deep_learning_transformer_split.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:32:33.358867Z",
     "iopub.status.busy": "2024-08-30T11:32:33.358795Z",
     "iopub.status.idle": "2024-08-30T11:32:33.361521Z",
     "shell.execute_reply": "2024-08-30T11:32:33.361295Z"
    },
    "tags": [
     "input"
    ]
   },
   "outputs": [],
   "source": [
    "splitPartCount = 5\n",
    "splitSeed = 27\n",
    "hoursPerWindow = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preproccess"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:32:33.362753Z",
     "iopub.status.busy": "2024-08-30T11:32:33.362686Z",
     "iopub.status.idle": "2024-08-30T11:32:34.517065Z",
     "shell.execute_reply": "2024-08-30T11:32:34.516674Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1213"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from utils.class_patient import Patients\n",
    "\n",
    "\n",
    "patients = Patients.loadPatients()\n",
    "len(patients)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## remove missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:32:34.530459Z",
     "iopub.status.busy": "2024-08-30T11:32:34.530293Z",
     "iopub.status.idle": "2024-08-30T11:32:34.540161Z",
     "shell.execute_reply": "2024-08-30T11:32:34.539980Z"
    }
   },
   "outputs": [],
   "source": [
    "# fill measures whose null represent false value\n",
    "\n",
    "from constants import NULLABLE_MEASURES\n",
    "\n",
    "\n",
    "nullableMeasures = NULLABLE_MEASURES\n",
    "\n",
    "for measureName in nullableMeasures:\n",
    "    patients.fillMissingMeasureValue(measureName, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:32:34.541152Z",
     "iopub.status.busy": "2024-08-30T11:32:34.541084Z",
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     "shell.execute_reply": "2024-08-30T11:32:34.553742Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pco2 917\n",
      "ph 954\n",
      "po2 917\n",
      "albumin 406\n",
      "hba1c 326\n",
      "lymphocyte 446\n",
      "height 415\n",
      "urine-ketone 294\n",
      "crp 19\n"
     ]
    }
   ],
   "source": [
    "# remove measures with less than 80% of data\n",
    "\n",
    "measures = patients.getMeasures()\n",
    "\n",
    "for measure, count in measures.items():\n",
    "    if count < len(patients) * 80 / 100:\n",
    "        patients.removeMeasures([measure])\n",
    "        print(measure, count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:32:34.555052Z",
     "iopub.status.busy": "2024-08-30T11:32:34.554966Z",
     "iopub.status.idle": "2024-08-30T11:32:34.559395Z",
     "shell.execute_reply": "2024-08-30T11:32:34.559246Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1206"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove patients with less than 80% of data\n",
    "\n",
    "patients.removePatientByMissingFeatures()\n",
    "len(patients)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:32:34.560073Z",
     "iopub.status.busy": "2024-08-30T11:32:34.560012Z",
     "iopub.status.idle": "2024-08-30T11:32:34.562504Z",
     "shell.execute_reply": "2024-08-30T11:32:34.562363Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # remove patients with positive tag in first 12 hours\n",
    "\n",
    "from pandas import Timedelta\n",
    "\n",
    "\n",
    "patients.removePatientAkiEarly(Timedelta(hours=12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:32:34.563145Z",
     "iopub.status.busy": "2024-08-30T11:32:34.563084Z",
     "iopub.status.idle": "2024-08-30T11:32:34.564849Z",
     "shell.execute_reply": "2024-08-30T11:32:34.564686Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total  1124\n",
      "AKI  392\n",
      "Ratio  0.3487544483985765\n"
     ]
    }
   ],
   "source": [
    "print(\"Total \", len(patients))\n",
    "print(\"AKI \", sum([1 for p in patients if p.akdPositive]))\n",
    "print(\"Ratio \", sum([1 for p in patients if p.akdPositive]) / len(patients))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## split patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:32:34.565493Z",
     "iopub.status.busy": "2024-08-30T11:32:34.565429Z",
     "iopub.status.idle": "2024-08-30T11:32:34.567292Z",
     "shell.execute_reply": "2024-08-30T11:32:34.567145Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "225"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splitedPatients = patients.split(splitPartCount, splitSeed)\n",
    "\n",
    "len(splitedPatients[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:32:34.567929Z",
     "iopub.status.busy": "2024-08-30T11:32:34.567866Z",
     "iopub.status.idle": "2024-08-30T11:32:34.570135Z",
     "shell.execute_reply": "2024-08-30T11:32:34.569980Z"
    }
   },
   "outputs": [],
   "source": [
    "splitedPatients = patients.split(splitPartCount, splitSeed)\n",
    "\n",
    "\n",
    "def trainTest():\n",
    "    for i in range(splitedPatients.__len__()):\n",
    "        testPatients = splitedPatients[i]\n",
    "\n",
    "        trainPatientsList = splitedPatients[:i] + splitedPatients[i + 1 :]\n",
    "        trainPatients = Patients(patients=[])\n",
    "        for trainPatientsElem in trainPatientsList:\n",
    "            trainPatients += trainPatientsElem\n",
    "\n",
    "        yield trainPatients, testPatients\n",
    "\n",
    "\n",
    "def trainValTest():\n",
    "    for i in range(splitedPatients.__len__()):\n",
    "        testPatients = splitedPatients[i]\n",
    "\n",
    "        trainPatientsList = splitedPatients[:i] + splitedPatients[i + 1 :]\n",
    "        trainPatients = Patients(patients=[])\n",
    "        for trainPatientsElem in trainPatientsList:\n",
    "            trainPatients += trainPatientsElem\n",
    "\n",
    "        *trainPatients, valPatients = trainPatients.split(5, 27)\n",
    "        tmpPatients = Patients(patients=[])\n",
    "        for trainPatientsElem in trainPatients:\n",
    "            tmpPatients += trainPatientsElem\n",
    "        trainPatients = tmpPatients\n",
    "\n",
    "        yield trainPatients, valPatients, testPatients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:32:34.570769Z",
     "iopub.status.busy": "2024-08-30T11:32:34.570699Z",
     "iopub.status.idle": "2024-08-30T11:32:34.572209Z",
     "shell.execute_reply": "2024-08-30T11:32:34.572058Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "899 225\n",
      "899 225\n",
      "899 225\n",
      "899 225\n",
      "900 224\n"
     ]
    }
   ],
   "source": [
    "for trainPatients, testPatients in trainTest():\n",
    "    print(len(trainPatients.patientList), len(testPatients.patientList))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seperate static and dynamic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:32:34.572959Z",
     "iopub.status.busy": "2024-08-30T11:32:34.572893Z",
     "iopub.status.idle": "2024-08-30T11:32:36.253985Z",
     "shell.execute_reply": "2024-08-30T11:32:36.253805Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-16 06:33:06.557033: I tensorflow/core/util/port.cc:153] 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",
      "2024-09-16 06:33:06.563190: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-09-16 06:33:06.569525: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-09-16 06:33:06.571790: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-09-16 06:33:06.577591: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1726443187.301441 3082414 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1726443187.305031 3082414 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1726443187.305125 3082414 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1726443187.305901 3082414 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1726443187.305974 3082414 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1726443187.306016 3082414 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1726443187.341363 3082414 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1726443187.341451 3082414 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1726443187.341505 3082414 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-09-16 06:33:07.341547: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1157 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4060 Ti, pci bus id: 0000:01:00.0, compute capability: 8.9\n",
      "WARNING:absl:The input size (31) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (31) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (31) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (31) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (31) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (31) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"split_transfomer\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"split_transfomer\"\u001b[0m\n"
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     },
     "metadata": {},
     "output_type": "display_data"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                </span>┃<span style=\"font-weight: bold\"> Output Shape          </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Trai… </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━┩\n",
       "│ global_average_pooling1d    │ (<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>)               │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │   <span style=\"font-weight: bold\">-</span>   │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling1D</span>)    │                       │            │       │\n",
       "├─────────────────────────────┼───────────────────────┼────────────┼───────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)               │ ?                     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │   <span style=\"font-weight: bold\">-</span>   │\n",
       "│                             │                       │  (unbuilt) │       │\n",
       "├─────────────────────────────┼───────────────────────┼────────────┼───────┤\n",
       "│ concatenate (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)   │ ?                     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │   <span style=\"font-weight: bold\">-</span>   │\n",
       "│                             │                       │  (unbuilt) │       │\n",
       "├─────────────────────────────┼───────────────────────┼────────────┼───────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ ?                     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │   <span style=\"font-weight: bold\">-</span>   │\n",
       "│                             │                       │  (unbuilt) │       │\n",
       "├─────────────────────────────┼───────────────────────┼────────────┼───────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)             │ ?                     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │   <span style=\"font-weight: bold\">-</span>   │\n",
       "│                             │                       │  (unbuilt) │       │\n",
       "├─────────────────────────────┼───────────────────────┼────────────┼───────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)             │ (<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                │         <span style=\"color: #00af00; text-decoration-color: #00af00\">99</span> │   <span style=\"color: #00af00; text-decoration-color: #00af00; font-weight: bold\">Y</span>   │\n",
       "└─────────────────────────────┴───────────────────────┴────────────┴───────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape         \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mTrai…\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━┩\n",
       "│ global_average_pooling1d    │ (\u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m31\u001b[0m)               │          \u001b[38;5;34m0\u001b[0m │   \u001b[1m-\u001b[0m   │\n",
       "│ (\u001b[38;5;33mGlobalAveragePooling1D\u001b[0m)    │                       │            │       │\n",
       "├─────────────────────────────┼───────────────────────┼────────────┼───────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)               │ ?                     │          \u001b[38;5;34m0\u001b[0m │   \u001b[1m-\u001b[0m   │\n",
       "│                             │                       │  (unbuilt) │       │\n",
       "├─────────────────────────────┼───────────────────────┼────────────┼───────┤\n",
       "│ concatenate (\u001b[38;5;33mConcatenate\u001b[0m)   │ ?                     │          \u001b[38;5;34m0\u001b[0m │   \u001b[1m-\u001b[0m   │\n",
       "│                             │                       │  (unbuilt) │       │\n",
       "├─────────────────────────────┼───────────────────────┼────────────┼───────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)           │ ?                     │          \u001b[38;5;34m0\u001b[0m │   \u001b[1m-\u001b[0m   │\n",
       "│                             │                       │  (unbuilt) │       │\n",
       "├─────────────────────────────┼───────────────────────┼────────────┼───────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)             │ ?                     │          \u001b[38;5;34m0\u001b[0m │   \u001b[1m-\u001b[0m   │\n",
       "│                             │                       │  (unbuilt) │       │\n",
       "├─────────────────────────────┼───────────────────────┼────────────┼───────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)             │ (\u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m)                │         \u001b[38;5;34m99\u001b[0m │   \u001b[1;38;5;34mY\u001b[0m   │\n",
       "└─────────────────────────────┴───────────────────────┴────────────┴───────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">99</span> (396.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m99\u001b[0m (396.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">99</span> (396.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m99\u001b[0m (396.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "import keras\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import (\n",
    "    LSTM,\n",
    "    Dense,\n",
    "    Dropout,\n",
    "    Input,\n",
    "    Concatenate,\n",
    "    LayerNormalization,\n",
    "    Conv1D,\n",
    "    GlobalAveragePooling1D,\n",
    "    BatchNormalization,\n",
    ")\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.regularizers import l2\n",
    "import tensorflow as tf\n",
    "from official.nlp.modeling.models import TransformerEncoder\n",
    "\n",
    "\n",
    "@keras.saving.register_keras_serializable()\n",
    "class SplitTransfomer(Model):\n",
    "\n",
    "    def __init__(self, timeSteps, timeFeatures, staticFeatures, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "\n",
    "        self.timeSteps = timeSteps\n",
    "        self.timeFeatures = timeFeatures\n",
    "        self.staticFeatures = staticFeatures\n",
    "\n",
    "        # time series layers\n",
    "        self.transformerEncoder = TransformerEncoder(\n",
    "            dropout_rate=0.1,\n",
    "            attention_dropout_rate=0.1,\n",
    "            # max_sequence_length=max_sequence_length,\n",
    "            # vocab_size=vocab_size,\n",
    "        )\n",
    "        self.pooling = GlobalAveragePooling1D()\n",
    "\n",
    "        # static layers\n",
    "        self.stDense = Dense(staticFeatures, activation=\"relu\")\n",
    "\n",
    "        # combine layers\n",
    "        self.concat = Concatenate(axis=1)\n",
    "        self.drop = Dropout(0.2)\n",
    "        self.dense1 = Dense(16, activation=\"relu\")\n",
    "        self.denseOut = Dense(1, activation=\"sigmoid\")\n",
    "\n",
    "        pass\n",
    "\n",
    "    def call(self, input, training=False):\n",
    "        seriesInputLayer, staticInputLayer = input\n",
    "\n",
    "        # series\n",
    "        seriesLayer = seriesInputLayer\n",
    "        seriesLayer = self.transformerEncoder(seriesLayer, training=training)\n",
    "        seriesLayer = self.pooling(seriesLayer)\n",
    "\n",
    "        # static\n",
    "        staticLayer = staticInputLayer\n",
    "\n",
    "        combined = tf.concat([seriesLayer, staticLayer], axis=-1)\n",
    "        combined = self.denseOut(combined)\n",
    "\n",
    "        return combined\n",
    "\n",
    "    def build(self, input_shape):\n",
    "        seriesInputLayer, staticInputLayer = input_shape\n",
    "\n",
    "        self.transformerEncoder.build(seriesInputLayer)\n",
    "\n",
    "        super().build(input_shape)\n",
    "\n",
    "    def get_config(self):\n",
    "        config = super().get_config()\n",
    "        config.update(\n",
    "            {\n",
    "                \"timeSteps\": self.timeSteps,\n",
    "                \"timeFeatures\": self.timeFeatures,\n",
    "                \"staticFeatures\": self.staticFeatures,\n",
    "            }\n",
    "        )\n",
    "        return config\n",
    "\n",
    "    @classmethod\n",
    "    def from_config(cls, config):\n",
    "        return cls(**config)\n",
    "\n",
    "\n",
    "def createModel2(timeSteps, timeFeatures, staticFeatures, model=None):\n",
    "    if model is None:\n",
    "        model = SplitTransfomer(timeSteps, timeFeatures, staticFeatures)\n",
    "    model.compile(\n",
    "        optimizer=Adam(learning_rate=0.0001),\n",
    "        loss=\"binary_crossentropy\",\n",
    "        metrics=[\"AUC\", \"accuracy\", \"precision\", \"recall\"],\n",
    "    )\n",
    "\n",
    "    return model\n",
    "\n",
    "\n",
    "x = createModel2(24, 10, 10)\n",
    "sample_series_input = tf.random.normal([1, 12, 31])  # (batch_size, timeSteps, timeFeatures)\n",
    "sample_static_input = tf.random.normal([1, 67])  \n",
    "_ = x([sample_series_input, sample_static_input])\n",
    "x.summary(expand_nested=True, show_trainable=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T11:32:36.254886Z",
     "iopub.status.busy": "2024-08-30T11:32:36.254817Z",
     "iopub.status.idle": "2024-08-30T12:04:20.262580Z",
     "shell.execute_reply": "2024-08-30T12:04:20.262377Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1726443190.071385 3082543 service.cc:146] XLA service 0x7e0964018960 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "I0000 00:00:1726443190.071406 3082543 service.cc:154]   StreamExecutor device (0): NVIDIA GeForce RTX 4060 Ti, Compute Capability 8.9\n",
      "2024-09-16 06:33:10.115668: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2024-09-16 06:33:10.140407: W tensorflow/compiler/tf2xla/kernels/random_ops.cc:61] Warning: Using tf.random.uniform with XLA compilation will ignore seeds; consider using tf.random.stateless_uniform instead if reproducible behavior is desired. split_transfomer_1_1/transformer_encoder_1/layer_0/self_attention/dropout_3/dropout/random_uniform/RandomUniform\n",
      "2024-09-16 06:33:10.519930: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 8907\n",
      "2024-09-16 06:33:11.018531: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 20 bytes spill stores, 20 bytes spill loads\n",
      "\n",
      "2024-09-16 06:33:11.037842: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 236 bytes spill stores, 236 bytes spill loads\n",
      "\n",
      "I0000 00:00:1726443192.434388 3082543 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n",
      "2024-09-16 06:33:13.336586: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 140 bytes spill stores, 140 bytes spill loads\n",
      "\n",
      "2024-09-16 06:33:13.375524: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 12 bytes spill stores, 12 bytes spill loads\n",
      "\n",
      "2024-09-16 06:33:13.398777: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 260 bytes spill stores, 260 bytes spill loads\n",
      "\n",
      "2024-09-16 06:33:16.352107: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 140 bytes spill stores, 140 bytes spill loads\n",
      "\n",
      "2024-09-16 06:33:16.443067: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 260 bytes spill stores, 260 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/8\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 545ms/step"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-16 06:33:41.424826: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 416 bytes spill stores, 408 bytes spill loads\n",
      "\n",
      "2024-09-16 06:33:41.487537: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 12 bytes spill stores, 12 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 130ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 70ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 17 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7e0acb0822a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 17 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7e0acb0822a0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 71ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 17 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7e0aca5623e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 17 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7e0aca5623e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 75ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 74ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 80ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 72ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
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     ]
    },
    {
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      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 72ms/step\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
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     ]
    },
    {
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     ]
    },
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     ]
    },
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     ]
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     ]
    },
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     ]
    },
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    },
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      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 74ms/step\n"
     ]
    },
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     "output_type": "stream",
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     ]
    },
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     ]
    },
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     "output_type": "stream",
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
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      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 74ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
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      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 75ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "2024-09-16 06:51:58.302762: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 20 bytes spill stores, 20 bytes spill loads\n",
      "\n",
      "2024-09-16 06:51:58.356913: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_7', 236 bytes spill stores, 236 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
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      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n",
      "WARNING:absl:The input size (19) is not a multiple of the number of attention heads (8)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step  \n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score,\n",
    "    recall_score,\n",
    "    precision_score,\n",
    "    confusion_matrix,\n",
    "    roc_auc_score,\n",
    "    roc_curve,\n",
    "    precision_recall_curve,\n",
    "    auc,\n",
    ")\n",
    "from utils.prepare_data import DeepLearningDataPreparer, trainValTestPatients\n",
    "from utils.class_voter import combineProbas\n",
    "from constants import TEMP_PATH\n",
    "import numpy as np\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "\n",
    "accuracy_score_list = []\n",
    "precision_score_list = []\n",
    "recall_score_list = []\n",
    "auc_score_list = []\n",
    "specificity_score_list = []\n",
    "sensitivity_score_list = []\n",
    "auc_pr_list = []\n",
    "\n",
    "train_loss_list = []\n",
    "val_loss_list = []\n",
    "\n",
    "for i, (trainValGenerator, testPatients) in enumerate(trainValTestPatients(patients, splitSeed)):\n",
    "    models = []\n",
    "    probas = []\n",
    "    for ii, (trainPatients, valPatients) in enumerate(trainValGenerator):\n",
    "        preparer = DeepLearningDataPreparer(\n",
    "            hoursPerWindows=hoursPerWindow,\n",
    "            fromHour=0,\n",
    "            toHour=12,\n",
    "        )\n",
    "\n",
    "        cacheFile = TEMP_PATH / f\"dl_train_data/{i}_{ii}.pkl\"\n",
    "        cacheFile.parent.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "        if cacheFile.exists():\n",
    "            (\n",
    "                (npTrainX, staticTrainX, trainY),\n",
    "                (npValX, staticValX, valY),\n",
    "                (npTestX, staticTestX, testY),\n",
    "            ) = pickle.loads(cacheFile.read_bytes())\n",
    "        else:\n",
    "            npTrainX, staticTrainX, trainY = preparer.fit_transform(trainPatients)\n",
    "            npValX, staticValX, valY = preparer.transform(valPatients)\n",
    "            npTestX, staticTestX, testY = preparer.transform(testPatients)\n",
    "            cacheFile.write_bytes(\n",
    "                pickle.dumps(\n",
    "                    (\n",
    "                        (npTrainX, staticTrainX, trainY),\n",
    "                        (npValX, staticValX, valY),\n",
    "                        (npTestX, staticTestX, testY),\n",
    "                    )\n",
    "                )\n",
    "            )\n",
    "\n",
    "        neg, pos = np.bincount(trainY)\n",
    "        weight0 = (1 / neg) * (len(trainY)) / 2.0\n",
    "        weight1 = (1 / pos) * (len(trainY)) / 2.0\n",
    "        weight = {0: weight0, 1: weight1}\n",
    "\n",
    "        early_stopping = EarlyStopping(\n",
    "            monitor=\"val_loss\", patience=250, restore_best_weights=True\n",
    "        )\n",
    "\n",
    "        model = createModel2(npTrainX.shape[1], npTrainX.shape[2], staticTrainX.shape[1])\n",
    "\n",
    "        history = model.fit(\n",
    "            [npTrainX, staticTrainX],\n",
    "            np.array(trainY),\n",
    "            epochs=5000,\n",
    "            batch_size=32,\n",
    "            validation_data=([npValX, staticValX], np.array(valY)),\n",
    "            class_weight=weight,\n",
    "            callbacks=[early_stopping],\n",
    "            verbose=0,\n",
    "        )\n",
    "        train_loss_list.append(history.history[\"loss\"])\n",
    "        val_loss_list.append(history.history[\"val_loss\"])\n",
    "        probas.append(model.predict([npTestX, staticTestX]))\n",
    "        # model.save(f\"result/transformer_split_model_{i}_{ii}.keras\")\n",
    "\n",
    "    # calculate final probas, auc, accuracy, precision, recal\n",
    "    finalYProbas = np.apply_along_axis(combineProbas, 0, np.array(probas))\n",
    "    finalYPred = np.where(finalYProbas > 0.5, 1, 0)\n",
    "    tn, fp, fn, tp = confusion_matrix(testY, finalYPred).ravel()\n",
    "    precision_vals, recall_vals, _ = precision_recall_curve(testY, finalYProbas)\n",
    "\n",
    "    accuracy_score_list.append(accuracy_score(testY, finalYPred))\n",
    "    specificity_score_list.append(tn / (tn + fp))\n",
    "    precision_score_list.append(precision_score(testY, finalYPred))\n",
    "    recall_score_list.append(recall_score(testY, finalYPred))\n",
    "    auc_score_list.append(roc_auc_score(testY, finalYProbas))\n",
    "    auc_pr_list.append(auc(recall_vals, precision_vals))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.8126984731904349 +- 0.011667640492285514 [0.8299319727891156, 0.8075178789464503, 0.8155886942951275, 0.8161088954395699, 0.7943449244819109]\n",
      "Accuracy: 0.7526547619047619 +- 0.02343757220531417 [0.7955555555555556, 0.7333333333333333, 0.76, 0.7377777777777778, 0.7366071428571429]\n",
      "Specificity: 0.7335942596216569 +- 0.03871963219125499 [0.7755102040816326, 0.7006802721088435, 0.7808219178082192, 0.726027397260274, 0.684931506849315]\n",
      "Precision: 0.6148411980575281 +- 0.03153716741333896 [0.6632653061224489, 0.5849056603773585, 0.6404494382022472, 0.6, 0.5855855855855856]\n",
      "Recall: 0.788510223953262 +- 0.04333238509056397 [0.8333333333333334, 0.7948717948717948, 0.7215189873417721, 0.759493670886076, 0.8333333333333334]\n",
      "AUC PR: 0.6917546739592506 +- 0.02570981691606199 [0.6878233740323112, 0.6778479267393064, 0.717475982620045, 0.7224790356636395, 0.653147050740951]\n"
     ]
    }
   ],
   "source": [
    "print(f\"AUC: {np.mean(auc_score_list)} +- {np.std(auc_score_list)}\", auc_score_list)\n",
    "print(f\"Accuracy: {np.mean(accuracy_score_list)} +- {np.std(accuracy_score_list)}\", accuracy_score_list)\n",
    "print(f\"Specificity: {np.mean(specificity_score_list)} +- {np.std(specificity_score_list)}\", specificity_score_list)\n",
    "print(f\"Precision: {np.mean(precision_score_list)} +- {np.std(precision_score_list)}\", precision_score_list)\n",
    "print(f\"Recall: {np.mean(recall_score_list)} +- {np.std(recall_score_list)}\", recall_score_list)\n",
    "print(f\"AUC PR: {np.mean(auc_pr_list)} +- {np.std(auc_pr_list)}\", auc_pr_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-30T12:04:20.266522Z",
     "iopub.status.busy": "2024-08-30T12:04:20.266459Z",
     "iopub.status.idle": "2024-08-30T12:04:20.869246Z",
     "shell.execute_reply": "2024-08-30T12:04:20.869035Z"
    }
   },
   "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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9f/vb33B0dKRXr140b96crKws3n//fZ588kksLCy4fPkyLVu2ZPjw4XTs2BFzc3N27tzJkSNHdEYh1MXSpUsJCgqiR48ehIWFaVuwWVlZsWDBgr/ylQDwz3/+s1JSDeWL03Xo0IG//e1vGBkZERISwksvvUR+fj7r1q3DwcGB8+fPa/UtLS157733eOGFF/Dz89P2av/uu++4fv06SUlJfznW2ggJCaFfv37Mnj2bU6dO0bFjR77++ms+//xzpk6dWmkNhlvNmDGD9evXM3DgQF599VVtC7aKUR9CCCGamEZbV14IIcRD5YMPPlABtVu3bpXOFRYWqq+99prq5OSkmpqaqr169VIPHjxYaXuz2mzBpqqqeuPGDTUiIkK1s7NTmzVrpoaEhKhnzpyptAXbH3/8oY4fP1595JFHVHNzczUwMFD9+eefK21hpaqqum7dOrVVq1aqvr6+znZst8eoqqr6+++/a/c1MjJS27dvrxPzrc+ydOnSSt/H7XFW5U5bjVVsu3bixAl1+PDhqrW1tWpiYqJ269ZN3bp1q8691q5dq/bp00e1s7NTjY2NVU9PTzUqKkq9cuWKqqrlW4lFRUWpHTt2VC0sLNRmzZqpHTt2VNesWVNjjLfGWdVWcTt37lR79eqlmpqaqpaWlmpISIh6/PhxnToVv9///Oc/d2yrNt/Lrd/rli1b1A4dOqgmJiaqu7u7+u6776rx8fFVbku2ZcsWtWfPnlqs3bp1U//xj39o5wMCAqrcxm7cuHGqm5vbHeN2c3NTn3zyyRrrXLt2TZ02bZrq7OysGhoaql5eXurSpUt1toKruNftf3+///57NSAgQDUxMVFbtGihvvnmm2pcXJxswSaEEE2QoqqymokQQgghhBBCCNEUyJx0IYQQQgghhBCiiZAkXQghhBBCCCGEaCIkSRdCCCGEEEIIIZoISdKFEEIIIYQQQogmQpJ0IYQQQgghhBCiiZAkXQghhBBCCCGEaCIMGjuAhlZWVsa5c+ewsLBAUZTGDkcIIYQQQgghxANOVVWuXbuGs7Mzeno195U/dEn6uXPncHFxaewwhBBCCCGEEEI8ZM6cOUPLli1rrPPQJekWFhZA+ZdjaWnZyNEIIYQQQgghhHjQXb16FRcXFy0frclDl6RXDHG3tLSUJF0IIYQQQgghRIOpzZRrWThOCCGEEEIIIYRoIiRJF0IIIYQQQgghmghJ0oUQQgghhBBCiCbioZuTLoQQQgghhKhZWVkZxcXFjR2GEPcVIyOjO26vVhuSpAshhBBCCCE0xcXFnDx5krKyssYORYj7ip6eHh4eHhgZGf2l+0iSLoQQQgghhABAVVXOnz+Pvr4+Li4u9dIrKMTDoKysjHPnznH+/HlcXV1rtYp7dSRJF0IIIYQQQgBQWlrK9evXcXZ2xszMrLHDEeK+Ym9vz7lz5ygtLcXQ0PCu7yOvxoQQQgghhBAA3Lx5E+AvD9cV4mFU8e+m4t/R3ZIkXQghhBBCCKHjrwzVFeJhVV//biRJF0IIIYQQQgghmghJ0oUQQgghhBAPtb59+zJ16tQa67i7u7Ny5coGiacuEhMTsba21j4vWLAAX1/fGq8JDQ1l6NChf7nt+rqP0CVJuhBCCCGEEOK+FhoaiqIolY7c3NwGi+Gnn35i2LBhuLu7oyjKHRP6tLQ09PX1OXv2bJXnvby8iIyMrHMc06dPZ9euXXW+rianTp1CURQyMzN1yletWkViYmK9tlUVRVHYvHnzPW+nqZAkXQghhBBCCHHfGzhwIOfPn9c5PDw8Gqz969ev06pVKxYvXoyjo+Md6w8ePBg7OzuSkpIqndu/fz+5ubmEhYXVOQ5zc3Ps7OzqfN3dsLKy0unFF/VDknQhhBBCCCHEfc/Y2BhHR0edQ19fH4B9+/bRrVs3jI2NcXJyIjo6mtLS0mrvdeHCBUJCQjA1NcXDw4Pk5OQ7tu/n58fSpUsZNWoUxsbGd6xvaGjImDFjquyJjo+Pp3v37rRr144VK1bQvn17mjVrhouLC1OmTCE/P7/a+94+3P3mzZtERkZibW2NnZ0dM2bMQFVVnWu++uor/P39tTqDBg3ixIkT2vmKlx2dOnVCURT69u0LVB7uXlRUREREBA4ODpiYmODv78+RI0e083v37kVRFHbt2kXXrl0xMzOjZ8+eZGdn3/H7qk5ZWRkLFy6kZcuWGBsb4+vry1dffaWdLy4uJjw8HCcnJ0xMTHBzc2PRokUAqKrKggULcHV1xdjYGGdnZyIiIu46lvoiSboQQgghhBCiSqqqcqOgsFGO2xPJu3X27FmCg4Px8/Pju+++IzY2lri4ON56661qrwkNDeXMmTPs2bOH1NRU1qxZw4ULF+olnluFhYWRk5PD/v37tbL8/HxSU1O1XnQ9PT1iYmL46aefSEpKYvfu3cyYMaPWbSxfvpzExETi4+P55ptvuHTpEp999plOnYKCAiIjIzl69Ci7du1CT0+Pp556irKyMgAOHz4MwM6dOzl//jybNm2qsq0ZM2aQlpZGUlIS3377La1btyYwMJBLly7p1Js9ezbLly/n6NGjGBgYMGHChFo/z+1WrVrF8uXLWbZsGd9//z2BgYEMHjyYnJwcAGJiYtiyZQsbNmwgOzub5ORk3N3dgfIpB++99x5r164lJyeHzZs30759+7uOpb4YNGbj+/fvZ+nSpRw7dozz58/z2Wef1bjwwKZNm4iNjSUzM5OioiLatWvHggULCAwMbLighRBCCCGEeEgUXi9isMWYRml7y7X1mDYzqXX9rVu3Ym5urn0OCgpi48aNrFmzBhcXF95//30URaFNmzacO3eOmTNnMm/ePPT0dPstf/nlF7Zt28bhw4fx8/MDIC4uDh8fn/p5sFu0bduWxx9/nPj4ePr06QPAhg0bUFWVUaNGAegsaOfu7s5bb73FpEmTWLNmTa3aWLlyJbNmzeLpp58G4MMPP2T79u06dYYNG6bzOT4+Hnt7e44fP85jjz2Gvb09AHZ2dtUO5S8oKCA2NpbExESCgoIAWLduHTt27CAuLo6oqCit7ttvv01AQAAA0dHRPPnkkxQWFmJiUvvfd4Vly5Yxc+ZM7ft699132bNnDytXruSDDz4gLy8PLy8v/P39URQFNzc37dq8vDwcHR0ZMGAAhoaGuLq60q1btzrHUN8atSe9oKCAjh078sEHH9Sq/v79+3niiSdIT0/n2LFj9OvXj5CQEP71r3/d40iFEEIIIYQQTVm/fv3IzMzUjpiYGACysrLo0aOHzh7WvXr1Ij8/n99++63SfbKysjAwMKBLly5aWZs2be7Z3OsJEyaQmprKtWvXgPIEecSIEVhYWADlvdf9+/enRYsWWFhYMGbMGC5evMj169fveO8rV65w/vx5unfvrpUZGBjQtWtXnXo5OTk8++yztGrVCktLS62nOS8vr9bPceLECUpKSujVq5dWZmhoSLdu3cjKytKp26FDB+1nJycngLsaqXD16lXOnTun0yaU/34r2gwNDSUzMxNvb28iIiL4+uuvtXojRozgxo0btGrViokTJ/LZZ5/VOA2ioTRqT3pQUJD2lqU2bl8h8Z133uHzzz/niy++oFOnTvUcXeNSi/ai5q8Fww7oWc5q7HCEEEIIIcRDyMTMmC3X1jda23XRrFkzWrdufY+iuXdGjRrFtGnT2LBhA3369CEjI0ObM33q1CkGDRrE5MmTefvtt7G1teWbb74hLCyM4uJizMzM6iWGkJAQ3NzcWLduHc7OzpSVlfHYY49RXFxcL/e/naGhofZzxcuTiqH19a1z586cPHmSbdu2sXPnTp555hkGDBhAamoqLi4uZGdns3PnTnbs2MGUKVNYunQp+/bt04mxoTVqkv5XlZWVce3aNWxtbautU1RURFFRkfb56tWrDRHaX1d2BUqOgVL3IR9CCCGEEELUB0VR6jTkvCny8fEhLS0NVVW1hDAjIwMLCwtatmxZqX6bNm0oLS3l2LFj2nD37OxsLl++fE/is7CwYMSIEcTHx3PixAkeffRRevfuDcCxY8coKytj+fLl2rD8DRs21PreVlZWODk5cejQIW04fcWzde7cGYCLFy+SnZ3NunXrtHa/+eYbnfsYGRkB5YvQVcfT0xMjIyMyMjK0IeUlJSUcOXLkjnvQ3y1LS0ucnZ3JyMjQhs9D+e/31mHrlpaWjBw5kpEjRzJ8+HAGDhzIpUuXsLW1xdTUlJCQEEJCQnj55Zdp06YNP/zwg/b9NIb7OklftmwZ+fn5PPPMM9XWWbRoEW+88UYDRlVPKpJz9UbjxiGEEEIIIcR9bMqUKaxcuZJXXnmF8PBwsrOzmT9/PpGRkZXmowN4e3szcOBAXnrpJWJjYzEwMGDq1KmYmprW2E5xcTHHjx/Xfj579iyZmZmYm5vfsYc/LCyM3r17k5WVxcyZM7Xy1q1bU1JSwurVqwkJCSEjI4MPP/ywTs//6quvsnjxYry8vGjTpg0rVqzQeeFgY2ODnZ0dH330EU5OTuTl5REdHa1zDwcHB0xNTfnqq69o2bIlJiYmWFlZ6dRp1qwZkydPJioqCltbW1xdXVmyZAnXr1+/q63kbnfy5MlK+7R7eXkRFRXF/Pnz8fT0xNfXl4SEBDIzM7UV+VesWIGTkxOdOnVCT0+PjRs34ujoiLW1NYmJidy8eZPu3btjZmbGxx9/jKmpqc689cZw367u/sknn/DGG2+wYcMGHBwcqq03a9Ysrly5oh1nzpxpwCj/Ai1JL6q5nhBCCCGEEKJaLVq0ID09ncOHD9OxY0cmTZpEWFgYc+bMqfaahIQEnJ2dCQgI4Omnn+bFF1+sMecAOHfuHJ06daJTp06cP3+eZcuW0alTJ1544YU7xujv74+3tzdXr15l7NixWnnHjh1ZsWIF7777Lo899hjJycnaUPjaeu211xgzZgzjxo2jR48eWFhY8NRTT2nn9fT0SElJ4dixYzz22GNMmzaNpUuX6tzDwMCAmJgY1q5di7OzM0OGDKmyrcWLFzNs2DDGjBlD586dyc3NZfv27djY2NQp5qpERkZq32/F8a9//YuIiAgiIyN57bXXaN++PV999RVbtmzBy8sLKB+psGTJErp27Yqfnx+nTp0iPT0dPT09rK2tWbduHb169aJDhw7s3LmTL774osH2ma+OotbX3gZ/kaIod1zdvUJKSgoTJkxg48aNPPnkk3Vq5+rVq1hZWXHlyhUsLS3vMtp7Ty06hPrHGND3RM9+W2OHI4QQQgghHgKFhYWcPHkSDw+Pu1ppW4iHWU3/fuqSh953Pen/+Mc/GD9+PP/4xz/qnKDfV7Se9MLGjUMIIYQQQgghRINp1Dnp+fn55Obmap8r5hlUzGGYNWsWZ8+e5e9//ztQPsR93LhxrFq1iu7du/Pvf/8bAFNT00pzIu572oJxkqQLIYQQQgghxMOiUXvSjx49qs0ngP/NM5g3bx4A58+f19mb76OPPqK0tJSXX34ZJycn7Xj11VcbJf57SvlzYQrpSRdCCCGEEEKIh0aj9qT37duXmqbEJyYm6nzeu3fvvQ2oKVH+3BdSLdTZLkIIIYQQQgghxIPrvpuT/tCo6EnnJlDSmJEIIYQQQgghhGggkqQ3VcotqwHKNmxCCCGEEEII8VCQJL3JMgT+HOKu3mjUSIQQQgghhBBCNAxJ0psoRVFk8TghhBBCCCGEeMhIkt6k/W/xOCGEEEIIIYQQDz5J0psybfE4SdKFEEIIIYS4V/r27cvUqVNrrOPu7s7KlSsbJJ66WLBgAb6+vtrn0NBQhg4dWuM1tXne2qiv+whdkqQ3ZYr0pAshhBBCCHEnoaGhKIpS6cjNzW2wGNatW0fv3r2xsbHBxsaGAQMGcPjw4WrrL1++HBsbGwoLK/+3/vXr17G0tCQmJqbOcaxatarSVtZ/1d69e1EUhcuXL+uUb9q0iTfffLNe27rdqVOnUBSFzMzMe9pOUyJJelMmc9KFEEIIIYSolYEDB3L+/Hmdw8PDo8Ha37t3L88++yx79uzh4MGDuLi48Le//Y2zZ89WWX/MmDEUFBSwadOmSudSU1MpLi7m+eefr3McVlZWWFtb1/m6u2Fra4uFhUWDtPUwkSS9KavYhk2SdCGEEEIIIWpkbGyMo6OjzqGvrw/Avn376NatG8bGxjg5OREdHU1paWm197pw4QIhISGYmpri4eFBcnLyHdtPTk5mypQp+Pr60qZNG/7f//t/lJWVsWvXrirrOzg4EBISQnx8fKVz8fHxDB06FFtbW2bOnMmjjz6KmZkZrVq1Yu7cuZSUlFQbx+3D3QsKChg7dizm5uY4OTmxfPnyStesX7+erl27YmFhgaOjI8899xwXLlwAynuy+/XrB4CNjQ2KohAaGgpUHu7+xx9/MHbsWGxsbDAzMyMoKIicnBztfGJiItbW1mzfvh0fHx/Mzc21lyt3q6ioiIiICBwcHDAxMcHf358jR47oxDR69Gjs7e0xNTXFy8uLhIQEAIqLiwkPD8fJyQkTExPc3NxYtGjRXcdSXwwaOwBRA0nShRBCCCFEI1JVlcLrRY3StomZcfmOR3/R2bNnCQ4OJjQ0lL///e/8/PPPTJw4ERMTExYsWFDlNaGhoZw7d449e/ZgaGhIRESElrTW1vXr1ykpKcHW1rbaOmFhYQwaNIjTp0/j5uYGwK+//sr+/fvZvn07ABYWFiQmJuLs7MwPP/zAxIkTsbCwYMaMGbWKIyoqin379vH555/j4ODA66+/zrfffqszj72kpIQ333wTb29vLly4QGRkJKGhoaSnp+Pi4kJaWhrDhg0jOzsbS0tLTE1Nq2wrNDSUnJwctmzZgqWlJTNnziQ4OJjjx49jaGiofS/Lli1j/fr16Onp8fzzzzN9+vRavQipyowZM0hLSyMpKQk3NzeWLFlCYGAgubm52NraMnfuXI4fP862bdt45JFHyM3N5caN8i2uY2Ji2LJlCxs2bMDV1ZUzZ85w5syZu4qjPkmS3pRJki6EEEIIIRpR4fUiBluMaZS2t1xbj2kzk1rX37p1K+bm5trnoKAgNm7cyJo1a3BxceH9999HURTatGnDuXPnmDlzJvPmzUNPT3dw8S+//MK2bds4fPgwfn5+AMTFxeHj41On+GfOnImzszMDBgyotk5gYCDOzs4kJCRoLwwSExNxcXGhf//+AMyZM0er7+7uzvTp00lJSalVkp6fn09cXBwff/yxdr+kpCRatmypU2/ChAnaz61atSImJgY/Pz/y8/MxNzfXXjQ4ODhUO5S+IjnPyMigZ8+eQPnoAhcXFzZv3syIESOA8hcCH374IZ6engCEh4ezcOHCOz5LVQoKCoiNjSUxMZGgoCCgfG2AHTt2EBcXR1RUFHl5eXTq1ImuXbsC5d9hhby8PLy8vPD390dRFO1FSWOT4e5NmSTpQgghhBBC1Eq/fv3IzMzUjopF17KysujRo4dOr3yvXr3Iz8/nt99+q3SfrKwsDAwM6NKli1bWpk2bOs3zXrx4MSkpKXz22WeYmFT/okFfX59x48aRmJiIqqqUlZWRlJTE+PHjtZcHn376Kb169cLR0RFzc3PmzJlDXl5ereI4ceIExcXFdO/eXSuztbXF29tbp96xY8cICQnB1dUVCwsLAgICAGrdDvzve7u1LTs7O7y9vcnKytLKzMzMtAQdwMnJqc6jFCqcOHGCkpISevXqpZUZGhrSrVs3rc3JkyeTkpKCr68vM2bM4MCBA1rd0NBQMjMz8fb2JiIigq+//vqu4qhv0pPelEmSLoQQQgghGpGJmTFbrq1vtLbrolmzZrRu3foeRVN7y5YtY/HixezcuZMOHTrcsf6ECRNYtGgRu3fvpqysjDNnzjB+/HgADh48yOjRo3njjTcIDAzEysqKlJSUKueV362CggICAwMJDAwkOTkZe3t78vLyCAwMpLi4uN7aqVAx7L2Coiioqlrv7VQICgri9OnTpKens2PHDvr378/LL7/MsmXL6Ny5MydPnmTbtm3s3LmTZ555hgEDBpCamnrP4qkNSdKbsj+TdFW9wV+fjSOEEEIIIUTdKIpSpyHnTZGPjw9paWmoqqr1pmdkZGBhYVFp2DeU95qXlpZy7Ngxbbh7dnZ2pe3HqrJkyRLefvtttm/frg2vvhNPT08CAgKIj49HVVUGDBigDbs+cOAAbm5uzJ49W6t/+vTpWt234t6GhoYcOnQIV1dXoHwhtV9++UXrLf/555+5ePEiixcvxsXFBYCjR4/q3MfIyAiAmzdvVtuWj48PpaWlHDp0SBvufvHiRbKzs2nbtm2tY64LT09PjIyMyMjI0L6zkpISjhw5orOgnb29PePGjWPcuHH07t2bqKgoli1bBoClpSUjR45k5MiRDB8+nIEDB3Lp0qUa1xK41yRJb9Iq/gexcRbrEEIIIYQQ4n43ZcoUVq5cySuvvEJ4eDjZ2dnMnz+fyMjISvPRAby9vRk4cCAvvfQSsbGxGBgYMHXq1GoXS6vw7rvvMm/ePD755BPc3d3597//DYC5ubnOXPmqhIWFMXHiRACdPc69vLzIy8sjJSUFPz8/vvzySz777LNaP7u5uTlhYWFERUVhZ2eHg4MDs2fP1nluV1dXjIyMWL16NZMmTeLHH3+stPe5m5sbiqKwdetWgoODMTU1rfRMXl5eDBkyhIkTJ7J27VosLCyIjo6mRYsWDBkypNYxVyc7O7tSWbt27Zg8eTJRUVHY2tri6urKkiVLuH79OmFhYQDMmzePLl260K5dO4qKiti6dau2vsCKFStwcnKiU6dO6OnpsXHjRhwdHRtsC7vqyJz0pkwb7n6jceMQQgghhBDiPtWiRQvS09M5fPgwHTt2ZNKkSYSFheksyHa7hIQEnJ2dCQgI4Omnn+bFF1/EwcGhxnZiY2MpLi5m+PDhODk5aUdFj21Nhg0bhrGxMWZmZjrbpw0ePJhp06YRHh6Or68vBw4cYO7cubV+doClS5fSu3dvQkJCGDBgAP7+/jrz7e3t7UlMTGTjxo20bduWxYsXV4q5RYsWvPHGG0RHR9O8eXPCw8OrbCshIYEuXbowaNAgevTogaqqpKenVxrifjdGjRpFp06ddI7ff/+dxYsXM2zYMMaMGUPnzp3Jzc1l+/bt2NjYAOWjAGbNmkWHDh3o06cP+vr6pKSkAOUr5y9ZsoSuXbvi5+fHqVOnSE9Pr/LlTUNS1Hs5AaAJunr1KlZWVly5cgVLS8vGDqdGav6HqPkrwHQ4elbvNHY4QgghhBDiAVdYWMjJkyfx8PCoccEzIURlNf37qUseKj3pTZn0pAshhBBCCCHEQ0WS9KZMVncXQgghhBBCiIeKJOlNmSTpQgghhBBCCPFQkSS9KVP+XEFSknQhhBBCCCGEeChIkt6UKcblf6qyBZsQQgghhBBCPAwkSW/SKnrSZeE4IYQQQgghhHgYSJLelFXMSUd60oUQQgghhBDiYSBJelMmW7AJIYQQQgghxENFkvSmTFZ3F0IIIYQQQoiHiiTpTZm2cJwk6UIIIYQQQtwrffv2ZerUqTXWcXd3Z+XKlQ0ST10sWLAAX19f7XNoaChDhw6t8ZraPG9t1Nd9hC5J0puyii3YuImqljRqKEIIIYQQQjRVoaGhKIpS6cjNzW2wGDZt2kTXrl2xtramWbNm+Pr6sn79+mrrL1++HBsbGwoLK3fIXb9+HUtLS2JiYuocx6pVq0hMTKzzdTXZu3cviqJw+fJlnfJNmzbx5ptv1mtbtzt16hSKopCZmXlP22lKJElvyrSF45DedCGEEEIIIWowcOBAzp8/r3N4eHg0WPu2trbMnj2bgwcP8v333zN+/HjGjx/P9u3bq6w/ZswYCgoK2LRpU6VzqampFBcX8/zzz9c5DisrK6ytret83d2wtbXFwsKiQdp6mEiS3qQZAUr5j5KkCyGEEEIIUS1jY2McHR11Dn19fQD27dtHt27dMDY2xsnJiejoaEpLS6u914ULFwgJCcHU1BQPDw+Sk5Pv2H7fvn156qmn8PHxwdPTk1dffZUOHTrwzTffVFnfwcGBkJAQ4uPjK52Lj49n6NCh2NraMnPmTB599FHMzMxo1aoVc+fOpaSk+lG2tw93LygoYOzYsZibm+Pk5MTy5csrXbN+/Xq6du2KhYUFjo6OPPfcc1y4cAEo78nu168fADY2NiiKQmhoqPbMtw53/+OPPxg7diw2NjaYmZkRFBRETk6Odj4xMRFra2u2b9+Oj48P5ubm2suVu1VUVERERAQODg6YmJjg7+/PkSNHdGIaPXo09vb2mJqa4uXlRUJCAgDFxcWEh4fj5OSEiYkJbm5uLFq06K5jqS8GjR2AqJ6iKKiKSfnq7rLCuxBCCCGEaGCqqlJ4vXG2AzYxM0ZRlL98n7NnzxIcHExoaCh///vf+fnnn5k4cSImJiYsWLCgymtCQ0M5d+4ce/bswdDQkIiICC1prQ1VVdm9ezfZ2dm8++671dYLCwtj0KBBnD59Gjc3NwB+/fVX9u/fr/XAW1hYkJiYiLOzMz/88AMTJ07EwsKCGTNm1CqWqKgo9u3bx+eff46DgwOvv/463377rc489pKSEt588028vb25cOECkZGRhIaGkp6ejouLC2lpaQwbNozs7GwsLS0xNTWtsq3Q0FBycnLYsmULlpaWzJw5k+DgYI4fP46hoSFQPpR/2bJlrF+/Hj09PZ5//nmmT59eqxchVZkxYwZpaWkkJSXh5ubGkiVLCAwMJDc3F1tbW+bOncvx48fZtm0bjzzyCLm5udy4UZ5bxcTEsGXLFjZs2ICrqytnzpzhzJkzdxVHfZIkvckzAW6AKnulCyGEEEKIhlV4vYjBFmMape0t19Zj2szkzhX/tHXrVszNzbXPQUFBbNy4kTVr1uDi4sL777+Poii0adOGc+fOMXPmTObNm4eenu7g4l9++YVt27Zx+PBh/Pz8AIiLi8PHx+eOMVy5coUWLVpQVFSEvr4+a9as4Yknnqi2fmBgIM7OziQkJGgvDBITE3FxcaF///4AzJkzR6vv7u7O9OnTSUlJqVWSnp+fT1xcHB9//LF2v6SkJFq2bKlTb8KECdrPrVq1IiYmBj8/P/Lz8zE3N8fW1hYo7/2vbih9RXKekZFBz549AUhOTsbFxYXNmzczYsQIoPyFwIcffoinpycA4eHhLFy48I7PUpWCggJiY2NJTEwkKCgIgHXr1rFjxw7i4uKIiooiLy+PTp060bVrV6D8O6yQl5eHl5cX/v7+KIqivShpbDLcvanT5qVLT7oQQgghhBDV6devH5mZmdpRsehaVlYWPXr00OmV79WrF/n5+fz222+V7pOVlYWBgQFdunTRytq0aVOred4WFhZkZmZy5MgR3n77bSIjI9m7d2+19fX19Rk3bhyJiYmoqkpZWRlJSUmMHz9ee3nw6aef0qtXLxwdHTE3N2fOnDnk5eXV6js5ceIExcXFdO/eXSuztbXF29tbp96xY8cICQnB1dUVCwsLAgICAGrdDvzve7u1LTs7O7y9vcnKytLKzMzMtAQdwMnJqU6jFG514sQJSkpK6NWrl1ZmaGhIt27dtDYnT55MSkoKvr6+zJgxgwMHDmh1Q0NDyczMxNvbm4iICL7++uu7iqO+SU96U6ftlS496UIIIYQQomGZmBmz5Vr1K5Tf67brolmzZrRu3foeRVM7enp6Wgy+vr5kZWWxaNEi+vbtW+01EyZMYNGiRezevZuysjLOnDnD+PHjATh48CCjR4/mjTfeIDAwECsrK1JSUqqcV363CgoKCAwMJDAwkOTkZOzt7cnLyyMwMJDi4uJ6a6dCxbD3CoqioKpqvbdTISgoiNOnT5Oens6OHTvo378/L7/8MsuWLaNz586cPHmSbdu2sXPnTp555hkGDBhAamrqPYunNiRJb+q0JF0WjhNCCCGEEA1LUZQ6DTlvinx8fEhLS0NVVa03PSMjAwsLi0rDvqG817y0tJRjx45pw92zs7MrbT9WG2VlZRQV1dzZ5unpSUBAAPHx8aiqyoABA7Rh1wcOHMDNzY3Zs2dr9U+fPl3r9j09PTE0NOTQoUO4uroC5Qup/fLLL1pv+c8//8zFixdZvHgxLi4uABw9elTnPkZGRgDcvHmz2rZ8fHwoLS3l0KFD2nD3ixcvkp2dTdu2bWsdc114enpiZGRERkaG9p2VlJRw5MgRnQXt7O3tGTduHOPGjaN3795ERUWxbNkyACwtLRk5ciQjR45k+PDhDBw4kEuXLmlD/BuDJOlNXcVe6bJwnBBCCCGEEHU2ZcoUVq5cySuvvEJ4eDjZ2dnMnz+fyMjISvPRAby9vRk4cCAvvfQSsbGxGBgYMHXq1GoXS6uwaNEiunbtiqenJ0VFRaSnp7N+/XpiY2PvGGNYWBgTJ04E0Nnj3MvLi7y8PFJSUvDz8+PLL7/ks88+q/Wzm5ubExYWRlRUFHZ2djg4ODB79myd53Z1dcXIyIjVq1czadIkfvzxx0p7n7u5uaEoClu3biU4OBhTU1Od+f8VsQ4ZMoSJEyeydu1aLCwsiI6OpkWLFgwZMqTWMVcnOzu7Ulm7du2YPHkyUVFR2Nra4urqypIlS7h+/TphYWEAzJs3jy5dutCuXTuKiorYunWrtr7AihUrcHJyolOnTujp6bFx40YcHR0bbAu76sic9KZO+XOYjwx3F0IIIYQQos5atGhBeno6hw8fpmPHjkyaNImwsDCdBdlul5CQgLOzMwEBATz99NO8+OKLODg41NhOQUEBU6ZMoV27dvTq1Yu0tDQ+/vhjXnjhhTvGOGzYMIyNjTEzM9PZPm3w4MFMmzaN8PBwfH19OXDgAHPnzq31swMsXbqU3r17ExISwoABA/D399eZb29vb09iYiIbN26kbdu2LF68WOtlrtCiRQveeOMNoqOjad68OeHh4VW2lZCQQJcuXRg0aBA9evRAVVXS09MrDXG/G6NGjaJTp046x++//87ixYsZNmwYY8aMoXPnzuTm5rJ9+3ZsbGyA8lEAs2bNokOHDvTp0wd9fX1SUlKA8jUElixZQteuXfHz8+PUqVOkp6dX+fKmISnqvZwA0ARdvXoVKysrrly5gqWlZWOHc0dlf0yCot0olm+hmD3T2OEIIYQQQogHWGFhISdPnsTDwwMTk/t7mLsQDa2mfz91yUOlJ72p03rSZU66EEIIIYQQQjzoJElv8mThOCGEEEIIIYR4WEiS3tT9uXCcKkm6EEIIIYQQQjzwJElv6iqGuyNJuhBCCCGEEEI86CRJb+pkCzYhhBBCCCGEeGhIkt7EKYrMSRdCCCGEEEKIh4Uk6U2dYlb+p/SkCyGEEEIIIcQDT5L0pk7rSZckXQghhBBCCCEedJKkN3XSky6EEEIIIYQQDw1J0ps6WThOCCGEEEKIe6pv375MnTq1xjru7u6sXLmyQeKpiwULFuDr66t9Dg0NZejQoTVeU5vnrY36uo/QJUl6UyfD3YUQQgghhKhRaGgoiqJUOnJzcxslnpSUFBRFqTFZXr58OTY2NhQWVl4g+vr161haWhITE1PntletWkViYmKdr6vJ3r17URSFy5cv65Rv2rSJN998s17but2pU6dQFIXMzMx72k5T0qhJ+v79+wkJCcHZ2RlFUdi8eXON9c+fP89zzz3Ho48+ip6e3sPx1kYb7n69ceMQQgghhBCiCRs4cCDnz5/XOTw8PBo8jlOnTjF9+nR69+5dY70xY8ZQUFDApk2bKp1LTU2luLiY559/vs7tW1lZYW1tXefr7oatrS0WFhYN0tbDpFGT9IKCAjp27MgHH3xQq/pFRUXY29szZ84cOnbseI+jayK04e6yBZsQQgghhBDVMTY2xtHRUefQ19cHYN++fXTr1g1jY2OcnJyIjo6mtLS02ntduHCBkJAQTE1N8fDwIDk5uVYx3Lx5k9GjR/PGG2/QqlWrGus6ODgQEhJCfHx8pXPx8fEMHToUW1tbZs6cyaOPPoqZmRmtWrVi7ty5lJSUVHvf24e7FxQUMHbsWMzNzXFycmL58uWVrlm/fj1du3bFwsICR0dHnnvuOS5cuACUv3To168fADY2NiiKQmhoKFB5uPsff/zB2LFjsbGxwczMjKCgIHJycrTziYmJWFtbs337dnx8fDA3N9dertytoqIiIiIicHBwwMTEBH9/f44cOaIT0+jRo7G3t8fU1BQvLy8SEhIAKC4uJjw8HCcnJ0xMTHBzc2PRokV3HUt9MWjMxoOCgggKCqp1fXd3d1atWgVQ5V/mB5LMSRdCCCGEEI1EVVUKrxc1StsmZsYoivKX73P27FmCg4MJDQ3l73//Oz///DMTJ07ExMSEBQsWVHlNaGgo586dY8+ePRgaGhIREaElrTVZuHAhDg4OhIWF8c9//vOO9cPCwhg0aBCnT5/Gzc0NgF9//ZX9+/ezfft2ACwsLEhMTMTZ2ZkffviBiRMnYmFhwYwZM2r1/FFRUezbt4/PP/8cBwcHXn/9db799ludeewlJSW8+eabeHt7c+HCBSIjIwkNDSU9PR0XFxfS0tIYNmwY2dnZWFpaYmpqWmVboaGh5OTksGXLFiwtLZk5cybBwcEcP34cQ0NDoHwo/7Jly1i/fj16eno8//zzTJ8+vdYvQm43Y8YM0tLSSEpKws3NjSVLlhAYGEhubi62trbMnTuX48ePs23bNh555BFyc3O5caM8t4qJiWHLli1s2LABV1dXzpw5w5kzZ+4qjvrUqEm6qAUtSb+Oqqr18j9UQgghhBBC1Ebh9SIGW4xplLa3XFuPaTOTWtffunUr5ubm2uegoCA2btzImjVrcHFx4f3330dRFNq0acO5c+eYOXMm8+bNQ09Pd3DxL7/8wrZt2zh8+DB+fn4AxMXF4ePjU2P733zzDXFxcXWaOx0YGIizszMJCQnaC4PExERcXFzo378/AHPmzNHqu7u7M336dFJSUmqVpOfn5xMXF8fHH3+s3S8pKYmWLVvq1JswYYL2c6tWrYiJicHPz4/8/HzMzc2xtbUFynv/qxtKX5GcZ2Rk0LNnTwCSk5NxcXFh8+bNjBgxAih/IfDhhx/i6ekJQHh4OAsXLrzjs1SloKCA2NhYEhMTtc7fdevWsWPHDuLi4oiKiiIvL49OnTrRtWtXoPw7rJCXl4eXlxf+/v4oiqK9KGlsD/zCcUVFRVy9elXnuK9UzElHBYobMxIhhBBCCCGarH79+pGZmakdFYuuZWVl0aNHD53Orl69epGfn89vv/1W6T5ZWVkYGBjQpUsXraxNmzY1zvO+du0aY8aMYd26dTzyyCO1jllfX59x48aRmJiIqqqUlZWRlJTE+PHjtZcHn376Kb169cLR0RFzc3PmzJlDXl5ere5/4sQJiouL6d69u1Zma2uLt7e3Tr1jx44REhKCq6srFhYWBAQEANS6Hfjf93ZrW3Z2dnh7e5OVlaWVmZmZaQk6gJOTU61GKVTlxIkTlJSU0KtXL63M0NCQbt26aW1OnjyZlJQUfH19mTFjBgcOHNDqhoaGkpmZibe3NxEREXz99dd3FUd9e+B70hctWsQbb7zR2GHcPeWWt4fqdVCMGy8WIYQQQgjxUDExM2bLtfWN1nZdNGvWjNatW9+jaGp24sQJTp06RUhIiFZWVlYGgIGBAdnZ2TqJ6a0mTJjAokWL2L17N2VlZZw5c4bx48cDcPDgQW2Oe2BgIFZWVqSkpFQ5r/xuFRQUEBgYSGBgIMnJydjb25OXl0dgYCDFxfXfSVgx7L2Coiioqlrv7VQICgri9OnTpKens2PHDvr378/LL7/MsmXL6Ny5MydPnmTbtm3s3LmTZ555hgEDBpCamnrP4qmNBz5JnzVrFpGRkdrnq1ev4uLi0ogR1Y2iGKBiCJT8OS/dprFDEkIIIYQQDwlFUeo05Lwp8vHxIS0tTWfqaEZGBhYWFpWGfUN5r3lpaSnHjh3ThrtnZ2dX2n7s9mt++OEHnbI5c+Zw7do1Vq1aVWP+4enpSUBAAPHx8aiqyoABA7Rh1wcOHMDNzY3Zs2dr9U+fPl3rZ/f09MTQ0JBDhw7h6uoKlC+k9ssvv2i95T///DMXL15k8eLFWpxHjx7VuY+RkRFQvjBedXx8fCgtLeXQoUPacPeLFy+SnZ1N27Ztax1zXXh6emJkZERGRob2nZWUlHDkyBGdBe3s7e0ZN24c48aNo3fv3kRFRbFs2TIALC0tGTlyJCNHjmT48OEMHDiQS5cuaUP8G8MDn6QbGxtjbHyf9z4rZqBekcXjhBBCCCGEqKMpU6awcuVKXnnlFcLDw8nOzmb+/PlERkZWmo8O4O3tzcCBA3nppZeIjY3FwMCAqVOnVrtYGoCJiQmPPfaYTlnF8Pjby6sSFhbGxIkTAXT2OPfy8iIvL4+UlBT8/Pz48ssv+eyzz2rx1OXMzc0JCwsjKioKOzs7HBwcmD17ts5zu7q6YmRkxOrVq5k0aRI//vhjpb3P3dzcUBSFrVu3EhwcjKmpqc78/4pYhwwZwsSJE1m7di0WFhZER0fTokULhgwZUuuYq5OdnV2prF27dkyePJmoqChsbW1xdXVlyZIlXL9+nbCwMADmzZtHly5daNeuHUVFRWzdulVbX2DFihU4OTnRqVMn9PT02LhxI46Ojg22hV11GnVOen5+vjZnBODkyZNkZmZqcx9mzZrF2LFjda6pqJ+fn89//vMfMjMzOX78eEOH3rAqhrxLki6EEEIIIUSdtGjRgvT0dA4fPkzHjh2ZNGkSYWFhOguy3S4hIQFnZ2cCAgJ4+umnefHFF3FwcLhnMQ4bNgxjY2PMzMx0tk8bPHgw06ZNIzw8HF9fXw4cOMDcuXPrdO+lS5fSu3dvQkJCGDBgAP7+/jrz7e3t7UlMTGTjxo20bduWxYsXa73MFVq0aMEbb7xBdHQ0zZs3Jzw8vMq2EhIS6NKlC4MGDaJHjx6oqkp6enqlIe53Y9SoUXTq1Enn+P3331m8eDHDhg1jzJgxdO7cmdzcXLZv346NTfkIZCMjI2bNmkWHDh3o06cP+vr6pKSkAOUr5y9ZsoSuXbvi5+fHqVOnSE9Pr/LlTUNS1Hs5AeAO9u7dq+25d6uKxRNCQ0M5deoUe/fu1c5Vtbq5m5sbp06dqlWbV69excrKiitXrmBpaXm3oTeosv8Ews2TKLbJKEZ+jR2OEEIIIYR4QBUWFnLy5Ek8PDwwMbm/h7kL0dBq+vdTlzy0UYe79+3bt8ZFAm4d6lGhEd8pNB7ZK10IIYQQQgghHgoP/BZsDwRJ0oUQQgghhBDioSBJ+v1AS9KvN24cQgghhBBCCCHuKUnS7wdakl7YuHEIIYQQQgghhLinJEm/H0hPuhBCCCGEEEI8FCRJvx/InHQhhBBCCCGEeChIkn4/+DNJVyVJF0IIIYQQQogHmiTp9wPFrPxPSdKFEEIIIYQQ4oEmSfp9QFFMyn+QJF0IIYQQQgghHmiSpN8PtJ50WThOCCGEEEKI+ta3b1+mTp1aYx13d3dWrlzZIPHUxYIFC/D19dU+h4aGMnTo0Bqvqc3z1kZ93UfokiT9fiBbsAkhhBBCCFGt0NBQFEWpdOTm5jZYDImJiZXaNzExqbb+8uXLsbGxobCw8n/jX79+HUtLS2JiYuocx6pVq0hMTKzzdTXZu3cviqJw+fJlnfJNmzbx5ptv1mtbtzt16hSKopCZmXlP22lKJEm/H8jq7kIIIYQQQtRo4MCBnD9/Xufw8PBo0BgsLS112j99+nS1dceMGUNBQQGbNm2qdC41NZXi4mKef/75OsdgZWWFtbV1na+7G7a2tlhYWDRIWw8TSdLvB7JPuhBCCCGEEDUyNjbG0dFR59DX1wdg3759dOvWDWNjY5ycnIiOjqa0tLTae124cIGQkBBMTU3x8PAgOTm5VjEoiqLTfvPmzaut6+DgQEhICPHx8ZXOxcfHM3ToUGxtbZk5cyaPPvooZmZmtGrVirlz51JSUlLtfW8f7l5QUMDYsWMxNzfHycmJ5cuXV7pm/fr1dO3aFQsLCxwdHXnuuee4cOECUN6T3a9fPwBsbGxQFIXQ0FCg8nD3P/74g7Fjx2JjY4OZmRlBQUHk5ORo5xMTE7G2tmb79u34+Phgbm6uvVy5W0VFRURERODg4ICJiQn+/v4cOXJEJ6bRo0djb2+PqakpXl5eJCQkAFBcXEx4eDhOTk6YmJjg5ubGokWL7jqW+mLQ2AGIWpDh7kIIIYQQohGoqkrh9aJGadvEzBhFUf7yfc6ePUtwcDChoaH8/e9/5+eff2bixImYmJiwYMGCKq8JDQ3l3Llz7NmzB0NDQyIiIrSktSb5+fm4ublRVlZG586deeedd2jXrl219cPCwhg0aBCnT5/Gzc0NgF9//ZX9+/ezfft2ACwsLEhMTMTZ2ZkffviBiRMnYmFhwYwZM2r1/FFRUezbt4/PP/8cBwcHXn/9db799ludeewlJSW8+eabeHt7c+HCBSIjIwkNDSU9PR0XFxfS0tIYNmwY2dnZWFpaYmpqWu33lpOTw5YtW7C0tGTmzJkEBwdz/PhxDA0NgfKh/MuWLWP9+vXo6enx/PPPM3369Fq/CLndjBkzSEtLIykpCTc3N5YsWUJgYCC5ubnY2toyd+5cjh8/zrZt23jkkUfIzc3lxo3yEcoxMTFs2bKFDRs24OrqypkzZzhz5sxdxVGfJEm/H0hPuhBCCCGEaASF14sYbDGmUdrecm09ps2qn9N9u61bt2Jubq59DgoKYuPGjaxZswYXFxfef/99FEWhTZs2nDt3jpkzZzJv3jz09HQHF//yyy9s27aNw4cP4+fnB0BcXBw+Pj41tu/t7U18fDwdOnTgypUrLFu2jJ49e/LTTz/RsmXLKq8JDAzE2dmZhIQE7YVBYmIiLi4u9O/fH4A5c+Zo9d3d3Zk+fTopKSm1StLz8/OJi4vj448/1u6XlJRUKZ4JEyZoP7dq1YqYmBj8/PzIz8/H3NwcW1tboLz3v7qh9BXJeUZGBj179gQgOTkZFxcXNm/ezIgRI4DyFwIffvghnp6eAISHh7Nw4cI7PktVCgoKiI2NJTExkaCgIADWrVvHjh07iIuLIyoqiry8PDp16kTXrl2B8u+wQl5eHl5eXvj7+6MoivaipLHJcPf7gcxJF0IIIYQQokb9+vUjMzNTOyoWXcvKyqJHjx46vfK9evUiPz+f3377rdJ9srKyMDAwoEuXLlpZmzZt7jjPu0ePHowdOxZfX18CAgLYtGkT9vb2rF27ttpr9PX1GTduHImJiaiqSllZGUlJSYwfP157efDpp5/Sq1cvHB0dMTc3Z86cOeTl5dXqOzlx4gTFxcV0795dK7O1tcXb21un3rFjxwgJCcHV1RULCwsCAgIAat0O/O97u7UtOzs7vL29ycrK0srMzMy0BB3AycmpVqMUqnLixAlKSkro1auXVmZoaEi3bt20NidPnkxKSgq+vr7MmDGDAwcOaHVDQ0PJzMzE29ubiIgIvv7667uKo75JT/r9QNuCTYa7CyGEEEKIhmNiZsyWa+sbre26aNasGa1bt75H0dSdoaEhnTp1uuMK8xMmTGDRokXs3r2bsrIyzpw5w/jx4wE4ePAgo0eP5o033iAwMBArKytSUlKqnFd+twoKCggMDCQwMJDk5GTs7e3Jy8sjMDCQ4uLiemunQsWw9wqKoqCqar23UyEoKIjTp0+Tnp7Ojh076N+/Py+//DLLli2jc+fOnDx5km3btrFz506eeeYZBgwYQGpq6j2LpzYkSb8fKH8O81Gvo6pqvczNEUIIIYQQ4k4URanTkPOmyMfHh7S0NJ3/js7IyMDCwqLKYeht2rShtLSUY8eOacPds7OzK20/dic3b97khx9+IDg4uMZ6np6eBAQEEB8fj6qqDBgwQBt2feDAAdzc3Jg9e7ZWv6YV46u6t6GhIYcOHcLV1RUoX0jtl19+0XrLf/75Zy5evMjixYtxcXEB4OjRozr3MTIy0p6pOj4+PpSWlnLo0CFtuPvFixfJzs6mbdu2tY65Ljw9PTEyMiIjI0P7zkpKSjhy5IjOgnb29vaMGzeOcePG0bt3b6Kioli2bBlQviL/yJEjGTlyJMOHD2fgwIFcunRJG+LfGCRJvx9U9KSjAkXA/f0/lEIIIYQQQjSUKVOmsHLlSl555RXCw8PJzs5m/vz5REZGVpqPDuVzywcOHMhLL71EbGwsBgYGTJ06tdrF0iosXLiQxx9/nNatW3P58mWWLl3K6dOneeGFF+4YY1hYGBMnTgTQ2ePcy8uLvLw8UlJS8PPz48svv+Szzz6r9bObm5sTFhZGVFQUdnZ2ODg4MHv2bJ3ndnV1xcjIiNWrVzNp0iR+/PHHSnufu7m5oSgKW7duJTg4GFNTU535/xWxDhkyhIkTJ7J27VosLCyIjo6mRYsWDBkypNYxVyc7O7tSWbt27Zg8eTJRUVHY2tri6urKkiVLuH79OmFhYQDMmzePLl260K5dO4qKiti6dau2vsCKFStwcnKiU6dO6OnpsXHjRhwdHRtsC7vqyJz0+4FyS1Iu89KFEEIIIYSotRYtWpCens7hw4fp2LEjkyZNIiwsTGdBttslJCTg7OxMQEAATz/9NC+++CIODg41tvPHH38wceJEfHx8CA4O5urVqxw4cKBWvcjDhg3D2NgYMzMzne3TBg8ezLRp0wgPD8fX15cDBw4wd+7cWj87wNKlS+nduzchISEMGDAAf39/nfn29vb2JCYmsnHjRtq2bcvixYu1XuYKLVq04I033iA6OprmzZsTHh5eZVsJCQl06dKFQYMG0aNHD1RVJT09vdIQ97sxatQoOnXqpHP8/vvvLF68mGHDhjFmzBg6d+5Mbm4u27dvx8bGBigfBTBr1iw6dOhAnz590NfXJyUlBShfOX/JkiV07doVPz8/Tp06RXp6epUvbxqSot7LCQBN0NWrV7GysuLKlStYWlo2dji1VvbvdkAJiv1eFH3nxg5HCCGEEEI8gAoLCzl58iQeHh6YmMjoTSHqoqZ/P3XJQ6Un/X6hLR4n27AJIYQQQgghxINKkvT7hbYNm6zwLoQQQgghhBAPKknS7xeyV7oQQgghhBBCPPAkSb9faEm6DHcXQgghhBBCiAeVJOn3CxnuLoQQQgghhBAPPEnS7xfSky6EEEIIIYQQDzxJ0u8XMiddCCGEEEIIIR54kqTfLyRJF0IIIYQQQogHniTp9wtJ0oUQQgghhBDigSdJ+v3izyRdlSRdCCGEEEKIetW3b1+mTp1aYx13d3dWrlzZIPHURWJiItbW1trnBQsW4OvrW+M1oaGhDB069C+3XV/3EbokSb9fKGblf0qSLoQQQgghhI7Q0FAURal05ObmNmgcly9f5uWXX8bJyQljY2MeffRR0tPTq6yblpaGvr4+Z8+erfK8l5cXkZGRdY5h+vTp7Nq1q87X1eTUqVMoikJmZqZO+apVq0hMTKzXtqqiKAqbN2++5+00FQaNHYCoHUUxQQVJ0oUQQgghhKjCwIEDSUhI0Cmzt7dvsPaLi4t54okncHBwIDU1lRYtWnD69GmdXu5bDR48GDs7O5KSknj99dd1zu3fv5/c3FzCwsLqHIe5uTnm5uZ38wh1ZmVl1SDtPGykJ/1+ofWkyxZsQgghhBBC3M7Y2BhHR0edQ19fH4B9+/bRrVs3jI2NcXJyIjo6mtLS0mrvdeHCBUJCQjA1NcXDw4Pk5OQ7th8fH8+lS5fYvHkzvXr1wt3dnYCAADp27FhlfUNDQ8aMGVNlT3R8fDzdu3enXbt2rFixgvbt29OsWTNcXFyYMmUK+fn51cZx+3D3mzdvEhkZibW1NXZ2dsyYMQNVVXWu+eqrr/D399fqDBo0iBMnTmjnPTw8AOjUqROKotC3b1+g8nD3oqIiIiIicHBwwMTEBH9/f44cOaKd37t3L4qisGvXLrp27YqZmRk9e/YkOzu72ue5k7KyMhYuXEjLli0xNjbG19eXr776SjtfXFxMeHg4Tk5OmJiY4ObmxqJFiwBQVZUFCxbg6uqKsbExzs7ORERE3HUs9UWS9PuFtnBcYePGIYQQQgghHhqqqnKjsLhRjtsTybt19uxZgoOD8fPz47vvviM2Npa4uDjeeuutaq8JDQ3lzJkz7Nmzh9TUVNasWcOFCxdqbGfLli306NGDl19+mebNm/PYY4/xzjvvcPPmzWqvCQsLIycnh/3792tl+fn5pKamar3oenp6xMTE8NNPP5GUlMTu3buZMWNGrZ9/+fLlJCYmEh8fzzfffMOlS5f47LPPdOoUFBQQGRnJ0aNH2bVrF3p6ejz11FOUlZUBcPjwYQB27tzJ+fPn2bRpU5VtzZgxg7S0NJKSkvj2229p3bo1gYGBXLp0Safe7NmzWb58OUePHsXAwIAJEybU+nlut2rVKpYvX86yZcv4/vvvCQwMZPDgweTk5AAQExPDli1b2LBhA9nZ2SQnJ+Pu7g6UTzl47733WLt2LTk5OWzevJn27dvfdSz1RYa73y+0JF160oUQQgghRMMoLCrhidExjdL2juQITE2Mal1/69atOsO8g4KC2LhxI2vWrMHFxYX3338fRVFo06YN586dY+bMmcybNw89Pd1+y19++YVt27Zx+PBh/Pz8AIiLi8PHx6fG9n/99Vd2797N6NGjSU9PJzc3lylTplBSUsL8+fOrvKZt27Y8/vjjxMfH06dPHwA2bNiAqqqMGjUKQGdBO3d3d9566y0mTZrEmjVravW9rFy5klmzZvH0008D8OGHH7J9+3adOsOGDdP5HB8fj729PcePH+exxx7Tpg3Y2dnh6OhYZTsFBQXExsaSmJhIUFAQAOvWrWPHjh3ExcURFRWl1X377bcJCAgAIDo6mieffJLCwkJMTExq9Uy3WrZsGTNnztS+r3fffZc9e/awcuVKPvjgA/Ly8vDy8sLf3x9FUXBzc9OuzcvLw9HRkQEDBmBoaIirqyvdunWrcwz1TXrS7xdKs/I/JUkXQgghhBCikn79+pGZmakdMTHlLxeysrLo0aMHiqJodXv16kV+fj6//fZbpftkZWVhYGBAly5dtLI2bdpUO7e8QllZGQ4ODnz00Ud06dKFkSNHMnv2bD788MMar5swYQKpqalcu3YNKE+QR4wYgYWFBVDee92/f39atGiBhYUFY8aM4eLFi1y/fue84MqVK5w/f57u3btrZQYGBnTt2lWnXk5ODs8++yytWrXC0tJS62nOy8u7YxsVTpw4QUlJCb169dLKDA0N6datG1lZWTp1O3TooP3s5OQEcMeRClW5evUq586d02kTyn+/FW2GhoaSmZmJt7c3ERERfP3111q9ESNGcOPGDVq1asXEiRP57LPPapwG0VCkJ/1+UTEnvUySdCGEEEII0TBMjA3Zkdw4c3RNjA3rVL9Zs2a0bt36HkVzZ05OThgaGmrz4AF8fHz497//TXFxMUZGVY8KGDVqFNOmTWPDhg306dOHjIwMbc70qVOnGDRoEJMnT+btt9/G1taWb775hrCwMIqLizEzM6uX2ENCQnBzc2PdunU4OztTVlbGY489RnFxcb3c/3aGhv/73Va8PKkYWl/fOnfuzMmTJ9m2bRs7d+7kmWeeYcCAAaSmpuLi4kJ2djY7d+5kx44dTJkyhaVLl7Jv3z6dGBua9KTfL/Rk4TghhBBCCNGwFEXB1MSoUY5be77/Ch8fHw4ePKgzxz0jIwMLCwtatmxZqX6bNm0oLS3l2LFjWll2djaXL1+usZ1evXqRm5urk2z+8ssvODk5VZugA1hYWDBixAji4+NJSEjg0UcfpXfv3gAcO3aMsrIyli9fzuOPP86jjz7KuXPnavvoWFlZ4eTkxKFDh7Sy25/t4sWLZGdnM2fOHPr374+Pjw9//PGHzn0q4q9pfr2npydGRkZkZGRoZSUlJRw5coS2bdvWOua6sLS0xNnZWadNKP/93tqmpaUlI0eOZN26dXz66aekpaVp8+RNTU0JCQkhJiaGvXv3cvDgQX744Yd7Em9tSU/6/UJWdxdCCCGEEKLOpkyZwsqVK3nllVcIDw8nOzub+fPnExkZWWk+OoC3tzcDBw7kpZdeIjY2FgMDA6ZOnYqpqWmN7UyePJn333+fV199lVdeeYWcnBzeeeedWq0WHhYWRu/evcnKymLmzJlaeevWrSkpKWH16tWEhISQkZFxx+Hzt3v11VdZvHgxXl5etGnThhUrVui8cLCxscHOzo6PPvoIJycn8vLyiI6O1rmHg4MDpqamfPXVV7Rs2RITE5NK2681a9aMyZMnExUVha2tLa6urixZsoTr16/f1VZytzt58mSlfdq9vLyIiopi/vz5eHp64uvrS0JCApmZmdqK/CtWrMDJyYlOnTqhp6fHxo0bcXR0xNramsTERG7evEn37t0xMzPj448/xtTUVGfeemOQJP1+IXPShRBCCCGEqLMWLVqQnp5OVFQUHTt2xNbWlrCwMObMmVPtNQkJCbzwwgsEBATQvHlz3nrrLebOnVtjOy4uLmzfvp1p06bRoUMHWrRowauvvqqTdFfH398fb29vcnNzGTt2rFbesWNHVqxYwbvvvsusWbPo06cPixYt0qlzJ6+99hrnz59n3Lhx6OnpMWHCBJ566imuXLkClK8en5KSQkREBI899hje3t7ExMRo26xB+Tz2mJgYFi5cyLx58+jduzd79+6t1NbixYspKytjzJgxXLt2ja5du7J9+3ZsbGxqHW91IiMjK5X985//JCIigitXrvDaa69x4cIF2rZty5YtW/Dy8gLKRyosWbKEnJwc9PX18fPzIz09HT09PaytrVm8eDGRkZHcvHmT9u3b88UXX2BnZ/eX4/0rFLW+9ja4T1y9ehUrKyuuXLmCpaVlY4dTa2rZVdQL5Qs8KM1/RFFqv9KlEEIIIYQQtVFYWMjJkyfx8PC4q5W2hXiY1fTvpy55qMxJv18otwyvkd50IYQQQgghhHggSZJ+n1AUQ+DP3nNJ0oUQQgghhBDigSRJ+v1EFo8TQgghhBBCiAeaJOn3k4rF48oKGjcOIYQQQgghhBD3hCTp9xPZK10IIYQQQgghHmiSpN9PZLi7EEIIIYQQQjzQJEm/n0iSLoQQQgghhBAPNEnS7ydaki5z0oUQQgghhBDiQSRJ+v2kYuE46UkXQgghhBCi3vTt25epU6fWWMfd3Z2VK1c2SDx1sWDBAnx9fbXPoaGhDB06tMZravO8tVFf9xG6GjVJ379/PyEhITg7O6MoCps3b77jNXv37qVz584YGxvTunVrEhMT73mcTYYMdxdCCCGEEKKS0NBQFEWpdOTm5jZYDH379q0yhieffLLK+suXL8fGxobCwsJK565fv46lpSUxMTF1jmPVqlX1niPt3bsXRVG4fPmyTvmmTZt4880367Wt2506dQpFUcjMzLyn7TQljZqkFxQU0LFjRz744INa1T958iRPPvkk/fr1IzMzk6lTp/LCCy+wffv2exxpE/Fnkq5Kki6EEEIIIYSOgQMHcv78eZ3Dw8OjwdrftGmTTts//vgj+vr6jBgxosr6Y8aMoaCggE2bNlU6l5qaSnFxMc8//3yd47CyssLa2rrO190NW1tbLCwsGqSth0mjJulBQUG89dZbPPXUU7Wq/+GHH+Lh4cHy5cvx8fEhPDyc4cOH8957793jSJuIip70MknShRBCCCGEuJWxsTGOjo46h76+PgD79u2jW7duGBsb4+TkRHR0NKWlpdXe68KFC4SEhGBqaoqHhwfJycl3bN/W1lan7R07dmBmZlZtku7g4EBISAjx8fGVzsXHxzN06FBsbW2ZOXMmjz76KGZmZrRq1Yq5c+dSUlJSbRy3D3cvKChg7NixmJub4+TkxPLlyytds379erp27YqFhQWOjo4899xzXLhwASjvye7Xrx8ANjY2KIpCaGgoUHm4+x9//MHYsWOxsbHBzMyMoKAgcnJytPOJiYlYW1uzfft2fHx8MDc3116u3K2ioiIiIiJwcHDAxMQEf39/jhw5ohPT6NGjsbe3x9TUFC8vLxISEgAoLi4mPDwcJycnTExMcHNzY9GiRXcdS325r+akHzx4kAEDBuiUBQYGcvDgwUaKqGEpehVz0mXhOCGEEEIIIWrj7NmzBAcH4+fnx3fffUdsbCxxcXG89dZb1V4TGhrKmTNn2LNnD6mpqaxZs0ZLWmsrLi6OUaNG0axZs2rrhIWFsXv3bk6fPq2V/frrr+zfv5+wsDAALCwsSExM5Pjx46xatYp169bVqZMyKiqKffv28fnnn/P111+zd+9evv32W506JSUlvPnmm3z33Xds3ryZU6dOaYm4i4sLaWlpAGRnZ3P+/HlWrVpVZVuhoaEcPXqULVu2cPDgQVRVJTg4WOelwvXr11m2bBnr169n//795OXlMX369Fo/z+1mzJhBWloaSUlJfPvtt7Ru3ZrAwEAuXboEwNy5czl+/Djbtm0jKyuL2NhYHnnkEQBiYmLYsmULGzZsIDs7m+TkZNzd3e86lvpi0NgB1MW///1vmjdvrlPWvHlzrl69yo0bNzA1Na10TVFREUVFRdrnq1ev3vM47xmZky6EEEIIIRqQqqoUFlXfa3svmRgboihKretv3boVc3Nz7XNQUBAbN25kzZo1uLi48P7776MoCm3atOHcuXPMnDmTefPmoaen22/5yy+/sG3bNg4fPoyfnx9QnnD7+PjUOpbDhw/z448/EhcXV2O9wMBAnJ2dSUhIYMGCBUB5b7OLiwv9+/cHYM6cOVp9d3d3pk+fTkpKCjNmzLhjHPn5+cTFxfHxxx9r90tKSqJly5Y69SZMmKD93KpVK2JiYvDz8yM/Px9zc3NsbW2B8t7/6obS5+TksGXLFjIyMujZsycAycnJuLi4sHnzZm1EQUlJCR9++CGenp4AhIeHs3Dhwjs+S1UKCgqIjY0lMTGRoKAgANatW8eOHTuIi4sjKiqKvLw8OnXqRNeuXQF0kvC8vDy8vLzw9/dHURTc3NzuKo76dl8l6Xdj0aJFvPHGG40dRv2QJF0IIYQQQjSgwqISnhhd98XL6sOO5AhMTYxqXb9fv37ExsZqnyt6sLOysujRo4dOwt+rVy/y8/P57bffcHV11blPVlYWBgYGdOnSRStr06ZNneZ5x8XF0b59e7p161ZjPX19fcaNG0diYiLz589HVVWSkpIYP3689vLg008/JSYmhhMnTpCfn09paSmWlpa1iuPEiRMUFxfTvXt3rczW1hZvb2+deseOHWPBggV89913/PHHH5SVlQHlSWzbtm1r1VbF93ZrW3Z2dnh7e5OVlaWVmZmZaQk6gJOTU51HKdz6fCUlJfTq1UsrMzQ0pFu3blqbkydPZtiwYXz77bf87W9/Y+jQodpLhNDQUJ544gm8vb0ZOHAggwYN4m9/+9tdxVKf7qvh7o6Ojvz+++86Zb///juWlpZV9qIDzJo1iytXrmjHmTNnGiLUe0OSdCGEEEIIIarUrFkzWrdurR1OTk6NEkdBQQEpKSnacPU7mTBhAnl5eezevZtdu3Zx5swZxo8fD5RP9x09ejTBwcFs3bqVf/3rX8yePZvi4uJ6jTcwMBBLS0uSk5M5cuQIn332GUC9tlPB0NBQ57OiKKiqWu/tVAgKCuL06dNMmzaNc+fO0b9/f214fefOnTl58iRvvvkmN27c4JlnnmH48OH3LJbauq960nv06EF6erpO2Y4dO+jRo0e11xgbG2NsbHyvQ2sYkqQLIYQQQogGZGJsyI7kiEZruz74+PiQlpaGqqpab3pGRgYWFhaVhn1Dea95aWkpx44d04a7Z2dnV9p+rDobN26kqKio1iuze3p6EhAQQHx8PKqqMmDAAG3Y9YEDB3Bzc2P27Nla/Vvnr9fm3oaGhhw6dEgbMfDHH3/wyy+/EBAQAMDPP//MxYsXWbx4MS4uLgAcPXpU5z5GRuUjGm7evFltWz4+PpSWlnLo0CGtp/rixYtkZ2fXuje+rjw9PTEyMiIjI0P7zkpKSjhy5IjOgnb29vaMGzeOcePG0bt3b6Kioli2bBkAlpaWjBw5kpEjRzJ8+HAGDhzIpUuXtCH+jaFRk/T8/HydvQtPnjxJZmYmtra2uLq6MmvWLM6ePcvf//53ACZNmsT777/PjBkzmDBhArt372bDhg18+eWXjfUIDUuRheOEEEIIIUTDURSlTkPOm6IpU6awcuVKXnnlFcLDw8nOzmb+/PlERkZWmo8OaEOfX3rpJWJjYzEwMGDq1KnVjty9XVxcHEOHDsXOzq7WMYaFhTFx4kQAnT3Ovby8yMvLIyUlBT8/P7788kutl7s2zM3NCQsLIyoqCjs7OxwcHJg9e7bOc7u6umJkZMTq1auZNGkSP/74Y6W9z93c3FAUha1btxIcHIypqanO/P+KWIcMGcLEiRNZu3YtFhYWREdH06JFC4YMGVLrmKuTnZ1dqaxdu3ZMnjyZqKgoLYdcsmQJ169f10YyzJs3jy5dutCuXTuKiorYunWrtr7AihUrcHJyolOnTujp6bFx40YcHR0bbAu76jTqcPejR4/SqVMnOnXqBEBkZCSdOnVi3rx5AJw/f568vDytvoeHB19++SU7duygY8eOLF++nP/3//4fgYGBjRJ/g5OedCGEEEIIIeqkRYsWpKenc/jwYTp27MikSZMICwvTWZDtdgkJCTg7OxMQEMDTTz/Niy++iIODwx3bys7O5ptvvqn1UPcKw4YNw9jYGDMzM53t0wYPHsy0adMIDw/H19eXAwcOMHfu3Drde+nSpfTu3ZuQkBAGDBiAv7+/znx7e3t7EhMT2bhxI23btmXx4sVaL3OFFi1a8MYbbxAdHU3z5s0JDw+vsq2EhAS6dOnCoEGD6NGjB6qqkp6eXmmI+90YNWqUljtWHL///juLFy9m2LBhjBkzhs6dO5Obm8v27duxsbEBykcBzJo1iw4dOtCnTx/09fVJSUkBylfOX7JkCV27dsXPz49Tp06Rnp5e5cubhqSo93ICQBN09epVrKysuHLlSq0XXGgq1NLfUP/7f4AJeo7fN3Y4QgghhBDiAVNYWMjJkyfx8PDAxMSkscMR4r5S07+fuuSh99XCcQ89vT970ilEVaufDyKEEEIIIYQQ4v4kSfr9pGJOOoB6o/HiEEIIIYQQQghxT0iSfl8xAvTLf5TF44QQQgghhBDigSNJ+n1EURRZPE4IIYQQQgghHmCSpN9vJEkXQgghhBBCiAeWJOn3G0nShRBCCCGEEOKBJUn6/aZi8TiZky6EEEIIIYQQDxxJ0u83FT3pZdKTLoQQQgghhBAPGknS7zd6MtxdCCGEEEIIIR5UkqTfb2ROuhBCCCGEEPWqb9++TJ06tcY67u7urFy5skHiqYvExESsra21zwsWLMDX17fGa0JDQxk6dOhfbru+7iN0SZJ+v9GSdJmTLoQQQgghBJQni4qiVDpyc3MbNI6VK1fi7e2NqakpLi4uTJs2jcLCwirrpqWloa+vz9mzZ6s87+XlRWRkZJ1jmD59Ort27arzdTU5deoUiqKQmZmpU75q1SoSExPrta2qKIrC5s2b73k7TYUk6febPxeOU6UnXQghhBBCCM3AgQM5f/68zuHh4dFg7X/yySdER0czf/58srKyiIuL49NPP+X111+vsv7gwYOxs7MjKSmp0rn9+/eTm5tLWFhYneMwNzfHzs6uztfdDSsrK51efFE/JEm/30hPuhBCCCGEEJUYGxvj6Oioc+jr6wOwb98+unXrhrGxMU5OTkRHR1NaWlrtvS5cuEBISAimpqZ4eHiQnJx8x/YPHDhAr169eO6553B3d+dvf/sbzz77LIcPH66yvqGhIWPGjKmyJzo+Pp7u3bvTrl07VqxYQfv27WnWrBkuLi5MmTKF/Pz8auO4fbj7zZs3iYyMxNraGjs7O2bMmIGqqjrXfPXVV/j7+2t1Bg0axIkTJ7TzFS87OnXqhKIo9O3bF6g83L2oqIiIiAgcHBwwMTHB39+fI0eOaOf37t2Loijs2rWLrl27YmZmRs+ePcnOzq72ee6krKyMhQsX0rJlS4yNjfH19eWrr77SzhcXFxMeHo6TkxMmJia4ubmxaNEiAFRVZcGCBbi6umJsbIyzszMRERF3HUt9kST9PqNoW7BJT7oQQgghhBB3cvbsWYKDg/Hz8+O7774jNjaWuLg43nrrrWqvCQ0N5cyZM+zZs4fU1FTWrFnDhQsXamynZ8+eHDt2TEvKf/31V9LT0wkODq72mrCwMHJycti/f79Wlp+fT2pqqtaLrqenR0xMDD/99BNJSUns3r2bGTNm1Pr5ly9fTmJiIvHx8XzzzTdcunSJzz77TKdOQUEBkZGRHD16lF27dqGnp8dTTz1FWVkZgPZMO3fu5Pz582zatKnKtmbMmEFaWhpJSUl8++23tG7dmsDAQC5duqRTb/bs2SxfvpyjR49iYGDAhAkTav08t1u1ahXLly9n2bJlfP/99wQGBjJ48GBycnIAiImJYcuWLWzYsIHs7GySk5Nxd3cHyqccvPfee6xdu5acnBw2b95M+/bt7zqWeqM+ZK5cuaIC6pUrVxo7lLtSVvCxevO8l3rz0suNHYoQQgghhHjA3LhxQz1+/Lh648YNVVVVtaysTL1+o6hRjrKyslrHPW7cOFVfX19t1qyZdgwfPlxVVVV9/fXXVW9vb537ffDBB6q5ubl68+ZNVVVVNSAgQH311VdVVVXV7OxsFVAPHz6s1c/KylIB9b333qsxjlWrVqmGhoaqgYGBCqiTJk26Y+yPP/64Om7cOO1zXFycamZmpl69erXK+hs3blTt7Oy0zwkJCaqVlZX2ef78+WrHjh21z05OTuqSJUu0zyUlJWrLli3VIUOGVBvTf/7zHxVQf/jhB1VVVfXkyZMqoP7rX//SqTdu3DjtPvn5+aqhoaGanJysnS8uLladnZ219vfs2aMC6s6dO7U6X375pQpof+eqAqifffZZleecnZ3Vt99+W6fMz89PnTJliqqqqvrKK6+o//d//1fl36fly5erjz76qFpcXFxt23Vx+7+fW9UlDzVorJcD4i4p5uV/qtUPcRFCCCGEEKI+FBaV8MTomEZpe0dyBKYmRrWu369fP2JjY7XPzZqVj0DNysqiR48eKIqinevVqxf5+fn89ttvuLq66twnKysLAwMDunTpopW1adPmjnOv9+7dyzvvvMOaNWvo3r07ubm5vPrqq7z55pvMnTu32usmTJjAtGnTWL16NRYWFsTHxzNixAgsLCyA8t7rRYsW8fPPP3P16lVKS0spLCzk+vXrmJmZ1RjTlStXOH/+PN27d9fKDAwM6Nq1q86Q95ycHObNm8ehQ4f473//q/Wg5+Xl8dhjj9XYRoUTJ05QUlJCr169tDJDQ0O6detGVlaWTt0OHTpoPzs5OQHlUwxu/13cydWrVzl37pxOm1D++/3uu++A8lERTzzxBN7e3gwcOJBBgwbxt7/9DYARI0awcuVKWrVqxcCBAwkODiYkJAQDg8ZNk2W4exOl3ryIWnwEteS47glJ0oUQQgghhKikWbNmtG7dWjsqkr+GMnfuXMaMGcMLL7xA+/bteeqpp3jnnXdYtGiRlvRWZdSoUQBs2LCBnJwcMjIytKHup06dYtCgQXTo0IG0tDSOHTvGBx98AJTPta4vISEhXLp0iXXr1nHo0CEOHTpU723cytDQUPu54uVJTd/RX9G5c2dOnjzJm2++yY0bN3jmmWcYPnw4AC4uLmRnZ7NmzRpMTU2ZMmUKffr0oaSk5J7EUlvSk95UFe1GvTobjPuh2Kz9X3lFkl4mSboQQgghhLi3TIwN2ZHcOAtpmRgb3rlSLfj4+JCWloaqqlpCmJGRgYWFBS1btqxUv02bNpSWlnLs2DH8/PwAyM7O5vLlyzW2c/36dfT0dPtAKxauU29bqO1WFhYWjBgxgvj4eE6cOMGjjz5K7969ATh27BhlZWUsX75cu/eGDRtq9+CUr77u5OTEoUOH6NOnD4D2bJ07dwbg4sWLZGdns27dOq3db775Ruc+RkblIxpu3rxZbVuenp4YGRmRkZGBm5sbACUlJRw5cuSOe9DfLUtLS5ydncnIyCAgIEArz8jIoFu3bjr1Ro4cyciRIxk+fDgDBw7k0qVL2NraYmpqSkhICCEhIbz88su0adOGH374Qft+GoMk6U2VXvnwFsqu3VYuPelCCCGEEKJhKIpSpyHnTdGUKVNYuXIlr7zyCuHh4WRnZzN//nwiIyMrJdWANiz6pZdeIjY2FgMDA6ZOnYqpqWmN7YSEhLBixQo6deqkDXefO3cuISEhWrJenbCwMHr37k1WVhYzZ87Uylu3bk1JSQmrV68mJCSEjIwMPvzwwzo9/6uvvsrixYvx8vKiTZs2rFixQueFg42NDXZ2dnz00Uc4OTmRl5dHdHS0zj0cHBwwNTXlq6++omXLlpiYmGBlZaVTp1mzZkyePJmoqChsbW1xdXVlyZIlXL9+/a62krvdyZMnK+3T7uXlRVRUFPPnz8fT0xNfX18SEhLIzMzUVuRfsWIFTk5OdOrUCT09PTZu3IijoyPW1tYkJiZy8+ZNunfvjpmZGR9//DGmpqbaS4bGIkl6U1XdsHatXLZgE0IIIYQQ4k5atGhBeno6UVFRdOzYEVtbW8LCwpgzZ0611yQkJPDCCy8QEBBA8+bNeeutt2qcVw4wZ84cFEVhzpw5nD17Fnt7e0JCQnj77bfvGKO/vz/e3t7k5uYyduxYrbxjx46sWLGCd999l1mzZtGnTx8WLVqkU+dOXnvtNc6fP8+4cePQ09NjwoQJPPXUU1y5cgUoXz0+JSWFiIgIHnvsMby9vYmJidG2WYPyeewxMTEsXLiQefPm0bt3b/bu3VuprcWLF1NWVsaYMWO4du0aXbt2Zfv27djY2NQ63upERkZWKvvnP/9JREQEV65c4bXXXuPChQu0bduWLVu24OXlBZSPVFiyZAk5OTno6+vj5+dHeno6enp6WFtbs3jxYiIjI7l58ybt27fniy++aLB95qujqDWNvXgAXb16FSsrK65cuYKlpWVjh1Mttfg71EsjQL8leva7/1d+8yLqf3oAoDT/GUWRZQWEEEIIIUT9KCws5OTJk3h4eGBiYtLY4QhxX6np309d8lDJ8JoqvT/3Q7997nnFcHeQvdKFEEIIIYQQ4gEjSXpTpfw5J129dttCE0bAn4toyLx0IYQQQgghhHigSJLeVFXMPecmUPi/YkUB5c9edknShRBCCCGEEOKBIkl6U6WYof16qlvhXbZhE0IIIYQQQogHiiTpTVR5j/mdVniXJF0IIYQQQgghHiSSpDdlFcn47T3pkqQLIYQQQgghxANJkvSmTK9i8bhqVniX4e5CCCGEEEII8UCRJL0pq26BOOlJF0IIIYQQQogHkiTpTVlFT/rtPeaSpAshhBBCCCHEA0mS9KZMS8Zvn5Ne3sOuynB3IYQQQggh/rK+ffsyderUGuu4u7uzcuXKBomnLhITE7G2ttY+L1iwAF9f3xqvCQ0NZejQoX+57fq6j9AlSXpTplQ9J13Rk550IYQQQgghKoSGhqIoSqUjNze3wWIoKSlh4cKFeHp6YmJiQseOHfnqq6+qrZ+Wloa+vj5nz56t8ryXlxeRkZF1jmP69Ons2rWrztfV5NSpUyiKQmZmpk75qlWrSExMrNe2qqIoCps3b77n7TQVkqQ3ZX8m42q1q7sXNHBAQgghhBBCNE0DBw7k/PnzOoeHh0eDtT9nzhzWrl3L6tWrOX78OJMmTeKpp57iX//6V5X1Bw8ejJ2dHUlJSZXO7d+/n9zcXMLCwuoch7m5OXZ2dnW+7m5YWVnp9OKL+iFJehOmVNOTLnPShRBCCCGE0GVsbIyjo6POoa+vD8C+ffvo1q0bxsbGODk5ER0dTWlpabX3unDhAiEhIZiamuLh4UFycvId21+/fj2vv/46wcHBtGrVismTJxMcHMzy5currG9oaMiYMWOq7ImOj4+ne/futGvXjhUrVtC+fXuaNWuGi4sLU6ZMIT+/+jzg9uHuN2/eJDIyEmtra+zs7JgxYwaqqupc89VXX+Hv76/VGTRoECdOnNDOV7zs6NSpE4qi0LdvX6DycPeioiIiIiJwcHDAxMQEf39/jhw5op3fu3cviqKwa9cuunbtipmZGT179iQ7O7va57mTsrIyFi5cSMuWLTE2NsbX11dnBENxcTHh4eE4OTlhYmKCm5sbixYtAkBVVRYsWICrqyvGxsY4OzsTERFx17HUF0nSm7LqVneXLdiEEEIIIUQDUFWVG4XFjXLcnkjerbNnzxIcHIyfnx/fffcdsbGxxMXF8dZbb1V7TWhoKGfOnGHPnj2kpqayZs0aLly4UGM7RUVFmJiY6JSZmpryzTffVHtNWFgYOTk57N+/XyvLz88nNTVV60XX09MjJiaGn376iaSkJHbv3s2MGTNq8+gALF++nMTEROLj4/nmm2+4dOkSn332mU6dgoICIiMjOXr0KLt27UJPT4+nnnqKsrIyAA4fPgzAzp07OX/+PJs2baqyrRkzZpCWlkZSUhLffvstrVu3JjAwkEuXLunUmz17NsuXL+fo0aMYGBgwYcKEWj/P7VatWsXy5ctZtmwZ33//PYGBgQwePJicnBwAYmJi2LJlCxs2bCA7O5vk5GTc3d2B8ikH7733HmvXriUnJ4fNmzfTvn37u46lvhg0dgCiBrK6uxBCCCGEaESFRSU8MTqmUdrekRyBqYlRretv3boVc3Nz7XNQUBAbN25kzZo1uLi48P7776MoCm3atOHcuXPMnDmTefPmoaen22/5yy+/sG3bNg4fPoyfnx8AcXFx+Pj41Nh+YGAgK1asoE+fPnh6erJr1y42bdrEzZs3q72mbdu2PP7448THx9OnTx8ANmzYgKqqjBo1CkBnQTt3d3feeustJk2axJo1a2r1vaxcuZJZs2bx9NNPA/Dhhx+yfft2nTrDhg3T+RwfH4+9vT3Hjx/nsccew97eHgA7OzscHR2rbKegoIDY2FgSExMJCgoCYN26dezYsYO4uDiioqK0um+//TYBAQEAREdH8+STT1JYWFjpJUdtLFu2jJkzZ2rf17vvvsuePXtYuXIlH3zwAXl5eXh5eeHv74+iKLi5uWnX5uXl4ejoyIABAzA0NMTV1ZVu3brVOYb6Jj3pTVl1ybjMSRdCCCGEEEJHv379yMzM1I6YmPKXC1lZWfTo0QNFUbS6vXr1Ij8/n99++63SfbKysjAwMKBLly5aWZs2be4493rVqlV4eXnRpk0bjIyMCA8PZ/z48ZVeAtxuwoQJpKamcu1a+TpU8fHxjBgxAguL8g67nTt30r9/f1q0aIGFhQVjxozh4sWLXL9+/Y7fyZUrVzh//jzdu3fXygwMDOjatatOvZycHJ599llatWqFpaWl1tOcl5d3xzYqnDhxgpKSEnr16qWVGRoa0q1bN7KysnTqdujQQfvZyckJ4I4jFapy9epVzp07p9MmlP9+K9oMDQ0lMzMTb29vIiIi+Prrr7V6I0aM4MaNG7Rq1YqJEyfy2Wef1TgNoqFIT3pTps1Jv23hOL0/h8HLcHchhBBCCHEPmRgbsiO5cebomhgb1ql+s2bNaN269T2K5s7s7e3ZvHkzhYWFXLx4EWdnZ6Kjo2nVqlWN140aNYpp06axYcMG+vTpQ0ZGhjZn+tSpUwwaNIjJkyfz9ttvY2tryzfffENYWBjFxcWYmZnVS+whISG4ubmxbt06nJ2dKSsr47HHHqO4uLhe7n87Q8P//W4rXp5UDK2vb507d+bkyZNs27aNnTt38swzzzBgwABSU1NxcXEhOzubnTt3smPHDqZMmcLSpUvZt2+fTowNTXrSm7Lq5p7f0sNeX3N1hBBCCCGEuJ2iKJiaGDXKcWvP91/h4+PDwYMHdf67OSMjAwsLC1q2bFmpfps2bSgtLeXYsWNaWXZ2NpcvX65VeyYmJrRo0YLS0lLS0tIYMmRIjfUtLCwYMWIE8fHxJCQk8Oijj9K7d28Ajh07RllZGcuXL+fxxx/n0Ucf5dy5c7WKA8pXX3dycuLQoUNa2e3PdvHiRbKzs5kzZw79+/fHx8eHP/74Q+c+Rkbl0w5qGrrv6emJkZERGRkZWllJSQlHjhyhbdu2tY65LiwtLXF2dtZpE8p/v7e2aWlpyciRI1m3bh2ffvopaWlp2jx5U1NTQkJCiImJYe/evRw8eJAffvjhnsRbW9KT3pRV15NekaRTChQBdZ+7IYQQQgghxMNgypQprFy5kldeeYXw8HCys7OZP38+kZGRVQ5F9/b2ZuDAgbz00kvExsZiYGDA1KlTMTU1rbGdQ4cOcfbsWXx9fTl79iwLFiygrKysVou8hYWF0bt3b7Kyspg5c6ZW3rp1a0pKSli9ejUhISFkZGTw4Ycf1un5X331VRYvXqwNxV+xYoXOCwcbGxvs7Oz46KOPcHJyIi8vj+joaJ17ODg4YGpqyldffUXLli0xMTHByspKp06zZs2YPHkyUVFR2Nra4urqypIlS7h+/fpdbSV3u5MnT1bap93Ly4uoqCjmz5+Pp6cnvr6+JCQkkJmZqa3Iv2LFCpycnOjUqRN6enps3LgRR0dHrK2tSUxM5ObNm3Tv3h0zMzM+/vhjTE1NdeatNwbpSW/Kbpl7rqq3DP9QzIA/3yzKkHchhBBCCCGq1aJFC9LT0zl8+DAdO3Zk0qRJhIWFMWfOnGqvSUhIwNnZmYCAAJ5++mlefPFFHBwcamynsLCQOXPm0LZtW5566ilatGjBN998U6t9xP39/fH29ubq1auMHTtWK+/YsSMrVqzg3Xff5bHHHiM5OVkbCl9br732GmPGjGHcuHH06NEDCwsLnnrqKe28np4eKSkpHDt2jMcee4xp06axdOlSnXsYGBgQExPD2rVrcXZ2rnZ0wOLFixk2bBhjxoyhc+fO5Obmsn37dmxsbOoUc1UiIyPp1KmTzvGvf/2LiIgIIiMjee2112jfvj1fffUVW7ZswcvLCygfqbBkyRK6du2Kn58fp06dIj09HT09PaytrVm3bh29evWiQ4cO7Ny5ky+++KLB9pmvjqI+ZOOlr169ipWVFVeuXMHS0rKxw6mRqhaj/v4YAIrDMZSK1d6Bst87g5qP8sjXKAbujRShEEIIIYR4kBQWFnLy5Ek8PDzuaqVtIR5mNf37qUseKj3pTZiiGAF/LlhQ7Qrv0pMuhBBCCCGEEA8KSdKbOm2v9NtXeK9mUTkhhBBCCCGEEPctSdKbOtkrXQghhBBCCCEeGpKkN3VaMl7NCu8y3F0IIYQQQgghHhiSpDd12nB3mZMuhBBCCCGEEA86SdKbuup60rU56VcbNh4hhBBCCCGEEPeMJOlNXXVzz5XyHnZVetKFEEIIIYQQ4oEhSXpT92ePuXrb6u6K3p9760lPuhBCCCGEEEI8MJpEkv7BBx/g7u6OiYkJ3bt35/Dhw9XWLSkpYeHChXh6emJiYkLHjh356quvGjDaBvZnj3nl1d0ryiVJF0IIIYQQQogHRaMn6Z9++imRkZHMnz+fb7/9lo4dOxIYGMiFCxeqrD9nzhzWrl3L6tWrOX78OJMmTeKpp57iX//6VwNH3jCUiuHulfZJt/qzXJJ0IYQQQggh/oq+ffsyderUGuu4u7uzcuXKBomnvoWGhjJ06FDtc0M+7/38vTWWRk/SV6xYwcSJExk/fjxt27blww8/xMzMjPj4+Crrr1+/ntdff53g4GBatWrF5MmTCQ4OZvny5Q0ceQPRq64n/c/h7tKTLoQQQgghHnKhoaEoilLpyM3NbbAYfvrpJ4YNG4a7uzuKolSbmNZlFPErr7yCj49Plefy8vLQ19dny5YtdY5106ZNvPnmm3W+riaJiYlYW1tXKj9y5AgvvvhivbZ1u71796IoCpcvX76n7TSURk3Si4uLOXbsGAMGDNDK9PT0GDBgAAcPHqzymqKiIkxMTHTKTE1N+eabb6qtf/XqVZ3jvlLt6u4VW7PdVi6EEEIIIcRDaODAgZw/f17n8PDwaLD2r1+/TqtWrVi8eDGOjo5V1qnrKOKwsDB+/vlnDhw4UOlcYmIiDg4OBAcH1zlWW1tbLCws6nzd3bC3t8fMzKxB2npQNGqS/t///pebN2/SvHlznfLmzZvz73//u8prAgMDWbFiBTk5OZSVlbFjxw42bdrE+fPnq6y/aNEirKystMPFxaXen+Oe0oa7V9OTXnalYeMRQgghhBCiCTI2NsbR0VHn0NfXB2Dfvn1069YNY2NjnJyciI6OprS0tNp7XbhwgZCQEExNTfHw8CA5OfmO7fv5+bF06VJGjRqFsbFxlXXqOorY19eXzp07VzqvqiqJiYmMGzcORVEICwvDw8MDU1NTvL29WbVqVY2x3j7cvTbPu2LFCtq3b0+zZs1wcXFhypQp5OeX5yh79+5l/PjxXLlyRRvFsGDBAqDycPe8vDyGDBmCubk5lpaWPPPMM/z+++/a+QULFuDr68v69etxd3fHysqKUaNGce3a3XdO/vHHH4wdOxYbGxvMzMwICgoiJydHO3/69GlCQkKwsbGhWbNmtGvXjvT0dO3a0aNHY29vj6mpKV5eXiQkJNx1LLXR6MPd62rVqlV4eXnRpk0bjIyMCA8PZ/z48ejpVf0os2bN4sqVK9px5syZBo74L6rYD/324e4Vq7ur11BVtWFjEkIIIYQQDwVVVblRWNwoR339N+7Zs2cJDg7Gz8+P7777jtjYWOLi4njrrbeqvSY0NJQzZ86wZ88eUlNTWbNmTbW93bV1N6OIobw3fcOGDRQU/G9L5r1793Ly5EkmTJhAWVkZLVu2ZOPGjRw/fpx58+bx+uuvs2HDhlrHVpvn1dPTIyYmhp9++omkpCR2797NjBkzAOjZsycrV67E0tJSG8Uwffr0Su2UlZUxZMgQLl26xL59+9ixYwe//vorI0eO1Kl34sQJNm/ezNatW9m6dSv79u1j8eLFtX6eqp7v6NGjbNmyhYMHD6KqKsHBwZSUlADw8ssvU1RUxP79+/nhhx949913MTcvz8Pmzp3L8ePH2bZtG1lZWcTGxvLII4/cdSy1YXBP734HjzzyCPr6+jpvTgB+//33aoeI2Nvbs3nzZgoLC7l48SLOzs5ER0fTqlWrKusbGxtX+ybrvlDt6u5/JumUgnoDFBlCIoQQQggh6ldhUQlPjI5plLZ3JEdgamJU6/pbt27VEiuAoKAgNm7cyJo1a3BxceH9999HURTatGnDuXPnmDlzJvPmzavU2ffLL7+wbds2Dh8+jJ+fHwBxcXHVzg2vrZpGEf/888/VXvfcc8/x2muvsXHjRkJDQwFISEjA39+fRx99FIA33nhDq+/h4cHBgwfZsGEDzzzzzB3jqu3z3trz7u7uzltvvcWkSZNYs2YNRkZGWFlZoShKtXkcwK5du/jhhx84efKkNsL573//O+3atePIkSNa+2VlZSQmJmpD8seMGcOuXbt4++237/g8t8vJyWHLli1kZGTQs2dPAJKTk3FxcWHz5s2MGDGCvLw8hg0bRvv27QF0csu8vDw6depE165dtWe/1xq1J93IyIguXf5/9u48vsYrf+D457lL9ptVIhEhsUUokcZSS6kfU2KoDmOZKlKxxjKqraVTS6c6tJam2tLqkJSaUUoxGlUUHcugOukgEVskIQiRfbu5y++PG1cjCUEQ9X2/XveV3Oc5z3POuUm8fJ/zPeeEsmvXLusxk8nErl27aN++/W2vtbOzw9fXF4PBwIYNG+jbt++Dbu6jUWm6uz3WZyyyeJwQQgghhHjCde3albi4OOtryRLLw4WEhATat2+PoijWsh07diQvL48LFy6Uu09CQgIajYbQ0FDrsaZNm1a4KNrD4OrqSr9+/awp7zk5OWzYsIGIiAhrmU8++YTQ0FA8PT1xcnJi+fLlpKSkVOn+Ve3vzp076datG76+vuh0OoYOHUpGRgYFBQVV7ktCQgJ+fn5lpiA3a9YMV1dXEhISrMf8/f3LzJn38fG550yGG/1r166d9ZiHhweBgYHWOidNmsTcuXPp2LEjs2fP5n//+5+17Lhx41i7di2tWrVi6tSpFa4PUN0e6Ug6wJQpUxg+fDitW7embdu2REVFkZ+fzyuvvALAsGHD8PX1Zd68eQAcOnSIixcv0qpVKy5evMicOXMwmUzWVIvfnBsLxFGE2VyComgBUBQFs6IDc6ZlGzZ15U+shBBCCCGEuBd2tlp2rJn0yOq+G46OjjRq1OgBteb+3UsW8Q0RERF069aNM2fOsHv3btRqNQMGDABg7dq1vP766yxatIj27duj0+lYsGABhw4dqra2nz9/nt69ezNu3Djeffdd3N3d2bdvHxEREej1+mpfGE6rLfuzVxQFk8lUrXX82siRI+nRowfffvst33//PfPmzWPRokVMnDiRsLAwkpOTiY2NZceOHXTr1o3x48ezcOHCB9aeRz4nfdCgQSxcuJBZs2bRqlUr4uLi+O6776xpICkpKWUWhSsqKuKtt96iWbNm/OEPf8DX15d9+/Y9sidbD0pCzkmWnvmMby/vvXmw3Lz00r3SZSRdCCGEEEI8AIqiYG9n80hevx75vh9BQUHWecg37N+/H51OR926dcuVb9q0KQaDgaNHj1qPJSYm3vf2XveTRdy1a1cCAgKIjo4mOjqawYMH4+joaO1Lhw4diIyMJCQkhEaNGnH27Nkqt6sq/T169Cgmk4lFixbxzDPP0KRJE9LS0sr1z2g03rauoKAgUlNTy6wTFh8fT1ZWFs2aNatym+9GUFAQBoOhzEOLjIwMEhMTy9Tp5+fH2LFj2bhxI6+99hqff/659ZynpyfDhw/nyy+/JCoqiuXLlz+Qtt7wyEfSASZMmMCECRMqPLdnz54y77t06UJ8fPxDaNWjdV2fyaHrh8kzNKOXmwOYCyzbrancbhZS6cCIbMMmhBBCCCFEJSIjI4mKimLixIlMmDCBxMREZs+ezZQpUypcfDowMJCePXsyZswYli1bhkajYfLkydjb29+2Hr1eb41T9Ho9Fy9eJC4uDicnJ+sI/52yiCujKAojRoxg8eLFZGZm8sEHH1jPNW7cmFWrVrF9+3YCAgJYvXo1R44cqfL2c1Xpb6NGjSgpKeGjjz6iT58+7N+/n08//bTMffz9/cnLy2PXrl0EBwfj4OBQboS9e/futGjRgiFDhhAVFYXBYCAyMpIuXbpY53zfj2PHjpVJk1cUheDgYPr27cuoUaP47LPP0Ol0TJ8+HV9fX+uU6cmTJxMWFkaTJk3IzMxk9+7d1jn5s2bNIjQ0lObNm1NcXMzWrVvve32CO3nkI+miYs4ayy9XriH3V3ulV7J4nFm2YRNCCCGEEKIivr6+xMbGcvjwYYKDgxk7diwRERG89dZblV4THR1NnTp16NKlC/369WP06NF4eXndtp60tDRCQkIICQnh0qVLLFy4kJCQEEaOHGktc6cs4tsJDw8nOzub5s2bl5lfPWbMGPr168egQYNo164dGRkZREZGVuGTqXp/g4ODWbx4Me+99x5PPfUUa9assU5HvqFDhw6MHTuWQYMG4enpyfvvv1+uHkVR2Lx5M25ubnTu3Jnu3bvToEEDvvrqq7tqb2U6d+5s/RmEhIRY59lHR0cTGhpK7969ad++PWazmdjYWGtavdFoZPz48QQFBdGzZ0+aNGnC0qVLAUuGwIwZM2jZsiWdO3dGrVazdu3aamlvZRTzE7Z/V05ODi4uLmRnZ+Ps7HznCx6R/5z4iWX5y3AwOvBJo9NgPIfi/iWKTVtrGVPWn6FoG4ruLRTHYY+wtUIIIYQQ4regqKiIpKQkAgICsLOze9TNEeKxcru/n7uJQ2UkvYZKO3oZgAIKbrPC+8290oUQQgghhBBCPP4kSK+hvNw8Ld+owaiUzge5NRhXWYJ0s0kWjhNCCCGEEEKI3wIJ0mso91qumHItMxFKzKVB+i0LxClK6aIIsrq7EEIIIYQQQvwmSJBeQ7l4OmPKsuwFWIyN5eCtwfiNLdhkJF0IIYQQQgghfhMkSK+hXGo5Y8q0jKQXGCw75ZVLa7eOpMucdCGEEEIIIYT4LZAgvYZydHHAlGX5Pquo9GC5kfTSheNMsgWbEEIIIYQQQvwW3FOQnpqayoULF6zvDx8+zOTJk1m+fHm1NexJpygKmiLLCHpWkSXtvVxau6zuLoQQQgghhBC/KfcUpL/00kvs3r0bgMuXL/O73/2Ow4cP85e//IW//vWv1drAJ5ltiS0AmYVGy4FKR9JlTroQQgghhBBC/BbcU5B+/Phx2rZtC8C6det46qmnOHDgAGvWrCEmJqY62/dEszdZVnW/ri8N0m8zkm42mx5iy4QQQgghhBBCPAj3FKSXlJRga2sZ5d25cycvvPACAE2bNuXSpUvV17onnJPKCYDMktIAvNxIeunCcZjBnPfwGiaEEEIIIcRvyHPPPcfkyZNvW8bf35+oqKiH0p7qNmfOHFq1amV9Hx4ezosvvnjba6rymVRFdd3nSXJPQXrz5s359NNP+fe//82OHTvo2bMnAGlpaXh4eFRrA59kOo1lpDzLZLAcuGUkXVFsAdsKzwkhhBBCCPGkCA8PR1GUcq8zZ848tDacOHGC/v374+/vj6IoFQb0P/74I3369KFOnTooisKmTZtue89Fixbh5uZGUVFRuXMFBQU4OzuzZMmSu27rhx9+WO0Z0Hv27EFRFLKyssoc37hxI++880611nWr8+fPoygKcXFxD7Seh+WegvT33nuPzz77jOeee44//elPBAcHA7BlyxZrGry4f+4OrgBkqixbsVWY1n5jr/RbR9mFEEIIIYR4gvTs2ZNLly6VeQUEBDy0+gsKCmjQoAHz58/H29u7wjL5+fkEBwfzySefVOmeQ4cOJT8/n40bN5Y79/XXX6PX63n55Zfvuq0uLi64urre9XX3wt3dHZ1Od+eCwuqegvTnnnuOa9euce3aNVauXGk9Pnr0aD799NNqa9yTzt3JHYBc7Y0jFaS139grXUbShRBCCCHEE8zW1hZvb+8yL7VaDcDevXtp27Yttra2+Pj4MH36dAwGQ6X3Sk9Pp0+fPtjb2xMQEMCaNWvuWH+bNm1YsGABgwcPtk4NvlVYWBhz587lD3/4Q5X65OXlRZ8+fcrEXDesXLmSF198EXd3d6ZNm0aTJk1wcHCgQYMGzJw5k5KSkkrve2u6e35+PsOGDcPJyQkfHx8WLVpU7prVq1fTunVrdDod3t7evPTSS6SnpwOWkeyuXbsC4ObmhqIohIeHA+XT3TMzMxk2bBhubm44ODgQFhbG6dOnredjYmJwdXVl+/btBAUF4eTkZH0Ac6+Ki4uZNGkSXl5e2NnZ0alTJ44cOVKmTUOGDMHT0xN7e3saN25MdHQ0AHq9ngkTJuDj44OdnR3169dn3rx599yWqtDcy0WFhYWYzWbc3NwASE5O5ptvviEoKIgePXpUawOfZJ4utQAwKCrM2KJQbNkT/caq7mD53giYZa90IYQQQghRvcxmM4W3CWYfJHuNBkVR7vs+Fy9epFevXoSHh7Nq1SpOnjzJqFGjsLOzY86cORVeEx4eTlpaGrt370ar1TJp0iRrQPqwRURE0Lt3b5KTk6lfvz4A586d48cff2T79u0A6HQ6YmJiqFOnDseOHWPUqFHodDqmTp1apTreeOMN9u7dy+bNm/Hy8uLNN9/k559/LjOPvaSkhHfeeYfAwEDS09OZMmUK4eHhxMbG4ufnx4YNG+jfvz+JiYk4Oztjb29fYV3h4eGcPn2aLVu24OzszLRp0+jVqxfx8fFotZbRyYKCAhYuXMjq1atRqVS8/PLLvP7661V6WFKRqVOnsmHDBr744gvq16/P+++/T48ePThz5gzu7u7MnDmT+Ph4tm3bRq1atThz5gyFhYUALFmyhC1btrBu3Trq1atHamoqqamp99SOqrqnIL1v377069ePsWPHkpWVRbt27dBqtVy7do3Fixczbty46m7nE8nN0xVjlgm1qwqT4oTaXFx+T/Qb6e4yki6EEEIIIapZocHAU8vufs5zdTg+bhIOWu2dC5baunUrTk5O1vdhYWGsX7+epUuX4ufnx8cff4yiKDRt2pS0tDSmTZvGrFmzUKnKJhefOnWKbdu2cfjwYdq0aQPAihUrCAoKqp6O3aUePXpQp04doqOjrQ8VYmJi8PPzo1u3bgC89dZb1vL+/v68/vrrrF27tkpBel5eHitWrODLL7+03u+LL76gbt26ZcqNGDHC+n2DBg1YsmQJbdq0IS8vDycnJ9zdLVnAXl5elabS3wjO9+/fT4cOHQBYs2YNfn5+bNq0iQEDBgCWBwKffvopDRs2BGDChAn3vNV3fn4+y5YtIyYmhrCwMAA+//xzduzYwYoVK3jjjTdISUkhJCSE1q1bA5bP8IaUlBQaN25Mp06dUBTF+qDkQbqndPeff/6ZZ599FrDMhahduzbJycmsWrXqnhYuEBVzqaXDlGWZj27AwXLQdMuIueJaejzrobVLCCGEEEKImqZr167ExcVZXzfikoSEBNq3b19mVL5jx47k5eVx4cKFcvdJSEhAo9EQGhpqPda0adOHNof7Vmq1muHDhxMTE4PZbMZkMvHFF1/wyiuvWB8wfPXVV3Ts2BFvb2+cnJx46623SElJqdL9z549i16vp127dtZj7u7uBAYGlil39OhR+vTpQ7169dDpdHTp0gWgyvXAzc/213V5eHgQGBhIQkKC9ZiDg4M1QAfw8fG550yGs2fPUlJSQseOHa3HtFotbdu2tdY5btw41q5dS6tWrZg6dSoHDhywlg0PDycuLo7AwEAmTZrE999/f0/tuBv3NJJeUFBgnfz//fff069fP1QqFc888wzJycnV2sAnmYunM6aDZvAHvdnOso57uW3YXAEwm7O4/2QgIYQQQgghbrLXaDg+btIjq/tuODo60qhRowfUmkdrxIgRzJs3jx9++AGTyURqaiqvvPIKAAcPHmTIkCG8/fbb9OjRAxcXF9auXVvhvPJ7lZ+fT48ePejRowdr1qzB09OTlJQUevTogV6vr7Z6btDekkGhKApms7na67khLCyM5ORkYmNj2bFjB926dWP8+PEsXLiQp59+mqSkJLZt28bOnTsZOHAg3bt35+uvv35g7bmnkfRGjRqxadMmUlNT2b59O88//zxgWWDB2dn5DleLqnL20GHKtPwyFhpLf1Fv3YZN5VZ6PPNhNk0IIYQQQjwBFEXBQat9JK/qmI8OEBQUxMGDB8sEefv370en05VL6QbLqLnBYODo0aPWY4mJieW2FnuYGjZsSJcuXVi5ciXR0dF0797dmnZ94MAB6tevz1/+8hdat25N48aN72rgtGHDhmi1Wg4dOmQ9lpmZyalTp6zvT548SUZGBvPnz+fZZ5+ladOm5Ua2bWxsADAajZXWFRQUhMFgKFNXRkYGiYmJNGvWrMptvhsNGzbExsaG/fv3W4+VlJRw5MiRMnV6enoyfPhwvvzyS6Kioli+fLn1nLOzM4MGDeLzzz/nq6++YsOGDVy/fv2BtBfucSR91qxZvPTSS7z66qv83//9H+3btwcso+ohISHV2sAnmY2tFlWB5TlKnl6Flz2VjqRLursQQgghhBDlRUZGEhUVxcSJE5kwYQKJiYnMnj2bKVOmlJuPDhAYGEjPnj0ZM2YMy5YtQ6PRMHny5EoXQrtBr9cTHx9v/f7ixYvExcXh5ORkHeHPy8srs3d7UlIScXFxuLu7U69evdvePyIiglGjRgGU2eO8cePGpKSksHbtWtq0acO3337LN998U6XPBsDJyYmIiAjeeOMNPDw88PLy4i9/+UuZz6ZevXrY2Njw0UcfMXbsWI4fP15u7/P69eujKApbt26lV69e2Nvbl1kj4EZb+/bty6hRo/jss8/Q6XRMnz4dX19f+vbtW+U2VyYxMbHcsebNmzNu3DjeeOMN6+f8/vvvU1BQQEREBGCJb0NDQ2nevDnFxcVs3brVugbB4sWL8fHxISQkBJVKxfr16/H29n6g0x/uaST9j3/8IykpKfz000/WFQUBunXrxgcffFBtjRNgo7ds35BTYvlRmW9dIE65sXBc1kNslRBCCCGEEI8HX19fYmNjOXz4MMHBwYwdO5aIiIgyi63dKjo6mjp16tClSxf69evH6NGj8fLyum09aWlphISEEBISwqVLl1i4cCEhISGMHDnSWuann36ylgGYMmUKISEhzJo164796N+/P7a2tjg4OJTZPu2FF17g1VdfZcKECbRq1YoDBw4wc+bMO97v1xYsWMCzzz5Lnz596N69O506dSozJ9/T05OYmBjWr19Ps2bNmD9/PgsXLixzD19fX95++22mT59O7dq1mTBhQoV1RUdHExoaSu/evWnfvj1ms5nY2NhyKe73YvDgwdbP98brypUrzJ8/n/79+zN06FCefvppzpw5w/bt2627ldnY2DBjxgxatmxJ586dUavVrF27FrCsnP/+++/TunVr2rRpw/nz54mNja3wAU91Ucz3mdx/Y7GFilJFaqKcnBxcXFzIzs5+LFLzx7wxmaIBubzikEXnWsfAYQgq59nW8+biA5gzw0HTCFWt2EfXUCGEEEII8dgrKioiKSmJgIAA7OzsHnVzhHis3O7v527i0HsK/00mE3/9619xcXGhfv361K9fH1dXV9555x1MJtO93FJUwlFxBCD7xtSOW0fSJd1dCCGEEEIIIX4z7mlO+l/+8hdWrFjB/PnzrUvZ79u3jzlz5lBUVMS7775brY18kjlrdWRwmWxjacJDuTnpNxaOy8JsNlfbAhtCCCGEEEIIIR6+ewrSv/jiC/7+97/zwgsvWI+1bNkSX19fIiMjJUivRq62ljnn1tC8spF0DGDOB6Xs4gxCCCGEEEIIIR4f95Tufv36dZo2bVrueNOmTR/oUvRPIg+dBwC56tIR8lu3YFPswbKDumzDJoQQQgghhBCPuXsK0oODg/n444/LHf/4449p2bLlfTdK3OTl7onZYKbAVJr0cGu6O9wcTTdnPaxmCSGEEEIIIYR4AO4p3f3999/n97//PTt37rTukX7w4EFSU1OJjZUVxqtD7OlTfHjoAPWwxZRpJt+29Ed1a7o7WIJ00xVZPE4IIYQQQgghHnP3NJLepUsXTp06xR/+8AeysrLIysqiX79+nDhxgtWrV1d3G59IRrOJ09czSDcXY7z2q5F0ijGbi8sW/tXicUIIIYQQQgghHl/3NJIOUKdOnXILxP3yyy+sWLGC5cuX33fDnnTeTpYF4K4b9dhlmSgyazCjoGC2jKarPW8WVlwtX2VOuhBCCCGEEEI81u5pJF08eL4OBfzONwl/p9MYr5oxo1BiLl0gzpxdtnDpnHSzjKQLIYQQQgghxGNNgvQaylPzE8s6fs+wRnGo9DYAFBotXzHlli0sC8cJIYQQQghxz5577jkmT5582zL+/v5ERUU9lPZUt/DwcF588UXr+4fZ38f5c3tUJEivoTTaOgD4OOShUjkAkG+8sXhcVpmyyo0gXUbShRBCCCHEEyg8PBxFUcq9zpw589DacOLECfr374+/vz+KolQYmM6bN482bdqg0+nw8vLixRdfJDExsdJ7Tpw4kaCgoArPpaSkoFar2bJly123dePGjbzzzjt3fd3txMTE4OrqWu74kSNHGD16dLXWdas9e/agKApZWVkPtJ6H5a7mpPfr1++2538rH0qNoPIGwNshH7XWESggz6i2nKsk3V2CdCGEEEII8aTq2bMn0dHRZY55enpWUrr6FRQU0KBBAwYMGMCrr75aYZm9e/cyfvx42rRpg8Fg4M033+T5558nPj4eR0fHcuUjIiL4+OOPOXDgAB06dChzLiYmBi8vL3r16nXXbXV3d7/ra+7Vw/wZ/Fbc1Ui6i4vLbV/169dn2LBhD6qtTxa1DwDutkVo7e0AyDGW/rhMtwTp1oXjsh5O24QQQgghhKhhbG1t8fb2LvNSqy2DXHv37qVt27bY2tri4+PD9OnTMRgMld4rPT2dPn36YG9vT0BAAGvWrLlj/W3atGHBggUMHjwYW1vbCst89913hIeH07x5c4KDg4mJiSElJYWjR49WWL5Vq1Y8/fTTrFy5ssxxs9lMTEwMw4cPR1EUIiIiCAgIwN7ensDAQD788MPbtvXWdPeq9Hfx4sW0aNECR0dH/Pz8iIyMJC8vD7CMZL/yyitkZ2dbsxjmzJkDlE93T0lJoW/fvjg5OeHs7MzAgQO5cuWK9fycOXNo1aoVq1evxt/fHxcXFwYPHkxu7i1Tfu9CZmYmw4YNw83NDQcHB8LCwjh9+rT1fHJyMn369MHNzQ1HR0eaN29u3Vo8MzOTIUOG4Onpib29PY0bNy73MKi63dVI+oNujPgVxZliky22qmJc3cxkAjmlz1TMpiyUX5eVOelCCCGEEOIBMJvNFN4mmH2Q7DUaFEW5c8E7uHjxIr169SI8PJxVq1Zx8uRJRo0ahZ2dnTWQvFV4eDhpaWns3r0brVbLpEmTSE9Pv++23Co72zL4druR7YiICKZPn86HH35oHW3fs2cPSUlJjBgxApPJRN26dVm/fj0eHh4cOHCA0aNH4+Pjw8CBA6vUjqr0V6VSsWTJEgICAjh37hyRkZFMnTqVpUuX0qFDB6Kiopg1a5Y1fd+pdLeqXzOZTNYAfe/evRgMBsaPH8+gQYPYs2ePtdzZs2fZtGkTW7duJTMzk4EDBzJ//vxyu4tVVXh4OKdPn2bLli04Ozszbdo0evXqRXx8PFqtlvHjx6PX6/nxxx9xdHQkPj7e2v6ZM2cSHx/Ptm3bqFWrFmfOnKGwsPCe2lFV97wFm3iwFEWhwOiOreoSzq4lXDeYyb+xV/qtwbh1n3TZgk0IIYQQQlSfQoOBp5YteSR1Hx83CQettsrlt27dWiYwDAsLY/369SxduhQ/Pz8+/vhjFEWhadOmpKWlMW3aNGbNmoVKVTa5+NSpU2zbto3Dhw/Tpk0bAFasWFHp3PB7ZTKZmDx5Mh07duSpp56qtNxLL73Ea6+9xvr16wkPDwcsg6edOnWiSZMmALz99tvW8gEBARw8eJB169ZVKUivan9/PfLu7+/P3LlzGTt2LEuXLsXGxgYXFxcURcHb27vSunbt2sWxY8dISkrCz88PgFWrVtG8eXOOHDlird9kMhETE4NOpwNg6NCh7Nq1656C9BvB+f79+61TBtasWYOfnx+bNm1iwIABpKSk0L9/f1q0aAFAgwYNrNenpKQQEhJC69atrX1/0GThuBqsBC8AdC55mK6byTOV/iN1a7q7ysPy1ZyP2Vz8EFsohBBCCCFEzdC1a1fi4uKsryVLLA8XEhISaN++fZlR+Y4dO5KXl8eFCxfK3SchIQGNRkNoaKj1WNOmTStcFO1+jB8/nuPHj7N27drblnN1daVfv37WlPecnBw2bNhARESEtcwnn3xCaGgonp6eODk5sXz5clJSUqrUjqr2d+fOnXTr1g1fX190Oh1Dhw4lIyODgoKCKvbYUpefn581QAdo1qwZrq6uJCQkWI/5+/tbA3QAHx+fe85kuNG/du3aWY95eHgQGBhorXPSpEnMnTuXjh07Mnv2bP73v/9Zy44bN461a9fSqlUrpk6dyoEDB+6pHXdDRtJrMrU38As6xxyMKZAfUPHq7ig6QAuUgCkD1HUebjuFEEIIIcRvkr1Gw/Fxkx5Z3XfD0dGRRo0aPaDWVK8JEyawdetWfvzxR+rWrXvH8hEREXTr1o0zZ86we/du1Go1AwYMAGDt2rW8/vrrLFq0iPbt26PT6ViwYAGHDh2qtvaeP3+e3r17M27cON59913c3d3Zt28fERER6PV6HBwcqq0uAO0tGRSKomAymaq1jl8bOXIkPXr04Ntvv+X7779n3rx5LFq0iIkTJxIWFkZycjKxsbHs2LGDbt26MX78eBYuXPjA2iMj6TWYjdYXABe7bIzXTOSZKtmCTVFAVTqPxZTxEFsohBBCCCF+yxRFwUGrfSSv6piPDhAUFMTBgwcxm83WY/v370en01UYIDdt2hSDwVBmMbfExMRq2cnKbDYzYcIEvvnmG3744QcCAgKqdF3Xrl0JCAggOjqa6OhoBg8ebJ2ffiONOzIykpCQEBo1asTZs2er3Kaq9Pfo0aOYTCYWLVrEM888Q5MmTUhLSytzHxsbG4xG423rCgoKIjU1ldTUVOux+Ph4srKyaNasWZXbfDeCgoIwGAxlHlpkZGSQmJhYpk4/Pz/Gjh3Lxo0bee211/j888+t5zw9PRk+fDhffvklUVFRLF++/IG09QYZSa/BHGz9oAA87XMxZurIN5Y+Ubp1CzawpLybrkiQLoQQQgghxK9ERkYSFRXFxIkTmTBhAomJicyePZspU6aUm48OEBgYSM+ePRkzZgzLli1Do9EwefJk7O3tb1uPXq8nPj7e+v3FixeJi4vDycnJOsI/fvx4/vGPf7B582Z0Oh2XL18GLLto3e7+iqIwYsQIFi9eTGZmJh988IH1XOPGjVm1ahXbt28nICCA1atXc+TIkSo/AKhKfxs1akRJSQkfffQRffr0Yf/+/Xz66adl7uPv709eXh67du0iODgYBweHciPs3bt3p0WLFgwZMoSoqCgMBgORkZF06dLFOuf7fhw7dqxMmryiKAQHB9O3b19GjRrFZ599hk6nY/r06fj6+tK3b1/AMt8+LCyMJk2akJmZye7du61z8mfNmkVoaCjNmzenuLiYrVu3Vvv6BLeSkfQazNbW8mTP2z6fknxVpSPpwM156cbrD6dxQgghhBBCPAZ8fX2JjY3l8OHDBAcHM3bsWCIiInjrrbcqvSY6Opo6derQpUsX+vXrx+jRo/Hy8rptPWlpaYSEhBASEsKlS5dYuHAhISEhjBw50lpm2bJlZGdn89xzz+Hj42N9ffXVV3fsR3h4ONnZ2TRv3rzM/OoxY8bQr18/Bg0aRLt27cjIyCAyMrIKn0zV+xscHMzixYt57733eOqpp1izZg3z5s0rc48OHTowduxYBg0ahKenJ++//365ehRFYfPmzbi5udG5c2e6d+9OgwYNqtT/qujcubP1ZxASEmKdZx8dHU1oaCi9e/emffv2mM1mYmNjrWn1RqOR8ePHExQURM+ePWnSpAlLly4FLBkCM2bMoGXLlnTu3Bm1Wn3HdQTul2L+dd7HEyAnJwcXFxeys7NxdnZ+1M25LXNJIuaMPmQW2/LKoo7UG5bPkrqWNA2ldjyKcjMRwpT1BhRtRnF6A8Vp1KNqshBCCCGEeIwVFRWRlJREQEAAdnZ2j7o5QjxWbvf3czdxqIyk12RqHwDcbIuxdbQj3/SrBRTMOWXLqmpZDku6uxBCCCGEEEI8tiRIr8kUHcVGWwDcPVSYUCisbPE4dWm6uwTpQgghhBBCCPHYkiC9hjp57SqfHj1MVokLAK7ulpUS80xqS4Fye6XfWN1d5qQLIYQQQgghxONKgvQa6nj6FRYc2EdagWVrBRcXPWaDmbwbK7zfunjcjYXjTNceXiOFEEIIIYQQQlQrCdJrKHd7y3YFVwqdAHBxzsN4zUz+jXR3c1bZC1SS7i6EEEIIIYQQjzsJ0msoj9I9BdPyLSPprg45GK+aybuxeFy5dPcbQfp1nrAF+4UQQgghhBDiN6NGBOmffPIJ/v7+2NnZ0a5dOw4fPnzb8lFRUQQGBmJvb4+fnx+vvvoqRUVFD6m1D0et0pH087mWpfu97fMxZGAdSTdXNicdQ/mV34UQQgghhBBCPBYeeZD+1VdfMWXKFGbPns3PP/9McHAwPXr0ID09vcLy//jHP5g+fTqzZ88mISGBFStW8NVXX/Hmm28+5JY/WO729gBcyLd89XbIQ5+r/lW6e2aZ8opiC4rO8kZS3oUQQgghhBDisfTIg/TFixczatQoXnnlFZo1a8ann36Kg4MDK1eurLD8gQMH6NixIy+99BL+/v48//zz/OlPf7rj6Pvjxl6rxUGr5VKBZU66j30+xYWaytPdQealCyGEEEIIIcRj7pEG6Xq9nqNHj9K9e3frMZVKRffu3Tl48GCF13To0IGjR49ag/Jz584RGxtLr169KixfXFxMTk5Omdfj4ODP56DYzKXC0jnptsVoNJqbI+kVBumyDZsQQgghhBB367nnnmPy5Mm3LePv709UVNRDaU91Cw8P58UXX7S+f5j9fZw/t0flkQbp165dw2g0Urt27TLHa9euzeXLlyu85qWXXuKvf/0rnTp1QqvV0rBhQ5577rlK093nzZuHi4uL9eXn51ft/XgQcvOLKckrIa/EliKjLQDu7mryjTeC9KzyF8k2bEIIIYQQ4gkUHh6OoijlXmfOnHlobThx4gT9+/fH398fRVEqDEyXLVtGy5YtcXZ2xtnZmfbt27Nt27ZK7zlx4kSCgoIqPJeSkoJarWbLli133daNGzfyzjvv3PV1txMTE4Orq2u540eOHGH06NHVWtet9uzZg6IoZGVlPdB6HpZHnu5+t/bs2cPf/vY3li5dys8//8zGjRv59ttvK/0lmzFjBtnZ2dZXamrqQ27xvfH2dEalt3yfXeIGgKvbzdXdzRWNlpcG6WajBOlCCCGEEOLJ0rNnTy5dulTmFRAQ8NDqLygooEGDBsyfPx9vb+8Ky9StW5f58+dz9OhRfvrpJ/7v//6Pvn37cuLEiQrLR0REcPLkSQ4cOFDuXExMDF5eXpVmFN+Ou7s7Op3urq+7F56enjiU7lwlquaRBum1atVCrVZz5cqVMsevXLlS6S/2zJkzGTp0KCNHjqRFixb84Q9/4G9/+xvz5s3DZDKVK29ra2t9UnXj9Tjw9nRGXWL5PrvEFQAX92Kys2+s7l5RkF7L8lXmpAshhBBCiCeMra0t3t7eZV5qtRqAvXv30rZtW2xtbfHx8WH69OkYDIZK75Wenk6fPn2wt7cnICCANWvW3LH+Nm3asGDBAgYPHoytrW2FZfr06UOvXr1o3LgxTZo04d1338XJyYn//Oc/FZZv1aoVTz/9dLn1usxmMzExMQwfPhxFUYiIiCAgIAB7e3sCAwP58MMPb9vWW9Pdq9LfxYsX06JFCxwdHfHz8yMyMpK8vDzAMpD6yiuvkJ2dbc1imDNnDlA+3T0lJYW+ffvi5OSEs7MzAwcOLBMPzpkzh1atWrF69Wr8/f1xcXFh8ODB5Obm3rZPt5OZmcmwYcNwc3PDwcGBsLAwTp8+bT2fnJxMnz59cHNzw9HRkebNmxMbG2u9dsiQIXh6emJvb0/jxo2Jjo6+57ZUxSMN0m1sbAgNDWXXrl3WYyaTiV27dtG+ffsKrykoKEClKtvsG398v6X9wT1cHVEbFACuF1keLLg755GZZumriiLMpoIy1yhqL8s3popXxhdCCCGEEOJumM1mCkpKHsmruv5vf/HiRXr16kWbNm345ZdfWLZsGStWrGDu3LmVXhMeHk5qaiq7d+/m66+/ZunSpZXuPnWvjEYja9euJT8/v9LYByyj6evWrSM/P996bM+ePSQlJTFixAhMJhN169Zl/fr1xMfHM2vWLN58803WrVtX5bZUpb8qlYolS5Zw4sQJvvjiC3744QemTp0KWNYNi4qKwtnZ2ZrF8Prrr5erx2Qy0bdvX65fv87evXvZsWMH586dY9CgQWXKnT17lk2bNrF161a2bt3K3r17mT9/fpX7U1H/fvrpJ7Zs2cLBgwcxm8306tWLkhLLqOj48eMpLi7mxx9/5NixY7z33ns4OVkW8J45cybx8fFs27aNhIQEli1bRq1ate65LVWheaB3r4IpU6YwfPhwWrduTdu2bYmKiiI/P59XXnkFgGHDhuHr68u8efMAy9OnxYsXExISQrt27Thz5gwzZ86kT58+1mD9t0CtVuFiY0suxVzOdwJPyzZsB9PdKTEraBWzZYE41a9SR1Slc/uNVyq+qRBCCCGEEHeh0GDgqWVLHkndx8dNwkGrrXL5rVu3WgMrgLCwMNavX8/SpUvx8/Pj448/RlEUmjZtSlpaGtOmTWPWrFnlBgBPnTrFtm3bOHz4MG3atAFgxYoVlc4Nv1vHjh2jffv2FBUV4eTkxDfffEOzZs0qLf/SSy/x2muvsX79esLDwwGIjo6mU6dONGnSBIC3337bWj4gIICDBw+ybt06Bg4ceMf2VLW/vx559/f3Z+7cuYwdO5alS5diY2ODi4sLiqJUmhENsGvXLo4dO0ZSUpJ1rbBVq1bRvHlzjhw5Yq3fZDIRExNjTckfOnQou3bt4t13371jf251+vRptmzZwv79++nQoQMAa9aswc/Pj02bNjFgwABSUlLo378/LVq0AKBBgwbW61NSUggJCaF169bWvj9ojzxIHzRoEFevXmXWrFlcvnyZVq1a8d1331kXk0tJSSnzh/PWW2+hKApvvfUWFy9exNPTkz59+tzTD6ymq+XgyAWKOZ9j2Svd1zGP4jxPcow2eGiKwXwdqHvzAnVpkC4j6UIIIYQQ4gnTtWtXli1bZn3v6GjZJSkhIYH27dujKIr1XMeOHcnLy+PChQvUq1evzH0SEhLQaDSEhoZajzVt2rTCRdHuRWBgIHFxcWRnZ/P1118zfPhw9u7dW2mg7urqSr9+/Vi5ciXh4eHk5OSwYcMGPvnkE2uZTz75hJUrV5KSkkJhYSF6vZ5WrVpVqT1V7e/OnTuZN28eJ0+eJCcnB4PBQFFREQUFBVWec56QkICfn1+ZxbybNWuGq6srCQkJ1iDd39+/zJx5Hx+fe85kuNG/du3aWY95eHgQGBhIQkICAJMmTWLcuHF8//33dO/enf79+9OyZUsAxo0bR//+/fn55595/vnnefHFF63B/oPyyIN0gAkTJjBhwoQKz+3Zs6fMe41Gw+zZs5k9e/ZDaNmj5e3sBObrnM22A8DXIZfiIi25Ji0eFIMxA379cFF1I939GmazAUWpET9eIYQQQgjxmLLXaDg+btIjq/tuODo60qhRowfUmupjY2NjbWdoaChHjhzhww8/5LPPPqv0moiICLp168aZM2fYvXs3arWaAQMGALB27Vpef/11Fi1aRPv27dHpdCxYsIBDhw5VW5vPnz9P7969GTduHO+++y7u7u7s27ePiIgI9Hp9tS8Mp70lg0JRlArXH6suI0eOpEePHnz77bd8//33zJs3j0WLFjFx4kTCwsJITk4mNjaWHTt20K1bN8aPH8/ChQsfWHseu9XdnyR13V0BOJ1j+aWv65hLsVFLjrH0l/bWxeNU7oAaMMs2bEIIIYQQ4r4pioKDVvtIXr8e+b4fQUFB1nnIN+zfvx+dTkfdunXLlW/atCkGg4GjR49ajyUmJj6w7b1MJhPFxcW3LdO1a1cCAgKIjo4mOjqawYMHWzMFbqRxR0ZGEhISQqNGjTh79myV669Kf48ePYrJZGLRokU888wzNGnShLS0tDL3sbGxwWg03rauoKAgUlNTy+y4FR8fT1ZW1m1T/u9HUFAQBoOhzEOLjIwMEhMTy9Tp5+fH2LFj2bhxI6+99hqff/659ZynpyfDhw/nyy+/JCoqiuXLlz+Qtt4gQ6011JXkqxSevQaucD7fDpNZhY3ahKOzQm4lQbqiqDCrPMF02ZLyrq58PogQQgghhBBPgsjISKKiopg4cSITJkwgMTGR2bNnM2XKlHLz0cGSjt6zZ0/GjBnDsmXL0Gg0TJ48GXt7+9vWo9friY+Pt35/8eJF4uLicHJyso6cz5gxg7CwMOrVq0dubi7/+Mc/2LNnD9u3b7/tvRVFYcSIESxevJjMzEw++OAD67nGjRuzatUqtm/fTkBAAKtXr+bIkSNV3n6uKv1t1KgRJSUlfPTRR/Tp04f9+/fz6aeflrmPv78/eXl57Nq1i+DgYBwcHMqNsHfv3p0WLVowZMgQoqKiMBgMREZG0qVLF+uc7/tx7NixMmnyiqIQHBxM3759GTVqFJ999hk6nY7p06fj6+tL3759Act8+7CwMJo0aUJmZia7d++2zsmfNWsWoaGhNG/enOLiYrZu3Vpt6xNURkbSa6iD//qJfUt3AmBARbHZEwAPTzO5pXulG41Xy194Y4V3WTxOCCGEEEIIfH19iY2N5fDhwwQHBzN27FgiIiJ46623Kr0mOjqaOnXq0KVLF/r168fo0aPx8vK6bT1paWmEhIQQEhLCpUuXWLhwISEhIYwcOdJaJj09nWHDhhEYGEi3bt04cuQI27dv53e/+90d+xEeHk52djbNmzcvM796zJgx9OvXj0GDBtGuXTsyMjKIjIyswidT9f4GBwezePFi3nvvPZ566inWrFljXdj7hg4dOjB27FgGDRqEp6cn77//frl6FEVh8+bNuLm50blzZ7p3706DBg346quv7qq9lencubP1ZxASEmKdZx8dHU1oaCi9e/emffv2mM1mYmNjrWn1RqOR8ePHExQURM+ePWnSpAlLly4FLBkCM2bMoGXLlnTu3Bm1Ws3atWurpb2VUcy/pX3LqiAnJwcXFxeys7Nr9J7pX8Ts5vOvDnCpuy1mrcLRPx7CRRXH6/95jjYNzzPI8zxF2h44eHxU5jpT5ngo3oHiPBvFYcgjar0QQgghhHgcFRUVkZSUREBAAHZ2do+6OUI8Vm7393M3caiMpNdQdRrUBgdbVJat+8grsYyk13XKI+u6ZZZCibGCFQ5LR9LNFZ0TQgghhBBCCFGjSZBeQwXUswTlar3lfVqO5WmLr2Mu1zNtLQdNGeWuU6wrvEuQLoQQQgghhBCPGwnSayg/H1cAVKVBelKmZdEFP8dcrpYG6WpzVvkLVaV7pctIuhBCCCGEEEI8diRIr6H0BcU4atXWkfTT1y1zGuo65pCeY9luwZY8yi0pcGPhOJMsHCeEEEIIIYQQjxsJ0muoPV8doPDidetIesI1y8qDPvb5pBc6AaBWTGDOL3uhjKQLIYQQQgghxGNLgvQayqOOG0pekXUk/cx1FSazFrXKjI2ThiKT5UdnNl4re+GNkXRzFmZz8UNssRBCCCGEEEKI+yVBeg3lUccdVV6hdSQ911SCSVUHAHcPAzlGy8h6fklK2QsVZ6B0YTnZK10IIYQQQgghHisSpNdQHnXcUHJvjqSXaM2oNH4A+DoVkFNsA0BOcXKZ6xRFAbWP5Y0x7aG1VwghhBBCCCHE/ZMgvYZy83JBXVCMujRj3aRVyC2ybMvm55hDdoFlJL2gJLX8xWpfy1cJ0oUQQgghhBDisSJBeg2l1qhxc7JD0ZvAZFnBPSVDB4C/LpvMPMtIenHJpQoutqTFY7r4UNoqhBBCCCHE4+y5555j8uTJty3j7+9PVFTUQ2lPdQsPD+fFF1+0vn+Y/X2cP7dHRYL0GqxWHTdUecXWlPe405Yfl79TNtfy7QEwVLCKu1I6km6WkXQhhBBCCPEECA8PR1GUcq8zZ848tDacOHGC/v374+/vj6IodwxM58+fj6Iotw2WJ06cSFBQUIXnUlJSUKvVbNmy5a7bunHjRt555527vu52YmJicHV1LXf8yJEjjB49ulrrutWePXtQFIWsrKwHWs/DIkF6DZWZno3GRovyq8Xjjp5VAMtI+tUCB8tB0/XyF98YSTfKSLoQQgghhHgy9OzZk0uXLpV5BQQEPLT6CwoKaNCgAfPnz8fb2/u2ZY8cOcJnn31Gy5Ytb1suIiKCkydPcuDAgXLnYmJi8PLyolevXnfdVnd3d3Q63V1fdy88PT1xcHB4KHX9VkiQXkPt/+YwCf85hepX27AdS1NjNqtw0BjIV+wA0JKDyWwqe7F1TroE6UIIIYQQ4slga2uLt7d3mZdarQZg7969tG3bFltbW3x8fJg+fToGg6HSe6Wnp9OnTx/s7e0JCAhgzZo1d6y/TZs2LFiwgMGDB2Nra1tpuby8PIYMGcLnn3+Om5vbbe/ZqlUrnn76aVauXFnmuNlsJiYmhuHDh6MoChEREQQEBGBvb09gYCAffvjhbe97a7p7Vfq7ePFiWrRogaOjI35+fkRGRpKXlwdYRrJfeeUVsrOzrVkMc+bMAcqnu6ekpNC3b1+cnJxwdnZm4MCBXLlyc1eqOXPm0KpVK1avXo2/vz8uLi4MHjyY3Nzc2/bpdjIzMxk2bBhubm44ODgQFhbG6dOnreeTk5Pp06cPbm5uODo60rx5c2JjY63XDhkyBE9PT+zt7WncuDHR0dH33JaqkCC9hnL1cUbTUIWNWwGq0sXjMs1mzOq6ljc2GgAcVXqu628ZTbcG6Zcx3xrACyGEEEIIUUVms5mCkpJH8jKbzdXSh4sXL9KrVy/atGnDL7/8wrJly1ixYgVz586t9Jrw8HBSU1PZvXs3X3/9NUuXLiU9vfw003sxfvx4fv/739O9e/cqlY+IiGDdunXk5+dbj+3Zs4ekpCRGjBiByWSibt26rF+/nvj4eGbNmsWbb77JunXrqtymqvRXpVKxZMkSTpw4wRdffMEPP/zA1KlTAejQoQNRUVE4Oztbsxhef/31cvWYTCb69u3L9evX2bt3Lzt27ODcuXMMGjSoTLmzZ8+yadMmtm7dytatW9m7dy/z58+vcn8q6t9PP/3Eli1bOHjwIGazmV69elFSUgJYfibFxcX8+OOPHDt2jPfeew8nJycAZs6cSXx8PNu2bSMhIYFly5ZRq1ate25LVWge6N3FPTvjcxqvNU4U/FvPlQuWYwZ7NcVFPtjbpOCktQyvO6v1XCy6TC3bX/2iqLwANVACpnRQ3z7dRgghhBBCiIoUGgw8tWzJI6n7+LhJOGi1VS6/detWa2AFEBYWxvr161m6dCl+fn58/PHHKIpC06ZNSUtLY9q0acyaNQuVquy45alTp9i2bRuHDx+mTZs2AKxYsaLSueF3Y+3atfz8888cOXKkyte89NJLvPbaa6xfv57w8HAAoqOj6dSpE02aNAHg7bfftpYPCAjg4MGDrFu3joEDB97x/lXt769H3v39/Zk7dy5jx45l6dKl2NjY4OLigqIot03137VrF8eOHSMpKQk/P8v20qtWraJ58+YcOXLEWr/JZCImJsaakj906FB27drFu+++e8f+3Or06dNs2bKF/fv306FDBwDWrFmDn58fmzZtYsCAAaSkpNC/f39atGgBQIMGDazXp6SkEBISQuvWra19f9BkJL2Gauph+YOzaWK2zkk32kLSWcuq7u52RQDoVCVcLiy7wruiaEBV+schKe9CCCGEEOIJ0LVrV+Li4qyvJUssDxcSEhJo3749iqJYy3bs2JG8vDwuXLhQ7j4JCQloNBpCQ0Otx5o2bVrhomh3IzU1lT//+c+sWbMGOzu7Kl/n6upKv379rCnvOTk5bNiwgYiICGuZTz75hNDQUDw9PXFycmL58uWkpKRU6f5V7e/OnTvp1q0bvr6+6HQ6hg4dSkZGBgUFBVXuS0JCAn5+ftYAHaBZs2a4urqSkJBgPebv719mzryPj889ZzLc6F+7du2sxzw8PAgMDLTWOWnSJObOnUvHjh2ZPXs2//vf/6xlx40bx9q1a2nVqhVTp06tcH2A6iYj6TWVthZmE2hqq7BT9GRji9EGThyDZkHgbWdJd1ErcLXwXPnr1b6WLdiMaUBo+fNCCCGEEELcgb1Gw/Fxkx5Z3XfD0dGRRo0aPaDW3L+jR4+Snp7O008/bT1mNBr58ccf+fjjjykuLrbOob9VREQE3bp148yZM+zevRu1Ws2AAQMAy+j866+/zqJFi2jfvj06nY4FCxZw6NChamv7+fPn6d27N+PGjePdd9/F3d2dffv2ERERgV6vr/aF4bS3ZFAoioLJ9OCm8Y4cOZIePXrw7bff8v333zNv3jwWLVrExIkTCQsLIzk5mdjYWHbs2EG3bt0YP348CxcufGDtkZH0GioxJw19Sem8c8ccAEy28Msxy9yc+roccostv7yZRWfL38C6wnv5p4NCCCGEEEJUhaIoOGi1j+T165Hv+xEUFGSdh3zD/v370el01K1bt1z5pk2bYjAYOHr0qPVYYmLifW/v1a1bN44dO1ZmtL9169YMGTKEuLi4SgN0sGQJBAQEEB0dTXR0NIMHD8bR0dHalw4dOhAZGUlISAiNGjXi7NkK4oNKVKW/R48exWQysWjRIp555hmaNGlCWlrZ7Z5tbGwwGo23rSsoKIjU1FRSU1Otx+Lj48nKyqJZs2ZVbvPdCAoKwmAwlHlokZGRQWJiYpk6/fz8GDt2LBs3buS1117j888/t57z9PRk+PDhfPnll0RFRbF8+fIH0tYbZCS9hirgCra2lhUnHdzyAU9MNgqnLlr+GP0cc7iU74jOtoT8klRMZhMq5eYzF0XjjxkwG85TPf+8CSGEEEII8fiJjIwkKiqKiRMnMmHCBBITE5k9ezZTpkwpNx8dIDAwkJ49ezJmzBiWLVuGRqNh8uTJ2Nvb37YevV5PfHy89fuLFy8SFxeHk5MTjRo1QqfT8dRTT5W5xtHREQ8Pj3LHb6UoCiNGjGDx4sVkZmbywQcfWM81btyYVatWsX37dgICAli9ejVHjhyp8vZzVelvo0aNKCkp4aOPPqJPnz7s37+fTz/9tMx9/P39ycvLY9euXQQHB+Pg4FBuhL179+60aNGCIUOGEBUVhcFgIDIyki5duljnfN+PY8eOlUmTVxSF4OBg+vbty6hRo/jss8/Q6XRMnz4dX19f+vbtC1jm24eFhdGkSRMyMzPZvXu3dU7+rFmzCA0NpXnz5hQXF7N169ZqWZ/gdmQkvYaqZedu/d6+TjGK0fLkL03tjsGgwUZtIsdg2drBTikgvfhq2Ruo/S1fjecfQmuFEEIIIYSomXx9fYmNjeXw4cMEBwczduxYIiIieOuttyq9Jjo6mjp16tClSxf69evH6NGj8fLyum09aWlphISEEBISwqVLl1i4cCEhISGMHDmyWvoRHh5OdnY2zZs3LzO/esyYMfTr149BgwbRrl07MjIyiIyMvKt736m/wcHBLF68mPfee4+nnnqKNWvWMG/evDL36NChA2PHjmXQoEF4enry/vvvl6tHURQ2b96Mm5sbnTt3pnv37jRo0ICvvvrqLj+NinXu3Nn6MwgJCbHOs4+OjiY0NJTevXvTvn17zGYzsbGx1rR6o9HI+PHjCQoKomfPnjRp0oSlS5cClgyBGTNm0LJlSzp37oxarWbt2rXV0t7KKObq2tvgMZGTk4OLiwvZ2dk4Ozs/6uZUau/FX1h50bLYhakIDh1sisFehcdPBjZP2oB33SyOXqtNaK0rrLrekObe79DG/ebTJ3NJPOaMF0FxRVX78CPqhRBCCCGEeJwUFRWRlJREQEDAXS1uJoS4/d/P3cShMpJeQ/k4WLZUM5tBZQc6rWWzdKOTmnMJZeerOKtLSC64ZfXGGyPp5izMpswH3VwhhBBCCCGEENVAgvQaysfBA5MJbqyX4aKzbG1gtIXkFMvcDo1iWeHQRaUntaDsAnGKyuHmNmyG8w+lzUIIIYQQQggh7o8E6TWUk40tJSU3tx5wrWXZcs1oB6nXXABw0JQA4KbRk1KQWv4mGn/LV2PSA22rEEIIIYQQQojqIUF6DaUoCoZfBemOngWAGaMtpF63LCrnblsEgKtaz3X9dfIMeWVvom4AWFZ4F0IIIYQQQghR80mQXpMZLau3m4rMqGxM6JwKMdpBSk5tADzsLEG6m0oPUD7l/cZIukFG0oUQQgghhBDicSBBeg2mwTL33HDdMjHd1SUfoy1kKq5kXLm5xb1OrUeNidRbU96t6e7nH0JrhRBCCCGEEELcLwnSazBbxREAQ+HNIN2sVjBqFc6fLj1nUlAp4KIuKT8vvTTdHcN5zGbTQ2u3EEIIIYQQQoh7I0F6DWavcgLAXLrCu7NTASqVCaMdJKVY9tYrMlpG1F3VxRUE6XUALVAMpssPqdVCCCGEEEIIIe6VBOk1mJPGEoirHMBwyYRKZdmKzWgLVwv9AMs+6gBuaj0XC9MwmAzW6xVFA2pLOZmXLoQQQgghhBA1nwTpNZiz2rLVmtYZig9bgm93t1yMdpBuCADARm0EoJbaiMFs4HLRLSPmGks5CdKFEEIIIYSo2HPPPcfkyZNvW8bf35+oqKiH0p7qFh4ezosvvmh9/zD7+zh/bo+KBOk1mLutKwAaBzNFByxBuodbLkY7M/GX3DCZwFZtmWvuYbJ8TbllhfcbQbpZ9koXQgghhBC/UeHh4SiKUu515syZh9aGEydO0L9/f/z9/VEUpcLAdM6cOeXa2LRp00rvOXHiRIKCgio8l5KSglqtZsuWLXfd1o0bN/LOO+/c9XW3ExMTg6ura7njR44cYfTo0dVa16327NmDoihkZWU90HoeFgnSazAPWxdMJsuEdMM5I+YSBVtbA3a1i7h0Vc/1a87Wsk7GG9uwlZ2XrqgbWb4xnH44jRZCCCGEEOIR6NmzJ5cuXSrzCggIeGj1FxQU0KBBA+bPn4+3t3el5Zo3b16mjfv27au0bEREBCdPnuTAgQPlzsXExODl5UWvXr3uuq3u7u7odLq7vu5eeHp64uDg8FDq+q2QIL2G+iXrf/y3ZIs1SFe5qDCesQPA2S8XgNzi+tbyTkohAOcLksveSBto+VpyEvONCexCCCGEEEL8xtja2uLt7V3mpVarAdi7dy9t27bF1tYWHx8fpk+fjsFgqPRe6enp9OnTB3t7ewICAlizZs0d62/Tpg0LFixg8ODB2NraVlpOo9GUaWOtWrUqLduqVSuefvppVq5cWea42WwmJiaG4cOHoygKERERBAQEYG9vT2BgIB9++OFt23pruntV+rt48WJatGiBo6Mjfn5+REZGkpeXB1hGsl955RWys7OtGQJz5swByqe7p6Sk0LdvX5ycnHB2dmbgwIFcuXLFen7OnDm0atWK1atX4+/vj4uLC4MHDyY3N/e2fbqdzMxMhg0bhpubGw4ODoSFhXH69M1BzOTkZPr06YObmxuOjo40b96c2NhY67VDhgzB09MTe3t7GjduTHR09D23pSo0dy4iHoUCQwFXDOe58RxF7aVQvNeAJgjcvXJJw4vrJfUJ4BgAOpsiKIJzeecwmo2oFcs/SGgaAWowZ4HpCqgrf6onhBBCCCHEr5nNZgpvE8w+SPYaDYqi3Pd9Ll68SK9evQgPD2fVqlWcPHmSUaNGYWdnZw0kbxUeHk5aWhq7d+9Gq9UyadIk0tPT77stAKdPn6ZOnTrY2dnRvn175s2bR7169SotHxERwfTp0/nwww9xdLRsw7xnzx6SkpIYMWIEJpOJunXrsn79ejw8PDhw4ACjR4/Gx8eHgQMHVqlNVemvSqViyZIlBAQEcO7cOSIjI5k6dSpLly6lQ4cOREVFMWvWLBITEwFwcnIqV4/JZLIG6Hv37sVgMDB+/HgGDRrEnj17rOXOnj3Lpk2b2Lp1K5mZmQwcOJD58+fz7rvvVqk/FfXv9OnTbNmyBWdnZ6ZNm0avXr2Ij49Hq9Uyfvx49Ho9P/74I46OjsTHx1vbP3PmTOLj49m2bRu1atXizJkzFBYW3lM7qkqC9BrK18EXAJXKMtdcXVtFzsZM7EfrcHDQo/Ut4sQ5HaGlW6HXcizCPs+eQlMhKQWpBDj6A6Aotpg1AWA4A4ZECdKFEEIIIUSVFRoMPLVsySOp+/i4SThotVUuv3Xr1jKBYVhYGOvXr2fp0qX4+fnx8ccfW+eAp6WlMW3aNGbNmoVKVTa5+NSpU2zbto3Dhw/Tpk0bAFasWFHp3PC70a5dO2JiYggMDOTSpUu8/fbbPPvssxw/frzS9POXXnqJ1157jfXr1xMeHg5AdHQ0nTp1okmTJgC8/fbb1vIBAQEcPHiQdevWVSlIr2p/fz3y7u/vz9y5cxk7dixLly7FxsYGFxcXFEW5bar/rl27OHbsGElJSfj5WXahWrVqFc2bN+fIkSPW+k0mEzExMdbPZOjQoezateuegvQbwfn+/fvp0KEDAGvWrMHPz49NmzYxYMAAUlJS6N+/Py1atACgQYMG1utTUlIICQmhdevW1r4/aJLuXkN523mjoHDj3wy1t4I5x0RupmXFd6fgLA7Eqa3lnbQleBZZ5qifyr1l/rmmdDGKkpMPvN1CCCGEEEI8Cl27diUuLs76WrLE8nAhISGB9u3blxmV79ixI3l5eVy4cKHcfRISEtBoNISGhlqPNW3atMJF0e5WWFgYAwYMoGXLlvTo0YPY2FiysrJYt25dpde4urrSr18/a8p7Tk4OGzZsICIiwlrmk08+ITQ0FE9PT5ycnFi+fDkpKSlValNV+7tz5066deuGr68vOp2OoUOHkpGRQUFBQZX7n5CQgJ+fnzVAB2jWrBmurq4kJCRYj/n7+5d5aOHj43PPmQw3+teuXTvrMQ8PDwIDA611Tpo0iblz59KxY0dmz57N//73P2vZcePGsXbtWlq1asXUqVMrXB+guslIeg1lo9JS29aLy8WW+RkqH0tAbs70Bo8sXBrnkLijFmZsULAsGqc/lw1N4VTuKXp4/856L0UTiJmtmA2J3H/CkBBCCCGEeFLYazQcHzfpkdV9NxwdHWnUqNEDas2D4erqSpMmTe64Cn1ERATdunXjzJkz7N69G7VazYABAwBYu3Ytr7/+OosWLaJ9+/bodDoWLFjAoUOHqq2d58+fp3fv3owbN453330Xd3d39u3bR0REBHq9vtoXhtPekkGhKAqm0t2sHoSRI0fSo0cPvv32W77//nvmzZvHokWLmDhxImFhYSQnJxMbG8uOHTvo1q0b48ePZ+HChQ+sPTKSXoPVLU15B1D7WH5ULlm1KC7WoLEzYteggLzim2WKr1sWUziZewqT+Ve/xNrSkXSDjKQLIYQQQoiqUxQFB632kbyqYz46QFBQEAcPHiyziPL+/fvR6XTUrVu3XPmmTZtiMBg4evSo9VhiYuID2d4rLy+Ps2fP4uPjc9tyXbt2JSAggOjoaKKjoxk8eLB1fvqNNO7IyEhCQkJo1KgRZ8+erXIbqtLfo0ePYjKZWLRoEc888wxNmjQhLS2tzH1sbGwwGo23rSsoKIjU1FRSU2/uSBUfH09WVhbNmjWrcpvvRlBQEAaDocxDi4yMDBITE8vU6efnx9ixY9m4cSOvvfYan3/+ufWcp6cnw4cP58svvyQqKorly5c/kLbeIEF6DXU27xxXizOs7zVeln+k7LMMXLnmCoCuRRZp12pby9g5g8akIc+QR+qv90u/ke5uOIfZVPV0FCGEEEIIIR53kZGRpKamMnHiRE6ePMnmzZuZPXs2U6ZMKTcfHSAwMJCePXsyZswYDh06xNGjRxk5ciT29va3rUev11tT7fV6PRcvXiQuLq7MKPnrr7/O3r17OX/+PAcOHOAPf/gDarWaP/3pT7e9t6IojBgxgmXLlnHw4MEyqe6NGzfmp59+Yvv27Zw6dYqZM2dy5MiRKn8+Velvo0aNKCkp4aOPPuLcuXOsXr2aTz/9tMx9/P39ycvLY9euXVy7dq3CNPju3bvTokULhgwZws8//8zhw4cZNmwYXbp0sc75vh/Hjh0rM+Xhl19+oXHjxvTt25dRo0axb98+fvnlF15++WV8fX3p27cvYJlvv337dpKSkvj555/ZvXu3dU7+rFmz2Lx5M2fOnOHEiRNs3bq1WtYnuB0J0muo1IJUkn+1nZraRQFbUC7lc+WqZV66Q0A+8Rdvppa41zGinLakBcXn3JzToahrg8oLMIEh/uF0QAghhBBCiBrA19eX2NhYDh8+THBwMGPHjiUiIoK33nqr0muio6OpU6cOXbp0oV+/fowePRovL6/b1pOWlkZISAghISFcunSJhQsXEhISwsiRI61lLly4wJ/+9CcCAwMZOHAgHh4e/Oc//8HT0/OO/QgPDyc7O5vmzZuXmV89ZswY+vXrx6BBg2jXrh0ZGRlERkZW4ZOpen+Dg4NZvHgx7733Hk899RRr1qxh3rx5Ze7RoUMHxo4dy6BBg/D09OT9998vV4+iKGzevBk3Nzc6d+5M9+7dadCgAV999dVdtbcynTt3tv4MQkJCrPPso6OjCQ0NpXfv3rRv3x6z2UxsbKw1rd5oNDJ+/HiCgoLo2bMnTZo0YenSpYAlQ2DGjBm0bNmSzp07o1arWbt2bbW0tzKK+QnbPDsnJwcXFxeys7NxdnZ+1M2p1LXia7z2yzQAzGZQFLgyII86zQLZE+ZCy6AkXFwKaXjOzFvP7QNg1enmfL3OFY+hKlq6tOC1wMnW+5kyx0HxLhTdDBTHVx5Fl4QQQgghRA1XVFREUlISAQEB2NnZPermCPFYud3fz93EoTKSXkM5mJxxwR2wBOhgWeE998RlAK5ccwMgrdbNa+o65pKVYRlZT8w9hcF0c09LRdsSAHPJsQfddCGEEEIIIYQQ90iC9Bpq0/e/kPxT2SQHdW0VWRcy8XF04mqGM6YShUJnhcISS2AeoMsix0aHMdNEsamYc/lJNy8uDdIp+R9CCCGEEEIIIWomCdJrqBaBdSg871jmmMbb8uPys3PCZFKRlWxJk0gusJTzts+npKEr+qOWVRVP5Pxq/rn2KctXYwpmU9aDbbwQQgghhBBCiHtSI4L0Tz75BH9/f+zs7GjXrh2HDx+utOxzzz2HoijlXr///e8fYosfvMCGtTGk6fj1Tmrq2pa8dy+DZYGDy0mWdPhEs2W+g73GiLaWicITlhH4ny/FWa9VVC6g9re8Kbl5XAghhBBCCCFEzfHIg/SvvvqKKVOmMHv2bH7++WeCg4Pp0aMH6enpFZbfuHEjly5dsr6OHz+OWq1mwIABD7nlD5aNVkNgvToUZ9tYj6lLR9J12Za55rlGO/ITdZzWu1jL+Drk4elj2XItpSSFYmPxr25q2dbAXPyfB918IYQQQgghhBD34JEH6YsXL2bUqFG88sorNGvWjE8//RQHBwdWrlxZYXl3d3e8vb2trx07duDg4PCbC9IBngqsQ16yk/X9jZF0zYV8AAwOcH1fLc7rb5Zp7JyJy9OBGC+bQA3b9u+wnlNsOli+0e9/CK0XQgghhBBCCHG3HmmQrtfrOXr0KN27d7ceU6lUdO/enYMHD1bpHitWrGDw4ME4OjreufBjpmVQXXKO3xwlV9e2/Lj0pzNQAJONQmG2HVcS3CkyWc49XesSiblZ1M6tDcDWg99hMpXmzNuWBumGRMzGaw+tH0IIIYQQQgghquaRBunXrl3DaDRSu3btMsdr167N5cuX73j94cOHOX78OCNHjqy0THFxMTk5OWVej4vQFvVQMuy5EWMrtgoqN4VrZ9Op5+IKgMERMvfXItNoSYtv7n6Ns0U59G5vmaNf2DSfvesPWK5XuYMmyHIzvaS8CyGEEEIIIURN88jT3e/HihUraNGiBW3btq20zLx583BxcbG+/Pz8HmIL74+DvQ2tg+pTVPyreek+CpfPpdPEwwOAEkcwXncgJdOy0ru/LpsSxYyzfX1URhUaHxXRy/9Bib7EcoPSlHezpLwLIYQQQgghRI3zSIP0WrVqoVaruXLlSpnjV65cwdvb+7bX5ufns3btWiIiIm5bbsaMGWRnZ1tfqamp993uh6lz28bkZtlb36t9VRQVFFPPTgdY5qUD/O8XXwBctHrs7YqIu3yVlq4tAMhrmMPmj78DQLHtaLmg+ABmc9l92IUQQgghhHgSPffcc0yePPm2Zfz9/YmKinoo7aluc+bMoVWrVtb34eHhvPjii7e9piqfSVVU132eJI80SLexsSE0NJRdu3ZZj5lMJnbt2kX79u1ve+369espLi7m5Zdfvm05W1tbnJ2dy7weJ13aNSIrXWd9bxuqBsC92PKjM+kUjEYTR36uD4BGMRPqf4H955Jo42lZzd2ui4YvZn1F2tnLYBMKaMF0CYxnH25nhBBCCCGEeADCw8Mr3Kb5zJkzD60NJ06coH///vj7+6MoSqUB/cWLF3n55Zfx8PDA3t6eFi1a8NNPP1VYdtGiRbi5uVFUVFTuXEFBAc7OzixZsuSu2/rhhx8SExNz19fdzp49e1AUhaysrDLHN27cyDvvvFOtdd3q/PnzKIpCXFzcA63nYXnk6e5Tpkzh888/54svviAhIYFx48aRn5/PK6+8AsCwYcOYMWNGuetWrFjBiy++iEdp2vdvlZuLIzYl7tb3Ni01ADhctWytZnBSMAOp6a7WMh3qpPC/66c5sCUTTAraxmryetRnzJQvmL90L2mZzQAwF91c+V0IIYQQQojHWc+ePcts1Xzp0iUCAgIeWv0FBQU0aNCA+fPnV5oVnJmZSceOHdFqtWzbto34+HhrIF6RoUOHkp+fz8aNG8ud+/rrr9Hr9XcctKyIi4sLrq6ud33dvXB3d0en0925oLB65EH6oEGDWLhwIbNmzaJVq1bExcXx3XffWReTS0lJ4dKlS2WuSUxMZN++fXdMdf+tCPT2w2gs3X7NzzKSbjibiY1KjUExY7QDVE7oSyw/zuZ22dSpf4kNu+MpSLGkyju2N5KpUfPtD8f5MtaSTXDm5D+YuXAL3/8YT2Z2wcPvmBBCCCGEENXE1ta2zFbN3t7eqNWW/zvv3buXtm3bYmtri4+PD9OnT8dgMFR6r/T0dPr06YO9vT0BAQGsWbPmjvW3adOGBQsWMHjwYGxtbSss89577+Hn50d0dDRt27YlICCA559/noYNG1ZY3svLiz59+lS4PfXKlSt58cUXcXd3Z9q0aTRp0gQHBwcaNGjAzJkzKSkpqbStt6a75+fnM2zYMJycnPDx8WHRokXlrlm9ejWtW7dGp9Ph7e3NSy+9RHp6OmAZye7atSsAbm5uKIpCeHg4UD7dPTMzk2HDhuHm5oaDgwNhYWGcPn3aej4mJgZXV1e2b99OUFAQTk5O1gcw96q4uJhJkybh5eWFnZ0dnTp14siRI2XaNGTIEDw9PbG3t6dx48ZER0cDlh3JJkyYgI+PD3Z2dtSvX5958+bdc1uqQvNA715FEyZMYMKECRWe27NnT7ljgYGBT9R86gBPdw7n2+HiXIiiBdXvPEk+eZEGTTw5mXmNEifIv6YnI8cBH488Gtrm4Oaaj1svezr4PscBYqkdmkve3/NR2duSGdwOk+nfNKp7hfjEo+w+eAq1SuHpFvXo1rEpPTo3Q6tVP+puCyGEEEKIR8xsNqM36R9J3TYqGxRFue/7XLx4kV69ehEeHs6qVas4efIko0aNws7Ojjlz5lR4TXh4OGlpaezevRutVsukSZOsAen92LJlCz169GDAgAHs3bsXX19fIiMjGTVqVKXXRERE0Lt3b5KTk6lf3zLF9dy5c/z4449s374dAJ1OR0xMDHXq1OHYsWOMGjUKnU7H1KlTq9SuN954g71797J582a8vLx48803+fnnn8vMYy8pKeGdd94hMDCQ9PR0pkyZQnh4OLGxsfj5+bFhwwb69+9PYmIizs7O2NvbV1hXeHg4p0+fZsuWLTg7OzNt2jR69epFfHw8Wq0WsGQlLFy4kNWrV6NSqXj55Zd5/fXXq/SwpCJTp05lw4YNfPHFF9SvX5/333+fHj16cObMGdzd3Zk5cybx8fFs27aNWrVqcebMGQoLCwFYsmQJW7ZsYd26ddSrV4/U1NQHvs5ZjQjSRcVuPIjwdHQk55oDLs6WXxS7Po78sBuyTlyFOgolOrC/BilXXPHxyMNLXYSdYqCofiIvt32FuF92U+BYSKuutTnx2WmuG02YxoSiMh/lzy8V8fctAZxLucaRX5I58ksyn635N893DqLv88HUq+N+uyYKIYQQQojfML1Jz+ijkY+k7uWhS7FVVzwiXZGtW7fi5ORkfR8WFsb69etZunQpfn5+fPzxxyiKQtOmTUlLS2PatGnMmjULlapscvGpU6fYtm0bhw8fpk2bNoBlqm1QUNB99+ncuXMsW7aMKVOm8Oabb3LkyBEmTZqEjY0Nw4cPr/CaHj16UKdOHaKjo60PFWJiYvDz86Nbt24AvPXWW9by/v7+vP7666xdu7ZKQXpeXh4rVqzgyy+/tN7viy++oG7dumXKjRgxwvp9gwYNWLJkCW3atCEvLw8nJyfc3S1xg5eXV6Wp9DeC8/3799Ohg2XXqTVr1uDn58emTZsYMGAAYHkg8Omnn1ozDCZMmMBf//rXO/alIvn5+SxbtoyYmBjCwsIA+Pzzz9mxYwcrVqzgjTfeICUlhZCQEFq3tqzp5e/vb70+JSWFxo0b06lTJxRFsT4oeZAeebq7qNiV5KvMfPE9dqzfj6eDI9m5N59EOTbJAczY5VueLCrulmctF69a5uerVOCvzgdNAdsufU97D8sifP7jfXCr7cL546kc+M4LgE4tTrLqg3BWfRDO6Jc64eHqSGZ2AV/96ygvTVzJy3+O5pvv4jCZnpzMBSGEEEII8fjp2rUrcXFx1teNBdUSEhJo3759mVH5jh07kpeXx4ULF8rdJyEhAY1GQ2hoqPVY06ZNq2UOt8lk4umnn+Zvf/sbISEhjB49mlGjRvHpp59Weo1arWb48OHExMRgNpsxmUx88cUXvPLKK9YHDF999RUdO3bE29sbJycn3nrrLVJSUqrUprNnz6LX62nXrp31mLu7O4GBgWXKHT16lD59+lCvXj10Oh1dunQBqHI9cPOz/XVdHh4eBAYGkpCQYD3m4OBQZgqAj4/PPWcynD17lpKSEjp27Gg9ptVqadu2rbXOcePGsXbtWlq1asXUqVM5cOCAtWx4eDhxcXEEBgYyadIkvv/++3tqx92QkfQaat4/trLlOTWxO7czu35/8vIdrOe0rgZ0p37m7RlvMerAd5ToLIvHZeTWspbxyS/mpC18eymWyIZj2JX+A8cKjxO5Ipx3e3/IkikpdDqmQmVIwFxyigb1mtCgXi3+9EIbDsWdZ+N3/+XosRTOX8hg0ec72bLzfwzsHUq3joHYaOXXRgghhBDiSWCjsmF56NJHVvfdcHR0pFGjRg+oNdXDx8eHZs2alTkWFBTEhg0bbnvdiBEjmDdvHj/88AMmk4nU1FTrQtsHDx5kyJAhvP322/To0QMXFxfWrl1b4bzye5Wfn0+PHj3o0aMHa9aswdPTk5SUFHr06IFeX/3TIW6kvd+gKMoDne4cFhZGcnIysbGx7Nixg27dujF+/HgWLlzI008/TVJSEtu2bWPnzp0MHDiQ7t278/XXXz+w9shIeg3VrW87UKsodFTzj9fXUFKiwWS6+fTPsacG+/QibFRqiswGjPZwOuXmaHuw9ioZmU6YMBF7+Tvq2dfDYDZQ/HQhf5rxB3IzNRzaYVlAzlyw2nqdVqumU5uGLJ75RzZ8OpqXXmyDvZ2W00npvPvRNvqMWErM+oMUFD6auUlCCCGEEOLhURQFW7XtI3lVx3x0sATBBw8eLBPk7d+/H51OVy6lGyyj5gaDgaNHj1qPJSYmltta7F507NiRxMTEMsdOnTp1xxTqhg0b0qVLF1auXEl0dDTdu3e3XnPgwAHq16/PX/7yF1q3bk3jxo1JTk6ucpsaNmyIVqvl0KFD1mOZmZmcOnXK+v7kyZNkZGQwf/58nn32WZo2bVpuZNvGxvJQxWg0VlpXUFAQBoOhTF0ZGRkkJiaWe3hRXRo2bIiNjQ379++3HispKeHIkSNl6vT09GT48OF8+eWXREVFsXz5cus5Z2dnBg0axOeff85XX33Fhg0buH79+gNpL0iQXmPVc7dsw2CqZcfVOEsaTlHxzRFs++c1XDh1gRalq+BrvW1IunRza4Pmda9zMaUWZqOGc/lJeNha5ojsvfoj4e8M5qlOTfn6U0t6vLlwE2ZT+V+yWu5ORA7twvploxgz5FlquTuRX6Dn72v3MzDyc2K+Pkh+QfGD+QCEEEIIIYSoBpGRkaSmpjJx4kROnjzJ5s2bmT17NlOmTCk3Hx0si1T37NmTMWPGcOjQIY4ePcrIkSMrXQjtBr1eb0211+v1XLx4kbi4uDJ7tb/66qv85z//4W9/+xtnzpzhH//4B8uXL2f8+PF37EdERAQbN27km2++KbPLVePGjUlJSWHt2rWcPXuWJUuW8M0331T583FyciIiIoI33niDH374gePHjxMeHl7ms6lXrx42NjZ89NFHnDt3ji1btpTb+7x+/fooisLWrVu5evUqeXl55epq3Lgxffv2ZdSoUezbt49ffvmFl19+GV9fX/r27VvlNlcmMTGxzJSHuLg4bGxsGDduHG+88Qbfffcd8fHxjBo1ioKCAuvnOGvWLDZv3syZM2c4ceIEW7duta5BsHjxYv75z39y8uRJTp06xfr16/H29n6gW9hJkF5D1dFZRrmNOi1enq5gNFNQaFk4w2w0o3JREV8QT+s6vgA41nMkPdOJEoNlVXaNBprprnM6yTL3/H/Zx9AoGi4WpnE6/wyz1r/GhaQ6JMbZo1CMOf/LStvi6uzA0H7t+OrjCCKHdsbd1YGsnEL+/s/99B+7nM/W/JvzFzIe4KchhBBCCCHEvfH19SU2NpbDhw8THBzM2LFjiYiIKLPY2q2io6OpU6cOXbp0oV+/fowePRovL6/b1pOWlkZISAghISFcunSJhQsXEhISwsiRI61l2rRpwzfffMM///lPnnrqKd555x2ioqIYMmTIHfvRv39/bG1tcXBwKLN92gsvvMCrr77KhAkTaNWqFQcOHGDmzJl3/mB+ZcGCBTz77LP06dOH7t2706lTpzJz8j09PYmJiWH9+vU0a9aM+fPns3DhwjL38PX15e2332b69OnUrl270t27oqOjCQ0NpXfv3rRv3x6z2UxsbGy5FPd7MXjwYOvP4MbrypUrzJ8/n/79+zN06FCefvppzpw5w/bt263709vY2DBjxgxatmxJ586dUavVrF27FrCsnP/+++/TunVr2rRpw/nz54mNja3wAU91UcxP0l5mQE5ODi4uLmRnZ+Ps7Pyom1Mpk9lMs08+RG8ysvWFQfzxq3/iHpBJQ/8rmE1mFJWCzQk7Bj83hVH/2oS3nSOq7Xmsmb0BPy9LwLz0UEsWJz/DoGf1pJWcxUnjRJ4hj+bOzZja9DVOHEjkXx/8makfnSM/zx7HBv9Bpbr9E0KAkhIjsbuPs/7bo5y/cHMEPrRFPf7Y62k6tWlYbelJQgghhBDi4SkqKiIpKYmAgADs7OwedXOEeKzc7u/nbuJQGUmvoVSKgnfpFhJ5WmhUz5vMLEcAFJUlAC4OLMSz9BnL5aJ8zLYKSWk3U97bel4CFEyZzXFUO5JnyENB4UROPGfyztK8QyDt/vgmVy5ocXQq5Jdt86rUNq1WTd/ng1n1wSvMnvx72gRb5sMcPZbCjPc2MezVGNZtPUrKxetP1H72QgghhBBCCHG/JEivwW6kvF/Ky8VHp6OwyBZKY17DZROKRuHt2X+lVollrrp3UzcupLtYr29ZLwM7dQkHU9IZWr9sCs2GCxsxm810HdSZS5d6AuBb52t2fLG1yu1TqRR+92wQH8wawFefjGTIi22xsdGQlJrBkujdvDRpJQMj/876b38mPSP3fj4KIYQQQgghhHgiSJBeg/noLKPiabk5eDvpAAXFZFk10ZxvidZtfqdCf+QiAJlFOaSWBunFJWpsNCZau13iQk4OnkpD2riFYi6N8uNzTvLfrF8AaNXrHTKvuVLLx0B+2t/4buUPd91WX29Xxg3tzNqPIxj5p460CKyDSqVwKT2bD1f+wB/HLmfCrLVs3XmMjMz8+/pchBBCCCGEEOK3Sja8rqFOXLxM0hXLfO9LubmlQTqojE4Y1ddRuSiYS8zYNFXjvw/+B1xU8rhw9cZIugYw0sU5lX3X6rEz6RzDgodyMvcUuQbLqPbalHW0dHkKjdoBt8ZRkBlO7+HXeO3FRZjNZsIiut11u708dIT/sT3hf2xPTl4R3+05wc59J4k/fYm4ExeIO3HBWrZX1+Z07xTE0y3qoVHL8yIhhBBCCCGEkMiohlqx9zBxmZcBSMvNtc5PNxRbUuBVHgpFBwwA9JrWDBVQUtuepHTLwm9atWVrtA510wDYee4MzlodoxpEoGCZ036l+Ao/pO+23M+2A9j9AZUKJr2XyofjlrFuweb76oOzkx0De4eyfP4Q/vHRCIb/8Rn86rhZz8fuPsGUd74mbNhHTHnna1ZvPMThuPOyB7sQQgghhBDiiSUj6TXU0/V82XTtNGCZk167NEjPyrHDTQeKoqA/bsS+i5afMg/Tzq8jB1MvowmoS36hFkf7EkwmCPS+jrd9Hv+9fImrBfkEu7agX90X2XDBsnfihgvf0N7jGXRaHYrzNMzFuwkIymLwxCt8Pu1LLp+/yvglr6BWq++rP/XquDPqT50Y9adOZGYX8MOBRA7HneeX+AvkFRRzOO48h+POA6Ao4F/Xg6cC69CxdSPq+brh5+MmK8YLIYQQQgghfvMkSK+hOjdtCD/vAeBCdjbejpYg/VIWuFm2RkftoWA8b6bYv5igegYOpoKxvj3nL7vRPCCdS2mO+NbNp4d7El9cbMG0WSsY0bEdXbp0Jsk1iZ+z4igyFfPp2c95PfBVFJU7OL+FOft1hr5+hYSfHfnXsu1kpWfxl3++ilpzf4H6DW4uDvQPC6F/WAgmk5mzKVf57/FUjp28yInTl0i/lktSagZJqRn8a+cxADzdnQhsWJv6vh4ENfImsGFtatdyRqWSwF0IIYQQQgjx2yFBeg1Vr5YrGoOCQWMmt0SPzsYWgOt5CpgAFdh10JC3vgiX1+zJUicC7iTmZJB6zYPmAenovAKBn/mdbzJfXGzB4dx0UvsvBGDQ7L54vOBORsl1juecYMeVXTzv3R3F/gXM+kMohet5Z9VFJvepx783HOKNbm/zzr+m4+jsUK39VKkUGvt70djfi4G9QzGbzaRn5PLvw2c4eiyFi5ezOJdyjavX87h6PY99R86Wub5hfU8a1q9Fs8Y+eHu6EODnga+3a7W2UQghhBBCCCEeFgnSazA3lS1XKQIgR1+Mk40NeXo9OpMLuapsNPXUFB0y4Gmy4ar+CsH1a/FLsplMrR+QwPWsIpy9oLVfGs7/LSYvyA2TjQqV3sRXb2/GaZ09Ln+3waw288+Ur2jh8hQ+9t4ozrMwG1PQcIgP/pXGX/4UwC//TmByp7d4+5up1Gno/cD6rCgKtWs588deT/PHXk8DkF9QTOLZKxz+5TxH/pdMVnYBV65ZFr87m3yVs8lX+f7HBOs9bG00ODnY4uBgQ8N6tQioVwutRk0tNycC6nng6a7D1cVBFqsTQgghhBBC1DgSpNdQZv1/md3uAIcybVh95inScnPxcdJx+noGrjZe5JqyAbB5SkOdc3U53+gctb0vQ7IXSSYvANRcorjEH1vtebrXOs/GS4FM/mUOxh3n2fzxNi4kXKJkegkeCxwwYWJu3Dyi2i5Eq7YF12WYM0egJY75607z8Zv+fLsqlQntZjBhyQj+76VnH9pn4ehgy9Mt6vF0i3qMLT2Wk1fEpfRs4k9dIu1KFudSr5Fy8TqX0nMo1hso1hvIyMonNS2TPf85Xe6eapWCq7MDikrBzkaDt5cLZrMZG60aezsbPN2dKNIb0GrUuLk44OJsj6vOHpVKISunEJ2TLU4OttSu5YyLsz1ajZqCIj1GowlXZwdsbeRPq7qZzZbtA7NzC3F2spepDkIIIYSoNs899xytWrUiKiqq0jL+/v5MnjyZyZMnP7R2VZfw8HCysrLYtGkT8HD7+zh/bo+KRBI1lTGJngFHcLL3ZfWZp7iQk42vszOnr2dgq3K3pLwDtm3VlGwBm9dtKDRl4OriyA9XNLwJ+HhkcTn/d9TVnuf52pYg/Zv4eFZO6M8LkT34765jbFm6neMx/8Mp3JY8dR4jv4hkmN1QnhvYEZXbSsxZk1Dp9zFpfiKtnq3PB1OMzHt5CfEHTzEuKvy+F5S7V85Odjg72RHYoHaZ44VFeq5nFZCVU8jZ5KscPZaM2Qy5+UWUlBhJTcskM6cAo8lMRtbN/dovXM6q9vY5Odri6GCLna0Wo9GEh6sjZsyYTGZUKgWTyYyiKKgUBRsbNQ72NphMZq5l5uHq7IBapXDlWi65eUXU9nTG0cEGo9GMg73lIUJhUQk6J1vsbbXk5BVhMpup4+WCwWhCURR0TnZkZRdgBlSKgsFoJCe3iJy8IlQqBbVKhYvODo1GjVqtwtZGg62NBq1GTXpGLk4OtqjVKgqL9NjaanFysKWgUI/BaMTWRkt2TgH5hXpy8orQatRoNCpUioJXLR1ms+VBislkwmy2bM2nqBSKi0tQqRTy8otJScvk2vU8iopLKCjSo1GrsbfTkpGZj9FowtHBhsKiEq5dz6NYb8BsNqPRqK2r/zs72eHt5YKNVo1GrUKtVmE2m8nOKcTGVoNGrcZgMGIym7HRarielY/JZCK/QI+NjQaF0gUYSwzonOxQq25mVtxYo/D5zs0Y2q9dtf5uCCGEEKL6hYeH88UXX5Q7fvr0aRo1avRQ2nDixAlmzZrF0aNHSU5O5oMPPigXmPr7+5OcnFzu2sjISD755JNyxydOnMjOnTtJSEgody4lJYWAgAC++eYbXnjhhbtq68aNG9FqtXd1zZ3ExMQwefJksrKyyhw/cuQIjo6O1VrXrfbs2UPXrl3JzMzE1dX1gdb1MEiQXlOp6wNQ3ykHgKTrmfg5W/ZALy62g9LY2LathnOfn+eF97qx/coOGtXL5KdjdSksscVeW8zBX2wZ8Cx0rJ+K7TEDP6Ymk1NchLOtHaG/Cyb0d8FcPp/Ouz/NJ6deNjxl4rMf/s66tlsY+FpfOv3hE7TajyH/czr/PpmnO+tYOMmDzZ9s4+wv53kjevwDTX+/W/Z2Nvh62+Dr7UrzJj688LuW5coYjSbSM3JJvnjdGnhmZOWjVqvQ6w3k5Rdz8UoWDnY26EsMFBWXkJVdSFZuIQaDEZVKITO7gOzcQor1BgwGk/XeN4LvnDxLMFxdTiWlV9u9Hmf6EqP1++r8jLNyCis8npGZX+FxIYQQQtQ8PXv2JDo6uswxT0/Ph1Z/QUEBDRo0YMCAAbz66qsVljly5AhG483/zxw/fpzf/e53DBgwoMLyERERfPzxxxw4cIAOHTqUORcTE4OXlxe9evW667a6u7vf9TX36mH+DH4rJEivqUqD9DoOediojBxPu0y3QMtTwMw8DbhY0n/V7ioy7HNop3qGXcpu7B1zcdYVcKHAi8YuqSQmXaKobS3sba/RxTWV7zMCiDlwlEldO1qr8vb34sP6C5n5vzlcKL6I/f9pydSmM3/YEjBD5wHt6TNiGi1C/omTLoU50bmcOebI5ujrTO5whr4T/8iLE3vi6PJgn5BVF7VahY+XCz5eLtVyP4PBSH6hHmcnO8ASPGZk5lNQqCe/oJi8gmIyMvNRqxRrCr1apWBrq8VsNmM0mcnKKcBoNKFWqXB1cSA3r4iCQj1ms5msnEKuXc+jQf1aAJw8cxlPDx06RzvyCorIyrYEmI4ONmRk5uNgb4PBaCI3rwgXnT1FxSUUlxhw1dljo9XgYG+Dq7M9BqOJ7NxCjEYTRpOZ4uIS61QBo9GEnZ0WrUaNgkJuQREKCk4Otigq0OuNaLVqdKXZAjZajTXdPyMzH4PBiKuzPXZ2WvIL9BQWlWAwGC0j2ArWqQK2thrqeLmgVqswGi0PO5x19mjUKor0BtQqhTq1XdFoVBgMJlQqBS8PHXkFxVzPyrfUZTRhNJqsX12d7S19MppKR9ctD2bcXBysD2I0WjU2WjWKolBSYkRfYrj5AzXf/LaWh1O1/I4IIYQQ4sGztbXF27viwaO9e/fyxhtv8Msvv+Du7s7w4cOZO3cuGk3F4VB6ejoRERHs3LkTb29v5s6de8f627RpQ5s2bQCYPn16hWVuDVjnz59Pw4YN6dKlS4XlW7VqxdNPP83KlSvLBOlms5mYmBiGDx+OoihERETwww8/cPnyZerVq0dkZCR//vOfK23rrenuVenv4sWLiY6O5ty5c7i7u9OnTx/ef/99nJyc2LNnD6+88gqAddvk2bNnM2fOnHLp7ikpKUycOJFdu3ahUqno2bMnH330EbVrWzJk58yZw6ZNm3jttdeYOXMmmZmZhIWF8fnnn6PT6Srt0+1kZmby5z//mX/9618UFxfTpUsXlixZQuPGjQFITk5mwoQJ7Nu3D71ej7+/PwsWLKBXr15kZmYyYcIEvv/+e/Ly8qhbty5vvvmmtb8PggTpNZXKAxQH1KoCfB1zOZt5nXAXS1CZdt2Ii7MCpSm5tm01XP0lg07NO7Ln6l78fK/x3wwnGruAf+1rpOW0pYFnLD1dzvB9RgArDx5hRNvWODna3qxOUfFOyzm8fWIu5wuSsX9Wi+pjheuvFfDj+oP8uB48/fyI/JsHz3Q9TqMW+by2OJ+CvDR+2HCemb3+Sdeho+g1qvsjS4F/VDQaNS46e+t7F519mfei+jnY2+DlcW//SAshhBCi6sxmM3qT/pHUbaOysQZ89+PixYv06tWL8PBwVq1axcmTJxk1ahR2dnbMmTOnwmvCw8NJS0tj9+7daLVaJk2aRHp69WY26vV6vvzyS6ZMmXLbfkZERDB9+nQ+/PBDa9r4nj17SEpKYsSIEZhMJurWrcv69evx8PDgwIEDjB49Gh8fHwYOHFiltlSlvyqViiVLlhAQEMC5c+eIjIxk6tSpLF26lA4dOhAVFcWsWbNITEwEwMmp/GCHyWSib9++ODk5sXfvXgwGA+PHj2fQoEHs2bPHWu7s2bNs2rSJrVu3kpmZycCBA5k/fz7vvvtulfpTUf9Onz7Nli1bcHZ2Ztq0afTq1Yv4+Hi0Wi3jx49Hr9fz448/4ujoSHx8vLX9M2fOJD4+nm3btlGrVi3OnDlDYWHFWZjVRYL0GkpRFMzqemA4SX2nbP6d42ZNd7+Qk0sDlQfXzNcAS5B++ug5ft89jB+v/ht313xOZTsD0MTvGlv3hzDpRejSKAXteSM5jmb++tk23nu1b5l/EFSKilnN/8JfT7zL+YJkbEM1NNtdn8bbg/jho/1cTc3l7aHg4t6E4dPz6PbHPBycMug93PK6eO419q7wwb3+H2jZbRCK2r1a/mEVYDblgTkPzHpAD+Ziy/dqXxS116NunhBCCCF+o/QmPaOPRj6SupeHLsVWbXvngqW2bt1aJjAMCwtj/fr1LF26FD8/Pz7++GMURaFp06akpaUxbdo0Zs2ahUpVdsefU6dOsW3bNg4fPmwdGV+xYgVBQUHV07FSmzZtIisri/Dw8NuWe+mll3jttddYv369tWx0dDSdOnWiSZMmALz99tvW8gEBARw8eJB169ZVKUivan9/Pb/e39+fuXPnMnbsWJYuXYqNjQ0uLi4oilJpNgPArl27OHbsGElJSfj5+QGwatUqmjdvzpEjR6z1m0wmYmJirCPnQ4cOZdeuXfcUpN8Izvfv32/NRlizZg1+fn5s2rSJAQMGkJKSQv/+/WnRogUADRo0sF6fkpJCSEgIrVu3tvb9QZMgvSZT1y8N0nPYo5jR2dgAkFFYiL9zANeyS4P0VmpOfniaoXYDeMajLQcy/sN1G8todpN615i6VMXoXq64uGbxO9uzxBY24fvzZ+j8w3F6d2tRtkpFzVvNZvBuwnyS8s+TqWRy/PdxzBz5Z658f51FEcvIvq5hyVRXlkx1IbijCwMiC2jVMR3fBnp8GyQDUXAtCoPRHrVdE9A0RtE2B3UD0DQB1W8veDebDWAusgTPlAbQlP6Dby4GCsFUWBpo5wBazCX/RVHXwWy6DpjBbATTNTBeAeNFMKUDJlBswFzx0zrF+W1w+NND6aMQQgghRE3WtWtXli1bZn1/Y9Q5ISGB9u3bl/n/Z8eOHcnLy+PChQvUq1evzH0SEhLQaDSEhoZajzVt2rTaFyRbsWIFYWFh1KlT57blXF1d6devHytXriQ8PJycnBw2bNhQZqG5Tz75hJUrV5KSkkJhYSF6vZ5WrVpVqR1V7e/OnTuZN28eJ0+eJCcnB4PBQFFREQUFBTg4OFS5Lj8/P2uADtCsWTNcXV1JSEiwBun+/v5lUtt9fHzuOZPhRv/atbu5GLCHhweBgYHWBfkmTZrEuHHj+P777+nevTv9+/enZUvL2lbjxo2jf//+/Pzzzzz//PO8+OKL5dYHqG4SpNdkmnpQDPWdLNutxSdfwdXOjqyiIlw0ljkbpiIzKjuFc6YkAHrX+T0HMw6Ra69QYlJwdSrC062A4ymhtG60iz94niA2pQkF3vDB33fRrLEPDerVKlOtVqVlRtOpzD+5gHP5SRQYC/jwwse88LvebCxaSdJ/Utm2Yhc7Vu3ll/06ftmvw96xFqFdcgl5No/WXXPxrqdHrS6Ekl+g5BfMhV/frEBxwaxyt6T0q30BM6g8UVSuoA0CdR1LUKo4gcoFFGcUpeJfVbPZBJSAucQSAKMGlROYMsFcAKY8UOwBFah0luDZlAnmTMs5SrDMG1DAlA3mYszmgtK7l45YGy9inahsulY6ip1vuT92lq/mXMB4a/PuyHznIr8K0DWg2AJay1fFBhS7u65TCCGEEKKqbFQ2LA9d+sjqvhuOjo4PbSX3+5WcnMzOnTvZuHFjlcpHRETQrVs3zpw5w+7du1Gr1dbF5tauXcvrr7/OokWLaN++PTqdjgULFnDo0KFqa+/58+fp3bs348aN491338Xd3Z19+/YRERGBXq+vcpBeVbeuPK8oCiaTqZLS92/kyJH06NGDb7/9lu+//5558+axaNEiJk6cSFhYGMnJycTGxrJjxw66devG+PHjWbhw4QNrjwTpNZiiro+Zmyu8HzqVTF1nF7KKilCXuFoKlf6uGoKLuJZ2Hd86dWjv8QwHMg6SWuxEA/tcmvhd4++batH6dej49FVcLxaR5WRHntbAtHkb+WzeENxdyy76Zqu2ZXrTN4g6/RHxOQmYMLHp4ha2X/qeAU3+yOvRkbwRPZ6zcee5ePoSV5Kvsusf/2bfDMuWElpbE3X8i6nfpJiAoEKaBBcR1FqPo64IzNlgzAZjEpT8ZK3zdgGrWdGB4gAYwGwoHZ0u4V4C4wfPBssoehFgZwmkFfvSl0NpYK1YAnuVK2iaAmpQFBSVB6hql65JYGPpq8oV1F4oKrdH2CchhBBCPIkURbmrlPOaKCgoiA0bNmA2m62j6fv370en01G3bt1y5Zs2bYrBYODo0aPWkd3ExMRyW4vdj+joaLy8vPj9739fpfJdu3YlICCA6Ohodu/ezeDBg62ZAjfSuCMjb05LOHv2bJXbUpX+Hj16FJPJxKJFi6zTA9atW1fmPjY2NmVWrq9IUFAQqamppKamWkfT4+PjycrKolmzZlVu890ICgrCYDBw6NAh6wh4RkYGiYmJZer08/Nj7NixjB07lhkzZvD5558zceJEwLLg3/Dhwxk+fDjPPvssb7zxhgTpTyzrNmyWkfT/pqVRr5EHx9OvUFBgbxmAdrD8Q2PXScuxA/F0/WMn+tXty6Hrh0k1OtKAXFoF5/DRqgAycnzxcL5IX/UxvjC2QdXAlkvHcpj6t40snvlHnG9Z7MxWbcuUJn/ms7N/50imJZguNBWxKvlLjmb+zB/r9qNRSACNQgIAGPhGX3Iz89j/zWFO/3yOozv+x4//usSP/3K13tPGzoRfYz1Ptfei68AGuHuZqF1PBxSA6QqUnLKkeSsOpaPVeZYLzbmlo9V3QXG13AM1KOrSEWkzKM6gqgVqT+DGUzoTKFrLOQyguFhHq5XSRfwsH3it0pF5DagcLSnqihaj2ZUSow6t1p7CYgN6vRG9voSs3EKCGvncXbuFEEIIIUS1iYyMJCoqiokTJzJhwgQSExOZPXs2U6ZMKTcfHSAwMJCePXsyZswYli1bhkajYfLkydjb335hYL1eT3x8vPX7ixcvEhcXh5OTU5kRfpPJRHR0NMOHD690dflbKYrCiBEjWLx4MZmZmXzwwQfWc40bN2bVqlVs376dgIAAVq9ezZEjRwgICKjSvavS30aNGlFSUsJHH31Enz592L9/P59++mmZ+/j7+5OXl8euXbsIDg7GwcGh3Ah79+7dadGiBUOGDCEqKgqDwUBkZCRdunSxzvm+H8eOHSuTJq8oCsHBwfTt25dRo0bx2WefodPpmD59Or6+vvTt2xewzLcPCwujSZMmZGZmsnv3buuc/FmzZhEaGkrz5s0pLi5m69at1b4+wa0kSK/JNJYg3dcxD7ViIikni3YulkUMLubk4+XkRbopHVOOGZWzwsGjh+lKJzxtPenm1ZXz+RfoAjRsYJm/se1QI17+3UX+0OQ0XxxvQ35t8E2x4+TZK0S+tZbFs/5YbsVsrUpLZKMxfHc5gHWpX2MuHe8+kRPPifh4PGzc6Vf3D3TweAaVokLn5kTPEf9HzxH/B0Da2cts+ngbJ/ad5PyJVPRFJZw9ZsfZYzlsXh5nrad2fU+6Du5Io5BB1G/uh629DXZOdji5atFoi8CUdTN9XeVuCZDRWgJrtKBosPw6qyzlFC2KYovZbEJRLP/4ms16QI2ilF19vqTEyNXruVy+msOVqzlcy8zHaDRRUKQnL78Yg8GIoihoNGpMJiMZmVc4ceoSRcUl2NlqMRpNFBbpMZrK5wL413Xnyw9H3M9vgRBCCCGEuA++vr7ExsbyxhtvEBwcjLu7OxEREbz11luVXhMdHc3IkSPp0qULtWvXu/xt0QAA4gRJREFUZu7cucycOfO29aSlpRESEmJ9v3DhQhYuXEiXLl3KrFy+c+dOUlJSGDHi7v6PGB4ezuzZs2nevHmZ+dVjxozhv//9L4MGDUJRFP70pz8RGRnJtm3bqnzvO/U3ODiYxYsX89577zFjxgw6d+7MvHnzGDZsmLVMhw4dGDt2LIMGDSIjI8O6BduvKYrC5s2bmThxIp07dy6zBVt16Ny5c5n3arUag8FAdHQ0f/7zn+nduzd6vZ7OnTsTGxtrTas3Go2MHz+eCxcu4OzsTM+ePa0PQmxsbJgxYwbnz5/H3t6eZ599lrVr11ZLeyujmM3mKk2L/a3IycnBxcWF7OxsnJ2dH3VzbstsNmG+EgwU0/XbP5F2VcfU5zoz7/C/ebZefTq2yGJ/xkH08QZsmmnQHLRlxZ8tc4bySvJYEj+O6bUPkaW34Y/vjcapIIsNc/+JWm1kwIY+/NdYh8nBz7D7y/9n77zjpCjvx/+eun2vd447moAIgoANlRiNqLHl61dNNApK/CYaY9RIbNFoNOWnJrZ8Y0kUbBF7DX7tJXYFwUJvdwfH9bvtu9Oe3x+zt3BSBGLBZN68ltudeWaeZ56Z2Z1P/5jOniSVZRGuuuAoxo6q2+J4Po59wl9W3k7aTqNKKpbYWFtal3VOHvx99i/bd5suUR+/vpi/XXI/S99fiWNtX1xJ7bAqIqVhSqqL2W3iMCrqy2nYfRDBaABfQKdiUBkAirrl0m/pjEFre4y2zhhtnXHWtnTT3ZeiL56hrSNGV2+SL+suGD2imhsuP2FAuTsPDw8PDw8Pj12VbDbLmjVrGDJkCH6/l/vGw2NH2Nb9syNyqGdJ34WRJBmhDgZrBQ3hGC2xCLmuHAAre3o4JTSUN7vfpl9WNsZmicfjRKNRwlqYSZUnYYn3KNYNyvZJ0vpkkPeXjWbf3T9hevl8PmyvZV7LCmb/9gdceM2jNK3v4aeXz2XGCftx2vH7oioD3X/GFu3BlWMu588r/0JzugUJieHhYaxKrsZwDOasvYf7mx5g9+goRkdHM6l0IhW+gUnpxh60Oze9+VuEEKxatJa3nvyAt558j1UL1251HlpXtcOqdgDeeXr+ZusFICIBQoPLidSW4KgKpl8nYdnYAZ2s+flx67qmUFkepboiSkVpGF1X0VSZSMi9uQzTxnYcQgGd4miQ4Y0VRMJ+TNPG51NRFZmiSADDtPD7NDRNRQIUZXMXKg8PDw8PDw8PDw8Pj63hWdJ3UT5Z3soLry/hxAPupKb4A349/wDuX7k735d2Yy4rAHji1CP544rrsTsdcCSUKonvZo7ixKnfA8ByLDas3486LcZVyybz7KMT2VNr4/ZZT2DkJKY+80M67SD3fe8E9iyr4k9/e4nnXnPjaIbUl3Hu6Qczec/GzcZmOAb3NT3Aa52vu21DjewR3YO3u9+hy+ga0HZwsJ49i8cxOFjPmOgYQuqWMz+uW97Km0+8z4bV7bz99Pv0bOgrrFN1ldrhVaQzJqIsQk5VSCPIKAooMk40ANrn6JtyJmrOJKQqhBWZkkiA6upiRo2pp7w4yKCGCup3qyFUFNr2fnZRunM99Bo95ByDjJ0lbaeRgA3Zdo6uPZKAsu0YKg8PDw8PDw8P8CzpHh7/Cp4l/d+cpnXdPPrsh4wbpFMzHhojMZAkPljRQum4AD2ZDFYujCQklAqZ3NMOytES78fe50RcIV2VVQL+SWC/xISqNp4aa/DxK5Wsbq1laG0rP9Tf54bMVO5etIDbjzqOy889kn3GN3LDnS+zpqWb83/zCONG13HS0ZOYMmlYwbKuyzpnDJnOmOho5qy9hzWptXTlupg55HTKfeV8GvuUD/sWsSyxnOZ0C83pFgCk/L/aQC27R0fRGGpgUKCOhlADg3ar5aRfHottOxx7yfd47O5X+eDdFazvTpItDrJMU0Hf+uWqqzIlQR8lYT9BTSXV1sva1xdTHNBJtHQhDNeabgA9+dcq4M3P7KekqohgNIiqKfRs6GXQyFp0v05VYwW+gI/d99uNYeMbKa4swhfQWftJM6U1JVTUl6EoW3a3/yxCCLJOlvZsB4Zj0GP0EFJDxMw4EtCZ66LPjNFn9NGaaSVjZynVS9BkDQmJdZn1AASVAGk7Q8bOFHIFbInxxePYLTJiu8bm4eHh4eHh4eHh4fH14gnpuyi7DXXroC9a4ePg8bBbUS8A7WqGxmA1PZkMzX0JqvQq2sw2sitz+AjQUdZOxs4ULKdloW8h4i+xmz9Gw5B1LGtv5P7nR3L5jFZO3GcVt7x2AC+uXkVLLEZ9URGHHbQ7++41lDvnvsnjzy3koyXr+WjJespKQhx1yFiOPHgPaquKkCSJfcr2ZkhoCLes/F+a0y3cuOIWJpdO4uTB3+fwmmkkzSQf9M5naWIZa1PNbMhuQCBYl1nHusy6Acer5oLYvT7i62WMuIKNjD1WJaCoCMtEWBYICZFwUJwkUsLEWmeDYSPH0shWDr24CKEGaZi0G+NOGs9et55KMBzk485PaVm/DhEHyZCxYjbrN7TSuayLrlV9GFmDnJUjkU6Swn3JUQnKoalsLepgmdXWShDwz5Wv4SwCLIE2TEEqknDeF6gRGaIQ8PnBB37bhy/ic8uzGwoCQSgaAEkiIRJ0m907dD3Erfhmy9J2esBnCYkyvQxNVin3lSOEoMJfQdCzont4eHh4eHh4eHh8Y/CE9F2UIYPK0DWFJWuLABiVF9KNItAzbptVvT0MrxpGW1cbclTGXGujNSq81/UBU6sOdBvp+wIw3JegoihJ54henv5gKOcm36OiMsVR0qc8IcZx70cfcumB3wIgGvZz/o8O4Yff25tHn/2QZ176mO7eFHc/8g53P/IOxdEAI4ZUMqKxkqGDy5mi/RdR9W0+ld7n/Z4PWNT7EXvL32KSf1/U9CDqYiX4+8ZSmuxmA0300EnCiRMckkQJuRZuy5eG6jSR6p2ZrXyGd2wsEnzEfD5iPvct+0yzUP5VAjQC+21c5QN8/Ouu7iZG4W+CfMm4vIzcaQ5sK0wBaQmpQ4aAQPEpBJQAeo+PXIvJoIpaakpqqK6qRAoL9KiOo9nImkKJVowqqZToJQTVAIqkEFEjyJIXA+/h4eHh4eHh4eHxTcYT0ndRVFVhWEMFa1uyAJT5U5ToGWJBP/ENKSiCFd3dnDB0CG90vYk+TsF40Ub7kcJzTS9sFNKVwSDXoDobGO6Lk67XmN8T4aF3RnLmoQs4Y49PeOKjsTy0+BN+vs/+hHS9MIaKsgg/+eFBzDxpCv98fyVPPr+IDz9toS+e4f1FTby/qGnAmPXKRioOa4O6DG/Yz/PS6tfofL6aXOtn49DDQBifrjJkWBH1o32EwiqRKpCiGfCZpOw0CTOBJMnYwsJyLGwcMlYGS5hYwsZwDBzhICGhSRqmY+JIm2eMt9sdrFYHOSKhVEggJGRkV6ANCBRZAVkipAeRJAkHm1K9DNMxiahhKvwV+GR3XlJWCgmJoBSkQqkgHAqTMTKsa2lFXq+Q7svQsaqLtq4OctkctmyTS+RI9my0egsDzKU2Ts+200GsoGWLy/0hH8IRVA+pRNEUJElC82mMnDQMSZawLYfOdV2k4xkkScK2bIaMbaCzpYu64dXYlkNJdTHldaX4Qz66W3uxTJtQUZDKweWUVhcjyRLFFVGKK4sQQmDmTGRFxjJtAiE/QggkSdrm+PtxHGeLNUg9PDw8PDw8PDw8PDbHE9J3YUYOrWLJyjZi6QqKgp2MLOrhnVwt3csTME5iWXcXo6KTAdDGKmRvEojpgvXaeppSTTSEGpAkCaHvC9nH2T8Ci7MOuw1rZfbS0ZxmfMTuoxJ8e8UqXs4M556PPuSsSftsNg5NU/j2/iP59v4jyeVM1rR0s2JtByvWdLCqqQtHCBLJLIZp4TxfQWpoG8F9W/FV5Rh0ahN6VwU1nWOpUwdTXhqmqjxC46Ay6qqLt1vQ2xr9eQ/795Ozc1jCQpN10vE0Kz9ew4rFa7ByFsueWcmHL32MkTW3tUtG7DWEQYc2Ei4JM3RcA1WNFZTVlAAQLt6Ktb122+N0HIdcOoesyCR6ksS7kzQtXoeqKfiCPlYtXIvu11i/so1cJkc6niEYDdC+tpMNq9vpaN6YkC+bcjP8Ny0eGDKw9N0VW+3/kzeWbnuAW0HVFKzPZMfXdBXTsJAVGX/IR2l1MbGuBP6gj0wySy6dwx/yUT6ojGRfis6WbkqqiqgeUsn6FW2U15VSXFVEIOynvLaU4kr3vSRJVDaU07K0lWwqi6qpRMsjdDR3YeZMdL+GcARGzqRmaBWBsB9VV2ld2UbrqjaGjG2guLKIcHEQRVUwMgYty1pRdZVoWYT1y1vJpnPYlkM6kWb0Pruh6irpeAYhBGU1JYRLQtQOr0ZRZOI9Sdav2EAoGiQQ8RMpDZOOZ5AVGSNj0LW+B1/QR3FllLLaUsrrSvEFdGJdccycxcf/XEK8K4E/5GPong2kYmmqGyupaqzAtmzi3UmiZWE0Xdts3i3TwnEEmq5iZA0kSUL365u1+zKwbXu78yt4eHh4eHh4eHh88XhC+i5Mf1x6c3s5Y4d0sltxD+901hWShDXF+ojKZYTlMEl/EjNkkH0VAt/ReKnjVc4YMh0AybcvIvs4k0MWc7pUSopTxGqDPPDp7syY8BHnjXyflxcO468LPuCHY8cT8W29prfPpzFqeDWjhm/bLz1uJnio5WH+2fUmRnknTeUvU1w0lsMGHceQUOMXMj/AZkK+T/Hhwx2/XlzEpAPHM+nA8YX1tm2zauFaVi5YQ6wrQduadrpae/jguUU4tmuFX7FgDSsWrNlif7IiU1xZRN3waoZPGELdiBqKK4soqy3BH/TRuEc96hYyzcuyTCDs+r376nyU15UxdFxDYf3eR0zY5nE6joNlWMS6EnQ0d5HsS2EZriDX2dLF8vmrsC0H3a9RWl1CUXkEIWDNx00s/2AVVY0VtK3pIBgNMu6g3elt76O3vY90Iktfex+5jEFReZS+zjjpWBokyKWNzQR0ANNwa/45tkM6niEdd+MvEj3JAW0SvanC5972GL3tMQDi3YltHuvO88YOtX7lgc+mDfz68AV0omUR9IBOLp2ja30PsIlCRJYYMq6Bkqoiutb1uIqEvBdFSWUR0bII4eIQiqaQ7E1hZA0qB5cT60qQ6ElSVlvCyoVriHXG8QV9aLpKvCdJUXkE23KIdydYuWANuYyBmTPxBXR8QR9ltSWEi0NUD60kHc+wfvkGxuw/kmQsRUdzF+V1pdQMqUKSJZa9v5LaYdW0re2guLKI8tpS7Pw9VV5XSndrLz1tvcS6EnS39iCcjQq2ZF+Kng29lNWWggTCEeQyBqXVxTi2g227Sq6KQWVkUzmKKqLUj6x1vTsMi1zGYMOaDtZ+3Iw/5EP3ayT70ji2g6qrVNSXcflDF1A3vOZrO8ceHh4eHh4eHtuLV4JtF2bZ6nZmzrqXs/7rQ35wyPs8uHoUl30wlfBaQW6QjKkKHjnhB7yVepL3ez4gfnsWY6FN+a0hdEnnpgl/JKgGEfYGROdUQOZd+RZuWzsXIaBlRRXPTH2CgGbx46e+zUvZEVyw7xTO2XvfL+wY1qXX8Vz7i7zR+SYO7gP7xJK9OKzqUHaLjNhlYqiFEHRv6MXMmrzzzHyaFq+jp62XlQvWkE5kSMXSn78TXKGqpLoYVVOIlIYpqS6mYfQgKurLKSqPEC4J4w/5qBteTbQsgqLuuhZL0zDpbY+h+TR0v4Zt2Ti2Q3drL4meJL6gj0RPEkmCaHmUtjUdxLsTlFQVofk0zJyJP+QnHU/T1xEnEPGTSWQpqS4mm8q6yobeFOl4mlhXHCNn0d3ag6qpVA+p5NM3l1I7vJqqhgqEI5AVGUmWMHMmiZ5k3mpvYFs2JVVFOI4gm8phZAyy6Rw9rb34w35G7T2cZF+KrnXdRMujlNeVsuz9lQwePQhVVwlG/AgB3a09ZFM5Nqxqx7EdjJyJYzsUV0RJxdLYtlNQ5CiqUugXINmbKigvNsUf9GHbDqqm4A/5iHXGcZz/qK/cXYa/fXoDDaMHfd3D8PDw8Njl8UqweXjsPF4Jtv8AhtaXo2sKS9e6J3FUsZs8ziyTkGMOlEks7uxgdNVI3u/5AN8EFfGYirnahqEGb3a9zXeqD0FSahBKI9hr2ScisahsH97ufpfqxm7+vnYUM0d8ws9Hv89LHw7nrwve55Sxe1IS+GIygg8KDmLmkBkcVXMET6x/ire732V+7wLm9y5gcLCe79YcweTSSSjS1yusSpJEeW0pAN8798jN1qfiacycSfOS9az5uJlYZ5z25k7ieatgoidJ1/oeTMPa6Jq+qh2Ad56ev8U+VU1h8OhBmDmT3SYPwx9wBbre9j5qh1Wj+zWKyqMomkIubTBotxr8YT+V9WUANOxe/yXMxEY0XaOyvnyz5cUVRVtsP3LSsC91PDvKjsTN7wipWIpgNDhg30IIetr6MDJGwZIbKto8NCKdyJBLu+EKvqCPvo4Yaz9toaqhAsu0SfYmSfamiJRF3HKAkQDhkhDtazv59K1lxLsSrFvRimXajNl/JI7t0LOhl562PsyciWXaCMeho7mLbCpHuCRU8P4oLo8yePdBmDmL9rUdyIpM9ZDKvKIigKzIDB3XQKIniT/sx8yZrP2kBTNn5veXZd2KDRSVRyivKyNcHCLZlyKTyGCaFoqiUFxZhJExiJSGSfQkUTWFrtYechkDRVWoH1lLzdAqiiuimIZFMBrEzJnEuxOYOYshYwe71m9NQZIlettjWIaFL+gjl87R1xEjXBIm3hWnc103siKj6Rp6QEfzqSBg8O6DMDIGRRVRJAnSiSy2aRXuGw8PDw8Pjy3xrW99i/Hjx3PjjTdutU1jYyPnnXce55133lc2ri+KK6+8kieeeIKFCxcCMGPGDPr6+njiiSe2us32zMn28EXt5z8JT0jfhdE0hTG71bJ6vSv07RbtQUJgRiSCTZArg0XtbXxr+Fi3/TiFtGySetSmeFaA59tf5JCqg11rtb4vZNaC+S4zGn9Bc7qF9bTyz2Q5P7BUdh+RYNqSZTyXHcVN777Fld865As9lip/FT8ediZH1X6Xp9Y/w/u9H9CcbuHWVXfwYMsjfKfqEL5VcRBB9bNJ5nYNQlF3XMUVRYw7aPcttrFtm3XLN5BJZOho7iIVS9PZ0k3b2g7amzqxLZtsKkeyN0V7UyeWabP6Izf5Xsuy1h0eU6Q0zJCxg0n2pdD9Oooq09seo7yulKLyCKl4hsEj6wjlY7TLakooqy2hdng1g3arRZIk+jpjCOG6rgshKKkqQpblL0W4/ar5so5hS8K3JEmFvAXbIhgJEIwEBnyuHfb5JQ2GjmsYEB7xVbHHlFFfeZ8eHh4eHh47w4wZM7j77rs3W75ixQqGDx/+lYzh008/5YorrmD+/Pk0NTVxww03bCbQ27bNlVdeyX333UdbWxu1tbXMmDGDX/3qV1t8dvnjH//INddcw4YNGzazzKbTaaqrq7nmmms499xzd2isN910E1+0Q/Wrr77KwQcfTG9vL8XFxYXljz32GJq2eQ6eL5K1a9cyZMgQPvzwQ8aPH/+l9vVV4AnpuzjjxwzinkeasGyFgGoyKJSgJRVFynvWvr9+HXWBaYQVNy7drjcx/2FT9JMAHXTwfs8H7FO2N5K+LyIzF4x38Ef9nD3sJ/xm8W/JhXM809nAiTWruORb83nthWHc99EifjhuPMNLv3jLU12glrOG/w+nmifzYsfLvNj+Mj1GDw+2PMyT65/ioIqDOKz6UCp8m1twd3UURSm4047ae8Q221qmRWdLNx+9vpiVC9awdnEL4eIQpdXFpBNucjLHdkjFXAu+4wh6WnvJpnO0rekA3Bjwj15bvNm+W1e2Fd4veOGjLfYvyxKKqmzRRVvzaRRXRpFluSDgb1jVTs3QSsrqSgkVBTf7ESmpLKKyoQJFVVA1hVzGLUXnC+gMGTuYaFkkb2HWC8n3hBAYWaNgDfXw8PDw8PDw+Fc4/PDDmT179oBlFRUVX1n/6XSaoUOHcsIJJ3D++edvsc3/+3//j1tvvZW7776bMWPG8MEHH3D66adTVFS0RUH71FNP5ZJLLuGxxx7j5JNPHrDukUcewTAMfvjDH+7wWIuKtuwZ+WVQWlr6lfX174InpO/iTBhTz+yHZFo6ShlS42Z4b0lFCUZ1Epg0x2N0ZzKMjo7k/d756Hsp2EsgMTdL9Ew/z2yYx96lk0HPZ223liHsbgYF6zh3xE/547IbedmqYmq2lUHRJGeNXMANi/fhNy+/xD3/feKXdlxhLcxxdcdwZM0RvNP9Dv/X9jzrM6083/4CL7a/xF4l49m/fD/2LBqHKv/7XaaqplIztIqaoVVMm3HwDm3rOA59HTE6mrtYv6KNUFEQx3GIdyVcF29Zpnt9D6sWrcHImgSjATSfxrrlG+ha101HcxeOI3A2EdAlSUKSwHHccmudLd3uik2q7G0rod6OEC2LECoKkssY9GxwQzgiJSEqGyoKCe/a13bg2A5tazupqC8jUhrGsRyMrIGqq2g+Da3/r08lXBKmo7mL5iXr2PeoiUTLIgCsW95K1/oe15OgtgTLtKmoK2P4hCE4jqsE6dnQi6zIVA6uIJPMUFpdQs3QSoSA4kq3DF0wEth6Zn8PDw8PDw+PXQKfz0d19ZY91F577TVmzZrFokWLKC0tZfr06VxzzTWo6pafMzs6Opg5cyYvvvhiwVr9eUyePJnJk93KSxdffPEW27z11lsce+yxfPe73wVcF/oHHniA9957b4vtKysrOfroo7nrrrs2E9LvuusujjvuOEpLS7nooot4/PHHWbduHdXV1ZxyyilcccUVW7Vgf9bdPZVKcdZZZ/HYY48RiUS48MILN9vm3nvv5aabbmLZsmWEQiG+/e1vc+ONN1JZWcnatWs5+GD3mbakxPUunD59OnPmzNnM3b23t5ef//znPP300+RyOaZOncrNN9/MiBGukWvOnDmcd955PPjgg5x33nm0tLRwwAEHMHv2bGpqdi4JbC6XY9asWcydO5d4PM6kSZO44YYbCuert7eXc845h+eff55kMsmgQYO49NJLOf300zEMgwsuuIBHH32U3t5eqqqq+MlPfsIll1yyU2PZHv79pJ9/M8aMqEFTFZa3FDGkppPdinp4sbURbZAftcfACknMb13PqOgo3u+dj28vFe3ZAH0Pxyk5PURzuoWPY58wrngsQh0J1jIw/gmB49ijaAw/bPgB9zTdz4PxRs71L+HM0R/xZMtuvNEKTy9ZwtGjR3+px6fLGgdVHMiB5QfwSexTnm17jk/ji/mgdwEf9C4gqkaZWDKBgyu/RUNo8Jc6lm8KsixTWl1CaXXJ51rst0Q6kaGnrS9fEqzCrbsu3Bj5WFcCI2vQvb4HWVVIxdL0tcfobe+jbkQNve0xMsnswB0KwcqFa1i1cC1mzqS0pgRFda3pRtagt62vkNkd3Ozun83wnuhNDcgGvymdLd0blQbbwct/33KW91UL1xbePzfnle3e36ZIkkRReYSSfPnAVCxNSXUxZTXFJHpTqJpCtCxCb3sMPaATLg6i6ir+gI8Pnl/kKkx0lVzGoKy2BFVTScXS+MN+huwxmEDYTzAawDZtZEWmu7WHzvU9CMdh6LhGwsUhLMOio6WrkBHetmwqB5dTUlWMbdlkkln0gE46nqGvI5aP75aJdcZZtWgN4aIQ6aSblT9cHCJaFiGTyGDkTIrKo1imje5T8Yf8aH4Nx3bwBXTSiQzFlUVUDCrDF/QR73YrDQhHIITAF9DR/RpIEvGuBEP3bKBtTQe1w6oIRoPIikwunSMVS5NN5VDz5eVSfe7xxzrj9LT1Ul5XhqoprF+xgb7OGKP3HUlxRRRZkSkfVMrgUXUEowE2rHYVOcWVUUqq3PPR295HvCdJX4d7vYWKgoWShfGuBL3tfcS6EoSKgpTWlFAzpJLiyij+kB9FlfGH/AghiHcniJZFaFm6nnQii2M7hEtCZBIZyutKUTQVWZYIl4Q2K1cnhKvo0nxaweNk0/wIpmEWEkZmU7kB4Q9fBbZl4zjOgCoUjuPQs6GP0upiFFUhncjg2G61iL7OOEbWRNUUZEUmUhIi3p0kEPaj+VQUTSXenSCbyhEI+ykqj2yxwkV/3y3LWjGyBtGyCJpPQwiBoiq0rmxDkiVKqorIJNzvmEDEz/oVbYzed0Qh5MjDw+OrRwiB4RhfS9+6rH8h4Wvr16/nyCOPZMaMGdxzzz0sXbqUM888E7/fz5VXXrnFbWbMmEFrayuvvPIKmqZx7rnn0tHR8S+PZf/99+eOO+5g+fLl7LbbbixatIg33niDP/3pT1vdZubMmRx11FE0NTXR0OCGv61evZrXX3+d5557DoBIJMKcOXOora3l448/5swzzyQSifDLX/5yu8Y1a9YsXnvtNZ588kkqKyu59NJLWbBgwQDXcdM0ufrqqxk5ciQdHR1ccMEFzJgxg3nz5lFfX8+jjz7K8ccfz7Jly4hGowS2kuNqxowZrFixgqeeeopoNMpFF13EkUceyeLFiwtKhXQ6zfXXX8+9996LLMv88Ic/5MILL+T+++/fruP5LL/85S959NFHufvuu2loaODaa69l2rRprFy5ktLSUi6//HIWL17Ms88+S3l5OStXriSTcSsY3XzzzTz11FM89NBDDB48mJaWFlpaWnZqHNuLJ6Tv4vh8GmN2q2H1etdNZGSRK6ysyyUIp2WSIcETCz7hsqPy9dLHKfRmMoi4gJdVOMzmifVPMbZoD/Ad4lrSsy8gBY4D4NuVB7M23czrna+zKF3CnsFertrrDU597SjOe34e65Jxpo/fi+CXHEciSRJji/dgbPEerEmt5c2ut3iz6y3iVpxXOl/jlc7XqPJVMrFkLxpDjexZPBa/4mUc3Rk+Gxe9Kf1x1TVDqr7QPm3bJh3P0PRpC76gDzNnkk0b+AI6ZbUl9GzoZcUC1/IfCPsJRvz0tsfoWt9DIOynor6MkqpiIqVhLMPCNCzMnJt9PtmbJNGbIlLiJjILRoN0NHXi2A7D9xqKP+RD1RTa1nZiZk0613UT746jaCpCCFRNIVwUoqOli1w6VxBoZUWma31PQeAD90GlrzNOX2e8sKy9qXOn5mTNx80DPr//7IfbbP/aQ2/vVD/fdP756Luf20aWJbcc2xZKBu4IsiKj+zSy+eR+20NReYRgNFhIotc/hv7wjv4KCP6wH4SrJNuUSGmYUFGQMVNG4vPr7r56k+SyBppPQ1EUutZ3k03nSMdd4TkQCRAuDpLsTaHqKplEBkVViJRFMLMGqXgGTVdJxzOuIiSWJlIapmudm8SvPwZR09W80L6xHJ6qq5g581+ax2AkgOM42JZDKBrAcVzFR39VhB3lupd+zfiD9/iXxuTh4bHzGI7B/8w/+2vp+46Jf8GnbL008Gd55plnCIfDhc9HHHEEDz/8MH/5y1+or6/nz3/+M5IkMWrUKFpbW7nooou44oorkOWB1YaWL1/Os88+y3vvvVewtN55552M/gKMVxdffDHxeJxRo0ahKAq2bfPb3/6WU045ZavbTJs2jdraWmbPnl1QKsyZM4f6+noOOcTNI/WrX/2q0L6xsZELL7yQuXPnbpeQnkwmufPOO7nvvvsK+7v77rsZNGhgVZQzzjij8H7o0KHcfPPNTJ48mWQySTgcLri1V1ZWDohJ35R+4fzNN99k//33B+D++++nvr6eJ554ghNOOAFwFQK33XYbw4a5iYnPOeccfvOb33zusWyJVCrFrbfeypw5czjiiCMA+Otf/8oLL7zAnXfeyaxZs2hubmbChAlMmjQJcOewn+bmZkaMGMEBBxyAJEkFRcmXiSekfwPYf9Iw3n3XjdEeW9pVWD5kUBkf57p4a00T1foxlGjF9NIHYxzk92TW39hF/WGlrEqtZlHfR+wZPgyR+gvk/olw0kiyG1t8euNpWI7F/b1ZRvsXsF9VKycPWczf14zhurff4K6FC/jxxMmcMnZPAl+ysA4wJNTIkFAjJww6no9iH/NO93t82LeQ9lwH89r+DwAJiYbgYPYsHseexeMYEmrcZcq5eWyOoihESsLsccCWf9yqGyvZfb+RX/Gotg/TcEu+xboSGBmjYFn0h3z05RUJ4eJgvsa56z3Quqod4TgUlUfRfJpbzu/DNew2cRjDJgxBksDIGDiOoGnxOhRVJtGbJNYZR9XUQghDcUURVQ0VZNM5VixYjSy7QlSiJ8nqj5pRNQXhCELFQVJ9aUJF7jgyiQyh4hDx7gTFFVGi5RH8QR+l1SVIskRpdTFCUChXJxyHcEl4Yw4E23HDEdp6MbImul9DURXWfNRE57oeqhrKKa0pIRgNEAj7CYT8GDkTI+tmgu9a35O39IexLYdsKks2lcNxRMG7wMxZVDWUEyoKkk5kiZSEKKkqJtYVp68jRjqRpbetj4bdB9HXEWN9PtdCoic54PxIkuSGbzgDhb9oWQRZcb8T/EHdzXBfVUSyN0Xbmg63nnxeGO0vrefYzgABXfNphIuDAzxBPkusK0GsK7HZ8mwqV7DiCwHpeGazNv3Hk+hJFnJNfFlsaYyfzUnR7wWwKYqqoPnUwrGA63WzLYXIpoqIvs6N+/usgL5pmE0g7McX0En2pZBkGTPnWu9rh1f/y0oDDw+P/xwOPvhgbr311sLnUMgNVVuyZAn77bffAKv8lClTSCaTrFu3jsGDB3prLlmyBFVVmThxYmHZqFGjtip47ggPPfQQ999/P3//+98ZM2YMCxcu5LzzzqO2tpbp06dvcRtFUQqu47/+9a8RQnD33Xdz+umnFxQMDz74IDfffDOrVq0imUxiWdZ2l5tetWoVhmGwzz77FJaVlpYycuTAZ7P58+dz5ZVXsmjRInp7e3Ec9/ezubmZ3XffcmLlz9I/t5v2VVZWxsiRI1myZElhWTAYLAjoADU1NTvtybBq1SpM02TKlCmFZZqmsffeexf6POusszj++ONZsGABhx12GMcdd1xBiTBjxgy+853vMHLkSA4//HCOOuooDjvssJ0ay/biCenfAL69/0jufqgSx4HB4Tilvgw9uQBqsQrtkPBZ/OP1Txk/ZE9e6XwN/wEqZZ3FtK5so3bVIJqGreHR9Y8zbvfLQRkE9jrX5d0/DQBZkvnR0NO5baXJY7Fuvl+yhsv3eosl8TI+7KqiO5Pmd2+8xh0L3ufHE/fm5D3GfSXCuk/xMbl0EpNLJ5E0kyzsW8Ty5EqWxJfSketgbbqJtekmnmx9Gk1SGRYexoSS8UwoHk+Vv/JLH5/HfwaarhXCCzy+fvpzCWg+DX/Qh5EvEZeKpamsL3MtyqXh7XaP7BdMjazJhtXtqJpC9ZBKWpa10rjHYHSfhuO41Q82rXyQSWXpWtdNe1MXQrhCZmV9GbbtoPt1Olu66Gzppmt9D3sdOpbOdT3k0jlGTByKZVhoPg3dr9G1vod1y1ppXrKeFR+uprzOTdhZ1VBBJpFBkiUCYT/hkjCR0jD+kI94V4LmJeuIdycoKneVMB1NndQMq0bV3FJ4QggSPUmKKqIEwn5kWaastsQtZ5cxWL9iA5X1ZUiyjOZTqagvp2tdN/HuBFWNleg+Fcu0B7j0Z5Kup0CoKEgunSPZlyaXMQrl7RRNIdGTpGdDH0IILNMm3hXPr1OJloVpHFOPqqnYtl14sMxlDPxBX+H8SpKEEALhiEJogIeHx9eHLuvcMfEvX1vfO0IoFPrKMrnvLLNmzeLiiy/m+9//PgBjx46lqamJ3//+91sV0sG1Yv/+97/n5ZdfxnEcWlpaOP300wF4++23OeWUU7jqqquYNm0aRUVFzJ07lz/+8Y9f2LhTqRTTpk1j2rRp3H///VRUVNDc3My0adMwjC8+HOKzsfT9vw1fFkcccQRNTU3MmzePF154gUMOOYSf/vSnXH/99ey1116sWbOGZ599lhdffJETTzyRQw89lEceeeRLG48npH8DqK6IMqShkbVtJQyt7WVCWTsvtTbyUUc7EUUngcEtz/yT3160L6/wGr4pKsbfXatHy5868d/qpzndwvy+D5noOwzSdyEy/0DKC+kAiqTw42FncssKk/fTc5kc7OLW/Z7n6BePp7tTJxoK0JVO89t/vsot773Nf43ane/vMY7dyr6aLOxhLcwBFVM4oGIKQgia0800pZtZ1PcRn8aXkLEzLE0sY2liGQ80P0iVr4o9i8dR7a9kYslEivWvLoOlh4fHl4csy0RKNroy6j6NQSN2LokMuD/6ul9H9+uM2GtoYfluEzdq7z/rBgkQCPmpH1lH/ci6Le63rGZgzoiG3eu32K6oPMqwPRt3cvQ7z5bmrHZY9TZLAgbCgQHvN/3cT3FFEcUVn/99u2ksf7+ADhvnWpIk8JyjPDx2CSRJ2iGX812R0aNH8+ijjw7IEfLmm28SiUQ2c+kG12puWRbz588vuLsvW7aMvr6+f3ks6XR6s98VRVEKVumtMWzYMKZOncpdd92FEIJDDz204Hb91ltv0dDQwGWXXVZo39TUtLVdbXHfmqbx7rvvFrwKent7Wb58OVOnTgVg6dKldHd384c//IH6evc37YMPPhiwH113lSq2vXVvq9GjR2NZFu+++27BUt3d3c2yZcu22xq/owwbNgxd13nzzTcLc2aaJu+///6AEnkVFRVMnz6d6dOnc+CBBzJr1iyuv/56AKLRKCeddBInnXQS//3f/83hhx9OT0/Pl5a53hPSvyF8e/+RLF5TydDaXsaXtfN+90jiuRyjqyp4r3U97WqGN//RhT5Wh2qDjmAMVVdZN7+V7/NdXuefPLbuCSaO+gGk74LcSwinB0neeGGpsso5I87iL8sy1KoPUBdMc9sB/8f3Xz4O+831HNrYwLKRPlriMeYs+pA5iz5kr+oajh45iv0HNTC8tPQrqa8tSRINoQYaQg0cVHEgpmOyIdvGkvhSFvYtZHF8Ke25dp5vfwGAe5rup1QvoSHYwO7R0YyIDGdQoA5N9sp+eXh4eHh4eHj8u3P22Wdz44038rOf/YxzzjmHZcuW8etf/5oLLrhgi4rYfrfmH//4x9x6662oqsp555231URo/RiGweLFiwvv169fz8KFCwmHwwUL/9FHH81vf/tbBg8ezJgxY/jwww/505/+NCDee2vMnDmTM888E3Bj0vsZMWIEzc3NzJ07l8mTJ/OPf/yDxx9/fHunh3A4zMyZM5k1axZlZWVUVlZy2WWXDZibwYMHo+s6t9xyCz/5yU/45JNPuPrqqwfsp6GhAUmSeOaZZzjyyCMJBAIDcgT0j/XYY4/lzDPP5PbbbycSiXDxxRdTV1fHscceu91j3hrLli3bbNmYMWM466yzmDVrFqWlpQwePJhrr72WdDrNzJkzAbjiiiuYOHEiY8aMIZfL8cwzzxRyEPzpT3+ipqaGCRMmIMsyDz/8MNXV1V9I+MPW8IT0bwjf3n8kf5tdxVFTljG+tIOAqhLP5bDzCX+ypfDYvI/5zp5DWG0vw3+gSrmvmrWfNNN3d5rQjBCt2Q08293MEdoYsD6FzFMQmjGgH03WOGvkBcxeluIU9WH2LO3id/u/zCznUD65fSGH7jaCA353LI+tXspLq1exoG0DC9o2AFATDnPwkGHsV1fPXjW11EQiX8ncaLLG4GA9g4P1TKv+Dr1GLx/0LmBJfCmrk2voNXvpMdzXh30LAddzoMZfzeBgPXWBOqr91VT7q6gL1H4lioavC8txkCUJ+d/4GD08PDw8PDw8NqWuro558+Yxa9Ys9txzT0pLS5k5c+aAZGufZfbs2fzoRz9i6tSpVFVVcc0113D55Zdvs5/W1lYmTJhQ+Hz99ddz/fXXM3XqVF599VUAbrnlFi6//HLOPvtsOjo6qK2t5cc//jFXXHHF5x7H8ccfzznnnIOiKBx33HGF5ccccwznn38+55xzDrlcju9+97tcfvnlW81cvyWuu+46kskkRx99NJFIhF/84hfEYhtzslRUVDBnzhwuvfRSbr75Zvbaay+uv/56jjnmmEKburo6rrrqKi6++GJOP/10TjvttAHKhH5mz57Nz3/+c4466igMw+Cggw5i3rx5Wy0XtyP0hxFsSktLC3/4wx9wHIdTTz2VRCLBpEmTeO655wrl4nRd55JLLmHt2rUEAgEOPPBA5s6dC7iZ86+99lpWrFiBoihMnjyZefPmbVHB80UhiS/TuX8XJB6PU1RURCwW2+5kCrsKv7vpZi4+6c+kTJX9nzqTlO0QVFVMx8F0HCreFzSMyxCYuhZzpY366zAbVrXjD/k499MZ3Nf2AKqkct2IcRRnbwJ1N6Syp7colNrC5pXmq/i2z704/7hmPH99YyK1/+9j6qNRLrr3XKrG1/PksiW82rSG+a2t5OyBSYgGRaM0FpUwuLiY+miUiO5jSn0Dg4uKvlJBOG7GWZlcxdpUE6tTa1idXE3KTm+xbVgNMyhQx9DwEIaGhjA4WE+xVvyluXn13349mQwt8RhW3tWpK51mdW8PXZk03ek0uqIQ0jRMxyGkafTkS0L4VBVdUUibJj2ZND5FJWkY2MLBEYKcZWHYNjnbJmdZbEgmePaU6Qwt+XJcczw8PDw8PDy+2WSzWdasWcOQIUPw+71KOh4eO8K27p8dkUM9S/o3iP33/g6pzO2EAiaDwp3E7UY2JBOMKq9gaVcn/iEB2j60GXKAhDZcocPupHZYFa2r2kk8kWP84XuysG8Rt65fz8VlPiRrOZgLQZ+wWV+KpHDI4KtY0RZnuDSPcxsXkVUlHlPHIv12Bb+YegUnzjqW6VedyI/2mkTWMnl7XQuvrl3Dh20bWNzZwbp4nHXxOLQMjIkJ6zo14QhRn4+Iz0dY0ykLBin2+ynxB/CrKpbjUOTzYwkHVZKxhEMgv7w04LYt9vsJaTp+VUXdJKHTZ4lqUfYqmcBeJe5xCiHoMXrzce0tNOeF94SVJGklC7HtA/ahRqnyV9IY2A1hhAnJJZRq5aiyQmcqxbpEDEWSyVoWOdsia218JYwcHckkkiQRz+UwbZuwrtOXy5I2TdLmV5u5eGVPtyeke3h4eHh4eHh4eOyi7BJC+v/+7/9y3XXX0dbWxp577sktt9zC3nvvvdX2fX19XHbZZTz22GP09PTQ0NDAjTfeyJFHHvkVjvqr54C9R/LpuzWMHdrM+LJ20uoUnl6+FCNf1iYwPITSDKm1QULDUvinaVStrKR1VTvP3Po8f/zJVfzqk1+zPNVOc8keNMjzEen7kLYgpIMb+z2i+k/EunqJ2m/zi7pFWN8SvBIeQfb6OA9c9yTv/d+HXHzvuQzZYzAHNw7l4EY38VI8l2VJZydNsT5a4jGaYn183N5OU6yPpGGwoqf7C52bIp8fRZLwqQol/gBBXafI50OR5AGu3Vbe6yBtGqzt66MzncqvqUWSHELBHKFglkg4SziUIRzKIkkQt+LEk3FWJFcW9uU4YDsyyWSAbE4jmfJjmirZnI7juH1mczpCbEF5kNp8UUUwhK4oyJJEWSBIWNcZXlZGTTiM7QhSpkHGtHAQlPoD6IpCzrbIWTaSBLqiENR0Svx+ZElGlkCWJDRFIahpaLJCfbToKwtD8PDw8PDw8PDw8PDYcb52If3BBx/kggsu4LbbbmOfffbhxhtvZNq0aSxbtozKys3LaBmGwXe+8x0qKyt55JFHqKuro6mp6UsN3N9VUBUZf3gy0Mz4sg6WZgNIwOq+HhRJYnlfNzf99HBue7qT0LAUweMCrP5RC8FIgJZlrax5vZkZe53Kn1feyux2hytrgOz/IeyLkJQtlyyTJJmi8r9h9pyO33yPS6sXUblfhueuGU7sgbEsn9/KmeN+wWGnTeW0K0+kutHdT9TnZ59B9ewzaGBG43gux/p4jO5MhoSRI5HLkTQMujNp+rJZejMZYrkcdt7tO22ZCCFQZZmMaaLKMlnbIpbNEsvlCu7hsVy20MeG5MA6ytuLEDLJVIBUKkBXp4SDQCBQFQe/3yASzhCNpCkJ26h6Bll2kGWHkuItSNz984eMjEJQjhBSIhRpRfikID4pQLFeTKleQokvyvBoHUW+MDJyXsD20hp7eHh4eHh4eHh4/Cfytcek77PPPkyePJk///nPgFsjtb6+np/97GdcfPHFm7W/7bbbuO6661i6dOlOJRf4JsekA5ipF1ESZ7MqXsy5L/2E8uoIb7Y0M6yklFW9PZw2bjyHFTVwa+L/IfsdWu+uYXhLmJXPzGf/Yydz1eO/5LZVf+Xt7nf4dfViGvVuCE5Hjl62zX6Fk0L0XQDGKwA8GRvMwy3DaH98EMZqGWVVG1pLNwd8dy+OP/8o9pgy6gs/9pxh0dYRw7QcUpkcbR0xetIZWjtjrE8l6O1LkXVsHEeQyGWJZbOoqkLGMF1vAwkkGyQHhAxKDtSU+15yQM6H1EtuU/e4ASQQEgjFbXPfDTNoGFRKR7aDjJNldXINPUYPK5IriZtxeo0+ZEnCFg45J7dTx6rLOn7ZjyIplPlKqfCVE1Gj+BSdsBpGQkKTNYRwiFsJVEklqAQJqkFCSpCQGiKoBCnzlaFKiif0e3h4eHh4eGwXXky6h8fO828Rk24YBvPnz+eSSy4pLJNlmUMPPZS33357i9s89dRT7Lfffvz0pz/lySefpKKigpNPPpmLLrpoQN3Vf1fUwGScuMSwaB8Jcx1HFR/Kmy3N9GbdRGKPL13CRTMPYvKSyczPvktkUoqP24qR9x7Bmy9+xMoP13Dq2JNZGl/Gw301zKrshvTfEaHTkZTarfYrySEo+Qsi+UdI/Y1ji5qpUdP87VSDtteq6QsOxtpjMC91xHn11FsYO6SSY8/4NlNP3A9V2/Zlls2ZdPem6OlL0RNL0xdLk84arFzbSSZrEotnWNfWS3fv1i3WWxwzYGOiA/pn1kXCfkIBnWCVTiigEwjoqIoryCqKjE9X6YtnsCybSNi9wSIhHxVlEYIBHVmSqQ649YSHhBq32L8jHHqNPnKWQVeum5gZo9fspT3eQ8ZJE3f6SDpJElaCpJMYsK3hGBiOAUCv2cvK5KodOvZN8ct+GkMN+GQ3+Z0qq8TMOJZjUu4rQ5EUwmqYMr0Un+InoATQZQ1ZkslYGRRZJapGUGWVYq0In+wHBLKkEFHDW80FEDcTSLhhE37Zjyp/7Y4730icvLJHkRRUSS0oXBzh+noo0vZ/7/XXh3WEQ8pK0WP0krJTlOqlVPurvqxD8PDw8PDw8PDw2AG+1qfmrq4ubNumqmrgw2FVVRVLly7d4jarV6/m5Zdf5pRTTmHevHmsXLmSs88+G9M0+fWvf71Z+1wuRy630ZoZj8e/2IP4ipHkImxlNKqzmL0b1vH0/fMJT9TpyWSoCAbpTKd5evlSvjv0UOYvfpfQiDiyXo1TV0q2toTzr3yIK37zfc5onMEfV/yJpdkiRvljiOT/IhX9dtt9SwpS5JfY8hDs2BXsHeqiQs1y86EmsTEGax4px5aiOBVRFtgOC299nsAt8xg8rJpxE4dSu1stXbE0qiLT1hmnqzdJS2sv7V1xdsSfQ5YlqsqjFEX8lJWECQd96LrC8IYKNE0lGNAJh3xUlIbJZE38Po2ykhA+XUXX1YIw3o9tO8STGbI5i5yxyavw2SSRypHJGvTFMrz4xlLCQR+242CaNp09SQzDwjBtMlmTnGG6yoVEhngiS188jWHaWzgSBShyX5JA9tlIikD2OSCBrDlIuoNWZCL7bdSIhRK0kP1uO2HJCFvCySogC0aOLKOoTCNtp0lbGXrNXmxhk3WymyXC62dtummLy3cETVJRZQ1VUjEdE1vYgMAUA7P9K5KCT/bhV3z4ZD8+WUfgKiQydgYJCQcbCZmgEiCkhsg5OUzHxCf7kCWZoBLEEhYZO4NAYAkbCTexn+EY5JwcCStJtb8Kv+xDV3w4wimMK6pF8ck+VEkhbiXI2BlyTo6klUICVEnDr/iwhU3aSqPJOiE1QMbOkrSSaJJGxs5iCZOgEiKqRYhqURRJIaD4USUNU5hISITUIJZjkcsrXCzHRACmY7Ah247hGBRpRYTzxxk3E3nPC4GU9+UwHYusk83PqYuMjCq7c93vUeEIB01WUSUNVVaRkZEkSFlpdFlDQiZjZ8g6WRRJQZGUghII4Njao/mvQcf9y9eCh4eHh4eHh4fHv843zrTlOA6VlZXccccdKIrCxIkTWb9+Pdddd90WhfTf//73XHXVVV/DSL881MABkFrM/lWt/NPZjWhHgGQJ+FXX/f/OD+dz/OjTqA3U0korxfoSRHZ3emWZXlXh/N88THE0QON3RvOoHuey6o8Q6UcRoZlI6tDP7V8JnUBG1JLuPZshviSXVy3kZnl3pJ8kkZfX0/RCkGwanKoiUsCSrMmSN5fBm1sWFMGNtw+HfETDfqLhAKoqU1IcpCgcIBTUKSsOUVNVjKLIRMI+/LqGz6diGDaJVJauniTxZJZkOkcilWV9m8OCT5pJpQ2SqRyJZBZVlclkTTq7E9iOIBL2E4un6Y1tTYj+8ggFdSzLwbRsNFUhZ1g4Wfd2DEp+KkvD+P0afp9GMKBjmBZWp4NPV6ksj6CpCkXRAJqqQASqK6LsufsgykvChT4c4dBn9hE3E6xLr8MQJiJfmq1EL0Yg6DZ6SFtpHOHQZXSRsbOYjokpTHK2gSopWMLGEiaWY9OZ68xH6m/UqpjCwvxM+b0tYQvbVSBspfzdpvSZfTs0n+tpHfA5Zsa20vKLI2Elac+1/0v72NHjBHBwCgK2QBRCKkzbBDKbtU9/5tK2hV0Q+jVJpdxXjl/x3Bk9PDw8PDw8PHYVvlYhvby8HEVRaG8f+KDb3t5OdXX1FrepqalB07QBru2jR4+mra0NwzDQ9YGOzZdccgkXXHBB4XM8Hqe+fmAys28akr4fInUH+1euR6/UsZdmkPaVaInHCGoaK3q6eXblcg6t/Db3NN1H5BiJrpPnc+jJB/HKwrU4Qyrpi2dY+ChUpAez8JB1jA/0sOzj0/jdPacTDgWJhPwURfwUFwWRkMjmTFKZHOs29GHbDjnDQpJO4LIzn2Z4SS+XVS1iXqKeJ0eZVNVpdL9aSXJxlEJ0txCwjdrolu3QF8/QF88AvV/JPG7oGCjI+XR1s5fuU12FgO5a6E3Txu/XyGQNFFlG0xQCfo3iaBC/TyPgd9sG/BrRSIDiaIBIyE8oqKMqCgJBOOjbzEXccQSW7QrsX1QNeVmSKdVLKdVLaQw1fCH77HeXtoWN5VgkrRSWMMnYroW2300eJEJKsGDVzTkGOSdL1s6Rc3Lk7BxZJ0vONlAkmRK9BCUfO28Lm2zecq3LPgQOOdvAEhYgUCUNTdYAgU92rd49Ri9hNYThmHTkOtBkDcuxCCpBFFlBApJWClVSyTpZUlaKMr2MiBZBRkaXNQJKACvfN4AlTBzhoEoqmqyjyRq2sAirYQJKgJSVImEliJtxYmYcSZIwnY3b5BwDTdbwyTq6rLtJCCUFXfZRppcS0SL0GL3EzBhBJVjINyAj4+DuQ5c1fLKfiBbByc+5JSxMYRWs54ZjokgKlmNhChPbsTHzYw+pIVJWCp/iI6gE3BAGO4suaZR6+Qo8PDw8PDw8PHZJvlYhXdd1Jk6cyEsvvcRxxx0HuJbyl156iXPOOWeL20yZMoW///3vOI6DLLsPl8uXL6empmYzAR3A5/Ph8/m+tGP4WtAnYgmN6mCKqQdqfLAO4u0GmWqJMj1A2jT5/Ruv8fQPTuZR5TFS9Wm0fWVK/BrFa9pJfdzED66fTnhYFYtXtXLPshS7jXud3Wo6GD/6NR57btx2DsTPuX84lllnv8rBDWs5tqiZfQJdzPENY9nRFhP/S+W/a/6bPSt3x7ItVi1ZzxsvfszCD1bR1NpLPGu6MryqgGWDqiBUBXQFSZbRJAnNp+JoeTd1Vcbv10kbFtmcSSZnEg758esqg2pKKIr48fk0FFlClmVCAZ1QyEcooOPzaciShN+nUlIUImeYrlU+5KekKEh1RRRF+fqEFVmW0L8BMdv9CgRFUlAUBZ+yffeWKquECH6ZQ/tGsmPKE82NkPDw8PDw8PDw8Pi35mvP7v7ggw8yffp0br/9dvbee29uvPFGHnroIZYuXUpVVRWnnXYadXV1/P73vwegpaWFMWPGMH36dH72s5+xYsUKzjjjDM4991wuu2zbGcrhm5/dvZ+e1hMplhdy85LvcMywqzjvxkf5pDEJQuAXKlnZZuaEiYxo7OIfG54lt8Ai/UuH4y84ivt+8wiVg8u5c/GN+IM+kmaSF9fM5Jjoh2QclXfW/45U31D64um8ZRv8PtdCXFddXHhfFPHTWF+GkODhBf+Po6sfIKK7brgLMxU80TeIJjPMXkUT+FbJIVRJdWRzJtmsK2CnMznWrWqnbV03ras76O6M0dOTorc7gbOly1KSQFfBsFzLvGHhU2XKB5URLgmhKAqKphApCeMP+ZAA3afhC+hESkIEw35XyHQcgpEAvoCOIsvIsoQiyxy0zwhk+YuxYu+qCCFw8mXrECDlFRqfxXEchCNAYrsTMuYyORI9SXIZg+KKKH2dcXo29JHoTRKMBNADOuV1pYSKgiAEwWjwC/Ma8PDw8PDw8Phi+E/N7v6tb32L8ePHc+ONN261TWNjI+eddx7nnXfeVzauL4orr7ySJ554goULFwIwY8YM+vr6eOKJJ7a6zfbMyfbwRe3nm8C/RXZ3gJNOOonOzk6uuOIK2traGD9+PP/3f/9XSCbX3Nw8QIior6/nueee4/zzz2fcuHHU1dXx85//nIsuuujrOoSvBV/gQMgtZHR0BeVVYe77zXS+N/te1hDHjllQLHHngvmc1NKAPFzGt5dKYlgKRVWoGFRGR3MXd1x4D+f+5UzCWpiDG29lbdtRNOpdjK6+Gm3s41T4x2GYFomkmzStsydJXzxNV2+SWDxDJuvGeyfTOZLpkTyu/YT/mvICJw1dwvhAJ+MDnSzMlPJyoocb1i4gvS5IfGExyaVRsLditS4Kuq/tJA00A2QtIB8X3ff5Mc9bovad5fh8Kqquovk0VE1B82komsKwPRsJRgJYhoUQAtOwiHXFkWUZSZZcYVeS80KvhJQX/iVZQvfrqLqKLEsEwn4UVaGnrY9ULI2RNchlDFKxtKuDCOgoioysKiiKjB5wvUMsw0JRFRI9SYLRAOV1ZdiWjaoppGJpUvE0Zs7CzJn5l4VpWMiKTLI3SbI3RTaVI5vK4jgDFSCRkhCR0jD+kJ+O5i5kRSbe7Wab9wd9BIuCZBKussYX0LFMG1mRyaVzhEtCCEfgD/noaO7C2oHY/qHjGrjmmUuoGFS2U+fLw8PDw8PDw6OfGTNmcPfdd2+2fMWKFQwfPvwrGcOnn37KFVdcwfz582lqauKGG27YTKBPJBJcfvnlPP7443R0dDBhwgRuuukmJk+evMV9/vGPf+Saa65hw4YNmwl96XSa6upqrrnmGs4999wdGutNN93EF22rffXVVzn44IPp7e2luLi4sPyxxx7bqdLZO8LatWsZMmQIH374IePHj/9S+/oq+NqFdIBzzjlnq+7tr7766mbL9ttvP955550veVS7NsHwoYjcLRxY1cJHHU3sWz+S//3BcRz593swiyUCPZAphYc3rGVMJkrx2D6Cs8q462+fsPuJ+7H+tSU8+t5Kllwwh0h5FFmW0AKncf5//S/VWoJP1p3A/9x+FF1rAjuUef2G5Qdy/75j+PG4Dzli0GrGB3oYH+ihy/LxarSafw6upm9qGLG0Dq1lEH7Fh6a6bueaplAUCRAM6Ch5i3a/pdV2BKZp4fe7N7jjCNraY8R7kyS6k1iGheM4WKZFrDMOkoQkSWTTOSzbwTJt1zqfX+7WP5fI1wgDCXo3bD0W/uPXl+zkmdr1SfSmSGylvF02nSOb3lgdIZPMDlifyxif3QRZkXFsB19Ap6yuFF9AJ5vKkexNkopncGzXkp+OpymrLfkCj8TDw8PDw8PjP5nDDz+c2bNnD1hWUVHxlfWfTqcZOnQoJ5xwAueff/4W2/zoRz/ik08+4d5776W2tpb77ruPQw89lMWLF1NXV7dZ+1NPPZVLLrmExx57jJNPPnnAukceeQTDMPjhD3+4w2MtKira4W12ltLS0q+sr38Xdgkh3WMnUEfRkS2n0t9FrPt5YCSjyiv4r1G789jSxTQMLyeZzrGOBE3JSorsPoLDs8QmwsLVnTC4HICPm7qgqauw287O7/Cns55lj3Avv/vpk1z+yMG0vV2JrmuUl4QoKwlTHAmg6wolRSEiYR/hoI9Q0EcoqBP06+Rkm5sXv8ctny7m9N0+5qjBaynXMvx3cRPfK2rm47IS3q1ax0pjHbsVTWT/sv0YU7T7l57AyhXibTRdJZvKkuhJEu9OYuRM/EEfju1gGhaW4Vqhc+kc6XiGeHeC1R81oagKju2Qy+RQdZXqxkqCkQCOIxCOk/8rCq7iwhEYWYNEbwpZcS3rmVQW27QJRoOuEFtbgm05+EM+jIxBqDiEYzvYlo1t2iR6k+TSOTSfhuZzFRS6X6N1VTuyIrtx9aVhQkVBdL/bpt8TQNNVHMchGA0SLQ3jD/vxh3yF/SuqgpkzMfKW/FQ8Q3ldKUIIIiVhV+HRlUBRFSIlIbKpnOspoMhkUzmCET+ZZBbbcrBNi/JBZVTUlyFJEvHuBNGyyGau9I7jYOZMsqkcbWs7t+hq7+Hh4eHh4eGxM/h8vq0mn37ttdeYNWsWixYtorS0lOnTp3PNNdegqlsWhzo6Opg5cyYvvvhiwVr9eUyePLlgEb/44os3W5/JZHj00Ud58sknOeiggwDXDf3pp5/m1ltv3WIflZWVHH300dx1112bCel33XUXxx13HKWlpVx00UU8/vjjrFu3jurqak455RSuuOKKrVqwP+vunkqlOOuss3jssceIRCJceOGFm21z7733ctNNN7Fs2TJCoRDf/va3ufHGG6msrGTt2rUcfPDBAJSUuEaY6dOnM2fOnM3c3Xt7e/n5z3/O008/TS6XY+rUqdx8882MGDECgDlz5nDeeefx4IMPct5559HS0sIBBxzA7Nmzqamp+bzTsEVyuRyzZs1i7ty5xONxJk2axA033FA4X729vZxzzjk8//zzJJNJBg0axKWXXsrpp5+OYRhccMEFPProo/T29lJVVcVPfvITLrnkkp0ay/bgCenfUCRJos3cj0r/05RIrwE/A+CiKQfx/KqVLO3p4uIpB/LXBR/QncmQitURLl1P+f4tdD4X4fjzjuH5e1+jt6Wb2iGVHH/+UQSCPlRFZnHnFHavvIKR/jh/OOElXjn2VE4eciYhNbTd4ztg3DBmL1zA796u4JqFWY4ZvJbz91xLpb6qYF13xDJW5j7ggw2PMq+lniHFh7F/2X7UBeq+lFhlWZbRfa5QGAgHCIQDVA7+6rSr30Tqhu/cF2FxxZa1s7Is4wv48AV8FJV/c3NCeHh4eHh4/KcghCiU/vyq0WX9C3kmXL9+PUceeSQzZszgnnvuYenSpZx55pn4/X6uvPLKLW4zY8YMWltbeeWVV9A0jXPPPZeOjo5/aRyWZWHb9mZu64FAgDfeeGOr282cOZOjjjqKpqYmGhrcpLOrV6/m9ddf57nnngMgEokwZ84camtr+fjjjznzzDOJRCL88pe/3K6xzZo1i9dee40nn3ySyspKLr30UhYsWDDAddw0Ta6++mpGjhxJR0cHF1xwATNmzGDevHnU19fz6KOPcvzxx7Ns2TKi0SiBQGCLfc2YMYMVK1bw1FNPEY1GueiiizjyyCNZvHhxQamQTqe5/vrruffee5FlmR/+8IdceOGF3H///dt1PJ/ll7/8JY8++ih33303DQ0NXHvttUybNo2VK1dSWlrK5ZdfzuLFi3n22WcpLy9n5cqVZDJuuOfNN9/MU089xUMPPcTgwYNpaWmhpaVlp8axvXhC+jeYQOQo4Gl2jy7BcdLIcpCKUIhz99mP373xGnfM/4D/PfJofvZ/z/DRKpvJERWtziK4Rydtz3zAjbf+D+fudykdq9tZUVvCrNk/zVs2R+MYIzG6T2WoL4km3c2fljRx2tDzadjObNSyJDFzwkQOGzqcS19+nofXqDy8Zjjfrj+USyYlaPS/h2yvZDd/nN38cWA1vdbbLG4t5lOpFj1wGKNKjqImsGVtqIeHh4eHh4eHx5eP4Rj8z/yzv5a+75j4l+2uJAPwzDPPEA6HC5+POOIIHn74Yf7yl79QX1/Pn//8ZyRJYtSoUbS2tnLRRRdxxRVXbObZt3z5cp599lnee++9gqX1zjvvZPTo0f/S8UQiEfbbbz+uvvpqRo8eTVVVFQ888ABvv/32NuPmp02bRm1tLbNnzy4oFebMmUN9fT2HHHIIAL/61a8K7RsbG7nwwguZO3fudgnpyWSSO++8k/vuu6+wv7vvvptBgwYNaHfGGWcU3g8dOpSbb76ZyZMnk0wmCYfDBbf2ysrKATHpm9IvnL/55pvsv//+ANx///3U19fzxBNPcMIJJwCuQuC2225j2LBhgBse/Zvf/OZzj2VLpFIpbr31VubMmcMRRxwBwF//+ldeeOEF7rzzTmbNmkVzczMTJkxg0qRJgDuH/TQ3NzNixAgOOOAAJEkqKEq+TDxf028wQ8oPZH0qQlA1aet5trB8+p4TGFlWTk82w/2fLOKRE37A+Mp61jS7VuPwWQH+uWAhaz9p5lcPXoAsS7x47+vcfPbfCgkkZH0PfOUPYUml1OtpLih7jpfWns2DzXPJ2bktjmdL1BcVcc9x/80fDjmMqM/Hyy3wnccjnPTKD1lk/R0pchFCn4JAoUQ1mBLu4DuhhUyVr6W491usap7Cktaf0Bm7Gyf3NsJuQwjni51IDw8PDw8PDw+PbzwHH3wwCxcuLLxuvvlmAJYsWcJ+++03wCo/ZcoUkskk69at22w/S5YsQVVVJk6cWFg2atSorQqeO8K9996LEIK6ujp8Ph8333wzP/jBD7YZAqgoSsF1vL9Sz913383pp59e2O7BBx9kypQpVFdXEw6H+dWvfkVzc/N2jWnVqlUYhsE+++xTWFZaWsrIkSMHtJs/fz5HH300gwcPJhKJMHXqVIDt7gc2zu2mfZWVlTFy5EiWLNmYAyoYDBYEdICampqd9mRYtWoVpmkyZcqUwjJN09h7770LfZ511lnMnTuX8ePH88tf/pK33nqr0HbGjBksXLiQkSNHcu655/L888/v1Dh2BM+S/g1GV1UW9o2jLvQmueQ/oPx4ADRF4drvHM5/PXg/zyxfxrShI5h7/En8dcF7vJi4l1Aoi35LJZfe9QSP3X4xv7z7Z/y/027hH3e8gKzI/PTm01EUBUkbgVb2IFbveQT4lBllK1hv/IHHVz7JxJorGBHdPm2iJEmcOGYs04aN4Pb57zNn0QIWtG3g+Mc2sN+gwczY8zIObhyEbM0nl3keM/saftGGT3YYoncCL0PmZciAABwUhFyHoo9HUoeBXAbKIFCHg1yO9CXHtnt4eHh4eHh4/Kegyzp3TPzL19b3jhAKhb6yTO47y7Bhw3jttddIpVLE43Fqamo46aSTGDp06Da3O+OMM/j973/Pyy+/jOM4tLS0cPrppwPw9ttvc8opp3DVVVcxbdo0ioqKmDt3Ln/84x+/sHGnUimmTZvGtGnTuP/++6moqKC5uZlp06ZhGF98OMRnY+klSfrCs9FvyhFHHEFTUxPz5s3jhRde4JBDDuGnP/0p119/PXvttRdr1qzh2Wef5cUXX+TEE0/k0EMP5ZFHHvnSxuNJM99wEpKboKFaew/hJAvLx1ZWcdYkV0N1ycvP05pI8JNJ+3L2bmcghERlVQLrBJ2D7ruLl2uyfO/2UxHA07c+x6+O+j3xHrcEl6Q2oJY/jBS5BIsQdXqaE4vmU574b95umk5z4oPtHmuR388vpxzIq9Nn8sOxe6LKMm+va+bH/3iSA+bM4fr5Dm2cR6T6VdSqBWSLn2Ct/AMWm7uzKhdlgxnAESBjozjNkH0KkbwBEf8VoncGovMARPseOJ2H4/RdiEjejsg8hjA+QNidX+qN7eHh4eHh4eHx74gkSfgU39fy+qJyFI0ePZq33357wLPgm2++SSQS2cylG1yruWVZzJ8/v7Bs2bJl9PX1fSHjAVehUFNTQ29vL8899xzHHnvsNtsPGzaMqVOnctdddzF79mwOPfTQgtv1W2+9RUNDA5dddhmTJk1ixIgRNDU1bfdYhg0bhqZpvPvuu4Vlvb29LF++vPB56dKldHd384c//IEDDzyQUaNGbWbZ1nVXqWLbWy/JO3r0aCzLGtBXd3c3y5YtY/fdd9/uMe8Iw4YNQ9d13nzzzcIy0zR5//33B/RZUVHB9OnTue+++7jxxhu54447Cuui0SgnnXQSf/3rX3nwwQd59NFH6enp+VLGC54l/RvPoLIDWRUvZli0D7LPQvCEwrqf7b0vb69rZv6GVn7+f//ggeNP5Nt1E4lzLI+vf4LhjRtYkAzwwCcfAVB5y1Tsl5p4Y/5Kzt7vEn7z8CyGjmtAklQInY4WOB4jeS9G8i6KlAT7KG9D6m1aY2UY2hRqi36A5pvwuZbsylCY3xx8KD+euDf3fryQRxd/Qkcqxa0fvMetH7zHfoPqOXHMWA4fthtDK68CIGtnWRxfwuvxxbSnPkS2V1KjpajT0hQpBqVKjgo1iyxZYK8GezWbi+Q6QqkEuQrkKEjFoFQhKbUgRUAKghwGVFCqQC4B/F9KEjshbFwdmQkiB0472B3gdLnjkPzgxMBuRjg94MRBJJCKb0SSdkyr7OHh4eHh4eHxn8zZZ5/NjTfeyM9+9jPOOeccli1bxq9//WsuuOCCLbqZjxw5ksMPP5wf//jH3HrrraiqynnnnbfVRGj9GIbB4sWLC+/Xr1/PwoULCYfDBQv/c889hxCCkSNHsnLlSmbNmsWoUaMKVvFtMXPmTM4880zAjUnvZ8SIETQ3NzN37lwmT57MP/7xDx5//PHtnR7C4TAzZ85k1qxZlJWVUVlZyWWXXTZgbgYPHoyu69xyyy385Cc/4ZNPPuHqq68esJ+GhgYkSeKZZ57hyCOPJBAIDMgR0D/WY489ljPPPJPbb7+dSCTCxRdfTF1d3ecqKraHZcuWbbZszJgxnHXWWcyaNYvS0lIGDx7MtddeSzqdZubMmQBcccUVTJw4kTFjxpDL5XjmmWcKOQj+9Kc/UVNTw4QJE5BlmYcffpjq6uovJPxha3hC+jecCdW1/O9rI/nluHfJJR/Ev4mQrikKN077Lt994B4Wtm/gV6+8yLWHTuOY2u+yJL6EpYlljI2uYtX71aTHV9Bh5+Bb1fCtalpNh2Nvv5PJjYM5+XtTGVtdQ1U4ii/6U/TI/9Ad/zvZxGyqlVaq1W4QT0HfU2SFH1sZRtB/AJJvH1f4VQYhKWWbjb0uGuXiKQdxwb5TeHnNah789GNeb1rD2+taeHtdC1f6/Bw3ajQn7r4Hoysq2atkAnuVTABOIWWlWBxfworESlYmV7E23QTCpEgxqdNSNOpJhvmylGkqpUqagNSHhAH2Ove1Cdu2r8sIKZAXnANA//sgSHJ+a8l9SX6QdBAWYIDIAs7Gz04aRBpEHEQyv90OWvetJtBG7Ng2/4G4mnIbsPLzb4HkQ5K2/ePq4eHh4eHh8e9HXV0d8+bNY9asWey5556UlpYyc+bMAcnWPsvs2bP50Y9+xNSpU6mqquKaa67h8ssv32Y/ra2tTJgwofD5+uuv5/rrr2fq1Km8+uqrAMRiMS655BLWrVtHaWkpxx9/PL/97W+3WiptU44//njOOeccFEXhuOOOKyw/5phjOP/88znnnHPI5XJ897vf5fLLL99q5votcd1115FMJjn66KOJRCL84he/IBaLFdZXVFQwZ84cLr30Um6++Wb22msvrr/+eo455phCm7q6Oq666iouvvhiTj/9dE477bQByoR+Zs+ezc9//nOOOuooDMPgoIMOYt68eds1B5/H97///c2WtbS08Ic//AHHcTj11FNJJBJMmjSJ5557rlAuTtd1LrnkEtauXUsgEODAAw9k7ty5gJvw79prr2XFihUoisLkyZOZN2/el1pKWBL/YT7A8XicoqIiYrEY0ei/RwmoMx67ndv2/ROqLJDK5yGpA2NxXm9ayxlPPYYjBLP2P4CzJu1Dj9HDrz6+kpSdIvOCif/BEo695wxe3dDMy2tWk7GszfoZU1HJlMENjCorpyYcYWxVNVlrLWt670UxXmeE3k5A3pp7iw7qMJCLQSrKW7LDeSt22LVayxHaMlEeX9bKg4uX0ppIYAtXAB5RWspBg2vZf/Bw9q2tw68aIIVwBTEb087Qml5Jc2oxLclP6c2twRAWCBBIRGSDcjVHSFGo0wV+RSeo+InIWXxSDhXDtWiTA2G7f3dUgP6XkNw5knTAcfsWAiQl/1lzFQPK4LyVv9SdN0x3a0kHVITIIEkBhEgjKTUIuxMwkORykPzuZxEDYbpKBikCwkKSgwjh5LfNIkn5L8n8fhFGvq+8Xk9SAQfhJFyFg90OyCDlFRbI7l+RBimaV14k3DFLQXcbp8/1FpD87lhEzt1OZNzxiZR7naCBHHHPi4jlx5MFjHw/svtZpNz94RTmZcAMhy9ACv/kCz5vHh4eHh4e/15ks1nWrFnDkCFDNisV5uHhsW22df/siBzqCem7MMJuA2EgqYO32e7P773DbsrlHFrXBMEzkKMXb9bmnkUfcuVrLwNw+UEHc/r4vfg0tpg/LrsRG5vkgznq3m3g9/MuIxgJ8FF7Gw898wb/985HJCt0zKoAyJu7fY8ur6A+WkRDSRGl0T7C+juY9qeM8nUzSE8RkGyKFGNLm24XlqOgbiL4O2KLw/iakPKCsgySBsLJC6mq+5JUwHYFXJHOC5abKz88vhqk8LlI4XO+7mF4eHh4eHjs0nhCuofHzuMJ6TvJN0VIF6m7EIk/gP9o5OJtZ2Zc3NnBn169mjsOfA4hRZErXkWSw5u1u/bNf3Lb/PcAuOSAgzhzr8m83f0ut61ykyLE/5KldnE9v/3HJRRXFLnLehI8fP3TPHLnc/QMDZGrDyEai7BqQ2TkLZdCUxSbUTUy5WVx8HejySkq1CylSo6gbBGSLUKySYlqUKYqRFWFIjmFJtkEpAQyW082sS0cIeNIERQ5hCTirnAsl+Wt9MUIKUTW6iHlBHCcTkw7TbclEbcMckIi6yhkhYIhFGQEWaHQa+sIQpTrUfyKRpkWQtdHEVBLCekNhLUSqv1V25V9VAgnL6gL1+qLA05vXoC3XCuxE8P1DtBBLsJVBMh5S7bqWp9FTz5OvRdQcBwdWbYBM2+xNtyX3eJapNWh7jon6c6FpLveDJLq9o2St0Tn3DFJQVwrtZTv13at2HJwk4PJW6rlYkBBUgbhWrX7rwnH3U7yu2PGVV4IkQEMJCmcDx9QQA7lvQY0XLf0kHuscsQ9RieenxPZ9SDAAnwg+ejP9+/+Ffl8A8VsVJJs8hflS8kv4OHh4eHh8e+EJ6R7eOw8X5SQ7sWk76qoo9y/xoLPbTq6vIIl8TGsir/rJpDLPAihmZu1m7X/AWiKzC3vvcPv33gdw3b46eR9iJkxHmh+kOjZftb/qYWfT/kVv5t3KXXDa4iWRpj5u5M54RdH89hN/+DpW58n3r0WAdjFOvKYcirH16OMKCEWVeh1DDYkE3y6DlhXBEQJBnKEQlmCfgNNsymNWPj8OZA3L9cgIfBJNqokkBH4JNBllapAPSG1DFWyiBsp2lIWiYxFW9IgkYVU1kfWUklnfAQ0hWHFlYwqq2JMRQ0H1Q+nKhRGAnzCRthZgmqQjJ3BZ6WpRdBr9NGWbSOZ3UBPrpv1mVbas+04/UJnxsAVXFNAx2fGLBFUggSUAKV6CWW+MoJKEFVWqfPXElQClOvlhKwQ6VSGvmQMM22ipjRySYN4KkE6nSHWFqcj2YHlM0l1Zsj05sjFc2iaiqIpJKQkUggkv4TmaBgZE9MwsByLk647FtMxCath/IoPWzj0GD20ZdtJWymSVgpZqsYRDhEtguEYxM04PUYPAcVPQAlS7qsjrIaxHIuck8MWNmE1hCbrmI6BQJC20jgISvVSbGEhI+NTfEwp258h4cbPvVY9EdnDw8PDw8PDw8Nj23iW9F0U4SQRHZMAB6nidSSlepvtL3v5BazUQ/x+8msgVyJVvLzVLOC3vPc2N7zzFgA/njiZWfsfyOPrn+Cp1mcASD6QQ3pA5+onL2H0PgOTlBlZg9ceeptXHnyTj19fTDaVG7BeliVKx9Tgn1SL1liCUR0grju05dJ0G1lMZ6P1XddNopE0qmKjaTa6ZhEN2QQ0UPzxHZ2yrSKE64UuHBVJskH6gi75/t14kidnD/sx+5Tt/XUPw8PDw8PDw+NfxLOke3jsPJ4l/d8cSQ4j1JFgLQFzISiHb7P9wY1DOGfeCC4YO58KfwdkHoHgyVts+7O990OTFa5965/cPv99VnR388fDjkCVVB5b/wThH/jIDrGYdfSvOeOyUzjm7Gmomnup6H6d75w2le+cNhXLtFj+wSoWvbqYBS99xKoP15DoTdH1cSt83DqgzxBQpCnIpQHEiBL04aUYlQGSxaVkdIk2dfNYbUW2kRUHTXWIhNMoiivgCyGhSRJ+3UYDdNVG92dRVAs5kANLAcV2PcXzL3dON+/DtiUkSSDLG4X57WYbbYUtXA/t/vxrO+tmvYk+QUEBAYqjICGDLJCQ8Kk+/JoPTdaQkTGFhS5rhNUwlb4Kyn3llPvK6cl1o0gqDja6rFPpqySshbGFTZ/RR8yMETPjBJUAPsWPIskkrRQ5J0efEaNYL0JGRpd1ck4OTVJxEGTtLHWB2p07Pg8PDw8PDw8PDw+PAXiW9F0YJ34VpO+H4Azk6KXbbJs2Tfa64385acgifr3XmyCXI5W/gCSHtrrNE0sXc/FLz2PYNnWRKH887AikQBt3rLoTQxhYzTY9szLUhWo589pT2fuICdsUNoUQ9LT10bxkHc1L1tP0aQvLPlhFz4Zekr0psuncVrd1NAnHp2IXa5ilfqxSH2aZD6vMhx3SsKIaTkhF6MrnzJpbEk2S3BhlSQJVsZEkN1beykpYORndZ2FLCqajI0ki3x4cR0JTbSTZ/WzbMrJw8JkGImYj9UFEgYhi4c/KhG2ZchHAl5CoiBRRUhpBD+pUDa4gHA2h+TSKa4vQSxXsgI3QBIYwSFsZuoxuMnaauBkHAX1WjFTeNd39m8QUm2cp/1eRkAgoAUJqiKgaIagGkZEIqSECSgBZkpGQCn8lSUJCxifrVPurCSh+NFkjYSWIqlF0WUcgUCSFUr2EgBIobKdIn3e+PDw8PDw8PHYlPEu6h8fO4yWO20m+SUK6yDyNiP0CtHHIZY98bvvTn3yMt1pW8u5xTxFVO5DCP0cK/3Sb23zU3sbPnn2GlngMCfjB2D35wfgG7my6g26jB5ESxP43S/pxk/HfGsP0q05izJRRO2wZti2b7g29GBmDdcs3kElm6euI0dPWRyaRQVZkhBCk4mkURUGWJXJZA9u0sUwby7BI9KUQqoQhCeKKjerX8JUEyUoCx6+gFPux/QoJYZIOSHTrDknFxvqK3dFlSUKVZDRFwacoBDQNn6KiKTKGZaEpChHdR8Tnw6+qKLKMLLnqBdsRhHSNkKZjCwdVMXBkE1tKYQkLSWjIagZZy6KrrvAsJAOf4sMRJmk7RdpJIEkyaTtJ1slgiq0rR75sFBSQJLe8GxKSpCBEf2JAGVlSUNFRJAUZBUkomBjkRAqdEDYmQghkdDRJwcYhIEXwSQH8moosuUoHRwiCqp+gGiSg6tg2WDYIIZO1c2RMQc6QkCQZRbaRRH8oiERECyI5ftJWloxlYNg2sbSDEAJN9qHgnouMaZEwUzQUF3HJ/gcjyQ6qrLrnAAdFUsjaWRRJwREOYTWMKnvOSh4eHh4e3yw8Id3DY+fxhPSd5BslpFvrEF3fBlSkyve3aRWHjWXWzh3Xw7mjHgYphFTxEpJcus3tErkc1/zzVR5e/AkAZYEgF06ZzCrpJZYllgNgfuoQuzmDscimbkQNh/7wIA754YHUDKn6Ig71SyVnWcRzOdKmScYySRoGbckEvdks3ek0adPAEeAIB0mSyFoWpmNT7PNj2DZtySS9mQwJI0c8lyNlmuRsC8txsJwtZ7jflZAkgarYWLZr1VZV280DoNqomo2q2kiI/PL88UgCTZbRZBlLOPhVGUc2UNQciuIgyw6WpaBpVt4LQUKWHXRt5zLz/7uiyzqapOFXfGTtLJqso8s6EhICgSMcFElGlVVUSUOVFBRZQZVUVEnFJ+tIkowtbEzHJGtncRD4ZR8AtrAxHKPgtVCsFeFT/ASVAFk7hy0sBBBUAuQcA9Mx0GQdv+zLR1IIRP87IQrRFWHV/a5xhINP8ZG0khiOQUgJEVSDWMLCEa4iA8BwDFRZJayGEcJBkzVsYaPJGpqkYeXHbzg5co6B4Rg4wsGv+PEpPjRJQ5EUFEnO/1VRZVdx0x+e4Zf9Ba+NtJXGEhaarKFKKmE1hCTJqJIbCiJJEnLem0POf5aQkSXXi8Qdi4kjHBzc43Bw3GNCoEruOTCFheVYWMLCJ/so0tzfDAcHTdJQZRUhBLawB3iNSJJUmBuBKHiWeHh4eHwT8IR0D4+dxxPSd5JvlJAuBKLrELDXIRX/Bcl/6Dbbr4vHOGjO31AkWPz9f6LYi8H/PeTi/7dd/b27roXLX3mRlb09AOxTV8d3xmq8G3+JrONaY413bWJ/yWAucwXaPQ4cxd6HT2DPg/dg5ORhyLL8rx30LogQglzGwLZsHNsh1pUgEPYTKgqi6SpZx2ZDIkFrb4yeVIqueILmrh7a+uJkVUg7FrFsFksSmLZDzrbIOjYGDiZf/O0nsTEG3hFiQOh8/zqRf2/v5O2vSjKKLKHIMpbtCuZh3UfKzCEkC1WWUWRAtghpGsJR0FQJVZaQJQlddj0LFFngYJG0MvhVmahfQ5JNFMVBFWFUWUbFj6zkSOQMZFngSDmEbJE2TEwHbMdBlSVsTGxhYjoWPk3Cp0qoCm4cvSahKY5bVc5xvQ8kybXACzkDkoOEgoKCkBwcTCQJbGHtUKLBfuG7/6/HvzebnmdFUrCFq1CQkcg6uc3W+2V//rpzywYGlRACB9Ox0GQVTdZwhKu80WSVYq0YWZKxhIUtbBzhYDiGq8SQlIICJ5f34CjSilAkmZxjkMsrQtx2Ev1JNCzHJGklCalholokv30OS9jYwsorUXLkbANZcnNQFGlRgkoQXdbRFZ24GXfzYcg6DgIZCVXW3O8XJDRZyytCDAzHvZckZEzHxEGQs7PYOJRoxQSUQEFB0z9XkhtwUwj3kQBFcpVA7hzZZJ0cqqRgOCaapJJ1cmiyhl/2IUsyMnJB8eIIB0OY2MImqARwCoqVfqXQwNemoTppK104r7awkSQJVVLxy358iu5+3yg+0lYKS9gEFD9ZO1doL3D7soWNJayCUieoBNBlH2Ft83KpYSW0TS8cy7FIWkns/PGRr1Di4CAjI0vu77Bf8eOXfUiSXPBmEgj8ip+wGsYRDnJ+Dvqvr6AaLCgSTcfAEnah2ofhGPQZfciSkv/9sAEJRXL7lCUFBVfpqMkaEhKWsFAkBcMxsB0bJ18u01WMCQKKn4ydda89O4df8WM6FqYwMR2zoADr/6vJGkK4e+k/TlVSCCpBBIKAEsTBRkLCcExUScURNkgSxVqRex8qfsJqiJASIutk6cx2osk6lrDc842NjIwma6Qs14vNr/jRJC1//bnKbE3WCorIrJN1z7FjFULFAkqAgOJ3r2ip/8p2veYydgYFmSK9CF/+mu0nZ+eQJImklUKVVPe7Ia/I7L8G+xWdASWAJSyydpasncNw3HvZcAxMx3S91Da5zvvfC+EQVEOElKCbz2aT/oUQ5HK5nRLSXWXvQAXlpvveWvt+BWn/9bupUlPkvxOBvMJ1c6XnpqLMZ7ftvwdl5AHrRf463HSZ0+/1h+TeT/mx9y/bXmWrEMJTzH4N7Crz7gnpO8k3SUgHcOJXQ/peCJyIXHTN57Y//P67Wd7dxZwjh3JA+GJAIBXfjuQ/eLv6M2ybvy74gD+/9w452020dkBDFWOHJ1iS/jD/owz60gAbbusi965dSG4WLYtQO6yKivoyyuvKiJSGiZSECRUH8Qd9RMsihEtChIqCBMJ+gtEAlmmTS+ewDAvTsLDyL9OwyKZyxLsTCAGJniRG1sDMWRhZA+EIiiuLcGwH27IJhP0gSYSKgiS6E2TTOYyMQS5jEO9OkIql0f06pmGSyxiYOZNMIks6kSEdz5DqS6H5NVRNRZIglzEwMgZG1m3/eWxqOdsRBCB0GSeg8D9/mcnIb40mkcuRMHIkDGPj+1yOpGGQNIyCRT9pGMRzWXK2Tdr84mPX/1PQZBlVlrEcZ0D1AReBojhu3gLAERK2LX8mwaD7IKIqgFBQJFd5oWsOSBZZ20BR7Px2AkURIAYWB5BkgSyJjX8lgSwLZNkpPNQ5joSd94aQZKewreNIrlJDtfOJEl3PCSEkHCEhhIQiO9iODCLfl7zpGKQBCQpl2UFVHRwnv23eC6N/P/0JHPvX96MoG9ch3LnqP4a6SBFjK+rQZR1f3ptAIDAcg5xtYAkTWzgFYWajUGMj4z6QuoJnDkfYRNQIVl7IMhyThJVARsJBIITzmb+C/n+fZdPcC5v+7RcO+h+QFUkla2e/lBwRHh4e/9lISES1KCCwHJuUnfpK+3eF9oHCTQnFfE87hprBtWg+NV8ix1WsSNvImivyaphN6f9e/axADBSE8y3RL1Rv6fu7XyHormfAejnvNdXvIbUjT2Y/OPz7jB63O1dce8Vm6/r7O2D3Azjjp6dzxk9n5hUR/WulwrNgf/leTVIHjN4RDjJSfsxOQeG1aSbiwv4+I2wWFBW4xo5+7zAp/9u36VxKn/m/X23y39P+m93H7c5vrrsaEOwzah9+9NMfMfOcH22c1/xvYf+x1QZruevB2Rx5zBHufoQoKNs2Owd5jzxXJ7VRKSUBdcE67px7J0ccc0ThmMQm87qx//z8iI1nr1/Z2r9HeRPPNVeR4u7JdEzqg4O2qRj6KvCyu/+HIPm+hUjfC7lXt0tD9O3GoSzv7uLvy2QOOPB0SN+FiF8K2pNISuXn9qcrCj+dvA/HjRzN/77/Do8s+ZQ3mtp5ownG1ExkVGMvnWIlxqgMZTeGCGVDaPP9NN/RRnxZgnh3gqXvrfyiDn+XJBD2k0vncJyNX/2bCuiyIhMpCZFOZImUhBBCUFJVjGlY6H6NSGkY3a+h6SqaT0PVVVRNZa/hDYys2bks6bbjkDJNUoZB1rbIWRam42DYFqbtYNg2pm2Ts21Mx31v5N8btk3OskmZhhvbrWkYtk0ilyNjWQU36L5sFsuxsRxByjTIWVbhC1tAQcB1HAdLODiOGPDXdhxsIbDzYQIhTSeoawQ13Y07z1u2P/sCNzGioF+w3PiD1z82ITb5kd5qmy0/CphbFM772SgYb8pAfYz7g2JYAP37scFw14HvM3sETXbzLiiShC0EQkBI0wjpOgFVxbAdDMPCsPOCqG2hyDKOI5BliYjPR0DVEEIQ9Os4joPjgKrIqMhIjqtwy5gmAU3DL6soSt7tOx/Hb+fPk19xfwZs4Z4X2xZYllNQNqjC/bHzqQo+VSVlmGQtC9vJu4bLCrIEjuW22XicGx8QDqwZyinD99zKHH819FswM3YGVVYLVu2ttYXNLTJZJ4uMXFAg9FsiwbWM2cIqPDj5ZB/9CRVd5YIohB/IeStx2kqjyzqqrGI6ruWw39KVtbPErUThe7//Yc8WNgElgC0cnLwiwaf4sByLPrMPgcAn+/DlLaiuYrX/XhKosoqMTMbOYAoLBTlfzUFBlRRXiaL4XEWKEOScHDEzRtrOYOaVKnY+1ECTdRxhI0kyjrDdexYH0zFdq7uso8tuuIMEeQutnK9EIREzYyStZOEBr98Dod8Kq0lueQyBQ9rOFCz+suSucwq5IHIEFPd8ZqxM4QFZQi5YedX8/JmOWZijgUohJ39eXQWNEG5Ygy7rBBQ/hmMihFPIsZGxs2TsDJqsYTgGQSWAJmtk7Rx+xUfGzhYs0v1z61oyFXJOFtOxtqL4EcTNJAJnkyWbXaAElAB+xY8qKdjCIdBvHc8rqGzhkHWyGI6Rn4uNXh0xM0bayuBXfDj541ZkFYQgY7vbCIR7bUoqDm5oSkD2U6QXYTuWazWXlIIV1BGuldwWFmY+RIT8tW4JG5/i7mtAUlIgZafxyW51El3WsRwTNW+ddr2ZbHTZhyopmMLCdMzCPvof2g0nV/D2y9hZ5Px3T3/YDYDpmPl71CbrZElaqcK6Yq2ocO0GlWBhXjJ2hqhWVPDYsISZ92Rxv+esTSz+7hhdpV7/vjJ2hqyd3WhZ3sTK3B92Ywr3eoyZsc2+h/q9QbYXn+zet1o+fEqXtcL3hC0cHDZe5zk7V7j2+udh0wvNxi7clzbOgHU76iXW/721yYIt8lkPtG0d+2cF835m/fhCHr3/0c2Wv7LoFRqGNW6xz/4xbly2tT7FgHcbPQb6l7nPKHNnP8Bjf3+M5UvccNE9xo9l1pUXsuek8QDYuMrjG6+5gblz5hKPxZm47ySuvvFqhgwfMvAg8/zohJlYpsWcJ+7ebN17b77H96edxD/emcfoPUZvZfQuTv67IZe/Xx5/7QmCoeDA76EtnB/DMUjbmW3uu58bf3sjLzzzPP94e96A5e+ueo9ocbRwr/4r2GKTa2OT8T5y3yP89qJr6Ovr+5f72BXwhPRdHX1vkILgdIC1GLQx22x+7KjR3Db/PV5es5rub51JmfomWMsQfedB6Zyt1k7/LHXRKL875DDOnrwPf3n/XR5bsphPNyT5dINGODCMicNtlHArKX8KpqSITtHYnUYGxQYTWhMmu8ok3ZcmGUuR6E2RS+eIdyVI9qVIxzObWadVTUHVVTRddYXWTd77Q35Kq4vR/Bq6X0P36a7beXc8v61KLmOQiqVxbIfiiij+kA89oOML+AqWe8uwCst1v+5a8yN+ApEAwUgA23awTQvHEfgCen57Hcu0iZaF0XQVRVXQ80JRNpXDMi0c28GxHXS/u42iyCjqV5vVXJFloj4fUZ/v8xv/hyKEwMi75vtUFdN2FRNp08R2BIrs/jT7FBVdUVBk91FPVxTs/LZBTSNpGHn3u343vX4B0FVCOI7AdOyCcBVQNfyq+1WryjJBTUP5NwwL2dXpd1OOyJHtarulZQElsNVtotrn79fDw2PXwHBMDDvnVjf5mqxu/Yq/rJ0lZsYLYRpRLYKMTEgNIRCFnB5mIXREwp93o09bKXyKbzOX+e2hP3wmbafzgruBT9GxHBtMQfe6bqr8lfh8voIcJG/hu3FTUXXTcIt+l/GCFXRAGF6/RdRVSvZbofvbApso26SCkq1/3vr/OcKt4tNvdQ8pIaZNm8Ztd96OnFfmyJJMVWVVIRyzPxdJ/3xvDEFyQ0SiWpTGYMMmx5ev9pNXNKiSSrFWxKBA3QDFQr/7/KK3FnHqKaey3777ofl1/nTdH5lx7AzmfzSfQXWDkJC4/rrruee2e/jb7L/R2NjIVb++ipnfO4MPP/4Qv99fMCz08z8z/4eTTzwZu8uiblDdRi8BIXj6708xYeIEpk48CIl+q/JA5YEQrsVZkzWCSoBKXwUSElWDKt1zkQ/HYJP9somHaFSNUKaX5s+bVLDgb+7hIBFWQ6iSSpVvo2FQAJWDKwZeN/lnJEvYKPSHO220+/cr8/rfy5LrDeheU6Lg0dhvzBHCDaH6t0L8hxGLxQQgYrHY1z2U7cbuOUvYG0YIO/6n7Wr/Xw/eL4bcdL249f13hWOuFnbbeHf73l8Ix7F3agy9mbS4d9GH4ntz3X0Puel6MeyWa8VBD1wlTv/nxeK0d2eK0949o/C64MNZ4m+rZ4sX2l4SqxNrhP2Zfi3TErGuuEgn0sK2d25MHh4eHh4eHh4eXyyZTEYsXrxYZDKZr3soO8T06dPFscceu9X1r776qpg8ebLQdV1UV1eLiy66SJimWVg/depU8fOf/7zwub29XRx11FHC7/eLxsZGcd9994mGhgZxww03bPeYLMsSkUhE3H333UIIIRzHEdXV1eK6664rtOnr6xM+n0888MADW9yHaZqiqqpKXH311QOWJxIJEQ6Hxa233iq6urrE97//fVFbWyv+f3t3HldVnT9+/HXuwnLZXQGlwAVRx8RQ+1q5JRNYmSWlmZNSgKmI2aLm5NpYaVpZmc5MkzA2FuWvrJncNVBTU3PLEi0NxZ1SEdnv8vn9cbknr6CCuWXv5+NxH8A5n3M+n3PuAe77s3p7e6s//elP6oMPPnBLf+71nXstP/zwg+rcubPy9PRULVu2VMuXL1eAWrhwoZ5m9OjRqnnz5srb21tFRESocePGqYqKCqWUUunp6c5OBme90tPTlVKqynm+/fZb1b17d+Xl5aXq1KmjUlJS1JkzZ/T9rvdy+vTpKjg4WNWpU0cNGzZMz6s66enpKiAg4Lz7Dxw4oO6//37l4+Oj/Pz81MMPP6yOHTum79++fbvq1q2b8vX1VX5+furWW29VmzdvVkoptX//fnXfffepwMBAZbFYVKtWrdSiRYuqzedCvz+1iUOlJf13QPO6B1W+Esr+h/IdedEu7/1at2HbsaN89P1Onox5Ai3wbdSpFCj7Lwo7BEyrcYu6S6CXN3+5JZq/3BLN3pMn+CTnez7bncPBfAMH830wmwOoX7eQxvXL8bQU8kvFCdb8vFY/3sPgQT2PutT3rE+4z82EWRoT4h1CQ68GN+Rkc1eDa5Iaq7JWfrW5/Wyr7Bpo07sfOme0LrOXUe4ox+qw0sCrAXfWu/1aX4oQQgghrlNKKX3oxNXmYfC4LJOBHT58mHvuuYfExETmzZvH7t27SUlJwcvLi0mTJlV7TGJiIkeOHCErKwuz2cyIESPIz8+vVb4lJSVYrVbq1HGutJSbm8uxY8eIjf11MuiAgABuu+02NmzYwCOPPFLlHCaTiYEDB5KRkcELL7yg348FCxZgt9vp378/RUVFxMTEMGbMGPz9/Vm0aBGPPfYYTZs2pWPHjhctp8PhoE+fPjRs2JCNGzdy+vRpRo4cWSWdn58fGRkZhIaGsnPnTlJSUvDz82P06NH069eP7777jqVLl7Jy5Ur92s5VXFxMXFwcnTp1YvPmzeTn55OcnMzw4cPJyMjQ02VlZRESEkJWVhZ79+6lX79+REdHk5KSctHrqe76evfuja+vL6tXr8Zms5Gamkq/fv3Izs4GYMCAAbRr1445c+ZgNBrZvn07ZrNz2FVqaioVFRWsWbMGHx8fdu3aha9v1Yk/LycJ0n8PPO9ydnm3HwLrN+DR4YLJ723egilrsjlwuoCvDx2kU9idEDAddXo0lC1COU5A4Gw0w6U9XM3q1GXMHV0YdXtndh4/Rtb+XLL2/8TOY8c5cgwMhmAC/Ivx9yshwLcCf78SKqjgSNlRjpQdZcfpb/VzaWgEmAOwmCwEmQOp51kXf5M/FpMFn8qZR31MPliMFnwqt509llRVM3b0tzq7u5VrJlGAckc5pfYyHMpOhcOqj+k6NxB2Le/kGpOn4exa55zZ2KGPi3Sc1ZVJ4aDcXk555RJV5XbnGLtyRzk2hw2HslcG4VZ9XN7lmD38loA2EqQLIYQQ4rwqHBUM3jLsmuT9z5jZeBprPpTviy++cAueevbsyYIFC5g9ezZhYWHMmjULTdOIioriyJEjjBkzhgkTJlRpMPrhhx9YsmQJmzZtokMH5+fu9957j5YtLzzu+1xjxowhNDRUD8qPHTsGQMOG7ksYN2zYUN9XnSeeeILp06ezevVqunXrBkB6ejoJCQkEBAQQEBDAc889p6dPS0tj2bJlfPzxxzUK0leuXMnu3btZtmwZoaHO+ZFefvllevbs6ZZu3Lhx+vfh4eE899xzZGZmMnr0aLy9vfH19cVkMhEcHHzevD744APKysqYN28ePj7OJV9nzZpFr169mDZtmn5vgoKCmDVrFkajkaioKO69915WrVp1SUH6qlWr2LlzJ7m5uYSFhQEwb948WrduzebNm+nQoQN5eXmMGjWKqKgoAJo3b64fn5eXR0JCAm3atAGgSZMmtS5DbUmQ/jugGSwor3uhdAGq5D9oFwnSfTw86NUiig+/+5aPdu2kU9hNaN73gSEQVZAGFV+jTj4KQf+q0WRy52PQNNoGh9A2OISR/3c7J0pK2HbsCN8cPcLWo0f4/uhxDthsgMLL04qXZwXe3hX4+Zbi7V2Oj3cFRqODAmsBBdYCjpQeqVm+lWOeXMvbuJb8cY7Tck7eYqhcksVQufQMuAJ5Z8BtrZwt2uawuUY26ROqlNZwcozriWvMlmvCGFPlki3On02VE/E4l8XxMnrpy8k0tjS61kUXQgghhLgsunfvzpw5c/SfXUFgTk4OnTp1cmvUueOOOygqKuLQoUPcdNNNbufJycnBZDIRExOjb4uKiiIwMLDGZZk6dSqZmZlkZ2f/5vXmo6KiuP3225k7dy7dunVj7969rF27lhdffBEAu93Oyy+/zMcff8zhw4epqKigvLwci8VSo/Pn5OQQFhamB+gAnTp1qpLuo48+4q233mLfvn0UFRVhs9lqvVpWTk4Obdu21d8bcL4XDoeDPXv26EF669atMRp/neMpJCSEnTt31iqvs/MMCwvTA3SAVq1aERgYSE5ODh06dOCZZ54hOTmZ999/n9jYWB5++GGaNm0KwIgRIxg6dCjLly8nNjaWhIQEbrnllksqS01JkP47oVkeQ5UugLLlKPsxNOP5a6jA2eX9w+++ZemPP/LzncXU9/FB87wT6vwHdSoZbLtRJx9xBuqmy1MbVNdiIbZJM2KbNAPA5nCw9+QJ9p08yf7TBeSdLuBAQQF5xwr4obgIUJjNNjw9bJhMdjw9bHh5VmAy2TEZHRhNdkwmOx4mB2aT82dNc+gTkbi4lnIC9BkrrxRD5WzBHgYPPI2emDUPfYkmU+Uarq4ZjY2VM9k6Z8k1V1YkGPRZjs+e5EWrnA3a0+BZOQmMB54Gz8rZe0367MSutZRNmjMIN1UG4a61fYUQQgghLicPgwf/jJl9zfKuDR8fH5o1a3aFSlNzM2bMYOrUqaxcudItmHO1MB8/fpyQkBB9+/Hjx4mOjr7gOZOSkkhLS+Odd94hPT2dpk2b0rVrVwCmT5/Om2++ycyZM2nTpg0+Pj6MHDmSiorLN0xhw4YNDBgwgMmTJxMXF0dAQACZmZm89tprly2Ps7m6mrtomobjvKvx/HaTJk3i0UcfZdGiRSxZsoSJEyeSmZnJgw8+SHJyMnFxcSxatIjly5fzyiuv8Nprr5GWlnbFyiNB+u+EZo5CmTuAdTOq5H00v1EXTN+mQUPaBYew7dhR/r1jG8/dfmfleVpDnY9Qp5LAfgB14iHwexa8+6Jp5gues7ZMBgNR9eoTVa9+lX1lNit5p087A/fTp8kvLuJESQknSks4UVz5taSUCofd7ThN+3U9aKU0nBNQOtd91kBf09qgKWdQz69rNHqaTDgcCqNRw6x5YMCIUTPhYTBiqnyZNRN2hwm7w7kckLfJjJfZiIfJiMXohbfJgwAPG/U8iwgwluJtsuNnKsHX7MCo2QAHSlVgpBA0jRPqfjw96mEyGJzLWVUua2WtnGVcq1xH0sNoxKNyVnEPoxEvkwkvk0kCbyGEEEJcU5qm1arL+fWoZcuWfPLJJ27LGa9btw4/Pz8aN25cJX1UVBQ2m40tW7bo3d337NlTo+W9Xn31VV566SWWLVtG+/bt3fZFREQQHBzMqlWr9KC8sLCQjRs3MnTo0Auet2/fvjz11FN88MEHzJs3j6FDh7pdS+/evfnLX/4COMdg//DDD7Rq1eqi5QXn/Tl48CBHjx7VKw++/vprtzTr16/n5ptv5oUXXtC3HThwwC2Nh4cHdrv7Z/fq8srIyKC4uFhvTV+3bh0Gg4EWLVrUqLy15bq+gwcP6q3pu3btoqCgwO0eRUZGEhkZydNPP03//v1JT0/nwQcfBCAsLIwhQ4YwZMgQxo4dy7vvvitBunDSfJJQBZuh5D8oSyKasWrwq6fVNAbHdGDoov/yn53bGdK+I74eztpIzXQT1MlEFaSCdSuqcDIU/xt8ngDvB9G0K/+H2MtkJrJuPSLr1jtvGqUUZyoqKgP2kipfS202CsvLOV1WhkMpSqwVnKmooKjyVW63nffcJs1Ofa8iGngXU9+rhDoeZQR5lhHoUYaP2YrFaMPbZMXH5Pwa6FFOQ+9iPAx2PIw1r8Xr/D8HR0svbey/q2LBy2TCbDDiUA48jEZAw9tswsNowli5RMbZwbzJYCDI2xtvkxmz0VC5DIlzf5nNhsGgYTYYsdqd6xq71ifXNNzWnrVV1laW2+1YzM7W/IKyUjyMzuVSPIxGiq0V2B0Kk/HXJVc8jEaCvLyclRIGA0qpykoQ5xIrBk3Dy2TC22RG07TK9dMdlWvfapgMBhxKUWazUW634WE0Vi6T5sBocPZkMBq0yq8GjJXX5mUyOStiKtdid65RbcDmcOjX7xoeYXM49OXYgry8MBgMOBwODAYDFpNzfV7XfARa5eokv37v3HD2zxq/VricvRb52dsVVFkD3tNoREFledHvjZfJRGF5OQZNw9tkwstsxtNoxGxwrktcYrViNhpRSmFzOCi32zEAHkYT3mYT5TYbFZXvn6Eyf3vle+1a/9yoaZVrSDvvuUHT9GXolKosa+WSMp5G5/I3PmYPzMaru7zg2VzPukMpKuw2NDTK7XbKbTa0ymszVN7/X1fA/fU9MGga5XZb5bI/zus1aM5KM03TsDnsWMweGLRff2dc+TqUotxup8Rq1c9rNhqwmD0wyeSXQghxXRs2bBgzZ84kLS2N4cOHs2fPHiZOnMgzzzxT7QTGLVq0ID4+nieffJI5c+ZgMpkYOXIk3t7nX4oTYNq0aUyYMIEPPviA8PBwfZy5r68vvr6+aJrGyJEjmTJlCs2bNyciIoLx48cTGhrKAw88cMFz+/r60q9fP8aOHUthYSGJiYn6vubNm/P//t//Y/369QQFBfH6669z/PjxGgfpsbGxREZGMmjQIKZPn05hYaFbMO7KIy8vj8zMTDp06MCiRYtYuHChW5rw8HByc3PZvn07jRs3xs/PD89zlgceMGAAEydOZNCgQUyaNImff/6ZtLQ0HnvssSpj9WvLbrezfft2t22enp7ExsbSpk0bBgwYwMyZM7HZbAwbNoyuXbvSvn17SktLGTVqFA899BAREREcOnSIzZs3k5CQAMDIkSPp2bMnkZGRnDp1iqysrFrPT1BbEqT/nnh2B/MtYP0WVfwPNP9xF0weG9GUiMAgcgtO8e8d20jtcJu+TzPWhTr/gZIPUcXvgH0/qnACFL2D8n4IzfsBNNPNFzj7ladpmr72d0Rg0HnTKWUDxylw/AyOE+D4BRwnsFmPY7P/jEGdAVUMjp/RcKCpUgycQvsNE6+V2C0U23wot5s5Y/WiyGbGrow4lAHQsDlM2JRGsF8jPDw07MqBze5cR9vucLgFOnblDBhdL33NR5xBdZnt/JUNQlxtZ1ceGTgrIK6smHAF++dWYniaTM5KG34Nek+Xl2F3OLA6nOueun4lf1131snucH5XbrfplTJ29dsnTrwQPw9PrA47NodDr7A6H5PBgKfRiLGyssPTaNIrjbxMzh4ypVYrZTYbVrtd791zduXar5U8YLM7MBkNmDRDZQVaZYWDplFus+FpMmFz2LHanX87bA6Hs8IBDQfOXkC2yko9g6Zhtf9aYeOq4HJV2BgMZ32vaXolmOt7g+HXfa4hOq5r0gBj5YdbTUOvODNVntf1VQNKrVbsSjl7COFeaeWsqKv8XjlwOJzfB3h54WkyUVpZMWI0GPS/h67eSa5KJ9M5lXemcyryTGelNWia/h5YHQ60yvMBWO12KhwOPAwGyivvrUG//l8rEo2V12U2GnEopT/Tzvv8631yVfIZNI0ymw2bw5nWrhy/HsfZFXgOHJWVZEr9WqFnr/zZde/Nle/t2e+NUX+vDBg0Kp8NV5mcz1iF3Y7d4cBiNmNzKGwOO16VlZuOyko/V6Wh615pUPmMGfAwGrE7nOW3n/V7oWkaZoMBpdB/b+xK4WE04ufhiY/ZrFdgehiMeJnNeJl+/Qjqqggts9twVFY8ehiNekVmuc2GtbJstspnxFZZBqvdQWFFOUZNw8NowlT5/CoFniYj5TY7pTZnRa+/lxems4aaud5PH1cjxll/s1yqq+B0Pd8mg1H/XTUZ3F8X4srX5nBgMhiq7TWnlNJ/T4yas5K73OacR8fTaNJ/ZyrszvfQWPn+Vtjt+trYZ3PPQqvmu1//JpVXVPz6u3nO37/znObcHy7K9Xfg1wmAcbvz1f2d/C0aNWrE4sWLGTVqFG3btqVOnTokJSW5TYR2rvT0dJKTk+natSsNGzZkypQpjB8//oL5zJkzh4qKCh566CG37RMnTtRnkR89ejTFxcUMHjyYgoIC7rzzTpYuXVqjcetJSUm899573HPPPW7jx8eNG8dPP/1EXFwcFouFwYMH88ADD3D69OmLnhPAYDCwcOFCkpKS6NixI+Hh4bz11lvEx8frae6//36efvpphg8fTnl5Offeey/jx493mx0/ISGBTz/9lO7du1NQUEB6erpbZQKAxWJh2bJlPPXUU3To0AGLxUJCQgKvv/56jcp6IUVFRbRr185tW9OmTdm7dy+ff/45aWlpdOnSBYPBQHx8PG+//TYARqOREydOMHDgQI4fP069evXo06cPkydPBpzBf2pqKocOHcLf35/4+HjeeOON31zeC9GUOvfX+MZWWFhIQEAAp0+frvVEB9cDVb4OdepxwIRW93M0c/MLpv98Tw5PL1uMr4cH2YOSqONddQIJ5ShyTkpXnA6Os2aWNLdD8+wGnneAqcVVaWHXy6QUqNPOgNt+AtRJsB9H2feD/RdQJeDIrwzIT8ElBdwmMDQAYz0w1AVDEGiBzlnvNW/njPqad+XLH4zBoHmCIQhNu3BN6m9hczgos9kotVopt9v0QN31oU2r/LDn+rB17j+wMpuV0+XllFqtWB0O/YOeQuFtMmNXzg9mJoMRg+YMgBxKYTYasDkUJoPzw42h8kOB2WCg1GZDKYWfp6f+Aa7MZsPXwwOTZsCuHPo7UGq1Ulhe7qyYcDhbyF0f2pQCB4pym41iq3OclKkyEAD0D14AFpMZD6ORCrsdb7MZs8GgV3LYKz9Muj7UK+XM11Z5P4yVHwatDgcehsoP0aBfr8locLYOK8WpsjJUZWBjV8577yqn68+j68OSM45U+gcL5dzp9rPrQ9XZ211cAZEB9A9chsoPflQeV2ytoMJux9/DE03TKLVZKbXa9A+pBs354VkppQcnHkYjDhRWu53C8nJ8PDwwG4yVw0KcH9YMBg2Hw/lh3/UB2/Uh70JBqOuDlBBCiMvPFYCrc/6X1Nbl/Fsd6m1h0i230qBRKJr58g6FrC1N06hnsRDs63dNyyFETZWVlZGbm0tERESVipfaxKHSkv47o3negfLsAeWrUIUToc5/0LTz19j2ioziX1u/4fuf83ln80bGd+le9ZwGX/B5HCyPQtkKVOlCqFgH1m0o6zYoctYUKVNzMDYB001oxia/BreaF2AGzQwYQVXg/FdhA1XufOEcr42joPJ1GqV+/R5HQWVQfsrZ6q0fU+M7A4Y6YKhX+aoLhnpoxnrOAFuzgLEB4AGaBxgaVgbb1183VZPBgK+Hhz48QYjaOHf4Q025Wk0AvcVQb1WprEwosVqpsNt/rTiqrMg4u7Li1+EG7h82S21WHA6lDxUwaBoBnp5669zZ3ctdrSmuTa4WSi+TSW8h8zabsTnseJvMeJvNemWUq8XR7Z6gcCjnsAJXq6GrwsdZQWXE5nDgWTm0wuZwUFhehmflUBNX65mrddzFardTbK2g3GavbLW041DOFv+zK9jKbXYsZjPeJhOmyiEoznKht3id3fJlNGiU2+z6e/FrBZDCbHBWXHkYjZgNBsyVLfiu6zdozuEcZqORCpsN+1nDKn5tlXXolXOuFl1XBY5DuXr8/Pq9Out+aZpGibVCb513He96n1yVZ66WVJvdOfTEYjY7hxuc1TNIO6vl/uwWfFcr5OmycspsNrzNJr2yz9tk1vN15eFq7XNV3tnP2n5uq6ut8rpcz5mrd4driIzrWXS9ZyajASpbtl33wtnz49fr1DRNH1akUHovD9e1uu6fa+iS63fL+Xv2a4WosbIFXN+H5pbW1evE2bvD7nyPcL13lZWW+jaHc0iUqwJUuYbYOJ/fCrsdo8F5/WU25xAQk+HXXgngrOR0hX0OpSi32Z3lMjgrb42V6Vy/bc5hI+jDmwyagQq7naKKckqsVv0+WB12Sq02Ks4akuaqSPUymjAYnC3i1spWYXC2iJsNxirzuxgrW7P9PD2wOZzndvXEACiz2/CqHAZk0AwUlpe5/X2wK+cwlrLKimjX+3X2dbmGg7nur0Lpz5LNYddb+C+1d4+9slL3t7pRK1P/YG2JQuikJf13SNmPoH7pCaoUfFIwXGQSubV5+xn02SeYDQaWDBhEk6A6NcgjH8pXoMrXQMU2UAWXqfS1pPn/2sptCAJjGFplAI6xwVlBeRCaJnVOQgghhLj6XJU2NRki46isKDMaNL33luaqsKmc98Q1nEhD0ysxXMM4ymy2ygomZw+5crsNq92Bp8mEp9GoV+y4nP1R/+wP/ecLAcrKSjmcd5DwiHC8PH9tCbxcAYOrElerrKgC954EgD5fjlIKg8FZGSPE74G0pP+BacZQ8J+MOj0ait9FGRujWfqfN33nm8LpHt6ErP0/MXn1l2T0TrhoS5tmbACWAWiWAc4/mI5fwLoT7AdR9lywHdTHfjtbzq2grDhbvz1AMwBGZyu75un8Hg0Mgc6X5vyqGQLBEKD/7NznW9mtvA6aJq3JQgghhLi+GTRNX6HlSvM7ZyIu78vcJV1VTuxq0AzVTqp2RVTzuVTCcvFHJkH675Tm/QDYD6OK3nTOzm4IRvOq2pXdZXyXbqzLO8DavAMs3fcjPZtF1jwvTQNjfTDe5fz5txZeCCGEEEIIIUS1rr8BuaLmfIaB90OAA3V6JMq687xJwwODeLK9c53HcV+u4HhR0VUqpBBCCCGEEEKImpIg/XdM0zQ0/8ngcQeoUtSpJ1HW786bPrXD/9G6fgNOlZXx3IolVZbVEEIIIYQQQghxbUmQ/junaWa0wLfBFAWOX1An+qNKv6g2rYfRyMy4e/AymVh3MI83vl5/lUsrhBBCCCGEEOJCJEi/AWgGX7Q688GjC1COOv0MjoLnUI6TVdI2rVOXV3rcDcDsbzbyn2+3X93CCiGEEEIIIYQ4LwnSbxCawQ8t6B/gMwQwQNl/UT/Ho0o+qbLERu8WLRne4f8AmJC9io+/P/9YdiGEEEIIIYQQV48E6TcQTTNi8HsGre7Hzu7vqgBVOBZ1ahDKluuW9un/u53Ho28FYOyq5Xy2e9e1KPINx+5w8HNxMTk/57Px0EG++GE3n+R8z/J9P17rogkhhBBCiPPo1q0bI0eOvGCa8PBwZs6ceVXKc7klJibywAMP6D9fzev9Pd+3a0WWYLsBaeZboO6nUPJv1Jk3oeJr1C+9UJa+aD5JaMZGaJrGuM7dKLfb+WDnDp5ZvoRDhYUMbd8R49VaE/N3otxm41DhaQ4VFnKqrJRTZWWUWK38XFzELyUl/FxSTHFFBafKSskvLsZ+Ts8FgJiQUO5u2vwalF4IIYQQ4saXmJjIv//97yrbf/zxR5o1a3ZVyvDuu+8yb948vvvOOZFzTEwML7/8Mh07dtTTfPrpp/z9739ny5YtnDx5km3bthEdHX3ec6alpbFy5UpycnKq7MvLyyMiIoKFCxdy//3316qsn376KWazuVbHXExGRgYjR46koKDAbfvmzZvx8fG5rHmdKzs7m+7du3Pq1CkCAwOvaF5XgwTpNyhNM4FPEnjejSqcBBVroeQ/qJIPUR6d0LzuBa8/82K3HngajaRv38rrX68ja/9PvHTXn4mqV/9aX8IV4VCK/OIijpw5w+nyMoyaAZvDQZnNypEzZ6iw2ymrDMoPVgbmx4trt1ydBtTx9ibAywuLyYy/lxet6ze4MhckhBBCCCEAiI+PJz093W1b/fpX7zNtdnY2/fv35/bbb8fLy4tp06Zx99138/3339OoUSMAiouLufPOO+nbty8pKSkXPWdSUhKzZs1i/fr13H777W77MjIyaNCgAffcc0+ty1qnTp1aH3OpruZ7cKOQIP0Gp5nCIOhfULEeVfxPqNgAFV+hKr6Cwgng2YVxHe6lVd0uTF7zNduOHeX+zP8w+NYOpHa4De/LXMN2JblavI+cOaO3cP9SUqx/f/jMGY4UFlLhsNf63D5mM439A/D39KShjy9eZhMNLL7Us1gI9PLCy2Qm2NeXYF9f6ll8MElvBCGEEEKIq8rT05Pg4OBq961evZpRo0axY8cO6tSpw6BBg5gyZQomU/XhUH5+PklJSaxcuZLg4GCmTJly0fznz5/v9vO//vUvPvnkE1atWsXAgQMBeOyxxwDYv39/ja4pOjqaW2+9lblz57oF6UopMjIyGDRoEJqmkZSUxJdffsmxY8e46aabGDZsGE899dR5z9utWzeio6P1bug1ud7XX3+d9PR0fvrpJ+rUqUOvXr149dVX8fX1JTs7m8cffxxwLhMNMHHiRCZNmkR4eDgjR47Uu9fn5eWRlpbGqlWrMBgMxMfH8/bbb9OwYUMAJk2axGeffcazzz7L+PHjOXXqFD179uTdd9/Fz8+vRvftXKdOneKpp57if//7H+Xl5XTt2pW33nqL5s2dPV0PHDjA8OHD+eqrr6ioqCA8PJzp06dzzz33cOrUKYYPH87y5cspKiqicePG/PWvf9Wv90qQIP0PQNM08LwDzfMOlO0AlC1GlS0C2w9QvgpVvooH63nT8+HO/OeHBszabmb2NxuZv3MHD7VqTZ+oVkTVq6//wl0LNoeD/OIifjp1ipOlJRwsPM2B0wUcKCggv7iYCruNY0VFVO1oXpVR06hv8cHf0xOjwYDRYMDDYKCejw+Bnl6YjUZC/fy4yT+QxgEBhPn7E+TlfU2vH0ApG2AEVQyqHCgHFJqx0TUtlxBCCCFuXEopUKXXJnPt8nz+Onz4MPfccw+JiYnMmzeP3bt3k5KSgpeXF5MmTar2mMTERI4cOUJWVhZms5kRI0aQn59fq3xLSkqwWq2/udU6KSmJ559/njfffFPvNp6dnU1ubi5PPPEEDoeDxo0bs2DBAurWrcv69esZPHgwISEh9O3bt0Z51OR6DQYDb731FhEREfz0008MGzaM0aNHM3v2bG6//XZmzpzJhAkT2LNnDwC+vr5V8nE4HPTu3RtfX19Wr16NzWYjNTWVfv36kZ2drafbt28fn332GV988QWnTp2ib9++TJ06lZdeeumS7mFiYiI//vgj//3vf/H392fMmDHcc8897Nq1C7PZTGpqKhUVFaxZswYfHx927dqll3/8+PHs2rWLJUuWUK9ePfbu3Utp6ZX9nZAg/Q9GM90MvkPRfIeirD84g/WyRWDPw8u2nOQm8ESEgX1n6rHtlzp8e3IrTy0KodjemM43R9A1PIKOoY2p4335gtZSq5VSm1Vv8f65uIRfSor5uaSYY0VF/HjyBHtPnsDmcFz0XD5mM438A6hvsVDP4qN/rWexEOLrR1hAAMG+ftekpds5y74VVAWoInCcAkcBqILK74tQqhAcJ51BuCoGx8+V+05U/oM0AGfdB+8EtIBXrvq1CCGEEOIPQpWi8qOvSdZag+2gWWqc/osvvnALDHv27MmCBQuYPXs2YWFhzJo1C03TiIqK4siRI4wZM4YJEyZgOOdz4Q8//MCSJUvYtGkTHTp0AOC9996jZcuWtSr/mDFjCA0NJTY2tlbHnevRRx/l2WefZcGCBSQmJgKQnp7OnXfeSWRkJACTJ0/W00dERLBhwwY+/vjjGgXpNb3esyeaCw8PZ8qUKQwZMoTZs2fj4eFBQEAAmqadtzcDwKpVq9i5cye5ubmEhYUBMG/ePFq3bs3mzZv1/B0OBxkZGXrL+WOPPcaqVasuKUh3Befr1q3TeyPMnz+fsLAwPvvsMx5++GHy8vJISEigTZs2ADRp0kQ/Pi8vj3bt2tG+fXv92q80CdL/wDRzJJo5EuU7EmzfoUq/gLIvMDh+prl/Ps398+nbZDcARVYzuwvqsutoXabl1ON0RSDHy8Px8KhLmH8A9SwWlFI4FJiNBixmM2U2m9vrdFkZhRXllFqtlFitFJaXUVhejrUGwTc4W8DDA4Oob/Ghoa8vTYKCCPMPpJ7Fgp+HB438A6h7GSoPlLI5A2JVXPm15KxXKThK9H3KtZ0KUFacLd1llQH28cogvOzXoBvbbyqbW4COARy1Gy8vhBBCCHGj6t69O3PmzNF/drU65+Tk0KlTJ7fPiHfccQdFRUUcOnSIm266ye08OTk5mEwmYmJi9G1RUVG1mpBs6tSpZGZmkp2djZeX1yVekVNgYCB9+vRh7ty5JCYmUlhYyCeffMI777yjp3nnnXeYO3cueXl5lJaWUlFRccEJ6c5W0+tduXIlr7zyCrt376awsBCbzUZZWRklJSVYLDWrTMnJySEsLEwP0AFatWpFYGAgOTk5epAeHh7u1rU9JCSk1j0Zzr2+2267Td9Wt25dWrRooU/IN2LECIYOHcry5cuJjY0lISGBW265BYChQ4eSkJDA1q1bufvuu3nggQeqzA9wuUmQLpx/sMxt0MxtUH7Pg/2wc0Z4+09g/Q5VsR1fcxnt6x+jff1j+nEOBT+dCeRkmTcHiv05U+FBkc2DMyUenLR6cMbqQZHVTJHre5sHJ8q8sCljteUI9PKinrc3DXx8qOfjS32LD/UtPjQJCqJlvQbU9/HBw1j1WKUqKluffwbbaZTjDM5g2RVoF+tBtlIl4CgGVQiOXyqD6RL3YJyKK3Snz2YCQ6DzpQVVfvUAQz3AgGasD5oPGBqAoY7zpXkCDtD8QfMCNDSt+nsphBBCCHFZaN7OFu1rlHdt+Pj4XLWZ3C9kxowZTJ06lZUrV+qB3m+VlJREjx492Lt3L1lZWRiNRh5++GEAMjMzee6553jttdfo1KkTfn5+TJ8+nY0bN16WvME5hv6+++5j6NChvPTSS9SpU4evvvqKpKQkKioqahyk19S5M89rmoajhg17lyI5OZm4uDgWLVrE8uXLeeWVV3jttddIS0ujZ8+eHDhwgMWLF7NixQp69OhBamoqM2bMuGLlkSBduNE0DUyNwfQQrrpGpexgzwXrLpT1exzW77FbD2DWjtPMvwD8C+jI0Rrn4VBmHHiAZsKAQqMcsKFpnpUZloIWCJoGrlKUmaDcCweu7khWZ0DtKMY5NvtKMDq7WGnezoBZ8676s8FS2Q3LA00zA3bACww+YKjrDLo178oA28O5XfOpTH9tx7gLIYQQQlyMpmm16nJ+PWrZsiWffPIJSin989e6devw8/OjcePGVdJHRUVhs9nYsmWL3rK7Z8+eKkuLVefVV1/lpZdeYtmyZXr36Muhe/fuREREkJ6eTlZWFo888ojeU8DVjXvYsGF6+n379tX43DW53i1btuBwOHjttdf04QEff/yx23k8PDyw2y88QXPLli05ePAgBw8e1FvTd+3aRUFBAa1atapxmWujZcuW2Gw2Nm7cqLeAnzhxgj179rjlGRYWxpAhQxgyZAhjx47l3XffJS0tDXDOUD9o0CAGDRpE586dGTVqlATp4trSNCOYmoGpGZr3/RhwPjjK/rNz8jnHL2A/glJFzu7XqgjUmbO+P2s7NgyaFQPWqhmpkrO+P0WNZoHTGZ2ty5o/GPxB2SqDaN/KwNqn8qvzpRl8wVAfNN9fA2/D2YG4D2CWQFoIIYQQ4ndu2LBhzJw5k7S0NIYPH86ePXuYOHEizzzzTJXx6AAtWrQgPj6eJ598kjlz5mAymRg5ciTe3hdu2Z82bRoTJkzggw8+IDw8nGPHnD1QfX199bHyJ0+eJC8vjyNHjgDok6wFBwdfcCy3pmk88cQTvP7665w6dYo33nhD39e8eXPmzZvHsmXLiIiI4P3332fz5s1ERETU6P7U5HqbNWuG1Wrl7bffplevXqxbt46///3vbucJDw+nqKiIVatW0bZtWywWS5UW9tjYWNq0acOAAQOYOXMmNpuNYcOG0bVr18tSqbFz5063bvKaptG2bVt69+5NSkoK//jHP/Dz8+P555+nUaNG9O7dG3COt+/ZsyeRkZGcOnWKrKwsfUz+hAkTiImJoXXr1pSXl/PFF1/Uen6C2pIgXVwyzVgfjL+ue3ixcFYph3OSNFXmHL+tKkAzAkbAivNxdDjPpKz8GqUrwFY5o7mrm4vJGUwbfCsDbT80TZY9E0IIIYQQ7ho1asTixYsZNWoUbdu2pU6dOiQlJTFu3LjzHpOenk5ycjJdu3alYcOGTJkyhfHjx18wnzlz5lBRUcFDDz3ktt21FBnAf//7X7elux555JEqac4nMTGRiRMn0rp1a7fx1U8++STbtm2jX79+aJpG//79GTZsGEuWLLng+WpzvW3btuX1119n2rRpjB07li5duvDKK6/oS8sB3H777QwZMoR+/fpx4sSJaq9J0zQ+//xz0tLS6NKli9sSbJdDly5d3H42Go3YbDbS09N56qmnuO+++6ioqKBLly4sXrxY71Zvt9tJTU3l0KFD+Pv7Ex8fr1eEeHh4MHbsWPbv34+3tzedO3cmMzPzspT3fDTlnHL6mnrnnXeYPn06x44do23btrz99tt07Nix2rQZGRlV1qTz9PSkrKysRnkVFhYSEBDA6dOn8ff3/81lF0IIIYQQ4kZRVlZGbm4uERERv3nCMyH+aC70+1ObOPSaNz1+9NFHPPPMM0ycOJGtW7fStm1b4uLiLjh7n7+/P0ePHtVfBw4cuIolFkIIIYQQQgghroxrHqS//vrrpKSk8Pjjj9OqVSv+/ve/Y7FYmDt37nmPca2/53o1bNjwKpZYCCGEEEIIIYS4Mq5pkF5RUcGWLVuIjY3VtxkMBmJjY9mwYcN5jysqKuLmm28mLCyM3r178/333583bXl5OYWFhW4vIYQQQgghhBDienRNg/RffvkFu91epSW8YcOG+myI52rRogVz587l888/5z//+Q8Oh4Pbb7+dQ4cOVZv+lVdeISAgQH+5pvoXQgghhBBCCCGuN9e8u3ttderUiYEDBxIdHU3Xrl359NNPqV+/Pv/4xz+qTT927FhOnz6tvw4ePHiVSyyEEEIIIYQQQtTMNV2CrV69ehiNRo4fP+62/fjx4xdcJ/BsZrOZdu3asXfv3mr3e3p64unp+ZvLKoQQQgghhBBCXGnXtCXdw8ODmJgYVq1apW9zOBysWrWKTp061egcdrudnTt3EhIScqWKKYQQQgghhBBCXBXXtCUd4JlnnmHQoEG0b9+ejh07MnPmTIqLi/W10AcOHEijRo145ZVXAHjxxRf5v//7P5o1a0ZBQQHTp0/nwIEDJCcnX8vLEEIIIYQQQgghfrNrHqT369ePn3/+mQkTJnDs2DGio6NZunSpPplcXl4eBsOvDf6nTp0iJSWFY8eOERQURExMDOvXr6dVq1bX6hKEEEIIIYQQQojL4rqYOG748OEcOHCA8vJyNm7cyG233abvy87OJiMjQ//5jTfe0NMeO3aMRYsW0a5du2tQaiGEEEIIIcSNoFu3bowcOfKCacLDw5k5c+ZVKc/ldu711eRaNE3js88++815X67z/JFcF0G6EEIIIYQQQlyqxMRENE2r8jrf5NJXwrvvvkvnzp0JCgoiKCiI2NhYNm3apO+3Wq2MGTOGNm3a4OPjQ2hoKAMHDuTIkSPnPWevXr2Ij4+vdt/atWvRNI1vv/221mXdvHkzgwcPrvVxFzJp0iSio6OrbD969Cg9e/a8rHmdKyMjg8DAwCuax9UkQboQQgghhBDidy8+Pp6jR4+6vSIiIq5a/tnZ2fTv35+srCw2bNhAWFgYd999N4cPHwagpKSErVu3Mn78eLZu3cqnn37Knj17uP/++897zqSkJFasWMGhQ4eq7EtPT6d9+/bccssttS5r/fr1sVgstT7uUgQHB8tqW7UkQboQQgghhBDid8/T05Pg4GC3l9FoBGD16tV07NgRT09PQkJCeP7557HZbOc9V35+Pr169cLb25uIiAjmz59/0fznz5/PsGHDiI6OJioqin/961/6ylUAAQEBrFixgr59+9KiRQv+7//+j1mzZrFlyxby8vKqPed9991H/fr13Yb/AhQVFbFgwQKSkpI4ceIE/fv3p1GjRlgsFtq0acOHH354wbKe2939xx9/pEuXLnh5edGqVStWrFhR5ZgxY8YQGRmJxWKhSZMmjB8/HqvVCjhbsidPnsyOHTv0XgyuMp/b3X3nzp3cddddeHt7U7duXQYPHkxRUZG+PzExkQceeIAZM2YQEhJC3bp1SU1N1fO6FHl5efTu3RtfX1/8/f3p27ev2zLgO3bsoHv37vj5+eHv709MTAzffPMNAAcOHKBXr14EBQXh4+ND69atWbx48SWXpSau+cRxQgghhBBCiOuTUgpU6bXJXPNG07TffJrDhw9zzz33kJiYyLx589i9ezcpKSl4eXkxadKkao9JTEzkyJEjZGVlYTabGTFiBPn5+bXKt6SkBKvVSp06dc6b5vTp02iadt6u2iaTiYEDB5KRkcELL7yg348FCxZgt9vp378/RUVFxMTEMGbMGPz9/Vm0aBGPPfYYTZs2pWPHjhctp8PhoE+fPjRs2JCNGzdy+vTpasfn+/n5kZGRQWhoKDt37iQlJQU/Pz9Gjx5Nv379+O6771i6dCkrV64EnJUS5youLiYuLo5OnTqxefNm8vPzSU5OZvjw4W4VEVlZWYSEhJCVlcXevXvp168f0dHRpKSkXPR6qrs+V4C+evVqbDYbqamp9OvXj+zsbAAGDBhAu3btmDNnDkajke3bt2M2mwFITU2loqKCNWvW4OPjw65du/D19a11OWpDgnQhhBBCCCFE9VQpKj/6mmStNdgOWs27ZH/xxRduwVPPnj1ZsGABs2fPJiwsjFmzZqFpGlFRURw5coQxY8YwYcIEt5WkAH744QeWLFnCpk2b6NChAwDvvfceLVu2rFX5x4wZQ2hoKLGxsdXuLysrY8yYMfTv3x9/f//znueJJ55g+vTprF69mm7dugHOru4JCQkEBAQQEBDAc889p6dPS0tj2bJlfPzxxzUK0leuXMnu3btZtmwZoaGhALz88stVxpGPGzdO/z48PJznnnuOzMxMRo8ejbe3N76+vphMJoKDg8+b1wcffEBZWRnz5s3Dx8cHgFmzZtGrVy+mTZumr/AVFBTErFmzMBqNREVFce+997Jq1apLCtJXrVrFzp07yc3NJSwsDIB58+bRunVrNm/eTIcOHcjLy2PUqFFERUUB0Lx5c/34vLw8EhISaNOmDQBNmjSpdRlqS7q7CyGEEEIIIX73unfvzvbt2/XXW2+9BUBOTg6dOnVya5W/4447KCoqqnasd05ODiaTiZiYGH1bVFRUrSYmmzp1KpmZmSxcuBAvL68q+61WK3379kUpxZw5cy54rqioKG6//Xbmzp0LwN69e1m7di1JSUkA2O12/va3v9GmTRvq1KmDr68vy5YtO28X+nPl5OQQFhamB+gAnTp1qpLuo48+4o477iA4OBhfX1/GjRtX4zzOzqtt27Z6gA7O98LhcLBnzx59W+vWrfWhCgAhISG17slwdp5hYWF6gA7QqlUrAgMDycnJAeCZZ54hOTmZ2NhYpk6dyr59+/S0I0aMYMqUKdxxxx1MnDjxkibqqy1pSRdCCCGEEEJUT/N2tmhfo7xrw8fHh2bNml2hwtTcjBkzmDp1KitXrqx2UjdXgH7gwAG+/PLLC7aiuyQlJZGWlsY777xDeno6TZs2pWvXrgBMnz6dN998k5kzZ+ozx48cOZKKiorLdk0bNmxgwIABTJ48mbi4OAICAsjMzOS11167bHmczdXV3EXTNBwOxxXJC5wz0z/66KMsWrSIJUuWMHHiRDIzM3nwwQdJTk4mLi6ORYsWsXz5cl555RVee+010tLSrlh5/nBBulIKgMLCwmtcEiGEEEIIIa4vFRUVOBwO7HY7dru9cus1mplb1TwoU0qhlDqrzL9q0aIFCxcuxGaz6a3pa9euxc/Pj5CQEOx2u9vxzZs3x2azuXV337NnDwUFBfq9OZ/p06fzyiuvsHjxYtq1a1clrdVq5ZFHHmHv3r2sXLmSwMDAC57PJSEhgaeeeor//Oc/zJs3jyeffFIPWr/66ivuv/9++vfvDzjHYP/www+0bNlSP3d198d1LZGRkRw8eJBDhw4REhICwLp169zSfPXVV9x88808//zz+vH79+8H0M9pMpnOeW5+5TpPixYtyMjIoLCwUG9NX7NmDQaDgWbNmlV5L1wu9P66zn92Wc7mur79+/frrem7du2ioKCAFi1a6Mc0bdqUESNGMGLECAYMGMDcuXP1mfdDQ0NJSUkhJSWFv/71r7z77rsMGzasSl52ux2Hw0FRUVGVShJX/OmKRy/kDxeknzlzBsCtu4MQQgghhBACbr75Zv7+979TWnqNJou7RCdOnKCoqIht27ZV2de5c2dmzpzJo48+qrdgT5kyhUceeYQdO3YAztnS8/Pz9eM7depEYmIizz//PEajkddffx1PT08OHTpUbR4A//73v/nHP/7BlClTOHPmjD5DusViwWKxYLPZGDNmDLt37+aNN95g+/bt+rEBAQFVWo/P1aNHD55//nmKi4uJiYnRyxEQEMDixYvJyMjA39+f+fPnc+TIEUJDQ/U0515fRUWFfi1169YlLCyMhx56iBEjRlBcXMwbb7wBwE8//cS2bdswGAwcOHCAadOm0apVK7766is++eQT7Ha72/3Yt28fmZmZNGzYEIvFgoeHh9t5XN3YH3zwQVJSUigoKGDKlCn07NmTI0eOcOTIkWrfy/z8/PO+v+Ccgd1qtZKZmem23cPDg/DwcJo2bUqfPn145plnsNvtTJs2jVtvvRWj0ciGDRt466236NGjB6GhoeTn57Nu3Truuusutm3bxmuvvcbtt9/OTTfdxJkzZ1i8eDHBwcHnLcsvv/zCvffey4EDB6rdf+bMmWon1TvbHy5IDw0N5eDBg/j5+V2W2SKvlMLCQsLCwjh48GCNusCIPwZ5LkR15LkQ55JnQlRHngtxruqeiYqKCo4fP054eHi1Y6mvV3Xr1sVoNNKuXbtq9y9atIgxY8YwYMAA6tSpQ0pKCn/7298wmZzhkK+vLw0aNNCPX7BgAYMHD2bIkCE0bNiQF198kYkTJ9K4cePz5vHQQw9htVoZM2aM2/bx48czceJE9u/fz5o1awDnbOJnW7lypT4p3Pk899xzfP755/Ts2ZO7775b3z5z5kySk5MZOXIkFouF5ORk/P39OX36tF7Wc6/Pw8PD7VoWLVpESkoKjz/+ODfffDNpaWmMGDGCJk2a0K5dO9q1a8fRo0d5/fXXKS8v55577mHChAm8+OKL+jlatWrFtm3bGD58OAUFBbz33nsMGjQIQD8PwIoVK3j66ad5/PHHsVgs9OnThxkzZuiT/lX3XjZo0IAjR46c995/++23lJSU8Je//MVte9OmTdmzZw9Lly7lqaeeYsiQIRgMBuLi4njzzTdp2LAhFRUVGAwGXnrpJY4fP069evV48MEHmTZtGl5eXtStW5c333yTQ4cO4e/vT1xcHK+99hp169atUo6ysjL279/PN998o1dQuCilOHPmjNvY//PRVE3a28VVV1hYSEBAAKdPn5Z/pEInz4WojjwX4lzyTIjqyHMhzlXdM1FWVkZubi4RERG/qyBdXD6u1vF27dq5Td4mLu5y/f7I7O5CCCGEEEIIIcR1QoJ0IYQQQgghhBDiOiFB+nXK09OTiRMn4ul5jWbTFNcleS5EdeS5EOeSZ0JUR54LcS55JkR1NE0jNDT0up6/60YnY9KFEEIIIYQQgIxJF+K3kDHpQgghhBBCCCHEDUaCdCGEEEIIIYQQ4johQboQQgghhBBCCHGdkCBdCCGEEEIIIYS4TkiQfp165513CA8Px8vLi9tuu41NmzZd6yKJK2TSpElomub2ioqK0veXlZWRmppK3bp18fX1JSEhgePHj7udIy8vj3vvvReLxUKDBg0YNWoUNpvtal+K+A3WrFlDr1699NlUP/vsM7f9SikmTJhASEgI3t7exMbG8uOPP7qlOXnyJAMGDMDf35/AwECSkpIoKipyS/Ptt9/SuXNnvLy8CAsL49VXX73SlyYu0cWeicTExCp/O+Lj493SyDNxY3nllVfo0KEDfn5+NGjQgAceeIA9e/a4pblc/zOys7O59dZb8fT0pFmzZmRkZFzpyxOXqCbPRbdu3ar8vRgyZIhbGtdzER0dzcGDBzl69Cjnzi995swZdu3axZYtW9i5cye//PLLFb8+UXv5+fl8//33bN26la1bt5KTk8Pp06f1/Q6HgwMHDrB9+3a2bt3K3r17sVqtbucoLy/nxx9/ZOvWrWzfvp2DBw/K83AVSZB+Hfroo4945plnmDhxIlu3bqVt27bExcWRn59/rYsmrpDWrVtz9OhR/fXVV1/p+55++mn+97//sWDBAlavXs2RI0fo06ePvt9ut3PvvfdSUVHB+vXr+fe//01GRgYTJky4FpciLlFxcTFt27blnXfeqXb/q6++yltvvcXf//53Nm7ciI+PD3FxcZSVlelpBgwYwPfff8+KFSv44osvWLNmDYMHD9b3FxYWcvfdd3PzzTezZcsWpk+fzqRJk/jnP/95xa9P1N7FngmA+Ph4t78dH374odt+eSZuLKtXryY1NZWvv/6aFStWYLVaufvuuykuLtbTXI7/Gbm5udx77710796d7du3M3LkSJKTk1m2bNlVvV5RMzV5LgBSUlLc/l6cXSF39nPx4YcfUq9ePQoKCjh8+LCexhW0+fn50apVKxo2bMj+/fvdgr/fs27dujFy5MgLpgkPD2fmzJlXpTy/hYeHB40bN6ZVq1a0atUKf39/7rrrLoYPHw7AwYMHuf3221m6dCktWrTAarWyb98+/XilFHv37iUyMpIff/yRiIgITpw4ccnPQ3UVzeIilLjudOzYUaWmpuo/2+12FRoaql555ZVrWCpxpUycOFG1bdu22n0FBQXKbDarBQsW6NtycnIUoDZs2KCUUmrx4sXKYDCoY8eO6WnmzJmj/P39VXl5+RUtu7gyALVw4UL9Z4fDoYKDg9X06dP1bQUFBcrT01N9+OGHSimldu3apQC1efNmPc2SJUuUpmnq8OHDSimlZs+erYKCgtyeizFjxqgWLVpc4SsSv9W5z4RSSg0aNEj17t37vMfIM3Hjy8/PV4BavXq1Uury/c8YPXq0at26tVte/fr1U3FxcVf6ksRlcO5zoZRSXbt2VU899dR5jzn7uSgtLVW7du1Shw4dUlu3blV2u10ppdTBgwfVd99953bc3r171Z49e67IddTWoEGDFFDl9eOPP9bo+IvdI6WUuvnmm9Ubb7xx3v3//Oc/1Z133qkCAwNVYGCg6tGjh9q4caNbmokTJ6oWLVooi8Wip/n666/Pe8777rvvvL97a9asUYDasWPHBcutlFIxMTFq8ODBymq1qm+++Ubt2bNHFRcXK6WUKikpUZs3b1ZnzpxRSjn/lmzevNntf8/x48cv+jykpaWpqKioKnkfPXpUlZWVXbSMv0V6eroKCAi4onnUhOv3p7S09DedR1rSrzMVFRVs2bKF2NhYfZvBYCA2NpYNGzZcw5KJK+nHH38kNDSUJk2aMGDAAPLy8gDYsmULVqvV7XmIioripptu0p+HDRs20KZNGxo2bKiniYuLo7CwkO+///7qXoi4InJzczl27JjbcxAQEMBtt93m9hwEBgbSvn17PU1sbCwGg4GNGzfqabp06YKHh4eeJi4ujj179nDq1KmrdDXicsrOzqZBgwa0aNGCoUOHcuLECX2fPBM3PleLVZ06dYDL9z9jw4YNbudwpZHPIb8P5z4XLvPnz6devXr86U9/YuzYsZSUlOj7qnsu/Pz8sNvteo+toqIi/Pz83M4ZEBBQpcX+Wjq3d9HRo0eJiIi4avlnZ2fTv39/srKy2LBhA2FhYdx9991uLdCRkZHMmjWLnTt38tVXXxEeHs7dd9/Nzz//XO05k5KSWLFiBYcOHaqyLz09nfbt23PLLbect0xKKU6ePIlSCrPZTElJCUopmjRpgsViAcDb2xsPDw/9vSwqKsLb29vtPAEBARd9Hry8vHA4HFXKEBwcjKen53nLKKqSIP0688svv2C3293+SAI0bNiQY8eOXaNSiSvptttuIyMjg6VLlzJnzhxyc3Pp3LkzZ86c4dixY3h4eBAYGOh2zNnPw7Fjx6p9Xlz7xO+f63280N+FY8eO0aBBA7f9JpOJOnXqyLNyg4qPj2fevHmsWrWKadOmsXr1anr27IndbgfkmbjRORwORo4cyR133MGf/vQngMv2P+N8aQoLCyktLb0SlyMuk+qeC4BHH32U//znP2RlZTF27Fjef/99/vKXv+j7q3vPjUYjgD5W2Wq1Yjab3dKYzWbsdnu1gdm14OnpSXBwsNvLdR2rV6+mY8eOeHp6EhISwvPPP3/B+Xvy8/Pp1asX3t7eREREMH/+/IvmP3/+fIYNG0Z0dDRRUVH861//wuFwsGrVKj3No48+SmxsLE2aNKF169a8/vrrFBYW8u2331Z7zvvuu4/69etXmReiqKiIBQsWkJSUxIkTJ+jfvz+NGjXCYrHQpk0bMjIy2Lp1K1u2bOHAgQN4e3tjMpmwWq1omkazZs3cuu4fPnyY+++/Hy8vL7p161ZlPiyTycTbb7/NLbfcgsViITY2ljfeeEN/PjIyMpg+fTo//PCDPu+Bq8zndnffuXMnd911F97e3tStW5fBgwe7zZeSmJjIAw88wIwZMwgJCaFu3bqkpqZWGTdfG3l5efTu3RtfX1/8/f3p27ev23wdO3bsoHv37vj5+eHv709MTAzffPMNAAcOHKBXr14EBQXh4+ND69atWbx48SWXpSZMV/TsQoiL6tmzp/79Lbfcwm233cbNN9/Mxx9/XKUWUwghXB555BH9+zZt2nDLLbfQtGlTsrOz6dGjxzUsmbgaUlNT+e6779zmMBHifM/F2XNRtGnThpCQEHr06MG+ffto2rTphU+qFMpRgkYZUIpylJy1rxSDVgaqBOW4Am1/mjeapv3m0xw+fJh77rmHxMRE5s2bx+7du0lJScHLy4tJkyZVe0xiYiJHjhwhKysLs9nMiBEjaj0/VElJCVartUqvBpeKigr++c9/EhAQQNu2batNYzKZGDhwIBkZGbzwwgv6/ViwYAF2u53+/ftTVFRETEwMY8aMwd/fn0WLFpGcnExWVha33norp06doqys7LyVEg6Hg6eeeoqGDRuyceNGdu/ezYsvvlglncViYfbs2URGRvK///2Pv/3tb4SEhDB69Gj69evHli1bWLx4MWvXrsVgMBAQEFDlHMXFxcTFxdGpUyc2b95Mfn4+ycnJDB8+3K0iIisri5CQELKysti7dy/9+vUjOjqalJSUi932aq/PFaCvXr0am81Gamoq/fr1Izs7G3DO4dKuXTvmzJmD0Whk+/bteqVUamoqFRUVrFmzBh8fH3bt2oWvr2+ty1EbEqRfZ+rVq4fRaKwyE+vx48cJDg6+RqUSV1NgYCCRkZHs3buXP//5z1RUVFBQUODWMnL28xAcHFylttP1/Mgzc2NwvY/Hjx8nJCRE3378+HGio6P1NOd+eLDZbJw8edLtWanub8vZeYjfryZNmlCvXj327t1Ljx495Jm4gQ0fPlyfCLBx48b69uDg4MvyP+N8z4W/v79UHl/HzvdcVOe2224DYO/evTRt2rTa58LVK8dstqPyo2lV37ldnfVnxQ+IbgT87BwAfrlpDbaDZqlx+i+++MIteOrZsycLFixg9uzZhIWFMWvWLH0VnSNHjjBmzBgmTJiAweBewfDDDz+wZMkSNm3aRIcOHQB47733aNmyZa3KP2bMGEJDQ6sMH/niiy945JFHKCkpISQkhBUrVlCvXr3znueJJ55g+vTprF69mm7dugHOru4JCQkEBAQQEBDAc889p6dPS0tj2bJlfP7553Tu3BkfHx8MBgMlJSWYzeYqs7SvXLmS3NxcFixYQNu2balXrx4jRoxwWwHAZrORlJREq1atsFgs/PnPf+bYsWN8/PHHjB49Gm9vb721PjQ09LzX8sEHH1BWVsa8efPw8fEBYNasWfTq1Ytp06bpPTqCgoKYNWsWRqORqKgo7r33XlatWnVJQfqqVavYuXMnubm5hIWFATBv3jxat27N5s2b6dChA3l5eYwaNUpfYal58+b68Xl5eSQkJNCmTRvA+f/2SpPu7tcZDw8PYmJi3LrFuLrJdOrU6RqWTFwtRUVF7Nu3j5CQEGJiYjCbzW7Pw549e8jLy9Ofh06dOrFz5063D+MrVqzA39+fVq1aXfXyi8svIiKC4OBgt+egsLCQjRs3uj0HBQUFbNmyRU/z5Zdf4nA49A9jnTp1Ys2aNW7dxVasWEGLFi0ICgq6SlcjrpRDhw5x4sQJvSJHnokbj1KK4cOHs3DhQr788ssqY20v1/+MTp06uZ3DlUY+h1yfLvZcVGf79u0Abn8vzn0uioqKMBqNeHn9fsYSu1YkcL3eeustAHJycujUqZNbq/wdd9xBUVFRtWO9c3JyMJlMxMTE6NuioqKqDCW5kKlTp5KZmcnChQvx8vKqtpzr168nPj6evn37XrCVPioqittvv525c+cCzsqVtWvXkpSUBDgrVP72t7/Rpk0b6tSpg6+vL8uWLdPnOHJRSmGxWNA0zW2IwrfffkuDBg30XhW+vr60aNHC7djCwkJWrlzJn//8Z4KDg4mJiWHGjBlueZSXl1ep8DhXTk4Obdu21QN0cL4XDofDbenA1q1b60MVwPmsXupKVzk5OYSFhekBOkCrVq0IDAwkJycHgGeeeYbk5GRiY2OZOnWq22z3I0aMYMqUKdxxxx1MnDjxvEMTLqvfOoOduPwyMzOVp6enysjIULt27VKDBw9WgYGBbjOxihvHs88+q7Kzs1Vubq5at26dio2NVfXq1VP5+flKKaWGDBmibrrpJvXll1+qb775RnXq1El16tRJP95ms6k//elP6u6771bbt29XS5cuVfXr11djx469VpckLsGZM2fUtm3b1LZt2xSgXn/9dbVt2zZ14MABpZRSU6dOVYGBgerzzz9X3377rerdu7eKiIhwmz00Pj5etWvXTm3cuFF99dVXqnnz5qp///76/oKCAtWwYUP12GOPqe+++05lZmYqi8Wi/vGPf1z16xUXd6Fn4syZM+q5555TGzZsULm5uWrlypXq1ltvVc2bN3ebQVeeiRvL0KFDVUBAgMrOzlZHjx7VXyUlJXqay/E/46efflIWi0WNGjVK5eTkqHfeeUcZjUa1dOnSq3q9omYu9lzs3btXvfjii+qbb75Rubm56vPPP1dNmjRRXbp00c9x9nOxbds2tWXLFrV161Z18OBB5XA4lMNerMpKT6ptW9epQwd/UCXFv6jjx/arLd+sVQWnjiqHvfjKvByOGt+HC6148eCDD6rExES3bdu3b1eA/n/27NndP/vsM2UymfSZzF0CAwMvOLu7y/Tp01VAQIDb6hoX0qxZM/Xyyy9fMM17772nLBaLKiwsVH/9619V06ZN9fvzyiuvqLp166r3339fbd++Xa1Zs0bFxcWpXr16qeLiYnXw4EF16623qqFDhyqllNq/f7++clRRUZEaO3asaty4sZ6Xw+FQGzZsUIDKzMxUBQUFKiMjQxmNRjVlyhS1efNm9d1336khQ4Yof39/VVJSoo4fP65SUlLUn/70pypl56xZ4p9++mnVrVs3t/0FBQVuKxJU914+9dRTqmvXrue9Pxea3f3NN99U4eHhVbYHBgaqf//73/rPe/bsUa+//rr685//rDw8PNSnn36q78vLy1Nz5sxRDz74oDKbzeqtt96qNq/LNbu7BOnXqbffflvddNNNysPDQ3Xs2PGCSzOI37d+/fqpkJAQ5eHhoRo1aqT69eun9u7dq+8vLS1Vw4YNU0FBQcpisagHH3xQHT161O0c+/fvVz179lTe3t6qXr166tlnn1VWq/VqX4r4DbKysqpdOmbQoEFKKec/zPHjx6uGDRsqT09P1aNHjyrL3pw4cUL1799f+fr6Kn9/f/X444/ry6m47NixQ915553K09NTNWrUSE2dOvVqXaKopQs9EyUlJeruu+9W9evXV2azWd18880qJSWlSmWuPBM3luqeB0Clp6fraS7X/4ysrCwVHR2tPDw8VJMmTdzyENeXiz0XeXl5qkuXLqpOnTrK09NTNWvWTI0aNUqdPn3a7Tyu56JFixZq2bJl6qeffqoSJBcWFqrvv/9effPNN+rbb79VP//889W6zIu6UJD+17/+VbVo0cLtet555x3l5+enB+JnB+m7d+9WgNq0aZOe3rXtYkH6tGnTlL+/v77sYU00adJETZw48YJpzpw5o3x9fdXf//531bhxY/XSSy/p++677z71xBNP6D/v27dP3XTTTapr165q27Ztavfu3erOO+/Ur89ut6tGjRqpZ599Vm3ZskXNnTtXmUwmdeTIEf0c//vf/xSgZsyYobZt26bGjRunmjRp4lamgQMHKj8/P/15+Otf/3rRIP2f//ynCgoKUkVFRfr+RYsWuS0NebmD9OXLlyuj0ajy8vL0bd9//32VZUrP9sgjj6hevXpVu+/5559Xbdq0qXafBOlCCCGEEEKIy+pyBRlX24WC9EOHDimLxaJSU1NVTk6O+uyzz1S9evXcAuNz10l39UT6+uuv1TfffKPuvPNO5e3tfcEgferUqcrDw0P9v//3/9x6NbgqR12t1hs2bFD79+9X33zzjXr88ceVp6dnlTXHq5OUlKSCgoKU0WhUhw8f1rc//fTTKiwsTK1bt07t2rVLJScnK39/f7f7ce71nb3mu91uV61atVJ//vOf9Zb4mJgYt+D6888/VyaTSX344Ydq79696s0331R16tRxC4znz5+vfHx81LZt29TPP/+s9+w6+zzFxcUqJCREJSQkqJ07d6ovv/xSNWnSRG+UUOrSg3RfX1+9B5rrtWvXLuVwOFR0dLTq3Lmz2rJli9q4caOKiYnRz1dSUqJSU1NVVlaW2r9/v/rqq69U06ZN1ejRo/W8ly5dqn766Se1ZcsWddttt6m+fftWWw5ZJ10IIYQQQgghLqJRo0YsXryYTZs20bZtW4YMGUJSUhLjxo077zHp6emEhobStWtX+vTpw+DBg6ssa3muOXPmUFFRwUMPPURISIj+mjFjBuBc1m737t0kJCQQGRlJr169OHHiBGvXrqV169YXvY6kpCROnTpFXFyc2+Rs48aN49ZbbyUuLo5u3boRHBzMAw88ULObAxgMBhYuXEhpaSkdO3YkOTmZl156yS3N/fffz9NPP83w4cOJjo5m/fr1jB8/3i1NQkIC8fHxdO/enfr16/Phhx9WyctisbBs2TJOnjxJhw4deOihh+jRowezZs2qcXnPp6ioiHbt2rm9evXqhaZpfP755wQFBdGlSxd9CbyPPvoIcL4vJ06cYODAgURGRtK3b1969uzJ5MmTAeeY/9TUVFq2bEl8fDyRkZHMnj37N5f3QjSlzpneTwghhBBCCPGHVFZWRm5uLhEREVUmPBNCXNjl+v2RlnQhhBBCCCGEEOI6IUG6EEIIIYQQQghxnZAgXQghhBBCCCGEuE5IkC6EEEIIIYQQQlwnJEgXQgghhBBCCCGuExKkCyGEEEIIIYQQ1wkJ0oUQQghx2WmaxmeffXatiyGEEEL87kiQLoQQQtxgEhMT0TStyis+Pv5aF00IIYQQF2G61gUQQgghxOUXHx9Penq62zZPT89rVBohhBBC1JS0pAshhBA3IE9PT4KDg91eQUFBgLMr+pw5c+jZsyfe3t40adKE//f//p/b8Tt37uSuu+7C29ubunXrMnjwYIqKitzSzJ07l9atW+Pp6UlISAjDhw932//LL7/w4IMPYrFYaN68Of/973/1fadOnWLAgAHUr18fb29vmjdvXqVSQQghrpZu3boxcuTIC6YJDw9n5syZV6U8l9u511eTa7lcw5Zk+FPtSZAuhBBC/AGNHz+ehIQEduzYwYABA3jkkUfIyckBoLi4mLi4OIKCgti8eTMLFixg5cqVbkH4nDlzSE1NZfDgwezcuZP//ve/NGvWzC2PyZMn07dvX7799lvuueceBgwYwMmTJ/X8d+3axZIlS8jJyWHOnDnUq1fv6t0AIcQN5XzDfPbu3XvVyvDuu+/SuXNngoKCCAoKIjY2lk2bNp03/ZAhQ9A07YLBcq9evc47VGnt2rVomsa3335b67Ju3ryZwYMH1/q4C5k0aRLR0dFVth89epSePXte1rzOlZGRQWBg4BXN42qSIF0IIYS4AX3xxRf4+vq6vV5++WV9/8MPP0xycjKRkZH87W9/o3379rz99tsAfPDBB5SVlTFv3jz+9Kc/cddddzFr1izef/99jh8/DsCUKVN49tlneeqpp4iMjKRDhw5VWqESExPp378/zZo14+WXX6aoqEj/wJqXl0e7du1o37494eHhxMbG0qtXr6tzc4QQN6T4+HiOHj3q9oqIiLhq+WdnZ9O/f3+ysrLYsGEDYWFh3H333Rw+fLhK2oULF/L1118TGhp6wXMmJSWxYsUKDh06VGVfeno67du355Zbbql1WevXr4/FYqn1cZciODhYhlvVkgTpQgghxA2oe/fubN++3e01ZMgQfX+nTp3c0nfq1ElvSc/JyaFt27b4+Pjo+++44w4cDgd79uwhPz+fI0eO0KNHjwuW4ewPjj4+Pvj7+5Ofnw/A0KFDyczMJDo6mtGjR7N+/frffM1CiD+26ob5GI1GAFavXk3Hjh314TnPP/88NpvtvOfKz8+nV69eeHt7ExERwfz58y+a//z58xk2bBjR0dFERUXxr3/9C4fDwapVq9zSHT58mLS0NObPn4/ZbL7gOe+77z7q169PRkaG2/aioiIWLFhAUlISJ06coH///jRq1AiLxUKbNm348MMPL3jec7u7//jjj3Tp0gUvLy9atWrFihUrqhwzZswYIiMjsVgsNGnShPHjx2O1WgFnS/bkyZPZsWOH3ovBVeZzu7tfbDhVYmIiDzzwADNmzCAkJIS6deuSmpqq53Up8vLy6N27N76+vvj7+9O3b1+90hlgx44ddO/eHT8/P/z9/YmJieGbb74B4MCBA/Tq1YugoCB8fHxo3bo1ixcvvuSy1IRMHCeEEELcgHx8fKp0P79cvL29a5Tu3A+fmqbhcDgA6NmzJwcOHGDx4sWsWLGCHj16kJqayowZMy57eYUQl04pBar02mSueaNp2m8+zeHDh7nnnntITExk3rx57N69m5SUFLy8vJg0aVK1xyQmJnLkyBGysrIwm82MGDFCr2SsqZKSEqxWK3Xq1NG3ORwOHnvsMUaNGkXr1q0veg6TycTAgQPJyMjghRde0O/HggULsNvt9O/fn6KiImJiYhgzZgz+/v4sWrSIxx57jKZNm9KxY8eL5uFwOOjTpw8NGzZk48aNnD59utrx+X5+fmRkZBAaGsrOnTtJSUnBz8+P0aNH069fP7777juWLl3KypUrAQgICKhyDtdwqk6dOrF582by8/NJTk5m+PDhbhURWVlZhISEkJWVxd69e+nXrx/R0dGkpKRc9Hqquz5XgL569WpsNhupqan069eP7OxsAAYMGEC7du2YM2cORqOR7du36//DUlNTqaioYM2aNfj4+LBr1y58fX1rXY7akCBdCCGE+AP6+uuvGThwoNvP7dq1A6Bly5ZkZGRQXFyst6avW7cOg8FAixYt8PPzIzw8nFWrVtG9e/dLLkP9+vUZNGgQgwYNonPnzowaNUqCdCGuN6oUlR99TbLWGmwHreZdsl3DfFx69uzJggULmD17NmFhYcyaNQtN04iKiuLIkSOMGTOGCRMmYDC4dy7+4YcfWLJkCZs2baJDhw4AvPfee7Rs2bJW5R8zZgyhoaHExsbq26ZNm4bJZGLEiBE1Ps8TTzzB9OnTWb16Nd26dQOcXd0TEhIICAggICCA5557Tk+flpbGsmXL+Pjjj2sUpK9cuZLdu3ezbNkyvfv9yy+/XGUc+bhx4/Tvw8PDee6558jMzGT06NF4e3vj6+uLyWQiODj4vHmdPZzK9f9l1qxZ9OrVi2nTptGwYUMAgoKCmDVrFkajkaioKO69915WrVp1SUH6qlWr2LlzJ7m5uYSFhQEwb948WrduzebNm+nQoQN5eXmMGjWKqKgoAJo3b64fn5eXR0JCAm3atAGgSZMmtS5DbUmQLoQQQtyAysvLOXbsmNs2k8mkT862YMEC2rdvz5133sn8+fPZtGkT7733HuBsUZg4cSKDBg1i0qRJ/Pzzz6SlpfHYY4/pH6AmTZrEkCFDaNCgAT179uTMmTOsW7eOtLS0GpVvwoQJxMTE0Lp1a8rLy/niiy9q/QFYCCHO1r17d+bMmaP/7AoCc3Jy6NSpk1ur/B133EFRURGHDh3ipptucjtPTk4OJpOJmJgYfVtUVFStJiabOnUqmZmZZGdn4+XlBcCWLVt488032bp1a616CERFRXH77bczd+5cunXrxt69e1m7di0vvvgiAHa7nZdffpmPP/6Yw4cPU1FRQXl5eY3HnOfk5BAWFuY2Pv7cIVEAH330EW+99Rb79u2jqKgIm82Gv79/ja/DldeFhlO5/se0bt1aH6oAEBISws6dO2uV19l5hoWF6QE6QKtWrQgMDCQnJ4cOHTrwzDPPkJyczPvvv09sbCwPP/wwTZs2BWDEiBEMHTqU5cuXExsbS0JCwiXNA1AbEqQLIYQQN6ClS5cSEhLitq1Fixbs3r0bcM68npmZybBhwwgJCeHDDz+kVatWAFgsFpYtW8ZTTz1Fhw4dsFgsJCQk8Prrr+vnGjRoEGVlZbzxxhs899xz1KtXj4ceeqjG5fPw8GDs2LHs378fb29vOnfuTGZm5mW4ciHEZaV5O1u0r1HetXElh/nUxowZM5g6dSorV650C+bWrl1Lfn6+W6WA3W7n2WefZebMmezfv/+850xKSiItLY133nmH9PR0mjZtSteuXQGYPn06b775JjNnzqRNmzb4+PgwcuRIKioqLts1bdiwgQEDBjB58mTi4uIICAggMzOT11577bLlcbYLDZe6EiZNmsSjjz7KokWLWLJkCRMnTiQzM5MHH3yQ5ORk4uLiWLRoEcuXL+eVV17htddeq3Gl9KWQIF0IIYS4wWRkZFSZZOhcoaGhLF++/Lz727Rpw5dffnnBczz55JM8+eST1e5TSlXZVlBQoH8/btw4t66TQojrk6Zptepyfj1q2bIln3zyCUopvQV73bp1+Pn50bhx4yrpo6KisNlsbNmyRe/uvmfPHre/Yefz6quv8tJLL7Fs2TLat2/vtu+xxx5z6/oOEBcXx2OPPcbjjz9+wfP27duXp556ig8++IB58+YxdOhQt2vp3bs3f/nLXwDnGOwffvhBr3i9mJYtW3Lw4EGOHj2qV+5+/fXXbmnWr1/PzTffzAsvvKBvO3DggFsaDw8P7Hb7RfO60HCqK8F1fQcPHtRb03ft2kVBQYHbPYqMjCQyMpKnn36a/v37k56ezoMPPghAWFgYQ4YMYciQIYwdO5Z33333igbpMru7EEIIIYQQ4oY1bNgwDh48SFpaGrt37+bzzz9n4sSJPPPMM1XGo4Oz11F8fDxPPvkkGzduZMuWLSQnJ1900sxp06Yxfvx45s6dS3h4OMeOHePYsWP6zOV169blT3/6k9vLbDYTHBx80QDV19eXfv36MXbsWI4ePUpiYqK+r3nz5qxYsYL169eTk5PDk08+6TZz+cXExsYSGRnJoEGD2LFjB2vXrnULxl155OXlkZmZyb59+3jrrbdYuHChW5rw8HByc3PZvn07v/zyC+Xl5VXyGjBgAF5eXgwaNIjvvvuOrKysKsOpLpXdbq+yqklOTg6xsbG0adOGAQMGsHXrVjZt2sTAgQPp2rUr7du3p7S0lOHDh5Odnc2BAwdYt24dmzdv1odgjRw5kmXLlpGbm8vWrVvJysq64sOzJEgXQgghhBBC3LAaNWrE4sWL2bRpE23btmXIkCEkJSVdsDdPeno6oaGhdO3alT59+jB48GAaNGhwwXzmzJlDRUUFDz30ECEhIfrrck2ImZSUxKlTp4iLi3MbPz5u3DhuvfVW4uLi6NatG8HBwTzwwAM1Pq/BYGDhwoWUlpbSsWNHkpOTeemll9zS3H///Tz99NMMHz6c6Oho1q9fz/jx493SJCQkEB8fT/fu3alfv361y8C5hlOdPHmSDh068NBDD9GjRw9mzZpVu5tRjaKiItq1a+f26tWrF5qm8fnnnxMUFESXLl2IjY2lSZMmfPTRRwAYjUZOnDjBwIEDiYyMpG/fvvTs2ZPJkycDzuA/NTWVli1bEh8fT2RkJLNnz/7N5b0QTVXXH00IIYQQQgjxh1NWVkZubi4RERH6hGdCiJq5XL8/0pIuhBBCCCGEEEJcJyRIF0IIIYQQQgghrhMSpAshhBBCCCGEENcJCdKFEEIIIYQQQojrhATpQgghhBBCCCHEdUKCdCGEEEIIIYQQ4johQboQQgghhBBCCHGdkCBdCCGEEEIIIYS4TkiQLoQQQgghhBBCXCckSBdCCCGEEEL8oXXr1o2RI0deME14eDgzZ868KuW53M69vppci6ZpfPbZZ78578t1nj8SCdKFEEIIIYQQv2uJiYlomlbltXfv3qtWhnfffZfOnTsTFBREUFAQsbGxbNq06aLljI+PP+85e/Xqdd79a9euRdM0vv3221qXdfPmzQwePLjWx13IpEmTiI6OrrL96NGj9OzZ87Lmda6MjAwCAwOvaB5XkwTpQgghhBBCiN+9+Ph4jh496vaKiIi4avlnZ2fTv39/srKy2LBhA2FhYdx9990cPnz4guX88MMPz3vOpKQkVqxYwaFDh6rsS09Pp3379txyyy21Lmv9+vWxWCy1Pu5SBAcH4+npeVXyulFIkC6EEEIIIYT43fP09CQ4ONjtZTQaAVi9ejUdO3bE09OTkJAQnn/+eWw223nPlZ+fT69evfD29iYiIoL58+dfNP/58+czbNgwoqOjiYqK4l//+hcOh4NVq1ZdsJxBQUHnPed9991H/fr1ycjIcNteVFTEggULSEpK4sSJE/Tv359GjRphsVho06bNBQN/qNrd/ccff6RLly54eXnRqlUrVqxYUeWYMWPGEBkZicVioUmTJowfPx6r1Qo4W7InT57Mjh079B4CrjKf2919586d3HXXXXh7e1O3bl0GDx5MUVGRvj8xMZEHHniAGTNmEBISQt26dUlNTdXzuhR5eXn07t0bX19f/P396du3L8ePH9f379ixg+7du+Pn54e/vz8xMTF88803ABw4cIBevXoRFBSEj48PrVu3ZvHixZdclpowXdGzCyGEEEIIIX63lFKgSq9N5po3mqb95tMcPnyYe+65h8TERObNm8fu3btJSUnBy8uLSZMmVXtMYmIiR44cISsrC7PZzIgRI8jPz69VviUlJVitVurUqeO2PTs7mwYNGhAUFMRdd93FlClTqFu3brXnMJlMDBw4kIyMDF544QX9fixYsAC73U7//v0pKioiJiaGMWPG4O/vz6JFi3jsscdo2rQpHTt2vGg5HQ4Hffr0oWHDhmzcuJHTp09XOz7fz8+PjIwMQkND2blzJykpKfj5+TF69Gj69evHd999x9KlS1m5ciUAAQEBVc5RXFxMXFwcnTp1YvPmzeTn55OcnMzw4cPdKiKysrIICQkhKyuLvXv30q9fP6Kjo0lJSbno9VR3fa4AffXq1dhsNlJTU+nXrx/Z2dkADBgwgHbt2jFnzhyMRiPbt2/HbDYDkJqaSkVFBWvWrMHHx4ddu3bh6+tb63LUhgTpQgghhBBCiOqpUlR+9DXJWmuwHbSad8n+4osv3IKnnj17smDBAmbPnk1YWBizZs1C0zSioqI4cuQIY8aMYcKECRgM7p2Lf/jhB5YsWcKmTZvo0KEDAO+99x4tW7asVfnHjBlDaGgosbGx+rb4+Hj69OlDREQE+/bt469//Ss9e/Zkw4YNeqv/uZ544gmmT5/O6tWr6datG+Ds6p6QkEBAQAABAQE899xzevq0tDSWLVvGxx9/XKMgfeXKlezevZtly5YRGhoKwMsvv1xlHPm4ceP078PDw3nuuefIzMxk9OjReHt74+vri8lkIjg4+Lx5ffDBB5SVlTFv3jx8fHwAmDVrFr169WLatGk0bNgQgKCgIGbNmoXRaCQqKop7772XVatWXVKQvmrVKnbu3Elubi5hYWEAzJs3j9atW7N582Y6dOhAXl4eo0aNIioqCoDmzZvrx+fl5ZGQkECbNm0AaNKkSa3LUFsSpAshhBBCCCF+97p3786cOXP0n11BYE5ODp06dXJrlb/jjjsoKiri0KFD3HTTTW7nycnJwWQyERMTo2+Lioqq1cRkU6dOJTMzk+zsbLy8vPTtjzzyiP59mzZtuOWWW2jatCnZ2dn06NGj2nNFRUVx++23M3fuXLp168bevXtZu3YtL774IgB2u52XX36Zjz/+mMOHD1NRUUF5eXmNx5zn5OQQFhamB+gAnTp1qpLuo48+4q233mLfvn0UFRVhs9nw9/evUR5n59W2bVv9vQHne+FwONizZ48epLdu3dqt0iIkJISdO3fWKq+z8wwLC9MDdIBWrVoRGBhITk4OHTp04JlnniE5OZn333+f2NhYHn74YZo2bQrAiBEjGDp0KMuXLyc2NpaEhIRLmgegNiRIF0IIIYQQQlRP83a2aF+jvGvDx8eHZs2aXaHC1NyMGTOYOnUqK1euvGgw16RJE+rVq8fevXvPG6SDcwK5tLQ03nnnHdLT02natCldu3YFYPr06bz55pvMnDmTNm3a4OPjw8iRI6moqLhs17RhwwYGDBjA5MmTiYuLIyAggMzMTF577bXLlsfZXF3NXTRNw+FwXJG8wDkz/aOPPsqiRYtYsmQJEydOJDMzkwcffJDk5GTi4uJYtGgRy5cv55VXXuG1114jLS3tipVHJo4TQgghhBBCVEvTNDSD5dq8LsN4dICWLVuyYcMG5/j6SuvWrcPPz4/GjRtXSR8VFYXNZmPLli36tj179lBQUHDRvF599VX+9re/sXTpUtq3b3/R9IcOHeLEiROEhIRcMF3fvn0xGAx88MEHzJs3jyeeeEK/P+vWraN379785S9/oW3btjRp0oQffvjhonm7tGzZkoMHD3L06FF929dff+2WZv369dx888288MILtG/fnubNm3PgwAG3NB4eHtjt9ovmtWPHDoqLi/Vt69atw2Aw0KJFixqXuTZc13fw4EF9265duygoKKBVq1b6tsjISJ5++mmWL19Onz59SE9P1/eFhYUxZMgQPv30U5599lnefffdK1JWFwnShRBCCCGEEDesYcOGcfDgQdLS0ti9ezeff/45EydO5JlnnqkyHh2gRYsWxMfH8+STT7Jx40a2bNlCcnIy3t4XbtmfNm0a48ePZ+7cuYSHh3Ps2DGOHTumz1xeVFTEqFGj+Prrr9m/fz+rVq2id+/eNGvWjLi4uAue29fXl379+jF27FiOHj1KYmKivq958+asWLGC9evXk5OTw5NPPuk2c/nFxMbGEhkZyaBBg9ixYwdr167lhRdecEvTvHlz8vLyyMzMZN++fbz11lssXLjQLU14eDi5ubls376dX375hfLy8ip5DRgwAC8vLwYNGsR3331HVlYWaWlpPPbYY3pX90tlt9vZvn272ysnJ4fY2FjatGnDgAED2Lp1K5s2bWLgwIF07dqV9u3bU1payvDhw8nOzubAgQOsW7eOzZs363MQjBw5kmXLlpGbm8vWrVvJysqq9fwEtSVBuhBCCCGEEOKG1ahRIxYvXsymTZto27YtQ4YMISkpyW0itHOlp6cTGhpK165d6dOnD4MHD6ZBgwYXzGfOnDlUVFTw0EMPERISor9mzJgBgNFo5Ntvv+X+++8nMjKSpKQkYmJiWLt2bY3WEU9KSuLUqVPExcW5jR8fN24ct956K3FxcXTr1o3g4GAeeOCBmt0cwGAwsHDhQkpLS+nYsSPJycm89NJLbmnuv/9+nn76aYYPH050dDTr169n/PjxbmkSEhKIj4+ne/fu1K9fv9pl4CwWC8uWLePkyZN06NCBhx56iB49ejBr1qwal/d8ioqKaNeundurV69eaJrG559/TlBQEF26dCE2NpYmTZrw0UcfAc735cSJEwwcOJDIyEj69u1Lz549mTx5MuAM/lNTU2nZsiXx8fFERkYye/bs31zeC9HU2f0+hBBCCCGEEH9YZWVl5ObmEhER4TbhmRDi4i7X74+0pAshhBBCCCGEENcJCdKFEEIIIYQQQojrhATpQgghhBBCCCHEdUKCdCGEEEIIIYQQ4johQboQQgghhBDCjcwtLUTtXa7fGwnShRBCCCGEEIBzOSqAioqKa1wSIX5/XL83rt+jS2W6HIURQgghhBBC/P6ZTCYsFgs///wzZrMZg0Ha9ISoCYfDwc8//4zFYsFk+m1htqyTLoQQQgghhNBVVFSQm5uLw+G41kUR4nfFYDAQERGBh4fHbzqPBOlCCCGEEEIINw6HQ7q8C1FLHh4el6X3iQTpQgghhBBCCCHEdUIGmQghhBBCCCGEENcJCdKFEEIIIYQQQojrhATpQgghhBBCCCHEdUKCdCGEEEIIIYQQ4johQboQQgghhBBCCHGdkCBdCCGEEEIIIYS4TkiQLoQQQgghhBBCXCf+P8kZKqdkiSoTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm \n",
    "\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "colors = cm.viridis(np.linspace(0,1,5))\n",
    "\n",
    "for i, train_loss in enumerate(train_loss_list):\n",
    "    plt.plot(\n",
    "        range(1, len(train_loss) + 1),\n",
    "        train_loss,\n",
    "        label=f\"Fold {i+1} Training Loss\",\n",
    "        color=colors[i // 5],\n",
    "    )\n",
    "plt.title(\"Training Loss for Each Fold\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.savefig(\"result/transformer_split_train_loss.svg\")\n",
    "plt.show()\n",
    "\n",
    "# Plot the validation loss for each fold\n",
    "plt.figure(figsize=(12, 6))\n",
    "for i, val_loss in enumerate(val_loss_list):\n",
    "    plt.plot(\n",
    "        range(1, len(val_loss) + 1),\n",
    "        val_loss,\n",
    "        label=f\"Fold {i+1} Validation Loss\",\n",
    "        color=colors[i // 5],\n",
    "    )\n",
    "plt.title(\"Validation Loss for Each Fold\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.savefig(\"result/transformer_split_val_loss.svg\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}