From c70635eafbb6a2fdae64088ccafea62ee1f8141a Mon Sep 17 00:00:00 2001
From: Mark Graham <markgraham539@gmail.com>
Date: Fri, 17 Mar 2023 11:39:41 -0600
Subject: [PATCH 1/4] Update likelihood calculation

---
 generative/inferers/inferer.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/generative/inferers/inferer.py b/generative/inferers/inferer.py
index eafefb5c..b9408637 100644
--- a/generative/inferers/inferer.py
+++ b/generative/inferers/inferer.py
@@ -584,6 +584,7 @@ def get_likelihood(
         target = latent[:, 1:]
         probs = torch.gather(probs, 2, target[:, : transformer_model.max_seq_len].unsqueeze(2)).squeeze(2)
 
+
         # if we have not covered the full sequence we continue with inefficient looping
         if logits.shape[1] < latent.shape[1]:
             if verbose and has_tqdm:
@@ -607,6 +608,7 @@ def get_likelihood(
         # convert to log-likelihood
         probs = torch.log(probs)
 
+
         # reshape
         probs = probs[:, ordering.get_revert_sequence_ordering()]
         probs_reshaped = probs.reshape((inputs.shape[0],) + latent_spatial_dim)

From d74252f7aa30eb35c1a82d93951fc879adab5ba0 Mon Sep 17 00:00:00 2001
From: Mark Graham <markgraham539@gmail.com>
Date: Fri, 17 Mar 2023 13:56:54 -0600
Subject: [PATCH 2/4] Fixes notebook

---
 .../anomaly_detection_with_transformers.ipynb | 58 +++++++------------
 .../anomaly_detection_with_transformers.py    |  2 +-
 2 files changed, 23 insertions(+), 37 deletions(-)

diff --git a/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.ipynb b/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.ipynb
index 52abd169..1fefde21 100644
--- a/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.ipynb
+++ b/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.ipynb
@@ -1,24 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fdc5edce",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Copyright (c) MONAI Consortium\n",
-    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-    "# you may not use this file except in compliance with the License.\n",
-    "# You may obtain a copy of the License at\n",
-    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
-    "# Unless required by applicable law or agreed to in writing, software\n",
-    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-    "# See the License for the specific language governing permissions and\n",
-    "# limitations under the License."
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "f6090d00",
@@ -32,7 +13,7 @@
     "\n",
     "Finally, we will compute the log-likelihood of images from the same class (in-distribution class) and images from other classes (out-of-distribution).\n",
     "\n",
-    "[1] - Pinaya et al. \"Unsupervised brain imaging 3D anomaly detection and segmentation with transformers\" https://doi.org/10.1016/j.media.2022.102475"
+    "[1] - [Pinaya et al. \"Unsupervised brain imaging 3D anomaly detection and segmentation with transformers\"](https://doi.org/10.1016/j.media.2022.102475)"
    ]
   },
   {
@@ -50,8 +31,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n",
-    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
     "!python -c \"import seaborn\" || pip install -q seaborn\n",
     "%matplotlib inline"
    ]
@@ -114,6 +93,16 @@
     }
    ],
    "source": [
+    "# Copyright 2020 MONAI Consortium\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License.\n",
     "import os\n",
     "import tempfile\n",
     "import time\n",
@@ -959,15 +948,16 @@
     "    progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=110)\n",
     "    progress_bar.set_description(f\"Epoch {epoch}\")\n",
     "    for step, batch in progress_bar:\n",
+    "\n",
     "        images = batch[\"image\"].to(device)\n",
     "\n",
     "        optimizer.zero_grad(set_to_none=True)\n",
     "\n",
     "        logits, quantizations_target, _ = inferer(images, vqvae_model, transformer_model, ordering, return_latent=True)\n",
     "        logits = logits.transpose(1, 2)\n",
-    "\n",
+    "        \n",
     "        # train the transformer to predict token n+1 using tokens 0-n\n",
-    "        loss = ce_loss(logits[:, :, :-1], quantizations_target[:, 1:])\n",
+    "        loss = ce_loss(logits[:,:,:-1], quantizations_target[:,1:])\n",
     "\n",
     "        loss.backward()\n",
     "        optimizer.step()\n",
@@ -982,6 +972,7 @@
     "        val_loss = 0\n",
     "        with torch.no_grad():\n",
     "            for val_step, batch in enumerate(val_loader, start=1):\n",
+    "\n",
     "                images = batch[\"image\"].to(device)\n",
     "\n",
     "                logits, quantizations_target, _ = inferer(\n",
@@ -989,19 +980,14 @@
     "                )\n",
     "                logits = logits.transpose(1, 2)\n",
     "\n",
-    "                loss = ce_loss(logits[:, :, :-1], quantizations_target[:, 1:])\n",
+    "                loss = ce_loss(logits[:,:,:-1], quantizations_target[:,1:])\n",
     "\n",
     "                val_loss += loss.item()\n",
     "        # get sample\n",
-    "        sample = inferer.sample(\n",
-    "            vqvae_model=vqvae_model,\n",
-    "            transformer_model=transformer_model,\n",
-    "            ordering=ordering,\n",
-    "            latent_spatial_dim=(spatial_shape[0], spatial_shape[1]),\n",
-    "            starting_tokens=vqvae_model.num_embeddings * torch.ones((1, 1), device=device),\n",
-    "        )\n",
-    "        plt.imshow(sample[0, 0, ...].cpu().detach())\n",
-    "        plt.title(f\"Sample epoch {epoch}\")\n",
+    "        sample = inferer.sample( vqvae_model=vqvae_model, transformer_model=transformer_model, ordering=ordering, latent_spatial_dim=(spatial_shape[0], spatial_shape[1]), starting_tokens=vqvae_model.num_embeddings * torch.ones((1, 1), device=device)\n",
+    "    )\n",
+    "        plt.imshow(sample[0,0,...].cpu().detach())\n",
+    "        plt.title(f'Sample epoch {epoch}')\n",
     "        plt.show()\n",
     "        val_loss /= val_step\n",
     "        val_epoch_losses.append(val_loss)\n",
@@ -1154,7 +1140,7 @@
    ],
    "source": [
     "sns.kdeplot(in_likelihoods, color=\"dodgerblue\", bw_adjust=1, label=\"In-distribution\")\n",
-    "sns.kdeplot(ood_likelihoods, color=\"deeppink\", bw_adjust=10, label=\"OOD\")\n",
+    "sns.kdeplot(ood_likelihoods, color=\"deeppink\", bw_adjust=40, label=\"OOD\")\n",
     "plt.legend()\n",
     "plt.xlabel(\"Log-likelihood\")"
    ]
@@ -1187,7 +1173,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.py b/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.py
index b28fda5a..33c7bc0e 100644
--- a/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.py
+++ b/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.py
@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.14.4
+#       jupytext_version: 1.14.1
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python

From fa3882b72ea770122b889d37ee7b79d2325b3e80 Mon Sep 17 00:00:00 2001
From: Mark Graham <markgraham539@gmail.com>
Date: Tue, 21 Mar 2023 11:44:49 -0600
Subject: [PATCH 3/4] Adds localised anomaly detection

---
 generative/inferers/inferer.py                |   2 -
 .../anomaly_detection_with_transformers.ipynb | 409 +++++++++++++-----
 .../anomaly_detection_with_transformers.py    | 222 +++++++---
 3 files changed, 469 insertions(+), 164 deletions(-)

diff --git a/generative/inferers/inferer.py b/generative/inferers/inferer.py
index b9408637..eafefb5c 100644
--- a/generative/inferers/inferer.py
+++ b/generative/inferers/inferer.py
@@ -584,7 +584,6 @@ def get_likelihood(
         target = latent[:, 1:]
         probs = torch.gather(probs, 2, target[:, : transformer_model.max_seq_len].unsqueeze(2)).squeeze(2)
 
-
         # if we have not covered the full sequence we continue with inefficient looping
         if logits.shape[1] < latent.shape[1]:
             if verbose and has_tqdm:
@@ -608,7 +607,6 @@ def get_likelihood(
         # convert to log-likelihood
         probs = torch.log(probs)
 
-
         # reshape
         probs = probs[:, ordering.get_revert_sequence_ordering()]
         probs_reshaped = probs.reshape((inputs.shape[0],) + latent_spatial_dim)
diff --git a/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.ipynb b/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.ipynb
index 1fefde21..280e81e8 100644
--- a/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.ipynb
+++ b/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.ipynb
@@ -7,11 +7,11 @@
    "source": [
     "# Anomaly Detection with Transformers\n",
     "\n",
-    "This tutorial illustrates how to use MONAI to perform image-wise anomaly detection with transformers based on the method proposed in Pinaya et al.[1].\n",
+    "This tutorial illustrates how to use MONAI to perform image-wise and localised anomaly detection with transformers based on the method proposed in Pinaya et al.[1].\n",
     "\n",
     "Here, we will work with the [MedNIST dataset](https://docs.monai.io/en/stable/apps.html#monai.apps.MedNISTDataset) available on MONAI, and similar to \"Experiment 2 – image-wise anomaly detection on 2D synthetic data\" from [1], we will train a general-purpose VQ-VAE (using all MEDNIST classes), and then a generative models (i.e., Transformer) on `HeadCT` images.\n",
     "\n",
-    "Finally, we will compute the log-likelihood of images from the same class (in-distribution class) and images from other classes (out-of-distribution).\n",
+    "We will compute the log-likelihood of images from the same class (in-distribution class) and images from other classes (out-of-distribution). We will also provide an example of performing localised anomaly detection with these trained models.\n",
     "\n",
     "[1] - [Pinaya et al. \"Unsupervised brain imaging 3D anomaly detection and segmentation with transformers\"](https://doi.org/10.1016/j.media.2022.102475)"
    ]
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 48,
    "id": "01787b4b",
    "metadata": {},
    "outputs": [],
@@ -45,18 +45,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 144,
    "id": "b6b0c79f",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/mark/Envs/monai-generative/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -111,6 +103,7 @@
     "import numpy as np\n",
     "import seaborn as sns\n",
     "import torch\n",
+    "import torch.nn.functional as F\n",
     "from monai import transforms\n",
     "from monai.apps import MedNISTDataset\n",
     "from monai.config import print_config\n",
@@ -118,7 +111,6 @@
     "from monai.utils import first, set_determinism\n",
     "from torch.nn import CrossEntropyLoss, L1Loss\n",
     "from tqdm import tqdm\n",
-    "\n",
     "from generative.inferers import VQVAETransformerInferer\n",
     "from generative.networks.nets import VQVAE, DecoderOnlyTransformer\n",
     "from generative.utils.enums import OrderingType\n",
@@ -179,44 +171,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 33,
    "id": "7db7ac32",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "MedNIST.tar.gz: 59.0MB [00:02, 24.7MB/s]                                                                                                                                                                                               "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2023-03-17 17:29:49,957 - INFO - Downloaded: /tmp/tmp1lues0wg/MedNIST.tar.gz\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-03-17 17:29:50,063 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-03-17 17:29:50,064 - INFO - Writing into directory: /tmp/tmp1lues0wg.\n"
+      "2023-03-17 20:10:01,229 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
+      "2023-03-17 20:10:01,230 - INFO - File exists: /tmp/tmp1lues0wg/MedNIST.tar.gz, skipped downloading.\n",
+      "2023-03-17 20:10:01,230 - INFO - Non-empty folder exists in /tmp/tmp1lues0wg/MedNIST, skipped extracting.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:27<00:00, 1734.23it/s]\n"
+      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:27<00:00, 1744.81it/s]\n"
      ]
     }
    ],
@@ -285,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 34,
    "id": "ec954b77",
    "metadata": {},
    "outputs": [
@@ -293,16 +265,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-03-17 17:30:22,567 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-03-17 17:30:22,568 - INFO - File exists: /tmp/tmp1lues0wg/MedNIST.tar.gz, skipped downloading.\n",
-      "2023-03-17 17:30:22,569 - INFO - Non-empty folder exists in /tmp/tmp1lues0wg/MedNIST, skipped extracting.\n"
+      "2023-03-17 20:10:28,891 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
+      "2023-03-17 20:10:28,892 - INFO - File exists: /tmp/tmp1lues0wg/MedNIST.tar.gz, skipped downloading.\n",
+      "2023-03-17 20:10:28,892 - INFO - Non-empty folder exists in /tmp/tmp1lues0wg/MedNIST, skipped extracting.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:03<00:00, 1751.59it/s]\n"
+      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:03<00:00, 1760.37it/s]\n"
      ]
     }
    ],
@@ -648,15 +620,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 35,
    "id": "0789cfcc",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Xr165uo6oocxAHNSrV886dOhgd911l/3yyy82YcIEq1ixopemHjJkiP3pT3+yHj162O2332579uyxF154wRo3bmyHDh1y3ytVqpQ1atTI3nzzTatfv75VqFDBmjRpYk2aNLHU1FQzMxs5cqT16NHDihUrZrfccoulpaXZ0KFDbfz48bZ8+XLr3r27lShRwjZs2GDTpk2zZ555xm644Qbnmx0/frz17t3bevbsacuWLbMPPvjAKlWqdMbvm5lZ9+7dLSkpyfr06WNDhw61I0eO2F//+lerUqWK12lWrVrVfv/739vTTz9t1157rV199dW2YsUKd+74fwt/+MMfbObMmda7d28bNGiQpaam2tGjR+3bb7+16dOn25YtWwrsekW0+MMf/mA33nijTZ482Z577jnr0KGDNW3a1O644w6rW7eu7d692xYtWmTbt2+3FStWuG2mT59uN954ow0ZMsRSU1PtwIEDNnPmTHvhhResefPm9rvf/c5efPFFGzRokC1dutRq165t06dPt4ULF9qECROsbNmyuTrP9evXW9euXe2mm26yRo0aWfHixe2dd96x3bt32y233OK+l5qaapMmTbJx48ZZvXr1rEqVKln+r5u55ZZb7L/+67+sX79+NnLkSDt27JhNmjTJ6tevb9988437Xr169eyhhx6yxx57zDp27GjXX3+9JScn29dff20XXXSRjR8/PlfnUKJECXv88cdt8ODBlpaWZrfeequzFtauXdtGjRqVq3sUOQrMx1BA5GQtLF26dJbvshUm07bz5JNPBk8//XRQs2bNIDk5OejYsWOwYsWKLNtPmTIlqFu3bpCUlBS0aNEimD17dra2my+++CJITU0NkpKSPJvhyZMngxEjRgSVK1cOihQpksVm+NJLLwWpqalBqVKlgrJlywZNmzYN7r///mDHjh3uO6dOnQrGjh0bXHjhhUGpUqWCTp06BatWrQpq1aqVJ2vhtGnTTntf8R7u3bvX/W3mzJlBs2bNgpIlSwa1a9cOHn/88eDll18OzCz4/vvv3fdOnjwZPPzww0G1atWCUqVKBV26dAnWrFkTVKxYMbjzzju94xw+fDh48MEHg3r16gVJSUlBpUqVgvbt2wdPPfWUZ4EUIq/k9KwHwf++bykpKUFKSkpw8uTJYNOmTcGAAQOCatWqBSVKlAiqV68e9O7dO5g+fbq33f79+4O77747qF69epCUlBTUqFEjGDhwYLBv3z73nd27dweDBw8OKlWqFCQlJQVNmzYNXnnlFW8/2Ecx2Lfs27cvGD58eNCgQYOgdOnSQbly5YK2bdsGU6dO9bbZtWtX0KtXr6Bs2bKBmTmLX9g9CIIg+Oijj4ImTZoESUlJwaWXXhpMmTIlS3+aycsvvxy0bNkySE5ODsqXLx+kpaUFc+bMOe05sLUwkzfffNPtr0KFCsFvfvMbzwoeBLH3+VGiSBBkM8tNZMuWLVusTp069uSTT9p9991X0KcTSQ4ePGjly5e3cePGuSIkQggh8obmDIhCy88//5zlb5nzHbIr9SyEECI+NGdAFFrefPNNmzx5svXs2dPKlCljn3/+ub3xxhvWvXt3u+KKKwr69IQQ4pxBgwFRaGnWrJkVL17cnnjiCTt06JCbVDhu3LiCPjUhhDin0JwBIYQQIuJozoAQQggRcTQYEEIIISKOBgNCCCFExIl5AuFFF12Un+chxFkJ1on/97//7cWwSiIuqWrml1vlsrAYS0pK8mLbt2+P/2QLiAsvvLCgT0GIAgHf5XjWeTCz0FLNxYv7/4RzH4RgCezsUGZACCGEiDgaDAghhBARR3UGhMgDWCUxcwXKTHC99NxIAWHSgxCiYAlz43MM32Vsm2VN/+e0H97u1KlTrs0rsqIUkVtZQpkBIYQQIuJoMCCEEEJEHMkEQuSBMmXKuPbRo0e9GKb4k5OTvRhKCCdOnPBimN7j7c4F8PpUAFUURjjFHpa2D3MM4GdM7/Nn7A8YlhNwn2HnEiZDZIcyA0IIIUTE0WBACCGEiDgaDAghhBARR3MGhMgDDRs2dO1y5cp5sUOHDrn2d99958UOHjzo2hdccIEXw6pi+L1zBc0TEIWd3NgHY40xVapUcW2u8Ltz507X3r17txfDuUhc2RTnIWjOgBBCCCFyhQYDQgghRMSRTCBEHqhTp45rp6SkeLFGjRq5NlcnXLt2rWvPmzfPi33zzTeuzRXGRGKRzVFkR5i1MDfbhaXqcQGv7t27e7EWLVq4Nlchxf5h7ty5Xmzjxo2unVuJUZkBIYQQIuJoMCCEEEJEHA0GhBBCiIhTJIhRDGHrgxDCX7UQLYFmfonRypUre7H27dtn2zbz7UI7duzwYqNGjYr/ZAsI1EaFOJcJ++eUSwfjXACea4B9R40aNbxYWlqaa7dr186LYUn0Tz75xIu9/PLLOZ6bmTIDQgghROTRYEAIIYSIOJIJhMgDaBlEycDMX42QLUb4mW2HYXbFV199Nf6TLSASJROE2QALa4zj50KM4+d6jOO5sR3id3NjO8R9sr0Y98OrHbZp08a1W7du7cUefvjhHI9npsyAEEIIEXk0GBBCCCEijgYDQgghRMTRnAEh8gCuEsb6HVsNEbQVcbnRsH3s2rUrt6dY4BQma2G8ZWaFyISfISTW+QO52Y5Bi2LYfAKcs2RmduDAgfD9xnwGQgghhDgn0WBACCGEiDhatVCIPMDSABK24iCm85KSkhJ6TmczhdVeJmth4bmnBf0bMrHet9xICLEej6sa4n6Sk5Nz3Ed2KDMghBBCRBwNBoQQQoiIo8GAEEIIEXE0Z0AIUWiIVStVrOBihe18zpZYmLUw7Lu5sR3GOkchO5QZEEIIISKOBgNCCCFExJFMIIQ4o8SbAi2s1rMoWgvjpbD+Tnn5DcOI974lQooIWxUxO5QZEEIIISKOBgNCCCFExNFgQAghhIg4mjMghDijnGkdVbHExvJCYbqOM31v4t0u3vkLXKr4dCgzIIQQQkQcDQaEEEKIiCOZQAhRaCis9rJz0VoYRn5ZC/ODs+U3DKMw3G9lBoQQQoiIo8GAEEIIEXE0GBBCCCEijuYMCCEKDYXJJnaux8LIL8063lLUYRSme1rY7nduUGZACCGEiDgaDAghhBARRzKBEOKsoLDaBwvaWljYCLs3sa64V5hWSeT4ufr7KjMghBBCRBwNBoQQQoiIo8GAEEIIEXE0Z0AIcVZQmGxihcmWFi+J0qkTpYXnx/UXpt/wTMTCVjE8HcoMCCGEEBFHgwEhhBAi4kgm+JV///vf3ucyZcp4n48ePerap06d8mKVK1d27ZMnT3qxgwcPunZSUpIXO++881z7yJEjXgzTP5UqVfJiW7Zsce0qVap4sf3797t2hQoVvFiJEiW8z7ht+fLlc9wPHs/Mv1clS5b0Ynj9pUuX9mJ79uxx7eLF/UcPz5V/i19++SXHa8DP+D1x7lFY7YNnIr2fH8fPD/ve6WKFyRIZ7z0sTLbDRN5fZQaEEEKIiKPBgBBCCBFxNBgQQgghIo7mDPwKa9+HDx/2PqPGXapUKS+G+jpvV7ZsWdeuWLGiF9u6datrsxZetOj/jdMOHDjgxfC7fN6ovaenp3uxMH1p+/btXuyCCy5w7dTU1BzPe/369Tken+ca1KhRw7V5/sRPP/3k2hkZGV4M51ZwDO8Nz1EQ5xaJ0MYTZXXLD+07THvPTVlfnNOE/Uhujp8bixq/kwjPDcL+6vjx417s2LFjrs39A55Pbq4pP/T1grYPxhrLLcoMCCGEEBFHgwEhhBAi4mgwIIQQQkQczRn4lZ9//tn7nJyc7H1GvYn97KiDodZu5vvud+3a5cVQ4+btsM7BoUOHcjyXbdu2ebFq1apluw8+F94Pz3XAYzZo0MCLDR061LV//PFHL7ZgwQLXvuiii7wYzjXge1iuXLkczwV1MZ4XUJh8yyJ/SYQvPN7yrQVdS4Brm8RbAwH1dr72eOcM8DFQ769Vq5YXa9iwoWvzNYXNRcI+geco8LwpJDc1EMIorLUEVGdACCGEEAlDgwEhhBAi4kgm+JVixYp5n9HOZubbXtgSg6k3LiuM8gOns1Aa2LdvnxfD/XBqHFNvS5Ys8WInTpxwbb4mPj6WB65Xr54XS0lJyfH4eN5XXnmlF2vdurVrs7Vw/vz5rr106VIvhuWe2bqJkgJaEM1861JeVuwShZ9E2K8KW3lcTHmzjIcWYrYe4zvIfRX2R9gfmPn9ER8P37PTySl4DP4uXlOLFi28GPZdfHyUI9nO/OWXX7o2Swh43izvxvpc5OY5KEz2QVkLhRBCCJEwNBgQQgghIo4GA0IIIUTE0ZyBX2E9nUsAY5yXFGYdG+nSpYtr9+7d24th6d6dO3d6MdQBeUlfnE/AcxT27t3r2mzf49KgYXr77t27XRu1fjOz9957z7VvuOEGL3bddde59vnnn+/Fbr75ZtfmkqJz5sxx7erVq3sxtCrt2LHDi+EyzFrC+OynMC1FnB9LGIdZBLF0uZlZy5YtXbtx48ZerG7duq7N7xmyefNm7/OmTZtce9WqVV4Mrc88LyrMosgxfCfr16/vxbBfRSuhmb+M+qWXXurFatas6drLly/3YnPnznVtnHvEhFkpT/fdgpxbImuhEEIIIc4IGgwIIYQQEUcywa9wipltL5iq5lQfSgi9evXyYrfddptrY5U9s3A7Ha5wyOl9lCmaNGnixd59913X/vrrr70Ypv7NfBvOunXrvBhagh555BEvhqlGbJv5FQk5fYn3iSUTlAa+/fZbL4YWRbY54u8imeDsJz9sVLFuF++KhrwdV8jDlDr3K5gaZ5tunz59XJv7DtwnV09FS3HlypW9WO3atXPcJ8oGnMLna8LrYKkSpQG2+uG7vHHjxhzPrWrVql4MVzzt2LGjF0Ob5YwZM7wYWrZZCsbfkPvY3FR8zG9kLRRCCCHEGUGDASGEECLiaDAghBBCRBzNGfgV1ohYa0OrDa+q16ZNG9e+6qqrvBjqVFhS08y3y6BVyMysUaNGrn3w4EEvhjpRt27dvBiWLe3QoYMXe+CBB7zPuDIhau9mfjlQLim6YsUK12abD1qgeDu0FaE+aGZ2ySWXuDav4IjnydZCtFKGrV4mzg7yw84X63bxHu90pXtRb+f3DMvz4hwBM79/YCsu9lccQ52etXfcJ/cPCxcudO2pU6d6MbYo4jvJVuBmzZq59vfff+/FVq9e7do8fwLfbb6HaK/m/gHnTbG1EC3LYaXM2b7N97SwlrSWtVAIIYQQCUODASGEECLiSCb4ldOl+jDFdNFFF3kxrLpXrVo1L4Yp/nbt2nkxtPbwdshnn33mff74449d++KLL/ZimN5ju0yZMmW8z2jXwdUGzcw6d+7s2pxCw9RmnTp1vNiUKVNce/HixV6sb9++rs02QEx7ssUKU6uvvfaaF0P5pqBXoBN5pzCt+hZravh01sIwuQHlMpYQ8P3lVQvxu9wfYb8SVnWUrX3btm1zbZZNue9AKzTKpGZ+BcKvvvrKi2F/yPcJ32WUIcx8iyBbIvH6ueIh7vPTTz/1Yihbnk5iDHtOwioZ5nefJGuhEEIIIRKGBgNCCCFExNFgQAghhIg4hXbOwIkTJ7zPqBOx1oTlMNkSgloQ6yt4DNbkeD94fFxt0MysYcOGrs12GbTdNG3a1Iuhbs5lO9G+x6skDhgwwLW55DDqgLjyoZnZwIEDvc/16tXL9jzNfN3+73//uxd76623XJtLDh87dsy12UqJOiBvh2WNR44c6cVQZ+TtsMQyzmXIDvxNUR8183U/LAXNx+ey0aytirxRmFYYjHc71umxT2Btes+ePa79zTff5LgftN6a+To5nxvOJ9iwYYMXW7lypWuzLfjtt992bS5xzPOdUlJSXJvnDOB94+vFe8FzkbA/5hi+5/x+oiWRSyNjH8fl2JcuXeraYasy8ufTzS+LlUSUyZa1UAghhBAJQ4MBIYQQIuIUWpmA07Fo7WNbGtpeODWO1hJOPWEKi2WJXbt2eZ9xW07N/Pd//7drc3rtwgsvdO0GDRp4MbTgYHrdzOzmm292bV7FD1P63333nRfD1QDXrFnjxfjccD9ccXHevHnZnqeZL4vwfUJJA++9mdn+/ftdm1dFw+2eeeYZL3b99ddne2wzP33HVdL4d7r66qtdu2XLll5swYIFrp2enu7Fwp4TXCFO5J38sAgiuUnphqVjc2MnQ1mTK+RhRT62+qHMdvnll3sxrDzK/RqmzflZxjQ9vy8Iy3EoC/D5cP+AkmvPnj29GPYJ3K/hvWG5Fe83V2TFfxv4/UQuvfRS7zP2XT/88EOOxzMLlwmQM73qpqyFQgghhEgYGgwIIYQQEUeDASGEECLiFNo5A2zZKlu2rGtzOUqE5xOgDsXlPkuXLu3arP2yPQj3yzbEZcuWuTZrXevXr3dt1smxlPBvf/tbL4arfV122WVeDG1x7777rhfDVRPZ8sPgSolcbvSjjz5ybSwjbObbIMN0vwkTJngxvN+syeGKbTzXAedFsAaJ9+b111/3YjhHwcxfwYztmu3bt3dtnluxZMkS1+bnQiWQzxyx2gfDiNdaGAbPjeF+BvX38uXLezGcj8Kr6qG9mPuOLVu2uHbbtm29GM4FYhs0ziFg+xzOt+Iy47wfnAfB87RwZUS2++KKgzgvycwvF4yrkfLxuI8Pu4e4wizPi8J3nudsYfljM//fB75vYRSk7TC3KDMghBBCRBwNBoQQQoiIU2hlgrAqXpyWw9QIp6wwLcXpX7SWcAVArtbXrFkz1+bUzKxZs1ybK36hfYcrZ91///2ujTIInyunHZ999lnXxjSYmb/CIack0aJn5lcx40phzZs3d22ufobnyrIIyhuYLjTzq63x8f7xj3+49jXXXOPFunfv7tp87zFlOHz4cC/217/+1fuMVQ45nfjhhx+6Nqc2UU5hWeR0q52JxBFrCjRRFi7sg9i+h889r5THUhbKgSzHYVVOtrctWrQox3PDfo1XLuWUPoIribKVES17p6vIh30A9zN4jVwtFi2JKBua+dIAVy/FyoIs02Jfyf82YLqfV4bF6oTcx/JKsfjb8DXhc8Lydn7bAmUtFEIIIUTC0GBACCGEiDgaDAghhBARp9DOGQiz63CpYixBydvhd1nbQ83owQcf9GJcOhhhnRxX+GM9a/bs2a7Nlhi8Ji6xiXMfJk6c6MXQysiaEeqHbMFs1aqV9xnnSfA1paWlWU6g9oYlfs3M1q1b59rPP/+8F8Nr4tXF8Fw+//xzL4Z6La/8iHYhLkU6atQo7zOutvjBBx94MdQ5cd6Dma8DtmjRwovhPASRvyRi9cEwWBdHLZrf6379+rk2z1VhWx6eKz/3GMM5NWZmrVu3dm0uK9ykSRPX5nlSO3fudG2e44KrnK5du9aL4TwE7qt4Dhcek/sZvlc57QfnYZn5c4N47hXOrWDQ9sd6/vbt23M8z6pVq7o2lznnuR3z5893bbSLM7kpUx3rd7VqoRBCCCHOCBoMCCGEEBGn0MoEbNnCClFhFb4OHDjgxTDd1aVLFy/22GOPuTZbWVatWuV9Rnsd2xDRQsdpG7QZsXUIr4PTeZgqf+ONN7xY7dq1XZvT+5ii5JQ2p9jxXFlewTQZpzbxt+HfCVcGGzx4sBd76KGHXHvAgAFeDKsqfvXVV14M7VB8LlhFjGUCtnKi9MG/4dKlS12bbYd4L/g3FGeO/FgtDmOcYkYJatiwYV4MVxXlVftY8kP5IUzixMqBZn7anp9tlK7w/TDzqxOy5IbVPDmdj1U5U1NTvRjLpvie8wqH2Cdxf8irNiLt2rVz7ZUrV3qxH3/80bW5r0KLIv/W+O8GyyJ4/R06dPBivEoj9ju8UitWOuV/m8KkAH7eckKrFgohhBDijKDBgBBCCBFxNBgQQgghIk6hnTPAugxqVFwaFHVcLKNr5tvL0Lpi5luHXnrpJS/GenusVkNecW/58uWuzfoR6lB/+ctfvBjainjVQrS+8cqEaJ/BuQVm4attsZUGdU/ULs18rYu1TNTMeF7Aa6+95tqsy2MJYrYcTZ482bXZ9odzQvhe8MpnaIlEa5aZv2Ia27hQO2Yr4YUXXmiicIPPK7+DaHVj6/E999zj2lwiG98lfpa5JC1q3GzRC7PMYT/H8xBQ+2d9HWO8fywdzKv49erVy7W7devmxRo3bpzjfhi8N6xp45yuMFv0bbfd5sXQasjaP95vvvfYN/PvhPeJ5xd17NjR+4w2dCzjbubfYz4+2h7DrIXxav+yFgohhBAiYWgwIIQQQkScQisT8Epcu3fvdm1Ozf6///f/XPs3v/mNF+MV6HJi0qRJ3meWIjDd8/7773sxTJXPmzfPi2GqmlONCKfFduzYkW2bj8f34tprr3VtTu+z7QVTSpx+Hzt2rGtjhUUz367DqbeWLVu6NqYdzczuuOMO1+bqhGwZRK677jrXnjNnjhcLey64Etwnn3zi2kuWLPFimPrjVB9WMWPLEd4LUTgIs2yxpRTlwNGjR3sxlOc4HYvSJFr5zLI+y5iq5ucFU8xsi8Znm98ztNqxHIbf5XuBciDbB1Fm4xVH2YaI18F9JcqvWPHQzGzq1KmuzRJG165dXfvGG2/0Yljx8W9/+5sXQ+mOpVD8vfk8sVIjrhBplnXlR6xWyHZqlCrx3wkz/7lJZEo/kfvIRJkBIYQQIuJoMCCEEEJEHA0GhBBCiIgT85wB1oxQl+LVoBAuP4maDus7qMXwdqjZsK3lzjvvdG3WehAuY4kWlZkzZ3oxthKi1ZH1JdRtsKSnmX9vcAVDM7+MJWr9ZmYLFy7M9ntm/ophmzdv9mJoQWILDNsH0UL32WefeTEsDYqauZnZ6tWrXZutdu+8845r88qHaDVEy6WZ2ZtvvunaXN4VLVA8lwStnF988YUXY5tp27ZtXXvr1q1eDOcQsCaIuiPrs3hPWb8LW10s1hXLYi1ZGnXwfob1K2xbHTlypGuzho5wuXC0ovKcHn5+sFwv90GoW/NqePjesd4dZkvD6+fj4fN0+PBhL4ZzG3h+EfcdOC8AywGb+dfBpdSx1Dj3lWg95vk/V111lWtj6XAzf74T/zuF18ul2/EzlkM38+dkmPn/rnDZaJwnEGZdDXuXE6n9x4syA0IIIUTE0WBACCGEiDgaDAghhBARJ+Y5A6xZoY7KGh1+l/Uz1HTQn2nm6y08DwG1L/ScmvmaHPtxkQkTJnifZ82a5dqdO3f2Yqyn4TVxmV/Ut/bs2ePF5s6d69q8/O0VV1zh2lx+GUuMsg6GcGlOrEHAS4syeI95zgLO2eBSyaiRchlP1NT/+c9/erHf/e53ro1arZl/n/iZwdoNfC9Qy2Odj5d2xeWO7733Xi/20UcfufbEiRO9GD7rPGcB512wJpgIvT9sboH4P1Bz5ZK/NWrUcG2ex9KoUSPX5voS69atc+0PP/zQi+GcE16WOOx9xfk+/F3+rbFOCHvysc/lugZ8HQj2axs3bvRi2HfxPWS9PWz5c5xTxPVLsL/ge4F9x7vvvuvFLr/8ctfmUskff/yxa+/bt8+LYV/C14R9Ol8DzwPBJewZPEbYUtOFHWUGhBBCiIijwYAQQggRcWKWCThNhKkRTm9h+oVTaJje4u0wrc3lMNHqwuVxMf3DtkO0zC1YsMCLodWNrYyY6mLYWoLXWLZsWS+Gq12xLILlPznVh3aVsPQWSzRh94KPgXYh/i4en1NfuOIfr/734osvujaW/zXzyxG3atXKi1199dWu/fbbb3uxMGsW2pr4d0Gbo5lvWeTfAu1KLANh+pItiSyTILIIZk8iVmtjsD/iMtxNmzZ1be478LnnlQHRBscWXpayEH5f8Xr53DD9zxJUmH0bZUXuA7Cv5n4bZQlO4aNMwOfJ14Sf+b5hP8PvJJZc5j4Xn4UwCblu3bpeDPtYtmGjBZKvAe8b93H8GY/PMhBKAfw84zFZMsgPGVGrFgohhBAibjQYEEIIISKOBgNCCCFExIl7CWPUlFkLCdNQUPNlDQdtPmztQ2sJzydAexdraxhj+x7bUBAuh4naG5fmRN0aLYFmvt2NS6Feeumlrs3lkMPuEy4fynohWmLC5nkwPJ8AdTGe94G6H1ueUAdjbQ+fizD7F9tY8f7yeaKuyjojlyNGKycvYbxs2bJsj2fm63Jsh8L7z896rOVHw+yD56K1MFZdM6y8M4PPdv369b0YltbmvgOfQ35+wkru4hymsKXBmbC5QdyP4lwZfs/w3Pg5QyssL6mL7xa/g/jdsPLHZrHPh+HzxnkCfE/x3vBcHH7vEPy3gvsqvE98L/C8ef9c1h6/y783Pnt8vXif8qPksJYwFkIIIUTC0GBACCGEiDgxywScJsIUNKeJOB2DYPq7WrVqXgxXDeMUPqa0OIUUVkkK4SpSKAVwtT6WKTi9iEybNs21sZKdmX/evM/rrrvOtTkth1IArz6IKS1Ob2G6HdOF2YErDrK80bJlS9dOSUnxYt98841r84qGmEJDy4+Zfy+w+qOZv7oZpw+xEhxLLWglwpUtzcyGDBnifd69e7dr40qIZma9e/d2ba5+hvcxrLqciI0wa2Gs8glLdfissd21Tp06rs3vBD6TLLlhipn7B+z/OL0flo5m6Q4ltzBbHEuc2M9x/4t9CZ8b3l9OqeM7wfsMszez/IgpdpZeMI3O7xLe4w4dOngx7FewIqmZX0mR+1h8hvh5wnvD8jKvnIpxrjKL8DWFHb8wrFSIKDMghBBCRBwNBoQQQoiIo8GAEEIIEXFinjMQVvKSY6ibcAz1NNa7UQfkcpCon7H2gnoSzxnA/Xz55ZderFatWq7dp08fL8bzEnA/fPyuXbu6NmtreEzWs95//33XZg0by4FiOVwz//5iqVUzX1P/9ttvvRh/F+8/z9HAUsKoa5r5ehqXNMV99uzZ04uhDfCpp57yYnh81nXxd7r55pu9GN5D1IbNsv5OqJGydouaLP9OeE28T9RWWWcN0wRjtQwWNl0xHnJzDWHzCbAvYY0XdWqec4Lzb/i3xfeMLWM4V4fn1ODzw88r9134XezH+DPHcD4OHyNstcMwazdq/TyHCu8hz2HifjVMC0fLNM/xWb58uWvj72JmdtVVV7k2r0yIcw2++OILL4aWRP7t8fr53yKkcePG3meep4X3lJ+hsPuNfQLH8qMsd15QZkAIIYSIOBoMCCGEEBEnbpkAUyMcC0uNYBqdq3FhFTjeDleSw1QTx3i1rX79+rn23/72Ny+Gq5Lx8SpVquR9RrsSp8wwbcUrcaWlpbk2p94w9ccVrzBNhufJx+d0FqbtOV2J6Tsz31p5++23e7H169e7NlY1NPNXGORj4HWw9PHggw+69vz5871YgwYNXHvLli1eDC2ovJokphp5hTSUJcx86yrKC2Z+RULeT07HM/N/wzCZgFOpYTJBYUgZJpKwaw9Lq/J22M/wO4i2UU7powTF0iSuWsh2RUwd79y504uhdMbXwPvhdxvB6w2TF9haiJICWwTxmeS+GVdVZVkEU/Hbtm3zYiwp4H7ZvtiwYUPX5nQ/9tUsxXbv3t212Z6J9u1//etfXgyfEz4XlFP4d8JVIrkf5fuG/fqaNWu8WFiVw/yuIKpVC4UQQgiRMDQYEEIIISKOBgNCCCFExCkSxCgycDle1LpYJ8ESlBzDw3GpSiwni3YyM1/D4dXoxo0b59psz0H97r333vNiWDqYtejLLrvM+4y2PNYrt27dajmBpTp5jkTHjh1d+5VXXvFiqK2xho4rIbIGijrjH//4Ry+G1h0z/zpY58xpn2b+vA+2JOJcCywjbGbWtm1b1+bfF+cFsD0I9WDWMgcNGuTabDfjsrRLly517VdffdWLhVkb8ZnluSRh2nEi5gzwPATWjs8GuOx4vLZK7ANY70bdnMvj4tyYK6+80ouhVZWfSXw/vvvuOy+G84QOHjzoxfhdwtU7+dnG8+Y5RThv57PPPvNiaBtm62/Y6qRh30Otn/sKXPnRzJ9/xCV48XfDVWPN/LkXOH/BzL/fH3/8sRf75z//6drcH+I8AX628J5ySWm8XrYW8u+E8wT4t8DzDitFXdBzgXjeC6PMgBBCCBFxNBgQQgghIk7M1kK2bCCcRg5btRDTOJymwpTSqlWrvBimGjn9PGPGDNfu37+/F8MUHq+ih1UH0VZj5qemzfxU3+bNm70YpiH53D7//HPX5vQzbscpblyZC2UBs9jv4cMPP+zF+Dfs0qWL5QRW2eJ0HlqgWJbB9Olf/vIXL4ZWQ5QMzMx++OEH1/7666+9GK6aiLKAmV8Nka2Mn376qfd5wYIFrs1VHRG2guHzHLYdkwj7YFjVtLOF3NirwqQVfH5ZYsR0LKfNEXwGzPz3h/sOlIQ4jYzPPT9nuIqemd8HodXYzLe38fWina9169ZeDI/JVju01LItGPsgljtXrFjh2iz/cZ+H1UXDKpuyZIJ9Hp8b/jZTp071YigPsr0XpRauAIvH5wqlKHXwe7ZhwwbvM/Zr3OfhPeV/C/PbQixroRBCCCEShgYDQgghRMTRYEAIIYSIODHPGTjThGkvrHUtXrzYtdGeaGbWokUL12aLE+rUAwcO9GJsX6lfv75rc2nOe+65x7Wx5C1/ZlsYzlMYMWKEF0Pdkc8F5xNgCWczf5UwPs+JEyd6n6+44grXZosizhPgMqlo63rrrbe8GJ4Pzpcw8+ch8NwKvF7UFc18Cybbv/A80Spq5mugZr7uyNYhnBewf/9+L4Y6HNuowubSiNwTprGGzZ/A34HnLKH+zXODcI4Nr0aHpcwbNWrkxVq2bOnarNOiDc7ML4/M38UyuPxs473AuTF8PjwXCkt98xwJnE/AtlXU23luDK8UiDo9223xM/9mOP+JV5HFlVLZQoy/TZh9kN9PnEfE/SH2eTwnYtGiRd7nsJLDbAnNifywFiZyn8oMCCGEEBFHgwEhhBAi4hRamYBteGj1Y5kALSqctkb7yG9/+1sv1qNHD9dm+xynt3AlO7bFYQqJU0aY0uIKiGiP6tq1qxfDVBunCFH6mDBhghfDSmlcNZJXN3vuuedcG1ci5P1gNUQz39Y0efJkL4b3gqUeXFFy7dq1XgztQm3atPFieJ/YZokpYE7tcaoRyc3KhJhO5XsomSD3xGu3wu1ys0IkP4cISnB///vfvRhWJWXbIdpy0XZsllUqxOdw06ZNXgxT+rhyp5n/3nP6G68RpUEzv+9kuy1W9uOKdPjccyqcP6Ndkq8Jj8/SC8qICxcu9GJo5+N3MNZ0OFsL0bLN93DPnj2uzXZmrhaLzxDvJ57zZOK1CMpaKIQQQoiEocGAEEIIEXE0GBBCCCEiTqGdM8DaB+pbrBfWqFHDtdm+N3v2bNdGW42ZvxIVrxrIehpagLhcLepJy5cv92KoCT711FNeDK19rBdimVIuDYpWottuu82LYQniOXPmeDG2LqHWyeU3UUudOXOmFytfvrxr81wLtOXh98x8aycfD/fz/fffezHU89BmaObPJeEV0rgUKmp9PO8EtX+eF4C6XKw2IpEz8eqaYfMCYp1rwHM8MMYWWrTl4YqXZmYdOnRwbSzNa5a1dDE+d6xNo+2QV8MLe+5wThGvMIjWXC5jjBr6G2+84cVQp+f7VLFiRe8zvltc/hktgmyLxrlIbC/G+899PBJmMeVY2HfRzsxzG/h5Qjt12HwVJtZnPRHvRF5RZkAIIYSIOBoMCCGEEBGnSBBjngHT5GcCthZiKj4shcTpQ0wT8aXiPtk+x/ZB3A+nn9FOx6k+rIKHqSYzP4XH6fb27du7Nq8Khqk+To2//fbbrj1lyhQv9tJLL3mfsQIXr36Ids1nnnnGi+GKkihnmPmV2c4//3wvhlYetgiiNMC/PVYOa9iwoRd7//33XZstP7wfhNOCmIbl80ZbFd9vlhTyG17p7WyAK0rmB2G2wzArY1gMbWrcd+C7w+81r0yIchyvhoppc5Y48X3hVDw+62iRNvNlCq66d/ToUddmey9a/dA6aGbWvHlz7zP2SbyCJFoN2d6LMiKv/oe/BdtBw/p8/G3Yhn3ZZZe5NtsjUcLl+8vfDZMbEiEThMle8cYYlr4ZZQaEEEKIiKPBgBBCCBFxNBgQQgghIk6htRbyynFobWH9CPVf1nZQ++HtUHdjmwtb39CKxpoyWnJYQ8ZjcAzPh608L774omuzRS8lJcW1ufQpzjVATcws6yqCeK+wVLCZb8PkY6AuxaWD0eaJeqiZf/1DhgzxYlOnTnVt1gtxRUnWIMM0X9RH+btcthSfE/4twlYtFAVDbnTUWLdj8Dnk72G5Wu5zuOx42Nwc7Fd4HgvOT+G+C/tD1oKxf+AS3bgffs9wjg2vYspzCFBjD5vrwO9gmDUX7w3P98F3ku8F3n+eI4H9Gt8L7OPC5prx8eOdPxBGbrbDc5W1UAghhBAJQ4MBIYQQIuIUWpmgatWq3mdMobGVpXTp0q7Nabiwlbgw1cYV6TDVZebb+dhKhCmzzp07ezFMjXN6C2P33nuvF8Nr4nQe2hfZLohpOa64yBUJMfXFqT6sssgyxY033ujaP/zwgxd75513XJutQ3Xq1HFtrmiG942Ph9LDt99+68UwncgpM77fKA2ErT4YtsodP19h3xXZk4g0J28Xts9YY2HnGVZ1jmNcyfDQoUOuHWZ/rV+/vhfDip1s7UZLL1c8xL6Sq3mipZdtjngdp+sP8X0Js+WFySLcH+P1ch+AskVYdUS2sS5evNi1WfoNk/9y85zkt30w7HhatVAIIYQQCUODASGEECLiaDAghBBCRJxCO2eALSmoZ7EOFbZdWDliLomLsEaH+jvbbLAc6N133+3Ftm/f7tpYltTMtwGyfoawRoa6WLt27bwY2vlWr17txfh6Z82a5do1a9b0YjgXALV+M7NWrVq5dosWLbwYlsvF8qa8HVqzzPwyxjzX4IknnnBttoCiZsb3kHVAnDPA805Q5+X9hK1sF2YzEtmTSDtULPuM93jY5+RGm2XbKn5mLR5tyldeeaUXu+6667I9FzO/fDnr66ihs56PqwjyO7hs2TLXxpUHzbLODcKSwzgnwswvCczlyvH6w3R5nlOEc3zYBo33kFdJxGvifpTnfiG5KWkdRn7MZUFkLRRCCCFEwtBgQAghhIg4hVYmYDj1hsSbzkO5IazCGO+Xj4GpOE5TYRqdK4UtWLDAtdmihufDKbNatWq5NtuRGjRo4Nr9+/f3YqNGjfI+X3755a6N6TQzf2VGTgO+9dZbro2WSzPf1tS7d28vhtUK0UbEx58xY4YXQ+mBV3fE5wJtpGZZfydOEyJhKUP8LSQL5C/x2g7j3S7MphXrPk+X4g3rn/CZ5ecXZT2uyIcVWvndxSp/2FeY+fInVio08/sOTsW/+uqr3mfsy/iaUKrlfg0lDe5X8Lz5nuJ7j7ZrM1+y+PDDD70Y9uP8bwi+y9w3JEIWMEvM6oPxrrqZWwlBPZsQQggRcTQYEEIIISKOBgNCCCFExDlr5gzkN6wF8+paYZoylrnEcry838suu8yLob7OehaW/9y4caMX++KLL1ybtS7cjksM8yqC+/btc+29e/d6MVypkC2RWOKTz7tPnz6uzSsaovY/ffp0L4bXxNZCnL/AuireQ7ZYibOPM7HqW6L3cTp9GfsAfl/weX733Xe9GNpYeeVQ7I/YTo2rHaK12cyfJ8V9Gur7X375pRdjO3VYf4jXy+dWqVIl1+Y+Ft9znhuEsZUrV3oxnHvFc73CVhkNm0PExDt/JCyWH2W584IyA0IIIUTE0WBACCGEiDgaDAghhBARR3MGfoWXtOXlf1GbYb0MdeslS5Z4MdTFVqxY4cVq167t2rz0JmprWN7TzNfFeJ+LFi1y7aVLl3oxXhYaS4Oy7rl27VrX5jLGl1xyiWvzvADUJHnOwrZt21ybywqjb7ly5cpeLNblS7mEqDi3SERp1/yaF4CElbLluUlY+pr7HPTMc2nxpk2buja/L6iv8zuINVHwfTTz/fpcrpvB8+Z7in0ea/g4r4frHGCM6xN89NFHrs3LmGOfwHMy8LfgMuNh9R+Y/FimONbt4j1eblFmQAghhIg4GgwIIYQQEadIEGNegVfxO9dgCwqnycJWxwuzqGDKDOUEM98+wzIFrvbVuHFjL4blONHWaGa2Zs0a12ZbD58nptR4NT6UQsJKdbK8gTIBXy/uk9OH+Jl/Czw3Th/iPTxdavNcAO2ZZwv8jESNWGUK7lfwuWfpAd+lChUqeDFMt/O7hLZDLGls5vdVLGfweedm1UakRo0ars3yJx5/69atXgyfe75PYceLNU0ftt3pSIQMlRvitTmyZMQoMyCEEEJEHA0GhBBCiIijwYAQQggRcTRn4FfQZmfm23PM/CVEuSQubstLjYbp8rj8L9uKsCQvWuvMfL2QdXn8OVlL5PKfqEPynAXU31nf79y5s2uzPWjatGmuzfo+7pN1v7A5CmFzMvAYfO/PRTRnIHsK0naYl3NDWKfH9zPMNsvvB75bYTE+XpgFkt/lMJ0e4f3gHB8+N5zjxH0swvMguC9Bwu5brM9FdvFY9snb5UcsN6B9OzuUGRBCCCEijgYDQgghRMRRBcJfYVkgLMXNaRtMW4Wl5VAWMPOlAZYCMG3P8gLCK/VhWoxlAb4mrFy2fv16L3bBBRe4dt++fb0Ypqlmz57txVBS4MpoYdXAMO3I9xDv7+ksoCKaJGoluTNNrKlqTrcjYWnysO3C4HM5ndUw1tihQ4dy/B72c3xNeHzuf7G/CJM+wshLKj43ckOs28V7LnlBmQEhhBAi4mgwIIQQQkQcDQaEEEKIiKM5A7/CKxGy1RA/h+n7rGGj9Y8teqjpYxlfM19P4jkDYXY6jPG58DFwvzVr1vRiaAfbvXu3F1u5cqVr792714vhXANe7RDvE2t7eH95bkPYPIgwS6I4t8gP+2B+2A5zszpdGPHay+LVmMNW8YtXUw9bwZHf87DjhR07N/c03n3mt0UwkasPxosyA0IIIUTE0WBACCGEiDiSCX7lwIED3mdcGZA/czoaU9ec3sHUOEsRsVbW4+0wpcRpc7TkVKxY0YthVUMzs+rVq7s2V0Bct25djueGKX6uLhdmh8LqYyyZ4LmF2SV5u1groYmzn/ywD+ZHuv1Mr1wXFotXFslN2jre9Hdu7IrxSgH5kaY/W561MMtpdigzIIQQQkQcDQaEEEKIiKPBgBBCCBFxtGqhEHkA5zpwSWuczxBWGjps3gPbr3bt2hXXeRYkiVq1sDBZC+Nd8S5RsXjJj/kMidDzs/sc6zHO9d8wL+eNnG7FU2UGhBBCiIijwYAQQggRceTLEiIPoH3z6NGjXgxtkGyjQrsoV2kr6JX0CiuFye5VmGLx2gAT9Zwlaj+F2T6YKLkhnnMLW0EyTGoJq/CYHcoMCCGEEBFHgwEhhBAi4mgwIIQQQkQczRkQIg/gypCs32EJayxLbeavGIkrW5r5miDbFaNMYbIPxnqeudk2Xr07EZa83JxnfpEfunxhthaGlX8OW0ESY7yiLVqYq1atarlBmQEhhBAi4mgwIIQQQkQcyQRC5IFDhw7lGMMUYXp6uhc777zzXLtx48ZerHv37q7drl27vJ7iOUNhsvOFkR/bFeYVFBNFIn6n/Fp9MBGSEa9Si/vhKqQoBfBqs7hdnTp1vFj//v1du1mzZjGfp5kyA0IIIUTk0WBACCGEiDgaDAghhBARR6sWCpEHzj//fNe+4IILvBjqeampqV6sefPmrs3liOfPn+/aH374oRdbvnx5vKdaYCRq1UKRewqbfTBezhbraLxlhbEfMTOrUaOGazds2NCLdenSxbXLlSvnxb788kvXXrBggRfjvoRRZkAIIYSIOBoMCCGEEBFH1kIh8sDgwYNdu3r16l4M0/+YvjMze+2111x7x44dXiws7ShEbjgXZAGzs8c6yjG0E3IV0rZt27p2mzZtvBiuasr9w8SJE11748aNXuzYsWOuzfLj6VBmQAghhIg4GgwIIYQQEUeDASGEECLiyFooRB44cuSIa6POx+BqYmZmxYoVc20sTWzmW46OHz/uxfbs2RPXeRYkshbGRqJsgImwzBXEXIN4VyZEErXaYdh+c7Mdzhng7UqUKOHaXI54//79ro19hZnfP3Cfg/s8efKkF+O5B4wyA0IIIUTE0WBACCGEiDiyFgqRB7ByGKflMLWYnJzsxTD1xxagEydOuLashdEhUan5s3WFw4K2AebHPjGlzzF871FuNPOlwzB5gfeZF1uyMgNCCCFExNFgQAghhIg4GgwIIYQQESdma6EQQgghzk2UGRBCCCEijgYDQgghRMTRYEAIIYSIOBoMCCGEEBFHgwEhhBAi4mgwIIQQQkQcDQaEEEKIiKPBgBBCCBFxNBgQQgghIs7/B8MFXSSOS4WNAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
+       "<Figure size 300x400 with 8 Axes>"
       ]
      },
      "metadata": {},
@@ -664,13 +636,17 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(nrows=1, ncols=2)\n",
-    "ax[0].imshow(images[0, 0].detach().cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
-    "ax[0].axis(\"off\")\n",
-    "ax[0].title.set_text(\"Inputted Image\")\n",
-    "ax[1].imshow(reconstruction[0, 0].detach().cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
-    "ax[1].axis(\"off\")\n",
-    "ax[1].title.set_text(\"Reconstruction\")\n",
+    "images = first(val_loader)[\"image\"].to(device)\n",
+    "reconstruction, quantization_loss = vqvae_model(images=images)\n",
+    "nrows = 4\n",
+    "fig, ax = plt.subplots(nrows=4, ncols=2, figsize=(3, 4))\n",
+    "for i in range(nrows):\n",
+    "    ax.flat[i * 2].imshow(images[i + 20, 0].detach().cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+    "    ax.flat[i * 2].axis(\"off\")\n",
+    "    ax.flat[i * 2 + 1].imshow(reconstruction[i + 20, 0].detach().cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+    "    ax.flat[i * 2 + 1].axis(\"off\")\n",
+    "ax.flat[0].title.set_text(\"Image\")\n",
+    "ax.flat[1].title.set_text(\"Reconstruction\")\n",
     "plt.show()"
    ]
   },
@@ -699,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 36,
    "id": "2b3c3a82",
    "metadata": {},
    "outputs": [
@@ -707,14 +683,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:14<00:00, 3264.50it/s]\n",
-      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:01<00:00, 3347.99it/s]\n"
+      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:14<00:00, 3184.50it/s]\n",
+      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:01<00:00, 3340.95it/s]\n"
      ]
     }
    ],
    "source": [
+    "in_distribution_class = \"HeadCT\"\n",
+    "\n",
     "train_data = MedNISTDataset(root_dir=root_dir, section=\"training\", seed=0)\n",
-    "train_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
+    "train_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == in_distribution_class]\n",
     "train_transforms = transforms.Compose(\n",
     "    [\n",
     "        transforms.LoadImaged(keys=[\"image\"]),\n",
@@ -735,7 +713,7 @@
     "train_loader = DataLoader(train_ds, batch_size=32, shuffle=True, num_workers=4, persistent_workers=True)\n",
     "\n",
     "val_data = MedNISTDataset(root_dir=root_dir, section=\"validation\", seed=0)\n",
-    "val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
+    "val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == in_distribution_class]\n",
     "val_transforms = transforms.Compose(\n",
     "    [\n",
     "        transforms.LoadImaged(keys=[\"image\"]),\n",
@@ -758,17 +736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "efab0cc5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "spatial_shape = next(iter(train_loader))[\"image\"].shape[2:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 37,
    "id": "f91086e3",
    "metadata": {
     "lines_to_next_cell": 2
@@ -776,9 +744,8 @@
    "outputs": [],
    "source": [
     "# Get spatial dimensions of data\n",
-    "# We divide the spatial shape by 4 as the vqvae downsamples the image by a factor of 4 along each dimension\n",
-    "spatial_shape = next(iter(train_loader))[\"image\"].shape[2:]\n",
-    "spatial_shape = (int(spatial_shape[0] / 4), int(spatial_shape[1] / 4))\n",
+    "test_data = next(iter(train_loader))[\"image\"].to(device)\n",
+    "spatial_shape = vqvae_model.encode_stage_2_inputs(test_data).shape[2:]\n",
     "\n",
     "ordering = Ordering(ordering_type=OrderingType.RASTER_SCAN.value, spatial_dims=2, dimensions=(1,) + spatial_shape)"
    ]
@@ -955,9 +922,9 @@
     "\n",
     "        logits, quantizations_target, _ = inferer(images, vqvae_model, transformer_model, ordering, return_latent=True)\n",
     "        logits = logits.transpose(1, 2)\n",
-    "        \n",
+    "\n",
     "        # train the transformer to predict token n+1 using tokens 0-n\n",
-    "        loss = ce_loss(logits[:,:,:-1], quantizations_target[:,1:])\n",
+    "        loss = ce_loss(logits[:, :, :-1], quantizations_target[:, 1:])\n",
     "\n",
     "        loss.backward()\n",
     "        optimizer.step()\n",
@@ -980,14 +947,19 @@
     "                )\n",
     "                logits = logits.transpose(1, 2)\n",
     "\n",
-    "                loss = ce_loss(logits[:,:,:-1], quantizations_target[:,1:])\n",
+    "                loss = ce_loss(logits[:, :, :-1], quantizations_target[:, 1:])\n",
     "\n",
     "                val_loss += loss.item()\n",
     "        # get sample\n",
-    "        sample = inferer.sample( vqvae_model=vqvae_model, transformer_model=transformer_model, ordering=ordering, latent_spatial_dim=(spatial_shape[0], spatial_shape[1]), starting_tokens=vqvae_model.num_embeddings * torch.ones((1, 1), device=device)\n",
-    "    )\n",
-    "        plt.imshow(sample[0,0,...].cpu().detach())\n",
-    "        plt.title(f'Sample epoch {epoch}')\n",
+    "        sample = inferer.sample(\n",
+    "            vqvae_model=vqvae_model,\n",
+    "            transformer_model=transformer_model,\n",
+    "            ordering=ordering,\n",
+    "            latent_spatial_dim=(spatial_shape[0], spatial_shape[1]),\n",
+    "            starting_tokens=vqvae_model.num_embeddings * torch.ones((1, 1), device=device),\n",
+    "        )\n",
+    "        plt.imshow(sample[0, 0, ...].cpu().detach())\n",
+    "        plt.title(f\"Sample epoch {epoch}\")\n",
     "        plt.show()\n",
     "        val_loss /= val_step\n",
     "        val_epoch_losses.append(val_loss)\n",
@@ -1010,7 +982,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 39,
    "id": "aa3938fe",
    "metadata": {},
    "outputs": [
@@ -1018,17 +990,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-03-17 19:50:26,144 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-03-17 19:50:26,144 - INFO - File exists: /tmp/tmp1lues0wg/MedNIST.tar.gz, skipped downloading.\n",
-      "2023-03-17 19:50:26,144 - INFO - Non-empty folder exists in /tmp/tmp1lues0wg/MedNIST, skipped extracting.\n"
+      "2023-03-17 20:12:36,970 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
+      "2023-03-17 20:12:36,970 - INFO - File exists: /tmp/tmp1lues0wg/MedNIST.tar.gz, skipped downloading.\n",
+      "2023-03-17 20:12:36,970 - INFO - Non-empty folder exists in /tmp/tmp1lues0wg/MedNIST, skipped extracting.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:01<00:00, 3244.75it/s]\n",
-      "In-distribution data: 100%|███████████████████████████████████████████████████| 17/17 [00:03<00:00,  4.86it/s]\n"
+      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:01<00:00, 3244.12it/s]\n",
+      "In-distribution data: 100%|███████████████████████████████████████████████████| 17/17 [00:02<00:00,  5.86it/s]\n"
      ]
     }
    ],
@@ -1038,7 +1010,9 @@
     "\n",
     "test_data = MedNISTDataset(root_dir=root_dir, section=\"test\", download=True, seed=0)\n",
     "\n",
-    "in_distribution_datalist = [{\"image\": item[\"image\"]} for item in test_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
+    "in_distribution_datalist = [\n",
+    "    {\"image\": item[\"image\"]} for item in test_data.data if item[\"class_name\"] == in_distribution_class\n",
+    "]\n",
     "in_distribution_ds = Dataset(data=in_distribution_datalist, transform=val_transforms)\n",
     "in_distribution_loader = DataLoader(\n",
     "    in_distribution_ds, batch_size=64, shuffle=False, num_workers=4, persistent_workers=True\n",
@@ -1069,7 +1043,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 40,
    "id": "f3e714ee",
    "metadata": {},
    "outputs": [
@@ -1077,28 +1051,38 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "out-of-distribution data: 100%|███████████████████████████████████████████████| 16/16 [00:03<00:00,  4.42it/s]\n"
+      "out-of-distribution data BreastMRI: 100%|█████████████████████████████████████| 15/15 [00:02<00:00,  6.02it/s]\n",
+      "out-of-distribution data CXR: 100%|███████████████████████████████████████████| 16/16 [00:02<00:00,  5.94it/s]\n",
+      "out-of-distribution data Hand: 100%|██████████████████████████████████████████| 16/16 [00:02<00:00,  5.70it/s]\n",
+      "out-of-distribution data ChestCT: 100%|███████████████████████████████████████| 16/16 [00:02<00:00,  5.71it/s]\n",
+      "out-of-distribution data AbdomenCT: 100%|█████████████████████████████████████| 16/16 [00:02<00:00,  5.72it/s]\n"
      ]
     }
    ],
    "source": [
-    "ood_datalist = [{\"image\": item[\"image\"]} for item in test_data.data if item[\"class_name\"] == \"ChestCT\"]\n",
-    "ood_ds = Dataset(data=ood_datalist, transform=val_transforms)\n",
-    "ood_loader = DataLoader(ood_ds, batch_size=64, shuffle=False, num_workers=4, persistent_workers=True)\n",
+    "all_classes = {item[\"class_name\"] for item in test_data.data}\n",
+    "all_classes.remove(in_distribution_class)\n",
     "\n",
-    "ood_likelihoods = []\n",
+    "all_likelihoods = {}\n",
+    "for c in all_classes:\n",
+    "    ood_datalist = [{\"image\": item[\"image\"]} for item in test_data.data if item[\"class_name\"] == c]\n",
+    "    ood_ds = Dataset(data=ood_datalist, transform=val_transforms)\n",
+    "    ood_loader = DataLoader(ood_ds, batch_size=64, shuffle=False, num_workers=4, persistent_workers=True)\n",
     "\n",
-    "progress_bar = tqdm(enumerate(ood_loader), total=len(ood_loader), ncols=110)\n",
-    "progress_bar.set_description(f\"out-of-distribution data\")\n",
-    "for step, batch in progress_bar:\n",
-    "    images = batch[\"image\"].to(device)\n",
+    "    ood_likelihoods = []\n",
     "\n",
-    "    log_likelihood = inferer.get_likelihood(\n",
-    "        inputs=images, vqvae_model=vqvae_model, transformer_model=transformer_model, ordering=ordering\n",
-    "    )\n",
-    "    ood_likelihoods.append(log_likelihood.sum(dim=(1, 2)).cpu().numpy())\n",
+    "    progress_bar = tqdm(enumerate(ood_loader), total=len(ood_loader), ncols=110)\n",
+    "    progress_bar.set_description(f\"out-of-distribution data {c}\")\n",
+    "    for step, batch in progress_bar:\n",
+    "        images = batch[\"image\"].to(device)\n",
     "\n",
-    "ood_likelihoods = np.concatenate(ood_likelihoods)"
+    "        log_likelihood = inferer.get_likelihood(\n",
+    "            inputs=images, vqvae_model=vqvae_model, transformer_model=transformer_model, ordering=ordering\n",
+    "        )\n",
+    "        ood_likelihoods.append(log_likelihood.sum(dim=(1, 2)).cpu().numpy())\n",
+    "\n",
+    "    ood_likelihoods = np.concatenate(ood_likelihoods)\n",
+    "    all_likelihoods[c] = ood_likelihoods"
    ]
   },
   {
@@ -1113,7 +1097,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 55,
    "id": "cd456a7c",
    "metadata": {},
    "outputs": [
@@ -1123,13 +1107,13 @@
        "Text(0.5, 0, 'Log-likelihood')"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1139,19 +1123,220 @@
     }
    ],
    "source": [
-    "sns.kdeplot(in_likelihoods, color=\"dodgerblue\", bw_adjust=1, label=\"In-distribution\")\n",
-    "sns.kdeplot(ood_likelihoods, color=\"deeppink\", bw_adjust=40, label=\"OOD\")\n",
-    "plt.legend()\n",
-    "plt.xlabel(\"Log-likelihood\")"
+    "sns.set_style(\"whitegrid\", {\"axes.grid\": False})\n",
+    "sns.kdeplot(in_likelihoods, bw_adjust=1, label=\"In-distribution\", fill=True, cut=True)\n",
+    "for c, l in all_likelihoods.items():\n",
+    "    sns.kdeplot(l, bw_adjust=20, label=f\"OOD {c}\", cut=True, fill=True)\n",
+    "plt.legend(loc=\"upper right\")\n",
+    "plt.xlabel(\"Log-likelihood\")\n",
+    "# plt.xlim([-200,10])\n",
+    "# plt.ylim([0,10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60ab6571-6f85-4c1b-8be8-896cee74bf48",
+   "metadata": {},
+   "source": [
+    "# Localised anomaly detection\n",
+    "First we create a synthetic corruption of an in-distribution image"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "89c3dc99",
+   "execution_count": 100,
+   "id": "884b152e-54a5-41b8-9d59-327991c58a20",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "input_image = first(in_distribution_loader)\n",
+    "image_clean = input_image[\"image\"][0, ...]\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.imshow(image_clean[0, ...], cmap=\"gray\")\n",
+    "plt.axis(\"off\")\n",
+    "plt.title(\"Clean image\")\n",
+    "image_corrupted = image_clean.clone()\n",
+    "image_corrupted[0, 25:40, 40:50] = 1\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.imshow(image_corrupted[0, ...], cmap=\"gray\")\n",
+    "plt.axis(\"off\")\n",
+    "plt.title(\"Corrupted image\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc3b8023-731c-4ac7-b335-9835e85bbabd",
+   "metadata": {},
+   "source": [
+    "Get the log-likelihood and convert into a mask of the 5% lowest-likelihood tokens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "3faeed43-59c1-4062-8180-2c8445b9e118",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "log_likelihood = inferer.get_likelihood(\n",
+    "    inputs=image_corrupted[None, ...].to(device),\n",
+    "    vqvae_model=vqvae_model,\n",
+    "    transformer_model=transformer_model,\n",
+    "    ordering=ordering,\n",
+    ")\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.imshow(log_likelihood.cpu()[0, ...], vmin=0.6, vmax=1)\n",
+    "plt.axis(\"off\")\n",
+    "plt.title(\"Log-likelihood\")\n",
+    "plt.subplot(1, 2, 2)\n",
+    "mask = log_likelihood.cpu()[0, ...] < torch.quantile(log_likelihood, 0.05).item()\n",
+    "plt.imshow(mask)\n",
+    "plt.axis(\"off\")\n",
+    "plt.title(\"Healing mask\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec053da6-8f95-4460-bbe5-577a4a956266",
+   "metadata": {},
+   "source": [
+    "Use this mask and the trained transformer to 'heal' the sequence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "dd08553f-0b51-48ff-860d-fc1fabadf77c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# flatten the mask\n",
+    "mask_flattened = mask.reshape(-1)\n",
+    "mask_flattened = mask_flattened[ordering.get_sequence_ordering()]\n",
+    "\n",
+    "latent = vqvae_model.index_quantize(image_corrupted[None, ...].to(device))\n",
+    "latent = latent.reshape(latent.shape[0], -1)\n",
+    "latent = latent[:, ordering.get_sequence_ordering()]\n",
+    "latent = F.pad(latent, (1, 0), \"constant\", vqvae_model.num_embeddings)\n",
+    "latent = latent.long()\n",
+    "latent_healed = latent.clone()\n",
+    "\n",
+    "# heal the sequence\n",
+    "# loop over tokens\n",
+    "for i in range(1, latent.shape[1]):\n",
+    "    if mask_flattened[i - 1]:\n",
+    "        # if token is low probability, replace with tranformer's most likely token\n",
+    "        logits = transformer_model(latent_healed[:, :i])\n",
+    "        probs = F.softmax(logits, dim=-1)\n",
+    "        # don't sample beginning of sequence token\n",
+    "        probs[:, :, vqvae_model.num_embeddings] = 0\n",
+    "        index = torch.argmax(probs[0, -1, :])\n",
+    "        latent_healed[:, i] = index\n",
+    "\n",
+    "\n",
+    "# reconstruct\n",
+    "latent_healed = latent_healed[:, 1:]\n",
+    "latent_healed = latent_healed[:, ordering.get_revert_sequence_ordering()]\n",
+    "latent_healed = latent_healed.reshape((16, 16))\n",
+    "\n",
+    "image_healed = vqvae_model.decode_samples(latent_healed[None, ...]).cpu().detach()\n",
+    "plt.imshow(image_healed[0, 0, ...], cmap=\"gray\")\n",
+    "plt.axis(\"off\")\n",
+    "plt.title(\"Healed image\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "082dd314-e328-4666-956e-2c831739537a",
+   "metadata": {},
+   "source": [
+    "## Create anomaly maps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "d629c1b6-0a66-4fb5-8722-3006404afe30",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get a naive anomaly map using the difference\n",
+    "difference_map = torch.abs(image_healed[0, 0, ...] - image_corrupted[0, ...])\n",
+    "\n",
+    "# Further mask with the healing mask\n",
+    "resizer = torch.nn.Upsample(size=(64, 64), mode=\"nearest\")\n",
+    "mask_upsampled = resizer(mask[None, None, ...].float()).int()\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 4, figsize=(14, 8))\n",
+    "plt.subplot(1, 4, 1)\n",
+    "plt.imshow(image_clean[0, ...], cmap=\"gray\")\n",
+    "plt.axis(\"off\")\n",
+    "plt.title(\"Clean image\")\n",
+    "image_corrupted = image_clean.clone()\n",
+    "image_corrupted[0, 25:40, 40:50] = 1\n",
+    "plt.subplot(1, 4, 2)\n",
+    "plt.imshow(image_corrupted[0, ...], cmap=\"gray\")\n",
+    "plt.axis(\"off\")\n",
+    "plt.title(\"Corrupted image\")\n",
+    "plt.subplot(1, 4, 3)\n",
+    "plt.imshow(image_corrupted[0, ...] - image_clean[0, ...], cmap=\"gray\")\n",
+    "plt.axis(\"off\")\n",
+    "plt.title(\"Ground-Truth anomaly mask\")\n",
+    "plt.subplot(1, 4, 4)\n",
+    "plt.imshow(mask_upsampled[0, 0, ...] * difference_map, cmap=\"gray\")\n",
+    "plt.axis(\"off\")\n",
+    "plt.title(\"Predicted anomaly mask\")\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {
diff --git a/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.py b/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.py
index 33c7bc0e..33641f09 100644
--- a/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.py
+++ b/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.py
@@ -13,35 +13,21 @@
 #     name: python3
 # ---
 
-# %%
-# Copyright (c) MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#     http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
 # %% [markdown]
 # # Anomaly Detection with Transformers
 #
-# This tutorial illustrates how to use MONAI to perform image-wise anomaly detection with transformers based on the method proposed in Pinaya et al.[1].
+# This tutorial illustrates how to use MONAI to perform image-wise and localised anomaly detection with transformers based on the method proposed in Pinaya et al.[1].
 #
 # Here, we will work with the [MedNIST dataset](https://docs.monai.io/en/stable/apps.html#monai.apps.MedNISTDataset) available on MONAI, and similar to "Experiment 2 – image-wise anomaly detection on 2D synthetic data" from [1], we will train a general-purpose VQ-VAE (using all MEDNIST classes), and then a generative models (i.e., Transformer) on `HeadCT` images.
 #
-# Finally, we will compute the log-likelihood of images from the same class (in-distribution class) and images from other classes (out-of-distribution).
+# We will compute the log-likelihood of images from the same class (in-distribution class) and images from other classes (out-of-distribution). We will also provide an example of performing localised anomaly detection with these trained models.
 #
-# [1] - Pinaya et al. "Unsupervised brain imaging 3D anomaly detection and segmentation with transformers" https://doi.org/10.1016/j.media.2022.102475
+# [1] - [Pinaya et al. "Unsupervised brain imaging 3D anomaly detection and segmentation with transformers"](https://doi.org/10.1016/j.media.2022.102475)
 
 # %% [markdown]
 # ### Setup environment
 
 # %%
-# !python -c "import monai" || pip install -q "monai-weekly[tqdm]"
-# !python -c "import matplotlib" || pip install -q matplotlib
 # !python -c "import seaborn" || pip install -q seaborn
 # %matplotlib inline
 
@@ -49,6 +35,16 @@
 # ### Setup imports
 
 # %%
+# Copyright 2020 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import os
 import tempfile
 import time
@@ -57,6 +53,7 @@
 import numpy as np
 import seaborn as sns
 import torch
+import torch.nn.functional as F
 from monai import transforms
 from monai.apps import MedNISTDataset
 from monai.config import print_config
@@ -64,7 +61,6 @@
 from monai.utils import first, set_determinism
 from torch.nn import CrossEntropyLoss, L1Loss
 from tqdm import tqdm
-
 from generative.inferers import VQVAETransformerInferer
 from generative.networks.nets import VQVAE, DecoderOnlyTransformer
 from generative.utils.enums import OrderingType
@@ -222,13 +218,17 @@
 # ### Plot reconstructions of final trained vqvae model
 
 # %%
-fig, ax = plt.subplots(nrows=1, ncols=2)
-ax[0].imshow(images[0, 0].detach().cpu(), vmin=0, vmax=1, cmap="gray")
-ax[0].axis("off")
-ax[0].title.set_text("Inputted Image")
-ax[1].imshow(reconstruction[0, 0].detach().cpu(), vmin=0, vmax=1, cmap="gray")
-ax[1].axis("off")
-ax[1].title.set_text("Reconstruction")
+images = first(val_loader)["image"].to(device)
+reconstruction, quantization_loss = vqvae_model(images=images)
+nrows = 4
+fig, ax = plt.subplots(nrows=4, ncols=2, figsize=(3, 4))
+for i in range(nrows):
+    ax.flat[i * 2].imshow(images[i + 20, 0].detach().cpu(), vmin=0, vmax=1, cmap="gray")
+    ax.flat[i * 2].axis("off")
+    ax.flat[i * 2 + 1].imshow(reconstruction[i + 20, 0].detach().cpu(), vmin=0, vmax=1, cmap="gray")
+    ax.flat[i * 2 + 1].axis("off")
+ax.flat[0].title.set_text("Image")
+ax.flat[1].title.set_text("Reconstruction")
 plt.show()
 
 # %% [markdown]
@@ -245,8 +245,10 @@
 # To train the transformer, we only use the `HeadCT` class.
 
 # %%
+in_distribution_class = "HeadCT"
+
 train_data = MedNISTDataset(root_dir=root_dir, section="training", seed=0)
-train_datalist = [{"image": item["image"]} for item in train_data.data if item["class_name"] == "HeadCT"]
+train_datalist = [{"image": item["image"]} for item in train_data.data if item["class_name"] == in_distribution_class]
 train_transforms = transforms.Compose(
     [
         transforms.LoadImaged(keys=["image"]),
@@ -267,7 +269,7 @@
 train_loader = DataLoader(train_ds, batch_size=32, shuffle=True, num_workers=4, persistent_workers=True)
 
 val_data = MedNISTDataset(root_dir=root_dir, section="validation", seed=0)
-val_datalist = [{"image": item["image"]} for item in val_data.data if item["class_name"] == "HeadCT"]
+val_datalist = [{"image": item["image"]} for item in val_data.data if item["class_name"] == in_distribution_class]
 val_transforms = transforms.Compose(
     [
         transforms.LoadImaged(keys=["image"]),
@@ -282,14 +284,10 @@
 # ### 2D latent representation -> 1D sequence
 # We need to define an ordering of which we convert our 2D latent space into a 1D sequence. For this we will use a simple raster scan.
 
-# %%
-spatial_shape = next(iter(train_loader))["image"].shape[2:]
-
 # %%
 # Get spatial dimensions of data
-# We divide the spatial shape by 4 as the vqvae downsamples the image by a factor of 4 along each dimension
-spatial_shape = next(iter(train_loader))["image"].shape[2:]
-spatial_shape = (int(spatial_shape[0] / 4), int(spatial_shape[1] / 4))
+test_data = next(iter(train_loader))["image"].to(device)
+spatial_shape = vqvae_model.encode_stage_2_inputs(test_data).shape[2:]
 
 ordering = Ordering(ordering_type=OrderingType.RASTER_SCAN.value, spatial_dims=2, dimensions=(1,) + spatial_shape)
 
@@ -333,6 +331,7 @@
     progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=110)
     progress_bar.set_description(f"Epoch {epoch}")
     for step, batch in progress_bar:
+
         images = batch["image"].to(device)
 
         optimizer.zero_grad(set_to_none=True)
@@ -356,6 +355,7 @@
         val_loss = 0
         with torch.no_grad():
             for val_step, batch in enumerate(val_loader, start=1):
+
                 images = batch["image"].to(device)
 
                 logits, quantizations_target, _ = inferer(
@@ -396,7 +396,9 @@
 
 test_data = MedNISTDataset(root_dir=root_dir, section="test", download=True, seed=0)
 
-in_distribution_datalist = [{"image": item["image"]} for item in test_data.data if item["class_name"] == "HeadCT"]
+in_distribution_datalist = [
+    {"image": item["image"]} for item in test_data.data if item["class_name"] == in_distribution_class
+]
 in_distribution_ds = Dataset(data=in_distribution_datalist, transform=val_transforms)
 in_distribution_loader = DataLoader(
     in_distribution_ds, batch_size=64, shuffle=False, num_workers=4, persistent_workers=True
@@ -420,23 +422,29 @@
 # We will use the "ChestCT" class of the dataset for the out-of-distribution examples.
 
 # %%
-ood_datalist = [{"image": item["image"]} for item in test_data.data if item["class_name"] == "ChestCT"]
-ood_ds = Dataset(data=ood_datalist, transform=val_transforms)
-ood_loader = DataLoader(ood_ds, batch_size=64, shuffle=False, num_workers=4, persistent_workers=True)
+all_classes = {item["class_name"] for item in test_data.data}
+all_classes.remove(in_distribution_class)
 
-ood_likelihoods = []
+all_likelihoods = {}
+for c in all_classes:
+    ood_datalist = [{"image": item["image"]} for item in test_data.data if item["class_name"] == c]
+    ood_ds = Dataset(data=ood_datalist, transform=val_transforms)
+    ood_loader = DataLoader(ood_ds, batch_size=64, shuffle=False, num_workers=4, persistent_workers=True)
 
-progress_bar = tqdm(enumerate(ood_loader), total=len(ood_loader), ncols=110)
-progress_bar.set_description(f"out-of-distribution data")
-for step, batch in progress_bar:
-    images = batch["image"].to(device)
+    ood_likelihoods = []
 
-    log_likelihood = inferer.get_likelihood(
-        inputs=images, vqvae_model=vqvae_model, transformer_model=transformer_model, ordering=ordering
-    )
-    ood_likelihoods.append(log_likelihood.sum(dim=(1, 2)).cpu().numpy())
+    progress_bar = tqdm(enumerate(ood_loader), total=len(ood_loader), ncols=110)
+    progress_bar.set_description(f"out-of-distribution data {c}")
+    for step, batch in progress_bar:
+        images = batch["image"].to(device)
+
+        log_likelihood = inferer.get_likelihood(
+            inputs=images, vqvae_model=vqvae_model, transformer_model=transformer_model, ordering=ordering
+        )
+        ood_likelihoods.append(log_likelihood.sum(dim=(1, 2)).cpu().numpy())
 
-ood_likelihoods = np.concatenate(ood_likelihoods)
+    ood_likelihoods = np.concatenate(ood_likelihoods)
+    all_likelihoods[c] = ood_likelihoods
 
 # %% [markdown]
 # ## Log-likelihood plot
@@ -444,9 +452,123 @@
 # Here, we plot the log-likelihood of the images. In this case, the lower the log-likelihood, the more unlikely the image belongs to the training set.
 
 # %%
-sns.kdeplot(in_likelihoods, color="dodgerblue", bw_adjust=1, label="In-distribution")
-sns.kdeplot(ood_likelihoods, color="deeppink", bw_adjust=10, label="OOD")
-plt.legend()
+sns.set_style("whitegrid", {"axes.grid": False})
+sns.kdeplot(in_likelihoods, bw_adjust=1, label="In-distribution", fill=True, cut=True)
+for c, l in all_likelihoods.items():
+    sns.kdeplot(l, bw_adjust=20, label=f"OOD {c}", cut=True, fill=True)
+plt.legend(loc="upper right")
 plt.xlabel("Log-likelihood")
+# plt.xlim([-200,10])
+# plt.ylim([0,10])
+
+# %% [markdown]
+# # Localised anomaly detection
+# First we create a synthetic corruption of an in-distribution image
+
+# %%
+input_image = first(in_distribution_loader)
+image_clean = input_image["image"][0, ...]
+plt.subplot(1, 2, 1)
+plt.imshow(image_clean[0, ...], cmap="gray")
+plt.axis("off")
+plt.title("Clean image")
+image_corrupted = image_clean.clone()
+image_corrupted[0, 25:40, 40:50] = 1
+plt.subplot(1, 2, 2)
+plt.imshow(image_corrupted[0, ...], cmap="gray")
+plt.axis("off")
+plt.title("Corrupted image")
+plt.show()
+
+# %% [markdown]
+# Get the log-likelihood and convert into a mask of the 5% lowest-likelihood tokens
 
 # %%
+log_likelihood = inferer.get_likelihood(
+    inputs=image_corrupted[None, ...].to(device),
+    vqvae_model=vqvae_model,
+    transformer_model=transformer_model,
+    ordering=ordering,
+)
+plt.subplot(1, 2, 1)
+plt.imshow(log_likelihood.cpu()[0, ...], vmin=0.6, vmax=1)
+plt.axis("off")
+plt.title("Log-likelihood")
+plt.subplot(1, 2, 2)
+mask = log_likelihood.cpu()[0, ...] < torch.quantile(log_likelihood, 0.05).item()
+plt.imshow(mask)
+plt.axis("off")
+plt.title("Healing mask")
+plt.show()
+
+# %% [markdown]
+# Use this mask and the trained transformer to 'heal' the sequence
+
+# %%
+# flatten the mask
+mask_flattened = mask.reshape(-1)
+mask_flattened = mask_flattened[ordering.get_sequence_ordering()]
+
+latent = vqvae_model.index_quantize(image_corrupted[None, ...].to(device))
+latent = latent.reshape(latent.shape[0], -1)
+latent = latent[:, ordering.get_sequence_ordering()]
+latent = F.pad(latent, (1, 0), "constant", vqvae_model.num_embeddings)
+latent = latent.long()
+latent_healed = latent.clone()
+
+# heal the sequence
+# loop over tokens
+for i in range(1, latent.shape[1]):
+    if mask_flattened[i - 1]:
+        # if token is low probability, replace with tranformer's most likely token
+        logits = transformer_model(latent_healed[:, :i])
+        probs = F.softmax(logits, dim=-1)
+        # don't sample beginning of sequence token
+        probs[:, :, vqvae_model.num_embeddings] = 0
+        index = torch.argmax(probs[0, -1, :])
+        latent_healed[:, i] = index
+
+
+# reconstruct
+latent_healed = latent_healed[:, 1:]
+latent_healed = latent_healed[:, ordering.get_revert_sequence_ordering()]
+latent_healed = latent_healed.reshape((16, 16))
+
+image_healed = vqvae_model.decode_samples(latent_healed[None, ...]).cpu().detach()
+plt.imshow(image_healed[0, 0, ...], cmap="gray")
+plt.axis("off")
+plt.title("Healed image")
+plt.show()
+
+# %% [markdown]
+# ## Create anomaly maps
+
+# %%
+# Get a naive anomaly map using the difference
+difference_map = torch.abs(image_healed[0, 0, ...] - image_corrupted[0, ...])
+
+# Further mask with the healing mask
+resizer = torch.nn.Upsample(size=(64, 64), mode="nearest")
+mask_upsampled = resizer(mask[None, None, ...].float()).int()
+
+fig, ax = plt.subplots(1, 4, figsize=(14, 8))
+plt.subplot(1, 4, 1)
+plt.imshow(image_clean[0, ...], cmap="gray")
+plt.axis("off")
+plt.title("Clean image")
+image_corrupted = image_clean.clone()
+image_corrupted[0, 25:40, 40:50] = 1
+plt.subplot(1, 4, 2)
+plt.imshow(image_corrupted[0, ...], cmap="gray")
+plt.axis("off")
+plt.title("Corrupted image")
+plt.subplot(1, 4, 3)
+plt.imshow(image_corrupted[0, ...] - image_clean[0, ...], cmap="gray")
+plt.axis("off")
+plt.title("Ground-Truth anomaly mask")
+plt.subplot(1, 4, 4)
+plt.imshow(mask_upsampled[0, 0, ...] * difference_map, cmap="gray")
+plt.axis("off")
+plt.title("Predicted anomaly mask")
+plt.show()
+

From 5c2941719b51230ed709af5688d4f058180c3243 Mon Sep 17 00:00:00 2001
From: Mark Graham <markgraham539@gmail.com>
Date: Tue, 21 Mar 2023 15:43:56 -0600
Subject: [PATCH 4/4] Address review comments

---
 .../anomaly_detection_with_transformers.ipynb | 264 ++++++++++--------
 .../anomaly_detection_with_transformers.py    |  32 ++-
 2 files changed, 163 insertions(+), 133 deletions(-)

diff --git a/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.ipynb b/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.ipynb
index 280e81e8..d33b281f 100644
--- a/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.ipynb
+++ b/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.ipynb
@@ -1,5 +1,24 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "73121f87-6d4c-413e-a9e9-ed5da13e2a33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copyright (c) MONAI Consortium\n",
+    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    "# you may not use this file except in compliance with the License.\n",
+    "# You may obtain a copy of the License at\n",
+    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
+    "# Unless required by applicable law or agreed to in writing, software\n",
+    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    "# See the License for the specific language governing permissions and\n",
+    "# limitations under the License."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "f6090d00",
@@ -26,12 +45,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 2,
    "id": "01787b4b",
    "metadata": {},
    "outputs": [],
    "source": [
     "!python -c \"import seaborn\" || pip install -q seaborn\n",
+    "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n",
+    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
     "%matplotlib inline"
    ]
   },
@@ -45,10 +66,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 3,
    "id": "b6b0c79f",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mark/Envs/monai-generative/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -85,16 +114,6 @@
     }
    ],
    "source": [
-    "# Copyright 2020 MONAI Consortium\n",
-    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-    "# you may not use this file except in compliance with the License.\n",
-    "# You may obtain a copy of the License at\n",
-    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
-    "# Unless required by applicable law or agreed to in writing, software\n",
-    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-    "# See the License for the specific language governing permissions and\n",
-    "# limitations under the License.\n",
     "import os\n",
     "import tempfile\n",
     "import time\n",
@@ -121,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "de0ed372",
    "metadata": {},
    "outputs": [],
@@ -143,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "42fa255d",
    "metadata": {},
    "outputs": [
@@ -151,7 +170,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/tmp/tmp1lues0wg\n"
+      "/tmp/tmp3g842skb\n"
      ]
     }
    ],
@@ -171,24 +190,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 6,
    "id": "7db7ac32",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "MedNIST.tar.gz: 59.0MB [00:03, 17.1MB/s]                                                                                                                                                                                               "
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-03-17 20:10:01,229 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-03-17 20:10:01,230 - INFO - File exists: /tmp/tmp1lues0wg/MedNIST.tar.gz, skipped downloading.\n",
-      "2023-03-17 20:10:01,230 - INFO - Non-empty folder exists in /tmp/tmp1lues0wg/MedNIST, skipped extracting.\n"
+      "2023-03-21 19:20:02,484 - INFO - Downloaded: /tmp/tmp3g842skb/MedNIST.tar.gz\n",
+      "2023-03-21 19:20:02,557 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
+      "2023-03-21 19:20:02,558 - INFO - Writing into directory: /tmp/tmp3g842skb.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:27<00:00, 1744.81it/s]\n"
+      "\n",
+      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:27<00:00, 1701.63it/s]\n"
      ]
     }
    ],
@@ -223,7 +250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "33d7c3dc",
    "metadata": {},
    "outputs": [
@@ -257,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 8,
    "id": "ec954b77",
    "metadata": {},
    "outputs": [
@@ -265,16 +292,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-03-17 20:10:28,891 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-03-17 20:10:28,892 - INFO - File exists: /tmp/tmp1lues0wg/MedNIST.tar.gz, skipped downloading.\n",
-      "2023-03-17 20:10:28,892 - INFO - Non-empty folder exists in /tmp/tmp1lues0wg/MedNIST, skipped extracting.\n"
+      "2023-03-21 19:20:35,512 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
+      "2023-03-21 19:20:35,512 - INFO - File exists: /tmp/tmp3g842skb/MedNIST.tar.gz, skipped downloading.\n",
+      "2023-03-21 19:20:35,513 - INFO - Non-empty folder exists in /tmp/tmp3g842skb/MedNIST, skipped extracting.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:03<00:00, 1760.37it/s]\n"
+      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:03<00:00, 1682.24it/s]\n"
      ]
     }
    ],
@@ -312,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "757d00ff",
    "metadata": {},
    "outputs": [
@@ -468,7 +495,7 @@
        ")"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -494,7 +521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "7611f596",
    "metadata": {},
    "outputs": [],
@@ -514,7 +541,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "fe7459e4",
    "metadata": {},
    "outputs": [
@@ -522,35 +549,35 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 0: 100%|████████████████| 185/185 [02:40<00:00,  1.15it/s, recons_loss=0.152, quantization_loss=1.31e-5]\n",
-      "Epoch 1: 100%|████████████████| 185/185 [03:15<00:00,  1.06s/it, recons_loss=0.036, quantization_loss=7.58e-6]\n",
-      "Epoch 2: 100%|███████████████| 185/185 [03:34<00:00,  1.16s/it, recons_loss=0.0292, quantization_loss=1.29e-5]\n",
-      "Epoch 3: 100%|███████████████| 185/185 [03:46<00:00,  1.22s/it, recons_loss=0.0302, quantization_loss=1.38e-5]\n",
-      "Epoch 4: 100%|███████████████| 185/185 [03:50<00:00,  1.24s/it, recons_loss=0.0278, quantization_loss=2.59e-5]\n",
-      "Epoch 5: 100%|███████████████| 185/185 [03:55<00:00,  1.27s/it, recons_loss=0.0263, quantization_loss=2.88e-5]\n",
-      "Epoch 6: 100%|████████████████| 185/185 [03:58<00:00,  1.29s/it, recons_loss=0.026, quantization_loss=2.77e-5]\n",
-      "Epoch 7: 100%|███████████████| 185/185 [03:59<00:00,  1.30s/it, recons_loss=0.0248, quantization_loss=2.68e-5]\n",
-      "Epoch 8: 100%|███████████████| 185/185 [03:58<00:00,  1.29s/it, recons_loss=0.0246, quantization_loss=3.16e-5]\n",
-      "Epoch 9: 100%|███████████████| 185/185 [03:59<00:00,  1.29s/it, recons_loss=0.0246, quantization_loss=2.58e-5]\n",
-      "Epoch 10: 100%|██████████████| 185/185 [03:58<00:00,  1.29s/it, recons_loss=0.0243, quantization_loss=3.26e-5]\n",
-      "Epoch 11: 100%|██████████████| 185/185 [04:00<00:00,  1.30s/it, recons_loss=0.0239, quantization_loss=3.42e-5]\n",
-      "Epoch 12: 100%|██████████████| 185/185 [04:00<00:00,  1.30s/it, recons_loss=0.0233, quantization_loss=3.16e-5]\n",
-      "Epoch 13: 100%|███████████████| 185/185 [03:58<00:00,  1.29s/it, recons_loss=0.0235, quantization_loss=3.8e-5]\n",
-      "Epoch 14: 100%|██████████████| 185/185 [03:59<00:00,  1.29s/it, recons_loss=0.0225, quantization_loss=3.29e-5]\n",
-      "Epoch 15: 100%|██████████████| 185/185 [03:56<00:00,  1.28s/it, recons_loss=0.0231, quantization_loss=2.51e-5]\n",
+      "Epoch 0: 100%|████████████████| 185/185 [02:38<00:00,  1.17it/s, recons_loss=0.152, quantization_loss=1.31e-5]\n",
+      "Epoch 1: 100%|████████████████| 185/185 [03:12<00:00,  1.04s/it, recons_loss=0.036, quantization_loss=7.58e-6]\n",
+      "Epoch 2: 100%|███████████████| 185/185 [03:30<00:00,  1.14s/it, recons_loss=0.0292, quantization_loss=1.29e-5]\n",
+      "Epoch 3: 100%|███████████████| 185/185 [03:42<00:00,  1.21s/it, recons_loss=0.0302, quantization_loss=1.38e-5]\n",
+      "Epoch 4: 100%|███████████████| 185/185 [03:47<00:00,  1.23s/it, recons_loss=0.0278, quantization_loss=2.59e-5]\n",
+      "Epoch 5: 100%|███████████████| 185/185 [03:51<00:00,  1.25s/it, recons_loss=0.0263, quantization_loss=2.88e-5]\n",
+      "Epoch 6: 100%|████████████████| 185/185 [03:55<00:00,  1.27s/it, recons_loss=0.026, quantization_loss=2.77e-5]\n",
+      "Epoch 7: 100%|███████████████| 185/185 [03:55<00:00,  1.27s/it, recons_loss=0.0248, quantization_loss=2.68e-5]\n",
+      "Epoch 8: 100%|███████████████| 185/185 [03:55<00:00,  1.27s/it, recons_loss=0.0246, quantization_loss=3.16e-5]\n",
+      "Epoch 9: 100%|███████████████| 185/185 [03:56<00:00,  1.28s/it, recons_loss=0.0246, quantization_loss=2.58e-5]\n",
+      "Epoch 10: 100%|██████████████| 185/185 [03:53<00:00,  1.26s/it, recons_loss=0.0243, quantization_loss=3.26e-5]\n",
+      "Epoch 11: 100%|██████████████| 185/185 [03:57<00:00,  1.29s/it, recons_loss=0.0239, quantization_loss=3.42e-5]\n",
+      "Epoch 12: 100%|██████████████| 185/185 [03:54<00:00,  1.27s/it, recons_loss=0.0233, quantization_loss=3.16e-5]\n",
+      "Epoch 13: 100%|███████████████| 185/185 [03:59<00:00,  1.29s/it, recons_loss=0.0235, quantization_loss=3.8e-5]\n",
+      "Epoch 14: 100%|██████████████| 185/185 [03:57<00:00,  1.28s/it, recons_loss=0.0225, quantization_loss=3.29e-5]\n",
+      "Epoch 15: 100%|██████████████| 185/185 [03:54<00:00,  1.27s/it, recons_loss=0.0231, quantization_loss=2.51e-5]\n",
       "Epoch 16: 100%|██████████████| 185/185 [03:58<00:00,  1.29s/it, recons_loss=0.0231, quantization_loss=2.55e-5]\n",
       "Epoch 17: 100%|██████████████| 185/185 [03:57<00:00,  1.28s/it, recons_loss=0.0225, quantization_loss=3.12e-5]\n",
-      "Epoch 18: 100%|███████████████| 185/185 [03:58<00:00,  1.29s/it, recons_loss=0.0222, quantization_loss=3.9e-5]\n",
+      "Epoch 18: 100%|███████████████| 185/185 [03:56<00:00,  1.28s/it, recons_loss=0.0222, quantization_loss=3.9e-5]\n",
       "Epoch 19: 100%|██████████████| 185/185 [03:58<00:00,  1.29s/it, recons_loss=0.0216, quantization_loss=3.22e-5]\n",
-      "Epoch 20: 100%|██████████████| 185/185 [03:58<00:00,  1.29s/it, recons_loss=0.0222, quantization_loss=3.17e-5]\n",
-      "Epoch 21: 100%|██████████████| 185/185 [04:00<00:00,  1.30s/it, recons_loss=0.0211, quantization_loss=3.31e-5]\n",
-      "Epoch 22: 100%|██████████████| 185/185 [03:56<00:00,  1.28s/it, recons_loss=0.0215, quantization_loss=4.93e-5]\n",
-      "Epoch 23: 100%|██████████████| 185/185 [03:56<00:00,  1.28s/it, recons_loss=0.0215, quantization_loss=3.38e-5]\n",
+      "Epoch 20: 100%|██████████████| 185/185 [03:55<00:00,  1.27s/it, recons_loss=0.0222, quantization_loss=3.17e-5]\n",
+      "Epoch 21: 100%|██████████████| 185/185 [03:55<00:00,  1.27s/it, recons_loss=0.0211, quantization_loss=3.31e-5]\n",
+      "Epoch 22: 100%|██████████████| 185/185 [03:55<00:00,  1.28s/it, recons_loss=0.0215, quantization_loss=4.93e-5]\n",
+      "Epoch 23: 100%|██████████████| 185/185 [03:59<00:00,  1.29s/it, recons_loss=0.0215, quantization_loss=3.38e-5]\n",
       "Epoch 24: 100%|██████████████| 185/185 [03:59<00:00,  1.29s/it, recons_loss=0.0214, quantization_loss=3.36e-5]\n",
       "Epoch 25: 100%|███████████████| 185/185 [03:56<00:00,  1.28s/it, recons_loss=0.0211, quantization_loss=3.3e-5]\n",
-      "Epoch 26: 100%|██████████████| 185/185 [03:57<00:00,  1.28s/it, recons_loss=0.0205, quantization_loss=3.57e-5]\n",
-      "Epoch 27: 100%|██████████████| 185/185 [03:58<00:00,  1.29s/it, recons_loss=0.0208, quantization_loss=3.33e-5]\n",
-      "Epoch 28: 100%|███████████████| 185/185 [03:58<00:00,  1.29s/it, recons_loss=0.021, quantization_loss=3.18e-5]\n",
+      "Epoch 26: 100%|██████████████| 185/185 [03:56<00:00,  1.28s/it, recons_loss=0.0205, quantization_loss=3.57e-5]\n",
+      "Epoch 27: 100%|██████████████| 185/185 [03:57<00:00,  1.28s/it, recons_loss=0.0208, quantization_loss=3.33e-5]\n",
+      "Epoch 28: 100%|███████████████| 185/185 [03:56<00:00,  1.28s/it, recons_loss=0.021, quantization_loss=3.18e-5]\n",
       "Epoch 29: 100%|███████████████| 185/185 [03:56<00:00,  1.28s/it, recons_loss=0.021, quantization_loss=2.49e-5]\n"
      ]
     },
@@ -558,7 +585,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "train completed, total time: 7012.97215795517.\n"
+      "train completed, total time: 6953.2038769721985.\n"
      ]
     }
    ],
@@ -620,13 +647,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 12,
    "id": "0789cfcc",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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D29/+drRaLTz00EMYGRlhR7SIf/Nv/g0ee+wxfPazn8UNN9yAt73tbRgaGsLq6iq+/e1v473vfS9nY+7k+qampvCJT3wCH/rQh3D77bfjJ3/yJzE0NIRHHnkEjz/+OA4fPtyTSV9xXFZsQAC2yPDb6DOvfvWre77XK+TSbDb1P/mTP9FvvPFGXVEUPRKJ6O9617v0+fl5/d3vfndXJhWh3W7r/+2//Tf90KFDuiRJ+vDwsP5v/+2/1XO5nO5yufRbbrml5/c/+OCD+pvf/GZ9aGhIdzgceiQS0e+88079t37rt9bFYq80et1LEbFYTNc0Tdc0TY/FYvz6s88+q7/73e/WJyYmdEmSdL/frx89elR///vfr3/rW99ad55UKqX/5m/+pj49Pa3Lsqx7vV79lltu0T/60Y/q5XK569gzZ87o73rXu/RoNKrb7XY9Go3q//pf/2v9zJkz6867WRZbrz3xxBNP6B/84Af1Y8eO6X6/X1cURT9w4ID+nve8h7NARfzzP/+z/vKXv1x3Op2XlUFH6HQ6+h/8wR/o+/fv1x0Ohz4+Pq7/xm/8hl4ulzfN8Pvc5z6nv+pVr9I9Ho8uy7I+NTWl/8zP/Iz+wx/+8Ipc39e//nX9gQce0H0+ny5Jkn7gwAH9N37jN3qGnSnU1wtbhRs3gkXXB6Nk7Ny5c5iensZP/dRPsW1pwsQg47q1+XeKWCy2zqlVqVRYlXz7299+Da7KhInrD9etzb9TfOITn8DnP/95HD9+HMPDw4jFYvjWt76F5eVlvOlNb8JP/MRPXOtLNGHiukDfEf8DDzyAZ555Bt/4xjeQyWRgt9sxPT2NX/mVX8GHP/xhswGJCRP/DwNj85swYaIbfWfzmzBhYnswid+EiQGFSfwmTAwotu3we//73381rwPAxTzvdrvNedIWiwVWqxVWq5UddVarlY8hd4X4vq7r/HmbzYZOp4NWqwWbzQZJktBsNhEMBqFpGgqFAuf0Dw0NYWhoCJVKBTMzM/B4PNB1HZVKhT/b6XRgsVjwqU996qrfi+sJH/jAB3bcQXgnn+vVGdpisfC5tjqnruv8efocfUaWZaiqina7jVKphFarBZ/Ph+HhYbRaLczNzfF5Op0ObDYbLBYL2u027HY7/vIv//Kyf8/1iuvK21+r1WCz2WC32zdMHyUCtNlsvKidToeZgdhHQNd1WK1WSJIE4GLxRavVQrlcRqvVQqVS4Y1Sq9WYEXg8HtRqNVitVmiaxszGZrNtWB3Wz+h0OnyfjI+bQSTSjT4rMvDNiJvWmtaU9oHxOug55XqI30P7pN1uo9lsotVqodlsolwuI5vNQpZlKIqCfD4Pq9UKh8OBdruNTqcDh8OxruR8r+O6UvtJgtNC0R8tGBEvEfpGEoK4NRGqJEmw2Wy8eNVqFYVCAc1mEzabDTabDfV6HblcDrVaDT6fD61WC61Wiz9LG3Krqrh+hShJxcetPiOuUS8GYHyfYGQA9Nx4DuP1ERPZ6DpbrRZqtRprgw6HA41GA+l0GuVyGV6vF1arFa1Wq6v5CzGcfsJ1JflJtSbiFpmBUZ0XNQO73c4NLum9RqOBZrOJdrvN5oOu63A4HGwK2O122O12ZjZE7JIkQdM0Pgdt0Ha7/ZIKWkxsTOAicRsJ16gRiMRNqrl4HnGtxO+g52L5rc1mY02gUqlAkiQ4nU5UKhW0Wi3eg51Op+/W/roifgIRsCzLvJCNRoNfF1VAAKyikW1OG6NerzNRA91aATEEWlji6q1WC41GA4FAALlcDtVqFaqqwmazoVarQVGUa3NTriGMUvZKnZNglNS0zrROrVara83pj44FurVGY2298bvIZBDNAwCs7QWDQVgsFhSLRdb8Go3GNe/gfKVxXf2aarXKUtflcnE9c61WQ6VSgdVqhd1uZ1ut0Wh0OQcdDgczDF3X0Ww216l4RvWx1Wox8ZO6V61WEQwGUavVUCgUIEkSM51+4/7XC+je2mw2yLLMGhlwSTrTOhEB02fo0eFwsHpeq9W6NEhRWND3EYhxtNttVKtV3nv5fL6LSfRbPtx1RfykwrlcLjidTlgsFjSbTdjtdjidTvb8kzpG3J+YAkl10hqcTiczAXL8Uc8/oxeYJAjZhI1Gg88rOhn7ze67ljBqFDabDU6ns0vjI7NNVPPFiBAA3heKovDsgVKphFKp1CUcemkw4j4gplGpVACA156+o99wXRG/zWaDoihdEr9Wq8FisUBRFCZ4igjY7fYu4ifvLGkBZLfX63Xoug673Y56vd61EURToNPpsJ+ANoAsy2z3k7/AxJWBqIUBF30+qqpCkiR27IomGmlntAZkHpDGIEkSaw0kOKrVKp+Hvsv4vXQtdO5SqdTlH+pXX8+uEr+R4EjtBi5xVkVRoKoqx9hbrRaH4ug4Uv98Pl9XyAYAawydTgd2ux2BQAC6rnNMV3TwGUNOolpfLpf5PWMbchNXHsT4FUXpUu9FKS/a64qicMdnEgQiY3A4HFAUhf0+QPf+E2F0LJKwML7fb9hV4hdvqLjAdHM1TYMsy9B1HdVqFdVqle11ss3tdjv8fj8mJycxMjKC2dlZWK1WpFIpFAoFAOgyDSh2W6lUmIMbpXcvL7KoVho3h4krB7q3DoeDIzbkfSfCpX2i6zokScLw8DCGh4eh6zoWFhZQKpVYMyOGYbFYWBMQnYDbSRYS98flJBftNew68ZPqTpJaTOghtb1araJcLqPRaADotsdbrRZCoRDuu+8+9rxXq1UMDw9jbm4Oq6urXaE52jCSJHVJfGA9UV8Nr7aJzUEqPTnrGo1GV5iWHH26rkNRFIyPj+PYsWOYmJhAvV6Hpmk4d+4cZ2IC6HL0KYrCgkP8zu2ucz/vh123+UXJa7fbeQhHvV6Hoihsf1PyRa1W42Qcu90OWZbhcrngcDiQSqWgaRo8Hg9kWUan08Hc3BxLELLXJEmCoiioVqtbXl+/qnjXG0SHrWjTE4M3JnlJkoRIJILDhw9jamqKc0JCoRCKxSJisRinh5NgIT+PLMvrGEAv9Fr77WQy7lXsus1PIK7sdruZw3s8HvYHkBZQLpeh6zo0TcPU1BQmJycRjUYRj8dx6tQpWK1WHD58GHa7HcFgEI1Gg1V+MTVTURT2G4jXIsaMTVw5bHVPRQecGGoTP29U9Q8fPozx8XE0Gg0sLS0hm82i2WyyQCACN0YQKMpDmuTlSPON/AT9gF2X/ESQFouly1FD3npy4NGieTweuN1uTE5O4tixYxgfH4fD4cDi4iLOnTvHKuLo6Cj/L47iFiW/LMtd471E9OsCXwtsRlwi0yWpLyb1AGDvfafTgaZpCAaDmJ6exoEDByDLMuLxOGZmZpBIJGC323lqUb1e5xRwMTxIwoQiCJeLy0lp3kvYdeInVY4SN4gYyQtPEQDyzI+MjGBsbAwHDhyApmlIJpNwOp1wOByIRCJYXFxEPB5HrVbDwsJC12fp+0j9I+8vsF7y02um3X9lsB1CEfMz6E8s2nG5XIhGo5icnMTY2Bjcbjfq9Trq9ToqlQpKpRKsViuHYmu1GocDxQiNKGiMPp9BXutdJX4xCcdisaDRaLAjR1EUWCwWqKrK2XuapiEajSIUCqHVamFxcRGdTgfDw8PQNA233HILlpaWUKlUeLwXefpF5yKVY8qy3OVzMGZ9mbgy2IrwiclKkgSHw9GVoCWafZFIBIcOHcLo6ChUVQVw0ekryzIikQjn4xMzoMpLShISGYDIXIyRnF5hvV77ot/2yq5lLpATT0zG6HQ6LIlVVUWpVEKz2eQS3EAggGg0Cq/XyxwfACfqDA0N4cYbb+TkEDHUQ2qlyOlpkxlhLP80ceVBhE332G63Q1VVaJrWldhFarvL5cLo6CjC4TBUVe3S5FwuF0ZGRjA6Ogqn07ku81LU4MS1dzgc616n48XHfiPyjbBrkp9i7tVqFW63G4FAAJlMBu12G263GzabjRnAyMgI0uk0Dh48CI/HA4/Hg4mJCTgcDlSrVZRKJebulIdtt9uRTqeZ+DudDufkA5c8/263G/l8ntN3jXZmr8IQE1cetB80TWPVndbIZrMhEAhgZGQEwWCQw7Ri+I5UeErjNUpwyvwTGTtpGbQ/CNuN4/ebcNg1yd/pdFCtVjnWnslkkMvlOAUzlUqhXq/DarViZWUFkUgEkiTB6/XC7XbD4/HA6/UiEAjA6/WyHT8+Po5QKMRqIgAOCxrztsVqLqC7kQQ9HxSuv9sQCYy0Msq/AMDO2k6nA6fTiWg0imAwyOtIpgD5hmw2G4d5xdJsYH1Gpuj4o/OZ2EXJ3+l0uF6a4vfBYBC33XYbOp0Ovv/973NRhizLcDgcUFUVxWKR7T0q63U6nWi1WlzIkUwmsba21uVMJBWP7H+6BpLwvXL7iUmYeOnYKnqi6zqXSpdKJRSLRbRaLWiaBr/fD7/f3xWxAcBefEr/1nUdTqcTmqbxhGUxRChmBtL6GjWCQcaue/slSUK1WkWz2cThw4fxzne+E6urqzh16hQWFhZwww03oN1u4+677+auKuVyGbFYDA6HA9FolBecavcjkQg0TWPvrwhxA5LUp/CiMSRkTDc2sXOIzFV8JFD0hZJyxJRcn88HVVVZSJAPh4iX+vCpqopcLofl5WV2ElNGX6/sTWMUoJeWN0h2/64RP0lYsutI/SqVSqjX69w3j5IxSN3XdR3ZbBalUomZxtjYGBO+1WpFNBrFjTfeiFarhUKhAF3XUa/X+RjRrqfwEjEJMbtsEBZ8tyAyUZHwSTOTZZmr9wiSJMHtdiMajUKWZdRqNWSz2a4KS5/PB5fLBVVVYbVa2T+0sLCARqPRVYUnNt8w+naMCUW9/u937Krkt1qtnGXncDhw+vRpJJNJdswdPHgQq6urCAQCKJfLCIVCWFtbQzqdRiqVQiwWY/V+3759vEFcLhf27duH5eVlZDIZlMtllMvlrnp80eYk209MAzUz/a4sNpOglNFZrVY5AkPSfP/+/QgGg2i321zSXa1WmVlTrwWS7k6nE4cPH8by8jLOnDnTtX5iLT5pDcTse13rRtfbr9jVImWyw+x2OyRJQiqVwsmTJ3H69GneDMPDw5iensbk5CQSiQTq9TqKxSLH8Cnnn7K1qOw3GAxiamoKgUAAALrKgSmZSOzwa+wQLDoETbx09HKein3wGo0GyuUyisUiOp0OVFVFOBzGyMgIp3mTA9DhcECSJHbqUcVnvV6H3W6H2+3G0aNH4Xa7WbszVpDS94rx/l7XK2qK/Y5dlfyk8pF31ufzwe12Q1EU6LrOGV333XcfkskkZmdnYbPZMDk5iRtuuIEXkRa3VCohl8vB6XRidHQUt99+O2ZnZ7lQiIiZYsfA+h5x4iYVXzPx0mBM4zVKXl3Xkc/nWTX3er0YGxuDx+Ph/gxiw1Zas3q9zu3ZXC4XXC4X+wmcTifq9fq6dGFR5adHo6Y3iOu+a8RPUt/pdPLi+Xw+XnxK8ohGo1zpR2G/paUlLtYBLpbwEhNRVRVjY2Ow2Wzwer0olUoIhULwer0AwAkjVC5qzACkawN6D4vYbgzYRG/0SqARC3moDoMGZ1C1XqFQ4AaqZMNTdh6Zjm63GyMjIwiHw6jVaj3NNlHNFxONNvL4D9I676rDT+yiI6ZyiqobETJ5cRcXFzkBiBw1pM7ruo5Go4Hl5WWcPn2a00XFXgDkT6ANR+cQ68QH1eFzLSCuG0lgr9eLcDiMUCjEyVrk46GOSmKqNmkFdrsdq6urnBxWKBSYsYvhXmP5cK/GHoOIXVX7Re9rs9lEJpOBLMsYHh7G6Ogojhw5Ap/Px9V8kUgEpVIJ5XK5K0FIZAYUy8/n82i323C5XOwkogQS4FIJMQCuKejV4MF0+l09iCFV4CKjdTgc2L9/Pw4dOgSn04lSqYS5uTmcO3cO+Xy+y9QT07Vp0AoxevID0XkpdGjs80cRASPRi9qf+Lyfsatqf6VSgSzLXTaZy+XCkSNHcP/998PpdGJtbY0l/vLyMkqlEtfjU7MHcgZRWJAWtNPpIJ/PI5/PcxJQo9FAtVrlHHKxB6CoTtLGMgn/6oEYq+hsm5qawrFjxzAyMoJUKoVTp07h+eefR6FQ6KryI4ZBhEsSXuz4Q+8Dl4iYQsokCMRmIXSckfAHBbvexosKd8QNQDn1jzzyCBYWFrC2toZiscihOHGIB/XtpzAQcXzi+tRxVazfpxJfYgZU829c9EFb/GsBuuckmYeGhqBpGubm5nDy5EnMzMzwjAbRRhdTtI0NPUUTjs5NDEJMCaZOzsQ4jAlgxmvsd+yqza9pGhMqcfRcLodnn30WuVwOc3NzKBQKPLCTbDYCfZYGaxj7ANKiUdovmQS0OUhboLpvui7x0cTVgzHZp9FoYGVlBcViEdlsFisrK6hWq7zuvbIzCWKbL2ORDv2JeRz0faK93wuDQvjALhO/SGhiyC6VSqFYLHLMF7gk7emRuLfYgx1AV2EIZfYB4LRQigVTSbDYm994HYMU490tiBJWJCxy3M7NzbEtLq4JMXSjmWDM29/IY09/4jBOMXIgHiNep2nzXwUQJyavP4CuXnvUXFMcvS1Kf6MNRznaYvEHfYYSQUjdp5pvKh4hJiJuMBNXD72cqESAYtddkejF6Mx27XKRcOk7NU2DpmnIZDI8AEYMNW7m/Ot3XLUMPyMXJduMavqppZbFYuEYrbGjCwBOCKJz0saguXzEDMimd7lcfB7yFIdCIYRCIbhcLgCXxkLRWDDRvDAZwdWBKGlF4hK7O4kSvpfEF9HrM0bYbDa43W6uJBXTfSlrUMwB6PU9/YwrRvy9iMZ4Q2niKSXpAJequ8ThCvQeOWnE9EwxzksMhSIA4pAG8t4D6OryA1xqJKEoCl/TIC36bqJXrr3obxFV+40Ehmjbb7RO4uui4HC5XBwlErUHYhyDrPntWm6/1WqFy+WCrl+s46Zx15TpRwtCNhnZf+SdF2092hTU6ZfOTyEfygkXu7ZWKhXOFaCagGq1ynMBzKq+qwfRjjcSPDF6Y5otOWppvXrZ6KLtD1wKA9Jz0ujIwStGDeicva51ULBrNr/NZkMwGOSGi8DF8VzUebdcLsPv9/N7FKYhKS62/KZwnpjyK2oIYtOOVqvFgxc1TePPkYNJbBFl4uqA1oKyK2lqMhV4VSqVdY5BkViNYTijk09cO2PiVrFY7PIrAJfCjOK5BjG5a9eI3+FwYGxsjMM7pOpT8s7IyAjb/kB3PBgAh+1InRfHaNN7lDFGzRqpciyVSvF3kbYhSoPN7EYTW2MjwhE96DR6u9Fo8Nx7q9UKVVWZuRtrLkgL7GWP92LYonZAQkCcuEvnBMAao7Hce5Cwq5I/EonA7XbjscceY6fLyMgIFEXB3XffjdnZWaiqyhKdwnPGkk5xscQxzjT+i7L7aPOIOd5U5GOc8T7oed5XA6LtHYlEEA6HkUgkuOV2q9WC0+nE2NgYqtUqstksZ21S5KZXBR6dWzQVxJx9KgPWdR21Wq1rOAyZExup/YOEXe3h12g0sG/fPgwPD/NUVtEGfM973oMLFy7g8ccfZ67t8/l4iKOY503x+lqthnw+z1lhYpeWTqcDr9cLv9/flS1IJgUAU+pfAfTKjhMz8yRJwvj4OA4fPozV1VXu2ARcXI/R0VF4vV4Ui0Ukk0nEYjHk8/l19r1RuxBDu+TjEb/bmNdPUSBjazD6TK//+xm7RvzNZhPxeBw+nw9Hjx7FhQsXmMBdLhdeeOEFHr38ile8AqVSCY8//jiq1Sq3cSLCpsIeauUk5v8T4TcaDVQqFe7kQxyfmoEavbyDsNhXC0aCN75O2lckEoGiKFhcXOQy7Ww2i0QiAafTiX379mFiYgLJZBLnzp3jZi7GVur0HcaiH3EEOxG6MWeAPt/LbBgUoidcVeIXNwWl8tpsNkxNTeG5554DABSLRYyOjiKfz+PZZ59FoVDA6uoqz18LhUIoFArcykmU+lTPPzo6ylWAy8vLXLu/vLzMnmNRShhNB/HRxM5glMoigVUqFayuriKVSjEzJkKs1WpYXV3lkGskEsHIyAiAi36iTCbDmZkE0txIe3O5XBzBIWZBMXzxusjc65VvAAye0++KEf9GrZBFaZDNZrG8vMzOvWw2C5fLhWw2C0VRkE6nMT09jTNnzsDv9+PgwYNsozebTWSzWeRyObYLFUVBMBjExMQEPB4PZmdnsbCwALvdjlwuB1VVMTQ0xNEBek2s6AO6nYobef4HaVNcCYj3sNFoYH5+Hs8//zwmJyc5aYvU8U6ng2Qyyeadx+OBy+XC0NAQ7HY7N+8gBgFcmr7kdDrRbrcxMzODbDaLRqPB5yFmT1JfDBUbnYe9/u937JraTxvg1KlTcLvdcDqdCIVCTFQ0jttut+Puu+9mBx5x82q1imAwiKNHj3J1XjabRTQaRTQaxZkzZ3DixAkUi0UMDQ1xYwiqAFQUhUN9Zs/2K4vNimTovVKphLNnz3LURdM09tOIoVfS1Mi0KxaLnKkXDofh9/s5UuRyudBsNnHu3DlUq1U0Go116j6ZBqKpsNVEpkFR/3eN+InbP/TQQ9i3bx/e8IY34N5778XXv/51PPbYYwAuzuDTdR2vetWrkM/nMT8/z919I5EIYrEY4vE47rjjDgQCAaytrWFtbQ0OhwNnz55llY9UQlL/qX57qzHNpnS/cjCmS3c6HWSzWSwuLsLtdkPTtC5iJy1MlmV4vV4UCgUO/1ESlqIo8Hg8POnHarWiWCzi7NmziMViTOjkSBYzCknDEBO6ehH5oBA+sMuz+jRNQz6fRzqdRiaTwZkzZ/D000+jWCziyJEjyGQyePHFFzExMYF6vQ63243JyUmEw2Ee30VhokKhwE6/RCKBtbU11Gq1rtlvlFhCG0VkBOLim5rAlYco9YmwO50O1tbW0Gw2OaQLgOcr0FqRI5cGcXg8HgQCAfj9fg4B01qeO3cOs7OzaLfbUBSlK43b6Hw0tm/b6Lo3+039hF2t6mu32xgdHYXVasVXv/pVRKNR6LqOsbEx1Ot1+P1+HuJBhT9i223g0sDFoaEhRKNRLC8v45FHHsHi4iL7ASwWC+f/i0U7rVaLmzmI10UYxOKOqwmjdKU+DPl8nnv1A5f6LJJTl3ox6rrOhVperxdOpxOdTofzP3K5HGKxGBeIiS2+RJC9T+ngO3Xs9ZtmuOvjuqixpsVigd/vh9frRbvdxvLyMsLhMJaXl/Hcc8/htttuw8TEBDMLkh5U7ANcXFRS/cWsLlL9SKWkdFKSJsZpLWJWmYkrCzFGT1pWo9HgHoyU708qOeVjSJLEMxiIGdBxFouFmUgqleLybNGpR45mMQOUkoc2C/FulE3Yj+bArhJ/q9VCOp2G1+vlybpra2s8lbVUKnUN6yS13fj5XC4HTdPQbDaRSCRYq2i1WuwRJjtSLAyhDSBuDnGzmNgZtiNJxYIa0QEnmgbVahXFYhHAJVNAjM+LFZ31eh2JRAKpVIpNPPoeI7GKa2+M7BiTe3qhX/fGrnbyIYcbJd1Qv3ar1cqdWn0+H+djU2ovSfRarYZUKsXNHePxOObm5pBKpZDL5bh2mxabyoVpJjstPiWBGK+vXxf5amOz+2aU+iTtZVlexwBqtRqSySTK5TJcLhdLejEfv16vo1QqYWVlpUvlNybwiCBmIQ5uEa/PeL3Ga+9XXDHi3yzcI/5PjhkatEAqHXBxCEMul4MkSZifn8e+ffvQ6XTYQUgZesPDwygWi5idneXXaWFtNhsKhQJL/Wq1ClmW0elcHBEuSnkxBkzPaZNSHQGZDGKbcBPbh7gvyAcjyzLXYACX0nTb7TbS6TQSiQQCgQAzCDL1aEJTLBbDhQsXeG5jr0Yg4iNpDPTadvYqHSsygH5jBLvexkvMsSZurKoqx+AbjQbS6TTOnDmDTqeDaDQKt9sNVVWxsrKCxcVFVKtVrK6u4uzZsyiXyxzXFVt+iX35aPHpudGu6/Vc7CUoHtNvG2A3YCQqcuqJzJYe8/k8Lly4AL/fj2g0ys1f4vE4VlZWEI/HEY/HkU6nUa1WmXmL9r2YzitqfMDlO3X7WSPcdYcfqeCUyEHJGaqq8oIUi0W8+OKLyGazOHDgAEZGRiBJEhKJBGZmZpDL5ZDJZFAoFLjaT/QQ08gnXb9YykkJRLqus7pvbPxgrBgTy31FVdJ0DF4+xPsnquDG6jqawTA/P89p4JqmIZfLYWlpCaurqzyymyQ5cGn0utgNSHTwUmy/X4l4p9j1iT1kuzebTbjdblQqFeRyOXbWiXUAlUoFpVIJi4uLsFgsKBQKXfPag8EgV2kBYKJ3OBxdC06dW0X1Uozv9yJo8b3Nwkgmtgcx2adYLHLfBVL9xbZs+XweZ86cQSKRgKZpXb4eUYqL5zVqAKT5iTMdgO2l8hoLlbZyCO5V7KrDjxx39XodlUoFqqpyWS5578kpRBlgmUyGR3EB4JAQSXFyHlL/Pur5L0oU4v7i5qD3jQsqbggxOYX+TMm/c4gdewF0DWQVW7LTMY1GgyM31Lxlo+xMYwSBGLY4nYfe26h+Y6PXTOJ/iSBnH3DRa1sulwGAy3G9Xi/XeZM32GivkdZAbbmJeDudDkt92kgAumw+cuKJuf0b2X9kLtBGIalEm9fEziA64Kh1F0VjjNmAALrCc0TEIgM21vAbG3Ju1LBlqzUU7XyRcfRbJuiuj+gm9YyytFqtFvx+P7dzEtVt0UFHBSC0CcSQHRWLUGGH6KEn+5CcgqJGYNwMondXlmVYrdau6T6m1L9yEMuyqcsOgC6GTs/FPyNEohdtfjGdl47bLuMmhzRpoka/T79g13azqDIRYYtNN0ldF4lUJHRS+6hVlzh0U0wVpRx+AGxTUgMQY/suUfIb7Tzq9yd+B5kNJnYO0cMvroXRUy+ujViLITJgcaoT0E3gxs+J37/RNdE5xLUXHb39xvx39dcYbyaF/ZrNJgqFQpfEFj8DXFLFnU4nVFXt6vNPm4XyBYhIqTCEzmMs5zVeC71GBSbApSaSYuqwiSsD0eMvolfhjdHuFiW+mAkq7heRyW9n3YjJq6rKJqLoP+g3xr+rO1lcUFGVp4ILY+01Lawsyzxhh0J3pL6rqspFQOTgoco+8g6Lnl9RLSSpIE4KAi7lkpN/gaQAxZVN7ByiVCd/jFhwY1Txxb1gdOiRCSmuHT2SibhVXr74Ou01SZK4wzP5fhqNRt+t/a6G+jZqoyR6/I0wbhYiyGazCYfDwYtPxE1de0SGYozhU+hH7OhKtQDUaIK0BnHjUNaficvDRslUwKVKPwBdjj/xONK6jBAzL0UHHaWGi1l94n4zmgJ0blp7yjcgiJGqfsKuEr9R6hJoAxSLxa6FNHqA6Thq+kFFPJQ4JHr7aRa7UZsgAiaCF0d1kekgSRJKpRK/TwyEVEETlwejxDQ+J98PJWwZ94lxZFcv7VDMxRC1SdFxLPobgEs+AyJsqgXJ5/OsMYr1CP3G+Hc9ww/oLqOlG9xqtVAoFACsV/VEiSGqcrTgInFSGjG1Zzba6WQHktYAdHeBBcDDPihhSDz/Vi2gTGwNUaLT/SQiN9ruvbz+tF7iAFc6hlLExQxAkXmQP0c0+4BLA2EpnCxOjKLrErWBfsA1IX5RstMNJ6LtddxGnyW1nIiUPP2k8okbi1Cr1bhWQCRyRVF4lHepVOqKPpCPwfT2XxmIppRxjFYv5x9BZOIUIaLXKLJDcxnJv2PUMCVJ4gQyyiuRJAlerxeKoqBYLHJTEUowEqNF/YRdJf5eGXVkT5EmIB5r/J80BLFOn8o8ZVnmkmH662U/+v1+aJrGUQbSJBRFwcjICDqdDs6cOdN1PWKkoN82wHbwUtTdXnb+RslVIrHSvTZOUKbXya9TKpXYYQuA11TUDOnciqLA7XZDlmWUSiVOIFJVFZFIBAC4F6Dxe8UMxH7Brkt+UZ0TFwlYX1xjhNEEEO1BYgoioYq2IyEWi+G+++5DOBzGqVOnUC6XcezYMRw6dAjLy8s4efIk+xMA8HnF0M8goxcD3+r4jbDVGhsfewkIkdGTGWFk0vS8XC7j5ptvht/vxwsvvMDPp6enEYvF8Mwzz7Aj2RjapW5B/YRd/zXiQoppuAC6iMsY1gHARCg2YRQ9/VQvTp/r1d0lEAjg7NmzyOVyuPfee3HkyBFYrVacPn0aCwsLXP9vt9u5dyA9pzZQg4heyTIvhRGK3vlefhnRUWf8jPj94iPtCzqv6KGn+Y2zs7MYHR3Fy1/+ckxPT/NrKysrXVWixs5Q/cj4Lfqg7mYTJgYcg2fAmjBhAoBJ/CZMDCxM4jdhYkBhEr8JEwMKk/hNmBhQmMRvwsSAwiR+EyYGFCbxmzAxoDCJ34SJAYVJ/CZMDChM4jdhYkBhEr8JEwOKbVf1XQ8VTRvVgVNl33ZbbFEVmFg5JvYI2AqDVgt1Paz99YJ+Wvs9VaC80dAGkWCp5tpIyNSZlcaC06hwsaGH2aKrP7BZt95exwL9RdTbxbZLeq837r/dRTMO2hQ7vBjHb4mTXTfDoG2U623tt8JmxL/Rb9numvbT2u85m9/YyYdeozHdRhjbhIvmgdgF1nhOE3sXxlbhxv/FfnziDIBBw55S+8VpLCKok494HEl145QeYxNI4FInIWrZZWLvQ2zDJXaLJjNR7BQ1qEx/TxF/r4aPxtcVReHhmtPT07jzzjsRjUZRKpWwtrbGAyLEkV7tdhsrKys4f/5837Vn7mcY11/svx8Oh3Hw4EEEg0F4PB6Uy2UeDU/deBVFAQDMz8/j7NmzPTv+9jP2PPGTVHc4HFBVFaVSCUeOHMGP/uiP4tWvfjUmJiZgs9lQrVZRqVSY+1Ovv1qthmw2i29/+9v40pe+hOXl5YFZ/L0O4zo5HA4cOHAAr3zlK/GKV7wCBw8e5GnLpVIJzWaT/Tk2mw0OhwP5fB5f+9rXEI/HkUqlBmrt9xTxGyHa7sFgEKOjo3jta1+Lw4cP44477kAoFEKlUkGpVILD4cDo6Ch0XUexWEShUECxWOQxzKQJDNLi9wuGhoZw00034b777sNtt92Gffv2cYtuADzBp1KpIJVKoVarwW63w+v1shN4ECcxXVfEv9lMNtGGJyK12WwYGRnBa1/7Wrzyla+Ez+fD0NAQfD4fnE4n6vU6rFYrNE1DNpvFysoKTp48iWKxiHK5jEKhwKOYzp8/j2KxeA1+tYmdwGazIRQK4eUvfzmOHz+Om266CSMjI3C5XGzOUbfldDqNmZkZnDt3DqurqygWi7BarfB4PFBVFXNzcwNp7u36oM7NnCuil1bkxkT0JJ3b7TY0TcNNN92EH/3RH8UrX/lKRCIRyLKMYrEITdNQLpcRiUSQz+eRSCSg6zqeffZZ/Mu//Avy+TxsNhvcbjf2798Pn8+HUCgEl8uFbDa7a/fDxPYheuNVVcXBgwfx+te/Hvfffz9uuOEGKIrC7bYpb6PZbCKRSOD06dN4/vnnsbCwgFwuxzMYXS4XVFVFPB5nJ+AgaX67Tvy9Zq8DvYc5GuPwxAQ0TcPNN9+Mn/iJn8Cb3/xm+Hw+5PN5HtNFgzwvXLiAxx9/HPF4HOFwGHNzc1BVlUc1kROwVqshlUqh2WzyeC4T1x8sFgtcLheOHDmCN7/5zXjta1+LaDQKAKjX66jVagAuagXJZBLnzp3D2bNnsbCwgJWVFZTLZTSbTfb8t9ttlMtllEqlLo1yULCrxL8Z4YuTUTZK1dV1HS6XC8eOHcNb3vIWHD9+nKV8u93mCb7ESB566CF87Wtf4wmwAOD1etFqtVAqlZBMJhGLxeB0OlGtVpHL5a7ejzfxkuF2u3HzzTfjR3/0R3H//fcjEAjwyK1Op8N7J5vN4tFHH8XJkyfZxiffDk1u7nQ6qFQqsFgsKBQKps1/tbHZDSYbXuTAYgjH4XAgEAjg5ptvxutf/3rcd9998Hq9LPFpLDcASJKERCKB1dVVZDIZ5HI5TgRyuVyo1+tdI8HF+X6DxPmvd4iTmnw+H44dO4Y3velNeNWrXgW/3981jZekeaFQwIkTJ/Dtb38bq6urXZOcaXozCRdK6SaGMGhrf00cfhuNXwYu2fv03OFwIBgMIhqN4ujRo3jFK16B22+/HYFAAPV6nWfoVSoVNJtNuFwuxGIxPPvsszzFtVgswufzoV6vM8GrqgpFUbpiv7SBBlEKXA/oZfrJsoxgMIjbbrsNr3/963H33XfzWpIXXyzSOnv2LL773e9iaWmJE7/q9ToajQZardY64SJqDIOGazKo02hbGR18NJNPkiREIhHccsstuPXWW3H06FGMjY3B6XTy50jaU9FONpvF3/3d32F5eRmZTAarq6uw2+2s2pHNT2ogfR9tAhPXDmLthSRJcLlcGBsbw8te9jK8+tWvxs033wyn08lzGY3rGI/H8f3vfx8vvvgiGo0GawbEIDaaCD2oxT27TvziCGaCw+GAw+Fg1V9VVfh8PoyMjGB6ehq33HILbrzxRgwNDQG4OIaZFq9cLkPXdZ6s+vDDD+PEiROs+lerVYRCIWSzWbhcLkiShEql0pX2S6OgTal/bUD33uFwMNEHg0HO17j11lsxOjoKSZK6UraJiEnd/8EPfoDnnnsOhUIBrVaLHYBAdx2HMZe/H8dvbwfXhPhFFV9VVU7BjEajcDgc8Hq9mJiYwA033IDx8XEEg0E4nU4O9QFAqVQCAM7qK5fLaDQa+P73vw9FUbC0tIR8Pg9JklCv1+F2uwFczOijMA8AVKtVVgl7jfQ2cfVgt9vhdrv5z+/3Y2hoCOPj45iYmMCBAwcQjUZZ0+tFzCTJ5+fnceLECaRSKdYIgPWFO+Jz0jYHVePbVeIXx2a3Wi24XC7ccsstuPvuu7F//35omsbjsGVZhtvthqZpbNfTgtnt9i4p3el04PF48OUvfxnLy8vsvFMUBZqmcRjI7XZ35fSTKqgoCmselPtv4uqAmKumadi/fz9uvfVW7N+/Hx6PBx6PB263G16vl/eCzWZDo9HgNRfPY7VaoSgKUqkUnnrqKczPz6NSqaDRaHTtNeCSn0l0ItJ+Is1j0JjArhE/5dN3Oh1Osrjvvvvwlre8BaFQCKVSiYle0zRomgan07muOQeFdTRNQ6vVQrVahd1ux7lz5/Dkk09yAUej0YDNZmOJTrn8YpYgqY7EEMyKvt2Boii46aab8MADD+DQoUPM8O12OxRFgc1m4+5MxKABcJUePaf98Oyzz+KFF15gh57oOxD/xOiRqDWYJb1XGbquo9Fo8I3ft28f7rnnHkxPT3MaJnF6Xdc5UUeSJH5dlmWW6jabjYnc4XDgueeeQ6lU6rL1SJqTA1G058VcAtoApt1/9UDEZ7FYEI1Gcfz4cRw7dgyqqnIpNa2TWLpt7L8gns9ut2N+fh5PPfUUCoUCCwby4YjH9roOej6IhA/sstpvt9t5QV0uF2RZ5sy6cDjM2XWiU9BqtUKSJGia1kXIpMpbLBZkMhl8//vfh8fjQSaTQa1WgyzLHMqjjULHE9c31vObuHqgNbXb7QiFQjhw4EDXeoseeNEBK2aF0p/dbofFYkGtVsOLL76IRCLBGmWn04HD4WDNTpT64v/GPJJB1Pp27ReTZCW7qtlsQtM0eDwe2O12ttVIXVdVFU6nE6qqQpZlztWm9F2K8bdaLTz33HN48sknkcvlmDk4HA7Y7XZWIQHwd9PmoigDvW9s/GHiykKUsvl8HgC6JL4x+iIeL8bj6X8q2CEfAgkLcZ2NDlxR6ovdnQbRyburan+9Xuf/M5kMzp49i2AwCEVReMFrtRonb9CGEDeD3W7v6rGXTCZx4sQJluqk8pPHV9wAvdp2iY+Dqv7tFmgPzM/P4/HHH4ckSRgZGeF8DYfDgUajAUmSWHpTBial8ZKPptVqYWZmBrOzs5y7QcxDUZQu6d6rRsT4/yCu/a4X9miaxgU1n/vc5zAzM4NbbrkF4+PjaLfbiMfjnHNNaZ2BQICl+djYGIaHhzlPu1Ao4Pnnn0coFMKtt96Kr33ta+xYbDQakGWZmQJJeGr7JTYCIc2EtAkTVx6kwmezWXznO9/B2toa9u3bB0mSYLVamcAps0+SJK6+HBkZwQ033ICJiQnIsox4PI5kMolSqQSXy8WOQorkiKackcmLr/fyBwwKdj23n+LzAJDL5fDNb34TTz75JNxuN6xWK5LJZFdoh1T/ZrMJr9eLt7/97fjxH/9xrgOw2WxYXl5GIBDA4cOH8c///M+QZZk3EmkP1LoJQFchCADOKCMzwST+qwOywdvtNnK5HE6ePIlnn322631jrn673YYkSTh06BDe+c53Ynx8vOscxWIR0WgUoVAIi4uLKJfL7BPYSNr3em5K/muATqeDXC7HNiAtChEgaQHAxYScTCbDIcFcLgdVVeH3+7ncc3p6GrOzs5znXywW0W63OVIAoCsBhEKIFEUYtFjvboOkLd33SqUCoLuRi9i4BbiYmz87O4tz586h1Woxc7fZbGg2m5BlGeFwGEtLS13mwWaSXHxvUG3+68bFuVWTD13XuTFHp9PB0NAQQqEQS21VVZFIJHDXXXfB4/FwjbaqqtyLnzQFSZKgKApUVWXVkrzFptS/+uilhov/k3dfrLRrNBpIpVIoFotQVZUzNKk02+VyweVyrXPeGiMF4ncZq0cHDdcN8W8Hun6x/142m+X6/UKhwJ14yuUyfD4fd3WhLr4U9qGSXQof0l8vr7CJ3cVmEppQKpWQzWZht9uhaRrcbjeazSZKpRIKhQLnddBaGgnfyAAGfb33FPEDQCqVwsLCAicG+Xw+HDlyBDabDYlEAmfOnEG9XofD4UClUkE+n2cnkug1Nob0xCQfE9cXaO3W1tawsrLCZt/Q0BAURUE+n0ehUFjX6xHoPbRlI3t/0JjBntvp6XQaq6urnAYcDAYRCoVw6tQpPPzww1hcXOTurJRVSIyCsgdFG5/69pOGYBL/9QOjWp7L5bC0tIRarQaHw4FIJIJwOIxWq4VYLMZZoptJe4KYOjyIzj5gjxG/xWJBpVJBJpPpyhiLRqO86H6/Hw6HA+12m5NHiNgphxwAmwG9tAAT1weMkrhcLnNbLlmWEYlEcPjwYei6zqag2CTWmM5Nj2JmH4WQBzHLc88Rf6vV4uIdqsg7evQo3vSmN+Hmm29GNBqFx+MBAA7viWm8ooon5vRTmG9QpcBeQKPRQKlUYk+/0+nEsWPH4PF4OI+D1pGwWbiPCr7I7zNo2FO/mFR28tyrqsoJIeFwGCsrK3A4HHC73VAUhdV6cuxRbF9U8x0OB9cBUKzfxPUJWnvS5Cjpy+fzAQATslGT28i5R45fVVW56nOQsKd2upiqWavVeKGpG286nQYAJmLq7kPz24y92ijc53K5WHpcuHDBrOm/TkFrSsQPXOzGHIlEuK5DTOXdLHWXqkSdTieHiwct4rOniB+4SLAul4ulPw3wCAaDOHr0KDvtaEEBcCmxWC1GGgP1DvB4POwQNHF9QtM0+Hw+6LrOnZtsNht8Ph/P5BMJXzTxxP9pH8iyDFVV2RlsEv91Dr/fj/HxcUiShFKpxAU9lMMvLiSp9sClzi+U90+boNVqoVgsotVqIZfLmb37r2N4vV4e0kF+HyreMlZuEsS4v9jYg4SBmFk4SIQP7DGbX5IkjI+PY2pqipM7Go0G8vk8Zmdn8cQTT7DtDlxy+JEZQAtPE31VVeXeADS003T4XZ+wWCwIBoMYHh7uKuumMm/qw9iLgI3aAEl9MhvFlm6DhD1F/H6/HzfccAMikQgKhQJyuRwP7LBYLOzlFws7qG6fVMJqtcpDGkldbDQabOfTZFcT1xccDgfC4TCCwSDK5TJqtRpneZbLZe7gJBZriTF8UfNzu93w+XxwuVy8VwYRe0rtlyQJHo8HkiRx6q6u69yKe3x8HCsrK6jX61AUBdVqFbqus3+AMsNCoRCGh4e5pTdN7SXnoYnrC1S0FYlEeDwbjeCi8F+tVkO1WuX1M9bw22w2aJqGQCCAcDgMr9fLWh8Viw0a9tQvJvW9XC5ztV+n00EqlcKJEycwOzuLVqvFEt1ut6PRaKBer8Pn88Hv92NkZARTU1MYGhricV92ux2JRIKjBSauL+j6xbZvPp+PPf2VSgX1eh3pdBqZTIZTt6lhK3ApQ9BqtcLlcmF4eBjj4+MIhULc8KNUKnE4eNA0gD1F/C6Xq2uophj6ozFcYjtvSuulyS9TU1Pw+Xzwer0cM/Z4PDh48CD8fj8uXLjAgz5MXD+wWCzQNI1zN8jeJ6mdTqfR6XS4CxR9hjo/uVwuRKNRjI2NIRAIdLWDpy7RJvFf56ABm6TClctlFItF1Ot1qKqK6elpRCIR2Gw2VKtVVCoVuN1uRKNR7N+/HwcOHMDa2hqWlpZQKBTgdDoxMjICn88Hn88HTdMGMtNrL4C6LxGBNxoNFItFJBIJNBqNrqpNyumwWCxwOp0IhUIYGhqCw+Hgjs+UCUiJY4PYv3FPET8N5KQwTy6XQ6PRwNzcHBqNBkKhEPbv348XX3wRqVSK23/dcsst0DQNqVQK9Xod8XgctVoN9Xodmqax95jChiauL5Dfplar8fy9Wq3G5l+73YaiKEy8YuZmKBTiZi/lchmlUokZhCzLkGV5YIe07iniLxaL3J+91WrxKK5YLIZCoQCXy4V0Os19/T0eDyYmJuD3+7G2toZisYhAIACv1wuv1wsA7DgCwAzAxPWHSqWCbDaLcrmMdrvNeyGVSqHRaMDtdnNxFxVx+Xw+BINBbuYiVnJSh2hVVbumNA8S9hTx5/N5JJNJzu4CLrb7okovh8PBTIEqtcLhMKxWK8rlMnuDFUWBLMtc70+boVAocIdhE9cXKpUKUqkU8vk8m2eNRoPDfAC6KvSo2QdpBKTuE4OnLEFN07jse9Cwp4i/Wq0imUwinU5zIo/Y8DEUCnEnXpfLxVWA1DSUIgW0IfL5PA/5pJDhoHH/vYJKpYJ4PI5MJgNFUViSU8IWVfSRLU8OPZoGRJ5/kvzE5GnKE7V9GyTsOe9WNptFLBYDAPb8ut1uzvMniUAqIHmFKTeAFj+XyyGdTrNEyGazKJVKps1/nYK69aZSqa5wHoV/iehlWeb4PyVvkSOQokGUCEQOxGq12jULYlCw54g/l8thZWWFvbm6rsPj8aDT6WB1dZWztqxWK9LpNDsHKVREUp8SRYwNH0zJf32CGriura2hVCqh3W7D5XLB6/VCVVUAF7MAXS4Xh+3Ijm+325zlKY7mosdB9PQDe4z4aQMsLi4ilUoBuOjZDQQCmJycRLFYRKPRwNDQEMLhMC8qqYfDw8OQZbkrE6xer6PT6fA4cDPD7/pFtVpFIpFAoVBgAhc7+VIyj8fjYWIHwBJfrOik142vDRL2FPEDF73zyWQSyWSS1Tev14sbb7wRkiRheXkZfr8fhw4dwvDwMIaGhgCAJQVwcRMRpyfp73a74Xa7r82PMrEtNJtNZDIZJJNJjuj4fD4e8kql206nE263mwu3SNpTfj/NbxTz/wdR49tzxK/rOtLpNM6fP49SqQRJktBsNjE2NgZVVfH888+jVCrBarVifHycq7fy+TwPA6UkEBoF5ff7AVx0Kg1iL7e9Ahrwsry8jGq1Ck3T4PV6EQ6Hoaoq0uk0Go0GFEWB0+nkqA5FAKhnH419pxTwQe3avKe8/QDYXl9eXsba2hrGxsY4UWN0dBSZTAaPP/44p/M6nU726GazWei6DkVRuMc7ZXfV6/UuR5KJ6w+Ui7+4uIjx8XFMT09DkiT4/X6EQiGkUinE43FEIhFm6FTVSeE9cSoQ5YtQGvigSf89R/zARfUvlUrh/PnzCAQCnKgxNjaGer2OZ599Fq1WC9FoFC6Xi1XAbDbL7b+oqUexWOQUYerpb+L6RavVQjKZxPnz5xGNRjmW7/P5oKoqcrkcZFnmCT40AVpsA2632zlbUKwOHTT1f0/pOqKntlQq4cKFC1hbW+uq5x8ZGcHY2Bin/ebzeU73DAaD8Pl88Hg8cLlckGWZ1Xxq4z1Ii79XUalUsLCwgKWlJbRaLdjtdng8HkQiEdjtduRyOcRiMeTzeY7zU+xfbN2mqirvgUFc9z0n+clB0263kU6nce7cOfh8PiZi6uiayWR4YOfo6Ch8Ph8ikQgsFgt7+Cku7HK52JygwZ4mrl+0221e30gkwqm9oVCI5zmura1x/Ybb7eaEL3H+3yAP7AD2GPF3Oh3m4tSVZ2FhAaOjo5iYmECn04EkSXA6nYhEIrwJMpkMfD4fUqkUrFYrEz+dUxzgMYgSYC+iXq9jeXkZi4uLOHz4MGt+Y2NjAMD9GcrlMsf+Rc2R1px6AAwi9hTxiyDnTTabxcLCAqLRKHtydV1nezAWi/Fwz3w+zxVi1LPd4XB0Nf0YxGSPvQhKz15YWMDY2Bg0TYMkSfD5fFzHT12aqD8DtewiLZHi/1QSPGiMf88RPzlvSAtoNBpYXl7GkSNHEIlEOP5L7bhVVeUKsHg8zhVcIvFT22cTewv1eh2rq6tYW1vD/v37ubDH4/FwEw+q+KQ2bQC64v3UwXkQmf6eIn7RPhNtf0r5JSdOOBxmL67L5UKlUsH58+d5Wg9J/1arBVVVWWrUarWBq+neyyDNb3Z2Fn6/H36/v0u9pyxAauteKBSY0KmZJw10HUTbf08RP1Vlic+Bi0M5nnvuOZTLZdx8880IBAKQZRnFYpFHMVGmXzabRaPRQKVSQbPZhM/nw/DwMGeIDeIm2Muo1+uYm5uDy+XCkSNHoKoqE73dbkcgEOjK6KtWq109G8gMGMR131PEL0IcvtDpdJDJZABcbL19+PBh+Hy+rvJNivtGo1E0Gg1kMhmkUim0221ks1mO+Zue/r2HYrGI559/Hs1mEwcPHkQoFOIsTlVVuXOzJEkoFAoolUpc0kutvQbR7NuTxE/JGOJEnlarhUQigWKxiFqthv3798PpdHald1LTDk3TIMsyZ4FZrVZO/y0UCqbav8dAzr+nn34aa2trOHToEEZHRzmNl3w81LOfyngp4Sefz3NLsEGCRd+mi/N6UYvI4SdKaGNmFjX0CAaDmJ6exsjICKv1ZNeTc0hRFLTbbSwtLeHFF1/kXgGbYdC8wtfL2m8GcSQXxfwnJycRDoe58484r48693Q6HSQSCczMzCAej2+Z5ttPa78niZ9UfZLQZLOR95/CfdSgQdM0TExMYGxsrGtENwDu+JLJZLCysoJsNrvlNfTTBtgOrpe13y7E66VBHdS3kca50f7pdDpIp9NYXl5GJpMxib/ngdfJBiAiB7BuwCKl8YoLSObBlVy0ftoA28H1svYvFWJZr1jgQwVe21nXflr7PUf8BNHht1GGlrFzC3F6muFHC09tnujYrTK++mkDbAfX29pfDjYq1hEZwOUU9PTT2u8p4jdKfOCiyk/2P81bE4s5jL3ZqMMv/exe59wK/bQBtoPrYe2vJF5K9V4/rf22id+ECRP9hcELbpowYQKASfwmTAwsTOI3YWJAYRK/CRMDCpP4TZgYUJjEb8LEgMIkfhMmBhQm8ZswMaAwid+EiQGFSfwmTAwoTOI3YWJAYRK/CRMDCpP4TZgYUGy7hx/NtqdGGcYSWGNx4GbFgjQeWxycSGW4Yvmo2JTD+B6wvpW3+J3G94yfM7Z12gjUN4A6wFqt1m21+uonOJ1OAN1lz2KzFONrIjY67nLKammdNnrc7jG9nm/0uvh5+ms2myiVStu65r2Ay2rgScTYa0GNx230XCQ46qBi7Msn3nDj8eKiiM97fad4DuOkVnEjG7/HeK29fvsgwTjLYLuEvxFEwjf2zN+oh/5WRG38vPF6es3l24oRiJ+h3oD9hG0Tv7hYRiLYjtQnQrfZbF0jkYGLzTeMN1wkdprQsxGREoybSPwM/U/de8TWX+LvEs81iITeC7ReRqLfDvFvd29s1TBkq7UwCgLj/+Je2up7e31e7BTdL7gikn8zdU98LjbfFP8nGPvzGbvtGM8rEqhRmvQ63shQ6HMbfdb4+qAyA1FT2uh+bHcPbPR8My1uo2vaDsMwHtNL0yBspHH067pfluTfSOpvh+vT/0TUxqaa4uYySvituK5RnTeaDaJ9T2aG8Xs3O7fxtwwiSPptxgw3u0e93iPmfzlEvBGTMBLpRteykW/IqAn2+u5+w2VL/pdyLKn+9L+uX2ykqaoq6vU6ms3munbcm31vL5XOqCWI30nnJYjmRy9zw/hbBpUBbMbwt6vui6/Tfbbb7ZBlGbVaracGJ65tr/fp+Wbf1+v7aY8MsjYH7GBiz2bq3WbONwDs4RelsNfr5cGaqVQKtVqNhyn2Urk2s9MAdNn0RqlusVjYvyCe29jEkxqBbvW7BwkbaX29jtvOuQDwCLVGo4FsNotms7mlpN2O2k7H0LrS6+LzXhrERgyo1/f2A3Y0rmsjLiy+1+u50dEHXByqEA6H0Wg0oOs6crkcCoXChjY4vbbRQoiaBW0AmsYqhuvIFKDn7XabJ/rQtfTbYu8Uvdb0crShXsdYLBYoioJwOMwMOZPJ8L3v9fntqOgibDYbJEmC3W7nY2kqb6vV6hrNLbZ13yyS0E+4bG//Zq9tdXN6SQ0iOrvdjvHxcaiqina7jXK53OWpF6VzL68tQdwgJMGHhoY4l0Ac001jvZrNJqxWKxRFYRVUPP9GEYFBw3Ykv/H4zdBqtWC32+HxeHjYSiaT6TmJ2YitGDPtEYfDweduNBo8r9Fut0NRFABgpt+PHv3NsCPJvx2Jv9nnRGKqVCooFotwu93weDwIhUJotVqIxWKoVqvr1P/tSn+73c4L7XQ60W63MTY2Br/fD4fDgUajwd9brVYRj8eRy+W6rlX8f5A2xVa4XGffRucol8vI5/NQVRVutxvDw8MAgEwms27eQq/Pb+UEJhPQbrfjyJEjiEQiaDabqFQqKJfLPLRzbm6O5/T1Sl7r17V/SWr/5aIX0yiXy8hms3A4HLwJHA4HAGBlZQXVapUXEUDPOL3xvCTxaUQzzWN/7Wtfi6NHj/LnTp06hUgkAofDge9+97v43ve+13Oxxdf6dSNshct17G0H9Xod6XQaTqcTfr8fw8PDPDsxl8t1DVfZyX0nrVGSJExMTOBlL3sZAoEAyuUyZmZmIMsyKpUKqtUq0uk0m4yDMqX5JY3o3gm3N0r/VquFXC7XpYoNDQ0BuLg5EokEms1mT9V7M4dgp9PhhCKyKVdXV9HpdFAul/HKV74SPp8P6XQa9XodqVSKU4zp3OL5xNdMvDTQurXbbRSLRaRSKTgcDgwPD2NkZISJnhjARtjKPtd1nSc6nTt3DtVqFQcPHsTBgwfh8Xhgs9kgyzLcbjdfl3jefpb6wA7i/OJz8dH4/0YQCdZms6HVajHntdlsUBQFHo8HkUgEjUYDjUYD6XS6yzEDoCtcQ+cloifHjd1uR7VaZafeP//zP6PT6aBUKiEYDKJarSKZTEKSJLhcLsiyvOVv6OfNsBHEtb5cZ99m5wPAjNdut8PpdCIYDGJsbIz9M4VCgX00l3Pv6Xga2XbmzBk899xzGBsbw+tf/3oezy5JEhRFYUHRS+r3KxN4SZKfcDmOMPKs02eIGVSrVaRSKWiaBr/fj2AwiOHhYZTLZdTrdRSLxa54PdCd3EPPycljdOAQo7Hb7VBVlb3/Ho8Ht956K0qlEhKJBObm5rpyBXr9VhNXBrR+lUoFqVQKTqcTTqcTXq8X4+PjzADK5fK6td/qnET4BKvVyjkFS0tLCAQCLPEpGmQkfuM+6Le131Gcf6PQzVafI1Bml7iguq6jVCohk8nA5/NB0zS4XC6Mj4+j2WxiZWUFpVJp3Wd6od1uw2KxdB1bqVQ4cajT6bBX+Xvf+x5OnTqFiYmJrqgCbaCNogqDhiv920VTitY+kUjA4/HA6XQiHA5zSC4Wi6FSqay7BuOeExm9GM4FLk1sbrVaWFhYQD6fh9/vh9PpRCaT6XnufpX4hF21+Tf6jGhrFQoFJBIJuFwuqKqKaDTKRLu6uopSqbTOEWT0B7TbbUiSBF3XUa/XWQMQy4ifeOIJdDodxGIxLC8vY3FxEdVqlaVDr+vb6W82sTmIIedyOaRSKZbKo6OjLI3j8Tiq1eo6jXEj4qRzirY/efNTqRRyuRwWFxdhsVhQLpf53P2c1GPES7b5xecUXiF1y1gNRq8B3V56MgWsVisqlQrS6TR8Ph+CwSCCwSA0TUOn00Gj0WAfAYWCjM44cvDU6/Wu5A5N0/gaHA4Hzp8/D+Billmz2UQ+n++qLuz12wYV28nxIIjS1li/Yfy88Z6S/e/z+eD1euHz+TA5OckMPR6Po16vb3qdFA1qt9u8Ryixh9BoNNgRTHkf5FgUNT7xrx/xkm1+ozrv8/ng8/lgsVhQrVZRr9dRq9VQr9e5pJeIjBgDET8hm80ilUohEokgHA4jEAig1WqhVqvBYrFgbW2Npb9YqkumBD1vNpuQJAnlchnNZrMrwYckCoV4RHuPmFcviW9K/t6gjD1N05jYqF6D7r3I+Ht9vtPpIJ/PY3V1FW63Gy6XC36/HwD4HMlkEo1GY8PziOtEtSK1Wo2vSdx32y3q6hVO7gdcEYcfcPEGqaqKUCiESCTCanen00G1WkUul0Mul0OpVGIVi0DMgGxuXddRLBaxvLwMSZJgtVrh9/sxOTnJxL68vIxqtcqOGsrSI5AEarVaSKfTXRKBrpeYBP0R0W9E6NvZMP0Ko6ffCIfDgUAggKGhIciyzNK01WqhVCqhVquhXC5z7QadE+iOALVaLWSzWSSTSfh8PiiKgkAggKmpKWb4qVSKpfpmWpnI0MUwLn2nEb2kfa8Es37BFU3vpbip0+lkNZtu+OjoKBqNBhKJBEqlEgqFAvL5PEqlEjqdDmRZRqvV4tAMhf7oXKFQCF6vFwcOHGAJs7KygkQiwVKHJAIRvlioQ84j0RQALi2oMTLQaxP0swq4XWwkAS2Wi51uqFjHbrd3rUO1WkWpVGIhUCwWOSIjngO46JyNxWJ8rkgkgkgkwt+xsLCAZDKJSqXCDGEzW118T3xufH+jNe7Xdb9ikp+Iq1QqIZ1Oo1arwe12w+v1stQdHx/H/v37US6XsbKygmQyyR7+ZDLJkpny8AuFAmKxGGRZhqIoGB0dhcfjgaZpCAQCCIfDmJ+fx8LCAmq1GktuSgqSZZl9BQA4c7AXtkrkGeREH6OvpxfxU0iuWCxyONXhcMDpdEKWZfbFEANIpVKIxWIoFosoFot8HmIutPZut5v3EdVoaJoGn8+HeDyOTCaDer3Okt1I/EbH4Fbru9lzU+3fBJVKBZlMpiu9tlarceiGcuxbrRb279+P0dFRpNNpFItFrK6u8iOphVTq6fF40Gg0ODGjXq+jUqkgFArB7/dj3759mJubY489bQLy2lNlH4AuR+FGjh1jyy+SXv24AbaD7fxmIuxkMolarQZVVblcl4jX7XZDVVX4fD4MDQ0hHA4jmUwiHo+jUCigXC6zqk7NMrPZLDKZDAuAQCAAu90Ol8uFUCiETCaDRCKBRCKBarXK19tLE7hcBm70+fQb47fo29zNkiQB6LZ7e32UCE3TNHbakDro9/vZJnS73UygdrsdqVQK58+fRyaTQbFYRCKRQL1ex9DQECYnJzE1NYXx8XH4fD4mymw2y5KmXq9jZWUF8/PziMViKBQKXMIrXquxC/B2bT/yCVitViSTycu5x3seord8M7vfYrHAbrfD4XBAlmVmAD6fD4FAAH6/Hx6Ph9O4yRm3urqKeDyOtbU19glZrVa4XC5Eo1FMTk5iZGQEwWAQkiQxYyAfQqVSQTKZxNraGjMSXde7nHziNW72SP+L606vWa3WruKvvY7LIn6j00f8qOhFFV8je9zv98PtdiMajbLEpo0yNDQEVVURj8cxOzuLhYUFNBoNyLLM0j4YDLLn3+/3s0+BvMPVapWrtJaXlzEzM4Pl5WWUy2UA3Y1Eetl44utiFyHxt5BjMJ1O7/R+70kQEV1uxIMYpqqq8Hq9GB4eRiQSQSgUQiAQgNfrZWJOJBKIxWJIJBLI5/Os6bndbgSDQTbzqCqTiL5SqXAUh3wFpAVWKpUuZm9ce8JG7/diAPl8/grc0esDOyb+zeL8YgiPNAXK2x8aGsLIyAhUVUUwGMSBAwfg8/kAgG30xcVFLC8vdxX0kP0YDAYxMjICn8+HZrPJqn25XEYsFoOqqtD1iymjCwsLeP7557GysoJWq8W5+5upgr080KLWYLVa+6p3+3ZAERXCVltmI8bpdDoRiUQwOjqKsbExhEIhhMNhKIrCBV6ZTAaFQgHFYpFLsh0OB1wuFwKBAAKBAGsPVKdRKpU4dq/rF0uF5+bm8Nxzz3Eo11iw1cu51yu0S/vAarVCVdW+YvzbtvlF7znZvw6Hg72tvZpt0A0k1btarWJpaQmZTAbRaJS9/O12G8FgELVajYtsDh06BKvVyrZgo9GAoiiwWq3I5/OQJAk+nw+qqqLT6bBquba2hmaziWg0yjXizz33HF544QWUSiXY7XbOBRCTUMg7TY4jKjFWVZVTjSnxZNBgjI9vhI1MKBIAhUIB9XqdHYNjY2Oo1WoIBoNQVRVOpxOSJMHr9aJarXLmHeVm0GuKokBRlK4ycAon6roOt9vNfoWZmRnMzs6yP0H8TeI1i8yAzEUqNiIHM+Uc9Asuu4GnSNjkRaf3RPSSqPR/sVhEpVJBvV7ngh6Xy4WpqSnIsoxmswmHw8G51/F4HJ1OpyumTw0fRkdHMTQ0hGaziVarBb/fz1GEcDgMu92OUCiEqakpnD59Gqurq8y4dF3nog6r1QpJkthXQc1F3G43e6zJjh1kbOb07BVKM2pP9Xod8Xgc5XIZhUIB2WwWgUCAidXpdMLj8UBVVXg8ni7V3mKxoF6vo1QqwWq1wuv1wul0sl+hVCpxfb4kSZienkY4HMa+fftw4cIFxGIxTjYjgUWdfmgfaJrGDIWYCEUX+o3xX5baT3F4unkEYzovn1xQq8SNQdLXarUiGAwiEokw1x8eHsbU1BT8fj8UReE2Xw6Ho6vCi6q07HY7gsEgxsfH+Tuq1SpvLCLqYrGIUqmEtbU1NBoN9gEQl6f/NU3r6v1mt9shSRJLJqfTiX/1r/7Vlbn7ewQbJT/1Wl9CL3+QCGqbRk5gSg6LRqOs0dHnms0mqtUqarUamwLkTAwGg/D5fCw0yuUyCxZK/KL07Xg8jmKxyEKLTApaZ4fDwSW+otbn9XoRDAbh9/tx3333XZ2bfA2woyQf4pputxuyLLO63Gq1OJ3TqCUAl3rm0/sAkEwmOZxHlVwA2NnXarWgKApL3tHRUTSbTWSzWRQKBVitVpTLZSwuLmLfvn2w2+0oFAqcZUj+hkAgAKvViomJCWSzWbRaLeb4qqryBid10uVysRSgpCJVVTnqMWgwEj5pQZQ0RU43CtWJfRXoc2KnHPLLiMeI0RnyEdDakMZHjj6q8chkMrBarYhGo7xWqqqyuUDnjUajGBsbQ7FYRL1eZ7Ve/JMkiX+XpmmsBXq9XmiaNrjjuoBuDq4oCoaHh9epQtSAQ1x0IkAqtqDXiEt3Oh0Ui0XOB6hWq4jFYmi1WrwgtBjkPBoZGcHY2Bg7iCRJwvLyMqampjiMNDQ0hGKxyKEnl8uFcDjMuQWk3pHksFgs0DSNNx1tCjEFuN9ivTuB3W7H0NAQAoEA50/QmhKRi8RPuR2kxTWbTTQaDSZOKtiivg1iHT4RIzXe0PWLNQOFQgGFQoEdhaRFUnKRpmmcL0LzAdrtNvsNyIFHNr0o7SVJYs1PkiRmFJv1ediL2HHffnK4RSIRAOBOuOINt1gsXVoA5eKT04e6ppLHPhQKQdM0SJLEx1AxEIX8KF+ANt3Q0BAKhQLHX0ulEoaGhjj053Q6UalU4Pf7OUfA5/MhkUjAbrfD6/XC6/Xy4lJegsPh6OodSL+9H5M9LhcOh4PNNcp9IEeZOIaNGALtAdIOK5UKS3Dy5bhcLq7joCabtAYAOGtQ0zSu9Wg0GiiXy+ztJ6J2Op3cmZfseqfTCbvdjlqtxvX7brcbfr+f15wavNBeIA3XTO/F+swp0dNPtpPFYmEbmfL0O50OHA4HisUiS9lqtcq5/VR1FwqFutoq0Xnb7TZyuRyXYYqOGIfDgVAoxBlmrVaLXydpIUkSxsbGmJu73W4uIvL5fHA6nbzAdN2DnM67FcR7QsRC9jJpanQPxQEsxCCIsZMAsFgscDqdbIaRD4YKwEgrqNVq7Hgjk5OESqFQ4HOR70a8JlLb6/U6rFYr6vU6q/ROp5O/k2D0WfQjLivUJz6S6kU2MnVJpcQOWgT6jKIo0HUdiqJw4sbw8DC3UW40GuxVpQYcxOXJwUhMgvLBxXbfqqp2FXkQI6K0UCJs4urkQ6CmIWISkFj5R5LMeB8GGaRqE4Ol+0rmlq7rrD1RghAxdSr5pToQis/T3iGI/5MDjzQ8v9/Prd7IOeh0OnngChE8aQD0nJg/MRWx0lPsEyhqMEB/pvYCL8HmbzQaSKVS6HQ6cDqd3DzDZrOhWCwil8sxYxBLPOnGilKCTAGXy4XR0VFks1lWBan/Pn2+VqtxRZiY4OHz+VhyyLKMQqHA0oG685DDkbi/cZHJWSk6g7ZKBR40kCmXTqfRbDbZXia7mpgt/UmSxCYhOUuJqZNdTtWWkiQxw6XvopAyrQ2tPVWFUuMPMvHIHKTroWsTs/Von5IJIo5wE0N/281k3Ku4LG+/KP2p8UK9XmcniRgyIakg5vdTCI0cPzSdhwib1HxS0yi5hlQ/UiOr1So7jEhraDQaiEQiUFWVtQXyH9B3U4iHHImUEkqSnr6DGEKvmX0m0KWFFQoFVtXFvH4ifpfLBafTCbfbzetUr9fZc04eftovpC2IsXfyHVBTGHIQ5nI5zhcIBoMYGhpiRkPnFSMI5ISk85M/goQC7WFg41TgfsKOQn3EkYn4yNFCBCNuAiJiCtdRhpbD4YDX6+VQIWkHtLBi6IhA3nngouZB7byIeMnrT45Fii6oqto1DZaiBsT1yVwQbUOT8DcHed3FrjrEOI3rLybiEEMmBxzlTtCwFiJUMiPIfCCJTn4f41xFygWg/AFx/YwhZ0rzFlV90W8l9iKg39qPDOCydnivJB56TlxVHLIgSnLyuJI5QLn+5BwMBoNsK1KfNuLOYnMNSvAQQ0wAuMyXvp+YBGkKZPeTlCFCF9tMiZKi38I6VwvG+D/tAerck8/nmYjdbjczflEzoPJeMiMoi1JcdyJqYsxkQtB3kcCgNTYSMD3S66TWixEcMgWNYb2BJ36SpMYJKqJTBOjujNPpdLqkA9VlU8iGCjQoX59ATjc6B2kOZP9ZLBbO0rPZbDzTjxqHAIDf72emQ8lHxEjI2UObiXr/iZmLGy14v26E7WIrO1h8n+4pleCSiUDrQo46asZByTRutxuapqFarXbF4GkdSXugLECPx8P9IkWb3tiRSQzfAd17FbhU7m3082y1J/Yqtk38pFqLVXtioYeoWvXK86csQFroXC6HZDLJUsBisSCbzbJEJr8CVdCRWUHH+/1+TgUlmxAAD94ELg1jIG4uFvOIGYhiim+/O3l2ClrbzXL7jccT6L6SIKD1oTwLUuvJ7COhUS6XmVFQtICInnwJXq+XY/p0TqMGZyzeMeZvGI8RG7f0K+EDl0H8xgaIBNEuohtFN5NeI4KjBRZzxavVKiqVChRFwdjYGMrlMjsG6Vz0vWLsOJVKIZPJQNM0RKNRHr/lcrmYyQQCATQaja7Fpe8yxnUJprq/MS6HMfYSCvRIORaUmFOtVrvCtKIDmIiRmAYRNO0Rl8sFj8fDvf6J0Rs7MBsJebPfJnZwEs/Rb9j2TidnzHZi3sY22GLYjGxCel+W5a44LDmDxJnqFCIUHTKi0ygWi2F+fp4depTfT+E9InTRSQh0Mymj2r8R+nUjbIWXIvlELUuUvmIRjSjVxfJe+k6j915sD055AMClwjFiEAQjEzB6840qf6/HftMKL1vt73WDxAwu8XVgfdsvq9UKj8fDDkBK+hgfH8fY2Bi3/6b8ejFLjM5BMWPSCqLRKEZHR7kzjM/n4+aQlUqFz0X2pNGZA6Bro4gYhEyvy8HlEIHxfpFEJiZP6nskEuFOPdSglZy6on1uZB6KovB4b7FrMAkL0ccj2voknIwaqsgg6JHCxhvtj72MbRO/JEnsuBNvjnhTCWJUQAz/UAnm0NAQF0xQdeDExAT279+Per0Or9fLnVs0TeMmkDQMhGr3qf6a2n5T3L5SqWBiYgK6frHPHzWOBNAVTqLr2wjihqAN04+bYDvYyJ+z1Wco7EetvMQqSUmSuJY/HA7D5/Nxjr9YaGNMGKK9I9ZgUPiPCB9Al21vZFric6MQo6iBaKKQA3qzDtB7DZc9rku04cSbQhBDLRSWo1pol8sFt9uNQCDAnXEUReE6bQAsmSmRpFAo8PdRtZZYcUfHFYtFVKtVBAIBABdDf4qi4MCBA9zWW9cvDoSk/n/UJ0DM5qLFJ7tT/G1i84dBgpFQejEAkYmKSTuUgk2dljweD6dUU6KP2OKbzmOMvNBx1OiDTECKANBaiZWEALqIlyCq+KI2QElItKfF30y/qZ+6+Vx2JovIJY1OElmWeaHJZqOOLETUgUAAo6OjXaE7atRAC14sFmGxWNgxR4kglIElFgtR/rbIlGjqy+TkZFcMuNO5OPAxEAhw5aDYDbZQKKBaraJYLPKmslgsLG3IdDh48OCVW4E+gBiFETMzqWiGJD7VdIhpuLVajWvvxbbqYpIW9WWo1+tIp9Ndgog0OdGpSJV9BGLkYh8JInrKEyiVStzog1K8RUlPaz+QxE+ONOASNyWpSBN1SG0j6UySnpw0Xq+XM7OIkKm4xmq1stc3n8+jUqmgVCpx5p3o/RfDOcQA/H4/vF4vh4hqtRrC4fDFH2m3I5lMIp/Pcw04fQ9tpEKhsG4CMHDJbKHf7HA4Bo74jT4cESSNqdKOiJ72ATFwWitjsw9aC6v1YmNUh8PB49Rp3cVoDe05YhBUT0D5IxQB8Hq9zPCJoVcqFc4IJDNBZPrioFYSJqRd0iSqm266aZfu+tXHZef2i/nRtBB+vx8TExPw+XxdZb2UzUVOO5qES6o6NV2k2L+YuCFJEndetVgsnBTkdrvZ9iNNgdKM4/E4gIsRhGAwiPPnzyMSifDwh0ajgWQyiVarxWnJpEWIab5iLbqxSnFQO/mIkhXodt6Oj49z2zXxT4zdUy0FFQXR/abUcGPjDABdqeKkUZDJJ+ZwUI4+qe7k36E8EcopIdOQ9hz1FKAqUqNZS4yOogeiNtEP2Dbxk7NL5LzAJZWPOCwtmFFNAy61ciIHHVUC6rrOTT0kSeI0ULIL6eZTlICcPGLYhxgTSYrR0VHU63U8//zzLL1zuVzXYAji/mLVGDl9iMmR9CBb0Gaz4Zd/+ZevwlJc/+hl7xubX4jNWYzHkapN2h+lVZM/R2QAojSnTFCqBiSiFyszgYsqPGmcwMVGsdS7L5FI8EAYcXI0rTutvZiVSNdGyUmapuHXfu3XrvJd3j1cVj2/OP5aJOhCoQCXy8XEIS4yJdkQQyBvLNnhdru9a/qKruusLlIHVb/fz+E/qhAU5wgQSJ3bt28fE63L5UKnc3EWQCaTwcLCAk/6oTZiYoGP+Ef+AvoDMJCSvxfRE/Ov1Wo8NZfsY9KgSHsSM+bI8UYER910SaiIdRfUq4EEj7FE2FhvTxmDVNxVKBSQTCYRi8UQi8WQSqWQy+WQz+d57alClHwCpIXS/hS1QWIq/YJtEz915SFJTfaRruvI5XJot9vslKNjxfJLUquMo7TFhCDaJGTrWywXG3CMjo4CAHuK6TvoM8ClzUhtxYCLLb1owYvFIp566imcPn2aR4WTDUgbUmQmomZDNqfY4WeQsJGHn9Rq6o4rFluJTlYxpVr8o2ON0SL6UxQFqVSKM/soX4Oar4omms1m4xwBqvZLp9NYW1vDysoKFhcXce7cOaytrXECmCjdjb9RzCwlRtNLo9nLuCybnxxfvVIfSXqLxGGMCNB5xIQgEQ6Hgwt26P1qtYqFhQUA4P59VLNPEQPi0NFoFC6XC+l0GvV6HbFYDAsLCyiXyzh16hS+9a1v8Rw3gtHBY/yj3y0ygEGEcb3oHorFW0bHYC+iEl8Tj++1b4g5k3ZAJcJiuJXyPWiaEw2HLRaLTPinT5/G008/jdXV1S77Xvxu0anYK9tvoz27l7HjTj69NoPIwTf6vPEmiv+32+2uJo5k07VaLSwtLUFRlK6ea3a7nWP1ExMTmJiYwMrKCm+Y+fl5ZDIZzM/P45FHHkGhUOjSFIypyhst9kbZi4MCY+ab+Bq9vtm+6HWuzfaB+LzVaiGdTndJflo3sVR4aGiIGVGtVsPa2hrm5+dx+vRpPPPMM1haWuqqH+i19sY/8TrI+dtP2FHHio1swMsljI02CsV7gUtSoFarIRaLYXl5GX6/nxfR7/djeHgYHo8H6XSa/RGzs7NYWlrCysoKnnrqKcTj8XUOol6b2nhdG23KQYV4z4z3b6N7s5nQMDIRUeMS8zYqlQry+Tz3bqSGHeQPstls7LlfXV3FhQsXcPbsWTzzzDNYXl5mn81mBN5LOIkZrP229pct+YHeVVHbuTFbbRIxD5vCPaQNUOMHCutRxiBN+6U20PV6HWfPnmUnz4kTJ3DhwoUuot9M5ey3Bb6SMBLxdgi612ubMQmgO33cYrGwdCeNj5x/lDFosVg4WSgej+PcuXN4/vnn8cILL/Dsxs20vK1+81bXvVex415VWy2wEUaC2+hz4hBNi8XCBE5JRKFQiFM8qeV2oVCAqqqo1+uYmZlBLBbDzMwMTp8+jaWlpXUJShvZ/MZr6WWbDqrNL6LXfdnu5zZCL4FCNQGBQADDw8Oc/+/xeHgPiOHFWCzWRfirq6vcn8+4rr2IeqtjBpb4d7rg4ue3Am0AagBJmXs+n4/bNdOMd+q9TyGjUqmEpaUlLC0tIRaL4amnnsLq6iqAS1mB5JTaSN0zXmcvidZvG2A76GWebWT67QSipKdEKorXezweHhASiUTg8Xi4lJvi9I1GA4lEAufOncMLL7yAs2fPIpFIMOEb1fbN1nojf08/rv2OHX47wVbOIF3XeSgiNWcgCU/DGjweDzt6KFmn0WiwnZdIJPDiiy9idXW1S9qTJmGUAr2uz6jSiq/3m9NnJ9iOINiKUESCp3OJuR2UKk5SnlR8Kv4Rk3QymQzOnz+PM2fO4MKFC0gmk+t6Pxqv6XKl/cATP+FK3QQjM1EUBZOTkxgaGmK1TpZllvpkDtAYLxq4WSwWMT8/j5WVFczPz3N7MApNktTfToLO5WoDgwCjk6+X5N/JfRHPoaoqwuEwq/ZUsEUMgHJIOp0OZ+5Rzv7KygrOnz+PhYUFjuhs1Zxjq/97aX39xvh3VNV3JaQ/SWLgkro3Pj6OqakpTu2kctChoSHIsszTfeLxOFdiVSoVxONxLC8vIxaLoVKpcGtmSkoCNrfVN7IBex0ziMRPMBL+TiW/Me5vt9sRDocxNjaGYDDIjTmoTx+V/tbrdeRyOc7JLxaLyGazWF1dRSwWQ6lUgsXSnYy11dpuR/LT38ASf68GlzthBEYpQnC5XNi3bx+r9IFAgLk9NWsolUqIx+NIpVLM+XO5HOLxOAqFwsUf9P/SiGkTiNcvfr/xeoyLLlby0edJixg0bCT5r9T5vF4vwuEw/H4/M3x6VBQFjUYD2WwWsVgMyWSSi8Ko8pOGx4hm3Wbq+lZ7wEj0vfZIP+Alqf10Y4xMYaPPiGXAFHojdXxoaIi7sJKHnxx6mUwGiUQCS0tLSKfTPJ6Z8vKp/pry/cXv3Ehib2X/GX9fv3L/y8FGKv92PiNCfG6327nZi7GYiypBU6kUFhcXsbq6ilwux/F8ytYTOyxtRqzb0QA20vr6cd2vSKhvK84qghgFcWgq7qEJvTRGm1J08/k8l2SmUikuA6aWYlQIIpaO0jX0IvzNiL7XRu2lEQwaNiKIy2EABCPzoD4P5NijnA0quslms4jH49yTUey0Q2tr7M9v/M7LYQa9pD9dd7+FebdN/GK/fmBzz32vm0zpv70kKZVwUluvWCyGeDzOan06neYSXOquQsM2xMIOVVXXFfpsdk2bPRqPpdcHkfiB9QRsfG076OUwJHOS/DSxWIxn8BWLRW6yInbn7WXXi+8Zr227GsBmav9mUaK9ih3Z/L1svs0YA8HYkcVisXB1FqVunjt3DufPn+cyUfFzFouFbXqjXU8FPr1iultx/s3MAmMTyEEk/o2Y+eXAuGfo89RTIZlMolAoYG1tjaM4YqnvRnb8dh97/Zat9oPx/X7z91yRX0MLuRVhkLovMhGy+8mRR95cYgxikoYxXi+mAAPriXO79n6vYzYa3NBvqt92sBHxb4cBGAWCca80m00sLy8jHo93SXha917Zeb1A67VZHv52zFPjfqDzifutX7BjyS9CVOONr4v/i1qDSEzUcJNysKlZAwAu0ew1fokaMtLCiK3FtiPVN+P05JwUZ/uJnxkk9Ir0bPS/EcbjRK2B1rJQKHSFZDdaF+PrxvMYe/z3wuVKfjHa02/YNvGTPb0R8W+0OYyvGbv9EFF3Oh2emitKFtG+p80jag/ic7GbzHbVv14bbDO1sh83wVYgpmpk4NsheiOxGbU/4BKjpf/F44FLDVuMe2oz23yn5l+vR6NW0S+w6C8lY8eECRN7Fv3FykyYMLFtmMRvwsSAwiR+EyYGFCbxmzAxoDCJ34SJAYVJ/CZMDChM4jdhYkBhEr8JEwMKk/hNmBhQmMRvwsSAwiR+EyYGFCbxmzAxoLisoR17BdTkg+aziaDy1K2Gim6GQauF2ktrf7XRT2vfX90J0D1XvReo8YcJE4OOviR+oJvIxeYPYj0+aQD9xM1NmNgu+o74e6n6oibQ630TJgYRfUf8Rlgsli7pTu2iqJOMaPvvpDGlCRN7FX1L/NTKyajWE7H3cviZhN+f6MXUTUbfh8Rv7L8m9nuTZRn1er2rdxz5AIw96kzsfYh7QIxY0MBPek9c+0FiCtvu4bcXwz3iQtL453379sHhcKDdbqPVaqFaraJUKqFQKKBcLnNH4M0wKJuDcK3WvhcDNz7vtRbk4CUCVxSlawKw1WpFvV7nmX/0Jw6G2WiN+2ntd5X4xQ6qtDAOh4OHcwDgGW0bgUJ57Xa7J6fu9R2hUAiHDh3CyMhI1xAOsXc8TQAuFApIJpNIp9Oo1WrrvnsQowO7Rfy9ZieIr9N6b7QGxm7Adrsdw8PDCIfD8Hg86HQ63ApeHATT6XSYGRQKBR782Qv9tPa7qvaLN85ut/NEFlK9xecOhwMOh4OntdIMPhre2Wq1UKlUkM1meTFtNltX62+Hw4HR0VFMT0/DarViZmYGNpsNtVoN9XoddrsdsizzZ+12OxRFgdfrRbFY5A3Qqw25iSsLYx9+aukuSRIkSYLdbuc90Wq1UKvVUK1WeY1arVYXY3C5XBgbG0M0GkWpVMLS0hLq9TqazSZPX6a28HR+p9PJQ2QajUbfr/eu2/yi1504sSRJLO0tFgui0SjGxsagKAosFgtUVYXT6YTf74fL5eoa9rCwsIB4PI5MJoNsNsvn9Hq9uOWWWzAyMoJqtYpYLIZarYZWq8XDQWRZRqPRQKFQQLVahSzLcLlcPAPAaCsOkj14LWC1WqEoCjRNg8vl4j8SADTC22azoVAoYGVlBcvLy8hkMiiXy2i329A0DW63G+Pj41BVFalUColEAvl8vuf0J5rw7HA4oChK19CYfl/rXSd+MQmHbi5JfLvdjmPHjuHgwYOQJAkWiwUejweRSATBYBChUIgnudrtdtRqNSwuLuLMmTNYXl7mCb6dTgfRaBQ33XQT8vk8Tpw4gXw+D6fTiXK5DIvFAqfTCZ/PBwA8NIQm/dI1Ad32pThcwsSVhd1uRzgcxvDwMHw+H7xeL3w+H/x+PwKBAILBIAKBALxeLyRJQq1WQzKZxOzsLGZmZrC6ugpd1xEMBuFyuaBpGhYXF5FKpdBqtSDLMk8DEif8Ape0SbqOzSb+9BOuCfHbbLaumWztdhvBYBB33XUXotEoFEWB3W5nlX1kZASapsFut3fZgZ1OB+FwGGNjYzh//jxWVlYQDoeZkNPpNM6fP8/flUgkeAOUy+Wu6TGKoqDVarFqSROKxEnA/S4JrgWsVivcbjcmJyexb98+RCIReDweeL1ehEIhDA0NwePxwOl0QlVVyLLMBHrgwAEcOHAAk5OTeOaZZ1AoFCBJEur1OhKJBJLJJE9xIq++0WkIdE8G2srh10/YdeJvt9s8Uovgcrlw11134Y1vfCN++MMfolQq4fjx47jzzjvhdrvRbDbZzidVnIg4FAph3759sFqtePTRR+Hz+ZDNZrG4uIjZ2VlkMhk2MWhhbTYb2u02qtUqMwAxC1BRFB4SCqwfF2XipYOkq8/nw+HDh3H06FG4XC7IsoxIJIKRkRFEIhH4fD72CdGYN1LTyVZPp9PQ9YvjvavVKorFIsrlMq83TQIWJT6d0zgIlATHIGBXiZ9ucqPRYK++rus4cuQI7rzzTnzve9+D0+nE8ePHce+990KSJJRKJVgsFl5ssttJC2i1Wmi1WiiVSpibm4PVasXp06eRTqdht9vRaDRQLBZhtVrhcrlQKpXQbrfZ7FAUBS6Xi8N/zWYT2WwWpVKJTQBCv81qu9ZQFAXT09O4+eabea1CoRCmpqYwMTEBj8fDa2w0t8gmT6fTeOGFF3Dy5EmsrKzwGtZqNXYq01qLQ2LFKcDk7B20PI9d9/ZLkoRGowGn04lWqwVVVXHXXXehXq/j7Nmz+MAHPoDXvOY1kGUZtVoNHo8HANgm83q9bKMRhz5//jy+//3v48KFCyiXy1hcXESlUln3/cRIiIh1XUej0UC1WoXL5YLP54MkSUin02wq9Aolmnjp0HUd0WgUhw4dQrvdRqFQwPj4OI4ePYpDhw5xaE7UukQpres6UqkUHn30UXzlK1/B3Nwc6vU6Go0GO3RFv5I40JWe02sU9SGNdFD8OrtK/DabDYqicLjFYrEgHA7j4MGD+Na3voXx8XHccccd8Hg8yGaz0DQNVquVVXeLxdLlvAGAeDyOf/zHf8SXv/xlNJtNnD17lr9LzN0nVZ8mBQPdmkgul0Oz2eSwoizL7AkW1UcTVwY2mw1jY2Nwu91YWVmBz+fD3Xffjdtvvx2apvE9JxVdZNhWqxW5XA6PPfYYvvCFL+CFF17gxB0K9YohX1rnXuo8HdtoNCBJEmsag4BdI36LxQK73c5OmWq1ClVVceutt6LdbmNxcRE//uM/jv379/O47nq9zs4/stVJWlssFpw9exaf/vSn8c1vfhPtdhulUom/z7jQvRbemPNfLBZRqVQ4/ZO+S/wNpuS/MlAUBQcOHEA+n0exWMStt96KW265BZqmddndon1Opl4ul8N3vvMdfOYzn8Hp06eh6zrq9fo6ou2VnyGuH71Oa03x/0FR/XeN+GmBxMWsVqsIhUKoVCrw+Xy45557oGkaKpUKJ99IkoRms8nOOcrQunDhAj75yU/iX/7lXyDL8hVT1baq8R+ETXG1Qaq2rutYXl6GrusYGRmB0+lkBk8quEiglKD15JNP4gtf+AJOnz4N4GKCj8jcN1sj43sioW8UDehX7LrDj9Rpipm73W7UajXoug6fz4dOp8Ne3HK5jGq1ysRIPoJUKoUvfvGLeOihh6DrOqfhXok4PH3PRq2+BmFT7Aaq1Sri8Tjy+TwikQi8Xm+Xqi8WaJFt3m63MTMzgy996Us4efIkms3mlungO8GgrPGuu6/JbifCymazcLlcTHDApaorcsQ4nU44nU54vV5UKhU89NBD+PrXvw6r1YpIJHLpx1wBbzxpF3R9tPkGIeNrt0D+k1qthpGREYyPjyMQCHCOPRGz6I232WxIJBJ45JFHcOLECQAX/QbGiMxLwaB1dtpV4iepSrDZbDh9+jQmJydx5MgRtvMpVVdVVaiqyvndNpsNTzzxBD7/+c8jnU6j0WggkUhw3v/VWDQzr//qwGq1olKpYHR0FLfffjtkWUa1WuWc+na73RWSK5fLOHnyJB599FFO4xZTwk1cPnZd8tOi0v8zMzNot9t41atexZJb0zS2+ajAxuVyYWZmBo888ghKpRLsdjvbh81mE6qqDkxyxl4HaX/Ly8uwWq0YHx/ntG16jx4bjQay2SxOnDiBhx56iPeLqCma2Bl2nfjJo0pOv3w+j69//etwu90oFotdOQCVSgU2mw0ulwvZbBaPP/445ufn4fF4OJSjKAqf92rB3GRXFhR+y2azuHDhAtLpNNrtNiqVCqv9lUqFszBfeOEFfPvb38bp06e55h7oHX0xIzLbx646/Mg5Q5V8pAX84z/+I+666y60Wi2Uy2U4nU7Mz89jfHwcN910E2w2Gx566CE8//zzaLfbWFtbY8lA59qo/nonGKRwz7UCOWpPnjyJyclJuN1uhEIhZDIZqKoKTdNQLBaRTCbx5JNP4tSpU4jH46jX613aI52rFxMw129z7HozD+PXiXX0H/nIRzA8PMxlvB6PB6FQCM8++yy++tWvYmVlBbVaDXNzcyiXy3xOsT0XJfMAgNvt5qwvOoZgJHBKJCGtpFKpdOX2i62/ByUJhHA1JanFYsHIyAjuuusuTE1NIRqNwufzcQLQM888g2984xs4ffo0stksisViV8XlZte2XX+NMRlI3Ke9QoP9gmvexouIjcpsqYorFApheHgYt956K1544QV84xvfQL1eh81mQywWW+c4FO19ImS3281eZUmSmGGIP5lSRo153cRUeoUR+2kDbAdXm/ipdNvj8UDTNIyNjeH222/H4cOHcfr0aXznO9/B2toaF+2QT2AjiU+PItPeSpMj56KYCdrrM/209te8gSdFAMgGLBQK3GgjGAzC6XRiaWkJxWKRF4OOFxcWuCTNybygegBxoxgXTyzndDgc3M2lUCiYDsRdAK1HoVBAqVSCw+HA6uoqMpkMACCZTKJUKnV14REJnED/0x4Qw74bEax4Hko9VxQFxWJxXQu3fsQ1L1Mj4u10Ol0ZXuVyGSsrK0gmk8hms5wdKJZkUlMPkvR2u53DflTNRW2ZqMoLAIcOKYefnI+SJGFsbAzHjh3j+n6SBqJ5YOLKQmzqQr305ubmcO7cOQDoigRs5OAjyW2xWLrW1ljMY/xe+nM6nRgfH8fo6OjAVG9eM8nfK42SuvCQhG61Wsjn8yyVVVXl9l1iJpixOwuZBKFQCJqmcfzY2EWI0kjdbjdcLhfGx8dx4MABDiWKlWT9pO5djzCm1pbLZeRyObjdbmiaBkmSuio1xTURidput0PTNM4kFTNE6XvEY6mr8/j4OIaHh5FKpQZmra+52i+C1GyxuUYul0MwGITFYuHiHWrhRcQptuGizEBVVTE8PAy/349UKsXhJLLlNU2Dx+NBIBDoyijM5XJdzMWY6WeaAlcXlMNPfRWmpqbg8/ngdrtRqVR6lmqLaj71AXQ6nV2aH609OZOJ6H0+H/cHpIpTsZt0P+OaEX8v7krhP5E7x2Ix3HDDDYhEIkilUlhdXWXVmzLBRNOBtATgYsGHzWZjjcHhcMBms8HpdCIYDCIajcLv98Nms6FcLqNSqSCXy7G0oHxy4BJDGpRa72sBUf0HwNqa1+uF1+tlP0CvvUM+nVarhXq9zinhAHgeA+WFRCIRhEIheL1eLhsHgEqlgmKxODBrfF1JfrFXHqnbyWQSmqbB7/ez155abBm972SbS5KEYrGIxcVFZhBUIwCAu/eoqsrfoygKe3rF2nExkjAom+J6AfltFEWBx+OBLMsAepfl0v9Umm21WhEKhbhJB+0pt9vNjN/tdgMAZxJSWHhQ1nnXq/qMtdUi4ZKt7nA4mBFQGFB08on914hZiBuC+v1ls1koisK2PZkGRPS6rrN0EZ1J1LabTAHAJPzdBO0Lat2+0b0X113cV9SOnVq+k/NWXN9qtcqaILWCG7Q13nXJLxK8Ua0G0GWbdTodDA8Po16vI5fLsdee1HujV5ZMALILO50OyuUyPB4PdF1HNpvljMBSqcT9AkQPfrvd5rAi0F9x3esd4t6QJAmBQADAxfqOUqnEoT6jtKfPiuehFGHq+kvpwuVyGbFYDJVKpUubANAVShyEdd/1Hn4kZY2LKKrbFGJTVRX79+9HqVTC8vIystksUqkUZ+oZObUxE6/T6aDRaHAfd+rhTsk++XyeO8GSekj+AlH1N3H1Yazf9/l8CIVCXPefzWa5oUsvGF+n8mDgUniWagVqtRoKhQJP66GBHQ6H44qWCF/vuCZ9+8Ux2b3sN1L3Dh8+jP3792NmZgZPP/00L6Yxj59MAnLwAeCxTnQ+MhmIIYhVYRTyoZxyCvPRNZrYXTgcDoyMjMDlcmFlZQUrKytc3WlMvd0ow4/WmdK+yZEstmerVCqwWCw8qkvTtK7ef/2OazKuy0hQoqqnqiomJiYQCATw2te+Fo1GA5lMBqVSie3/jcJtYrKH+D1kBojvidN4qYEjzfBTVXXg8vevBUR7nbRCu92OQCCAAwcOQNcv9uKnXA/RL7MZAyCQ2Uc5I2JkSAQxBgoJDgp2nfhFwjfa/5qm4Y477sA999yDYDAIh8OBH/zgByiVSpAkib31lUqlS7UnR57dbucJLRSr7XQ6iMfj7MUV2zmLIJWQegSK6p8Z4rt6oHRsmrFHRT1jY2NIJBKcZi06d41O414gs61arQLojiT1ytcXJ/UMgtQHrpHkJzWdbjZNaXnzm9+M22+/HYlEAul0GmfOnEG1WsX+/ftZjaeRWtTBV5QERjOC3qfpLeT17ZVMREyB+r4Pyga4ViDGL8syp1SHw2EOwTqdTlQqFXi9XtbMSDvYrnSmbj+054wmg9Fx2Cty0M+4pkk+5LE/dOgQ7rnnHhw5cgSJRALZbBYHDx6E1Wrl2H4oFMLMzAxWVla67HGSyOJMdnok250cOpQHUKvVupKJem0E47WauPJwOBy47bbb8KY3vQkjIyPsi6EJStTU8+mnn8bq6ip/bjs2Oe0vqgTdbH03eq3fsetDO4yDM1wuF44ePYoHHniAx2zde++9uO222yDLMjKZDPL5PL70pS8hlUohl8sxN6ewIA1bIJtN7L9OIR1N07hRCGX0GbMJRWxUwjsoRR9XEyRhfT4fjh8/jkgkwvMcxsbGEA6H4Xa7ubtzoVBAPB7vqZYbJbaxglPcayJMBrDLVX20CJRuCwD3338/fuRHfgQnTpzAiy++iHvuuQf79u1DtVpFOp3u8sCLBTxithfN8BPLOoFL6b2VSgWJRALlchnJZHJbKZy93r+ade2DAtFUe8UrXoGpqSmcOnUKs7OzcLvd2LdvHwKBAGdk3njjjbjxxhs5V6NXZZ8IY5xetPMHqTPvdrBrxE8FFQA4XjsyMoLp6WnMzc3hySefxKFDhxAMBpnQSXpbLBZ4vV54PJ6uIh7K2qtUKlAUpSucs9EfwdwE1wZEhJqm4YYbbkAymUQsFgMAHtxBSTqUlj00NMR9HY3e/V4+HnECj/i3kcNvULFrxK/rOnteibj3798Pp9OJkydPIpPJ4NixY1zBJxK/w+HAxMQEIpEIJ2UYS0A3UuPETbDTRe8VYjKxM9B93LdvH/txqH9/KBTqSvcGAFmWEQgE4PV6uwZp9nLgiY8iNnLyDTquiQFLNvr+/fsBAGfPnsXIyAiGh4e7BmMC4HTc/fv3M7Mgpx4N7dT1i6PAKA9/I+yU82/FZExsH8TM9+3bxzn4fr8ft99+e9cEZrELUygUQjgc5rmNYlVnr8de32mq/Ouxq2o/ddmhzKrR0VE4HA6Uy2VMT0/Dbrdzd1YAvBHa7TaCwSAOHTqEUCjUld5L3lzgyjnjTNv+6sFisUDTNIRCIXQ6HW6iEggEOApDiVzkHPb5fBgZGeFx7UYi3swP0Cv338RF7KraLzZJ0DSNM7ja7TYOHz7MxE6LT6WWjUYDNpsN09PTOHToEOfiS5LE5gGNbhK7+xj/tgPKMttODzgTlw9R+6KIC+VfGIu1ms0marUaZFnG6OgowuEwrzvlaIhe/a2+dytc7l7Z69j1cV02m437q5VKJcRiMX5d5PZiMg4xgpGREdxxxx2YmJjo6q1HhUDAeilwOao+MRLqFyCew8SVgcViQbPZRD6fRyKRQDweRyaT6dL2yHFLYTpd1xEOhzE2Nsbao9HmvxLXJc4FHAQGsOtqP8XjqS++pmmw2Wy4cOECl1SKmVm0EBTzPXToEPbv38/HUgiItAqR2C9nY4hVZbQBTFx50Folk0mUy2Xk83mk02kUi0UAYHuftEBKs56YmMDNN9+McDjMRVpEsC+VUEWmr6rqlr6jfsGuq/3tdhvhcBjVahXPPvsshoeH4XQ68fzzz7MvgNJrxdg+ff7gwYN43eteB5/PxxvF6/V2leAaNwQxnV4QiV2WZXi9Xvj9fjYpjMeaeOmgRivk1C0Wi0gkEqhWq0z4JPFp7QOBAO68807cddddHPJVFKVL/d/J+pCZR92dSBgNAnZV8suyjE6nw1Va8/PzyOfzmJycxNzcHFKpFJrNJnt1yflDzy0WC1wuF2655Ra88pWv5EKeVqvVxa3pc/QaMRNK8TUuLjV99Hg8UBSFq/u2Sgk1sTNYLBfbs9VqNQQCAVitVsRiMe6cK64PMQKbzYaJiQncfffduOGGG/gYRVG4VXcvid2LIZCkp1bvlEhGpeQbFX/1G66Jw4+89Wtra3jmmWcwNTWFG2+8ET/4wQ9Y3c9ms0zstFAUDRgfH8fP/dzP4ciRI8wcjh07Bo/Hw4lExoQesh8pRixJEnd5dTqdkGWZk0tqtdrAdHC9FrBYLMjn84jH43A6nfB4PCgWi1hbW0M6ne5qt03MutPpQJIkHDx4EC972cswNjbGklqW5a7GHEaTQMwbofM5HI4upkFrP0hlvbtq3FBWXrPZhM1mQ71ex3PPPYfR0VHcfPPNuHDhAs6dO4ebb74ZHo8H9Xod2WwWqqpyJl+r1YLT6UQoFML09DT27dvHUtvv9+OZZ57hxdM0DZ1OB7VajSsCgYtJRrIsw263d3X6JfuSTAdjB99BkAa7AcrLWFhY4DLeer2OpaUllMtlBINB1sJItScNz2q1IhqNYt++ffD5fDyZiSb+1Ov1rjJekfDpURwRR2Yl9fgbFMIHrkEDT4Ku6zyJ9YknnoDdbkc0GsVzzz2HWq2GO+64A16vl6MD5NXXdR2ZTAZLS0vw+/0YGRnhMJHL5YIkSVheXkYul+NGjZqmAbg4FIS4PmUPil5mMWtw0MI+uwlK0c1ms1haWmKzjhzBxWIRwWAQwWAQLpeL6/mr1SrW1taQTCbhcDgwNDQEn88Hi8XC8xby+TxSqRRqtVpX8ZZRC6DrEIuAgN7Zg/2KXR3USeobALbByQk4PDyM22+/HW63G4qiIBQK4cCBA4hEIkysrVYLuVwOsVgMKysriMfj3MRjamoKbrebxzqXSiVks1nOI7BYLCgWi8jlcnw9xmgCLbzYAYaei+j3TWHE1WCARFwulwujo6MYHx9HMBjkYSp+vx+BQAAejwcOhwPVahWJRALz8/NYWFhAPp+HqqqcGarrOnd9SiQSvNblcpkjCcb8/l51AaSVbNTLr5/W/po08yDVnySx1WpFIpHA97//fezfvx9utxurq6tYWVnhOW1UuZdKpbC2toZUKoVOp8PDHcvlMsbGxhAMBlEqlZDL5WC1WjE8PAyr1YpKpcJqIQC2/UgiUC4BvUbXKi72IEiD3QLdx0qlgpWVFVQqFQwNDcHr9cLtdiORSMDlckGWZY4OxGIxxONx5PN5dvKura1h3759nAFI6d8A4HQ6u8w+4BID6LWWg1aufU1GdFNXHrHppuhlDYfDGBkZAXDJcUeLWKvVuohbkiQO901MTOCee+7Bt771LczPz8Pn8yEQCMDlcqFarWJxcZFLfynEJ2oVVCugqiqy2ey6Sa3bzSbrN1xN00e0ySncpigK+2RIEpfLZZTLZdTr9S5Cttls8Hq9GBsbw4EDByBJEmZnZxGPxznrkxy4FDoUQWtJDkBdv9gDYqMpvf209tckm6HZbLIH39hWS9d1JBIJJBIJAJc674qLDlyav069+TKZDGq1GoaHh9lGJBWQiFzMAaC6gFar1TXcgeYAkgeYFtts5X11QPez1WqhVCqhXC53aWTGcmxiRGTHdzodHrFWLpcxNTXFDkBy4olEb7TtgYuET1GDRqPBDsN+xzVLZSJJbnzNiK04sPiZarWKF198kZt/xmKxrnbfon0PXCRoWZbh9/sRCoXYZzA7O7uuj5+p8l89GPMpxOk6xnsuPhd9NPV6Haurqxy+9fv9nEgkRmtI5aeQIDUN0TSNB30Misf/mqj9VxMWiwVHjx7F6OgoarUacrkcUqkUyuUyVFVlf0Oz2YTdbofP54PX60W9XkcymeQ8c7GsmNTSQevuStgra2+1WuH3+zE6OspTmcjPQ0yATEWXywWn08kmaLFYRDab5clAm5UH9wv6jvgBcJru+Pg4pqenIcsy549TjYCqqiiXy4jH4+xEonJiowYixoOB/toA28FeWHvSzIihiyXAVO1JnZ9pRFu1WkUul0M6nUY+n+fjNlvfflr7viR+gt1uh9/vh9frRSAQQDAYRLFYRLFYRDqdRjqdRq1W23RBe22GftoA28FeXHua7Ozz+bhYp9lsol6vs9OYtALRl3QlSoP3CvqO+C/XNqcsP6oiMyZ59AoHDYpNSNgraw+sH7BCjlrRgUhaALD+t5nE3+vAPbIBZFnumuXXK0tvM7udQoB0jNEEMIl/b4CYgJixeSU0OJP49xCoeINUPuBSbwGSACQFKPGH0ogHwemzHezFtSdi3yhqsFP009r3HfFT4QY1DSEYU4sv95wiM+inDbAd7JW13w3009pvm/hNmDDRXxisZGYTJkwwTOI3YWJAYRK/CRMDCpP4TZgYUJjEb8LEgMIkfhMmBhQm8ZswMaAwid+EiQGFSfwmTAwo/n+WmeUbpA+/QwAAAABJRU5ErkJggg==\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 300x400 with 8 Axes>"
       ]
@@ -675,7 +702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 13,
    "id": "2b3c3a82",
    "metadata": {},
    "outputs": [
@@ -683,8 +710,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:14<00:00, 3184.50it/s]\n",
-      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:01<00:00, 3340.95it/s]\n"
+      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:14<00:00, 3269.35it/s]\n",
+      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:01<00:00, 3347.86it/s]\n"
      ]
     }
    ],
@@ -736,7 +763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 14,
    "id": "f91086e3",
    "metadata": {
     "lines_to_next_cell": 2
@@ -809,17 +836,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 0: 100%|████████████████████████████████████████████████| 250/250 [01:00<00:00,  4.11it/s, ce_loss=1.82]\n",
-      "Epoch 1: 100%|████████████████████████████████████████████████| 250/250 [01:05<00:00,  3.81it/s, ce_loss=1.53]\n",
-      "Epoch 2: 100%|████████████████████████████████████████████████| 250/250 [01:08<00:00,  3.66it/s, ce_loss=1.43]\n",
-      "Epoch 3: 100%|████████████████████████████████████████████████| 250/250 [01:07<00:00,  3.70it/s, ce_loss=1.35]\n",
-      "Epoch 4: 100%|████████████████████████████████████████████████| 250/250 [01:07<00:00,  3.72it/s, ce_loss=1.29]\n",
-      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:02<00:00, 114.38it/s]\n"
+      "Epoch 0: 100%|█████████████████████████████████████████████████| 250/250 [01:00<00:00,  4.12it/s, ce_loss=1.8]\n",
+      "Epoch 1: 100%|████████████████████████████████████████████████| 250/250 [01:06<00:00,  3.76it/s, ce_loss=1.51]\n",
+      "Epoch 2: 100%|████████████████████████████████████████████████| 250/250 [01:07<00:00,  3.69it/s, ce_loss=1.42]\n",
+      "Epoch 3: 100%|████████████████████████████████████████████████| 250/250 [01:06<00:00,  3.76it/s, ce_loss=1.34]\n",
+      "Epoch 4: 100%|████████████████████████████████████████████████| 250/250 [01:10<00:00,  3.54it/s, ce_loss=1.28]\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:02<00:00, 117.05it/s]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -831,17 +858,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 5: 100%|████████████████████████████████████████████████| 250/250 [01:05<00:00,  3.79it/s, ce_loss=1.24]\n",
-      "Epoch 6: 100%|████████████████████████████████████████████████| 250/250 [01:06<00:00,  3.76it/s, ce_loss=1.21]\n",
-      "Epoch 7: 100%|████████████████████████████████████████████████| 250/250 [01:09<00:00,  3.59it/s, ce_loss=1.18]\n",
-      "Epoch 8: 100%|████████████████████████████████████████████████| 250/250 [01:07<00:00,  3.70it/s, ce_loss=1.16]\n",
-      "Epoch 9: 100%|████████████████████████████████████████████████| 250/250 [01:07<00:00,  3.71it/s, ce_loss=1.14]\n",
-      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:02<00:00, 118.28it/s]\n"
+      "Epoch 5: 100%|████████████████████████████████████████████████| 250/250 [01:05<00:00,  3.83it/s, ce_loss=1.24]\n",
+      "Epoch 6: 100%|████████████████████████████████████████████████| 250/250 [01:08<00:00,  3.66it/s, ce_loss=1.21]\n",
+      "Epoch 7: 100%|████████████████████████████████████████████████| 250/250 [01:06<00:00,  3.76it/s, ce_loss=1.18]\n",
+      "Epoch 8: 100%|████████████████████████████████████████████████| 250/250 [01:07<00:00,  3.69it/s, ce_loss=1.16]\n",
+      "Epoch 9: 100%|████████████████████████████████████████████████| 250/250 [01:06<00:00,  3.78it/s, ce_loss=1.14]\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:02<00:00, 118.55it/s]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -853,17 +880,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 10: 100%|███████████████████████████████████████████████| 250/250 [01:05<00:00,  3.80it/s, ce_loss=1.12]\n",
+      "Epoch 10: 100%|███████████████████████████████████████████████| 250/250 [01:07<00:00,  3.69it/s, ce_loss=1.12]\n",
       "Epoch 11: 100%|███████████████████████████████████████████████| 250/250 [01:07<00:00,  3.71it/s, ce_loss=1.11]\n",
-      "Epoch 12: 100%|████████████████████████████████████████████████| 250/250 [01:07<00:00,  3.71it/s, ce_loss=1.1]\n",
-      "Epoch 13: 100%|███████████████████████████████████████████████| 250/250 [01:09<00:00,  3.60it/s, ce_loss=1.09]\n",
-      "Epoch 14: 100%|███████████████████████████████████████████████| 250/250 [01:07<00:00,  3.71it/s, ce_loss=1.08]\n",
-      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:02<00:00, 117.65it/s]\n"
+      "Epoch 12: 100%|████████████████████████████████████████████████| 250/250 [01:06<00:00,  3.78it/s, ce_loss=1.1]\n",
+      "Epoch 13: 100%|███████████████████████████████████████████████| 250/250 [01:08<00:00,  3.68it/s, ce_loss=1.09]\n",
+      "Epoch 14: 100%|███████████████████████████████████████████████| 250/250 [01:08<00:00,  3.66it/s, ce_loss=1.08]\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:02<00:00, 115.16it/s]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -875,17 +902,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 15: 100%|███████████████████████████████████████████████| 250/250 [01:07<00:00,  3.72it/s, ce_loss=1.06]\n",
-      "Epoch 16: 100%|███████████████████████████████████████████████| 250/250 [01:07<00:00,  3.70it/s, ce_loss=1.06]\n",
-      "Epoch 17: 100%|███████████████████████████████████████████████| 250/250 [01:07<00:00,  3.69it/s, ce_loss=1.05]\n",
-      "Epoch 18: 100%|███████████████████████████████████████████████| 250/250 [01:09<00:00,  3.61it/s, ce_loss=1.04]\n",
-      "Epoch 19: 100%|███████████████████████████████████████████████| 250/250 [01:07<00:00,  3.69it/s, ce_loss=1.03]\n",
-      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:02<00:00, 123.74it/s]\n"
+      "Epoch 15: 100%|███████████████████████████████████████████████| 250/250 [01:07<00:00,  3.71it/s, ce_loss=1.06]\n",
+      "Epoch 16: 100%|███████████████████████████████████████████████| 250/250 [01:07<00:00,  3.69it/s, ce_loss=1.06]\n",
+      "Epoch 17: 100%|███████████████████████████████████████████████| 250/250 [01:07<00:00,  3.71it/s, ce_loss=1.05]\n",
+      "Epoch 18: 100%|███████████████████████████████████████████████| 250/250 [01:07<00:00,  3.68it/s, ce_loss=1.04]\n",
+      "Epoch 19: 100%|███████████████████████████████████████████████| 250/250 [01:07<00:00,  3.72it/s, ce_loss=1.03]\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:02<00:00, 113.08it/s]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -897,7 +924,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "train completed, total time: 1368.4766018390656.\n"
+      "train completed, total time: 1365.2433605194092.\n"
      ]
     }
    ],
@@ -982,7 +1009,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 18,
    "id": "aa3938fe",
    "metadata": {},
    "outputs": [
@@ -990,17 +1017,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-03-17 20:12:36,970 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-03-17 20:12:36,970 - INFO - File exists: /tmp/tmp1lues0wg/MedNIST.tar.gz, skipped downloading.\n",
-      "2023-03-17 20:12:36,970 - INFO - Non-empty folder exists in /tmp/tmp1lues0wg/MedNIST, skipped extracting.\n"
+      "2023-03-21 21:39:36,491 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
+      "2023-03-21 21:39:36,491 - INFO - File exists: /tmp/tmp3g842skb/MedNIST.tar.gz, skipped downloading.\n",
+      "2023-03-21 21:39:36,492 - INFO - Non-empty folder exists in /tmp/tmp3g842skb/MedNIST, skipped extracting.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:01<00:00, 3244.12it/s]\n",
-      "In-distribution data: 100%|███████████████████████████████████████████████████| 17/17 [00:02<00:00,  5.86it/s]\n"
+      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:01<00:00, 3284.82it/s]\n",
+      "In-distribution data: 100%|███████████████████████████████████████████████████| 17/17 [00:03<00:00,  4.93it/s]\n"
      ]
     }
    ],
@@ -1038,12 +1065,12 @@
    "id": "19541717",
    "metadata": {},
    "source": [
-    "We will use the \"ChestCT\" class of the dataset for the out-of-distribution examples."
+    "We will use all other classes for the out-of-distribution examples."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 19,
    "id": "f3e714ee",
    "metadata": {},
    "outputs": [
@@ -1051,11 +1078,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "out-of-distribution data BreastMRI: 100%|█████████████████████████████████████| 15/15 [00:02<00:00,  6.02it/s]\n",
-      "out-of-distribution data CXR: 100%|███████████████████████████████████████████| 16/16 [00:02<00:00,  5.94it/s]\n",
-      "out-of-distribution data Hand: 100%|██████████████████████████████████████████| 16/16 [00:02<00:00,  5.70it/s]\n",
-      "out-of-distribution data ChestCT: 100%|███████████████████████████████████████| 16/16 [00:02<00:00,  5.71it/s]\n",
-      "out-of-distribution data AbdomenCT: 100%|█████████████████████████████████████| 16/16 [00:02<00:00,  5.72it/s]\n"
+      "out-of-distribution data BreastMRI: 100%|█████████████████████████████████████| 15/15 [00:03<00:00,  4.89it/s]\n",
+      "out-of-distribution data AbdomenCT: 100%|█████████████████████████████████████| 16/16 [00:03<00:00,  4.54it/s]\n",
+      "out-of-distribution data Hand: 100%|██████████████████████████████████████████| 16/16 [00:03<00:00,  4.31it/s]\n",
+      "out-of-distribution data ChestCT: 100%|███████████████████████████████████████| 16/16 [00:03<00:00,  4.38it/s]\n",
+      "out-of-distribution data CXR: 100%|███████████████████████████████████████████| 16/16 [00:03<00:00,  4.45it/s]\n"
      ]
     }
    ],
@@ -1097,7 +1124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 22,
    "id": "cd456a7c",
    "metadata": {},
    "outputs": [
@@ -1107,13 +1134,13 @@
        "Text(0.5, 0, 'Log-likelihood')"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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lVoe6Tpw4kaFDh6LT6UhOTmb9+vXExcXx9NNPM27cuArHNXToUCZOnIjJZOL8+fO89dZbPP/88yxfvrzM9iaTCYPBgFptv2/dISEhrFmzxipASkhIIC0tDXd391LtSz4/hYWFbN68mVdeeYUGDRrQs2fPGznsGpEAyUmVrGIDySAJIWzApwkENLf3KMo0Y8YMNBoNixYtspzL1ahRI9q0aUPfvn2ZO3cuM2bMwGQy8fLLLxMREcH8+fNRKs3/RzZu3JhmzZoxePBglixZwuOPP27p28PDg6CgIEufnTt3JigoiPj4eO666y4iIiLKHZerq6vl3gYNGvCvf/2LV1991fJ6SXZl4cKFfPDBBxw7doxPP/2Uzp078/HHH7NixQrS0tIIDw/nySef5O677wbMZ41NmzaNnTt3kpaWRkhICA8//DCPPPKIVd/vvvsuJ06cQK1W07x5c9577z127drF/PnzAWjVqhWAVVZo0KBBLFmyhJSUFEJCQgBYvXo1gwYNYt26daXe47Wfn8cff5xPP/2U7du33xQBkhRpOyldkQG11ryCTaVWYNBLgCSEcDxZWVls27aNhx9+uNShpUFBQQwaNIjNmzdjMpk4fPgwJ06cIC4uzhIclYiKiiI2NpaNGzdW+syRI0diMpnYsmVLtca5efNmOnToUOq19957j8mTJ7Np0yZatWrFRx99xLp165gxYwYbN25k1KhRPP/885YpO6PRSMOGDfnggw/YuHEj48ePZ+7cuWzatAkAvV7P+PHj6dy5M+vXr2fFihU89NBDKBQKBgwYwOjRo2nRogXbtm1j27ZtDBgwwDKWgIAAunfvztq1awEoKChg06ZNPPDAAxW+P6PRyHfffUd2djYajabKnxd7kgySk9IXGVH5XjPFJgGSEMIBnTlzBpPJRGRkZJmvR0ZGkp2dTUZGBqdPn7ZcK0tERAS7d++u9Jm+vr4EBARY6m/K8+WXX7Jq1SpMJhMFBQWEh4fz6aeflmo3ceJEunXrBkBxcTEfffQRixcvJiYmBoAmTZqwe/duVqxYwW233YZGo2HixImW+5s0aUJCQgLffvstAwYMIDc3l8uXL/OPf/yDpk2blnrP7u7uqFQqS+bneg888ACzZs1i3LhxfPfddzRt2pTWrVuX2Xb27Nl88MEHFBcXo9fr8fX1tapBqs8kQHJSumLrIm2DTLEJIRyYyWSqk7YV9aFQKCpsM2jQIJ544gkA0tLS+OijjxgzZgyrV6/G09PT0q59+/aW3585c4aCggJGjx5t1ZdOp7MKUr744gtWr17N+fPnKSoqQqfTERUVBZgDuPvvv58xY8bQrVs3unbtSv/+/WnQoEGV3tudd97Jq6++yp9//snq1asrzB6NGTOG+++/n9TUVN555x0efvhhwsLCqvQce5MAyUnprynSVqkU6HUSIAkhHE/Tpk1RKBQkJibSt2/fUq8nJibi4+ODv78/4eHhlmtt2rQp1fbkyZOWNhXJzMwkIyOD0NDQCtt5enpagoWwsDDefPNNunfvzubNm62yLNeunMvPzwfgo48+Ijg42Kq/kuLxjRs3MmvWLF544QViYmLw8PDg008/Zd++fZa2M2fOZMSIEWzdupXNmzfz/vvvs3jxYqKjoyt9f2q1mnvvvZd58+axb98+S81SWfz8/AgLCyMsLIwPPviAQYMG0a5dO5o3r5/1ateSGiQnpdcZr65iU0kNkhDCMfn5+dGtWzeWL19OYWGh1Wupqals2LCB/v37o1AoaN26NRERESxZsgSj0fr/xCNHjrB9+/ZSy9vLsnTpUpRKJX369KnWWFUqc13o9eO8VmRkJFqtlvPnz1sCj5KPkqLpPXv2EBMTw7/+9S/atGlDWFgYZ8+eLdVXmzZtGDt2LP/73/9o2bIl33zzDQAajabU+7/egw8+yB9//EHv3r3x8fGp0vsLCQlhwIABvPfee1Vqb2+SQXJCJpPJPMWmLgmQZJm/EMJxTZs2jWHDhjFmzBgmTZpktcw/ODiYZ555BgCFQsGbb77J6NGjmTBhAmPHjiUwMJB9+/Yxa9YsoqOjGTVqlFXfeXl5pKamotfrSUpKYv369Xz11Vc8++yzlU4lFRYWkpqaCkB6ejr/+c9/cHFxsdQblcXT05PRo0czc+ZMTCYTt9xyC5cvX2bPnj14enpy3333ERYWxrp169i6dSuhoaF8/fXXHDhwwJLROnfuHCtXrqRXr140aNCAU6dOcfr0af7v//4PMK/aS0pK4vDhwwQHB+Pp6Wm1tQGYA7WdO3daZbeqYuTIkdxzzz0cOHDAauqwPpIAyQkZ9EYwIRkkIYTtZJ+rt88JDw9n9erVzJs3j0mTJpGdnU1gYCB9+vRh/Pjxlj2QADp16sSKFStYsGABjz32GHl5eYSEhDB48GDGjh1bKlCIj48nPj4ejUZDUFAQHTt2ZMmSJdx+++2VjmvlypWsXLkSAB8fH1q1asXChQsr3BoAYNKkSfj7+/PRRx+RlJSEl5cXbdq0sdQzDRs2jMOHD/PMM8+gUCgYOHAgDz/8ML/99htgnrI7efIka9euJSsry7LFwLBhwwC46667+OGHHxg5ciQ5OTlWy/yvdf0Gm1XRvHlzunXrRnx8PB9//HG177+RFCZbVKM5IYPBQEJCAtHR0Za06M2iME/Hp5O3cts9zWjUwpe9P5yl4HIxQ17sbO+hCSHqqcLCQk6dOkWzZs2sl8vfJDtpC+dS7tcrVf/+LRkkJ1QynVZyUK1kkIQQNebbxBysyFlswsFIgOSESnbNVl27zF8viUQhRA35NpGARTgcWcXmhPS6kgzSNTVIssxfCCGEsJAAyQmVZJCUainSFkIIIcoiAZITKtkU8tpl/hIgCSGEEFdJgOSESoq0JYMkhBBClE0CJCdUEgxZr2KTIm0hhBCihARITsiyiu2aKTaT0YTJKEGSEEIIARIgOaWSDJJSZc4gqa78KtNsQgghhFm92Afpiy++4NNPPyU1NZWoqCimTZtGhw4dym2/efNmPvjgA5KTkwkPD+e5556jZ8+eltfnzZvHxo0buXDhAhqNhrZt2/LMM8/QsWNHS5usrCzeeOMNfv75Z5RKJf369ePll1/Gw8OjTt9rfaAvNqJUKVAork6xgTlAUmtvrl3BhRD2l5KbQmZR5g17np+LHyGeITfsefayZs0a3nrrLf76669q3Tdv3jx+/PFHvv766zoamXOwe4C0adMmZs6cyYwZM+jYsSOfffYZY8aM4dtvvyUgIKBU+z179jB58mSeffZZ/vGPf7BhwwbGjx/PmjVraNmyJWA+d2f69Ok0adKEwsJClixZwujRo/nhhx/w9/cH4LnnniM1NZXFixej0+l46aWXmD59+k1zynBtGHRGy/QamKfYAKlDEkJUW0puCveuu5dCQ/kn0Nuaq8qV9YPXVytISklJIT4+nq1bt5KVlUVQUBC9e/dm/Pjxpc4UO378OPPnz2fXrl3k5ubSuHFjBgwYwOOPP251OGuvXr1ITk4GwMXFhcDAQNq3b8+wYcPo2rVrlcb1zTff8PzzzzNs2DBeffXVKr8fR2IymVi5ciWrVq3ixIkTqFQqmjZtyr333stDDz3EwIEDLZ/nstx33328/fbbNh+X3QOkxYsXM3ToUB544AEAZsyYwS+//MLq1at5/PHHS7VfunQpPXr04NFHHwXMh/Zt376dZcuW8frrrwMwaNAgq3tefPFFVq1axdGjR+natSuJiYls3bqVVatWWU4TfuWVV3j88ceZMmUKwcHBdfmW7c6gN6C8UqAN1hkkIYSojsyiTAoNhTzW/jEaeTaq8+edzz3Pxwc+JrMos8oB0rlz53jooYcIDw9nzpw5hIaGcvz4cd599122bt3KihUrLAfWJiQkEBcXR9euXVm4cCEBAQHs37+fWbNmsWPHDpYuXWp1YO3EiRMZOnQoOp2O5ORk1q9fT1xcHE8//TTjxo2rdGyrVq3i0UcfZcWKFUydOhUXF5cafV5uZs8//zw//PAD48aNY9q0afj7+3PkyBE+++wzQkNDWbVqFQaDefX13r17mTBhAt9++y2enp4Apc5asxW7BkjFxcUcOnSIsWPHWq4plUpiY2PZu3dvmfckJCQwatQoq2vdu3fnxx9/LPcZK1aswMvLi1atWgHmT7C3t7clOAKIjY1FqVSyf/9++vbtW8t3Vr/pdUZUqmszSOYAyWiQAEkIUTONPBsR5h1m72GUacaMGWg0GhYtWmT5ZtqoUSPatGlD3759mTt3LjNmzMBkMvHyyy8TERHB/PnzUSrN/082btyYZs2aMXjwYJYsWWL1w7uHhwdBQUGWPjt37kxQUBDx8fHcddddRERElDuuc+fOsXfvXubNm8euXbv4/vvvS/2AD/Djjz/yzjvvkJKSwm233ca///1vQkKuBocLFy5kyZIlFBQU0L9/f8tMSQmj0ch//vMfVq5cSUZGBpGRkUyePJk77rgDgKSkJHr37s3cuXNZtmwZBw8epEWLFsyePZvLly/z2muvcerUKW655Rbeeecdq/6/+uorFi1aRFJSEo0bN2bEiBH861//sup33rx5fP755+zfv5+wsDBmzJhBTEwMYJ5F2rBhAwsWLKBPnz6WfkNDQ+nduze5ubl4eXlZrvv4+AAQEBCAt7d3uZ9bW7BrkXZmZiYGg6HUVFpAQABpaWll3pOWlkZgYGCl7X/++WdiYmLo0KEDS5YsYdGiRZa/1LS0tFJfQGq1Gh8fH1JTU2v7tuo9g85onUFSlmSQZIpNCOFYsrKy2LZtGw8//HCpTENQUBCDBg1i8+bNmEwmDh8+zIkTJ4iLi7MERyWioqKIjY1l48aNlT5z5MiRmEwmtmzZUmG7NWvW0LNnT7y8vLj33ntZtWpVqTaFhYX897//ZdasWXz55Zfk5OTwzDPPWF7ftGkT8+bN45lnnmH16tUEBQWxfPlyqz6WLl3K4sWLeeGFF1i/fj3du3fnySef5PTp01bt5s2bx7hx41i7di1qtZrJkyfz7rvv8vLLL/PFF19w9uxZPvjgA0v79evX88EHH/DMM8+wadMmnn32WeLj41m7dq1Vv3PnzmXMmDGsW7eO8PBwJk+ejF6vB2DDhg00a9bMKjgqoVAorIKjG81hV7F16dKFdevW8b///Y8ePXowadIk0tNv4GnT9Vj5NUiSQRJCOJYzZ85gMpmIjIws8/XIyEiys7PJyMiwBAzltY2IiCgVVJTF19eXgICACutmjEYja9eu5d577wVgwIAB7N69m3Pnzlm10+l0TJ8+nZiYGNq1a8fbb7/N3r172b9/P2AOfh588EGGDBlCREQEzzzzDM2bN7fq49NPP+Wxxx5j4MCBRERE8PzzzxMVFcVnn31m1W706NH06NGDyMhIRo4cyaFDh3jyySe55ZZbaNOmDQ8++CC7du2ytJ83bx5Tp06lX79+NGnShH79+vHII4+wYsWKUv3eeeedNGvWjIkTJ5KcnMyZM2cA899Ps2bNKv2c2oNdAyQ/Pz9UKlWpwCU9Pb1UlqhEYGBgqWxRWe3d3d0JCwsjOjqat956C7VabYnOAwMDycjIsGqv1+vJzs62pEodmUFvtEyrwTVTbJJBEkI4KJOp6v+/VadtRX2UrBQuy++//05BQYFlBba/vz/dunVj9erVVu3UarVVOUhkZCTe3t4kJiYCkJiYaLVCGyA6Otry+9zcXC5dukSnTp2s2nTq1MnSR4mSMhTAMrNz/bWS7535+fmcPXuWl19+mZiYGMvHf//7X86ePVtuvyXfY0v6scXnuq7YtQZJq9XStm1bduzYYUmvGY1GduzYwfDhw8u8Jzo6mp07d1rVIW3fvt3qC6IsRqOR4uJiAGJiYsjJyeHgwYO0a9cOgJ07d2I0GivcXsBR6PVGS9YIrinSlhokIYSDadq0KQqFgsTExDLrSxMTE/Hx8cHf35/w8HDLtTZt2pRqe/LkSUubimRmZpKRkUFoaGi5bVatWkVWVpZVcGM0Gjl69CgTJ04sNcV3I2g0GsvvS4I7tVptdc1oNH+fyM/PB+CNN94oFaBdP/ay+i3pJzw8nJMnT9rqLdiU3afY4uLiWLlyJWvXriUxMZHXXnuNgoIC7r//fgCmTJlitfR+5MiRbN26lUWLFpGYmMi8efM4ePCgJaDKz89nzpw5JCQkkJyczMGDB3nxxRe5ePEid999N2COwHv06MG0adPYv38/u3fv5o033mDgwIEOv4INrkyxlZFBkik2IYSj8fPzo1u3bixfvpzCQuutCFJTU9mwYQP9+/dHoVDQunVrIiIiWLJkieUbeIkjR46wfft2Bg4cWOkzly5dilKpLLOuBswB1JYtW5g7dy7r1q2z+sjOzmbbtm2Wtnq9noMHD1r+fPLkSXJycizTgJGRkezbt8+q/2v/7OnpSYMGDdizZ49Vmz179pSaiquOwMBAGjRowLlz5wgLC7P6aNKkSZX7GTRoEKdPny5zoZXJZOLy5cs1HmNt2X2Z/4ABA8jIyCA+Pp7U1FRat27NJ598YpkyS0lJsYpGO3XqxOzZs3n//feZM2cO4eHhLFiwwLIHkkql4uTJk6xdu5bMzEx8fX1p3749X3zxBS1atLD0M3v2bN544w0eeeQRy0aRr7zyyo1983Zi0Jtkik0I4TSmTZvGsGHDGDNmDJMmTbJa5h8cHGwpelYoFLz55puMHj2aCRMmMHbsWAIDA9m3bx+zZs0iOjq61CrqvLw8UlNT0ev1JCUlsX79er766iueffZZwsLKXtX39ddf4+vrawnMrtWzZ09WrVplWWGm0Wh44403eOWVV1CpVLzxxhtER0dbZjtGjhzJ1KlTadeuHZ06dWLDhg0cP37cKkgZM2YM8+bNo2nTpkRFRbFmzRqOHDnC7Nmza/V5nThxIv/+97/x8vKiR48eFBcXc/DgQXJycoiLi6tSH/379+eHH35g8uTJjBs3jm7duuHv78+xY8dYsmQJI0aMKDfQrGt2D5AAhg8fXu6U2ueff17qWv/+/enfv3+Z7V1cXJg/f36lz/T19XWKTSHLoi82WE+xKSWDJISonfO55+vtc8LDw1m9ejXz5s1j0qRJZGdnExgYSJ8+fRg/frxlDyQw/xC+YsUKFixYwGOPPUZeXh4hISEMHjyYsWPHWu2BBBAfH098fDwajYagoCA6duzIkiVLuP3228sdz+rVq+nbt2+ZNUr9+vVjypQplhodV1dXHnvsMSZPnszFixe59dZbefPNNy3tBwwYwNmzZ3n33XcpKirirrvu4p///KdVFmrkyJHk5uby9ttvW5b5/+c//6nSdGFFhgwZgqurK59++invvPMO7u7utGzZkkceeaTKfSgUCt577z1WrFjB6tWr+fDDD1GpVISFhTF48GC6d+9eqzHWhsJUnyuk6jGDwUBCQgLR0dGoVDfX8RxrZu9GpVZya/9wAHRFBjb+Zz93PdaO5rc0sO/ghBD1UmFhIadOnaJZs2ZWy+Vvlp20hXMp7+sVqv79u15kkMSNZdAb0bpe/auXGiQhRE2FeIawfvB6OYtNOBwJkJxQqRokpeykLYSouRDPEAlYhMOx+yo2ceMZdNb7ICmUChQK2UlbCCGEKCEBkhMyXLcPEph305YpNiGEEMJMAiQnZLxuJ20w1yEZDZJBEkIIIUACJKd0fQ0SlARIkkESQgghQAIkp3T9WWxgDpCkBkkIIYQwkwDJCRkNRlTX1SAplAqMUoMkhBBCABIgOR2TyWSeYlOWkUGSGiQhhBACkH2QnE5JIXapKTbJIAkhakh3/jz6zBu3UaTazw9No0Y37HmOKCkpid69e7Nu3Tpat25t7+HUSxIgOZmSpfxKdRnL/CWDJISoJt358yQOGIip8MYdNaJwdSVy08ZqBUkpKSnEx8ezdetWsrKyCAoKonfv3owfPx4/Pz+rtsePH2f+/Pns2rWL3NxcGjduzIABA3j88cdxc3OztOvVqxfJycmA+RzQwMBA2rdvz7Bhw+jatWuF4xkxYgRRUVG8/PLLVtfXrFnDW2+9xV9//VXl9ybqhgRITsZ4pRC71BSbUiH7IAkhqk2fmYmpsJCAsWNvSFZHd/486R99hD4zs8rPO3fuHA899BDh4eHMmTOH0NBQjh8/zrvvvsvWrVtZsWKF5cDahIQE4uLi6Nq1KwsXLiQgIID9+/cza9YsduzYwdKlS60OrJ04cSJDhw5Fp9ORnJzM+vXriYuL4+mnn2bcuHF18SkQN4gESE7GcGUp//VTbApZ5i+EqAVNo0Zoa3k6fF2ZMWMGGo2GRYsWWQ4ubdSoEW3atKFv377MnTuXGTNmYDKZePnll4mIiGD+/PkoleZMe+PGjWnWrBmDBw9myZIlPP7445a+PTw8CAoKsvTZuXNngoKCiI+P56677iIiIqJWYz979iwzZ85k3759FBQUEBERweTJk4mNjbW06dWrF0OHDuXMmTN8++23+Pj4MG7cOB566CFLm/379zN9+nQSExNp0aKFBG9VIEXaTsYyxVZmDZJMsQkhHEtWVhbbtm3j4YcfLnWqe1BQEIMGDWLz5s2YTCYOHz7MiRMniIuLswRHJaKiooiNjWXjxo2VPnPkyJGYTCa2bNlS6/Hn5+fTs2dPlixZwtq1a+nRowdPPPEE58+ft2q3ePFi2rVrx7p163j44Yd57bXXOHnyJAB5eXmMHTuWyMhI1qxZw4QJE5g1a1atx+boJIPkZCxTbKWOGpEpNiGE4zlz5gwmk4nIyMgyX4+MjCQ7O5uMjAxOnz5tuVaWiIgIdu/eXekzfX19CQgIsNQnlefLL79k1apVVtf0ej0uLi6WP0dFRREVFWX586RJk/jxxx/56aefGD58uOX6HXfcwb/+9S8AHnvsMZYsWcKuXbuIiIjgm2++wWg08tZbb+Hi4kKLFi24cOECr732WqXvxZlJgORkKsogGWSKTQjhoEymqmfIq9O2oj4UCkWFbQYNGsQTTzxhde3777/no48+svw5Ly+P+fPn88svv5CamorBYKCwsLBUBqlVq1aW3ysUCgIDA0lPTwcgMTGRVq1aWQVeMTExNX5vzkICJCdjWeZfxj5IMsUmhHA0TZs2RaFQkJiYSN++fUu9npiYiI+PD/7+/oRfqaFKTEykTZs2pdqePHnS0qYimZmZZGRkEBoaWmE7T09PwsLCrK4FBARY/XnWrFls376dF154gaZNm+Lq6srEiRPR6XRW7dRq62/nCoXCJoGeM5MaJCdTXgZJoZQibSGE4/Hz86Nbt24sX76cwuu2IkhNTWXDhg30798fhUJB69atiYiIYMmSJRiN1v8fHjlyhO3btzNw4MBKn7l06VKUSiV9+vSp9fj37t3LfffdR9++fWnVqhWBgYGVTt1dLzIykqNHj1JUVGS5lpCQUOuxOTrJIDkZYzmr2JQqBboi+WlDCFEzuuumfOrTc6ZNm8awYcMYM2YMkyZNslrmHxwczDPPPAOYsy5vvvkmo0ePZsKECYwdO5bAwED27dvHrFmziI6OZtSoUVZ95+XlkZqail6vJykpifXr1/PVV1/x7LPPlsoO1URYWBg//PADvXr1QqFQ8P7775cK3ipzzz33MHfuXF555RXGjh1LcnIyixYtqvXYHJ0ESE7GUF6RtuyDJISoAbWfHwpXV9KvqZupawpXV9TXbe5YkfDwcFavXs28efOYNGkS2dnZBAYG0qdPH8aPH2/ZAwmgU6dOrFixggULFvDYY4+Rl5dHSEgIgwcPZuzYsVZ7IAHEx8cTHx+PRqMhKCiIjh07smTJEm6//XabvNepU6fy0ksvMWzYMPz8/Cxjqg4PDw8+/PBDXn31VQYPHkzz5s157rnnmDBhgk3G6KgUJpmkrBGDwUBCQgLR0dGoVCp7D6fKTh9IY+OC/dz9WDtcPTWW6/t/TiLrUj7/nN7FjqMTQtRXhYWFnDp1imbNmpVaLi9HjYj6pqKv16p+/5YMkpMp9yw2lcLymhBCVIemUSMJWITDkSJtJ1MyjaYoa5m/TLEJIYQQgARITsdYsopNKavYhBBCiPJIgORkDBVNsck+SEIIIQQgAZLTMeqNKJSKUju8KlUKS/AkhBBCODsJkJyMQW8qlT2CK4fVyhSbEEIIAUiA5HQMBmPZAZIcViuEEEJYSIDkZIx6U6kCbQCFSonJCCajTLMJIYQQEiA5mXIzSFeCJtkLSQghhJCNIp2OUW8qdcwIXF3VZjAYUWkkbhZCVN3ljEIKc3WVN7QRV08NXv6ulTe8ySUlJdG7d2/WrVtH69at7T0cpyMBkpMxGIwoyoh/JIMkhKiJyxmFLH9tJ/riG1fDqNYqefi126sVJKWkpBAfH8/WrVvJysoiKCiI3r17M378ePyuO9ft+PHjzJ8/n127dpGbm0vjxo0ZMGAAjz/+OG5ubpZ2vXr1Ijk5GQAXFxcCAwNp3749w4YNo2vXrpWO6cyZM3z44Yf8/vvvZGRk0KBBA6Kjo4mLi6N9+/ZVfm+1MWLECKKionj55ZdLvfbdd9+xbNky/v77b4xGI6Ghodx1110MHz6cCRMm8Mcff5Tb72233cbnn39el0OvcxIgOZnyapAsGSQp1BZCVENhrg59sZFb7g67IVmdyxmF7P72DIW5uio/79y5czz00EOEh4czZ84cQkNDOX78OO+++y5bt25lxYoVlgNrExISiIuLo2vXrixcuJCAgAD279/PrFmz2LFjB0uXLrU6sHbixIkMHToUnU5HcnIy69evJy4ujqeffppx48aVO6YDBw4watQoWrRoweuvv05ERAR5eXls2bKFWbNmsWzZslp9nmpr7ty5fPzxxzzyyCM888wzNGjQgDNnzvC///2Pr7/+mnnz5qHTmbOGKSkpDBkyhCVLltC8eXMANBpNRd3fFCRAcjJGg7HCKTbJIAkhasLL3xXfYHd7D6NMM2bMQKPRsGjRIsvBpY0aNaJNmzb07duXuXPnMmPGDEwmEy+//DIRERHMnz8fpdL8f2Xjxo1p1qwZgwcPZsmSJTz++OOWvj08PAgKCrL02blzZ4KCgoiPj+euu+4iIiKi1HhMJhMvvvgiYWFhLF++3PIcgNatWzNy5Eir9ufOneOtt95i//79hIWFMWPGDGJiYiyv//XXX8yZM4eDBw/i5+dH3759efbZZ3F3N/99fPHFF3z22WekpKTg5eXFrbfeSnx8PFOnTuWPP/7gjz/+YOnSpQBs2bKFjIwMPvzwQ1566SUeeeQRy3NCQ0Pp1q0bOTk5eHt7W64XFRUB4Ovra/lcOAIpNnEyBkPZ+yAplJJBEkI4nqysLLZt28bDDz9c6lT3oKAgBg0axObNmzGZTBw+fJgTJ04QFxdnFbQAREVFERsby8aNGyt95siRIzGZTGzZsqXM1w8fPszx48cZPXp0qecAVsEHmLM5Y8aMYd26dYSHhzN58mT0ej0AZ8+e5bHHHqNfv36sX7+euXPnsnv3bt544w3AnKl68803mThxIt9++y2ffPIJt956KwAvv/wyMTExDB06lG3btrFt2zZCQkJYv3497u7uPPzww2WO//rxOSoJkJyMUW+scIpNjhsRQjiSM2fOYDKZiIyMLPP1yMhIsrOzycjI4PTp05ZrZYmIiLC0qYivry8BAQGW+qTrlfRRVnapLKNHj+bOO++kWbNmTJw4keTkZM6cOQPARx99xKBBgxg1ahTh4eF06tSJl19+mXXr1lFUVERKSgpubm7ceeedNG7cmDZt2lgyVF5eXmg0GlxdXQkKCiIoKAiVSsWZM2do0qSJQ0yT1YZMsTmZ8nfSNsfKRqNkkIQQjsdkqvoPf9VpW1Ef1x/pVFOtWrWy/L5kCisjI4PIyEiOHDnC0aNH2bBhg9WzjUYjSUlJxMbG0qhRI/r06UOPHj3o0aMHffv2tSo2L2vsQgIkp2M0GC3Tade6WqQt/zCEEI6jadOmKBQKEhMT6du3b6nXExMT8fHxwd/fn/DwcMu1Nm3alGp78uRJS5uKZGZmkpGRQWhoaJmvl/Rx8uTJMp9zvWszOSVBV8kPs/n5+QwbNowRI0aUui8kJAStVsvatWv5448/2LZtG/Hx8cyfP59Vq1aVO1UWHh7O7t270el0Tp1Fkik2J1PRUSNgnoITQghH4efnR7du3Vi+fDmFhYVWr6WmprJhwwb69++PQqGgdevWREREsGTJklLZ9CNHjrB9+3YGDhxY6TOXLl2KUqmkT58+Zb7eunVrmjdvzqJFi8rM2ufk5FT5/bVp04YTJ04QFhZW6qNktZ1arSY2NpYpU6awfv16kpOT2blzJ2AOvq4fw6BBg8jPz2f58uVlPrM647uZSQbJyZS7zL+kSFtWsQkhauByRmHljez0nGnTpjFs2DDGjBnDpEmTrJb5BwcH88wzzwDm7Mybb77J6NGjmTBhAmPHjiUwMJB9+/Yxa9YsoqOjGTVqlFXfeXl5pKamotfrSUpKYv369Xz11Vc8++yzhIWFlTkehULBzJkzGTVqFA8//DDjxo2zLPP/+eef+f3336u8zP+xxx7joYce4vXXX2fIkCG4ublx4sQJtm/fzvTp0/n55585d+4cnTt3xtvbm19//RWj0UizZs0A8wq9ffv2kZSUhLu7O76+vnTs2JFHH32UWbNmcfHiRfr27UuDBg04e/YsX375JbfccovV6jZHVS8CpC+++IJPP/2U1NRUoqKimDZtGh06dCi3/ebNm/nggw9ITk4mPDyc5557jp49ewKg0+l4//33+e233zh37hyenp7ExsYyefJkgoODLX1cu8FXicmTJ1st33REBr0RlbqCZf6SQRJCVIOrpwa1Vsnub8/csGeqtUpcPas+9RMeHs7q1auZN28ekyZNIjs7m8DAQPr06cP48eMteyABdOrUiRUrVrBgwQIee+wx8vLyCAkJYfDgwYwdO9ZqDySA+Ph44uPj0Wg0BAUF0bFjR5YsWcLtt99e4Zg6dOjA6tWr+fDDD3nllVfIzMykQYMGxMTE8NJLL1X5vUVFRfH555/z/vvvW1adNWnShAEDBgDmQuwffviB+fPnU1RURFhYGO+99x4tWrQAzAXgU6dOZeDAgRQWFrJlyxZCQ0N5/vnnadu2LcuXL+d///sfJpOJJk2acNddd3HfffdVeXw3M4XJztVYmzZtYsqUKcyYMYOOHTvy2Wef8e233/Ltt98SEBBQqv2ePXsYPnw4zz77LP/4xz/YsGEDn3zyCWvWrKFly5ZcvnyZiRMnMmTIEKKiosjJyeHNN9/EYDCwZs0aSz+9evXigQceYOjQoZZrHh4eln0jKmMwGEhISCA6OhqVSlX7T8QNsmrWX7i4q+nUz/onm6J8HZs/OsiAJzvQrEOgnUYnhKivCgsLOXXqFM2aNSu1XF6OGhH1TUVfr1X9/m33DNLixYsZOnQoDzzwAGDe0OuXX35h9erVZWZzli5dSo8ePXj00UcBmDRpEtu3b2fZsmW8/vrreHl5sXjxYqt7pk2bxpAhQzh//jyNGjWyXL92gy9nUd4y/5LCbckgCSGqy8vfVQIW4XDsWqRdXFzMoUOHiI2NtVxTKpXExsayd+/eMu9JSEgodcZN9+7dSUhIKPc5ubm5KBSKUhX7H3/8MV26dGHw4MF88sknlo23HJl5o8iyptjM12SjSCGEEMLOGaTMzEwMBkOpqbSAgABOnjxZ5j1paWkEBgaWap+WllZm+6KiImbPns3AgQPx9PS0XB8xYgRt2rTBx8eHvXv3MmfOHFJTU3nxxRdr+a7qN4O+klVsUqQthBBC2H+KrS7pdDqefvppTCYTM2bMsHotLi7O8vuoqCg0Gg2vvvoqkydPLlWE50iMhrJXsZXsZyYZJCGEEMLOU2x+fn6oVCrS09Otrqenp5fKEpUIDAwslS0qq71Op2PSpEmcP3+eRYsWWWWPytKxY0fLMk1HVt5GkQqFAqVKIRtFCiGEENg5QNJqtbRt25YdO3ZYrhmNRnbs2GF1UvG1oqOjLRtcldi+fTvR0dGWP5cER2fOnGHJkiX4+flVOpbDhw+jVCrLXDnnSIzlHDUC5mk2o0EySEIIIYTdp9ji4uJ44YUXaNeuHR06dOCzzz6joKCA+++/H4ApU6YQHBzM5MmTAfMpySNGjGDRokX07NmTTZs2cfDgQV5//XXAHBxNnDiRv//+m48++giDwUBqaioAPj4+aLVa9u7dy759+7j99tvx8PBg7969zJw5k3vvvRcfHx/7fCJuEEM5U2xgLtSWKTYhhBCiHgRIAwYMICMjg/j4eFJTU2ndujWffPKJZcosJSXFcpAqmDfxmj17Nu+//z5z5swhPDycBQsW0LJlSwAuXrzITz/9BMD//d//WT1r6dKldOnSBa1Wy6ZNm5g/fz7FxcWEhoYyatQoq7okR2U0GFFUmEGSKTYhhBDC7gESwPDhwxk+fHiZr33++eelrvXv35/+/fuX2T40NJSjR49W+Ly2bduycuXK6g/UARgNFUyxKRUYdJJBEkIIIepFgCRuDJPJVO4qNjBnkOQsNiFEdeWkXaLgBh5g6ubtjXdggxv2POGcJEByIiXTZxUFSLKTthCiOnLSLrH4mXHoi4tu2DPVWhfi5v63WkFSSkoK8fHxbN26laysLIKCgujduzfjx48vtZDn+PHjzJ8/n127dpGbm0vjxo0ZMGAAjz/+OG5ubpZ2157p6eLiQmBgIO3bt2fYsGGlNjQuy5kzZ/jwww/5/fffycjIoEGDBkRHRxMXF0f79u05cuQIDz74IB988AG9e/e23Pfdd9/x3HPPsXr1alq2bMm8efOYP38+YN5suUGDBtxxxx1MnjzZ6pw5UT0SIDkRS4BUbg2SUjJIQohqKcjJQV9cRJf7huIdWPdHN+WkpbJr7UoKcnKqHCCdO3eOhx56iPDwcObMmUNoaCjHjx/n3XffZevWraxYscISSCQkJBAXF0fXrl1ZuHAhAQEB7N+/n1mzZrFjxw6WLl1qtVfexIkTGTp0KDqdjuTkZNavX09cXBxPP/0048aNK3dMBw4cYNSoUbRo0YLXX3+diIgI8vLy2LJlC7NmzWLZsmVERUXx5JNPMn36dDp16oSfnx/p6em8+uqrTJgwwVJ7C9CiRQsWL16M0WgkMTGRl156icuXL/P+++/X6PMsJEByKiUr1MraBwmuTLFJBkkIUQPegUH4hTS29zDKNGPGDDQaDYsWLbIcXNqoUSPatGlD3759mTt3LjNmzMBkMvHyyy8TERHB/PnzLQuEGjduTLNmzRg8eDBLliyxOif02jM9GzVqROfOnQkKCiI+Pp677rqLiIiIUuMxmUy8+OKLhIWFsXz5cquFSK1bt2bkyJGWP48dO5affvqJ119/nblz5zJ9+nTCw8MZM2aMVZ8qlcoyjuDgYO6++26rA9pF9dl1HyRxY13NIJX9165UyhSbEMKxZGVlsW3bNh5++OFSp7oHBQUxaNAgNm/ejMlk4vDhw5w4cYK4uDiroAXMJy7ExsaycePGSp85cuRITCYTW7ZsKfP1w4cPc/z4cUaPHl3qOYDVuaEqlYpZs2axZcsWJk+ezLZt25g5c2aFp9AnJSWxbds2NBpNpWMV5ZMMkhMp2QSyjH+P5uuyk7YQwsGcOXMGk8lEZGRkma9HRkaSnZ1NRkYGp0+ftlwrS0REBLt37670mb6+vgQEBFjqk65X8pyyskvljfGRRx5h4cKFPPfcczRr1qxUm2PHjhETE4PBYKCoyFwP5uhni9Y1ySA5kZLgp7wMkkJ20hZCOCiTqeo//FWnbUV9KBRllzNUV15eHps2bcLNza3cAK1Zs2asW7eOVatW8dhjj9G9e/dyt88RVSMBkhMpCX7KrUGSfZCEEA6madOmKBQKEhMTy3w9MTERHx8f/P39CQ8Pt1wry8mTJy1tKpKZmUlGRgahoaFlvl7Sx8mTJyvtC+Cdd97BxcWF//3vf2zfvp1169aVaqPRaAgLC6Nly5Y899xzqFQqy8o2UTMSIDmRylexKTBIBkkI4UD8/Pzo1q0by5cvp7Cw0Oq11NRUNmzYQP/+/VEoFLRu3ZqIiAiWLFmC0Wj9f+GRI0fYvn07AwcOrPSZS5cuRalU0qdPnzJfb926Nc2bN2fRokWlngOQc82eUr///jurVq3i7bffJioqikmTJvHWW29x6dKlCscwbtw4Fi1axMWLFysdryib1CA5kZIVahUt89cVGW7kkIQQDiInLbXePmfatGkMGzaMMWPGMGnSJKtl/sHBwTzzzDMAKBQK3nzzTUaPHs2ECRMYO3YsgYGB7Nu3j1mzZhEdHc2oUaOs+s7LyyM1NRW9Xk9SUhLr16/nq6++4tlnnyUsLKzM8SgUCmbOnMmoUaN4+OGHGTdunGWZ/88//8zvv//OsmXLyM3N5eWXX2bMmDF06NABgFGjRvHDDz8wffp0Pvzww3Lfc0xMDK1ateKjjz5i+vTp1f6cCQmQnEqlG0UqZZm/EKJ63Ly9UWtd2LX2xh3fpNa64HbNSq/KhIeHs3r1aubNm8ekSZPIzs4mMDCQPn36MH78eKvNFDt16sSKFStYsGABjz32GHl5eYSEhDB48GDGjh1rtQcSQHx8PPHx8Wg0GoKCgujYsSNLlizh9ttvr3BMHTp0YPXq1Xz44Ye88sorZGZm0qBBA2JiYnjppZcAePPNN/Hy8uKpp56y3KdUKpk5cyaDBw9m3bp1DB48uNxnjBo1iqlTp/LYY48REhJS5c+XMFOYbFGN5oQMBgMJCQlER0dXuNyyPjl/PJO17+2lz6jWePq5lnp938/nyEktYNi0LnYYnRCiPissLOTUqVM0a9as1HJ5OWpE1DcVfb1W9fu3ZJCcSMkqtgqLtGWZvxCimrwDG0jAIhyOFGk7kSodNSJTbEIIIYQESM7EUqQth9UKIYQQFZIAyYlUetSISiGH1QohhBBIgORUKl3mL6vYhBCVkHU94mZgi69TCZCcSEkGqdwibZUSoxRpCyHKUHLwaX5+vp1HIkTlSr5Oa3Ngr6xicyKWo0bKOR6oZCdtW54hJIRwDCqVCl9fX8sOzu7u7vL/hKh3TCYT+fn5XLp0CV9f31ptwyMBkhMx6E0oVYpy/1NTqhRgApPRhKKcaTghhPNq2LAhQKXHXAhhb76+vpav15qSAMmJGA3GcuuP4GptkjmQulGjEkLcLBQKBSEhITRo0ACdTmfv4QhRJo1GY5MNnCVAciJGg6ncJf5wdXWbQW9E4yIRkhCibCqV6qY5QUCImpIibSdi0BvLXeIPV/dHkpVsQgghnJ0ESE7EaKi4tkilVljaCSGEEM5MAiQnYjQYK5liu5JB0kkGSQghhHOTAMmJlKxiK49SeaUGySABkhBCCOcmAZITMeqrtopNNosUQgjh7CRAciIGo6ncXbTh2mX+kkESQgjh3CRAciJGfRVrkCRAEkII4eQkQHIiRkMlNUhXtgCQKTYhhBDOTgIkJ2LQV7ZRpGSQhBBCCJAAyakYDUapQRJCCCGqQAIkJ2Ko6llsssxfCCGEk5MAyYkYK5tiK9kHSSc1SEIIIZybBEhOxKCveIpNceWrwSgZJCGEEE5OAiQnUtkqNoVCgVKlkBokIYQQTk8CJCdS2T5IwJUASabYhBBCODcJkJyIwWCy7HVUHpVKKVNsQgghnJ4ESE7EaDBZ6ozKo5AMkhBCCCEBkjMx6I1VyCBJDZIQQgghAZITMRoqXuYP5gySUQIkIYQQTq5eBEhffPEFvXr1on379gwZMoT9+/dX2H7z5s3cfffdtG/fnkGDBvHrr79aXtPpdLz77rsMGjSI6OhounfvzpQpU7h48aJVH1lZWUyePJlOnTpx66238tJLL5GXl1cn76++MFayUSSYz2MzGGSKTQghhHOze4C0adMmZs6cyfjx41m7di1RUVGMGTOG9PT0Mtvv2bOHyZMn8+CDD7Ju3Tp69+7N+PHjOXbsGACFhYX8/fffjBs3jjVr1jB//nxOnTrFuHHjrPp57rnnOHHiBIsXL+bDDz/kr7/+Yvr06XX+fu2pKhkkWeYvhBBC1IMAafHixQwdOpQHHniA5s2bM2PGDFxdXVm9enWZ7ZcuXUqPHj149NFHiYyMZNKkSbRp04Zly5YB4OXlxeLFixkwYAARERFER0czbdo0Dh06xPnz5wFITExk69at/Pvf/6Zjx47ceuutvPLKK2zcuLFUpsmRGA0mFJVlkJQKjDoJkIQQQji3GgVI586ds8nDi4uLOXToELGxsVcHpFQSGxvL3r17y7wnISGBrl27Wl3r3r07CQkJ5T4nNzcXhUKBt7c3AHv37sXb25v27dtb2sTGxqJUKiud3ruZGaq6D5JMsQkhhHByNQqQ+vbty4gRI/j6668pKiqq8cMzMzMxGAwEBARYXQ8ICCAtLa3Me9LS0ggMDKxy+6KiImbPns3AgQPx9PS09OHv72/VTq1W4+PjQ2pqak3fTr1mMpmqNsWmVMg+SEIIIZxejQKktWvX0qpVK95++226devG9OnT62XmRafT8fTTT2MymZgxY4a9h2NXJqM5K1TpFJvsgySEEELULEBq3bo1r7zyClu3buWtt97i0qVLPPzww9xzzz0sXryYjIyMKvXj5+eHSqUqVZCdnp5eKktUIjAwsFS2qKz2Op2OSZMmcf78eRYtWmTJHpX0cf0Y9Xo92dnZBAUFVWnsN5uSabNKV7EplbLMXwghhNOrVZG2Wq2mX79+xMfH89xzz3HmzBlmzZpFz549mTJlCpcuXarwfq1WS9u2bdmxY4flmtFoZMeOHcTExJR5T3R0NDt37rS6tn37dqKjoy1/LgmOzpw5w5IlS/Dz87NqHxMTQ05ODgcPHrRc27lzJ0ajkQ4dOlT17d9USoKeqtUgSYAkhBDCudUqQDpw4ACvvfYa3bt3Z/HixYwePZoffviBxYsXc+nSJZ588slK+4iLi2PlypWsXbuWxMREXnvtNQoKCrj//vsBmDJlCu+9956l/ciRI9m6dSuLFi0iMTGRefPmcfDgQYYPHw6Yg6OJEydy8OBBZs+ejcFgIDU1ldTUVIqLiwGIjIykR48eTJs2jf3797N7927eeOMNBg4cSHBwcG0+JfWWsYoZJDlqRAghhAB1TW5avHgxa9as4dSpU9xxxx2WrJFSaY63mjRpwttvv02vXr0q7WvAgAFkZGQQHx9PamoqrVu35pNPPrFMmaWkpFj6BejUqROzZ8/m/fffZ86cOYSHh7NgwQJatmwJwMWLF/npp58A+L//+z+rZy1dupQuXboAMHv2bN544w0eeeQRlEol/fr145VXXqnJp+OmUBL0KKpQpC37IAkhhHB2CpPJVO10Qb9+/XjggQe47777aNCgQZltiouL2bhxI/fdd1+tB1kfGQwGEhISiI6ORqVS2Xs4lcpJK+DzV3YQe38kDcK8y22376dz5KQXMuyV227g6IQQQogbo6rfv2uUQVq0aBGNGjWyyuyAeSl5SkoKjRo1QqvVOmxwdDO6OsVW8ayqUilnsQkhhBA13gcpMzOz1PWsrCx69+5d60EJ2yspvJaNIoUQQojK1ShAKm9WLj8/HxcXl1oNSNQNYxVrkBQqySAJIYQQ1ZpimzlzJgAKhYIPPvgANzc3y2sGg4H9+/cTFRVl2xEKm6jqKjalUikZJCGEEE6vWgHS33//DZgzSMeOHUOj0Vhe02q1REVFMXr0aNuOUNiEZYqtCjtpSwZJCCGEs6tWgPT5558D8OKLL/Lyyy9b7U4t6jdLBqkKNUhGySAJIYRwcjVaxVYy1SZuHiVZIdkHSQghhKhclQOkp556irfffhtPT0+eeuqpCtvOnz+/1gMTtlX1GiRzBslkMqFQVNxWCCGEcFRVDpC8vLzK/L24OVR1mb/iSgBlMposvxdCCCGcTZUDpGun1WSK7eZTkkGqLOgpyTAZDCaU9X+DcCGEEKJO1GgfpMLCQgoKCix/Tk5OZsmSJWzbts1mAxO2VVKDVGmR9pXXpVBbCCGEM6tRgPTkk0+ybt06AHJychgyZAiLFy/mySefZPny5bYcn7ARQzVWsQGy1F8IIYRTq1GAdOjQIW699VYAvvvuOwIDA/n555+ZNWuWZSsAUb8YDSYUiqqtYgMw6CWDJIQQwnnVeIrNw8MDgG3bttGvXz+USiXR0dGcP3/epgMUtmE0GCsNjuDqYbZGg2SQhBBCOK8aBUhNmzblxx9/JCUlhW3bttGtWzcA0tPTZfPIespoMFW6xB+uZpikBkkIIYQzq1GANH78eN555x169epFx44diYmJAeD333+ndevWNh2gsA2D3lilAOnqKjbJIAkhhHBeNdpJ++677+aWW24hNTXV6nDarl270qdPH5sNTtiO0WCq2hRbSQZJapCEEEI4sRoFSABBQUEEBQVZXevQoUOtByTqhnmKrfKEoUyxCSGEEDUMkPLz81m4cCE7d+4kPT0do9F6OmbLli02GZywHaPBWOkSf5ApNiGEEAJqGCC98sor/PHHH/zf//0fQUFBcmbXTcBgMKGoQsXZ1Sk2CZCEEEI4rxoFSL/99hsfffQRt9xyi63HI+qI0WCqUgap5CgSmWITQgjhzGq0is3b2xtfX18bD0XUJaPeWKUaJMtGkRIgCSGEcGI1CpCefvppPvjgA6vz2ET9ZqzqFJslgyRTbEIIIZxXjabYFi9ezNmzZ4mNjSU0NBS12rqbtWvX2mRwwnaqvYpNlvkLIYRwYjUKkGSvo5tPlVexKSWDJIQQQtQoQHrqqadsPQ5RxwxXDqutjNQgCSGEEDWsQQLIycnhq6++4r333iMrKwuAQ4cOcfHiRVuNTdhQdc5iUyhkFZsQQgjnVqMM0pEjR4iLi8PLy4vk5GSGDh2Kr68v33//PSkpKbzzzju2HqeoJaPeWKWjRsAcJMkUmxBCCGdWowzS22+/zX333cf333+PVqu1XO/Zsyd//fWXzQYnbMdQxX2QwLySTTJIQgghnFmNAqQDBw4wbNiwUteDg4NJTU2t9aCE7Zn3QapigKSUAEkIIYRzq1GApNVqyc3NLXX99OnT+Pv713pQwvaMRlPVp9hUCgxy1IgQQggnVqMAqVevXixYsACdTme5dv78eWbPnk2/fv1sNjhhO0Z91Zb5g2SQhBBCiBoFSFOnTiU/P5+uXbtSVFTEiBEj6NevHx4eHjzzzDO2HqOwAYPBZDlnrTISIAkhhHB2NVrF5uXlxeLFi9m9ezdHjhwhPz+ftm3bEhsba+vxCRup6mG1YJ5ik1VsQgghnFm1AySj0ciaNWv44YcfSE5ORqFQ0LhxY4KCgjCZTCiqshuhuOGqupM2mDNIslGkEEIIZ1atAMlkMjFu3Dh+/fVXoqKiaNmyJSaTicTERKZOncr333/Pf/7zn7oaq6gFg16m2IQQQoiqqlaAtGbNGv7880+WLFnC7bffbvXajh07GD9+POvWrWPw4MG2HKOwAaNRptiEEEKIqqpWkfbGjRt54oknSgVHAF27duXxxx9nw4YNNhucsJ3qTrFJBkkIIYQzq1aAdPToUXr06FHu63fccQdHjhyp9aCE7RmrsYpNIQGSEEIIJ1etACk7O5uAgIByXw8ICCA7O7vWgxK2Z9RX46gRpQKjbBQphBDCiVUrQDIYDKjV5ZctqVQqDAZDrQclbM9orPoUm0JWsQkhhHBy1V7FNnXqVKsDaq9VXFxc7QF88cUXfPrpp6SmphIVFcW0adPo0KFDue03b97MBx98QHJyMuHh4Tz33HP07NnT8vr333/P//73Pw4dOkRWVhbr1q2jdevWVn2MGDGCP/74w+raQw89xOuvv17t8d8MTCYTJiMoVFWLh5VSpC2EEMLJVSuDdN999xEQEICXl1eZHwEBAdVawbZp0yZmzpzJ+PHjWbt2LVFRUYwZM4b09PQy2+/Zs4fJkyfz4IMPsm7dOnr37s348eM5duyYpU1+fj6dOnXiueeeq/DZQ4cOZdu2bZaPKVOmVHncN5uSeiJlFf+2pUhbCCGEs6tWBmnmzJk2ffjixYsZOnQoDzzwAAAzZszgl19+YfXq1Tz++OOl2i9dupQePXrw6KOPAjBp0iS2b9/OsmXLLNmfkgAtKSmpwme7uroSFBRkw3dTf10NkKoxxSY1SEIIIZxYjc5is4Xi4mIOHTpkdTyJUqkkNjaWvXv3lnlPQkICXbt2tbrWvXt3EhISqv38DRs20KVLF+655x7ee+89CgoKqt3HzaIk2FFUtUhbJRkkIYQQzq1GZ7HZQmZmJgaDodSquICAAE6ePFnmPWlpaQQGBpZqn5aWVq1n33PPPTRq1IgGDRpw9OhRZs+ezalTp5g/f3713sRNwpJBqmoNkkyxCSGEcHJ2C5Ds6aGHHrL8vlWrVgQFBTFq1CjOnj1L06ZN7TiyulHdGiRnnWLLLMxk48mN9G7amxDPEHsPRwghhB3ZbYrNz88PlUpVqiA7PT29VJaoRGBgYKlsUUXtq6pjx44AnDlzplb91FclK9KqPMXmhBkkk8nEpJ8nMevPWTzx4xMYTc4XIAohhLjKbgGSVqulbdu27Nixw3LNaDSyY8cOYmJiyrwnOjqanTt3Wl3bvn070dHRtRrL4cOHARy2aPvqFFs1dtI2OleAlJCawJ5Le7gn4h5OZp9k5/mdld8khBDCYdktQAKIi4tj5cqVrF27lsTERF577TUKCgq4//77AZgyZQrvvfeepf3IkSPZunUrixYtIjExkXnz5nHw4EGGDx9uaZOVlcXhw4dJTEwE4NSpUxw+fJjU1FQAzp49y4IFCzh48CBJSUls2bKFF154gc6dOxMVFXUD3/2NY7iSQVJWcY5NqXK+nbS/PfUt/q7+DG4+mADXAH5J+sXeQxJCCGFHdq1BGjBgABkZGcTHx5Oamkrr1q355JNPLFNmKSkpVt/UO3XqxOzZs3n//feZM2cO4eHhLFiwgJYtW1ra/PTTT7z44ouWPz/zzDMAPPXUU0yYMAGNRsOOHTtYunQp+fn5hISE0K9fP5588skb9K5vvJIMkkyxle/XpF/pGNQRpUJJ64DW7Lm4x95DEkIIYUd2L9IePny4VQboWp9//nmpa/3796d///7l9nf//fdbMlBlCQkJYdmyZdUf6E3s6hRb1dornGyZ//nc8yTnJjO4+WAAIn0i2X5+O/m6fNw17vYdnBBCCLuw6xSbuDFqlkFynim2vZfM+2619DNnIsO8wzCajCRmJdpzWEIIIexIAiQnYLTUIFU9QHKmw2r3pe6joXtDvLReAIR4hqBAwYmsE3YemRBCCHuRAMkJGPXV3CjSyabYDqYdJNwn3PJnF5ULQe5BkkESQggnJgGSEzBUcx8khVKByWjCZHL8IElv1HMs8xhh3mFW1xu4NeDs5bN2GpUQQgh7kwDJCVR3J+2SqThn2AvpbM5ZigxFNPWy3kE92COYMzmOuXGoEEKIykmA5ASqfRbblQ0lS6bmHNnRzKMANPFqYnW9gXsDknKTnCKLJoQQojQJkJxAdY8aKWnnDCvZjmcex8/FD0+tp9X1QLdAig3FpBeml3OnEEIIRyYBkhO4OsVWxVVsVzJNzlCofTzzOI09G5e6HuAaAJj3SBJCCOF8JEByAgZ9SQapau1LapUMTjDFdjzrOI08G5W6Huhm3s1dAiQhhHBOEiA5AaPBhFKpQKGobgbJsafYCvQFnM89T2Ov0hkkd407bmo3LuZftMPIhBBC2JsESE7AaDChUFUtOIJra5AcO4N0Ovs0Jkw08iidQQLwc/XjQt6FGzwqIYQQ9YEESE7AYDBWuf4Irq5iMzh4Bikx27wRZIhHSJmv+7n4SQZJCCGclARITqBkiq2qLPsgOXgN0qnsU/i5+JV7IK2fqx8peSk3eFRCCCHqAwmQnIBRb6zeFJvKOabYTmadLDd7BODr4ktqfuoNHJEQQoj6QgIkJ2A0mCzTZlVRkkFy9Cm2k9knCfYILvd1Pxc/0grSMJoc+/MghBCiNAmQnIChplNsDpxB0hv1nLt8rtwCbQAfFx8MJgOZhZk3cGRCCCHqAwmQnIDRYKxeBsly1IjjZk6Sc5PRGXU09GhYbhtfF18AUgtkmk0IIZyNBEhOwKivXgZJYZlic9wM0uns0wCEeFZcgwSQVpB2A0YkhBCiPpEAyQkYDdUr0naGDNKp7FO4qFzwc/Ert42XixcgAZIQQjgjCZCcQLWX+TvBPkinck7R0KNhhbuLa5QaPDWeEiAJIYQTkgDJCRgMJsu0WVUorxzG5sj7IJ3KPlXhEv8SPi4+EiAJIYQTkgDJCVS3SLvkUFtHXsV2KvtUhQXaJby13hIgCSGEE5IAyQkY9KYqH1QLoFAoUCoVGBy0BimrMIusoiwJkIQQQpRLAiQnYNRXL4ME5jokR80gnco5BVDhHkglvF28SS9Ir+shCSGEqGckQHIChmpOsYE5QHLUDNKp7FMoURLsXv4u2iW8td6kF0qAJIQQzkYCJCdgqOY+SABKldJhA6STWScJdA9Eo9JU2tbbxZvLxZfRGXQ3YGRCCCHqCwmQnIC5SLt6f9WOPMV2MvtkleqPAHy0PgCSRRJCCCcjAZITqO5O2uDYU2yJ2YlVqj8C8xQbQEZhRl0OSQghRD0jAZITMBiM1doHCczHjTjiPkgF+gJSclOqtAcS3KAAyWiEH2fAO5Hw2SDISam7ZwkhhKgSCZCcgFFvqnaRtkqlcMidtE9nn8aEiUaeVcsgeWnNx43UaYD0+1zYNheadoVLh+Gze0BXUHfPE0IIUSkJkJxATVaxKRy0SPtk9kmAKmeQNCoNbmo3MgrqKEC6fBF+mw1t7oUuY6Hv65B1Fn5+s26eJ4QQokokQHICUoN0VWJWIv6u/rhr3Kt8j4/Wp+6KtP/61Pxrh39eeVgTaD8Edn0EOefr5plCCCEqJQGSE6jRPkhKBQad49Ugncg6UeXsUQlPrWfdTLEZjZDwBYR3BxfPq9db3wtqF/g93vbPFEIIUSUSIDkBo8GEokY7aTteBulE1gkaezau1j1eWq+6CZDObofsJIjsbX1d6wEt+0PCMijKtf1zhRBCVEoCJCdgnmKr/j5IBp1jBUiF+kKSLidVuUC7hLe2jo4bOfYtuAdAgzalX2t5NxTnwYGVtn+uEEKISkmA5ASMNTxqRO9gNUinsk9hwlR/MkjHvodGnaCsg4Q9G0DjW2HvMts/VwghRKUkQHJwRoMRkwk5agQ4nnUcoEYZpMzCTEwmG9ZkZZ6BtKMQemv5bSJ7QfJuSDtuu+cKIYSoEgmQHJzhynEhNdkHyehgU2wnMk8Q5BaEm9qtWvd5ab0oNhaTr8+33WBObwUU0LBj+W2a3AZaT9gv02xCCHGjSYDk4IxXskDVn2JTYnCwnbSPZR6rdvYIrtlN25Z7IZ3ZDv7NrFevXU+lNQdJf6+z3XOFEEJUiQRIDq4kyKnJYbWONsV2LPMYoV6h1b6vZDdtm+6FdHobBLetvF1YN0g7BpeO2O7ZQgghKiUBkoMz1DSDpFagd6AptuyibFILUgn1rHmAZLNC7ZzzkHUGGlQhQGoUAxo3OLLBNs8WQghRJXYPkL744gt69epF+/btGTJkCPv376+w/ebNm7n77rtp3749gwYN4tdff7V6/fvvv2f06NF06dKFVq1acfjw4VJ9FBUVMWPGDLp06UJMTAwTJkwgLS3Npu+rvijZy6j6NUhKy/ScIziWeQygRhkkT40nChS2C5DO/WH+tazl/ddTac1B0rHvbPNsIYQQVWLXAGnTpk3MnDmT8ePHs3btWqKiohgzZgzp6WVPZezZs4fJkyfz4IMPsm7dOnr37s348eM5duyYpU1+fj6dOnXiueeeK/e5b731Fj///DPvv/8+n3/+OZcuXeKpp56y+furD0p2w67JMn9HmmI7lnkMtVJNQ/eG1b5XpVThqfEkszDTNoNJ/gs8GoC7f9XaN74Vkv6CvDo67kQIIUQpdg2QFi9ezNChQ3nggQdo3rw5M2bMwNXVldWrV5fZfunSpfTo0YNHH32UyMhIJk2aRJs2bVi27OpeMYMHD+app56ia9euZfZx+fJlVq9ezdSpU+natSvt2rXjrbfeYu/evSQkJNTF27QrQ0kGqZobRarUjrXM/1jmMRp7NkalVNXofi8XG+6FlPQXBLWsevvQzoAJEn+yzfOFEEJUym4BUnFxMYcOHSI2NvbqYJRKYmNj2bt3b5n3JCQklAp8unfvXq3A5uDBg+h0OqvnRkZG0qhRI8cMkEpqkNQ1ySCZbLv3jx0dyThSo/qjEl4aL9sUaRv0cH4vBFYjQHLzA79mcPKX2j9fCCFEldgtQMrMzMRgMBAQEGB1PSAgoNx6oLS0NAIDA6vcvrw+NBoN3t7epfpJTU2tcj83i5I6IlV1a5DU5i8NR8giGYwGErMSaeLVpMZ9eGm9bDPFlnoY9IXVC5AAGnaAkz+DgwSsQghR39m9SFvUras1SNVf5m++/+YPkM5cPkORoajWAZJNzmM7nwAowD+yeveFdIScZMg8VfsxCCGEqJTdAiQ/Pz9UKlWpguz09PRSWaISgYGBpbJFFbUvrw+dTkdOTk6pfoKCgqrcz81CX8Nl/iUZJEdY6n8so+Yr2Ep4a73JLLJBBiklAXybmJfuV0dwO1AozfsnCSGEqHN2C5C0Wi1t27Zlx44dlmtGo5EdO3YQExNT5j3R0dHs3LnT6tr27duJjo6u8nPbtWuHRqOxeu7Jkyc5f/58tfq5WZRkgGoaIDlCBulo5lH8Xf0t+xnVRMl5bEZTLT8fyXuqnz0C0Lqb65DO7qrd84UQQlSJ2p4Pj4uL44UXXqBdu3Z06NCBzz77jIKCAu6//34ApkyZQnBwMJMnTwZg5MiRjBgxgkWLFtGzZ082bdrEwYMHef311y19ZmVlkZKSwqVLlwA4dco8JREYGEhQUBBeXl488MADvP322/j4+ODp6cm///1vYmJiHDNAsmSQajbF5ggZpNoWaIN5is1gMnC5+DI+Lj4168Sgg4sHodMjNbs/KArO7qi8nRBCiFqza4A0YMAAMjIyiI+PJzU1ldatW/PJJ59YpsxSUlKslqd36tSJ2bNn8/777zNnzhzCw8NZsGABLVteLXj96aefePHFFy1/fuaZZwB46qmnmDBhAgAvvfQSSqWSiRMnUlxcTPfu3Xn11VdvxFu+4Wq6k7ZDZZAyjnJbw9tq1ce1u2nXOEBKPQqGYgioQQYJzBtLHt0IeWngUfVpZSGEENVn1wAJYPjw4QwfPrzM1z7//PNS1/r370///v3L7e/++++3ZKDK4+LiwquvvuqwQdG1jHojCqUCpbKGNUjFhroY1g2TUZhBakEqTbxrXqANVw+sTS9Ip5lPs5p1cuHKLvF+ETW7v0Fr869Jf0Kr8v8NCCGEqD1Zxebg9DpjtZf4w9V9k/TFN3cG6WjGUYBarWCDqxmkWhVqp+wD78bmeqKa8Agy74mUvKfmYxBCCFElEiA5OIPeiFJd/b/mq6vYbu4M0tGMo7ioXAh2D65VP+4ad1QKVe32QkrZB/41zB4BKBTm6bnk3TXvQwghRJVIgOTgDDojqmruog3XTrHd3BmkI5lHaOLVBKWidl/qSoXSvBdSTXfTNhrhwoHaBUgAAS3g/B7ZMFIIIeqYBEgOzqA3VXsFG4BK4xgZpMPph2s9vVbCW+tNRkENz2PLPAXFuTVb4n+twJZQkAlZZ2rXjxBCiApJgOTgDDpjtVewASiVCpQqxU2dQSrUF3I65zRNvZrapD9PrWfNM0gp+8y/1nQFW4mSAOvCgdr1I4QQokISIDk4vc5gmS6rLpVGie4mXsV2LPMYRpORpt62CZC8td5kFNYwg3Rhv3lpvmsNtwgo4e5vLtSWAEkIIeqUBEgOzqAzWlakVZdKrURfdPMGSEcyjqBUKGu9SWQJL61XzafYUvbXfHn/9fwirmakhBBC1AkJkByceZl/zf6a1RrlTT3FdjjjMI09G6NRaWzSX43PYzOZar+C7Vr+za7uqSSEEKJOSIDk4PQ1rEGCm3+K7VDaIZvVH4E5g5RdlI3eqK/ejZcvQH6abQOknPOQX8NslhBCiEpJgOTg9MW1qEG6iafYdAYdJ7JOEO4dbrM+vbXemDCRVZRVvRtLsj21LdAu4XdlJ+9Lf9umPyGEEKVIgOTg9DpjjQMk9U2cQTqedRydUUeYT5jN+rz2PLZqSdkPLl7g0cA2A/FuBEo1XJQASQgh6ooESA7OnEGqxRRb4c0ZIB1KP4RSobTZHkhw9Ty26gdICebpNUXN/h5KUarBtylcOmSb/oQQQpQiAZKD0xcbLZs+Vpdao0R3k06xHUw7SKhnKC4qF5v1aQmQqruSLWWf7VawlfBtKhkkIYSoQxIgObja1CCpNaqbNkDan7rfpvVHAC5qF1xULtXLIOVnQPY5CGhu07HgGw6ph+XIESGEqCMSIDm42tQgqTRKim/CAClPl0diViIRvjbO2mDOIlVrN+2UBPOvtirQLuEXBkWXITvJtv0KIYQAJEByePpiQ82n2LQ35xTbgbQDmDAR6WvjoIQrm0VWJ4OUsg80bubCalvyvbJ9QeoR2/YrhBACkADJoZmMJvTFRtQ1DpBUN+Uy/72X9uKh8SDEI8TmfXtpvUgvqEYG6XyC+fw0hY3/qXkEgdoNLh22bb9CCCEACZAcml5n3gW7tkXaJuPNVeey5+Iemvs2R2nroISaBEh7rx4wa0sKJfg2kQySEELUEQmQHFjJ9FhtMkjATbUXks6oI+FSAi39WtZJ/9WqQcrPgKwzENiiTsaCTxPZLFIIIeqIBEgOTF9cEiCpanR/SWB1M9UhHUg9QKGhkCj/qDrp31vrXfUapPN7zb8G1FGA5NsUUo+C8eY9L08IIeorCZAcWPGVTR7V2lpmkG6izSJ3pOzAQ+NBmLftdtC+lrfWmyJDEfm6/Mobn98LWk/wtn0tFGAOkHT5kJNcN/0LIYQTU9t7AKLu6ArNh6rWZhUbQHFhNQ9nvQFy0i5x/uhhUCgIjmiOX0PzKrHfkn6jbUDbOqk/AvByMR83kl6YjrvGveLG5/eYl/fX0ViurmQ7aq5HEkIIYTMSIDmwqzVINZxiu5JBKq5HGSS9Tsevn3/Cvu83Ybpmk8TApuE0u6Mbxy8eZlSnR+vs+T5aH8B83Eilx5gk7Ybw7nU2FvNKNldzoXaLPnX3HCGEcEISIDmwogJz5kfjUrMASXMlg6SrJxkko8HAhjlvcXr/Xjr07U94x04oUHDpzEnO7N/Ln18s5yFFE9zPnySppQKPhkG4+Png4uuNykVrkzFYDqwt57gR3cVLFJ85jTH1HBzNBFd3TNt2Y8jJRXcxnaKkixQnXUCfnoVJb0Ad4IvnLW3xv78v2oaB1RuMQmku1E6Vpf5CCGFrEiA5sOIrAVJta5DqSwZp+1fLObX3L7r/8xFCml9dpRYa1ZbQqLb8++dX8b+oxCfzMqfW/4jJcHXcLn4++EVFEBJ7K15Na75po5fWCwUKq5VsJpOJnE2bSP9oIUXHjl3TOgC2/gj8CAoFKh8vNEF+aBsH4962OahU6NMzyf7lD7I2/0bwuGH4DehZvQH5hMIlWeovhBC2JgGSAysuMKDWKlHU8BR5pUqBQqmwBFr2dPHkCf5Yt5K2PXtbBUclTuec4WTxOTredj+Rvi0wGY0UX85Dl5uH7nIeBakZZBw6zoUdewnuEk3zBweg0mqqPQ6lQmm1m7ZJp+P8Sy+Ts2EDbtEdCRw/Hk1oKMoTX8OprdD1KVCrULq6oFCWHaj6DuhJ1sZfuTDvC4z5hQQ8eFfVB+TbFA6tMZ/JVsO/ZyGEEKVJgOTAigp0aF1r/lesUCjQuNj/uBGTycRPSxbiHdSAqG5lZ1h+OP0D3lofIn3M568plEpcfLxw8TFPiflFRRLS7RYy/j5Byu9/knf+Eu2fHI7G3a3a4/HWepNekI7JZOL81BfJ+e47Ap54Ao+uXa822ncKgkPB27PS/pRaDf739UHp7sqlT1ejCQnCu1unqg3Gp6n5TLac8+DTuNrvRQghRNlkmb8DK8rX17j+qIRao7J7BunU3r84f/RvOvTpj1JV+v1kFGaw68IuOgV3Qqko//0qlEoC2rUk4r67KUhL59DCLzHqqv/eSjJImZ8vI2fjRgLHjrUOjowGSDtuDl6qwadfN9zatSBl7mfoUqu415KcySaEEHVCAiQHZpMAyUVl1xokk8nE9q+WE9g0nIaRZW+4+O3pb9GqtHQM6lilPt0bBNBsYG8unz3PidWbqz0mL60X+nNJXJo9G8++fXG/7TbrBpmnwFB8NXipIoVCQcAD/VCoVFz4z5dVu8mzAai0EiAJIYSNSYDkwApzi9G41jaDpLTrKrZzh/Zz8eRx2nS/s8xaqtziXH499xudGnTCReVS5X7dGwbR6I7buLBjD2n7qrcKzFvrTY+vjqH08sJ3yJDSDVKPgFIF3tWf8lK6u+I36B/k7txH7h8HqnCD6sqO2hIgCSGELUmA5MAKc2tXgwTmFXD2zCD9tWEtvg1DCC4ne/Rr0q+YTCZigmOq3bd/mxZ4N2vC8ZUb0eUXVPm+iDNFtD6aj+9DD6F0KSMou3QYvBqBqvpF4ABu7VvgEtmEi5+sslqJVy6fJuZnCiGEsBkJkBxYQZ4OF/faBUgarf1qkNKTz3Eq4S9adulWZvbIaDLxS9KvRPm3wkPtUe3+FQoFjXvejqG4mNPf/FTl+6LXHyHFD1xuLaeQ+tIh8Kv5UScKhQLfAXdQfC6F7J92VX5DSQbpmo0zhRBC1I4ESA6s4LIOrVttM0gqiuw0xZbw3Te4enjSpG2HMl8/nnWc9II02ge1r/EzNJ7uBN/WkZTtu8lNvlhpe+WRkwT+ncLWtgpy9XmlG+SlQm4q+NbuLDiX0Ia4tW1O2vJvMOkr+fz7hl1ZySZnsgkhhK1IgOSgigv1GHRGXGodICntclhtUX4+h37ZQkSnzqjUZb+H3Rf/wkvrTWPP0Fo9K7B9a1x8vTi1/odK22pWfovO34vDoZBTnFO6waW/zb/WIoNUwqdvLLoLaWT//EfFDUueJdNsQghhMxIgOaiCy8UAuHrUrA6mhFqrssthtYd+3YJeV0zkrV3KfN1kgr0XE4j0jaz1wbQKlZKGt3ci80gimcdOld8uPQv1r39Q0K0DJqWCnKLs0o0u/Q0egXDlUNva0IYE4dYmkvQVmzEZjOU39AgCjdvV4EwIB3MmPY+3Nx9hyIfbuSd+KxO/3Mu3By9gNMq0sqg7EiA5qPwcHYBNapBudAbJZDSy99sNhLZui5uXd5ltLuZfIL0wjYgrG0PWlndEU9yCAzn9zRarQ3Cvpd74C6hU0MVce3S5+HLpRhcOgm+4TcYE4P2PLhQnX+Tyjr3lN1IozdNskkESDkZnMPLud0foNftXvth1BpVSQZCXKweTs3li2W7unb+N4xfL+HcohA3ITtoOqiDHnEFyqXUGybyTtsloQqG8MUdZnDmQQNaF88TcNbDcNoczjqBQKGni1cQmz1QoFDS8LZpTG34k80gi/q2bWzcwGNFs+AVDTBs0Hp64qFzILrpuiq34MmSegdDONhkTgEvTEFwim5D+1Xd4detU/rExvk3h4kGbPVcIe8sr0vPEst1sT0zn/k6NuadDI7Tqqz/TH7mQw6fbTjFo/jb+869O9IoKtuNohSOSDJKDys8pQqFUoK3tPkhXDqy9kceN7P12A74NQwhsGl5um6OZR2no3rBaex9VxrNpI9wbBnHm219LZZFUfx1EmZqBvms0AO4aD3KKr5tiu3gYMIFf+eOuCe87OlN47DT5B46V38gvHFKPgsH+5+YJUVuFOgNjPvuLv05nMvXuKO7vFGoVHAFENfTm34Pb0b6xD48t3c13hy7YabTCUUmA5KDysotx9VDX+KDaEhqt+UvkRu2FlJmSzMm9f9G8c9cKx34i4ziNPW179phCoaDBrR24fDqJrOOnrV7TbPoVY0gQpqYhAHio3UsXaV/YD24+4OZn03G5tgpH0zCQ9NXfl9/IL9y8e3f6CZs+W4gbzWQy8cKq/ew5k8mUu1rRrrFPuW1d1Cqe7t2SzuF+PLV8D7tOpt/AkQpHJwGSg8rPKcbFvXbTa3A1g3Sj9kLas3kDLu4ehLUv/9iQzKIsMooybB4gAXiFNcY1yJ9zP2y9ejH7Mqrf96C/rQNcCdrcNR5kX1+kfWE/+EVY2tiKQqHAq8ct5P1xgKKzKWU3Kql7kmk2cZNb9Ptpvt53nid6RhAVUnYN4rVUSgXj72xOy2AvHv98N2fT82/AKIUzqBcB0hdffEGvXr1o3749Q4YMYf/+/RW237x5M3fffTft27dn0KBB/Prrr1avm0wmPvjgA7p3706HDh0YNWoUp0+ftmrTq1cvWrVqZfWxcOFCW781uzEHSLUvMVNfOcutuKjuA6SC3Msc/PkHIm+5DZW6/ODuVLZ5pVmIR4jNx6BQKGjQqR1Zx05x+Zw5GNH8tAtMJgy3tLW089Bcl0EqzoWMU+DfzOZjAvCIjkLl7UnG2nK2InD1Nq+eu3ioTp4vxI1wICmbtzYdZkD7ELpGBlb5PrVKyaQ+LXHTqHj8878o1Nlv93/hOOweIG3atImZM2cyfvx41q5dS1RUFGPGjCE9vexU6Z49e5g8eTIPPvgg69ato3fv3owfP55jx67WZ3z88cd8/vnnvPbaa6xcuRI3NzfGjBlDUVGRVV8TJ05k27Ztlo/hw4fX6Xu9kQouF9d6DyQwr2ID0BXU/X84Cd99g8lopHnn2ytsdyb7NO5qD7y0tV9KXxafyDC0Pl4kbfkdAPXm3zC0jgSvq7t1e6ivyyBdPAQmI/hH1smYFGo1nrHRZP+4A31mGfsvAfg1kwySuGkV6gw8vWIvYf7u/LNz9RdfeLqoeaZvS06l5fHqevlBQdSe3QOkxYsXM3ToUB544AGaN2/OjBkzcHV1ZfXq1WW2X7p0KT169ODRRx8lMjKSSZMm0aZNG5YtWwaYs0dLly5l3Lhx9OnTh6ioKN555x0uXbrEjz/+aNWXh4cHQUFBlg93d/c6f783is0ySFdqkIrqeIpNV1jInk3raRZzC64enhW2PZNzlmCP4FrXV5VHoVQS2LE1qfv+pmjfEVTHTmPobL1bt4fWg1xdLkbTlcAxZZ+59sjG9UfX8urSEZRKMjf8XHYDv3DzOIS4Cb3/43HOZeQz7s5I1KqafWtq6u/OI7HhrPjzHBv2nbfxCIWzsWuAVFxczKFDh4iNjbVcUyqVxMbGsndv2fu+JCQk0LVrV6tr3bt3JyEhAYCkpCRSU1Ot+vTy8qJjx46l+vz444/p0qULgwcP5pNPPkFf2ZEON5HCXJ1Na5B0dTzFtu+HTRTl59Gqa49K2565fIZg9wZ1Oh7/1s1RabWkfP0DJg83jG2sM0MeGg+MJiOXdbnmC+cTzNmjOgraAJTurnh0bkfGN79gLCwq3cA/EnIvQu6lOhuDEHXh0PlsPv7tJPfFhBLqV7sfVO9sGURsZAAvrjnAuQypRxI1Z9cAKTMzE4PBQEBAgNX1gIAA0tLSyrwnLS2NwMDActunpqZarlXU54gRI5gzZw6fffYZDz30EB999BHvvvturd9TfaAvNqArMtT6HDYApVKBSq2kuA6n2HRFhfyxfjXhHWLw8K04A5NTfJnsoiyC6jhAUmo0+LdpQXJqOkUdW8F1x524XzkcN6coGwoyIesMBNTN9Nq1vLvfgjE3n+wfd5R+sWR6L6XiGj4h6hOD0cRLaw7QyNeVQR1rX1eoUCgY3a0ZbloVz6xIwCC7bYsasvsUm73ExcXRpUsXoqKi+Oc//8kLL7zAsmXLKC4utvfQaq0g98ou2m612wOphNpFWafHjSR8v4nC3Mu07vGPStsmXT4HQIM6DpAAgjw8MCrgXEjpYlFPbUmAlHN1WusGBEhqfx/c2rUgfe2PpY8f8QoGrQdckGk2cfNY+dc59iVlM7p7M9RK23xL8nBR82TPSPaczeTDXxNt0qdwPnYNkPz8/FCpVKUKstPT00tliUoEBgaWyi5d2z4oKMhyrap9AnTs2BG9Xk9SUlK130d9U3glQNK61X6KDer2uJHignz++PormsXciqeff6Xtz11OQq3Q4OdSd7U+Jdz2HcWvWM+5C5cwGa2DEY8rGaTs4mw4vwe8Q2xy/lpVePe4Bd35S+T+cV2mSKE0F2pLHZK4SWTlFzPr2yPc0SKQqIaVL+mvjqgQb+7t2Ig5Pxxjf1KWTfsWzsGuAZJWq6Vt27bs2HF1usBoNLJjxw5iYmLKvCc6OpqdO3daXdu+fTvR0dEAhIaGEhQUZNVnbm4u+/btK7dPgMOHD6NUKktNzd2MCvNKAiQbZZDq8MDav75ZR3FBAW2qkD0CSM5NItAtsNYH1FaqoBDVgaM0aBBEYW4e6UnW+w+plWpcVa5kF2VB8h4IaFG347mGS1gjXMIakb6mjCX/Ac3hfAXntglRj8z94RhFOiP/vK1pnfT/QKdQwgPcmfjlXvJuwFYlwrHYfYotLi6OlStXsnbtWhITE3nttdcoKCjg/vvvB2DKlCm89957lvYjR45k69atLFq0iMTERObNm8fBgwctS/QVCgUjR47kv//9L1u2bOHo0aNMmTKFBg0a0KdPHwD27t3LkiVLOHLkCOfOnWP9+vXMnDmTe++9Fx+f8ndtvVlYAiRX2xy1p9Yq62Qn7fycbP76Zg3Nb70dd++qfd6TLicR6Fb3Qaxq72HQG3Bt3xJ3Hy+SDh8v1cZD40F29jlzDVLgjQuQALx63ELBweMUHDtt/UJAC8g6C3myo7Co345dvMyynWe5L6Yxvu7aOnmGWqVk/J3NSckuZMYGWfovqsfuh9UOGDCAjIwM4uPjSU1NpXXr1nzyySeW6bCUlBSU18xLd+rUidmzZ/P+++8zZ84cwsPDWbBgAS1btrS0eeyxxygoKGD69Onk5ORwyy238Mknn+DiYj63S6vVsmnTJubPn09xcTGhoaGMGjWKuLi4G/vm60hhrg6FUmFZol9baq2KogKdTfq61q61K8FkonX3nlVqbzSZOJ97ntjG3Ww+luupd+3H1LQReLgT2LQxZw8cIS8rBw/fq9MAHloPsrPPgNrl6k7WN4hb2+ao/X3IWPsjjV949OoLJYFayl5o3ueGjkmIqjKZTLzxzd8EebvQv13DOn1WiK8bj8SGs/C3k3RvEcS9HRvV6fOE47B7gAQwfPjwcjdp/Pzzz0td69+/P/379y+3P4VCwdNPP83TTz9d5utt27Zl5cqVNRvsTaAoX4fWVWWzfYI0WtuvYstJvUTC9xtp0/1OXNw9Kr8BSCtIo9hYTKBr1XfYrQlFSirKM8no7jJvOeDbsAHnj54k+cgJWt7eydLOQ+1JZvYx87SW6sb+U1IolXh170TmN7/SYPT9aIKu1G95hYDWE5IlQBL1189HL7H1eBqT+7as8Z5H1XFnyyAOJmfz4ur9tG/sQ7PAqv2fI5yb3afYhO0V5uvRuNqm/gjMx43Y+iy27V99gdbFlRa3Vz0blJybDECQe5BNx3I91c4ETO6uGJuFAua9uQJCG3LhxGn0uquZNE+llmxDEQS2qtPxlMfj1nYotBoyN/xy9aJCcaUOabddxiREZXQGI//eeJi2jby5JazuF1uA+YfmMd2b4e2mYdyy3RQUy1EkonISIDmgojwdWhfbZTQ0WtsGSOlJ5/j7t5+I6n4nGq1Lle9Lzk3GReWKp6binbZrRadD/edBDK2awTU/2QaENsKg13Mx8YzlmkdxHtkqJQTZJ0BSumjx7NyOzM2/WW8cGdQKzv0JJtn/RdQ/y3ed5VRqHsNvD6uz3fDL4q5VM6mP+SiSl9YewCT/PkQlJEByQEX5ejQutssgabQqmxZpb/9qGW7evkTeclu17kvKNRdo1+V/qqqEIyjyCzC2bW51Xevmik9wIEmHT1j+Y/XMTaVQoaBI41Zn46mMV2wMxrwCsn+6ZmVnUBTkp5k3rxSiHsnO1zH3x2Pc2SqI8IAbP83V1N+dx3pEsHZvMp9uO3XDny9uLhIgOaAiW0+xaVXoCvU2+Ykr9cwpju38nTY97kSlrl6WK/lyMkFudTu9pt62B2PTEEw+pfdkCWzSiPzsHLIupoJBh2fOBQCy9bl1OqaKqP19cGsTSca6n67+/QReWbCQ9JfdxiVEWeb/fJxCnYEht1b/MFpb6dY8kHs6hPDWpsP8fFSO5RHlkwDJARXl6WybQXJRYjKBrqj2WaTtq5bj6RdAeMdOlTe+ht5k4ELeBQLc6q5AW3H2PMozyRjalr1k39PfDxcPd5KPnIC0o3jqzbuuZ+ry6mxMVeHVrRPF51LITzhivuDqA96N4dwuu45LiGudTstj8e+nGdShEX51tKy/qv7ZuSkxTfwY/8UeDp3PtutYRP0lAZIDKiqw7RSb+kpftV3JlnrmFCf+2EFU954oVdUb36W8SxhM+jot0FZv/QujtyfGZo3LfF2hUBDYpBFpZ5IpOncATxfz3k3Zdg6QXCJC0TQMJGP9T1cvBrWGs2Wc1yaEnczcfBhfdw33dLD/MnulUsFTvZrT0MeVUYv/lENtRZkkQHJAxTYOkDTakgCpdoXaO9f8Dw8/f8I7lL+jeXmScs1nsAXWUQZJkZOLas9hjO1amI/sKIdfo2AUSgXnz6Sh9QtHo1STaccpNjAHbl6xMeTu2k/xxSvH8AS3gYuHoDDHrmMTAmBHYjrfHbrIQ52bolXXj287rhoVz/drhUqhYMSnu0i9XFT5TcKp1I+vVGEzJqOJ4iKDjafYrgRItThuJD3pHMd2bSeqW/WzRwBJl5Px1Hjirnav8Rgqotq6G1RKDNcVZ19PrdHg5+/G+Tw/TL6ReKvcyCy2b4AE4B7TGqWrC1nf/Gq+0KANmIyQ9Kd9ByacnsFo3hSyRQNPYiPr11FOvu5apvaPIrtAx/BPdpGZd/MfVi5sRwIkB1NcqAcTdRIgFdUig7Rr3Urcvb1rlD0CSLp8rs6yRxQVo962G2PrSNBWXhsR6JJFsVFLWrYJT7Wb3TNIAEqtBo9b25L57VaMRcXmGiRXXziz3d5DE05u1e5z/J2Sw/Dbw1DewGX9VRXs7cpLA1pzIaeQf32yi6x8CZKEmQRIDqYkiKmTGqT8mgVIWRcvcOT3X2l5e49qr1wrcfbyuTqrP1Lv2AvFxRiioypvbCjCreAsHm6QdPISnipXMnX2D5AAPLtGY8wrIOfXP80bRga3hdPb7D0s4cQuF+p457ujdGseQMtgL3sPp1yhfu682D+Kc5n5/PPjnZJJEoAESA6npE5IrbVhgKRRolDUPIP059ercHFzJ6LTrTW6P19fQEZhOkHuDWp0f4V0OlQ/78LYshkmzyrsy5JxEkwmAhsFkJ2Ri0eRC5nFl20/rhrQBPji1iqcjPVXlvw3bA/Ju6FYClCFfcz/6QS5hXr+2bmpvYdSqbAAD14Z2IbzWYU8tHAHl3IK7T0kYWcSIDmY4jrIICkUCjSuNdtN+3J6Ggd/+YGWt3dHranZ0t5zl80F2g3cbB8gqXbtR5GTh6FTm6rdkHYcPALwDfJHo1WjuaAkS5+PyWS0+dhqwrNrDEWJ5yg8cgqC24NRB+d2Vn6jEDaWmJrLp9tOcW/HRgR4Vn3HfHtq6u/OtHvakJ5bzIMf7uBsuvxw4cwkQHIwRfm2D5DM/aktfVfHH19/hVrrQuStXWr87LM5Z1Ep1AS42bjAU6dD/cN2jC3CMPmW3hiylKIcuHwBvBujUCoIbOiD7lIxCp2JbF2BbcdWQ64tw1EH+JKx4SfwbQpufnDqN3sPSzgZk8nE6xv+xt9DWy+W9VdHY183Xh3UhmKDkfv/+7vsk+TEJEByMHWRQSrpryhfV3nDa1xOT+PAlu9peXs3NC41/wnyTM4ZgtyDUCls+57Uv+9FcTkPQ+f2Vbsh7TioNOAVAkBAsC8KoEGmN5n6+jHNplAq8OwaTc5vu9Fn5kDDDpD4U+U3CmFDPx6+xK/HUhneJazeLOuvjiAvV14b1BYfNw1DPtzBr8dS7T0kYQc331euqFBRgQGlSoHKxv8paVyU1c4g7Vq7ApVGQ4vbYmv17NM5pwi2df1RYRGqH7djbNWsatkjjJB2DLwagtIcqKk1KnwCvQhO9yatoP78lOl5a1sUKiWZm3+DRjGQsh/yM+w9LOEkCnUGZmw4RMdQH24N97P3cGrMx03DKwPbENXQi9GL/+TznXK2obORAMnB2HqTyBIaFzWFeVXPIGVdSOHAT98TFXtHrbJHRYZiUvIu0NC9YY37KIv6p50oCovQ31bF7NHlC1B0GXysz5AKbhSA2qDkzNmLNh1fbSjdXPHo1IbMb37BGNgOMMHJn+09LOEk/vPzCS7mFPJIbHidHix9I7hqVDzbtxV92wQzbd1BXll3gGJ9/ag3FHVPAiQHY+tjRkpoXVUUVSNA2vrlZ7i4e9D8tq61eu6ZnDOYTEYaetouQFJkZqP+eReGjlFQlZVrYM4eaT3Azd/qspu7lkIPPemnszAaa3+Yr614xcZgyLpMzl+nwC8Cjv9g7yEJJ3AyNZf//prIwPaNCPFxs/dwbEKlVPBIbDiPdm/G//44x7CFO7iQLSvcnIEESA6muEBv2bfIljSuagqrOMWWfORvju3cRrt/9EWt0dTquSezT6JWaAhys90eSOr1P4FWg6FT26rdYCiGjFPg0xjK+IFY72/CVGDkVNJ5m42xtjTBAbi2Cidj7Q+YGsXA8e/BKD/5irpjMpmY9vVB/D203BdT9nmGN7PerYOZdk8bzqTnMyB+K78cvWTvIYk6JgGSgynK11vOTrMlrUvVMkhGg4Etiz/Ev1Eo4R1rtmv2tRIzT9DQsyFKGxVoK4+eQr33MPqu0aCtYvCWcRIM+lLTayVcvbQUuxvYffCoef+hesKr+y0UnUwi/3JDyE+H83vsPSThwNbuTeb3E+mMig2/KQuzq6JlsBdv3deepv7ujFr8J6+tP0RBce0O8Rb1l2N+FTux4gJdnQRIGjc1xYUGjIaKsxB7Nn1N6plTdOp/L4oKDn2tCpMJTmSfoLGnjX4aLS5G89W3GBsHY2zVrOr3pR0Dz0DQlD1l4KlyJdX7MhlZOZxOSrHNWG3AtUUYmkYNSN9yFFy84chGew9JOKj03CJe/+ZvYiMDiG5y8xZmV4W3m4bn72rFI13DWL7rLP0/+I2dJ9PtPSxRByRAcjBF+Xo0rrYPkFzczH0W5pU/zZZxPpnfVy6jxW1d8W8cWutnphakkl2UbbMASb15K4rMHPR33kaZc2VlKcy+svdR+e/HQ+1GjmsBfr5e/HngSL3JIikUCrzvuJW83X9TqO0AhzfYe0jCQb2+4W8MBhMju4bbeyg3hFKh4O52Icy8vz0uGhXDFu7k+a/2kZ5bZO+hCRuSAMnB1NUqNq2r+Qy1wtyyp9kMeh2b5r2Lm5c37f/RzybPPJpxFFAQ6ln7YEt5/AzqX/7A0KVDFZf1X5F29MreR+UXiXuqzJml4Cb+ZGTlcOJMcm2HazPuHVqh9vchLcEE6cch9ai9hyQczA9/X+TrfecZ0TUMH7fa1RzebBr5ujH9njaM6d6MTQdTuPPdX/hk60lZ6eYgJEByMEUFepuew1ZC63YlQCrnEMffvlhC6plTdLlvKGptzY4Uud7hjL9p6B6Mq9q1dh3l5qNZth5TowZVO5C2hMkIqcfAu7Fl76OyeF0Zn87NSHCgH3/s+xtDJVORN4pCpcSrZ2cu/3WaogIvOLTW3kMSDiQzr5gX1+ynU1NfujcPtPdw7EKpUNCndTBzhkbTJcKftzYdpvd7v7B+3/l6tbJVVJ8ESA6muECPti6m2NzNAVJ+TukM0uHff2XPpq/p2Lc//o1qn+0Bc/3R3+mHaepdy0MujUa0y75GUaxD1ycWqlMXlX0WdPngW3ZxdgmNUo2rUkuWLpfWkeHk5uVz6Pip2o3bhjxvbYvKx5O0E43gwCrzJ1eIWjKZTLy87gBFOiOP9oi46fc8qi1vVw1jukcw64EOBHm5MPHLvdwzbxs/H71Ub6bdRfVIgORA9MUGDHpTHW0UqUKpVFBw2TqDdCHxON/9933COsTQvHPt9jy61rncc+QUZxPuU41i6jKoN/yM8uhp9H1jwdO9ejdfOgJuPuDqU2lTb7U7GbpcvDzdadIomL8OHKGwqOxs242mUKvxvvM2cv6+TGHiKUjZZ+8hCQewZk8ymw5cIK5bM/zcbZM1dgShfu48f1cUrw5qg9FkIm7xnwz9aAd/nJLd7G82EiA5kLo6qBbMBb8uHmryc65+07+cnsa6d17HJ7ght94z2KY/Qe5L3YdWqSXUq+YZKdXvu9H8vAt9txiMTUKqd3PxZcg6Bz5hVWrupXYjvTgHgKiIphhNRv7Yf7i6Q64znp3bo/b3IfVQAOz70t7DETe5U2l5TPv6IHe0CKRrpI0PkXYQUQ29mX5PG164uxVpucUM/WgHIz/dxYGk+nMskaiYBEgOpPDKYbJ1sYoNwNVDQ162eZVGcWEBa2fNwGSCbkOHo1Lbtjhz78U9hPuEo1aoa3S/au/faFZ9j6FjK4wdq1F3VOLSEVCpwadqJ5F7q93I0F0GkwkXFy0tmzXh8PFTpGZkVf/ZdUChVuHTrxu551Tkff8V6OtHdkvcfAp1BsZ/sQcfNw2jYmuX4XV0CoWC6CZ+/HtwO57u3YITqXkMmr+NJz7/i+MX68cB16J8EiA5kKIrS/C1LjULKirj6qEhN7MIo9HAxg/eIevCeboPG4Gbp5dNn5NakMbpnNO09GtVo/uV+4+iWbYeY8tw9N1uocpL+ksY9ZB6GHxCQVm1z6WP2gOdyUCWPh+AZqEheHm689sfCfWmUNO9QxTaxoFc3KHAdGi9vYcjblIzNhzi+KXLTOzdArc6WBDiiJQKBbdHBPDOAx14omcEu89m0W/ubzz9v72cTM219/BEOSRAciAlh8nWVQbJzUtLbkYhvy79lFN7/+L2B/6Jb7BtD5EF+CNlF2qFhkjfyGrfq9r7N9olazFGNEXf63aoybRf+nHQFYFveJVv8dGYz3RLKzanz5VKJe1bRZKakcWh4yerP4Y6oFAq8Bvcj6IsLZmfvG/v4Yib0Jd/nOXLP84R160Z4QFVPMdQWKiUCnq2bMCcIR0Z1S2cbcfT6DPnVyZ+uYe/z+fYe3jiOhIgOZCiK1NsJXsW2Zq7t5b0pO3s2byemLsHEdK8pc2fYTLB1uRttPRvgYvKpVr3qrbvQbP0a4wtwtD36QrKmnx5G81FzF7B4FL1bwBuShdcFGouFWVZrvn7ehPWuCG7Ev4mJzevBmOxPZemIXi2a0zqz6noDmy193DETWTnyXSmrTtIn9bB/KNVA3sP56amVinp16Yhc4ZG80hsODsS0xkQv5V/LtzJ5gMpso9SPSEBkgMpzNWj1ipRqupmuW1BzlGKL/9MxC1dad759jp5xt8Zf3Mp/yIdgzpW/SaTCfXm39Cu/BZj+xboe9c0OALST0JhDgQ0r9ZtCgX4aby4VGRdgNm6eRgajZpfdu6tN0t9fe+7B4VaQcorL9abMYn6LTE1l8c//4uohl48Elu1hQuiclr1lUDpoWie+kdzMvKKGffFHm799w88/9U+Nh9IIbOcvedE3aubVIOwi8K8Ylzc62Yn29QzRzn822IUqkY0adezTp4B8P3p7whyb0CoZ8V7D1no9Wj+twn1XwfR3x6N4ZY2VLvmqITJAMl/gWcwuPlW+3Y/rSfnCtKsrmnUajq2bs7OvYc4cDSRDlHVC7zqgtLTE/87m5P6bSKZn36I/6Pj7D0kUY9dzClkxKe78HbVMKlPS9Q1/eFDlEutVNKteSDdmgdyLiOf3xPT2J6Yzle7kwAI9XOjZbAXTf3dCfZ2xdddg6eLGjeNCheNEq1KiatGhZtWhbtWhbebBi8XtdPvTVVbEiA5kIJcHVo329cfpScl8uuy2XgHNaKg4FayLxUQHFaN4zqq6GTWKQ6kHeCeiEFV+oetuJyLdtFqFGcvoOvXDWOL8NoN4OIhKLwMzaJrdHuAxovDuee4rCvA65qDbYP8fWnWJISdCX8T0iCQIH/f2o3TBty63YXnofe5NHc+brd2xS062t5DEvVQWm4RD3+8k2K9kdcGtcWjjhaAiKua+LszzL8pwzo3JS23iKMXLnMyLY/zWQUcu3iZrHwdeUV6Ksv9qpUK/Dy0NPByIcTHlVA/d0L93AgP8CA80IOm/u5o1RLsVkS+2h1IwWWd5UgQW7l48hBbl7+Ph28g7fs8yNEdaaSevUzLzsE2fY7JBCuOriDQLYgo/8qX5StOJ6NdvBqF/v/bu+/4qKq88eOfmfTeQwslBJPApNMkG0ARIoosFuDBVemy6uOz664FXiqsbNalLLsK6KJSLAi7KmJZYfkpu9KEAAKhJSQkRCEhvfcp9/z+iI6mAAEGQsL3/XrNH3PvuefeOZnc+51TLZjuHY3qepXLHBirIfcQ+PQG5ysL/vwdGyeUzK0vJtyhaQ1Y/359KC2v4svdB3hg7G04O7XzxHqOLviMisb02QnO/e+TBH/0IQ7d2zalgbg5/BgclVQbWXDPAPzcL69PoLh6/u5O+Pdz4hfNlnHRlKLBpNFgtmCyKEwWDaNFw2jWqDdZqGmwUN1goqLORHmtieJqI6fyqyisbMD4wzJIdjodvfxcCe3iTlhXTwZ08yQqyItuXs5S8/QDCZA6kdrKBpzdbNPEppTi9P4vObJtI95dexMx6gHsHRzx6+FOVkoh1eUNuHvb7oa5J3cPp8szmBw2Gf3FlgNRCvudB7H/19eoAF+MYxPA7TJnyG5BgzM7Gof0B1zZ1AIALnaOeNq5cra+kHCPpgGSnV7PoMgwdh88ype7DzDu9njs7Nr315suZAT+kQcoOFrD2dmP0nv9e9j7yaR/AvIq6nhozX7Kaoy8OG4A3bxdLn2QuG70Oh0ujnaXPc2CphTltSbyK+rILa/nfHkdOWW1JJ8ppaKucZBPF08nBvfxJT7EnxGh/gT5XO39teOSAKkTqa004h1w9V/m8oJzHN6ynsLv0ggaMJiQwaPQ/7BYa0BvD86dKuX4zlyGTeh71ecCyKvJZ+OpjUT4RdDH88ITz+kqqnD45xbs0s5giQ7HPCwWbBFknDsAVfnQcwjYXV2A2dXJh+yaAvCnRVcoVxdnBkaGs//ISb5OPswd8QPb95eakzt2t9xKgGU/BScc+X7qNHqtW4dDFxmhdDNLz69i2roDWDSN+fcMoJuXBEedhV6nw9fNEV83RwZ0/2kJJaUUZbUmsoqqySioIj2/iq3H89AUhHX14K6IroyP7k5IgHs7Xv31JwFSJ6E0RW2FEaerqEEqyT3DqT1bOHfyIC6ePkQnTsG3R9MgyM5eT58IP9IPFFBRUoeX39XdPMsaynn18Ku4O7hzR+/RrSdSGnbJR3H4/L+g12MafztaLxs1B+Ufhbxj0GUAuF39auTdnH3JqM2l2FiBv1PLNdz8fbyINYRy+EQ6ep2O226NRd+enV77jsQh91u63NmDwv8W8N3//A9Br7+Gi8HQftck2s1/0gr4zT+OEODhxLN3DsDXTdZYuxnorIGTL4P7+AJQ02DmeG4Fh74v482dZ3h1+2kM3T15IC6IX8Z0x/8maHKVAKmTqKs2oVkULh6XFyBpmkZO6kHS926jJCcTFw8fQofdSbdbotHbtV5969/TnTMpReSml+MVf+UBUnpZBquPvoVJmZkSPqXlvEdKoc/4Dod/fY0+Jx9LeF/Mv4gDZ1v8Y2qQ8y2cT2kc0u9jm9qwLo5eOOjsOVWTS0IrARJA9y7+KKU4knqauoYGRv9iEE6O7fQgcnCBkFE4pH1Bl8f/QPHGLXw/5UECfvc7fKc+gs5ebhE3A7NFY/l/TrPyv5kM6u3D/97eD2cHmSX7ZubmZM+tff24ta8fRrNGyrly9mQW8eetaby8NY3bQgO4Py6IO/oHdtrvitz9OonqsnoAXD3a9qDVNI2zx/dx4utPqC4twLtrLyJGPYB/z1vQXaJGQ6fX493VjfysCgbEX94isDXmWo4WHuWb3D2klabRw70Hk/v+Ek+nn3WMNpmwO5qO/a6D6M/moXX1x3R/Ilq3gMs61wUZayB7F1TkQGB/8A254pkBmrPT29HLOYATld/xC5/+F2xC69E1AEdHBw6dSOejrV9z261xBHW10ee7XD1vhdxD2Ke+Q5e5f6F886cU/uUvVHz2GYHPPI1bQoJ02uzEviuu4XcfpnD0XDn/M6gnv4zpjl7+3uJnHO31DAn2ZUiwL1X1JvZllbA7s5j/3XgYFwc7bg8PYMyALiT0CyDAo/PULEmA1ElUFNUB4Op16QApP+skKds2UF5wDv+etxD2i7vx9L+8QMeniysZBwuorzXj7Nr0a6QUlDWUUtFQgUkzU2uqIbf6PGmlqaSXpmNRFoI8enJP3/GE+4Y3dsqursEu43v0J05jd/I0ugYjWs+uPzSndcMmEYzSoOgUnDsIeh30GgJutg9KQty6kVWXR0ZNLmHuQRdMF+DrzYjB0aSknuaL/35D357dGRI9AG/P69zOr9eD4QHY/zq6kx/g86sZuA4dSvk//sG5R+fgdMsteE+ahMediTh0se3oRdF+jGaNtXuyWb49Ay9XBxbcYyCsq23XVRSdj4ezA4mGriQaupJXUUfymVK+/b6UrcfzAejt50p0kDdhXT3o4+dGd29nung64+/u1OGmFdApmUr3ilgsFlJSUoiJicHuAk1R19O3W7M58tVZ7n4s6oJpKovzOPr//klu+mG8AoMIGTwKr8ALP8Avxlhv5sC/shl4V2969W9ss643N/DV2a/YeXYHpQ2lTdI76h3p4d6DEO8Q+nn1w6vMiP7sefTf5aI/cw59QQkAWoAvWp8eaGF9UF62mmtJg9LsxmH8dRXgHQQB/cH+2jVrfV18DJMyMz0oETv9xYM7pRQ5+UWcyvqehgYjwb26ExXWjy7+Pte35ubMTsjYBqNegF7xKKVoSE2l6j/bqUs5ChYLjiEhuA4ahLNhAM5hYTiG9MPOXdbk6kg0TbHleB7LvkznXGktYyO6MWlgUKdtJhHXR3mtkZPnK8koqOL7klpyymupabA0SePhZI+fuyNdPJ3p4eNCL19XQgLc6d/Nk77+bugvca+0lbY+v2+IAGnDhg2sXbuWoqIiwsPDmT9/PlFRF37Q//vf/2b58uXk5ubSp08fnnnmGUaO/Gl2Z6UUK1as4KOPPqKyspK4uDheeukl+vTpY01TXl5OUlISX3/9NXq9nsTERF544QXc3Np2s7/RAqRtbx2nvLCO4ZNuabGvqrSAtN1fkH14F05uHvQdeBuBwQOu+uGbsv0sHn7O3PrLvqQUHuW91PeoMlZh8DcQ4h2Cp4MHjjUmXMpqcS2pQZ9XhF1OAbrcQnRGI0qnQ/l5obr4o3UPROvRxQZD9n/GXA/F6VCQCg1V4B7YOIzfufW+QbZUZqrmy6LDDPUOY7hfRJuOsVg0zuUVcObseWrq6vH19qR/SG/69Q7CxSb9ri5BKTj6j8Zatjv+AN1jrbu0mhrqjh2jPi0NY1YWpvPnQWucT8W+WzecwsJwDg/D2WDAJTISh662X8RYXJ3qBjOfpeSydk82Z4pqiO3pzYNDetHT9+Ydxi2uHaUU1Q1mSmqMlNcaqagzUVFnpqLORFmtkZLqBgoqG6zTC7g52hHby4ehwb4MC/Ejuqc3DtdoKpQOEyBt3bqV5557joULFxIdHc27777Ltm3b2LZtG36tzMly+PBhHn74YX7/+99z++23869//Ys1a9awefNmQkMbF0996623eOutt1i8eDFBQUEsX76cjIwMtm7dipNT44Nm9uzZFBUV8cc//hGTycTzzz9PZGQkf/3rX9t03TdSgKSU4p2539D9Fm8iRvQAwFRfR97po2Sn7CHv9DEcnF3pFXkrPcIHYnfVHW8VWm0t50+V8P2Zehy8v6Gw9DDdNE+C9f441BjRVVSjK69EZzI3HqHToXw8UX7eKH9fVBc/tABfcLTx0igWI1Scg9IsKD/X+ND37Aa+fa9LYPRzJ6vOcqwqmzH+scR6hbT5OKUUhSXlnD1fQEFxY01cjy7+9OnRjaBuAXh5uF+7miWLGVLWN65JN+RRCB9Ha82bmtGIOTcXY24uptxcTDk5mM6exVJeDoB9YCAusTG4xMTiEh2Nc/9w9C4dZLi4Zmnsn1Zd2Bhk2zs1Lj/jFQT69v8xdDkqak3sySzmy5P5fJlaQIPZwsDePoyP6s4tXaQ5TbS/ynoT35fUklVUzemCKk7lV1FrtODqaMfQYF8WjDcQ7G/bWuoOEyBNmjSJyMhIFixYADR2Hh45ciSPPPIIc+bMaZH+qaeeoq6ujjfffNO6bfLkyYSHh/PHP/4RpRTDhw9nxowZzJo1C4Cqqiri4+NZvHgx48aNIysri7vvvptNmzYRGRkJwK5du5gzZw47d+6kSxv6WbR3gJSXWc6+T7PQNEWX3vakfHWckFh7LMYiSnKyKD2fjdI0PAO6061fFF36Gn4KjJRqnKZeAUpDaRpoGspsAbMJZTShGRuw1Nah1VSjamrRqqqwVFZgqahEq6xEmc0onZ6cHrdR7+yLZ/V39KpIxt4JlIsTys0V5eYCnu4oL3eUtyfYvJwUmGqhtrTxYVZ1HqoKGvsauXiDZ4/G1zVsSrvo1SlIqczkVE0uYW49GOkXhbfD5f2jNxhN5BUWk19USnFZBUopnBwd8PfxxsfLAw93V9xdXXB1dsLZyQlnJ0ecHB2vrqpas8CpL+BscuMIv4j7IWgwOFy6psFcVoYxK4uGrCyMWZkYs79DGY2g1+PYqxeOISE49u6NQ7du2Af4Y+ftg52nB3oPD/Rubujd3dFfzxF9pvrGGrO8o3D+CJw/DIVpjYF2c/ZOEGhoLIugwdAjDnyCr3xhZBvRNEV5nYnCqnpyy+rILq4ho6CKozkVZORXoYBevq4MDfZlRGjATTE8W3RcmqbILqnhRG4FH357jgX3DGD6Ly48P96VaOvzu107aRuNRk6ePMmvf/1r6za9Xk98fDxHjhxp9ZiUlBSmT5/eZFtCQgLbt28HICcnh6KiIuLj4637PTw8iI6O5siRI4wbN44jR47g6elpDY4A4uPj0ev1HDt2jDFjxlzy2n+MKy0WyyVSXr7S99ZTtGLFRdPsHzSPai0Vi/E4Occbt2Uk/7Tf3s4ZJ507lEBeyTHy9h+zzcU5O6FcAtB0oOlB059Cr7yodHckw603jhQ2plP1UF0P1WVw3janbsJcB+bmDzF70P3wK79WByV1QOY1OHnb+QJxyg8zdaScPkig3vmK8vEE3Jz0VCo9lUpHflkl+WWVbTo2QGdhuIORy46ZavtBfimcXAOsaSWBDnR6sNZmNT2Boz049gNjtQOmKj0NBQU0FBTA3r2XeSGATtHzjlpc/bU2HqAaI1RUY8CsmRsXI24LZ7/GwNrFpzG4thihtgwqc6HwVOPr8PqL56F3+CFw0v1wDVzeNfzMDOOzHNQuvfxOa3r7ONO/u6d1PqPkrKIrykeI681OB052OvQomz9nf8zvUvVD7RoglZWVYbFYWjSl+fn5cebMmVaPKS4uxt/fv0X64uLGVdSLioqs2y6Upri4GF9f3yb77e3t8fLysh5/KdoP/S+OHz/epvSXJSoS1qy+eBIAQoDxtj+/6HQ6w2Mxo70voJ381ia5mG2SixDX0/29uwBlpKSUXZP8f3yOX4gM879C9vb2REZGotfrZY4YIYQQooNQSqFpGvaX6I/brgGSj48PdnZ2lJSUNNleUlLSopboR/7+/taaoNbSBwQEWLcFBgY2SRMeHm7No7S06TB0s9lMRUWF9fhL0ev1OLbX7MdCCCGEuKbatXeho6MjBoOBffv2Wbdpmsa+ffuIjY1t9ZiYmBiSk5ObbNu7dy8xMTEABAUFERAQ0CTP6upqjh49as0zNjaWyspKTpw4YU2TnJyMpmkXnV5ACCGEEDeHdp/WcsaMGXz44Yd88sknZGVl8dJLL1FXV8f9998PwHPPPddk6P3UqVPZvXs369atIysri5UrV3LixAkefvhhoHHRvalTp7Jq1Sr+85//kJ6eznPPPUdgYCCjRzcuhhoSEsLw4cOZP38+x44d49ChQyQlJTFu3Lg2jWATQgghROfW7n2Q7r77bkpLS1mxYgVFRUX079+fNWvWWJvM8vLymqx2HhcXx7Jly3j11Vf529/+Rp8+fXj99detcyABPProo9TV1bFgwQIqKysZOHAga9assc6BBLBs2TKSkpKYNm2adaLIF1988fp9cCGEEELcsNp9HiQhhBBCiBtNuzexCSGEEELcaCRAEkIIIYRoRgIkIYQQQohmJEASQgghhGhGAiRxVVatWsWUKVOIjo5m0KBBraY5f/48c+bMITo6mmHDhrFkyRLM5qZLH+zfv5/77ruPiIgIxowZw+bNm1vks2HDBkaNGkVkZCSTJk3i2DEbrS93g8jOzubxxx9n6NChxMXF8eCDD7aY88tWZdnZ7Nixg0mTJhEVFcXgwYN54oknmuyXcrswo9HIhAkTCAsLIy0trcm+U6dO8atf/YrIyEhGjhzJ6tUtl0D697//zdixY4mMjGT8+PHs3Lnzel36dZeTk8Pzzz/PqFGjiIqKYvTo0axYsQKjsem6kFJubXPD39OVEFdh+fLl6u2331aLFi1SAwcObLHfbDare+65R02fPl2lpqaqHTt2qKFDh6q//vWv1jRnz55V0dHRatGiRSozM1OtX79e9e/fX+3atcuaZsuWLcpgMKhNmzap06dPqxdffFENGjRIFRcXX5fPeT0kJiaqRx99VKWlpans7Gz10ksvqejoaFVYWKiUsl1Zdjbbtm1TgwcPVhs3blRnzpxRp0+fVlu2bLHul3K7uKSkJDV79mwVGhqqUlNTrdurqqpUfHy8evrpp1VGRob64osvVFRUlPrnP/9pTXPo0CHVv39/tXr1apWZmaleeeUVZTAYVHp6ent8lGtu586dat68eWr37t3q7Nmzavv27WrYsGFq8eLF1jRSbm3TEe7pEiAJm/j4449bDZB27NihwsPDVVFRkXXbxo0bVVxcnGpoaFBKKbV06VI1bty4Jsc99dRTaubMmdb3EydOVAsXLrS+t1gsKiEhQb355pu2/ijtoqSkRIWGhqqDBw9at1VVVanQ0FD1zTffKKVsV5adiclkUsOHD1cffvjhBdNIuV3Yjh071NixY9Xp06dbBEgbNmxQgwcPtpaRUkr95S9/UXfeeaf1/W9/+1s1Z86cJnlOmjRJzZ8//9pf/A1i9erVatSoUdb3Um5t0xHu6dLEJq6plJQUQkNDm6ytl5CQQHV1NZmZmdY0w4YNa3JcQkICKSkpQGMTwMmTJ4mPj7fu1+v1xMfHc+TIkWv/Ia4DHx8fgoOD+fTTT6mtrcVsNvPBBx/g5+eHwWAAbFOWnU1qaioFBQXo9XruvfdeEhISmD17NhkZGdY0Um6tKy4uZv78+SxduhRnZ+cW+1NSUhg0aFCTNScTEhLIzs6moqLCmuZmK7fmqqqq8PLysr6Xcru0jnJPlwBJXFPFxcUtFh7+8X1RUdFF01RXV1NfX09ZWRkWiwU/P78mafz8/FosXNxR6XQ63nnnHVJTU4mLiyMqKoq3336bNWvWWG++tijLzubcuXMAvPbaazz++OO88cYbeHl58cgjj1BeXg5IubVGKcW8efOYMmUKkZGRraa5WLn9+H/XWprO9H95Kd9//z3vv/8+U6ZMsW6Tcru0jnJPb/elRsSNZ9myZa12Kvy5rVu3EhIScp2uqONqa1n27duXhQsX4ufnx4YNG3B2duajjz7iscceY9OmTQQGBl6nK74xtLXcNE0D4LHHHuPOO+8EYNGiRYwYMYJt27Y1eXDdDNpabt988w01NTX8+te/vk5XdmO7knteQUEBs2fPZuzYsUyePPlaX6JoBxIgiRZmzpzJfffdd9E0PXv2bFNe/v7+LUYm/PgLISAgwJqm+a+G4uJi3N3dcXZ2Rq/XY2dnR0lJSZM0JSUlLX6F3WjaWpbJycns2LGDgwcP4u7uDoDBYGDv3r18+umnzJkzxyZl2VG0tdx+rAH6+YPL0dGRnj17kpeXB9jmO9hRXM73LSUlpUXt0QMPPMD48eNZsmTJBcsEfqoRaS1NR/i/bO5y73kFBQVMnTqV2NhYkpKSmqS7mcrtSvn4+HSIe7oESKIFX19ffH19bZJXTEwMb7zxBiUlJdbq1L179+Lu7k6/fv2saXbt2tXkuL179xITEwM0PvAMBgP79u1j9OjRAGiaxr59+3j44Ydtcp3XSlvLsq6uDmhsavs5nU5nrSWxRVl2FG0tt4iICBwdHcnOzrZOM2EymcjNzaV79+6AlFtrXnzxRZ566inr+8LCQmbNmsUrr7xCdHQ00Fgmr776KiaTCQcHB6CxTIKDg63NvjExMSQnJzN9+nRrXp253OCn4MhgMLBo0aImi6nDzVVuV6rD3NPbu5e46Nhyc3NVamqqWrlypYqJiVGpqakqNTVVVVdXK6V+GmI9c+ZMlZaWpnbt2qVuvfXWVodYL1myRGVmZqr333+/1WH+ERERavPmzSozM1PNnz9fDRo0qMnIpI6spKREDRkyRD355JMqLS1NnTlzRi1evFgZDAaVlpamlLJdWXY2f/rTn9Tw4cPV7t27VVZWlnr++efVsGHDVHl5uVJKyq0tzp0712IUW2VlpYqPj1fPPvusysjIUFu2bFHR0dEthqsPGDBArV27VmVmZqoVK1Z06uHq+fn5asyYMWratGkqPz9fFRYWWl8/knJrm45wT5cASVyVuXPnqtDQ0Bav5ORka5qcnBw1e/ZsFRUVpYYOHaoWL16sTCZTk3ySk5PVhAkTlMFgUHfccYf6+OOPW5xr/fr16rbbblMGg0FNnDhRpaSkXPPPdz0dO3ZMzZw5Uw0ZMkTFxsaqyZMnqx07djRJY6uy7EyMRqNavHixGjZsmIqNjVXTp09XGRkZTdJIuV1cawGSUkqlpaWpBx98UEVERKjhw4e3OgR769atKjExURkMBjVu3LgW39nO5OOPP271fhcaGtoknZRb29zo93SdUkq1dy2WEEIIIcSNRIb5CyGEEEI0IwGSEEIIIUQzEiAJIYQQQjQjAZIQQgghRDMSIAkhhBBCNCMBkhBCCCFEMxIgCSGEEEI0IwGSEEIIIUQzEiAJITqVsLAwtm/fDkBOTg5hYWGkpaUBsH//fsLCwqisrLyivC+V3+bNm61rwl1v8+bN44knnmiXcwvRGclitUKIa2bevHlUVlby97//vV3O361bN/bs2YOPj881yT82NpY9e/bg4eFxTfIXQrQfCZCEEJ2WnZ0dAQEB1yx/R0fHa5q/EKL9SBObEKJdHDhwgIkTJxIREUFCQgLLli3DbDZb91dXV/P0008TExNDQkIC77zzDo888ggvv/xym8/RvEmsubq6OmbPns2UKVOszWQfffQRd911F5GRkYwdO5YNGzZcMP8LNdnt3r2bu+66i9jYWGbNmkVhYaF1n6ZpvPbaa4wYMYKIiAgmTJjArl27mhyfnp7O1KlTiYqKYujQocyfP5+amhrrfovFwqJFixg0aBBDhw5l6dKlyLKaQtiWBEhCiOuuoKCAOXPmEBkZyWeffcZLL73Epk2bWLVqlTXN4sWLOXLkCKtWrWLdunV8++23nDx50mbXUFlZyYwZM1BK8fbbb+Pp6cnnn3/O8uXL+d3vfsfWrVv5/e9/z4oVK/jkk0/anG99fT3r1q1j6dKlvP/+++Tl5bFkyRLr/vfee4+3336buXPn8vnnn5OQkMATTzzBd999B0BtbS2zZs3Cy8uLTZs28eqrr7J3716SkpKseaxbt45PPvmEP//5z2zcuJGKigq++uorm5WNEEICJCFEO9i4cSNdu3ZlwYIFhISEMHr0aP7v//6PdevWoWka1dXVfPrppzz33HMMGzaM0NBQFi1ahKZpNjl/UVERDz/8MIGBgaxatQoXFxcAVq5cybx580hMTKRnz54kJiYybdo0PvjggzbnbTKZWLhwIZGRkRgMBh566CGSk5Ot+9euXcujjz7KuHHj6Nu3L88++yzh4eG8++67AHzxxRcYjUaWLFlCaGgow4YNY8GCBXz22WcUFxcD8O677zJnzhwSExMJCQlh4cKF0g9KCBuTPkhCiOsuKyuL2NhYdDqdddvAgQOpra0lPz+fyspKTCYTUVFR1v0eHh4EBwdb37/xxhu8+eab1vdbtmyhe/fubTr/zJkziYqK4pVXXsHOzg5orLk5e/YsL7zwAvPnz7emNZvNlxV8uLi40KtXL+v7wMBASkpKgMZmw8LCQuLi4pocExcXx6lTp4DGsgkLC8PV1bXJfk3TyM7OxsnJiaKiIqKjo6377e3tiYiIkGY2IWxIAiQhRIc0ZcoU7rrrLuv7wMDANh87cuRIvvzySzIzMwkLCwMaAySApKSkJsEHgF7f9sp2e/umt1WdTieBixAdkDSxCSGuu5CQEI4cOdIkcDh06BBubm507dqVoKAgHBwcOH78uHV/VVWVtZ8OgLe3N71797a+mgcmF/PMM89w3333MX36dDIzMwHw9/cnMDCQc+fONcm3d+/e9OzZ8+o/NODu7k5gYCCHDx9usv3w4cP069cPaCyb9PR0a8D24369Xk9wcDAeHh4EBARw9OhR636z2WzT/llCCKlBEkJcY1VVVS1GkU2ePJl3332XpKQkHnroIbKzs1m5ciUzZsxAr9fj7u7Ovffey9KlS/Hy8sLPz4+VK1ei0+maNMtdjblz52KxWJg2bRrvvfceISEh/OY3v+FPf/oTHh4eDB8+HKPRyIkTJ6wdum1h1qxZrFy5kl69ehEeHs7mzZs5deoUy5YtA2D8+PGsWLGCefPm8eSTT1JaWkpSUhITJkzA398fgKlTp7J69Wr69OlDcHAw77zzzhVPfimEaJ0ESEKIa+rAgQPce++9TbZNnDiRt956i6VLl/Lhhx/i7e3NxIkTefzxx61p5s2bxx/+8Acee+wx3N3dmT17Nnl5eTg5Odns2p5//nk0TWPatGmsX7+eSZMm4ezszNq1a1m6dCmurq6EhoYybdo0m51z6tSpVFdXs3jxYkpLSwkJCeHvf/87ffr0ARr7MK1du5aXX36ZiRMn4uLiQmJiIvPmzbPmMXPmTIqKipg7dy56vZ4HHniAMWPGUFVVZbPrFOJmp1PSOC6E6ABqa2sZMWIEc+fOZdKkSe19OUKITk5qkIQQN6TU1FTOnDlDVFQUVVVVvP766wDccccd7XxlQoibgQRIQogb1rp168jOzsbBwQGDwcCGDRvw9fVt78sSQtwEpIlNCCGEEKIZGeYvhBBCCNGMBEhCCCGEEM1IgCSEEEII0YwESEIIIYQQzUiAJIQQQgjRjARIQgghhBDNSIAkhBBCCNGMBEhCCCGEEM38f6/w7j2k5iSOAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1126,7 +1153,7 @@
     "sns.set_style(\"whitegrid\", {\"axes.grid\": False})\n",
     "sns.kdeplot(in_likelihoods, bw_adjust=1, label=\"In-distribution\", fill=True, cut=True)\n",
     "for c, l in all_likelihoods.items():\n",
-    "    sns.kdeplot(l, bw_adjust=20, label=f\"OOD {c}\", cut=True, fill=True)\n",
+    "    sns.kdeplot(l, bw_adjust=1, label=f\"OOD {c}\", cut=True, fill=True)\n",
     "plt.legend(loc=\"upper right\")\n",
     "plt.xlabel(\"Log-likelihood\")\n",
     "# plt.xlim([-200,10])\n",
@@ -1144,13 +1171,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 23,
    "id": "884b152e-54a5-41b8-9d59-327991c58a20",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -1185,13 +1212,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 30,
    "id": "3faeed43-59c1-4062-8180-2c8445b9e118",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -1207,8 +1234,9 @@
     "    transformer_model=transformer_model,\n",
     "    ordering=ordering,\n",
     ")\n",
+    "likelihood = torch.exp(log_likelihood)\n",
     "plt.subplot(1, 2, 1)\n",
-    "plt.imshow(log_likelihood.cpu()[0, ...], vmin=0.6, vmax=1)\n",
+    "plt.imshow(likelihood.cpu()[0, ...])\n",
     "plt.axis(\"off\")\n",
     "plt.title(\"Log-likelihood\")\n",
     "plt.subplot(1, 2, 2)\n",
@@ -1229,13 +1257,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 31,
    "id": "dd08553f-0b51-48ff-860d-fc1fabadf77c",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1291,15 +1319,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 32,
    "id": "d629c1b6-0a66-4fb5-8722-3006404afe30",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Cvy9fvjySkJAQGTVqVPC3ffv2RQ4ePJjhfpKSkiKjR48O/vbyyy9HEhISIp999lnwt71790YuvvjiSEJCQuSbb76JRCKRyKFDhyIXXXRR5KqrroocOnTIe37at28fufLKK6M+RuBkyekckf45HT58uPf3Rx55JJKQkBCZO3du8Ld27dpFEhISIl9++aV32W+++SaSkJAQueCCCzLMGaNGjYokJCRkuN/3338/kpCQEFmzZk2G2//kk0+8x9OqVatIt27dgr99/PHH3ucz3a5duyJNmjSJ3HPPPd7f//jjj0jjxo29v3ft2jXSqlWryM6dO4O/ffXVV5GEhIRIu3btMj5RRr9+/SL9+vXL8Bx07tw5kpaWFvz9tttuiyQmJkYGDRrkXb9nz54Z7sc+d5FIJHLVVVdFLrjgAu+xnHPOOZEbbrjBu9yzzz4bSUhIOOq5Hrkj/fNqX89I5PB33pYtW4J/7vsj/TvyiSee8K7z2WefRRISEiLPP/+89/ebbropkpiYGHxHZ/a9m85+n6Z/hocNG+Zd7u9//3ukWbNmQZw+l9x///3e5W677bYMtxnNwoULM4wtfb7o3bt35MCBA97l77rrrkw/s5nNPQ0aNPA+Izl9jFnJ7LM7ZsyYSGJiYmTdunXeWBMSErzjkEgkEunWrVuke/fuQRz2dYxEDr9eSUlJ3lz69ttvRxISEiKtWrWKpKSkBH9/8sknM8y7Ycdun8+dO3dG6tWrF3n88ce96/7zn/+MNGjQILJ79+4Mt+tKn++///774G+zZ8+OJCQkRJKTk737Tn887pyf2binTJkSSUhIiHz33XfB3xo3bhwZMWJE1LH069cvcumll0YikUjkk08+idStWzdyzz33ZDiO/DNL/wx+/fXXkS1btkQ2bNgQ+eijjyLNmjWLJCcnR37//fdIJJL13PTdd99FEhISIpMmTfL+/uWXX3p/37JlS6Ru3bqRa6+91jvefuqppzJ8x6V/76a/Lw4cOBBp3759pF27dpEdO3Z49+Pe1ogRIzI9LjkRY8zK8f59dcMNN0Tq1q0bWb16dfC3jRs3Rho2bBjp27dv8Lf019H+nunZs2ckMTExcu+99wZ/O3DgQKRNmzbesU5Oxm5/f6Z/jpctW+Zdt3nz5tk+ZyfrGCt97l24cGGWY0n//hw7dmxk//79kSFDhkSSk5Mjs2fPjvoYjjfSeXJo165dkqTixYsf9W1MmzZNjRs3VlxcnLZu3Rr8O/fcc3Xw4EF99913wWXdWgc7duxQSkqKGjdunGFZpSSde+653v/q1K5dW7GxsVqzZs1Rj/Vvf/tb0I6Li1O1atVUtGhRXXLJJcHfq1evrri4OO9+ChUqFPxvxcGDB7Vt2zYVK1ZM1apV88Y+e/ZsVahQwTv7Wbhw4eB/qdItWbJEK1euVJcuXbRt27bgOUtNTVXLli313XffsTweeUJO54hZs2ZJkq688krv71dddZXXn65y5cpq3bp1prfVrVu3Y97xp3z58rrwwguDODY2Vt26ddPixYv1xx9/RL3u119/rZ07d+rSSy/15rb8+fOrfv36wQqzTZs2acmSJerevbtKlCgRXL9Vq1aqWbPmMY2/a9eu3gqc5ORkRSIRXXbZZd7lkpOTtWHDBm+ZuPvcpaSkaOvWrWrWrJnWrFmjlJQUSYdXxB04cCD4H+d0/fr1yzCWnMz1yB3pn9fM6gX0799fLVu2DP6lL0F3pf9Pfbovv/xSMTExGbbfvuqqqxSJREKl2GalV69eXtykSRNt3749eAzpc4W97+zSi3Pi8ssvP6GrArJ7jFlxP7upqanaunWrGjZsqEgkkunxkn3dGjdu7KXZ5PR1bNmypVdkO/1/nS+66CLFxsYGf09fMeseL+V07OlKlCihCy64QB999FGw3P/gwYP6+OOPdcEFF4SqgVGzZk2vHk76uFu0aKGKFStm+HtW4963b5+2bt0aXM5N1YmLi9OPP/6ojRs3ZjueKVOm6NZbb1XPnj31wAMPnLY1vY7FFVdcEaTT3XrrrSpevLhGjx4drPBOZ9/j06ZNU4kSJdSqVSvv+6hu3boqVqxY8P389ddfa//+/erXr1+wmkgKN48sXrxYa9eu1YABAxQXF+f1ubeVlZMxxnTH8/fVwYMHNWfOHHXo0CFYkS8dPp7q3Lmz5s+fn2EO++tf/+qNPf1Y5a9//Wvwt5iYGCUlJWX4HZeTsbsuueQSFS5c2CvB8NVXX2nbtm1ZpgZaJ/oYK/2Y8Isvvsh2VdH+/ft1yy236IsvvtCLL76o8847L9RjOF5I58mh9C/D3bt3H/VtrFq1SkuXLvWq4bu2bt0atGfOnKl///vfWrJkiZdbltlk5OZrpytZsqR27tx5VOMsXLiw4uPjvb+VKFFCZ5xxRob7L1GihHc/hw4d0uuvv6633npLa9eu9XKN3SWH69atU9WqVTPcnl3iu3LlSknSXXfdleV4U1JSVLJkyVCPDThRcjpHrFu3Tvnz58/wni9Xrpzi4uK0bt067+/RdsM5HjvlnHXWWRk+j2effXYw1nLlymV53fTPaVYHMunPzfr164P7suyJ1pxyD/ylI1/Idn4sUaKEDh06pJSUlGA57vz58/Xss8/qhx9+yFCHISUlRSVKlAjGbl+vUqVKZZh/cjLXI3ekn+zMLDXlgQce0O7du7V58+ZM00kKFCiQIf1s3bp1Kl++vPfDWTqS7mY/zzlh39vpP1J27Nih2NjYLOeS9JS7dGlpaRnqC8THx4c6OXKid+PK7jFmZf369Ro1apRmzJiR4bHZHy+ZHduULFnSu15OX0c7v6Rfz74/0ucj93gpJ2O3unXrpqlTp2revHlq2rSpvv76a23evDlqQeBo404fnx13+uNxx719+3aNHj1aU6dO1ZYtW7zLp/8gkg4XZh46dKjOP/981a1bV23btlW3bt28H5vS4Zoe/+///T9dfPHF+sc//hFq/H9G9957r6pVq6aYmBiVLVtW1apVy3CyKbO5adWqVUpJScny+yj9NUz/jkv/3k8XHx+f7TF2+o99N70sJ07GGNMdz99XW7du1Z49e7z06nQ1atTQoUOHtGHDBtWqVSv4e06OVey8kJOxu+Li4tSuXTtNmTJFQ4YMkXQ4ladChQpBbZ3snOhjrGbNmqljx44aPXq0Xn31VTVr1kwdOnRQly5dMmwoMGbMGKWmpuqll15S8+bNQ43/eOIkSg7FxsaqfPnyOSpeZB06dEitWrXSoEGDMu1PnxTmzZun66+/Xk2bNtV9992ncuXKqWDBgnr//fczLf6T1QFQxBQkCiur2wtzPy+88IL+9a9/6bLLLtMtt9yikiVLKn/+/HrooYeOajzp17nzzjtVp06dTC/DLgHIC452jgjzvzSSoq40yawvq9u1RRSPh/TP6WOPPZbpyZaTkdee1f9cZvX39DGvXr1aV1xxhapXr66hQ4fqzDPPVMGCBTVr1iy9+uqrR7XSLexcj9xTokSJoJ6Dlf6/6rYQaDp3xWVOHc3nMrv3cFgLFizQgAEDvL9Nnz491AmSzHbiOJ5zzNE8xoMHD+rKK6/Ujh07NGjQIFWvXl3FihXTxo0bNXTo0Ayf3RMxDx3t8VJOx26dd955Klu2rCZNmqSmTZtq0qRJKleunM4999wTOm7pcP2aBQsW6Oqrr1adOnVUrFgxHTp0SIMGDfIu16lTJzVp0kSfffaZ5syZo3Hjxumll17Ss88+GxQnlg7/x0G5cuU0a9YsLVq0KNiBBr7k5ORsn5vM5qZDhw6pTJkyeuKJJzK9jj2xmBtO1hhz+/eVlPNjlXQ5HbvVrVs3TZs2Td9//70SEhI0Y8YM9e7dO/R32Yk+xsqXL59GjRqlH374QTNnztTs2bN1991365VXXtE777zjrfJu3bq1Zs+erbFjx6p58+YnfacoTqIchXbt2umdd97RggULvGWQYVWtWlWpqanZfsl98sknKly4sMaNG+edfXv//fdzfJ8n2yeffKLmzZvroYce8v6+c+dOr8htpUqVtGzZMkUiEe9ALL1QVrr0/7GIjY0NfXAA5JaczBGVKlXSoUOHtGrVquB/OSVp8+bN2rlzpypVqnRMY0n/n9ydO3d6y2vT/yfHWrVqVYbPY/oKk/SxZPWjKf1zWqZMmaif0/T/yVi1alWGvt9++y3L651IM2bMUFpamv797397/9Nii1yn961evdr7n9Rt27Zl+N+isHM9ctf555+v9957TwsXLjzmAuWVKlXS3LlztWvXLm8Vw4oVK4J+ScH/ltqVoll9LsPe96FDh7R69Wpv9Un6faerXbu2XnnlFe9v0VaYZScuLi7TFa/H8lhy4pdfftHKlSv16KOPesUX58yZc9S3GfZ1PFbHOvaYmBh17txZEydO1B133KHPP//8hKdcSYdXBs2dO1c33XSTVzgy/bvCKl++vPr27au+fftqy5Yt6t69u1544QXvJErhwoU1ZswYDRw4UIMGDdL48eO9/7nHsalatarmzp2rRo0aRf3PmPTvuJUrV3rfcVu3bs3wHWelX/6XX36J+r2X1THEyRijdPx/X8XHx6to0aKZHr+sWLFC+fPnz3Q1y9E41rG3bt1a8fHxmjx5surXr689e/aEXrl2LMIeY6Vr0KCBGjRooFtvvVWTJ0/WHXfcoalTp3plJurXr69evXrpuuuu0y233KLRo0erQIGTd2qDZMOjMGjQIBUrVkz33HOPNm/enKF/9erVwVZembnkkku0YMECzZ49O0Pfzp07g/yxmJgY5cuXz/vfnLVr12r69OnH4VGcWDExMRnO0H788ccZcmLPO+88bdy40XtM+/bt07vvvutdLikpSVWrVtXLL7+caZoEy+KRl+Rkjkg/iLRzRvqPHPcg82ikL+1362+kpqbqww8/zPTymzZt8qqg79q1Sx9++KHq1KkT/NAqWrSoJH/JtnT4yzk2NlZjxozJNJc1/XNavnx51alTRxMnTvRuY86cOVq2bNlRPMpjl/6jw523UlJSMhyYtGzZUgUKFNCECRO8v2dWLyPsXI/cNWjQIBUtWlR33313pp/XnPxvY5s2bXTw4MEM74dXX31V+fLlU5s2bSQd/g+B0qVLa968ed7lMtvuMSf3LUlvvPGG93c7t5QsWVLnnnuu9+9Y/gevatWqSklJ0c8//xz8zc4j6YoVK3bUKcZZSf8fUPd1ikQi3g44ORX2dTxWx2PsXbt21Y4dO3TvvfcqNTU1dG2DY5HVSRr7Xjt48GCG74kyZcqofPnyGbY/lQ6vDBs7dqzKlCmjK6+8MsN/qOHoXXLJJTp48KCef/75DH0HDhwIPpfnnnuuChYsqPHjx3vvy2i/a9LVrVtXlStX1uuvv57hc+7eVvoxhL3MyRijdPx/X8XExKhVq1aaPn26t3Jx8+bNmjJliho3bhw1HTGn93UsYy9QoIAuvfRSffzxx/rggw+UkJCg2rVrH5exRRP2GGvHjh0ZvnPTMxAymzPOPfdcPf3005o9e7buvPPOk1ofk5UoR6Fq1ap64okndOutt6pTp07q2rWrEhISlJaWpgULFmjatGnq0aNHlte/+uqrNWPGDA0ePFjdu3dX3bp1tWfPHv3yyy/65JNPNH36dMXHx6tt27Z65ZVXNGjQIHXu3FlbtmzRW2+9papVq2rp0qUn8RHn3Pnnn6/nnntOw4YNU8OGDfXLL79o8uTJGXJge/bsqfHjx+v222/XgAEDVK5cOU2ePDk4oEs/W50/f36NHDlS11xzjTp37qwePXqoQoUK2rhxo7799lvFxsbqhRdeOOmPE8hMTuaI2rVrq3v37nrnnXe0c+dONW3aVIsWLdLEiRPVoUOH0HmqWWnVqpUqVqyo4cOHa8WKFYqJidH777+v0qVLZ/o/xWeffbaGDx+uRYsWqUyZMnr//fe1ZcsWPfzww8Fl6tSpo5iYGL300ktKSUlRoUKF1KJFC5UpU0b333+/7rzzTvXo0UOdOnVSfHy81q9fr1mzZqlRo0a69957JUm33XabrrvuOvXp00eXXXaZtm/fHvzPY063Tj0eWrVqpYIFC2rw4MHq1auXdu/erffee09lypTxCuqWLVtWAwYMCLYCbd26tZYuXaovv/xSpUuX9v6HLexcj9x19tln64knntDtt9+uiy++WF26dFHt2rUViUS0du1aTZkyRfnz5w+1/Xb79u3VvHlzPf3001q3bp0SExM1Z84cTZ8+XQMHDvTqlfztb3/Tiy++qOHDhyspKUnz5s07ppVYderUUefOnfXWW28pJSVFDRs21DfffJPpiq/jqVOnTnriiSd04403qn///tq7d68mTJigatWqeQVGpcM/subOnatXXnlF5cuXV+XKlXO8tbRVvXp1Va1aVY8++qg2btyo2NhYffLJJ8d0siYnr2Nuj/2cc85RQkKCpk2bpho1amS5bfDxFBsbq6ZNm2rs2LHav3+/KlSooDlz5mRIfdu9e7fatm2rjh07qnbt2ipWrJi+/vprLVq0SEOHDs30tuPj4/XKK6+od+/euuKKKzRhwoQMRVORc82aNVPPnj01ZswYLVmyJPjOW7lypaZNm6bhw4fr4osvVnx8vK666iqNGTNG1113ndq2bavFixcH33HR5M+fX/fff7+uv/56devWLdj+esWKFVq2bJnGjRsnScF7dOTIkTrvvPMUExOjSy+99KSMUdIJ+X01ZMgQff311+rTp4/69OmjmJgYvfPOO0pLS8t2i/acOB5j79atm9544w19++23uuOOO47b2KIJe4w1ceJETZgwQR06dFDVqlW1e/duvfvuu4qNjc3y5HWHDh300EMP6a677lJsbKweeOCBk/KYOIlylC644AJNmjRJ48aN0/Tp0zVhwgQVKlRIiYmJGjp0aIbdZVxFixbVG2+8oTFjxmjatGn68MMPFRsbq7PPPls33XRTUKSnZcuWevDBB/XSSy/poYceUuXKlXXHHXdo3bp1ef4kyuDBg7Vnzx5NnjxZU6dO1TnnnKMxY8boySef9C5XvHhxvfbaaxo5cqRef/11FStWTN26dVPDhg110003ef871rx5c73zzjt6/vnnNX78eKWmpqpcuXJKTk5Wz549T/ZDBKLKyRwxcuRIVa5cWRMnTtTnn3+usmXL6rrrrvOWSR+tggULavTo0RoxYoT+9a9/qVy5cho4cKDi4uI0bNiwDJc/++yz9Y9//EOPPfaYfvvtN1WuXFlPP/20tyNQuXLlNGLECI0ZM0bDhw/XwYMH9frrr6tMmTLq0qWLypcvrxdffFHjxo1TWlqaKlSooCZNmngnl9u0aaN//etfeuaZZ/Tkk0+qatWqevjhhzV9+nT973//O+bHnVPVq1fXqFGj9Mwzz+jRRx9V2bJl1bt3b8XHx+vuu+/2LnvHHXeoSJEieu+99zR37lw1aNBA48aNU58+fbzltWHneuS+Dh06aPLkyXr55Zc1Z84cvf/++8qXL58qVqyotm3bqnfv3qH+ty5//vz697//rVGjRmnq1Kn64IMPVKlSJd15553Bjlvp/v73v2vr1q365JNP9PHHH6tNmzYaO3ZslkUVw3jooYdUunRpTZ48WdOnT1fz5s314osvHvOKtmhKly6t0aNH65FHHtHjjz+uypUr67bbbtOqVasynEQZOnSo7r33Xj3zzDPau3evunfvfswnUQoWLKgXXnhBI0eO1JgxY1S4cGFdeOGF6tu371EvU8/J65gXxt61a1c9/vjjJ2VZfronn3xS//znP/XWW28pEomoVatWeumll7zviiJFiqh3796aM2eOPv30U0UiEVWtWlX33Xdfhh3OXBUqVNCrr76qPn366Morr9T48eM54XwcPPDAA0pKStLbb7+tp59+WjExMapUqZL+8pe/qFGjRsHlhgwZokKFCuntt9/Wt99+q+TkZL388su67rrrsr2P1q1b67XXXtNzzz2nl19+WZFIRFWqVPGOeS666CL1799fH330kSZNmqRIJKJLL730pI3xRPy+qlWrlt588009+eSTGjNmjCKRiJKTk/X4448f8xx3vMeelJSkWrVqafny5Sdl5ZoU/hirWbNmWrRokaZOnarNmzerRIkSSk5O1hNPPJHhP+JdXbt21e7duzVixAgVL1486kYkx0u+yLFUxQFOkFdffVUPP/ywvvzyS/4HAjhJ2rdvr1q1amnMmDG5PZRTTvoqoiFDhuj666/P7eEA+BN57bXX9PDDD2vGjBkZds8AAKtbt24qWbJk6BQoZERNFOS6vXv3evG+ffv0zjvv6Oyzz+YECoA8x85Z0pFc7GbNmp3s4QD4E4tEIvrPf/6jpk2bcgIFQLYWLVqkJUuWeMWskXOk8yDX3XjjjapYsaJq166tXbt2adKkSVqxYkWWW5wBQG6aOnWqJk6cqDZt2qhYsWL6/vvvNWXKFJ133nlq3Lhxbg8PwJ9AamqqZsyYoW+//Va//PJLpsU4ASDdL7/8op9++kkvv/yyypUrp06dOuX2kE5pnERBrjvvvPP0n//8R5MnT9bBgwdVs2ZNPf3003y4AeRJiYmJiomJ0dixY7V7926VKVNGAwYM0JAhQ3J7aAD+JLZu3arbb79dcXFxGjx4sC644ILcHhKAPOyTTz7Rc889p2rVqumpp546pl3ZQE0UAAAAAACAUKiJAgAAAAAAEAInUQAAAAAAAELgJAoAAAAAAEAIoQvLsm0aTnWHDh3y4nz58mXZlz9//izj/fv3R72fYsWKBe1du3Z5fQUKFIgau/djxxRt/Ha8a9asiTrGvIy5Bqc65ppTg/u4AORtp3IJR+Ya4NQRdq5hJQoAAAAAAEAInEQBAAAAAAAIgZMoAAAAAAAAIYSuiQKc6mwuf0xMTNC2NQAs97KWrVuwd+/eoB0bG+v12fs5ePCgF+/bt++4jAlA7mGuAQAAOH2xEgUAAAAAACAETqIAAAAAAACEQDoP/jTsUnh3ibrdfs4ufXcvW7BgQa/PLoU/cOBA0E5NTc3RGN00gEKFCmXZByDvYq4BAAA4fXGkBAAAAAAAEAInUQAAAAAAAELgJAoAAAAAAEAI1ETBn5ab95+Wlub17dy5M8vLFi9e3OuzdQvcOBKJeH1uDYPsYntdW0vB7c9ui1IAuYe5BgAA4PTBShQAAAAAAIAQOIkCAAAAAAAQAidRAAAAAAAAQqAmCv40qlat6sWJiYlBu0aNGl5fmTJlvLhUqVJBu0iRIl7f/v37vditeVC4cOGoY7L1BXbt2hW0f//9d69v2bJlXrxkyZIsLwsg9zDXAAAAnL5YiQIAAAAAABACJ1EAAAAAAABCIJ0Hfxru8nVJWrFiRdBet26d17d3714vdpfNR+uT/GXzdgm9u32pJBUrVsyL4+Pjg3aVKlW8vrPOOsuLzz333KBdsmRJAcgbmGsAAABOX6xEAQAAAAAACIGTKAAAAAAAACFwEgUAAAAAACCEfJFIJBLmghUrVjzRYwFOqNTUVC8uWLBg6OsWKJB1+SBbi8C9XXsfdovSffv2efGBAweCdnY1DeLi4oJ20aJFvb558+ZlOd68jrkGpzrmmlNDvnz5cnsIAEIK+XMlT2KuAU4dYecaVqIAAAAAAACEwEkUAAAAAACAEDiJAgAAAAAAEELWydfAaaZ06dJenJaWFrT37t3r9dkaAW6dgoMHD3p9NnfOrVuQkpLi9dm6Bba+gJs3a+/HrWEgSZs3bxaAvIe5BgAA4PTFShQAAAAAAIAQOIkCAAAAAAAQAuk8mcjJVmTRtkHKyXZs9j7tdW3sXv5YxmuXkrv97hL0zO6nePHiWfbZJevu8vCYmBivr1ChQlHH5C5Zt1t82stmdT0p4+NxH2t2Y3DZLUjt43Hv196uZZfNR2OfY3u/OPUw1xzGXJM55hoAAPKmaMdT9jvZHhPt37//xA0sj4l2DBEtTTmvYyUKAAAAAABACJxEAQAAAAAACIGTKAAAAAAAACFQEyWPiFYvILPYlV2Ng2hs7pm7LabNx7fbYLr5fNndZ7R8fXu70WI7Xhu747B9hQsX9uJotR7s43HrCUSrGZFZDOQlzDXhYuYaAACQFfsd7R7L2OMCe7zxZ+I+9mi16E41rEQBAAAAAAAIgZMoAAAAAAAAIXASBQAAAAAAIARqouRQTmoAHEv9gJzcVnb5ZDmpceDelr3daHua2zoEtiaAmydobyctLc2Lbd6gmz9n78fuPe7ej1tzQZK2b9+e5e1Gex5sbJ9Pm9/nxsfymuPPjbkmc8w1mcfMNQAA5J5oxzX2OCHacc7pxj1+yq6W26lUI4WVKAAAAAAAACFwEgUAAAAAACAETqIAAAAAAACEQE2UTJyo3PJoeWAHDhyIetnscsjC3q+9HZvn7z52m39vaw+417X1A+zjcWsE2Nu1dQmyG6Nr3759Xrxr164sL1u0aNEs++xrnpMxRHutqFOAaJhrDmOuCTcG5hoAAPIGtzaaFP24xh5//JlEe+z2uMZ93vJ6fRRWogAAAAAAAITASRQAAAAAAIAQSOfJI6ItZ8oszgn3utktL3OXTtkx2WXz7nah9nbtEjd3eX60Zf12DJYdk70f97bt7dr7dR9PdikO7nXtY83J+IHcxlyT+Rgs5hoAAJAV+32eE+4xhT2+sGnLu3fvDtqnW1qQPa45lVKTWYkCAAAAAAAQAidRAAAAAAAAQuAkCgAAAAAAQAjURMlEtHys47X9Z3Z9Nqc+2raX2eWPRcuxt3l40eoUpKamevHevXuDdrFixby+uLg4L46NjQ3aq1at8vps7p99PO7Wonb87u1KGbdGde3fv9+L3duyjzVa7YHs8vfcy0Z73QDmmszHxFyTeR9zDQAApz73WGbPnj1eX/HixbOMN2zYcGIHhtBYiQIAAAAAABACJ1EAAAAAAABC4CQKAAAAAABACNREySGbk56T2gPH837d2O4Z7ubJS34+vq1LYGO3JkDBggW9vjJlynhx3bp1g3bz5s29vqSkJC/OSZ2FtWvXevGCBQuC9sqVK70+u0e7+9jdfdUz49Y0sM9ZWlqaF7vPi613kJOaEkBYzDVHMNccxlwDAMCpz631VqtWLa+vUqVKXuzWTKEmSt7BShQAAAAAAIAQOIkCAAAAAAAQAuk8eYRdKm6XndvtNt1l9XbJt11y7143u+Xf7hKyDh06eH0XX3yxF7vL6EuUKOH1LVmyxItnzJgRtIsWLer1tW7d2osvv/zyLMe3ceNGL3aX30vSxx9/nOl9SlKRIkW82N3u1KZD2OX57nNsl+PbNAX3du1SfSC3MdccwVwDAABOtnr16gXt9evXe332OOzSSy8N2l999dWJHRhCYyUKAAAAAABACJxEAQAAAAAACIGTKAAAAAAAACHki9iE+CxUrFjxRI8lA5tvb3PhU1JSvNjNIStevLjXt2vXLi92885t7r7NO3efIjcHXZK2bt3qxRUqVAjalStX9vpWrVrlxW6+u30Z3O2spIz57gkJCUG7ZMmSXl+VKlW8+I8//gjatiZA7dq1vdjtj4+PV1g7duzw4l69ennxl19+meXtPvbYY17csGHDLMeYXT0Hd6vUxYsXe31DhgzxYjev0D6HZ5xxhhefffbZQXvZsmVe3/Lly73YfQ/Y92ipUqW82NaYcOsj2G1fo20vaz8bv/32m05VzDWHMddkjrnmCOaaY3M8twYHcGKF/LmSJzHXwHJrp9n3x3//+18vdmvI1a1b98QODKHnGlaiAAAAAAAAhMBJFAAAAAAAgBA4iQIAAAAAABBCgewvknfYXH1bT8DN4962bVvUy5YuXTrT62V22S1btgRtN4dNkrp06eLFcXFxQfunn37y+mxOuluLwObud+7c2YsvuOACLy5btqyyYnPs3cdXo0YNr8/WXdi0aVPQtnuRN2/e3IvdPPrvvvvO67O5/FWrVg3aqampXp/7Wkh+/r0kbd++XVkpXLhwlmNyazlI0ttvv+3F9913X9CeMGGC12ffP7///nvQdvdrlzI+p998803Qtu8Xe7t2L/hixYoFbZuTZ2M3h3Lv3r3C8cNccwRzzWHMNYcx1wAAcGzKly8ftNesWeP1zZs3z4vHjRt3UsaEnGElCgAAAAAAQAicRAEAAAAAAAghT29xbNktJ+3yZXcpud3usWjRol7sLmV2t3qUMm411ahRo6C9c+dOr89dki75y8ftUvdatWp58dKlS4O2XVJvH2udOnW82N0G027T6T42y26x+vXXX3uxu1WnXcbfrFkzL77rrruC9owZM7w+m7bgLoV/7733ot7uxIkTvdh9jpOTk72+pKQkL7bbb7rs0n43fvPNN72++++/34vd8dvHZsfgbi26cuVKr8/G9r3mphvs27fP67PvCTcVxF7W3Wr2VMNccxhzzRHMNZmPgbnm2LDtKHDqYItjnE4GDBgQtO3xhv2edY8hHnzwwRM7MLDFMQAAAAAAwPHESRQAAAAAAIAQOIkCAAAAAAAQQp7e4tjm1NscMbs9pZu3bfPVbY63u5Wl3brSrUsgScOGDQvaNifdbq+5ZMmSTO9DkjZs2ODFa9euDdru1qaSVL9+fS/u3r27F7tjttuxfv75517s5nbZPC93DJK/vabdktTWqqhUqVLQtvUbbK0Ed1tV+7xs3brVi+1r5Y55+fLlXp/Nx3e3fa1SpYrXV7169SzH2KtXryzHK0nPP/980HbrS0jS6tWrvditRWC3JLXbg9p6Gm6/3abWfh7cLUvd7VaRc8w1RzDXHMZccwRzDQAAx88XX3wRtJs2ber15YXagMgeK1EAAAAAAABC4CQKAAAAAABACJxEAQAAAAAACCFP10SxOfVuHYLM+t389qJFi3p97h7bklS8ePGg3a5dO6/v1Vdf9WI3r75IkSJen81jq1OnTtD+8MMPvb7Fixd78WWXXRa0K1So4PW1b9/ei23dhQkTJgRtmydvc+HPOuusoO3WIZAy1nooV65c0K5cubLXZ+tEuPn49rWw+Xzua2PrKrh5gZI0f/58L3ZrBtgaBvZ1desWbNq0yetbsWKFF7uv1RlnnOH19e/f34s3btwYtMeMGeP12ec7NjY2aNuaDLb+geXej61LYN//7mvn1ixAzjHXHMFck/F2JOaadMw1AAAcG/d46qabbjop92lryB08eNCL3WMBexxg69GBlSgAAAAAAAChcBIFAAAAAAAghDydzmOXGdmlRTZ2t3C0y5Pt1qLu8vFu3bp5fVOmTPHin3/+OWjbJfZ2K8sWLVoE7Y4dO3p9dtm/O6YGDRp4fXbJt93e1F2KbbentFtQ7tq1K2jv2bPH67NLs91l23ZZv30OO3XqFLQHDx7s9SUmJnqxm7ZQqlQpr89dqi9lXG7mLvu3S9bt43HfE/Z5yZcvnxdPnTo1aNttXd37lKQrrrgiaGe3pM3dRvXdd9/1+qpVq+bFdmvXaEvp7GvlpjzYx4acYa45grnmMOaaI5hrAAA4vdiUZ77fc4aVKAAAAAAAACFwEgUAAAAAACAETqIAAAAAAACEkC9i94vMgt1GMjfYLTLd7R0lafPmzUHb1gQoX768F7vbfPbu3dvrGzt2rBe7+fk2L969T0lq0qRJ0B46dKjXV79+fS92c9FsvrpbW0CSXn/9dS92t9S09QPs9qDu/ZQuXdrrs9fN6nqSn6svSXFxcUHbjt/WmHDfZhMnTvT63K1CJT/PX/Jfuw0bNnh9Nnfffd3t+8M+p+ecc07QtrUHmjdv7sVufQG3boUkjRgxwovdGhN2C1u7vax9/t1x2O1Mo20tardjdetYnGqYaw5jrjmCueYw5prji/xv4NQR8udKnsRcg7zAfrdH+0zZY9vU1NQTMqa8KOxcw0oUAAAAAACAEDiJAgAAAAAAEAInUQAAAAAAAEIokNsDiKZAAX94Ns/c5hi6l7fXLVasmBdv27YtaE+bNs3rs3nybg5ZkSJFvL74+Hgvnj59etDesWOH1/fEE094sZsnb2sL2Bx7WwfAHZOtnWDz2Hbv3p3l/cTExCgrlStX9uKyZct6sZtTb8dnb9e9X3c8Usb6EzZ2H5+93eLFi3uxe9v2fuxzunTp0qBt6wcsW7bMi906BfZ5Oeuss7zYfQ+UKlXK67PPf8GCBb3YfTz2/W65lz2Vc4XzAuaaI5hrMr9d5prDmGsAADj12ONKWytt+/btQdse2yIjVqIAAAAAAACEwEkUAAAAAACAEPL0Wp3slp27y+Rtv12KvX79ei+uWrVq0N6/f7/XZ5eLu0vW7fJ1G69YsSJof/fdd17fU0895cXutqRVqlRRNNWrV/fiL774ImifeeaZXp/dwspdkpWSkhL1ftwtrJKTk6Ne1j5v0cyZMydo2yX0dnm4fU7dpfw2ncBuzekuVbPL1+37yU0ZcJewSVKJEiW8eO3atUHbLrHv27evF69bty5oT5o0yetzt2q145X8bWztUrpoW7na5wE5w1xzBHPNYcw1RzDXAACQ+xo0aODFa9as8WJ7XOam69ptiu1xjns86x4PIXOsRAEAAAAAAAiBkygAAAAAAAAhcBIFAAAAAAAghDxdE8Xms9v6AWlpaV7s5njbXPJo+eC2BoDdgtLNIbP54HaLUjcX3tZVmDp1qhe7eWzjx4/3+ux2lXXq1PFit86CHYOtReA+T2eccYbXZ7dGdbdndbdFzUyZMmWy7Pv111+92H2s9nWz9Q7sc+zm7NlcfZvL7+YC2vePzRN0b8uOydYtcGtiVKxY0euzdSIaN24ctGfNmuX12RoMtkaD+761eY7RtiG1268iZ5hrjjid5pqrrroq6u3mhmeeecaLmWuAU9/w4cO92M7J0WJ7HGPrFLhxvnz5vD77uXa/b/75z39mN2wAp5gBAwZ48YEDB7zYblscbatie2zr3pb9bXb33XfnaJx/BqxEAQAAAAAACIGTKAAAAAAAACFwEgUAAAAAACCEPF0Txebqr1+/3ovdXH3JzxW1tTNq1arlxWvXrg3ay5Yt8/ri4uK82M0tt5e1uau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\n",
       "text/plain": [
        "<Figure size 1400x800 with 4 Axes>"
       ]
@@ -1332,7 +1358,7 @@
     "plt.axis(\"off\")\n",
     "plt.title(\"Ground-Truth anomaly mask\")\n",
     "plt.subplot(1, 4, 4)\n",
-    "plt.imshow(mask_upsampled[0, 0, ...] * difference_map, cmap=\"gray\")\n",
+    "plt.imshow(mask_upsampled[0, 0,...] * difference_map, cmap=\"gray\")\n",
     "plt.axis(\"off\")\n",
     "plt.title(\"Predicted anomaly mask\")\n",
     "plt.show()"
diff --git a/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.py b/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.py
index 33641f09..f5536cd2 100644
--- a/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.py
+++ b/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.py
@@ -13,6 +13,18 @@
 #     name: python3
 # ---
 
+# %%
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 # %% [markdown]
 # # Anomaly Detection with Transformers
 #
@@ -29,22 +41,14 @@
 
 # %%
 # !python -c "import seaborn" || pip install -q seaborn
+# !python -c "import monai" || pip install -q "monai-weekly[tqdm]"
+# !python -c "import matplotlib" || pip install -q matplotlib
 # %matplotlib inline
 
 # %% [markdown]
 # ### Setup imports
 
 # %%
-# Copyright 2020 MONAI Consortium
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#     http://www.apache.org/licenses/LICENSE-2.0
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
 import os
 import tempfile
 import time
@@ -419,7 +423,7 @@
 in_likelihoods = np.concatenate(in_likelihoods)
 
 # %% [markdown]
-# We will use the "ChestCT" class of the dataset for the out-of-distribution examples.
+# We will use all other classes for the out-of-distribution examples.
 
 # %%
 all_classes = {item["class_name"] for item in test_data.data}
@@ -455,7 +459,7 @@
 sns.set_style("whitegrid", {"axes.grid": False})
 sns.kdeplot(in_likelihoods, bw_adjust=1, label="In-distribution", fill=True, cut=True)
 for c, l in all_likelihoods.items():
-    sns.kdeplot(l, bw_adjust=20, label=f"OOD {c}", cut=True, fill=True)
+    sns.kdeplot(l, bw_adjust=1, label=f"OOD {c}", cut=True, fill=True)
 plt.legend(loc="upper right")
 plt.xlabel("Log-likelihood")
 # plt.xlim([-200,10])
@@ -490,8 +494,9 @@
     transformer_model=transformer_model,
     ordering=ordering,
 )
+likelihood = torch.exp(log_likelihood)
 plt.subplot(1, 2, 1)
-plt.imshow(log_likelihood.cpu()[0, ...], vmin=0.6, vmax=1)
+plt.imshow(likelihood.cpu()[0, ...])
 plt.axis("off")
 plt.title("Log-likelihood")
 plt.subplot(1, 2, 2)
@@ -571,4 +576,3 @@
 plt.axis("off")
 plt.title("Predicted anomaly mask")
 plt.show()
-