26dcO9e/dw4MABhZWo/fr1g5aWFnR0dBS+FsU5ffo0zp07h379+ikcMzQ0ROfOnSGVSos9X1NTE+PGjcPnn3/+VqsviYiIiIiIiN4F5kvMlyqC+RLzJSIiIiJSPyKZTCZTdRFERFS658+fo3fv3vjjjz+QlpaGWrVqlev8xMREhISEIDs7+90UqMYCAwORnZ2tsNV5gfz8fAQFBWH//v1IS0tDo0aN3m+BVCRP8QBVl6B29v95TtUlqBVuF09E9PYO5ier9PquB8NUev2q5AfPOFWXQEREYL6kSsyXqibmS8rFbEn5mC8REb095ktVR1XIl7gTFhFRFVGjRg3s2LEDw4YNw9GjRys0xsOHD6Gnp4ewMP5lXhbHjh2Dnp4evvnmmxL7icViSKVShIWF4dixY++pOiIiIiIiIiKi8mG+9P4xXyIiIiIi+nBwJywiog/E48eP5VuQGxkZwdTUVMUVVX7Pnj3DnTt3AAB6enowNzdXcUVUHlypqHxcrUiVHVd/En14uFKx6qgKKxWJiKh0zJfKj/lS1cZ8Sbk0LeurugS181nqdlWXoHZmW7VUdQlE9J4xX6o6qkK+VLYP8yYioipPX18f+vr6qi6jStHW1oatra2qyyAiNcUJQ0REREREVNUwXyo/5ktE/9mTsVvVJagdr7qcMERERFSZcBIWEREREZUJJw1RZcfd2pSP3/dEJcuXiVRdAhERERFRlcGfMakqYL6kXPy+Jyod8yX1wklYREREREQqwEBH+RjqEBEREREREdGHhPmS8jFfIiKit8FJWET0QUpNTYVEIgEA9O7dG9u3b1dtQWoiMTERw4cPBwBMnDgRS5YsUW1BShIZGYmoqCgAwOLFixESEqLagtREYGAg1q9fDwDYtm0b+vTpo9qCqFQMdYiIiIiIiP7DfOndYL5E5cF8qWrRNKul6hLUzq8vn6i6BCIiIiqEk7CIlOzevXuIjY3Fnj17cPv2bRgaGsLW1hb+/v4ICAiAjo4OGjZsiFu3bgEAtLW1YWNjg4kTJ2LkyJEK4yUlJcHf3x+ffvopVqxYIThWOOh50927d2Fubo6nT59i7ty5+Pbbb3Hnzh3o6+vDyckJkydPRu/evQEAN27cQEREBFJTU3H//n2YmpqiZcuWiIuLg4ODAwBAJFLcBtHV1RXHjx+XHy/qh9zRo0dj3bp12LRpEwYMGAAAWLhwIaKjo3H37l3UqFFD0P/p06cwNzdHdHQ0JkyYAAA4c+YM5s+fj6NHj+L+/fswNzdHkyZNMHr0aPTs2RMikQg3b96ElZUVxGIxfv/9d9SrV0/wLOrXr4+8vDzcuHEDDRs2lB+7cuUKzMzM5K/d3d3RvHlzhXAnMTERISEhyM7OBvBfaDJ69GisXr1a3u/s2bNo0aKF/DoFdZ05cwbNmzevcJ3r16/H8uXLcfHiRWhoaOCjjz5CaGgoevbsKe9T+P0gEomgr68Pa2treHp6YtKkSahTp47C1/DNr9WhQ4fw559/Qk9PD+3atRO8BwrGfVNSUhIGDx4MABg0aBC6du2Kvn37FnmN2NhYzJw5E/Pnz0doaGiJtbz5vilQOLDS0NCAhYUFfHx8MHfuXOjp6QF4HbjExcXh0qVLyM/PR4MGDeDp6Sn/uhaEeQ4ODrh06ZJg/OTkZAwcOBCWlpa4efOmvN3Z2RmHDh2CgYGB4HkU9b4PDAxEdna2PHx1d3dHWlqa4FkBwJIlS7BkyRL5dd58n1WkzmfPnmH+/PlISkrCrVu3oK+vD4lEgsjISDg7Oxf7HI2MjODk5IS+fftizJgx0NLSKuarA2RlZcHPzw8///wzsrKyYGZmht69eyMmJkb+fIr786ngzyYAWLp0KebPny94b775PQMAjx8/Rq9evfDXX3/hwIEDsLCwKLY2oqqEq+qIiIiIqDjMl/oI+jBfYr5UGPMl5kvMl6quPWcPqboEteNV11XVJRAREVEhYlUXQKROrl+/jhYtWuDAgQOIiYnBmTNnkJGRgWnTpmH37t04dOi/HzDmzJmDu3fv4sKFC/D398eoUaOwd+9ehTGlUimmTZuGpKQkPH/+vMjrXrlyBXfv3hX8Kgh+Pv30U2zduhVffvklLl++jH379qF///7IysoCALx8+RKenp54+PAhtm7diitXrmDz5s1o0qSJ/Af1AgkJCYJr7Ny5s8Tn8fTpU2zatAnTpk1DfHy8vH3o0KHIycnB1q1bFc7ZsmULXrx4AX9/fwDAjh074OLigidPnmD9+vW4dOkS9u3bBx8fH8ycORMPHz4UnF+vXj18/fXXgrb169cLwqjCzMzMYGRkVOJ9FKdGjRqQSqX49ddfy31ueeqcOnUqRo8ejUGDBuHnn3/GyZMn0b59e/Tu3RvLly9X6H/lyhX8+eefOHXqFMLCwnDo0CE0btwY58+fL7Gmli1bIiEhAZcuXcL+/fshk8nQpUsX5OXlCfq9+T4oHBBpa2vD3Nwc1atXL/Ia8fHxCu+HNxX3vinM2dkZd+/exc2bNxEXF4c1a9ZgypQpAIDDhw9j0KBB6NevH06ePInTp09j3rx5ePnypWAMXV1d/P3338jIyBC0S6VSNGjQQOGampqaMDc3h46OTrG1l6RGjRqYOXOmQh2lKU+dubm58PDwQHx8PKKjo3H16lV8//33ePXqFT7++GNkZmYK+hc8x99//x0pKSkYMGAAYmNj0a5dOzx+/LjYmsRiMXr37o2dO3fi6tWrSExMxKFDh/Dpp58q9H3zz6fCobShoaE8MCvOP//8A4lEgpycHBw7dowBGRERERERqT3mS0LMl4rHfIn5UlkxX2K+RERERETvBydhESlRcHAwNDU18eOPP2LgwIFwdHSEtbU1evfujT179sDb21veV19fH+bm5rC2tkZYWBiMjY1x8OBBwXg3btxAeno6pk+fDjs7uyJDJeB10GNubi74JRa//vbeuXMnZsyYge7du6Nhw4Zo2bIlxo8fjxEjRgAALl68iGvXrmHlypVwcXGBpaUlXF1dER0dDRcXF8F1jIyMBNcwNjYu8XkkJyfDyckJ06dPx9GjR/HHH3/I6/X29i4yAImPj0efPn1gbGyMnJwcBAUFoUePHtizZw+6dOkCa2trODo6IigoCOfOnYOhoaHg/ICAACQkJAjaEhISEBAQUGKtFWFvbw+JRIKIiIhyn1vWOjMzM7Fw4UIsWLAAU6dOha2tLRwdHTFv3jyEhIRg8uTJ8udaoOD9YGdnh8GDB+OHH35ArVq1MGbMmBJr+uSTT9ChQwc0bNgQH330EaKjo/HHH38IVsEBiu+DN1ebFictLQ3Pnj3DnDlz8OjRI6SnpxfZr7j3TWEFgZWFhQUGDRoEPz8/eWi7a9cuuLq6IjQ0FPb29rCzs0OfPn0UVvpqampiyJAhgvfh7du3kZqaiiFDhpTpnsrD19cX2dnZWLt2bbnOK0+dS5YsQUZGBnbv3i1fxdimTRt899138u8bmUwmGNvc3Bx169ZFkyZNMH78eKSlpeHChQuIi4srtqaaNWtizJgxaNWqFSwtLdG5c2cEBwfj2LFjCn3f/POp4M+msvjjjz/g5uYGQ0NDHDlyBCYmJmU+l4iIiIiIqKpiviTEfKl4zJeYL5UV8yXmS0RERET0fvDjCImUJCsrS75CUVdXt8g+RW21nZ+fj23btuHBgwcKq7sSEhLQo0cPGBoawt/fH1KptNw/vJubm+P7779H3759oa+vr3C8Vq1aEIvF2LJlC0JCQqChoVGu8UsilUrh7+8PQ0NDdOvWDYmJifjss88AAEFBQejZsydu3boFS0tLAK9Xeh49ehT79+8HABw4cABZWVmYNm1asdd485n26tULq1evxvHjx9G+fXscP34cDx48gLe3N+bOnau0eyswf/58tG7dGj/++CNatWpV5vPKWmdSUhL09PQwevRohTGmTJmCRYsW4bvvvkNISEix19LW1sann36KSZMm4e+//4aZmZl8K+83t6UvkJOTg4SEBFhZWaF+/fqCY2PHjsXIkSNhbW2NTz/9FMOHDy/yvf0mqVQKX19fVKtWDb6+vpBKpWjXrl2R/Yp735R0jy9evADw+j2/ceNGXLhwAY0bNy7xvBEjRsDd3R1Lly6Fjo4OEhMT0bVrV9SuXbvU+ykvAwMDREREYM6cOQgICCj2z4m3qXPjxo3w9PREs2bCjzgTi8WYNGkS/Pz8cO7cOfk27EVxcHBAt27dsHXrVkRHRwN4vbV8YmKiQmBa4M8//8TWrVvRsWNHhWPNmzdHbm4uGjdujMjISLi6lm177CtXriA0NBStWrVCUlJSidvXF2f/n+fKfQ6VjB+fR0REJCSTlf7vYCKi8mC+pIj5UvGYLzFfYr5UMuZLlQ+zJSIiIkXMl9QLJ2ERKclvv/0GmUwGe3t7Qbupqal8m/exY8fKV/+EhYVh5syZyM3NxatXr2BsbIyRI0fKz8vPz0diYiK+/PJLAMDgwYMxZcoU3LhxA1ZWVoJrvLl9sqWlJS5evAgAWLNmDfz8/GBiYoJmzZqhffv26N+/v/wH1Xr16mHZsmWYNm0aoqKi0KpVK0gkEvj5+cHa2lowrq+vryBE27Bhg2Cr8MJ+/fVXZGZmyldX+vv7Y/LkyZg5cyZEIhG8vLxQt25dJCQkIDIyEgCQmJiI+vXro3PnzgCAq1evAoDgmZ46dQoSiUT+etOmTejZs6f8dbVq1eDv74/4+Hi0b98e8fHx8Pf3R7Vq1Yqs82199NFHGDhwIMLCwnD48OEyn1fWOq9evQobG5sit1+vW7cuDAwM5M+pJA4ODgCAmzdvwszMDDo6OrC3t1e43sqVKzFt2jTk5OTA3t4eBw8eFFx7zpw56NSpE3R0dHDgwAEEBwfjyZMnmDBhQonXf/ToEbZs2SLf8tzf3x9ubm5YunQp9PT05P1Ke98U5fTp09i4cSM6deoEABg/fjyOHTuGJk2awNLSEi4uLujSpQv8/PwUgpYWLVrA2toaW7ZswdChQ5GYmIhFixbh+vXrpT7TiggODsbSpUuxaNGiUoO/itR59epVwfdHYY6OjvI+JYVkwOv3y4EDB+SvTU1NYWNjo9DP19cXO3bswLNnz+Dt7Y1169bJj9WpUwerV69Gq1atkJubi3Xr1sHd3R0nTpzARx99VOo9Dxs2DK6urkhOTi5TeJ+bm4vc3FxBm0+dxhCLlBf8Eykbg1yqChiQExERvV/Ml4SYL5WM+RLzJeZLJWO+RB+C6xtbqLoEtaP1s7aqS1Ar9eYXvWslEZG64iQsonfs5MmTyM/Ph5+fn+AHuNDQUAQGBuLu3bsIDQ1FcHAwbG1t5ccPHjyInJwcdO/eHcDrH1I9PT0RHx+vsOLu2LFjglWIhYOPDh064Pr168jMzER6ejoOHz6MpUuXIioqSv5D+tixYzFs2DCkpqYiMzMTycnJiImJwc6dO+Hp6Skfa/HixfDw8JC/rlOnTrH3HR8fDy8vL5iamgIAunfvjqCgIBw5cgSdO3eGhoYGAgICkJiYiNmzZ0Mmk2H9+vUYPnx4idtJN23aFGfPngUANGrUCK9evVLoM2LECLRr1w4xMTFITk5GRkZGkf2UJTo6Go6Ojjhw4ADMzMzKfF5Z6yy8vXdFFYxREDS1adMGly9fVujn5+cHT09P3L17F1988QUGDhyIH374Qb4lfOFgp0WLFsjJycGCBQtKDcmSkpJgY2MjX0HXvHlzWFpaYvPmzQgKCpL3K+19U+D8+fPQ09NDXl4eXrx4gR49emD58uUAAF1dXezZswfXrl1DSkoKMjMzMWXKFCxduhQZGRnQ0dER1DZixAgkJCSgQYMG8u+5grGUTUtLC3PmzMH48eNL3b7/TWWtU1nvl8Kh5Lhx4zBu3DiFfosXL8bs2bNx9epVhIeHY/LkyVi5ciWA1+F24YC7Xbt2uHbtGhYvXoz//e9/pdbQq1cvbN++HVu3bsWAAQNK7R8bG4uoqChBmxUcYQPnUs+lsuOkIeXi5BYiIiIiKivmS8yXisN8iflSeTBfKhnzpXeP2ZLyedVVdQVERERUGCdhESmJra0tRCIRrly5ImgvWO2nrS2cOW9qagpbW1vY2toiOTkZTZo0QatWreDk5ATg9ZbZ9+/fF5yXn5+Pn3/+GVFRUYIgycrKCkZGRsXWVq1aNbi5ucHNzQ1hYWGIjo7GnDlzEBYWJl+Fpq+vD29vb3h7eyM6OhpeXl6Ijo4WhGTm5uaCIK84eXl5WL9+Pe7duwdNTU1Be3x8vDzsGDFiBGJjY3HkyBHk5+fjjz/+wPDhw+X9GzVqBOD1ttEuLi4AXocMpdXQpEkTODg4wNfXF46OjmjcuLE8WCuNgYEBHj58qNCenZ0NQ0PDIs+xsbHBqFGjMH36dEil0jJdp6x12tnZ4fjx43jx4oXCasU///wTjx49gp2dXanXunTpEgAUuTV8YYaGhjA0NESjRo3g4uKCmjVrYtu2bfD19S2y/8cff4y5c+ciNze3xO28pVIpLl68KHg/5OfnIz4+Xh6SlfV9A7wOYHbu3AlNTU3UrVu3yJWcNjY2sLGxwciRIxEREQE7Ozts3rxZ8B4DXgeD06ZNQ2RkJIYOHSq4dmn09fXL/X7x9/fHF198gejo6FK/HuWt087OTv61flNBe1nfL2+uiC6Kubk5zM3N4eDgAGNjY7i5ueGzzz4rNkBv06YNjh8/Xuq4ABAREYGmTZtiyJAhkMlkGDhwYIn9C0K6wnwMA8t0LSJVYfCofJzYRkRERFUd86X/MF8qG+ZLzJeYLxWP+VLl083GRdUlqJ3k2ymqLkHtDLDg+5SIiCqOk7CIlMTExASenp5Yvnw5xo8fD11d3TKfW79+fQwaNAjh4eHYsWMHsrKysGPHDmzatAnOzv+tssnLy0P79u1x4MABdO3atcK1Ojk54dWrV3j+/HmR4YJIJIKDgwPS0yu2Rej333+Px48f48yZM4Jtni9cuIDhw4cjOzsbRkZGsLGxQceOHREfHw+ZTAYPDw9YWlrK+3fp0gXGxsaIi4vDtm3bylXDiBEjEBwcjFWrVpXrPHt7e8E22QV++umnEsOFWbNmwcbGBps2bVJqnYMHD8ayZcvw1VdfYfz48YJjX3zxBapVq4Z+/fqVeI1nz55hzZo16NChA2rVqlXm2mQyGWQymcIW3IWdPXsWNWvWLDEgO3/+PH788UekpqbC2NhY3n7//n24u7vj8uXLcHBwKPP7BgCqV69epsC2QMOGDaGjo4OcnByFY8bGxujVqxe+/fZbrF69usxjAq/fL6dPn0ZAQIC8LS8vD+fOnRN8/ENhYrEYsbGx6Nu3b7lWK5alzsGDByMiIgLnzp2TrwoFXgeSixcvhpOTk6C9KJcvX8a+ffsQHh5e5toKrgGg1PdLSSuc3/TZZ59BLBbDz88PMpkMgwYNKravlpaWwvuQW8VTZccJQ0RERET0JuZL/2G+pLw6mS8xXypPncyXmC+9S3uvZaq6BLXjVZcThoiIiCoTTsIiUqKVK1fC1dUVrVq1QmRkJJo2bQqxWIxTp07h8uXLaNmyZbHnTpw4EY0bN8aPP/6I48ePw8TEBAMHDhRs2Qy83j5bKpUKQrK///4bz58/F/QzMTFBtWrV4O7uDl9fX7Rq1QomJib45ZdfMGPGDEgkEhgYGODs2bOYPXs2hg4dCicnJ1SvXh1paWmIj49HWFhYhZ6DVCpFjx49FH4Yd3JywqRJk/DNN99g7NixAICgoCCMGjUKAJCYmCjor6enh3Xr1mHQoEHo0aMHJkyYgEaNGuHJkyfYt28fAAjClMJGjRqFAQMGlLiCsyhjxozB8uXLMWHCBIwcORJaWlrYs2cPkpKSsGvXrmLPq127NiZPnowFCxaU63ql1dm2bVtMnDgRoaGhePHiBfr06YOXL19iw4YNWLp0KZYsWYL69esLzil4Pzx+/BinT5/G559/jn///Rdbt26V9zl58iSGDRuGw4cPo169erh+/To2b96MLl26oFatWrh9+zbmz58PbW1t+UcW7Nq1C3/99RdcXFxQo0YNHDx4EDExMZg6dWqJ9yiVStGmTRt06NBB4Vjr1q0hlUqxYMGCcr1vShIZGYmnT5+ie/fusLS0RHZ2NpYtW4aXL18KVt4WlpiYiJUrV8LExKTU8QubPHkygoKC4ODgAE9PT+Tk5ODLL7/EgwcPig3JAKBHjx74+OOP8dVXX6F27dplvl5pdU6aNAk7duyAt7c3Fi5ciI8//hh//fUXYmJicOnSJRw6dEjwZ8qrV69w79495OfnIysrC6mpqYiOjkbz5s0RGhoq77d8+XJs27YNhw8fBvA6CP/rr7/QunVr6Onp4eLFiwgNDYWrq6t89eWSJUtgZWUFZ2dnPH/+HOvWrcORI0eKDKFLEhERAQ0NDfj5+SE/P7/YVbNERERERETqgvnSa8yXyo75EvMl5kslY75ERERERO8LJ2ERKZGNjQ3OnDmDmJgYhIeH4/bt29DS0oKTkxOmTp2K4ODgYs91cnJCly5dMGvWLNy+fRs+Pj4KARkA9OvXD0OHDsW///4rb7O3t1fol5GRARcXF3h5eWH9+vWYMWMGnj59irp166Jnz56YNWsWAMDCwgINGzZEVFQUbt68CZFIJH89adKkcj+Dv/76C3v27MHGjRsVjonFYvj4+EAqlcrDjn79+mHcuHHQ0NBAnz59FM7x8fFBeno64uLiMGzYMNy/fx+GhoZo1aoVNm3ahJ49exZZh6amJkxNTctdv7W1NY4ePYqIiAh4eHjgxYsXcHBwQHJycqmrQ6dOnYpVq1YpBJYlKUudS5YsQdOmTbFy5UrMnDkTGhoa+Oijj7B9+3Z4e3sr9Le3t4dIJIKenh6sra3RpUsXTJ48Gebm5vI+T58+xZUrV/Dy5UsAQI0aNXDs2DEsWbIEDx48QO3atdGhQwekp6fDzMwMwOuPHVixYgUmTZoEmUwGW1tbLFq0SB5yFuXFixfYsGFDsYFrv379sHDhQkydOrVc75uSdOzYEStWrMCwYcPw119/oWbNmmjRogUOHDhQ5PcK8PrjHN78SIey8PX1hUwmw6JFizB9+nTo6OigZcuWOHr0aKnhV1xcHNq1a1eu65VWZ40aNXDkyBHExMRgxowZuHXrFvT19SGRSJCZmYnGjRsL+l+8eBF16tSBhoYGDA0N4eTkhPDwcIwZM0aw6u/ff//FtWvXBHWsXbsWkyZNQm5uLurXr4++ffti+vTp8j4vXrzAlClTcOfOHejo6KBp06Y4dOgQJBJJue4ZAKZPnw6xWIyhQ4dCJpNhyJAh5R6DqDLixxEqH3cXI6L3LV+m+DMbEdHbYr7EfIn5khDzpeIxXyo75kuVw9qHZd/FjMpG1KqJqktQO7Ifz6u6BCL6wDBfUi8imUwmU3URRETvW2pqKiQSCR48eFDu1YxUOnd3dzRv3hxLlixRdSlKERkZie3bt+Ps2bOqLkUtiUQibNu2rcig/G14igcodTzipCFl44QhIqK3dzA/WaXXb7NvhkqvX5Wc7Bqj6hKIiJSO+dK7xXyJyoP5UtXAbEn5mC8REb095ktVR1XIl7gTFhF90CwsLODt7Y2kpCRVl6IWvvnmG4wePRrPnj1D8+bNVV2OUp0/fx56enr4/PPPS1x1TGX36aefYsOGDaoug0hlGDwqH4NHIiIiIlIF5kvKxXyJyoP5UtXi4TdC1SWoHddzJ1RdgtrJbMb/fU5ERBXHnbCI6IP07Nkz3LlzBwCgp6cn2EqdKu7x48f466+/AABGRkYV2rK/Mrp//z7u378PAKhVqxYMDQ1VXJF6+Pvvv/Ho0SMAQJ06daCrq6vU8blSUfk4aYgqO07CIvrwcKVi1VEVVioSEZUX86V3g/kSlQfzpapl/R8/qLoEtTOicXdVl6B28h4+VHUJRPSeMV+qOqpCvsSpvET0QdLW1oatra2qy1A7+vr60NfXV3UZSmdsbAxjY2NVl6F2zMzMYGZmpuoyiFSGE4aIiIiIiKo25kvvBvMlKg/mS1WLuYaeqktQO5wwREREVLmIVV0AERERERERERERERERERERERFRVcadsIiIiIiIiIiIKkAmU3UFRERERERERERUlTFfUi/cCYuIiIiIiIiIiIiIiIiIiIiIiOgtcCcsIiKSu3fvHmJjY7Fnzx7cvn0bhoaGsLW1hb+/P/z8/GBtbY2pU6di+vTpCufOnTsXy5cvx+3bt1GtWjUAgIODA27cuIFbt27B3Ny8yGs+e/YM9erVg1gsxp07d6ClpaXQ57vvvsOKFStw5swZPH/+HA0aNICrqyvGjx+PFi1alGu8hg0b4tatWwrXiI2NxfTp03Hz5k1YWVnJ242NjdGyZUvExcXJr+Xu7o60tDQAgJaWFqytrTFu3DgEBwdj4cKFiI6Oxt27d1GjRg3BNZ4+fQpzc3NER0djwoQJglp0dHRgb2+P8PBwDBgwAAAQGRmJqKgoAICGhgYsLCzg4+ODuXPnQk9PT6HWwjIyMuDi4qKU5/L8+XN5HcWRyWQIDAxEdnY2tm/fLm/fsmUL/P39MW/ePEyZMgWBgYFYv349AKBatWpo0KABhg0bhhkzZkBTUxOpqamQSCTy801NTdG6dWvExcWhSZMmJdZAVNXs//OcqktQO151m6m6BCIiIiKiDx7zJeZLRT0X5ksEAF59h6m6BLXT85dUVZegdnY71VR1CUREVIVxEhYREQEArl+/DldXVxgZGSEmJgZNmjSBlpYWzp8/jzVr1qBevXrw9/dHQkKCQkgmk8mQmJiIYcOGyQOy48eP49mzZ+jfvz/Wr1+PsLCwIq/73XffwdnZGTKZDNu3b8egQYMEx8PCwrBw4UJMmDABUVFRsLS0xD///IO9e/ciPDwc+/btK9d4ADBnzhyMGjVK0Kavry94fejQITg7O+P27duYMGECunXrhsuXL8PIyAgAMGrUKMyZMwdPnz7F119/jbFjx6JmzZoYOnQowsPDsXXrVgwZMkQw5pYtW/DixQv4+/sr1PLo0SMsXLgQgwYNQr169dCuXTsAgLOzMw4dOoRXr17hhx9+wIgRI/D06VN89dVXCrUWZmJiorTnIpPJ8Omnn8rbWrdujU8++USh75vWrVuHsWPHYvXq1Rg+fLi8vWvXrkhISEBubi6+//57jB07FtWqVUN4eLi8z5UrV2BgYIA///wToaGh6NGjB3777TdUr169xGsSVSWcMEREREREROqG+RLzJeZLVJL9W79WdQlqh/kSERFR5cJJWEREBAAIDg6GpqYmfvzxR+jq6srbra2t0bt3b8hkMlhZWWHp0qU4fvw42rdvL++TlpaG69evIygoSN4mlUoxZMgQdOzYERMnTiw2JJNKpfD394dMJoNUKhWEN5mZmfj888+xdOlSTJgwQd7eoEEDtGzZErIiPiS5pPEK6OvrF7tysoCJiQnMzc1hbm6OL774Aq6urjhx4gS8vLwAvF5ZWDBGZGQkNm7ciJ07d8LX1xfe3t6Ij49XCMni4+PRp08fGBsbK9Ribm6OFStWYMOGDdi1a5c8JNPU1JRfZ9CgQTh8+DB27twpCMkKai3J2z4XPT09+e81NDRKfYaff/45Zs+ejU2bNsHHx0dwTEtLS37umDFjsG3bNuzcuVMQkpmZmcHIyAjm5uYICQlBr169cPnyZTRt2rTE+yQiIiIiIiIi1WG+JMR8SYj5EhERERGpO07CIiIiZGVl4cCBA4iJiREEZIWJRCI0adIErVu3Rnx8vCAkS0hIQLt27eDg4AAAePz4MZKTk3HixAk4ODjg4cOHOHbsGNzc3ARjXrt2DRkZGdi6dStkMhkmTZqEW7duwdLSEgCQlJQEPT09BAcHF1tTecarKG1tbQDAixcvSuxTcDwoKAg9e/YUXPv69es4evQo9u/fX+wYmpqaqFatWpmvU1bv6rkUJywsDCtXrsTu3bvRuXPnUvtra2sjKyuryGMPHz7Epk2bAICrFCsBrqxTLn4cofLxPUpEREREpDrMl0rGfKl8mC+pJ/7crnzMl5SP71MiInobnIRFRET47bffIJPJYG9vL2g3NTXF8+fPAQBjx45FXFwcgoKCMHXqVCxbtgx6enp4/PgxtmzZgmXLlsnP27RpExo1aiTfwnzw4MGQSqUKIVl8fDy6deuGmjVff8a6l5cXEhISEBkZCQC4evUqrK2toan5319XixYtwqxZs+Sv79y5A0NDwzKNVyAsLAwzZ84UtO3du1ehPgDIzs7G3LlzoaenhzZt2igcz8vLQ1JSEn7++Wd88skn8uvWrVtXcO3ExETUr1+/2NDoxYsXWLhwIR4+fIhOnToV2ef06dPYuHGjwvF27dpBLBYL2p48eSL//bt4LsXZu3cvduzYgcOHDxd7HwVkMhkOHz6M/fv3Y/z48YJjFhYWAICcnBwAQK9eveQhbFFyc3ORm5sraMuX5UEs0ihz7URU9TF4VD4Gj0Qly4eo9E5EREQfCOZLzJcA5ktE71uLHxV3pKO3NM649D5UZmbL01VdAlGlx3xJvXASFhERFevkyZPIz8+Hn5+fPIDw9fXFpEmT8O2332LEiBHYvHkzxGKxYPvx+Ph4+Pv7y1/7+/ujY8eO+PLLL6Gvrw/gdbi0fv16LF26VNBv6tSpmDVrlkLoU2DEiBHo1asXTpw4Id/+vLzjhYaGIjAwUDBuvXr1BK8LgqecnBxYW1tj8+bNqF27tvz4ypUrsW7dOrx48QIaGhqYNGkSxowZA+D1duoBAQFITEzE7NmzIZPJsH79egwfPlzhvgqCqefPn0NPTw/z589Hjx495MfPnz8PPT095OXl4cWLF+jRoweWL18uGGPz5s1wdHQs8nkp+7mUpmnTpvj3338xe/ZstGnTRrDNfIHdu3dDT08PL1++RH5+PoYMGaIQ2B07dgw6OjrIzMxETEwMVq9eXeJ1Y2NjERUVJWizgiNs4Fyu+qlknOCiXJzcQkREREREHwLmS8yXmC9RAWZLyudVV9UVEBERUWGchEVERLC1tYVIJMKVK1cE7dbW1gD+2y4dAAwMDNC/f38kJCRgxIgRSEhIwMCBA+VhyC+//ILMzEycPHkSYWFh8vPy8vKwadMmjBo1CgCwf/9+3LlzRxCuFfQ7fPgwPD090ahRIxw/fhwvX75EtWrVAABGRkYwMjLC7du3BeeVZbwCpqamsLW1LfGZbN68GU5OTjAxMYGRkZHCcT8/P0REREBbWxt16tRRCL9GjBiB2NhYHDlyBPn5+fjjjz8wfPhwhXEKgik9PT3Url1bYQt8e3t77Ny5E5qamqhbt26RW6bXr1+/2PtR9nMpTb169bBlyxZIJBJ07doVe/fulQejBSQSCVatWoXq1aujbt26gpWoBaysrGBkZAR7e3v8/fffGDRoEI4ePVrsdcPDwzF58mRBm49h4FvdCxERERERERGVHfMlRcyXKob5EhERERFVVZyERUREMDExgaenJ5YvX47x48dDV1e3xP5BQUFwd3fH7t27kZ6ejgULFsiPSaVSdOjQAStWrBCck5CQAKlUKg/JpFIpBg8ejIiICEG/efPmQSqVwtPTE76+vvjyyy+xcuVKTJw4scSayjJeedSvXx82NjbFHjc0NCwxULKxsUHHjh0RHx8PmUwGDw8PWFpaKvQrLZiqXr36WwVXyn4uZWFpaYm0tDR5ULZv3z5BUKarq1uuexo7dixiY2Oxbds2+Pj4FNlHS0sLWlpagraDdy9U7AaIiIiIiIiIqNyYLylivlRxlSVfEourVewGqEj5yFd1CURERETvFCdhERERgNfbn7u6uqJVq1aIjIxE06ZNIRaLcerUKVy+fBktW7aU9+3QoQNsbW0xbNgwODg4oF27dgCAly9f4n//+x/mzJmDxo0bC8YfOXIkFi1ahIsXL8LMzAy7du3Czp07FfoNGzYMPj4+uH//Ptq2bYspU6ZgypQpuHXrFvr27Yv69evj7t27kEqlEIlEEIvF+Oeff8o0nrHx689yf/z4Me7duyfop6OjAwMDA6U9T+B1mFgQCiYmJip17MKysrIU7sfIyAiPHz9W2XOpX78+UlNTIZFI4OXlhX379lX4+ero6GDUqFGYPXs2+vTpo7Caszj8qDfl45bxysXnqXz8viciIiIiUi3mS8yX1C1fgoyThpTp37ynqi5B7Wi8sUscvb28x49VXQIREVVhnIRFREQAXq+sO3PmDGJiYhAeHo7bt29DS0sLTk5OmDp1KoKDg+V9RSIRRowYgRkzZiA8PFzevnPnTmRlZRW5mszR0RGOjo6QSqWoV68edHV10blzZ4V+nTt3hra2NjZs2IAJEybgiy++QJs2bbBq1SrEx8fj6dOnqF27Njp06ICMjAwYGBhg4cKFZR4PAGbNmoVZs2YJ+o0ePRqrV6+u8PMrSr9+/TBu3DhoaGigT58+Sh27MA8PD4W2pKQk3LlzR6XPxcLCQhCU7d+/v0LjAMC4ceOwaNEiJCcnY+DAgRUeh6gy4YQhIqKqTyYr4/+8IyIi+kAwX2K+xHyJSmKmoafqEtQOJwwREVV9zJfUi0gmk8lUXQQRERGRsnmKB6i6BLXDnZuUi5OwiIje3sH8ZJVe/6PvZ6r0+lXJT92jVV0CERERUbkxX1IuZkvKx3yJiOjtMV+qOqpCviRWdQFERERERERERERERERERERERERVGT+OkIiIiIjKhCvrlIurP6kq4Pc9ERERERERKQt/xlQ+zVqmqi5B7Sw7vUPVJaiV4Aauqi6BiOi94iQsIiIiIiIiIiIiIiIiIqIq5vo4O1WXoHY8D4aougS10ginVF0CEdF7xUlYREREpJa4yxDRh4craomIiIiIiEipRGJVV6BWHn9vreoS1I7ed6quQP00mM1JQ0REVHGchEVERERqiZMxqLLjREEioqovXyZSdQlERERE9C7J8lVdgVrR7/abqktQO/v/5CwsZfNax1yZiN4v5kvqhZOwiIiIiKhMOGmIiIiIiIiIiIgq6slgF1WXoHb6XTNTdQlq6G9VF0BERFUYJ2EREVGVFxgYiOzsbGzfvl3QnpqaColEggcPHsDIyAgymQzr1q1DfHw8Ll68iPz8fFhaWsLDwwPjx4+Hra0tACAyMhLbt2/H2bNnS7zus2fPUK9ePYjFYty5cwdaWlqC4w0bNkRISAhCQkLkbZcvX4ajoyMyMjLg4vJf6ODi4oKzZ88iOzsbNWrUAAA8f/4cRkZGWLFiBYKCghAYGIj169cr1OHl5YV9+/YJ2mJjYzFz5kzMnz8foaGhgmOJiYkICQlBdna2vO3SpUvo0qULXFxc8M0332Djxo0YPnw4HBwccOnSJcH5ycnJGDhwICwtLXHz5s1yP5Nbt24p3ENsbCymT5+OmzdvwsrKCmfOnEHz5s0BAI8fP0avXr3w119/4cCBA7CwsFA4n6gq4m5tRERERERElQfzJeZL9O79sOgrVZegdpgvERERVS78MGsiIvogyGQyDBkyBBMmTED37t1x4MAB/PLLL5BKpahRowaio6PLPeZ3330HZ2dnODg4KAR0xXFwcIC5uTlSU1PlbY8fP8ZPP/2EWrVqITMzU96ekZGB3NxcdOrUSd7WtWtX3L17V/ArKSlJ4Trx8fGYNm0a4uPjS63p1KlTcHNzQ9euXbF582ZUr14dAKCrq4u///4bGRkZgv5SqRQNGjQocqyyPJM5c+Yo3MP48eOL7PvPP/9AIpEgJycHx44dY0BGRERERERERCrDfKl4zJeIiIiIiDgJi4iIPhCbN2/Gpk2bsHnzZnz22WdwcXFBgwYN4OLigri4OCQkJJR7TKlUCn9/f/j7+0MqlZb5PIlEIgjJjh8/Djs7O3h7ewvaU1NTYWlpCSsrK3mblpYWzM3NBb9q1qwpGD8tLQ3Pnj3DnDlz8OjRI6Snpxdby5EjR9CpUycEBQVh7dq1EIv/+6eBpqYmhgwZIgjabt++jdTUVAwZMqTI8cryTPT19RXuQVdXV6HfH3/8ATc3NxgaGuLIkSMwMTEp9j6IiIiIiIiIiN415ktFY75ERERERPQaP46QiIg+CElJSbC3t0evXr2KPC4Sico13rVr15CRkYGtW7dCJpNh0qRJuHXrFiwtLUs9VyKRYNKkSXj16hU0NTWRkpICd3d3uLm5YeXKlYiMjAQApKSkQCKRlKsu4HVQ5evri2rVqsHX1xdSqRTt2rVT6Ldt2zYMGTIEkZGRCAsLK3KsESNGwN3dHUuXLoWOjg4SExPRtWtX1K5dW6Hv2zyTN125cgWhoaFo1aoVkpKSFLadJ9Xg9uZU2e3/85yqSyAqEf8cJSIiIqramC8xX6K3w5+JqCrYeLv4SadUfkMduqi6BLWT9+SJqksgohJwEhYREamF3bt3Q09PT9CWl5cn//3Vq1dhb28vOB4SEoJ169YBAIyMjHD79u0yXy8+Ph7dunWTrxL08vJCQkKCPOAqScH256dOnULbtm2RmpqK0NBQtG/fHgEBAXj+/DlkMhlOnjyJkSNHlnqfM2bMwIwZMwAAjx49wpYtW+RbvPv7+8PNzQ1Lly4VnPfkyRMMGDAAM2bMKDYgA4AWLVrA2toaW7ZswdChQ5GYmIhFixbh+vXrFX4mYWFhmDlzpqBt7969cHNzk78eNmwYXF1dkZycDA0NjWLrK5Cbm4vc3FxBW74sD2JR6edS2XGCi3IxeFQ+PlMiet9kMlVXQERERMrEfIn5EvOld4vZkvIxC1G+IRaKE07pbXDCEFFpmC+pF07CIiIitSCRSLBq1SpB24kTJ+Dv71/sORERERg3bhy2bt2KmJiYMl8rLy8P69evx9KlS+Vt/v7+mDp1KmbNmiXYcr0otra2sLCwQGpqKpydnXHmzBl07NgRZmZmaNCgATIyMiCTyZCbm6uwUrGo+zQ2Npb/PikpCTY2NmjW7PUP382bN4elpSU2b96MoKAgeT9tbW20b98ea9euha+vLxwdHYutd8SIEUhISECDBg2Qk5OD7t27Y/ny5RV+JqGhoQgMDBScX69ePcHrXr16Yfv27di6dSsGDBhQbG0FYmNjERUVJWizgiNs4FzquVR2DHWUi8Gj8vE9SkREREREb4P50mvMl/7DfEm5+HO78jFfUj6+T4mI6G1wEhYREakFXV1d2NraCtoKrzxs1KgRrly5Ijheq1Yt1KpVC2ZmZuW61v79+3Hnzh0MGjRI0J6Xl4fDhw/D09Oz1DHc3d2RkpKCpk2bolGjRvIaOnbsiJSUFMhkMtja2qJ+/fql3mdhUqkUFy9ehKbmf3/F5+fnIz4+XhCSaWhoYPv27ejbty8kEglSUlKKDcr8/Pwwbdo0REZGYujQoYKxK/JMTE1NS7wH4HWA2bRpUwwZMgQymQwDBw4ssX94eDgmT54saPMxDCzxHCo/hjpERERERESkzpgvvcZ86T/Ml5RLw7imqktQO7+/eqzqEoiIiKgQTsIiIqIPgq+vL4YMGYIdO3agd+/ebzWWVCrF4MGDERERIWifN28epFJpmUIyiUSCCRMmwMnJCe7u7vL2Dh06YO3atZDJZAqrFEtz/vx5/Pjjj0hNTRWsXrx//z7c3d1x+fJlODg4yNu1tLSwdetW9O/fHxKJBEeOHIGTk5PCuMbGxujVqxe+/fZbrF69ushrK+OZvOmzzz6DWCyGn58fZDKZQgBXmJaWFrS0tARt3CqeKjuuqiMiIiIiIqpamC8xX6K38/2FVFWXoHa86rZXdQlERERUCCdhERHRB2Hw4MHYunUrBg8ejPDwcHh5eaF27dq4desWNm/eDA0NYaDy7NkznD17VtCmr68PAwMD7Nq1Czt37kTjxo0Fx4cNGwYfHx/cv39fHlLduXNHYRxLS0tIJBLk5OQgPj4ea9eulR/r2LEjRo4cCQAIDg5WuI/c3Fzcu3dP0KapqQlTU1NIpVK0adMGHTp0UDivdevWkEqlWLBggaBdS0sL3333HQYMGCAPypydFbdYT0xMxMqVK2FiYqJw7J9//inXM3n8+LHCPejo6MDAwEBh7IiICGhoaMDPzw/5+fnw9fVV6ENUVXFnMeXjxDYiIiIiInqXmC8xX6K3092po6pLUDvxf+xWdQlqZ0R9TmwjIqKK4yQsIiL6IIhEImzevBlr165FQkICPv/8c7x8+RIWFhbo3LkzFi1aJOh/9epVtGjRQtDWuXNndOvWDbq6uujcubPCNTp37gxtbW1s2LABEyZMAAB88cUX+OKLLwT9/ve//8Hf3x+Wlpa4desWOnb8L3xo0KAB6tati5s3bwpWMBbYt28f6tSpI2izt7fHzz//jA0bNiAsLKzI++/Xrx8WLlyImJgYhWPVq1fHli1bMHDgQHlQ9iZtbW1oa2sXOfbXX39drmcya9YszJo1S9Bv9OjRxa6CnD59OsRiMYYOHQqZTIYhQ4YU2Y/ePU5wUS5OwiIiIiIiIqpamC8xX6K3k5edreoS1E49DX1Vl0BERESFiGQymUzVRRAREREpm6d4gKpLUDucNKRcnNRGRPT2DuYnq/T6TXfNKr0TAQB+9p6j6hKIiIiIyo35knIxW1I+5ktERG+P+VLVURXyJe6ERURERERlwlCHKjuGucrH73siIiIiIiJSFv6MSVUB8yXl4vc9EX1oOAmLiIiIiEgFGOgoH0MdIiIiIiIiIvqQMF9SPuZLRET0NjgJi4iIiIjKhKGOcjHQISIiIiIiIqIPCbMl5WO+REREVLlwEhYRERERlQlDHeVi8Kh8fI8SERERERERVV78uV35mC8pH9+nRET0NsSqLoCIiIiIiIiIiIiIiIiIiIiIiKgq405YRERERFQmXFmnXFxVR0RU9clkIlWXQERERERUZTBbUj7mS0REVR/zJfXCSVhEREQqFhgYiOzsbGzfvl3QnpqaColEggcPHsDIyAhr167F8uXLce3aNWhqasLKygoDBw5EeHg4ACAyMhJRUVEAAA0NDRgZGcHJyQl9+/bFmDFjoKWlJR/b3d0daWlpAAAtLS1YW1tj3LhxCA4OBgDk5eVhwYIFSExMxK1bt6CtrY1GjRph1KhRGDlyZJF1F3cfhZ05cwbz58/H0aNHcf/+fZibm6NJkyYYPXo0XFxc0KRJE0yYMAEzZswQnDdw4ED8/vvv+OGHH6ChoVHhZ01UmTB4VD4Gj0RERERE9KFivsR8qSro0cFH1SWonWnXdqq6BLXzuU1jVZdARERVGCdhERERVQHx8fEICQnBsmXL0LFjR+Tm5uLnn3/GhQsXBP2cnZ1x6NAh5OfnIysrC6mpqYiOjsb//vc/pKamQl9fX9531KhRmDNnDp4+fYqvv/4aY8eORc2aNeHr64uoqCh89dVXWL58OVq1aoVHjx7hxx9/xIMHDyp8Dzt27MDAgQPh4eGB9evXw9bWFrm5uUhPT8fMmTORlpaGNWvWYMCAAfD29kaTJk0AAMnJydi9ezfOnDnDgEzFOMFFuTgJi4iIiIiIiN4n5kvMl1Tt1W/XVV2C2umsnafqEtTO56ougIiIqjROwiIiIqoCdu7ciYEDByIoKEje5uzsrNBPU1MT5ubmAIC6deuiSZMm8PT0RLNmzRAXF4fo6Gh5Xx0dHXnfyMhIbNy4ETt37oSvry927tyJ4OBgDBgwQN6/WbOKT8DJyclBUFAQevToga1btwqOOTo6IigoCDKZDL169cKQIUMQEBCAEydOIDs7G2PHjsX8+fNhb29f4euTcnDSkHJxUhsRERERERG9T8yXmC+pGrMl5WO+REREVLlwEhYREVEVYG5ujrS0NNy6dQuWlpblOtfBwQHdunXD1q1bBSHZm7S1tfHixQv59Y4cOYLg4GDUqlXrrWoHgAMHDiArKwvTpk0rto9I9Pozr5cuXYomTZpg7ty5uHTpEho3bozx48e/dQ1ElQ2DR+Vj8EhERERERFQ85kvMl1Stu+eA0jtRuYz59XtVl6B2VjWyVXUJRERUhXESFhERUSWwe/du6OnpCdry8v7bSnr27Nno27cvGjZsCDs7O7Rt2xbdu3dH//79IRaLSx3fwcEBBw4cKPJYXl4ekpKS8PPPP+OTTz4BACxatAj9+/eHubk5nJ2d0a5dO/Tu3RvdunWr0P1dvXoVAASrDU+dOgWJRCJ/vWnTJvTs2RMGBgZISEhAly5doKuri59//lkeoBUnNzcXubm5grZ8WR7EIm4vT0REREREREQfBuZLzJcqO9G9LFWXoHaaa91TdQlqiJOwiIio4jgJi4iIqBKQSCRYtWqVoO3EiRPw9/cHANSpUwcZGRm4cOECjh49ivT0dAQEBGDdunXYt29fqUGZTCZTCJpWrlyJdevW4cWLF9DQ0MCkSZMwZswYAICTkxMuXLiA06dP44cffsDRo0fh7e2NwMBArFu3Tin33LRpU5w9exYA0KhRI7x69Up+rFOnTnBxcUHz5s3LtDIzNjYWUVFRgrZZU4wxe6qJUmolehe4axMRUdWXLyv5f+QRERERvU/Ml5SfL9lUbwZbrRZKqZWAr8/tUnUJameIRXtVl0BERG+J+ZJ64SQsIiKiSkBXVxe2tsIVNrdv31bo17hxYzRu3BjBwcH49NNP4ebmhrS0NMGKv6JcunQJVlZWgjY/Pz9ERERAW1sbderUUQjaxGIxWrdujdatWyMkJAQbNmzA0KFDERERoTBWaRo1agQAuHLlClxcXAAAWlpaCvdcmKamJjQ1y/ZPlfDwcEyePFnQ5mMYCK9FXKlIlRc/jlD5OLGNiIiIiIg+ZMyXFCkjX8p7+aRcdVLxhli0U3UJaof5kvIxXyIiordR+v6yREREVCk5OTkBAHJyckrsd/nyZezbtw/9+vUTtBsaGsLW1hb16tUr05bzZb1eUbp06QJjY2PExcWV+9yy0NLSgoGBgeAXt4onIiIiIiIiIioZ86X/MF8iIiIiorfFnbCIiIiqgDFjxqBu3bro1KkTLCwscPfuXURHR6NWrVpo27atvN+rV69w79495OfnIysrC6mpqYiOjkbz5s0RGhpa5uv1798frq6uaNeuHczNzXHjxg2Eh4fDzs4ODg4OxZ738OFD+RbwBUxMTFC/fn2sW7cOgwYNQo8ePTBhwgQ0atQIT548wb59+wAAGhoMtSo7rqxTLq6qIyIiIiIioveJ+RKpGrMl5WO+REREVLlwEhYREVEV4OHhgfj4eKxatQpZWVkwNTVF27ZtcfjwYZiYmMj7Xbx4EXXq1IGGhgYMDQ3h5OSE8PBwjBkzBlpaWmW+npeXF5KSkhAbG4uHDx/C3NwcnTp1QmRkZIlbuKempqJFixaCtqCgIKxbtw4+Pj5IT09HXFwchg0bhvv378PQ0BCtWrXCpvpZAvgAAQAASURBVE2b0LNnz/I/GHqvGOooF4NH5eN7lIiIiIiIqHjMl0jV+HO78jFfUj6+T4mI6G2IZDKZTNVFEBERESmbp3iAqktQOwx1lIuBDhHR2zuYn6zS6zvviFTp9auSi70jVV0CERERUbkxX1IuZkvKx3yJiOjtMV+qOqpCvsSdsIiIiIiIVIDBo/IxeCSi943L2oiIiIiIyq67fXtVl6B2/vfHPlWXoHaG1ndVdQlE9IFhvqReOAmLiIiIiEgFOGGIiIiIiIiIiD4k3185ruoS1I5XXU4YIiIiqkw4CYuIiIjUEncZIiIiIiIiIiKit8F8Sbm4II2IiIjUHSdhERERkVpiqKN8DB6JiIiIiIiI6EPCfEm5mC0pH9+jRERElYtY1QUQERERERERERERERERERERERFVZdwJi4iIiIhIBbj6U/m4+pOIiIiIiIio8vK90UnVJaidnP6NVF2C2tHdkqnqEoiIqArjJCwiIiIiIhXghCEioqpPJhOpugQiIiIioiojyeqIqktQO15bslRdAhERvSXmS+qFk7CIiIhUKDAwENnZ2di+fTsCAwOxfv16hT5eXl7Yt28fAKBhw4a4deuWQp/Y2FhMnz4dN2/ehJWVFc6cOYPmzZsrvH5TYmIihg8fDgAQi8UwMDCAnZ0devTogYkTJ8LQ0BBz587FypUrcfHiRRgbG8vPPXfuHNq0aYPvvvsOPXv2hEgkwrZt29CnTx+F66SmpkIikchfm5qaonXr1oiLi0OTJk0Ez6PgGWhqasLCwgIDBgzAnDlzUKNGjbI9VKIqgjthKR8nthERERER0YeI+RLzpaqiu1NHVZegduL/2K3qEtTOiPrtVV0CERFVYZyERUREVIl07doVCQkJgjYtLS3B6zlz5mDUqFGCNn19/Qpf08DAAFeuXIFMJkN2djbS09MRGxuLhIQE/PDDDwgPD8euXbswduxYJCUlAQBevnyJgIAA+Pv7o2fPnmW+1pUrV2BgYIA///wToaGh6NGjB3777TdUr15d3qfgGbx8+RKnT59GQEAARCIR4uLiKnyPRJURJwwRERERERHRu8B8iflSZfX9L2mqLkHteNXlhCEiIqLKhJOwiIiIKhEtLS2Ym5uX2EdfX7/UPuUhEonk49WpUweOjo7w9vaGs7Mzpk2bhg0bNuDrr79GixYtsGXLFvTv3x/z5s1DdnY2Fi9eXK5rmZmZwcjICObm5ggJCUGvXr1w+fJlNG3aVN6n8DOoX78+PDw8cPDgQYZklQAnDSkXd8KiqoDf90REREREVQ/zJeZLlRV/xlQ+sRZ3d1O25GucLKhM/Sw+VnUJRETvFSdhERERkQIzMzP4+fkhPj4eeXl5cHBwQGxsLMaMGQN9fX3ExsZi3759MDAwqND4Dx8+xKZNmwBAsErxTRcuXEB6ejosLS0rdB1SLk4aUi4Gj0RERERERKTOmC/Rm5gtKR/zJeXjpCEiInobnIRFRERUiezevRt6enqCthkzZmDGjBny12FhYZg5c6agz969e+Hm5qbUWhwcHPD48WNkZWXBzMwMEydOxI4dO9C9e3eMHz8eEomk3GNaWFgAAHJycgAAvXr1goODg6BPwTN49eoVcnNzIRaLsXz58hLHzc3NRW5urqAtX5YHsUij3DUSvS8MHpWPwSMRvW8ymUjVJRAREREpYL7EfKmy+ih6jKpLUDvi3VmqLkHtmPS8ouoSiOgDw3xJvXASFhERUSUikUiwatUqQZuxsbHgdWhoKAIDAwVt9erVU3otMpkMwOvt5Av+GxERgdTUVIWQrqyOHTsGHR0dZGZmIiYmBqtXr1boU/AMcnJysHjxYmhqaqJfv34ljhsbG4uoqChBmxUcYQPnCtVJ9D5wwhARERERERG9C8yXmC9VVj/NXFV6JyoX5ktERESVCydhERERVSK6urqwtbUtsY+pqWmpfZTh0qVLMDAwgImJibxNU1NT8N/ysrKygpGREezt7fH3339j0KBBOHr0qKBP4WcQHx+PZs2aQSqVIigoqNhxw8PDMXnyZEGbj2FghWokel+4E5byMXgkIiIiIiJivgQwX6qsvPoOU3UJaqfnL6mqLkHt7HaqqeoSiIioCuMkLCIiIlLw999/Y+PGjejTpw/EYvE7ucbYsWMRGxuLbdu2wcfHp8g+YrEYM2bMwOTJkzFkyBBoa2sX2U9LSwtaWlrCc7lVPNEHhxPblI8T24iIiIiIqKKYL9GbNK7+ruoS1M6axB6qLkHtPFn3UtUlqBW7kadUXQIR0XvFSVhERESVSG5uLu7duydo09TUhKmpqfz148ePFfro6OjAwMCg2HGvXFH8HHtn59dbqctkMty7dw8ymQzZ2dnIyMhATEwMDA0NMX/+/HLVf+PGDZw9e1bQ1qhRoyL76ujoYNSoUZg9ezb69Okj35b+TQMGDEBoaChWrFiBqVOnlqseosqMk1uIiIiIiIjoXWC+pIj5UuXw/YVUVZegdrzqPlB1CURERFQIJ2ERERFVIvv27UOdOnUEbfb29rh8+bL89axZszBr1ixBn9GjR2P16tXFjjt48GCFtj/++AMA8OjRI9SpUwcikQgGBgawt7dHQEAAJk6cWGLwVpQ3t2wHgGPHjhXbf9y4cVi0aBGSk5MxcODAIvtoampi3Lhx+PzzzzFmzBjo6uqWqyaiyoq7NikfJ7YRERERERExXyoK8yUiIiIieh9EMplMpuoiiIiIiJTNUzxA1SWoHU4aUi5OGCIiensH85NVen37rXNUev2q5ErfWaV3IiIiIqpkmC8pF7Ml5WO+RET09pgvVR1VIV96Nx/CTURERERERERERERERERERERE9IHgxxESEREREakAV38qH1d/EhEREREREVVePVp4qroEtbP01nZVl6B2Jlq2U3UJRERUhXESFhERERGVCSe4KBcnYSkfn6ny8fueiIiIiIiIlOXVX3+rugS1MyxyiqpLUDt6B/9UdQlqpZrnLVWXQET0XnESFhEREaklTsagyo6TW4iIiIiIiIgqN7G2tqpLUCubf01RdQlqZ4CFqitQQwmqLoCIiKoyTsIiIiIitcQJLsrHiW3KxeepfPy+JyIiIiIiImXKf/ZM1SWolQEWLqouQe0wX1I+5ktERPQ2OAmLiIiIiIiIiKgCZDKRqksgIiIiIqo6RGJVV6B28pGv6hKIiOgtMV9SL5yERUREZRYYGIjs7Gxs375d8PsCf/zxB2bPno19+/bh33//RZ06ddCnTx/MmjULJiYm8n7u7u5IS0tTGH/06NFYvXo1AEAkEmHbtm3o06cPAODly5cYNmwYjh49ij//LPkz2WUyGf755x/MmjULe/bswV9//YWaNWuiWbNmmDVrFlxdXQEADRs2xK1bt5CRkQEXl/9WYYWEhODs2bNITU0FAERGRiIqKkrhOgcPHoSHh4f8eOH6AeDs2bNo0aIFbty4gYYNGwrO9fLywqFDh5CZmYnWrVsXey8ODg64ceMGbt26BXNzc8Exd3d3NG/eHEuWLAEA3LhxAxEREUhNTcX9+/dhamqKli1bIi4uDg4ODkU+18JSU1MhkUjw4MEDGBkZAQD+/PNPeHl5oWbNmti1axfOnDkDiUSicG5ERAQ2bNiAW7eK/3z3gIAAJCYmQiT67x+TBgYGaNy4MebOnYtOnToJjhVl9uzZiIyMLLEPvTtcBUZE9Ha4QpmIiIiI+dKbmC/9h/nSB0DGCUPK1q1uC1WXQETvGfMlosqNk7CIiEgprl+/jrZt28LOzg5JSUmwsrLCxYsXERoair179yIzMxPGxsby/qNGjcKcOXMEY+jo6BQ59tOnT9GvXz/8+uuv2LdvH2rVqiU/1rp1a3zyyScYNWqU4Jx+/frhxYsXWL9+PaytrfHXX3/h8OHDyMrKEvSrUaMGwsLCigztCnN2dsahQ4cEbYXvp0aNGpBKpZgyZQoaNWpU4li///470tPTMW7cOMTHxxcbkh0/fhzPnj1D//79sX79eoSFhRU75suXL+Hp6Ql7e3ts3boVderUwe3bt7F3715kZ2eXWE9xrl27Bk9PTzg5OSE5ORna2tryY1euXIGBgYH8tZ6eHiZOnIi8vDwAQHp6Ovr16yfoV/j8hIQEdO3aFf/++y8iIiLQs2dPXLhwAXfv3pX32bx5M2bNmoUrV64IrkOqwx/uqLLjREGq7PgeVb6D/H84REREaoX5EvMl5kvqTdOinqpLUDtzjm9XdQlqZ0bD4ie1ElUGzJeUj/kSKRMnYRERkVKMHTsW1atXx4EDB+RhSIMGDdCiRQvY2NggIiICq1atkvfX0dFRWHlXlOzsbPTo0QNPnjzB8ePHFc7R0NCAvr6+oD07OxvHjh1DamoqOnbsCACwtLREmzZtFMb/5JNPsHr1anz//ffo3r17sXVoamqWWK+9vT3MzMwQERGBb7/9tsR7SkhIQM+ePTFmzBi4uLhg0aJFggCpgFQqxZAhQ9CxY0dMnDixxJDs4sWLuHbtGg4fPgxLS0sAr++5YFVmef3888/w8vJCp06dsH79emhqCv/JYGZmJl/NWKBwgFUQIBbVDwCMjIxgbm4Oc3NzrFq1CvXq1cPBgwcxevRoeR9DQ0OIRKIyvU+IqiL+sExERERERCTEfIn5EvMl9bbn5PeqLkHteNXlhCEiIqLKhJOwiIjord2/fx/79+/HvHnzFMIec3Nz+Pn5YfPmzVi5cmWp24EXdu/ePXTs2BF6enpIS0srMmwpip6eHvT09LB9+3a4uLhAS0ur2L5WVlb49NNPER4ejq5du0IsFpe5vjfNnz8frVu3xo8//ohWrVoV2UcmkyEhIQErVqyAg4MDbG1tsWXLFgwdOlTQ7/Hjx0hOTsaJEyfg4OCAhw8f4tixY3Bzcyty3Fq1akEsFmPLli0ICQmBhoZGhe8jPT0dfn5+8PPzw5dfflmur1lFFLxnXrx48U6vQ2+Pk4aUizuLKR/fo0REREREVRfzpdeYL1UM86WqgT+3Kx/zJeXj+5SIiN5GxX8SICIi+n+//vorZDIZHB0dizzu6OiIBw8e4J9//pG3rVy5Uh5mFfz65ptvBOdNnDgRL168wMGDB8sckAGvVxUmJiZi/fr1MDIygqurK2bMmIGff/65yP4zZ87EjRs3FK5f2Pnz5wW1FrXq8aOPPsLAgQNLXFF46NAhPH36FF5eXgAAf39/SKVShX6bNm1Co0aN4OzsDA0NDQwePLjIfgXq1auHZcuWYdasWahZsyY6deqEuXPn4vr168WeUxwfHx94e3tj+fLlxQZkFhYWgufx5jb8ZfX06VPMnDkTGhoa8lWlFZGbm4tHjx4JfuXL8io8HhERERERERG9X8yXXmO+VH7Ml4iIiIiosuBOWEREpDQymazE49WrV5f/3s/PDxEREYLjtWvXFrzu2bMntm/fjq+++gqTJk0qVy39+vVDjx49cOzYMWRmZmLv3r34/PPPsW7dOgQGBgr61qpVC1OnTsWsWbMwaNCgIsezt7fHzp075a+LW/0YHR0NR0dHHDhwAGZmZgrH4+PjMWjQIPn2676+vggNDcW1a9dgY2Mj6Ofv7y9/7e/vj44dO+LLL7+Evr5+kdceO3Yshg0bhtTUVGRmZiI5ORkxMTHYuXMnPD09i35QRejduze2bdtW4srIY8eOCeqoWbNmmccHXt+3hoYGnj17hlq1akEq/T/27jssimt/A/i7C1KUpiBSBQQRu8ZeohAl2COxIggIMfaCDcECYkEx1ihqlGISCxEBvSbYgyVqEo0aY4wdjVFMLKixgML+/vDHhnEXWGBhYHk/99nnucycOfPd2Z2bzHvPORuDZs2aFauP/CIjIzFv3jzBNgc0hCMal7hPUsSZderFWXVERBqg8H/9JSIiohJgvsR8SVXMlyofZkvqx3yJiEgDMF/SKByERUREpebk5ASJRIJLly7B09NTYf+lS5dQu3ZtwWxDY2NjODk5Fdrv8OHD0a9fPwQEBEAmk2HKlCnFqktPTw/u7u5wd3fHnDlz8MknnyAsLEwhJAOAKVOmIDo6GtHR0Ur70tHRKbJeAHB0dMTIkSMxc+ZMhZmFjx49QnJyMl6/fo1169bJt+fk5CA2NhYLFy4EAPz+++84deoUfvrpJ8Gsx5ycHGzfvh0jR44s8PyGhobo27cv+vbtiwULFsDDwwMLFiwoVki2YcMGzJgxAz179sR3332HLl26KLRxcHAo1uzRd61YsQLdu3eHsbExateuXeJ+8oSEhCh8PzyN/UvdLwkx1FEvBo/qx+8oEREREVHlxXzpP8yXVMN8qfLhc7v6MV9SP35PiYioNDgIi4iISs3U1BTu7u6Ijo5GUFAQ9PX15fsyMjKwZcsWjBs3rkR9+/n5QSqVYsSIEcjNzcW0adNKXGejRo2QkpKidJ+BgQHmzJmD8PBw9OvXr8TnAIC5c+fC0dER27dvF2zfsmULbGxsFGrYv38/li1bhoiICGhpaSEmJgZdunTB2rVrBe3i4uIQExNTaEiWn0QigYuLC06cOFGs+iUSCb744gtIpVL06tUL3377bamWclfGwsJCpdBRVbq6ugqzR6USLbX1T28x1CEiIiIiIqKywnxJiPlS0ZgvVT5apRh0R8r9lfNM7BKIiIgoHw7CIiIitVizZg06duwonxnn4OCAixcvYvr06XB2dsbcuXMF7V+8eIGMjAzBNl1dXaXLjg8fPhxSqRR+fn6QyWSYPn16obU8fPgQgwYNQkBAAJo1awZDQ0OcPn0aUVFR+Oijjwo87tNPP8WKFSuwdetWtGvXrhjvXqhOnTqYMmUKli5dKtgeExODgQMHokmTJoLttra2CAkJwd69e/Hhhx/iq6++QkREhEK7Tz75BMuXL8fFixfRuLFwGfRz584hLCwMw4cPR6NGjaCjo4MjR44gNjZWMNsRAG7evIlz584JttWvX1/wt0Qiwfr166GlpSUPylxdXUtwNcTDAUPqx1lgREREREREVJaYL/2H+VLFwHxJvZgtqV+AbWexSyAiIqJ8OAiLiIjUon79+vj5558RHh6OwYMH4++//4ZMJsPHH3+Mr776CtWrVxe037hxIzZu3CjY5uHhgb179yrt39vbG1KpFMOHD0dubq5C8JOfgYEB2rVrhxUrVuD69et4/fo1bG1tMXLkSISGhhZ4XLVq1TB//nwMGzasGO9cuWnTpmHdunV49eoVAODMmTM4f/68wnsG3i6d361bN8TExODVq1d4+PCh0mX3GzZsiIYNGyImJgbLly8X7LOxsYG9vT3mzZuH9PR0SCQS+d9BQUGCtsqW3T927JjCNolEgrVr10IqlaJ3797Ys2cPJBJJsa6DmBjqqB+DR/Xid5SIiIiIiEiI+ZIQ8yXx8dldvZgtqR+/o0RERBWLRCaTycQugoiINFNYWBiWL1+OAwcOoH379mKXQ1WMu3SQ2CVoHAZl6sWQjIio9A7k7hD1/M6J80U9f2VyZeAcsUsgIqJKivkSiYn5knoxW1I/5ktERKXHfKnyqAz5ElfCIiKiMjNv3jzY29vj1KlTaNu2LaRSqdglURXCUIeIiIjKmkxWeVZxICIiqqyYL5GYUu+eFbsEjeJh3UrsEjRQrtgFEBFRKTFf0iwchEVERGVqxIgRYpdAVRRngVFFx4GC6sf7noiIiIhIMzFfIrH0tGopdgkahgOG1I35kvoxXyIiotLgICwiIiIiUglDHfVioENEREREREREVQmzJfVjvkRERFSxcBAWEREREamEoQ5VdAxz1Y/3PREREREREakLnzGpMmC+pF6874moquEgLCIiIiJSCQMI9WIAoX68pkREREREREQVF7Ml9WMWon68pkREVBpSsQsgIiIiIiIiIiIiIiIiIiIiIiKqzLgSFhERUSXm7++PzMxMpKSkwN/fH5s3bwYAVKtWDXXr1oWvry9CQ0Ohrf3fP/I9PDxw8OBBnDp1Cm3atFHoL6+P/Dw8PLB7925YWVlh2rRpmDlzpkKb+fPnY82aNbhz5w62bNmCESNGAAAkEgmsrKzg7u6OJUuWwNzcXL49OTkZ/fv3L/A9KfubxMNZYFTRcUYtVXT831HNI5OJXQERERFR6TFfovLCZyKqDMZcvSZ2CRrli/69xC5B4+RcvCJ2CaRmzJc0CwdhERERaZAePXogLi4OWVlZ+O677zBu3DhUq1YNISEhAIDbt2/jxIkTGD9+PGJjYxVCsvx95KerqwsdHR34+PggLi5OISSTyWSIj4+Hr68vqlWrBgAwMjLC5cuXkZubi/Pnz2PEiBG4e/cu9u3bV0bvnsoaB7ioF4NH9eM1JSIiIiIiKj3mS1RWmC2pH7MQ9VtX30nsEjQMBwwRUdXCnyMkIiLSILq6urCwsICdnR3GjBmD7t27Y/fu3fL9cXFx6NOnD8aMGYNt27bh5cuXBfaR/1WzZk0AQGBgIK5cuYLjx48Ljjly5Ahu3LiBwMBA+TaJRAILCwtYWVmhZ8+emDhxIg4ePKj0nEREREREREREVDEwXyIiIiIiKhkOwiIiItJg+vr6yM7OBvB2NmFcXBx8fHzg4uICJycnJCYmFqu/pk2bok2bNoiNjRVsj4uLQ8eOHeHi4lJoLbm5uXjz5k3x3wgREREREREREYmC+RIRERERkWr4c4REREQaSCaT4dChQ9i3bx8mTJgAADh48CBevHgBDw8PAICPjw9iYmIwfPhwwbF79uyBgYGBYFtoaChCQ0MBvJ2tOG3aNKxevRoGBgZ49uwZEhMTsXr16gLruXr1KtavX4/WrVvD0NBQvt3LywtaWlqCtllZWejdu3ex3m9WVhaysrIE23JlOZBKtAo4gkh8XIJf/bgEPxERERERkfowX2K+pG693AeJXYLGGXP1O7FL0Dj8OUIiIioNDsIiIiLSIHkB1+vXr5Gbm4thw4YhPDwcABAbG4shQ4ZAW/vtP/69vLwwffp0XL9+HY6OjvI+3NzcsG7dOkG/tWrVkv93Ly8vBAUF4ZtvvkFAQAASEhIglUoxZMgQwTFPnjyBgYEBcnNz8erVK3Tu3BmbNm0StFmxYgW6d+8u2BYcHIycnJxive/IyEjMmzdPsG3u1FoIm2ZarH6ocBzgQkREROVp7dq1WLp0KTIyMtC8eXN8/vnnaNu2bYHtMzMzMWvWLCQlJeHRo0ews7PDypUr0atXr3KsmoiIqPJjvvQf5kvq5WEldgWahwOGiIiICiZGtsRBWERERBokL+DS0dGBlZWVPBB79OgRkpOT8fr1a0EAlpOTg9jYWCxcuFC+rUaNGnByKvjh3cjICAMHDkRcXBwCAgIQFxeHwYMHK8xuNDQ0xC+//AKpVApLS0vo6+sr9GVhYaFwLkNDQ2RmZhbrfYeEhGDKlCmCbZ7G/vBYzpmKVHFxJSyqDDj4kqhwMplE7BI0VkJCAqZMmYL169ejXbt2WLlyJTw8PHD58mWYm5srtM/Ozoa7uzvMzc2RmJgIa2tr3Lp1CyYmJuVfPBERUSXHfOk/zJeoopNWry52CRpn65VDYpegUYbadBC7BKIKj/lS2RArW+IgLCIiIg1SUMC1ZcsW2NjYICUlRbB9//79WLZsGSIiIhSWbS9MYGAgXF1dsWfPHpw4cQJLly5VaCOVSgsN29RJV1cXurq6gm0H7v1WLueuSjgYQ714PYmIqCpR9vM+yv4dLs/y5csxcuRIjBgxAgCwfv16fPvtt4iNjcXMmTMV2sfGxuLRo0c4ceIEqlWrBgCwt7dX75sgIiKqIpgv/Yf5knoxC1G/3BcvxC5B43DQEBFRxVWcfEmsbEla7COIiIio0omJicHAgQPRpEkTwSswMBAPHjzA3r175W2zsrKQkZEheD148EDQX5cuXeDk5ARfX1+4uLigY8eO5f2WiIiIiKgSiYyMhLGxseAVGRmptG12djbOnDkj+FkhqVSK7t274+TJk0qP2b17Nzp06IBx48ahTp06aNKkCRYtWlTsnyEiIiKigjFfIiIiIiIxqZoviZktcRAWERGRhjtz5gzOnz+PAQMGKOwzNjZGt27dEBMTI9+2d+9eWFpaCl6dO3cWHCeRSBAQEIDHjx8jICCgzN8DEREREVVuISEhePLkieAVEhKitO2DBw+Qk5ODOnXqCLbXqVMHGRkZSo+5ceMGEhMTkZOTg++++w5z5szBsmXLsGDBArW/FyIioqqI+RIRERERiU3VfEnMbEkik8lkxTqCiIiIqBJwlw4SuwSNs+/uebFL0Chcgp+IqPQO5O4Q9fyOCQtFPX9lcn3ILJXb3r17F9bW1jhx4gQ6dPjvp0BmzJiBI0eO4Mcff1Q4xtnZGa9evcLNmzflP4O0fPlyLF26FPfu3Sv9GyAiIqIqifmSejFbUj/mS0REpcd8qfJQNV8SM1vSVrklERERERERERFRGTMzM4OWlhbu378v2H7//n1YWFgoPcbS0hLVqlWTh2QA0LBhQ2RkZCA7Oxs6OjplWjMREREREREREVUMYmZL/DlCIiIiIiIiIqKSkEn4UvVVDDo6OmjVqhUOHTok35abm4tDhw4JZi/m16lTJ1y7dg25ubnybVeuXIGlpSUHYBERERERERFRxSV2ZlOZXioSM1viSlhEREREpBIub04VHX/WQP143xORWKZMmQI/Pz+0bt0abdu2xcqVK/H8+XOMGDECAODr6wtra2tERkYCAMaMGYM1a9Zg0qRJmDBhAq5evYpFixZh4sSJYr4NIiIiIsqHz5hUGTBfUi/e90QkFrGyJQ7CIiIiIiKVMICgio6hDhGR5hgyZAj++ecfzJ07FxkZGWjRogX27t2LOnXqAABu374NqfS/Bd5tbW2xb98+BAUFoVmzZrC2tsakSZMQHBws1lsgIiIionfMvXFW7BI0zqIP+otdgsbxsBK7AiIiUgexsiWJTCaTqfWdEBEREVUA7tJBYpegcTgIiyo6DsIiqnoO5O4Q9fyO2xeJev7K5PrQULFLICIiIio25kvqJXu/pdglaJxu646LXYLGOdykhtglEFE5Y75UeVSGfIkrYRERERGRSjjARb04qE39eE3Vj/c9ERERERERqYvkGFfCUretMe5il6BxXic+E7sEjWI78ILYJRARlSsOwiIiIqpE/P39kZmZiZSUFPkSmt9++y3u37+PmjVronnz5pg7dy46deoEADh//jzmzJmDU6dO4enTp7CwsEC7du3w+eefw9zcHOnp6XBwcFB6rpMnT6J9+/aIj4/H5MmTkZmZKd936dIlfPjhh2jfvj1at26NxYsX4969e9DT0xP08eLFC1hYWGDBggWIiIjAtGnTMHPmTIVzzZ8/H2vWrMGdO3ewZcsW+e8xSyQSWFlZwd3dHUuWLIG5ubmariSVBAe4qBcHtxARVX5cW5yIiIgqI+ZLzJfEwmxJ/fjTeWVghdgFEFFVw3xJs3AQFhERUSU1YMAAZGdnY/PmzahXrx7u37+PQ4cO4eHDhwCAf/75B926dUOfPn2wb98+mJiYID09Hbt378bz588FfR08eBCNGzcWbDM1NVV63p9//hk9e/aEp6cnNmzYgAcPHmDOnDlISkrCsGHDBG0TExORnZ0NHx8f3LhxA3FxcQohmUwmQ3x8PHx9fVGtWjUAgJGRES5fvozc3FycP38eI0aMwN27d7Fv375SXTOiioTBo/pxYBsREREREVHxMF+i8uRhzZ8jVLfUu2fELkHj9LTi95SIiEqOg7CIiIgqoczMTBw7dgxpaWno2rUrAMDOzg5t27aVt/nhhx/w5MkTbNq0Cdrab/+R7+DgADc3N4X+TE1NYWFhUeR5Dx8+jI8++ghjx47FkiVLAADm5ubo27cvYmNjFUKy2NhY9O/fH7Vq1UJgYCBWrVqF48ePo3PnzvI2R44cwY0bNxAYGCjfJpFI5PVYWVlh4sSJmDNnDl6+fAl9fX2VrhEHuFBFxwFDREREREREJCbmS0WT6uio1I5Uk3TzuNglaJyeVm2LbkRERETlhoOwiIiIKiEDAwMYGBggJSUF7du3h66urkIbCwsLvHnzBsnJyRg4cCAkEkmpzpmcnIxhw4YhPDwcwcHBgn2BgYHo06cPbt26BTs7OwDAjRs3cPToUfnswqZNm6JNmzaIjY0VhGRxcXHo2LEjXFxcCjy3vr4+cnNz8ebNG5Xr5QAX9ePANiIiIiIiIiLNwXypaLnZ2cV5e1QEfYnid4yIiIhIk0jFLoCIiIiKT1tbG/Hx8di8eTNMTEzQqVMnhIaG4tdff5W3ad++PUJDQzFs2DCYmZmhZ8+eWLp0Ke7fv6/QX8eOHeXBW94rv3///ReDBg3C9OnTFQIyAPDw8ICVlRXi4uLk2+Lj42Fra4tu3brJtwUGBmLHjh34999/AQDPnj1DYmIiAgICCnyvV69exfr169G6dWsYGhoqbZOVlYWnT58KXrmynAL7JCIiIiIiIiKq6pgvCTFfIiIiIqLS4kpYREREldSAAQPQu3dvHDt2DKdOnUJqaiqioqKwadMm+Pv7AwAWLlyIKVOm4PDhw/jxxx+xfv16LFq0CEePHkXTpk3lfSUkJKBhw4YFnktfXx+dO3fGxo0b4eXlpdBWS0sLfn5+iI+PR1hYGGQyGTZv3owRI0ZAKv1vzLeXlxeCgoLwzTffICAgAAkJCZBKpRgyZIigvydPnsDAwAC5ubl49eoVOnfujE2bNhVYX2RkJObNmyfY5oCGcETjIq8jkVi4spj6cQU8IiIiIiKi4mG+9B/mS2Xv2CuxK9A82vXsxS5B47y5kS52CUREVIlJZDKZTOwiiIiISDX+/v7IzMxESkqK0v2ffPIJDhw4gFu3bindn52djZYtW6J169bYvHkz0tPT4eDggLNnz6JFixZKj4mPj8fkyZNx//59fPzxxzhz5gy+//57haDs+vXrqF+/Pg4cOIDc3Fx4eHjg5s2b8uXj8/j6+uLmzZs4duwYOnXqBBcXF8TExAjON3HiRPzyyy+QSqWwtLSEvr5+odclKysLWVlZgm2exv6QSrQKPY6Kh4OG1IsDhoiISu9A7g5Rz19v6yJRz1+Z3BgWKnYJRERE9P+YLynHfKnsMVtSP+ZLRESlx3yp8qgM+RJ/jpCIiEiDNGrUCM+fPy9wv46ODhwdHQttUxBdXV0kJSWhTZs2cHNzw++//y7Y7+joiK5duyI2NhZxcXHo3r27QkAGvF0y/vjx49izZw9OnDiBwMBAhTZSqRROTk6oV69ekQFZXm1GRkaCFwMyIiIiIiIiIqLiY77EfImIiIiISoY/R0hERFQJPXz4EIMGDUJAQACaNWsGQ0NDnD59GlFRUfjoo48AAHv27MH27dsxdOhQODs7QyaT4X//+x++++47xMXFKfSXkZEh2GZiYgI9PT3BNl1dXezcuRODBg2Cm5sbDh8+jMaN/1uSPTAwECNHjgTwdsahMl26dIGTkxN8fX3h4uKCjh07lvZyEFVKnP2pfpz9SUREREREpDrmS1TeejXsInYJGmfzn9+JXYLG8bPtJHYJRERUiXEQFhERUSVkYGCAdu3aYcWKFbh+/Tpev34NW1tbjBw5EqGhb5fibNSoEapXr46pU6fizz//hK6uLurXr49NmzZh+PDhgv66d++ucI5t27Zh6NChCtt1dHSQmJiIwYMHy4OyJk2aAAAGDBiA8ePHQ0tLC/3791dau0QiQUBAAEJDQxESElLKK0FUeXHAEBEREREREYmJ+RKVt+8uHRW7BI3jYcUBQ0RERBWJRCaTycQugoiIiEjd3KWDxC5B43DlJvXiICwiotI7kLtD1PPX27pI1PNXJjeGhYpdAhEREVGxMV9SL2ZL6sd8iYio9JgvVR6VIV/iSlhERERERCJg8Kh+DB6JiIiIiIiIKq6WkWPELkHjyHY9FrsEjVPno0til0BERJUYB2ERERERkUo4wEW9OAiLiKjyk8kkYpdARERERFRpmH9+QuwSNM6+EOZL6uYBZqBEVL6YL2kWDsIiIiIiIpVw0JB6cVAbEREREREREVUlzJbUj/kSERFRxcJBWEREREREImDwqH4MHomIiIiIiIgqLj63qx/zJfXj95SIiEqDg7CIiIiIiETAQIeIiIiIiIiIqhIOGFI/5ktEREQVi1TsAoiIqOoJDw+HRCKBRCLBypUrxS5HY/j7+8uva0pKitjlEBERERERERGVGeZLZYP5EhERERFRyXEQFhFRFePv74/+/fsLtiUmJkJPTw/Lli2Tb4uMjISWlhaWLl2q0Ed8fDxMTEwEf0skEjRs2FCh7Y4dOyCRSGBvby/Y3rhxY9y7dw+ffvqpfFtB4c67Nbu6ukIikWD79u2CditXrhScRx11vnz5EmFhYXB2doauri7MzMwwaNAgXLx4UdAuf/Cnra0NMzMzdOnSBStXrkRWVpbC+fJ7+PAhevToASsrK+jq6sLW1hbjx4/H06dP5W3S0tLk/ed/ZWRkyNusWrUK9+7dE/Sdnp4uaG9oaIjGjRtj3LhxuHr1qtJ6Tp48CS0tLfTu3Vvp/uzsbCxduhTvvfceatSoAWNjYzRv3hyzZ8/G3bt35e2UfdeK6j+v3nPnzsm3PXv2DG5ubmjUqBHu3LmjtCYiIiIiIiIiKj/Ml5gvMV8iIiIiIlLEnyMkIqriNm3ahHHjxmH9+vUYMWKEfHtsbCxmzJiB2NhYTJ8+vch+atSogb///hsnT55Ehw4d5NtjYmJQt25dhfba2tqwsLAocd16enqYPXs2BgwYgGrVqql8XHHqzMrKQvfu3XH79m0sW7YM7dq1w/379xEZGYl27drh4MGDaN++vbx948aNcfDgQeTm5uLhw4dIS0vDggUL8NVXXyEtLQ2GhoZKa5JKpfjoo4+wYMEC1K5dG9euXcO4cePw6NEjbN26VdD28uXLMDIykv9tbm4u/+/GxsYwNjZWeo6DBw+icePGePHiBS5cuIBVq1ahefPm+N///odu3boJ2sbExGDChAmIiYnB3bt3YWVlJbgmH374IX799VfMmzcPnTp1Qu3atXHz5k1s27YNn3/+OSIjIwu6/EX2/65//vkHPXv2hFQqxbFjx2Bqalpo3/lxeXMiIiIqczKxCyAiIqoYmC8VXCfzpcqdLxERERGVOeZLGoWDsIiIqrCoqCiEhYVh+/bt8PT0lG8/cuQIXr58iYiICHz55Zc4ceIEOnbsWGhf2traGDZsGGJjY+Xh0507d5CWloagoCBs27ZNrbV7eXlh9+7d2LhxI8aOHavyccWpc+XKlTh58iTOnj2L5s2bAwDs7Oywc+dOtGvXDoGBgfjtt98gkUjkfecFf1ZWVmjatCnc3d3RvHlzLFmyBAsWLFBaU82aNTFmzBj533Z2dhg7dqzSWaLm5uaC2ZeqMjU1lddWr1499O3bF926dUNgYCCuX78OLS0tAMC///6LhIQEnD59GhkZGYiPj0doaKi8nxUrVuD48eM4ffo0WrZsKd9et25ddO3aFTJZ4f+mWFT/+f35559wd3eHtbU1du3aBQMDg2K9Zw+r5sVqT1TeOFBQ/XjfExERERGVP+ZLzJc0OV8i9eJzu/oxX1I/fk+JiKg0OAiLiKiKCg4ORnR0NPbs2aN0ppqXlxeqVasGLy8vxMTEFBmSAUBAQABcXV2xatUqVK9eHfHx8ejRowfq1Kmj9vqNjIwwa9YsREREwM/PDzVq1FD5WFXr3Lp1qzzkyk8qlSIoKAje3t44f/48WrRoUeC5XFxc0LNnTyQlJclDsvDwcMTHxyM9PV3pMXfv3kVSUhK6du2qsK9FixbIyspCkyZNEB4ejk6dOqn8vt99D5MmTYKnpyfOnDmDtm3bAgC++eYbuLi4oEGDBvDx8cHkyZMREhIiDwK3bdsGd3d3QUCWX167ghTVf57Lly9j+vTpaN26NbZt2wZdXd0SvU9SL4Y6RERERERElB/zJeZLzJeoODJSGoldgsZpubjo/12l4jHHCbFLICKiSoyDsIiIqqDU1FTs2rULhw4dwgcffCDY9/TpUyQmJuLkyZMAAB8fH7z//vtYtWpVkTPFWrZsiXr16iExMRHDhw9HfHw8li9fjhs3bpTJ+xg7dixWrVqF5cuXY86cOSofp2qdV65cgZubm9I+GjZsKG9TWEgGvA3K9u/fL//bzMwMjo6OCu28vLywa9cuvHz5En379sWmTZvk+ywtLbF+/Xq0bt0aWVlZ2LRpE1xdXfHjjz/ivffeU/WtK9QFAOnp6fKQLCYmBj4+PgCAHj164MmTJzhy5AhcXV3l7zfvv+fx9PTEgQMHAADNmjXDiRMFP6QW1X8eX19fdOrUCTt27JDPoixMVlYWsrKyBNtyZTmQSoo+llTHWWDqxUFt6sdrqn6874mIiIioIMyXmC/l1QUwXyLVWPT/XewSNM6i9J/FLkHjzE3qL3YJGuXNnb/ELoGIqFxxEBYRURXUrFkzPHjwAGFhYWjbtq0g/Nq2bRscHR3ls/NatGgBOzs7JCQkIDAwsMi+AwICEBcXh7p16+L58+fo1asX1qxZUybvQ1dXFxEREZgwYYJguXVVqFpnUcufq0Imkwlm4o0fPx7jx49XaLdixQqEhYXhypUrCAkJwZQpUxAdHQ0AaNCgARo0aCBv27FjR1y/fh0rVqzAV199VeK6gP9mF16+fBk//fQTkpOTAbxd/n7IkCGIiYlRCLHyi46OxvPnz7F69WocPXq0wHbF6b9fv35ISUlBUlISBg0aVOR7iYyMxLx58wTbHNAQjmhc5LGkOg5woYqOA4aIiIiIiMoP8yXmS3l1AcyXSDVatWqKXYLGqS3NKroRFQsHDRERUWlwEBYRURVkbW2NxMREuLm5oUePHkhNTYWhoSGAtzPJLl68CG3t//4RkZubi9jYWJVCMm9vb8yYMQPh4eEYPny4oJ+iGBoa4smTJwrbMzMzYWxsrPQYHx8ffPbZZ1iwYAHs7e1VPpcqdTo7O+PSpUtKj8/b7uzsXOS5Ll26BAcHhyLbWVhYwMLCAi4uLqhVqxbef/99zJkzB5aWlkrbt23bFsePHy+y38LqAiCvLSYmBm/evIGVlZW8jUwmg66uLtasWQNjY2PUr18fly9fFvSTV1+tWrUKPZ8q/eeZNWsWmjVrhmHDhkEmk2Hw4MGF9p0XKubnaexf6DFEYuOAISIiIiIiqsyYLzFfyqsLYL5EqvnutzSxS9A4HladxS6BiIiI8uEgLCKiKsrOzg5HjhyRB2V79+5Feno6Tp8+jbS0NEHg8ejRI7i6uuKPP/6QLzFekFq1aqFfv3745ptvsH79+mLV1KBBA5w5cwZ+fn7ybTk5OTh//jw++eQTpcdIpVJERkbi448/LtZsRVXqHDp0KGbNmoXz58/LZ24Cb0PDFStWoFGjRoLtyvzxxx/Yu3cvQkJCVK4t7xwAFJZAz+/cuXMFBmiq9L969Wo4ODigZcuWePPmDb788kssW7YMH374oaBt//79sW3bNowePRpeXl6YPXs2zp49i5YtW6p8PlX7z2/OnDmQSqXw9vaGTCbDkCFDCuxfV1cXurq6gm1cKl79OGiIKjqu1qZ+vO+JCieTSYpuREREpMGYLzFfYr5ExcFnTKoMmC+pF+97oqIxX9IsHIRFRFSF2draIi0tDW5ubvDw8ICLiwvatm2LLl26KLRt06YNYmJisHTp0iL7jY+PR3R0NExNTYtVz5QpUxAYGAgXFxe4u7vj+fPn+Pzzz/H48eMCQzIA6N27N9q1a4cNGzagTp06Kp+vqDqDgoKwa9cu9O3bF8uWLUO7du1w//59LFq0CJcuXcLBgwcFy8C/efMGGRkZyM3NxcOHD5GWloYFCxagRYsWmD59urzdmjVrkJycjEOHDgEAvvvuO9y/fx9t2rSBgYEBLl68iOnTp6NTp07y2ZcrV66Eg4MDGjdujFevXmHTpk04fPgw9u/fr9J7ffjwITIyMvDixQv89ttvWLlyJX766Sd8++230NLSQkpKCh4/fozAwECFWaEDBgxATEwMRo8ejaCgIHz77bfo1q0bwsLC8P7776NmzZq4cuUKUlNToaWlPJjas2ePSv2/a9asWdDS0oK3tzdyc3Ph5eWl0vsF+LBMFR8DCPXjNSUiIiIiKn/Ml5gvaXK+BIlU9bZUpNS/zohdgsbpaaX6QEZSDfMlIiIqDQ7CIiKq4mxsbJCWlob3338fly5dwuTJk5W2GzBgAJYtW4ZFixYV2ae+vj709fWLXYuXlxdkMhmWL1+OmTNnonr16mjVqhWOHj1aZPi1ZMkSdOzYsVjnK6pOPT09HD58GIsWLUJoaChu3boFQ0NDuLm54dSpU2jSpImg/cWLF2FpaQktLS0YGxujUaNGCAkJwZgxYwSz6B48eIDr168L6ti4cSOCgoKQlZUFW1tbfPzxx5g5c6a8TXZ2NqZOnYq//voL1atXR7NmzXDw4EG4ubmp9F67d+8OAKhevTrs7Ozg5uaGL774Ak5OTgDeLuXevXt3pcvyDxgwAFFRUfj111/RrFkzHDp0CCtXrkRcXBxCQkKQm5sLBwcH9OzZE0FBQUrPr2r/RkZGCvtnzpwJqVSK4cOHQyaTYdiwYSq9Zz4sqx8HtqkXr6f68b4nIiIiIhIH8yXmS4Bm5kuQ5arWjlTSx73glcioZMZc/U7sEjTOuvpOYpdARESVmEQmk8nELoKIiKqW8PBwpKSk4Ny5c2KXopEkEgmSk5PRv39/sUsRlbt0kNglEBWKg7DUj4OwiKqeA7k7RD2//VeLRT1/ZZI+fGbRjYiIiIqB+VLZYr70FvMlquiYL6kf8yWiqof5UuVRGfIlroRFRESiuHDhAgwMDBAVFYWxY8eKXY5GGD16NL7++muxyyAiEg2DR/Vj8EhEREREFRnzJfVjvkRUubgP9Re7BI3zwYUTYpegUdKaFn9VSyKiyowrYRERUbl79OgRHj16BACoXbu20iXEqfj+/vtvPH36FABgaWmJGjVqiFyRuDhTUf04wEW9OLiFiKj0OFOx8qgMMxWJiKhyYb5UNpgvCTFfUi9mS+rHfImIqPSYL1UelSFf4kpYRERU7mrVqoVatWqJXYbGMTc3h7m5udhlEJGKGDyqH4NHIip3nNZGREQkGuZLZYP5EpWlJqeGiV2CxtGezAGo6lZnJVfCIqJyxnxJo3AQFhERERGRCDhgiIiIiIiIiIiqkt/abxW7BI3j8THzJSIiooqEg7CIiIiIiETAlbCoMuBgQSIiIiIiIlIXPmOq30e/PxS7BI2z299V7BI0iuynC2KXQERUrjgIi4iIiIhUwqBMvTgIi4iIiIiIiIiISmN9TF+xS9A42dP/FbsEjWI3SOwKiIjKFwdhERFRhePv74/MzEykpKTItyUmJsLHxwcLFy7EhQsXsHnzZoXjPDw8sHfvXgCAvb09Jk+ejMmTJwvaREZGYvbs2Vi8eDGmT58u2BcfH48RI0Yo9Ltx40Z88skngv0SiQRWVlZwd3fHkiVLYG5uLt+ex9DQEA0aNMDs2bPx0UcfwdXVFUeOHCnwfXft2hVpaWmwt7fHrVu3AAB6enqoU6cO2rZti9GjR+ODDz6Qt09PT4eDg4PSvk6ePIn27dsjPj4ekydPRmZmptJ2RV3rqVOnwt/fH5s3b0ZkZCRmzpwpb5eSkgJPT0/IZG9/rDotLQ1ubm7y62BoaIh69erB3d0dQUFBsLS0lB8bHh6OlJQUnDt3Tmldrq6uaNGiBVauXFng9SoKB7hQRcdBbURERERERGWH+RLzJXXkS6RezOvUz8NK7AqIiIgoP6nYBRARERVl06ZN8Pb2xrp16zB16lQAQI8ePXDv3j3Ba9u2bUX2FRsbixkzZiA2NlbpfiMjI4V+vb29FfbfuXMHGzduRGpqKoYPHy7oIy4uDvfu3cPp06fRqVMnDBw4EBcuXEBSUpK8z59++gkAcPDgQfm2pKQkeR8RERG4d+8eLl++jC+//BImJibo3r07Fi5cqFBz/j7yXq1atSr6wiqh7FoDb8O6JUuW4PHjx0X2cfnyZdy9exc///wzgoODcfDgQTRp0gQXLnDZYSIiIiIiIiISB/Ml5ktERERERGWNK2EREVGFFhUVhbCwMGzfvh2enp7y7bq6urCwsChWX0eOHMHLly8RERGBL7/8EidOnEDHjh0FbSQSSaH95t9vZWWFiRMnYs6cOXj58iX09fUBACYmJrCwsICFhQXmz5+PVatW4fvvv8fEiRPl/bx69QoAYGpqqvR8hoaG8u1169ZFly5dYGlpiblz52LgwIFo0KCBvG1BfRRXQdcaALp3745r164hMjISUVFRhfZjbm4uvwbOzs746KOP0LJlS4wZMwbHjx8vdZ2q4ipD6sfZiurF66l+vO+JqPxJim5CREREomO+xHyJKoZufoFil6Bx2pw9LXYJGudsS7ErIKKqh/mSJuEgLCIiqrCCg4MRHR2NPXv2oFu3bqXuLyYmBl5eXqhWrRq8vLwQExOjEJIVl76+PnJzc/HmzRuFfW/evEFMTAwAQEdHp1TnAYBJkyZh/vz52LVrF2bMmFHq/vIr6lpraWlh0aJFGDZsGCZOnAgbGxuV+9bX18fo0aMRFBSEv//+W760PlU+HOCiXhyERUREREREVPaYLwkxXyIxaR/ggCF1W1yH+ZK6eYAZKBERlRwHYRERUYWUmpqKXbt24dChQ/jggw8U9u/ZswcGBgaCbaGhoQgNDVXa39OnT5GYmIiTJ08CAHx8fPD+++9j1apVgn6ePHki+NvAwAAZGRlK+7x69SrWr1+P1q1bw9DQUL7dy8sLWlpaePnyJXJzc2Fvb4/Bgwer/uYLUKtWLZibmyM9PV2wvWPHjpBKhb8w/O+//6rcb1HXOo+npydatGiBsLAwefinKhcXFwBAenp6mYRkWVlZyMrKEmzLleVAKtFS+7mIqOLiwDb14+BLIiIiIqrMmC8pYr5UMOZLVBn1sH1P7BI0zp6/fhK7BI3Sx7pkP21LRFRZcRAWERFVSM2aNcODBw8QFhaGtm3bKgRibm5uWLdunWBbrVq1Cuxv27ZtcHR0RPPmb//P5BYtWsDOzg4JCQkIDPxvGWxDQ0P88ssv8r/fDZ/yQrTc3Fy8evUKnTt3xqZNmwRtVqxYge7du+PGjRsICgrC6tWrC62tOGQyGSQS4bKkCQkJaNiwYYn7LOpa57dkyRJ88MEHmDZtWrHOIZPJAEChdnWJjIzEvHnzBNsc0BCOaFwm5yOiiokDhoiIiIiIKD/mS8oxX1KO+RJVRnv//KXoRlQsHlYcNERERCXHQVhERFQhWVtbIzExEW5ubujRowdSU1MFswFr1KgBJycnlfuLiYnBxYsXoa393z/6cnNzERsbKwjJpFJpof3mhWhSqRSWlpbQ19dXaGNhYQEnJyc4OTkhLi4OvXr1wu+//17qGXoPHz7EP//8AwcHB8F2W1vbYl2LdxV1rfPr0qULPDw8EBISAn9/f5XPcenSJQCAvb19iessTEhICKZMmSLY5mnsXybnqsq4yhARERERERFVJsyXFDFfKhjzpbInrV5d7BI0zuPcF2KXQERERPlwEBYREVVYdnZ2OHLkiDy82bt3b4HhTWEuXLiA06dPIy0tTTBj8NGjR3B1dcUff/whX868KEWFaO9q27YtWrVqhYULF2LVqlXFrj2/VatWQSqVon///qXqR5niXOvFixejRYsWaNCggUp9v3z5El988QW6dOmC2rVrq7NsOV1dXejq6gq2cal4IiIiIiIiImK+JMR8qWDMl8pBTq7YFWicapAW3YiIiIjKDQdhERFRhWZra4u0tDS4ubnBw8MDe/fuBQBkZWUhIyND0FZbWxtmZmYKfcTExKBt27bo0qWLwr42bdogJiYGS5cuLZs3AGDy5Mnw9PTEjBkzYG1trdIxz549Q0ZGBl6/fo2bN2/i66+/xqZNmxAZGakQ0j18+FDhWpiYmEBPTw8AkJOTg3Pnzgn26+rqKiwxr+xaGxkZKdTWtGlTeHt7Y/Xq1Upr//vvv/Hq1Ss8e/YMZ86cQVRUFB48eICkpCRBu5cvXyrUZWhoCEdHRwDAP//8o7Df0tISderUUXpeKnv8qTf14spiREQaQCZ2AURERKQK5kvMl/JjviSe3KxXYpegcQykemKXQEREpcV8SaNwEBYREVV4NjY2gvDG0tISe/fuhaWlpaBdgwYN8Mcffwi2ZWdn4+uvv0ZwcLDSvgcMGIBly5Zh0aJFZVZ/jx494ODggIULFyI6OlqlY+bOnYu5c+dCR0cHFhYWaN++PQ4dOgQ3NzeFtt27d1fYtm3bNgwdOhQA8O+//6Jly5aC/Y6Ojrh27ZrCce9e63379imtLyIiAgkJCUr3NWjQABKJBAYGBqhXrx4+/PBDTJkyBRYWFoJ2V65cUairW7duOHjwIABg69at2Lp1q2D//PnzMXv2bKXnpbLHQUNU0fE7qn4cfElEREREmoL5EvOlPMyXxKNtZyt2CRrnVNYZsUsgIiKifCQymYzj6oiIiEjj5GY4i10CUaE4uIWIqPQO5O4Q9fz2m5eIev7KJN1P+f9pTURERFSRuUsHiV2CRuHkKfVjvkREVHrMlyqPypAvcSUsIiIi0kgMINSPQRkRERERERERERERERGRclKxCyAiIiIiIiIiIiIiIiIiIiIiIqrMuBIWERERaSSu2kRERERERERERKUh0a4mdgkaJUv2WuwSiIiIiMoUB2ERERGRRuLPEaofB7YRERG9QyZ2AURERERUlmRvOGhInXQlHNRGRESkgPmSRuHPERIREREREREREREREREREREREZUCB2ERESmRnp4OiUQCiUSCFi1aiF2OxkhLS5Nf1/79+6u9//j4eHn/kydPVnv/mqisPxMiIiIiIiKiqor5UtlgvlTxMF8iIiIiInqLP0dIpIH8/f2RmZmJlJQU+bbExET4+Phg4cKFmDp1KjIyMhAZGYlvv/0Wd+7cgbGxMZycnODj4wM/Pz9oa2vDysoK06ZNw8yZMxXOMX/+fKxZswZTpkzB4sWLce/ePejp6QnavHjxAhYWFliwYAEmTpwIe3t7TJ48WSG8CA8PR0pKCs6dOyf/e968eQAALS0tmJiYoFGjRvj4448xZswY6Orqyo91dXXFkSNHAAA6OjowMzPDe++9hxEjRuDjjz8WnEcikQAATp48ifbt28u3Z2VlwcrKCo8ePcL3338PV1dX+b6DBw8KQjJl1xZ4GzS4ubnh8ePHMDExQXx8PEaMGAEPDw/s3btX3i4zMxM1a9YUnEcikSA5OVkeUJSkzj179mDp0qX45ZdfkJOTg8aNG2PcuHHw9/eXt0lPT4eDg4P8bwMDA9StWxeurq6YPHky6tevj8KEh4dj+/bt+PPPP6Gjo4NWrVph4cKFaNeunbyNvb09bt26JTguMjJS/h3q2LEj7t27h0mTJiErK0veJv/nmN+oUaOwfv16+d/ff/89li5dih9//BEvX76Evb09evbsiSlTpsDa2hoAYGRkhMuXL6NGjRqCulT57vn7+2Pz5s2CmgEgJSUFnp6ekMnergf67ued97eJiYnCvfDzzz+jbdu2ACA/HgBycnKwevVqxMbG4urVq9DX10f79u0xe/ZsdOrUSd4u77sEAFKpFEZGRnB2dkbv3r0xadIkGBsbK1w3ZbKystCuXTucP38eZ8+elX+vC/pMbt68iVmzZiEtLQ2PHj2CmZkZWrVqhSVLlsDFxQWA4nc3T959kpycDHd3d2hpaWHfvn2CNtHR0QgNDcVvv/2Ga9euFfv6qYI/nUdERERERFRyzJeYLzFfYr70rqqYL23+84ditafC9WrYS+wSNNATsQsgIiKifDgIi6gK2LRpE8aNG4f169djxIgRuHHjBjp16gQTExMsWrQITZs2ha6uLi5cuIAvvvgC1tbW6NevH3x8fBAXF6cQkslkMsTHx8PX1xcjRozAnDlzkJSUhGHDhgnaJSYmIjs7Gz4+PsWuuXHjxjh48CByc3Px8OFDpKWlYcGCBfjqq6+QlpYGQ0NDeduRI0ciIiICb968wZ07d5CcnIyhQ4fC398fX3zxhaBfW1tbxMXFCcKn5ORkGBgY4NGjRwp1mJqawtTUtNj1A4C2tjYOHjyI77//Hm5ubsU6tjh1fv7555g8eTKCg4Oxbt066OjoYNeuXRg9ejR+++03fPbZZ4L2Bw8eROPGjfHixQtcuHABq1atQvPmzfG///0P3bp1K7AmZ2dnrFmzBvXq1cPLly+xYsUKfPjhh7h27Rpq164tbxcREYGRI0fK/87/Weno6MDCwgL6+vqCQAb473PMr3r16vL/vmHDBowdOxZ+fn7YuXMn7O3tcfv2bXz55ZdYtmwZli9fDuBtcGNhYVHg+yiKnp4elixZglGjRqFmzZrFOtbQ0BDJycnw8vKSb4uJiUHdunVx+/Zt+TaZTIahQ4fi4MGDWLp0Kbp164anT59i7dq1cHV1xY4dOwTBU17wJ5PJkJmZiRMnTiAyMhJxcXH44YcfYGVlVWRtM2bMgJWVFc6fFw5MUvaZvH79Gu7u7mjQoAGSkpJgaWmJO3fuIDU1FZmZmSpfD4lEgri4ODRt2hQbNmzAqFGjALwN4GbMmIF169bBxsYG165dK9b1U5WHVfNiH0OF48A2IiIiIqKqi/nSf5gvMV8qCvMlzcmX/Gw7Fd2IVLbv7lGxS9A4zECJiIgqFg7CItJwUVFRCAsLw/bt2+Hp6QkAGDt2LLS1tXH69GnBbK569erho48+ks8GCgwMxKpVq3D8+HF07txZ3u7IkSO4ceMGAgMDYW5ujr59+yI2NlYhJIuNjUX//v1Rq1atYtetra0tDzqsrKzQtGlTuLu7o3nz5liyZAkWLFggb1u9enV5WxsbG7Rv3x4uLi4ICAjA4MGD0b17d3lbPz8/rF69GitXroS+vr68Tj8/P8yfP7/YdRamRo0aGDx4MGbOnIkff/yxWMeqWueff/6JqVOnYvLkyVi0aJF8+9SpU6Gjo4OJEydi0KBBgtmEpqam8utVr1499O3bF926dUNgYCCuX78OLS0tpTW9+/kuX74cMTEx+PXXXwXhmqGhYYlCqvyf47vu3LmDiRMnYuLEiVixYoV8u729Pbp06VKs4KYo3bt3x7Vr1xAZGYmoqKhiHevn54fY2Fh5yPPy5Uts374dEydOFHxu33zzDRITE7F792707dtXvv2LL77Aw4cP8cknn8Dd3V1+f+YP/iwtLdGwYUP07dsXjRs3xowZM/D1118XWldqair279+PnTt3IjU1tcj3cfHiRVy/fh2HDh2CnZ0dAMDOzk4wg1JVtra2WLVqFcaPH48PP/wQ9vb2CAwMxIcffojhw4cL2qp6/Ug8DHXUi4Pa1I/fUSIqdzKJ2BUQEZUL5kvMl5gvFQ/zJeZLpByf29WP+ZL68XtKROWO+ZJG4SAsIg0WHByM6Oho7NmzRx5iPHz4EPv378eiRYsEAVl+ecuVN23aFG3atEFsbKwgJIuLi0PHjh3lS0YHBgaiT58+uHXrlvyB+saNGzh69KjCEtGl4eLigp49eyIpKUkQkinj5+eHqVOnIikpSRCStWrVCvb29ti5cyd8fHxw+/ZtHD16FGvXri2Th/Dw8HA4OTkhMTERAwcOVPk4VetMTEzE69evMW3aNIU+Ro0ahdDQUGzbtk0Qkr1LKpVi0qRJ8PT0xJkzZ9C2bVv50vLvLkufJzs7G1988QWMjY3RvLnwgWTx4sWYP38+6tati2HDhiEoKAja2qX7x82OHTuQnZ2NGTNmKN1vYmJSqv7z09LSwqJFizBs2DBMnDgRNjY2Kh87fPhwLF26FLdv30bdunXlMyrfe+89QbutW7fC2dlZEJDlyfveHjhwQGEZ9vzMzc3h7e2N2NhY5OTkQEtLS760fP5l1e/fv4+RI0ciJSVFMPOzMLVr14ZUKkViYiImT55cYHCqKj8/PyQnJyMgIAAff/wxfvvtN1y8eFGhnarXj8TDUIeIiIiIqOphvsR8iflS8TFfYr5EyklV/P6Q6h7nvhC7BCIiIsqHg7CINFRqaip27dqFQ4cO4YMPPpBvv3btGmQyGRo0aCBob2ZmhlevXgEAxo0bhyVLlgB4G4BNmzYNq1evhoGBAZ49e4bExESsXr1afqyHhwesrKwQFxeH8PBwAEB8fDxsbW0Vlh8PDg7G7NmzBduys7PRqFEjld6Xi4sL9u/fX2Q7qVQKZ2dnpKenK+wLCAhAbGwsfHx8EB8fj169egmWO1cnKysrTJo0CbNmzSo08FBGlTqvXLkCY2NjWFpaKhyvo6ODevXq4cqVK0WeKy/wTE9PR9u2bVGtWjU0aNBAIVTZs2cPhg4dihcvXsDS0hIHDhyAmZmZfP/EiRPx3nvvoVatWjhx4gRCQkJw7949+VLuhYmOjsamTZsE2zZs2ABvb29cvXoVRkZGSt9nWfD09ESLFi0QFhaGmJgYlY8zNzdHz549ER8fj7lz5yI2NhYBAQEK7a5cuYKGDRsq7SNvu6qf27Nnz/Dw4UOYm5vD2NhYcG/LZDL4+/tj9OjRaN26tdL7QRlra2usXr0aM2bMwLx589C6dWu4ubnB29sb9erVE7T18vJSCNGysrLQu3dvwbYvvvgCjRs3xtGjR7Fz506l95yq10+ZrKwshZ8gyJXlQCopXcBHVJY4q46IiIiIKjrmS8yXmC+VHPMl5kukKPXaSbFL0DgeVh3ELoGIiIjy4SAsIg3VrFkzPHjwAGFhYWjbti0MDAwKbf/TTz8hNzcX3t7eggdNLy8vBAUF4ZtvvkFAQAASEhIglUoxZMgQeRstLS34+fkhPj4eYWFhkMlk2Lx5M0aMGAGpVCo4z/Tp0+Hv7y/Ytnr1ahw9qtpvwctkMvlMypK29fHxwcyZM3Hjxg3Ex8cLAr+yEBwcjA0bNiA2NhaDBw9W+bjyrDNvZlve9bK2tsYff/yh0M7NzQ3nzp3DgwcPsHHjRgwePBg//vgjzM3NAQBTpkyRt23WrBl0dHQwatQoREZGQldXt9AavL29MWvWLMG2OnXqyOtT9XNXlyVLluCDDz5QOgu0MAEBAZg0aRJ8fHxw8uRJ7NixA8eOHVNol382YUm9+7l5enrKfxYCAD7//HM8e/YMISEhxe573Lhx8PX1RVpaGk6dOoUdO3Zg0aJF2L17N9zd3eXtVqxYIZgNDLz9zufk5Ai2mZubY9SoUUhJSSk0MFb1+r0rMjIS8+bNE2xzQEM4orEK75ZUxUFDVNFxtTb1431PRERUtTFfYr5UHMyXFDFfYr5EQnzGpMqA+ZJ68b4noqqGg7CINJS1tTUSExPh5uaGHj16IDU1FYaGhnBycoJEIsHly5cF7fNmH+nr6wu2GxkZYeDAgYiLi0NAQADi4uIwePBghdAtICAAkZGROHz4MHJzc/Hnn39ixIgRCnWZmZnByclJsK1WrVoqv69Lly7BwcGhyHY5OTm4evUq2rRpo7DP1NQUffr0QWBgIF69eoWePXvi2bNnKp3fyMgIt27dUtiemZkJLS0tpUvwm5iYICQkBPPmzUOfPn1UOo+qdTo7O+PJkye4e/curKysBPuys7Nx/fp1uLm5FXmuS5cuAUCR17ZGjRpwcnKCk5MT2rdvj/r16yMmJqbAEKZdu3Z48+YN0tPTFWbHvsvY2Fjhu5En733eu3evRLMVjYyM8OTJE4XtmZmZMDY2VnpMly5d4OHhgZCQEIVgtzA9e/bEp59+isDAQPTt2xempqYKbZydneXX/F15252dnYs816VLl2BkZKT0HABw+PBhnDx5UiGgbN26Nby9vbF58+ZC+zc0NETfvn3Rt29fLFiwAB4eHliwYIEgJLOwsFD43AwNDZGZmanQn7a2dpE/HaDK9VMmJCREENICgKexv0rHEomFgQ4RERERVXTMl5gvMV/6D/Ml5ktEFVHIjV/FLkHj9H6/v9glaJibYhdARFSuOAiLSIPZ2dnhyJEj8qBs7969MDU1hbu7O9asWYMJEyYoDXXeFRgYCFdXV+zZswcnTpzA0qVLFdo4Ojqia9euiI2NhUwmQ/fu3WFnZ6fW9/PHH39g7969Ks262rx5Mx4/fowBAwYo3R8QEIBevXohODhYYanrwjRo0ADbt29HVlaWIHj45Zdf4ODggGrVqik9bsKECVi9ejVWrVql8rlUqXPAgAEIDg7GsmXLsGzZMsG+9evX4/nz5/Dy8ir0HLm5uVi9ejUcHBzQsmXLYtWXm5ursER3fufOnYNUKpXPZCypgQMHYubMmYiKisKKFSsU9mdmZsLExKTA4xs0aIAzZ84obP/ll18KDe8WL16MFi1aFBnw5aetrQ1fX19ERUUhNTVVaZuhQ4di2LBh+N///oe+ffsK9i1btkx+nxbm77//xtatW9G/f3+FGcF5Vq9ejQULFsj/vnv3Ljw8PJCQkIB27dqp/J6At7MhXVxccOLEiWIdV1yqXD9ldHV1FcJALhWvfhw0RBUdZ9YRERERqR/zJeZLzJfeYr70FvMlKg1JMf63klTTSfe12CVonMjrHDREREQlx0FYRBrO1tYWaWlpcHNzg4eHB/bu3Yvo6Gh06tQJrVu3Rnh4OJo1awapVIqff/4Zf/zxB1q1aiXoo0uXLnBycoKvry9cXFzQsWNHpecKDAzEyJEjAQDx8fGlqvvNmzfIyMhAbm4uHj58iLS0NCxYsAAtWrTA9OnTBW1fvHiBjIwMvHnzBnfu3EFycjJWrFiBMWPGFDhLr0ePHvjnn39gZGRUrLq8vb0REREBX19fzJgxA8bGxjh69ChWrlyJqKioAo/T09PDvHnzMG7cuGKdr6g669ati6ioKEydOhV6enoYPnw4qlWrhl27diE0NBRTp05VCEMePnyIjIwMvHjxAr/99htWrlyJn376Cd9++608iPvrr7/QrVs3fPnll2jbti2eP3+OhQsXol+/frC0tMSDBw+wdu1a/PXXXxg0aBAA4OTJk/jxxx/h5uYGQ0NDnDx5EkFBQfDx8UHNmjWLfK95n2N+urq6qFmzJmxtbbFixQqMHz8eT58+ha+vL+zt7XHnzh18+eWXMDAwUAgJ8wsKCsL777+PhQsX4uOPP0ZOTg62bduGkydPIjo6usDjmjZtCm9v72Iv1T9//nxMnz69wFl2Q4cOxY4dO+Dn54elS5eiW7duePr0KdauXYvdu3djx44dggBbJpMhIyMDMpkMmZmZOHnyJBYtWgRjY2MsXrxY3i45ORkhISHypf7r1q0rOG/eDGNHR0fY2NgUWP+5c+cQFhaG4cOHo1GjRtDR0cGRI0cQGxuL4ODgYl2Lkijq+pF4OMBFvTiojYio8lPDLwAREVUKzJeYLzFfYr6Uh/kSlYbsnZ+YpNKrJuH/1UtEVNkxX9Is/CczURVgY2MjCMr27duHs2fPYtGiRQgJCcGdO3egq6uLRo0aYdq0aRg7dqzgeIlEgoCAAISGhhY6S3DAgAEYP348tLS00L9//1LVfPHiRVhaWkJLSwvGxsZo1KgRQkJCMGbMGIXZSBs3bsTGjRuho6MDU1NTtGrVCgkJCfD09Cywf4lEAjMzs2LXZWJigmPHjmHmzJno168fnjx5AicnJyxfvhyBgYGFHuvn54dly5bh999/V/l8qtQ5efJk1KtXD5999hlWrVqFnJwcNG7cGOvWrVO6ZH/37t0BANWrV4ednR3c3NzwxRdfCJb8fv36NS5fvowXL14AALS0tPDHH39g8+bNePDgAUxNTdGmTRscO3YMjRs3BvA20Nq+fTvCw8ORlZUFBwcHBAUFKSzhXZC8zzG/vGAXAMaOHQtnZ2d89tln8PT0xMuXL2Fvb48+ffoUeY6OHTsiNTUVERERWLZsGaRSKZo2bYpDhw6hSZMmhR4bERGBhIQEld5DHh0dnUI/N4lEgm+++QYrV67EihUrMHbsWOjp6aFDhw5IS0tDp06dBO2fPn0KS0tLSCQSGBkZoUGDBvDz88OkSZMEAeqTJ08UfgqiJGxsbGBvb4958+YhPT0dEolE/ndQUFCp+y9KUdePxMNBQ1TR8Tuqfhx8SURERHmYLylivsR8iflSwZgvkTLaHBSndu7DFP83mkrH5tQ1sUvQKHfbPxW7BCKiciWRyTiujojoXenp6XBwcMDZs2fRokULscvROP7+/sjMzERKSopa+42Pj8fkyZORmZmp1n6rgrL6TMTkLh0kdgkahwNcqKLjgCGiqudA7g5Rz28XW/BqJSR0K2CG2CUQEZU75ktli/lSxcN8iYqSe8hW7BI0ztMvC16RjkrGZPNJsUsgonLGfKnyqAz5ElfCIiIqRMeOHdGiRQucOHFC7FI0wrFjx9CzZ09kZWWhd+/eZXKOJ0+ewMDAAOPGjcOSJUvK5ByapDw+E9IcHOBCFR0HCqof73siIiKi0mO+pF7Mlyoe5kukKmm3P8UuQeOYgNdU3ZgvqRezJSKqajgIi4hICRsbG1y9ehUAFJanp5Jr3bo1zp07BwAwMDBQe/8DBgxA586dAbxd2p+KVtafCWkWBhBU0THUISIiIqKKhPlS2WC+VPEwXyJVvfRsJ3YJGsdhxh9il6BxmC8REVFpcBAWEZES2tracHJyErsMjaOvr1+m19XQ0BCGhoZl1r8mKuvPhIgKxkCHiIiIiEizMV8qG8yXKh7mS6Sqo2u/ELsEjcN8iYiIqGLhICwiIiIiIiIiopKQiV0AERERERERERFVasyXNAoHYRERERGRSjizTr34845EVQ//d5SIiIiIiKoyPhOVgfa8purWfN0FsUvQKL91Mxa7BI2T8+ix2CUQUSE4CIuIiIiIiDQCw1wiIiIiIiIiqkr2JX0pdgkah/mSunHAEBFVLVKxCyAiIiIiIiIiIiIiIiIiIiIiIqrMuBIWERFRJeDv74/MzEykpKTItyUmJsLHxwcLFy7EhQsXFPa/6+zZs1i8eDGOHj2KR48ewcLCAk2bNsWoUaPQp08fSCQSpKenw8HBAWfPnkWLFi0KrWnUqFHYtGkTtm/fjkGDBgn2hYeHY968eQAALS0t2NjYwNPTE/Pnz4eBgYG83ebNm7FmzRpcvHgRWlpaeO+99zB9+nT06dNH3iYtLQ1ubm54/PgxTExMVL5m/Kk3qug4q46IiIiIiIjKE/Ol4udLL/bVU7ktFe3VDguxS9A4HlZiV0BERET5cRAWERFRJbRp0yaMGzcO69evx4gRI+Dv719o+127dmHw4MHo3r07Nm/eDCcnJ2RlZeHEiROYPXs23n///eIFUC9eYPv27ZgxYwZiY2MVQjIAaNy4MQ4ePIg3b97ghx9+QEBAAF68eIENGzYAAKZNm4Y1a9ZgwYIF6N+/P16/fo2vv/4aH330EVatWoXx48cX55Io4AAX9ePANiIiIiIiIiLNwXypaNU9bpTqeBI6djdZ7BI0jscmZqBEREQVCQdhERERVTJRUVEICwvD9u3b4enpWWT758+fIzAwEL1790ZSUpJgX8OGDREYGAiZTFasGnbs2IFGjRph5syZsLKywp9//glbW1tBG21tbVhYvJ3dNmTIEBw6dAi7d+/Ghg0bcOrUKSxbtgyrV6/GhAkT5McsXLgQr169wpQpU/DRRx8p9EmkSTioTf04+JKIyp1MInYFRERERCXCfInE0Lt9b7FL0DgLbqaIXYLGme3QWuwSiKiqYb6kUTgIi4iIqBIJDg5GdHQ09uzZg27duql0zP79+/Hw4UPMmDGjwDYSSfH+BS8mJgY+Pj4wNjZGz549ER8fjzlz5hR6jL6+PrKzswEA27Ztg4GBAUaNGqXQburUqVi+fDl27tyJyZMnq1RPVlYWsrKyBNtyZTmQSrRUe0NERERERERERFUE8yXlmC+VvX9b8Lfz1G3jP13FLkEDPRe7ACIiqsQ4CIuIiKiSSE1Nxa5du3Do0CF88MEHKh935coVAECDBg3k237++We4ubnJ/96+fTv69OmjUn9Xr17FqVOn5LMefXx8MGXKFMyePbvAsO3MmTPYunWrvO4rV67A0dEROjo6Cm2trKxgZGQkr1sVkZGRmDdvnmCbAxrCEY1V7oOKxlWG1IsrYREREREREVF5Y75UMOZLZU9v909il6BxvljPfEndPMAMlIiISo6DsIiIiCqJZs2a4cGDBwgLC0Pbtm1hYGBQqr7OnTsHAKhfvz7evHmj8rGxsbHw8PCAmZkZAKBXr14IDAzE4cOHBbMnL1y4AAMDA+Tk5CA7Oxu9e/fGmjVr5PuLWqJeWYBWkJCQEEyZMkWwzdPYX+XjSTUcNKReHNRGRERERERE5Y35UsGYL5U9Zkvqx3yJiIioYuEgLCIiokrC2toaiYmJcHNzQ48ePZCamgpDQ8Mij6tfvz4A4PLly2jfvj0AQFdXF05OTsWuIScnB5s3b0ZGRga0tbUF22NjYwUhWYMGDbB7925oa2vDyspKEHrVr18fx48fR3Z2tkIYdvfuXTx9+hTOzs4q16WrqwtdXV3BNi4Vr34MddSLwSNVBrzviYiIiIg0C/OlgjFfKnt8xlS/q/GtxS5B4+jdVH3wJhXNdt4JsUsgIipXHIRFRERUidjZ2eHIkSPyoGzv3r1FBmUffvghatWqhSVLliA5OblU5//uu+/w7NkznD17Flpa/4VQv/32G0aMGIHMzEyYmJgAeDvTsKAgzsvLC59//jk2bNiACRMmCPZ99tln0NPTw5AhQ0pVKxFVPQxziYiIiIiIisZ8iUhz3PgwRuwSNA7zJSIiKg0OwiIiIqpkbG1tkZaWBjc3N3h4eGDv3r0AgCdPnsiXgM9jamoKW1tbbNq0CUOGDEHv3r0xceJE1K9fH//++6/82PyBF/B2VuO7GjdujJiYGPTu3RvNmwsfRBs1aoSgoCBs2bIF48aNK/I9dOjQAZMmTcL06dORnZ2N/v374/Xr1/j666+xevVqxMfHw9TUtDiXhcoBV24iIiISkhT+6zdEREREFRbzJRKDtnltsUvQOFdf/yt2CUREVErMlzQLB2ERERFVQjY2NoKgzNLSEmlpaWjZsqWgXWBgIDZt2gRPT0+cOHECS5Ysga+vLx49egRjY2O0bt0a27dvR58+fQTHDR06VOGc6enp+Pbbb7F161aFfVKpFJ6enoiJiVEpJAOAlStXolmzZoiOjsbs2bPx6tUr6Ojo4PDhw+jSpUsxroZyHDBEFR1n1REREREREZGYmC8VTaJdrdR90H+SzqaKXYLG6WfdSewSiIiIKB+JTCbjuDoiIiISXXp6Orp27YoOHTpgy5YtCrMni8tdOkhNlVEeDmyjio4D24iqngO5O0Q9v/3GpaKevzJJHzld7BKIiIioCmC+VLFpc2UytVt7dpfYJWicUXU7i10CEZUz5kuVR2XIl7gSFhEREVUI9vb2SEtLw+bNm3Hu3Dm0atVK7JLoHRzgol4c1EZERERERESkXsyXKrY3Dx+KXYLGsdc2FLsEIiIiyoeDsIiIiKjCcHBwQHh4uNhlUAE4aEi9OKiNiIiIiIiISP2YL1VczJbUj/kSERFRxcJBWEREREREImDwqH4MHomIiIiIiIgqrp6O7cUuQePsuPO92CVonEE2/J4SEVHJcRAWEREREZEIOGCIiEgDyMQugIiIiIio8ki9fkrsEjSOhxUHDBERVXrMlzSKVOwCiIiIiIiIiIiIiIiIiIiIiIiIKjMOwiIiIipn/v7+6N+/v2BbYmIi9PT0sGzZMvj7+0MikWDx4sWCNikpKZBIJPK/09LSIJFIFF6zZ89WaT8AyGQybNy4ER06dICRkREMDAzQuHFjTJo0CdeuXZO3Cw8Plx+vpaUFW1tbfPrpp3j06JHC+3v58iVq1aoFMzMzZGVlKey3t7dXqMnGxqbQ/cquBxERERERERFRVcV8ifkSEREREVU8/DlCIiIikW3atAnjxo3D+vXrMWLECPj7+0NPTw9LlizBqFGjULNmzUKPv3z5MoyMjOR/GxgYqLRfJpNh2LBhSElJQWhoKFasWAErKyvcvXsXycnJWLBgAeLj4+XHNW7cGAcPHkROTg4uXbqEgIAAPHnyBAkJCYLz7dy5E40bN4ZMJkNKSgqGDBmiUHNERARGjhwp/1tLS6vQ/QBgaGhY6HUgqmz23T0vdgkahz/xSEREREREVRXzJeZLlUHDjWPELkHjZMVmi12CxqkfcFrsEoiIqBLjICwiIiIRRUVFISwsDNu3b4enp6d8e/fu3XHt2jVERkYiKiqq0D7Mzc1hYmJS7P0JCQnYvn07du3ahX79+sm3161bF+3bt4dMJvwRam1tbVhYWAAArK2tMWjQIMTFxSn0GxMTAx8fH8hkMsTExCgNyQwNDeV9KVPUfiJNwAFDREREREREpA7Ml5RjvlTxXBq5TuwSNA7zJSIiooqFP0dIREQkkuDgYMyfPx979uwRBGTA25l7ixYtwueff447d+6Uyfm3bduGBg0aCAKy/PIvTf+u9PR07Nu3Dzo6OoLt169fx8mTJzF48GAMHjwYx44dw61bt9RaNxERERERERERvcV8iYiIiIio4uAgLCIiIhGkpqYiKioKu3btQrdu3ZS28fT0RIsWLRAWFlZoXzY2NjAwMJC/Hj58qNL+K1euoEGDBoK2kydPlrezsbER7Ltw4QIMDAygr68PBwcHXLx4EcHBwYI2sbGx6NmzJ2rWrIlatWrBw8ND6WzG4OBgQU2rV68udL+BgQGOHTtW4DXIysrC06dPBa9cWU6h142IiIio1GQSvlR9ERERkdoxX2K+RERERBpA7MymMr0qAf4cIRERkQiaNWuGBw8eICwsDG3btoWBgYHSdkuWLMEHH3yAadOmFdjXsWPHYGhoKP+7Zs2axdqf36xZszB+/HgkJSVh0aJFgn0NGjTA7t278erVK3z99dc4d+4cJkyYIN+fk5ODzZs3Y9WqVfJtPj4+mDZtGubOnQup9L+x39OnT4e/v7/8bzMzM8G53t0PvF2iviCRkZGYN2+eYJsDGsIRjQs8hkhs++6eF7sEjcMl+ImIiIiIqCphvsR8qbLp1air2CVonNg/94hdgsYJsO0sdglERFSJcRAWERGRCKytrZGYmAg3Nzf06NEDqampgiArT5cuXeDh4YGQkBCF0CiPg4MDTExMCjxXQfvr16+Py5cvC7bVrl0btWvXhrm5uUJ7HR0dODk5AQAWL16M3r17Y968eZg/fz4AYN++ffjrr78wZMgQwXE5OTk4dOgQ3N3d5dvMzMzkfSlT1P53hYSEYMqUKYJtnsb+Kh9PJAYOGCIiIiIiIqLSYL7EfKmy+e73I2KXoHE8rDhgiIiIqCLhICwiIiKR2NnZ4ciRI/KgbO/evUqDssWLF6NFixYKS7uXlpeXF4YNG4Zdu3bho48+Kvbxs2fPxgcffIAxY8bAysoKMTExGDp0KGbNmiVot3DhQsTExAhCMnXT1dWFrq6uYJtUolVm5yNSB66EpX4c2EZERERERFUN8yX1Yb5U9v54/VzsEjSOdh3FwY5UOm/u/y12CUREVIlxEBYREZGIbG1tkZaWBjc3N3h4eGDv3r0KbZo2bQpvb2+sXr1areceOnQokpKSMHToUISEhMDDwwN16tTBrVu3kJCQAC2twkOmDh06oFmzZli0aBHCwsLwv//9D7t370aTJk0E7Xx9feHp6YlHjx6hVq1aKtX27NkzZGRkCLZVr14dRkZGxXuTRBUYBwwRERERERGROjBfUo75UsXjUq2G2CVoHA4YIiIiqlg4CIuIiEhkNjY2gqDM0tJSoU1ERAQSEhLUel6JRIKEhARs3LgRcXFxiIqKwuvXr2FjY4Nu3bph+fLlRfYRFBQEf39/1K5dGzVq1EC3bt0U2nTr1g36+vr4+uuvMXHiRJVqmzt3LubOnSvYNmrUKKxfv161N0dlgoOG1IsrYakfv6NERERERFRVMV9SxHyp4uFzu/oxX1I/fk+JiKg0JDKZTCZ2EURERETq5i4dJHYJRFTOGDxSRccgV/0O5O4Q9fz26z8T9fyVSfroaWKXQERERFRszJeIqp4ct1Zil6BROq38UewSNM6p5lxnR92YL1UelSFf4h1KRERERCrhABf14mAM9eM1JSIiIiIiIqq4mC2pH7MQ9dP6/ozYJWgUDhgioqqG/6tHRERERCphqKNeDB7Vj99RIiIiIiIiooqLz+3qx3xJ/fg9JSKi0uAgLCIiIiJSCUMdIiIiIiIiIiIqKa2mLmKXoHF2/JsudglERESUDwdhERERkUbigCGq6DirjoiIiIiIiIiqku/2JYhdgsZhvkRERFSxcBAWEREREREREVFJyMQugIiIiIiIiIiIKjXmSxqFg7CIiIhII3EWGFV0XK1N/XjfExEREREREVVcfG5XP+ZL6sfvKRERlQYHYRERVVKurq44cuQIAODs2bNo0aKFuAVpCHt7e9y6dQsA8PjxY5iYmKh8LD+T4vP398fmzZsBAMnJyejfv7+4BVGhGOqoFwMdIiIiIiISE3OMssFsqXwxW6pcmC2pH/MlIiKiikUqdgFERKXh7++v8GCdmJgIPT09LFu2DP7+/pBIJJBIJKhWrRocHBwwY8YMvHr1SnCMRCJBSkqK/O/Xr1/Dy8sL1tbW+O233+RtJBIJTp06JTg2KysLpqamkEgkSEtLU+jzypUrqF69OrZu3So4Ljc3Fx07dsTAgQPl70UikWD06NEK73PcuHGQSCTw9/cXbB85ciTu3buHJk2aAADS0tIgkUiQmZmp0Ie9vT1WrlwpqE9PT08eCuXp37+/4DzvXuOS1Pnnn38iICAAVlZW0NHRgZ2dHSZNmoSHDx8K2rm6usqvs66uLqytrdG3b18kJSUpnOtdx48fR6dOnWBqagp9fX24uLhgxYoVgjbh4eHy/vNeLi4ugjY///wzdu7cqdB/dnY2oqKi0Lx5c1SvXh1mZmbo1KkT4uLi8Pr1a3k7fibK/fDDD9DW1lYID1etWoV79+7J/z5y5AiqVauG48ePC9o9f/4c9erVw7Rp01Q+57675/lS84uIiIiIiEjTMFtijpGH2VLF+0zyEyNbAgBIpHyp8ZXL/6j9P0RERFSxcCUsItIomzZtwrhx47B+/XqMGDEC/v7+6NGjhzzMOHPmDPz8/CCRSLBkyRKlfbx48QIDBgzA1atXcfz4cTg4OMj32draIi4uDu3bt5dvS05OhoGBAR49eqS0P2dnZyxevBgTJkyAm5sbLC0tAQDLli3DjRs3sHv3bkH/27dvx4oVK6Cvrw8AePXqFbZu3Yq6desq9F29enVYWFgU/0L9P4lEgrlz58pni6mqOHXeuHEDHTp0gLOzM7Zt2wYHBwdcvHgR06dPR2pqKk6dOoVatWrJ248cORIRERF48+YN7ty5g+TkZAwdOhT+/v744osvCqypRo0aGD9+PJo1a4YaNWrg+PHjGDVqFGrUqIFPP/1U3q5x48Y4ePCg/G9tbeE/CmvXri2oB3gbknl4eOD8+fOYP38+OnXqBCMjI5w6dQqfffYZWrZsKQ+A+JkoyszMhK+vL7p164b79+8L9hkbG8PY2Fj+d9euXTFhwgT4+/vj/PnzqFGjBgBgxowZ0NfXx4IFC1S+JpwFpn4ciEVERERERJqO2VLxaFKOwWyp4n0mecTKlgAAMg5yUScp14YgIiIiDcdBWESkMaKiohAWFobt27fD09NTvl1XV1ceXNja2qJ79+44cOCA0qAsMzMTvXv3xr///ovjx48rBB5+fn5YvXo1Vq5cKQ8jYmNj4efnh/nz5xdY24QJE5CSkoKRI0diz549+OOPPzB37lwkJCTAzMxM3u69997D9evXkZSUBG9vbwBAUlIS6tatKwjs1GX8+PFYvnw5pk+fLp9dp4ri1Dlu3Djo6Ohg//798mtWt25dtGzZEo6Ojpg1axbWrVsnb58/aLKxsUH79u3h4uKCgIAADB48GN27d1daU8uWLdGyZUv53/b29khKSsKxY8cEQZm2tnaxg6yVK1fi6NGjOH36tOAc9erVw6BBg5CdnV2s/gqjSZ9JntGjR2PYsGHQ0tISzAouyKJFi7B3714EBwdjzZo1+P7777Fp0yacOHECenp6Kl8TUj8ObFMvDmqjyoD3PREREVUlzJaKT5NyDGZLFe8zycNsSXPwGVP9cru+J3YJGqfr5yfFLkGjHGumK3YJRETlioOwiEgjBAcHIzo6Gnv27EG3bt0KbPfbb7/hxIkTsLOzU9iXkZGBrl27wsDAAEeOHIGJiYlCm1atWsHe3h47d+6Ej48Pbt++jaNHj2Lt2rWFBmUSiQRxcXFo1qwZNm7ciJiYGAwdOhT9+vVTaBsQEIC4uDh52BEbG4sRI0YIlqNXl06dOuHKlSuYOXMm9uzZU6xjVanz0aNH2LdvHxYuXCgPZPJYWFjA29sbCQkJiI6OhkQiKfBcfn5+mDp1KpKSkuShjKurK+zt7REfH6/0mLNnz+LEiRMKs9uuXr0KKysr6OnpoUOHDoiMjFQ6EzS/LVu2oHv37oKQLE+1atVQrVq1Qo8vDk37TOLi4nDjxg18/fXXKs801NPTw5dffomOHTvC3d0dkydPRmhoKFq1alXgMVlZWcjKyhJsy5XlQCrRUumcpBoOGlIvBo9ERBpAJnYBRESkLsyWSkbTcoz8mC1VjM+kvLIlgPlSeWC2pH4eVmJXoHk4aIiIyh3zJY3CQVhEVOmlpqZi165dOHToED744AOF/Xv27IGBgQHevHmDrKwsSKVSrFmzRqHdpEmTUK9ePRw4cADVq1cv8HwBAQGIjY2Fj48P4uPj0atXL9SuXbvIOu3s7LBy5Up88sknsLGxwf79+5W28/HxQUhICG7dugUA+OGHH7B9+/YyCcoAIDIyEs2aNcOxY8fw/vvvq3ycKnVevXoVMpkMDRs2VNpHw4YN8fjxY/zzzz8wNzcv8FxSqRTOzs5IT0+Xb6tbt658+f38bGxs8M8//+DNmzcIDw/HJ598It/Xrl07xMfHo0GDBrh37x7mzZuH999/H7/99hsMDQ0LPP/Vq1fh6upa4H5105TP5OrVq5g5cyaOHTumsDR/UVq3bo2QkBB8/PHHaNmyJWbNmlVo+8jISMybN0+wzQEN4YjGxTovUXli8Kh+HNhGRERERCXBbKl0NCXHyMNsqeJ8JuWZLQHMl8qDh7XiQEQqndS7Z8QuQeP0tOL3lIiISo6DsIio0mvWrBkePHiAsLAwtG3bFgYGBoL9bm5uWLduHZ4/f44VK1ZAW1sbAwYMUOinT58+SElJwYYNGxAUFFTg+Xx8fDBz5kzcuHED8fHxWL16tcq1jhgxAnPmzMGECRNgZGSktE3t2rXRu3dvxMfHQyaToXfv3oJl5dWtUaNG8PX1xcyZM/HDDz+ofFxx6pTJSj+EWyaTCWbPffnll0rbHTt2DP/++y9OnTqFmTNnwsnJCV5eXgCAnj17yts1a9YM7dq1g52dHb755hsEBgYWeu7ypAmfSU5ODoYNG4Z58+bB2dm5RP3PmTMHERERmDlzZpFBW0hICKZMmSLY5mnsX6LzUsE4wEW9OAhL/XhN1Y/3PREREVUFzJZKRxNyjPyYLVWMz6S8syWA+VK5kOWKXYHGuZfzXOwSNI6WkpUsqeRyMjPFLoGIqFxxEBYRVXrW1tZITEyEm5sbevTogdTUVMHMsxo1asDJyQnA2yW0mzdvjpiYGIVgZPjw4ejXrx8CAgIgk8kUHrjzmJqaok+fPggMDMSrV6/Qs2dPPHv2TOV6tbW1i3zoDwgIwPjx4wEAa9euVbnvvPDtyZMnCkveZ2ZmwtjYWOlxeWFGSkqKyudSpU4nJydIJBJcunQJnp6eCvsvXbqEmjVrFjnbMycnB1evXkWbNm2KrMnBwQEA0LRpU9y/fx/h4eHyoOxdJiYmcHZ2xrVr1wrt09nZGX/88UeR51amqn4mz549w+nTp3H27Fl5Pbm5uZDJZNDW1sb+/fuVzi7OL+8+USUk09XVha6ucJloLhWvfhzgQhUdBwwRERERUUkwW/pPVc0x8mO2VDE+k/LOlgDmS+VBq1ZNsUvQODn8CSu146AhIiIqDQ7CIiKNYGdnhyNHjsjDsr179ypdAlwqlSI0NBRTpkzBsGHDoK+vL9jv5+cHqVSKESNGIDc3F9OmTVN6voCAAPTq1QvBwcHQ0lL/g3iPHj2QnZ0NiUQCDw8PlY+rX78+pFIpzpw5Azs7O/n2Gzdu4MmTJwXOGrO1tcX48eMRGhoKR0dHtdVpamoKd3d3REdHIygoSHC9MzIysGXLFvj6+gpmvCmzefNmPH78WOks08Lk5uYiKyurwP3//vsvrl+/juHDhxfaz7BhwxAaGoqzZ8+iZUvhUsSvX79GdnY2atSoofTYqvqZGBkZ4cKFC4Jt0dHROHz4MBITE+WBJlUuHOCiXhzUpn68purH+56IiIiqCmZLb1XVHKMgzJaYLZF65Tx6LHYJGsf10GSxS9A4uuE6YpegUeqGnxC7BCKiclXiQViZmZlITEzE9evXMX36dNSqVQu//PIL6tSpA2tra3XWSESkEltbW6SlpcHNzQ0eHh7Yu3ev0naDBg3C9OnTsXbtWqVB2PDhwyGVSuHn5weZTIbp06crtOnRowf++eefApd9Ly0tLS1cunRJ/t9VZWhoiE8++QRTp06FtrY2mjZtij///BPBwcFo3749OnbsWOCxISEh2LhxI27evIkhQ4aorc41a9agY8eO8PDwwIIFC+Dg4ICLFy9i+vTpsLa2xsKFCwXtX7x4gYyMDLx58wZ37txBcnIyVqxYgTFjxsDNzU3eztfXF9bW1oiMjATwdmZe3bp14eLiAgA4evQoPvvsM0ycOFF+zLRp09C3b1/Y2dnh7t27CAsLg5aWVoGzGfNMnjwZ3377Lbp164b58+ejc+fOMDQ0xOnTp7FkyRLExMSgRYsWSo+tqp+JVCpFkyZNBP2Ym5tDT09PYTtVHhzgol4c3EJERMR8iYgqFmZLVTfHAJgt5akonwmzJc3EbEn9PKzEroCIiIjyK9EgrF9//RXdu3eHsbEx0tPTMXLkSNSqVQtJSUm4fft2gb+lTkRU1mxsbARhmaWlpUIbbW1tjB8/HlFRURgzZozSWWbe3t6QSqUYPnw4cnNzERwcLNgvkUhgZmZWZu8DQIlDuFWrVmHx4sUIDg7GrVu3YGFhAXd3dyxcuLDQGWi1atVCcHAwQkND1Vpn/fr1cfr0aYSFhWHw4MF49OgRLCws0L9/f4SFhaFWrVqC9hs3bsTGjRuho6MDU1NTtGrVCgkJCQrLm9++fRtSqVT+d25uLkJCQnDz5k1oa2vD0dERS5YswahRo+Rt7ty5Ay8vLzx8+BC1a9dG586dcerUqSKXR9fV1cWBAwewYsUKbNiwAdOmTUP16tXRsGFDTJw4scjgp6p+JkRUOAaP6seBbURU7mSFr/BAhWO+REQVEbOlqptjMFv6T0X5TEjznMp6I3YJGkfbzlbsEjTOm1t/il0CEVU1zJc0ikQmkxX714K7d++O9957D1FRUTA0NMT58+dRr149nDhxAsOGDUN6enoZlEpERPm5urqiRYsWWLlypdilaJy8sPXx48cwMTFR+Th+JiUnkUiQnJyM/v37q61Pd+kgtfVFb3HQEFV0HIRFVPUcyN0h6vnt1y4T9fyVSfq4qQrbmC8REYmLOUbZYbZU/soiWwKYL6mb9J2fcKXSS7j6vdglaJxBNu3FLoGIyhnzpcpDWb5U0ZRoJayff/4ZGzZsUNhubW2NjIyMUhdFRESqiY6OxqZNm3Dy5Ek0bdpU7HI0QuPGjXHjxo0SH8/PpHhGjx6Nr7/+WuwySEUc4EIVHQcKqh/veyIqS8yXiIjExxxD/ZgtlS9mS5VL7suXYpegcThgSP2YL6kXsyUiqmpKNAhLV1cXT58+Vdh+5cqVIpfdJSIi9diyZQte/v9Da926dUWuRnN89913eP36NYDiL9vPz6T4IiIiMG3aNABQ+hMPRJqMgY76MdQhIqpcmC8REYmLOUbZYLZUvpgtUVXHfEn9mC8REVFplGgQVr9+/RAREYFvvvkGwNtlXm/fvo3g4GAMGDBArQUSEZFy1tbWYpegkezs7Ep8LD+T4jM3N4e5uXmZ9M0Agio6BjpERFTVMV8iIhIXc4yywWypfJVltgQAEi2tMuu7Kvrf7Z/ELkHjeFi1ErsEIiIiyqdEg7CWLVuGgQMHwtzcHC9fvkTXrl2RkZGBDh06YOHChequkYiIiKjYOMCFKjoOFFQ/3vdERJUL8yUiIiKq6GQ5OWKXoFH6WHPAkLoxX1I/5ktERFQaJRqEZWxsjAMHDuCHH37A+fPn8e+//+K9995D9+7d1V0fEREREVUQDHWoouN3VP0YPBIVTiITu4LKjfkSERERUdUi1dERuwSNc/3Nv2KXoHG0DA3FLkGj5Dx7JnYJRBUe8yXNUqJBWHk6deqETp06qauWMhUeHo558+YBAFasWIHJkyeLW1AlEB8fjxEjRgAAJk2ahJUrV6r9HK6urjhy5AgA4OzZs2jRooXaz6Fp/P39sXnzZgBAcnIy+vfvr/KxvA+Krzzug6qE93zxleaeJyKqajhgiIiocmK+pNmYL1VMzJfKF/Ml9eI9X3zMl6iikHJwi9ppgf/Pvbpx0BAREZWGtCQHTZw4EatXr1bYvmbNmmI9dPv7+yv8y35iYiL09PRgaGiIHj16KD3u2LFjkEgk+PXXX+Xbdu7cCVdXVxgbG8PAwADNmjVDREQEHj16JG/TuHFj3Lt3D59++ql8m0QiQUpKSpG1ubq6QiKRYPv27YJ2K1euhL29vfzv+Ph4mJiYCP6WSCRo2LChwjl27NgBiUQiOB4AXr58ibCwMDg7O0NXVxdmZmYYNGgQLl68KGgXHh4OiUQCiUQCbW1tmJmZoUuXLli5ciWysrKUXTqlHj58CBsbG0gkEmRmZsq3DxkyBPfu3UOHDh0AANnZ2TAzM8PixYuV9jN//nzUqVMHr1+/lrePiopC8+bNUb16dZiZmaFTp06Ii4uTtwGAkSNH4t69e2jSpAkAIC0tTaGWPPb29oKQQiKRQE9PD7du3RK069+/P/z9/eV/v/t5+vv7QyKRYPTo0QrnGDduHCQSieB4APjzzz8REBAAKysr6OjowM7ODpMmTcLDhw8F7fK+KxKJBLq6urC2tkbfvn2RlJSk9LoV5IcffoC2trZCiLBq1Srcu3dP6TG8D8r+Pshjb28vP2+NGjXw3nvvYceOHfL9L168QEhICBwdHaGnp4fatWuja9eu2LVrFwCgadOmSr9/APDVV19BV1cXDx48kN8Pea86depgwIABuHHjhqCWgsK79PR0SCQSnDt3Tun+vM9GIpFAKpXC0tISQ4YMwe3btwXt8n+v9fT00KhRI0RHRwva8J4vv3s+73PNe5mamuLDDz/E2bNnldaV/5X/Ghw5cgQffPABatWqherVq6N+/frw8/NDdna2vI1MJsPGjRvRoUMHGBkZwcDAAI0bN8akSZNw7do1QV2PHj3C5MmTYWdnBx0dHVhZWSEgIEDh+5T3ebz7yv/P/cK+1yQeD6vmfKnxReq37+55vtT8IiIqS8yX/lOVnquZL1XcZ808vA+YL72L+dJ/f2vSPc98icTw5uFDvtT8stbS50vNL4mWFl9qfBERVTUlGoS1c+dOpTMUO3bsiMTExBIXs2nTJnh7e2PdunX46quvcODAAdy5c0ehXVxcHFq3bo1mzZoBAGbNmoUhQ4agTZs2SE1NxW+//YZly5bh/Pnz+Oqrr+THaWtrw8LCAtWrVy9RfXp6epg9e7bgYU8VNWrUwN9//42TJ08KtsfExKBu3bqCbVlZWejevTtiY2OxYMECXLlyBd999x3evHmDdu3a4dSpU4L2eYHH7du38f3332PQoEGIjIxEx44d8UzFkdqBgYHya5mfvr4+LCwsoPP/y8Pq6OjAx8cHcXFxCm1lMhni4+Ph6+uLatWqITs7Gx4eHli8eDE+/fRTnDhxAj/99BPGjRuHzz//XBB0VK9eHRYWFtDWLtnCbBKJBHPnzi32cba2tti+fTtevnwp3/bq1Sts3bpV4XO5ceMGWrdujatXr2Lbtm24du0a1q9fj0OHDqFDhw6CEAr4LwS4fv06du7ciUaNGmHo0KGCYKowmZmZ8PX1Rbdu3RT2GRsbw8LCQmE774PyuQ/yi4iIwL1793D27Fm0adMGQ4YMwYkTJwAAo0ePRlJSEj7//HP88ccf2Lt3LwYOHCgPWAIDAxW+f3ni4uLQr18/mJmZybddvnwZd+/exY4dO3Dx4kX07dsXOTk5Kr23ohgZGeHevXv466+/sHPnTly+fBmDBg1SaJf3vf79998xePBgjBs3Dtu2bQMA3vMi3PMAcPDgQdy7dw/79u3Dv//+i549ewrCxry68r+ioqIAAL///jt69OiB1q1b4+jRo7hw4QI+//xz6OjoyL9bMpkMw4YNw8SJE9GrVy/s378fv//+O2JiYqCnp4cFCxbIz/Xo0SO0b98eBw8exPr163Ht2jVs374d165dQ5s2bQTBLgD06NFDoba87xMRUUmJPbBOE19ERGWJ+VLVfK5mvlSxnzV5HzBfKinmS5XzngeYLxFVdtUk2nyp+SXLyeFLjS8ioqqmRE8oDx8+hLGxscJ2IyMjPHjwoESFREVFISwsDNu3b4enpyfevHmD2rVrIz4+HrNnz5a3+/fff7Fjxw4sXboUAPDTTz9h0aJFWLlyJSZNmiRvZ29vD3d3d6UzX0rKy8sLu3fvxsaNGzF27FiVj9PW1sawYcMQGxsrn+10584dpKWlISgoSPBQsHLlSpw8eRJnz55F8+Zv/08POzs77Ny5E+3atUNgYCB+++03SCQSed95D09WVlZo2rQp3N3d0bx5cyxZskTwAKPMunXrkJmZiblz5yI1NbXI9xIYGIhVq1bh+PHj6Ny5s3z7kSNHcOPGDQQGBsrfx9GjR3H69Gm0bNlS3q5evXoYNGiQYBZMaY0fPx7Lly/H9OnT5TOfVPHee+/h+vXrSEpKgre3NwAgKSkJdevWhYODg6DtuHHjoKOjg/3790NfXx8AULduXbRs2RKOjo6YNWsW1q1bJ2+fFwIAgI2NDdq3bw8XFxcEBARg8ODB6N69e6G1jR49GsOGDYOWlpbSGYTv4n1QvvdBHkNDQ1hYWMDCwgJr167F119/jf/973/o2LEjdu/ejVWrVqFXr14A3n4WrVq1kh/r4+OD4OBg7Ny5Ez4+PvLtN2/eRFpaGr777jvBuczNzWFiYgJLS0vMnTsX3t7euHbtGho0aKByvQWRSCTy62dpaYnAwEBMnDgRT58+hZGRkbxd/u91eHg4tm7dit27d8PLy4v3fDnf83lMTU3l38HPPvsMnTp1wo8//ggPDw+Fut61f/9+WFhYyEMzAHB0dBTMFkxISMD27duxa9cu9OvXT769bt26aN++PWSy/5a6njVrFu7evYtr167Jz1m3bl3s27cP9evXx7hx4wT3l66uboG1UcXFVXGIiIhInZgvVd3nauZLFfNZk/cB86XSYL5U+e75PMyXqDxJWzYSuwSN89Wzv8QugYiIiPIp0SAsJycn7N27F+PHjxdsT01NRb169YrdX3BwMKKjo7Fnzx75LA1tbW34+voiPj4es2bNkj8M79ixAzk5OfDy8gIAbNmyBQYGBgU+rOdfsrq0jIyMMGvWLERERMDPzw81atRQ+diAgAC4urpi1apVqF69OuLj49GjRw/UqVNH0G7r1q3yh/v8pFIpgoKC4O3tjfPnzxf6O/cuLi7o2bMnkpKS5OFAeHg44uPjkZ6eLm/3+++/IyIiAj/++KPCDJKCNG3aFG3atEFsbKwgJIuLi0PHjh3h4uIC4O3n0r17d8HDcp5q1aqhWrVqKp1PFZ06dcKVK1cwc+ZM7Nmzp1jHBgQEIC4uTv7AHBsbixEjRiAtLU3e5tGjR9i3bx8WLlwof1jOY2FhAW9vbyQkJCA6Olr+PVXGz88PU6dORVJSkvyB2dXVFfb29oiPj5e3i4uLw40bN/D1118XGe7k4X2gqCzvA2W0tbXls3SBt9+N7777Dh9//DEMlfzOvZmZGT766CPExsYKQrL4+HjY2Njgww8/LPBced9DdQZPef7++28kJydDS0sLWkUsE6uvry+vgfe8orK855Up7vfCwsIC9+7dw9GjR9GlSxelbbZt24YGDRoIArL88t5/bm4utm/fDm9vb4XgS19fH2PHjsXs2bPx6NEj1KpVS9W3VCxZWVkKPxGRK8uBVMLljqni4ipDRERU1TFfqrrP1cyXKuazJu8DRcyXSob5UuW455VhvsR8qaylfsuVytSN+RIREVHFUqJBWFOmTMH48ePxzz//4IMPPgAAHDp0CMuWLSv272unpqZi165dOHTokLyvPAEBAVi6dCmOHDkCV1dXAG8fJgYMGCCfKXn16lXUq1dPrQ9ghRk7dixWrVqF5cuXY86cOSof17JlS9SrVw+JiYkYPnw44uPjsXz5coWH8itXrsDNzU1pHw0bNpS3KSwcAN4GBPv375f/bWZmBkdHR/nfWVlZ8PLywtKlS1G3bt1ihQOBgYGYNm0aVq9eDQMDAzx79gyJiYlYvXq1vM3Vq1fln1l5iIyMRLNmzXDs2DG8//77Kh/n4+ODkJAQ3Lp1CwDwww8/YPv27YIH5qtXr0Imk8mv/7saNmyIx48f459//oG5uXmB55JKpXB2dhYENHXr1oWlpaXgXDNnzsSxY8eKtZQ27wPlyvI+yC87OxvLli3DkydP5P879sUXX8Db2xumpqZo3rw5OnfujIEDBwp+aiMwMBA9e/bEzZs34eDgAJlMhs2bN8PPzw9SqfJfi7137x4+++wzWFtbq2WWIgA8efIEBgYGkMlkePHiBQBg4sSJBQagOTk52LZtG3799Vf5cui85xWV5T3/rszMTMyfPx8GBgZo27atfHt0dDQ2bdokaLthwwZ4e3tj0KBB2LdvH7p27QoLCwu0b98e3bp1g6+vr3yG6pUrVxS+Z5MnT5b3aWJigjt37uCff/5BZmZmoddMJpPh2rVr8vr27NkDAwMDQbvQ0FCEhoaW6BpERkZi3rx5gm0OaAhHNC5Rf0TlgSuLqR+DRyIqd7Kim1DBmC9V7edq5ktCFeFZk/eBcsyXVMN8qfLd8+9ivsR8qTz0blH4im5UfGtupYhdgsYZb6f4k+lERGWK+ZJGKdG/kQcEBCArKwsLFy7E/PnzAbxdCnndunXw9fUtVl/NmjXDgwcPEBYWhrZt2wr+hdnFxQUdO3ZEbGwsXF1dce3aNRw7dgwRERHyNvmXqi0Purq6iIiIwIQJEzBmzJhiHZs3Q6Zu3bp4/vw5evXqhTVr1ii0U8d7kslkghk048ePF8wsDQkJQcOGDQUzpFTl5eWFoKAgfPPNNwgICEBCQgKkUimGDBkiOH95atSoEXx9fTFz5kz88MMPKh9Xu3Zt9O7dG/Hx8ZDJZOjduzfMzMyUti2Lz+XLL7+U//ecnBwMGzYM8+bNg7Ozc7H7LU+8D94KDg7G7Nmz8erVKxgYGGDx4sXo3bs3AKBLly64ceMGTp06hRMnTuDQoUNYtWoV5s2bJw8W3d3dYWNjg7i4OERERODQoUO4ffs2RowYoXAuGxsbeYjVvHlz7Ny5Ezo6OiWq+12Ghob45Zdf8Pr1a6SmpmLLli1YuHChQru8wCU7OxtaWloICgqSf/6855Urq3s+T8eOHSGVSvH8+XPUq1cPCQkJgpm/3t7emDVrluCYvP1aWlqIi4vDggULcPjwYfz4449YtGgRlixZgp9++kkQ5uU3a9YsjB8/HklJSVi0aJHC+y1M/u+sm5ubYMl9AKWaxRgSEoIpU6YItnka+5e4PyKqnDiwTf04sI2IyhLzpar9XM18qeSYL2nOfQAwX2K+VDTmS0LMlyq3N/88FLsEjWOvrSd2CZpHonwQM5WQLFfsCoiIylWJp0WMGTMGY8aMwT///AN9fX2F2Qaqsra2RmJiItzc3NCjRw+kpqYKllYODAzEhAkTsHbtWsTFxcHR0RFdu3aV73d2dsbx48fx+vXrEs3SMjQ0xJMnTxS2Z2ZmymdDvsvHxwefffYZFixYAHt7e5XP5e3tjRkzZiA8PBzDhw9XOivF2dkZly5dUnp83nZVHqYuXbqk8Bv0+R0+fBgXLlxAYmIigP8ebMzMzDBr1iyF2R75GRkZYeDAgYiLi5MHHoMHDxZ8B5ydnfHHH38UWWdB/QNvZ0+9u8x5YZ9L3oNmcX7vHngb2uQFJ2vXrlXY7+TkBIlEgkuXLsHT01Nh/6VLl1CzZk3Url270PPk5OTg6tWraNOmjdL9z549w+nTp3H27Fl5Pbm5uZDJZNDW1sb+/fsVZvPm4X2gXFneBwAwffp0+Pv7w8DAAHXq1FFYOrxatWp4//338f777yM4OBgLFixAREQEgoODoaOjA6lUCn9/f2zevBnh4eGIi4uDm5ub0p/dOHbsGIyMjGBubq50+fnSkEqlcHJyAvB2Rtn169cxZswYfPXVV4J2eYGLvr4+LC0tBbMpec8rKst7Pk9CQgIaNWoEU1NTpT8LYWxsLP9sC2JtbY3hw4dj+PDhmD9/PpydnbF+/XrMmzcP9evXx+XLlwXta9eujdq1awtmadauXRsmJiaF3rfa2tqC+7FGjRpF1lYcurq60NXVFWzjUvFEREREFR/zpar7XM18SagiPGvyPlCO+ZJqmC8JVYZ7Pg/zpf8wXyp7Wkbq/d8eAv7OfSl2CZqHg4aIiKgUSr427f8r6iFBFXZ2djhy5Ig8KNu7d6/8IXDw4MGYNGkStm7dii+//BJjxowRPIgOGzYMq1evRnR0NCZNmqTQd2ZmptIHhzwNGjTAmTNn4OfnJ9+Wk5OD8+fP45NPPlF6jFQqRWRkJD7++ONizdKqVasW+vXrh2+++Qbr169X2mbo0KGYNWsWzp8/j+bN/5t1npubixUrVqBRo0aC7cr88ccf2Lt3L0JCQgpss3PnTrx8+d+/mP38888ICAjAsWPHBMtpFyQwMBCurq7Ys2cPTpw4gaVLlwr2Dxs2DKGhoTh79ixatmwp2Pf69WtkZ2cXuBR1/fr1IZVKcebMGdjZ2cm337hxA0+ePCkwHLG1tcX48eMRGhqq0nvI06NHD2RnZ0MikcDDw0Nhv6mpKdzd3REdHY2goCDo6+vL92VkZGDLli3w9fVVCEjetXnzZjx+/BgDBgxQut/IyAgXLlwQbIuOjsbhw4eRmJhYaNjD+0BRedwHZmZmxXrIb9SoEd68eYNXr17JZ2yNGDECCxYsQFJSEpKTkxWW9s7j4OBQ6GeoTjNnzoSjoyOCgoLw3nvvybcXFrjwnldUlvd8Hltb22K996LUrFkTlpaWeP78OYC3M9OHDRuGXbt24aOPPirwOKlUisGDB2PLli2IiIiAhYWFfN/Lly8RHR0NT0/PAgNPqjy4Ig5VdFwJi4io8mK+VDWfq5kvvVVRnjV5HyhivlRyzJcq/j2fh/kSlaccJYN1qXQCbDuLXYLGYb6kXsyUiaiqKdEgrPv372PatGk4dOgQ/v77b4XlYXNycordp62tLdLS0uDm5gYPDw/s3bsXRkZGMDAwwJAhQxASEoKnT5/C399fcFy7du0wY8YMTJ06FX/99Rc8PT1hZWWFa9euYf369ejcubPS0CDPlClTEBgYCBcXF7i7u+P58+f4/PPP8fjx4wLDAQDo3bs32rVrhw0bNgiW5i1KfHw8oqOjYWpqqnR/UFAQdu3ahb59+2LZsmVo164d7t+/j0WLFuHSpUs4ePCg4MHszZs3yMjIQG5uLh4+fIi0tDQsWLAALVq0wPTp0+Xt1qxZg+TkZBw6dAgAFB6qHjx4AODtLCVVHsS7dOkCJycn+Pr6ypf1z2/y5Mn49ttv0a1bN8yfPx+dO3eGoaEhTp8+jSVLliAmJgYtWrRQ2rehoSE++eQTTJ06Fdra2mjatCn+/PNPBAcHo3379grnyi8kJAQbN27EzZs3BcvXF0ZLS0s+u0ZLS/msljVr1qBjx47w8PDAggUL4ODggIsXL2L69OmwtrZWWFr7xYsXyMjIwJs3b3Dnzh0kJydjxYoVGDNmDNzc3OTtfH19YW1tjcjISEilUjRp0kTQj7m5OfT09BS2v4v3gTj3QWFcXV3h5eWF1q1bw9TUFL///jtCQ0Ph5uYmn5kHvA2/PvjgA3z66afQ1dXFxx9/XKLz/fXXXzh37pxgW/7A6d3ZZgDQuHFjpX3Z2trC09MTc+fOxZ49e1Q6P+/58r3nVZVXV366urqoWbMmNmzYgHPnzsHT0xOOjo549eoVvvzyS1y8eBGff/45gLeBdVJSEoYOHYqQkBB4eHigTp06uHXrFhISEgTXb+HChTh06BDc3d0RFRWFJk2a4ObNm5g9ezakUilWrVolqCMrK0uhNm1tbcHy/QV9r2vWrKmOy0MlwACCKjqGOkRElQvzJT5XM1+qWM+avA+YLzFfqlr3vKqYL5E6aVtbiV2Cxln4Q7LYJWgcD6t2YpdARESVWIkGYfn7++P27duYM2cOLC0ti5ytoSobGxtBULZv3z4YGRkhMDAQMTEx6NWrF6ysFP8FbcmSJWjVqhXWrl2L9evXIzc3F46Ojhg4cKBg5pUyXl5ekMlkWL58OWbOnInq1aujVatWOHr0aJEP/UuWLCn04U0ZfX19wayXd+np6eHw4cNYtGgRQkNDcevWLRgaGsLNzQ2nTp1SeHC6ePEiLC0toaWlBWNjYzRq1AghISEYM2aMYNncBw8e4Pr168WqtTASiQQBAQEIDQ1VOhNMV1cXBw4cwIoVK7BhwwZMmzYN1atXR8OGDTFx4sQiHwBXrVqFxYsXIzg4GLdu3YKFhQXc3d2xcOHCQr9vtWrVQnBwMEJDQ4v1fvKHFsrUr18fp0+fRlhYGAYPHoxHjx7BwsIC/fv3R1hYmMLvzG/cuBEbN26Ejo4OTE1N0apVKyQkJCgsPX379m3Bktulwfug/O+Dwnh4eGDz5s0IDQ3FixcvYGVlhT59+mDu3LkKbQMDA3Ho0CGMHTsWenol+/32zz77DJ999plg21dffYXOnd/Oghk6dKjCMX/++WeB/QUFBaFDhw746aef0LZt2yLPz3u+/O95VeTVlV/e/xHVtm1bHD9+HKNHj8bdu3dhYGCAxo0bIyUlRf6zLBKJBAkJCdi4cSPi4uIQFRWF169fw8bGBt26dcPy5cvl/ZqZmeHUqVOIiIjAqFGjcPfuXeTk5KBjx444d+6cwjXbu3cvLC0tBdsaNGgg+NmBgr7XPj4+ark+RERERCQu5kv/nbeqPlczX6p4z5q8D5gvMV9SnSbc86pgvkTqlGtRq+hGVCy/vLIruhERERGVG4ns3WmGKjA0NMSxY8cKnHVSEYWHhyMlJUVhxgMVzdXVFS1atMDKlSsrVd+aTiKRIDk5Gf3791f5GN4HJcfvqnrwOpZcSe55scXExGDs2LFISEgQpW536aByPydRcXBlMaoMuLoYVXQHcneIen6Hz5eJev7K5OaEqQrbmC9VLcyXKibmS+WL31X14HUsOeZLxcd8iSq6q/GtxC5B4+jd0C26EanMNuKE2CUQFYn5UuWhLF+qaEq0Epatra3CEvGVwYULF2BgYICoqCiMHTtW7HIqvC1btmDUqFF4+fJlmQai0dHR2LRpE06ePImmTZuW2Xk0xejRo/H111+X+HjeB8VTXvdBVcJ7vnhKe8+LKTAwELVq1cKlS5fg4eFR6Axlqhw4aIgqOg4YIiKqXJgvVQ3Mlyom5kvli/mS+vGeLx7mS1RRaNe1EbsEjZPQdYPYJWic2f6txS6BiIgqsRKthLV//34sW7YMGzZsgL29fRmUpX6PHj3Co0ePAAC1a9eGsbGxyBVVfM+ePcP9+/cBACYmJoLfUVeXv/76Cy9fvgQA1K1bFzo6Omo/h6b5+++/8fTpUwCApaUlatSoofKxvA+Krzzug6qE93zxleaer+o4U1H9OAhLvThgiIio9DhTsfJQNlOR+VLVwHypYmK+VL6YL6kX7/niY75UcsyX1IvZkvoxXyIiKj3mS5WHxq6ENWTIELx48QKOjo6oXr06qlWrJtif9xBekdSqVUvh98KpcIaGhjA0NCzTc1hbW5dp/5rI3Nwc5ubmJTqW90Hxlcd9UJXwni++0tzzRERERP/H3p3H1Zj+/wN/nVOKlKIkpVIqKUuWsS+lyD5Zooi28bUTIcVQtjCSfQxOi/GxK/swMmVfxtBgpmGmkTUztkIR1fn90a8zjtNyqsOp4/V8PHo85lz3dd/3+77POem85rquQ1SZMV/6PDBfqpyYL31azJcUi+/5smO+RERERET0aZRrEBa/a52IiIjo88OZdYrF2Z9UFfB9T0QfE/MlIiIios8LP2Mq3qa7Z5RdgsoZ69Bf2SWolLxnz5VdAhHRJ1WuQVje3t6KroOIiIhIoTjAhSo7Bo9ERPS5Y75ERERE9HlhXqd4rsadlV2CCuKgISIiKr9yDcICgNTUVERHRyM1NRWrVq2CoaEhfvjhB5iZmcHe3l6RNRIREclIS0uDhYUFAKBFixZITk5WbkEqIikpCU5OTgCAL7/8Evv27VNuQRXAAS6Kx6CMiIhImkCs7AqqPuZLRESkTMyXPg5VypeIiIiIPjbmS6qlXIOwTp48id69e6NTp044deoUFi1aBENDQ/z6668QiUTYs2ePouskIqJy8vHxQUZGRolhx/3792FpaQkbGxvcuHFDZrtAIEB8fDzc3NwAAO/evcOoUaNw6tQpHDt2DE2bNoVAIJD0r1WrFpo2bYoFCxage/fuZToPAJw/fx7t27eXtOfk5MDY2BjPnj1DYmIiHB0dJdsSEhLg4OBQ6vUWhj/Pnz+Hnp4eYmJi4OvrC1dXVxw9elTSLyMjA7Vr15Y6z4fXX546Dx06hG+++QZXrlxBXl4e7O3tMWHCBPj4+Ej6vB/8AYC2tjbMzMzg6OiIgIAAWFtby9yz94WGhmLHjh24d+8eNDQ00Lp1ayxatAjt2rWT9GnYsCHu3LkjtV94eDhmzZoFAOjYsSPS09MxZcoU5OTkSPo4OjrCwcGh2K+MEQgE0NTUxM2bN2Fubi5pd3Nzk9xvoOD5iY2NBQBUq1YNZmZmGDVqFEJCQqCuri7zPAHAw4cP4erqitq1a+PgwYPQ1dUt8T4QVRUc1KZ4HHxJRFS1MF8iIqo6mC8VYL5UgPkSlZfLSD9ll6ByOiRfUnYJKueSg5qySyAioipMWJ6dZs2ahYULF+L48ePQ0NCQtHfv3h0XLlxQWHFERPRpxMTEYOjQoXjx4gUuXrxYYt/s7GwMGDAAP//8M86cOYOmTZtKtkVHRyM9PR1nz56FgYEB+vXrh7///rtM5zE1NUV0dLRUW3x8PLS1tYvsr6+vD319fXkvVYq6ujoSEhKQmJhY5n3LUueaNWvw5ZdfolOnTrh48SKuXbsGDw8PjB07FtOnT5fpn5CQgPT0dPz6669YvHgxUlJS0KJFC5w4caLEmmxsbLB27Vpcv34dZ86cQcOGDdGzZ088fvxYqt/8+fORnp4u+Zk0aZJkm4aGBoyMjFCjRo2y3A4ABUHZ3LlzS+3Xq1cvpKen488//0RgYCBCQ0PxzTffFNk3NTUVnTt3hrm5OY4dO8aAjIiIiEiFMF8iIlItzJeYLxVivkTFEeSJ+aPgn2rCPP4o+IeIiKgiyrUS1vXr17Ft2zaZdkNDQzx58qTCRRER0acjFosRHR2N9evXo0GDBhCJRFIz296XkZGBvn374tWrVzhz5gyMjIyktuvp6cHIyAhGRkb49ttvYWJiguPHj2PMmDFyn8fb2xurV6/GypUrJUFNVFQUvL29sWDBAoVee82aNTF06FDMmjWr1HCwvHXeu3cPgYGBCAgIwOLFiyXtgYGB0NDQwOTJk+Hu7i51L/T19SX31tLSEv3794ezszP8/f2RmpoKNbWiZ+IMHz5c6vGKFSsgEolw7do1ODs7S9p1dHRknjtFmDhxIlasWIEZM2ZIhacf0tTUlJx/3LhxiI+Px4EDBxAcHCzV79q1a3B1dUX37t0RGxsLdfVyf4syKQhXGVIsroRFRESfO+ZLRESqg/kS8yVFYb6k2oRJV5RdgsqZa5Ci7BJUjiuYgRIRUfmV669NPT09pKenSy1pCwBXr16FiYmJQgojIqJPIzExEdnZ2XBxcYGJiQk6duyIyMhI1KxZU6rfo0eP0K1bN2hra+PkyZOSpbyLUxgcvX37tkznad26NRo2bIi9e/fCy8sLd+/exalTp7Bu3TqFh2RAwRLrVlZW2LNnD4YMGSL3fvLWuWfPHrx7967IGYljxoxBSEgItm/fXmwwCQBCoRBTpkzBwIED8csvv6Bt27aSpeU/XJa+0Nu3b7Fx40bo6uqiRQvpD41LlizBggULYGZmhuHDh2Pq1KkKCaA6deqEW7duYdasWTh06JDc+9WoUQNPnz6Vajt37hxGjBiBESNGYM2aNVJfR0DKw0FDisVBbURE9LljvkREpDqYLzFfYr5E8mC2pHjMl4iIiCqXcv1F7OHhgaCgIOzevRsCgQD5+fk4e/Yspk+fjlGjRim6RiIi+ohEIhE8PDygpqaGpk2bwtLSErt374aPj49UvylTpsDS0hLHjx+HlpZWicfMzs7GnDlzoKamhm7dupXpPADg5+eHqKgoeHl5ISYmBn369EHdunUVdclSjI2NMWXKFMyePRtubm5l2leeOm/dugVdXV3Ur19fZn8NDQ1YWlri1q1bpZ7L1tYWAJCWloa2bduiWrVqaNy4scxzcejQIXh4eCA7Oxv169fH8ePHYWBgINk+efJktGrVCnXq1MG5c+cQHByM9PR0rFixokzXXpzw8HA0b94cp0+fRpcuXUrsKxaLceLECRw7dkxqyXoAGDhwIIYNG4a1a9fKdd6cnBzk5ORIteWL8yAUFD2rk8qHoY5iMXhUPL5GiYiqFuZLRESqg/kS8yXmSyQPfm5XPOZLisfXKRERVUS5BmEtXrwYEyZMgKmpKfLy8mBnZ4e8vDwMHz4cc+bMUXSNRET0kWRkZCAuLg5nzpyRtHl5eUEkEsmEV/369cO+ffvw3XffYerUqUUez9PTE2pqanj9+jXq1q0LkUiE5s2bl+k8hdtmzZqFv//+GzExMVi9erVCrrc4QUFB+O677xAVFYWhQ4fKvd+nrFMsFgOAZMaeiYkJ/vjjD5l+Tk5OSE5OxpMnT7Bp0yYMHToUFy9ehKGhIQBg2rRpkr7NmzeHhoYGxowZg/DwcGhqala4Tjs7O4waNQqzZs3C2bNni+xz6NAhaGtr4927d8jPz8fw4cMRGhoq1efLL79EfHy8XGEbUBDOhYWFSbVZoAkawb7c10KyGOoQERF9QMzVFCqC+RIRkWpgvlSA+RLzJSqdur6+sktQOWm5L5VdAhERVRTzJZVSrkFYGhoa2LRpE77++mvcuHEDr169QsuWLWFtba3o+oiI6CPatm0b3rx5I7VUuVgsRn5+Pm7dugUbGxtJ+8iRIzFgwAD4+flBLBZLhS2FIiMj4eLiAl1dXakZe2U5DwDo6+ujX79+8Pf3x5s3b9C7d2+8fCnfh8latWrhzp07Mu0ZGRlQU1OTWZ4eKPgalODgYISFhaFfv35ynUfeOm1sbJCZmYmHDx/C2NhYatvbt2+RmpoKJyenUs+VkpICADJf1fKhmjVrwsrKClZWVmjfvj2sra0hEokQHBxcZP927dohNzcXaWlpaNy4cal1yCMsLAw2NjbYt29fkdudnJzw7bffQkNDA8bGxkUuVf/dd99h5syZ6N27N44cOYKuXbuWeM7g4GCZ1+RAXZ/yXgIVg7PAFIuD2hSP91Tx+L4noo+J+RIRkWpgvlSA+RLzJSpd7gdfGUkVN6HjMGWXoHIWp+1TdgkqJaThF8ougYjok6rQF3SbmZnBzMxMUbUQEdEnJhKJEBgYKDNbcPz48YiKisKSJUuk2r29vSEUCuHr64v8/HxMnz5daruRkRGsrKwqfB6gYCn2Pn36ICgoCGpq8i/53bhxY+zYsQM5OTlSM++uXLkCCwsLVKtWrcj9Jk2ahNWrV2PVqlVyn0ueOgcPHoygoCBEREQgIiJCatuGDRuQlZUFT0/PEs+Rn5+P1atXw8LCAi1btixTffn5+TLLqL8vOTkZQqFQMpNREUxNTTFx4kSEhISgUaNGMtsLg7ySCAQCbNy4EUKhEH369MHhw4clXz1QFE1NTZmZllwqXvE4wEWxOLiFiIioAPMlIqKqjfnSf5gvMV+ikjFbUjxX49L7UNlw0BAREVVEuQZh+fn5lbg9KiqqXMUQEdHHkZmZieTkZKm2ly9f4sqVK/jf//4HW1tbqW2enp6YP38+Fi5cKDOLbOTIkRAKhfD29oZYLMaMGTNKPHdycnK5ztOrVy88fvwYtWrVKtO1jhgxAvPnz8eoUaMwc+ZM6Orq4tSpU1i5ciWWLVtW7H7Vq1dHWFgYJkyYUKbzlVanmZkZli1bhsDAQFSvXh0jR45EtWrVsH//foSEhCAwMFBqBicAPH36FI8ePUJ2djZu3LiBlStX4tKlSzh8+LAkiHvw4AGcnZ2xZcsWtG3bFllZWVi0aBEGDBiA+vXr48mTJ1i3bh0ePHgAd3d3AMD58+dx8eJFODk5QUdHB+fPn8fUqVPh5eWF2rVrl3qtjx8/lnkd1a9fH/Xq1ZPpGxwcjE2bNuH27dsYNqx8s7EEAgE2bNgANTU1SVDm6OhYrmMRERERUeXDfImIqGphvsR8CWC+RERERERUknINwnr+/LnU43fv3uHGjRvIyMhA9+7dFVIYEREpTlJSkswMNx8fH9jZ2ckEVwAwcOBATJw4EUeOHMGAAQNkto8YMQJCoRAjR45Efn4+goKCij23SCQq13kEAgEMDAzkvUQJPT09nD59GrNmzcKAAQOQmZkJKysrrFixAv7+/iXu6+3tjYiICPz+++9yn0+eOgMCAmBpaYnly5dj1apVyMvLg729Pb799lv4+vrK9HdxcQEAaGlpwdzcHE5OTti4caPU7L53797h5s2byM7OBgCoqanhjz/+QGxsLJ48eQJ9fX188cUXOH36NOzt7QEUzObbsWMHQkNDkZOTAwsLC0ydOrXIpf+Lsm3bNmzbtk2qbcGCBZgzZ45M3zp16iAoKAghISFyHbs4AoEA69atg1AoRN++fXHo0CG5ltcnIiIiosqP+RIRUdXCfIn5EsB8iYiIiIioJAKxWCxWxIHy8/Mxbtw4NGrUCDNnzlTEIYmIiIqVlpYGCwsLXL16FQ4ODsouR+X4+PggIyMD+/btU3Yp5dZD6K7sElQOl4ynyo5f8Uj0+Tmev1up57dcuUKp569K/g6Q73/MMl8iIqJPifnSx8V8iT707wHZgZRUQT/WUXYFKsdw7Tlll0BEnxjzpapD3nxJmcq1ElZRhEIhpk2bBkdHR4ZkRET0yXTs2BEODg44d44fjBTh9OnT6N27N3JyctC3b19ll0OVDAe4UGXHgYKKx/c9EX1qzJeIiEgZmC8pFvMlKo7hgD+UXQJRqZgvKRazJSL63ChsEBYApKamIjc3V5GHJCIiKlKDBg3w559/AihYBp0Uo02bNkhOTgYAaGtrK7cYqnQYQCgWAwjF4z0lIlINzJeIiOhTYb70cTBfouIwW1I8ZiGKx3tKREQVUa5BWB9+t7dYLEZ6ejoOHz4Mb29vhRRGRERUEnV1dVhZWSm7DJVTo0YN3lciIiIi+iSYLxERkbIxX/o4mC8RERER0eeqXIOwrl69KvVYKBSibt26iIiIgJ+fn0IKIyIiIiIiIiIi1cV8iYiIiIiIiIiIVEm5BmElJiYqug4iIiIiIiIiIvqMMF8iIiIiIiIiIiJVIlR2AURUtTk6OkIgEEAgECA5OVnZ5aiMhg0bSu5rRkZGuY4RGhoqOcbKlSsVWt/nzMfHR3Jf9+3bp+xyiIiIiEiZxPyR+4eIqATMlz4O5kuVF/MlIiIiIpJQdmZTlX6qgHKthNWyZUsIBAK5+l65cqU8pyBSWT4+PsjIyCjyw3XDhg1x584dmfbw8HDMmjVL8njv3r1Yt24drl69ijdv3sDMzAydOnXCpEmT0LJlS/zxxx9o0qQJzp8/j/bt20v2a9++PZKTk5GRkYHq1asDAN68eQM9PT2sW7cO/v7+8PHxQWxsLMaMGYMNGzZI1TFhwgSsX78e3t7eiImJkbSPHj0a8+fPh4GBAQAgKSkJTk5OeP78OfT09GSuMSAgAAEBAQAAgUAATU1N3Lx5E+bm5pJ+bm5u0NPTk5znw/tWnjrv3buHefPm4ejRo3jy5Anq168PNzc3zJ07F/r6+pJ+jo6OOHnyJABAQ0MDBgYGaNWqFXx9fTFo0CCZ5+d9Z86cQVBQEP744w9kZ2fD3NwcY8aMwdSpUyV9QkNDERYWJrVf48aN8ccff0ge//zzzzh9+jQGDx4saYuJiYGvry9sbW2RkpIitf/u3bsxdOhQmJubIy0tTdJub2+PhIQE1KpVS9ImEAgQHx8PNzc3qWN8eI8L78P27dvh4eEh6bdy5UqsXLlScp6YmBgEBARIwrzy1Pn69WssWbIE27dvx507d6CjowMnJyeEhobC3t6+yHunpqYGPT092NnZYdCgQRg3bhw0NTVRnKdPn2LEiBG4du0anj59CkNDQ3z55ZdYvHix5P4UvnY/lJ6eDiMjIwDAqlWrsGTJEtSvX1+yPS0tDRYWFpLH2traMDMzg6OjIwICAmBtbS3ZVnh/XF1dcfToUUl7RkYGateujcTERDg6OgIo2/sjNjZWpu4///wTVlZW+PXXX/H111/jwoULePHiBYyMjNCuXTusWbMGhoaGkvqvXr0KBwcHqWM4OjrCwcFBErR++Pj96yp8HVT0d9CHvyP37NkDLy8vLFq0CIGBgTLXSZ+Oq3ELZZegUo49/FXZJRCViu97IvqYmC8RlR/zJeZLzJeYLzFfYr5UFfEzpuL9vd1B2SWonOrJWsouQaUYLz2n7BKIiD6pcg3C6tWrF9avXw87Ozt06NABAHDhwgX89ttvGDduHGrUqKHQIok+J/Pnz8fo0aOl2nR0dCT/HRQUhIiICEyePBlhYWEwNzfH48eP8cMPPyA4OBhHjx6Fra0tjIyMkJSUJPmA+vLlS1y5cgX16tXDhQsXJB/Cz58/j5ycHHTv3l1yDlNTU+zYsQORkZGS9/ObN2+wbds2mJmZydSspaUlCRDKQyAQYO7cuUV+yC9JWer8+++/0aFDB9jY2GD79u2wsLDAb7/9hhkzZuCHH37AhQsXUKdOHUn/wuAvNzcX9+/fR3x8PDw8PODj44ONGzcWW1PNmjUxceJENG/eHDVr1sSZM2cwZswY1KxZE//3f/8n6VcYXhVSV5f+dVy3bl2pet4//r///ovz589Lfv8CgEgkKvK5UVdXr9BzU716dcyZMweDBw9GtWrV5N6vLHXm5OTAxcUFd+/eRUREBNq1a4d//vkH4eHhaNeuHRISEqSClsJ7l5+fj6dPnyIpKQkLFy7E999/j6SkJKn3y/uEQiG+/PJLLFy4EHXr1sVff/2FCRMm4NmzZ9i2bZtU35s3b0oFi4aGhpL/1tXVha6ubpHnSEhIgL29PbKzs3H9+nWsWrUKLVq0wMGDB+Hs7Czpp66ujoSEBCQmJhYZyr1P3vdHr169EB0dLdVWt25dPH78GM7OzujXrx+OHTsGPT09pKWl4cCBA8jKyirxmOVV0d9B79u8eTMmTJiADRs2wNfX96PUS/LjoCEiIiJSJOZLRB8P8yX5MV9ivsR86T/Ml4iqHqtv3im7BJUzbNteZZegUrYvrV96JyIiFVKuQViPHz/G5MmTsWDBAqn2efPm4d69e4iKilJIcUSfIx0dnWJDjQsXLmDZsmVYtWoVJk+eLGk3MzND69atIRb/twafk5MTkpKSJDMcz5w5AxsbG3Tt2hVJSUmSD6hJSUkwNzeXmmXVqlUrpKamIi4uDiNGjAAAxMXFwczMTKqfokycOBErVqzAjBkz0LRpU7n3K0udEyZMgIaGBn788UdJoGZmZoaWLVuiUaNGmD17Nr799ltJ//eDvwYNGqB9+/awtbWFn58fhg4dChcXlyJratmyJVq2bCl53LBhQ8TFxeH06dNSIVl5wyt1dXUMHz4cUVFRkvDp/v37SEpKwtSpU7F9+/YyH7Mknp6eOHDgADZt2oTx48d/lDpXrlyJ8+fP4+rVq2jRomAmlLm5Ofbu3Yt27drB398fN27ckMyQf//eGRsbo1mzZujRowdatGiBpUuXYuHChUXWVLt2bYwbN07y2NzcHOPHj8c333wj09fQ0FBmlq089PX1JbVZWlqif//+cHZ2hr+/P1JTU6GmpgagIEQcOnQoZs2ahYsXL5Z4THnfH5qamkW+ps6ePYvMzExs3rxZEsZaWFiUGs5VVEV+BxVatmwZ5s2bhx07dmDgwIEftV4iUg2cUUtEVLUwXyL6eJgvMV8qCfMl5kvFYb5EH5tQiysMKdq2A5uVXYLK8WjQofRORERExSjXIKzdu3fj8uXLMu1eXl5o06YNQzKij2T79u3Q1tYuNqx4/2scnJycMHXqVOTm5kJdXV2yBHWXLl2wfv16hIaGAkCxM6X8/PwQHR0tCZ+ioqLg6+uLpKQkhV9Xp06dcOvWLcyaNQuHDh0q077y1Pns2TMcO3YMixYtkplJbWRkhBEjRmDnzp1Yv359iV+F4e3tjcDAQMTFxUlCMkdHRzRs2FBqWfr3Xb16FefOnZMJbv78808YGxujevXq6NChA8LDw4ucaVjcNTs6OmLVqlXQ0tJCTEwMevXqhXr16sm1f1nUqlULs2fPxvz58+Ht7Y2aNWvKva+8dW7btk0Scr1PKBRi6tSpGDFiBH799VeZZczfZ2tri969eyMuLk5yr0NDQxETEyO1LP37Hj58iLi4OHTr1k1mm4ODA3JyctC0aVOEhoaiU6dOcl/3h9cwZcoUDBw4EL/88gvatm0r2RYaGgorKyvs2bMHQ4YMKfYYFXl/AAWv8dzcXMTHx2PIkCFyf91LRVX0d1BQUBDWr1+PQ4cOSc3yJFIlHDBERESfO+ZLRMrBfEkW8yXmSwDzpZIwXyJF+eGv88ouQeW4GnPAEBERUWUiLM9ONWrUwNmzZ2Xaz549K/kObiIqn6CgIGhra0v9nD59GgBw69YtWFpaSi0tvmLFCqm+mZmZAAo+oGZlZeHnn38GUDAbqFu3bujatSsuXryIN2/e4PXr17h06VKRH1C9vLxw5swZ3LlzB3fu3MHZs2fh5eX10a47PDwcR48elVyrvOSp888//4RYLEaTJk2KPEaTJk3w/PlzPH78uMRzCYVC2NjYSIUuZmZmqF9fdinVBg0aQFNTE23atMGECRPw1VdfSba1a9cOMTExOHr0KL799lvcvn0bXbp0wcuXL+W65pYtW8LS0hJ79uyBWCxGTEwM/Pz85Nq3PMaPH4/q1atjxYoVZdpP3jpv3bpV4nNT2Kc0tra2Us+NgYEBGjVqJNPP09MTWlpaMDExQa1atbB5838zherXr48NGzZg79692Lt3L0xNTeHo6IgrV66Uev6S6gIgE9YZGxtjypQpmD17NnJzc0s8hjzvj0OHDkn9LnB3dwcAtG/fHiEhIRg+fDgMDAzQu3dvfPPNN/jnn39kjtGxY8dif/+8b/369TL9xo4dK9WnIr+DfvjhByxbtgz79++XOyDLycnBixcvpH7yxXly7UtEREREysF8iejjYb7EfKk0zJeKxnyJ+RLzJSIiIiKqiHKthBUQEIBx48bhypUrklkXFy9eRFRUFL7++muFFkj0uZkxYwZ8fHyk2kxMTIrt7+fnhwEDBuDixYvw8vKSLBlvZWWFBg0aICkpCfb29rh69Sq6desGQ0NDmJmZ4fz58xCLxcjJySkyJKtbty769u2LmJgYiMVi9O3bFwYGBgq91vfZ2dlh1KhRmDVrVpEhfHHKUuf7y+mXl1gslprptWXLliL7nT59Gq9evcKFCxcwa9YsWFlZwdPTEwDQu3dvSb/mzZujXbt2MDc3x65du+Dv7y9XHYUzNM3MzJCVlYU+ffpg7dq1Fbiy4mlqamL+/PmYNGmS1HLriqzzYzw3EydOxMSJE2X6RUZGYt68ebh16xaCg4Mxbdo0rF+/HgDQuHFjNG7cWNK3Y8eOSE1NRWRkJL7//vty1wWgyBmCQUFB+O677xAVFYWhQ4cWewx53h9OTk5SX3nw/qzSRYsWYdq0afjpp59w8eJFbNiwAYsXL8apU6fQrFkzSb+dO3fKBJaFs4A/bJs9e7ZUW1xcHBYvXix5XJHfQc2bN8eTJ08wb948tG3bFtra2sXem0Lh4eEICwuTarNAEzSCfan7EhEREZWXoOJ/xn7WmC8RfTzMl5gvyYP5kizmS8yXmC8RERHRp8Z8SbWUaxDWrFmzYGlpiVWrVmHr1q0ACmaTREdHl/hHPhGVzsDAAFZWVkVus7a2xpkzZ/Du3TtUq1YNAKCnpwc9PT3cv39fpr+joyMSExPRvHlzWFtbw9DQEADQrVs3JCYmQiwWw8rKCqampkWez8/PTxIyrFu3Tu5rqFWrFgAgMzMTenp6UtsyMjKgq6tb5H5hYWGwsbHBvn375D6XPHVaWVlBIBAgJSUFAwcOlNmekpKC2rVro27duiWeJy8vD3/++Se++OKLUmuysLAAADRr1gz//PMPQkNDJSHZh/T09GBjY4O//vqr1OMWGjFiBGbOnInQ0FCMHDlSavZqaXR0dCQzWt9X0nPj5eWF5cuXY+HChWjYsKFC67SxsUFKSkqR+xe229jYlHqulJQUyX0viZGREYyMjGBra4s6deqgS5cu+Prrr4uccQoAbdu2xZkzZ0o9bkl1ASiyNj09PQQHByMsLAz9+vUr8TilvT9q1qxZ7O8OANDX14e7uzvc3d2xePFitGzZEsuXL0dsbKykj6mpqcwxPvyKBQDQ1dWV6Vf4++V95f0dZGJigj179sDJyQm9evXCDz/8AB0dnWKvDYAk8HzfQF2fEvehsuPX51Fld+zhr8ouQeXwfU9EHxPzJaKPh/kS8yV5MF8qui/zJeZL72O+pFj8jElVAfMlxeL7nog+N+UahAUAQ4cOZSBG9Il5enpizZo1WL9+PaZMmVJqfycnJ0yePBl2dnZwdHSUtHft2hWbNm2CWCwucpZioV69euHt27cQCARwdXWVu05ra2sIhUL88ssvMDc3l7T//fffyMzMLDbwMDU1xcSJExESElLkMt/lrVNfXx89evTA+vXrMXXqVKkP/Y8ePcL//vc/jBo1qsiZZO+LjY3F8+fPMXjwYLlrA4D8/Hzk5OQUu/3Vq1dITU3FyJEj5T5mnTp1MGDAAOzatQsbNmwoUz2NGzfGL7/8Am9vb0lbXl4efv31V6ll7d8nFAoRHh6OQYMGlWm2ojx1enh4YPbs2fj111/RosV/f4zn5+cjMjISdnZ2Uu1F+eOPP3D06FEEBwfLXVvhOQCU+PwkJycXG6DJc/zVq1fDwsICLVu2LLLPpEmTsHr1aqxatarEY5X3/VEUDQ0NNGrUCFlZWRU6Tmkq8jvI3NwcJ0+elARlR48eLTEo09TUhKamplSbUKCmkOug/zCAUCwGEIrHe0pEVPUwXyL69Jgvla9O5kvSmC/9h/kS8yWSH7MlxWMWoni8p0REVBHlHoSVkZGBPXv24O+//8b06dNRp04dXLlyBfXq1StxaWsiKpjBl5ycLNWmr68PAHj58iUePXoktU1LSwu1atVChw4dEBgYiMDAQNy5cweDBg2Cqakp0tPTIRKJIBAIIBQKJfs5OTkhKysLUVFR2LRpk6S9W7dukjBk/PjxxdappqYmmWWlpib/h00dHR189dVXCAwMhLq6Opo1a4Z79+4hKCgI7du3R8eOHYvdNzg4GJs2bcLt27cxbNgwuc4nT51r165Fx44d4erqioULF8LCwgK//fYbZsyYARMTEyxatEiqf3Z2Nh49eoTc3Fzcv38f8fHxiIyMxLhx46Q+1I8aNQomJiYIDw8HUDBT0szMDLa2tgCAU6dOYfny5Zg8ebJkn+nTp6N///4wNzfHw4cPMW/ePKipqRU7k7E4MTExWL9+veS1I69p06bB398ftra26NGjB7KysrBmzRo8f/682JAMAPr27Yt27drhu+++Q7169RRW59SpU7F//370798fERERaNeuHf755x8sXrwYKSkpSEhIkAowc3Nz8ejRI+Tn5+Pp06dISkrCwoUL4eDggBkzZkj6rV27FvHx8Thx4gQA4MiRI/jnn3/wxRdfQFtbW/L8d+rUSTL7cuXKlbCwsIC9vT3evHmDzZs346effsKPP/4o17U+ffoUjx49QnZ2Nm7cuIGVK1fi0qVLOHz4cLGvzerVqyMsLAwTJkwo9fjleX8cOnQIO3bsgIeHB2xsbCAWi3Hw4EEcOXIE0dHRch2jvCr6O8jU1BRJSUlwcnKCq6srjh49KpkJTURERESqgfkSUfkxX2K+xHzpP8yXmC8Vh/kSEREREX1K5RqEde3aNbi4uEBXVxdpaWn46quvUKdOHcTFxeHu3bvFfoc9ERVISkqSmbXk7+8PAJg7dy7mzp0rtW3MmDGSWV7Lly9H27Zt8e233yIqKgrZ2dmoV68eunbtivPnz0t9gLSwsIC5uTnu3LmDbt26SdrNzMxgbGyMtLQ0qdlDRSnvB9JVq1ZhyZIlCAoKwp07d2BkZIQePXpg0aJFJc4IrFOnDoKCghASElKm85VWp7W1NS5fvox58+Zh6NChePbsGYyMjODm5oZ58+ahTp06Uv03bdqETZs2QUNDA/r6+mjdujV27twps9z83bt3pYLJ/Px8BAcH4/bt21BXV0ejRo2wdOlSjBkzRtLn/v378PT0xNOnT1G3bl107twZFy5cKHW5+g/VqFGjyKW8S+Pp6QmxWIwVK1Zg1qxZ0NLSQuvWrXHq1KlSw6+lS5eWGHKWp87q1avjp59+wuLFixESEoI7d+5AR0cHTk5OuHDhApo2bSrV/7fffkP9+vWhpqYGXV1d2NnZITg4GOPGjZOaqfbkyROkpqZK1bFp0yZMnToVOTk5MDU1xaBBgzBr1ixJn7dv3yIwMBAPHjyAlpYWmjdvjoSEhBJn9L7PxcUFQEGwbW5uDicnJ2zcuLHEZdwBwNvbGxEREfj9999L7Fee94ednR20tLQQGBiIe/fuQVNTE9bW1ti8eXOZZseWhyJ+BzVo0EAqKDt27BiDMiXiLDCq7DijVvH4vieij4n5ElHFMF9ivsR86T/Ml5gvlYT5UuXBz5hUFTBfUiy+74nocyMQi8Xisu7k4uKCVq1aYdmyZdDR0cGvv/4KS0tLnDt3DsOHD0daWtpHKJWIKiNHR0c4ODhg5cqVyi5F5RQGA8+fP4eenl6Z9w8NDcW+fftkZsWSYggEAsTHx8PNzU3ZpVAxegjdlV2CymEAoVgMIIiIKu54/m6lnr/RihVKPX9Vkjptmkwb8yUiKsR86eNhvlS5MV+q/JgvKRazJcVjvkREVHHMl6qOovKlykZYehdZP//8s9Ssm0ImJiYyy1wTkepbv349tLW1cf36dWWXojLs7e3Ru3fvCh/n+vXr0NbWxvr16xVQFQHA2LFjoa2trewyiIiIiKgyEPNH7p8iMF8iovcxX1I85kuVF/MlIiIiIpJQdmZTlX6qgHJ9HaGmpiZevHgh037r1q0yL3dMRFXb//73P7x+/RpAwRLQpBhHjhzBu3fvAJR/yf7JkyfDy8sLAPi7WYHmz5+P6dOnAwDq16+v5GqIPi3OrFMszv5UPL5GiYiqFuZLRFSI+dLHwXyp8mK+RJ8rfm5XPOZLisfXKRERVUS5BmENGDAA8+fPx65duwAULJl79+5dBAUFYfDgwQotkIgqNxMTE2WXoJLMzc0rfIw6deqgTp06CqiG3mdoaAhDQ0Nll0GkFAx1FIuBDhERfe6YLxFRIeZLHwfzpcqL+RJ9rpgtKR7zJSIiosqlXF9HGBERgVevXsHQ0BCvX79Gt27d0KhRI2hra2PRokWKrpGIiIiIiIiIiFQM8yUiIiIiIiIiIlIl5VoJS1dXF8ePH8eZM2dw7do1vHr1Cq1bt4azs7Oi6yMiIiIiIiIiIhXEfImIiIiIiIiIiFRJmVbCOn/+PA4dOiR53LlzZ9SsWRPr16+Hp6cn/u///g85OTkKL5KIiCqfhg0bQiAQQCAQICMjQ9nlqIzCe6qnp6fsUoiIiIiIPgrmS0REVIj50sfBfImIiIiISDnKtBLW/Pnz4ejoiH79+gEArl+/jtGjR8Pb2xtNmjTBN998A2NjY4SGhn6MWomIqgQfHx9kZGRg3759RW6/evUqlixZglOnTuHZs2cwMjJCs2bNMGbMGPTr1w8CgUDSNzY2FmvXrsVvv/0GNTU1tGrVCjNmzJD8Hi60adMmrF27FqmpqVBXV4eFhQWGDh2K4OBgAEB2djYWLFiAXbt24cGDB9DR0YGdnR2mTZuGL7/8EgDg6OiIkydPIjw8HLNmzZI6ft++fXHkyBHMmzdP6nf8/PnzMXr0aOjq6gIAYmJiEBAQUGRoJhAIEB8fDzc3N6SlpcHCwgJ169ZFamoqdHR0JP0cHBzg5uYmOY+joyMcHBywcuXKctf522+/ISwsDImJiXjx4gXMzc3h4eGBWbNmQUtLS9KvYcOGuHPnDgCgevXqqFevHtq2bYuxY8eie/fuRT6fheLi4rB48WL89ddfePfuHaytrREYGIiRI0dK+vj4+CA2NlZqP1dXVxw9elTyOD09HTt37sS8efMkbaGhodi3bx+Sk5OLPf9ff/2FxYsXIyEhAf/88w8MDAxga2sLPz8/DBs2DOrqBf/kv//6et/27dvh4eEBAMjLy8Pq1asRFRWFP//8EzVq1ED79u0xZ84cdOrUSWq/t2/fYtWqVdi+fTtu3rwJdXV1NGzYEP3798f48eNhbGws1f/8+fPo3LkzevXqhcOHD0ttK3xdFKpduzaaNWuGhQsXokuXLsVeO1FVdezhr8ouQeW4GrdQdglE9LkRK7uAqon5EhFR6ZgvMV9ivsR8SRX1adxZ2SWonO/vHS29E5XJSNNOpXciIlIk5ksqpUyDsJKTk7FgwQLJ4x07dqBt27bYtGkTAMDU1FTmgwkREf1n//79GDp0KFxcXBAbGwsrKyvk5OTg3LlzmDNnDrp06SKZoTZ9+nSsXbsWCxcuhJubG969e4etW7fiyy+/xKpVqzBx4kQAQFRUFAICArB69Wp069YNOTk5uHbtGm7cuCE579ixY3Hx4kWsWbMGdnZ2ePr0Kc6dO4enT59K1WdqaoqYmBip8OnBgwc4ceIE6tevL3M9Ojo6MDIyKvf9ePnyJZYvX46wsLAy7VeWOi9cuAAXFxe4uLjg8OHDqFevHi5duoTAwECcOHECiYmJ0NDQkPQvDP7evn2LtLQ0bN26FS4uLliwYAFmz55dbE116tTB7NmzYWtrCw0NDRw6dAi+vr4wNDSEq6urpF+vXr0QHR0teaypqSl1HCMjI0noKK9Lly7BxcUF9vb2WLduHWxtbQEAly9fxrp169C0aVO0aPHfwITo6Gj06tVL6hiFrzuxWAwPDw8kJCTgm2++gbOzM168eIF169bB0dERu3fvhpubGwAgJycHPXv2xLVr1xAWFoZOnTqhbt26uH37NrZv3441a9YgPDxc6jwikQiTJk2CSCTCw4cPZUI0AEhISIC9vT2ePHmCRYsWoV+/frh16xbq1atXpvtCVNlxwBAREX2umC8REVUM8yVpzJeYLzFfqjqO3Dyj7BJUjqsxBwwRERFVJmUahPX8+XOpP1BPnjyJ3r17Sx5/8cUXuHfvnuKqIyJSIVlZWfD390ffvn0RFxcnta1Jkybw9/eHWFww1PnChQuIiIjA6tWrMWnSJEm/RYsW4c2bN5IZhqampjhw4ACGDh0Kf39/ST97e3up4x84cACrVq1Cnz59ABTMyGvdurVMjf369cOuXbtw9uxZyYy02NhY9OzZE3fv3lXMjXjPpEmTsGLFCkyYMAGGhoZy7ydvnWKxGP7+/mjSpAni4uIgFBZ8C6+5uTlsbGzQsmVLREZGIigoSLLP+8GfmZkZunbtivr162Pu3LkYMmQIGjduXGRNjo6OUo+nTJmC2NhYnDlzRiok09TUrFCw+CGxWAwfHx/Y2Njg7NmzkmsEAGtra3h6ekpeV4X09PSKrWHXrl3Ys2cPDhw4gP79+0vaN27ciKdPn+Krr75Cjx49ULNmTURGRuLMmTO4fPkyWrZsKelrZmaGbt26yZz31atX2LlzJy5fvoxHjx4hJiYGISEhMjXo6+vDyMgIRkZGCAkJwY4dO3Dx4kUMGDCgXPeIFIeDhhSLK2EpHl+jRERVA/MlIqLyY74ki/lSxTFfok+Fn9sVj/mS4vF1SkREFSEsvct/6tWrh9u3bwMoWB72ypUraN++vWT7y5cvUa1aNcVWSESkIn788Uc8ffoUM2fOLLZP4VLe27dvh7a2NsaMGSPTJzAwEO/evcPevXsBFMxsu3DhgmSZ86IYGRnhyJEjePnyZYk1amhoYMSIEVIz6WJiYuDn51fifuXl6ekJKysrzJ8/v0z7yVtncnIyfv/9d0ybNk0qPAKAFi1awMXFBdu3by/1fFOmTIFYLMb+/fslbQ0bNix2Zr5YLMaJEydw8+ZNdO3aVWpbUlISDA0N0bhxY4wbN05mtmhZJScnIyUlBdOnT5e5xkLFLRFflG3btsHGxkYqICsUGBiIp0+f4vjx4wAKXqc9evSQCshKOu+uXbtga2uLxo0bw8vLC1FRUTJB2vtev36NLVu2AIDUbFIiIiIiqtqYLxERlR/zJVnMl5gvMV8iIiIiosqiTCth9enTB7NmzcLSpUuxb98+aGlpSX2H9rVr19CoUSOFF0lEpApu3boFAFIz3X7++Wc4OTlJHu/YsUOyNHajRo2KDAaMjY1Rq1YtyfHmzZuHQYMGoWHDhrCxsUGHDh3Qp08fDBkyRBKabNy4ESNGjIC+vj5atGiBzp07Y8iQIZJZfu/z8/NDly5dsGrVKvzyyy/IzMxEv379PspXgQgEAixZsgT9+/fH1KlTy/RviDx1Ft6jJk2aFHmMJk2a4MyZ0pfArlOnDgwNDZGWliZpa9SoEQwMDKT6ZWZmwsTEBDk5OVBTU8P69evRo0cPyfZevXph0KBBsLCwQGpqKkJCQtC7d2+cP38eampqcl/7+4p6Xf3777+wtLSUPF62bBnGjx8veezp6Slzvt9//x1mZma4detWiffr/XPeunVLZobmwIEDJSFa8+bNce7cOck2kUgELy8vAAX3IjMzEydPnpQ5RseOHSEUCpGdnQ2xWIzWrVvD2dm5xPuQk5ODnJwcqbZ8cR6EgvLdVyoaZ9YpFmfVERHR54r5EhFR+TFfksV8ifkS86Wqg9mS4jFfIiIiqlzKtBLWggULoK6ujm7dumHTpk3YtGmT1Ae4qKgo9OzZU+FFEhGpqubNmyM5ORnJycnIyspCbm6uZFtJM7iA/2Zu1a9fH+fPn8f169cxZcoU5ObmwtvbG7169UJ+fj4AoGvXrvj7779x4sQJDBkyBL/99hu6dOmCBQsWyBy3RYsWsLa2xp49exAVFYWRI0dCXb1MY3bLxNXVFZ07d8bXX39dpv3KUmdp91IeYrFYaubdiRMnMHHiRKk+Ojo6SE5Oxs8//4xFixZh2rRpSEpKkmz38PDAgAED0KxZM7i5ueHQoUP4+eefpfoogr6+vuR1paenh7dv30ptj4yMlGwv/DE2Npa61pKUNGtw/fr1SE5Ohp+fH7KzsyXtN2/exKVLl+Dp6QkAUFdXx7BhwyASiWSOsXPnTly9ehV79+6FlZUVYmJiSl0JITw8HLq6ulI/t/FHifsQERERkXIwXyIiUizmS8yXmC8xXyIiIiKiyqFMn3oMDAxw6tQpZGZmQltbW2aWw+7du6Gtra3QAomIVIW1tTWAgrCg8Ks2NDU1YWVlVWTfM2fO4O3btzKBxMOHD/HixQvY2NhItTdt2hRNmzbF+PHjMXbsWHTp0gUnT56UzISsVq0aunTpgi5duiAoKAgLFy7E/PnzERQUJHMOPz8/rFu3Dr///jsuXbok9zXWqlULWVlZyM/Pl1q6PCMjAwCgq6tb5H5LlixBhw4dMGPGDLnPJU+dhfcoJSWlyCXNU1JSZO5jUZ4+fYrHjx/DwsKixH5CoVDyfDo4OCAlJQXh4eEyM/EKWVpawsDAAH/99VepM/GK8/7rqvAa1dTUJHUUFRwaGRkV+borPF5KSkqR2wrbC++ZtbU1bt68KdWnfv36AApmd75PJBIhNzdXJozT1NTE2rVrpV4bpqamsLa2hrW1NXJzczFw4EDcuHEDmpqaxdwFIDg4GNOmTZNqG6jrU2x/osqAsz8Vj7M/iehTE1T8/8V+lpgvERGVH/Ml5ksfYr7EfKkq6ePsruwSVM7Evw4ruwSVs9bKWtklENFnhvmSainX1JPiPuR8+AcxERH9p2fPnqhTpw6WLl2K+Pj4Evt6enpizZo1+O677zBp0iSpbcuXL0f16tUxbNiwYve3s7MDAGRlZZXYJzc3F2/evJEJyYYPH47p06ejRYsWkmPJo3HjxsjNzUVycjJatWolab9y5QoAFBtItW3bFoMGDcKsWbPkPpc8dTo4OMDW1haRkZHw8PCQCu5+/fVXJCQkIDw8vNTzrFq1CkKhEG5ubmWqLz8/X2YJ8/fdv38fT58+lQRL5dGyZUvY2tpi+fLlGDp0qNQ1loenpyeGDx+OgwcPon///lLbIiIiYGxsLFkC39PTE3PmzMHVq1eLDCEL5ebmYsuWLYiIiJBZ0cDNzQ3bt2/H2LFji9x3yJAhmDt3LtavX4+pU6cWew5NTU2ZEI1LxVNlxwFDRET0uWO+RERUdsyXmC99iPkS86Wq5MiJ3couQeUwXyIiIqpcPt76v0REn7HMzEwkJydLtenr62Pz5s0YNmwY+vbti8mTJ8Pa2hqvXr3C0aNHAUAyA7xDhw6YMmUKZsyYgbdv38LNzQ3v3r3D1q1bsXr1asTExEBfXx8AMG7cOBgbG6N79+5o0KAB0tPTsXDhQtStWxcdOnQAADg6OsLT0xNt2rSBvr4+fv/9d4SEhMDJyQm1atWSqb927dpIT08vdYnuD9nb26Nnz57w8/NDREQELC0tcfPmTQQEBGDYsGEwMTEpdt9FixbB3t6+TEvTl1anQCCASCRCjx49MHjwYAQHB8PIyAgXL15EYGAgOnTogICAAKl9Xr58iUePHuHdu3e4ffs2tm7dis2bNyM8PFxqdp+zszMGDhwoWTI+PDwcbdq0QaNGjZCTk4MjR47g+++/x7fffgsAePXqFcLCwjB48GAYGRkhNTUVM2fOhJWVFVxdXUu91tevX8u8pnR0dNCoUSNER0ejR48e6NSpE4KDg9GkSRO8e/cOp06dwuPHj2VWFsjIyMCjR49kjlWzZk14eHhg165d8Pb2xjfffANnZ2e8ePEC69atw6FDh3D06FHJ/Z46dSoOHz4MZ2dnzJs3D126dEHt2rVx69Yt/PDDD5LzHjp0CM+fP4e/v7/M/2gbPHgwRCJRsSGZQCDA5MmTERoaijFjxkBLS6vUe0VUVXAlLMVj8EhEREREqoT5EvMl5kvMl1RNk03jlF2CysmJelt6JyoTa7/Lyi6BiIiqMA7CIiL6CJKSkmRmbvn7+2Pz5s04d+4cli5dilGjRuHZs2fQ1dVFmzZtsGPHDvTr10/Sf+XKlWjevDnWr1+POXPmSGYU/vTTT+jataukn4uLC6KiovDtt9/i6dOnMDAwQIcOHXDixAlJkObq6orY2FiEhIQgOzsbxsbG6NevH+bOnVvsNejp6ZXr2nfu3Il58+ZhzJgxePjwIRo0aICBAwfi66+/LnE/Gxsb+Pn5YePGjWU6X2l1duzYERcuXEBYWBh69+6Nly9fwszMDN7e3ggODpaZ3TZ37lzMnTsXGhoaMDIyQvv27XHixAnJsvuFUlNT8eTJE8njrKwsjB8/Hvfv30eNGjVga2uLrVu3SmaUqqmp4dq1a4iNjUVGRgaMjY3Rs2dPLFiwoMRl0AvdunVL5jXl7OyMhIQEtG/fHr/88gsWL16MCRMm4NGjR6hZsyZatGiByMhI+Pn5Se3n6+src/zw8HDMmjULAoEAu3fvxsqVKxEZGYnx48fj7du3qFOnDq5evSo1I7R69eo4ceIEVq5ciejoaAQHByM/Px8WFhbo3bu3ZGahSCSCi4tLkSsdDB48GMuWLcO1a9eKDGwBwNvbG7Nnz8batWsxc+bMUu8VfTwc4KJYHISleLynisf3PREREZHyMF9ivsR8ifmSqjGbd07ZJaiczCP86jxFezqmo7JLUCn63/F9T0SfF4FYLOY3TBIRVQFpaWno1q0bOnTogP/9738ys88+tYYNGyIgIEBmph9VXExMDAICApCRkaHsUgAULPfv4uICf39/fPPNN8ouR275j4r+egKiyoKDW4iIKu54vnK/zsTqmxVKPX9V8teMacougYiIwHzpc8J8STGO3G6q7BJUypo+/UvvRGWSe/MvZZdARFTlMV+qOqpCvsSVsIiIqoiGDRsiKSkJsbGxSE5ORuvWrZVdEoKCgjBnzhw8ePCgyFloVHba2trIzc1F9erVlV2KRKtWrXDixAns378fqampaNSokbJLkgsHuCgeVxkiIiIiIiKq2pgvfR6YLylOZKMmyi5BpRx7uFfZJagcZqBERESVCwdhERFVIRYWFggNDVV2GQCAkydP4t27dwAAHR0dJVejOpKTkwFA6TNRP9SyZUuZ5eqJiIiIPntigbIrICIiKjPmS6qP+RIRERFRFcJ8SaVwEBYREZWLubm5sktQSVZWVsougahYnFlHlR1Xa1M8vu+JiIiI6GNivvRxMF+iyoqfMakqYL6kWHzfE9HnhoOwiIiIiEguDCCosmOoQ0RERERERFR5qXHFO4WL+f2osktQOa7GnZRdAhERVWEchEVEREREpAQcMEREREREREREn5MjN88ouwSVwwFDRERElYtQ2QUQERERERERERERERERERERERFVZVwJi4iISEF8fHyQkZGBffv2yWxr2LAhAgICEBAQILMtLS0NFhYWksfa2towMzODo6MjAgICYG1tLdkWExODgIAAZGRkyBxHIBAgPj4ebm5ukrZDhw7hm2++wZUrV5CXlwd7e3tMmDABPj4+Mue/evUqHBwcpI7p6OgIBwcHrFy5Uqp9+/bt8PLywtixY7Fu3TqpbUlJSXBycsLz58+hp6dX4n0o6vGdO3cAADVq1ECjRo0wZcoUfPXVVzLXS58eV25SLH69I1UFfN8TEREREX1azJcKMF9STfyMqXivB7ZTdgkqx2LmH8ouQaU86pCp7BKIiD4pDsIiIiKqJBISEmBvb4/s7Gxcv34dq1atQosWLXDw4EE4OzuX+Xhr1qxBQEAAgoKC8O2330JDQwP79+/H2LFjcePGDSxfvrzctYpEIsycORPfffcdIiIiUL169XIf60Pz58/H6NGjkZ2djd27d2P06NEwMTFB7969FXYOIiIiIoUQK7sAIiIiImnMlwowX6LPxrh/lV2Byvl9s72yS1ApdXBO2SUQVX7Ml1QKB2ERERFVEvr6+jAyMgIAWFpaon///nB2doa/vz9SU1OhpqYm97Hu3buHwMBABAQEYPHixZL2wMBAaGhoYPLkyXB3d0e7dmWfKXX79m2cO3cOe/fuRWJiIuLi4jB8+PAyH6c4Ojo6kvsQFBSEZcuW4fjx4wzJKgGu3ESVHWfUEhERERHR5475UgHmS5XTn+u5apOiqV+R/z1N8rEQcdAQERGVHwdhERERVVJCoRBTpkzBwIED8csvv6Bt27Zy77tnzx68e/cO06dPl9k2ZswYhISEYPv27eUKyaKjo9G3b1/o6urCy8sLIpFIoSFZofz8fMTHx+P58+fQ0NAo8/4cMKR4HOBCREREREREVLUwX2K+VJm4Giu7AiIiIqKPi4OwiIiIKjFbW1sAQFpamiQky8zMhLa2don73bp1C7q6uqhfv77MNg0NDVhaWuLWrVtS7R07doRQKJRqe/36NRwcHCSP8/PzERMTgzVr1gAAPDw8EBgYiNu3b8PCwkJq3wYNGsicOzs7u8S6gYLZiXPmzEFOTg5yc3NRp04dfPXVVyXuk5OTg5ycHKm2gfWbQijgTDBFYvCoWBzURkRERERERJ8C8yXmS5UFsyXFY75ERERUuXAQFhERUSUmFhd8EbRAIJC06ejo4MqVKzJ9ra2ty3TsD2f/7dy5E02aNJFqGzFihNTj48ePIysrC3369AEAGBgYoEePHoiKisKCBQuk+p4+fRo6OjpSbY6OjqXWNWPGDPj4+CA9PR0zZszA+PHjYWVlVeI+4eHhCAsLk2qbG1gH86brl3o+kh9DHSIiIiIiIqKqh/kS86XKgtkSERERqToOwiIiIqrEUlJSAEBqFqBQKCw1NLK2tkZmZiYePnwIY2Ppdb7fvn2L1NRUuLq6SrWbmprKHLdGjRpSj0UiEZ49eybVnp+fj2vXriEsLExqpqOFhQX09PSk9ldXL/1PDwMDA1hZWcHKygq7d+9Gs2bN0KZNG9jZ2RW7T3BwMKZNmybVNlDXB64rOFNRkThbkSo7hrlE9KkJxMqugIiIiKh0zJeYL1UWtzZ9oewSVI7mo2rKLkHlmH99TtklENFnhvmSauEgLCIiokoqPz8fq1evhoWFBVq2bFmmfYcMGYKgoCBEREQgIiJCatuGDRuQnZ2NUaNGlemYT58+xf79+7Fjxw7Y29tL2vPy8tC5c2f8+OOP6NWrV5mOWRpTU1MMGzYMwcHB2L9/f7H9NDU1oampKdXGpeKpsuOAISIiIiIiIvrYmC8xX6pMbvfdrOwSVA7zJSIiosqFg7CIiIgUKDMzE8nJyVJt+voFS5Y/ePBAZpu5ubnkv58+fYpHjx4hOzsbN27cwMqVK3Hp0iUcPnwYamplC3zMzMywbNkyTJ8+HdWrV8fIkSNRrVo17N+/HyEhIVi4cCGaNm1apmN+//330NfXx9ChQ6WWrweAPn36QCQSKTwkA4ApU6agadOmuHz5Mtq0aaPw4xMRERERERERVSbMl5gvEREREVHVxEFYRERECpSUlCQzq9Df3x8AsHz5cixfvlxq2/fff4/OnTsDAFxcXAAAWlpaMDc3h5OTEzZu3Fjq0vDFmTp1KiwtLREREYFVq1YhKysLALB9+3Z4eHiU+XhRUVEYOHCgTEAGAIMHD8bIkSPx5MmTctVaEjs7O/Ts2RNz587FkSNHFH58ImXh1zsqHmd/EhERERGRKmC+xHxJVbWfNVbZJagc3Z/uKbsE1dP9vrIrICKiKkwgFov5DZNERESfgWfPnsHZ2Rm1atXCDz/8AC0tLWWX9FH1ELoruwSVw0FDisUBQ0REFXc8f7dSz2+9JFKp569K/pw1VdklEBERkQIwX6KKYLakeMyXiIgqjvlS1VEV8iWhsgsgIiKiT6NOnTpISEiAs7Mzzp8/r+xyiIiIiIiIiIioimG+RERERERUPH4dIRER0WdEX18fc+fOVXYZRERERKqBa4sTERHRZ4j5EhEREZECMV9SKVwJi4iIiIiIiIiIiIiIiIiIiIiIqAI4CIuIiIiIiIiIiIiIiIiIiIiIiKgC+HWEREREpJKOPfxV2SWoHFfjFsougYiIiIiIiIjok2G+pFjMloiIiEjVcRAWERERqSSGOorH4JEqO77viYiIiIiISJH4OVOx1C0bKrsElTMrYb+yS1A5Cy35viciovLjICwiIqIy8vHxQUZGBvbt2yezrWHDhrhz5w62b98ODw8PqW329vb4/fffER0dDR8fH0n/gIAABAQEyBwrLS0NFhYWksd16tRB69atsXTpUrRs2RIA4OjoiJMnTwIANDQ0YGBggFatWsHX1xeDBg2SOWZiYiIiIiJw8eJFvHz5EiYmJmjTpg0mTJiArl27AgCSkpLg5OSE58+fIyAgALGxscXeC3Nzc+jo6KBTp07YsGGDzPbvv/8eX331FR48eIAbN27AycmpyOOkp6dj0aJFSEhIQEpKisz2u3fvwsLCAvHx8RgwYECx9RBVJQxyiYiIiIiIPl/Ml/7DfOnzcfgMBwwpGvMlIiKiykWo7AKIiIhUjampKaKjo6XaLly4gEePHqFmzZplPl5CQgLS09Nx7NgxvHr1Cr1790ZGRoZk++jRo5Geno7U1FTs3bsXdnZ28PDwwP/93/9JHWf9+vVwdnaGvr4+du7ciZs3byI+Ph4dO3bE1KlTizz3qlWrkJ6eLvkBgOjoaMnjn3/+Gf7+/tixYwdev34ts390dDQGDBgAAwMDSdvNmzeljpmeng5DQ0P4+/vjjz/+wLlz52SOExMTA0NDQ/Tp06fM94+IiIjoYxGI+SPvDxEREZUN86X/MF8iIiIiVabszKYq/VQFHIRFRESkYCNGjMDJkydx7949SVtUVBRGjBgBdfWyL0Kpr68PIyMjtGnTBsuXL8c///yDixcvSrZraWnByMgIDRo0QPv27bF06VJ899132LRpExISEgAUzPQrnBEZGxuL7t27w9zcHM2bN8eUKVNw+fLlIs+tq6sLIyMjyQ8A6OnpSR7XrVsXXl5eeP36Nfbu3Su17+3bt5GUlAR/f3+pdkNDQ6ljGhkZQSgUwsHBAa1atUJUVJRUf7FYjJiYGHh7e5fr/hERERERERERVTXMlwowXyIiIiKiqoSDsIiIiBSsXr16cHV1lSyznp2djZ07d8LPz6/Cx65RowYA4O3btyX28/b2Ru3atREXFwcA2Lt3L969e4eZM2cW2V8gEJS7JgMDA3z55Zcy4VZMTAwaNGiAnj17yn0sf39/7Nq1C1lZWZK2pKQk3L59WyH3j4iIiIiIiIioKmC+VID5EhERERFVJRzuT0RE9BH4+fkhMDAQs2fPxp49e9CoUSM4ODhU6JgZGRlYsGABtLW10bZt2xL7CoVC2NjYIC0tDQBw69Yt1KpVSzLbECgIzry9vSWPz58/j2bNmpWrNn9/f/Tu3Ru3b9+GhYUFxGIxYmNj4e3tDaFQesx3gwYNpB6bm5vjt99+AwAMHz4cgYGB2L17N3x8fAAULDnfuXNn2NjYFHv+nJwc5OTkSLXli/MgFKiV63qoaK7GLZRdgko59vBXZZegcvgaJSIiIiIiVcJ8ifmSquHndsVjvqR4fJ0SEVFFcBAWERHRR9C3b1+MGTMGp06dQlRUVIVm2XXs2BFCoRBZWVmwtLTEzp07Ua9evVL3E4vFUjMQP5yN6OrqiuTkZDx48ACOjo7Iy8srd409evRAgwYNEB0djfnz5+PEiRO4e/cufH19ZfqePn0aOjo6ksfVqlWT/Leenh4GDRqEqKgo+Pj44MWLF9i7dy/WrVtX4vnDw8MRFhYm1WaBJmgE+3JfE8liqENERERERET06TBfYr6kav7e1lLZJaicpqs7KrsElWOCc8ougYiIqjAOwiIiIvoI1NXVMXLkSMybNw8XL15EfHx8uY+1c+dO2NnZQV9fH3p6enLtk5eXhz///BNffPEFAMDa2hqZmZl49OiRZLaitrY2rKysoK5e8T8HhEIhfHx8EBsbi9DQUERHR8PJyQmWlpYyfS0sLEq8Dn9/fzg7O+Ovv/5CYmIi1NTU4O7uXuL5g4ODMW3aNKm2gbo+5bkUok+Gs+qIiIiIiIioJMyXmC+pmj8dY5RdgspxHc58iYiIqDIRlt6FiIiIysPPzw8nT57El19+idq1a5f7OKampmjUqJHcARkAxMbG4vnz5xg8eDAAYMiQIahWrRqWLl1a7jpK4+vri3v37iEuLg7x8fHw9/cv13GcnJxgYWGB6OhoREdHw8PDAzVr1ixxH01NTdSqVUvqh0vFExER0Ucn5o/cP0RERFQuzJeYLxEREZGKU3ZmU5V+qgCuhEVERFQOmZmZSE5OlmrT19eXetykSRM8efIEWlpaJR7rwYMHMscyNzeXu5bs7Gw8evQIubm5uH//PuLj4xEZGYlx48bByckJAGBmZoaIiAhMmTIFz549g4+PDywsLPDs2TNs3boVAKCmVrFQycLCAt27d8f//d//QVNTE4MGDSqy37///os3b95Itenr60uWjRcIBPDz88OKFSvw/PlzREZGVqguIiIiIiIiIqLKiPmSLOZLRERERFSVcRAWERFROSQlJaFly5ZSbUXNzPswOCvK8uXLsXz5cqm277//Hp07d5arlk2bNmHTpk3Q0NCAvr4+WrdujZ07d2LgwIFS/SZNmoQmTZpgxYoVGDJkCF68eAF9fX106NABR48eRbNmzeQ6X0n8/f1x4sQJjB8/HtWrVy+yT+PGjWXazp8/j/bt20se+/j4YN68ebC3t0e7du0qXBdRZXTs4a/KLkHl8CseiYiIiIioKmG+VDTmS6rL7ttxyi5B5byJzVF2CSrH2vsXZZdARERVmEAsFleRRbuIiIiI5NdD6K7sElQOBw0pFgcMERFV3PH83Uo9v80irqggr1uzpyq7BCIiIqIyY76kWMyWFI/5EhFRxTFfqjqqQr4kVHYBREREREREREREREREREREREREVRm/jpCIiIiISAk4+1PxOPuTiIiIiIiIqPLq3ah96Z2oTHbfT1R2CSrHvQFfp0REVH4chEVEREREpAQcMEREpALEyi6AiIiIiKjq+CH1grJLUDmuxhwwRERU5TFfUin8OkIiIiIiIiIiIiIiIiIiIiIiIqIK4EpYRERERERKwK8jVDyuLkZERERERERUefFzu+IxX1I8vk6JiKgiOAiLiIiIiIiIiIiIiIiIiD6qFyM6KLsEldP/Vn1ll6CC0pVdABERVWEchEVERERERCqBsz8Vj7M/iYiIiIiISFH04vm5XdG0x9dQdgkqJ71/W2WXoFI0D15SdglERJ8UB2ERERFVAj4+PsjIyMC+fftktjVs2BB37twBAGhpaaFx48YIDg6Gu7s7ACA7OxsLFizArl278ODBA+jo6MDOzg7Tpk3Dl19+CQBwdHTEyZMnAQAaGhowMDBAq1at4Ovri0GDBgEA9u7di6FDh+Lu3bswMTGRqcPa2hr9+/fHihUrAAC//fYbwsLCkJiYiBcvXsDc3BweHh6YNWsWtLS0kJSUBCcnpxKvOzExEY6Ojrh//z4sLS1hY2ODGzduyPQTCASIj4+Hm5ubfDeUqArg4BYiIiIiIiJSJOZLzJcqux/+Oq/sElQO8yXF08RTZZdARERVGAdhERERVQHz58/H6NGj8eLFC0RERGDYsGEwMTFBx44dMXbsWFy8eBFr1qyBnZ0dnj59inPnzuHpU+kPi6NHj8b8+fORm5uL+/fvIz4+Hh4eHvDx8cHGjRsxYMAA6OvrIzY2FiEhIVL7njp1Cn/99Rf8/f0BABcuXICLiwtcXFxw+PBh1KtXD5cuXUJgYCBOnDiBxMREdOzYEenp/y3dPGXKFLx48QLR0dGStjp16gAAYmJiMHToUJw6dQoXL15Eu3btPtatpApgqKNYXLWJqgK+74lKJhAruwIiIiIi+TFfImXjZ0zFE2poKLsElRN3+4yyS1ApbiZcWYyoNMyXVAsHYREREVUBOjo6MDIygpGREdatW4etW7fi4MGD6NixIw4cOIBVq1ahT58+AApmNrZu3VrmGFpaWjAyMgIANGjQAO3bt4etrS38/PwwdOhQuLi4YOTIkYiJiZEJyaKiotCuXTvY29tDLBbD398fTZo0QVxcHIRCIQDA3NwcNjY2aNmyJSIjIxEUFCQ5HwDUqFEDOTk5Um0AIBaLER0djfXr16NBgwYQiUQMySopDhqiyo5hLhERERERUfGYL5Gyve31hbJLUDkOi64quwSVw0FDRERUERyERUREVMWoq6ujWrVqePv2LQDAyMgIR44cwaBBg6Cjo1OmY3l7eyMwMBBxcXFwcXGBv78/VqxYgVOnTqFr164AgFevXmHPnj2IjIwEACQnJ+P333/Htm3bJAFZoRYtWsDFxQXbt29HUFCQXDUkJiYiOzsbLi4uktmXkZGRqFmzptzXkZOTg5ycHKm2fHEehAI1uY9B9KlxwBAREREREREpC/MlWcyXPr7EqM3KLkHlMF8iIiKqXDgIi4iIqAp5+/YtIiIikJmZie7duwMANm7ciBEjRkBfXx8tWrRA586dMWTIEHTq1KnU4wmFQtjY2CAtLQ0AYGdnh/bt2yMqKkoSku3atQtisRgeHh4AgFu3bgEAmjRpUuQxmzRpgjNn5F+yWSQSwcPDA2pqamjatCksLS2xe/du+Pj4yH2M8PBwhIWFSbXNDayDedP15T4GEREREREREdHngPlS0YrKlyzQBI1gL/cxiIiIiOjzxkFYREREVUBQUBDmzJmDN2/eQFtbG0uWLEHfvn0BAF27dsXff/+NCxcu4Ny5czhx4gRWrVqFsLAwfP3116UeWywWQyAQSB77+flh6tSpWLNmDXR0dBAVFQV3d3eZWZBicfFfUq2hoSHXdWVkZCAuLk4qVPPy8oJIJCpTSBYcHIxp06ZJtQ3U9YHrCs5UJPqc8CszFY8zaomIiIiIVAfzpZIVly+R4vAzJlUFzJcUi+97IvrccBAWERFRFTBjxgz4+PhAW1sb9erVkwq1AKBatWro0qULunTpgqCgICxcuBDz589HUFBQiYFVXl4e/vzzT3zxxReSNg8PD0ydOhW7du1C165dcfbsWYSHh0u2W1tbAwBSUlLQsmVLmWOmpKTAxsZGruvatm0b3rx5g3bt2knaxGIx8vPzcevWLbmPo6mpCU1NTam24+k35NqXSFkYQCge7ykREREREVHxmC+VrKh8iV9FqFgc3KJ4zEIUj/eUiIgqgoOwiIiIqgADAwNYWVnJ3d/Ozg65ubl48+ZNiSFZbGwsnj9/jsGDB0vadHR04O7ujqioKKSmpsLGxgZdunSRbG/ZsiVsbW0RGRkJDw8PCIVCybZff/0VCQkJWLt2rVx1ikQiBAYGysxKHD9+PKKiorBkyRI5r1gWPywrHoMyIiIiIiIioqqL+RIRERER0cfFQVhERESVRGZmJpKTk6Xa9PX1S93P0dERnp6eaNOmDfT19fH7778jJCQETk5OqFWrlqRfdnY2Hj16hNzcXNy/fx/x8fGIjIzEuHHj4OTkJHVMf39/dOnSBSkpKQgKCpLaJhAIsHnzZvTs2RODBw9GcHAwjIyMcPHiRQQGBsLV1RVjxowpte7k5GRcuXIF//vf/2Brayu1zdPTE/Pnz8fChQuhrs4/V4iIiIiIiIiI5MF86T/Ml4iIiIjoU+NfnURERJVEUlKSzPLr/v7+pe7n6uqK2NhYhISEIDs7G8bGxujXrx/mzp0r1W/Tpk3YtGkTNDQ0oK+vj9atW2Pnzp0YOHCgzDE7d+6Mxo0b46+//sKoUaNktnfq1AkXLlxAWFgYevfujWfPngEAJk6ciMjISKiplb5Uu0gkgp2dnUxABgADBw7ExIkTceTIEQwYMKDUY9GnwdXFqLLjam1U2fH3KBERERF9bMyX/sN8qfLhZyKqCt65flF6J5Jby/Aryi5B5dxolafsEoioBAKxWCxWdhFERERUteXn58Pf3x/Hjh3DyZMnYW1treyS0EPoruwSVA4HuCgWg0ciooo7nr9bqedvvCBSqeevSm5+PVXZJRAREVElx3xJ9TFbUjzmS0REFcd8qeqoCvkSV8IiIiKiChMKhRCJRFizZg1Onz5dKUIyosqOwaPiMXgkIiIiIiKqupgvqb6+3QYpuwSVMz31gLJLUDnLG9kruwQiIqrCOAiLiIiIFEIoFGLKlCnKLoOIiIiIiIiIiKoo5kuqTfzvE2WXoHKaaWQouwQiIiJ6DwdhEREREZFcuMqQYnElLMXjPVU8vu+JSiFWdgFERERERFVHXmamsktQOb6Neyi7BJWz4/4JZZegUjwadFB2CUSVH/MllcJBWEREREQkFw5wocqOA4aIiIiIiIiIKi81XV1ll6Byom4cUXYJKsejQSdll0BERFUYB2ERERERkVw4wIUqOw4UpMqOv0eJiIiIiOhzxpWwFM/blAOGFG33/QvKLkGlDLN2UnYJKif/9Wtll0BEJeAgLCIiIgA+Pj7IyMjAvn37ZLb9+uuv+Prrr3HhwgW8ePECRkZGaNeuHdasWQNDQ0OkpaXBwsJC0l9bWxtmZmZwdHREQEAArK2tJdtiYmLg6+sLABAKhahVqxZsbGzQt29fTJkyBbrvzQYrrqakpCQ4OTnh+fPn0NPTAwCIxWJs3rwZUVFR+O2335Cfnw9zc3O4uLhg0qRJsLKykjrG/fv3YWlpCRsbG9y4cUPmmk+ePImwsDAkJyfjzZs3MDExQceOHbFp0ya8ffsWtWvXxvfffw8PDw/JPh4eHti5cydu376Nhg0bStobNmyIkSNHYsGCBQgNDUVYWJjk+o2NjdG7d28sWbIENWvWRKtWrdCpUyds3LhRqp6ZM2di9+7duHbtGnR0dIp4BulT4AAXquw4wIWIiIiIiJSJ+ZI05kv0oXc92yi7BJXTZukVZZegctwbtFd2CSqGA4aI6PPCQVhEREQlePz4MZydndGvXz8cO3YMenp6SEtLw4EDB5CVlSXVNyEhAfb29sjOzsb169exatUqtGjRAgcPHoSzs7OkX61atXDz5k2IxWJkZGTg3LlzCA8PR3R0NM6ePQtjY+My1SgWizF8+HDs27cPISEhiIyMhLGxMR4+fIj4+HgsXLgQMTExUvvExMRg6NChOHXqFC5evIh27dpJtv3+++/o1asXJk2ahNWrV6NGjRr4888/sXfvXuTl5UFbWxtt2rRBUlKSVEiWlJQEU1NTJCUlwcfHBwBw+/Zt3LlzB927d5f0s7e3R0JCAvLy8pCSkgI/Pz9kZmZi586d2LJlCzp06IDBgwfD1dUVAHDhwgVERkYiISGhTAEZBwxRZccBQ0RERERERJ8H5ktVN18ixfopRqTsElQO8yUiIqLKhYOwiIiISnD27FlkZmZi8+bNUFcv+GfTwsICTk6yS+jq6+vDyMgIAGBpaYn+/fvD2dkZ/v7+SE1NhZqaGgBAIBBI+tWvXx9NmjRB//79YW9vj5kzZ2Lr1q1lqnHnzp3YsWMH9u/fjwEDBkjazczM0L59e4jFYqn+YrEY0dHRWL9+PRo0aACRSCQVkv34448wMjLCsmXLJG2NGjVCr169JI+dnJwQFxcneZySkoI3b95gypQpUiFZUlISNDU10aFDB0lfdXV1yfWbmJjA3d0d0dHRAIDWrVtj9uzZ8Pf3x40bN1C9enX4+vpi0qRJ6NatW5nuCwMIxePANiIiImkCcel9iIiIiJgvFaiK+RIRERHRx8Z8SbVwEBYREVEJjIyMkJubi/j4eAwZMgQCgUDufYVCIaZMmYKBAwfil19+Qdu2bYvta2hoiBEjRiAqKgp5eXmSQE0e27dvR+PGjaUCsvd9WHNiYiKys7Ph4uIiWQY+MjISNWvWBFBwzenp6Th16hS6du1a5DGdnJwQHh6O9PR01K9fH4mJiejcuTO6d++O7777TupcHTp0QPXq1Ys8TlpaGo4dOwYNDQ1J2+zZs3Hw4EFMnjwZhoaGEAgEWLx4sdz3g6iq4KA2xePgSyIiIiIiqoyYLzFfogKWP/oruwSVU32eRumdqExMw84puwQiIqrCOAiLiIioBO3bt0dISAiGDx+OsWPHom3btujevTtGjRqFevXqlbq/ra0tgIIwqKSQrLDvy5cv8fTpUxgaGgIADh06BG1tbal+eXl5Uo9v3bqFxo0bS7UFBARg8+bNAAA9PT3cv39fsk0kEsHDwwNqampo2rQpLC0tsXv3bsnsQnd3dxw7dgzdunWDkZER2rdvD2dnZ4waNQq1atUCAHTq1AkaGhpISkqCp6cnkpKS0K1bN7Ru3RpPnjzB7du3YWFhgZMnT8LfXzpcuX79OrS1tZGXl4c3b94AAFasWCHZrq6uji1btqB169bIz8/H2bNniw3ZCuXk5CAnJ0eq7cDtq9DUFJa4H5UNB7gQERERERERlR3zJeZLVMC1bN+SSURERFTlcBAWERFRKRYtWoRp06bhp59+wsWLF7FhwwYsXrwYp06dQrNmzUrct3CpdnlmOBbV18nJCd9++61Uv4sXL8LLy6vEY82ePRsTJ05EXFyc1Cy/jIwMxMXF4cyZM5I2Ly8viEQiSUimpqaG6OhoLFy4UHLNixcvxtKlS3Hp0iXUr18fWlpa+OKLLyQh2cmTJzFjxgyoq6ujY8eOSEpKglgsxt27d2WW1m/cuDEOHDiAN2/eYOvWrUhOTsakSZOk+tjZ2WHw4MHIyMhAmzZtSr134eHhCAsLk2qbG1gH86brl7ovyY8rNykWB7URERERERF9PpgvVc18yQJN0EhgX+q+JB9mS4rHfImIiKhy4SAsIiIiOejr68Pd3R3u7u5YvHgxWrZsieXLlyM2NrbE/VJSUgAAFhYWpZ4jJSUFtWrVgr7+fwOHatasCSsrK6l+7886BABra2vcvHlTqq1u3bqoW7euZMZjoW3btuHNmzdo166dpE0sFiM/Px+3bt2CjY2NpN3ExAQjR47EyJEjsWDBAtjY2GDDhg2SMMrJyQk7d+7Eb7/9htevX6NVq1YAgG7duiExMRH5+fnQ0tKSOhcAaGhoSK5pyZIl6Nu3L8LCwrBgwQKpfurq6lBXl+9PleDgYEybNk2qbaCuD1xXyL/sPpWOQRkRERERERFR+TFfqpr5EhERERGRvDgIi4iIqIw0NDTQqFEjZGVlldgvPz8fq1evhoWFBVq2bFli33///Rfbtm2Dm5sbhMKyLXHu6emJ4cOHY//+/fjyyy9L7CsSiRAYGCiZlVho/PjxiIqKwpIlS4rcr3bt2qhfv77UNTs5OWHhwoXYtm0bOnfuDDW1ggFPXbt2xcaNGyEWiyXLypdkzpw56N69O8aNGwdj4/KtSa6pqQlNTU2pNqGAA7CocuOgNsXj7E8iIiIiIqoqmC8VYL70eenbvq+yS1A5C2/vU3YJKmeORekr5xERERWHg7CIiIj+v8zMTCQnJ0u1Xb9+HceOHYOHhwdsbGwgFotx8OBBHDlyBNHR0VJ9nz59ikePHiE7Oxs3btzAypUrcenSJRw+fFgSIAEFMwMfPXoEsViMjIwMnD9/HosXL4aurm6xIVVJPDw8EBcXBw8PDwQHB8PV1RX16tXDnTt3sHPnTsm5k5OTceXKFfzvf/+Dra2t1DE8PT0xf/58LFy4ECKRCMnJyRg4cCAaNWqEN2/eYMuWLfjtt9+wZs0ayT4dO3aEpqYm1qxZg9mzZ0va27Zti3///Rf79+9HcHBwqfV36NABzZs3x+LFi7F27doyXz9RVcUBQ0REKkCs7AKIiIiosmG+xHyJinf4wmFll6ByXI05YIiIqMpjvqRSOAiLiIjo/0tKSpKZUejk5AQrKysEBgbi3r170NTUhLW1NTZv3oyRI0dK9XVxcQEAaGlpwdzcHE5OTti4caPMcu8vXrxA/fr1IRAIUKtWLTRu3Bje3t6YMmUKatWqVea6BQIBdu7ciU2bNiE6OhrLli3Du3fv0KBBAzg7O2PFihUACmYp2tnZyQRkADBw4EBMnDgRR44cQdu2bXHmzBmMHTsWDx8+hLa2Nuzt7bFv3z5069ZNsk/16tXRvn17nDx5Eo6OjpJ2TU1NtG/fHklJSXBycpLrGqZOnQofHx8EBQXB1NS0zPeAPg0OGlIsroRFVQHf90REREREZcN8ifkSFY+fMakqYGanWHzfE9HnRiAWizmujoiIiFROD6G7sktQOQwgqLJjqEP0+Tmev1up57cNjVTq+auSP0KnKrsEIiIiojJjvqRYwlK+VpLKLu72GWWXoHLcTNoquwQi+sSYL1UdVSFf4kpYRERERERKwAFDRERERERERPQ5+SHtZ2WXoHJcjTlgiIiIqDLhICwiIiIiIiXgymKKx4FtRERERERERJVX75t9lF2CyskcaabsElSO7vfnlV0CERFVYRyERURERESkBBwwRERERERERESfkx8aH1F2CSrH9XvmS0RERJUJB2ERERERkVw4aEixuBKW4vE1SkSfnFjZBRARERERVR383K54zJcUj69TIvrkmC+pFA7CIiIiIiK5MNQhIiIiIiIiIqLyEqhXU3YJKidH/E7ZJRAREdF7OAiLiIiIiEgJOKuOiIiIiIiIiD4nR+9eVnYJKsfVuI2ySyAiIqL3cBAWERGRkvn4+CAjIwP79u0DAERERGDhwoVIT09H9erVpfpmZ2fDyMgICxcuxOTJkwEA27dvh5eXF8aOHYt169ZJ9U9KSoKTkxPs7Oxw7do1qKmpSbbp6elh5cqV8PHxAQA0bNgQd+7ckdrfxMQE9+/fL/N2LS0tNG7cGMHBwXB3dwcAhIaGYt++fUhOTpbsf/r0afTv3x8+Pj6IjIxEWFhYqX0EAoG8t5YUjIOGqLLjam2Kx/c9EREREVHVwHyJ+VJVwM+YVBUwX1Isvu+J6HPDQVhERESVzMiRIxEcHIy4uDgMHz5catuePXvw9u1beHl5SdpEIhFmzpyJ7777DhERETLBGgD8/fff2LJlC3x9fUs89/z58zF69GjJ4/dDtbJsf/HiBSIiIjBs2DCYmJigY8eOMuc6fPgw3N3dMWvWLMydO7fIeuTpQ58OAwjFYgCheLynREREREREBZgvla0PfRrMlhSPWYji8Z4SEVFFcBAWERFRJWNoaIj+/fsjKipKJiSLioqCm5sb6tSpAwC4ffs2zp07h7179yIxMbHIYA0AJk2ahHnz5mH48OHQ1NQs9tw6OjowMjKq8HYjIyOsW7cOW7duxcGDB2VCsm3btsHX1xcRERGYOHFikceSpw9RVcbgUfEYkhERERERERVgviR/H/p0XE1aKrsElfPDw1+UXYLK6W3M1ykREZUfB2ERERFVQv7+/ujXrx/u3LkDc3NzAAWzDU+dOoVjx45J+kVHR6Nv377Q1dWFl5cXRCJRkSFZQEAAtm7dijVr1mD69Omf5BrU1dVRrVo1vH37Vqp93bp1mDZtGqKiojBixIgi95WnD1FVxwFDRERVn0Cs7AqIiIiIisd8iflSZXPswVVll6ByXDlgiIioymO+pFo4CIuIiKgScnV1hbGxMaKjoxEaGgoAiImJgampKZydnQEA+fn5iImJwZo1awAAHh4eCAwMxO3bt2FhYSF1PC0tLcybNw8hISEYPXo0dHV1izxvUFAQ5syA7QBXAAEAAElEQVSZI3m8ePFiTJ48We7thd6+fYuIiAhkZmaie/fukvaUlBRMnDgRIpGo2PBLnj4fysnJQU5OjlRbvjgPQoFaMXtQeXDQkGJxJSyqCvi+JyIiIiKqupgvMV+qbPgZU/HuzJf9mk6qmBzjd8ouQaXYfPWzsksgIvqkOAiLiIioElJTU4O3tzdiYmIwb948iMVixMbGwtfXF0KhEABw/PhxZGVloU+fPgAAAwMD9OjRA1FRUViwYIHMMf39/REREYGlS5di8eLFRZ53xowZ8PHxkTw2MDAo0/bCEO3NmzfQ1tbGkiVL0LdvX8n2Bg0aQE9PD9988w169+6N+vXry9QgT58PhYeHIywsTKrNAk3QCPal7kvy46AhquwY5hIREREREf2H+RLzpcpGrZmtsktQOQs8tim7BJWz2aahsksgIqIqjIOwiIiIKik/Pz+Eh4fjp59+Qn5+Pu7duwdfX1/JdpFIhGfPnqFGjRqStvz8fFy7dg1hYWGSMK2Quro6Fi1aBB8fH0ycOLHIcxoYGMDKyqrYmkrbXhiiaWtro169ehAIBFLbdXR0kJCQgB49esDJyQmJiYkyIZg8fT4UHByMadOmSbUN1PUpcR8iZeOAISIiIiIiIvrYmC8xX6pMjhzbqewSVA7zJSIiosqFg7CIiIgqqUaNGqFbt26IioqCWCyGi4sLzM3NAQBPnz7F/v37sWPHDtjb/zcbLy8vD507d8aPP/6IXr16yRzT3d0d33zzjcysPkUpLUQDgNq1ayMhIQE9e/aEo6MjEhMTYWxsXOY+79PU1ISmpqZU2/H0G+W/ECIiIiIiIiIiFcB8qWL50rH0a+W/EJLhatJa2SWooHxlF0BERETv4SAsIiKiSiAzMxPJyclSbfr6+vD398fo0aMBADExMZJt33//PfT19TF06FCZ2YB9+vSBSCQqMiQDgCVLlsDV1VWh9ZeVnp4ejh8/DldXVzg6OiIpKUkmBJOnT0k4C0zx+HWERERERERERJUX8yXF50u9jVsquuzP2rGHV5VdgsphBkpERFS5cBAWERFRJZCUlISWLaVDHX9/f6xZswYTJ06Empoa3NzcJNuioqIwcOBAmYAMAAYPHoyRI0fiyZMnRZ6re/fu6N69O3788UeFXkNZ6erqSmZUduvWDUlJSXL1MTEx+fTFEgCGOorGQW2Kx9coEX1yYmUXQERERPQf5kvMlyo7fm5XPOZLisfXKRF9csyXVIpALBbzKSUiIiKV00PoruwSVA5DHcVioENEVHHH83cr9fxNvo5U6vmrkpQFU5VdAhEREVGZMV9SLGZLisd8iYio4pgvVR1VIV8SKrsAIiIiIiIiIiIiIiIiIiIiIiKiqoyDsIiIiIiIiIiIiIiIiIiIiIiIiCpAXdkFEBERERF9jrgEv+JxCX4iIiIiIiKiyqtv+77KLkHlLLy9T9klqJw5Fm2UXQIREVVhHIRFREREKokDXKiy44AhIiIiIiIiosqN+ZJiuRoruwLVwwFDRERElQsHYREREZFK4gAXxWPwSEREJE0gVnYFRERERPQxMV9SLGZLisfXKBFR1cd8SbVwEBYRERERyYWhDlV2DHMVj+97IiIiIiIiUhR+xqSqgPmSYvF9T0SfGw7CIiIiUhIfHx/ExsZizJgx2LBhg9S2CRMmYP369fD29kZMTIyk/fz58+jcuTN69eqFw4cPAwD++ecfNGjQAN9//z08PDxkzuPv74+rV6/iypUrCA0Nxb59+5CcnFxiba9fv4aJiQmEQiEePHgATU1Nqe0NGzZEQEAAAgICAAC//vorvv76a1y4cAEvXryAkZER2rVrhzVr1sDQ0BBpaWmwsLDA1atX4eDgIHUsR0dHODg4YOnSpWjVqhU6deqEjRs3SvWZOXMmdu/ejWvXrkFHR6fE2gvxwzJVdgwgFI/3lIiIiIiIPjfMlwp8rHxJoKYmVz+Sz8G7l5RdgsrpZ9Ja2SWoHOZLRERUERyERUREpESmpqbYsWMHIiMjUaNGDQDAmzdvsG3bNpiZmcn0F4lEmDRpEkQiER4+fAhjY2PUq1cPffv2RVRUlExIlpWVhV27dmHJkiVlqmvv3r2wt7eHWCzGvn37MGzYsGL7Pn78GM7OzujXrx+OHTsGPT09pKWl4cCBA8jKypL7nJqamtiyZQs6dOiAwYMHw9XVFQBw4cIFREZGIiEhQe6ADOCHZar8OFBQ8fi+JyIiIiKizxHzpf8oOl8S5+XJ3ZdKxwFDisd8SfGYLxERUUVwEBYREZEStWrVCqmpqYiLi8OIESMAAHFxcTAzM4OFhYVU31evXmHnzp24fPkyHj16hJiYGISEhAAomI3o5uaGu3fvSoVru3fvRm5uruTY8hKJRPDy8oJYLIZIJCoxJDt79iwyMzOxefNmqKsX/GlhYWEBJyenMp0TAFq3bo3Zs2fD398fN27cQPXq1eHr64tJkyahW7duZT4eKRZDHcVioENERERERESKwHxJGvOlyovZkuIxXyIiIqpcOAiLiIhIyfz8/BAdHS0JsqKiouDr64ukpCSpfrt27YKtrS0aN24MLy8vBAQEIDg4GAKBAH369EG9evUQExODuXPnSvaJjo7GoEGDoKenJ3c9qampOH/+POLi4iAWizF16lTcuXMH5ubmRfY3MjJCbm4u4uPjMWTIEAgEgjLfg/fNnj0bBw8exOTJk2FoaAiBQIDFixdX6JikGAx1qLJjmKt4fN8TEREREVUNzJekMV+qnPgZk6oC5kuKxfc9EX1uOAiLiIhIyby8vBAcHIw7d+4AKJj5t2PHDpmQrHD2IAD06tULmZmZOHnyJBwdHaGmpgZvb2/ExMTg66+/hkAgQGpqKk6fPo3jx4+XqZ6oqCj07t0btWvXBgC4uroiOjoaoaGhRfZv3749QkJCMHz4cIwdOxZt27ZF9+7dMWrUKNSrV0+qb8eOHSEUCqXaXr9+DQcHB8ljdXV1bNmyBa1bt0Z+fj7Onj2L6tWrl1hzTk4OcnJypNryxXkQCtTkvGqSBwMIxWIAoXi8p0T0yYmVXQARERFRAeZLzJeqAmZLiscsRPF4T4nok2O+pFKEpXchIiKij6lu3bro27cvYmJiEB0djb59+8LAwECqz82bN3Hp0iV4enoCKAiShg0bBpFIJOnj5+eH27dvIzExEUDBLMWGDRuie/fucteSl5eH2NhYSRgHFIR4MTExyM/PL3a/RYsW4dGjR9iwYQPs7e2xYcMG2Nra4vr161L9du7cieTkZKmfNm3ayBzPzs4OgwcPRo8ePYrc/qHw8HDo6upK/dzGH3JfNxERERERERFRVcZ8ifkSERERESkfB2ERERFVAn5+foiJiUFsbCz8/PxktotEIuTm5sLY2Bjq6upQV1fHt99+i7179yIzMxMAYG1tjS5duiA6Ohr5+fnYsmULfH19y7R8+7Fjx/DgwQMMGzZMch4PDw/cuXMHJ06cKHFffX19uLu7Y/ny5UhJSYGxsTGWL18u1cfU1BRWVlZSPzVq1CjyeIXnl0dwcDAyMzOlfixgK99FExERERERERGpAOZLspgvEREREdGnxK8jJCIiqgR69eqFt2/fQiAQwNXVVWpbbm4utmzZgoiICPTs2VNqm5ubG7Zv346xY8cCAPz9/TFu3DgMGDAADx48gI+PT5nqEIlE8PDwwOzZs6XaFy1aBJFIhB49esh1HA0NDTRq1AhZWVllOn95aWpqQlNTU6qNS8VTZccl+BWPy8UTEREREdHnjPlSxTBf+vh6DfAqvROVyZCUkgc2UtntaWKo7BKIiKgK4yAsIiKiSkBNTQ0pKSmS/37foUOH8Pz5c/j7+0NXV1dq2+DBgyESiSQhmbu7OyZPnowxY8agZ8+eMDU1lTnX69evkZycLNWmo6ODWrVq4eDBgzhw4ACaNm0qtX3UqFEYOHAgnj17hjp16sjUt2PHDnh4eMDGxgZisRgHDx7EkSNHEB0dXa77QZUTB7goFgdhERERERERkSIxX6LKTnz5eumdqExG66YruwSVswcchEVEROXHQVhERESVRK1atYpsF4lEcHFxkQnIgIKQbNmyZbh27RqaN28OLS0teHh4YOPGjUUuOw8At27dQsuWLaXanJ2d0bt3b9SsWRPOzs4y+zg7O6NGjRrYunUrJk+eLLXNzs4OWlpaCAwMxL1796CpqQlra2ts3rwZI0eOlPfyqQrgoCEiIiIiIiKiyo35ElVm6mYNlF2Cyvk557KySyAiIqL3CMRisVjZRRAREREpWg+hu7JLUDkchKVYXFmMiKjijufvVur57UIilXr+quT3xVOVXQIRERFRmTFfUixmS4rHfImIqOKYL1UdVSFf4kpYRERERCQXhjpU2THMVTy+74mIiIiIiEhR+BmTqgLmS4rF9z0RfW44CIuIiIiI5MIAgio7hjpERERERERElZewpZ2yS1A5w3ccV3YJKof5EhERVQQHYRERERERKQEDHSIiIiIiIiL6nPxweLuyS1A5zJeIiIgqFw7CIiIiIiJSAq4spngMHomIiIiIiIgqr74OLsouQeWsvbNP2SWonInmnZRdAhERVWEchEVEREREcuEAF8XiICwioqpPIFZ2Bapt3bp1+Oabb/Do0SO0aNECa9asQdu2bUvdb8eOHfD09MSXX36Jffv2ffxCiYiIiEguuf8+VnYJKse6mraySyAiogpivvTxKCNb4iAsIiIiIpILBw0pFge1ERERFW/nzp2YNm0aNmzYgHbt2mHlypVwdXXFzZs3YWhoWOx+aWlpmD59Orp06fIJqyUiIiIieTBbUjzmS0REREVTVrYkLG/BRERUNmlpaRAIBBAIBHBwcFB2OSojKSlJcl/d3NzKtC+fk/IpvGd6enrKLoWIiIiIVNSKFSswevRo+Pr6ws7ODhs2bICWlhaioqKK3ScvLw8jRoxAWFgYLC0tP2G1RESfDrOMj4P50qfHfImIiIiIPiZlZUschEVEFebj4wOBQICxY8fKbJswYQIEAgF8fHzK3Lewf1HBx/nz56Gmpoa+ffvKbCsMPpKTk4usNy8vD0uWLIGtrS1q1KiBOnXqoF27dti8ebPMNQkEAlSrVg0WFhaYOXMm3rx5I3Wswj4XLlyQas/JyYG+vj4EAgGSkpKktiUkJODEiROlXmNh+JORkQEAiImJgUAgQK9evaT6ZWRkyJxHIBBILY1YnjoPHTqEbt26QUdHB1paWvjiiy8QExMj1ef9kEkgEEBHRwf29vaYMGEC/vzzT5lr+lBoaChsbW1Rs2ZN1K5dGy4uLrh48aJUn4YNG0qdQyAQYMmSJZLtHTt2RHp6OoYOHSpz/L/++gu+vr5o0KABNDU1YWFhAU9PT1y+fFmqH5+Too0dOxYCgQArV66Uak9PT5dpCw0NlZxTXV0dBgYG6Nq1K1auXImcnBwAgL+/P5o1a4a3b99K7XvkyBFoaGjgypUrRb5/X758CScnJ9jZ2eH+/ftlugYiIiIiqhxycnLw4sULqZ/CvxM/9PbtW/zyyy9wcXGRtAmFQri4uOD8+fPFnmP+/PkwNDSEv7+/wusnoo+P+RLzJeZLqvOcvI/5EhEREREpirz5kjKzJX4dIREphKmpKXbs2IHIyEjUqFEDAPDmzRts27YNZmZm5e5bHJFIhEmTJkEkEuHhw4cwNjaWu9awsDB89913WLt2Ldq0aYMXL17g8uXLeP78uVS/Xr16ITo6Gu/evcMvv/wCb29vCAQCLF26VOZ6oqOj0b59e0lbfHw8tLW18ezZM5nz6+vrQ19fX+5636euro6EhAQkJibCycmpTPuWpc41a9YgICAAQUFB+Pbbb6GhoYH9+/dj7NixuHHjBpYvXy7VPyEhAfb29sjOzsb169exatUqtGjRAgcPHoSzs3OxNdnY2GDt2rWwtLTE69evERkZiZ49e+Kvv/5C3bp1Jf3mz5+P0aNHSx7r6OhI/ltDQwNGRkaoUaOG1D+yly9fhrOzM5o2bYrvvvsOtra2ePnyJfbv34/AwECcPHlS0pfPiaz4+HhcuHChyPeWkZERdHV1Zdrt7e2RkJCA/Px8PH36FElJSVi4cCG+//57JCUlITIyEs2bN8e8efMQHh4OoCBQHD16NL7++mu0atUKaWlpUsd8/PgxevfuDaFQiNOnT5f7eSKqjLgEv+JxCX4iosorPDwcYWFhUm3z5s1DaGioTN8nT54gLy8P9erVk2qvV68e/vjjjyKPf+bMGYhEomIHSxBR1cB8ifkS8yXVeE7er4P5Er0vH/nKLkH1CLjehsKJ+TolIqqs5M2XlJktcRAWESlEq1atkJqairi4OIwYMQIAEBcXBzMzM1hYWJS7b1FevXqFnTt34vLly3j06BFiYmIQEhIid60HDhzA+PHj4e7uLmlr0UL2f9pqamrCyMgIQEGY4eLiguPHj8uEZN7e3li9ejVWrlwpCf2ioqLg7e2NBQsWyF2XPGrWrImhQ4di1qxZMjP6SiNvnffu3UNgYCACAgKwePFiSXtgYCA0NDQwefJkuLu7o127dpJt+vr6kntlaWmJ/v37w9nZGf7+/khNTYWamlqRNQ0fPlzq8YoVKyASiXDt2jWpIEdHR0dyfHmIxWL4+PjA2toap0+fhlD43wdRBwcHTJkyRe5jlUbVnhMAePDgASZNmoRjx44VORu4OOrq6pJzGhsbo1mzZujRowdatGiBpUuXYuHChYiOjoarqyvc3NzQrl07BAQEwMTEBMHBwTLHu3fvHnr06AETExPs378f2tractdCVBVwwBAREX1OgoODMW3aNKk2TU1NhRz75cuXGDlyJDZt2gQDAwOFHJOIlIP5EvMl5kuq8ZwAzJeoaEJ+QY/iccAQERF9Rj5WvqTIbImDsIhIYfz8/BAdHS0JvqKiouDr6yuz5HVZ+35o165dsLW1RePGjeHl5YWAgAAEBwdDIBDIVaeRkRF++uknjB8/Xmo2XElu3LiBc+fOwdzcXGZb69at0bBhQ+zduxdeXl64e/cuTp06hXXr1ik8JAMKluW2srLCnj17MGTIELn3k7fOPXv24N27d5g+fbrMMcaMGYOQkBBs375dKpD5kFAoxJQpUzBw4ED88ssvaNu2LdLS0mBhYYHExEQ4OjrK7PP27Vts3LgRurq6MqHlkiVLsGDBApiZmWH48OGYOnUq1NWL/ycsOTkZv/32G7Zt2yYVkBXS09Mrdt/yUKXnJD8/HyNHjsSMGTNgb28v/00ohq2tLXr37o24uDgsXLgQTk5OGD9+vCQI3LVrF65cuSLzfN68eRMzZsxAmzZtsH379lL/gMrJyZFZbjRfnAehoPgwkMqOg4aosuPqYorH9z1RKcTKLqDq0NTUlDsUMzAwgJqaGv755x+p9n/++afI/3mempqKtLQ09O/fX9KWn1/wP6PU1dVx8+ZNNGrUqALVE9GnxHyJ+VIh5kvyqYzPCfMlKg4/Y1JVwHxJsfi+J5ID8yW5yZsvKTNb4iAsIlIYLy8vBAcH486dOwCAs2fPYseOHUUGX2Xp+yGRSAQvLy8ABUu6Z2Zm4uTJk0UGL0VZsWIFhgwZAiMjI9jb26Njx4748ssv0bt3b6l+hw4dgra2NnJzc5GTkwOhUIi1a9cWeUw/Pz9ERUXBy8sLMTEx6NOnj9wBXFkZGxtjypQpmD17Ntzc3Mq0rzx13rp1C7q6uqhfv77M/hoaGrC0tMStW7dKPZetrS0AIC0tDW3btkW1atXQuHFjaGlpSfU7dOgQPDw8kJ2djfr16+P48eNSI4wnT56MVq1aoU6dOjh37hyCg4ORnp6OFStWFHvuP//8U6qGj02VnpOlS5dCXV0dkydPLtN1lHbeH3/8UfI4PDwcR48ehYeHByIiIop8nkaNGoVOnTph9+7dJc6qfP+YHy4/aoEmaISKB330HwYQisUAQvF4T4mIVIOGhgZat26NEydOSP6+zs/Px4kTJzBx4kSZ/ra2trh+/bpU25w5c/Dy5UusWrUKpqamn6JsIlIQ5kvMl97HfEk+le05Yb5ExWG2pHjMQhSP95SIqOpTZrbEdT+JSGHq1q2Lvn37IiYmBtHR0ejbt2+xy/WVpe/7bt68iUuXLsHT0xNAwcjTYcOGQSQSyV2nnZ0dbty4gQsXLsDPzw///vsv+vfvj6+++kqqn5OTE5KTk3Hx4kV4e3vD19cXgwcPLvKYXl5eOH/+PP7++2/ExMTAz89P7nrKIygoCI8fP0ZUVFSZ9vuUdYrFBcO2C2eQmpiY4I8//kDbtm2l+hXe53PnzqFXr14YOnQo/v33X8n2adOmwdHREc2bN8fYsWMRERGBNWvWyMxKK+rcn5IqPCe//PILVq1ahZiYGLln/sp73vePV6NGDUyfPh1aWlrFLt0/YMAAnD59GnFxcXKdIzg4GJmZmVI/Fvg0ISkRERERKd60adOwadMmxMbGIiUlBePGjUNWVhZ8fX0BFPxP1cKvHKpevTqaNm0q9aOnpwcdHR00bdoUGhoayrwUIioj5kvMl97HfEk+lek5Yb5ERERERJWBsrIlroRFRArl5+cnGT26bt06hfUtJBKJkJubC2NjY0mbWCyGpqYm1q5dC11dXbmOIxQK8cUXX+CLL75AQEAAtm7dipEjR2L27NmwsLAAANSsWRNWVlYACpazb9GiBUQiEfz9/WWOp6+vj379+sHf3x9v3rxB79698fLlS7lqqVWrlmTG5vsyMjKgpqaGmjVrymzT09NDcHAwwsLC0K9fP7nOI2+dNjY2yMzMxMOHD6XuM1CwpHtqaiqcnJxKPVdKSgoASO5ncQrvs5WVFdq3bw9ra2uIRCLJP3ofateuHXJzc5GWlobGjRsX2cfGxgYA8Mcff6Bly5al1vqhz/U5OX36NP7991+YmZlJ2vLy8hAYGIiVK1ciLS1N7uv68LwfnlNdXR1qamrFhnGzZ89G8+bNMXz4cIjFYgwdOrTEcxS1/CiXiqfKjrM/FY8zFYmIVMewYcPw+PFjzJ07F48ePYKDgwOOHj2KevXqAQDu3r1b5FdDEZFqYL7EfKkQ8yX5VKbnhPkSlcRl5McdXPo56pB8SdklqJxLDnzfExGpAmVlSxyERUQK1atXL7x9+xYCgQCurq4K6wsAubm52LJlCyIiItCzZ0+pbW5ubti+fTvGjh1brrrt7OwAAFlZWUVuFwqFCAkJwbRp0zB8+HDUqFFDpo+fnx/69OmDoKAguZa3LtS4cWPs2LEDOTk5Uh/yr1y5AgsLC1SrVq3I/SZNmoTVq1dj1apVcp9LnjoHDx6MoKAgREREICIiQmrbhg0bkJWVJZkpWpz8/HysXr0aFhYWZQ6p8vPzS5yFmJycDKFQCENDw2L7ODg4wM7ODhERERg2bJjMP6AZGRnQ09Mrdv/P9TkZOXIkXFxcpNpcXV0xcuRIyajwsvrjjz9w9OjRYkPPknz99dcQCoUYMWIExGIxhg0bVq4aiCorDhgiIiIq2cSJE4tcIh5AqV81FhMTo/iCiOiTYb7EfAlgvlRVnxPmS1SShO/LtsoblY75EhERUfGUkS1xEBYRKZSamppkNlRpQVFZ+gLAoUOH8Pz5c/j7+8vMSBw8eDBEIpFUSHbz5k2ZY9jb28PT0xOdOnVCx44dYWRkhNu3byM4OBg2NjawtS1+eWl3d3fMmDED69atw/Tp02W29+rVC48fP0atWrVKvZb3jRgxAvPnz8eoUaMwc+ZM6Orq4tSpU1i5ciWWLVtW7H7Vq1dHWFgYJkyYUKbzlVanmZkZli1bhsDAQFSvXh0jR45EtWrVsH//foSEhCAwMBDt2rWT2ufp06d49OgRsrOzcePGDaxcuRKXLl3C4cOHJc/tgwcP4OzsjC1btqBt27bIysrCokWLMGDAANSvXx9PnjzBunXr8ODBA7i7uwMAzp8/j4sXL8LJyQk6Ojo4f/48pk6dCi8vL9SuXbvYaxQIBIiOjoaLiwu6dOmC2bNnw9bWFq9evcLBgwfx448/4uTJk8Xu/7k+J/r6+tDX15c6TrVq1WBkZFTsrND35ebm4tGjR8jPz8fTp0+RlJSEhQsXwsHBATNmzCjTPSk0e/ZsqKmpYcSIEcjPzy81DHwfVxlSPIY6RERERET0KTBfYr7EfKnqPifMl6gkzJaIiIhI1XEQFhEpXFlCorL0FYlEcHFxKXJJ+MGDB2PZsmW4du2a5JgeHh4y/e7duwdXV1ds374d4eHhyMzMhJGREbp37/7/2LvzuKiqvw/gnwFkkVVRA0EW2UUUyhU3UAnUNMkNFAVByl3QEDGTRQU3RC0pTTZbEHHPX+6JomhlaZnividWLuCOCvP84cPEdYZNBgaGz/v1mtcT5557z/feGXri8zvnDKKioqCmVva/FtXU1DB58mQsXrwYEyZMkNoyXCQSoVmzZpW+nxIGBgbIzs7GrFmzMGjQIBQUFMDa2hrLli2TuTV9af7+/oiPj8eZM2cqPV5l6gwJCUHr1q2xdOlSrFixAkVFRXB0dMQXX3whc8Vayeq2xo0bw9zcHO7u7lizZo1ku30AePHiBc6dO4cnT54AeBWMnj17Fmlpabhz5w4MDQ3RsWNHZGdnw9HREcCrLcDXr1+PqKgoFBYWwtLSEqGhoZg+fXqF99mpUyccP34cCxYsQHBwMO7cuQNjY2O4urpi+fLl5Z7bUN+T6jp9+jSMjY2hqqoKfX19tGnTBhEREZgwYYLUVu5VMWvWLKioqGD06NEQi8UYOXJkpc5jqEN1HYNc+ePvPRHVOrGiCyAiUl7Ml6qmoWYZzJf+U1fek+pivkRUNcyX5I+/90RU65gvKRWRWCzmW0pEVAuuXr0KS0tLnDhxAs7OzoouR+kEBAQgPz8fW7durfQ5fE/eXGpqKkJCQpCfn6/oUsrkoTJM0SUQlYshmfwxJCNqePYWZyp0fMfwBIWOX5+cXhSq6BKIiJQCs4yaxXypdjFfIqo+5kvyx3yJqOFhvlR/1Id8iTthERHVMldXVzg7OyMnJ0fRpSiF7Oxs9OvXD4WFhRgwYMAbXYPvSdXo6Ojg5cuX0NTUVHQpVMsY6sgXAx0iIiIiInpTzDLki/lS7WO+1DAxW5I/5ktERER1CydhERHVElNTU1y4cAEAqrV1Ngl16NABJ0+eBPAqvKkKvidvpuR5q6qqKrYQonqOwSPVBwxziYiIiOoWZhk1g/lS7WO+1DD1t+2m6BKUzurruxRdgtKZYNNX0SUoleLCZ4ougYioVnESFhFRLVFTU4O1tbWiy1A6Wlpab/xc+Z68GT4zIqqrOGGIiIiIiJQds4yawXyp9vGZNUzbzh1UdAlKZ6BZL0WXoHTERZw0REREb46TsIiIiIiIFIAThoiIiIiIiIioIWkk4v8sKW/ioiJFl0BERESl8L92iIiIiIgUgF9HKH+c2EZEtU0kVnQFRERERET1x4Ce3oouQenMvLRd0SUoncVWbRVdAhE1MMyXlAsnYRERUbW5ubnh4MFXW0mfOHECzs7Oii1ISVhYWODatWsAgPv378PAwECxBRGRXHHCEBERERER0X+YL9UM5ktUl/zv0BZFl6B0mC8RERHVLZyERUREMgUEBCAtLQ0fffQRvvzyS8GxSZMmITExEf7+/khNTQUABAcHIyYmBs2aNQMAZGVlwd3dXWa4Y2FhgZCQEISEhAAARCIRNDQ0cO7cOZibm0v6DR48GAYGBpIxAgICkJ+fj61bt75RjQBw48YNREZGYteuXbhz5w6MjY0xePBgzJ07F4aGhpJ+pYM/dXV1NGvWDG+//TbGjh2LDz74oNxnd/jwYYSHh+Ps2bN48uQJzM3N8dFHHyE0NFTSJyoqCtHR0YLz7OzscPbsWcnPv/zyC7KzszFkyBCpMZ4+fQoTExOoqKjgr7/+goaGhtQzLgnYSouLi8OzZ8+kxn6dWCyWet6vKxnj6NGj6NKli6Q9JCQEJ0+eRFZWlqTt3r17iImJwZYtW5CXl4dmzZrBy8sLUVFRMDMzk/STNebGjRvh5+eHBQsWYMaMGeXWTTWLoY58cScs+eNnlIiIiIiobmG+xHyJ+RKVxr/b5Y/5kvzxc0pERNXBSVhERFSmVq1aYf369UhISICWlhYA4NmzZ/juu+8EwQYANG7cGEZGRm88lkgkwty5c5GWllZjNV6+fBldu3aFra0t0tPTYWlpidOnTyMsLAw7d+7EsWPH0LRpU0n/kuDv5cuXuHnzJrZs2QIfHx8EBARgzZo1Zdakra2NyZMno127dtDW1sbhw4fx0UcfQVtbGx9++KGkn6OjI/bt2yf5WU1N+P+WmzdvLqintE2bNsHR0RFisRhbt27FiBEjpPrExMQgODhY0KarqwuxWIzx48dL2jp27IgPP/xQqm9laGpqIjw8XBIoynLv3j106dIF6urq+PLLL+Ho6IirV69izpw56NixI44ePYrWrVvLPHft2rWYNGkSvvzyS4wdO7bK9ZF8MdQhIiIiIiKiqmK+xHypIsyXGg7Vpk0UXYLSuf7yoaJLICIiolI4CYuIiMr09ttv49KlS9i8eTNGjRoFANi8eTPMzMxgaWkp17EmT56MZcuWISwsDG3bVv4716tS46RJk6Curo49e/ZIAjUzMzO4uLjAysoKn3zyCb744gtJ/9LBn6mpKbp06QJ7e3sEBgZi+PDh6Nu3r8yaXFxc4OLiIvnZwsICmzdvRnZ2tiAkU1NTe+NgMSkpCX5+fhCLxUhKSpIZkunq6pZ5fR0dHck/q6qqltu3PB9++CG+/PJL/PDDD+jfv7/MPp988glu3bqFixcvSsYwMzPD7t27YWNjg0mTJmHnzp1S5y1evBiRkZFYv349vL29q1wbyR9XgckXJ7UREREREVFDwHyJ+VJFmC81HEX37iu6BKVjpqar6BKIiIioFE7CIiKicgUGBiIlJUUSQCUnJ2Ps2LGCrcDloVu3bjh//jxmzZqFHTt2yL3Ge/fuYffu3ViwYIEkICthZGSEUaNGISMjA4mJiRCJRGWO5e/vjxkzZmDz5s2SkMzNzQ0WFhaCbelLO3HiBHJycjB//nxB+4ULF9CyZUtoamqia9euiIuLk1pdKculS5dw9OhRbN68GWKxGKGhobh27Zpgq/3aYmlpifHjxyMiIgJeXl5QUVERHC8uLsb69esxatQoqRBOS0sLEydOxJw5c3Dv3j3Bqszw8HAkJiZix44d6NOnT63cC1WMk4aIiIiIiIjoTTBfEmK+JMR8qeEocn9H0SUonch/Xyq6BCIiIiqFk7CIiKhcfn5+iIiIwLVr1wAAR44cwfr16+UekgFAXFwc2rVrh+zsbPTo0UOuNV64cAFisRgODg4yr+Hg4ID79+/j33//RYsWLcocS0VFBba2trh69aqkzczMDMbGxlJ9TU1N8e+//+Lly5eIiorCuHHjJMc6d+6M1NRU2NnZIS8vD9HR0ejRowf+/PNP6OqWv3opOTkZ/fr1Q5Mmr7bv9vT0REpKCqKiogT9wsPDMWfOHEHbzp07q/RsK2POnDlISUnBt99+i9GjRwuO/fvvv8jPzy/3uYvFYly8eBGdOnWS1Lht2zbs378fvXv3rlQNhYWFKCwsFLQVi4ugIlJ9gzsiIiIiqiSxogsgIiKqH5gvCTFfksZ8qWFQPfCroktQOtHNTyu6BKXjCX4bABHVMuZLSoWTsIiIqFzNmzfHgAEDkJqaCrFYjAEDBqBZs2Y1MlabNm0wZswYzJo1C0eOHKmRGsXi6v+XjFgsFqxmXLduncx+2dnZePToEY4dO4ZZs2bB2toavr6+AIB+/fpJ+rVr1w6dO3eGubk5NmzYgKCgoDLHLioqQlpaGlasWCFp8/Pzw8cff4y5c+cKVgqGhYUhICBAcL6JiUmV7rUymjdvLhlf1rb1QMXPXV1dXfLP7dq1w507dxAZGYlOnToJtrYvS1xcHKKjowVtlnCAFRwrcQdEREREREREVJOYL8m+BvOl/zBfahjGnLuh6BKUTr9+voouQQmdUXQBRERUj3ESFhERVSgwMBCTJ08GAKxatapS5+jp6QEACgoKYGBgIDiWn58PfX19medFR0fD1tYWW7dulWuN1tbWEIlEyM3Nhbe3t9Tx3NxcNGnSBM2bNy93nKKiIly4cAEdO3assCZLS0sAgJOTE/7++29ERUVJQrLXGRgYwNbWFhcvXiz3mrt378Zff/0lFUYVFRVh//798PDwkLQ1a9YM1tbWFdYpD9OnT0diYiISExMF7c2bN4eBgQFyc3Nlnpebmws1NTXJswJeBXkbN26Eu7s7vLy8sHPnzgpXb0ZERGD69OmCNm/9gDe7GSIiIiIiIiKSO+ZL/2G+JBvzJeX3nauzoktQOl+cWKPoEpTOR2bdFV0CERHVY5yERUREFfLy8sLz588hEong6elZqXNsbGygoqKCX3/9Febm5pL2y5cvo6CgALa2tjLPa9WqFSZPnozZs2fDyspKbjUaGhrCw8MDiYmJCA0NhZaWluTY7du38e2332LMmDGCFYiypKWl4f79+xgyZEilawOA4uJiqe3MS3v06BEuXboktd3665KSkuDj44NPPvlE0L5gwQIkJSUJQrLapKOjg08//RRRUVEYNGiQpF1FRQXDhw/Ht99+i5iYGBgZGUmOPX36FImJifD29pYKTc3NzXHw4EFJULZr165ygzINDQ1oaGgI2rhVPNV1ni25tTkRERERETUczJf+w3xJNuZLyu9/p35UdAlKx7MlJwwRERHVJSoVdyEiooZOVVUVubm5OHPmDFRVKxc86OrqYty4cZgxYwa2b9+OK1eu4NChQxg1ahS6dOkCV1fXMs+NiIjArVu3sG/fPrnW+Pnnn6OwsBCenp44dOgQbty4gV27dsHDwwMmJiZYsGCBoP+TJ09w+/Zt3Lx5E8eOHUN4eDjGjx+PCRMmwN3dXdJvzJgxiIiIkPy8atUqfP/997hw4QIuXLiApKQkLF26FH5+fpI+H3/8MQ4ePIirV68iJycH3t7eUFVVLXMlIwD8+++/+P777+Hv74+2bdsKXmPGjMHWrVtx7949Sf+HDx/i9u3bgteDBw8q/UyBVytNT548KXjduCF72/APP/wQ+vr6+O677wTtCxYsgJGRETw8PLBz507cuHEDhw4dgqenJ1RUVARb35fWqlUrZGVl4Z9//oGnp2eVayciIiIiIiKiuoP5EvMl5ktEREREpOw4CYuIiCpFT09PsgV8Za1YsQL+/v4IDw+Ho6MjAgIC0K5dO3z//fflrghs2rQpwsPD8ezZM7nWaGNjg+PHj6N169YYPnw4rKys8OGHH8Ld3R1Hjx5F06ZNBf2/+uorGBsbw8rKCh988AHOnDmDjIwMqS3Rr1+/jry8PMnPxcXFiIiIgLOzMzp06IBVq1Zh0aJFiImJkfS5efMmfH19YWdnh+HDh8PQ0BDHjh0rd7v6devWQVtbG3369JE61qdPH2hpaeGbb76RtM2dOxfGxsaC18yZM8t+gDJkZWXBxcVF8IqOjpbZt1GjRpg3b57U+9asWTMcO3YM7u7u+Oijj2BpaYlevXqhqKgIJ0+ehLGxcZnjm5qaIisrC3fu3GFQRkRERERERFTPMV9ivsR8iYiIiIiUmUgsFosVXQQREdVvbm5ucHZ2xvLlyxVditLJysqCu7s77t+/DwMDA0WXIzdJSUmYOHEiMjIyMHjw4BoZw0NlWI1ctyHbfet3RZegVPh1hERE1be3OFOh4zt9nKDQ8euTU0tDFV0CERHVccyXag7zpTfHfEm+mC3JH/MlIqLqY75Uf9SHfIk7YRERkVwkJiZCR0cHp06dUnQpSsPR0RH9+vVTdBk1IigoCOvXr0dubi6ePn2q6HKIiIiIiIiIqA5gviR/zJeIiIiIiGqPmqILICKi+u/bb7+VBB1mZmYKrkZ5/PDDD3jx4gUAVHmr/vrA29tb0SUQKRRXf8ofV38SEREREdVfzJdqBvMlqkta7w5SdAlKRyNKXdElKB2zqBxFl0BERPUYJ2EREVG1mZiYKLoEpWRubq7oEoiIiIiIiIiIagXzpZrBfInqkuReyYouQeks8Xhf0SUonZeKLoCIiOo1TsIiIiIiIlIA7tpERERERERERA2Jm6ZY0SUonbhLVxRdAhEREZXCSVhEREREVCmcNCRf/DpC+eNnlIiIiIiIiKju4t/t8sd8Sf74OSUiourgJCwiIqoxbm5uOHjwIADgxIkTcHZ2VmxBSsLCwgLXrl0DANy/fx8GBgaKLYgaDIY6REREr+FCfiIiohrHfKlmMF8iRSju9baiS1A68+8UKroEIiKqLuZLSkVF0QUQEVH9EhAQAJFIhPHjx0sdmzRpEkQiEQICAiRtwcHByMvLQ9u2bQEAWVlZEIlEyM/PlzrfwsICy5cvl/wsEomgqakpCYRKDB48WDBGQEAABg8e/MY1AsCNGzcQGBiIli1bQl1dHebm5pg2bRru3r0r6Ofm5gaRSASRSAQNDQ2YmJhg4MCB2Lx5s9RYrzt8+DC6desGQ0NDaGlpwd7eHgkJCYI+UVFRkuuXvOzt7QV9fvnlF2zatEnQlpqaWm5YVvJMRCIR1NXVYW1tjZiYGLx8+eob7kvel5LXW2+9hSFDhuDy5cuSa7z+/pR29epVwfmGhoZ49913ceLECUmfK1euYOTIkWjZsiU0NTVhamqK999/H2fPnpX0KX0NPT09dOzYEdu2bSv3uRIRERERERFR/cJ8ifmSLMyXGgCxmC85v1RFxXzJ+UVERFQd3AmLiIiqrFWrVli/fj0SEhKgpaUFAHj27Bm+++47mJmZCfo2btwYRkZGbzyWSCTC3LlzkZaWVmM1Xr58GV27doWtrS3S09NhaWmJ06dPIywsDDt37sSxY8fQtGlTSf/g4GBJwHTz5k1s2bIFPj4+CAgIwJo1a8qsSVtbG5MnT0a7du2gra2Nw4cP46OPPoK2tjY+/PBDST9HR0fs27dP8rOamvD/XTdv3lxQT2V5eXkhJSUFhYWF+OGHHzBp0iQ0atQIERERkj7nzp2Drq4uLly4gA8//BADBw7EH3/8AVVV1UqNsW/fPjg6OuLmzZuYOnUq+vXrh7Nnz0JbWxseHh6ws7PD5s2bYWxsjJs3b2Lnzp1SgWlKSgq8vLzw4MEDJCYmYujQofjtt9/g5ORU5Xsm+eJW3PLFncWIiIiIiKghY77EfKkszJeUl8qhExV3oiqJMDyv6BKUThaYgRIR0ZvjJCwiIqqyt99+G5cuXcLmzZsxatQoAMDmzZthZmYGS0tLuY41efJkLFu2DGFhYZLVjvKucdKkSVBXV8eePXskgZqZmRlcXFxgZWWFTz75BF988YWkf+ngz9TUFF26dIG9vT0CAwMxfPhw9O3bV2ZNLi4ucHFxkfxsYWGBzZs3Izs7WxCSqampVStYLIuGhobkuhMmTMCWLVuwfft2QUjWokULGBgYwNjYGHPnzsWoUaNw8eJF2NnZVWoMQ0NDGBkZwcjICEuXLkW3bt3w008/4a233sKlS5ewf/9+mJubAwDMzc3RrVs3qWsYGBhIrjFv3jysWLECBw4cqHJIxgkuVNdxUhsRERERETVkzJeYL5WlLuVLJF/M6+SP+RIREVHdwklYRET0RgIDA5GSkiIJoJKTkzF27FhkZWXJdZxu3brh/PnzmDVrFnbs2CH3Gu/du4fdu3djwYIFkoCshJGREUaNGoWMjAwkJiZCJBKVOZa/vz9mzJiBzZs3S0IyNzc3WFhYIDU1VeY5J06cQE5ODubPny9ov3DhgmRL9a5duyIuLk5qdaU8aGlpSW2H//pxAHj+/PkbX7/k/ObNm0NFRQUbN25ESEhIpVY+vnz5EklJSQAAdXX1Ko/PAEL+GJQRERERERGRPDFfEmK+VP75isiXiIiIiIiqgpOwiIjojfj5+SEiIgLXrl0DABw5cgTr16+Xe0gGAHFxcWjXrh2ys7PRo0cPudZ44cIFiMViODg4yLyGg4MD7t+/j3///RctWrQocywVFRXY2tri6tWrkjYzMzMYGxtL9TU1NcW///6Lly9fIioqCuPGjZMc69y5M1JTU2FnZ4e8vDxER0ejR48e+PPPP6Grq1vpey+PWCzG/v37sXv3bkyZMkVmn7y8PCxduhQmJiaVXqVYWn5+PubNmwcdHR106tQJb731FlauXImZM2ciOjoaHTp0gLu7O0aNGoXWrVsLzvX19YWqqiqePn2K4uJiWFhYYPjw4eWOV1hYiMLCQkFbsbgIKqLKbXNPlcOJbfLFSW3yx88oEREREVH9wnxJiPmSEPMl5cO/2+WP+ZL88XNKRETVwUlYRET0Rpo3b44BAwYgNTUVYrEYAwYMQLNmzWpkrDZt2mDMmDGYNWsWjhw5UiM1isXiatcpFosFqxnXrVsns192djYePXqEY8eOYdasWbC2toavry8AoF+/fpJ+7dq1Q+fOnWFubo4NGzYgKCioWvXt2LEDOjo6ePHiBYqLizFy5EhERUUJ+piamkIsFuPJkydo3749Nm3aVKVVgq6urlBRUcHjx4/RunVrZGRk4K233gLwalv+MWPGICsrC8eOHUNmZiZiY2Oxfft2eHh4SK6RkJCAvn374vLlywgNDcXKlSvRtGnTcseNi4tDdHS0oM0SDrCCY6Vrp4ox1CEiInpN9f8TkoiIqEFjviT7GsyXmC8pK1Eldi+jqnkhfqnoEoiIqLqYLykVTsIiIqI3FhgYiMmTJwMAVq1aValz9PT0AAAFBQUwMDAQHMvPz4e+vr7M86Kjo2Fra4utW7fKtUZra2uIRCLk5ubC29tb6nhubi6aNGmC5s2blztOUVERLly4gI4dO1ZYk6WlJQDAyckJf//9N6KioiQh2esMDAxga2uLixcvVnjdiri7u+OLL76Auro6WrZsCTU16f8MyM7Ohp6eHlq0aPFGKyMzMjLQpk0bGBoaSr2/AKCrq4uBAwdi4MCBmD9/Pjw9PTF//nxBSGZkZARra2tYW1sjJSUF/fv3x5kzZ8pdKRoREYHp06cL2rz1A6pcP5WPq8CoruNEQfnj7z0RERER1TTmS/9hvvQK8yXlJS4qUnQJSuc9k3cUXYLSYb4kX8yWiKih4SQsIiJ6Y15eXnj+/DlEIhE8PT0rdY6NjQ1UVFTw66+/wtzcXNJ++fJlFBQUwNbWVuZ5rVq1wuTJkzF79mxYWVnJrUZDQ0N4eHggMTERoaGh0NLSkhy7ffs2vv32W4wZM0awAlGWtLQ03L9/H0OGDKl0bQBQXFwstc15aY8ePcKlS5cwevToKl1XFm1tbVhbW5fbx9LSUma4VVmtWrWq9PsjEolgb2+PnJycMvt06tQJ77zzDhYsWIAVK1aU2U9DQwMaGhqCNm4VL38MIOSLAYT88ZkSEREREdU/zJf+w3zpFeZLyovZkvwxC5E/PlMiIqoOTsIiIqI3pqqqitzcXMk/V4auri7GjRuHGTNmQE1NDU5OTrhx4wbCw8PRpUsXuLq6lnluREQEvvrqK1y5cgUjRoyQW42ff/45XF1dJavmLC0tcfr0aYSFhcHExAQLFiwQ9H/y5Alu376Nly9f4ubNm9iyZQsSEhIwYcIEuLu7S/qNGTMGJiYmiIuLA/BqpaSZmRns7e0BAIcOHcLSpUsxdepUyTkff/wxBg4cCHNzc9y6dQuRkZFQVVUtcyVjaUVFRTh58qSgTUNDAw4ODhU/qEr666+/pMYoHXaW5eTJk4iMjMTo0aPRpk0bqKur4+DBg0hOTkZ4eHi554aEhMDb2xszZ86EiYlJpWtlqCN/DCCIiIiIiIhI3pgvMV8CmC81FMyWiIiISNlxEhYREVVLyfbvVbFixQosXLgQ4eHhuHbtGoyMjODh4YEFCxaUuyKwadOmCA8Px+zZs+Vao42NDY4fP47IyEgMHz4c9+7dg5GREQYPHozIyEg0bdpU0P+rr77CV199BXV1dRgaGuKdd95BRkaG1Hbz169fh4qKiuTn4uJiRERE4MqVK1BTU4OVlRUWLVqEjz76SNLn5s2b8PX1xd27d9G8eXN0794dx44dq3C7euDVqkYXFxdBm5WVlVy2mi+xdOlSLF26VND29ddfo3v37uWeZ2pqCgsLC0RHR+Pq1asQiUSSn0NDQ8s918vLC5aWlliwYAESExMrXStDHflj8Chf/IwSERERERG9wnyJ+RLzpYaB2ZL88TNKRERUt4jEYrFY0UUQEZFycnNzg7OzM5YvX67oUpROVlYW3N3dcf/+/Wpt767MPFSGKboEpcOgjOo6Bo9EDc/e4kyFjt9ueoJCx69P/lhW/v8wSkREVBbmSzWH+VLFmC/J19c3jii6BKUT0MZL0SUonaKHDxVdAhHVMuZL9Ud9yJe4ExYREdWoxMRErF27FkePHoWTk5Oiy1EKjo6OuHz5sqLLoAaIE1zki5PaiIiIiIiIKof5kvwxXyJFGN2qm6JLUDq7bx1WdAlKhxkoERFVBydhERFRjfn222/x9OlTAICZmZmCq1EeP/zwA168eAHgzbbrJyIiIiIiIiKqL5gv1QzmS0RERERE8sdJWEREVGNMTEwUXYJSMjc3V3QJ1EBx5yb54qo6IiIiIiKiijFfqhnMl0gRmC3JH/MlIiKiuoWTsIiIiIiIFIDBo/wxeCQiIiIiIiKqu74qMFZ0CUpH1IFf0Spv4uOnFF0CERHVY5yERURERESVwgku8sVJWPLHZyp//L0nIiIiIiIiedno0ELRJSidZ4O0FF2C0rH7WVvRJSiVa50eK7oEIqJaxUlYRPVcamoqxo4dCwCYNm0ali9frtiC6oGsrCy4u7sDAN5//31s3bpVsQXVE1evXoWlpSUAoH379jh58qRiC6onRCIRAEBfXx/5+fmKLYaomjjBRb44uYWISAmIFV0AEZF8MF+qOuZLb4b50pthvkTKgtmS/Hm2VHQFyufadkVXQEQNDvMlpaKi6AKIlE1AQABEIhEWLlwoaN+6davkj+XS7O3toaGhgdu3b8u83oEDB9C/f38YGhqicePGaNOmDWbMmIG//vpL0kdPTw95eXmYN2+epM3CwkJmYBYVFQVnZ+cq15uVlQWRSCT5I7/k5yZNmuDZs2eCc3/55ReIRCKp+y0qKkJCQgKcnJygqamJJk2aoF+/fjhy5IigX2pqquR8VVVVNGnSBJ07d0ZMTAwKCgpkPidZCgsL4ezsDJFIJAh0XF1dkZeXh+HDh0udc/r0aQwfPhzNmzeHhoYGbG1tMXfuXDx58kTmGHFxcVBVVcWSJUukjpW+j9KvtWvXSh1XUVGBqakpxo4di3/++UdyDZFIVGaIV/IelJyvr68PFxcXzJw5E3l5eYK+r7/vsly8eBFjx46FqakpNDQ0YGlpCV9fXxw/flzQb9++fdi/f7/k54CAAAwePLjM+ko+MyX36+XlJeiXn58PkUiErKysMu+75D6PHTsmOLewsBCGhoZS5wPAjh070KtXL+jq6qJx48bo2LEjUlNTBX2uXr0qeG90dXXh6OiISZMm4cKFC+U+r9eNHz8eIpFI6vcuLy9P0GZhYSHzc1HyCggIENyzSCSCtrY2bGxsEBAQgF9//VVq7K+++grt27eHjo4ODAwM4OLigri4OMnxqKgomWPt27ev3ONr164tt1aRSCR5plWpQVVVFa1atcKHH36Ie/fuCe6l9PNp3LgxnJycJL8zREREREREDQXzJeZLsu6D+RLzpRLMl5gvEREREVHdxJ2wiGqApqYmFi1ahI8++ghNmjQps9/hw4fx9OlTDB06FGlpaQgPDxccX716NSZOnAh/f39s2rQJFhYWuH79OtatW4f4+HgsW7YMwKs/po2MjGq8Xll0dXWxZcsW+Pr6StqSkpJgZmaG69evS9rEYjF8fHywb98+LFmyBH369MGDBw+watUquLm5ITMzUxC06Onp4dy5cxCLxcjPz0dOTg7i4uKQkpKCI0eOoGXLipd3zJw5Ey1btsTvvwtX16irq8PIyAhaWlooLCyUtB87dgx9+/ZF37598b///Q9vvfUWfv75Z8yYMQP79+/HgQMHoK6uLrhWcnIyZs6cieTkZISFhUnVUHIfpenr60sdLy4uxu+//46xY8fi1q1b2L17d4X3V+LcuXPQ09PDgwcP8Ntvv2Hx4sVISkpCVlYWnJwq933wx48fR58+fdC2bVusXr0a9vb2ePjwIbZt24YZM2bg4MGDkr6GhoYwNDSsdH2lqampYd++fThw4IBktWhltWrVCikpKejSpYukbcuWLdDR0ZEKWj777DOEhIQgPDwcX3zxBdTV1bFt2zaMHz8ef/75J5YuXSrov2/fPjg6OuLJkyc4deoUVqxYgfbt2+P7779Hnz59Kqxty5YtOHbsmMzPpZGRkeA9/+WXX1BUVAQAyMnJwZAhQyTvIQBoaf23fXRKSgq8vLzw7NkznD9/HmvWrEHnzp2RnJyMMWPGAHj1GQwJCcHKlSvRq1cvFBYW4o8//sCff/4pqMPR0VESipVo2rRpucebNGmC9957T/Lz0qVLsWvXLkE/fX39KtdQVFSE3NxcBAYGoqCgABkZGYJ+MTExCA4OxpMnT5CZmYng4GCYmJigX79+Us+Xahd3bpIvrv6UP35GiYiISJkwX2K+9Pp9lMZ8ifkS8yXmS/UR/26XP+ZL8sfPKRERVQcnYRHVgL59++LixYuIi4vD4sWLy+yXlJSEkSNHolevXpg2bZogJLt58yamTp2KqVOnIiEhQdJuYWGBnj17ynXb6crWK4u/vz+Sk5MlIdnTp0+xfv16TJ06VbBycsOGDdi4cSO2b9+OgQMHStrXrFmDu3fvYty4cfDw8IC29qvv2i4d/BkbG8PBwQEDBw6Eo6MjZs6ciW+++abcunbu3Ik9e/Zg06ZN2LlzZ4X3IRaLERQUBAcHB2zevBkqKq82CjQ3N4etrS1cXFyQkJAgeI8OHjyIp0+fIiYmBuvWrUNOTg5cXV0F160owCx9vGXLlpg6dSo+/fRTPH36VBCWlKdFixYwMDCAkZERbG1t8f7778PFxQUTJkzA4cOHK3XvAQEBsLGxQXZ2tuTeAcDZ2RnTpk2rVB2Voa2tjeHDh2PWrFn46aefqnSuv78/Vq5cieXLl0ueTXJyMvz9/QWftRs3bmDGjBkICQlBbGyspH3GjBlQV1fH1KlTMWzYMHTu3FlyzNDQUPI+tG7dGgMHDkSfPn0QFBSES5cuQVVVtcy6/vrrL0yZMgW7d+/GgAEDKryP5s2bS/65JKQqeQ9fV/K+Aq9+99999134+/tj8uTJGDhwIJo0aYLt27dj+PDhCAoKkpzn6OgodS01NbVyP4tlHS/dpqOjI7Pfm9RgYmKCYcOGISUlRaqfrq6upF94eDgWL16MvXv3MiQjIiIiIqIGhfkS86USzJeEmC8xX2K+RERERER1Fb+OkKgGqKqqIjY2Fp999hlu3rwps8/Dhw+RmZkJPz8/eHh4oKCgANnZ2ZLjmZmZeP78OWbOnCnzfFl/UNdkvWUZPXo0srOzJasSS1ZUvv3224J+3333HWxtbQUBWYkZM2bg7t272Lt3b7ljtWjRAqNGjcL27dslK71KtiAv7e+//0ZwcDC+/vprNG7cuFL3cfLkSZw5cwbTp08XhEQA0L59e/Tt2xfp6emC9qSkJPj6+qJRo0bw9fVFUlJSpcYqj5aWFoqLi/Hy5ctqXWP8+PE4cuSIYOv5spw8eRKnT5/GjBkzpO4dkO9nDXi1bfipU6ewcePGKp33zjvvwMLCAps2bQIAXL9+HYcOHcLo0aMF/TZu3IgXL17g448/lrrGRx99BB0dHan38nUqKiqYNm0arl27JtmevWRr+dLb0hcXF2P06NEICwuTGQrVhNDQUDx8+FDy+2JkZIRjx47h2rVrtTK+LG9Sw9WrV7F7926p1b+lFRcXY9OmTbh//365/YiIiIiIiJQR8yXmS2+K+VLZmC+9wnyJiIiIiKhmcCcsohri7e0NZ2dnREZGygxP1q9fDxsbG8kf1j4+PkhKSkKPHj0AABcuXICenh6MjY3rRL1ladGiBfr164fU1FTMnTsXycnJCAwMlOp3/vx5ODg4yLxGSfv58+crHK9kG/O7d++iRYsW0NfXh52dneR4yaq78ePHo0OHDrh69Wql7qNk7PJqLL3q78GDB9i4cSOOHj0KAPDz80OPHj2wYsUK6OjoSPoVFBQIftbR0cHt27dljnHhwgV8+eWX6NChA3R1dStVd1ns7e0BvAoiWrRoUW7fCxcuCM6paS1btsS0adPwySefCL4ioDICAwORnJwMPz8/pKamon///oKVf8Cr91JfX1/m7466ujpat25d6c8a8OoZdurUCY0aNYKdnZ0geF20aBHU1NQwderUKt1HdZSuCwAiIyPxwQcfwMLCAra2tujatSv69++PoUOHCkLPU6dOCT6Lbdq0wc8//1zp4+Wpag1FRUV49uwZAEi+9qK08PBwzJkzB4WFhXj58iWaNm2KcePGlVtDYWGh4OsfAGD7lRPQ0OB8c3niVtzyxedJRERERBVhviTEfOkV5kvMl6qL+ZJszJdqHrMQ+eMzJSIiqls4CYuoBi1atAi9e/eWuWKq5A/9En5+fujVqxc+++wz6OrqQiwWS63Aq2nl1VuewMBATJs2DX5+fjh69CgyMzMFqy5LiMXiatdYco2SZ+Pt7Q1vb2/J8c8++wwPHz5EREREta4vS+mVUunp6bCyskL79q/+wHF2doa5uTkyMjIEW2br6urit99+k/z8+krAkhCtuLgYz549Q/fu3bF27do3ql3WfVTmMySP96WqwsPDsXr1aiQnJ2P48OGVPs/Pzw+zZs3C5cuXkZqaipUrV9ZYja8/QxMTE5w9e1Zy/Ndff8WKFSvw22+/1erv6ut1GRsb4+jRo/jzzz9x6NAh5OTkwN/fH2vXrsWuXbsknzk7Ozts375dch0NDQ3BdSs6Xp6q1vDs2TN88803OHnyJKZMmSJ1vbCwMAQEBCAvLw9hYWGYOHEirK2ty60hLi4O0dHRgjZLOMBKVDsrSInexO5bvyu6BKXD4JGIapuo9v9TmogaIOZLQsyXmC+VYL4kv7qYL73CfInqI+ZL8sd8iYhqG/Ml5cLp+0Q1qGfPnvD09JQKbM6cOYNjx45h5syZUFNTg5qaGrp06YInT55g/fr1AABbW1sUFBQgLy/vjcbW09NDQUGBVHt+fj709fWrVG9F+vXrh6dPnyIoKAgDBw6EoaGhVB9bW1vk5ubKPL+k3dbWtsKxcnNzoaenJ3MMAPjxxx9x9OhRaGhoQE1NTfKHdYcOHeDv71/mdW1sbAS1yBq3dH1JSUk4ffq05P1TU1PDmTNnkJycLDhPRUUF1tbWklfr1q0Fx3V1dXHy5En8+eefePz4MQ4dOlSp51CRkvuwsLCosG/JeKUDoKoo77OmqqoKbW1tqWMGBgaIiIhAdHQ0njx5UumxDA0N8d577yEoKAjPnj1Dv379pPqU/O7cunVL6tjz589x6dKlSn/WAMDS0lLm8ezsbPzzzz8wMzOTfAauXbuGGTNmVOq5v6my6mrbti0mTpyIb775Bnv37sXevXtx8OBByXF1dXXBZ7FVq1aC8ys6XhmVraFt27ZYuHAhVFVVpYItAGjWrBmsra3Ro0cPZGZmYurUqThz5ky5Y0dERKCgoEDwskTtrL4lIiIiIiKqScyX/sN8iflSacyX3hzzJdmYLxERERFRdXEnLKIatnDhQjg7Owu2NE9KSkLPnj2xatUqQd+UlBQkJSUhODgYQ4cOxaxZs7B48WIkJCRIXTc/Px8GBgZljmtnZ4dff/1Vqv23334T1FKZeiuipqaGMWPGYPHixdi5c6fMPj4+Phg5ciS+//57DBw4UHAsPj4ehoaG8PDwKHecf/75B9999x0GDx4steKvxMqVKzF//nzJz7du3YKnpycyMjLQuXPnMq/t4uICe3t7JCQkwMfHR3D933//Hfv27cPnn38O4NWW18ePH0dWVhaaNm0q6Xfv3j24ubnh7Nmzld56vSREk6enT59izZo16Nmzp9RW6rI4OzujTZs2iI+Px4gRI6SebWU+a+vXr0dhYaFgddtvv/0GS0tLNGrUSOZ5U6ZMwcqVK7FixYrK3dj/CwwMRP/+/REeHg5VVVWp40OGDEF4eDji4+MRHx8vOPbll1/i8ePH8PX1LXeM4uJirFy5EpaWlnBxcZHZZ/To0ejbt6+gzdPTE6NHj8bYsWOrdE9VsXz5cujp6UmNXVqbNm0AAI8fP66xOipSmRrmzJmD3r17Y8KECWjZsqXMPq1atcKIESMQERGBbdu2lXktDQ0NqdWVKiLpzwdVD1fWyRdX1RERERFRZTFfeoX5kmzMl5gvVRXzJdmYL9U8Zkvyx3yJiIiobuEkLKIa5uTkhFGjRkm2tX7x4gW+/vprxMTEoG3btoK+48aNw7Jly3D69Gk4OjoiISEBkydPxoMHDzBmzBhYWFjg5s2bWLduHXR0dKQCgNJCQ0PRo0cPLFiwAB988AGKioqQnp6Oo0ePIjExsdL1Vta8efMQFhZW5gpCHx8fZGZmwt/fH0uWLEGfPn3w4MEDrFq1Ctu3b0dmZqZgRZtYLMbt27chFouRn5+Po0ePIjY2Fvr6+li4cKGk35YtWxARESFZZWdmZiYYV0dHBwBgZWUFU1PTMusXiURYu3Yt3n33XQwZMgQREREwMjLCTz/9hBkzZsDT0xMfffQRgFchZ6dOndCzZ0+p63Ts2BFJSUlYsmRJJZ9cxa5cuYKTJ08K2kpWVgKvwsNnz57h4cOH+PXXX7F48WLcuXMHmzdvFpzz9OlTqevo6urCysoKKSkp6Nu3L3r06IFPPvkE9vb2ePToEb7//nvs2bNHsNrsdaNGjUJMTAzGjBmDmTNnQl9fH4cOHcLy5cuxePHiMs/T1NREdHQ0Jk2aVPmHAcDLywv//vsv9PT0ZB43MzPD4sWLMWPGDGhqamL06NFo1KgRtm3bhtmzZ2PGjBlSgendu3dx+/ZtPHnyBH/++SeWL1+On3/+Gf/73/8kQdxff/2FPn36YN26dejUqRMMDQ2lPu+NGjWCkZFRlULm8uTn5+P27dsoLCzE+fPnsXr1amzduhXr1q2TBJclAVPv3r1hamqKvLw8zJ8/H82bN0fXrl3lUkdF3rSGrl27ol27doiNjZWE0LJMmzYNbdu2xfHjx9GhQ4eauAUihWDwSPUBw1wiIqK6gfnSK8yX3hzzJSHmS8yXSDH4N6b8qTVvpugSlM7y42VP1qSqm2zeTdElEBHVKk7CIqoFMTExyMjIAABs374dd+/ehbe3t1Q/BwcHODg4ICkpCcuWLcPEiRNha2uLpUuXwtvbG0+fPoWFhQXee+89TJ8+vdwxXV1dsXPnTsTExCA+Ph4qKipwcnLC/v37pcK58uqtLHV1dTRrVvZ/7ItEImzYsAHLly9HQkICJk6cCE1NTXTt2hVZWVno1k34H2EPHjyAsbExRCIR9PT0YGdnB39/f0ybNk0QjhQUFODcuXNVqrUs3bp1w7FjxxAdHY1+/frh3r17AIDJkycjISEBqqqqeP78Ob755huEh4fLvMaQIUMQHx+P2NhYudQEQOZ7nZ2dLflnOzs7iEQi6OjooHXr1nj33Xcxffp0GBkZCc45f/681Kq7Pn36YN++fejUqROOHz+OBQsWIDg4GHfu3IGxsTFcXV2xfPnycuszMDBAdnY2Zs2ahUGDBqGgoADW1tZYtmwZgoKCyj3X398f8fHxFW4FXppIJCr3swYAISEhaN26NZYuXYoVK1agqKgIjo6O+OKLL2SuIixZ9de4cWOYm5vD3d0da9asEawiffHiBc6dO1el7e2rq6RWTU1NmJiYoHv37vj555/x9ttvC2pPTk7GF198gbt376JZs2bo2rUr9u/fX2ZoLW/VqSE0NBQBAQEIDw8vc4v6Nm3a4N1338XcuXPxww8/1MQtEJGSYJhLREREyoz5EvOl6mC+JMR8ifkSKYaajZWiS1A6Ibu2K7oEpcNJQ0REVB0isVgsVnQRRPTmUlNTERISgvz8fEWXUu8EBAQgPz8fW7dulXm8uLgYQUFB2L17Nw4ePChYHdgQXb16FZaWljhx4gScnZ0VXU69wt9TxfBQGaboEpQOd26SL04YIiKqvr3FmQodv32I9Fd7kWy/Lw9VdAlEVA7+3frmmC9VDfOlN8ffU8VgviRfzJbkj/kSEVH1MV+qP+pDviT7S++JqF4pKCiAjo5OmavnSCg7Oxs6Ojr49ttvy+2noqKCpKQkhIeHC1YGNnSurq5wdXVVdBn1ho6ODsaPH6/oMoiIiIioJoj5qvSLiOo85ktVw3ypepgvVQ3zJSIiIiIlpujMpj696gF+HSFRPTdkyBB0794dwKttu6liHTp0wMmTJwG8CjDKo6KigmnTptVCVXWfqakpLly4AADQ0NBQcDX1R8lnTVVVVbGFEBEREREREZWB+VLVMV96M8yX3gzzJSIiIiKi+oGTsIjqOV1dXejq6iq6jHpFS0sL1tbWii6j3lFTU+NzewN8ZkRERERERFTXMV+qOuZLb4b50pvhMyMiIiIiqh/4dYRERERERERERERERERERERERETVwElYRERERERERERERERERERERERE1cCvIyQiIiKiSvFs2V7RJSiV3bd+V3QJSoefUSIiIiIiIqK6i3+3yx/zJfnj55SIiKqDk7CIiIhqUEBAANLS0hAXF4dZs2ZJ2rdu3Qpvb2+IxWJkZWXB3d0d9+/fh4GBgdTPr4uKikJ0dDQAQFVVFaampvD29sa8efNw7tw5dOjQAUePHkWXLl2kzu3Tpw/09fWxefNmSW0AoKamhqZNm6Jdu3bw9fVFQEAAVFT+2zDz999/x6effopjx47hwYMHMDIyQufOnfHZZ5+hRYsWgjHi4uIwZ84cLFy4EGFhYQCATZs2Yfjw4bh+/TpMTEyk6rKxscHAgQOxbNkyuLm5wdnZGcuXL5ccX7FiBWbOnIm0tDT4+PhU/g0guWKoI18MdIiIlIBY0QUQERGRsmO2xGxJmTBbkj/mS0RESoD5klLhJCwiIqIapqmpiUWLFuGjjz5CkyZN5HJNR0dH7Nu3Dy9fvsSRI0cQGBiIJ0+eYPXq1Wjfvj2Sk5OlgrKrV6/iwIED+P777yVtXl5eSElJQVFREf7++2/s2rUL06ZNw8aNG7F9+3aoqanh33//RZ8+ffDee+9h9+7dMDAwwNWrV7F9+3Y8fvxYqrbk5GTMnDkTycnJkqBs0KBBMDQ0RFpaGmbPni3of+jQIVy8eBFBQUEy7zUyMhJLly7Ftm3b4OXlVelnxFCHiIiIiIiIiJQBsyXFZEsAAJFKxX2o0opRrOgSiIiIiGoUJ2ERERHVsL59++LixYuIi4vD4sWL5XJNNTU1GBkZAQBGjBiB/fv3Y/v27Vi9ejWCgoIwZ84cLF++HI0bN5ack5qaCmNjY0HYpKGhIbmOiYkJ3n77bXTp0gV9+vRBamoqxo0bhyNHjqCgoABr166Fmtqr/3SwtLSEu7u7VF0HDx7E06dPERMTg3Xr1iEnJweurq5o1KgRRo8ejdTUVKmgLDk5GZ07d4ajo6OgXSwWY+rUqfjmm2+wd+9euLq6VukZcRWY/HFiGxEREREREVHtY7akmGzp1UU4aUieVMBJbURERKTcOAmLiIiohqmqqiI2NhYjR47E1KlTYWpqKvcxtLS08Pz5cwDAqFGjEBYWho0bN2LMmDEAXoVOaWlpCAgIgKqqarnX6t27N9q3b4/Nmzdj3LhxMDIywsuXL7FlyxYMHToUIpGozHOTkpLg6+uLRo0awdfXF0lJSZKAKygoCMuWLcOhQ4fQs2dPAMCjR4+wceNGJCQkCK7z8uVL+Pn54ccff8TBgwfRrl27N342RHUVJ7XJHydfEhERERGRMmK2xGxJWTwofqroEpSOipaWoktQOsVP+TklIqI3x0lYREREtcDb2xvOzs6IjIxEUlKSXK/966+/4rvvvkPv3r0BAE2bNoW3tzeSk5MlQdmBAwdw9epVjB07tlLXtLe3xx9//AEA6NKlC2bPno2RI0di/Pjx6NSpE3r37o0xY8bgrbfekpzz4MEDbNy4EUePHgUA+Pn5oUePHlixYgV0dHTQpk0bdOnSBcnJyZKgbMOGDRCLxfDx8RGM/9VXXwEAfv/9d9jb21dYb2FhIQoLCwVtxeIiqIjKDwWJiIiIiIiIiOoDZks1my0BzJdqg+uq6YouQek8W8sJQ/JmNeqEoksgIqJ6jPt+EhER1ZJFixYhLS0Nubm51b7WqVOnoKOjAy0tLXTq1Aldu3bF559/LjkeGBiIQ4cO4dKlSwBebcveq1cvWFtbV+r6YrFYsCpxwYIFuH37Nr788ks4Ojriyy+/hL29PU6dOiXpk56eDisrK7Rv/2onGmdnZ5ibmyMjI0NQ18aNG/Hw4UNJXcOGDYOurq5g/O7du0NHRweffvopXr58WWG9cXFx0NfXF7yu4Gyl7pWIiIiIiIiIqD5gtlRz2RLAfKk2qD7lS94vDc0XfMn5RUREVB3cCYuIiKiW9OzZE56enoiIiEBAQEC1rmVnZ4ft27dDTU0NLVu2hLq6uuB4nz59YGZmhtTUVISFhWHz5s1YvXp1pa+fm5sLS0tLQZuhoSGGDRuGYcOGITY2Fi4uLli6dCnS0tIAvNou/vTp01BT++8/L4qLi5GcnIygoCAAgI+PD0JDQ7Fhwwb07NkTR44cQVxcnNT4Tk5OiI+PR9++fTFixAhkZGQIrvu6iIgITJ8uXEnnrR9Q6fulyuFXvVFdx694lD/+3hOVTyRWdAVERETUkDBbqrlsCWC+VBuMlucougTls1zRBSgf5kvyxWyJqGLMl5QLJ2ERERHVooULF8LZ2Rl2dnbVuo66unq5Kw9VVFQwduxYJCUlwcTEBOrq6hg6dGilrv3jjz/i1KlTCA0NLXd8KysrPH78GMCr1ZPHjx9HVlYWmjZtKul37949uLm54ezZs7C3t4euri6GDRuG5ORkXLp0Cba2tujRo4fMMZydnbF//3707dsXw4cPR0ZGBho1aiSzr4aGBjQ0NITPgFvFyx0DCPliACF/fKZERERERKTsmC3VTLYEMF+qDcyW5I9ZiPzxmRIRUXVwEhYREVEtcnJywqhRo7By5coK+546dUqwlbpIJJJsx14ZY8eORUxMDGbPng1fX19oaWlJ9SksLMTt27dRVFSEv//+G7t27UJcXBzee+89jBkzBgCwY8cOrF+/Hj4+PrC1tYVYLMb333+PH374ASkpKQBerVTs1KkTevbsKTVGx44dkZSUhCVLlgAAgoKC0KNHD+Tm5iI8PLzce2jfvj1+/PFH9OnTB8OHD8eGDRvKDcuI6hMGj1QfMHgkIiIiIqpbmC0xW6rP+ll2VnQJSmf9zQOKLkHp+Jp3V3QJSkVcVKToEoiIahUnYREREdWymJgYZGRkVNjv9dBJVVUVL1++rPQ4ZmZm6Nu3L/bs2YPAwECZfXbt2gVjY2OoqamhSZMmaN++PVauXAl/f3+oqKgAANq0aYPGjRtjxowZuHHjBjQ0NGBjY4O1a9di9OjReP78Ob755psyQ68hQ4YgPj4esbGxaNSoEbp37w47OztcvHhREsaVx8nJSRKWDRs2DBs2bJDaIp9qBydjyBcnYckfP6NERERERNQQMFtitlRfFRc+U3QJSqeJSmNFl6B0OGmIiIiqQyQWi/kNk0RERKR0PFSGKboEpcNJQ/LFCUNERNW3tzhToeM7T0lQ6Pj1ycnPyv46IiIiIqK6ivmSfDFbkj/mS0RE1cd8qf6oD/mSiqILICIiIiIiIiIiIiIiIiIiIiIiqs/4dYRERESklLiyjoiIiGoc9xYnIiIiUmqXvnVRdAlKpe1nroouQemYIEfRJRARUXUxX1IqnIRFRERESolbccsfJ7bJF58n1Qf8dykRERERETVkVqNOKLoEpSJSa6ToEpTOlIvnFF2C0kn06qfoEpTKy4uXFV0CEVGt4iQsIiIiIiIF4OQWIiIiIiIiImpIdl0/rugSlA7zpZrASUNERPTmVBRdABERERERERERERERERERERERUX3GnbCIiIiIiBSAX0cof1z9SURERERERFR3uRwfoegSlM/kpoquQOm0+DxH0SUQEVE9xklYRERENSQgIABpaWmIi4vDrFmzJO1bt26Ft7c3xGIxsrKy4O7uLjnWokULdO/eHUuWLMGNGzfQt29fHDhwAN27d5f0efz4MZycnPDBBx9g6dKlcHNzw8GDB6XGf/HiBdTU1ATHNTQ00Lp1a0yePBkTJ06U9H3+/DlWrFiB9PR0nDt3DmpqarCwsMDAgQMxceJEtGzZUtL3xo0biIyMxK5du3Dnzh0YGxtj8ODBmDt3LgwNDaXqSE9Ph5+fH8aPH49Vq1YJjpXc//3792FgYCA4Zm9vjytXruDatWswMjKq5FMnqj84YYiIiIiIiIjKw2zpFWZLyuNEhwxFl6B0PAcxXyIiIqpL+HWERERENUhTUxOLFi3C/fv3y+137tw53Lp1C5mZmTh9+jQGDhyI7t27Y8qUKQgICMDjx48lfWfOnAktLS3Mnz9f0hYcHIy8vDzBS01NTer4mTNnMHz4cEyaNAnp6ekAgMLCQnh4eCA2NhYBAQE4dOgQTp06hZUrV+LOnTv47LPPJNe5fPkyOnTogAsXLiA9PR0XL17El19+if3796Nr1664d++e1L0lJSVh5syZSE9Px7Nnzyr13A4fPoynT59i6NChSEtLq9Q5RERERLVNJOarsi8iIiJ6M8yWmC0RERGRclN0ZlOfXvUBd8IiIiKqQX379sXFixcRFxeHxYsXl9mvRYsWMDAwgLGxMebOnYtRo0bh4sWLiI2Nxa5duxAeHo7PP/8cBw4cwNq1a5GTkwNNTU3J+Y0bNy53RV/p41FRUfjuu++wfft2+Pr6IiEhAYcPH8bx48fh4uIiOcfMzAy9evWCWPzff9VMmjQJ6urq2LNnD7S0tCT9XFxcYGVlhU8++QRffPGFpP+VK1eQk5ODTZs24cCBA9i8eTNGjhxZ4XNLSkrCyJEj0atXL0ybNg3h4eEVnkNU3/DrCOWPu4sREREREZGyYbbEbEmZLLtvqegSlI+rs6IrUD45JxVdARER1WOchEVERFSDVFVVERsbi5EjR2Lq1KkwNTWt8JySAOr58+fQ1NTEunXr4OrqCg8PD4SEhGD27Nl45513qlWXlpYWnj9/DuDVlu4eHh6CkKw0kUgEALh37x52796NBQsWSGosYWRkhFGjRiEjIwOJiYmSc1JSUjBgwADo6+vDz89PEoCV5+HDh8jMzMRPP/0Ee3t7FBQUIDs7Gz169KjWPRPVNZwwRERERERERBVhtsRsSZlMb3JF0SUond05eoougYiIiErhJCwiIqIa5u3tDWdnZ0RGRiIpKancvnl5eVi6dClMTExgZ2cHAOjQoQMiIiLwwQcfwMXFBZ988onUeYmJiVi7dq3k548++gjx8fFS/YqKipCeno4//vgDH374IQDg/PnzcHNzk6p57969AIB27dohJycHFy5cgFgshoODg8zaHRwccP/+ffz7779o0aIFiouLkZqaKtly3sfHBzNmzMCVK1dgaVn2qrf169fDxsYGjo6OkvOSkpLKDcoKCwtRWFgoaCsWF0FFpFrmOUSKxp2w5I8T24iIiIiISBkxW6r5bAlgvlQbBjj1VnQJSmf19W2KLkHpfGTWXdElEBFRPcZJWERERLVg0aJF6N27Nz7++GOZx01NTSEWi/HkyRO0b98emzZtgrq6uuT4p59+ipiYGMyaNQtqatL/73vUqFGCAM3AwEBwvCRIe/78OVRVVREaGooJEyaUWW9iYiIeP36MlStX4tChQ4JjpbeQl6Wk7r179+Lx48fo378/AKBZs2bw8PBAcnIy5s2bV+b5ycnJ8PPzk/zs5+eHXr164bPPPoOurq7Mc+Li4hAdHS1os4QDrOBYbq1EisQJQ0RERERERFRZzJZqNlsCmC/Vhv+d+lHRJSgdz5acMERERFSXcBIWERFRLejZsyc8PT0RERGBgIAAqePZ2dnQ09NDixYtZIZBJeGYrJAMAPT19WFtbV3m+CVBmpaWFoyNjaGioiI5ZmNjg3Pnzgn6GxsbAwCaNm0qabO2toZIJEJubi68vb2lxsjNzUXz5s0lIV1SUhLu3bsn2F6+uLgYf/zxB6KjowU1lDhz5gyOHTuGn3/+GeHh4ZL2oqIirF+/HsHBwTLvLyIiAtOnTxe0eesHlPE0iOoG7oQlf5zYRkS1rvz//ZCIiIhIbpgtvVJT2RLAfKk29B47TtElKB2X335TdAlK58+3ixRdAhE1NMyXlAonYREREdWShQsXwtnZWbIVfGmWlpZSKwzlqbwgzdfXF3PmzMGJEyfg4uJS5jUMDQ3h4eGBxMREhIaGCgKw27dv49tvv8WkSZMAAHfv3sW2bduwfv16ydbvwKvAq3v37tizZw+8vLykxkhKSkLPnj2xatUqQXtKSgqSkpLKDMo0NDSgoaEhaONW8URERERERESkTJgt1Vy2BDBfqg1ap24qugSlE9D0iKJLUDofo4uiSyAionqMk7CIiIhqiZOTE0aNGoWVK1cquhSB0NBQ/O9//0OfPn0QGRmJHj16oEmTJjh//jx27twJVdX/wqbPP/8crq6u8PT0xPz582FpaYnTp08jLCwMtra2mDt3LgDg66+/hqGhIYYPHw6RSCQYr3///khKSpIKyl68eIGvv/4aMTExaNu2reDYuHHjsGzZMpw+fVoQvBHVZ9y1iYiIiIiIiKqC2dIrzJbqr/8d36XoEpSOZ0tOGCIiIqpLpPdqJSIiohoTExOD4uJiRZchoKmpif379yM8PBwpKSno3r07HBwcEBISgm7dumHr1q2SvjY2Nvjll1/QunVrDB8+HObm5ujXrx9sbW1x5MgR6OjoAACSk5Ph7e0tFZIBwJAhQ7B9+3bcuXNH0L59+3bcvXtX5nb0Dg4OcHBwQFJSknxvnoiIiIiIiIioHmG2xGyJiIiIiOoukVgs5jdMEhER0RuLjIzEsmXLsHfvXnTpUndWXnmoDFN0CUpn963fFV0CUbm4uxhRw7O3OFOh47tMTFDo+PXJicRQRZdAREREdVRdzZYA5kvydumbsr+ukt6M5hmtijtRlZjE5ii6BCKqZcyX6o/6kC/x6wiJiIioWqKjo2FhYYFjx46hU6dOUFHhRptElcEJQ0RERERERETMlhqSi71TFV2C0vH0Y75ERERUl3ASFhEREVXb2LFjFV0CUb3DncXkjxPbiKi2ibi3OBEREZFcMFtqGLxava3oEpTOjr9+VnQJSuc9k3cUXQIRNTDMl5QLJ2ERERERESkAJwwRERERERERUUOy68Zvii5B6Xi25IQhIiKiuoR7uhIREREREREREREREREREREREVUDJ2ERERERERERERERERERERERERFVA7+OkIiIiJTS7lu/K7oEIiIiIiIiIiKqz0Tcy0CeilGs6BKIiIiIahQnYREREZFS8mzZXtElKB1ObCMiIiIiIiKiBkXMSUPypMIv6CEiIiIlx//aISJSYgEBARCJRFi4cKGgfevWrRCJRPj666+hra2NixcvCo7funULTZo0weeffy5py8nJQf/+/dGkSRNoamrCyckJy5YtQ1FREQDg/PnzaNy4Mb777jvBtYqLi+Hq6oqhQ4dCJBKV+4qKisLVq1chEolw8uRJmfeUmpoKAwMDmcdEIhG2bt0q+FlTUxPXrl0T9Bs8eDACAgIEz2nw4MFSz00kEqFRo0Z466234OHhgeTkZBQXSwcvFT2bsuorLSsrCyKRCPn5+TKPR0VFwdnZucyfS7z+/Ep+VlVVxV9//SXom5eXBzU1NYhEIly9elXQX9br2LFjAF69ByKRCA4ODlLjZ2ZmQiQSwcLCQtD+/PlzLFmyBG+//Ta0tbWhr6+P9u3bY86cObh165ak3+vvBQBs3LgRmpqaiI+Pl/lsiIiIiBRGzFelX0REVG8xX2K+xHyJiIiIqAYpOrOpT696gJOwiIiUnKamJhYtWoT79+9LHRs9ejQ8PT0REBAgCH+Cg4PxzjvvYNKkSQCALVu2oFevXjA1NcWBAwdw9uxZTJs2DfPnz4ePjw/EYjFsbW2xcOFCTJkyBXl5eZJrxcfH4/Lly/jyyy+Rl5cneS1fvhx6enqCto8//lju9y8SiTB37twqn+fl5YW8vDxcvXoVO3fuhLu7O6ZNm4b33nsPL1++lPSrzLOpC0xMTLBu3TpBW1paGkxMTGT237dvn+C9ycvLwzvvvCM5rq2tjX/++QdHjx4VnJeUlAQzMzNBW2FhITw8PBAbG4uAgAAcOnQIp06dwsqVK3Hnzh189tlnZda9du1ajBo1Cl988QVmzJhR1dsmIiIiIiIiIjlgvsR8CWC+RERERERUEX4dIRGRkuvbty8uXryIuLg4LF68WOr46tWr4ejoiGXLluHjjz9Gamoqjhw5glOnTkEkEuHx48cIDg7GoEGDsGbNGsl548aNw1tvvYVBgwZhw4YNGDFiBKZMmYKtW7ciODgYO3bswNmzZzF37lxkZGSgWbNmgnH19fUhEolgZGQkaL9z545c73/y5MlYtmwZwsLC0LZt20qfp6GhIanNxMQEb7/9Nrp06YI+ffogNTUV48aNq9KzUTR/f3+kpKQgIiJC0paSkgJ/f3/MmzdPqr+hoaHUe1OampoaRo4cieTkZHTt2hUAcPPmTWRlZSE0NBTp6emSvgkJCTh8+DCOHz8OFxcXSbuZmRl69epVZpC4ePFiREZGYv369fD29q7yPfOr84iIiIiIiIjkg/kS8yWgYeZLRERERERVwUlYRERKTlVVFbGxsRg5ciSmTp0KU1NTwfHmzZtjzZo18PX1Rfv27REaGooVK1agVatWAIA9e/bg7t27MlcRDhw4ELa2tkhPT8eIESMgEomQkpKCdu3a4auvvkJSUhJ8fHwwaNCgWrlXWbp164bz589j1qxZ2LFjR7Wu1bt3b7Rv3x6bN2/GuHHjqvRsFG3QoEH48ssvcfjwYXTv3h2HDx/G/fv3MXDgQJkhWWUEBgbCzc0NK1asQOPGjZGamgovLy+89dZbgn7p6enw8PAQBGSliUQiqbbw8HAkJiZix44d6NOnT4W1FBYWorCwUNDmbdwWKiLVKtwRVYQT24iIiIiIiBom5kvMl4CGmS8Vi4uYLxERERFRpfHrCImIGgBvb284OzsjMjJS5vHBgwdj+PDh8PLyQq9eveDv7y85dv78eQCAg4ODzHPt7e0lfQDA3Nwcy5cvx/jx45GXl4cVK1bI8U5eKSgogI6OjtSrLHFxcdi1axeys7OrPba9vT2uXr0KoOrPRt5OnTol9QwcHR1l9m3UqBH8/PyQnJwMAEhOToafnx8aNWoks7+rq2uFz9fFxQWtW7fGxo0bIRaLkZqaisDAQKl+58+fh52dnaDN29tbcl1XV1fBsZ07d2Lx4sXYtm1bpQIy4NV7rK+vL3hdwdlKnUtEREREREREFWO+xHyJ+RIRERERUfk4CYuIqIFYtGgR0tLSkJubK/P4p59+iuLiYsyZM0fm8bK29AYAdXV1wc9jx46FsbExpkyZAj09vTcvugy6uro4efKk1Kssbdq0wZgxYzBr1qxqjy0Wi6VW1lXl2ciTnZ2d1DP44YcfyuwfGBiIzMxM3L59G5mZmTIDrRIZGRmVer6BgYFISUnBwYMH8fjxY/Tv379StScmJuLkyZMIDAzEkydPBMfatWsHCwsLREZG4tGjR5W6XkREBAoKCgQvS9hX6lwiIiIiIiIiqhzmS8yXmC8REREREZWNX0dIRNRA9OzZE56enoiIiEBAQIDUcTU1NcH/LWFjYwMAyM3NlVpRVtLu7Ows83qvX0teVFRUYG1tXaVzoqOjYWtri61bt1Zr7NzcXFhaWgJ482cjL+rq6lLPobxn7uTkBHt7e/j6+sLBwQFt27YtM/xq1apVpZ7xqFGjMHPmTERFRWH06NEyx7exscG5c+cEbcbGxgCApk2bSvU3MTHBxo0b4e7uDi8vL+zcuRO6urrl1qGhoQENDQ1BG7eKJyIiohpX9v9WSkREpJSYLzFfYr5EREREJGfMl5QKd8IiImpAFi5ciO+//x5Hjx6t9Dmenp5o2rQp4uPjpY5t374dFy5ckBm61TWtWrXC5MmTMXv2bBQVFb3RNX788UecOnUKQ4YMAVA/n01gYCCysrLKXaVYFU2bNsWgQYNw8ODBMq/p6+uLvXv34sSJE5W+rrm5OQ4ePIjbt2/Dy8sLDx8+lEu9RERERERERFQ9zJeYLzFfIiIiIiKSjTthERE1IE5OThg1ahRWrlxZ6XO0tbWxevVq+Pj44MMPP8TkyZOhp6eH/fv3IywsDMHBwZXeIrwqXl/ZBgCOjo7VumZERAS++uorXLlyBSNGjCi3b2FhIW7fvo2ioiL8/fff2LVrF+Li4vDee+9hzJgxAN7s2Vy5ckVqdWDJikcAOHXqlGBVnkgkQvv27at136UFBwdj2LBhMDAwKLff3bt3cfv2bUGbgYEBNDU1pfqmpqYiMTERhoaGMq8VGhqK//3vf+jTpw8iIyPRo0cPNGnSBOfPn8fOnTuhqip7RWGrVq2QlZUFd3d3eHp6YteuXTXy9QNEREREREREVHnMl5gvMV8iIiIiIpKNk7CIiBqYmJgYZGRkVOmcoUOH4sCBA1iwYAF69OiBBw8eAAAWLVqEmTNn1kSZ8PHxkWq7ceNGta7ZtGlThIeHY/bs2RX23bVrF4yNjaGmpoYmTZqgffv2WLlyJfz9/aGi8t9GklV9NtOnT5dqy87Olvxzz549BcdUVVXx8uXLSt9jRdTU1NCsWbMK+/Xt21eqLT09Xeb7oqWlBS0trTKvpampif3792P58uVISUlBREQEiouLYWlpiX79+iE0NLTMc01NTQVB2e7duxmUERERERERESkY8yXmS8yXiIiIiIikicRiMb9hkoiIquTZs2d4//33cePGDRw8eBDNmzdXdEl1Bp9N3eGhMkzRJRCVa/et3xVdgtLxbCm/ld1EVD/sLc5U6Phvf5Sg0PHrk99Wl/0/jBIRUcPEDKVsfDZ1B/MlquuYL8kf8yWihof5Uv1RH/Il7oRFRERVpqmpiW3btmH58uU4dOgQhgwZouiS6gw+G1JmDHXki4EOERERERE1ZMxQysZnQ8qK2ZL8MV8iIiKqWzgJi4iI3oimpiZmzZql6DLqJD4bIiIiooZBxL3FiYiIqoUZStn4bIiIiIgaBuZLykWl4i5ERERERERERERERERERERERERUFk7CIiIiIiIiIiIiIiIiIiIiIiIiqgZ+HSEREREppd23fld0CUrHs2V7RZdARERERERERFRrmC/JF7MlIiIiUnachEVERERElcLgUb4YPBIRERERERHVbf0deiq6BKWSduMHRZegdPxbdVN0CURERFQKJ2EREZHcBQQEIC0tDQDQqFEjmJmZYcyYMZg9ezYOHz4Md3d33L9/HwYGBoLzLCwsEBISgpCQEEF7XFwc5syZg4ULFyIsLExwLDU1FSEhIcjPzy+zntu3byMuLg7/+9//cPPmTejr68Pa2hp+fn7w9/dH48aNJeNfu3YNAKClpQUrKytMmzYN48aNk1wrKysL7u7uMsfJy8uDkZERnjx5gnnz5mHDhg3466+/oKurizZt2mD69Olo3749LC0ty31+S5cuxcyZM3H9+nWYmJhIHbexscHAgQOxbNkyuLm54eDBgwAAdXV1NGvWDG+//TbGjh2LDz74QOrcAwcOID4+Hj/99BMePnwIExMTdOjQAZMmTULPnj0F99imTRv88ccfUFVVlZxvYGCA5cuXY+TIkWjZsiU+/vhjzJo1S2qcefPm4fPPP8fNmzfRqFEjPH36FAsXLkR6ejquXbsGXV1duLu7IyoqCo6OjpLzoqKiEB0dDQBQUVFBy5Yt0a9fPyxcuBBNmzYt97m9jhNciBoeThSUP/67lIiIiIgUgdkSs6W6kC0BQFFBQZXPobJxwhDVB8yX5IvZEhE1NJyERURENcLLywspKSkoLCzEDz/8gEmTJqFRo0bo2rVrla+VnJyMmTNnIjk5WSooq8jly5fRrVs3GBgYIDY2Fk5OTtDQ0MCpU6ewZs0amJiYYNCgQZL+MTExCA4OxpMnT5CZmYng4GCYmJigX79+guueO3cOenp6grYWLVoAAMaPH4+ffvoJn332Gdq0aYO7d+8iJycHd+/eRatWrZCXlyc5Z+nSpdi1axf27dsnadPW1saiRYuQlpaG2bNnC8Y4dOgQLl68iKCgIElbcHAwYmJi8PLlS9y8eRNbtmyBj48PAgICsGbNGkm/xMRETJ48GaNHj0ZGRgasrKxQUFCAAwcOIDQ0FL/++qvUs1u3bh3Gjh0r9VzV1dXh5+eHlJQUqaBMLBYjNTUVY8aMQaNGjVBYWIi+ffvi+vXriI+PR+fOnfH3338jLi4OnTt3xr59+9ClSxfJ+Y6Ojti3bx+KioqQm5uLwMBAFBQUICMjQ6oOql0MIKiuY6hDRLVOrOgCiIiIlBezJWZLzJaUkEhF0RUonZ1//VpxJ6oSz5Yuii6BiBoa5ktKhZOwiIioRmhoaMDIyAgAMGHCBGzZsgXbt2+vclB28OBBPH36FDExMVi3bh1ycnLg6upa6fMnTpwINTU1HD9+HNra2pL21q1b4/3334dYLPwvG11dXUnd4eHhWLx4Mfbu3SsVlLVo0UJqtWWJ7du3Y8WKFejfvz+AV6sg33nnHcnxkusDgI6ODtTU1ARtADB69GikpqZKBWXJycno3LmzYIVf48aNJeebmpqiS5cusLe3R2BgIIYPHy4JqUpWgi5btkxwzXbt2mHq1KlS9zFlyhRERkZi5MiR0NDQkDoeFBSEFStW4PDhw+jevbuk/eDBg7h8+bIkzFu+fDmOHj2KEydOoH37VxMkzM3NsWnTJnTu3BlBQUH4888/IRKJAEDwPExMTDBs2DCkpKTIfNZE9RknDBEREREREZWN2RKzJWZLymf3XycUXYLS4YQhIiKiuoVTzomIqFZoaWnh+fPnVT4vKSkJvr6+aNSoEXx9fZGUlFTpc+/evYs9e/Zg0qRJgpCstJJw5nXFxcXYtGkT7t+/D3V19SrVbGRkhB9++AEPHz6s0nmlBQUF4cKFCzh06JCk7dGjR9i4caNgpWJZ/P390aRJE2zevBkAsGnTJrx48QIzZ86U2V/WcwgJCcHLly/x2WefyTzHyckJHTt2RHJysqA9JSUFrq6usLe3BwB899138PDwkIRkJVRUVBAaGoozZ87g999l77B09epV7N69u8L3oLCwEA8ePBC8isVF5Z5DRERERERERPUHs6WqYbb0SmWzJYD5EhERERFVHydhERFRjRKLxdi3bx92796N3r17S9pNTU2ho6MjeF2/fl1w7oMHD7Bx40b4+fkBAPz8/LBhwwY8evSoUmNfvHgRYrEYdnZ2gvZmzZpJxgwPDxccCw8Ph46ODjQ0NDB06FA0adIE48aNk7r26/WXXj24Zs0a5OTkwNDQEB07dkRoaCiOHDlSqZpLtGnTBl26dBGEUBs2bIBYLIaPj0+F56uoqMDW1hZXr14FAJw/fx56enqCVZGbNm0S3MOpU6cE12jcuDEiIyMRFxeHgoICmeMEBQUhMzNT8p48fPgQGzduRGBgoKTP+fPn4eDgIPP8kvbz589L2k6dOgUdHR1oaWnB0tISp0+flnqfXhcXFwd9fX3B6wrOlnsOEREREREREdV9zJaYLdVGtgQwXyIiIiKi6uPXERIRUY3YsWMHdHR08OLFCxQXF2PkyJGIiorCL7/8AgDIzs6Grq6u4Bw3NzfBz+np6bCyspKscnN2doa5uTkyMjIqtWKvLD///DOKi4sxatQoFBYWCo6FhYUhICAAeXl5CAsLw8SJE2FtbS11jdfrb9SokeSfe/bsicuXL+PYsWPIycnB/v37sWLFCkRHR+PTTz+tdJ2BgYEIDQ3FZ599Bl1dXSQnJ2PYsGFSz60sYrFYsArx9RWJnp6eOHnyJP766y+4ubmhqEh6ZV9QUBDi4+OxaNEixMbGSh339fVFaGgoNmzYgMDAQGRkZEBFRQUjRoyQqqU8pVcj2tnZYfv27Xj27Bm++eYbnDx5ElOmTCn3/IiICEyfPl3Q5q0fUO45RIq2+5bsVbr05vgVj0REREREyoPZErOl12spT3WzJYD5Um0Y0L6PoktQOonXtym6BKUz0ayboksgIqJ6jJOwiIioRri7u+OLL76Auro6WrZsCTU14f/LsbS0hIGBgaDt9T5JSUk4ffq0oL24uBjJycmVCsqsra0hEolw7tw5QXvr1q0BvNrG/nXNmjWDtbU1rK2tkZmZCScnJ3To0AFt2rSpsP7SGjVqhB49eqBHjx4IDw/H/PnzERMTg/Dw8EpvQe/j4yMJoXr27IkjR44gLi6uUucWFRXhwoUL6NixIwDAxsYGBQUFuH37tmTFoo6ODqytraWee2lqampYsGABAgICMHnyZKnjenp6GDp0KFJSUhAYGIiUlBQMHz4cOjo6kj42NjbIzc2Vef2SdltbW0mburq6JJxcuHAhBgwYgOjoaMybN6/MOjU0NKChoSFoUxGpltmfqC7ghCEiIiIiIqKyMVtitlSiNrIlgPlSbfjf7/sVXYLS8WzJCUNERER1CSdhERFRjdDW1pa5yq+yTp06hePHjyMrKwtNmzaVtN+7dw9ubm44e/Ys7O3ty72GoaEhPDw88Pnnn2PKlCnQ1tauUg2tWrXCiBEjEBERgW3bqreiqE2bNnj58iWePXtW6aBMV1cXw4YNQ3JyMi5dugRbW1v06NGjUuempaXh/v37GDJkCABg6NChmDVrFhYtWoSEhIQq1T5s2DAsWbIE0dHRMo8HBQXBzc0NO3bsQE5ODpYsWSI47uvri08++QS///67ZOUp8Cr0TEhIkBlEljZnzhz07t0bEyZMQMuWLatUO8kXJw3JF3fCkj9+Romotokq2JGBiIiI3hyzJSFmS8yWlAH/bpc/5kvyx88pEdU25kvKhZOwiIioTkpKSkKnTp3Qs2dPqWMdO3ZEUlKSJJApKirCyZMnBX00NDTg4OCAxMREdOvWDR06dEBUVBTatWsHFRUV/PLLLzh79izeeeedcuuYNm0a2rZti+PHj6NDhw6S9n/++QfPnj0T9DU0NESjRo3g5uYGX19fdOjQAYaGhjhz5gxmz54Nd3d36OnpVek5BAUFoUePHsjNzUV4eLjMPk+ePMHt27fx8uVL3Lx5E1u2bEFCQgImTJgAd3d3AICZmRni4+Mxbdo03Lt3DwEBAbC0tMS9e/fwzTffAABUVcte2bdw4UJ4enrKPNazZ09YW1tjzJgxsLe3h6urq+B4aGgotm3bhoEDByI+Ph6dO3fG33//jdjYWFy4cAE5OTnlPoOuXbuiXbt2iI2Nxeeff15uX6pZDHXki4EOERERERFRzWG29AqzJWZLdQmzJfljvkRERFS3cBIWERHVOc+fP8c333xTZjA0ZMgQxMfHIzY2FgDw6NEjuLi4CPpYWVnh4sWLsLKywokTJxAbG4uIiAjcvHkTGhoaaNOmDT7++GNMnDix3FratGmDd999F3PnzsUPP/wgabezs5Pqe/ToUXTp0gWenp5IS0vD7Nmz8eTJE7Rs2RLvvfce5s6dW9VHge7du8POzg4XL17EmDFjZPb56quv8NVXX0FdXR2GhoZ45513kJGRAW9vb0G/KVOmwMHBAcuWLcPQoUPx4MEDGBoaomvXrti1axecnJzKrKN3797o3bs39uzZI3VMJBIhMDAQs2fPRkREhNRxTU1N7N+/H3FxcYiIiMC1a9fw8uVLWFtb488//4SpqWmFzyE0NBQBAQEIDw9Hq1atKuxPVB8weKT6gGEuEREREdVHzJb+w2zpFWZLdcMA576KLkHp3A61UXQJSudhu+eKLkGp2Iw9rugSiIhqlUgs5t5mREREVLt27twJb29vLF26FJMnT66RMTxUhtXIdRsyThqSL05uISKqvr3FmQodv8O4ZQodvz45vna6oksgIiIiJVIb2RLAfEnemC3JH/MlIqLqY75Uf9SHfElF0QUQERFRw9OvXz/s3LkT9+7dw507dxRdDhERERERERER1SPMloiIiIioLuLXERIREZFCuLu7w93dXdFlECkMV3/KH1d/EhERERERNRzMluof/t0uf8yX5I+fUyIiqg5OwiIiIiIiIiIiehNiRRdARERERFR/qDRurOgSlM794ieKLoGIiKqL+ZJS4SQsIiIiIiIF4Ko6IiIiIiIiImpIdl48qugSlI5ny66KLoGIiIhK4SQsIiKqt1JTUzF27FgAwLRp07B8+XLFFlQPZGVlSbZpf//997F161bFFkTUgHG7ePnjxDYiIiIiIqoq5ktVx3yJ3pRXq7cVXYLS2fHXz4ouQem8Z/KOoksgIqJ6jJOwiEjhAgICkJaWBgBo1KgRzMzMMGbMGMyePRuHDx+W/EEPAM2aNUPHjh2xaNEiODk5SV3L09MT+/btw7Fjx9CxY0epcfLz86VCgZLQ4P79+zAwMBCECCKRCLq6umjdujU8PDwQGhoKY2NjwfkPHjzAkiVLsHnzZly+fBmNGzdG69atMWzYMAQHB6NJkyaSvhcvXkRsbCz27duHv//+G82aNYO9vT0CAwMxYsQIqKmpScaVJT09HT4+PlI1l2ZhYYGQkBCEhIRIfr527RoAoHHjxrCzs0NERASGDRsGAIiKikJ0dDQAQFVVFQYGBmjTpg0++OADTJgwARoaGpJru7m54eDBgwAADQ0NtG7dGpMnT8bEiRMBvAqtQkJCkJ+fL6inZHxZ/P39kZqaCpFIhC1btmDw4MGSYwcOHMCSJUvw008/4enTp7CwsEC/fv0wffp0mJiYAAD09PRw7tw5aGtrl/kMSkRFRWHr1q04efIkgP8+e3FxcZg1a5ak39atW+Ht7Q2x+NX+n2V9RgwMDJCXlwdNTU3Jub/88gs6deoEAJLzAaCoqAgrV65EcnIyLly4AC0tLXTp0gVz5sxBt27dJP1KB38qKirQ09ODra0tBgwYgGnTpkFfX7/MZ1laYWEhOnfujN9//x0nTpyAs7MzAMDV1RV5eXmYNm0aCgsLJf3f5L0trbzf423btmH48OG4fv265H0rzcbGBgMHDsSyZcuk6jAzM8PYsWMxa9asMn8vqPZwgot8cRKW/PGZyh9/74mIiKi+YL7EfIn5EvMl5kt1n7ioSNElKJ33TDtW3ImqZOetXxVdglLp19JF0SUQEdUqTsIiojrBy8sLKSkpKCwsxA8//IBJkyahUaNG6Nr11Va6586dg56eHm7duoWwsDAMGDAAFy9ehLq6uuQa169fR05ODiZPnozk5GSpkKyqSsZ88OABfvvtNyxevBhJSUnIysqSBHT37t1D9+7d8eDBA8ybNw/vvPMO9PX1ce7cOaSkpOC7777DpEmTAAA///wz+vbtC0dHR6xatQr29vYAgOPHj2PVqlVo27Yt2rf/73/oTElJgZeXl6Cm1wOxyoqJiUFwcDAePHiA+Ph4jBgxAiYmJnB1dQUAODo6Yt++fSguLsbdu3eRlZWF+fPn4+uvv0ZWVhZ0dXUl1woODkZMTAyePHmCdevWYdKkSWjSpAl8fX1ljv3LL7+g6P//uM7JycGQIUMkzxYAtLS0ZJ63evVqTJw4Ef7+/ti0aRMsLCxw/fp1rFu3DvHx8Vi2bBmAV4GikZHRGz0XANDU1MSiRYvw0UcfCQLNytDV1cWWLVsE956UlAQzMzNcv35d0iYWi+Hj44N9+/ZhyZIl6NOnDx48eIBVq1bBzc0NmZmZgnCwJPgTi8XIz89HTk4O4uLikJKSgiNHjqBly5YV1jZz5ky0bNkSv/8unJCgrq4OIyMjaGlpCUIyoOrv7evK+j3++OOPYWhoiLS0NMyePVtwzqFDh3Dx4kUEBQVJ1VFYWIgff/wRH374IQwMDDBhwoRK1UFEDRcnDBERERE1bMyXmC+9jvkS8yXmS6Tsdv91QtElKB1PThoiIqJqUFF0AUREwKsVSUZGRjA3N8eECRPQt29fbN++XXK8RYsWMDIywttvv42QkBDcuHEDZ8+eFVwjJSUF7733HiZMmID09HQ8ffq0WjWVjGlrawsfHx8cOXIEzZs3F/yhPnv2bFy/fh0///wzxo4di3bt2sHc3Bzvvvsu0tPTJau8xGIxAgICYGtriyNHjmDgwIGwsbGBjY0NfH19cfjwYbRr104wvoGBAYyMjASv0iviqkJXV1dyL6tWrYKWlha+//57yXE1NTUYGRmhZcuWcHJywpQpU3Dw4EH8+eefWLRokeBajRs3hpGREVq3bo2oqCjY2NgI3qvXNW/eXFJ/06ZNBc/WyMhI5sq7mzdvYurUqZg6dSqSk5Ph5uYGCwsL9OzZE2vXrsXcuXPf6DnI0rdvXxgZGSEuLq7K5/r7+yM5OVny89OnT7F+/Xr4+/sL+m3YsAEbN27EunXrMG7cOFhaWqJ9+/ZYs2YNBg0ahHHjxuHx48eS/iXBn7GxMRwcHBAUFIScnBw8evQIM2fOrLCunTt3Ys+ePVi6dGmV7qeq7+3ryvo9btSoEUaPHo3U1FSpc5KTk9G5c2c4OjpK1WFubi75vdq7d2+V7oWIiIioNojEfFX2RURUG5gvMV8qjfkS8yXmS0RERFQfKDqzqU+v+oA7YRFRnaSlpYW7d+9KtRcUFGD9+vUAIFilKBaLkZKSIlkBaG1tjY0bN2L06NFyrWn8+PEIDQ3FP//8g2bNmiEjIwN+fn5lrhwr2d765MmTyM3NRXp6OlRUZM9/ra2tsNXU1NCoUSM8f/683H729vbo168fNm/ejPnz55fZT0tLq8JrVVVmZiaeP39eZiD0pis2ZVFVVUVsbCxGjhyJqVOnwtTUtNLnjh49GkuWLMH169dhZmYmWVH59ttvC/p99913sLW1xcCBA6WuMWPGDGzevBl79+4VrFZ8XYsWLTBq1CgkJyejqKgIqqqqkq3lS29L//fffyM4OBhbt25F48aNK30vslT3vS39exwUFIRly5bh0KFD6NmzJwDg0aNH2LhxIxISEmSeLxaLcfjwYZw9exY2NjZvXAfJD7/qjYiIiIiI6hPmSzWH+ZIQ86WyMV+i0lRK/TuX5OOpuLDiTkRERFRrOAmLiOoUsViM/fv3Y/fu3ZgyZYqkvSS4KFnNNWjQIMl26wCwb98+PHnyBJ6engAAPz8/JCUlSYVkO3bsgI6OjqCtqArfQ18y5tWrVyVbedvZ2Qn6vPPOOzh37hwAYODAgUhPT8f58+cBQND3n3/+QevWrSU/L168WLKyEQB8fX2hqqoquPaZM2dgZmYm+VlWoPPkyZMy63/+/Dni4+NRUFCA3r17V+p+9+zZI/NYUVER0tPT8ccff+DDDz+s8FpVceHCBejp6cHY2Fiu1y2Lt7c3nJ2dERkZiaSkpEqf16JFC/Tr1w+pqamYO3cukpOTERgYKNXv/PnzcHBwkHmNkvaSz0h57O3t8fDhQ9y9exctWrSAvr6+4DNVsiJ2/Pjx6NChA65evVrpeymtuu+trN/jNm3aoEuXLkhOTpaEZBs2bJBspV9aYmIi1q5di+fPn+PFixfQ1NTE1KlTyx2zsLBQavv7YnERVESqZZxBpHj86jwiIiIioprBfIn5EsB8qSzMl8rGfKnm7bz6i6JLUDqeLTspugQiIiIqhZOwiKhOKAmvXrx4geLiYowcORJRUVH45ZdXf5RlZ2ejcePGOHbsGGJjY/Hll18Kzk9OTsaIESOgpvbqX2u+vr4ICwvDpUuXYGVlJenn7u6OL774QnDuTz/9BD8/v0rVWbIirLxVhVu2bMHz588RHh5e7pb1hoaGOHnyJADAzc1NakVYQkIC+vbtK2h7fUVkdnY2dHV1BW1ubm5SY4WHh2POnDl49uwZdHR0sHDhQgwYMKDM2kqIxWKpey0dYKiqqiI0NFSwhb48yBq3pi1atAi9e/fGxx9/XKXzAgMDMW3aNPj5+eHo0aPIzMxEdna2VL/Sqwnf1OufP29vb3h7e0uOf/bZZ3j48CEiIiLe6PrVfW/L+j0uERgYiNDQUHz22WfQ1dVFcnIyhg0bJvUZHjVqFD755BPcv38fkZGRcHV1haura7ljx8XFITo6WtBmCQdYwbGMM4gUjzuLyR8nthERERE1bMyXmC9VNG5NY77EfInK5/nBGEWXoHTeO5Ol6BKUzo42TRRdAhER1WOchEVEdUJJeKWuro6WLVtKwq4SlpaWMDAwgJ2dHf755x+MGDEChw4dAgDcu3cPW7ZswYsXLwQBWFFREZKTk7FgwQJJm7a2NqytrQXXvnnzZqXrzM3NBQBYWFjA0NAQBgYGklWJJUpWEurq6iI/Px8AJFtdnzt3Di4uLgBebVNeUsvr9wsARkZGUrW+ruS5lCbrWmFhYQgICICOjg7eeuutSgdQubm5sLS0FLSVBBhaWlowNjYuc/v76rC1tUVBQQHy8vLeaLWinp4eCgoKpNrz8/Ohr68v85yePXvC09MTERERCAgIqPRY/fr1w4cffoigoCAMHDgQhoaGUn1sbW0ln53XlbTb2tpWOFZubi709PRkjgEAP/74I44ePQoNDQ1Be4cOHTBq1CikpaWVe/3qvrcV/R77+PggNDQUGzZsQM+ePXHkyBHExcVJXUdfX1/y2d+wYQOsra3RpUsXqdC4tIiICEyfPl3Q5q0fUKX6qWKc4CJfnIRFRERERCRfzJeYL5XGfEk25kvMlxTqGLMQeZticF3RJSidHeAkLCIienOchEVEdYKs8KoskyZNQlxcHLZs2QJvb298++23MDU1xdatWwX99uzZg/j4eMTExEhtu/4mnj59ijVr1qBnz55o3rw5AGD48OH45ptvMHfuXKlVhKW5uLjA3t4eS5cuxfDhw2skWCpPs2bNKv18S5w9exa7du2SWvVWOsCoKUOHDsWsWbOwePFiJCQkSB3Pz8+XCgdLs7Ozw6+//irV/ttvv0lt71/awoUL4ezsXG6f16mpqWHMmDFYvHgxdu7cKbOPj48PRo4cie+//x4DBw4UHIuPj4ehoSE8PDzKHeeff/7Bd999h8GDB5f5+Vm5ciXmz58v+fnWrVvw9PRERkYGOnfuXOG9VPe9rej3WFdXF8OGDUNycjIuXboEW1tb9OjRo9xr6ujoYNq0afj4449x4sSJMgNeDQ0NqXCQW8XLHycNERERERFRXcZ8qWYxX3qF+VL5mC9ReVQdKp4oSFXz/ZMLii6BiIiISuEkLCKqdxo3bozg4GBERkZi8ODBSEpKwtChQ9G2bVtBv1atWiEiIgK7du2q1Nbor/vnn3/w7NkzPHz4EL/++isWL16MO3fuYPPmzZI+sbGxyMrKQqdOnRATE4MOHTpAW1sbf/zxB44ePSqpSSQSISUlBR4eHujWrRsiIiLg4OCAFy9e4NChQ/j333+lgrz8/Hzcvn1b0Karqwttbe0q30tFXr58idu3b6O4uBh3795FVlYW5s+fD2dnZ4SFhVXpWkVFRZJt8EtoaGjAwcGh0tdo1aoVEhISMHnyZDx48ABjxoyBhYUFbt68iXXr1kFHRwfx8fFlnh8aGooePXpgwYIF+OCDD1BUVIT09HQcPXoUiYmJZZ7n5OSEUaNGYeXKlZWuFQDmzZuHsLCwMlcQ+vj4IDMzE/7+/liyZAn69OmDBw8eYNWqVdi+fTsyMzMF76tYLMbt27chFouRn5+Po0ePIjY2Fvr6+li4cKGk35YtWxAREYGzZ88C+G+VbAkdHR0AgJWVFUxNTat0T7LI470NCgpCjx49kJubi/Dw8Eqd89FHH2HevHnYtGkThg4dWpWSiYiIiGpW9b8RiIiIFIT5EvMl5kuvMF+i2vSymfz/3dPQnXlW9uRdIiKqJ5gvKRVOwiKiemny5MlYtmwZFi9ejN9//x1fffWVVB99fX306dMHSUlJbxSS2dnZQSQSQUdHB61bt8a7776L6dOnw8jISNLH0NAQP//8MxYtWoQlS5bgypUrUFFRgY2NDUaMGIGQkBBJ3y5duuDXX39FbGwsJk2ahNu3b0NbWxvt27dHQkICAgMDBeOPHTtWqqa4uDjMmjWryvdSkdOnT8PY2BiqqqrQ19dHmzZtEBERgQkTJkit/qrIo0ePJFvil7CyssLFixerdJ2JEyfC1tYWS5cuhbe3N54+fQoLCwu89957UtuCv87V1RU7d+5ETEwM4uPjoaKiAicnJ+zfv18qTH1dTEwMMjIyqlSruro6mjVrVuZxkUiEDRs2YPny5UhISMDEiROhqamJrl27IisrC926dRP0f/DgAYyNjSESiaCnpwc7Ozv4+/tj2rRp0NPTk/QrKCiQ+rqCmiSP97Z79+6ws7PDxYsXMWbMmEqd07RpU4wZMwZRUVH44IMPan2lLxERERERESkn5kvyxXzpP8yXysZ8qWFTyflD0SUonelNziq6BKXzI95RdAlERFSPicRiMefVERFRvZSamoqQkBDk5+crupR6JyAgAPn5+VJfs6BMPFSGKboEonLx6x3lz7Nle0WXQES1bG9xpkLH7zh2mULHr09+SSn/f+gmIiJSFOZLb475EpHiMV+SP+ZLRA0P86X6oz7kS9wJi4iI6rWCggLo6Ohg0qRJWLRokaLLqfOys7PRr18/FBYWvtEKXmrYGOoQERERERGRMmK+VDXMl4iIiIiIZOMkLCIiqreGDBmC7t27AwAMDAwUW0w90aFDB5w8eRIAoKOjo9hiqN7hKjCq6zhRUP74e09EREREyo75UtUxXyKqO/h3u/wxX5IvfkaJqKHhJCwiIqq3dHV1oaurq+gy6hUtLS1YW1srugwiAgOdmsBQh4iIiIiIqor5UtUxXyKqO5gvyR/zJSIiqg5OwiIiIiKiSmGoI18MdIiI6j+RWNEVEBERERHVH8yW5I/5EhFR/cd8SblwEhYRERERkQIweJQ/Bo9EREREREREdRf/bpc/5kvyx88pERFVBydhEREREREpAAMdIiIiIiIiImpIOGFI/pgvERER1S0qii6AiIiIiIiIiIiIiIiIiIiIiIioPuNOWERERHXEjRs3EBkZiV27duHOnTswNjbG4MGDMXfuXBgaGgr6pqenw8/PD+PHj8eqVasEx7KysuDu7o779+/DwMBA8nOJZs2aoWPHjli0aBGcnJwk7QEBAUhLS5Oqy9PTE7NmzRJcQ5alS5di/vz5yMvLg6ampuDYkydPYGRkhPnz52Pq1KmwsLDAtWvXpK4RFxeHWbNm4erVq7C0tMSJEyfg7Oxc7rhUe7iyTr64+lP++BklIiIiIqKGjNkSs6W6jn+3yx/zJfnj55SIiKqDk7CIiIjqgMuXL6Nr166wtbVFeno6LC0tcfr0aYSFhWHnzp04duwYmjZtKumflJSEmTNnYvXq1YiPj5cKpmQ5d+4c9PT0cOvWLYSFhWHAgAG4ePEi1NXVJX28vLyQkpIiOE9DQwPa2trIy8uTtE2bNg0PHjwQ9FVRUUFERAQ2b96MkSNHCq6xceNGPH/+HH5+fpK2mJgYBAcHC/rp6upWeB+kOAx15IuBDhEREREREckLs6VXmC3VbcyW5I/5EhERUd3CSVhERER1wKRJk6Curo49e/ZAS0sLAGBmZgYXFxdYWVnhk08+wRdffAEAuHLlCnJycrBp0yYcOHBAZjAlS4sWLWBgYAAjIyOEhIRg0KBBOHv2LNq1ayfpo6GhASMjI5nnl27X0tJCYWGhVN+BAwciOTlZqp7k5GQMHjxYEPbp6uqWORbVTQx15IvBo/zxM0pEtU6s6AKIiIiIXmG2RPUB/26XP+ZL8sfPKRHVOuZLSoWTsIiIiBTs3r172L17NxYsWCAJyUoYGRlh1KhRyMjIQGJiIkQiEVJSUjBgwADo6+vDz88PSUlJlQrKShQUFGD9+vUAIFipKA9BQUF47733cO3aNZibmwN4tRLz0KFD2L17t1zHKq2wsBCFhYWCtmJxEVREqjU2ZkPEUEe+GOgQERERERGRPDBbkg/mSzWP2ZL8MV8iIiKqW1QUXQAREVFDd+HCBYjFYjg4OMg87uDggPv37+Pff/9FcXExUlNTJVuv+/j44PDhw7hy5UqF45iamkJHRwcGBgb47rvvMGjQINjb2wv67NixAzo6OoJXbGxspe/F09MTLVu2FGwln5qailatWqFPnz6CvuHh4VJjZWdnV3qs0uLi4qCvry94XcHZN7oWEREREREREVF9wmyp+tkSwHyJiIiIiKqPO2ERERHVEWJx+fuNqqurY+/evXj8+DH69+8PAGjWrBk8PDyQnJyMefPmlXt+dnY2GjdujGPHjiE2NhZffvmlVB93d3fJ1vQlSm/zXhFVVVX4+/sjNTUVkZGREIvFSEtLw9ixY6GiIpz7HRYWhoCAAEGbiYlJpccqLSIiAtOnTxe0eesHyO5MVEdw9af8cfUnERERERE1ZMyW3jxbApgv1QavVm8rugSls+OvnxVdgtJ5z+QdRZdARET1GCdhERERKZi1tTVEIhFyc3Ph7e0tdTw3NxfNmzeHgYEBkpKScO/ePcHW8sXFxfjjjz8QHR0tFUaVZmlpCQMDA9jZ2eGff/7BiBEjcOjQIUEfbW1tWFtbV+t+AgMDERcXhx9//BHFxcW4ceMGxo4dK9WvWbNm1R6rhIaGBjQ0NARt3Cqe6jpOGCIiIiIiIiJ5YLYkH8yXat6uG78pugSl49mSE4aIiIjqEn4dIRERkYIZGhrCw8MDiYmJePr0qeDY7du38e233yIgIAB3797Ftm3bsH79epw8eVLyOnHiBO7fv489e/ZUesxJkybhzz//xJYtW+R9O7CyskKvXr2QnJyMlJQU9O3bF+bm5nIfh4iIiIiIiIiImC0REREREdUV3AmLiIioDvj888/h6uoKT09PzJ8/H5aWljh9+jTCwsJga2uLuXPnYu3atTA0NMTw4cMhEokE5/fv3x9JSUnw8vKq1HiNGzdGcHAwIiMjMXjwYMn1CgsLcfv2bUFfNTU1NGvWrEr3ExQUhODgYABAamqqzD4PHz6UGqtx48bQ09OT/Hzu3Dmp8xwdHdGoUaMq1UNERERUE0Tlf+MPERERUa1htvRfXcyWiIiIqD5hvqRcuBMWERFRHWBjY4NffvkFrVu3xvDhw2Fubo5+/frB1tYWR44cgY6ODpKTk+Ht7S0VkgHAkCFDsH37dty5c6fSY06ePBm5ubnIzMyUtO3atQvGxsaCV/fu3at8P0OGDIGGhgYaN26MwYMHy+wzd+5cqbFmzpwp6OPj4wMXFxfB6++//65yPUREREREREREyozZErMlIiIiIlI8kVgs5rw6IiKiOigyMhLLli3D3r170aVLF0WXU+94qAxTdAlKZ/et3xVdglLxbNle0SUQEdV7e4szK+5UgzqPWabQ8euTn9ZNV3QJREREDQ6zpepjviRfzJbkj/kSEVH1MV+qP+pDvsSvIyQiIqqjoqOjYWFhgWPHjqFTp05QUeEGlkTKhMGj/DF4JCIiIiIi+g+zJapr7JMnKLoEpVO45qWiS1A6th/+rOgSiIioHuMkLCIiojps7Nixii6BSIITXOSLk7CIiIiIiIiopjFborrEfE6OoktQOsyX5M8TzECJiOjNcRIWERERKSUGEPLHSVjyxedJREREREREVLcxX5IvZiHyx2dKRERUt3ASFhERESklBhDyx+CRqOHhv0uJKiBWdAFEREREVJP4N5F8qRkaKroEpeOT84eiS1A669/tougSlMrLazcUXQJR3cd8SalwEhYRERERVQqDR/nipDYiIiIiIiIiakhe3r2r6BKUTvoH7oouQel8uO8HRZegVL6wsVZ0CUREtYqTsIiIiIioUjhpSL44qY2IiIiIiIiIGhJmS/Ln2VLRFSgfThoiIqLq4CQsIiKiarpx4wYiIyOxa9cu3LlzB8bGxhg8eDDmzp0Lw//fYtvNzQ3Ozs5Yvny55LwVK1Zg5syZSEtLg4+Pj8w+rztw4ADi4+Px008/4eHDhzAxMUGHDh0wadIk9OzZEwCQlZUFd3d33L9/HwYGBlLXiIqKwtatW3Hy5ElJm0gkKvceIyMjsXr1akybNg2zZs2StM+aNQuLFi3CgQMH4ObmJml3c3NDq1at8PXXXyM1NRVjx46Fvb09cnNzBdfNzMzE8OHDYW5ujqtXrwIAUlNTERISgvz8fEm/3NxcvPvuu+jSpQu+/fZbqKurl1svUX3A4FH+OLGNiIiIiIjqI2ZLzJYaCv7dLn/Ml+SPn1MiIqoOTsIiIiKqhsuXL6Nr166wtbVFeno6LC0tcfr0aYSFhWHnzp04duwYmjZtKnVeZGQkli5dim3btsHLy6tSYyUmJmLy5MkYPXo0MjIyYGVlhYKCAhw4cAChoaH49ddf3/g+8vLyJP+ckZGBuXPn4ty5c5I2HR0dnDt3DllZWYKg7MCBA2jVqhWysrIkQdmzZ89w7Ngx+Pv7S/ppa2vjn3/+wdGjR9G1a1dJe1JSEszMzMqt7ZdffkG/fv3g7e2N1atXQ0VF5Y3vk6guYaBDREREREREzJaYLTUknDAkf8yXiIiI6hZOwiIiIqqGSZMmQV1dHXv27IGWlhYAwMzMDC4uLrCyssInn3yCL774QtJfLBZj6tSp+Oabb7B37164urpWapzr168jJCQEISEhWLZsmeBYu3btMHXq1Grdh5GRkeSf9fX1IRKJBG0A4O7ujhkzZuDly5dQU1PDw4cPceLECSQkJCAzM1PS7+jRoygsLIS7u7ukTU1NDSNHjkRycrIkKLt58yaysrIQGhqK9PR0mXX9+OOPeP/99zFx4kQsWrSoWvdIRERERERERFTXMFtitkREREREyoPT/YmIiN7QvXv3sHv3bkycOFESkpUwMjLCqFGjkJGRAbFYDAB4+fIl/Pz8sHHjRhw8eLDSIRkAbNq0CS9evMDMmTNlHq9oy3d5cHd3x6NHj/DLL78AALKzs2Fra4shQ4bgp59+wrNnzwC8WsFoYWEBCwsLwfmBgYHYsGEDnjx5AuDV1vBeXl546623ZI63ZcsWDBgwAHPmzKkwJCssLMSDBw8Er2JxUTXvmIiIiKh8IjFflX0RERGRNGZLdSdbApgvERERkWIoOrOpT6/6gDthERERvaELFy5ALBbDwcFB5nEHBwfcv38f//77LwDgq6++AgD8/vvvsLe3r9JY58+fh56enmAF4aZNmwTbsh89ehROTk5VvY1Ks7GxgYmJCbKystC1a1dkZWWhV69eMDIygpmZGY4ePQp3d3dkZWUJViqWcHFxQevWrbFx40aMHj0aqampWLZsGS5fvizV99GjRxg2bBhmz56N8PDwCmuLi4tDdHS0oM0SDrCC45vfMFEN4xb88sct+ImIiIiIqD5htlR3siWA+VJt6O/lo+gSlM7Y87sVXYLSSbEt/ytOiYiIysNJWERERNVUshqxLOrq6gCA7t274+TJk/j000+Rnp4ONbWq/b/h11ckenp64uTJk/jrr7/g5uaGoqKaX5nn5uaGrKwsREREICsrC2FhYQCAXr3+j717j8+5/v84/ry2MYdrsxAzZmPMKWcxh2zDIhRiDjmNWTmTQxqaM5FjIZUdlJxFIiQ2Vk6pKHIopZNDwiiHHez6/eHn+rraxmYX13Z53G+36/bt+nzen/fn9fns0teevT7vy19xcXHy8/PTvn37FBYWlu7xvXv3VnR0tEqXLq2rV6+qZcuWmj9/fppx+fPnV6NGjfTee++pS5cuGYaRt4WHh2vYsGEW29oVCrm/iwQeEhqGAAAAAAAS2ZJk+2xJIl96GD7dssLWJdgd8iUAAHIWvo4QAID7VK5cORkMBh09ejTd/UePHtXjjz8uNzc3SVLVqlW1fft2xcbGqlOnTkpJScn0ucqXL6/Lly/r7Nmz5m1Go1HlypWTl5dXtq4jKwIDA/Xll1/qwoUL+vbbb+Xv7y/pVlAWGxur3bt3KykpSU2aNEn3+K5du2rv3r0aP368unfvnmFY6OjoqPXr16tWrVoKDAzM8B7f5uzsLFdXV4uXg8ExexcLAAAAAADwAJEt5ZxsSSJfAgAAQPbRhAUAwH0qUqSIgoKCtHDhQl2/ft1i39mzZ/Xhhx8qJCTEYnuNGjW0fft27dq1Sx07dlRycnKmztWhQwflyZNH06dPt1b59yUwMFBXr17V7NmzVb58eRUrVkyS1LhxY+3fv1+bN282Ly2fnsKFC+u5557Tzp071bt377uey9nZWR999JGefPJJBQYG6ocffrD69QAAAAAAANgK2RLZEgAAAOwLX0cIAEA2zJ8/Xw0aNFDz5s01efJklSlTRkeOHNHIkSPl6+uriIiINMdUr15dO3bsUNOmTdWxY0etWrVKefLkkSSdP39eBw8etBhfokQJlS5dWrNmzdKQIUN08eJFhYSEqEyZMrp48aKWLl0q6dYTfnf6/vvv5eLiYn5vMBhUvfqt5amvX7+e5jwuLi7y8fG56/WWLVtWpUuX1ltvvaWuXbuat3t6esrDw0PvvvuuunTpctc5YmJitHDhQhUpUuSu46RbYdnatWsVHByswMBA7dixQ1WqVLnncUBusPX0IVuXYHdYgh8AAABAbkO2dAvZ0qOhVZMOti7B7gz+aaOtS7A7b5arYOsSAAC5GE1YAABkQ/ny5fXVV19p/Pjx6tixo/766y+ZTCY9//zz+uCDD1SgQIF0j6tatao5LAsODtaqVaskScuWLdOyZcssxk6aNEljx47VoEGDVKlSJc2ePVsdOnTQlStXVKRIEdWvX19btmxR1apVLY5r3LixxXtHR0fzMvUnTpxQzZo1LfY3bdpUn3/++T2vOTAwUEuWLFFAQIDFdn9/f8XExCgwMPCux+fPn1/58+e/53luy5s3r9asWaOOHTuaw7Innngi08cDORUNQwBgB0wmW1cAAAByObKl/yFbsn+bdqyxdQl2h3wJAOwA+ZJdMZhM/EQBALCmcePGafbs2dq2bZv8/PxsXc4jK8gh2NYl2B1WbrIuQjIAyL5tqatten6/rrNsev7cZO+Hw21dAgAAuQbZUs5BvmRdZEvWR74EANlHvpR75IZ8iZWwAACwsgkTJsjb21t79+5V3bp15eDgYOuSAAAAAAAAkEuQLQEAAAC5E01YAAA8AL169bJ1CQByOJ7+tD6e/gQAAABgL8iWYI9aVmhk6xLszge/b7F1CXanu2dDW5cAAMjFaMICAAAAbICGIQAAAAAA8Cj59PgXti7B7jT3oGEIAICchCYsAAAAAACA+2Aw2boCAAAAAAAA5GbkS/aFJiwAAABkCis3WRdfR2h9fEYBAAAAAMi5+L3d+siXrI/PKQAgO2jCAgAAQKYQ6lgXgQ4AAAAAAHiUkC1ZH/kSAAA5i4OtCwAAIDf5/fff1bt3b3l4eChv3rzy8vLSkCFDdOHCBfOYgIAAGQwGGQwG5cuXT76+vpo2bZpMJpNOnDihAgUKaNmyZRbzpqamqkGDBurQoYN52/Lly+Xo6KgBAwakqSMuLk4Gg0EJCQnp1jl+/HgZDAb17dvXYvvBgwdlMBh06tQpSdKpU6fMtRoMBhUuXFj+/v6Kj49PM1+NGjUkSd7e3hbH/Pf1zDPPKE+ePFqxYkW6tYWGhqpWrVrasmWLDAaDzp49a7G/RIkS8vb2tth2u87t27enOycAAAAAAEBuQb5EvgQAAAD7xEpYAABk0s8//6z69evL19dXy5cvV5kyZXTkyBGNHDlSmzdv1t69e1W4cGFJUlhYmCZOnKjExETt2LFDL774otzc3NSvXz+9/vrrGjRokAIDA1WiRAlJ0qxZs/Tzzz9rw4YN5vNFRkbqlVde0TvvvKNZs2YpX758Wao3X758ioyM1PDhw1W+fPm7jv38889VpUoV/f3335oyZYpat26tEydOqHjx4mnGfvXVV7p586Ykaffu3Wrfvr2OHz8uV1dXSVL+/PnVs2dPRUVFqXPnzhbHXr16VatWrdLrr7+uRo0aycnJSXFxceZxR48e1fXr13Xt2jWdOnXKHJbFxsbK2dlZDRs2zNI9gHXxZJ118fQncgP+3AMAAADWRb50C/nSo4nfMa2v2/E/bV2C3VnWOcjWJdiV1G9/sHUJAPBQ0YQFAEAmDRgwQHnz5tVnn32m/PnzS5JKly6tmjVrysfHR2PGjNHbb78tSSpQoIDc3d0lSb169dL8+fO1bds29evXT4MGDdL69esVFhamjRs36tixY4qIiNDKlStVtGhRSdIvv/yi3bt3a+3atYqNjdVHH32kF154IUv1VqhQQcWKFdOYMWO0atWqu44tUqSI3N3d5e7urtGjR2vFihXat2+fnnvuuTRjH3/8cfM/3w4FixUrJjc3N/P20NBQtW3bVr/99ptKly5t3r569WqlpKSoa9euMhqNevLJJy1Csri4ODVq1EipqamKi4tTSEiIebufn1+Wg0JYF01D1kXwCAAAAACPHvKlW8iXHk1kS9ZHvvQg0DQEALh/NGEBAJAJFy9e1NatWzVlyhRzQHabu7u7unbtqpUrV2rhwoUW+0wmk7744gsdO3bM/LSgwWBQdHS0qlWrpvfee0+RkZHq3LmzRSAVHR2tVq1aqVChQurWrZsiIyOzHJJJ0uuvv64nn3xSBw4cUJ06de45/vr163r//fclSXnz5s3y+W5r2bKlihcvrpiYGEVERJi3R0dH6/nnnzcHaoGBgVqzZo15f2xsrAICAnTz5k3FxsZahGS9e/fO8HyJiYlKTEy02JZquikHg+N9XwPwoBE8Wh/BI4CHzmTrAgAAQG5CvpQ15Ev2p+UTAbYuwe6899snti7B7oSVbmTrEgA8asiX7IqDrQsAACA3+PHHH2UymVSpUqV091eqVEmXLl3S+fPnJUkLFy6U0WiUs7OzGjdurNTUVA0ePNg83svLS3PnzlXfvn115swZzZs3z7wvNTVVMTEx6tatmySpc+fO+uKLL/TLL79kue5atWqpY8eOGjVq1F3HNWjQQEajUQULFtTMmTNVu3ZtNW3aNMvnu83R0VE9e/ZUTEyMTKZbf3s8efKk4uPjLcKuwMBAnThxQmfOnJEk7dy5U/7+/mrcuLF27twp6dYy/b/99psCAwMzPN+0adNUqFAhi9cvOnbf9QMAAAAAAFgb+VLWkC/ZH4PBgZeVX6kSLyu/AADIDlbCAgAgC24HPhm5/XRf165dNWbMGF26dEnjxo1TgwYN1KBBA4uxvXr10muvvaZBgwbJ1dXVvH3btm26evWqWrZsKUkqWrSogoKCFBUVpUmTJmW55smTJ6tSpUr67LPPVKxYsXTHrFy5UhUrVtThw4f1yiuvKCYmRnny5Mnyue7Uu3dvvf7664qNjVWTJk0UHR0tb29vNWnSxDymQYMGyps3r+Li4lS9enVdv35dtWrVUmpqqs6fP69ffvlFcXFxyp8/v/z8/DI8V3h4uIYNG2axrV2hkGzVDwAAAAAA8CCQL2Ue+ZKdebywrSuwO0eSHr/3IAAA8NDQhAUAQCaUK1dOBoNBR48eVbt27dLsP3r0qB5//HHzMuiFChVSuXLlJEmrVq1SuXLl5Ofnp2bNmlkc5+TkJCcny/87joyM1MWLFy2WpU9NTdV3332nCRMmyMEhawtZ+vj4KCwsTK+++qoiIyPTHePp6any5curfPnySklJUbt27XT48GE5Oztn6Vx3Kl++vJ566ilFR0crICBA77//vsLCwmQwGMxjChQooLp16yo2NlYXL15Uo0aN5OjoKEdHRzVo0ECxsbGKjY1Vw4YN77p8vbOzc5paWSre+viqN+vi6wgBAAAA4NFCvpR15Ev2JeXYj7Yuwe60KnDD1iXYnTdtXQAAIFejCQsAgEwoUqSIgoKCtHDhQr388ssWAdbZs2f14YcfasCAAekeazQaNWTIEI0YMULffvutRUj0XxcuXNDHH3+sFStWqEqVKubtN2/eVKNGjfTZZ5+pRYsWWa4/IiJCPj4+WrFixT3HdujQQREREeZrzY7Q0FD169dPzz33nP7880+FhISkGRMYGKgVK1bo0qVLCggIMG9v3Lix4uLitHPnTvXt2zdbdcA6aBqyLpraAAAAAODRQr50f8iX7AfZkvWRLwEAkLNk7VEHAAAeYfPnz1diYqKaN2+uXbt26ffff9eWLVsUFBQkX19fRUREZHjsSy+9pBMnTmjt2rV3PccHH3ygIkWKqGPHjnriiSfMr+rVq6tly5ZpnjT8/vvvdfDgQfPr0KH0g4zixYtr2LBhevPNez/HYzAYNHjwYL3++uu6du3aPcffTXBwsPLkyaOXXnpJTz/9tDw9PdOMCQwM1I8//qitW7fK39/fvN3f31/r16/X77//rsDAwGzVAQAAAAAAkBOQL2Ud+RIAAAByC5qwAADIpPLly+urr75S2bJl1bFjR3l5eemZZ56Rr6+vvvzySxmNxgyPLVy4sHr06KHx48crNTU1w3FRUVFq165duk8ztm/fXhs2bNDff/9t3ta4cWPVrFnT/Kpdu3aGc48YMeKuNd6pZ8+eSk5O1vz58zM1PiMFChRQ586ddenSJfXu3TvdMfXr15ezs7NMJpNF/fXq1VNycrKMRqOefPLJbNUBAADwIBhSeWX2BQAAbiFfyjryJQAAYM9sndnkplduYDCZTCZbFwEAQG41btw4zZ49W9u2bZOfn5+ty8EdghyCbV0CgIeMrzWwPr7WADndttTVNj1/g06zbHr+3GT3yuG2LgEAgByLfCnnIl8CHj3kS9ZFtoTcgHwp98gN+ZKTrQsAACA3mzBhgry9vbV3717VrVtXDg4sMgn7RQCBnI5QBwAAAEBuRL6ER4VjJldRQ+Z9cOwzW5dgd5p7NLB1CQCAXIwmLAAAsqlXr162LgFALkTDEAAAAADgNvIlPAo+PfGlrUuwOzQMAQCQs9CEBQAAANgAK4tZH41tAAAAAADkXOWX9bV1CXYn9U2TrUuwO+UG77V1CQCAXIwmLAAAAMAGaBgCAAAAAACPkh9fWGTrEuwO+RIAADkLTVgAAAAAAAD3g4fOAQAAAAAAkB3kS3aFJiwAAGCX+Ko3AAAAAAAAAAAAAA8LTVgAADwAISEhWrJkiSTJyclJpUqVUnBwsCZOnKh8+fJJkgwGQ7rHLl++XJ07d1ZcXJwCAwN16dIlubm5pRk3fvx4TZgwQZLk6OgoNzc3Va5cWc8//7z69esnZ2dn89iAgADt3LlTkuTs7KyyZctq4MCB6t+/vyTp5s2beuONNxQTE6Nff/1V+fPnV/ny5RUWFqY+ffqYrykhIUHr1683zztr1ixNnjxZZ86cMV/XbdeuXZO7u7smT56swYMH69ChQ3rttde0d+9eXblyRe7u7qpXr57eeustFStWzOLYadOmaezYsXr99dc1cuTIzN52CyzFbX00tgEAAAAA8PCQL9k+XwIAAACywsHWBQAAYK9atGihM2fO6Oeff9acOXP0zjvvaNy4cRZjoqOjdebMGYtX27ZtM32OKlWq6MyZM/rtt98UGxur4OBgTZs2TQ0aNNA///xjMTYsLExnzpzRDz/8oI4dO2rAgAFavny5JGnChAmaM2eOJk2apB9++EGxsbF68cUXlZCQcNfzd+/eXVevXtVHH32UZt+aNWuUlJSkbt266fz582ratKkKFy6srVu36ujRo4qOjpaHh4euXr2a5tioqCi98sorioqKyvS9AAAAAAAAsDfkS+RLAAAAyD1YCQsAgAfE2dlZ7u7ukiRPT081a9ZM27Zt0/Tp081j3NzczGPuh5OTk/l4Dw8PVa1aVUFBQapevbqmT5+uyZMnm8cWKFDAPHb8+PFatmyZNmzYoC5dumjDhg3q37+/goODzeOrV7/3SlLFihXTs88+q6ioKL3wwgsW+6KiotS2bVsVLlxY69ev1+XLl7V48WI5Od3660eZMmUUGBiYZs6dO3fq+vXrmjhxot5//33t3r1bDRo0yPrNAQAAAAAAyOXIl8iXAAAAkHuwEhYAAA/B4cOHtXv3buXNm/eBn6tixYp65pln0n168E758+dXUlKSJMnd3V07duzQ+fPns3y+0NBQ7dixQ7/++qt5288//6xdu3YpNDTUPH9KSorWrVsnk8l01/kiIyPVpUsX5cmTR126dFFkZGSWawIAAAAAALA35EvkSwAAAMjZWAkLAIAHZOPGjTIajUpJSVFiYqIcHBw0f/58izFdunSRo6OjxbYffvhBpUuXzta5K1asqM8++yzdfTdv3tTy5cv13Xff6cUXX5QkzZ49Wx06dJC7u7uqVKmiBg0aqE2bNnrmmWfuea7mzZvLw8ND0dHRGj9+vCQpJiZGnp6eatq0qSTJz89Po0eP1gsvvKC+ffuqbt26atKkiXr06KHixYub57py5YrWrFmjPXv2SJK6deump556SvPmzZPRaMywhsTERCUmJlpsSzXdlIPBMYMjANvbevqQrUuwO8097v2ENQBYk+Hu/+0PAAAg28iXyJfsSfOSNW1dgt3ZfPprW5dgd57x4HMK4OEiX7IvNGEBAPCABAYG6u2339bVq1c1Z84cOTk5qX379hZj5syZo2bNmlls8/DwyPa5TSaTDAaDxbaFCxdq8eLFSkpKkqOjo15++WX169dPklS5cmUdPnxYX3/9tb788kvt2rVLzz77rEJCQrR48eK7nsvR0VE9e/ZUTEyMxo0bJ5PJpCVLlqhXr15ycPjfoptTpkzRsGHDtGPHDu3bt0+LFi3S1KlTtWvXLlWtWlWStHz5cvn4+JiXqq9Ro4a8vLy0cuVK81OP6Zk2bZomTJhgsa2MKslHVTJ/03BPNLhYF01YAAAAAIB7IV8iX7IrplRbV2B3HPjSIwAAchSD6V5rtgIAgCwLCQlRQkKC1q9fL0lKTU1V9erVNXToUHPYYzAYtG7dOrVt2zbdOeLi4hQYGKhLly7Jzc0tzf7x48dr/fr1OnjwYJp9zz33nH7++WcdPnxYkhQQEKAyZcpozJgxyp8/v0qUKGERYKVn6dKl6t69u37++WeVKVMmzTXd6eTJkypfvry2bdum1NRUNW/eXL/88ou8vLwynD8pKUk1a9ZUnTp1tGTJEklS3bp1deDAAYvaUlNTVb9+fX355ZcZzpXek4rtCoXwpKKV0TSEnI5GQeDRsy11tU3P3zB4lk3Pn5t8uXq4rUsAACDXIV8iX7I3ZEvWRxYCANlHvpR75IZ8iZWwAAB4CBwcHDR69GgNGzZML7zwgvLnz//AznXs2DFt2bJF4eHhFtsLFSqkcuXKZXqeypUrS5KuXr16z7E+Pj7y9/dXVFSUTCaTmjVrdteATJLy5s0rHx8f8/zff/+9Dhw4oLi4OBUuXNg87uLFiwoICNCxY8dUsWLFdOdydnaWs7OzxTYCMusj1LEugkcAAAAAQFaQL6VFvpS7kC1ZH/mS9fE5BQBkB01YAAA8JMHBwRo5cqQWLFigESNGSJISEhJ09uxZi3EuLi4qWLCg+f33338vFxcX83uDwWBeTj0lJUVnz55VamqqLly4oLi4OE2ePFk1atTQyJEjM11bhw4d1LBhQzVo0EDu7u765ZdfFB4eLl9fX4tg6vLly2mejCxSpIg8PT0VGhqqsLAwSVJMTIzFmI0bN2rFihXq3LmzfH19ZTKZ9Mknn+jTTz9VdHS0JCkyMlJ169ZV48aN09T35JNPKjIyUm+88UamrwnWR6gDAAAAAIBtkS+RL+VmH/ye8UpkuD8tK7SwdQl26B9bFwAAyMVowgIA4CFxcnLSwIEDNWPGDPXr10+S1KtXrzTjpk2bpldffdX8/r+hkaOjo1JSUiRJR44cUYkSJeTo6KhChQqpcuXKCg8PV79+/dI8uXc3zZs31/LlyzVt2jRdvnxZ7u7uatKkicaPHy8np//9dSEuLk41a9a0ODY0NFSLFy9W+/btNXDgQDk6OqZZAr9y5coqUKCAhg8frt9//13Ozs4qX768Fi9erO7duyspKUlLly7VqFGj0q2vffv2mjVrlqZOnao8efJk+rpgXTwFZl00tQEAAAAAsop8iXwpN+vu2dDWJdidrae/sHUJdocMFACQHQaTyWSydREAAADWFuQQbOsSADxkNLZZH8Ejcrptqattev6GHWba9Py5yZdrRti6BAAAgCwjXwIePeRL1kW2hNyAfCn3yA35EithAQAAADZAoIPcgM+pdRE8AgAAAACsyaFAAVuXYHf+uvmvrUuwLwYHW1dgf0yptq4AwF3QhAUAAADALtDgAgAAAAAAHiWbf9pj6xLsTnMPvjbTumgYAvBooQkLAAAAmcKKONZFwxAAAAAAAHiUkC1ZH/kSAAA5C01YAAAAyBRCHesieERuwJ97AAAAAIC18Dum9W0+/a2tS7A7z5SsbesS7AtfnQfgEUMTFgAAADKFpiHrIngEAAAAAACPErIl62vuUdPWJdghmoYAAPePJiwAAADABggerY/GNgAPm8Fk6woAAACA3CPZlGLrEuyOwdHR1iXYHdPNm7YuAcAjhnzJvtCEBQCwiZCQEC1ZskSS5OTkpFKlSik4OFgTJ05Uvnz5JEkGg0Hr1q1T27ZtJUnjx4/XhAkT7jrv22+/rZEjR+rSpUtycrr1f3P//vuvHnvsMTVs2FBxcXHmsXFxcQoMDNRPP/0kHx8fSdKePXvUqFEjtWjRQps2bUoz/7p16zR9+nQdPXpUqampKl26tIKCgjR37lxJUkxMjIYOHaqEhIQ0x/73egwGQ7rXsHz5cnXu3Nlc36VLl+Tm5iZJOn36tJo3b67HHntMn3zyib799lsFBgbKzc1NZ86cMd87Sfrqq69Ut25dSZLJZLK45jvnvM3b21tDhw7V0KFDze9//fXXNPVNmzZNr776qvn92rVrtWDBAn377be6ceOGSpcurYYNG2rQoEGqWbOm+b706tVLkuTg4CBXV1f5+vqqVatWGjJkiAoVKmSe787Pxp2aN2+uLVu2pHvPgNyIhiEAAAAAuH9kS2RLZEu5Tx4D/1nS2mgYAgAgZ3GwdQEAgEdXixYtdObMGf3888+aM2eO3nnnHY0bNy7D8SNGjNCZM2fMr1KlSmnixIkW2wIDA/Xvv//qwIED5uPi4+Pl7u6uffv26caNG+btsbGxKl26tDkkk6TIyEgNGjRIu3bt0unTpy3Ov337dnXq1Ent27fX/v379fXXX2vKlClKTk6+73sQHR1tUf+ZM2fMQdp/nTx5Uo0aNZKXl5e2bt1qES65uLho3bp1FuMjIyNVunTp+65NUpr7e+bMGQ0aNMi8f9SoUerUqZNq1KihDRs26Pjx41q2bJnKli2r8PBwi7lcXV115swZ/fHHH9q9e7defPFFvf/++6pRo0aae337s3Hna/ny5dm6FgAAAAAAYF/IlsiWyJYAAACQk9ByDgCwGWdnZ7m7u0uSPD091axZM23btk3Tp09Pd7zRaJTRaDS/d3R0lIuLi3kOSXJ3d1eJEiUUFxcnPz8/Sbee0GvTpo127NihvXv3KiAgwLw9MDDQfOy///6rlStX6sCBAzp79qxiYmI0evRo8/5PPvlEDRs21MiRI83bfH19Mwy2MsPNzc2i/ox89913at68uZo0aaIlS5aYn8S8rWfPnoqKilKXLl0kSdevX9eKFSs0ePBgTZo06b7r++/9vdPevXs1Y8YMzZs3T4MHDzZvL126tGrXrm1+QvI2g8FgnqtEiRKqVKmSnn32WVWpUkWvvPKKli5dah5752fjfvFVbwAAAAAA2DeyJbKlB5kt3TopaxlYU6pSbV0CAADAA0UTFgAgRzh8+LB2794tLy+vbM8VGBio2NhY87LmsbGxeuWVV3Tz5k3FxsYqICBA169f1759+9S7d2/zcatWrVLFihVVoUIFdevWTUOHDlV4eLh5aXd3d3ctW7ZMhw8f1hNPPJHtOjNr9+7d6tq1q7p27aq33nor3aXmu3fvrjfeeEO//fabSpcurbVr18rb21u1atV6YHUtX75cRqNR/fv3T3d/Rkvi36lYsWLq2rWroqKidPPmTTk6OlqtPr7qzfpobLMu7qf18eceAAAAeHSRLWWMbCkbTDQNWVPAgL62LsHulNlzzNYl2J2z9S/bugQAQC5GExYAwGY2btwoo9GolJQUJSYmysHBQfPnz8/2vIGBgRo6dKhSUlJ0/fp1ffvtt/L391dycrIWLVokSdqzZ48SExMtnlaMjIxUt27dJN1asvzy5cvauXOn+enGQYMGKT4+XlWrVpWXl5f8/Pz09NNPq2vXrnJ2djbPc/nyZYunKu+mS5cuacKhH374wWKp93bt2qlTp053vTfFihXTM888o5iYGEVERCgqKsoiBPyvUqVKpdl27dq1NNtGjRqlsWPHWmzbvHmznnrqKZ04cUJly5a1eHJy9uzZioiIML//888/LZa2T0/FihX1zz//6MKFCypWrJik/3027jR69GiLp0fvlJiYqMTERIttqaabcjBYOXgDrIiGIQAAAADIHrIlsiXJOtmSRL70MOxa8K6tS7A75EsAAOQsNGEBAGwmMDBQb7/9tq5evao5c+bIyclJ7du3z/a8AQEBunr1qr766itdunRJvr6+evzxx+Xv769evXrpxo0biouLU9myZc2B1PHjx7V//36tW7dOkuTk5KROnTopMjLSHJQVLFhQmzZt0smTJxUbG6u9e/dq+PDhmjdvnvbs2aMCBQpIurXM+jfffJOmrvLly6fZNmfOHDVr1sxim4eHh8X7Nm3aaN26dYqPj9dTTz2V4XX37t1bQ4YMUbdu3bRnzx6tXr1a8fHx6Y6Nj4+Xi4tLmvv2XyNHjlRISIjFtpIlS961hueee0779u1Tt27d0iwbn57bY+58uvH2Z+NOhQsXznCOadOmacKECRbbyqiSfFTlnucHAAC4b/f+qw4AAHiAyJbIliTrZEsS+RIAALAR8iW7QhMWAMBmChYsqHLlykmSoqKiVL16dUVGRio0NDRb85YrV06lSpVSbGysLl26JH9/f0m3AihPT0/t3r1bsbGxatKkifmYyMhIpaSkWIRUJpNJzs7Omj9/vsUTdz4+PvLx8VGfPn00ZswY+fr6auXKlerVq5ckycHBwXxd9+Lu7n7Pse+8845eeeUVPfPMM/r000/VuHHjdMc988wzevHFFxUaGqpnn31WRYoUyXDOMmXKyM3NzWLbnU8d3la0aNEM6ytfvry++OILJScnK0+ePJIkNzc3ubm56Y8//rjrNd3p6NGjcnV1taj3zs9GZoSHh2vYsGEW29oVCsn08QAAAAAAIPchWyJbkqyTLUnkSwAAAMg+mrAAADmCg4ODRo8erWHDhumFF15Q/vz5szVfYGCg4uLidOnSJY0cOdK8vXHjxtq8ebP279+vfv36SZJSUlL0/vvva9asWXr66act5mnbtq2WL1+uvn37pnseb29vFShQQFevXs1WvXdjMBj07rvvysHBQS1bttSmTZvM4d+dnJyc1KNHD82YMUObN29+YPXc1qVLF7311ltauHChhgwZcl9z/PXXX1q2bJnatm0rBweH+67F2dnZYtl+SSwV/wCwvLl1bT19yNYlAPfEn3sAAADkFmRLGSNbyhzypQeP3zGt78foOrYuwe44/5rX1iXYldLjd9u6BAB4qGjCAgDkGMHBwRo5cqQWLFigESNGSJJ++eUXHTx40GJc+fLlVbBgwbvOFRgYqAEDBig5OdkiVPL399fAgQOVlJSkwMBASdLGjRt16dIlhYaGWjyVKEnt27dXZGSk+vbtq/Hjx+vatWtq2bKlvLy8lJCQoDfffFPJyckKCgq6r2tOSEjQ2bNnLba5uLikuT6DwaBFixbJ0dHRHJalt8T7pEmTNHLkyLs+qZgV//zzT5r6ChQoIFdXV9WvX1/Dhw/X8OHD9euvv+r555+Xp6enzpw5o8jISBkMBovwy2Qy6ezZszKZTEpISNCePXs0depUFSpUSK+//rrFORITE9Oc18nJSUWLFrXKdQGwT4S5AAAAwKONbOkWsiWyJTw6fm4eaesS7A75EgAgO2jCAgDkGE5OTho4cKBmzJhhfpLwv0uAS1J8fLwaNWp017kCAwN1/fp1VaxYUcWLFzdv9/f31z///KMKFSqoRIkSkm4tF9+sWbM0IZl0KyibMWOGvvvuO/n7+2vBggXq0aOHzp07p8cee0w1a9bUZ599pgoVKtzXNd9eZv5O06ZN06uvvppmu8Fg0IIFC+Tg4KBWrVpp48aNMhgMFmPy5s1r1TApIiJCERERFtteeuklLVq0SJI0c+ZM1a1bV2+//baioqJ07do1FS9eXI0bN9aePXvk6upqPu7KlSsqUaKEDAaDXF1dVaFCBfXs2VNDhgyxGCdJW7ZsMf98bqtQoYKOHTtmtWtD1rFyEwAAAAAgJyNbuoVsiWwpp3Iq4W7rEuzOkeQ9ti4BAADcwWAymUy2LgIAAMDaghyCbV0CAOR6NF9aF0/TWt+21NU2PX+j52fa9Py5yRcfjbB1CQAAAFlGvgQA2UO2ZH3kS9ZHvpR75IZ8iZWwAAAAABsggLA+Agjr454Cd2fgsTYAAAAANkS+ZH1kIdbF/QTujXzJvtCEBQAAgEwh1LEuAggAAAAAAPAoIVuyPvIlAAByFgdbFwAAAAAAAAAAAAAAAAAAuRkrYQEAAAA2wNOf1sfTnwAAAAAA5FytnnzG1iXYnemn1tm6BLszyruerUsAAORiNGEBAAAAAAAAAAAAeKASfUvYugS7s+5ybVuXAAAA7kATFgAAADKFVYasi5WwkBvwObUu/j1qh0wmW1cAAAAA5BqOsV/bugS7c+BSaVuXYIdO27oAu3KlW31bl2B3XJfusXUJsDbyJbtCExYA2EhISIiWLFkiSXJyclKpUqUUHBysiRMnKl++fJKknTt3asKECTp48KBu3LihkiVLqkGDBnrvvfeUN29excXFKTAwMN35z5w5I3d3d127dk2TJk3SqlWr9Oeff8rFxUWVK1fWsGHD1KZNG/P4n376SVOnTtXnn3+uc+fOqWjRoqpYsaJ69+6tTp06ycnJ6b5qKlasmBo1aqQ33nhDZcuWlSQdOnRIr732mvbu3asrV67I3d1d9erV01tvvaVixYpZXMe0adM0duxYvf766xo5cmSa6zx79qymTZumTZs26Y8//lChQoVUrlw5devWTT179lSBAgXMY7/99lu9/vrr2rVrly5evCh3d3dVrVpVL730klq3bi2DwaBTp06pTJky+vbbb1WjRg2LcwUEBKhGjRqaO3euJOmXX37RmDFjFBcXp4sXL6po0aKqXbu2pk+frooVK0qSDAaD1q1bp7Zt20qSkpOT1aNHD+3atUtbt27VE088IYPBYD6Hq6urnnjiCU2aNElNmjRJ81nJkyePSpcurR49emj06NFycnIy3/NLly7Jzc3NomZvb28NHTpUQ4cOzdS9v339txUuXNh8TTVr1kz3PkjSoEGD9Pnnn+vo0aNpfka//fabypQpo3Xr1um5555Lc0/ulN61nD59Ws2bN9djjz2mTz75RIUKFUpzHB4OmjGsi2YM5AZ8TgEAAHI28iXyJfKltmnGki/lXGRL1tfcw9YVAHdHwxCARw1NWABgQy1atFB0dLSSk5P19ddfq2fPnjIYDJo+fbp++OEHtWjRQoMGDdKbb76p/Pnz68cff9TatWt18+ZNi3mOHz8uV1dXi223w6a+fftq3759euutt1S5cmVduHBBu3fv1oULF8xj9+/fr2bNmqlKlSpasGCBOeA5cOCAFixYoCeeeELVq1fPck0uLi768ccf9eKLL+rZZ5/Vd999p4sXL6pp06Zq3bq1tm7dKjc3N506dUobNmzQ1atX09yjqKgovfLKK4qKikoTkv38889q2LCh3NzcNHXqVFWtWlXOzs76/vvv9e6776pkyZJ67rnnJEkff/yxOnbsqGbNmmnJkiUqV66cEhMTtXv3bo0dO1ZPPfVUmoDpbpKTkxUUFKQKFSroo48+UokSJfTHH39o8+bNSkhISPeYa9euqX379vrxxx/1xRdfWIRR0dHRatGihf7++2+NGTNGrVu31uHDh83B4u3PSmJioj799FMNGDBAefLkUXh4eKZrPn/+fKbv/eeff64qVarojz/+0ODBg/XMM8/o2LFjGd6j0NBQzZ8/X7t371aDBg0s9sXExKhYsWJq2bJlpmu97eTJkwoKClLlypW1evVq5c+fP8tzwHpoxrAugkfr4zMKAACARxH5EvnSbeRL6SNfyjn4vd36yJesj88pACA7aMICABtydnaWu7u7JMnT01PNmjXTtm3bNH36dH322Wdyd3fXjBkzzON9fHzUokWLNPMUK1Ysw/Biw4YNmjdvnjmg8Pb2Vu3a//ueeJPJpJCQEPn6+urLL7+Ug4ODeV/58uXVpUsXmf5/Gcz7qalEiRKKiIhQ165d9dNPP+no0aO6fPmyFi9ebH76sUyZMuk+cblz505dv35dEydO1Pvvv58mgOnfv7+cnJx04MABFSxY0Ly9bNmyatOmjbnuq1evKjQ0VK1atdJHH31kcY5KlSopNDTUPDazjhw5opMnT2r79u3y8vKSJHl5ealhw4bpjk9ISFCrVq3077//6osvvjD/3G9zc3OTu7u73N3d9fbbb6tkyZLatm2bXnrpJUmWn5V+/fpp3bp12rBhQ5ZCsi+//DLT975IkSLmembOnKmGDRtq3759at68ebpz16hRQ7Vq1VJUVJTFz8hkMikmJkY9e/Y0nzOzvvvuOzVv3lxNmjTRkiVLsnw8rI9QBwAAAAByHvIl8qXbyJfSIl/KWX5/rcG9ByFLym6rfe9ByJLy4mszAQD3j79tAkAOcfjwYe3evdscuLi7u+vMmTPatWuXGjdufN/zuru769NPP9Xzzz8vFxeXNPsPHjyoo0ePavny5RYB2Z1uL2d+vzXdfrosKSlJ7u7uSklJ0bp169ShQweLpdL/KzIyUl26dFGePHnUpUsXRUZGmgOYCxcu6LPPPtPUqVMtArL06v7ss8904cIFvfLKKxme6251pOfxxx+Xg4OD1qxZo6FDh8rR0THDsWfPnpW/v7+MRqN27tx5zyci77xfdxtz59OmmZGVe5/VeqRbTyu++uqrmjdvnvlnEhcXp19++UW9e/fOUq27d+9W165d1bVrV7311ltZ/vngweApMOuiqQ0AAACAtZEvpUW+RL5EvpRzeE7abesS7A75kvU1FxkoAOD+0YQFADa0ceNGGY1GpaSkKDExUQ4ODpo/f74kKTg4WFu3bpW/v7/c3d3l5+enpk2bqkePHmmWhi9VqpTFey8vLx05ckSS9O6776pr164qUqSIqlevrkaNGqlDhw7mJ+pOnDghSapQoYL5+L/++su8TLkkzZgxQ/37989STbedOXNGM2fOVMmSJVWhQgXlzZtXo0eP1gsvvKC+ffuqbt26atKkiXr06KHixYubj7ty5YrWrFmjPXtufV94t27d9NRTT2nevHkyGo366aefZDKZLOqWpKJFi+rGjRuSpAEDBmj69OnpXuNXX31l8YTeihUr1Lp1a/P7Bg0apAkNr1+/rho1akiSSpYsqTfffFOvvPKKJkyYoDp16igwMFBdu3a1uHeSNGTIEJUtW1bbtm1TgQIF0r1Pt127dk1jx46Vo6Oj/P390+w3mUzavn27tm7dqkGDBlns++/n4PZ8t/n5+WXq3t8pISFBkyZNktFoVN26de9a+wsvvKDhw4dr9erVCgkJkXRrGfxGjRrJ19f3rsf+V7t27dSpUyfzn4d7SUxMVGJiosW2VNNNORgyDi+RdYQ61kVTGwAAAABrIF8iX/ov8qVbyJdyHrIl6yNfAgAgZ6EJCwBsKDAwUG+//bauXr2qOXPmyMnJSe3bt5ckOTo6Kjo6WpMnT9aOHTu0b98+TZ06VdOnT9f+/ftVokQJ8zzx8fEWTyHmyZPH/M+NGzfWzz//rL1792r37t3avn275s2bpwkTJui1115Lt64iRYro4MGDkqSAgADzE2pZqalUqVIymUy6du2aqlevrrVr1ypv3rySpClTpmjYsGHmORYtWqSpU6dq165dqlq1qiRp+fLl8vHxUfXqt36JrFGjhry8vLRy5UqFhoZmeE/379+v1NRUde3aNU1ocqdq1aqZr7F8+fJKSUmx2L9y5UpVqlTJYlvXrl0t3g8YMEA9evRQXFyc9u7dq9WrV2vq1KnasGGDgoKCzONat26t9evX65133tHLL7+cbj1dunSRo6Ojrl+/rscff1yRkZGqVq2aef/tQDU5OVmpqal64YUXNH78eIs5/vs5kG79/O6UmXsv/S8kvHr1qsqWLauVK1dmGKTd5ubmpueff15RUVEKCQnRlStXtHbtWi1YsOCux6WnTZs2WrduneLj4/XUU0/dc/y0adM0YcIEi21lVEk+qpLlcyNjhDrWRfBofXxGATxshqx94xAAAA8E+RL50m3kS5bIl3Iefm+3PvIl6+NzCuBhI1+yL+mvCwwAeCgKFiyocuXKqXr16oqKitK+ffsUGRlpMaZkyZLq3r275s+fryNHjujGjRtatGiRxZgyZcqoXLly5tftJedvy5Mnj5566imNGjVKn332mSZOnKhJkyYpKSlJ5cuXlyQdP37cPN7R0dE8l5NT2n7dzNQUHx+v7777TleuXNHBgwdVr149i/1FihRRcHCwZs6cqaNHj8rDw0MzZ84074+MjNSRI0fk5ORkfv3www+KioqSJJUrV04Gg8GibkkqW7asypUrZ17iXFK61+js7Gy+xvR4enpa3NP/znmbi4uLnn32WU2ZMkWHDh3SU089pcmTJ1uM6d69u6KiojRixAjNnj073fPNmTNHBw8e1NmzZ3X27Fn17NnTYn9gYKAOHjyoH3/8UdevX9eSJUvSLJP/389BRj+/e9176VZIeOjQIV26dEknT55Uy5Yt0637v0JDQxUfH6+ffvpJK1eulKOjo4KDgzN17J3eeecdde7cWc8884x27dp1z/Hh4eG6fPmyxauMKmb5vAAAAAAA5DbkS+RLt5EvWSJfAgAAwMPGSlgAkEM4ODho9OjRGjZsmF544YV0A5nHHntMJUqU0NWrV7N1rsqVKyslJUU3btxQzZo1VbFiRc2cOVMdO3ZMs0T6vWRUU5kyZeTm5papOfLmzSsfHx/zHN9//70OHDiguLg4FS5c2Dzu4sWLCggI0LFjx1SxYkUFBQVp/vz5GjRoUJrA6E5PP/20ChcurOnTp2vdunVZur6sMBgMqlixonbv3p1mX8+ePeXg4KBevXopNTVVI0aMsNjv7u6eYWAn/S9Qtbb/3vvbPD095ePjk+X5AgMDVaZMGUVHRys2NladO3e+688mIwaDQe+++64cHBzUsmVLbdq0Kd3l829zdnaWs7OzxTaWirc+nqyzLp6qAwAAAGBt5EvkS+RL/0O+lPOQLVkf+RIAADkLTVgAkIMEBwdr5MiRWrBggVxcXHTw4EG1a9dOPj4+unHjht5//30dOXJEb731lsVxf/31l27cuGGxrUiRIsqTJ48CAgLUpUsX1alTR0WKFNEPP/yg0aNHKzAwUK6urpKk6OhoBQUFqWHDhgoPD1elSpWUnJysXbt26fz583J0vBU2vPPOO5muKSMbN27UihUr1LlzZ/n6+spkMumTTz7Rp59+qujoaEm3nlKsW7euGjdunOb4J598UpGRkXrjjTe0cOFCNWzYUHXq1NH48eNVrVo1OTg46KuvvtKxY8dUu3ZtSZLRaNTixYvVqVMntWrVSoMHD1b58uX177//asuWLZJkvsbMOnjwoMaNG6fu3burcuXKyps3r3bu3KmoqCiNGjUq3WO6d+8uBwcH9ezZUyaTSSNHjszSObMrM/c+s86fP29ebv+2EiVKqHjx4urdu7dmz56tS5cuac6cOeke/8svv6Q5/vYTpbcZDAYtWrRIjo6O5qDsv8vfA7kZwaP1ETwCAAAA5EvkSw8W+RKyo2WLzrYuwe70OrHV1iXYnWjf0rYuAQCQi9GEBQA5iJOTkwYOHKgZM2Zo3bp1+uKLL9S3b1+dPn1aRqNRVapU0fr169M8sVWhQoU0c+3Zs0d+fn5q3ry5lixZotGjR+vatWvy8PBQ69atFRERYR7r5+enr7/+WlOnTtWAAQN09uxZFSxYUNWrV9ecOXPUu3dvSVLdunUzXVNGKleurAIFCmj48OH6/fff5ezsrPLly2vx4sXq3r27kpKStHTp0gyDpvbt22vWrFmaOnWqfHx89O2332rq1KkKDw/XH3/8IWdnZ1WuXFkjRoxQ//79zce1a9dOu3fv1vTp09WjRw9dvHhRhQoVUp06dbRixQq1bt06U/XfVqpUKXl7e2vChAk6deqUDAaD+f3LL7+c4XFdu3aVg4ODunfvrtTU1Ayv80G4173PimXLlmnZsmUW2yZNmqSxY8cqJCRE48aNU5UqVdJ8TcBtw4YNS7MtPj4+zTaDwaAFCxbIwcFBrVq10saNGxUYGJilWoGcioYhAAAAAA8C+RL50oNEvoTs+HTLCluXYHfIlwAAyFkMJpPJZOsiAAAArC3IIdjWJdgdVm6yLkIyAMi+bamrbXr+xm3esOn5c5NdHz/cVToAAACsgXzJusiWrI98CQCyj3wp98gN+RIrYQEAACBTCHWsi+DR+viMAnjoeKwNAAAAyDR+b7c+8iXr43MK4KEjX7IrNGEBAAAgUwh1rItABwAAAAAAPErIlqyPfAkAgJzFwdYFAAAAAAAAAAAAAAAAAEBuRhMWAAAAAAAAAAAAAAAAAGQDX0cIAAAA2ABL8FsfS/ADAAAAAJBztQpsb+sS7M7LJz+xdQl2Z45PJVuXAADIxWjCAgAAAGyAhiEAAAAAAPAo2RS71tYl2B3yJQAAcha+jhAAkG3nz59Xv379VLp0aTk7O8vd3V3NmzfXl19+aTFuz549cnR0VKtWrdKdZ926dfLz81OhQoXk4uKiKlWqaOjQoeb9MTExMhgMaV758uWTpHT33fkaP378Pc8TEBBw1zkCAgIkSd7e3uZtBQsWVK1atbR69WpzrePHj0/3+IoVK5rHBAQEWFzfndfp5uZmse369esaN26cfH195ezsrKJFiyo4OFhHjhxJc/yVK1f02muvqUqVKsqfP7+KFCmiJ598UjNmzNClS5csxv7000/q3bu3+WdXsmRJNW3aVB9++KFSUlIsxsbGxqp169Z6/PHHlS9fPvn4+KhTp07atWuXeUxcXJwMBoMSEhLS+xFr/PjxqlGjRrr7bt+TO3+uvr6+mjZtmkwmU4bHAAAA2IrBxCuzLwAA7oV8iXzpNvIlAADwKLF1ZpObXrkBK2EBALKtffv2SkpK0pIlS1S2bFmdO3dO27dv14ULFyzGRUZGatCgQYqMjNTp06fl4eFh3rd9+3Z16tRJU6ZM0XPPPSeDwaAffvhB27Zts5jD1dVVx48ft9hmMBgkSWfOnDFvW7lypSIiIizGGo3Ge57no48+UlJSkiTp999/V926dfX555+rSpUqkqS8efOa55s4caLCwsJ05coVzZo1S506dVLJkiXVoEEDSVKVKlX0+eefW9Tq5JT1/+tNTExUs2bN9Ntvv2nWrFmqV6+ezp07p2nTpqlevXr6/PPP5efnJ0m6ePGiGjVqpCtXrmjSpEmqXbu2ChUqpOPHjys6OlrLli3TgAEDJEn79+9Xs2bNVKVKFS1YsMAc4B04cEALFizQE088oerVq0uSFi5cqIEDB6p79+5auXKlfHx8dPnyZcXGxurll1/W119/neXrykhYWJgmTpyoxMRE7dixQy+++KLc3NzUr1+/LM3DV71ZH0/WAQAAAAAeFPIl8iXyJftHtgQAAOwdTVgAgGxJSEhQfHy84uLi5O/vL0ny8vJS3bp1Lcb9+++/WrlypQ4cOKCzZ88qJiZGo0ePNu//5JNP1LBhQ40cOdK8zdfXV23btrWYx2AwyN3dPd1a7txeqFChdMfe6zyFCxc2b79x44YkqUiRIume08XFRe7u7nJ3d9eCBQu0dOlSffLJJ+aQzMnJKcNas2Lu3Lnas2ePvv32W3No5eXlpbVr16pevXoKDQ3V4cOHZTAYNHr0aP322286ceKERQjp5eWlp59+2vzEn8lkUkhIiHx9ffXll1/KweF/i2OWL19eXbp0MY/97bffNHToUA0dOlSzZ8+2qK1atWoaPHhwtq/xTgUKFDDft169emn+/Pnatm1blkMyQh3kdAS5yA34dykAAAAeBvIl8iXyJQA5BZmddfHvUQCPGpqwAADZYjQaZTQatX79evn5+cnZ2TndcatWrVLFihVVoUIFdevWTUOHDlV4eLj5KUN3d3ctW7ZMhw8f1hNPPPHA6n1Q53FyclKePHnMTzla07JlyxQUFGQOyG5zcHDQyy+/rK5du+rQoUOqVq2aVq5cqW7dulkEZHe6fb8PHjyoo0ePavny5RYBWXpj165dq+TkZL3yyit3HWdtJpNJX3zxhY4dO6by5cs/kHMAsC+EOgAAAEDuRL50C/mS9ZEvwd7RMGR95EsAgOygCQsAkC1OTk6KiYlRWFiYFi1apFq1asnf31+dO3dWtWrVzOMiIyPVrVs3SVKLFi10+fJl7dy5UwEBAZKkQYMGKT4+XlWrVpWXl5f8/Pz09NNPq2vXrhbB2+XLl2U0Gi1qeOqpp7R58+ZM1ZvZ82RFUlKSZs2apcuXL6tJkybm7d9//32aWrt166ZFixaZ3y9cuFCLFy+2GJOSkqJ8+fKZ3584cUKBgYHpnrtSpUrmMSVKlFBCQoIqVKhgMaZ27drmZfOfffZZLV++XCdOnJAki7F//fWXypYta34/Y8YM9e/fXydOnJCrq6vFU5dr165Vz549ze/37NmjqlWrpltjVt2+J0lJSUpOTla+fPnu+TRkYmKiEhMTLbalmm7KweBolZpwC6GOdRHoAAAAAMAt5EvkSxL50qOAbMn6yJcAAMhZaMICAGRb+/bt1apVK8XHx2vv3r3avHmzZsyYocWLFyskJETHjx/X/v37tW7dOkm3grVOnTopMjLSHJIVLFhQmzZt0smTJxUbG6u9e/dq+PDhmjdvnvbs2aMCBQpIurVE+zfffGNx/vz582e61syeJzNGjRqlsWPH6saNGzIajXr99dfVqlUr8/4KFSpow4YNFse4urpavO/atavGjBljse2jjz7S1KlTLbbdXro9I3nz5s1w37p165SUlKRRo0bp+vXrGY4rUqSIDh48KEkKCAiweOryv08jNm/eXAcPHtSff/6pgIAA3bx58671ZcXte3Lp0iWNGzdODRo0MC/Bn5Fp06ZpwoQJFtvKqJJ8VMVqdYFQx9oIHq2PzygAAACQe5EvkS+RL9k/fm+3PvIl6+NzCgDIDpqwAABWkS9fPgUFBSkoKEivvfaa+vTpo3HjxikkJESRkZFKSUmxWMLcZDLJ2dlZ8+fPV6FChczbfXx85OPjoz59+mjMmDHy9fXVypUr1atXL0m3lkgvV65ctuu913kyY+TIkQoJCZHRaFTx4sXTBEl58+a9Z62FChVKM6ZYsWIW78uXL6+jR4+me/zt7b6+vnr88cfl5uZmfirxttKlS0u6FTAmJCSY55Sk48ePq2bNmpIkR0dHcy1OTv/7K0L58uV1+fJlnT171vy0otFoVLly5SzGWcud92TVqlUqV66c/Pz81KxZswyPCQ8P17Bhwyy25blUS87OhBDIuQh0AMAOpN79P2QCAICsIV8iX7IWa+VL7QqFWL22RxkNQ9ZHvgQAdoB8ya7QhAUAeCAqV66s9evXKyUlRe+//75mzZqlp59+2mJM27ZttXz5cvXt2zfdOby9vVWgQAFdvXr1gdZ6v+cpWrSoVQK7e+nSpYvGjBmjQ4cOqXr1//1SnZqaqjlz5qhOnTqqXLmyJKljx45aunSpIiIiLELJ/6pZs6YqVqyomTNnqmPHjnJwcMhwbIcOHfTqq69q+vTpmjNnjvUuLBOMRqOGDBmiESNG6Ntvv00TRN7m7OycZrn/ILeaD6NE4L4RPFofwSMAAABgX8iXrId86f7yJb6K0Lr4vd36yJesj88pACA7aMICAGTLhQsXFBwcrN69e6tatWpycXHRgQMHNGPGDLVp00YbN27UpUuXFBoaavFEonRrmfnIyEj17dtX48eP17Vr19SyZUt5eXkpISFBb775ppKTkxUUFGQ+xmQy6ezZs2nqKFas2F2Dntsyex5rSElJSVOrwWBQ8eLFszTPyy+/rI8//ljPPvusZs2apXr16uncuXOaOnWqfvzxR+3evds8durUqYqLi1PdunU1ceJE1alTRwULFtR3332nPXv26IknnjDXER0draCgIDVs2FDh4eGqVKmSkpOTtWvXLp0/f16OjrdCptKlS2vWrFkaMmSILl68qJCQEJUpU0YXL17U0qVLJck89rbvv/9eLi4uFtd9O+C7fv26eVn621xcXOTj45Pu9b/00kuaNGmS1q5dqw4dOmTp3sG6CHWsi0AHAAAAAG4hX8oY+RL5kj0hW7I+8iUAAHIWmrAAANliNBpVr149zZkzRydPnlRycrI8PT0VFham0aNHq2PHjmrWrFmagEy6FZLNmDFD3333nfz9/bVgwQL16NFD586d02OPPaaaNWvqs88+U4UKFczHXLlyRSVKlEgz15kzZ8xLmd9NZs9jDUeOHElTq7Ozs27cuJGlefLly6ft27dr2rRpCg8P16+//qqUlBSVK1dOhw8fVqlSpcxjixQpov3792v69Ol644039Msvv8jBwUHly5dXp06dNHToUPNYPz8/ff3115o6daoGDBigs2fPqmDBgqpevbrmzJmj3r17m8cOGjRIlSpV0uzZs9WhQwdduXJFRYoUUf369bVlyxZVrVrVoubGjRtbvHd0dFRKSook6cSJE+Yl6m9r2rSpPv/883Svv3DhwurRo4fGjx+v559/PlNhKAAAAAAAyD3IlzJGvvQ/5EsAAADI6Qwmk4kvmAQAIJfZvHmz2rVrp5kzZ2rgwIG2LidHCnIItnUJdoenFZHT8fQn8OjZlrrapuf3bzXDpufPTXZuesXWJQAAgP8gX7o38iXrcipSxNYl2J0F335s6xLszkulG9m6BAAPGflS7pEb8iVWwgIAIBd65plntHnzZsXHx+vvv/9W0aJFbV0SANgcjYLWR2MbAAAAYL/Il/Cw3bx8xdYl2J3Az162dQl2J/+4PLYuwa54Tth970EAYEdowgIAIJcKDAxUYGCgrcsAgByDhiEADx1riwMAgFyOfAkP09m15Wxdgt1x+5yGIWsr9hZNQwAeMvIlu0ITFgAAsEusiIOcjoYhAAAAAADwKDn45Apbl2B3mrchXwIAICehCQsAANglGlysj8Y26+J+Wh9/7gEAAAAAyLla1Wlh6xLszsxT62xdgt0Z4e1n6xIAALkYTVgAAAAAAAAAAAAAHqjfXyhj6xLsTvD+F21dgt3x0ne2LgEAkIvRhAUAAADYAKs2AQAAAACAR8l3w962dQl2h3wJAICchSYsAAByufPnzysiIkKbNm3SuXPn9Nhjj6l69eqKiIhQw4YN5e3trV9//TXNcdOmTVP9+vXVrFkzxcbGqlGjRuZ9V69eVdWqVfX8889r5syZCggIUI0aNTR37lzzmHnz5umVV17RkiVL1LlzZ/3yyy8aM2aM4uLidPHiRRUtWlS1a9fW9OnT5eDgoBo1amjx4sV64YUXzHOkpqaqUaNG8vDw0Jo1axQSEqIlS5ZIkpycnFSqVCkFBwdr4sSJypcv34O7iYAN8HWE1kfwCAAAAAD3h3wJD0PcDYOtS7A7Tj6sLmZtKSd/sXUJAIBcjCYsAAByufbt2yspKUlLlixR2bJlde7cOW3fvl0XLlwwj5k4caLCwsIsjnNxcVHBggU1aNAghYSE6NChQypYsKAk6ZVXXlH+/Pk1efLkdM85btw4zZw5Ux9//LFatGih5ORkBQUFqUKFCvroo49UokQJ/fHHH9q8ebMSEhLk5+en119/XYMGDVJgYKBKlCghSZo1a5Z+/vlnbdiwwTx3ixYtFB0dreTkZH399dfq2bOnDAaDpk+fbu1bB9gUDUMAkPsZTLauAAAAwDrIl/AwBOTjL9DWNo2GIQDI9ciX7AtNWAAA5GIJCQmKj49XXFyc/P39JUleXl6qW7euxTgXFxe5u7unO8fUqVO1ZcsWjRo1SvPnz1dsbKwWL16s3bt3p3k60GQyafDgwVq6dKm2bdumBg0aSJKOHDmikydPavv27fLy8jLX0bBhQ/OxgwYN0vr16xUWFqaNGzfq2LFjioiI0MqVK1W0aFHzOGdnZ3Otnp6eatasmbZt20ZIBrvDSljWR2MbAAAAAGQd+RIellb+z9u6BLsz4uSGew9Clsz0qWLrEgAAuRhNWAAA5GJGo1FGo1Hr16+Xn5+fnJ2dszxHvnz59P7776tBgwYKCgrS0KFDNXr0aNWuXdtiXEpKirp166YdO3Zo586dqlatmnnf448/LgcHB61Zs0ZDhw6Vo6NjmvMYDAZFR0erWrVqeu+99xQZGanOnTvrueeey7C2w4cPa/fu3ebgDQAAAAAAANZFvoSHJfWP07Yuwe7Uc/7H1iUAAIA7GEwmE4ubAQCQi61du1ZhYWG6fv26atWqJX9/f3Xu3NkcYnl7e+vMmTPKkyePxXGbN2/WU089ZX4/btw4TZ48WTVr1tTevXvl5PS/Xu2AgADt2bNHknTo0CFVrFgxTR0LFizQK6+8IkdHR9WpU0eBgYHq2rWrypYtazEuOjpaffr0UalSpfT999/L1dXVvC8kJERLly5Vvnz5lJKSosTERDk4OGjVqlVq3759hvcgMTFRiYmJFtvyXKolZ2eHe90+wGZYtQkAsm9b6mqbnj/gmRk2PX9uErf5FVuXAAAA7oJ8Kf18qV2hEDkY0jaD4f6wKrj1kS8BQPaRL+UeuSFfogkLAAA7cOPGDcXHx2vv3r3avHmz9u/fr8WLFyskJETe3t7q1q2bQkJCLI4pWbKk8ufPb36fkpKiPHnyaPXq1erQoYPF2ICAADk6OurgwYNq0qSJli9fbhGi3fbPP/8oLi5Oe/fu1SeffKIff/xRGzZsUFBQkMW4UqVKaejQoRoxYoTF9pCQEP355596++23dfXqVc2ZM0dOTk5avHjxXa9//PjxmjBhgsW2MqokHwNLR1sTQZl1EZIBQPYRkuUeuSEkAwDgUUe+RL70oJEtWR/5EgBkH/lS7pEb8iWWhwAAwA7ky5dPQUFBeu2117R7926FhIRo3Lhx5v1FixZVuXLlLF53BmSSzKFXeuGXJFWtWlXbt29XbGysOnXqpJSUlDRjXFxc9Oyzz2rKlCk6dOiQnnrqKU2ePDnNOCcnpwzPU7BgQZUrV07Vq1dXVFSU9u3bp8jIyLtef3h4uC5fvmzxKqO0T1MCAAAAAAAgfeRL5EsAAADIHpqwAACwQ5UrV9bVq1etPm+NGjW0fft27dq1Sx07dlRycnKGYw0GgypWrJitOhwcHDR69GiNHTtW169fz3Ccs7OzXF1dLV4sFQ8AAB44k4lXZl8AACDXIV8iXwIAAA+BrTOb3PTKBdJ/RAAAAOQKFy5cUHBwsHr37q1q1arJxcVFBw4c0IwZM9SmTRvzuH/++Udnz561OLZAgQJydXXN8jmrV6+uHTt2qGnTpurYsaNWrVqlI0eOaNy4cerevbsqV66svHnzaufOnYqKitKoUaOydY3BwcEaOXKkFixYkGZ5eTxcLG9uXSzBb318RgEAAAAg68iX8LDwe7v1kS9ZH59TAEB20IQFAEAuZjQaVa9ePc2ZM0cnT55UcnKyPD09FRYWptGjR5vHRUREKCIiwuLYl156SYsWLbqv81atWtUclAUHB+vdd9+Vt7e3JkyYoFOnTslgMJjfv/zyy9m6RicnJw0cOFAzZsxQv379VLBgwWzNh/tHqGNdBDoAAAAAgJyAfAkPC9mS9ZEvAQCQsxhMplyyZhcAAEAWBDkE27oE4K4IHq2P4BF49GxLXW3T8we0mG7T8+cmcVuyt3oFAACALZAvIacjX7I+8iXg0UO+lHvkhnyJlbAAAACQKYQ6AAAAAAAAuF9OpUvZugS781XiAVuXAAAA7kATFgAAAGADPFUHALmfgbXFAQAAgEzbtHeTrUuwO8096ti6BABANpEv2ReasAAAAAAbYGUx66OxDQAAAACAnKuFZy1bl2B3Nv6539Yl2J3WJWvbugQAQC5GExYAAABgAzQMAQAAAACAR8mW37+xdQl2p7kHDUMAAOQkNGEBAAAANsBKWNZHYxsAAAAAADkXv7dbH/mS9fE5BQBkB01YAAAAyBQCCOsiJAMAAAAAAAAAALAfDrYuAAAAAAAAAAAAAAAAAAByM1bCAgDAhs6fP6+IiAht2rRJ586d02OPPabq1asrIiJCDRs2lLe3t4YOHaqhQ4dKUpr36VmyZInmz5+vI0eOyNHRUbVq1dLIkSPVunVr85i4uDgFBgame/yZM2fk7u6u8ePHa8KECWn2V6hQQceOHZMkBQQEaOfOnVq+fLk6d+5sHjN37lzNnTtXp06dkiTFxMSoV69ekiSDwaDixYurcePGeuONN1S6dGnzcbfnkyRnZ2eVLl1avXr10quvviqDwXDvGwrkIqwsZn2sLmZ9fE6BezDZugAAAADyJfIlPMr4vd36yJesi88okAnkS3aFJiwAAGyoffv2SkpK0pIlS1S2bFmdO3dO27dv14ULF+5rvhEjRmj+/PmaPHmy2rZtq+TkZC1dulRt2rTRvHnzNHDgQIvxx48fl6urq8W2YsWKmf+5SpUq+vzzzy32OzlZ/vUhX758Gjt2rNq3b688efJkWJurq6uOHz8uk8mkX375Rf3791dwcLD27dtnMS4sLEwTJ05UYmKiduzYoRdffFFubm7q169flu4FrI8AAjkdoQ4AAACARxH5EvlSbvHOb1/YugS7M6BmG1uXYHfIlwAA2UETFgAANpKQkKD4+HjFxcXJ399fkuTl5aW6deve13x79+7VrFmz9Oabb2rQoEHm7VOmTNGNGzc0bNgwtWnTRp6enuZ9xYoVk5ubW4ZzOjk5yd3d/a7n7dKlizZs2KD33ntP/fv3z3CcwWAwz1WiRAmFhoZq8ODBunLlikVQV6BAAfO4Xr16af78+dq2bRshGewOgQ4AAAAAILvIl8iXchNvJxdbl2B3Uu6z2RIAADwYNGEBAGAjRqNRRqNR69evl5+fn5ydnbM13/Lly2U0GvXSSy+l2Td8+HDNnj1ba9euvetS8/fD1dVVY8aM0cSJE9WzZ08VLFjwnsf89ddfWrdunRwdHeXo6JjuGJPJpC+++ELHjh1T+fLl7zpfYmKiEhMTLbalmm7KwZD+3EBOwMpi1kdjGwAAAIBHDfkS+VJuUnsSTXDW5rDpb1uXYHcKtzph6xIAALkYTVgAANiIk5OTYmJiFBYWpkWLFqlWrVry9/dX586dVa1atSzPd+LECfn4+Chv3rxp9nl4eMjV1VUnTlj+AlmqVCmL915eXjpy5Ij5/ffffy+j0Wgxplu3blq0aJHFtv79+2vevHmaPXu2XnvttXTru3z5soxGo0wmk65duyZJGjx4cJpQbeHChVq8eLGSkpKUnJysfPnyafDgwXe99mnTpmnChAkW28qoknxU5a7HIWtocEFOR2Ob9fHnHgAAAMjZyJfIl3KTom/vtnUJ9udtWxdgf8iXrItsCcCjhiYsAABsqH379mrVqpXi4+O1d+9ebd68WTNmzNDixYsVEhKS5flMJtNd9/83QIuPj5eLy/+WAc+TJ4/F/goVKmjDhg0W2+5c2v02Z2dnTZw4UYMGDcpwWXcXFxd98803Sk5O1ubNm/Xhhx9qypQpacZ17dpVY8aM0aVLlzRu3Dg1aNBADRo0uOt1hYeHa9iwYRbb2hUKuesxyDoCCOsigLA+7ikAAACARxH5EvlSbkG2ZH1kIdbHPQUAZAdNWAAA2Fi+fPkUFBSkoKAgvfbaa+rTp4/GjRuX5ZCsfPny+uKLL5SUlJQmDDt9+rSuXLkiX19fi+1lypSRm5tbhnPmzZtX5cqVy9T5u3XrppkzZ2ry5Mny9vZOs9/BwcE8V6VKlXTy5En169dPH3zwgcW4QoUKmcetWrVK5cqVk5+fn5o1a5bhuZ2dndMst89S8cjpCB6tj5AMwMNmuMd/oAQAAHhYyJfIl3KD9Vfv/TWTyBrHKr73HoQsuXmEryME8HCRL9kXmrAAAMhhKleurPXr12f5uC5duuitt97SO++8o0GDBlnsmzlzpvLly6dOnTpZqcq0HBwcNG3aND3//PMZPq14p1dffVU+Pj56+eWXVatWrXTHGI1GDRkyRCNGjNC3334rg8Fg7bKRBTS4IKejsQ05Hf8eBbJmwYIFeuONN3T27FlVr15db731lurWrZvu2Pfee0/vv/++Dh8+LEmqXbu2pk6dmuF4AADsHfnS/5Av5Rxvl89cMx6ygoYha3OoxVeQWlOnZZ/bugS7s7xiCVuXAOQatsiWaMICAMBGLly4oODgYPXu3VvVqlWTi4uLDhw4oBkzZqhNmzYZHvfnn3/q4MGDFtu8vLxUv359DRkyRCNHjlRSUpLatm2r5ORkLV26VG+++aZiYmJUpEgRi+P++usv3bhxw2JbkSJFzMvGp6Sk6OzZsxb7DQaDihcvnm5trVq1Ur169fTOO+9kOOY2T09PtWvXThEREdq4cWOG41566SVNmjRJa9euVYcOHe46Jx4sGlyQ09HgAgD2Y+XKlRo2bJgWLVqkevXqae7cuWrevLmOHz+uYsWKpRkfFxenLl26qEGDBsqXL5+mT5+up59+WkeOHFHJkiVtcAUAADwc5EvkS7mJkweNA9b2+u51ti7B7ozwtnUF9oWGIQC2YqtsiSYsAABsxGg0ql69epozZ45Onjyp5ORkeXp6KiwsTKNHj87wuJkzZ2rmzJkW2z744AN169ZNc+fOVbVq1bRw4UKNHTtWN27cUN68ebVjxw41btw4zVwVKlRIs23Pnj3y8/OTJB05ckQlSlj+kuTs7JwmWLvT9OnT1aBBg7te+20vv/yy6tevr/3792fYSV64cGH16NFD48eP1/PPPy8HB4dMzQ3kdDQMAQCQsdmzZyssLEy9evWSJC1atEibNm1SVFSUXn311TTjP/zwQ4v3ixcv1tq1a7V9+3b16NHjodQMAIAtkC+RL+Ummw5ssXUJdqe5h5+tSwAAIEeyVbZkMJn4gkkAAOzVqVOn5O/vr/r16+vDDz+Uo6OjrUt6aIIcgm1dgt1hJSzrogkLALJvW+pqm56/SdDrNj1/brJ548tKTEy02Obs7CxnZ+c0Y5OSklSgQAGtWbNGbdu2NW/v2bOnEhIS9PHHH9/zfP/884+KFSum1atXq3Xr1tmuHwCARxn5EqyFbMn6yJcAIPvIl3KPzOZLtsyWWAkLAAA75u3trbi4OC1ZskQHDx5U7dq1bV0SgP9H8Gh9BI8AkHNNmzZNEyZMsNg2btw4jR8/Ps3Yv//+Wzdv3kzz9UPFixfXsWPHMnW+UaNGycPDQ82aNbvvmgEAwC3kS7CWFu2627oEu9Pmh1hbl2B3Pq5c5N6DAAA2kdl8yZbZEk1YAADYuTJlyqT7H7cA2BYNQwBgB1JtXUDuER4ermHDhllsS28VLGt4/fXXtWLFCsXFxSlfvnwP5BwAADxqyJdgDVvWfWDrEuwO+RIA2AHypUx7WPlSdrIlmrAAAACQKYQ6yOlYXcz6+HMPwFoy+urB9BQtWlSOjo46d+6cxfZz587J3d39rsfOnDlTr7/+uj7//HNVq1btvusFAACA9fE7JnID8iXr4s89AGvKbL5ky2yJJiwAAABkCgGEdRFAWB/3FADsQ968eVW7dm1t375dbdu2lSSlpqZq+/btGjhwYIbHzZgxQ1OmTNHWrVtVp06dh1QtAAAAMotsyfrIQqyPewoAuZ8tsyWasAAAAAAbIHi0PkIyALAfw4YNU8+ePVWnTh3VrVtXc+fO1dWrV9WrVy9JUo8ePVSyZElNmzZNkjR9+nRFRERo2bJl8vb21tmzZyVJRqNRRqPRZtcBAACA//nr5r+2LsHuOLq42LoEu3Pzn39sXQIAwApslS3RhAUAQDrOnz+viIgIbdq0SefOndNjjz2m6tWrKyIiQg0bNpS3t7eGDh2qoUOHpnv8unXrNH36dB09elSpqakqXbq0goKCNHfuXE2aNEkLFy7UkSNHVLhwYfMxhw4dUt26dbV27Vq1bt1aBoNB69atM3doGwwG81hXV1c98cQTmjRpkpo0aXLPmpOTkxUYGHjXa46NjdWpU6fMf/m403vvvac+ffooJibGvN9gMMjDw0NBQUGaPn26ihUrlqk6Q0JClJCQoPXr16dbh7e3t3799VdJUv78+eXj46MhQ4aoT58+d63/v2hwQU5HwxAAABnr1KmT+e+3Z8+eVY0aNbRlyxYVL15ckvTbb7/JwcHBPP7tt99WUlKSOnToYDHPuHHjNH78+IdZOgAAksiW/is3ZkuS5JA3b5aPQcZcHPLYugS7Q8MQAADps1W2RBMWAADpaN++vZKSkrRkyRKVLVtW586d0/bt23XhwoV7Hrt9+3Z16tRJU6ZM0XPPPSeDwaAffvhB27ZtkySFh4frk08+0YABA7R8+XJJUnJysnr27Klu3bqpdevWGc4dHR2tFi1a6O+//9aYMWPUunVrHT58WGXLlr1rzS1atNCZM2fM8wwZMkRXrlxRdHS0eVvhwoV16tQpubq66vjx4xbnLVSokPmfb+9PTU3VoUOH1KtXL50+fVpbt27NVJ2ZMXHiRIWFhenatWtavXq1wsLCVLJkST3zzDOZOl6iweVBoLHNurif1sefewCwLwMHDsxwifi4uDiL96dOnXrwBQEAkAVkS7k/W5Kk1KSkLI3H3T1fPsDWJdidFX9st3UJdqdzqfq2LgEAYCW2yJZowgIA4D8SEhIUHx+vuLg4+fv7S5K8vLxUt27dTB3/ySefqGHDhho5cqR5m6+vr/mpQycnJ73//vuqWbOm1qxZow4dOmjKlClKSEjQnDlz7jq3m5ub3N3d5e7urrffflslS5bUtm3b1KlTp3vW7O7ubv7n/PnzKzEx0WLbbQaDId3t6e338PDQ4MGD9dprr+n69evKnz//Fx0+NwAA6XBJREFUXet86aWX7nH3bnFxcTGfY9SoUZoxY4a2bduW5aAM1kWDC3I6Gtusjz/3wN0ZTCZblwAAAHIgsiWyJaQv9do1W5dgd2gYsj7yJesiWwLujXzJvtCEBQDAf9z+bt/169fLz89Pzs7OWTre3d1dy5Yt0+HDh/XEE0+kO6ZixYqaNm2a+vXrJxcXF02bNk1btmyRq6trps9zO5RKSkrKds3ZkT9/fqWmpiolJeWedWZVamqq1q1bp0uXLikvy7/bHAEEcjpCHQAAAAA5AdlS1pAtPTr+HN3A1iXYnRuVr9u6BLvT3MPWFQAAcjOasAAA+A8nJyfFxMQoLCxMixYtUq1ateTv76/OnTurWrVq9zx+0KBBio+PV9WqVeXl5SU/Pz89/fTT6tq1q0WANWTIEH388cdq2bKlBg0apMDAwEzXeO3aNY0dO1aOjo7y9/fPds13unz5soxGo/m90WjU2bNn0x37448/atGiRapTp45cXFzuWWdmjRo1SmPHjlViYqJSUlJUuHBh9enTJ8PxiYmJSkxMtNiWaropB4Njps8JPGw0DAEAAACAfSJbyn3ZkkS+9DAcHvi2rUuwO+RLAADkLA62LgAAgJyoffv2On36tDZs2KAWLVooLi5OtWrVUkxMzD2PLViwoDZt2qSffvpJY8eOldFo1PDhw1W3bl1du2PJbYPBoDFjxig1NVVjx47NVF1dunSR0WiUi4uL1q5dq8jISHMQlp2a7+Ti4qKDBw+aX7t377bYfztIK1CggCpUqKDixYvrww8/zHSdmTFy5EgdPHhQO3bsUL169TRnzhyVK1cuw/HTpk1ToUKFLF6/6FiWrhsAAAAAAMBayJZyV7YkkS8BAAAg+1gJCwCADOTLl09BQUEKCgrSa6+9pj59+mjcuHEKCQnJ1PE+Pj7y8fFRnz59NGbMGPn6+mrlypXq1auXeYyTk5PF/97LnDlz1KxZMxUqVEiPP/641WuWJAcHh7uGUi4uLvrmm2/k4OCgEiVKmJeEz0qd91K0aFGVK1dO5cqV0+rVq1W1alXVqVNHlStXTnd8eHi4hg0bZrGtXaGQLJ8XeJj4ekfr4+lPAAAAADkJ2VL6cmK2JJEvPQwtStexdQl2Z8Ofe2xdgt15riSfUwDA/aMJCwCATKpcubLWr19/X8d6e3urQIECunr1arZqcHd3v+dTe3fKTs0ZuVeQJmW9zrvx9PRUp06dFB4ero8//jjdMc7OzhbL8UtiqXjkeDQMAQAAAMCjhWzplpyYLUnkSw/Dlt8O2LoEu9Pcg4YhAAByEpqwAAD4jwsXLig4OFi9e/dWtWrV5OLiogMHDmjGjBlq06aNedyff/6pgwcPWhzr5eWlefPm6dq1a2rZsqW8vLyUkJCgN998U8nJyQoKCrJpzTnF5cuX09y7IkWKyNPTM93xQ4YM0RNPPKEDBw6oTh2CBQAAkEOYbF0AAADIiciWHjyyJQAAYDfIl+wKTVgAAPyH0WhUvXr1NGfOHJ08eVLJycny9PRUWFiYRo8ebR43c+ZMzZw50+LYDz74QP7+/lqwYIF69Oihc+fO6bHHHlPNmjX12WefqUKFCjatOaeIi4tTzZo1LbaFhoZq8eLF6Y6vXLmynn76aUVEROjTTz99GCUCAAAAAADcF7KlB49sCQAAADmRwWQy0VcHAADsTpBDsK1LAO5q6+lDti7B7vAVj8CjZ1vqapuev2ngNJuePzfZHhtu6xIAAACyjHwJOR35kvWRLwGPHvKl3CM35EsOti4AAAAAAAAAAAAAAAAAAHIzvo4QAAAAmcKTddbFU3UAAAAAAOBRQrZkfeRLAADkLDRhAQAAADZA8Gh9BI8AAAAAAORcLUrXsXUJdmfDn3tsXYLdea4kn1MAwP2jCQsAAACwARqGAMAOmEy2rgAAAADINbb8dsDWJdid5h40DAFArke+ZFdowgIAAECm0DSEnI7VxZDT8e9RAAAAAI8yfidCbjDz1F5bl2BXXm3QztYl2J2U02dsXQKAu6AJCwAA2CWaMayPoAw5HZ9RAAAAAIA1kS9ZF7+3IzcY4e1n6xLsDA1DAB4tNGEBAAAgUwgerYvgEQAAAACAnC1VqbYuwb4YHGxdgf0x8RkFACAnoQkLAID7cP78eUVERGjTpk06d+6cHnvsMVWvXl0RERFq2LChvL299euvv0qSChQooAoVKig8PFzBwcGSpGvXrmnSpElatWqV/vzzT7m4uKhy5coaNmyY2rRpY3GuP/74Q2XLlpWvr68OHz6cYU179uxRo0aN1KJFC23atMli36lTp1SmTBl9++23Wr9+vSZMmHDX62vdurWSk5O1ZcuWNPvi4+PVuHFjHTp0SNWqVZMkLVmyRPPnz9eRI0fk6OioWrVqaeTIkWrdurX5uLi4OAUGBprfFy1aVE8++aSmT5+uqlWratasWZo8ebLOnDmjfPnyWZzz2rVrcnd31+TJkzV48OC71n4bDS7WRxOWdXE/rY8/9wAAAAByE/KlnJ8vPeNRM1PjkDlXutWzdQl2p3CfX21dgt1JCTht6xIAALkYTVgAANyH9u3bKykpSUuWLFHZsmV17tw5bd++XRcuXDCPmThxosLCwnTlyhXNmjVLnTp1UsmSJdWgQQP17dtX+/bt01tvvaXKlSvrwoUL2r17t8Xxt8XExKhjx47atWuX9u3bp3r10g8rIiMjNWjQIEVGRur06dPy8PBId9yIESPUt29f8/snn3xSL774osLCwszb9u7dq/bt2+uPP/5QqVKlLI6Pjo5WnTp1zAHZiBEjNH/+fE2ePFlt27ZVcnKyli5dqjZt2mjevHkaOHCgxfHHjx+Xq6urTp8+rZEjR6pVq1b66aef1L17d4WHh+ujjz7SCy+8YHHMmjVrlJSUpG7duqV7TemhwcX6aHABAAAAAMB6yJfIlx41zdP/OCEbUpbaugIAAHAnmrAAAMiihIQExcfHKy4uTv7+/pIkLy8v1a1b12Kci4uL3N3d5e7urgULFmjp0qX65JNP1KBBA23YsEHz5s1Ty5YtJUne3t6qXbt2mnOZTCZFR0dr4cKFKlWqlCIjI9MNyf7991+tXLlSBw4c0NmzZxUTE6PRo0enW7/RaJTRaDS/d3R0NNd6W+vWrfX4448rJiZGY8eOtTjP6tWr9cYbb0i6FabNmjVLb775pgYNGmQeN2XKFN24ccP85KWnp6d5X7FixeTm5iZ3d3cNHTpUzz33nI4dO6Zq1arp2WefVVRUVJqQLCoqSm3btlXhwoXTvab00DBkfQSP1sVnFAByP4PJ1hUAAIDcinyJfOlRRLZkfXxGASD3I1+yLzRhAQCQRbdDpvXr18vPz0/Ozs73PMbJyUl58uRRUlKSJMnd3V2ffvqpnn/+ebm4uGR4XGxsrK5du6ZmzZqZn3KcM2eOChYsaDFu1apVqlixoipUqKBu3bpp6NChCg8Pl8FguK9rdHJyUo8ePRQTE6MxY8aY51m9erVu3rypLl26SJKWL18uo9Gol156Kc0cw4cP1+zZs7V27VoNHTo0zf7Lly9rxYoVkqS8efNKkkJDQ9W6dWv9+uuv8vLykiT9/PPP2rVrl7Zu3ZphvYmJiUpMTLTYlmq6KQeDY9YvHhki1LEugkfr4zMKAAAAILcgXyJfehTxe7v1kS9ZH59TAEB20IQFAEAWOTk5KSYmRmFhYVq0aJFq1aolf39/de7c2byE+p2SkpI0a9YsXb58WU2aNJEkvfvuu+ratauKFCmi6tWrq1GjRurQoYMaNmxocWxkZKQ6d+4sR0dHPfHEEypbtqxWr16tkJCQNONuL6XeokULXb58WTt37lRAQMB9X2fv3r31xhtvWMwTHR2t9u3bq1ChQpKkEydOyMfHxxxy3cnDw0Ourq46ceKExfbby89fvXpVkvTcc8+pYsWKkqTmzZvLw8ND0dHRGj9+vKRby+V7enqqadOmGdY6bdo0TZgwwWJbxPDCGjeiSNYvHBkigLAu7icAAAAAPLrIl8iXHkVkIdbHPQUAIGdxsHUBAADkRu3bt9fp06e1YcMGtWjRQnFxcapVq5ZiYmLMY0aNGiWj0agCBQpo+vTpev3119WqVStJUuPGjfXzzz9r+/bt6tChg44cOaKnnnpKkyZNMh+fkJCgjz76yBx+SVK3bt0UGRlpUcvx48e1f/9+89ODTk5O6tSpU5pxWVWxYkU1aNBAUVFRkqSffvpJ8fHxCg0NtRhnMt19ndT/Bmjx8fH6+uuvFRMTI19fXy1atMi8z9HRUT179lRMTIxMJpNSU1O1ZMkS9erVSw4OGf+1JTw8XJcvX7Z4vTrosaxeMgAAAAAAwENDvvQ/5EsAAACwB6yEBQDAfcqXL5+CgoIUFBSk1157TX369NG4cePMTxGOHDlSISEhMhqNKl68eJql2/PkyaOnnnpKTz31lEaNGqXJkydr4sSJGjVqlPLmzatly5bpxo0bqlevnvmY28HRiRMn5OvrK+nWU4opKSny8PCwGOfs7Kz58+ebnyq8H6GhoRo0aJAWLFig6Oho+fj4yN/f37y/fPny+uKLL5SUlJQmDDt9+rSuXLlirvO2MmXKyM3NTRUqVNBff/2lTp06adeuXeb9vXv31rRp07Rjxw6lpqbq999/V69eve5ap7Ozc5pl+4Pcat7vZSMDLG9uXTypCAAAAAAgXyJfepSQLVkf+RIAADkLTVgAAFhJ5cqVtX79evP7okWLqly5clk6PiUlRTdu3FDevHkVGRmp4cOHp1kavn///oqKitLrr7+ulJQUvf/++5o1a5aefvppi3Ft27bV8uXL1bdv3/u+po4dO2rIkCFatmyZ3n//ffXr188i7OvSpYveeustvfPOOxo0aJDFsTNnzlS+fPnUqVOnDOcfMGCApk2bpnXr1qldu3aSZA7ioqKiZDKZ1KxZM3l5eWW5dkId6yPUAQDgP+6xYgMAAEBWkS+RL9kzsiUAANJBvmRXaMICACCLLly4oODgYPXu3VvVqlWTi4uLDhw4oBkzZqhNmzaZmiMgIEBdunRRnTp1VKRIEf3www8aPXq0AgMD5erqqoMHD+qbb77Rhx9+qIoVK1oc26VLF02cOFGTJ0/Wxo0bdenSJYWGhqZ5IrF9+/aKjIzMVkhmNBrVqVMnhYeH68qVK2kCu/r162vIkCEaOXKkkpKS1LZtWyUnJ2vp0qV68803FRMToyJFimQ4f4ECBRQWFqZx48apbdu25gAuNDRUYWFhkmSxBH9WEOpYH8Ejcjr+3AMAAADILciX/od86dFRdDdf72htv82qYOsS7E6BtXttXQIAIBejCQsAgCwyGo2qV6+e5syZo5MnTyo5OVmenp4KCwvT6NGjMzVH8+bNtWTJEo0ePVrXrl2Th4eHWrdurYiICEm3loCvXLlymoBMktq1a6eBAwfq008/VWRkpJo1a5bukvDt27fXjBkz9N1338nV1fW+rzc0NFSRkZFq2bKlxZL0t82dO1fVqlXTwoULNXbsWPOTljt27FDjxo3vOf/AgQM1e/ZsrV69Wh07djTXPnDgQDk6Oqpt27b3XTuQkxHkAgAAAMCji3zJEvnSo+FD7zhbl2B3mq+9ZOsSAADAHQwmE2ubAQAA6zl16pT8/f1Vv359ffjhh3J0dLRJHUEOwTY5rz1jJSzrogkLALJvW+pqm56/WeMpNj1/bvL5rjG2LgEAAOQi5Ev2iWzJ+siXACD7yJdyj9yQL7ESFgAAsCpvb2/FxcVpyZIlOnjwoGrXrm2TOgh1AAAAAAAAcqecki8BAAAAWUETFgAAsLoyZcpo/PjxNq2Bp8Csj8Y2AAAAAADwsOSEfAkAAADICpqwAAAAAAAA7oMh1dYVAAAAAAAAIDcjX7IvNGEBAAAANsDKYtbHCngAAAAAAORcdcb3s3UJdifv5r9sXYLdcXnmJ1uXAADIxWjCAgAAQKbQ4GJdNGEBAAAAAIBHSZF3d9u6BLuzdTz5krU1FxkoAOD+0YQFAMB9OH/+vCIiIrRp0yadO3dOjz32mKpXr66IiAg1bNhQhw4d0muvvaa9e/fqypUrcnd3V7169fTWW2+pWLFi5nnWrl2rBQsW6Ntvv9WNGzdUunRpNWzYUIMGDVLNmjV17NgxVapUSXv27JGfn5/5OD8/Px08eFAJCQnKly+fJOnGjRtyc3PTggULFBoaqpCQECUkJGj9+vWSlOa9JK1Zs0bdunXTlClTNHz48HTH3Mnb21u//vqrJKlAgQKqUKGCwsPDFRwcLEm6du2aJk2apFWrVunPP/+Ui4uLKleurGHDhqlNmzaSpICAAO3cuTPN3MnJyXJyclJAQIBq1KihuXPnmvfNmzdPr7zyipYsWaLOnTtn+ecF5EQ0tVkfjW3I6fhzDwAAgDuRL5EvAdnF75nWV2qf0dYl2JWfplaxdQl2J9/H+2xdAoC7oAkLAID70L59eyUlJWnJkiUqW7aszp07p+3bt+vChQs6f/68mjZtqtatW2vr1q1yc3PTqVOntGHDBl29etU8x6hRozRr1iwNHjxYEyZMkJeXl86fP6/NmzcrPDxcW7ZsUcWKFeXu7q64uDhzSPbPP//om2++UfHixbV3714FBARIkvbs2aPExEQ1adIkU9ewePFiDRgwQIsWLVKvXr0yfe0TJ05UWFiYrly5olmzZqlTp04qWbKkGjRooL59+2rfvn166623VLlyZV24cEG7d+/WhQsXLOYICwvTxIkTLbY5OaX/15Jx48Zp5syZ+vjjj9WiRYtM1wkAyD7CXAAAAODBIV8iXwKQ80R6fmnrEuxK84//tXUJAPBQ0YQFAEAWJSQkKD4+XnFxcfL395ckeXl5qW7dupKk9evX6/Lly1q8eLE5+ClTpowCAwPNc+zdu1czZszQvHnzNHjwYPP20qVLq3bt2jKZTOZtgYGBiouL06uvvipJ+uKLL+Tr66vGjRsrLi7OHJLFxcXJy8tLZcqUuec1zJgxQ+PGjdOKFSvUrl27LF2/i4uL3N3d5e7urgULFmjp0qX65JNP1KBBA23YsEHz5s1Ty5YtJd16srF27dpp5ihQoIDc3d3veh6TyaTBgwdr6dKl2rZtmxo0aJClOgEAAAAAAHIq8iXyJQAAANgfmrAAAMgio9Eoo9Go9evXy8/PT87Ozhb73d3dlZKSonXr1qlDhw4yGAxp5li+fLmMRqP69++f7jnuPCYwMFAvv/yyUlJS5OTkpNjYWAUEBOipp57SwoULNX78eElSbGysRRCXkVGjRmnhwoXauHGjmjZtmoUrT8vJyUl58uRRUlKSpFvX/umnn+r555+Xi4vLfc+bkpKibt26aceOHdq5c6eqVauWrTqBnIivzgMAO3DHf9gEAADICvKl/yFfAu6fU7myti7B7my/ftjWJQB41JAv2RWasAAAyCInJyfFxMQoLCxMixYtUq1ateTv76/OnTurWrVq8vPz0+jRo/XCCy+ob9++qlu3rpo0aaIePXqoePHikqQTJ06obNmyFkukz549WxEREeb3f/75pwoVKqTAwEBdvXpVX331lerXr6+4uDiNHDlSjRo1Us+ePXXjxg2ZTCbt379fffr0uWvtmzdv1scff6zt27dneln5jCQlJWnWrFm6fPmyea53331XXbt2VZEiRVS9enU1atRIHTp0UMOGDS2OXbhwoRYvXmx+/9JLL2nWrFnm9++9954k6dChQ6pYseI9a0lMTFRiYqLFtlTTTTkYHO/7+gDkPnx1HgAAAIDcgnzpFvIlIHs27Vpn6xLsDvkSACA7DCYTbXUAANyPGzduKD4+Xnv37tXmzZu1f/9+LV68WCEhIZKkCxcuaMeOHdq3b5/WrVunixcvateuXapataqeeeYZnT59WocO/W8lnISEBP3999/at2+funXrpkuXLsnNzU2S5Onpqf79+2vAgAEqUqSI/vzzTxUrVky+vr565513ZDKZ1LRpU/3222/y9PSUJIWEhCghIUHr1683vz9y5Ij+/vtvlSpVSps3b5bRaLS4pv8e81/e3t46c+aM8uTJoxs3bshoNCo8PFyjRo0yj0lOTtbevXu1e/dubd++XZ9//rkmTJig1157TZIUEBCgMmXKaMyYMeZj3NzcVLRoUfN+R0dHHTx4UE2aNNHy5cstwsT0jB8/XhMmTLDYVkaV5GOoctfjkDWs3IScjpAMePRsS11t0/MHNZxs0/PnJtu+HGvrEgAAyJHIl8iXHiWGOlVtXYLdaf/BdluXYHfWVCpm6xIAPGTkS7lHbsiXaMICAMBK+vTpo23btunXX39Nsy8pKUk1a9ZUnTp1tGTJEg0ePFjR0dG6ePGi8uTJYzE2Li5OgYGBFiFZ9+7dde7cOQ0ZMkQjR47UDz/8IEkKCwtTiRIlZDKZtGLFCv3444/medILyRISEjRv3jwFBgbKw8NDmzdvtljWPTMhWbdu3RQSEiKj0ajixYunuxz+nSZPnqyJEyfq33//Vd68eRUQEKAaNWpo7ty56Y6/vT8kJETNmjWTv7+/Vq5cedegLL0nFdsVCuFJRSujCcu6aBgCgOwjJMs9ckNIBgBATkC+lD7yJftAtmR95EsAkH3kS7lHbsiXHGxdAAAA9qJy5cq6evVquvvy5s0rHx8f8/4uXbro33//1cKFCzM1d2BgoHbv3q1t27YpICDAvL1x48aKi4szB2uZ4eXlpZ07d+rs2bNq0aKF/vnnn0wdd1vRokVVrlw5ubu73zMgk27dl5SUFN24cSNL56lRo4a2b9+uXbt2qWPHjkpOTs5wrLOzs1xdXS1eBGQAAAAAACC3IV9KH/kSAAAAcoO7r70KAADSuHDhgoKDg9W7d29Vq1ZNLi4uOnDggGbMmKE2bdpo48aNWrFihTp37ixfX1+ZTCZ98skn+vTTTxUdHS1Jql+/voYPH67hw4fr119/1fPPPy9PT0+dOXNGkZGRMhgMcnD4X690YGCgrl69qqioKL333nvm7f7+/urTp48kqX///pm+Bk9PT3Ow1rx5c23ZskWurq6SpMuXL+vgwYMW44sUKWJehv5uAgIC1KVLF9WpU0dFihTRDz/8oNGjRyswMNA8f1ZUr15dO3bsUNOmTdWxY0etWrUqzZOdAAAAAAAAuQ35UsbIlwAAAJBb0YQFAEAWGY1G1atXT3PmzNHJkyeVnJwsT09PhYWFafTo0Tpz5owKFCig4cOH6/fff5ezs7PKly+vxYsXq3v37uZ5Zs6cqbp16+rtt99WVFSUrl27puLFi6tx48bas2ePRahUpkwZeXl56ddff5W/v795e+nSpeXh4aFTp05ZPMGYGaVKlbIIyrZu3Srp1nL1NWvWtBgbGhqqxYsX33PO5s2ba8mSJRo9erSuXbsmDw8PtW7dWhEREVmq7U5Vq1Y1B2XBwcFatWqV8ubNe9/zAQAAWI3J1gUAAIDcinwpY+RLAADgkUK+ZFcMJpOJHykAALA7QQ7Bti4BuKutpw/ZugS709yjuq1LAPCQbUtdbdPzBzWYbNPz5ybbdo+1dQkAAABZRr6EnI58yfrIl4BHD/lS7pEb8iVWwgIAAECmEOpYF4EOAAAAAAB4lJAtWR/5EgAAOYvDvYcAAAAAAAAAAAAAAAAAADLCSlgAAACADfD0p/Xx9CcAAAAAADlXs669bV2C3Wl4aJ+tS7A7e6vzn88BAPeP/xcBAAAAbICGIQAAAAAA8Cj5/MMoW5dgd8iXAADIWWjCAgAAQKYQ6lgXK2FZH59RAA+bwWSydQkAAABArsHv7dZHvmR9fE4BPGzkS/aFJiwAAAAAdoHg0foIHgEAAAAAyLmCuvSydQl2x/+7PbYuwa7EV3O2dQkA8FDRhAUAAIBMocHFumhuAQAAAAAAjxKyJetr7mHrCuwPTUMAgOygCQsAgBzq/PnzioiI0KZNm3Tu3Dk99thjql69ukaPHq327dtrxIgRevXVV9McN2nSJM2fP19//PGHPvzwQ/Xq1UvNmzfXli1bzGMSEhL02GOPKTY2VgEBAZIkg8Fg3l+gQAF5eHioYcOGGjRokGrXrm3eFxcXp8DAQF26dElubm6SpJs3b+rNN99UVFSUfvzxR+XPn19+fn4aO3asGjZsaD42JiZGvXr1Mp+vePHiaty4sd544w2VLl3aPC4gIEA1atTQ3LlzrXErgRyJ4NH6aGwDAAAAAEvkS+RLOUnz9j1sXYLdaXlkl61LsDufVilk6xIAALkYTVgAAORQ7du3V1JSkpYsWaKyZcvq3Llz2r59uy5fvqxu3bopOjo6TUhmMpkUExOjHj16KE+ePJIkJycnff7554qNjVVgYOBdzxkdHa0WLVroxo0bOnHihN59913Vq1dPUVFR6tEj/ZDEZDKpc+fO+vzzz/XGG2+oadOmunLlihYsWKCAgACtXr1abdu2NY93dXXV8ePHZTKZ9Msvv6h///4KDg7Wvn37snfDgFyGhiEAAAAAwINGvoScZOva921dgt0hXwIAIGehCQsAgBwoISFB8fHxiouLk7+/vyTJy8tLdevWlSSVKVNG8+bN0xdffKFGjRqZj9u5c6d+/vlnhYaGmrcVLFhQHTt21KuvvnrPIMrNzU3u7u6SJG9vbz399NPq2bOnBg4cqGeffVaPPfZYmmNWrVqlNWvWaMOGDXr22WfN2999911duHBBffr0UVBQkAoWLCjp1hOKt89RokQJhYaGavDgwbpy5YpcXV3v53bhISHUsS5WwrI+PqMAAAAA8D/kS8hp+L3d+siXrI/PKQAgO2jCAgAgBzIajTIajVq/fr38/Pzk7Gz5PfRVq1bVk08+qaioKIuQLDo6Wg0aNFDFihUtxo8fP17lypXTmjVr1KFDhyzV8vLLL+v999/Xtm3b1LFjxzT7ly1bJl9fX4uA7Lbhw4fro48+0rZt2yyeVrztr7/+0rp16+To6ChHR8cs1XWnxMREJSYmWmxLNd2Ug+H+50RahDoAAPyHyWTrCgAAADJEvpQ15EsPnkPevLYuwe5cNyXeexAAIGcjX7IrNGEBAJADOTk5KSYmRmFhYVq0aJFq1aolf39/de7cWdWqVZMkhYaGasSIEXrzzTdlNBr1zz//aM2aNXrzzTfTzOfh4aEhQ4ZozJgx6YZVd3M7cDt16lS6+0+cOKFKlSqlu+/29hMnTpi3Xb58WUajUSaTSdeuXZMkDR482Pwk4/2YNm2aJkyYYLGtjCrJR1Xue07gQeOpOgAAAADAg0S+lDXkSw/e5lNf2boEu9Pco66tSwAAAHegCQsAgByqffv2atWqleLj47V3715t3rxZM2bM0OLFixUSEqIuXbro5Zdf1qpVq9S7d2+tXLlSDg4O6tSpU7rzjRo1Su+8846ioqLSfeIwI6b/78A3GAz3HJORvHc85ebi4qJvvvlGycnJ2rx5sz788ENNmTIl0/WkJzw8XMOGDbPYludSLTk7s3ITAAAAAAB4dJEvZV56+dLzhUNlYCUsq0k2pdi6BAAAgAeKJiwAAHKwfPnyKSgoSEFBQXrttdfUp08fjRs3TiEhIXJ1dVWHDh0UHR2t3r17Kzo6Wh07dpTRaEx3Ljc3N4WHh2vChAlq3bp1pms4evSoJKlMmTLp7i9fvrx5TEbH+vr6mrc5ODioXLlykm49yXjy5En169dPH3zwQaZr+i9nZ+c0S+oHudW87/mQPr6OEAAAAACA3Id8KXPSy5cMqZJJN+97TljKY+A/SwIAAPvmYOsCAABA5lWuXFlXr141vw8NDdUXX3yhjRs3avfu3QoNDb3r8YMGDZKDg4PmzZuX6XPOnTtXrq6uatasWbr7u3Tpoh9//FGffPJJmn2zZs2Sh4eHgoKCMpz/1Vdf1cqVK/XNN99kuiYAAAAAAADcH/IlAAAA4MGg5RwAgBzowoULCg4OVu/evVWtWjW5uLjowIEDmjFjhtq0aWMe17hxY5UrV049evRQxYoV1aBBg7vOmy9fPk2YMEEDBgxId39CQoLOnj2rxMREnThxQu+8847Wr1+v999/X25ubuke07lzZ61atUo9e/bUG2+8oaZNm+rKlStasGCBNm7cqC1btihPnjwZ1uTp6al27dopIiJCGzduNG8/f/68Dh48aDG2RIkSKl68+F2v8TZWbbK+5h7VbV0CAAAAAADIJPIl8qWchmwJAADYO5qwAADIgYxGo+rVq6c5c+bo5MmTSk5Olqenp8LCwjR69GjzOIPBoN69e2v06NEKDw/P1Nw9e/bUrFmz9MMPP6TZ16tXL0m3wrSSJUuqUaNG2r9/v2rVqpXhfAaDQatXr9bcuXM1Z84c9e/fX0lJSSpcuLC+/fZbVa5c+Z41vfzyy6pfv772/x97dx5XY/r/D/x1KqWkRSIltKjIvjZlVEK20NBGSI2vNbLLWvZ9X2ecztHHSEKGMWOEImMbS2bMULbGGMxG9pI6vz/8usftVEqHU8fr+X2cx1fXfd339b7vc8zMeX2u6+rMGbRp0wYAsG3bNmzbtk3Ub86cOZg+fXqJ7pOhjuoxeFQtfkaJiDRAvroLICIiIioa8yXmS+UNsyXV42eUiEgDMF/SKBKFQqFQdxFERESkWc6fP4+OHTsiLCwMS5YsUUsNnbT81DKuJmNQploMyYiIyi4pP0Gt43duM1ut41ckB8/MVHcJREREVMEwX9I8zJZUj/kSEVHZMV+qOCpCvsSdsIiIiEjlWrRogcOHD+Prr7/G9evXYWdn98FrYKijegx1iIiIiIiIiOhDYb6keZgtERERkabjJCwiIiJ6L5o3b47mzZuruwxSIQaPqsXgkYiIiIiIiKh46s6XulgX/SsUqfS++eOMukvQOD2sWqq7BCIiInoNJ2ERERGRRuIEF9XjJCwiIiIiIiIi+pgo8vLUXYJGqSTh/yxJREREmo3/tUNEREREJcKJbarFSW2qx88oERERERERUfnF7+2qx3xJ9fg5JSKisuAkLCIiIiIqEYY6qsVAh4io4pMoFOougYiIiIiowmC2pHrMl4iIKj7mS5pFS90FEBERERERERERERERERERERERVWSchEVERBpNIpEU+4qKikJmZqaorWrVqnB2dsbIkSNx9epVAMDRo0dRqVIlHD9+XHT9p0+fwtbWFhMmTAAAeHh4QCKRYPv27aJ+K1euRL169YSf8/LysHDhQjg5OUFfXx/VqlVD27ZtsXnzZqFPSEgIevfuXeS91atXT6hZX18f9erVg7+/P44cOSLq9+b9VatWDe7u7khNTRX1i4qKQrNmzUQ/SyQSdOnSRWnsJUuWQCKRwMPDQ+nY7du3oauri0aNGhVa99GjR9GhQwdUq1YNBgYGqF+/PgYNGoQXL14AAFJSUiCRSJCVlSWcc+fOHTRu3Bjt27fHw4cPi3wmRERERERERESqxGyJ2RIRERERUUnx1xESEZFGu3v3rvDn+Ph4zJw5E+np6UKboaEh/vnnHwDAoUOH4OzsjGfPnuHnn3/GqlWr0LRpU+zbtw9eXl4IDw9HSEgILl68iCpVqgAAJk2aBH19fcydO1e4ZuXKlTF9+nT06dMHlSpVKrSu6OhobNq0CWvXrkWrVq3w6NEjnD17Fg8ePCjV/c2ePRtDhgzBixcvkJmZia1bt6Jjx46YM2cOpk2bJupbcH///PMP5s2bhx49eiAjIwM1a9Ys8vq1atVCcnIybt++jdq1awvtMTExqFOnTqHnyOVy+Pv749ixYzh9+jTatm0rHPv111/RpUsXhIeHY/Xq1dDX18fVq1exa9cu5OXlFXq969evo1OnTmjYsCESEhKgr69fmkdEKsTtzVWLW/CrHj+jRERERESkasyW/sNsicqK39tVj/mS6vFzSkREZcFJWEREpNEsLCyEPxsbG0MikYjaAAhBmZmZmXDM1tYWPj4+8PLyQlhYGK5fv4758+fjwIEDmDx5MtauXYvk5GRs3rwZJ06cQOXKlYXrBQUFYe/evfjyyy8xYsSIQuvau3cvRowYAT8/P6GtadPSf7mrWrWqUHOdOnXQvn171KpVCzNnzkTfvn3h6Ogo9C24PwsLC0ydOhXbt2/H6dOn0bNnzyKvX6NGDbRs2RJbtmwRgrcTJ07gn3/+gZ+fH3799VdRf4VCAZlMhvXr16N27dqQSqWioOzgwYOwsLDA4sWLhTY7O7tCV0QCwE8//QRvb2906NABW7ZsgY4O/9NFnRjqEBERERER0ceG2RKzJVKd+5+7qrsEjfPpz7bqLkHjGOCGuksgIqIKjP+1SUREVAQtLS2MGTMGvr6+OHfuHNq0aYPY2Fi4urqiU6dOiIiIwNSpU9GyZUvReUZGRpg2bRpmz56NQYMGCSsbX2dhYYEjR45gxIgRMDc3V2ndY8aMwZw5c/D1119j0qRJSsefP3+O2NhYAICuru5brxcaGopJkyYJQVlMTAz69+9faN/k5GQ8e/YMHTt2hJWVFVxdXbFixQrhGVhYWODu3bs4duwY2rdvX+y4J06cQP/+/dG/f3+sWbMGEonkrbUSVSRcVUdEpAEUCnVXQEREROUYs6VXmC1RgR9nb1B3CRqH+RIRkQZgvqRROAmLiIioGE5OTgCAzMxMtGnTBq1atUJkZCQ+++wzNG/eXGlb9gIjRozAqlWrsHz5csyYMUPp+PLly9G3b19YWFjA2dkZrq6u6NWrF7p27VrmmqtVq4YaNWogMzNT1O7q6gotLS08e/YMCoUCLVu2hJeX11uv16NHDwwbNgzHjh1Dy5YtsWPHDhw/fhwxMTFKfaVSKQIDA6GtrY1GjRrB1tYWCQkJCAkJAQD4+fnh+++/h7u7OywsLODi4gIvLy8MHDgQRkZGomv5+voiICAAa9eufWuNOTk5yMnJEbXlK/KgJdF+67lE6sKdxVSPwSMREREREZU3zJYqRrYEMF/6EGwT/0/dJWgcnUVa6i5B49hMPqnuEoiIqALjJCwiIqJiKP7/7PPXV8rNmDEDs2fPxpQpU4rcwlxPTw+zZ89GeHg4hg8frnS8YcOGuHTpEs6dO4cffvgBx44dg4+PD0JCQrB582aV1P3m6r74+Hg4OTnh0qVLmDRpEuRyOSpVqvTWa1WqVAnBwcGQyWS4ceMGHBwc0KRJE6V+WVlZ2L17N44fPy60BQcHQyqVCkGZtrY2ZDIZ5s6diyNHjuD06dOYP38+Fi1ahDNnzqBWrVrCub169UJiYiJSU1Px6aefFlvjggULEB0dLWqzQQPYwfmt90ekLpwwREREREREpPmYLVWMbAlgvvQh3PD9Qt0laBzmS0REROULp0cTEREV4/LlywAAGxsboa0gHCsqJCsQHByMunXrYu7cuYUe19LSQuvWrREREYHdu3dDLpdDKpXi5s2bZar533//xd9//y2qGQCsra1Rv359+Pr6Yv78+fD19VVa3VeU0NBQJCQkYN26dQgNDS20z7Zt25CdnY22bdtCR0cHOjo6mDx5Mo4fP46MjAxRXysrKwwYMABr167FL7/8guzsbGzcuFHUZ9OmTQgMDETXrl1x7NixYuuLjIzEw4cPRS8bOJXo3oiIiIiIiIiI3hdmS6+U92wJYL5ERERERGXHSVhERERFyM/Px+rVq2FjY4PmzZuX+nwtLS0sWLAAGzZsUNq+vTANGzYEADx9+rTUY71u1apV0NLSQu/evYvs07dvX+jo6GD9+vUluqazszOcnZ1x6dIl9OvXr9A+UqkU48ePR1pamvC6ePEiPv3000K3ly9gamqKWrVqKd23RCLBF198gf79+6Nbt244evRokdfQ09ODkZGR6MWt4omIiIiIiIhInZgt/ae8Z0sA8yUiIiIiKjv+OkIiIqL/799//8W9e/fw7NkzXLp0CStXrsSZM2ewf/9+aGu/W+DSvXt3tG3bFps2bULNmjWF9r59+8LNzQ2urq6wsLDAzZs3ERkZCQcHBzg5/bfC7uHDh0hLSxNd08zMDNbW1gCAx48f4969e8jNzcXNmzexdetWbN68GQsWLIC9vX2RdUkkEowePRpRUVEYOnQoDAwM3novR44cQW5uLkxMTJSOpaWl4fz58/jqq69E9QNAUFAQZs+ejblz50IqlSItLQ2+vr6ws7NDdnY2YmNj8csvv2DNmjWF1rlx40Zoa2ujW7du2L9/Pzw8PN5aKxERERERERHRh8ZsqXjMloiIiIhI03EnLCIiov+vY8eOqFWrFho3bowpU6agQYMG+Omnn+Dp6Vmm6y5atAjZ2dmiNm9vb+zbtw8+Pj5wcHDAoEGD4OTkhIMHD4q2ok9JSUHz5s1Fr+joaOH4zJkzUatWLdjb22PAgAF4+PAhDh8+jMmTJ7+1rkGDBiE3Nxdr164t0X1UqVKl0JAMeLVSsWHDhkohGQD4+vrir7/+wrfffos2bdrgyZMnGDZsGJydneHu7o5Tp05hz549cHd3L/TaEokE69atw+DBg9G9e3ckJyeXqF4iIiKi906h4KukLyIioo8As6XiMVsiIiIiKoS6M5uK9KoAJApFBamUiIiIqBQ6afmpuwSN8/2di+ouQaN4WzZVdwlERBVeUn6CWsf3bjlLreNXJN+fi357JyIiIqJyhvmSajFbUj3mS0REZcd8qeKoCPkSfx0hERERaSSGOqrHUIeIiIiIiIiIPibMl1SL2RIRERFpOk7CIiIiIo3EUEf1GDyqFj+jREREREREROUbv7urFrMl1eNnlIiIqHzhJCwiIiIiKhGGOqrF4FH1+BklIiIiIiIiKr/4vV31mC+pHj+nRERUFpyERUREREQlwlBHtRjoEBEREREREdHHhNmS6jFfIiIiKl84CYuIiIiISoShjmoxeFQ9fkaJ6IPLV3cBREREREQVB7+3qx7zJdXj55SIPjjmSxqFk7CIiIhURCKRAACMjY2RlZWl3mIqiHr16uG3334DADx48AAmJibqLYiKxVCHiIiIiIiI6P1ivlR6zJeIiIiIqLzQUncBRESkehKJpNhXVFQUMjMzIZFIkJaWpnS+h4cHIiIihD7FveRyOQBAoVDgyy+/xCeffAIjIyMYGhrC2dkZY8aMwbVr14RrR0VFFXodJycnpfFft2rVKujp6WH79u1Cn4JzK1eujIYNG2L9+vVCf7lcXug4mzdvFvq8ePECS5YsQYsWLVClShUYGxujadOmmD59Ou7cuSMa/969ewgPD4etrS309PRgbW0NHx8fHD58WNRPJpMhIyNDdL/NmjVTesZvPv+UlBRIJBI4OzsjLy9P1NfExER4zsCrYGnlypWinyUSifBsXufs7Cx6nwqcOHEC3bp1g6mpKSpXrozGjRtj+fLlSmO//uyqVKmC+vXrIyQkBOfOnVMaqzgLFy6ERCJRel9//PFH7Nq1S9Qml8uVwrLLly/D2toafn5+ePHiRanGJiIiIiIiIqLSY77EfKkA8yVSGYkWXyp+5fP/VP5/REREZcGdsIiINNDdu3eFP8fHx2PmzJlIT08X2gwNDfHPP/+89TrW1taiay1duhQHDhzAoUOHhDZjY2MoFAr069cPe/bswdSpU7FixQpYWlrizp07SExMxNy5c0UhjbOzs+gaAKCjU/S/kmbNmoWlS5fi66+/RpcuXYT2IUOGYPbs2Xj27BliY2MxcuRImJqaIigoCABgZGQkuu+CegEgJycHnTt3xk8//YTo6Gi4ubnB3NwcN2/eRFxcHNasWYMFCxYAeBVoubm5wcTEBEuWLEHjxo2Rm5uL77//HiNHjsSVK1eE65uYmKBGjRpvfbZFuXHjBmJjYzF48OBSnWdtbQ2ZTIbAwECh7dSpU7h37x6qVKki6puYmAh/f38MHjwYycnJMDExwaFDhzBp0iScPHkSO3bsEFZdAq+Cvy5duiA7OxsZGRn44osv0LZtW8TExGDgwIFvre3HH3/Epk2b0KRJE6Vj5ubmqFat2lvP79q1K3x9fbFp0yZoaXEOubpwK27V4s5iRERERERUnjFfYr4EMF8iFVNwgouqaXG/DSIionKFk7CIiDSQhYWF8GdjY2NIJBJRG4AShWTa2tqi8wwNDaGjo6N0re3bt2P79u34+uuv0bNnT6G9Tp06cHFxgUKhEPUv7BqFUSgUGD16NLZu3YqkpCS4urqKjhsYGAjXiYqKwrZt27B3714hJCvsvgusWLECx48fx9mzZ9G8eXNRze7u7qKaR4wYAYlEgjNnzogCJ2dnZ4SGhr71PkojPDwcs2bNQr9+/aCnp1fi8/r3748VK1bg999/h7W1NQAgJiYG/fv3R2xsrNDv6dOnGDJkCHr27IkvvvhCaP/8889Rs2ZN9OzZEzt27EBAQIBwzMTERHiO9erVQ+fOnTFo0CCMGjUKPj4+MDU1LbKuJ0+eoH///vjyyy8xd+7cEt9PgSNHjqBXr14YMWIEFi1aVKpzOcGFyjtOaiMiIiIiovKM+RLzJYD5EiSc4KJK3/1Rut3P6O28LZu/vRMRERF9MJyERUREZRYXFwdHR0dRQPa611e9ldTLly8RHByMI0eO4OjRo4WucnuTvr5+ibcSj4uLQ6dOnUQB2esKar5//z4OHDiAefPmKa34A6C0rXlZRUREYOvWrVizZg0mTJhQ4vNq1qwJb29vbNmyBdOnT8ezZ88QHx+Po0ePikKygwcP4t9//y302j4+PnBwcEBcXJwoJCvM2LFjERsbi6SkJPj7+wN49cxkMhlCQkKEfiNHjkT37t3RsWPHUodkiYmJ6NevH6KiojB58uRSnQtwgsv7wIltqsXnqXr8e09EREREVHExX1Id5kv/KWu+xJ2bVKsrJwypHPMl1WO+REREZcFJWEREHzlXV1el7befP3+OZs2alfgaGRkZcHR0FLVFRERg8+bNAF4FSbdv3xaO/fzzzzA0NBT1Dw4OxsaNG4Wfv/zySwDAxYsX4eTkVOz4eXl5iIuLw08//YT/+7//E9ofPnwoGsfQ0BD37t0Tavbw8BBdx9fXF0lJSQCAJk2a4MSJE7h27RoUCsVba1AVAwMDzJo1C1OnTsWQIUOE7e1LIjQ0FOPHj8e0adOwc+dO2NnZKb2PGRkZAIAGDRoUeg0nJyehT3EKnkdmZqbQ5ujoKKp3+/btOH/+PH788ccS30OBJ0+ewM/PD1OnTi1RQJaTk4OcnBxRW74iD1oS7VKPTURERFRSkjd25CAiIvpYMV/6D/Ml5ktUNIlOJXWXoHFyFLnqLoGIiMqI+ZJm4SQsIqKPXHx8vFJg0r9//zJfd9q0aRg1ahR2796N+fPni445Ojpi7969ojYjIyPRz+3atUNaWhpmzJiBuLg46Ogo/ytr/fr12Lx5M168eAFtbW2MHTsWw4cPF45XrVoV58+fF35+Mwws7HpPnz7F6tWrcezYMQBQ2ur+QwgLC8OyZcuwaNEipWdXnO7du2Po0KE4duwYYmJiit3Kvqz3VXD+66tQr1y5Ivz5999/x5gxY5CUlITKlSuX+vr6+vpo164dvvzySwQFBRUZ6hVYsGABoqOjRW02aAA7OJd6bKIPhavqiIiIiIhIUzBfEl+P+VLJMF/6+By4dVbdJWgcb8tW6i6BiIiIXsNJWEREHzlra2vY29uL2vT19Ut1jfr16yM9PV3UZm5uDnNzc9SoUUOpv66urtKYb2rcuDGWLVuGjh07IiAgAPHx8UpBWf/+/TFt2jTo6+ujVq1aSiGYlpZWkeMUVnOtWrUAANWqVRP1k0gkogCoNIyMjPDw4UOl9qysLAAodCWijo4O5s2bh5CQEIwaNarEY+no6GDAgAGYNWsWTp8+jcTERKU+Dg4OAIDLly/D1dVV6fjly5fRsGHDt451+fJlAICNjU2hx8+dO4e//voLLVq0ENry8vJw7NgxrF27Fjk5OdDWLnoVoba2Nvbs2YPPPvsMnp6eSE5OLjYoi4yMxLhx40RtvsYhb70PIiIiIiIiIio75kv/Yb7EfImIiIiIPl7FL9kgIiIqgaCgIKSnp+Prr79W6XWbNWuGw4cP49ixY/D390durnhrZWNjY9jb28PKyuqtqxDfFBQUhKSkJFy4cKHYftWqVYO3tzfWrVuHp0+fKh0vCLuK4ujoiNu3b+PPP/8UtZ8/fx6VK1dGnTp1Cj3Pz88Pzs7OSqvv3iY0NBRHjx5Fr169YGpqqnS8c+fOqFatGpYtW6Z0bO/evbh69SqCgoLeOs7KlSthZGSEjh07Fnrcy8sLP//8M9LS0oRXq1at0L9/f6SlpRUbkBXQ09PD7t270bp1a3h6euLXX38ttq+RkZHoxa3iiYiIiIiIiCoO5ktFY77EfImIiIiIKgbuhEVERGUWGBiI3bt3IzAwEJGRkfD29kbNmjXx22+/IT4+XikQefnyJe7duydqk0gkqFmzptK1mzZtiiNHjsDLywv+/v7YsWMHKlWqVOaax44di/3798PLywuzZs3Cp59+ClNTU2RkZOC7774T1bxu3Tq4ubmhTZs2mD17Npo0aYKXL18iKSkJGzZsEFbtFcbb2xuOjo4ICgrC3LlzYWFhgfPnz2P69OkYM2ZMsWHRwoUL4e3tXar7atCgAf755x8YGBgUerxKlSrYtGkTAgMD8X//938YNWoUjIyMcPjwYUycOBF9+/aFv7+/6JysrCzcu3cPOTk5yMjIwKZNm7Bnzx7ExsbCxMRE6Ofk5IQFCxbA19cXVatWRaNGjZTGNjMzU2ovjp6eHnbt2gU/Pz94enriyJEjcHbmFvDqwl+fp1rf37mo7hI0Dj+jREREREQVF/Ml5ksA8yVNx+/tqsd8SfX4OSUiorLgJCwiIioziUSC+Ph4fPnll5DJZFi8eDFyc3NRu3ZteHl5Yfny5aL+v/zyi7A1ewE9PT1kZ2cXev3GjRsLQZmfnx927NhR5porV66Mw4cPY+XKlZDJZIiMjER+fj5sbGzQtWtXjB07Vuhra2uL8+fPY968eRg/fjzu3r0Lc3NztGzZEhs2bCh2HB0dHRw8eBBTp05FUFAQ/v77b9jY2GDMmDFK25u/qUOHDujQoQMOHjxYqnszMzMr9njfvn2RnJyMefPm4dNPP0V2djbq16+PadOmISIiAhKJRNR/8ODBAF49MysrK7Rr1w5nzpwRbQUPAOnp6YVujV9Wurq62LlzJ/z9/YWgrDRBG6kOQx3VYqBDRERERET0H+ZLRWO+VHbMl8oHZkuqx3yJiIiofJEoFAqFuosgIiLSBBKJBImJiejdu7e6S6lQUlJS4OnpiQcPHohWPpZVJy0/lV2LXmFQRuUdg0eij09SfoJax+/SdIZax69IDlyco+4SiIiIKgTmS++G+VLFoFPDXN0laJyVP+5RdwkaZ1RdN3WXQEQfGPOliqMi5EvcCYuIiEiFgoKCYGZmhtu3b6u7lArB2dkZN27cUHcZRERERERERETlBvOl0mG+VIHo8H+WVLVsRdG/jpSIiIg+PP7XDhERkYpcvXoVAKCtzS++JfXtt98iNzcXAGBkZKTmaog+LO7aRERERERERG9ivlR6zJcqjv1nD6i7BI3jbemi7hKIiIjoNZyERUREpCL29vbqLqHCqVu3rrpLoFLgpCHV4q93VD1+RomIiIiIqKJjvlR6zJcqDn5vVz3mS6rHzykREZUFJ2ERERERUYkw1FEtBjpERERERERE9DFhtqR6zJeIiIjKF07CIiIiIqISYahD5R3DXNXj33siIiIiIiJSFX7HpIqA+ZJq8e89EX1sOAmLiIgEKSkp8PT0BAD06tULe/bsUW9BGkIul2Pw4MEAgDFjxmDlypXqLegjwS/LVN4xgFA9PlMi+uAUCnVXQEREVO4wX3o/mC+px3d3Lqi7BI3S1aqlukvQPIp8dVegcZgvEdEHx3xJo3ASFhFRBSWRSIo9PmvWLISEhMDGxgZaWlq4desWrKyshON3796FtbU18vLycPPmTdSrV084lp6ejho1agg/e3h4oFmzZkrhjlwuR0REBLKysgAAUVFRiI6OxtChQ7Fx40ahX1paGpo3by6Mk5mZCRsbG1y4cAHNmjUTfi5tnVu2bMHatWvxyy+/QFtbGy1atMDEiRPRo0cPoc/rwZ9EIkHVqlVha2uLTp06YezYsahVq1axz3Ho0KE4dOgQ7ty5A0NDQ7i6umLRokVwcnIS+hT2XsTFxSEwMBAAEBAQgC5duuCzzz4TjgcGBiIrKwsHDhwQ2g4cOICuXbti1qxZiIqKEtqjoqIQExODW7duCc/qTf3798fWrVsBAM+fP4eVlRW0tLTwxx9/QE9PT6n/rl27sG7dOly4cAHZ2dmoU6cO3NzcEB4ejubNmwv9Xrx4gVWrViEuLg7p6enQ0dFBvXr14OPjgxEjRsDS0hIAEBISgi1btiiNc/XqVdjb2yMkJARZWVlKwWvB+/PgwQOYmJiI3i8AqF69Olq3bo1FixahcePGStcvDr8sqx4ntqkWn6fq8e89EREREVHpMF9ivvQ65kvKulo2f3snKjEtPV11l6BxEq4fVXcJGqdP7bbqLoGIiCowTsIiIqqg7t69K/w5Pj4eM2fORHp6utBmaGiIf/75BwBgZWWF2NhYREZGCse3bNkCKysr3Lp1S+naNWrUgImJyTvVVblyZUilUowfPx7169cv1bmlqXPChAlYu3Yt5s6di969eyM3Nxdbt25Fr169sGrVKowaNUrUPz09HUZGRnj06BHOnz+PxYsXQyqVIiUlpdjwpWXLlujfvz/q1KmD+/fvIyoqCp07d8bNmzehra0t9JPJZOjSpYvw8+vPT19fH/r6+tDV/S9k8PT0xIQJE/Dy5Uvo6Lz613FycjKsra2RkpIiqiE5OVkUHAHAoUOH4OzsLBqjwK5du+Ds7AyFQoE9e/YgICBAdO7kyZOxbNkyjB49GtHR0ahbty7+/vtvfPfdd4iMjBSCu5ycHHTu3Bk//fQToqOj4ebmBnNzc9y8eRNxcXFYs2YNFixYIFy3S5cukMlkorHMzc2LfLbFKXi/7ty5g4kTJ6J79+64du2a6BkSVXScMEREREREROrGfIn5EvMl+pC+u3la3SVoHG9LThgiIiIqTzgJi4iogrKwsBD+bGxsDIlEImoDIIRkgwYNgkwmE4VPMpkMgwYNwpw5c1Ral6OjI2rUqIFp06Zhx44dpTq3pHWeOnUKy5Ytw+rVqxEeHi60z5s3D9nZ2Rg3bhx69eoFa2tr4VhB8GdhYQEHBwf06tULzZs3x/Dhw3H8+PEia/q///s/4c/16tXD3Llz0bRpU2RmZsLOzk44VnDtkvL09MSTJ09w9uxZuLi4AHi1am/KlCkYP348srOzUblyZWRnZ+P06dPCdvMFzMzMihxPKpUiODgYCoUCUqlUFJKdOnUKixcvxqpVqzB69GihvU6dOmjZsiUUr215umLFChw/fhxnz54VrV6sU6cO3N3dRX0BQE9Pr1TPoDivv18RERHo2bMnrly5giZNmqjk+kRERERERETEfIn5EvMlIiIiIiJV4iQsIqKPQM+ePbFx40YcP34c7dq1w/Hjx/HgwQP4+PioPCQDgIULF6J169Y4e/YsWrVqpfI64+LiYGhoiKFDhypdY/z48Vi+fDl27dqFiIiIIsfS19fHsGHDMHbsWPz111+oUaOGsFX5m9vSF3j69ClkMhlsbGxEARwAjBw5Ep9//jlsbW0xbNgwDB48uNgt/R0cHGBpaYnk5GS4uLjg8ePHOH/+PL755husWbMGJ0+ehKenJ06cOIGcnByllYpFuX79Ok6ePIndu3dDoVBg7Nix+O2331C3bl3RsxsxYkSh579ec1xcHDp16iQKyIrq+748fPgQ27dvBwCuUiSNw19HqHrcXYyIiIiI6P1hvqSM+RLzJSqd5mcD3t6JSmdUNXVXoHFqrD2h7hKIiKgC4yQsIqKPQKVKlRAcHIyYmBi0a9cOMTExCA4ORqVKld7LeC1atIC/vz8mT56Mw4cPq7zOjIwM2NnZFRqaWFpawsjICBkZGW8dz8nJCQCQmZmJGjVqwMDAAI6OjkrjrV+/HpMmTcLTp0/h6OiIpKQk0dizZ89Ghw4dYGBggIMHD2LEiBF48uSJaCVgYTw9PZGSkoLIyEikpqbCwcEB5ubmaN++vRDYpaSkwMbGRgi5Cri6ukJLS0v4OTU1Fc2bN0dMTAy6du0KU1NTAIC3tzdkMhmioqKEZ2draytsUQ8Ay5cvx8yZM4Wf//jjDxgbGyMjIwMeHh6icX19fZGUlAQAaNKkCU6c+O8L6TfffANDQ0Ph565duyIhIaHI4wCQl5dX6LOpXbs2gFfBJPAqQC14vwqTk5ODnJwcUVu+Ig9aEu0iziBSP04YIiIiIiKiioT5UuGYL73CfIlK4kKreHWXoHG8ezJfIiIiKk84CYuI6CMRGhoKV1dXzJ8/HwkJCTh58iRevnz53sabO3cuGjRogIMHD6JGjRoqr/PNrcrfRcE1ClbctWnTBleuXFHq179/f3Tq1Al3797F0qVL4e/vjx9++AGVK1cGAMyYMUPo27x5czx9+hRLlix5a0jm4eGBiIgI5ObmIiUlRQik3N3dsWnTJgAQwrI3xcfHo0GDBsLP1tbWyMvLw5YtW7Bq1SqhPTg4GBMmTMDMmTNFodrrQkND0bNnT5w+fVrYZr4o69evx9OnT7F69WocO3ZMdMzT0xMbNmwQfq5SpUqxxwEIY74pNTUVBgYGOHXqFObPn4+NGzcWWRMALFiwANHR0aI2GzSAHZyLPY9InbgTlupxYhsRfXAq+G9SIiKiioT5kjLmS68wX6KS6N6hr7pL0Dijr32j7hI0zmp7R3WXQEQfG+ZLGoWTsIiIPhKNGzeGk5MTgoKC0KBBAzRq1AhpaWklOtfIyAgPHz5Uas/KyoKxsXGh59jZ2WHIkCGYMmUKpFKpSut0cHDA8ePH8eLFC6XVinfu3MGjR4/g4ODw1rEuX74MAIVuDf86Y2NjGBsbo379+nBxcYGpqSkSExMRFBRUaP+2bdtizpw5yMnJgZ6eXpHX9fT0xNOnT/Hjjz8iOTkZEydOBPAqJAsNDcX9+/dx+vTpQrfFt7a2hr29vajt22+/xR9//IGAAPG23nl5eTh8+DA6deqE+vXr4/jx48jNzRVWZJqYmMDExAS3b98WnVe/fn2kp6eL2mrVqgUAqFZNeZvrKlWqKNX0tuNvjlnAxsYGJiYmcHR0xF9//YWAgAClUO51kZGRGDdunKjN1zikyP5E5QEnDBERERERUUXDfEkZ8yXmS1Ry+4/sVHcJGof5EhERUflS+JIFIiLSSKGhoUhJSUFoaGipznN0dMT58+eV2s+fP19sGDVz5kxkZGRg+/btKq0zMDAQT548EVbzvW7p0qWoVKkS+vTpU+wYz58/xxdffIH27dvD3Ny8xLUpFAooFAqlrclfl5aWBlNT02IDMuBVkGhtbY29e/ciLS0N7u7uAAArKytYWVlh2bJlePHiRaErFQsjlUoRGBiItLQ00SswMFAIKoOCgvDkyROsX7/+rdcLCgpCUlISLly4UKLx35eRI0fi0qVLSExMLLKPnp4ejIyMRC9uFU9ERERERESkesyX/sN8ifkSEREREdHruBMWEdFHZMiQIfDz84OJiUmpzhs+fDjWrl2L0aNH4/PPP4eenh7279+PuLg47Nu3r8jzatasiXHjxmHJkiUqrfOTTz7BmDFjMHHiRLx48QK9e/dGbm4utm7dilWrVmHlypWwtrYWnfPXX38hOzsbjx8/xrlz57B48WL8888/2L17t9DnzJkzGDhwIA4fPgwrKyvcuHED8fHx6Ny5M8zNzXH79m0sXLgQ+vr66NatGwBg3759+PPPP+Hi4oLKlSsjKSkJ8+fPx4QJE0p0r56enli/fj3s7e1Rs2ZNod3d3R1r1qyBg4MDLC0t33qdv//+G/v27cPevXvRqFEj0bGBAwfC19cX9+/fxyeffILx48dj/Pjx+O233/DZZ5/B2toad+/ehVQqhUQiEbaVHzt2LPbv3w8vLy/MmjULn376KUxNTZGRkYHvvvsO2tofJoQyMDDAkCFDMGvWLPTu3VvY3p+IiIiIiIiIPjzmS8yXmC8RERERERWOk7CIiD4iOjo6qF69eqnPs7W1xbFjxzBt2jR07NgRL168gJOTExISEtClS5diz50wYQI2bNiA7Oxslda5cuVKNGnSBOvXr8f06dOhra2NFi1aYM+ePfDx8VHq7+joCIlEAkNDQ9ja2qJz584YN24cLCwshD7Pnj1Deno6cnNzAQCVK1dGamoqVq5ciQcPHqBmzZpo3749Tpw4gRo1agAAKlWqhHXr1mHs2LFQKBSwt7fH8uXLMWTIkBLdq6enJ2JjY+Hh4SFqd3d3h0wmQ79+/Up0ndjYWFSpUgVeXl5Kx7y8vKCvr4+tW7di9OjRWLp0Kdq0aYMNGzYgJiYGz549E+7t5MmTMDIyEu7/8OHDWLlyJWQyGSIjI5Gfnw8bGxt07doVY8eOLVFtqjBq1CgsX74cCQkJ8Pf3L9E539+5+J6rIiIiIiIiIvr4MF9ivvQx5UtERERERKUhUSgUCnUXQURE5UNKSgo8PT3x4MGDUq9mpLfz8PBAs2bNsHLlSnWX8lHopOWn7hI0Die2qZa3ZVN1l0BEVOEl5SeodfwujaepdfyK5MDP89RdAhERfSDMl94v5ksfFvMl1WK2pHrMl4iIyo75UsVREfIl7oRFRERKateuDR8fH8TFxam7FI3w1VdfYejQoXj+/DmaNWum7nI+Ggx1iIiI6L3LV3cBRERE5RfzJdVivqQeEp1K6i5Bo+QoctVdAhERUfnDfEmjcBIWEREJ2rZti6tXrwIADA0N1VyN5ujZsyfatm0LAFwB+gFxFZjqcWKbavF5UkXAf5YSERERUWkxX3o/mC+ph+IlJw2pUq86bdVdgsZ5EtBa3SVonBojb6q7BI3yrP2f6i6BiOiD4iQsIiIS6Ovrw97eXt1laJyqVauiatWq6i6DqMw4GUO1OAlL9fgZJSIiIiJSP+ZL7wfzJdIEirw8dZegcX5YsUndJWgc5ktERFQWnIRFRERERCXCSUNU3vEzqnoMHomIiIiIiEhlJFrqrkDjuI0bqu4SNE71o5nqLkGjZLvfU3cJREQfFCdhEREREVGJcDKGanHCkOrxM0pERERERERUjiny1V2BxvlhOXfCUjXmS0REVBachEVEREREJcJJQ6rFQIeIqOKTKBTqLoGIiIiIqMJgtqR6zJeIiCo+5kuahZOwiIioXJNIJMUenzVrFqKiopCYmIhFixbh8uXLyM/PR506ddCpUyesXLkSACCXyxEREYGsrKxCrxMSEoItW7YotXt7e2Pv3r2wtLTEhAkTMGXKFKU+c+bMwdq1a3H79m189dVXGDx4sFC7paUlOnXqhEWLFqFGjRrF3lNcXBwCAwORkpICT09PPHjwACYmJsLPDRs2xE8//QRtbW3hHBMTE6xcuRIhISGiay1YsADTp0/HwoULMXHiRNGxkjyLrKws7Nmzp9Dj9erVQ0REBCIiIoQ2Hx8f5Obm4sCBA0r9U1NT0b59e1y8eBFGRkawsbERjlWrVg0tW7bEokWL0Lx5cwCAh4cHmjVrJrx3ALBq1SpMmjQJW7ZsQWBgYKF1EVU0DB5Vj8EjEREREREVhvkS8yWA+VJ5YL+DvzpP5VYU/883Kj27sSfVXQIREVVgnIRFRETl2t27d4U/x8fHY+bMmUhPTxfaDA0NcfjwYQQEBGDevHno2bMnJBIJfv31VyQlJZVqrC5dukAmk4na9PT0oKuri+DgYMhkMqWQTKFQQC6XY+DAgahUqRIAwMjICOnp6cjPz8fFixcxePBg3LlzB99//71wnkwmQ5cuXUTXMjExKba+GzduIDY2VgjhihMTE4NJkyYhJiZGKSR7H8LCwtCnTx/cvn0btWvXFh2TyWRo1aoVmjRpgszMTADAoUOH4OzsjNu3b2P06NHo2rUrrly5UugzmDVrFpYuXYqvv/5a6ZnRh8UJLqrFSVhEREREREQfBvOl/zBfYr6kTnYRp9RdgsZhvqR63mOZgRIR0bvjJCwiIirXLCwshD8bGxtDIpGI2gBg3759cHNzE4VBDg4O6N27d6nG0tPTU7p2gbCwMKxatQrHjx9Hu3bthPajR4/ixo0bCAsLE9per9HS0hKjR4/GjBkz8Pz5c+jr6wN4FYgVNVZRwsPDMWvWLPTr1w96enpF9jt69CieP3+O2bNnIzY2FidOnICrq2upxiqtHj16wNzcHHK5HNOnTxfanzx5goSEBCxZskTU38zMDBYWFrCwsMDSpUvh5uaG06dPw9vbW+ijUCgwevRobN26FUlJSe/9HujtGOqoFie1ERERERERfRjMl/7DfIn5kjoxW1I95ktERETli5a6CyAiIiorCwsL/PLLL7h06dJ7G6Nx48Zo3bo1YmJiRO0ymQyurq5wcnIq8lx9fX3k5+fj5cuXZaohIiICL1++xJo1a4rtJ5VKERQUhEqVKiEoKAhSqbRM45aEjo4OBg4cCLlcDsVrv7s6ISEBeXl5CAoKKvLcguDwxYsXQtvLly8RHByMnTt34ujRowzIiIiIiIiIiOi9Yr4kxnyJiIiIiKj0uBMWERFVeOHh4UhNTUXjxo1Rt25duLi4oHPnzujfv3+xK/re9M0338DQ0FDUNnXqVEydOhXAq9WKEyZMwOrVq2FoaIjHjx9j586dWL16dZHXvHr1KjZu3IhWrVqhatWqQntQUBC0tbVFfX/99VfUqVOnyGsZGBhg1qxZmDp1KoYMGQJjY2OlPo8ePcLOnTtx8uSr31sfHByMTz/9FKtWrVK6N1ULDQ3FkiVLcPToUXh4eAB4FSL26dOn0FoBICsrC3PmzIGhoSHatGkjtH/55ZcAgIsXLxYbQBbIyclBTk6OqG3vzQvQ0+N8cyIiIiIiIiJ6O+ZL/2G+9J98KKAl0S7iDCqtfOSruwQiIiKi94qTsIiIqMKrUqUK9u/fj+vXryM5ORmnTp3C+PHjsWrVKpw8eRIGBgYluo6npyc2bNggaqtWrZrw56CgIIwdOxY7duxAaGgo4uPjoaWlhYCAANE5Dx8+hKGhIfLz85GdnY127dph8+bNoj4rVqxAx44dRW2WlpZvrTEsLAzLli3DokWLMH/+fKXjcXFxsLOzQ9Omr7ahbtasGerWrYv4+HjRlvbvg5OTE1xdXRETEwMPDw9cu3YNqampmD17tlJfV1dXaGlp4enTp7C1tUV8fDxq1qwpHG/Xrh3S0tIwY8YMxMXFQUen+P9kWbBgAaKjo0VtNmgAO4mzam6O6D3gFvyqxy34ieiDe22HBiIiIqrYmC/9h/nSf2zQAHZgvqQqXS2bq7sEjcN8SfWYLxHRB8d8SaNwEhYREWkMOzs72NnZ4fPPP8e0adPg4OCA+Ph4DB48uETnV6lSBfb29kUeNzIyQt++fSGTyRAaGgqZTAZ/f3+lFYBVq1bF+fPnoaWlhVq1agnbob/OwsKi2LGKoqOjg3nz5iEkJASjRo1SOi6VSvHLL7+IQqX8/HzExMS895AMeBXihYeHY926dZDJZLCzs4O7u7tSv/j4eDRs2BBmZmYwMTFROt64cWMsW7YMHTt2REBAAOLj44sNyiIjIzFu3DhRm69xSFlvh97AUEe1GOgQERERERGVP8yXmC+9jvmSajFbUj3mS0REROULf0cPERFppHr16sHAwABPnz5V6XXDwsJw/PhxfPPNNzhx4kShwZOWlhbs7e1ha2tbaEBWVn5+fnB2dlZamffzzz/j7NmzSElJQVpamvBKSUnByZMnceXKFZXX8iZ/f39oaWlh27ZtiI2NRWhoKCQSiVI/a2tr2NnZFRqQFWjWrBkOHz6MY8eOwd/fH7m5uUX21dPTg5GRkejFreKJiIiIiIiIqCyYLzFfYr5ERERERKXBnbCIiKjCi4qKwrNnz9CtWzfUrVsXWVlZWL16NXJzc9GpUyehX15eHtLS0kTn6unpoUGDBgCAnJwc3Lt3T3RcR0cH1atXF35u37497O3tMXDgQGF79HeRlZWlNFbVqlVRpUqVEp2/cOFCeHt7i9qkUinatGmD9u3bK/Vv3bo1pFIplixZAuDtz+Lhw4dKx83MzGBtbQ0A+OOPP5SO161bF6ampggICEBkZCQePXqEkJCQEt1PUZo2bYojR47Ay8sL/v7+2LFjBypVqlSmaxIRERERERERvYn50ivMl4iIiIiI3h0nYRERUYXn7u6OdevWYeDAgfjzzz9hamqK5s2b4+DBg3B0dBT6PXnyBM2bNxeda2dnh2vXrgEADhw4gFq1aomOOzo6ilb4SSQShIaGYurUqYiMjHznmgvbwn7BggWYMmVKic7v0KEDOnTogIMHDwIAXrx4ga1bt2Ly5MmF9u/Tpw+WLVuG+fPnA3j7s0hJSVE6HhYWhs2bNwMAli5diqVLl4qO/+9//0NwcDDCwsIglUrRrVs3WFpaluh+itO4cWMhKPPz88OOHTugq6tb5usSqRu34Fc9bsFPRERERETvivkS8yV6/5zXD1d3CRonOzZb3SVoHPuB59VdAhERVWAShUKhUHcRRERERKrWSctP3SVoHE4aovKOk7CIPj5J+QlqHb9rg3f/H00/Nt9dXqDuEoiIiIhKjfmSaunUqa3uEjRO1NE96i5B40y3aaXuEojoA2O+VHFUhHyJO2EREREREakBJwwREWmAfK5rIyIiIiIqqf2n9qu7BI3jbckJQ0REFR7zJY2ipe4CiIiIiIiIiIiIiIiIiIiIiIiIKjLuhEVEREQaib86T/W4cxMRERERERERfUyYL6kWsyUiIiLSdJyERURERBqJoQ6VdwxyqSLgP0uJiIiIiOhjxu9EVN59d+eCukvQOF2tWqq7BM2iyFd3BUREHxQnYRERERFRiXDSkGoxyCUiIiIiIiKijwmzJdXztmyu7hI0ECcNERHRu+MkLCIiIiIqEU4aUi0Gj6rHzygRERERERFR+cXv7arHfEn1+DklIqKy4CQsIiIqFR8fH+Tm5uLAgQNKx1JTU9G+fXtcvHgRTZo0wdChQ7F582Zs374dfn5+RV7T29sbhw4dwqlTp9C6dWvRsb///hszZ87E/v378eeff8LU1BRNmzbFzJkz4ebmBgCoV68eIiIiEBERoXTtzMxM2NjYCD9Xq1YNLVu2xKJFi9C8+atVQjdv3sS0adOQkpKC+/fvo3r16kIfJycnAIBEIim09ri4OAQGBgIAFAoFNm/ejJiYGPzyyy/Iz89H3bp10bFjR4SHh8Pe3l4479GjR1iyZAl2796NGzduwMDAALa2tvDz88OQIUNgamqKKVOmYM+ePbhy5Ypw3pUrV9CgQQMMGjQIcrlcaJfL5Rg6dChWrlyJESNGFPmsC+63Ro0amDNnDnbs2IE//vgDVatWRcOGDTFu3Dj06tVL6Hvt2jXMnz8fhw4dwp9//onq1avDyckJoaGhCAgIgI6O+D8linvPo6KiEB0dDQDQ1tZG7dq14evrizlz5sDQ0FDpvTI1NUXjxo0xd+5cfPrpp8XeE30YDHVUi4EOEZEGUCjUXQEREVGFxHxJjPkS86WPBbMl1WO+RESkAZgvaRROwiIiolIJCwtDnz59cPv2bdSuXVt0TCaToVWrVmjSpAmePXuG7du3Y9KkSYiJiSkyJLt16xZOnDiBUaNGISYmRikk69OnD168eIEtW7bA1tYWf/75Jw4fPox///23VHUfOnQIzs7OuH37NkaPHo2uXbviypUrqFKlCjp16gRHR0fs3r0btWrVwu3bt/Hdd98hKytL6f66dOkiajMxMQHwKiDr168f9uzZg6lTp2LFihWwtLTEnTt3kJiYiLlz5wqh1v3799GuXTs8evQIc+bMQcuWLWFsbIz09HTIZDJs27YNI0eOhKenJxYtWoR79+7BwsICAJCcnAxra2ukpKSI6khOToaLiwtCQkLg6+srtH/22Wdo1KgRZs+eLbSZm5tj8ODBOH36NNasWYOGDRvi33//xYkTJ0TP9cyZM+jYsSOcnZ2xbt06ITA8e/Ys1q1bh0aNGqFp0/++5JfkPXd2dsahQ4fw8uVL/PDDDwgNDcWzZ8+wadMmpffqn3/+wbx589CjRw9kZGSgZs2ab3mXiSoWBo+qx+CRiIiIiKhiYL7EfIn50seJ39tVj/mS6vFzSkREZcFJWEREVCo9evSAubk55HI5pk+fLrQ/efIECQkJWLJkCQAgISEBDRs2xJQpU2BpaYnff/8d1tbWSteTyWTo0aMHhg8fDhcXFyxfvhz6+voAgKysLKSmpiIlJQXu7u4AgLp166JNmzalrtvMzAwWFhawsLDA0qVL4ebmhtOnT6NmzZq4fv06Dh8+jLp16wpjFKyCfJ2JiYkQVr0pPj4e27dvx9dff42ePXsK7XXq1IGLiwsUr81inzp1Km7duoWMjAxYWloK7XXr1kXnzp2Fvu3atUOlSpWQkpIirIZMSUnByJEjMW/ePGRmZqJevXpC++DBg6Gvry88PwDQ1dWFgYGBUt179+7FqlWr0K1bNwCvVnu2bNlSOK5QKBASEgIHBwf88MMP0NLSEo7Vr18fQUFBonsCSvae6+joCLUEBATg8OHD2Lt3rygke/29mjp1KrZv347Tp0+LnisRERERERERVVzMl5gvMV/6OO26fVrdJWicrjbu6i5BA2WruwAiIqrAOAmLiIhKRUdHBwMHDoRcLse0adOEbdQTEhKQl5eHoKAgAIBUKkVwcDCMjY3RtWtXyOVyzJgxQ3QthUIBmUwmrIKzt7fHzp07MWDAAACAoaEhDA0NsWfPHri4uEBPT08l91AQIr148QLm5ubQ0tLCzp07ERERAW1t7Xe6ZlxcHBwdHYsMcgqeU35+PuLj4xEcHCwKyArrW6VKFbRu3RrJycmikGzixIlISUlBcnIyBg8ejBs3buDWrVvw9PQscb0WFhb49ttv8dlnn6Fq1apKx9PS0nD58mXExcWJArLC6ixQkvf8Tfr6+njx4kWhx54/f47Y2FgAr8I+Ik3DVXVERERERPSxYr5UOOZLzJc0naFWZXWXoHHyczhhiIiIqDwp/L96iYiIihEaGorr16/j6NGjQptMJkOfPn1gbGyMq1ev4tSpUwgICAAABAcHQyaTKa1sO3ToEJ49ewZvb2+hn1QqFY7r6OhALpdjy5YtMDExgZubG6ZOnYqffvrpnWvPysrCnDlzYGhoiDZt2sDKygqrV6/GzJkzYWpqig4dOmDOnDm4ceOG0rlBQUFCcFfwunXrFgAgIyMDjo6Oov4RERFCv4Kt9f/++29kZWUp9W3ZsqXQtyBoBABPT09ha/hff/0V2dnZaN68Odq3by+0p6SkoHLlynBxcSnxc/jiiy9w4sQJmJmZoXXr1hg7dix++OEH4XhGRgYAiOr866+/RPe+fv164VhJ3/PXnTt3Dtu2bUOHDh1E7a6urjA0NESVKlWwdOlStGzZEl5eXsXeT05ODh49eiR65SvySvw8iIiIiIiIiOjDYr7EfIn5EhERERFpGk7CIiKiUnNycoKrqytiYmIAANeuXUNqairCwsIAADExMfD29kb16tUBAN26dcPDhw9x5MgR0XViYmIQEBAAHZ1XGzMGBQXhhx9+wPXr14U+ffr0wZ07d7B371506dIFKSkpaNGiBeRyealqLgheTE1NcfHiRcTHx6NmzZoAgJEjR+LevXv46quv8MknnyAhIQHOzs5ISkoSXWPFihVIS0sTvYpabQgA06ZNQ1paGmbOnIknT54UW19iYiLS0tLg7e2N58+fC+0eHh7IyMjA3bt3kZKSgnbt2kFbWxvu7u6ikMzV1bVUKznbt2+PGzdu4PDhw+jbty9++eUXfPrpp5gzZ06R55iZmQn3bWJiIlphWNL3/Oeff4ahoSH09fXRpk0bfPLJJ1i7dq2oT3x8PC5cuIBdu3bB3t4ecrkclSpVKvZ+FixYAGNjY9HrJq6U+HkQERERERER0YfFfIn5EvMlIiIiItI0/HWERET0TsLCwhAeHo5169ZBJpPBzs4O7u7uyMvLw5YtW3Dv3j0h/AKAvLw8xMTECCvO7t+/j8TEROTm5mLDhg1K/ebNmye0Va5cGZ06dUKnTp0wY8YMfP7555g1axZCQkJKXG98fDwaNmwIMzMzmJiYKB2vWrUqfHx84OPjg7lz58Lb2xtz585Fp06dhD4WFhawt7cv9Pr169dHenq6qM3c3Bzm5uaoUaOGqM3ExESpb506dYQ6srKyhHY3Nzfo6uoiOTkZycnJcHd3BwC0bt0a//zzD27cuIGUlBQMHTq0xM+iQKVKlfDpp5/i008/xeTJkzF37lzMnj0bkydPRv369QEA6enpaN68OQBAW1tbuP8339uSvOfAq5WPe/fuhY6ODiwtLQvdBt7a2hr169dH/fr18fLlS/j6+uLSpUvFhoCRkZEYN26cqM3XOKTUz4ToQ/r+zkV1l6Bx+CseieiDK2ZXBiIiIno75ktizJeYL2k6b6vm6i5B43x355y6S9A4XS35OSWiD4z5kkbhJCwiInon/v7+GDNmDLZt24bY2FgMHz4cEokE3377LR4/fowLFy5AW1tb6H/p0iUMHjwYWVlZMDExwVdffYXatWtjz549ousePHgQy5Ytw+zZs0Xnv65hw4ZK572NtbU17OzsStRXIpHAyckJJ06cKPH1g4KC0K9fP3z99dfo1atXkf20tLTg7++PrVu3YubMmcWudAQAfX19tG3bFikpKTh69CgmTpwI4FXA5eLiAqlUit9//x2enp4lrrUoDRs2xMuXL4Ut6Z2cnLB06VL4+/tDS6vozTNL+p4DgK6ubpFBY2H69u2LmTNnYv369Rg7dmyR/fT09JRCNC1J4Z8fenec4EL08eFkQSIiIiJ6n5gviTFfYr6k8RT56q5A43DCEJV3mfNc1V2Cxsmt/lLdJRBRMTgJi4iI3omhoSECAgIQGRmJR48eCasGpVIpunfvjqZNxZM1GjZsiLFjx+Krr77CyJEjIZVK0bdvXzRq1EjUz9raGpGRkThw4ABcXFzg5+eH0NBQNGnSBFWrVsXZs2exePFipSDqjz/+QFpamqitbt26b72PtLQ0zJo1CwMGDEDDhg2hq6uLo0ePIiYmBpMnTxb1zcrKwr1790RtVatWRZUqVRAYGIjdu3cjMDAQkZGR8Pb2Rs2aNfHbb78hPj5eFB7Nnz8fKSkpaNOmDWbPno1WrVqhSpUq+Omnn3Dy5EmlZ+Lp6YkVK1YAAFq0aCG0u7u7Y+nSpahSpQpat2791nt9nYeHB4KCgtCqVSuYmZnh119/xdSpU+Hp6QkjIyMAgEwmQ6dOneDm5obIyEg0aNAAubm5OHbsGP7++2/hnkr6nr8LiUSC0aNHIyoqCkOHDoWBgcE7XYdUg5MxVIuT2qgi4OeUyrsk/m84REREFRrzpVeYLzFf+lgwW1I9fm+n8q7etJJPRiZSmyHqLoA0SdHLDoiIiN4iLCwMDx48gLe3NywtLfHnn39i//796NOnj1JfLS0t+Pr6QiqV4ty5c7h48WKh/YyNjeHl5QWpVApDQ0O0bdsWK1asQPv27dGoUSPMmDEDQ4YMwdq1a0XnLV26FM2bNxe99u/f/9Z7qF27NurVq4fo6Gi0bdsWLVq0wKpVqxAdHY1p06aJ+g4ePBi1atUSvdasWQPgVZgTHx+PlStX4ttvv4WXlxccHR0RGhoKa2trHD9+XLiOmZkZzpw5g4EDB2LJkiVo06YNGjdujKioKAQEBODLL78Ujevp6YnHjx/Dzc1NtB27u7s7Hj9+jHbt2qFSpUpvvdfXeXt7Y8uWLejcuTMaNGiA8PBweHt7Y8eOHUIfFxcXnDt3Do6Ojhg5ciQaNmwIV1dXxMXFYcWKFRg+fHiJ3/OyGDRoEHJzc5XecyIiIiIiIiKq+JgvMV9ivkREREREmkKiUPAXTBIREZHm6aTlp+4SiIrF1Z+qx9WfRB+fpPwEtY7ftf4ktY5fkXx3dbG6SyAiIiIqNeZLVN4xX1I95ktEHx/mSxVHRciX+OsIiYiIiKhEGOqoFgMdIiIiIiIiIvqYMFtSPeZLRERE5QsnYRERERERERERvQtuLk5ERERERERERGXBfEmjaKm7ACIiIiIiIiIiIiIiIiIiIiIiooqMk7CIiIiIiIiIiIiIiIiIiIiIiIjKgL+OkIiIiDTS93cuqrsEIiIiIiIiIiKqwKqfMFV3CRrl0/Ch6i5B4xjglLpLICIiotdwEhYRERFpJG/LpuougahYnChIFQH/WUpERERERB+zf1wfqLsEjcIJQ6q3548z6i5B43xm007dJWiU/Bcv1F0CEdEHxUlYREREhfDx8UFubi4OHDigdCw1NRXt27fHxYsX0bRp4f/j9MmTJ+Hi4oK8vDwsWbIEcrkcv/32G/T19VG/fn0MGTIEn3/+eYnHMTIygo2NDbS0tHDr1i1YWVkJ/e7evQtra2vk5eXh5s2bqFevHjIzM2FjYwNzc3Ncv34dVatWFfo3a9YMvXv3RlRUFADg5s2bmDZtGlJSUnD//n1Ur14dLVu2xKJFi2BqaoratWvjf//7HwIDA5VqDAsLw4ULF+Dm5oZDhw7h8uXLSn1u3boFGxsbJCYmomfPnjh69Ciio6ORlpaG7OxsWFlZwdXVFV9++SV0dXWRkpICT09PPHjwACYmJm97q+gD4qQh1eLkFiIiIiIiIs3GfIn5EokxW1I9b8s26i5BA3HSEBERvTtOwiIiIipEWFgY+vTpg9u3b6N27dqiYzKZDK1atYKRkREA4NChQ3B2dhb1MTMzAwBER0dj06ZNWLt2LVq1aoVHjx7h7NmzePDgQYnHadKkCTIzMwEAVlZWiI2NRWRkpNBvy5YtsLKywq1bt5Tu4/Hjx1i6dCmio6MLvc/c3Fx06tQJjo6O2L17N2rVqoXbt2/ju+++Q1ZWFpycnNC9e3fExMQohWRPnz7Fjh07sHDhQri5uWHt2rU4ceIEXF1dRf3kcjlq1KiBbt264ddff0WXLl0QHh6O1atXQ19fH1evXsWuXbuQl5dXaI1EmorBo+pxYhsRfXD5CnVXQEREROUY8yXmSyT2ycRh6i5B41RPyVR3CRrnhcdddZdARB8b5ksahZOwiIiICtGjRw+Ym5tDLpdj+vTpQvuTJ0+QkJCAJUuWCG1mZmawsLAo9Dp79+7FiBEj4OfnJ7S9vrqxNOMAwKBBgyCTyUQhmUwmw6BBgzBnzhyl8cPDw7F8+XKMHDkSNWrUUDr+yy+/4Pr16zh8+DDq1q0LAKhbty7c3NyEPmFhYejduzdu3bqFOnXqCO0JCQl4+fIl+vfvDxMTE7Ro0QIxMTGikEyhUEAul2PQoEHQ0dHBwYMHYWFhgcWLFwt97Ozs0KVLl0KfHxERERERERFRRcV8ifkSiVU7clPdJWichfMT1V2CxhmHT9RdAhERVWCchEVERFQIHR0dDBw4EHK5HNOmTYNEIgHwKhjKy8tDUFCQsNqwOBYWFjhy5AhGjBgBc3PzdxrndT179sTGjRtx/PhxtGvXDsePH8eDBw/g4+NTaEgWFBSEpKQkzJ49G2vXrlU6bm5uDi0tLezcuRMRERHQ1tZW6tOtWzfUrFkTcrkcM2fOFNplMhk+++wzYUv3sLAwTJkyBatWrUKVKlUAACkpKbh58yZCQ0OF53H37l0cO3YM7du3f+vzI9Jk3LWJiIiIiIhIszFf+g/zJQKA/ee+V3cJGsfbkhOGiIiIyhNOwiIiIipCaGgolixZgqNHj8LDwwPAq2CoT58+MDY2FkIyV1dXaGlpic598uQJAGD58uXo27cvLCws4OzsDFdXV/Tq1Qtdu3Yt8Tivq1SpEoKDgxETE4N27dohJiYGwcHBqFSpUqH3IJFIsHDhQvj4+GDs2LGws7MTHbeyssLq1asxadIkREdHo1WrVvD09ET//v1ha2sLANDW1sagQYMgl8sxY8YMSCQSXL9+HampqUhKShKu1a9fP4wfPx4JCQkICQkR7qNdu3ZwcHAAAPj5+eH777+Hu7s7LCws4OLiAi8vLwwcOFDYfv9d5OTkICcnR9SWr8iDlkQ59KN3x0lDqsVfR6h6/IwSEREREVF5w3yJ+RL9h9/bVY/5kurxc0pERGUhUSgU/AWTRERERXBzc4OdnR1iY2Nx7do11K9fH8nJyfDw8EBmZiZsbGywd+9eNGjQQHSevb298Of8/HycO3cOP/zwA44dO4a9e/ciJCQEmzdvLtE4AISxLly4AG1tbbi6uuLq1auoX78+Tp48iZcvX6J58+a4efMm6tWrJ+rfrFkzdOjQARYWFti2bRuaNWuG3r17IyoqShj/8ePHSElJwalTp7Bv3z5cvXoVe/fuRadOnQAAV69ehYODAw4fPowOHTpg+vTp2LZtG65fvy6srgSA/v374/fff8exY8fw6NEj1KpVC+vWrRNCswJ//PEHjhw5gtOnT2P37t3Q1tbGmTNnUKtWLaSkpMDT0xMPHjwQVkG+TVRUFKKjo0VtNmgAO4lzic6nkmGoo1oMdIiIyi4pP0Gt43e1naDW8SuS724sVXcJREREasN8ifkSvcJsSfWYLxERlR3zpYqjIuRLWm/vQkRE9PEKCwvDrl278PjxY8hkMtjZ2cHd3V3Ux9raGvb29qLX67S0tNC6dWtERERg9+7dkMvlkEqluHnzZqnGKdC4cWM4OTkhKCgIDRo0QKNGjd56HwsXLkR8fDwuXLhQ6PGqVavCx8cH8+bNw8WLF/Hpp59i7ty5wvH69evj008/hUwmQ35+PmJjYzF48GBRQFZwH6mpqbh27Rri4+Ohra0NPz8/pfGsrKwwYMAArF27Fr/88guys7OxcePGt95HUSIjI/Hw4UPRywZO73w9IiIiIiIiIiJVYb70CvMlIiIiItJ0/HWERERExfD398eYMWOwbds2xMbGYvjw4UrBUGk1bNgQAPD06dN3Hic0NBQjRozAhg0bSjRmmzZt8Nlnn2HKlClv7SuRSODk5IQTJ06I2sPCwjB8+HD07NkTf/zxh9LqQwDw9PSEjY0NZDIZkpOTERgYiCpVqhQ7nqmpKWrVqiV6HqWlp6cHPT09URu3ilc9rqxTLa7+VD1+Ronog1Pkq7sCIiIiqgCYL/2H+dLHjd/bVY/5kurxc0pEHxzzJY3CSVhERETFMDQ0REBAACIjI/Ho0aNCg6F///0X9+7dE7WZmJigcuXK6Nu3L9zc3ODq6goLCwvcvHkTkZGRcHBwgJPTfyvpSjLO64YMGQI/P78Sb6cOAPPmzYOzszN0dP77139aWhpmzZqFAQMGoGHDhtDV1cXRo0cRExODyZMni8738/PD6NGjMXToUHTu3BnW1tZKY0gkEoSGhmL58uV48OABVqxYITq+adMmpKWlwdfXF3Z2dsjOzkZsbCx++eUXrFmzRtT3559/RtWqVUXXbtqUX4DViaEOERERERERUekxX/oP86WPm5a+vrpL0DiP8p+ruwQiIiJ6DSdhERERvUVYWBikUim6desGS0tLpeMdO3ZUaouLi0NgYCC8vb0RFxeHBQsW4OHDh7CwsECHDh0QFRUlCqtKMs7rdHR0UL169VLdh4ODA0JDQ/HFF18IbbVr10a9evUQHR2NzMxMSCQS4eexY8eKzjcwMEBgYCC++OILhIaGFjlOSEgIZs2aBWdnZ7Rt21Z0rE2bNjh+/DiGDRuGO3fuwNDQEM7OztizZ4/S9vjt27cX/aytrY2XL1+W6p6JyjOuqiMiIiIiIvp4MF96hfnSx+2766fUXYLG8bZ0UXcJRERE9BqJQqFQqLsIIiIiIlXrpOWn7hI0DnfCovKOE9uIPj5J+QlqHb+rzTi1jl+RfHdzubpLICIiIio15ktU3jGvUz3mS0QfH+ZLFUdFyJe4ExYRERERkRow0CEiIiIiIiKijwknDKke8yUiIqLyhZOwiIiISCMx1FE9hjpERERv4ObiRERERBqN+ZJqMVsiIiIqBPMljcJJWERERKSRGOqoHoNHKu/4956IiIiIiIhUid8zVUu7SQN1l6BxBu78Xt0laByZQx11l0BERBUYJ2EREREREakBg1wiIiIiIiIi+ph8e2C7ukvQOMyXiIiIyhdOwiIiIiKiEmGoo1rcWUz1+BklIiIiIiIiKr/4vV31mC+pHj+nRERUFpyERUREREQlwlBHtRjoEBEREREREdHHhNmS6jFfIiIiKl84CYuIiEjFfHx8kJubiwMHDigdS01NRfv27XHx4kU0bdoUFy5cQLNmzQAAiYmJWLRoES5fvoz8/HzUqVMHnTp1wsqVKwEAcrkcERERyMrKUrquRCJBYmIievfuLfxcwMjICI0aNcKcOXPQoUMHAEBISAi2bNmCBQsWYMqUKULfPXv2wNfXFwqFAv/73/8wbNgwXLx4Efb29kKfO3fuwNnZGXPmzMGoUaNQr149/PbbbwCAypUro2bNmmjTpg2GDRsmjFfgXe7x8uXL6Ny5M1xcXPDVV19BV1e3RO8DUXnH4JEqAoa5RERERETqwXyJ+ZIm4ndM1dv7x1l1l6BxetVpq+4SNIoiL0/dJRARfVCchEVERKRiYWFh6NOnD27fvo3atWuLjslkMrRq1QpGRkai9sOHDyMgIADz5s1Dz549IZFI8OuvvyIpKemd65DJZOjSpQv++ecfTJs2DT169MClS5dga2sL4FWgtWjRIgwdOhSmpqZK5w8YMACJiYkICQnBsWPHoKWlBQAYMmQIWrZsiZEjRwp9Z8+ejSFDhuDFixfIzMzE1q1b0bFjR8yZMwfTpk1753v88ccf0bVrV/j6+mLTpk1CDUSagMEjEZEGyFeouwIiIiLSUMyXmC9pIi5IUz1vy1bqLkEDcdIQEX1gzJc0CidhERERqViPHj1gbm4OuVyO6dOnC+1PnjxBQkIClixZonTOvn374ObmhokTJwptDg4OwsrDd2FiYgILCwtYWFhgw4YNsLKyQlJSEoYOHQoA6NixI65du4YFCxZg8eLFhV5j06ZNcHZ2xvLlyzFhwgTI5XL88MMP+Pnnn0WrIatWrQoLCwsAQJ06ddC+fXvUqlULM2fORN++feHo6Fjqezxy5Ah69eqFESNGYNGiRe/8HEh1OGlItRg8UkXAv/dEREREROrBfIn5kibid0zV09LXV3cJGif+arK6S9AofrVd1F0CEdEHxUlYREREKqajo4OBAwdCLpdj2rRpQpiUkJCAvLw8BAUF4cGDB6JzLCwssG3bNly6dAmNGjVSeU36///L+IsXL4Q2bW1tzJ8/H/369cPo0aOVVlUCgLm5Ob744gsEBQWhadOmGDt2LFatWgVra+u3jjlmzBjMmTMHX3/9NSZNmlSqe0xMTES/fv0QFRWFyZMnl/Ju6X3hpCHVYvBIRERERERERWG+9ArzJc3CbEn1mC+pHicNERFRWXASFhER0XsQGhqKJUuW4OjRo/Dw8ADwavv2Pn36wNjYWCkkCw8PR2pqKho3boy6devCxcUFnTt3Rv/+/aGnpyf0e/jwIQwNDUtVy7NnzzB9+nRoa2vD3d1ddMzX1xfNmjXDrFmzIJVKCz2/d+/e8Pf3R5cuXeDj44NBgwaVaNxq1aqhRo0ayMzMLNU9PnnyBH5+fpg6dWqJA7KcnBzk5OSI2vbevAA9PW4vT0REREREREQVE/Ml9edL+VBAS6JdovPp7fKRr+4SiIiIiN4rTsIiIiJ6D5ycnODq6oqYmBh4eHjg2rVrSE1NxezZswvtX6VKFezfvx/Xr19HcnIyTp06hfHjx2PVqlU4efIkDAwMALzalv38+fNK59evX1+pLSgoCNra2nj+/DnMzc0hlUrRpEkTpX6LFi1Chw4dMGHChCLvZ8aMGYiNjRVtf18SCoVCWKlZ0nvU19dHu3bt8OWXXyIoKAgNGjR46zgLFixAdHS0qM0GDWAncS5VvVQ8rlYkIiIiIiIi+nCYL72i9nwJzJdURQtcMElERESajZOwiIiI3pOwsDCEh4dj3bp1kMlksLOzU1op+CY7OzvY2dnh888/x7Rp0+Dg4ID4+HgMHjwYAKClpQV7e/sSjb9ixQp07NgRxsbGMDc3L7Jf+/bt4e3tjcjISISEhBTaR0dHR/T/S+Lff//F33//DRsbG1H72+5RW1sbe/bswWeffQZPT08kJye/NSiLjIzEuHHjRG2+xoXfC1F5wUltqsct+ImIiIiISNMwX2K+pElsD4apuwSNU3mWrrpL0DjW0SfUXQIREVVgnIRFRET0nvj7+2PMmDHYtm0bYmNjMXz4cGHVXknUq1cPBgYGePr06TuNb2FhUeJAbeHChWjWrBkcHR3faazCrFq1ClpaWujdu3eRfYq6Rz09PezevRt9+/aFp6cnjhw5goYNGxZ5HT09PdGW8wC4VTwRERG9fwqFuisgIiIiDcd8ifmSJrHf/FLdJWiczhtT1F2CxkmKLt2vayUiKjPmSxqFk7CIiIjeE0NDQwQEBCAyMhKPHj0qchUgAERFReHZs2fo1q0b6tati6ysLKxevRq5ubno1KnTe6+1cePG6N+/P1avXv1O5z9+/Bj37t1Dbm4ubt68ia1bt2Lz5s1YsGCBENSV9h719PSwa9cu+Pn5CUGZszO3f1cn7jKkWtwJS/X4TFWPf++JiIiIiNSL+RLzJU0iOZ6m7hI0Ts1KD9VdgsbRat5G3SVolPwLv6q7BCKiD4qTsIiIiN6jsLAwSKVSdOvWDZaWlkX2c3d3x7p16zBw4ED8+eefMDU1RfPmzXHw4EGVrh4szuzZsxEfH/9O586cORMzZ86Erq4uLCws4OLigsOHD8PT01Po8y73qKuri507d8Lf318Iyho1alSimjgZQ/U4GUO1+DyJiIiIiIioJJgvMV/SFMxCVG+ro5W6S9BAnDRERETvTqJQcG8zIiIi0jz59xzUXQJRsRg8EhGVXVJ+glrH72o9Rq3jVyTf/b5K3SUQERERlZq34SB1l6BRtmUcVncJGiew9ifqLoGIqMJjvlRxVIR8iTthERERkUbiBBfV4+pPIiIiIiIiIvqY5D97pu4SNIqploG6SyAiIiJ6r7TUXQAREREREREREREREREREREREVFFxp2wiIiIiIiIiIjehUKh7gqIiIiIiIiIiKgiY76kUbgTFhERERERERERERERERERERERURlwEhYREREREREREREREREREREREVEZcBIWEZGapaSkQCKRQCKRoHfv3uouR2PI5XLhuUZERKi7HCIiIiIiIiKi94b50vvBfImIiIiIiEqDk7CIiErBx8cHXbp0KfRYamoqJBIJfvrpJ0gkEmhra+OPP/4Q9bl79y50dHQgkUiQmZkpOpaeng65XC787OHhUWi4I5fLYWJiIvwcFRUFiUSCYcOGifqlpaWJxsnMzIREIkFaWpro59LWuWXLFrRu3RoGBgaoWrUq3N3d8c0334j6vB78aWlpwdjYGM2bN8ekSZNw9+7dQp/f64YOHQo7Ozvo6+vD3NwcvXr1wpUrV0R9Cq7/+mv79u3C8YCAANy9exeffPKJ6Lx69eph5cqVSmNGRUWhWbNmAICwsDA0btwYL168EPX59ttvoauri/PnzwvP781XcHCw6PkWPO83vR7iaWtrw9TUFG3btsXs2bPx8OFDpf737t3DmDFjYG9vj8qVK6NmzZpwc3PDhg0b8OzZM3h4eBRaT8HLw8Oj2Pt/veaCl5mZGTp37owLFy4IfV4fp3LlymjYsCHWr1+vdKy4Gi5evIiePXuiRo0aqFy5MurVq4eAgAD89ddfJXp2JfX9nYt8qfhFREREREREZcd8ifkS86WKky9JtLX5UuErV/GSLxW/iIiIqHzRUXcBREQVSVhYGPr06YPbt2+jdu3aomMymQytWrWCkZERAMDKygqxsbGIjIwU+mzZsgVWVla4deuW0rVr1KghCr9Ko3LlypBKpRg/fjzq169fqnNLU+eECROwdu1azJ07F71790Zubi62bt2KXr16YdWqVRg1apSof3p6OoyMjPDo0SOcP38eixcvhlQqRUpKCho3blxkTS1btkT//v1Rp04d3L9/H1FRUejcuTNu3rwJbW1toZ9MJhOFlq8/P319fejr60NXV7dUzwMAVqxYgSZNmmDWrFlYsGABACArKwtDhgzBjBkz0KJFCyE8PHToEJydnUXjlpSRkRHS09OhUCiQlZWFEydOYMGCBZDJZPjhhx9gaWkJALhx4wbc3NxgYmKC+fPno3HjxtDT08PPP/+ML774AlZWVti9e7cQ6v3+++9o06aNqLbSPIeC827fvo3Ro0eja9euuHLlivB8hwwZgtmzZ+PZs2eIjY3FyJEjYWpqWqIa/v77b3h5eaFHjx74/vvvYWJigszMTOzduxdPnz4tcY0l4W3ZVKXXI1I1TmxTPf69JyIiIqKKgPkS8yXmSxUnX1Lk5an0eh+7HlYt1V2CxmG+pHrMl4iIqCw4CYuIqBR69OgBc3NzyOVyTJ8+XWh/8uQJEhISsGTJEqFt0KBBkMlkovBJJpNh0KBBmDNnjkrrcnR0RI0aNTBt2jTs2LGjVOeWtM5Tp05h2bJlWL16NcLDw4X2efPmITs7G+PGjUOvXr1gbW0tHCsI/iwsLODg4IBevXqhefPmGD58OI4fP15kTf/3f/8n/LlevXqYO3cumjZtiszMTNjZ2QnHCq6takZGRpDJZPD29kbv3r3Rtm1bREREwMrKSvScAMDMzOyda5BIJMK5tWrVQoMGDeDj4wNnZ2dMmjQJW7duBQCMGDECOjo6OHv2LKpUqSKcb2tri169ekGhUEAikQjt2dnZZaqt4DwLCwssXboUbm5uOH36NLy9vQEABgYGwnWjoqKwbds27N27F0FBQW+tYc+ePXj48CE2b94MHZ1X/xliY2MDT0/PUtdJHx5DHdVioENEpAEUCnVXQEREFRDzJeZLzJeYL32smC2pHvMlIiINwHxJo/DXERIRlYKOjg4GDhwIuVwOxWv/QkxISEBeXp4oJOjZsycePHgghEHHjx/HgwcP4OPj815qW7hwIXbt2oWzZ8+W6ryS1hkXFwdDQ0MMHTpU6Rrjx49Hbm4udu3aVexY+vr6GDZsGH744Qdha/CCreXf3Ja+wNOnTyGTyWBjYyMK4ABg5MiRqF69Otq0aYOYmBjRe1JWnp6eGDFiBAYNGoSEhATs2LEDsbGxQrDzvtSoUQP9+/fH3r17kZeXh3///RcHDx7EyJEjRQHZ614PyFStYOXlm1vnv9mnuOOvs7CwwMuXL5GYmKjS94uIiIiIiIioomC+xHyJ+VLhfZgvEREREVFFx0lYRESlFBoaiuvXr+Po0aNCm0wmQ58+fWBsbCy0VapUCcHBwYiJiQEAxMTEIDg4GJUqVXovdbVo0QL+/v6YPHlyqc4raZ0ZGRmws7MrdNtxS0tLGBkZISMj463jOTk5AYAQihkYGMDR0VFpvPXr18PQ0BCGhob47rvvkJSUJBp79uzZ2LFjB5KSktCnTx+MGDECa9asKdE9T548Wbh2wWv+/PlK/Qq2ig8MDMT8+fOF2l/n6uoqus6FCxdKVENxnJyc8PjxY/z777+4du0aFAoFHB0dRX2qV68ujFna97yksrKyMGfOHBgaGqJNmzZKx/Py8rB161b89NNP6NChQ4mu6eLigqlTp6Jfv36oXr06unbtiiVLluDPP/8sU605OTl49OiR6JWv4HbxREREREREVD4xX2K+9DrmS8yXiIiIiEgzcBIWEVEpOTk5wdXVVQiVrl27htTUVISFhSn1DQ0NRUJCAu7du4eEhASEhoa+19rmzp2L1NRUHDx4sFTnlbROVawsK7hGweq6Nm3a4MqVK7CyshL169+/Py5cuICjR4/CwcEB/v7+whbkADBjxgy4ubmhefPmmDx5MiZNmiTarr84EydORFpamug1bNgwpX76+vqYMGECDAwMMGbMmEKvFR8fL7pOw4YNS1RDcd58RoU5c+YM0tLS4OzsjJycnDKP+bqC4M/U1BQXL15EfHw8atasKRwvCDD19fUxZMgQjB07FsOHDy/x9efNm4d79+5h48aNcHZ2xsaNG+Hk5ISff/75nWtesGABjI2NRa+buPLO1yMiIiIiIiJ6n5gvlQ3zpbdjvlR6zJeIiIiIqKze7563REQaKiwsDOHh4Vi3bh1kMhns7Ozg7u6u1K9x48ZwcnJCUFAQGjRogEaNGiEtLa1EYxgZGeHhw4dK7VlZWaIVka+zs7PDkCFDMGXKFEil0hLfT0nqdHBwwPHjx/HixQul1Yp37tzBo0eP4ODg8NaxLl++DACoV69esf0Kgo769evDxcUFpqamSExMFG3J/7q2bdtizpw5yMnJgZ6eXrHXrl69Ouzt7UVt1apVK7Svjo4OtLW1iwysrK2tla5VVpcvX4aRkRHMzMygpaUFiUSC9PR0UR9bW1sA/23nrkrx8fFo2LAhzMzMYGJionS8f//+mDZtGvT19VGrVi1oaZV+TreZmRn8/Pzg5+eH+fPno3nz5li6dCm2bNnyTjVHRkZi3LhxojZf45B3uhbRh/L9nYvqLkHjeFs2VXcJREREREQlxnyJ+VIB5kvMlz4W3n0HqbsEjeP9yzF1l6Bxvnc2UncJRERUgXESFhHRO/D398eYMWOwbds2xMbGYvjw4UWGKKGhoRgxYgQ2bNhQqjEcHR0LXXF4/vz5YsOomTNnws7ODtu3by/VeG+rMzAwEKtXr8amTZsQHh4uOrZ06VJUqlQJffr0KXaM58+f44svvkD79u1hbm5e4toUCgUUCkWxK/LS0tJgamr61oCsvPvrr7+wbds29O7dG1paWjAzM0OnTp2wdu1ahIeHo0qVKu+9Bmtra9jZ2RV53NjYWKXBoK6uLuzs7PD06dN3voaenp7Se68l0S5raURERERERETvDfMl5kvvC/Old8N86f3T+VN5UiiVTQv9THWXoHG+RxN1l0BERBUYJ2EREb0DQ0NDBAQEIDIyEo8ePUJISEiRfYcMGQI/P79CV3wVZ/jw4Vi7di1Gjx6Nzz//HHp6eti/fz/i4uKwb9++Is+rWbMmxo0bV+Kt00ta5yeffIIxY8Zg4sSJePHiBXr37o3c3Fxs3boVq1atwsqVK2FtbS0656+//kJ2djYeP36Mc+fOYfHixfjnn3+we/duoc+ZM2cwcOBAHD58GFZWVrhx4wbi4+PRuXNnmJub4/bt21i4cCH09fXRrVs3AMC+ffvw559/wsXFBZUrV0ZSUhLmz5+PCRMmlOqeP4Q3VxgCgLOzM4BX4d+9e/egUCiQlZWFkydPYv78+TA2NsbChQuF/uvXr4ebmxtatWqFqKgoNGnSBFpaWvjxxx9x5coVtGzZslQ1/fHHH0orUevWrVv6m3sH33zzDbZv347AwEA4ODhAoVBg3759+PbbbyGTyUR9i3p2lSpV+iC1Er1v3LWJiEgD5OeruwIiIqrAmC8xXyop5ktizJcqrv2pe9RdgsZhvkREpAGYL2kUTsIiInpHYWFhkEql6NatGywtLYvsp6Ojg+rVq5f6+ra2tjh27BimTZuGjh074sWLF3ByckJCQgK6dOlS7LkTJkzAhg0bkJ2dXeLxSlLnypUr0aRJE6xfvx7Tp0+HtrY2WrRogT179sDHx0epv6OjIyQSCQwNDWFra4vOnTtj3LhxsLCwEPo8e/YM6enpyM3NBQBUrlwZqampWLlyJR48eICaNWuiffv2OHHiBGrUqAEAqFSpEtatW4exY8dCoVDA3t4ey5cvx5AhQ0p8vx9KYGCgUtvvv/8OAHj06BFq1aoFiUQCIyMjODo6YtCgQRgzZgyMjP7b8tjOzg4XLlzA/PnzERkZidu3b0NPTw8NGzbEhAkTMGLEiFLVtHTpUixdulTU9r///Q/t2rV7hzssnYYNG8LAwADjx4/H77//Dj09PdSvXx+bN2/GgAEDRH2Lena1a9d+73USERERERERfQjMl5gvlQTzJTHmS0RERERUXkkUCoVC3UUQEX3MUlJS4OnpiQcPHpR6NSO9nYeHB5o1a4aVK1equxT6wPLvFf1rFejdcGUdERGVN0n5CWodv2utkWodvyL57u46dZdARKTRmC+9X8yXPl7Ml1SL2RIREZVHzJcqjoqQL3EnLCKicqJ27drw8fFBXFycukvRCF999RWGDh2K58+fo1mzZuouh9SAoY7qfX/norpL0Cj8jBIRERERkaoxX1It5kvE7+6qxWxJ9fgZJSIiKl84CYuISM3atm2Lq1evAgAMDQ3VXI3m6NmzJ9q2bQsAXAFKRERERERERBqN+dL7wXyJiIiIiIhKg5OwiIjUTF9fH/b29uouQ+NUrVoVVatWVXcZRERF4upP1ePqTyIiIiL6WDFfej+YLxGpVtd6rdVdgsbZ88dxdZegcXpbtVF3CUREVIFxEhYRERERkRpwwhARkQZQKNRdARERERFRhfFd5o/qLkHjeFtywhARUYXHfEmjaKm7ACIiIiIiIiIiIiIiIiIiIiIiooqMO2EREREREakBfx2h6nF3MSIiIiIiIqLyq5tjO3WXoHH+9/sBdZegcQZYu6m7BCIiqsA4CYuIiKgYPj4+yM3NxYEDyl9mU1NT0b59e1y8eBFNm/73P/ybmpqicePGmDt3Lj799FOhPSoqCtHR0QAAbW1t1K5dG76+vpgzZw7S09PRqlUrnDx5Ei4uLkpjeXl5wdjYGLt37wYA3L59G7a2tnBwcMClS5eKrP/kyZNo164dunTpgv379xfZLy4uDsHBwRg2bBjWrVsnOpaSkgJPT0/h5+rVq6N169ZYtGgRGjdurHQtb29vHDp0CKdOnULr1q0BAOHh4Th06BAuX76s1P/WrVuwsbFBYmIievbsCYlEgsTERPTu3RsAkJubi4EDB+LYsWP4/vvv0ahRoyLvg94vTnCh8o4T21SPf++JiIiIiMqG2RKzJfpP3uPH6i5B43DCkOoxX1ItZktE9LHhJCwiIqJihIWFoU+fPrh9+zZq164tOiaTydCqVSsYGRkBAA4dOgRnZ2f8888/mDdvHnr06IGMjAzUrFlTOMfZ2RmHDh3Cy5cv8cMPPyA0NBTPnj3Dpk2b0LRpU8TExCgFZZmZmUhOTsa+ffuENrlcDn9/fxw7dgynT59G27ZtC61fKpUiPDwcUqkUd+7cgaWlZZH9Jk2ahE2bNmHZsmWoXLmyUp/09HQYGRnhzp07mDhxIrp3745r165BV1dX6HPr1i2cOHECo0aNQkxMjBCUhYWFYe3atThx4gRcXV1F15XL5ahRowa6deumNOazZ8/Qp08fXL16FcePH4eNjU2h9dOHwQBCtRhAqB6fKRERERERlTfMlv7DbImYLakesxDV4zMlIqKy4CQsIiKiYvTo0QPm5uaQy+WYPn260P7kyRMkJCRgyZIlQpuZmRksLCxgYWGBqVOnYvv27Th9+jR69uwp9NHR0YGFhQUAICAgAIcPH8bevXuxadMmhIWFYfr06Vi5ciUMDAyEc+RyOWrVqoUuXboAABQKBWQyGdavX4/atWtDKpUWGpQ9efIE8fHxOHv2LO7duwe5XI6pU6cq9bt58yZOnDiBXbt2ITk5Gbt370a/fv2U+tWoUQMmJiawsLBAREQEevbsiStXrqBJkyZCH5lMhh49emD48OFwcXHB8uXLoa+vj2bNmqFFixaIiYkRBWUKhQJyuRyDBg2Cjo74P0uysrLQvXt3PHnyBMePHxeeG6kPAwjVYvCoevyMEhERERFRecNs6T/Mlojf21WP+ZLq8XNKRERlwUlYRERExdDR0cHAgQMhl8sxbdo0SCQSAEBCQgLy8vIQFBSEBw8eiM55/vw5YmNjAUC0kq8w+vr6ePHiBQCgf//+mDhxInbu3ImBAwcCeBUkbdmyBSEhIdDW1gYAJCcn49mzZ+jYsSOsrKzg6uqKFStWoEqVKqJr79ixA05OTnB0dERwcDAiIiIQGRkp3EMBmUyG7t27w9jYGMHBwZBKpYUGZQUePnyI7du3K91fQYC3bt06ODk5wd7eHjt37sSAAQMAvFqxOGXKFKxatUqoNSUlBTdv3kRoaKhojHv37sHd3R2GhoY4evQoTExMin2O9GEw1KHyjp9R1WPwSPQWCoW6KyAiIqJyjtmSMmZLRKrTcs5wdZegcbT2/6PuEjRKte4Z6i6BqPxjvqRROAmLiIjoLUJDQ7FkyRIcPXoUHh4eAF6FS3369IGxsbEQlLm6ukJLSwvPnj2DQqFAy5Yt4eXlVeR1z507h23btqFDhw4AgGrVqsHX1xcxMTFCUJacnIzMzEwMHjxYOE8qlSIwMBDa2tpo1KgRbG1tkZCQgJCQENH1pVIpgoODAQBdunTBw4cPRfcAAPn5+ZDL5VizZg0AIDAwEOPHj8fNmzeVtmcv2DL/6dOnAICePXvCyclJOH7o0CE8e/YM3t7eACCEbgVBWb9+/TB+/HhRrTKZDO3atYODg4NorDFjxsDW1hZJSUmilZtFycnJQU5OjqgtX5EHLYn2W88lUhdObiEiIiIiIvo4MFt6pTxnSwDzpQ+Bi6dUz7vw3xBKZbFB3QUQEVFFpqXuAoiIiMo7JycnuLq6IiYmBgBw7do1pKamIiwsTNQvPj4eFy5cwK5du2Bvbw+5XI5KlSqJ+vz8888wNDSEvr4+2rRpg08++QRr164VjoeGhuLYsWO4fv06ACAmJgbu7u6wt7cH8Gob9d27dwsBGPBfIPW69PR0nDlzBkFBQQBerboMCAhQ6peUlISnT5+iW7duAIDq1aujU6dOwr2+LjU1FefOnYNcLoeDgwM2btwoOh4TE4OAgABh6/egoCD88MMPwr2YmJjgs88+E6796NEj7Nq1S+k5Aq+26s/IyMCmTZuUjhVmwYIFMDY2Fr1u4kqJziUiIiIiIiIiep+YLb1SnrMlgPkSEREREZUdd8IiIiIqgbCwMISHh2PdunWQyWSws7ODu7u7qI+1tTXq16+P+vXr4+XLl/D19cWlS5egp6cn9HF0dMTevXuho6MDS0tLpS3lvby8UKdOHcjlckycOBG7d+8WhUXbtm1DdnY22rZtK7QpFArk5+cjIyNDWPUnlUrx8uVLWFpaivrp6elh7dq1MDY2Fvrdv38f+vr6Qr/8/Hz89NNPiI6OhpbWf/O1bWxsYGJiAkdHR/z1118ICAjAsWPHAAD3799HYmIicnNzsWHDf0uF8vLyEBMTg3nz5gnP0cvLC9euXUNycjK0tbXh5+en9LwHDBiAnj17IjQ0FAqFAuPGjSv2/YmMjFTq42scUuw5VHrcuUm1uPpT9fgZJSIiIiKi8orZUvnOlgDmSx8Cv7erHvMl1ePnlIiIyoKTsIiIiErA398fY8aMwbZt2xAbG4vhw4dDIpEU2b9v376YOXMm1q9fj7Fjxwrturq6wsrDwmhpaWHw4MGQSqWwsrKCrq4u+vbtKxyXSqUYP3680vbwI0aMQExMDBYuXIiXL18iNjYWy5YtQ+fOnUX9evfujbi4OAwbNgz//vsvvv76a2zfvh3Ozs5Cn7y8PLRr1w4HDx5Ely5dCq1z5MiRWLBgARITE+Hr64uvvvoKtWvXxp49e0T9Dh48iGXLlmH27NnQ1taGp6cnbGxsIJPJkJycjMDAQFSpUqXQMQYNGiQ8j/z8fEyYMKHI56anpycKJAFwq/j3gKEOERERERER0bthtiRW3rIlgPnSh3Bnkqu6S9A4Dsc4YUjVbMAMlIiI3h0nYREREZWAoaEhAgICEBkZiUePHikFVW+SSCQYPXo0oqKiMHToUBgYGJR4rMGDB2P27NmYOnUqgoKChJWEaWlpOH/+PL766is4OTmJzgkKCsLs2bMxd+5cfPPNN3jw4AHCwsKEVYkF+vTpA6lUimHDhuF///sfzMzM4O/vrxT6devWDVKptMigzMDAAEOGDMGsWbPQu3dvSKVS9O3bF40aNRL1s7a2RmRkJA4cOIDu3btDIpEgNDQUy5cvx4MHD7BixYpin8WAAQOgpaWFQYMGQaFQYOLEiSV6hkQVAVfVERFpgHyFuisgIiKiCoLZkhizpY/TzxEb3t6JSoX5EhGRBmC+pFE4CYuIiKiEwsLCIJVK0a1bN9FW7EUZNGgQpk2bhrVr12LSpEklHqdOnTro2LEjDh48iNDQUKFdKpWiYcOGSiEZAPj6+mLUqFH49ttvIZVK0bFjR6WQDHgVlC1evBg//fQTYmJi4OvrW+iqyz59+mDAgAH4559/iqxz1KhRWL58ORYvXoyLFy/iyy+/VOpjbGwMLy8vSKVSdO/eHQAQEhKCWbNmwdnZWbT1fVH69+8PLS0tDBgwAPn5+Zg8efJbzyGqCLizmOoxeCQiIiIiovKM2ZIYs6WPD7+3qx7zJdXj55SIiMpColAoOK2OiIiINE4nLT91l6BxGOqoFgMdIqKyS8pPUOv4Xc2HqXX8iuS7vzequwQiIiKiUmO+pFrMllSP+RIRUdkxX6o4KkK+pKXuAoiIiIiIiIiIiIiIiIiIiIiIiCoyTsIiIiIiIiIiIiIiIiIiIiIiIiIqA07CIiIiIiIiIiIiIiIiIiIiIiIiKgMddRdARERE9D58f+eiuksgIiIiDadQ5Ku7BCIiIiIiIiIiqsCYL2kWTsIiIiIijeRt2VTdJRAVixMFVY9/74mIiIiIiIjKL35vVz3mS6rHzykREZUFJ2ERERERUYkw1FEtBjpERERERERE9DFhtqR6zJeIiIjKF07CIiIijZOZmQkbGxsAQNOmTZGWlqbegioIiUQCADA2NkZWVpZ6iyH6CDB4VD0Gj0REREREpCrMl94N8yUqTscBoeouQeN8knZG3SVonDPNtNVdAhERVWCchEVEVEY+Pj7Izc3FgQMHlI6lpqaiffv2AICTJ0/CxcVFqY+XlxeMjY2xe/duhISEICsrC3v27Cl2zNu3b8PW1hYODg64dOmS0vGjR48iOjoaaWlpyM7OhpWVFVxdXfHll19CV1cXAJCXl4fVq1cjJiYGV69ehb6+PlxcXDB9+nS4ubkJ15LL5Rg8eDCAVyGKpaUlOnXqhEWLFqFGjRpCewEDAwNYWlrCzc0N4eHhaNmypXAsJSUFnp6eePDgAUxMTJTqjoqKQnR0tFK7o6Mjrly5Ivx87do1zJs3D0lJSfj7779haWkJFxcXjB8/Hq1atRL6HTp0CM2aNRN+Lur5vllXwT17e3uL3tesrCyYmpoiOTkZHh4ewr0nJiaid+/eomfx5vudk5MDS0tL3L9/X3Q+AHzzzTdYsmQJzp8/j7y8PDg7O2PkyJEICQkR+rwe/AGAoaEh6tSpAw8PD0RERKB+/fpKz60ow4YNw6ZNm7BixQpEREQI7Xfv3kV8fDxmzZol6v/o0SMsWbIEu3fvxo0bN2BgYABbW1v4+flhyJAhMDU1BQB4eHjg6NGjSuPl5uZCR0fnrZ/LgvehQPXq1dG6dWssWrQIjRs3LvH90fvDCS5U3nFiG5V3/OcoERERFYX5EvMl5kvMlz4G2ofPqbsEjcMJQ6o36bryvxPp3S337qnuEjTOy2s31F0CERWDk7CIiMooLCwMffr0we3bt1G7dm3RMZlMhlatWiE3NxcxMTFKIVlmZiaSk5Oxb9++Uo0pl8vh7++PY8eO4fTp02jbtq1w7Ndff0WXLl0QHh6O1atXQ19fH1evXsWuXbuQl5cHAFAoFAgMDMShQ4ewZMkSeHl54dGjR1i3bh08PDyQkJAghD4AYGRkhPT0dOTn5+PixYsYPHgw7ty5g++//150r126dEF2djYyMjLwxRdfoG3btoiJicHAgQNLfG/Ozs44dOiQqE1H579/XZ09exZeXl5o1KgRNm3aBCcnJzx+/Bhff/01xo8fLwppzMzMYGZmVuKx3xzz0KFDSE5OFgU3JWFtbQ2ZTCZ6vxMTE2FoaIj79++L+q5ZswYRERGYPHkyNmzYAF1dXXz99dcYNmwYLl26hKVLl4r6Hzp0CM7Oznj27Bl+/vlnrFq1Ck2bNsW+ffvg5eX11toSExNx6tQpWFpaKh2zsLCAsbGxqO3+/fto164dHj16hDlz5qBly5YwNjZGeno6ZDIZtm3bhpEjRwr9hwwZgtmzZ4uuoaOjU6LPZYH09HQYGRnhzp07mDhxIrp3745r164JAS+pDye4qBYnY6genykRERERVVTMl/67V+ZLrzBfYr6kiZgtqR6zENVbbNdI3SVoGE4YIqKPCydhERGVUY8ePWBubg65XI7p06cL7U+ePEFCQgKWLFmC3NxcTJ8+HStXroSBgYHQRy6Xo1atWujSpUuJx1MoFJDJZFi/fj1q164NqVQqCskOHjwICwsLLF68WGizs7MTjbFjxw7s3LkTe/fuhY+Pj9D+xRdf4N9//8Xnn3+OTp06oUqVKgBerb6zsLAAAFhaWmL06NGYMWMGnj9/Dn19fQCAiYmJ0KdevXro3LkzBg0ahFGjRsHHx0dYzfY2Ojo6wnUKu/eQkBDUr18fqamp0NLSEo41a9YMY8aMKdEYJVGlShX4+/tjypQpOH36dKnOHTRoEFavXo2VK1cKzycmJgaDBg3CnDlzhH6///47xo8fj4iICMyfP19oHz9+PHR1dTF69Gj4+fmJ3l8zMzPh+dja2sLHxwdeXl4ICwvD9evXoa1d9MqnP/74A+Hh4fj+++/RvXv3Et3L1KlTcevWLWRkZIiCtbp166Jz585QKBSi/gYGBoW+fyX5XBaoUaOG8HmKiIhAz549ceXKFTRp0qRENRMRERF9MPmKt/chIiIqAeZLzJfexHyJ+RIRERF9JJgvaRROwiIiKiMdHR0MHDgQcrkc06ZNE7YLT0hIQF5eHoKCgpCXl4eJEydi586dwqo9hUKBLVu2ICQkpNhg403Jycl49uwZOnbsKGy3vWLFCiHQsrCwwN27d3Hs2DFhq/o3bdu2DQ4ODqKArMD48eOxe/duJCUliVYrvk5fXx/5+fl4+fJlsbWOHTsWsbGxSEpKgr+/f4nvsShpaWn45ZdfsG3bNlFAVqCwLejLIioqCvb29ti5cyf69u1b4vNatmyJevXqYdeuXQgODsatW7dw7NgxrFu3ThSS7dy5E7m5uZgwYYLSNYYOHYqpU6ciLi5OFJK9SUtLC2PGjIGvry/OnTuHNm3aCFvLv74tfX5+PgYMGICJEyfC2dm5RPeRn5+P+Ph4BAcHF7qyERD/qoDilORz+aaHDx9i+/btAPDWVYo5OTnIyckRteUr8qAl4XbcqsSVdVTecUWt6vHvPREREdGHwXypaMyXmC8VYL5U8fE7JlUEzJdUi3/viehjw0lYREQqEBoaiiVLluDo0aNCKCGTydCnTx9h+21fX1/R1unJycnIzMzE4MGDSzWWVCpFYGAgtLW10ahRI9ja2iIhIQEhISEAAD8/P3z//fdwd3eHhYUFXFxc4OXlhYEDB8LIyAgAkJGRgQYNGhR6/YL2jIyMQo9fvXoVGzduRKtWrVC1atVia3VycgLwalv8kvr5559haGgoagsODsbGjRtx9epV0XXfN0tLS4wZMwbTpk0rMjAsSmhoKGJiYhAcHAy5XI5u3brB3Nxc1CcjIwPGxsaoVauW0vm6urqwtbUt8n143evPuU2bNqhUqRIcHR1Fq2IXLVoEHR0djB49usT38PfffyMrKwuOjo6i9pYtWyI9PR0A4OPjg7i4OOHY+vXrsXnzZuHnoUOHYtmyZSX6XBYo+LULT58+BQD07Nnzre/5ggULEB0dLWqzQQPYoWSBIJUMAwgq7xjqEBEREVFFxnypcMyXmC8xX9IckrbciUzVesqS1V2CxmG+REREZcFJWEREKuDk5ARXV1fExMTAw8MD165dQ2pqKmbPni30CQ0Nhbe3N65fvw47OzvExMTA3d0d9vb2JR4nKysLu3fvxvHjx4W24OBgSKVSISTT1taGTCbD3LlzceTIEZw+fRrz58/HokWLcObMGSGQeXOb7ze9vjLs4cOHMDQ0RH5+PrKzs9GuXTtREFKUgjFKupoNABwdHbF3715RW0GI8raa34fJkydj06ZNiImJKdVqy+DgYEyZMgU3btyAXC7H6tWr31uNbz5nKysrXLlyRTh+7tw5rFq1CufPny/Ve1GUxMREvHjxApMnT8bz589Fx/r3749p06YJPxesHi3p5xIAUlNTYWBggFOnTmH+/PnYuHHjW2uKjIzEuHHjRG2+xiHvfpNEREREREREHxjzpcIxX2K+xHxJc2j/+VDdJWicppVvqbsEjfM1zNRdAhERVWCchEVEpCJhYWEIDw/HunXrIJPJYGdnB3d3d+G4l5cX6tSpA7lcjokTJ2L37t3YtGlTqcbYtm0bsrOzRduHKxQK5OfnIyMjAw4ODkK7lZUVBgwYgAEDBmDOnDlwcHDAxo0bER0djfr16+Py5cuFjlHQ/vq1qlativPnz0NLSwu1atWCvr5+ieotuJaNjU2J71FXV7fI4LCgpitXrqB58+YlvmYBIyMj/Pbbb0rtWVlZ0NbWFrbcf52JiQkiIyMRHR2NHj16lHgsMzMz9OjRA2FhYcjOzkbXrl3x+PFjUR8HBwc8fPgQd+7cUdqO/cWLF7h+/To8PT3fOtbbnnNqair++usv1KlTR2jLy8vD+PHjsXLlyiJXkpqbm8PExERYlVig4DpVq1ZFVlaW6JixsXGxwW9xn8sCNjY2MDExgaOjI/766y8EBATg2LFjRV4TAPT09KCnpydq41bxVN5xVR0REREREb2J+VLR12K+xHypAPOlimv/ib1v70Sl4m1Z+n+OERER0fvDSVhERCri7++PMWPGYNu2bYiNjcXw4cNFq8K0tLQwePBgSKVSWFlZQVdXF3379i3VGFKpFOPHjxdWJRYYMWIEYmJisHDhwkLPMzU1Ra1atYTtt4OCgtCvXz/s27cPPj4+or7Lli2DpaUlOnXqJKq9NCsqC6xcuRJGRkbo2LFjqc8tTLNmzdCwYUMsW7YMAQEB0NLSEh3PysoSVsYVxtHREdu3b8f/a+/O46qq9v+Pvw+ggAKaiOIsKqCiOWTmkBqionmdMhXngayuaDmTViqWes0cC6dk0K8Zas7dlMxEc8pMqRyuUw6VWubE1RQUzu8Pf57rCVSMI5tzfD0fDx6Pztpr7/3ZC7oX3q21dmpqqlWgsnfvXvn5+SlfvnxZnjdo0CDNmjVLM2fOfKh6+/Xrp+eff16RkZFyds4c2HTs2FGRkZGaOnWqpk6danVs7ty5unbtmrp27Xrfe2RkZGjWrFny8/O7Z3DYs2fPTN+D0NBQ9ezZ876vK3ByclLnzp21ePFijRkzJlOQl1N//bnMSkREhCZNmqRVq1apQ4cOD3V9Xp1ne0waAgAAAIBHi3wpM/Il8qX7IV+yL2RLAADA0TEJCwBsxMPDQ126dNGoUaOUkpKSKciSpL59+2r8+PEaPXq0unbtmuWKvytXrig5OdmqzdvbWxcuXNDevXv18ccfq3LlylbHu3btqvHjx+vdd99VTEyMkpOT1aFDB1WsWFE3btzQokWLdODAAX3wwQeSpLCwMC1btky9e/fWlClTFBISopSUFEVHR+uzzz7Thg0b7hkY3cvly5d17tw5paam6siRI5o3b55Wr16tRYsWZQqufvzxR3l6elo+m0wm1ahx+w/wW7du6dy5c1b9TSaTihcvLpPJpLi4ODVr1kyNGjXSm2++qcqVK+vq1atat26dvvjiC23ZsuWeNXbv3l3jx49Xr169NHLkSBUqVEhbt27VjBkz9N57793zPDc3N0VFRSkiIuKhxqRly5Y6f/68Zbv7vypbtqzee+89DRs2TG5uburZs6fy5cunNWvWaPTo0Ro2bJjVqlRJunDhgs6dO6c///xT+/fv14wZM7R79279+9//tgRxv/76q0JCQrRo0SLVrVtX3t7e8va23kI5X7588vX1VWBg4H2fYeLEiUpKSlLdunU1fvx41alTRwULFtQPP/ygnTt3qlq1atkai3nz5j3w5zIrBQoUUP/+/TV27Fi1b9/+oba7J9QBHj+E47bH/5YCD2DA64wAAI6NfIl86a/Il/6HfAlAbiBfsi3+dxTIBvIlh8IkLACwofDwcMXExOj555/PclVX2bJl1axZM33xxRfq169fltdISkrKtOIsPDxc7u7uqlq1aqaATJI6dOiggQMH6vPPP1fdunW1bds2vfrqqzpz5ow8PDwUFBSk1atXW7avN5lMWr58uWbMmKHp06drwIABSktLU5EiRbRv3z5VrVr1oZ/9zoo3Nzc3lSpVSs8++6x2796t2rVrZ+rbuHFjq8/Ozs66deuWJOnAgQMqUaKE1XFXV1fduHFDklS3bl3t2bNHEyZMUP/+/fXHH3+oRIkSatCggWbMmHHfGgsXLqyvv/5ab7zxhtq2basrV66oUqVKmjZtmsLDw+97bu/evTV16lQdPHjwvv3uZjKZVLRo0fv2GTx4sCpUqKD3339fM2fOVHp6uoKCgjRnzpwsVxHeWXFYoEABlStXTsHBwZo/f77VStKbN2/q8OHD+vPPP7Nd6714e3tr9+7dmjx5sqZMmaITJ07IyclJ/v7+6tKliwYPHpyt62Tn5/JeBg4cqGnTpmn58uXq3Llzjp8JgOMi1LE9gkfb4mcUAABkB/kS+dLdyJf+h3wJQG7gb3fbIluyPX5GgbzNZDYzrQ4AcHvL9GbNmik8PFxTpkwxupwcOXnypPz8/LRv3z7VrFnT6HLsSnx8vAYPHqzLly8bXUqONXfqZHQJDoc/mG2LP5YBIOc2Ziw39P4tn3jJ0Pvbkw2XFhhdAgAgF5AvQSJfwr2RLdke+RIA5Bz5kv2wh3yJnbAAAJKk2rVra9OmTVqzZo2OHz+uihUrGl1SjjVo0EA1a9bUjh07jC7FLnh4eOjWrVtyc3MzuhSbINSxPUIdAAAAAMD9kC+BfAn3Q7YEAAAcHZOwAAAWtWrVyrRVvT0qXbq0jh49Kun2VvPInuTkZEm3t+93BIQ6tkfwaFv8jAIAAABwRORLjzfyJdwP2ZLt8TMKAEDewiQsAIDDcXFxUaVKlYwuw+4wZkDuIni0PYJHAAAAALZCvvT3MGa4n2dGvWp0CQ7Ha9MvRpfgcJxCfja6BACAHWMSFgAAAGAAJgwBgAPIyDC6AgAAAMBufDNprtElOBzyJQBwAORLDsXJ6AIAAAAAAAAAAAAAAAAAwJ4xCQsAAAAAAAAAAAAAAAAAcoDXEQIAkEvmzp2rESNG6NKlS3Jxuf1/wVevXtUTTzyhhg0bKikpydI3KSlJwcHBOnbsmEJCQnTq1ClJUoECBRQYGKhRo0apU6dOlv4XL17U+PHjtWrVKp09e1ZFixZVy5YtNW7cOJUtW9aqjp9//lljx47Vhg0b9Mcff6hEiRJq3769xowZI29vb0lS9erV1bBhQ82dm3mL8P/7v//TSy+9pEWLFqlbt246ffq0SpUqlamfv7+/2rRpo2nTpkmSDhw4oKioKG3evFkpKSkqV66cwsLC9MYbb6hAgQKW88qXL6/Bgwdr8ODBkiSz2awRI0Zo/vz5Wrt2rZ577rmHH3zYBNub21bime+NLsHh8DMKAAAAwNGRL5Ev2TP+brc98iXb4+cUAJAT7IQFAEAuCQ4O1tWrV7Vnzx5L29dffy1fX1998803unHjhqV98+bNKlu2rCpWrChJGj9+vM6ePat9+/bp6aefVpcuXbRjxw5JtwOyevXq6csvv9TcuXN17NgxJSQk6NixY3r66af1008/Wa77008/qU6dOjp69Kg++eQTHTt2THPnztWmTZtUv359Xbx4UZIUHh6uhIQEXb9+PdNzxMXFqW3btnrhhRfk7e2thQsXZuqzdetWHTt2TOHh4ZKkXbt26ZlnnlFaWpr+/e9/68iRI5owYYLi4+PVvHlzpaWlZTlm6enpCg8P16JFi7R582YCMgAAAAAA8FgjXyJfAgAAQN7FTlgAAOSSwMBAlShRQklJSapXr56k2ysS27Vrp6+++kq7du2yhEB3Vire4enpKV9fX/n6+io6OlqLFy/WunXr1KBBA7355ps6c+aMjh07Jl9fX0lS2bJllZiYKH9/f0VERGj9+vWSpIiICOXPn19ffPGF3N3dLX1r1aqlihUr6s0339ScOXPUo0cPRUZGasWKFerRo4eljhMnTigpKUmff/658uXLp549eyo+Pl6jR4+2etbY2Fg988wzCgoKktlsVnh4uKpUqaKVK1fKyen2HPBy5copICBAtWrV0vTp0xUZGWl1jdTUVHXt2lV79uzR119/rcDAQBt+NwA4IlZ/2h6rPwEAAIC8hXyJfAm4W8uydYwuweGs/XWn0SU4lLal+BkF8HhhEhYAALkoODhYmzdv1htvvCHp9orEkSNHKj093bIS7/r16/rmm2/Ur1+/LK/h4uKifPnyKS0tTRkZGUpISFD37t0tAdkd7u7uGjBggN566y3LCsTExERNmDDBEpDd4evrq+7du2vp0qWaPXu2ihYtqnbt2ik2NtYqJIuPj1fp0qXVokULSbdXNE6bNk1bt25V48aNJd3eAv/TTz/V9OnTJUnJyck6ePCglixZYgnI7qhRo4aaNWumTz75xCoku3r1qlq3bq1ffvlF27dvV5kyZR56rGF7THCxLSa3AIADMJuNrgAAADyGyJfIl+wV2ZLtkS/ZHpOGAOQ68iWHwiQsAAByUXBwsAYPHqxbt27p+vXr2rdvn5o0aaKbN29q7ty5kqSdO3cqNTXVaqXiHWlpaZo6daquXLmipk2b6vz587p8+bKqVKmS5f2qVKkis9msY8eOyWw2y2w237fvpUuXdP78eRUrVkzh4eFq1aqVTpw4IT8/P5nNZi1cuFC9e/e2hF1Vq1ZVvXr1FBsbawnJli1bJrPZrLCwMEnSkSNHLNe/1323bdtm1fbOO+/I09NThw4dko+Pz4OGVampqUpNTbVqyzCny8nk/MBzAaMQPNoewSMAAACAxwH5Utb3JV/K+1rXaWl0CQ7n/ZOrjC7B4QwvX8/oEgAAdoxJWAAA5KLnnntO165d07fffqtLly4pICBAPj4+atKkifr27asbN24oKSlJFSpUUNmyZS3nRUZG6q233tKNGzfk4eGhf/3rX2rdurV+++03SZL5AbPk8+fPbwmRstNXkpo3b67SpUsrLi5O48eP16ZNm3T69Gn17dvXqn+/fv00ZMgQffDBB/L09FRsbKw6deokT09Pq373u++de97RokULffnll5o4caJlxeP9TJo0SVFRUVZtfqqiigp64LnIPia42BaTsGyPMbU9/r0HAAAA8h7ypfvf8w7ypbzn1pmzRpfgcNxM6UaX4HBcij140iay79bv540uAQByFZOwAADIRZUqVVLp0qW1efNmXbp0SU2aNJEklSxZUmXKlNGOHTu0efNmNW3a1Oq8ESNGqE+fPvLw8FDx4sVlMpkkST4+PipcuLAOHTqU5f0OHTokFxcX+fn56datWzKZTDp06JA6dOiQZd8715MkJycn9enTRwsXLtS4ceMUFxen4OBgVahQweq8sLAwDRkyRMuWLVPjxo21fft2TZo0yXLc39/fcv1atWpled+AgACrtpCQEA0aNEjt2rVTRkaGZs6ceb9h1ahRozR06FCrtg6F+tz3HDw8Jrggr2PCEAAAAIDHAfkS+ZK9cqlU4cGd8FBO39pvdAkOh0lDAICcYBIWAAC5LDg4WElJSbp06ZJGjBhhaW/cuLHWr1+v3bt365///KfVOUWLFlWlSpUyXcvJyUmdO3fWxx9/rPHjx8vX19dy7Pr165o9e7Y6dOigQoUKSbq9+nD27NkaMmSI3N3dLX3PnTunjz/+WBEREVbX79u3r959912tXLlSq1at0oIFCzLV4OnpqU6dOik2NlbHjx9XQECAGjVqZDleq1YtVa5cWdOnT1dYWJhlq3lJ+v777/Xll1/qww8/zHTdFi1aaN26dWrbtq3MZrNmzZp1zzF1dXWVq6ur9diwVTzyOCYMAQAAAAD+LvIl8iV79O+tvDrP1siXAADIW5we3AUAANhScHCwtm3bpuTkZMtKRUlq0qSJ5s2bp7S0NAUHB2f7ehMmTJCvr6+aN2+u9evX6+eff9bWrVsVGhoqJycnq1V+H374oVJTUxUaGqqtW7fq559/1oYNG9S8eXMFBARozJgxVtf28/NT06ZN9fLLL8vV1VUvvPBCljWEh4drx44dmjt3rvr162d1zGQyacGCBTp48KA6duyo3bt36/Tp01q+fLnatGmj0NBQvfLKK1let1mzZvrss88UExOjgQMHZntMAAAAAAAAHBn5EvkSAAAA8h4mYQEAkMuCg4N1/fp1VapUScWLF7e0N2nSRP/9738VGBioEiVKZPt6RYsW1a5duxQcHKxXXnlFfn5+atKkidLT05WcnGx1LX9/f3377beqUKGCOnfurHLlyqlVq1YKCAjQ9u3b5eHhken64eHhunTpkrp16yY3N7csa3j22WcVGBiolJQU9erVK9Pxhg0bateuXXJ2dlarVq1Urlw5de7cWe3atdO6devk7HzvVYVNmzbVv//9b8XHxysiIkJmsznbYwMAAPAomTMy+MrmFwAAsC3yJfIlAADgGIzObOzpyx6YzPymCQCAw4mJidGAAQO0dOlStW/f/r59x44dq2nTpmnjxo2qV69ertSXkZGh8PBwJSYmasuWLfL397f5PZo7dbL5NR93iWe+N7oEh8J28QCQcxszlht6/1CP3obe354kXl1odAkAAOAhkS+RL9ka2ZLtkS8BQM6RL9kPe8iX2AkLAAAHFB4eroSEBB06dEjXr1+/b9+oqCjNmjVLu3btUkYuzSJ3cnJSTEyMIiMj9fXXX+fKPQEAAAAAAJB95EsAAADAw2EnLAAA4JBYqWh7rFYEHj+sqEVex0pF+2EPKxUBAAD+inzJtpzc3Y0uweHMObzR6BIczj8DmxtdgkPJeMAkXiAvIF+yH/aQL7kYXQAAAAAAAAAAAAAAx5baKMjoEhxOy4W1jC7B4Xj0MroCx+I9b4fRJQBArmISFgAAAGAAdhgCAAAAAACPk6/iY4wuweGQLwEAkLcwCQsAADgkXp0HAAAeObPZ6AoAAAAAAABgz8iXHAqTsAAAgENiFZjtMbHNthhP2+PfewAAAAAA8q5Wfs8YXYLDWfHLFqNLcDgdS/NzCgD4+5iEBQB4pMxms5o3by5nZ2clJiZaHZs9e7ZGjx6t/fv3q1SpUlqwYIFiY2N14MABZWRkqFy5cmrWrJkGDRqkbdu2qW/fvve914kTJxQfH6+oqChJkrOzs0qXLq0OHTronXfekYeHh06ePCk/P78sz9+5c6fq1aun+Ph4DR48WJcvX37g81WuXFknTpzQqVOn5Ovrm2Wf69evq1SpUnJyctKvv/4qV1fXTH0WLlyoDz/8UAcOHJCzs7Nq166tESNG6B//+IelT1JSkoKDg3Xp0iUVLlw402dJSk9P16xZsxQbG6ujR4/K3d1d9erV01tvvaWGDRs+dG3ff/+93n77be3atUspKSny9fXVM888ow8++EDFihWz9FuxYoWio6O1b98+3bhxQ2XLllXDhg01aNAg1apVS5IUHx+f5ffQ1dVVN27ckCT16dNHCxculCS5uLiodOnS6tSpk8aPHy83N7f7fSsAAAAAAICDIl8iXyJfcgznXqltdAkOp+GeCkaX4HB8ddDoEgAAdoxJWACAR8pkMikuLk7Vq1fXvHnz9Morr0i6HWiNHDlSc+bMUalSpdStWzetXr1ao0eP1vTp01WyZEmdOXNGq1at0rvvvqs5c+aoZcuWluu+8MILqlatmsaPH29p8/HxkSQFBQXpyy+/1K1bt7R9+3b169dPf/75p+bNm2fp++WXXyooKMiqVm9v74d6tm3btun69et68cUXtXDhQkVGRmbZb8WKFQoKCpLZbNbq1avVpUsXq+PDhw/Xhx9+qHfffVft27fXzZs3tXjxYrVr104zZ87UwIEDs1WP2WxWWFiYvvzyS02ZMkUhISFKSUlRdHS0nnvuOS1fvlzt27fPdm3nz59XSEiI/vGPfygxMVGFCxfWyZMntXbtWl27ds3SLzIyUlOnTtVrr72mqKgolStXTufPn9f69es1atQobdiwwdLXy8tLhw8ftqrBZDJZfW7ZsqXi4uJ08+ZNfffdd+rdu7dMJpMmT56crXEAAAAAAACOhXyJfIl8yTGUiPvR6BIcTvyIDQ/uhIfSU5knmwIAkF1MwgIAPHJlypSxhD0tWrRQ+fLlFR4erhYtWqhnz55KSEhQQkKC1qxZo7Zt21rOK1u2rOrVqyez2SyTySR3d3fLsfz586tAgQJZrg50cXGxtHfp0kWbNm3S2rVrrUIyb2/ve64szK6YmBh169ZNTZo00euvv37PkCwmJkY9evSQ2WxWTEyMVRC1a9cuTZ06VbNmzdKgQYMs7RMmTNCNGzc0dOhQtWvXTmXKlHlgPcuWLdOnn36qtWvXqk2bNpb2+fPn68KFC3rppZfUvHlzFSxYMFu1bd++XVeuXNGCBQvk4nL7VwY/Pz8FBwdb1f/ee+9p5syZeu211yztZcuW1VNPPSXzX95jbTKZHjjurq6ulj5lypRRs2bNtHHjRkIyOBxenQcAAAAA2Ue+RL50B/mS/fr88DajS3A4oSWZMAQAQF7CJCwAQK7o3bu3Vq1apX79+umFF17Q/v37deDAAUnSJ598osDAQKuA7G5/Xcn2sNzd3ZWWlpaja/zVf//7Xy1fvlzffPONKleurCtXrujrr79Wo0aNrPodP35cO3fu1MqVK2U2mzVkyBCdOnVK5cqVk3T72T08PCwrOO82bNgwTZs2TStWrNDgwYMfWNOSJUsUEBBgFZDdfa2VK1dq48aNltWKD6rN19dXt27d0qpVq/Tiiy9m+X24U/+AAQOyrCmn37v9+/drx44dlpoAR5J45nujS3A4TGwDkOsyzA/uAwAAbIZ8iXzp7yBfyjtadO5tdAkOp8V+JrbZ2sZqHkaXAOBxQ77kUJiEBQDINfPnz1dQUJC2bt2qFStWWLZ3P3LkiAIDA636Dh48WAsWLJAkFS5cWL/88svfuud3332nJUuWqGnTplbtDRo0kJOTk1Xb1atXs33dhIQE+fv7W7acDwsLU0xMTKaQLDY2Vq1atdITTzwhSQoNDVVcXJzGjRsn6fazV6xYUfnz5890j5IlS8rLy0tHjhzJVk1HjhxRlSpVsjx2p/3uaz2otnr16mn06NHq1q2bXn31VdWtW1dNmzZVr169VLx4ccv1KlSoYFnJKEnTpk3TmDFjLJ9//fVXFSpUSJJ05coVeXhY/xHbqFEjrV+/3vL5s88+k4eHh27duqXU1FQ5OTnpww8/vO+zp6amKjU11aotw5wuJ5Pzfc8DAAAAAAD2hXyJfIl8yX65fH/c6BIcTq9CLPKztY28jhAAkANMwgIA5JpixYrplVde0erVqy2r5e7lzTff1MCBA7Vy5UpNnDjxoe7z448/ysPDQ+np6UpLS1Pr1q0zhSxLly69Z6CUHbGxserRo4flc48ePdSkSRN98MEH8vT0lCSlp6dr4cKFmjlzplW/4cOHa8yYMZaQ7q9bqv9VVgHavWT3WtmtbcKECRo6dKi++uorffPNN5o7d64mTpyorVu3qnr16lneo1+/fmrbtq2++eYby1b0d3h6emrv3r1W/e9+DYAkBQcHa86cObp27ZqmT58uFxcXdezY8b7PNWnSJEVFRVm1+amKKirovucBcCzsLmZ77C4GAACAvIZ8iXyJfMmO5eM/S9paw20RRpfgcDwjChhdgkPxid5hdAkAkKv4bQcAkKtcXFysVrVJkr+/vw4fPmzV5uPjIx8fHxUrVuyh7xEYGKi1a9fKxcVFJUuWzDJkKlOmjCpVqvTQ15akgwcPateuXdq9e7ciIyMt7enp6UpISFD//v0lSYmJifr111/VpUsXq/PT09O1adMmNW/eXP7+/tq2bZvS0tIy1XnmzBmlpKQoICAgW3X5+/vr0KFDWR67037nWtmp7Q5vb2916tRJnTp10sSJE1WrVi29//77WrhwoaX+mzdvKl++fJJuryy91+pSJyenB457wYIFLX1iY2NVo0YNxcTEKDw8/J7njBo1SkOHDrVq61Coz33vAwAAAAAA7BP5EvkS+ZJ9yriSYnQJDiffQSYM2drlOrZ99ezjzsfoAgAglzEJCwBguK5du6pbt25as2aN2rVrl+Pr5c+f/28HYNkRExOjxo0bKzo62qo9Li5OMTExlpAsJiZGYWFhevPNN636TZgwQTExMWrevLm6du2qDz74QPPmzdOgQYOs+r3//vtyc3PLFGTdy51xXLdundq0aWN1bOrUqSpZsqQl/MpObVnJnz+/KlasqGvXrlnu+cEHH2j27Nl6/fXXs1Xnw3ByctLo0aM1dOhQdevWLdOqxjtcXV3l6upqfS5bxQOPHXZtAgAAAB5f5EvkS/dCvpR3HJ7zlNElOBzX342uwPH4991jdAkAADvGJCwAgOHCwsK0cuVKhYWFadSoUQoNDVXx4sV16tQpLV26VM7Otg87Lly4oHPnzlm1FS5cWG5ubpJur9hLTk62Ou7q6qpKlSrp//7v/zR+/HhVq1bN6vhLL72kadOm6cCBAypWrJjWrVuntWvXZurXq1cvdejQQRcvXlT9+vX1+uuva8SIEUpLS1P79u118+ZNLV68WLNmzVJ8fLy8vb2z9UxhYWFatmyZevfurSlTpigkJEQpKSmKjo7WZ599pg0bNihfvnw6f/58tmrbsWOHEhISFBYWpoCAAJnNZq1bt06ff/654uLiJEn169fXsGHDNGzYMJ06dUovvPCCypQpo7NnzyomJkYmk8my9bx0ezv7v467dPtVAnf3u1unTp00YsQIRUdHa/jw4dkaCzwaTHCxLV6dBwAAAAC2Q75EvkS+lPcFvLzb6BIcDvmS7YW+RQYKAPj7mIQFADCcyWTS0qVL9dFHHykuLk7vvfeebt68qdKlSyskJETTpk2z+T2bNWuWqe2TTz5RWFiYJOnq1auqVauW1fGKFStq8uTJunDhgjp06JDp/CpVqqhKlSqKiYlRqVKlVLBgQYWEhGTqFxISInd3dy1evFivvfaaZsyYoSeffFKzZ8/WW2+9pRs3bih//vz66quv1Lhx42w/k8lk0vLlyzVjxgxNnz5dAwYMUFpamooUKaJ9+/apatWqkqRFixZlq7Z//OMfKlCggIYNG6aff/5Zrq6u8vf314IFC9SzZ0/LOe+//77q1q2rOXPmKDY2Vn/++aeKFy+uxo0ba+fOnfLy8rL0TUlJUYkSJTLd9+zZs/L19c3yuVxcXDRw4EC99957+uc//6mCBQtme0xgW4Q6AAD8hTnD6AoAAMD/R75EvkS+lPe5JJU0ugSHU39kfaNLcDhe2ml0CQAeN+RLDsVkNpvNRhcBAAD+5+TJk2rSpInq16+vjz/+OEcrNffu3atmzZopPDxcU6ZMsWGVeV9zp05Gl+BwmIRlW+wsBgA5tzFjuaH3D3Xrbuj97UnijY+NLgEAgMcK+ZJtkC/ZFtmS7ZEvAUDOkS/ZD3vIl9gJCwCAPKZ8+fJKSkrSwoULlZycrKeeeupvX6t27dratGmT1qxZo+PHj6tixYo2rDRvI9QBAAAAAACPK/IlGzFl/XpD/D0ZYqcPAADg2JiEBQBAHuTn56dx48bZ5Fq1atXKtPX944BVYLbHxDYAAAAAAOwH+ZIN8Hogm3ISk9oAAIBj47cdAAAAAAAAAAAAAAAAAMgBJmEBAAAAAAAAAAAAAAAAQA7wOkIAAOCQeHWe7fGKRwAArJkzzEaXAAAAgEeIfMm2yJYAAMiMfMmxMAkLAAA4JEId2yN4tC1+RgEAAAAAyNv42922yJZsj59RAADyFl5HCAB4JMxms5o1a6bQ0NBMx2bPnq3ChQtr8eLFMplMlq/ixYurY8eO+umnnyx9y5cvbzlesGBB1a5dW8uXL7e6XkpKit5++20FBQXJ3d1d3t7eevrpp/Xee+/p0qVLln4nTpxQt27dVLJkSbm5ual06dJq166d/vOf/1j6bNmyRU2bNlWRIkVUoEAB+fv7q3fv3kpLS7P0SU9P1/Tp01W9enW5ubnpiSeeUKtWrbR9+/Ysx+L69esqUqSIihYtqtTU1EzH737GO1+lS5fO8niBAgVUvXp1LViwINN1sltXfHx8pvuZTCa5ublZ+vTp00cmk0mvvvpqpvtERETIZDKpT58+Vv3bt2+f5fPfsWPHDj3//PN64okn5ObmpurVq2vatGlKT0+36nd3TV5eXnr66ae1Zs2a+14bAAAAAAA4FrKl/yFbuo1sCQAAAHkdO2EBAB4Jk8mkuLg4Va9eXfPmzdMrr7wi6XZYNXLkSM2ZM8cSBh0+fFienp46evSoXn75ZbVp00Y//PCDnJ2dJUnjx49X//79lZKSoqlTp6pLly4qVaqUGjRooIsXL+rZZ59VSkqK3nnnHT311FMqVKiQDh8+rLi4OC1ZskQRERG6efOmmjdvrsDAQK1cuVIlSpTQL7/8ovXr1+vy5cuSpIMHD6ply5YaNGiQZs2aJXd3dx09elQrVqywhDlms1lhYWH68ssvNWXKFIWEhCglJUXR0dF67rnntHz58kyB0YoVKxQUFCSz2azVq1erS5cumcbrzjPecefZ/3r8zz//1PLly9W/f3+VKlVKrVq1+lt1eXl56fDhw5m+Z3crU6aMEhISNH36dLm7u0uSbty4oSVLlqhs2bIP/Bm426pVq9S5c2f17dtXmzdvVuHChfXll19q5MiR2rlzp5YtW2Z1/7i4OLVs2VIpKSmaPXu2XnzxRe3du1fVq1fP9j1ZWQcAAAAAgP0iW/ofsiVjsqXbD8VeBraUoQyjSwAAAHikmIQFAHhkypQpo5kzZ2rgwIFq0aKFypcvr/DwcLVo0UI9e/ZUUlKSJKlYsWIqXLiwSpQooTFjxqh79+46duyYAgMDJUmenp7y9fWVr6+voqOjtXjxYq1bt04NGjTQ6NGjdfr0aR05ckQlS5a03LtcuXJq0aKFzObb71E+cOCAjh8/rk2bNqlcuXKWPg0bNrSc88UXX8jX11fvvfeepa1ixYpq2bKl5fOyZcv06aefau3atWrTpo2lff78+bpw4YJeeuklNW/eXAULFrQci4mJUY8ePWQ2mxUTE5NlUHbnGe/l7uORkZF67733tHHjRktQ9rB1mUym+95PkmrXrq3jx49r5cqV6t69uyRp5cqVKlu2rPz8/O577t2uXbum/v37q23btpo/f76l/aWXXlLx4sXVtm1bLVu2zGpcChcubPmev/POO5o5c6Y2b9788EEZbIrtzQEAAAAAuYls6TayJeOypcRf9z1Uf9xfaMlaRpcAAADwSDEJCwDwSPXu3VurVq1Sv3799MILL2j//v06cODAPfvfWRV39xbtd3NxcVG+fPmUlpamjIwMLV26VD169LAKye52ZwWcj4+PnJyc9Omnn2rw4MGZVgNKkq+vr86ePautW7eqcePGWV5vyZIlCggIsAqj7hg2bJhWrlypjRs3WlYGHj9+XDt37tTKlStlNps1ZMgQnTp1yhLWPayMjAytWrVKly5dUv78+f92XdnVr18/xcXFWYKy2NhY9e3b1xJyZscXX3yhCxcuaPjw4ZmOtWnTRgEBAfrkk0+yDBBv3bqlmJgYSbJ63uxgwpDtsbsY8jr+vQcAAAAcD9kS2ZJR2ZLE35m2ZnLJZ3QJDmfNqZ1Gl+Bw2paqY3QJAAA7xiQsAMAjN3/+fAUFBWnr1q1asWKFfHx8sux39uxZvf/++ypVqpRlpeLd0tLSNHXqVF25ckVNmzbV+fPndfny5Ux9n3rqKct26G3atNEnn3yiUqVKadasWRo5cqSioqJUp04dBQcHq3v37qpQoYIkqVOnTkpMTFSTJk3k6+urevXqKSQkRL169ZKXl5ck6ciRI6pSpUqW9d9pP3LkiKUtNjZWrVq10hNPPCFJCg0NVVxcnMaNG2d1bmRkpN566y3L54kTJ+q1117LdDw1NVW3bt1SkSJF9NJLL1mOP2xdV65ckYeHh1W/Ro0aaf369VZtPXr00KhRo3Tq1ClJ0vbt25WQkPBQQdmd+96rvsqVK1vVJkldu3aVs7Ozrl+/royMDJUvX16dO3e+5z1SU1OVmppq1ZZhTpeTKXMgCuQVBLkA4ADMvE4FAIDcQLZEtnR3HX9li2xJIl/KDRtO7zG6BIcTWpIJQwBg98iXHAqTsAAAj1yxYsX0yiuvaPXq1VmulitdurTMZrP+/PNP1ahRQytWrLBamXYnJLpx44Y8PDz0r3/9S61bt9Zvv/2W5f1WrVqltLQ0RUZG6vr165b2iIgI9erVS0lJSdq1a5eWL1+uiRMnau3atWrevLmcnZ0VFxend999V1999ZW++eYbTZw4UZMnT9bu3btVokQJSbJsQ38vd2pPT0/XwoULNXPmTMuxHj16aPjw4RozZoycnJws7SNGjFCfPn0sn4sWLWp1zTvHz549qxEjRmjAgAGqVKmSVZ/s1iXd3oJ+7969VsfvrBS9m4+Pj1q3bq34+HiZzWa1bt06U23Zdb/6/roScfr06WrWrJl++uknDRkyRLNmzVKRIkXuef6kSZMUFRVl1eanKqqooL9VK5Ab2FnM9pjYBgAAADgmsiWypQfVl9NsSSJfyg3TLmX/NZTIpgY1ja7A8exINroCAIAdYxIWACBXuLi4yMUl6//b+frrr+Xl5aVixYrJ09Mz0/E7IZGHh4eKFy9utQ184cKFLSsT7yhbtqyk22HQ5cuXrY55enqqTZs2atOmjd59912Fhobq3XffVfPmzS19SpUqpZ49e6pnz5565513FBAQoLlz5yoqKkr+/v46dOhQls9xpz0gIECSlJiYqF9//TXTVujp6enatGmT1T2LFi2aKfi6253jlSpV0vLly1W9enXVqVNHVatWlaSHqkuSnJyc7nu/u/Xr108DBw6UJEVHR2frnLv5+/tb6mjQoEGW9dWsWdOqzdfX1/K8cXFxev7553Xw4EEVK1Ysy3uMGjVKQ4cOtWrrUKjPQ9cK5CYmDAEAAABA9pEt/Q/ZUub6cpotSeRLuWHoEyeMLsHhJO7wMroEAABwF6cHdwEA4NHy8/NTxYoVswzJpP+FRL6+vpaQTLod9nTu3FmLFy/WmTNnHvq+JpNJlStX1rVr1+7Z54knnlCJEiUsfbp27aqjR49q3bp1mfpOnTpVJUuWtARgMTExCgsLU3JystVXWFiYYmJiHrreO8qUKaMuXbpo1KhRlraHqethtWzZUmlpabp586ZCQ0Mf+vzQ0FAVKVJEU6dOzXRs7dq1Onr0qNVKzb+qW7eunnrqKU2YMOGefVxdXeXl5WX1xVbxAAAAAAA8HsiWHg7ZUtbIlwAAAJBT7IQFALBrEydOVFJSkurWravx48erTp06KliwoH744Qft3LlT1apVkyQlJydr7Nix6tmzp6pWrar8+fNry5Ytio2NVWRkpCRp3rx5Sk5OVocOHVSxYkXduHFDixYt0oEDB/TBBx9IksLCwrRs2TL17t1bU6ZMUUhIiFJSUhQdHa3PPvtMGzZsUL58+XT+/HmtW7dOa9eutdRwR69evdShQwddvHjxgdug38vrr7+uatWqac+ePapTp06267rDbDbr3Llzma5brFgxq63sJcnZ2dmy4tHZ+d7B05UrV5ScnGzV5u3trTJlymjevHkKCwvTyy+/rIEDB8rLy0ubNm3SiBEj1L9/fz3//PP3fd7BgwerQ4cOGjlypEqVKvWg4QEAAAAAAMgWsiWyJQAAAMBWmIQFALBr3t7e2r17tyZPnqwpU6boxIkTcnJykr+/v7p06aLBgwdLkkqXLq3y5csrKipKJ0+elMlksnweMmSIpNur4rZt26ZXX31VZ86ckYeHh4KCgrR69Wo1adJE0u0VjsuXL9eMGTM0ffp0DRgwQGlpaSpSpIj27dtn2cJ90aJFKliwoEJCQjLVHBISInd3dy1evFivvfba33ruqlWrqkWLFhozZow+//zzbNd1R0pKikqUKJHpumfPnpWvr2+mdi+vB29rnZSUpFq1alm1hYeHa8GCBXrxxRe1efNmTZgwQY0aNVJKSookafLkyRo5cuQDr92yZUv5+flpwoQJmj179gP7AwAAAAAAZAfZEtkSAAAAYCsms9lsNroIAADs2d69e9WsWTOFh4drypQpRpdjkVfrkqQbN26oXbt2+vnnn7Vlyxb5+PjY/B7NnTrZ/JqPu8Qz3xtdAnBfoSVrGF0CgFy2MWO5ofdv7tzF0Pvbk43pS40uAQCAPCuvZjh5tS4pd7IliXzJ1pyrBRpdgsMJX7Xe6BIcznz/CkaXACCXkS/ZD3vIl9gJCwCAHKpdu7Y2bdqkNWvW6Pjx46pYsaLRJUnKu3VJkpubm9asWaMZM2Zo69at6tixo9ElAQAAAAAAGCKvZjh5tS6JbMlunTpjdAUOp5HbWaNLcDjzxSQsAMDfx05YAADAIbFSEXkdO4vZHjthAY8fViraD3tYqQgAAPBX5EvI68iXbI98CXj8kC/ZD3vIl9gJCwAAANlCqGNbBDoAAAAAAOBxQrZke+RLAADkLUzCAgAAAAxA8Gh7BI8AAAAAAORdQbP/aXQJDufGohtGl+BwKvXaa3QJAAA7xiQsAAAAwABMGAIAAAAAAI+TAwPmGF2CwyFfAgAgb2ESFgAAAAAAwN9hzjC6AgAAAAAAANgz8iWHwiQsAAAAwAC8jtD2WP0JAAAAAEDe1apVV6NLcDi9DicaXYLDWRRYxugSAAB2jElYAAAAgAGYMAQAwP1FR0drypQpOnfunGrUqKEPPvhAdevWvWf/5cuX6+2339bJkyfl7++vyZMn6/nnn8/FigEAAHA/69d/YnQJDod8CQCAezMiW3LKadEAAAAAAACALS1dulRDhw7V2LFjtXfvXtWoUUOhoaH6/fffs+y/Y8cOde3aVeHh4dq3b5/at2+v9u3ba//+/blcOQAAAAAAAIxmVLZkMpvNZls8AAAAQF7S3KmT0SU4HF6fZ1usVASAnNuYsdzQ+/P7RvY97PfqmWee0dNPP60PP/xQkpSRkaEyZcpo0KBBeuONNzL179Kli65du6bPPvvM0lavXj3VrFlTc+fOzVnxAADgscXve7ZFtmR75EsAkHPkS/bjYb5XRmVLvI4QAAAAMADBo+0RPAJA3pWamqrU1FSrNldXV7m6umbqm5aWpu+++06jRo2ytDk5OalZs2bauXNnltffuXOnhg4datUWGhqq1atX57x4AAAA2MTpW/81ugSH41zkCaNLcDjpFy8ZXQIA4B6ymy8ZmS0xCQsAADgko1cuZEdqaqomTZqkUaNGZfkfIPHwGFPbs6cx3ZhhdAUPZk/jaS8YU9tjTLPPHn7fyCvGjRunqKgoq7axY8dq3Lhxmfr+8ccfSk9PV/Hixa3aixcvrv/85z9ZXv/cuXNZ9j937lzOCgcAAI81e/h9j9/fbc+exnTDH0ZX8GD2NJ72gjG1PcbU9hjT7LOH3zfyiuzmS0ZmS04P1RsAAAA2k5qaqqioqEyz9vH3Maa2x5jaFuNpe4yp7TGmeBRGjRqlK1euWH3dvRoRAAAAfw+/v9seY2pbjKftMaa2x5jaHmOKR8Ee8iV2wgIAAAAAAMAjda9XD2alaNGicnZ21m+//WbV/ttvv8nX1zfLc3x9fR+qPwAAAAAAAOxLdvMlI7MldsICAAAAAABAnpE/f3499dRT2rRpk6UtIyNDmzZtUv369bM8p379+lb9JWnjxo337A8AAAAAAADHZGS2xE5YAAAAAAAAyFOGDh2q3r17q06dOqpbt65mzJiha9euqW/fvpKkXr16qVSpUpo0aZIk6fXXX1eTJk00depUtW7dWgkJCdqzZ4/mz59v5GMAAAAAAADAAEZlS0zCAgAAMIirq6vGjh2b7Vfz4MEYU9tjTG2L8bQ9xtT2GFPkBV26dNH58+c1ZswYnTt3TjVr1tSGDRtUvHhxSdLp06fl5PS/Dd4bNGigJUuW6K233tLo0aPl7++v1atXq1q1akY9AgAAQK7g93fbY0xti/G0PcbU9hhT22NMYTSjsiWT2Ww22/RJAAAAAAAAAAAAAAAAAOAx4vTgLgAAAAAAAAAAAAAAAACAe2ESFgAAAAAAAAAAAAAAAADkAJOwAAAAAAAAAAAAAAAAACAHmIQFAAAAAAAAAAAAAAAAADnAJCwAAIBctnXrVrVp00YlS5aUyWTS6tWrjS7Jrk2aNElPP/20PD09VaxYMbVv316HDx82uiy7NmfOHD355JPy8vKSl5eX6tevr/Xr1xtdlkP517/+JZPJpMGDBxtdit0aN26cTCaT1VflypWNLsuu/frrr+rRo4e8vb3l7u6u6tWra8+ePUaXBQAAACAL5Eu2Rb5ke+RLjxbZUs6RLT0a5Et43DEJCwAAIJddu3ZNNWrUUHR0tNGlOIQtW7YoIiJCu3bt0saNG3Xz5k21aNFC165dM7o0u1W6dGn961//0nfffac9e/aoadOmateunQ4cOGB0aQ7h22+/1bx58/Tkk08aXYrdCwoK0tmzZy1f27ZtM7oku3Xp0iU1bNhQ+fLl0/r163Xw4EFNnTpVTzzxhNGlAQAAAMgC+ZJtkS/ZHvnSo0O2ZDtkS7ZFvgRILkYXAAAA8Lhp1aqVWrVqZXQZDmPDhg1Wn+Pj41WsWDF99913aty4sUFV2bc2bdpYfZ4wYYLmzJmjXbt2KSgoyKCqHMPVq1fVvXt3ffTRR3r33XeNLsfuubi4yNfX1+gyHMLkyZNVpkwZxcXFWdr8/PwMrAgAAADA/ZAv2Rb5ku2RLz0aZEu2RbZkW+RLADthAQAAwMFcuXJFklSkSBGDK3EM6enpSkhI0LVr11S/fn2jy7F7ERERat26tZo1a2Z0KQ7h6NGjKlmypCpUqKDu3bvr9OnTRpdkt9auXas6deqoU6dOKlasmGrVqqWPPvrI6LIAAAAAwBDkS7ZFvmQ7ZEu2RbZkW+RLADthAQAAwIFkZGRo8ODBatiwoapVq2Z0OXbtxx9/VP369XXjxg15eHho1apVqlq1qtFl2bWEhATt3btX3377rdGlOIRnnnlG8fHxCgwM1NmzZxUVFaVGjRpp//798vT0NLo8u/PTTz9pzpw5Gjp0qEaPHq1vv/1Wr732mvLnz6/evXsbXR4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+      "text/plain": [
+       "<Figure size 2400x1600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_df = processed_data.copy()\n",
+    "\n",
+    "charge_state_df = pd.DataFrame(plot_df['charge_state_vector'].tolist(), index=plot_df['modified_sequence'], columns=[f'{i}' for i in range(1, 7)])\n",
+    "intensity_dist_df = pd.DataFrame(plot_df['normalized_intensity_distribution'].tolist(), index=plot_df['modified_sequence'], columns=[f'{i}' for i in range(1, 7)])\n",
+    "\n",
+    "plt.figure(figsize=(24, 16))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.heatmap(charge_state_df, cmap='viridis', cbar=True)\n",
+    "plt.title('Charge State Vector')\n",
+    "plt.xlabel('Charge State')\n",
+    "plt.ylabel('Sequence')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.heatmap(intensity_dist_df, cmap='viridis', cbar=True)\n",
+    "plt.title('Normalized Intensity Distribution')\n",
+    "plt.xlabel('Charge State')\n",
+    "plt.ylabel('Sequence')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2400x1600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "plot_df = processed_data.copy()\n",
+    "charge_state_df = pd.DataFrame(plot_df['charge_state_vector'].tolist(), index=plot_df['modified_sequence'], columns=[f'{i}' for i in range(1, 7)])\n",
+    "intensity_dist_df = pd.DataFrame(plot_df['normalized_intensity_distribution'].tolist(), index=plot_df['modified_sequence'], columns=[f'{i}' for i in range(1, 7)])\n",
+    "\n",
+    "# Function to sort charge state vectors\n",
+    "def sort_charge_state_vectors(row):\n",
+    "    values = row.values\n",
+    "    num_ones = sum(values)\n",
+    "    order = f\"{num_ones:02d}\" + \"\".join(map(str, values))\n",
+    "    return order\n",
+    "\n",
+    "# Sort the DataFrames\n",
+    "sorted_indices = charge_state_df.apply(sort_charge_state_vectors, axis=1).sort_values().index\n",
+    "charge_state_df = charge_state_df.loc[sorted_indices]\n",
+    "intensity_dist_df = intensity_dist_df.loc[sorted_indices]\n",
+    "\n",
+    "plt.figure(figsize=(24, 16))\n",
+    "\n",
+    "# Plot charge state vector heatmap\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.heatmap(charge_state_df, cmap='viridis', cbar=True, yticklabels=False)\n",
+    "plt.title('Charge State Vector')\n",
+    "plt.xlabel('Charge State')\n",
+    "\n",
+    "# Plot normalized intensity distribution heatmap\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.heatmap(intensity_dist_df, cmap='viridis', cbar=True, yticklabels=False)\n",
+    "plt.title('Normalized Intensity Distribution')\n",
+    "plt.xlabel('Charge State')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2400x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "\n",
+    "plot_df = processed_data.copy()\n",
+    "charge_state_df = pd.DataFrame(plot_df['charge_state_vector'].tolist(), index=plot_df['modified_sequence'], columns=[f'{i}' for i in range(1, 7)])\n",
+    "intensity_dist_df = pd.DataFrame(plot_df['normalized_intensity_distribution'].tolist(), index=plot_df['modified_sequence'], columns=[f'{i}' for i in range(1, 7)])\n",
+    "\n",
+    "# Function to sort charge state vectors\n",
+    "def sort_charge_state_vectors(row):\n",
+    "    values = row.values\n",
+    "    num_ones = int(sum(values))  # Convert to integer\n",
+    "    order = f\"{num_ones:02d}\" + \"\".join(map(str, map(int, values)))  # Convert values to integers\n",
+    "    return order\n",
+    "\n",
+    "# Sort the DataFrames\n",
+    "sorted_indices = charge_state_df.apply(sort_charge_state_vectors, axis=1).sort_values().index\n",
+    "charge_state_df = charge_state_df.loc[sorted_indices]\n",
+    "intensity_dist_df = intensity_dist_df.loc[sorted_indices]\n",
+    "\n",
+    "# Split into single and multiple charge DataFrames based on charge_state_df\n",
+    "single_charge_df = intensity_dist_df[charge_state_df.sum(axis=1) == 1]\n",
+    "multiple_charge_df = intensity_dist_df[charge_state_df.sum(axis=1) > 1]\n",
+    "\n",
+    "plt.figure(figsize=(24, 8))\n",
+    "\n",
+    "# Plot single charge heatmap\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.heatmap(single_charge_df, cmap='viridis', cbar=True, yticklabels=False)\n",
+    "plt.title('Single Charge')\n",
+    "plt.xlabel('Charge State')\n",
+    "\n",
+    "# Plot multiple charge heatmap\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.heatmap(multiple_charge_df, cmap='viridis', cbar=True, yticklabels=False)\n",
+    "plt.title('Multiple Charges')\n",
+    "plt.xlabel('Charge State')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Sampling some of the data\n",
+    "sampled_df = plot_df.sample(n=200, random_state=42)  \n",
+    "\n",
+    "charge_state_sampled_df = pd.DataFrame(sampled_df['charge_state_vector'].tolist(), index=sampled_df['modified_sequence'], columns=[f'{i}' for i in range(1, 7)])\n",
+    "intensity_dist_sampled_df = pd.DataFrame(sampled_df['normalized_intensity_distribution'].tolist(), index=sampled_df['modified_sequence'], columns=[f'{i}' for i in range(1, 7)])\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.heatmap(charge_state_sampled_df, cmap='viridis', cbar=True)\n",
+    "plt.title('Charge State Vector')\n",
+    "plt.xlabel('Charge State')\n",
+    "plt.ylabel('Sequence')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.heatmap(intensity_dist_sampled_df, cmap='viridis', cbar=True)\n",
+    "plt.title('Normalized Intensity Distribution')\n",
+    "plt.xlabel('Charge State')\n",
+    "plt.ylabel('Sequence')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Idea: group by unique charge state vector\n",
+    "#### --> mean over each group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_df = processed_data.copy()\n",
+    "plot_df['charge_state_vector_str'] = plot_df['charge_state_vector'].apply(lambda x: str(x))\n",
+    "\n",
+    "# Group by charge state vector string\n",
+    "grouped = plot_df.groupby('charge_state_vector_str')\n",
+    "# Calculate mean of normalized intensity distribution\n",
+    "agg_intensity_dist_df = grouped['normalized_intensity_distribution'].apply(lambda x: np.mean(np.vstack(x), axis=0)).reset_index(drop=True)\n",
+    "\n",
+    "unique_charge_state_vectors = grouped['charge_state_vector'].first().apply(pd.Series).reset_index(drop=True)\n",
+    "unique_charge_state_vectors.columns = [i for i in range(1, 7)]\n",
+    "agg_intensity_dist_df = pd.DataFrame(agg_intensity_dist_df.tolist(), columns=[i for i in range(1, 7)])\n",
+    "\n",
+    "# Charge state vectors as labels\n",
+    "ytick_labels = [str(vec) for vec in grouped['charge_state_vector'].first().index]\n",
+    "\n",
+    "plt.figure(figsize=(18, 8))\n",
+    "\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.heatmap(unique_charge_state_vectors, cmap='viridis', cbar=True, yticklabels=ytick_labels)\n",
+    "plt.title('Charge State Vector')\n",
+    "plt.xlabel('Charge State')\n",
+    "plt.ylabel('Charge State Vector')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.heatmap(agg_intensity_dist_df, cmap='viridis', cbar=True, yticklabels=ytick_labels)\n",
+    "plt.title('Mean Normalized Intensity Distribution')\n",
+    "plt.xlabel('Charge State')\n",
+    "plt.ylabel('Charge State Vector')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Assuming processed_data is already defined and populated\n",
+    "plot_df = processed_data.copy()\n",
+    "plot_df['charge_state_vector_str'] = plot_df['charge_state_vector'].apply(lambda x: str(x))\n",
+    "\n",
+    "# Group by charge state vector string\n",
+    "grouped = plot_df.groupby('charge_state_vector_str')\n",
+    "\n",
+    "# Calculate mean of normalized intensity distribution\n",
+    "agg_intensity_dist_df = grouped['normalized_intensity_distribution'].apply(\n",
+    "    lambda x: np.mean(np.vstack(x), axis=0)).reset_index(drop=True)\n",
+    "\n",
+    "# Extract unique charge state vectors\n",
+    "unique_charge_state_vectors = grouped['charge_state_vector'].first().apply(pd.Series).reset_index(drop=True)\n",
+    "unique_charge_state_vectors.columns = [i for i in range(1, 7)]\n",
+    "\n",
+    "# Convert aggregated intensity distribution to DataFrame\n",
+    "agg_intensity_dist_df = pd.DataFrame(agg_intensity_dist_df.tolist(), columns=[i for i in range(1, 7)])\n",
+    "\n",
+    "# Charge state vectors as labels\n",
+    "ytick_labels = [str(vec) for vec in grouped['charge_state_vector'].first().index]\n",
+    "\n",
+    "# Add sum of charge state vector to sort\n",
+    "unique_charge_state_vectors['sum'] = unique_charge_state_vectors.sum(axis=1)\n",
+    "sorted_indices = unique_charge_state_vectors.sort_values(by='sum').index\n",
+    "\n",
+    "# Sort dataframes and labels\n",
+    "unique_charge_state_vectors = unique_charge_state_vectors.loc[sorted_indices].drop(columns='sum')\n",
+    "agg_intensity_dist_df = agg_intensity_dist_df.loc[sorted_indices]\n",
+    "ytick_labels = [ytick_labels[i] for i in sorted_indices]\n",
+    "\n",
+    "plt.figure(figsize=(18, 8))\n",
+    "\n",
+    "# Plot charge state vector heatmap\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.heatmap(unique_charge_state_vectors, cmap='viridis', cbar=True, yticklabels=ytick_labels)\n",
+    "plt.title('Charge State Vector')\n",
+    "plt.xlabel('Charge State')\n",
+    "plt.ylabel('Charge State Vector')\n",
+    "\n",
+    "# Plot mean normalized intensity distribution heatmap\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.heatmap(agg_intensity_dist_df, cmap='viridis', cbar=True, yticklabels=ytick_labels)\n",
+    "plt.title('Mean Normalized Intensity Distribution')\n",
+    "plt.xlabel('Charge State')\n",
+    "plt.ylabel('Charge State Vector')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### sorted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Yt9xyi8fbe0gXVm6jo6OdDrb3AAAAgD80NQXO4a3Zs2frt7/9rTZs2KBPP/1UU6dO1dmzZzVx4kRJ0rhx4zRv3jzH56dOnaqqqio99thj+uyzz/T222/r2Wef1bRp0/x1Of2CvEZeAwAAACTjM1ow5jWvtmSUpNjYWA0fPtzbMo9lZWUpJSVFY8eO1dKlS2W325Wbm6tp06a5vWWvoqJCVVVVqqioUENDg2w2mySpZ8+eioqKuuTzJpNJM2fO1NNPP62bbrpJycnJ+vWvf63ExEQNGzbM5TyzZ8/W+PHj1b9/f/3oRz/SypUrnf6imlNfX69Dhw45/vzFF1/IZrMpKipKPXv2bLZm9OjRWrBggSZNmqS5c+fqk08+0apVq7RixQqX8wAAAADwzMiRI3Xq1Cnl5eXJbrcrLS1NW7dudTyHqqKiQiEh3/2+8Prrr9e2bds0a9Ys3Xbbberatasee+wxzZ0716iv0CzyGnkNAAAACHZG5TWvF8xeffVVb0u8Ehoaqs2bN2vq1KmyWCzq1KmTxo8fr4ULF7qty8vL04YNGxyv09PTJUnvvfeeMjIymq154okndPbsWU2ZMkWnT5/W3Xffra1bt6pjx44u57ncX1Rzjh8/7uhHkpYtW6Zly5bp3nvv1c6dO5utiYmJ0fbt2zVt2jT169dP11xzjfLy8jRlyhQ3VwEAAACAp6ZPn67p06c3+15z/063WCz68MMPW7mrliGvkdcAAACAtsCIvOb1gtlFp06d0pEjRyRJt9xyi6699toWNfJ9PXr00JYtW7yqKSwsVGFhoVc1JpNJCxcuvGy4+yF3f1HNSUpKUpMP9x3edttt2r17t9d1AAAAwBXhw9YauDLIa+Q1AAAAtHPkNa95/Qyzs2fP6pFHHtF1112nQYMGadCgQUpMTNSkSZP0zTffeN3A2rVrFRUVpdLSUq9r27tnn31WUVFRqqioMLoVAAAAAAGAvBY4yGsAAABAcPH6DrPZs2fr/fff11/+8hfdddddkqQPPvhAM2bM0K9+9Su9/PLLHp+rqKhItbW1kqTu3bt720q79+ijj+rhhx+WJL/+YhQAAABAcCKvBQ7yGgAAABBcvF4w++///m/96U9/ctpn/v7771dERIQefvhhrwJY165dvZ0e3xMXF6e4uDij2wAAAEA71dRkMroF/AB5LXCQ1wAAAGAk8pr3vN6S8Ztvvmn2gcnx8fE+bfEBAAAAAPAP8hoAAAAA+MbrBTOLxaL58+fr22+/dYzV1tZqwYIFslgsfm0OAAAAAOA58hoAAAAA+MbrLRlXrlypIUOGqFu3burbt68k6cCBA+rYsaO2bdvm9wYBAAAABKgmoxvAD5HXAAAAAEgir/nA6wWz1NRU/eMf/1BRUZEOHz4sSfrZz36mMWPGKCIiwu8NAgDgqW3HDxjdAlywJvY1ugUAaBfIa0DrSnqzyugW4EaD0Q3ArcmfHTO6Bbjxys1GdwAAxvN6wWzXrl0aOHCgcnJynMbPnz+vXbt2adCgQX5rDgAAAEDg4iHSgYe8BgAAAEAir/nC62eYDR48WFVVl/6i6syZMxo8eLBfmgIAAAAAeI+8BgAAAAC+8XrBrKmpSSbTpSuT//73v9WpUye/NAUAAAAA8B55DQAAAAB84/GWjA899JAkyWQyacKECQoPD3e819DQoIMHD2rgwIFeTZ6RkaH3339fkvTxxx8rLS3Nq/r2Lj8/XwsWLJAkrVixQjNnzjS2IQAAALQvPEQ6YJDXAg95DQAAAIYir3nN4zvMYmJiFBMTo6amJnXu3NnxOiYmRmazWVOmTNEf/vAHrxvIyclRZWWl+vTp4xirqKhQdna2IiMjFR8frzlz5uj8+fNuz/PMM89o4MCBioyMVGxsrEdzNzU1KS8vT9ddd50iIiKUmZmpf/zjH25rdu3apQceeECJiYkymUzatGmTR3Pt3LlTt99+u8LDw9WzZ08VFhZetubgwYO655571LFjR11//fVaunSp0/uPP/64Kisr1a1bN496AAAAANA2kdcuIK8BAAAA8JXHd5i9+uqrkqSkpCTNmTNHkZGRfmkgMjJSZrPZ8bqhoUHZ2dkym83as2ePKisrNW7cOF111VV69tlnXZ6nvr5eI0aMkMVi0e9+9zuP5l66dKlWr16tDRs2KDk5Wb/+9a9ltVp16NAhdezYsdmas2fPqm/fvnrkkUccv+K8nPLycmVnZ+vRRx9VUVGRduzYocmTJ+u6666T1Wpttqa6ulpZWVnKzMxUQUGBSktL9cgjjyg2NlZTpkyRJEVFRSkqKkqhoaEe9QEAAACgbSKvXUBeAwAAAOArjxfMLho3bpy++OIL3XTTTU7j//jHP3TVVVcpKSmpRQ1t375dhw4d0jvvvKOEhASlpaVp0aJFmjt3rvLz8xUWFtZs3cWtLjz5JaB04deKK1euVG5urn7yk59Ikn7/+98rISFBmzZt0qhRo5qtGzp0qIYOHerVdyooKFBycrKWL18uSerdu7c++OADrVixwmUAKyoqUn19vdavX6+wsDDdeuutstlseuGFFxwBDAAAADDWpc/KgrHIa+Q1AAAA4ALymrc83pLxogkTJmjPnj2XjO/du1cTJkxocUMlJSVKTU1VQkKCY8xqtaq6ulplZWUtPv9F5eXlstvtyszMdIzFxMRowIABKikp8ds80oXv9P15pAvfyd08JSUlGjRokFPgtFqtOnLkiL766iu/9gcAAACgbSCveY+8BgAAAEDyYcHs448/1l133XXJ+J133imbzdbihux2u1P4kuR4bbfbW3z+78/z/XN/fy5/znNxrubmqa6uVm1trVc1F98DAAAAgB8ir/k2F3kNAAAAgNdbMppMJn399deXjJ85c0YNDQ1+aQr+U1dXp7q6OqexxqYGhZjYRx8AAAAt1GR0A/gh8lpwIa8BAACg1ZDXvOb1HWaDBg3S4sWLncJWQ0ODFi9erLvvvrvFDZnNZp04ccJp7OLr7z9s2h/zfP/c35/Ln/NcnKu5eaKjoxUREeFVzcX3PLV48WLFxMQ4HeU67OU3AAAAABAMyGu+zUVeAwAAAOD1gtmSJUv07rvv6pZbbtHEiRM1ceJE3XLLLdq1a5eef/75FjdksVhUWlqqkydPOsaKi4sVHR2tlJSUFp//ouTkZJnNZu3YscMxVl1drb1798pisfhtHunCd/r+PNKF7+RuHovFol27duncuXNONbfccou6dOni8dzz5s3TmTNnnI5k9fL+SwAAAAAIeOQ175HXAAAAAEg+LJilpKTo4MGDevjhh3Xy5El9/fXXGjdunA4fPqw+ffq0uKGsrCylpKRo7NixOnDggLZt26bc3FxNmzZN4eHhLusqKipks9lUUVGhhoYG2Ww22Ww21dTUNPt5k8mkmTNn6umnn9Zbb72l0tJSjRs3TomJiRo2bJjLeWpqahznli48jPrivK48+uij+vzzz/XEE0/o8OHDWrt2rV5//XXNmjXLZc3o0aMVFhamSZMmqaysTK+99ppWrVql2bNnu6xpTnh4uKKjo50OtvcAAACAXzQF0AFJ5DXyGgAAAPD/GJ3RgjCvef0MM0lKTEzUs88+6+9eJEmhoaHavHmzpk6dKovFok6dOmn8+PFauHCh27q8vDxt2LDB8To9PV2S9N577ykjI6PZmieeeEJnz57VlClTdPr0ad19993aunWrOnbs6HKeffv2afDgwY7XFwPR+PHjVVhY2GxNcnKy3n77bc2aNUurVq1St27d9Morr8hqtbqcJyYmRtu3b9e0adPUr18/XXPNNcrLy9OUKVNc1gAAAAAAeY28BgAAAMB7Pi2Y7d69W7/5zW/0+eef64033lDXrl31v//3/1ZycrJf9sXv0aOHtmzZ4lVNYWGhywDkislk0sKFCy8b7r4vIyNDTU3eL4lmZGTo448/9qrmtttu0+7du72eCwAAALgimkxGd4BmkNfIawAAAAB5zXteb8n43//937JarYqIiNDf//531dXVSZLOnDnj068Y165dq6ioKJWWlnpd2949++yzioqKcru9CAAAAID2g7wWOMhrAAAAQHDx+g6zp59+WgUFBRo3bpz++Mc/OsbvuusuPf30016dq6ioSLW1tZKk7t27e9tKu/foo4/q4YcfliRde+21BncDAAAAwGjktcBBXgMAAACCi9cLZkeOHNGgQYMuGY+JidHp06e9OlfXrl29nR7fExcXp7i4OKPbAAAAQDvlw853aGXktcBBXgMAAICRyGve83pLRrPZrKNHj14y/sEHH+iGG27wS1MAAAAAAO+R1wAAAADAN14vmOXk5Oixxx7T3r17ZTKZdPz4cRUVFenxxx/X1KlTW6NHAAAAAIAHyGsAAAAA4Buvt2R88skn1djYqPvuu0/ffPONBg0apPDwcD3++OP65S9/2Ro9AgAAAAhEbPERcMhrAAAAACSR13zg8YJZ//79NXnyZI0ePVr/9V//pTlz5ujo0aOqqalRSkqKoqKiWrNPAAgI244fMLoFuGFN7Gt0CwAAGIK8BlwZX/bvYnQLcCPqhR5GtwA3XrnZ6A4AAHDP4y0Z+/btqyeeeELXXXedxo0bpz179iglJUU/+tGPCF8AAAAAYCDyGgAAAAC0jMcLZr/73e9kt9u1Zs0aVVRU6L777lPPnj317LPP6osvvmjNHgEAAAAEoiZT4BztHHkNAAAAgBOjM1oQ5jWPF8wkKTIyUhMmTNDOnTv12WefadSoUfrNb36jpKQkZWdn680332ytPgEAAAAAbpDXAAAAAMB3Xi2Yfd+NN96op59+WseOHdP/+T//Rx9++KFGjBjhz94AAAAABDBTU+AccEZeAwAAANo3ozNaMOY1nxfMJGnnzp2aMGGCJkyYoIaGBuXk5HhVn5GRIZPJJJPJJJvN1pJW2qX8/HzH9Vu5cqXR7QAAAAAIIOQ1Y5HXAAAAgODi9YLZv/71Lz399NPq2bOnfvzjH+vYsWNau3atKisrVVBQ4HUDOTk5qqysVJ8+fRxjFRUVys7OVmRkpOLj4zVnzhydP3/e7XmeeeYZDRw4UJGRkYqNjfVo7jfffFNZWVm6+uqrvQqBb7zxhnr16qWOHTsqNTVVW7ZsuWzNzp07dfvttys8PFw9e/ZUYWHhZWsOHjyoe+65Rx07dtT111+vpUuXOr3/+OOPq7KyUt26dfOobwAAAABtG3mNvAYAAADANx4vmL3++usaMmSIkpOT9fLLL+vhhx/WZ599pvfff1/jxo1TRESETw1ERkbKbDarQ4cOkqSGhgZlZ2ervr5ee/bs0YYNG1RYWKi8vDy356mvr9eIESM0depUj+c+e/as7r77bi1ZssTjmj179uhnP/uZJk2apI8//ljDhg3TsGHD9Mknn7isKS8vV3Z2tgYPHiybzaaZM2dq8uTJ2rZtm8ua6upqZWVlqUePHtq/f7+ef/555efna926dY7PREVFyWw2KzQ01OP+AQAAAL9pCqCjnSOvXUBeAwAAAP4fozNaEOa1Dp5+8Oc//7mys7P15z//Wffff79CQlq0m6NL27dv16FDh/TOO+8oISFBaWlpWrRokebOnav8/HyFhYU1W7dgwQJJ8uiXgBeNHTtWknTs2DGPa1atWqUhQ4Zozpw5kqRFixapuLhYL730kstfbBYUFCg5OVnLly+XJPXu3VsffPCBVqxYIavV2mxNUVGR6uvrtX79eoWFhenWW2+VzWbTCy+8oClTpnjcLwAAAIC2j7x2AXkNAAAAgK88TlH/+te/9Oc//1n/8R//0WrhS5JKSkqUmpqqhIQEx5jValV1dbXKyspabV5PlZSUKDMz02nMarWqpKTE7zWDBg1yCpxWq1VHjhzRV1995WP3AAAAANoi8toF5DUAAAAAvvI4ScXHx7dmHw52u90pfElyvLbb7VekB3dc9eeuN1c11dXVqq2t9arm4nsAAACA4ZpMgXO0c+Q1OXogrwEAAAAyPqMFYV7zeEtGBKe6ujrV1dU5jTU2NSjExD76AAAAAGAk8hoAAAAQOFpvrw4fmc1mnThxwmns4muz2WxES05c9eeuN1c10dHRLh++7a/rsHjxYsXExDgd5TrscT0AAAAAXERec19z8T1PkdcAAACAwBFwC2YWi0WlpaU6efKkY6y4uFjR0dFKSUkxsLMLLBaLduzY4TRWXFwsi8Xi95pdu3bp3LlzTjW33HKLunTp4nG/8+bN05kzZ5yOZPXyuB4AAABwqSmADlwR5LXvashrAAAACGhGZ7QgzGs+LZidPn1ar7zyiubNm6eqqipJ0t///nd98cUXLW4oKytLKSkpGjt2rA4cOKBt27YpNzdX06ZNU3h4uMu6iooK2Ww2VVRUqKGhQTabTTabTTU1NS5rqqqqZLPZdOjQIUnSkSNHZLPZ3O45/9hjj2nr1q1avny5Dh8+rPz8fO3bt0/Tp093WfPoo4/q888/1xNPPKHDhw9r7dq1ev311zVr1iyXNaNHj1ZYWJgmTZqksrIyvfbaa1q1apVmz57tsqY54eHhio6OdjrY3gMAAABou8hr5DUAAAAA3vN6wezgwYO6+eabtWTJEi1btkynT5+WJL355puaN29eixsKDQ3V5s2bFRoaKovFop///OcaN26cFi5c6LYuLy9P6enpmj9/vmpqapSenq709HTt27fPZc1bb72l9PR0ZWdnS5JGjRql9PR0FRQUuKwZOHCgNm7cqHXr1qlv377605/+pE2bNqlPnz4ua5KTk/X222+ruLhYffv21fLly/XKK6/IarW6rImJidH27dtVXl6ufv366Ve/+pXy8vI0ZcoUt9cBAAAAuGKM/pViEP5isbWR18hrAAAAgCTjM1oQ5jVTU1OTV+1mZmbq9ttv19KlS9W5c2cdOHBAN9xwg/bs2aPRo0fr2LFjHp8rIyNDaWlpWrlypZdt4/uSkpI0c+ZMzZw506PP/6+QEa3bEFpk2/EDRrcABC1rYl+jWwAAvytufMPoFlxKenmZ0S04HJv6uNEtBATyWuAhr7UtX010vVUnjBc1+rjRLcCNq/7XP41uAQD8jrzmmWDJa17fYfbRRx/pF7/4xSXjXbt2dbs1hitr165VVFSUSktLva5t75599llFRUWpoqLC6FYAAAAABADyWuAgrwEAAADBpYO3BeHh4aqurr5k/LPPPtO1117r1bmKiopUW1srSerevbu3rbR7jz76qB5++GFJ8vraAwAAAC0WRFtrtBfktcBBXgMAAIChyGte83rB7MEHH9TChQv1+uuvS5JMJpMqKio0d+5cDR8+3Ktzde3a1dvp8T1xcXGKi4szug0AAAAAAYK8FjjIawAAAEBw8XpLxuXLl6umpkbx8fGqra3Vvffeq549e6pz58565plnWqNHAAAAAIAHyGsAAAAA4Buv7zCLiYlRcXGx/vrXv+rAgQOqqanR7bffrszMzNboDwAAAECgajIZ3QF+gLwGAAAAQBJ5zQdeL5j9/ve/18iRI3XXXXfprrvucozX19frj3/8o8aNG+fXBgEAAAAAniGvAQAAAIBvTE1NTV49+i00NFSVlZWKj493Gv/3v/+t+Ph4NTQ0+LVB+F+j/WajW4Ab1sS+RrcAN7YdP2B0C3CD/34AtEXFjW8Y3YJLSWuWG92Cw7FpvzK6hYBAXgt+Q3vPM7oFuHH+yFGjW4Abs/7vp0a3ADdW3Njb6BYAwO/Ia54Jlrzm9R1mTU1NMpkuvZXvX//6l2JiYvzSFAAAAIDAZ/Lqp3e4EshrAAAAACTymi88XjBLT0+XyWSSyWTSfffdpw4dvittaGhQeXm5hgwZ0ipNAgAAAABcI68BAAAAQMt4vGA2bNgwSZLNZpPValVUVJTjvbCwMCUlJWn48OF+bxAAAABAgOIXiwGDvAYAAADACXnNax4vmM2fP1+SlJSUpJEjR6pjx46t1hQAAAAAwHPkNQAAAABomRBvC8aPH++38JWRkeHYNsRms/nlnO1Jfn6+4/qtXLnS6HYAAAAAGIy8FjjIawAAAEBw8XrBrKGhQcuWLdOPfvQjmc1mxcXFOR3eysnJUWVlpfr06eMYmzFjhvr166fw8HClpaV5dJ5vv/1W06ZN09VXX62oqCgNHz5cJ06ccFvz5ptvKisrS1dffbVXIfCNN95Qr1691LFjR6WmpmrLli2Xrdm5c6duv/12hYeHq2fPniosLLxszcGDB3XPPfeoY8eOuv7667V06VKn9x9//HFVVlaqW7duHvUNAAAAoG0jr5HXAAAAAPjG6wWzBQsW6IUXXtDIkSN15swZzZ49Ww899JBCQkKUn5/vdQORkZEym81OD6WWpEceeUQjR470+DyzZs3SX/7yF73xxht6//33dfz4cT300ENua86ePau7775bS5Ys8XiePXv26Gc/+5kmTZqkjz/+WMOGDdOwYcP0ySefuKwpLy9Xdna2Bg8eLJvNppkzZ2ry5Mnatm2by5rq6mplZWWpR48e2r9/v55//nnl5+dr3bp1js9ERUXJbDYrNDTU4/4BAAAAtF3kNfIaAAAAAN94/Ayzi4qKivTb3/5W2dnZys/P189+9jPdeOONuu222/Thhx9qxowZLW5q9erVkqRTp07p4MGDl/38mTNn9Lvf/U4bN27Uj3/8Y0nSq6++qt69e+vDDz/UnXfe2Wzd2LFjJUnHjh3zuLdVq1ZpyJAhmjNnjiRp0aJFKi4u1ksvvaSCgoJmawoKCpScnKzly5dLknr37q0PPvhAK1askNVqbbamqKhI9fX1Wr9+vcLCwnTrrbfKZrPphRde0JQpUzzuFwAAAED7QV4jrwEAAADwjdd3mNntdqWmpkq68Iu5M2fOSJL+4z/+Q2+//bZ/u/PQ/v37de7cOWVmZjrGevXqpe7du6ukpMSvc5WUlDjNI0lWq9XtPL7WDBo0SGFhYU41R44c0VdffeVj9wAAAID/mJoC58AF5DXyGgAAACAZn9GCMa95vWDWrVs3VVZWSpJuvPFGbd++XZL00UcfKTw83L/dechutyssLEyxsbFO4wkJCbLb7X6fKyEhwat5XNVUV1ertrbWq5qL7wEAAADAD5HXyGsAAAAAfOP1low//elPtWPHDg0YMEC//OUv9fOf/1y/+93vVFFRoVmzZrVGj2iBuro61dXVOY1dVdeo8HCv10oBAAAABDjyWnBpLq81Np5XSIjXUR0AAABAC3n9r/DnnnvO8eeRI0eqR48e2rNnj2666SY98MADfm3OU2azWfX19Tp9+rTTrxZPnDghs9ns97lOnDjhNHa5eVzVREdHKyIiwquai+95avHixVqwYIHTWN6v4jT/8as9PgcAAADQrCaT0R3gB8hrwZ/Xbrz6Lt107T0enwMAAABoFnnNa17fZrRr1y6dP3/e8frOO+/U7NmzNXToUO3atcuvzXmqX79+uuqqq7Rjxw7H2JEjR1RRUSGLxeLXuSwWi9M8klRcXOx2Hl9rdu3apXPnzjnV3HLLLerSpYvH/c6bN09nzpxxOp78pef1AAAAAIIHeS3489qNV/v3mgAAAADwjNd3mA0ePFiVlZWKj493Gj9z5owGDx6shoaGFjd19OhR1dTUyG63q7a2VjabTZKUkpLi9FDli2JiYjRp0iTNnj1bcXFxio6O1i9/+UtZLBbdeeedLuepqqpSRUWFjh8/LulCaJMu/CLQ1a8CH3vsMd17771avny5srOz9cc//lH79u3TunXrXM7z6KOP6qWXXtITTzyhRx55RO+++65ef/11tw/dHj16tBYsWKBJkyZp7ty5+uSTT7Rq1SqtWLHCZU1zwsPDL3lWQeM3bMcIAAAAPwiihze3F+S14M9rbMcIAAAAvyCvec3rf4k3NTXJZLr0Vr5///vf6tSpk1+amjx5st5//33H6/T0dElSeXm5kpKSmq1ZsWKFQkJCNHz4cNXV1clqtWrt2rVu53nrrbc0ceJEx+tRo0ZJkubPn6/8/PxmawYOHKiNGzcqNzdXTz31lG666SZt2rRJffr0cTlPcnKy3n77bc2aNUurVq1St27d9Morr8hqtbqsiYmJ0fbt2zVt2jT169dP11xzjfLy8jRlyhS33wkAAABA+0VeI68BAAAA8I3HC2YPPfSQJMlkMmnChAlOv4JraGjQwYMHNXDgQL80tXPnTq9rOnbsqDVr1mjNmjUe10yYMEETJkzweq4RI0ZoxIgRXtVkZGTo448/9qrmtttu0+7du72qAQAAAND+kNe+Q14DAAAA4AuP9+aLiYlRTEyMmpqa1LlzZ8frmJgYmc1mTZkyRX/4wx+8bmDt2rWKiopSaWmp17Xt3bPPPquoqChVVFQY3QoAAADao6YAOto58lrgIa8BAADAUEZntCDMax7fYfbqq69KkpKSkvT444/7ZTuPoqIi1dbWSpK6d+/e4vO1N48++qgefvhhSdK1115rcDcAAAAAjEJeCzzkNQAAACC4eP0Ms/nz5zu9fv/993X27FlZLBZ16dLFq3N17drV2+nxPXFxcYqLizO6DQAAAAABgrwWOMhrAAAAQHDxeMFsyZIlqqmp0aJFiyRdeJj00KFDtX37dklSfHy8duzYoVtvvbV1OgUAAAAQUExBtLVGW0deAwAAAPB95DXvefwMs9dee019+vRxvP7Tn/6kXbt2affu3fryyy/Vv39/LViwoFWaBAAAAAC4Rl4DAAAAgJbxeMGsvLxct912m+P1li1b9J//+Z+66667FBcXp9zcXJWUlLRKkwAAAAAA18hrAAAAANAyHm/JeP78eYWHhztel5SUaObMmY7XiYmJ+vLLL/3aHNAebTt+wOgW4IY1sa/RLQAAEDjY4iNgkNfajvNxnYxuAW7cX3bG6BbgxoobexvdAgAAgYO85jWP7zC78cYbtWvXLklSRUWFPvvsMw0aNMjx/r/+9S9dffXV/u8QAAAAAOAWeQ0AAAAAWsbjO8ymTZum6dOna/fu3frwww9lsViUkpLieP/dd99Venp6qzQJAAAAIADxi8WAQV4DAAAA4IS85jWPF8xycnIUGhqqv/zlLxo0aJDmz5/v9P7x48f1yCOP+L1BAAAAAIB75DUAAAAAaBmPt2SUpEceeUR//vOf9fLLL8tsNju9t3btWv30pz/1avKMjAyZTCaZTCbZbDavaiHl5+c7rt/KlSuNbgcAAACAgchrgYW8BgAAAAQXrxbMWkNOTo4qKyvVp08fx9iMGTPUr18/hYeHKy0tzaPzrFu3ThkZGYqOjpbJZNLp06c9qluzZo2SkpLUsWNHDRgwQH/7298uW/PGG2+oV69e6tixo1JTU7Vly5bL1uzcuVO33367wsPD1bNnTxUWFl625uDBg7rnnnvUsWNHXX/99Vq6dKnT+48//rgqKyvVrVu3y54LAAAA8DdTU+AcaB3kNdfIawAAAAhkRme0YMxrhi+YRUZGymw2q0MH590hH3nkEY0cOdLj83zzzTcaMmSInnrqKY9rXnvtNc2ePVvz58/X3//+d/Xt21dWq1UnT550WbNnzx797Gc/06RJk/Txxx9r2LBhGjZsmD755BOXNeXl5crOztbgwYNls9k0c+ZMTZ48Wdu2bXNZU11draysLPXo0UP79+/X888/r/z8fK1bt87xmaioKJnNZoWGhnr8nQEAAADAU+S15pHXAAAAgLbH42eYXUmrV6+WJJ06dUoHDx70qGbmzJmSLvwy0FMvvPCCcnJyNHHiRElSQUGB3n77ba1fv15PPvlkszWrVq3SkCFDNGfOHEnSokWLVFxcrJdeekkFBQXN1hQUFCg5OVnLly+XJPXu3VsffPCBVqxYIavV2mxNUVGR6uvrtX79eoWFhenWW2+VzWbTCy+8oClTpnj8HQEAAADAn8hr5DUAAACgLTL8DjOj1NfXa//+/crMzHSMhYSEKDMzUyUlJS7rSkpKnGokyWq1tkrNoEGDFBYW5lRz5MgRffXVVy7rAAAAgCumyRQ4B9oU8hoAAADQQkZntCDMaz4vmB09elTbtm1TbW2tJKmpKYg2opT05ZdfqqGhQQkJCU7jCQkJstvtLuvsdrvfaqqrqx3Xz9Oai+8BAAAAgCvktZbXkNcAAACA9sXrLRn//e9/a+TIkXr33XdlMpn0j3/8QzfccIMmTZqkLl26OLaxQGCoq6tTXV2d09hVdY0KD2+3NxcCAAAAbRZ5Lbg0l9caG88rJCQgn54AAAAAtGler5rMmjVLHTp0UEVFhSIjIx3jI0eO1NatW/3aXGu65pprFBoaqhMnTjiNnzhxQmaz2WWd2Wz2W010dLQiIiK8qrn4nqcWL16smJgYp+O5F9kiBAAAAH7QFEAHJJHX2kJeK//XLo/rAQAAAJeMzmhBmNe8XjDbvn27lixZom7dujmN33TTTfrnP//pt8ZaW1hYmPr166cdO3Y4xhobG7Vjxw5ZLBaXdRaLxalGkoqLi1ulZteuXTp37pxTzS233KIuXbq4rPuhefPm6cyZM07Hk7/0vB4AAABA8CCvBX9eS+42yON6AAAAAP7j9YLZ2bNnnX6peFFVVZXCw8P90tTRo0dls9lkt9tVW1srm80mm82m+vp6lzV2u102m01Hjx6VJJWWlspms6mqqsplzezZs/Xb3/5WGzZs0KeffqqpU6fq7Nmzmjhxosuaxx57TFu3btXy5ct1+PBh5efna9++fZo+fbrLmkcffVSff/65nnjiCR0+fFhr167V66+/rlmzZrmsGT16tMLCwjRp0iSVlZXptdde06pVqzR79myXNc0JDw9XdHS008F2jAAAAPAHU1PgHLiAvBb8eY3tGAEAAOAPRme0YMxrXv9L/J577tHvf/97LVq0SJJkMpnU2NiopUuXavDgwX5pavLkyXr//fcdr9PT0yVJ5eXlSkpKaramoKBACxYscLweNOjCr/JeffVVTZgwodmakSNH6tSpU8rLy5PdbldaWpq2bt16ycObv2/gwIHauHGjcnNz9dRTT+mmm27Spk2b1KdPH5c1ycnJevvttzVr1iytWrVK3bp10yuvvCKr1eqyJiYmRtu3b9e0adPUr18/XXPNNcrLy9OUKVNc1gAAAABo38hr5DUAAAAAvjE1NTV5tb73ySef6L777tPtt9+ud999Vw8++KDKyspUVVWlv/71r7rxxhs9PldGRobS0tK0cuVKb/vG9yQlJWnmzJmaOXOmR59vtN/cug0BbZg1sa/RLQAA2pnixjeMbsGlm55bYXQLDv940vXdQO0JeS3weJvX/tddT7duQ2iR+1/hGXOBbMutMUa3AABoZ8hrngmWvOb13nx9+vTRZ599prvvvls/+clPdPbsWT300EP6+OOPvQpfF61du1ZRUVEqLS31ura9e/bZZxUVFaWKigqjWwEAAEB7ZPSDo4PwIdKtjbwWOMhrAAAAMJTRGS0I85pPm6PHxMTov/7rv1o8eVFRkWprayVJ3bt3b/H52ptHH31UDz/8sCTp2muvNbgbAAAAAIGAvBYYyGsAAABAcPF6wezgwYPNjptMJnXs2FHdu3f3+GHSXbt29XZ6fE9cXJzi4uKMbgMAAABAgCCvBQ7yGgAAABBcvF4wS0tLk8lkkiRdfPzZxdeSdNVVV2nkyJH6zW9+o44dO/qpTQAAAACBxhREW2u0F+Q1AAAAABJ5zRdeP8Psz3/+s2666SatW7dOBw4c0IEDB7Ru3Trdcsst2rhxo373u9/p3XffVW5ubmv0CwAAAABwgbwGAAAAAL7x+g6zZ555RqtWrZLVanWMpaamqlu3bvr1r3+tv/3tb+rUqZN+9atfadmyZX5tFgAAAADgGnkNAAAAAHzj9YJZaWmpevToccl4jx49VFpaKunCNiCVlZUt7w4AAABA4GKLj4BDXgMAAAAgibzmA68XzHr16qXnnntO69atU1hYmCTp3Llzeu6559SrVy9J0hdffKGEhAT/dgq/sSb2NboFAGgV244fMLoFAAAMRV5rA0r490wg23JrjNEtAEErpF8fo1uAG0em82zTQGUuvsroFoB2w+sFszVr1ujBBx9Ut27ddNttt0m68CvGhoYGbd68WZL0+eef6//7//4//3YKAAAAILDwi8WAQ14DAAAAIIm85gOvF8wGDhyo8vJyFRUV6bPPPpMkjRgxQqNHj1bnzp0lSWPHjvVvlwAAAACAyyKvAQAAAIBvvFowO3funHr16qXNmzfr0Ucfba2eAAAAAABeIq8BAAAAgO9CvPnwVVddpW+//dZvk2dkZMhkMslkMslms/ntvO1FYWGh4/rNnDnT6HYAAADQzpiaAucAeS3QkNcAAABgJKMzWjDmNa8WzCRp2rRpWrJkic6fP++XBnJyclRZWak+fb578GdFRYWys7MVGRmp+Ph4zZkz57LzVVVVacyYMYqOjlZsbKwmTZqkmpoatzXffvutpk2bpquvvlpRUVEaPny4Tpw44bbmzTffVFZWlq6++mqPg2NZWZmGDx+upKQkmUwmrVy58rI13377rSZMmKDU1FR16NBBw4YNu+QzI0eOVGVlpSwWy2XPBwAAAKDtI6+R1wAAAAD4xutnmH300UfasWOHtm/frtTUVHXq1Mnp/TfffNOr80VGRspsNjteNzQ0KDs7W2azWXv27FFlZaXGjRunq666Ss8++6zL84wZM0aVlZUqLi7WuXPnNHHiRE2ZMkUbN250WTNr1iy9/fbbeuONNxQTE6Pp06froYce0l//+leXNWfPntXdd9+thx9+WDk5OR59x2+++UY33HCDRowYoVmzZnlU09DQoIiICM2YMUP//d//3exnIiIiFBERobCwMI/OCQAAAKBtI6+R1wAAAAD4xusFs9jYWA0fPrw1epEkbd++XYcOHdI777yjhIQEpaWladGiRZo7d67y8/ObDRuffvqptm7dqo8++kj9+/eXJL344ou6//77tWzZMiUmJl5Sc+bMGf3ud7/Txo0b9eMf/1iS9Oqrr6p379768MMPdeeddzbb38UHZB87dszj73THHXfojjvukCQ9+eSTHtV06tRJL7/8siTpr3/9q06fPu3xfAAAAADaJ/IaeQ0AAACAb7xeMHv11Vdbow+HkpISpaamKiEhwTFmtVo1depUlZWVKT09vdma2NhYR/iSpMzMTIWEhGjv3r366U9/eknN/v37de7cOWVmZjrGevXqpe7du6ukpMRlAAMAAACAQEVeAwAAAADfeP0Ms9Zmt9udwpckx2u73e6yJj4+3mmsQ4cOiouLc1sTFham2NjYS+ZyVQMAAAAA7Rl5DQAAAEBb5fUdZpL0pz/9Sa+//roqKipUX1/v9N7f//53vzQG/6irq1NdXZ3TWGNTg0JMoQZ1BAAAgDajyegG0BzyWvAgrwEAAKDVkNe85vUdZqtXr9bEiROVkJCgjz/+WD/60Y909dVX6/PPP9fQoUNb3JDZbNaJEyecxi6+/v7Dpn9Yc/LkSaex8+fPq6qqym1NfX39JXvNnzhxwmVNMFq8eLFiYmKcjnIdNrotAAAAAK2AvBZcyGsAAABA4PB6wWzt2rVat26dXnzxRYWFhemJJ55QcXGxZsyYoTNnzrS4IYvFotLSUqdAVVxcrOjoaKWkpLisOX36tPbv3+8Ye/fdd9XY2KgBAwY0W9OvXz9dddVV2rFjh2PsyJEjqqiokMViafH3CBTz5s3TmTNnnI5k9TK6LQAAALQBpqbAOXABeS24kNcAAADQWozOaMGY17zekrGiokIDBw6UJEVEROjrr7+WJI0dO1Z33nmnXnrppRY1lJWVpZSUFI0dO1ZLly6V3W5Xbm6upk2bpvDw8GZrevfurSFDhignJ0cFBQU6d+6cpk+frlGjRikxMbHZmpiYGE2aNEmzZ89WXFycoqOj9ctf/lIWi8XtA6SrqqpUUVGh48ePS7oQ2qQLv4B09UvH+vp6HTp0yPHnL774QjabTVFRUerZs6fLuQ4dOqT6+npVVVXp66+/ls1mkySlpaW5rPmh8PDwS64b23sAAAAAbRN5jbwGAAAAwDdeL5iZzWZVVVWpR48e6t69uz788EP17dtX5eXlampq+VJhaGioNm/erKlTp8pisahTp04aP368Fi5c6LauqKhI06dP13333aeQkBANHz5cq1evdluzYsUKx2fr6upktVq1du1atzVvvfWWJk6c6Hg9atQoSdL8+fOVn5/fbM3x48eVnp7ueL1s2TItW7ZM9957r3bu3Olyrvvvv1///Oc/Ha8vnsMf1xkAAABA20NeI68BAAAA8I3XC2Y//vGP9dZbbyk9PV0TJ07UrFmz9Kc//Un79u3TQw895JemevTooS1btnhVExcXp40bN3pV07FjR61Zs0Zr1qzxuGbChAmaMGGCV/MkJSX5FJqOHTvmdQ0AAABwxbAuEHDIa+Q1AAAAQBJ5zQdeL5itW7dOjY2NkqRp06bp6quv1p49e/Tggw/qF7/4hdcNrF27Vq+88opKSkqUmprqdX17VlRUpF/84heqra31atsPAAAAAG0TeS1wkNcAAACA4OL1gllISIhCQkIcr0eNGuXY5sJbRUVFqq2tlSR1797dp3O0Zw8++KDjIdmxsbHGNgMAAADAcOS1wEFeAwAAAIKL1wtmknT69Gn97W9/08mTJx2/Xrxo3LhxHp+na9euvkyP/6dz587q3Lmz0W0AAACgvWKLj4BEXgsM5DUAAAAYirzmNa8XzP7yl79ozJgxqqmpUXR0tEwmk+M9k8nkVQADAAAAAPgPeQ0AAAAAfBNy+Y84+9WvfqVHHnlENTU1On36tL766ivHUVVV1Ro9AgAAAAA8QF4DAAAAAN94fYfZF198oRkzZigyMrI1+gEAAAAQJExs8RFwyGsAAAAAJPKaL7xeMLNardq3b59uuOGG1ugHAALatuMHjG4BblgT+xrdAlzgvx0AuDLIawDasw7duxndAtw4v/8To1uAG527pBjdAlxpija6A6Dd8GjB7K233nL8OTs7W3PmzNGhQ4eUmpqqq666yumzDz74oH87BAAAABCY+MViQCCvAQAAALgEec1rHi2YDRs27JKxhQsXXjJmMpnU0NDQ4qYAAAAAAJ4hrwEAAABAy3m0YNbY2NjafQAAAAAAfEBeAwAAAICW8/oZZgAAAAAg8RBpAAAAAAhU5DXvhXj6wXfffVcpKSmqrq6+5L0zZ87o1ltv1a5du7yaPCMjQyaTSSaTSTabzataSIWFhY7rN3PmTKPbAQAAAGAQ8lrgIa8BAAAAwcXjBbOVK1cqJydH0dHRl7wXExOjX/ziF1qxYoXXDeTk5KiyslJ9+vRxjFVUVCg7O1uRkZGKj4/XnDlzdP78ebfnqaqq0pgxYxQdHa3Y2FhNmjRJNTU1bmvWrVunjIwMRUdHy2Qy6fTp0x71vGbNGiUlJaljx44aMGCA/va3v7n9fFlZmYYPH66kpCSZTCatXLnysnN8++23mjBhglJTU9WhQ4dmn0swcuRIVVZWymKxeNQ3AAAAgLaJvPYd8hoAAAAAX3i8YHbgwAENGTLE5ftZWVnav3+/1w1ERkbKbDarQ4cLu0M2NDQoOztb9fX12rNnjzZs2KDCwkLl5eW5Pc+YMWNUVlam4uJibd68Wbt27dKUKVPc1nzzzTcaMmSInnrqKY/7fe211zR79mzNnz9ff//739W3b19ZrVadPHnS7Tw33HCDnnvuOZnNZo/maWhoUEREhGbMmKHMzMxmPxMRESGz2aywsDCP+wcAAAD8pimAjnaOvHYBeQ0AAAD4f4zOaEGY1zxeMDtx4oSuuuoql+936NBBp06danFD27dv16FDh/SHP/xBaWlpGjp0qBYtWqQ1a9aovr6+2ZpPP/1UW7du1SuvvKIBAwbo7rvv1osvvqg//vGPOn78uMu5Zs6cqSeffFJ33nmnx/298MILysnJ0cSJE5WSkqKCggJFRkZq/fr1LmvuuOMOPf/88xo1apTCw8M9mqdTp056+eWXlZOT43FoAwAAANA+kdcuIK8BAAAA8JXHC2Zdu3bVJ5984vL9gwcP6rrrrmtxQyUlJUpNTVVCQoJjzGq1qrq6WmVlZS5rYmNj1b9/f8dYZmamQkJCtHfv3hb3dFF9fb3279/v9AvCkJAQZWZmqqSkxG/zAAAAAGh93m7dd9Ef//hHmUymZrfhMwp5jbwGAAAAtCVG5DWPF8zuv/9+/frXv9a33357yXu1tbWaP3++/uM//sPrBn7Ibrc7hS9Jjtd2u91lTXx8vNNYhw4dFBcX57LGF19++aUaGhqa7c+f8wAAAABBwehtPVqwxYcvW/dJ0rFjx/T444/rnnvu8X7SVkReI68BAAAATozOaEGY1zxeMMvNzVVVVZVuvvlmLV26VP/zP/+j//mf/9GSJUt0yy23qKqqSv/1X//lUxNoPXV1daqurnY6GpsajG4LAAAAMJQvW/c1NDRozJgxWrBggW644YYr2O3lkdeCE3kNAAAAuJRRec3jBbOEhATt2bNHffr00bx58/TTn/5UP/3pT/XUU0+pT58++uCDDy75JZ8vzGazTpw44TR28bWrveHNZvMlK4vnz59XVVWVX/eTv+aaaxQaGtpsf4G6b/3ixYsVExPjdJTrsNFtAQAAoA0wNQXO4Q1ft+5buHCh4uPjNWnSJF8vWashr5HXAAAAgO8zOqMFY17zeMFMknr06KEtW7boyy+/1N69e/Xhhx/qyy+/1JYtW5ScnOxzE99nsVhUWlrqFKiKi4sVHR2tlJQUlzWnT5/W/v37HWPvvvuuGhsbNWDAAL/0JUlhYWHq16+fduzY4RhrbGzUjh07ZLFY/DaPP82bN09nzpxxOpLVy+i2AAAAAL9q7k6durq6Zj/ry9Z9H3zwgX73u9/pt7/9rd979xfyGnkNAAAACETBktc6+FLUpUsX3XHHHS2a2JWsrCylpKRo7NixWrp0qex2u3JzczVt2jSFh4c3W9O7d28NGTJEOTk5Kigo0Llz5zR9+nSNGjVKiYmJLuey2+2y2+06evSoJKm0tFSdO3dW9+7dFRcX12zN7NmzNX78ePXv318/+tGPtHLlSp09e1YTJ050OU99fb0OHTrk+PMXX3whm82mqKgo9ezZ02XdoUOHVF9fr6qqKn399dey2WySpLS0NJc1PxQeHn7JdQsxhXpcDwAAAASDxYsXa8GCBU5j8+fPV35+fovP/fXXX2vs2LH67W9/q2uuuabF52tt5DXyGgAAABBIgiWv+bRg1ppCQ0O1efNmTZ06VRaLRZ06ddL48eO1cOFCt3VFRUWaPn267rvvPoWEhGj48OFavXq125qCggKnv6RBgwZJkl599VVNmDCh2ZqRI0fq1KlTysvLk91uV1pamrZu3ep2e5Pjx48rPT3d8XrZsmVatmyZ7r33Xu3cudNl3f33369//vOfjtcXz9HU5MNT8gAAAAB/C6B/ls6bN0+zZ892GnO1gOPt1n3/9//+Xx07dkwPPPCAY6yxsVGS1KFDBx05ckQ33nhjS79CUCCvfYe8BgAAgIAWQP8sDZa8FnALZtJ3W4l4Iy4uThs3bvSqJj8/36cVzOnTp2v69Okefz4pKcmn0HTs2DGvawAAAID2qLk7dVz5/tZ9w4YNk/Td1n3N/Tu/V69eKi0tdRrLzc3V119/rVWrVun6669vcf/BhLx2AXkNAAAA8Eyw5DWvnmHWGtauXauoqKhLvhAur6ioSFFRUdq9e7fRrQAAAABBZfbs2frtb3+rDRs26NNPP9XUqVOdtu4bN26c5s2bJ0nq2LGj+vTp43TExsaqc+fO6tOnj8LCwoz8Kq2KvOY78hoAAADgG6PymqF3mBUVFam2tlaS1L17dyNbCUoPPvig4yHZsbGxxjYDAACA9ieAtvjw1uW27quoqFBIiOG/LzQUea1lyGsAAAAwFHnNa6YmNlhvd/5XyAijWwCC1rbjB4xuAW5YE/sa3QJc4L8dwHch5s+MbsGlW+euMLoFh7Ils4xuAfAL8hrguw7duxndAtw4X/Evo1uAG/Z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+      "text/plain": [
+       "<Figure size 1800x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Assuming processed_data is already defined and populated\n",
+    "plot_df = processed_data.copy()\n",
+    "plot_df['charge_state_vector_str'] = plot_df['charge_state_vector'].apply(lambda x: str(x))\n",
+    "\n",
+    "# Group by charge state vector string\n",
+    "grouped = plot_df.groupby('charge_state_vector_str')\n",
+    "\n",
+    "# Calculate mean of normalized intensity distribution\n",
+    "agg_intensity_dist_df = grouped['normalized_intensity_distribution'].apply(\n",
+    "    lambda x: np.mean(np.vstack(x), axis=0)).reset_index(drop=True)\n",
+    "\n",
+    "# Extract unique charge state vectors\n",
+    "unique_charge_state_vectors = grouped['charge_state_vector'].first().apply(pd.Series).reset_index(drop=True)\n",
+    "unique_charge_state_vectors.columns = [i for i in range(1, 7)]\n",
+    "\n",
+    "# Convert aggregated intensity distribution to DataFrame\n",
+    "agg_intensity_dist_df = pd.DataFrame(agg_intensity_dist_df.tolist(), columns=[i for i in range(1, 7)])\n",
+    "\n",
+    "# Charge state vectors as labels\n",
+    "ytick_labels = [str(vec) for vec in grouped['charge_state_vector'].first().index]\n",
+    "\n",
+    "# Function to sort charge state vectors\n",
+    "def sort_charge_state_vectors(row):\n",
+    "    values = row.values\n",
+    "    num_ones = sum(values)\n",
+    "    order = f\"{num_ones:02d}\" + \"\".join(map(str, values))\n",
+    "    return order\n",
+    "\n",
+    "# Sort the DataFrames\n",
+    "sorted_indices = unique_charge_state_vectors.apply(sort_charge_state_vectors, axis=1).sort_values().index\n",
+    "unique_charge_state_vectors = unique_charge_state_vectors.loc[sorted_indices]\n",
+    "agg_intensity_dist_df = agg_intensity_dist_df.loc[sorted_indices]\n",
+    "ytick_labels = [ytick_labels[i] for i in sorted_indices]\n",
+    "\n",
+    "plt.figure(figsize=(18, 8))\n",
+    "\n",
+    "# Plot charge state vector heatmap\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.heatmap(unique_charge_state_vectors, cmap='viridis', cbar=True, yticklabels=ytick_labels)\n",
+    "plt.title('Charge State Vector')\n",
+    "plt.xlabel('Charge State')\n",
+    "plt.ylabel('Charge State Vector')\n",
+    "\n",
+    "# Plot mean normalized intensity distribution heatmap\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.heatmap(agg_intensity_dist_df, cmap='viridis', cbar=True, yticklabels=ytick_labels)\n",
+    "plt.title('Mean Normalized Intensity Distribution')\n",
+    "plt.xlabel('Charge State')\n",
+    "plt.ylabel('Charge State Vector')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pcp_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/precursor_charge_state_dev/model_dev.ipynb b/notebooks/precursor_charge_state_dev/model_dev.ipynb
new file mode 100644
index 00000000..97420e30
--- /dev/null
+++ b/notebooks/precursor_charge_state_dev/model_dev.ipynb
@@ -0,0 +1,3758 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-08-16 14:44:42.484467: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
+      "2024-08-16 14:44:42.570758: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "2024-08-16 14:44:42.570780: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+      "2024-08-16 14:44:42.609395: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2024-08-16 14:44:42.635793: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    },
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'dlomix'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 8\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrandom\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# Models \u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdlomix\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mchargestate\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DominantChargeStatePredictor, ObservedChargeStatePredictor, ChargeStateDistributionPredictor\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# Constants\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdlomix\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconstants\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ALPHABET_UNMOD\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'dlomix'"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import tensorflow as tf\n",
+    "import pandas as pd \n",
+    "import numpy as np \n",
+    "import random\n",
+    "\n",
+    "# Models \n",
+    "from dlomix.models.chargestate import DominantChargeStatePredictor, ObservedChargeStatePredictor, ChargeStateDistributionPredictor\n",
+    "\n",
+    "# Constants\n",
+    "from dlomix.constants import ALPHABET_UNMOD\n",
+    "\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "from tensorflow.keras.callbacks import Callback\n",
+    "from tensorflow.keras.models import load_model\n",
+    "from tensorflow.keras.losses import binary_crossentropy\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
+    "from scipy.spatial.distance import cosine\n",
+    "from scipy.spatial.distance import cosine\n",
+    "from scipy.stats import entropy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create dummy data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    modified_sequence  precursor_intensity  max_intensity  avg_intensity  \\\n",
+      "0            AGFRQVCK         36196.371320   24319.484879    1517.503342   \n",
+      "1               CFPNR         19182.155002   11931.658496    2579.836566   \n",
+      "2  FNTATQHEYHCCTPTRMY         42095.180602    3259.879645    1349.622333   \n",
+      "3   CGPVIECQSYVVILTEI        101015.561318    1129.572852    2190.835968   \n",
+      "4       RQCPMFLGWQFHS         17428.230714    8495.865993     448.713829   \n",
+      "\n",
+      "  precursor_charge  charge_by_max_intensity charge_state_vector  \\\n",
+      "0                6                        2  [1, 0, 0, 1, 0, 1]   \n",
+      "1                4                        5  [0, 1, 1, 0, 0, 1]   \n",
+      "2                2                        1  [1, 1, 0, 0, 0, 0]   \n",
+      "3                6                        1  [1, 1, 1, 1, 0, 1]   \n",
+      "4                1                        1  [1, 0, 0, 1, 0, 1]   \n",
+      "\n",
+      "     one_hot_most_abundant_charge  \\\n",
+      "0  [0.0, 0.0, 0.0, 1.0, 0.0, 0.0]   \n",
+      "1  [0.0, 1.0, 0.0, 0.0, 0.0, 0.0]   \n",
+      "2  [0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   \n",
+      "3  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0]   \n",
+      "4  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0]   \n",
+      "\n",
+      "                   normalized_intensity_distribution  \n",
+      "0  [0.15078066308210442, 0.07508449834781106, 0.0...  \n",
+      "1  [0.43770826930017936, 0.07011928254284626, 0.1...  \n",
+      "2  [0.24889653251053998, 0.2059262824144272, 0.30...  \n",
+      "3  [0.2946234524639446, 0.10439225743579525, 0.02...  \n",
+      "4  [0.20629753240620344, 0.16707228468155383, 0.0...  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set the random seeds\n",
+    "np.random.seed(42)\n",
+    "random.seed(42)\n",
+    "\n",
+    "# Dummy Data Size\n",
+    "num_samples = 1000\n",
+    "amino_acids = \"ACDEFGHIKLMNPQRSTVWY\"\n",
+    "min_seq_len = 5\n",
+    "max_seq_len = 20\n",
+    "\n",
+    "# Generate random sequences\n",
+    "def generate_random_sequence(length):\n",
+    "    return \"\".join(random.choices(amino_acids, k=length))\n",
+    "\n",
+    "dummy_sequences = [generate_random_sequence(random.randint(min_seq_len, max_seq_len)) for _ in range(num_samples)]\n",
+    "\n",
+    "# Create dummy data\n",
+    "dummy_data = {\n",
+    "    'modified_sequence': dummy_sequences,\n",
+    "    'precursor_intensity': np.random.lognormal(mean=10, sigma=1, size=num_samples),\n",
+    "    'max_intensity': np.random.lognormal(mean=8, sigma=1.5, size=num_samples),\n",
+    "    'avg_intensity': np.random.lognormal(mean=8, sigma=1, size=num_samples),\n",
+    "    'precursor_charge': np.random.choice(['1', '2', '3', '4', '5', '6'], size=num_samples),\n",
+    "    'charge_by_max_intensity': np.random.randint(1, 6, size=num_samples),\n",
+    "    'charge_state_vector': [list(np.random.choice([0, 1], size=6)) for _ in range(num_samples)],\n",
+    "    'one_hot_most_abundant_charge': [list(np.eye(6)[np.random.randint(0, 6)]) for _ in range(num_samples)],\n",
+    "    'normalized_intensity_distribution': [list(np.random.dirichlet(np.ones(6))) for _ in range(num_samples)],\n",
+    "}\n",
+    "\n",
+    "dummy_df = pd.DataFrame(dummy_data)\n",
+    "print(dummy_df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# encode the sequences\n",
+    "dummy_df.modified_sequence = dummy_df.modified_sequence.apply(list).apply(lambda x: np.array([ALPHABET_UNMOD[aa] for aa in x]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# determine the maximum sequence length in the dummy-dataset\n",
+    "max_len = dummy_df.modified_sequence.apply(len).max()\n",
+    "max_len"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pad sequences to max length with 0s\n",
+    "dummy_df.modified_sequence = dummy_df.modified_sequence.apply(lambda x: np.pad(x, (0, max_len - len(x)), constant_values=0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Split dummy data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "precursor_charge\n",
+      "4    109\n",
+      "6    108\n",
+      "3    102\n",
+      "5     98\n",
+      "2     98\n",
+      "1     85\n",
+      "Name: count, dtype: int64\n",
+      "precursor_charge\n",
+      "4    37\n",
+      "6    36\n",
+      "3    34\n",
+      "2    32\n",
+      "5    32\n",
+      "1    29\n",
+      "Name: count, dtype: int64\n",
+      "precursor_charge\n",
+      "4    36\n",
+      "6    36\n",
+      "3    34\n",
+      "5    33\n",
+      "2    32\n",
+      "1    29\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Split the data into train, validation and test sets\n",
+    "dummy_train_df, dummy_test_df = train_test_split(dummy_df, test_size=0.2, stratify=dummy_df['precursor_charge'])\n",
+    "dummy_train_df, dummy_val_df = train_test_split(dummy_train_df, test_size=0.25, stratify=dummy_train_df['precursor_charge'])\n",
+    "\n",
+    "# Check distribution of the stratified column\n",
+    "print(dummy_train_df['precursor_charge'].value_counts())\n",
+    "print(dummy_val_df['precursor_charge'].value_counts())\n",
+    "print(dummy_test_df['precursor_charge'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>modified_sequence</th>\n",
+       "      <th>precursor_intensity</th>\n",
+       "      <th>max_intensity</th>\n",
+       "      <th>avg_intensity</th>\n",
+       "      <th>precursor_charge</th>\n",
+       "      <th>charge_by_max_intensity</th>\n",
+       "      <th>charge_state_vector</th>\n",
+       "      <th>one_hot_most_abundant_charge</th>\n",
+       "      <th>normalized_intensity_distribution</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[15, 14, 2, 13, 11, 5, 10, 6, 19, 14, 5, 7, 16...</td>\n",
+       "      <td>17428.230714</td>\n",
+       "      <td>8495.865993</td>\n",
+       "      <td>448.713829</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[1, 0, 0, 1, 0, 1]</td>\n",
+       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0]</td>\n",
+       "      <td>[0.20629753240620344, 0.16707228468155383, 0.0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>919</th>\n",
+       "      <td>[5, 13, 7, 15, 1, 9, 17, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
+       "      <td>26744.656969</td>\n",
+       "      <td>1468.305265</td>\n",
+       "      <td>10892.957755</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[0, 1, 0, 0, 0, 0]</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 1.0]</td>\n",
+       "      <td>[0.21908741505324741, 0.08152975019136677, 0.0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>471</th>\n",
+       "      <td>[14, 20, 18, 19, 8, 15, 17, 1, 20, 13, 17, 14,...</td>\n",
+       "      <td>2204.109464</td>\n",
+       "      <td>1194.312092</td>\n",
+       "      <td>31693.677582</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[0, 0, 0, 1, 1, 0]</td>\n",
+       "      <td>[0.0, 0.0, 1.0, 0.0, 0.0, 0.0]</td>\n",
+       "      <td>[0.19002376067759677, 0.0588641505747201, 0.13...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>654</th>\n",
+       "      <td>[8, 13, 3, 19, 19, 18, 13, 4, 11, 12, 14, 4, 1...</td>\n",
+       "      <td>288762.492297</td>\n",
+       "      <td>4952.886244</td>\n",
+       "      <td>1544.052058</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[1, 0, 1, 1, 0, 0]</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 1.0, 0.0, 0.0]</td>\n",
+       "      <td>[0.18419068909704223, 0.13271286637116517, 0.0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>518</th>\n",
+       "      <td>[2, 1, 10, 7, 10, 7, 8, 17, 17, 3, 3, 13, 16, ...</td>\n",
+       "      <td>24326.761154</td>\n",
+       "      <td>641.442639</td>\n",
+       "      <td>2368.945905</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[0, 0, 1, 1, 1, 0]</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 1.0, 0.0, 0.0]</td>\n",
+       "      <td>[0.22154311271089955, 0.12158967605324313, 0.1...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     modified_sequence  precursor_intensity  \\\n",
+       "4    [15, 14, 2, 13, 11, 5, 10, 6, 19, 14, 5, 7, 16...         17428.230714   \n",
+       "919  [5, 13, 7, 15, 1, 9, 17, 0, 0, 0, 0, 0, 0, 0, ...         26744.656969   \n",
+       "471  [14, 20, 18, 19, 8, 15, 17, 1, 20, 13, 17, 14,...          2204.109464   \n",
+       "654  [8, 13, 3, 19, 19, 18, 13, 4, 11, 12, 14, 4, 1...        288762.492297   \n",
+       "518  [2, 1, 10, 7, 10, 7, 8, 17, 17, 3, 3, 13, 16, ...         24326.761154   \n",
+       "\n",
+       "     max_intensity  avg_intensity precursor_charge  charge_by_max_intensity  \\\n",
+       "4      8495.865993     448.713829                1                        1   \n",
+       "919    1468.305265   10892.957755                4                        5   \n",
+       "471    1194.312092   31693.677582                6                        1   \n",
+       "654    4952.886244    1544.052058                6                        3   \n",
+       "518     641.442639    2368.945905                6                        1   \n",
+       "\n",
+       "    charge_state_vector    one_hot_most_abundant_charge  \\\n",
+       "4    [1, 0, 0, 1, 0, 1]  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0]   \n",
+       "919  [0, 1, 0, 0, 0, 0]  [0.0, 0.0, 0.0, 0.0, 0.0, 1.0]   \n",
+       "471  [0, 0, 0, 1, 1, 0]  [0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   \n",
+       "654  [1, 0, 1, 1, 0, 0]  [0.0, 0.0, 0.0, 1.0, 0.0, 0.0]   \n",
+       "518  [0, 0, 1, 1, 1, 0]  [0.0, 0.0, 0.0, 1.0, 0.0, 0.0]   \n",
+       "\n",
+       "                     normalized_intensity_distribution  \n",
+       "4    [0.20629753240620344, 0.16707228468155383, 0.0...  \n",
+       "919  [0.21908741505324741, 0.08152975019136677, 0.0...  \n",
+       "471  [0.19002376067759677, 0.0588641505747201, 0.13...  \n",
+       "654  [0.18419068909704223, 0.13271286637116517, 0.0...  \n",
+       "518  [0.22154311271089955, 0.12158967605324313, 0.1...  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dummy_train_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load preprocessed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "processed_data = pd.read_parquet(\"/mnt/c/Users/Florian/Desktop/Uni/MSc/FoPr/preprocessed_pcp_data.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>modified_sequence</th>\n",
+       "      <th>precursor_charge</th>\n",
+       "      <th>precursor_intensity</th>\n",
+       "      <th>charge_by_max_intensity</th>\n",
+       "      <th>max_intensity</th>\n",
+       "      <th>charge_by_avg_intensity</th>\n",
+       "      <th>avg_intensity</th>\n",
+       "      <th>one_hot_most_abundant_charge</th>\n",
+       "      <th>charge_state_vector</th>\n",
+       "      <th>normalized_intensity_distribution</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AAAAAAAAAAAAAAAAGLGLGP</td>\n",
+       "      <td>[2, 3, 2, 1, 1, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, ...</td>\n",
+       "      <td>[12746190.0, 2009914.0, 12746190.0, 791369.9, ...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>84118390.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4.312718e+07</td>\n",
+       "      <td>[0, 1, 0, 0, 0, 0]</td>\n",
+       "      <td>[1, 1, 1, 0, 0, 0]</td>\n",
+       "      <td>[0.0014942136737631886, 0.977159622342211, 0.0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AAAAAAAAAAAAAAAASAGGK</td>\n",
+       "      <td>[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]</td>\n",
+       "      <td>[5944787.0, 5944787.0, 8502878.0, 6425294.0, 8...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8824163.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7.635637e+06</td>\n",
+       "      <td>[0, 1, 0, 0, 0, 0]</td>\n",
+       "      <td>[0, 1, 0, 0, 0, 0]</td>\n",
+       "      <td>[0.0, 1.0, 0.0, 0.0, 0.0, 0.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AAAAAAAAAAAAAAAGAGAGAK</td>\n",
+       "      <td>[2, 2, 2, 2]</td>\n",
+       "      <td>[3067981.0, 3067981.0, 3067981.0, 3067981.0]</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3067981.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3.067981e+06</td>\n",
+       "      <td>[0, 1, 0, 0, 0, 0]</td>\n",
+       "      <td>[0, 1, 0, 0, 0, 0]</td>\n",
+       "      <td>[0.0, 1.0, 0.0, 0.0, 0.0, 0.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AAAAAAAAAAAAAAASGFAYPGTSER</td>\n",
+       "      <td>[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3]</td>\n",
+       "      <td>[11913300.0, 11913300.0, 11913300.0, 11913300....</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11913300.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8.273927e+06</td>\n",
+       "      <td>[0, 1, 0, 0, 0, 0]</td>\n",
+       "      <td>[0, 1, 1, 0, 0, 0]</td>\n",
+       "      <td>[0.0, 0.9879325512697509, 0.012067448730249095...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>AAAAAAAAAAAAAAG</td>\n",
+       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+       "      <td>[1573785.0, 4638296.0, 7934979.0, 2019978.0, 4...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>16573010.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8.562738e+06</td>\n",
+       "      <td>[1, 0, 0, 0, 0, 0]</td>\n",
+       "      <td>[1, 0, 0, 0, 0, 0]</td>\n",
+       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            modified_sequence  \\\n",
+       "0      AAAAAAAAAAAAAAAAGLGLGP   \n",
+       "1       AAAAAAAAAAAAAAAASAGGK   \n",
+       "2      AAAAAAAAAAAAAAAGAGAGAK   \n",
+       "3  AAAAAAAAAAAAAAASGFAYPGTSER   \n",
+       "4             AAAAAAAAAAAAAAG   \n",
+       "\n",
+       "                                    precursor_charge  \\\n",
+       "0  [2, 3, 2, 1, 1, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, ...   \n",
+       "1         [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]   \n",
+       "2                                       [2, 2, 2, 2]   \n",
+       "3      [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3]   \n",
+       "4  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...   \n",
+       "\n",
+       "                                 precursor_intensity  charge_by_max_intensity  \\\n",
+       "0  [12746190.0, 2009914.0, 12746190.0, 791369.9, ...                        2   \n",
+       "1  [5944787.0, 5944787.0, 8502878.0, 6425294.0, 8...                        2   \n",
+       "2       [3067981.0, 3067981.0, 3067981.0, 3067981.0]                        2   \n",
+       "3  [11913300.0, 11913300.0, 11913300.0, 11913300....                        2   \n",
+       "4  [1573785.0, 4638296.0, 7934979.0, 2019978.0, 4...                        1   \n",
+       "\n",
+       "   max_intensity  charge_by_avg_intensity  avg_intensity  \\\n",
+       "0     84118390.0                        2   4.312718e+07   \n",
+       "1      8824163.0                        2   7.635637e+06   \n",
+       "2      3067981.0                        2   3.067981e+06   \n",
+       "3     11913300.0                        2   8.273927e+06   \n",
+       "4     16573010.0                        1   8.562738e+06   \n",
+       "\n",
+       "  one_hot_most_abundant_charge charge_state_vector  \\\n",
+       "0           [0, 1, 0, 0, 0, 0]  [1, 1, 1, 0, 0, 0]   \n",
+       "1           [0, 1, 0, 0, 0, 0]  [0, 1, 0, 0, 0, 0]   \n",
+       "2           [0, 1, 0, 0, 0, 0]  [0, 1, 0, 0, 0, 0]   \n",
+       "3           [0, 1, 0, 0, 0, 0]  [0, 1, 1, 0, 0, 0]   \n",
+       "4           [1, 0, 0, 0, 0, 0]  [1, 0, 0, 0, 0, 0]   \n",
+       "\n",
+       "                   normalized_intensity_distribution  \n",
+       "0  [0.0014942136737631886, 0.977159622342211, 0.0...  \n",
+       "1                     [0.0, 1.0, 0.0, 0.0, 0.0, 0.0]  \n",
+       "2                     [0.0, 1.0, 0.0, 0.0, 0.0, 0.0]  \n",
+       "3  [0.0, 0.9879325512697509, 0.012067448730249095...  \n",
+       "4                     [1.0, 0.0, 0.0, 0.0, 0.0, 0.0]  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "processed_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 757820 entries, 0 to 758525\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column                             Non-Null Count   Dtype  \n",
+      "---  ------                             --------------   -----  \n",
+      " 0   modified_sequence                  757820 non-null  object \n",
+      " 1   precursor_charge                   757820 non-null  object \n",
+      " 2   precursor_intensity                757820 non-null  object \n",
+      " 3   charge_by_max_intensity            757820 non-null  int64  \n",
+      " 4   max_intensity                      757820 non-null  float64\n",
+      " 5   charge_by_avg_intensity            757820 non-null  int64  \n",
+      " 6   avg_intensity                      757820 non-null  float64\n",
+      " 7   one_hot_most_abundant_charge       757820 non-null  object \n",
+      " 8   charge_state_vector                757820 non-null  object \n",
+      " 9   normalized_intensity_distribution  757820 non-null  object \n",
+      "dtypes: float64(2), int64(2), object(6)\n",
+      "memory usage: 63.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "processed_data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remove UNIMOD sequences\n",
+    "processed_data = processed_data[~processed_data.modified_sequence.str.contains('UNIMOD')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# encode the sequences\n",
+    "processed_data.modified_sequence = processed_data.modified_sequence.apply(list).apply(lambda x: np.array([ALPHABET_UNMOD[aa] for aa in x]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "40"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# determine the maximum sequence length in the dummy-dataset\n",
+    "max_len = processed_data.modified_sequence.apply(len).max()\n",
+    "max_len"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pad sequences to max length with 0s\n",
+    "processed_data.modified_sequence = processed_data.modified_sequence.apply(lambda x: np.pad(x, (0, max_len - len(x)), constant_values=0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# drop all column exept modified_sequence and precursor_charge\n",
+    "processed_data = processed_data[['modified_sequence', 'charge_by_max_intensity', 'one_hot_most_abundant_charge', 'charge_state_vector', 'normalized_intensity_distribution']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "529065"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(processed_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>modified_sequence</th>\n",
+       "      <th>charge_by_max_intensity</th>\n",
+       "      <th>one_hot_most_abundant_charge</th>\n",
+       "      <th>charge_state_vector</th>\n",
+       "      <th>normalized_intensity_distribution</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>[0, 1, 0, 0, 0, 0]</td>\n",
+       "      <td>[1, 1, 1, 0, 0, 0]</td>\n",
+       "      <td>[0.0014942136737631886, 0.977159622342211, 0.0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>[0, 1, 0, 0, 0, 0]</td>\n",
+       "      <td>[0, 1, 0, 0, 0, 0]</td>\n",
+       "      <td>[0.0, 1.0, 0.0, 0.0, 0.0, 0.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>[0, 1, 0, 0, 0, 0]</td>\n",
+       "      <td>[0, 1, 0, 0, 0, 0]</td>\n",
+       "      <td>[0.0, 1.0, 0.0, 0.0, 0.0, 0.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>[0, 1, 0, 0, 0, 0]</td>\n",
+       "      <td>[0, 1, 1, 0, 0, 0]</td>\n",
+       "      <td>[0.0, 0.9879325512697509, 0.012067448730249095...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 6, ...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[1, 0, 0, 0, 0, 0]</td>\n",
+       "      <td>[1, 0, 0, 0, 0, 0]</td>\n",
+       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   modified_sequence  charge_by_max_intensity  \\\n",
+       "0  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...                        2   \n",
+       "1  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...                        2   \n",
+       "2  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...                        2   \n",
+       "3  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...                        2   \n",
+       "4  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 6, ...                        1   \n",
+       "\n",
+       "  one_hot_most_abundant_charge charge_state_vector  \\\n",
+       "0           [0, 1, 0, 0, 0, 0]  [1, 1, 1, 0, 0, 0]   \n",
+       "1           [0, 1, 0, 0, 0, 0]  [0, 1, 0, 0, 0, 0]   \n",
+       "2           [0, 1, 0, 0, 0, 0]  [0, 1, 0, 0, 0, 0]   \n",
+       "3           [0, 1, 0, 0, 0, 0]  [0, 1, 1, 0, 0, 0]   \n",
+       "4           [1, 0, 0, 0, 0, 0]  [1, 0, 0, 0, 0, 0]   \n",
+       "\n",
+       "                   normalized_intensity_distribution  \n",
+       "0  [0.0014942136737631886, 0.977159622342211, 0.0...  \n",
+       "1                     [0.0, 1.0, 0.0, 0.0, 0.0, 0.0]  \n",
+       "2                     [0.0, 1.0, 0.0, 0.0, 0.0, 0.0]  \n",
+       "3  [0.0, 0.9879325512697509, 0.012067448730249095...  \n",
+       "4                     [1.0, 0.0, 0.0, 0.0, 0.0, 0.0]  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "processed_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### majority model data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train set distribution:\n",
+      "charge_by_max_intensity\n",
+      "2    0.558872\n",
+      "3    0.318821\n",
+      "4    0.062008\n",
+      "1    0.052990\n",
+      "5    0.006807\n",
+      "6    0.000502\n",
+      "Name: proportion, dtype: float64\n",
+      "Training set shape: (370355, 40) (370355, 6)\n",
+      "--------------------------------------------------\n",
+      "Validation set distribution:\n",
+      "charge_by_max_intensity\n",
+      "2    0.558869\n",
+      "3    0.318819\n",
+      "4    0.062011\n",
+      "1    0.052995\n",
+      "5    0.006805\n",
+      "6    0.000501\n",
+      "Name: proportion, dtype: float64\n",
+      "Validation set shape: (105803, 40) (105803, 6)\n",
+      "--------------------------------------------------\n",
+      "Test set distribution:\n",
+      "charge_by_max_intensity\n",
+      "2    0.558867\n",
+      "3    0.318824\n",
+      "4    0.062014\n",
+      "1    0.052980\n",
+      "5    0.006804\n",
+      "6    0.000510\n",
+      "Name: proportion, dtype: float64\n",
+      "Test set shape: (52907, 40) (52907, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Features and labels for the majority prediction\n",
+    "majority_X = np.array(processed_data['modified_sequence'].tolist())\n",
+    "majority_y = np.array(processed_data['one_hot_most_abundant_charge'].tolist())\n",
+    "\n",
+    "# Dataframe for stratified split\n",
+    "data = pd.DataFrame({\n",
+    "    'X': list(majority_X),\n",
+    "    'y': list(majority_y),\n",
+    "    'charge_by_max_intensity': processed_data['charge_by_max_intensity']\n",
+    "})\n",
+    "\n",
+    "# train/test split with stratification\n",
+    "train_val_data, test_data = train_test_split(\n",
+    "    data,\n",
+    "    test_size=0.1,\n",
+    "    stratify=data['charge_by_max_intensity'],\n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "# Split the remaining data into train and validation \n",
+    "train_data, val_data = train_test_split(\n",
+    "    train_val_data,\n",
+    "    test_size=0.2222,\n",
+    "    stratify=train_val_data['charge_by_max_intensity'],\n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "# Extract features and labels for each split\n",
+    "majority_train_X = np.array(train_data['X'].tolist())\n",
+    "majority_train_y = np.array(train_data['y'].tolist())\n",
+    "\n",
+    "majority_val_X = np.array(val_data['X'].tolist())\n",
+    "majority_val_y = np.array(val_data['y'].tolist())\n",
+    "\n",
+    "majority_test_X = np.array(test_data['X'].tolist())\n",
+    "majority_test_y = np.array(test_data['y'].tolist())\n",
+    "\n",
+    "# Distribution across splits and shapes of the splits\n",
+    "print(f\"Train set distribution:\\n{train_data['charge_by_max_intensity'].value_counts(normalize=True)}\")\n",
+    "print(\"Training set shape:\", majority_train_X.shape, majority_train_y.shape)\n",
+    "print(\"-\"*50)\n",
+    "print(f\"Validation set distribution:\\n{val_data['charge_by_max_intensity'].value_counts(normalize=True)}\")\n",
+    "print(\"Validation set shape:\", majority_val_X.shape, majority_val_y.shape)\n",
+    "print(\"-\"*50)\n",
+    "print(f\"Test set distribution:\\n{test_data['charge_by_max_intensity'].value_counts(normalize=True)}\")\n",
+    "print(\"Test set shape:\", majority_test_X.shape, majority_test_y.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### oserved model data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train label distribution:\n",
+      "[0.11708496 0.78455806 0.41369767 0.09109098 0.01166718 0.00103144]\n",
+      "Training set shape: (370355, 40) (370355, 6)\n",
+      "--------------------------------------------------\n",
+      "Validation label distribution:\n",
+      "[0.11804013 0.78616863 0.41348544 0.09141518 0.01121896 0.00100186]\n",
+      "Validation set shape: (105803, 40) (105803, 6)\n",
+      "--------------------------------------------------\n",
+      "Test label distribution:\n",
+      "[0.11765929 0.783299   0.41487894 0.09165139 0.01145406 0.00100176]\n",
+      "Test set shape: (52907, 40) (52907, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extract observed_X and observed_y\n",
+    "observed_X = np.array(processed_data['modified_sequence'].tolist())\n",
+    "observed_y = np.array(processed_data['charge_state_vector'].tolist())\n",
+    "\n",
+    "# Create a DataFrame containing the features, labels, and stratification column\n",
+    "observed_data = pd.DataFrame({\n",
+    "    'X': list(observed_X),\n",
+    "    'y': list(observed_y),\n",
+    "    'charge_by_max_intensity': processed_data['charge_by_max_intensity']\n",
+    "})\n",
+    "\n",
+    "# train/test split with stratification\n",
+    "train_val_data_obs, test_data_obs = train_test_split(\n",
+    "    observed_data,\n",
+    "    test_size=0.1,\n",
+    "    stratify=observed_data['charge_by_max_intensity'],\n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "# Split the remaining data into train and validation \n",
+    "train_data_obs, val_data_obs = train_test_split(\n",
+    "    train_val_data_obs,\n",
+    "    test_size=0.2222,\n",
+    "    stratify=train_val_data_obs['charge_by_max_intensity'],\n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "# Extract features and labels for each split\n",
+    "observed_train_X = np.array(train_data_obs['X'].tolist())\n",
+    "observed_train_y = np.array(train_data_obs['y'].tolist())\n",
+    "\n",
+    "observed_val_X = np.array(val_data_obs['X'].tolist())\n",
+    "observed_val_y = np.array(val_data_obs['y'].tolist())\n",
+    "\n",
+    "observed_test_X = np.array(test_data_obs['X'].tolist())\n",
+    "observed_test_y = np.array(test_data_obs['y'].tolist())\n",
+    "\n",
+    "# Frequencies of each charge state in the dataset\n",
+    "def calculate_label_distribution(binarized_vectors):\n",
+    "    return np.mean(binarized_vectors, axis=0)\n",
+    "\n",
+    "# Calculate label distribution across training, validation, and test sets\n",
+    "train_label_distribution = calculate_label_distribution(observed_train_y)\n",
+    "val_label_distribution = calculate_label_distribution(observed_val_y)\n",
+    "test_label_distribution = calculate_label_distribution(observed_test_y)\n",
+    "\n",
+    "print(f\"Train label distribution:\\n{train_label_distribution}\")\n",
+    "print(\"Training set shape:\", observed_train_X.shape, observed_train_y.shape)\n",
+    "print(\"-\"*50)\n",
+    "print(f\"Validation label distribution:\\n{val_label_distribution}\")\n",
+    "print(\"Validation set shape:\", observed_val_X.shape, observed_val_y.shape)\n",
+    "print(\"-\"*50)\n",
+    "print(f\"Test label distribution:\\n{test_label_distribution}\")\n",
+    "print(\"Test set shape:\", observed_test_X.shape, observed_test_y.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### distribution model data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set shape: (370345, 40) (370345, 6)\n",
+      "Validation set shape: (105813, 40) (105813, 6)\n",
+      "Test set shape: (52907, 40) (52907, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "distribution_X = np.array(processed_data.modified_sequence.to_list())\n",
+    "distribution_y = np.array(processed_data['normalized_intensity_distribution'].tolist())\n",
+    "\n",
+    "# 70/20/10 split for training, validation and test\n",
+    "# First split: training and test+validation\n",
+    "distribution_X_train, distribution_X_temp, distribution_y_train, distribution_y_temp = train_test_split(distribution_X, distribution_y, test_size=0.3, random_state=42)\n",
+    "\n",
+    "# Second split: validation and test\n",
+    "distribution_X_val, distribution_X_test, distribution_y_val, distribution_y_test = train_test_split(distribution_X_temp, distribution_y_temp, test_size=1/3, random_state=42)\n",
+    "\n",
+    "# Check the shape of the splits\n",
+    "print(\"Training set shape:\", distribution_X_train.shape, distribution_y_train.shape)\n",
+    "print(\"Validation set shape:\", distribution_X_val.shape, distribution_y_val.shape)\n",
+    "print(\"Test set shape:\", distribution_X_test.shape, distribution_y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train label statistics:\n",
+      "{'mean': array([5.40789637e-02, 5.89132753e-01, 2.94278501e-01, 5.62228980e-02,\n",
+      "       5.80632792e-03, 4.80555624e-04]), 'median': array([0.       , 0.9881562, 0.       , 0.       , 0.       , 0.       ]), 'std_dev': array([0.22128287, 0.46047764, 0.41442661, 0.20871807, 0.06602074,\n",
+      "       0.01896303])}\n",
+      "Training set shape: (370355, 40) (370355, 6)\n",
+      "--------------------------------------------------\n",
+      "Validation label statistics:\n",
+      "{'mean': array([5.40803262e-02, 5.89320365e-01, 2.94164204e-01, 5.61885862e-02,\n",
+      "       5.79558962e-03, 4.50929266e-04]), 'median': array([0.        , 0.98814883, 0.        , 0.        , 0.        ,\n",
+      "       0.        ]), 'std_dev': array([0.22121924, 0.4602745 , 0.41412042, 0.20844984, 0.06642831,\n",
+      "       0.01828028])}\n",
+      "Validation set shape: (105803, 40) (105803, 6)\n",
+      "--------------------------------------------------\n",
+      "Test label statistics:\n",
+      "{'mean': array([5.39260536e-02, 5.88920043e-01, 2.94752981e-01, 5.62129452e-02,\n",
+      "       5.75463968e-03, 4.33336987e-04]), 'median': array([0.        , 0.98781312, 0.        , 0.        , 0.        ,\n",
+      "       0.        ]), 'std_dev': array([0.22111104, 0.46031441, 0.4143227 , 0.20853051, 0.06626251,\n",
+      "       0.01769372])}\n",
+      "Test set shape: (52907, 40) (52907, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extract distribution_X and distribution_y\n",
+    "distribution_X = np.array(processed_data['modified_sequence'].tolist())\n",
+    "distribution_y = np.array(processed_data['normalized_intensity_distribution'].tolist())\n",
+    "\n",
+    "# Create a DataFrame containing features, labels, and the stratification column\n",
+    "distribution_data = pd.DataFrame({\n",
+    "    'X': list(distribution_X),\n",
+    "    'y': list(distribution_y),\n",
+    "    'charge_by_max_intensity': processed_data['charge_by_max_intensity']\n",
+    "})\n",
+    "\n",
+    "# Initial train/test split with stratification over charge_by_max_intensity\n",
+    "train_val_data_dist, test_data_dist = train_test_split(\n",
+    "    distribution_data,\n",
+    "    test_size=0.1,\n",
+    "    stratify=distribution_data['charge_by_max_intensity'],\n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "# Split the remaining data into train and validation sets\n",
+    "train_data_dist, val_data_dist = train_test_split(\n",
+    "    train_val_data_dist,\n",
+    "    test_size=0.2222,\n",
+    "    stratify=train_val_data_dist['charge_by_max_intensity'],\n",
+    "    random_state=42\n",
+    ")\n",
+    "\n",
+    "# Extract features and labels for each split\n",
+    "distribution_train_X = np.array(train_data_dist['X'].tolist())\n",
+    "distribution_train_y = np.array(train_data_dist['y'].tolist())\n",
+    "\n",
+    "distribution_val_X = np.array(val_data_dist['X'].tolist())\n",
+    "distribution_val_y = np.array(val_data_dist['y'].tolist())\n",
+    "\n",
+    "distribution_test_X = np.array(test_data_dist['X'].tolist())\n",
+    "distribution_test_y = np.array(test_data_dist['y'].tolist())\n",
+    "\n",
+    "# Function to calculate summary statistics for continuous vectors\n",
+    "def calculate_summary_statistics(continuous_vectors):\n",
+    "    return {\n",
+    "        'mean': np.mean(continuous_vectors, axis=0),\n",
+    "        'median': np.median(continuous_vectors, axis=0),\n",
+    "        'std_dev': np.std(continuous_vectors, axis=0)\n",
+    "    }\n",
+    "\n",
+    "# Calculate statistics across training, validation, and test sets\n",
+    "train_summary_stats = calculate_summary_statistics(distribution_train_y)\n",
+    "val_summary_stats = calculate_summary_statistics(distribution_val_y)\n",
+    "test_summary_stats = calculate_summary_statistics(distribution_test_y)\n",
+    "\n",
+    "# Display summary statistics\n",
+    "print(f\"Train label statistics:\\n{train_summary_stats}\")\n",
+    "print(\"Training set shape:\", distribution_train_X.shape, distribution_train_y.shape)\n",
+    "print(\"-\" * 50)\n",
+    "\n",
+    "print(f\"Validation label statistics:\\n{val_summary_stats}\")\n",
+    "print(\"Validation set shape:\", distribution_val_X.shape, distribution_val_y.shape)\n",
+    "print(\"-\" * 50)\n",
+    "\n",
+    "print(f\"Test label statistics:\\n{test_summary_stats}\")\n",
+    "print(\"Test set shape:\", distribution_test_X.shape, distribution_test_y.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "notebookRunGroups": {
+     "groupValue": "1"
+    }
+   },
+   "source": [
+    "## TASK MODELS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-06-12 13:32:24.835951: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
+      "Your kernel may have been built without NUMA support.\n",
+      "2024-06-12 13:32:24.942204: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
+      "Your kernel may have been built without NUMA support.\n",
+      "2024-06-12 13:32:24.942243: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
+      "Your kernel may have been built without NUMA support.\n",
+      "2024-06-12 13:32:24.947622: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
+      "Your kernel may have been built without NUMA support.\n",
+      "2024-06-12 13:32:24.947658: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
+      "Your kernel may have been built without NUMA support.\n",
+      "2024-06-12 13:32:24.947675: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
+      "Your kernel may have been built without NUMA support.\n",
+      "2024-06-12 13:32:25.133105: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
+      "Your kernel may have been built without NUMA support.\n",
+      "2024-06-12 13:32:25.133341: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
+      "Your kernel may have been built without NUMA support.\n",
+      "2024-06-12 13:32:25.133349: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2022] Could not identify NUMA node of platform GPU id 0, defaulting to 0.  Your kernel may not have been built with NUMA support.\n",
+      "2024-06-12 13:32:25.133375: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:887] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node\n",
+      "Your kernel may have been built without NUMA support.\n",
+      "2024-06-12 13:32:25.133517: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6701 MB memory:  -> device: 0, name: NVIDIA GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "optimizer = Adam(learning_rate=0.0001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "majority_charge_model = DominantChargeStatePredictor()\n",
+    "majority_charge_model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_charges_model = ObservedChargeStatePredictor()\n",
+    "observed_charges_model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "charge_distribution_model_linear = ChargeStateDistributionPredictor()\n",
+    "charge_distribution_model_linear.compile(optimizer=optimizer, loss='mean_absolute_error', metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Regularization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Early stopping\n",
+    "from tensorflow.keras.callbacks import EarlyStopping\n",
+    "early_stopping = EarlyStopping(monitor='val_loss', patience=5, verbose=1, mode='min', restore_best_weights=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.callbacks import Callback\n",
+    "\n",
+    "class LearningRateTracker(Callback):\n",
+    "    def on_epoch_end(self, epoch, logs=None):\n",
+    "        logs = logs or {}\n",
+    "        optimizer = self.model.optimizer\n",
+    "        lr = tf.keras.backend.get_value(optimizer.learning_rate)\n",
+    "        logs['lr'] = lr\n",
+    "\n",
+    "lr_tracker = LearningRateTracker()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_metrics(y_true, y_pred):\n",
+    "    mse = mean_squared_error(y_true, y_pred) # mean squared error\n",
+    "    mae = mean_absolute_error(y_true, y_pred) # mean absolute error\n",
+    "    cos_sim = np.mean([1 - cosine(y_t, y_p) for y_t, y_p in zip(y_true, y_pred)]) # cosine similarity\n",
+    "    \n",
+    "    return {\n",
+    "        \"mse\": mse,\n",
+    "        \"mae\": mae,\n",
+    "        \"cosine_similarity\": cos_sim\n",
+    "    }\n",
+    "\n",
+    "class Task3Metrics(Callback):\n",
+    "    def on_epoch_end(self, epoch, logs=None):\n",
+    "        val_outputs = self.model.predict(self.validation_data[0])\n",
+    "        val_targets = self.validation_data[1]\n",
+    "        \n",
+    "        metrics = calculate_metrics(val_targets, val_outputs)\n",
+    "        \n",
+    "        print(f\"Epoch {epoch+1}, \"\n",
+    "              f\"MSE: {metrics['mse']}, \"\n",
+    "              f\"MAE: {metrics['mae']}, \"\n",
+    "              f\"Cosine Similarity: {metrics['cosine_similarity']}, EMD: {metrics['emd']}\")\n",
+    "\n",
+    "class ModifiedTask3Metrics(Task3Metrics):\n",
+    "    def __init__(self, validation_data):\n",
+    "        super().__init__()\n",
+    "        self.validation_data = validation_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## -----------------------------------------------------------------------------------------------------------------------------------------------------\n",
+    "# TRAINING"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### TASK 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(40,)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "majority_train_X[0, :].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-06-07 12:51:44.327498: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8907\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tf.Tensor: shape=(1, 6), dtype=float32, numpy=\n",
+       "array([[0.16687633, 0.16652647, 0.16699576, 0.16658245, 0.16601186,\n",
+       "        0.16700715]], dtype=float32)>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "majority_charge_model(majority_train_X[0].reshape(1, -1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "majority_charge_model_history = majority_charge_model.fit(majority_train_X, majority_train_y, validation_data=(majority_val_X, majority_val_y), epochs=50, batch_size=32,  callbacks=[lr_tracker])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = observed_charges_model(observed_train_X[0].reshape(1, -1)).numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.13334474, 0.794806  , 0.37976646, 0.06208049, 0.00831781,\n",
+       "        0.00132354]], dtype=float32)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.794806], dtype=float32)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions[predictions >= 0.5] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 0, 0, 0, 0])"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "observed_train_y[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0]), array([1]))"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.where(predictions > 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/root/miniforge3/envs/pcp_env/lib/python3.11/site-packages/keras/src/backend.py:5818: UserWarning: \"`binary_crossentropy` received `from_logits=True`, but the `output` argument was produced by a Sigmoid activation and thus does not represent logits. Was this intended?\n",
+      "  output, from_logits = _get_logits(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tf.Tensor: shape=(1,), dtype=float32, numpy=array([0.69167405], dtype=float32)>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "binary_crossentropy(observed_train_y[0].reshape(1, -1), observed_charges_model(observed_train_X[0].reshape(1, -1)), from_logits=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<tf.Tensor: shape=(), dtype=float64, numpy=0.665368962631303>"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.array([0, 1, 1, 0, 0,0])\n",
+    "y = np.array([0.1,0.9,0.9,0.4,0.2,0.3])\n",
+    "y_p = np.where(y >= 0.5, 1, 0)\n",
+    "binary_crossentropy(x, y, from_logits=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 1, 0, 0, 0])"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### TASK 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_charges_model_history = observed_charges_model.fit(observed_train_X, observed_train_y, validation_data=(observed_val_X, observed_val_y), epochs=25, batch_size=32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"observed_charge_state_predictor\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " embedding_1 (Embedding)     multiple                  352       \n",
+      "                                                                 \n",
+      " sequential_2 (Sequential)   (None, 40, 512)           1996800   \n",
+      "                                                                 \n",
+      " attention_layer_1 (Attenti  multiple                  552       \n",
+      " onLayer)                                                        \n",
+      "                                                                 \n",
+      " sequential_3 (Sequential)   (None, 512)               262656    \n",
+      "                                                                 \n",
+      " dense_3 (Dense)             multiple                  3078      \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 2263438 (8.63 MB)\n",
+      "Trainable params: 2263438 (8.63 MB)\n",
+      "Non-trainable params: 0 (0.00 Byte)\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "observed_charges_model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### TASK 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-06-12 13:32:54.541643: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8907\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tf.Tensor: shape=(1, 6), dtype=float32, numpy=\n",
+       "array([[-0.00569904, -0.00055099, -0.00276928, -0.00384324, -0.00343191,\n",
+       "         0.00470454]], dtype=float32)>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "charge_distribution_model_linear(distribution_train_X[0].reshape(1, -1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/25\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-06-05 20:51:19.583524: I external/local_xla/xla/service/service.cc:168] XLA service 0x7ff2b41cb030 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
+      "2024-06-05 20:51:19.583551: I external/local_xla/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce GTX 1080, Compute Capability 6.1\n",
+      "2024-06-05 20:51:19.594950: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "I0000 00:00:1717613479.660135  348435 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11574/11574 [==============================] - 215s 18ms/step - loss: 0.0570 - accuracy: 0.8660 - val_loss: 0.0390 - val_accuracy: 0.8919\n",
+      "Epoch 2/25\n",
+      "11574/11574 [==============================] - 227s 20ms/step - loss: 0.0403 - accuracy: 0.8975 - val_loss: 0.0330 - val_accuracy: 0.9091\n",
+      "Epoch 3/25\n",
+      "11574/11574 [==============================] - 244s 21ms/step - loss: 0.0365 - accuracy: 0.9077 - val_loss: 0.0319 - val_accuracy: 0.9158\n",
+      "Epoch 4/25\n",
+      "11574/11574 [==============================] - 230s 20ms/step - loss: 0.0346 - accuracy: 0.9126 - val_loss: 0.0303 - val_accuracy: 0.9193\n",
+      "Epoch 5/25\n",
+      "11574/11574 [==============================] - 204s 18ms/step - loss: 0.0331 - accuracy: 0.9168 - val_loss: 0.0294 - val_accuracy: 0.9204\n",
+      "Epoch 6/25\n",
+      "11574/11574 [==============================] - 206s 18ms/step - loss: 0.0322 - accuracy: 0.9196 - val_loss: 0.0289 - val_accuracy: 0.9240\n",
+      "Epoch 7/25\n",
+      "11574/11574 [==============================] - 204s 18ms/step - loss: 0.0316 - accuracy: 0.9207 - val_loss: 0.0287 - val_accuracy: 0.9245\n",
+      "Epoch 8/25\n",
+      "11574/11574 [==============================] - 195s 17ms/step - loss: 0.0311 - accuracy: 0.9221 - val_loss: 0.0281 - val_accuracy: 0.9239\n",
+      "Epoch 9/25\n",
+      "11574/11574 [==============================] - 189s 16ms/step - loss: 0.0306 - accuracy: 0.9230 - val_loss: 0.0273 - val_accuracy: 0.9276\n",
+      "Epoch 10/25\n",
+      "11574/11574 [==============================] - 184s 16ms/step - loss: 0.0303 - accuracy: 0.9242 - val_loss: 0.0273 - val_accuracy: 0.9271\n",
+      "Epoch 11/25\n",
+      "11574/11574 [==============================] - 184s 16ms/step - loss: 0.0301 - accuracy: 0.9250 - val_loss: 0.0280 - val_accuracy: 0.9260\n",
+      "Epoch 12/25\n",
+      "11574/11574 [==============================] - 191s 16ms/step - loss: 0.0298 - accuracy: 0.9259 - val_loss: 0.0274 - val_accuracy: 0.9278\n",
+      "Epoch 13/25\n",
+      "11574/11574 [==============================] - 189s 16ms/step - loss: 0.0296 - accuracy: 0.9261 - val_loss: 0.0265 - val_accuracy: 0.9293\n",
+      "Epoch 14/25\n",
+      "11574/11574 [==============================] - 186s 16ms/step - loss: 0.0294 - accuracy: 0.9269 - val_loss: 0.0269 - val_accuracy: 0.9297\n",
+      "Epoch 15/25\n",
+      "11574/11574 [==============================] - 183s 16ms/step - loss: 0.0292 - accuracy: 0.9270 - val_loss: 0.0263 - val_accuracy: 0.9294\n",
+      "Epoch 16/25\n",
+      "11574/11574 [==============================] - 185s 16ms/step - loss: 0.0290 - accuracy: 0.9279 - val_loss: 0.0272 - val_accuracy: 0.9260\n",
+      "Epoch 17/25\n",
+      "11574/11574 [==============================] - 186s 16ms/step - loss: 0.0288 - accuracy: 0.9281 - val_loss: 0.0266 - val_accuracy: 0.9304\n",
+      "Epoch 18/25\n",
+      "11574/11574 [==============================] - 182s 16ms/step - loss: 0.0286 - accuracy: 0.9288 - val_loss: 0.0271 - val_accuracy: 0.9270\n",
+      "Epoch 19/25\n",
+      "11574/11574 [==============================] - 183s 16ms/step - loss: 0.0285 - accuracy: 0.9293 - val_loss: 0.0262 - val_accuracy: 0.9310\n",
+      "Epoch 20/25\n",
+      "11574/11574 [==============================] - 182s 16ms/step - loss: 0.0284 - accuracy: 0.9295 - val_loss: 0.0263 - val_accuracy: 0.9308\n",
+      "Epoch 21/25\n",
+      "11574/11574 [==============================] - 183s 16ms/step - loss: 0.0282 - accuracy: 0.9298 - val_loss: 0.0263 - val_accuracy: 0.9302\n",
+      "Epoch 22/25\n",
+      "11574/11574 [==============================] - 182s 16ms/step - loss: 0.0281 - accuracy: 0.9302 - val_loss: 0.0257 - val_accuracy: 0.9305\n",
+      "Epoch 23/25\n",
+      "11574/11574 [==============================] - 183s 16ms/step - loss: 0.0279 - accuracy: 0.9307 - val_loss: 0.0260 - val_accuracy: 0.9298\n",
+      "Epoch 24/25\n",
+      "11574/11574 [==============================] - 182s 16ms/step - loss: 0.0278 - accuracy: 0.9311 - val_loss: 0.0266 - val_accuracy: 0.9294\n",
+      "Epoch 25/25\n",
+      "11574/11574 [==============================] - 183s 16ms/step - loss: 0.0276 - accuracy: 0.9317 - val_loss: 0.0257 - val_accuracy: 0.9318\n"
+     ]
+    }
+   ],
+   "source": [
+    "charge_distribution_model_history = charge_distribution_model_linear.fit(distribution_train_X, distribution_train_y, validation_data=(distribution_val_X, distribution_val_y), epochs=25, batch_size=32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: charge_distribution_model_linear/assets\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: charge_distribution_model_linear/assets\n"
+     ]
+    }
+   ],
+   "source": [
+    "# save model \n",
+    "charge_distribution_model_linear.save(\"charge_distribution_model_linear\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load model\n",
+    "charge_distribution_model_linear = load_model(\"/mnt/c/Users/Florian/Desktop/Uni/MSc/FoPr/dlomix/charge_distribution_model_linear\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1654/1654 [==============================] - 11s 6ms/step - loss: 0.0261 - mean_absolute_error: 0.0261\n",
+      "Test MAE: 0.026057051494717598\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.metrics import MeanAbsoluteError\n",
+    "# evaluation with MAE \n",
+    "charge_distribution_model_linear.compile(optimizer=optimizer, loss='mean_absolute_error')\n",
+    "results  = charge_distribution_model_linear.evaluate(distribution_test_X, distribution_test_y)\n",
+    "print(f\"Test MAE: {results[0]}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "holdout = pd.read_parquet(\"/mnt/c/Users/Florian/Desktop/Uni/MSc/FoPr/data/holdout/test-00000-of-00001.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>modified_sequence</th>\n",
+       "      <th>raw_file</th>\n",
+       "      <th>scan_number</th>\n",
+       "      <th>charge_by_max_intensity</th>\n",
+       "      <th>one_hot_most_abundant_charge</th>\n",
+       "      <th>charge_state_vector</th>\n",
+       "      <th>normalized_intensity_distribution</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[]-AAAAAAAK[UNIMOD:737]NK[UNIMOD:737][UNIMOD:1...</td>\n",
+       "      <td>[02526_BA12-TMT_TUM_mod_ubi_12_01_01-TMT1-1h-R3]</td>\n",
+       "      <td>[10609]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0.0, 0.0, 1.0, 0.0, 0.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[]-AAAGEDYK[UNIMOD:737]-[]</td>\n",
+       "      <td>[02526_BC5-TMT_TUM_mod_ubi_29_01_01-TMT1-1h-R3]</td>\n",
+       "      <td>[7709]</td>\n",
+       "      <td>2</td>\n",
+       "      <td>[0, 1, 0, 0, 0]</td>\n",
+       "      <td>[0, 1, 0, 0, 0]</td>\n",
+       "      <td>[0.0, 1.0, 0.0, 0.0, 0.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[]-AAAM[UNIMOD:35]ANNLQK[UNIMOD:737][UNIMOD:12...</td>\n",
+       "      <td>[02526_BE4-TMT_TUM_mod_ubi_52_01_01-TMT1-1h-R3]</td>\n",
+       "      <td>[4432]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0.0, 0.0, 1.0, 0.0, 0.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[]-AAEEPSK[UNIMOD:737]VEEK[UNIMOD:737][UNIMOD:...</td>\n",
+       "      <td>[02526_BC3-TMT_TUM_mod_ubi_27_01_01-TMT1-1h-R3...</td>\n",
+       "      <td>[9980, 9983, 9915]</td>\n",
+       "      <td>4</td>\n",
+       "      <td>[0, 0, 0, 1, 0]</td>\n",
+       "      <td>[0, 0, 0, 1, 0]</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 1.0, 0.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[]-AAEQYTPK[UNIMOD:737][UNIMOD:121]AK[UNIMOD:7...</td>\n",
+       "      <td>[02526_BA12-TMT_TUM_mod_ubi_12_01_01-TMT1-1h-R...</td>\n",
+       "      <td>[15083, 15188]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0, 1, 1, 0, 0]</td>\n",
+       "      <td>[0.0, 0.09967955495951202, 0.900320445040488, ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   modified_sequence  \\\n",
+       "0  []-AAAAAAAK[UNIMOD:737]NK[UNIMOD:737][UNIMOD:1...   \n",
+       "1                         []-AAAGEDYK[UNIMOD:737]-[]   \n",
+       "2  []-AAAM[UNIMOD:35]ANNLQK[UNIMOD:737][UNIMOD:12...   \n",
+       "3  []-AAEEPSK[UNIMOD:737]VEEK[UNIMOD:737][UNIMOD:...   \n",
+       "4  []-AAEQYTPK[UNIMOD:737][UNIMOD:121]AK[UNIMOD:7...   \n",
+       "\n",
+       "                                            raw_file         scan_number  \\\n",
+       "0   [02526_BA12-TMT_TUM_mod_ubi_12_01_01-TMT1-1h-R3]             [10609]   \n",
+       "1    [02526_BC5-TMT_TUM_mod_ubi_29_01_01-TMT1-1h-R3]              [7709]   \n",
+       "2    [02526_BE4-TMT_TUM_mod_ubi_52_01_01-TMT1-1h-R3]              [4432]   \n",
+       "3  [02526_BC3-TMT_TUM_mod_ubi_27_01_01-TMT1-1h-R3...  [9980, 9983, 9915]   \n",
+       "4  [02526_BA12-TMT_TUM_mod_ubi_12_01_01-TMT1-1h-R...      [15083, 15188]   \n",
+       "\n",
+       "   charge_by_max_intensity one_hot_most_abundant_charge charge_state_vector  \\\n",
+       "0                        3              [0, 0, 1, 0, 0]     [0, 0, 1, 0, 0]   \n",
+       "1                        2              [0, 1, 0, 0, 0]     [0, 1, 0, 0, 0]   \n",
+       "2                        3              [0, 0, 1, 0, 0]     [0, 0, 1, 0, 0]   \n",
+       "3                        4              [0, 0, 0, 1, 0]     [0, 0, 0, 1, 0]   \n",
+       "4                        3              [0, 0, 1, 0, 0]     [0, 1, 1, 0, 0]   \n",
+       "\n",
+       "                   normalized_intensity_distribution  \n",
+       "0                          [0.0, 0.0, 1.0, 0.0, 0.0]  \n",
+       "1                          [0.0, 1.0, 0.0, 0.0, 0.0]  \n",
+       "2                          [0.0, 0.0, 1.0, 0.0, 0.0]  \n",
+       "3                          [0.0, 0.0, 0.0, 1.0, 0.0]  \n",
+       "4  [0.0, 0.09967955495951202, 0.900320445040488, ...  "
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "holdout.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42270"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(holdout)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "\n",
+    "def clean_sequence(sequence):\n",
+    "    # Remove '[]-', '-[]' patterns\n",
+    "    sequence = re.sub(r'\\[\\]-', '', sequence)\n",
+    "    sequence = re.sub(r'-\\[\\]', '', sequence)\n",
+    "    \n",
+    "    # Remove '[UNIMOD:<number>]' with optional '-'\n",
+    "    sequence = re.sub(r'-?\\[UNIMOD:\\d+\\]-?', '', sequence)\n",
+    "    \n",
+    "    return sequence\n",
+    "\n",
+    "# Apply the function to the 'modified_sequence' column\n",
+    "holdout['unmod_sequence'] = holdout['modified_sequence'].apply(clean_sequence)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>modified_sequence</th>\n",
+       "      <th>raw_file</th>\n",
+       "      <th>scan_number</th>\n",
+       "      <th>charge_by_max_intensity</th>\n",
+       "      <th>one_hot_most_abundant_charge</th>\n",
+       "      <th>charge_state_vector</th>\n",
+       "      <th>normalized_intensity_distribution</th>\n",
+       "      <th>unmod_sequence</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[]-AAAAAAAK[UNIMOD:737]NK[UNIMOD:737][UNIMOD:1...</td>\n",
+       "      <td>[02526_BA12-TMT_TUM_mod_ubi_12_01_01-TMT1-1h-R3]</td>\n",
+       "      <td>[10609]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0.0, 0.0, 1.0, 0.0, 0.0]</td>\n",
+       "      <td>AAAAAAAKNK</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[]-AAAGEDYK[UNIMOD:737]-[]</td>\n",
+       "      <td>[02526_BC5-TMT_TUM_mod_ubi_29_01_01-TMT1-1h-R3]</td>\n",
+       "      <td>[7709]</td>\n",
+       "      <td>2</td>\n",
+       "      <td>[0, 1, 0, 0, 0]</td>\n",
+       "      <td>[0, 1, 0, 0, 0]</td>\n",
+       "      <td>[0.0, 1.0, 0.0, 0.0, 0.0]</td>\n",
+       "      <td>AAAGEDYK</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[]-AAAM[UNIMOD:35]ANNLQK[UNIMOD:737][UNIMOD:12...</td>\n",
+       "      <td>[02526_BE4-TMT_TUM_mod_ubi_52_01_01-TMT1-1h-R3]</td>\n",
+       "      <td>[4432]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0.0, 0.0, 1.0, 0.0, 0.0]</td>\n",
+       "      <td>AAAMANNLQKGSAGPMR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[]-AAEEPSK[UNIMOD:737]VEEK[UNIMOD:737][UNIMOD:...</td>\n",
+       "      <td>[02526_BC3-TMT_TUM_mod_ubi_27_01_01-TMT1-1h-R3...</td>\n",
+       "      <td>[9980, 9983, 9915]</td>\n",
+       "      <td>4</td>\n",
+       "      <td>[0, 0, 0, 1, 0]</td>\n",
+       "      <td>[0, 0, 0, 1, 0]</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 1.0, 0.0]</td>\n",
+       "      <td>AAEEPSKVEEKK</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[]-AAEQYTPK[UNIMOD:737][UNIMOD:121]AK[UNIMOD:7...</td>\n",
+       "      <td>[02526_BA12-TMT_TUM_mod_ubi_12_01_01-TMT1-1h-R...</td>\n",
+       "      <td>[15083, 15188]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0, 1, 1, 0, 0]</td>\n",
+       "      <td>[0.0, 0.09967955495951202, 0.900320445040488, ...</td>\n",
+       "      <td>AAEQYTPKAK</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   modified_sequence  \\\n",
+       "0  []-AAAAAAAK[UNIMOD:737]NK[UNIMOD:737][UNIMOD:1...   \n",
+       "1                         []-AAAGEDYK[UNIMOD:737]-[]   \n",
+       "2  []-AAAM[UNIMOD:35]ANNLQK[UNIMOD:737][UNIMOD:12...   \n",
+       "3  []-AAEEPSK[UNIMOD:737]VEEK[UNIMOD:737][UNIMOD:...   \n",
+       "4  []-AAEQYTPK[UNIMOD:737][UNIMOD:121]AK[UNIMOD:7...   \n",
+       "\n",
+       "                                            raw_file         scan_number  \\\n",
+       "0   [02526_BA12-TMT_TUM_mod_ubi_12_01_01-TMT1-1h-R3]             [10609]   \n",
+       "1    [02526_BC5-TMT_TUM_mod_ubi_29_01_01-TMT1-1h-R3]              [7709]   \n",
+       "2    [02526_BE4-TMT_TUM_mod_ubi_52_01_01-TMT1-1h-R3]              [4432]   \n",
+       "3  [02526_BC3-TMT_TUM_mod_ubi_27_01_01-TMT1-1h-R3...  [9980, 9983, 9915]   \n",
+       "4  [02526_BA12-TMT_TUM_mod_ubi_12_01_01-TMT1-1h-R...      [15083, 15188]   \n",
+       "\n",
+       "   charge_by_max_intensity one_hot_most_abundant_charge charge_state_vector  \\\n",
+       "0                        3              [0, 0, 1, 0, 0]     [0, 0, 1, 0, 0]   \n",
+       "1                        2              [0, 1, 0, 0, 0]     [0, 1, 0, 0, 0]   \n",
+       "2                        3              [0, 0, 1, 0, 0]     [0, 0, 1, 0, 0]   \n",
+       "3                        4              [0, 0, 0, 1, 0]     [0, 0, 0, 1, 0]   \n",
+       "4                        3              [0, 0, 1, 0, 0]     [0, 1, 1, 0, 0]   \n",
+       "\n",
+       "                   normalized_intensity_distribution     unmod_sequence  \n",
+       "0                          [0.0, 0.0, 1.0, 0.0, 0.0]         AAAAAAAKNK  \n",
+       "1                          [0.0, 1.0, 0.0, 0.0, 0.0]           AAAGEDYK  \n",
+       "2                          [0.0, 0.0, 1.0, 0.0, 0.0]  AAAMANNLQKGSAGPMR  \n",
+       "3                          [0.0, 0.0, 0.0, 1.0, 0.0]       AAEEPSKVEEKK  \n",
+       "4  [0.0, 0.09967955495951202, 0.900320445040488, ...         AAEQYTPKAK  "
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "holdout.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# encode the sequences\n",
+    "holdout['unmod_sequence_encoded'] = holdout['unmod_sequence'].apply(list).apply(lambda x: np.array([ALPHABET_UNMOD[aa] for aa in x]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>modified_sequence</th>\n",
+       "      <th>raw_file</th>\n",
+       "      <th>scan_number</th>\n",
+       "      <th>charge_by_max_intensity</th>\n",
+       "      <th>one_hot_most_abundant_charge</th>\n",
+       "      <th>charge_state_vector</th>\n",
+       "      <th>normalized_intensity_distribution</th>\n",
+       "      <th>unmod_sequence</th>\n",
+       "      <th>unmod_sequence_encoded</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[]-AAAAAAAK[UNIMOD:737]NK[UNIMOD:737][UNIMOD:1...</td>\n",
+       "      <td>[02526_BA12-TMT_TUM_mod_ubi_12_01_01-TMT1-1h-R3]</td>\n",
+       "      <td>[10609]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0.0, 0.0, 1.0, 0.0, 0.0]</td>\n",
+       "      <td>AAAAAAAKNK</td>\n",
+       "      <td>[1, 1, 1, 1, 1, 1, 1, 9, 12, 9]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[]-AAAGEDYK[UNIMOD:737]-[]</td>\n",
+       "      <td>[02526_BC5-TMT_TUM_mod_ubi_29_01_01-TMT1-1h-R3]</td>\n",
+       "      <td>[7709]</td>\n",
+       "      <td>2</td>\n",
+       "      <td>[0, 1, 0, 0, 0]</td>\n",
+       "      <td>[0, 1, 0, 0, 0]</td>\n",
+       "      <td>[0.0, 1.0, 0.0, 0.0, 0.0]</td>\n",
+       "      <td>AAAGEDYK</td>\n",
+       "      <td>[1, 1, 1, 6, 4, 3, 20, 9]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[]-AAAM[UNIMOD:35]ANNLQK[UNIMOD:737][UNIMOD:12...</td>\n",
+       "      <td>[02526_BE4-TMT_TUM_mod_ubi_52_01_01-TMT1-1h-R3]</td>\n",
+       "      <td>[4432]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0.0, 0.0, 1.0, 0.0, 0.0]</td>\n",
+       "      <td>AAAMANNLQKGSAGPMR</td>\n",
+       "      <td>[1, 1, 1, 11, 1, 12, 12, 10, 14, 9, 6, 16, 1, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[]-AAEEPSK[UNIMOD:737]VEEK[UNIMOD:737][UNIMOD:...</td>\n",
+       "      <td>[02526_BC3-TMT_TUM_mod_ubi_27_01_01-TMT1-1h-R3...</td>\n",
+       "      <td>[9980, 9983, 9915]</td>\n",
+       "      <td>4</td>\n",
+       "      <td>[0, 0, 0, 1, 0]</td>\n",
+       "      <td>[0, 0, 0, 1, 0]</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 1.0, 0.0]</td>\n",
+       "      <td>AAEEPSKVEEKK</td>\n",
+       "      <td>[1, 1, 4, 4, 13, 16, 9, 18, 4, 4, 9, 9]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[]-AAEQYTPK[UNIMOD:737][UNIMOD:121]AK[UNIMOD:7...</td>\n",
+       "      <td>[02526_BA12-TMT_TUM_mod_ubi_12_01_01-TMT1-1h-R...</td>\n",
+       "      <td>[15083, 15188]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0, 1, 1, 0, 0]</td>\n",
+       "      <td>[0.0, 0.09967955495951202, 0.900320445040488, ...</td>\n",
+       "      <td>AAEQYTPKAK</td>\n",
+       "      <td>[1, 1, 4, 14, 20, 17, 13, 9, 1, 9]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   modified_sequence  \\\n",
+       "0  []-AAAAAAAK[UNIMOD:737]NK[UNIMOD:737][UNIMOD:1...   \n",
+       "1                         []-AAAGEDYK[UNIMOD:737]-[]   \n",
+       "2  []-AAAM[UNIMOD:35]ANNLQK[UNIMOD:737][UNIMOD:12...   \n",
+       "3  []-AAEEPSK[UNIMOD:737]VEEK[UNIMOD:737][UNIMOD:...   \n",
+       "4  []-AAEQYTPK[UNIMOD:737][UNIMOD:121]AK[UNIMOD:7...   \n",
+       "\n",
+       "                                            raw_file         scan_number  \\\n",
+       "0   [02526_BA12-TMT_TUM_mod_ubi_12_01_01-TMT1-1h-R3]             [10609]   \n",
+       "1    [02526_BC5-TMT_TUM_mod_ubi_29_01_01-TMT1-1h-R3]              [7709]   \n",
+       "2    [02526_BE4-TMT_TUM_mod_ubi_52_01_01-TMT1-1h-R3]              [4432]   \n",
+       "3  [02526_BC3-TMT_TUM_mod_ubi_27_01_01-TMT1-1h-R3...  [9980, 9983, 9915]   \n",
+       "4  [02526_BA12-TMT_TUM_mod_ubi_12_01_01-TMT1-1h-R...      [15083, 15188]   \n",
+       "\n",
+       "   charge_by_max_intensity one_hot_most_abundant_charge charge_state_vector  \\\n",
+       "0                        3              [0, 0, 1, 0, 0]     [0, 0, 1, 0, 0]   \n",
+       "1                        2              [0, 1, 0, 0, 0]     [0, 1, 0, 0, 0]   \n",
+       "2                        3              [0, 0, 1, 0, 0]     [0, 0, 1, 0, 0]   \n",
+       "3                        4              [0, 0, 0, 1, 0]     [0, 0, 0, 1, 0]   \n",
+       "4                        3              [0, 0, 1, 0, 0]     [0, 1, 1, 0, 0]   \n",
+       "\n",
+       "                   normalized_intensity_distribution     unmod_sequence  \\\n",
+       "0                          [0.0, 0.0, 1.0, 0.0, 0.0]         AAAAAAAKNK   \n",
+       "1                          [0.0, 1.0, 0.0, 0.0, 0.0]           AAAGEDYK   \n",
+       "2                          [0.0, 0.0, 1.0, 0.0, 0.0]  AAAMANNLQKGSAGPMR   \n",
+       "3                          [0.0, 0.0, 0.0, 1.0, 0.0]       AAEEPSKVEEKK   \n",
+       "4  [0.0, 0.09967955495951202, 0.900320445040488, ...         AAEQYTPKAK   \n",
+       "\n",
+       "                              unmod_sequence_encoded  \n",
+       "0                    [1, 1, 1, 1, 1, 1, 1, 9, 12, 9]  \n",
+       "1                          [1, 1, 1, 6, 4, 3, 20, 9]  \n",
+       "2  [1, 1, 1, 11, 1, 12, 12, 10, 14, 9, 6, 16, 1, ...  \n",
+       "3            [1, 1, 4, 4, 13, 16, 9, 18, 4, 4, 9, 9]  \n",
+       "4                 [1, 1, 4, 14, 20, 17, 13, 9, 1, 9]  "
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "holdout.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pad sequences to max length with 0s to a length of 40 \n",
+    "holdout['unmod_sequence_encoded'] = holdout['unmod_sequence_encoded'].apply(lambda x: np.pad(x, (0, 40 - len(x)), constant_values=0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "2/2 [==============================] - 0s 6ms/step\n",
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+      "2/2 [==============================] - 0s 8ms/step\n",
+      "2/2 [==============================] - 0s 6ms/step\n",
+      "1/1 [==============================] - 0s 13ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "def batch_predict(model, data, batch_size=64):\n",
+    "    predictions = []\n",
+    "    for start in range(0, len(data), batch_size):\n",
+    "        end = min(start + batch_size, len(data))\n",
+    "        batch_data = np.array(data[start:end])\n",
+    "        batch_pred = model.predict(batch_data)\n",
+    "        predictions.extend(batch_pred)\n",
+    "    return predictions\n",
+    "\n",
+    "# Apply batch prediction on holdout data\n",
+    "holdout_sequences = holdout['unmod_sequence_encoded'].to_list()\n",
+    "holdout['charge_dist_pred'] = batch_predict(charge_distribution_model_linear, holdout_sequences)\n",
+    "\n",
+    "# Save predictions to a new column\n",
+    "holdout['charge_dist_pred'] = np.array(holdout['charge_dist_pred'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>modified_sequence</th>\n",
+       "      <th>raw_file</th>\n",
+       "      <th>scan_number</th>\n",
+       "      <th>charge_by_max_intensity</th>\n",
+       "      <th>one_hot_most_abundant_charge</th>\n",
+       "      <th>charge_state_vector</th>\n",
+       "      <th>normalized_intensity_distribution</th>\n",
+       "      <th>unmod_sequence</th>\n",
+       "      <th>unmod_sequence_encoded</th>\n",
+       "      <th>charge_dist_pred</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[]-AAAAAAAK[UNIMOD:737]NK[UNIMOD:737][UNIMOD:1...</td>\n",
+       "      <td>[02526_BA12-TMT_TUM_mod_ubi_12_01_01-TMT1-1h-R3]</td>\n",
+       "      <td>[10609]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0.0, 0.0, 1.0, 0.0, 0.0]</td>\n",
+       "      <td>AAAAAAAKNK</td>\n",
+       "      <td>[1, 1, 1, 1, 1, 1, 1, 9, 12, 9, 0, 0, 0, 0, 0,...</td>\n",
+       "      <td>[2.7353148e-05, 0.9977683, 0.00019761967, -4.4...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[]-AAAGEDYK[UNIMOD:737]-[]</td>\n",
+       "      <td>[02526_BC5-TMT_TUM_mod_ubi_29_01_01-TMT1-1h-R3]</td>\n",
+       "      <td>[7709]</td>\n",
+       "      <td>2</td>\n",
+       "      <td>[0, 1, 0, 0, 0]</td>\n",
+       "      <td>[0, 1, 0, 0, 0]</td>\n",
+       "      <td>[0.0, 1.0, 0.0, 0.0, 0.0]</td>\n",
+       "      <td>AAAGEDYK</td>\n",
+       "      <td>[1, 1, 1, 6, 4, 3, 20, 9, 0, 0, 0, 0, 0, 0, 0,...</td>\n",
+       "      <td>[3.406231e-05, 0.99788773, 0.00018831156, -7.1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[]-AAAM[UNIMOD:35]ANNLQK[UNIMOD:737][UNIMOD:12...</td>\n",
+       "      <td>[02526_BE4-TMT_TUM_mod_ubi_52_01_01-TMT1-1h-R3]</td>\n",
+       "      <td>[4432]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0.0, 0.0, 1.0, 0.0, 0.0]</td>\n",
+       "      <td>AAAMANNLQKGSAGPMR</td>\n",
+       "      <td>[1, 1, 1, 11, 1, 12, 12, 10, 14, 9, 6, 16, 1, ...</td>\n",
+       "      <td>[-0.0007836181, 0.030643761, 0.967537, 0.00046...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[]-AAEEPSK[UNIMOD:737]VEEK[UNIMOD:737][UNIMOD:...</td>\n",
+       "      <td>[02526_BC3-TMT_TUM_mod_ubi_27_01_01-TMT1-1h-R3...</td>\n",
+       "      <td>[9980, 9983, 9915]</td>\n",
+       "      <td>4</td>\n",
+       "      <td>[0, 0, 0, 1, 0]</td>\n",
+       "      <td>[0, 0, 0, 1, 0]</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 1.0, 0.0]</td>\n",
+       "      <td>AAEEPSKVEEKK</td>\n",
+       "      <td>[1, 1, 4, 4, 13, 16, 9, 18, 4, 4, 9, 9, 0, 0, ...</td>\n",
+       "      <td>[-0.0008068746, 0.026914954, 0.967738, 0.00050...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[]-AAEQYTPK[UNIMOD:737][UNIMOD:121]AK[UNIMOD:7...</td>\n",
+       "      <td>[02526_BA12-TMT_TUM_mod_ubi_12_01_01-TMT1-1h-R...</td>\n",
+       "      <td>[15083, 15188]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0, 1, 1, 0, 0]</td>\n",
+       "      <td>[0.0, 0.09967955495951202, 0.900320445040488, ...</td>\n",
+       "      <td>AAEQYTPKAK</td>\n",
+       "      <td>[1, 1, 4, 14, 20, 17, 13, 9, 1, 9, 0, 0, 0, 0,...</td>\n",
+       "      <td>[-0.00012928512, 0.9521399, 0.030967759, 1.708...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   modified_sequence  \\\n",
+       "0  []-AAAAAAAK[UNIMOD:737]NK[UNIMOD:737][UNIMOD:1...   \n",
+       "1                         []-AAAGEDYK[UNIMOD:737]-[]   \n",
+       "2  []-AAAM[UNIMOD:35]ANNLQK[UNIMOD:737][UNIMOD:12...   \n",
+       "3  []-AAEEPSK[UNIMOD:737]VEEK[UNIMOD:737][UNIMOD:...   \n",
+       "4  []-AAEQYTPK[UNIMOD:737][UNIMOD:121]AK[UNIMOD:7...   \n",
+       "\n",
+       "                                            raw_file         scan_number  \\\n",
+       "0   [02526_BA12-TMT_TUM_mod_ubi_12_01_01-TMT1-1h-R3]             [10609]   \n",
+       "1    [02526_BC5-TMT_TUM_mod_ubi_29_01_01-TMT1-1h-R3]              [7709]   \n",
+       "2    [02526_BE4-TMT_TUM_mod_ubi_52_01_01-TMT1-1h-R3]              [4432]   \n",
+       "3  [02526_BC3-TMT_TUM_mod_ubi_27_01_01-TMT1-1h-R3...  [9980, 9983, 9915]   \n",
+       "4  [02526_BA12-TMT_TUM_mod_ubi_12_01_01-TMT1-1h-R...      [15083, 15188]   \n",
+       "\n",
+       "   charge_by_max_intensity one_hot_most_abundant_charge charge_state_vector  \\\n",
+       "0                        3              [0, 0, 1, 0, 0]     [0, 0, 1, 0, 0]   \n",
+       "1                        2              [0, 1, 0, 0, 0]     [0, 1, 0, 0, 0]   \n",
+       "2                        3              [0, 0, 1, 0, 0]     [0, 0, 1, 0, 0]   \n",
+       "3                        4              [0, 0, 0, 1, 0]     [0, 0, 0, 1, 0]   \n",
+       "4                        3              [0, 0, 1, 0, 0]     [0, 1, 1, 0, 0]   \n",
+       "\n",
+       "                   normalized_intensity_distribution     unmod_sequence  \\\n",
+       "0                          [0.0, 0.0, 1.0, 0.0, 0.0]         AAAAAAAKNK   \n",
+       "1                          [0.0, 1.0, 0.0, 0.0, 0.0]           AAAGEDYK   \n",
+       "2                          [0.0, 0.0, 1.0, 0.0, 0.0]  AAAMANNLQKGSAGPMR   \n",
+       "3                          [0.0, 0.0, 0.0, 1.0, 0.0]       AAEEPSKVEEKK   \n",
+       "4  [0.0, 0.09967955495951202, 0.900320445040488, ...         AAEQYTPKAK   \n",
+       "\n",
+       "                              unmod_sequence_encoded  \\\n",
+       "0  [1, 1, 1, 1, 1, 1, 1, 9, 12, 9, 0, 0, 0, 0, 0,...   \n",
+       "1  [1, 1, 1, 6, 4, 3, 20, 9, 0, 0, 0, 0, 0, 0, 0,...   \n",
+       "2  [1, 1, 1, 11, 1, 12, 12, 10, 14, 9, 6, 16, 1, ...   \n",
+       "3  [1, 1, 4, 4, 13, 16, 9, 18, 4, 4, 9, 9, 0, 0, ...   \n",
+       "4  [1, 1, 4, 14, 20, 17, 13, 9, 1, 9, 0, 0, 0, 0,...   \n",
+       "\n",
+       "                                    charge_dist_pred  \n",
+       "0  [2.7353148e-05, 0.9977683, 0.00019761967, -4.4...  \n",
+       "1  [3.406231e-05, 0.99788773, 0.00018831156, -7.1...  \n",
+       "2  [-0.0007836181, 0.030643761, 0.967537, 0.00046...  \n",
+       "3  [-0.0008068746, 0.026914954, 0.967738, 0.00050...  \n",
+       "4  [-0.00012928512, 0.9521399, 0.030967759, 1.708...  "
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "holdout.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save holdout data with predictions to a new parquet file \n",
+    "holdout.to_parquet(\"/mnt/c/Users/Florian/Desktop/Uni/MSc/FoPr/data/holdout/holdout_with_predictions.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>modified_sequence</th>\n",
+       "      <th>raw_file</th>\n",
+       "      <th>scan_number</th>\n",
+       "      <th>charge_by_max_intensity</th>\n",
+       "      <th>one_hot_most_abundant_charge</th>\n",
+       "      <th>charge_state_vector</th>\n",
+       "      <th>normalized_intensity_distribution</th>\n",
+       "      <th>unmod_sequence</th>\n",
+       "      <th>unmod_sequence_encoded</th>\n",
+       "      <th>charge_dist_pred</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[]-AAAAAAAK[UNIMOD:737]NK[UNIMOD:737][UNIMOD:1...</td>\n",
+       "      <td>[02526_BA12-TMT_TUM_mod_ubi_12_01_01-TMT1-1h-R3]</td>\n",
+       "      <td>[10609]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0.0, 0.0, 1.0, 0.0, 0.0]</td>\n",
+       "      <td>AAAAAAAKNK</td>\n",
+       "      <td>[1, 1, 1, 1, 1, 1, 1, 9, 12, 9, 0, 0, 0, 0, 0,...</td>\n",
+       "      <td>[2.7353148e-05, 0.9977683, 0.00019761967, -4.4...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[]-AAAGEDYK[UNIMOD:737]-[]</td>\n",
+       "      <td>[02526_BC5-TMT_TUM_mod_ubi_29_01_01-TMT1-1h-R3]</td>\n",
+       "      <td>[7709]</td>\n",
+       "      <td>2</td>\n",
+       "      <td>[0, 1, 0, 0, 0]</td>\n",
+       "      <td>[0, 1, 0, 0, 0]</td>\n",
+       "      <td>[0.0, 1.0, 0.0, 0.0, 0.0]</td>\n",
+       "      <td>AAAGEDYK</td>\n",
+       "      <td>[1, 1, 1, 6, 4, 3, 20, 9, 0, 0, 0, 0, 0, 0, 0,...</td>\n",
+       "      <td>[3.406231e-05, 0.99788773, 0.00018831156, -7.1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[]-AAAM[UNIMOD:35]ANNLQK[UNIMOD:737][UNIMOD:12...</td>\n",
+       "      <td>[02526_BE4-TMT_TUM_mod_ubi_52_01_01-TMT1-1h-R3]</td>\n",
+       "      <td>[4432]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0.0, 0.0, 1.0, 0.0, 0.0]</td>\n",
+       "      <td>AAAMANNLQKGSAGPMR</td>\n",
+       "      <td>[1, 1, 1, 11, 1, 12, 12, 10, 14, 9, 6, 16, 1, ...</td>\n",
+       "      <td>[-0.0007836181, 0.030643761, 0.967537, 0.00046...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[]-AAEEPSK[UNIMOD:737]VEEK[UNIMOD:737][UNIMOD:...</td>\n",
+       "      <td>[02526_BC3-TMT_TUM_mod_ubi_27_01_01-TMT1-1h-R3...</td>\n",
+       "      <td>[9980, 9983, 9915]</td>\n",
+       "      <td>4</td>\n",
+       "      <td>[0, 0, 0, 1, 0]</td>\n",
+       "      <td>[0, 0, 0, 1, 0]</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 1.0, 0.0]</td>\n",
+       "      <td>AAEEPSKVEEKK</td>\n",
+       "      <td>[1, 1, 4, 4, 13, 16, 9, 18, 4, 4, 9, 9, 0, 0, ...</td>\n",
+       "      <td>[-0.0008068746, 0.026914954, 0.967738, 0.00050...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[]-AAEQYTPK[UNIMOD:737][UNIMOD:121]AK[UNIMOD:7...</td>\n",
+       "      <td>[02526_BA12-TMT_TUM_mod_ubi_12_01_01-TMT1-1h-R...</td>\n",
+       "      <td>[15083, 15188]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[0, 0, 1, 0, 0]</td>\n",
+       "      <td>[0, 1, 1, 0, 0]</td>\n",
+       "      <td>[0.0, 0.09967955495951202, 0.900320445040488, ...</td>\n",
+       "      <td>AAEQYTPKAK</td>\n",
+       "      <td>[1, 1, 4, 14, 20, 17, 13, 9, 1, 9, 0, 0, 0, 0,...</td>\n",
+       "      <td>[-0.00012928512, 0.9521399, 0.030967759, 1.708...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   modified_sequence  \\\n",
+       "0  []-AAAAAAAK[UNIMOD:737]NK[UNIMOD:737][UNIMOD:1...   \n",
+       "1                         []-AAAGEDYK[UNIMOD:737]-[]   \n",
+       "2  []-AAAM[UNIMOD:35]ANNLQK[UNIMOD:737][UNIMOD:12...   \n",
+       "3  []-AAEEPSK[UNIMOD:737]VEEK[UNIMOD:737][UNIMOD:...   \n",
+       "4  []-AAEQYTPK[UNIMOD:737][UNIMOD:121]AK[UNIMOD:7...   \n",
+       "\n",
+       "                                            raw_file         scan_number  \\\n",
+       "0   [02526_BA12-TMT_TUM_mod_ubi_12_01_01-TMT1-1h-R3]             [10609]   \n",
+       "1    [02526_BC5-TMT_TUM_mod_ubi_29_01_01-TMT1-1h-R3]              [7709]   \n",
+       "2    [02526_BE4-TMT_TUM_mod_ubi_52_01_01-TMT1-1h-R3]              [4432]   \n",
+       "3  [02526_BC3-TMT_TUM_mod_ubi_27_01_01-TMT1-1h-R3...  [9980, 9983, 9915]   \n",
+       "4  [02526_BA12-TMT_TUM_mod_ubi_12_01_01-TMT1-1h-R...      [15083, 15188]   \n",
+       "\n",
+       "   charge_by_max_intensity one_hot_most_abundant_charge charge_state_vector  \\\n",
+       "0                        3              [0, 0, 1, 0, 0]     [0, 0, 1, 0, 0]   \n",
+       "1                        2              [0, 1, 0, 0, 0]     [0, 1, 0, 0, 0]   \n",
+       "2                        3              [0, 0, 1, 0, 0]     [0, 0, 1, 0, 0]   \n",
+       "3                        4              [0, 0, 0, 1, 0]     [0, 0, 0, 1, 0]   \n",
+       "4                        3              [0, 0, 1, 0, 0]     [0, 1, 1, 0, 0]   \n",
+       "\n",
+       "                   normalized_intensity_distribution     unmod_sequence  \\\n",
+       "0                          [0.0, 0.0, 1.0, 0.0, 0.0]         AAAAAAAKNK   \n",
+       "1                          [0.0, 1.0, 0.0, 0.0, 0.0]           AAAGEDYK   \n",
+       "2                          [0.0, 0.0, 1.0, 0.0, 0.0]  AAAMANNLQKGSAGPMR   \n",
+       "3                          [0.0, 0.0, 0.0, 1.0, 0.0]       AAEEPSKVEEKK   \n",
+       "4  [0.0, 0.09967955495951202, 0.900320445040488, ...         AAEQYTPKAK   \n",
+       "\n",
+       "                              unmod_sequence_encoded  \\\n",
+       "0  [1, 1, 1, 1, 1, 1, 1, 9, 12, 9, 0, 0, 0, 0, 0,...   \n",
+       "1  [1, 1, 1, 6, 4, 3, 20, 9, 0, 0, 0, 0, 0, 0, 0,...   \n",
+       "2  [1, 1, 1, 11, 1, 12, 12, 10, 14, 9, 6, 16, 1, ...   \n",
+       "3  [1, 1, 4, 4, 13, 16, 9, 18, 4, 4, 9, 9, 0, 0, ...   \n",
+       "4  [1, 1, 4, 14, 20, 17, 13, 9, 1, 9, 0, 0, 0, 0,...   \n",
+       "\n",
+       "                                    charge_dist_pred  \n",
+       "0  [2.7353148e-05, 0.9977683, 0.00019761967, -4.4...  \n",
+       "1  [3.406231e-05, 0.99788773, 0.00018831156, -7.1...  \n",
+       "2  [-0.0007836181, 0.030643761, 0.967537, 0.00046...  \n",
+       "3  [-0.0008068746, 0.026914954, 0.967738, 0.00050...  \n",
+       "4  [-0.00012928512, 0.9521399, 0.030967759, 1.708...  "
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test = pd.read_parquet(\"/mnt/c/Users/Florian/Desktop/Uni/MSc/FoPr/data/holdout/holdout_with_predictions.parquet\")\n",
+    "test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6\n",
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(test['charge_dist_pred'][1]))\n",
+    "print(len(test['charge_state_vector'][1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>modified_sequence</th>\n",
+       "      <th>raw_file</th>\n",
+       "      <th>scan_number</th>\n",
+       "      <th>charge_by_max_intensity</th>\n",
+       "      <th>one_hot_most_abundant_charge</th>\n",
+       "      <th>charge_state_vector</th>\n",
+       "      <th>normalized_intensity_distribution</th>\n",
+       "      <th>unmod_sequence</th>\n",
+       "      <th>unmod_sequence_encoded</th>\n",
+       "      <th>charge_dist_pred</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8790</th>\n",
+       "      <td>[]-SC[UNIMOD:4]EHQEYGER[UNIMOD:34]PYTHR[UNIMOD...</td>\n",
+       "      <td>[03512a_BE3-TUM_combPTM_3_01_01-2xIT_2xHCD-1h-...</td>\n",
+       "      <td>[6975, 6899, 7007, 7014, 6898, 7129, 7003, 708...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[0, 0, 0, 0, 1]</td>\n",
+       "      <td>[0, 0, 1, 1, 1]</td>\n",
+       "      <td>[0.0, 0.0, 0.3503564294219207, 0.0115840428653...</td>\n",
+       "      <td>SCEHQEYGERPYTHR</td>\n",
+       "      <td>[16, 2, 4, 7, 14, 4, 20, 6, 4, 15, 13, 20, 17,...</td>\n",
+       "      <td>[-0.00053760095, -0.02590537, 0.03422918, 0.96...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15093</th>\n",
+       "      <td>[UNIMOD:737]-AVVAEDNNIK[UNIMOD:737][UNIMOD:121...</td>\n",
+       "      <td>[02526_BF2-TMT_TUM_mod_ubi_62_01_01-TMT1-1h-R3...</td>\n",
+       "      <td>[16507, 16508]</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[0, 0, 0, 0, 1]</td>\n",
+       "      <td>[0, 0, 0, 0, 1]</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 1.0]</td>\n",
+       "      <td>AVVAEDNNIKENDYKDHK</td>\n",
+       "      <td>[1, 18, 18, 1, 4, 3, 12, 12, 8, 9, 4, 12, 3, 2...</td>\n",
+       "      <td>[-0.0006585169, -0.009884775, 0.2430393, 0.747...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17171</th>\n",
+       "      <td>[UNIMOD:737]-DYEDGILHENETSHEM[UNIMOD:35]K[UNIM...</td>\n",
+       "      <td>[02526_BE11-TMT_TUM_mod_ubi_59_01_01-TMT1-1h-R...</td>\n",
+       "      <td>[16962, 16963]</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[0, 0, 0, 0, 1]</td>\n",
+       "      <td>[0, 0, 0, 0, 1]</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 1.0]</td>\n",
+       "      <td>DYEDGILHENETSHEMKK</td>\n",
+       "      <td>[3, 20, 4, 3, 6, 8, 10, 7, 4, 12, 4, 17, 16, 7...</td>\n",
+       "      <td>[-0.00058012153, 0.0062743425, 0.88822603, 0.0...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25143</th>\n",
+       "      <td>[UNIMOD:737]-K[UNIMOD:737]NNHQEDK[UNIMOD:737][...</td>\n",
+       "      <td>[02526_BE6-TMT_TUM_mod_ubi_54_01_01-TMT2-1h-R3]</td>\n",
+       "      <td>[19146]</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[0, 0, 0, 0, 1]</td>\n",
+       "      <td>[0, 0, 0, 0, 1]</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 1.0]</td>\n",
+       "      <td>KNNHQEDKIEFDNEVDK</td>\n",
+       "      <td>[9, 12, 12, 7, 14, 4, 3, 9, 8, 4, 5, 3, 12, 4,...</td>\n",
+       "      <td>[-0.000559118, -0.010482311, 0.080475375, 0.89...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32906</th>\n",
+       "      <td>[UNIMOD:737]-NQTAEK[UNIMOD:737][UNIMOD:121]EEF...</td>\n",
+       "      <td>[02526_BE10-TMT_TUM_mod_ubi_58_01_01-TMT1-1h-R...</td>\n",
+       "      <td>[18124, 18125]</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[0, 0, 0, 0, 1]</td>\n",
+       "      <td>[0, 0, 0, 0, 1]</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 1.0]</td>\n",
+       "      <td>NQTAEKEEFEHQQKELEK</td>\n",
+       "      <td>[12, 14, 17, 1, 4, 9, 4, 4, 5, 4, 7, 14, 14, 9...</td>\n",
+       "      <td>[-0.000541562, -0.009713888, 0.1835658, 0.7955...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35409</th>\n",
+       "      <td>[UNIMOD:737]-SHPFIK[UNIMOD:737]EYQAK[UNIMOD:73...</td>\n",
+       "      <td>[02526_BE5-TMT_TUM_mod_ubi_53_01_01-TMT1-1h-R3...</td>\n",
+       "      <td>[20569, 20570]</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[0, 0, 0, 0, 1]</td>\n",
+       "      <td>[0, 0, 0, 0, 1]</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 1.0]</td>\n",
+       "      <td>SHPFIKEYQAKENDFDR</td>\n",
+       "      <td>[16, 7, 13, 5, 8, 9, 4, 20, 14, 1, 9, 4, 12, 3...</td>\n",
+       "      <td>[-0.0005206787, -0.013860166, 0.03778573, 0.93...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39282</th>\n",
+       "      <td>[UNIMOD:737]-VFYYK[UNIMOD:737][UNIMOD:121]M[UN...</td>\n",
+       "      <td>[02526_BC1-TMT_TUM_mod_ubi_25_01_01-TMT2-1h-R3]</td>\n",
+       "      <td>[18060]</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[0, 0, 0, 0, 1]</td>\n",
+       "      <td>[0, 0, 0, 0, 1]</td>\n",
+       "      <td>[0.0, 0.0, 0.0, 0.0, 1.0]</td>\n",
+       "      <td>VFYYKMKGDYHR</td>\n",
+       "      <td>[18, 5, 20, 20, 9, 11, 9, 6, 3, 20, 7, 15, 0, ...</td>\n",
+       "      <td>[-0.0005873069, -0.0012048483, 0.40841085, 0.5...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                       modified_sequence  \\\n",
+       "8790   []-SC[UNIMOD:4]EHQEYGER[UNIMOD:34]PYTHR[UNIMOD...   \n",
+       "15093  [UNIMOD:737]-AVVAEDNNIK[UNIMOD:737][UNIMOD:121...   \n",
+       "17171  [UNIMOD:737]-DYEDGILHENETSHEM[UNIMOD:35]K[UNIM...   \n",
+       "25143  [UNIMOD:737]-K[UNIMOD:737]NNHQEDK[UNIMOD:737][...   \n",
+       "32906  [UNIMOD:737]-NQTAEK[UNIMOD:737][UNIMOD:121]EEF...   \n",
+       "35409  [UNIMOD:737]-SHPFIK[UNIMOD:737]EYQAK[UNIMOD:73...   \n",
+       "39282  [UNIMOD:737]-VFYYK[UNIMOD:737][UNIMOD:121]M[UN...   \n",
+       "\n",
+       "                                                raw_file  \\\n",
+       "8790   [03512a_BE3-TUM_combPTM_3_01_01-2xIT_2xHCD-1h-...   \n",
+       "15093  [02526_BF2-TMT_TUM_mod_ubi_62_01_01-TMT1-1h-R3...   \n",
+       "17171  [02526_BE11-TMT_TUM_mod_ubi_59_01_01-TMT1-1h-R...   \n",
+       "25143    [02526_BE6-TMT_TUM_mod_ubi_54_01_01-TMT2-1h-R3]   \n",
+       "32906  [02526_BE10-TMT_TUM_mod_ubi_58_01_01-TMT1-1h-R...   \n",
+       "35409  [02526_BE5-TMT_TUM_mod_ubi_53_01_01-TMT1-1h-R3...   \n",
+       "39282    [02526_BC1-TMT_TUM_mod_ubi_25_01_01-TMT2-1h-R3]   \n",
+       "\n",
+       "                                             scan_number  \\\n",
+       "8790   [6975, 6899, 7007, 7014, 6898, 7129, 7003, 708...   \n",
+       "15093                                     [16507, 16508]   \n",
+       "17171                                     [16962, 16963]   \n",
+       "25143                                            [19146]   \n",
+       "32906                                     [18124, 18125]   \n",
+       "35409                                     [20569, 20570]   \n",
+       "39282                                            [18060]   \n",
+       "\n",
+       "       charge_by_max_intensity one_hot_most_abundant_charge  \\\n",
+       "8790                         5              [0, 0, 0, 0, 1]   \n",
+       "15093                        5              [0, 0, 0, 0, 1]   \n",
+       "17171                        5              [0, 0, 0, 0, 1]   \n",
+       "25143                        5              [0, 0, 0, 0, 1]   \n",
+       "32906                        5              [0, 0, 0, 0, 1]   \n",
+       "35409                        5              [0, 0, 0, 0, 1]   \n",
+       "39282                        5              [0, 0, 0, 0, 1]   \n",
+       "\n",
+       "      charge_state_vector                  normalized_intensity_distribution  \\\n",
+       "8790      [0, 0, 1, 1, 1]  [0.0, 0.0, 0.3503564294219207, 0.0115840428653...   \n",
+       "15093     [0, 0, 0, 0, 1]                          [0.0, 0.0, 0.0, 0.0, 1.0]   \n",
+       "17171     [0, 0, 0, 0, 1]                          [0.0, 0.0, 0.0, 0.0, 1.0]   \n",
+       "25143     [0, 0, 0, 0, 1]                          [0.0, 0.0, 0.0, 0.0, 1.0]   \n",
+       "32906     [0, 0, 0, 0, 1]                          [0.0, 0.0, 0.0, 0.0, 1.0]   \n",
+       "35409     [0, 0, 0, 0, 1]                          [0.0, 0.0, 0.0, 0.0, 1.0]   \n",
+       "39282     [0, 0, 0, 0, 1]                          [0.0, 0.0, 0.0, 0.0, 1.0]   \n",
+       "\n",
+       "           unmod_sequence                             unmod_sequence_encoded  \\\n",
+       "8790      SCEHQEYGERPYTHR  [16, 2, 4, 7, 14, 4, 20, 6, 4, 15, 13, 20, 17,...   \n",
+       "15093  AVVAEDNNIKENDYKDHK  [1, 18, 18, 1, 4, 3, 12, 12, 8, 9, 4, 12, 3, 2...   \n",
+       "17171  DYEDGILHENETSHEMKK  [3, 20, 4, 3, 6, 8, 10, 7, 4, 12, 4, 17, 16, 7...   \n",
+       "25143   KNNHQEDKIEFDNEVDK  [9, 12, 12, 7, 14, 4, 3, 9, 8, 4, 5, 3, 12, 4,...   \n",
+       "32906  NQTAEKEEFEHQQKELEK  [12, 14, 17, 1, 4, 9, 4, 4, 5, 4, 7, 14, 14, 9...   \n",
+       "35409   SHPFIKEYQAKENDFDR  [16, 7, 13, 5, 8, 9, 4, 20, 14, 1, 9, 4, 12, 3...   \n",
+       "39282        VFYYKMKGDYHR  [18, 5, 20, 20, 9, 11, 9, 6, 3, 20, 7, 15, 0, ...   \n",
+       "\n",
+       "                                        charge_dist_pred  \n",
+       "8790   [-0.00053760095, -0.02590537, 0.03422918, 0.96...  \n",
+       "15093  [-0.0006585169, -0.009884775, 0.2430393, 0.747...  \n",
+       "17171  [-0.00058012153, 0.0062743425, 0.88822603, 0.0...  \n",
+       "25143  [-0.000559118, -0.010482311, 0.080475375, 0.89...  \n",
+       "32906  [-0.000541562, -0.009713888, 0.1835658, 0.7955...  \n",
+       "35409  [-0.0005206787, -0.013860166, 0.03778573, 0.93...  \n",
+       "39282  [-0.0005873069, -0.0012048483, 0.40841085, 0.5...  "
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# print the rwos where the charge_by_max_intensity is 6\n",
+    "test[test['charge_by_max_intensity'] == 5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## -----------------------------------------------------------------------------------------------------------------------------------------------------\n",
+    "# EVALUATION\n",
+    "## -----------------------------------------------------------------------------------------------------------------------------------------------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_learning_curves(history, title='Learning Curves'):\n",
+    "    \"\"\"Plot the learning curves for training and validation loss and accuracy. \n",
+    "    Args:\n",
+    "    - history: Return value from model.fit().\n",
+    "    - title (optional): Base title for the plots. It will be appended with \"Loss\" and \"Accuracy\" for the respective plots.\n",
+    "    \"\"\"\n",
+    "    history_dict = history.history\n",
+    "    loss = history_dict['loss']\n",
+    "    val_loss = history_dict.get('val_loss', [])\n",
+    "    \n",
+    "    epochs = range(1, len(loss) + 1)\n",
+    "    \n",
+    "    accuracy_key = next((key for key in history_dict.keys() if 'acc' in key and not key.startswith('val_')), None)\n",
+    "    val_accuracy_key = next((key for key in history_dict.keys() if 'acc' in key and key.startswith('val_')), None)\n",
+    "    learning_rate = history_dict.get('lr', [])\n",
+    "    \n",
+    "    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(18, 6))\n",
+    "    \n",
+    "    # Plot training and validation loss\n",
+    "    ax1.plot(epochs, loss, 'b', label='Training Loss')\n",
+    "    if val_loss:\n",
+    "        ax1.plot(epochs, val_loss, 'r', label='Validation Loss')\n",
+    "    ax1.set_title(f'{title} - Loss')\n",
+    "    ax1.set_xlabel('Epochs')\n",
+    "    ax1.set_ylabel('Loss')\n",
+    "    ax1.legend()\n",
+    "    \n",
+    "    # Plot training and validation accuracy if keys exist\n",
+    "    if accuracy_key and val_accuracy_key:\n",
+    "        accuracy = history_dict[accuracy_key]\n",
+    "        val_accuracy = history_dict[val_accuracy_key]\n",
+    "        ax2.plot(epochs, accuracy, 'b', label='Training Accuracy')\n",
+    "        ax2.plot(epochs, val_accuracy, 'r', label='Validation Accuracy')\n",
+    "        ax2.set_title(f'{title} - Accuracy')\n",
+    "        ax2.set_xlabel('Epochs')\n",
+    "        ax2.set_ylabel('Accuracy')\n",
+    "        ax2.legend()\n",
+    "    else:\n",
+    "        ax2.set_title(f'{title} - Accuracy Not Available')\n",
+    "        ax2.set_xlabel('Epochs')\n",
+    "        ax2.set_ylabel('Accuracy')\n",
+    "        ax2.text(0.5, 0.5, 'Accuracy data not found in history', horizontalalignment='center', verticalalignment='center', transform=ax2.transAxes)\n",
+    "    \n",
+    "    # Plot learning rate\n",
+    "    if learning_rate:\n",
+    "        ax3.plot(epochs, learning_rate, 'g', label='Learning Rate')\n",
+    "        ax3.set_title(f'{title} - Learning Rate')\n",
+    "        ax3.set_xlabel('Epochs')\n",
+    "        ax3.set_ylabel('Learning Rate')\n",
+    "        ax3.legend()\n",
+    "    else:\n",
+    "        ax3.set_title(f'{title} - Learning Rate Not Available')\n",
+    "        ax3.set_xlabel('Epochs')\n",
+    "        ax3.set_ylabel('Learning Rate')\n",
+    "        ax3.text(0.5, 0.5, 'Learning rate data not found in history', horizontalalignment='center', verticalalignment='center', transform=ax3.transAxes)\n",
+    "\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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HDnHgwIEnXmkbGhoKgKen51Pt85133rFZr1atmk13c3i641ejRg2KFi1qU7ZmzRoqV65M6dKlrWWZMmWKcYXZ+vXrCQ4OpnXr1ly9etW6ODo6UqlSJTZt2hTv51WvXj0yZcrEokWLMAyDRYsW0bp161jr/vrrrwD07dvXprxfv34A1mE3fvvtNyIiIujZs6dNt/4+ffrE2OaSJUuoVq0aGTNmtHkudevWJTIykj/++CPezyUx/Prrrzg6OtKrVy+b8n79+mEYBqtXrwYgQ4YMgDkUQ1zDR2TIkIFz587FGFJBRETsR22lmNRWspXW20oHDhzg4MGDNs85+riuXbvWWrZs2TKioqIYOnRojPn0op/j+vXruXnzJh9++GGMOYeedSgoiP3/xdnZ2RpHZGQk165dsw6//vD7benSpWTJkoWePXvG2G50TIUKFaJSpUo2/0/Xr19n9erVtG3b9rliFxFJ7tRWikltJVtpva30JEuWLMHb25tXXnnFJr5y5crh4eFhc6wffv1v3rzJ1atXqVatGrdv3+aff/6x2a6zszOdOnWKdZ8tW7a06S1XrVo1gBjvt9jUrVuXAgUKWNdLliyJl5eX9bGRkZH89ttvNG3a1DpyAkDBggWtPYLj49atW2TNmpWsWbNSsGBB+vfvT9WqVVm+fLnNa/7wMbl37x7Xrl2jYMGCZMiQ4YmfK5Cw73VJfBreUyQesmbNSt26dVmwYAG3b98mMjKS5s2bx1l/5cqVjB49mn379tmMpf2kH7KnT5+mUqVKMcqju1GfPn2a4sWLW8vz5cv3tE/FhoODA23btmXatGnWiWrnz5+Pi4uLdQznuHh5eQHml2d0ouZJXFxcYnRRz5gxY4yxt5/m+MV2DE6fPk3lypVjlBcsWNBm/dixYwDUrl071nijn2N8pE+fnhYtWrBgwQIqVqzI2bNn42zAnz59GgcHhxjx5MiRgwwZMnD69GlrPQA/Pz+belmzZrVpdEQ/lwMHDsQ5BMDly5fj/VwAgoKCbNa9vb2f64fA6dOn8fHxidGYf/i9DWaDaubMmXTp0oUPP/yQOnXq8Nprr9G8eXPrCaeBAwfy22+/UbFiRQoWLEi9evVo06YNVatWfeb4RETk+aitFJPaSrbSelvp+++/x93dnfz583P8+HHAfL19fX2ZP3++daixEydO4ODgEOPk48NOnDgBYPNeTwixvVeioqL44osv+Oqrrzh58qTNXDjRQ1NFx/Tiiy+SLt3jTzG0b9+eHj16cPr0afLmzcuSJUu4d+/eUw3tJiKSEqmtFJPaSrbSelvpSY4dO0ZISAjZsmV7YnyHDx9myJAhbNy40ZpcjhYSEmKz/sILL8Q6rC1Anjx5bNajj9mj77f4PDb68dGPvXz5Mnfu3InxGkLM99njuLi48MsvvwDmXJqfffYZly9fjnGs79y5w5gxY5g9ezbnz5/HMAzrfY8ek9gk5HtdEp+SfiLx1KZNG7p27UpQUBANGjSIs0GyZcsWXn31VapXr85XX31Fzpw5SZ8+PbNnz2bBggUJGtPzfFlGa9++PePGjWPZsmW0bt2aBQsW0KhRI7y9vR/7uMKFCwNw8OBB65UuTxLbxMOPetrj9zzHILon2bx588iRI0eM+5900uJRbdq0Yfr06QwfPpxSpUo99mQNPN+V2I+KiorilVdeYcCAAbHeX6hQoafaXs6cOW3WZ8+eHWNy8MTg6urKH3/8waZNm1i1ahVr1qxh8eLF1K5dm3Xr1uHo6EiRIkU4evQoK1euZM2aNSxdupSvvvqKoUOHMmLEiESPUUREYqe2ki21lWJKq20lwzBYuHAht27divU5X758mbCwMDw8PJ4qhieJ6/hFRkbG+l6L7b3yySef8PHHH/PWW28xatQoMmXKhIODA3369IlzVIbHadWqFe+//z7z58/no48+4vvvv6d8+fK8+OKLT70tEZGURm0lW2orxZRW20rxERUVRbZs2Wx6lT4sOlkZHBxMjRo18PLyYuTIkRQoUAAXFxf27NnDwIEDY7RfHvf6x/V+ezhhlhiPfRqOjo7UrVvXuu7v70/hwoXp1q2bdd5MgJ49ezJ79mz69OlD5cqV8fb2xmKx0KpVq3i16RL6vS6JS6+GSDw1a9aMbt268ddff7F48eI46y1duhQXFxfWrl1rM8Hv7Nmzn7iPvHnzcvTo0Rjl0V3P8+bN+wyRP74RULx4ccqUKcP8+fPJlSsXZ86cYcqUKU/cZuPGjRkzZgzff/99vBtn8fE8xy9a3rx5rVdQP+zRsuhu9tmyZbP5gnxWL7/8Mnny5GHz5s2MHTv2sfFFRUVx7Ngx69V2YE64HRwcbH2do/8eO3aM/PnzW+tduXIlxlVFBQoUICwsLEGeB5jd9h9WrFix59pe3rx5+e2337h586ZNb7/Y3tsODg7UqVOHOnXqMHHiRD755BMGDx7Mpk2brM/P3d2dli1b0rJlSyIiInjttdf43//+x6BBg2IMcyUiIklDbSVbaivFlFbbSr///jvnzp1j5MiRNs8HzCvF3377bZYtW0a7du0oUKAAUVFRHDlyxGZIsYdFvy6HDh167JXgGTNmJDg4OEb56dOnbY7X4/z444/UqlWLWbNm2ZQHBweTJUsWm5h27NjBvXv3SJ8+fZzby5QpEw0bNmT+/Pm0bduWrVu3MmnSpHjFIiKS0qmtZEttpZjSalspPgoUKMBvv/1G1apVH5uo27x5M9euXeOnn36ievXq1vKTJ08+1/4TWrZs2XBxcYnX++xp5MyZk/fff58RI0bw119/8dJLLwFmm65Dhw5MmDDBWvfu3bsx2opx/a8n9HtdEpfm9BOJJw8PD6ZNm8bw4cNp3LhxnPUcHR2xWCw2Q9+cOnWKZcuWPXEfAQEB/P3332zfvt1aduvWLb755ht8fX2feIVPXNzd3R/bVfvNN99k3bp1TJo0icyZM8dr7OjKlStTv359Zs6cGetzi4iIoH///k8d6/Mcv2j+/v5s376dffv2WcuuX78e42ogf39/vLy8+OSTT7h3716M7Vy5cuWpYrdYLEyePJlhw4Y9doiigIAAgBgnOCZOnAhgHd6pbt26pE+fnilTpthcCRTbiZE33niD7du328wJEy04OJj79+8/1XOpW7euzfLoFVpPKyAggMjISKZOnWpT/vnnn2OxWKzvuevXr8d4bPQJr+ghOa5du2Zzv5OTE0WLFsUwjFhfRxERSRpqK9lSWymmtNpWih7a84MPPqB58+Y2S9euXfHz87Me+6ZNm+Lg4MDIkSNjXHUd/Rzr1auHp6cnY8aM4e7du7HWAfPkzF9//UVERIS1bOXKlZw9ezbez9PR0THGFelLlizh/PnzNmWvv/46V69ejdHWezQmMP+fjhw5wgcffICjoyOtWrWKdzwiIimZ2kq21FaKKa22leLjjTfeIDIyklGjRsW47/79+9bkVXQPu4efb0REBF999dVz7T+hRffQW7ZsGRcuXLCWHz9+nNWrVz/Xtnv27Imbmxuffvqpzf4ebZNNmTLF5v8EzP91IEYyMKHf65K41NNP5Cl06NDhiXUaNmzIxIkTqV+/Pm3atOHy5ct8+eWXFCxYkAMHDjz2sR9++CELFy6kQYMG9OrVi0yZMjF37lxOnjzJ0qVLrXOaPa1y5cqxePFi+vbtS4UKFfDw8LBpYLZp04YBAwbw888/8+677z726tyHfffdd9SrV4/XXnuNxo0bU6dOHdzd3Tl27BiLFi3i4sWLjB8//qlifZ7jF23AgAF8//33vPLKK/Ts2RN3d3dmzpxJnjx5uH79uvWqFS8vL6ZNm8abb75J2bJladWqFVmzZuXMmTOsWrWKqlWrxnri4nGaNGlCkyZNHlunVKlSdOjQgW+++cY67MDff//N3Llzadq0KbVq1QLMoQn69+/PmDFjaNSoEQEBAezdu5fVq1fbXFkN8MEHH7BixQoaNWpEx44dKVeuHLdu3eLgwYP8+OOPnDp1KsZjEtquXbsYPXp0jPKaNWvSuHFjatWqxeDBgzl16hSlSpVi3bp1LF++nD59+livGBo5ciR//PEHDRs2JG/evFy+fJmvvvqKXLlyWScir1evHjly5KBq1apkz56dwMBApk6dSsOGDZ96AnAREUlYaivZUlspprTWVgoPD2fp0qW88sorcY5G8Oqrr/LFF19w+fJlChYsyODBgxk1ahTVqlXjtddew9nZmZ07d+Lj48OYMWPw8vLi888/p0uXLlSoUIE2bdqQMWNG9u/fz+3bt5k7dy4AXbp04ccff6R+/fq88cYbnDhxgu+//97a7oqPRo0aMXLkSDp16kSVKlU4ePAg8+fPj9FTsH379nz33Xf07duXv//+m2rVqnHr1i1+++033nvvPZvXvGHDhmTOnJklS5bQoEGDOOfmERFJjdRWsqW2Ukxpra30sMedV6pRowbdunVjzJgx7Nu3j3r16pE+fXqOHTvGkiVL+OKLL2jevDlVqlQhY8aMdOjQgV69emGxWJg3b16CD6uZEIYPH866deuoWrUq7777rvVi+eLFi9sknJ9W5syZ6dSpE1999RWBgYEUKVKERo0aMW/ePLy9vSlatCjbt2/nt99+s5mjGcwL7x0dHRk7diwhISE4OztTu3ZtsmXLluDvdUlEhojEavbs2QZg7Ny587H18ubNazRs2NCmbNasWYafn5/h7OxsFC5c2Jg9e7YxbNgw49F/ubx58xodOnSwKTtx4oTRvHlzI0OGDIaLi4tRsWJFY+XKlTZ1Nm3aZADGkiVLYsQTfd+mTZusZWFhYUabNm2MDBkyGICRN2/eGI8LCAgwAGPbtm2Pfb6Pun37tjF+/HijQoUKhoeHh+Hk5GT4+fkZPXv2NI4fP26t16FDB8Pd3T3G42M7LvE9foDRvXv3WOPau3evUa1aNcPZ2dnIlSuXMWbMGGPy5MkGYAQFBdnU3bRpk+Hv7294e3sbLi4uRoECBYyOHTsau3bteuxzf9zr8LAaNWoYxYoVsym7d++eMWLECCNfvnxG+vTpjdy5cxuDBg0y7t69a1MvMjLSGDFihJEzZ07D1dXVqFmzpnHo0KFY3zs3b940Bg0aZBQsWNBwcnIysmTJYlSpUsUYP368ERERYXPchg0b9tiYnxYQ5zJq1ChrfO+//77h4+NjpE+f3vDz8zPGjRtnREVFWbezYcMGo0mTJoaPj4/h5ORk+Pj4GK1btzb+/fdfa52vv/7aqF69upE5c2bD2dnZKFCggPHBBx8YISEhCfqcRETk8dRWih+1ldJ2W2np0qUGYMyaNSvOOps3bzYA44svvrCWffvtt0aZMmUMZ2dnI2PGjEaNGjWM9evX2zxuxYoVRpUqVQxXV1fDy8vLqFixorFw4UKbOhMmTDBeeOEFw9nZ2ahataqxa9cuo0aNGkaNGjWsdR73Ot29e9fo16+f9fhWrVrV2L59e4xtGIb5Xh88eLD1NcuRI4fRvHlz48SJEzG2+9577xmAsWDBgscdPhGRFE1tpfhRWyltt5Ue3uaTzisZhmF88803Rrly5QxXV1fD09PTKFGihDFgwADjwoUL1jpbt241XnrpJcPV1dXw8fExBgwYYKxduzbG+zq242oYhnHy5EkDMMaNGxdrnA8/96d5X8V2zDds2GCUKVPGcHJyMgoUKGDMnDnT6Nevn+Hi4vKkQxbn/4RhmJ8Djo6O1v3duHHD6NSpk5ElSxbDw8PD8Pf3N/75559YY5oxY4aRP39+w9HRMcYxe9b3uiQti2EkwzS3SBqRO3du/P39mTlzpr1DoVmzZhw8ePC5xo1O7vr06cPXX39NWFhYvCZ/FhEREftSWylpqa0kSeX9999n1qxZBAUF4ebmZu9wRERSLLWVkpbaSpIUmjZtyuHDhzl27Ji9Q5EUSnP6idjJvXv3uHbtWqJ3jY+PixcvsmrVqseOF57S3Llzx2b92rVrzJs3j5dfflkNMxERkRRAbaXEpbaS2Mvdu3f5/vvvef3115XwExF5DmorJS61lSQpPPo+O3bsGL/++is1a9a0T0CSKmhOPxE7WLt2LYsWLeLOnTvUqVPHbnGcPHmSrVu3MnPmTNKnT0+3bt3sFktCq1y5MjVr1qRIkSJcunSJWbNmERoayscff2zv0EREROQJ1FZKfGorSVK7fPkyv/32Gz/++CPXrl2jd+/e9g5JRCTFUlsp8amtJEkhf/78dOzYkfz583P69GmmTZuGk5MTAwYMsHdokoIp6SdiB59++inHjx/nf//7H6+88ord4vj999/p1KkTefLkYe7cueTIkcNusSS0gIAAfvzxR7755hssFgtly5Zl1qxZVK9e3d6hiYiIyBOorZT41FaSpHbkyBHatm1LtmzZmDx5MqVLl7Z3SCIiKZbaSolPbSVJCvXr12fhwoUEBQXh7OxM5cqV+eSTT/Dz87N3aJKCaU4/ERERERERERERERERkRROc/qJiIiIiIiIiIiIiIiIpHBK+omIiIiIiIiIiIiIiIikcJrTLxZRUVFcuHABT09PLBaLvcMRERGReDIMg5s3b+Lj44ODg65tSinU9hIREUl51O5KmdTuEhERSZni2/ZS0i8WFy5cIHfu3PYOQ0RERJ7R2bNnyZUrl73DkHhS20tERCTlUrsrZVG7S0REJGV7UttLSb9YeHp6AubB8/LysnM0IiIiEl+hoaHkzp3b+l0uKYPaXiIiIimP2l0pk9pdIiIiKVN8215K+sUiengDLy8vNYBERERSIA1VlLKo7SUiIpJyqd2VsqjdJSIikrI9qe2lQddFREREREREREREREREUjgl/URERERERERERERERERSOCX9RERERERERERERERERFI4zeknIiKJIjIyknv37tk7DEll0qdPj6Ojo73DEDuIiooiIiLC3mGIJAonJyccHHQ9poiIiIhISqfzYfKsEuqcl5J+IiKSoAzDICgoiODgYHuHIqlUhgwZyJEjxxMnLpbUIyIigpMnTxIVFWXvUEQShYODA/ny5cPJycneoYiIiIiIyDPQ+TBJCAlxzktJPxERSVDRDZxs2bLh5uamxIwkGMMwuH37NpcvXwYgZ86cdo5IkoJhGFy8eBFHR0dy586t3lCS6kRFRXHhwgUuXrxInjx59L0pIiIiIpIC6XyYPI+EPOelpJ+IiCSYyMhIawMnc+bM9g5HUiFXV1cALl++TLZs2TTUZxpw//59bt++jY+PD25ubvYORyRRZM2alQsXLnD//n3Sp09v73BEREREROQp6HyYJISEOuelS6VFRCTBRI9ZrhPzkpii318aIz9tiIyMBNCwh5KqRb+/o9/vIiIiIiKScuh8mCSUhDjnpaSfiIgkOA1hIIlJ76+0Sa+7pGZ6f4uIiIiIpHxq18vzSoj3kJJ+IiIiIiIiIiIiIiIiIimckn4iIiKJxNfXl0mTJsW7/ubNm7FYLAQHBydaTCKScukzRURERERERNKKp/0NLCYl/UREJM2zWCyPXYYPH/5M2925cydvv/12vOtXqVKFixcv4u3t/Uz7iy8lAkQSV1r7THlY4cKFcXZ2JigoKMn2KSIiIiIiIs+mY8eONG3a1N5hxOppfwM/K19fX+vvdTc3N0qUKMHMmTOfejsWi4Vly5YlfIBPKVkk/b788kt8fX1xcXGhUqVK/P3333HW/emnnyhfvjwZMmTA3d2d0qVLM2/ePJs6hmEwdOhQcubMiaurK3Xr1uXYsWOJ/TRERCSFunjxonWZNGkSXl5eNmX9+/e31jUMg/v378dru1mzZn2qSZydnJzIkSOHxoAXSeHS6mfKn3/+yZ07d2jevDlz585Nkn0+zvNMfC4iIiIiIiKJI76/1Z72N/DzGDlyJBcvXuTQoUO0a9eOrl27snr16iTZd0Kze9Jv8eLF9O3bl2HDhrFnzx5KlSqFv78/ly9fjrV+pkyZGDx4MNu3b+fAgQN06tSJTp06sXbtWmudzz77jMmTJzN9+nR27NiBu7s7/v7+3L17N6meloiIpCA5cuSwLt7e3lgsFuv6P//8g6enJ6tXr6ZcuXI4Ozvz559/cuLECZo0aUL27Nnx8PCgQoUK/PbbbzbbfXQYAovFwsyZM2nWrBlubm74+fmxYsUK6/2P9sCbM2cOGTJkYO3atRQpUgQPDw/q16/PxYsXrY+5f/8+vXr1IkOGDGTOnJmBAwfSoUOH57pK68aNG7Rv356MGTPi5uZGgwYNbC6eOX36NI0bNyZjxoy4u7tTrFgxfv31V+tj27ZtS9asWXF1dcXPz4/Zs2c/cywiKVFa/UyZNWsWbdq04c033+Tbb7+Ncf+5c+do3bo1mTJlwt3dnfLly7Njxw7r/b/88gsVKlTAxcWFLFmy0KxZM5vn+ugVkxkyZGDOnDkAnDp1CovFwuLFi6lRowYuLi7Mnz+fa9eu0bp1a1544QXrFZsLFy602U5UVBSfffYZBQsWxNnZmTx58vC///0PgNq1a9OjRw+b+leuXMHJyYkNGzY88ZiIiIiIiIikZIcOHaJBgwZ4eHiQPXt23nzzTa5evWq9f82aNbz88svW35CNGjXixIkT1vvj+q0W3cNw/Pjx5MyZk8yZM9O9e3ebhODT/gYGWLFiBX5+fri4uFCrVi3mzp0br9GuPD09yZEjB/nz52fgwIFkypSJ9evXW+/fuXMnr7zyClmyZMHb25saNWqwZ88em1gBmjVrhsVisa4DLF++nLJly+Li4kL+/PkZMWJEvC/+fRZ2T/pNnDiRrl270qlTJ4oWLcr06dNxc3OL9UQBQM2aNWnWrBlFihShQIEC9O7dm5IlS/Lnn38C5tXSkyZNYsiQITRp0oSSJUvy3XffceHChWTRtVJEJK0xDLh1K+kXw0jY5/Hhhx/y6aefEhgYSMmSJQkLCyMgIIANGzawd+9e6tevT+PGjTlz5sxjtzNixAjeeOMNDhw4QEBAAG3btuX69etx1r99+zbjx49n3rx5/PHHH5w5c8aml9DYsWOZP38+s2fPZuvWrYSGhj73913Hjh3ZtWsXK1asYPv27RiGQUBAgLXh1b17d8LDw/njjz84ePAgY8eOxcPDA4CPP/6YI0eOsHr1agIDA5k2bRpZsmR5rnhEHmavz5SE/lxJbZ8pN2/eZMmSJbRr145XXnmFkJAQtmzZYr0/LCyMGjVqcP78eVasWMH+/fsZMGAAUVFRAKxatYpmzZoREBDA3r172bBhAxUrVnzifh/14Ycf0rt3bwIDA60X/ZUrV45Vq1Zx6NAh3n77bd58802bkUUGDRrEp59+av38WrBgAdmzZwegS5cuLFiwgPDwcGv977//nhdeeIHatWs/dXwiIiIiIpI2GIbBrYhbdlmMBPrxGhwcTO3atSlTpgy7du1izZo1XLp0iTfeeMNa59atW/Tt25ddu3axYcMGHBwcaNasmfW3XrRHf6sBbNq0iRMnTrBp0ybmzp3LnDlzrBd2xuVxv4FPnjxJ8+bNadq0Kfv376dbt24MHjz4qZ5zVFQUS5cu5caNGzg5OVnLb968SYcOHfjzzz/566+/8PPzIyAggJs3bwJmUhBg9uzZXLx40bq+ZcsW2rdvT+/evTly5Ahff/01c+bMsV5omigMOwoPDzccHR2Nn3/+2aa8ffv2xquvvvrEx0dFRRm//fab4ebmZqxbt84wDMM4ceKEARh79+61qVu9enWjV69e8YorJCTEAIyQkJB41RcREdOdO3eMI0eOGHfu3LGWhYUZhnmqPGmXsLBnew6zZ882vL29reubNm0yAGPZsmVPfGyxYsWMKVOmWNfz5s1rfP7559Z1wBgyZMhDxybMAIzVq1fb7OvGjRvWWADj+PHj1sd8+eWXRvbs2a3r2bNnN8aNG2ddv3//vpEnTx6jSZMmccb56H4e9u+//xqAsXXrVmvZ1atXDVdXV+OHH34wDMMwSpQoYQwfPjzWbTdu3Njo1KlTnPtOCLG9z6LpOzxletzr9ujrba/PlGf9XEkLnymGYRjffPONUbp0aet67969jQ4dOljXv/76a8PT09O4du1arI+vXLmy0bZt2zi3D8T4zeDt7W3Mnj3bMAzDOHnypAEYkyZNemychmEYDRs2NPr162cYhmGEhoYazs7OxowZM2Kte+fOHSNjxozG4sWLrWUlS5aM8zPwWT3uc01EJLlSuytl0usmIpLwYj0fFh5mMBy7LGHh8f/x2qFDhzh/740aNcqoV6+eTdnZs2cNwDh69Gisj7ly5YoBGAcPHjQMI+7fah06dDDy5s1r3L9/31rWokULo2XLltb1p/0NPHDgQKN48eI2+xk8eHCc58Ae3o+Tk5Ph7u5upEuXzgCMTJkyGceOHYvzMZGRkYanp6fxyy+/2MT36O/WOnXqGJ988olN2bx584ycOXPGut2EOOdl155+V69eJTIy0nolbbTs2bMTFBQU5+NCQkLw8PDAycmJhg0bMmXKFF555RUA6+OeZpvh4eGEhobaLCIiIg8rX768zXpYWBj9+/enSJEiZMiQAQ8PDwIDA5/YK6dkyZLW2+7u7nh5ecU5pDWAm5sbBQoUsK7nzJnTWj8kJIRLly7Z9IZxdHSkXLlyT/XcHhYYGEi6dOmoVKmStSxz5sy8+OKLBAYGAtCrVy9Gjx5N1apVGTZsGAcOHLDWfffdd1m0aBGlS5dmwIABbNu27ZljEUnNUttnyrfffku7du2s6+3atWPJkiXWqx737dtHmTJlyJQpU6yP37dvH3Xq1Hnifp7k0eMaGRnJqFGjKFGiBJkyZcLDw4O1a9daj2tgYCDh4eFx7tvFxcVmuNI9e/Zw6NAhOnbs+NyxioiIiIiIJGf79+9n06ZNeHh4WJfChQsDWIfwPHbsGK1btyZ//vx4eXlZh7V89Lfso7/VAIoVK4ajo6N1/eHfp3F53G/go0ePUqFCBZv68R1B5oMPPmDfvn1s3LiRSpUq8fnnn1OwYEHr/ZcuXaJr1674+fnh7e2Nl5cXYWFhT/zNvn//fkaOHGlzDLt27crFixe5fft2vGJ7WukSZauJzNPTk3379hEWFsaGDRvo27cv+fPnp2bNms+0vTFjxjBixIiEDVJERABwc4OwMPvsNyG5u7vbrPfv35/169czfvx4ChYsiKurK82bNyciIuKx20mfPr3NusViiTHkwZPqGwk9dulT6tKlC/7+/qxatYp169YxZswYJkyYQM+ePWnQoAGnT5/m119/Zf369dSpU4fu3bszfvx4u8YsqYe9PlOi951QUtNnypEjR/jrr7/4+++/GThwoLU8MjKSRYsW0bVrV1xdXR+7jSfdH1ucsU3+/uhxHTduHF988QWTJk2iRIkSuLu706dPH+txfdJ+wfzMK126NOfOnWP27NnUrl2bvHnzPvFxIiIiIiKSdrmldyNskH1+vLqlT5gfr2FhYTRu3JixY8fGuC9nzpwANG7cmLx58zJjxgx8fHyIioqiePHiMX7LPvpbDZ7+9+yzPiY+smTJQsGCBSlYsCBLliyhRIkSlC9fnqJFiwLQoUMHrl27xhdffEHevHlxdnamcuXKT/zNHhYWxogRI3jttddi3Ofi4vLcccfGrkm/LFmy4OjoyKVLl2zKL126RI4cOeJ8nIODgzXLWrp0aQIDAxkzZgw1a9a0Pu7SpUvWN170eunSpWPd3qBBg+jbt691PTQ0lNy5cz/r0xIRkYdYLBDL93qKt3XrVjp27EizZs0A80v81KlTSRqDt7c32bNnZ+fOnVSvXh0wT7Lv2bMnzu+8JylSpAj3799nx44dVKlSBYBr165x9OhRa0MHIHfu3Lzzzju88847DBo0iBkzZtCzZ08AsmbNSocOHejQoQPVqlXjgw8+UNJPEow+UxLPs36mzJo1i+rVq/Pll1/alM+ePZtZs2bRtWtXSpYsycyZM7l+/Xqsvf1KlizJhg0b6NSpU6z7yJo1KxcvXrSuHzt2LF5XRW7dupUmTZpYeyFGRUXx77//Wj/P/Pz8cHV1ZcOGDXTp0iXWbUT/2JsxYwYLFixg6tSpT9yviEhycucOODuDg13HehIREUlbLBYL7k4p+8dr2bJlWbp0Kb6+vqRLFzOVFH2+aMaMGVSrVg2AP//8M6nDtHrxxRf59ddfbcqi59Z7Grlz56Zly5YMGjSI5cuXA+Zvy6+++oqAgAAAzp49y9WrV20elz59eiIjI23KypYty9GjR216DSY2uzb5nJycKFeuHBs2bLCWRUVFsWHDBipXrhzv7URFRREeHg5Avnz5yJEjh802Q0ND2bFjR5zbdHZ2xsvLy2ZJDPPnw0svgToVioikfH5+fvz000/s27eP/fv306ZNmwS5suhp9ezZkzFjxrB8+XKOHj1K7969uXHjBhaL5YmPPXjwIPv27bMu+/fvx8/PjyZNmtC1a1f+/PNP9u/fT7t27XjhhRdo0qQJAH369GHt2rWcPHmSPXv2sGnTJooUKQLA0KFDWb58OcePH+fw4cOsXLnSep+IxC2lfqbcu3ePefPm0bp1a4oXL26zdOnShR07dnD48GFat25Njhw5aNq0KVu3buW///5j6dKlbN++HYBhw4axcOFChg0bRmBgIAcPHrS5mrR27dpMnTqVvXv3smvXLt55550YV3jGxs/Pj/Xr17Nt2zYCAwPp1q2bzQWHLi4uDBw4kAEDBvDdd99x4sQJ/vrrL2bNmmWznS5duvDpp59iGIY1MSsikhIcPAjlysHEifaORERERJKrkJAQm/ND+/bt4+zZs3Tv3p3r16/TunVrdu7cyYkTJ1i7di2dOnUiMjKSjBkzkjlzZr755huOHz/Oxo0bbTpXJbVu3brxzz//MHDgQP79919++OEH5syZAxCv82QP6927N7/88gu7du0CzN+W8+bNIzAwkB07dtC2bdsYI8f4+vqyYcMGgoKCuHHjBmCeJ/vuu+8YMWIEhw8fJjAwkEWLFjFkyJDnf8JxsPt1Xn379mXGjBnMnTuXwMBA3n33XW7dumW9yrd9+/YMGjTIWn/MmDGsX7+e//77j8DAQCZMmMC8efOsV+9aLBb69OnD6NGjWbFiBQcPHqR9+/b4+PjQtGlTezxFq6tXYccO+P8pkUREJAWbOHEiGTNmpEqVKjRu3Bh/f3/Kli2b5HEMHDiQ1q1b0759eypXroyHhwf+/v7xGiKgevXqlClTxrpEz9s1e/ZsypUrR6NGjahcuTKGYfDrr79aT7BHRkbSvXt3ihQpQv369SlUqBBfffUVYF7QM2jQIEqWLEn16tVxdHRk0aJFiXcARFKJlPqZsmLFCq5duxZrIqxIkSIUKVKEWbNm4eTkxLp168iWLRsBAQGUKFGCTz/91Dp/Q82aNVmyZAkrVqygdOnS1K5dm7///tu6rQkTJpA7d26qVatGmzZt6N+/P27xGG91yJAhlC1bFn9/f+uoII/+Jvj444/p168fQ4cOpUiRIrRs2TLGPBKtW7cmXbp0tG7dOtGGYBERSUiGAV9/DRUrmucgpk41e/yJiIiIPGrz5s0254fKlCnDiBEj8PHxYevWrURGRlKvXj1KlChBnz59yJAhAw4ODjg4OLBo0SJ2795N8eLFef/99xk3bpzdnke+fPn48ccf+emnnyhZsiTTpk1j8ODBgNnx62kULVqUevXqMXToUMAc4ebGjRuULVuWN998k169epEtWzabx0yYMIH169eTO3duypQpA4C/vz8rV65k3bp1VKhQgZdeeonPP/88UaeMsBj2nhgImDp1KuPGjSMoKIjSpUszefJkKlWqBJgnAHx9fa0Z2SFDhrB48WLOnTuHq6srhQsXpnfv3rRs2dK6PcMwGDZsGN988w3BwcG8/PLLfPXVVxQqVChe8YSGhuLt7U1ISEiC9vqbOxc6doT69WH16gTbrIhIsnH37l1OnjxJvnz5dFLUTqKioihSpAhvvPEGo0aNsnc4ieJx77PE+g6XxPW4102fK/aVFj5T4uPUqVMUKFCAnTt3JkoyVu9zEUlIwcHw9tuwZIm53qCBeT4ia9aE3Y/aXSmTXjcRkYSn9nzy9b///Y/p06dz9uxZe4cSLwlxzsuuc/pF69GjBz169Ij1vs2bN9usjx49mtGjRz92exaLhZEjRzJy5MiECjFBeHubf4OD7RqGiIikIqdPn2bdunXUqFGD8PBwpk6dysmTJ2nTpo29QxORFEifKbbu3bvHtWvXGDJkCC+99JJdel+KiDyNHTugVSs4dQrSp4dPP4U+fTSfn4iIiKQNX331FRUqVCBz5sxs3bqVcePGxZl7Sq2SRdIvrciQwfwbEmLXMEREJBVxcHBgzpw59O/fH8MwKF68OL/99pvm0RORZ6LPFFtbt26lVq1aFCpUiB9//NHe4YiIxCkqCsaPh8GD4f59yJ8fFi2CChXsHZmIiIhI0jl27BijR4/m+vXr5MmTh379+tlMH5cWKOmXhNTTT0REElru3LnZunWrvcMQkVRCnym2atasSTKYDUFE5LEuX4b27WHtWnO9ZUtzPr/ocxAiIiIiacXnn3/O559/bu8w7EoDPCQh9fQTERERERERkYSyYQOUKmUm/FxdYcYMWLhQCT8RERGRtEo9/ZJQdNLv9m2IiAAnJ7uGIyIiIiIiIiIpzN278MsvMHcu/PorGAYUKwaLF5t/RURERCTtUtIvCXl5PbgdEgJZs9ovFhERERERERFJGQwDduwwE32LFtlOG9K1K0yaBG5u9opOREREAKKiouwdgqRwCfEeUtIvCTk6gqcn3LyppJ+IiIiIiIiIPN7ZszBvHnz3HRw9+qA8Vy54801zLr/Che0Xn4iIiICTkxMODg5cuHCBrFmz4uTkhMVisXdYkoIYhkFERARXrlzBwcEBp+cYJlJJvyTm7W0m/R6+Kk9EREREREREJNrmzTB6NGzcaPbyA7Mn32uvQYcOUKuWeWGxiIiI2J+DgwP58uXj4sWLXLhwwd7hSArm5uZGnjx5cHBweOZtKOmXxDJkgHPnzJ5+IiIiIiIiIiLR7t+HkSPNhF90sq9mTTPR9/rr5uhBIiIikvw4OTmRJ08e7t+/T2RkpL3DkRTI0dGRdOnSPXcvUSX9kpi3t/lXPf1ERFKfmjVrUrp0aSZNmgSAr68vffr0oU+fPnE+xmKx8PPPP9O0adPn2ndCbUdEkg99poiIpC3nz0ObNvDHH+Z6584wZAj4+to1LBEREYkni8VC+vTpSZ8+vb1DkTTs2fsIyjPJkMH8q55+IiLJR+PGjalfv36s923ZsgWLxcKBAweeers7d+7k7bffft7wbAwfPpzSpUvHKL948SINGjRI0H09as6cOWSI/iITkTjpM+Xp3Llzh0yZMpElSxbCw8OTZJ8iIsnNmjVQurSZ8PPwgIULYeZMJfxERERE5Oko6ZfEos+VqqefiEjy0blzZ9avX8+5c+di3Dd79mzKly9PyZIln3q7WbNmxc3NLSFCfKIcOXLg7OycJPsSkcfTZ8rTWbp0KcWKFaNw4cIsW7YsSfYZF8MwuH//vl1jEJG05d49+PBDaNAArl6FMmVgzx5o1crekYmIiIhISqSkXxLT8J4iIslPo0aNyJo1K3PmzLEpDwsLY8mSJXTu3Jlr167RunVrXnjhBdzc3ChRogQLFy587HZ9fX2tw/IBHDt2jOrVq+Pi4kLRokVZv359jMcMHDiQQoUK4ebmRv78+fn444+5d+8eYPa0GzFiBPv378disWCxWKwxWywWm5PlBw8epHbt2ri6upI5c2befvttwsLCrPd37NiRpk2bMn78eHLmzEnmzJnp3r27dV/P4syZMzRp0gQPDw+8vLx44403uHTpkvX+/fv3U6tWLTw9PfHy8qJcuXLs2rULgNOnT9O4cWMyZsyIu7s7xYoV49dff33mWETsSZ8pT/eZMmvWLNq1a0e7du2YNWtWjPsPHz5Mo0aN8PLywtPTk2rVqnHixAnr/d9++y3FihXD2dmZnDlz0qNHDwBOnTqFxWJh37591rrBwcFYLBY2b94MwObNm7FYLKxevZpy5crh7OzMn3/+yYkTJ2jSpAnZs2fHw8ODChUq8Ntvv9nEFR4ezsCBA8mdOzfOzs4ULFiQWbNmYRgGBQsWZPz48Tb19+3bh8Vi4fjx4088JiKSNpw5Y87XN3asud6jB2zbBn5+dg1LRERERFIwzemXxDS8p4ikOYYBt28n/X7d3CCeE9+mS5eO9u3bM2fOHAYPHmydMHfJkiVERkbSunVrwsLCKFeuHAMHDsTLy4tVq1bx5ptvUqBAASpWrPjEfURFRfHaa6+RPXt2duzYQUhISKzzcnl6ejJnzhx8fHw4ePAgXbt2xdPTkwEDBtCyZUsOHTrEmjVrrCefvaOvJnnIrVu38Pf3p3LlyuzcuZPLly/TpUsXevToYZOE2LRpEzlz5mTTpk0cP36cli1bUrp0abp27Rqv4/bo84tO+P3+++/cv3+f7t2707JlS+vJ9bZt21KmTBmmTZuGo6Mj+/bts45z3717dyIiIvjjjz9wd3fnyJEjeHh4PHUckgbY6zMF4v25os+U+H+mnDhxgu3bt/PTTz9hGAbvv/8+p0+fJm/evACcP3+e6tWrU7NmTTZu3IiXlxdbt2619sabNm0affv25dNPP6VBgwaEhISwdevWJx6/R3344YeMHz+e/PnzkzFjRs6ePUtAQAD/+9//cHZ25rvvvqNx48YcPXqUPHnyANC+fXu2b9/O5MmTKVWqFCdPnuTq1atYLBbeeustZs+eTf/+/a37mD17NtWrV6dgwYJPHZ+IpD4rVkDHjnDjhnlx8KxZ8Prr9o5KRERERFI8Q2IICQkxACMkJCTBtz12rGGAYbRvn+CbFhGxuzt37hhHjhwx7ty586AwLMz84EvqJSzsqWIPDAw0AGPTpk3WsmrVqhnt2rWL8zENGzY0+vXrZ12vUaOG0bt3b+t63rx5jc8//9wwDMNYu3atkS5dOuP8+fPW+1evXm0Axs8//xznPsaNG2eUK1fOuj5s2DCjVKlSMeo9vJ1vvvnGyJgxoxH20DFYtWqV4eDgYAQFBRmGYRgdOnQw8ubNa9y/f99ap0WLFkbLli3jjGX27NmGt7d3rPetW7fOcHR0NM6cOWMtO3z4sAEYf//9t2EYhuHp6WnMmTMn1seXKFHCGD58eJz7flis77P/l5jf4ZJ4Hve6xXi97fWZ8pSfK/pMefJnimEYxkcffWQ0bdrUut6kSRNj2LBh1vVBgwYZ+fLlMyIiImJ9vI+PjzF48OBY7zt58qQBGHv37rWW3bhxw+Z12bRpkwEYy5Yte2ychmEYxYoVM6ZMmWIYhmEcPXrUAIz169fHWvf8+fOGo6OjsWPHDsMwDCMiIsLIkiVLnJ+Bj/tcE5HUJSLCMPr2ffDVUqGCYZw4Ye+ono3aXSmTXjcREZGUKb7f4RreM4mpp5+ISPJUuHBhqlSpwrfffgvA8ePH2bJlC507dwYgMjKSUaNGUaJECTJlyoSHhwdr167lzJkz8dp+YGAguXPnxsfHx1pWuXLlGPUWL15M1apVyZEjBx4eHgwZMiTe+3h4X6VKlcLd3d1aVrVqVaKiojh69Ki1rFixYjg6OlrXc+bMyeXLl59qXw/vM3fu3OTOndtaVrRoUTJkyEBgYCAAffv2pUuXLtStW5dPP/3UZni+Xr16MXr0aKpWrcqwYcM4cODAM8UhklzoM+XJnymRkZHMnTuXdu3aWcvatWvHnDlziIqKAswhMatVq2btFfywy5cvc+HCBerUqfNUzyc25cuXt1kPCwujf//+FClShAwZMuDh4UFgYKD12O3btw9HR0dq1KgR6/Z8fHxo2LCh9fX/5ZdfCA8Pp0WLFs8dq4ikXEFBUKcOTJxorr//Pvz5J+TPb9+4RERERCT1UNIviWlOPxFJc9zcICws6Rc3t6cOtXPnzixdupSbN28ye/ZsChQoYD2hO27cOL744gsGDhzIpk2b2LdvH/7+/kRERCTYodq+fTtt27YlICCAlStXsnfvXgYPHpyg+3jYoyfRLRaL9UR7Yhg+fDiHDx+mYcOGbNy4kaJFi/Lzzz8D0KVLF/777z/efPNNDh48SPny5ZkyZUqixSIpmL0+U57hc0WfKY//TFm7di3nz5+nZcuWpEuXjnTp0tGqVStOnz7Nhg0bAHB1dY3z8Y+7D8DBwfypYxiGtSyuOQYfTmgC9O/fn59//plPPvmELVu2sG/fPkqUKGE9dk/aN5ifa4sWLeLOnTvMnj2bli1b4vYM300ikjr8+SeULQtbtoCXF/z8s5n8c3Kyd2QiIiIikpoo6ZfE1NNPRNIciwXc3ZN+ied8fg974403cHBwYMGCBXz33Xe89dZb1rm4tm7dSpMmTWjXrh2lSpUif/78/Pvvv/HedpEiRTh79iwXL160lv311182dbZt20bevHkZPHgw5cuXx8/Pj9OnT9vUcXJyIjIy8on72r9/P7du3bKWbd26FQcHB1588cV4x/w0op/f2bNnrWVHjhwhODiYokWLWssKFSrE+++/z7p163jttdeYPXu29b7cuXPzzjvv8NNPP9GvXz9mzJiRKLFKCmevz5Rn+FzRZ8rjzZo1i1atWrFv3z6bpVWrVsyaNQuAkiVLsmXLlliTdZ6envj6+loThI/KmjUrgM0x2rdvX7xi27p1Kx07dqRZs2aUKFGCHDlycOrUKev9JUqUICoqit9//z3ObQQEBODu7s60adNYs2YNb731Vrz2LSKpi2HA5MlQqxZcvAjFisHOndC0qb0jExEREZHUSEm/JBad9FNPPxGR5MfDw4OWLVsyaNAgLl68SMeOHa33+fn5sX79erZt20ZgYCDdunXj0qVL8d523bp1KVSoEB06dGD//v1s2bKFwYMH29Tx8/PjzJkzLFq0iBMnTjB58mRrT7hovr6+nDx5kn379nH16lXCw8Nj7Ktt27a4uLjQoUMHDh06xKZNm+jZsydvvvkm2bNnf7qD8ojIyMgYJ+gDAwOpW7cuJUqUoG3btuzZs4e///6b9u3bU6NGDcqXL8+dO3fo0aMHmzdv5vTp02zdupWdO3dSpEgRAPr06cPatWs5efIke/bsYdOmTdb7RFIqfabE7cqVK/zyyy906NCB4sWL2yzt27dn2bJlXL9+nR49ehAaGkqrVq3YtWsXx44dY968edZhRYcPH86ECROYPHkyx44dY8+ePdZewq6urrz00kt8+umnBAYG8vvvvzNkyJB4xefn58dPP/3Evn372L9/P23atLHptejr60uHDh146623WLZsGSdPnmTz5s388MMP1jqOjo507NiRQYMG4efnF+vwqyKSut26BW3bQu/ecP8+tGoFO3ZAoUL2jkxEREREUisl/ZKYhvcUEUneOnfuzI0bN/D397eZK2vIkCGULVsWf39/atasSY4cOWj6FJdoOzg48PPPP3Pnzh0qVqxIly5d+N///mdT59VXX+X999+nR48elC5dmm3btvHxxx/b1Hn99depX78+tWrVImvWrCxcuDDGvtzc3Fi7di3Xr1+nQoUKNG/enDp16jB16tSnOxixCAsLo0yZMjZL48aNsVgsLF++nIwZM1K9enXq1q1L/vz5Wbx4MWCe/L527Rrt27enUKFCvPHGGzRo0IARI0YAZjKxe/fuFClShPr161OoUCG++uqr545XxN70mRK77777Dnd391jn46tTpw6urq58//33ZM6cmY0bNxIWFkaNGjUoV64cM2bMsA4l2qFDByZNmsRXX31FsWLFaNSoEceOHbNu69tvv+X+/fuUK1eOPn36MHr06HjFN3HiRDJmzEiVKlVo3Lgx/v7+lC1b1qbOtGnTaN68Oe+99x6FCxema9euNr0hwXz9IyIi6NSp09MeIhFJ4Y4dg5degoULIV06+OILWLDA7DguIiIiIpJYLMbDk1wIAKGhoXh7exMSEoKXl1eCbjsoCHLmBAcHuHfP/CsiklrcvXuXkydPki9fPlxcXOwdjqRSj3ufJeZ3uCSex71u+lyRlGzLli3UqVOHs2fPPrZXpN7nIqnL8uXQvj2EhkKOHLBkCbz8sr2jSnhqd6VMet1ERERSpvh+hyvllMSie/pFRUFYmH1jERERERGRhBceHs65c+cYPnw4LVq0eO6hlUUkZbh1CwYONOfrCw01E3179qTOhJ+IiIiIJE9K+iUxFxdwcjJvh4TYNxYREREREUl4CxcuJG/evAQHB/PZZ5/ZOxwRSWT37sH06VCwIET/y7//PmzcaI70IyIiIiKSVJT0S2IWi+b1ExERERFJzTp27EhkZCS7d+/mhRdesHc4IpJIoqJg8WIoWhTefdecziNfPli6FCZOhP+fflREREREJMko6WcHGTKYf9XTT0RERERERCTlWb8eKlSAVq3g+HHIlg2mTIF//oHXXrN3dCIiIiKSVqWzdwBpkXr6iYiIiIiIiKQ8O3fCoEGwYYO57ukJ/fubw3l6eto3NhERERERJf3sILqnn5J+IpJaRUVF2TsEScX0/kqbDMOwdwgiiUbvb5Hk7+JF6NULfvzRXHdygvfeg48+gqxZ7RubiIiIiEg0Jf3sQMN7ikhq5eTkhIODAxcuXCBr1qw4OTlhsVjsHZakEoZhEBERwZUrV3BwcMDJycneIUkSSJ8+PRaLhStXrpA1a1Z9pkiqYxgGV65cwWKxkF4TgIkkS7/9Bm3bwuXLYLFA+/YwfDj4+to7MhERERERW0r62YGG9xSR1MrBwYF8+fJx8eJFLly4YO9wJJVyc3MjT548ODhoauK0wNHRkVy5cnHu3DlOnTpl73BEEoXFYiFXrlw4OjraOxQReUhkJIwcCaNGgWFAyZIwb575V0REREQkOVLSzw7U009EUjMnJyfy5MnD/fv3iYyMtHc4kso4OjqSLl069fZKYzw8PPDz8+PevXv2DkUkUaRPn14JP5Fk5uJFaNMGNm82199+GyZNAldXe0YlIiIiIvJ4SvrZgXr6iUhqFz1EmYYpE5GE4ujoqKSIiIgkiYeH8/TwgK+/NhOAIiIiIiLJncbFsgP19BMRERERERFJXiIjYehQqFfPTPiVLAm7dinhJyIiIiIph3r62YF6+omIiIiIiIgkHxrOU0RERERSA/X0s4Ponn5K+omIiIiIiIjYT1QULFoEpUubCT8PD5g/3xzSUwk/EREREUlp1NPPDjS8p4iIiIiIiIj9GAb8+isMHgz795tlJUvCDz/Aiy/aNzYRERERkWelnn52oOE9RUREREREROzjjz+gWjVo1MhM+Hl5wciR8NdfSviJiIiISMqmnn52oJ5+IiIiIiIiIklrzx6zZ9+aNea6iwv06gUDBkDmzPaNTUREREQkISjpZwfRPf3u3oXwcHB2tm88IiIiIiIiIqnVP//Axx/Djz+a6+nSQdeuMGQI+PjYNzYRERERkYSk4T3twMsLLBbztnr7iYiIiIiIiCS8qCj48EMoVsxM+Fks0K6dmQT86isl/EREREQk9VHSzw4cHMDT07ytef1EREREREREElZkpNmbb+xYM/nXpIk5f9+8eVCggL2jExERERFJHEr62Ynm9RMRERERERFJeBER0KYNfPutedHt3LmwbBmUKGHvyCSpfPnll/j6+uLi4kKlSpX4+++/H1t/yZIlFC5cGBcXF0qUKMGvv/5qc79hGAwdOpScOXPi6upK3bp1OXbsmE2d69ev07ZtW7y8vMiQIQOdO3cmLCzMev/du3fp2LEjJUqUIF26dDRt2jTWWDZv3kzZsmVxdnamYMGCzJkzJ864P/30UywWC3369Hns8xMREZG0Q0k/O4lO+qmnn4iIiIiIiEjCuHMHXnsNfvgB0qc3/7Zvb++oJCktXryYvn37MmzYMPbs2UOpUqXw9/fn8uXLsdbftm0brVu3pnPnzuzdu5emTZvStGlTDh06ZK3z2WefMXnyZKZPn86OHTtwd3fH39+fu3fvWuu0bduWw4cPs379elauXMkff/zB22+/bb0/MjISV1dXevXqRd26dWON5eTJkzRs2JBatWqxb98++vTpQ5cuXVi7dm2Mujt37uTrr7+mZMmSz3qoREREJBWyGIZh2DuI5CY0NBRvb29CQkLw8vJKlH1Urw5btpg/QFq0SJRdiIiIpDlJ8R0uCU+vm4iIJISwMHj1Vdi0CVxc4OefoX59e0eVeiXX7+9KlSpRoUIFpk6dCkBUVBS5c+emZ8+efPjhhzHqt2zZklu3brFy5Upr2UsvvUTp0qWZPn06hmHg4+NDv3796N+/PwAhISFkz56dOXPm0KpVKwIDAylatCg7d+6kfPnyAKxZs4aAgADOnTuHzyMTSHbs2JHg4GCWLVtmUz5w4EBWrVplk3Bs1aoVwcHBrFmzxloWFhZG2bJl+eqrrxg9ejSlS5dm0qRJ8To+yfV1ExERkceL73e4evrZiYb3FBEREREREUkYN27AK6+YCT9PT1i7Vgm/tCgiIoLdu3fb9KRzcHCgbt26bN++PdbHbN++PUbPO39/f2v9kydPEhQUZFPH29ubSpUqWets376dDBkyWBN+AHXr1sXBwYEdO3bEO/4nxRKte/fuNGzYMM4egw8LDw8nNDTUZhEREZHUS0k/O/H2Nv9qeE8RERERERGRZ3f5MtSqBX/9BRkzwoYN5ug6kvZcvXqVyMhIsmfPblOePXt2goKCYn1MUFDQY+tH/31SnWzZstncny5dOjJlyhTnfp8mltDQUO7cuQPAokWL2LNnD2PGjInXNseMGYO3t7d1yZ07d7zjERERkZRHST87UU8/ERERERERkedz9ixUqwb790P27PD771Chgr2jEkkcZ8+epXfv3syfPx8XF5d4PWbQoEGEhIRYl7NnzyZylCIiImJP6ewdQFqlnn4iIiIiIiIiz+7ECahTB06fhty5zR5+fn72jkrsKUuWLDg6OnLp0iWb8kuXLpEjR45YH5MjR47H1o/+e+nSJXLmzGlTp3Tp0tY6ly9fttnG/fv3uX79epz7fZpYvLy8cHV1Zffu3Vy+fJmyZcta74+MjOSPP/5g6tSphIeH4+joaPN4Z2dnnJ2d4x2DiIiIpGzq6Wcn6uknIiIiIiIi8vSuXoWPP4ayZc2EX8GC8OefSvgJODk5Ua5cOTZs2GAti4qKYsOGDVSuXDnWx1SuXNmmPsD69eut9fPly0eOHDls6oSGhrJjxw5rncqVKxMcHMzu3butdTZu3EhUVBSVKlWKd/xPiqVOnTocPHiQffv2WZfy5cvTtm1b9u3bFyPhJyIiImmPevrZiXr6iYiIiIiIiMRfUBBMmADTpsGtW2ZZ+fLwyy/wFJ2pJJXr27cvHTp0oHz58lSsWJFJkyZx69YtOnXqBED79u154YUXrHPi9e7dmxo1ajBhwgQaNmzIokWL2LVrF9988w0AFouFPn36MHr0aPz8/MiXLx8ff/wxPj4+NG3aFIAiRYpQv359unbtyvTp07l37x49evSgVatW+Pj4WGM7cuQIERERXL9+nZs3b7Jv3z4Aa4/Bd955h6lTpzJgwADeeustNm7cyA8//MCqVasA8PT0pHjx4jbP193dncyZM8coFxERkbRJST87ie7pp6SfiIiIiIiISNzOnoVx42DGDLh71ywrUwaGDIGmTcFBYxjJQ1q2bMmVK1cYOnQoQUFBlC5dmjVr1pA9e3YAzpw5g8NDb5oqVaqwYMEChgwZwkcffYSfnx/Lli2zSaINGDCAW7du8fbbbxMcHMzLL7/MmjVrbObVmz9/Pj169KBOnTo4ODjw+uuvM3nyZJvYAgICOH36tHW9TJkyABiGAZi9CletWsX777/PF198Qa5cuZg5cyb+/v4Jf6BEREQkVbIY0S0LsQoNDcXb25uQkBC8vLwSZR/r10O9elCypDnhuIiIiDy/pPgOl4Sn101ERGLz338wdizMng337pllL71kDu3ZoAFYLPaNL63T93fKpNdNREQkZYrvd7h6+tmJhvcUERERERERiSk4GPr1g7lzITLSLKtRw0z21a6tZJ+IiIiISFyU9LOT6OE9Q0LsGoaIiIiIiIhIsnHwILz2Ghw/bq77+8PgwVCtmn3jEhERERFJCZT0s5Ponn6hoRAVpTkIREREREREJG1bsAC6dIE7dyBvXpg/H6pWtXdUIiIiIiIph1JNdhKd9DMMuHnTvrGIiIiIiIiI2Mu9e9C7N7Rtayb8XnkFdu9Wwk9ERERE5Gkp6WcnLi7g7Gze1rx+IiIiIiIikhZdvGjO0zd5srn+0UewejVkzmzfuEREREREUiIl/ewoel4/Jf1EREREREQkrdm6FcqWhT//BC8vWLYM/vc/cHS0d2QiIiIiIimTkn52FJ30CwmxaxgiIiIiIiIiScYwzJ59NWtCUBAUKwY7d0KTJvaOTEREREQkZVPSz46i5/VTTz8RERERERFJC27fhnbtzDn87t+Hli3hr7+gUCF7RyYiIiIikvKls3cAaZl6+omIiIiIiEhaERQEr75q9upzdIRx46BPH7BY7B2ZiIiIiEjqoKSfHamnn4iIiIiIiKQFhw5Bw4Zw5gxkygRLl5rDe4qIiIiISMLR8J52pJ5+IiIiIiIiktqtWwdVq5oJPz8/czhPJfxERERERBKekn52pJ5+IiIiIiIikpp98w0EBEBoKFSvDtu3m4k/ERERERFJeEr62VF0Tz8l/URERERERCQ1iYqCAQOgWzeIjIR27cwef5kz2zsyEREREZHUS0k/O9LwniIiIpLUvvzyS3x9fXFxcaFSpUr8/fffcda9d+8eI0eOpECBAri4uFCqVCnWrFkTo9758+dp164dmTNnxtXVlRIlSrBr167EfBoiIpKM3b4NLVrAuHHm+vDh8N134Oxs17BERERERFI9Jf3sSMN7ioiISFJavHgxffv2ZdiwYezZs4dSpUrh7+/P5cuXY60/ZMgQvv76a6ZMmcKRI0d45513aNasGXv37rXWuXHjBlWrViV9+vSsXr2aI0eOMGHCBDJmzJhUT0tERJKRoCBzvr6ffgInJ5g3D4YNA4vF3pGJiIiIiKR+SvrZkXr6iYhIqhIUBJUqQZs2cOWKvaORWEycOJGuXbvSqVMnihYtyvTp03Fzc+Pbb7+Ntf68efP46KOPCAgIIH/+/Lz77rsEBAQwYcIEa52xY8eSO3duZs+eTcWKFcmXLx/16tWjQIECSfW0REQkmdixA156CXbuhEyZ4LffzGE9RUREREQkaSjpZ0fq6SciIqnKp5/C33/DwoVQsqQ5cY8kGxEREezevZu6detayxwcHKhbty7bt2+P9THh4eG4uLjYlLm6uvLnn39a11esWEH58uVp0aIF2bJlo0yZMsyYMSNxnoSIiCRLgYHw+utmwu/0afDzg7/+gmrV7B2ZiIiIiEjaoqSfHamnn4iIpBpBQfD11+bt3LnNdX9/eP99uHvXvrEJAFevXiUyMpLs2bPblGfPnp2goKBYH+Pv78/EiRM5duwYUVFRrF+/np9++omLFy9a6/z3339MmzYNPz8/1q5dy7vvvkuvXr2YO3dunLGEh4cTGhpqs4iISMpz5gy89RYUL24O52mxQPv2sH27mfgTEREREZGkpaSfHT3c088w7BqKiIjI8xk/3kzuVa4MR49Cjx5m+aRJULEiHDpk1/Dk2XzxxRf4+flRuHBhnJyc6NGjB506dcLB4UETMioqirJly/LJJ59QpkwZ3n77bbp27cr06dPj3O6YMWPw9va2Lrlz506KpyMiIgnk8mXo08dM7M2eDVFR0LQpHDwIc+dC5sz2jlBEREREJG1S0s+Oonv6RUSoE4SIiKRgly/DtGnm7aFDwdUVpkyBlSshWzbzDGD58maZrnKxmyxZsuDo6MilS5dsyi9dukSOHDlifUzWrFlZtmwZt27d4vTp0/zzzz94eHiQP39+a52cOXNStGhRm8cVKVKEM2fOxBnLoEGDCAkJsS5nz559jmcmIiJJJTQUhg2DAgXgiy/M37K1apk9+37+GYoVs3eEIiIiIiJpm5J+duThYQ5/AhriU0REUrCJE+H2bahQwRzSM1rDhnDgAAQEQHg49Opl3o5jKElJXE5OTpQrV44NGzZYy6KiotiwYQOVK1d+7GNdXFx44YUXuH//PkuXLqVJkybW+6pWrcrRo0dt6v/777/kzZs3zu05Ozvj5eVls4iISPJ1+zZMmAD588PIkRAWBuXKmdP3bthgzuUnIiIiIiL2p6SfHTk42A7xKSIikuJcvQpTp5q3P/74wdUs0bJnN3v8TZ0KLi6wZg2ULGmWSZLr27cvM2bMYO7cuQQGBvLuu+9y69YtOnXqBED79u0ZNGiQtf6OHTv46aef+O+//9iyZQv169cnKiqKAQMGWOu8//77/PXXX3zyySccP36cBQsW8M0339C9e/ckf34iIpKw7t41e/Tlzw/9+8O1a1C4MPz4I+zcCa+8EvOrX0RERERE7CedvQNI6zJkMBN+6uknIiIp0qRJcOsWlC4NjRrFXsdige7doWZNaNPG7P3XuDH8+SdUrZqEwUrLli25cuUKQ4cOJSgoiNKlS7NmzRqyZ88OwJkzZ2zm67t79y5Dhgzhv//+w8PDg4CAAObNm0eG6DHKgQoVKvDzzz8zaNAgRo4cSb58+Zg0aRJt27ZN6qcnIiIJJDwcZs2C//0PLlwwy3x9zVG833wT0ulMgoiIiIhIsmQxDE2u86jQ0FC8vb0JCQlJ9OGmSpeG/fvNjg8Pj4gmIiKS7N24AXnzws2b8NNP0KzZkx8THg4ffQSnT8OSJQnePSApv8Ml4eh1ExFJHu7dgzlzYPRoiJ6aNXduszN/hw7g5GTX8CSZ0fd3yqTXTUREJGWK73e4rs+zs+gL5dXTT0REUpwvvjATfiVKwENzvD2Ws7M5KVBkpMYDExERSSbu34d582DUKDh50izz8YHBg6FzZ/PrW0REREREkj8l/exMc/qJiEiKFBJiDu0J5uX/Dk85TbCjY4KHJCIiIk/v77/NXnz//GOuZ88OgwbB22+Dq6t9YxMRERERkaejpJ+dqaefiIikSFOmmF9eRYvC66/bOxoRERF5ShER5jCen3xidsDPkgUGDoT33gM3N3tHJyIiIiIiz0JJPztTTz8REUlxbt6EiRPN20OGPH0vPxEREbGrw4ehfXvYs8dcb90apk6FTJnsG5eIiIiIiDwfnaWzs+iefkr6iYhIivHll3DjBhQqBG+8Ye9oREREJJ6ioszrdsqVMxN+mTLB4sWwYIESfiIiIiIiqYF6+tmZhvcUEZEUJSwMJkwwbw8Zorn5REREUohTp6BjR/j9d3O9QQOYORN8fOwZlYiIiIiIJCT19LMzDe8pIiIpyvTpcPUqFChgjgUmIiIiyZphwLffQokSZsLP3R2+/hpWrVLCT0REREQktVFPPztTTz8REUkxbt+GcePM24MHQzo1I0RERJKzCxfgnXfgl1/M9apVYe5c89odERERERFJfdTTz87U009ERFKMb76By5fB1xfatbN3NCIiIhKHqCizN1+RImbCz8kJxo41e/op4SciIiIiknrpEn07U08/ERFJEe7cMc8WAnz0EaRPb994REREJFZHj0LXrrBli7leoQLMmmUO7ykiIiIiIqmbevrZmXr6iYhIivDZZxAUBLlzQ4cO9o5GREREHhERAaNHQ8mSZsLP3R0mTYLt25XwExERERFJK9TTz86ie/rdvAmRkeDoaNdwREREYlq/HkaMMG9/8ok5RpiIiIgkGzt2QJcucOiQud6gAUybBnnz2jcuERERERFJWurpZ2fRPf0AQkPtF4eIiEiszp6F1q3BMMyziZrLT0REJNm4eRN694bKlc2EX5YsMH8+rFqlhJ+IiIiISFqULJJ+X375Jb6+vri4uFCpUiX+/vvvOOvOmDGDatWqkTFjRjJmzEjdunVj1O/YsSMWi8VmqV+/fmI/jWfi5ASuruZtDfEpIiLJSkQEtGgB165B2bIwZYq9IxIREZH/t2ULFC8Okyeb1+a0bw+BgdCmDVgs9o5ORERERETswe5Jv8WLF9O3b1+GDRvGnj17KFWqFP7+/ly+fDnW+ps3b6Z169Zs2rSJ7du3kzt3burVq8f58+dt6tWvX5+LFy9al4ULFybF03km0UN8hoTYNQwRERFb/fqZ44VlyAA//gguLvaOSEREJM0zDDPRV7s2nDkD+fLB2rUwd67Z009ERERERNIuuyf9Jk6cSNeuXenUqRNFixZl+vTpuLm58e2338Zaf/78+bz33nuULl2awoULM3PmTKKiotiwYYNNPWdnZ3LkyGFdMmbMmBRP55lED/Gpnn4iIpJsLFwIU6eat+fNM88oioiIiF3dvm326OvdG+7fN3v1HTwI9erZOzIREREREUkO7Jr0i4iIYPfu3dStW9da5uDgQN26ddm+fXu8tnH79m3u3btHpkyZbMo3b95MtmzZePHFF3n33Xe5du1agsaekNTTT0REkpXDh835+wAGD4ZGjewbj4iIiHDyJFStCt9/D46O8Pnn5m13d3tHJiIiIiIiyUU6e+786tWrREZGkj17dpvy7Nmz888//8RrGwMHDsTHx8cmcVi/fn1ee+018uXLx4kTJ/joo49o0KAB27dvx9HRMcY2wsPDCQ8Pt66HhoY+4zN6NurpJyIiycbNm/D662ZXgjp1YMQIe0ckIiKS5q1bB61bw/XrkDUr/PAD1Kxp76hERERERCS5sWvS73l9+umnLFq0iM2bN+Py0DxDrVq1st4uUaIEJUuWpECBAmzevJk6derE2M6YMWMYYceTmtE9/ZT0ExERuzIMs4ff0aPwwguwYIHZlUBERETswjBg7Fiz431UFFSsCEuXQq5c9o5MRERERESSI7sO75klSxYcHR25dOmSTfmlS5fIkSPHYx87fvx4Pv30U9atW0fJkiUfWzd//vxkyZKF48ePx3r/oEGDCAkJsS5nz559uifynKJ7+ml4TxERsavJk82uA+nSwZIlkC2bvSMSERFJs27ehBYtYNAgM+HXuTP8/rsSfiIiIiIiEje7Jv2cnJwoV64cGzZssJZFRUWxYcMGKleuHOfjPvvsM0aNGsWaNWsoX778E/dz7tw5rl27Rs6cOWO939nZGS8vL5slKamnn4iI2N22bdC/v3l7wgR4zPewiIiIJK4DB+Cll8xefenTw/TpMGMGPDTAjYiIiIiISAx2TfoB9O3blxkzZjB37lwCAwN59913uXXrFp06dQKgffv2DBo0yFp/7NixfPzxx3z77bf4+voSFBREUFAQYWFhAISFhfHBBx/w119/cerUKTZs2ECTJk0oWLAg/v7+dnmOTxKd9FNPPxERsYvLl82uBPfvQ8uW0LOnvSMSERFJkw4eNL+SS5WCI0fAx8fs3detG1gs9o5ORERERESSO7vP6deyZUuuXLnC0KFDCQoKonTp0qxZs4bs2bMDcObMGRwcHuQmp02bRkREBM2bN7fZzrBhwxg+fDiOjo4cOHCAuXPnEhwcjI+PD/Xq1WPUqFE4Ozsn6XOLr+jhPdXTT0REklx4uHl28cIFKFwYZs7UWUUREZEkduAAjBxp9uyL1rw5TJkCT5j5QkRERERExMruST+AHj160KNHj1jv27x5s836qVOnHrstV1dX1q5dm0CRJQ319BMREbswDHj7bfjjD/Dygp9+Ag8Pe0clIiKSZuzfbyb7fvrJXLdYzGTfxx9DiRL2jU1ERERERFKeZJH0S+vU009EROzik0/gu+/A0RGWLIEiRewdkYiISJqwb5+Z7Pv5Z3PdYjE73n/8MRQvbtfQREREREQkBVPSLxmI7umnpJ+IiCSZxYthyBDz9pdfQr169o1HREQkDbhyBXr1gkWLzHWLxZxOd8gQKFbMvrGJiIiIiEjKp6RfMhDd00/De4qISJLYvh06dDBvv/8+dOtm33hERETSgGXLzK/cy5fNZF+rVmayr2hRe0cmIiIiIiKphZJ+ycDDPf0Mw/wBKCIikihOnoQmTSA8HBo3hnHj7B2RiIhIqnbjBvTuDfPmmevFi8PcuVC2rH3jEhERERGR1MfB3gHIg6Tf/ftw545dQxERkdQsJAQaNTLHFitdGhYsMOfzExERkUSxZo2Z5Js3Dxwc4MMPYdcuJfxERERERCRxqKdfMuDubp5zjYw0e/u5udk7IhERSXXu34c33oAjR8DHB375BTw87B2ViIhIqnTzJvTvD998Y64XKmT27nvpJfvGJSIiIiIiqZt6+iUDFovm9RMRkURkGNCzJ6xbZ15Z8ssvkCuXvaMSERFJlTZvhpIlHyT8eveGvXuV8BMRERERkcSnpF8yEZ30Cw62axgiIpIaffEFTJ9uXmWyYIHGFBMREUkEJ0/Ce+9BrVpw6hT4+sKmTTBpkkZzERERERGRpKHhPZOJ6Hn9lPQTEZEE9csv0LeveXvcOGjSxL7xiIiIpCJRUWZH+i+/hFWrzM71AN26mV+7np72jU9ERERERNIWJf2SCQ3vKSIiCS4qCt591zwD+fbbD5J/IiIi8lxu3IDZs2HaNDh+/EG5vz8MGAC1a9svNhERERERSbuU9Esm1NNPREQS3K5dcP682c1g8mRzeE8RERF5Zvv2mb365s+HO3fMMm9v6NTJvM6mUCG7hiciIiIiImmckn7JRHTSTz39REQkwSxfbv5t0ACcne0bi4iISAp1/z78/LM5Re7WrQ/KS5aE7t2hbVtwd7dffCIiIiIiItGU9Esmoof3VE8/ERFJMCtWmH9ffdW+cYiIiKRAISEwcyZMmQKnT5tl6dLB669Djx5Qtao60YuIiIiISPKipF8yoZ5+IiKSoP77Dw4dAkdHCAiwdzQiIiIpxokT5qjY334LYWFmWZYs5vCd774LOXPaNz4REREREZG4KOmXTKinn4iIJKjooT1r1ICMGe0bi4iISDJnGLBlC3z+ufkVahhmebFi0KePOYSnq6tdQxQREREREXkiJf2Sieiefkr6iYhIgtDQniIiIk9kGLByJYwYAbt3Pyhv0MBM9r3yiobwFBERERGRlENJv2QiuqefhvcUEZHndv262V0BoEkT+8YiIiKSTO3fD/36wYYN5rqrK7RvD717Q5Ei9o1NRERERETkWSjpl0yop5+IiCSYVasgMhJKlgRfX3tHIyIikqwEBcHHH8OsWWZPPycns1ffgAGQObO9oxMREREREXl2SvolE9FJP/X0ExGR56ahPUVERGK4c8ecs2/MGAgLM8veeAM+/RTy5bNvbCIiIiIiIglBSb9kInp4T/X0ExGR5xIeDmvWmLc1tKeIiAiGAYsWwYcfwpkzZlnFimYCsEoV+8YmIiIiIiKSkBzsHYCYonv63boF9+/bNRQREUnJNm40uy/4+EC5cvaORkRExG4MA37/3UzstWljJvxy5YLvv4ft25XwExERERGR1EdJv2TCy+vBbQ3xKSIiz+zhoT0tFvvGIiIiYgdRUfDLL1C1KtSsCX/9Be7uMGoUHD0KbduCg34Ji4iIiIhIKqThPZOJ9OnNH6K3bplJP00gLyIiTy0q6kHST0N7iohIGnP/vjmM59ixcOiQWebsDJ06wdChkDOnfeMTERERERFJbEr6JSPe3mbST/P6iYjIM9m9Gy5cAA8PqFXL3tGIiIgkiTt3YPZsGDcOTp0yyzw94d13oU8fJftERERERCTtUNIvGcmQwTxXq6SfiIg8k+hefvXrm10bREREUrHgYJg2DSZNgsuXzbKsWc1E33vvPZg3XUREREREJK1Q0i8Z8fY2/2pOPxEReSbLl5t/NbSniIikUqGh5jUuS5bA2rUQHm6W580LH3xgDuXp5mbfGEVEREREROxFSb9kJPpKVPX0ExGRp3byJBw8CI6OEBBg72hEREQSTEiIbaIvIuLBfcWLw4AB0KqVOU+6iIiIiIhIWqakXzISnfRTTz8REXlq0UN7VqsGmTLZNxYREZHnFBJidmBfsgTWrbNN9BUuDC1amEvx4mCx2C9OERERERGR5ERJv2QkenhP9fQTEZGnpqE9RUQkFTh5EiZMgG+/hTt3HpQXKfIg0VesmBJ9IiIiIiIisVHSLxlRTz8REXkm16/DH3+Yt1991b6xiIiIPIP9+2HsWPjhB4iMNMuKFIE33niQ6BMREREREZHHU9IvGVFPPxEReSarV5tnSIsXh/z57R2NiIhIvBgGbN5sJvvWrn1QXq8eDBwItWqpR5+IiIiIiMjTUNIvGYnu6aekn4iIPBUN7SkiIilIZCQsW2Ym+3buNMscHMxefQMGQJkydg1PREREREQkxXKwdwDyQHRPPw3vKSIi8RYebvb0Aw3tKSIiyc61a/DXXzBvHgwdCq1bQ6FC0Ly5mfBzcYH33oNjx2DhQiX8RCRhfPnll/j6+uLi4kKlSpX4+++/H1t/yZIlFC5cGBcXF0qUKMGvv/5qc79hGAwdOpScOXPi6upK3bp1OXbsmE2d69ev07ZtW7y8vMiQIQOdO3cmLCzMev/du3fp2LEjJUqUIF26dDRt2jTWWDZv3kzZsmVxdnamYMGCzJkzx+b+MWPGUKFCBTw9PcmWLRtNmzbl6NGj8T84IiIikqop6ZeMqKefiIg8tU2bICwMcuaE8uXtHY2IiKRhR4/C6NHw5pvw0kuQKRNkyQKVK0P79jBqFCxaBP/9BxkzwpAhcPo0fPmlRqcWkYSzePFi+vbty7Bhw9izZw+lSpXC39+fy5cvx1p/27ZttG7dms6dO7N3716aNm1K06ZNOXTokLXOZ599xuTJk5k+fTo7duzA3d0df39/7t69a63Ttm1bDh8+zPr161m5ciV//PEHb7/9tvX+yMhIXF1d6dWrF3Xr1o01lpMnT9KwYUNq1arFvn376NOnD126dGHtQ2Mg//7773Tv3p2//vqL9evXc+/ePerVq8etW7ee99CJiIhIKmAxDMOwdxDJTWhoKN7e3oSEhODl5ZVk+92+HapUMX/wnjiRZLsVEZGU7L33YNo06NYNpk+3dzR2Z6/vcHk+et1EUraDB+F//4MffjDn6XvUCy+An5/Zwy/6b61a4OmZ9LGKSMJJrt/flSpVokKFCkydOhWAqKgocufOTc+ePfnwww9j1G/ZsiW3bt1i5cqV1rKXXnqJ0qVLM336dAzDwMfHh379+tG/f38AQkJCyJ49O3PmzKFVq1YEBgZStGhRdu7cSfn/vxBvzZo1BAQEcO7cOXx8fGz22bFjR4KDg1m2bJlN+cCBA1m1apVNwrFVq1YEBwezZs2aWJ/vlStXyJYtG7///jvVq1d/4vFJrq+biIiIPF58v8PV0y8Z8fYGZ+4y8Hwv8xeziIjI4xgGrFhh3tbQniIiksR27YKmTaFkSVi82PxaCgiATz6BJUtg/36zM/q5c2bH9K+/hv79za8sJfxEJDFERESwe/dum550Dg4O1K1bl+3bt8f6mO3bt8foeefv72+tf/LkSYKCgmzqeHt7U6lSJWud7du3kyFDBmvCD6Bu3bo4ODiwY8eOeMf/pFhiE/L/c8RkypQp3vsRERGR1CudvQOQBzJkgA7M5e3wKRgfLMfyxhv2DklERJKz3bvh/Hlwd4fate0djYiIpBHbtplDdUZ3OrFYoEUL+OgjKFXKvrGJSNp29epVIiMjyZ49u0159uzZ+eeff2J9TFBQUKz1g4KCrPdHlz2uTrZs2WzuT5cuHZkyZbLWiY+4YgkNDeXOnTu4urra3BcVFUWfPn2oWrUqxYsXj3Wb4eHhhIeHW9dDQ0PjHY+IiIikPOrpl4x4exl042tz5dw5uH/fvgGJiEjyFt3Lr359cHGxbywiIpKqGQZs3GheY1K1qpnwc3Q05+87csTs6aeEn4hI0urevTuHDh1i0aJFcdYZM2YM3t7e1iV37txJGKGIiIgkNfX0S0bcDu+kLHsBsERFwcWLoMaYiIjExjBg+XLztob2FBGRBGQYcOYM7N0L+/aZf/fuhbNnzfvTp4eOHWHgQChQwJ6RiojYypIlC46Ojly6dMmm/NKlS+TIkSPWx+TIkeOx9aP/Xrp0iZw5c9rUKV26tLXO5cuXbbZx//59rl+/Hud+nyYWLy+vGL38evTowcqVK/njjz/IlStXnNscNGgQffv2ta6HhoYq8SciIpKKqadfMmL5erptQfSvahERkYdFREC3bnDggNnNomFDe0ckIiIplGHAv//CvHnQt6/Zky9zZvD1hWbNYMQIs2P52bPg7Aw9esCJE/DNN0r4iUjy4+TkRLly5diwYYO1LCoqig0bNlC5cuVYH1O5cmWb+gDr16+31s+XLx85cuSwqRMaGsqOHTusdSpXrkxwcDC7d++21tm4cSNRUVFUqlQp3vE/KRYAwzDo0aMHP//8Mxs3biRfvnyP3aazszNeXl42i4iIiKRe6umXXAQHw/8Px3CdjGTihpJ+IiIS06VL8PrrsHWrOYnS55+bZ2dFRETi6e5d2LwZfv3VXE6ciFknXTooVgzKlIHSpR/81bliEUnu+vbtS4cOHShfvjwVK1Zk0qRJ3Lp1i06dOgHQvn17XnjhBcaMGQNA7969qVGjBhMmTKBhw4YsWrSIXbt28c033wBgsVjo06cPo0ePxs/Pj3z58vHxxx/j4+ND06ZNAShSpAj169ena9euTJ8+nXv37tGjRw9atWqFj4+PNbYjR44QERHB9evXuXnzJvv27QOw9hh85513mDp1KgMGDOCtt95i48aN/PDDD6xatcq6je7du7NgwQKWL1+Op6endc5Ab2/vGL0BRUREJO1R0i+5mDcP7tzhuEtxdtwtSVsWmGPqiIiIRNuzB5o2NS8K8fKChQshIMDeUYmISApw5oyZ4Fu1CjZsgDt3HtyXPj1UrAhlyz5I7hUtavbsExFJaVq2bMmVK1cYOnQoQUFBlC5dmjVr1pA9e3YAzpw5g4PDg4GvqlSpwoIFCxgyZAgfffQRfn5+LFu2jOLFi1vrDBgwgFu3bvH2228THBzMyy+/zJo1a3B5aF7t+fPn06NHD+rUqYODgwOvv/46kydPtoktICCA06dPW9fLlCkDmL33wOxVuGrVKt5//32++OILcuXKxcyZM/H397c+Ztq0aQDUrFnTZtuzZ8+mY8eOz3HkREREJDWwGNEtC7EKDQ3F29ubkJCQpBn2wDCgeHE4coTJhaZy+9+zfMhY6NkTHmkgiohIGrV4MXTqZJ6lLVTInM+vcGF7R5XsJPl3uCQIvW4iCc8wzHn4fvgBVq6Ew4dt73/hBfO6kYAAqFMHPD3tE6eIpFz6/k6Z9LqJiIikTPH9DldPv+Tgzz/hyBFwc2Pni+3w/He+Wa7hPUVEJCoKhgyB/x9+iPr1zR5+GTLYNSwREUmeAgPNWQMWLTLn6ovm4ABVqjxI9JUsaY4SLSIiIiIiIqmHkn7JwfTp5t/WrUkf5c1ZcpvrGt5TRCRtCw2Ftm3NLhoAH3xgJv8cHe0bl4iIJCv//Wd2CF+0CA4ceFDu4gKNG0OzZuDvD5ky2S9GERERERERSXxK+tnb1avw44/m7XfeIcMC2BOd9FNPPxGRtOvYMWjSxOyy4ewMM2dCu3b2jkpERJKJU6dg2TIz0bdjx4Py9OnNBF+rVvDqq88xbGdkJFy4YGYUT540/969C0WKQIkS5qR/bm7x29bNm+ZYo7t2mUtgIJQqZWYj69UDV9dnDFJEREREREQepqSfvc2ZAxERUK4clC+P9yo4Qx7zvitXzB/WD00MLSIiacDRo/DSSxAcDD4+5lndChXsHZWIiDzJ3r3g5QUFCiT4pg0D9uwxp3Rdvty2R5+DA9SubSb6mjV7yh599+/D5s3mxqOTeydPmlnFe/fifpzFYj7PEiXM+cmj/77wAhw69CDBt2sX/POP+QQetm8fzJ0L7u7QoAG89ho0bGgePxEREREREXkmSvrZU1QUfP21efuddwBziqYbZOSuoxsukbfh3DkoWNB+MYqISNKKioLOnc2EX/nysGIF5Mxp76hERORxrlyBvn3h++/N3tlffml+lj+n8HAzH7d8ufl1cP78g/scHODll6FFC2jeHHLkeIoNGwbs3w/z5sGCBRAUFHu9dOnA1xfy5YP8+cHJCQ4fhoMHzed8/Li5/Pzzk/eZO7f5vVa+PLz4ojmv+U8/mVMa/PijuTg5Qd26ZuaySRPImvUpntRTun7dfB4HDpjL2bPmRIfVq0PVqpAxY+LtW0REREREJJEo6WdPGzeaP5K9vMzLcgFvbwALV1xyk/vWUfNHsJJ+IiJpx/TpsHUreHjA0qVK+ImIJGeGYY7c0b+/mUQCM1PXpQts2wZTpz7V0JWGYf48+P13WLcO1qwxR8aM5u4O9eubw3Y2bAiZMz9lvOfPw/z5ZrLv0KEH5ZkymcNsFiz4IMGXP7/Zay+ueWQvXzaTZgcPmts6eNBMCN66ZWYgK1R4kOQrVw6yZ7d9/Ouvw8SJZg/Dn34yv/OOHoVffzWXbt0gb17zSWbKZP6N7banpxljunTmEtvtmzdtE3wHDpgXVz5q7VoYN87sxRidAKxeHapVixm/iIiIiIhIMqSknz1Nn27+ffNN8+QuZk8/gIvpcpObo5rXT0QkLTl7FgYONG9/+inkyWPfeERE0oKICDMJlD//042L+e+/ZmJq82ZzvVQpcxSPTZtg8GD49lszobV0qbntWBiGOfLl778/WC5etK2TM6eZ5Hv1VXMITxcXzCE5g4Jg9yWzUvr0cS+RkbBypZno27jxwTCbTk7mRtu1M4fXdHJ6qsNGtmxQp465RIuKgpCQ+PeSs1jMhGC5cvC//5lz/f30k7lEDzd68uTTxfU0fH3N5F6JEmZPxN27zRfh33/NnpD798OUKWbdF180E4CtW0PNmmbsIiIiIiIiyYySfvZy8aI5Tg+YJwv+X3TS73RUHiqCkn4iImmFYcC770JYGFSpYt4WEZHEdeQItGljJnccHc3xMhs3hkaNzCRPbCIiYOxYM0kVHm725BsxAvr0MZNslSqZvdxatzbnrStXzky4NWpEZKTZKW7LFjO39McfZoe5hzk5Qc3yYbQqsJPq+c+Rz+kcDhfOw4zzMPy82UPt0iUzwfYsqlUzLzps0eLBj4+E4uDwfMNiFiliJkwHDzZ7JZ4+bfagvHbtwd9Hb9+6ZSY27983l9huOztDsWJmgi96KV48epiVmIKCzBfpjz/M5eBBsxfi0aMwY4b5mg4YYM5DmE4/qUVEREREJPnQLxR7+fZb8wdolSrmlaX/r2hR8++Rm7nNG0r6iYikDYsWwapV5tnemTPNE6ciIpI4DAOmTYN+/eDuXTMpFB7+oLtd//7g52cmABs3NpOB6dKZ89C9/bbZIw3MsTa/+socEvNhderAnj3cf/0N0v29HRo3Zr7vYLpfHUFImO1wmS4uULkyvFIplCaOK3nxwBIc16+BbXcf/xzSpTN72zk4wL17MZfIyAd1CxUyE31t28aMNbl64QVzsYccOcykaIsW5vqNG+bQ26tWwdy5Zo/Ali3NHpx9+0KnTuDmFr9t379vZn49PDSNg4iIiIiIJDiLYUSP7yLRQkND8fb2JiQkBC8vr4TfQWSk+QPxzBn47jvzB/hDChWCasdmMYsu5omE1asTPgYREUk+rl41ezdcvQojR8LHH9s7ohQr0b/DJVHodZMkdfkydO5sDnkJ5lx2c+bAnTtm2cqV5pCd9+49eEyGDFCmjDl0J5jJtkmTzHm5Hxrm8cwZs8q2beby76EIxtGfXphDRK6nLm+7L8CvSlZq1IDaZYMpf2EF6Zf/aM4nFxHxYJ9585qJx+jkV/SSK5f5NzrhF5eoqAc93lxdNRxlQrl6Fb780hz289o1syxzZujRw1yyZLGtf+EC/PXXg2X3brh927yvVy8YMyb+CUORZEbf3ymTXjcREZGUKb7f4Ur6xSLRG0CrVplDBmXKZA7P4+pqc/dbb8G52etYh785DM2hQwkfg4iIJB9vvgnff28ONbZ799PPqyRWOomRMul1kySzZg107GgOj+nkBJ99Bj17xkyehYbCunXwyy/w669moidaly7m8J7/P/9feDgsW2aO+rhhQ8xdFigAfXMupOvfXUgfcRsjVy4s/frB+vXm8nBy8cUXH/QwK1FCibrk7PZtmD0bJkx4MO+gq6v5Yy5fPtixw0zyxTZyi6cn3Lxp3i5UyLwQtFKlpItdJIHo+ztl0usmIiKSMsX3O1zDe9rD11+bfzt2jJHwA6haFcbNzmOuaHhPEZHUbc0aM+Hn4ACzZinhJyKSGO7ehYEDYfJkc71YMViwwJzbLTZeXtC8ublERpoJnG3bzKH5q1QBzOkAZ8408zXRHb4sFjN38/LLD6pmzw7QGg6XhNdfx3L0KLz//oN9FStm7qdFC3OsfyX6UgY3N+je3Zyf/aefzATy7t1mL8CHOTiYCdyXXjLfHC+9ZCZ3160ze5z++6/5RvnwQxg2TO0AERERERF5LurpF4tEverpzBnzys+oKPjnH/MH3yOOHoWyhW9xCw+zIDg47knmRUQk5bp50+zdd+aMeQJ44kR7R5Ti6crllEmvmySqgwehTZsHo2f07Gn21Ivl4rsnuXULliwxe/Vt2/ag/IUXzA5eb70Fvr6P2cDNm+b+Dx+GV181k31Fijx1HJIMGYY5tuu0aeaQqtFJvvLlzfn7YnPjhvl+mD/fXC9Vyswix5WMjq+oKHPeyS1bzGXXLsiTxxxtpmFDzSUoCUbf3ymTXjcREZGUST39kquZM80fYbVqxZrwA3OEF7cs7ly/mpFM3DB7+ynpJyKS+gwZYib8fH1h1Ch7RyMiknoEB5vz8q1bB99+a47BmS2bORxjQMATHx4VBUFB5kf06dPm38BAWLrUHPkTwNHRzKF07Qr+/pAuPr+sPD3N+QMl9bFYoHZtc4mvjBnN3v7NmsE778D+/WaScORI6N8/nm8qzLkg9+x5kOTbuhWuX7et8++/8Ntv0KeP+Ts0OgH48suQPn38YxYRERERkWRNSb+kdO+emfQD80ddHCwWc4jPM8vzPEj6FS+eREGKiEiS2L4dpkwxb3/9Nbi72zceEZHEduWKmYjbuRPy5jWTDcWLm9mz5xUebs6f9ttv5jx5O3eambtoDRuayb9s2WweZhhm1VWrzGnZzpwxl3PnbKfae1j+/Oa0fh06gI/P84cuwuuvm/8P3brB8uUwaJD5d9YsyJDBnFPy0eXaNfPvuXPmm/jOHdtturmZvQ1fftnscfjPP7BypZkUPHrUXCZMMC8u9fc3/0fq1DHf1BpiVkREREQkxVLSLyn98gtcvGiebGja9LFVq1aFs8tzU5r9mtdPRCS1CQ83zxgbhnnWuF49e0ckIpLwgoPhjz9g40Zz2MMDB2LW8fKCypXNxm/VqmZy4nEXQRiGud1Ll8yueHv2mEm+P/6A27dt6774ItStCw0amL37HkpknDtndrCaO9fMhcTG0dEctjNPHnPJm9fMidSqZU7TJpKgsmeHn382h/fs1ctMYhcrFv/HZ85sJviqVTP/li1r24MvIAD69oWQELMH7KpV8OuvZjL+hx/MBSBrVihTBkqXNpcyZcDPL2GS8yIiIiIikuiU9EtKhw6ZZwg6d37iBO0vvwx7yQ2AcfoMutZSRCQVGTMGjhwxLwLRPH6SxL788kvGjRtHUFAQpUqVYsqUKVSsWDHWuvfu3WPMmDHMnTuX8+fP8+KLLzJ27Fjq168fa/1PP/2UQYMG0bt3byZNmpSIz0KSpbt34fffHyT5du+27W0HZs++KlXg1Cmzx3NoKKxday5gJhbKlDETgJ6eZmIvOsEX/TciIvb9Z8tmJvleecXMzuXObXP37dtmTmXuXLNDYPTM5i4u0KSJmd/Im/dBki9nzviPriiSICwW82Kg2rXNi4PWrTP/JzJnhixZbJfosqxZoVw5KFw4ftlob29o0cJcIiPN+f5WrjSTgAcOmEnAdevMJZqrqznPYOnSUKCAGWdUlPlP9PASXeblZT6PDBkS60iJiIiIiEgc9DM2KQ0dCh078n/s3Xt8zvX/x/HHtTFz2tDYjDkr5HwahZLVQsqhoi85JEoOSSVnknKWYw4VKfxQoZKGVkgJOeWc83kbYcsws+v6/fFuYxm22fbZde15v90+t+tzfa739ble165cn6vP6/N6vcmR465Dq1eHFe4BEAdRe06gGf1ERFzE7t3wwQdmfcoUKFDA2ngkS1m0aBF9+vRhxowZBAYGMnHiRIKDg9m/fz+F/tP2EGDQoEHMmzePjz/+mHLlyrFy5UpatGjBb7/9RrVq1RKN3bx5MzNnzqRy5coZ9XYkM7h40SQLli2DH36A6OjEj99/vymNe+wxePTRxO014+Jg504z/9j69WY5edIkIf74486v6+0Nfn5QpoxJ8AUFmYTif9oSxsWZ3c+dC19+Cf/8c+Ox+vVNXuK550yOQiTTCAgwifDoaJNwS6/SUnd3U10bGGjmFr5yxVyoun07bNtmbnfsMBnzjRvNklxjx5qpLW5zkYiIiIiIiKQPm8MRf42rxIuKisLb25vIyEi8LDwD8F65+Qze344z5RpSeO9PlsUhIiJpqEULc3K8WTMzX4/mzUlTmeUYnlkFBgZSq1Ytpk6dCoDdbicgIICePXvSr1+/W8b7+/szcOBAunfvnrCtVatW5MyZk3nz5iVsu3TpEtWrV+ejjz5ixIgRVK1aNUWVfvrcnMzp0+b7a9kyU9V3/fqNx4oUMS2LGzY0S9GiKdv38eMmS7dhg8nY+fmZtof/vfX0vOWply6ZHGJ8nmL7dnP/5q6fJUtC+/ZmKVUqNW9eJIuJi4ODB28kAk+fNr9dbl7c3BLf//lnOHDAPP/ll83cgfpud0k6fjsnfW4iIiLOKbnHcFX6ZWKFagTAfnA7rTn9RERcwp495iS5zQajRyvhJxnq2rVrbNmyhf79+ydsc3NzIygoiA0bNiT5nJiYGDz/k1zJmTMn69evT7Ste/fuNG3alKCgIEaMGJH2wYv1Tp0yk+AtW2bmGrtZhQrmgobmzU2bwXv5bovvrfnCC3ccFhlp8oJbttxI8h08eKNl583y5jXVfB06mBb6mo9PJAXc3c38mA88AK1bJ+85ly/DwIEwaZKp9lu5EmbPNhW5IiIiIiKSrpT0y8TKPhYACyDfPyfMGQydHBYRcW6jR5vb5s2hfHlLQ5Gs59y5c8TFxeHr65tou6+vL/v27UvyOcHBwUyYMIEGDRpQunRpQkNDWbJkCXFxcQljFi5cyNatW9m8eXOyY4mJiSEmJibhflRUVArfjWSYmBhTpfP++4lL5urUuZHou//+dA/j+HHT/TO+E+jOnUkn+AoXNtOOValy47ZsWZO3EJEMkisXfPih+Y7o1AkOHzZzbXbrBmPGQJ48VkcoIiIiIuKylPTLxKo2LYIdGzkcMZzbexafCrfOtSMiIk7i6FGYP9+s31RpJZKZTZo0iS5dulCuXDlsNhulS5emU6dOzJ49G4ATJ07w+uuvs3r16lsqAu9k5MiRvPvuu+kVtqSV5cuhd284dMjcr1PHlMs984zJrqWj3btNh8D4RN/Jk7eOKV3ahBSf3KtSJfGUgSJisQYNTBnuO+/ARx/B9OkQEgJz5sAjj1gdnYiIiIiIS1LSLxMr4OdBRDY/Cl0/w64fTvCokn4iIs5r3DgzL05QENSqZXU0kgX5+Pjg7u5OeHh4ou3h4eH4+fkl+ZyCBQuybNkyrl69yt9//42/vz/9+vWj1L+ToW3ZsoWIiAiqV6+e8Jy4uDjWrVvH1KlTiYmJwT2JEqv+/fvTp0+fhPtRUVEEBASkxduUtHDggEn2rVhh7vv7w9ixpuVmOnaeOHfOXBvx2WemZefN3N2henV4+GHTovPhh830fiKSyeXJA9OmQcuW8NJLcOQIPPoo9OoFI0eaqkAREREREUkzSvplctH5A+DsGY79chzerGF1OCIikhrh4fDpp2ZdVX5iEQ8PD2rUqEFoaCjNmzcHwG63ExoaSo8ePe74XE9PT4oUKUJsbCxff/01zz//PACNGjVi586dicZ26tSJcuXK8c477ySZ8APIkSMHOXLkuPc3JWnr0iX44APTzvPaNcieHfr0MXNz5c2bLi8ZGws//GASfcuXm/tgXrphQ5Pgq1cPateG3LnTJQQRyQiNGpm+vG+/DbNmweTJsG8ffP89ZNNpCRERERGRtKJf15mcW4kAOLuJv7efsDoUERFJrUmT4OpVc9a6YUOro5EsrE+fPnTo0IGaNWtSu3ZtJk6cSHR0NJ06dQKgffv2FClShJEjRwKwceNGTp06RdWqVTl16hTDhg3DbrfTt29fAPLmzUvFihUTvUbu3Lm57777btkumZjDAYsWwVtvwalTZtuTT8LEifDAA+nykjt2mETf/Plw9uyN7TVqQMeOpqjwvvvS5aVFxCpeXjBzppnrr2VLWLXKXAw1dmzq9nf+PDz1FEREmNbDnTubymQRERERkSxMSb9MzrtSMdgMjhMnuHIFcua0OiIREUmRyEjT1grMia10bI0ncjetW7fm7NmzDBkyhLCwMKpWrUpISAi+vr4AHD9+HDc3t4TxV69eZdCgQRw+fJg8efLQpEkTvvjiC/Lly2fRO5A0FxEBbdvCjz+a+yVLmmRfs2Zp8n1lt8OxY7B3L+zZY5bNm2HXrhtjfH2hXTtzzr5SpXt+SRHJ7J580szr16aNaX9etar5HkqJy5dNwm/DBnN/yBB49114+ml45RV4/HG46XgmIiIiIpJV2BwOh8PqIDKbqKgovL29iYyMxMvLy9JYHBM+xPZmHxbSGv+1C2nQwNJwREQkpUaNMsm+ChVMWyudgEpXmekYLsmnz80iW7dC8+Zw4oS5sqx/f9N6z9MzVbu7cAHWrbuR3Nuzx3Tvu3z51rHZs5tz8x07QnCwuS8iWcyAAWZeP09PWL/elPomR2ys+e5asQLy54dhw2DxYvj11xtjSpSALl3MPIKaADTd6PjtnPS5iYiIOKfkHsNV6ZfJ2YoFAFCM46z9FSX9REScyZUr8OGHZr1fPyX8RCTz+L//M63wrlyBsmXhm2+gfPkU7+bQIfj2W7P88gvExd06xsPDdAmtUMEs5cvDY4+pfadIlvfee6bX74oVJon3xx+m9PdO7HaTyFuxwlyssHw5PPQQ9OoFu3eb+QI//xyOHjXzkQ4dCs88Y77v6teHPHlSH29YGGzbBnXrgireRURERCSTUtIvswswSb8ATrB+vcWxiIhIysyebVrnFS9uWliJiFgtLi7xHFqNG8OCBck+gR0XB5s23Uj07dmT+PEKFaB69RsJvgoVTMfQbPq/DhH5L3d38/1Tuzb89Rc8+yyEhporBZLicJi5R+fNM8/98kuT8Iv34INmHuWRI81jM2ea9p9ff20Wd3fTSrRePXj4YbPcbg5AhwMOHjRXM8Qvhw6Zx+6/H1avhmLF0vTPISIiIiKSFtTeMwmZqtXBmTPg708cbvh6xxBxPpsKRUREnEFsrKmeOXYMpk6F7t2tjihLyFTHcEk2fW4Z5MIFeOEFWLnS3O/XD0aMMCfC78DhMG07P//cFNVERNx4LFs2eOQR06qzWTOT4BMRSZF9+yAwEKKi4NVXYfr0pMeNHm2+twDmzoX27e++7507TfLv229NK+P/KlnyRhKwfHlTyffLL6bdaHh44rE2G+TODZcumYtzV682ZcxZmI7fzkmfm4iIiHNK7jFcSb8kZKofQHY7Dk9PbLGxBHCcH3YGULGitSGJiEgyfPGFORlVqJBpMZUzp9URZQmZ6hguyabPLQPs2WNa3B08aL6P5syB1q3v+BSHw+QHR4xIPFWWtzc0aWISfU8+qS53IpIGvv/eXDngcMCMGfDKK4kfnz3btOgEGD8e+vRJ+WscP26+zH791ST1/vzTvN7teHiYKsT69c1St65J+D3+uElUFixoviSrVUt5LC5Cx2/npM9NRETEOWlOP1fh5oatSBE4epRiHOfXX5X0ExHJ9Ox2GDXKrPfurYSfiKS9AwdM5UqePKbFXLFipvIkqR/+33wD7dqZk9XFi8OyZabF3W3Y7WbXI0bAli1mW44c0KGDyRPWrw/Zs6fLuxKRrKppU/OlM3Ag9OhhWnXWq2ce+/Zb6NLFrL/zTuoSfnDju/KFF8z9yEj4/fcbScC//oLKlW8k+WrWBE/PxPvIl8+UPj/5JGzdCo8+akqg69dPXUwiIiIiImlMST9nUKwYHD2aMK/ffy96FBGRTOa770xVjZcXvPaa1dGIiKuIi4OQENMyOCQk6THe3omTgHY7zJplHnv0UVi82FSn3Gb3X34J778Pu3aZbblymW57b755+6mvRETSRP/+sH27+SJq1Qr++AOOHDFXG9jt0KmTma8vrXh7Q3CwWVKiYEH46SdT7rxunXn+11+bOVJFRERERCympJ8zCAgwN5zg61/vMlZERKzlcMAHH5j17t3NCSURkXtx/rxpbTd9Ohw+bLbZbKbFnKenaVl3/LgZFxlp5rDauTPxPnr2NC3xkijRi42F+fPNV9eBA2abl5cptund+7Y5QhGRtGWzmdbDf/0FO3aY6r/jx+HqVZNgmzXLjMkMvL3NxRfPPWdakz79NMybd9e2ySIiIiIi6U1JP2fwb9KvOMc5cgROn9aV1iIimdbPP8OmTeZEfO/eVkcjIs5s2zaYNs1k5K5eNdvy5zfzWnXrBqVKJR4fHQ0nTtxIAh4/DmfOmCqUZ59NNDQ2FtasgSVLYOlSCA832wsUMF9dPXtqrj4RsUDu3KYFcc2aNy5eqFcPFi6EbJns9EXOnOYLtEMH+L//M21Do6JutCIVEREREbFAJvvVLEn6N+n3oNcJiDJTDjz3nMUxiYhI0uLbTnXuDIUKWRuLiDgfh8NUjYwaZX70xatSxWTiXnjB9NxMSu7cUK6cWZJw5QqsXm260H33HVy4cOOxQoXgrbdMK8+8edPw/YiIpFSJEvDVV6bSr1w584WVWedHzp4dvvjCVP7NmAFdu5ov1759zeN2O5w7Zy7AiF/CwsxtzpymYrt+fTNxqoiIiIhIGlDSzxkUKwZA6RwnADPHuJJ+IiKZ0B9/wI8/gru7OXsuIpIS69dDv343kn3ZspkKvR494KGHUtXWLioKVqwwFX0rVphiwHiFCkHz5tCyJTRsCB4eafM2RETu2aOPmhY3efOCm5vV0dyZuzt89JGpxB45Et55Bz7/3CT/wsPNhKm3M2aMuZCjYUN48kmzlCmTcbGLiIiIiMtR0s8Z/FvpVyjGJP1+1bx+IiKZx/Xr8NNPpq3TkiVmW9u25ip1EZHk2LkTBgyA5cvNfU9P6NXL9NksXDjVu50/33QB/eefG9sCAkySr1Urk0d0d7+30EVE0o0zzYtss5mJUfPlM0m/3bsTP1awIPj5me/0+CUszMwLeOaMqfD+/nszvnTpGwnAhg1NFbeIiIiISDIp6ecM/k36eUadxZMrbN+ek0uXIE8ei+MSEcmqHA7YsMEk+hYvhoiIG4+VLQvvvmtdbCLiPI4cgaFDYd48873i7g4vvwyDB0ORIqne7eXLJmf46afmftmypmCwZUuoUSNVBYMiIpIcffvCE0+Y+VXjk3uFCpk2oElxOMyFHyEhZlm/Hg4dMvO5TptmqgCHDzcXgWSRqzSuXr2Kp6en1WGIiIiIOK1M3idDANMm5N+5W2oXPklcHGzcaHFMIiJZ0c6d0L8/lCoFDz8MU6eahN9995lymnXrYN8+VfmJyJ1FRJis3AMPmLmgHA54/nnYs8fMCXUPCb+9eyEw0CT8bDYYNsxs++ADqFlTCT8RkXRXtSo0a2a+dIsUuX3CD8yXcuXKJln400/w99/wzTfmd2WJEuYqjrfeMqXZN1cPuhi73c57771HkSJFyJMnD4cPHwZg8ODBfBp/BYuIiIiIJIuSfs7AZkuY1++J8jfm9RMRkQzicJiWnZUrw6hRcPSoKbdu185MknXmjJnLpX79zD/vjIhYa8YMc+HAlCkQGwuPPw6bN8OiRXD//fe0688/N+eYd+0CX18zxejQoVmmOERExPnlzQtPP21+Vx4+DJ98Al5esGkTVKsG771njh0uZsSIEXz22WeMGTMGj5smmK1YsSKffPKJhZGJiIiIOB+dmXQW/7b4rFNE8/qJiGS4TZtgwQJz5rx5c3NyPjzcVOg0bnznK7hFROL9+iu89hpER5vs3I8/wqpVZv0eXL4ML70EHTqY9UaNYPt2eOyxtAlbREQsYLNB586mCrxZM5PsGzLEHDO2bLE6ujT1+eefM2vWLNq2bYv7TVeqVKlShX379lkYmYiIiIjzUdLPWfyb9Hsw73HATCV1/bqVAYmIZCFTp5rbdu1g6VLThu/ftssiIskSHW2ycg4HtG9vLiZo1Oied7tnD9SuDXPmmELj4cNh5Urw80uDmEVExHpFipiWnwsWmJbyf/5p+jj37w9Xryb9HIcDTp828wSOGWN+ww4blqFhp8SpU6coU6bMLdvtdjuxLljZKCIiIpKeslkdgCTTv+09C107gZcXREWZqaWqVbM4LhERVxcRAYsXm/UePayNRUSc1zvvwKFD5kKuyZPTZHK9uXNN4eDlyybJt2ABNGyYBrGKiEjmYrPBCy+Yi0V69TJdJ0aNMhejzZgBuXObZGD8snOnmR/wZjVqZNrEX4UKFfjll18oXrx4ou1fffUV1XTSQ0RERCRFlPRzFv9W+rmdPMFDD5kL9tavV9JPRCTdffIJXLtmSmnusQWfiGRRP/4I06aZ9dmzwdv7nnZnt8Pbb8OECeZ+UBDMm2fm8RMRERdWqBAsXAht2kC3brB//+2v9nBzgwceMHNSV64M1atnbKwpMGTIEDp06MCpU6ew2+0sWbKE/fv38/nnn7N8+XKrwxMRERFxKpmivee0adMoUaIEnp6eBAYGsmnTptuO/fjjj6lfvz758+cnf/78BAUF3TLe4XAwZMgQChcuTM6cOQkKCuLAgQPp/TbS179JP44f5+GHzer69daFIyKSJVy/DtOnm3VV+YlIakRGmgn3wJTlBQXd0+5iY02X0PiE37vvmovBlPATEclCmjc3/Z07dTJzThcsaI4vb7xh+j1v2QKXLpkxCxfCgAHw5JNWR31bzzzzDN999x0//vgjuXPnZsiQIezdu5fvvvuOxx9/3OrwRERERJyK5ZV+ixYtok+fPsyYMYPAwEAmTpxIcHAw+/fvp1ChQreMX7NmDS+88AIPPfQQnp6ejB49mieeeILdu3dTpEgRAMaMGcPkyZOZO3cuJUuWZPDgwQQHB7Nnzx48PT0z+i2mjfik34kT1KtnVtevN63606A7lIiIJOXbb+HkSXMi5bnnrI5GRJzRG2/AiRNQujSMHn1Pu4qONl9FP/wA2bKZosEXX0yjOEVExLnkz28OBDNnQvbsVkdzz+rXr8/q1autDkNERETE6Vle6TdhwgS6dOlCp06dqFChAjNmzCBXrlzMnj07yfHz58/ntddeo2rVqpQrV45PPvkEu91OaGgoYKr8Jk6cyKBBg3jmmWeoXLkyn3/+OadPn2bZsmUZ+M7SWHzS759/qP1AJB4eZl7unTutDUtExKXFt+Pr0gWc9aIREbHOd9+ZigubDT77DPLkSfWuzp83RRw//AA5c8I33yjhJyIiuETCr1SpUvz93zkIgYsXL1KqVCkLIhIRERFxXpYm/a5du8aWLVsIuqnNkZubG0FBQWzYsCFZ+7h8+TKxsbEUKFAAgCNHjhAWFpZon97e3gQGBt52nzExMURFRSVaMp3cueHf95jr7xM89ZTZPH++hTGJiLiyPXvgp5/MfCivvmp1NCLibP7+21wwANCnDwmtGlLh5EmoXx9+/90UdoSGQpMmaRSniIiIxY4ePUpcXNwt22NiYjh16pQFEYmIiIg4L0vbe547d464uDh8/zMJia+vL/v27UvWPt555x38/f0TknxhYWEJ+/jvPuMf+6+RI0fy7rvvpjT8jBcQYC7zPn6cdu0qsmSJSfqNHGnOSYuISBqKr/Jr3vxGtbWISHJ17w7h4VC+PIwYkerd7NsHTzxhOoQWKQIrV8KDD6ZhnCIiIhb59ttvE9ZXrlyJt7d3wv24uDhCQ0MpUaKEBZGJiIiIOC/L5/S7F6NGjWLhwoWsWbPmnubq69+/P3369Em4HxUVRUBmPMEbEAA7dsCJEzTpCPnywalTsHYtNGxodXAiIi4kMhLmzjXr3btbG4uIOJ9Fi8zi7m6+S1L5O3XTJlPR9/ff8MADsGoVFCuWxrGKiIhYpHnz5gDYbDY6dOiQ6LHs2bNTokQJxo8fb0FkIiIiIs7L0vowHx8f3N3dCQ8PT7Q9PDwcPz+/Oz533LhxjBo1ilWrVlG5cuWE7fHPS8k+c+TIgZeXV6IlU4o/y3PiBDlywPPPm7vz5lkXkoiIS/r8c4iONhU6uqpCRFIiLAxee82sDxgAtWqlajerVsFjj5mEX61asH69En4iIuJa7HY7drudYsWKERERkXDfbrcTExPD/v37eSp+bhMRERERSRZLk34eHh7UqFGD0NDQhG12u53Q0FDq1q172+eNGTOG9957j5CQEGrWrJnosZIlS+Ln55don1FRUWzcuPGO+3QK8dWHJ04A0K6dufvVV3DlikUxiYi4GofjRmvPHj3AZrM2HhFxHg6Hmcfv/HmoWhUGDUrVbr76Cp56ylx78PjjZnpRH5+0DVVERCSzOHLkCD460ImIiIikCcvbe/bp04cOHTpQs2ZNateuzcSJE4mOjqZTp04AtG/fniJFijBy5EgARo8ezZAhQ1iwYAElSpRImKcvT5485MmTB5vNRu/evRkxYgRly5alZMmSDB48GH9//4TWEU4rPul3/DgADz8MxYvDsWOwfDk895yFsYmIuIrQUNi/H/LmhRdftDoaEXEmc+eaH2XZs5uKYQ+PFO8iJAT+9z+IjYXWrVO9GxEREacSHR3N2rVrOX78ONeuXUv0WK9evSyKSkRERMT5WJ70a926NWfPnmXIkCGEhYVRtWpVQkJC8PX1BeD48eO4ud0oSJw+fTrXrl3j2WefTbSfoUOHMmzYMAD69u1LdHQ0Xbt25eLFi9SrV4+QkJB7mvcvU7ipvSeAm5s5KTRypGnxqaSfiEgamDrV3HbsaBJ/IiLJceECvP66WR8+HCpVSvEufv0VWrY0Cb82bczvO3f3NI5TREQkk9m2bRtNmjTh8uXLREdHU6BAAc6dO0euXLkoVKiQkn4iIiIiKWBzOBwOq4PIbKKiovD29iYyMjJzze939CiULAk5cph+njYbe/bAgw9Ctmxw5oxaP4mI3JOjR6F0abDbYe9eKFfO6ogkhTLtMVzuyCU+t/Hj4a23oGJF2LbN/DhLgR074JFHIDISGjeGZctU4SciIplbWh2/H330Ue6//35mzJiBt7c3O3bsIHv27LRr147XX3+dli1bpmHU4hK/u0RERLKg5B7DLZ3TT1KoSBEzt1RMDJw9C0CFClCtGly/Dl9+aXF8IiLObsYMk/ALClLCTzKNEiVKMHz4cI7/295bMqG4uBtVwq+/nuKE34EDEBxsEn4PP2zm9FPCT0REsort27fz5ptv4ubmhru7OzExMQQEBDBmzBgGDBhgdXgiIiIiTkVJP2eSPTv4+Zn1m078tWtnbufNsyAmERFXcfUqfPKJWe/Rw9pYRG7Su3dvlixZQqlSpXj88cdZuHAhMTExVoclN/v+e1MpXKCA6b2eAqdOweOPQ3g4VKlipgTMlSt9whQREcmMsmfPnjCtS6FChRIudPL29ubEv9ObiIiIiEjyKOnnbP4zrx+YOV/c3OC33+DwYYviEhFxdosWwd9/m+/Zp56yOhqRBL1792b79u1s2rSJ8uXL07NnTwoXLkyPHj3YunWr1eEJwOTJ5vbll1OUsTt3ziT8jh2DsmVh5UrIly99QhQREcmsqlWrxubNmwF45JFHGDJkCPPnz6d3795UrFjR4uhEREREnIuSfs4mIMDc3pT08/eHRo3M+vz5FsQkIuLsHA6YMsWsv/YauLtbG49IEqpXr87kyZM5ffo0Q4cO5ZNPPqFWrVpUrVqV2bNno2maLbJnD4SGmiuwXnst2U/75x9o0sRMH1qkCKxeDb6+6RiniIhIJvXBBx9QuHBhAN5//33y589Pt27dOHv2LDNnzrQ4OhERERHnkrIJR8R68Um//8zr066dOVk0bx4MGmSm/hMRkWTatAm2bIEcOaBzZ6ujEUlSbGwsS5cuZc6cOaxevZo6derQuXNnTp48yYABA/jxxx9ZsGCB1WFmPfEXDDzzDBQvnqynXL1qhm/eDPfdZ37DJfOpIiIiLqdmzZoJ64UKFSIkJMTCaEREREScmyr9nE0SlX4ALVpAzpzw11/wxx8WxCUi4symTjW3bdqAj4+1sYj8x9atWxO19HzwwQfZtWsX69evp1OnTgwePJgff/yRpUuXWh1q1nPxInz+uVnv1StZT7l+3XzV/Pwz5MkDISFQvnz6hSgiIuKstm7dylNquy8iIiKSIkr6OZsk5vQDyJsXmjc36/PmZWxIIiJO7cQJWLzYrPfoYW0sIkmoVasWBw4cYPr06Zw6dYpx48ZRrly5RGNKlixJmzZtLIowC5s9Gy5fhooV4ZFHkvWUHj3gm29MYfG338JNxQ0iIiJZzsqVK3nrrbcYMGAAhw8fBmDfvn00b96cWrVqYbfbLY5QRERExLko6edsblPpB6bFJ8D//R/ExmZgTCIizqxXL7h2DRo00Nl3yZQOHz5MSEgIzz33HNmzZ09yTO7cuZkzZ04GR5bFxcXBtGlmvWfPZPVW/+UXmDnTDF20CBo2TOcYRUREMrFPP/2Uxo0b89lnnzF69Gjq1KnDvHnzqFu3Ln5+fuzatYsVK1ZYHaaIiIiIU1HSz9nEJ/1Onzb9oW7y+ONQsCCcPQs//mhBbCIizubbb2HZMsiWDT76yOpoRJIUERHBxo0bb9m+ceNG/lBPb+usWAGHD0P+/NC27V2Hx8WZ3CBAly5mTj8REZGsbNKkSYwePZpz586xePFizp07x0cffcTOnTuZMWMG5dX/WkRERCTFlPRzNr6+kD072O0m8XeT7NnNHDGgFp8iInd16dKNdp5vvQUPPmhtPCK30b17d04kUeF/6tQpunfvbkFEAsCUKea2c2fInfuuwz/5BHbsgHz5YMSI9A1NRETEGRw6dIjnnnsOgJYtW5ItWzbGjh1L0aJFLY5MRERExHkp6eds3Nwg/gfwHVp8Ll0K//yTgXGJiDibYcPM92iJEjB4sNXRiNzWnj17qF69+i3bq1Wrxp49eyyISNi7F1avNr/LkpF4PX8eBg4068OHm84MIiIiWd2VK1fIlSsXADabjRw5clC4cGGLoxIRERFxbtmsDkBSISAAjhxJMulXqxaULQsHDpiOdS++mPHhiYhkejt2wMSJZn3aNPj3ZINIZpQjRw7Cw8MpVapUou1nzpwhWzb9lLPE1Knmtlkzc+HAXQwdCn//bQqKu3VL39BEREScySeffEKePHkAuH79Op999hk+Pj6JxvTq1cuK0EREREScks4UOaP4ef2OH7/lIZvNVPsNHWpafCrpJyLyH3Fx8Mor5vbZZ6FJE6sjErmjJ554gv79+/PNN9/g7e0NwMWLFxkwYACPP/64xdFlQZGRMHeuWU/GScidO2H6dLM+aZKZQlRERESgWLFifPzxxwn3/fz8+OKLLxKNsdlsSvqJiIiIpIBOOzijYsXMbRKVfgBt25qk348/wpkzoO4YIiI3mTULNm6EvHnNGXiRTG7cuHE0aNCA4sWLU61aNQC2b9+Or6/vLSfGJAPMmQPR0aZsr2HDOw51OOD11801Bq1aQaNGGRSjiIiIEzh69KjVIYiIiIi4HM3p54ziK/1uk/QrXRrq1gW7HRYuzMC4REQyu7Aw6N/frL//Pvj7WxuPSDIUKVKEP//8kzFjxlChQgVq1KjBpEmT2LlzJwHxvwkkY9jtN1p79uxpWizcwZIl8PPP4OkJ48ZlQHwiIiIiIiIikqWp0s8Z3aG9Z7x27WDDBtPi8403MiguEZHM7o03TGu+mjXhtdesjkYk2XLnzk3Xrl2tDkN++AEOHYJ8+cyPrTu4cgXefNOs9+2brKn/RERERERERETuiZJ+zugulX4Azz9v2klt3QrbtsG/3cBERLKuVatM+bObG8ycCe7uVkckkiJ79uzh+PHjXLt2LdH2p59+2qKIsqDJk81t586QO/cdh44dC8eOmZ9t77yTAbGJiIiIiIiISJanpJ8zip/T79w5U7Hi7X3LEB8fM3fMokWmmOXXX815bhGRLOnKFejWzaz37AnVq1sbj0gKHD58mBYtWrBz505sNhsOhwMA27+tJePi4qwML+vYt89cPGCz3bVS+NgxGDnSrI8bB7lyZUB8IiIiIiIiIpLlpSoNdOLECU6ePJlwf9OmTfTu3ZtZs2alWWByB/nywQMPmPUlS247bNw4yJsXfv8dZszImNBERDKlDz6Aw4ehSBF47z2roxFJkddff52SJUsSERFBrly52L17N+vWraNmzZqsWbPG6vCyjvi5/Jo1g1Kl7jj07bfh6lV45BF47rkMiE1EREREREREhFQm/f73v//x888/AxAWFsbjjz/Opk2bGDhwIMOHD0/TACUJNhu0b2/WP//8tsOKFr1xlXm/fnDqVAbEJiKS2ezdC6NHm/XJk83VECJOZMOGDQwfPhwfHx/c3Nxwc3OjXr16jBw5kl69elkdXtYQGQlz55r1nj3vOHTNGvjyS9NhYfJk87NNREREbi8qKirJ5Z9//rmlrbmIiIiI3Fmqkn67du2idu3aACxevJiKFSvy22+/MX/+fD777LO0jE9up107cxZpzRo4evS2w159FerUgX/+ues5KhER13D9Opw/D0eOwPbt5oswNhaeegpatLA6OpEUi4uLI++/yWofHx9Onz4NQPHixdm/f7+VoWUdP/0Ely5B2bLQqNFth12/DvF52FdfhcqVMyg+ERERJ5YvXz7y589/y5IvXz5y5sxJ8eLFGTp0KHa73epQRURERDK9VM3pFxsbS44cOQD48ccfefrppwEoV64cZ86cSbvo5PaKFYOGDc1JqHnzYNCgJIe5u8OsWWb6qqVLzaJz3iLiEn77zVTwRUSYKpz4JTr61rG5cpnWfCq5ESdUsWJFduzYQcmSJQkMDGTMmDF4eHgwa9YsSt2lzaSkkfi29pUr3/F7ZNYs2LkTChQANb8QERFJns8++4yBAwfSsWPHhAvMN23axNy5cxk0aBBnz55l3Lhx5MiRgwEDBlgcrYiIiEjmlqqk34MPPsiMGTNo2rQpq1ev5r1/50c6ffo09913X5oGKHfQoYNJ+n3+OQwceNuTUJUqQd++ZkqrHj3MBepeXhkcq4hIWvrqK1PxHBNz+zE5c4K3N+TPb3ocFy+ecfGJpKFBgwYR/W8ye/jw4Tz11FPUr1+f++67j0WLFlkcXRbxb3Ul/v63HRIdDYMHm/URI0A/iUVERJJn7ty5jB8/nueffz5hW7NmzahUqRIzZ84kNDSUYsWK8f777yvpJyIiInIXqUr6jR49mhYtWjB27Fg6dOhAlSpVAPj2228TrsqSDNCyJbz2Ghw4ABs2wEMP3XbooEGweDEcPAgDBpiCFxERpzR1qumf53DAM89Ax44muXfz4uUFHh5WRyqSJoKDgxPWy5Qpw759+zh//jz58+fHpurVjBGf9CtS5LZDQkNNZ+HixaFLlwyKS0RExAX89ttvzJgx45bt1apVY8OGDQDUq1eP48ePZ3RoIiIiIk4nVXP6Pfroo5w7d45z584xe/bshO1du3ZN8oeapJM8eaBVK7P++ed3HJozJ8ycadY/+sjkCEVEnIrDAf37mwlKHQ7o1g2+/hqaNzftjqtXh9KlwcdHCT9xGbGxsWTLlo1du3Yl2l6gQAEl/DJSMir9vv/e3DZrBtlSdVmdiIhI1hQQEMCnn356y/ZPP/2UgIAAAP7++2/y58+f0aGJiIiIOJ1UJf2uXLlCTExMwg+uY8eOMXHiRPbv30+hQoXSNEC5iw4dzO2iRXD16h2HPvaYKYhxOKBrV7h2Lf3DExFJE9eume+7UaPM/REjYNo0M3GpiAvLnj07xYoVIy4uzupQsra7JP0cDlixwqw3bZpBMYmIiLiIcePG8eGHH1KlShVefvllXn75ZapWrcrEiRMZP348AJs3b6Z169bJ3ue0adMoUaIEnp6eBAYGsmnTpjuO//LLLylXrhyenp5UqlSJFfEH9n85HA6GDBlC4cKFyZkzJ0FBQRw4cCDRmPPnz9O2bVu8vLzIly8fnTt35tKlSwmPX716lY4dO1KpUiWyZctG8+bNk4xlzZo1VK9enRw5clCmTBk+++yze35/IiIiknWkKun3zDPP8Pm/lWUXL14kMDCQ8ePH07x5c6ZPn56mAcpdPPooBATAxYvw3Xd3HT5unCmC2bXLrIuIZHr//GNKZ774wiT55sy54zymIq5m4MCBDBgwgPPnz1sdStZ1l6Tfn3/CyZOQK5f5aSYiIiLJ9/TTT7Nv3z4aN27M+fPnOX/+PI0bN2bfvn089dRTAHTr1o0JEyYka3+LFi2iT58+DB06lK1bt1KlShWCg4OJiIhIcvxvv/3GCy+8QOfOndm2bRvNmzenefPmiTotjBkzhsmTJzNjxgw2btxI7ty5CQ4O5upNF1+3bduW3bt3s3r1apYvX866devo2rVrwuNxcXHkzJmTXr16ERQUlGQsR44coWnTpjRs2JDt27fTu3dvXn75ZVauXJnq9yciIiJZi83hcDhS+iQfHx/Wrl3Lgw8+yCeffMKUKVPYtm0bX3/9NUOGDGHv3r3pEWuGiYqKwtvbm8jISLy8vKwO5+4GDoQPPjCXli9fftfh8+dDu3aQIwfs3Ally2ZAjCIiqREWZr7btm6F3Lnhyy+hcWOro5JMzOmO4clQrVo1Dh48SGxsLMWLFyd37tyJHt+6datFkaWdTP25Xb5svn/AXGTl7X3LkA8+MD/HmjWDb7/N2PBERESsklmP34GBgdSqVYupU6cCYLfbCQgIoGfPnvTr1++W8a1btyY6OprlN51PqVOnDlWrVmXGjBk4HA78/f158803eeuttwCIjIzE19eXzz77jDZt2rB3714qVKjA5s2bqVmzJgAhISE0adKEkydP4v+fC4c6duzIxYsXWbZsWaLt77zzDt9//32ihGObNm24ePEiISEhqXp//5VZPzcRERG5s+Qew1M148jly5fJmzcvAKtWraJly5a4ublRp04djh07lrqIJfXatzdnm0JCIDwcfH3vOPx//zNTAK5aBa+8AqGhKpgRkUzowAEIDoYjR6BgQTNhVq1aVkclkuFu1/pJMkh8lV+uXHCbH9Xx8/mptaeIiEjqXLx4kU2bNhEREYHdbk/0WPv27ZO9n2vXrrFlyxb69++fsM3NzY2goCA2bNiQ5HM2bNhAnz59Em0LDg5OSMgdOXKEsLCwRNV53t7eBAYGsmHDBtq0acOGDRvIly9fQsIPICgoCDc3NzZu3EiLFi2SFf+GDRtuqQIMDg6md+/eqX5/GcXhcHA59rKlMYiIiGQWubLnwmZR0iVVSb8yZcqwbNkyWrRowcqVK3njjTcAiIiI0FVCVnjgAQgMhI0bYcEC+PfzuB2bDaZPh4oV4eefYe5cM9efiEimsXkzNGkC585B6dLmooYyZayOSsQSQ4cOtTqErO3m1p5J/GD/+2/4/Xez3qRJBsYlIiLiIr777jvatm3LpUuX8PLySnSCzGazpSjpd+7cOeLi4vD9z8XQvr6+7Nu3L8nnhIWFJTk+LCws4fH4bXcaU6hQoUSPZ8uWjQIFCiSMSY7bxRIVFcWVK1e4cOFCit9fTEwMMTExCfejoqKSHU9KXI69TJ6RedJl3yIiIs7mUv9L5PbIffeB6SBVc/oNGTKEt956ixIlSlC7dm3q1q0LmKq/atWqpWmAkkzxP4Lnzk3W8FKl4N13zfqbb5oCQRGRTOHkyRsJv5o14bfflPATEevcZT6/kBCw26FyZTPNsoiIiKTMm2++yUsvvcSlS5e4ePEiFy5cSFg0p/G9GzlyJN7e3glLgH6wiIiIuLRUVfo9++yz1KtXjzNnzlClSpWE7Y0aNUp2ywJJY23aQO/esGOHWW76XG7njTdMYeD27aaD3qpV8J8L00REMta1a/DccybhV7WqKUfOo6tFJWtzc3O7Y0uIuLi4DIwmC7pL0k+tPUVERO7NqVOn6NWrF7ly5brnffn4+ODu7k74f65sDg8Px8/PL8nn+Pn53XF8/G14eDiFCxdONKZq1aoJYyIiIhLt4/r165w/f/62r5uSWLy8vMiZMyfu7u4pfn/9+/dP1L40KioqXRJ/ubLn4lL/S2m+XxEREWeUK/u9/65JrVQl/cD8EPHz8+PkyZMAFC1alNq1a6dZYJJCBQpAs2awZImZsG/8+Ls+JVs2k/Rr2NDkCR95xMzvd5tzWiIi6e+tt0yfvHz54OuvlfATAZYuXZrofmxsLNu2bWPu3Lm8G1+2L+nnDkm/69dNpR8o6SciIpJawcHB/PHHH5QqVeqe9+Xh4UGNGjUIDQ1NmBfZbrcTGhpKjx49knxO3bp1CQ0NTZg3D2D16tUJXa1KliyJn58foaGhCUm+qKgoNm7cSLdu3RL2cfHiRbZs2UKNGjUA+Omnn7Db7QQGBiY7/rp167JixYpE226OJTXvL0eOHOTIkSPZMaSWzWazrI2ZiIiI3JCqpJ/dbmfEiBGMHz+eS5fMVTx58+blzTffZODAgbi5paprqNyrDh1M0m/+fBg92mT17qJ8eVi3Dho1gn37oH59k/grUSL9wxURSeT//g+mTDHrX3xh+hCLCM8888wt25599lkefPBBFi1aROfOnS2IKguJT/oVKXLLQ7//DhcumGuv6tTJ4LhERERcRNOmTXn77bfZs2cPlSpVInv27Ikef/rpp1O0vz59+tChQwdq1qxJ7dq1mThxItHR0XTq1AmA9u3bU6RIEUaOHAnA66+/ziOPPML48eNp2rQpCxcu5I8//mDWrFmASWb17t2bESNGULZsWUqWLMngwYPx9/dPSLyVL1+eJ598ki5dujBjxgxiY2Pp0aMHbdq0wf+mC4f27NnDtWvXOH/+PP/88w/bt28HSEgmvvrqq0ydOpW+ffvy0ksv8dNPP7F48WK+j28tkIz3JyIiIllbqpJ+AwcO5NNPP2XUqFE8/PDDAKxfv55hw4Zx9epV3n///TQNUpKpcWPw8TET9K1aZebESob774dffjGJv8OHoUED+PFHs11EJEPs3g0vv2zWBw6Ep56yNh4RJ1CnTh26du1qdRiu7w6VfvHn3558EtzdMzAmERERF9KlSxcAhg8ffstjNpstxa3MW7duzdmzZxkyZAhhYWFUrVqVkJAQfH19ATh+/Hiii9UfeughFixYwKBBgxgwYABly5Zl2bJlVKxYMWFM3759iY6OpmvXrly8eJF69eoREhKCp6dnwpj58+fTo0cPGjVqhJubG61atWLy5MmJYmvSpAnHjh1LuF+tWjUAHA4HYKoKv//+e9544w0mTZpE0aJF+eSTTwgODk72+xMREZGszeaI/2WRAv7+/syYMeOWq62++eYbXnvtNU6dOpVmAVohKioKb29vIiMj8fLysjqclHn9dZg8GZ5/HhYtStFTT52CoCBT8efraxJ/N/3GFRFJH//8A7Vqwf795uqDlSt19lxSzamP4Slw5coV+vfvzw8//MD+/futDueeZerP7f774cABWLvWXBl1k8qVYedO02Thf/+zKD4RERGLZOrjt9yWPjcRERHnlNxjeKoq/c6fP0+5cuVu2V6uXDnOnz+fml1KWunQwST9vvkGLl4082IlU5Ei5nzWE0/cmONv1Sr4tx29iEjaczigc2eT8Cta1LT4VMJPJJH8+fNjs9kS7jscDv755x9y5crFvHnzLIwsC3A4blvpd/y4Sfi5uZlKPxERERERERERq6Uq6VelShWmTp16S5uCqVOnUrly5TQJTFKpWjV48EHTKm/xYkhh269CheDnn02n0I0b4bHHYMUK+LeLq4hI2po0Cb78ErJnN7cFC1odkUim8+GHHyZK+rm5uVGwYEECAwPJnz+/hZFlAf/8A9HRZr1w4UQPrVhhbuvWNXP6iYiISPJNnjyZrl274unpecu5pf/q1atXBkUlIiIi4vxSlfQbM2YMTZs25ccff6Ru3boAbNiwgRMnTrAi/gyIWMNmM9V+ffvC3LkpTvoB5M8Pq1ebKbXWrTOVf99+a7ruiYikmfXr4e23zfqHH0KdOtbGI5JJdezY0eoQsq74lvXe3pA7d6KH4ufza9o0g2MSERFxAR9++CFt27bF09OTDz/88LbjbDabkn4iIiIiKeB29yG3euSRR/jrr79o0aIFFy9e5OLFi7Rs2ZLdu3fzxRdfpHWMklJt25peU7/9BgcPpmoXefPCDz+YhN/ly+aE1tKlaRyniGRdYWFm7tHr181EWK+9ZnVEIpnWnDlz+PLLL2/Z/uWXXzJ37lwLIspCbtPa88oVCA0160r6iYiIpNyRI0e47777EtZvtxw+fNjiSEVEREScS6qSfgD+/v68//77fP3113z99deMGDGCCxcu8Omnn6ZlfJIa/v7w+ONm/fPPU72bXLlMhd8zz0BMDLRsCUOHgt2eRnGKSNZ0/Tq0aQNnzph2xLNmmSplEUnSyJEj8fHxuWV7oUKF+OCDDyyIKAu5TdLv559N4q9oUahUyYK4RERERERERESSkKr2nuIE2reHlSvhiy9g2DBT+ZcKOXKYabbefBOmTIHhw+GPP2D+fMiXL00jFpGswOEwLT3XroU8eeDrr29pmSciiR0/fpySJUvesr148eIcP37cgoiykNsk/W5u7alrFkRERO5NXFwcn332GaGhoURERGD/z5XGP/30k0WRiYiIiDifVFf6SSbXvLnp0Xn0KIwde0+7yp4dJk82RYOenrBiBdSqBbt2pUmkIpJVnD0LzZrBxInm/pw58MADloYk4gwKFSrEn3/+ecv2HTt2JLTFknSSRNLP4dB8fiIiImnp9ddf5/XXXycuLo6KFStSpUqVRIuIiIiIJJ8q/VxVrlwwaBC88w706wexseb+PXjxRdOJr2VLM1VgnTowe7aZlktE5I5CQ6FdOzOXX44cMGkSPPus1VGJOIUXXniBXr16kTdvXho0aADA2rVref3112nTpo3F0bm4+KRfkSIJm/bsgWPHzFfZY49ZFJeIiIgLWbhwIYsXL6ZJkyZWhyIiIiLi9FKU9GvZsuUdH7948eK9xCJprW9fuHYNBg82y9Wr8N5799SHqnp1097zhRfgxx+hdWvYsgXefx+yKYUsIv8VG2u+f8aMMeUxFSrAwoWaBEskBd577z2OHj1Ko0aNyPbvwdZut9O+fXvN6Zfekqj0i6/ya9hQ3YlFRETSgoeHB2XKlLE6DBERERGXkKL2nt7e3ndcihcvTvv27dMrVkmNQYNutPd8/30zl5bDcU+79PGBH34wOUUw5/IbN4Zz5+4xVhFxLYcPQ716MHq0+d555RXYvFkJP5EU8vDwYNGiRezfv5/58+ezZMkSDh06xOzZs/Hw8LA6PNd2h6SfWnuKiIikjTfffJNJkybhuMdzFSIiIiKSwkq/OXPmpFcckp7eestMxtezJ4wfbyr+Jk8Gt9RP6ZgtmzmPX6MGvPSSqfqrWRMWL4batdMwdhFxTgsWwKuvwj//QL588OmnpjewiKRa2bJlKVu2rNVhZB0Oxy1JvwsX4NdfzSYl/URERNLG+vXr+fnnn/nhhx948MEHyZ49e6LHlyxZYlFkIiIiIs4n9VkfcS49esCsWaa157RppuLGbr/n3T7/PPz+O5QpY+a3CQyEZs1gw4Y0iFlEnM+lS9CxI7RtaxJ+9erBjh1K+Incg1atWjF69Ohbto8ZM4bnnnvOgoiyiPPnTZt0AD8/AFatgrg4KF8eSpa0MDYREREXki9fPlq0aMEjjzyCj4/PLV2lRERERCT5NAtbVtKlC+TIAZ06wSefQEwMzJ59z5PxVaxoOvb17GmKe5YvN8ujj8LAgdCo0T1NIygizuLXX833y4EDppJ4yBDzJaAJP0Xuybp16xg2bNgt2xs3bsz48eMzPqCsIr7Kz8fH/H5CrT1FRETS2vXr12nYsCFPPPEEfv9eZCMiIiIiqadKv6ymfXuTmXN3hy++MNU4sbH3vNt8+czu9u2Dzp0he3ZYswYefxzq1IFvvkmTwkIRyYwuX4Y33oD69U3CLyDAfAEMHaqEn0gauHTpUpJz92XPnp2oqCgLIsoiTp0yt/+29oyLM3Mag5J+IiIiaSVbtmy8+uqrxMTEWB2KiIiIiEtQ0i8rat0avvzSZOYWL4bnnjMt+dJA2bKmiPDQIejVC3LmhE2boHlzqFLF5BuvX0+TlxKRzOCXX8w/7okTzfxXnTrBn3+aBKCIpIlKlSqxaNGiW7YvXLiQChUqWBBRFvGf+fw2b4Zz58DbGx5+2MK4REREXEzt2rXZtm2b1WGIiIiIuASVYGRVLVrAsmVmnq1vvjGVOa+8Yub+K1r0nncfEACTJpnOfhMnmmkEd+0yhYWDB0OfPmbar9y57/mlRMQK0dHQvz9MnWqSfUWLmnlDGze2OjIRlzN48GBatmzJoUOHeOyxxwAIDQ1lwYIFfPXVVxZH58L+k/SLb+35xBPmuikRERFJG6+99hpvvvkmJ0+epEaNGuT+z4mCypUrWxSZiIiIiPNRpV9W1qSJ6VNVpgxcvAijR0PJkvDii7B1a5q8RKFC8MEHcOwYjBgBrbxWU+XwEnr0gGLFTAIwPDxNXkpEMsqaNVC5MkyZYhJ+L79ssvpK+Imki2bNmrFs2TIOHjyYcFLs1KlT/PTTT5QpU8bq8FxXfNKvSBFA8/mJiIiklzZt2nDkyBF69erFww8/TNWqValWrVrCrYiIiIgkn5J+WV3DhrB/v6n2e+QR03tz3jyoUcM89t13aTIZXz7HBQbubcdXUU+whFb0K/gp58+bRGDx4tC1q5kPUEQysUuXoHt3891w+LAp6V25Ej7+2PS7E5F007RpU3799Veio6M5fPgwzz//PG+99RZVqlSxOjTXdVOl3+nTsG0b2Gy6vkFERCStHTly5Jbl8OHDCbciIiIiknxK+gm4ucHTT5vqnT/+MD04s2Uz959+GsqVg+nTTTu/1Fi9GipVgvnzEzZ9EPkaoSM3ERgIMTEmZ1C+vHm5detM8ZCIZCLr15t/xx99ZO6/8oqp7nviCWvjEslC1q1bR4cOHfD392f8+PE89thj/P7771aH5bpuSvr99JNZrVHDdDEQERGRtFO8ePE7LiIiIiKSfEr6SWI1aphKvyNHoG9fU71z4AC89pqp6hkwAE6dSt6+Ll+Gnj1NUuDUKShbFn77DZo3x3btGo9Na8WGbyJYvx6aNzdXz3/3nSk4DAyETz6BqKh0fbcicjdxcfD++/Doo3D0qCnN/fFHmDEDvLysjk7E5YWFhTFq1CjKli3Lc889h5eXFzExMSxbtoxRo0ZRq1atFO9z2rRplChRAk9PTwIDA9m0adNtx8bGxjJ8+HBKly6Np6cnVapUISQkJNGYkSNHUqtWLfLmzUuhQoVo3rw5+/fvT3Fcmc5NSb8//jCrDz1kXTgiIiKubs+ePYSEhPDtt98mWkREREQk+ZT0k6QVLWrm+Dt5EiZPhlKl4MIFGDkSSpSAdu1gy5bbP3/jRqhWDaZONfe7dzd9serWhblz4YEH4ORJbG1a83DgdZYuNe09X30VPD1h82bo0gUKF4aOHVX9J2KJM2cgOBgGDTLJv3btYOdOaNTI6shEsoRmzZrxwAMP8OeffzJx4kROnz7NlClT7mmfixYtok+fPgwdOpStW7dSpUoVgoODiYiISHL8oEGDmDlzJlOmTGHPnj28+uqrtGjRgm3btiWMWbt2Ld27d+f3339n9erVxMbG8sQTTxCd2g4BmUFcHISFmfWbkn6pyLGKiIjIXRw+fJgqVapQsWJFmjZtSvPmzWnevDktWrSgRYsWVocnIiIi4lRsDodSKf8VFRWFt7c3kZGReKmSxYiLM2V4H35oMnDxGjSAN96AZs3A3R2uXYP33oMPPjBzARYpArNn39oCcO9eqF3bzBHWpw+MH5/w0NmzJi/46aeJ5/krXRo6dYIOHUxOUkTS0cqV0L49RERArlymrWf79qYkVyQTc6VjeLZs2ejVqxfdunWjbNmyCduzZ8/Ojh07qFChQor3GRgYSK1atZj670U5drudgIAAevbsSb9+/W4Z7+/vz8CBA+nevXvCtlatWpEzZ07mzZuX5GucPXuWQoUKsXbtWho0aJCsuDLd5xYWZq48cnPjenQMXgWyceWK+flSrpzVwYmIiGQOaXX8btasGe7u7nzyySeULFmSTZs28ffff/Pmm28ybtw46tevn4ZRS6b73SUiIiLJktxjuCr9JHnc3U0PzrVrE8/7t24dtGhhKvfGjDGVfCNGmITf//5nqoKSmvOrfHmT2QOYMAH+7/8SHipYEN56C/bsgQ0bTMVf3rxw6JApOCpeHBo3hsWLTQdREUlDsbHwzjvw5JMm4Ve5sqnq7dBBCT+RDLZ+/Xr++ecfatSoQWBgIFOnTuXcuXOp3t+1a9fYsmULQUFBCdvc3NwICgpiw4YNST4nJiYGT0/PRNty5szJ+vXrb/s6kZGRABQoUOC2Y2JiYoiKikq0ZCrxrT19fdl7wCT88uaF+++3NiwRERFXtGHDBoYPH46Pjw9ubm64ublRr149Ro4cSa9evawOT0RERMSpKOknKRc/79/Ro9C/P+TPbzJy77wDW7dCgQKwaBHMn28eu52WLc3zATp3hj//TPSwzQZ16sCsWabL4Ny5Zr4/ux1CQqB1a5MgbNkSvvjCdB8VkXtw9Kip3h0zxtzv1g1+/11lLSIWqVOnDh9//DFnzpzhlVdeYeHChfj7+2O321m9ejX//PNPivZ37tw54uLi8PX1TbTd19eXsPhWlv8RHBzMhAkTOHDgQMLrLlmyhDNnziQ53m6307t3bx5++GEqVqx421hGjhyJt7d3whIQEJCi95LubprPb/Nms1qjBrjpl7OIiEiai4uLI2/evAD4+Phw+t/jcPHixV1jnmARERGRDKRTF5J6RYqYNp4nTpjWf1WqmKq/nTvh+eeTt4/33jOVgFeumOfeJnOXO7fpLLhmDRw8eKPi7/JlWLrUPFaoEDz+uAkl/lydiCTT11+beTh//x28veGrr8w/ppw5rY5MJMvLnTs3L730EuvXr2fnzp28+eabjBo1ikKFCvH000+n62tPmjSJsmXLUq5cOTw8POjRowedOnXC7TbZr+7du7Nr1y4WLlx4x/3279+fyMjIhOXEiRPpEX7qnTplbm+az69mTevCERERcWUVK1Zkx44dgGlFPmbMGH799VeGDx9OqVKlLI5ORERExLko6Sf3LnduUxG0fTssWQL+/sl/rrs7LFgAJUrA4cOmbWhc3B2fUrq0yRUeOWIKCwcPhooV4fp1+PFH6N7d5CPr1IHRo0EXBorcJCoKNm6EOXOgb18zH2fp0vDss3DxIgQGmn/LrVpZHamIJOGBBx5gzJgxnDx5kv+7qTV2cvj4+ODu7k54eHii7eHh4fj5+SX5nIIFC7Js2TKio6M5duwY+/btI0+ePEmegOvRowfLly/n559/puhdJt/NkSMHXl5eiZZMJYlKv1q1rAtHRETElQ0aNAi73Q7A8OHDOXLkCPXr12fFihVMnjzZ4uhEREREnEs2qwMQ4b77TLle3brwww/w7rswfPhdn2azmcKkatXM8IMHzW6WLjVzAW7caJZ+/cwcPM88A08/bV7G3T0D3peI1eLizL+pVatg716zxFev/Jebm5lMc8QIyJ49Y+MUkRRzd3enefPmNG/ePNnP8fDwoEaNGoSGhiY8z263ExoaSo8ePe74XE9PT4oUKUJsbCxff/01z99U0e9wOOjZsydLly5lzZo1lCxZMjVvKXP5N+l33bcI/xYeqNJPREQknQQHByeslylThn379nH+/Hny58+PTfOKi4iIiKSIkn6SOVStCh9/DC++aMr4atQwWboUKFMG3n7bLGfOwDffmATgzz/DX3/B2LFm8fGBp54yu3/8cVOoKOJSLl6E2bNh6lRTEvtf/v5QvrxZKlQwtxUrmn8cIuLS+vTpQ4cOHahZsya1a9dm4sSJREdH06lTJwDat29PkSJFGDlyJAAbN27k1KlTVK1alVOnTjFs2DDsdjt9+/ZN2Gf37t1ZsGAB33zzDXnz5k2YH9Db25ucztoi+N+k30m7P7GxZrpiV8hlioiIZGYHDx7k0KFDNGjQgAIFCuBwOKwOSURERMTpKOknmUe7drB5M0yebJJ/48ZBx47g4ZHiXRUuDK++apaoKAgJgW+/he+/h3Pn4LPPzJIjBwQFwZNPmq6GVaqk6uVEMoe9e2HKFJg710x4CZA/v2mbW62aSfCVKwf58lkapohYp3Xr1pw9e5YhQ4YQFhZG1apVCQkJwdfXF4Djx48nmq/v6tWrDBo0iMOHD5MnTx6aNGnCF198Qb6bvkemT58OwKOPPprotebMmUPHjh3T+y2lj3+TfrsvmJblNWuaDgMiIiKS9v7++2+ef/55fv75Z2w2GwcOHKBUqVJ07tyZ/PnzM378eKtDFBEREXEaNocunbpFVFQU3t7eREZGZr45ZlxdbKwpv1u71twPCID+/eGll0yGLg12v369qQL85hs4ejTx4zlymNxIYOCNpWRJneiTTCwuDlasMMm+1atvbK9YEXr1Mgm/XLmsi08kg+kY7pwy3efm6wsREQxrvp13l1Vh4EDT/VhERERuSKvjd/v27YmIiOCTTz6hfPny7Nixg1KlSrFy5Ur69OnD7t270zBqyXS/u0RERCRZknsMV6WfZC7Zs5uyvFmzYPRoOHECXnsN3n/fTM738svg6XlPu2/Y0Cwffgi7d5sKwPXrzfx/58/D77+bJZ6PD9SuDfXrQ6tWULZsGrxPkXtlt8PMmaYi9vBhs83NzUxc2asXPPqostUiIqkRGwsREQCs+etGpZ+IiIikj1WrVrFy5UqKFi2aaHvZsmU5duyYRVGJiIiIOCe3uw8RyWCeniZpceiQmZOsaFE4dQp69oRSpWDixButC++BzWaKoQYMMIVS587BgQMwb555qdq1TavPc+fM4/37w/33Q/XqJh+Z1FRpIhkiIgKaNjUJ8cOHTbvOt982/2aWLjVZbSX8RERS5985CR3Zs/PrvvsAqFXLyoBERERcW3R0NLmS6E5y/vx5cqRBxx8RERGRrERJP8m8PD2he3c4eBCmT4dixeDMGXjjDdNzc9w4uHQpzV7OZoMyZUw3xMmTTeVfVJSp+ps0CZ54AtzdYds2U3RYqpRp/zlhgilIFMkQoaFm8smQEPNv5MMP4eRJGDMGSpSwOjoREef373x+1woU5rrdDT8/8Pe3OCYREREXVr9+fT7//POE+zabDbvdzpgxY2jYsKGFkYmIiIg4HyX9JPPLkQNefdWU4X38sUlsRESYyqYSJeCDDyAyMt1eOjDQFB6uXGlyjjNmmEIqNzfYtAnefNPkI+vVM0WIv/6abuGIK1izBnr0gDlz4OrV5D/v+nUYNMjMeRkWBhUqwObN0Ls35M6dXtGKiGQ9/yb9zue80dpTxdMiIiLpZ8yYMcyaNYvGjRtz7do1+vbtS8WKFVm3bh2jR4+2OjwRERERp6KknzgPDw8zp99ff8Hs2VC6NPz9NwwcaJJ/Q4eaSfnSit1ukiubN8OSJRAaSkEfB6+8Aj/9ZDqOTplikn1gkn1vvGHu58sHxYvDU0+ZtqALFsDOnXDtWtqFJ07m8GFo2dJkjKdNg5deMq1r+/eH48fv/Nzjx80cfe+/Dw4HdOli/rusWDFDQhcRyVL+TfqdjDNJP7X2FBERSV8VK1bkr7/+ol69ejzzzDNER0fTsmVLtm3bRunSpa0OT0RERMSp2BwOh8PqIDKbqKgovL29iYyMxMvLy+pw5HauX4dFi0wiZO9esy1PHjPPWZ8+4Ot75+fb7aYv5549ZoK+EycSL6dO3Zqlq1PH9POsWzfR5lOn4MsvYdUqk9w7eTLpl8yWDcqXN3mfZs2gQQOTyxQXFhVl/hudONH89+TmBs89Bxs23Ej2ublB8+ZmMslHHklcUrJsmUkQXrgAXl4waxa0bm3BGxFxDjqGO6dM9bkNGAAjRzIvXw9evDiF77+HJk2sDUlERCQzSu/j98mTJxk+fDizZs1K831nZZnqd5eIiIgkW3KP4Ur6JUE/gJyM3Q5Ll8KIEbB9u9mWMyd07WpagBYqBIcOmcTg3r0mybd3L+zbB5cv33nfNhsULgwBAbBrF0RHm+2tW8OoUbedQ+3CBTN8587ES1RU4nF585q5Aps1g8aNTajiIuLi4LPPTCVqeLjZ9vjjJmlcsaJJWn/3nSkX/fnnG8+rWNG0/3zuORg2zDwOptRk4UIzmaSI3JaO4c4pU31uHTvC3Ln0ZySj6Ed4uI7PIiIiSUnv4/eOHTuoXr06cXFxab7vrCxT/e4SERGRZFPS7x7oB5CTcjjg++/hvffMZHsA2bOb29jYpJ+TPTuULQtlypiJ+QICEi/+/jf2ceYMDB5sWos6HGbCv969TXtGb++7h3fyFH9/9RPn1u9n0fVWTP+9WkIuCEx+MTDQJACfegoqVdIcQk5r3Trz38a2beZ+2bIm2de0adIf6u7dMHUqfP75jUS0zWb+OwN46y1TLaiyUJG70jHcOWWqz+2JJ2D1atozl7XF2nPsmLXhiIiIZFZK+jmnTPW7S0RERJJNSb97oB9ATs7hgB9/NJV/69aZbblzQ7lyprdm+fJQoYK5LVXqRlIvuXbsgDffhNBQc79gQRg+3Mw3mC3bjXHnz5sKrp9+MmP377/xWPbs2N//gC0N+rB8hRvffXcjPxTPxwdq1zaJwMBAs54/f8r/HJJBLlyALVtg5kz46iuzzdvbzDXZvXvyEnYXL5rqwKlTTXWqj49JBDZunJ6Ri7gUHcOdU6b63CpWhN27CWI1+VoFJXyli4iISGJK+jmnTPW7S0RERJJNSb97oB9ALuSvv0xFXkCAmTctrTgcsGKFqcDat89sq1DBzCW4b59J8m3ffqNSC8zr16hh5mWLTxg+8QTMnQt+fpw6ZQoVly83OcsrV2592fvvv5EEDAyEypUzYfGXw+H6JYrR0SZLu3nzjeXgwRuPu7nBK6/Au++apHBK2e3w++/mA/fxSbu4RbIAHcOdU6b63AoUgAsXqMBu2o+sQL9+1oYjIiKSWSnp55wy1e8uERERSTYl/e6BfgBJssXGwqxZpprr779vfbxCBWjUyCyPPAL58pmk2KefQq9eJrNXsKBJ/N1UzRUTYwoKN268sdycU4rn7Q3Nm8Pzz0NQkIUJwPPnYeJE+OgjU+34xBNmefxx8PW1KKg05HCY+fe++cYk+HbvNom5/ypVCurWhXfeMf1ZRSTD6RjunDLN53blCuTKBUA+LvD1j/lo1Mi6cERERDKzez1+t2zZ8o6PX7x4kbVr1yrpl8Yyze8uERERSREl/e6BfgBJil28CCNHmgq+KlVMku+xx8DP7/bP2bsXXnjBZPfAzAE3apSpTEzC33+bqQpvTgReuHDj8fz5oUULkwB87LEUdC11OOCff0wFYkqdPWvmqps6FS5dSnpM1aoQHGyWhx/OhKWJd/Hrr9C3L/z2W+LthQtDrVo3lpo14b77rIlRRBLoGO6cMs3ndvgwlC7NZXKSm2jOn7eptbaIiMht3Ovxu1OnTskaN2fOnBTvW24v0/zuEhERkRRR0u8e6AeQZJirV01V2OTJ5n7VqrBwITzwQNLjHQ6IiIBdu7Dv2s2BcC8+PdecL77LR1jYjWEFCkDLliYB2LBh4qkGE0RHw/z5JmG3cyeULg1PPmmWhg3NPIi3Ex4O48aZyr7Ll822KlVg8GDz4qtWwcqVt05UmDs3PPooNGliMpSFCyfzD2WB/fuhf39YutTcz5nTtOx89FGT5PP3tzQ8EUmajuHOKdN8buvXQ/36HKQ0jcsc5MAB60IRERHJ7DLN8VtSRJ+biIiIc1LS7x7oB5BkuOXLoVMnOHfOtBWbMsX07dy9G3btMkv8+n/biHp44Gj6FHuqtWXmiSYs+saTiIgbD+fPDw0amOWRR6Bq3kO4z/wIZs82FYpJ8fCA+vVNy9EnnzRtSm02OH0axoyBmTNNwhLMPIVDhkCzZrfO5RceDqtXmyTgqlXmfjybDerVg2efNRnKokXv9a9oOBym6vDCBbNERZk5HYsXT95cg+HhZi6+WbMgLs7Mz9e5MwwbpkSfiBPQMdw5ZZrPbfFiaN2addRnxgvrWLDAulBEREQyu0xz/JYU0ecmIiLinJT0uwf6ASSWOH0a2rc3LULvxGaDMmVMIu7gQZMMjOftjb3ls2yv2I6P9zfgqyVunDsHNuw8wSp6MJUmrMAN88/+apHSZO/dHffWz5qqvB9+MMuxY4lfs2hRqF7dVO/FxJhtgYFmLsMnn0xeMs1uhz//NPtYtgx+/z3x4w89ZBKArVpBsWK3Pv/qVRPX0aNw5IhZjh838wleuGASmPG316/f+vz8+aFaNbNUr25u778f3N3N45cuwfjxMHasqYIEk8gcNcr8rUXEKegY7pwyzec2cSK88QYLac3p8Qvp08e6UERERDK7THP8lhTR5yYiIuKclPS7B/oBJJax203SafBgiI011WkVK5rlwQfNbblyptUkmKq2P/80bTr/7//g5Mkb+ypaFHvrFzgR60feBTMocO5Gj7IfeJIp9CSEJ8mV242HHzZT7jVtCveXdWA78BeEhJgE4Jo1NxJ9YOblGzoUgoKSl+y7nRMnYMkS+PJLM2/ezQIDTaXhmTM3knynT6ds/x4eJtGXJ49JDsbG3jomVy7TlvTBB+G7725UItaqZT6HRx5J1VsTEevoGO6cMs3n1rcvjB3LBN6g1roJ1K9vXSgiIiKZXaY5fkuK6HMTERFxTkr63QP9ABLLXbpkEnp58yb/OXY7rFtnEoBffgmRkYkf9/LC3rEText2Z9WRsqxdC7/8YgrlblaqlEn+NWlipq/ztF82+9282bTjfPTRe0v2JeXUKTN33ldfmde63ddS7txQsuSNpXhx8PGBfPlMgu/mJWfOG3Feu2YqIrduNRWN27bB9u035iOMV7o0jBxpKg7T+j2KSIbQMdw5pevntnevmSvXze2uQ6+0bEvOpQt4yzaOYVFvkidP2oYiIiLiSvS7yznpcxMREXFOSvrdA/0AEqd39SqsWAELFpjqtf/9D158kf+evbTbTS4sNNQMX7vW5Mfi5coFjRqZBGCTJkl33UxzYWGmAnDvXtNWND7BV6KESfClVTIuLg4OHDCJwD//NAm/Dh1MhaCIOC0dw51TunxuDoeZ0Hb9erM8/PBdn3KuckN8dq7h7aILGHvihbSJQ0RExEXpd5dz0ucmIiLinJJ7DM+WgTGJSEbx9ISWLc1yB25uUKmSWXr3NgWGoaHw/fcmCXjqlOl6+d13ZnzFiib517ixOXeaPXs6xO7nB6+9lg47/g93d9MqtVw5kxQVERHXYrOZCzrWr4eFC5OV9ItvJV3gQf90Dk5EREREREREJO3dvc+RiGQZefLAM8/ArFlmyr3t2+GDD8x5Ujc32LULxoyBhg1N0d2zz8Ls2Smfbk9ERCRDtGljbhcvhuvX7zo810VzQAsIVNJPRERERERERJyPkn4ikiSbDapUgf79TZHE2bPwf/8H7dtDwYIQFQVffw2dO0ORIlCtGgwcCBs33n5KPhERkQzVqBHcdx9ERJge1nfgiPqHXHGXAHjg0cIZEZ2IiIiIiIiISJqyPOk3bdo0SpQogaenJ4GBgWzatOm2Y3fv3k2rVq0oUaIENpuNiRMn3jJm2LBh2Gy2REu5cuXS8R2IZA0FCpiCiblzzbR7mzbBsGFQu7ZJEMZXBdapY6bg69sXtmxRAlBERCyUPbspSwfT4vMOzmwxVX6ReFGpbp47jhURERERERERyYwsTfotWrSIPn36MHToULZu3UqVKlUIDg4mIiIiyfGXL1+mVKlSjBo1Cj8/v9vu98EHH+TMmTMJy/r169PrLYhkSW5uUKsWDB1qKvvCw+GLL6B1a8idG44dg7FjoWZNuP9+UwH4559KAIqIiAXiW3x+/TVcu3bbYQfXmaTf+Rz+eHpmRGAiIiIiIiIiImnL0qTfhAkT6NKlC506daJChQrMmDGDXLlyMXv27CTH16pVi7Fjx9KmTRty5Mhx2/1my5YNPz+/hMXHxye93oKIYNp9tmtniigiIuCrr+C55yBnTjh40FQAVqkCFSqYROGuXUoAiohIBqlfHwoXhgsXYPXq2w6Lr/S7ep/m8xMRERERERER52RZ0u/atWts2bKFoKCgG8G4uREUFMSGDRvuad8HDhzA39+fUqVK0bZtW44fP37H8TExMURFRSVaRCR1cuWCVq1g8WKTAPy//4PmzSFHDti3D4YPh0qVTAvQ116D5cvh8mWroxYREZfl7g7PP2/W79DiM3KvSfplC1DST0RERERERESck2VJv3PnzhEXF4evr2+i7b6+voSFhaV6v4GBgXz22WeEhIQwffp0jhw5Qv369fnnn39u+5yRI0fi7e2dsAQEBKT69UXkhjx5TFe1pUtNAvCLL+Cpp0wC8NgxmD4dmjUz8wU2bgxTp8Lhw1ZHLSIiLie+xeeyZXDlyi0POxxw/YRJ+nmVU9JPRERERERERJyTpe0900Pjxo157rnnqFy5MsHBwaxYsYKLFy+yePHi2z6nf//+REZGJiwnTpzIwIhFsgYvL9MC9Lvv4Px5c9utGxQrBjExEBICPXtC6dJQvjy8/Tbs3m111CIi4hICA6F4cbh0CVasuOXhQ4fgvhiT9POpUiSjoxMRERERERERSROWJf18fHxwd3cnPDw80fbw8HD8/PzS7HXy5cvH/fffz8GDB287JkeOHHh5eSVaRCT95MplKv4++giOHjVz/I0eDY88Yrqw7dsH48ZBxYrw0EMwe7Y5TysiIpIqNhu0bm3Wk2jxuXkzFOEUAO5q7ykiIiIiIiIiTsqypJ+Hhwc1atQgNDQ0YZvdbic0NJS6deum2etcunSJQ4cOUbhw4TTbp4ikHZsNHnwQ+vaFNWvg3DlYtAhatDAJwA0boHNn8PeHV14xJ2YdDqujFhERpxPf4nP5cvhP2/c//gB/TKUf/kr6iYiIiIiIiIhzsrS9Z58+ffj444+ZO3cue/fupVu3bkRHR9OpUycA2rdvT//+/RPGX7t2je3bt7N9+3auXbvGqVOn2L59e6Iqvrfeeou1a9dy9OhRfvvtN1q0aIG7uzsvvPBChr8/EUm5fPng+edhyRI4eRJGjYIyZcz52VmzoHZtqFbNzP934YLV0YqIiNOoWhXuvx+uXoVvv0300OZNDiX9RERERERERMTpWZr0a926NePGjWPIkCFUrVqV7du3ExISgq+vLwDHjx/nzJkzCeNPnz5NtWrVqFatGmfOnGHcuHFUq1aNl19+OWHMyZMneeGFF3jggQd4/vnnue+++/j9998pWLBghr8/Ebk3fn7wzjvw11/w88/Qti3kyAE7dpj5//z9oUMHUw2o6j8REbkjm+1Gtd9NLT7j4uDI1gt4EmM2qDuEiIiIiIiIiDgpm8OhU+X/FRUVhbe3N5GRkZrfTySTOX8e5s+Hjz+GnTtvbK9UCV59Fdq1A/2zFcm6dAx3Thn2ue3ZY3pKZ88OYWFQoAB79sDzD+5iF5Vw3HcftnPn0u/1RUREXIh+dzknfW4iIiLOKbnHcEsr/UREUqpAAVPlt2OHqfDr2BE8PU0CsHt3U/3XpQts2WJ1pCIikulUqACVK0NsLCxdCpi5YuNbe9rU2lNEREREREREnJiSfiLilGw2qFMH5syB06dh0iQoXx6io+GTT6BmTahVy6xfumR1tCIikmn8p8Xn3r1oPj8RERERERERcQlK+omI08ufH3r1gt27Ye1aeOEF8PCAP/4wVX+FCplt330H165ZHa2IiFiqdWtz+9NPEB7OxYtK+omIiIiIiIiIa1DST0Rchs0GDRrAggVw8iSMGQNly8KVK6ag4+mnoXBhM/ffunVgt1sdsYiIZLhSpaB2bXMQ+OorIiNvSvoVKWJtbCIiIiIiIiIi90BJPxFxSQULwttvw/79sGkT9O4Nfn5w/jzMnAmPPAIlSsA775j5AR0OqyMWEZEMc1OLz6goVfqJiIiIiIiIiGtQ0k9EXJrNZub2+/BDU/23ejV07Ah588KJE6YasGpVqF4d5s6FmBirIxYRkXT3/PPmALF+PTkiTijpJyIiIiIiIiIuQUk/Ecky3N0hKAjmzIHwcPjyS2jRwsz/t327SQaWKAEjRsC5cxYHKyIi6adIEahfH4C6JxdThFNmu5J+IiIiIiIiIuLElPQTkSwpZ0549llYsgROn4YPPjDnesPCYPBgCAiArl1hzx6rIxURkXTxb4vPJ/9egB9hZpuSfiIiIiIiIiLixJT0E5Es7777oH9/OHIE5s2DGjXg6lX4+GN48EF48klYtUrz/omIuJRWrcDdnUqxW8nOdRw2G/j6Wh2ViIiIiIiIiEiqKeknIvIvDw9o2xY2b4Z160zrT5sNVq6E4GCoXBnmz4fr162OVERE7lmhQjgea5Rw117QF7JlszAgEREREREREZF7o6SfiMh/2GxmqqclS+DgQXj9dciTB3btgnbt4P77YeZMUw0oIiLO62rzNgnrNrX2FBEREREREREnp6SfiMgdlCoFEyfCiRNm3r+CBU0b0FdfNY+NHw+XLlkdpYiIpMbfDVpwjewA2Ioq6SciIiIiIiIizk1JPxGRZMiXz8z7d/QoTJoERYvCmTPw1ltQvDgMHw7nz1sdpYiIpMRF8vEDjQFV+omIiIiIiIiI81PST0QkBXLlgl694NAh+PRTKFvWJPuGDjXJv759ISzM6ihFRCQ5oqJgKO/yu+ej0KWL1eGIiIiIiIiIiNwTJf1ERFLBwwNeegn27oVFi6BKFdPmc+xYKFECunc3VYEiIpJ5RUbCDqrSvcLPULOm1eGIiIiIiIiIiNwTJf1ERO6Buzs8/zxs2wbLl8NDD0FMDHz0kakC7NgR9u2zOkoREUlKZKS59fKyNg4RERERERERkbSgpJ+ISBqw2aBpU1i/Hn7+GYKC4Pp1mDsXKlSA554ziUEREck84pN+3t7WxiEiIiIiIiIikhaU9BMRSUM2Gzz6KKxeDRs3wjPPgMMBX30F1atDkybw669WRykiIqCkn4iIiIiIiIi4FiX9RETSSe3asGwZ/Pkn/O9/4OYGP/wA9eqZ5N/evVZHKCKStUVFmVsl/URERERERETEFSjpJyKSzipVgvnzYf9+6NIFsmUzyb9KlaBHDzh3zuoIRUSyJlX6iYiIuKZp06ZRokQJPD09CQwMZNOmTXcc/+WXX1KuXDk8PT2pVKkSK1asSPS4w+FgyJAhFC5cmJw5cxIUFMSBAwcSjTl//jxt27bFy8uLfPny0blzZy5dupRozJ9//kn9+vXx9PQkICCAMWPGJHo8NjaW4cOHU7p0aTw9PalSpQohISGJxsTFxTF48GBKlixJzpw5KV26NO+99x4OhyOlfyYRERFxQUr6iYhkkDJlYNYs2LPHtP2Mi4Np08z2CRPg2jWrIxQRyVrik35eXtbGISIiImln0aJF9OnTh6FDh7J161aqVKlCcHAwERERSY7/7bffeOGFF+jcuTPbtm2jefPmNG/enF27diWMGTNmDJMnT2bGjBls3LiR3LlzExwczNWrVxPGtG3blt27d7N69WqWL1/OunXr6Nq1a8LjUVFRPPHEExQvXpwtW7YwduxYhg0bxqxZsxLGDBo0iJkzZzJlyhT27NnDq6++SosWLdh20wTxo0ePZvr06UydOpW9e/cyevRoxowZw5QpU9LyzygiIiJOyubQpUC3iIqKwtvbm8jISLx0FkhE0snPP8Mbb8COHeZ+6dIwdiw0b27mBhSRlNMx3DlZ9bk98wx8+y3MnAk3nZMTERGRZMisv7sCAwOpVasWU6dOBcButxMQEEDPnj3p16/fLeNbt25NdHQ0y5cvT9hWp04dqlatyowZM3A4HPj7+/Pmm2/y1ltvARAZGYmvry+fffYZbdq0Ye/evVSoUIHNmzdTs2ZNAEJCQmjSpAknT57E39+f6dOnM3DgQMLCwvDw8ACgX79+LFu2jH379gHg7+/PwIED6d69e0IsrVq1ImfOnMybNw+Ap556Cl9fXz799NPbjrmTzPq5iYiIyJ0l9xiuSj8REYs0bAhbtsCnn4KfHxw6BC1bwmOPwU0XcoqISDpRe08RERHXcu3aNbZs2UJQUFDCNjc3N4KCgtiwYUOSz9mwYUOi8QDBwcEJ448cOUJYWFiiMd7e3gQGBiaM2bBhA/ny5UtI+AEEBQXh5ubGxo0bE8Y0aNAgIeEX/zr79+/nwoULAMTExODp6Zkolpw5c7J+/fqE+w899BChoaH89ddfAOzYsYP169fTuHHjJN9fTEwMUVFRiRYRERFxXUr6iYhYyN0dXnoJ/voLBg4ET09YswZq1IBu3eA/U0CIiEgaij/npaSfiIiIazh37hxxcXH4+vom2u7r60tYWFiSzwkLC7vj+Pjbu40pVKhQosezZctGgQIFEo1Jah83v0ZwcDATJkzgwIED2O12Vq9ezZIlSzhz5kzCc/r160ebNm0oV64c2bNnp1q1avTu3Zu2bdsm+f5GjhyJt7d3whIQEJDkOBEREXENSvqJiGQCefPCiBGwfz/873/gcMCMGVClCtx0UaeIiKQhVfqJiIhIZjJp0iTKli1LuXLl8PDwoEePHnTq1Ak3txun7xYvXsz8+fNZsGABW7duZe7cuYwbN465c+cmuc/+/fsTGRmZsJw4cSKj3o6IiIhYQEk/EZFMpFgxmD8ffvrJrB8+DA0aQN++cNMc8SIikgbik36azkZERMQ1+Pj44O7uTnh4eKLt4eHh+Pn5JfkcPz+/O46Pv73bmIiIiESPX79+nfPnzycak9Q+bn6NggULsmzZMqKjozl27Bj79u0jT548lCpVKuE5b7/9dkK1X6VKlXjxxRd54403GDlyZJLvL0eOHHh5eSVaRERExHUp6Scikgk1bAg7d0KnTqbqb+xYqFlTc/2JiKQVh0OVfiIiIq7Gw8ODGjVqEBoamrDNbrcTGhpK3bp1k3xO3bp1E40HWL16dcL4kiVL4ufnl2hMVFQUGzduTBhTt25dLl68yJYtWxLG/PTTT9jtdgIDAxPGrFu3jtjY2ESv88ADD5A/f/5Er+/p6UmRIkW4fv06X3/9Nc8880zCY5cvX05U+Qfg7u6O3W6/+x9IREREXJ6SfiIimZSXF8yeDd98A4UKwe7dULu2aQN6/brV0YmIOLcrV258lyrpJyIi4jr69OnDxx9/zNy5c9m7dy/dunUjOjqaTp06AdC+fXv69++fMP71118nJCSE8ePHs2/fPoYNG8Yff/xBjx49ALDZbPTu3ZsRI0bw7bffsnPnTtq3b4+/vz/NmzcHoHz58jz55JN06dKFTZs28euvv9KjRw/atGmDv78/AP/73//w8PCgc+fO7N69m0WLFjFp0iT69OmTEMvGjRtZsmQJhw8f5pdffuHJJ5/EbrfTt2/fhDHNmjXj/fff5/vvv+fo0aMsXbqUCRMm0KJFi/T+04qIiIgTyGZ1ACIicmdPPw1160K3bvD11zB4MHz3HXz+OTzwgNXRiYg4p6goc2uzQZ481sYiIiIiaad169acPXuWIUOGEBYWRtWqVQkJCcHX1xeA48ePJ6qUe+ihh1iwYAGDBg1iwIABlC1blmXLllGxYsWEMX379iU6OpquXbty8eJF6tWrR0hICJ6englj5s+fT48ePWjUqBFubm60atWKyZMnJzzu7e3NqlWr6N69OzVq1MDHx4chQ4bQtWvXhDFXr15l0KBBHD58mDx58tCkSRO++OIL8uXLlzBmypQpDB48mNdee42IiAj8/f155ZVXGDJkSHr8OUVERMTJ2BwOh8PqIDKbqKgovL29iYyMVK9zEck0HA5YsAB69ICLF8HTE4YOhVdfhZv+H1AkS9Mx3DlZ8bnt3w/lypkqv4sXM+QlRUREXIp+dzknfW4iIiLOKbnHcLX3FBFxEjYbtG1r5vp7/HG4ehX694ciRUzib+dOqyMUEXEems9PRERERERERFyNkn4iIk6maFFYuRLmzIGKFeHyZZg5EypXhkcfha++gpvmhhcRkSQo6SciIiIiIiIirkZJPxERJ2SzQceO8OefsGYNPPssuLvD2rXw3HNQogS89x6EhVkcqIhIJqWkn4iIiIiIiIi4GiX9REScmM0GjzwCX34JR4/CoEFQqBCcPg1DhkCxYtChA5w/b3WkIiKZS1SUuVXST0RERERERERchZJ+IiIuomhRU913/DjMmwd16pg2n59/DjVrmqpAEREx4iv97jD3tYiIiIiIiIiIU1HST0TExeTIAW3bwoYN8NtvptXnkSNQty4sXGh1dCIimYPae4qIiIiIiIiIq1HST0TEhdWtC3/8AY8/DpcvwwsvwNtvw/XrVkcmImItJf1ERERERERExNUo6Sci4uLuuw9++AHeecfcHzcOnnwSzp2zNi4RESsp6SciIiIiIiIirkZJPxGRLMDdHUaNgkWLIHduCA018/xt22Z1ZCIi1oiKMrdK+omIiIiIiIiIq1DST0QkC3n+efj9dyhdGo4dg4cegnnzrI5KRCTjxVf6eXlZG4eIiIiIiIiISFpR0k9EJIupWBE2b4bGjeHqVXjxRejdG2JjrY5MRCTjqL2niIiIiIiIiLgaJf1ERLKg/Pnhu+9g0CBzf9Ik0+5zyxZr4xIRyShK+omIiIiIiIiIq1HST0Qki3J3h/feg6VL4b774M8/ITAQ+vWDK1esjk5EJH1pTj8RERERERERcTVK+omIZHHNm8OePdC6NcTFwejRULUqrF9vdWQiIulHlX4iIiIiIiIi4mqU9BMREQoVgoULYdkyKFwY/voLGjSAnj3h0iWroxMRSVuxsTcqmr28rI1FRERERERERCStKOknIiIJnnkGdu+Gl14ChwOmToVKlWD1aqsjExFJO/FVfqCkn4iIiIiIiIi4DiX9REQkkfz54dNPYeVKKF4cjh6FJ56Azp3h4kWroxMRuXfxSb/cuSFbNmtjERERERERERFJK0r6iYhIkp54Anbtgh49zP3Zs6FWLVMJKCLizKKizK3m8xMRERERERERV6Kkn4iI3FaePDBlCvzyCxQrBgcPQp06Zu4/ERFnFV/pp6SfiIiIiIiIiLgSJf1EROSu6tWDP/6ARx+FS5egRQsYOhTsdqsjExFJufikn+bzExERERERERFXoqSfiIgkS8GCsGoVvP66uT98uEn+xbfJExFxFqr0ExERERERERFXpKSfiIgkW/bsMHEizJkDOXLAt99CYCD89ZfVkYlIck2bNo0SJUrg6elJYGAgmzZtuu3Y2NhYhg8fTunSpfH09KRKlSqEhITc0z4zAyX9RERERERERMQVKeknIiIp1rEjrFsHRYrAvn1QuzasWGF1VCJyN4sWLaJPnz4MHTqUrVu3UqVKFYKDg4mIiEhy/KBBg5g5cyZTpkxhz549vPrqq7Ro0YJt27alep+ZQXyFspJ+IiIiIiIiIuJKlPQTEZFUqV3bzPP38MOmauapp2DkSHA4rI5MRG5nwoQJdOnShU6dOlGhQgVmzJhBrly5mD17dpLjv/jiCwYMGECTJk0oVaoU3bp1o0mTJowfPz7V+8wMVOknIiIiIiIiIq5IST8REUk1Pz/46Sd49VWT7BswAJ5/Hi5dsjoyEfmva9eusWXLFoKCghK2ubm5ERQUxIYNG5J8TkxMDJ6enom25cyZk/Xr16d6n5lBfNLPy8vaOERERERERERE0pKSfiIick88PGD6dJg1y8z599VXUKcOHDhgdWQicrNz584RFxeHr69vou2+vr6EhYUl+Zzg4GAmTJjAgQMHsNvtrF69miVLlnDmzJlU7xNMMjEqKirRkpFU6SciIiIiIiIirkhJPxERSRNdusDatVC4MOzeDbVqwfffWx2ViNyLSZMmUbZsWcqVK4eHhwc9evSgU6dOuLnd20/IkSNH4u3tnbAEBASkUcTJo6SfiIiIiIiIiLgiJf1ERCTN1K0LW7bcmOevWTMYPhzsdqsjExEfHx/c3d0JDw9PtD08PBw/P78kn1OwYEGWLVtGdHQ0x44dY9++feTJk4dSpUqlep8A/fv3JzIyMmE5ceLEPb67lIkvLFTST0RERERERERciZJ+IiKSpgoXNvP8de9u5vkbOhRatLhRWSMi1vDw8KBGjRqEhoYmbLPb7YSGhlK3bt07PtfT05MiRYpw/fp1vv76a5555pl72meOHDnw8vJKtGQkVfqJiIiIiIiIiCtS0k9ERNKchwdMnQpz5kCOHPDttxAYCHv3Wh2ZSNbWp08fPv74Y+bOncvevXvp1q0b0dHRdOrUCYD27dvTv3//hPEbN25kyZIlHD58mF9++YUnn3wSu91O3759k73PzCg+6ZfBuUYRERERERERkXSVzeoARETEdXXsCBUrQsuWsH8/1K4Nn39uKv9EJOO1bt2as2fPMmTIEMLCwqhatSohISH4+voCcPz48UTz9V29epVBgwZx+PBh8uTJQ5MmTfjiiy/Ily9fsveZGanST0RERERERERckc3hcDisDiKziYqKwtvbm8jIyAxvNyUi4ooiIqB1a1izxtwfMMDM9efubmlY4oJ0DHdOGfm5xcVBtn8vewsPh0KF0vXlREREXJZ+dzknfW4iIiLOKbnHcLX3FBGRdFeoEKxeDW+8Ye5/8AE88YQ54S4ikpEuXbqxrko/EREREREREXElSvqJiEiGyJYNJkyABQsgd2746SeoVg3WrbM6MhHJSuJbe3p4mDlHRURERERERERchZJ+IiKSoV54ATZvhgoV4MwZeOwxGD0a7HarIxORrEDz+YmIiIiIiIiIq1LST0REMlz58rBpE7z4oplfq18/eOYZOH/e6shExNUp6SciIiIiIiIirkpJPxERsUTu3DB3LsyaZVrsLV8O1aubKkARkfSipJ+IiIiIiIiIuCol/URExDI2G3TpAhs2QOnScOwYPPwwTJ0KDofV0YmIK4qKMrdK+omIiIiIiIiIq1HST0RELFetGmzZAi1aQGws9Oxp5v6LPzkvIpJW4iv9vLysjUNEREREREREJK0p6SciIpmCtzd8/TVMmADZssGiRabd5x9/WB2ZiLgStfcUEREREREREVelpJ+IiGQaNhu88QasWwfFisGhQ/DQQzBxotp9ikjaUNJPRERERERERFyVkn4iIpLp1K0L27ffaPf5xhvw9NNw7pzVkYmIs9OcfiIiIiIiIiLiqpT0ExGRTCl/ftPuc9o0yJEDli+HKlVg7VqrIxMRZ6ZKPxERERERERFxVUr6iYhIpmWzwWuvwe+/wwMPwOnT8Nhj8O67EBdndXQi4ozik35eXtbGISIiIiIiIiKS1pT0ExGRTK9qVfjjD+jYEex2GDYMGjWCU6csDkxEnI4q/URERERERETEVSnpJyIiTiFPHpgzB774AnLnNm0+q1SB1autjkxEnImSfiIiIiIiIiLiqpT0ExERp9KuHWzdCtWqwd9/Q+PGMHUqOBxWRyYiziAqytwq6SciIiIiIiIirkZJPxERcTr33w+//QYvvmjm9uvZ08z9FxtrdWQiktmp0k9EREREREREXJWSfiIi4pQ8PWHuXBg9Gmw2mDEDgoNN9Z+ISFIcjhtJPy8va2MREREREREREUlrSvqJiIjTstmgb19YtszM+ffzzxAYCHv3Wh2ZiGRGly+b6mBQpZ+IiIiIiIiIuB4l/URExOk9/bRp91miBBw6BHXqQEiI1VGJSGYTX+Xn7g65c1sbi4iIiIiIiIhIWlPST0REXEKlSrBpE9SrB1FR0LQpTJxo2vmJiID5bgDT2tNmszYWEREREREREZG0pqSfiIi4jIIFITQUXnoJ7HZ44w3o2hWuXbM6MhHJDOIr/dTaU0RERERERERckZJ+IiLiUjw84JNPYPx4cHMz6w0bwunTVkcmIlaLT/p5eVkbh4iIiIiIiIhIelDST0REXI7NBn36wHffmZP7v/0G1arBmjVWRyYiVlKln4iIiIiIiIi4MiX9RETEZTVpAlu2QOXKEBEBQUEwdqzm+RPJqpT0ExERERERERFXpqSfiIi4tDJlYMMGePFFiIuDvn2hVasbJ/9FJOuIijK3SvqJiIiIiIiIiCuyPOk3bdo0SpQogaenJ4GBgWzatOm2Y3fv3k2rVq0oUaIENpuNiRMn3vM+RUTE9eXKBXPnwvTpZs6/pUuhVi3YtcvqyEQkI6nST0RERERERERcmaVJv0WLFtGnTx+GDh3K1q1bqVKlCsHBwURERCQ5/vLly5QqVYpRo0bh5+eXJvsUEZGswWaDV1+FX36BgAA4cAACA2H+fKsjE5GMEp/08/KyNg4RERERERERkfRgadJvwoQJdOnShU6dOlGhQgVmzJhBrly5mD17dpLja9WqxdixY2nTpg05cuRIk32KiEjWUrs2bN0Kjz8Oly9Du3bQowdcu2Z1ZCKS3lTpJyIiIiIiIiKuzLKk37Vr19iyZQtBQUE3gnFzIygoiA0bNmToPmNiYoiKikq0iIiI6/LxgR9+gMGDzf1p0+DRR+HMGUvDEpF0pqSfiIiIiIiIiLgyy5J+586dIy4uDl9f30TbfX19CQsLy9B9jhw5Em9v74QlICAgVa8vIiLOw90dhg+H5cshXz7YsMHM8/fHH1ZHJiLpJf66LiX9RERERERERMQVWdreM7Po378/kZGRCcuJEyesDklERDJI06awaROULw+nTkH9+rBggdVRiUh6UKWfiIiIiIiIiLgyy5J+Pj4+uLu7Ex4enmh7eHg4fn5+GbrPHDly4OXllWgREZGso2xZU+nXtClcvQpt20L//hAXZ3VkIpKW4pN++qknIiIiIiIiIq7IsqSfh4cHNWrUIDQ0NGGb3W4nNDSUunXrZpp9iohI1uDtDd98A++8Y+6PGgXPPHOjHaCIOD9V+omIiIiIiIiIK7O0vWefPn34+OOPmTt3Lnv37qVbt25ER0fTqVMnANq3b0///v0Txl+7do3t27ezfft2rl27xqlTp9i+fTsHDx5M9j5FRERux93dJPvmzwdPT/j+e6hTB246zIiIE1PST0RERERERERcWTYrX7x169acPXuWIUOGEBYWRtWqVQkJCcHX1xeA48eP4+Z2Iy95+vRpqlWrlnB/3LhxjBs3jkceeYQ1a9Yka58iIiJ387//mZafzZvD3r1QuzYsXgxBQVZHJiKpFRNjFlDST0RERERERERck83hcDisDiKziYqKwtvbm8jISM3vJyKShZ05Ay1awMaN4OYG48fD66+DzWZ1ZHI7OoY7p4z43M6ehUKFzPr166ayV0RERFJPv7uckz43ERER55TcY7il7T1FREQys8KFYc0a6NAB7HZ44w1o00bz/Ik4o/jWnnnyKOEnIiIiIiIiIq5JST8REZE78PSEOXPgww8hWzbT5rNmTdixw+rIRCQlNJ+fiIiIiIiIiLg6Jf1ERETuwmaD3r1h3TooWhQOHIA6deDTT0FNskWcQ3yFrpJ+IiIiIiIiIuKqlPQTERFJprp1Yds2aNwYrl6Fl1+Gjh0hOtrqyETkblTpJyIiIiIiIiKuTkk/ERGRFPDxgeXL4YMPwM0NPv8cateGvXutjkxE7iQ+6XeHua5FRERERERERJyakn4iIiIp5OYG/fvDTz+Bnx/s2WPm+Zs3z+rIROR2VOknIiIiIiIiIq5OST8REZFUeuQR2L4dGjWCy5fhxReha1eIibE6MhH5LyX9RERERERERMTVKeknIiJyD3x9YeVKGDoUbDb4+GNo2hT++cfqyETkZlFR5lZJPxERERERERFxVUr6iYiI3CN3dxg2DFasgDx5IDQUGjaEiAirIxOReKr0ExERERERERFXp6SfiIhIGnnySfj5Z/DxgS1boF49OHLE6qhEBG4k/by8rI1DRERERERERCS9KOknIiKShmrWhF9/heLF4cABePhh2LnT6qhERJV+IiIiIiIiIuLqlPQTERFJY/ffbxJ/FSvCmTPQoAGsX291VCJZm5J+IiIiIiIiIuLqlPQTERFJB0WKwLp1ptLv4kV4/HH47juroxLJuqKizK2SfiIiIq5v2rRplChRAk9PTwIDA9m0adMdx3/55ZeUK1cOT09PKlWqxIoVKxI97nA4GDJkCIULFyZnzpwEBQVx4MCBRGPOnz9P27Zt8fLyIl++fHTu3JlLly4lGvPnn39SaCRcnwAAIfhJREFUv359PD09CQgIYMyYMYkej42NZfjw4ZQuXRpPT0+qVKlCSEjILfGeOnWKdu3acd9995EzZ04qVarEH3/8kZI/kYiIiLgoJf1ERETSSf78sGoVNG0KV69Cixbw2WdWRyWSNanST0REJGtYtGgRffr0YejQoWzdupUqVaoQHBxMREREkuN/++03XnjhBTp37sy2bdto3rw5zZs3Z9euXQljxowZw+TJk5kxYwYbN24kd+7cBAcHc/Xq1YQxbdu2Zffu3axevZrly5ezbt06unbtmvB4VFQUTzzxBMWLF2fLli2MHTuWYcOGMWvWrIQxgwYNYubMmUyZMoU9e/bw6quv0qJFC7Zt25Yw5sKFCzz88MNkz56dH374gT179jB+/Hjy58+fln9GERERcVI2h8PhsDqIzCYqKgpvb28iIyPx8vKyOhwREXFysbHQpQvMnWvujxkDb79tbUyuSsdw55QRn1vevHDpkplrs0yZdHkJERGRLCWz/u4KDAykVq1aTJ06FQC73U5AQAA9e/akX79+t4xv3bo10dHRLF++PGFbnTp1qFq1KjNmzMDhcODv78+bb77JW2+9BUBkZCS+vr589tlntGnThr1791KhQgU2b95MzZo1AQgJCaFJkyacPHkSf39/pk+fzsCBAwkLC8PDwwOAfv36sWzZMvbt2weAv78/AwcOpHv37gmxtGrVipw5czJv3ryE5/z666/88ssvqfr7ZNbPTURERO4sucdwVfqJiIiks+zZYc4c+PccAX37Qq9ecP26tXGJZBVxcSbhB6r0ExERcWXXrl1jy5YtBAUFJWxzc3MjKCiIDRs2JPmcDRs2JBoPEBwcnDD+yJEjhIWFJRrj7e1NYGBgwpgNGzaQL1++hIQfQFBQEG5ubmzcuDFhTIMGDRISfvGvs3//fi5cuABATEwMnp6eiWLJmTMn62+aIPzbb7+lZs2aPPfccxQqVIhq1arx8ccfJ/+PJCIiIi5NST8REZEMYLPB2LGmyg9gyhTT9vPiRUvDEskS4ufzAyX9REREXNm5c+eIi4vD19c30XZfX1/CwsKSfE5YWNgdx8ff3m1MoUKFEj2eLVs2ChQokGhMUvu4+TWCg4OZMGECBw4cwG63s3r1apYsWcKZM2cSnnP48GGmT59O2bJlWblyJd26daNXr17MjW8r8h8xMTFERUUlWkRERMR1KeknIiKSgd5+G776CnLlMvP91alj2g2KSPqJP7fl6Qk3XVwvIiIikqlMmjSJsmXLUq5cOTw8POjRowedOnXCze3G6Tu73U716tX54IMPqFatGl27dqVLly7MmDEjyX2OHDkSb2/vhCUgICCj3o6IiIhYQEk/ERGRDNaqFaxfD0WLwv79EBgIP/1kdVQirisy0tyqyk9ERMS1+fj44O7uTnh4eKLt4eHh+Pn5JfkcPz+/O46Pv73bmIiIiESPX79+nfPnzycak9Q+bn6NggULsmzZMqKjozl27Bj79u0jT548lCpVKuE5hQsXpkKFCon2U758eY7/f3v3HlVVnfdx/HMQQbyAFMalvFCZliUVGoPa44ywvPW41GilyZhjU6ZC42WaHmlUzCa1y5jlOHSZLk5lGKXmZDoSJaV5RU0tNC1Nn0nEnlKQRM3ze/44j+eZU9Y4wt6bfc77tdZZwN6/A9/zBRef5Zff3vv3n/X15eXl6ejRo/7HgQMHzroOAAAEB4Z+AAA44LrrpA0bfAO/b76ReveWCgqcrgoITmeGfj9xn2sAABAEIiIilJqaqpKSEv8xr9erkpISpaenn/U56enpAeslqbi42L8+OTlZCQkJAWuqqqq0fv16/5r09HQdOXJEZWVl/jXvvvuuvF6v0tLS/Gvef/99nTp1KuDrdOjQQbGxsQFfv0mTJrr44ov13Xff6Y033tDAgQP957p3765du3YFrP/000/Vtm3bs76+yMhIRUdHBzwAAEDwYugHAIBDEhOlVauk7Gzp9Glp7FgpN1f67junKwOCCzv9AAAIHRMnTtSzzz6r+fPnq7y8XGPGjFFNTY1GjhwpSbr99tuVl5fnXz9u3DitWLFCf/zjH7Vz505NmzZNmzZtUm5uriTJ4/Fo/Pjx+sMf/qClS5dq+/btuv3225WUlKRBgwZJ8u2069u3r+666y5t2LBBa9asUW5uroYOHaqkpCRJ0rBhwxQREaFf//rX+vjjj7Vw4UI98cQTmjhxor+W9evXa9GiRfr888/1wQcfqG/fvvJ6vbrvvvv8ayZMmKB169ZpxowZ2rNnjxYsWKBnnnlGOTk5VrcWAAC4QLjTBQAAEMqaNJFeeknq1Em6/35p3jzfJT9fe0363h/8AjhPDP0AAAgdQ4YM0eHDhzV16lRVVFTo2muv1YoVKxQfHy9J2r9/f8A98rp166YFCxZo8uTJuv/++9W+fXstWbJ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+      "text/plain": [
+       "<Figure size 1800x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_learning_curves(majority_charge_model_history, title='Majority Charge Model')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7Xrx40QgMDDQeffRR67YjR44YkgwfHx/j7NmzNuM7dOhgeHp6GidPnrRuO3DggOHi4mKk/DN59OhRw9nZ2Xjttdds9t+1a5fh4uJi3b59+3ZDkvHNN99k+fla9OvXz/Dy8jIMwzC6du1qtG7d2jAMw0hKSjKCgoKMiRMnWp/L22+/bd1v1KhRhiSbn+Ply5eNChUqGCEhIUZSUpJhGIYxdepUQ5Lx9ddfW8fFxsYalStXNiQZa9asMQzDMJKTk40qVaoY4eHhRnJysnVsXFycUaFCBePee++1bps9e7YhyThy5Ei2n296LMfcvHlzumM6depkuLq6GocOHbJuO3XqlFG0aFGjefPm1m116tQx2rdvn+5xLl68mOr1BICCjvlO2udivsN8xzDybr5jGIaxZcsWQ5KxatUqa0xlypQxRo4caTNu3LhxhiRj0aJFqY5hiX3WrFmGJGPKlCnpjlmzZo3Na2Bheb1nz55t3davXz9DkvHcc8+lOl5cXFyqbZMmTTJMJpNx7Ngx67bmzZsbRYsWtdmWMh7DMIyxY8cabm5uxqVLl6zbzp49a7i4uGT6Owfg1jAPSPtczAOYBxiGY33uMXDgQCM4ONiIioqy2d6zZ0/D19fX+u9xTrxHLL/fKccbhmF07tzZKF68uM228uXLG/369Uv1XNq0aWPzWj755JOGs7Oz9d/4M2fOGC4uLkanTp1sjjdhwgRDks0x0yPJGDhwoHHu3Dnj7NmzxpYtW4y2bdum+blOWvOVxx57zPD09DSuXbtm3da+fXujfPnyqcZm5+9UWlK+1+fOnZtqLifJGDZsmPW+5b375ZdfWrclJCQYjRs3Nry9va1/i8+dO5elv0kpRUZGGi4uLsann35q3dakSRObfwf2799vSDI++OADm33/97//Gd7e3jav582vbUJCglGrVi3jnnvusdl+83slrbloVueVlrnpE088Yd2WnJxstG/f3nB1dbX5N+fm1yerv0sFCa0+gZtcvnxZklS0aNEMx1kej4mJsdk+bNgwm/tPPPGEJGnZsmWSpGLFikkyl5snJyeneexvvvlG1atXV7Vq1RQVFWW93HPPPZKUqlS/RYsWqlGjhs22e+65RwEBATbfqrl48aJWrVqlHj16ZPtclvhHjBhhc56sfiMiJiYm09f0Zt7e3jY9u11dXdWwYUMdPnzYus3Z2dn6zb7k5GRduHBBiYmJql+/vrZt25bqmF26dLH5RltSUpJ++eUXderUSaVKlbJur1y5svXbdRaLFi1ScnKyunfvbvNaBQUFqUqVKtbXyvLNtpUrV6Zqh5AdvXv31tq1a3XmzBn9+uuvOnPmTLrtLpYtW6aGDRvatBPx9vbWkCFDdPToUWtrj2XLlik4ONimPN/T01NDhgyxOV5ERIS1vcb58+etzzU2NlatW7fW77//nu77Ny8kJSXp559/VqdOnWzWmwgODlbv3r21bt066+9msWLFtGfPHh04cCDNY3l4eMjV1VVr167VxYsX8yR+ALA35jvMdyTmO44w35k3b54CAwOtbZVMJpN69OihBQsW2LSh+u6771SnTp1UVXGWfSxjAgICrL+PaY25FY8//niqbR4eHtbbsbGxioqKUpMmTWQYhrZv3y5JOnfunH7//Xc9+uijKleuXLrx9O3bV/Hx8TbtoRYuXKjExESb3w0AOYd5APMAiXmAI8wDMmIYhr777jt16NBBhmHY/DzCw8MVHR1t/fnf7nskpaFDh9rcb9asmc6fP5/q70BahgwZYvNvfLNmzZSUlKRjx45JklavXq3ExET973//s9kvrblLRmbOnKkSJUqoZMmSql+/vlavXq1nn31Wo0ePthmXcr5y+fJlRUVFqVmzZoqLi9Pff/+d6Xmy+3cqIw8//LCqVKmSYVvKZcuWKSgoSL169bJuK1KkiEaMGKErV67ot99+y/L5brZgwQI5OTmpS5cu1m29evXS8uXLrZ+FVa1aVXXr1rX5m5qUlKRvv/1WHTp0sHk9U96+ePGioqOj1axZszTfb5nJyrwypeHDh1tvWyr4EhIS9Msvv6R5/Oz8LhUkJP6Am1gmaZaJcHrSmyhXqVLF5n6lSpXk5ORkXSusR48eatq0qQYNGqTAwED17NlTX3/9tc1k4sCBA9qzZ49KlChhc6lataokc7/nlCpUqJAqPhcXF3Xp0kXff/+9te/0okWLdP36dZsJcFbPdezYMTk5OaUqm7/jjjsyfJ0sfHx8Mn1Nb1amTJlUH1L4+fmlSs7MnTtXtWvXtq7fVqJECS1dujTNvvI3v1Znz57V1atXVbly5VRjb9524MABGYahKlWqpHq99u3bZ32tKlSooNGjR+uzzz5TQECAwsPDNW3atCz3ubewrKWycOFCzZs3Tw0aNEgzTsn880nrZ1G9enXr45brypUrp3pdb97XkiTr169fquf62WefKT4+PlvP5+rVqzpz5ozN5XacO3dOcXFx6T7n5ORk6/oDL7/8si5duqSqVasqNDRUzzzzjHbu3Gkd7+bmpjfffFPLly9XYGCgmjdvrrfeeuu2YwQAR8Z8h/mOBfMd+813kpKStGDBArVq1UpHjhzRwYMHdfDgQTVq1EiRkZFavXq1deyhQ4dUq1atDI936NAh3XHHHXJxybkVPVxcXFSmTJlU248fP67+/fvL399f3t7eKlGihFq0aCFJ1tfM8qF1ZnFXq1ZNDRo0sFnbcN68ebrrrrvSfS8AuD3MA5gHWDAPcOzPPS5dumRdhzPlxbK2Zcrfk9t5j6R085d1/Pz8JClLX5TObF/Lz+jmn7G/v791bFZ07NhRq1at0tKlS61rE8bFxcnJyTbVsWfPHnXu3Fm+vr7y8fFRiRIlrIn2rPxss/t3KiPOzs568cUXFRERkW6L0GPHjqlKlSqpnsfN7/Fb8eWXX6phw4Y6f/68dc5Zr149JSQk2LTT7NGjh9avX6+TJ09KMq8NevbsWZu/qZL0008/6a677pK7u7v8/f2tLUiz+zdAytq80sLJycmmAECS9edh+TfoZtn9XSooWOMPuImvr6+Cg4NtEgNp2blzp0qXLi0fH58Mx9080fDw8NDvv/+uNWvWaOnSpVqxYoUWLlyoe+65Rz///LOcnZ2VnJys0NBQTZkyJc1jli1bNtUx09KzZ099/PHHWr58uTp16qSvv/5a1apVU506daxjsnuuW1WtWjVt375dJ06cyPIxnZ2d09ye8psxX375pfr3769OnTrpmWeeUcmSJeXs7KxJkyalWtRXSv+1york5GSZTCYtX748zdgsPcAl6Z133lH//v31/fff6+eff9aIESOs6x+k9eFJWtzc3PTQQw9p7ty5Onz4cJYW6s0plv+Qvf3226l6wVukfL6ZWbhwYapF19P7hlNOa968uQ4dOmT9WXz22Wd69913NWPGDA0aNEiS+RucHTp00JIlS7Ry5Uq99NJLmjRpkn799VfVq1cvT+IEgLzEfCfjc90q5jvMd7Iz3/n11191+vRpLViwQAsWLEj1+Lx583Tfffdl+fxZkV7lX8rqwpTc3NxSffiUlJSke++9VxcuXNCYMWNUrVo1eXl56eTJk+rfv/8tVUf07dtXI0eO1L///qv4+Hj99ddf+vDDD7N9HABZwzwg43PdKuYBzANy8nMPS3x9+vRRv3790hxjWdctJ98jWXlP5sa+2VGmTBm1adNGkjl5HBAQoOHDh6tVq1bWNe8uXbqkFi1ayMfHRy+//LIqVaokd3d3bdu2TWPGjMnSfCWn/3Y8/PDD1rX+OnXqlK19b8eBAwe0efNmSam/uCGZ55yWitgePXpo7Nix+uabbzRq1Ch9/fXX8vX1Vdu2ba3j//jjDz344INq3ry5PvroIwUHB6tIkSKaPXu25s+fn63YcmNeebPs/C4VJCT+gDQ88MAD+vTTT7Vu3TqbFgIWf/zxh44eParHHnss1WMHDhyw+ebMwYMHlZycrJCQEOs2JycntW7dWq1bt9aUKVP0+uuv64UXXtCaNWvUpk0bVapUSTt27FDr1q1vqy1P8+bNFRwcrIULF+ruu+/Wr7/+qhdeeMFmTFbPVb58eSUnJ1u/SWyxf//+LMXSoUMHffXVV/ryyy81duzYW3tCafj2229VsWJFLVq0yCb+8ePHZ2n/kiVLyt3dXQcPHkz12M3bKlWqJMMwVKFCBeu3STISGhqq0NBQvfjii/rzzz/VtGlTzZgxQ6+++mqWYpPMbS9mzZolJycnm0V0b1a+fPk0fxaW1gXly5e3Xu/evVuGYdi8Xjfva/mGo4+Pj3UydTvCw8O1atWq2z6ORYkSJeTp6Znuc3ZycrKZgPn7+2vAgAEaMGCArly5oubNm2vChAnWxJ9kfs5PPfWUnnrqKR04cEB169bVO++8oy+//DLH4gYAR8J8JzXmO8x3bkd25zvz5s1TyZIlNW3atFSPLVq0SIsXL9aMGTPk4eGhSpUqaffu3Rker1KlStq4caOuX7+uIkWKpDnG8m36S5cu2WzPzjfId+3apX/++Udz585V3759rdtvfu6Wb2NnFrdk/uB+9OjR+uqrr3T16lUVKVIk1TfLAeQs5gGpMQ9gHnA7cuNzj6JFiyopKSnT+G73PZJXLD+jgwcP2vwNOX/+/G0tvfLYY4/p3Xff1YsvvqjOnTvLZDJp7dq1On/+vBYtWqTmzZtbxx45ciTV/un9Xcipv1MWlqo/S8L8ZuXLl9fOnTuVnJxs88Wrm9/j2Y1l3rx5KlKkiL744otUydl169bp/fff1/Hjx1WuXDlVqFBBDRs21MKFCzV8+HAtWrRInTp1kpubm3Wf7777Tu7u7lq5cqXN9tmzZ2crLinr80qL5ORkHT582Obv0z///CNJNv8GpZSd36WChFafQBqeeeYZeXh46LHHHtP58+dtHrtw4YKGDh0qT09PPfPMM6n2vfk/7h988IEkWfumX7hwIdU+lm8WWVpTdO/eXSdPntSnn36aauzVq1cVGxubpefh5OSkrl276scff9QXX3yhxMTEVP+Bzuq5LPG///77NmOmTp2apVi6du2q0NBQvfbaa9qwYUOqxy9fvpxqcp4Vln+wUn57aOPGjWmeI73927RpoyVLlujUqVPW7QcPHtTy5cttxj700ENydnbWxIkTU31byTAM63slJiZGiYmJNo+HhobKycnJ+jPOqlatWumVV17Rhx9+qKCgoHTHtWvXTps2bbJ53rGxsfrkk08UEhJiXQuhXbt2OnXqlM0aKnFxcfrkk09sjhcWFqZKlSpp8uTJunLlSqrznTt3LlvPIzg4WG3atLG53A5nZ2fdd999+v77721K+SMjIzV//nzdfffd1m+l3vw77O3trcqVK1t/FnFxcbp27ZrNmEqVKqlo0aLZ/nkBQH7CfCf1uZjvMN9JKTfnO1evXtWiRYv0wAMPqGvXrqkuw4cP1+XLl/XDDz9IMq/Fs2PHDi1evDjVsSw/py5duigqKirNSjnLmPLly8vZ2Vm///67zeMfffRRlp9nWu9HwzD03nvv2YwrUaKEmjdvrlmzZun48eNpxmMREBCg+++/X19++aXmzZuntm3bKiAgIMsxAcg+5gGpz8U8gHlASo7wuUeXLl303XffpfklmpTx3e57JK+0bt1aLi4umj59us32263yd3Fx0VNPPaV9+/ZZE2ppvSYJCQlpznm8vLzSbFGZU3+nUurTp48qV66siRMnpnqsXbt2OnPmjM0ae4mJifrggw/k7e1tbX/p6ekpKfUXudIzb948NWvWTD169Eg157T8jf/qq6+s43v06KG//vpLs2bNUlRUVKq/qc7OzjKZTDYdI44ePZpuC9OMZHVemVLK94thGPrwww9VpEgRtW7dOt1zZPV3qSCh4g9IQ5UqVTR37lw9/PDDCg0N1cCBA1WhQgUdPXpUM2fOVFRUlL766qtUfd8l8zdHHnzwQbVt21YbNmzQl19+qd69e1vbTLz88sv6/fff1b59e5UvX15nz57VRx99pDJlyli/ZffII4/o66+/1tChQ7VmzRo1bdpUSUlJ+vvvv/X1119r5cqVql+/fpaeS48ePfTBBx9o/PjxCg0NtfaFtsjquerWratevXrpo48+UnR0tJo0aaLVq1en+Y2xtBQpUkSLFi1SmzZt1Lx5c3Xv3l1NmzZVkSJFtGfPHs2fP19+fn567bXXsnQ8iwceeECLFi1S586d1b59ex05ckQzZsxQjRo10py4pWXChAn6+eef1bRpUz3++ONKSkrShx9+qFq1aikiIsI6rlKlSnr11Vc1duxYHT16VJ06dVLRokV15MgRLV68WEOGDNHTTz+tX3/9VcOHD1e3bt1UtWpVJSYmWr9Vk3IR3axwcnLSiy++mOm45557Tl999ZXuv/9+jRgxQv7+/po7d66OHDmi7777zvpNocGDB+vDDz9U3759tXXrVgUHB+uLL76wThpSnvezzz7T/fffr5o1a2rAgAEqXbq0Tp48qTVr1sjHx0c//vhjtp7LrZg1a5ZWrFiRavvIkSP16quvatWqVbr77rv1v//9Ty4uLvr4448VHx+vt956yzq2Ro0aatmypcLCwuTv768tW7bo22+/tS4G/M8//6h169bq3r27atSoIRcXFy1evFiRkZEZftsQAPI75jvMd5jv2G++88MPP+jy5ct68MEH03z8rrvuUokSJTRv3jz16NFDzzzzjL799lt169ZNjz76qMLCwnThwgX98MMPmjFjhurUqaO+ffvq888/1+jRo7Vp0yY1a9ZMsbGx+uWXX/S///1PHTt2lK+vr7p166YPPvhAJpNJlSpV0k8//ZStdU2qVaumSpUq6emnn9bJkyfl4+Oj7777Ls1v6r///vu6++67deedd2rIkCHWvzFLly61ed9J5nafXbt2lSS98sorWX8xAdwS5gHMA5gHOP7nHm+88YbWrFmjRo0aafDgwapRo4YuXLigbdu26ZdffrEm2XPiPZIXAgMDNXLkSL3zzjvWvyE7duzQ8uXLFRAQcFtVdf3799e4ceP05ptvqlOnTmrSpIn8/PzUr18/jRgxQiaTSV988UWabUfDwsK0cOFCjR49Wg0aNJC3t7c6dOiQo3+nLJydnfXCCy+kagsrSUOGDNHHH3+s/v37a+vWrQoJCdG3336r9evXa+rUqdb1Vj08PFSjRg0tXLhQVatWlb+/v2rVqpXmusobN27UwYMHrZ+B3ax06dK68847NW/ePI0ZM0aSOeH59NNP6+mnn5a/v3+qJHb79u01ZcoUtW3bVr1799bZs2c1bdo0Va5cOdMW0jfLzrxSktzd3bVixQr169dPjRo10vLly7V06VI9//zzKlGiRLrnyervUoFiAEjXzp07jV69ehnBwcFGkSJFjKCgIKNXr17Grl27Uo0dP368IcnYu3ev0bVrV6No0aKGn5+fMXz4cOPq1avWcatXrzY6duxolCpVynB1dTVKlSpl9OrVy/jnn39sjpeQkGC8+eabRs2aNQ03NzfDz8/PCAsLMyZOnGhER0dbx0kyhg0blu5zSE5ONsqWLWtIMl599dU0x2T1XFevXjVGjBhhFC9e3PDy8jI6dOhgnDhxwpBkjB8/Pkuv6cWLF41x48YZoaGhhqenp+Hu7m7UqlXLGDt2rHH69GnruBYtWhg1a9ZMtX+/fv2M8uXL2zy/119/3Shfvrzh5uZm1KtXz/jpp59SjTty5IghyXj77bfTjGv16tVGvXr1DFdXV6NSpUrGZ599Zjz11FOGu7t7qrHfffedcffddxteXl6Gl5eXUa1aNWPYsGHG/v37DcMwjMOHDxuPPvqoUalSJcPd3d3w9/c3WrVqZfzyyy+Zvj79+vUzvLy8MhyT3nM5dOiQ0bVrV6NYsWKGu7u70bBhQ+Onn35Ktf+xY8eMBx980PD09DQCAgKMkSNHGitWrDAkGWvWrLEZu337duOhhx4yihcvbri5uRnly5c3unfvbqxevdo6Zvbs2YYk48iRI5k+v6yyHDO9y4kTJwzDMIxt27YZ4eHhhre3t+Hp6Wm0atXK+PPPP22O9eqrrxoNGzY0ihUrZnh4eBjVqlUzXnvtNSMhIcEwDMOIiooyhg0bZlSrVs3w8vIyfH19jUaNGhlff/11jj0fAHBkzHeY7zDfyfv5TocOHQx3d3cjNjY23TH9+/c3ihQpYkRFRRmGYRjnz583hg8fbpQuXdpwdXU1ypQpY/Tr18/6uGEYRlxcnPHCCy8YFSpUsP4+d+3a1Th06JB1zLlz54wuXboYnp6ehp+fn/HYY48Zu3fvNiQZs2fPto7L6Oe0d+9eo02bNoa3t7cREBBgDB482NixY0eqYxiGYezevdvo3Lmz9Wd2xx13GC+99FKqY8bHxxt+fn6Gr6+vzd8TALmLeQDzAOYBjv25R2RkpDFs2DCjbNmy1t/R1q1bG5988on1WDnxHrH8fp87dy7NOFM+9/Llyxv9+vVLNWbz5s02+65ZsybVa56YmGi89NJLRlBQkOHh4WHcc889xr59+4zixYsbQ4cOzfR1y+jvwYQJE2zOt379euOuu+4yPDw8jFKlShnPPvussXLlylQxXblyxejdu7dRrFgxQ5LNa5bVvx1pSe+9fv36daNSpUppPpfIyEhjwIABRkBAgOHq6mqEhoammlsZhmH8+eefRlhYmOHq6prh36cnnnjCkGQzF7yZ5XXbsWOHdVvTpk0NScagQYPS3GfmzJlGlSpVDDc3N6NatWrG7Nmzre+hlG5+r6T1nsjqvNLyeh46dMi47777DE9PTyMwMNAYP368kZSUZHPetF6TrPwuFSQmw8jh1TUBoIDo1KmT9uzZowMHDtg7FAAAgFzBfAeOIjExUaVKlVKHDh00c+ZMe4cDAIUC8wA4gkuXLsnPz0+vvvrqLbXDBZAaa/wBgMy9uVM6cOCAli1bppYtW9onIAAAgBzGfAeObMmSJTp37pz69u1r71AAoEBiHgBHcPP7ULqxjibvRSDnUPEHADIvwty/f39VrFhRx44d0/Tp0xUfH6/t27erSpUq9g4PAADgtjHfgSPauHGjdu7cqVdeeUUBAQHatm2bvUMCgAKJeQAcwZw5czRnzhy1a9dO3t7eWrdunb766ivdd999Wrlypb3DAwoMF3sHAACOoG3btvrqq6905swZubm5qXHjxnr99deZ/AIAgAKD+Q4c0fTp0/Xll1+qbt26mjNnjr3DAYACi3kAHEHt2rXl4uKit956SzExMQoMDNTIkSP16quv2js0oECh4g8AAAAAAACF1u+//663335bW7du1enTp7V48WJ16tQpw33Wrl2r0aNHa8+ePSpbtqxefPFF9e/fP0/iBQAAyAhr/AEAAAAAAKDQio2NVZ06dTRt2rQsjT9y5Ijat2+vVq1aKSIiQqNGjdKgQYNoUwcAABwCFX8AAAAAAACAJJPJlGnF35gxY7R06VLt3r3buq1nz566dOmSVqxYkQdRAgAApI81/tKQnJysU6dOqWjRojKZTPYOBwAA2IFhGLp8+bJKlSolJyeaJGSEuRMAAJAKz/xpw4YNatOmjc228PBwjRo1Kt194uPjFR8fb72fnJysCxcuqHjx4syfAAAopHJr7kTiLw2nTp1S2bJl7R0GAABwACdOnFCZMmXsHYZDY+4EAABSKujzpzNnzigwMNBmW2BgoGJiYnT16lV5eHik2mfSpEmaOHFiXoUIAADykZyeO5H4S0PRokUlmV9sHx8fO0cDAADsISYmRmXLlrXOC5A+5k4AAEBi/pSRsWPHavTo0db70dHRKleuHPMnAAAKsdyaO5H4S4OlxYKPjw+TLwAACjlaL2WOuRMAAEipoM+fgoKCFBkZabMtMjJSPj4+aVb7SZKbm5vc3NxSbWf+BAAAcnruVHAbrgMAAAAAAAA5rHHjxlq9erXNtlWrVqlx48Z2iggAAOAGEn8AAAAAAAAotK5cuaKIiAhFRERIko4cOaKIiAgdP35ckrlNZ9++fa3jhw4dqsOHD+vZZ5/V33//rY8++khff/21nnzySXuEDwAAYIPEHwAAAAAAAAqtLVu2qF69eqpXr54kafTo0apXr57GjRsnSTp9+rQ1CShJFSpU0NKlS7Vq1SrVqVNH77zzjj777DOFh4fbJX4AAICUWOMPAJAvJCUl6fr16/YOAwWMq6urnJz4HhQAIGcxb0FBVaRIETk7O9s7jBzXsmVLGYaR7uNz5sxJc5/t27fnYlQAAAC3hsQfAMChGYahM2fO6NKlS/YOBQWQk5OTKlSoIFdXV3uHAgAoAJi3oDAoVqyYgoKCZDKZ7B0KAAAA0kDiDwDg0CwfnpUsWVKenp58wIAck5ycrFOnTun06dMqV64c7y0AwG1j3oKCzDAMxcXF6ezZs5Kk4OBgO0cEAACAtJD4AwA4rKSkJOuHZ8WLF7d3OCiASpQooVOnTikxMVFFihSxdzgAgHyMeQsKAw8PD0nS2bNnVbJkyQLZ9hMAACC/Y1EbAIDDsqyN4+npaedIUFBZWnwmJSXZORIAQH7HvAWFheU9zjqWAAAAjonEHwDA4dEmC7mF9xYAIKfxbwsKOt7jAAAAjo3EHwAAAAAAAAAAAFAAkPgDACAfCAkJ0dSpU7M8fu3atTKZTLp06VKuxQQAAJAW5i0AAACA/ZD4AwAgB5lMpgwvEyZMuKXjbt68WUOGDMny+CZNmuj06dPy9fW9pfNlFR/UAQCQfxW2eUtK1apVk5ubm86cOZNn5wQAAADygou9AwAAoCA5ffq09fbChQs1btw47d+/37rN29vbetswDCUlJcnFJfN/jkuUKJGtOFxdXRUUFJStfQAAQOFSWOct69at09WrV9W1a1fNnTtXY8aMybNzp+X69esqUqSIXWMAAABAwUHFHwAAOSgoKMh68fX1lclkst7/+++/VbRoUS1fvlxhYWFyc3PTunXrdOjQIXXs2FGBgYHy9vZWgwYN9Msvv9gc9+aWWSaTSZ999pk6d+4sT09PValSRT/88IP18Zsr8ebMmaNixYpp5cqVql69ury9vdW2bVubD/wSExM1YsQIFStWTMWLF9eYMWPUr18/derU6ZZfj4sXL6pv377y8/OTp6en7r//fh04cMD6+LFjx9ShQwf5+fnJy8tLNWvW1LJly6z7PvzwwypRooQ8PDxUpUoVzZ49+5ZjAQAAtgrrvGXmzJnq3bu3HnnkEc2aNSvV4//++6969eolf39/eXl5qX79+tq4caP18R9//FENGjSQu7u7AgIC1LlzZ5vnumTJEpvjFStWTHPmzJEkHT16VCaTSQsXLlSLFi3k7u6uefPm6fz58+rVq5dKly4tT09PhYaG6quvvrI5TnJyst566y1VrlxZbm5uKleunF577TVJ0j333KPhw4fbjD937pxcXV21evXqTF8TAAAAFBwk/gAA+YZhSLGx9rkYRs49j+eee05vvPGG9u3bp9q1a+vKlStq166dVq9ere3bt6tt27bq0KGDjh8/nuFxJk6cqO7du2vnzp1q166dHn74YV24cCHd8XFxcZo8ebK++OIL/f777zp+/Liefvpp6+Nvvvmm5s2bp9mzZ2v9+vWKiYlJ9cFVdvXv319btmzRDz/8oA0bNsgwDLVr107Xr1+XJA0bNkzx8fH6/ffftWvXLr355pvW6oKXXnpJe/fu1fLly7Vv3z5Nnz5dAQEBtxUPAAB5yV5zF+Yt6bt8+bK++eYb9enTR/fee6+io6P1xx9/WB+/cuWKWrRooZMnT+qHH37Qjh079Oyzzyo5OVmStHTpUnXu3Fnt2rXT9u3btXr1ajVs2DDT897sueee08iRI7Vv3z6Fh4fr2rVrCgsL09KlS7V7924NGTJEjzzyiDZt2mTdZ+zYsXrjjTesc6T58+crMDBQkjRo0CDNnz9f8fHx1vFffvmlSpcurXvuuSfb8QEAACAfM5BKdHS0IcmIjo62dygAUKhdvXrV2Lt3r3H16lXDMAzjyhXDMH+UlfeXK1eyH//s2bMNX19f6/01a9YYkowlS5Zkum/NmjWNDz74wHq/fPnyxrvvvmu9L8l48cUXrfevXLliSDKWL19uc66LFy9aY5FkHDx40LrPtGnTjMDAQOv9wMBA4+2337beT0xMNMqVK2d07Ngx3ThvPk9K//zzjyHJWL9+vXVbVFSU4eHhYXz99deGYRhGaGioMWHChDSP3aFDB2PAgAHpnjsn3PweS4n5QNbxWgFA2v+m2GvuwrwlfZ988olRt25d6/2RI0ca/fr1s97/+OOPjaJFixrnz59Pc//GjRsbDz/8cLrHl2QsXrzYZpuvr68xe/ZswzAM48iRI4YkY+rUqRnGaRiG0b59e+Opp54yDMMwYmJiDDc3N+PTTz9Nc+zVq1cNPz8/Y+HChdZttWvXTneedTuYP+UMXisAAJBb8wEq/gAAyGP169e3uX/lyhU9/fTTql69uooVKyZvb2/t27cv02/O165d23rby8tLPj4+Onv2bLrjPT09ValSJev94OBg6/jo6GhFRkbafGPd2dlZYWFh2XpuKe3bt08uLi5q1KiRdVvx4sV1xx13aN++fZKkESNG6NVXX1XTpk01fvx47dy50zr28ccf14IFC1S3bl09++yz+vPPP285FgAAcGsK2rxl1qxZ6tOnj/V+nz599M033+jy5cuSpIiICNWrV0/+/v5p7h8REaHWrVtnep7M3Py6JiUl6ZVXXlFoaKj8/f3l7e2tlStXWl/Xffv2KT4+Pt1zu7u727Qu3bZtm3bv3q3+/fvfdqwAAADIXzJflRsAAAfh6SlduWK/c+cULy8vm/tPP/20Vq1apcmTJ6ty5cry8PBQ165dlZCQkOFxihQpYnPfZDJZ21BldbyRk73AbsGgQYMUHh6upUuX6ueff9akSZP0zjvv6IknntD999+vY8eOadmyZVq1apVat26tYcOGafLkyXaNGQCArLLX3IV5S9r27t2rv/76S5s2bdKYMWOs25OSkrRgwQINHjxYHh4eGR4js8fTitPS4jylm1/Xt99+W++9956mTp2q0NBQeXl5adSoUdbXNbPzSuZ5Vd26dfXvv/9q9uzZuueee1S+fPlM9wMAAEDBQsUfACDfMJkkLy/7XEym3Hte69evV//+/dW5c2eFhoYqKChIR48ezb0TpsHX11eBgYHavHmzdVtSUpK2bdt2y8esXr26EhMTtXHjRuu28+fPa//+/apRo4Z1W9myZTV06FAtWrRITz31lD799FPrYyVKlFC/fv305ZdfaurUqfrkk09uOR4AAPKaveYuzFvSNnPmTDVv3lw7duxQRESE9TJ69GjNnDlTkrkyMSIiIt31B2vXrq3Vq1ene44SJUro9OnT1vsHDhxQXFxcps9p/fr16tixo/r06aM6deqoYsWK+ueff6yPV6lSRR4eHhmeOzQ0VPXr19enn36q+fPn69FHH830vAAAACh4qPjLY199Jb3/vnTvvdLLL9s7GgCAI6hSpYoWLVqkDh06yGQy6aWXXsrwG/C55YknntCkSZNUuXJlVatWTR988IEuXrwoUxY+Pdy1a5eKFi1qvW8ymVSnTh117NhRgwcP1scff6yiRYvqueeeU+nSpdWxY0dJ0qhRo3T//feratWqunjxotasWaPq1atLksaNG6ewsDDVrFlT8fHx+umnn6yPoRA5dUrq1UtKSJA2bLB3NABQ6OXXecv169f1xRdf6OWXX1atWrVsHhs0aJCmTJmiPXv2qFevXnr99dfVqVMnTZo0ScHBwdq+fbtKlSqlxo0ba/z48WrdurUqVaqknj17KjExUcuWLbNWEN5zzz368MMP1bhxYyUlJWnMmDGpqhfTUqVKFX377bf6888/5efnpylTpigyMtL6ZSl3d3eNGTNGzz77rFxdXdW0aVOdO3dOe/bs0cCBA22ey/Dhw+Xl5aXOnTvf6ssLAACAfIyKvzx26ZL011/Srl32jgQA4CimTJkiPz8/NWnSRB06dFB4eLjuvPPOPI9jzJgx6tWrl/r27avGjRvL29tb4eHhcnd3z3Tf5s2bq169etaLZY2d2bNnKywsTA888IAaN24swzC0bNky6wdgSUlJGjZsmKpXr662bduqatWq+uijjyRJrq6uGjt2rGrXrq3mzZvL2dlZCxYsyL0XAI7J1VX6/XfzBCox0d7RAEChl1/nLT/88IPOnz+fZjKsevXqql69umbOnClXV1f9/PPPKlmypNq1a6fQ0FC98cYbcnZ2liS1bNlS33zzjX744QfVrVtX99xzjzZt2mQ91jvvvKOyZcuqWbNm6t27t55++ml5ZqH36osvvqg777xT4eHhatmypYKCgtSpUyebMS+99JKeeuopjRs3TtWrV1ePHj1SrZPYq1cvubi4qFevXlmawwEAAKDgMRn2XtzHAcXExMjX11fR0dHy8fHJ0WMvXiw99JDUqJH58ysAQPquXbumI0eOqEKFCnxwYQfJycmqXr26unfvrldeecXe4eSKjN5juTkfKGhy9bVKTJQslRKRkVLJkjl7fADIIcxb7KswzFuy4ujRo6pUqZI2b96cawlZ5k85g9cKAADk1nyAVp95LDjYfH3mjH3jAADgZseOHdPPP/+sFi1aKD4+Xh9++KGOHDmi3r172zs0FGYuLlKxYua2CRcukPgDAEhi3nKz69ev6/z583rxxRd111132aUKEwAAAI6BVp95LCjIfH3mjEStJQDAkTg5OWnOnDlq0KCBmjZtql27dumXX35hXT3Yn7+/+fr8efvGAQBwGMxbbK1fv17BwcHavHmzZsyYYe9wAAAAYEdU/OUxS+IvPt78xXU/P7uGAwCAVdmyZbV+/Xp7hwGkVry4dPiwueIPAAAxb7lZy5YtxUouAAAAkKj4y3Pu7uZuVRLtPgEAALKkeHHzNRV/AAAAAAAAGSLxZwcp230CAAAgE7T6BAAAAAAAyBISf3YQHGy+Pn3avnEAAADkC5aKP1p9AgAAAAAAZIjEnx1Q8QcAAJANVPwBAAAAAABkCYk/OyDxBwAAkA1U/AEAAAAAAGQJiT87oNUnAABANlDxBwAAAAAAkCUk/uyAij8AQGZatmypUaNGWe+HhIRo6tSpGe5jMpm0ZMmS2z53Th0HuWfatGkKCQmRu7u7GjVqpE2bNqU7tmXLljKZTKku7du3T3P80KFDZTKZMn2/5Skq/gDAoTFvAQAAABwHiT87IPEHAAVXhw4d1LZt2zQf++OPP2QymbRz585sH3fz5s0aMmTI7YZnY8KECapbt26q7adPn9b999+fo+e62Zw5c1SsWLFcPUdBtXDhQo0ePVrjx4/Xtm3bVKdOHYWHh+vs2bNpjl+0aJFOnz5tvezevVvOzs7q1q1bqrGLFy/WX3/9pVKlSuX208geS+KPij8AyFHMW7Ln6tWr8vf3V0BAgOLj4/PknAAAAEB2kfizA1p9AkDBNXDgQK1atUr//vtvqsdmz56t+vXrq3bt2tk+bokSJeTp6ZkTIWYqKChIbm5ueXIuZN+UKVM0ePBgDRgwQDVq1NCMGTPk6empWbNmpTne399fQUFB1suqVavk6emZKvF38uRJPfHEE5o3b56KFCmSF08l62j1CQC5gnlL9nz33XeqWbOmqlWrZvcqQ8MwlJiYaNcYAAAA4JhI/NmBpeLv/HkpIcG+sQAActYDDzygEiVKaM6cOTbbr1y5om+++UYDBw7U+fPn1atXL5UuXVqenp4KDQ3VV199leFxb26ZdeDAATVv3lzu7u6qUaOGVq1alWqfMWPGqGrVqvL09FTFihX10ksv6fr165LMFXcTJ07Ujh07rK0fLTHf3DJr165duueee+Th4aHixYtryJAhunLlivXx/v37q1OnTpo8ebKCg4NVvHhxDRs2zHquW3H8+HF17NhR3t7e8vHxUffu3RUZGWl9fMeOHWrVqpWKFi0qHx8fhYWFacuWLZKkY8eOqUOHDvLz85OXl5dq1qypZcuW3XIsjiQhIUFbt25VmzZtrNucnJzUpk0bbdiwIUvHmDlzpnr27CkvLy/rtuTkZD3yyCN65plnVLNmzUyPER8fr5iYGJtLrrJU/MXFSdeu5e65AKAQYd6SvXnLzJkz1adPH/Xp00czZ85M9fiePXv0wAMPyMfHR0WLFlWzZs106NAh6+OzZs1SzZo15ebmpuDgYA0fPlySdPToUZlMJkVERFjHXrp0SSaTSWvXrpUkrV27ViaTScuXL1dYWJjc3Ny0bt06HTp0SB07dlRgYKC8vb3VoEED/fLLLzZxxcfHa8yYMSpbtqzc3NxUuXJlzZw5U4ZhqHLlypo8ebLN+IiICJlMJh08eDDT1wQAAACOx8XeARRG/v6Si4uUmChFRkply9o7IgDIJwzD/MG/PXh6SiZTpsNcXFzUt29fzZkzRy+88IJM/+3zzTffKCkpSb169dKVK1cUFhamMWPGyMfHR0uXLtUjjzyiSpUqqWHDhpmeIzk5WQ899JACAwO1ceNGRUdH26yrY1G0aFHNmTNHpUqV0q5duzR48GAVLVpUzz77rHr06KHdu3drxYoV1g+HfH19Ux0jNjZW4eHhaty4sTZv3qyzZ89q0KBBGj58uM2HhGvWrFFwcLDWrFmjgwcPqkePHqpbt64GDx6c6fNJ6/lZkn6//fabEhMTNWzYMPXo0cP64dfDDz+sevXqafr06XJ2dlZERIS1Sm3YsGFKSEjQ77//Li8vL+3du1fe3t7ZjsMRRUVFKSkpSYGBgTbbAwMD9ffff2e6/6ZNm7R79+5UH1a++eabcnFx0YgRI7IUx6RJkzRx4sSsB367fHwkJycpOdm8zp+jtSIFgPTYa+7CvCXH5y2HDh3Shg0btGjRIhmGoSeffFLHjh1T+fLlJZkr55s3b66WLVvq119/lY+Pj9avX2+typs+fbpGjx6tN954Q/fff7+io6O1fv36TF+/mz333HOaPHmyKlasKD8/P504cULt2rXTa6+9Jjc3N33++efq0KGD9u/fr3LlykmS+vbtqw0bNuj9999XnTp1dOTIEUVFRclkMunRRx/V7Nmz9fTTT1vPMXv2bDVv3lyVK1fOdnwAAABwAAZSiY6ONiQZ0dHRuXaOMmUMQzKMTZty7RQAkO9dvXrV2Lt3r3H16lXzhitXzH887XG5ciXLce/bt8+QZKxZs8a6rVmzZkafPn3S3ad9+/bGU089Zb3fokULY+TIkdb75cuXN959913DMAxj5cqVhouLi3Hy5Enr48uXLzckGYsXL073HG+//bYRFhZmvT9+/HijTp06qcalPM4nn3xi+Pn5GVdSPP+lS5caTk5OxpkzZwzDMIx+/foZ5cuXNxITE61junXrZvTo0SPdWGbPnm34+vqm+djPP/9sODs7G8ePH7du27NnjyHJ2PTfP5xFixY15syZk+b+oaGhxoQJE9I9d0qp3mMp5MV8ILtOnjxpSDL+/PNPm+3PPPOM0bBhw0z3HzJkiBEaGmqzbcuWLUZgYKDN+ynl+y0t165dM6Kjo62XEydO5P5rFRBg/l3ctSv3zgEAtyHNf1PsNXdh3mJ9PCfmLYZhGM8//7zRqVMn6/2OHTsa48ePt94fO3asUaFCBSMhISHN/UuVKmW88MILaT525MgRQ5Kxfft267aLFy/a/FzWrFljSDKWLFmSYZyGYRg1a9Y0PvjgA8MwDGP//v2GJGPVqlVpjj158qTh7OxsbNy40TAMw0hISDACAgLSnWcZRv6bPzkqXisAAJBb8wFafdqJpd3nmTP2jQMAkPOqVaumJk2aWNdcO3jwoP744w8NHDhQkpSUlKRXXnlFoaGh8vf3l7e3t1auXKnjx49n6fj79u1T2bJlVSpF1VPjxo1TjVu4cKGaNm2qoKAgeXt768UXX8zyOVKeq06dOjZtIZs2bark5GTt37/fuq1mzZpydna23g8ODtbZs2ezda6U5yxbtqzKpiiJr1GjhooVK6Z9+/ZJkkaPHq1BgwapTZs2euONN2zaaI0YMUKvvvqqmjZtqvHjx2vnzp23FIcjCggIkLOzs03bU0mKjIxUkGVykY7Y2FgtWLDA+j60+OOPP3T27FmVK1dOLi4ucnFx0bFjx/TUU08pJCQkzWO5ubnJx8fH5pLrLO0+WecPAHIU85bM5y1JSUmaO3eu+vTpY93Wp08fzZkzR8nJyZLM7TGbNWuW5jq5Z8+e1alTp9S6detsPZ+01K9f3+b+lStX9PTTT6t69eoqVqyYvL29tW/fPutrFxERIWdnZ7Vo0SLN45UqVUrt27e3/vx//PFHxcfHp1oLGAAAAPkHiT87sXw2d/q0feMAgHzF01O6csU+F0/PbIU6cOBAfffdd7p8+bJmz56tSpUqWT9wefvtt/Xee+9pzJgxWrNmjSIiIhQeHq6EHFz4dcOGDXr44YfVrl07/fTTT9q+fbteeOGFHD1HSjd/yGUymawfhOWGCRMmaM+ePWrfvr1+/fVX1ahRQ4sXL5YkDRo0SIcPH9YjjzyiXbt2qX79+vrggw9yLZa85OrqqrCwMK1evdq6LTk5WatXr07zQ9SUvvnmG8XHx9t8aClJjzzyiHbu3KmIiAjrpVSpUnrmmWe0cuXKXHket8Tf33xN4g9AfmKvuQvzlgxld96ycuVKnTx5Uj169LB+SaZnz546duyY9d9kDw+PdPfP6DHJvF6vJBmGYd2W3pqDKZOakvT0009r8eLFev311/XHH38oIiJCoaGh1tcus3NL5rnTggULdPXqVc2ePVs9evSQZzbfQwAAAHAcrPFnJ8HB5msq/gAgG0wm6aYPOxxV9+7dNXLkSM2fP1+ff/65Hn/8ceu6OevXr1fHjh2tCZjk5GT9888/qlGjRpaOXb16dZ04cUKnT59W8H//oPz11182Y/7880+VL19eL7zwgnXbsWPHbMa4uroqKSkp03PNmTNHsbGx1g+a1q9fLycnJ91xxx1Zije7LM/vxIkT1qq/vXv36tKlSzavUdWqVVW1alU9+eST6tWrl2bPnq3OnTtLksqWLauhQ4dq6NChGjt2rD799FM98cQTuRJvXhs9erT69eun+vXrq2HDhpo6dapiY2M1YMAASeZ1fEqXLq1JkybZ7Ddz5kx16tRJxS2Vc/8pXrx4qm1FihRRUFBQrv2Mb4klxgsX7BsHAGRHPpm7MG/J2MyZM9WzZ0+b+CTptdde08yZM3Xvvfeqdu3amjt3rq5fv54qsVi0aFGFhIRo9erVatWqVarjlyhRQpJ0+vRp1atXT5K5Ui8r1q9fr/79+1vnQFeuXNHRo0etj4eGhio5OVm//fab2rRpk+Yx2rVrJy8vL02fPl0rVqzQ77//nqVzAwAAwDFR8WcntPoEgILN29tbPXr00NixY3X69Gn179/f+liVKlW0atUq/fnnn9q3b58ee+yxVK0bM9KmTRtVrVpV/fr1044dO/THH3+k+iCqSpUqOn78uBYsWKBDhw7p/ffft1bEWYSEhOjIkSOKiIhQVFSU4uPjU53r4Ycflru7u/r166fdu3drzZo1euKJJ/TII48oMDAwey/KTZKSkmyqzCIiIrRv3z61adNGoaGhevjhh7Vt2zZt2rRJffv2VYsWLVS/fn1dvXpVw4cP19q1a3Xs2DGtX79emzdvVvXq1SVJo0aN0sqVK3XkyBFt27ZNa9assT5WEPTo0UOTJ0/WuHHjVLduXUVERGjFihXWn8fx48d1+qaWAvv379e6detStfnMV6j4A4Bcw7wlfefOndOPP/6ofv36qVatWjaXvn37asmSJbpw4YKGDx+umJgY9ezZU1u2bNGBAwf0xRdfWFuMTpgwQe+8847ef/99HThwQNu2bbN2JPDw8NBdd92lN954Q/v27dNvv/2mF198MUvxValSRYsWLVJERIR27Nih3r1721QvhoSEqF+/fnr00Ue1ZMkSHTlyRGvXrtXXX39tHePs7Kz+/ftr7NixqlKlSqZdBAAAAODYSPzZiaX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+      "text/plain": [
+       "<Figure size 1800x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_learning_curves(observed_charges_model_history, title='Observed Charges Model')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_learning_curves(charge_distribution_model_history, title='Charge Distribution Model (Linear)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def evaluate_model(model, X_test, y_test):\n",
+    "    # Predict classes\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    y_pred_classes = np.argmax(y_pred, axis=1)\n",
+    "    y_true = np.argmax(y_test, axis=1)\n",
+    "    print('#################################')\n",
+    "    print(model.summary())\n",
+    "    print('#################################')\n",
+    "    # Print the classification report\n",
+    "    print(classification_report(y_true, y_pred_classes))\n",
+    "    print('#################################')\n",
+    "    # Compute confusion matrix\n",
+    "    cm = confusion_matrix(y_true, y_pred_classes, normalize='true')\n",
+    "    print(\"Confusion Matrix:\")\n",
+    "    print(cm)\n",
+    "\n",
+    "    # Plot confusion matrix\n",
+    "    plt.figure(figsize=(8, 8))\n",
+    "    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
+    "    plt.title('Confusion matrix')\n",
+    "    plt.colorbar()\n",
+    "    tick_marks = np.arange(len(np.unique(y_true)))\n",
+    "    plt.xticks(tick_marks, np.unique(y_true), rotation=45)\n",
+    "    plt.yticks(tick_marks, np.unique(y_true))\n",
+    "    plt.tight_layout()\n",
+    "    plt.ylabel('True label')\n",
+    "    plt.xlabel('Predicted label')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3324/3324 [==============================] - 3s 1ms/step\n",
+      "#################################\n",
+      "Model: \"dominant_charge_state_predictor\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " embedding (Embedding)       multiple                  352       \n",
+      "                                                                 \n",
+      " sequential (Sequential)     (None, 19, 512)           406272    \n",
+      "                                                                 \n",
+      " flatten (Flatten)           multiple                  0         \n",
+      "                                                                 \n",
+      " sequential_1 (Sequential)   (None, 64)                1253568   \n",
+      "                                                                 \n",
+      " dense_2 (Dense)             multiple                  390       \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 1660582 (6.33 MB)\n",
+      "Trainable params: 1660582 (6.33 MB)\n",
+      "Non-trainable params: 0 (0.00 Byte)\n",
+      "_________________________________________________________________\n",
+      "None\n",
+      "#################################\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.96      0.94      0.95      5630\n",
+      "           1       0.95      0.95      0.95     59374\n",
+      "           2       0.89      0.90      0.90     34048\n",
+      "           3       0.80      0.82      0.81      6499\n",
+      "           4       0.71      0.50      0.59       732\n",
+      "           5       0.58      0.12      0.20        59\n",
+      "\n",
+      "    accuracy                           0.92    106342\n",
+      "   macro avg       0.82      0.71      0.73    106342\n",
+      "weighted avg       0.92      0.92      0.92    106342\n",
+      "\n",
+      "#################################\n",
+      "Confusion Matrix:\n",
+      "[[ 5296   321    13     0     0     0]\n",
+      " [  228 56574  2561    10     1     0]\n",
+      " [    0  2537 30538   965     8     0]\n",
+      " [    0     1  1049  5358    91     0]\n",
+      " [    0     0    19   342   366     5]\n",
+      " [    0     0     2     1    49     7]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "evaluate_model(majority_charge_model, majority_X_test, majority_y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/run_scripts/run_prosit_chargestate_distribution.py b/run_scripts/run_prosit_chargestate_distribution.py
new file mode 100644
index 00000000..e35ecf40
--- /dev/null
+++ b/run_scripts/run_prosit_chargestate_distribution.py
@@ -0,0 +1,64 @@
+import keras
+import tensorflow as tf
+
+from dlomix.constants import PTMS_ALPHABET
+from dlomix.data import ChargeStateDataset
+from dlomix.eval import adjusted_mean_absolute_error
+from dlomix.models import ChargeStateDistributionPredictor
+
+# Model
+
+model = model = ChargeStateDistributionPredictor(
+    num_classes=6, seq_length=32, alphabet=PTMS_ALPHABET
+)
+
+print(model)
+
+optimizer = tf.keras.optimizers.Adam(lr=0.0001)
+
+d = ChargeStateDataset(
+    data_format="hub",
+    data_source="Wilhelmlab/prospect-ptms-charge",
+    sequence_column="modified_sequence",
+    label_column="charge_state_dist",
+    max_seq_len=30,
+    batch_size=512,
+)
+
+print(d)
+
+for x in d.tensor_train_data:
+    print(x)
+    break
+
+test_targets = d["test"]["charge_state_dist"]
+test_sequences = d["test"]["modified_sequence"]
+
+# callbacks
+
+weights_file = "./output/prosit_charge_dist_ms1_test"
+checkpoint = tf.keras.callbacks.ModelCheckpoint(
+    weights_file, save_best_only=True, save_weights_only=True
+)
+
+stop = keras.callbacks.EarlyStopping(patience=20)
+
+callbacks = [checkpoint, stop]
+
+metrics = [adjusted_mean_absolute_error]
+
+model.compile(optimizer=optimizer, metrics=metrics, loss="mean_squared_error")
+
+history = model.fit(
+    d.tensor_train_data,
+    epochs=2,
+    validation_data=d.tensor_val_data,
+    callbacks=callbacks,
+)
+
+predictions = model.predict(test_sequences)
+predictions = predictions.ravel()
+
+print(test_sequences[:5])
+print(test_targets[:5])
+print(predictions[:5])
diff --git a/run_scripts/run_prosit_chargestate_dominant.py b/run_scripts/run_prosit_chargestate_dominant.py
new file mode 100644
index 00000000..7a3175bc
--- /dev/null
+++ b/run_scripts/run_prosit_chargestate_dominant.py
@@ -0,0 +1,55 @@
+import tensorflow as tf
+
+from dlomix.data import ChargeStateDataset
+from dlomix.models import DominantChargeStatePredictor
+
+model = DominantChargeStatePredictor(seq_length=30)
+
+optimizer = tf.keras.optimizers.Adam(lr=0.0001)
+
+TRAIN_DATAPATH = "../example_dataset/proteomTools_train_val.csv"
+TEST_DATAPATH = "../example_dataset/proteomTools_test.csv"
+
+d = ChargeStateDataset(
+    data_format="hub",
+    data_source="Wilhelmlab/prospect-ptms-charge",
+    sequence_column="modified_sequence",
+    label_column="most_abundant_charge_state",
+    max_seq_len=30,
+    batch_size=512,
+)
+
+
+print(d)
+
+test_targets = d["test"]["most_abundant_charge_state"]
+test_sequences = d["test"]["modified_sequence"]
+
+model.compile(
+    optimizer=optimizer, loss="categorical_crossentropy", metrics=["accuracy"]
+)
+
+weights_file = "./prosit_charge_test"
+checkpoint = tf.keras.callbacks.ModelCheckpoint(
+    weights_file, save_best_only=True, save_weights_only=True
+)
+decay = tf.keras.callbacks.ReduceLROnPlateau(
+    monitor="val_loss", factor=0.1, patience=10, verbose=1, min_lr=0
+)
+early_stop = tf.keras.callbacks.EarlyStopping(patience=20)
+callbacks = [checkpoint, early_stop, decay]
+
+
+history = model.fit(
+    d.tensor_train_data,
+    epochs=2,
+    validation_data=d.tensor_val_data,
+    callbacks=callbacks,
+)
+
+predictions = model.predict(test_sequences)
+predictions = predictions.ravel()
+
+print(test_sequences[:5])
+print(test_targets[:5])
+print(predictions[:5])
diff --git a/setup.py b/setup.py
index 6b80e1ba..a8bb6105 100644
--- a/setup.py
+++ b/setup.py
@@ -22,6 +22,7 @@
     package_data={"": ["data/processing/pickled_feature_dicts/*"]},
     install_requires=[
         "datasets",
+        "huggingface_hub",
         "fpdf",
         "pandas",
         "numpy",
diff --git a/src/dlomix/constants.py b/src/dlomix/constants.py
index 71d087be..973b67e1 100644
--- a/src/dlomix/constants.py
+++ b/src/dlomix/constants.py
@@ -89,6 +89,12 @@
     "Y[UNIMOD:354]": 54,
 }
 
+# ToDo: double check chargestate data additional tokens for the alphabet
+
+# PTMS_ALPHABET['W[UNIMOD:425]'] = 57
+# PTMS_ALPHABET['K[UNIMOD:1342]'] = 58
+# PTMS_ALPHABET['[UNIMOD:27]-'] = 59
+
 
 # ---- detectability_model_constants.py ----
 CLASSES_LABELS = ["Non-Flyer", "Weak Flyer", "Intermediate Flyer", "Strong Flyer"]
diff --git a/src/dlomix/eval/__init__.py b/src/dlomix/eval/__init__.py
index e3eed22a..025e837d 100644
--- a/src/dlomix/eval/__init__.py
+++ b/src/dlomix/eval/__init__.py
@@ -1,3 +1,4 @@
+from .chargestate import adjusted_mean_absolute_error
 from .rt_eval import TimeDeltaMetric, TimeDeltaMetric2
 
-__all__ = ["TimeDeltaMetric", "TimeDeltaMetric2"]
+__all__ = ["TimeDeltaMetric", "TimeDeltaMetric2", "adjusted_mean_absolute_error"]
diff --git a/src/dlomix/eval/chargestate.py b/src/dlomix/eval/chargestate.py
new file mode 100644
index 00000000..101e4232
--- /dev/null
+++ b/src/dlomix/eval/chargestate.py
@@ -0,0 +1,61 @@
+from keras import backend as K
+
+
+def adjusted_mean_absolute_error(y_true, y_pred):
+    """
+    Used as an evaluation metric for charge state prediction.
+
+    For two vectors, discard those components that
+    are 0 in both vectors and compute the mean
+    absolute error for the adjusted vector.
+    """
+    # Convert y_true and y_pred to float tensors
+    y_true = K.cast(y_true, dtype="float32")
+    y_pred = K.cast(y_pred, dtype="float32")
+
+    # Create a mask for elements that are not both zero
+    mask = K.cast(K.not_equal(y_true + y_pred, 0.0), dtype="float32")
+
+    # Apply mask to both y_true and y_pred
+    y_true_adjusted = y_true * mask
+    y_pred_adjusted = y_pred * mask
+
+    # Compute the mean absolute error
+    absolute_errors = K.abs(y_true_adjusted - y_pred_adjusted)
+    sum_absolute_errors = K.sum(absolute_errors)
+    count_non_zero = K.sum(mask)
+
+    # Avoid division by zero by adding a small epsilon to the denominator
+    epsilon = K.epsilon()
+    mean_absolute_error = sum_absolute_errors / (count_non_zero + epsilon)
+
+    return mean_absolute_error
+
+
+def adjusted_mean_squared_error(y_true, y_pred):
+    """
+    For two vectors, discard those components that
+    are 0 in both vectors and compute the mean
+    squared error for the adjusted vector.
+    """
+    # Convert y_true and y_pred to float tensors
+    y_true = K.cast(y_true, dtype="float32")
+    y_pred = K.cast(y_pred, dtype="float32")
+
+    # Create a mask for elements that are not both zero
+    mask = K.cast(K.not_equal(y_true + y_pred, 0.0), dtype="float32")
+
+    # Apply mask to both y_true and y_pred
+    y_true_adjusted = y_true * mask
+    y_pred_adjusted = y_pred * mask
+
+    # Compute the mean squared error
+    squared_errors = K.square(y_true_adjusted - y_pred_adjusted)
+    sum_squared_errors = K.sum(squared_errors)
+    count_non_zero = K.sum(mask)
+
+    # Avoid division by zero by adding a small epsilon to the denominator
+    epsilon = K.epsilon()
+    mean_squared_error = sum_squared_errors / (count_non_zero + epsilon)
+
+    return mean_squared_error
diff --git a/src/dlomix/models/__init__.py b/src/dlomix/models/__init__.py
index 8bf7ec47..29b2b979 100644
--- a/src/dlomix/models/__init__.py
+++ b/src/dlomix/models/__init__.py
@@ -1,4 +1,5 @@
 from .base import *
+from .chargestate import *
 from .deepLC import *
 from .detectability import *
 from .prosit import *
@@ -8,5 +9,8 @@
     "PrositRetentionTimePredictor",
     "DeepLCRetentionTimePredictor",
     "PrositIntensityPredictor",
+    "DominantChargeStatePredictor",
+    "ObservedChargeStatePredictor",
+    "ChargeStateDistributionPredictor",
     "DetectabilityModel",
 ]
diff --git a/src/dlomix/models/chargestate.py b/src/dlomix/models/chargestate.py
new file mode 100644
index 00000000..b11c69dc
--- /dev/null
+++ b/src/dlomix/models/chargestate.py
@@ -0,0 +1,263 @@
+import tensorflow as tf
+
+from ..constants import ALPHABET_UNMOD
+from ..layers.attention import AttentionLayer
+
+"""
+This module contains models for predicting charge states for peptide sequences in mass spectrometry data.
+There are three task formulations, each with a corresponding model:
+
+1. DominantChargeStatePredictor:
+   - Task: Predict the dominant charge state of a given peptide sequence.
+   - Model: Uses a deep learning model (RNN-based) inspired by Prosit's architecture to predict the most likely charge state.
+
+2. ObservedChargeStatePredictor:
+   - Task: Predict the observed charge states for a given peptide sequence.
+   - Model: Uses a multi-label classification approach to predict all possible charge states.
+
+3. ChargeStateProportionPredictor:
+   - Task: Predict the proportion of each charge state for a given peptide sequence.
+   - Model: Uses a regression approach to predict the proportion of each charge state.
+"""
+
+
+class DominantChargeStatePredictor(tf.keras.Model):
+    """
+    Charge State Prediction Model for predicting the dominant charge state of a peptide sequence.
+
+    Args:
+        embedding_output_dim (int): The size of the embedding output dimension. Defaults to 16.
+        seq_length (int): The length of the input sequence. Defaults to 30.
+        alphabet (dict): Dictionary mapping for the alphabet (the amino acids in this case). Defaults to ALPHABET_UNMOD.
+        dropout_rate (float): The dropout rate used in the encoder layers. Defaults to 0.5.
+        latent_dropout_rate (float): The dropout rate for the latent space. Defaults to 0.1.
+        recurrent_layers_sizes (tuple): The sizes of the recurrent layers. Defaults to (256, 512).
+        regressor_layer_size (int): The size of the regressor layer. Defaults to 512.
+        num_classes (int): The number of classes for the output corresponding to charge states available in the data. Defaults to 6.
+    """
+
+    def __init__(
+        self,
+        embedding_output_dim=16,
+        seq_length=30,
+        alphabet=ALPHABET_UNMOD,
+        dropout_rate=0.5,
+        latent_dropout_rate=0.1,
+        recurrent_layers_sizes=(256, 512),
+        regressor_layer_size=512,
+        num_classes=6,
+    ):
+        super(DominantChargeStatePredictor, self).__init__()
+
+        # tie the count of embeddings to the size of the vocabulary (count of amino acids)
+        self.embeddings_count = len(alphabet)
+
+        self.dropout_rate = dropout_rate
+        self.latent_dropout_rate = latent_dropout_rate
+        self.regressor_layer_size = regressor_layer_size
+        self.recurrent_layers_sizes = recurrent_layers_sizes
+
+        self.embedding = tf.keras.layers.Embedding(
+            input_dim=self.embeddings_count,
+            output_dim=embedding_output_dim,
+            input_length=seq_length,
+        )
+        self._build_encoder()
+
+        self.attention = AttentionLayer()
+
+        self.regressor = tf.keras.Sequential(
+            [
+                tf.keras.layers.Dense(self.regressor_layer_size, activation="relu"),
+                tf.keras.layers.Dropout(rate=self.latent_dropout_rate),
+            ]
+        )
+
+        self.output_layer = tf.keras.layers.Dense(num_classes, activation="softmax")
+
+    def _build_encoder(self):
+        self.encoder = tf.keras.Sequential(
+            [
+                tf.keras.layers.Bidirectional(
+                    tf.keras.layers.GRU(
+                        units=self.recurrent_layers_sizes[0], return_sequences=True
+                    )
+                ),
+                tf.keras.layers.Dropout(rate=self.dropout_rate),
+                tf.keras.layers.GRU(
+                    units=self.recurrent_layers_sizes[1], return_sequences=True
+                ),
+                tf.keras.layers.Dropout(rate=self.dropout_rate),
+            ]
+        )
+
+    def call(self, inputs):
+        x = self.embedding(inputs)
+        x = self.encoder(x)
+        x = self.attention(x)
+        x = self.regressor(x)
+        x = self.output_layer(x)
+        return x
+
+
+class ObservedChargeStatePredictor(tf.keras.Model):
+    """
+    Charge State Prediction Model for predicting all observed charge states of a peptide sequence.
+
+    Args:
+        embedding_output_dim (int): The size of the embedding output dimension. Defaults to 16.
+        seq_length (int): The length of the input sequence. Defaults to 30.
+        alphabet (dict): Dictionary mapping for the alphabet (the amino acids in this case). Defaults to ALPHABET_UNMOD.
+        dropout_rate (float): The dropout rate used in the encoder layers. Defaults to 0.5.
+        latent_dropout_rate (float): The dropout rate for the latent space. Defaults to 0.1.
+        recurrent_layers_sizes (tuple): The sizes of the recurrent layers. Defaults to (256, 512).
+        regressor_layer_size (int): The size of the regressor layer. Defaults to 512.
+        num_classes (int): The number of classes for the output corresponding to charge states available in the data. Defaults to 6.
+    """
+
+    def __init__(
+        self,
+        embedding_output_dim=16,
+        seq_length=30,
+        alphabet=ALPHABET_UNMOD,
+        dropout_rate=0.5,
+        latent_dropout_rate=0.1,
+        recurrent_layers_sizes=(256, 512),
+        regressor_layer_size=512,
+        num_classes=6,
+    ):
+        super(ObservedChargeStatePredictor, self).__init__()
+
+        # tie the count of embeddings to the size of the vocabulary (count of amino acids)
+        self.embeddings_count = len(alphabet)
+
+        self.dropout_rate = dropout_rate
+        self.latent_dropout_rate = latent_dropout_rate
+        self.regressor_layer_size = regressor_layer_size
+        self.recurrent_layers_sizes = recurrent_layers_sizes
+
+        self.embedding = tf.keras.layers.Embedding(
+            input_dim=self.embeddings_count,
+            output_dim=embedding_output_dim,
+            input_length=seq_length,
+        )
+        self._build_encoder()
+
+        self.attention = AttentionLayer()
+
+        self.regressor = tf.keras.Sequential(
+            [
+                tf.keras.layers.Dense(self.regressor_layer_size, activation="relu"),
+                tf.keras.layers.Dropout(rate=self.latent_dropout_rate),
+            ]
+        )
+
+        self.output_layer = tf.keras.layers.Dense(num_classes, activation="sigmoid")
+
+    def _build_encoder(self):
+        self.encoder = tf.keras.Sequential(
+            [
+                tf.keras.layers.Bidirectional(
+                    tf.keras.layers.GRU(
+                        units=self.recurrent_layers_sizes[0], return_sequences=True
+                    )
+                ),
+                tf.keras.layers.Dropout(rate=self.dropout_rate),
+                tf.keras.layers.GRU(
+                    units=self.recurrent_layers_sizes[1], return_sequences=True
+                ),
+                tf.keras.layers.Dropout(rate=self.dropout_rate),
+            ]
+        )
+
+    def call(self, inputs):
+        x = self.embedding(inputs)
+        x = self.encoder(x)
+        x = self.attention(x)
+        x = self.regressor(x)
+        x = self.output_layer(x)
+        return x
+
+
+class ChargeStateDistributionPredictor(tf.keras.Model):
+    """
+    Charge State Prediction Model for predicting the proportion of all observed charge states of a peptide sequence.
+
+    Args:
+        embedding_output_dim (int): The size of the embedding output dimension. Defaults to 16.
+        seq_length (int): The length of the input sequence. Defaults to 30.
+        alphabet (dict): Dictionary mapping for the alphabet (the amino acids in this case). Defaults to ALPHABET_UNMOD.
+        dropout_rate (float): The dropout rate used in the encoder layers. Defaults to 0.5.
+        latent_dropout_rate (float): The dropout rate for the latent space. Defaults to 0.1.
+        recurrent_layers_sizes (tuple): The sizes of the recurrent layers. Defaults to (256, 512).
+        regressor_layer_size (int): The size of the regressor layer. Defaults to 512.
+        num_classes (int): The number of classes for the output corresponding to charge states available in the data. Defaults to 6.
+        output_activation_fn (str): The activation function for the output layer. Defaults to "linear".
+    """
+
+    def __init__(
+        self,
+        embedding_output_dim=16,
+        seq_length=30,
+        alphabet=ALPHABET_UNMOD,
+        dropout_rate=0.5,
+        latent_dropout_rate=0.1,
+        recurrent_layers_sizes=(256, 512),
+        regressor_layer_size=512,
+        num_classes=6,  # number of charge states
+        output_activation_fn="linear",
+    ):
+        super(ChargeStateDistributionPredictor, self).__init__()
+
+        # tie the count of embeddings to the size of the vocabulary (count of amino acids)
+        self.embeddings_count = len(alphabet)
+
+        self.dropout_rate = dropout_rate
+        self.latent_dropout_rate = latent_dropout_rate
+        self.regressor_layer_size = regressor_layer_size
+        self.recurrent_layers_sizes = recurrent_layers_sizes
+        self.output_activation_fn = output_activation_fn
+
+        self.embedding = tf.keras.layers.Embedding(
+            input_dim=self.embeddings_count,
+            output_dim=embedding_output_dim,
+            input_length=seq_length,
+        )
+        self._build_encoder()
+
+        self.attention = AttentionLayer()
+
+        self.regressor = tf.keras.Sequential(
+            [
+                tf.keras.layers.Dense(self.regressor_layer_size, activation="relu"),
+                tf.keras.layers.Dropout(rate=self.latent_dropout_rate),
+            ]
+        )
+
+        self.output_layer = tf.keras.layers.Dense(
+            num_classes, activation=self.output_activation_fn
+        )
+
+    def _build_encoder(self):
+        self.encoder = tf.keras.Sequential(
+            [
+                tf.keras.layers.Bidirectional(
+                    tf.keras.layers.GRU(
+                        units=self.recurrent_layers_sizes[0], return_sequences=True
+                    )
+                ),
+                tf.keras.layers.Dropout(rate=self.dropout_rate),
+                tf.keras.layers.GRU(
+                    units=self.recurrent_layers_sizes[1], return_sequences=True
+                ),
+                tf.keras.layers.Dropout(rate=self.dropout_rate),
+            ]
+        )
+
+    def call(self, inputs):
+        x = self.embedding(inputs)
+        x = self.encoder(x)
+        x = self.attention(x)
+        x = self.regressor(x)
+        x = self.output_layer(x)
+        return x
diff --git a/tests/test_models.py b/tests/test_models.py
index c62beac8..b6c283b8 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -2,7 +2,13 @@
 
 import pytest
 
-from dlomix.models import PrositIntensityPredictor, PrositRetentionTimePredictor
+from dlomix.models import (
+    ChargeStateDistributionPredictor,
+    DominantChargeStatePredictor,
+    ObservedChargeStatePredictor,
+    PrositIntensityPredictor,
+    PrositRetentionTimePredictor,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -118,3 +124,21 @@ def test_prosit_intensity_model_encoding_metadata_missing():
                 # no meta-data while expected
             }
         )
+
+
+def test_dominant_chargestate_model():
+    model = DominantChargeStatePredictor()
+    logger.info(model)
+    assert model is not None
+
+
+def test_observed_chargestate_model():
+    model = ObservedChargeStatePredictor()
+    logger.info(model)
+    assert model is not None
+
+
+def test_chargestate_distribution_model():
+    model = ChargeStateDistributionPredictor()
+    logger.info(model)
+    assert model is not None

From e101d2af83c8390cba00026b8fcd95b4ae6baf9c Mon Sep 17 00:00:00 2001
From: Omar Shouman <o.shouman@tum.de>
Date: Fri, 31 Jan 2025 17:12:32 +0100
Subject: [PATCH 2/4] Feature/dev container (#57)

* First version of the Dev Container

* Add some apt packages to the container

* trim down dev-requirements to new requirements + minor dockerfile changes

* guide + folder for devcontainer

* reorder

* changes in dev setup & contribution instructions in DEVELOPMENT_GUIDE.md (#56)

* testing setup and basic commands in dev container. The minor changes here were introduced by the pre-commit hook

* added 2 links with instructions for docker install and post-install preparations. Adjusted DEVELOPMENT_GUIDE.md to have a dedicated & streamlined section for Implementation, separated from the setup

* minor fix in code example

---------

Co-authored-by: omsh <omar.shouman@gmail.com>

* pre-commit hooks note

* notes about cpu torch

* cleanup commented code

---------

Co-authored-by: Armin Soleymaniniya <armin.solem@gmail.com>
Co-authored-by: Ayla <131137738+ayla-s@users.noreply.github.com>
---
 .devcontainer/DEVELOPMENT_GUIDE.md           | 221 +++++++++++++++++++
 .devcontainer/Dockerfile                     |  31 +++
 .devcontainer/dev-requirements.txt           |   5 +
 .devcontainer/devcontainer.json              |  34 +++
 .devcontainer/vscode-screenshot.png          | Bin 0 -> 43110 bytes
 src/dlomix/data/dataset.py                   |  64 +-----
 src/dlomix/data/processing/feature_tables.py |   2 +-
 src/dlomix/detectability_model_constants.py  |  28 ++-
 8 files changed, 318 insertions(+), 67 deletions(-)
 create mode 100644 .devcontainer/DEVELOPMENT_GUIDE.md
 create mode 100644 .devcontainer/Dockerfile
 create mode 100644 .devcontainer/dev-requirements.txt
 create mode 100644 .devcontainer/devcontainer.json
 create mode 100644 .devcontainer/vscode-screenshot.png

diff --git a/.devcontainer/DEVELOPMENT_GUIDE.md b/.devcontainer/DEVELOPMENT_GUIDE.md
new file mode 100644
index 00000000..54162f1a
--- /dev/null
+++ b/.devcontainer/DEVELOPMENT_GUIDE.md
@@ -0,0 +1,221 @@
+# Development Guides for dlomix PyTorch Implementation
+
+This file provides guidelines for contributing PyTorch implementations to the dlomix project, a deep learning framework for proteomics.
+
+Based on your environment, please follow the respective setup guide:
+
+- [Dev Containers in VSCode](#dev-containers-in-vscode)
+- [Local Development Guide](#local-development-guide)
+- [Google Colab Development Guide](#google-colab-development-guide)
+
+For contributing, please follow our [implementation guidelines](#implementation-guidelines)
+
+
+## Dev Containers in VSCode
+
+### Steps
+
+1. Ensure you have Docker installed on your system and the docker daemon is running. To validate, please run the following command and ensure you do not get a listing of CONTAINER ID and other details:
+```bash
+docker ps
+```
+2. If you don't yet have docker installed, follow these instructions: https://docs.docker.com/engine/install/ubuntu/#install-using-the-repository
+
+3. To run docker without sudo (VSCode requirement), follow these post-installation steps: https://docs.docker.com/engine/install/linux-postinstall/
+
+4. Open VSCode and install the Devcontainers extensions from the extensions tab
+
+5. Clone the forked GitHub repository of DLOmix https://github.com/omsh/dlomix
+
+6. Open the repository in a DevContainer by clicking on the arrows in the botton left corner, and choosing "Reopen in Container".
+
+![alt text](vscode-screenshot.png)
+
+7. During the first time, the container build will take some time and then VSCode will connect to the running container. Once it is done, please run the following command in the VSCode Terminal to install DLOmix with development packages in the editable mode:
+
+```bash
+make install-dev
+```
+8. You are now ready to make changes to the source code and see the impact directly. Once you make the changes, they should be reflected in the editable install inside your dev container.
+
+VSCode Official Tutorial: https://code.visualstudio.com/docs/devcontainers/tutorial
+VSCode documentaion for DevContainers: https://code.visualstudio.com/docs/devcontainers/containers
+
+
+## Local Development Guide
+
+### Environment Setup
+
+#### Option 1: Using venv (Recommended)
+
+1a. Create and activate a virtual environment:
+```bash
+python -m venv venv
+# On Windows
+.\venv\Scripts\activate
+# On Unix or MacOS
+source venv/bin/activate
+```
+
+#### Option 2: Using conda
+
+1b. Create and activate a conda environment:
+```bash
+conda create -n dlomix python=3.9
+conda activate dlomix
+```
+
+2. Clone the repository and `cd` into the directory of the cloned repo:
+```bash
+git clone https://github.com/omsh/dlomix.git
+cd dlomix
+```
+
+3. Install development dependencies and ensure torch-related packages are in this file, otherwise extend it:
+```bash
+pip install -r ./.devcontainer/dev-requirements.txt
+```
+
+### DLOmix Editable installation
+
+Install the package with the dev option and in editable mode:
+```bash
+pip install -e .[dev]
+```
+
+
+## Google Colab Development Guide
+
+### Initial Setup
+
+1. Create a new Colab notebook and mount your Google Drive:
+```python
+from google.colab import drive
+drive.mount('/content/drive')
+```
+
+2. Clone the forked dlomix repository:
+```bash
+!git clone https://github.com/omsh/dlomix.git
+```
+
+3. Install development dependencies and ensure torch-related packages are in this file, otherwise extend it:
+```bash
+pip install -r ./dlomix/.devcontainer/dev-requirements.txt
+```
+
+4. Install the package in development mode:
+```bash
+!pip install -e "./dlomix[dev]"
+```
+
+
+## Implementation Guidelines
+
+1. Add PyTorch implementations following the current project structure:
+```
+dlomix/
+├── models/
+│   ├── pytorch/
+│   │   ├── __init__.py
+│   │   └── model.py
+│   └── existing_models/
+```
+
+2. Ensure compatibility with existing APIs:
+```python
+# dlomix/models/pytorch/model.py
+import torch
+import torch.nn as nn
+
+# Example of maintaining consistent API
+class PrositRTPyTorch(nn.Module):
+    """PyTorch implementation of Prosit retention time model"""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__()
+        # PyTorch implementation here
+
+    def forward(self, sequences):
+        # Maintain same input/output structure as TensorFlow version
+
+        return retention_times
+```
+
+3. Add corresponding tests:
+```python
+# tests/test_pytorch_models.py
+import torch
+import pytest
+from dlomix.models.pytorch import PrositRTPyTorch
+
+def test_model_compatibility():
+    tf_model = PrositRT()  # Existing TF implementation
+    pt_model = PrositRTPyTorch()
+
+    # Test with same input
+    sequence_input = "PEPTIDE"
+    tf_output = tf_model.predict(sequence_input)
+    pt_output = pt_model(torch.tensor(encoded_sequence))
+
+    assert tf_output.shape == pt_output.detach().numpy().shape
+
+def test_model_forward_pass():
+    model = PrositRTPyTorch()
+    expected_shape = (128, 1)
+    input_size = 30
+
+    x = torch.randn(128, input_size)  # Match existing input dimensions
+    output = model(x)
+    assert output.shape == expected_shape
+```
+
+4. Add a usage example of the new PyTorch implementation, preferably in a notebook under `./notebooks`
+
+
+### Development Workflow
+
+#### (Optional, but recommended) Pre-commit hooks
+We use some simple pre-commit hooks to ensure consistency in file and code formatting. To use pre-commit hooks:
+- install pre-commit with `pip install pre-commit`
+- add the hooks by running in the root directory of the repo `pre-commit install`
+- If you like, you can manually run the checks after staging but before commiting using `pre-commit run` to run the hooks against youur changes.
+
+1. Create a new branch:
+```bash
+git checkout -b feature/FEATURE_NAME
+```
+
+2. Add your implementation
+
+3. Write tests under `./tests` to ensure your code runs as expected.
+
+4. Run the test suite using make:
+```bash
+make test-local
+```
+
+For google Colab you can run:
+```bash
+!python -m pytest tests/
+```
+
+5. Format your code using the project's style guidelines:
+```bash
+make format
+```
+
+6. Create a pull request with:
+- Clear description of changes
+- Any new dependencies added
+- Mention the usage example under `./notebooks`
+
+
+### General Considerations
+
+1. Sequence Data :
+   - Assume the same sequence encoding schemes
+
+2. Model Architecture:
+   - Closely mimic the existing Keras implementations or the original implementations of papers
+   - Maintain similar model inputs and outputs (datatype, shape, etc..)
diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
new file mode 100644
index 00000000..270d72b5
--- /dev/null
+++ b/.devcontainer/Dockerfile
@@ -0,0 +1,31 @@
+FROM python:3.9-slim
+
+ENV PYTHONUNBUFFERED=1
+ENV PYTHONDONTWRITEBYTECODE=1
+
+# Install extra development dependencies
+COPY dev-requirements.txt /tmp/dev-requirements.txt
+RUN pip install --upgrade --no-cache-dir pip && \
+    pip install --no-cache-dir -r /tmp/dev-requirements.txt
+
+# Install system dependencies
+RUN set -ex && \
+    apt-get update && \
+    apt-get install -y --no-install-recommends \
+    build-essential \
+    bash-completion \
+    git \
+    openssh-client \
+    ca-certificates \
+    rsync \
+    vim \
+    nano \
+    wget \
+    curl \
+    && update-ca-certificates \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set the working directory
+WORKDIR /workspaces/dlomix
+
+USER root
diff --git a/.devcontainer/dev-requirements.txt b/.devcontainer/dev-requirements.txt
new file mode 100644
index 00000000..4f765343
--- /dev/null
+++ b/.devcontainer/dev-requirements.txt
@@ -0,0 +1,5 @@
+# comment the following line to install cuda with pytorch, otherwise if left uncommented it will install cpu version
+--index-url https://download.pytorch.org/whl/cpu
+
+torch
+#wandb >= 0.15 # enable this line to install wandb
diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
new file mode 100644
index 00000000..fa86ece1
--- /dev/null
+++ b/.devcontainer/devcontainer.json
@@ -0,0 +1,34 @@
+{
+    "name": "DLOmix Dev Container",
+    "build": {
+        "dockerfile": "Dockerfile"
+    },
+    "runArgs": [
+        "--platform=linux/amd64"
+    ],
+    "remoteUser": "root",
+    "containerUser": "root",
+    "workspaceFolder": "/workspaces/dlomix",
+    "workspaceMount": "source=${localWorkspaceFolder},target=/workspaces/dlomix,type=bind,consistency=cached",
+    "customizations": {
+        "vscode": {
+            "extensions": [
+                "ms-azuretools.vscode-docker",
+                "eamodio.gitlens",
+                "ms-python.python",
+                "ms-python.black-formatter",
+                "ms-python.vscode-pylance",
+                "tamasfe.even-better-toml",
+                "ms-toolsai.jupyter"
+            ],
+            "settings": {
+                "git.path": "/usr/bin/git",
+                "[python]": {
+                    "python.pythonPath": "/usr/local/bin/python",
+                    "editor.defaultFormatter": "ms-python.black-formatter"
+                }
+            },
+            "postCreateCommand": "pip install -e .[dev]"
+        }
+    }
+}
diff --git a/.devcontainer/vscode-screenshot.png b/.devcontainer/vscode-screenshot.png
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N&}urUVpWTfe*mW=>o5QS

literal 0
HcmV?d00001

diff --git a/src/dlomix/data/dataset.py b/src/dlomix/data/dataset.py
index 704a5341..b120c4db 100644
--- a/src/dlomix/data/dataset.py
+++ b/src/dlomix/data/dataset.py
@@ -110,37 +110,14 @@ def __init__(self, config: DatasetConfig):
         super(PeptideDataset, self).__init__()
         self.__dict__.update(**config.__dict__)
 
-        # self.data_source = data_source
-        # self.val_data_source = val_data_source
-        # self.test_data_source = test_data_source
-
-        # self.data_format = data_format
-
-        # self.sequence_column = sequence_column
-        # self.label_column = label_column
-
-        # self.val_ratio = val_ratio
-        # self.max_seq_len = max_seq_len
-        # self.dataset_type = dataset_type
-        # self.batch_size = batch_size
-        # self.model_features = model_features
-
         # to be kept in the hf dataset, but not returned in the tensor dataset
         if config.dataset_columns_to_keep is None:
             self.dataset_columns_to_keep = []
         else:
             self.dataset_columns_to_keep = config.dataset_columns_to_keep
 
-        # self.features_to_extract = features_to_extract
-        # self.pad = pad
-        # self.padding_value = padding_value
-        # self.alphabet = alphabet
-        # self.with_termini = with_termini
         self.encoding_scheme = EncodingScheme(config.encoding_scheme)
-        # self.processed = processed
-        # self.enable_tf_dataset_cache = enable_tf_dataset_cache
-        # self.disable_cache = disable_cache
-        # self.auto_cleanup_cache = auto_cleanup_cache
+
         self._set_hf_cache_management()
 
         self.extended_alphabet = None
@@ -159,7 +136,6 @@ def __init__(self, config: DatasetConfig):
             self._test_set_only = False
             self._num_proc = config.num_proc
             self._set_num_proc()
-            # self.batch_processing_size = config.batch_processing_size
 
             self._data_files_available_splits = {}
             self._load_dataset()
@@ -199,25 +175,6 @@ def _set_hf_cache_management(self):
 
     def _refresh_config(self):
         self._config = DatasetConfig(**self._config.__dict__)
-        #     data_source=self.data_source,
-        #     val_data_source=self.val_data_source,
-        #     test_data_source=self.test_data_source,
-        #     data_format=self.data_format,
-        #     sequence_column=self.sequence_column,
-        #     label_column=self.label_column,
-        #     val_ratio=self.val_ratio,
-        #     max_seq_len=self.max_seq_len,
-        #     dataset_type=self.dataset_type,
-        #     batch_size=self.batch_size,
-        #     model_features=self.model_features,
-        #     dataset_columns_to_keep=self.dataset_columns_to_keep,
-        #     features_to_extract=self.features_to_extract,
-        #     pad=self.pad,
-        #     padding_value=self.padding_value,
-        #     alphabet=self.alphabet,
-        #     encoding_scheme=self.encoding_scheme,
-        #     processed=self.processed,
-        # )
 
         self._config._additional_data.update(
             {
@@ -633,25 +590,6 @@ def from_dataset_config(cls, config: DatasetConfig):
         config_dict.pop("_additional_data")
 
         d = cls(**config_dict)
-        # data_source=config.data_source,
-        # val_data_source=config.val_data_source,
-        # test_data_source=config.test_data_source,
-        # data_format=config.data_format,
-        # sequence_column=config.sequence_column,
-        # label_column=config.label_column,
-        # val_ratio=config.val_ratio,
-        # max_seq_len=config.max_seq_len,
-        # dataset_type=config.dataset_type,
-        # batch_size=config.batch_size,
-        # model_features=config.model_features,
-        # dataset_columns_to_keep=config.dataset_columns_to_keep,
-        # features_to_extract=config.features_to_extract,
-        # pad=config.pad,
-        # padding_value=config.padding_value,
-        # alphabet=config.alphabet,
-        # encoding_scheme=config.encoding_scheme,
-        # processed=config.processed,
-        # )
 
         for k, v in config._additional_data.items():
             setattr(d, k, v)
diff --git a/src/dlomix/data/processing/feature_tables.py b/src/dlomix/data/processing/feature_tables.py
index b21a56b6..981b66c5 100644
--- a/src/dlomix/data/processing/feature_tables.py
+++ b/src/dlomix/data/processing/feature_tables.py
@@ -1,4 +1,4 @@
-""" Module to load pickled feature dictionaries. """
+"""Module to load pickled feature dictionaries."""
 
 import os
 import pickle
diff --git a/src/dlomix/detectability_model_constants.py b/src/dlomix/detectability_model_constants.py
index 807aef84..f0b2e79e 100644
--- a/src/dlomix/detectability_model_constants.py
+++ b/src/dlomix/detectability_model_constants.py
@@ -1,12 +1,34 @@
 import numpy as np
 
-CLASSES_LABELS = ['Non-Flyer', 'Weak Flyer', 'Intermediate Flyer', 'Strong Flyer']
+CLASSES_LABELS = ["Non-Flyer", "Weak Flyer", "Intermediate Flyer", "Strong Flyer"]
 
-alphabet = ['0', 'A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y']
+alphabet = [
+    "0",
+    "A",
+    "C",
+    "D",
+    "E",
+    "F",
+    "G",
+    "H",
+    "I",
+    "K",
+    "L",
+    "M",
+    "N",
+    "P",
+    "Q",
+    "R",
+    "S",
+    "T",
+    "V",
+    "W",
+    "Y",
+]
 
 aa_to_int_dict = dict((aa, i) for i, aa in enumerate(alphabet))
 
 int_to_aa_dict = dict((i, aa) for i, aa in enumerate(alphabet))
 
 padding_char = np.zeros(len(alphabet))
-padding_char[0] = 1
\ No newline at end of file
+padding_char[0] = 1

From 8e7c236618b8bad1ccb4caf0635ac2b589ec1f87 Mon Sep 17 00:00:00 2001
From: Omar Shouman <o.shouman@tum.de>
Date: Sun, 2 Feb 2025 11:28:16 +0100
Subject: [PATCH 3/4] gitignore and guide updates (#58)

---
 .devcontainer/DEVELOPMENT_GUIDE.md | 26 +++++++++++++++++++++++---
 .gitignore                         |  2 +-
 2 files changed, 24 insertions(+), 4 deletions(-)

diff --git a/.devcontainer/DEVELOPMENT_GUIDE.md b/.devcontainer/DEVELOPMENT_GUIDE.md
index 54162f1a..7ba0c597 100644
--- a/.devcontainer/DEVELOPMENT_GUIDE.md
+++ b/.devcontainer/DEVELOPMENT_GUIDE.md
@@ -4,9 +4,11 @@ This file provides guidelines for contributing PyTorch implementations to the dl
 
 Based on your environment, please follow the respective setup guide:
 
-- [Dev Containers in VSCode](#dev-containers-in-vscode)
-- [Local Development Guide](#local-development-guide)
-- [Google Colab Development Guide](#google-colab-development-guide)
+- [Dev Containers in VSCode](#dev-containers-in-vscode): Recommended if you would like to isolate everything in a Docker container. If you have Apple Silicon, local development would be a better option.
+- [Local Development Guide](#local-development-guide): Recommended if you have good command of your Python virtual environments, dependencies, etc..
+- [Google Colab Development Guide](#google-colab-development-guide): More explorative and would not provide full control on the development environment (temrinal, etc..)
+
+Other options: GitHub Codespaces or similar, please follow the local development guide.
 
 For contributing, please follow our [implementation guidelines](#implementation-guidelines)
 
@@ -219,3 +221,21 @@ make format
 2. Model Architecture:
    - Closely mimic the existing Keras implementations or the original implementations of papers
    - Maintain similar model inputs and outputs (datatype, shape, etc..)
+
+### Resources
+
+#### PyTorch
+- PyTorch Installation https://pytorch.org/get-started/locally/
+- PyTorch Documentation, please always ensure  you have the right version on the top left corner https://pytorch.org/docs/stable/index.html
+
+### Keras and TensorFlow
+- TensorFlow API Documentation 2.15 (Version 2.16 introduced some breaking changes with respect to Keras) https://www.tensorflow.org/versions/r2.15/api_docs/python/tf
+- TensorFlow Keras Guide https://www.tensorflow.org/guide/keras
+
+### HuggingFace Datasets
+
+- PROSPECT PTMs is available on HuggingFace for Retention time, Fragment ion intensity, and Charge state prediction https://huggingface.co/collections/Wilhelmlab/prospect-ptms-665db48431a7e844634660ba
+
+
+### Python Environments
+- If you like to use conda, try out miniforge https://github.com/conda-forge/miniforge
diff --git a/.gitignore b/.gitignore
index 053ba50e..f8d4a36a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -163,7 +163,7 @@ run_scripts/*.index
 run_scripts/*.data-*
 run_scripts/*.csv
 run_scripts/*.pkl
-
+run_scripts/output/
 
 # test assets (will be downloaded the first time tests are run and then ignore by git)
 assets/

From 2576044e6c4fffca11784d76c3188d8cf7047a36 Mon Sep 17 00:00:00 2001
From: omsh <omar.shouman@gmail.com>
Date: Sun, 2 Feb 2025 11:33:48 +0100
Subject: [PATCH 4/4] version 0.1.9

---
 src/dlomix/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/dlomix/__init__.py b/src/dlomix/__init__.py
index f39339e0..4fc85197 100644
--- a/src/dlomix/__init__.py
+++ b/src/dlomix/__init__.py
@@ -1,4 +1,4 @@
-__version__ = "0.1.8"
+__version__ = "0.1.9"
 
 META_DATA = {
     "author": "Wilhelm Lab",