diff --git a/tests/test_integration_workflows_adversarial.py b/tests/test_integration_workflows_adversarial.py
index 4ce46554..7369aa9c 100644
--- a/tests/test_integration_workflows_adversarial.py
+++ b/tests/test_integration_workflows_adversarial.py
@@ -24,7 +24,7 @@
 from monai.data import create_test_image_2d
 from monai.handlers import CheckpointSaver, StatsHandler, TensorBoardStatsHandler
 from monai.networks.nets import AutoEncoder, Discriminator
-from monai.transforms import AsChannelFirstd, Compose, LoadImaged, RandFlipd, ScaleIntensityd
+from monai.transforms import Compose, EnsureChannelFirstd, LoadImaged, RandFlipd, ScaleIntensityd
 from monai.utils import CommonKeys, set_determinism
 
 from generative.engines import AdversarialTrainer
@@ -44,7 +44,7 @@ def run_training_test(root_dir, device="cuda:0"):
     train_transforms = Compose(
         [
             LoadImaged(keys=[CommonKeys.IMAGE, CommonKeys.LABEL]),
-            AsChannelFirstd(keys=[CommonKeys.IMAGE, CommonKeys.LABEL]),
+            EnsureChannelFirstd(keys=[CommonKeys.IMAGE, CommonKeys.LABEL], channel_dim=2),
             ScaleIntensityd(keys=[CommonKeys.IMAGE]),
             RandFlipd(keys=[CommonKeys.IMAGE, CommonKeys.LABEL], prob=0.5),
         ]
diff --git a/tutorials/generative/2d_controlnet/2d_controlnet.ipynb b/tutorials/generative/2d_controlnet/2d_controlnet.ipynb
index def0ef48..8ff9ff29 100644
--- a/tutorials/generative/2d_controlnet/2d_controlnet.ipynb
+++ b/tutorials/generative/2d_controlnet/2d_controlnet.ipynb
@@ -1,1423 +1,1422 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "70eef519",
-   "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": "63d95da6",
-   "metadata": {},
-   "source": [
-    "# Using ControlNet to control image generation\n",
-    "\n",
-    "This tutorial illustrates how to use MONAI Generative Models to train a ControlNet [1]. ControlNets are hypernetworks that allow for supplying extra conditioning to ready-trained diffusion models. In this example, we will walk through training a ControlNet that allows us to specify a whole-brain mask that the sampled image must respect.\n",
-    "\n",
-    "\n",
-    "\n",
-    "In summary, the tutorial will cover the following:\n",
-    "1. Loading and preprocessing a dataset (we extract the brain MRI dataset 2D slices from 3D volumes from the BraTS dataset)\n",
-    "2. Training a 2D diffusion model\n",
-    "3. Freeze the diffusion model and train a ControlNet\n",
-    "3. Conditional sampling with the ControlNet\n",
-    "\n",
-    "[1] - Zhang et al. [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "022890b1-ea44-4c60-8a80-ed1fc755f90b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n",
-    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6b766027",
-   "metadata": {},
-   "source": [
-    "## Setup environment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "972ed3f3",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
+    "cells": [
+        {
+            "cell_type": "code",
+            "execution_count": 1,
+            "id": "70eef519",
+            "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": "63d95da6",
+            "metadata": {},
+            "source": [
+                "# Using ControlNet to control image generation\n",
+                "\n",
+                "This tutorial illustrates how to use MONAI Generative Models to train a ControlNet [1]. ControlNets are hypernetworks that allow for supplying extra conditioning to ready-trained diffusion models. In this example, we will walk through training a ControlNet that allows us to specify a whole-brain mask that the sampled image must respect.\n",
+                "\n",
+                "\n",
+                "\n",
+                "In summary, the tutorial will cover the following:\n",
+                "1. Loading and preprocessing a dataset (we extract the brain MRI dataset 2D slices from 3D volumes from the BraTS dataset)\n",
+                "2. Training a 2D diffusion model\n",
+                "3. Freeze the diffusion model and train a ControlNet\n",
+                "3. Conditional sampling with the ControlNet\n",
+                "\n",
+                "[1] - Zhang et al. [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 2,
+            "id": "022890b1-ea44-4c60-8a80-ed1fc755f90b",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm]\"\n",
+                "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
+                "%matplotlib inline"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "6b766027",
+            "metadata": {},
+            "source": [
+                "## Setup environment"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 3,
+            "id": "972ed3f3",
+            "metadata": {
+                "collapsed": false,
+                "jupyter": {
+                    "outputs_hidden": false
+                },
+                "lines_to_next_cell": 2
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "2023-05-04 18:42:25,456 - A matching Triton is not available, some optimizations will not be enabled.\n",
+                        "Error caught was: No module named 'triton'\n",
+                        "MONAI version: 1.2.dev2304\n",
+                        "Numpy version: 1.23.4\n",
+                        "Pytorch version: 1.13.1+cu117\n",
+                        "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
+                        "MONAI rev id: 9a57be5aab9f2c2a134768c0c146399150e247a0\n",
+                        "MONAI __file__: /home/mark/Envs/monai-generative/lib/python3.8/site-packages/monai/__init__.py\n",
+                        "\n",
+                        "Optional dependencies:\n",
+                        "Pytorch Ignite version: 0.4.10\n",
+                        "ITK version: 5.3.0\n",
+                        "Nibabel version: 5.0.0\n",
+                        "scikit-image version: 0.19.3\n",
+                        "Pillow version: 9.3.0\n",
+                        "Tensorboard version: 2.12.0\n",
+                        "gdown version: 4.6.0\n",
+                        "TorchVision version: 0.14.1+cu117\n",
+                        "tqdm version: 4.64.1\n",
+                        "lmdb version: 1.4.0\n",
+                        "psutil version: 5.9.4\n",
+                        "pandas version: 1.5.3\n",
+                        "einops version: 0.6.0\n",
+                        "transformers version: 4.21.3\n",
+                        "mlflow version: 2.1.1\n",
+                        "pynrrd version: 1.0.0\n",
+                        "\n",
+                        "For details about installing the optional dependencies, please visit:\n",
+                        "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
+                        "\n"
+                    ]
+                }
+            ],
+            "source": [
+                "import os\n",
+                "import tempfile\n",
+                "import time\n",
+                "import os\n",
+                "import matplotlib.pyplot as plt\n",
+                "import numpy as np\n",
+                "import torch\n",
+                "import torch.nn.functional as F\n",
+                "from monai import transforms\n",
+                "from monai.apps import DecathlonDataset\n",
+                "from monai.config import print_config\n",
+                "from monai.data import DataLoader\n",
+                "from monai.utils import first, set_determinism\n",
+                "from torch.cuda.amp import GradScaler, autocast\n",
+                "from tqdm import tqdm\n",
+                "\n",
+                "\n",
+                "from generative.inferers import DiffusionInferer\n",
+                "from generative.networks.nets import DiffusionModelUNet, ControlNet\n",
+                "from generative.networks.schedulers import DDPMScheduler\n",
+                "\n",
+                "print_config()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "7d4ff515",
+            "metadata": {},
+            "source": [
+                "### Setup data directory"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 4,
+            "id": "8b4323e7",
+            "metadata": {
+                "collapsed": false,
+                "jupyter": {
+                    "outputs_hidden": false
+                }
+            },
+            "outputs": [],
+            "source": [
+                "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
+                "root_dir = tempfile.mkdtemp() if directory is None else directory"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "99175d50",
+            "metadata": {},
+            "source": [
+                "### Set deterministic training for reproducibility"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 5,
+            "id": "34ea510f",
+            "metadata": {
+                "collapsed": false,
+                "jupyter": {
+                    "outputs_hidden": false
+                }
+            },
+            "outputs": [],
+            "source": [
+                "set_determinism(42)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "c3f70dd1-236a-47ff-a244-575729ad92ba",
+            "metadata": {
+                "tags": []
+            },
+            "source": [
+                "## Setup BRATS dataset\n",
+                "\n",
+                "We now download the BraTS dataset and extract the 2D slices from the 3D volumes.\n"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "87977bac-ff5e-4612-b9f2-b069d6ad9e9a",
+            "metadata": {},
+            "source": [
+                "### Specify transforms\n",
+                "We create a rough brain mask by thresholding the image."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 6,
+            "id": "c68d2d91-9a0b-4ac1-ae49-f4a64edbd82a",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "<class 'monai.transforms.utility.array.AddChannel'>: Class `AddChannel` has been deprecated since version 0.8. please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead.\n"
+                    ]
+                }
+            ],
+            "source": [
+                "channel = 0\n",
+                "assert channel in [0, 1, 2, 3], \"Choose a valid channel\"\n",
+                "\n",
+                "train_transforms = transforms.Compose(\n",
+                "    [\n",
+                "        transforms.LoadImaged(keys=[\"image\"]),\n",
+                "        transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
+                "        transforms.Lambdad(keys=[\"image\"], func=lambda x: x[channel, :, :, :]),\n",
+                "        transforms.EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n",
+                "        transforms.EnsureTyped(keys=[\"image\"]),\n",
+                "        transforms.Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n",
+                "        transforms.Spacingd(keys=[\"image\"], pixdim=(3.0, 3.0, 2.0), mode=\"bilinear\"),\n",
+                "        transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=(64, 64, 44)),\n",
+                "        transforms.ScaleIntensityRangePercentilesd(keys=\"image\", lower=0, upper=99.5, b_min=0, b_max=1),\n",
+                "        transforms.RandSpatialCropd(keys=[\"image\"], roi_size=(64, 64, 1), random_size=False),\n",
+                "        transforms.Lambdad(keys=[\"image\"], func=lambda x: x.squeeze(-1)),\n",
+                "        transforms.CopyItemsd(keys=[\"image\"], times=1, names=[\"mask\"]),\n",
+                "        transforms.Lambdad(keys=[\"mask\"], func=lambda x: torch.where(x > 0.1, 1, 0)),\n",
+                "        transforms.FillHolesd(keys=[\"mask\"]),\n",
+                "        transforms.CastToTyped(keys=[\"mask\"], dtype=np.float32),\n",
+                "    ]\n",
+                ")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "9d378ac6",
+            "metadata": {},
+            "source": [
+                "### Load training and validation datasets"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 7,
+            "id": "da1927b0",
+            "metadata": {
+                "collapsed": false,
+                "jupyter": {
+                    "outputs_hidden": false
+                }
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "2023-05-04 18:42:34,233 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n",
+                        "2023-05-04 18:42:34,233 - INFO - File exists: /home/mark/data_drive/monai_data_dir/Task01_BrainTumour.tar, skipped downloading.\n",
+                        "2023-05-04 18:42:34,233 - INFO - Non-empty folder exists in /home/mark/data_drive/monai_data_dir/Task01_BrainTumour, skipped extracting.\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Loading dataset: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 388/388 [01:36<00:00,  4.02it/s]\n"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Length of training data: 388\n",
+                        "Train image shape torch.Size([1, 64, 64])\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Loading dataset: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 96/96 [00:24<00:00,  3.88it/s]"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Length of val data: 96\n",
+                        "Validation Image shape torch.Size([1, 64, 64])\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "\n"
+                    ]
+                }
+            ],
+            "source": [
+                "train_ds = DecathlonDataset(\n",
+                "    root_dir=root_dir,\n",
+                "    task=\"Task01_BrainTumour\",\n",
+                "    section=\"training\",\n",
+                "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
+                "    num_workers=4,\n",
+                "    download=True,\n",
+                "    seed=0,\n",
+                "    transform=train_transforms,\n",
+                ")\n",
+                "print(f\"Length of training data: {len(train_ds)}\")\n",
+                "print(f'Train image shape {train_ds[0][\"image\"].shape}')\n",
+                "\n",
+                "val_ds = DecathlonDataset(\n",
+                "    root_dir=root_dir,\n",
+                "    task=\"Task01_BrainTumour\",\n",
+                "    section=\"validation\",\n",
+                "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
+                "    num_workers=4,\n",
+                "    download=False,\n",
+                "    seed=0,\n",
+                "    transform=train_transforms,\n",
+                ")\n",
+                "print(f\"Length of val data: {len(val_ds)}\")\n",
+                "print(f'Validation image shape {val_ds[0][\"image\"].shape}')"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 8,
+            "id": "8e4d6164-00e5-4663-a678-1391438574e9",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "train_loader = DataLoader(train_ds, batch_size=64, shuffle=True, num_workers=4, drop_last=True, persistent_workers=True)\n",
+                "val_loader = DataLoader(val_ds, batch_size=64, shuffle=False, num_workers=4, drop_last=True, persistent_workers=True)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "5d86ba60-84d2-49f2-95c1-2ab611310d84",
+            "metadata": {},
+            "source": [
+                "### Visualise the images and masks"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 9,
+            "id": "17a5e9a4-9756-400b-8dbd-0f1d457ad3dd",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Batch shape: torch.Size([64, 1, 64, 64])\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 600x300 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "check_data = first(train_loader)\n",
+                "print(f\"Batch shape: {check_data['image'].shape}\")\n",
+                "image_visualisation = torch.cat(\n",
+                "    (\n",
+                "        torch.cat(\n",
+                "            [\n",
+                "                check_data[\"image\"][0, 0],\n",
+                "                check_data[\"image\"][1, 0],\n",
+                "                check_data[\"image\"][2, 0],\n",
+                "                check_data[\"image\"][3, 0],\n",
+                "            ],\n",
+                "            dim=1,\n",
+                "        ),\n",
+                "        torch.cat(\n",
+                "            [check_data[\"mask\"][0, 0], check_data[\"mask\"][1, 0], check_data[\"mask\"][2, 0], check_data[\"mask\"][3, 0]],\n",
+                "            dim=1,\n",
+                "        ),\n",
+                "    ),\n",
+                "    dim=0,\n",
+                ")\n",
+                "plt.figure(figsize=(6, 3))\n",
+                "plt.imshow(image_visualisation, vmin=0, vmax=1, cmap=\"gray\")\n",
+                "plt.axis(\"off\")\n",
+                "plt.tight_layout()\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "de29d929-bc99-4235-aea6-d6867c3d360c",
+            "metadata": {},
+            "source": [
+                "## Train the Diffusion model\n",
+                "In general, a ControlNet can be trained in combination with a pre-trained, frozen diffusion model. In this case we will quickly train the diffusion model first."
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "08428bc6",
+            "metadata": {},
+            "source": [
+                "### Define network, scheduler, optimizer, and inferer"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 10,
+            "id": "bee5913e",
+            "metadata": {
+                "lines_to_next_cell": 2,
+                "tags": []
+            },
+            "outputs": [],
+            "source": [
+                "device = torch.device(\"cuda\")\n",
+                "\n",
+                "model = DiffusionModelUNet(\n",
+                "    spatial_dims=2,\n",
+                "    in_channels=1,\n",
+                "    out_channels=1,\n",
+                "    num_channels=(128, 256, 256),\n",
+                "    attention_levels=(False, True, True),\n",
+                "    num_res_blocks=1,\n",
+                "    num_head_channels=256,\n",
+                ")\n",
+                "model.to(device)\n",
+                "\n",
+                "scheduler = DDPMScheduler(num_train_timesteps=1000)\n",
+                "\n",
+                "optimizer = torch.optim.Adam(params=model.parameters(), lr=2.5e-5)\n",
+                "\n",
+                "inferer = DiffusionInferer(scheduler)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "f815ff34",
+            "metadata": {},
+            "source": [
+                "### Run training\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 11,
+            "id": "9a4fc901",
+            "metadata": {
+                "tags": []
+            },
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.73it/s, loss=0.987]\n",
+                        "Epoch 1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.44it/s, loss=0.946]\n",
+                        "Epoch 2: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.41it/s, loss=0.893]\n",
+                        "Epoch 3: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.38it/s, loss=0.836]\n",
+                        "Epoch 4: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.43it/s, loss=0.78]\n",
+                        "Epoch 5: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.33it/s, loss=0.723]\n",
+                        "Epoch 6: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.34it/s, loss=0.673]\n",
+                        "Epoch 7: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.43it/s, loss=0.617]\n",
+                        "Epoch 8: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.34it/s, loss=0.567]\n",
+                        "Epoch 9: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.41it/s, loss=0.52]\n",
+                        "Epoch 10: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.32it/s, loss=0.478]\n",
+                        "Epoch 11: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.41it/s, loss=0.434]\n",
+                        "Epoch 12: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.41it/s, loss=0.389]\n",
+                        "Epoch 13: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.40it/s, loss=0.357]\n",
+                        "Epoch 14: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.38it/s, loss=0.321]\n",
+                        "Epoch 15: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.31it/s, loss=0.284]\n",
+                        "Epoch 16: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.39it/s, loss=0.252]\n",
+                        "Epoch 17: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.40it/s, loss=0.227]\n",
+                        "Epoch 18: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.39it/s, loss=0.205]\n",
+                        "Epoch 19: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.38it/s, loss=0.197]\n",
+                        "Epoch 20: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.31it/s, loss=0.167]\n",
+                        "Epoch 21: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.38it/s, loss=0.152]\n",
+                        "Epoch 22: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.38it/s, loss=0.137]\n",
+                        "Epoch 23: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.31it/s, loss=0.123]\n",
+                        "Epoch 24: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.37it/s, loss=0.112]\n",
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:09<00:00, 101.87it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 200x200 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 25: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.31it/s, loss=0.104]\n",
+                        "Epoch 26: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.35it/s, loss=0.0922]\n",
+                        "Epoch 27: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.39it/s, loss=0.0875]\n",
+                        "Epoch 28: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.37it/s, loss=0.0778]\n",
+                        "Epoch 29: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.30it/s, loss=0.0702]\n",
+                        "Epoch 30: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.37it/s, loss=0.0606]\n",
+                        "Epoch 31: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.36it/s, loss=0.0573]\n",
+                        "Epoch 32: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.33it/s, loss=0.0535]\n",
+                        "Epoch 33: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.33it/s, loss=0.0452]\n",
+                        "Epoch 34: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.24it/s, loss=0.0497]\n",
+                        "Epoch 35: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.29it/s, loss=0.0469]\n",
+                        "Epoch 36: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.28it/s, loss=0.0377]\n",
+                        "Epoch 37: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.30it/s, loss=0.0381]\n",
+                        "Epoch 38: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.27it/s, loss=0.0413]\n",
+                        "Epoch 39: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.23it/s, loss=0.0318]\n",
+                        "Epoch 40: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.26it/s, loss=0.0379]\n",
+                        "Epoch 41: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.14it/s, loss=0.0338]\n",
+                        "Epoch 42: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.22it/s, loss=0.03]\n",
+                        "Epoch 43: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.23it/s, loss=0.0287]\n",
+                        "Epoch 44: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.14it/s, loss=0.0269]\n",
+                        "Epoch 45: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.16it/s, loss=0.0255]\n",
+                        "Epoch 46: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.23it/s, loss=0.0304]\n",
+                        "Epoch 47: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.17it/s, loss=0.0265]\n",
+                        "Epoch 48: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.13it/s, loss=0.0266]\n",
+                        "Epoch 49: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.10it/s, loss=0.0256]\n",
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:09<00:00, 101.68it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 200x200 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 50: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.26it/s, loss=0.0249]\n",
+                        "Epoch 51: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.16it/s, loss=0.0228]\n",
+                        "Epoch 52: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.23it/s, loss=0.0297]\n",
+                        "Epoch 53: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.23it/s, loss=0.0228]\n",
+                        "Epoch 54: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.17it/s, loss=0.0285]\n",
+                        "Epoch 55: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.19it/s, loss=0.0258]\n",
+                        "Epoch 56: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.13it/s, loss=0.0205]\n",
+                        "Epoch 57: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.14it/s, loss=0.0265]\n",
+                        "Epoch 58: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.08it/s, loss=0.0237]\n",
+                        "Epoch 59: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.05it/s, loss=0.0226]\n",
+                        "Epoch 60: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.16it/s, loss=0.0272]\n",
+                        "Epoch 61: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.16it/s, loss=0.0236]\n",
+                        "Epoch 62: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.10it/s, loss=0.0234]\n",
+                        "Epoch 63: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.11it/s, loss=0.0211]\n",
+                        "Epoch 64: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.06it/s, loss=0.0245]\n",
+                        "Epoch 65: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.08it/s, loss=0.0246]\n",
+                        "Epoch 66: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.13it/s, loss=0.0195]\n",
+                        "Epoch 67: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.10it/s, loss=0.0227]\n",
+                        "Epoch 68: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.11it/s, loss=0.0251]\n",
+                        "Epoch 69: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.07it/s, loss=0.0209]\n",
+                        "Epoch 70: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.08it/s, loss=0.0236]\n",
+                        "Epoch 71: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.03it/s, loss=0.0261]\n",
+                        "Epoch 72: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.11it/s, loss=0.0255]\n",
+                        "Epoch 73: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.16it/s, loss=0.0232]\n",
+                        "Epoch 74: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.98it/s, loss=0.0229]\n",
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:09<00:00, 103.82it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 200x200 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 75: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.15it/s, loss=0.0198]\n",
+                        "Epoch 76: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.22it/s, loss=0.0264]\n",
+                        "Epoch 77: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.18it/s, loss=0.0208]\n",
+                        "Epoch 78: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.18it/s, loss=0.0192]\n",
+                        "Epoch 79: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.01it/s, loss=0.0257]\n",
+                        "Epoch 80: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.11it/s, loss=0.0226]\n",
+                        "Epoch 81: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.07it/s, loss=0.0226]\n",
+                        "Epoch 82: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.10it/s, loss=0.0216]\n",
+                        "Epoch 83: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.03it/s, loss=0.0228]\n",
+                        "Epoch 84: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.08it/s, loss=0.0247]\n",
+                        "Epoch 85: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.99it/s, loss=0.0234]\n",
+                        "Epoch 86: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.02it/s, loss=0.022]\n",
+                        "Epoch 87: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.12it/s, loss=0.023]\n",
+                        "Epoch 88: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.01it/s, loss=0.0232]\n",
+                        "Epoch 89: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.95it/s, loss=0.021]\n",
+                        "Epoch 90: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.01it/s, loss=0.0217]\n",
+                        "Epoch 91: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.02it/s, loss=0.02]\n",
+                        "Epoch 92: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.09it/s, loss=0.0226]\n",
+                        "Epoch 93: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.07it/s, loss=0.0232]\n",
+                        "Epoch 94: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.85it/s, loss=0.02]\n",
+                        "Epoch 95: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.01it/s, loss=0.0179]\n",
+                        "Epoch 96: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.03it/s, loss=0.0225]\n",
+                        "Epoch 97: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.00it/s, loss=0.0144]\n",
+                        "Epoch 98: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.05it/s, loss=0.0214]\n",
+                        "Epoch 99: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.93it/s, loss=0.0137]\n",
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:09<00:00, 103.43it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 200x200 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 100: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.13it/s, loss=0.0221]\n",
+                        "Epoch 101: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.11it/s, loss=0.0137]\n",
+                        "Epoch 102: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.03it/s, loss=0.0217]\n",
+                        "Epoch 103: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.05it/s, loss=0.0199]\n",
+                        "Epoch 104: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.11it/s, loss=0.0196]\n",
+                        "Epoch 105: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.97it/s, loss=0.0167]\n",
+                        "Epoch 106: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.01it/s, loss=0.0199]\n",
+                        "Epoch 107: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.97it/s, loss=0.0201]\n",
+                        "Epoch 108: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.99it/s, loss=0.0183]\n",
+                        "Epoch 109: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.03it/s, loss=0.0213]\n",
+                        "Epoch 110: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.99it/s, loss=0.0194]\n",
+                        "Epoch 111: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.90it/s, loss=0.0198]\n",
+                        "Epoch 112: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.01it/s, loss=0.0227]\n",
+                        "Epoch 113: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.05it/s, loss=0.019]\n",
+                        "Epoch 114: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.89it/s, loss=0.0189]\n",
+                        "Epoch 115: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.93it/s, loss=0.0176]\n",
+                        "Epoch 116: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.90it/s, loss=0.0247]\n",
+                        "Epoch 117: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.05it/s, loss=0.0226]\n",
+                        "Epoch 118: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.02it/s, loss=0.0199]\n",
+                        "Epoch 119: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.84it/s, loss=0.0207]\n",
+                        "Epoch 120: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.93it/s, loss=0.017]\n",
+                        "Epoch 121: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.00it/s, loss=0.0213]\n",
+                        "Epoch 122: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.91it/s, loss=0.0204]\n",
+                        "Epoch 123: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.98it/s, loss=0.0242]\n",
+                        "Epoch 124: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.98it/s, loss=0.0196]\n",
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:09<00:00, 100.61it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKEAAAChCAYAAACvUd+2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAsb0lEQVR4nO2deXNcx3XFz5t9H6wESVnyKm9l+59UPkA+cypfIxWXIid2JNuSKAokCMy+b/kD9es50xxSgInBAPK7VVMggZn3erpP3+Xce/slq9VqpVRS2aNk9j2AVFJJQZjK3iUFYSp7lxSEqexdUhCmsndJQZjK3iUFYSp7lxSEqexdcjd9Y7FY1Gq10nw+10Plt5MkkSStVquNf2/7+00lk1nv03d97kPnI5PJaLVa3cu8JknywfdhHpH4ev735XL5vde7MQgfKvBieSzjdHlsY77r8d4YhIvF4k5vvAvxydk2Uf/I5G275l1okw8d10O/VyaTeUtjvkv+IU1414uwD9lmUvx38fd77N/3PiVJkvC6ifzgzPFN5aaa8q406j+TMD83nacbgzCVVG4jt9moKQhT2YmkIExlr3JbdyUlq1PZu6QgTOXOhaj4ptFxCsJUdiY3NcspCFO5c0l9wlQenaQgTGXvkoIwlb1LCsJUdiZpdJzK3uQ2xQtSmjFJZQdy2+g4BWEqO5FbVa/vcByppHIjSUGYyt4lBWEqe5fUJ0xlJ3Kb6DjVhKnsXf6pNGHcd/yP9CGncjNJK6vfIbGJyGQyymQyWiwW72w+T4G6e7kxCB9jm6eDLpvNqlgsKpPJqFQqbfTF9vt9DQaDt4oxvXUxn88rl8tpuVxquVxqsVhoNpt9b69zKt8vt9KEDxGI7+sVBkCZTEb5fF7ValWFQkHHx8fK5/MBUOfn5wGEvLLZ7MbnG42GKpWKZrOZZrOZJpOJ+v3+xj3RqK49H9p8PUT5QfQdZzIZFQqF8DObzapQKCifz4f38LtcLqdaraZsNqvpdKr5fK5KpaKDg4MAOGkN7ul0qtVqpXK5rFqtFuZhOp2qVqsFjbhcLsPP6XT6lpb8Z5PbRMfJTU/vv81F71MA3vHxscrlsn70ox+pXq+rVqupUqlosVhosVhoPp9vHOa0Wq3U6XQ0Ho+DRuR6/H8+n+v169eaTqf6+OOPdXJyolqtpnq9rvl8rvF4rNlspsvLS81mM41GI02nU11cXOjy8nJjnNlsVqvVausBQT9Ev5PNfJPjYx5VYIIfh5krFArBxJbLZZVKpaDt8vl88ONWq5UWi4Umk0n492KxCIufz+fDpAHCxWKh6XSqUqmkXC6nYrGofD6vQqGgYrGoXO566rLZrGq1mmazmXK5nGazmXq9XjDnjDmbzUq6PqVqtVoFrYl45UnskyKLxeJGp1w9NnnwIMxkMioWi8pmsyqXyyoUChqNRhqNRvrxj3+sf/mXf9FyuQzaaDabqd1uazweq1AoqFarqdFoKEmSAIA4Gi4WiyqVSmHRl8ulJpOJSqWSms2mkiQJIASwaNYkSfTkyZPwWa45mUxUKBRCEJTL5ZTNZoOL8N1336nT6Wg2m2mxWAQ3IZfLqVqtKpPJhL8x1uFwqG63u7e12JU8aBBms9mwcA5CtEOj0dCTJ0+C1ppMJhqNRprP58En41xFScrlcprP55I2D0AicJGutQ+gkBR8TDRaHAj5JkHbVSoVFYtFFYtFVSqVjci8XC5LktrttgaDQdC6uVxO5XJZ+XxetVpNmUxGk8lkA4SLxUL5fP5RaMT4gKn3yb2B8Psi6+PjYx0cHKjb7arVaqlSqej58+fKZDLq9XpaLpd69uyZDg8PVSqVVCqVVCwWNZ1OJUmnp6eSFPy5VqulwWCg8Xisq6urYLpzuZySJFEul1Oj0VCtVgufcwFQcIlsBHhFSeFafB7QFAoFnZ2dBdBWKhU9efJE+XxexWJR0rWmzGazGgwG6vf7Ojk50a9//evgPhDgoL2R1Wqly8tLff755w/ah7wNM3AvIIxN1Tap1+s6PT1VkiTq9/th4SQF09dsNnV8fKzj42MdHR1pOByq3W4rm82qXq8HwEjSeDzWYDAIvtdqtQoahiABEzydTgOYkThKRtN5gOFcI37ebDYL40FjlUolHR4eBpMuSc1mU8PhMJx+22w29dFHHymTyWg0GoWo2x173IZ8Pq8///nPms1md7RC+5V7AeH7dsVHH32ker2uk5MT1et1LZdLjUYjHR4e6uzsTNlsVtVqVfP5XPV6XdI1PQLBTBAAuFj4XC6nw8PDDcDA643HY0kKJnNbdLpcLgNJzXUBGGAvlUparVYbGotxoAExsbwH7VkqlVSr1VSr1fT8+XNVq9UwLucbc7lcuB+frdfr+tWvfqV+v6/z8/PwuYckD7q83zViNpvVxx9/rGfPngVeb7lcajgc6vj4WGdnZ8rn8zo+PtZisQiUyGQyUZIkAYREw9lsVpPJZIP7wz+czWYaDAYhMsbEViqVDeqEn0TPgHQ0GgUTio9XLBa1WCw0GAw0n88DYND85XJZJycnWi6XGo/HWiwWqlar4fP1el2NRkONRkOTyST4iB5Re7YGMDabTf36179Wr9dTv9/XfD4PwH0o8qBB6JIkiQ4ODnR6ehoc9FqtpuPj45CdcMBiCok08fM88mXRYpojm81uLKR0rVEx2dK12QfgkN+uZbl/7KuhNfl3LpcL1A7vI7AYDoch2Go0GiqVSuEeABA3AQHw+Lt8vlQq6eTkRMViMXCUk8nkQZjpBxmYbJNMJqNnz57p5z//uVqtlvr9vnK5nCqViiRpNBopm82GCBN6BK2JNpvP58HUlsvlAFJJwaRJbz+BYDgcajAYBE0znU7VbreD9s3lcgG4aOnxeKzRaKTVaqVWq7WRgUGLQuXE3OBisVCn01GSJGo2m8FdkBQ2XJIkgaoh0MFsTyYTDYfDYM6z2ax++tOfajwe6+LiQv1+X+12W+12+55Xcrs8qMDEJY70ut2uLi8vNRqNwkKgcRC0l6fV0D5oLEDiAUWsYfgcL1wD15aAh4yJazkfB/+PH5eAX8hY3bfza2JC3Q1w0PNZSPYkSYIr4p8j2o5dgYdgmh+FJpzP5/rjH/+or776SicnJ2o2m8G/AzQsDMEBizefz4NfhY/nqTmIYoTPzGYzdbvdQDTHPCSg9UiXTYFJZ+EdjJJCNOv+Lb5ivV4PqcLZbKbxeBwAySapVCqBxkmSJPCe3W43+KgADdCRory8vAymGl91n2b50fiE0rXJ9UWS9E5NgxBIvCsXyzX8s07NABpPrTnQXIP59eIxxWlEf59/HrCjVR3ovFdS8GWhZfg9m8c3pkfMfJYNwoaL73Nfcts6g72BEA1EWq3ZbKrZbG4UE0hr/o2djaZbLpfq9/vq9/uSFDQbGQ7MGKYLDeEmnJ/cB0BRuuVA9sIHxkBEOpvNgp9HWpG0IVodwJZKpTC2yWSy4TsSGLVaLUlSuVxWvV5XoVDQfD7XaDTSYDBQoVBQpVLZMN8fffSRjo6OQkqTsXa7Xf3P//zPg34OzV5AiDbAlysUCuHliX00B4sNvYJ4YSngIa2HKcR8E016MUHs/7l24b0eVEhva2dAyXWLxeJblTkeSaO5+CzX5PsQQUsKHCPfbTqdhk3lc4gpp7iC1GCsnR+q7AWEbr7cFOKHYT4AoJsnFoAXvhfvwXcCuNAWRJvcr1AoSNIGWHK5XNCOriG5DrWFHgQBwOVyqV6vF3hKChBarVYgyj1wwGflO/n3rNfr4Tvgrrh/i98Mf0mkP51OA7twdXWlb775ZqNi/KHK3syxgxAfDLNLkYFzb9s+I639ujjbgcYYj8eBm5vNZsrn8zo4OFAul3urEMBNNuAD9NQLxpU0aFUyMePxOGjY2WwWtBpmk6qag4MDlUolzWazoOEWi8VGgDQej4PJZtN4JO/56sFgoMlkomazqWKxqNlspm+//TZsaC/euC958NGxJ+nxoXCqY9PHC5B6MOHUi7T2DR2wOO28n0VnkqhBJBImdRjTLby8uIEonQjdr4fG8rFAYjuP6XwiuWxJwaz3ej2Nx+OwMXjhJ/rGBfjL5VLValXT6TRka+47q/JgeULEMwj5fF6j0ShkBRDP0yZJovl8rul0Ghbbi0u5pu92AONZCRbZgxj6TxCoEYhitC0Ujd+T8iwvuXKahXER3ZbL5Y0iCCfTCUDIyABCCh24Lpt3Pp8H+obCBjTjYrEI6UA23H20HGzLw3+f7BWETsbi+7i/gzCJnsLjve4bcl3eg1bj87wAIxrLtQ9A5vO8lzo+/D9/Setgi3sj0CSYU65JwBTTPbxfUjDdvJegAz9XUsh94wIwp5SqkW/Hn3WSfBeAvE26DtkrT4gmZDH7/b4ajYaOjo42TC1+DWa6WCyqWq1uVMjguDO5aAbMEQKgqM4pFouhEMKvVy6Xg5YCsIwF7cJ1PYXoWs4LEIjaPW89nU7DhnKzjJycnKhUKoXiiUqlokajoW63q3a7rSRJQp0iwBoMBhoMBiGvPB6PlcvlNB6P1e/3Q+oPLfsQgLh3shozxULg00mbhQHSZqEpmjGuepHWfpZrL37vmhBASGsfFbOKZo2DIk+5MdksJgWp/l4IcHzGmED3+/v3dM3N351fRLM5DeSBlrMA5KIZI9E2FBb85L5k7yAcDAYaDofBZKCBJAUSmMiSEnkiW16Q2JDBRLDlcjkQy2iq2Mf0tBnXcrDwdwdBv98PQF+tVoEc9goXCiLwYVl8fMLYXQDcSZKEukkvQRuNRhtdfZeXl+FexWIxUEOuUTHLP/7xj4OlIY2YzWY1HA7V7/d1dXWlL7744t6jZ2RvIGSRMbF0qwEad9z9MzF/xyJisp2A9tSeR7ixFnTxlCDjc4Kc+zm/6ZG5tOkTunYipx1rOf8+0lpDskm84Z6SLe91JuKfz+cbGRiuBfGOT8hGpTcGt+CuoudH5RNKm1wg/lun09koVHXeDSqnUCiERZjP5yGCpOig2+0qm80Gc1OtVtVsNoOpkxTokn6/v0FzSNoofCV6p5qlUqkEzYWWhbPDrAEECGm+qwcFvjnoY16tViEVCRihsHzMXM+JdNdy+KHMgc83LkqpVAplc7QbcO+7WtebyIMCIRqNvKubrWw2q/F4HHatH+Ph/RiY1sFgEHw20nkEAdzXe5O35YgBWFw+Rg2j1wC6NgJ47ruiTbkGWo5N4RVCrtW9OggfDwAhcaS+LSsUp/CI2KvVqvr9fvAR9yF7ByGCZiiVSnry5MlGESu+IOkwNKFrtJOTE0na8PMIMMipwpVdXl5qsViErAVZlH6/H8CM5gGgnqMGeOVyOWjf1WrdSEXhggPb89VOxfAej5K9MgaWgNww90RTAzQ2AACkoobx8V1Wq+tyssFgEHp3VquVPv74Y/V6PfV6vTupunlUmlBam5Xlcql8Ph8oGv5GpOoFrLFZpSkq7rkolUqqVqvBdFOFDEVBO0Gj0QhVJwQgaCoA49ymE8fcq1qtho3jkbiT1QCN37NhCCicmvJqn9XqumqcKnPAxgYFhJT3x5yjb4RXr17p4uIikPZU4XQ6HX355ZcfDMLb+pV7ByELRsP48fGxTk5OtFgsQsm+t2O6efbjOXznxTWGvtjkbUm3JUkS+kwIjjCNvnBe9gUgGZcT5U4xxQGIR8BsJMw6Wgow8tMDMUDlZWhxAQZjQ6Niyt1k04AlSb1eL/S7QDfdhTyKjIm0jnaz2ayazaYODg70/PlzffLJJ4FQnU6nIejAdALCYrGoWq22YfbiL4/mQmNls1k9f/5ckkKUeXV1FczTwcHBRlULAOesG6Tf76vX6ylJEjUajQ1t5CZbUkgdck9JISjgfBw4PNeWCGD0YlXKtfr9fvDv3N8FqIxnPp+HbkPm+vXr1/r2229Vq9V0cHDwVlvFh8iDByG7NJvNBl/n8PBQR0dHoXsOasHNIf+m9tDNMmCLF1S6ji6dpPZuNvy9drsdUmNUrzjvxgLxORbX04lc710LENNLXqjh/9+mQb0AAdfCqaZtvStcEz8Xre4V5QiWJZ/P33sf885A+L7FAHzFYlGffPKJ6vW6fvSjH4Wj1/BVTk9PN3i7wWAQTsrCN2L3O+FLozy9F69evVKlUgn9zQCdPOyXX36pv/zlLzo6OgrZiDdv3oSFy2SueznYFHEkDeXBWAAEgnZzQSMSaOEOsIGcdO73+6EcDQ6Ua1YqFWUyGbXb7UCaMzaAy3zW63VVq9Xwe8bkbQHNZjNU53yI3CZ1t5fmdxaMamfOEuSYN/dppLXmIGfrZVkx2PGtWLBtm4Hro/FI8vvRITGQHHBxidk24nsb5cP7Pa0XX8tfvpDcZ1u0Hn83rh3/zX3MJElCmwDcY6FQ0MHBgSTp/Pz83iibez8kM0muTzU4PT1VpVLRp59+qqOjI52enga/JDYXHgigneIyr5jfQ+gtBtTusP/pT3/Sq1evNnpVJIXCUjRNLpfT8fGx6vX6W+cKepDgh3DCBeLDeQEDc+AngeG3udaknIxzF3kR4UsKRx9zX/xcgIzplhS6+RgDqVIYB7TveDzWv//7v+uLL7744PW+Cbz2Fpgw2ZwuEB/vK20eFilt9tbG7+H3AA2qgqDAMxyYvjdv3ui7777bIJshynH80RAcRkTmxu8fFyLEYwRgHtm7tmOhvDTN/Tm0FbQQEbWn7bAOnlZ0ky6tNSFjKBQKajQaIeDJ5/M6PT3VfD4Pp5Xdh+ztLBpOV6jX6zo7Owtm2AGAo45mQBN6FiPW0P57zy+7Q99ut0PfyfHxsbrdbtCEaIR+vx8oI0q+WESnZEg3unaMz0AEqARbvtk8ko6ruil6YL4IsHAf0HQUwPo9PePDiWWMkTw0RR5o/OVyqdevX2s4HAYKK9b8t1nnm8reGp0mk0ko0mw2myGwoMwI7SFpI+kPp0a0ui0d5X6RC1qi2+1qNBqFBSLg8cZ2zzJgOuMqbsZITaRvjriQljFyLT+F1X1DafOwdrg/yHM/4AmAeiWOE+GAkOCFhnt8Xs/iEKS9fv1a3W43JA6ch7yp3NZ12wsIvTKE07PY3fhWnPHiDn68WLEvyGJBdHNqq2cGstmsTk5OgtbN5/P67W9/G+4JRXFxcaHFYrGRHpTWWgQT6BkJFizmC90lAAj+mZjiQVPGFUGeG/aAxjfiNmqHs2m8Z9ujZ+beT4bgPncVC7xP7hWEPjk40NVqVUdHR+r1eqEzDf8EEhhgSdrwe7imTywEd6fTCWYFgHEs29OnT5XP53V2dhbu32g0AuXTbrf13//93+p2u/rmm2/CIUSSQj2hmzDn/QAaFTFoFMDlbQxorHiOyAcTRDgIARk9MbGmioHJsSe+gbEk3peNpqUix+/1oev9fbIXTZjP51Wv13VwcBAAQ6mUtEmhOGfmZ9RI6zSYAwET5eX+ZD44LpifVNYgHBVH81W5XFaj0QiaCW7P/UNpvfBoQLI9cd4Zzi72a32D+fXQ3NtMPG6DazfEKS7nUuPInDGQjXH/j8+9q+7yfXIbjlDaU2BSKpX0k5/8JFSufPXVV+Hv7GAOpCwWi+F4DcCAJgE0XrDg/ptTJl7mDtHtgct0OlW329WbN2+0XC43Fmo6ner8/FztdluFQiFUPksKlNFyuQwuQL/f13g8Dj0hfC96atCKnDjh2Renn7zpyykfvoOkoLW8pI1yN/g/7xiEtC6VShoMBup2u28FH2SnCoVCoHC2cZLfJzd9/72bY15eR8cXjakOqINt5sFTefhdkjb8NPenYoI7plC8VUDSRrO5pDAO127+d8bEGJza8ZRfHKj43ABAL+b1fDDiWZt4TpkT/77MAQQ3kS9de+7X+u9i4nxXcu8gZAIpVcd/oqLFnW6vmobeYIGZZCeKAZubAw619LwtFdIABOd8m6nEB6TDjYDGSWUWCuDir1EQ4X5ZvV7f6EtmnE6Q1+v1jQ2DxEGFbwTPC/uzU2azmV6/fq3xeKxvv/1WvV4vfPb09FTPnz8PvS/T6VQvX74MzfZcl+vcBogPsoCBCffTByRtmBJfFLQg2lLaLACgciTWLpgO3hun91hA/g/Xxj19rD4+1xbxC4mzPVRux+PxKhnfmG6CfY64L99vWzASz7NrMEj4bre7cV4OwZ+3S8TlY7vUgMjOQegTUqlUdHJyonK5rMPDw8BfYeokBV8pk8lsRMtJkujs7Gwjn+x+nXffYUp4n1cYs+geMLhPRHGr+2qZTEZnZ2ehDtHr8xDARYROIQT8I1XX/n0kBV4U0LExfHzemul0k7QONnyTMO/0reCrovGozh6NRvr88891cHCgn/zkJ6pUKvq3f/s3LZdL/cd//If+93//Nxy0xL12AcydgjDeoZxHSIWw90D44vpCsAhMamySPLhwECJoF29kiontuIeDCmXny6rVqsrlcjDNXp3ih6x7ZgYplUqBbvJIHm1OpMvYYp/Vgw44Sm8TQBwg/t2gw/gcmrfVaunNmzdarVZ69uyZqtWqPvnkk8AkxJ2G+Nt3XdiwcxC6XwHlUalUVK/XAzjQePhrBABMFmfOYJoB42g0CtkKsgzbiF8/F4b7OFg8jSUpRMqYLAIEyq4Ytzc6SWtf8OzsbMO3xA2J3QbG5yX/bqrjiBcWwL/fNp4x5i3ZJETUXBuflfrK+XyudrutcrmsP/zhD3r69Kn+67/+S59//nmY+5tqwQdD0TgIKduq1+uhbAuT6w063jHnDeNoTU9bxf6a9PbpBc6pxVoDMBMRYnp5WM9yuVSj0QjaynlKNoNrBmgkXArGRn0e1/dxOFfnIHTt7ubYSed4oR2UPi8AHWrKx+Xc7Hw+D9/7N7/5jX73u9+p1+vp//7v/8JYbmOKHwRZjZZoNpuhYFXSBi3D7mZiAaFHi2gfFo+FZhLjhZU2F8HLvubz9UlWNCZ5lM7z8OjIIztCEQPm2LWpLzbcprchON2BVvJAw1kBBxgb08us+JsXpvq8AVanuAAP3KsD3q+ZyWRCXh1u9uzsTP/6r/+qV69e6e9///utgPggeEIW6uTkRL///e/DkRY4zGid1Wp9qhSTBimM4+5A4+GKnLsnrSttPNpGQ/E3auVarZZGo5GOjo5ULpfDMRt8Zjwe69WrVxtBzsHBgarVqobDoXq93gZFEwOK8VGwy98w5x5QxRso5v34OyBiI8X5Xcy1pwwZG8DBt6aq3VtT8YXJmS8W18fi/exnP9PPfvYz/ed//qdevHjxVq77LmQnIGQSMaOk0Dxi9YmOKQyABUntnBf8IakmjxJdnGtEm1FEwFiurq4kbZ43QxGEm78kSUK5O34i72PM+HdoP58HtEz8f58T/+mkN78j9SgpnDbG32Iwe4TsP5lX3B/Xrsy1F03M53P1ej0NBgO12+1wj9tW1Xyf3BiEtwnPAd+TJ09Cjpgv5VrDFyzOEa9WK11dXSmbvX6yOuaXQMMJYydr3USuVtd9KZeXl6pUKjo7O9NyuQynDXz22Wc6Pz8PTVaMJ64mkRTqEA8ODnR4eKh+v683b95IUhgfD1RkHJg7TDRUCoQ338k5UiJY5kdSINLJdTOXi8UidNB55AsofTMQvAFEHiruvif1nVBNw+FQf/zjH/XZZ59tFM/eNDreq0+IY06BKmYWbs/JWYAdawAHuwNrW0WJtFk9QjQqrZuOPEGPn+mEMIsKCOOuNe7jC+Bcn1d9xxGwtC4+4HpeReMRLd/X58VpHfcffVwu7l/6NREn5+NoGxcAWgc/OdauN5E79wlvooIxSQcHByqXy6GFc7lcqtPpBL6MvKqk4CPC0bkTHjvN7G7Xom5WXDNSi4j/tlwudXl5qVwuFzRfkiT69NNP1W631el0QlbB+5Q7nY7m83kITHq9XtBqT58+VTabDaT7cnldFV2tVkMkKl27FVdXVxugQZPXarVwHAfpSTYr4IuP+HAyO36RtpPWZ2p7OtI3OxRVqVTS0dFRcHHoxebIvnq9HsDooH2f7CVtF0dzmEwCCDShP6LBgwjfaX7yqX8h1zDbfKwYjNlsNpgxDhRHC7FZKGJttVqBLwQsztHRXM6iEnigVZNkXXYvbRYZEDAALgeaE/BsRhY6jnhd+8WV5dL6sKW4wAEwu2bm/cy9U2CMF9C51t5FGu9OzTELjxrPZDIhUqRolMXlzBi+GD4HKSy0CjxfXOLEGSzvepXL5cAPElmyABwN/O2336rdbuvvf/+7vvnmmxDNUkTA4jgIGYtH7f49ILIpAOD7ssh07/EZ5+YYK6X/MAfOnfIeQO2tnx7s+Hji4ltScYxvNpup0WhouVwG5uCvf/2rrq6uQpC2LRt1Z7i5qwu5NvLig5gvY1KHw+FGDzE73r8kXCKAA0TOnbmJcbMNOY7mymTWFdp04rVaLbVaLb169UovX77U8fGxms1mACNZhLg9gH8DrNFotOGjEgS42eSzPKXJH7DoR/UCTMDtppa0mQM0zhD5Pd30x2lNP26Y2kwCOYKui4uLsHbuH98GEzeROwMhk+cD8AgtDg5IFXl5FuByc8XC+bEf+C6YfRfG0O12g0+DyfFsBP7e+fm5lstlIGchqzlnBvH7AExoGxbQaSYHjM8NJjd+cT2/DuLuC9fx3LBvYIh8OFnG4xqWHD6g5aE+fC8KZzlumTXzbMtNMXET2RkIPbCQ1k4wZtpr5+IyciYHcEKuOnnLTgccTI6X2DNxUD6elyVPen5+rnw+r8PDQy2Xy5BGHAwGymQyOj4+Ds3nvpiSQlO9l+ejCRmTa7vVahXOu242mxvnVxO8eTTOvMTnHUrrYgKCDjQrWaDpdKpWq6UkScJjyrAE5XI59NugJDC7RMXwu2xkdwfuWu7UJ3RqQVLgzvhyXu0b1xV6uo5XXMDqdXbub6HdpHWKznuBnZtks8zn82B2CaKQ+XweFu7k5OSth/xwT3p9+Rv+LCaf9/Ly6BiymXnxFk6PQP3/bm7dp2Q8gJmzF7E27kt6AS9ak3GiJBhLtVrdcBsevE+IOL1RqVR0eHgYNIb7R2gWAMROJE+MBoTO8UZtJhOn2iNCTv93zew5ZBZpMpmoXq/r6dOnQdNxX2ldAtZsNpXP53V1dRWeA0K60NNjaGgCmbjMDNBRiMvxJACMY+4kvQUwL+p1n5cxLBaLsJEw67lcLvS3uFuAxvXnomA58NUBcKVSCe+9jRmW9lxFA6+FX+I1fD4wN6uuxZwH9AQ71yCy3FYtw/2d2OV3/pN7YHLc3/TAw6kg5ykpkgUcMdnuY4jH4uNAnHxnfAQMAMdTczERHc+vU1q4QV7M4PMvrdtCoaNWq1XwzTHFu6BmkJ1kTFqtlvr9vi4vL3V4eLgxQR4p4pCz89CCnm3xiNvJahbFiwjQqGgPNKKb5dVqFaLtg4ODt3Ys70Gj05/C/ekRccoDLcfY+J78TJIkHHMC4Ny18MjavxN+YHxgPPfyKhrnST1bFAOMucZ3zOfzury81J/+9KcNGovNiGnfpewEhJgfWgohdqXNhXJN4cD0TAhAcrC4ZnQt5ybDzb/fyyeUBYt9Wd/1Tu/ElSu8CFjisW7THj6+bZoc2aaFtwUG2z7r9/I8M9d1VgFw4qLE97/rKuptspOj4ZgYotpf/OIX+vTTT1UsFsMDAYkWnYgm54ymAoxojXq9vvF+P0oYIKOpcKY9D0zVNiQsZxG69oOjg1u7uLjQaDQKJC5ArtVqOj09Ddokjlzdt4TXk7TxZFH4OSgRKp8BqhfRJkkSwIL4hiITRXTLshLpSwrPQqbe8cWLF/rrX/8aijygdbAi0oc3O93kszsr5ZIUzv07PDwMvQsQyCTFY5qCQbOL3TzhYLtJ9soQBy2Lz0S4piS4cDPovhv3pCK61+up2+2GPDfRPod68pk4CndgolE80+MRrWvtmOry7w/AGLvzqsg2fzSTWffJsKkXi4Wurq6CWfe122YVdiU7M8eZTEaNRiMQoZ999pk++ugjHR8fh0lzh9srgH2HOwiJBEnvkTYDnE7bSOsiVrQlC899Ab9Xd9OJhv9EMevh4eF7D5+Mn2/si4dJdLB7NgRt59kMrgN1wlxwnC9RrD94xzMrfM4DGuap2+2GQ59Go1EwyU4lfajsNTpGVquVDg4OdHR0pDdv3uhvf/vbRolWDDZPRUnrjAATSk4ashWT6Y+A5bO83ykZT7m5+UZjAkrO5IOW8FZNovN8Pq9Op6Ovv/5aq9UqHGIJyOlZ4XvyfeKfaDMCFEDgDVRsWO7Dmd7+3JJY6zrr4JEyoO50Oup0Omq32xtHJe86AHmX7LS8n9ML8Hmm06levHgRFp7oy3cfYHSt5kx/LpcL6TJPC7JYXAufC3DhhxJoeL4aQK9WqzAeNBUbg0WGh0ySJGQd0EgAH4AjTjNJaxprNpsF8+ji34l5g/aCPGaMzK+nNrme00bz+Vxff/11yBTxCAznNPclO9WEOPLkK4fDob744gsVi0UdHh4Ghx7tAgGLaQQYfnJ9JpMJJ2d5VgVNBIg4ERU6hGjdq5AxRUTvw+EwkLa8+D8gQssmyfr5JSxinMd2Teu0knR9ljan1fI0Acw8WsyPhkM7YxnYrK1WKxDvZIDYCPh9mcx1V+Of//xnXV1dBcrnfVH8fcpONSE+CIvuqTEm2oMNfDnSd85/OXcXmykWznPH+EYcs0s0TLDhyfrZbBY2gKf2ENcozgOiRQmupLVfx4vvxe8hfzHDrjEBaRwl+/09debcIvPLqWDMq9dE8r3jdOa+ZacgxBlvNBqBXvGTplardWWHLzzOOt1lVNqQm/UiS96PT8NZKwB0MBio0+kEs+4mDGFDcGAm9wZEmEU0trTuOSmVSnr+/LlKpZIuLy+Dq8DY8A8Zn2t03Asn1enNyWQywWfLZtdP7MTXxKWgOINTLf72t7/pyy+/DIWpPhb/6RzrvmXnmjBOfAPEbYf+IDFJi7ip5nqe7PcUFcBC8/jv4uDH890+Zu6Fy8AG4DEOAAI/08+CZoxxygvT7icv8DlOJ8PXxIclcIipILQbVePS+sE7BC2I59cfmuxcE7Kg/X5fz58/1y9/+cuNHhMcY7RMnKPFT5IUfDSCg9gMAyhps6CAc1XQboVCIUSJTv7OZjP1ej3N53N1Oh1lMtelXO7r0cjPkWuIF97in7Eh0OYeGdfrddXrdbVaLZ2fn6vT6ejFixfK5XJ68uRJ8FPRiLSt4udSYnV5eRkqcfD9AKRL7GLsWuKI/X2y8+Z3tAsRHbQKGs4rkP3lJK1rriRJgrZxH8vpHWmzZ9eb38nj4s/xHvfpvNNO2uxpppoHDnK5vC6JL5VKwbx6ub9f30ls34hoMlpcpfUhSoVCIZDlzB1kP5vG6amHYmKlB3QCAz8BDERs/B78HqI+AgbPEwMcEu2UMnmECZ3jESbPbfNHPTgv6HwjUiqVtFgs9PLlS0kKT3RCuyZJEs6mHg6HyuVy4USHi4uLkKGgFOzw8HAjU0LQlMtdP0uFIzrm8+tTEJJk3VtDYOWkM5vPe7kfkql9EGT1NkHLvGuARJKYDt7rJDM+FFzbaDQKz2jjb54v5gg615auGUklYi692HO1WoVAp1KphKgVzTiZTDaOBQFEbCKqsimVJ7iBH/XiUQpQl8tlSGkC2Ps0o/uQewXhcDjUN998E55dAgigGOIWTwQqAr/HU15oPo708EMdEXjCOMeMNuSaFILiNgAyNsFisdCrV6/U7Xb11VdfBX+Pl5Pl0nVX39dff61SqaROpxMe48V3ZtNARtfr9fBwISJ93JWbmLbHaIqlewbhaDTSy5cvVavVgg8lrX0mb9HclkJyasY5MC/G9C4+D3A8SPB8NOTvfD4PhzBBdk8mE3U6HUkK0W+n01G/31er1drK9zkI+/2+Li4ulM/ng994dnYWjsYrFosajUaBRKeymuADcD4kOmUXcu/mmCzKq1evQmkXdI2XP22beMDp/iI9tSy++5BkK/wB2lzHgw9MH6ZQWpPRXIssDaABONs2S1zFzPXIcAwGA7VarZDhIRUHx8h3p4jihwxA6Z5BiCbp9Xpqt9vB/Dl9US6XVavVNigFwAPYIH85RWGxWIRo0SuD0Sg8PBF/DK7OWyCJajl9Cv8UMHuPCZkHr/rxoggAFfODy+VSL1++DByet1NCXkubPSZxRc5jkgdB0WwTj5Tx6aR1yyGRLybTKRbXLETKHl0TDUPDQGc46cxP14oOIv7v9A3Agjh2AHr2gVMkvLeG8cXfk+DF6Sc+49f+oQNQ2hMI3ayS43369Gngy8bjsarVqp4/f658Ph+e60HQQUWMl2M5/7hYLILP1el0dHl5uXFCmLeFSpvFooDKgyHKyEj3eWAkrRuK2u120LSxmfaHejMPsQDUH4IP+KBBGAvaDb7NS9jjo0JcE0qbC4lP55XP0vpJ6F4eFRcNeBkZJt0LTOOj4lxzOmDe57/dBlSPHYC3lRv3mLBwu5ogDpgkk+DmmHzzL37xC52enoanNHFAEcAYj8f6/PPP1W63N/pW8B1//vOfB025XC6Db0dR6vn5uf7yl79s5KeJwjkexIOgfzaw3EacLfg+2bsmRMh5ck4KJtJTXM+ePdPR0ZGk9SHlfFlomk6nEzIOSXJ9TiGPrQA03ofrpU1+YHqcBtumBVN5t9wmd7yTbrsPEffDSKk9efJE5XJZv/nNb/TkyZPgN2I+Kc+icrvf7wfQ0F3mZhgweWQKDRMfZsmYPFBIAfj94ozG98mD0YQIUSb1faVSSScnJ2o0Gmo0GiqXyxu+H3lVql+ovOHvAK/f7+u7775LAfQA5cGBEHGf7cWLFyGy5WxBHnhzcXGxUbovvf14LYIKihIeO//2Q5MHDULq4jqdTkjD+WNjW62Wvv7669AELyk0v/txwdA55XI5VNP80IsCHpM8WBDGMp9fP4nJ6RWeXulpPHhE/s/fnJ/cV2tjKtvlwQUm7xMvl/IaOv8KHLNBBBxLaoJ3L56RelQUzU1kmxaLQeWtACngHoc8KhDeRFJfb/9y281/40OIH4I5TuXxyG3YhxtrwtS0pXIbeVQFDKn8MOU2INzNMwFS+aeX21jOFISp7ExuCsQUhKnsXVKfMJWdSGqOU3lUkmrCVHYiaXScyqOSVBOmshNJNWEqj0pSEKayd0lBmMreJfUJU9mJpDzhA5K0BO77ZWcgTCc/lZvKP2yOY5C5+vUeg/hvd32vbe/x5vXb3jvuWYmvs21z+XtuMrb4b++6Z/yZ911729/86Ja7rAe9ydz+oMyxH3p528986D13fc2b/v0m9/6Q6+9b0srqVPYuD14TpvLDlxSEqexdUhCmsndJQZjK3iUFYSp7lxSEqexdUhCmsndJQZjK3iUFYSp7l/8Hht4tucQPqNoAAAAASUVORK5CYII=\n",
+                        "text/plain": [
+                            "<Figure size 200x200 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 125: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.11it/s, loss=0.0176]\n",
+                        "Epoch 126: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.10it/s, loss=0.02]\n",
+                        "Epoch 127: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.01it/s, loss=0.0246]\n",
+                        "Epoch 128: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.16it/s, loss=0.0189]\n",
+                        "Epoch 129: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.99it/s, loss=0.0174]\n",
+                        "Epoch 130: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.99it/s, loss=0.0165]\n",
+                        "Epoch 131: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.95it/s, loss=0.0229]\n",
+                        "Epoch 132: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.89it/s, loss=0.0174]\n",
+                        "Epoch 133: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.79it/s, loss=0.0172]\n",
+                        "Epoch 134: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.04it/s, loss=0.0193]\n",
+                        "Epoch 135: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.94it/s, loss=0.018]\n",
+                        "Epoch 136: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.87it/s, loss=0.02]\n",
+                        "Epoch 137: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.87it/s, loss=0.022]\n",
+                        "Epoch 138: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.13it/s, loss=0.0204]\n",
+                        "Epoch 139: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.94it/s, loss=0.0192]\n",
+                        "Epoch 140: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.88it/s, loss=0.0184]\n",
+                        "Epoch 141: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.92it/s, loss=0.0175]\n",
+                        "Epoch 142: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.80it/s, loss=0.0198]\n",
+                        "Epoch 143: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.88it/s, loss=0.0166]\n",
+                        "Epoch 144: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.94it/s, loss=0.0237]\n",
+                        "Epoch 145: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.83it/s, loss=0.0195]\n",
+                        "Epoch 146: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.94it/s, loss=0.0171]\n",
+                        "Epoch 147: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.96it/s, loss=0.0187]\n",
+                        "Epoch 148: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.88it/s, loss=0.0161]\n",
+                        "Epoch 149: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.93it/s, loss=0.022]\n",
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:10<00:00, 95.66it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 200x200 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "train completed, total time: 489.2791540622711.\n"
+                    ]
+                }
+            ],
+            "source": [
+                "n_epochs = 150\n",
+                "val_interval = 25\n",
+                "epoch_loss_list = []\n",
+                "val_epoch_loss_list = []\n",
+                "\n",
+                "scaler = GradScaler()\n",
+                "total_start = time.time()\n",
+                "for epoch in range(n_epochs):\n",
+                "    model.train()\n",
+                "    epoch_loss = 0\n",
+                "    progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=70)\n",
+                "    progress_bar.set_description(f\"Epoch {epoch}\")\n",
+                "    for step, batch in progress_bar:\n",
+                "        images = batch[\"image\"].to(device)\n",
+                "        optimizer.zero_grad(set_to_none=True)\n",
+                "\n",
+                "        with autocast(enabled=True):\n",
+                "            # Generate random noise\n",
+                "            noise = torch.randn_like(images).to(device)\n",
+                "\n",
+                "            # Create timesteps\n",
+                "            timesteps = torch.randint(\n",
+                "                0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n",
+                "            ).long()\n",
+                "\n",
+                "            # Get model prediction\n",
+                "            noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n",
+                "\n",
+                "            loss = F.mse_loss(noise_pred.float(), noise.float())\n",
+                "\n",
+                "        scaler.scale(loss).backward()\n",
+                "        scaler.step(optimizer)\n",
+                "        scaler.update()\n",
+                "\n",
+                "        epoch_loss += loss.item()\n",
+                "\n",
+                "        progress_bar.set_postfix({\"loss\": epoch_loss / (step + 1)})\n",
+                "    epoch_loss_list.append(epoch_loss / (step + 1))\n",
+                "\n",
+                "    if (epoch + 1) % val_interval == 0:\n",
+                "        model.eval()\n",
+                "        val_epoch_loss = 0\n",
+                "        for step, batch in enumerate(val_loader):\n",
+                "            images = batch[\"image\"].to(device)\n",
+                "            with torch.no_grad():\n",
+                "                with autocast(enabled=True):\n",
+                "                    noise = torch.randn_like(images).to(device)\n",
+                "                    timesteps = torch.randint(\n",
+                "                        0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n",
+                "                    ).long()\n",
+                "                    noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n",
+                "                    val_loss = F.mse_loss(noise_pred.float(), noise.float())\n",
+                "\n",
+                "            val_epoch_loss += val_loss.item()\n",
+                "            progress_bar.set_postfix({\"val_loss\": val_epoch_loss / (step + 1)})\n",
+                "        val_epoch_loss_list.append(val_epoch_loss / (step + 1))\n",
+                "\n",
+                "        # Sampling image during training\n",
+                "        noise = torch.randn((1, 1, 64, 64))\n",
+                "        noise = noise.to(device)\n",
+                "        scheduler.set_timesteps(num_inference_steps=1000)\n",
+                "        with autocast(enabled=True):\n",
+                "            image = inferer.sample(input_noise=noise, diffusion_model=model, scheduler=scheduler)\n",
+                "\n",
+                "        plt.figure(figsize=(2, 2))\n",
+                "        plt.imshow(image[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+                "        plt.tight_layout()\n",
+                "        plt.axis(\"off\")\n",
+                "        plt.show()\n",
+                "\n",
+                "total_time = time.time() - total_start\n",
+                "print(f\"train completed, total time: {total_time}.\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "fd2b79a4",
+            "metadata": {},
+            "source": [
+                "## Train the ControlNet"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "73524090-2924-4967-8774-45e795f45bb4",
+            "metadata": {},
+            "source": [
+                "### Set up models"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 12,
+            "id": "06181aa6-1c4b-415d-9973-df6f44693935",
+            "metadata": {
+                "lines_to_next_cell": 2
+            },
+            "outputs": [],
+            "source": [
+                "# Create control net\n",
+                "controlnet = ControlNet(\n",
+                "    spatial_dims=2,\n",
+                "    in_channels=1,\n",
+                "    num_channels=(128, 256, 256),\n",
+                "    attention_levels=(False, True, True),\n",
+                "    num_res_blocks=1,\n",
+                "    num_head_channels=256,\n",
+                "    conditioning_embedding_num_channels=(16,),\n",
+                ")\n",
+                "# Copy weights from the DM to the controlnet\n",
+                "controlnet.load_state_dict(model.state_dict(), strict=False)\n",
+                "controlnet = controlnet.to(device)\n",
+                "# Now, we freeze the parameters of the diffusion model.\n",
+                "for p in model.parameters():\n",
+                "    p.requires_grad = False\n",
+                "optimizer = torch.optim.Adam(params=controlnet.parameters(), lr=2.5e-5)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "94d2e5e7-8633-4d1d-a323-7e74c963641c",
+            "metadata": {
+                "tags": []
+            },
+            "source": [
+                "### Run ControlNet training"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 13,
+            "id": "78053aaf-2009-405b-904e-0e5d301018eb",
+            "metadata": {
+                "tags": []
+            },
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.42it/s, loss=0.0229]\n",
+                        "Epoch 1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.42it/s, loss=0.0182]\n",
+                        "Epoch 2: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.32it/s, loss=0.0206]\n",
+                        "Epoch 3: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.37it/s, loss=0.0223]\n",
+                        "Epoch 4: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.34it/s, loss=0.0193]\n",
+                        "Epoch 5: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.29it/s, loss=0.0216]\n",
+                        "Epoch 6: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.22it/s, loss=0.019]\n",
+                        "Epoch 7: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.26it/s, loss=0.0179]\n",
+                        "Epoch 8: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.28it/s, loss=0.0188]\n",
+                        "Epoch 9: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.20it/s, loss=0.0219]\n",
+                        "Epoch 10: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.29it/s, loss=0.0185]\n",
+                        "Epoch 11: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.32it/s, loss=0.0202]\n",
+                        "Epoch 12: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.62it/s, loss=0.021]\n",
+                        "Epoch 13: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.15it/s, loss=0.0239]\n",
+                        "Epoch 14: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.23it/s, loss=0.0182]\n",
+                        "Epoch 15: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.24it/s, loss=0.0192]\n",
+                        "Epoch 16: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.19it/s, loss=0.0192]\n",
+                        "Epoch 17: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.29it/s, loss=0.0223]\n",
+                        "Epoch 18: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.16it/s, loss=0.0224]\n",
+                        "Epoch 19: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.13it/s, loss=0.0215]\n",
+                        "Epoch 20: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.24it/s, loss=0.0186]\n",
+                        "Epoch 21: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.19it/s, loss=0.0191]\n",
+                        "Epoch 22: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.16it/s, loss=0.0159]\n",
+                        "Epoch 23: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.15it/s, loss=0.0179]\n",
+                        "Epoch 24: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.23it/s, loss=0.018]\n",
+                        "sampling...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:31<00:00, 31.75it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 400x200 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 25: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.47it/s, loss=0.0192]\n",
+                        "Epoch 26: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.44it/s, loss=0.0151]\n",
+                        "Epoch 27: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.50it/s, loss=0.0205]\n",
+                        "Epoch 28: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.42it/s, loss=0.0161]\n",
+                        "Epoch 29: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.45it/s, loss=0.0193]\n",
+                        "Epoch 30: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.41it/s, loss=0.0176]\n",
+                        "Epoch 31: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.41it/s, loss=0.0202]\n",
+                        "Epoch 32: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.38it/s, loss=0.0161]\n",
+                        "Epoch 33: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.37it/s, loss=0.0186]\n",
+                        "Epoch 34: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.30it/s, loss=0.0204]\n",
+                        "Epoch 35: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.35it/s, loss=0.0161]\n",
+                        "Epoch 36: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.21it/s, loss=0.0129]\n",
+                        "Epoch 37: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.18it/s, loss=0.0174]\n",
+                        "Epoch 38: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.25it/s, loss=0.0201]\n",
+                        "Epoch 39: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.24it/s, loss=0.02]\n",
+                        "Epoch 40: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.10it/s, loss=0.0183]\n",
+                        "Epoch 41: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.20it/s, loss=0.0199]\n",
+                        "Epoch 42: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.18it/s, loss=0.0228]\n",
+                        "Epoch 43: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.18it/s, loss=0.0151]\n",
+                        "Epoch 44: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.23it/s, loss=0.0135]\n",
+                        "Epoch 45: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.23it/s, loss=0.016]\n",
+                        "Epoch 46: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.28it/s, loss=0.0205]\n",
+                        "Epoch 47: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.05it/s, loss=0.0194]\n",
+                        "Epoch 48: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.16it/s, loss=0.0188]\n",
+                        "Epoch 49: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.28it/s, loss=0.0194]\n",
+                        "sampling...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:32<00:00, 30.98it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 400x200 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 50: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.51it/s, loss=0.0175]\n",
+                        "Epoch 51: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.47it/s, loss=0.019]\n",
+                        "Epoch 52: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.37it/s, loss=0.0191]\n",
+                        "Epoch 53: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.46it/s, loss=0.0197]\n",
+                        "Epoch 54: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.41it/s, loss=0.0194]\n",
+                        "Epoch 55: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.42it/s, loss=0.0202]\n",
+                        "Epoch 56: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.38it/s, loss=0.0144]\n",
+                        "Epoch 57: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.30it/s, loss=0.0186]\n",
+                        "Epoch 58: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.29it/s, loss=0.0186]\n",
+                        "Epoch 59: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.33it/s, loss=0.0154]\n",
+                        "Epoch 60: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.32it/s, loss=0.0152]\n",
+                        "Epoch 61: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.21it/s, loss=0.0178]\n",
+                        "Epoch 62: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.17it/s, loss=0.0198]\n",
+                        "Epoch 63: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.17it/s, loss=0.0176]\n",
+                        "Epoch 64: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.44it/s, loss=0.0169]\n",
+                        "Epoch 65: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.86it/s, loss=0.0167]\n",
+                        "Epoch 66: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.21it/s, loss=0.017]\n",
+                        "Epoch 67: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.03it/s, loss=0.0191]\n",
+                        "Epoch 68: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.11it/s, loss=0.0132]\n",
+                        "Epoch 69: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.32it/s, loss=0.017]\n",
+                        "Epoch 70: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.24it/s, loss=0.0207]\n",
+                        "Epoch 71: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.25it/s, loss=0.0195]\n",
+                        "Epoch 72: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.11it/s, loss=0.0189]\n",
+                        "Epoch 73: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.06it/s, loss=0.0144]\n",
+                        "Epoch 74: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.10it/s, loss=0.0199]\n",
+                        "sampling...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:32<00:00, 30.69it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 400x200 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 75: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.49it/s, loss=0.0199]\n",
+                        "Epoch 76: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.49it/s, loss=0.0174]\n",
+                        "Epoch 77: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.45it/s, loss=0.0218]\n",
+                        "Epoch 78: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.49it/s, loss=0.0138]\n",
+                        "Epoch 79: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.36it/s, loss=0.0186]\n",
+                        "Epoch 80: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.33it/s, loss=0.0192]\n",
+                        "Epoch 81: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.33it/s, loss=0.0174]\n",
+                        "Epoch 82: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.24it/s, loss=0.0228]\n",
+                        "Epoch 83: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.31it/s, loss=0.0183]\n",
+                        "Epoch 84: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.21it/s, loss=0.0201]\n",
+                        "Epoch 85: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.27it/s, loss=0.0141]\n",
+                        "Epoch 86: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.18it/s, loss=0.0181]\n",
+                        "Epoch 87: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.26it/s, loss=0.0211]\n",
+                        "Epoch 88: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.24it/s, loss=0.0178]\n",
+                        "Epoch 89: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.18it/s, loss=0.0167]\n",
+                        "Epoch 90: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.30it/s, loss=0.0213]\n",
+                        "Epoch 91: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.19it/s, loss=0.0114]\n",
+                        "Epoch 92: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.14it/s, loss=0.0137]\n",
+                        "Epoch 93: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.15it/s, loss=0.0128]\n",
+                        "Epoch 94: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.86it/s, loss=0.0183]\n",
+                        "Epoch 95: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.16it/s, loss=0.0125]\n",
+                        "Epoch 96: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.26it/s, loss=0.0176]\n",
+                        "Epoch 97: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.25it/s, loss=0.0193]\n",
+                        "Epoch 98: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.10it/s, loss=0.0198]\n",
+                        "Epoch 99: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.90it/s, loss=0.0183]\n",
+                        "sampling...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:32<00:00, 31.08it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 400x200 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 100: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.50it/s, loss=0.0171]\n",
+                        "Epoch 101: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.40it/s, loss=0.0172]\n",
+                        "Epoch 102: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.44it/s, loss=0.0167]\n",
+                        "Epoch 103: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.46it/s, loss=0.0167]\n",
+                        "Epoch 104: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.46it/s, loss=0.0146]\n",
+                        "Epoch 105: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.49it/s, loss=0.0157]\n",
+                        "Epoch 106: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.28it/s, loss=0.0152]\n",
+                        "Epoch 107: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.26it/s, loss=0.0166]\n",
+                        "Epoch 108: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.26it/s, loss=0.0155]\n",
+                        "Epoch 109: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.26it/s, loss=0.0154]\n",
+                        "Epoch 110: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.26it/s, loss=0.0185]\n",
+                        "Epoch 111: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.21it/s, loss=0.0159]\n",
+                        "Epoch 112: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.25it/s, loss=0.0143]\n",
+                        "Epoch 113: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.30it/s, loss=0.015]\n",
+                        "Epoch 114: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.19it/s, loss=0.0215]\n",
+                        "Epoch 115: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.02it/s, loss=0.0169]\n",
+                        "Epoch 116: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.05it/s, loss=0.0141]\n",
+                        "Epoch 117: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.16it/s, loss=0.0189]\n",
+                        "Epoch 118: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.18it/s, loss=0.0162]\n",
+                        "Epoch 119: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.09it/s, loss=0.0159]\n",
+                        "Epoch 120: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.10it/s, loss=0.015]\n",
+                        "Epoch 121: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.08it/s, loss=0.0177]\n",
+                        "Epoch 122: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.27it/s, loss=0.0164]\n",
+                        "Epoch 123: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.02it/s, loss=0.017]\n",
+                        "Epoch 124: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.10it/s, loss=0.0195]\n",
+                        "sampling...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:32<00:00, 30.41it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 400x200 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 125: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.48it/s, loss=0.0163]\n",
+                        "Epoch 126: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.45it/s, loss=0.02]\n",
+                        "Epoch 127: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.36it/s, loss=0.0159]\n",
+                        "Epoch 128: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.41it/s, loss=0.0201]\n",
+                        "Epoch 129: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.40it/s, loss=0.0148]\n",
+                        "Epoch 130: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.36it/s, loss=0.0153]\n",
+                        "Epoch 131: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.37it/s, loss=0.0175]\n",
+                        "Epoch 132: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.38it/s, loss=0.0178]\n",
+                        "Epoch 133: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.23it/s, loss=0.018]\n",
+                        "Epoch 134: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.29it/s, loss=0.0179]\n",
+                        "Epoch 135: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.22it/s, loss=0.0153]\n",
+                        "Epoch 136: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.24it/s, loss=0.0173]\n",
+                        "Epoch 137: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.11it/s, loss=0.0164]\n",
+                        "Epoch 138: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.16it/s, loss=0.0165]\n",
+                        "Epoch 139: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  1.97it/s, loss=0.0146]\n",
+                        "Epoch 140: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.02it/s, loss=0.0188]\n",
+                        "Epoch 141: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.08it/s, loss=0.0194]\n",
+                        "Epoch 142: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.26it/s, loss=0.0143]\n",
+                        "Epoch 143: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:03<00:00,  2.00it/s, loss=0.0154]\n",
+                        "Epoch 144: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.12it/s, loss=0.012]\n",
+                        "Epoch 145: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.00it/s, loss=0.0198]\n",
+                        "Epoch 146: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.16it/s, loss=0.0224]\n",
+                        "Epoch 147: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.18it/s, loss=0.0152]\n",
+                        "Epoch 148: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.11it/s, loss=0.018]\n",
+                        "Epoch 149: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [00:02<00:00,  2.09it/s, loss=0.018]\n",
+                        "sampling...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:32<00:00, 30.67it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 400x200 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "train completed, total time: 599.0462603569031.\n"
+                    ]
+                }
+            ],
+            "source": [
+                "n_epochs = 150\n",
+                "val_interval = 25\n",
+                "epoch_loss_list = []\n",
+                "val_epoch_loss_list = []\n",
+                "\n",
+                "scaler = GradScaler()\n",
+                "total_start = time.time()\n",
+                "for epoch in range(n_epochs):\n",
+                "    model.train()\n",
+                "    epoch_loss = 0\n",
+                "    progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=70)\n",
+                "    progress_bar.set_description(f\"Epoch {epoch}\")\n",
+                "    for step, batch in progress_bar:\n",
+                "        images = batch[\"image\"].to(device)\n",
+                "        masks = batch[\"mask\"].to(device)\n",
+                "\n",
+                "        optimizer.zero_grad(set_to_none=True)\n",
+                "\n",
+                "        with autocast(enabled=True):\n",
+                "            # Generate random noise\n",
+                "            noise = torch.randn_like(images).to(device)\n",
+                "\n",
+                "            # Create timesteps\n",
+                "            timesteps = torch.randint(\n",
+                "                0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n",
+                "            ).long()\n",
+                "\n",
+                "            images_noised = scheduler.add_noise(images, noise=noise, timesteps=timesteps)\n",
+                "\n",
+                "            # Get controlnet output\n",
+                "            down_block_res_samples, mid_block_res_sample = controlnet(\n",
+                "                x=images_noised, timesteps=timesteps, controlnet_cond=masks\n",
+                "            )\n",
+                "            # Get model prediction\n",
+                "            noise_pred = model(\n",
+                "                x=images_noised,\n",
+                "                timesteps=timesteps,\n",
+                "                down_block_additional_residuals=down_block_res_samples,\n",
+                "                mid_block_additional_residual=mid_block_res_sample,\n",
+                "            )\n",
+                "\n",
+                "            loss = F.mse_loss(noise_pred.float(), noise.float())\n",
+                "\n",
+                "        scaler.scale(loss).backward()\n",
+                "        scaler.step(optimizer)\n",
+                "        scaler.update()\n",
+                "\n",
+                "        epoch_loss += loss.item()\n",
+                "\n",
+                "        progress_bar.set_postfix({\"loss\": epoch_loss / (step + 1)})\n",
+                "    epoch_loss_list.append(epoch_loss / (step + 1))\n",
+                "\n",
+                "    if (epoch + 1) % val_interval == 0:\n",
+                "        model.eval()\n",
+                "        val_epoch_loss = 0\n",
+                "        for step, batch in enumerate(val_loader):\n",
+                "            images = batch[\"image\"].to(device)\n",
+                "            masks = batch[\"mask\"].to(device)\n",
+                "\n",
+                "            with torch.no_grad():\n",
+                "                with autocast(enabled=True):\n",
+                "                    noise = torch.randn_like(images).to(device)\n",
+                "                    timesteps = torch.randint(\n",
+                "                        0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n",
+                "                    ).long()\n",
+                "                    noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n",
+                "                    val_loss = F.mse_loss(noise_pred.float(), noise.float())\n",
+                "\n",
+                "            val_epoch_loss += val_loss.item()\n",
+                "            progress_bar.set_postfix({\"val_loss\": val_epoch_loss / (step + 1)})\n",
+                "            break\n",
+                "        val_epoch_loss_list.append(val_epoch_loss / (step + 1))\n",
+                "\n",
+                "        # Sampling image during training with controlnet conditioning\n",
+                "        progress_bar_sampling = tqdm(scheduler.timesteps, total=len(scheduler.timesteps), ncols=110)\n",
+                "        progress_bar_sampling.set_description(\"sampling...\")\n",
+                "        sample = torch.randn((1, 1, 64, 64)).to(device)\n",
+                "        for t in progress_bar_sampling:\n",
+                "            with torch.no_grad():\n",
+                "                with autocast(enabled=True):\n",
+                "                    down_block_res_samples, mid_block_res_sample = controlnet(\n",
+                "                        x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=masks[0, None, ...]\n",
+                "                    )\n",
+                "                    noise_pred = model(\n",
+                "                        sample,\n",
+                "                        timesteps=torch.Tensor((t,)).to(device),\n",
+                "                        down_block_additional_residuals=down_block_res_samples,\n",
+                "                        mid_block_additional_residual=mid_block_res_sample,\n",
+                "                    )\n",
+                "                    sample, _ = scheduler.step(model_output=noise_pred, timestep=t, sample=sample)\n",
+                "\n",
+                "        plt.subplots(1, 2, figsize=(4, 2))\n",
+                "        plt.subplot(1, 2, 1)\n",
+                "        plt.imshow(masks[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+                "        plt.axis(\"off\")\n",
+                "        plt.title(\"Conditioning mask\")\n",
+                "        plt.subplot(1, 2, 2)\n",
+                "        plt.imshow(sample[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+                "        plt.axis(\"off\")\n",
+                "        plt.title(\"Sample image\")\n",
+                "        plt.tight_layout()\n",
+                "        plt.axis(\"off\")\n",
+                "        plt.show()\n",
+                "\n",
+                "total_time = time.time() - total_start\n",
+                "print(f\"train completed, total time: {total_time}.\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "b005e3bd-54b9-44bc-964d-ca0c9585a139",
+            "metadata": {},
+            "source": [
+                "## Sample with ControlNet conditioning\n",
+                "First we'll provide a few different masks from the validation data as conditioning. The samples should respect the shape of the conditioning mask, but don't need to have the same content as the corresponding validation image."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 14,
+            "id": "262a5129-9445-4ecc-a37a-a97c59386747",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "sampling...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:37<00:00, 27.00it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 600x800 with 24 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "progress_bar_sampling = tqdm(scheduler.timesteps, total=len(scheduler.timesteps), ncols=110, position=0, leave=True)\n",
+                "progress_bar_sampling.set_description(\"sampling...\")\n",
+                "num_samples = 8\n",
+                "sample = torch.randn((num_samples, 1, 64, 64)).to(device)\n",
+                "\n",
+                "val_batch = first(val_loader)\n",
+                "val_images = val_batch[\"image\"].to(device)\n",
+                "val_masks = val_batch[\"mask\"].to(device)\n",
+                "for t in progress_bar_sampling:\n",
+                "    with torch.no_grad():\n",
+                "        with autocast(enabled=True):\n",
+                "            down_block_res_samples, mid_block_res_sample = controlnet(\n",
+                "                x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=val_masks[:num_samples, ...]\n",
+                "            )\n",
+                "            noise_pred = model(\n",
+                "                sample,\n",
+                "                timesteps=torch.Tensor((t,)).to(device),\n",
+                "                down_block_additional_residuals=down_block_res_samples,\n",
+                "                mid_block_additional_residual=mid_block_res_sample,\n",
+                "            )\n",
+                "            sample, _ = scheduler.step(model_output=noise_pred, timestep=t, sample=sample)\n",
+                "\n",
+                "plt.subplots(num_samples, 3, figsize=(6, 8))\n",
+                "for k in range(num_samples):\n",
+                "    plt.subplot(num_samples, 3, k * 3 + 1)\n",
+                "    plt.imshow(val_masks[k, 0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+                "    plt.axis(\"off\")\n",
+                "    if k == 0:\n",
+                "        plt.title(\"Conditioning mask\")\n",
+                "    plt.subplot(num_samples, 3, k * 3 + 2)\n",
+                "    plt.imshow(val_images[k, 0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+                "    plt.axis(\"off\")\n",
+                "    if k == 0:\n",
+                "        plt.title(\"Actual val image\")\n",
+                "    plt.subplot(num_samples, 3, k * 3 + 3)\n",
+                "    plt.imshow(sample[k, 0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+                "    plt.axis(\"off\")\n",
+                "    if k == 0:\n",
+                "        plt.title(\"Sampled image\")\n",
+                "plt.tight_layout()\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "a1ca8274-d85c-4dcc-9c16-08ac2b6ce0fd",
+            "metadata": {},
+            "source": [
+                "What happens if we invent some masks? Let's try a circle, and a square"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 15,
+            "id": "393fca6c-2446-4822-8aad-44403761b40e",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "sampling...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:16<00:00, 61.51it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 400x400 with 4 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "xx, yy = np.mgrid[:64, :64]\n",
+                "circle = ((xx - 32) ** 2 + (yy - 32) ** 2) < 30**2\n",
+                "\n",
+                "square = np.zeros((64, 64))\n",
+                "square[10:50, 10:50] = 1\n",
+                "\n",
+                "mask = np.concatenate((circle[None, None, ...], square[None, None, ...]), axis=0)\n",
+                "mask = torch.from_numpy(mask.astype(np.float32)).to(device)\n",
+                "\n",
+                "\n",
+                "progress_bar_sampling = tqdm(scheduler.timesteps, total=len(scheduler.timesteps), ncols=110, position=0, leave=True)\n",
+                "progress_bar_sampling.set_description(\"sampling...\")\n",
+                "num_samples = 2\n",
+                "sample = torch.randn((num_samples, 1, 64, 64)).to(device)\n",
+                "\n",
+                "for t in progress_bar_sampling:\n",
+                "    with torch.no_grad():\n",
+                "        with autocast(enabled=True):\n",
+                "            down_block_res_samples, mid_block_res_sample = controlnet(\n",
+                "                x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=mask\n",
+                "            )\n",
+                "            noise_pred = model(\n",
+                "                sample,\n",
+                "                timesteps=torch.Tensor((t,)).to(device),\n",
+                "                down_block_additional_residuals=down_block_res_samples,\n",
+                "                mid_block_additional_residual=mid_block_res_sample,\n",
+                "            )\n",
+                "            sample, _ = scheduler.step(model_output=noise_pred, timestep=t, sample=sample)\n",
+                "\n",
+                "plt.subplots(num_samples, 2, figsize=(4, 4))\n",
+                "for k in range(num_samples):\n",
+                "    plt.subplot(num_samples, 2, k * 2 + 1)\n",
+                "    plt.imshow(mask[k, 0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+                "    plt.axis(\"off\")\n",
+                "    if k == 0:\n",
+                "        plt.title(\"Conditioning mask\")\n",
+                "    plt.subplot(num_samples, 2, k * 2 + 2)\n",
+                "    plt.imshow(sample[k, 0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+                "    plt.axis(\"off\")\n",
+                "    if k == 0:\n",
+                "        plt.title(\"Sampled image\")\n",
+                "plt.tight_layout()\n",
+                "plt.show()"
+            ]
+        }
+    ],
+    "metadata": {
+        "jupytext": {
+            "formats": "py:percent,ipynb"
+        },
+        "kernelspec": {
+            "display_name": "Python 3 (ipykernel)",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.8.13"
+        },
+        "vscode": {
+            "interpreter": {
+                "hash": "4f1513a79f82193cb81c96943579af15c6a44d6347609348bde584197ab7b1ab"
+            }
+        }
     },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2023-05-04 18:42:25,456 - A matching Triton is not available, some optimizations will not be enabled.\n",
-      "Error caught was: No module named 'triton'\n",
-      "MONAI version: 1.2.dev2304\n",
-      "Numpy version: 1.23.4\n",
-      "Pytorch version: 1.13.1+cu117\n",
-      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
-      "MONAI rev id: 9a57be5aab9f2c2a134768c0c146399150e247a0\n",
-      "MONAI __file__: /home/mark/Envs/monai-generative/lib/python3.8/site-packages/monai/__init__.py\n",
-      "\n",
-      "Optional dependencies:\n",
-      "Pytorch Ignite version: 0.4.10\n",
-      "ITK version: 5.3.0\n",
-      "Nibabel version: 5.0.0\n",
-      "scikit-image version: 0.19.3\n",
-      "Pillow version: 9.3.0\n",
-      "Tensorboard version: 2.12.0\n",
-      "gdown version: 4.6.0\n",
-      "TorchVision version: 0.14.1+cu117\n",
-      "tqdm version: 4.64.1\n",
-      "lmdb version: 1.4.0\n",
-      "psutil version: 5.9.4\n",
-      "pandas version: 1.5.3\n",
-      "einops version: 0.6.0\n",
-      "transformers version: 4.21.3\n",
-      "mlflow version: 2.1.1\n",
-      "pynrrd version: 1.0.0\n",
-      "\n",
-      "For details about installing the optional dependencies, please visit:\n",
-      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import tempfile\n",
-    "import time\n",
-    "import os\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "import torch.nn.functional as F\n",
-    "from monai import transforms\n",
-    "from monai.apps import DecathlonDataset\n",
-    "from monai.config import print_config\n",
-    "from monai.data import DataLoader\n",
-    "from monai.utils import first, set_determinism\n",
-    "from torch.cuda.amp import GradScaler, autocast\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "\n",
-    "from generative.inferers import DiffusionInferer\n",
-    "from generative.networks.nets import DiffusionModelUNet, ControlNet\n",
-    "from generative.networks.schedulers import DDPMScheduler\n",
-    "\n",
-    "print_config()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7d4ff515",
-   "metadata": {},
-   "source": [
-    "### Setup data directory"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "8b4323e7",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
-    "root_dir = tempfile.mkdtemp() if directory is None else directory"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "99175d50",
-   "metadata": {},
-   "source": [
-    "### Set deterministic training for reproducibility"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "34ea510f",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "set_determinism(42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c3f70dd1-236a-47ff-a244-575729ad92ba",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Setup BRATS dataset\n",
-    "\n",
-    "We now download the BraTS dataset and extract the 2D slices from the 3D volumes.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "87977bac-ff5e-4612-b9f2-b069d6ad9e9a",
-   "metadata": {},
-   "source": [
-    "### Specify transforms\n",
-    "We create a rough brain mask by thresholding the image."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "c68d2d91-9a0b-4ac1-ae49-f4a64edbd82a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<class 'monai.transforms.utility.array.AddChannel'>: Class `AddChannel` has been deprecated since version 0.8. please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead.\n"
-     ]
-    }
-   ],
-   "source": [
-    "channel = 0\n",
-    "assert channel in [0, 1, 2, 3], \"Choose a valid channel\"\n",
-    "\n",
-    "train_transforms = transforms.Compose(\n",
-    "    [\n",
-    "        transforms.LoadImaged(keys=[\"image\"]),\n",
-    "        transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
-    "        transforms.Lambdad(keys=[\"image\"], func=lambda x: x[channel, :, :, :]),\n",
-    "        transforms.AddChanneld(keys=[\"image\"]),\n",
-    "        transforms.EnsureTyped(keys=[\"image\"]),\n",
-    "        transforms.Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n",
-    "        transforms.Spacingd(keys=[\"image\"], pixdim=(3.0, 3.0, 2.0), mode=\"bilinear\"),\n",
-    "        transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=(64, 64, 44)),\n",
-    "        transforms.ScaleIntensityRangePercentilesd(keys=\"image\", lower=0, upper=99.5, b_min=0, b_max=1),\n",
-    "        transforms.RandSpatialCropd(keys=[\"image\"], roi_size=(64, 64, 1), random_size=False),\n",
-    "        transforms.Lambdad(keys=[\"image\"], func=lambda x: x.squeeze(-1)),\n",
-    "        transforms.CopyItemsd(keys=[\"image\"], times=1, names=[\"mask\"]),\n",
-    "        transforms.Lambdad(keys=[\"mask\"], func=lambda x: torch.where(x > 0.1, 1, 0)),\n",
-    "        transforms.FillHolesd(keys=[\"mask\"]),\n",
-    "        transforms.CastToTyped(keys=[\"mask\"], dtype=np.float32),\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d378ac6",
-   "metadata": {},
-   "source": [
-    "### Load training and validation datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "da1927b0",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2023-05-04 18:42:34,233 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n",
-      "2023-05-04 18:42:34,233 - INFO - File exists: /home/mark/data_drive/monai_data_dir/Task01_BrainTumour.tar, skipped downloading.\n",
-      "2023-05-04 18:42:34,233 - INFO - Non-empty folder exists in /home/mark/data_drive/monai_data_dir/Task01_BrainTumour, skipped extracting.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 388/388 [01:36<00:00,  4.02it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Length of training data: 388\n",
-      "Train image shape torch.Size([1, 64, 64])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 96/96 [00:24<00:00,  3.88it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Length of val data: 96\n",
-      "Validation Image shape torch.Size([1, 64, 64])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_ds = DecathlonDataset(\n",
-    "    root_dir=root_dir,\n",
-    "    task=\"Task01_BrainTumour\",\n",
-    "    section=\"training\",\n",
-    "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
-    "    num_workers=4,\n",
-    "    download=True,\n",
-    "    seed=0,\n",
-    "    transform=train_transforms,\n",
-    ")\n",
-    "print(f\"Length of training data: {len(train_ds)}\")\n",
-    "print(f'Train image shape {train_ds[0][\"image\"].shape}')\n",
-    "\n",
-    "val_ds = DecathlonDataset(\n",
-    "    root_dir=root_dir,\n",
-    "    task=\"Task01_BrainTumour\",\n",
-    "    section=\"validation\",\n",
-    "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
-    "    num_workers=4,\n",
-    "    download=False,\n",
-    "    seed=0,\n",
-    "    transform=train_transforms,\n",
-    ")\n",
-    "print(f\"Length of val data: {len(val_ds)}\")\n",
-    "print(f'Validation image shape {val_ds[0][\"image\"].shape}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "8e4d6164-00e5-4663-a678-1391438574e9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_loader = DataLoader(train_ds, batch_size=64, shuffle=True, num_workers=4, drop_last=True, persistent_workers=True)\n",
-    "val_loader = DataLoader(val_ds, batch_size=64, shuffle=False, num_workers=4, drop_last=True, persistent_workers=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5d86ba60-84d2-49f2-95c1-2ab611310d84",
-   "metadata": {},
-   "source": [
-    "### Visualise the images and masks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "17a5e9a4-9756-400b-8dbd-0f1d457ad3dd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Batch shape: torch.Size([64, 1, 64, 64])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 600x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "check_data = first(train_loader)\n",
-    "print(f\"Batch shape: {check_data['image'].shape}\")\n",
-    "image_visualisation = torch.cat(\n",
-    "    (\n",
-    "        torch.cat(\n",
-    "            [\n",
-    "                check_data[\"image\"][0, 0],\n",
-    "                check_data[\"image\"][1, 0],\n",
-    "                check_data[\"image\"][2, 0],\n",
-    "                check_data[\"image\"][3, 0],\n",
-    "            ],\n",
-    "            dim=1,\n",
-    "        ),\n",
-    "        torch.cat(\n",
-    "            [check_data[\"mask\"][0, 0], check_data[\"mask\"][1, 0], check_data[\"mask\"][2, 0], check_data[\"mask\"][3, 0]],\n",
-    "            dim=1,\n",
-    "        ),\n",
-    "    ),\n",
-    "    dim=0,\n",
-    ")\n",
-    "plt.figure(figsize=(6, 3))\n",
-    "plt.imshow(image_visualisation, vmin=0, vmax=1, cmap=\"gray\")\n",
-    "plt.axis(\"off\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "de29d929-bc99-4235-aea6-d6867c3d360c",
-   "metadata": {},
-   "source": [
-    "## Train the Diffusion model\n",
-    "In general, a ControlNet can be trained in combination with a pre-trained, frozen diffusion model. In this case we will quickly train the diffusion model first."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "08428bc6",
-   "metadata": {},
-   "source": [
-    "### Define network, scheduler, optimizer, and inferer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "bee5913e",
-   "metadata": {
-    "lines_to_next_cell": 2,
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "device = torch.device(\"cuda\")\n",
-    "\n",
-    "model = DiffusionModelUNet(\n",
-    "    spatial_dims=2,\n",
-    "    in_channels=1,\n",
-    "    out_channels=1,\n",
-    "    num_channels=(128, 256, 256),\n",
-    "    attention_levels=(False, True, True),\n",
-    "    num_res_blocks=1,\n",
-    "    num_head_channels=256,\n",
-    ")\n",
-    "model.to(device)\n",
-    "\n",
-    "scheduler = DDPMScheduler(num_train_timesteps=1000)\n",
-    "\n",
-    "optimizer = torch.optim.Adam(params=model.parameters(), lr=2.5e-5)\n",
-    "\n",
-    "inferer = DiffusionInferer(scheduler)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f815ff34",
-   "metadata": {},
-   "source": [
-    "### Run training\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "9a4fc901",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0: 100%|██████████████| 6/6 [00:03<00:00,  1.73it/s, loss=0.987]\n",
-      "Epoch 1: 100%|██████████████| 6/6 [00:02<00:00,  2.44it/s, loss=0.946]\n",
-      "Epoch 2: 100%|██████████████| 6/6 [00:02<00:00,  2.41it/s, loss=0.893]\n",
-      "Epoch 3: 100%|██████████████| 6/6 [00:02<00:00,  2.38it/s, loss=0.836]\n",
-      "Epoch 4: 100%|███████████████| 6/6 [00:02<00:00,  2.43it/s, loss=0.78]\n",
-      "Epoch 5: 100%|██████████████| 6/6 [00:02<00:00,  2.33it/s, loss=0.723]\n",
-      "Epoch 6: 100%|██████████████| 6/6 [00:02<00:00,  2.34it/s, loss=0.673]\n",
-      "Epoch 7: 100%|██████████████| 6/6 [00:02<00:00,  2.43it/s, loss=0.617]\n",
-      "Epoch 8: 100%|██████████████| 6/6 [00:02<00:00,  2.34it/s, loss=0.567]\n",
-      "Epoch 9: 100%|███████████████| 6/6 [00:02<00:00,  2.41it/s, loss=0.52]\n",
-      "Epoch 10: 100%|█████████████| 6/6 [00:02<00:00,  2.32it/s, loss=0.478]\n",
-      "Epoch 11: 100%|█████████████| 6/6 [00:02<00:00,  2.41it/s, loss=0.434]\n",
-      "Epoch 12: 100%|█████████████| 6/6 [00:02<00:00,  2.41it/s, loss=0.389]\n",
-      "Epoch 13: 100%|█████████████| 6/6 [00:02<00:00,  2.40it/s, loss=0.357]\n",
-      "Epoch 14: 100%|█████████████| 6/6 [00:02<00:00,  2.38it/s, loss=0.321]\n",
-      "Epoch 15: 100%|█████████████| 6/6 [00:02<00:00,  2.31it/s, loss=0.284]\n",
-      "Epoch 16: 100%|█████████████| 6/6 [00:02<00:00,  2.39it/s, loss=0.252]\n",
-      "Epoch 17: 100%|█████████████| 6/6 [00:02<00:00,  2.40it/s, loss=0.227]\n",
-      "Epoch 18: 100%|█████████████| 6/6 [00:02<00:00,  2.39it/s, loss=0.205]\n",
-      "Epoch 19: 100%|█████████████| 6/6 [00:02<00:00,  2.38it/s, loss=0.197]\n",
-      "Epoch 20: 100%|█████████████| 6/6 [00:02<00:00,  2.31it/s, loss=0.167]\n",
-      "Epoch 21: 100%|█████████████| 6/6 [00:02<00:00,  2.38it/s, loss=0.152]\n",
-      "Epoch 22: 100%|█████████████| 6/6 [00:02<00:00,  2.38it/s, loss=0.137]\n",
-      "Epoch 23: 100%|█████████████| 6/6 [00:02<00:00,  2.31it/s, loss=0.123]\n",
-      "Epoch 24: 100%|█████████████| 6/6 [00:02<00:00,  2.37it/s, loss=0.112]\n",
-      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:09<00:00, 101.87it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 200x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 25: 100%|█████████████| 6/6 [00:02<00:00,  2.31it/s, loss=0.104]\n",
-      "Epoch 26: 100%|████████████| 6/6 [00:02<00:00,  2.35it/s, loss=0.0922]\n",
-      "Epoch 27: 100%|████████████| 6/6 [00:02<00:00,  2.39it/s, loss=0.0875]\n",
-      "Epoch 28: 100%|████████████| 6/6 [00:02<00:00,  2.37it/s, loss=0.0778]\n",
-      "Epoch 29: 100%|████████████| 6/6 [00:02<00:00,  2.30it/s, loss=0.0702]\n",
-      "Epoch 30: 100%|████████████| 6/6 [00:02<00:00,  2.37it/s, loss=0.0606]\n",
-      "Epoch 31: 100%|████████████| 6/6 [00:02<00:00,  2.36it/s, loss=0.0573]\n",
-      "Epoch 32: 100%|████████████| 6/6 [00:02<00:00,  2.33it/s, loss=0.0535]\n",
-      "Epoch 33: 100%|████████████| 6/6 [00:02<00:00,  2.33it/s, loss=0.0452]\n",
-      "Epoch 34: 100%|████████████| 6/6 [00:02<00:00,  2.24it/s, loss=0.0497]\n",
-      "Epoch 35: 100%|████████████| 6/6 [00:02<00:00,  2.29it/s, loss=0.0469]\n",
-      "Epoch 36: 100%|████████████| 6/6 [00:02<00:00,  2.28it/s, loss=0.0377]\n",
-      "Epoch 37: 100%|████████████| 6/6 [00:02<00:00,  2.30it/s, loss=0.0381]\n",
-      "Epoch 38: 100%|████████████| 6/6 [00:02<00:00,  2.27it/s, loss=0.0413]\n",
-      "Epoch 39: 100%|████████████| 6/6 [00:02<00:00,  2.23it/s, loss=0.0318]\n",
-      "Epoch 40: 100%|████████████| 6/6 [00:02<00:00,  2.26it/s, loss=0.0379]\n",
-      "Epoch 41: 100%|████████████| 6/6 [00:02<00:00,  2.14it/s, loss=0.0338]\n",
-      "Epoch 42: 100%|██████████████| 6/6 [00:02<00:00,  2.22it/s, loss=0.03]\n",
-      "Epoch 43: 100%|████████████| 6/6 [00:02<00:00,  2.23it/s, loss=0.0287]\n",
-      "Epoch 44: 100%|████████████| 6/6 [00:02<00:00,  2.14it/s, loss=0.0269]\n",
-      "Epoch 45: 100%|████████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0255]\n",
-      "Epoch 46: 100%|████████████| 6/6 [00:02<00:00,  2.23it/s, loss=0.0304]\n",
-      "Epoch 47: 100%|████████████| 6/6 [00:02<00:00,  2.17it/s, loss=0.0265]\n",
-      "Epoch 48: 100%|████████████| 6/6 [00:02<00:00,  2.13it/s, loss=0.0266]\n",
-      "Epoch 49: 100%|████████████| 6/6 [00:02<00:00,  2.10it/s, loss=0.0256]\n",
-      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:09<00:00, 101.68it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 200x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 50: 100%|████████████| 6/6 [00:02<00:00,  2.26it/s, loss=0.0249]\n",
-      "Epoch 51: 100%|████████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0228]\n",
-      "Epoch 52: 100%|████████████| 6/6 [00:02<00:00,  2.23it/s, loss=0.0297]\n",
-      "Epoch 53: 100%|████████████| 6/6 [00:02<00:00,  2.23it/s, loss=0.0228]\n",
-      "Epoch 54: 100%|████████████| 6/6 [00:02<00:00,  2.17it/s, loss=0.0285]\n",
-      "Epoch 55: 100%|████████████| 6/6 [00:02<00:00,  2.19it/s, loss=0.0258]\n",
-      "Epoch 56: 100%|████████████| 6/6 [00:02<00:00,  2.13it/s, loss=0.0205]\n",
-      "Epoch 57: 100%|████████████| 6/6 [00:02<00:00,  2.14it/s, loss=0.0265]\n",
-      "Epoch 58: 100%|████████████| 6/6 [00:02<00:00,  2.08it/s, loss=0.0237]\n",
-      "Epoch 59: 100%|████████████| 6/6 [00:02<00:00,  2.05it/s, loss=0.0226]\n",
-      "Epoch 60: 100%|████████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0272]\n",
-      "Epoch 61: 100%|████████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0236]\n",
-      "Epoch 62: 100%|████████████| 6/6 [00:02<00:00,  2.10it/s, loss=0.0234]\n",
-      "Epoch 63: 100%|████████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.0211]\n",
-      "Epoch 64: 100%|████████████| 6/6 [00:02<00:00,  2.06it/s, loss=0.0245]\n",
-      "Epoch 65: 100%|████████████| 6/6 [00:02<00:00,  2.08it/s, loss=0.0246]\n",
-      "Epoch 66: 100%|████████████| 6/6 [00:02<00:00,  2.13it/s, loss=0.0195]\n",
-      "Epoch 67: 100%|████████████| 6/6 [00:02<00:00,  2.10it/s, loss=0.0227]\n",
-      "Epoch 68: 100%|████████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.0251]\n",
-      "Epoch 69: 100%|████████████| 6/6 [00:02<00:00,  2.07it/s, loss=0.0209]\n",
-      "Epoch 70: 100%|████████████| 6/6 [00:02<00:00,  2.08it/s, loss=0.0236]\n",
-      "Epoch 71: 100%|████████████| 6/6 [00:02<00:00,  2.03it/s, loss=0.0261]\n",
-      "Epoch 72: 100%|████████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.0255]\n",
-      "Epoch 73: 100%|████████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0232]\n",
-      "Epoch 74: 100%|████████████| 6/6 [00:03<00:00,  1.98it/s, loss=0.0229]\n",
-      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:09<00:00, 103.82it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 200x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 75: 100%|████████████| 6/6 [00:02<00:00,  2.15it/s, loss=0.0198]\n",
-      "Epoch 76: 100%|████████████| 6/6 [00:02<00:00,  2.22it/s, loss=0.0264]\n",
-      "Epoch 77: 100%|████████████| 6/6 [00:02<00:00,  2.18it/s, loss=0.0208]\n",
-      "Epoch 78: 100%|████████████| 6/6 [00:02<00:00,  2.18it/s, loss=0.0192]\n",
-      "Epoch 79: 100%|████████████| 6/6 [00:02<00:00,  2.01it/s, loss=0.0257]\n",
-      "Epoch 80: 100%|████████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.0226]\n",
-      "Epoch 81: 100%|████████████| 6/6 [00:02<00:00,  2.07it/s, loss=0.0226]\n",
-      "Epoch 82: 100%|████████████| 6/6 [00:02<00:00,  2.10it/s, loss=0.0216]\n",
-      "Epoch 83: 100%|████████████| 6/6 [00:02<00:00,  2.03it/s, loss=0.0228]\n",
-      "Epoch 84: 100%|████████████| 6/6 [00:02<00:00,  2.08it/s, loss=0.0247]\n",
-      "Epoch 85: 100%|████████████| 6/6 [00:03<00:00,  1.99it/s, loss=0.0234]\n",
-      "Epoch 86: 100%|█████████████| 6/6 [00:02<00:00,  2.02it/s, loss=0.022]\n",
-      "Epoch 87: 100%|█████████████| 6/6 [00:02<00:00,  2.12it/s, loss=0.023]\n",
-      "Epoch 88: 100%|████████████| 6/6 [00:02<00:00,  2.01it/s, loss=0.0232]\n",
-      "Epoch 89: 100%|█████████████| 6/6 [00:03<00:00,  1.95it/s, loss=0.021]\n",
-      "Epoch 90: 100%|████████████| 6/6 [00:02<00:00,  2.01it/s, loss=0.0217]\n",
-      "Epoch 91: 100%|██████████████| 6/6 [00:02<00:00,  2.02it/s, loss=0.02]\n",
-      "Epoch 92: 100%|████████████| 6/6 [00:02<00:00,  2.09it/s, loss=0.0226]\n",
-      "Epoch 93: 100%|████████████| 6/6 [00:02<00:00,  2.07it/s, loss=0.0232]\n",
-      "Epoch 94: 100%|██████████████| 6/6 [00:03<00:00,  1.85it/s, loss=0.02]\n",
-      "Epoch 95: 100%|████████████| 6/6 [00:02<00:00,  2.01it/s, loss=0.0179]\n",
-      "Epoch 96: 100%|████████████| 6/6 [00:02<00:00,  2.03it/s, loss=0.0225]\n",
-      "Epoch 97: 100%|████████████| 6/6 [00:02<00:00,  2.00it/s, loss=0.0144]\n",
-      "Epoch 98: 100%|████████████| 6/6 [00:02<00:00,  2.05it/s, loss=0.0214]\n",
-      "Epoch 99: 100%|████████████| 6/6 [00:03<00:00,  1.93it/s, loss=0.0137]\n",
-      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:09<00:00, 103.43it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 200x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 100: 100%|███████████| 6/6 [00:02<00:00,  2.13it/s, loss=0.0221]\n",
-      "Epoch 101: 100%|███████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.0137]\n",
-      "Epoch 102: 100%|███████████| 6/6 [00:02<00:00,  2.03it/s, loss=0.0217]\n",
-      "Epoch 103: 100%|███████████| 6/6 [00:02<00:00,  2.05it/s, loss=0.0199]\n",
-      "Epoch 104: 100%|███████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.0196]\n",
-      "Epoch 105: 100%|███████████| 6/6 [00:03<00:00,  1.97it/s, loss=0.0167]\n",
-      "Epoch 106: 100%|███████████| 6/6 [00:02<00:00,  2.01it/s, loss=0.0199]\n",
-      "Epoch 107: 100%|███████████| 6/6 [00:03<00:00,  1.97it/s, loss=0.0201]\n",
-      "Epoch 108: 100%|███████████| 6/6 [00:03<00:00,  1.99it/s, loss=0.0183]\n",
-      "Epoch 109: 100%|███████████| 6/6 [00:02<00:00,  2.03it/s, loss=0.0213]\n",
-      "Epoch 110: 100%|███████████| 6/6 [00:03<00:00,  1.99it/s, loss=0.0194]\n",
-      "Epoch 111: 100%|███████████| 6/6 [00:03<00:00,  1.90it/s, loss=0.0198]\n",
-      "Epoch 112: 100%|███████████| 6/6 [00:02<00:00,  2.01it/s, loss=0.0227]\n",
-      "Epoch 113: 100%|████████████| 6/6 [00:02<00:00,  2.05it/s, loss=0.019]\n",
-      "Epoch 114: 100%|███████████| 6/6 [00:03<00:00,  1.89it/s, loss=0.0189]\n",
-      "Epoch 115: 100%|███████████| 6/6 [00:03<00:00,  1.93it/s, loss=0.0176]\n",
-      "Epoch 116: 100%|███████████| 6/6 [00:03<00:00,  1.90it/s, loss=0.0247]\n",
-      "Epoch 117: 100%|███████████| 6/6 [00:02<00:00,  2.05it/s, loss=0.0226]\n",
-      "Epoch 118: 100%|███████████| 6/6 [00:02<00:00,  2.02it/s, loss=0.0199]\n",
-      "Epoch 119: 100%|███████████| 6/6 [00:03<00:00,  1.84it/s, loss=0.0207]\n",
-      "Epoch 120: 100%|████████████| 6/6 [00:03<00:00,  1.93it/s, loss=0.017]\n",
-      "Epoch 121: 100%|███████████| 6/6 [00:02<00:00,  2.00it/s, loss=0.0213]\n",
-      "Epoch 122: 100%|███████████| 6/6 [00:03<00:00,  1.91it/s, loss=0.0204]\n",
-      "Epoch 123: 100%|███████████| 6/6 [00:03<00:00,  1.98it/s, loss=0.0242]\n",
-      "Epoch 124: 100%|███████████| 6/6 [00:03<00:00,  1.98it/s, loss=0.0196]\n",
-      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:09<00:00, 100.61it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 200x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 125: 100%|███████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.0176]\n",
-      "Epoch 126: 100%|█████████████| 6/6 [00:02<00:00,  2.10it/s, loss=0.02]\n",
-      "Epoch 127: 100%|███████████| 6/6 [00:02<00:00,  2.01it/s, loss=0.0246]\n",
-      "Epoch 128: 100%|███████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0189]\n",
-      "Epoch 129: 100%|███████████| 6/6 [00:03<00:00,  1.99it/s, loss=0.0174]\n",
-      "Epoch 130: 100%|███████████| 6/6 [00:03<00:00,  1.99it/s, loss=0.0165]\n",
-      "Epoch 131: 100%|███████████| 6/6 [00:03<00:00,  1.95it/s, loss=0.0229]\n",
-      "Epoch 132: 100%|███████████| 6/6 [00:03<00:00,  1.89it/s, loss=0.0174]\n",
-      "Epoch 133: 100%|███████████| 6/6 [00:03<00:00,  1.79it/s, loss=0.0172]\n",
-      "Epoch 134: 100%|███████████| 6/6 [00:02<00:00,  2.04it/s, loss=0.0193]\n",
-      "Epoch 135: 100%|████████████| 6/6 [00:03<00:00,  1.94it/s, loss=0.018]\n",
-      "Epoch 136: 100%|█████████████| 6/6 [00:03<00:00,  1.87it/s, loss=0.02]\n",
-      "Epoch 137: 100%|████████████| 6/6 [00:03<00:00,  1.87it/s, loss=0.022]\n",
-      "Epoch 138: 100%|███████████| 6/6 [00:02<00:00,  2.13it/s, loss=0.0204]\n",
-      "Epoch 139: 100%|███████████| 6/6 [00:03<00:00,  1.94it/s, loss=0.0192]\n",
-      "Epoch 140: 100%|███████████| 6/6 [00:03<00:00,  1.88it/s, loss=0.0184]\n",
-      "Epoch 141: 100%|███████████| 6/6 [00:03<00:00,  1.92it/s, loss=0.0175]\n",
-      "Epoch 142: 100%|███████████| 6/6 [00:03<00:00,  1.80it/s, loss=0.0198]\n",
-      "Epoch 143: 100%|███████████| 6/6 [00:03<00:00,  1.88it/s, loss=0.0166]\n",
-      "Epoch 144: 100%|███████████| 6/6 [00:03<00:00,  1.94it/s, loss=0.0237]\n",
-      "Epoch 145: 100%|███████████| 6/6 [00:03<00:00,  1.83it/s, loss=0.0195]\n",
-      "Epoch 146: 100%|███████████| 6/6 [00:03<00:00,  1.94it/s, loss=0.0171]\n",
-      "Epoch 147: 100%|███████████| 6/6 [00:03<00:00,  1.96it/s, loss=0.0187]\n",
-      "Epoch 148: 100%|███████████| 6/6 [00:03<00:00,  1.88it/s, loss=0.0161]\n",
-      "Epoch 149: 100%|████████████| 6/6 [00:03<00:00,  1.93it/s, loss=0.022]\n",
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:10<00:00, 95.66it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 200x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train completed, total time: 489.2791540622711.\n"
-     ]
-    }
-   ],
-   "source": [
-    "n_epochs = 150\n",
-    "val_interval = 25\n",
-    "epoch_loss_list = []\n",
-    "val_epoch_loss_list = []\n",
-    "\n",
-    "scaler = GradScaler()\n",
-    "total_start = time.time()\n",
-    "for epoch in range(n_epochs):\n",
-    "    model.train()\n",
-    "    epoch_loss = 0\n",
-    "    progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=70)\n",
-    "    progress_bar.set_description(f\"Epoch {epoch}\")\n",
-    "    for step, batch in progress_bar:\n",
-    "        images = batch[\"image\"].to(device)\n",
-    "        optimizer.zero_grad(set_to_none=True)\n",
-    "\n",
-    "        with autocast(enabled=True):\n",
-    "            # Generate random noise\n",
-    "            noise = torch.randn_like(images).to(device)\n",
-    "\n",
-    "            # Create timesteps\n",
-    "            timesteps = torch.randint(\n",
-    "                0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n",
-    "            ).long()\n",
-    "\n",
-    "            # Get model prediction\n",
-    "            noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n",
-    "\n",
-    "            loss = F.mse_loss(noise_pred.float(), noise.float())\n",
-    "\n",
-    "        scaler.scale(loss).backward()\n",
-    "        scaler.step(optimizer)\n",
-    "        scaler.update()\n",
-    "\n",
-    "        epoch_loss += loss.item()\n",
-    "\n",
-    "        progress_bar.set_postfix({\"loss\": epoch_loss / (step + 1)})\n",
-    "    epoch_loss_list.append(epoch_loss / (step + 1))\n",
-    "\n",
-    "    if (epoch + 1) % val_interval == 0:\n",
-    "        model.eval()\n",
-    "        val_epoch_loss = 0\n",
-    "        for step, batch in enumerate(val_loader):\n",
-    "            images = batch[\"image\"].to(device)\n",
-    "            with torch.no_grad():\n",
-    "                with autocast(enabled=True):\n",
-    "                    noise = torch.randn_like(images).to(device)\n",
-    "                    timesteps = torch.randint(\n",
-    "                        0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n",
-    "                    ).long()\n",
-    "                    noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n",
-    "                    val_loss = F.mse_loss(noise_pred.float(), noise.float())\n",
-    "\n",
-    "            val_epoch_loss += val_loss.item()\n",
-    "            progress_bar.set_postfix({\"val_loss\": val_epoch_loss / (step + 1)})\n",
-    "        val_epoch_loss_list.append(val_epoch_loss / (step + 1))\n",
-    "\n",
-    "        # Sampling image during training\n",
-    "        noise = torch.randn((1, 1, 64, 64))\n",
-    "        noise = noise.to(device)\n",
-    "        scheduler.set_timesteps(num_inference_steps=1000)\n",
-    "        with autocast(enabled=True):\n",
-    "            image = inferer.sample(input_noise=noise, diffusion_model=model, scheduler=scheduler)\n",
-    "\n",
-    "        plt.figure(figsize=(2, 2))\n",
-    "        plt.imshow(image[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
-    "        plt.tight_layout()\n",
-    "        plt.axis(\"off\")\n",
-    "        plt.show()\n",
-    "\n",
-    "total_time = time.time() - total_start\n",
-    "print(f\"train completed, total time: {total_time}.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fd2b79a4",
-   "metadata": {},
-   "source": [
-    "## Train the ControlNet"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "73524090-2924-4967-8774-45e795f45bb4",
-   "metadata": {},
-   "source": [
-    "### Set up models"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "06181aa6-1c4b-415d-9973-df6f44693935",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "# Create control net\n",
-    "controlnet = ControlNet(\n",
-    "    spatial_dims=2,\n",
-    "    in_channels=1,\n",
-    "    num_channels=(128, 256, 256),\n",
-    "    attention_levels=(False, True, True),\n",
-    "    num_res_blocks=1,\n",
-    "    num_head_channels=256,\n",
-    "    conditioning_embedding_num_channels=(16,),\n",
-    ")\n",
-    "# Copy weights from the DM to the controlnet\n",
-    "controlnet.load_state_dict(model.state_dict(), strict=False)\n",
-    "controlnet = controlnet.to(device)\n",
-    "# Now, we freeze the parameters of the diffusion model.\n",
-    "for p in model.parameters():\n",
-    "    p.requires_grad = False\n",
-    "optimizer = torch.optim.Adam(params=controlnet.parameters(), lr=2.5e-5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "94d2e5e7-8633-4d1d-a323-7e74c963641c",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### Run ControlNet training"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "78053aaf-2009-405b-904e-0e5d301018eb",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0: 100%|█████████████| 6/6 [00:02<00:00,  2.42it/s, loss=0.0229]\n",
-      "Epoch 1: 100%|█████████████| 6/6 [00:02<00:00,  2.42it/s, loss=0.0182]\n",
-      "Epoch 2: 100%|█████████████| 6/6 [00:02<00:00,  2.32it/s, loss=0.0206]\n",
-      "Epoch 3: 100%|█████████████| 6/6 [00:02<00:00,  2.37it/s, loss=0.0223]\n",
-      "Epoch 4: 100%|█████████████| 6/6 [00:02<00:00,  2.34it/s, loss=0.0193]\n",
-      "Epoch 5: 100%|█████████████| 6/6 [00:02<00:00,  2.29it/s, loss=0.0216]\n",
-      "Epoch 6: 100%|██████████████| 6/6 [00:02<00:00,  2.22it/s, loss=0.019]\n",
-      "Epoch 7: 100%|█████████████| 6/6 [00:02<00:00,  2.26it/s, loss=0.0179]\n",
-      "Epoch 8: 100%|█████████████| 6/6 [00:02<00:00,  2.28it/s, loss=0.0188]\n",
-      "Epoch 9: 100%|█████████████| 6/6 [00:02<00:00,  2.20it/s, loss=0.0219]\n",
-      "Epoch 10: 100%|████████████| 6/6 [00:02<00:00,  2.29it/s, loss=0.0185]\n",
-      "Epoch 11: 100%|████████████| 6/6 [00:02<00:00,  2.32it/s, loss=0.0202]\n",
-      "Epoch 12: 100%|█████████████| 6/6 [00:03<00:00,  1.62it/s, loss=0.021]\n",
-      "Epoch 13: 100%|████████████| 6/6 [00:02<00:00,  2.15it/s, loss=0.0239]\n",
-      "Epoch 14: 100%|████████████| 6/6 [00:02<00:00,  2.23it/s, loss=0.0182]\n",
-      "Epoch 15: 100%|████████████| 6/6 [00:02<00:00,  2.24it/s, loss=0.0192]\n",
-      "Epoch 16: 100%|████████████| 6/6 [00:02<00:00,  2.19it/s, loss=0.0192]\n",
-      "Epoch 17: 100%|████████████| 6/6 [00:02<00:00,  2.29it/s, loss=0.0223]\n",
-      "Epoch 18: 100%|████████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0224]\n",
-      "Epoch 19: 100%|████████████| 6/6 [00:02<00:00,  2.13it/s, loss=0.0215]\n",
-      "Epoch 20: 100%|████████████| 6/6 [00:02<00:00,  2.24it/s, loss=0.0186]\n",
-      "Epoch 21: 100%|████████████| 6/6 [00:02<00:00,  2.19it/s, loss=0.0191]\n",
-      "Epoch 22: 100%|████████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0159]\n",
-      "Epoch 23: 100%|████████████| 6/6 [00:02<00:00,  2.15it/s, loss=0.0179]\n",
-      "Epoch 24: 100%|█████████████| 6/6 [00:02<00:00,  2.23it/s, loss=0.018]\n",
-      "sampling...: 100%|████████████████████████████████████████████████████████| 1000/1000 [00:31<00:00, 31.75it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 400x200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 25: 100%|████████████| 6/6 [00:02<00:00,  2.47it/s, loss=0.0192]\n",
-      "Epoch 26: 100%|████████████| 6/6 [00:02<00:00,  2.44it/s, loss=0.0151]\n",
-      "Epoch 27: 100%|████████████| 6/6 [00:02<00:00,  2.50it/s, loss=0.0205]\n",
-      "Epoch 28: 100%|████████████| 6/6 [00:02<00:00,  2.42it/s, loss=0.0161]\n",
-      "Epoch 29: 100%|████████████| 6/6 [00:02<00:00,  2.45it/s, loss=0.0193]\n",
-      "Epoch 30: 100%|████████████| 6/6 [00:02<00:00,  2.41it/s, loss=0.0176]\n",
-      "Epoch 31: 100%|████████████| 6/6 [00:02<00:00,  2.41it/s, loss=0.0202]\n",
-      "Epoch 32: 100%|████████████| 6/6 [00:02<00:00,  2.38it/s, loss=0.0161]\n",
-      "Epoch 33: 100%|████████████| 6/6 [00:02<00:00,  2.37it/s, loss=0.0186]\n",
-      "Epoch 34: 100%|████████████| 6/6 [00:02<00:00,  2.30it/s, loss=0.0204]\n",
-      "Epoch 35: 100%|████████████| 6/6 [00:02<00:00,  2.35it/s, loss=0.0161]\n",
-      "Epoch 36: 100%|████████████| 6/6 [00:02<00:00,  2.21it/s, loss=0.0129]\n",
-      "Epoch 37: 100%|████████████| 6/6 [00:02<00:00,  2.18it/s, loss=0.0174]\n",
-      "Epoch 38: 100%|████████████| 6/6 [00:02<00:00,  2.25it/s, loss=0.0201]\n",
-      "Epoch 39: 100%|██████████████| 6/6 [00:02<00:00,  2.24it/s, loss=0.02]\n",
-      "Epoch 40: 100%|████████████| 6/6 [00:02<00:00,  2.10it/s, loss=0.0183]\n",
-      "Epoch 41: 100%|████████████| 6/6 [00:02<00:00,  2.20it/s, loss=0.0199]\n",
-      "Epoch 42: 100%|████████████| 6/6 [00:02<00:00,  2.18it/s, loss=0.0228]\n",
-      "Epoch 43: 100%|████████████| 6/6 [00:02<00:00,  2.18it/s, loss=0.0151]\n",
-      "Epoch 44: 100%|████████████| 6/6 [00:02<00:00,  2.23it/s, loss=0.0135]\n",
-      "Epoch 45: 100%|█████████████| 6/6 [00:02<00:00,  2.23it/s, loss=0.016]\n",
-      "Epoch 46: 100%|████████████| 6/6 [00:02<00:00,  2.28it/s, loss=0.0205]\n",
-      "Epoch 47: 100%|████████████| 6/6 [00:02<00:00,  2.05it/s, loss=0.0194]\n",
-      "Epoch 48: 100%|████████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0188]\n",
-      "Epoch 49: 100%|████████████| 6/6 [00:02<00:00,  2.28it/s, loss=0.0194]\n",
-      "sampling...: 100%|████████████████████████████████████████████████████████| 1000/1000 [00:32<00:00, 30.98it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 400x200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 50: 100%|████████████| 6/6 [00:02<00:00,  2.51it/s, loss=0.0175]\n",
-      "Epoch 51: 100%|█████████████| 6/6 [00:02<00:00,  2.47it/s, loss=0.019]\n",
-      "Epoch 52: 100%|████████████| 6/6 [00:02<00:00,  2.37it/s, loss=0.0191]\n",
-      "Epoch 53: 100%|████████████| 6/6 [00:02<00:00,  2.46it/s, loss=0.0197]\n",
-      "Epoch 54: 100%|████████████| 6/6 [00:02<00:00,  2.41it/s, loss=0.0194]\n",
-      "Epoch 55: 100%|████████████| 6/6 [00:02<00:00,  2.42it/s, loss=0.0202]\n",
-      "Epoch 56: 100%|████████████| 6/6 [00:02<00:00,  2.38it/s, loss=0.0144]\n",
-      "Epoch 57: 100%|████████████| 6/6 [00:02<00:00,  2.30it/s, loss=0.0186]\n",
-      "Epoch 58: 100%|████████████| 6/6 [00:02<00:00,  2.29it/s, loss=0.0186]\n",
-      "Epoch 59: 100%|████████████| 6/6 [00:02<00:00,  2.33it/s, loss=0.0154]\n",
-      "Epoch 60: 100%|████████████| 6/6 [00:02<00:00,  2.32it/s, loss=0.0152]\n",
-      "Epoch 61: 100%|████████████| 6/6 [00:02<00:00,  2.21it/s, loss=0.0178]\n",
-      "Epoch 62: 100%|████████████| 6/6 [00:02<00:00,  2.17it/s, loss=0.0198]\n",
-      "Epoch 63: 100%|████████████| 6/6 [00:02<00:00,  2.17it/s, loss=0.0176]\n",
-      "Epoch 64: 100%|████████████| 6/6 [00:02<00:00,  2.44it/s, loss=0.0169]\n",
-      "Epoch 65: 100%|████████████| 6/6 [00:03<00:00,  1.86it/s, loss=0.0167]\n",
-      "Epoch 66: 100%|█████████████| 6/6 [00:02<00:00,  2.21it/s, loss=0.017]\n",
-      "Epoch 67: 100%|████████████| 6/6 [00:02<00:00,  2.03it/s, loss=0.0191]\n",
-      "Epoch 68: 100%|████████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.0132]\n",
-      "Epoch 69: 100%|█████████████| 6/6 [00:02<00:00,  2.32it/s, loss=0.017]\n",
-      "Epoch 70: 100%|████████████| 6/6 [00:02<00:00,  2.24it/s, loss=0.0207]\n",
-      "Epoch 71: 100%|████████████| 6/6 [00:02<00:00,  2.25it/s, loss=0.0195]\n",
-      "Epoch 72: 100%|████████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.0189]\n",
-      "Epoch 73: 100%|████████████| 6/6 [00:02<00:00,  2.06it/s, loss=0.0144]\n",
-      "Epoch 74: 100%|████████████| 6/6 [00:02<00:00,  2.10it/s, loss=0.0199]\n",
-      "sampling...: 100%|████████████████████████████████████████████████████████| 1000/1000 [00:32<00:00, 30.69it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 400x200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 75: 100%|████████████| 6/6 [00:02<00:00,  2.49it/s, loss=0.0199]\n",
-      "Epoch 76: 100%|████████████| 6/6 [00:02<00:00,  2.49it/s, loss=0.0174]\n",
-      "Epoch 77: 100%|████████████| 6/6 [00:02<00:00,  2.45it/s, loss=0.0218]\n",
-      "Epoch 78: 100%|████████████| 6/6 [00:02<00:00,  2.49it/s, loss=0.0138]\n",
-      "Epoch 79: 100%|████████████| 6/6 [00:02<00:00,  2.36it/s, loss=0.0186]\n",
-      "Epoch 80: 100%|████████████| 6/6 [00:02<00:00,  2.33it/s, loss=0.0192]\n",
-      "Epoch 81: 100%|████████████| 6/6 [00:02<00:00,  2.33it/s, loss=0.0174]\n",
-      "Epoch 82: 100%|████████████| 6/6 [00:02<00:00,  2.24it/s, loss=0.0228]\n",
-      "Epoch 83: 100%|████████████| 6/6 [00:02<00:00,  2.31it/s, loss=0.0183]\n",
-      "Epoch 84: 100%|████████████| 6/6 [00:02<00:00,  2.21it/s, loss=0.0201]\n",
-      "Epoch 85: 100%|████████████| 6/6 [00:02<00:00,  2.27it/s, loss=0.0141]\n",
-      "Epoch 86: 100%|████████████| 6/6 [00:02<00:00,  2.18it/s, loss=0.0181]\n",
-      "Epoch 87: 100%|████████████| 6/6 [00:02<00:00,  2.26it/s, loss=0.0211]\n",
-      "Epoch 88: 100%|████████████| 6/6 [00:02<00:00,  2.24it/s, loss=0.0178]\n",
-      "Epoch 89: 100%|████████████| 6/6 [00:02<00:00,  2.18it/s, loss=0.0167]\n",
-      "Epoch 90: 100%|████████████| 6/6 [00:02<00:00,  2.30it/s, loss=0.0213]\n",
-      "Epoch 91: 100%|████████████| 6/6 [00:02<00:00,  2.19it/s, loss=0.0114]\n",
-      "Epoch 92: 100%|████████████| 6/6 [00:02<00:00,  2.14it/s, loss=0.0137]\n",
-      "Epoch 93: 100%|████████████| 6/6 [00:02<00:00,  2.15it/s, loss=0.0128]\n",
-      "Epoch 94: 100%|████████████| 6/6 [00:03<00:00,  1.86it/s, loss=0.0183]\n",
-      "Epoch 95: 100%|████████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0125]\n",
-      "Epoch 96: 100%|████████████| 6/6 [00:02<00:00,  2.26it/s, loss=0.0176]\n",
-      "Epoch 97: 100%|████████████| 6/6 [00:02<00:00,  2.25it/s, loss=0.0193]\n",
-      "Epoch 98: 100%|████████████| 6/6 [00:02<00:00,  2.10it/s, loss=0.0198]\n",
-      "Epoch 99: 100%|████████████| 6/6 [00:03<00:00,  1.90it/s, loss=0.0183]\n",
-      "sampling...: 100%|████████████████████████████████████████████████████████| 1000/1000 [00:32<00:00, 31.08it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 400x200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 100: 100%|███████████| 6/6 [00:02<00:00,  2.50it/s, loss=0.0171]\n",
-      "Epoch 101: 100%|███████████| 6/6 [00:02<00:00,  2.40it/s, loss=0.0172]\n",
-      "Epoch 102: 100%|███████████| 6/6 [00:02<00:00,  2.44it/s, loss=0.0167]\n",
-      "Epoch 103: 100%|███████████| 6/6 [00:02<00:00,  2.46it/s, loss=0.0167]\n",
-      "Epoch 104: 100%|███████████| 6/6 [00:02<00:00,  2.46it/s, loss=0.0146]\n",
-      "Epoch 105: 100%|███████████| 6/6 [00:02<00:00,  2.49it/s, loss=0.0157]\n",
-      "Epoch 106: 100%|███████████| 6/6 [00:02<00:00,  2.28it/s, loss=0.0152]\n",
-      "Epoch 107: 100%|███████████| 6/6 [00:02<00:00,  2.26it/s, loss=0.0166]\n",
-      "Epoch 108: 100%|███████████| 6/6 [00:02<00:00,  2.26it/s, loss=0.0155]\n",
-      "Epoch 109: 100%|███████████| 6/6 [00:02<00:00,  2.26it/s, loss=0.0154]\n",
-      "Epoch 110: 100%|███████████| 6/6 [00:02<00:00,  2.26it/s, loss=0.0185]\n",
-      "Epoch 111: 100%|███████████| 6/6 [00:02<00:00,  2.21it/s, loss=0.0159]\n",
-      "Epoch 112: 100%|███████████| 6/6 [00:02<00:00,  2.25it/s, loss=0.0143]\n",
-      "Epoch 113: 100%|████████████| 6/6 [00:02<00:00,  2.30it/s, loss=0.015]\n",
-      "Epoch 114: 100%|███████████| 6/6 [00:02<00:00,  2.19it/s, loss=0.0215]\n",
-      "Epoch 115: 100%|███████████| 6/6 [00:02<00:00,  2.02it/s, loss=0.0169]\n",
-      "Epoch 116: 100%|███████████| 6/6 [00:02<00:00,  2.05it/s, loss=0.0141]\n",
-      "Epoch 117: 100%|███████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0189]\n",
-      "Epoch 118: 100%|███████████| 6/6 [00:02<00:00,  2.18it/s, loss=0.0162]\n",
-      "Epoch 119: 100%|███████████| 6/6 [00:02<00:00,  2.09it/s, loss=0.0159]\n",
-      "Epoch 120: 100%|████████████| 6/6 [00:02<00:00,  2.10it/s, loss=0.015]\n",
-      "Epoch 121: 100%|███████████| 6/6 [00:02<00:00,  2.08it/s, loss=0.0177]\n",
-      "Epoch 122: 100%|███████████| 6/6 [00:02<00:00,  2.27it/s, loss=0.0164]\n",
-      "Epoch 123: 100%|████████████| 6/6 [00:02<00:00,  2.02it/s, loss=0.017]\n",
-      "Epoch 124: 100%|███████████| 6/6 [00:02<00:00,  2.10it/s, loss=0.0195]\n",
-      "sampling...: 100%|████████████████████████████████████████████████████████| 1000/1000 [00:32<00:00, 30.41it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 400x200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 125: 100%|███████████| 6/6 [00:02<00:00,  2.48it/s, loss=0.0163]\n",
-      "Epoch 126: 100%|█████████████| 6/6 [00:02<00:00,  2.45it/s, loss=0.02]\n",
-      "Epoch 127: 100%|███████████| 6/6 [00:02<00:00,  2.36it/s, loss=0.0159]\n",
-      "Epoch 128: 100%|███████████| 6/6 [00:02<00:00,  2.41it/s, loss=0.0201]\n",
-      "Epoch 129: 100%|███████████| 6/6 [00:02<00:00,  2.40it/s, loss=0.0148]\n",
-      "Epoch 130: 100%|███████████| 6/6 [00:02<00:00,  2.36it/s, loss=0.0153]\n",
-      "Epoch 131: 100%|███████████| 6/6 [00:02<00:00,  2.37it/s, loss=0.0175]\n",
-      "Epoch 132: 100%|███████████| 6/6 [00:02<00:00,  2.38it/s, loss=0.0178]\n",
-      "Epoch 133: 100%|████████████| 6/6 [00:02<00:00,  2.23it/s, loss=0.018]\n",
-      "Epoch 134: 100%|███████████| 6/6 [00:02<00:00,  2.29it/s, loss=0.0179]\n",
-      "Epoch 135: 100%|███████████| 6/6 [00:02<00:00,  2.22it/s, loss=0.0153]\n",
-      "Epoch 136: 100%|███████████| 6/6 [00:02<00:00,  2.24it/s, loss=0.0173]\n",
-      "Epoch 137: 100%|███████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.0164]\n",
-      "Epoch 138: 100%|███████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0165]\n",
-      "Epoch 139: 100%|███████████| 6/6 [00:03<00:00,  1.97it/s, loss=0.0146]\n",
-      "Epoch 140: 100%|███████████| 6/6 [00:02<00:00,  2.02it/s, loss=0.0188]\n",
-      "Epoch 141: 100%|███████████| 6/6 [00:02<00:00,  2.08it/s, loss=0.0194]\n",
-      "Epoch 142: 100%|███████████| 6/6 [00:02<00:00,  2.26it/s, loss=0.0143]\n",
-      "Epoch 143: 100%|███████████| 6/6 [00:03<00:00,  2.00it/s, loss=0.0154]\n",
-      "Epoch 144: 100%|████████████| 6/6 [00:02<00:00,  2.12it/s, loss=0.012]\n",
-      "Epoch 145: 100%|███████████| 6/6 [00:02<00:00,  2.00it/s, loss=0.0198]\n",
-      "Epoch 146: 100%|███████████| 6/6 [00:02<00:00,  2.16it/s, loss=0.0224]\n",
-      "Epoch 147: 100%|███████████| 6/6 [00:02<00:00,  2.18it/s, loss=0.0152]\n",
-      "Epoch 148: 100%|████████████| 6/6 [00:02<00:00,  2.11it/s, loss=0.018]\n",
-      "Epoch 149: 100%|████████████| 6/6 [00:02<00:00,  2.09it/s, loss=0.018]\n",
-      "sampling...: 100%|████████████████████████████████████████████████████████| 1000/1000 [00:32<00:00, 30.67it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 400x200 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train completed, total time: 599.0462603569031.\n"
-     ]
-    }
-   ],
-   "source": [
-    "n_epochs = 150\n",
-    "val_interval = 25\n",
-    "epoch_loss_list = []\n",
-    "val_epoch_loss_list = []\n",
-    "\n",
-    "scaler = GradScaler()\n",
-    "total_start = time.time()\n",
-    "for epoch in range(n_epochs):\n",
-    "    model.train()\n",
-    "    epoch_loss = 0\n",
-    "    progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=70)\n",
-    "    progress_bar.set_description(f\"Epoch {epoch}\")\n",
-    "    for step, batch in progress_bar:\n",
-    "        images = batch[\"image\"].to(device)\n",
-    "        masks = batch[\"mask\"].to(device)\n",
-    "\n",
-    "        optimizer.zero_grad(set_to_none=True)\n",
-    "\n",
-    "        with autocast(enabled=True):\n",
-    "\n",
-    "            # Generate random noise\n",
-    "            noise = torch.randn_like(images).to(device)\n",
-    "\n",
-    "            # Create timesteps\n",
-    "            timesteps = torch.randint(\n",
-    "                0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n",
-    "            ).long()\n",
-    "\n",
-    "            images_noised = scheduler.add_noise(images, noise=noise, timesteps=timesteps)\n",
-    "\n",
-    "            # Get controlnet output\n",
-    "            down_block_res_samples, mid_block_res_sample = controlnet(\n",
-    "                x=images_noised, timesteps=timesteps, controlnet_cond=masks\n",
-    "            )\n",
-    "            # Get model prediction\n",
-    "            noise_pred = model(\n",
-    "                x=images_noised,\n",
-    "                timesteps=timesteps,\n",
-    "                down_block_additional_residuals=down_block_res_samples,\n",
-    "                mid_block_additional_residual=mid_block_res_sample,\n",
-    "            )\n",
-    "\n",
-    "            loss = F.mse_loss(noise_pred.float(), noise.float())\n",
-    "\n",
-    "        scaler.scale(loss).backward()\n",
-    "        scaler.step(optimizer)\n",
-    "        scaler.update()\n",
-    "\n",
-    "        epoch_loss += loss.item()\n",
-    "\n",
-    "        progress_bar.set_postfix({\"loss\": epoch_loss / (step + 1)})\n",
-    "    epoch_loss_list.append(epoch_loss / (step + 1))\n",
-    "\n",
-    "    if (epoch + 1) % val_interval == 0:\n",
-    "        model.eval()\n",
-    "        val_epoch_loss = 0\n",
-    "        for step, batch in enumerate(val_loader):\n",
-    "            images = batch[\"image\"].to(device)\n",
-    "            masks = batch[\"mask\"].to(device)\n",
-    "\n",
-    "            with torch.no_grad():\n",
-    "                with autocast(enabled=True):\n",
-    "                    noise = torch.randn_like(images).to(device)\n",
-    "                    timesteps = torch.randint(\n",
-    "                        0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n",
-    "                    ).long()\n",
-    "                    noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n",
-    "                    val_loss = F.mse_loss(noise_pred.float(), noise.float())\n",
-    "\n",
-    "            val_epoch_loss += val_loss.item()\n",
-    "            progress_bar.set_postfix({\"val_loss\": val_epoch_loss / (step + 1)})\n",
-    "            break\n",
-    "        val_epoch_loss_list.append(val_epoch_loss / (step + 1))\n",
-    "\n",
-    "        # Sampling image during training with controlnet conditioning\n",
-    "        progress_bar_sampling = tqdm(scheduler.timesteps, total=len(scheduler.timesteps), ncols=110)\n",
-    "        progress_bar_sampling.set_description(\"sampling...\")\n",
-    "        sample = torch.randn((1, 1, 64, 64)).to(device)\n",
-    "        for t in progress_bar_sampling:\n",
-    "            with torch.no_grad():\n",
-    "                with autocast(enabled=True):\n",
-    "                    down_block_res_samples, mid_block_res_sample = controlnet(\n",
-    "                        x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=masks[0, None, ...]\n",
-    "                    )\n",
-    "                    noise_pred = model(\n",
-    "                        sample,\n",
-    "                        timesteps=torch.Tensor((t,)).to(device),\n",
-    "                        down_block_additional_residuals=down_block_res_samples,\n",
-    "                        mid_block_additional_residual=mid_block_res_sample,\n",
-    "                    )\n",
-    "                    sample, _ = scheduler.step(model_output=noise_pred, timestep=t, sample=sample)\n",
-    "\n",
-    "        plt.subplots(1, 2, figsize=(4, 2))\n",
-    "        plt.subplot(1, 2, 1)\n",
-    "        plt.imshow(masks[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
-    "        plt.axis(\"off\")\n",
-    "        plt.title(\"Conditioning mask\")\n",
-    "        plt.subplot(1, 2, 2)\n",
-    "        plt.imshow(sample[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
-    "        plt.axis(\"off\")\n",
-    "        plt.title(\"Sample image\")\n",
-    "        plt.tight_layout()\n",
-    "        plt.axis(\"off\")\n",
-    "        plt.show()\n",
-    "\n",
-    "total_time = time.time() - total_start\n",
-    "print(f\"train completed, total time: {total_time}.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b005e3bd-54b9-44bc-964d-ca0c9585a139",
-   "metadata": {},
-   "source": [
-    "## Sample with ControlNet conditioning\n",
-    "First we'll provide a few different masks from the validation data as conditioning. The samples should respect the shape of the conditioning mask, but don't need to have the same content as the corresponding validation image."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "262a5129-9445-4ecc-a37a-a97c59386747",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "sampling...: 100%|████████████████████████████████████████████████████████| 1000/1000 [00:37<00:00, 27.00it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 600x800 with 24 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "progress_bar_sampling = tqdm(scheduler.timesteps, total=len(scheduler.timesteps), ncols=110, position=0, leave=True)\n",
-    "progress_bar_sampling.set_description(\"sampling...\")\n",
-    "num_samples = 8\n",
-    "sample = torch.randn((num_samples, 1, 64, 64)).to(device)\n",
-    "\n",
-    "val_batch = first(val_loader)\n",
-    "val_images = val_batch[\"image\"].to(device)\n",
-    "val_masks = val_batch[\"mask\"].to(device)\n",
-    "for t in progress_bar_sampling:\n",
-    "    with torch.no_grad():\n",
-    "        with autocast(enabled=True):\n",
-    "            down_block_res_samples, mid_block_res_sample = controlnet(\n",
-    "                x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=val_masks[:num_samples, ...]\n",
-    "            )\n",
-    "            noise_pred = model(\n",
-    "                sample,\n",
-    "                timesteps=torch.Tensor((t,)).to(device),\n",
-    "                down_block_additional_residuals=down_block_res_samples,\n",
-    "                mid_block_additional_residual=mid_block_res_sample,\n",
-    "            )\n",
-    "            sample, _ = scheduler.step(model_output=noise_pred, timestep=t, sample=sample)\n",
-    "\n",
-    "plt.subplots(num_samples, 3, figsize=(6, 8))\n",
-    "for k in range(num_samples):\n",
-    "    plt.subplot(num_samples, 3, k * 3 + 1)\n",
-    "    plt.imshow(val_masks[k, 0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
-    "    plt.axis(\"off\")\n",
-    "    if k == 0:\n",
-    "        plt.title(\"Conditioning mask\")\n",
-    "    plt.subplot(num_samples, 3, k * 3 + 2)\n",
-    "    plt.imshow(val_images[k, 0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
-    "    plt.axis(\"off\")\n",
-    "    if k == 0:\n",
-    "        plt.title(\"Actual val image\")\n",
-    "    plt.subplot(num_samples, 3, k * 3 + 3)\n",
-    "    plt.imshow(sample[k, 0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
-    "    plt.axis(\"off\")\n",
-    "    if k == 0:\n",
-    "        plt.title(\"Sampled image\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a1ca8274-d85c-4dcc-9c16-08ac2b6ce0fd",
-   "metadata": {},
-   "source": [
-    "What happens if we invent some masks? Let's try a circle, and a square"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "393fca6c-2446-4822-8aad-44403761b40e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "sampling...: 100%|████████████████████████████████████████████████████████| 1000/1000 [00:16<00:00, 61.51it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 400x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xx, yy = np.mgrid[:64, :64]\n",
-    "circle = ((xx - 32) ** 2 + (yy - 32) ** 2) < 30**2\n",
-    "\n",
-    "square = np.zeros((64, 64))\n",
-    "square[10:50, 10:50] = 1\n",
-    "\n",
-    "mask = np.concatenate((circle[None, None, ...], square[None, None, ...]), axis=0)\n",
-    "mask = torch.from_numpy(mask.astype(np.float32)).to(device)\n",
-    "\n",
-    "\n",
-    "progress_bar_sampling = tqdm(scheduler.timesteps, total=len(scheduler.timesteps), ncols=110, position=0, leave=True)\n",
-    "progress_bar_sampling.set_description(\"sampling...\")\n",
-    "num_samples = 2\n",
-    "sample = torch.randn((num_samples, 1, 64, 64)).to(device)\n",
-    "\n",
-    "for t in progress_bar_sampling:\n",
-    "    with torch.no_grad():\n",
-    "        with autocast(enabled=True):\n",
-    "            down_block_res_samples, mid_block_res_sample = controlnet(\n",
-    "                x=sample, timesteps=torch.Tensor((t,)).to(device).long(), controlnet_cond=mask\n",
-    "            )\n",
-    "            noise_pred = model(\n",
-    "                sample,\n",
-    "                timesteps=torch.Tensor((t,)).to(device),\n",
-    "                down_block_additional_residuals=down_block_res_samples,\n",
-    "                mid_block_additional_residual=mid_block_res_sample,\n",
-    "            )\n",
-    "            sample, _ = scheduler.step(model_output=noise_pred, timestep=t, sample=sample)\n",
-    "\n",
-    "plt.subplots(num_samples, 2, figsize=(4, 4))\n",
-    "for k in range(num_samples):\n",
-    "    plt.subplot(num_samples, 2, k * 2 + 1)\n",
-    "    plt.imshow(mask[k, 0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
-    "    plt.axis(\"off\")\n",
-    "    if k == 0:\n",
-    "        plt.title(\"Conditioning mask\")\n",
-    "    plt.subplot(num_samples, 2, k * 2 + 2)\n",
-    "    plt.imshow(sample[k, 0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
-    "    plt.axis(\"off\")\n",
-    "    if k == 0:\n",
-    "        plt.title(\"Sampled image\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "py:percent,ipynb"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.13"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "4f1513a79f82193cb81c96943579af15c6a44d6347609348bde584197ab7b1ab"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+    "nbformat": 4,
+    "nbformat_minor": 5
 }
diff --git a/tutorials/generative/2d_controlnet/2d_controlnet.py b/tutorials/generative/2d_controlnet/2d_controlnet.py
index da08911f..8f48892a 100644
--- a/tutorials/generative/2d_controlnet/2d_controlnet.py
+++ b/tutorials/generative/2d_controlnet/2d_controlnet.py
@@ -105,7 +105,7 @@
         transforms.LoadImaged(keys=["image"]),
         transforms.EnsureChannelFirstd(keys=["image"]),
         transforms.Lambdad(keys=["image"], func=lambda x: x[channel, :, :, :]),
-        transforms.AddChanneld(keys=["image"]),
+        transforms.EnsureChannelFirstd(keys=["image"], channel_dim="no_channel"),
         transforms.EnsureTyped(keys=["image"]),
         transforms.Orientationd(keys=["image"], axcodes="RAS"),
         transforms.Spacingd(keys=["image"], pixdim=(3.0, 3.0, 2.0), mode="bilinear"),
@@ -339,7 +339,6 @@
         optimizer.zero_grad(set_to_none=True)
 
         with autocast(enabled=True):
-
             # Generate random noise
             noise = torch.randn_like(images).to(device)
 
diff --git a/tutorials/generative/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb b/tutorials/generative/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb
index e8455bd3..e2ebb048 100644
--- a/tutorials/generative/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb
+++ b/tutorials/generative/3d_autoencoderkl/3d_autoencoderkl_tutorial.ipynb
@@ -1,982 +1,982 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8a7e6369",
-   "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": "eca65c39",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "source": [
-    "# 3D AutoencoderKL\n",
-    "\n",
-    "This demo is a toy example of how to use MONAI's AutoencoderKL. In particular, it uses the Autoencoder with a Kullback-Leibler regularisation as implemented by Rombach et. al [1].\n",
-    "\n",
-    "[1] Rombach et. al \"High-Resolution Image Synthesis with Latent Diffusion Models\" https://arxiv.org/pdf/2112.10752.pdf\n",
-    "\n",
-    "This tutorial was based on:\n",
-    "\n",
-    "[Brain tumor 3D segmentation with MONAI](https://github.com/Project-MONAI/tutorials/blob/main/3d_segmentation/brats_segmentation_3d.ipynb)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0f82c364",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm, nibabel]\"\n",
-    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7325d9ae",
-   "metadata": {},
-   "source": [
-    "## Setup imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a44e7a6e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jdafflon/miniconda3/envs/genmodels/lib/python3.9/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"
-     ]
+    "cells": [
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "8a7e6369",
+            "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": "eca65c39",
+            "metadata": {
+                "lines_to_next_cell": 2
+            },
+            "source": [
+                "# 3D AutoencoderKL\n",
+                "\n",
+                "This demo is a toy example of how to use MONAI's AutoencoderKL. In particular, it uses the Autoencoder with a Kullback-Leibler regularisation as implemented by Rombach et. al [1].\n",
+                "\n",
+                "[1] Rombach et. al \"High-Resolution Image Synthesis with Latent Diffusion Models\" https://arxiv.org/pdf/2112.10752.pdf\n",
+                "\n",
+                "This tutorial was based on:\n",
+                "\n",
+                "[Brain tumor 3D segmentation with MONAI](https://github.com/Project-MONAI/tutorials/blob/main/3d_segmentation/brats_segmentation_3d.ipynb)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "0f82c364",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm, nibabel]\"\n",
+                "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
+                "%matplotlib inline"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "7325d9ae",
+            "metadata": {},
+            "source": [
+                "## Setup imports"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 2,
+            "id": "a44e7a6e",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "/home/jdafflon/miniconda3/envs/genmodels/lib/python3.9/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",
+                    "text": [
+                        "MONAI version: 1.1.dev2239\n",
+                        "Numpy version: 1.23.4\n",
+                        "Pytorch version: 1.13.0\n",
+                        "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
+                        "MONAI rev id: 13b24fa92b9d98bd0dc6d5cdcb52504fd09e297b\n",
+                        "MONAI __file__: /home/jdafflon/miniconda3/envs/genmodels/lib/python3.9/site-packages/monai/__init__.py\n",
+                        "\n",
+                        "Optional dependencies:\n",
+                        "Pytorch Ignite version: NOT INSTALLED or UNKNOWN VERSION.\n",
+                        "Nibabel version: 4.0.2\n",
+                        "scikit-image version: NOT INSTALLED or UNKNOWN VERSION.\n",
+                        "Pillow version: 9.2.0\n",
+                        "Tensorboard version: NOT INSTALLED or UNKNOWN VERSION.\n",
+                        "gdown version: NOT INSTALLED or UNKNOWN VERSION.\n",
+                        "TorchVision version: 0.14.0\n",
+                        "tqdm version: 4.64.1\n",
+                        "lmdb version: NOT INSTALLED or UNKNOWN VERSION.\n",
+                        "psutil version: 5.9.4\n",
+                        "pandas version: NOT INSTALLED or UNKNOWN VERSION.\n",
+                        "einops version: 0.6.0\n",
+                        "transformers version: NOT INSTALLED or UNKNOWN VERSION.\n",
+                        "mlflow version: NOT INSTALLED or UNKNOWN VERSION.\n",
+                        "pynrrd version: NOT INSTALLED or UNKNOWN VERSION.\n",
+                        "\n",
+                        "For details about installing the optional dependencies, please visit:\n",
+                        "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
+                        "\n"
+                    ]
+                }
+            ],
+            "source": [
+                "import os\n",
+                "import shutil\n",
+                "import tempfile\n",
+                "\n",
+                "import matplotlib.pyplot as plt\n",
+                "import numpy as np\n",
+                "import torch\n",
+                "import torch.nn.functional as F\n",
+                "from monai import transforms\n",
+                "from monai.apps import DecathlonDataset\n",
+                "from monai.config import print_config\n",
+                "from monai.data import DataLoader\n",
+                "from monai.networks.layers import Act\n",
+                "from monai.utils import first, set_determinism\n",
+                "from torch.cuda.amp import autocast\n",
+                "from tqdm import tqdm\n",
+                "\n",
+                "from generative.losses import PatchAdversarialLoss, PerceptualLoss\n",
+                "from generative.networks.nets import AutoencoderKL, PatchDiscriminator\n",
+                "\n",
+                "print_config()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 3,
+            "id": "1aaa77a6",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# for reproducibility purposes set a seed\n",
+                "set_determinism(42)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "72bae2d5",
+            "metadata": {},
+            "source": [
+                "## Setup a data directory and download dataset\n",
+                "\n",
+                "Specify a `MONAI_DATA_DIRECTORY` variable, where the data will be downloaded. If not specified a temporary directory will be used."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 4,
+            "id": "48155dfa",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "/tmp/tmpyxyg6wxs\n"
+                    ]
+                }
+            ],
+            "source": [
+                "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
+                "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
+                "print(root_dir)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "319bff04",
+            "metadata": {},
+            "source": [
+                "## Download the training set"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "053fdee1",
+            "metadata": {},
+            "source": [
+                "Note: The DecatholonDataset has 7GB. So make sure that you have enought space when running the next line"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 5,
+            "id": "1dbaf6af",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "/home/jdafflon/miniconda3/envs/genmodels/lib/python3.9/site-packages/monai/utils/deprecate_utils.py:107: FutureWarning: <class 'monai.transforms.utility.array.AddChannel'>: Class `AddChannel` has been deprecated since version 0.8. please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead.\n",
+                        "  warn_deprecated(obj, msg, warning_category)\n"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "2022-12-08 20:46:51,956 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n",
+                        "2022-12-08 20:46:51,958 - INFO - File exists: /tmp/tmpyxyg6wxs/Task01_BrainTumour.tar, skipped downloading.\n",
+                        "2022-12-08 20:46:51,959 - INFO - Non-empty folder exists in /tmp/tmpyxyg6wxs/Task01_BrainTumour, skipped extracting.\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Loading dataset: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 388/388 [02:39<00:00,  2.43it/s]"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Image shape (1, 96, 96, 64)\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "\n"
+                    ]
+                }
+            ],
+            "source": [
+                "channel = 0  # 0 = Flair\n",
+                "assert channel in [0, 1, 2, 3], \"Choose a valid channel\"\n",
+                "\n",
+                "train_transforms = transforms.Compose(\n",
+                "    [\n",
+                "        transforms.LoadImaged(keys=[\"image\"]),\n",
+                "        transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
+                "        transforms.Lambdad(keys=\"image\", func=lambda x: x[channel, :, :, :]),\n",
+                "        transforms.EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n",
+                "        transforms.EnsureTyped(keys=[\"image\"]),\n",
+                "        transforms.Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n",
+                "        transforms.Spacingd(keys=[\"image\"], pixdim=(2.4, 2.4, 2.2), mode=(\"bilinear\")),\n",
+                "        transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=(96, 96, 64)),\n",
+                "        transforms.ScaleIntensityRangePercentilesd(keys=\"image\", lower=0, upper=99.5, b_min=0, b_max=1),\n",
+                "    ]\n",
+                ")\n",
+                "train_ds = DecathlonDataset(\n",
+                "    root_dir=root_dir,\n",
+                "    task=\"Task01_BrainTumour\",\n",
+                "    section=\"training\",\n",
+                "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
+                "    num_workers=4,\n",
+                "    download=True,\n",
+                "    seed=0,\n",
+                "    transform=train_transforms,\n",
+                ")\n",
+                "train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=4, persistent_workers=True)\n",
+                "print(f'Image shape {train_ds[0][\"image\"].shape}')"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "617a46a9",
+            "metadata": {},
+            "source": [
+                "## Visualise examples from the training set"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 6,
+            "id": "8902c0a4",
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "<matplotlib.image.AxesImage at 0x7f555f9f39d0>"
+                        ]
+                    },
+                    "execution_count": 6,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 3 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "check_data = first(train_loader)\n",
+                "\n",
+                "# Select the first image from the batch\n",
+                "img = check_data[\"image\"][0]\n",
+                "fig, axs = plt.subplots(nrows=1, ncols=3)\n",
+                "for ax in axs:\n",
+                "    ax.axis(\"off\")\n",
+                "ax = axs[0]\n",
+                "ax.imshow(img[0, ..., img.shape[3] // 2].rot90(), cmap=\"gray\")\n",
+                "ax = axs[1]\n",
+                "ax.imshow(img[0, :, img.shape[2] // 2, ...].rot90(), cmap=\"gray\")\n",
+                "ax = axs[2]\n",
+                "ax.imshow(img[0, img.shape[1] // 2, ...].rot90(), cmap=\"gray\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "902e37b5",
+            "metadata": {},
+            "source": [
+                "## Download the validation set"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 7,
+            "id": "0550cac3",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "2022-12-08 20:49:45,062 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n",
+                        "2022-12-08 20:49:45,064 - INFO - File exists: /tmp/tmpyxyg6wxs/Task01_BrainTumour.tar, skipped downloading.\n",
+                        "2022-12-08 20:49:45,065 - INFO - Non-empty folder exists in /tmp/tmpyxyg6wxs/Task01_BrainTumour, skipped extracting.\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Loading dataset: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 96/96 [00:36<00:00,  2.60it/s]"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Image shape (1, 96, 96, 64)\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "\n"
+                    ]
+                }
+            ],
+            "source": [
+                "val_transforms = transforms.Compose(\n",
+                "    [\n",
+                "        transforms.LoadImaged(keys=[\"image\"]),\n",
+                "        transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
+                "        transforms.Lambdad(keys=\"image\", func=lambda x: x[channel, :, :, :]),\n",
+                "        transforms.EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n",
+                "        transforms.EnsureTyped(keys=[\"image\"]),\n",
+                "        transforms.Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n",
+                "        transforms.Spacingd(keys=[\"image\"], pixdim=(2.4, 2.4, 2.2), mode=(\"bilinear\")),\n",
+                "        transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=(96, 96, 64)),\n",
+                "        transforms.ScaleIntensityRangePercentilesd(keys=\"image\", lower=0, upper=99.5, b_min=0, b_max=1),\n",
+                "    ]\n",
+                ")\n",
+                "val_ds = DecathlonDataset(\n",
+                "    root_dir=root_dir,\n",
+                "    task=\"Task01_BrainTumour\",\n",
+                "    section=\"validation\",\n",
+                "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
+                "    num_workers=4,\n",
+                "    download=True,\n",
+                "    seed=0,\n",
+                "    transform=val_transforms,\n",
+                ")\n",
+                "val_loader = DataLoader(val_ds, batch_size=2, shuffle=False, num_workers=4, persistent_workers=True)\n",
+                "print(f'Image shape {val_ds[0][\"image\"].shape}')"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 8,
+            "id": "8e21e0ce",
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "<matplotlib.image.AxesImage at 0x7f555ff98190>"
+                        ]
+                    },
+                    "execution_count": 8,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 3 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "check_data = first(val_loader)\n",
+                "\n",
+                "img = check_data[\"image\"][0]\n",
+                "fig, axs = plt.subplots(nrows=1, ncols=3)\n",
+                "for ax in axs:\n",
+                "    ax.axis(\"off\")\n",
+                "ax = axs[0]\n",
+                "ax.imshow(img[0, ..., img.shape[3] // 2].rot90(), cmap=\"gray\")\n",
+                "ax = axs[1]\n",
+                "ax.imshow(img[0, :, img.shape[2] // 2, ...].rot90(), cmap=\"gray\")\n",
+                "ax = axs[2]\n",
+                "ax.imshow(img[0, img.shape[1] // 2, ...].rot90(), cmap=\"gray\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "19532ecb",
+            "metadata": {},
+            "source": [
+                "## Define the network"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 10,
+            "id": "5e0514e5",
+            "metadata": {
+                "lines_to_next_cell": 2
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Using cuda\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "text/plain": [
+                            "PatchDiscriminator(\n",
+                            "  (initial_conv): Convolution(\n",
+                            "    (conv): Conv3d(1, 32, kernel_size=(4, 4, 4), stride=(2, 2, 2), padding=(1, 1, 1))\n",
+                            "    (adn): ADN(\n",
+                            "      (D): Dropout(p=0.0, inplace=False)\n",
+                            "      (A): LeakyReLU(negative_slope=0.2)\n",
+                            "    )\n",
+                            "  )\n",
+                            "  (0): Convolution(\n",
+                            "    (conv): Conv3d(32, 64, kernel_size=(4, 4, 4), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)\n",
+                            "    (adn): ADN(\n",
+                            "      (N): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+                            "      (D): Dropout(p=0.0, inplace=False)\n",
+                            "      (A): LeakyReLU(negative_slope=0.2)\n",
+                            "    )\n",
+                            "  )\n",
+                            "  (1): Convolution(\n",
+                            "    (conv): Conv3d(64, 128, kernel_size=(4, 4, 4), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)\n",
+                            "    (adn): ADN(\n",
+                            "      (N): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+                            "      (D): Dropout(p=0.0, inplace=False)\n",
+                            "      (A): LeakyReLU(negative_slope=0.2)\n",
+                            "    )\n",
+                            "  )\n",
+                            "  (2): Convolution(\n",
+                            "    (conv): Conv3d(128, 256, kernel_size=(4, 4, 4), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
+                            "    (adn): ADN(\n",
+                            "      (N): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+                            "      (D): Dropout(p=0.0, inplace=False)\n",
+                            "      (A): LeakyReLU(negative_slope=0.2)\n",
+                            "    )\n",
+                            "  )\n",
+                            "  (final_conv): Convolution(\n",
+                            "    (conv): Conv3d(256, 1, kernel_size=(4, 4, 4), stride=(1, 1, 1), padding=(1, 1, 1))\n",
+                            "  )\n",
+                            ")"
+                        ]
+                    },
+                    "execution_count": 10,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+                "print(f\"Using {device}\")\n",
+                "\n",
+                "model = AutoencoderKL(\n",
+                "    spatial_dims=3,\n",
+                "    in_channels=1,\n",
+                "    out_channels=1,\n",
+                "    num_channels=(32, 64, 64),\n",
+                "    latent_channels=3,\n",
+                "    num_res_blocks=1,\n",
+                "    norm_num_groups=32,\n",
+                "    attention_levels=(False, False, True),\n",
+                ")\n",
+                "model.to(device)\n",
+                "\n",
+                "discriminator = PatchDiscriminator(\n",
+                "    spatial_dims=3,\n",
+                "    num_layers_d=3,\n",
+                "    num_channels=32,\n",
+                "    in_channels=1,\n",
+                "    out_channels=1,\n",
+                "    kernel_size=4,\n",
+                "    activation=(Act.LEAKYRELU, {\"negative_slope\": 0.2}),\n",
+                "    norm=\"BATCH\",\n",
+                "    bias=False,\n",
+                "    padding=1,\n",
+                ")\n",
+                "discriminator.to(device)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 11,
+            "id": "da14911d",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "/home/jdafflon/miniconda3/envs/genmodels/lib/python3.9/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+                        "  warnings.warn(\n",
+                        "/home/jdafflon/miniconda3/envs/genmodels/lib/python3.9/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=SqueezeNet1_1_Weights.IMAGENET1K_V1`. You can also use `weights=SqueezeNet1_1_Weights.DEFAULT` to get the most up-to-date weights.\n",
+                        "  warnings.warn(msg)\n"
+                    ]
+                }
+            ],
+            "source": [
+                "perceptual_loss = PerceptualLoss(spatial_dims=3, network_type=\"squeeze\", fake_3d_ratio=0.25)\n",
+                "perceptual_loss.to(device)\n",
+                "\n",
+                "adv_loss = PatchAdversarialLoss(criterion=\"least_squares\")\n",
+                "adv_weight = 0.01\n",
+                "perceptual_weight = 0.001\n",
+                "\n",
+                "optimizer_g = torch.optim.Adam(model.parameters(), 1e-4)\n",
+                "optimizer_d = torch.optim.Adam(discriminator.parameters(), lr=5e-4)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 12,
+            "id": "5c0b87e9",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "scaler_g = torch.cuda.amp.GradScaler()\n",
+                "scaler_d = torch.cuda.amp.GradScaler()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "7d19616e",
+            "metadata": {},
+            "source": [
+                "## Model training"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 13,
+            "id": "aa98bfa9",
+            "metadata": {
+                "lines_to_next_cell": 0
+            },
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 0: 100%|\u2588| 194/194 [04:33<00:00,  1.41s/it, recons_loss=0.078, g\n",
+                        "Epoch 1: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0408, \n",
+                        "Epoch 2: 100%|\u2588| 194/194 [04:33<00:00,  1.41s/it, recons_loss=0.0368, \n",
+                        "Epoch 3: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0347, \n",
+                        "Epoch 4: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0333, \n",
+                        "Epoch 5: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0314, \n",
+                        "Epoch 6: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0312, \n",
+                        "Epoch 7: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0301, \n",
+                        "Epoch 8: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0289, \n",
+                        "Epoch 9: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0307, \n",
+                        "Epoch 10: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0293,\n",
+                        "Epoch 11: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0294,\n",
+                        "Epoch 12: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0298,\n",
+                        "Epoch 13: 100%|\u2588| 194/194 [04:33<00:00,  1.41s/it, recons_loss=0.0284,\n",
+                        "Epoch 14: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0283,\n",
+                        "Epoch 15: 100%|\u2588| 194/194 [04:33<00:00,  1.41s/it, recons_loss=0.029, \n",
+                        "Epoch 16: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0293,\n",
+                        "Epoch 17: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0286,\n",
+                        "Epoch 18: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0276,\n",
+                        "Epoch 19: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0285,\n",
+                        "Epoch 20: 100%|\u2588| 194/194 [04:32<00:00,  1.40s/it, recons_loss=0.0275,\n",
+                        "Epoch 21: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0279,\n",
+                        "Epoch 22: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0271,\n",
+                        "Epoch 23: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0278,\n",
+                        "Epoch 24: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.028, \n",
+                        "Epoch 25: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0261,\n",
+                        "Epoch 26: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0266,\n",
+                        "Epoch 27: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0264,\n",
+                        "Epoch 28: 100%|\u2588| 194/194 [04:33<00:00,  1.41s/it, recons_loss=0.0277,\n",
+                        "Epoch 29: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0279,\n",
+                        "Epoch 30: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0266,\n",
+                        "Epoch 31: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0273,\n",
+                        "Epoch 32: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.027, \n",
+                        "Epoch 33: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.027, \n",
+                        "Epoch 34: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.029, \n",
+                        "Epoch 35: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0285,\n",
+                        "Epoch 36: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0271,\n",
+                        "Epoch 37: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0267,\n",
+                        "Epoch 38: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0248,\n",
+                        "Epoch 39: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0246,\n",
+                        "Epoch 40: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0248,\n",
+                        "Epoch 41: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0258,\n",
+                        "Epoch 42: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0254,\n",
+                        "Epoch 43: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0245,\n",
+                        "Epoch 44: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0248,\n",
+                        "Epoch 45: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0247,\n",
+                        "Epoch 46: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0247,\n",
+                        "Epoch 47: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0248,\n",
+                        "Epoch 48: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0256,\n",
+                        "Epoch 49: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0238,\n",
+                        "Epoch 50: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0239,\n",
+                        "Epoch 51: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.025, \n",
+                        "Epoch 52: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0239,\n",
+                        "Epoch 53: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0234,\n",
+                        "Epoch 54: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0242,\n",
+                        "Epoch 55: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0236,\n",
+                        "Epoch 56: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0242,\n",
+                        "Epoch 57: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0227,\n",
+                        "Epoch 58: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0216,\n",
+                        "Epoch 59: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.022, \n",
+                        "Epoch 60: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0234,\n",
+                        "Epoch 61: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.024, \n",
+                        "Epoch 62: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0229,\n",
+                        "Epoch 63: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0238,\n",
+                        "Epoch 64: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.023, \n",
+                        "Epoch 65: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0241,\n",
+                        "Epoch 66: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0223,\n",
+                        "Epoch 67: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0218,\n",
+                        "Epoch 68: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.022, \n",
+                        "Epoch 69: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0221,\n",
+                        "Epoch 70: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0238,\n",
+                        "Epoch 71: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0229,\n",
+                        "Epoch 72: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.022, \n",
+                        "Epoch 73: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0222,\n",
+                        "Epoch 74: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0238,\n",
+                        "Epoch 75: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0217,\n",
+                        "Epoch 76: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0228,\n",
+                        "Epoch 77: 100%|\u2588| 194/194 [04:32<00:00,  1.40s/it, recons_loss=0.0218,\n",
+                        "Epoch 78: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0231,\n",
+                        "Epoch 79: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0224,\n",
+                        "Epoch 80: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0217,\n",
+                        "Epoch 81: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0222,\n",
+                        "Epoch 82: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0232,\n",
+                        "Epoch 83: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.021, \n",
+                        "Epoch 84: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0208,\n",
+                        "Epoch 85: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0212,\n",
+                        "Epoch 86: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0219,\n",
+                        "Epoch 87: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0214,\n",
+                        "Epoch 88: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0224,\n",
+                        "Epoch 89: 100%|\u2588| 194/194 [04:32<00:00,  1.40s/it, recons_loss=0.0219,\n",
+                        "Epoch 90: 100%|\u2588| 194/194 [04:32<00:00,  1.40s/it, recons_loss=0.0223,\n",
+                        "Epoch 91: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0229,\n",
+                        "Epoch 92: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0218,\n",
+                        "Epoch 93: 100%|\u2588| 194/194 [04:32<00:00,  1.40s/it, recons_loss=0.022, \n",
+                        "Epoch 94: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0215,\n",
+                        "Epoch 95: 100%|\u2588| 194/194 [04:32<00:00,  1.40s/it, recons_loss=0.0207,\n",
+                        "Epoch 96: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0207,\n",
+                        "Epoch 97: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0214,\n",
+                        "Epoch 98: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0215,\n",
+                        "Epoch 99: 100%|\u2588| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0223,\n"
+                    ]
+                }
+            ],
+            "source": [
+                "kl_weight = 1e-6\n",
+                "n_epochs = 100\n",
+                "val_interval = 6\n",
+                "epoch_recon_loss_list = []\n",
+                "epoch_gen_loss_list = []\n",
+                "epoch_disc_loss_list = []\n",
+                "val_recon_epoch_loss_list = []\n",
+                "intermediary_images = []\n",
+                "n_example_images = 4\n",
+                "\n",
+                "for epoch in range(n_epochs):\n",
+                "    model.train()\n",
+                "    discriminator.train()\n",
+                "    epoch_loss = 0\n",
+                "    gen_epoch_loss = 0\n",
+                "    disc_epoch_loss = 0\n",
+                "    progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=70)\n",
+                "    progress_bar.set_description(f\"Epoch {epoch}\")\n",
+                "    for step, batch in progress_bar:\n",
+                "        images = batch[\"image\"].to(device)\n",
+                "        optimizer_g.zero_grad(set_to_none=True)\n",
+                "\n",
+                "        # Generator part\n",
+                "        with autocast(enabled=True):\n",
+                "            reconstruction, z_mu, z_sigma = model(images)\n",
+                "            logits_fake = discriminator(reconstruction.contiguous().float())[-1]\n",
+                "\n",
+                "            recons_loss = F.l1_loss(reconstruction.float(), images.float())\n",
+                "            p_loss = perceptual_loss(reconstruction.float(), images.float())\n",
+                "            generator_loss = adv_loss(logits_fake, target_is_real=True, for_discriminator=False)\n",
+                "\n",
+                "            kl_loss = 0.5 * torch.sum(z_mu.pow(2) + z_sigma.pow(2) - torch.log(z_sigma.pow(2)) - 1, dim=[1, 2, 3, 4])\n",
+                "            kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]\n",
+                "\n",
+                "            loss_g = recons_loss + (kl_weight * kl_loss) + (perceptual_weight * p_loss) + (adv_weight * generator_loss)\n",
+                "\n",
+                "        scaler_g.scale(loss_g).backward()\n",
+                "        scaler_g.step(optimizer_g)\n",
+                "        scaler_g.update()\n",
+                "\n",
+                "        # Discriminator part\n",
+                "        optimizer_d.zero_grad(set_to_none=True)\n",
+                "\n",
+                "        with autocast(enabled=True):\n",
+                "            logits_fake = discriminator(reconstruction.contiguous().detach())[-1]\n",
+                "            loss_d_fake = adv_loss(logits_fake, target_is_real=False, for_discriminator=True)\n",
+                "            logits_real = discriminator(images.contiguous().detach())[-1]\n",
+                "            loss_d_real = adv_loss(logits_real, target_is_real=True, for_discriminator=True)\n",
+                "            discriminator_loss = (loss_d_fake + loss_d_real) * 0.5\n",
+                "\n",
+                "            loss_d = adv_weight * discriminator_loss\n",
+                "\n",
+                "        scaler_d.scale(loss_d).backward()\n",
+                "        scaler_d.step(optimizer_d)\n",
+                "        scaler_d.update()\n",
+                "\n",
+                "        epoch_loss += recons_loss.item()\n",
+                "        gen_epoch_loss += generator_loss.item()\n",
+                "        disc_epoch_loss += discriminator_loss.item()\n",
+                "\n",
+                "        progress_bar.set_postfix(\n",
+                "            {\n",
+                "                \"recons_loss\": epoch_loss / (step + 1),\n",
+                "                \"gen_loss\": gen_epoch_loss / (step + 1),\n",
+                "                \"disc_loss\": disc_epoch_loss / (step + 1),\n",
+                "            }\n",
+                "        )\n",
+                "    epoch_recon_loss_list.append(epoch_loss / (step + 1))\n",
+                "    epoch_gen_loss_list.append(gen_epoch_loss / (step + 1))\n",
+                "    epoch_disc_loss_list.append(disc_epoch_loss / (step + 1))\n",
+                "\n",
+                "    if (epoch + 1) % val_interval == 0:\n",
+                "        model.eval()\n",
+                "        val_loss = 0\n",
+                "        with torch.no_grad():\n",
+                "            for val_step, batch in enumerate(val_loader, start=1):\n",
+                "                images = batch[\"image\"].to(device)\n",
+                "                optimizer_g.zero_grad(set_to_none=True)\n",
+                "\n",
+                "                reconstruction, z_mu, z_sigma = model(images)\n",
+                "                # get the first sammple from the first validation batch for visualisation\n",
+                "                # purposes\n",
+                "                if val_step == 1:\n",
+                "                    intermediary_images.append(reconstruction[:n_example_images, 0])\n",
+                "\n",
+                "                recons_loss = F.l1_loss(reconstruction.float(), images.float())\n",
+                "\n",
+                "                val_loss += recons_loss.item()\n",
+                "\n",
+                "        val_loss /= val_step\n",
+                "        val_recon_epoch_loss_list.append(val_loss)\n",
+                "\n",
+                "progress_bar.close()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "a28c94e3",
+            "metadata": {},
+            "source": [
+                "## Evaluate the trainig"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 14,
+            "id": "066417fe",
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "plt.figure()\n",
+                "val_samples = np.linspace(val_interval, n_epochs, int(n_epochs / val_interval))\n",
+                "plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_recon_loss_list, label=\"Train\")\n",
+                "plt.plot(val_samples, val_recon_epoch_loss_list, label=\"Validation\")\n",
+                "plt.xlabel(\"Epochs\")\n",
+                "plt.ylabel(\"Loss\")\n",
+                "plt.legend()\n",
+                "plt.tight_layout()\n",
+                "plt.show()\n",
+                "plt.close()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 15,
+            "id": "bb1b6dd8",
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "plt.title(\"Adversarial Training Curves\", fontsize=20)\n",
+                "plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_gen_loss_list, color=\"C0\", linewidth=2.0, label=\"Generator\")\n",
+                "plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_disc_loss_list, color=\"C1\", linewidth=2.0, label=\"Discriminator\")\n",
+                "plt.yticks(fontsize=12)\n",
+                "plt.xticks(fontsize=12)\n",
+                "plt.xlabel(\"Epochs\", fontsize=16)\n",
+                "plt.ylabel(\"Loss\", fontsize=16)\n",
+                "plt.legend(prop={\"size\": 14})\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "21bbecae",
+            "metadata": {},
+            "source": [
+                "### Visualise some reconstruction images"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 16,
+            "id": "caf2b1e1",
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 800x600 with 15 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "# get the first 5 examples to plot\n",
+                "n_evaluations = 5\n",
+                "\n",
+                "fig, axs = plt.subplots(nrows=n_evaluations, ncols=3, constrained_layout=True, figsize=(8, 6))\n",
+                "\n",
+                "\n",
+                "# Remove ticks\n",
+                "for ax in axs.flatten():\n",
+                "    ax.set_xticks([])\n",
+                "    ax.set_yticks([])\n",
+                "\n",
+                "\n",
+                "for image_n in range(n_evaluations):\n",
+                "    axs[image_n, 0].imshow(\n",
+                "        intermediary_images[image_n][0, ..., intermediary_images[image_n].shape[3] // 2].cpu(), cmap=\"gray\"\n",
+                "    )\n",
+                "    axs[image_n, 1].imshow(\n",
+                "        intermediary_images[image_n][0, :, intermediary_images[image_n].shape[2] // 2, ...].cpu().rot90(), cmap=\"gray\"\n",
+                "    )\n",
+                "    axs[image_n, 2].imshow(\n",
+                "        intermediary_images[image_n][0, intermediary_images[image_n].shape[1] // 2, ...].cpu().rot90(), cmap=\"gray\"\n",
+                "    )\n",
+                "    axs[image_n, 0].set_ylabel(f\"Epoch {val_samples[image_n]:.0f}\")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 18,
+            "id": "dd03417f",
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgMAAAELCAYAAABEYIWnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAChBElEQVR4nO19d5Bl113m93JO/TpNT0/o6Uka5WDJyBaSLWOBbVjbGAO7W7ZsljXY2EARFpaykY2rVAYMorAxoQp7dw1bDoQVu2YdMDJgtLbkIMmSRpNTz3Tul/N7d/8YvtO/e/rc1z2jMNP9zlf16nW/d8O5993z+32/eHyO4ziwsLCwsLCwGFj4r/QALCwsLCwsLK4sLBmwsLCwsLAYcFgyYGFhYWFhMeCwZMDCwsLCwmLAYcmAhYWFhYXFgMOSAQsLCwsLiwGHJQMWFhYWFhYDDksGLCwsLCwsBhyWDFhYWFhYWAw4LBkYUOzevRv333//lR6GhYWFxWXhkUcegc/nwyOPPHKlh7IlMHBk4FOf+hR8Ph8ef/zxKz0UAECtVsMDDzxgfKC/8IUv4IEHHnjJxyTh8/nwcz/3c1d0DBYWVxMoQ/gKBoPYvn077r//fszMzFzp4b2g+KM/+iN86lOfGvgxDAKCV3oAg45arYYPfvCDAIB77rnH9d0XvvAFfPzjH7/ihMDCwmItPvShD2FqagqNRgP/7//9P3zqU5/Cv/zLv+B73/seotHolR7eC4I/+qM/wvDw8BX1InqN4fu///tRr9cRDoevzMC2GCwZsLCwsLgM/NAP/RBuu+02AMB/+k//CcPDw/jIRz6Chx9+GG9961uv8OheelSrVSQSiZfsfH6/f8uQrqsBAxcmMOH+++9HMpnEzMwM3vjGNyKZTGJkZAS//Mu/jG63q7Y7deoUfD4ffvd3fxe///u/j127diEWi+Huu+/G9773Pdcx77nnnjWWPs+1e/dudbyRkREAwAc/+EHldnzggQdw//334+Mf/zgAuFySRK/Xw0MPPYRrr70W0WgUY2NjeNe73oWVlRXX+RzHwYc//GFMTk4iHo/jVa96FZ5++unLvleM0332s5/FBz/4QWzfvh2pVApvectbUCwW0Ww28Qu/8AsYHR1FMpnEO97xDjSbTdcxPvnJT+LVr341RkdHEYlEcOjQIXziE59Yc65er4cHHngAExMTauzPPPOMMd+hUCjgF37hF7Bjxw5EIhHs3bsXH/nIR9Dr9S77Wi0sLgV33XUXAOD48ePqs8OHD+Mtb3kLhoaGEI1Gcdttt+Hhhx9es2+hUMAv/uIvYvfu3YhEIpicnMTb3vY2LC4uqm3m5+fxUz/1UxgbG0M0GsWNN96I//bf/pvrOFJG/emf/immp6cRiUTwspe9DI899phr29nZWbzjHe/A5OQkIpEItm3bhn/37/4dTp06BeBiXtHTTz+Nr33ta0r+UKYxVPK1r30N7373uzE6OorJyUkAbhkn8cADD7hkGPHpT38at99+O+LxOHK5HL7/+78fX/rSl9Ydg1fOwOc+9znceuutiMViGB4exn/8j/9xTfhmozJ/kGA9A/+GbreL++67D3fccQd+93d/F1/5ylfw0Y9+FNPT0/jZn/1Z17b//b//d5TLZbznPe9Bo9HAH/zBH+DVr341nnrqKYyNjW34nCMjI/jEJz6Bn/3Zn8Wb3vQmvPnNbwYA3HDDDahWqzh//jy+/OUv43/8j/+xZt93vetd+NSnPoV3vOMdeN/73oeTJ0/iYx/7GL7zne/g61//OkKhEADgAx/4AD784Q/jda97HV73utfh29/+Nl772tei1Wo9j7sFPPjgg4jFYvi1X/s1HDt2DH/4h3+IUCgEv9+PlZUVPPDAA8p1OjU1hQ984ANq30984hO49tpr8SM/8iMIBoP4u7/7O7z73e9Gr9fDe97zHrXdr//6r+O3f/u38cM//MO477778MQTT+C+++5Do9FwjaVWq+Huu+/GzMwM3vWud2Hnzp3413/9V/z6r/86Lly4gIceeuh5XauFxUZAJZrL5QAATz/9NF7xildg+/bt+LVf+zUkEgl89rOfxRvf+Eb81V/9Fd70pjcBACqVCu666y48++yzeOc734lbbrkFi4uLePjhh3Hu3DkMDw+jXq/jnnvuwbFjx/BzP/dzmJqawuc+9zncf//9KBQK+Pmf/3nXWP7yL/8S5XIZ73rXu+Dz+fDbv/3bePOb34wTJ04o2fCjP/qjePrpp/He974Xu3fvxvz8PL785S/jzJkz2L17Nx566CG8973vRTKZxG/8xm8AwBr59u53vxsjIyP4wAc+gGq1esn37IMf/CAeeOAB3HnnnfjQhz6EcDiMb3zjG/jqV7+K1772tRsagwRl4ste9jI8+OCDmJubwx/8wR/g61//Or7zne8gm82qbS9F5g8EnAHDJz/5SQeA89hjj6nP3v72tzsAnA996EOubW+++Wbn1ltvVf+fPHnSAeDEYjHn3Llz6vNvfOMbDgDnF3/xF9Vnd999t3P33XevOf/b3/52Z9euXer/hYUFB4Dzm7/5m2u2fc973uOYfqJ//ud/dgA4f/EXf+H6/P/+3//r+nx+ft4Jh8PO61//eqfX66nt/ut//a8OAOftb3/7mmPrAOC85z3vUf//4z/+owPAue6665xWq6U+/8mf/EnH5/M5P/RDP+Ta//u+7/tc1+s4jlOr1dac57777nP27Nmj/p+dnXWCwaDzxje+0bXdAw88sGbsv/Vbv+UkEgnnyJEjrm1/7dd+zQkEAs6ZM2fWvU4Li42CMuQrX/mKs7Cw4Jw9e9b5/Oc/74yMjDiRSMQ5e/as4ziOc++99zrXX3+902g01L69Xs+58847nX379qnPPvCBDzgAnL/+679ecy7O24ceesgB4Hz6059W37VaLef7vu/7nGQy6ZRKJcdxVmVUPp93lpeX1bb/63/9LweA83d/93eO4zjOysqKA8D5nd/5nb7Xeu211xrlGO/BK1/5SqfT6bi+02Uc8Zu/+ZsueXb06FHH7/c7b3rTm5xut2u87n5joCz6x3/8R3U/RkdHneuuu86p1+tqu//9v/+3A8D5wAc+4BrjRmT+IMGGCQR+5md+xvX/XXfdhRMnTqzZ7o1vfCO2b9+u/r/99ttxxx134Atf+MKLPkbgohssk8ngB37gB7C4uKhet956K5LJJP7xH/8RAPCVr3wFrVYL733ve13uuV/4hV943mN429vepiwMALjjjjvgOA7e+c53ura74447cPbsWXQ6HfVZLBZTfxeLRSwuLuLuu+/GiRMnUCwWAQD/8A//gE6ng3e/+92u4733ve9dM5bPfe5zuOuuu5DL5Vz34zWveQ263S7+6Z/+6Xlfr4WFjte85jUYGRnBjh078Ja3vAWJRAIPP/wwJicnsby8jK9+9at461vfinK5rJ7JpaUl3HfffTh69KhyXf/VX/0VbrzxRuUpkOC8/cIXvoDx8XH85E/+pPouFArhfe97HyqVCr72ta+59vvxH/9x5aEAVkMYlGexWAzhcBiPPPLImtDipeCnf/qnEQgELmvfv/3bv0Wv18MHPvAB+P1uVWQKJ6yHxx9/HPPz83j3u9/tyiV4/etfj4MHD+L//J//s2afjcr8QYANE/wbotGoit8TuVzOOFH27du35rP9+/fjs5/97Is2PomjR4+iWCxidHTU+P38/DwA4PTp0wDWjndkZMQlKC4HO3fudP2fyWQAADt27Fjzea/XQ7FYRD6fBwB8/etfx2/+5m/i0UcfRa1Wc21fLBaRyWTU2Pfu3ev6fmhoaM3Yjx49iieffHLN70fwflhYvJD4+Mc/jv3796NYLOLP//zP8U//9E+IRCIAgGPHjsFxHLz//e/H+9//fuP+8/Pz2L59O44fP44f/dEf7Xuu06dPY9++fWuU5jXXXKO+l9DnJ+cM5VkkEsFHPvIR/NIv/RLGxsbw8pe/HG94wxvwtre9DePj4xu8A8DU1NSGt9Vx/Phx+P1+HDp06LKPIcF7cODAgTXfHTx4EP/yL//i+uxSZP4gwJKBf8Plslsv+Hw+OI6z5vMXIjml1+thdHQUf/EXf2H83kspvpDwul9en/NeHD9+HPfeey8OHjyI3/u938OOHTsQDofxhS98Ab//+79/WQl/vV4PP/ADP4Bf/dVfNX6/f//+Sz6mhcV6uP3221U1wRvf+Ea88pWvxL//9/8ezz33nHqOf/mXfxn33XefcX+d6L6QWG8eAhc9hD/8wz+Mv/3bv8UXv/hFvP/978eDDz6Ir371q7j55ps3dB7p5SO8rPqrLTHvhZb5mx2WDFwGjh49uuazI0eOuDJoc7mc0d2kM/h+7jCv76anp/GVr3wFr3jFK4yTkdi1a5ca7549e9TnCwsLV4z9/t3f/R2azSYefvhhl/XC0AbBsR87dsxlfSwtLa0Z+/T0NCqVCl7zmte8iCO3sPBGIBDAgw8+iFe96lX42Mc+psJloVBo3edyenp6TTWSjl27duHJJ59Er9dzeQcOHz6svr8cTE9P45d+6ZfwS7/0Szh69ChuuukmfPSjH8WnP/1pAJfnrs/lcigUCms+12Xf9PQ0er0ennnmGdx0002ex9voGHgPnnvuObz61a92fffcc89d9j0aFNicgcvA3/7t37pKVb75zW/iG9/4Bn7oh35IfTY9PY3Dhw9jYWFBffbEE0/g61//uutY8XgcAIyThzW7+ndvfetb0e128Vu/9Vtr9ul0Omr717zmNQiFQvjDP/xDl0VwJbPrycbleIrFIj75yU+6trv33nsRDAbXlBx+7GMfW3PMt771rXj00UfxxS9+cc13hULBla9gYfFi4Z577sHtt9+Ohx56COl0Gvfccw/+5E/+BBcuXFizrZQLP/qjP4onnngCf/M3f7NmO86T173udZidncVnPvMZ9V2n08Ef/uEfIplM4u67776ksdZqtTVVOdPT00ilUq5S4EQiYZRN/TA9PY1isYgnn3xSfXbhwoU11/fGN74Rfr8fH/rQh9Z4BKV82OgYbrvtNoyOjuKP//iPXdfw93//93j22Wfx+te//pKuY9BgPQOXgb179+KVr3wlfvZnfxbNZhMPPfQQ8vm8y039zne+E7/3e7+H++67Dz/1Uz+F+fl5/PEf/zGuvfZalEoltV0sFsOhQ4fwmc98Bvv378fQ0BCuu+46XHfddbj11lsBAO973/tw3333IRAI4Cd+4idw9913413vehcefPBBfPe738VrX/tahEIhHD16FJ/73OfwB3/wB3jLW96i6mYffPBBvOENb8DrXvc6fOc738Hf//3fY3h4+CW/bwDw2te+FuFwGD/8wz+Md73rXahUKvizP/szjI6OuoTm2NgYfv7nfx4f/ehH8SM/8iP4wR/8QTzxxBNq7NJa+JVf+RU8/PDDeMMb3oD7778ft956K6rVKp566il8/vOfx6lTp67Y9VoMFn7lV34FP/ZjP4ZPfepT+PjHP45XvvKVuP766/HTP/3T2LNnD+bm5vDoo4/i3LlzeOKJJ9Q+n//85/FjP/ZjeOc734lbb70Vy8vLePjhh/HHf/zHuPHGG/Gf//N/xp/8yZ/g/vvvx7e+9S3s3r0bn//85/H1r38dDz30EFKp1CWN88iRI7j33nvx1re+FYcOHUIwGMTf/M3fYG5uDj/xEz+htrv11lvxiU98Ah/+8Iexd+9ejI6OrrG6dfzET/wE/st/+S9405vehPe9732o1Wr4xCc+gf379+Pb3/622m7v3r34jd/4DfzWb/0W7rrrLrz5zW9GJBLBY489homJCTz44IOXNIZQKISPfOQjeMc73oG7774bP/mTP6lKC3fv3o1f/MVfvKR7NHC4YnUMVwhepYWJRGLNtnopDMt2fud3fsf56Ec/6uzYscOJRCLOXXfd5TzxxBNr9v/0pz/t7NmzxwmHw85NN93kfPGLXzSW3fzrv/6rc+uttzrhcNhVZtjpdJz3vve9zsjIiOPz+daUGf7pn/6pc+uttzqxWMxJpVLO9ddf7/zqr/6qc/78ebVNt9t1PvjBDzrbtm1zYrGYc8899zjf+973nF27dj2v0sLPfe5z695XeQ8XFhbUZw8//LBzww03ONFo1Nm9e7fzkY98xPnzP/9zB4Bz8uRJtV2n03He//73O+Pj404sFnNe/epXO88++6yTz+edn/mZn3Gdp1wuO7/+67/u7N271wmHw87w8LBz5513Or/7u7/rKoG0sHi+8HrWHefifJuennamp6edTqfjHD9+3Hnb297mjI+PO6FQyNm+fbvzhje8wfn85z/v2m9pacn5uZ/7OWf79u1OOBx2Jicnnbe//e3O4uKi2mZubs55xzve4QwPDzvhcNi5/vrrnU9+8pOu40gZpUPKlsXFRec973mPc/DgQSeRSDiZTMa54447nM9+9rOufWZnZ53Xv/71TiqVcgCoEr9+98BxHOdLX/qSc9111znhcNg5cOCA8+lPf3qNPCX+/M//3Ln55pudSCTi5HI55+6773a+/OUvrzsGvbSQ+MxnPqOONzQ05PyH//AfXKXgjrNxmT9I8DmOIcvNwohTp05hamoKv/M7v4Nf/uVfvtLDGUgUCgXkcjl8+MMfVk1ILCwsLCyeH2zOgMVVi3q9vuYz5juYWj1bWFhYWFwebM6AxVWLz3zmM/jUpz6F173udUgmk/iXf/kX/M//+T/x2te+Fq94xSuu9PAsLCwstgwsGbC4anHDDTcgGAzit3/7t1EqlVRS4Yc//OErPTQLCwuLLQWbM2BhYWFhYTHgsDkDFhYWFhYWAw5LBiwsLCwsLAYclgxYWFhYWFgMODacQHg5PaotLCxeWGzGFB8rOywsrjzWkx3WM2BhYWFhYTHgsKWFFhYWLypMy3lz5T3HcfpaLF5LgW/0vFcr9GviWDdyrabrer4eI697td5vc7kwXb/XPXmhzrER6Oe81Ot/Ic7phY2MhdtcyvOkjrHR0sKreWJZWAwKtkqY4HKE1UuJF0PhWli8VDDNr/WeX+sZsLCwsNBgFb/FVsCleNZszoCFhcUVgVW4FhYvDji3LmWOWTJgYWHxksMSAQuLqwuWDFhYWFhYWGwxXCrhtmTAwsLCwsJii+FSk/4tGbCwsLCwsBhwWDJgYWFhYWEx4LBkwMLCwsLCYsBhyYCFhYWFhcWAw5IBCwsLCwuLAYclAxYWFhYWFlsIl7N8gCUDFhYWFhYWWwiX09TLkgELCwsLC4sBhyUDFhYWFhYWAw5LBiwsLCwsLAYclgxYWFhYWFgMOCwZsLCwsLCwGHBYMmBhYWFhYTHgsGTAwsLCwsJiwGHJgIWFhYWFxYDDkgELCwsLC4sBhyUDFhYWFhYWAw5LBiwsLCwsLAYclgxYWFhYWFgMOCwZsLCwsLCwGHBYMmBhYWFhYTHgsGTAwsLCwsJiC8Hn813yPpYMWFhYWFhYDDgsGbCwsLCwsNhCcBznkvexZMDCwsLCwmLAYcmAhYWFhYXFFoLNGbCwsLCwsLC4ZFgyYGFhYWFhMeCwZMDCwsLCwmLAYcmAhYWFhYXFgMOSAQsLCwsLiy0EW1poYWFhYWFhccmwZMDCwsLCwmLAYcmAhYWFhYXFgMOSAQsLCwsLiwGHJQMWFhYWFhYDjuCVHoDFxhCJRBCNRtHr9dBut+E4DtrtNnq93pUemoWFhYXFJoclA5sEu3btwv79+1Gv1zE3N6feK5XKlR6ahYXF8wR7yV9OSZg8hs/ng9/vX3NMvqzxYOEFSwZeYvj9/jWLSOj/c+JKJJNJjIyMoFqtolqtIhAIYGVlRU18ua/XsU3HtbCwuPK43HnJ+U254vP5EAgEXN9JIuDz+TZECnRiwe2tDNm68Dkb/GUvZxUki4vw+/2IRCIIhUKYmprCxMSEmpB+vx+xWAzB4EVe5jgOGo0GCoUCWq0WSqUS2u02hoeHMTIygna7jUqlgm63q47fbrfRbDbR6XTQaDTgOI76vZLJJNLpNBqNBs6cOYN6vQ6/3w+/349Wq4V6vX5F7onF5WEzCmIrO15Y+P1+hMNhBAIBxGIxBAIB9X8wGEQwGFRzHAA6nQ4cx1HyodfrodVqodvtuvZJJBIIBoOIx+MIh8Pw+/0IBALodrvodDrodDqo1WpoNBqo1WoolUrodrtotVqb8rnc6iDxI9b7jaxn4CWAz+dDNBpFNBrFgQMHcOONN6LX66nJmM1mEY1GFUEolUpKcc/OzqJaraLT6WBxcVFN8lgshomJCaTTaVSrVZTLZbRaLRSLRXQ6HQQCAfh8PuTzeUxMTKBQKKBUKgEAQqEQAoEAqtWqIg8WFhZXP3w+H0KhEKLRKILBILLZLMLhsJIvwWAQkUjERQa63S7a7bYyJEgK2u02QqGQykcaHh5GJBLB0NAQEokEAoEAAoEAer0earWakkGlUgkrKytotVpot9uKLFhsblgy8CIgEokgGAwiFoshnU4jFAohlUohGo0im80iEAgo1h0KhZBOpxGNRtX+Pp8PqVRK7Q+4XX2tVgs+nw+ZTAaRSATDw8Po9XqKudPj4PP5EIvFkEwmEY/HMTw8jGAwiF6vh16vp84v0Wg00Gw27QS3sLiCkNY+LfZUKoVEIoFoNIpUKoVQKIREIoFQKIRgMIhQKKTkivTGSMu+0WgocuA4jiIC4XAYmUwGoVAI8Xgc0WhUhQp6vZ7aL5vNKs/l6Oioyl1qNBool8uoVqtKvlhsLlgy8AKDijyZTGJychIHDx5EJBJBKpVSBIGMOxAIIBqNYnR0FMlkUinwZDKJVqsFv9+P/fv3Y2hoSE32er2OhYUFtFotFRrIZrPI5/MutxCFQb1eR6VSwcLCApaXl7GwsKAmbTAYRDgcVrFBx3EwOzuLxcVFNJtNVCoV6zWwsHgJ4ff7lULOZDJIJBLYuXMnkskkpqamsG3bNqTTaYyOjiIYDLoUv+M4LkXf7XbVe6/Xc+UKkDjE43EkEgkVeqDyp+eSx2Cogceh8i8Wi/jud7+LxcVFnDx5EjMzM2i1WooUWGweWDJwGaDbny62WCymFDHJQDQaRT6fRy6Xc5EBuu/o6otGo4jH44jFYuq7druNTCajQgjZbFaRgUgkglarpSZcu91GMplENptVTB5YjQ/R8m82m8jn8wCAWCyGRqOBcDisxs7JTvLgOI6KF1pCYGHxwoJznV5CqZzD4TASiQSy2Szi8TjGx8eRSqUwNjaGsbExJJNJ5PN5RQSkAu90OqrkmCTAZKmHw2FlnMTjcfj9fpW31Ol0FBGQ+QYS9EYEAgGMjIzA7/ejWq2i1WqhVqup4zA3weLqhyUDl4FQKIQbb7wRu3btwsGDB3HbbbchEAioSTQ/P49isahiecFgEOl0GsFgULHrbDaL8fFxtQ0FAifXtm3b4DgO4vE4gsGgst6j0SiGhobQarUQjUbRbDYxNDSEXC6nBIzjOMprwISgdDoNv9+PRqOh3IK0PjqdDhYWFlCpVPDII4/AcRyVHNRut5WL0MLC4vkjkUggFoshm81ieHgY6XQa09PTSCQS6v9IJKLCAwwFSPc9FTctdSrrQCCgQop+v9/lEaAM8fl8KgQRCoWUAcE5Lr2LPAbBYwcCAcTjcUQiEbz85S9Hu93G4uIiVlZWMDs7i6eeegrlchnnzp1T+UztdvulvM0WlwhLBjYIThhOxLGxMUxNTeHGG2/EPffcg2AwiHa7jU6ng6NHj+L8+fNKmQYCAZU70Ol0FBnYtm2bIgiO4yi23ev1kEwmXS4+IhAIqJwE5gZEo1FEIhE1NunOI+MPBoPYvn07Op0OcrkckskkEokEMpkMut0uzp49i2KxiMOHDyOTyaDX6ymSIpsbWS+BhcXzQzQaVZb/+Pg4crkcDh48qP6XSYEyB4Av3WKX1UOyxJCfkwiw7FAaHnqYQb4TOimgXGFeQyaTgc/nQzabRaVSQSKRwNLSEpaXl1WiIb0WVn5cvbBkwAM+n08x8lQqpZL8qEjvvPNOTE9PY3JyUil4egZisRjy+TyazSbq9boqAQoGg2g2m2i1WgDgcuGREEj2zMmuu/o4kVOpFLrdrvIeyP3q9Trq9boSHNyXXgYmIjIkQIvjzjvvxM6dO1GpVLC4uIhyuYynnnoKS0tLqFQqqNVqLqFBN2C9XlfuQQuLQQcV79jYGLZt24ZUKoXt27cjFoshk8kgFospMi4z+VOpFCKRiOoVIK16GQakB5BKWnoECH6mGxM0GmTuEscMQIUGpaeR7/L69L4GlJHMRapWq9i5cycKhQJOnz6NmZkZNBoNFItFFYawuHpgyYAH/H4/EokEEokEJiYmMDk5iUwmg927dyOdTuPmm2/G5OQkgsGgYryceIzDNxoNVykgY/BU+NxeT9Ih6wagJg2VOicaPQLS5QesEgyZU9BsNlWvA5IBACqUwO9isRi+7/u+D6FQCI1GA9VqFUtLS3j44Ydx8uRJzM/PY2FhQY271+uhWq2i2WwCgCUDFhb/BuYETU1N4WUvexnGxsZw6623Ip1OK/e6jPfT+yYte4KyRX4uSQG3ofI3NTGTYQIAKn9J9w7w2PRKkgRIwiDDDRLhcBgAMDQ0hN27d6PZbGLXrl0oFAr49re/DQAqZMAE6BczyfCF6Oo4SLBkQEMikcD4+DhisRh27dqFdDqtPANk9yzxofudLnuZxUsvgZw8rB7w+XyIRCIAVieqZPWSzcskINlWlBNUTmjdg8DvOPGZNCRdh2Tneocxhhfi8Th27dqlCIheOsT1EhhO6HQ6qNfrdgJaDBz8fr+K9+/YsQPZbBbT09PYtWuX8igy8VjvHCoVtVT+Url7kQQ53037cT5zG77r3gZ9PxovetjBNHZ9PFI+JJNJjI2NYe/evSgWiwiFQmi1WiiXy8ogkf0KZCXE85EjJmL0YkNv9LOZYMmAhvHxcfzIj/wIxsfHccstt2B8fBwrKytYWFhQ5T10+TNmFo1GlcXf7XbRbDZRq9XUA81GIcwSpoKWxEG6AqXbTw8T8EVXHz0FZPe1Wg3tdlspfyp1v9+PZDKJYDCoPA1ykgOrgoLEIxgMIpPJ4J577kG73caXvvQl5eIjAchms4hEIjh9+jTC4TAqlQpmZmZsspBFX2xmoemFXC6Hm2++GSMjI3jVq16Fqakp1ROAuT5SkXL+A24XPRWprvh1JSy/04kCsKrwdcIg3037SblAgrGR30oaLvR0sF9KIpHAwYMHUSwWcebMGdRqNczMzKBcLqNUKqFYLKqOqzQoZFXE5T4rOil6MaF7bTbb823JwL8hFoshFoupTP7x8XFVygNcbMbDLGA2COKDRqucn/UDM3hlDE5aAoSMp3kdU3fXcQLLySvdfLogkvtIDwHJAPdPJpPo9XpIJBLKo0FCMzQ0hGg0ikqlglKphGAwiGKxiEaj4SIym21iWFhsFAzZZbNZjI2NYWRkRMmOUCikynt1y99U8tdP8UqrfyNj0nMMADfB2KjSulzlJhMWZTiT+UXtdlvJ03A4jGaziVAopDolsgJBT0C81NCClT0bgyUDgEqcu/vuuzE8PIxrrrlG1fsyDp5IJBAOh1Ufbj3xhha94zgqHkiFyEQ7WtuhUMglCKRilrX9UiHTEpeufwoaOQ5uw7wBYDUEwPwAhjB0giA9Bjwex9fr9VTm8PDwMK699lrEYjHlfThw4AAuXLiApaUlfO9731NlRcViUbVLtrDYavD5fDh06BCuu+46jI+Pq7wAVgrJkBznoWzqo9fvS+JsyguQkMRfuvx5Lmlw6ApUzvt+ytWLRMixSK+DntDo9/uVrGGfFBoX11xzjUo+bjabaDabKJVKaLVaqtR5eXkZ8/PzqNfrqhka85SutqZGVyIs8ULCkgFcfIh37NiBO++8E4lEAvl83pUHAECxWk5ePY4mFSeFgFSs/NtUs6tn6TL+LiEVNJm2TDSU18LzSMFisv5NbkcZhpDj5ISOxWIYGhrCvn37kEwm1fepVAqZTAYLCwuoVqtYWVlRayp0Oh3bzdBiy4Hhv/HxcRw8eBDj4+PYv38/4vG4yyIGsEZ56pVEhP73esqlnyeBc9ykNHU5pH8mSQWwdrVV6YWUn8kKBD1xkaFMdlvlPaKMaDabKo/g/PnzKBaLmJ2dhd/vR6VSQaPRQCAQcPUssITghcNAkgHG+uPxOK677jqMjIzg1ltvxejoqIrty1gb4+5sKOT3+zEyMqIYLhcBYcyMCTF+vx+pVArAWrcd4I7X6ZNNTijTPqZYHwBVYhiLxdT4uXARJ5/u6gdWBRTgJimdTgeFQgGNRgOxWAzXXnsthoaG1D3iGJgP0Wg0VOhgbGwMvV4Px44dwzPPPINqtYr5+XnlsbAYXGxmYsgGQNdccw2Gh4dx/fXX4+DBg6rzqKzskYpW5gLohoRpLuvf6/dMT/6TxIN/m8IQ+ruXUaKPzUQIdFIgmxzJ8+hjkAYHx8tFlgKBAPL5vArdZjIZNBoN7NixA61WS5U8l0olLC0tod1uqwXarobn6moYw+VgYMlAPB7HyMgIXvva1+LQoUPYuXOn6vonM1oBKDLQbDYxPz+v4uepVEqRAcbHHcdR5CAUCiGTycBxLnYE1LN69cxeXWDwXboN+wkPAMptH4vFAECFCzjZaM3ISgSOT4LX1Ww2VaLP8PAwbrnlFtfKaBwHO6Z1Oh2MjY2h2+1i3759GB4exqOPPgoAWFhYUEszW1hsRgQCAdUO+K677sL+/fuxY8cObN++XSUVA6stfaUHgKFCmfArX7qnTk/6o3zZSAxfzwniZzoZ0CuVuA/Pq4ce9dCoHKdOAkwkQz+3NHjoeex2u6pke2xsTBlaXDlxdnYWxWIRMzMzOHLkCCqVCtrttko63KzK+EpjIMkArdbR0VEMDw9jaGgIsVhMPZy6O52fs/cAH14qNcbF+BBSwcqyQ9kxUJ/0OiPXJ5IpJqiHIfg/VxxkyY4expAuOznJZWxQDy+wxzjvgUkY0c3XarWUIGSns6GhIezZs0ctuFQul3H69GksLi4iFospUlWv15XL0DYksbja4PNdXCl0586dGB4exsjIiGogROWuK1tTwp5pLuueQ+Yl6fsCWONh0L83fdePRJjCFHIfKYNM1yX/pzwxeSxMJYm6YcP7qF8j5UG320Uul0M4HFayqVKpwOe7WElVKBRQqVRsU6PLwECSgeHhYdxxxx2YmJjAoUOHsGfPHhdzJ6OXFn+n00EoFMKuXbvUw1kqlVRpIbA6ScPhsKrp50PLicQyPxNLp1CQ8TYqZpksyEnVaDSUVV+v19U1yAREJu1Q8dPLwQYhgDtBiSxcejHYgKjdbrusAUkcKpWK6lLIZEHmV0xPT2N8fFxdZ7lcxsc+9jF88YtfxNjYGG688Ua0Wi0cP34clUpFJR1aWFwtiEQiiMfj2LdvH+677z7k83kcOHBALUQGmJP0JNFmVYFcsKyfK15XvFJeyKRgwktR68cwhRz4nTyHHL+05mU/Ax5b/i8JgamCie/SMOIxeGwaWFKp85rz+bySe9VqFZVKBYcPH0ahUMDTTz+NU6dOoVqtolAorLnGQcKlekgGigxw0kWjUYyMjGB4eFjF+dgVixNNj4PRRUY3PFv9mjLwTRNbThCvSS5hcu3J7cmKe70ems2mWkxIdkPkeSWx8Dqf3rBE95KYQhP6dnqpFL+PRCLKNRqNRlGtVjE0NKT6s4+MjKDVaqnFndizgSTMwuJKIxaLIZ1Oq+c1n8+rUlt6AiV0YiAVoyQCemnwRskA32WCoFfo0AvrKQuTZ0B+57U/r4PyTt+P2/QjLSYiwf/pPSBBi8ViWFlZQSQSwdzcHAqFAgKBgKrmsjJkYxgoMsCa1h07duC2227D6Oio6u/PpD+TxU6l1ul0UKvVFGsFVssBdfc+oStzPpwssQGgrPlut+uKp8vjSUXPDn/NZnNNYw5JAng+utE4hnq9rhIl2TpZehx6vR5qtRoWFxfR7XaRyWSQSqWQzWbXuEPpSfD7/Wop1PHxcXUNrCKgV4TX9fKXvxypVArj4+OYmppCIBDAvffei06ng0cffRTPPPMM5ufncfLkSevus7hiiMViiEQiuPnmm3Ho0CGMj49jcnIS8XgcoVBIedNkojGwGg83hQXlIkGmVsCmNQUAtzyQ+QP832ue6IaNfiz+z/N7HUMaM6Z3ub8kBNK4kv+beqlwfHq+hT5WkolgMIhUKoX9+/ej3W5j+/btWFlZwczMDJ5++mmUSiWcPHkS5XJ54BqheXmAvDBQZIBZwLlcDrt27UI+n1ftdPUYkymuRotVn7ymEj2vH4FKOxKJqH4E+gSQ++sCgISh2+0q5iuhxwp5XZLdM0EnFospwUTXJfdtNpsoFAro9XrI5/OIRCJqFTX9nnAMrDBIp9Nq/Eyc7PV6KmTh8/mwe/du5RkYHR1Vi7X4fD7l+uv1ejhz5owlAxZXDNFoFLFYDDt27MD111+vvANMFpRzQBJq2X0UcFcTyUWCGDogdMMCWKvA5f9seNZP6EsZYpIrRD/lYfpcJyuS2OgyUiZjcz8vYsLvJAnQyRHlLvOy4vE4ACgv4/Hjx1GtVl19Cq6WaoOrFQNDBthLYHp6Gvv27VNNhSRDZ8MfZtbzgZONffQHElhbAiRZrexLwHd9e1oTjUZDJQBKLwWtarnQkc6s5Xh1QqBPUE4suZqidMEBF2Ok2WwWAJQVJDsn6rXSvMc8nxQ6MpTCxkfpdFqVOXJ7ehh2794Nx3EwNTWFnTt3YmVlBd/+9rextLT0gjwLFhYbQSQSwfbt2zE0NISxsTHVWld60IBVpeyVPEclaZqbcjsARk+CrsxNIQjZU8DLkPDyeurHMr3r20q3vS5XvPYx/W+6Pv3e8hzyfHzxnsrqjFAohG3btuHmm29WeQMLCws4c+YMLly4YI0LDwwUGdi7dy9e/epXY3JyUi0ownI72bOfiXlsHiIbDtVqNTXxdDIgmw3JlQb9fv8aqxpws1uGHtimk6EA+dBTEZNgyFImbmtKSjLF+tlEiKEKfk5SxNbMPI88Lq9N96joSVFSEDH8wZLLXC6H4eFhtFottSwyvzt48CAOHjyomhedOXMG58+ft2TA4iVFJBLB3r17sX37duzcuRPpdFrlDAEXk/c47xky0MOCcu7qpEDfhkpNDx0QMmRIAiC9fZyHOmmQHkedSHAM+rsuP2RIQHfX69tIQqMTCNM16dcm/5ceTZ1MUf7JMCeTpVOpFHbs2IFyuYxUKoW5uTk8+uijag0EvZR6q8Er3NMPA0EGOFlTqRTy+TxSqdQaJi4fND0pR58cOtaLtxEUHH7/6iJCPt/Fnt0sP1zvPHICAqtZ/Tob78fw+42P5EROfNO6BiaCobsr9UQpjpdhCp5TLtcMrJIPhjIymQz27dsHn+9ivkOj0UC9XldhDAuLFxIkw5lMBtlsFtlsFtFo1EX2Zd2917OvK3qdqMtGYFRqUsHpc47nkH1Q9F4EOjbiCdDns5choYcU+Jl+DOkd8Oo74PWS5zKFNuT9o1dSJ0Y8Nw25bDaLXq+H8fFxbN++HZVKBXNzcwOXQ7AetjwZoPUbj8exe/duXH/99Sq2zcnMxTE4QcnypdVNmFxUhM6eGWKQ26VSKSSTyTXsn2y2VCrB5/OpvAB+pz/kgFt5msYq0Y8wSMTj8TWtmOXEliWX8hgydCBJBUsrac3IhEOC29AbwgztXu/ieggTExN497vfjWaziSNHjuDUqVM4duwYHnnkEdTr9XWeAAuLS0M6ncb09DRGRkZw4MABjI+PI5PJqJVGm80mfD6fKimWYTCpyClLSG45RzmfZR6O9DhsBMwX8vtX1xoxhS/0JDxT+aPuESD6GRC6B8S0j0nZm3IBpEdAjtNEBjhmmZwIuL000tAIhULYu3cvWq0Wkskk9uzZg5MnT+Kf//mfVRdDi4vYcmRAV9bBYBDJZBLJZBKpVEotIQyYy1y4n8kFpp9H31cek9AnKL0UJkjPgG4Z6Of1unYTU9dhYuJyvF5uShkW0L0p8tq9LCJ5HximkNckLR5ZhhWJRLBz504AUE2NuC46QzoWFi8UIpEIhoaGkM1mkUwmkUgkEAqF1HOqk1kqL6nwdc+jl2eARP5SiACwKivY9tw0X72s7hcLcv73s/z7vffbr1+IA4CLIMj7HI/HVYJyq9VCpVJBLBZTqyNuRe9iP/nvhS1FBnw+H3K5nFL6uVwO6XQaN910E0ZGRrBv3z7UajVVVsc4NSczJ2gkElnjcpLbkGSQ+Uvlzv1oRQBQyqvX66mkPBP4cJpq9vUFiYDVJiYml50eQ5RuTGY6c+UvKYhkFrS+9Ko8JoWQbNTEccbjcbU/7xePxevgKmX8jvdMugB5bknOJiYmkEwm0e12MT4+jkgkouKAFhbPB7FYDIlEAtPT07j11luRyWQwOjqqDAh6+6LRqHpOZTKazP9hnpFU9rT++X80GvU0DNYDLWASk2azqcYorW2v8jwJuR+wVp7w7/Vg2lb3Cnh5CSh3+C7d/vJYwGoOBJW/zFEwGVO8Pv6Wfr8f58+fx+LiIp588kmsrKxc8v3fithyZCCZTGJ4eBi5XA6Tk5MYHh7GPffcg4mJCVcmPq1fJuNJpWhKBtIbe8iJLt32FBj6Q8/+2f0yWXu9HhqNhidT1SebySLQ2bb8XE44uvvb7bZyq3EMvAaZocvzUwByH5nwxMkrhZ50mcproIUvu7Lp1QqmRKShoSHk83nMzc0hl8uh0+mgWq1aMmDxvBGNRlWp6/T0NJLJpEoalMqJYUadCEhFrxMBGg4ydHi5RECCy6Hr+Ua6wtUTCPXtvEKIXvJEh5dnQv4tx8SxSMOFnkedtHh5XuUxGdqU++jhi2w2qxY92rlzJ6LRKI4fP27JwL9hy5GB0dFR7Nu3Ty2cEwqFVM9qYDVjvtlsKhbPDoSyZl/G/wjmGACrk5+NewCseZCbzabKlOc+UvHqkN2ypJtcsl2OTX83ueqld0NOambcRqNR12SU20uhIcsq9Xgkt+fiRXpYgPvRkyAbD8nOjZLt89plFYMujHK5HG655RbMzs6iXq+jVqt53lcLi/Xg9/uxa9cuTE1NYc+ePaozKeeCfK5NcXe+pMeQRFn/jGTihQAJBZf3lcl7ujUuYXIje8mOfjAZC/oxTS+v3ACdMADu5Mh+x+e4TcnfEslkElNTU0ilUjh9+jR8Ph8KhYJqoz6o2FJkIBAIYGpqCi9/+ctRr9dRLBaVG5kxauluZ+MfurtbrZbLnUdXH7DWKqfyikajaplixrzJ0kulEsrlslJ6PKYX5BikItQJgYnFm0p5OG7pZqf3hJ3VHMdRiwx1u13X+gcULNxXlkzq55G9GPRQBUsLKRh5LCY+6ddM4UnyJeOv9Fps27YNP/iDP4jz58/j+PHjuHDhwqU/MBYWgFoq97rrrsMdd9yBbDaLoaEhV3KeviIh4CbqfLalwpeew2AwaCwvfiHAsCfnDOeu7h2QkHF9WSptkh+m/3Wrm+/6MfRwgCk0oH8mIY0GPRSge2+BtdULJvmZy+Vw0003YWVlBQsLC0ilUjh69Kjqljqo2BJkwO/3q1bD7KpHyAV+dDLAMAG9AlL58Xu9NE63ZPXjSWUdCoWQTCYRDodViMAU+5fYqNWvQ3fn6YRBn1QUVpKde0Ee2yRUZOzOtJ8cjywFknka/SwYEhbHcdQiUD7fxWzsWCymEkRJpiwsNgqfz6cqfFKplMsjwO/5LhWQTs7ly0TepcfrxboOk4I2bafvY7K8vY63EXIAmMML8jP9bylbvEITJhnjdT6TLCGkR3doaEiVGkYikYFey2BLkIFYLIaDBw8il8upRjnZbBZ79uxRjFxamJIUsPUt2TWw+iDJBEHG+fpNaCksOC6GELZv3452u61WODNBj9NL96NUnjwXYcoxkFUMjMWxDG90dBTZbNa1HoOMd+rg9chj8ZwyX4KTSG7Pkh/mYbRaLfR6F5crbjaba7pA6lUKJAJzc3NoNpsYGhpCJpNR5V2RSAR79uwBAJw9exZnz571vL8WgweTtSsRj8dxzTXXYGRkBLt378bQ0JDq9GkyBggSAlr90gugVxBwmxcTJqJuGruJ5MiQpNxPDwHwXRonXiSAf+svGUqVCY4sY5bf64aN9HyYQrjcRjc8dAPM57sY1kwkEmq9iU6ng6WlJVSrVSwuLm70tm8pbAkyEAwGkU6nMTQ0pFxxkUhEtbyV7XaB1eS/Xq+HarXqUljSSr7UydzPgterDrzgFRogvFiznJjStS/dcHJxJamcSXz0c+nn0a133UIiaZH7clt+R+Ytk4VM91GGPdgVslarIR6PI5FIKEFCy25oaGhgJ7HF5SMUCiGbzSKfzysvHrC2Da4pNMfvTR4B+Z1X6d+LCRm+kPNeV+762Lwsf/2466GfZa4re921b3L/czuv8a13Pp3AUC9QR6TTacRiMdW7YSuWG66HTU0GqKQTiQTGx8fVa3R0VMXD5QNCRRQOh1XoIB6PK7apu5hIBNZz7ZNMeNXnbwTNZhOVSmXN5/oCShyffO8HbhMIBJBKpVTpJAmBvp3pHLIhiMyqlu5+GW6g9a93SGO7ZVpd7OomiQiPCVy8r1xghL8RF1BiB8J6vY58Po9QKISFhYV1LUGLwYLXs8COpKOjo5iamsK2bduQSqXWZNv3UwpS+eseLS93+4sFyirHcZRRI/uCSBki54gcN7/Tr0tCkn6v6zLdcy9lbQoX9AtHUs7qRpPutZW/g5RhUmb1ej1XuGDHjh1YWVlBpVJBo9FQq1EOCjY1GZCxn3w+j5GREVVWCKx9AJkfIB922UWMcWk5UdYjA45zsVeB13aNRgMAXCVGOgqFAiqVCmq12hrhIZOV9CRBPX4pvQESnCyRSMQVDuFnwNrOX6b6ZJmIJJm29KbQ5Sf35bkAqGWXuVCUbk3JY5IMyHJHEo1qtYqlpSV0uxeXWE6n03j22Wc9fycLCwl6DvP5PCYmJrBt2zbE43HPhkImT4HJI/BSewAIyh/p5SPRprEioV8P/+a71/VIhawfTw9jbtRoMREC/u8VpjB5UOXx5HXpCl2GKxhGzuVyGBsbg99/sQeBJA2Dgk1NBuLxuCIATBxk/E5aprobiisTytiTVIK0XEkc+hECHk+2DpYhBUk2vCZFs9lUSYy664zQJ4W8Hn08eiyNkzQajboqJCToGdGVv4ztyQoAmQnM62OSJPMQ9DHQBafnYch+BJJk+P1+xONxZe10u11FaBKJBGKxmAohtFotda8tLNYDe9an02lXy2BdsfezaHVlp38m93spSIJuzTNBWB+Lrmz7kQHTOeTfppDoRsYpjRh6GqRckoaO7hGQv5XJO8OXXt3UL5RA70Cr1VLeYnoHBgWbmgxks1kcOHBATepoNIpIJIJwOOxSrnonLsbJaRnrypH7stmILJvTIePujUYDPp8PsVhMZesTtGr13INWq4VqtYpqtepSovpk0V2PUmHLsiBpift8PqWcuUYDSwpNkG56WRYoSwCp8Om6pyLnSossUyTr9vv9aLfbqtshQxPRaFRlbUsGL6+LXh957by3/J/JheVyGel02oYJLDaEWCyG7du3Y2RkRC3jLd3PfI70mnVgbQxad2/ryuel9BhQWVK+8V23knUPn67k5bspVCD/7uf+94IeguBxTMl+fEmPB/flZ9xPJnBS1kqjRoYL5PEzmQx27NgBv9+PU6dOIRAIoFarKVm22XA5z9umJAOcuLQSZW080P9B1GNLenxKTnS5EEg/6IKD+0nFb3Kt6Q+nnCD9LP6NTD5JDGj58HOe10RwdI+KHirQY3q69SNjl3oHQmn5y+9MMUhprejsX/ckRCIRtNttVb3BzoqbcRJbvDSge5h9RoD149rApSfQrWeRvlAwHd9L0evhxPWuR+b9eJ3LyyPidd1eIQmTN8UUEvBKtO4X4jCNl+DzwBebOA2ScbHpyIDf78fIyAgymQzy+fyaNpy0ULktLV3AvUgQlaPMEyCLlk13GHaIx+NrGDJwUalFo1FVusf3RqOBRqOBeDzuajwCQDX4oZXN2DlftMJlWMM0oSXLl4pdKvBEIoFsNqssdJYTckVBlj7S0mfIQk5qGb7Q+5/zPpOQmQSgrBzgZzJsIl2F+kSXnQkZutEnZyqVQiwWw65du3Dw4EEUCgWcOXNmoFx8Fm6sJ8Sj0agqU+VzxufclNujzz/dW+cVspNhxxervJCyRC/t1cetw+s7r/vWr5W6ac6vRwTouTB5CQhTPoZXmMBEEkzGii6L/P6LfWqy2Szq9TpGR0cRCoVUqeGgGBabjgzQDZ/JZIzNQTj59IQ7WqrSqpRWrswZ4HGYrGZadldCLkoErDYk4mfhcBjxeFyNRcbWqehIOnRvgnQ1bvSBlEqUZAWAa0liSZCAVYXfbrfXNO6RE8fEuPVj8Xs56XRPQrPZVMJLCgdZRyzPK9/1zGg2ImJCGL0elgwMNvp50mgJypATySjnh+5BvFxXv5QxXjLk+UBflIzwkhkblSUblTcmr4CXp0CeXw978m/93eQh8CIDkgjI4+jn1/9m18hoNIpEIoFms+laft6SgasQPt/FdrpDQ0MIBoOqXLDRaKgfLxaLuaxRWfIGwMX89Zie/OF5jFqthuXlZcTjcdV6WILKPBKJuJLspIeBoYNqtao8CCQDukufx5TCieOR7nPp+udnLBskAQiFQkopUuFT0UoPRKPRcI1dKl2dCMhmRVKY6vE4mbdgijtKTwAhhYTsjWDKm9CtgGuuuQbBYBDPPPMMTp8+rSo5LAYP6wlvJqHG43FlaerGArD2WTaRYZ3wEjKspfc6eaFA8i475+luep/Pp+Y8sOpJ9LpHJjf/Rlz//QgAYZJr+vcmxa7Pffk9PyOknDR5BqSRpcvUbDaLyclJxONxnD17FpVKRZUabnVsSjIQj8ddHfSYtEbFFI1GlfUtyYC0lhleMDXbkZPG5/Oh0WioJYhNZIDEgmSEx5APXrVaVUpXhgn0h5WVEPIBpeLkiyRAhkFIPmRHP6mkpWKV94Pej1qt5hIm9IhI5czjUvA4jqPyN2Q4Q75ktYacvNxHenBk+EOSOXmt8p7ouQN79+7Fnj17EI/H8dd//dfP70Gz2NKgty4Wi62xOHXoz7SE7h43WakvZgdCluqSEHgpWd3C9iqb05X+ela/6VwmAsExAHAZYvyc913/LbwU/nrQ88DkPXAcx9VgjrKJHQ7Hx8dVueHKygparZYlA1crSALoHms0GpidnUW5XMbo6Cjy+fwaZUJFqysUqWT0h5cPiVSeXpCJbfKcunucCpvWMuP0Mlvf57vYEEhXpjL8QW8EcwH4nV67z0ksBZ3MLJb3UYY2mEMgPScyyY8eDOn9kKU8+rtXRrIUnPJcMmdDbgdAXSvfZdzR57uYGXzw4EFkMhnMzMwM/GpkFqvgvGFoiVU/61ntJsXXz1rWldmL6RUgEZBeNKB/mITbeZUw94Ocq17f87wb8RboRMXLI6CP3QtevQF0g4/yVd4vLjzX7XbVmiebdUVUqYs2gk1HBujur1ar6v96va4aRaRSKaTTaZUsyOQ2v9+vyg6peKl0dTe1VIxMtqMLvdFoKIUrwWNR8YZCIdXkSE6ccDisuiNSGddqNTQaDSwtLWFpaQnZbBZTU1NqW5ngJN2Z8XgcoVAIzWZTNefR75Xcj14DqfA5Plr6Mn9CX52Nx6R3Rp7HdD90RQ+4J6q0nKSFJUmL/jCTvMgYLAkDyciOHTvw5je/GbOzs/irv/orHD58eGMPl8WWBysIuDBRIpFwrRBK6Bn0klhzDvD5k4pOKjN6BNbrYHqp6PV6KBaL6HQ6KJfLqnpGX91Pegd169jL2pbhDpNSl8fSvZr6Pjo2QgZMxKDfMb2IggwD8LcjKZO9V9iATZaR93o9xGIxjI+Po1KpoF6vY2FhwTj2rYRNRwaAVTc3laXMdgegatoZH9cXKQLMVmu/h026lPo91IB7wQ9pucrzybgZ2SlJCq9Hjtm0vXzxGiWRkcSGkJNdLxuU16ZPdH3SmSZoP6tCEgJ5bV6uRP28Uthy/PT8cHlkeZ5sNotms9l3YSiLwQNLkuUSw7rS8YJuWcp307aX4ta+FFD5M1zH3COZm6NbhV6WvIks6B4Dk0Wtf67/byIZ/TwJ8rv1EgAvBbqsNZEhmbjc611clp5NzKLR6IbWlNkK2JRkgA/Lrl27cNNNN7m66jWbTdRqNaXcgsEghoeHEY1GlSUtHwYKBL2uHoAql5OJehsRGIz7MyQArCouuUgP2TybniwuLiprnZY+z9dqtdBsNl1NNqrVquuh7nQ6WF5eXtMXgROA7Y71MkCOR1oEPIe83/LaZTKm7jXQPS18ly/eD6nEdeIlEz0lufP7/eoeUSCyqyQbPyWTSbTb7YGZyBYbQzqdRi6XQzabRTweV+tj8FkjedetY/7tZUnrHgJ9mxcC9CJyHRN97Q4Z+5aeC3kdujHE8enXpXv6TPdD33Y9AtQvXOHlWdhImEGeW/eA8G8pOwKBgJILOknodDoqjDQ6OoparTYQXgFgk5IBKpxMJqMSxui2Pn78OGZmZpRSDYfDLqUgLWn5v8kNJrOAL3VC65NJuqdkQp/jOCqXgdvIroHchxYAxycTAjnOXu/i0sAMgfBe6Q8+3WG6AjYJDzlReC6Zi8F9+nkO9NihJF1SaOmeAF1I6Yye1hFXnuT6DtFoVLWo5r19oQSyxeZGKBRCMplUFp9es26KowPeFqrMzDcprBfquXOc1eRj2b5chs10q11XsBv1fHiRA700Uj+uSQ7I8Xt5AfV5Dbg7Jcr99N/GZKjI3CP9fPr2Uq7JkCWTxeVKllsdm4oM8MeisiQhYN/6breLUCiEdDqt9gkEAkopdDodl3Xs9/uVZSCX1pXVAWSRZIvAxia4HlagJS2rH2j9080XDAYxOjqqLJZIJKLaB/MYZK/MgQgEAmg0GiiVSmg0GiiXy0pgtNttdQyZXKhb6RImZi3JET0fUgDwO1mZ4WVByX4GdGtSWZvirvxObsP/WUKaSCQArNZbS4/H9PS0Yvezs7OWFAw48vk8duzYgXw+70oe0yGVjiQIUonIRFaSWpkYvF4oYaOgvJNEoNVqqXwgL0+gV96DvA6J9SxvGW7Vv/OCybLXZcx6x5SyVKJfUrJ+DF2WyfHJewVA5ZXl83m0Wi0cP35cGZVbWX5sGjJA5UBlRss5nU4jEAhgaWlJeQIymYxS4vKBoBVJwqCTASplkgFaDcxN4PE28kDQFc4EPZbwSZc2LdpGo+EKaUQiEWW5xGIxVSrJ8haSAZKGSqWC5eVlRQparZZa3jcejyOTyailnqWlzLCFTLKUile30vVcBTmZ2RZaTm6pzOkJkPkc/F31+yZzIEwTmxZKNBpV94FkLRQKoVqtYmFhQZEBv9+Pp59+GnNzc1t6Mlv0h9/vRz6fx+TkpOpeasr013NbTPFz/dkkEZUVLiYFdjkgsWdXU1YRUJ7IiiVpwevk2ZRDYPKImmC67o3sx+/li+M1lRh6kRSZ2+TlnTApf9Ox5DaSpPAYNPpGR0fh9/sxNDSk+rboDdm2EjYNGQBWXfqhUEgtgas/aLRcpbLSFZfJLS+ZvyQDdMHLEiQva0JCd4dLYSEtYD2+TuEjGwXJuDiw6qpfWlqC4ziYm5vDzMwMGo0GVlZW0Gw2US6X0Wg0kEgkVLfGkZERRCIRtFotlTXL2mQ9kcp03+QaB7LagsKH10JSIFcllN4EXivhxeBNAkQXfry3Pt9qq2J5n9iVUFY/WAwm+Ewyd0hXKiZLVYbD9GfT5GXTc4/kZxuRGzr4PEu5AaxmzMvkZ338cky68pfbyOoheT6eR8pMykQpJ+U9lOOWx6T8kvdGHlPKZAk5FkKWM0uDQ/5eujEjx2U6j27E0KNMg0tWoW1VbBoywB8wGAwil8thdHQUyWRSPch80GSMX08E1B9sAK74Os+jhxBoqZOIrAcqKKnE2eFPKlmeh9ayZL+Oc7ExBqslWH5IT0W73cbhw4exsLCAc+fO4eTJk2g0GlheXlZNMlqtlurWmMlkcO211yqPCN2bMg9BvmRpJssbSQY4nlarpfp3M0cjGAyqsEwikUA4HEYqlUI2m0UkEsHQ0JBaE4HdEvV4oRSg+vik9UWhVK/XXYlRFEChUAiTk5NIJBI4ffp0X3emxeaHlyuY8Pv9SCQSyOVySCQSrtwhr2NI5an3tZBKlt/TZc/nludgnk6/uDoAl/FAoi27g+ohO0l+TdcgZYp+XdxXz73h3KackHOayjEUCql28DqBkh6LRqPhkl/0itLTyCRrNozju35MjsPnu9hlVZZskhh4eW11w0N6E2RIUiYXBgIB5HI5hEIh5PN5JJNJ+Hw+VdK+GXCpXtBNQwYAd40sHwRatlKRAe4sX31fk5tLWuuEnlh0OcxejoVdAmUsj+zedGxOVtkASB6rVCphfn4e8/PzWFpaUp4BxhTl4iW9Xg8rKyvodDouQaJbNNxWhg5IBjgB2+02qtUqms0mlpaWVNIiyQDDEc1mU7nXyKxTqZQrIdPLNahDkiXd8qEgltvy2JJ48N7bUMFggtasXvsvnykJXXGSAOitzPt5B2RDLyotff7Jc8nkYv1dnlP34Hl52nTPmt6FlFVKsmcB57YkA5zbMjFbehXlOaUXgNUOtVpNdTmVS5yTDFDGkAxI7wPlvYm8mX4v/W9d3urf66Aslo2pwuHwlu9CuKnIABV5pVLBwsIC5ufnMTs7qxpGkInKeBrZJ7C6kA6P5TgOotEoAoGLa1dXq1UXC6aivlSLUnoQOClk62ROYDLQRCKBbrerYt69Xk+5oyQRiEQi6li1Wg3Hjh3DM888g0KhoBQ98w84wfn38vIyFhcXlacjGAwilUphZGTElfAkvRn6CoGm2mY50ah82Rqa9zgWixndnPxt9GxsGZbgvaHHg/3kWV5FoSJ/J0k0IpEI4vE4kskkstksGo0GKpXKCxLLtdhcoDua8kDvUAqsEnCpqPn5ep4Hzh9pEfPl9/tV0rOeuCjli2yhLgmw3FYaNTI/QB8L3+U1MXGX4yuXyygWi2g0GlhcXFS5TFTYvG5a5QzPsp2zzN2RIQveB0kGKJv0suRoNIpYLKaIAWUTm0Ol02nlJdXDjvr6LP2UvJRVMuwjk0Cl4ReLxeDz+VSDKnqRt6oxsWnIgGTCfGC5iIRM7iNzpfuLnQBZescfk3+T+crkNpl46EUEOFG8vAV6op1k9wBUwh0t716vp5IF6aKj4pedB4HVNqRLS0s4f/68IjJy0uv1947jYGFhQU2+UCiEkZERRUg4NjY0obLluB3HcU1oTh6GUiggZMdGrvyltzrWLRvpZpX3nMSMWbxMGo1Goy6rSQ8zyGeG4Q5WVfA6LBnYeljP2tM9fKbEQH6ukwHKDZNXUf4t55FOfikvpIGiu8T1PAP9OTW5uU0eBsocfia9jCQDzWYTpVIJS0tLqNVqmJubU/OeLdL19UZoSEQiESQSCdUJVYZh5bkYGmDyI2WaBOUe5yk9EQzlkHRIZa17JHRPwHpeXJMHWZIFAConjfplqzcw2zRkAFhl5ywPGhoaWqPcuTQpXfIkD7RqpaUeCASQzWYVs+WDKBffYdxa5ibwnS4/mWVPpSbjfqYEIB6PcUzGqkKhkGLHbCrC88t7EA6HsX//foTDYZw6dQpHjx51EQApFPTEPhlrZxtn6Ubk+MngeSy56iEtfzZ8Yl4F2Xw0GkUmk0EikUA6nUYymUQsFlPkY724qSQ09GyUSiVUKhVFQBjHM1lFXlaVxdZFP8udCpHhLOar9Hsm9GfLtK2JHPDZkwt/yYQ3Pv+SmOhkQIYDdYJvsoIlQdC9HdKbYLov/UJnOhnh/KdBIpuf8RzyGmS7c31JcXn/ZT4XFa9M3qNMpGyWsky/b9JDwWPL6+hHFqTXhe3luaxxrVZTXoStiE1DBqTLamRkBHv37kU+nzeSAcazGCOmq6pSqaBaraLVaqFcLiMQCGB0dBSJRALj4+MYHx9XDxofFpnIQ3cXFS4Jhx7H0pWdtBgkSyeZSKfTygXGh43XIOuI5aQNh8O47rrrMDU1hUcffRQzMzPK4jWRAZkDQFQqFZRKJZdA4ETQk2tIGCgMksmkq7dALBZTLxKsoaEhJJNJ1fFNEh2vmKkUpj7fxcZC9Xod8/PzWF5eVtuNj4/jhhtuQCgUQq1WM95fkxvYEoLBhFSuzWYT8XhcPecmi7+fO9iLREirXnrD+CzLEKFMWNPH6XUsmVmvP9emsUuvAY0bL/JiCkXIbXheadXrFvpG5pYMOegKmV5ahiASiYTK96GhRLIgu0bKa5DeFBl6NBECnpPb8hpo3NHgicViSCQSqFQqlgxcLeAPGw6HVaY6wQmnP5gkA0yUAS66n9mLIJFIKPapx69lvF667KX1zO8AcwcvE7Mm6EokwdDdbLLkUZbTyBAHY3bZbFbdDypQMnfJpnl9HJtcB0EXMP0sEEkUJJsPh8PKQyD7M+ilmfIccnJJgcK/WeYTi8VQrVZRr9dVghN/M94T6ZmR47QkwIKhwFqthng87oobmxSlxHrhAe7PY7LyQO7PbSQZkGOQx5UKTuYRmMiznLf65/rclTF3usDphk8kEq7VSOVCZjKPQipOaSyYrsN0Hxnnl31cqOylvNA7RErypstVnQCYxqCTDz1MKe+jbjzoRtZWxKYhA47jKFd/KpXC+Pi4q1GOrC2XyqbdbmNubg6VSkWtX57NZjExMaEeNp/Ph3Q6jUQiodzj0iKXk4HJiborD3An0cnMW8ba5MMomwiVy2XXA0i3O7Ca/UsXmazVpettaGgI1157Ler1Oubm5tBoNDAzM4NCoeCKwzG2J0MdvEb5LoWQDhIujocJmKlUCqlUCrFYDJlMBpFIBJlMRpUVsjmUTJ7idVBwcuLz/HTVAcC2bduQTCbx3HPPYW5uTnka2JCJ1g+JAYWZXIzmcpJBLbYO2u22yrMJBALYtm2b63nwUhY6JHmWcgCAy4tnCgWYPG76uU2KTveYybHoRMZLYdE7SJnX7XbVHG42m0gkEmi1WiiVSi4DirJGeil4PGnhSyNDykg5HjnPOV+ZQEijJhKJIJVKqWRFfUEpyi/T7yPPp1v/UsHrv4FuhFDOUQ6y2ZMlA1cB5KSjkl0vSYT7yXgVcwNSqZQrUVD+Lctv9HPrD7lMsuHxZfKPrE2Wk1YSDD2hhsxZCgs+nIx3yUkYiUSUZ6Ber6sJLnMQZJyfpEJv3iEFmm5x6C5V6eaUCzpJNx7f+TJ5BaRQ1Rm5nKDst8B7ID0B+nh476QgtrBwHEdVkzCZTT5HXtioZ0k+x7oS5HG4nXTl8zP9WITJ6vUKs5mOoZ/PcRyXVc525c1mUxkxJOKcb1SE9IxKBSuvgXJNEhgvMsB3yih6EqVXwCQ35D3R57cpMdiUI2AiVKbfTPcKWzJwlaDXu1iy88gjj+Dpp5/GLbfcgte85jWKPQKrHgQ+FKFQCLlcDrFYDLlcTnkAWHcuCQBzC2QdLF1/eqxOWuicHKyR5TFo4etud6nA5EOod+tiCIDVDvye18rJMzIyglQqhUqlotrx+v0X22hyclJZ64mOMsFHkhh6LvSJTOUvs4ppofPFUIF08+mCjO9yXLL0k8KLY6DXgfkHsupC5kPwXDLkwnsvrRqLwYPjOJidnUWn00E4HMaBAwfU8yuVmx7H34gikaRCDxuYYuPA6noePKbumpbn013z+rlM12ran//LucYwHKuBWEZHo0v3DOjGEiE9llKWSRkn56r0DDBZkE3JON8pa6SckNfk5UXxkjd8ybAs7wGNJJKharWKRqOBQqGA5eVllMvlLS0/NhUZoKJ/7rnn8NxzzyGVSuHVr361yyKUiWP8nPkFmUxGubDlg8WkELkSGB8kfifrUKWXgApGupZYngdANd0xufN0ksDxSyEiWy7L/AHpFk8mk8hkMojH4yiVSgiFQmqRImb9ynOS8dONLhUlXywtkuOTuQe6F0DG/fiZTDDUr1/POZBkQE7mdrsNn8+n7iP7DLBklF4VmSsiX/LZ8WL/FoODQqGAVquFnTt3unJqJEyEoB90K173AOjhNl2heD2z/I7vOiHQ5YkcjyTC8jg6MaG3kKG1Xq+n5AaTp6WxIKuVpDteyhATiZDJx/TCct7Ltr/02MoQgST6lL+ma5ZyWd5nExkw5Wvwt+LvxTBJvV5HpVJBvV5fc96rFV7PRj9sKjKg4+zZs/jyl7+MkZER3HLLLchms+pHrtVqKBQKAKBKQ9g+E3BPSD4A8mEG3Gt/c6IwhiaVP3Dx5hcKBTiO41KG+gTvF2fTrWNgNclQt5oBKIucn5FZs4FHKpVCo9FAvV5fIxjIjrkiou7pYBav/Fy6/GUeQjgcRjKZVPeY++ouP70BlF6WqYNkTk5eCm9aDOzRICGFcblcxuLiIlZWVhTTt2RgcEGv3/z8PE6cOIFMJoOdO3eqtSs2IkB15SLjziYLX1fMhBcxNe3f7329cUroVQVyvPxOrgIrQ5kyLCpDADI8QHkhK7BMngFJBigzKEdk3xI9pu8VJtHDEhyP/H2kPNHLOymL6B1ot9s4ceIEFhcXcfbsWdRqNZWAvlWxqcnAkSNHcPbsWUxNTWF6ehqjo6PqYSwUCpidnUUoFML09DQymYzaj5a3fHikR4APkUwMZCMMlu/RDc4HqtFoYG5uDp1OB6Ojo8hms66kF/ngSVYqY2r8H1glK/QyMKFQPtD8TCbcjY2Nod1uI5vNquZMpVLJNTGlO49hAmmJ6xOaBIjXw8nKWn+WAunsnklBJAUyRCETmUh0dOElBQdfPDebCLGnhG75M7SyvLyMM2fOYG5uDsVicU1+hsXWwUasIc6nmZkZPPHEExgbG8PIyIhqcCNJqXyeJIEH3EloUll5hRRMLn3Z0Ed3f5usWY5Jh37NUvHqrnKvfUz7S/koCYB86d+ZyINuiMh5zxwu9nqhnNC7NJqMBV3x6+Nmx0fZk4BeBklI9ATjdruNSqWC733vezhy5AgOHz6MYrG4pk/CVsOmJgNsylMul3Hq1CkAwNDQkMpc5w/Nd6l49YddPliEielzgpnc3dKroCf/6Ylz3AZwL+VJQiCtCTlxZbKfJBj8nyVTTLhLp9NIp9OuCcNMYZkXICsM+KJLsFqtqhwGOamYlEhlL70AMlzA6ySh0N11vHaGJorFIiqVCpLJJPL5vLrn3W4XlUoFhUIBkUgEJ0+edJWFylCK3+9HrVbD8vIylpeXUa1Wt3S8z+IiNur14ToeoVAIxWIR4XAY6XRaheUAs3tZ/1zCpJT5uSlEJee63N8r6VXub7KU9W36hcV097i8TtMx9PCoSXZKL6LMxJfHkla5TgpMoT557XJuc1wMQzAsIZvLMcQoZRXJgOx1oMvnXq+HSqWi1nyp1Wqe93ErYVOTAf74MzMz+LM/+zOk02n8+I//OO69917lMqdLmw+aVHrA6vrUusuLD62M2ZPF8mEHoLbRj6uXOPLhY5yb43Kc1XJF6abngyzdWXwY6cHgRKLylQ813fcybs/rnJmZUQ95uVxW+RJ66ISdG7kIksyd4PXlcrk1yYPJZFKtK0DGz1bCwNpWzT7fxS6R58+fR6VSwVNPPYVjx47h4MGDuPvuuxEKhdSaE0ePHsW3v/1tBINB/NM//ROi0Sj27NmDTCbjyqmIRqNoNBp4/PHHcebMGZw/f37LT2aLjWN5eRmNRgOzs7PI5XIYHx/HgQMH1nS15LzdqEteV8A6kfcyNiTRln/L73QlLWEiAF5VCNL7IF8yLKqjnyGlhwkAuOSh3F4eW79meWyTN0R6cYHVmH673UahUECz2UShUEC5XFZeXABK/tNokUYIW9dzLGwsV6/X8eSTT2J2dlYtFb/eM7DZsanJALBaLnTmzBlEo1EsLCwoy5fweri5v+lBlw+kaQJK653vtKbldvJ70/Hl+EyMmJ/LyS2PA6yGDuj+YldD2f5XgmVV7BFAMiDdlgwhkGlHIhFXUiEnlMwNIPPWwyOS6DAhE3C7SZmkUy6Xsby8jIWFBYyNjaFeryuPQLPZRLlcVuswABfzQXK5nDoPa6cjkQgajQbK5bLa18KC6Ha7KJfLCIVCWFlZQTgcVkv4Au5mVdKFD/T3PuhzX85/Xc7I+a4rG+ntM53Xy0NgGo9pP13+yPNII8br+uT+MlFPVvNIGanLL9N5TWP28nRQ3jJBmjH9UqmkiEGtVoPP51NyjsaK9KZyQTeSEq7aSI9zrVbb8uEBYtOTAYl2u41HHnkEp06dUnHsTCaDV7ziFRgdHVXbSRZM5d1sNl0KQzawoZLhkr18eACo5JdisYgTJ06gVqu5yuqkxQ5Axdoli5bxNRnP53fAapKjTDKUCY9UtKzFZ70wxwFAVUvU63WVxSsTEHVywgnMFc642Igsv5T3i0KEJIKlf3I/tg5uNpvqe90yyWazePnLX454PI6TJ0+i2WzixIkTyouxb98+jIyM4MCBA6rJUTgcRrlcRrlcRr1ex+LiIgBgZGREVU7Mzs6+MA+axZYBY8OnTp1CpVLB7Ows8vk8du7cqWSIrrD05F/T37pFLF3afNc9Aj7fasMy5sZI97mEPgbdpS5JiMnFbVL23EeGIglTXo5+bOkp0DP55Zj1ccrqL74Dq3JR9whw30ajoZKCuZT6ysoKSqWSIgosmwwGLy6tLvOZpLeVnk16m+v1OuLxOM6dO4eTJ0+qvKuNQhqAmwVbigx0u108+eSTePLJJ5HL5bBjxw6Mj4/j2muvRTabdTF9/lB8EMkkgVWywOoDKmg+bOwqCEDVyBYKBVy4cAG1Wg0TExPI5XIqtk74/X7Vb1tONj1eD6ztZ0CXGK1xjkuGNGTGPXMB5CqCtHxIZmTiDjskynskE/scx1FrkpOBU7nLBY2o6DmpaNVzmdSFhQXlzqtWqy4Pw7Zt2xCPx7F//35MTk6iVCphdnYWxWIR3/nOd7CysoKDBw9iz5492Lt3L17zmtcgHo8rocFlrVdWVrC0tAQAqlPh7OzsppygFi8uWq0WTp06pWLJ9Xodu3btwvDwMIBVFzPgJsxy/npZ3jQiTAm5eg4O569UpAxREF5eQ/3c/Fy3zLmNKVShQyYyS4UtvRsmT6X+mWmc+j7cj/eD8tbn87nOq5c00ptIeURrnp5DeX6ugJpKpRCNRl15ZX6/H6lUSoWIAKBarWJlZQXBYBCFQqGvt2SrYEuRAYlWq6V+RFqXpslMN5EsLZGrFzabTVcNfS6Xc8WqWq2Wimdv27YNvV4P6XTalZlMYsF35grok0FWNEhFT5BcsAyK23B80tKg4paJgHS5yyQfmbwDmCcwxyjXZaBnQMbp9TJB3jOy806ng1wup7wT0jMQCoVUDwguHgVcJEOhUAjZbBbdbhfDw8PYtm0bMpmMImUcJ68tGo1iamoKjUYD0WgUxWIRp0+fVkmVW72TmMWlgXOPFUiBwMXVTOPxOEZGRtSclZ02TYrBlB8gFZxudZtyBmRJMrA2rLiR51bKOK8wpVdMXkKSAL0pmZz7Jje+PEa/+y7HIsMxlCPyePxeKma/36/ynZrNJpLJpDLqer2ekivhcFi1RefaNjIRmuXQNIJCoRB27typDAlWm8jwcz9sRvmyZclAvV7HhQsXUKlU8M1vflMxfcBdZ7+8vIxWq4Xx8XGMjo4iHo+rLljtdls9ROxiyPUL5ubmVHxqYWEB8XgcBw8edK3tzRcXRpJ9thmTl9n7XGOcChxYtUz8fr8SSiQbVH6ycQon2MrKikoQXF5edrknAW+3plSWepmQJEmcRLK0ULYM5nWzXIgTjV4NmcVryp52HEc1UyoUCjh9+jQCgQB27dqlwgP0SvDYJCupVAp79+5Fr9fD4cOHsbCwgDNnziCZTCrX4macrBYvHrrdLubm5lAul1VPimQyid27d6t1N5gYS2+a/gybMuJNFqVuqRO6l4CQLnddgeueA9O7fl792dc/lwYKjRN6NaSnQ7fUTV4H/b0fYdLLAKWnkkSJcoUKnHIql8spg4fl0sBFshCPx1WLdpI6mVQoK85obHFsu3fvxsLCAp544gkUi0WUSqW+z9FmxpYlA9IFLyerdO8xjsS1CtLptMoBkN30ZFhBVhHwYUomk4jH40gmkyruBLgT+6R1zXHJSSVjiMDqBNIX9ZD9uk1xOWB1PQZ6MCTBME14EgVOevm5JAPdblcpfJIQKneSCBk3lLkAslmRJEn8Xloacgy8btlkiC2aeS/5G8lrlDFYadUNgrvP4vJA5SeTzyqVCgC4FBKw2gNDEgDOYa/yWaKf5SwVIL/3UuD9sJ7nwnQM3VvJMJ+sNpJtieVCbnJ/XfnrL9P56L2QREA/lk4O5DXxt5AVC9xfdjL0WutA3mOei7KD8oPyaqsaEluWDACrde3btm3D5OSk6nJFd7zff7F/fzQaRSaTQTqdVvvqD5903fNBicfjyOfzOHTokEsxMvQg4+71el0l7snjAVCsOxAIKPeVLGmSQoVKEYA6D8cLQFnHbKMpvQ08j5zsLBnkd6YJK3MaaPlz7QXmHuguPzlePYTAz3UyICckz8UxMTcgnU4jmUyqHAgZgul0OqhUKvD5fOpeh0IhRdZIZCwZsDCBBJg5LY1GQyXa0jPAVU9DoRDS6bSroZZ8zk2Wbb/nTld8HA//18vyCJNy1BWpVHxyfunn1xOZGQKlxU25Kev45THlePTESFnirF+H/J7XLeWu3i1QnlP3GgBwJX5LWSN/E14j7w/PTw8oj8OFnFKplMrZ0u+dCZuRNGxpMgBAKatYLKaUAjPjQ6EQxsfHkUwmVTtd6RqT3gX9AaDCTiQSGB4eVg+Y3I8PmbS49QmvkwxawUxe9HroZZxP/k0lyk5rMtYnLXd9spP5y0nJ88uqB7JveQy/3++qHtAXFDG5LfsJSZNlpLc3lt4MmSglPS7yd5KuSAsLE+QzxznBCiM579j1ku5nL9e4l1Wsk239Mx16fgH304/j9Z3pGqVskURcbzpGGULDg5VC0gunyyipuGkMcc7KOSjJCqFXLchxe12vfk5T8yL9/pkqJvi5vB4Z8vTKFdkq2LJkgD9kt9vF8ePHUSwWsXv3bmzfvl1Z4HxQdaWpQ394qTRZKcAGQVKps783E05MyTEUJLTK5cMsPQjAqlJzHEcpcOBiqSKw2oBjYWEBjUYDi4uLKJfL6hpksqR8Z3tlehjoUpOuNI5BEgbW89M6YCmUJBKsKuD4ZdMiehb0REveRxINNhAZGxvD8PCwai4kBQUJDJMMGUqgVaFbbRYWOvx+P7LZrHpt27YNkUgEuVzOFWoyLaZj6q1BmEICG1UoVHhSJkkFJueoTjakdc5tCamUpeKnXGk0GspryAqkSqWiPI4MCeo5A3I8dNvTa8KQK+Uyt9cVebfbVQYUXfuUOfIcktxLYiC3Y4hYyhn999F/KxoXUhbLPi2WDGxCSDJw9uxZLC8vKyueq2TJXgO0JvW4kZw4cmLKmLfe7156BBqNhvJOSNZK5UnyoE94eX4AamIwE7/X66nPSGZqtRrm5uZUG95qtaqSHoGLypwNOlj7z1abvOZut+uawFLQUHBItz4z+vUsfXoLZFgFgIs8SOYtY7A8HxuAAMDw8LBy+etWA/dPpVLqeCRj/QSBhQWwOl/T6TRGRkZUnwGWoMk8F9aoMzwlc3i8PF2AuQvgRmDyABB6AzB9W6l4pddAehZl+I9Wf7VaVWHGcrmsyAC9jTKXQF6XJN6cywzhMbwpZaBOVihXSFx4fMpdOW7TfZReh/UIk4TpM55P9wZs1JjYbCECYIuQAb9/NWOUaxNEIhHE43EXu8vlcmo7mRwHQCW3SZjcytL654Oix9qlYqcSlIxWj1FxPIRsViTDA/RkSBLjOA4qlQpKpRKKxaLqdcByR45N9gSoVCrq+0aj4bpehklo9ZsUqQwD0CvCsAuvScYI6SGR95Oxe8nCpYXA7Rjry2azSviyCoPnoCvX77/Yx0GWLvJeRKNRTE5OYnl5GYVCwS5YZAHg4vM1PDyMaDSqPIfpdBrj4+MuxU/IsJOuZEyhMcKUrHcphIDHNYUk5DmlkuX8k6FLYDWcKHOL2u22WoynXC6r1U7pEWCZslzIjMqakHNXXxqZ5FySfjnnJaHRZUe/fAn5N++pTjLkuda7z/RKUjbJxm6bUcFfCrYEGQiFQhgeHkYymcRtt92G/fv3I5VKYWRkBK1WC6dPn0atVlOrCdIFzYY4tFZl32pa8/yfXgaZrU7lTMVGxS8t6Fgs5hqrTGQJhUJrMmm5Dceil9oEAgEVnyeDv3DhAs6ePYuVlRUcO3YMjUZDJUuS7bfbbSwvL6uWvkwIkss1SwHCUhypoAGoic6JKj0AJDayvBJwxwa5L++jvDaZ8CMti0gkgsnJSSSTSaXMmdwDQJX7jI2NIZ/Po1gs4vz586pxSK1WQyaTwU033YSzZ8/izJkzm2ptcosXD9FoFAcPHsTw8DAOHTqE3bt3q3U1AHc/fObMUD5IUtBP6UgrnNDnvAkbJRBSRunGBueaHv6kl7Barbq6djabTSwvL6vmYuxNwlCmrDKSuUry2iVhkk3NGEbkZ7rXjhVCeiIh5azJs6LfD0mYdANjvXtN5U8Dhx5jGRbZythSZCCXy2FkZEQRAzYIKhaLrqQ8KhLGvflwy1WuyFL1ZBYTG+Vk4GcygU1/kLmNTHKR7jH+L18ci0yO6/V6qFarahU/tuGsVCrKCpcuQOkZYJWBTA7USYmMuUkLnueWSYXA6tKw8jMSIsm2TezalDAErGbzynXN9XspP+fxZVKmTJ6kVbPVGb7FxkDCn81mkc/nkclkkEqlXF5CSVrlc9bP1W9S2htR7Ho4wPS8y23XCzdIEqJ7BuSqpezFT+OCDcvoOZTJx/I4UiZKt7ycy5IwUAbIRGs9b0iXdXzJ4zLvSMoTr3vkBd3bKWWQHKfMjdBzlbYatgQZyOVyeP3rX4+pqSnkcjkkk0mlSOn+Y8Jcq9VS5UEAVAkRANdSpgBc9fR8QHTrnxNFtu5lsh6VvPQ2yJUEpRUOQCk+afVykrHvPl3ftVoN3/3ud3Hu3DksLCxgdnZWxfuYq0BXP0sL5TKfVOZ6/I6eC46H2+jsularKcJAQtXr9VwJPzKOyfwIGYuTIRj5GfdlbgfDEBRe3I/H5sqJjuOoNscy4bLT6WBmZgbf+MY3XL+3xdaFF/EkIpEIMpkMJiYmcMstt2BychLZbBapVMpF0KkIZLKsbnlKZUhF1S9ZUFfi/Sxd/TNJTPR9pEKTSlR2DuX6JPSYLS4u4sKFC2i1WlheXlYyhJ5TegTkuXUvopRjnMeUYzIkwHExqdrkymeosdfrqTJw/i2Pzf0oq0zhEy9lL++fzEmgLKcxwxBjvV5XusOUYL5VsCXIQCQSwe7du7F//36lZKVLK5PJoNvtolAooNVqqWoCAGoxHy5bKRmoqcxEd2EBbuYtvQVkl/oE112K/J5Zy5IMyCYffCD5gM7Pz6vliBcWFtawdr//4iJBlUpljXuPoQ5Ckhk5mXSXox5LY4ig1Wq5qiuka1V3K8pjc6w8trwn8j7ofQfkGFk6yqRI3nN5/Gq1irm5ORUesRhsMJyVTCYxOjqKsbEx5cqWkIqC80vOdZMHQPck9INOCkx/y2Pr3+veA13pSU8iFXG73UatVlMhAsrFcrmsjAdZPijB0KZ0wcvyOyp0qbylnJH3T16X9K4yiZkyQLYRl78HYF6wySucIu+L6XN6MmkoyYRJU6hlq2FTkwE2/pHteaXbW4/xcLEKx7m4qA0f3EQi4VJGJAVkt3x4TP0GZDaxtIQ5WeT/svwIWC1R5Hni8bgrx4BKrNFooFAooFAooFgs4uTJkygWi3juuedw7tw55eKT4HhkkiCvkeWDbKbBfgnBYNDVoCeZTLqsd+m2I0vmS3YEZPmRrJaQ95JWu7QMZFhD/q1XWcjkJAoaKeQoxHjP9fDCVnbzWWwcIyMjuO222zA2NoahoaE1q9jJZ1B/Dr3c05wLeujK5AmQ3+mfeUF+Z0q+k3JKjo35AY1GAysrK6oEuVqtolAooFKpuGLi7OkhFTz/50qnclVFOR8p52gYkAhIVzv/li2OZc8CKR+lkpYyQQ8t6mEHed9N91vmVuhGD4/HJdCZYLnVCcGmJgN09UmlJS1pKgXZIx+4qKwWFxcRCAQwMTGhFv7h/tKNLJktrV3Hcda4+wnpSpOQ8X+dzTJhTk829Pl8an0BVgvMzs7iySefxMrKCo4ePYqFhQVjToNku2TUdNmRdGQyGYyOjqp66kgkguHhYWSzWUW0gNX+AnTVc4nPTqfjcinKUqBAIKDijQzX6JNNn6wcMxO1fD6fqmiQwkUPJ8gYqO49kB4Cec8tBhujo6O4+eabMTQ0pMJMUmbIZ9GUOa+77uXzLL2BupGgK/v1vAESMi9J96TprniZL0OPWbVaxcLCgvIqVioV1Go1VCoV1zVwDRIu6BMMBlVvhUwmo/orsNJCrrkivZ68N8BqLxfK116vp5SsTFSUCwHJayAJICnQc5BMStqLbElPpxwvDReGVuhVZZiAHs+tik1NBkKhEFKplFpfgOUrMpYPuJNxGNOOxWJq4vIBDIfDSrHIzHZgNbYOrG3gwXPIB0Vn7KaQgkxyo+Chu56lf4VCAeVyWYUCFhYWsLS0hFKppBIedUZN658Kk94LMnou45nJZDAyMqJIVTQaRS6XQzqddpETWtusYpBkQK8/JulgKIZCgy1BZTMSVmvork6TZSW/18H7SWHDcbIRE0mMbuFZDC74fLOVtt7W2zSfTc+O3MYrT8DkmvayWOX5dKxn7epjltazDBPK66cyl4oxGo2q+UmZQVIQj8fVZ/QW6Gs0SJknQ5LSMKEyp8yjZ5GLDJFYyBVjSTikYaXLZJMRYLpnulyWvxufB5kvoN+/rYhNTQYSiQS2bduG8fFxpNNpxONxV7tMMmQqe93FRQVcKpVUcx6p4JrNpnKdA3A1zgBWQxFSqQNwudBoCfPBk3F2Jt/RRcmkvGaziZWVFTQaDZw5cwbFYhEzMzM4d+4c5ufncfToUcWqOYlkQqDf71ckKZ1Or7H+E4mEul6u0MhVFRkmkK4+6dajstU9A1x7gczb5/MpEkDQ3RgIBJBKpZTngULDVMuruwzlZ7znJCFU/OylwB7zJFS2mmBw4PUbc07G43GVbMyEX5m8pmfO0w0uFaxXqZl8TvVKI/nOv00KTX83KTN9fx3SQyrllM/nU/OPiphyiPIuGo26VvfTLX99DBy/11gkIdGTE0namRcl7zvPzWow/i2JhyRB+vj0e6aHB+TvwHFS9hcKBSwuLqJUKim5slFIr8WVwHpeJhM2NRmIRCLI5/NKoXFiSFe0VNKmG2RKapPVA/q2pkkg9+e77AsgzyPHpDN3blOv11UmKxMbWUHAh5KJkGTovHa647PZrKqaGB0dRTgcRi6XUxYBM/UzmYzq7McWq9KlT4HGvABOYuZo0JXPpCFJBihIeI3SitATi/i9LoCldaFPLj1uKwUOrQyZxGg9AxZSGcj1BnSrz0RKTfJjI0J/PYue4/IiCfr++rHWA+ektOZpDDFBT5IBelqlbGG/E308+nn6gTKDyhxwu/cpT6Rs9PIEmEIAuiw3kS/9e30fmdfAPAvZY2ErY1OTgenpabz5zW9GJpNRpYKpVAqxWMwl/PVlNxuNBorFIvx+v7KW6bKKRCLYtWuXK3tdJgjqmfV6+MDnczcjAVaXRpVjksyVrjMuN1ytVjE/P49Go4GlpSXVT0B2FQwEAhgbG0M6ncbk5CSmpqYQj8eV4uf56erTBQ+vhV4JJvTJrop6Ih4A5TGgezWdTq+x2mVlAt+lhU9IC0tOSsYgeX97vR5qtdqahYnk/eVE5v1kFzXZaZHH2OqT2sIbdH8zf8jnW617Nyl2k5XJ553k1WRhyn1M73IOAquJdl4EQI5Ft4i9wgMk3EyS7vV6GBkZWXN9Up5JmSbnvUn566EML6Urx0X56TiOa8Exlm3K6gddUZOQ6PkIpr/10Kk8jsnI03/fTqeD5eVlzM3NoVAoqI6ml4KrwTtwKeff1GQgm83iwIEDKjzQ6/WUhSofKrqHG42GK4mFDzu3Z9Yq+wyw8YYeX5ITR04mWW8rPQIcA/83PXx0kbHkh8qfDYL0bFa62oeGhrBr1y7ceOONSKVS2LFjByKRiIt0UKEycUdmPXspblP5jiQ+evhDJvTxmPI8HANJESsOdFerTroYBpH3S/cI6H9L16MskZI10xaDCd3lDaxNVPNSIDIRkJ/z3YtEXMpLP6auiKVyJhHx8pbJY0gSYlpHQVrEXl44Hespfv1a5PgkcacslB5EPfwqQwCm48jrNhGAfjBdJ+8Fu7VSb2y0A+GlnP9qwqYmAxT6ZJim76XiZcxPWsLxeNzYK5vEgq4tUzaxvo9MpOExqJDZmEe6u/jO65DdwKi46LoLhULIZrMIBAK49957XecZGxvDyMiIcv3J9sCScEgFz+uXyl0SGpn5z3HKkiU9BCPjiTwG7xk/59gArLmfPI/0FPj9q62Oec95DXQ10s0ZCASwsrKiyizL5TJqtZrqysj1CywsiH5JYbrC8oIMbZkMBpN1bbJO+b2+nel/jh1wz0U5T3SDRTdovEiEyWqW51sPureAc9oE6fHQz6eTA7m96TpM1+4Fme+hXx+NMlYSUI5cikdxI8/NS4FLPf+mJgOMC9MtDqz2yZYPEr+jazAWi2FoaMj10ND6BFbdWcDFpEFa5vLmygkGrGbwS5ebVGokIqx6IBzHUQk1LPORcSp24Mvn8wgEAhgfH8edd96JYDCIc+fOoVAoqGxbPuRUuCYiwLCHjNdLi8HkwpQCU7rqqdyZCEmCIX8LTgz9N5GTWbew5DVIIUYvAY/LcyYSCYRCIRQKBeVZKRaLqNVqKBaLqleDhYUpv0TKjI3s288jQJgs/34ud9N+/N8rR4ljksRc7qe7+02EwjQWL8Kg3wcvq1of63rER8+XMF2f6VheYRJ9f/3YsnpEH7esQCqXy0qObNQrsJmxqckAY+rtdhvpdNoV7wbWZucyDKC7yQC3C1w+XNxHts81eQgoGPjw6cfXY+7cV56HzT4AKEuWGb88TiwWU4oSWG16JNdY0CeHFHrSBae73nWhwvso3/UJSKGjbyfvk+kchH48nqPfMfg/P2MohXE9VpSwKmN5eVnVUlsMNkg0GXZjtzsvhdTvmdGVPIA13jRdEesywUQaTK9+Y/CynHVSId91ObXRa5ZEwIsMSK+KrKaQ0D0b60Efv/6dbrV7Xbd+HfJ/vliaTJki5e1WxqYmA8vLy3jqqaeQz+dxzTXXIJVKAXBXBUg3Hb8D3EpFWsZS4TWbTU/XFPeX4QEqWhnXo7uLE5/ljdINT88Ax8EyJ7/fj/HxcSSTSVcFwdzcHHw+n1rhi2U3MiYvkyZle2B97LKfuE5OdDYs25NyO+5HLw2wdi1x6UqVyt6rokInC1Joycxi9oWYm5tTXRpLpRLK5TKq1SpKpRKee+45nDlzBrVazZIBC+V1q1QqqFQq8Pl8inDrhEDOd6nUpDLXPQpeSYPyuHoioGk7k8Figi6XJDk3eQT6KUkvRW8KSeife41Jvz9ex9XH5DVmk1KXBEQPK8iXKe+If1Mece2XarWK5eVlLCwsoFKpmG9+H2xGWbOpyQDX3g6Hw6jVairbVJIBk+UKmCcDt5VZ9DIBTke/ycp9TYJAkgUSBYYm6O6Xy32yGoAPPJW716SR6GeNS+Wvb6e78nn/5ASWFoDpOJKxyzCBnr+gkwHT+E1WGL9jQqhsLERixjXZB4XdW/QHnwt2vWO+0Ua8AnJO6PNOn49eytzr8/WUnyTU+v/6+eUxJExEZz14xcnlfJfv8nupoE37m0iCvJZ+49c/1+XQevAiA1zQjt5GJg9uNnjdw37Y1GSgVqthYWFBLcfLJjdU6FzKl80qhoaGVHOdRCKxJo7NEj9CWsz6JJeWhK6smPAnLWvW99KKpueBwoljYFkda/Z7vYtLFcvqCAovXpfjOKjX6+oapNUtV+GSJImQEwFY7WtOj4fjOGrxH/1YwGp4Rd5HTjT9vBI6meA55fb9vAXNZhOlUkldIz/n+GVVCe+zhQVw8TlZWFjAt7/9bYyMjKjum6ZneD2rV/9MJ62m8EO/hEKT5StJNWFKWuS2+rn1Y3p5BfT5SEPFi6R7hQX1e+WllEzb811XZlLRm+6Rfgz5OT+T45X5TMBqu+RCoYATJ05geXkZ58+fx8LCAmq1mnH8Ww2bmgywAx5/ZFlDzHp9AEgmk0q50kVPpSj7eAPoO5ElGdDDD/J76T7k35KkyBX/9InE/AQKJ4YHpHBiSIHEgzFQhgX0iUryoFslclJwG/7N8kCOgfFVEiZ5DPm/7kbUGbvJ+pH3j/dAWj46KeD/sk2ovFb+dgzRDELyj8WloVKp4Ny5c8Z++HL+SOhKystL4OUZ0JWWFxHQlXs/9BvLett5XVs/4mwiShslT4TMvdLJB8+vh3b1bS8X0kiS52Hpca1Ww/LyMhYXF1EsFlEulwdGfmxqMsAwgc/nw/bt25FMJlWbTcZ+fL7V8sF8Pq9W6aOS0GPYulXKMIHuIZAPJRUwrWRZF0/ywXcqVn3VPymE5BiAtcyWD7BcIEjmA+iufL1nOI8pY4o6IeL5eF1ybIRUzqYETLmPLlxN8VYpUPSQjkmw0lOxtLTk6sfACV0oFGxJ4YBiPaXG5ONIJIJarYZms6n64HN/0/FMz7JJJpgS59bzTnlZ56bxmK5XP48pkVp6MU0EwaRw9ZCE11i9xnUpCvxSFb40QgDz8sj6PZTyiYq+3W6rCoKZmRmVK2Bqkb5VsanJQKvVQrFYRCgUQjqdxsjICPL5PDKZjOqf7/f7lSJOJBKIx+PKgpaQ5SYyGZCue3oWCNPDx7+p9NnyV1YQ+P1+1Yeb49AbbJgsDd2qoPveC/JYspRRQu/PTY+DPmmkF0UXUnqyoClB0JSMSE+JPlkpbHVBqrtW+Xs1Gg3Mzc2hWCwqr0q5XMbs7CwKhYItKbQwol6vY25uDqFQSJWeynlKrKds5DbyxSZbeo6LaV9+pyvBS1GMpu14Pn0VRb7TIFjvOPr3Ju+I13j7Hd9LyeoywXRcyg5CyiFdlurn0o0Vx3FUnsDKygpOnz6NhYUFFYYcFGx6MlAul1WdOWPofMhlJzxpSeqWp+OsLk4hJ4gsQ/SyViV0tknXk+M4Ssjoiq/fJNaP6+WKM+3rJbQkZDhAekZ4HPm3VyKRdNGTPPVbDEgfn9xOegr6kSJ6Qti+WZaKcZGilZUVFAoFmzhoYQSfV7nSpSk/RUJ3o/PdVDonvQjSs6Zvb3K5b9Ti1s+3nlyQXg3p/fM6lq4wub0esusnl0xGjOk8JlAemLwk/RaJMp3D9Lvxb5k4yMZvbDQ0SNjUZKBcLuPkyZNwnIvJg2zMI9323W5XxX048WlBAqsPd6lUUtYlk/hyuZxa54Bxez6EXMhDdw3y4WOMnQ15HMdR78DFB1rvi6BPMDkZvSAFi2mi6taKHKdMJJRWPI9Lli1JjSRGcnyyhFEm8elJmvxtpBdGTxwkaKlJYkcCIFuFsksYV06cmZnBd7/7XZTLZRsmGFCsZ9ExZ4cexEajgWg0ikgk4lIoenxbNyK4DeeY9Kzxb6m4+pECk9ve9JLnNhF2LwUrjyFzfUz3TlfU+nw3ySX9nFIBmwiHzPHRofeD4Wdy7CZ4kRP5mS6nHcdBpVJRy8RfuHABy8vLA+dV3NRkoNPpqIksFxuRSo+TUbrjgdWJKJP5OGmp3Di59UQ7biMfdCo6qdjkMp0mlmlytUnGLt915e4FL8auCyrTpPFi7boQMBEVObHXY+ImYbWR2KLM5aAXQOZeNBoNlEolV68BCwsTJIk1eQZ0y9w0X6Qik6RB7rOeW1zOlfW8ARI6KdAVt2kMjuO4rG0vgmEiFaa5r1+PKS/IBNN5TNvoMo//ex2337F0r4Q+DpJCrgyrL6U8CNjUZIA/VrFYxGOPPYYLFy7gjjvuwDXXXINarYZCoYBOp4NUKoVer6dK5ai4e70e6vU6Op0OYrEYMpmMirHTGpVJfrTmgdXQApWsfNBY1sZtut0uSqXSmkoDuQ+P7ff7VQcsne1LRizPqwsEk7KW90s2PJIkxe/3u6oGOAaZ46BPEAoJOWElQZDj1QWThN/vRyKRcHkr9NbKumuXCYNcn+D8+fN44oknUK1WrUfAoi8cx1Fd5kggU6nUmhwB034mos78APmMyz4l3I6f839p9QJrw4cmBS7HQuhkXDcETN4G+b88nslT6WWc8Bg6pMdTXrM8hhf50scn9zOFGkxjMH0ujysNxU6ng4WFBRw5cgTnzp1TLeEHDZuaDABQCv3EiROoVCq48cYbMTQ0hEAgoDqM6bFo6aJizDmRSCCXy7mW2GS2vnzwpatKegJIMEgmODbux5I8rgsgPRjSxSjbCssJLo/FfaSg6EcKeAzp7dAVtyQpJAgsuZJEQJY1ynHp5EQeWy77rI+ff7P8Ui4ty6YfHIPf71+TeCnbRa+srODIkSOegtzCQgerT9h/3sttbvpMKjE9496kzAidJANwyZJ+25u8bNxGJycSMnFQntdELnRZoudSmAwZCUn8dU+nPE8/MqATKdM16/ejHygrTWSLS57Pzs5iaWlJre46aNj0ZACAWnsaAI4cOYJsNotwOIxoNLpGaUklKxUhHwoqGgCu0IHeXMdkJfMBk9Y2PQmmBjySaJBMcFuTla+zXS9B4yUwKHDk9ethE6+lOvUOjHoYQO+tIM+rCyB5DBIg3id2DdQtexKTZrOp1h6g5+X8+fMoFotYWVlZM24Li35otVqYnZ2F4zjI5XLI5/MA1jbsMc0pXUEC7oXS+LdJTuhWvkn5mazd9cZjsuqBVUPANAdN0K/LdK0bOY4XvGQbIe+drDrqF37UvZBe27ARWbvdxtLSEmq1Gs6fP4+ZmRkUCoWB6SugY0uQgXa7jfn5eVQqFTz22GNYWVnBnj17cPvtt6ukPbnaHRW+nHy9Xk91LOTDJBcyYYiBitpUDshj0c0vy+cYk2SZHrD60PI4dH/LLoJSuZuEgZ4DIJtpmMgLLWnp3ZDH04kIPR2mdszyvNLjIK0BSTZMbJsufknIVlZWsLKygng8jlwuBwCqLShDA8wVaDabOH78OM6dO6eEuoXFRtFoNFTHufHxcUxMTKgOotLdT5hi5/xcWrT63xLyuLoSM1nDgHcejj6/pTzRvQX6+b3+N32+UQIgvQLy2vRrkX/r8kYaFIDbiDFZ9vp5TceVBgt7CpAErKys4NixYzh8+LCqSuqHfmGkqwWXSs6ALUIGmADCWlGuaV+pVFTsXoLWp640dfZMpcwKBOmeBtwPopcVIaFPLn2i6kSFBIUKXCpkn8/ncuNLNisnjiynNE0uuZ8UIvp19LsmPStabmuqM+aklfeL99lxHFXiI5emlmBoaH5+XnkEbOWAxeWg1+uhVqshEAigXC6jXC4jEokglUq5Em4lNiJodSXEz3QZIAk7jy2tYV25yn3lefTzrreNvBZ9bq8nx3SYCMBGCcHzUawmj4LpO92gkmXJxWIRhUIB1Wp1w0Rgq2JLkAGWDzYaDRSLRVQqFczNzeGZZ55BIpHA5OQk4vG4Uu61Wg1nz54FAIyOjiIWi6kHRMa+a7Ua2u22WhHQcRyVVMfYP2PdUrnJygZ+pk9uEhjmEVC5yxa7dGPV63VkMhlks1lXuRJdXMyGDofDyOfzyosBQBEjn8+nLHAmU0rLQXoLpGUjhRVJiiQ/dO/L34L3iCWapvilXNtBelu63a5qB8o1GsjmOY52u40zZ87gS1/6EgqFAubm5tRvZWFxKeh2uzh//jzC4TAmJycRi8UwMjKCPXv2uDxi8hmW3kPdsgfWls2ZCHG//Tm/N0oETN4K00vua4JU0HKs+tj1z3Xvh04E+oU1N3o+eb36/ZPb6aWgUm7xxeTyUqmEw4cP4/z58zh37hzq9brnvTGN+Wr3DlwqtgQZAFbd23QBVatVFf8h0ybro8UNwGXl65NG9gqQbnbHcVyuft2CMMWu+L8UGLTQ5Tn1PAK6xPU4lrxWekVkQp2cCJJ08PrlNUnlLhMCTW5Ck8DQx6UnXcrv5D2RsVV6Bvhi22ZZgeHz+VQJUKlUwszMjCojtETAwoSNCG16CullonEgQ3pex9b/NlnEXsl8Xla86Vg6JBHQP9PJgpcFbZqf+hi8rtskB0xjN/293rF0T6tX2ESSA/1eye8p0ygfKUOq1ar6zQdlMaJ+2DJkALg46c6cOYNisYgdO3aoVesikQgymQzK5TIqlQpCoRCGhoaUUu90OqqcEFh9KLmMsM/nU14DWvF6L34Zs6NFQXe23+9HJBJxlfPJBBkqMu4ricLExAR6vR4ikYjyULTbbfj9fmSzWcTjcRSLRUVuOA56LVh6R8HGZD1p0csJozf70Juo6NvLCWmKkeoWFd8Z9pBhDOl54O9BIsNJ/Pjjj+Oxxx7D4uIilpeXFQmysDDhUqy306dPo16vY2pqCtFoFMlkUnkOCX0+9FOc+ncci7T6HcdZY0wQunLUlbspKdr0vRf0c8ptpaFisuxleFAqb5Nlr18Hz2XqzSC3o4zdiEdBvw5puNGYYv+AlZUVnDx5EoVCARcuXLiklQn7EbSrCesROhO2FBlwnItLky4sLMBxHOX2C4VCSCaTaDQayl3O3gO0hoG1DztDAQwhhEIhl+tQZ/hUanSP0+0fCAQQi8UQDAaN2frcj+fkJPb7/chkMsqlz3OwTDGRSCCZTCqiIsdCVzyVrszal2SADzZd+9xOn7jclgpfKnDpKTG58UwWlCxPlG2MSZI4Bl4rWf2xY8fwD//wD1f9ZLTYfJibm8Py8jJ6vR527NiBbDaLdDq9Jgyo5+4A3gmyuqXKffTnVw/JmRSoCbryB9YmOcpx9lOg3JeQlRG6t0Ne/3pkwHRe3cDQEyf7EQGTXJHf6Z5P5gKwqdDKygrOnz+PQqGApaUlFIvFS8o32qqyZ0uRAYlms4nFxUX4/X48++yzKBQKalXDYDCoYvNUSrTkgbWucOnu02Ps/JzWrM6YZUtemUsgJ9/y8jJarRay2axaZInZ8vQasMxRutgpQOLxOLLZrLL8pfKnN0F6NXheeR0kB/K4DDeQwPBz2Q+BZEoKMh6bE5LkQ3oN5HbsHyATIdlKulQqodls4tlnn8XS0hJOnz69ZSejxZWF41wMvRWLRRw/fhz5fB7ZbBYAEIvFVKmyVGASOiHo5+LX302ufPm/rvhM4QHT39x3PTLAscp1FExeQZPXsB8ZkPfGixDI48vxbsQroBtB8kV5wlLkQqGgcozm5uZQKpVU0zkTkRs0bFkyUKlUcObMGSwvL6PT6SCXy+FVr3oVbr/9dlQqFUUGqKylu5pgkhuwGguXsUQpFGj9yxp7hgeoME2Mn8lwxWIRhw4dwrZt21Cv11GpVNRx6eKq1+sIBoOIxWJK8TNcIJMG6f2gJyMWi7mUugxDcBwkMtIrQY8EyyJ5TygIpDWvv3g+AIhGo8qjIls+8z7J8kuSn3A4rEpG5+bm8Jd/+Zc4fvy4rRiweFHhOA4uXLiARqOBfD6PoaEhdLtdjI6OIh6PeyooKlMTUfAiBFLp6n/Ll8wrksczkQATqeD4gLVKVx8Px65b5tIQkG789Y6t76+PSRoRpv1M0MmNNCykZ0B6BJrNJmZnZ3HmzBksLCzg2LFjqNVqKJVKyvDaSrjUEAGwhclAp9NRvemLxSIAqJXsms2my8IOBALqwaGCA6AUlYxlM14vlzTeiBsPcLcc1RksQxH1el3F/2UIo9lsolarIRKJIBaLrXG3y1wD+TkJCCeIXq9LyIku95GTVCdAcgxeVo1M3jElAvG88tws9+Hni4uLKBQKqFQqfX9zC4sXAt1uV801JhTG43FkMhljN81+kJar9BT0c6kTuuvcBC/lb/IqmOa8vp0+TtO4TJ4GPd9qvfCAaTz99vOCHiaR7cqZhMz1BuS6JSQI0ts66NiyZIDrldMyT6fTiMfjWFpaQiaTwfbt2xGJRABAKflKpaIa2wBAPB5HKBRS7iZau1S+0WhUKUmfbzW+zQeZCt7v96skJMmGJcnw+/1YXl7GkSNHEIvFkMvl4PP5VP90rqY1NDSkPAFUoGySAqwKAZkQyQnOcfZzydGb0Gq1UK1WXSGQUCik3P2yxFK3TuSKhCRTrVZrTUmh3IbnaLVaePzxx/H1r39djbHdbmNubu7FeVAsLDQ4jqPqzr/5zW8im83ijjvuQDwed61hovfXAFaVmMyh0ZNqCd3N3g86kZDnlYpQNwh0l75XsqJXyMJLucsx614EXeHr2+ufmZSxaZz62OQ1s/SYxlSr1VIrmZ48eRLlchlHjx7F6dOnVaWZV7fVrQCvnIp+2LJkgOVC3W5XWZTz8/MIh8MYHx/H+Pi4slp1txJd0foyx9xGxgalUJCJLdIaly5Agp/RDRgIBNBoNLCysoJer4d8Pu9yH7ZaLbVCI8+rT0h9Ent5AUx1zwTDD3TZO46jiIWp1IrH1vu6y3FIN57pvFKgdTodzM3N4dixYxv4lS0sXjy0Wi2Vac7yM84p6cki1rP0Cd2KXm97+X2/kIOXl4D76jLDa/z9jmG6FtNx9X0u1QvgRbb0a5ahSRodTNxml8FSqYTl5WXlGW40GlsyNPB8sWXJANHtdrGysoJqtYpIJKIenO3btyMUCqk2t4lEQjH/XC7nYqbsTBYKhZDL5ZSFzLI9E1MH3G6+er0On8/nWjOdyXU7d+5Es9nEM888g29961vYvXs3tm3bpnIQgsEgxsbGkM1mVZUCyw3pEeD5WEZDC5zKXZ/0OiGQCTcMsczPz8NxHKTTadWwKBqNAoAr4RKAy+I3JeTo7jxuv7y8rDJ7n3rqKSwvL+Pw4cMvxE9vYfG8wc6Wzz77LDqdDnbs2IGXvexliEajKlm3Hxh/Xw/SgyCNB5NXwStPwIt0S/RT2tKY0c+rkwnpZfA6pj5WfRuT9So9hrrRRdCip9eUoYBOp4NKpYJarYZKpYKlpSVUq1WcOHECxWIRi4uLqNfrysixcGMgyECxWITP51NKOBgMolgsIhgM4vz586q2OJPJIBaLYWhoCH6/XzX04QOWTCaRyWQQjUaV0gTcLkGpVOUKiExYDAQCrlLBQCCAbdu2wXEcfPOb38Tjjz+ObreL7//+71fK3HEcDA8PIx6Po9FoqPp6fcI4zmpNLRPxZN9/3WvAvzlm6WKs1+tYXFxU1+c4jkpcpDdDChCZZa0v6iTvjfScBAIBFItFHDlyBOfPn8eXvvQlGw4YEHhZuVcb6CE7efIkKpUKWq0Wrr/+epUztJ7bHVhbdaB/r88TmUiob2vaX+7ntT3Q32LnHNa9mKZ9TMddL/Sghzn4vxfhkHlElEmSRJAMMJzDRMBSqYRqtarKB6vVKk6fPo1yuYxarTYwfUkuNUQADAAZkGg2m6hUKigUCjh37pxatyAWiyGVSqmljwF3P2sqZVl2I8HP9C54fKhlCIIWtaylpzDI5XLYu3cvtm3bptrwyokmyYYsy5Pn47ZU7vQc6MxfJwP6NQWDQSQSCbRaLZw9exbdbhfXXHMNhoeH1Xll1rFspSzHIic8SVG328Xi4iLK5TLOnj2LI0eOYGVlZSDXEB9UbAYiIMFW5wsLCzh16hSy2Sy2b9+OZDIJ4PKEL6F7FuWcNIX4ZHjTFBqQ1n0/V7uXIpbH0Lc3vUyQ3lH93KbzSEIi95Pv9DiyGqxUKmFpaUk1Emq1WqhUKqoaq1AoqBwCKTOfz2+1lTEwZMBxHFQqFVXD3263kclkcOjQIQwPD2NiYgJ79uxBs9lU5Ygyph+JRFTynFzQSDa24Jro0n1PFz1Zab1eRyAQQDKZxMjIiCprdBwHe/bsQTQaRT6fRzweV25IWbfPLFlOomAw6KrNJ6NmJi1LBqXwkBYNhQtf/CwajWJsbAxLS0v46le/inPnziEUCuGGG25Q7jmGPWRjJjm55STnZK5Wq6jVavjKV76CJ598EoVCAYuLi+q6LCyuRlSrVVSrVXS7XbV+wd13361CeV7EfT0r3cvC93K/yzi59OhJYsAXZYec19I6J/SQoaxC8sJ6yY+SDEhCIA0R+S49LNJbyc8oO7heC9efOXXqFGq1GhYWFlQuAOU7ewhwP73Pien+DzIGhgwAUBZ6o9FAuVwGcLEfQSKRQLPZdLUJ1l1mbE1MhcyYt3yIeQz9gePDybCCtMTlRGK+Qjwed7FZ/XhybKb4vJxMLLPRz+UFKYzC4bCrJXOvd7G9sp48qSdTybgfALWQUqPRUB6B+fl5Namr1aqdlBabAkzy9fv9KBQKSKVSKtcIwBqF5hUO8fpMt5j7EYoXGl4x+vWgEwc95MG/dW8Bz6OHTPRjA6uyu9lsolqtolQqqRLker2OQqGgDCBZWijXltHvpZU5bgwUGWAXP8abotEoWq0W0um0SrpLJpMYGxuDz+dTyyAHAgGMjo6i0+mgVCoBAPL5vFpvgJUAc3NzqNfrqoyRvQHI0n0+H2KxmEpklLErn8+H4eFh5PN5lEolHD16FAAwMTGBZDKpCEAgEEAmk1GKuVqtIhwOq3wIhjnYqIikIBQKIZ1OK+8Gcw5ka2WGNFqtFnw+nzrvy1/+cuzbtw+xWAxPPfUUEokExsbGXFUGMuyhJ0ytrKxgZmYGs7Oz+NrXvqZWJSyVSrbO12JToVwu49lnn1VzY3x8HNdffz2uu+4617oe0psnrXdg1Vo29RDQcw1MZEJ3ufNdeiv17aVho3sFTF6CfomI0juhe0Tk+OR5pYdC5gzp45eVWbx/NCRY2XHkyBFcuHAB8/PzmJmZUbkDUpbI80q5L70qFm4MFBmQbjUqy1AohEqlgtnZWczPz6PX62FiYkK52Nrttmo6wrihnAjSi1Cr1VCtVpXXoFKpKCsimUy6EgclU+WEiEajCIfDyvoALi6xLIUGQxYsnWQZJJsOSa+BXLGQ37EaQbZGlpOQ+wUCAYTDYcRiMYyPjyOZTKLbvbhscq/Xw+jo6BorhoJBXw650WigUChgfn4ezz33HBYWFl7Kn93C4gWD4ziqac25c+fQbDYxOTm5RjFKpSYVq04KdNe96W/uZyIGehhB/1vfn397QY/dc5zyOHKMktTox5UkQIYL9Pskj6kfg15FegSq1SqWlpbUOhIM6cr28vL6Kc8kCbBEwIyBIgOETPgrFAqo1Wr41re+hYWFBWzfvh3lchmJREJZ7rL3wNDQEHy+i4sEya5/Pt/FxkbhcFg1I0omk+pvZvWTKLRaLczPzyMUCiGfz6uSPbq3eD6ZCBgKhVwtheVaBMxNYBJjMBhULX1rtZoKQXCxpHa7rfZ1HMcVSgiHw66QQC6XQzqdViWWzWYTp06dcp2f3gne11arhVOnTqn1BJgkyK6QFhabGZ1OBzMzM4rsLy8vI5/P48CBA8p4kOQfWK3K8SoZlDB5CABzm1/CFFaQSlfP5fFaDEhXzDJ8YPreFCbgi7F6yhxeD++HXoFEryKteYYEKpWKahh04sQJLCwsqBCj9L7oIUvKNj3XwmItBpYM8AFiq+LFxUV85zvfwfT0NMLhsEoqTKVS6Ha7aDabCIfDSqEyli6JQjqdVusAAFCWtZwofDC5IEo0GsXQ0JDqdCjj6z7fas1+IBBQ3RSZdU9ywVUSqYiZWEgvQ7FYVMcNh8OuiUlCw9pbJj+yyZHf71e9Ffx+v2rmsbCwoPoFBAIBTExMYMeOHYp8VKtVPPPMMzh+/DhOnDiBw4cP25CAxZYCS28LhQKOHTuGqakpJJNJVZpM76KeOCc9ePyf8HK3y+9MSYW00k1Wtk4CdHJg6jnCd50QmOL+pnFzWyp1Wvey6kpa8TKh2XEc1Q9gZWUFCwsLKnRaqVQwPz+vZJps3S6vVRISyh0re/pjIMmACXxQqtUqTp06hVKppBQlmT6z7GWjH2A1kS4ej7sWO6JCluybAoKTlySAFQK0+JPJpCqjAYDh4eE1xILnlpOPzJpWSa/XQ7FYVMeWoQ1ZXshJSOLjOA4SiYQaO/MOEomEa7tSqaRaOHMpZZ/Ph3q9rtx55XLZEgGLLYtWq4VyuYyFhQU899xzyGQymJycRCaTQTweV5VBUg5IpeWVZCiVt/xMvsv99TBAv3CBDFGYchJMcX/5f788BmmF00UvE/sajcaa0IG8B5R79Nwy0bhUKqFWq6mma3rZou4RMCUvWnjDkgENCwsL+MpXvoJUKoVWq4U9e/ZgbGxMdRzL5XKIRCKoVCqu1sCRSES5+vXJJNm37PLHEAIfbiYjhsNhTExMoNFoYGZmBvV6Hddee62q8ZeMnpNNegQoeKLRKM6dO4eTJ09ieHgYN954o2p6ROLAcZE8cFGPTCaDiYkJ+Hw+rKysoNlsIhaLIZFIYG5uDmfPnkWpVMLjjz+OCxcuYGRkBGNjY8hkMti9ezc6nQ6eeeYZPP3002tWg7Sw2EpoNBpoNBpYWlrCmTNnEI/Hcd1112F0dBT79u3DoUOHXMuby3knE+Xk/zoBMLn65fbAqrVuUvK6kpShCr0JjynPQZIC07H1/ynn6BmgR5H5Q0zGpgzlPqwIKBaLql9AsVhEu91GuVx2LUAkPRVeYxpkIuBFMr1gyYCGTqejLNmVlRUsLy8jGo0ilUopFzvj7nqcSu/KJx9EaQkQ/JvKkjEzv391YSN+T3e/46x2M9QzlvWMfk5eTkIAqo2yXhYJYI2AkjFFn8+n2jAzLAFATVh+1ul0VLUDG4BYWAwCer2eKnWbn5+Hz+dDPp9HoVBQi6IFAgFEo1FXjb0ec5cweQL7YT0F0C/ebwoB6LKsH1GQ94FGijR+mEvEfgAkA9KLwDJsJmkyaVAvFfS6Rp0cDCoRAC69dNKSAQ80m01861vfwuHDh7Fjxw5MT09jZGQEjUYDyWRSKchkMolsNgsArmZAMrNeTg65ciDj/GxWxJyDeDyOcDisjlculzE7O4vnnnsO0WgUw8PDCIVCSCaTKjFRJgSSULByYGlpCZFIBJFIRHVMA1YXJZLWAUMRgUAAtVoNgUAAiUQCiUTC1dCIqz4mk0lVWrmwsIDl5WWcO3cOnU7HVg1YDCSazSaOHz+OmZkZnDt3Dk8//TTy+TxuuOEGpNNpTExMqOWQ9U6ghFS6ejxe9xpImaMrd5NClPO9H7mQXgBTEp6sijB9x4onrh1Qq9VQLBZx5swZ1Go1zM/Pq5VimcDMY9Dy53f820ROvLwDg4x+BNMLlgx4oNvt4sKFCwCgEl7q9TomJyeRTqdV7gDLAemq10sOqfBlRrAUALJBBrcJhUKq4Q+Fxvnz5/Hcc88hlUqh1+upOCSPT3BCSCLC7odMQmw2m64lhTkWx3HUcsg+n09dD89F0FPSaDRUToSMCZLcMN/BwuKlgCn57kqBlTdcMGd8fBxjY2PodDrIZrOqT4GUCdKL6JX17hVGkNualIDMUyJxkH97nUcvhZYNfEgoZB8F/dVsNlU3wHq9jnK5jOXlZbUQGl3/plbuchxyLF7jtHh+sGRgAyiVSjh79iyKxSIajQYSiQTGx8eRSqUwPT3t8gzIpBXZ1Ee6tuRklZ+z06EESxVzuRwmJiZQr9fx5JNPwufzYffu3cjlchgbG8Pk5KRy48sWw5lMBvv27VO9Aqi89XghJzOPUavVcOrUKXQ6HcXuZczv9OnTKJVKOH78OEqlkiI/3I7uPAuLlwpXmgCYwCRbAHj88ceRTCbV2gZDQ0OYmJhQ7Y0ZauP8ZajPBF0Jmix16daXLctlyJLnkKSEskFa3JzPsnkPZReteoYD5DmZCLi4uIjl5WUUCgWcP39ehT0ZJvDyVOheDvm39QS8sLBkYAPgOth+vx/PPPMMIpEIrrnmGmzfvh2BQABTU1OqzFA2uJDhAoJuecBdA8vvSAa4P2v3h4eH0e12cfLkSXzrW99CvV7H8vKy6gR48OBBJUi4PwDkcjkcPHgQ+XxeLUVMMgCsZeDhcBihUAjFYhHHjx9XiwmVy2UsLS2pe3Hu3DlXBQS9FEwWskzdwgLKM1er1TA7O4tAIIChoSHE43Hs3bsXN9xwA4aHh5FKpRAKhVwJvrpV7uWa9/qOoALXqw5k91G+S68BAJdilwl+UvGTFLDluiQP5XIZrVYLs7OzaqnymZkZNJtN4wqCJjKgyyhT3pXF84clAxsEJwXdWYVCAcFgECdPnlStibnEr8z+lSV7juO4wgp0jy0tLanEmlKppFois9EQOwLKXv+tVkstw8zFj3TiAayWDdbrdTzzzDOIx+NKeLCbIZMSJfPnKoLdbheJRALBYFCRgWq1qoQCzyEFhp2gFhZmMI7uOA6WlpYU0WYOUCqVUg3N4vG4ygGSuUamskAqefmZdNfrrc95DIYZGVaUx6B8YvMf9j9h6TF7k5B8yAQ/EgMuLWySHSaDQQ93mAiA6X+L5w9LBi4BMmZ26tQpnD17FkePHsXXvvY1ZLNZXHvttUin0yoRkJNXViiMjIwgl8upxZLq9TpOnTqFSqWCaDSKeDyOa6+9Fj/2Yz+GRCKhMv/p3gegrO8zZ85gbm4OBw4cUO592eSEY+71ejh//jy+9KUvodFo4Pbbb8eBAwcQiURUbwT2/T537hxmZ2eRSCQwNDSEWCyGffv2IRKJ4Ny5czhz5ozKDdDjeSRKdqJarIdLLXvaSqhUKqpu/sSJEwiHwxgaGkI4HMauXbswPDyMbdu2Yf/+/YjH46olOV+06CUBkB5JwL3kLxv+yDg/DQdZisx3yhA2VKMyZ8ijWq2i1+u5PIPSM8DGY+12G8vLyyokUCwWlXdhI/AiAhYvDiwZuAw4jqNK9ZrNpkoUyufzqmxGZt+z0yGXP43FYqjVaiiXyyrDltZBoVDA6OioSr5rNBpot9vq1ev1VJIf4F7WWAoCTkxOfrn0J0t6GDOU+QBcI7zdbiuvgUwU4n6me2JhcSkYZEIAQC236/P5VDKuLP2lPGEPE4YRaMUDazP5dW8AZQFlB615ei9NZEC6/ekJKBQKyoBhC2Aei78hPQOtVks1ByoUCmg2m7bMeBPA52xwNl5qmcKggNY/2T3dfSwPjMfjaoL5/X5cc801mJqaQrFYxIULF9BsNlXrzUKhgGKxiOHhYRw8eBCxWEz1NyCzZklOr9dDKpVCNBrF2NgYJiYmEI1Gkc/nEQgElMKnVcBVFVutFsbGxpDL5VSYgILAcRycOXMGFy5cwOLiIo4dO+YKE5w5cwZnz5690rd8oLEZladJdpiyxQcZNBzi8bjyEGazWUQiEdXobGhoCJlMBqlUSiUcylwDucR6r9dTckWuKVIul13NibgfsJpDwKXYuS5As9lEsVhUoUyZF0BI7wQJAXuccEwMK1q8NPDqa+MF6xl4nuAkqNfrmJmZAQDE43Hlgk+lUiopiPF9MnS60pgbsLy8rDobLi4uIhwOY8eOHUilUspdFwwGMTY2hmAwqBKRKpUKjh07hng8rtYfYPlONBpVfQJ27dqlmDwtEp4/n88jFAohk8mg0Whgfn4eR48eRa1Wu8J32MJi68DLG0JrvlQqqeoDgiuebt++HSMjI8pjwPlOw4NdROkRZJy/WCyqHKClpSWVTEgPg1xK2HEurgvAnCR2H2U/ACp8wlT6JysNLNnbPLBk4EWAbKDBiUcW/vTTT2NhYUFVAzCRx+fzIZPJqHXRWevPdsUAXBZAIBBQ9bt0AVKJO46DkydPYnFxUfUsALCGyVMABINBJBIJBAIBFAoFlMtlzM3Nran9tbCweH64HOVI9/vi4qJa3nxlZQXhcFitlCpDh1TC0sInMahUKmsS92QOAs/H0CF7hrBCyKTg9Z4Etuxvc8KGCV5EeJXJmHqHBwIB3HfffbjnnntU1UCr1cKJEydQKBSU9yAYDKp2pjKhkK5BuvIee+wxnDp1asO/m6lrme0TcPVhMwpYGyZ4YSHlCMuJ2X2URoesCjD1BjApcMDcrpjnlOfm37b2/+qFDRNcRfBi0CYl6zgOVlZWcP78eTXZ2u226tJFMsCFjKSAlclCTEqq1Wo2RmdhsQUh+wowF6nT6aypNiD0NsG68pc9BZ7PeCwR2NywnoGrCKlUyrV2AF19rAPW2xnL7fjOF/MRLLYWNqOw9fIMbMZr2QyQlrsXLsViXO/4tgTw6sSlegYsGbCw2ETYjALXyo6tCRvqubphwwQWFhYWFi86LAnYWli7XJWFhYWFhYXFQMGSAQsLCwsLiwGHJQMWFhYWFhYDDksGLCwsLCwsBhyWDFhYWFhYWAw4LBmwsLCwsLAYcFgyYGFhYWFhMeCwZMDCwsLCwmLAYcmAhYWFhYXFgMOSAQsLCwsLiwGHJQMWFhYWFhYDDksGLCwsLCwsBhyWDFhYWFhYWAw4LBmwsLCwsLAYcFgyYGFhYWFhMeCwZMDCwsLCwmLAYcmAhYWFhYXFgMOSAQsLCwsLiwGHJQMWFhYWFhYDDksGLCwsLCwsBhyWDFhYWFhYWAw4LBmwsLB4yeHz+a70ECwsLAQsGbCwsLCwsBhwWDJgYWFhYWEx4LBkwMLCwsLCYsBhyYCFhYWFhcWAw5IBCwsLCwuLAYclAxYWFi85HMe50kOwsLAQsGTAwsLCwsJii+FSCbclAxYWFhYWFgMOSwYsLCwsLCy2GC61sZclAxYWFhYWFgMOSwYsLCwsLCy2GGzOgIWFhYWFhcUlwZIBCwsLCwuLAUfwSg/AwsJia2O9RKZLcWfKYw1irwKfz/eSXrd+vn733+s7/ffnd4P+W76Y4L29lPtqyYCFhcUVxeUquMsReJsJXiTK6355Kd2NnsdLgV/O79OPAJq+u1p/y8shLF7X/kJd24tFCH3O1Xb3LSwsLCwsLF5S2JwBCwsLCwuLAYclAxYWFhYWFgMOSwYsLCwsLCwGHJYMWFhYWFhYDDgsGbCwsLCwsBhwWDJgYWFhYWEx4LBkwMLCwsLCYsBhyYCFhYWFhcWAw5IBCwsLCwuLAcf/B4nkCsdisD1KAAAAAElFTkSuQmCC\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "fig, ax = plt.subplots(nrows=1, ncols=2)\n",
+                "ax[0].imshow(images[0, channel, ..., images.shape[2] // 2].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, channel, ..., reconstruction.shape[2] // 2].detach().cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+                "ax[1].axis(\"off\")\n",
+                "ax[1].title.set_text(\"Reconstruction\")\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "292506bf",
+            "metadata": {},
+            "source": [
+                "## Clean up data directory\n",
+                "\n",
+                "Remove directory if a temporary storage was used"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "25551b82",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "if directory is None:\n",
+                "    shutil.rmtree(root_dir)"
+            ]
+        }
+    ],
+    "metadata": {
+        "jupytext": {
+            "formats": "ipynb,py"
+        },
+        "kernelspec": {
+            "display_name": "Python 3 (ipykernel)",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.8.13"
+        }
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MONAI version: 1.1.dev2239\n",
-      "Numpy version: 1.23.4\n",
-      "Pytorch version: 1.13.0\n",
-      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
-      "MONAI rev id: 13b24fa92b9d98bd0dc6d5cdcb52504fd09e297b\n",
-      "MONAI __file__: /home/jdafflon/miniconda3/envs/genmodels/lib/python3.9/site-packages/monai/__init__.py\n",
-      "\n",
-      "Optional dependencies:\n",
-      "Pytorch Ignite version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "Nibabel version: 4.0.2\n",
-      "scikit-image version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "Pillow version: 9.2.0\n",
-      "Tensorboard version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "gdown version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "TorchVision version: 0.14.0\n",
-      "tqdm version: 4.64.1\n",
-      "lmdb version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "psutil version: 5.9.4\n",
-      "pandas version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "einops version: 0.6.0\n",
-      "transformers version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "mlflow version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "pynrrd version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "\n",
-      "For details about installing the optional dependencies, please visit:\n",
-      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import shutil\n",
-    "import tempfile\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "import torch.nn.functional as F\n",
-    "from monai import transforms\n",
-    "from monai.apps import DecathlonDataset\n",
-    "from monai.config import print_config\n",
-    "from monai.data import DataLoader\n",
-    "from monai.networks.layers import Act\n",
-    "from monai.utils import first, set_determinism\n",
-    "from torch.cuda.amp import autocast\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "from generative.losses import PatchAdversarialLoss, PerceptualLoss\n",
-    "from generative.networks.nets import AutoencoderKL, PatchDiscriminator\n",
-    "\n",
-    "print_config()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "1aaa77a6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for reproducibility purposes set a seed\n",
-    "set_determinism(42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "72bae2d5",
-   "metadata": {},
-   "source": [
-    "## Setup a data directory and download dataset\n",
-    "\n",
-    "Specify a `MONAI_DATA_DIRECTORY` variable, where the data will be downloaded. If not specified a temporary directory will be used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "48155dfa",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/tmp/tmpyxyg6wxs\n"
-     ]
-    }
-   ],
-   "source": [
-    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
-    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
-    "print(root_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "319bff04",
-   "metadata": {},
-   "source": [
-    "## Download the training set"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "053fdee1",
-   "metadata": {},
-   "source": [
-    "Note: The DecatholonDataset has 7GB. So make sure that you have enought space when running the next line"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "1dbaf6af",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jdafflon/miniconda3/envs/genmodels/lib/python3.9/site-packages/monai/utils/deprecate_utils.py:107: FutureWarning: <class 'monai.transforms.utility.array.AddChannel'>: Class `AddChannel` has been deprecated since version 0.8. please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead.\n",
-      "  warn_deprecated(obj, msg, warning_category)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2022-12-08 20:46:51,956 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n",
-      "2022-12-08 20:46:51,958 - INFO - File exists: /tmp/tmpyxyg6wxs/Task01_BrainTumour.tar, skipped downloading.\n",
-      "2022-12-08 20:46:51,959 - INFO - Non-empty folder exists in /tmp/tmpyxyg6wxs/Task01_BrainTumour, skipped extracting.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 388/388 [02:39<00:00,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Image shape (1, 96, 96, 64)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "channel = 0  # 0 = Flair\n",
-    "assert channel in [0, 1, 2, 3], \"Choose a valid channel\"\n",
-    "\n",
-    "train_transforms = transforms.Compose(\n",
-    "    [\n",
-    "        transforms.LoadImaged(keys=[\"image\"]),\n",
-    "        transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
-    "        transforms.Lambdad(keys=\"image\", func=lambda x: x[channel, :, :, :]),\n",
-    "        transforms.AddChanneld(keys=[\"image\"]),\n",
-    "        transforms.EnsureTyped(keys=[\"image\"]),\n",
-    "        transforms.Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n",
-    "        transforms.Spacingd(keys=[\"image\"], pixdim=(2.4, 2.4, 2.2), mode=(\"bilinear\")),\n",
-    "        transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=(96, 96, 64)),\n",
-    "        transforms.ScaleIntensityRangePercentilesd(keys=\"image\", lower=0, upper=99.5, b_min=0, b_max=1),\n",
-    "    ]\n",
-    ")\n",
-    "train_ds = DecathlonDataset(\n",
-    "    root_dir=root_dir,\n",
-    "    task=\"Task01_BrainTumour\",\n",
-    "    section=\"training\",\n",
-    "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
-    "    num_workers=4,\n",
-    "    download=True,\n",
-    "    seed=0,\n",
-    "    transform=train_transforms,\n",
-    ")\n",
-    "train_loader = DataLoader(train_ds, batch_size=2, shuffle=True, num_workers=4, persistent_workers=True)\n",
-    "print(f'Image shape {train_ds[0][\"image\"].shape}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "617a46a9",
-   "metadata": {},
-   "source": [
-    "## Visualise examples from the training set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "8902c0a4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f555f9f39d0>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "check_data = first(train_loader)\n",
-    "\n",
-    "# Select the first image from the batch\n",
-    "img = check_data[\"image\"][0]\n",
-    "fig, axs = plt.subplots(nrows=1, ncols=3)\n",
-    "for ax in axs:\n",
-    "    ax.axis(\"off\")\n",
-    "ax = axs[0]\n",
-    "ax.imshow(img[0, ..., img.shape[3] // 2].rot90(), cmap=\"gray\")\n",
-    "ax = axs[1]\n",
-    "ax.imshow(img[0, :, img.shape[2] // 2, ...].rot90(), cmap=\"gray\")\n",
-    "ax = axs[2]\n",
-    "ax.imshow(img[0, img.shape[1] // 2, ...].rot90(), cmap=\"gray\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "902e37b5",
-   "metadata": {},
-   "source": [
-    "## Download the validation set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "0550cac3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2022-12-08 20:49:45,062 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n",
-      "2022-12-08 20:49:45,064 - INFO - File exists: /tmp/tmpyxyg6wxs/Task01_BrainTumour.tar, skipped downloading.\n",
-      "2022-12-08 20:49:45,065 - INFO - Non-empty folder exists in /tmp/tmpyxyg6wxs/Task01_BrainTumour, skipped extracting.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 96/96 [00:36<00:00,  2.60it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Image shape (1, 96, 96, 64)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "val_transforms = transforms.Compose(\n",
-    "    [\n",
-    "        transforms.LoadImaged(keys=[\"image\"]),\n",
-    "        transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
-    "        transforms.Lambdad(keys=\"image\", func=lambda x: x[channel, :, :, :]),\n",
-    "        transforms.AddChanneld(keys=[\"image\"]),\n",
-    "        transforms.EnsureTyped(keys=[\"image\"]),\n",
-    "        transforms.Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n",
-    "        transforms.Spacingd(keys=[\"image\"], pixdim=(2.4, 2.4, 2.2), mode=(\"bilinear\")),\n",
-    "        transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=(96, 96, 64)),\n",
-    "        transforms.ScaleIntensityRangePercentilesd(keys=\"image\", lower=0, upper=99.5, b_min=0, b_max=1),\n",
-    "    ]\n",
-    ")\n",
-    "val_ds = DecathlonDataset(\n",
-    "    root_dir=root_dir,\n",
-    "    task=\"Task01_BrainTumour\",\n",
-    "    section=\"validation\",\n",
-    "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
-    "    num_workers=4,\n",
-    "    download=True,\n",
-    "    seed=0,\n",
-    "    transform=val_transforms,\n",
-    ")\n",
-    "val_loader = DataLoader(val_ds, batch_size=2, shuffle=False, num_workers=4, persistent_workers=True)\n",
-    "print(f'Image shape {val_ds[0][\"image\"].shape}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "8e21e0ce",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f555ff98190>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "check_data = first(val_loader)\n",
-    "\n",
-    "img = check_data[\"image\"][0]\n",
-    "fig, axs = plt.subplots(nrows=1, ncols=3)\n",
-    "for ax in axs:\n",
-    "    ax.axis(\"off\")\n",
-    "ax = axs[0]\n",
-    "ax.imshow(img[0, ..., img.shape[3] // 2].rot90(), cmap=\"gray\")\n",
-    "ax = axs[1]\n",
-    "ax.imshow(img[0, :, img.shape[2] // 2, ...].rot90(), cmap=\"gray\")\n",
-    "ax = axs[2]\n",
-    "ax.imshow(img[0, img.shape[1] // 2, ...].rot90(), cmap=\"gray\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "19532ecb",
-   "metadata": {},
-   "source": [
-    "## Define the network"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "5e0514e5",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using cuda\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "PatchDiscriminator(\n",
-       "  (initial_conv): Convolution(\n",
-       "    (conv): Conv3d(1, 32, kernel_size=(4, 4, 4), stride=(2, 2, 2), padding=(1, 1, 1))\n",
-       "    (adn): ADN(\n",
-       "      (D): Dropout(p=0.0, inplace=False)\n",
-       "      (A): LeakyReLU(negative_slope=0.2)\n",
-       "    )\n",
-       "  )\n",
-       "  (0): Convolution(\n",
-       "    (conv): Conv3d(32, 64, kernel_size=(4, 4, 4), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)\n",
-       "    (adn): ADN(\n",
-       "      (N): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (D): Dropout(p=0.0, inplace=False)\n",
-       "      (A): LeakyReLU(negative_slope=0.2)\n",
-       "    )\n",
-       "  )\n",
-       "  (1): Convolution(\n",
-       "    (conv): Conv3d(64, 128, kernel_size=(4, 4, 4), stride=(2, 2, 2), padding=(1, 1, 1), bias=False)\n",
-       "    (adn): ADN(\n",
-       "      (N): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (D): Dropout(p=0.0, inplace=False)\n",
-       "      (A): LeakyReLU(negative_slope=0.2)\n",
-       "    )\n",
-       "  )\n",
-       "  (2): Convolution(\n",
-       "    (conv): Conv3d(128, 256, kernel_size=(4, 4, 4), stride=(1, 1, 1), padding=(1, 1, 1), bias=False)\n",
-       "    (adn): ADN(\n",
-       "      (N): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-       "      (D): Dropout(p=0.0, inplace=False)\n",
-       "      (A): LeakyReLU(negative_slope=0.2)\n",
-       "    )\n",
-       "  )\n",
-       "  (final_conv): Convolution(\n",
-       "    (conv): Conv3d(256, 1, kernel_size=(4, 4, 4), stride=(1, 1, 1), padding=(1, 1, 1))\n",
-       "  )\n",
-       ")"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "print(f\"Using {device}\")\n",
-    "\n",
-    "model = AutoencoderKL(\n",
-    "    spatial_dims=3,\n",
-    "    in_channels=1,\n",
-    "    out_channels=1,\n",
-    "    num_channels=(32, 64, 64),\n",
-    "    latent_channels=3,\n",
-    "    num_res_blocks=1,\n",
-    "    norm_num_groups=32,\n",
-    "    attention_levels=(False, False, True),\n",
-    ")\n",
-    "model.to(device)\n",
-    "\n",
-    "discriminator = PatchDiscriminator(\n",
-    "    spatial_dims=3,\n",
-    "    num_layers_d=3,\n",
-    "    num_channels=32,\n",
-    "    in_channels=1,\n",
-    "    out_channels=1,\n",
-    "    kernel_size=4,\n",
-    "    activation=(Act.LEAKYRELU, {\"negative_slope\": 0.2}),\n",
-    "    norm=\"BATCH\",\n",
-    "    bias=False,\n",
-    "    padding=1,\n",
-    ")\n",
-    "discriminator.to(device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "da14911d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jdafflon/miniconda3/envs/genmodels/lib/python3.9/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
-      "  warnings.warn(\n",
-      "/home/jdafflon/miniconda3/envs/genmodels/lib/python3.9/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=SqueezeNet1_1_Weights.IMAGENET1K_V1`. You can also use `weights=SqueezeNet1_1_Weights.DEFAULT` to get the most up-to-date weights.\n",
-      "  warnings.warn(msg)\n"
-     ]
-    }
-   ],
-   "source": [
-    "perceptual_loss = PerceptualLoss(spatial_dims=3, network_type=\"squeeze\", fake_3d_ratio=0.25)\n",
-    "perceptual_loss.to(device)\n",
-    "\n",
-    "adv_loss = PatchAdversarialLoss(criterion=\"least_squares\")\n",
-    "adv_weight = 0.01\n",
-    "perceptual_weight = 0.001\n",
-    "\n",
-    "optimizer_g = torch.optim.Adam(model.parameters(), 1e-4)\n",
-    "optimizer_d = torch.optim.Adam(discriminator.parameters(), lr=5e-4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "5c0b87e9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scaler_g = torch.cuda.amp.GradScaler()\n",
-    "scaler_d = torch.cuda.amp.GradScaler()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7d19616e",
-   "metadata": {},
-   "source": [
-    "## Model training"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "aa98bfa9",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0: 100%|█| 194/194 [04:33<00:00,  1.41s/it, recons_loss=0.078, g\n",
-      "Epoch 1: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0408, \n",
-      "Epoch 2: 100%|█| 194/194 [04:33<00:00,  1.41s/it, recons_loss=0.0368, \n",
-      "Epoch 3: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0347, \n",
-      "Epoch 4: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0333, \n",
-      "Epoch 5: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0314, \n",
-      "Epoch 6: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0312, \n",
-      "Epoch 7: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0301, \n",
-      "Epoch 8: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0289, \n",
-      "Epoch 9: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0307, \n",
-      "Epoch 10: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0293,\n",
-      "Epoch 11: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0294,\n",
-      "Epoch 12: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0298,\n",
-      "Epoch 13: 100%|█| 194/194 [04:33<00:00,  1.41s/it, recons_loss=0.0284,\n",
-      "Epoch 14: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0283,\n",
-      "Epoch 15: 100%|█| 194/194 [04:33<00:00,  1.41s/it, recons_loss=0.029, \n",
-      "Epoch 16: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0293,\n",
-      "Epoch 17: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0286,\n",
-      "Epoch 18: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0276,\n",
-      "Epoch 19: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0285,\n",
-      "Epoch 20: 100%|█| 194/194 [04:32<00:00,  1.40s/it, recons_loss=0.0275,\n",
-      "Epoch 21: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0279,\n",
-      "Epoch 22: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0271,\n",
-      "Epoch 23: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0278,\n",
-      "Epoch 24: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.028, \n",
-      "Epoch 25: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0261,\n",
-      "Epoch 26: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0266,\n",
-      "Epoch 27: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0264,\n",
-      "Epoch 28: 100%|█| 194/194 [04:33<00:00,  1.41s/it, recons_loss=0.0277,\n",
-      "Epoch 29: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0279,\n",
-      "Epoch 30: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0266,\n",
-      "Epoch 31: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0273,\n",
-      "Epoch 32: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.027, \n",
-      "Epoch 33: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.027, \n",
-      "Epoch 34: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.029, \n",
-      "Epoch 35: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0285,\n",
-      "Epoch 36: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0271,\n",
-      "Epoch 37: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0267,\n",
-      "Epoch 38: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0248,\n",
-      "Epoch 39: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0246,\n",
-      "Epoch 40: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0248,\n",
-      "Epoch 41: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0258,\n",
-      "Epoch 42: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0254,\n",
-      "Epoch 43: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0245,\n",
-      "Epoch 44: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0248,\n",
-      "Epoch 45: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0247,\n",
-      "Epoch 46: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0247,\n",
-      "Epoch 47: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0248,\n",
-      "Epoch 48: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0256,\n",
-      "Epoch 49: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0238,\n",
-      "Epoch 50: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0239,\n",
-      "Epoch 51: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.025, \n",
-      "Epoch 52: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0239,\n",
-      "Epoch 53: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0234,\n",
-      "Epoch 54: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0242,\n",
-      "Epoch 55: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0236,\n",
-      "Epoch 56: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0242,\n",
-      "Epoch 57: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0227,\n",
-      "Epoch 58: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0216,\n",
-      "Epoch 59: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.022, \n",
-      "Epoch 60: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0234,\n",
-      "Epoch 61: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.024, \n",
-      "Epoch 62: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0229,\n",
-      "Epoch 63: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0238,\n",
-      "Epoch 64: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.023, \n",
-      "Epoch 65: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0241,\n",
-      "Epoch 66: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0223,\n",
-      "Epoch 67: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0218,\n",
-      "Epoch 68: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.022, \n",
-      "Epoch 69: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0221,\n",
-      "Epoch 70: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0238,\n",
-      "Epoch 71: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0229,\n",
-      "Epoch 72: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.022, \n",
-      "Epoch 73: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0222,\n",
-      "Epoch 74: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0238,\n",
-      "Epoch 75: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0217,\n",
-      "Epoch 76: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0228,\n",
-      "Epoch 77: 100%|█| 194/194 [04:32<00:00,  1.40s/it, recons_loss=0.0218,\n",
-      "Epoch 78: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0231,\n",
-      "Epoch 79: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0224,\n",
-      "Epoch 80: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0217,\n",
-      "Epoch 81: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0222,\n",
-      "Epoch 82: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0232,\n",
-      "Epoch 83: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.021, \n",
-      "Epoch 84: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0208,\n",
-      "Epoch 85: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0212,\n",
-      "Epoch 86: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0219,\n",
-      "Epoch 87: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0214,\n",
-      "Epoch 88: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0224,\n",
-      "Epoch 89: 100%|█| 194/194 [04:32<00:00,  1.40s/it, recons_loss=0.0219,\n",
-      "Epoch 90: 100%|█| 194/194 [04:32<00:00,  1.40s/it, recons_loss=0.0223,\n",
-      "Epoch 91: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0229,\n",
-      "Epoch 92: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0218,\n",
-      "Epoch 93: 100%|█| 194/194 [04:32<00:00,  1.40s/it, recons_loss=0.022, \n",
-      "Epoch 94: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0215,\n",
-      "Epoch 95: 100%|█| 194/194 [04:32<00:00,  1.40s/it, recons_loss=0.0207,\n",
-      "Epoch 96: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0207,\n",
-      "Epoch 97: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0214,\n",
-      "Epoch 98: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0215,\n",
-      "Epoch 99: 100%|█| 194/194 [04:32<00:00,  1.41s/it, recons_loss=0.0223,\n"
-     ]
-    }
-   ],
-   "source": [
-    "kl_weight = 1e-6\n",
-    "n_epochs = 100\n",
-    "val_interval = 6\n",
-    "epoch_recon_loss_list = []\n",
-    "epoch_gen_loss_list = []\n",
-    "epoch_disc_loss_list = []\n",
-    "val_recon_epoch_loss_list = []\n",
-    "intermediary_images = []\n",
-    "n_example_images = 4\n",
-    "\n",
-    "for epoch in range(n_epochs):\n",
-    "    model.train()\n",
-    "    discriminator.train()\n",
-    "    epoch_loss = 0\n",
-    "    gen_epoch_loss = 0\n",
-    "    disc_epoch_loss = 0\n",
-    "    progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=70)\n",
-    "    progress_bar.set_description(f\"Epoch {epoch}\")\n",
-    "    for step, batch in progress_bar:\n",
-    "        images = batch[\"image\"].to(device)\n",
-    "        optimizer_g.zero_grad(set_to_none=True)\n",
-    "\n",
-    "        # Generator part\n",
-    "        with autocast(enabled=True):\n",
-    "            reconstruction, z_mu, z_sigma = model(images)\n",
-    "            logits_fake = discriminator(reconstruction.contiguous().float())[-1]\n",
-    "\n",
-    "            recons_loss = F.l1_loss(reconstruction.float(), images.float())\n",
-    "            p_loss = perceptual_loss(reconstruction.float(), images.float())\n",
-    "            generator_loss = adv_loss(logits_fake, target_is_real=True, for_discriminator=False)\n",
-    "\n",
-    "            kl_loss = 0.5 * torch.sum(z_mu.pow(2) + z_sigma.pow(2) - torch.log(z_sigma.pow(2)) - 1, dim=[1, 2, 3, 4])\n",
-    "            kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]\n",
-    "\n",
-    "            loss_g = recons_loss + (kl_weight * kl_loss) + (perceptual_weight * p_loss) + (adv_weight * generator_loss)\n",
-    "\n",
-    "        scaler_g.scale(loss_g).backward()\n",
-    "        scaler_g.step(optimizer_g)\n",
-    "        scaler_g.update()\n",
-    "\n",
-    "        # Discriminator part\n",
-    "        optimizer_d.zero_grad(set_to_none=True)\n",
-    "\n",
-    "        with autocast(enabled=True):\n",
-    "            logits_fake = discriminator(reconstruction.contiguous().detach())[-1]\n",
-    "            loss_d_fake = adv_loss(logits_fake, target_is_real=False, for_discriminator=True)\n",
-    "            logits_real = discriminator(images.contiguous().detach())[-1]\n",
-    "            loss_d_real = adv_loss(logits_real, target_is_real=True, for_discriminator=True)\n",
-    "            discriminator_loss = (loss_d_fake + loss_d_real) * 0.5\n",
-    "\n",
-    "            loss_d = adv_weight * discriminator_loss\n",
-    "\n",
-    "        scaler_d.scale(loss_d).backward()\n",
-    "        scaler_d.step(optimizer_d)\n",
-    "        scaler_d.update()\n",
-    "\n",
-    "        epoch_loss += recons_loss.item()\n",
-    "        gen_epoch_loss += generator_loss.item()\n",
-    "        disc_epoch_loss += discriminator_loss.item()\n",
-    "\n",
-    "        progress_bar.set_postfix(\n",
-    "            {\n",
-    "                \"recons_loss\": epoch_loss / (step + 1),\n",
-    "                \"gen_loss\": gen_epoch_loss / (step + 1),\n",
-    "                \"disc_loss\": disc_epoch_loss / (step + 1),\n",
-    "            }\n",
-    "        )\n",
-    "    epoch_recon_loss_list.append(epoch_loss / (step + 1))\n",
-    "    epoch_gen_loss_list.append(gen_epoch_loss / (step + 1))\n",
-    "    epoch_disc_loss_list.append(disc_epoch_loss / (step + 1))\n",
-    "\n",
-    "    if (epoch + 1) % val_interval == 0:\n",
-    "        model.eval()\n",
-    "        val_loss = 0\n",
-    "        with torch.no_grad():\n",
-    "            for val_step, batch in enumerate(val_loader, start=1):\n",
-    "                images = batch[\"image\"].to(device)\n",
-    "                optimizer_g.zero_grad(set_to_none=True)\n",
-    "\n",
-    "                reconstruction, z_mu, z_sigma = model(images)\n",
-    "                # get the first sammple from the first validation batch for visualisation\n",
-    "                # purposes\n",
-    "                if val_step == 1:\n",
-    "                    intermediary_images.append(reconstruction[:n_example_images, 0])\n",
-    "\n",
-    "                recons_loss = F.l1_loss(reconstruction.float(), images.float())\n",
-    "\n",
-    "                val_loss += recons_loss.item()\n",
-    "\n",
-    "        val_loss /= val_step\n",
-    "        val_recon_epoch_loss_list.append(val_loss)\n",
-    "\n",
-    "progress_bar.close()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a28c94e3",
-   "metadata": {},
-   "source": [
-    "## Evaluate the trainig"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "066417fe",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure()\n",
-    "val_samples = np.linspace(val_interval, n_epochs, int(n_epochs / val_interval))\n",
-    "plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_recon_loss_list, label=\"Train\")\n",
-    "plt.plot(val_samples, val_recon_epoch_loss_list, label=\"Validation\")\n",
-    "plt.xlabel(\"Epochs\")\n",
-    "plt.ylabel(\"Loss\")\n",
-    "plt.legend()\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "plt.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "bb1b6dd8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkEAAAHZCAYAAACB2e8eAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAADCY0lEQVR4nOzdd3hTZfsH8G+SpnvTTUsHpcxCWS17qUwBkamgCCg4UByIoCLgePEVRVB++oqyVZCpDEX2HmWUvaED2tK9R5om5/fH6Tk5pxlN26RJ2/tzXVykyTnJk47kzv3cz/1IGIZhQAghhBDSyEgtPQBCCCGEEEugIIgQQgghjRIFQYQQQghplCgIIoQQQkijREEQIYQQQholCoIIIYQQ0ihREEQIIYSQRomCIEIIIYQ0ShQEEUIIIaRRoiCIWFxISAgkEgleeuklSw+l0Vm7di0kEgkkEgkSEhLM8hgvvfQSJBIJQkJCzHL/1qJfv36QSCTo16+f2R6jLn5ehDQmFASRKh09epR/4ZVIJDh16pSlh0QaAS6oqM2/tWvXWvppkGooKirCqlWrMG7cOLRo0QLu7u6wtbWFj48PunfvjnfffRdnzpyx9DBJA0JBEKnSunXrRF+vX7/eQiMhhDRUP/30E8LCwvDyyy9jy5YtuHfvHvLy8qBUKpGRkYEzZ87g22+/Rffu3REdHU0fxohJSGgDVWJISUkJfH19UVBQAGdnZxQWFsLDwwOpqamws7MzyWOEhIQgMTERkydPpk/uDdBLL72EdevWITg4uFpTOPHx8SgqKtJ528cff4y//voLAPDvv/8iICBA53GBgYFwd3ev7pBJHVKr1XjttdewcuVKAIBUKsXIkSMxdOhQhIeHw9XVFRkZGbh69Sr++usvnDhxAgAwcuRI/PnnnxYcOWkIbCw9AGLdduzYgYKCAgDAd999h6lTpyInJwe7du3CmDFjLDw60pCFhobqvU0Y2ERERDT4eqOGbNGiRXwA1KJFC2zduhXt27fXOm7QoEGYPXs2Tp48ibfeequuh0kaKJoOIwZxU1/t27fHlClT0LJlS9H1hBBSUxcuXMDnn38OAAgICMCJEyd0BkBCPXv2xKlTp/D888/XxRBJA0dBENErNTUVBw4cAABMmjRJ9P/evXuRkZFh1P38888/GDp0KLy9veHo6IiIiAi8++67SE5ONnhe8+bNIZFI0LNnzyofIzk5GTKZDBKJBHPmzNF5TF5eHhYvXoyePXvC29sbtra28Pf3x/Dhw7F161YYmhnmCm0XLlwIADh06BDGjh2LoKAgyOVyrUxESkoK5s6di06dOsHNzQ1yuRy+vr6IjIzEc889h7Vr1yI/P1/rcYqKivDHH3/g5ZdfRlRUFH+ut7c3+vbti6+//hqFhYUGvxfVGWtVq43UajUOHTqE2bNno2fPnvDy8oJcLoe7uzuioqIwe/ZsJCUlGRyPJVR+XgqFAsuWLUO3bt3g5eUl+v4AQFlZGXbt2oWZM2eia9eu8PDwgFwuR5MmTRATE4OFCxciMzPT4GMaWh2WkJCgVay9f/9+DB8+HH5+frCzs0NoaChee+01PHr0yOjnVdUYkpOT8e677yI8PBwODg5o0qQJBg0ahH/++aeqbyEA9sNO37594eHhAWdnZ0RGRuLTTz/lf3cr/65V1+LFi6FWqwEA33//PXx8fIw6z87ODuPGjRNdZ+zKOV0/C6HKqxlTU1PxwQcfoG3btnBxcYFEIsGRI0cwdepUSCQSODg48NlyQ1q2bAmJRILo6Gidt6tUKqxbtw5PP/00AgICYGdnhyZNmqBXr15YunQpSkpKDN7/hQsXMG3aNERERMDJyQn29vYICgpC586d8cYbb2Dnzp0GX+MaLYYQPZYsWcIAYKRSKfPo0SOGYRjmwYMHjEQiYQAwy5cvr/I+3nnnHQaAzn/e3t7MuXPnmODgYAYAM3nyZNG5H3/8MQOAkUgkTHx8vFFjBcBcunRJ6/YDBw4wTZo00TsWAMzQoUOZgoICnffPHbNgwQLmww8/1Do3ODiYP/bYsWOMq6urwccCwOzatUvrcfr27VvleaGhoczNmzf1fi+qM9Y1a9bw1+v6Hi9YsKDK8Tg6OjLbt2/XO57JkydrPW5tcfepb9zC53Xu3DkmKipKa9wLFizQeX/6/jVp0oQ5ceKE3jFxP7u+fftq3RYfH8/fz5o1a5i5c+ca/Lu4ceOGzseo6uclHMOJEycYLy8vvY+zZMkSvc+lrKyMGTlypN5zW7RowSQkJOj8XhorJyeHkclkDAAmJCSEUalU1b4Poaq+N5zKP4vKhL+vp0+f1vk9PHz4MHPgwAH+67Vr1xoc27lz5/hjly1bpnV7YmIi06FDB4O/f+Hh4czt27d13v/SpUsZqVRa5e+wvte3xoxqgoheGzZsAMB+umzatCkAtk6jR48eOHnyJNavX29wbn7ZsmX49ttvAbCp7nnz5iE6OhqlpaXYs2cPli1bhrFjx6K4uFjn+RMnTsTnn38OhmHw+++/48MPP9T7WL///jsAoG3btujQoYPotpMnT2LIkCFQKpXw9fXFm2++iQ4dOiAgIAApKSn4448/8Ouvv+Lvv//G5MmTsW3bNr2Ps337dly9ehWRkZF455130K5dO5SUlODSpUsAAIVCgQkTJiA/Px8uLi547bXX0L9/f/j4+KCsrAzx8fE4deoUduzYofP+y8vLERkZiREjRqBLly4ICAgAwzBITEzEjh07sHnzZsTHx+OZZ57BpUuXYG9vX+OxGqO8vBz+/v4YNWoUunfvjrCwMNjb2+Phw4c4deoUfvjhBxQWFuL555/HxYsX0bp1a6Pvu65MmzYNV69exYsvvojx48fDz88PSUlJosL+8vJyhIWFYdSoUYiOjkazZs1gY2ODxMREHDhwAKtXr0ZWVhZGjRqFa9euGZ2x0OXnn3/GqVOn0LdvX8yYMQMRERHIzc3F+vXrsX79emRkZGDq1Kk4ffp0jR8jNTUVzzzzDKRSKb788kv06tULtra2OHHiBD799FPk5uZi3rx5GDJkCNq2bat1/qxZs/jC87Zt22L27Nlo164d8vPzsWPHDvz4448YP358jccHACdOnIBKpQIADB48GFKpdU1MFBYWYvTo0SgtLcVHH32Ep556Co6Ojrh69Sr8/f3RokUL/jXkt99+w+TJk/XeF/f6JJPJMGHCBNFtWVlZ6NWrFx4+fAg7Ozu88sor6Nu3L0JCQlBYWIh9+/Zh+fLluHfvHoYMGYKLFy/Czc2NP//KlSuYPXs21Go1QkNDMXPmTERFRcHT0xMFBQW4ffs2Dh8+zP88SSWWjsKIdYqLi+M/PaxevVp0248//sjfdv36dZ3np6WlMY6OjvwnqtTUVK1jDh48yNjY2PD3VTkTxDAM06lTJwYA07ZtW71jvXnzJn8f//nPf0S3lZWVMSEhIQwAZvDgwUxRUZHO+1i5ciV/H/v27dO6HYJPU0888QRTWlqq834OHjxoMNPDUSqVTF5entb1d+7c0XsOwzDM/v37+U98v/zyi85jjB0rw1T96Tk+Pp4pKyvTe/7Dhw+Zpk2bMgCYSZMm6TzG0pkgQ98rzr179xi1Wq339itXrjDOzs4MAObjjz/WeYyxmSAAzCuvvKLz8V5++WX+mIsXLxp8XoYyQdz3m8vgCh0/fpzP5r711ltat1+8eJG/vXv37kxxcbHWMVu2bNGbVTPW559/zp//888/V/v8ykydCQLAODs768wsc959910GACOTyZjHjx/rPEalUjEBAQEMAGbgwIFatz///PP8z+vBgwc67+PixYuMk5MTA4D58MMPRbfNnz+fAcA4OTnpHQPDMExubm6ts20NkXWF3sRqcIXPDg4OGD16tOi2cePGwdbWVnRcZevWreMzPN988w38/Py0jhkwYABeeeUVg+OYOHEiAOD69eu4fPmyzmN+++03AGx9QuViyU2bNiEhIQH29vZYv349HB0ddd7HK6+8ws/VG1qmL5VK8csvv+htD/D48WP+cp8+ffTej42NDVxdXbWub9Gihd5zAODJJ5/EiBEjAKDK5cFVjdUYISEhkMvlem8PDAzE+++/DwBWW3MwYMAATJs2zeAxXP2ZPpGRkXj55ZcBVP19r4q/vz++//57nY83e/Zs/vLx48dr9Tjff/89n8EV6tWrF2JiYvQ+xsqVK/mf488//wwHBwetY8aMGYNRo0bVanxZWVn85dpk1sxpzpw5WpllIe71SaVSYdOmTTqPOXz4MFJSUkTHcxISEvDHH38AAFasWKF3RWTHjh3xxhtvANB+feJecyIiIuDr66t3rG5ublaXbbMG9B0hWsrLy/n07fDhw7XerD09PTF06FAAbADCFTYKcQXVHh4eGDlypN7Hmjp1qsGxTJgwgf/D5cZU2caNGwGwq0aCg4NFt+3cuRMA0LdvX3h7ext8LC5oMTQN0bNnT4PLsf39/fnLa9asMfh4xsjIyMDdu3dx7do1/h/3PPQFhcaOtSby8/MRHx+P69ev8+PhAkvuNmtT+Y3HGDk5Obh//77oeXLL8m/cuAGlUlnj8YwZM0ZvYNqyZUs4OzsDAB48eFDjx3B3d8ewYcP03t65c2e9j8H97Xbs2FHnVBnnxRdfrPH4AIiKiZ2cnGp1X+ZS1e9Op06d0KpVKwD6X5+46x0cHLQCxz179kClUsHR0RFDhgwx+Fjc61NKSopoMQL3mnPjxg3ExsYavA+ijWqCiJZ///0XaWlpADSrwSqbNGkS/vzzTzx69AiHDx/GE088Ibr96tWrANgXUhsb/b9mUVFRsLW1RVlZmc7bAwIC0L9/fxw8eBAbN27El19+KfoEffbsWdy/fx+A7hes8+fP88/J0Cd9IWE2p7Kqlu/26tULYWFhePDgAd5++2389ttvGDVqFPr06YOuXbvyGTRDTp48ie+++w4HDhxAdna23uOqWq1U1ViNlZiYiK+//hq7du1CYmKiwWMzMzMRFhZmksc1FWO/D1evXsW3336Lf/75x+DvgFqtRk5OTo2zF9ybpj4eHh4oLCw0asWRPi1atDD4qd/T0xMAtB6jtLQU9+7dA6AJlPTp0qVLjccHAC4uLvxlfU0xLcnZ2dmo3+WJEydi/vz5iI2Nxb179xAeHs7fplAosH37dgDAiBEjRM8Z0Lw+FRcXG3ydrOzx48do1qwZAOC5557D4sWLoVAo0LNnTwwePBjDhg1Dr1690LZtW6Nf9xorygQRLdwUV5MmTTB48GCdxzz99NP8J2NdU2Lcm3dVbxQ2Njb8C7I+XHDz8OFDHDt2THQbNxUml8sxduxYrXPT09MN3rcuhpaienh4GDxXLpdj165dfIHwuXPn8OGHH6JXr15wd3fH4MGD8fvvv/MFoZUtXLgQvXr1wubNmw0GQFWN05ixGuOff/5BmzZtsGLFiioDIGPGZAnGfB9WrVqFTp06Yc2aNQYDIE5tnqe+KVkOF7zo+x0x5WNUzuLm5ubyl6vKnFZ1e1WaNGnCX+Y+dFkTYzuNC6fgudcjzp49e/jvqa4PaTV5fQIgWkzSqlUrbNy4ER4eHigvL8fu3bvx2muvITIyEj4+PnjhhRdqPbXakFEQRETy8vL4KaSsrCzY2trq3JjS3t6e/+Pevn273k9ypvgUMnr0aH4VlDDlrFKpsHnzZgDs6hLhi6rwGAAYMmQIrl69avQ/fWQyWZXjbdOmDa5evYodO3Zg6tSp/CfDkpIS/Pvvv5g4cSJiYmK0XgAPHjyIRYsWAQDCwsLwww8/4MqVK8jNzYVSqQTDMGAYBvPnz69yDMaO1ZDMzEw8//zzKC4uhrOzMxYuXIjTp08jPT0dCoWCH8/Bgwf5c6yxJqiq78OtW7fw6quvory8HD4+PliyZAkuXLiArKwslJWV8c9z1apV/DnW+DzrG2GtzcWLFy04Et2M/fsJCwtD9+7dAWhPiXFf6/tAyb0+eXl5Vev1qWvXrqL7GT16NOLj4/HTTz/h2Wef5QPUzMxM/Prrr+jTpw9eeuklnaULjR1NhxGRzZs3o7S0tFrnFBYWYvv27XjhhRf46zw8PPD48eMqP+GVl5dXmfFwdXXF008/ja1bt2Lr1q1YsWIF5HI5Dh48yN+/vrn7Jk2aICUlBWVlZWjXrl21nldtyGQyPPPMM3jmmWcAsEuW9+7di//7v//DhQsXcOHCBcyYMUO0VP7nn38GwH7vzpw5o/eTdlXfL1PZunUrH+ju2LEDTz75pEXHYy5r165FeXk5ZDIZjh49qne6qr4/T2MIsx9VNUM1tlmqPr169YJMJoNKpcI///wDtVpdq8Jd4bmG3uzNMfU2ceJEnD59Gnfu3MH58+fRpUsX5OfnY8+ePQCAsWPH6lxgwH1wKygoQOvWrWv1wcXNzQ3Tp0/H9OnTAQA3b97EX3/9he+//x4pKSlYt24dOnbsiFmzZtX4MRoiygQREW5qy9/fHxs3bqzyX2BgoOg8TmRkJADg0qVLKC8v1/t4ly9f1lsPJMQFOdnZ2Xy3Wy717OLiwq+Yqqxjx44A2Ll3Yx7HXPz9/TFlyhScPn0anTp1AgDs3r1bNK1y/fp1AED//v0NTjVwdQTmxo3H09NTbwBUl+MxF+55dujQwWC9Tn1/nsawt7dH8+bNAbAdiA2p7ffD3d2d/5CQmJhY61V3wnqbnJwcvcfduXOnVo+jy7hx4/iaHi77s23bNv4Dpb4Padzrk0KhMPnvV+vWrTF37lycOXOGLzznMudEg4IgwouPj8fJkycBsOnVCRMmVPmPWz5/6NAh0TYY3JtmdnY2du3apfcxV69ebdTYhg4dytd2/PbbbygtLeWzKKNGjdK5jBcAHxzl5eWZZLVWbcnlcvTt2xcAmwUT1mBwwaKhT6pxcXE4e/asWcdYeTylpaV6P1kXFxfzTTXrK2O+76mpqfw0cUPHLXKIi4vjA0RdTLF/4Lx58/gMzptvvml0jYxCodB6QxcuLzcUUHCrSU3J29sbAwcOBMC25VCr1XwwFBwcrHfrn+HDh/MlA8uWLTP5uAAgKCgIERERAKpeTNEYURBEeOvXr+drHYzdIZ47Tq1W49dff+Wvnzx5Mh+YvPvuuzqnxY4ePcrvHl0VW1tb/rF27dqF33//nV/ZYmgZ6+TJkxEUFASA7cFSubC6shMnTuDo0aNGjUmX48eP86trdCkrK+Pv39nZWZTx4XoEnThxQud9ZGRkiKYczY0bT3Fxsc5PkCqVCi+//DLfA6W+4p7n3bt3cerUKa3bi4uL8fzzz1tl0bc5TJ8+nX9jfuWVV3Q+723btuntel4dnTt3xscffwyAXfrdq1cvgzV5ANvComfPnlr1N+3ateMXWaxYsQIKhULr3M2bN2PLli21Hrcu3OtQamoqfv/9dxw+fBgAWzitrzayZcuW/IKOTZs2YenSpQYfIz4+XiuI+/PPP0Ufpip7+PAhbt26BQB6+xA1ZhQEER73id7Hxwe9e/c26pwePXrwfSqEGQFfX1989tlnANiGYJ07d8b//d//4dy5czh+/DjmzZuHQYMGoWnTpkavMuFeZEpKSvDee+/xj1N5eb6QnZ0dNm/eDDs7OxQWFmLAgAGYNGkStm7digsXLuDcuXPYuXMnFixYgPbt26N3795VvggbcvDgQbRs2RL9+vXDkiVL8O+//+LixYs4efIk1qxZg969e/NFoNOmTRMti+X6rhQVFaFv3774/vvvcerUKZw6dQpff/01OnTogBs3bvBFmOY2btw4vp/NlClTMHfuXBw8eBDnz5/HunXrEBMTg40bNxq1wa014wJLtVqNYcOG4T//+Q+OHTuG2NhY/Pjjj4iKisKRI0fq/fM0VufOnfkmpqdPn0bXrl2xbt06XLhwAYcPH8abb76J8ePHizYCrc0CiAULFvB1LHfv3kVUVBRGjx6N1atX49ixY4iLi8O+ffuwdOlS9OvXDz169NA5VWdjY4MZM2YAAK5du4YBAwbgr7/+QlxcHPbu3Ytp06bhueeeQ48ePWo8VkNGjhzJTzu9+eabfNFzVb2GfvzxR34p/nvvvYe+ffti1apVOHPmDOLi4nDgwAF88803eOqppxAeHq61rc+yZcvQtGlTjBs3Dv/73/9w9OhRXLp0CYcPH8aSJUvQs2dPPpB99dVXTf206z8LdaomVubEiRN8q/gZM2ZU69zXX3+dP/f8+fOi29566y29m/l5eXkxsbGxejdQrUytVjNBQUGi+5g1a5ZRYzx9+rTWufr+rVu3Tut87raqtgcwZsNRAMzIkSN1bkcwZcoUvefIZDJm2bJlosfQxdixMkzVWw2sXr3a4MaM48ePF20kefjwYa37sPS2GVVtvsswDLNo0SKDP6/33nuvWpuXVlbVVg1Chv4eajMGoap+hxQKBfP000/r/X6EhoYy9+7d47/+8ssvDT6eMX788UfG29vbqL+fHj16MLGxsVr3UVRUxHTr1k3vef369WOuXbtm1LYZNfl9nThxoujxOnToYNR5qampTO/evY167lOmTBGda8ymy1KplPnss8+q/XwaA8oEEQDi+f3K22RURXh85TqB5cuXY8+ePRg0aBA8PT1hb2+P8PBwvPXWW4iLi9Na6mmIRCLBc889J7rO2G7A3bp1w927d/G///0Pw4YNQ0BAAGxtbWFvb4+goCAMHDgQX3zxBW7dulWrTrizZ8/Gtm3b8Nprr6Fbt25o1qwZ7O3tYW9vj5CQEIwbNw67d+/Gn3/+qbOOafXq1diwYQN69+4NFxcX2NnZITg4GC+88AJOnTpV5ys7pkyZguPHj+OZZ56Bt7c35HI5/P39MXjwYPzxxx/YtGlTrZfiW4NPPvkEe/bswcCBA+Hh4QFbW1sEBgbi2Wefxb59+/D1119beoh1ytbWFjt37sSaNWvQq1cvuLm5wdHREa1bt8aHH36ICxcuiFpSCDf0rKlXX30VDx48wM8//4wxY8agefPmcHV1hVwuh7e3N7p164Z3330XsbGxOHnypM7XDkdHRxw6dAhffPEFIiMj4eDgAFdXV3Tt2hUrVqzAgQMHzNqduvLrkbGvT35+fjh27Bh2796NiRMnIiwsDI6Ojvxz79GjB9577z0cPXpUq45y48aNWLlyJZ5//nlERUXBz88PNjY2cHZ2Rtu2bfHaa68hLi6On3YkYhKGoYYXhBBCqufEiRP8tPmBAwcMTksTYq0oE0QIIaTauAJduVxe5RYbhFgrCoIIIYSIZGZmGlxx9O+//+Knn34CwLahMHaLCUKsDU2HEUIIETly5AhGjhyJsWPH4sknn0Tz5s0hlUqRmJiInTt34tdff4VKpYKDgwMuXbrE96EhpL6hIIgQQojIkSNH0L9/f4PHuLq6YsuWLXyTQELqIwqCCCGEiBQWFmLbtm3Yu3cvLl++jIyMDOTm5sLV1RXh4eEYPHgwZs6cWeud5AmxNAqCCCGEENIo0S7yBqjVaqSkpMDFxaVWHVEJIYQQUncYhkFBQQECAgL4/el0oSDIgJSUFH7fKUIIIYTULw8fPkRgYKDe2ykIMsDFxQUA+010dXW18GgIIYQQYoz8/HwEBQXx7+P6UBBkADcF5urqSkEQIYQQUs9UVcpCzRIJIYQQ0ihREEQIIYSQRomCIEIIIYQ0ShQEEUIIIaRRoiCIEEIIIY0SBUGEEEIIaZQoCCKEEEJIo0RBECGEEEIaJQqCCCGEENIoURBECCGEkEaJgiBCCCGENEoUBBFCCCGkUaIgiBBCCDGz4rJy/H01FbcfF1h6KESAdpEnhBBCzOjkvUzM3X4FD7NLYGsjxa6ZvdDSz8XSw6qx3OIy2MtlsJfLLD2UWqMgiBBCCDGDvGIlvvj7Bjaff8RfV1auxvy/ruGP6d0gkUgsODrjqNUMbqTm42JSDi4k5uBiUg4eZpfAz9Ue/77dB26OcksPsVYoCCKEEEJM7PCtdMzZdgUZBQr+OplUApWaQWx8Nv66lIJnOja14AirdvROBj7ffQN30wu1bnucX4qT9zMxNNLfAiMzHQqCCCGEEBNKyirGjA0XUKZSAwCc7Wwwd0grBLjbY+ra8wCAL/6+iQGtfeBqb32ZlAcZhfh8z00cupWudZtEAjAMezk+s6iOR2Z6FAQRQghpcLacf4h/r6fhnadaoG2AW50+9m9nE/kAqHcLL3w1pj383RwAAAPb+GLfjTRkFCiwbP9dfDK8TZ2OzZBSpQrf7LuNNScTUK5m+OujgtwxokMAOgd7QM0wGPXDKQBAYhYFQYQQQohVScktwQfbrkDNAIpyFTZMi6mzxy5VqvDH+YcAAFuZFN+Oj4KXsx1/+/yn2+DY3QyUKtVYdzoBY7sEorW/a52Nz5Afj9zHz8fj+a/9XO0xd0grjIwK4OuXCkqV/O0JmcV1PkZToyXyhBBCqkWpUoveDK3NlvOPwCUyLj3MBcMwhk8woT1XUpFbzH5vhkb6iQIgAAjydMTM/uEAAJWawfw/r9Xp+Ay5m65Zvv9q3+Y4NLsvnunYVFTA7WIvh5ezLQAgnjJBhBBCdCkpU2H3lRR0CHJHhG/9XQ5dqlRhy/mHuJqch4fZJUjKLkZqXgnUDPBG/+aYPbClVa1yUqsZbK7IxABAQWk5krKLEdzEqU4ef/2ZRP7yC92DdR7zSp8wbLuYjPjMIpxPzMH2i8kY3TmwTsZnSGZhGX951hMt4GCrewl8SBMnZBaWIaNAgSJFOZzs6m8oQZkgQggxg6/33cb7W69g9A+nkF5Qaunh1NiakwmY/9d1bD7/CKcfZCE5t4TPsvzf4fv4Ys9Nq8lkAMDJ+5lIzi0RXXctOb9OHvvKo1xcfpgLAGjt74pOzTx0HmdnI8PCEW35r5cdvIPyihoiS8oqZFeyOdnK9AZAAEQBZUItskFKK3jOFAQRQogZnLqfBQAoUJTjh8P3LTyamouNzxJ97eYgF9Ww/HIiHot23bCaQGjTuYda111LyauTx/5VmAXqFmwwQ9Y3whu9W3gBAB5ml+Dva4/NPr6qZBWxmaAmlabwKgv1cuQvJ2bVrC6oXKXGoG+P4Z0/LvGBoyVQEEQIISamVKlxT1Bf8fvZJDzKqZ9FpPcy2B4xTrYyXF4wEJcXDMQ/s3rjv6Mjwb3Hrz2VgAU7r1cZCJk7UMouKsO+64/58XKuJZs/CMorVuKvSykAABc7G4yMCqjynNf6Nucv/3T0vkUDSaVKzdcyNamo+dEnxEuTCarpMvkDN9PwILMIO+KS8e2BOzW6D1OgIIgQQkzsQUYRlCrNG1qZSo3vDt614IhqplSpwqMcdmop3McZbg6anjbjuzbDkjEd+EBo/elEfPTnNajVut/I4zOLMOy7Exj07TFkF5XpPKa2dsQl89/356Kb8QW815LzzB5gbLnwEIpydnpndOdAo+pkujdvgsim7PL96yn5OHEv06xjNCRH8DNp4mQ4ExQimA6r6TL5NScT+MtTeobW6D5MgYIgQggxsVuPtWtQtl1Mxv0M7c67lZWr1EjILLKK6aX7GYV8Y7zmPs5at4/pHIil4zpAWhEI/X42CZ/t0Z4ayypU4KU1sbiRmo/baQXYczXV5GNlGAZ/nEviv54QHcT3B8opViIlz3x1WWo1I5oKm9RNd0F0ZRKJBK8KskH/O2q6adMiRTnS841/zsKiaK9qZIJqskz+ekoezsZnAwCaezuhT8W0oCVQEEQIISZ2S7BTeJdgtjhWpWbw7X7Daf9HOcUYvPw4+n19pMpj68I9wXYJLXx0r3Ab1TEQ346P4gOhNScT8O0BTdarVKnCK+vPi2pHMgVbSZhK3MNc3Eljx9sl2APhPi58lgUw75TYiXuZSKh4fj2aN0G4joBRn8Ht/BDShK2xOXkvC1cf1X6cOUVl6PnfQ+i2+CBiK4KNqmQVaX4mVU2HOdvZ8Ev/a7JMfq0gC/RSjxCLri6kIIgQQkzstiAIWvxsJP/JeveVVNxI0b1S6dbjfIz+8RQfePx07AEyC00fLFSHMAgy9MY+Mqop/ju6Pf/1dwfv4pfjD6BWM3h38yVcTMoVHZ9bbPrpsM2CgujxXYMAAO2aagq4jQmC0vJL8cKqs5j4y5lq9UHaflGzQeoLRmaBODKpBK/0CeO/NkU26OT9TOQWK6FmgP03jCu4zio0fjoMAB+4ccvkjZVVqMBflytqp+xt8Gwny7YGoCCIEEJM7FYqG+i42Nsg3McZr/cL52/7Zt9trePPJWRj3P9OIy1fE/QoytWiT8yWYGwQBABjuwRhgWALiM/33MSkVWfx91X2TdhGqvm0n11s2kaLhYpy7Kx4Y3W2s8Gw9uymnsLtMqoKghTlKszYcAHH72bi5L0sbBHs/F4VrnhcJpXgida+1R0+RncK5APlf66lIqGWe3IJs27C3ylDhAF3VZkgoNKUWDWyQRtjk1BWUTs1oWuQxXsMWWUQVFhYiAULFmDw4MHw9PSERCLB2rVrjT4/NzcX06dPh7e3N5ycnNC/f39cvHjRfAMmhJAKeYL6k1Z+LpBIJHg+phn83ewBAAdvpePDHVex5mQ8Dt9Ox59xyZj0y1nkl7KfptsGuEIuYwOG9acTUFiNT9mmxgVBtjIpgjwcqjx+Ss9QvPtUBP811yZAJpVg6fgo/npTZ4L+vpKK4jIVAGBEVAAcbdk31kAPB76Y+5qeDBzA1hPN//MaLgmWal95lKv3+MpSctmft5+rPWxtqv+2ai+X8cXBagb4+fgDvccqVWos3HkdC/66prfPTpIgCHpsZF1QVpGwJsj4TBBg/DJ5pUqNDRW1U1IJ8GL3EKPOMyerDIIyMzPx6aef4ubNm+jQoUO1zlWr1Rg2bBh+//13zJw5E1999RXS09PRr18/3L1b/1ZnEELql9tpmqmwVn7sdIy9XIZZT7Tgr//9bBIW7bqBKWvO4e0/LvGrinq38MLmGd0xMqopACC/tBybYpNgCUqVml/+HObtBBuZcW8Xbw4Ixyu9xat9Ph3ZFsPb+/PZoBwTB0En72tWVY0WTK9IJBK+LiijQKG3UPjXM4nYXCnzYyhoEiouK+dXuwW421dr3EKTYoL5Zf1bLjziGxdWtvfaY6w9lYB1pxOx54ruAvPEbE1mxtji6KxaZIKMXSb/z7XHfGbqyda+CPJ0rOIM87PKIMjf3x+pqalITEzEkiVLqnXu1q1bcerUKaxduxYLFizAG2+8gSNHjkAmk2HBggVmGjEhhLCEK8Na+mmKicd0DkSvcP2rYEZGBWDV5K5wsrPBq301NSK/HI/npw/qUmJWMb+TuK6VYfpIJBJ8OLQ1ZvQJg4u9Dd4f1BITY9jGge6O7JtrTpFpp8Pupmmmo4R1QADQVvD1VR1TYmcfZGHRrhv81y4V0zP3MwqNqnXhskAA0NS96myZPm6OcoyrqGUqK1frLWi+Iwiyb6TqDtQeZms6Zj/OLzVqpWH1a4Kqv0x+zUnN5qyWXBYvZJUbftjZ2cHPz69G527duhW+vr549tln+eu8vb0xbtw4/Prrr1AoFLCzq/oHTAghNXEzVfMm1dpfEwTZyKTYMC0aGYUKJGQWIyGrCAmZRXiYU4LIpq54uVcYpBWZknAfFzzZ2hcHbqbhcX4p/rqUjLFdgur0eYjqgbyND4IANhCaN7Q15g1tLbrew1GOzEKFSTNBajWDB5nsWIM9HWFnI97uoZ2oLihfVLOTkluC13+7yAd70/uEoaBUiY2xD8EwbJDRNcTT4OMLt+hoasSUoSFRQe785Yd6mms+zNZc/yBDO/hQlKuQkqcZU6lSjfzSclGPJ10yK7JZEgn7c6pKdZfJX3qYi7iKAvlWfi7oFmb4+1pXrDITVBtxcXHo1KkTpFLxU4uOjkZxcTHu3LH8slNCiPklZBZh3P9O4+M/r+pt4GcOtwWZoMobp0okEvi42CM61BPjugRhzuBW+P65jpjepzkfAHFe66fJBv107EGdPgcAoo7XLXyrFwTp41GRCSouU6FUqTLJfSbnlqBUyWbKdGWs2gmXyQu2z1CrGcz8/SJfC9O7hRfmDGopKqY2Zrl6co4m4AioRSYIgGh6SJjNEXokeDwu+Kt8e+XET5oRU2LcdJiHo61RU5/VXSa/VpQFsuyyeKEGFwSlpqbC399f63ruupSUFL3nKhQK5Ofni/4RQsyrXKU2S2PANSfjEZuQjV/PJIlqRsxJrWb45fGBHg5wsa/6E7U+nYM90TWE7TF0L70QB2+lm2SMxqrOyjBjeThpvh+5JlohVtU4gz0d4VwxxXVdMB229cIjful+kKcDvn+uI2xkUnFvISP2HEsRZoJqGwR5aIKgpGw9mSBBhigpq1irOFrXecYFQRX7hjlVXQ/E4fYQq2qZfF6Jkt8bzcNRzte8WYMGFwSVlJTonO6yt7fnb9dn8eLFcHNz4/8FBdVt+pkQa6RWM/h89w2MWHHC5A3nEjKLMHDZMXRbfBCpefr/NmtC2LDwwI00k963Po9ySlBUsUqJK4quDXN1EzYGt+xbKgFCBVMftcFlggDTFUdXNW0nlUrQNoD9WaTklSKrUIG8EiX+u/cWf8x/n23P1yu19HPhC7iN+X1PNmEQ5OVsCwc5O533UEcwU6pUiZa8l6sZreOSdKzUelxFt+zisnKUVGTmjCmK5hi7m/yuyyl8XdszHZvCXq5/h/q61uCCIAcHBygU2lX1paWl/O36zJs3D3l5efy/hw+1dyMmpLH55cQD/HIiHlce5Zn0jVhRrsLMjRfxIKMIafkK/BmnP0tbE8ItKg7cTK+TbSiERdHCeqCa6t/SBxEVU1EXEnPqZCNQgA1876ezb2rBTZy06mxqyt3cQZCejJV4Siwf3+6/w0+DDYv0Rw9Bwbq9XMZPY95LL0RxmeHiaGEQVNvpMIlEgmYVU2KPckq0pkCFj8WpvDJL13L19Co6dIuKoo1YHs8RBseGlslvvaBZeTe2s3UlFxpcEMStLKuMuy4gQP/OvnZ2dnB1dRX9I6Qxu5achyX/apr76fp0WlNf7b2Na8maoMGUmaCcojLRXkjJuSV6V9KYkjD7JFwZVlNSqQTjBAXRwpVB5pScW8JnBppXsyjaEE/BdJipVojdEwS7+laxCVeMbbvwiO9V4yCX4cNhrfUer2bEhe66cDVB7o5ykzT+C/JkA6kylRppBeIMjq6/v8rF0UnZ2hmZqjJBwkaJXtWYDgsW9ArSt0z+XnoB33+pjb8r2gRY1/tqgwuCoqKicPHiRajV4nnSs2fPwtHREREREXrOJIQIlZSp8PYfl0S7oSfnmmYTysO30rHqRLzoulQTbnB5T8dGpQdumL+mRpgJMsV0GAD4u2myCxlm2HNLF+H3z1T1QIDpM0EMw/CZoAA3e772pzLhCrGdl1OgqsiwvNG/uc4pLGP3HFOpGb4ZYW2nwjiGiqMf5mh/UKhcHF2TmqCaZoKMWSa/9UIyf3lMZ8tukaFLvQ6CUlNTcevWLSiVmk8UY8aMQVpaGrZv385fl5mZiS1btmD48OG0PJ4QIy3+56ZoqgFgPzEqymu3qictvxTvbbmsdb0pM0GVxw0AB26avy6IywTZ2UhFHXVrw9tF85pVV0HQfTMURQPimiBTdI3OqKjvAQz3MgrzduZrbTjBTRzxcu8wnccLp8909RbipOWX8gFVbafCOIaKox/pCHDuCzJBDMPw5wQ3cQS3AKvKIKgam6cKVbVMXqVmsCOOnQqzkUowMkr/TIylWGWfIABYsWIFcnNz+dVcu3btwqNH7DfzzTffhJubG+bNm4d169YhPj4eISEhANggqFu3bpgyZQpu3LgBLy8v/PDDD1CpVFi0aJGlng4h9crhW+lYf5qdMrCzkSLcxxnXKzropuUp0MyIN/giRTkW7ryOnGIl2jV1RWRTN7Rr6oa3N13iO+w+2doX15Lz8Di/tMqUfXUIgyBbGynKytW4mpyH1LwSUWbFlEqVKn7Ppxa+zkZ3WK6KKAgysKGqWs2gTKU2SdEp13wQAFqYMAgSTodlm2A6zNgVbDKpBG0CXHEhMYe/7pOn2+j9XrX2d4VMKoFKzRjMBJlyZRinmSgTJA4shCvDuPEJp6HSCxR8u4AwLycUl6mQUaCocuuMzGo2SuRwy+QzCxU6l8kfv5vBF3IPaOVTrSxTXbHaIOjrr79GYmIi//X27dv57M6kSZPg5uam8zyZTIa///4b77//Pr777juUlJSga9euWLt2LVq2bFknYyekPsssVOD9rZpMzcfDWiMhq5gPglLySowKgr4/dA9bKgoidWVh/N3ssWRMe0xddw6P80uRWVgGRbnKJEW4dwVvjmM6B+L3s0kV40iv9i7fRj9mWiG4OlZTTYUBxmWCsovKMGjZMQDAqsld0D7QvVaPaUydTU24mzgTVJ2MVTtBEDSglY/BjU7t5TK08HHGrccFuJteiFKlSmfAZMqVYRzh35ZWEFQxPSaVAO0D3RCXlIuMAgUKSpVwsZeLipODmzgho1CBjAL2n0rNQCbV3ZtHOB3mVY1MEMAuk8+seJwiRbmoLmqLoCDaGqfCACueDktISADDMDr/cVmftWvXir7meHh44JdffkFmZiaKiopw5MgRdOnSpe6fBCH10Bd7bvKfDAe08sGkbsH85p+AcdNWpUoVNp3Tv+eVVAIsGx8FDydb0X2n5Zlmuod7c3Sxs8Hz0c346825VP6mqB6o9kXRHCdbGT+Voy8I2nf9Mf9m984fl2rViFBYZ+NvoM6mJoTTYdkmCIKq09V6bJcg2Egl8HO1F+12rw83JaZSM7ipp6jelN2iOYGC+6k8HcZlgvzdHNBS0IiTK44WHt/M0xF+ruzflpoRFz9XJp4Oq162JkTPMvm8YiX2X2f/3po42aJ/K59q3W9dsdogiBBS9249zsefl9hCRndHOf47uj0kEomo3iHFiOLovy4l883wBrX1xYrnO2JGnzB0D2uC4CaO+OyZdogJawJAXPhrirqgIkU5/+YU7uuMtgGuCKgItE7fzzLbruy3UrU3TjUFiUTCZ4P0TYcJi8rvZxTh2/0174yfWVjG19mYsh4IANwc5HydSo4JmiVWp4C7XVM3XF4wEPvf7SPqb6OPMcXRpuwWzXG01XRiFk5/FZQq+b+pQA8HhHlrb2CaJAhCgps4wtdV8AHDwJSYuDC6epmgED3L5HdeSUFZRSPHkVFNITfR9LCpWe10GCGk7n39722+5f7M/uH8m291MkEMw2DNyQT+69f7haNDkDuebq+7KFJ837WvCxIuGQ73doZEIsGTbXyx/nQiylRqHLuTgaGR2l3lqyOnqAy7r6SguY8zYkKbQCaV4HaaIBNkgh5BQt4udkjKLkZusVLnlGHlN7iVxx9gYFtfdA6u/v5MdwXbZZg6CJJJJXBzkCO3WGmS6TAuE+ThKDcqg1GdJezGFEeboyYIYJfJZxYqkJav4KfihCvFgjwdEeql+dk8qAgGEytlgoRB0OO8UrTXMyPFZYlsZVJ+A1ljCTNBwvqkrfVgKgygIIgQUuF8QjYO3GSXkfu72WOSoHZG+Ck3tYpM0Nn4bH6VVFSQOzoINoXUxc/EQZCuN/EnW/vyhd4HbqTVOgiau/0K/q1I9Xu72GFYpD9fM+XlbMd/kjcVb8H9ZRWWaWUdKhe+Mgwwe8sV/P1WbzjY6q+xupdegK//vYMuIR6Y0jMUMqnEbCvDOB6OtsgtVvLF8TWVX6rki27NMc42/q6QStipJGE/KyEu42hrI612LY0hzTwd+c1GH+WUINzHWZQVCvJwFGWC7lcEH8JMTJBgOgwA0gysLOQaRzZxtq32nl7CXkH/XEvFvfRC3Esv5ANHa+wNJGSd+SlCSJ1iGAZfCZoiznqihagQ1MvZjt9KIKWKQGXdqQT+8pSeIVU+tnA67LEJpsN0rRjqFtaEr205dDsd5ZX2W6oOtZrBqXtZ/NcZBQqsPZXAT1WYsh6I4+WieYPVVdvBrayTyyT8TuTxmUX4et9trWM5inIVZmy4gL3XH+PzPTfx0ppYZBUqRN+/Fj6mfy7cDuUFpeW1+jmIgzXTj9PBVsb//txJK9Cqs2IYhp8Oa+ruYNINQYXL5LniaGGRdJCnA5p5OvJ/k1z2kzvGz9Ue9nIZfFw1wXOanr9btZrhA9LqToUB4umwa8n52BGXLMqcje1ivVkggIIgQgiAo3cyEBufDYBdWls5fS2TSvjUuqHpsOTcEvx7nd0o0dvFDkPaVZ1xEU6HVRVgGUPXm7itjRR9W3oDYDfuPC9YKl1dSdnFKKioK/JwlMPWRvwy2tYMn3q9nTXfI13F0dy2CD4u9vh6bAd+TKtPxvM/18r+d+SBqMfM8buZGPbdCRy/q9ls1lyZIE5uSc3rgu6aOWMFaKbEygUb43LyS8r5feJMORUGiJfJc8XOwt3jgzwdIZdJ+eMSMotQUKrkMzrc9cIsq76aoPxSJd/rqDrL4znOdjZoHyherS2VsBmiCV2D8HxMMz1nWgeaDiOkkVOrGdHWGO8OjNDZ4ybA3R7JuSXILVaipEylc5plw+lEfpn4xJhmWgGCLj4udvy0gyl6BXHFsnY2UtGKnada+2LPFXb7nAM30tCtojC7uoQ7i0/uEYKpvUKx/3oa/rmWCkW5Gi/2CKn54PUwtExeUa7iP8n7udkj3McZ7w9siS/+vgmGAd7eFIetr/UQTaHdzyjE/x2+B4ANcD0c5cgsLBNNq3k62cKzGlsoGEvUNbqorMZTh+aetgPY4ujtF9mFAtdS8kRTu49yNZmZAHf7yqfWSpCOXkGiTFBFpijUywkPMotQolSJgl1umb2vi6AmSE8QlFmLomjOj5M649CtdLg7yBHu44xQLyer2iTVEMoEEdLI/X0tla9naRvgiqF6sjfCaasUHdkg4bJ4uUxi9CdAG5kUPi5clql2QVBZuZqviwjzdhb1RenX0pv/WpjtqC5hqr9dgBtc7eUY3TkQv0zuig3TYkyeFQAMB0Hpgl3FfSumP6b2CkWXYA8AbHZt0i9n+Wk0hmHw0Y6r/Mqdl3uH4u+3eiM6VFxEXdWS85oS7R9WixVixjZKrI12BlaICVdJNnU3TXdwDrd/GKDJBHE1QbY2UvhU/D4I64IO39ZsCxNcEUS5CzKVwt8ToSzhvmE1DEibujvghW7BGN4hAK39XetNAARQEERIo1auUmPpPs1y6vcHtYRUT0M1f8GnXV3F0cJl8cMi/fnAxhhc2r6223IkZBXxqf3Kb4zujrb8zu530gv4ZeDVdV1QJBsZqLtpq6kZ6hot/ITPTVnKpBL8MKkTv3XHg8wivLgqFnklSmy58AhnHrBZgyBPB7z9RAR8XO3x+8sxeLVvc/6+YsKqv7LMGKbaP4zL+DnayvgWCKbWxt+VX9JfeYVYco75MkH+bg58vc/DnBIwDMNPhwW6O/B/o2GCQPXwrQz+MpcJkkgkfGCsLxOUJShQb2KGzJ+1oyCIkEbsYlIuHlSsLIkO9UTfCG+9xwZUkQlad0rT4f2lnqHVGofwTUTfJ1ZjiOuBtLMDnZux2RGGAb+zdXUwDMO/GXo52/GfyM3NUCZIOIUoXA3k42KPX1+O4WuubqTm46U1sfjP3zf5Yz5/JpKf1rSRSTF3SCtse60HFj8bidf6aQIiUxJOseXUcIVYqVLFTw81r2iDYA5OdjZ8RuxmagHyBJkrYf2aqRolcmRSCX+fD7OLkV1UhuKK+qNAwVRZmKAoWdi4UVhTxP1O5JUodTbRFGaCrHFbC3OjIIiQRkzYCXdEhwCDbyaGlslnFSpwo+K+2jV15VcoGcvPVdiMseYrxKqaIulUMUUEQLSPlLEe5ZTwGaTIpq5me/OtTLj8unIQJCx49auUEQn0cMSvL8fwn/DjknL5bN2IDgE6g97OwR54LroZHG3NUzLKrQ4Daj4dFp9ZxNeemWsqjNOn4nukUjOiKSdho0RzTIFygUyhohxXBFkoYUfpUG/dTR+FzSB9qmiYaIqaoPqMgiBCGrFbj4Vdjg0vMzbUMPGmoFty15DqT6MIM0FVbfZoSFUrhroIxnYhUfeqKUOEdSHCehFzs7ORwc2BDR4qT4el6ZgOE2ru7Yx1U6PhYq8JalztbTD/6aq3jjAHU+wfVhf1QJyBbTR7jO278Zi/zGVeJBLt4NMUAgXL5E/d09SwCZfPezvbaTU3dLGzEQWaol5BOrKswi0zvGqwOqy+oyCIkEbstmC/q4gqgiDR1hmVCphvpGqCgzb+1V8ibqqGidybo0wqEXWy5QS42fNvCpeScqvdp0a4MqwugyBAMyWmNR0mKozW/Wbcrqkb1rzUle+VtGhkW9EUW10STofVtGFiXQZBnYM9+KDiyO0MfkqJC4K8ne1MsulvZcIprVP3NX2phEXTEolEVBzN3u4oylD6CnoF6fqAUZstMxoCCoIIaaQYhsGdNPbNpKm7A1zt5QaP93CUw65ipUlqpSmrGymaYKom3WFFWaYaToep1Ay/fUBwE0edy/MlEgk6V0yJFZWpcDutQOsYQ64KiqLrPAiqqNcoLlOhSLD/WZqemqDKuoR44sj7/XDovb4Y1dFyDezcTTAdVpdBkI1Myu84X1ymwqn7mVCUq/hg1NT1QBxhEHRDMG0tzAQB7DJ5IWEHZ0AcGOtqmCgMgszREsHaURBESCP1KKeE30y0pRFdjoUbqabksitWONyLtFwmqVGXYfEmqjXLBCXnlEBRzmZ2DC3v7iyoC7pYjboghmFwvWI6zNPJ1mwrkvTxEmRuhF2juU/3rvY2BrfIANhi7jAzLX03lrtD7VeHcUGQXCbhl4Obk2hK7HqaqCbOVBunVibM+Aj+1EQ9hABo/TybGQqCdNUEVUyHudjZ1Kul7aZCQRAhjZSwA64xQRCgydgUlamQX8oGUKVKFd95ONzHxagGiZVxDROBmtcEGbvxpzAIqk7n6NS8Un45cduAuiuK5gj3D+OyEAzD8G9s5qhLMQdbG80mnTUJgspVan6jzpAmTjobe5pa7xbesJezj3PgZppoH69AMwVBzXQEd062MlG9DwCt6bBgT/HXwuygoekwz0Y4FQZQEERIoyWcCjJ2vytxxoadtrqTVsD35qlJPRAgbpiYUsUGrfqIlsf76g+C2gS48m9o1VkhJiyKjqzjqTBA9zL5vBIln/3SVw9kjdwrGibm1mA6LCm7mG/0aO6pMI6DrQx9WrCrxDILy7D7cip/m7kyQW4Ocq2i58r1PgAQ5lUpE+SpPxNUuf1EWbmaX+3YGHsEARQEEdJoiVeGGRe8BOhomFjbeiCOsGFiWXn1N9YU1Yl46w/q5DIpOgS6A2CnBPXtqVSZpVaGcXQ1TNTVKLE+4PYPyy0ug1rNVHG02HXB71vrGgbdNTGwrR9/+c9LyfxlcyyPB9jp58pTX4Ee2tmhEC/xdZVrghxsZXCtWBlYORMkzMQ1xh5BAAVBhDRa3MowuUx7hYk+urbOEBZt1jQTxN531Zs9GiJcHt/cx/Dz6VyDfkHXBG++1pIJ0tco0dpxQZCaYTfwrA7xCr26C4KeaOXDT9kqBEG6uQqjAe2sjrBOiONoa8PXp9lIJaK/Iw4XIKfll4pq+TJFW2ZQJogQ0kiUlavxoKKOp7m3M+RG1lXo2jpDlAmqVRBU8+JohmH4DTWbujtU2eivJkEQ1ynazUEualhXV3TVBIl6BNWTmiCgdg0ThduWtAuou2DUw8lWa381wHzTYYB20FN5ZRjnxR4hkErYTYt11UhxWVaFYPoLqLQ8vhH2CAJoF3lCGqX7GYUor5iGMLYoGtDeOkOtZviu003dHeDmaHiZvSGGmjFWJTWvFAUVK92MqRPp1Kx6QVB6fikfeLSrw07RQrozQZpP8vUpEyRsmJhdVKa1zFsfhmH4TJCXs52oG3JdGNjGj993DWBXVHFNLM1BOxOkOwh6tW9zTO4eond1oG+lhonc91/YKLEx9ggCKBNESKNUk5VhgHYmKCm7GEUVexrVph5I676rmQkSrvIyZorEw8kWzSumAK+n5OncU0mo8s7xluDpZMtPx3A1QWkFwpqg+vNJXtiPpjpdo5NzS/hi6rqcCuM8JVgqD5g3CwSI9wkDdE+HcQy1R9DXMFHcKLH+/P6YEgVBhDRC1dkuQ8jVXrNiJTWvxGT1QIA4E/S4mkHQ2QeajroxoU2MOoebElOqGFx5lGfw2GsWbJLIkUkl/BsVPx1Wb2uCajYdds1CU2GcIE9H0e+5OeuBAB2ZID3TYVXx09MrSLhvmBetDiOENDR30wpwVccbvHC7jJZGrgzjcBmb1LxSXBcUqdY2E+SnY/m9sWLj2SkKmVQi2iTVkC7Bwn3EDE+JXbXwyjCOV0UQlFmoAMMw/Kd6YYBUHwinw6qzk/wNCxVFCw1sq8kGCVdLmoNw5Zmnky2c7GpWweKjp2t0Y99BHqAgiJAG635GIQYtO4bhK07g0K000W3cdJiLvU21Ox9zBcyKcjVO3NNkYGqbCRI2TKzOdFhWoYJfGdauqRu/P1ZVxDvKZ/P3teZkPN7dfAk/HLmHm6n5bKfoijdfFzubOulQrA9XF6RUMcgrUfKf6n1c7CCT1n2dUk0Jp8Oq0zBRuEKvrYWmJUd0COAbgkYbmXWsKXu5DF0qfk+7hVV/Y2KOKBMkmELNKhJOhzXOTBAVRhPSQF1+mAuuBcv3h+5hQCv2E2xeiZLfALWlr0u1i3yFn34vP8wFwAZTtV0xJZdJ4e1ih7R8RbWCoHMJmkLVGB2rd/QJ83KCu6McucVKxMZnY/r68zh0K50vGAeAr/behq+rHb/7dpsAV0gtGGwIV4gl55bw0xl1XSBcW+L9w6oRBFl4hR7AblPx1xs9kVmoQK9wL7M/3soXu+Dsgyz0alHzxxIWRguL6blMkESiaVvQ2FAmiJAGSrjJZlxSLi4msVM+d9JqVhTNES5l57TxN82KKe6+q9Mw8Wx8zYIgqVSCzhWrxPJLy7HvRpooAOKkCbrsWqI/kJBwhZiwNYFfPSqKBsRvuDlFxtUEpeeXIr2iFsoS25YItfZ3Re8W3nUyBk8nWwyJ9IdLFRscG+LlrCmqTy/QrgnydLStV5lEU6IgiJAGqlAhXvG0+kQ8gJoXRXN0NWOrbT1Q5ftmGOMbJp6tWLIskYjrfIxRue+Lj4sdZvQNw5ZXu2PRiLbo19IbdoK90Pq29K7W/ZuaMAgSdrCuT0XRQKUgyMhMkLBTtCXrsuojG5mUrydLzSuFWs2AYRh+iXxjnQoDaDqMkAZLmAkCgH+uPUZKbkmtiqIB3cuCTbV9gXAT0Mf5pXr7onDySpS4WfF8Wvm5VrtP0XMxzfj6qOFRAegd7sU3m+sa4onJPUJQqlThXEI2HOQydAmpeV2GKQiDIGGxdn1qlAiwy7nt5VKUKtVGB0HCoK+tiYLuxsTPzR7pBQpkFCjQbuG/aOHrglIlm21trI0SAQqCCGmwCisFQSo1g/WnE2vcI4ijMxNkoiBI1Iwxt+oVYhcSs8HtAlCdqTCOq70cS8dHGTzGXi5D7xaWzQBxhDVBwvYE9S0TBLDZoNS8UqOXyFMmqHbaBrjxrSCKy1R8PR9AmSBCSANUORMEABtjk/gNKwPc7GvU7bZyTZCNVGJw1/bq8Ktmr6CzD2pWD1RfCTNB3Kd4oH5tnsrhgqDc4jIwDFNlfQ3XKdrRVobQJsZ1mCYac4e0QqCHAy49zMXtxwVIyi7mb9O1HUhjQUEQIQ1UUZkmCOoe1gSnH2SJ9g2qSRYIYKcyPBzl/Cf4cB9n2Nno71ZbHQHV7BotLIpuDC/kwiBIqF4GQU5sAK5UMShUlBss/M0tLsOjHDYz2Mbfsiv06is3Bzne6B/Of12oKMftxwVQMwy/DL8xosJoQhooYWH0m0+Ea91ek3ogjjAbZKqiaKB6DROLFOV8XUy4j3OjaPbmam/D96gR8qtnNUGAuGFibhVTYjQVZnrOdjboHOyBriGeFl1pZ2kUBBHSQAmnw6JDPLWmi2qyMowjzNiYqh4IYFdnca/HVU2HXUzKgapiaq8xTIUBgEQiEdUFAeybmbENIq2JZzVWiFFRNDEXCoIIaaC4IMjORgobmRTTeoWKbq/pdBgABAr2MDJl5165TAofF81SXkNiG9lUGMer0pRYfdo4VUi4f1h2FVtnXKNMEDETCoIIaaC41WFcluCJ1r78hoz2cimae9e8mHlSt2Zo5eeC4R0CTJ6F4fZL4pbz6iMuijbv9gXWpHImqD7WAwHVnQ5jM0G2NlKE+5imCJ8QgIIgQhosLhPEbbook0rw7fgo9G7hhf+MitRZW2KscB8X7H27D75/rqPJi1S7hWkCmiO303UeU6pU4VLFEt/gJo71siampioXR9fH5fGA8fuHFSrKEZ9ZBICdwpXL6G2LmA79NhHSQBVVFEYLd57uHOyBDdNi8GynQEsNq0oDWvnwlw/d0h0EXXqYizIVu0Q82sINDOta5SCovjVK5Ij2DzMwHcZuYstettSmqaThoiCIkAaorFzNBwnOdqZZvl5XOjbz4N8gj9/N1LmHmLAeKCas8UyFAQ01E6R/OkxYFN2uKRVFE9OiIIiQBki4Msypnq0ckkkl6BfBdmguVJTjvGCXeABgGAb/Xn/Mf91YVoZxtGuC6mthtCYIyjYwHSbcHqQdZYKIiVEQREgDVFiPgyAA6G9gSuxqch7fN6ZDoFuV+4s1NFrTYfU0EyScDsvVEwSp1QyO380EwK5yrM2KRkJ0oSCIkAZI2C3a2bb+BUF9I7zB1VsfqlQcvTE2ib/8XHSzuhyWVfCpPB1WT2uCnO1sIJexP+ScIt3TYVeT8/gVgr1beMFeXr+mdon1oyCojj3IKMS87Vfx+m8XsO3CI0sPhzRQ9Xk6DGCXT3euaOX/IKMIiVns6qBCRTl2XkoBADjZyjC8Q4DFxmgpXoLpMKlEe3qsvpBIJPwyeX2rww7cTOMvP9Hat07GRRoXCoLqWF6JEhtjk/D31ceiuW5CTEm4ZUZ9K4zm6JoS23U5BUVl7HMbEdW0XgZ4teVgK4NLxfP2craDTT1eMt6kojg6o0CBPB3F0QduarKATwh+Hwgxlfr711NPiRuEGe6SSkhN1fdMEKB7qbxwKuz5RjgVxhnfNQgSCTChnn8PujdnV/aVqxnsiBNnxh/lFONmqqb2y6ee1j4R60ZBUB1zc9AUAwp39CbElOp7YTQAtPR1QUBFvcvZB9k4l5CNK4/Y7Gm7pq6IDGy8K4U+froNri4chHefirD0UGpFWNO1MfYhGK4hEMQF8U/SVBgxEwqC6pirveYNiYIgYi7CTFB93FwTYGtGuCmxMpUa72+5zN82oWv9zoCYQn39uQpF+LrwtV+30wpwMSmXv23/DU090JNtKAgi5kFBUB2zkUn5+fxcCoKImTSE6TAAeKK1ZkosIasYAOAgl2FkVOMriG6ohNmgTRXTnQWlSpx5kAWA3UuuFS2NJ2ZCQZAFuFZMieVTEETMRFgY7VRPC6MBoHuYF+wq7XE2okMAXOzles4g9c2wSH+4VGTId11JQX6pEsfvZkKpYqfGnmztA4nEtPvTEcKhIMgCuCZhucVK0Rw4IabSEKbDAHYlVI/m4m0xJkQHWWg0xBwcbGUY1bEpAKBUqcZfcck4cIOWxpO6QUGQBXDF0eVqBsVlqiqOJqT6Gsp0GCBeJdbKzwVRQe6WGwwxC2GN129nk3C4okGms50NYsIa17YopG5ZbRCkUCjwwQcfICAgAA4ODoiJicH+/fuNOvfAgQPo378/vLy84O7ujujoaGzYsMHMIzYerRAj5lbYQDJBADC4nWa65LV+zWlqpAFqE+CKDhXB7a3HBfyGqn0jvGFnU3+nc4n1s9og6KWXXsLSpUsxceJELF++HDKZDEOHDsWJEycMnrdz504MHDgQZWVlWLhwIb744gs4ODjgxRdfxLfffltHozdMvGcOBUHE9ITbZtT3TJC3ix0OvtsXe97qhZFRTS09HGImz+uY5hQWxhNiDlYZBMXGxmLTpk1YvHgxlixZgunTp+PQoUMIDg7GnDlzDJ67YsUK+Pv749ChQ5g5cybeeOMNHDx4EM2bN8fatWvr5glUwZUyQcTMhIXRjg1gvyUfV3u0pR3EG7Sn2wfAyVbzuyqVAP1bUhBEzMsqg6CtW7dCJpNh+vTp/HX29vaYNm0aTp8+jYcPH+o9Nz8/Hx4eHrCz0+ynY2NjAy8vLzg4OJh13Mai6TBiblxNkJOtDFIpTR8R6+dkZ4ORHTWZvi4hnvBwsjVwBiG1Z5VBUFxcHCIiIuDq6iq6Pjo6GgBw6dIlvef269cP169fx/z583Hv3j3cv38fn332Gc6fP19lFkmhUCA/P1/0zxzcHTR/2HkltHUGMT0+CKrnU2GkcXmhWzBkFUE79YIidcEqXyFTU1Ph7++vdT13XUpKit5z58+fj/j4eHzxxRf4/PPPAQCOjo7Ytm0bRo4cafBxFy9ejEWLFtVi5MahTBAxN64wur4XRZPGpbW/KzbP6IaU3FIMi9R+DyDE1KwyE1RSUiKazuLY29vzt+tjZ2eHiIgIjBkzBhs3bsSvv/6KLl26YNKkSThz5ozBx503bx7y8vL4f4am3WqDCqOJOTEMQ5kgUm91DvbE8A4BNI1L6oRVvkI6ODhAoVBoXV9aWsrfrs/MmTNx5swZXLx4EVIpG+ONGzcObdu2xaxZs3D27Fm959rZ2ekMvkyNMkHEnEqVaqgrenDW527RhBBiblaZCfL390dqaqrW9dx1AQG654rLysqwatUqDBs2jA+AAEAul2PIkCE4f/48ysosX4NDQRAxp4bUI4gQQszJKoOgqKgo3LlzR6swmcviREVF6TwvKysL5eXlUKm0uzArlUqo1Wqdt9U1N0cKgoj5NKRu0YQQYk5WGQSNGTMGKpUKK1eu5K9TKBRYs2YNYmJiEBTENtVKSkrCrVu3+GN8fHzg7u6OHTt2iDI+hYWF2LVrF1q1amUVy+SdbW3ATXdTEERMrZCCIEIIMYpVvkLGxMRg7NixmDdvHtLT0xEeHo5169YhISEBq1at4o978cUXcfToUX4TUplMhtmzZ+Pjjz9Gt27d8OKLL0KlUmHVqlV49OgRfv31V0s9JRGpVAI3BzlyipVUGE1MrqFsnkoIIeZmta+Q69evx/z587Fhwwbk5OSgffv22L17N/r06WPwvI8++gihoaFYvnw5Fi1aBIVCgfbt22Pr1q0YPXp0HY2+alwQRJkgYmqiLTNsrfZPnBBCLM5qXyHt7e2xZMkSLFmyRO8xR44c0Xn9888/j+eff95MIzMNrjg6v1QJtZqh5aDEZIRbZtDqMEII0c8qa4IaAzdHtms0wwAFpeVVHE2I8Wg6jBBCjENBkIXQMnliLrQ6jBBCjENBkIW4OWjenCgIIqZEfYIIIcQ4FARZiHAT1VzaRJWYEGWCCCHEOBQEWQhNhxFzocJoQggxDgVBFuJGm6gSM6HCaEIIMQ4FQRZCmSBiLjQdRgghxqEgyEKEQVA+BUHEhETbZlCzREII0YuCIAtxp+kwYiZcx2ipBLCX0584IYToQ6+QFkLTYcRciioKo53sbCCRUCdyQgjRh4IgCxEGQbREnpgSVxNERdGEEGIYBUEW4iCXwVbGfvvzSmjbDGI6XBBERdGEEGIYBUEWIpFI4MptokrTYcRE1GoGRWWa6TBCCCH6URBkQVxxdG4xTYcR0yhWaholOlOjREIIMYiCIAvi6oKKylRQqtQWHg1pCIpoeTwhhBiNgiALol5BxNRo81RCCDEeBUEW5C5aIUZBEKk96hZNCCHGoyDIglypVxAxsUIKggghxGgUBFmQqGEidY0mJlCkoMJoQggxFgVBFiTcOoMyQcQUaDqMEEKMR0GQBdHWGcTUaDqMEEKMR0GQBdEmqsTUimh1GCGEGI2CIAuiTBAxNZoOI4QQ41EQZEG0iSoxtUIqjCaEEKNREGRBbg62/GVqlkhMgTJBhBBiPAqCLIimw4ipFZbRthmEEGIsCoIsyNZGCkdbdsqCCqOJKVBhNCGEGI+CIAvjskGUCSKmQNNhhBBiPAqCLIyCIGJKXGG0rUwKWxv68yaEEEPoVdLCuCBIUa5GqVJVxdGEGMZlgpxoZRghhFSJgiALo+JoYkqaIIimwgghpCoUBFmYqFcQFUeTWuK2zaCiaEIIqRoFQRZGm6gSUylXqaEoVwOgTBAhhBiDgiALo+kwYipFgm7RFAQRQkjVKAiyMDdHTdfo3GLaOoPUnLBRIm2ZQQghVaMgyMIoE0RMRdQjiLpFE0JIlSgIsjAKgoipFFKjREIIqRYKgizMnYIgYiK0ZQYhhFQPBUEWRpkgYiq0ZQYhhFQPBUEWRn2CiKkUClaHUWE0IYRUjYIgC3O1gkzQw+xi/OfvmzjzIMsij09MgzJBhBBSPRQEWZhMKoGLPfuGlW+hIOiLPTex8tgDvPrrBZRVNNsj9Q8VRhNCSPXQK6UVcHeUo6C0HLkWCoIuPcwFwE7HJWUXI9zH2SLjILVDhdENj1KphEpFGysTIpPJIJfLqz6wmuiV0gq4OcjxECXIK1GCYRhIJJI6e+xCRTke55fyXz/IKKQgqJ6i6bCGIz8/H5mZmVAoFJYeCiFWw87ODl5eXnB1dTXZfdIrpRXgiqNVagaFinK42Js+2tUnPqNI9PWDzCI9RxJrR4XRDUN+fj6Sk5Ph7OwMLy8vyOXyOv1gRIi1YRgGSqUSeXl5SE5OBgCTBUIUBFkBdwfh1hlKo4OgUqUKtx8XILKpG6TSmr1IPsgsFH19P71Qz5HE2lEmqGHIzMyEs7MzAgMDKfghpIKDgwNcXFzw6NEjZGZmmiwIosJoKxDo4cBfvmdkEHI+IRv9vz6Ckf93Ep/uvlHjx64c9FAmqP4qKqMgqL5TKpVQKBRwc3OjAIiQSiQSCdzc3KBQKKBUmqaGloIgK9AmQBPRXk/JM3isWs3gf0fvY/zKM0jNY2t5Tt7LrPFj368U9DzIoExQfVVIe4fVe1wRtDkKQAlpCLi/DVMtGKBXSivQVhAE3UjN13tcTlEZ3ttyGYdupYuuF775VVflTFBOsRLZRWXwdLLVcwaxVtx0mINcBlkNp0eJdaAsECG6mfpvw2ozQQqFAh988AECAgLg4OCAmJgY7N+/3+jz//jjD3Tv3h1OTk5wd3dHjx49cOjQITOOuOZCvZxhL2d/FNdTdAdBafmlGPbdcT4AkkgAWxv2nMLSmgVBajWDhCzt6S/KBtU/Zx5kITGrGADgbE+fbQghxBhWGwS99NJLWLp0KSZOnIjly5dDJpNh6NChOHHiRJXnLly4EM899xyCgoKwdOlSfP7552jfvj1fVW5tZFIJWvmx2aDErGIUlGrPda489gApFdNfnk62WDclGq39XAAAhWXlUKuZaj9uSl4JSpXazRHvUxBUrxy5nY7Jq2OhqGh0+VQbXwuPiBBC6ger/MgYGxuLTZs2YcmSJZg9ezYA4MUXX0S7du0wZ84cnDp1Su+5Z86cwaeffopvvvkG77zzTl0NudbaBLjyTQtvphYgOtRTdHtsfDYANgO0c2ZPBHo44qdj9wEADAMUK1XVbpB3X7A8vo2/Kz8V9yCDiqPri3+vP8bM3y9CqWKD4P4tvfHJ020sPCpCCKkfrDITtHXrVshkMkyfPp2/zt7eHtOmTcPp06fx8OFDvecuW7YMfn5+mDVrFhiGQWFh/chqiOqCKhVHFyrK+YLplr4uCPRwBCDuClyTKTHhtJcwe3CfgqB64a9LyXj9N00ANKSdH356oQvs5dQjiNR/ly5dwquvvoo2bdrA1dUVtra28PPzw1NPPYVvvvkGGRkZlh5inQoJCUFISIilh9HgWGUQFBcXh4iICK0+ANHR0QDYPw59Dh48iK5du+K7776Dt7c3XFxc4O/vjxUrVphzyLXWxl9/cfSlpFxws11dQjz4653tNCtIChXVXy4onPbq1cKLr0uimiDrd+ZBFt7+4xJUFb8Yz3Zsiu+f68jXiRFSX6nVasyePRsdO3bEL7/8Aj8/P0ydOhXvv/8+hg8fjtTUVMyePRuhoaFWW+JA6g+rnA5LTU2Fv7+/1vXcdSkpKTrPy8nJQWZmJk6ePIlDhw5hwYIFaNasGdasWYM333wTcrkcM2bM0Pu4CoVC1KY+P1//Si1Ta+XnCqkEUDPaxdGxCdn85a4hmmkyF0EBbEGNMkGajE+4tzNCvZxxMzUfSdnFUKrUkMvoDdVaLT9wF0xFYPxcdDN88Uy7GjfMJMSafPTRR/jmm2/QqVMn/PHHHwgPD9c65uLFi/jggw9QUlJigRGShsQq3+VKSkpgZ2endb29vT1/uy7c1FdWVhZ++eUXzJ49G+PGjcOePXvQpk0bfP755wYfd/HixXBzc+P/BQUF1fKZGM/BVoYwb3bPrrtphaLd3M/rCYJE02E1WCbPZYI8nWzh4WSLMG8nAEC5mkFSdnG174/UjSuPcnH6QRYAINTLCZ9TAEQaiDt37mDJkiXw9vbG3r17dQZAANCpUyfs379fa3roypUrmDBhAvz9/WFra4vg4GC8+eabyMrKEh2XkJAAiUSCl156Cffu3cOoUaPg4eEBJycnPPnkk7h8+bLOx01PT8c777yD8PBwfh+r0aNH49q1a1rHctNXubm5mDlzJoKCgmBjY4O1a9cCAC5cuICZM2eiXbt2cHNzg4ODAyIjI/Hll1+KGgFyY01MTERiYiIkEgn/b+HChaLHXLNmDWJiYuDs7AxnZ2fExMTwjyd05MgR/vxTp05h4MCBcHd3b5StGWoVBKlUKuTn56O8XPwGXFJSgkWLFmHUqFF455139GZu9HFwcNC5cWBpaSl/u77zALaZ0pgxY/jrpVIpxo8fj0ePHiEpKUnv486bNw95eXn8P0O1R+bATYmVqdR852ilSo24pFwAQFN3BwS4a567cCl0dWuCChXlSMtnv8dhXmzw07zif4C2z7BmPx17wF9+pXcY9QQiDca6deugUqkwY8YMeHt7V3m8jY3mNXDnzp2Ijo7Gzp070a9fP7z99tuIjIzEihUr0L17d+Tk5Gidn5CQgG7duiE7OxtTp07FU089hYMHD6J///5IS0sTHXv//n107twZy5YtQ/PmzfHmm29i6NCh2Lt3L7p164azZ89q3b9CocCAAQOwb98+jBgxAm+88QZ8fdn6y59//hk7duxAZGQkZsyYgWnTpoFhGMybNw8TJkzg78Pd3R0LFizgP5wvWLCA/9evXz/+uLfeegtTp05FcnIypk2bhmnTpiE5ORlTpkzBrFmzdH7/Tp06hX79+kEikWD69OkYP358ld/zBoephU8++YSRSqXMsWPH+OvUajUTHR3NSKVSRiKRMFKplGnWrBmTnZ1t9P0++eSTTOvWrbWuP3DgAAOA2blzp87zVCoVY29vz/j5+Wnd9uOPPzIAmEuXLhk9jry8PAYAk5eXZ/Q5tfG/I/eY4A92M8Ef7Ga2nH/IMAzDXErK4a97a+NF0fG/nkngb/vjXFK1HuvyQ839ztlymWEYhvkz7hF/3Y9H7pnmSRGTSsgsZELnsj+jzp/tY0rKyi09JGJCJSUlzI0bN5iSkhJLD8Ui+vfvzwBgDh48WK3zMjMzGVdXV6Zp06ZMQkKC6LaNGzcyAJiZM2fy18XHxzMAGADMl19+KTr+448/ZgAwixcvFl3fo0cPRiaTMXv37hVdf/v2bcbFxYWJjIwUXR8cHMwAYAYNGsQUFxdrjTkxMZEpLxf//arVambq1KkMAObEiRNa9xccHKzz+R89epQBwLRu3ZrJzc3lr8/OzmYiIiIYAKL36cOHD/PPf/Xq1Trv01oZ+zdi7Pt3rWqCDh48CD8/P/Tu3Zu/bteuXTh37hwiIiLw+uuv459//sG+ffvw888/Y86cOUbdb1RUFA4fPoz8/HxRcTQXaUdFRek8TyqVIioqCufOnUNZWRlsbTVdj7lslDGfLiyl8vYZYzoH4pxgKqxLiHjZfG1WhwnrgbhpsDAvZ8HtlAmyRr8cj+eL5Kf0DKWVYI3M8O9PIKNAO0tuLbxd7LDrzV41Pv/x48cAgICAAK3bjhw5giNHjoiu69evH/r164f169cjPz8fK1asQHBwsOiYCRMmYMmSJdi0aRO+//570W2hoaF4//33RddNmzYNn3/+Oc6dO8dfFxcXh1OnTmHq1KkYNGiQ6PiIiAi88sorWLp0Ka5du4Z27dqJbv/qq690zl40a9ZM6zqJRII33ngDq1evxoEDB9CzZ0+tY3RZt24dALZHnpubG3+9h4cHFixYgIkTJ2Lt2rWi92qAnVacMmWKUY/RUNUqCIqPj0erVq1E1/3111+QSCT47bff0LlzZ7z++usIDAzE1q1bjQ6CxowZg6+//horV67k+wQpFAp+vpOr1UlKSkJxcbFoDOPHj8eZM2ewbt06vPLKKwDYabTffvsNbdq00fnHZS1EK8QqiqPPJ2hSuF0FK8OA2hVGC1eGNa+oReKCIfZ2WiZvbbIKFdh8np2idbSVYVJMcBVnkIYmo0CBx/mllh6GRRw5cgSLFi3Sur5fv344c+YMAPaD8v3797WOKS0tRWZmJjIzM+Hl5cVfHxUVBalUXBUSGBgIAMjNzeWv4+4/LS1Nqw4HAG7dusX/LwyC7O3tERkZqfP5lJWVYcWKFdi0aRNu3bqFwsJCMIym6W11ykji4uIAQDQ9xunfvz8A3auqu3btavRjNFS1CoKysrLg5+cnuu7kyZNo2rQpOnfuzD6AjQ26devG/xIZIyYmBmPHjsW8efOQnp6O8PBwrFu3DgkJCVi1ahV/3IsvvoijR4+KfnFmzJiBX375BW+88Qbu3LmDZs2aYcOGDUhMTMSuXbtq83TNromzHfxc7fE4vxQ3UvOhVjM4n8hmglzsbRDh4yI6vjZL5HVlgpzsbPjHp0yQ9Vl3OpHvCv1cdDO4OdImm42Nt4v2ghFrUtvx+fr64ubNm0hJSdH6gL1w4UI+ANm0aROee+45/rbsbPZ18v/+7/8M3n9RUZEoCKrchgXQ1BkJN+jk7n/Pnj3Ys2ePwfsX8vHx0VtsPGbMGOzatQsREREYP348fHx8IJfLkZubi+XLl+usi9UnPz8fUqlU50yHr68vJBKJztXOXH1SY1arIMjGxkb0Q8/JycHdu3cxbtw40XEuLi7IyzO8O3pl69evx/z587Fhwwbk5OSgffv22L17N/r06WPwPAcHBxw6dAhz5szB6tWrUVRUhKioKOzZs0crjWmN2gS44nF+KQpKy3HiXiYyC8sAAF2CPbRWANVmdRiXCZLLJAjydOSvD/N2wuP8UtpI1coUl5Vj/ekEAOw2K1N7hVp2QMQiajPVVB/06NEDR44cweHDhzFgwACjz+OCmatXr2pNR5kCd//ff/89Zs6cafR5+gKgc+fOYdeuXRg0aBD27NkDmUwzrX3mzBksX7682uNTq9XIyMiAj4+P6Lb09HQwDKMz4GuMq8Eqq9XqsLCwMJw5cwZqNfvpdPfu3WAYBr16if9Q09PTq12LY29vjyVLliA1NRWlpaWIjY3VCmKOHDkiygJxfHx8sHbtWmRlZaG0tBRnzpypFwEQIO4cve5UAn+5cj0QUPPpMJWaQXwmG7w283QU9QPipsYAqguyJlvOP0JuMZvtG9EhAE3dda+QJKQ+mzx5MqRSKVauXInMzEyjz4uJiQEAnD592izjMvX9c1N2w4YNEwVAAHD8+HGd58hkMlF2Sqhjx44AoFUzJbxOXy1tY1erIGjEiBFIT0/HyJEjsXz5cnzwwQeQyWQYPnw4fwzDMIiLi0NoKH1yNYawLujQ7XT+clcdQVBNM0EpuSX8tIow6AHEdUG0h5h1YBgGq07E819P7xNmwdEQYj4RERGYM2cO0tPTMWTIENy7d0/nccJ6HQCYMmUKXFxc8NFHH+H69etaxxcXF1erJKOy6OhoxMTEYOPGjfjjjz+0bler1Th69KjR98cVb1feEPz69etYvHixznM8PT2RmZnJt4oRmjx5MgBg0aJFommvvLw8vo6KO4aI1Wo6bM6cOfjrr79E86Rz584VVb2fOHECmZmZWtkholvbAE1lP5fkspVJ0T7QTetYpxquDhMWRYdpBUHOOo8jlvMgs4hvXtk9rAla+2untQlpKL744guUlZVh6dKlaNWqFfr06YMOHTrA0dER6enpuHLlCmJjY+Hs7MxnN7y9vbFx40aMHTsWHTp0wODBg9GqVSsoFAokJCTg6NGj6NGjB/bu3VvjcW3cuBH9+/fHhAkTsGzZMnTq1AkODg5ISkrC6dOnkZGRoTNA0SU6OhrR0dHYvHkzUlNT0a1bNyQlJWHnzp0YNmwYtm7dqnXOgAEDcP78eQwZMgS9e/eGra0t+vTpw/9788038f3336Ndu3YYPXo0GIbBtm3b8OjRI7z11ltVlpI0VrUKglxdXREbG4utW7ciLS0NXbt2Rd++fUXHZGVlYdasWY2zCVMNBHo4wMXOBgWCzE5koJvOpdC2NlLY2UihKFdXKxN0X0dRNKc5rRCzOmceaLrd9m1pvS0eCDEFqVSKb775BpMmTcL//vc/HDt2DOfOnYNCoYCnpyfatm2LJUuW4MUXXxTVvwwbNgxxcXFYsmQJDhw4gP3798PJyQmBgYGYMmUKJk2aVKtxhYaGIi4uDkuXLsWff/6JNWvWQCaTwd/fH3369BE16K2KTCbD7t27MXfuXOzduxfnzp1DixYt8PXXX2PIkCE6g6D58+cjJycHu3fvxvHjx6FSqbBgwQI+uPnuu+/QsWNH/Pjjj1i5ciUAoG3btvj0008b/TJ4QySMrqIaAoCtuHdzc0NeXp7OojJzGffTacTGa/oDvdq3OeYOaaXz2C6fH0BmoQJN3R1wcq5xhYQf7biK386ynbO3vdYDnYM1S+/VagZtFuxFqVKNMG8nHHqvX82fCDGJtzbGYedldrnsjtd7oGMzjyrOIPVVaWkp4uPjERoaym8TRAjRMPZvxNj3b7PuHZaXl6ezcJkY1qbSdEfl/kBCXHF09TJBwh5B4kyQVCpBSBP2uqQsdiNVYjkMw/CZICdbGdo11Z4WJYQQUjO1CoKuXbuG7777Dnfu3BFdf/jwYYSGhsLT05NfqUWMJ+wcDUCUqamMK44uVJQbHXByBc9NnGzh7qi9BL65D1sXRBupWl5CVjHSKzoEdw7xFK3kI4QQUju1ekX97rvv8O6774pagmdlZeGZZ55BYmIiGIZBVlYWXn75Zb6jJamacJl8hK+zzkCFwwVBKjWDUmXVWZuCUiX/plq5Hogj3Ej12J0MHLqVhg1nEvHt/juibTyI+QnrgbqFaa8QJIQQUnO1CoJOnjyJtm3b8ttYAMCGDRtQUFCAGTNmIDc3F+vXr4dardbas4XoF+HrwgcoI6OaGjzWWdQrqOqu0cJl75WXx3OEK8QW7bqBqWvPY/6f17D84F28sOos0hpp235LOCsIgmJCm1hwJIQQ0vDUKghKS0vT2gRu//79kMlk+Pzzz+Hq6opJkyahY8eOZmti1RDJZVLsnNkLu2b2wmt9mxs81kWwTL7AiLqghCxNEBTqpTsTVHk6TqhUqcauy8bvaUNqjq0HYjNvDnKZzjYJhBBCaq5WQRBXfS109uxZREVFoUkTzafWFi1aIDk5uTYP1eg429kgMtBNa6sMrePsq9crKClLU+MT3ER3EBTh64KPh7VGnwhvjOkciLeeaIEPh2pWp1EQVDeSsov5zTK7hHhQPRAhhJhYrfsECYObmzdvIjs7GxMnTtQ6lvYoMY/qdo0WFjo3E+wZVtnLvcPwcm9xZ+I/41JwIzUflx/lITGrSG8QRUxDXA9EU2GEEGJqtfpoGRUVhVOnTvGtzVetWgWJRKLVMDE+Ph7+/v61eSiih3M19w9LFARBQZ7V239qeIcA/jJlg8zv7ANNEXpMKBVFE0KIqdUqCJoxYwaUSiU6d+6Mjh074ttvv4WPjw+GDRvGH1NQUIBLly6ZZWdfArjYy/nLxmSCHlYEQZ5OtqJzjTG8gyaQ3UlBkFkJ+wPZy6VoH+hu2QERQkgDVKsgaOzYsVi4cCHKy8tx+fJlBAcHY8uWLbCzs+OP2bx5M5RKpVZ2iJiGi2j/MMOrw0qVKr7GJMjAVJg+gR6OfM+iO2mFuP24oNr3QYzzKKcEKXnsz6pzsAdsbageiBBCTK3Wr6yffPIJcnJykJ6ejgcPHmhtlPrUU08hLi6O9i4xk+rUBCXnlvCbsgbXIAgCgBGCKbGdl6nY3VxOC+uBaGk8IYSYhUk+Xtra2sLLy0vnbc2aNUOHDh3g7Ky7Jw2pHVFNUBVBkHBlmKGiaEOGRvqDW7C263IqbYtiJqJ6ICqKJoQQs6jV6jChsrIyXLhwgV8t1rRpU3Tu3Bm2tvq7HZPaE2WCqiiMNnZlmCHeLnbo0dwLJ+5lIim7GJcf5SEqyL1G90X04+qB7Gyk6BBE/YEIIcQcap0JKi8vx/z58+Hj44NevXph/PjxGD9+PHr16gUfHx988sknKC83fnNPUj0u9sZPhyWJVobVLAgCKk2JXaICaVN7mF2M5NwSAECnZh6ws5FZeESEWL8jR45AIpFg4cKFlh4K+vXrZ9a2MGvXroVEIqF9OU2gVkGQWq3GiBEj8J///Af5+flwd3dHx44d0bFjR7i7uyM/Px9ffPEFRo4cCbWadiM3B2EmqKol8qJMUJOaB0GD2vpBLmP/wHdfSYFKTVNipkT9gUhjlpCQAIlEIvrn6OiIgIAAPPHEE/jkk09w//59Sw+zUQoJCUFISIilh2FStQqCfvnlF+zduxfBwcHYunUrsrKycP78eZw/fx5ZWVnYtm0bgoODsXfvXqxatcpUYyYC1ekYzS2Pt5VJ4edqX+PHdHOUo2+EDwAgvUCB2Hj9m6reSMnH0v13kFKR2SCGlZSp8MMRzQs8bZpKGqvmzZtjwYIFWLBgAWbNmoUhQ4YgPT0dn332GVq2bIkPP/xQVJMYHR2NmzdvYubMmRYcNWv9+vW4efOm2e5/1KhRuHnzJkaNGmW2x2gsalUTtH79ejg4OODQoUM6o8NRo0YhKioKbdu2xbp16/DKK6/U5uGIDnY2MtjKpChTqQ0WRjMMw2eCAj0cIKtiO46qjIgKwIGbaQCAzecfontz7YxFRoEC4386jQJFOeKScrBhWkytHrMxWLr/NuIz2f3dOjZzR9cQCoJI4xQeHq5zauvEiRN44YUXsHjxYshkMnz22WcAAEdHR7Rq1UrreEuovKemqbm5uWltWUVqplaZoGvXrqFfv34G02OhoaEYMGAArl27VpuHIgZw2aBChf4+QZmFZSguUwGoXT0Q58nWPnCteNwdccm4/DBX65il++/wgdnp+1lGNXNszC4m5WDViXgAgK2NFEvGdKhy7zhCGptevXph7969sLOzw1dffYWHDx8C0F8TdPfuXUyZMgWhoaGws7ODp6cnOnTogLfffltrdWtBQQEWLVqE9u3bw9HREW5ubujYsSPmz58PpVLz+iqRSNCvXz8kJyfjxRdfhJ+fH6RSKY4cOQJAd02QsI5n165diImJgaOjI5o2bYr58+fzJSPr1q1Dhw4d4ODggGbNmmHJkiVa3wN9NUHcuNLS0jB58mR4eXnBwcEB3bp148cmdOHCBcycORPt2rWDm5sbHBwcEBkZiS+//FL0fLkpysTERCQmJoqmKit/v9esWYOYmBg4OzvD2dkZMTExOmuXhD+vU6dOYeDAgXB3d6/zLbZqFQQpFAqjolEXFxcoFIraPBQxgCuONjQdZoqVYUKOtjaY9WQE//WiXddFLyi3Hufjj3NJ/NflagbnEvRPmzV2pUoV5my9Aq686p0nIxDuQ20lCNGlZcuWGDduHMrKyvDnn3/qPS4lJQXR0dH47bffEBUVhXfeeQcTJ06Ev78/fvjhB6hUKv7Y9PR0REdHY+HChZDJZHjttdcwdepU+Pn54b///S+KiopE952VlYXu3bvjypUrmDBhAqZPnw5XV9cqx75jxw6MGzcOYWFhePXVV+Hs7IzPP/8cn3zyCZYsWYK33noLHTp0wPTp06FWqzFnzhysX7/e6O9Nbm4uevXqhevXr+OFF17As88+i/Pnz2PQoEFayYiff/4ZO3bsQGRkJGbMmIFp06aBYRjMmzcPEyZM4I9zd3fHggUL+AwUN025YMEC9OvXjz/urbfewtSpU5GcnIxp06Zh2rRpSE5OxpQpUzBr1iyd4z116hQfNE6fPh3jx483+rmaBFMLLVq0YEJCQpjy8nK9x5SXlzMhISFMeHh4bR7KIvLy8hgATF5enqWHYtDQ5ceY4A92M+Ef7mHUarXOY3ZcfMQEf7CbCf5gN7Py6H2TPG5ZuYoZ8PVh/n53XHzEMAzDqNVqZuLPZ/jruX9f7LlhksdtiP77z03++zT8++OMslxl6SERCygpKWFu3LjBlJSUWHooFhMfH88AYAYNGmTwuFWrVjEAmBdeeIFhGIY5fPgwA4BZsGABf8x3333HAGCWLVumdX5WVpbo69GjRzMAmA8//FDr2MePHzNKpZL/GgADgJkyZYrO97++ffsyld9e16xZwwBg5HI5Exsby1+fn5/P+Pj4MI6Ojoyfnx9z/77m9TkpKYmxtbVlIiMjdd7XmjVrRNdz43r99dcZlUrzGvLLL78wAJgZM2aIjk9MTNQav1qtZqZOncoAYE6cOCG6LTg4mAkODtZ6vgzDMEePHmUAMK1bt2Zyc3P567Ozs5mIiAgGAHPs2DH+eu7nBYBZvXq1zvvUxdi/EWPfv2tVEzRo0CD88MMPmDVrFr799lvI5eK9qMrKyvDOO+8gKSkJb7zxRm0eihjArRBTqhgoytWwl2svqU7MMs3KMCG5TIpPhrfF5NWxAIDF/9zEU218cTY+CyfuZQIA/FztkVZQCoYBTt3PNMnjWoOycjXe23IZRYpyfDO2Azycat4P6+qjPPx07AEAQC6TYMmYDrCR0TYZRIef+gKF6ZYehX7OPsCMo3XyUAEBbKuOzMyqX1ccHLQ3i/b01NTbPX78GNu3b0fz5s111iH5+vpqXWdra4uvvvoKMln1WlhMmjQJXbt25b92cXHB008/jdWrV+ODDz5AWFgYf1tQUBB69eqFo0ePory8HDY2Vb9lOzk54b///S+kUs1ryOTJk/Hqq6/i3LlzomN11S5JJBK88cYbWL16NQ4cOICePXsa9bzWrVsHAFi4cKFohsjDwwMLFizAxIkTsXbtWvTu3Vt0XqdOnSy6o0StgqC5c+fi999/x48//oi//voLEyZMQGhoKADgwYMH+OOPP5CSkgJPT0988MEHJhkw0Va5V5CuIMjU02GcvhHeeLK1Dw7cTEdavgLfH7qH/Tce87d/NKw1fjxyHzdS83E9JR+5xWVwd6z/DTQP3EzDropNZFccvof5T7ep0f2o1QzmbLvCtxl4c0ALtPRzMdk4SQNTmA4UUG8uYw0fPhzz5s3DG2+8gYMHD2Lw4MHo27evKNAAgPPnz4NhGPTv31/rw7w+oaGhendKMCQqKkrrOn9/f4O3qVQqpKWloWnTplXef0REhNYODTY2NvD19UVubq7o+rKyMqxYsQKbNm3CrVu3UFhYKCprSEkx/nctLi4OAETTY5z+/fsDAC5duqR1mzAgtIRaBUFNmzbF3r17MXbsWCQlJWHp0qWi2xmGQbNmzbBt2zajfnikZip3jfZyttM65qGJGiXq8tGwNjh6JwNKFYP/HdUs7+4c7IGn2/vj8sNc3EjNB8MAZx5kY3A7P5M+vikwDFOtgrwbKfn85S3nH2L2wJZwsK1+U8PYhGzcTGXvq7W/K17r17za90EaEWcfS4/AsDocH/cG7e3trfeYkJAQnDlzBgsXLsTff/+NzZs3AwBatWqFTz/9FGPHjgUA5OXlAUC13qd0ZYeMoatuiMvwGLpNWKhc3fvn7kdYAwUAY8aMwa5duxAREYHx48fDx8cHcrkcubm5WL58ebVqefPz8yGVSnX+PHx9fSGRSJCfn6/zNkuq9bYZXbt2xZ07d7BlyxYcOXJEtG1Gv379MHbsWNy4cQPHjh1Dnz59aj1gos3ZiK7RXCaoiZOtKGgyhVAvJ0ztGcpP6XA+HtYaEokEPcKb4JeKVU+n72daXRD037238Me5h/jk6TZ4pqNxL4J30gr4y/ml5dh5ORnju1Z/WSyXTQKAV/uGQU7TYMSQOppqqg+41U5VZRLatWuHrVu3QqlU4sKFC/jnn3/w3XffYfz48QgICEDPnj3h7u4OAPz7lzHqehWTqZ07dw67du3CoEGDsGfPHtG03pkzZ7B8+fJq3Z+rqyvUajUyMjLg4yMOhtPT08EwjM4AzdLfR5O8G9ra2mLixImYOHGizttfe+01nDt3jrbPMBNnO036VlfX6FKlCo/zSwGYPgvEmTkgHNsuJiOzkP3kMDIqAB2beQAAuoZ4QiaVQKVmcOp+lta5DMMgv7QcrvY2df4HkVmowI8VzQm/O3i3RkEQAKw/nYhxXYKqNX6lSo1/rrFTh/ZyKZ5sbdlPRITUF3fu3MHmzZthZ2dndMNAuVyObt26oVu3bggPD8eLL76I3bt3o2fPnujSpQukUikOHz4MpVJp9JRYfcZ13R42bJhWXdPx48d1niOTyVBWVqbzto4dOyIuLg5HjhzBuHHjRLdxAauu6T5Lq7OPnQztNm42wpqgglLtlOmjHM1UWLCJiqK1xyDHpyPbQiaVwNvFDnMGtxLd1j6QLZS7m16I9IJS0bmf7r6BDov24aM/676X1LE7GfzlB5lFyCnS/QcuVFKmQqJgehEArqfkI05HryRDTt3PQnbF4z3R2hdOJs7QEdIQnTx5EoMGDYJCocDcuXMNTmFduHBB5xRMWhrb6NXenu2c7+vri9GjR+P+/ftYtGiR1vHp6ekN7kN8cHAwALb5pND169exePFined4enoiMzMTpaWlWrdNnjwZALBo0SLR9zwvL4//nnLHWBN61W0ARDVBOqbDzFUUXdnQSH9EBbnDydYGbo7iT1I9mjdBXFIuALZx4sgo9oXrQmIO1pxMAAD8fjYJozs1RefguuuSfOR2hujruIc5GNDKcEbmXnohuJje3VGO3GI28Pz1dCI6VWS/jCGcChvePsDAkYQ0Pvfu3eNXapWVlSE9PR2xsbG4evUqZDIZPv74YyxYsMDgfWzYsAE//fQT+vTpg+bNm8PV1RU3btzA33//DU9PT9GqpB9++AHXrl3DF198gb///hsDBgwAwzC4c+cO9u3bh7S0NH7arCGIjo5GdHQ0Nm/ejNTUVHTr1g1JSUnYuXMnhg0bhq1bt2qdM2DAAJw/fx5DhgxB7969YWtriz59+vD/3nzzTXz//fdo164dRo8eDYZhsG3bNjx69AhvvfWWVZbEUBDUAFS1k3xSlvmKoisLcNdeigoAPZp74f8Os+lXLghiGAZf7LkhOu6rvbexaXq3OpkWU6kZHL9bKQhKyq0yCLotmAp7uVcofj4ej7wSJXZfScVHw1qjiY7C9MoU5Sr8WzEV5mxng34t9Rd3EtIYCbMyDg4OcHd3R6tWrTB//nxMnjwZzZtXvYjgueeeQ2lpKU6ePInY2FgoFAoEBgbitddew/vvvy9aIu7l5YUzZ87g66+/xpYtW7BixQrY29sjNDQUc+fOhZOTk9meqyXIZDLs3r0bc+fOxd69e3Hu3Dm0aNECX3/9NYYMGaIzCJo/fz5ycnKwe/duHD9+HCqVCgsWLOCDm++++w4dO3bEjz/+iJUrVwIA2rZti08//dSiy+ANkTB1ME/VvXt3xMbGalWmW7v8/Hy4ubkhLy/PqE6glrLv+mNM33ABAPD+oJZ4o3+46PZPd93A6pNsYfKm6d0ssjN5qVKF9gv3oUylRjNPRxyb0x9/X03F679d1Dp23dRo9I0wf1AQl5SDUT+cEl3XM7wJfnu5m8Hz/vP3TaysKAJfPzUax+9m4Ofj7Pf3g8GtjFrhJfyZPduxKZaOj6rBMyANTWlpKeLj4xEaGspP1RBCNIz9GzH2/ZuWojQAVa0OS8rWtHs353SYIfZyGToFu1eMpxgPMgrx5T+3+NvHdg7kLy/59xbUavPXkB29k6F13aWkXL5njz63H2syQS39XDCpWzD/9a9nEqs8HwB2XUnlLw/vQFNhhBBiCRQENQAugtVhuvYP42qCbGVS+Lla7tNlj+aaxmLv/HGJH1eP5k3w39Ht0cafjdavJefzq6bMSVgPFBXkDgAoKlPhbnqBnjNY3MowNwc5fFzsENzEic9cJeeW4Mhtwx19i8vKceAGW5jp7ihHz/DqN1wjhBBSe9WqCarOJm5CGRnan7iJ6RjKBDEMwwcbgZ4OFt2VvEfzJli6n718+RHbnEwiYbtKS6USvD+4JaasYdu6f7P/Nga19YWNTIoiRTnWnkrA7iupeCYqADP61r6hYE5RGS4/ygUARPg6Y1ikPy5VrO66mJiLVn6606d5JUqk5pXy53G1Sy92D+YzS+tOJ+IJA8vdD91KR4mSnRoe0s4Ptjb0WYQQQiyhWkHQSy+9VKOC1ep24yXVI1wdVrlPUEahAqVKNQDLTYVx2ge6w9FWhuIyTW3Ysx0D0TaAXT7fL8Ib0SGeiE3IxoOMImyMTUKZisGPR+4hs5BdSn7rcT5GRAXA3013Abaxjt3N4Fd49Wvpg47N3PnbLibl4PkY3Y0P7wqKoiN8Ndtb9Gvpg6buDkjOLcGxOxk4eidDb10TrQojhBDrUK0gqFmzZhTMWCHx6jBxn6CHdbQ83hi2NlJ0DfHkMyZ2NlLMHhTB3y6RSDBncEuM+d9pAMD8v65r3QfDsEHE9D61ywYdFUyF9YvwRrumbpDLJFCqGMQl5eg9T7gyTLjHl0wqwawnWmDOtisAgI92XMW+d/rA0Vb8J5ZfqsThisf2crZDjAWK1AkhhLCqFQQlJCSYaRikNuxspLCRSlCuZrSmw0S7x1s4CAKAXuFefBD0Su8wrYxOlxBPDGjlg0O3xHU1T7b2xYGbbB3NjrjaBUFqNYNjFUvjHW1l6BziATsbGdoEuOHyw1zczyjSu9Hrnce6M0EAMLZLILbHPcKZB9l4lFOCZQfu4sOhrUXH7L36GGXlbGZuWKQfZBacniSEkMaOihEaAIlEwtcFVS6MrqtGicZ6oXswnu3UFM9FN9Nays+ZO6QV3BzYYu8nWvlgz1u98MvkLnzx8s3UfNEKreq6npLPT6/1aO4FOxu2ZXzHivsHoLf78520Qv5y5SBIIpHgP6Mi+RqfX44/wLXkPP72gzfT8MlOTVdsWhVGCCGWRUFQA8HVBVWuCRIFQWbaMqM67OUyLB0XhcXPRurddT3C1wUH3u2LU3MHYNVLXfmaoVGCfb3+vGT8RoeVHb2jyTL1FTQp7BSs6fbMdbeujFsZ5u1iB08n7UxRmLcz3hrABndqBpi7/QrKVWpsjE3CK+vP8/VZ/Vp6V6u7NGlcaJshQnQz9d8GBUENhIs9mzkpqDQdJqwJCvKwfBBkLG8XO63u08Pa+/PTRzsvpdS4l9CRSvVAnE6C4mhddUGZhQpkVez11bJSFkhoep/m/O3XkvPx/M9nMW/7VXDDfbq9P356obNFV+oR68RtZKlUau8BSAjR/G1U3vS1pigIaiBcKjJBZeVqKMrZ1VcMw+BBBtso0cvZrt5v0OnlbIc+LdieOsm5JTiXkF3t+8grVuJiRYAT5u0k2kakqbsDvF3YLS8uJeVqBVmG6oGEbG2kWDw6EtwagljBOF/uFYrvJnTkp+AIEZLL5bCzs0NeXh5lgwiphGEY5OXlwc7ODnK5vOoTjFC/3xUJT9grqEihgp2NDOkFmsxFa3/9b9r1yTMdm/Krq/68lGL06iqGYXD5UR7Wn0rgMzL9InxEx0gkEnRq5o5/r6ehQFGOexmFomBHvDLM2eDjdWrmgRe7BWPd6UT+uo+HtcbLvcOMGi9pvLy8vJCcnIxHjx7Bzc0NcrmcVuWSRo1hGCiVSuTl5aGwsBBNmzat+iQjURBU19RqIP8RkHUPcGsGeOkuDq4u0U7ypeXwdLLFjZR8/jquG3N991QbX77X0J4rKVg4oo3BrEpqXgnWn07E7ispeJhdIrpN16alnZp54N/r7Cq0i4k5oiDojp4eQfrMHtQSlx/lISGrCJ+NbEeF0MQo3D5HmZmZSE6uee0bIQ2NnZ0dmjZtatK9PCkIqmu3/wb+mMhe7jcP6DfXJHcrzAQVVPQKupGqCYJaN5AgyNHWBoPa+mFHXDLyS8tx5HYGBrX103lsRoECw78/icxCheh6uUyCUR2bopeO7So6CoqVLyblYEK0pmmicEVaCyOCIBd7Of58oyfUaobqf0i1uLq6wtXVFUqlst5tPE2IOchkMpNNgQlREFTXmggyP5l3TXa3LpUyQYA4CGoT0DCCIAAYGRWAHXHsJ+Q/45L1BkGL/7nJB0AyqQQ9mjfB8PYBGNTWD26Ouv+Y2ge68T2XhCvEGIbhl8c3dXcQZd6qQgEQqSm5XG6WF35CCIuCoLrmGQpIpACjZqfETEQ0HVaxQuxmxXSYrY0UYV5OJnssS+sV7gUvZ1tkFpbh4K105JUo+b5CnDMPsrD9IhsouTnI8fes3mjqXvVWG/ZyGdoEuOLKozzcTS/k7zslr5T/vgo7RRNCCKm/aHVYXbOxA9wrpliy7gEmWgFSeRPV4rJyxGexK8Na+bnARtZwftQ2Mimerthzq6xcjXWnEkS3l5WrMf9PTVPCOYNbGhUAcYRNE9/fchlZhQqjV4YRQgipP6z2nVGhUOCDDz5AQEAAHBwcEBMTg/3791f7fp566ilIJBLMnDnTDKOsoSYt2P/LCoHCNJPcZeVNVG89LuDjq9Z6dkSvz0Z3CuQvL91/B98dvMsvKV59Mh5309mpqw5B7pjQVfdmqPoIC5j33UjDoGXH8NvZJP66qlaGEUIIqR+sNgh66aWXsHTpUkycOBHLly+HTCbD0KFDceLECaPvY/v27Th9+rQZR1lDZqgL4polAmwmSLQyrAHVA3EiA934zswAGwh9tvsmHuUUY/kB9nsqlQCfj2xX7f25uoR44seJneBRUTeUWVjG71sGUCaIEEIaCqsMgmJjY7Fp0yYsXrwYS5YswfTp03Ho0CEEBwdjzpw5Rt1HaWkp3nvvPXzwwQdmHm0NCJfFm6guSLiTfEGpssEWRQu9O7AlPhzaiv969cl4jFhxEiVKdjXNC92CERnoVqP7HhLpj3/f6YMnWol7CUklQHNvygQRQkhDYJVB0NatWyGTyTB9+nT+Ont7e0ybNg2nT5/Gw4cPq7yPr776Cmq1GrNnzzbnUGumiemDoMp9gm4KgqBWDbiQd3qf5vjv6EhwyZ7siuaQXs52eG9Qy1rdt4+LPX6Z3AVfjW4Pp4p9zroEe8JeTt2eCSGkIbDK1WFxcXGIiIjQaogUHR0NALh06RKCgoL0np+UlIQvv/wSq1evhoOD8QWxdYarCQJMFwQJMkF5JUrcSmULeZt5Ooqmyhqi8V2bwdVejlmbLqFMxW5QOv/p1nA1wfOWSCQY1zUIvSO8cPxups4Gi4QQQuonqwyCUlNT4e/vr3U9d11KSorB89977z107NgREyZMqNbjKhQKKBSaxnr5+fkGjq4FF39A7ggoi01XEyTIBF1LyeenhBpKp+iqDIn0h5uDHF/9extdgj0wwsTdmf3dHDCui/7AmxBCSP1jlUFQSUkJ7OzstK63t7fnb9fn8OHD2LZtG86ePVvtx128eDEWLVpU7fOqTSoFmjQHHl8FchIAlRKQ1S5rIcwE3atYGQU03HogXXqEe+FPHV2gCSGEEF2ssibIwcFBlJHhlJaW8rfrUl5ejrfeegsvvPACunbtWu3HnTdvHvLy8vh/xtQe1RhXF8So2EColhzkMuhaBNVYMkGEEEJIdVllJsjf31/nxoGpqakAgIAA3VMd69evx+3bt/HTTz8hISFBdFtBQQESEhLg4+MDR0dHnefb2dnpzECZReW6IK8W+o81gkQigbOdDfIrtszgtG5EmSBCCCGkOqwyExQVFYU7d+5o1eRwU1xRUVE6z0tKSoJSqUTPnj0RGhrK/wPYACk0NBT79u0z69iNZuZeQQC7XUSAm71J7psQQghpaKwyEzRmzBh8/fXXWLlyJb/EXaFQYM2aNYiJieFXhiUlJaG4uBitWrG9YiZMmKAzQBo1ahSGDh2KV155BTExMXX2PAwyQ6+gypt6tvF3hURCm3cSQgghulhlEBQTE4OxY8di3rx5SE9PR3h4ONatW4eEhASsWrWKP+7FF1/E0aNH+e0SWrVqxQdElYWGhuKZZ56pi+Ebxwy9goQNE4HGVRRNCCGEVJdVBkEAO301f/58bNiwATk5OWjfvj12796NPn36WHpopmHvBjj5AEXpZukVBACtqSiaEEII0ctqgyB7e3ssWbIES5Ys0XvMkSNHjLovxkQ7tZucVws2CCpMA0rzAfvaBS26psMIIYQQoptVFkY3Gk2aay6bIBsknA6TyyQI96E9rgghhBB9KAiyJBNvnyHMBLXwcYGtDf14CSGEEH3oXdKSTFwc7WynWSJPRdGEEEKIYRQEWZKwQaIJegUJp8OoKJoQQggxjIIgS3IPBiQy9rIJMkH9W/nA2c4GLvY2GBrpV+v7I4QQQhoyq10d1ijY2AIeIUD2fSDrPsAwQC2aG4Z6OeH0vAGQy6Swl8tMN05CCCGkAaJMkKVxdUHKIqAgtdZ352IvpwCIEEIIMQIFQZZm4rogQgghhBiHgiBLM3GvIEIIIYQYh4IgSzNxryBCCCGEGIeCIEszw0aqhBBCCKkaBUGW5uIH2FZsb0E1QYQQQkidoSDI0iQSTV1QbiJQXmbZ8RBCCCGNBAVB1sArgv2fUQOply07FkIIIaSRoCDIGoT20Vy+84/lxkEIIYQ0IhQEWYOIwQAqOkXf+tuiQyGEEEIaCwqCrIGzDxDYlb2ccRPIfmDZ8RBCCCGNAAVB1qLVUM3l2zQlRgghhJgbBUHWoiUFQYQQQkhdoiDIWnhFAJ4VS+UTTwHF2ZYdDyGEENLAURBkLSQSoOUQ9jKjAu7ut+x4CCGEkAaOgiBr0mqY5vLtPZYbByGEENIIUBBkTQKjAQdP9vK9g0C5wrLjIYQQQhowCoKsicymomcQgLJCIP543Txufirw70dAwom6eTxCCCHEClAQZG24uiAAuF1HjRO3TQNOrwB+G0sF2YQQQhoNCoKsTfMBgMyOvXz7H4BhzPt4GXeAxJPsZWUxcG2beR+PEEIIsRIUBFkbO2cgrC97uSAFSL1k3seL22D4a0IIIaSBoiDIGgmnxA5+CmTerfqc/FTg2BLg/2KAX54ESnKrPkelBC5vEl+Xehl4fLVawyWEEELqIwqCrFHEEEAqZy/fPwSs6ApsnQak39IcoyoHCjOAO/uATROBb9sChz4HMm4Bj84Bl36r+nHu7gOK0tnLdq6a6+OMOJcQQgip5yQMY+6ik/orPz8fbm5uyMvLg6ura9UnmNK1bcCe94CSHMGVEsAtCCjNBRT5hs8P7QtM3mn4mI3PaYqvR68C/nwdUCnYZfrv3QJs7GrzDAghhBCLMPb9mzJB1qrdaODtq8CTCwHHJhVXMkBeku4AyNkP6D2bDZIAduuNUgOBUsFj4M6/7GWXAKDtKKD1cPbrkmzav4wQQkiDZ2PpARAD7FyAXu8A0dOB86uBc6vYAMjBE3DwABw9AScvdvPVFoPYPkOKfCB2JaBWAg8OA21G6r7vy5vY7TkAIOp5QCoDOk4Erm1lr4v7FWj7TJ08TUIIIcQSKAiqD2ydgB5vsv+q0mIQGwQBbL2QriCIYdggh9NxIvt/aF82k5T3ELh/EMhPAVwD2NtK84E7ewH/DoB3y9o9H0IIIcQK0HRYQxPSC5A7spfv/guo1drHPDwLZFWsOAvpDXiGsZelMjYrBACMGri8kQ2Yrmxmi7O3vwL8rxfwMNb8z4MQQggxMwqCGhq5PRDWj71clAGkxmkfI+wF1HGS+DYuCAKAC2uBtcPY4KfwMXudqoxdjZaXbMpRE0IIIXWOgqCGKGKQ5jJX/MxRFADXdrCX7VyB1iPEt3uEAKF92Mu5SZpu0gDg6MX+X5QObHoeKCuueiy39gB/zzGu1xEhhBBShygIaohaDNRcrhwEnVgGKIvYy+1GA7aO2udHVcoOeYQCz28B3ogF3Jux16VeAnbO1L+tR0kusO1lNliK/YnNKBU8rsGTIYQQQsyDgqCGyDUA8ItkL6de0gQf6beAk8vZy1I50O113ee3GQkE9wTs3YD+HwOvnwEiBgJOTYDnNgFyJ/a4a9uAE0u1z48/DvzYE7i6RXNdYRqwZQrbpboyRSFw7yCgLKnR0yWEEEJqglaHNVQRgzXbX9zdx2Z3dr/DLp0HgJ6zAO8I3efK7YEpf7NF1dJKcbJvW+DZlcAfFSvKDn4GXP8TcPEDnH0AtapiK46KDJGdG3t/hWlA0ingwEJg0Bea+0u+CGyezPY/8ooApv7LLv0nhBBCzIwyQQ1Vi0p1QZd+Y4MQgJ3e6jO76vuoHABxWj/NZogAAAzw+AobaMX9yq4o4wKgkN7AayeBCb9rtgE5vQK4tp29fGEdsHoQGwABQOYdtou1srQ6z5QQQgipEQqCGqqmnTSFzPcPA/vna24b9g0gd6jd/feZDfT7kO02LZGJb5PKgac+A17cCbgHAYFdgMGLNbf/NRPYOhXY9Ra72kzo4RlgxwzdS/tNTa0C7u4H/pgEfN8ZuLrV/I9JCCHEatDeYQZYdO8wU9jxakVmRqDdGGDMKtM+jlrNbrVRmAYUZQI+rdmpMSGGYYObK39onx89HYgcC6x/RlO03X2meNrMlPIesVmruF/ZxpAciQyYuBkIf9K4+ykrZvsp2TmbZ5yEEEJqhPYOI+Kl8gBbnzPoP6Z/HKmU3b7Dty0Q1lc7AAIAiQR4ehng01ZznY0DMGolMHQJEBQNjF2rySqdXgGc+dG048xJBP56A1jWHjiyWBwAAew2IptfAtKuV31fiaeBZe2AryOAhJNVH08IIcTqUBDUkDUfAEgFte9PLQRcfC02HNg6AhN+BQKj2dVnLx8AOozX3B4xEHhasNps7zxg1yygML12j5ufAux+l53yivtVs2eaRMrWTk34HWj1NHtdWQHw+3igIE3//d0/BGwYBRRnsZmr7dPZlgCEEELqFZoOM6DeT4cBbOYj7lc2IJq4TX+xszU59DlwbInma1tnoPe77JL+qmqZSvPYxoyZd9h/GXeAewcAlUJzjJ0bEDMD6DwZcAtkrysrBtYOBVIqOmwHdARe+lu7j9Ktv4Etk7VrmSLHAqN/qdnzJYQQYlLGvn9TEGRAgwiCysvYfcK8WrK7zNcHDMNOhx35L5uZ4bgFAeFPsEGR3JHdWFZVBmQ/ALLus/8XZ+q/X1tnoNtrQPc3AAcP7dsLHgM/PwHkP2K/DusHdHge8G3DLt+/uYvN+nCZpPCn2H3UFHns16NXAZFjTPItIIQQUnMUBJlAgwiC6rPCDODIf9g9zJharBazdQG6TAF6vs02fDQk7TqwapA4+ALYaUW1Cvzy/8ixwDM/Ajf+ArZNY6+zcwNeP6XJLlWmLGH7IqVdY2ugAjrW/DkRQgjRi4IgE6AgyEqk3wT2fcxOaxni4g94hgFNwtnMjVcE2xDSLQiQygyfK3TvILDlJUCRr/v2TpOBp7/V3OfWacC1iuX1Ib3Z1gCMis1Mpd8EUi4CSWfYqTZuGk1mC0zdCzTtrH3/henA0a8ArxbstB0hhJBqoSDIBCgIsjL5KewSfGUxUFYIlBWxxc0eoYBnKDs9ZirF2UDyBTZrk3YDSL/BLq3vOg0YMJ9d7cYpyQF+7KWZRnMPZseq1rFFiJBbEDDjmLhDdmkesGYo+7gA8MKfQPP+pntehBDSCFAQZAIUBBGjxR8D1o0AP12mS5NwoFk34PE1dk83AGj+BDBxC5tVKlcAv44GEo5rzmkxiO1dRAghxGj1vk+QQqHABx98gICAADg4OCAmJgb79++v8rzt27dj/PjxCAsLg6OjI1q2bIn33nsPubm55h80abxC+wD9P2IvS+WAd2ug7Si2q/b434DZd4E3LwAj/49dks938z7ITn2p1cCfr4kDIAC4+y+Qea9unwshhDQSVpsJeu6557B161a8/fbbaNGiBdauXYtz587h8OHD6NWrl97zvLy8EBAQgGeeeQbNmjXD1atX8b///Q9hYWG4ePEiHByM3y6CMkGk2kpy2Wk5mdzwcQ+OAhueqSj4lgAtBrIBD8A2kWw9HLhakQGKns42lCSEEGKUej0dFhsbi5iYGCxZsgSzZ7MbfZaWlqJdu3bw8fHBqVOn9J575MgR9OvXT3Td+vXrMXnyZPz88894+eWXjR4HBUHErI4vBQ4uEl8nkbKZombdgKVt2PonW2fg3RuAvZtlxkkIIfVMvZ4O27p1K2QyGaZPn85fZ29vj2nTpuH06dN4+PCh3nMrB0AAMGrUKADAzZs3TT5WQmqs1ztAy2Hi655eBrQcwvYx6jCBva6skG14SQghxKSsMgiKi4tDRESEVvQWHR0NALh06VK17u/x48cA2KkyQqyGRAI88wPgGwlAAjzxCdvFmhPzquby2Z8q+hQRQggxFatsIZyamgp/f3+t67nrUlJSqnV///3vfyGTyTBmjOFuvgqFAgqFZnuF/Hw9fWIIMRUHd+CVQ4CiQLuRo3dLdvXY/YNAbiJwZy/QapjOuyGEEFJ9VpkJKikpgZ2dndb19vb2/O3G+v3337Fq1Sq89957aNGihcFjFy9eDDc3N/5fUFBQ9QZOSE3Y2OrvZN3tNc3lMz/WzXgIIaSRsMogyMHBQZSR4ZSWlvK3G+P48eOYNm0aBg0ahC+++KLK4+fNm4e8vDz+n6HaI0LqRPMn2P5CALt8/vE1y46HEEIaEKsMgvz9/ZGamqp1PXddQEBAlfdx+fJljBgxAu3atcPWrVthY1P1zJ+dnR1cXV1F/wixKKlUXBt06DOqDSKEEBOxyiAoKioKd+7c0arJOXv2LH+7Iffv38fgwYPh4+ODv//+G87OzuYaKiHm1+E5wN6dvXxnL7DzTba5IiGEkFqxyiBozJgxUKlUWLlyJX+dQqHAmjVrEBMTw9fqJCUl4datW6JzHz9+jIEDB0IqleLff/+Ft7d3nY6dEJOzcwZGr2I7UQPApd+AvR8A1tfiixBC6hWrbJYIAOPGjcOOHTvwzjvvIDw8HOvWrUNsbCwOHjyIPn36AGB7Ah09ehTCpxAVFYXLly9jzpw5iIyMFN2nr68vnnrqKaPHQM0SiVW58Re7uz1TkQXq9S7w5AKLDokQQqxRve4YDbBF0PPnz8evv/6KnJwctG/fHp999hkGDRrEH6MrCJIId/eupG/fvjhy5IjRY6AgiFidSxuBPwU1QgM+Bvq8b7nxEEKIFar3QZA1oCCIWKVzvwB73tN83fUVYPDiqvcrI4SQRqJeb5tBCDGg68vAU59qvj73M7B+JFCYYbkxEUJIPURBECH1Uc9ZwIgVgMyW/TrxJLCyL5ASZ9lxEUJIPUJBECH1VacXgJf+BlwqtpjJTwZWDQLu7LPsuAghpJ6gIIiQ+iyoKzD9CBAUw36tUgD/zrPokAghpL6gIIiQ+s7FD5i8G/BuxX6ddR8o1952hhBCiBgFQYQ0BDa2gE+bii8YIJf2vSOEkKpQEERIQ+ERormck2CpURBCSL1BQRAhDYUoCIq32DAIIaS+oCCIkIbCI1hzOTfRcuMghJB6goIgQhoKmg4jhJBqoSCIkIbCNRCQyNjLFAQRQkiVKAgipKGQ2QDuQezlnESAtgUkhBCDKAgipCFxr6gLUuQDJTmWHQshhFg5CoIIaUioLogQQoxGQRAhDQkFQYQQYjQKgghpSGiZPCGEGI2CIEIaEsoEEUKI0SgIIqQh8QjVXKYgiBBCDKIgiJCGxMEDsHNlL1MQRAghBlEQREhDIpFolsnnPQJU5ZYdDyGEWDEKgghpaLjiaHU5kJ9s2bEQQogVoyCIkIaGiqMJIcQoNpYeACHExLSCoL4WGgghVUg8BRz7GnBvBoT1BUJ6A05elh4VaUQoCCKkoREGQQ2hV5BaDZxYygZ0Ty6kN8mGZO88IPUSe/nCGvZ/33ZAq6eB3u8CNnYWGxppHCgIIqShaWjTYbE/AYc+Yy/nPQJe2MEWgJP6TaUE0q5rX592jf2X9xB45oe6HxdpVKgmiJCGxi0IQEWQUN+DoPRbwP4Fmq8fHAbiNlhuPNbi/Grgu07ApY2WHknNZd0D1Er2crPuQM9ZQEBH8L+7l34Dbv9jseGRxoGCIEIaGrk94BrAXs6px9Nh5WXA9lcAlUJ8/b8fAXmNeNWbWsUGhtn3gX0fAwxj6RHVTPoNzeXwJ4CnPgWmHwFGfK+5fudbQHF2nQ+NNB4UBBHSEHG9goozAUWBZcdSU0e/BB5fYS97twIix7KXFfnArln1982/tjJusd8DgP35Ztyy7HhqKk0QBPm00VzuOAloMYi9XJQO7HmvbsdFGhUKgghpiER1QfUwG5R0FjjxLXtZagM8uxIYugRw9mOvu7cfuPS75cZnSY/Oib9OOGGZcdRWup4gSCIBRnwH2LuzX1/fDlzbXqdDI40HBUGENET1uThaUQjsmA4wavbrfvMA/w7sliDDl2mO2zsPyE+xyBAt6tF58dcJxy0zjtriiqLlTprMJcfFDxj2jebrPe8BBWl1NzbSaFAQREhDZO5l8uUK4OxK4P4h09/3oc81gVtgNNDzbc1tLYcA7SewlxV57LSYWm36MVgzrSDoRP2bGlQUaH4vfVoDUh1vRe1G/397dx4XdbX+Afwz7IssooAggguhiQuiQeCa3hRzSXPXa5maSyrpTdMW7SZq95pXypsiWj/NJa00LTXJJSVzQVRwuypuCAEuibLIzpzfHw8z3xmGZQZm2OZ5v168/M53mTlzwO88c85zzgHav0rbuenAvtD69z5ZncdBEGMNUWOVb9aGaAk6/SVwcAGwbaR6bkd1FRcBl3bStpk1MHw9YFpqJo+QT4FGrrR98xDwy3zj+XDMy9DMAcp5DDy8VjvlqaqHKu/BtX3Z58hkwKDVgK0zPU6IAn7/zPBlY0aFgyDGGqLyusPkciDlAnU5Vce1ffSvKAYubKnec6n6MxbIfULbPv2BJm00z7FxovljZKb0+NzX1HpkDFIuACgJ+Mxtpf31LS/oocr8QC6+5Z9n2xQYFgHlsPljy4Fr+w1atDonLxO4dRQoyKntkjRIHAQx1hA1cgXMrGhbkRidlwFsfRXY+BKw+ZWqrzD/7C8gNV56fOk7Gs6uDwlR0rbPwPLP8/4btRIpnFgFnPpv+ec3FKpdYd3elLbrW16QastVeS1BCs+9DPRbIj3eM12/rY913bdjgG2vAXtn1HZJGiSeMZqxhkgmo9agR9cp9yIzFdg+GnhwmY6nXQQu7gD8J+r+3LePQdkaAVC+RsJBKX+jOhJ+LdmQ0YdfRTqNBnKfUrccQHPmWDnq/p6yHwFPk4DCZ0BhLlDwDCjIBp49omPPHgLZD2kSyoH/BqzsdXxTeqQ6MqzrJJo4Mi8DuHeSWvnKyq2pi1RninapJAgCgB7z6Joru+h3s3Mc8NYxahWsafnZwKk19Dfx8lLD/j08TQKSTtH2tX2UHG7narjXM0IcBDHWUDl6URBUlAds6ANklxpdE/1vCiR0XZ/p9lHNfXHbqh8EPUkEHpW0EHi8oN0aYYHTqPvs+Ap6vC+URhZVFkAp3IgCvp8IFGvZkmVuDQxerd25+iaEFARZOwFNvAGv7sCNXygv6NH1yltV6gIhpOHxti7a/Z5lMppE8fFNCuCfJAI/TAL+/qNmzpgh3T0B/DRLSuq2sqdAyFBuH5O2hRz430/0N19axp/A+c30f7BZx7KfSy6n5UggAPvmgE0TXn4G3B3GWMOlmhekCIAcPIEWgbSdkUw3Tl3I5ZSfAADmNoC9B23fOlL94eoJh6RtnwHaX9f7PSBwJm0LOXBosXYjxh5cBXZP0T4AAmiRz/uXtT9fn57cpVY3gIJEmQxo2UM6Xl/ygrIfUtAG6Ba0WdgAY7+lwAkA7kZLc0kZWsEz4Jf3gG8Gq4+2vPhd1buVtVF69OWV3ZrnCEGtvL9/Rv8WF5b9XL9/BkT2BCJ7AZ+1AZa5Al/4ATvGU4uTkeIgiLGGSjUIAgDXjsDUw8DAldK+31fRDV5bD65Q9xAAtOxJs/sCFHxcrOY6Vmr5QCHaXyeTAQNW0HB6gFqTbh2u+JpnfwE7xlLXCgB49aCh+C99CPRfBgwOB0ZvBSYfAkLjgJc+ovOEHDi4sHZGo6nmA3l0o3/VgqB6khekbVJ0WRw8gDFbaQJNADizzvAJw/evABHdaSFfBdOS1tPs+8Dd44Z5XXkxcKfUcyefAZ4mq++7/ZtUp1mpZf8dFBeqlx+g5Wie3AVuHFBfn8/IcBDEWEPV3F/abtULePMAdRW5+wHPD6X9zx4CZzdo/5yqXWHe/QC/8dLjuG1VDw7ys6Sbt70H4Krjh6OJCdBjrvT4j8/LP7eoAPhuovTt180PmPAD8PIn1KoUPAfoNhloPxTwDAScWgPdQ+lfgPJvru7RrXz6oJoPpAiCXDsAVg5SuVRbwDJSgOP/An5bTsHuqS+Bsxtp9uWs+zVX7tJUk5qr0n3n+SLg+xpt56YDFw04c7i8mLrdntylx2bWQMi/gRFfSecYahHb1Dgg7yltm6h0+ZX+24tZr/64rL/Nu9FS61tTHxpY4NIeMDGnfdcPUG6ZEeIgiLGGyvNFYHgk8MoqYMIu6cMSoBYPxbDjPz7X/gZ4SzUI+hvNR9SqNz1OvwMkna5aWe8cl7qlfAZULVfBZyDd4AFKJk0+q3mOEMCBf0jJpo2aAeN2UFdLRcwsgQGfSo8PLVZvgcj4E9gzE9gyjLYNQRkEyYDmXWnTxJTyggApLwig1oKv+wPHPwV+Xwn8FgYc+pDmVNr1JvCftsCXATQT89W91DWYmUaTYBpaectl6CJ4trR9ei0FK4Zw4yDlIQGA8/PAzJPAizOopdK6JCn7+n7DBBCqXWFBs6Rt1S6xv27RXFmqru3T7BK7rHJNvyXA33cDb5+WRhgW51O+kRHiIIixhqzzWCDgLc3kZ5d2dAygb5un11b+XPnZQNIZ2nb0klpGFF1iALUGVUVVu8JUmZgAwaHS45NfaJ5zZh2NqAJoCoGx3wL27to9v88ACvwAIPNP4OTn9OF7JgJYG0gtEneOUYCkb4W5Ui6Sc1v1gLZ0XlDWA2DLUCpjRf66AcR+BfzwBhARDKxuByxzAVY0B9YFl8xJZADKIEhGC+NWhVtn9eD7xkG9FE3DqTXSdv8wad4qMwug40jaLsqjQFLfVIOgF6ZSiyUApMUDj2/TtmoXl2LeqNwn1PKjUJhHgRoAWNoD3iqDBhT3AIDym4wQB0GMGaveC6Vm9tNrqSWgIol/APKSb5je/aTWmueHAJYlH8pX9+q+ar1cLiVFm1kDrXrqdr2qTqMBOzfavn4AeJQgHTu/Gfj1A+nxq2sBj67aP7dMRq1Bijo7+QWwsS8QtUjKLQKA/+2lD2Z9SrsIyEsScBVdYQqqQdD1/cDW4dLrO7UBxv8AjNsJjNoMDFsP9Hy3JLHatOzXKsimHJN9ofpfkkReLM0W7dSq8ha4igTPkbYNMUdUUgyQHEPbLu2lAFhBLYDYqd/XzsuUWjKbPAc4etIyIgpXdlPrk2IRYXMbYOC/pOOqXWK3jgD5mbTdbjBgbiUdc/en5weAe39UPUH62j6aPf6X9+gekP2oas9TCzgIYsxYObUC/F+n7YJsas2oKFH61hFpu00/advcGuhYcoMufAac+A/w7LH25UiLk5Kt27xEz1dVZpbAiyUjxSCA0yUfjue/oXXGFHq9J32T14WzDxBYMmldUR59K1dQDE0Wcsq/0Se1fKAX1I+p5gXdjZaSZB1aAK//RDNvtx0I+A4H/MZRd8jUI8CiexQg9XyXWvPaDgI8g2ihWoBanq7puYvkSSJQlEvbVe0KU/D+m9SSlHwGSI6t+HxdqbYCBc/R7KJ19weatqXtpFNA+l39vXbiCZqNHQDa9KV/fYdLxy/volZXRfDdeSzQcZTUGnRtvzSBqWr3mWogBdB76jxGenypCq1B2Y+A3VNpMMLZSGpZXOUNfPkCtYrmZer+nDWIgyDGjFmv96S1mfIzKHdkTRfqJimdV6BIijYxo0RrVapdYn+E0xDcr/4GRK+kCRBvHwMST9IH1f0r6s+tnCARug2NL0/XSdTsD9A39BP/oVYNheA5wEsflHmpVnqr1BlAH8STfwXe2AdYNKJ9cdtoKLi+VBQEqeYFKTRypQDIsUX5z2lpRwFSvyXUKjbuW2ByFPCaStLvb8v1OwRcdZJEXZPfS5PJ1FuDTlfSGpSZCuycQInOt45W3Mr11y1qSQSoZbFDGQGzTEZBpYI+W4NUc+8UQZBjC6DFiyXlu6G+jlrgDPry0LZklvW8pxQQFzyTupqtnYDWvTVfq+Noafvid7oPbjizjr4QlPZXAgWSkT01F/2tQzgIYsyY2bsB06KBLhMBWcntIPsBJcxGBFOXAEDfchVdLB4BmrPkuvtLN2sAQMnEfseWA9+OBrYOo6U6vv4bsL47sLIN8P0b1JyvuhbUc/2r/56sHGh0F0DJ1kdVJrMLmg28HFa9SeKsHIDx31HLyctLgeknKAndujEFYAAlmpYetVMdf56nfy0alZ1Ho9olZt0YmLi37HXXtOHdD/AMpu3HN6vWOlAefSRFq+o4SlpM99q+8rshC3JoSoTr+6mraNtrwNoXgDPry05qPv0llLOiB86gHKAyX380lAMMLu7Q39QJinwgE3P1361qS45ijb02fSlPDFBvLbq6h3KlCksS+Nu/Cpiaa75WYy8piH58E0jVIRcsL4O+MCnKOuJroPs7QPNuUnfrk0Tg/wbQlxFDJbBXAwdBjBk7h+bAq18Cb8dIQ+cB+ib3fwOon//az9J+736azyGT0Qi0SQfoJuj8fMWvmZ9BuTN7Z0rdN26dtU9SrkzgDMC01AdX0GyaA0gfs+Q270otJ93fUf+ADJolDTuO/Ur3/KiyZKZKSc7uXajlp7TO46hrpnFLGvlTnZmjZTKgn0pyd/S/9Lc2nD5bggDq/gwomUFZyClJvTQhqCUw7aL6/se3gKiFwH+eB6Lel3Lish9JuTYWduprtJXm0Bxo3Ye2n96r+uhIVel3pCH5LQIBy0bSsfavSl9WFBTdswB1EVrY0fa1/epzd1XU/dtJpUtMlwTp2K+lfCO/cfQaLy8F3joKhF6QWi3lRfRlZMurNHVDHcJBEGOMOPvQRHRTf5OGYENQP/9hlQUsywqCAPpwbtmDboKzzgBzLwNDvqDh+L0W0MitwJn0bdXKUfP6qo4KK4u9m3ri6ouz9BcAVfi67tIHSl6G+ozcOenArx8C3wwBDi6ilouc9IqfTwj1lpjSXWEKNk7A7LPAnDiV3101eAVLeV9Pk4AL3+h2vRA0kjAmkloTFS0kipYgMytpdGF1dZtMicEAcGELcGGreovMqTXA5R9o29yWpoxoqZJ8X/iMunS+6EwtoMc/pZY8AOj6hvpIvLKozpWlCJ6qQ3WpjDYvqR+zc1Uvu1Mb9dFe5lZAu1doOz9DyuOzc6N8r/L4DpMmgLyyq/xZp1UV5lK9ARSYdZ+rfrxxS+DNg0DP+VC2liWeAL7sBhz/t26TtBoQrx3GGFPn0RWYcpi+Vf+2TEpkBQCbpkCzzto9j6On1D1UWnERdZfdPETDyq0cgRffrm7J1Q1YQblBTbypHDW1TlL3UCB+Oygxey3wwlvA5e9pVl7Fshd3fwdiSlotXNpTLpT/G5SsrpD7BNg/T32kT8tSuT+l6XMB1b4fSXlgv68C/CZUPpqrKJ8ScWPWq7e8NHmOWgoU3VXObctu0aoKGyf6/SpyU36eTXU2dA2NRDvyT+nc4etpEsyAt2gl+7MbKHApyqPAJ1YlH8rETCXJvgLtBlE3ZUE2BayuvkDA9Kr/LlSHxqt1MZfoPFYaAv/iTM3X8R2u2YXpO7zi+rZyoODp6h6ab+rWESm/qDxx22iRYYBaqMrqfjU1p1bF1r2BH6fTjNaFObTW3/nNlI/WaUytLvwrE6I25n+vHzIzM+Hg4ICMjAzY29fiytGM1Zb0O8DPodJszn5/B4ZpMaeQsds5QZqbxaEFrdNWKRm1snWbQsHG3reBTJWug26TgUGra3bRS9X30Xsh5aTIi+inKJ9asnIe009mCgVAig/GinQeRwGJvhTmUsCo2v1jYUd1peiu6b0IeOl9zWuzH1IO0NmvqFVIodNY4LVIzfPLcnCRFNQCQOuXgGHrdO/eLS4CVraiMls7AQtuawYIQlB5haAu3tLHi/KBz7yl9w0AU49qTq1Q2o0oYEdJK2b7YcDoClr/iguBNf5ARsmQ+uknALdOFT9/TjrNYB77lTTyDaBu8AGfVh7g60jbz+86GwTl5+djyZIl2Lp1K548eYJOnTph2bJlePnlyleHTklJwbx583Do0CHI5XK89NJLCA8PR+vWujW/chDEGOhme3kXrRsWNBto5Fz5NcYuOZaSwEvrMJJGlz2+TctcJP4B3L9E+SzlsXKkbkXfYYYqbfkeXgPWBUGZJKwLNz8q862jmutZ9V+mPrJLXxJ+pakQskrNedVuMK0FV1GLQ046tSbFfkXdZpP2q7fMVaQon1qcFN1DAP3eBodTK4w2gWthLrWu/DKfHncYAYz8P+1ev7Q9M6SA0NELeOdi5WUoLgT+0w7I+YtawQJnAD3mAbZNNc+N3wHsLclF8n4Z+Psu7cv2KIG61xNUJrhs2ZPqW4/qfRA0btw47Nq1C3PnzsVzzz2HzZs3IzY2FseOHUOPHj3KvS47Oxv+/v7IyMjAu+++C3Nzc4SHh0MIgfj4eDRp0kTrMnAQxBirsk2DaAI6gJbzeGVV2UOUsx/RLNbnNknfrBVa9aIJDh2aG7685flxmvYjxGSm1N0UOBNoESB98KbfoW6nyz9QPtDrP9E6doaQ+5Ryr+JLZi93fp4WDra00/45hKhai9vtY9SCl5Uq7XN+nqaQ6DxWM6AozKNk6ss/UI6YauvNq2vVp57Qxc3DwPaSROie89UT3Sty5J80xYWCuS11uQXPpqCu4BmVcetwaYmWNw9SDpmubh8DDn1EyfLTf6+8JUlH9ToIOnv2LAIDA/HZZ59h/nyKivPy8tChQwe4uLjg1KlT5V67cuVKLFy4EGfPnsULL1AS4fXr19GhQwe89957WLFihdbl4CCIMVZl6XcoAdStMy17UN4wawV5MbWanPuaEogDplFCdy3mSwCgBO/olcCzkhYCExP619SCumxsnACbJvTj3I6Sd+uCxD9ofpouEwFb7b/8VltOOq1PV3ohUxMzSv5v5Aqk3wYe3ynpJi3jI7ipD01oWVlSdnmEoCHpmSk0JYTqCLOKFBcCRz+hhXZV5/4xMafWStVuLICSrSdHocrkxZRAr+euMKCeB0HvvfceVq9ejfT0dLXCf/rpp/jggw+QlJSEFi3KngQsICAAAAVSqgYMGIDbt2/j1q1bWpeDgyDGGGM6E4IWJD0TQbNZa8PCjlrROo6iFkB9JY5XRWYaBVHnN0tL5ZRlwm7guTK6fesAbT+/6+TosLi4OPj4+GgUXBHgxMfHlxkEyeVyXLp0CZMnT9Y4FhAQgEOHDiErKwt2djo0izLGGGO6kMkoH8p3GPDXTcr1ubiDJiJVsHKgIe5NfWgkls+A6i0Zo0/2bsCgVTTS8cRq6rIzs6JJUi1Lflr2qLMBkC7qZBCUlpYGNzc3jf2KfampqRrHACA9PR35+fmVXtu2bdsyr8/Pz0d+fr7ycWZm3V7zhDHGWB3X9Dng5U+AvotpWggTUwp+bJxqdqRfVTh6AkM+r+1SGFSdnCwxNzcXlpaWGvutrKyUx8u7DkCVrgWou83BwUH5U16XG2OMMaYTUzPAK4gSxm2b1P0AyEjUySDI2tparUVGIS8vT3m8vOsAVOlaAHj//feRkZGh/ElO1mZuD8YYY4zVR3WyO8zNzQ0pKZrri6Sl0dwP7u5lT0Dl5OQES0tL5Xm6XAtQC1JZrUiMMcYYa3jqZEuQn58fEhISNHJyYmJilMfLYmJigo4dO+LcuXMax2JiYtC6dWtOimaMMcYYgDoaBI0cORLFxcXYsGGDcl9+fj42bdqEwMBAZa5OUlISrl+/rnFtbGysWiB048YN/Pbbbxg1alTNvAHGGGOM1Xl1cp4gABg9ejT27NmDefPmwdvbG9988w3Onj2Lo0ePolevXgCAPn36IDo6GqpvISsrC126dEFWVhbmz58Pc3NzrF69GsXFxYiPj4ezs/ZT/vM8QYwxxlj9U6/nCQKALVu2YPHixWprh+3fv18ZAJXHzs4Ox48fx7x587Bs2TLI5XL06dMH4eHhOgVAjDHGGGvY6mxLUF3ALUGMMcZY/aPt53edzAlijDHGGDM0DoIYY4wxZpQ4CGKMMcaYUeIgiDHGGGNGiYMgxhhjjBklDoIYY4wxZpTq7DxBdYFi9oDSy3cwxhhjrO5SfG5XNgsQB0EVyMrKAgDlMh2MMcYYqz+ysrLg4OBQ7nGeLLECcrkcqampsLOzg0wmq/LzZGZmokWLFkhOTuZJFw2M67rmcF3XHK7rmsN1XXMMWddCCGRlZcHd3R0mJuVn/nBLUAVMTEzg4eGht+ezt7fn/1Q1hOu65nBd1xyu65rDdV1zDFXXFbUAKXBiNGOMMcaMEgdBjDHGGDNKHATVAEtLS3z88cewtLSs7aI0eFzXNYfruuZwXdccruuaUxfqmhOjGWOMMWaUuCWIMcYYY0aJgyDGGGOMGSUOghhjjDFmlDgIYowxxphR4iDIgPLz87Fw4UK4u7vD2toagYGBOHz4cG0Xq96KjY3F7Nmz4evrC1tbW3h6emL06NFISEjQOPfatWsICQlBo0aN4OTkhIkTJ+LRo0e1UOqGY/ny5ZDJZOjQoYPGsVOnTqFHjx6wsbFBs2bNEBoaiuzs7FooZf124cIFDB06FE5OTrCxsUGHDh2wZs0atXO4rqvv5s2bGDt2LDw8PGBjY4N27dph6dKlyMnJUTuP61p72dnZ+PjjjxESEgInJyfIZDJs3ry5zHO1vT/L5XKsXLkSrVq1gpWVFTp16oQdO3bot+CCGczYsWOFmZmZmD9/voiMjBRBQUHCzMxMnDhxoraLVi+NGDFCNGvWTMyZM0ds3LhRhIWFCVdXV2FraysuX76sPC85OVk0bdpUtGnTRnzxxRdi+fLlonHjxqJz584iPz+/Ft9B/ZWcnCxsbGyEra2t8PX1VTsWFxcnrKysRJcuXURERIT48MMPhaWlpQgJCaml0tZPv/76q7CwsBCBgYFi9erVYsOGDWLhwoViwYIFynO4rqsvKSlJODo6Ci8vL/Hpp5+KyMhIMWnSJAFADB06VHke17Vu7t69KwAIT09P0adPHwFAbNq0SeM8Xe7PixYtEgDEW2+9JTZs2CAGDRokAIgdO3bordwcBBlITEyMACA+++wz5b7c3FzRpk0bERQUVIslq79Onjyp8Z8kISFBWFpaigkTJij3zZw5U1hbW4t79+4p9x0+fFgAEJGRkTVW3oZkzJgxom/fvqJ3794aQdDAgQOFm5ubyMjIUO7buHGjACB+/fXXmi5qvZSRkSFcXV3F8OHDRXFxcbnncV1X3/LlywUAceXKFbX9r7/+ugAg0tPThRBc17rKy8sTaWlpQgghYmNjyw2CtL0///nnn8Lc3FzMmjVLuU8ul4uePXsKDw8PUVRUpJdycxBkIAsWLBCmpqZq/4GEEGLFihUCgEhKSqqlkjU8/v7+wt/fX/nYxcVFjBo1SuM8Hx8f0a9fv5osWoMQHR0tTE1NxaVLlzSCoIyMDGFmZqbWWiGEEPn5+aJRo0ZiypQpNV3ceikiIkIAEP/73/+EEEJkZ2drBENc1/qxcOFCAUA8evRIY7+JiYnIzs7muq6mioIgbe/Pa9euFQDE1atX1c779ttvBQC99ahwTpCBxMXFwcfHR2NRuICAAABAfHx8LZSq4RFC4MGDB2jatCkAICUlBQ8fPkS3bt00zg0ICEBcXFxNF7FeKy4uxpw5czB16lR07NhR4/jly5dRVFSkUd8WFhbw8/Pj+tbSkSNHYG9vj5SUFLRt2xaNGjWCvb09Zs6ciby8PABc1/rSp08fAMCUKVMQHx+P5ORkfPfdd4iIiEBoaChsbW25rg1El/tzXFwcbG1t8fzzz2ucpziuDxwEGUhaWhrc3Nw09iv2paam1nSRGqTt27cjJSUFY8aMAUD1DqDcuk9PT0d+fn6NlrE+W79+Pe7du4ewsLAyj1dW3/x3rp2bN2+iqKgIr776KgYMGIDdu3dj8uTJWL9+Pd58800AXNf6EhISgrCwMBw+fBhdunSBp6cnxo4dizlz5iA8PBwA17Wh6HJ/TktLg6urK2QymcZ5gP4+Q8308ixMQ25ubpnroVhZWSmPs+q5fv06Zs2ahaCgILzxxhsApHqtrO55XaDKPX78GEuWLMHixYvh7Oxc5jmV1Tf/nWsnOzsbOTk5mDFjhnI02GuvvYaCggJERkZi6dKlXNd61LJlS/Tq1QsjRoxAkyZNcODAAaxYsQLNmjXD7Nmzua4NRJf7c019hnIQZCDW1tZltjgomratra1rukgNyv379zFo0CA4ODhg165dMDU1BSDVK9d99X300UdwcnLCnDlzyj2nsvrmutaOop7GjRuntn/8+PGIjIzE6dOnYWNjA4Drurp27tyJadOmISEhAR4eHgAo4JTL5Vi4cCHGjRvHf9cGosv9uaY+Q7k7zEDc3NyUTX+qFPvc3d1rukgNRkZGBgYOHIinT58iKipKrS4VTaXl1b2TkxO3Amnh5s2b2LBhA0JDQ5GamorExEQkJiYiLy8PhYWFSExMRHp6eqX1zX/n2lHUk6urq9p+FxcXAMCTJ0+4rvVk3bp16NKlizIAUhg6dChycnIQFxfHdW0gutyf3dzccP/+fYhSa7zr+zOUgyAD8fPzQ0JCAjIzM9X2x8TEKI8z3eXl5WHIkCFISEjA/v370b59e7XjzZs3h7OzM86dO6dx7dmzZ7netZSSkgK5XI7Q0FC0atVK+RMTE4OEhAS0atUKS5cuRYcOHWBmZqZR3wUFBYiPj+f61lLXrl0BUL2rUuQ9ODs7c13ryYMHD1BcXKyxv7CwEABQVFTEdW0gutyf/fz8kJOTg2vXrqmdp/fPUL2MMWMazpw5ozFPUF5envD29haBgYG1WLL6q6ioSAwdOlSYmZmJAwcOlHvejBkzhLW1tdo0BEeOHBEARERERE0Utd579OiR2LNnj8aPr6+v8PT0FHv27BGXLl0SQggREhIi3NzcRGZmpvL6r776SgAQBw8erK23UK9cuHBBABDjx49X2z9u3DhhZmYmUlJShBBc1/owePBgYWFhIW7cuKG2f9iwYcLExITrWg8qGiKv7f05OTm53HmCmjdvzvME1QejRo1SzjURGRkpgoODhZmZmYiOjq7totVL77zzjgAghgwZIrZu3arxo5CUlCSaNGki2rRpI9asWSNWrFghGjduLDp27Cjy8vJq8R3Uf2VNlnj+/HlhaWmpNrOulZWV6N+/fy2Vsn6aPHmyACBGjx4t1q5dK0aNGiUAiPfff195Dtd19SnmvXJxcRFLly4Va9euFQMHDhQAxNSpU5XncV3r7r///a8ICwsTM2fOFADEa6+9JsLCwkRYWJh4+vSpEEK3+/OCBQsEADFt2jSxceNG5YzR27dv11uZOQgyoNzcXDF//nzRrFkzYWlpKV544QURFRVV28Wqt3r37i0AlPuj6sqVK6J///7CxsZGODo6igkTJoj79+/XUskbjrKCICGEOHHihAgODhZWVlbC2dlZzJo1S+0bNKtcQUGB+Oc//ym8vLyEubm58Pb2FuHh4RrncV1XX0xMjBg4cKBo1qyZMDc3Fz4+PmL58uWisLBQ7Tyua914eXmVe3++e/eu8jxt78/FxcVixYoVwsvLS1hYWAhfX1+xbds2vZZZJkSprCPGGGOMMSPAidGMMcYYM0ocBDHGGGPMKHEQxBhjjDGjxEEQY4wxxowSB0GMMcYYM0ocBDHGGGPMKHEQxBhjjDGjxEEQY4wxxowSB0GMMYNr2bIlZDJZpT+bN2+u7aJqTVFmxlj9ZVbbBWCMGY/u3bvD29u73OMVHWOMMX3jIIgxVmOmTp2KSZMm1XYxGGMMAHeHMcYYY8xIcRDEGKuTVHNuNm7ciK5du8LW1haOjo545ZVXcObMmXKvTU9PxwcffABfX1/Y2NjAzs4OXbt2xcqVK5Gbm1vudSkpKViwYAE6duwIOzs72NrawsfHB5MmTcKpU6fKvW737t3o0aMH7O3tYWtri+7du+OXX34p89y0tDS888478PHxgZWVFWxsbNCiRQv069cPq1at0rJ2GGP6wKvIM8YMrmXLlrh37x42bdqkdXeYIgCaN28ePv/8c3Tv3h0tWrTA5cuXceXKFZiZmeH777/H8OHD1a67c+cO+vbti3v37sHZ2Rm9evVCYWEhjh07hqysLPj7++PIkSNo3Lix2nVHjx7FyJEj8fTpU7i4uCAoKAgWFhZITExEfHw8xo8fr5a4rSjfkiVLEBYWhuDgYHh4eOD69eu4ePEiZDIZdu/erVa++/fvo2vXrkhNTYWnpye6dOkCKysrpKam4urVqyguLsbTp091r2DGWNUIxhgzMC8vLwFAbNq0SetrAAgAwtraWhw9elTt2MqVKwUA4eDgIB48eKB2LDAwUAAQQ4cOFdnZ2cr9Dx8+FP7+/gKAGD9+vNo1SUlJwsHBQQAQixYtEvn5+WrHHzx4IE6cOFFm+RwdHcWZM2fUjn388ccCgPDx8VHb/8knnwgAYtq0aUIul6sdKygoEEeOHNGiZhhj+sJBEGPM4BRBUGU/T548UV6j2Dd37twyn7Nbt24CgFi+fLly34kTJwQAYWNjI+7fv69xzblz5wQAYWJiIpKTk5X7586dKwCIIUOGaP2eFOVbs2aNxrG8vDxlUJWUlKTc//bbbwsA4scff9T6dRhjhsOjwxhjNaayIfIWFhYa+954440yz3399ddx7tw5HD9+HB988AEA4Pjx4wCAkJAQuLq6alzTtWtXdO7cGRcvXkR0dDQmTJgAAIiKigIATJs2Taf3AwBDhgzR2GdpaYnWrVsjLi4OKSkpaNGiBQAgICAA69atw6JFiyCEQP/+/dGoUSOdX5Mxph8cBDHGakxVhsi3atWqwv1//vmncl9KSkqF1wBAmzZtcPHiReW5AHDv3j0AQLt27XQqGwB4enqWud/e3h4AkJeXp9w3ceJEHD58GNu3b8eIESNgamqK9u3bo0ePHhg5ciT69u2r8+szxqqOR4cxxuo1UctjO0xMtL+NmpiYYNu2bbh69SpWrlyJwYMHIy0tDREREejXrx+GDh2K4uJiA5aWMaaKgyDGWJ129+7dMvcnJiYCADw8PJT7mjdvDoBGiJVHcUxxLiC15ly/fr1aZdVW+/btsWDBAuzduxcPHz7EkSNH4OLign379mHLli01UgbGGAdBjLE6buvWrRXu79Onj3KfYjsqKgoPHjzQuCYuLg7x8fEwMTFBr169lPtDQkIA0HxENU0mk6Ffv34YP348ACA+Pr7Gy8CYseIgiDFWp0VERCgTnhXCw8Nx9uxZ2NnZYcqUKcr9PXr0QGBgIHJzczF9+nTk5OQoj/3111+YPn06AGDs2LHKZGUA+Mc//gE7Ozv8/PPP+Oijj1BYWKj2eg8fPsQff/xR7feyZcsWnD9/XmN/VlaW8j16eXlV+3UYY9rhyRIZYwanmCyxstFh/fv3V7aIKCYjnDt3Lr744gv07NkTzZs3x5UrV3D58mWYmppi586dGDlypNpzqE6W6OLiojZZYmZmZrmTJR46dAgjR45EVlYWXF1dERQUBHNzc9y7dw9xcXHlTpZY3i20T58+iI6OxrFjx5QtVMOGDcNPP/0Ed3d3+Pn5oXHjxnjy5AlOnjyJjIwMdOjQAadOnYKdnZ1O9csYqxoeHcYYqzEnT57EyZMnyz3u6OioDIIUwsPD0bZtW0RGRiI2Nhbm5uYICQnB4sWLERwcrPEcrVu3xoULF7Bq1Srs3bsX+/fvh4mJCdq2bYsxY8YgNDQU1tbWGtf1798fV65cwerVqxEVFYWoqCiYmZnB3d0dEydOxFtvvVXt9//uu++iVatWOHXqFC5cuID09HQ4OTmhffv2GD9+PN58803Y2tpW+3UYY9rhliDGWJ1UWUsLY4xVF+cEMcYYY8wocRDEGGOMMaPEQRBjjDHGjBInRjPG6iTOBWKMGRq3BDHGGGPMKHEQxBhjjDGjxEEQY4wxxowSB0GMMcYYM0ocBDHGGGPMKHEQxBhjjDGjxEEQY4wxxowSB0GMMcYYM0ocBDHGGGPMKP0/DKCeVTLLLTQAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.title(\"Adversarial Training Curves\", fontsize=20)\n",
-    "plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_gen_loss_list, color=\"C0\", linewidth=2.0, label=\"Generator\")\n",
-    "plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_disc_loss_list, color=\"C1\", linewidth=2.0, label=\"Discriminator\")\n",
-    "plt.yticks(fontsize=12)\n",
-    "plt.xticks(fontsize=12)\n",
-    "plt.xlabel(\"Epochs\", fontsize=16)\n",
-    "plt.ylabel(\"Loss\", fontsize=16)\n",
-    "plt.legend(prop={\"size\": 14})\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "21bbecae",
-   "metadata": {},
-   "source": [
-    "### Visualise some reconstruction images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "caf2b1e1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 800x600 with 15 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# get the first 5 examples to plot\n",
-    "n_evaluations = 5\n",
-    "\n",
-    "fig, axs = plt.subplots(nrows=n_evaluations, ncols=3, constrained_layout=True, figsize=(8, 6))\n",
-    "\n",
-    "\n",
-    "# Remove ticks\n",
-    "for ax in axs.flatten():\n",
-    "    ax.set_xticks([])\n",
-    "    ax.set_yticks([])\n",
-    "\n",
-    "\n",
-    "for image_n in range(n_evaluations):\n",
-    "    axs[image_n, 0].imshow(\n",
-    "        intermediary_images[image_n][0, ..., intermediary_images[image_n].shape[3] // 2].cpu(), cmap=\"gray\"\n",
-    "    )\n",
-    "    axs[image_n, 1].imshow(\n",
-    "        intermediary_images[image_n][0, :, intermediary_images[image_n].shape[2] // 2, ...].cpu().rot90(), cmap=\"gray\"\n",
-    "    )\n",
-    "    axs[image_n, 2].imshow(\n",
-    "        intermediary_images[image_n][0, intermediary_images[image_n].shape[1] // 2, ...].cpu().rot90(), cmap=\"gray\"\n",
-    "    )\n",
-    "    axs[image_n, 0].set_ylabel(f\"Epoch {val_samples[image_n]:.0f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "dd03417f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(nrows=1, ncols=2)\n",
-    "ax[0].imshow(images[0, channel, ..., images.shape[2] // 2].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, channel, ..., reconstruction.shape[2] // 2].detach().cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
-    "ax[1].axis(\"off\")\n",
-    "ax[1].title.set_text(\"Reconstruction\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "292506bf",
-   "metadata": {},
-   "source": [
-    "## Clean up data directory\n",
-    "\n",
-    "Remove directory if a temporary storage was used"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "25551b82",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if directory is None:\n",
-    "    shutil.rmtree(root_dir)"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,py"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+    "nbformat": 4,
+    "nbformat_minor": 5
 }
diff --git a/tutorials/generative/3d_autoencoderkl/3d_autoencoderkl_tutorial.py b/tutorials/generative/3d_autoencoderkl/3d_autoencoderkl_tutorial.py
index 8d94dedf..59011823 100644
--- a/tutorials/generative/3d_autoencoderkl/3d_autoencoderkl_tutorial.py
+++ b/tutorials/generative/3d_autoencoderkl/3d_autoencoderkl_tutorial.py
@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: light
 #       format_version: '1.5'
-#       jupytext_version: 1.14.4
+#       jupytext_version: 1.14.1
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
@@ -91,7 +91,7 @@
         transforms.LoadImaged(keys=["image"]),
         transforms.EnsureChannelFirstd(keys=["image"]),
         transforms.Lambdad(keys="image", func=lambda x: x[channel, :, :, :]),
-        transforms.AddChanneld(keys=["image"]),
+        transforms.EnsureChannelFirstd(keys=["image"], channel_dim="no_channel"),
         transforms.EnsureTyped(keys=["image"]),
         transforms.Orientationd(keys=["image"], axcodes="RAS"),
         transforms.Spacingd(keys=["image"], pixdim=(2.4, 2.4, 2.2), mode=("bilinear")),
@@ -138,7 +138,7 @@
         transforms.LoadImaged(keys=["image"]),
         transforms.EnsureChannelFirstd(keys=["image"]),
         transforms.Lambdad(keys="image", func=lambda x: x[channel, :, :, :]),
-        transforms.AddChanneld(keys=["image"]),
+        transforms.EnsureChannelFirstd(keys=["image"], channel_dim="no_channel"),
         transforms.EnsureTyped(keys=["image"]),
         transforms.Orientationd(keys=["image"], axcodes="RAS"),
         transforms.Spacingd(keys=["image"], pixdim=(2.4, 2.4, 2.2), mode=("bilinear")),
diff --git a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb
index 1174e567..6c1af36a 100644
--- a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb
+++ b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.ipynb
@@ -1,976 +1,984 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fa57bdf5",
-   "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": "6286986e",
-   "metadata": {},
-   "source": [
-    "# Denoising Diffusion Probabilistic Model on 3D data\n",
-    "\n",
-    "This tutorial illustrates how to use MONAI for training a denoising diffusion probabilistic model (DDPM)[1] to create synthetic 3D images.\n",
-    "\n",
-    "[1] - [Ho et al. \"Denoising Diffusion Probabilistic Models\"](https://arxiv.org/abs/2006.11239)\n",
-    "\n",
-    "\n",
-    "## Setup environment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f96b6f31",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!python -c \"import monai\" || pip install -q \"monai-weekly[nibabel, tqdm]\"\n",
-    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cbc01d24",
-   "metadata": {},
-   "source": [
-    "## Setup imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "cdea37d5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MONAI version: 1.2.dev2304\n",
-      "Numpy version: 1.23.5\n",
-      "Pytorch version: 1.13.1+cu117\n",
-      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
-      "MONAI rev id: 9a57be5aab9f2c2a134768c0c146399150e247a0\n",
-      "MONAI __file__: /media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.10/site-packages/monai/__init__.py\n",
-      "\n",
-      "Optional dependencies:\n",
-      "Pytorch Ignite version: 0.4.10\n",
-      "ITK version: 5.3.0\n",
-      "Nibabel version: 4.0.2\n",
-      "scikit-image version: 0.19.3\n",
-      "Pillow version: 9.3.0\n",
-      "Tensorboard version: 2.11.0\n",
-      "gdown version: 4.6.0\n",
-      "TorchVision version: 0.14.1+cu117\n",
-      "tqdm version: 4.64.1\n",
-      "lmdb version: 1.4.0\n",
-      "psutil version: 5.9.4\n",
-      "pandas version: 1.5.3\n",
-      "einops version: 0.6.0\n",
-      "transformers version: 4.21.3\n",
-      "mlflow version: 2.1.1\n",
-      "pynrrd version: 1.0.0\n",
-      "\n",
-      "For details about installing the optional dependencies, please visit:\n",
-      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import tempfile\n",
-    "import time\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "import torch.nn.functional as F\n",
-    "from monai.apps import DecathlonDataset\n",
-    "from monai.config import print_config\n",
-    "from monai.data import DataLoader\n",
-    "from monai.transforms import AddChanneld, CenterSpatialCropd, Compose, Lambdad, LoadImaged, Resized, ScaleIntensityd\n",
-    "from monai.utils import set_determinism\n",
-    "from torch.cuda.amp import GradScaler, autocast\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "from generative.inferers import DiffusionInferer\n",
-    "from generative.networks.nets import DiffusionModelUNet\n",
-    "from generative.networks.schedulers import DDPMScheduler, DDIMScheduler\n",
-    "\n",
-    "print_config()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "50e37a43",
-   "metadata": {},
-   "source": [
-    "## Setup data directory\n",
-    "\n",
-    "You can specify a directory with the MONAI_DATA_DIRECTORY environment variable.\n",
-    "\n",
-    "This allows you to save results and reuse downloads.\n",
-    "\n",
-    "If not specified a temporary directory will be used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "c38b4c33",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/media/walter/Storage/Projects/GTC_2023_presentation/data\n"
-     ]
-    }
-   ],
-   "source": [
-    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
-    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
-    "print(root_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "41af1391",
-   "metadata": {},
-   "source": [
-    "## Set deterministic training for reproducibility"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "515d8583",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "set_determinism(42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "29d8c601",
-   "metadata": {},
-   "source": [
-    "## Setup Decathlon Dataset and training and validation data loaders\n",
-    "\n",
-    "In this tutorial, we will use the 3D T1 weighted brain images from the [2016 and 2017 Brain Tumor Segmentation (BraTS) challenges](https://www.med.upenn.edu/sbia/brats2017/data.html). This dataset can be easily downloaded using the [DecathlonDataset](https://docs.monai.io/en/stable/apps.html#monai.apps.DecathlonDataset) from MONAI (`task=\"Task01_BrainTumour\"`). To load the training and validation images, we are using the `data_transform` transformations that are responsible for the following:\n",
-    "\n",
-    "1. `LoadImaged`:  Loads the brain images from files.\n",
-    "2. `Lambdad`: Choose channel 1 of the image, which is the T1-weighted image.\n",
-    "3. `AddChanneld`: Add the channel dimension of the input data.\n",
-    "4. `ScaleIntensityd`: Apply a min-max scaling in the intensity values of each image to be in the `[0, 1]` range.\n",
-    "5. `CenterSpatialCropd`: Crop the background of the images using a roi of size `[160, 200, 155]`.\n",
-    "6. `Resized`: Resize the images to a volume with size `[32, 40, 32]`.\n",
-    "\n",
-    "For the data loader, we are using mini-batches of 8 images, which consumes about 21GB of GPU memory during training. Please, reduce this value to run on smaller GPUs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "f640d7ac",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<class 'monai.transforms.utility.array.AddChannel'>: Class `AddChannel` has been deprecated since version 0.8. please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead.\n"
-     ]
-    }
-   ],
-   "source": [
-    "data_transform = Compose(\n",
-    "    [\n",
-    "        LoadImaged(keys=[\"image\"]),\n",
-    "        Lambdad(keys=\"image\", func=lambda x: x[:, :, :, 1]),\n",
-    "        AddChanneld(keys=[\"image\"]),\n",
-    "        ScaleIntensityd(keys=[\"image\"]),\n",
-    "        CenterSpatialCropd(keys=[\"image\"], roi_size=[160, 200, 155]),\n",
-    "        Resized(keys=[\"image\"], spatial_size=(32, 40, 32)),\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "ddd61e60",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2023-03-20 14:39:26,630 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n",
-      "2023-03-20 14:39:26,630 - INFO - File exists: /media/walter/Storage/Projects/GTC_2023_presentation/data/Task01_BrainTumour.tar, skipped downloading.\n",
-      "2023-03-20 14:39:26,631 - INFO - Non-empty folder exists in /media/walter/Storage/Projects/GTC_2023_presentation/data/Task01_BrainTumour, skipped extracting.\n"
-     ]
+    "cells": [
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "fa57bdf5",
+            "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": "6286986e",
+            "metadata": {},
+            "source": [
+                "# Denoising Diffusion Probabilistic Model on 3D data\n",
+                "\n",
+                "This tutorial illustrates how to use MONAI for training a denoising diffusion probabilistic model (DDPM)[1] to create synthetic 3D images.\n",
+                "\n",
+                "[1] - [Ho et al. \"Denoising Diffusion Probabilistic Models\"](https://arxiv.org/abs/2006.11239)\n",
+                "\n",
+                "\n",
+                "## Setup environment"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 1,
+            "id": "f96b6f31",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "!python -c \"import monai\" || pip install -q \"monai-weekly[nibabel, tqdm]\"\n",
+                "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
+                "%matplotlib inline"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "cbc01d24",
+            "metadata": {},
+            "source": [
+                "## Setup imports"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 13,
+            "id": "cdea37d5",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "MONAI version: 1.2.dev2304\n",
+                        "Numpy version: 1.23.5\n",
+                        "Pytorch version: 1.13.1+cu117\n",
+                        "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
+                        "MONAI rev id: 9a57be5aab9f2c2a134768c0c146399150e247a0\n",
+                        "MONAI __file__: /media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.10/site-packages/monai/__init__.py\n",
+                        "\n",
+                        "Optional dependencies:\n",
+                        "Pytorch Ignite version: 0.4.10\n",
+                        "ITK version: 5.3.0\n",
+                        "Nibabel version: 4.0.2\n",
+                        "scikit-image version: 0.19.3\n",
+                        "Pillow version: 9.3.0\n",
+                        "Tensorboard version: 2.11.0\n",
+                        "gdown version: 4.6.0\n",
+                        "TorchVision version: 0.14.1+cu117\n",
+                        "tqdm version: 4.64.1\n",
+                        "lmdb version: 1.4.0\n",
+                        "psutil version: 5.9.4\n",
+                        "pandas version: 1.5.3\n",
+                        "einops version: 0.6.0\n",
+                        "transformers version: 4.21.3\n",
+                        "mlflow version: 2.1.1\n",
+                        "pynrrd version: 1.0.0\n",
+                        "\n",
+                        "For details about installing the optional dependencies, please visit:\n",
+                        "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
+                        "\n"
+                    ]
+                }
+            ],
+            "source": [
+                "import os\n",
+                "import tempfile\n",
+                "import time\n",
+                "\n",
+                "import matplotlib.pyplot as plt\n",
+                "import numpy as np\n",
+                "import torch\n",
+                "import torch.nn.functional as F\n",
+                "from monai.apps import DecathlonDataset\n",
+                "from monai.config import print_config\n",
+                "from monai.data import DataLoader\n",
+                "from monai.transforms import (\n",
+                "    EnsureChannelFirstd,\n",
+                "    CenterSpatialCropd,\n",
+                "    Compose,\n",
+                "    Lambdad,\n",
+                "    LoadImaged,\n",
+                "    Resized,\n",
+                "    ScaleIntensityd,\n",
+                ")\n",
+                "from monai.utils import set_determinism\n",
+                "from torch.cuda.amp import GradScaler, autocast\n",
+                "from tqdm import tqdm\n",
+                "\n",
+                "from generative.inferers import DiffusionInferer\n",
+                "from generative.networks.nets import DiffusionModelUNet\n",
+                "from generative.networks.schedulers import DDPMScheduler, DDIMScheduler\n",
+                "\n",
+                "print_config()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "50e37a43",
+            "metadata": {},
+            "source": [
+                "## Setup data directory\n",
+                "\n",
+                "You can specify a directory with the MONAI_DATA_DIRECTORY environment variable.\n",
+                "\n",
+                "This allows you to save results and reuse downloads.\n",
+                "\n",
+                "If not specified a temporary directory will be used."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 3,
+            "id": "c38b4c33",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "/media/walter/Storage/Projects/GTC_2023_presentation/data\n"
+                    ]
+                }
+            ],
+            "source": [
+                "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
+                "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
+                "print(root_dir)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "41af1391",
+            "metadata": {},
+            "source": [
+                "## Set deterministic training for reproducibility"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 4,
+            "id": "515d8583",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "set_determinism(42)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "29d8c601",
+            "metadata": {},
+            "source": [
+                "## Setup Decathlon Dataset and training and validation data loaders\n",
+                "\n",
+                "In this tutorial, we will use the 3D T1 weighted brain images from the [2016 and 2017 Brain Tumor Segmentation (BraTS) challenges](https://www.med.upenn.edu/sbia/brats2017/data.html). This dataset can be easily downloaded using the [DecathlonDataset](https://docs.monai.io/en/stable/apps.html#monai.apps.DecathlonDataset) from MONAI (`task=\"Task01_BrainTumour\"`). To load the training and validation images, we are using the `data_transform` transformations that are responsible for the following:\n",
+                "\n",
+                "1. `LoadImaged`:  Loads the brain images from files.\n",
+                "2. `Lambdad`: Choose channel 1 of the image, which is the T1-weighted image.\n",
+                "3. `EnsureChannelFirstd`: Add the channel dimension of the input data.\n",
+                "4. `ScaleIntensityd`: Apply a min-max scaling in the intensity values of each image to be in the `[0, 1]` range.\n",
+                "5. `CenterSpatialCropd`: Crop the background of the images using a roi of size `[160, 200, 155]`.\n",
+                "6. `Resized`: Resize the images to a volume with size `[32, 40, 32]`.\n",
+                "\n",
+                "For the data loader, we are using mini-batches of 8 images, which consumes about 21GB of GPU memory during training. Please, reduce this value to run on smaller GPUs."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 5,
+            "id": "f640d7ac",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "<class 'monai.transforms.utility.array.AddChannel'>: Class `AddChannel` has been deprecated since version 0.8. please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead.\n"
+                    ]
+                }
+            ],
+            "source": [
+                "data_transform = Compose(\n",
+                "    [\n",
+                "        LoadImaged(keys=[\"image\"]),\n",
+                "        Lambdad(keys=\"image\", func=lambda x: x[:, :, :, 1]),\n",
+                "        EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n",
+                "        ScaleIntensityd(keys=[\"image\"]),\n",
+                "        CenterSpatialCropd(keys=[\"image\"], roi_size=[160, 200, 155]),\n",
+                "        Resized(keys=[\"image\"], spatial_size=(32, 40, 32)),\n",
+                "    ]\n",
+                ")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 6,
+            "id": "ddd61e60",
+            "metadata": {
+                "lines_to_next_cell": 2
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "2023-03-20 14:39:26,630 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n",
+                        "2023-03-20 14:39:26,630 - INFO - File exists: /media/walter/Storage/Projects/GTC_2023_presentation/data/Task01_BrainTumour.tar, skipped downloading.\n",
+                        "2023-03-20 14:39:26,631 - INFO - Non-empty folder exists in /media/walter/Storage/Projects/GTC_2023_presentation/data/Task01_BrainTumour, skipped extracting.\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Loading dataset: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 388/388 [03:32<00:00,  1.83it/s]\n"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "2023-03-20 14:43:06,832 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n",
+                        "2023-03-20 14:43:06,832 - INFO - File exists: /media/walter/Storage/Projects/GTC_2023_presentation/data/Task01_BrainTumour.tar, skipped downloading.\n",
+                        "2023-03-20 14:43:06,833 - INFO - Non-empty folder exists in /media/walter/Storage/Projects/GTC_2023_presentation/data/Task01_BrainTumour, skipped extracting.\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Loading dataset: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 96/96 [00:52<00:00,  1.83it/s]\n"
+                    ]
+                }
+            ],
+            "source": [
+                "train_ds = DecathlonDataset(\n",
+                "    root_dir=root_dir, task=\"Task01_BrainTumour\", transform=data_transform, section=\"training\", download=True\n",
+                ")\n",
+                "\n",
+                "train_loader = DataLoader(train_ds, batch_size=8, shuffle=True, num_workers=8, persistent_workers=True)\n",
+                "\n",
+                "val_ds = DecathlonDataset(\n",
+                "    root_dir=root_dir, task=\"Task01_BrainTumour\", transform=data_transform, section=\"validation\", download=True\n",
+                ")\n",
+                "\n",
+                "val_loader = DataLoader(val_ds, batch_size=8, shuffle=False, num_workers=8, persistent_workers=True)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "50efe5ef",
+            "metadata": {},
+            "source": [
+                "### Visualization of the training images"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 7,
+            "id": "bffb4abc",
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 1000x600 with 4 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "plt.subplots(1, 4, figsize=(10, 6))\n",
+                "for i in range(4):\n",
+                "    plt.subplot(1, 4, i + 1)\n",
+                "    plt.imshow(train_ds[i * 20][\"image\"][0, :, :, 15].detach().cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+                "    plt.axis(\"off\")\n",
+                "plt.tight_layout()\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "d22296e5",
+            "metadata": {},
+            "source": [
+                "### Define network, scheduler, optimizer, and inferer\n",
+                "\n",
+                "We will use a DDPM in this example; for that, we need to define a `DiffusionModelUNet` network that will have as input the noisy images and the values for the timestep `t`, and it will predict the noise that is present in the image.\n",
+                "\n",
+                "In this example, we have a network with three levels (with 256, 256, and 512 channels in each). In every level, we will have two residual blocks, and only the last one will have an attention block with a single attention head (with 512 channels)."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 8,
+            "id": "d499f7b1",
+            "metadata": {
+                "lines_to_next_cell": 2
+            },
+            "outputs": [],
+            "source": [
+                "device = torch.device(\"cuda\")\n",
+                "\n",
+                "model = DiffusionModelUNet(\n",
+                "    spatial_dims=3,\n",
+                "    in_channels=1,\n",
+                "    out_channels=1,\n",
+                "    num_channels=[256, 256, 512],\n",
+                "    attention_levels=[False, False, True],\n",
+                "    num_head_channels=[0, 0, 512],\n",
+                "    num_res_blocks=2,\n",
+                ")\n",
+                "model.to(device)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "47ad91ff",
+            "metadata": {},
+            "source": [
+                "Together with our U-net, we need to define the Noise Scheduler for the diffusion model. This scheduler is responsible for defining the amount of noise that should be added in each timestep `t` of the diffusion model's Markov chain. Besides that, it has the operations to perform the reverse process, which will remove the noise of the images (a.k.a. denoising process). In this case, we are using a `DDPMScheduler`. Here we are using 1000 timesteps and a `scaled_linear` profile for the beta values (proposed in [Rombach et al. \"High-Resolution Image Synthesis with Latent Diffusion Models\"](https://arxiv.org/abs/2112.10752)). This profile had better results than the `linear, proposed in the original DDPM's paper. In `beta_start` and `beta_end`, we define the limits for the beta values. These are important to determine how accentuated is the addition of noise in the image."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "6c1de5ad",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "scheduler = DDPMScheduler(num_train_timesteps=1000, schedule=\"scaled_linear_beta\", beta_start=0.0005, beta_end=0.0195)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 57,
+            "id": "36d3e99a",
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "Text(0, 0.5, 'alpha cumprod')"
+                        ]
+                    },
+                    "execution_count": 57,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "plt.plot(scheduler.alphas_cumprod.cpu(), color=(2 / 255, 163 / 255, 163 / 255), linewidth=2)\n",
+                "plt.xlabel(\"Timestep [t]\")\n",
+                "plt.ylabel(\"alpha cumprod\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "9125f7c8",
+            "metadata": {},
+            "source": [
+                "Finally, we define the Inferer, which contains functions that will help during the training and sampling of the model, and the optimizer."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "8685da6e",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "inferer = DiffusionInferer(scheduler)\n",
+                "\n",
+                "optimizer = torch.optim.Adam(params=model.parameters(), lr=5e-5)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "9f371ad8",
+            "metadata": {},
+            "source": [
+                "## Model training\n",
+                "\n",
+                "In this part, we will train the diffusion model to predict the noise added to the images. For this, we are using an MSE loss between the prediction and the original noise. During the training, we are also sampling brain images to evaluate the evolution of the model. In this training, we use Automatic Mixed Precision to save memory and speed up the training."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 9,
+            "id": "bd10b595",
+            "metadata": {
+                "lines_to_next_cell": 0
+            },
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:54<00:00,  1.12s/it, loss=0.263]\n",
+                        "Epoch 1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:54<00:00,  1.12s/it, loss=0.0245]\n",
+                        "Epoch 2: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.13s/it, loss=0.014]\n",
+                        "Epoch 3: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.13s/it, loss=0.0103]\n",
+                        "Epoch 4: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.14s/it, loss=0.00888]\n",
+                        "Epoch 5: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.15s/it, loss=0.0125]\n",
+                        "Epoch 6: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.15s/it, loss=0.00897]\n",
+                        "Epoch 7: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00892]\n",
+                        "Epoch 8: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00569]\n",
+                        "Epoch 9: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.0075]\n",
+                        "Epoch 10: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00929]\n",
+                        "Epoch 11: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00622]\n",
+                        "Epoch 12: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00633]\n",
+                        "Epoch 13: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00664]\n",
+                        "Epoch 14: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00597]\n",
+                        "Epoch 15: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00646]\n",
+                        "Epoch 16: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00721]\n",
+                        "Epoch 17: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00895]\n",
+                        "Epoch 18: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00681]\n",
+                        "Epoch 19: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00657]\n",
+                        "Epoch 20: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00635]\n",
+                        "Epoch 21: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00578]\n",
+                        "Epoch 22: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00691]\n",
+                        "Epoch 23: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00602]\n",
+                        "Epoch 24: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00668]\n",
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [01:01<00:00, 16.22it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 200x200 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 25: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00884]\n",
+                        "Epoch 26: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00494]\n",
+                        "Epoch 27: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00529]\n",
+                        "Epoch 28: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.14s/it, loss=0.00513]\n",
+                        "Epoch 29: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00503]\n",
+                        "Epoch 30: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00852]\n",
+                        "Epoch 31: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00525]\n",
+                        "Epoch 32: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00571]\n",
+                        "Epoch 33: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00495]\n",
+                        "Epoch 34: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00574]\n",
+                        "Epoch 35: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.0048]\n",
+                        "Epoch 36: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00593]\n",
+                        "Epoch 37: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.0054]\n",
+                        "Epoch 38: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00569]\n",
+                        "Epoch 39: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00641]\n",
+                        "Epoch 40: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.0047]\n",
+                        "Epoch 41: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00498]\n",
+                        "Epoch 42: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00431]\n",
+                        "Epoch 43: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00484]\n",
+                        "Epoch 44: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00421]\n",
+                        "Epoch 45: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00462]\n",
+                        "Epoch 46: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00404]\n",
+                        "Epoch 47: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00435]\n",
+                        "Epoch 48: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00626]\n",
+                        "Epoch 49: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00365]\n",
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [01:01<00:00, 16.22it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 200x200 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 50: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.0064]\n",
+                        "Epoch 51: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00488]\n",
+                        "Epoch 52: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00691]\n",
+                        "Epoch 53: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00507]\n",
+                        "Epoch 54: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00373]\n",
+                        "Epoch 55: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00488]\n",
+                        "Epoch 56: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.14s/it, loss=0.00374]\n",
+                        "Epoch 57: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00416]\n",
+                        "Epoch 58: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00564]\n",
+                        "Epoch 59: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00428]\n",
+                        "Epoch 60: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00456]\n",
+                        "Epoch 61: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00506]\n",
+                        "Epoch 62: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.0036]\n",
+                        "Epoch 63: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00306]\n",
+                        "Epoch 64: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00442]\n",
+                        "Epoch 65: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00393]\n",
+                        "Epoch 66: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00361]\n",
+                        "Epoch 67: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.0035]\n",
+                        "Epoch 68: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00457]\n",
+                        "Epoch 69: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00455]\n",
+                        "Epoch 70: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00524]\n",
+                        "Epoch 71: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00394]\n",
+                        "Epoch 72: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00577]\n",
+                        "Epoch 73: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00558]\n",
+                        "Epoch 74: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00465]\n",
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [01:01<00:00, 16.23it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 200x200 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 75: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.17s/it, loss=0.00369]\n",
+                        "Epoch 76: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.16s/it, loss=0.00296]\n",
+                        "Epoch 77: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.17s/it, loss=0.00641]\n",
+                        "Epoch 78: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.15s/it, loss=0.00384]\n",
+                        "Epoch 79: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.18s/it, loss=0.00447]\n",
+                        "Epoch 80: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.17s/it, loss=0.00444]\n",
+                        "Epoch 81: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.17s/it, loss=0.0046]\n",
+                        "Epoch 82: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.17s/it, loss=0.0053]\n",
+                        "Epoch 83: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.17s/it, loss=0.0032]\n",
+                        "Epoch 84: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.17s/it, loss=0.00467]\n",
+                        "Epoch 85: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.15s/it, loss=0.00346]\n",
+                        "Epoch 86: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.17s/it, loss=0.00382]\n",
+                        "Epoch 87: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.18s/it, loss=0.0034]\n",
+                        "Epoch 88: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:58<00:00,  1.18s/it, loss=0.00355]\n",
+                        "Epoch 89: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.15s/it, loss=0.00478]\n",
+                        "Epoch 90: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00405]\n",
+                        "Epoch 91: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.17s/it, loss=0.00465]\n",
+                        "Epoch 92: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.16s/it, loss=0.00416]\n",
+                        "Epoch 93: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.16s/it, loss=0.00372]\n",
+                        "Epoch 94: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.16s/it, loss=0.00372]\n",
+                        "Epoch 95: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.18s/it, loss=0.00492]\n",
+                        "Epoch 96: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.16s/it, loss=0.00495]\n",
+                        "Epoch 97: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.18s/it, loss=0.00486]\n",
+                        "Epoch 98: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.16s/it, loss=0.00418]\n",
+                        "Epoch 99: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:58<00:00,  1.18s/it, loss=0.00563]\n",
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [01:02<00:00, 16.06it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKIAAACGCAYAAABez1E7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAANqElEQVR4nO2dSWtWzRaFK5993zfEPhrEiagDByKiEZyLIHGakT8gP8A/Ig6CKI4EMWgGig0oiDhxopFobDDGvu/1Ti7nrnrMu3Py3u/y1XtZa1TFPu9pKpvaK2vvqmr7/fv372QY/zD++qdfwDBSsiMahcCOaBQBO6JRBOyIRhGwIxpFwI5oFAE7olEE7IhGEZhc98Jly5Zl/S9fvlTtKVOmZLbv379X7V+/fmW2v/76j+/zd2pLKaUfP35U7WnTpmW2qVOnjnldSinNnDmzas+ePTuz8T4/f/5s+K7fvn2r2u/fv89sbW1tY94jpfz7eV8mstTG50+aNKlhn9fqfTkeOq4TSaRNntzYPT5//pz1dTyId+/ejfssz4hGEbAjGkWgdmhmuNHp/uvXr5lNp3+GFw2pDMUzZsxo+Ez9XUop7dmzp2p3dXVlttWrV1ftWbNmZbYohBC3b9+u2k+fPs1sb968qdrPnz/PbNeuXcv6GkYZ0jT8ffr0KXwfHVeOnY4VQ6r+jjb+XfVvQMqhfdIqBd+tDjwjGkXAjmgUgdqhefr06Vlfp3RO7xqOGZo1NDLcEhs3bqzaPT09mW3nzp0Nn6H/NTIUMTTPmTOnas+bNy+zdXZ2Vm0NxSml9Pbt26rN0Lxo0aKsPzw8XLUZfleuXFm1P3z4kNkeP36c9UdHR6s26ZD2o//aGTYjpYChWRH998171oFnRKMI2BGNImBHNIpAbY5ItV775HrKH2hTeYC888iRI1l/3bp1VVu5XEp5hoScTLM+5FLMtOj7kAcqv1y+fHnD58+fPz+zsT80NFS1yWcjqYsccXBwsGrPnTu34TOuX7+e2TQrRI4YcWjywCgrpnxSeWZdeEY0ioAd0SgCf0tmhZKIhlxO4SrJHDhwILPt2LEj6z969KhqM8R+/PixarOQQcP4eKGIxQwKfXd+f0Q/Fi9e3PB9FixYkNk000La8OrVq6y/efPmqq2yT0q5LKQhPKWUjh07VrVv3bqV2UgH9Dv5d9WQywyRjnNULNEInhGNImBHNIqAHdEoAm11txyhRKL8iTxDJZH169dntt7e3qrNR2/fvj3rKy9kFY3+liklTdWplJPSn9KCyiDkoco9la+mlPNApubIGTUdyGfote3t7ZlNeXBKOX+jnKUcjbKPFqb29/dntr6+vobXRlVVUYEveTm57ljwjGgUATuiUQTsiEYRmLjg829E6R5NzR0+fLihjVXPLKdS7hlxImp8qnFRC6NWp6AeqXyJ2qByJFaWkyMqZyXX1FQp+SzTePot/C5NsZGj6bvv27cvs3HML1y40NCm30xezP8TJgrPiEYRsCMaRaB2aGbaRqd/Vpvs37+/am/YsCGzafpPwzTvmVIu2XDq1zBKKUHfNVrkQ1Da0fXRDP9qmwh4Hw2prN5m+NXxIR3Q8Vm7dm1m0+/iWB06dCjrq9R08eLFzKbUIVro5hSf0bKwIxpFwI5oFIHawZx8RTnB7t27M1t3d3fVfvLkSWaLJBlWbOszyZ+isqNISuA+LJqa4iq+SBKZCFTqYDpS+3w3pvhU6mE6MNq4INpDiGVpWmp28+bNzBZVXv+3Y+UZ0SgCdkSjCDQdmnW637VrV2bTqmdKNBpGGVLZjxZo6bWsFtbQzGwF+5ppobQSZTL0Pnw30gitPiH9qFvtwt/SpmGT93n58mXVppxFOqAV9Fu3bs1sAwMDqQ44jnXgGdEoAnZEowjYEY0i0HT1jabf9F/+lPJtjlmloRIJ+QnTRtqP0kZRuo1yBSUa5TPkXcpRya2UI1KuYF9lKm5UoNyOVejknhFnVV5KSUY5Ozky/z76nVwpqGNJKSfay7IOPCMaRcCOaBQBO6JRBGpzxGjVFnmP6njkC9FRB+SBykN4n2jVmIIaI/kkOWRdTKTSW/kcx0pLtmjTkizeh1qlfic32NTx4e/4HS9evKjaTCPq3uT379/PbFFZXh14RjSKgB3RKAJNyzcK3ZsvpXwx/pIlSzKbhg1ugMRqE638jvZlZkpLwz/lCt5HQz7Dv4Y7yjdKI16/fp3ZmOLSxUuUqPQZDOmkKiq18FqlHFzQrmPA8SAd0Pvy+To+0bEl/DvWgWdEowjYEY0iYEc0isDfwhGvXLmS9bdt29bwWpUSmNIit1Kux8XmCvIl5aXkRORzmn4jf9Syq+joNKbUohNZCeVhlD0ow2jqNFpgT4lKeSjfJfoupkN1LPluyjVdoW20LOyIRhGoHZqjBe4Mf7o/H0OqhiJO4ZRPNLPCao+owkPDBsM26YD2o32yo4Oyo8XmfAeGNJWF+I18dx2vKPzzbxVtFMAjPfSbKYtp+OW7OjQb/xewIxpFwI5oFIGmV/Fp+o17Xy9cuLBqj4yMZDblDzxWLNpXkNxG011MvylHIreLqp4j3klJRLke02TRxk+UaFROYsqT36yyFJ+haTVWOGlFDVcRshpHx46pOh0fpv+i81nqwDOiUQTsiEYRaHp/RK0o6ejoyGwamrk4JzoNlEWaGsYpEansEoVbhjeGPw2V/EaVL/huGsIopTDc6RbNDOO6+J3SUlTxw+/S99HxTykfc4btVatWZf3R0dGqzTDOTItCpSafYG+0LOyIRhGwIxpFoDZHZLpHeQj3yVYuwwptvQ/TREx/KaJUYfQ78hx+h/Zp428VyjV5ZMWdO3eyvvIn3lPvwwVJnZ2dWV+lKB5J1+i6lHIeSslsIgvfVKbjN6oM1cxRF54RjSJgRzSKgB3RKAK1OSI1LdXKqM1pn5wo4nbkJFEaLdIK9d2YmuOZMApyVl3gTm1QORF1s02bNmV91SBXrFiR2fSYMU3FjfVM/c6oDIzjoc/nODLlqH3+fXQzgKVLl2Y25aVexWe0LOyIRhGoHZpZhawLhiYie2j4o5TAf/s1/DI1peGH4UafP95effru0SKs6GgH3RMmpT9DmoZRVqZoiGO4U9klpXwMov1looVm4+3lqCk+pmf1qBKmZzUFa/nGaFnYEY0iYEc0ikBtjkjeo5yA/En5ZLT4PCq7SinnQeRvutEQOaLKFeOtKItW2OlveR8tieJ7k09HXDeqEOd99F2ZxtNryUN1HMc7Z0WlF3K9/v7+qk3+qM/ge9eBZ0SjCNgRjSLQdIW2ZhaopGs4ZrjRa8dbUM6MjULDMbMnGn6YZWBoUqoQyQ78fv0d35uhWn/LsKXXMpPCa5Ue8Jk6HgzN0ZEilNq0YptjF1Ve67h662KjZWFHNIqAHdEoArU5IlfRadXIw4cPM5sejUteodIK+Qrlk6jahLKQQqWNaM/qlHKOxG9UkOsqX4uklPHuq9yO1Te8T7Q/onJNjpVyNvI3cl+tOOLxGirZ8G+lz3eKz2hZ2BGNImBHNIpAbY5IzU854tGjRzPbli1bqjZX8SnIc6hHqj5I3Uz5I3+nvItcZiL8Rb852rxId3JI6c/jZaO9p5V3kYeyDEy/me+j99VdOFKKNwONdpM4f/58ZtPSr2ijJeqPdeAZ0SgCdkSjCNQOzaxwUfmCi60HBgaq9po1azKbTunc2Ij9aPGUTv96RAWv5XFg3EgoekaUxozSiLqHeEr5N0cnsFJ2IY1QmYgbNkWnrOp9KQlRMjp79mzVvnTpUsN7svpGx84n2BstCzuiUQTsiEYRaPoEe+VFlACOHz9etbu6ujKbSgss3yJHVD5FbqUcNdpDm8+g7KBchymtaNG4vg/fO1pxSI6mpW6UVvjNURW2pjzJdbXPsTp16lTWP3HiRNXWFX0p5TJUtBowSmk2gmdEowjYEY0i0Pa7ZjktZQ8Nf1T51bZ3797M1tvbW7Up7VCGUTSbIWHVcxTGuWhcK0oePHiQ2TQURntL85kcK6UGlFJY6a329vb2zKZ/Rr6PyjdnzpzJbH19fQ2fQYqhIDVQ+kFKwQzRWPCMaBQBO6JRBOyIRhFoehWf/rtOLqPpJp5ur9Xb3d3dmY17F0YVycqJKHvou1HKiEC6rPfleSQq5zDdRY6mvJAcVb+L56MQysNu3LiR2S5fvly1mWJUjsbxIA/Ubx5vAwSFcm2v4jNaFnZEowjYEY0iUJsjEsoXWPajqTHyjNOnT1dt6oYHDx5s+AzyjmgzTtXNmIpiik3TWFEak8/QynNqnEwH6vtwE01Nhw0PD2c2rg68evVq1T558mTD+zDlqN/BNGZ0Rg3HQ68l19Vx9jG5RsvCjmgUgaYX2GtojNJvDFMq7Zw7dy6zMR3X09NTtbm/tILP0DDBMEVoGHn27Flm08od3T86pTzcMoQODQ1lfQ1xTBWqnHLv3r3MxiPRVCaiZKV9VqVHoZLURa9lxXi00ZLex8fkGi0LO6JRBOyIRhGoXQYWLZSP9p4moiNjKa10dHRUbeWLKeWV3ywzUkmG3JKfqxsFaJospZwvjYyMZLa7d+9W7cHBwcxGPq08kJxVbSxt4+pA5YEcc71PdMzZeOesRBKNymlRGRhXFLoMzGgZ2BGNIlA7NHMRkk6/UbU0Q4hmWjiFMwujv+WCcj3BnfdRGSiqGEkpD3cMU3ofZlaiUEg5Jwp3ei2rdihLaTjkd0WL+JUq0Ma+fgupU0QN1Ad4Ty7CGgueEY0iYEc0ioAd0SgCtTmiYfwv4RnRKAJ2RKMI2BGNImBHNIqAHdEoAnZEowjYEY0iYEc0ioAd0SgC/wIFLRYCJpOjGwAAAABJRU5ErkJggg==\n",
+                        "text/plain": [
+                            "<Figure size 200x200 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 100: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.16s/it, loss=0.00425]\n",
+                        "Epoch 101: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.18s/it, loss=0.00327]\n",
+                        "Epoch 102: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.15s/it, loss=0.0045]\n",
+                        "Epoch 103: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.17s/it, loss=0.00335]\n",
+                        "Epoch 104: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.16s/it, loss=0.00341]\n",
+                        "Epoch 105: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.16s/it, loss=0.00418]\n",
+                        "Epoch 106: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.16s/it, loss=0.0032]\n",
+                        "Epoch 107: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.18s/it, loss=0.00503]\n",
+                        "Epoch 108: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.16s/it, loss=0.00341]\n",
+                        "Epoch 109: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.16s/it, loss=0.00545]\n",
+                        "Epoch 110: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.18s/it, loss=0.00523]\n",
+                        "Epoch 111: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:59<00:00,  1.21s/it, loss=0.00306]\n",
+                        "Epoch 112: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.15s/it, loss=0.00472]\n",
+                        "Epoch 113: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:59<00:00,  1.21s/it, loss=0.00513]\n",
+                        "Epoch 114: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:59<00:00,  1.21s/it, loss=0.00339]\n",
+                        "Epoch 115: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.18s/it, loss=0.00388]\n",
+                        "Epoch 116: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.16s/it, loss=0.00398]\n",
+                        "Epoch 117: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.17s/it, loss=0.0026]\n",
+                        "Epoch 118: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.18s/it, loss=0.00355]\n",
+                        "Epoch 119: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.15s/it, loss=0.00315]\n",
+                        "Epoch 120: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.15s/it, loss=0.00346]\n",
+                        "Epoch 121: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.16s/it, loss=0.00506]\n",
+                        "Epoch 122: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.18s/it, loss=0.00301]\n",
+                        "Epoch 123: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:58<00:00,  1.19s/it, loss=0.0051]\n",
+                        "Epoch 124: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.18s/it, loss=0.0032]\n",
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [01:03<00:00, 15.74it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKIAAACGCAYAAABez1E7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAANKUlEQVR4nO2dOYhVyxaGVzvP86zthFPigFEjGimYiBoogiKCiYGhkQqCgZGZCIIogoFgooEgYiy0swji2M7zPM/Ti9zvX3+fvU6dfd99t/ryf1EVdU7t2rtX7/WfVauqmn7//v3bhPiH6fRPD0AIMxmiyAQZosgCGaLIAhmiyAIZosgCGaLIAhmiyAIZosiCLqkf7Natm6vjhAxPzjQ1NdUsm5n9+vWrKHfqFP8f4Heja3Ab1utdA/n582fpNXDc3G90fe6HnwfXI7DfqB8ea/TMeazR80r9m3fp4s3q8+fPpX0W1637CSH+D8gQRRYku2Z+3SPsJlJf4Qy7RoRdBvYTuZtobPWuGfXTyPOIro9j5z6ruk2mc+fOpddoRBpE91zlc4jeiCILZIgiC5Jdc0Tk3hpxU/xrK/WXMboeM7OePXsW5b59+7q2yG3wr7v3798XZXaL379/L+2zEZeaGmGoVU/pk8fDY4tcdVWJUQW9EUUWyBBFFsgQRRYka8RIL9QLkZS18WxNpIEmTpzo6gsXLizKs2bNcm2DBg0qykOHDnVt3759K62zRmxtbS3Kr1+/dm0PHz4s/d6ZM2dcHdv5nlFboe40qz5j1MisBxP9DaLr/9WlT3ojiiyQIYosSHbN9X72I5Ebx1f6jx8/XNvgwYNdffny5UV51apVrm306NFFmd0dXh9DMGZmL1++dHW8r5EjR7o2dPEcIurVq1fp9flZHT9+vCgfPHjQtbW1tdUct5lZjx49XB1lxJcvX1xb165drQwcDz/zRpInyvqs1U+j6I0oskCGKLJAhiiyoCl1yxH+uR4lhqJe4Ok/nH5bv369a1u5cqWrDxkypOb3zHxIhPUJju3Vq1eu7d27d66O42ONiBotyv5h3cXaDsd66tQp1/bo0aOi/OnTJ9fW3Nzs6ji+I0eOuLYrV64U5Vu3brm2Z8+elV6DSQ3RRNOarKc5LFULvRFFFsgQRRbIEEUWJGtEnhqKpphQd40YMcK1bdy4sSgvWrTItbHWwqGxXsL4IMfxsB+e0uPpOLxG9+7dXRvqII7boQ7i+CfrMIzx8VQh6jfW09OnTy/tJ0q9u379uqsfOHCgKO/bt8+18TPH59VIpnWUMc9/g1rojSiyQIYosqBy9g26NHYTw4YNK8p79+51bWPGjCnK7Aoj988uBMM5UYiod+/ero1DCZHE+PDhQ82ymXdb9TLNcew8VnRbLDG+fv1a2i9nnqNU4YyjNWvW1ByLmdmePXtcHe8ryt7mEE2VBVOI3ogiC2SIIgtkiCILKodvUBPwlNb+/fuLcktLi2vr06fPfy9OurN///6ujqEW1lbYD+s+DHNw2OXJkyeujjosyp6O0q5wLGbt9RLqMtZoeI8cWuJnPnny5KIcTSOynu3Xr19pnzt37nT1DRs2FOVIP0d7AWnvG9FhkSGKLEh2zZwBjK/iOXPmuDaM5GMGTb0+I9ilYfggcs0fP350bfxZDJ9EoZXhw4e7Nvwsfy8Ke/Djxvu6ffu2a+PPYvbN2LFjSz/LkgfHd//+/fAaixcvLsqY0cNEmd3s0lnW1EJvRJEFMkSRBTJEkQWV90fEVWxr1651bagJ+Hs8rRcRZYKg7uGpsCi0wpoINSRfA0MkHNpB+PpRRjJfA6cgJ02a5No4i4ezfJBoFV2UKcSZMUuWLCnKN27ccG3RxlOoC3n6LwW9EUUWyBBFFsgQRRYka0TWdjgdN2/ePNeGGiHShKylOB4XpYGh9os2c+I4Ise4cKyoe2t9tgy+R66jhsSMbDM/HYY7S5i1n/J88eJFUeZVjQg/R3zO/Mz52WFWeLSDRZQhXgW9EUUWyBBFFiS7Zs6gwE2QONsC67zAHV/30XSXmXdxHCJB18ChBHTHz58/L20zMxs/fnxRZteM98GuEF0cfy/KNokyxtltnz171tUx8xqz4M3i7Gmsc5iHp994H8qya0ToeAvRYZEhiiyQIYosqHzOCv7MZ/2EYQjWFThtxVqC08KwzhoRtSZrS1wpyFrqwYMHro6aMdqzmjdvGjBgQFHmaTLWaPgM+D5w49DLly+7Nu53/vz5Nfs081ov2hQqmprjOqea4ZRfdM5KlU079UYUWSBDFFmQ7JrZbc6ePbso8z7VGKLgRT7omngfGM4MwUVAUaiHw0fo0vh7HD7BkAnPOmC/AwcOdG34PDhExddEOcB7eON98IzMggULSq/55s0b1/b06dOiHGUqcaY5y6pRo0bVLJuZ3bx5syizS8drKvtGdFhkiCILZIgiC5I1IuswDJ9wlgbrQgR1Dn8u2nuZQwKoS6PjdTmjhTUSfvbixYul42FNdO/evaLM04Y8rYhjZT2N2nPGjBmujTNcUKNxGAp1MetZ1JP1Frvj2HmKMZq6w+ejKT7RYZEhiiyofAQa7yGDREmT6O44JMSZIOjy2I1jGIRDIphRwwv8edYB6+z+MdTDGTY4e8P3y2Gp8+fPF2UMs5j55FeWOLwHYpRkjKelslRAV8mumSUHhte4nygxtpETUGuhN6LIAhmiyAIZosiC5E2YOCSwZcuWorx06VLXxns4l1FvYTrqtyizmfUK6kleYM/XwGwg1jaoWaPsbQ5XcB3HwPeMWjvSj2Z+f0S+BoZoONMbp/Ewa8is/XNFDcmL4q5evVp6/Sj7RkegiQ6DDFFkgQxRZEFywIezhVGHsbZCTRJt2shxRN77OTqWFWNsHH+LNvGMUtaic184jhllIUdHyPKzwixovgYeoWvmp994qhJ1KOtAvGa97GlcxM/6Ee+Df1ooDUz8K5AhiixIds3Ryec8FYQull/v0cnv0TXxiAazOHyC3+NxYwjCzO9LzS4NXXV0zBm7/3r7yyAYzuFMIXajKGX42aEEihZEMTzlh9KF/644nioLpCL0RhRZIEMUWSBDFFlQeRUfai/WINjG34vO42AdiNqKdRfqQp42w/NBMD3KrP2qNbwGp13hNfkakb5lzYqaOTovJjo6zSx9T8gofBQdwWYWP7soCzt1L8ky9EYUWSBDFFmQ7JrZFZw7d64oL1u2zLWhu+HvYdiDs2bYbWD2SXSsGGetYAgCM6nNzKZNm+bqGKJh9xLtwYgzTW/fvnVt7MYxLMRhIHSN/D2GZ4WQKHsbn1W0v5CZz+Lh2TR8BtE+QVXctN6IIgtkiCILZIgiC5I1IuuX1tbWonz37l3XhrqMNQhmmHCWBmemRFkcOB7OZJ45c2ZR5rAP6xds51WEuLkSa7BoNWKUccQ6D0M7nAXO2hP1Nu9dyBsmpcJjP336dFFmjZi6B6L2RxQdFhmiyAIZosiCyntoP378uCjv2rXLtW3evLkos5aJspVZo0VpRzhVFx2Fi+M0a6/RUPdwyhjq0Ei/sibkTHOMFbLuQljP8r7duJEpa9bUY844/tfW1ubqR48eLcp8z9gvt2GMURpRdFhkiCILkl1z9Lo9dOiQq+OREtu2bSv9Hu89zfCi/rLxcJgHXTy7zWiDoGihPoevcCFTdAScWSxHoiM02I2iO+Z+cOy8KRVuRHXt2jXXtnv3blfH++LnES1m+6unleqNKLJAhiiyQIYosiB5EyZe/I2aIFpttmPHDteGGzZxRjb3gzqIwx4YauHVd/hZDsmwlsFwDqehodbk+8fHxmEn1qw4dcdaE/vldDbWerionjUa9sNnsOBnt2/f7tqOHTtmqeDfJ0rL479jFLL6g96IIgtkiCILksM3jRxZgC5u06ZNrg1nGVasWOHaeF9F7CdaUB7BboEXC2E7z4jg7A27G3S30cIyrvOeNeh+OVzFx21g6IVnT3CsfMopZkpdunTJtfF9oeSITlmNMrSroDeiyAIZosgCGaLIguTwDesFrLPOwBBJtGps6tSprm3r1q2uPnfu3KLMmhBDFLwwHq/PYRfOgsYzUVhPYj/RAns+ywVPeudrjBs3zrXhGSyHDx92bXfu3HF1DFNxyAifB2801cjp8nhfkQ5X+Eb8K5EhiiyQIYosSNaIrBeimFKUolX2ObP2mdarV68uyuvWrXNtqN84/oj1SMvw2Fl34Xc5Nsgr7JCTJ0+6+oULF4ryiRMnXBvGBlmHNrKqEXUYT2NGep6J4sVRGhhS74zFWuiNKLJAhiiyINk1R4tl2G2nLqThPiP3jwuHzMxaWlqKcnNzs2ubMGFCUeZNoKZMmeLqGN7hz2LYhUMyuKkAbkhl5kMyZt41sfuN3G3VabTIbfLfo5GF8lG/eB98T9H+63/QG1FkgQxRZIEMUWRBskbkn+Suk0BLRN2zRmxEr0SrxrDfehsk4WejMEN0lgr3yRopOjosamuEKJyG1DuDJXWsEfx35dS7muNK6lmIvxkZosiCygvsU91o5KYit8Ckhg64n3rHrEX7ZON9NDLLER0v8b/ee/oPUfZL1WfXyKL56NiSFPRGFFkgQxRZIEMUWZAcvhHi70RvRJEFMkSRBTJEkQUyRJEFMkSRBTJEkQUyRJEFMkSRBTJEkQX/AYc/ETQ93p53AAAAAElFTkSuQmCC\n",
+                        "text/plain": [
+                            "<Figure size 200x200 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 125: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.17s/it, loss=0.00394]\n",
+                        "Epoch 126: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.18s/it, loss=0.00387]\n",
+                        "Epoch 127: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:58<00:00,  1.18s/it, loss=0.00382]\n",
+                        "Epoch 128: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:57<00:00,  1.18s/it, loss=0.00439]\n",
+                        "Epoch 129: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:56<00:00,  1.15s/it, loss=0.00654]\n",
+                        "Epoch 130: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00585]\n",
+                        "Epoch 131: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00495]\n",
+                        "Epoch 132: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00375]\n",
+                        "Epoch 133: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00406]\n",
+                        "Epoch 134: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00394]\n",
+                        "Epoch 135: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00369]\n",
+                        "Epoch 136: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00354]\n",
+                        "Epoch 137: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00384]\n",
+                        "Epoch 138: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00457]\n",
+                        "Epoch 139: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00427]\n",
+                        "Epoch 140: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00319]\n",
+                        "Epoch 141: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00419]\n",
+                        "Epoch 142: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00412]\n",
+                        "Epoch 143: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00397]\n",
+                        "Epoch 144: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00292]\n",
+                        "Epoch 145: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00397]\n",
+                        "Epoch 146: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00364]\n",
+                        "Epoch 147: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00406]\n",
+                        "Epoch 148: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00449]\n",
+                        "Epoch 149: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 49/49 [00:55<00:00,  1.14s/it, loss=0.00239]\n",
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [01:01<00:00, 16.37it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 200x200 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "train completed, total time: 8859.028432130814.\n"
+                    ]
+                }
+            ],
+            "source": [
+                "n_epochs = 150\n",
+                "val_interval = 25\n",
+                "epoch_loss_list = []\n",
+                "val_epoch_loss_list = []\n",
+                "\n",
+                "scaler = GradScaler()\n",
+                "total_start = time.time()\n",
+                "for epoch in range(n_epochs):\n",
+                "    model.train()\n",
+                "    epoch_loss = 0\n",
+                "    progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=70)\n",
+                "    progress_bar.set_description(f\"Epoch {epoch}\")\n",
+                "    for step, batch in progress_bar:\n",
+                "        images = batch[\"image\"].to(device)\n",
+                "        optimizer.zero_grad(set_to_none=True)\n",
+                "\n",
+                "        with autocast(enabled=True):\n",
+                "            # Generate random noise\n",
+                "            noise = torch.randn_like(images).to(device)\n",
+                "\n",
+                "            # Create timesteps\n",
+                "            timesteps = torch.randint(\n",
+                "                0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n",
+                "            ).long()\n",
+                "\n",
+                "            # Get model prediction\n",
+                "            noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n",
+                "\n",
+                "            loss = F.mse_loss(noise_pred.float(), noise.float())\n",
+                "\n",
+                "        scaler.scale(loss).backward()\n",
+                "        scaler.step(optimizer)\n",
+                "        scaler.update()\n",
+                "\n",
+                "        epoch_loss += loss.item()\n",
+                "\n",
+                "        progress_bar.set_postfix({\"loss\": epoch_loss / (step + 1)})\n",
+                "    epoch_loss_list.append(epoch_loss / (step + 1))\n",
+                "\n",
+                "    if (epoch + 1) % val_interval == 0:\n",
+                "        model.eval()\n",
+                "        val_epoch_loss = 0\n",
+                "        for step, batch in enumerate(val_loader):\n",
+                "            images = batch[\"image\"].to(device)\n",
+                "            noise = torch.randn_like(images).to(device)\n",
+                "            with torch.no_grad():\n",
+                "                with autocast(enabled=True):\n",
+                "                    timesteps = torch.randint(\n",
+                "                        0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n",
+                "                    ).long()\n",
+                "\n",
+                "                    # Get model prediction\n",
+                "                    noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n",
+                "                    val_loss = F.mse_loss(noise_pred.float(), noise.float())\n",
+                "\n",
+                "            val_epoch_loss += val_loss.item()\n",
+                "            progress_bar.set_postfix({\"val_loss\": val_epoch_loss / (step + 1)})\n",
+                "        val_epoch_loss_list.append(val_epoch_loss / (step + 1))\n",
+                "\n",
+                "        # Sampling image during training\n",
+                "        image = torch.randn((1, 1, 32, 40, 32))\n",
+                "        image = image.to(device)\n",
+                "        scheduler.set_timesteps(num_inference_steps=1000)\n",
+                "        with autocast(enabled=True):\n",
+                "            image = inferer.sample(input_noise=image, diffusion_model=model, scheduler=scheduler)\n",
+                "\n",
+                "        plt.figure(figsize=(2, 2))\n",
+                "        plt.imshow(image[0, 0, :, :, 15].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+                "        plt.tight_layout()\n",
+                "        plt.axis(\"off\")\n",
+                "        plt.show()\n",
+                "\n",
+                "total_time = time.time() - total_start\n",
+                "print(f\"train completed, total time: {total_time}.\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "3e263b67",
+            "metadata": {},
+            "source": [
+                "### Learning curves"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 10,
+            "id": "c7520419",
+            "metadata": {
+                "lines_to_next_cell": 2
+            },
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 800x550 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "plt.style.use(\"seaborn-v0_8\")\n",
+                "plt.title(\"Learning Curves\", fontsize=20)\n",
+                "plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n",
+                "plt.plot(\n",
+                "    np.linspace(val_interval, n_epochs, int(n_epochs / val_interval)),\n",
+                "    val_epoch_loss_list,\n",
+                "    color=\"C1\",\n",
+                "    linewidth=2.0,\n",
+                "    label=\"Validation\",\n",
+                ")\n",
+                "plt.yticks(fontsize=12)\n",
+                "plt.xticks(fontsize=12)\n",
+                "plt.xlabel(\"Epochs\", fontsize=16)\n",
+                "plt.ylabel(\"Loss\", fontsize=16)\n",
+                "plt.legend(prop={\"size\": 14})\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "38724c9b",
+            "metadata": {},
+            "source": [
+                "## Sampling Brain Image\n",
+                "\n",
+                "In order to sample the brain images, we need to pass the model an image containing just noise and use it to remove the noise of the image iteratively. For that, we will use the `.sample()` function of the `inferer`."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 11,
+            "id": "092eb6a0",
+            "metadata": {
+                "lines_to_next_cell": 2
+            },
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [03:45<00:00,  4.44it/s]\n"
+                    ]
+                }
+            ],
+            "source": [
+                "model.eval()\n",
+                "noise = torch.randn((1, 1, 32, 40, 32))\n",
+                "noise = noise.to(device)\n",
+                "scheduler.set_timesteps(num_inference_steps=1000)\n",
+                "image = inferer.sample(input_noise=noise, diffusion_model=model, scheduler=scheduler)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 12,
+            "id": "5dc3e69d",
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "plt.style.use(\"default\")\n",
+                "plotting_image_0 = np.concatenate([image[0, 0, :, :, 15].cpu(), np.flipud(image[0, 0, :, 20, :].cpu().T)], axis=1)\n",
+                "plotting_image_1 = np.concatenate([np.flipud(image[0, 0, 15, :, :].cpu().T), np.zeros((32, 32))], axis=1)\n",
+                "plt.imshow(np.concatenate([plotting_image_0, plotting_image_1], axis=0), vmin=0, vmax=1, cmap=\"gray\")\n",
+                "plt.tight_layout()\n",
+                "plt.axis(\"off\")\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "f0acc27a",
+            "metadata": {},
+            "source": [
+                "### Sampling with Denoising Diffusion Implicit Model Scheduler\n",
+                "\n",
+                "Recent papers have proposed different ways to improve the sampling speed by using fewer steps in the denoising process. In this example, we are using a `DDIMScheduler` (from [Song et al. \"Denoising Diffusion Implicit Models\"](https://arxiv.org/abs/2010.02502)) to reduce the original number of steps from 1000 to 250."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 46,
+            "id": "e3e43b95",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 250/250 [00:55<00:00,  4.52it/s]\n"
+                    ]
+                }
+            ],
+            "source": [
+                "scheduler_ddim = DDIMScheduler(\n",
+                "    num_train_timesteps=1000, schedule=\"scaled_linear_beta\", beta_start=0.0005, beta_end=0.0195, clip_sample=False\n",
+                ")\n",
+                "\n",
+                "scheduler_ddim.set_timesteps(num_inference_steps=250)\n",
+                "\n",
+                "model.eval()\n",
+                "noise = torch.randn((1, 1, 32, 40, 32))\n",
+                "noise = noise.to(device)\n",
+                "\n",
+                "image = inferer.sample(input_noise=noise, diffusion_model=model, scheduler=scheduler_ddim)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 47,
+            "id": "89f93ab8",
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "plt.style.use(\"default\")\n",
+                "plotting_image_0 = np.concatenate([image[0, 0, :, :, 15].cpu(), np.flipud(image[0, 0, :, 20, :].cpu().T)], axis=1)\n",
+                "plotting_image_1 = np.concatenate([np.flipud(image[0, 0, 15, :, :].cpu().T), np.zeros((32, 32))], axis=1)\n",
+                "plt.imshow(np.concatenate([plotting_image_0, plotting_image_1], axis=0), vmin=0, vmax=1, cmap=\"gray\")\n",
+                "plt.tight_layout()\n",
+                "plt.axis(\"off\")\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "a39c881c",
+            "metadata": {},
+            "outputs": [],
+            "source": []
+        }
+    ],
+    "metadata": {
+        "jupytext": {
+            "formats": "py:percent,ipynb"
+        },
+        "kernelspec": {
+            "display_name": "Python 3 (ipykernel)",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.8.13"
+        }
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 388/388 [03:32<00:00,  1.83it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2023-03-20 14:43:06,832 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n",
-      "2023-03-20 14:43:06,832 - INFO - File exists: /media/walter/Storage/Projects/GTC_2023_presentation/data/Task01_BrainTumour.tar, skipped downloading.\n",
-      "2023-03-20 14:43:06,833 - INFO - Non-empty folder exists in /media/walter/Storage/Projects/GTC_2023_presentation/data/Task01_BrainTumour, skipped extracting.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 96/96 [00:52<00:00,  1.83it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_ds = DecathlonDataset(\n",
-    "    root_dir=root_dir, task=\"Task01_BrainTumour\", transform=data_transform, section=\"training\", download=True\n",
-    ")\n",
-    "\n",
-    "train_loader = DataLoader(train_ds, batch_size=8, shuffle=True, num_workers=8, persistent_workers=True)\n",
-    "\n",
-    "val_ds = DecathlonDataset(\n",
-    "    root_dir=root_dir, task=\"Task01_BrainTumour\", transform=data_transform, section=\"validation\", download=True\n",
-    ")\n",
-    "\n",
-    "val_loader = DataLoader(val_ds, batch_size=8, shuffle=False, num_workers=8, persistent_workers=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "50efe5ef",
-   "metadata": {},
-   "source": [
-    "### Visualization of the training images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "bffb4abc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 1000x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.subplots(1, 4, figsize=(10, 6))\n",
-    "for i in range(4):\n",
-    "    plt.subplot(1, 4, i + 1)\n",
-    "    plt.imshow(train_ds[i * 20][\"image\"][0, :, :, 15].detach().cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
-    "    plt.axis(\"off\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d22296e5",
-   "metadata": {},
-   "source": [
-    "### Define network, scheduler, optimizer, and inferer\n",
-    "\n",
-    "We will use a DDPM in this example; for that, we need to define a `DiffusionModelUNet` network that will have as input the noisy images and the values for the timestep `t`, and it will predict the noise that is present in the image.\n",
-    "\n",
-    "In this example, we have a network with three levels (with 256, 256, and 512 channels in each). In every level, we will have two residual blocks, and only the last one will have an attention block with a single attention head (with 512 channels)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "d499f7b1",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "device = torch.device(\"cuda\")\n",
-    "\n",
-    "model = DiffusionModelUNet(\n",
-    "    spatial_dims=3,\n",
-    "    in_channels=1,\n",
-    "    out_channels=1,\n",
-    "    num_channels=[256, 256, 512],\n",
-    "    attention_levels=[False, False, True],\n",
-    "    num_head_channels=[0, 0, 512],\n",
-    "    num_res_blocks=2,\n",
-    ")\n",
-    "model.to(device)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "47ad91ff",
-   "metadata": {},
-   "source": [
-    "Together with our U-net, we need to define the Noise Scheduler for the diffusion model. This scheduler is responsible for defining the amount of noise that should be added in each timestep `t` of the diffusion model's Markov chain. Besides that, it has the operations to perform the reverse process, which will remove the noise of the images (a.k.a. denoising process). In this case, we are using a `DDPMScheduler`. Here we are using 1000 timesteps and a `scaled_linear` profile for the beta values (proposed in [Rombach et al. \"High-Resolution Image Synthesis with Latent Diffusion Models\"](https://arxiv.org/abs/2112.10752)). This profile had better results than the `linear, proposed in the original DDPM's paper. In `beta_start` and `beta_end`, we define the limits for the beta values. These are important to determine how accentuated is the addition of noise in the image."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6c1de5ad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scheduler = DDPMScheduler(num_train_timesteps=1000, schedule=\"scaled_linear_beta\", beta_start=0.0005, beta_end=0.0195)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "36d3e99a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'alpha cumprod')"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(scheduler.alphas_cumprod.cpu(), color=(2 / 255, 163 / 255, 163 / 255), linewidth=2)\n",
-    "plt.xlabel(\"Timestep [t]\")\n",
-    "plt.ylabel(\"alpha cumprod\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9125f7c8",
-   "metadata": {},
-   "source": [
-    "Finally, we define the Inferer, which contains functions that will help during the training and sampling of the model, and the optimizer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8685da6e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inferer = DiffusionInferer(scheduler)\n",
-    "\n",
-    "optimizer = torch.optim.Adam(params=model.parameters(), lr=5e-5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9f371ad8",
-   "metadata": {},
-   "source": [
-    "## Model training\n",
-    "\n",
-    "In this part, we will train the diffusion model to predict the noise added to the images. For this, we are using an MSE loss between the prediction and the original noise. During the training, we are also sampling brain images to evaluate the evolution of the model. In this training, we use Automatic Mixed Precision to save memory and speed up the training."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "bd10b595",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0: 100%|████████████| 49/49 [00:54<00:00,  1.12s/it, loss=0.263]\n",
-      "Epoch 1: 100%|███████████| 49/49 [00:54<00:00,  1.12s/it, loss=0.0245]\n",
-      "Epoch 2: 100%|████████████| 49/49 [00:55<00:00,  1.13s/it, loss=0.014]\n",
-      "Epoch 3: 100%|███████████| 49/49 [00:55<00:00,  1.13s/it, loss=0.0103]\n",
-      "Epoch 4: 100%|██████████| 49/49 [00:56<00:00,  1.14s/it, loss=0.00888]\n",
-      "Epoch 5: 100%|███████████| 49/49 [00:56<00:00,  1.15s/it, loss=0.0125]\n",
-      "Epoch 6: 100%|██████████| 49/49 [00:56<00:00,  1.15s/it, loss=0.00897]\n",
-      "Epoch 7: 100%|██████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00892]\n",
-      "Epoch 8: 100%|██████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00569]\n",
-      "Epoch 9: 100%|███████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.0075]\n",
-      "Epoch 10: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00929]\n",
-      "Epoch 11: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00622]\n",
-      "Epoch 12: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00633]\n",
-      "Epoch 13: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00664]\n",
-      "Epoch 14: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00597]\n",
-      "Epoch 15: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00646]\n",
-      "Epoch 16: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00721]\n",
-      "Epoch 17: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00895]\n",
-      "Epoch 18: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00681]\n",
-      "Epoch 19: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00657]\n",
-      "Epoch 20: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00635]\n",
-      "Epoch 21: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00578]\n",
-      "Epoch 22: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00691]\n",
-      "Epoch 23: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00602]\n",
-      "Epoch 24: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00668]\n",
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:01<00:00, 16.22it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 200x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 25: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00884]\n",
-      "Epoch 26: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00494]\n",
-      "Epoch 27: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00529]\n",
-      "Epoch 28: 100%|█████████| 49/49 [00:56<00:00,  1.14s/it, loss=0.00513]\n",
-      "Epoch 29: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00503]\n",
-      "Epoch 30: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00852]\n",
-      "Epoch 31: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00525]\n",
-      "Epoch 32: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00571]\n",
-      "Epoch 33: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00495]\n",
-      "Epoch 34: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00574]\n",
-      "Epoch 35: 100%|██████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.0048]\n",
-      "Epoch 36: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00593]\n",
-      "Epoch 37: 100%|██████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.0054]\n",
-      "Epoch 38: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00569]\n",
-      "Epoch 39: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00641]\n",
-      "Epoch 40: 100%|██████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.0047]\n",
-      "Epoch 41: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00498]\n",
-      "Epoch 42: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00431]\n",
-      "Epoch 43: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00484]\n",
-      "Epoch 44: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00421]\n",
-      "Epoch 45: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00462]\n",
-      "Epoch 46: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00404]\n",
-      "Epoch 47: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00435]\n",
-      "Epoch 48: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00626]\n",
-      "Epoch 49: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00365]\n",
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:01<00:00, 16.22it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 200x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 50: 100%|██████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.0064]\n",
-      "Epoch 51: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00488]\n",
-      "Epoch 52: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00691]\n",
-      "Epoch 53: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00507]\n",
-      "Epoch 54: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00373]\n",
-      "Epoch 55: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00488]\n",
-      "Epoch 56: 100%|█████████| 49/49 [00:56<00:00,  1.14s/it, loss=0.00374]\n",
-      "Epoch 57: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00416]\n",
-      "Epoch 58: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00564]\n",
-      "Epoch 59: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00428]\n",
-      "Epoch 60: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00456]\n",
-      "Epoch 61: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00506]\n",
-      "Epoch 62: 100%|██████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.0036]\n",
-      "Epoch 63: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00306]\n",
-      "Epoch 64: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00442]\n",
-      "Epoch 65: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00393]\n",
-      "Epoch 66: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00361]\n",
-      "Epoch 67: 100%|██████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.0035]\n",
-      "Epoch 68: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00457]\n",
-      "Epoch 69: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00455]\n",
-      "Epoch 70: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00524]\n",
-      "Epoch 71: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00394]\n",
-      "Epoch 72: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00577]\n",
-      "Epoch 73: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00558]\n",
-      "Epoch 74: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00465]\n",
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:01<00:00, 16.23it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 200x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 75: 100%|█████████| 49/49 [00:57<00:00,  1.17s/it, loss=0.00369]\n",
-      "Epoch 76: 100%|█████████| 49/49 [00:56<00:00,  1.16s/it, loss=0.00296]\n",
-      "Epoch 77: 100%|█████████| 49/49 [00:57<00:00,  1.17s/it, loss=0.00641]\n",
-      "Epoch 78: 100%|█████████| 49/49 [00:56<00:00,  1.15s/it, loss=0.00384]\n",
-      "Epoch 79: 100%|█████████| 49/49 [00:57<00:00,  1.18s/it, loss=0.00447]\n",
-      "Epoch 80: 100%|█████████| 49/49 [00:57<00:00,  1.17s/it, loss=0.00444]\n",
-      "Epoch 81: 100%|██████████| 49/49 [00:57<00:00,  1.17s/it, loss=0.0046]\n",
-      "Epoch 82: 100%|██████████| 49/49 [00:57<00:00,  1.17s/it, loss=0.0053]\n",
-      "Epoch 83: 100%|██████████| 49/49 [00:57<00:00,  1.17s/it, loss=0.0032]\n",
-      "Epoch 84: 100%|█████████| 49/49 [00:57<00:00,  1.17s/it, loss=0.00467]\n",
-      "Epoch 85: 100%|█████████| 49/49 [00:56<00:00,  1.15s/it, loss=0.00346]\n",
-      "Epoch 86: 100%|█████████| 49/49 [00:57<00:00,  1.17s/it, loss=0.00382]\n",
-      "Epoch 87: 100%|██████████| 49/49 [00:57<00:00,  1.18s/it, loss=0.0034]\n",
-      "Epoch 88: 100%|█████████| 49/49 [00:58<00:00,  1.18s/it, loss=0.00355]\n",
-      "Epoch 89: 100%|█████████| 49/49 [00:56<00:00,  1.15s/it, loss=0.00478]\n",
-      "Epoch 90: 100%|█████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00405]\n",
-      "Epoch 91: 100%|█████████| 49/49 [00:57<00:00,  1.17s/it, loss=0.00465]\n",
-      "Epoch 92: 100%|█████████| 49/49 [00:57<00:00,  1.16s/it, loss=0.00416]\n",
-      "Epoch 93: 100%|█████████| 49/49 [00:56<00:00,  1.16s/it, loss=0.00372]\n",
-      "Epoch 94: 100%|█████████| 49/49 [00:56<00:00,  1.16s/it, loss=0.00372]\n",
-      "Epoch 95: 100%|█████████| 49/49 [00:57<00:00,  1.18s/it, loss=0.00492]\n",
-      "Epoch 96: 100%|█████████| 49/49 [00:56<00:00,  1.16s/it, loss=0.00495]\n",
-      "Epoch 97: 100%|█████████| 49/49 [00:57<00:00,  1.18s/it, loss=0.00486]\n",
-      "Epoch 98: 100%|█████████| 49/49 [00:56<00:00,  1.16s/it, loss=0.00418]\n",
-      "Epoch 99: 100%|█████████| 49/49 [00:58<00:00,  1.18s/it, loss=0.00563]\n",
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:02<00:00, 16.06it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 200x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 100: 100%|████████| 49/49 [00:56<00:00,  1.16s/it, loss=0.00425]\n",
-      "Epoch 101: 100%|████████| 49/49 [00:57<00:00,  1.18s/it, loss=0.00327]\n",
-      "Epoch 102: 100%|█████████| 49/49 [00:56<00:00,  1.15s/it, loss=0.0045]\n",
-      "Epoch 103: 100%|████████| 49/49 [00:57<00:00,  1.17s/it, loss=0.00335]\n",
-      "Epoch 104: 100%|████████| 49/49 [00:56<00:00,  1.16s/it, loss=0.00341]\n",
-      "Epoch 105: 100%|████████| 49/49 [00:56<00:00,  1.16s/it, loss=0.00418]\n",
-      "Epoch 106: 100%|█████████| 49/49 [00:56<00:00,  1.16s/it, loss=0.0032]\n",
-      "Epoch 107: 100%|████████| 49/49 [00:57<00:00,  1.18s/it, loss=0.00503]\n",
-      "Epoch 108: 100%|████████| 49/49 [00:56<00:00,  1.16s/it, loss=0.00341]\n",
-      "Epoch 109: 100%|████████| 49/49 [00:56<00:00,  1.16s/it, loss=0.00545]\n",
-      "Epoch 110: 100%|████████| 49/49 [00:57<00:00,  1.18s/it, loss=0.00523]\n",
-      "Epoch 111: 100%|████████| 49/49 [00:59<00:00,  1.21s/it, loss=0.00306]\n",
-      "Epoch 112: 100%|████████| 49/49 [00:56<00:00,  1.15s/it, loss=0.00472]\n",
-      "Epoch 113: 100%|████████| 49/49 [00:59<00:00,  1.21s/it, loss=0.00513]\n",
-      "Epoch 114: 100%|████████| 49/49 [00:59<00:00,  1.21s/it, loss=0.00339]\n",
-      "Epoch 115: 100%|████████| 49/49 [00:57<00:00,  1.18s/it, loss=0.00388]\n",
-      "Epoch 116: 100%|████████| 49/49 [00:56<00:00,  1.16s/it, loss=0.00398]\n",
-      "Epoch 117: 100%|█████████| 49/49 [00:57<00:00,  1.17s/it, loss=0.0026]\n",
-      "Epoch 118: 100%|████████| 49/49 [00:57<00:00,  1.18s/it, loss=0.00355]\n",
-      "Epoch 119: 100%|████████| 49/49 [00:56<00:00,  1.15s/it, loss=0.00315]\n",
-      "Epoch 120: 100%|████████| 49/49 [00:56<00:00,  1.15s/it, loss=0.00346]\n",
-      "Epoch 121: 100%|████████| 49/49 [00:56<00:00,  1.16s/it, loss=0.00506]\n",
-      "Epoch 122: 100%|████████| 49/49 [00:57<00:00,  1.18s/it, loss=0.00301]\n",
-      "Epoch 123: 100%|█████████| 49/49 [00:58<00:00,  1.19s/it, loss=0.0051]\n",
-      "Epoch 124: 100%|█████████| 49/49 [00:57<00:00,  1.18s/it, loss=0.0032]\n",
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:03<00:00, 15.74it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKIAAACGCAYAAABez1E7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAANKUlEQVR4nO2dOYhVyxaGVzvP86zthFPigFEjGimYiBoogiKCiYGhkQqCgZGZCIIogoFgooEgYiy0swji2M7zPM/Ti9zvX3+fvU6dfd99t/ryf1EVdU7t2rtX7/WfVauqmn7//v3bhPiH6fRPD0AIMxmiyAQZosgCGaLIAhmiyAIZosgCGaLIAhmiyAIZosiCLqkf7Natm6vjhAxPzjQ1NdUsm5n9+vWrKHfqFP8f4Heja3Ab1utdA/n582fpNXDc3G90fe6HnwfXI7DfqB8ea/TMeazR80r9m3fp4s3q8+fPpX0W1637CSH+D8gQRRYku2Z+3SPsJlJf4Qy7RoRdBvYTuZtobPWuGfXTyPOIro9j5z6ruk2mc+fOpddoRBpE91zlc4jeiCILZIgiC5Jdc0Tk3hpxU/xrK/WXMboeM7OePXsW5b59+7q2yG3wr7v3798XZXaL379/L+2zEZeaGmGoVU/pk8fDY4tcdVWJUQW9EUUWyBBFFsgQRRYka8RIL9QLkZS18WxNpIEmTpzo6gsXLizKs2bNcm2DBg0qykOHDnVt3759K62zRmxtbS3Kr1+/dm0PHz4s/d6ZM2dcHdv5nlFboe40qz5j1MisBxP9DaLr/9WlT3ojiiyQIYosSHbN9X72I5Ebx1f6jx8/XNvgwYNdffny5UV51apVrm306NFFmd0dXh9DMGZmL1++dHW8r5EjR7o2dPEcIurVq1fp9flZHT9+vCgfPHjQtbW1tdUct5lZjx49XB1lxJcvX1xb165drQwcDz/zRpInyvqs1U+j6I0oskCGKLJAhiiyoCl1yxH+uR4lhqJe4Ok/nH5bv369a1u5cqWrDxkypOb3zHxIhPUJju3Vq1eu7d27d66O42ONiBotyv5h3cXaDsd66tQp1/bo0aOi/OnTJ9fW3Nzs6ji+I0eOuLYrV64U5Vu3brm2Z8+elV6DSQ3RRNOarKc5LFULvRFFFsgQRRbIEEUWJGtEnhqKpphQd40YMcK1bdy4sSgvWrTItbHWwqGxXsL4IMfxsB+e0uPpOLxG9+7dXRvqII7boQ7i+CfrMIzx8VQh6jfW09OnTy/tJ0q9u379uqsfOHCgKO/bt8+18TPH59VIpnWUMc9/g1rojSiyQIYosqBy9g26NHYTw4YNK8p79+51bWPGjCnK7Aoj988uBMM5UYiod+/ero1DCZHE+PDhQ82ymXdb9TLNcew8VnRbLDG+fv1a2i9nnqNU4YyjNWvW1ByLmdmePXtcHe8ryt7mEE2VBVOI3ogiC2SIIgtkiCILKodvUBPwlNb+/fuLcktLi2vr06fPfy9OurN///6ujqEW1lbYD+s+DHNw2OXJkyeujjosyp6O0q5wLGbt9RLqMtZoeI8cWuJnPnny5KIcTSOynu3Xr19pnzt37nT1DRs2FOVIP0d7AWnvG9FhkSGKLEh2zZwBjK/iOXPmuDaM5GMGTb0+I9ilYfggcs0fP350bfxZDJ9EoZXhw4e7Nvwsfy8Ke/Djxvu6ffu2a+PPYvbN2LFjSz/LkgfHd//+/fAaixcvLsqY0cNEmd3s0lnW1EJvRJEFMkSRBTJEkQWV90fEVWxr1651bagJ+Hs8rRcRZYKg7uGpsCi0wpoINSRfA0MkHNpB+PpRRjJfA6cgJ02a5No4i4ezfJBoFV2UKcSZMUuWLCnKN27ccG3RxlOoC3n6LwW9EUUWyBBFFsgQRRYka0TWdjgdN2/ePNeGGiHShKylOB4XpYGh9os2c+I4Ise4cKyoe2t9tgy+R66jhsSMbDM/HYY7S5i1n/J88eJFUeZVjQg/R3zO/Mz52WFWeLSDRZQhXgW9EUUWyBBFFiS7Zs6gwE2QONsC67zAHV/30XSXmXdxHCJB18ChBHTHz58/L20zMxs/fnxRZteM98GuEF0cfy/KNokyxtltnz171tUx8xqz4M3i7Gmsc5iHp994H8qya0ToeAvRYZEhiiyQIYosqHzOCv7MZ/2EYQjWFThtxVqC08KwzhoRtSZrS1wpyFrqwYMHro6aMdqzmjdvGjBgQFHmaTLWaPgM+D5w49DLly+7Nu53/vz5Nfs081ov2hQqmprjOqea4ZRfdM5KlU079UYUWSBDFFmQ7JrZbc6ePbso8z7VGKLgRT7omngfGM4MwUVAUaiHw0fo0vh7HD7BkAnPOmC/AwcOdG34PDhExddEOcB7eON98IzMggULSq/55s0b1/b06dOiHGUqcaY5y6pRo0bVLJuZ3bx5syizS8drKvtGdFhkiCILZIgiC5I1IuswDJ9wlgbrQgR1Dn8u2nuZQwKoS6PjdTmjhTUSfvbixYul42FNdO/evaLM04Y8rYhjZT2N2nPGjBmujTNcUKNxGAp1MetZ1JP1Frvj2HmKMZq6w+ejKT7RYZEhiiyofAQa7yGDREmT6O44JMSZIOjy2I1jGIRDIphRwwv8edYB6+z+MdTDGTY4e8P3y2Gp8+fPF2UMs5j55FeWOLwHYpRkjKelslRAV8mumSUHhte4nygxtpETUGuhN6LIAhmiyAIZosiC5E2YOCSwZcuWorx06VLXxns4l1FvYTrqtyizmfUK6kleYM/XwGwg1jaoWaPsbQ5XcB3HwPeMWjvSj2Z+f0S+BoZoONMbp/Ewa8is/XNFDcmL4q5evVp6/Sj7RkegiQ6DDFFkgQxRZEFywIezhVGHsbZCTRJt2shxRN77OTqWFWNsHH+LNvGMUtaic184jhllIUdHyPKzwixovgYeoWvmp994qhJ1KOtAvGa97GlcxM/6Ee+Df1ooDUz8K5AhiixIds3Ryec8FYQull/v0cnv0TXxiAazOHyC3+NxYwjCzO9LzS4NXXV0zBm7/3r7yyAYzuFMIXajKGX42aEEihZEMTzlh9KF/644nioLpCL0RhRZIEMUWSBDFFlQeRUfai/WINjG34vO42AdiNqKdRfqQp42w/NBMD3KrP2qNbwGp13hNfkakb5lzYqaOTovJjo6zSx9T8gofBQdwWYWP7soCzt1L8ky9EYUWSBDFFmQ7JrZFZw7d64oL1u2zLWhu+HvYdiDs2bYbWD2SXSsGGetYAgCM6nNzKZNm+bqGKJh9xLtwYgzTW/fvnVt7MYxLMRhIHSN/D2GZ4WQKHsbn1W0v5CZz+Lh2TR8BtE+QVXctN6IIgtkiCILZIgiC5I1IuuX1tbWonz37l3XhrqMNQhmmHCWBmemRFkcOB7OZJ45c2ZR5rAP6xds51WEuLkSa7BoNWKUccQ6D0M7nAXO2hP1Nu9dyBsmpcJjP336dFFmjZi6B6L2RxQdFhmiyAIZosiCyntoP378uCjv2rXLtW3evLkos5aJspVZo0VpRzhVFx2Fi+M0a6/RUPdwyhjq0Ei/sibkTHOMFbLuQljP8r7duJEpa9bUY844/tfW1ubqR48eLcp8z9gvt2GMURpRdFhkiCILkl1z9Lo9dOiQq+OREtu2bSv9Hu89zfCi/rLxcJgHXTy7zWiDoGihPoevcCFTdAScWSxHoiM02I2iO+Z+cOy8KRVuRHXt2jXXtnv3blfH++LnES1m+6unleqNKLJAhiiyQIYosiB5EyZe/I2aIFpttmPHDteGGzZxRjb3gzqIwx4YauHVd/hZDsmwlsFwDqehodbk+8fHxmEn1qw4dcdaE/vldDbWerionjUa9sNnsOBnt2/f7tqOHTtmqeDfJ0rL479jFLL6g96IIgtkiCILksM3jRxZgC5u06ZNrg1nGVasWOHaeF9F7CdaUB7BboEXC2E7z4jg7A27G3S30cIyrvOeNeh+OVzFx21g6IVnT3CsfMopZkpdunTJtfF9oeSITlmNMrSroDeiyAIZosgCGaLIguTwDesFrLPOwBBJtGps6tSprm3r1q2uPnfu3KLMmhBDFLwwHq/PYRfOgsYzUVhPYj/RAns+ywVPeudrjBs3zrXhGSyHDx92bXfu3HF1DFNxyAifB2801cjp8nhfkQ5X+Eb8K5EhiiyQIYosSNaIrBeimFKUolX2ObP2mdarV68uyuvWrXNtqN84/oj1SMvw2Fl34Xc5Nsgr7JCTJ0+6+oULF4ryiRMnXBvGBlmHNrKqEXUYT2NGep6J4sVRGhhS74zFWuiNKLJAhiiyINk1R4tl2G2nLqThPiP3jwuHzMxaWlqKcnNzs2ubMGFCUeZNoKZMmeLqGN7hz2LYhUMyuKkAbkhl5kMyZt41sfuN3G3VabTIbfLfo5GF8lG/eB98T9H+63/QG1FkgQxRZIEMUWRBskbkn+Suk0BLRN2zRmxEr0SrxrDfehsk4WejMEN0lgr3yRopOjosamuEKJyG1DuDJXWsEfx35dS7muNK6lmIvxkZosiCygvsU91o5KYit8Ckhg64n3rHrEX7ZON9NDLLER0v8b/ee/oPUfZL1WfXyKL56NiSFPRGFFkgQxRZIEMUWZAcvhHi70RvRJEFMkSRBTJEkQUyRJEFMkSRBTJEkQUyRJEFMkSRBTJEkQX/AYc/ETQ93p53AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 200x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 125: 100%|████████| 49/49 [00:57<00:00,  1.17s/it, loss=0.00394]\n",
-      "Epoch 126: 100%|████████| 49/49 [00:57<00:00,  1.18s/it, loss=0.00387]\n",
-      "Epoch 127: 100%|████████| 49/49 [00:58<00:00,  1.18s/it, loss=0.00382]\n",
-      "Epoch 128: 100%|████████| 49/49 [00:57<00:00,  1.18s/it, loss=0.00439]\n",
-      "Epoch 129: 100%|████████| 49/49 [00:56<00:00,  1.15s/it, loss=0.00654]\n",
-      "Epoch 130: 100%|████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00585]\n",
-      "Epoch 131: 100%|████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00495]\n",
-      "Epoch 132: 100%|████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00375]\n",
-      "Epoch 133: 100%|████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00406]\n",
-      "Epoch 134: 100%|████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00394]\n",
-      "Epoch 135: 100%|████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00369]\n",
-      "Epoch 136: 100%|████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00354]\n",
-      "Epoch 137: 100%|████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00384]\n",
-      "Epoch 138: 100%|████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00457]\n",
-      "Epoch 139: 100%|████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00427]\n",
-      "Epoch 140: 100%|████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00319]\n",
-      "Epoch 141: 100%|████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00419]\n",
-      "Epoch 142: 100%|████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00412]\n",
-      "Epoch 143: 100%|████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00397]\n",
-      "Epoch 144: 100%|████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00292]\n",
-      "Epoch 145: 100%|████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00397]\n",
-      "Epoch 146: 100%|████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00364]\n",
-      "Epoch 147: 100%|████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00406]\n",
-      "Epoch 148: 100%|████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00449]\n",
-      "Epoch 149: 100%|████████| 49/49 [00:55<00:00,  1.14s/it, loss=0.00239]\n",
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [01:01<00:00, 16.37it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKIAAACGCAYAAABez1E7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAANh0lEQVR4nO2dyatVRxfFS41d7LvYt6hgP3CgiEIQbCbqwIEIjp0J4sT/QRCcOlQQdGoXIhIwg6iggh1q7LFvE3tNbDIrVv18d7/z3mdIvY+1RlXsc6rq1N3UXnfvXVXdvnz58iUZxn+M7v/1AAwjJSuiUQmsiEYVsCIaVcCKaFQBK6JRBayIRhWwIhpVwIpoVIHvmj44YMCAot6tW7dcZnCme/fW+q3Pfv78uZD16NGjZf3Tp08t24xk331XfuLff/9d1HUM7L/pWHUu2uozGl/0XNN55Ph69+5dyN69e5fLvXr1atznx48fW8p69uzZUsY5fvXq1dcfAHhFNKqAFdGoAo1NM5dwNUc0TbqkRzkVNGFsR/ukrOlY2zOLNMetxhOZbY6NpinqQ8fHuYrMZkSHSCMiWTSvHLe+y/71m6PvbQWviEYVsCIaVaCxaSYi0xRBl20u73/99VfjZyOTr/8M+W+f/wTfvn3bsn/9Lr6ntKK9sWmd/4QjM8Z2dDw029oOxxqZf9Z17jhWNc008doO+28Cr4hGFbAiGlXAimhUgcYckTxQ61HUgVxG63yP7pzZs2fn8owZMwrZDz/8kMt9+vQpZLNmzcrloUOHFrJnz54V9QsXLqRWUL706NGjlu3cu3evkL1//76o37lzJ5cZZYjcYBGfiyIrRMRnCXXDcDzKGSOO2pH/DLntDr9hGP8CrIhGFejWdDvpwIEDi3r0WvQ3//vvv8/lxYsXF7INGzYU9cmTJ+cyoxW6/L948aKQqammKaS7ZPjw4bn84MGDQqYmtm/fvoVMxzNz5swU4ffff8/lEydOFLJffvkll9WEp5TSn3/+WdR1LiOqFLl9CM7Hhw8fcjmKfEVRHyZE8DvagldEowpYEY0qYEU0qkBjjjhs2LCiHmW1KJcht9y8eXMu//jjj4VMEzhTKsNzyl3Yxx9//FHIRo8enctv3rwpZOQ9Go7iN+nUMNlU2xk/fnwhIy99+vRpLjPkqOOjK+nu3bst27l48WIhUxcSXU3KAzmPEZ+MsojI/ZUXss3Xr1+n9uAV0agCVkSjClgRjSrwTTK0KVP+sHTp0kKmvsMoyzelklswVKd9kHdFoTCmKGk75MHqRxw1alQh69+/fy6T2w4aNKioK59kOFL9k9OmTStky5Ytazl2pqwp97t06VIh27dvXy4fP368kHHsUYhP+yfX1nmkrAm8IhpVwIpoVIFvEuLjEj5x4sRc3r17dyHTv/kMBXFJ19DQpEmTCpmaoiizW10efC+l0n3CjGSt8/s5dgWnVM2vhhRTSunx48e5TIozePDgoq5mk2N9/vx5LnMeX758mctqplNKadeuXUVdzW9EY9h/tNFM+28Fr4hGFbAiGlXAimhUgU5naCsP6tevXyHbtGlTLpNLKH+hjO0oD4tCc0zRUhcJQ3MM+SnvYUhN3TnkiNEZMVH4iy4qHStDYZHLjK4mzSbn+TZaX7lyZSHjfPz000+5TG6n3xllhHsXn9FlYUU0qkCnTbMuzRs3bixkc+bMyWWaX82UoXsiyp6mTKMX3KwUmTua6ujMFu1fIylt9amg2dJnOR6dH45NXTIcA+d1xIgRuRxtsJ87d24hY1RKM4cOHDhQyCJzrPoRHafXCl4RjSpgRTSqgBXRqAKNOSJ5h25wX7VqVSFTVwddK/qe7uhL6euwmdabnmNIkNvxO7TO8TBTRqHupPaOR1buF2Wt0CVDrkUOqVB3GudRv4vfz7FOnTq1zTY59o6c3dgEXhGNKmBFNKqAFdGoAp3miOvXr89lcinlMnxPOSNDUeRAyp+iQ3/4noamGKZipreOnXxJD/Ek11S+xDQ01nV85H06BxEnZbucu4gnR3POzPNFixbl8sGDBwvZ9evXc7kzWdgRvCIaVcCKaFSBxusrs090ExTDXWrGaNI0w6a985yjsJGa6uggoXPnzhUyujYWLlyYy9EmrIgasE2aP0W0IYk0hmNXmsHDCVq1mVJpRtm/utNSKunQunXrCtm2bdtyOTqf0ddbGF0WVkSjClgRjSrQmCNy07gedES3g/KFKKRGdwm5VbShW3kI3SWaErVkyZJCdvPmzaKuY4iuFYuysMkRo1s9mUqlc8cDRy9fvlzUV6xYkZqAqWbqhmrvejYd6/z581uOlX3o3JkjGl0WVkSjCjQ2zbq8p1SemULvfHQ7aHSuIqMgakaYxaNuIZp0NRN0yeiVGeyTZ8aMGzcul6NIAk0650q/g2ZbXV83btwoZFOmTCnqzM5RPHz4MJd5FrfOVeSGopzzqnPAgwo0etT0onSFV0SjClgRjSpgRTSqQGOOyMOMjh07lssTJkwoZJoRTHdBdGY13SBRZoy2wxCjyuh2iq6pZf90CymUI/HMbLqIlLPpnSspld/IA5p4PqJCD29KqQwH8htv377dsg+eP64b7s+cOVPI9DfgXOlvGR1Q1QpeEY0qYEU0qkBj00z3xeHDh3N5+fLlhUzNIZdpXd7pHuBVWfpsdD5hFB2Ibk5lOzTjam54Lk8UPRgyZEhRV7fLmDFjCtmtW7datkN3zsiRI3OZWTMLFizIZbpvNBmYppluGHU98fdR8//bb78VsmhjVRN4RTSqgBXRqAJWRKMKNOaIUYbJkydPCpmGe8jtNNzEDe10Oyi/Y4ZPtFkoOuv5/v37Lfsk79PxNb3yLaWv+bRekcawnWaIa5gupZTOnj1b1NVNxNClclhyXeWB3GjG79Lfh+eWz5o1K5fp2lFXV3RYUyt4RTSqgBXRqAJWRKMKNOaI5CRbt27N5Xnz5hUy9f+RL2k75BIM46mcnDDajK5pWTzs8uTJk0Vdrx1jqDLacajcMjrrOqVyDpgipt9I/yP5pHJE7o7UudMM9ZTKlD3OOVPLNOTHb452Jyq/jvh7K3hFNKqAFdGoAo1NM00BzYhCw0/RxipmzdDEqashMgtsJ8og4XUO+l0cq5o7ZuJEGeKEuk/Yh5rqBw8eFDKaRu2T4VA1hxyPmk1SJc65fifnSjOHOB+aQe8ztI0uCyuiUQWsiEYVaMwReSeK7tzjLehNz3qOrpNNKT486NGjR7nMVCZtV3fipZTS2LFji7py3WjHYeRaokuG36Xh0eh8afI3nuWo80M3TJT6pmjvLEd1dzGb/MqVK7nM30N/A2+wN7osrIhGFWhsmpn1e+/evVxm1rFu7KG7Qk08N5szeqPLP8+FUddCZNIYLaHZUlPJ8aiJ4Xd0JBtHzRZl2icznCK3FL9Zo0B0u0RXcbAPpVncoKUb6KLzKn29hdFlYUU0qoAV0agCjTkiN2Lv3bs3l7ds2VLIdIM5d5upa4dhQroEIq6hvJScSF0t5HLkaOq+IF9SzsZ2NIwWXUubUsnfuBlfn6WMme+a7c7dePouXU2sRzJ1Rf3666+FTOeH89iZjBuFV0SjClgRjSpgRTSqQKd38R06dCiXp0+fXsjWrFnTqM32rvxS/hTt+OOutY5cz6V+M3JE5XbR4U1R+lhKpQ+UMuXBHDc5tPZDjhZdpabfyJAew5OnT5/OZabQRVCO6F18RpeFFdGoAo1tGF0S6jLZsWNHIdON2HrbZUqlKaC7gmE8zTaJzo+OrlKjW4GZQtEmfr3CgRk+6pZqrw81uTTx+mx0YBXlNLHaDvvX9/gdp06dKup79uxp+Wx0GIHPRzT+L2BFNKqAFdGoAt2+NMzZiVK0yB9nzpyZy9u3by9kc+bMyWWGxsi11CVB903kItB2mS0e9Un3iXIi8lmdtvZ2rUUhR+XM7J/3zkTfpdnT5Ija588//1zI1A2XUsn9O5JNrt/IsXHHYVvwimhUASuiUQUam2ZubFKTFt3KrmcDppTSzp07c5m3X9J9oxGTKIuGZ72o24UHAxBqUnRDFvsnNdErI7ixjK4NdbVEmTHMcOJ4NPJDV5NGQXgu9/nz59sspxRnmvN31WcpU3pCqmLTbHQZWBGNKmBFNKpApzmiInJfROcKrl27tpCtX7++qOsOPPJA5WVRtgvfo9tBueb169cLmfJLckR1czBDnOeGK58kD1YXzf79+wsZD2VS7snsbc2Kj86djHgf+4iugCP0PfbP+WkLXhGNKmBFNKqAFdGoAo05IkNs0a6tqEkN20VpRimVqVarV68uZCtWrMhlnjQR7XZjHzoe3n3XNPx37dq1lm0SR48eLepHjhzJ5atXrxYycrTIV6e8jLwvSouLOGK0qzLyI/L3Z3i0LXhFNKqAFdGoAo1NM6+JjV7TZZqulejGeJoJfTfaLMQQm9ZJKXjmoPbBjUQaqqR50Y1NdLPQZaShOYbx1H3DzKCIDnD+9V2abX2Wc8zfIPrtmp6NzXE7xGd0GVgRjSpgRTSqQGOO2F46lSJy0TR1D6QU7wyL0peUy3DTPLOHVU5uo7wr2rTf3j0rUTZ5xN8IHUPUJmU655G7huOJVKMju/jMEY0uAyuiUQUab7DvyBKu5jhyJdA0RyYl8uRHJo1mky4SNSPRlRFsJ/p+jifamB5FRKLMd7YTbWaL+iD0WdIR7ZPz2Jlrz4r3/6e3DeMbwYpoVAErolEFGrtvDOPfhFdEowpYEY0qYEU0qoAV0agCVkSjClgRjSpgRTSqgBXRqAJWRKMK/APf6koH82ftcwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 200x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train completed, total time: 8859.028432130814.\n"
-     ]
-    }
-   ],
-   "source": [
-    "n_epochs = 150\n",
-    "val_interval = 25\n",
-    "epoch_loss_list = []\n",
-    "val_epoch_loss_list = []\n",
-    "\n",
-    "scaler = GradScaler()\n",
-    "total_start = time.time()\n",
-    "for epoch in range(n_epochs):\n",
-    "    model.train()\n",
-    "    epoch_loss = 0\n",
-    "    progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=70)\n",
-    "    progress_bar.set_description(f\"Epoch {epoch}\")\n",
-    "    for step, batch in progress_bar:\n",
-    "        images = batch[\"image\"].to(device)\n",
-    "        optimizer.zero_grad(set_to_none=True)\n",
-    "\n",
-    "        with autocast(enabled=True):\n",
-    "            # Generate random noise\n",
-    "            noise = torch.randn_like(images).to(device)\n",
-    "\n",
-    "            # Create timesteps\n",
-    "            timesteps = torch.randint(\n",
-    "                0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n",
-    "            ).long()\n",
-    "\n",
-    "            # Get model prediction\n",
-    "            noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n",
-    "\n",
-    "            loss = F.mse_loss(noise_pred.float(), noise.float())\n",
-    "\n",
-    "        scaler.scale(loss).backward()\n",
-    "        scaler.step(optimizer)\n",
-    "        scaler.update()\n",
-    "\n",
-    "        epoch_loss += loss.item()\n",
-    "\n",
-    "        progress_bar.set_postfix({\"loss\": epoch_loss / (step + 1)})\n",
-    "    epoch_loss_list.append(epoch_loss / (step + 1))\n",
-    "\n",
-    "    if (epoch + 1) % val_interval == 0:\n",
-    "        model.eval()\n",
-    "        val_epoch_loss = 0\n",
-    "        for step, batch in enumerate(val_loader):\n",
-    "            images = batch[\"image\"].to(device)\n",
-    "            noise = torch.randn_like(images).to(device)\n",
-    "            with torch.no_grad():\n",
-    "                with autocast(enabled=True):\n",
-    "                    timesteps = torch.randint(\n",
-    "                        0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n",
-    "                    ).long()\n",
-    "\n",
-    "                    # Get model prediction\n",
-    "                    noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n",
-    "                    val_loss = F.mse_loss(noise_pred.float(), noise.float())\n",
-    "\n",
-    "            val_epoch_loss += val_loss.item()\n",
-    "            progress_bar.set_postfix({\"val_loss\": val_epoch_loss / (step + 1)})\n",
-    "        val_epoch_loss_list.append(val_epoch_loss / (step + 1))\n",
-    "\n",
-    "        # Sampling image during training\n",
-    "        image = torch.randn((1, 1, 32, 40, 32))\n",
-    "        image = image.to(device)\n",
-    "        scheduler.set_timesteps(num_inference_steps=1000)\n",
-    "        with autocast(enabled=True):\n",
-    "            image = inferer.sample(input_noise=image, diffusion_model=model, scheduler=scheduler)\n",
-    "\n",
-    "        plt.figure(figsize=(2, 2))\n",
-    "        plt.imshow(image[0, 0, :, :, 15].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
-    "        plt.tight_layout()\n",
-    "        plt.axis(\"off\")\n",
-    "        plt.show()\n",
-    "\n",
-    "total_time = time.time() - total_start\n",
-    "print(f\"train completed, total time: {total_time}.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3e263b67",
-   "metadata": {},
-   "source": [
-    "### Learning curves"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "c7520419",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 800x550 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.style.use(\"seaborn-v0_8\")\n",
-    "plt.title(\"Learning Curves\", fontsize=20)\n",
-    "plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n",
-    "plt.plot(\n",
-    "    np.linspace(val_interval, n_epochs, int(n_epochs / val_interval)),\n",
-    "    val_epoch_loss_list,\n",
-    "    color=\"C1\",\n",
-    "    linewidth=2.0,\n",
-    "    label=\"Validation\",\n",
-    ")\n",
-    "plt.yticks(fontsize=12)\n",
-    "plt.xticks(fontsize=12)\n",
-    "plt.xlabel(\"Epochs\", fontsize=16)\n",
-    "plt.ylabel(\"Loss\", fontsize=16)\n",
-    "plt.legend(prop={\"size\": 14})\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "38724c9b",
-   "metadata": {},
-   "source": [
-    "## Sampling Brain Image\n",
-    "\n",
-    "In order to sample the brain images, we need to pass the model an image containing just noise and use it to remove the noise of the image iteratively. For that, we will use the `.sample()` function of the `inferer`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "092eb6a0",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [03:45<00:00,  4.44it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "model.eval()\n",
-    "noise = torch.randn((1, 1, 32, 40, 32))\n",
-    "noise = noise.to(device)\n",
-    "scheduler.set_timesteps(num_inference_steps=1000)\n",
-    "image = inferer.sample(input_noise=noise, diffusion_model=model, scheduler=scheduler)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "5dc3e69d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.style.use(\"default\")\n",
-    "plotting_image_0 = np.concatenate([image[0, 0, :, :, 15].cpu(), np.flipud(image[0, 0, :, 20, :].cpu().T)], axis=1)\n",
-    "plotting_image_1 = np.concatenate([np.flipud(image[0, 0, 15, :, :].cpu().T), np.zeros((32, 32))], axis=1)\n",
-    "plt.imshow(np.concatenate([plotting_image_0, plotting_image_1], axis=0), vmin=0, vmax=1, cmap=\"gray\")\n",
-    "plt.tight_layout()\n",
-    "plt.axis(\"off\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f0acc27a",
-   "metadata": {},
-   "source": [
-    "### Sampling with Denoising Diffusion Implicit Model Scheduler\n",
-    "\n",
-    "Recent papers have proposed different ways to improve the sampling speed by using fewer steps in the denoising process. In this example, we are using a `DDIMScheduler` (from [Song et al. \"Denoising Diffusion Implicit Models\"](https://arxiv.org/abs/2010.02502)) to reduce the original number of steps from 1000 to 250."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "e3e43b95",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 250/250 [00:55<00:00,  4.52it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "scheduler_ddim = DDIMScheduler(\n",
-    "    num_train_timesteps=1000, schedule=\"scaled_linear_beta\", beta_start=0.0005, beta_end=0.0195, clip_sample=False\n",
-    ")\n",
-    "\n",
-    "scheduler_ddim.set_timesteps(num_inference_steps=250)\n",
-    "\n",
-    "model.eval()\n",
-    "noise = torch.randn((1, 1, 32, 40, 32))\n",
-    "noise = noise.to(device)\n",
-    "\n",
-    "image = inferer.sample(input_noise=noise, diffusion_model=model, scheduler=scheduler_ddim)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "89f93ab8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.style.use(\"default\")\n",
-    "plotting_image_0 = np.concatenate([image[0, 0, :, :, 15].cpu(), np.flipud(image[0, 0, :, 20, :].cpu().T)], axis=1)\n",
-    "plotting_image_1 = np.concatenate([np.flipud(image[0, 0, 15, :, :].cpu().T), np.zeros((32, 32))], axis=1)\n",
-    "plt.imshow(np.concatenate([plotting_image_0, plotting_image_1], axis=0), vmin=0, vmax=1, cmap=\"gray\")\n",
-    "plt.tight_layout()\n",
-    "plt.axis(\"off\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a39c881c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "py:percent,ipynb"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+    "nbformat": 4,
+    "nbformat_minor": 5
 }
diff --git a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py
index 527b96d6..7e862f05 100644
--- a/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.py
+++ b/tutorials/generative/3d_ddpm/3d_ddpm_tutorial.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
@@ -55,7 +55,15 @@
 from monai.apps import DecathlonDataset
 from monai.config import print_config
 from monai.data import DataLoader
-from monai.transforms import AddChanneld, CenterSpatialCropd, Compose, Lambdad, LoadImaged, Resized, ScaleIntensityd
+from monai.transforms import (
+    EnsureChannelFirstd,
+    CenterSpatialCropd,
+    Compose,
+    Lambdad,
+    LoadImaged,
+    Resized,
+    ScaleIntensityd,
+)
 from monai.utils import set_determinism
 from torch.cuda.amp import GradScaler, autocast
 from tqdm import tqdm
@@ -93,7 +101,7 @@
 #
 # 1. `LoadImaged`:  Loads the brain images from files.
 # 2. `Lambdad`: Choose channel 1 of the image, which is the T1-weighted image.
-# 3. `AddChanneld`: Add the channel dimension of the input data.
+# 3. `EnsureChannelFirstd`: Add the channel dimension of the input data.
 # 4. `ScaleIntensityd`: Apply a min-max scaling in the intensity values of each image to be in the `[0, 1]` range.
 # 5. `CenterSpatialCropd`: Crop the background of the images using a roi of size `[160, 200, 155]`.
 # 6. `Resized`: Resize the images to a volume with size `[32, 40, 32]`.
@@ -105,7 +113,7 @@
     [
         LoadImaged(keys=["image"]),
         Lambdad(keys="image", func=lambda x: x[:, :, :, 1]),
-        AddChanneld(keys=["image"]),
+        EnsureChannelFirstd(keys=["image"], channel_dim="no_channel"),
         ScaleIntensityd(keys=["image"]),
         CenterSpatialCropd(keys=["image"], roi_size=[160, 200, 155]),
         Resized(keys=["image"], spatial_size=(32, 40, 32)),
diff --git a/tutorials/generative/3d_ldm/3d_ldm_tutorial.ipynb b/tutorials/generative/3d_ldm/3d_ldm_tutorial.ipynb
index 5e07974f..8be2b780 100644
--- a/tutorials/generative/3d_ldm/3d_ldm_tutorial.ipynb
+++ b/tutorials/generative/3d_ldm/3d_ldm_tutorial.ipynb
@@ -1,1196 +1,1196 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8efe4285",
-   "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": "e0a3f076",
-   "metadata": {},
-   "source": [
-    "# 3D Latent Diffusion Model\n",
-    "In this tutorial, we will walk through the process of using the MONAI Generative Models package to generate synthetic data using Latent Diffusion Models (LDM)  [1, 2]. Specifically, we will focus on training an LDM to create synthetic brain images from the Brats dataset.\n",
-    "\n",
-    "[1] - Rombach et al. \"High-Resolution Image Synthesis with Latent Diffusion Models\" https://arxiv.org/abs/2112.10752\n",
-    "\n",
-    "[2] - Pinaya et al. \"Brain imaging generation with latent diffusion models\" https://arxiv.org/abs/2209.07162"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "da9e6b23",
-   "metadata": {},
-   "source": [
-    "### Set up imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b44c4689",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.10/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"
-     ]
+    "cells": [
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "8efe4285",
+            "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": "e0a3f076",
+            "metadata": {},
+            "source": [
+                "# 3D Latent Diffusion Model\n",
+                "In this tutorial, we will walk through the process of using the MONAI Generative Models package to generate synthetic data using Latent Diffusion Models (LDM)  [1, 2]. Specifically, we will focus on training an LDM to create synthetic brain images from the Brats dataset.\n",
+                "\n",
+                "[1] - Rombach et al. \"High-Resolution Image Synthesis with Latent Diffusion Models\" https://arxiv.org/abs/2112.10752\n",
+                "\n",
+                "[2] - Pinaya et al. \"Brain imaging generation with latent diffusion models\" https://arxiv.org/abs/2209.07162"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "da9e6b23",
+            "metadata": {},
+            "source": [
+                "### Set up imports"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 1,
+            "id": "b44c4689",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "/media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.10/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",
+                    "text": [
+                        "MONAI version: 1.2.dev2304\n",
+                        "Numpy version: 1.23.5\n",
+                        "Pytorch version: 1.13.1+cu117\n",
+                        "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
+                        "MONAI rev id: 9a57be5aab9f2c2a134768c0c146399150e247a0\n",
+                        "MONAI __file__: /media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.10/site-packages/monai/__init__.py\n",
+                        "\n",
+                        "Optional dependencies:\n",
+                        "Pytorch Ignite version: 0.4.10\n",
+                        "ITK version: 5.3.0\n",
+                        "Nibabel version: 4.0.2\n",
+                        "scikit-image version: 0.19.3\n",
+                        "Pillow version: 9.3.0\n",
+                        "Tensorboard version: 2.11.0\n",
+                        "gdown version: 4.6.0\n",
+                        "TorchVision version: 0.14.1+cu117\n",
+                        "tqdm version: 4.64.1\n",
+                        "lmdb version: 1.4.0\n",
+                        "psutil version: 5.9.4\n",
+                        "pandas version: 1.5.3\n",
+                        "einops version: 0.6.0\n",
+                        "transformers version: 4.21.3\n",
+                        "mlflow version: 2.1.1\n",
+                        "pynrrd version: 1.0.0\n",
+                        "\n",
+                        "For details about installing the optional dependencies, please visit:\n",
+                        "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
+                        "\n"
+                    ]
+                }
+            ],
+            "source": [
+                "import os\n",
+                "import shutil\n",
+                "import tempfile\n",
+                "\n",
+                "import matplotlib.pyplot as plt\n",
+                "import torch\n",
+                "import torch.nn.functional as F\n",
+                "from monai import transforms\n",
+                "from monai.apps import DecathlonDataset\n",
+                "from monai.config import print_config\n",
+                "from monai.data import DataLoader\n",
+                "from monai.utils import first, set_determinism\n",
+                "from torch.cuda.amp import GradScaler, autocast\n",
+                "from torch.nn import L1Loss\n",
+                "from tqdm import tqdm\n",
+                "\n",
+                "from generative.inferers import LatentDiffusionInferer\n",
+                "from generative.losses import PatchAdversarialLoss, PerceptualLoss\n",
+                "from generative.networks.nets import AutoencoderKL, DiffusionModelUNet, PatchDiscriminator\n",
+                "from generative.networks.schedulers import DDPMScheduler\n",
+                "\n",
+                "print_config()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 2,
+            "id": "a21c1f6a",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# for reproducibility purposes set a seed\n",
+                "set_determinism(42)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "2b02aa6c",
+            "metadata": {},
+            "source": [
+                "### Setup a data directory and download dataset\n",
+                "Specify a MONAI_DATA_DIRECTORY variable, where the data will be downloaded. If not specified a temporary directory will be used."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 3,
+            "id": "5d450e1d",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "/tmp/tmp5nw3g3c4\n"
+                    ]
+                }
+            ],
+            "source": [
+                "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
+                "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
+                "print(root_dir)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "74302407",
+            "metadata": {},
+            "source": [
+                "### Prepare data loader for the training set\n",
+                "Here we will download the Brats dataset using MONAI's `DecathlonDataset` class, and we prepare the data loader for the training set."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 4,
+            "id": "c34a9ba3",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "<class 'monai.transforms.utility.array.AddChannel'>: Class `AddChannel` has been deprecated since version 0.8. please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead.\n"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "2023-02-19 09:07:46,210 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n",
+                        "2023-02-19 09:07:46,210 - INFO - File exists: /tmp/tmp5nw3g3c4/Task01_BrainTumour.tar, skipped downloading.\n",
+                        "2023-02-19 09:07:46,211 - INFO - Non-empty folder exists in /tmp/tmp5nw3g3c4/Task01_BrainTumour, skipped extracting.\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Loading dataset: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 388/388 [01:32<00:00,  4.21it/s]"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Image shape torch.Size([1, 96, 96, 64])\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "\n"
+                    ]
+                }
+            ],
+            "source": [
+                "batch_size = 2\n",
+                "channel = 0  # 0 = Flair\n",
+                "assert channel in [0, 1, 2, 3], \"Choose a valid channel\"\n",
+                "\n",
+                "train_transforms = transforms.Compose(\n",
+                "    [\n",
+                "        transforms.LoadImaged(keys=[\"image\"]),\n",
+                "        transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
+                "        transforms.Lambdad(keys=\"image\", func=lambda x: x[channel, :, :, :]),\n",
+                "        transforms.EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n",
+                "        transforms.EnsureTyped(keys=[\"image\"]),\n",
+                "        transforms.Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n",
+                "        transforms.Spacingd(keys=[\"image\"], pixdim=(2.4, 2.4, 2.2), mode=(\"bilinear\")),\n",
+                "        transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=(96, 96, 64)),\n",
+                "        transforms.ScaleIntensityRangePercentilesd(keys=\"image\", lower=0, upper=99.5, b_min=0, b_max=1),\n",
+                "    ]\n",
+                ")\n",
+                "train_ds = DecathlonDataset(\n",
+                "    root_dir=root_dir,\n",
+                "    task=\"Task01_BrainTumour\",\n",
+                "    section=\"training\",  # validation\n",
+                "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
+                "    num_workers=8,\n",
+                "    download=True,  # Set download to True if the dataset hasnt been downloaded yet\n",
+                "    seed=0,\n",
+                "    transform=train_transforms,\n",
+                ")\n",
+                "train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=8, persistent_workers=True)\n",
+                "print(f'Image shape {train_ds[0][\"image\"].shape}')"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "1d36e0c4",
+            "metadata": {},
+            "source": [
+                "### Visualise examples from the training set"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 5,
+            "id": "723c2dad",
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "<matplotlib.image.AxesImage at 0x7f5030f90af0>"
+                        ]
+                    },
+                    "execution_count": 5,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 3 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "# Plot axial, coronal and sagittal slices of a training sample\n",
+                "check_data = first(train_loader)\n",
+                "idx = 0\n",
+                "\n",
+                "img = check_data[\"image\"][idx, 0]\n",
+                "fig, axs = plt.subplots(nrows=1, ncols=3)\n",
+                "for ax in axs:\n",
+                "    ax.axis(\"off\")\n",
+                "ax = axs[0]\n",
+                "ax.imshow(img[..., img.shape[2] // 2], cmap=\"gray\")\n",
+                "ax = axs[1]\n",
+                "ax.imshow(img[:, img.shape[1] // 2, ...], cmap=\"gray\")\n",
+                "ax = axs[2]\n",
+                "ax.imshow(img[img.shape[0] // 2, ...], cmap=\"gray\")\n",
+                "# plt.savefig(\"training_examples.png\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "513d7eee",
+            "metadata": {},
+            "source": [
+                "## Autoencoder KL\n",
+                "\n",
+                "### Define Autoencoder KL network\n",
+                "\n",
+                "In this section, we will define an autoencoder with KL-regularization for the LDM. The autoencoder's primary purpose is to transform input images into a latent representation that the diffusion model will subsequently learn. By doing so, we can decrease the computational resources required to train the diffusion component, making this approach suitable for learning high-resolution medical images.\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 6,
+            "id": "1042ebac",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Using cuda\n"
+                    ]
+                }
+            ],
+            "source": [
+                "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+                "print(f\"Using {device}\")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 7,
+            "id": "383a2043",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "autoencoder = AutoencoderKL(\n",
+                "    spatial_dims=3,\n",
+                "    in_channels=1,\n",
+                "    out_channels=1,\n",
+                "    num_channels=(32, 64, 64),\n",
+                "    latent_channels=3,\n",
+                "    num_res_blocks=1,\n",
+                "    norm_num_groups=16,\n",
+                "    attention_levels=(False, False, True),\n",
+                ")\n",
+                "autoencoder.to(device)\n",
+                "\n",
+                "\n",
+                "discriminator = PatchDiscriminator(spatial_dims=3, num_layers_d=3, num_channels=32, in_channels=1, out_channels=1)\n",
+                "discriminator.to(device)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "67f94d1b",
+            "metadata": {},
+            "source": [
+                "### Defining Losses\n",
+                "\n",
+                "We will also specify the perceptual and adversarial losses, including the involved networks, and the optimizers to use during the training process."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 8,
+            "id": "7594daa3",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+                        "Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=SqueezeNet1_1_Weights.IMAGENET1K_V1`. You can also use `weights=SqueezeNet1_1_Weights.DEFAULT` to get the most up-to-date weights.\n"
+                    ]
+                }
+            ],
+            "source": [
+                "l1_loss = L1Loss()\n",
+                "adv_loss = PatchAdversarialLoss(criterion=\"least_squares\")\n",
+                "loss_perceptual = PerceptualLoss(spatial_dims=3, network_type=\"squeeze\", is_fake_3d=True, fake_3d_ratio=0.2)\n",
+                "loss_perceptual.to(device)\n",
+                "\n",
+                "\n",
+                "def KL_loss(z_mu, z_sigma):\n",
+                "    kl_loss = 0.5 * torch.sum(z_mu.pow(2) + z_sigma.pow(2) - torch.log(z_sigma.pow(2)) - 1, dim=[1, 2, 3, 4])\n",
+                "    return torch.sum(kl_loss) / kl_loss.shape[0]\n",
+                "\n",
+                "\n",
+                "adv_weight = 0.01\n",
+                "perceptual_weight = 0.001\n",
+                "kl_weight = 1e-6"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 9,
+            "id": "354a3057",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "optimizer_g = torch.optim.Adam(params=autoencoder.parameters(), lr=1e-4)\n",
+                "optimizer_d = torch.optim.Adam(params=discriminator.parameters(), lr=1e-4)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "be4fe2d4",
+            "metadata": {},
+            "source": [
+                "### Train model"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 10,
+            "id": "047c1bc4",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:27<00:00,  1.31it/s, recons_loss=0.0642, gen_loss=0, disc_loss=0]\n",
+                        "Epoch 1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:29<00:00,  1.30it/s, recons_loss=0.0421, gen_loss=0, disc_loss=0]\n",
+                        "Epoch 2: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:30<00:00,  1.29it/s, recons_loss=0.0337, gen_loss=0, disc_loss=0]\n",
+                        "Epoch 3: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:31<00:00,  1.28it/s, recons_loss=0.0325, gen_loss=0, disc_loss=0]\n",
+                        "Epoch 4: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:31<00:00,  1.28it/s, recons_loss=0.0307, gen_loss=0, disc_loss=0]\n",
+                        "Epoch 5: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:31<00:00,  1.28it/s, recons_loss=0.0277, gen_loss=0, disc_loss=0]\n",
+                        "Epoch 6: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.027, gen_loss=0.528, disc_loss=0.342]\n",
+                        "Epoch 7: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0282, gen_loss=0.594, disc_loss=0.228]\n",
+                        "Epoch 8: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0291, gen_loss=0.572, disc_loss=0.238]\n",
+                        "Epoch 9: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0284, gen_loss=0.511, disc_loss=0.246]\n",
+                        "Epoch 10: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0287, gen_loss=0.389, disc_loss=0.223]\n",
+                        "Epoch 11: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0279, gen_loss=0.425, disc_loss=0.218]\n",
+                        "Epoch 12: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0277, gen_loss=0.406, disc_loss=0.23]\n",
+                        "Epoch 13: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0269, gen_loss=0.384, disc_loss=0.221]\n",
+                        "Epoch 14: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0259, gen_loss=0.432, disc_loss=0.231]\n",
+                        "Epoch 15: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0259, gen_loss=0.375, disc_loss=0.225]\n",
+                        "Epoch 16: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0257, gen_loss=0.41, disc_loss=0.226]\n",
+                        "Epoch 17: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0255, gen_loss=0.394, disc_loss=0.218]\n",
+                        "Epoch 18: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0254, gen_loss=0.403, disc_loss=0.221]\n",
+                        "Epoch 19: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0256, gen_loss=0.389, disc_loss=0.224]\n",
+                        "Epoch 20: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0257, gen_loss=0.403, disc_loss=0.221]\n",
+                        "Epoch 21: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0252, gen_loss=0.406, disc_loss=0.22]\n",
+                        "Epoch 22: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0253, gen_loss=0.388, disc_loss=0.214]\n",
+                        "Epoch 23: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0246, gen_loss=0.387, disc_loss=0.215]\n",
+                        "Epoch 24: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.13it/s, recons_loss=0.0239, gen_loss=0.411, disc_loss=0.214]\n",
+                        "Epoch 25: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.13it/s, recons_loss=0.0243, gen_loss=0.415, disc_loss=0.211]\n",
+                        "Epoch 26: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0243, gen_loss=0.41, disc_loss=0.209]\n",
+                        "Epoch 27: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.13it/s, recons_loss=0.0234, gen_loss=0.461, disc_loss=0.227]\n",
+                        "Epoch 28: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0237, gen_loss=0.426, disc_loss=0.207]\n",
+                        "Epoch 29: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.024, gen_loss=0.421, disc_loss=0.21]\n",
+                        "Epoch 30: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0236, gen_loss=0.447, disc_loss=0.209]\n",
+                        "Epoch 31: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0236, gen_loss=0.414, disc_loss=0.208]\n",
+                        "Epoch 32: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0231, gen_loss=0.418, disc_loss=0.206]\n",
+                        "Epoch 33: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0236, gen_loss=0.412, disc_loss=0.212]\n",
+                        "Epoch 34: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0234, gen_loss=0.435, disc_loss=0.206]\n",
+                        "Epoch 35: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0231, gen_loss=0.423, disc_loss=0.207]\n",
+                        "Epoch 36: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0231, gen_loss=0.424, disc_loss=0.205]\n",
+                        "Epoch 37: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0232, gen_loss=0.427, disc_loss=0.214]\n",
+                        "Epoch 38: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0222, gen_loss=0.476, disc_loss=0.217]\n",
+                        "Epoch 39: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0225, gen_loss=0.446, disc_loss=0.206]\n",
+                        "Epoch 40: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0226, gen_loss=0.437, disc_loss=0.207]\n",
+                        "Epoch 41: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0229, gen_loss=0.426, disc_loss=0.207]\n",
+                        "Epoch 42: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0221, gen_loss=0.468, disc_loss=0.198]\n",
+                        "Epoch 43: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.023, gen_loss=0.455, disc_loss=0.201]\n",
+                        "Epoch 44: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0225, gen_loss=0.456, disc_loss=0.198]\n",
+                        "Epoch 45: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0221, gen_loss=0.501, disc_loss=0.196]\n",
+                        "Epoch 46: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.13it/s, recons_loss=0.022, gen_loss=0.476, disc_loss=0.194]\n",
+                        "Epoch 47: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0226, gen_loss=0.487, disc_loss=0.197]\n",
+                        "Epoch 48: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0225, gen_loss=0.486, disc_loss=0.186]\n",
+                        "Epoch 49: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0226, gen_loss=0.508, disc_loss=0.187]\n",
+                        "Epoch 50: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0226, gen_loss=0.511, disc_loss=0.189]\n",
+                        "Epoch 51: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0224, gen_loss=0.564, disc_loss=0.182]\n",
+                        "Epoch 52: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0224, gen_loss=0.508, disc_loss=0.183]\n",
+                        "Epoch 53: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0221, gen_loss=0.526, disc_loss=0.175]\n",
+                        "Epoch 54: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0227, gen_loss=0.521, disc_loss=0.181]\n",
+                        "Epoch 55: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0224, gen_loss=0.56, disc_loss=0.182]\n",
+                        "Epoch 56: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0232, gen_loss=0.543, disc_loss=0.182]\n",
+                        "Epoch 57: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0228, gen_loss=0.525, disc_loss=0.168]\n",
+                        "Epoch 58: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.023, gen_loss=0.539, disc_loss=0.165]\n",
+                        "Epoch 59: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0228, gen_loss=0.572, disc_loss=0.178]\n",
+                        "Epoch 60: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0231, gen_loss=0.536, disc_loss=0.165]\n",
+                        "Epoch 61: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0231, gen_loss=0.579, disc_loss=0.158]\n",
+                        "Epoch 62: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.024, gen_loss=0.549, disc_loss=0.162]\n",
+                        "Epoch 63: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0235, gen_loss=0.565, disc_loss=0.153]\n",
+                        "Epoch 64: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0234, gen_loss=0.598, disc_loss=0.152]\n",
+                        "Epoch 65: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0235, gen_loss=0.591, disc_loss=0.163]\n",
+                        "Epoch 66: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0232, gen_loss=0.604, disc_loss=0.156]\n",
+                        "Epoch 67: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0229, gen_loss=0.625, disc_loss=0.152]\n",
+                        "Epoch 68: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.023, gen_loss=0.589, disc_loss=0.152]\n",
+                        "Epoch 69: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.13it/s, recons_loss=0.0234, gen_loss=0.617, disc_loss=0.148]\n",
+                        "Epoch 70: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0235, gen_loss=0.635, disc_loss=0.156]\n",
+                        "Epoch 71: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0234, gen_loss=0.61, disc_loss=0.161]\n",
+                        "Epoch 72: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0235, gen_loss=0.587, disc_loss=0.142]\n",
+                        "Epoch 73: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0236, gen_loss=0.637, disc_loss=0.149]\n",
+                        "Epoch 74: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0238, gen_loss=0.615, disc_loss=0.149]\n",
+                        "Epoch 75: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0242, gen_loss=0.609, disc_loss=0.142]\n",
+                        "Epoch 76: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0235, gen_loss=0.643, disc_loss=0.143]\n",
+                        "Epoch 77: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0237, gen_loss=0.65, disc_loss=0.145]\n",
+                        "Epoch 78: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0231, gen_loss=0.704, disc_loss=0.121]\n",
+                        "Epoch 79: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0233, gen_loss=0.649, disc_loss=0.125]\n",
+                        "Epoch 80: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0237, gen_loss=0.656, disc_loss=0.132]\n",
+                        "Epoch 81: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0238, gen_loss=0.651, disc_loss=0.142]\n",
+                        "Epoch 82: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0235, gen_loss=0.669, disc_loss=0.13]\n",
+                        "Epoch 83: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.13it/s, recons_loss=0.0238, gen_loss=0.653, disc_loss=0.13]\n",
+                        "Epoch 84: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0232, gen_loss=0.688, disc_loss=0.126]\n",
+                        "Epoch 85: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0223, gen_loss=0.763, disc_loss=0.1]\n",
+                        "Epoch 86: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0234, gen_loss=0.655, disc_loss=0.136]\n",
+                        "Epoch 87: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0236, gen_loss=0.664, disc_loss=0.121]\n",
+                        "Epoch 88: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0234, gen_loss=0.697, disc_loss=0.117]\n",
+                        "Epoch 89: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0228, gen_loss=0.721, disc_loss=0.101]\n",
+                        "Epoch 90: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0235, gen_loss=0.704, disc_loss=0.113]\n",
+                        "Epoch 91: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0243, gen_loss=0.674, disc_loss=0.127]\n",
+                        "Epoch 92: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0215, gen_loss=0.833, disc_loss=0.0804]\n",
+                        "Epoch 93: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0221, gen_loss=0.742, disc_loss=0.106]\n",
+                        "Epoch 94: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0233, gen_loss=0.707, disc_loss=0.107]\n",
+                        "Epoch 95: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0231, gen_loss=0.736, disc_loss=0.106]\n",
+                        "Epoch 96: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0231, gen_loss=0.729, disc_loss=0.113]\n",
+                        "Epoch 97: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0237, gen_loss=0.702, disc_loss=0.112]\n",
+                        "Epoch 98: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0226, gen_loss=0.735, disc_loss=0.105]\n",
+                        "Epoch 99: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [02:49<00:00,  1.14it/s, recons_loss=0.0225, gen_loss=0.736, disc_loss=0.108]\n"
+                    ]
+                }
+            ],
+            "source": [
+                "n_epochs = 100\n",
+                "autoencoder_warm_up_n_epochs = 5\n",
+                "val_interval = 10\n",
+                "epoch_recon_loss_list = []\n",
+                "epoch_gen_loss_list = []\n",
+                "epoch_disc_loss_list = []\n",
+                "val_recon_epoch_loss_list = []\n",
+                "intermediary_images = []\n",
+                "n_example_images = 4\n",
+                "\n",
+                "for epoch in range(n_epochs):\n",
+                "    autoencoder.train()\n",
+                "    discriminator.train()\n",
+                "    epoch_loss = 0\n",
+                "    gen_epoch_loss = 0\n",
+                "    disc_epoch_loss = 0\n",
+                "    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",
+                "        images = batch[\"image\"].to(device)  # choose only one of Brats channels\n",
+                "\n",
+                "        # Generator part\n",
+                "        optimizer_g.zero_grad(set_to_none=True)\n",
+                "        reconstruction, z_mu, z_sigma = autoencoder(images)\n",
+                "        kl_loss = KL_loss(z_mu, z_sigma)\n",
+                "\n",
+                "        recons_loss = l1_loss(reconstruction.float(), images.float())\n",
+                "        p_loss = loss_perceptual(reconstruction.float(), images.float())\n",
+                "        loss_g = recons_loss + kl_weight * kl_loss + perceptual_weight * p_loss\n",
+                "\n",
+                "        if epoch > autoencoder_warm_up_n_epochs:\n",
+                "            logits_fake = discriminator(reconstruction.contiguous().float())[-1]\n",
+                "            generator_loss = adv_loss(logits_fake, target_is_real=True, for_discriminator=False)\n",
+                "            loss_g += adv_weight * generator_loss\n",
+                "\n",
+                "        loss_g.backward()\n",
+                "        optimizer_g.step()\n",
+                "\n",
+                "        if epoch > autoencoder_warm_up_n_epochs:\n",
+                "            # Discriminator part\n",
+                "            optimizer_d.zero_grad(set_to_none=True)\n",
+                "            logits_fake = discriminator(reconstruction.contiguous().detach())[-1]\n",
+                "            loss_d_fake = adv_loss(logits_fake, target_is_real=False, for_discriminator=True)\n",
+                "            logits_real = discriminator(images.contiguous().detach())[-1]\n",
+                "            loss_d_real = adv_loss(logits_real, target_is_real=True, for_discriminator=True)\n",
+                "            discriminator_loss = (loss_d_fake + loss_d_real) * 0.5\n",
+                "\n",
+                "            loss_d = adv_weight * discriminator_loss\n",
+                "\n",
+                "            loss_d.backward()\n",
+                "            optimizer_d.step()\n",
+                "\n",
+                "        epoch_loss += recons_loss.item()\n",
+                "        if epoch > autoencoder_warm_up_n_epochs:\n",
+                "            gen_epoch_loss += generator_loss.item()\n",
+                "            disc_epoch_loss += discriminator_loss.item()\n",
+                "\n",
+                "        progress_bar.set_postfix(\n",
+                "            {\n",
+                "                \"recons_loss\": epoch_loss / (step + 1),\n",
+                "                \"gen_loss\": gen_epoch_loss / (step + 1),\n",
+                "                \"disc_loss\": disc_epoch_loss / (step + 1),\n",
+                "            }\n",
+                "        )\n",
+                "    epoch_recon_loss_list.append(epoch_loss / (step + 1))\n",
+                "    epoch_gen_loss_list.append(gen_epoch_loss / (step + 1))\n",
+                "    epoch_disc_loss_list.append(disc_epoch_loss / (step + 1))\n",
+                "\n",
+                "del discriminator\n",
+                "del loss_perceptual\n",
+                "torch.cuda.empty_cache()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 11,
+            "id": "a27064b6",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "2023-02-19 13:52:44,991 - No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "plt.style.use(\"ggplot\")\n",
+                "plt.title(\"Learning Curves\", fontsize=20)\n",
+                "plt.plot(epoch_recon_loss_list)\n",
+                "plt.yticks(fontsize=12)\n",
+                "plt.xticks(fontsize=12)\n",
+                "plt.xlabel(\"Epochs\", fontsize=16)\n",
+                "plt.ylabel(\"Loss\", fontsize=16)\n",
+                "plt.legend(prop={\"size\": 14})\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 12,
+            "id": "fd710efe",
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkEAAAHZCAYAAACB2e8eAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAACy0klEQVR4nOzdd3hUVfrA8e+dyaRXkkBCKKGE3nsHAQURsRfsvfwsq8DaXd21rb2hrg2UlVUQRUBEEOnSe+8dEkghvUwyc35/3ExLZtIbyft5Hh5mbjn3zM1k8s457zlHU0ophBBCCCEaGENtV0AIIYQQojZIECSEEEKIBkmCICGEEEI0SBIECSGEEKJBkiBICCGEEA2SBEFCCCGEaJAkCBJCCCFEgyRBkBBCCCEaJAmChBBCCNEgSRAkal1sbCyapnHXXXfVdlUanG+++QZN09A0jePHj1fLNe666y40TSM2NrZayq8rRowYgaZpjBgxotquURM/LyEaEgmCRKlWrlxp/+DVNI21a9fWdpVEA2ALKirz75tvvqntlyHKISsri6+//pobb7yRuLg4QkND8fb2pnHjxgwcOJBJkyaxfv362q6mqEckCBKl+vbbb12ez5gxo5ZqIoSorz7//HNat27Nfffdx48//sjhw4dJS0sjPz+fxMRE1q9fz/vvv8/AgQPp16+ffBkTVUKTBVRFSXJycmjSpAkZGRkEBgaSmZlJWFgY8fHx+Pj4VMk1YmNjOXHiBHfeead8c6+H7rrrLr799ltatmxZri6cY8eOkZWV5XbfCy+8wLx58wBYvHgxTZs2dXtcs2bNCA0NLW+VRQ2yWq08/PDDfPHFFwAYDAauuuoqxo0bR9u2bQkODiYxMZFdu3Yxb9481qxZA8BVV13FL7/8Uos1F/WBV21XQNRtc+fOJSMjA4CPPvqIe+65hwsXLrBgwQKuv/76Wq6dqM9atWrlcZ9zYNOuXbt6n29Un/3zn/+0B0BxcXHMmTOHbt26FTtuzJgxTJkyhb/++ovHH3+8pqsp6inpDhMlsnV9devWjbvvvpv27du7bBdCiIrasmULr776KgBNmzZlzZo1bgMgZ4MHD2bt2rXccsstNVFFUc9JECQ8io+PZ+nSpQDcdtttLv///vvvJCYmlqmcRYsWMW7cOCIjI/H396ddu3ZMmjSJM2fOlHhemzZt0DSNwYMHl3qNM2fOYDQa0TSNp556yu0xaWlpvPHGGwwePJjIyEi8vb2Jjo7myiuvZM6cOZTUM2xLtH355ZcBWLZsGTfccAPNmzfHZDIVa4k4e/YszzzzDL169SIkJASTyUSTJk3o2rUrEydO5JtvviE9Pb3YdbKyspg1axb33XcfPXr0sJ8bGRnJ8OHDeeedd8jMzCzxXpSnrqWNNrJarSxbtowpU6YwePBgIiIiMJlMhIaG0qNHD6ZMmcLJkydLrE9tKPq68vLy+OCDDxgwYAAREREu9wfAbDazYMECHn30Ufr27UtYWBgmk4nw8HD69+/Pyy+/TFJSUonXLGl02PHjx4sla//xxx9ceeWVREVF4ePjQ6tWrXj44Yc5ffp0mV9XaXU4c+YMkyZNom3btvj5+REeHs6YMWNYtGhRabcQ0L/sDB8+nLCwMAIDA+natSv/+te/7O/dou+18nrjjTewWq0AfPzxxzRu3LhM5/n4+HDjjTe6bCvryDl3PwtnRUczxsfH8/TTT9O5c2eCgoLQNI0VK1Zwzz33oGkafn5+9tbykrRv3x5N0+jXr5/b/RaLhW+//Zbx48fTtGlTfHx8CA8PZ8iQIbz33nvk5OSUWP6WLVu49957adeuHQEBAfj6+tK8eXN69+7NI488wvz580v8jGuwlBAevP322wpQBoNBnT59Wiml1NGjR5WmaQpQH374YallPPnkkwpw+y8yMlJt2rRJtWzZUgHqzjvvdDn3hRdeUIDSNE0dO3asTHUF1Pbt24vtX7p0qQoPD/dYF0CNGzdOZWRkuC3fdsxLL72knnvuuWLntmzZ0n7sqlWrVHBwcInXAtSCBQuKXWf48OGlnteqVSu1b98+j/eiPHWdPn26fbu7e/zSSy+VWh9/f3/1888/e6zPnXfeWey6lWUr01O9nV/Xpk2bVI8ePYrV+6WXXnJbnqd/4eHhas2aNR7rZPvZDR8+vNi+Y8eO2cuZPn26euaZZ0r8vdi7d6/ba5T283Kuw5o1a1RERITH67z99tseX4vZbFZXXXWVx3Pj4uLU8ePH3d7Lsrpw4YIyGo0KULGxscpisZS7DGel3Ruboj+Lopzfr+vWrXN7D5cvX66WLl1qf/7NN9+UWLdNmzbZj/3ggw+K7T9x4oTq3r17ie+/tm3bqgMHDrgt/7333lMGg6HU97Cnz7eGTHKChEf//e9/Af3bZUxMDKDnaQwaNIi//vqLGTNmlNg3/8EHH/D+++8DelP3s88+S79+/cjNzWXhwoV88MEH3HDDDWRnZ7s9/9Zbb+XVV19FKcX//vc/nnvuOY/X+t///gdA586d6d69u8u+v/76i8svv5z8/HyaNGnCY489Rvfu3WnatClnz55l1qxZfPfdd/z222/ceeed/PTTTx6v8/PPP7Nr1y66du3Kk08+SZcuXcjJyWH79u0A5OXlcfPNN5Oenk5QUBAPP/wwl1xyCY0bN8ZsNnPs2DHWrl3L3Llz3ZZfUFBA165dmTBhAn369KFp06YopThx4gRz585l9uzZHDt2jKuvvprt27fj6+tb4bqWRUFBAdHR0VxzzTUMHDiQ1q1b4+vry6lTp1i7di2ffvopmZmZ3HLLLWzdupWOHTuWueyacu+997Jr1y7uuOMObrrpJqKiojh58qRLYn9BQQGtW7fmmmuuoV+/frRo0QIvLy9OnDjB0qVLmTZtGsnJyVxzzTXs3r27zC0W7nz55ZesXbuW4cOH8+CDD9KuXTtSU1OZMWMGM2bMIDExkXvuuYd169ZV+Brx8fFcffXVGAwG/v3vfzNkyBC8vb1Zs2YN//rXv0hNTeXZZ5/l8ssvp3PnzsXO/9vf/mZPPO/cuTNTpkyhS5cupKenM3fuXD777DNuuummCtcPYM2aNVgsFgDGjh2LwVC3OiYyMzO57rrryM3N5fnnn+fSSy/F39+fXbt2ER0dTVxcnP0zZObMmdx5550ey7J9PhmNRm6++WaXfcnJyQwZMoRTp07h4+PD/fffz/Dhw4mNjSUzM5MlS5bw4YcfcvjwYS6//HK2bt1KSEiI/fydO3cyZcoUrFYrrVq14tFHH6VHjx40atSIjIwMDhw4wPLly+0/T1FEbUdhom7atm2b/dvDtGnTXPZ99tln9n179uxxe/65c+eUv7+//RtVfHx8sWP+/PNP5eXlZS+raEuQUkr16tVLAapz584e67pv3z57Ga+//rrLPrPZrGJjYxWgxo4dq7KystyW8cUXX9jLWLJkSbH9OH2bGjVqlMrNzXVbzp9//lliS49Nfn6+SktLK7b94MGDHs9RSqk//vjD/o3vq6++cntMWeuqVOnfno8dO6bMZrPH80+dOqViYmIUoG677Ta3x9R2S1BJ98rm8OHDymq1ety/c+dOFRgYqAD1wgsvuD2mrC1BgLr//vvdXu++++6zH7N169YSX1dJLUG2+21rwXW2evVqe2vu448/Xmz/1q1b7fsHDhyosrOzix3z448/emxVK6tXX33Vfv6XX35Z7vOLquqWIEAFBga6bVm2mTRpkgKU0WhUCQkJbo+xWCyqadOmClCXXXZZsf233HKL/ed19OhRt2Vs3bpVBQQEKEA999xzLvtefPFFBaiAgACPdVBKqdTU1Eq3ttVHdSv0FnWGLfHZz8+P6667zmXfjTfeiLe3t8txRX377bf2Fp53332XqKioYseMHDmS+++/v8R63HrrrQDs2bOHHTt2uD1m5syZgJ6fUDRZ8ocffuD48eP4+voyY8YM/P393ZZx//332/vqSxqmbzAY+OqrrzxOD5CQkGB/PGzYMI/leHl5ERwcXGx7XFycx3MARo8ezYQJEwBKHR5cWl3LIjY2FpPJ5HF/s2bN+Pvf/w5QZ3MORo4cyb333lviMbb8M0+6du3KfffdB5R+30sTHR3Nxx9/7PZ6U6ZMsT9evXp1pa7z8ccf21twnQ0ZMoT+/ft7vMYXX3xh/zl++eWX+Pn5FTvm+uuv55prrqlU/ZKTk+2PK9OyVp2eeuqpYi3LzmyfTxaLhR9++MHtMcuXL+fs2bMux9scP36cWbNmATB16lSPIyJ79uzJI488AhT/fLJ95rRr144mTZp4rGtISEida22rC+SOiGIKCgrszbdXXnllsT/WjRo1Yty4cYAegNgSG53ZEqrDwsK46qqrPF7rnnvuKbEuN998s/0X11anor7//ntAHzXSsmVLl33z588HYPjw4URGRpZ4LVvQUlI3xODBg0scjh0dHW1/PH369BKvVxaJiYkcOnSI3bt32//ZXoenoLCsda2I9PR0jh07xp49e+z1sQWWtn11TdE/PGVx4cIFjhw54vI6bcPy9+7dS35+foXrc/3113sMTNu3b09gYCAAR48erfA1QkNDueKKKzzu7927t8dr2H53e/bs6barzOaOO+6ocP0Al2TigICASpVVXUp77/Tq1YsOHToAnj+fbNv9/PyKBY4LFy7EYrHg7+/P5ZdfXuK1bJ9PZ8+edRmMYPvM2bt3Lxs3biyxDFGc5ASJYhYvXsy5c+cAx2iwom677TZ++eUXTp8+zfLlyxk1apTL/l27dgH6B6mXl+e3WY8ePfD29sZsNrvd37RpUy655BL+/PNPvv/+e/7973+7fIPesGEDR44cAdx/YG3evNn+mkr6pu/MuTWnqNKG7w4ZMoTWrVtz9OhRnnjiCWbOnMk111zDsGHD6Nu3r70FrSR//fUXH330EUuXLiUlJcXjcaWNViqtrmV14sQJ3nnnHRYsWMCJEydKPDYpKYnWrVtXyXWrSlnvw65du3j//fdZtGhRie8Bq9XKhQsXKtx6Yfuj6UlYWBiZmZllGnHkSVxcXInf+hs1agRQ7Bq5ubkcPnwYcARKnvTp06fC9QMICgqyP/Y0KWZtCgwMLNN7+dZbb+XFF19k48aNHD58mLZt29r35eXl8fPPPwMwYcIEl9cMjs+n7OzsEj8ni0pISKBFixYATJw4kTfeeIO8vDwGDx7M2LFjueKKKxgyZAidO3cu8+deQyUtQaIYWxdXeHg4Y8eOdXvM+PHj7d+M3XWJ2f54l/aHwsvLy/6B7IktuDl16hSrVq1y2WfrCjOZTNxwww3Fzj1//nyJZbtT0lDUsLCwEs81mUwsWLDAniC8adMmnnvuOYYMGUJoaChjx47lf//7nz0htKiXX36ZIUOGMHv27BIDoNLqWZa6lsWiRYvo1KkTU6dOLTUAKkudakNZ7sPXX39Nr169mD59eokBkE1lXqenLlkbW/Di6T1Sldco2oqbmppqf1xay2lp+0sTHh5uf2z70lWXlHWmcecueNvnkc3ChQvt99Tdl7SKfD4BLoNJOnTowPfff09YWBgFBQX8+uuvPPzww3Tt2pXGjRtz++23V7prtT6TIEi4SEtLs3chJScn4+3t7XZhSl9fX/sv988//+zxm1xVfAu57rrr7KOgnJucLRYLs2fPBvTRJc4fqs7HAFx++eXs2rWrzP88MRqNpda3U6dO7Nq1i7lz53LPPffYvxnm5OSwePFibr31Vvr371/sA/DPP//kn//8JwCtW7fm008/ZefOnaSmppKfn49SCqUUL774Yql1KGtdS5KUlMQtt9xCdnY2gYGBvPzyy6xbt47z58+Tl5dnr8+ff/5pP6cu5gSVdh/279/PQw89REFBAY0bN+btt99my5YtJCcnYzab7a/z66+/tp9TF1/nxcY512br1q21WBP3yvr707p1awYOHAgU7xKzPff0hdL2+RQREVGuz6e+ffu6lHPddddx7NgxPv/8c6699lp7gJqUlMR3333HsGHDuOuuu9ymLjR00h0mXMyePZvc3NxynZOZmcnPP//M7bffbt8WFhZGQkJCqd/wCgoKSm3xCA4OZvz48cyZM4c5c+YwdepUTCYTf/75p718T3334eHhnD17FrPZTJcuXcr1uirDaDRy9dVXc/XVVwP6kOXff/+dTz75hC1btrBlyxYefPBBl6HyX375JaDfu/Xr13v8pl3a/aoqc+bMsQe6c+fOZfTo0bVan+ryzTffUFBQgNFoZOXKlR67qy7211kWzq0fpU2GWtbJUj0ZMmQIRqMRi8XCokWLsFqtlUrcdT63pD/21dH1duutt7Ju3ToOHjzI5s2b6dOnD+np6SxcuBCAG264we0AA9sXt4yMDDp27FipLy4hISE88MADPPDAAwDs27ePefPm8fHHH3P27Fm+/fZbevbsyd/+9rcKX6M+kpYg4cLWtRUdHc33339f6r9mzZq5nGfTtWtXALZv305BQYHH6+3YscNjPpAzW5CTkpJin+3W1vQcFBRkHzFVVM+ePQG9770s16ku0dHR3H333axbt45evXoB8Ouvv7p0q+zZsweASy65pMSuBlseQXWz1adRo0YeA6CarE91sb3O7t27l5ivc7G/zrLw9fWlTZs2gD4DcUkqez9CQ0PtXxJOnDhR6VF3zvk2Fy5c8HjcwYMHK3Udd2688UZ7To+t9eenn36yf6H09CXN9vmUl5dX5e+vjh078swzz7B+/Xp74rmt5Vw4SBAk7I4dO8Zff/0F6M2rN998c6n/bMPnly1b5rIMhu2PZkpKCgsWLPB4zWnTppWpbuPGjbPndsycOZPc3Fx7K8o111zjdhgvYA+O0tLSqmS0VmWZTCaGDx8O6K1gzjkYtmCxpG+q27ZtY8OGDdVax6L1yc3N9fjNOjs72z6p5sWqLPc9Pj7e3k1c39kGOWzbts0eILpTFesHPvvss/YWnMcee6zMOTJ5eXnF/qA7Dy8vKaCwjSatSpGRkVx22WWAPi2H1Wq1B0MtW7b0uPTPlVdeaU8Z+OCDD6q8XgDNmzenXbt2QOmDKRoiCYKE3YwZM+y5DmVdId52nNVq5bvvvrNvv/POO+2ByaRJk9x2i61cudK+enRpvL297ddasGAB//vf/+wjW0oaxnrnnXfSvHlzQJ+DpWhidVFr1qxh5cqVZaqTO6tXr7aPrnHHbDbbyw8MDHRp8bHNEbRmzRq3ZSQmJrp0OVY3W32ys7PdfoO0WCzcd9999jlQLla213no0CHWrl1bbH92dja33HJLnUz6rg4PPPCA/Q/z/fff7/Z1//TTTx5nPS+P3r1788ILLwD60O8hQ4aUmJMH+hQWgwcPLpZ/06VLF/sgi6lTp5KXl1fs3NmzZ/Pjjz9Wut7u2D6H4uPj+d///sfy5csBPXHaU25k+/bt7QM6fvjhB957770Sr3Hs2LFiQdwvv/zi8mWqqFOnTrF//34Aj/MQNWQSBAk72zf6xo0bM3To0DKdM2jQIPs8Fc4tAk2aNOGVV14B9AnBevfuzSeffMKmTZtYvXo1zz77LGPGjCEmJqbMo0xsHzI5OTlMnjzZfp2iw/Od+fj4MHv2bHx8fMjMzGTkyJHcdtttzJkzhy1btrBp0ybmz5/PSy+9RLdu3Rg6dGipH8Il+fPPP2nfvj0jRozg7bffZvHixWzdupW//vqL6dOnM3ToUHsS6L333usyLNY270pWVhbDhw/n448/Zu3ataxdu5Z33nmH7t27s3fvXnsSZnW78cYb7fPZ3H333TzzzDP8+eefbN68mW+//Zb+/fvz/fffl2mB27rMFlharVauuOIKXn/9dVatWsXGjRv57LPP6NGjBytWrLjoX2dZ9e7d2z6J6bp16+jbty/ffvstW7ZsYfny5Tz22GPcdNNNLguBVmYAxEsvvWTPYzl06BA9evTguuuuY9q0aaxatYpt27axZMkS3nvvPUaMGMGgQYPcdtV5eXnx4IMPArB7925GjhzJvHnz2LZtG7///jv33nsvEydOZNCgQRWua0muuuoqe7fTY489Zk96Lm2uoc8++8w+FH/y5MkMHz6cr7/+mvXr17Nt2zaWLl3Ku+++y6WXXkrbtm2LLevzwQcfEBMTw4033sh//vMfVq5cyfbt21m+fDlvv/02gwcPtgeyDz30UFW/7ItfLc1ULeqYNWvW2KeKf/DBB8t17v/93//Zz928ebPLvscff9zjYn4RERFq48aNHhdQLcpqtarmzZu7lPG3v/2tTHVct25dsXM9/fv222+LnW/bV9ryAGVZcBRQV111ldvlCO6++26P5xiNRvXBBx+4XMOdstZVqdKXGpg2bVqJCzPedNNNLgtJLl++vFgZtb1sRmmL7yql1D//+c8Sf16TJ08u1+KlRZW2VIOzkn4fKlMHZ6W9h/Ly8tT48eM93o9WrVqpw4cP25//+9//LvF6ZfHZZ5+pyMjIMv3+DBo0SG3cuLFYGVlZWWrAgAEezxsxYoTavXt3mZbNqMj79dZbb3W5Xvfu3ct0Xnx8vBo6dGiZXvvdd9/tcm5ZFl02GAzqlVdeKffraQikJUgArv37RZfJKI3z8UXzBD788EMWLlzImDFjaNSoEb6+vrRt25bHH3+cbdu2FRvqWRJN05g4caLLtrLOBjxgwAAOHTrEf/7zH6644gqaNm2Kt7c3vr6+NG/enMsuu4zXXnuN/fv3V2om3ClTpvDTTz/x8MMPM2DAAFq0aIGvry++vr7ExsZy44038uuvv/LLL7+4zWOaNm0a//3vfxk6dChBQUH4+PjQsmVLbr/9dtauXVvjIzvuvvtuVq9ezdVXX01kZCQmk4no6GjGjh3LrFmz+OGHHyo9FL8u+Mc//sHChQu57LLLCAsLw9vbm2bNmnHttdeyZMkS3nnnndquYo3y9vZm/vz5TJ8+nSFDhhASEoK/vz8dO3bkueeeY8uWLS5TUjgv6FlRDz30EEePHuXLL7/k+uuvp02bNgQHB2MymYiMjGTAgAFMmjSJjRs38tdff7n97PD392fZsmW89tprdO3aFT8/P4KDg+nbty9Tp05l6dKl1To7ddHPo7J+PkVFRbFq1Sp+/fVXbr31Vlq3bo2/v7/9tQ8aNIjJkyezcuXKYnmU33//PV988QW33HILPXr0ICoqCi8vLwIDA+ncuTMPP/ww27Zts3c7CleaUjLhhRBCiPJZs2aNvdt86dKlJXZLC1FXSUuQEEKIcrMl6JpMplKX2BCirpIgSAghhIukpKQSRxwtXryYzz//HNCnoSjrEhNC1DXSHSaEEMLFihUruOqqq7jhhhsYPXo0bdq0wWAwcOLECebPn893332HxWLBz8+P7du32+ehEeJiI0GQEEIIFytWrOCSSy4p8Zjg4GB+/PFH+ySBQlyMJAgSQgjhIjMzk59++onff/+dHTt2kJiYSGpqKsHBwbRt25axY8fy6KOPVnoleSFqmwRBQgghhGiQJDFaCCGEEA2SBEFCCCGEaJC8Sj9EXLhwwb7SdFWJjIwkMTGxSssU7sm9rjlyr2uO3OuaJfe75lTFvfby8iIsLKz04yp1lQaioKCA/Pz8KivPtthgQUEBkpJVveRe1xy51zVH7nXNkvtdc2r6Xkt3mBBCCCEaJAmChBBCCNEgSRAkhBBCiAZJgiAhhBBCNEgSBAkhhBCiQZIgSAghhBANkgRBQgghhGiQZJ6gKmK1WsnNzS3zpIo5OTmYzeZqrpUAudc1yd299vLywtfXF4NBvnMJIeoWCYKqgNVqJSMjA19fX/z8/OyTPZXEZDJV6QSMwjO51zWn6L1WSmE2m8nIyCAoKEgCISFEnSKfSFUgNzcXX19ffHx8yhQACdFQaJqGj48Pvr6+5Obm1nZ1hBDChQRBVaCgoABvb+/aroYQdZa3t3eVr78nhBCVJUFQFZEWICE8k98PIURdJEGQEEII0QCp08exblyFasA5k5IYLYQQQjQwKjsL67+fhrwcuP5utDHX1HaVaoW0BAkhhBANzbkzegAEqEN7arkytUeCICGEEKKhycpwPE4+X3v1qGXSHSaq1O7du/nvf//Lhg0biI+PJy8vj9DQUDp06MCIESO44YYbCA8Pr+1q1pj+/fsDsGHDhlquiRBCOKisTMeT5PMopRrkAAYJgkSVsFqtvPrqq3z++ecYjUb69+/PsGHD8Pf3Jzk5mS1btvDKK6/w7rvvsmrVKqKjo2u7ykII0XA5twTlZEN2FgQE1l59aokEQaJKvPnmm3z++ed07dqVzz77jFatWhU7ZteuXbz22msyaZ4QQtQ255YggORzDTIIkpwgUWlHjhzhs88+Izw8nJkzZ7oNgAC6du3KDz/8QPPmzV227927l4cffpiePXsSGxtLv379eOGFF0hJSXE57tSpU8TExPDEE09w7Ngx7r33Xjp16kTbtm256aab2LPHfXJfYmIiL730EoMHD6ZVq1Z06dKF+++/n/379xc7tn///vTv35+0tDSef/55+vTpQ4sWLZg1axYAO3fu5Pnnn2fkyJF06NCBNm3aMGrUKKZOneqyXIStrqdPn+b06dPExMTY/7377rsu15w1axbjx48nLi6OuLg4xo8fb7+es7Vr19rP37RpExMnTqRjx47ExMS4fd1CCOGRc0sQQFLDzAuSliBRaT/++CMWi4XbbrutTPk+Xl6Ot92SJUt46KGH0DSNMWPG0LRpUw4ePMj06dNZsWIFv/76K6GhoS7nnz59miuvvJL27dtz0003ceLECRYvXsyNN97IihUriIyMtB97/PhxbrjhBs6ePcvw4cMZM2YMSUlJ/Pbbb6xYsYJZs2bRq1cvl/LNZjM33ngjWVlZXHbZZXh5ednLnDlzJkuXLqV///6MHDmSnJwc1q1bxxtvvMGOHTv48ssvAQgODmbSpEl89dVXANx333328gcOHGh//OKLLzJt2jSioqKYOHEiAAsXLmTSpEns2bOHf/3rX8Xu3+bNm/n4448ZNGgQt956K2fOnCn1ngshhIsiQZBKOU/Dywiqw0FQfn4+s2bNYvXq1WRmZtKyZUtuvvlmunXrVuq5O3fuZO7cuZw8eRKLxUJ0dDSXX345w4YNq4GaNzxbtmwBYNCgQeU6LyUlhccff5ywsDDmzZtHs2bN7PvmzZvH//3f//HOO+/w6quvupy3bt06nnvuOR555BH7trfeeosPP/yQWbNm8eijj9q3/+1vf+PcuXPMnDmTESNGuGwfN24cTz31FEuXLnUp//z583Tq1IlffvkFPz8/l32PP/44r7/+Okaj0b5NKcWUKVP44Ycf2LRpE3379iUkJITJkycze/ZsACZPnlzs9a9fv55p06YRFxfH/PnzCQ4OBmDSpElceeWVfP3111xxxRX25GqbVatW8d5773HTTTeVeH+FEMITVbQ7TFqC6pZPPvmEDRs2MG7cOKKjo1mxYgVvvPEGL730Eh06dPB43ubNm3n77bdp164dN9xwA6D/0Zw6dSrp6emMHz++pl4CAJZXJ0HaheLbNUDVaFU8CwnD+MJ7FT49MTERgKioqGL71q5dy7p161y2DRw4kEGDBjFnzhwyMjJ49dVXXQIggKuuuorPPvuMefPmFQuCWrRowcMPP+yybeLEiXz44Yfs2LHDvm337t1s3ryZW265xSUAAmjTpg233HILX3zxBfv37y/2nnr++eeLBUCA264nTdO46667+OGHH1i9ejV9+/Ytdow7P/74I6AHPbYACCA0NJRJkybx6KOPMnv27GJBUNeuXSUAEkJUTtGWoAY6TL5OBkGHDx9m7dq13HbbbUyYMAGAYcOGMXnyZL777rtifxSd/f7774SGhvKPf/wDk8kEwKWXXsoTTzzBypUrazwIIu0CpCbX7DXrkHXr1vHee64B1qRJkxg0aBBbt24FYNu2bZw4caLYuXl5eaSkpJCSkkKjRo3s2zt37ozB4JrOZhttlpaWZt9ma6FKTEwslocD+vvM9r9zEOTr60vHjh3dvh6z2cz06dOZP38+hw8fJisrC6Uc0WxCQoLb89zZvXs34L4FzbbNXZ5T9+7dy3wNIYRwS1qCgDoaBK1fvx6DwcDo0aPt27y9vRk5ciTff/89SUlJREREuD03JyeHwMBAewAEYDQaXb5p16iQMPfb61hLUGVERERw6NAhEhISaNu2rcu+yZMn27uCbF1cNqmpqQB88803JZafnZ3tEgQFBhYfwWDLM7JarcXK/+OPP/jjjz9KLN9ZeHi4x/kyHnjgAf744w9at27NhAkTCA8Px2QykZaWxtdff43ZbC7xtTjLzMzEYDC4zaOKjIxE0zQyMjLc7hNCiEopmhgtLUF1x7Fjx4iOjsbf399lu+0P7PHjxz0GQZ06dWLevHn88MMP9i6QNWvWcOTIEZ588slqrbc7nrqZTCaTy2iii1mfPn1Yt24da9euZciQIWU+LygoCIA///yzxC7OirKV/8Ybb3DHHXeU+TxPAdD27dv5448/GDFiBDNmzHDJC9qyZQtff/11ueoXGBiI1WolOTm52Ps5KSkJpZT9NZSlfkIIURbKai3eEpSThcrORPNvWMPk6+QQ+dTUVMLCirdO2LZduFA8x8bmuuuuY+DAgcydO5fHH3+cxx9/nHnz5jF58uRiuRVF5efnk52dbf+Xk5Nj36dpmsd/Dd0NN9yAwWBg5syZxYa1l6Rnz56Ao9uqqtnK37RpU5WUZ+uyGzVqlEsABLBx40a35xiNRiwWi9t9Xbp0AfS8qaJseVSdO3eucH3ropJ+j+Rf+f7J/ZT7XeF/eTmgrMV/P1OSar9uVXSvy6pOtgSZzWaX7iwb27aSuhxMJhPR0dH2+V6sVitLly7l448/5oUXXqBdu3Yez507dy5z5syxP2/VqhVvvvlmqd0POTk5butbmoqcUxd16NCBRx99lI8++ojbb7+d//znP7Ru3brYcZmZ+jcPg8GAyWTi1ltv5cMPP+Stt95iwIABxVqDsrOz2bt3L3369AEcXV62893RNM2+r1+/fvTu3Zu5c+cyduxYrr76apdjrVYr69evd8nJsf3yuCu/ZcuWgJ58/+CDD9q379+/n6lTp7qtW1hYGPv378diseDr6+tS3sSJE/nhhx/44IMPuOyyy+ytPunp6bz//vv2Y2zlleX11wWe6ubt7S0zhVcxd4MRRPWpL/e7IP408W62h1nM+NWR39Gautd1Mgjy9vZ221Vk2+bt7e3x3K+//ppDhw7x5ptv2pNnBw4cyOTJk/nmm294/fXXPZ57zTXXuCRO2/4gJiYmUlBQ4PE8s9lc7q6t+tQdBvD3v/+d3NxcvvjiCwYPHkz//v3p1KkTfn5+JCcns2/fPrZt20ZAQAAdO3YkPz+fkJAQPvnkEx588EEuueQSRowYQdu2bTGbzZw6dYr169fTp08fZs6cCWD/GVitVo/3Tinlsm/q1KnceOONPPDAA/znP/+ha9eu+Pr6cubMGbZs2UJKSgpHjx51OR9wW37Xrl3p2bMn8+bNIyEhgV69enHmzBmWLFnCqFGjWLhwYbG6DRo0iO3bt3PTTTfRv39/TCYTAwYMYMCAAfTp04d77rmHadOmMXToUMaNG4dSit9++434+Hjuvfde+vTpYy+vLK+/tpX0vjabzcTHu/voFeWlaRpRUVEkJCS4JOaL6lHf7rc67vjMw9cPcvVej5TDBzDEtq+lWumq6l47z+9W4nEVvkI1Cg0NddutYusGc9dVBvofieXLlzNhwgSX0UNeXl706NGD33//nYKCApfJ+pyZTCaP32Lrwxu/OhkMBl566SWuu+46ZsyYwYYNG9ixYwdms5nQ0FDatWvHiy++yPXXX++S/zJ69GgWL17Mf/7zH1avXs3q1avx9/cnOjqam266iWuvvbZS9WrRogV//vknn3zyCYsXL2bWrFkYjUYaN27MgAEDuOKKK8pcltFo5Ntvv+X1119nxYoV7Nixg1atWvHiiy8ycuRIFi5cWOycJ554grS0NJYuXcrGjRuxWCxMmjSJAQMGAPDKK6/QpUsXZsyYYQ/22rdvz9///vd6OQxefo+qllJK7mkNqi/3W2U6JUW3aA0H9VGoKulcnXl9NXWv62QQFBsby549e8jOznZJjj506JB9vzsZGRlYLBaXEUI2FosFpZTbfaLqdOnShbfeeqtc57Rt25Z33nmn1OOaN29e4uzInvaFhoby1FNP8dRTT5V6jdJWew8PD3c73N7T9QMCAkq9HzfddFOZAp5BgwbJ7NBCiEpTTiPDtBZtUbYgqAGOEKuTidEDBgyw5/LY5Ofns2LFCuLi4uwtCUlJSS5/FEJCQggICGDjxo0u3Ve5ubls2bKFmJiYErvShBBCiHrPeWRYTAuwDfRogEFQnWwJiouLY8CAAXz//fekp6cTFRXFypUrSUxM5KGHHrIfN3XqVPbu3WtfmsBgMHDllVfyww8/8PzzzzNs2DCsVivLli0jOTmZxx57rLZekhBCCFE3ZKXbH2pBoajQcD0AaoATJtbJIAjg0UcfZdasWaxatYqsrCxatGjB008/TadOnUo879prr6Vx48b89ttvzJkzh/z8fFq2bOmShyGEEEI0WM4tQQGBENFED4KyM1HZWWj+AbVXtxpWZ4Mgb29vbr/9dm6//XaPx7z88stutw8ZMqRck/YJIYQQDYbzbNEBQWjhjR0LGKScB/9WtVGrWlEnc4KEEEIIUT1U0Zag8MaO5w2sS0yCICGEEKIhcW4J8g+ECEcQpJITa6FCtUeCICGEEKIhsbUE+fmjeXmhhTdx7Es+Vzt1qiUSBAkhhBANia0lyLZYarhjZuWGNleQBEFCCCFEA6GUguzClqAAfb1CwiLAtsqC5AQJIYQQorqp1BTUzk2omlwLMDcHLBb9cYDeEqQZjXogBA1uwkQJgoQQQogapqwWrG8/i/XjV1C//LfmLuy8ZIatJQgcI8SyMlC52TVXn1omQZAQQghR05LOwfl4APvaXTWi6PD4QprzMPkGNEJMgiAhhBCipiU4LYacmlxz13WZKDHY8TiiYc4VJEGQqDVr164lJibG46rsNen6668nJiam2sqfNWsWMTExzJo1q9quIYS4eCjnICgtFWXL06nu63poCXKeMFE1oGHydXbZDHHxOHXqVLF12Xx9fQkJCaFt27b07duXG264gdjY2NqpYAPWv39/ADZs2FDLNRFCuDjnFAQpK6SnQlh49V/XafFUnHKCXJbOaEDJ0RIEiSoTGxvLtddeC4DZbCYpKYnt27fzwQcf8PHHH/Pwww/zzDPPoGkaAD179mTlypU0atSoNqsNwIcffkhOTk61lX/55ZfTu3dvGjduXPrBQoh6T50767ohNaWGgiBHS5DmsSVIgiAhyi02NpbJkycX275x40Yef/xxpk6ditFo5KmnngLAz8+Ptm3b1nQ13arOrjCA4OBggoODSz9QCNEwOHeHQWFeUFz1X7fI4ql2YRGgGfRWKckJEqLq9OvXj++++w4fHx8+++wzzpzRf/k95QQdPXqUJ598kgEDBtCqVSs6d+7M6NGj+cc//qFP9OUkMzOT9957j9GjR9OmTRs6dOjAZZddxltvvUW+09wbMTExXH/99cTHx/P444/To0cPmjVrxtq1awH3OUHOeTxLlixh/PjxtGnTht69e/PWW29htVoBmD17tv36ffv25bPPPit2DzzlBNnqlZiYyN/+9je6dOlCmzZtGD9+vL1uznbu3Mnzzz/PyJEj6dChA23atGHUqFFMnTrV5fWeOnWKmJgYTp8+zenTp4mJibH/K3q/Z82axfjx44mLiyMuLo7x48e7zV1y/nlt2rSJiRMn0rFjx2oPIIWob1RONqSluG6rqeRoT6PDvLwcLVHSEiRE1Wrbti3jx4/np59+YvHixdxzzz1uj0tISGD8+PFkZ2czatQoJkyYQHZ2NseOHWPGjBn84x//wMtLf9smJSVx3XXXcfjwYTp37swdd9yB1WrlyJEjfPrppzz44IOEhITYy75w4QITJkwgNDSUCRMmkJeXR1BQkNt6OPv9999ZtWoVY8aMoU+fPvz55598+OGHKKUIDg7mww8/5LLLLmPQoEEsXLiQV199lYiICG644YYy3Zu0tDSuvvpqgoODue6660hOTmb+/PnceuutLFq0iA4dOtiPnTlzJkuXLqV///6MHDmSnJwc1q1bxxtvvMGOHTv48ssvAb3ladKkSXz11VcA3HffffYyBg4caH/84osvMm3aNKKiopg4cSIACxcuZNKkSezZs4d//etfxeq7efNmPv74YwYNGsStt95qD2qFEGV0/mzxbRdqJghSnlqCQB8hlpIImemo3Bw0X78aqVNtkiBI1JhBgwbx008/sX37do/H/Pbbb6SlpfHPf/7T5Q836EGMLQACePbZZzl8+DCPPfYYzzzzjMuxiYmJBAQEuGzbv38/N910E2+//TZGo7HM9V6+fDm//PILPXr0AGDKlCkMHjyYL7/8kqCgIBYvXkzLli0BePDBBxkyZAiff/55mYOgvXv3cuedd/Lqq69iKJy6fvDgwUyZMoXp06fz5ptv2o99/PHHef31113qr5RiypQp/PDDD2zatIm+ffsSEhLC5MmTmT17NoDbbsr169czbdo04uLimD9/vr27btKkSVx55ZV8/fXXXHHFFfbkaptVq1bx3nvvcdNNN5XxDgohnKmiXWGg5wTVBE+jw7AlRxfOWXTmBLTpQH0nQVA1m7ToOKk5BcV3aIAqvrk2hPp58d7lsdV+nSZN9JWKL1y4UOqxvr6+xbaFhYXZH58/f55FixZ5zEOKjIwsts3b25sXXnihXAEQwLXXXmsPgAACAwMZPXo0P/zwA4888og9AAK9e6tv376sX7+egoICl6DNE39/f55//nl7AARwww038Mwzz7Bjxw6XY911PWmaxl133cUPP/zA6tWr6du3b5le148//gjoQY9zvlJoaCiTJk3i0UcfZfbs2cWCoK5du0oAJERluAmCaq47rLAlyMcPzcvkui+uM6xbrtdn23o0D0GQSk4EHx+0wPLlOarUFMhMg9xcyMuFvBxUbi5ai9ZozWLL+0qqhARB1Sw1p4Bkd0GQcOvSSy/ljTfe4Pnnn2fNmjVccsklDBgwwCXQANixYwdKKQYNGoTJZPJQmqvmzZtXaCRa586di22zjfJyt69JkyZYLBYSExOJjo4utfzWrVsXa7Xy8vIiIiKC9PR0l+1ms5np06czf/58Dh8+TFZWlkueVEJCQpleE8Du3bsBvYWuKNu2PXuKz2TbvXv3Ml9DCOHGudpsCSoMgoq0AgFoPQag/vspKCtq61rUdXfaR/PaqD3bsH7wEnh5oV1yBdq4G8oUDFl/+Q61cLb7ndfeIUFQfRXq5+EW17GWoJpw7pw+AVdJgUjz5s1ZsGAB7777LsuWLWPBggWAnlM0ZcoUrrzySgAyMvRf5KioqDJf313rUFkEBhb/sLC18LjbZ2tpKigoW/DrKS/Jy8sLS5EJ1B544AH++OMPWrduzYQJEwgPD8dkMpGWlsbXX3+N2Wwu0zVBTyo3GAyEhxcflhsZGYmmafb7XHSfEKLilC0IMhggNFzPw6mBliCllKM7zF0QFBQM7TrDgV2QmACnj0PzVi7HWBfN0R8UFKD+mIdasxTt8uvRRo1H8/Zxf928XNQf8zxXLDe3Ii+nSkgQVM08dTOZTCaX0TwNgW20k3PXkjsdOnTgyy+/JD8/n507d7J8+XKmTZvGww8/TFRUFH379rV335Sn5aPoN5qLzfbt2/njjz8YMWIEM2bMcOnW27JlC19//XW5ygsMDMRqtZKcnExERITLvqSkJJRSbgO0i/0+ClGblFJgmyMoogmENtKDoJzs6k9GzssFS+GXs6JJ0YW03oNQB3bpdd26Fs0pCFLnzuoBkrOcLNTP36KWL8Tw4FNuu9DUjo1gztOftGyLFtcZfH3Bx1fvloutvalSZIi8qBFHjhzh119/xcfHh7Fjx5bpHJPJRO/evZkyZQr/+te/UEqxdOlSQO+SMRgMrF27tsEEkydOnABg1KhRxfKaNm7c6PYco9FYrDXJpkuXLgBuh+KvW7cOcN/dJ4SohNQUPRgBaBKDFhruuq86lZAUbaP1dMz+r7auc9mnVi9xHHfZNWiDR+tzCwFcSML6xdsoa/HPG7Vpjf2x4fq7MNx0L4arbsUw9joMl4xDa9WuIq+mSkgQJKrdpk2buPXWW8nLy+ORRx4pMU9m586dbrtgkpKSAPDx0ZtbIyMjGTduHMePH+e9995ze3xZu6MuFrak6KIBz4EDB5g6darbc0JDQ7lw4QK5bpqbbaPX3n//fZd7np6ebr+nZR3hJoQoo4TT9odakxi9O8ymurvEnIbHa55agkLDHaPCzp5EFdZXFeSj1v6pbzd6oY29FsNdj2N46UNo0VrfnpIIOze5lKeys2D3Fv1JSJje3VaHSHeYqDLHjx+3T8RnNptJTk5m+/bt7Nu3D6PRyN/+9jcmTZpUYhlz5sxh5syZ9O/fn5YtWxIUFMTBgwdZtmwZoaGhLqOS3njjDQ4cOMBHH33EsmXLGDx4MEopjh49yqpVq9i+fbvLPEEXu549e9KzZ08WLFjA+fPn6dWrF2fOnGHJkiWMGjWKhQsXFjtn8ODB7Nixg9tuu43+/ftjMpkYMGCA/d8999zDtGnTGDlyJOPGjUMpxW+//UZ8fDz33ntvsTXhhBCVo5yToqNiID/PsS81mYp0NiuLBc6ehOhmxUd8OStpjiAnWq9BqCP79bK3rEW74kbYsREy0vT9PQegBemfrVpMSwxX34b1I31OMevyRRh7OLUmbd8ABXprvdZ7MJqhfKNzq5sEQaLKOLfK2BZQbdOmDU888USZF1C9+uqrycvLY/PmzWzfvp28vDyio6O54447ePjhh12GiDdq1IgFCxbwn//8h19//ZVvvvkGHx8fmjdvziOPPIK/v391vdRaYTQa+fbbb3n99ddZsWIFO3bsoFWrVrz44ouMHDnSbRD0xBNPkJaWxtKlS9m4cSMWi4VJkybZg5tXXnmFLl26MGPGDGbOnAlA+/bt+fvf/y7D4IWoDk5rhmlRMaj0NMe+CnaHqZmf6V1VcZ0wTHpVn/3ZnWzn7rASgqCeA1A/TtPL3roOrrgR6yqnrrChl7me0LmXnt+UdA72bkOdO4vWpKl+/mZHV5jWd0g5X1n101TRdQhEMYmJiSXmnaSnp5d7XaiGmBhdW+Re15yS7nVFfk+Ee5qmER0dTXx8fLGlZETVq8r7bfnwn/buIcPb0yHpHNY39cletVFXYrj5/nKVp+JPYf3HI466Xn0bhitudHusdeXvqO8+1Y+78zEMQy71XM9XnoSTR/R6Tn4V67sv6DsimmB47XM0g2s2jXXxz6g53+hlX3oVhhvvRWVlYJ18B1gsEBaB4d9fFTuvqKq61yaTqUwjWSUnSAghhKgptu4wHz8IaeSSE6QqsHSGWvST6/Nff0CdPen+4DLkBNn393Isr2P9+n3H9qGXuQ1ktEGjobArTv31JyovT29FKhyYofUdUmoAVBvqXo2EEEKIekjl5ztWaI+K0aebCHGaN62cidEqORG1caXrxoICrN985HaUVllGh9lovZ0mUbXVy2BAGzTK/fFBwY7uruxM1KZVqE2rHfv7DC3xerVFgiAhhBCiJiTGg7IC2HNmNJMJbDMulzMnSP3xi6OlZcw10KQwZ/LYQdTS+cVPKGNiNIAW1QyatnDd2K0fWqjnyW61S65w1G3xz7C/cE6hyCioxbmASiJBkBBCCFETnNcMa+K0DqCtSywtBWW1lqkolZHumLfH2xttzHUY7noMCiczVb/M1Cc3dD6nHC1B4NolBmAYNqbkE2LjoGVhsJNwxhHw9RlSZydZlSBICCFEg6TSLmBdvxyVVXxusmq5XtHh8TZhhUGQxaIvMFqWspb9ap+FWRs6Ru+OatsJbeR4/YB8M9ZvP3INqrLL3hIE+lB5u0aR0LlHycdrGtqIy4tv71s3u8JAgiAhhBANlPWT11Bfv4912gc1c0GnIEhzagly6WIqQ5eYys3RgyAAoxHt0qsdZV1zu979BHBoL2r9cseJmYVBkLcPmsm79Po2i0Ubcin4B2KYeH+Z5vjR+g4Df6dWpqhmUEuLo5aFBEFCCCEaHJWRBscO6k/2bkcVVP80GsqlO6yp47FzEHShDEHQ6iX2OX+0fsPRwh1DwTUfXwy3O4bMq1WLHSfaF08tvRUI9JYdw52PYfzwf2g9yjZxqubjgzbYkTyt9a27XWEgQVCVkbk6hPBMfj9EnXP8kONxQT6c8TCsvCrZWoJCw10XSnUeJl/KCDGVn49a8ov9uTb22uIHdegGMS31x0f2oxLOFK4gX9gSVIZ8oMrQLr9eX3qjTQe0UVdW67UqS4KgKuDl5YXZbK7taghRZ5nNZrw8zWIrRC1QtlYg2/MThzwcWUXXy0x3dEc5twIBWljZ1w9TW9c6jukxAK3oCC4Kc3OchrKrdcvAbLYvX1HWlqCK0oJCMD7zFsZn3ip1PqLaJkFQFfD19SU3N5e8vDz5xiuEE6UUeXl55Obm4uvrW9vVEcKuaBDE8cPVe8Eiy2W4KMdK8mqDY14gw+gJHo/TBgyHwskJ1brlrgnX1dwSdDGRr2ZVwGAwEBQURG5urtsV0N3x9vaW1qMaIve65ri7115eXgQFBWGog7PFioZJKQXHXFt+1PFqbglK8DAyDFxygkrqDlOZ6bB3m/4kLALiOnk8VgsOg6599IVPLyShNv/l2FfHW2dqkgRBVcRgMJR5wU5Z96fmyL2uOXKvxUUjMcF14kCAMydQ5jw0b5/quea50/aHziPDAH2yRC8vKCiAEpbOcF2GYmipy1AYBo3EumOjfu6yBY4dEgTZyVczIYQQDYpLV5ht5JLVCqeOVd819+10PGna0mWfy/IZJXSHqY2rHOf0K8PcO936QmBhwJOS5Ngu3WF2EgQJIYRoWJy7vrr1tT9U1ZQXpFKSHNdsFusypN3OlhydlYHKL959r1KT4eBu/UnjptCiTanX1bxMaP1HFN8hLUF2EgQJIYRoUJxbggzOMxxX0wgxtX29/bHWc6DbY7SQkidMVJv/gsJuZq3f0DLPvaMNGll8m7QE2UkQJIQQosFQBQVw8qj+pHE0tO8GRj09ttpagrY5BUG9PEw66DxM3k1ekEtXWHmWoWjeuviMzdISZCdBkBBCiIbjzAko7G7SYtvpq7jbJhZMOI3Kza7Sy6nMdEc3VmQUxMS6P7CECRNVYoJjdutmsW7nBvKk6JxBgOQEOZEgSAghRIPhkhTdKg4ALTaucKdytBJV1fV2btKTrgGt5wDP3VglrB+mNq22P9b6DSt3HbT+w8HotO6Xv7QE2UgQJIQQouE47giCtFbt9Aexbe3bKjJfkLJaUQd3660+RfdtXee4Xk/P629poZ5njXbpCuszpNz104JDHWt/BQZBcEi5y6ivZJ4gIYQQDYayTZJoNELzVoDeEmSf2aoCeUHq+89RKxZBowgML7yPFqQHGSovF/Zu1w8KDoXWHTwXEua+JUidOal34QG0bo9mWyG+nLTb/w9atkHr0B3Ny1ShMuojaQkSQgjRIKjcbIg/pT+JiXVMjBjdHEze+jHlbAlSB3bpARBAShLWbz92TBa6e6sj/6jHgJInN3TOCXJKjFabKpgQXYQWEITh8uvRCrsAhU6CICGEEA3DiSOOYeZOwYDm5QUtWutPEhNQWZllKk7l52P97lPXjTs2olYv0fdvK1tXGKAHZP6FCctpekuQ2rcDtXxh4QFahbrCRMkkCBJCCNEguCZFt3PZp7V05AWVdb4gtWgO2NYEC2/s2D7rK9SZk6idm/UNfgHQoWvpBdqSoy8kY108F+v7L0F2lr6te3805+RpUSUkCBJCCNEgKKdFU7VY1yCIls7J0Y68IJWfjzq4G2um61pjKuE0atGP+hOjEcOjL6ANG6s/N+dhffd5yNEDGK1rn7Ll4di6xAryUXOmg9JHldG1D4a7Hy/DKxTlJYnRQgghGgZbS5CPH0S7LmKqtXIkR9vygtSxg1invQ8JZzjrFwAjr0AbPQECgrB+95m+4CmgXXo1WrNYuPEe1IFdcO4MZKQ5yvY0QWIRWlgjii49rI2/Ce3KiaUulioqRoIgIYQQ9Z5KTYYLhYuIxrZFMxhdD2jSVA+O8nLg2CGsc7/Tu7sKW2NUThYsnI1aOh86dIMDu/TzIpqgjb8ZAM3HF8N9k7D++yn7au94maBzr7JVspHTmmK+fhjuebLUXCJRORJaCiGEqPfU9o32x1qRfCBAD4paFi5KmpqM+m22ozuqSVPwKmwzyMuFHY6yDLc+hObj4ygnNg7tyomOgjv3RPP1K1MdtYEjIaoZtO2I4bl3JACqAdISJIQQot5S6amoWV+jNq60b9Nat3d7rBbbFmVb4gLA6IV25c0YLr+exj5eJHz7GWr1Ykc3WN+haF16Fy/n8usgPRV14jCG6+4qc121yCiMr3xa+oGiykgQJIQQot5RSqHW/IGa8w1kOw1579YXuvd1e47WoTtqyS/6k5iWendUi9ZomoZXZBTGWx/Cevn1qBW/gTkPbcIt7ssxGNEmPlC1L0hUCwmChBBC1Dtq+oeodcscGwKC0G64G23QKM/rd3XphXbX42ApQBs4Sl9ctQgtLBztmturqdaipkkQJIQQol5R8adcAiCt/3C0G+9FCw4t8TxN09AGj67m2om6RIIgIYQQdZbKykStXozWvqvbhGa352xZa3+sXX0bhiturK7qiYucBEFCCCGqncrLRc36CiwWtFseRPPxLdt5P32DWr0EZfLG8MpnaOGRpZ+z1SkIGnBJhess6j8ZIi+EEKLaqbn/1YOZtX/q8++U5RylULsKl57IN6MWzir9nPPxcOqY/iQ2rkxBk2i4JAgSQghRrdTJo6hlCx3PVyxC5eWVfmJKIqSmOM5b+6ce5JR0LedWoF6Dyl9Z0aBIECSEEKLaKKsV68zPHBMPAmRloNb+Wfq5R/a7brBYUL/+UPI5W51Wbu89sFx1FQ2PBEFCCCHKxbp+BZZPX0edOVHqseqvpXD0gP7EaRV0tXQeymop+WTnIKhwWLtavxIVf9r9tZITHeuDNWuF1rhpqfUTDZsEQUIIIcpMnTmBmvY+bFuP9b+flHxsRjrqp2/tzw33ToKO3fUn5+PBaSkLt+c7BUHamGsLN1pRC753f/w2p64waQUSZSBBkBBCiDKzzv0vqMK1zo/sRznl7BSlfvoGsjKAwrl6OnTDcOnVjrL++MXzuXl5cLowwblpC7QrboSgEH3f5jWo08eLn7PFqStM8oFEGUgQJIQQokzU4X0ui4cCqJ2bPBy7V+8KA/DzR7vhHv1xl14Q3Vx/fHhf8bwfmxOH7Cuxa206oPn6oY29rrBwhXX+/1yvl5oCR/bpT6KaoTVtUb4XJxokCYKEEEKUSimFde6M4tu3b3B/7A9f2Z9rV9+GFhKmP9Y0tMuutu/z1BrkEhy16aCfO+JyCCnMK9q2HnVgl+P4bevtLVRab2kFEmUjQZAQQojS7dkKB/foj5vEOJKc9+1A5ea4HnvsIJw4rD9uFos24nKX3Vr/EWBbwmLrelRiQrHLueQDtS4Mgrx90MZdb99uffcFrP/9VM89kqHxogLq7IzR+fn5zJo1i9WrV5OZmUnLli25+eab6datW5nOX7t2LQsXLuTkyZMYjUaaNWvGzTffTJcuXaq55kIIUb8oq1XPBSqkXXUr7N+JWvU7FOTD3u3Qy5GIrFYtdhw7egKawehSnmYyoY0cj/rlOz3R+c8FaDff7zhfKcfIsIAgaOIY5aUNHYPasFIfcaYUatXvqM2rwRaIRUZB81ZV+OpFfVZnW4I++eQTFi5cyJAhQ7j77rsxGAy88cYb7N/vof/YyezZs/nwww8JDw/njjvu4Oabb6ZFixakpHhO4BNCCOGe2vIXnDyqP2nRBq33ILQe/R37nbrEVHYWatNq/YmfP1qfIW7L1IaPBW9v/ZzVS1AZ6Y6difGQWfi8dXs0g+NPlWYyYfj762jX3w0+fvrG7Cyw6vMQab0Gel4lXogi6mRL0OHDh1m7di233XYbEyZMAGDYsGFMnjyZ7777jldffdXjuQcPHuSnn37i9ttvZ/z48TVVZSGEqJdUQYHeYlPIcO0daAYDqkM3PQjJy0Ht2oSyWNCMRr2VxqzPBq0NGOFxjTAtMBhtyGWoZb+COQ+1dD7aNbfp1zzs1BVWmA/kcq6XCW3MNaj+w1E/f4tat9yxr7f7oEsId+pkS9D69esxGAyMHj3avs3b25uRI0dy8OBBkpKSPJ7722+/ERoayrhx41BKkZubWxNVFkKIekn9OV+f0wegfVfo1APQW2To0lPfnpmhD5cv7J6y0YaNKbFsbcw1YNS/i6vlv6KyM/UdR53zgdp7Pj+0EYZ7nsTwzFtofYeiXX8XWqu4cr5C0ZDVyZagY8eOER0djb+/v8v2tm3bAnD8+HEiIiLcnrtr1y7at2/PokWL+Pnnn8nIyCA0NJRrr72WsWPHVnvdhRCivlA7NqJ+cowIM1xzu0tXk9a9P2rL2sJjN6B5eYFt/p7W7dGalZybozWKRBs0ErV6CeRko5YtRBt/kyMpWjNAq3al1lNr08Fti5EQpamTQVBqaiphYWHFttu2Xbhwwe15mZmZZGRkcODAAXbv3s31119PREQEK1asYNq0aRiNRi699FKP183Pzyc/P9/+XNM0/Pz87I+riq0s6beufnKva47c65pTVfdaHdqLZeZnaLFxGK67E61wMkIAdfII1i/fsa/5pY27AUPbji7nG7r1xWIwgNWq5wVlZTr2DRtbpvoZxt2A5a+lehlL58PQy+DMSX1ns1gMfv4lF1AD5L1dc2r6XtfJIMhsNmMymYptt20zm81uz7N1fWVkZPDEE08waJA+THLAgAFMmTKFn3/+ucQgaO7cucyZM8f+vFWrVrz55ptERkZW+LWUJCoqqlrKFcXJva45cq9rTmXv9bl3nsNy+jjq9HHU7i2E/t8z+A8eSUHSOc5/8hrk6Z+pfsMuJfzhv7skKOuiOd+5J3m7tsD5eFTyeQC0gECir7wBg6/7fCDXIqJJHjGW7GW/QVYGpu8/x1IYeAV07UWj6OhKvcaqJO/tmlNT97pOBkHe3t4uLTI2tm3ehSMK3J0HYDQaGTBggH27wWBg0KBBzJ49m6SkJI9daddcc41LMrUtEk1MTKSgoKBiL8YNTdOIiooiISFBHwpag1RuDpa3n4PsLIx/ewktKqZGr1/TavNeNzRyr2tOVdxrlXwey37HZIPW1BSSX3+KlD5DUOfOQHKivqNNB8wTHyLh3Dm35Vg79oBdW/QnhTM802845zy02Luty4jxsHwRKEXuFsd8PznRLYiPjy/X66oO8t6uOVV1r728vMrUgFEng6DQ0FC3w9lt3WDuusoAAgMDMZlMBAQEYCjyjSU4OBjQu8w8BUEmk8ltCxRQLW98pVSN/0JZt2+wT2JmmfUVxsf/UaPXry21ca8bKrnXNacy99q6eY3jSVgEXNAHnCjn7ZFRGB55HrxMnq/Toz/M/tplkzZsTPnqFd0MrdcgfSi+s9bt69R7Sd7bNaem7nWdHB0WGxtLfHw82dnZLtsPHTpk3++OwWAgNjaW9PT0Yi03tgDKFgw1WGdOOB7v2ow6vLf26iKEqDVqsyPgMEx6Be2+yfrEhDb+ARge+4dLnpA7WmQUxLR0bGjdHq1ZbLnro427wXVDUIg+8aEQ1ahOBkEDBgzAarWydOlS+7b8/HxWrFhBXFycvSUnKSmJM2fOuJw7aNAgrFYrK1assG8zm82sWbOGZs2a0ahRoxp5DXWVOnvS5bl17nfyzUaIBkYlndOXtgBo3gotKgZD/+EY/jkVbeBIaNVOD4Cim5WpPOeJE7VhFRuFq7VoDd36Oja06SCJyKLa1cnusLi4OAYMGMD3339Peno6UVFRrFy5ksTERB566CH7cVOnTmXv3r3Mnj3bvu3SSy9l2bJlfP3118THxxMREcGqVatITEzk6aefro2XU7c4twQBHNwN+7ZDp561Uh0hRM1z7nZyntFZCwlDu+eJcpenXXY1KuE0WnAo2oARFa6XYfzNWHdvAasVrWvvCpcjRFnVySAI4NFHH2XWrFmsWrWKrKwsWrRowdNPP02nTp1KPM/b25t//OMffPfddyxfvpy8vDxiY2N55pln6NGjR81Uvo5SuTmQVJjcaPKGfH2UnXXudxg69pBvXUJcxFRqClgtaI1KTwZVmxx5P1qfwZW+tuYfiPGhZypfTqs4DJNfQ11I8rjchhBVqc4GQd7e3tx+++3cfvvtHo95+eWX3W4PCQnhkUceqaaaXcTiT9kfan2Hok4e0Sc2O34Itm+AngM8nyuEqLNUwmms/3wclMLw9JtoJUwwqBITHCu8t2iD1ripx2Nrg9auM/J1TNSUOpkTJKqHcu4KaxaL4erb7E+t82airJZaqJUQorLUxlVQUAAWC2rpgpKP3ey+K0yIhkiCoIbEKSlai2mhJyHavjGeOeHSRC6EqDuU1VLiAAZ1wDHfj9q+DpWT7fnYzVXbFSbExUyCoAZEnXEaGda0JZqmYbjG0d2oFvwgI8WEqGNUwmmsf78b60uPopyWpbDvz8uDIwccG8xm1Na1xY4DUOfPwskj+pOWbfXh7UI0YBIENSRnC7vDAoIgRJ9wUuvYHWwLD547Y58wTQhRN6glv0B6KsSfQq1fXvyAI3vB4jovmlrn5jiKdIX1la4wISQIaiBUVgakFs7CHdPCdSXodp0dB546VsM1E6LuUYVrZtU2VZBvX6UdQG1bX/yY/TsdT7TCj/QDu+zreLkc6zwqrLd0hQkhQVBD4dQVpjVt6bqveWv7QyVBkGjgrLO/xvrYTVjnflfbVYG92yHbqQvs0B5UZrrLIcpp/S/tknGO7etXuB539ACcLvz9btUOLaJJVddWiIuOBEENhDrrNDIspoXLPq1ZK8dxpyUIEg2XKshHLf8NlEItW4CyWiteVl4u1hWLUKePV7yMooMVrFbUzk2O/dlZcLxwuHvTFmiXXe3Yt365PcdP5eVhnf6BfZ/Wf0SF6yREfSJBUENRUktQk2jw9tYfnzpec3USoq45cwIK8vXHuTlw/myFi1JzvkHN/Azr61NQCafLf745D7W9sPtLc3xUq20bHI8P7gGlB2pah25o4Y2hfVd9Z8IZfQ4wQM2Zrj8HPSF6eMWWthCivpEgqIEosSXIYARbYJQYr88sLUQDpI4edH1+4kjFyrFaHUPR881Yv/24/K1Ku7fogRigDRgOwaH69r1b7TlL6oAjH0jr0E3/f+AljnqsW4batQW14jd9g7c3hvsmoXnV2XlyhahREgQ1AEopR0tQSCM055WiC2nNW9kOLr6+mBANxTHXIMje1VReJw6Dc+7O4X16N1s5qI2r7Y+1/iPQuvfTn5jNqL3b9GNsSdGaBu266A97DbK37KqNq7F++5GjnBvuQYsq26KoQjQEEgQ1BOmpkJWhPy7SCmTX3CkvSJKjRQOlCruP7M9PViwIUru2FN82d4a+entZzs/NRu0qzP0JDIYO3dCclrVR29ZjSUt1jOZs0QYtIBAAzc8frcdAfXtWBqRd0B937YM2/PIKvR4h6isJghoCp5adYvlAtu1OydFIcrRogFR2FhTN3TlxtELLyajdTkFQj8LgJS8X64ypZZqQVO3YBGZ9gWOtz2A0oxE6dAcfP/v+vO2O3CCtQ1eX87WBI1wLDAzGcOdjskiyEEVIENQAlJQPZNcs1nG8tASJhujEYb072FleDpwrX3K0ykizJyQT0xLDPU9Aowj9+b4dqL+Wll7GJqeusL5D9f9NJrSuvfWNWRmkz5rmOKZ9N9cCOvawT4gKYLjjUTSn50IInQRBDYHzyLAYDy1Bfv5gm0L/zIlKDQ0W4mKknPOBomIc20+Ur0tM7dlmD6a0Lr3Q/Pwx3PaIY//saajUZM/nZ2XC7q36k9BG0LaTY2eP/vaH+bakbaMR4jq6lKEZjRhufRiimqFde6dLV5oQwkGCoAbAZfX46OaeD7S1BuXlQmJCtdZJiLpGHXPkA2kjHJMOljs52qkrTOuit9xoXXs7Rm3lZGGd+bnnemxfb18GQ+szBM3g+JjWuvYBY5GRXbFxaL7+xcrReg7A+MqnGC6/rnz1F6IBkSConlNWK5w9pT+JaILm6+fxWJe8oFNHq7lmQtQdSinHyDC/ALR+wxz7yjFMXlkteksQgK8ftHW00Gg33QdBIfqT7etRW9e5L2PjKsc5hV1h9uf+AVA0/6dDka4wIUSZSRBU36Uk6nkNAB66wmw0lxFix6uxUkLUMReSIa1wbb3YtmhBIRDeWH9+8kjZk6OPOw2N79gdzctk36UFBKFNfMD+3Pq/z/VkbCfWdcv1pTJAv36rdsUuofVw7dqSIEiIipMgqL5zmSnaQ1K0TXNZPkM0UMcd+UCaLfCIbav/b86D+DNlKka56QpzpvUZAl376E/SUlA/f+s499Be1IyPHceOu97taC7NKS8ILxO06VCmugkhipMgqJ5zGRlWWhAU3hj8AvTHEgSJBsQlH6hVnP5/yzjH/jImRytbQjMegiBN0xOWfXz141f+rgc/iQlYP30dCgpzgYaPRRs6xu01tNBG9hXgtQEj0EzeZaqbEKI4mTu9vnOeI6i07jBNg2Yt4dBeSElCZWW4nV1aiPrGZWRYrN4SpLVsg33A/InDMGhkyWUUGRqv2YbFF6GFR6JdfRtq1lcAWP/7ib7DuRvt5gdKnNPH8ODfibQ+SaLB5PEYIUTppCWonlNnC7vDDAaXYb+euCZHS2uQqP+U1eIYAdYoAi20kf64ZVvHMUVaglR2JtYVv+mtOLaV2l2GxhdvBXKmjbwCYgtbmuJP6f8AoppheOjpUtf20gxGTM1jXUaOCSHKT1qC6rusTP3/wOCyNZsXyQuSpEtR78WfcQweiHUkImsBgfrcWYkJcOooymJBMxpRFgvW9/4BJw7rLUUxLdFGXuEYFQaOSQ090AxGDHc8ivXVJ8E2J1dgEIbHXkTzD6ziFyiE8ES+RtR3lsJRLWVcNdp5hBgyQkw0AMo5Kbq162gszdYaZDbbW2vUkrl695jNmROo/34KtiHvvn7QxnXyQne05q3QLr9ef+LlheHhZ9EaR1f8hQghyk1aguo7S77+f9EJ1jxp2gI0AygrSuYKEg3BUacgKLbIkPTYtrB5DVA4X5DRiJr/feHBBmjR2jUggsKh8WX80nHVrWhtO0J4E7RoWd1diJomQVB9Z2sJKmMQpHn76LlDhXkKqqCgzB/oQlyM7C1BmgFatnHZp7Vs60iOPnYA6+rFUKB/sdAuvQrDDXejThxGLV+I2rgaLAUYho0t87U1TYNS8oeEENVH/rrVd4VDbjEay3yK1rwVKv6Ufm7CaZfFVYWoT5Q5D04f1580bV58RvUWjqBIrVlqX86Cxk3RrroF0AMl7a6/oW6+H8xmtODQ6q+4EKJKSE5QfWf70PYqx1DaZs4zR8sIMVH/KKVQCWdQfy6wJyZr7mZn9g+Axk31J7bfJcBw52N6q6nzsb7+EgAJcZGRlqB6TFmtjpEn5WoJinV0Adi+JQtRD6i927Eu+xWO7HfMy2PTKs7tOVpsW9T5s47nl1yB1q5zdVZTCFFDpCWoPrM4rXdUnrwe5xEqqclVVx8hapFKu4D1k1dhx8biAVBoo2Jrctk5zRdEeGO0a++ovkoKIWqUtATVZ7aRYVD20WEATk36Ku1C1dVHiFqkfv9JH+oOEBAErdujtemA1qaD/rhI95aN1nswasH3oMBw1+PF84aEEBctCYLqM+eWoPIEQT5+4O2jLxyZnlrl1RKivKyL58K5M2jX31WhyQRVagpq5e/6E29vDP+cihYSVqZztfBIDG9Pl6RnIeohCYLqswJHIme5coI0DULC9JlyJQgStUwdPYCaM11/omlotz9S/jJ+/wny9VYgbfjlZQ6AbDRff/D1L/d1hRB1m+QE1WdOo1nKlRMEji6xrAxUfn6JhwpRndRex3IUat1yVEZ6CUe7OT812aUVSBt7bVVWTwhxEZMgqD5zagnSytMdBi55QWSkVkl1hKgIdWC340m+GbVyUfnO//1nxwSHI65ACy5fK5AQov6SIKg+q2hOELh2F0iXmKglKj9fH87uvG3Fb2VunVQXnFuBfNDGXFPVVRRCXMQkCKrPnEeHlbc7LCjU8TgttSpqI0T5HTtgz+WxS7uA2rSq2KHKakXlZqOUcmxbNMfRCnTJOElsFkK4kMTo+qwSLUE4tQSp9AtoVVQlIcrDuStMGzYGtWqxvv2P+aiBI/UkfkBlpGP96J9w/JA+sjEsAsLC4fBe/WRvH7QxkgskhHAlLUH1WQVHhwFoIaGOJ9IdJmqJOrDL/li7/HqwLW1x+hgU7lNZGVjff1EPgECf2uHcGdi/0/47oF1yBVpQSI3WXQhR90kQVJ9VZnSYS3eYTJgoap7KN8PRA/qT8MZoEU3QRk+w77f+MQ+VnYX1/ZfAtsZdYBBEN9fnurJpFCmtQEIIt6Q7rD6rwu4wIWrc0YOOuX3ad9X/7zUI1SgCUpJg5yasbz/rWN8uOBTD319Hi2qm5wXlZOn5bI0i0XzczwYthGjYpCWoPnPpDqvEEHnpDmvwVNoF1L4dKKul9IOr6ppOXWHYgiAvL7SR4x3bbQFQUAiGya+iRTXTj9M0NP9AtOhmEgAJITySIKg+s1QiJ8jbB/wKZ8iV0WENmsrLw/rqk1jfexG18Meau+5Bp6To9l0cj4deBj6+jgMDgjBM+hda0xY1VjchRP0gQVB95pITZCr/+bZJ5aQ7rGE7uh9SUwBQyxeiCqp/BnGVb3bMDxTRBC28sX2f5h+INupK/Yl/oB4ANWtV7XUSQtQ/khNUj6lKjA4DICRUH2WTm4PKy5NuhQZKHdrreJKRBjs3Q6+BFSsrN1tPdo7rjGby9nzg0QOO+X2cWoFstKtuQevQDZrFyqgvIUSFSUtQfVaZ0WGA5jxCTFqDGix1ZJ/Lc+tfSytclvWLd7C+/xLWN/6uB0SerumSD9St2H7NYETr2F0CICFEpUgQVJ9VZnQYuIwQk+TohklZLHDkgOvG3VtQhd1j5SorIw12bdafnDqG9fO39fLdHes8SWK74i1BQghRFSQIqs8qMzoMZIRYFVBnTqJ2bEJZrbVdlYo5cxzycvTHWuHHhdWKWr+83EWpvdtdN+zegpr1pcsyFwDKnKfnIQFERqGFR5b7WkIIURaVCoKsVivZ2dlYinybM5vN/Pjjj7z99tt88803pKSU/1ujqAKVGB0GuARBSiZMLDeVmoz19UlYp77iWMTzIqMOObrCtJFXOLb/tbRY8FKqfduLl7/8N9SfC1w3Hj3gmOm5cGi8EEJUh0oFQXPmzOHuu+/m4MGD9m1KKV5++WXmzJnD5s2bWbRoES+88AKZmZmVrqwoJ6cgSKtITpBLd5gEQeWl9u4Asz7Zn1rzRy3XpoKc8oG0gSPB1jWVcKbY6u4lUUrp9wPA5I122/859s3+GuuGlagj+7FuXIXVOShykxQthBBVpVKjw3bt2kVoaCgdO3a0b9uyZQtHjhwhOjqayy67jO3bt7Njxw7+/PNPrrrqqkpXWJRDZbvDJCeoco46BQknj6ASE9Aio2qvPuWklHKMDPPx1UdiDR5tn79H/bUUrW3HEkpwknAGLiTpj+M6YRg+FmtKEuq32aAU6qt3cdeuJPlAQojqVKmWoPPnzxMTE+OybdOmTQA8/vjjjBs3jqeffprg4GDWr19fmUuJirBUMghyGh2mZMLEclNHXROK1da1tVSTCkpJhNRk/XHr9mhGI1rvQeCrr8ulNq1B5eWWqSjnfCCtU0/9/6tuQes71PNJXfugNZJ8ICFE9alUS1BmZiahoaEu2w4cOECjRo1o3bo1AEajkbi4OA4dOlSZS4mKcM7VqkB3GMFOw4+lO6xcVG4OnD7hum3LWriIFvJ0nh/I1uKj+fii9R2KWr0E8nJQW/5CGzSq9LKc8oG0Tj30/w0GuPtvEByKSjqH1igCwhtDI32xVFq2qdLXI4QQRVUqCDIYDOTmOr4JZmZmEh8fz8CBrhOp+fn5kZ3teU4QUU0qOVmi5mXSV+XOzJDusPI6cRhUkRFhxw6ikhNLHe2kUlOwfvcpWlQztOvuRNO0aqxoCZzzgdp2cjwePFoPgtC7xCglCFIFBbC/cN6foBCIaekoy+SNdvP9VVhpIYQou0p1hzVp0oRDhw5hLRz+u3XrVgA6dOjgclx6ejrBwcGVuZSoiMp2h4GjSyz9QvlHAzVgLl1hUY4uY7Wt9C4x9cOXsGMjavHPsHtLdVSvTOwtQZoBWrdz7GjdHgoXKuXgHqzzZpa8sOrRA/Zh9lrHHnoLkBBC1AGV+jTq06cP6enpvPXWW/z222/MnDkTg8FAnz597McopTh27BiNGzcuoSRRLSo5YzTgSI42myE3p/J1aiCU08gpw7V3OrZvKTkIUimJqG3rHM83rq76ypWBys6Esyf1J81j0Xz97fs0TUMbPcFx7K+zsH7wMspDa6FzVxiFXWFCCFEXVCoImjBhAs2aNWPbtm18++23pKamcuWVVxIREWE/Zv/+/WRkZBRrHRI1oLIzRgNasIwQKy+llN76AeAfCN37QXRz/fnhfagLyZ7PXf4bOE2sqLat1ycPrGlHDkBhy59zV5iNNmwM2nV3gq1VZ98OLP/6G3l7thc71jUpukc1VFYIISqmUjlB/v7+vPHGG6xfv57U1FTatm1Lp06uH5gZGRlcfvnlDBo0qFIVFRXgvNp3RbvDQkIdj9MuQJOmlapSg5B0Tl9oFKB1OzSDAa33YNSvPwCgtq1DGzm+2GkqLw+1arHrxrwc2LUFelf890cpVe68InXYab0wN8PgNU1DG3sdqnV7rF+8rb83UlM4/8yDGG55EG34WL2c7Ew4VjgoIro5Wlh4hV+HEEJUtUp3znt7ezNs2DAmTJhQLAAC6NevH3fddRctW7Z0c7aoVpUdHQZFls6QEWJl4ZwPpLXWW0A1pyBGbV1X7BwAtWE5ZBdOKtrEkUdk3biqYvXISMfyyhNYn38Qdax8ozPVYaeRYW08zwWkteuC4cUPwDazs9WC9btPsS6drz/fv8ueIC6tQEKIuqZaMxSzs7MlmbYWqcoumwHg1B3mKeejPJTVgnXRT1j/XFB/3xtO+UBa6/b6g5iWjsDm4B5UkYBSKYVa6pgp2XDvk/pIKoBdm1E55R9dqX6fAyePQmIC1g9eQp08WrbzCvLhWOEs8OGN9aHrJdBCwjA8+S+0S692lDHrK6yL57odGi+EEHVFpYKgkydP8ttvv3H27FmX7bt37+aRRx7h7rvv5r777mPFihWVuYyoqILKJ0a7LJ1RBRMmqvUrUT9/q4+A2rO10uXVRfaWIE2DVu0KH2qO1iBlRW3b4HrSvu0Qf0p/3LYTWqt2aL0H68/zzagdRY4vrQ6Z6a7rlWVnYn3/H6gzJ0s/+eRRyNeX+yjrjNCa0YjhxnsInugY7q7mTEf99af+xOjlWHJDCCHqiEoFQYsWLWLGjBl4e3vbt2VkZPD222+TlKRPkZ+Zmcl//vMfjh07VrmaivKrgsToKu8O27PN/tC2/EJ9osx5cLrwvR7VDM0/wL5P6+XUJbZptUtLmNW5FWj0lfrx/YY5ji/nKDH15wKwzeZsKvz9zEzH+t4LqIQz7s9RCnX6GGrZr46NZV0WAz3QC7ntQQxX3+bYWBhM0aY9WuFM00IIUVdUKjH6wIEDNG/e3GU02KpVq8jNzWX06NHceuutbN68mU8++YRFixbxf//3fyWUJqpcVcwT5JQYXbQ7zLr8N9TuLRiuvRMtpkWZinPONVEnjlSsTnXZiSP24FNrU2REZIvWENFET5w+sAvrq0+ijbkWrXlr2LVZP6ZRJPQYoD9u0wHCIvQ1t/ZuQ2WmowWWPt+Wysl2BDJGI4bn3sH6zUf6BI7pqVjffQHtujv0UWgF+ZCfD2dOoHZtcSyTUajMa4M5MYy/CWU0on761lFO4VIZQghRl1QqCEpLSyMuLs5l286dOzEYDNx88834+/szbNgwFi5cKMtm1IaqCIICg/XJ8pRVHwFUSJ2PR/3vPwBYE85gePljNJOpxKJUcqK+HpXNiSMVGrlUlynnRVNt+UCFNE1Du+wa+33j5FHUl++gvBz3TRt5BVph/pZmMOhLVCyZCxYLautatGFjS6/DikWQnaWXMWAEWrNYDE/+E+s7z8Pp45CajPr6/dJfTK+BEBNb+nFuGMZeh9XLCzV7GphMJa8RJoQQtaRS3WHZ2dn4+/u7bDt8+DCxsbEEBQXZt0VHR5OSklKZS4mKqOSyGQCawQhBha0PTi1BarvTgrjnz+qzG5fCuRUIgKwMSD5foXrVVe5GhjkzXDIOw8PPQMu2jo22qQy8fdCGXOZyvNbPETyUpUtMmfNQf/xSeLIBbez1+sOAIAyTXnHMV+SOyRu69Eab+ACG1z7H+PCzlQpQDaOvwvDa5xhe+wKtcXSFyxFCiOpS6XmCLlxwtA6cPn2azMxMhgwZUumKiSpgywkyGCq3VEFwmB4ApafaW27UtvUuh6jffkT1H44WGeW5nKJBEOjdRxFNKl63OkQppU8yCODnD9HN3B6n9RqEoedA2L8T6+8/QeFkgtol49ACAl0PbtEGGkfD+Xg4uBt17iwknEbt3oo6tAcCgjCMuUZfcV3TUKv/sM9RpPUZjOa0ZIcWFILh6X+j1q/Qu8JMJj3w8TKhBYVA245o3j5Vek9KfD8IIUQtq1QQFBsby759+0hISCAqKoply5YBFJsv6Pz584SFhbkrQlQnWwtDRecIsgkJhdPo3WvZmfrQe6dh4ADkm7HO+grjoy94LMZ5VXL7thOHXebQuRioC8mobetQOzfprS2DRqH1GgipKZBW2OIZG1di4KlpGnTsjrFjd9TpY5CSBF16uz1O6zcM9essUArrCw8VO8Z6cDe07YThqltQSxwtctrl1xcvLyAIbdSVFXjVQghR/1Tqr+Po0aPZvXs3Tz/9NE2aNOHEiROEhITQq1cv+zE5OTkcP36c3r2Lf8CLamZrCapoPlAhLTgU+zimtAv6bMK2JRUuuUKf/C8tRV/0c8dGtO79ipWhspzWogptpAcMXDzJ0So7E7XmD/21FgkA1e4tqIgmLt1fxZKiS6A1awXNWnne33eoHgQV26HZfw4c3ov1XacAtFtftOaeyxRCCFHJIGjgwIGcOXOGefPmceLECSIjI3n00UcxOSXIrlu3DovF4nY26ZLk5+cza9YsVq9eTWZmJi1btuTmm2+mW7du5SrnlVdeYdeuXYwZM4Z77723XOde9GyJ0ZUMgiiyfpja7pizRhs4Etp2RH35DgDW77/A0KE7mk+RbpUjToFT78GoDSshMx1OHi6WHK1ys7F+8DJkZWJ4+Bm0pmUbeVZdlFJYP34FnJeSKCrpHCrpnP2pViQpujK0pi3Q+g1HbVwJEU3QuvRC69xLn6V533asc/8LRYa9G8bdUGXXF0KI+qqSfx3h+uuv5+qrryY7O5vg4OLDd7t168abb75JVFT5cgM++eQTNmzYwLhx44iOjmbFihW88cYbvPTSS2VejHXDhg0cPHiwXNetV6oqCHKaMFGdO6tP7AcQGg6xbdFi26JWL4H9OyH5PGrRj2jOc8VQZBmGuE6ohNP6nEGZGfqIsfDGjmP/WmZvbbF+8TaG599FM3lTFVRqih6QNWmqt8CUxf6drgFQTEu0XgP1LrCMdKyLf3aZ/wiwT5JYVQz3T0bd8Sh4e7smK/cahKF7f9S6Zaj538OFJLR+w8rVEiWEEA1VpYMgAC8vL7cBEEBERITLPEJlcfjwYdauXcttt93GhAkTABg2bBiTJ0/mu+++49VXXy21DLPZzIwZM7jqqquYPXt2ua5fb9hGh1V0yQwbpwkT1do/7eVqPfvb/yAbbnkI6z8fB0sBavHPqMGjXZJi1SHnBTk7oZ08irIFDicOuwZB25zW1jpzAvXLTLQb7q5Q1ZXFQu7OzVhW/oHavdUxkaGXF4Zn3kJzHqXlgfV3pzybu/6GYfAol/22vB615Be9O3Dw6DLN51NexVrXbNuNRrQhl6L6j9DnIGoiI7GEEKIsqmztsIKCAg4ePMj69etZv349Bw8epMB5iHY5rF+/HoPBwOjRo+3bvL29GTlyJAcPHrTPRl2S+fPno5SyB1ENki0nqJKJ0ZrzrNHOQ8B7DnQ8jm6GdtlV+pOCAtRCR+Cp8s1wvLBFrnE0WkiYS/DhnBekMtLg4B6X66s/fkEd2OW6Le0C1i/fxfLvpzyuiaVys7G8+TSJzz6E+v0nRwBUWEfrjE9QzrNquyvj5BHYWxisRTRBGzDC7XFas1YY7nkS44ffY7ixdrpdNZNJ/zkYKhn0CiFEA1HpliCLxcKPP/7I77//Tk5Ojss+Pz8/Lr/8cq6//nqM5WiNOHbsGNHR0cXmIGrbVv/Defz48RJbl5KSkvjll194+OGHXZb0aHAKqr47zM4/EOI6u2zSxl6nr1eVnaV3z4y7Hq1xUzh+2NF61LYwN8wlCDrseLxjo33VcSKjIDFBHxU17QMML32E5h+A2r8T65fv2Octsn70LwwvvIsWGu4oRyms0z9yCdrs183K0FtMTh5BLV+INtpzoKycW4HGXGOfyFAIIcTFr1J/Ha1WK2+99Rbbt28HICAggMaN9W6N8+fPk5WVxc8//8zRo0d5+umnMZRxrprU1FS3Q+pt25znJnJnxowZtGrVisGDB5fj1ejJ2Pn5+fbnmqbh5+dnf1xVbGVV+0zJtpwgL6/KXctNEKR174uhyAzRWkAQXHYN1l++A6sV9etsDPc+WSwfSNM0CI/UZ6POTNfnCqLwfjh1hRnvm4z15xl6K1BKIup/n0N0M6zz/ucIlADSUrB+8jrGp96wz3OjFs2BrWsL6xWI4eYHoHNPfaTb4X1Y/v2UftwvM6H3YLcrpavz8ajNf+lPgkIxDB5dr2a3rmo19r4Wcq9rmNzvmlPT97pSQdCyZcvYvn07kZGR3H777fTv399l/8aNG5kxYwbbt29n2bJlLt1bJTGbzS4jzGxs28xms8dzd+/ezYYNG3jttdfK8Up0c+fOZc6cOfbnrVq14s033yQyMrLcZZVFeZPFy+tUYRBk8vUjKrrieSLK2oTTXl4uM1A3uuRy/N2Uab31PuL/XIA1Iw21YQURd/0fqaeOUriUJ40Hj8BUeF5iu87kbl0Hmek09jJgCAzkzN4dABgjmhA9aDiWdh1IeORmVHYWasMKx1B9wKdHPwrOnsJyPh6OH8Jn9lc0mvIKeds2kPjLd/pBmkb4lFfx6+c0gWd0NCk7riVr0c+Ql4P33BlEvPB2sdeS8vM3ZBUGWyFXTyS4ZWyF72FDUt3va+Eg97pmyf2uOTV1rysVBK1cuRJvb2/+8Y9/2FuAnPXr14/Y2FgmTZrEypUryxwEeXt7u7TI2Ni2eerislgsTJ8+naFDh9q7zsrjmmuuYfz48fbntkg0MTGxwvlN7miaRlRUFAkJCS4riVclZbXYh6TnWxXx8fGVKzAoVF/IE8DkTWpMK9I8lXnpVfDzDLBaOff1R44E6KAQEjUTWuF5lijHjMrnNq3VJ3csnODR2q0vCef0IefaxAdRX7/nKF/TMEy4hYIrboAzJ+HfT0FeLtkrfifHyxu1brk+IzJgmHALfv2GFLvXauwN8NcySE8lZ91yzvw2F0PPAY79aRewLJmvP/HxI7PPMLIqew/ruZp4Xwud3OuaJfe75lTVvfby8ipTA0algqBTp07RqVMntwGQTePGjenSpQv79+/3eExRoaGhbtcas3WDeZp9euXKlZw9e5YHHniA8+dd16TKycnh/PnzhISE4ONhlI3JZHLbAgVUyxtfKVV9QVCRdcMqfZ3gUEcQ1KkHePt4LvOScbBkLmRmoDaucmwvXJHcdp7Wso29ZUcdPwznHHPdaL0GOsrvPxxt7zY9uAkOxXDfZLSO3fV9zWIx3PMk1s/e0MtZOt9xve790K640X5Nl/r6B6DdeC/qq3cBsP7vc2jfFc1Pz0Oz/rnAHpBpw8eAf4B8+JVRdb6vhSu51zVL7nfNqal7XakgKD8/v1jysju+vr5uW3Y8iY2NZc+ePcUWaLWtRB8bG+v2vKSkJCwWCy+++GKxfatWrWLVqlVMmTKFfv2Kz2hc7zgHQZVdNgNchsk7jwpzR/P1RxtzLeqnb123ty0yYaZzcvSRfXBc//kSGAROx2qaBnc/gTZqAkTFoPn4upbbayDaVbeg5v3PsbFxUwz3PFny0hX9hqHWLtNHf11Iwvq3W/QlQkLDIf6UfpDRC230VSW+XiGEEBenSv11jIiI4ODBg1itVo9Jz1arlUOHDhEeHu52vzsDBgxgwYIFLF261D7EPT8/nxUrVhAXF2cfGZaUlEReXh4xMfoikYMHD3YbIL3zzjv07NmTUaNGERcXV85XeZGyOLcEVT4I0lq3Q+3aDP6BaN37ln78JVeglvxiX8wTQCsymoxGkXrAk5kBTkPgte79i43C0jQNWrbxfL0rboIzJ1Gb14CPH4b/exbNP6DkOmoahlsfwvryY5Bv1pOtU1PsS3oAaANGoIWV/b0rhBDi4lGpv47du3dn8eLFTJ8+nTvvvBOvIi0OBQUFfPPNNyQlJTFmzJgylxsXF8eAAQP4/vvvSU9PJyoqipUrV5KYmMhDDzkWkJw6dSp79+61T4YYExNjD4iKaty4ccNoAbIpqOIgaOx10KQZWkyLMk0EqPn4oo29FvXjdH2Dtw8UWctK0zRo0dYxD49te6+SW5rcXk/T4P7JaP2HQ9MWaI3LlgiuNY7G8H/PYV31u76IaWqyPvReKQgMsnenCSGEqH8q9dfx6quvZs2aNSxZsoTNmzczaNAge37QuXPnWLduHSkpKQQGBnL11VeXq+xHH32UWbNmsWrVKrKysmjRogVPP/10udcga7CcJgGsirltNC8TWt8hpR/ofM7wcahlCyH5PFqP/mhuuuW0lm1QzkGQrx/Y8n3KW0eDEXr0L/3Aoud16YWxi2PRX1VQAOkXICikypbrEEIIUfdUKghq1KgRzz33HO+//z5JSUn8+uuvxY6JiIhg8uTJNGrUqFxle3t7c/vtt3P77bd7PObll18uU1kNctkMi1MOVlXkBFWA5uOD4Zk3Uft3onVz34WmtWzrMuxd69qn1gMPzctL76oTQghRr1X6r2Pbtm358MMPWbduHXv27HEZwdW5c2cGDhzI6dOn2bt3r7Ti1CTn5SCqoDusorTQcLQBl3g+oEieT0W6woQQQoiKqLIFVIcOHcrQoUPd7v/yyy85cuQIP/zwQ1VcTpRFVY8Oqy7hjR3J0V4m6NK7tmskhBCigaiyBVRLI3Mr1DCL6zxBdZWmaWjjb9ZHnU2YiObrV9tVEkII0UDU4SYCUSkuo8PcTwBZVxhGXYkaOV7W5RFCCFGjaqwlSNQwl5ygutsSZCMBkBBCiJomQVB9VQdGhwkhhBB1mQRB9VUdGR0mhBBC1FUSBNVXF8voMCGEEKKWlOuv48qVKyt0kfT09AqdJyrhIhkdJoQQQtSWcgVBn376aXXVQ1QxdRGNDhNCCCFqQ7mCINvq7eIicJGNDhNCCCFqWrmCoE8++aS66iGqmnSHCSGEECWSxOj6yjkI8pLuMCGEEKIoCYLqq8Ig6LxPKHPTgojPMNdyhYQQQoi6RYKg+qowMfrT9tczIzmQd/86W8sVEkIIIeoWCYLqq8LE6NMBjQE4mpKLVRaxFUIIIewkCKqvCluC8g16PpBFQXqepaQzhBBCiAZFgqD6qjAnyGxwJEWnZBd4OloIIYRocCQIqq8sBSgg3+CYBeFCjgRBQgghhI0EQfWVxYJFM2DVHD/iC7kSBAkhhBA2EgTVVwX5Ll1hACnSEiSEEELYSRBUX1ksmA2uE4JLd5gQQgjhIEFQfVVQIC1BQgghRAkkCKqvLAX24fE20hIkhBBCOEgQVE8pSwFmo3SHCSGEEJ5IEFRfWSxuusMsKJk1WgghhAAkCKq/3IwOK7AqMszWWqqQEEIIUbdIEFRfWSwuEyXaSJeYEEIIoZMgqL4qKCg2RB4kCBJCCCFsJAiqryzFh8iDDJMXQgghbCQIqq+kO0wIIYQokQRB9ZW0BAkhhBAlkiCovvIQBElLkBBCCKGTIKi+Kig+WSJIECSEEELYSBBUX1ksxZbNAOkOE0IIIWwkCKqvCvLdDpFPySmQWaOFEEIIJAiqv4osmxFg0n/UZosiO19mjRZCCCEkCKqvLAUuQ+SbBDoCIskLEkIIISQIqr+KjA5rEuhtfyx5QUIIIYQEQfWSUqrYshnOLUESBAkhhBASBNVPVj3nx7klKEq6w4QQQggXEgTVRwV6kJPv0h0mQZAQQgjhTIKg+siiBzl5RufuMEdO0IUcS41XSQghhKhrJAiqjyyuLUEmg0YjP0dAlJIrLUFCCCGEBEH1kT0I0gMfb6OGn8mAr5f+407JliBICCGEkCCoPirMCcqztQQZNQAa+RkByQkSQgghQIKg+smi5/w4WoL0H7OtSyynwEpugcwaLYQQomGTIKg+KuwOsw2R9y5sCQpzyguS1iAhhBANnQRB9VFB8ZwgcA2CJC9ICCFEQydBUH1ksWBBo6AwCDIVdoe5BEHSEiSEEKKBkyCoPrLkuyye6mNPjHbqDpNh8kIIIRo4CYLqI4sFs9ExQ7TJXRAkLUFCCCEaOK/SDxEXnQLXFeS9y9kdlmm2MGNbIvGZZh7pF0VUkLfHY4UQQoiLlbQE1UeWApfuMLeJ0R6CoH2J2Tyx8BiLD6eyMyGbLzefq966CiGEELVEWoLqoyItQbbusACTAW+jhtmiinWHWayKn/Yk8/2uJKzKsX3z2SyOpuTSupFvjVRdCCGEqCnSElQPKYsFs5vEaE1zrCHmHASl5BTwj2WnmLnTEQCFO7UazdmTXAO1FkIIIWqWBEH1kSW/SEuQ48ds6xLLNFsxW6ycSTfz9OLj7D6XDYBBg5u7hvPJla0J9dWX2Vh7MoPT6Xk1+AKEEEKI6idBUH1ksbjNCQLXvKDNZzJ5dskJzmfprULh/l68OqoFE7tF4mcycFWHRgAo4Kc9KTVTdyGEEKKGSBBUHxUbHeY+CHp7zVnS8vR1xmJDfXhnbCydm/jb949tF0qgt/4WWXksjfOZ+SVe1qoUW89mcvxCrsdjLFbFimNprDiWhtki65cJIYSoPZIYXR9Z3A+RB9e5gmz5P50i/Xh+RDMCvY0uxfibjIxvH8YPu5KxKJi7L5kH+0a5vaRSik82JLD0SBoAt3WP4PrO4WiaIwDLybfy3tqzbDydCcA32xK5umMYY9qG4Weqn/F4ltlCToGVCH9T6QcLIYSoURIE1UcF7ofIg2sQBNA3JoC/D4nBx8t9EHJF+0b8si+F3ALFH4fTuLFLhEtrks2M7Yn2AAjgux1JnEoz8+iAKLyNBpKz83l1xWmOXnDkFl3IKWD61kTm7E7m8nZhBPsYSc21kJpbQFpuAeH+Jq7vHE5kwMUZQCRn5/P3309wIbeASYOaMjQ2uLarJIQQwokEQfVRkRmjnYOgdhG+aOh5Ppe0CubRAdF4GbTiZRQK9jEyNi6MX/alkG9VzNuXwl29Grsc88u+ZH7eq+cM2coGWHk8nYRMMxO7RfLxuniSC0ekBZgMdGrsx6YzWQBkmK3M3u1+BNqq4+k80KcJI1oFu7QqOcu3KA4n57DrXDaHUnIJ9jHSubE/nSL9anWix6+3nLe/5q+2nKN3TAD+JmMpZwkhhKgpEgTVR5Z8lyHyzqPDmgX78NaYlmTlW+ke5Y/BQ2Dh7KqOjVh44AL5VsXcfSkcu5DL2HZh9IsJZOXxdKZvTbQf+2DfJoT6evH+2rPkWRQHknJ5edkp+/7GASZevKQZLUJ8OJWWx897k1lxLN1lbiJn2flWPlgXz8YzmTzcL4pgHyPZ+RYOJedyIDGHPYk57DufTZ7FtQBbq1QjPy+6N0vGV8snwGQkyMdAoLeRdhF+tAjxKdPtrIjt8Vn8dTLD/jw118JPe1K4vUdktV1TCCFE+UgQVB9ZLB4TowHaRfiVq7hGfl6MaxfKvP0XANiekM32hGwa+XmR6rQQ68RuEVzeLgyAxoEteW3FaXtLCED7CF+eG96MUF/9bdc8xIe/DWzKzV0j2JGQjbdRI9TXi1BfI34mA9/vTGL5sXRAH6a/93w2ob5enEzL8xg0FZWSU8DyQ4lu9w1pGcSt3SJpGly1rUX5Fiufbyo+0/a8fSmMaRtK48CLs3tPCCHqGwmC6qMScoIq6s6ejWnk78Wig6kkFI4Sc15644p2odzUJdz+vE0jX965PJY3V51hf1IOI2KD+b/+UW5zj5oEenNZ2+KByBODmtK3WSCfbUggw2wtzBeyFDsu3M+Lrk386dLEn06N/UnJyWfv+Rz2ns9mf1IOuQXuI6Y1JzJYezKDS9uEclPXcBr5eZFnUeTmW8kpsOJl0Aj1Nbq0pJXFL/tSOJthBqBDhB/tI3yZt19vSfvvjkQmD25arvJqg1UpTqeb2Z+Yw77EbPYn5pBltnJfnyYMk9wmIUQ9IUFQfVTC6LCKMho0ru4YzoQOjdgen8Xvh1LZdCYTq4JhscHc16dJsZydRn5e/PuyFmSarQT5VCwXZnCLYDpG+jN1fTxbzmZh0KBlqA/tI/xoH+FHx0g/ogJNLteOCfama5MAoHAEXEAYR0/Hk5lnISPPQkKmmQX7L5CWZ8GqYPHhVP44kopSjnwmZwEmAyG+XjTyM9K6kS+dCvONQnyL//qcyzTb85sMGjzUrwmRASaWH0snPc/CquPpjG8fRvtytsaVVZbZQoB35fKOVhxLY/rW824Dzvf+OovZYmV0m9BKXUMIIeqCOhsE5efnM2vWLFavXk1mZiYtW7bk5ptvplu3biWet2HDBtauXcuRI0dITU0lPDyc3r17c9111xEQEFBDta9lRZbNqIqWIBuDptGraSC9mgaSnJ1PYlYB7SN8PSYta5pW4QDIppGfFy+OaEZCZj6hvl7lGk5vNGhEh/phzPFDKUeIc0X7MBbsv8DcvSnkFFhL7F7LyreSlW/mbAbsPp/D/MJuwWbB3nRq7EdcuB9x4b60CPHh6y3nMRfmJ13RPoxWYfqaaxO7Rdi7yL7ecp43L2uBpmlYrIrDKbmcz8wn3N+LJoEmwvy8ypSr5WxHQhYzdyRyICmXzo39uLd3E9qUc703i1Xx7bbz9m5PZwZNDygV8PH6BCxWGBMXWq7yhRCirqmzQdAnn3zChg0bGDduHNHR0axYsYI33niDl156iQ4dOng874svviAsLIyhQ4cSERHByZMn+f3339m2bRtvvvkm3t61N1qoxhQUYDY4Ar6qDIKchfubCK+h+W80TSO6Ckd6+ZuM3NQ1gsvjQvl5bwrbE7LwMmj4ehnwMxnwNRrIt1rtQ/ZTc/T5fpydTjdzOt3MksN6ErZtcVrQJ6W8pVuE/dgxbUNZeOACp9PNHEjK4cvN50jOKWBXQjZZ+a7lmgwakQEmGgeaiPT3onGAiYgAE5EBXkT4m2jk52XvVtyfmMN3OxLZVbjsCcCe8zlMXnScUW1CuK17pMuUBrkFVpKy8wn3M7kEk+l5Ft5Zc4YdCY5yekT50z0qgI6N/WjTyJcZ2xJZcEAPkD7dmECBVXFF+zC391cpRZ5FkZFnoZGfF8YSRiAKIURtqZNB0OHDh1m7di233XYbEyZMAGDYsGFMnjyZ7777jldffdXjuZMmTaJz584u21q3bs0nn3zC6tWrGTVqVLXWvU6wFM0Jqp8TEVaFYF+vYkP+PUnPs7AvMZu953PYcz6bIym5Li1IZqcRavf0auwyHN5o0Li7V2NeWXEagIUHUz1eJ9+qOJthtucVuRPkYyTI21jsGFsgptBHyP11IoOBLQJJyi7gTLqZ5Gw9j0sDmoV406aRL63CfPjtYCrnCnO9jBrc36eJPcnd5t7ejTEaNH7Zp0+H8MXmc+xPysGA3lqWnW/FrE6RkpVLeq6F/MKbE+Jr5ImB0fRqGujx9QghRG2ok0HQ+vXrMRgMjB492r7N29ubkSNH8v3335OUlERERITbc4sGQAD9+vXjk08+4cyZM9VW5zqlSE6QqZpaghqaYB8j/ZsF0b9ZEKC3qhxJyeVwci6HknM4lJzLucx8hscGM7RlULHzezcNoEd0ANvjs+zbgrwNdIsKoHWYLym5BZzPNHMuM59zmfnFhv07yyjMb7KJDjIxsWsEA1sEsehgKrN2JZFVmOC97Gh6sfMVcCrNzKk0MyuOObaH+Bp5emgMnRv7FztH0zTu6hmJl0Fjzh4972nV8eJlF5WWa+GVFae5r3cTjy1HQghRG+pkEHTs2DGio6Px93f9IG7bti0Ax48f9xgEuZOamgpAUFDxP0z1UpHJEn0kCKoWvl4GOjf2dwkYLFblsetH0zQmD27K3L3JBJiMdI/2p3WYr9vjlVJkmK0kZuW7/EvOKSA5W/+XkpNPZICJazuFM7J1iH3Sy6s6NmJEq2Bm7kjijyOp9taqIG8DTYN9iAzwIj7DzInUPJx7+No08uXZYTElztCtaRq3dY/AZND4fldSsf0mo0aQt1FvqfIxkldg5VCy3mL2xeZznErL474+Tex1zTRbOJiUQ4FV0aaRb411rwohBNTRICg1NZWwsOLfGG3bLlwonrhZknnz5mEwGBgwYECJx+Xn55Of71gkVNM0/Pz87I+riq2sqizTRdEh8l7G6rtWHVft97oIr1ICzhBfL+7q1aTUcjRNI8RXH5XWNtz9SDKllMfXFepn4pEB0dzcLYKk7AKig0wE+7j+uudbrBxPzeNwci5KKUa1CfW4fErRuk3sHsnQ2GBy8q34exvwNxkJ9DHSvGk0586dsyehW5Xiv9sT+amw5WjRoVTOZJiJDvRmf1IOJ1PzXEbkNfLzom0jX9qG+9Iy1IdmIT5EBXpLa2YRNf2+bujkftecGv/MrpGrlJPZbMZkKv6N0LbNbPacK1HUmjVrWLZsGRMmTCA6OrrEY+fOncucOXPsz1u1asWbb75JZGT1zPIbFeV+MdLKSjR5kZfvuH8tmkXjZWjYeUHVda/rupLf8dCiGQyraNkeCi96r59p2pSuLeJ5bcl+8i2KnQnZ7CTb7bkpOQVsPJPJxjOZ9m1GTSMm1JfY8ADaRgTQJjKQuMhAmof5cSE7n/3nMjhwLoMD5zPJzbfwf0Nb0zGqYcxl1FDf17VF7nfNqal7XSeDIG9vb5cWGRvbtrKO8Nq3bx+fffYZ3bt3Z+LEiaUef8011zB+/Hj7c1skmpiYSEFBgafTyk3TNKKiokhISHAZtl1VLFmZ5BfOYWPUIPFc8dmLG4rqvtfCoaR73SscXhnVgtdXnia9MJfJoEHrMF86Rvrh7WXgcHIOh5Nzi42WsyjFyQs5nLyQw6rDji4427D9onacTuX5Ec3oFlV/p8SQ93XNkvtdc6rqXnt5eZWpAaNOBkGhoaGkpKQU227rBnPXVVbU8ePHeeutt2jRogWTJ0/GaCx9rhqTyeS2BQqolje+Uqp6yi1wJEabjJr80lJ991oU5+led4z044NxsWw6k0nTIG/aRfjhW6T7zaoUCRn5HEnJ5Uy6mdPpeZxJN3Mm3VwsUdzT3E45BVb+uewUfx/SlP7N63ceoLyva5bc75pTU/e6TgZBsbGx7Nmzh+zsbJfk6EOHDtn3lyQhIYHXX3+d4OBgnn32WXx9yzdp3EXPaYi8jwyPF3VIuL+JsXGev8QYNI2mwd7F1nOzKkViVj7HU/M4kZrH8Qt5nM0wE+xjpHWYL60b6TlE/92eyKYzmeRbFf9efYbHB0RzSeuQYtexWBUJmfmcTNODrAh/Lwa3CJbcIyEamDoZBA0YMIAFCxawdOlS+zxB+fn5rFixgri4OPvIsKSkJPLy8oiJibGfm5qaymuvvYamaTz//PMEBzeM3AAXTguoyoe6qA8MmkaTQG+aBHrbpyhw55lhMXy0Lp6Vx9OxKvhgXTzb47PQNL2FKCffSnqehdNpZvs8Rjbf70zilm4RDI0NLveM3UKIi1OdDILi4uIYMGAA33//Penp6URFRbFy5UoSExN56KGH7MdNnTqVvXv3Mnv2bPu21157jXPnzjFhwgT279/P/v377ftCQ0NLXXajXijIty+bIRMliobEy6DxxKBo/E0GFh1KBWBFGeYyAkjIzOe9tfHM3ZfCbd0jaR7iTYEVCqyKAquicYCp3EvAFFgVRk1GFQlRV9XJIAjg0UcfZdasWaxatYqsrCxatGjB008/TadOnUo878SJEwDMnz+/2L5OnTo1jCDIYiG/sCWoupbMEKKuMmgaD/ZtQqC3kTl7kostimvQoGmQNy1CfWgRorcuLT+Wxs7CJUOOXcizz+ztzKjBzd0iuK5TeLG5nbLzLczff4F957PJMOsTWabnWcgtUBg08DMZCDDpUwnEBHtza/dIYoIbwBI+QtRxmpIsr1IlJia6Ha1WUZqmER0dTXx8fLUkfhU8/xDXdXoCgHbhvrw9NrbKr3GxqO57LRzq4r1Ozs4nJacAP5MBv8J14XyMBrcTVG6Pz2LG9vMcSckrscxOkX48MSiaJoHeKKVYfSKD6VvPk5JT9hGkEf5evDM21mVdN0+UUqw7lYG/yUiPaH3EW1281/WZ3O+aU1X32mQyXbyjw0TlOOc6SEuQaMjKs8hvj+gAukXFsvZkBhtOZ6KUPvu3l0Ffj23NCT3PaG9iDk/8dpxbukWw4XSmy+K1oLc02dZ2C/Q2UmBVZOdbyMq3kmW2UGCFpOwCXl95mldHtyh1gspvtiXa12t7pH8Ul7UNrdC9EEIUJ0FQPWR2Cp4lJ0iIsjNoGkNaBjOkZfEBFZfHhfL+2njOZ+WTnW/lqy3nXfb3jQng7l5NaBpk8pgDdCGngCm/Hycpu4CDybl8tD6eKYObejz+j8Op9gAI4D8bE4gKNNE9WhajFaIqyF/IeshsdfxYZXSYEFWjU2N/PhgXy4hY1wApKtDEC8Ob8cKI5sQEe5eYBB3m58ULI5rh66Ufs+ZEBj+4WYMNYNe5LD7bmOCyzaLg36vPcDqt5C47IUTZSBBUDzkvKiLdYUJUnQBvI08ObsqUwU3pHuXPbd0j+Hh8K/o2K3vLTKswXyYPbortN/OHXcksOZyKxakb+2y6mX+vOoNtfsgr2ofRN0bPB8oyW/nX8lOkZpd9+SAhhHvSHVYP5VsdgY90hwlR9YbGBjM0tuJzkPVrFsRdvSKZvjURgE82JDBj23l6RAfQMzqAOXtSyDTry4f0bhrAvb0ak2ex8uySkxxPzSMhM5+n5u3ihaFReLlJ8hZClI38hayHzMrxY5WWICHqpqs6NOKyto7ZrDPMVlafyOCj9QmczdBbeVqEeDNlSFOMBg1/k5EXRjQjzFefq2jb6TReXXGa5OyqG7kK+mg0s8Va+oFC1AMSBNUzSinMOLcESRAkRF2kaRoP94ti8uCmDG4RRIDJ9eM4xEcPevxNjgkaIwNMPD+imf33elt8Fo/9eoylR1JdhhMnZ+fz055k/rnsFIsOXijzUOPErHwmLTrOzbMO8vH6+CoPsIo6lJzDj7uTOCU5TqKWSHdYfeM0USKASbrDhKizDJrGsNhghsUGU2BVHEjKYfOZTJKyC7iuUyOaBBafUDEu3I8XRjTnw/UJJGeZycq38vH6BP46kcGQlkGsOpHBzoQs+wKzW+Oz2BafxeMDogksYcbrk2l5vLzsFMnZ+nxHS4+ksep4Old3bMQ1nRq5BGOVdSAph1m7kthyNguAeftS+HRCG4LLOSO3EJUlQVB9YynAbHQEQT7SEiTERcHLoNG5sT+dG/uXemyP6ABm3d2f137byfKjaYAe7GyNz3J7/IbTmTy56Bh/HxJDuwi/YvsPJOXwyvJTZJhdu8HMFsXs3cksPpzKdZ3CGdEqmBDfiv/ZOJCUw/92JrG9SD0zzFb+tyORh/pFVbhsISpCgqD6xlJgXzcMZIi8EPVViJ+JJwc1ZUiLID7ZkOAyY3WTQBOXtAomKtCbr7ecI8Ns5XxWAc8sOcHN3SLoFOlPqJ+RMF8vDiTl8O9VZ8grHIrWppEPTw5qyuJDqfx28AIWBWm5FqZtPc+3287Tq2kAl7QKoW+zwDIPvMi3WJmxPZH5+y+4bI/09yLDrC8vsvhwKmPjQokN8626myREKSQIqm8KCuwryIOMDhOivusTE8jH41vx055kcgusDGkZTMdIPwyF8xV1aeLPO2vOsj8pB4uCmTvcz0sE0K2JP88Oj8HfZOS+Pk24on0YM7YnsvZkBqDPU7TpTBabzmThbdTwNxnwKpxV28ugERPszSWtQ+gbE2gftXYyLY/3/jrLsQuOvJ/GASZu6BLOJa1CmLcvhf/uSMSq4Kst53llVHNZcFbUGAmC6huLhXynliBJjBai/gv0NnJnz8Zu90UGmHjt0hbM3JHIz3tT3B4DMKhFEJMGRbvkEUYHefP00BhOpuax/FgaK4+lk1zY4mS2KMwWi0sZp9PNbDidSYivkUtahRDmZ2TmjiTMha1MXgaNO3pEckX7MHuQNKFjGH8cSSUhM59d57JZdyqDQS0c0w/sO5/NwoMXyC2w4uNlwNfLgI+XgQg/L0a1CalU95wQ8u6pbwryXVqCpDtMCOFl0LizZ2MGtQhi17ls0nItXMgpIDW3gPQ8C72iA7i1e6TbhWUBWoT6cGfPxtzWPZLd57NZcSyNA0m55Fus5FuhwKrIK7Dag520XIvLch8AzUO8mTK4abHuLm+jgXt6Neb1VWcAmL71PL2bBqKA/25PZOGBC3ga2zZnTzITu0VwebswmS9JVIgEQfWNxeKSE+Qj3WFCiEJx4X7EhRdPjC4ro0Gje1QA3aMCiu2zWBU7ErL440gaG09nUOCUYz2uXSh39WzscbHYfs0C6R7lz46EbM5nFfDJhgT2J+VwLrPkIfpZhWu4LT6cyn29m9AjOgCzxUparoXU3AIu5BSQnF34LyeflOwCWoX5cnsPzwGfaFgkCKpvLAVFhsjLL7oQovoZDRq9mgbSq2kgabkFrDiWzqH/b+++o6Ms876Bf+9p6b0RSKMkoQhBQAkSEAIuRUCRDSI+a3t31XU57NoWV0XKY+Px4K713Ud313LkFRBFKa6GoBSRsEQMYEL6hEAykEkmkzJJpt7vH2HuzJBCQpJJMvl+zvEcuOa+kys/YObrdV+luglzRwZg6ojOjxURBAG/nRaBP+5XwyYCh0vrpNdUcgH/lRSG2XH+MFltaLaIaDLbcKBYj4zilpVxF2pN2PDdBXgpZGiydL7RY/alRozwV+G2MYHX9XNabCL25umgb7ZixYQQLusf5BiC3M1Vq8M4J4iIXC3AU4E7xgV3656YAA8sTgjCvvzWFWQTwr2wZnokhvu33S9pbJgXFsYH4v2sy8ivagaAawYgu89zq5E6KqDbo0GNZiu2HCnHz1eW+J+9bMB/z4uBj4pBaLBiCHI3XB1GRIPUPRNDUaJrhtZgxl0TQrAwPlBa5dae+BAvvPqrWBxS12Fvng7NFhGBnnIEeCoQ6ClHoKcCId4t/4V6K/Fe1mWcvdwITb0Zx8rqMbsb579VNRjx3IEyFOuapbZinREvHrqIjanRHT7q666aJgsUMgF+HGFyCYYgd2O1Om2WyJEgIhosfD3kePm2mG4tkZcJAlJHBSB1VMA1r115QwjOXm4E0DKpOiXWr9OQZVdeZ8R/7/0JFbUtAchHJYNMEFBvtCJX24QtR8vxl9lRPZ5+kHO5Ec8fLIOPUobN82IwKph7JvU1DhO4G4uZS+SJaNDqyz2CJkZ4IzG0JVic1xtxsrzhmvecvWzAum/PSwEo1FuBV2+LxabUaHhfOe/tpwoD/vpjBay2rp3R1pG9+TWwiS07aL9ypBx1Ruu1b6Ie4UiQu7FaYeTjMCKiNgRBQNqEULx4+CIA4LNfqnHzCN92g1ed0YqPfq6UJl8DQGygBzbMjUKId8t77PNzorDxuwswWUUcK6vHOW0xFDIBNlGEKAJeShmmDPdBSqw/EkI8Ow14RosNpypaQ1mlwYytxyrwwpwormTrQwxB7sZq4UgQEVEHpo3wQVygB0r1RhRWN+PM5UanJf+iKOJ7dR0+OFXpNBIzLSYITyaHSaM/ADAh3Bt/mT0CLx2+CIsNTkeXAACaWjaQ3JNXg1BvBWbE+OFXYwIRE+DRpl/ZlwzS0SVSm8aA/3emCr+ZHNbjn7ugqgmfnqnC9GhfLIwP6vHXcxccJnA3V4UgLpEnImolCAJ+PSFE+v1nv1QDABqMVmQU6/GXA2V447hGCkDeShkeuSkCb6dNbncV2JThvng6ZQTCfZQI8JAjyFOOYK+WydiOb79VjRbszavB09+Uorqx7f5HmRdaR4GWjwuGffBnV041jl85tuR6Xao3YdP3F3BKY8D/nrwMfbPl2jcNERwJcjOi1crVYUREnbglxg/DzyhRUd9yVMfzGWU4p23E1SvsZ8b44f9MDUeoj6rTR1LJ0X5IjvZr095gtOLExXocK6vH6UsGWGxAs0XEN4V63JvUOrpjtYnS/CRPhQyrk0IR5KXAv05VAgD+dlyDEG8FEkK7v9Gl0WLDq0fL0WBq+eFsInCusgkzYtr2dyjiJ6S7sbTuEyRARC+t2iQichtymYAVDqNBZy87B6BIPyXWz4nCn2eNkOb/XA9fDznmjQ7EC3Oj8e7SUdLI0LeFepisrd8wp7IR9VdGnqYO94FKLsOysUGYFdsSVJotNjz97Xk8/U0p0ov0aDR3bcK0KIp4+8Qlp8Nr7d+PWvAj0t1YW/cJUgkiT2MmImrHrXEBGObbGnBCvBRYOjYI/7MgFv936ShMu8Yu190V4avCLVdGX2qNVhx12BU782LrozD7iJIgCFiTHIlRQa3zhwqqm/HOiUt48IsivJ912SlItWdPXg2OXPk+ngoZ7J8GuVqGIDs+DnM3FgvMspYhUyXzDxFRu5RyAS/Oj8F/LjZgZJAHxoZ5dWnPoJ5YOjYYR8+3zO/Zm18j7W104kJLm0LWMhJk56mQ4ZVfxSKjWI/0olqc17eM6DRbRGln7d9Ni2j3e525ZMCHP1dKv//TjEhsP1uFUr0R6hojDCYrd7oGQ5D7sVphlLf8sXoIPduzgojInYX5KHF7outWSiWEeCI+xBOF1c1Q1xiRq22CSi6gqrFlovKkCJ82wcRTIcOSxGDcnhCEIl0zDhTV4mBJLSy2liB00whfTI50PtBWU2/Caz9UwL5tUdqEEMyI8cPZywaU6o2wiUCetumaZ7p1Rb3Rik/PaHHiYgNEsSVcKmQClHIBMQEe+K+kMIT7Xv8jxb7Gx2HuxmqWDlBV8k+XiGjAEAQBSxxC1968GqdVYe1Nrna8Nz7EC49NH4YHbmydVP3mcQ0aHJbyVzea8cLBC9LqtimRPrhnUiiAliX9drnaph79LDZRREaxHo/tLcH+Aj2qGi2obrLgUoMZF+tMUNcYcbi0Dmv3q5FRrIcotv2fcpPVhqzyBjR38cy3vsCRIHfjsDpMxRBERDSgzIzxx4enKlHT3LJyLMjTvpAFmB7VtZGZ2xOD8J/yBpy51IjqJgv+9+RlPJkyHA1GKzZ9dxGVhpYl+LEBHnhy5nBpZdt4hxDU0eRoo8WGywYzqgxmaA0WaA1mNJisCLhyFluglwJKmYCdv1RJB9cCLXvS+XnIYbGKMNtEGC02WMWWQ23fyryEzAv1+MP0SChlArIqGpB5oQE/axrQbBHxzOwRmNFJAOxLDEFuRjS37hPEEERENLAo5QIWJgTh0zNVsIlA9ZUNFseGeSHQq2sfyTJBwB9nRGLtfjUMJhuOnK9DUqQ3Moprcb62Zd5QuI8SG1Kj4OtwEGuQlwLD/Vq2BiisboLRYnM6+PVURQNePVLeZtPGa0mJ9cODU8IR6rCSzmCy4p8/VeJgScuO2yfLDfj9nhIYrTZcfbrIiQv1/RaC+DHpZqxWK2xCyx+rilutExENOAvHBEJx1ftzcnT35ueEeivx6E3DpN+/lXkJ56484grwlGNTanS7y/vto0EWG1BY3TqSI4oiPjyl7VYAivJXYfO8aDydMsIpAAGAj0qOtTMi8eytIxDg2RLEmizOAcjfQ455owIwO86/y9+zt3EkyM2YHJ6tKjnxn4howAn0UmBWrB++V7cuk0+O6v5IyOw4f/znYr204gwAvBQybJwbjeH+qnbvmRDuLZ2HllPZiBsiWkLRzxqDNIoU6adESow/Qn0UCPNWwtdDjnqjFfpmC/RNVuiNFsQEeGDuyIBrnkowPcoP40K98PeTl3GsrB7DfJWYHuWL6dF+GBvq1e/nojEEuRmTQ4rnbtFERAPTksRgKQTFBnpgmF/7oeVaHrlpGHIrm1DdZIFSJuC5OSMwKtizw+snhLfuOu04L+irvBrp1/dNDsMtMb03OuPvqcCfZ41As8UGD7kwoPavYwhyMyZr6yoBPg4jIhqYxoR4YuUNIci8UI9HOtjrpyv8POTYNC8a/y7UY1asH8aFeXd6fbiPEiHeClQ3WpBf1QSLTcTFWiOyNQYAuDJS0zfzczwH4BEGDEFuxmyDNNNLpWAIIiIaqO5NCnM6Q+x6RQd44OEuBilBEDAh3BtHSuvQbBFRomvGvwv10utLxwb1+yMqVxp4sYx6hI/DiIioM+PDWh+JHSurx5HSljlCPioZ5o0K7Kde9Q9+SroZxxCkHIBDj0RE1L8mRLQ+MtuTp5MOj104JhBeQ2yX3aH10w4BZof1hx4cCSIioqtE+6vgf2X/IPtHhlyAS48QGSj4KelmjLbWZ7lKrpEnIqKrCIKAcQ6PxABgVpx/u/sKuTuGIDdjctiISsXHYURE1A7Hc8QA4M5xwf3Uk/7FT0k3Y3I4h06l4OI/IiJq6waHeUGThnljZFDHewu5M35KuhmzyMdhRETUuVFBHrhzXDDyq5p6tE/RYMcQ5GZMDiFIxRBERETtEAQBD04J7+9u9Ds+DnMzjiHIQ8EQRERE1BGGIDfjNBLEJfJEREQd4qekmzE7/JFe63RfIiKioYwhyM2Y4PA4jCGIiIioQwxBbsaE1nlASj4OIyIi6hA/Jd2M4+MwniJPRETUMYYgN+M4EqSSMQQRERF1hCHIzZgEx8dhDEFEREQdYQhyM44hiKfIExERdYyfkm7GzJEgIiKiLmEIciOizQajrPUkFBVDEBERUYcYgtyJ1QqzTAkAUIpWCAJDEBERUUcYgtyJ1QLzlZEglWjp584QERENbAxB7sRqhdE+EgRbP3eGiIhoYGMIcidWszQS5CEyBBEREXWGIcidWKwwSSNB1n7uDBER0cDGEOROHOcECWI/d4aIiGhgYwhyI1aLGRZ7COKcICIiok4xBLkRs6l1RZiSI0FERESdYghyIyZz6zwgPg4jIiLqHEOQGzE5jASpwBBERETUGYYgN2K2OIwEyRiCiIiIOsMQ5EaMDiFIyRMziIiIOsUQ5EYcR4I8OBJERETUKcW1L+kfZrMZO3bswNGjR9HQ0IDY2FisWrUKkyZNuua9Op0OH374Ic6cOQNRFDFhwgTcf//9iIiIcEHP+4/jxGgl4y0REVGnBuxH5TvvvIP9+/cjJSUFDz74IGQyGV555RXk5eV1el9zczM2bdqEc+fOYfny5UhLS4NarcbGjRtRX1/vot73D5O1dW8glYzPw4iIiDozIENQUVERfvzxR9xzzz34zW9+g/nz5+OFF15AaGgoPvnkk07v/fbbb6HRaLBu3TrccccdWLJkCZ5//nnU1NRg7969LvoJ+ofR7BCC5P3YESIiokFgQIagzMxMyGQyzJ8/X2pTqVRITU1FQUEBqqqqOr139OjRGDNmjNQ2YsQITJw4EcePH+/Tfvc3s8NIkFI+IP9oiYiIBowBOSdIrVYjMjIS3t7eTu32YFNaWorQ0NA299lsNpSVlWHu3LltXhs9ejROnz6NpqYmeHl59U3Hu6BGW4XvfsiBykMFk9EEsRfnLxfXmQFlMAA+DiMiIrqWARmC9Ho9goKC2rTb22pqatq9r6GhAWazGYGBgZ3e21EIMpvNMJvN0u8FQZCuFYTeCRU1VXp83BgBNPbKl3OmbP2lSi70Wp8HM3sNWIu+x1q7DmvtWqy367i61gMyBJlMJiiVyjbt9jaTydThfY7XdedeANi9ezd27dol/X7kyJHYsmULwsLCut75a6iuqAKg7bWv1x6ZaMXsmTciMjKyT7/PYDJs2LD+7sKQwVq7DmvtWqy367iq1gMyBKlUKqcRGTt7m0ql6vA+x+u6cy8ALF++HEuWLJF+b0+iWq0WFoulo9u6ReWtwl+iDfDx9YWhoaFPDrcYMyYKHn5e0Gg0ffDVBxdBEDBs2DBcunQJYm8+e6Q2WGvXYa1di/V2nd6qtUKh6NIAxoAMQYGBgdDpdG3a7Y/B2ntUBgC+vr5QKpXQ6/XdvhdoGS1qbxQJQK/9xfcN8MOMW6chMjISGo2mz/5B8R+qM1EUWRMXYa1dh7V2LdbbdVxV6wG5hCguLg4ajQaNjc4TZwoLC6XX2yOTyRATE4Pi4uI2rxUVFSEiIqJfJ0UTERHRwDEgQ1BycjJsNhsyMjKkNrPZjEOHDiE+Pl5aGVZVVYXy8nKne6dPn47i4mKnIFRRUYFffvkFycnJrvkBiIiIaMAbkI/D4uPjkZycjE8//RR1dXUYNmwYDh8+DK1Wi0cffVS67u2330Zubi527twptS1YsAAHDx7Eq6++iqVLl0Iul2Pfvn0ICAjA0qVL++PHISIiogFoQIYgAFizZg127NiBI0eOwGAwICYmBuvWrcP48eM7vc/LywsbN27ERx99hM8//xyiKGL8+PF44IEH4O/v76LeExER0UAniJzldU1arbbdFWfXSxCEPp8YTS1Ya9dhrV2HtXYt1tt1eqvWSqWyS6vDBuScICIiIqK+xhBEREREQxJDEBEREQ1JDEFEREQ0JDEEERER0ZDEEERERERDEkMQERERDUkMQURERDQkDdgdowcShaJvytRXX5faYq1dh7V2HdbatVhv1+lprbt6P3eMJiIioiGJj8P6QVNTE9atW4empqb+7orbY61dh7V2HdbatVhv13F1rRmC+oEoilCr1TyDxgVYa9dhrV2HtXYt1tt1XF1rhiAiIiIakhiCiIiIaEhiCOoHSqUSv/71r6FUKvu7K26PtXYd1tp1WGvXYr1dx9W15uowIiIiGpI4EkRERERDEkMQERERDUkMQURERDQkMQQRERHRkMSDUFzIbDZjx44dOHr0KBoaGhAbG4tVq1Zh0qRJ/d21QauoqAiHDx9GTk4OtFotfH19ER8fj1WrVmH48OFO1168eBEfffQR8vLyoFAoMGXKFNx///3w9/fvp94Pfl988QW2b9+O6OhobN261em1/Px8fPLJJ1Cr1fDy8sKMGTOwevVqeHp69lNvB5+SkhJ89tlnyMvLg9lsRkREBObNm4fFixdL17DOPafRaLBjxw7k5eWhoaEBoaGhSElJwdKlS+Hh4SFdx1p3T3NzM/bs2YPCwkIUFRXBYDDgsccew5w5c9pc29X3Z5vNhr179yI9PR16vR6RkZG48847kZKScl19ZAhyoXfeeQcnTpzA4sWLERkZiUOHDuGVV17Bhg0bMHbs2P7u3qD01VdfIT8/H8nJyYiNjYVer8c333yDdevW4aWXXkJMTAwAoLq6Ghs2bIC3tzfuueceNDc3Y+/evSgrK8Mrr7zCgxGvQ3V1NXbv3u30IWFXWlqKzZs3IyoqCvfddx90Oh327t2LS5cu4dlnn+2H3g4+p0+fxpYtWzBy5EisWLECnp6euHz5MnQ6nXQN69xzVVVVePbZZ+Ht7Y2FCxfC19cXBQUF2LlzJ0pKSvDnP/8ZAGt9Perq6rBr1y6EhoYiLi4OOTk57V7Xnffn7du348svv8S8efMwevRoZGVl4c0334QgCJg5c2b3OymSSxQWFoppaWniV199JbUZjUZxzZo14nPPPdePPRvc8vLyRLPZ7NRWUVEhrl69WnzjjTektvfff1+89957Ra1WK7WdPn1aTEtLEw8cOOCy/rqTv/71r+KmTZvEDRs2iE888YTTay+//LL48MMPiwaDQWrLyMgQ09LSxOzsbFd3ddAxGAzib3/7W/G1114TrVZrh9exzj33+eefi2lpaWJZWZlT+1tvvSWmpaWJ9fX1oiiy1tfDZDKJNTU1oiiKYlFRkZiWliZ+//33ba7r6vtzdXW1uGrVKvEf//iH1Gaz2cQXXnhBfPTRRzv9t9IRzglykczMTMhkMsyfP19qU6lUSE1NRUFBAaqqqvqxd4NXYmJim1GcyMhIREVFoby8XGo7ceIEpkyZgtDQUKlt0qRJiIyMxPHjx13WX3eRm5uLzMxMPPDAA21ea2xsxJkzZzBr1ix4e3tL7bfeeis8PT1Z7y744YcfUFtbi1WrVkEmk6G5uRk2m83pGta5d9gP6gwICHBqDwoKgiAIUCgUrPV1UiqVCAwMvOZ1XX1/PnnyJKxWKxYsWCC1CYKA2267DdXV1SgoKOh2HxmCXEStViMyMtLpHxAAjBkzBkDLUCv1DlEUUVtbKz1L1ul0qK2txejRo9tcO2bMGKjVald3cVCz2Wz44IMPkJqaKj1udFRWVgar1YpRo0Y5tSsUCsTFxbHeXXD27Fl4eXlBp9Phj3/8I+677z7cf//9eP/992EymQCwzr1lwoQJAIC///3vKC0tRVVVFX788Uekp6dj0aJF8PT0ZK37UHfen9VqNTw8PDBixIg219lf7y5OhHARvV6PoKCgNu32tpqaGld3yW0dPXoUOp0OK1euBNBa247q39DQALPZzC3xuyg9PR1arRbr169v93W9Xg+g/XoHBgYiLy+vL7vnFi5dugSbzYbXXnsNc+fOxerVq5GTk4NvvvkGBoMBf/rTn1jnXjJ58mTcfffd2L17N7KysqT2u+66C6tWrQLAv9N9qTvvz3q9HoGBgRAEoc11jl+rOxiCXMRkMrX7IWtvs//fHfVMeXk5/vnPfyIhIUFagWCvbXuTnx3rzxB0bfX19di5cydWrFjR4ao6e73bq6dKpeLf9S5obm6G0WjEbbfdhoceeggAMH36dFgsFmRkZODuu+9mnXtRWFgYxo0bh+nTp8PPzw+nTp3C7t27ERgYiIULF7LWfag7788mk+ma13UXQ5CLqFQqmM3mNu32NpVK5eouuR29Xo9XX30V3t7eeOKJJyCTtTzttdfWYrG0uYf1757t27fD19cXixYt6vAaey3b+/tuMplY6y6w1+jq1S4pKSnIyMhAQUGBtCqPde6ZY8eO4b333sMbb7yBkJAQAC2BUxRFbNu2DTNnzuTf6T7UnfdnlUrV6+/jnBPkIoGBge0O1XU2FEhd19jYiJdffhkGgwHPPfccgoODpdc6GyqtqamBr68vR4G6QKPRICMjA4sWLYJOp0NlZSUqKythNpthsVhQWVmJhoYGaSJke/Xu6LEwObPX6OpJpfbJuwaDgXXuJenp6Rg5cqQUgOymTZsGo9EItVrNWveh7rw/BwYGQq/XQ7zq3PeefI4yBLlIXFwcNBoNGhsbndoLCwul1+n6mEwmbNmyBRqNBs888wyioqKcXg8ODoa/vz+Ki4vb3FtUVMTad5FOp4Moivjggw+wZs0a6b/CwkJoNBqsWbMGu3btQkxMDORyOUpKSpzut1gsKC0tZb27wD4B13FPIKD1zd7f35917iV6vb7NyjugdWTCZrOx1n2oO+/PcXFxMBqNTit/7dfZX+8uhiAXSU5Ohs1mQ0ZGhtRmNptx6NAhxMfHOy0NpK6z2Wz429/+hoKCAjz++ONISEho97rp06fj1KlTTlsRnD17FhqNBsnJya7q7qAWHR2Np556qs1/0dHRCA0NxVNPPYXU1FR4e3tj4sSJOHr0qLT8GACOHDmC5uZmzJgxox9/isHBXqPvvvvOqf3gwYOQy+UYP34869xLIiMjoVarUVFR4dR+7NgxCIKAmJgY1rqPdfX9+aabboJcLse3334rtYmiiAMHDiA4OBiJiYnd/t6cE+Qi8fHxSE5Oxqeffoq6ujoMGzYMhw8fhlarxaOPPtrf3Ru0Pv74Y2RlZWHq1KloaGjAkSNHnF6fPXs2AGD58uXIzMzEpk2bsHjxYmk795iYGMydO7c/uj7o+Pv74+abb27T/vXXXwOA02urVq3C+vXrsXHjRsybN0/aXTcpKQmTJ092VZcHrZEjR2Lu3Ln4/vvvYbVaMX78eOTk5CAzMxN33nmn9LiXde65ZcuWITs7Gxs2bMCCBQukidE///wzUlNTWesesq9otI9iZmVlobq6GgCwaNEieHt7d/n9OSQkBLfffjv27NkDq9WK0aNH4+TJkzh37hzWrl0rzQPtDkG8+uEa9RmTySSdHWYwGBATE4O7776b/4B6YOPGjcjNze3w9Z07d0q/vnDhAj7++GPpbJobb7wR9913X5c286KObdy4EfX19W3ODsvLy8O2bdtQUlLidM6Sl5dXP/V0cLFYLNi9ezcOHToEnU6HsLAwLFiwALfffrvTdaxzzxUVFeGzzz6DWq1GfX09wsPDceutt+KOO+6AXC6XrmOtu+8Pf/gDtFptu6+9/fbbCA8PB9D192ebzYavvvoKGRkZqKmpkc4OmzVr1nX1jyGIiIiIhiTOCSIiIqIhiSGIiIiIhiSGICIiIhqSGIKIiIhoSGIIIiIioiGJIYiIiIiGJIYgIiIiGpIYgoiIiGhI4rEZRNTnOts11tFjjz2GOXPm9H2HesHKlSsBOO9KTkSDC0MQEblMYmIihg0b1uHrnb1GRNTbGIKIyGXmzZs3aEZ6iMj9cU4QERERDUkcCSKiAclxzk1GRgYOHDiAiooKyOVyJCYmYsWKFUhISGj33oaGBuzZswdZWVmorKyETCZDZGQkbrnlFixatAgqlard+3Q6Hfbv34/s7GxotVqIoojg4GAkJCRg/vz5SExMbPe+zMxM7N+/H2VlZbDZbIiLi8Py5csxZcqUNtfW1NTgyy+/RHZ2NqqqqiAIAvz8/BAZGYnJkydj2bJl11kxIuouhiAiGtA++ugjfP3110hMTMS0adNQVlaGn3/+GWfOnMHjjz+Om2++2en6y5cvY/PmzdBqtfD398eNN94Iq9WKnJwcbNu2DT/++CPWr18PX19fp/vOnj2L119/HQaDAQEBAbjhhhugUCig1Wrxww8/AEC7IWjnzp34/PPPkZCQgBtvvBHl5eXIz8/Hli1b8OSTTzr1T6/X45lnnkFNTQ1CQ0ORlJQElUqFmpoalJaWoqSkhCGIyIUYgohoQDtw4ADWr1+PG264QWrbs2cPPvnkE7z77rtITExEQECA9Nqbb74JrVaLadOmYe3atfD09AQA1NXV4aWXXoJarca//vUvrF27VrqnqqoKW7duRWNjI+68806sXLkSCkXr22NtbS00Gk27/fv3v/+NF198EfHx8VLbzp07sWvXLmzbts0pBGVkZKCmpgbz58/H7373OwiCIL1msVhw7ty5HlSKiLqLIYiIXObdd9/Fu+++2+HrH3zwAXx8fJza5s+f7xSAAGDZsmU4fvw4iouLcfDgQdx1110AgLy8PBQWFsLDwwMPP/ywFIAAwN/fH4888gieeeYZHDt2DPfeey9CQkIAAPv27UNjYyOmTp2K1atXt+lXQECAU9BytHLlSqcABADLly/H119/DY1Gg6qqKoSGhgJoGQkCgMmTJzsFIABQKBSYOHFih7Uhot7HEERELnOtJfKOoy92Ha0mmz17NoqLi5GbmyuFoJycHABAUlISAgMD29wzatQoxMbG4vz588jNzcWsWbMAAKdPnwbQEri6a+rUqW3alEolIiIioFarodPppBA0ZswYpKenY9u2bRBFEUlJSU5BjYhciyGIiFzmepbIh4eHd9peXV0ttel0uk7vAYCIiAicP39euhaAtJHjiBEjutU3AFLAuZqXlxcAwGw2S22zZ8/GmTNn8MMPP2Dr1q2QyWSIiorC2LFjkZyc3GbEi4j6FkMQEVEPyGRd32lEJpNh7dq1uOuuu3Dq1Cnk5eUhPz8f6enpSE9Px9SpU/H0009362sS0fVjCCKiAa2yshJxcXFt2u2jN8HBwVKb/deVlZWdfr2r7wsNDUVFRQXKy8tdsmt1VFQUoqKisGzZMoiiiF9++QVvvvkmfvrpJxw+fBhz587t8z4QETdLJKIB7siRI522T5gwQWqz/zo7O1uahOxIrVajtLQUgiBg3LhxUvvkyZMBAAcPHuylXnedIAiYOHEiZs6cCQAoLS11eR+IhiqGICIa0NLT06UJz3b79u1DUVERvLy8kJqaKrWPHTsW8fHxMJlMeO+992A0GqXX6urq8N577wEAZs6c6TSXZ8mSJfDy8kJWVha2b98Oi8Xi9P1qa2uRl5fX45/l8OHDKCkpadPe1NSE3NxcAEBYWFiPvw8RdQ0fhxGRyxw8eLBNoHGUlJSElJQUp7b58+dj8+bNGDt2LIKDg3HhwgWUlZVBJpPh97//fZtVYGvXrsXmzZuRlZWFNWvWYNy4cbBYLMjJyUFTUxNGjhyJhx56yOme0NBQPPHEE3j99dfxxRdf4ODBg0hISIBcLkdVVRXUajVSUlIwduzYHv38J06cwDvvvIOgoCDExcXBx8cHBoMB+fn5aGxsRHR0NObNm9ej70FEXccQREQuk5+fj/z8/A5f9/HxaROCHnjgAQwfPhwZGRk4efIk5HI5Jk+ejBUrVrS7g3NERAS2bNmCPXv24OTJk/jpp58gk8kwfPhwzJgxA4sXL2732IykpCRs3boV+/btQ3Z2NrKzsyGXyxEUFITZs2f3SjhZunQpwsPDUVBQALVajYaGBvj6+iIqKgopKSmYM2cOl8wTuZAgiqLY350gIrqa49lhRER9gXOCiIiIaEhiCCIiIqIhiSGIiIiIhiTOCSIiIqIhiSNBRERENCQxBBEREdGQxBBEREREQxJDEBEREQ1JDEFEREQ0JDEEERER0ZDEEERERERDEkMQERERDUkMQURERDQk/X/BXnRjzjoRrwAAAABJRU5ErkJggg==\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "plt.title(\"Adversarial Training Curves\", fontsize=20)\n",
+                "plt.plot(epoch_gen_loss_list, color=\"C0\", linewidth=2.0, label=\"Generator\")\n",
+                "plt.plot(epoch_disc_loss_list, color=\"C1\", linewidth=2.0, label=\"Discriminator\")\n",
+                "plt.yticks(fontsize=12)\n",
+                "plt.xticks(fontsize=12)\n",
+                "plt.xlabel(\"Epochs\", fontsize=16)\n",
+                "plt.ylabel(\"Loss\", fontsize=16)\n",
+                "plt.legend(prop={\"size\": 14})\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "92e38b28",
+            "metadata": {},
+            "source": [
+                "### Visualise reconstructions"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 13,
+            "id": "ec9685bb",
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "<matplotlib.image.AxesImage at 0x7f506a8ce0b0>"
+                        ]
+                    },
+                    "execution_count": 13,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 3 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "# Plot axial, coronal and sagittal slices of a training sample\n",
+                "idx = 0\n",
+                "img = reconstruction[idx, channel].detach().cpu().numpy()\n",
+                "fig, axs = plt.subplots(nrows=1, ncols=3)\n",
+                "for ax in axs:\n",
+                "    ax.axis(\"off\")\n",
+                "ax = axs[0]\n",
+                "ax.imshow(img[..., img.shape[2] // 2], cmap=\"gray\")\n",
+                "ax = axs[1]\n",
+                "ax.imshow(img[:, img.shape[1] // 2, ...], cmap=\"gray\")\n",
+                "ax = axs[2]\n",
+                "ax.imshow(img[img.shape[0] // 2, ...], cmap=\"gray\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "fe436141",
+            "metadata": {},
+            "source": [
+                "## Diffusion Model\n",
+                "\n",
+                "### Define diffusion model and scheduler\n",
+                "\n",
+                "In this section, we will define the diffusion model that will learn data distribution of the latent representation of the autoencoder. Together with the diffusion model, we define a beta scheduler responsible for defining the amount of noise tahat is added across the diffusion's model Markov chain."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 42,
+            "id": "88cbe609",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "unet = DiffusionModelUNet(\n",
+                "    spatial_dims=3,\n",
+                "    in_channels=3,\n",
+                "    out_channels=3,\n",
+                "    num_res_blocks=1,\n",
+                "    num_channels=(32, 64, 64),\n",
+                "    attention_levels=(False, True, True),\n",
+                "    num_head_channels=(0, 64, 64),\n",
+                ")\n",
+                "unet.to(device)\n",
+                "\n",
+                "\n",
+                "scheduler = DDPMScheduler(num_train_timesteps=1000, schedule=\"scaled_linear_beta\", beta_start=0.0015, beta_end=0.0195)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "243ddf9e",
+            "metadata": {},
+            "source": [
+                "### Scaling factor\n",
+                "\n",
+                "As mentioned in Rombach et al. [1] Section 4.3.2 and D.1, the signal-to-noise ratio (induced by the scale of the latent space) can affect the results obtained with the LDM, if the standard deviation of the latent space distribution drifts too much from that of a Gaussian. For this reason, it is best practice to use a scaling factor to adapt this standard deviation.\n",
+                "\n",
+                "_Note: In case where the latent space is close to a Gaussian distribution, the scaling factor will be close to one, and the results will not differ from those obtained when it is not used._\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 14,
+            "id": "c5fedcea",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Scaling factor set to tensor(1.0026, device='cuda:0')\n"
+                    ]
+                }
+            ],
+            "source": [
+                "with torch.no_grad():\n",
+                "    with autocast(enabled=True):\n",
+                "        z = autoencoder.encode_stage_2_inputs(check_data[\"image\"].to(device))\n",
+                "\n",
+                "print(f\"Scaling factor set to {1/torch.std(z)}\")\n",
+                "scale_factor = 1 / torch.std(z)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "439ff2d8",
+            "metadata": {},
+            "source": [
+                "We define the inferer using the scale factor:"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "7de37f3a",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "inferer = LatentDiffusionInferer(scheduler, scale_factor=scale_factor)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 43,
+            "id": "5eef3ec7",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "optimizer_diff = torch.optim.Adam(params=unet.parameters(), lr=1e-4)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "4705c795",
+            "metadata": {},
+            "source": [
+                "### Train diffusion model"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 44,
+            "id": "0a7f6459",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.24it/s, loss=0.58]\n",
+                        "Epoch 1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  7.85it/s, loss=0.356]\n",
+                        "Epoch 2: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.30it/s, loss=0.315]\n",
+                        "Epoch 3: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  8.05it/s, loss=0.29]\n",
+                        "Epoch 4: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.41it/s, loss=0.277]\n",
+                        "Epoch 5: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.43it/s, loss=0.253]\n",
+                        "Epoch 6: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:22<00:00,  8.45it/s, loss=0.276]\n",
+                        "Epoch 7: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:22<00:00,  8.47it/s, loss=0.302]\n",
+                        "Epoch 8: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  8.02it/s, loss=0.258]\n",
+                        "Epoch 9: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.28it/s, loss=0.26]\n",
+                        "Epoch 10: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.18it/s, loss=0.247]\n",
+                        "Epoch 11: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:22<00:00,  8.47it/s, loss=0.25]\n",
+                        "Epoch 12: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.36it/s, loss=0.286]\n",
+                        "Epoch 13: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.40it/s, loss=0.26]\n",
+                        "Epoch 14: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.38it/s, loss=0.307]\n",
+                        "Epoch 15: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.41it/s, loss=0.28]\n",
+                        "Epoch 16: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.43it/s, loss=0.291]\n",
+                        "Epoch 17: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.40it/s, loss=0.274]\n",
+                        "Epoch 18: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.43it/s, loss=0.257]\n",
+                        "Epoch 19: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  8.02it/s, loss=0.276]\n",
+                        "Epoch 20: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.21it/s, loss=0.255]\n",
+                        "Epoch 21: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.28it/s, loss=0.245]\n",
+                        "Epoch 22: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.34it/s, loss=0.276]\n",
+                        "Epoch 23: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.17it/s, loss=0.232]\n",
+                        "Epoch 24: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.43it/s, loss=0.256]\n",
+                        "Epoch 25: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.21it/s, loss=0.243]\n",
+                        "Epoch 26: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  8.06it/s, loss=0.249]\n",
+                        "Epoch 27: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.26it/s, loss=0.283]\n",
+                        "Epoch 28: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.28it/s, loss=0.277]\n",
+                        "Epoch 29: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.30it/s, loss=0.279]\n",
+                        "Epoch 30: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.09it/s, loss=0.251]\n",
+                        "Epoch 31: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.17it/s, loss=0.257]\n",
+                        "Epoch 32: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.20it/s, loss=0.278]\n",
+                        "Epoch 33: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.33it/s, loss=0.264]\n",
+                        "Epoch 34: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.20it/s, loss=0.256]\n",
+                        "Epoch 35: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  7.92it/s, loss=0.266]\n",
+                        "Epoch 36: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.33it/s, loss=0.253]\n",
+                        "Epoch 37: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.13it/s, loss=0.268]\n",
+                        "Epoch 38: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.13it/s, loss=0.257]\n",
+                        "Epoch 39: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.25it/s, loss=0.246]\n",
+                        "Epoch 40: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.37it/s, loss=0.254]\n",
+                        "Epoch 41: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.15it/s, loss=0.277]\n",
+                        "Epoch 42: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  7.79it/s, loss=0.277]\n",
+                        "Epoch 43: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.11it/s, loss=0.259]\n",
+                        "Epoch 44: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  7.82it/s, loss=0.258]\n",
+                        "Epoch 45: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.30it/s, loss=0.249]\n",
+                        "Epoch 46: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.31it/s, loss=0.26]\n",
+                        "Epoch 47: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.19it/s, loss=0.261]\n",
+                        "Epoch 48: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.16it/s, loss=0.275]\n",
+                        "Epoch 49: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.38it/s, loss=0.259]\n",
+                        "Epoch 50: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.28it/s, loss=0.291]\n",
+                        "Epoch 51: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.23it/s, loss=0.268]\n",
+                        "Epoch 52: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.29it/s, loss=0.272]\n",
+                        "Epoch 53: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.15it/s, loss=0.251]\n",
+                        "Epoch 54: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.13it/s, loss=0.289]\n",
+                        "Epoch 55: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  8.03it/s, loss=0.261]\n",
+                        "Epoch 56: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.18it/s, loss=0.28]\n",
+                        "Epoch 57: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.22it/s, loss=0.259]\n",
+                        "Epoch 58: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  7.83it/s, loss=0.248]\n",
+                        "Epoch 59: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.33it/s, loss=0.25]\n",
+                        "Epoch 60: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  8.00it/s, loss=0.273]\n",
+                        "Epoch 61: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  7.79it/s, loss=0.259]\n",
+                        "Epoch 62: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  8.08it/s, loss=0.257]\n",
+                        "Epoch 63: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:25<00:00,  7.75it/s, loss=0.263]\n",
+                        "Epoch 64: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.31it/s, loss=0.251]\n",
+                        "Epoch 65: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  7.98it/s, loss=0.278]\n",
+                        "Epoch 66: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  7.81it/s, loss=0.258]\n",
+                        "Epoch 67: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.14it/s, loss=0.288]\n",
+                        "Epoch 68: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  8.05it/s, loss=0.248]\n",
+                        "Epoch 69: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  8.03it/s, loss=0.257]\n",
+                        "Epoch 70: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.39it/s, loss=0.246]\n",
+                        "Epoch 71: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  7.88it/s, loss=0.288]\n",
+                        "Epoch 72: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.38it/s, loss=0.282]\n",
+                        "Epoch 73: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:22<00:00,  8.44it/s, loss=0.287]\n",
+                        "Epoch 74: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.29it/s, loss=0.282]\n",
+                        "Epoch 75: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.42it/s, loss=0.263]\n",
+                        "Epoch 76: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.11it/s, loss=0.286]\n",
+                        "Epoch 77: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:25<00:00,  7.68it/s, loss=0.252]\n",
+                        "Epoch 78: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.18it/s, loss=0.273]\n",
+                        "Epoch 79: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.33it/s, loss=0.235]\n",
+                        "Epoch 80: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.34it/s, loss=0.265]\n",
+                        "Epoch 81: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.21it/s, loss=0.258]\n",
+                        "Epoch 82: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.36it/s, loss=0.243]\n",
+                        "Epoch 83: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.11it/s, loss=0.251]\n",
+                        "Epoch 84: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  8.01it/s, loss=0.306]\n",
+                        "Epoch 85: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  8.07it/s, loss=0.265]\n",
+                        "Epoch 86: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.12it/s, loss=0.243]\n",
+                        "Epoch 87: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.17it/s, loss=0.257]\n",
+                        "Epoch 88: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.32it/s, loss=0.268]\n",
+                        "Epoch 89: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.16it/s, loss=0.263]\n",
+                        "Epoch 90: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.19it/s, loss=0.244]\n",
+                        "Epoch 91: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.39it/s, loss=0.278]\n",
+                        "Epoch 92: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.28it/s, loss=0.274]\n",
+                        "Epoch 93: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.15it/s, loss=0.24]\n",
+                        "Epoch 94: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  8.01it/s, loss=0.275]\n",
+                        "Epoch 95: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  8.06it/s, loss=0.259]\n",
+                        "Epoch 96: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  8.00it/s, loss=0.247]\n",
+                        "Epoch 97: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.32it/s, loss=0.273]\n",
+                        "Epoch 98: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:25<00:00,  7.75it/s, loss=0.262]\n",
+                        "Epoch 99: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:25<00:00,  7.71it/s, loss=0.281]\n",
+                        "Epoch 100: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  7.82it/s, loss=0.301]\n",
+                        "Epoch 101: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.39it/s, loss=0.276]\n",
+                        "Epoch 102: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  7.83it/s, loss=0.279]\n",
+                        "Epoch 103: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.34it/s, loss=0.289]\n",
+                        "Epoch 104: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.35it/s, loss=0.277]\n",
+                        "Epoch 105: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.42it/s, loss=0.251]\n",
+                        "Epoch 106: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.36it/s, loss=0.262]\n",
+                        "Epoch 107: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.41it/s, loss=0.273]\n",
+                        "Epoch 108: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.41it/s, loss=0.283]\n",
+                        "Epoch 109: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.38it/s, loss=0.31]\n",
+                        "Epoch 110: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.43it/s, loss=0.278]\n",
+                        "Epoch 111: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.35it/s, loss=0.256]\n",
+                        "Epoch 112: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.43it/s, loss=0.26]\n",
+                        "Epoch 113: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  7.92it/s, loss=0.251]\n",
+                        "Epoch 114: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:25<00:00,  7.74it/s, loss=0.274]\n",
+                        "Epoch 115: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.12it/s, loss=0.289]\n",
+                        "Epoch 116: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  7.88it/s, loss=0.262]\n",
+                        "Epoch 117: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  8.04it/s, loss=0.247]\n",
+                        "Epoch 118: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.41it/s, loss=0.25]\n",
+                        "Epoch 119: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.43it/s, loss=0.263]\n",
+                        "Epoch 120: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.40it/s, loss=0.259]\n",
+                        "Epoch 121: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.42it/s, loss=0.258]\n",
+                        "Epoch 122: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.37it/s, loss=0.272]\n",
+                        "Epoch 123: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.42it/s, loss=0.248]\n",
+                        "Epoch 124: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:22<00:00,  8.44it/s, loss=0.286]\n",
+                        "Epoch 125: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.36it/s, loss=0.288]\n",
+                        "Epoch 126: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.43it/s, loss=0.283]\n",
+                        "Epoch 127: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.39it/s, loss=0.283]\n",
+                        "Epoch 128: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  7.86it/s, loss=0.256]\n",
+                        "Epoch 129: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  7.91it/s, loss=0.268]\n",
+                        "Epoch 130: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  8.05it/s, loss=0.266]\n",
+                        "Epoch 131: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:22<00:00,  8.47it/s, loss=0.276]\n",
+                        "Epoch 132: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.24it/s, loss=0.25]\n",
+                        "Epoch 133: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.24it/s, loss=0.295]\n",
+                        "Epoch 134: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.22it/s, loss=0.263]\n",
+                        "Epoch 135: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.28it/s, loss=0.248]\n",
+                        "Epoch 136: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:22<00:00,  8.44it/s, loss=0.234]\n",
+                        "Epoch 137: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.41it/s, loss=0.265]\n",
+                        "Epoch 138: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:25<00:00,  7.67it/s, loss=0.288]\n",
+                        "Epoch 139: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.35it/s, loss=0.232]\n",
+                        "Epoch 140: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.37it/s, loss=0.275]\n",
+                        "Epoch 141: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.38it/s, loss=0.267]\n",
+                        "Epoch 142: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.31it/s, loss=0.247]\n",
+                        "Epoch 143: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.39it/s, loss=0.261]\n",
+                        "Epoch 144: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.23it/s, loss=0.263]\n",
+                        "Epoch 145: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:24<00:00,  7.80it/s, loss=0.265]\n",
+                        "Epoch 146: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.29it/s, loss=0.276]\n",
+                        "Epoch 147: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.27it/s, loss=0.263]\n",
+                        "Epoch 148: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.14it/s, loss=0.275]\n",
+                        "Epoch 149: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 194/194 [00:23<00:00,  8.36it/s, loss=0.266]\n"
+                    ]
+                }
+            ],
+            "source": [
+                "n_epochs = 150\n",
+                "epoch_loss_list = []\n",
+                "autoencoder.eval()\n",
+                "scaler = GradScaler()\n",
+                "\n",
+                "first_batch = first(train_loader)\n",
+                "z = autoencoder.encode_stage_2_inputs(first_batch[\"image\"].to(device))\n",
+                "\n",
+                "for epoch in range(n_epochs):\n",
+                "    unet.train()\n",
+                "    epoch_loss = 0\n",
+                "    progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=70)\n",
+                "    progress_bar.set_description(f\"Epoch {epoch}\")\n",
+                "    for step, batch in progress_bar:\n",
+                "        images = batch[\"image\"].to(device)\n",
+                "        optimizer_diff.zero_grad(set_to_none=True)\n",
+                "\n",
+                "        with autocast(enabled=True):\n",
+                "            # Generate random noise\n",
+                "            noise = torch.randn_like(z).to(device)\n",
+                "\n",
+                "            # Create timesteps\n",
+                "            timesteps = torch.randint(\n",
+                "                0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n",
+                "            ).long()\n",
+                "\n",
+                "            # Get model prediction\n",
+                "            noise_pred = inferer(\n",
+                "                inputs=images, autoencoder_model=autoencoder, diffusion_model=unet, noise=noise, timesteps=timesteps\n",
+                "            )\n",
+                "\n",
+                "            loss = F.mse_loss(noise_pred.float(), noise.float())\n",
+                "\n",
+                "        scaler.scale(loss).backward()\n",
+                "        scaler.step(optimizer_diff)\n",
+                "        scaler.update()\n",
+                "\n",
+                "        epoch_loss += loss.item()\n",
+                "\n",
+                "        progress_bar.set_postfix({\"loss\": epoch_loss / (step + 1)})\n",
+                "    epoch_loss_list.append(epoch_loss / (step + 1))"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 18,
+            "id": "93b93696",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "2023-02-19 14:12:22,536 - No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "plt.plot(epoch_loss_list)\n",
+                "plt.title(\"Learning Curves\", fontsize=20)\n",
+                "plt.plot(epoch_loss_list)\n",
+                "plt.yticks(fontsize=12)\n",
+                "plt.xticks(fontsize=12)\n",
+                "plt.xlabel(\"Epochs\", fontsize=16)\n",
+                "plt.ylabel(\"Loss\", fontsize=16)\n",
+                "plt.legend(prop={\"size\": 14})\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "c9de4288",
+            "metadata": {},
+            "source": [
+                "### Plotting sampling example\n",
+                "\n",
+                "Finally, we generate an image with our LDM. For that, we will initialize a latent representation with just noise. Then, we will use the `unet` to perform 1000 denoising steps. In the last step, we decode the latent representation and plot the sampled image."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 51,
+            "id": "bc946d70",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:15<00:00, 64.03it/s]\n"
+                    ]
+                }
+            ],
+            "source": [
+                "autoencoder.eval()\n",
+                "unet.eval()\n",
+                "\n",
+                "noise = torch.randn((1, 3, 24, 24, 16))\n",
+                "noise = noise.to(device)\n",
+                "scheduler.set_timesteps(num_inference_steps=1000)\n",
+                "synthetic_images = inferer.sample(\n",
+                "    input_noise=noise, autoencoder_model=autoencoder, diffusion_model=unet, scheduler=scheduler\n",
+                ")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "fed68b96",
+            "metadata": {},
+            "source": [
+                "### Visualise synthetic data"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 52,
+            "id": "0763caa1",
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "<matplotlib.image.AxesImage at 0x7f510c1672e0>"
+                        ]
+                    },
+                    "execution_count": 52,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 3 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "idx = 0\n",
+                "img = synthetic_images[idx, channel].detach().cpu().numpy()  # images\n",
+                "fig, axs = plt.subplots(nrows=1, ncols=3)\n",
+                "for ax in axs:\n",
+                "    ax.axis(\"off\")\n",
+                "ax = axs[0]\n",
+                "ax.imshow(img[..., img.shape[2] // 2], cmap=\"gray\")\n",
+                "ax = axs[1]\n",
+                "ax.imshow(img[:, img.shape[1] // 2, ...], cmap=\"gray\")\n",
+                "ax = axs[2]\n",
+                "ax.imshow(img[img.shape[0] // 2, ...], cmap=\"gray\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "d3ab7b79",
+            "metadata": {},
+            "source": [
+                "## Clean-up data"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 21,
+            "id": "52b71f99",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "if directory is None:\n",
+                "    shutil.rmtree(root_dir)"
+            ]
+        }
+    ],
+    "metadata": {
+        "jupytext": {
+            "cell_metadata_filter": "-all",
+            "formats": "ipynb,py"
+        },
+        "kernelspec": {
+            "display_name": "Python 3 (ipykernel)",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.8.13"
+        },
+        "vscode": {
+            "interpreter": {
+                "hash": "a7e6f8385898884a13cbe220eefefb32cba5012927a94186742ddc14746e4dba"
+            }
+        }
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MONAI version: 1.2.dev2304\n",
-      "Numpy version: 1.23.5\n",
-      "Pytorch version: 1.13.1+cu117\n",
-      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
-      "MONAI rev id: 9a57be5aab9f2c2a134768c0c146399150e247a0\n",
-      "MONAI __file__: /media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.10/site-packages/monai/__init__.py\n",
-      "\n",
-      "Optional dependencies:\n",
-      "Pytorch Ignite version: 0.4.10\n",
-      "ITK version: 5.3.0\n",
-      "Nibabel version: 4.0.2\n",
-      "scikit-image version: 0.19.3\n",
-      "Pillow version: 9.3.0\n",
-      "Tensorboard version: 2.11.0\n",
-      "gdown version: 4.6.0\n",
-      "TorchVision version: 0.14.1+cu117\n",
-      "tqdm version: 4.64.1\n",
-      "lmdb version: 1.4.0\n",
-      "psutil version: 5.9.4\n",
-      "pandas version: 1.5.3\n",
-      "einops version: 0.6.0\n",
-      "transformers version: 4.21.3\n",
-      "mlflow version: 2.1.1\n",
-      "pynrrd version: 1.0.0\n",
-      "\n",
-      "For details about installing the optional dependencies, please visit:\n",
-      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import shutil\n",
-    "import tempfile\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import torch\n",
-    "import torch.nn.functional as F\n",
-    "from monai import transforms\n",
-    "from monai.apps import DecathlonDataset\n",
-    "from monai.config import print_config\n",
-    "from monai.data import DataLoader\n",
-    "from monai.utils import first, set_determinism\n",
-    "from torch.cuda.amp import GradScaler, autocast\n",
-    "from torch.nn import L1Loss\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "from generative.inferers import LatentDiffusionInferer\n",
-    "from generative.losses import PatchAdversarialLoss, PerceptualLoss\n",
-    "from generative.networks.nets import AutoencoderKL, DiffusionModelUNet, PatchDiscriminator\n",
-    "from generative.networks.schedulers import DDPMScheduler\n",
-    "\n",
-    "print_config()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "a21c1f6a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for reproducibility purposes set a seed\n",
-    "set_determinism(42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b02aa6c",
-   "metadata": {},
-   "source": [
-    "### Setup a data directory and download dataset\n",
-    "Specify a MONAI_DATA_DIRECTORY variable, where the data will be downloaded. If not specified a temporary directory will be used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "5d450e1d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/tmp/tmp5nw3g3c4\n"
-     ]
-    }
-   ],
-   "source": [
-    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
-    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
-    "print(root_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "74302407",
-   "metadata": {},
-   "source": [
-    "### Prepare data loader for the training set\n",
-    "Here we will download the Brats dataset using MONAI's `DecathlonDataset` class, and we prepare the data loader for the training set."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "c34a9ba3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<class 'monai.transforms.utility.array.AddChannel'>: Class `AddChannel` has been deprecated since version 0.8. please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2023-02-19 09:07:46,210 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n",
-      "2023-02-19 09:07:46,210 - INFO - File exists: /tmp/tmp5nw3g3c4/Task01_BrainTumour.tar, skipped downloading.\n",
-      "2023-02-19 09:07:46,211 - INFO - Non-empty folder exists in /tmp/tmp5nw3g3c4/Task01_BrainTumour, skipped extracting.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 388/388 [01:32<00:00,  4.21it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Image shape torch.Size([1, 96, 96, 64])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "batch_size = 2\n",
-    "channel = 0  # 0 = Flair\n",
-    "assert channel in [0, 1, 2, 3], \"Choose a valid channel\"\n",
-    "\n",
-    "train_transforms = transforms.Compose(\n",
-    "    [\n",
-    "        transforms.LoadImaged(keys=[\"image\"]),\n",
-    "        transforms.EnsureChannelFirstd(keys=[\"image\"]),\n",
-    "        transforms.Lambdad(keys=\"image\", func=lambda x: x[channel, :, :, :]),\n",
-    "        transforms.AddChanneld(keys=[\"image\"]),\n",
-    "        transforms.EnsureTyped(keys=[\"image\"]),\n",
-    "        transforms.Orientationd(keys=[\"image\"], axcodes=\"RAS\"),\n",
-    "        transforms.Spacingd(keys=[\"image\"], pixdim=(2.4, 2.4, 2.2), mode=(\"bilinear\")),\n",
-    "        transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=(96, 96, 64)),\n",
-    "        transforms.ScaleIntensityRangePercentilesd(keys=\"image\", lower=0, upper=99.5, b_min=0, b_max=1),\n",
-    "    ]\n",
-    ")\n",
-    "train_ds = DecathlonDataset(\n",
-    "    root_dir=root_dir,\n",
-    "    task=\"Task01_BrainTumour\",\n",
-    "    section=\"training\",  # validation\n",
-    "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
-    "    num_workers=8,\n",
-    "    download=True,  # Set download to True if the dataset hasnt been downloaded yet\n",
-    "    seed=0,\n",
-    "    transform=train_transforms,\n",
-    ")\n",
-    "train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=8, persistent_workers=True)\n",
-    "print(f'Image shape {train_ds[0][\"image\"].shape}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1d36e0c4",
-   "metadata": {},
-   "source": [
-    "### Visualise examples from the training set"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "723c2dad",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f5030f90af0>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot axial, coronal and sagittal slices of a training sample\n",
-    "check_data = first(train_loader)\n",
-    "idx = 0\n",
-    "\n",
-    "img = check_data[\"image\"][idx, 0]\n",
-    "fig, axs = plt.subplots(nrows=1, ncols=3)\n",
-    "for ax in axs:\n",
-    "    ax.axis(\"off\")\n",
-    "ax = axs[0]\n",
-    "ax.imshow(img[..., img.shape[2] // 2], cmap=\"gray\")\n",
-    "ax = axs[1]\n",
-    "ax.imshow(img[:, img.shape[1] // 2, ...], cmap=\"gray\")\n",
-    "ax = axs[2]\n",
-    "ax.imshow(img[img.shape[0] // 2, ...], cmap=\"gray\")\n",
-    "# plt.savefig(\"training_examples.png\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "513d7eee",
-   "metadata": {},
-   "source": [
-    "## Autoencoder KL\n",
-    "\n",
-    "### Define Autoencoder KL network\n",
-    "\n",
-    "In this section, we will define an autoencoder with KL-regularization for the LDM. The autoencoder's primary purpose is to transform input images into a latent representation that the diffusion model will subsequently learn. By doing so, we can decrease the computational resources required to train the diffusion component, making this approach suitable for learning high-resolution medical images.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "1042ebac",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using cuda\n"
-     ]
-    }
-   ],
-   "source": [
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "print(f\"Using {device}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "383a2043",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "autoencoder = AutoencoderKL(\n",
-    "    spatial_dims=3,\n",
-    "    in_channels=1,\n",
-    "    out_channels=1,\n",
-    "    num_channels=(32, 64, 64),\n",
-    "    latent_channels=3,\n",
-    "    num_res_blocks=1,\n",
-    "    norm_num_groups=16,\n",
-    "    attention_levels=(False, False, True),\n",
-    ")\n",
-    "autoencoder.to(device)\n",
-    "\n",
-    "\n",
-    "discriminator = PatchDiscriminator(spatial_dims=3, num_layers_d=3, num_channels=32, in_channels=1, out_channels=1)\n",
-    "discriminator.to(device)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "67f94d1b",
-   "metadata": {},
-   "source": [
-    "### Defining Losses\n",
-    "\n",
-    "We will also specify the perceptual and adversarial losses, including the involved networks, and the optimizers to use during the training process."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "7594daa3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
-      "Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=SqueezeNet1_1_Weights.IMAGENET1K_V1`. You can also use `weights=SqueezeNet1_1_Weights.DEFAULT` to get the most up-to-date weights.\n"
-     ]
-    }
-   ],
-   "source": [
-    "l1_loss = L1Loss()\n",
-    "adv_loss = PatchAdversarialLoss(criterion=\"least_squares\")\n",
-    "loss_perceptual = PerceptualLoss(spatial_dims=3, network_type=\"squeeze\", is_fake_3d=True, fake_3d_ratio=0.2)\n",
-    "loss_perceptual.to(device)\n",
-    "\n",
-    "\n",
-    "def KL_loss(z_mu, z_sigma):\n",
-    "    kl_loss = 0.5 * torch.sum(z_mu.pow(2) + z_sigma.pow(2) - torch.log(z_sigma.pow(2)) - 1, dim=[1, 2, 3, 4])\n",
-    "    return torch.sum(kl_loss) / kl_loss.shape[0]\n",
-    "\n",
-    "\n",
-    "adv_weight = 0.01\n",
-    "perceptual_weight = 0.001\n",
-    "kl_weight = 1e-6"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "354a3057",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "optimizer_g = torch.optim.Adam(params=autoencoder.parameters(), lr=1e-4)\n",
-    "optimizer_d = torch.optim.Adam(params=discriminator.parameters(), lr=1e-4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "be4fe2d4",
-   "metadata": {},
-   "source": [
-    "### Train model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "047c1bc4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0: 100%|█████████████████| 194/194 [02:27<00:00,  1.31it/s, recons_loss=0.0642, gen_loss=0, disc_loss=0]\n",
-      "Epoch 1: 100%|█████████████████| 194/194 [02:29<00:00,  1.30it/s, recons_loss=0.0421, gen_loss=0, disc_loss=0]\n",
-      "Epoch 2: 100%|█████████████████| 194/194 [02:30<00:00,  1.29it/s, recons_loss=0.0337, gen_loss=0, disc_loss=0]\n",
-      "Epoch 3: 100%|█████████████████| 194/194 [02:31<00:00,  1.28it/s, recons_loss=0.0325, gen_loss=0, disc_loss=0]\n",
-      "Epoch 4: 100%|█████████████████| 194/194 [02:31<00:00,  1.28it/s, recons_loss=0.0307, gen_loss=0, disc_loss=0]\n",
-      "Epoch 5: 100%|█████████████████| 194/194 [02:31<00:00,  1.28it/s, recons_loss=0.0277, gen_loss=0, disc_loss=0]\n",
-      "Epoch 6: 100%|██████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.027, gen_loss=0.528, disc_loss=0.342]\n",
-      "Epoch 7: 100%|█████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0282, gen_loss=0.594, disc_loss=0.228]\n",
-      "Epoch 8: 100%|█████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0291, gen_loss=0.572, disc_loss=0.238]\n",
-      "Epoch 9: 100%|█████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0284, gen_loss=0.511, disc_loss=0.246]\n",
-      "Epoch 10: 100%|████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0287, gen_loss=0.389, disc_loss=0.223]\n",
-      "Epoch 11: 100%|████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0279, gen_loss=0.425, disc_loss=0.218]\n",
-      "Epoch 12: 100%|█████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0277, gen_loss=0.406, disc_loss=0.23]\n",
-      "Epoch 13: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0269, gen_loss=0.384, disc_loss=0.221]\n",
-      "Epoch 14: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0259, gen_loss=0.432, disc_loss=0.231]\n",
-      "Epoch 15: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0259, gen_loss=0.375, disc_loss=0.225]\n",
-      "Epoch 16: 100%|█████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0257, gen_loss=0.41, disc_loss=0.226]\n",
-      "Epoch 17: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0255, gen_loss=0.394, disc_loss=0.218]\n",
-      "Epoch 18: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0254, gen_loss=0.403, disc_loss=0.221]\n",
-      "Epoch 19: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0256, gen_loss=0.389, disc_loss=0.224]\n",
-      "Epoch 20: 100%|████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0257, gen_loss=0.403, disc_loss=0.221]\n",
-      "Epoch 21: 100%|█████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0252, gen_loss=0.406, disc_loss=0.22]\n",
-      "Epoch 22: 100%|████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0253, gen_loss=0.388, disc_loss=0.214]\n",
-      "Epoch 23: 100%|████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0246, gen_loss=0.387, disc_loss=0.215]\n",
-      "Epoch 24: 100%|████████| 194/194 [02:50<00:00,  1.13it/s, recons_loss=0.0239, gen_loss=0.411, disc_loss=0.214]\n",
-      "Epoch 25: 100%|████████| 194/194 [02:50<00:00,  1.13it/s, recons_loss=0.0243, gen_loss=0.415, disc_loss=0.211]\n",
-      "Epoch 26: 100%|█████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0243, gen_loss=0.41, disc_loss=0.209]\n",
-      "Epoch 27: 100%|████████| 194/194 [02:50<00:00,  1.13it/s, recons_loss=0.0234, gen_loss=0.461, disc_loss=0.227]\n",
-      "Epoch 28: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0237, gen_loss=0.426, disc_loss=0.207]\n",
-      "Epoch 29: 100%|██████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.024, gen_loss=0.421, disc_loss=0.21]\n",
-      "Epoch 30: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0236, gen_loss=0.447, disc_loss=0.209]\n",
-      "Epoch 31: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0236, gen_loss=0.414, disc_loss=0.208]\n",
-      "Epoch 32: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0231, gen_loss=0.418, disc_loss=0.206]\n",
-      "Epoch 33: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0236, gen_loss=0.412, disc_loss=0.212]\n",
-      "Epoch 34: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0234, gen_loss=0.435, disc_loss=0.206]\n",
-      "Epoch 35: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0231, gen_loss=0.423, disc_loss=0.207]\n",
-      "Epoch 36: 100%|████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0231, gen_loss=0.424, disc_loss=0.205]\n",
-      "Epoch 37: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0232, gen_loss=0.427, disc_loss=0.214]\n",
-      "Epoch 38: 100%|████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0222, gen_loss=0.476, disc_loss=0.217]\n",
-      "Epoch 39: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0225, gen_loss=0.446, disc_loss=0.206]\n",
-      "Epoch 40: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0226, gen_loss=0.437, disc_loss=0.207]\n",
-      "Epoch 41: 100%|████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0229, gen_loss=0.426, disc_loss=0.207]\n",
-      "Epoch 42: 100%|████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0221, gen_loss=0.468, disc_loss=0.198]\n",
-      "Epoch 43: 100%|█████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.023, gen_loss=0.455, disc_loss=0.201]\n",
-      "Epoch 44: 100%|████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0225, gen_loss=0.456, disc_loss=0.198]\n",
-      "Epoch 45: 100%|████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0221, gen_loss=0.501, disc_loss=0.196]\n",
-      "Epoch 46: 100%|█████████| 194/194 [02:50<00:00,  1.13it/s, recons_loss=0.022, gen_loss=0.476, disc_loss=0.194]\n",
-      "Epoch 47: 100%|████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0226, gen_loss=0.487, disc_loss=0.197]\n",
-      "Epoch 48: 100%|████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0225, gen_loss=0.486, disc_loss=0.186]\n",
-      "Epoch 49: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0226, gen_loss=0.508, disc_loss=0.187]\n",
-      "Epoch 50: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0226, gen_loss=0.511, disc_loss=0.189]\n",
-      "Epoch 51: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0224, gen_loss=0.564, disc_loss=0.182]\n",
-      "Epoch 52: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0224, gen_loss=0.508, disc_loss=0.183]\n",
-      "Epoch 53: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0221, gen_loss=0.526, disc_loss=0.175]\n",
-      "Epoch 54: 100%|████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0227, gen_loss=0.521, disc_loss=0.181]\n",
-      "Epoch 55: 100%|█████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0224, gen_loss=0.56, disc_loss=0.182]\n",
-      "Epoch 56: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0232, gen_loss=0.543, disc_loss=0.182]\n",
-      "Epoch 57: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0228, gen_loss=0.525, disc_loss=0.168]\n",
-      "Epoch 58: 100%|█████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.023, gen_loss=0.539, disc_loss=0.165]\n",
-      "Epoch 59: 100%|████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0228, gen_loss=0.572, disc_loss=0.178]\n",
-      "Epoch 60: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0231, gen_loss=0.536, disc_loss=0.165]\n",
-      "Epoch 61: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0231, gen_loss=0.579, disc_loss=0.158]\n",
-      "Epoch 62: 100%|█████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.024, gen_loss=0.549, disc_loss=0.162]\n",
-      "Epoch 63: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0235, gen_loss=0.565, disc_loss=0.153]\n",
-      "Epoch 64: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0234, gen_loss=0.598, disc_loss=0.152]\n",
-      "Epoch 65: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0235, gen_loss=0.591, disc_loss=0.163]\n",
-      "Epoch 66: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0232, gen_loss=0.604, disc_loss=0.156]\n",
-      "Epoch 67: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0229, gen_loss=0.625, disc_loss=0.152]\n",
-      "Epoch 68: 100%|█████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.023, gen_loss=0.589, disc_loss=0.152]\n",
-      "Epoch 69: 100%|████████| 194/194 [02:50<00:00,  1.13it/s, recons_loss=0.0234, gen_loss=0.617, disc_loss=0.148]\n",
-      "Epoch 70: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0235, gen_loss=0.635, disc_loss=0.156]\n",
-      "Epoch 71: 100%|█████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0234, gen_loss=0.61, disc_loss=0.161]\n",
-      "Epoch 72: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0235, gen_loss=0.587, disc_loss=0.142]\n",
-      "Epoch 73: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0236, gen_loss=0.637, disc_loss=0.149]\n",
-      "Epoch 74: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0238, gen_loss=0.615, disc_loss=0.149]\n",
-      "Epoch 75: 100%|████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0242, gen_loss=0.609, disc_loss=0.142]\n",
-      "Epoch 76: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0235, gen_loss=0.643, disc_loss=0.143]\n",
-      "Epoch 77: 100%|█████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0237, gen_loss=0.65, disc_loss=0.145]\n",
-      "Epoch 78: 100%|████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0231, gen_loss=0.704, disc_loss=0.121]\n",
-      "Epoch 79: 100%|████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0233, gen_loss=0.649, disc_loss=0.125]\n",
-      "Epoch 80: 100%|████████| 194/194 [02:51<00:00,  1.13it/s, recons_loss=0.0237, gen_loss=0.656, disc_loss=0.132]\n",
-      "Epoch 81: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0238, gen_loss=0.651, disc_loss=0.142]\n",
-      "Epoch 82: 100%|█████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0235, gen_loss=0.669, disc_loss=0.13]\n",
-      "Epoch 83: 100%|█████████| 194/194 [02:50<00:00,  1.13it/s, recons_loss=0.0238, gen_loss=0.653, disc_loss=0.13]\n",
-      "Epoch 84: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0232, gen_loss=0.688, disc_loss=0.126]\n",
-      "Epoch 85: 100%|██████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0223, gen_loss=0.763, disc_loss=0.1]\n",
-      "Epoch 86: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0234, gen_loss=0.655, disc_loss=0.136]\n",
-      "Epoch 87: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0236, gen_loss=0.664, disc_loss=0.121]\n",
-      "Epoch 88: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0234, gen_loss=0.697, disc_loss=0.117]\n",
-      "Epoch 89: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0228, gen_loss=0.721, disc_loss=0.101]\n",
-      "Epoch 90: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0235, gen_loss=0.704, disc_loss=0.113]\n",
-      "Epoch 91: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0243, gen_loss=0.674, disc_loss=0.127]\n",
-      "Epoch 92: 100%|███████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0215, gen_loss=0.833, disc_loss=0.0804]\n",
-      "Epoch 93: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0221, gen_loss=0.742, disc_loss=0.106]\n",
-      "Epoch 94: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0233, gen_loss=0.707, disc_loss=0.107]\n",
-      "Epoch 95: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0231, gen_loss=0.736, disc_loss=0.106]\n",
-      "Epoch 96: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0231, gen_loss=0.729, disc_loss=0.113]\n",
-      "Epoch 97: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0237, gen_loss=0.702, disc_loss=0.112]\n",
-      "Epoch 98: 100%|████████| 194/194 [02:50<00:00,  1.14it/s, recons_loss=0.0226, gen_loss=0.735, disc_loss=0.105]\n",
-      "Epoch 99: 100%|████████| 194/194 [02:49<00:00,  1.14it/s, recons_loss=0.0225, gen_loss=0.736, disc_loss=0.108]\n"
-     ]
-    }
-   ],
-   "source": [
-    "n_epochs = 100\n",
-    "autoencoder_warm_up_n_epochs = 5\n",
-    "val_interval = 10\n",
-    "epoch_recon_loss_list = []\n",
-    "epoch_gen_loss_list = []\n",
-    "epoch_disc_loss_list = []\n",
-    "val_recon_epoch_loss_list = []\n",
-    "intermediary_images = []\n",
-    "n_example_images = 4\n",
-    "\n",
-    "for epoch in range(n_epochs):\n",
-    "    autoencoder.train()\n",
-    "    discriminator.train()\n",
-    "    epoch_loss = 0\n",
-    "    gen_epoch_loss = 0\n",
-    "    disc_epoch_loss = 0\n",
-    "    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",
-    "        images = batch[\"image\"].to(device)  # choose only one of Brats channels\n",
-    "\n",
-    "        # Generator part\n",
-    "        optimizer_g.zero_grad(set_to_none=True)\n",
-    "        reconstruction, z_mu, z_sigma = autoencoder(images)\n",
-    "        kl_loss = KL_loss(z_mu, z_sigma)\n",
-    "\n",
-    "        recons_loss = l1_loss(reconstruction.float(), images.float())\n",
-    "        p_loss = loss_perceptual(reconstruction.float(), images.float())\n",
-    "        loss_g = recons_loss + kl_weight * kl_loss + perceptual_weight * p_loss\n",
-    "\n",
-    "        if epoch > autoencoder_warm_up_n_epochs:\n",
-    "            logits_fake = discriminator(reconstruction.contiguous().float())[-1]\n",
-    "            generator_loss = adv_loss(logits_fake, target_is_real=True, for_discriminator=False)\n",
-    "            loss_g += adv_weight * generator_loss\n",
-    "\n",
-    "        loss_g.backward()\n",
-    "        optimizer_g.step()\n",
-    "\n",
-    "        if epoch > autoencoder_warm_up_n_epochs:\n",
-    "            # Discriminator part\n",
-    "            optimizer_d.zero_grad(set_to_none=True)\n",
-    "            logits_fake = discriminator(reconstruction.contiguous().detach())[-1]\n",
-    "            loss_d_fake = adv_loss(logits_fake, target_is_real=False, for_discriminator=True)\n",
-    "            logits_real = discriminator(images.contiguous().detach())[-1]\n",
-    "            loss_d_real = adv_loss(logits_real, target_is_real=True, for_discriminator=True)\n",
-    "            discriminator_loss = (loss_d_fake + loss_d_real) * 0.5\n",
-    "\n",
-    "            loss_d = adv_weight * discriminator_loss\n",
-    "\n",
-    "            loss_d.backward()\n",
-    "            optimizer_d.step()\n",
-    "\n",
-    "        epoch_loss += recons_loss.item()\n",
-    "        if epoch > autoencoder_warm_up_n_epochs:\n",
-    "            gen_epoch_loss += generator_loss.item()\n",
-    "            disc_epoch_loss += discriminator_loss.item()\n",
-    "\n",
-    "        progress_bar.set_postfix(\n",
-    "            {\n",
-    "                \"recons_loss\": epoch_loss / (step + 1),\n",
-    "                \"gen_loss\": gen_epoch_loss / (step + 1),\n",
-    "                \"disc_loss\": disc_epoch_loss / (step + 1),\n",
-    "            }\n",
-    "        )\n",
-    "    epoch_recon_loss_list.append(epoch_loss / (step + 1))\n",
-    "    epoch_gen_loss_list.append(gen_epoch_loss / (step + 1))\n",
-    "    epoch_disc_loss_list.append(disc_epoch_loss / (step + 1))\n",
-    "\n",
-    "del discriminator\n",
-    "del loss_perceptual\n",
-    "torch.cuda.empty_cache()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "a27064b6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2023-02-19 13:52:44,991 - No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.style.use(\"ggplot\")\n",
-    "plt.title(\"Learning Curves\", fontsize=20)\n",
-    "plt.plot(epoch_recon_loss_list)\n",
-    "plt.yticks(fontsize=12)\n",
-    "plt.xticks(fontsize=12)\n",
-    "plt.xlabel(\"Epochs\", fontsize=16)\n",
-    "plt.ylabel(\"Loss\", fontsize=16)\n",
-    "plt.legend(prop={\"size\": 14})\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "fd710efe",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.title(\"Adversarial Training Curves\", fontsize=20)\n",
-    "plt.plot(epoch_gen_loss_list, color=\"C0\", linewidth=2.0, label=\"Generator\")\n",
-    "plt.plot(epoch_disc_loss_list, color=\"C1\", linewidth=2.0, label=\"Discriminator\")\n",
-    "plt.yticks(fontsize=12)\n",
-    "plt.xticks(fontsize=12)\n",
-    "plt.xlabel(\"Epochs\", fontsize=16)\n",
-    "plt.ylabel(\"Loss\", fontsize=16)\n",
-    "plt.legend(prop={\"size\": 14})\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "92e38b28",
-   "metadata": {},
-   "source": [
-    "### Visualise reconstructions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "ec9685bb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f506a8ce0b0>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot axial, coronal and sagittal slices of a training sample\n",
-    "idx = 0\n",
-    "img = reconstruction[idx, channel].detach().cpu().numpy()\n",
-    "fig, axs = plt.subplots(nrows=1, ncols=3)\n",
-    "for ax in axs:\n",
-    "    ax.axis(\"off\")\n",
-    "ax = axs[0]\n",
-    "ax.imshow(img[..., img.shape[2] // 2], cmap=\"gray\")\n",
-    "ax = axs[1]\n",
-    "ax.imshow(img[:, img.shape[1] // 2, ...], cmap=\"gray\")\n",
-    "ax = axs[2]\n",
-    "ax.imshow(img[img.shape[0] // 2, ...], cmap=\"gray\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fe436141",
-   "metadata": {},
-   "source": [
-    "## Diffusion Model\n",
-    "\n",
-    "### Define diffusion model and scheduler\n",
-    "\n",
-    "In this section, we will define the diffusion model that will learn data distribution of the latent representation of the autoencoder. Together with the diffusion model, we define a beta scheduler responsible for defining the amount of noise tahat is added across the diffusion's model Markov chain."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "88cbe609",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "unet = DiffusionModelUNet(\n",
-    "    spatial_dims=3,\n",
-    "    in_channels=3,\n",
-    "    out_channels=3,\n",
-    "    num_res_blocks=1,\n",
-    "    num_channels=(32, 64, 64),\n",
-    "    attention_levels=(False, True, True),\n",
-    "    num_head_channels=(0, 64, 64),\n",
-    ")\n",
-    "unet.to(device)\n",
-    "\n",
-    "\n",
-    "scheduler = DDPMScheduler(num_train_timesteps=1000, schedule=\"scaled_linear_beta\", beta_start=0.0015, beta_end=0.0195)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "243ddf9e",
-   "metadata": {},
-   "source": [
-    "### Scaling factor\n",
-    "\n",
-    "As mentioned in Rombach et al. [1] Section 4.3.2 and D.1, the signal-to-noise ratio (induced by the scale of the latent space) can affect the results obtained with the LDM, if the standard deviation of the latent space distribution drifts too much from that of a Gaussian. For this reason, it is best practice to use a scaling factor to adapt this standard deviation.\n",
-    "\n",
-    "_Note: In case where the latent space is close to a Gaussian distribution, the scaling factor will be close to one, and the results will not differ from those obtained when it is not used._\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "c5fedcea",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Scaling factor set to tensor(1.0026, device='cuda:0')\n"
-     ]
-    }
-   ],
-   "source": [
-    "with torch.no_grad():\n",
-    "    with autocast(enabled=True):\n",
-    "        z = autoencoder.encode_stage_2_inputs(check_data[\"image\"].to(device))\n",
-    "\n",
-    "print(f\"Scaling factor set to {1/torch.std(z)}\")\n",
-    "scale_factor = 1 / torch.std(z)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "439ff2d8",
-   "metadata": {},
-   "source": [
-    "We define the inferer using the scale factor:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7de37f3a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "inferer = LatentDiffusionInferer(scheduler, scale_factor=scale_factor)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "5eef3ec7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "optimizer_diff = torch.optim.Adam(params=unet.parameters(), lr=1e-4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4705c795",
-   "metadata": {},
-   "source": [
-    "### Train diffusion model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "0a7f6459",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0: 100%|███████████| 194/194 [00:23<00:00,  8.24it/s, loss=0.58]\n",
-      "Epoch 1: 100%|██████████| 194/194 [00:24<00:00,  7.85it/s, loss=0.356]\n",
-      "Epoch 2: 100%|██████████| 194/194 [00:23<00:00,  8.30it/s, loss=0.315]\n",
-      "Epoch 3: 100%|███████████| 194/194 [00:24<00:00,  8.05it/s, loss=0.29]\n",
-      "Epoch 4: 100%|██████████| 194/194 [00:23<00:00,  8.41it/s, loss=0.277]\n",
-      "Epoch 5: 100%|██████████| 194/194 [00:23<00:00,  8.43it/s, loss=0.253]\n",
-      "Epoch 6: 100%|██████████| 194/194 [00:22<00:00,  8.45it/s, loss=0.276]\n",
-      "Epoch 7: 100%|██████████| 194/194 [00:22<00:00,  8.47it/s, loss=0.302]\n",
-      "Epoch 8: 100%|██████████| 194/194 [00:24<00:00,  8.02it/s, loss=0.258]\n",
-      "Epoch 9: 100%|███████████| 194/194 [00:23<00:00,  8.28it/s, loss=0.26]\n",
-      "Epoch 10: 100%|█████████| 194/194 [00:23<00:00,  8.18it/s, loss=0.247]\n",
-      "Epoch 11: 100%|██████████| 194/194 [00:22<00:00,  8.47it/s, loss=0.25]\n",
-      "Epoch 12: 100%|█████████| 194/194 [00:23<00:00,  8.36it/s, loss=0.286]\n",
-      "Epoch 13: 100%|██████████| 194/194 [00:23<00:00,  8.40it/s, loss=0.26]\n",
-      "Epoch 14: 100%|█████████| 194/194 [00:23<00:00,  8.38it/s, loss=0.307]\n",
-      "Epoch 15: 100%|██████████| 194/194 [00:23<00:00,  8.41it/s, loss=0.28]\n",
-      "Epoch 16: 100%|█████████| 194/194 [00:23<00:00,  8.43it/s, loss=0.291]\n",
-      "Epoch 17: 100%|█████████| 194/194 [00:23<00:00,  8.40it/s, loss=0.274]\n",
-      "Epoch 18: 100%|█████████| 194/194 [00:23<00:00,  8.43it/s, loss=0.257]\n",
-      "Epoch 19: 100%|█████████| 194/194 [00:24<00:00,  8.02it/s, loss=0.276]\n",
-      "Epoch 20: 100%|█████████| 194/194 [00:23<00:00,  8.21it/s, loss=0.255]\n",
-      "Epoch 21: 100%|█████████| 194/194 [00:23<00:00,  8.28it/s, loss=0.245]\n",
-      "Epoch 22: 100%|█████████| 194/194 [00:23<00:00,  8.34it/s, loss=0.276]\n",
-      "Epoch 23: 100%|█████████| 194/194 [00:23<00:00,  8.17it/s, loss=0.232]\n",
-      "Epoch 24: 100%|█████████| 194/194 [00:23<00:00,  8.43it/s, loss=0.256]\n",
-      "Epoch 25: 100%|█████████| 194/194 [00:23<00:00,  8.21it/s, loss=0.243]\n",
-      "Epoch 26: 100%|█████████| 194/194 [00:24<00:00,  8.06it/s, loss=0.249]\n",
-      "Epoch 27: 100%|█████████| 194/194 [00:23<00:00,  8.26it/s, loss=0.283]\n",
-      "Epoch 28: 100%|█████████| 194/194 [00:23<00:00,  8.28it/s, loss=0.277]\n",
-      "Epoch 29: 100%|█████████| 194/194 [00:23<00:00,  8.30it/s, loss=0.279]\n",
-      "Epoch 30: 100%|█████████| 194/194 [00:23<00:00,  8.09it/s, loss=0.251]\n",
-      "Epoch 31: 100%|█████████| 194/194 [00:23<00:00,  8.17it/s, loss=0.257]\n",
-      "Epoch 32: 100%|█████████| 194/194 [00:23<00:00,  8.20it/s, loss=0.278]\n",
-      "Epoch 33: 100%|█████████| 194/194 [00:23<00:00,  8.33it/s, loss=0.264]\n",
-      "Epoch 34: 100%|█████████| 194/194 [00:23<00:00,  8.20it/s, loss=0.256]\n",
-      "Epoch 35: 100%|█████████| 194/194 [00:24<00:00,  7.92it/s, loss=0.266]\n",
-      "Epoch 36: 100%|█████████| 194/194 [00:23<00:00,  8.33it/s, loss=0.253]\n",
-      "Epoch 37: 100%|█████████| 194/194 [00:23<00:00,  8.13it/s, loss=0.268]\n",
-      "Epoch 38: 100%|█████████| 194/194 [00:23<00:00,  8.13it/s, loss=0.257]\n",
-      "Epoch 39: 100%|█████████| 194/194 [00:23<00:00,  8.25it/s, loss=0.246]\n",
-      "Epoch 40: 100%|█████████| 194/194 [00:23<00:00,  8.37it/s, loss=0.254]\n",
-      "Epoch 41: 100%|█████████| 194/194 [00:23<00:00,  8.15it/s, loss=0.277]\n",
-      "Epoch 42: 100%|█████████| 194/194 [00:24<00:00,  7.79it/s, loss=0.277]\n",
-      "Epoch 43: 100%|█████████| 194/194 [00:23<00:00,  8.11it/s, loss=0.259]\n",
-      "Epoch 44: 100%|█████████| 194/194 [00:24<00:00,  7.82it/s, loss=0.258]\n",
-      "Epoch 45: 100%|█████████| 194/194 [00:23<00:00,  8.30it/s, loss=0.249]\n",
-      "Epoch 46: 100%|██████████| 194/194 [00:23<00:00,  8.31it/s, loss=0.26]\n",
-      "Epoch 47: 100%|█████████| 194/194 [00:23<00:00,  8.19it/s, loss=0.261]\n",
-      "Epoch 48: 100%|█████████| 194/194 [00:23<00:00,  8.16it/s, loss=0.275]\n",
-      "Epoch 49: 100%|█████████| 194/194 [00:23<00:00,  8.38it/s, loss=0.259]\n",
-      "Epoch 50: 100%|█████████| 194/194 [00:23<00:00,  8.28it/s, loss=0.291]\n",
-      "Epoch 51: 100%|█████████| 194/194 [00:23<00:00,  8.23it/s, loss=0.268]\n",
-      "Epoch 52: 100%|█████████| 194/194 [00:23<00:00,  8.29it/s, loss=0.272]\n",
-      "Epoch 53: 100%|█████████| 194/194 [00:23<00:00,  8.15it/s, loss=0.251]\n",
-      "Epoch 54: 100%|█████████| 194/194 [00:23<00:00,  8.13it/s, loss=0.289]\n",
-      "Epoch 55: 100%|█████████| 194/194 [00:24<00:00,  8.03it/s, loss=0.261]\n",
-      "Epoch 56: 100%|██████████| 194/194 [00:23<00:00,  8.18it/s, loss=0.28]\n",
-      "Epoch 57: 100%|█████████| 194/194 [00:23<00:00,  8.22it/s, loss=0.259]\n",
-      "Epoch 58: 100%|█████████| 194/194 [00:24<00:00,  7.83it/s, loss=0.248]\n",
-      "Epoch 59: 100%|██████████| 194/194 [00:23<00:00,  8.33it/s, loss=0.25]\n",
-      "Epoch 60: 100%|█████████| 194/194 [00:24<00:00,  8.00it/s, loss=0.273]\n",
-      "Epoch 61: 100%|█████████| 194/194 [00:24<00:00,  7.79it/s, loss=0.259]\n",
-      "Epoch 62: 100%|█████████| 194/194 [00:24<00:00,  8.08it/s, loss=0.257]\n",
-      "Epoch 63: 100%|█████████| 194/194 [00:25<00:00,  7.75it/s, loss=0.263]\n",
-      "Epoch 64: 100%|█████████| 194/194 [00:23<00:00,  8.31it/s, loss=0.251]\n",
-      "Epoch 65: 100%|█████████| 194/194 [00:24<00:00,  7.98it/s, loss=0.278]\n",
-      "Epoch 66: 100%|█████████| 194/194 [00:24<00:00,  7.81it/s, loss=0.258]\n",
-      "Epoch 67: 100%|█████████| 194/194 [00:23<00:00,  8.14it/s, loss=0.288]\n",
-      "Epoch 68: 100%|█████████| 194/194 [00:24<00:00,  8.05it/s, loss=0.248]\n",
-      "Epoch 69: 100%|█████████| 194/194 [00:24<00:00,  8.03it/s, loss=0.257]\n",
-      "Epoch 70: 100%|█████████| 194/194 [00:23<00:00,  8.39it/s, loss=0.246]\n",
-      "Epoch 71: 100%|█████████| 194/194 [00:24<00:00,  7.88it/s, loss=0.288]\n",
-      "Epoch 72: 100%|█████████| 194/194 [00:23<00:00,  8.38it/s, loss=0.282]\n",
-      "Epoch 73: 100%|█████████| 194/194 [00:22<00:00,  8.44it/s, loss=0.287]\n",
-      "Epoch 74: 100%|█████████| 194/194 [00:23<00:00,  8.29it/s, loss=0.282]\n",
-      "Epoch 75: 100%|█████████| 194/194 [00:23<00:00,  8.42it/s, loss=0.263]\n",
-      "Epoch 76: 100%|█████████| 194/194 [00:23<00:00,  8.11it/s, loss=0.286]\n",
-      "Epoch 77: 100%|█████████| 194/194 [00:25<00:00,  7.68it/s, loss=0.252]\n",
-      "Epoch 78: 100%|█████████| 194/194 [00:23<00:00,  8.18it/s, loss=0.273]\n",
-      "Epoch 79: 100%|█████████| 194/194 [00:23<00:00,  8.33it/s, loss=0.235]\n",
-      "Epoch 80: 100%|█████████| 194/194 [00:23<00:00,  8.34it/s, loss=0.265]\n",
-      "Epoch 81: 100%|█████████| 194/194 [00:23<00:00,  8.21it/s, loss=0.258]\n",
-      "Epoch 82: 100%|█████████| 194/194 [00:23<00:00,  8.36it/s, loss=0.243]\n",
-      "Epoch 83: 100%|█████████| 194/194 [00:23<00:00,  8.11it/s, loss=0.251]\n",
-      "Epoch 84: 100%|█████████| 194/194 [00:24<00:00,  8.01it/s, loss=0.306]\n",
-      "Epoch 85: 100%|█████████| 194/194 [00:24<00:00,  8.07it/s, loss=0.265]\n",
-      "Epoch 86: 100%|█████████| 194/194 [00:23<00:00,  8.12it/s, loss=0.243]\n",
-      "Epoch 87: 100%|█████████| 194/194 [00:23<00:00,  8.17it/s, loss=0.257]\n",
-      "Epoch 88: 100%|█████████| 194/194 [00:23<00:00,  8.32it/s, loss=0.268]\n",
-      "Epoch 89: 100%|█████████| 194/194 [00:23<00:00,  8.16it/s, loss=0.263]\n",
-      "Epoch 90: 100%|█████████| 194/194 [00:23<00:00,  8.19it/s, loss=0.244]\n",
-      "Epoch 91: 100%|█████████| 194/194 [00:23<00:00,  8.39it/s, loss=0.278]\n",
-      "Epoch 92: 100%|█████████| 194/194 [00:23<00:00,  8.28it/s, loss=0.274]\n",
-      "Epoch 93: 100%|██████████| 194/194 [00:23<00:00,  8.15it/s, loss=0.24]\n",
-      "Epoch 94: 100%|█████████| 194/194 [00:24<00:00,  8.01it/s, loss=0.275]\n",
-      "Epoch 95: 100%|█████████| 194/194 [00:24<00:00,  8.06it/s, loss=0.259]\n",
-      "Epoch 96: 100%|█████████| 194/194 [00:24<00:00,  8.00it/s, loss=0.247]\n",
-      "Epoch 97: 100%|█████████| 194/194 [00:23<00:00,  8.32it/s, loss=0.273]\n",
-      "Epoch 98: 100%|█████████| 194/194 [00:25<00:00,  7.75it/s, loss=0.262]\n",
-      "Epoch 99: 100%|█████████| 194/194 [00:25<00:00,  7.71it/s, loss=0.281]\n",
-      "Epoch 100: 100%|████████| 194/194 [00:24<00:00,  7.82it/s, loss=0.301]\n",
-      "Epoch 101: 100%|████████| 194/194 [00:23<00:00,  8.39it/s, loss=0.276]\n",
-      "Epoch 102: 100%|████████| 194/194 [00:24<00:00,  7.83it/s, loss=0.279]\n",
-      "Epoch 103: 100%|████████| 194/194 [00:23<00:00,  8.34it/s, loss=0.289]\n",
-      "Epoch 104: 100%|████████| 194/194 [00:23<00:00,  8.35it/s, loss=0.277]\n",
-      "Epoch 105: 100%|████████| 194/194 [00:23<00:00,  8.42it/s, loss=0.251]\n",
-      "Epoch 106: 100%|████████| 194/194 [00:23<00:00,  8.36it/s, loss=0.262]\n",
-      "Epoch 107: 100%|████████| 194/194 [00:23<00:00,  8.41it/s, loss=0.273]\n",
-      "Epoch 108: 100%|████████| 194/194 [00:23<00:00,  8.41it/s, loss=0.283]\n",
-      "Epoch 109: 100%|█████████| 194/194 [00:23<00:00,  8.38it/s, loss=0.31]\n",
-      "Epoch 110: 100%|████████| 194/194 [00:23<00:00,  8.43it/s, loss=0.278]\n",
-      "Epoch 111: 100%|████████| 194/194 [00:23<00:00,  8.35it/s, loss=0.256]\n",
-      "Epoch 112: 100%|█████████| 194/194 [00:23<00:00,  8.43it/s, loss=0.26]\n",
-      "Epoch 113: 100%|████████| 194/194 [00:24<00:00,  7.92it/s, loss=0.251]\n",
-      "Epoch 114: 100%|████████| 194/194 [00:25<00:00,  7.74it/s, loss=0.274]\n",
-      "Epoch 115: 100%|████████| 194/194 [00:23<00:00,  8.12it/s, loss=0.289]\n",
-      "Epoch 116: 100%|████████| 194/194 [00:24<00:00,  7.88it/s, loss=0.262]\n",
-      "Epoch 117: 100%|████████| 194/194 [00:24<00:00,  8.04it/s, loss=0.247]\n",
-      "Epoch 118: 100%|█████████| 194/194 [00:23<00:00,  8.41it/s, loss=0.25]\n",
-      "Epoch 119: 100%|████████| 194/194 [00:23<00:00,  8.43it/s, loss=0.263]\n",
-      "Epoch 120: 100%|████████| 194/194 [00:23<00:00,  8.40it/s, loss=0.259]\n",
-      "Epoch 121: 100%|████████| 194/194 [00:23<00:00,  8.42it/s, loss=0.258]\n",
-      "Epoch 122: 100%|████████| 194/194 [00:23<00:00,  8.37it/s, loss=0.272]\n",
-      "Epoch 123: 100%|████████| 194/194 [00:23<00:00,  8.42it/s, loss=0.248]\n",
-      "Epoch 124: 100%|████████| 194/194 [00:22<00:00,  8.44it/s, loss=0.286]\n",
-      "Epoch 125: 100%|████████| 194/194 [00:23<00:00,  8.36it/s, loss=0.288]\n",
-      "Epoch 126: 100%|████████| 194/194 [00:23<00:00,  8.43it/s, loss=0.283]\n",
-      "Epoch 127: 100%|████████| 194/194 [00:23<00:00,  8.39it/s, loss=0.283]\n",
-      "Epoch 128: 100%|████████| 194/194 [00:24<00:00,  7.86it/s, loss=0.256]\n",
-      "Epoch 129: 100%|████████| 194/194 [00:24<00:00,  7.91it/s, loss=0.268]\n",
-      "Epoch 130: 100%|████████| 194/194 [00:24<00:00,  8.05it/s, loss=0.266]\n",
-      "Epoch 131: 100%|████████| 194/194 [00:22<00:00,  8.47it/s, loss=0.276]\n",
-      "Epoch 132: 100%|█████████| 194/194 [00:23<00:00,  8.24it/s, loss=0.25]\n",
-      "Epoch 133: 100%|████████| 194/194 [00:23<00:00,  8.24it/s, loss=0.295]\n",
-      "Epoch 134: 100%|████████| 194/194 [00:23<00:00,  8.22it/s, loss=0.263]\n",
-      "Epoch 135: 100%|████████| 194/194 [00:23<00:00,  8.28it/s, loss=0.248]\n",
-      "Epoch 136: 100%|████████| 194/194 [00:22<00:00,  8.44it/s, loss=0.234]\n",
-      "Epoch 137: 100%|████████| 194/194 [00:23<00:00,  8.41it/s, loss=0.265]\n",
-      "Epoch 138: 100%|████████| 194/194 [00:25<00:00,  7.67it/s, loss=0.288]\n",
-      "Epoch 139: 100%|████████| 194/194 [00:23<00:00,  8.35it/s, loss=0.232]\n",
-      "Epoch 140: 100%|████████| 194/194 [00:23<00:00,  8.37it/s, loss=0.275]\n",
-      "Epoch 141: 100%|████████| 194/194 [00:23<00:00,  8.38it/s, loss=0.267]\n",
-      "Epoch 142: 100%|████████| 194/194 [00:23<00:00,  8.31it/s, loss=0.247]\n",
-      "Epoch 143: 100%|████████| 194/194 [00:23<00:00,  8.39it/s, loss=0.261]\n",
-      "Epoch 144: 100%|████████| 194/194 [00:23<00:00,  8.23it/s, loss=0.263]\n",
-      "Epoch 145: 100%|████████| 194/194 [00:24<00:00,  7.80it/s, loss=0.265]\n",
-      "Epoch 146: 100%|████████| 194/194 [00:23<00:00,  8.29it/s, loss=0.276]\n",
-      "Epoch 147: 100%|████████| 194/194 [00:23<00:00,  8.27it/s, loss=0.263]\n",
-      "Epoch 148: 100%|████████| 194/194 [00:23<00:00,  8.14it/s, loss=0.275]\n",
-      "Epoch 149: 100%|████████| 194/194 [00:23<00:00,  8.36it/s, loss=0.266]\n"
-     ]
-    }
-   ],
-   "source": [
-    "n_epochs = 150\n",
-    "epoch_loss_list = []\n",
-    "autoencoder.eval()\n",
-    "scaler = GradScaler()\n",
-    "\n",
-    "first_batch = first(train_loader)\n",
-    "z = autoencoder.encode_stage_2_inputs(first_batch[\"image\"].to(device))\n",
-    "\n",
-    "for epoch in range(n_epochs):\n",
-    "    unet.train()\n",
-    "    epoch_loss = 0\n",
-    "    progress_bar = tqdm(enumerate(train_loader), total=len(train_loader), ncols=70)\n",
-    "    progress_bar.set_description(f\"Epoch {epoch}\")\n",
-    "    for step, batch in progress_bar:\n",
-    "        images = batch[\"image\"].to(device)\n",
-    "        optimizer_diff.zero_grad(set_to_none=True)\n",
-    "\n",
-    "        with autocast(enabled=True):\n",
-    "            # Generate random noise\n",
-    "            noise = torch.randn_like(z).to(device)\n",
-    "\n",
-    "            # Create timesteps\n",
-    "            timesteps = torch.randint(\n",
-    "                0, inferer.scheduler.num_train_timesteps, (images.shape[0],), device=images.device\n",
-    "            ).long()\n",
-    "\n",
-    "            # Get model prediction\n",
-    "            noise_pred = inferer(\n",
-    "                inputs=images, autoencoder_model=autoencoder, diffusion_model=unet, noise=noise, timesteps=timesteps\n",
-    "            )\n",
-    "\n",
-    "            loss = F.mse_loss(noise_pred.float(), noise.float())\n",
-    "\n",
-    "        scaler.scale(loss).backward()\n",
-    "        scaler.step(optimizer_diff)\n",
-    "        scaler.update()\n",
-    "\n",
-    "        epoch_loss += loss.item()\n",
-    "\n",
-    "        progress_bar.set_postfix({\"loss\": epoch_loss / (step + 1)})\n",
-    "    epoch_loss_list.append(epoch_loss / (step + 1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "93b93696",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2023-02-19 14:12:22,536 - No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(epoch_loss_list)\n",
-    "plt.title(\"Learning Curves\", fontsize=20)\n",
-    "plt.plot(epoch_loss_list)\n",
-    "plt.yticks(fontsize=12)\n",
-    "plt.xticks(fontsize=12)\n",
-    "plt.xlabel(\"Epochs\", fontsize=16)\n",
-    "plt.ylabel(\"Loss\", fontsize=16)\n",
-    "plt.legend(prop={\"size\": 14})\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c9de4288",
-   "metadata": {},
-   "source": [
-    "### Plotting sampling example\n",
-    "\n",
-    "Finally, we generate an image with our LDM. For that, we will initialize a latent representation with just noise. Then, we will use the `unet` to perform 1000 denoising steps. In the last step, we decode the latent representation and plot the sampled image."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "bc946d70",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:15<00:00, 64.03it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "autoencoder.eval()\n",
-    "unet.eval()\n",
-    "\n",
-    "noise = torch.randn((1, 3, 24, 24, 16))\n",
-    "noise = noise.to(device)\n",
-    "scheduler.set_timesteps(num_inference_steps=1000)\n",
-    "synthetic_images = inferer.sample(\n",
-    "    input_noise=noise, autoencoder_model=autoencoder, diffusion_model=unet, scheduler=scheduler\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fed68b96",
-   "metadata": {},
-   "source": [
-    "### Visualise synthetic data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "0763caa1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f510c1672e0>"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "idx = 0\n",
-    "img = synthetic_images[idx, channel].detach().cpu().numpy()  # images\n",
-    "fig, axs = plt.subplots(nrows=1, ncols=3)\n",
-    "for ax in axs:\n",
-    "    ax.axis(\"off\")\n",
-    "ax = axs[0]\n",
-    "ax.imshow(img[..., img.shape[2] // 2], cmap=\"gray\")\n",
-    "ax = axs[1]\n",
-    "ax.imshow(img[:, img.shape[1] // 2, ...], cmap=\"gray\")\n",
-    "ax = axs[2]\n",
-    "ax.imshow(img[img.shape[0] // 2, ...], cmap=\"gray\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d3ab7b79",
-   "metadata": {},
-   "source": [
-    "## Clean-up data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "52b71f99",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if directory is None:\n",
-    "    shutil.rmtree(root_dir)"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "formats": "ipynb,py"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "a7e6f8385898884a13cbe220eefefb32cba5012927a94186742ddc14746e4dba"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+    "nbformat": 4,
+    "nbformat_minor": 5
 }
diff --git a/tutorials/generative/3d_ldm/3d_ldm_tutorial.py b/tutorials/generative/3d_ldm/3d_ldm_tutorial.py
index 6ea8cfb0..14bd0c73 100644
--- a/tutorials/generative/3d_ldm/3d_ldm_tutorial.py
+++ b/tutorials/generative/3d_ldm/3d_ldm_tutorial.py
@@ -7,7 +7,7 @@
 #       extension: .py
 #       format_name: light
 #       format_version: '1.5'
-#       jupytext_version: 1.14.4
+#       jupytext_version: 1.14.1
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
@@ -84,7 +84,7 @@
         transforms.LoadImaged(keys=["image"]),
         transforms.EnsureChannelFirstd(keys=["image"]),
         transforms.Lambdad(keys="image", func=lambda x: x[channel, :, :, :]),
-        transforms.AddChanneld(keys=["image"]),
+        transforms.EnsureChannelFirstd(keys=["image"], channel_dim="no_channel"),
         transforms.EnsureTyped(keys=["image"]),
         transforms.Orientationd(keys=["image"], axcodes="RAS"),
         transforms.Spacingd(keys=["image"], pixdim=(2.4, 2.4, 2.2), mode=("bilinear")),
diff --git a/tutorials/generative/3d_vqvae/3d_vqvae_tutorial.ipynb b/tutorials/generative/3d_vqvae/3d_vqvae_tutorial.ipynb
index 7f66d076..a078cb08 100644
--- a/tutorials/generative/3d_vqvae/3d_vqvae_tutorial.ipynb
+++ b/tutorials/generative/3d_vqvae/3d_vqvae_tutorial.ipynb
@@ -1,896 +1,896 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8bd4c6b4",
-   "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": "0b4285e3",
-   "metadata": {},
-   "source": [
-    "# Vector Quantized Variational Autoencoders for 3D reconstruction of images\n",
-    "\n",
-    "This tutorial illustrates how to use MONAI for training a Vector Quantized Variational Autoencoder (VQVAE)[1] on 3D images.\n",
-    "\n",
-    "Here, we will train our VQVAE model to be able to reconstruct the input images.  We will work with the Decathlon Dataset available on [MONAI](https://docs.monai.io/en/stable/apps.html#monai.apps.DecathlonDataset). In order to train faster, we will select just one of the available tasks (\"Task01_BrainTumour\").\n",
-    "\n",
-    "The VQVAE can also be used as a generative model if an autoregressor model (e.g., PixelCNN, Decoder Transformer) is trained on the discrete latent representations of the VQVAE bottleneck. This falls outside of the scope of this tutorial.\n",
-    "\n",
-    "[1] - Oord et al. \"Neural Discrete Representation Learning\" https://arxiv.org/abs/1711.00937\n",
-    "\n",
-    "\n",
-    "### Set up environment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2859b87c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm, nibabel]\"\n",
-    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e6a4ca0e",
-   "metadata": {},
-   "source": [
-    "### Setup imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "bb14df03",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MONAI version: 1.1.dev2248\n",
-      "Numpy version: 1.23.3\n",
-      "Pytorch version: 1.8.0+cu111\n",
-      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
-      "MONAI rev id: 3400bd91422ccba9ccc3aa2ffe7fecd4eb5596bf\n",
-      "MONAI __file__: /media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.8/site-packages/monai/__init__.py\n",
-      "\n",
-      "Optional dependencies:\n",
-      "Pytorch Ignite version: 0.4.10\n",
-      "Nibabel version: 4.0.2\n",
-      "scikit-image version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "Pillow version: 9.2.0\n",
-      "Tensorboard version: 2.11.0\n",
-      "gdown version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "TorchVision version: 0.9.0+cu111\n",
-      "tqdm version: 4.64.1\n",
-      "lmdb version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "psutil version: 5.9.3\n",
-      "pandas version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "einops version: 0.6.0\n",
-      "transformers version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "mlflow version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "pynrrd version: NOT INSTALLED or UNKNOWN VERSION.\n",
-      "\n",
-      "For details about installing the optional dependencies, please visit:\n",
-      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import shutil\n",
-    "import tempfile\n",
-    "import time\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "from monai import transforms\n",
-    "from monai.apps import DecathlonDataset\n",
-    "from monai.config import print_config\n",
-    "from monai.data import DataLoader\n",
-    "from monai.utils import set_determinism\n",
-    "from torch.nn import L1Loss\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "from generative.networks.nets import VQVAE\n",
-    "\n",
-    "print_config()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "352bd8ea",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# for reproducibility purposes set a seed\n",
-    "set_determinism(42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d0618c17",
-   "metadata": {},
-   "source": [
-    "### Setup a data directory\n",
-    "\n",
-    "Specify a `MONAI_DATA_DIRECTORY` variable, where the data will be downloaded. If not\n",
-    "specified a temporary directory will be used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "4fc6c2f9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/tmp/tmpc398pj0s\n"
-     ]
-    }
-   ],
-   "source": [
-    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
-    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
-    "print(root_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a342ff79",
-   "metadata": {},
-   "source": [
-    "### Setup used transforms and download dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "1e1b3bd0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:107: FutureWarning: <class 'monai.transforms.utility.array.AddChannel'>: Class `AddChannel` has been deprecated since version 0.8. please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead.\n",
-      "  warn_deprecated(obj, msg, warning_category)\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_transform = transforms.Compose(\n",
-    "    [\n",
-    "        transforms.LoadImaged(keys=[\"image\"]),\n",
-    "        transforms.Lambdad(keys=\"image\", func=lambda x: x[:, :, :, 1]),\n",
-    "        transforms.AddChanneld(keys=[\"image\"]),\n",
-    "        transforms.ScaleIntensityd(keys=[\"image\"]),\n",
-    "        transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=[176, 224, 155]),\n",
-    "        transforms.Resized(keys=[\"image\"], spatial_size=(32, 48, 32)),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "val_transform = transforms.Compose(\n",
-    "    [\n",
-    "        transforms.LoadImaged(keys=[\"image\"]),\n",
-    "        transforms.Lambdad(keys=\"image\", func=lambda x: x[:, :, :, 1]),\n",
-    "        transforms.AddChanneld(keys=[\"image\"]),\n",
-    "        transforms.ScaleIntensityd(keys=[\"image\"]),\n",
-    "        transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=[176, 224, 155]),\n",
-    "        transforms.Resized(keys=[\"image\"], spatial_size=(32, 48, 32)),\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "399ab576",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Task01_BrainTumour.tar: 7.09GB [07:59, 15.9MB/s]                                                                                                                                                                                "
-     ]
+    "cells": [
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "8bd4c6b4",
+            "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": "0b4285e3",
+            "metadata": {},
+            "source": [
+                "# Vector Quantized Variational Autoencoders for 3D reconstruction of images\n",
+                "\n",
+                "This tutorial illustrates how to use MONAI for training a Vector Quantized Variational Autoencoder (VQVAE)[1] on 3D images.\n",
+                "\n",
+                "Here, we will train our VQVAE model to be able to reconstruct the input images.  We will work with the Decathlon Dataset available on [MONAI](https://docs.monai.io/en/stable/apps.html#monai.apps.DecathlonDataset). In order to train faster, we will select just one of the available tasks (\"Task01_BrainTumour\").\n",
+                "\n",
+                "The VQVAE can also be used as a generative model if an autoregressor model (e.g., PixelCNN, Decoder Transformer) is trained on the discrete latent representations of the VQVAE bottleneck. This falls outside of the scope of this tutorial.\n",
+                "\n",
+                "[1] - Oord et al. \"Neural Discrete Representation Learning\" https://arxiv.org/abs/1711.00937\n",
+                "\n",
+                "\n",
+                "### Set up environment"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "2859b87c",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "!python -c \"import monai\" || pip install -q \"monai-weekly[tqdm, nibabel]\"\n",
+                "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
+                "%matplotlib inline"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "e6a4ca0e",
+            "metadata": {},
+            "source": [
+                "### Setup imports"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 1,
+            "id": "bb14df03",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "MONAI version: 1.1.dev2248\n",
+                        "Numpy version: 1.23.3\n",
+                        "Pytorch version: 1.8.0+cu111\n",
+                        "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
+                        "MONAI rev id: 3400bd91422ccba9ccc3aa2ffe7fecd4eb5596bf\n",
+                        "MONAI __file__: /media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.8/site-packages/monai/__init__.py\n",
+                        "\n",
+                        "Optional dependencies:\n",
+                        "Pytorch Ignite version: 0.4.10\n",
+                        "Nibabel version: 4.0.2\n",
+                        "scikit-image version: NOT INSTALLED or UNKNOWN VERSION.\n",
+                        "Pillow version: 9.2.0\n",
+                        "Tensorboard version: 2.11.0\n",
+                        "gdown version: NOT INSTALLED or UNKNOWN VERSION.\n",
+                        "TorchVision version: 0.9.0+cu111\n",
+                        "tqdm version: 4.64.1\n",
+                        "lmdb version: NOT INSTALLED or UNKNOWN VERSION.\n",
+                        "psutil version: 5.9.3\n",
+                        "pandas version: NOT INSTALLED or UNKNOWN VERSION.\n",
+                        "einops version: 0.6.0\n",
+                        "transformers version: NOT INSTALLED or UNKNOWN VERSION.\n",
+                        "mlflow version: NOT INSTALLED or UNKNOWN VERSION.\n",
+                        "pynrrd version: NOT INSTALLED or UNKNOWN VERSION.\n",
+                        "\n",
+                        "For details about installing the optional dependencies, please visit:\n",
+                        "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
+                        "\n"
+                    ]
+                }
+            ],
+            "source": [
+                "import os\n",
+                "import shutil\n",
+                "import tempfile\n",
+                "import time\n",
+                "\n",
+                "import matplotlib.pyplot as plt\n",
+                "import numpy as np\n",
+                "import torch\n",
+                "from monai import transforms\n",
+                "from monai.apps import DecathlonDataset\n",
+                "from monai.config import print_config\n",
+                "from monai.data import DataLoader\n",
+                "from monai.utils import set_determinism\n",
+                "from torch.nn import L1Loss\n",
+                "from tqdm import tqdm\n",
+                "\n",
+                "from generative.networks.nets import VQVAE\n",
+                "\n",
+                "print_config()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 2,
+            "id": "352bd8ea",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "# for reproducibility purposes set a seed\n",
+                "set_determinism(42)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "d0618c17",
+            "metadata": {},
+            "source": [
+                "### Setup a data directory\n",
+                "\n",
+                "Specify a `MONAI_DATA_DIRECTORY` variable, where the data will be downloaded. If not\n",
+                "specified a temporary directory will be used."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 3,
+            "id": "4fc6c2f9",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "/tmp/tmpc398pj0s\n"
+                    ]
+                }
+            ],
+            "source": [
+                "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
+                "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
+                "print(root_dir)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "a342ff79",
+            "metadata": {},
+            "source": [
+                "### Setup used transforms and download dataset"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 4,
+            "id": "1e1b3bd0",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "/media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.8/site-packages/monai/utils/deprecate_utils.py:107: FutureWarning: <class 'monai.transforms.utility.array.AddChannel'>: Class `AddChannel` has been deprecated since version 0.8. please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead.\n",
+                        "  warn_deprecated(obj, msg, warning_category)\n"
+                    ]
+                }
+            ],
+            "source": [
+                "train_transform = transforms.Compose(\n",
+                "    [\n",
+                "        transforms.LoadImaged(keys=[\"image\"]),\n",
+                "        transforms.Lambdad(keys=\"image\", func=lambda x: x[:, :, :, 1]),\n",
+                "        transforms.EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n",
+                "        transforms.ScaleIntensityd(keys=[\"image\"]),\n",
+                "        transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=[176, 224, 155]),\n",
+                "        transforms.Resized(keys=[\"image\"], spatial_size=(32, 48, 32)),\n",
+                "    ]\n",
+                ")\n",
+                "\n",
+                "val_transform = transforms.Compose(\n",
+                "    [\n",
+                "        transforms.LoadImaged(keys=[\"image\"]),\n",
+                "        transforms.Lambdad(keys=\"image\", func=lambda x: x[:, :, :, 1]),\n",
+                "        transforms.EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n",
+                "        transforms.ScaleIntensityd(keys=[\"image\"]),\n",
+                "        transforms.CenterSpatialCropd(keys=[\"image\"], roi_size=[176, 224, 155]),\n",
+                "        transforms.Resized(keys=[\"image\"], spatial_size=(32, 48, 32)),\n",
+                "    ]\n",
+                ")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 5,
+            "id": "399ab576",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Task01_BrainTumour.tar: 7.09GB [07:59, 15.9MB/s]                                                                                                                                                                                "
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "2022-11-30 00:27:21,262 - INFO - Downloaded: /tmp/tmpc398pj0s/Task01_BrainTumour.tar\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "\n"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "2022-11-30 00:27:29,463 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n",
+                        "2022-11-30 00:27:29,464 - INFO - Writing into directory: /tmp/tmpc398pj0s.\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Loading dataset: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 388/388 [03:31<00:00,  1.84it/s]\n"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "2022-11-30 00:31:12,636 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n",
+                        "2022-11-30 00:31:12,636 - INFO - File exists: /tmp/tmpc398pj0s/Task01_BrainTumour.tar, skipped downloading.\n",
+                        "2022-11-30 00:31:12,637 - INFO - Non-empty folder exists in /tmp/tmpc398pj0s/Task01_BrainTumour, skipped extracting.\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Loading dataset: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 96/96 [00:52<00:00,  1.82it/s]\n"
+                    ]
+                }
+            ],
+            "source": [
+                "train_ds = DecathlonDataset(\n",
+                "    root_dir=root_dir, task=\"Task01_BrainTumour\", transform=train_transform, section=\"training\", download=True\n",
+                ")\n",
+                "\n",
+                "train_loader = DataLoader(train_ds, batch_size=16, shuffle=True, num_workers=8)\n",
+                "\n",
+                "val_ds = DecathlonDataset(\n",
+                "    root_dir=root_dir, task=\"Task01_BrainTumour\", transform=val_transform, section=\"validation\", download=True\n",
+                ")\n",
+                "\n",
+                "val_loader = DataLoader(val_ds, batch_size=16, shuffle=False, num_workers=8, persistent_workers=True)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "7c896e0a",
+            "metadata": {},
+            "source": [
+                "### Visualize the training images"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 6,
+            "id": "5a32be9f",
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 1000x600 with 4 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "plt.subplots(1, 4, figsize=(10, 6))\n",
+                "for i in range(4):\n",
+                "    plt.subplot(1, 4, i + 1)\n",
+                "    plt.imshow(train_ds[i * 20][\"image\"][0, :, :, 15].detach().cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+                "    plt.axis(\"off\")\n",
+                "plt.tight_layout()\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "5ae4f2c7",
+            "metadata": {},
+            "source": [
+                "### Define network, optimizer and losses"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 8,
+            "id": "b28d46a4",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Using cuda\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "text/plain": [
+                            "VQVAE(\n",
+                            "  (encoder): Sequential(\n",
+                            "    (0): Convolution(\n",
+                            "      (conv): Conv3d(1, 256, kernel_size=(4, 4, 4), stride=(2, 2, 2), padding=(1, 1, 1))\n",
+                            "      (adn): ADN(\n",
+                            "        (A): ReLU()\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (1): VQVAEResidualUnit(\n",
+                            "      (conv1): Convolution(\n",
+                            "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
+                            "        (adn): ADN(\n",
+                            "          (D): Dropout(p=0.1, inplace=False)\n",
+                            "          (A): ReLU()\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (conv2): Convolution(\n",
+                            "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (2): VQVAEResidualUnit(\n",
+                            "      (conv1): Convolution(\n",
+                            "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
+                            "        (adn): ADN(\n",
+                            "          (D): Dropout(p=0.1, inplace=False)\n",
+                            "          (A): ReLU()\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (conv2): Convolution(\n",
+                            "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (3): Convolution(\n",
+                            "      (conv): Conv3d(256, 256, kernel_size=(4, 4, 4), stride=(2, 2, 2), padding=(1, 1, 1))\n",
+                            "      (adn): ADN(\n",
+                            "        (D): Dropout(p=0.1, inplace=False)\n",
+                            "        (A): ReLU()\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (4): VQVAEResidualUnit(\n",
+                            "      (conv1): Convolution(\n",
+                            "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
+                            "        (adn): ADN(\n",
+                            "          (D): Dropout(p=0.1, inplace=False)\n",
+                            "          (A): ReLU()\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (conv2): Convolution(\n",
+                            "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (5): VQVAEResidualUnit(\n",
+                            "      (conv1): Convolution(\n",
+                            "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
+                            "        (adn): ADN(\n",
+                            "          (D): Dropout(p=0.1, inplace=False)\n",
+                            "          (A): ReLU()\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (conv2): Convolution(\n",
+                            "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (6): Convolution(\n",
+                            "      (conv): Conv3d(256, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
+                            "    )\n",
+                            "  )\n",
+                            "  (quantizer): VectorQuantizer(\n",
+                            "    (quantizer): EMAQuantizer(\n",
+                            "      (embedding): Embedding(256, 32)\n",
+                            "    )\n",
+                            "  )\n",
+                            "  (decoder): Sequential(\n",
+                            "    (0): Convolution(\n",
+                            "      (conv): Conv3d(32, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
+                            "    )\n",
+                            "    (1): VQVAEResidualUnit(\n",
+                            "      (conv1): Convolution(\n",
+                            "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
+                            "        (adn): ADN(\n",
+                            "          (D): Dropout(p=0.1, inplace=False)\n",
+                            "          (A): ReLU()\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (conv2): Convolution(\n",
+                            "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (2): VQVAEResidualUnit(\n",
+                            "      (conv1): Convolution(\n",
+                            "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
+                            "        (adn): ADN(\n",
+                            "          (D): Dropout(p=0.1, inplace=False)\n",
+                            "          (A): ReLU()\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (conv2): Convolution(\n",
+                            "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (3): Convolution(\n",
+                            "      (conv): ConvTranspose3d(256, 256, kernel_size=(4, 4, 4), stride=(2, 2, 2), padding=(1, 1, 1))\n",
+                            "      (adn): ADN(\n",
+                            "        (D): Dropout(p=0.1, inplace=False)\n",
+                            "        (A): ReLU()\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (4): VQVAEResidualUnit(\n",
+                            "      (conv1): Convolution(\n",
+                            "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
+                            "        (adn): ADN(\n",
+                            "          (D): Dropout(p=0.1, inplace=False)\n",
+                            "          (A): ReLU()\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (conv2): Convolution(\n",
+                            "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (5): VQVAEResidualUnit(\n",
+                            "      (conv1): Convolution(\n",
+                            "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
+                            "        (adn): ADN(\n",
+                            "          (D): Dropout(p=0.1, inplace=False)\n",
+                            "          (A): ReLU()\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (conv2): Convolution(\n",
+                            "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (6): Convolution(\n",
+                            "      (conv): ConvTranspose3d(256, 1, kernel_size=(4, 4, 4), stride=(2, 2, 2), padding=(1, 1, 1))\n",
+                            "    )\n",
+                            "  )\n",
+                            ")"
+                        ]
+                    },
+                    "execution_count": 8,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+                "print(f\"Using {device}\")\n",
+                "model = VQVAE(\n",
+                "    spatial_dims=3,\n",
+                "    in_channels=1,\n",
+                "    out_channels=1,\n",
+                "    num_channels=(256, 256),\n",
+                "    num_res_channels=256,\n",
+                "    num_res_layers=2,\n",
+                "    downsample_parameters=((2, 4, 1, 1), (2, 4, 1, 1)),\n",
+                "    upsample_parameters=((2, 4, 1, 1, 0), (2, 4, 1, 1, 0)),\n",
+                "    num_embeddings=256,\n",
+                "    embedding_dim=32,\n",
+                ")\n",
+                "model.to(device)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 9,
+            "id": "dbefd7a9",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "optimizer = torch.optim.Adam(params=model.parameters(), lr=1e-4)\n",
+                "l1_loss = L1Loss()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "8fe3cb3c",
+            "metadata": {},
+            "source": [
+                "### Model training\n",
+                "Here, we are training our model for 100 epochs (training time: ~60 minutes)."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 10,
+            "id": "7ba11fab",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 0: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:35<00:00,  1.44s/it, recons_loss=0.0964, quantization_loss=1.45e-5]\n",
+                        "Epoch 1: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:35<00:00,  1.43s/it, recons_loss=0.0776, quantization_loss=1.04e-5]\n",
+                        "Epoch 2: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:36<00:00,  1.45s/it, recons_loss=0.0441, quantization_loss=8.18e-6]\n",
+                        "Epoch 3: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:36<00:00,  1.46s/it, recons_loss=0.0312, quantization_loss=3.03e-5]\n",
+                        "Epoch 4: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:36<00:00,  1.47s/it, recons_loss=0.0239, quantization_loss=1.14e-5]\n",
+                        "Epoch 5: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.48s/it, recons_loss=0.0213, quantization_loss=1.51e-5]\n",
+                        "Epoch 6: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.48s/it, recons_loss=0.0194, quantization_loss=9.73e-6]\n",
+                        "Epoch 7: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.49s/it, recons_loss=0.018, quantization_loss=1.46e-5]\n",
+                        "Epoch 8: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.49s/it, recons_loss=0.0167, quantization_loss=9.45e-6]\n",
+                        "Epoch 9: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.49s/it, recons_loss=0.0156, quantization_loss=1.3e-5]\n",
+                        "Epoch 10: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.50s/it, recons_loss=0.0156, quantization_loss=7.13e-6]\n",
+                        "Epoch 11: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.50s/it, recons_loss=0.0146, quantization_loss=7.13e-6]\n",
+                        "Epoch 12: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0139, quantization_loss=1.28e-5]\n",
+                        "Epoch 13: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0142, quantization_loss=8.03e-6]\n",
+                        "Epoch 14: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0135, quantization_loss=8.1e-6]\n",
+                        "Epoch 15: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0135, quantization_loss=7.39e-6]\n",
+                        "Epoch 16: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0143, quantization_loss=1.17e-5]\n",
+                        "Epoch 17: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0128, quantization_loss=6.57e-6]\n",
+                        "Epoch 18: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0125, quantization_loss=8.15e-6]\n",
+                        "Epoch 19: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0127, quantization_loss=8.6e-6]\n",
+                        "Epoch 20: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0121, quantization_loss=7.32e-6]\n",
+                        "Epoch 21: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0123, quantization_loss=5.92e-6]\n",
+                        "Epoch 22: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.012, quantization_loss=4.29e-6]\n",
+                        "Epoch 23: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0119, quantization_loss=3.72e-6]\n",
+                        "Epoch 24: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0119, quantization_loss=9.14e-6]\n",
+                        "Epoch 25: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0115, quantization_loss=3.31e-6]\n",
+                        "Epoch 26: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0118, quantization_loss=5.89e-6]\n",
+                        "Epoch 27: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0112, quantization_loss=9.95e-6]\n",
+                        "Epoch 28: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0111, quantization_loss=6.78e-6]\n",
+                        "Epoch 29: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0108, quantization_loss=3.85e-6]\n",
+                        "Epoch 30: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0121, quantization_loss=5.7e-6]\n",
+                        "Epoch 31: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0112, quantization_loss=7.31e-6]\n",
+                        "Epoch 32: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0108, quantization_loss=4.53e-6]\n",
+                        "Epoch 33: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0107, quantization_loss=5.36e-6]\n",
+                        "Epoch 34: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0106, quantization_loss=6.23e-6]\n",
+                        "Epoch 35: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0107, quantization_loss=2.98e-6]\n",
+                        "Epoch 36: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0103, quantization_loss=4.57e-6]\n",
+                        "Epoch 37: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0102, quantization_loss=3.09e-6]\n",
+                        "Epoch 38: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0106, quantization_loss=3.28e-6]\n",
+                        "Epoch 39: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0103, quantization_loss=2.81e-6]\n",
+                        "Epoch 40: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.01, quantization_loss=6.5e-6]\n",
+                        "Epoch 41: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0099, quantization_loss=2.46e-6]\n",
+                        "Epoch 42: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0101, quantization_loss=3.62e-6]\n",
+                        "Epoch 43: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0107, quantization_loss=6.03e-6]\n",
+                        "Epoch 44: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0108, quantization_loss=2.58e-6]\n",
+                        "Epoch 45: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0105, quantization_loss=4.09e-6]\n",
+                        "Epoch 46: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00974, quantization_loss=4.14e-6]\n",
+                        "Epoch 47: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00961, quantization_loss=3.92e-6]\n",
+                        "Epoch 48: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00958, quantization_loss=6.57e-6]\n",
+                        "Epoch 49: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00974, quantization_loss=5.11e-6]\n",
+                        "Epoch 50: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0098, quantization_loss=2.66e-6]\n",
+                        "Epoch 51: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00948, quantization_loss=4.26e-6]\n",
+                        "Epoch 52: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00932, quantization_loss=2.78e-6]\n",
+                        "Epoch 53: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00915, quantization_loss=3.76e-6]\n",
+                        "Epoch 54: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00942, quantization_loss=2.41e-6]\n",
+                        "Epoch 55: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00929, quantization_loss=2.18e-6]\n",
+                        "Epoch 56: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00981, quantization_loss=3.42e-6]\n",
+                        "Epoch 57: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0103, quantization_loss=2.66e-6]\n",
+                        "Epoch 58: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00952, quantization_loss=2.19e-6]\n",
+                        "Epoch 59: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00972, quantization_loss=5.11e-6]\n",
+                        "Epoch 60: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00919, quantization_loss=3.34e-6]\n",
+                        "Epoch 61: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00894, quantization_loss=4.7e-6]\n",
+                        "Epoch 62: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00897, quantization_loss=2.94e-6]\n",
+                        "Epoch 63: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00898, quantization_loss=2.08e-6]\n",
+                        "Epoch 64: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00895, quantization_loss=6.23e-6]\n",
+                        "Epoch 65: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00874, quantization_loss=3.02e-6]\n",
+                        "Epoch 66: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0086, quantization_loss=1.7e-6]\n",
+                        "Epoch 67: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00869, quantization_loss=5.49e-6]\n",
+                        "Epoch 68: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00863, quantization_loss=2.99e-6]\n",
+                        "Epoch 69: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00888, quantization_loss=5.46e-6]\n",
+                        "Epoch 70: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00874, quantization_loss=6.97e-6]\n",
+                        "Epoch 71: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00852, quantization_loss=4.5e-6]\n",
+                        "Epoch 72: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00837, quantization_loss=3.29e-6]\n",
+                        "Epoch 73: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00836, quantization_loss=3.99e-6]\n",
+                        "Epoch 74: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00851, quantization_loss=4.51e-6]\n",
+                        "Epoch 75: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00852, quantization_loss=2.96e-6]\n",
+                        "Epoch 76: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00838, quantization_loss=3.18e-6]\n",
+                        "Epoch 77: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00892, quantization_loss=3.5e-6]\n",
+                        "Epoch 78: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00853, quantization_loss=5.36e-6]\n",
+                        "Epoch 79: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00818, quantization_loss=1.95e-6]\n",
+                        "Epoch 80: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00829, quantization_loss=3.55e-6]\n",
+                        "Epoch 81: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00874, quantization_loss=4.41e-6]\n",
+                        "Epoch 82: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00853, quantization_loss=3.41e-6]\n",
+                        "Epoch 83: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00816, quantization_loss=4.04e-6]\n",
+                        "Epoch 84: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00837, quantization_loss=3.13e-6]\n",
+                        "Epoch 85: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00913, quantization_loss=2.3e-6]\n",
+                        "Epoch 86: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00876, quantization_loss=3.61e-6]\n",
+                        "Epoch 87: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00825, quantization_loss=3.02e-6]\n",
+                        "Epoch 88: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00823, quantization_loss=3.47e-6]\n",
+                        "Epoch 89: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00834, quantization_loss=3.9e-6]\n",
+                        "Epoch 90: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00843, quantization_loss=2.41e-6]\n",
+                        "Epoch 91: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00912, quantization_loss=4.24e-6]\n",
+                        "Epoch 92: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00994, quantization_loss=2.73e-6]\n",
+                        "Epoch 93: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00875, quantization_loss=3.5e-6]\n",
+                        "Epoch 94: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00875, quantization_loss=2.9e-6]\n",
+                        "Epoch 95: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00835, quantization_loss=3.81e-6]\n",
+                        "Epoch 96: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00813, quantization_loss=2.94e-6]\n",
+                        "Epoch 97: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00793, quantization_loss=3.69e-6]\n",
+                        "Epoch 98: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00791, quantization_loss=4.25e-6]\n",
+                        "Epoch 99: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00768, quantization_loss=1.91e-6]\n"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "train completed, total time: 3827.494425058365.\n"
+                    ]
+                }
+            ],
+            "source": [
+                "n_epochs = 100\n",
+                "val_interval = 10\n",
+                "epoch_recon_loss_list = []\n",
+                "epoch_quant_loss_list = []\n",
+                "val_recon_epoch_loss_list = []\n",
+                "intermediary_images = []\n",
+                "n_example_images = 4\n",
+                "\n",
+                "total_start = time.time()\n",
+                "for epoch in range(n_epochs):\n",
+                "    model.train()\n",
+                "    epoch_loss = 0\n",
+                "    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",
+                "        images = batch[\"image\"].to(device)\n",
+                "        optimizer.zero_grad(set_to_none=True)\n",
+                "\n",
+                "        # model outputs reconstruction and the quantization error\n",
+                "        reconstruction, quantization_loss = model(images=images)\n",
+                "\n",
+                "        recons_loss = l1_loss(reconstruction.float(), images.float())\n",
+                "\n",
+                "        loss = recons_loss + quantization_loss\n",
+                "\n",
+                "        loss.backward()\n",
+                "        optimizer.step()\n",
+                "\n",
+                "        epoch_loss += recons_loss.item()\n",
+                "\n",
+                "        progress_bar.set_postfix(\n",
+                "            {\"recons_loss\": epoch_loss / (step + 1), \"quantization_loss\": quantization_loss.item() / (step + 1)}\n",
+                "        )\n",
+                "    epoch_recon_loss_list.append(epoch_loss / (step + 1))\n",
+                "    epoch_quant_loss_list.append(quantization_loss.item() / (step + 1))\n",
+                "\n",
+                "    if (epoch + 1) % val_interval == 0:\n",
+                "        model.eval()\n",
+                "        val_loss = 0\n",
+                "        with torch.no_grad():\n",
+                "            for val_step, batch in enumerate(val_loader, start=1):\n",
+                "                images = batch[\"image\"].to(device)\n",
+                "\n",
+                "                reconstruction, quantization_loss = model(images=images)\n",
+                "\n",
+                "                # get the first sample from the first validation batch for\n",
+                "                # visualizing how the training evolves\n",
+                "                if val_step == 1:\n",
+                "                    intermediary_images.append(reconstruction[:n_example_images, 0])\n",
+                "\n",
+                "                recons_loss = l1_loss(reconstruction.float(), images.float())\n",
+                "\n",
+                "                val_loss += recons_loss.item()\n",
+                "\n",
+                "        val_loss /= val_step\n",
+                "        val_recon_epoch_loss_list.append(val_loss)\n",
+                "\n",
+                "total_time = time.time() - total_start\n",
+                "print(f\"train completed, total time: {total_time}.\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "5fc01098",
+            "metadata": {},
+            "source": [
+                "### Learning curves"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 11,
+            "id": "ac5f7c56",
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "plt.style.use(\"ggplot\")\n",
+                "plt.title(\"Learning Curves\", fontsize=20)\n",
+                "plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_recon_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n",
+                "plt.plot(\n",
+                "    np.linspace(val_interval, n_epochs, int(n_epochs / val_interval)),\n",
+                "    val_recon_epoch_loss_list,\n",
+                "    color=\"C1\",\n",
+                "    linewidth=2.0,\n",
+                "    label=\"Validation\",\n",
+                ")\n",
+                "plt.yticks(fontsize=12)\n",
+                "plt.xticks(fontsize=12)\n",
+                "plt.xlabel(\"Epochs\", fontsize=16)\n",
+                "plt.ylabel(\"Loss\", fontsize=16)\n",
+                "plt.legend(prop={\"size\": 14})\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "6df78259",
+            "metadata": {},
+            "source": [
+                "###  Plotting  evolution of reconstructed images"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 19,
+            "id": "040b52ba",
+            "metadata": {
+                "lines_to_next_cell": 2
+            },
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 1850x3050 with 10 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "# Plot every evaluation as a new line and example as columns\n",
+                "val_samples = np.linspace(val_interval, n_epochs, int(n_epochs / val_interval))\n",
+                "fig, ax = plt.subplots(nrows=len(val_samples), ncols=1, sharey=True)\n",
+                "fig.set_size_inches(18.5, 30.5)\n",
+                "for image_n in range(len(val_samples)):\n",
+                "    reconstructions = intermediary_images[image_n]\n",
+                "    reconstructions = np.concatenate(\n",
+                "        [\n",
+                "            reconstructions[0, :, :, 15],\n",
+                "            np.flipud(reconstructions[0, :, 24, :].T),\n",
+                "            np.flipud(reconstructions[0, 15, :, :].T),\n",
+                "        ],\n",
+                "        axis=1,\n",
+                "    )\n",
+                "\n",
+                "    ax[image_n].imshow(reconstructions, cmap=\"gray\")\n",
+                "    ax[image_n].set_xticks([])\n",
+                "    ax[image_n].set_yticks([])\n",
+                "    ax[image_n].set_ylabel(f\"Epoch {val_samples[image_n]:.0f}\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "1c3b5cff",
+            "metadata": {},
+            "source": [
+                "### Plotting the reconstructions from final trained model"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 20,
+            "id": "709f9c57",
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 2 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "fig, ax = plt.subplots(nrows=1, ncols=2)\n",
+                "plt.style.use(\"default\")\n",
+                "plotting_image_0 = np.concatenate([images[0, 0, :, :, 15].cpu(), np.flipud(images[0, 0, :, 24, :].cpu().T)], axis=1)\n",
+                "plotting_image_1 = np.concatenate([np.flipud(images[0, 0, 15, :, :].cpu().T), np.zeros((32, 32))], axis=1)\n",
+                "image = np.concatenate([plotting_image_0, plotting_image_1], axis=0)\n",
+                "\n",
+                "ax[0].imshow(image, vmin=0, vmax=1, cmap=\"gray\")\n",
+                "ax[0].axis(\"off\")\n",
+                "ax[0].title.set_text(\"Inputted Image\")\n",
+                "\n",
+                "plotting_image_2 = np.concatenate(\n",
+                "    [reconstruction[0, 0, :, :, 15].cpu(), np.flipud(reconstruction[0, 0, :, 24, :].cpu().T)], axis=1\n",
+                ")\n",
+                "plotting_image_3 = np.concatenate([np.flipud(reconstruction[0, 0, 15, :, :].cpu().T), np.zeros((32, 32))], axis=1)\n",
+                "reconstruction_3d = np.concatenate([plotting_image_2, plotting_image_3], axis=0)\n",
+                "ax[1].imshow(reconstruction_3d, vmin=0, vmax=1, cmap=\"gray\")\n",
+                "ax[1].axis(\"off\")\n",
+                "ax[1].title.set_text(\"Reconstruction\")\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "54ef9b14",
+            "metadata": {},
+            "source": [
+                "### Cleanup data directory\n",
+                "\n",
+                "Remove directory if a temporary was used."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "d53b24f4",
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "if directory is None:\n",
+                "    shutil.rmtree(root_dir)"
+            ]
+        }
+    ],
+    "metadata": {
+        "jupytext": {
+            "cell_metadata_filter": "-all",
+            "formats": "auto:percent,ipynb",
+            "main_language": "python",
+            "notebook_metadata_filter": "-all"
+        },
+        "kernelspec": {
+            "display_name": "Python 3 (ipykernel)",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.8.13"
+        }
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2022-11-30 00:27:21,262 - INFO - Downloaded: /tmp/tmpc398pj0s/Task01_BrainTumour.tar\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2022-11-30 00:27:29,463 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n",
-      "2022-11-30 00:27:29,464 - INFO - Writing into directory: /tmp/tmpc398pj0s.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 388/388 [03:31<00:00,  1.84it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2022-11-30 00:31:12,636 - INFO - Verified 'Task01_BrainTumour.tar', md5: 240a19d752f0d9e9101544901065d872.\n",
-      "2022-11-30 00:31:12,636 - INFO - File exists: /tmp/tmpc398pj0s/Task01_BrainTumour.tar, skipped downloading.\n",
-      "2022-11-30 00:31:12,637 - INFO - Non-empty folder exists in /tmp/tmpc398pj0s/Task01_BrainTumour, skipped extracting.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 96/96 [00:52<00:00,  1.82it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_ds = DecathlonDataset(\n",
-    "    root_dir=root_dir, task=\"Task01_BrainTumour\", transform=train_transform, section=\"training\", download=True\n",
-    ")\n",
-    "\n",
-    "train_loader = DataLoader(train_ds, batch_size=16, shuffle=True, num_workers=8)\n",
-    "\n",
-    "val_ds = DecathlonDataset(\n",
-    "    root_dir=root_dir, task=\"Task01_BrainTumour\", transform=val_transform, section=\"validation\", download=True\n",
-    ")\n",
-    "\n",
-    "val_loader = DataLoader(val_ds, batch_size=16, shuffle=False, num_workers=8, persistent_workers=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7c896e0a",
-   "metadata": {},
-   "source": [
-    "### Visualize the training images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "5a32be9f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 1000x600 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.subplots(1, 4, figsize=(10, 6))\n",
-    "for i in range(4):\n",
-    "    plt.subplot(1, 4, i + 1)\n",
-    "    plt.imshow(train_ds[i * 20][\"image\"][0, :, :, 15].detach().cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
-    "    plt.axis(\"off\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5ae4f2c7",
-   "metadata": {},
-   "source": [
-    "### Define network, optimizer and losses"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "b28d46a4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using cuda\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "VQVAE(\n",
-       "  (encoder): Sequential(\n",
-       "    (0): Convolution(\n",
-       "      (conv): Conv3d(1, 256, kernel_size=(4, 4, 4), stride=(2, 2, 2), padding=(1, 1, 1))\n",
-       "      (adn): ADN(\n",
-       "        (A): ReLU()\n",
-       "      )\n",
-       "    )\n",
-       "    (1): VQVAEResidualUnit(\n",
-       "      (conv1): Convolution(\n",
-       "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
-       "        (adn): ADN(\n",
-       "          (D): Dropout(p=0.1, inplace=False)\n",
-       "          (A): ReLU()\n",
-       "        )\n",
-       "      )\n",
-       "      (conv2): Convolution(\n",
-       "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
-       "      )\n",
-       "    )\n",
-       "    (2): VQVAEResidualUnit(\n",
-       "      (conv1): Convolution(\n",
-       "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
-       "        (adn): ADN(\n",
-       "          (D): Dropout(p=0.1, inplace=False)\n",
-       "          (A): ReLU()\n",
-       "        )\n",
-       "      )\n",
-       "      (conv2): Convolution(\n",
-       "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
-       "      )\n",
-       "    )\n",
-       "    (3): Convolution(\n",
-       "      (conv): Conv3d(256, 256, kernel_size=(4, 4, 4), stride=(2, 2, 2), padding=(1, 1, 1))\n",
-       "      (adn): ADN(\n",
-       "        (D): Dropout(p=0.1, inplace=False)\n",
-       "        (A): ReLU()\n",
-       "      )\n",
-       "    )\n",
-       "    (4): VQVAEResidualUnit(\n",
-       "      (conv1): Convolution(\n",
-       "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
-       "        (adn): ADN(\n",
-       "          (D): Dropout(p=0.1, inplace=False)\n",
-       "          (A): ReLU()\n",
-       "        )\n",
-       "      )\n",
-       "      (conv2): Convolution(\n",
-       "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
-       "      )\n",
-       "    )\n",
-       "    (5): VQVAEResidualUnit(\n",
-       "      (conv1): Convolution(\n",
-       "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
-       "        (adn): ADN(\n",
-       "          (D): Dropout(p=0.1, inplace=False)\n",
-       "          (A): ReLU()\n",
-       "        )\n",
-       "      )\n",
-       "      (conv2): Convolution(\n",
-       "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
-       "      )\n",
-       "    )\n",
-       "    (6): Convolution(\n",
-       "      (conv): Conv3d(256, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
-       "    )\n",
-       "  )\n",
-       "  (quantizer): VectorQuantizer(\n",
-       "    (quantizer): EMAQuantizer(\n",
-       "      (embedding): Embedding(256, 32)\n",
-       "    )\n",
-       "  )\n",
-       "  (decoder): Sequential(\n",
-       "    (0): Convolution(\n",
-       "      (conv): Conv3d(32, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
-       "    )\n",
-       "    (1): VQVAEResidualUnit(\n",
-       "      (conv1): Convolution(\n",
-       "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
-       "        (adn): ADN(\n",
-       "          (D): Dropout(p=0.1, inplace=False)\n",
-       "          (A): ReLU()\n",
-       "        )\n",
-       "      )\n",
-       "      (conv2): Convolution(\n",
-       "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
-       "      )\n",
-       "    )\n",
-       "    (2): VQVAEResidualUnit(\n",
-       "      (conv1): Convolution(\n",
-       "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
-       "        (adn): ADN(\n",
-       "          (D): Dropout(p=0.1, inplace=False)\n",
-       "          (A): ReLU()\n",
-       "        )\n",
-       "      )\n",
-       "      (conv2): Convolution(\n",
-       "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
-       "      )\n",
-       "    )\n",
-       "    (3): Convolution(\n",
-       "      (conv): ConvTranspose3d(256, 256, kernel_size=(4, 4, 4), stride=(2, 2, 2), padding=(1, 1, 1))\n",
-       "      (adn): ADN(\n",
-       "        (D): Dropout(p=0.1, inplace=False)\n",
-       "        (A): ReLU()\n",
-       "      )\n",
-       "    )\n",
-       "    (4): VQVAEResidualUnit(\n",
-       "      (conv1): Convolution(\n",
-       "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
-       "        (adn): ADN(\n",
-       "          (D): Dropout(p=0.1, inplace=False)\n",
-       "          (A): ReLU()\n",
-       "        )\n",
-       "      )\n",
-       "      (conv2): Convolution(\n",
-       "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
-       "      )\n",
-       "    )\n",
-       "    (5): VQVAEResidualUnit(\n",
-       "      (conv1): Convolution(\n",
-       "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
-       "        (adn): ADN(\n",
-       "          (D): Dropout(p=0.1, inplace=False)\n",
-       "          (A): ReLU()\n",
-       "        )\n",
-       "      )\n",
-       "      (conv2): Convolution(\n",
-       "        (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
-       "      )\n",
-       "    )\n",
-       "    (6): Convolution(\n",
-       "      (conv): ConvTranspose3d(256, 1, kernel_size=(4, 4, 4), stride=(2, 2, 2), padding=(1, 1, 1))\n",
-       "    )\n",
-       "  )\n",
-       ")"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "print(f\"Using {device}\")\n",
-    "model = VQVAE(\n",
-    "    spatial_dims=3,\n",
-    "    in_channels=1,\n",
-    "    out_channels=1,\n",
-    "    num_channels=(256, 256),\n",
-    "    num_res_channels=256,\n",
-    "    num_res_layers=2,\n",
-    "    downsample_parameters=((2, 4, 1, 1), (2, 4, 1, 1)),\n",
-    "    upsample_parameters=((2, 4, 1, 1, 0), (2, 4, 1, 1, 0)),\n",
-    "    num_embeddings=256,\n",
-    "    embedding_dim=32,\n",
-    ")\n",
-    "model.to(device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "dbefd7a9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "optimizer = torch.optim.Adam(params=model.parameters(), lr=1e-4)\n",
-    "l1_loss = L1Loss()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8fe3cb3c",
-   "metadata": {},
-   "source": [
-    "### Model training\n",
-    "Here, we are training our model for 100 epochs (training time: ~60 minutes)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "7ba11fab",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0: 100%|█████████████████| 25/25 [00:35<00:00,  1.44s/it, recons_loss=0.0964, quantization_loss=1.45e-5]\n",
-      "Epoch 1: 100%|█████████████████| 25/25 [00:35<00:00,  1.43s/it, recons_loss=0.0776, quantization_loss=1.04e-5]\n",
-      "Epoch 2: 100%|█████████████████| 25/25 [00:36<00:00,  1.45s/it, recons_loss=0.0441, quantization_loss=8.18e-6]\n",
-      "Epoch 3: 100%|█████████████████| 25/25 [00:36<00:00,  1.46s/it, recons_loss=0.0312, quantization_loss=3.03e-5]\n",
-      "Epoch 4: 100%|█████████████████| 25/25 [00:36<00:00,  1.47s/it, recons_loss=0.0239, quantization_loss=1.14e-5]\n",
-      "Epoch 5: 100%|█████████████████| 25/25 [00:37<00:00,  1.48s/it, recons_loss=0.0213, quantization_loss=1.51e-5]\n",
-      "Epoch 6: 100%|█████████████████| 25/25 [00:37<00:00,  1.48s/it, recons_loss=0.0194, quantization_loss=9.73e-6]\n",
-      "Epoch 7: 100%|██████████████████| 25/25 [00:37<00:00,  1.49s/it, recons_loss=0.018, quantization_loss=1.46e-5]\n",
-      "Epoch 8: 100%|█████████████████| 25/25 [00:37<00:00,  1.49s/it, recons_loss=0.0167, quantization_loss=9.45e-6]\n",
-      "Epoch 9: 100%|██████████████████| 25/25 [00:37<00:00,  1.49s/it, recons_loss=0.0156, quantization_loss=1.3e-5]\n",
-      "Epoch 10: 100%|████████████████| 25/25 [00:37<00:00,  1.50s/it, recons_loss=0.0156, quantization_loss=7.13e-6]\n",
-      "Epoch 11: 100%|████████████████| 25/25 [00:37<00:00,  1.50s/it, recons_loss=0.0146, quantization_loss=7.13e-6]\n",
-      "Epoch 12: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0139, quantization_loss=1.28e-5]\n",
-      "Epoch 13: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0142, quantization_loss=8.03e-6]\n",
-      "Epoch 14: 100%|█████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0135, quantization_loss=8.1e-6]\n",
-      "Epoch 15: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0135, quantization_loss=7.39e-6]\n",
-      "Epoch 16: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0143, quantization_loss=1.17e-5]\n",
-      "Epoch 17: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0128, quantization_loss=6.57e-6]\n",
-      "Epoch 18: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0125, quantization_loss=8.15e-6]\n",
-      "Epoch 19: 100%|█████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0127, quantization_loss=8.6e-6]\n",
-      "Epoch 20: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0121, quantization_loss=7.32e-6]\n",
-      "Epoch 21: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0123, quantization_loss=5.92e-6]\n",
-      "Epoch 22: 100%|█████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.012, quantization_loss=4.29e-6]\n",
-      "Epoch 23: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0119, quantization_loss=3.72e-6]\n",
-      "Epoch 24: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0119, quantization_loss=9.14e-6]\n",
-      "Epoch 25: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0115, quantization_loss=3.31e-6]\n",
-      "Epoch 26: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0118, quantization_loss=5.89e-6]\n",
-      "Epoch 27: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0112, quantization_loss=9.95e-6]\n",
-      "Epoch 28: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0111, quantization_loss=6.78e-6]\n",
-      "Epoch 29: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0108, quantization_loss=3.85e-6]\n",
-      "Epoch 30: 100%|█████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0121, quantization_loss=5.7e-6]\n",
-      "Epoch 31: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0112, quantization_loss=7.31e-6]\n",
-      "Epoch 32: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0108, quantization_loss=4.53e-6]\n",
-      "Epoch 33: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0107, quantization_loss=5.36e-6]\n",
-      "Epoch 34: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0106, quantization_loss=6.23e-6]\n",
-      "Epoch 35: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0107, quantization_loss=2.98e-6]\n",
-      "Epoch 36: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0103, quantization_loss=4.57e-6]\n",
-      "Epoch 37: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0102, quantization_loss=3.09e-6]\n",
-      "Epoch 38: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0106, quantization_loss=3.28e-6]\n",
-      "Epoch 39: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0103, quantization_loss=2.81e-6]\n",
-      "Epoch 40: 100%|███████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.01, quantization_loss=6.5e-6]\n",
-      "Epoch 41: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0099, quantization_loss=2.46e-6]\n",
-      "Epoch 42: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0101, quantization_loss=3.62e-6]\n",
-      "Epoch 43: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0107, quantization_loss=6.03e-6]\n",
-      "Epoch 44: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0108, quantization_loss=2.58e-6]\n",
-      "Epoch 45: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0105, quantization_loss=4.09e-6]\n",
-      "Epoch 46: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00974, quantization_loss=4.14e-6]\n",
-      "Epoch 47: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00961, quantization_loss=3.92e-6]\n",
-      "Epoch 48: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00958, quantization_loss=6.57e-6]\n",
-      "Epoch 49: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00974, quantization_loss=5.11e-6]\n",
-      "Epoch 50: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0098, quantization_loss=2.66e-6]\n",
-      "Epoch 51: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00948, quantization_loss=4.26e-6]\n",
-      "Epoch 52: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00932, quantization_loss=2.78e-6]\n",
-      "Epoch 53: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00915, quantization_loss=3.76e-6]\n",
-      "Epoch 54: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00942, quantization_loss=2.41e-6]\n",
-      "Epoch 55: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00929, quantization_loss=2.18e-6]\n",
-      "Epoch 56: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00981, quantization_loss=3.42e-6]\n",
-      "Epoch 57: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0103, quantization_loss=2.66e-6]\n",
-      "Epoch 58: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00952, quantization_loss=2.19e-6]\n",
-      "Epoch 59: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00972, quantization_loss=5.11e-6]\n",
-      "Epoch 60: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00919, quantization_loss=3.34e-6]\n",
-      "Epoch 61: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00894, quantization_loss=4.7e-6]\n",
-      "Epoch 62: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00897, quantization_loss=2.94e-6]\n",
-      "Epoch 63: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00898, quantization_loss=2.08e-6]\n",
-      "Epoch 64: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00895, quantization_loss=6.23e-6]\n",
-      "Epoch 65: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00874, quantization_loss=3.02e-6]\n",
-      "Epoch 66: 100%|█████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.0086, quantization_loss=1.7e-6]\n",
-      "Epoch 67: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00869, quantization_loss=5.49e-6]\n",
-      "Epoch 68: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00863, quantization_loss=2.99e-6]\n",
-      "Epoch 69: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00888, quantization_loss=5.46e-6]\n",
-      "Epoch 70: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00874, quantization_loss=6.97e-6]\n",
-      "Epoch 71: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00852, quantization_loss=4.5e-6]\n",
-      "Epoch 72: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00837, quantization_loss=3.29e-6]\n",
-      "Epoch 73: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00836, quantization_loss=3.99e-6]\n",
-      "Epoch 74: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00851, quantization_loss=4.51e-6]\n",
-      "Epoch 75: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00852, quantization_loss=2.96e-6]\n",
-      "Epoch 76: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00838, quantization_loss=3.18e-6]\n",
-      "Epoch 77: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00892, quantization_loss=3.5e-6]\n",
-      "Epoch 78: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00853, quantization_loss=5.36e-6]\n",
-      "Epoch 79: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00818, quantization_loss=1.95e-6]\n",
-      "Epoch 80: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00829, quantization_loss=3.55e-6]\n",
-      "Epoch 81: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00874, quantization_loss=4.41e-6]\n",
-      "Epoch 82: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00853, quantization_loss=3.41e-6]\n",
-      "Epoch 83: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00816, quantization_loss=4.04e-6]\n",
-      "Epoch 84: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00837, quantization_loss=3.13e-6]\n",
-      "Epoch 85: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00913, quantization_loss=2.3e-6]\n",
-      "Epoch 86: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00876, quantization_loss=3.61e-6]\n",
-      "Epoch 87: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00825, quantization_loss=3.02e-6]\n",
-      "Epoch 88: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00823, quantization_loss=3.47e-6]\n",
-      "Epoch 89: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00834, quantization_loss=3.9e-6]\n",
-      "Epoch 90: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00843, quantization_loss=2.41e-6]\n",
-      "Epoch 91: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00912, quantization_loss=4.24e-6]\n",
-      "Epoch 92: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00994, quantization_loss=2.73e-6]\n",
-      "Epoch 93: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00875, quantization_loss=3.5e-6]\n",
-      "Epoch 94: 100%|████████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00875, quantization_loss=2.9e-6]\n",
-      "Epoch 95: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00835, quantization_loss=3.81e-6]\n",
-      "Epoch 96: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00813, quantization_loss=2.94e-6]\n",
-      "Epoch 97: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00793, quantization_loss=3.69e-6]\n",
-      "Epoch 98: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00791, quantization_loss=4.25e-6]\n",
-      "Epoch 99: 100%|███████████████| 25/25 [00:37<00:00,  1.51s/it, recons_loss=0.00768, quantization_loss=1.91e-6]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train completed, total time: 3827.494425058365.\n"
-     ]
-    }
-   ],
-   "source": [
-    "n_epochs = 100\n",
-    "val_interval = 10\n",
-    "epoch_recon_loss_list = []\n",
-    "epoch_quant_loss_list = []\n",
-    "val_recon_epoch_loss_list = []\n",
-    "intermediary_images = []\n",
-    "n_example_images = 4\n",
-    "\n",
-    "total_start = time.time()\n",
-    "for epoch in range(n_epochs):\n",
-    "    model.train()\n",
-    "    epoch_loss = 0\n",
-    "    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",
-    "        images = batch[\"image\"].to(device)\n",
-    "        optimizer.zero_grad(set_to_none=True)\n",
-    "\n",
-    "        # model outputs reconstruction and the quantization error\n",
-    "        reconstruction, quantization_loss = model(images=images)\n",
-    "\n",
-    "        recons_loss = l1_loss(reconstruction.float(), images.float())\n",
-    "\n",
-    "        loss = recons_loss + quantization_loss\n",
-    "\n",
-    "        loss.backward()\n",
-    "        optimizer.step()\n",
-    "\n",
-    "        epoch_loss += recons_loss.item()\n",
-    "\n",
-    "        progress_bar.set_postfix(\n",
-    "            {\"recons_loss\": epoch_loss / (step + 1), \"quantization_loss\": quantization_loss.item() / (step + 1)}\n",
-    "        )\n",
-    "    epoch_recon_loss_list.append(epoch_loss / (step + 1))\n",
-    "    epoch_quant_loss_list.append(quantization_loss.item() / (step + 1))\n",
-    "\n",
-    "    if (epoch + 1) % val_interval == 0:\n",
-    "        model.eval()\n",
-    "        val_loss = 0\n",
-    "        with torch.no_grad():\n",
-    "            for val_step, batch in enumerate(val_loader, start=1):\n",
-    "                images = batch[\"image\"].to(device)\n",
-    "\n",
-    "                reconstruction, quantization_loss = model(images=images)\n",
-    "\n",
-    "                # get the first sample from the first validation batch for\n",
-    "                # visualizing how the training evolves\n",
-    "                if val_step == 1:\n",
-    "                    intermediary_images.append(reconstruction[:n_example_images, 0])\n",
-    "\n",
-    "                recons_loss = l1_loss(reconstruction.float(), images.float())\n",
-    "\n",
-    "                val_loss += recons_loss.item()\n",
-    "\n",
-    "        val_loss /= val_step\n",
-    "        val_recon_epoch_loss_list.append(val_loss)\n",
-    "\n",
-    "total_time = time.time() - total_start\n",
-    "print(f\"train completed, total time: {total_time}.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5fc01098",
-   "metadata": {},
-   "source": [
-    "### Learning curves"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "ac5f7c56",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.style.use(\"ggplot\")\n",
-    "plt.title(\"Learning Curves\", fontsize=20)\n",
-    "plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_recon_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n",
-    "plt.plot(\n",
-    "    np.linspace(val_interval, n_epochs, int(n_epochs / val_interval)),\n",
-    "    val_recon_epoch_loss_list,\n",
-    "    color=\"C1\",\n",
-    "    linewidth=2.0,\n",
-    "    label=\"Validation\",\n",
-    ")\n",
-    "plt.yticks(fontsize=12)\n",
-    "plt.xticks(fontsize=12)\n",
-    "plt.xlabel(\"Epochs\", fontsize=16)\n",
-    "plt.ylabel(\"Loss\", fontsize=16)\n",
-    "plt.legend(prop={\"size\": 14})\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6df78259",
-   "metadata": {},
-   "source": [
-    "###  Plotting  evolution of reconstructed images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "040b52ba",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 1850x3050 with 10 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot every evaluation as a new line and example as columns\n",
-    "val_samples = np.linspace(val_interval, n_epochs, int(n_epochs / val_interval))\n",
-    "fig, ax = plt.subplots(nrows=len(val_samples), ncols=1, sharey=True)\n",
-    "fig.set_size_inches(18.5, 30.5)\n",
-    "for image_n in range(len(val_samples)):\n",
-    "    reconstructions = intermediary_images[image_n]\n",
-    "    reconstructions = np.concatenate(\n",
-    "        [\n",
-    "            reconstructions[0, :, :, 15],\n",
-    "            np.flipud(reconstructions[0, :, 24, :].T),\n",
-    "            np.flipud(reconstructions[0, 15, :, :].T),\n",
-    "        ],\n",
-    "        axis=1,\n",
-    "    )\n",
-    "\n",
-    "    ax[image_n].imshow(reconstructions, cmap=\"gray\")\n",
-    "    ax[image_n].set_xticks([])\n",
-    "    ax[image_n].set_yticks([])\n",
-    "    ax[image_n].set_ylabel(f\"Epoch {val_samples[image_n]:.0f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c3b5cff",
-   "metadata": {},
-   "source": [
-    "### Plotting the reconstructions from final trained model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "709f9c57",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(nrows=1, ncols=2)\n",
-    "plt.style.use(\"default\")\n",
-    "plotting_image_0 = np.concatenate([images[0, 0, :, :, 15].cpu(), np.flipud(images[0, 0, :, 24, :].cpu().T)], axis=1)\n",
-    "plotting_image_1 = np.concatenate([np.flipud(images[0, 0, 15, :, :].cpu().T), np.zeros((32, 32))], axis=1)\n",
-    "image = np.concatenate([plotting_image_0, plotting_image_1], axis=0)\n",
-    "\n",
-    "ax[0].imshow(image, vmin=0, vmax=1, cmap=\"gray\")\n",
-    "ax[0].axis(\"off\")\n",
-    "ax[0].title.set_text(\"Inputted Image\")\n",
-    "\n",
-    "plotting_image_2 = np.concatenate(\n",
-    "    [reconstruction[0, 0, :, :, 15].cpu(), np.flipud(reconstruction[0, 0, :, 24, :].cpu().T)], axis=1\n",
-    ")\n",
-    "plotting_image_3 = np.concatenate([np.flipud(reconstruction[0, 0, 15, :, :].cpu().T), np.zeros((32, 32))], axis=1)\n",
-    "reconstruction_3d = np.concatenate([plotting_image_2, plotting_image_3], axis=0)\n",
-    "ax[1].imshow(reconstruction_3d, vmin=0, vmax=1, cmap=\"gray\")\n",
-    "ax[1].axis(\"off\")\n",
-    "ax[1].title.set_text(\"Reconstruction\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "54ef9b14",
-   "metadata": {},
-   "source": [
-    "### Cleanup data directory\n",
-    "\n",
-    "Remove directory if a temporary was used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d53b24f4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if directory is None:\n",
-    "    shutil.rmtree(root_dir)"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "formats": "auto:percent,ipynb",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+    "nbformat": 4,
+    "nbformat_minor": 5
 }
diff --git a/tutorials/generative/3d_vqvae/3d_vqvae_tutorial.py b/tutorials/generative/3d_vqvae/3d_vqvae_tutorial.py
index d5a59d7d..d3adcde2 100644
--- a/tutorials/generative/3d_vqvae/3d_vqvae_tutorial.py
+++ b/tutorials/generative/3d_vqvae/3d_vqvae_tutorial.py
@@ -76,7 +76,7 @@
     [
         transforms.LoadImaged(keys=["image"]),
         transforms.Lambdad(keys="image", func=lambda x: x[:, :, :, 1]),
-        transforms.AddChanneld(keys=["image"]),
+        transforms.EnsureChannelFirstd(keys=["image"], channel_dim="no_channel"),
         transforms.ScaleIntensityd(keys=["image"]),
         transforms.CenterSpatialCropd(keys=["image"], roi_size=[176, 224, 155]),
         transforms.Resized(keys=["image"], spatial_size=(32, 48, 32)),
@@ -87,7 +87,7 @@
     [
         transforms.LoadImaged(keys=["image"]),
         transforms.Lambdad(keys="image", func=lambda x: x[:, :, :, 1]),
-        transforms.AddChanneld(keys=["image"]),
+        transforms.EnsureChannelFirstd(keys=["image"], channel_dim="no_channel"),
         transforms.ScaleIntensityd(keys=["image"]),
         transforms.CenterSpatialCropd(keys=["image"], roi_size=[176, 224, 155]),
         transforms.Resized(keys=["image"], spatial_size=(32, 48, 32)),
diff --git a/tutorials/generative/anomaly_detection/anomalydetection_tutorial_classifier_guidance.ipynb b/tutorials/generative/anomaly_detection/anomalydetection_tutorial_classifier_guidance.ipynb
index 71e58a54..de47717b 100644
--- a/tutorials/generative/anomaly_detection/anomalydetection_tutorial_classifier_guidance.ipynb
+++ b/tutorials/generative/anomaly_detection/anomalydetection_tutorial_classifier_guidance.ipynb
@@ -1,1458 +1,1465 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2470cf02",
-   "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": "63d95da6",
-   "metadata": {},
-   "source": [
-    "# Diffusion Models for Medical Anomaly Detection with Classifier Guidance\n",
-    "\n",
-    "This tutorial illustrates how to use MONAI for training a 2D gradient-guided anomaly detection using DDIMs [1].\n",
-    "\n",
-    "We train a diffusion model on 2D slices of brain MR images. A classification model is trained to predict whether the given slice shows a tumor or not.\\\n",
-    "We then translate an input slice to its healthy reconstruction using DDIMs.\\\n",
-    "Anomaly detection is performed by taking the difference between input and output, as proposed in [1].\n",
-    "\n",
-    "[1] - Wolleb et al. \"Diffusion Models for Medical Anomaly Detection\" https://arxiv.org/abs/2203.04306\n",
-    "\n",
-    "## Setup environment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "id": "75f2d5f3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "running install\n",
-      "/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\n",
-      "  warnings.warn(\n",
-      "/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/setuptools/command/easy_install.py:144: EasyInstallDeprecationWarning: easy_install command is deprecated. Use build and pip and other standards-based tools.\n",
-      "  warnings.warn(\n",
-      "running bdist_egg\n",
-      "running egg_info\n",
-      "writing generative.egg-info/PKG-INFO\n",
-      "writing dependency_links to generative.egg-info/dependency_links.txt\n",
-      "writing requirements to generative.egg-info/requires.txt\n",
-      "writing top-level names to generative.egg-info/top_level.txt\n",
-      "reading manifest file 'generative.egg-info/SOURCES.txt'\n",
-      "writing manifest file 'generative.egg-info/SOURCES.txt'\n",
-      "installing library code to build/bdist.linux-x86_64/egg\n",
-      "running install_lib\n",
-      "warning: install_lib: 'build/lib' does not exist -- no Python modules to install\n",
-      "\n",
-      "creating build/bdist.linux-x86_64/egg\n",
-      "creating build/bdist.linux-x86_64/egg/EGG-INFO\n",
-      "copying generative.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
-      "copying generative.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
-      "copying generative.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
-      "copying generative.egg-info/requires.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
-      "copying generative.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
-      "zip_safe flag not set; analyzing archive contents...\n",
-      "creating 'dist/generative-0.1.0-py3.10.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n",
-      "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n",
-      "Processing generative-0.1.0-py3.10.egg\n",
-      "Removing /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/generative-0.1.0-py3.10.egg\n",
-      "Copying generative-0.1.0-py3.10.egg to /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages\n",
-      "generative 0.1.0 is already the active version in easy-install.pth\n",
-      "\n",
-      "Installed /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/generative-0.1.0-py3.10.egg\n",
-      "Processing dependencies for generative==0.1.0\n",
-      "Searching for monai-weekly==1.2.dev2304\n",
-      "Best match: monai-weekly 1.2.dev2304\n",
-      "Adding monai-weekly 1.2.dev2304 to easy-install.pth file\n",
-      "\n",
-      "Using /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages\n",
-      "Searching for numpy==1.23.2\n",
-      "Best match: numpy 1.23.2\n",
-      "Adding numpy 1.23.2 to easy-install.pth file\n",
-      "Installing f2py script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
-      "Installing f2py3 script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
-      "Installing f2py3.10 script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
-      "\n",
-      "Using /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages\n",
-      "Searching for torch==1.12.1\n",
-      "Best match: torch 1.12.1\n",
-      "Adding torch 1.12.1 to easy-install.pth file\n",
-      "Installing convert-caffe2-to-onnx script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
-      "Installing convert-onnx-to-caffe2 script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
-      "Installing torchrun script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
-      "\n",
-      "Using /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages\n",
-      "Searching for typing-extensions==4.3.0\n",
-      "Best match: typing-extensions 4.3.0\n",
-      "Adding typing-extensions 4.3.0 to easy-install.pth file\n",
-      "\n",
-      "Using /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages\n",
-      "Finished processing dependencies for generative==0.1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "!python -c \"import monai\" || pip install -q \"monai-weekly[pillow, tqdm]\"\n",
-    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
-    "!python -c \"import seaborn\" || pip install -q seaborn"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6b766027",
-   "metadata": {},
-   "source": [
-    "## Setup imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "id": "972ed3f3",
-   "metadata": {
-    "jupyter": {
-     "outputs_hidden": false
+    "cells": [
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "2470cf02",
+            "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": "63d95da6",
+            "metadata": {},
+            "source": [
+                "# Diffusion Models for Medical Anomaly Detection with Classifier Guidance\n",
+                "\n",
+                "This tutorial illustrates how to use MONAI for training a 2D gradient-guided anomaly detection using DDIMs [1].\n",
+                "\n",
+                "We train a diffusion model on 2D slices of brain MR images. A classification model is trained to predict whether the given slice shows a tumor or not.\\\n",
+                "We then translate an input slice to its healthy reconstruction using DDIMs.\\\n",
+                "Anomaly detection is performed by taking the difference between input and output, as proposed in [1].\n",
+                "\n",
+                "[1] - Wolleb et al. \"Diffusion Models for Medical Anomaly Detection\" https://arxiv.org/abs/2203.04306\n",
+                "\n",
+                "## Setup environment"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 90,
+            "id": "75f2d5f3",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "running install\n",
+                        "/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\n",
+                        "  warnings.warn(\n",
+                        "/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/setuptools/command/easy_install.py:144: EasyInstallDeprecationWarning: easy_install command is deprecated. Use build and pip and other standards-based tools.\n",
+                        "  warnings.warn(\n",
+                        "running bdist_egg\n",
+                        "running egg_info\n",
+                        "writing generative.egg-info/PKG-INFO\n",
+                        "writing dependency_links to generative.egg-info/dependency_links.txt\n",
+                        "writing requirements to generative.egg-info/requires.txt\n",
+                        "writing top-level names to generative.egg-info/top_level.txt\n",
+                        "reading manifest file 'generative.egg-info/SOURCES.txt'\n",
+                        "writing manifest file 'generative.egg-info/SOURCES.txt'\n",
+                        "installing library code to build/bdist.linux-x86_64/egg\n",
+                        "running install_lib\n",
+                        "warning: install_lib: 'build/lib' does not exist -- no Python modules to install\n",
+                        "\n",
+                        "creating build/bdist.linux-x86_64/egg\n",
+                        "creating build/bdist.linux-x86_64/egg/EGG-INFO\n",
+                        "copying generative.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
+                        "copying generative.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
+                        "copying generative.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
+                        "copying generative.egg-info/requires.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
+                        "copying generative.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
+                        "zip_safe flag not set; analyzing archive contents...\n",
+                        "creating 'dist/generative-0.1.0-py3.10.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n",
+                        "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n",
+                        "Processing generative-0.1.0-py3.10.egg\n",
+                        "Removing /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/generative-0.1.0-py3.10.egg\n",
+                        "Copying generative-0.1.0-py3.10.egg to /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages\n",
+                        "generative 0.1.0 is already the active version in easy-install.pth\n",
+                        "\n",
+                        "Installed /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/generative-0.1.0-py3.10.egg\n",
+                        "Processing dependencies for generative==0.1.0\n",
+                        "Searching for monai-weekly==1.2.dev2304\n",
+                        "Best match: monai-weekly 1.2.dev2304\n",
+                        "Adding monai-weekly 1.2.dev2304 to easy-install.pth file\n",
+                        "\n",
+                        "Using /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages\n",
+                        "Searching for numpy==1.23.2\n",
+                        "Best match: numpy 1.23.2\n",
+                        "Adding numpy 1.23.2 to easy-install.pth file\n",
+                        "Installing f2py script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
+                        "Installing f2py3 script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
+                        "Installing f2py3.10 script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
+                        "\n",
+                        "Using /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages\n",
+                        "Searching for torch==1.12.1\n",
+                        "Best match: torch 1.12.1\n",
+                        "Adding torch 1.12.1 to easy-install.pth file\n",
+                        "Installing convert-caffe2-to-onnx script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
+                        "Installing convert-onnx-to-caffe2 script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
+                        "Installing torchrun script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
+                        "\n",
+                        "Using /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages\n",
+                        "Searching for typing-extensions==4.3.0\n",
+                        "Best match: typing-extensions 4.3.0\n",
+                        "Adding typing-extensions 4.3.0 to easy-install.pth file\n",
+                        "\n",
+                        "Using /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages\n",
+                        "Finished processing dependencies for generative==0.1.0\n"
+                    ]
+                }
+            ],
+            "source": [
+                "!python -c \"import monai\" || pip install -q \"monai-weekly[pillow, tqdm]\"\n",
+                "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
+                "!python -c \"import seaborn\" || pip install -q seaborn"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "6b766027",
+            "metadata": {},
+            "source": [
+                "## Setup imports"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 91,
+            "id": "972ed3f3",
+            "metadata": {
+                "collapsed": false,
+                "jupyter": {
+                    "outputs_hidden": false
+                },
+                "lines_to_next_cell": 2
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "path ['/home/juliawolleb/PycharmProjects/MONAI/GenerativeModels/tutorials/generative/anomaly_detection/classifier_guidance_anomalydetection', '/home/juliawolleb/anaconda3/envs/experiment/lib/python310.zip', '/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10', '/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/lib-dynload', '', '/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages', '/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/PyYAML-6.0-py3.10-linux-x86_64.egg', '/home/juliawolleb/PycharmProjects/Python_Tutorials/Calgary_Infants/calgary/HD-BET', '/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/lpips-0.1.4-py3.10.egg', '/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/tqdm-4.64.1-py3.10.egg', '/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/generative-0.1.0-py3.10.egg', '/home/juliawolleb/PycharmProjects/MONAI/GenerativeModels/', '/home/juliawolleb/PycharmProjects/MONAI/GenerativeModels/', '/home/juliawolleb/PycharmProjects/MONAI/GenerativeModels/']\n",
+                        "MONAI version: 1.2.dev2304\n",
+                        "Numpy version: 1.23.2\n",
+                        "Pytorch version: 1.12.1\n",
+                        "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
+                        "MONAI rev id: 9a57be5aab9f2c2a134768c0c146399150e247a0\n",
+                        "MONAI __file__: /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/monai/__init__.py\n",
+                        "\n",
+                        "Optional dependencies:\n",
+                        "Pytorch Ignite version: 0.4.10\n",
+                        "ITK version: 5.3.0\n",
+                        "Nibabel version: 4.0.1\n",
+                        "scikit-image version: 0.19.3\n",
+                        "Pillow version: 9.2.0\n",
+                        "Tensorboard version: 2.12.0\n",
+                        "gdown version: 4.6.4\n",
+                        "TorchVision version: 0.13.1\n",
+                        "tqdm version: 4.64.1\n",
+                        "lmdb version: 1.4.0\n",
+                        "psutil version: 5.9.4\n",
+                        "pandas version: 1.5.3\n",
+                        "einops version: 0.6.0\n",
+                        "transformers version: 4.21.3\n",
+                        "mlflow version: 2.1.1\n",
+                        "pynrrd version: 1.0.0\n",
+                        "\n",
+                        "For details about installing the optional dependencies, please visit:\n",
+                        "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
+                        "\n"
+                    ]
+                }
+            ],
+            "source": [
+                "import os\n",
+                "import time\n",
+                "import tempfile\n",
+                "import matplotlib.pyplot as plt\n",
+                "import numpy as np\n",
+                "import torch\n",
+                "import torch.nn.functional as F\n",
+                "from monai import transforms\n",
+                "from monai.apps import DecathlonDataset\n",
+                "from monai.config import print_config\n",
+                "from monai.data import DataLoader\n",
+                "from monai.utils import set_determinism\n",
+                "from torch.cuda.amp import GradScaler, autocast\n",
+                "from tqdm import tqdm\n",
+                "\n",
+                "from generative.inferers import DiffusionInferer\n",
+                "from generative.networks.nets.diffusion_model_unet import DiffusionModelEncoder, DiffusionModelUNet\n",
+                "from generative.networks.schedulers.ddim import DDIMScheduler\n",
+                "\n",
+                "torch.multiprocessing.set_sharing_strategy(\"file_system\")\n",
+                "\n",
+                "print_config()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "7d4ff515",
+            "metadata": {},
+            "source": [
+                "## Setup data directory"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 92,
+            "id": "8b4323e7",
+            "metadata": {
+                "collapsed": false,
+                "jupyter": {
+                    "outputs_hidden": false
+                }
+            },
+            "outputs": [],
+            "source": [
+                "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
+                "root_dir = tempfile.mkdtemp() if directory is None else directory"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "99175d50",
+            "metadata": {},
+            "source": [
+                "## Set deterministic training for reproducibility"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 93,
+            "id": "34ea510f",
+            "metadata": {
+                "collapsed": false,
+                "jupyter": {
+                    "outputs_hidden": false
+                }
+            },
+            "outputs": [],
+            "source": [
+                "set_determinism(42)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "c3f70dd1-236a-47ff-a244-575729ad92ba",
+            "metadata": {
+                "tags": []
+            },
+            "source": [
+                "## Preprocessing of the BRATS Dataset in 2D slices for training\n",
+                "We download the BRATS training dataset from the Decathlon dataset. \\\n",
+                "We slice the volumes in axial 2D slices, and assign slice-wise labels (0 for healthy, 1 for diseased) to all slices.\n",
+                "Here we use transforms to augment the training dataset:\n",
+                "\n",
+                "1. `LoadImaged` loads the brain MR images from files.\n",
+                "1. `EnsureChannelFirstd` ensures the original data to construct \"channel first\" shape.\n",
+                "1. `ScaleIntensityRangePercentilesd` takes the lower and upper intensity percentiles and scales them to [0, 1].\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 94,
+            "id": "c68d2d91-9a0b-4ac1-ae49-f4a64edbd82a",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "<class 'monai.transforms.utility.array.AddChannel'>: Class `AddChannel` has been deprecated since version 0.8. please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead.\n"
+                    ]
+                }
+            ],
+            "source": [
+                "channel = 0  # 0 = Flair\n",
+                "assert channel in [0, 1, 2, 3], \"Choose a valid channel\"\n",
+                "\n",
+                "train_transforms = transforms.Compose(\n",
+                "    [\n",
+                "        transforms.LoadImaged(keys=[\"image\", \"label\"]),\n",
+                "        transforms.EnsureChannelFirstd(keys=[\"image\", \"label\"]),\n",
+                "        transforms.Lambdad(keys=[\"image\"], func=lambda x: x[channel, :, :, :]),\n",
+                "        transforms.EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n",
+                "        transforms.EnsureTyped(keys=[\"image\", \"label\"]),\n",
+                "        transforms.Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
+                "        transforms.Spacingd(keys=[\"image\", \"label\"], pixdim=(3.0, 3.0, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
+                "        transforms.CenterSpatialCropd(keys=[\"image\", \"label\"], roi_size=(64, 64, 44)),\n",
+                "        transforms.ScaleIntensityRangePercentilesd(keys=\"image\", lower=0, upper=99.5, b_min=0, b_max=1),\n",
+                "        transforms.RandSpatialCropd(keys=[\"image\", \"label\"], roi_size=(64, 64, 1), random_size=False),\n",
+                "        transforms.Lambdad(keys=[\"image\", \"label\"], func=lambda x: x.squeeze(-1)),\n",
+                "        transforms.CopyItemsd(keys=[\"label\"], times=1, names=[\"slice_label\"]),\n",
+                "        transforms.Lambdad(keys=[\"slice_label\"], func=lambda x: 0.0 if x.sum() > 0 else 1.0),\n",
+                "    ]\n",
+                ")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 107,
+            "id": "da1927b0",
+            "metadata": {
+                "collapsed": false,
+                "jupyter": {
+                    "outputs_hidden": false
+                }
+            },
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Loading dataset: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 388/388 [03:02<00:00,  2.13it/s]"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Length of training data: 388\n",
+                        "Train image shape torch.Size([1, 64, 64])\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "\n"
+                    ]
+                }
+            ],
+            "source": [
+                "batch_size = 64\n",
+                "\n",
+                "train_ds = DecathlonDataset(\n",
+                "    root_dir=root_dir,\n",
+                "    task=\"Task01_BrainTumour\",\n",
+                "    section=\"training\",  # validation\n",
+                "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
+                "    num_workers=4,\n",
+                "    download=False,  # Set download to True if the dataset hasnt been downloaded yet\n",
+                "    seed=0,\n",
+                "    transform=train_transforms,\n",
+                ")\n",
+                "\n",
+                "print(f\"Length of training data: {len(train_ds)}\")  # this gives the number of patients in the training set\n",
+                "print(f'Train image shape {train_ds[0][\"image\"].shape}')\n",
+                "\n",
+                "train_loader = DataLoader(\n",
+                "    train_ds, batch_size=batch_size, shuffle=True, num_workers=4, drop_last=True, persistent_workers=True\n",
+                ")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "fac55e9d",
+            "metadata": {
+                "tags": []
+            },
+            "source": [
+                "## Preprocessing of the BRATS Dataset in 2D slices for validation\n",
+                "We download the BRATS validation dataset from the Decathlon dataset, and define the dataloader to load 2D slices for validation.\n",
+                "\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 77,
+            "id": "73d72110-a8b3-4e03-91cc-1dab4d5a7b87",
+            "metadata": {
+                "lines_to_next_cell": 2
+            },
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Loading dataset: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 96/96 [00:48<00:00,  2.00it/s]"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Length of training data: 96\n",
+                        "Validation Image shape torch.Size([1, 64, 64])\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "\n"
+                    ]
+                }
+            ],
+            "source": [
+                "val_ds = DecathlonDataset(\n",
+                "    root_dir=root_dir,\n",
+                "    task=\"Task01_BrainTumour\",\n",
+                "    section=\"validation\",\n",
+                "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
+                "    num_workers=4,\n",
+                "    download=False,  # Set download to True if the dataset hasnt been downloaded yet\n",
+                "    seed=0,\n",
+                "    transform=train_transforms,\n",
+                ")\n",
+                "print(f\"Length of training data: {len(val_ds)}\")\n",
+                "print(f'Validation Image shape {val_ds[0][\"image\"].shape}')\n",
+                "\n",
+                "val_loader = DataLoader(\n",
+                "    val_ds, batch_size=batch_size, shuffle=False, num_workers=4, drop_last=True, persistent_workers=True\n",
+                ")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "08428bc6",
+            "metadata": {},
+            "source": [
+                "## Define network, scheduler, optimizer, and inferer\n",
+                "At this step, we instantiate the MONAI components to create a DDIM, the UNET, the noise scheduler, and the inferer used for training and sampling. We are using\n",
+                "the deterministic DDIM scheduler containing 1000 timesteps, and a 2D UNET with attention mechanisms\n",
+                "in the 3rd level (`num_head_channels=64`).\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 108,
+            "id": "bee5913e",
+            "metadata": {
+                "collapsed": false,
+                "jupyter": {
+                    "outputs_hidden": false
+                },
+                "lines_to_next_cell": 2
+            },
+            "outputs": [],
+            "source": [
+                "device = torch.device(\"cuda\")\n",
+                "\n",
+                "model = DiffusionModelUNet(\n",
+                "    spatial_dims=2,\n",
+                "    in_channels=1,\n",
+                "    out_channels=1,\n",
+                "    num_channels=(64, 64, 64),\n",
+                "    attention_levels=(False, False, True),\n",
+                "    num_res_blocks=1,\n",
+                "    num_head_channels=64,\n",
+                "    with_conditioning=False,\n",
+                ")\n",
+                "model.to(device)\n",
+                "\n",
+                "scheduler = DDIMScheduler(num_train_timesteps=1000)\n",
+                "\n",
+                "optimizer = torch.optim.Adam(params=model.parameters(), lr=2.5e-5)\n",
+                "\n",
+                "inferer = DiffusionInferer(scheduler)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "2a4d3ab2",
+            "metadata": {
+                "tags": []
+            },
+            "source": [
+                "## Model training of the diffusion model\n",
+                "We train our diffusion model for 2000 epochs."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 109,
+            "id": "6c0ed909",
+            "metadata": {
+                "collapsed": false,
+                "jupyter": {
+                    "outputs_hidden": false
+                },
+                "lines_to_next_cell": 2
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 0 Validation loss 0.9828271865844727\n",
+                        "Epoch 20 Validation loss 0.45277565717697144\n",
+                        "Epoch 40 Validation loss 0.16044068336486816\n",
+                        "Epoch 60 Validation loss 0.06908729672431946\n",
+                        "Epoch 80 Validation loss 0.037922561168670654\n",
+                        "Epoch 100 Validation loss 0.024700244888663292\n",
+                        "Epoch 120 Validation loss 0.02825773134827614\n",
+                        "Epoch 140 Validation loss 0.01575350947678089\n",
+                        "Epoch 160 Validation loss 0.02807718887925148\n",
+                        "Epoch 180 Validation loss 0.03635002672672272\n",
+                        "Epoch 200 Validation loss 0.018522320315241814\n",
+                        "Epoch 220 Validation loss 0.020984284579753876\n",
+                        "Epoch 240 Validation loss 0.02985953912138939\n",
+                        "Epoch 260 Validation loss 0.018604595214128494\n",
+                        "Epoch 280 Validation loss 0.02505004033446312\n",
+                        "Epoch 300 Validation loss 0.018166495487093925\n",
+                        "Epoch 320 Validation loss 0.012706207111477852\n",
+                        "Epoch 340 Validation loss 0.03222103416919708\n",
+                        "Epoch 360 Validation loss 0.010545151308178902\n",
+                        "Epoch 380 Validation loss 0.017768580466508865\n",
+                        "Epoch 400 Validation loss 0.023036960512399673\n",
+                        "Epoch 420 Validation loss 0.023991823196411133\n",
+                        "Epoch 440 Validation loss 0.014143284410238266\n",
+                        "Epoch 460 Validation loss 0.010133783333003521\n",
+                        "Epoch 480 Validation loss 0.019768211990594864\n",
+                        "Epoch 500 Validation loss 0.016018100082874298\n",
+                        "Epoch 520 Validation loss 0.016411196440458298\n",
+                        "Epoch 540 Validation loss 0.012067019008100033\n",
+                        "Epoch 560 Validation loss 0.017793692648410797\n",
+                        "Epoch 580 Validation loss 0.015390219166874886\n",
+                        "Epoch 600 Validation loss 0.015438873320817947\n",
+                        "Epoch 620 Validation loss 0.019228052347898483\n",
+                        "Epoch 640 Validation loss 0.022589124739170074\n",
+                        "Epoch 660 Validation loss 0.022526469081640244\n",
+                        "Epoch 680 Validation loss 0.0310574471950531\n",
+                        "Epoch 700 Validation loss 0.016018839552998543\n",
+                        "Epoch 720 Validation loss 0.018153013661503792\n",
+                        "Epoch 740 Validation loss 0.01506253331899643\n",
+                        "Epoch 760 Validation loss 0.00914084818214178\n",
+                        "Epoch 780 Validation loss 0.017407484352588654\n",
+                        "Epoch 800 Validation loss 0.013946758583188057\n",
+                        "Epoch 820 Validation loss 0.013289306312799454\n",
+                        "Epoch 840 Validation loss 0.007855996489524841\n",
+                        "Epoch 860 Validation loss 0.01187637448310852\n",
+                        "Epoch 880 Validation loss 0.018494905903935432\n",
+                        "Epoch 900 Validation loss 0.009516816586256027\n",
+                        "Epoch 920 Validation loss 0.030950400978326797\n",
+                        "Epoch 940 Validation loss 0.017931077629327774\n",
+                        "Epoch 960 Validation loss 0.017525378614664078\n",
+                        "Epoch 980 Validation loss 0.016576599329710007\n",
+                        "Epoch 1000 Validation loss 0.007525463588535786\n",
+                        "Epoch 1020 Validation loss 0.008745957165956497\n",
+                        "Epoch 1040 Validation loss 0.023068588227033615\n",
+                        "Epoch 1060 Validation loss 0.023049402981996536\n",
+                        "Epoch 1080 Validation loss 0.020367465913295746\n",
+                        "Epoch 1100 Validation loss 0.026941468939185143\n",
+                        "Epoch 1120 Validation loss 0.019598377868533134\n",
+                        "Epoch 1140 Validation loss 0.023052945733070374\n",
+                        "Epoch 1160 Validation loss 0.020239276811480522\n",
+                        "Epoch 1180 Validation loss 0.009076420217752457\n",
+                        "Epoch 1200 Validation loss 0.011559909209609032\n",
+                        "Epoch 1220 Validation loss 0.023455770686268806\n",
+                        "Epoch 1240 Validation loss 0.015224231407046318\n",
+                        "Epoch 1260 Validation loss 0.020417172461748123\n",
+                        "Epoch 1280 Validation loss 0.025817634537816048\n",
+                        "Epoch 1300 Validation loss 0.012675277888774872\n",
+                        "Epoch 1320 Validation loss 0.014165625907480717\n",
+                        "Epoch 1340 Validation loss 0.021743204444646835\n",
+                        "Epoch 1360 Validation loss 0.00959782674908638\n",
+                        "Epoch 1380 Validation loss 0.014942880719900131\n",
+                        "Epoch 1400 Validation loss 0.033313099294900894\n",
+                        "Epoch 1420 Validation loss 0.025836177170276642\n",
+                        "Epoch 1440 Validation loss 0.015067282132804394\n",
+                        "Epoch 1460 Validation loss 0.01235564611852169\n",
+                        "Epoch 1480 Validation loss 0.012111244723200798\n",
+                        "Epoch 1500 Validation loss 0.00833088904619217\n",
+                        "Epoch 1520 Validation loss 0.01528056338429451\n",
+                        "Epoch 1540 Validation loss 0.017444560304284096\n",
+                        "Epoch 1560 Validation loss 0.014621825888752937\n",
+                        "Epoch 1580 Validation loss 0.019431518390774727\n",
+                        "Epoch 1600 Validation loss 0.016186822205781937\n",
+                        "Epoch 1620 Validation loss 0.02027059532701969\n",
+                        "Epoch 1640 Validation loss 0.01720491796731949\n",
+                        "Epoch 1660 Validation loss 0.011756360530853271\n",
+                        "Epoch 1680 Validation loss 0.02627478912472725\n",
+                        "Epoch 1700 Validation loss 0.023451916873455048\n",
+                        "Epoch 1720 Validation loss 0.011613328941166401\n",
+                        "Epoch 1740 Validation loss 0.026256393641233444\n",
+                        "Epoch 1760 Validation loss 0.008156227879226208\n",
+                        "Epoch 1780 Validation loss 0.01597723178565502\n",
+                        "Epoch 1800 Validation loss 0.013070507906377316\n",
+                        "Epoch 1820 Validation loss 0.01726200059056282\n",
+                        "Epoch 1840 Validation loss 0.009824991226196289\n",
+                        "Epoch 1860 Validation loss 0.014878236688673496\n",
+                        "Epoch 1880 Validation loss 0.017673484981060028\n",
+                        "Epoch 1900 Validation loss 0.016455603763461113\n",
+                        "Epoch 1920 Validation loss 0.02442217618227005\n",
+                        "Epoch 1940 Validation loss 0.026278261095285416\n",
+                        "Epoch 1960 Validation loss 0.02376818098127842\n",
+                        "Epoch 1980 Validation loss 0.016214493662118912\n",
+                        "train diffusion completed, total time: 6097.77689909935.\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "n_epochs = 2000\n",
+                "val_interval = 20\n",
+                "epoch_loss_list = []\n",
+                "val_epoch_loss_list = []\n",
+                "\n",
+                "scaler = GradScaler()\n",
+                "total_start = time.time()\n",
+                "\n",
+                "for epoch in range(n_epochs):\n",
+                "    model.train()\n",
+                "    epoch_loss = 0\n",
+                "\n",
+                "    for step, data in enumerate(train_loader):\n",
+                "        images = data[\"image\"].to(device)\n",
+                "        classes = data[\"slice_label\"].to(device)\n",
+                "        optimizer.zero_grad(set_to_none=True)\n",
+                "        timesteps = torch.randint(0, 1000, (len(images),)).to(device)  # pick a random time step t\n",
+                "\n",
+                "        with autocast(enabled=True):\n",
+                "            # Generate random noise\n",
+                "            noise = torch.randn_like(images).to(device)\n",
+                "\n",
+                "            # Get model prediction\n",
+                "            noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n",
+                "            loss = F.mse_loss(noise_pred.float(), noise.float())\n",
+                "\n",
+                "        scaler.scale(loss).backward()\n",
+                "        scaler.step(optimizer)\n",
+                "        scaler.update()\n",
+                "        epoch_loss += loss.item()\n",
+                "    epoch_loss_list.append(epoch_loss / (step + 1))\n",
+                "\n",
+                "    if (epoch) % val_interval == 0:\n",
+                "        model.eval()\n",
+                "        val_epoch_loss = 0\n",
+                "\n",
+                "        for step, data in enumerate(val_loader):\n",
+                "            images = data[\"image\"].to(device)\n",
+                "            classes = data[\"slice_label\"].to(device)\n",
+                "            timesteps = torch.randint(0, 1000, (len(images),)).to(device)\n",
+                "            with torch.no_grad():\n",
+                "                with autocast(enabled=True):\n",
+                "                    noise = torch.randn_like(images).to(device)\n",
+                "                    noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n",
+                "                    val_loss = F.mse_loss(noise_pred.float(), noise.float())\n",
+                "\n",
+                "            val_epoch_loss += val_loss.item()\n",
+                "        val_epoch_loss_list.append(val_epoch_loss / (step + 1))\n",
+                "        print(\"Epoch\", epoch, \"Validation loss\", val_epoch_loss / (step + 1))\n",
+                "\n",
+                "total_time = time.time() - total_start\n",
+                "print(f\"train diffusion completed, total time: {total_time}.\")\n",
+                "\n",
+                "plt.style.use(\"seaborn-bright\")\n",
+                "plt.title(\"Learning Curves Diffusion Model\", fontsize=20)\n",
+                "plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n",
+                "plt.plot(\n",
+                "    np.linspace(val_interval, n_epochs, int(n_epochs / val_interval)),\n",
+                "    val_epoch_loss_list,\n",
+                "    color=\"C1\",\n",
+                "    linewidth=2.0,\n",
+                "    label=\"Validation\",\n",
+                ")\n",
+                "plt.yticks(fontsize=12)\n",
+                "plt.xticks(fontsize=12)\n",
+                "plt.xlabel(\"Epochs\", fontsize=16)\n",
+                "plt.ylabel(\"Loss\", fontsize=16)\n",
+                "plt.legend(prop={\"size\": 14})\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "326101ed-333b-44a9-933f-55760b5d93a4",
+            "metadata": {},
+            "source": [
+                "## Check the performance of the diffusion model\n",
+                "\n",
+                "We generate a random image from noise to check whether our diffusion model works properly for an image generation task.\n",
+                "\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 161,
+            "id": "8f7a9e99-a8a4-4c8f-a42f-17ef91b18585",
+            "metadata": {
+                "lines_to_next_cell": 2
+            },
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:23<00:00, 42.86it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "model.eval()\n",
+                "noise = torch.randn((1, 1, 64, 64))\n",
+                "noise = noise.to(device)\n",
+                "scheduler.set_timesteps(num_inference_steps=1000)\n",
+                "with autocast(enabled=True):\n",
+                "    image, intermediates = inferer.sample(\n",
+                "        input_noise=noise, diffusion_model=model, scheduler=scheduler, save_intermediates=True, intermediate_steps=100\n",
+                "    )\n",
+                "\n",
+                "chain = torch.cat(intermediates, dim=-1)\n",
+                "\n",
+                "plt.style.use(\"default\")\n",
+                "plt.imshow(chain[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+                "plt.tight_layout()\n",
+                "plt.axis(\"off\")\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "546f9983-c2e2-4c24-b03a-ebe34627638a",
+            "metadata": {},
+            "source": [
+                "## Define the classification model\n",
+                "First, we define the classification model. It follows the encoder architecture of the diffusion model, combined with linear layers for binary classification between healthy and diseased slices.\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 174,
+            "id": "44cc6928-2525-4e61-8805-15b409097bbb",
+            "metadata": {
+                "lines_to_next_cell": 2
+            },
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "DiffusionModelEncoder(\n",
+                            "  (conv_in): Convolution(\n",
+                            "    (conv): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "  )\n",
+                            "  (time_embed): Sequential(\n",
+                            "    (0): Linear(in_features=32, out_features=128, bias=True)\n",
+                            "    (1): SiLU()\n",
+                            "    (2): Linear(in_features=128, out_features=128, bias=True)\n",
+                            "  )\n",
+                            "  (down_blocks): ModuleList(\n",
+                            "    (0): DownBlock(\n",
+                            "      (resnets): ModuleList(\n",
+                            "        (0): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 32, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=128, out_features=32, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 32, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Identity()\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (downsampler): Downsample(\n",
+                            "        (op): Convolution(\n",
+                            "          (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+                            "        )\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (1): AttnDownBlock(\n",
+                            "      (attentions): ModuleList(\n",
+                            "        (0): AttentionBlock(\n",
+                            "          (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (resnets): ModuleList(\n",
+                            "        (0): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 32, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=128, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Convolution(\n",
+                            "            (conv): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1))\n",
+                            "          )\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (downsampler): Downsample(\n",
+                            "        (op): Convolution(\n",
+                            "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+                            "        )\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (2): AttnDownBlock(\n",
+                            "      (attentions): ModuleList(\n",
+                            "        (0): AttentionBlock(\n",
+                            "          (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (resnets): ModuleList(\n",
+                            "        (0): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=128, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Identity()\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (downsampler): Downsample(\n",
+                            "        (op): Convolution(\n",
+                            "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+                            "        )\n",
+                            "      )\n",
+                            "    )\n",
+                            "  )\n",
+                            "  (out): Sequential(\n",
+                            "    (0): Linear(in_features=4096, out_features=512, bias=True)\n",
+                            "    (1): ReLU()\n",
+                            "    (2): Dropout(p=0.1, inplace=False)\n",
+                            "    (3): Linear(in_features=512, out_features=2, bias=True)\n",
+                            "  )\n",
+                            ")"
+                        ]
+                    },
+                    "execution_count": 174,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "device = torch.device(\"cuda\")\n",
+                "classifier = DiffusionModelEncoder(\n",
+                "    spatial_dims=2,\n",
+                "    in_channels=1,\n",
+                "    out_channels=2,\n",
+                "    num_channels=(32, 64, 64),\n",
+                "    attention_levels=(False, True, True),\n",
+                "    num_res_blocks=(1, 1, 1),\n",
+                "    num_head_channels=64,\n",
+                "    with_conditioning=False,\n",
+                ")\n",
+                "\n",
+                "classifier.to(device)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "45fab83a-b4c8-42cb-96c9-4e9f1e191111",
+            "metadata": {},
+            "source": [
+                "## Model training of the classification model\n",
+                "We train our classification model for 1000 epochs.\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 24,
+            "id": "de18d5cb-68e7-407c-afe9-8efd7a5a904a",
+            "metadata": {
+                "lines_to_next_cell": 0
+            },
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 9 Validation loss 0.2536351333061854\n",
+                        "Epoch 19 Validation loss 0.3019549027085304\n",
+                        "Epoch 29 Validation loss 0.34552596261103946\n",
+                        "Epoch 39 Validation loss 0.2783070926864942\n",
+                        "Epoch 49 Validation loss 0.28460513055324554\n",
+                        "Epoch 59 Validation loss 0.25296298414468765\n",
+                        "Epoch 69 Validation loss 0.3343521902958552\n",
+                        "Epoch 79 Validation loss 0.2634535978237788\n",
+                        "Epoch 89 Validation loss 0.2862999041875203\n",
+                        "Epoch 99 Validation loss 0.22700381030639014\n",
+                        "Epoch 109 Validation loss 0.27035540093978244\n",
+                        "Epoch 119 Validation loss 0.2451721504330635\n",
+                        "Epoch 129 Validation loss 0.2890484283367793\n",
+                        "Epoch 139 Validation loss 0.27566688507795334\n",
+                        "Epoch 149 Validation loss 0.28788923223813373\n",
+                        "Epoch 159 Validation loss 0.2524748469392459\n",
+                        "Epoch 169 Validation loss 0.3107323000828425\n",
+                        "Epoch 179 Validation loss 0.21660694728295007\n",
+                        "Epoch 189 Validation loss 0.2702282816171646\n",
+                        "Epoch 199 Validation loss 0.2677164326111476\n",
+                        "Epoch 209 Validation loss 0.33349836121002835\n",
+                        "Epoch 219 Validation loss 0.2969249188899994\n",
+                        "Epoch 229 Validation loss 0.268981905033191\n",
+                        "Epoch 239 Validation loss 0.29199230174223584\n",
+                        "Epoch 249 Validation loss 0.2806356226404508\n",
+                        "Epoch 259 Validation loss 0.301661084095637\n",
+                        "Epoch 269 Validation loss 0.25811708470185596\n",
+                        "Epoch 279 Validation loss 0.2599738910794258\n",
+                        "Epoch 289 Validation loss 0.23392533014218012\n",
+                        "Epoch 299 Validation loss 0.2580989971756935\n",
+                        "Epoch 309 Validation loss 0.22807281464338303\n",
+                        "Epoch 319 Validation loss 0.2510971352458\n",
+                        "Epoch 329 Validation loss 0.25221700221300125\n",
+                        "Epoch 339 Validation loss 0.25722870975732803\n",
+                        "Epoch 349 Validation loss 0.2516109471519788\n",
+                        "Epoch 359 Validation loss 0.22627043972412744\n",
+                        "Epoch 369 Validation loss 0.28725822021563846\n",
+                        "Epoch 379 Validation loss 0.2712069054444631\n",
+                        "Epoch 389 Validation loss 0.29460274676481885\n",
+                        "Epoch 399 Validation loss 0.2599460730950038\n",
+                        "Epoch 409 Validation loss 0.22882529348134995\n",
+                        "Epoch 419 Validation loss 0.24265126883983612\n",
+                        "Epoch 429 Validation loss 0.23436561226844788\n",
+                        "Epoch 439 Validation loss 0.25520699471235275\n",
+                        "Epoch 449 Validation loss 0.22466829667488733\n",
+                        "Epoch 459 Validation loss 0.26379595696926117\n",
+                        "Epoch 469 Validation loss 0.23318989326556525\n",
+                        "Epoch 479 Validation loss 0.264743114511172\n",
+                        "Epoch 489 Validation loss 0.25179669509331387\n",
+                        "Epoch 499 Validation loss 0.20064709583918253\n",
+                        "Epoch 509 Validation loss 0.2527008851369222\n",
+                        "Epoch 519 Validation loss 0.24675505111614862\n",
+                        "Epoch 529 Validation loss 0.2267578070362409\n",
+                        "Epoch 539 Validation loss 0.2342942381898562\n",
+                        "Epoch 549 Validation loss 0.2587633654475212\n",
+                        "Epoch 559 Validation loss 0.21963710337877274\n",
+                        "Epoch 569 Validation loss 0.2676527574658394\n",
+                        "Epoch 579 Validation loss 0.25124627848466236\n",
+                        "Epoch 589 Validation loss 0.22307553887367249\n",
+                        "Epoch 599 Validation loss 0.28288981815179187\n",
+                        "Epoch 609 Validation loss 0.2745586136976878\n",
+                        "Epoch 619 Validation loss 0.2356488679846128\n",
+                        "Epoch 629 Validation loss 0.191768117249012\n",
+                        "Epoch 639 Validation loss 0.23102722316980362\n",
+                        "Epoch 649 Validation loss 0.2544248104095459\n",
+                        "Epoch 659 Validation loss 0.23119398951530457\n",
+                        "Epoch 669 Validation loss 0.20733060439427695\n",
+                        "Epoch 679 Validation loss 0.22538802524407706\n",
+                        "Epoch 689 Validation loss 0.216872605184714\n",
+                        "Epoch 699 Validation loss 0.22977381944656372\n",
+                        "Epoch 709 Validation loss 0.21891566862662634\n",
+                        "Epoch 719 Validation loss 0.223398727675279\n",
+                        "Epoch 729 Validation loss 0.24623310069243112\n",
+                        "Epoch 739 Validation loss 0.23960118989149728\n",
+                        "Epoch 749 Validation loss 0.21641289939483008\n",
+                        "Epoch 759 Validation loss 0.21971949686606726\n",
+                        "Epoch 769 Validation loss 0.22835112363100052\n",
+                        "Epoch 779 Validation loss 0.2273434673746427\n",
+                        "Epoch 789 Validation loss 0.18299358462293944\n",
+                        "Epoch 799 Validation loss 0.1827801006535689\n",
+                        "Epoch 809 Validation loss 0.21519174302617708\n",
+                        "Epoch 819 Validation loss 0.1936649220685164\n",
+                        "Epoch 829 Validation loss 0.23625890165567398\n",
+                        "Epoch 839 Validation loss 0.2425163264075915\n",
+                        "Epoch 849 Validation loss 0.16746311262249947\n",
+                        "Epoch 859 Validation loss 0.20408761004606882\n",
+                        "Epoch 869 Validation loss 0.2144848903020223\n",
+                        "Epoch 879 Validation loss 0.23374033719301224\n",
+                        "Epoch 889 Validation loss 0.23659739891688028\n",
+                        "Epoch 899 Validation loss 0.24609535684188208\n",
+                        "Epoch 909 Validation loss 0.2324757898847262\n",
+                        "Epoch 919 Validation loss 0.24446949362754822\n",
+                        "Epoch 929 Validation loss 0.19177630295356116\n",
+                        "Epoch 939 Validation loss 0.2438896174232165\n",
+                        "Epoch 949 Validation loss 0.2519366617004077\n",
+                        "Epoch 959 Validation loss 0.20046784232060114\n",
+                        "Epoch 969 Validation loss 0.21268909921248755\n",
+                        "Epoch 979 Validation loss 0.2184151684244474\n",
+                        "Epoch 989 Validation loss 0.21281357357899347\n",
+                        "Epoch 999 Validation loss 0.21612912913163504\n",
+                        "train completed, total time: 1351.5848128795624.\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "\n",
+                "n_epochs = 1000\n",
+                "val_interval = 10\n",
+                "epoch_loss_list = []\n",
+                "val_epoch_loss_list = []\n",
+                "optimizer_cls = torch.optim.Adam(params=classifier.parameters(), lr=2.5e-5)\n",
+                "\n",
+                "\n",
+                "scaler = GradScaler()\n",
+                "total_start = time.time()\n",
+                "for epoch in range(n_epochs):\n",
+                "    classifier.train()\n",
+                "    epoch_loss = 0\n",
+                "\n",
+                "    for step, data in enumerate(train_loader):\n",
+                "        images = data[\"image\"].to(device)\n",
+                "        classes = data[\"slice_label\"].to(device)\n",
+                "        # classes[classes==2]=0\n",
+                "\n",
+                "        optimizer_cls.zero_grad(set_to_none=True)\n",
+                "        timesteps = torch.randint(0, 1000, (len(images),)).to(device)\n",
+                "\n",
+                "        with autocast(enabled=False):\n",
+                "            # Generate random noise\n",
+                "            noise = torch.randn_like(images).to(device)\n",
+                "\n",
+                "            # Get model prediction\n",
+                "            noisy_img = scheduler.add_noise(images, noise, timesteps)  # add t steps of noise to the input image\n",
+                "            pred = classifier(noisy_img, timesteps)\n",
+                "\n",
+                "            loss = F.cross_entropy(pred, classes.long())\n",
+                "\n",
+                "            loss.backward()\n",
+                "            optimizer_cls.step()\n",
+                "\n",
+                "        epoch_loss += loss.item()\n",
+                "    epoch_loss_list.append(epoch_loss / (step + 1))\n",
+                "\n",
+                "    if (epoch + 1) % val_interval == 0:\n",
+                "        classifier.eval()\n",
+                "        val_epoch_loss = 0\n",
+                "\n",
+                "        for step, data_val in enumerate(val_loader):\n",
+                "            images = data_val[\"image\"].to(device)\n",
+                "            classes = data_val[\"slice_label\"].to(device)\n",
+                "            timesteps = torch.randint(0, 1, (len(images),)).to(\n",
+                "                device\n",
+                "            )  # check validation accuracy on the original images, i.e., do not add noise\n",
+                "\n",
+                "            with torch.no_grad():\n",
+                "                with autocast(enabled=False):\n",
+                "                    noise = torch.randn_like(images).to(device)\n",
+                "                    pred = classifier(images, timesteps)\n",
+                "                    val_loss = F.cross_entropy(pred, classes.long(), reduction=\"mean\")\n",
+                "\n",
+                "            val_epoch_loss += val_loss.item()\n",
+                "            _, predicted = torch.max(pred, 1)\n",
+                "            val_epoch_loss_list.append(val_epoch_loss / (step + 1))\n",
+                "        print(\"Epoch\", epoch, \"Validation loss\", val_epoch_loss / (step + 1))\n",
+                "\n",
+                "total_time = time.time() - total_start\n",
+                "print(f\"train completed, total time: {total_time}.\")\n",
+                "\n",
+                "## Learning curves for the Classifier\n",
+                "\n",
+                "plt.style.use(\"seaborn-bright\")\n",
+                "plt.title(\"Learning Curves\", fontsize=20)\n",
+                "plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n",
+                "plt.plot(\n",
+                "    np.linspace(val_interval, n_epochs, int(n_epochs / val_interval)),\n",
+                "    val_epoch_loss_list,\n",
+                "    color=\"C1\",\n",
+                "    linewidth=2.0,\n",
+                "    label=\"Validation\",\n",
+                ")\n",
+                "plt.yticks(fontsize=12)\n",
+                "plt.xticks(fontsize=12)\n",
+                "plt.xlabel(\"Epochs\", fontsize=16)\n",
+                "plt.ylabel(\"Loss\", fontsize=16)\n",
+                "plt.legend(prop={\"size\": 14})\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "a676b3fe",
+            "metadata": {},
+            "source": [
+                "# Image-to-image translation to a healthy subject\n",
+                "We pick a diseased subject of the validation set as input image. We want to translate it to its healthy reconstruction."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 162,
+            "id": "fe0d9eac-1477-4d6d-a885-d3c4acb4a781",
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "minmax tensor(0.) tensor(1.3396)\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdUAAAHWCAYAAAAhLRNZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAVUklEQVR4nO3dW2wW9P3H8QIFCthyEsTDRJgMhOF504ERFzzhPEzN5jHRaXSL0SzTuMULs8VdLNHtSp2LGi+2Oc1OThZP0WhkxgSmqBBEBZUtFFCrLadSDqX/2yX///cre/5fKoXX6/ZN26dPH/rxSfj5G9TX19fXBAD8vw3+oh8AAOwvjCoAFDGqAFDEqAJAEaMKAEWMKgAUMaoAUMSoAkARowoARZr39A8OGjRobz4OANin7cn/gNA7VQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAo0vxFPwDYH/X19YVt2bJlYVuxYkXY3n///bCdc845YRszZkzYvvKVr4QN+O95pwoARYwqABQxqgBQxKgCQBGjCgBFjCoAFBnUl/3b///8g4MG7e3Hwn4mOwKybt26sH3wwQdhGzduXNg6OzvD9vvf/z5sEydODNt5550XtsMPPzxsgwfH/726Zs2asL388sthu/XWW8PW09PTUGttbQ3b7NmzwwYHoj2ZS+9UAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAijtTwue6+++6wzZ07N2xdXV1h2759e9iyIyBDhgwJ2wsvvBC2BQsWhG3Xrl0Nfb0RI0aELfv+fvvb34btkksuCVt2o0z2WEaNGhW2f/zjH2HbsWNH2MaPHx+21157LWy/+MUvwgb7OkdqAKAfGVUAKGJUAaCIUQWAIkYVAIoYVQAo4kjNAWLTpk1pb2tra+jzPvnkk2H797//HbZnn302bDfddFPYtmzZErZf//rXYbviiivCduihh4YtO47S3d0dti996Uthu+WWW8J23333hW3btm1ha25uDtvWrVvDNnr06IY+Z/a8ZEdxsiNK2Wv0pJNOChv0F0dqAKAfGVUAKGJUAaCIUQWAIkYVAIoYVQAo4kjNfiQ74rF27dr0Y9evXx+266+/Pmw//OEPwzZy5Mj0a0buuOOOsJ166qlhy16j2ff/k5/8JGzZ0ZHs6/X29obtxRdfDNt3vvOdsDV6a0x2W1Cjsl8bu3fvDlt2fGnz5s1hy15LLS0tYZs6dWrY4L/lSA0A9COjCgBFjCoAFDGqAFDEqAJAEaMKAEUcqdmP3HnnnWG78cYb04995ZVXwtbR0RG2559/PmwLFiwI2xFHHBG27DjKxo0bw7Zz586wjRkzJmzZ7S/ZkZpZs2aFbeXKlWFrb28P24wZM8KW/VW9//77w5YdK8l+RtlRquz3wV/+8pewHXTQQWHLfg7Zz6+1tTVsCxcuDNv3vve9sMH/xZEaAOhHRhUAihhVAChiVAGgiFEFgCJGFQCKOFIzwDz88MNhy45HrFq1Kv28y5cvD1t2JOOjjz4K25AhQ8KWHalZt25d2AYPjv87sK2tLWzZDT6ZI488MmyTJk0KW3YrTnZrzPbt28OW/YyyY09PPvlk2Hp6esL2zDPPhO3iiy8O2/Dhw8OW/Ryy5yW7pSb73ZS9JqZNmxa25557LmwcuBypAYB+ZFQBoIhRBYAiRhUAihhVAChiVAGgiCM1+6CnnnoqbMOGDQtbdnxgxYoV6dfMjodkRx2y18WuXbvClh23+etf/xq27373u2E7+OCDw5bdYNPc3By27Baeiy66KGzZbTpDhw4NW/Z8Zsdtsr/Gs2fPDtuIESPCtmbNmrBlLrzwwrC1tLSELfv+5s+fH7bFixeHbcuWLWHLbsx59913w8aBy5EaAOhHRhUAihhVAChiVAGgiFEFgCJGFQCKxGcJ+MJ861vfCtsLL7wQtk8//TRs2dGQpqampkWLFoUtO3YxZsyYsN18880NfdzWrVvDlv2T9h07doQtu90mO3Zx1VVXha29vT1svb29YRs1alTYsmMl2c8h+95ff/31sGVHXLLPmR1HyY64ZD+/n/70p2G75557wpY91w888EDYbrvttrAtXbo0bPPmzQsbeKcKAEWMKgAUMaoAUMSoAkARowoARYwqABRxS80Ak93g8vjjj4fthhtuSD/vhg0bwtbZ2Rm27GacTEdHR9hmzpwZtp6enrBlR1Uyra2tYXv66afDdsYZZ4St0eMv2fGQ7Gaf3bt3N9Sy23SGDx8etuxmn+wWns2bN4ctu2Wou7s7bNmvsOyxZK+l7Ial7OjPm2++GTYGPrfUAEA/MqoAUMSoAkARowoARYwqABQxqgBQxJGaASa7+eXss88O27Zt29LPm92Okt0ok33cpEmTwtbV1RW27DhDduRk9OjRYcte5tnxl+wIyB7+1flfRo4cGbbsuc6+948//jhs2fM5bdq0sGWvmU2bNoUtO6qS/R7JjvBkstdgdjtRdhys0RuPsp/R6aefHjYGBkdqAKAfGVUAKGJUAaCIUQWAIkYVAIoYVQAoEl81wT5pwoQJYcuOamTHKpqampqWLFkStm9/+9thW7duXdheeumlsM2aNStsjR45yW5cyT5ndrwnk92AkrXsca5duzZs2T/nP+WUU8L2/vvvhy07OpIdccmOxmSPM/u47Jah7KakQw45JGzZzyE7hpQdJ8puNWr0tcT+wztVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIIzUDzFe/+tWwZTeHfN4NIHPnzg1bdmxm2LBhYZsxY0bYWlpawpYd/1m0aFHYpk+fHrbsaEV2q0p2hCe75SS7rWT16tVhy25cWbZsWdimTJkStux73717d9juu+++sF166aVhy56z7Ov94Q9/CNvll18etsmTJ4ft4osvDtsDDzwQtoMPPjhsZ555Zti2bt0aNg4M3qkCQBGjCgBFjCoAFDGqAFDEqAJAEaMKAEUG9WVXSvznH0xul2DfcM0114QtO8rweT07PpEdPciO4mQflx0N+vGPfxy2p59+OmzZUYcf/ehHYctuJDnttNPC9tRTTzX0Of/1r3+F7aCDDgpbdtvM7bffHra//e1vYctucTnxxBPDdvzxx4dt6dKlYVu+fHnYsmNWY8eODVt2g032cdmRqOy53rBhQ9gef/zxsDEw7MlceqcKAEWMKgAUMaoAUMSoAkARowoARYwqABRxpGY/ctxxx4Vt9OjR6cdmxwSOOeaYsB122GFhe+aZZ8KWHZHIbr7JXq7f//73wzZq1KiwZbfGnHfeeWHLbmPJjvdkN6dkN+a88847YfvlL38ZtrPOOits2dGY7DjKo48+Gra///3vYfvjH/8YtnHjxoXt6quvDlt2C88ll1wStsGD4/cUHR0dYcv+rrS3t4dt1qxZYWtqyo8+sW9wpAYA+pFRBYAiRhUAihhVAChiVAGgiFEFgCLNX/QDoM5dd90Vtp07d6Yf293dHbbm5vhlkt02k92qcvLJJ4ctu1FmxIgRYdu+fXtDj2XixIlhy56X7FhQduvPqlWrwpb9k/2urq6wzZ8/P2yTJk0K29y5c8OWHcNav3592B577LGwbdy4MWwnnHBC2P785z+HLbtNZ/bs2WFbs2ZN2LKf+6233hq27HWWvV7Yf3inCgBFjCoAFDGqAFDEqAJAEaMKAEWMKgAUcUvNPuif//xn2D744IOwjR07NmzZcZOmpqambdu2hS27NSY75pHd4tLS0hK24cOHh623tzds2TGW7IhE9rxlfz2yG2Wyoz+dnZ0NfdzSpUvD9o1vfCNsQ4YMCdtpp50Wtka9+uqrYctuxcleE43KjvC89957YbvuuuvClj2fZ555Zth+9atfhY2BwS01ANCPjCoAFDGqAFDEqAJAEaMKAEWMKgAUcUvNPii7waW9vT1s2bGn7FhMU1N+E03WBg+O/7vso48+Cttxxx0Xtux4T3aMZceOHWHLblxZt25d2I466qiwZc93ditQdnQk+yf7s2bNClt2E8306dPDtjfMmTOnX79eJjtKlv2Msuds5MiRYXNsBu9UAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAibqnZBz322GNhy25U2bRpU9iGDh2afs3seMGSJUvCNnfu3LBlt9SsXbs2bIccckjYsu8jeynv2rUrbNlre8OGDWHLbpTJjhpljyX7nOeff37YDmSLFi0KW/ZzePDBB8N2ww03hO3tt98O2w9+8IOwMfC5pQYA+pFRBYAiRhUAihhVAChiVAGgiFEFgCJuqdkHXXHFFWG7++67w/bGG2+E7ZZbbkm/5ubNm8M2bdq0sC1btixs2a0qhx12WNi6u7vDNnz48LB1dnaGLbtZZOPGjWFra2sL24QJE8KW3dCTHZv5vKNP+7P169eHrbW1NWyvvvpq2LKfbXbkq6urK2yOzZDxThUAihhVAChiVAGgiFEFgCJGFQCKGFUAKOJIzQBz+umnh23y5Mlhu+uuu9LPe+ihh4ZtypQpYTvjjDPC1t7eHrbs+Et2C8iMGTPC1twcv5y3b98etq1bt4YtuxXoZz/7WdimTp0atnnz5oUtO1Lz3HPPhS272WfLli1hy46OZK+J7FjQu+++G7bx48eH7cQTTwzb6tWrw5bdRLNixYqw3XzzzWHLjktBxjtVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIIzUDTHa8Zdy4cWHLjrc0NTU1Pfvss2H72te+Fra1a9eGLTvm8eGHH4Ytu5Fk5cqVYdu5c2fYTjnllIa+XnZ0JDuOctZZZ4Xtk08+CdvEiRPDlh2NefTRRxt6LK+//nrYent7w3bZZZeFra+vL2yLFy8OW3a0KXvOVq1aFbbs2MySJUvCduedd4YNMt6pAkARowoARYwqABQxqgBQxKgCQBGjCgBFHKkZYLJjKsuXLw/bp59+2vDnfeSRR8J29NFHh23Dhg1hu/7668PW1tYWtpkzZ4btd7/7XdimT58etuwoUkdHR9iyYzrZjTlHHnlk2N54442wvfbaa2HLjo4MGTIkbCeccELYdu/eHbZjjz02bNktNdmtMU888UTYenp6wjZnzpywnXPOOWGDvcE7VQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiCM1+5HZs2eH7dprr00/dvLkyWHLbkfJbvq47bbb0q8Z6e7uDtsrr7wStnnz5jX09TZt2hS27DjKW2+9Fbazzz47bNnxpuy4zfjx48OWHbeZMWNG2LJbeL7+9a+HLTu+9ac//Sls2evsqKOOClt2y9KwYcPCBv3NO1UAKGJUAaCIUQWAIkYVAIoYVQAoYlQBoMigvr6+vj36g4MG7e3Hwhdo4cKFYbvwwgvDdu+994bty1/+ctjuueeesF100UVhe/jhh8N24403hq2lpSVsra2tYdu1a1fYJkyYELalS5eGbdq0aQ19vewIT3Z7T3YcpbOzM2zZEa0333wzbL29vWHLbgT67LPPwnbBBReEbcqUKWGbOnVq2OC/tSdz6Z0qABQxqgBQxKgCQBGjCgBFjCoAFDGqAFDEkRr2moceeihsJ510Utiy4ygPPvhg2LLjKN/85jfDdvTRR4dt6NChYctueHnppZfClh0Z2rx5c9h+85vfhG3UqFFhy47NnHrqqWHLvr/sVpwrr7wybGPHjg3bggULwpbdRON3E/3FkRoA6EdGFQCKGFUAKGJUAaCIUQWAIkYVAIo4UsM+J3tJvvfee2FbvHhx2FavXh22c889N2yDB8f/3bly5cqGPu7nP/952O64446wdXR0hK2rqytsPT09YTv88MPDlt0oc+mll4atra0tbJkXX3wxbPPnz2/oc0IlR2oAoB8ZVQAoYlQBoIhRBYAiRhUAihhVACjiSA0HhOxlvnDhwrB98sknYZs5c2bY5syZE7Zrr702bNnfs6uvvjpsRxxxRNimT58etkZlz6ffFeyvHKkBgH5kVAGgiFEFgCJGFQCKGFUAKGJUAaCIIzUAsAccqQGAfmRUAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAoYlQBoIhRBYAiRhUAihhVAChiVAGgiFEFgCLNe/oH+/r69ubjAIABzztVAChiVAGgiFEFgCJGFQCKGFUAKGJUAaCIUQWAIkYVAIoYVQAo8j/bDQpRm1Wv1wAAAABJRU5ErkJggg==\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/plain": [
+                            "DiffusionModelEncoder(\n",
+                            "  (conv_in): Convolution(\n",
+                            "    (conv): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "  )\n",
+                            "  (time_embed): Sequential(\n",
+                            "    (0): Linear(in_features=32, out_features=128, bias=True)\n",
+                            "    (1): SiLU()\n",
+                            "    (2): Linear(in_features=128, out_features=128, bias=True)\n",
+                            "  )\n",
+                            "  (down_blocks): ModuleList(\n",
+                            "    (0): DownBlock(\n",
+                            "      (resnets): ModuleList(\n",
+                            "        (0): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 32, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=128, out_features=32, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 32, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Identity()\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (downsampler): Downsample(\n",
+                            "        (op): Convolution(\n",
+                            "          (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+                            "        )\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (1): AttnDownBlock(\n",
+                            "      (attentions): ModuleList(\n",
+                            "        (0): AttentionBlock(\n",
+                            "          (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (resnets): ModuleList(\n",
+                            "        (0): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 32, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=128, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Convolution(\n",
+                            "            (conv): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1))\n",
+                            "          )\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (downsampler): Downsample(\n",
+                            "        (op): Convolution(\n",
+                            "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+                            "        )\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (2): AttnDownBlock(\n",
+                            "      (attentions): ModuleList(\n",
+                            "        (0): AttentionBlock(\n",
+                            "          (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (resnets): ModuleList(\n",
+                            "        (0): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=128, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Identity()\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (downsampler): Downsample(\n",
+                            "        (op): Convolution(\n",
+                            "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+                            "        )\n",
+                            "      )\n",
+                            "    )\n",
+                            "  )\n",
+                            "  (out): Sequential(\n",
+                            "    (0): Linear(in_features=4096, out_features=512, bias=True)\n",
+                            "    (1): ReLU()\n",
+                            "    (2): Dropout(p=0.1, inplace=False)\n",
+                            "    (3): Linear(in_features=512, out_features=2, bias=True)\n",
+                            "  )\n",
+                            ")"
+                        ]
+                    },
+                    "execution_count": 162,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "idx_unhealthy = np.argwhere(data_val[\"slice_label\"].numpy() == 0).squeeze()\n",
+                "idx = idx_unhealthy[4]  # Pick a random slice of the validation set to be transformed\n",
+                "inputimg = data_val[\"image\"][idx]  # Pick an input slice of the validation set to be transformed\n",
+                "inputlabel = data_val[\"slice_label\"][idx]  # Check whether it is healthy or diseased\n",
+                "print(\"minmax\", inputimg.min(), inputimg.max())\n",
+                "\n",
+                "plt.figure(\"input\" + str(inputlabel))\n",
+                "plt.imshow(inputimg[0, ...], vmin=0, vmax=1, cmap=\"gray\")\n",
+                "plt.axis(\"off\")\n",
+                "plt.tight_layout()\n",
+                "plt.show()\n",
+                "\n",
+                "model.eval()\n",
+                "classifier.eval()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "0cd48c2d",
+            "metadata": {},
+            "source": [
+                "### Encoding the input image in noise with the reversed DDIM sampling scheme\n",
+                "In order to sample using gradient guidance, we first need to encode the input image in noise by using the reversed DDIM sampling scheme.\\\n",
+                "We define the number of steps in the noising and denoising process by L.\\\n",
+                "The encoding process is presented in Equation 6 of the paper \"Diffusion Models for Medical Anomaly Detection\" (https://arxiv.org/pdf/2203.04306.pdf).\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 176,
+            "id": "f71e4924",
+            "metadata": {
+                "collapsed": false,
+                "jupyter": {
+                    "outputs_hidden": false
+                },
+                "lines_to_next_cell": 2
+            },
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:05<00:00, 33.36it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "L = 200\n",
+                "current_img = inputimg[None, ...].to(device)\n",
+                "scheduler.set_timesteps(num_inference_steps=1000)\n",
+                "\n",
+                "progress_bar = tqdm(range(L))  # go back and forth L timesteps\n",
+                "for t in progress_bar:  # go through the noising process\n",
+                "    with autocast(enabled=False):\n",
+                "        with torch.no_grad():\n",
+                "            model_output = model(current_img, timesteps=torch.Tensor((t,)).to(current_img.device))\n",
+                "    current_img, _ = scheduler.reversed_step(model_output, t, current_img)\n",
+                "\n",
+                "plt.style.use(\"default\")\n",
+                "plt.imshow(current_img[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+                "plt.tight_layout()\n",
+                "plt.axis(\"off\")\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "a7c8346a-6296-4800-b978-c10fcdf09779",
+            "metadata": {},
+            "source": [
+                "### Denoising process using gradient guidance\n",
+                "From the noisy image, we apply DDIM sampling scheme for denoising for L steps.\\\n",
+                "Additionally, we apply gradient guidance using the classifier network towards the desired class label y=0 (healthy). This is presented in Algorithm 2 of https://arxiv.org/pdf/2105.05233.pdf, and in Algorithm 1 of https://arxiv.org/pdf/2203.04306.pdf. \\\n",
+                "The scale s is used to amplify the gradient."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 173,
+            "id": "7ab274bd-ea60-4674-b59b-d41de98fee5b",
+            "metadata": {
+                "lines_to_next_cell": 2
+            },
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:15<00:00, 12.79it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "y = torch.tensor(0)  # define the desired class label\n",
+                "scale = 6  # define the desired gradient scale s\n",
+                "progress_bar = tqdm(range(L))  # go back and forth L timesteps\n",
+                "\n",
+                "for i in progress_bar:  # go through the denoising process\n",
+                "    t = L - i\n",
+                "    with autocast(enabled=True):\n",
+                "        with torch.no_grad():\n",
+                "            model_output = model(\n",
+                "                current_img, timesteps=torch.Tensor((t,)).to(current_img.device)\n",
+                "            ).detach()  # this is supposed to be epsilon\n",
+                "\n",
+                "        with torch.enable_grad():\n",
+                "            x_in = current_img.detach().requires_grad_(True)\n",
+                "            logits = classifier(x_in, timesteps=torch.Tensor((t,)).to(current_img.device))\n",
+                "            log_probs = F.log_softmax(logits, dim=-1)\n",
+                "            selected = log_probs[range(len(logits)), y.view(-1)]\n",
+                "            a = torch.autograd.grad(selected.sum(), x_in)[0]\n",
+                "            alpha_prod_t = scheduler.alphas_cumprod[t]\n",
+                "            updated_noise = (\n",
+                "                model_output - (1 - alpha_prod_t).sqrt() * scale * a\n",
+                "            )  # update the predicted noise epsilon with the gradient of the classifier\n",
+                "\n",
+                "    current_img, _ = scheduler.step(updated_noise, t, current_img)\n",
+                "    torch.cuda.empty_cache()\n",
+                "\n",
+                "plt.style.use(\"default\")\n",
+                "plt.imshow(current_img[0, 0].cpu().detach().numpy(), vmin=0, vmax=1, cmap=\"gray\")\n",
+                "plt.tight_layout()\n",
+                "plt.axis(\"off\")\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "id": "d2e343f8-c6f3-4071-a5e6-771e2343c3bc",
+            "metadata": {
+                "lines_to_next_cell": 2
+            },
+            "source": [
+                "# Anomaly Detection\n",
+                "To get the anomaly map, we compute the difference between the input image the output of our image-to-image translation model towards the healthy reconstruction."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 175,
+            "id": "ecffaaf3-a7df-453e-81a9-757113d85084",
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "\n",
+                "diff = abs(inputimg.cpu() - current_img[0, 0].cpu()).detach().numpy()\n",
+                "plt.style.use(\"default\")\n",
+                "plt.imshow(diff[0, ...], cmap=\"jet\")\n",
+                "plt.tight_layout()\n",
+                "plt.axis(\"off\")\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "id": "c459ab23-459d-4063-824e-39dac93abb43",
+            "metadata": {},
+            "outputs": [],
+            "source": []
+        }
+    ],
+    "metadata": {
+        "jupytext": {
+            "formats": "py:percent,ipynb"
+        },
+        "kernelspec": {
+            "display_name": "Python 3 (ipykernel)",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.8.13"
+        }
     },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "path ['/home/juliawolleb/PycharmProjects/MONAI/GenerativeModels/tutorials/generative/anomaly_detection/classifier_guidance_anomalydetection', '/home/juliawolleb/anaconda3/envs/experiment/lib/python310.zip', '/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10', '/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/lib-dynload', '', '/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages', '/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/PyYAML-6.0-py3.10-linux-x86_64.egg', '/home/juliawolleb/PycharmProjects/Python_Tutorials/Calgary_Infants/calgary/HD-BET', '/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/lpips-0.1.4-py3.10.egg', '/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/tqdm-4.64.1-py3.10.egg', '/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/generative-0.1.0-py3.10.egg', '/home/juliawolleb/PycharmProjects/MONAI/GenerativeModels/', '/home/juliawolleb/PycharmProjects/MONAI/GenerativeModels/', '/home/juliawolleb/PycharmProjects/MONAI/GenerativeModels/']\n",
-      "MONAI version: 1.2.dev2304\n",
-      "Numpy version: 1.23.2\n",
-      "Pytorch version: 1.12.1\n",
-      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
-      "MONAI rev id: 9a57be5aab9f2c2a134768c0c146399150e247a0\n",
-      "MONAI __file__: /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/monai/__init__.py\n",
-      "\n",
-      "Optional dependencies:\n",
-      "Pytorch Ignite version: 0.4.10\n",
-      "ITK version: 5.3.0\n",
-      "Nibabel version: 4.0.1\n",
-      "scikit-image version: 0.19.3\n",
-      "Pillow version: 9.2.0\n",
-      "Tensorboard version: 2.12.0\n",
-      "gdown version: 4.6.4\n",
-      "TorchVision version: 0.13.1\n",
-      "tqdm version: 4.64.1\n",
-      "lmdb version: 1.4.0\n",
-      "psutil version: 5.9.4\n",
-      "pandas version: 1.5.3\n",
-      "einops version: 0.6.0\n",
-      "transformers version: 4.21.3\n",
-      "mlflow version: 2.1.1\n",
-      "pynrrd version: 1.0.0\n",
-      "\n",
-      "For details about installing the optional dependencies, please visit:\n",
-      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import time\n",
-    "import tempfile\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "import torch.nn.functional as F\n",
-    "from monai import transforms\n",
-    "from monai.apps import DecathlonDataset\n",
-    "from monai.config import print_config\n",
-    "from monai.data import DataLoader\n",
-    "from monai.utils import set_determinism\n",
-    "from torch.cuda.amp import GradScaler, autocast\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "from generative.inferers import DiffusionInferer\n",
-    "from generative.networks.nets.diffusion_model_unet import DiffusionModelEncoder, DiffusionModelUNet\n",
-    "from generative.networks.schedulers.ddim import DDIMScheduler\n",
-    "\n",
-    "torch.multiprocessing.set_sharing_strategy(\"file_system\")\n",
-    "\n",
-    "print_config()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7d4ff515",
-   "metadata": {},
-   "source": [
-    "## Setup data directory"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "8b4323e7",
-   "metadata": {
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
-    "root_dir = tempfile.mkdtemp() if directory is None else directory"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "99175d50",
-   "metadata": {},
-   "source": [
-    "## Set deterministic training for reproducibility"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "34ea510f",
-   "metadata": {
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "set_determinism(42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c3f70dd1-236a-47ff-a244-575729ad92ba",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Preprocessing of the BRATS Dataset in 2D slices for training\n",
-    "We download the BRATS training dataset from the Decathlon dataset. \\\n",
-    "We slice the volumes in axial 2D slices, and assign slice-wise labels (0 for healthy, 1 for diseased) to all slices.\n",
-    "Here we use transforms to augment the training dataset:\n",
-    "\n",
-    "1. `LoadImaged` loads the brain MR images from files.\n",
-    "1. `EnsureChannelFirstd` ensures the original data to construct \"channel first\" shape.\n",
-    "1. `ScaleIntensityRangePercentilesd` takes the lower and upper intensity percentiles and scales them to [0, 1].\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "id": "c68d2d91-9a0b-4ac1-ae49-f4a64edbd82a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<class 'monai.transforms.utility.array.AddChannel'>: Class `AddChannel` has been deprecated since version 0.8. please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead.\n"
-     ]
-    }
-   ],
-   "source": [
-    "channel = 0  # 0 = Flair\n",
-    "assert channel in [0, 1, 2, 3], \"Choose a valid channel\"\n",
-    "\n",
-    "train_transforms = transforms.Compose(\n",
-    "    [\n",
-    "        transforms.LoadImaged(keys=[\"image\", \"label\"]),\n",
-    "        transforms.EnsureChannelFirstd(keys=[\"image\", \"label\"]),\n",
-    "        transforms.Lambdad(keys=[\"image\"], func=lambda x: x[channel, :, :, :]),\n",
-    "        transforms.AddChanneld(keys=[\"image\"]),\n",
-    "        transforms.EnsureTyped(keys=[\"image\", \"label\"]),\n",
-    "        transforms.Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
-    "        transforms.Spacingd(keys=[\"image\", \"label\"], pixdim=(3.0, 3.0, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
-    "        transforms.CenterSpatialCropd(keys=[\"image\", \"label\"], roi_size=(64, 64, 44)),\n",
-    "        transforms.ScaleIntensityRangePercentilesd(keys=\"image\", lower=0, upper=99.5, b_min=0, b_max=1),\n",
-    "        transforms.RandSpatialCropd(keys=[\"image\", \"label\"], roi_size=(64, 64, 1), random_size=False),\n",
-    "        transforms.Lambdad(keys=[\"image\", \"label\"], func=lambda x: x.squeeze(-1)),\n",
-    "        transforms.CopyItemsd(keys=[\"label\"], times=1, names=[\"slice_label\"]),\n",
-    "        transforms.Lambdad(keys=[\"slice_label\"], func=lambda x: 0.0 if x.sum() > 0 else 1.0),\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "id": "da1927b0",
-   "metadata": {
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset: 100%|████████████████████████| 388/388 [03:02<00:00,  2.13it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Length of training data: 388\n",
-      "Train image shape torch.Size([1, 64, 64])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "batch_size = 64\n",
-    "\n",
-    "train_ds = DecathlonDataset(\n",
-    "    root_dir=root_dir,\n",
-    "    task=\"Task01_BrainTumour\",\n",
-    "    section=\"training\",  # validation\n",
-    "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
-    "    num_workers=4,\n",
-    "    download=False,  # Set download to True if the dataset hasnt been downloaded yet\n",
-    "    seed=0,\n",
-    "    transform=train_transforms,\n",
-    ")\n",
-    "\n",
-    "print(f\"Length of training data: {len(train_ds)}\")  # this gives the number of patients in the training set\n",
-    "print(f'Train image shape {train_ds[0][\"image\"].shape}')\n",
-    "\n",
-    "train_loader = DataLoader(\n",
-    "    train_ds, batch_size=batch_size, shuffle=True, num_workers=4, drop_last=True, persistent_workers=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fac55e9d",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Preprocessing of the BRATS Dataset in 2D slices for validation\n",
-    "We download the BRATS validation dataset from the Decathlon dataset, and define the dataloader to load 2D slices for validation.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "73d72110-a8b3-4e03-91cc-1dab4d5a7b87",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset: 100%|██████████████████████████| 96/96 [00:48<00:00,  2.00it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Length of training data: 96\n",
-      "Validation Image shape torch.Size([1, 64, 64])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "val_ds = DecathlonDataset(\n",
-    "    root_dir=root_dir,\n",
-    "    task=\"Task01_BrainTumour\",\n",
-    "    section=\"validation\",\n",
-    "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
-    "    num_workers=4,\n",
-    "    download=False,  # Set download to True if the dataset hasnt been downloaded yet\n",
-    "    seed=0,\n",
-    "    transform=train_transforms,\n",
-    ")\n",
-    "print(f\"Length of training data: {len(val_ds)}\")\n",
-    "print(f'Validation Image shape {val_ds[0][\"image\"].shape}')\n",
-    "\n",
-    "val_loader = DataLoader(\n",
-    "    val_ds, batch_size=batch_size, shuffle=False, num_workers=4, drop_last=True, persistent_workers=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "08428bc6",
-   "metadata": {},
-   "source": [
-    "## Define network, scheduler, optimizer, and inferer\n",
-    "At this step, we instantiate the MONAI components to create a DDIM, the UNET, the noise scheduler, and the inferer used for training and sampling. We are using\n",
-    "the deterministic DDIM scheduler containing 1000 timesteps, and a 2D UNET with attention mechanisms\n",
-    "in the 3rd level (`num_head_channels=64`).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "id": "bee5913e",
-   "metadata": {
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "device = torch.device(\"cuda\")\n",
-    "\n",
-    "model = DiffusionModelUNet(\n",
-    "    spatial_dims=2,\n",
-    "    in_channels=1,\n",
-    "    out_channels=1,\n",
-    "    num_channels=(64, 64, 64),\n",
-    "    attention_levels=(False, False, True),\n",
-    "    num_res_blocks=1,\n",
-    "    num_head_channels=64,\n",
-    "    with_conditioning=False,\n",
-    ")\n",
-    "model.to(device)\n",
-    "\n",
-    "scheduler = DDIMScheduler(num_train_timesteps=1000)\n",
-    "\n",
-    "optimizer = torch.optim.Adam(params=model.parameters(), lr=2.5e-5)\n",
-    "\n",
-    "inferer = DiffusionInferer(scheduler)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2a4d3ab2",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Model training of the diffusion model\n",
-    "We train our diffusion model for 2000 epochs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "id": "6c0ed909",
-   "metadata": {
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0 Validation loss 0.9828271865844727\n",
-      "Epoch 20 Validation loss 0.45277565717697144\n",
-      "Epoch 40 Validation loss 0.16044068336486816\n",
-      "Epoch 60 Validation loss 0.06908729672431946\n",
-      "Epoch 80 Validation loss 0.037922561168670654\n",
-      "Epoch 100 Validation loss 0.024700244888663292\n",
-      "Epoch 120 Validation loss 0.02825773134827614\n",
-      "Epoch 140 Validation loss 0.01575350947678089\n",
-      "Epoch 160 Validation loss 0.02807718887925148\n",
-      "Epoch 180 Validation loss 0.03635002672672272\n",
-      "Epoch 200 Validation loss 0.018522320315241814\n",
-      "Epoch 220 Validation loss 0.020984284579753876\n",
-      "Epoch 240 Validation loss 0.02985953912138939\n",
-      "Epoch 260 Validation loss 0.018604595214128494\n",
-      "Epoch 280 Validation loss 0.02505004033446312\n",
-      "Epoch 300 Validation loss 0.018166495487093925\n",
-      "Epoch 320 Validation loss 0.012706207111477852\n",
-      "Epoch 340 Validation loss 0.03222103416919708\n",
-      "Epoch 360 Validation loss 0.010545151308178902\n",
-      "Epoch 380 Validation loss 0.017768580466508865\n",
-      "Epoch 400 Validation loss 0.023036960512399673\n",
-      "Epoch 420 Validation loss 0.023991823196411133\n",
-      "Epoch 440 Validation loss 0.014143284410238266\n",
-      "Epoch 460 Validation loss 0.010133783333003521\n",
-      "Epoch 480 Validation loss 0.019768211990594864\n",
-      "Epoch 500 Validation loss 0.016018100082874298\n",
-      "Epoch 520 Validation loss 0.016411196440458298\n",
-      "Epoch 540 Validation loss 0.012067019008100033\n",
-      "Epoch 560 Validation loss 0.017793692648410797\n",
-      "Epoch 580 Validation loss 0.015390219166874886\n",
-      "Epoch 600 Validation loss 0.015438873320817947\n",
-      "Epoch 620 Validation loss 0.019228052347898483\n",
-      "Epoch 640 Validation loss 0.022589124739170074\n",
-      "Epoch 660 Validation loss 0.022526469081640244\n",
-      "Epoch 680 Validation loss 0.0310574471950531\n",
-      "Epoch 700 Validation loss 0.016018839552998543\n",
-      "Epoch 720 Validation loss 0.018153013661503792\n",
-      "Epoch 740 Validation loss 0.01506253331899643\n",
-      "Epoch 760 Validation loss 0.00914084818214178\n",
-      "Epoch 780 Validation loss 0.017407484352588654\n",
-      "Epoch 800 Validation loss 0.013946758583188057\n",
-      "Epoch 820 Validation loss 0.013289306312799454\n",
-      "Epoch 840 Validation loss 0.007855996489524841\n",
-      "Epoch 860 Validation loss 0.01187637448310852\n",
-      "Epoch 880 Validation loss 0.018494905903935432\n",
-      "Epoch 900 Validation loss 0.009516816586256027\n",
-      "Epoch 920 Validation loss 0.030950400978326797\n",
-      "Epoch 940 Validation loss 0.017931077629327774\n",
-      "Epoch 960 Validation loss 0.017525378614664078\n",
-      "Epoch 980 Validation loss 0.016576599329710007\n",
-      "Epoch 1000 Validation loss 0.007525463588535786\n",
-      "Epoch 1020 Validation loss 0.008745957165956497\n",
-      "Epoch 1040 Validation loss 0.023068588227033615\n",
-      "Epoch 1060 Validation loss 0.023049402981996536\n",
-      "Epoch 1080 Validation loss 0.020367465913295746\n",
-      "Epoch 1100 Validation loss 0.026941468939185143\n",
-      "Epoch 1120 Validation loss 0.019598377868533134\n",
-      "Epoch 1140 Validation loss 0.023052945733070374\n",
-      "Epoch 1160 Validation loss 0.020239276811480522\n",
-      "Epoch 1180 Validation loss 0.009076420217752457\n",
-      "Epoch 1200 Validation loss 0.011559909209609032\n",
-      "Epoch 1220 Validation loss 0.023455770686268806\n",
-      "Epoch 1240 Validation loss 0.015224231407046318\n",
-      "Epoch 1260 Validation loss 0.020417172461748123\n",
-      "Epoch 1280 Validation loss 0.025817634537816048\n",
-      "Epoch 1300 Validation loss 0.012675277888774872\n",
-      "Epoch 1320 Validation loss 0.014165625907480717\n",
-      "Epoch 1340 Validation loss 0.021743204444646835\n",
-      "Epoch 1360 Validation loss 0.00959782674908638\n",
-      "Epoch 1380 Validation loss 0.014942880719900131\n",
-      "Epoch 1400 Validation loss 0.033313099294900894\n",
-      "Epoch 1420 Validation loss 0.025836177170276642\n",
-      "Epoch 1440 Validation loss 0.015067282132804394\n",
-      "Epoch 1460 Validation loss 0.01235564611852169\n",
-      "Epoch 1480 Validation loss 0.012111244723200798\n",
-      "Epoch 1500 Validation loss 0.00833088904619217\n",
-      "Epoch 1520 Validation loss 0.01528056338429451\n",
-      "Epoch 1540 Validation loss 0.017444560304284096\n",
-      "Epoch 1560 Validation loss 0.014621825888752937\n",
-      "Epoch 1580 Validation loss 0.019431518390774727\n",
-      "Epoch 1600 Validation loss 0.016186822205781937\n",
-      "Epoch 1620 Validation loss 0.02027059532701969\n",
-      "Epoch 1640 Validation loss 0.01720491796731949\n",
-      "Epoch 1660 Validation loss 0.011756360530853271\n",
-      "Epoch 1680 Validation loss 0.02627478912472725\n",
-      "Epoch 1700 Validation loss 0.023451916873455048\n",
-      "Epoch 1720 Validation loss 0.011613328941166401\n",
-      "Epoch 1740 Validation loss 0.026256393641233444\n",
-      "Epoch 1760 Validation loss 0.008156227879226208\n",
-      "Epoch 1780 Validation loss 0.01597723178565502\n",
-      "Epoch 1800 Validation loss 0.013070507906377316\n",
-      "Epoch 1820 Validation loss 0.01726200059056282\n",
-      "Epoch 1840 Validation loss 0.009824991226196289\n",
-      "Epoch 1860 Validation loss 0.014878236688673496\n",
-      "Epoch 1880 Validation loss 0.017673484981060028\n",
-      "Epoch 1900 Validation loss 0.016455603763461113\n",
-      "Epoch 1920 Validation loss 0.02442217618227005\n",
-      "Epoch 1940 Validation loss 0.026278261095285416\n",
-      "Epoch 1960 Validation loss 0.02376818098127842\n",
-      "Epoch 1980 Validation loss 0.016214493662118912\n",
-      "train diffusion completed, total time: 6097.77689909935.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "n_epochs = 2000\n",
-    "val_interval = 20\n",
-    "epoch_loss_list = []\n",
-    "val_epoch_loss_list = []\n",
-    "\n",
-    "scaler = GradScaler()\n",
-    "total_start = time.time()\n",
-    "\n",
-    "for epoch in range(n_epochs):\n",
-    "    model.train()\n",
-    "    epoch_loss = 0\n",
-    "\n",
-    "    for step, data in enumerate(train_loader):\n",
-    "        images = data[\"image\"].to(device)\n",
-    "        classes = data[\"slice_label\"].to(device)\n",
-    "        optimizer.zero_grad(set_to_none=True)\n",
-    "        timesteps = torch.randint(0, 1000, (len(images),)).to(device)  # pick a random time step t\n",
-    "\n",
-    "        with autocast(enabled=True):\n",
-    "            # Generate random noise\n",
-    "            noise = torch.randn_like(images).to(device)\n",
-    "\n",
-    "            # Get model prediction\n",
-    "            noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n",
-    "            loss = F.mse_loss(noise_pred.float(), noise.float())\n",
-    "\n",
-    "        scaler.scale(loss).backward()\n",
-    "        scaler.step(optimizer)\n",
-    "        scaler.update()\n",
-    "        epoch_loss += loss.item()\n",
-    "    epoch_loss_list.append(epoch_loss / (step + 1))\n",
-    "\n",
-    "    if (epoch) % val_interval == 0:\n",
-    "        model.eval()\n",
-    "        val_epoch_loss = 0\n",
-    "\n",
-    "        for step, data in enumerate(val_loader):\n",
-    "            images = data[\"image\"].to(device)\n",
-    "            classes = data[\"slice_label\"].to(device)\n",
-    "            timesteps = torch.randint(0, 1000, (len(images),)).to(device)\n",
-    "            with torch.no_grad():\n",
-    "                with autocast(enabled=True):\n",
-    "                    noise = torch.randn_like(images).to(device)\n",
-    "                    noise_pred = inferer(inputs=images, diffusion_model=model, noise=noise, timesteps=timesteps)\n",
-    "                    val_loss = F.mse_loss(noise_pred.float(), noise.float())\n",
-    "\n",
-    "            val_epoch_loss += val_loss.item()\n",
-    "        val_epoch_loss_list.append(val_epoch_loss / (step + 1))\n",
-    "        print(\"Epoch\", epoch, \"Validation loss\", val_epoch_loss / (step + 1))\n",
-    "\n",
-    "total_time = time.time() - total_start\n",
-    "print(f\"train diffusion completed, total time: {total_time}.\")\n",
-    "\n",
-    "plt.style.use(\"seaborn-bright\")\n",
-    "plt.title(\"Learning Curves Diffusion Model\", fontsize=20)\n",
-    "plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n",
-    "plt.plot(\n",
-    "    np.linspace(val_interval, n_epochs, int(n_epochs / val_interval)),\n",
-    "    val_epoch_loss_list,\n",
-    "    color=\"C1\",\n",
-    "    linewidth=2.0,\n",
-    "    label=\"Validation\",\n",
-    ")\n",
-    "plt.yticks(fontsize=12)\n",
-    "plt.xticks(fontsize=12)\n",
-    "plt.xlabel(\"Epochs\", fontsize=16)\n",
-    "plt.ylabel(\"Loss\", fontsize=16)\n",
-    "plt.legend(prop={\"size\": 14})\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "326101ed-333b-44a9-933f-55760b5d93a4",
-   "metadata": {},
-   "source": [
-    "## Check the performance of the diffusion model\n",
-    "\n",
-    "We generate a random image from noise to check whether our diffusion model works properly for an image generation task.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "id": "8f7a9e99-a8a4-4c8f-a42f-17ef91b18585",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████████████| 1000/1000 [00:23<00:00, 42.86it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "model.eval()\n",
-    "noise = torch.randn((1, 1, 64, 64))\n",
-    "noise = noise.to(device)\n",
-    "scheduler.set_timesteps(num_inference_steps=1000)\n",
-    "with autocast(enabled=True):\n",
-    "    image, intermediates = inferer.sample(\n",
-    "        input_noise=noise, diffusion_model=model, scheduler=scheduler, save_intermediates=True, intermediate_steps=100\n",
-    "    )\n",
-    "\n",
-    "chain = torch.cat(intermediates, dim=-1)\n",
-    "\n",
-    "plt.style.use(\"default\")\n",
-    "plt.imshow(chain[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
-    "plt.tight_layout()\n",
-    "plt.axis(\"off\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "546f9983-c2e2-4c24-b03a-ebe34627638a",
-   "metadata": {},
-   "source": [
-    "## Define the classification model\n",
-    "First, we define the classification model. It follows the encoder architecture of the diffusion model, combined with linear layers for binary classification between healthy and diseased slices.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 174,
-   "id": "44cc6928-2525-4e61-8805-15b409097bbb",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DiffusionModelEncoder(\n",
-       "  (conv_in): Convolution(\n",
-       "    (conv): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "  )\n",
-       "  (time_embed): Sequential(\n",
-       "    (0): Linear(in_features=32, out_features=128, bias=True)\n",
-       "    (1): SiLU()\n",
-       "    (2): Linear(in_features=128, out_features=128, bias=True)\n",
-       "  )\n",
-       "  (down_blocks): ModuleList(\n",
-       "    (0): DownBlock(\n",
-       "      (resnets): ModuleList(\n",
-       "        (0): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 32, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=128, out_features=32, bias=True)\n",
-       "          (norm2): GroupNorm(32, 32, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Identity()\n",
-       "        )\n",
-       "      )\n",
-       "      (downsampler): Downsample(\n",
-       "        (op): Convolution(\n",
-       "          (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (1): AttnDownBlock(\n",
-       "      (attentions): ModuleList(\n",
-       "        (0): AttentionBlock(\n",
-       "          (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
-       "        )\n",
-       "      )\n",
-       "      (resnets): ModuleList(\n",
-       "        (0): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 32, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=128, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Convolution(\n",
-       "            (conv): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1))\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "      (downsampler): Downsample(\n",
-       "        (op): Convolution(\n",
-       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (2): AttnDownBlock(\n",
-       "      (attentions): ModuleList(\n",
-       "        (0): AttentionBlock(\n",
-       "          (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
-       "        )\n",
-       "      )\n",
-       "      (resnets): ModuleList(\n",
-       "        (0): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=128, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Identity()\n",
-       "        )\n",
-       "      )\n",
-       "      (downsampler): Downsample(\n",
-       "        (op): Convolution(\n",
-       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       "  (out): Sequential(\n",
-       "    (0): Linear(in_features=4096, out_features=512, bias=True)\n",
-       "    (1): ReLU()\n",
-       "    (2): Dropout(p=0.1, inplace=False)\n",
-       "    (3): Linear(in_features=512, out_features=2, bias=True)\n",
-       "  )\n",
-       ")"
-      ]
-     },
-     "execution_count": 174,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "device = torch.device(\"cuda\")\n",
-    "classifier = DiffusionModelEncoder(\n",
-    "    spatial_dims=2,\n",
-    "    in_channels=1,\n",
-    "    out_channels=2,\n",
-    "    num_channels=(32, 64, 64),\n",
-    "    attention_levels=(False, True, True),\n",
-    "    num_res_blocks=(1, 1, 1),\n",
-    "    num_head_channels=64,\n",
-    "    with_conditioning=False,\n",
-    ")\n",
-    "\n",
-    "classifier.to(device)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45fab83a-b4c8-42cb-96c9-4e9f1e191111",
-   "metadata": {},
-   "source": [
-    "## Model training of the classification model\n",
-    "We train our classification model for 1000 epochs.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "de18d5cb-68e7-407c-afe9-8efd7a5a904a",
-   "metadata": {
-    "lines_to_next_cell": 0
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 9 Validation loss 0.2536351333061854\n",
-      "Epoch 19 Validation loss 0.3019549027085304\n",
-      "Epoch 29 Validation loss 0.34552596261103946\n",
-      "Epoch 39 Validation loss 0.2783070926864942\n",
-      "Epoch 49 Validation loss 0.28460513055324554\n",
-      "Epoch 59 Validation loss 0.25296298414468765\n",
-      "Epoch 69 Validation loss 0.3343521902958552\n",
-      "Epoch 79 Validation loss 0.2634535978237788\n",
-      "Epoch 89 Validation loss 0.2862999041875203\n",
-      "Epoch 99 Validation loss 0.22700381030639014\n",
-      "Epoch 109 Validation loss 0.27035540093978244\n",
-      "Epoch 119 Validation loss 0.2451721504330635\n",
-      "Epoch 129 Validation loss 0.2890484283367793\n",
-      "Epoch 139 Validation loss 0.27566688507795334\n",
-      "Epoch 149 Validation loss 0.28788923223813373\n",
-      "Epoch 159 Validation loss 0.2524748469392459\n",
-      "Epoch 169 Validation loss 0.3107323000828425\n",
-      "Epoch 179 Validation loss 0.21660694728295007\n",
-      "Epoch 189 Validation loss 0.2702282816171646\n",
-      "Epoch 199 Validation loss 0.2677164326111476\n",
-      "Epoch 209 Validation loss 0.33349836121002835\n",
-      "Epoch 219 Validation loss 0.2969249188899994\n",
-      "Epoch 229 Validation loss 0.268981905033191\n",
-      "Epoch 239 Validation loss 0.29199230174223584\n",
-      "Epoch 249 Validation loss 0.2806356226404508\n",
-      "Epoch 259 Validation loss 0.301661084095637\n",
-      "Epoch 269 Validation loss 0.25811708470185596\n",
-      "Epoch 279 Validation loss 0.2599738910794258\n",
-      "Epoch 289 Validation loss 0.23392533014218012\n",
-      "Epoch 299 Validation loss 0.2580989971756935\n",
-      "Epoch 309 Validation loss 0.22807281464338303\n",
-      "Epoch 319 Validation loss 0.2510971352458\n",
-      "Epoch 329 Validation loss 0.25221700221300125\n",
-      "Epoch 339 Validation loss 0.25722870975732803\n",
-      "Epoch 349 Validation loss 0.2516109471519788\n",
-      "Epoch 359 Validation loss 0.22627043972412744\n",
-      "Epoch 369 Validation loss 0.28725822021563846\n",
-      "Epoch 379 Validation loss 0.2712069054444631\n",
-      "Epoch 389 Validation loss 0.29460274676481885\n",
-      "Epoch 399 Validation loss 0.2599460730950038\n",
-      "Epoch 409 Validation loss 0.22882529348134995\n",
-      "Epoch 419 Validation loss 0.24265126883983612\n",
-      "Epoch 429 Validation loss 0.23436561226844788\n",
-      "Epoch 439 Validation loss 0.25520699471235275\n",
-      "Epoch 449 Validation loss 0.22466829667488733\n",
-      "Epoch 459 Validation loss 0.26379595696926117\n",
-      "Epoch 469 Validation loss 0.23318989326556525\n",
-      "Epoch 479 Validation loss 0.264743114511172\n",
-      "Epoch 489 Validation loss 0.25179669509331387\n",
-      "Epoch 499 Validation loss 0.20064709583918253\n",
-      "Epoch 509 Validation loss 0.2527008851369222\n",
-      "Epoch 519 Validation loss 0.24675505111614862\n",
-      "Epoch 529 Validation loss 0.2267578070362409\n",
-      "Epoch 539 Validation loss 0.2342942381898562\n",
-      "Epoch 549 Validation loss 0.2587633654475212\n",
-      "Epoch 559 Validation loss 0.21963710337877274\n",
-      "Epoch 569 Validation loss 0.2676527574658394\n",
-      "Epoch 579 Validation loss 0.25124627848466236\n",
-      "Epoch 589 Validation loss 0.22307553887367249\n",
-      "Epoch 599 Validation loss 0.28288981815179187\n",
-      "Epoch 609 Validation loss 0.2745586136976878\n",
-      "Epoch 619 Validation loss 0.2356488679846128\n",
-      "Epoch 629 Validation loss 0.191768117249012\n",
-      "Epoch 639 Validation loss 0.23102722316980362\n",
-      "Epoch 649 Validation loss 0.2544248104095459\n",
-      "Epoch 659 Validation loss 0.23119398951530457\n",
-      "Epoch 669 Validation loss 0.20733060439427695\n",
-      "Epoch 679 Validation loss 0.22538802524407706\n",
-      "Epoch 689 Validation loss 0.216872605184714\n",
-      "Epoch 699 Validation loss 0.22977381944656372\n",
-      "Epoch 709 Validation loss 0.21891566862662634\n",
-      "Epoch 719 Validation loss 0.223398727675279\n",
-      "Epoch 729 Validation loss 0.24623310069243112\n",
-      "Epoch 739 Validation loss 0.23960118989149728\n",
-      "Epoch 749 Validation loss 0.21641289939483008\n",
-      "Epoch 759 Validation loss 0.21971949686606726\n",
-      "Epoch 769 Validation loss 0.22835112363100052\n",
-      "Epoch 779 Validation loss 0.2273434673746427\n",
-      "Epoch 789 Validation loss 0.18299358462293944\n",
-      "Epoch 799 Validation loss 0.1827801006535689\n",
-      "Epoch 809 Validation loss 0.21519174302617708\n",
-      "Epoch 819 Validation loss 0.1936649220685164\n",
-      "Epoch 829 Validation loss 0.23625890165567398\n",
-      "Epoch 839 Validation loss 0.2425163264075915\n",
-      "Epoch 849 Validation loss 0.16746311262249947\n",
-      "Epoch 859 Validation loss 0.20408761004606882\n",
-      "Epoch 869 Validation loss 0.2144848903020223\n",
-      "Epoch 879 Validation loss 0.23374033719301224\n",
-      "Epoch 889 Validation loss 0.23659739891688028\n",
-      "Epoch 899 Validation loss 0.24609535684188208\n",
-      "Epoch 909 Validation loss 0.2324757898847262\n",
-      "Epoch 919 Validation loss 0.24446949362754822\n",
-      "Epoch 929 Validation loss 0.19177630295356116\n",
-      "Epoch 939 Validation loss 0.2438896174232165\n",
-      "Epoch 949 Validation loss 0.2519366617004077\n",
-      "Epoch 959 Validation loss 0.20046784232060114\n",
-      "Epoch 969 Validation loss 0.21268909921248755\n",
-      "Epoch 979 Validation loss 0.2184151684244474\n",
-      "Epoch 989 Validation loss 0.21281357357899347\n",
-      "Epoch 999 Validation loss 0.21612912913163504\n",
-      "train completed, total time: 1351.5848128795624.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "n_epochs = 1000\n",
-    "val_interval = 10\n",
-    "epoch_loss_list = []\n",
-    "val_epoch_loss_list = []\n",
-    "optimizer_cls = torch.optim.Adam(params=classifier.parameters(), lr=2.5e-5)\n",
-    "\n",
-    "\n",
-    "scaler = GradScaler()\n",
-    "total_start = time.time()\n",
-    "for epoch in range(n_epochs):\n",
-    "    classifier.train()\n",
-    "    epoch_loss = 0\n",
-    "\n",
-    "    for step, data in enumerate(train_loader):\n",
-    "        images = data[\"image\"].to(device)\n",
-    "        classes = data[\"slice_label\"].to(device)\n",
-    "        # classes[classes==2]=0\n",
-    "\n",
-    "        optimizer_cls.zero_grad(set_to_none=True)\n",
-    "        timesteps = torch.randint(0, 1000, (len(images),)).to(device)\n",
-    "\n",
-    "        with autocast(enabled=False):\n",
-    "            # Generate random noise\n",
-    "            noise = torch.randn_like(images).to(device)\n",
-    "\n",
-    "            # Get model prediction\n",
-    "            noisy_img = scheduler.add_noise(images, noise, timesteps)  # add t steps of noise to the input image\n",
-    "            pred = classifier(noisy_img, timesteps)\n",
-    "\n",
-    "            loss = F.cross_entropy(pred, classes.long())\n",
-    "\n",
-    "            loss.backward()\n",
-    "            optimizer_cls.step()\n",
-    "\n",
-    "        epoch_loss += loss.item()\n",
-    "    epoch_loss_list.append(epoch_loss / (step + 1))\n",
-    "\n",
-    "    if (epoch + 1) % val_interval == 0:\n",
-    "        classifier.eval()\n",
-    "        val_epoch_loss = 0\n",
-    "\n",
-    "        for step, data_val in enumerate(val_loader):\n",
-    "            images = data_val[\"image\"].to(device)\n",
-    "            classes = data_val[\"slice_label\"].to(device)\n",
-    "            timesteps = torch.randint(0, 1, (len(images),)).to(\n",
-    "                device\n",
-    "            )  # check validation accuracy on the original images, i.e., do not add noise\n",
-    "\n",
-    "            with torch.no_grad():\n",
-    "                with autocast(enabled=False):\n",
-    "                    noise = torch.randn_like(images).to(device)\n",
-    "                    pred = classifier(images, timesteps)\n",
-    "                    val_loss = F.cross_entropy(pred, classes.long(), reduction=\"mean\")\n",
-    "\n",
-    "            val_epoch_loss += val_loss.item()\n",
-    "            _, predicted = torch.max(pred, 1)\n",
-    "            val_epoch_loss_list.append(val_epoch_loss / (step + 1))\n",
-    "        print(\"Epoch\", epoch, \"Validation loss\", val_epoch_loss / (step + 1))\n",
-    "\n",
-    "total_time = time.time() - total_start\n",
-    "print(f\"train completed, total time: {total_time}.\")\n",
-    "\n",
-    "## Learning curves for the Classifier\n",
-    "\n",
-    "plt.style.use(\"seaborn-bright\")\n",
-    "plt.title(\"Learning Curves\", fontsize=20)\n",
-    "plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n",
-    "plt.plot(\n",
-    "    np.linspace(val_interval, n_epochs, int(n_epochs / val_interval)),\n",
-    "    val_epoch_loss_list,\n",
-    "    color=\"C1\",\n",
-    "    linewidth=2.0,\n",
-    "    label=\"Validation\",\n",
-    ")\n",
-    "plt.yticks(fontsize=12)\n",
-    "plt.xticks(fontsize=12)\n",
-    "plt.xlabel(\"Epochs\", fontsize=16)\n",
-    "plt.ylabel(\"Loss\", fontsize=16)\n",
-    "plt.legend(prop={\"size\": 14})\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a676b3fe",
-   "metadata": {},
-   "source": [
-    "# Image-to-image translation to a healthy subject\n",
-    "We pick a diseased subject of the validation set as input image. We want to translate it to its healthy reconstruction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "id": "fe0d9eac-1477-4d6d-a885-d3c4acb4a781",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "minmax tensor(0.) tensor(1.3396)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "DiffusionModelEncoder(\n",
-       "  (conv_in): Convolution(\n",
-       "    (conv): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "  )\n",
-       "  (time_embed): Sequential(\n",
-       "    (0): Linear(in_features=32, out_features=128, bias=True)\n",
-       "    (1): SiLU()\n",
-       "    (2): Linear(in_features=128, out_features=128, bias=True)\n",
-       "  )\n",
-       "  (down_blocks): ModuleList(\n",
-       "    (0): DownBlock(\n",
-       "      (resnets): ModuleList(\n",
-       "        (0): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 32, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=128, out_features=32, bias=True)\n",
-       "          (norm2): GroupNorm(32, 32, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Identity()\n",
-       "        )\n",
-       "      )\n",
-       "      (downsampler): Downsample(\n",
-       "        (op): Convolution(\n",
-       "          (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (1): AttnDownBlock(\n",
-       "      (attentions): ModuleList(\n",
-       "        (0): AttentionBlock(\n",
-       "          (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
-       "        )\n",
-       "      )\n",
-       "      (resnets): ModuleList(\n",
-       "        (0): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 32, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=128, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Convolution(\n",
-       "            (conv): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1))\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "      (downsampler): Downsample(\n",
-       "        (op): Convolution(\n",
-       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (2): AttnDownBlock(\n",
-       "      (attentions): ModuleList(\n",
-       "        (0): AttentionBlock(\n",
-       "          (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
-       "        )\n",
-       "      )\n",
-       "      (resnets): ModuleList(\n",
-       "        (0): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=128, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Identity()\n",
-       "        )\n",
-       "      )\n",
-       "      (downsampler): Downsample(\n",
-       "        (op): Convolution(\n",
-       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       "  (out): Sequential(\n",
-       "    (0): Linear(in_features=4096, out_features=512, bias=True)\n",
-       "    (1): ReLU()\n",
-       "    (2): Dropout(p=0.1, inplace=False)\n",
-       "    (3): Linear(in_features=512, out_features=2, bias=True)\n",
-       "  )\n",
-       ")"
-      ]
-     },
-     "execution_count": 162,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idx_unhealthy = np.argwhere(data_val[\"slice_label\"].numpy() == 0).squeeze()\n",
-    "idx = idx_unhealthy[4]  # Pick a random slice of the validation set to be transformed\n",
-    "inputimg = data_val[\"image\"][idx]  # Pick an input slice of the validation set to be transformed\n",
-    "inputlabel = data_val[\"slice_label\"][idx]  # Check whether it is healthy or diseased\n",
-    "print(\"minmax\", inputimg.min(), inputimg.max())\n",
-    "\n",
-    "plt.figure(\"input\" + str(inputlabel))\n",
-    "plt.imshow(inputimg[0, ...], vmin=0, vmax=1, cmap=\"gray\")\n",
-    "plt.axis(\"off\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "model.eval()\n",
-    "classifier.eval()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0cd48c2d",
-   "metadata": {},
-   "source": [
-    "### Encoding the input image in noise with the reversed DDIM sampling scheme\n",
-    "In order to sample using gradient guidance, we first need to encode the input image in noise by using the reversed DDIM sampling scheme.\\\n",
-    "We define the number of steps in the noising and denoising process by L.\\\n",
-    "The encoding process is presented in Equation 6 of the paper \"Diffusion Models for Medical Anomaly Detection\" (https://arxiv.org/pdf/2203.04306.pdf).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 176,
-   "id": "f71e4924",
-   "metadata": {
-    "jupyter": {
-     "outputs_hidden": false
-    },
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████████████████████████████████████| 200/200 [00:05<00:00, 33.36it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "L = 200\n",
-    "current_img = inputimg[None, ...].to(device)\n",
-    "scheduler.set_timesteps(num_inference_steps=1000)\n",
-    "\n",
-    "progress_bar = tqdm(range(L))  # go back and forth L timesteps\n",
-    "for t in progress_bar:  # go through the noising process\n",
-    "    with autocast(enabled=False):\n",
-    "        with torch.no_grad():\n",
-    "            model_output = model(current_img, timesteps=torch.Tensor((t,)).to(current_img.device))\n",
-    "    current_img, _ = scheduler.reversed_step(model_output, t, current_img)\n",
-    "\n",
-    "plt.style.use(\"default\")\n",
-    "plt.imshow(current_img[0, 0].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
-    "plt.tight_layout()\n",
-    "plt.axis(\"off\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a7c8346a-6296-4800-b978-c10fcdf09779",
-   "metadata": {},
-   "source": [
-    "### Denoising process using gradient guidance\n",
-    "From the noisy image, we apply DDIM sampling scheme for denoising for L steps.\\\n",
-    "Additionally, we apply gradient guidance using the classifier network towards the desired class label y=0 (healthy). This is presented in Algorithm 2 of https://arxiv.org/pdf/2105.05233.pdf, and in Algorithm 1 of https://arxiv.org/pdf/2203.04306.pdf. \\\n",
-    "The scale s is used to amplify the gradient."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "id": "7ab274bd-ea60-4674-b59b-d41de98fee5b",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████████████████████████████████████| 200/200 [00:15<00:00, 12.79it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "y = torch.tensor(0)  # define the desired class label\n",
-    "scale = 6  # define the desired gradient scale s\n",
-    "progress_bar = tqdm(range(L))  # go back and forth L timesteps\n",
-    "\n",
-    "for i in progress_bar:  # go through the denoising process\n",
-    "    t = L - i\n",
-    "    with autocast(enabled=True):\n",
-    "        with torch.no_grad():\n",
-    "            model_output = model(\n",
-    "                current_img, timesteps=torch.Tensor((t,)).to(current_img.device)\n",
-    "            ).detach()  # this is supposed to be epsilon\n",
-    "\n",
-    "        with torch.enable_grad():\n",
-    "            x_in = current_img.detach().requires_grad_(True)\n",
-    "            logits = classifier(x_in, timesteps=torch.Tensor((t,)).to(current_img.device))\n",
-    "            log_probs = F.log_softmax(logits, dim=-1)\n",
-    "            selected = log_probs[range(len(logits)), y.view(-1)]\n",
-    "            a = torch.autograd.grad(selected.sum(), x_in)[0]\n",
-    "            alpha_prod_t = scheduler.alphas_cumprod[t]\n",
-    "            updated_noise = (\n",
-    "                model_output - (1 - alpha_prod_t).sqrt() * scale * a\n",
-    "            )  # update the predicted noise epsilon with the gradient of the classifier\n",
-    "\n",
-    "    current_img, _ = scheduler.step(updated_noise, t, current_img)\n",
-    "    torch.cuda.empty_cache()\n",
-    "\n",
-    "plt.style.use(\"default\")\n",
-    "plt.imshow(current_img[0, 0].cpu().detach().numpy(), vmin=0, vmax=1, cmap=\"gray\")\n",
-    "plt.tight_layout()\n",
-    "plt.axis(\"off\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d2e343f8-c6f3-4071-a5e6-771e2343c3bc",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "source": [
-    "# Anomaly Detection\n",
-    "To get the anomaly map, we compute the difference between the input image the output of our image-to-image translation model towards the healthy reconstruction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "id": "ecffaaf3-a7df-453e-81a9-757113d85084",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "diff = abs(inputimg.cpu() - current_img[0, 0].cpu()).detach().numpy()\n",
-    "plt.style.use(\"default\")\n",
-    "plt.imshow(diff[0, ...], cmap=\"jet\")\n",
-    "plt.tight_layout()\n",
-    "plt.axis(\"off\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c459ab23-459d-4063-824e-39dac93abb43",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "py:percent,ipynb"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+    "nbformat": 4,
+    "nbformat_minor": 5
 }
diff --git a/tutorials/generative/anomaly_detection/anomalydetection_tutorial_classifier_guidance.py b/tutorials/generative/anomaly_detection/anomalydetection_tutorial_classifier_guidance.py
index 54fdb6f6..7add440f 100644
--- a/tutorials/generative/anomaly_detection/anomalydetection_tutorial_classifier_guidance.py
+++ b/tutorials/generative/anomaly_detection/anomalydetection_tutorial_classifier_guidance.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
@@ -104,7 +104,7 @@
         transforms.LoadImaged(keys=["image", "label"]),
         transforms.EnsureChannelFirstd(keys=["image", "label"]),
         transforms.Lambdad(keys=["image"], func=lambda x: x[channel, :, :, :]),
-        transforms.AddChanneld(keys=["image"]),
+        transforms.EnsureChannelFirstd(keys=["image"], channel_dim="no_channel"),
         transforms.EnsureTyped(keys=["image", "label"]),
         transforms.Orientationd(keys=["image", "label"], axcodes="RAS"),
         transforms.Spacingd(keys=["image", "label"], pixdim=(3.0, 3.0, 2.0), mode=("bilinear", "nearest")),
diff --git a/tutorials/generative/image_to_image_translation/tutorial_segmentation_with_ddpm.ipynb b/tutorials/generative/image_to_image_translation/tutorial_segmentation_with_ddpm.ipynb
index b16613ef..dc5629a0 100644
--- a/tutorials/generative/image_to_image_translation/tutorial_segmentation_with_ddpm.ipynb
+++ b/tutorials/generative/image_to_image_translation/tutorial_segmentation_with_ddpm.ipynb
@@ -1,1491 +1,1490 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "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",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Diffusion Models for Implicit Image Segmentation Ensembles<br>\n",
-    "<br>\n",
-    "This tutorial illustrates how to use MONAI for 2D segmentation of images using DDPMs, as proposed in [1].<br>\n",
-    "The same structure can also be used for conditional image generation, or image-to-image translation, as proposed in [2,3].\n",
-    "<br>\n",
-    "<br>\n",
-    "[1] - Wolleb et al. \"Diffusion Models for Implicit Image Segmentation Ensembles\", https://arxiv.org/abs/2112.03145<br>\n",
-    "[2] - Waibel et al. \"A Diffusion Model Predicts 3D Shapes from 2D Microscopy Images\", https://arxiv.org/abs/2208.14125<br>\n",
-    "[3] - Durrer et al. \"Diffusion Models for Contrast Harmonization of Magnetic Resonance Images\", https://aps.arxiv.org/abs/2303.08189\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup environment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "running install\n",
-      "/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\n",
-      "  warnings.warn(\n",
-      "/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/setuptools/command/easy_install.py:144: EasyInstallDeprecationWarning: easy_install command is deprecated. Use build and pip and other standards-based tools.\n",
-      "  warnings.warn(\n",
-      "running bdist_egg\n",
-      "running egg_info\n",
-      "writing generative.egg-info/PKG-INFO\n",
-      "writing dependency_links to generative.egg-info/dependency_links.txt\n",
-      "writing requirements to generative.egg-info/requires.txt\n",
-      "writing top-level names to generative.egg-info/top_level.txt\n",
-      "reading manifest file 'generative.egg-info/SOURCES.txt'\n",
-      "writing manifest file 'generative.egg-info/SOURCES.txt'\n",
-      "installing library code to build/bdist.linux-x86_64/egg\n",
-      "running install_lib\n",
-      "warning: install_lib: 'build/lib' does not exist -- no Python modules to install\n",
-      "\n",
-      "creating build/bdist.linux-x86_64/egg\n",
-      "creating build/bdist.linux-x86_64/egg/EGG-INFO\n",
-      "copying generative.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
-      "copying generative.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
-      "copying generative.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
-      "copying generative.egg-info/requires.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
-      "copying generative.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
-      "zip_safe flag not set; analyzing archive contents...\n",
-      "creating 'dist/generative-0.1.0-py3.10.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n",
-      "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n",
-      "Processing generative-0.1.0-py3.10.egg\n",
-      "Removing /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/generative-0.1.0-py3.10.egg\n",
-      "Copying generative-0.1.0-py3.10.egg to /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages\n",
-      "generative 0.1.0 is already the active version in easy-install.pth\n",
-      "\n",
-      "Installed /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/generative-0.1.0-py3.10.egg\n",
-      "Processing dependencies for generative==0.1.0\n",
-      "Searching for monai-weekly==1.2.dev2304\n",
-      "Best match: monai-weekly 1.2.dev2304\n",
-      "Adding monai-weekly 1.2.dev2304 to easy-install.pth file\n",
-      "\n",
-      "Using /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages\n",
-      "Searching for numpy==1.23.2\n",
-      "Best match: numpy 1.23.2\n",
-      "Adding numpy 1.23.2 to easy-install.pth file\n",
-      "Installing f2py script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
-      "Installing f2py3 script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
-      "Installing f2py3.10 script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
-      "\n",
-      "Using /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages\n",
-      "Searching for torch==1.12.1\n",
-      "Best match: torch 1.12.1\n",
-      "Adding torch 1.12.1 to easy-install.pth file\n",
-      "Installing convert-caffe2-to-onnx script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
-      "Installing convert-onnx-to-caffe2 script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
-      "Installing torchrun script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
-      "\n",
-      "Using /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages\n",
-      "Searching for typing-extensions==4.3.0\n",
-      "Best match: typing-extensions 4.3.0\n",
-      "Adding typing-extensions 4.3.0 to easy-install.pth file\n",
-      "\n",
-      "Using /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages\n",
-      "Finished processing dependencies for generative==0.1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "!python -c \"import monai\" || pip install -q \"monai-weekly[pillow, tqdm]\"\n",
-    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
-    "!python -c \"import seaborn\" || pip install -q seaborn"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Setup imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MONAI version: 1.2.dev2304\n",
-      "Numpy version: 1.23.2\n",
-      "Pytorch version: 1.12.1\n",
-      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
-      "MONAI rev id: 9a57be5aab9f2c2a134768c0c146399150e247a0\n",
-      "MONAI __file__: /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/monai/__init__.py\n",
-      "\n",
-      "Optional dependencies:\n",
-      "Pytorch Ignite version: 0.4.10\n",
-      "ITK version: 5.3.0\n",
-      "Nibabel version: 4.0.1\n",
-      "scikit-image version: 0.19.3\n",
-      "Pillow version: 9.2.0\n",
-      "Tensorboard version: 2.12.0\n",
-      "gdown version: 4.6.4\n",
-      "TorchVision version: 0.13.1\n",
-      "tqdm version: 4.64.1\n",
-      "lmdb version: 1.4.0\n",
-      "psutil version: 5.9.4\n",
-      "pandas version: 1.5.3\n",
-      "einops version: 0.6.0\n",
-      "transformers version: 4.21.3\n",
-      "mlflow version: 2.1.1\n",
-      "pynrrd version: 1.0.0\n",
-      "\n",
-      "For details about installing the optional dependencies, please visit:\n",
-      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import tempfile\n",
-    "import time\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "import torch.nn.functional as F\n",
-    "from monai import transforms\n",
-    "from monai.apps import DecathlonDataset\n",
-    "from monai.config import print_config\n",
-    "from monai.data import DataLoader\n",
-    "from monai.utils import set_determinism\n",
-    "from torch.cuda.amp import GradScaler, autocast\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "from generative.inferers import DiffusionInferer\n",
-    "from generative.networks.nets.diffusion_model_unet import DiffusionModelUNet\n",
-    "from generative.networks.schedulers.ddpm import DDPMScheduler\n",
-    "\n",
-    "torch.multiprocessing.set_sharing_strategy(\"file_system\")\n",
-    "print_config()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup data directory"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [],
-   "source": [
-    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
-    "root_dir = tempfile.mkdtemp() if directory is None else directory"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Set deterministic training for reproducibility"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "set_determinism(42)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Preprocessing of the BRATS Dataset in 2D slices for training\n",
-    "We download the BRATS training dataset from the Decathlon dataset. \\\n",
-    "We slice the volumes in axial 2D slices, and assign slice-wise ground truth segmentations of the tumor to all slices.\n",
-    "Here we use transforms to augment the training dataset:\n",
-    "\n",
-    "1. `LoadImaged` loads the brain MR images from files.\n",
-    "1. `EnsureChannelFirstd` ensures the original data to construct \"channel first\" shape.\n",
-    "1. `ScaleIntensityRangePercentilesd` takes the lower and upper intensity percentiles and scales them to [0, 1].\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<class 'monai.transforms.utility.array.AddChannel'>: Class `AddChannel` has been deprecated since version 0.8. please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead.\n"
-     ]
-    }
-   ],
-   "source": [
-    "channel = 0  # 0 = Flair\n",
-    "assert channel in [0, 1, 2, 3], \"Choose a valid channel\"\n",
-    "\n",
-    "\n",
-    "train_transforms = transforms.Compose(\n",
-    "    [\n",
-    "        transforms.LoadImaged(keys=[\"image\", \"label\"]),\n",
-    "        transforms.EnsureChannelFirstd(keys=[\"image\", \"label\"]),\n",
-    "        transforms.Lambdad(keys=[\"image\"], func=lambda x: x[channel, :, :, :]),\n",
-    "        transforms.AddChanneld(keys=[\"image\"]),\n",
-    "        transforms.EnsureTyped(keys=[\"image\", \"label\"]),\n",
-    "        transforms.Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
-    "        transforms.Spacingd(keys=[\"image\", \"label\"], pixdim=(3.0, 3.0, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
-    "        transforms.CenterSpatialCropd(keys=[\"image\", \"label\"], roi_size=(64, 64, 64)),\n",
-    "        transforms.ScaleIntensityRangePercentilesd(keys=\"image\", lower=0, upper=99.5, b_min=0, b_max=1),\n",
-    "        transforms.RandSpatialCropd(keys=[\"image\", \"label\"], roi_size=(64, 64, 1), random_size=False),\n",
-    "        transforms.Lambdad(keys=[\"image\", \"label\"], func=lambda x: x.squeeze(-1)),\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████| 388/388 [02:59<00:00,  2.16it/s]\n"
-     ]
+    "cells": [
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "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",
+            "metadata": {},
+            "source": [
+                "\n",
+                "# Diffusion Models for Implicit Image Segmentation Ensembles<br>\n",
+                "<br>\n",
+                "This tutorial illustrates how to use MONAI for 2D segmentation of images using DDPMs, as proposed in [1].<br>\n",
+                "The same structure can also be used for conditional image generation, or image-to-image translation, as proposed in [2,3].\n",
+                "<br>\n",
+                "<br>\n",
+                "[1] - Wolleb et al. \"Diffusion Models for Implicit Image Segmentation Ensembles\", https://arxiv.org/abs/2112.03145<br>\n",
+                "[2] - Waibel et al. \"A Diffusion Model Predicts 3D Shapes from 2D Microscopy Images\", https://arxiv.org/abs/2208.14125<br>\n",
+                "[3] - Durrer et al. \"Diffusion Models for Contrast Harmonization of Magnetic Resonance Images\", https://aps.arxiv.org/abs/2303.08189\n",
+                "\n"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## Setup environment"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 59,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "running install\n",
+                        "/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\n",
+                        "  warnings.warn(\n",
+                        "/home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/setuptools/command/easy_install.py:144: EasyInstallDeprecationWarning: easy_install command is deprecated. Use build and pip and other standards-based tools.\n",
+                        "  warnings.warn(\n",
+                        "running bdist_egg\n",
+                        "running egg_info\n",
+                        "writing generative.egg-info/PKG-INFO\n",
+                        "writing dependency_links to generative.egg-info/dependency_links.txt\n",
+                        "writing requirements to generative.egg-info/requires.txt\n",
+                        "writing top-level names to generative.egg-info/top_level.txt\n",
+                        "reading manifest file 'generative.egg-info/SOURCES.txt'\n",
+                        "writing manifest file 'generative.egg-info/SOURCES.txt'\n",
+                        "installing library code to build/bdist.linux-x86_64/egg\n",
+                        "running install_lib\n",
+                        "warning: install_lib: 'build/lib' does not exist -- no Python modules to install\n",
+                        "\n",
+                        "creating build/bdist.linux-x86_64/egg\n",
+                        "creating build/bdist.linux-x86_64/egg/EGG-INFO\n",
+                        "copying generative.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
+                        "copying generative.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
+                        "copying generative.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
+                        "copying generative.egg-info/requires.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
+                        "copying generative.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
+                        "zip_safe flag not set; analyzing archive contents...\n",
+                        "creating 'dist/generative-0.1.0-py3.10.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n",
+                        "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n",
+                        "Processing generative-0.1.0-py3.10.egg\n",
+                        "Removing /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/generative-0.1.0-py3.10.egg\n",
+                        "Copying generative-0.1.0-py3.10.egg to /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages\n",
+                        "generative 0.1.0 is already the active version in easy-install.pth\n",
+                        "\n",
+                        "Installed /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/generative-0.1.0-py3.10.egg\n",
+                        "Processing dependencies for generative==0.1.0\n",
+                        "Searching for monai-weekly==1.2.dev2304\n",
+                        "Best match: monai-weekly 1.2.dev2304\n",
+                        "Adding monai-weekly 1.2.dev2304 to easy-install.pth file\n",
+                        "\n",
+                        "Using /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages\n",
+                        "Searching for numpy==1.23.2\n",
+                        "Best match: numpy 1.23.2\n",
+                        "Adding numpy 1.23.2 to easy-install.pth file\n",
+                        "Installing f2py script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
+                        "Installing f2py3 script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
+                        "Installing f2py3.10 script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
+                        "\n",
+                        "Using /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages\n",
+                        "Searching for torch==1.12.1\n",
+                        "Best match: torch 1.12.1\n",
+                        "Adding torch 1.12.1 to easy-install.pth file\n",
+                        "Installing convert-caffe2-to-onnx script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
+                        "Installing convert-onnx-to-caffe2 script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
+                        "Installing torchrun script to /home/juliawolleb/anaconda3/envs/experiment/bin\n",
+                        "\n",
+                        "Using /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages\n",
+                        "Searching for typing-extensions==4.3.0\n",
+                        "Best match: typing-extensions 4.3.0\n",
+                        "Adding typing-extensions 4.3.0 to easy-install.pth file\n",
+                        "\n",
+                        "Using /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages\n",
+                        "Finished processing dependencies for generative==0.1.0\n"
+                    ]
+                }
+            ],
+            "source": [
+                "!python -c \"import monai\" || pip install -q \"monai-weekly[pillow, tqdm]\"\n",
+                "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
+                "!python -c \"import seaborn\" || pip install -q seaborn"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "\n",
+                "## Setup imports"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 60,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "MONAI version: 1.2.dev2304\n",
+                        "Numpy version: 1.23.2\n",
+                        "Pytorch version: 1.12.1\n",
+                        "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
+                        "MONAI rev id: 9a57be5aab9f2c2a134768c0c146399150e247a0\n",
+                        "MONAI __file__: /home/juliawolleb/anaconda3/envs/experiment/lib/python3.10/site-packages/monai/__init__.py\n",
+                        "\n",
+                        "Optional dependencies:\n",
+                        "Pytorch Ignite version: 0.4.10\n",
+                        "ITK version: 5.3.0\n",
+                        "Nibabel version: 4.0.1\n",
+                        "scikit-image version: 0.19.3\n",
+                        "Pillow version: 9.2.0\n",
+                        "Tensorboard version: 2.12.0\n",
+                        "gdown version: 4.6.4\n",
+                        "TorchVision version: 0.13.1\n",
+                        "tqdm version: 4.64.1\n",
+                        "lmdb version: 1.4.0\n",
+                        "psutil version: 5.9.4\n",
+                        "pandas version: 1.5.3\n",
+                        "einops version: 0.6.0\n",
+                        "transformers version: 4.21.3\n",
+                        "mlflow version: 2.1.1\n",
+                        "pynrrd version: 1.0.0\n",
+                        "\n",
+                        "For details about installing the optional dependencies, please visit:\n",
+                        "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
+                        "\n"
+                    ]
+                }
+            ],
+            "source": [
+                "import os\n",
+                "import tempfile\n",
+                "import time\n",
+                "\n",
+                "import matplotlib.pyplot as plt\n",
+                "import numpy as np\n",
+                "import torch\n",
+                "import torch.nn.functional as F\n",
+                "from monai import transforms\n",
+                "from monai.apps import DecathlonDataset\n",
+                "from monai.config import print_config\n",
+                "from monai.data import DataLoader\n",
+                "from monai.utils import set_determinism\n",
+                "from torch.cuda.amp import GradScaler, autocast\n",
+                "from tqdm import tqdm\n",
+                "\n",
+                "from generative.inferers import DiffusionInferer\n",
+                "from generative.networks.nets.diffusion_model_unet import DiffusionModelUNet\n",
+                "from generative.networks.schedulers.ddpm import DDPMScheduler\n",
+                "\n",
+                "torch.multiprocessing.set_sharing_strategy(\"file_system\")\n",
+                "print_config()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## Setup data directory"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 61,
+            "metadata": {
+                "lines_to_next_cell": 2
+            },
+            "outputs": [],
+            "source": [
+                "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
+                "root_dir = tempfile.mkdtemp() if directory is None else directory"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "\n",
+                "## Set deterministic training for reproducibility"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 62,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "set_determinism(42)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "\n",
+                "# Preprocessing of the BRATS Dataset in 2D slices for training\n",
+                "We download the BRATS training dataset from the Decathlon dataset. \\\n",
+                "We slice the volumes in axial 2D slices, and assign slice-wise ground truth segmentations of the tumor to all slices.\n",
+                "Here we use transforms to augment the training dataset:\n",
+                "\n",
+                "1. `LoadImaged` loads the brain MR images from files.\n",
+                "1. `EnsureChannelFirstd` ensures the original data to construct \"channel first\" shape.\n",
+                "1. `ScaleIntensityRangePercentilesd` takes the lower and upper intensity percentiles and scales them to [0, 1].\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 63,
+            "metadata": {
+                "lines_to_next_cell": 2
+            },
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "<class 'monai.transforms.utility.array.AddChannel'>: Class `AddChannel` has been deprecated since version 0.8. please use MetaTensor data type and monai.transforms.EnsureChannelFirst instead.\n"
+                    ]
+                }
+            ],
+            "source": [
+                "channel = 0  # 0 = Flair\n",
+                "assert channel in [0, 1, 2, 3], \"Choose a valid channel\"\n",
+                "\n",
+                "\n",
+                "train_transforms = transforms.Compose(\n",
+                "    [\n",
+                "        transforms.LoadImaged(keys=[\"image\", \"label\"]),\n",
+                "        transforms.EnsureChannelFirstd(keys=[\"image\", \"label\"]),\n",
+                "        transforms.Lambdad(keys=[\"image\"], func=lambda x: x[channel, :, :, :]),\n",
+                "        transforms.EnsureChannelFirstd(keys=[\"image\"], channel_dim=\"no_channel\"),\n",
+                "        transforms.EnsureTyped(keys=[\"image\", \"label\"]),\n",
+                "        transforms.Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
+                "        transforms.Spacingd(keys=[\"image\", \"label\"], pixdim=(3.0, 3.0, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
+                "        transforms.CenterSpatialCropd(keys=[\"image\", \"label\"], roi_size=(64, 64, 64)),\n",
+                "        transforms.ScaleIntensityRangePercentilesd(keys=\"image\", lower=0, upper=99.5, b_min=0, b_max=1),\n",
+                "        transforms.RandSpatialCropd(keys=[\"image\", \"label\"], roi_size=(64, 64, 1), random_size=False),\n",
+                "        transforms.Lambdad(keys=[\"image\", \"label\"], func=lambda x: x.squeeze(-1)),\n",
+                "    ]\n",
+                ")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 64,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Loading dataset: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 388/388 [02:59<00:00,  2.16it/s]\n"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Length of training data: 388\n",
+                        "Train image shape torch.Size([1, 64, 64])\n",
+                        "Train label shape torch.Size([1, 64, 64])\n"
+                    ]
+                }
+            ],
+            "source": [
+                "batch_size = 32\n",
+                "\n",
+                "train_ds = DecathlonDataset(\n",
+                "    root_dir=root_dir,\n",
+                "    task=\"Task01_BrainTumour\",\n",
+                "    section=\"training\",  # validation\n",
+                "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
+                "    num_workers=4,\n",
+                "    download=False,  # Set download to True if the dataset hasnt been downloaded yet\n",
+                "    seed=0,\n",
+                "    transform=train_transforms,\n",
+                ")\n",
+                "\n",
+                "print(f\"Length of training data: {len(train_ds)}\")  # this gives the number of patients in the training set\n",
+                "print(f'Train image shape {train_ds[0][\"image\"].shape}')\n",
+                "print(f'Train label shape {train_ds[0][\"label\"].shape}')\n",
+                "\n",
+                "\n",
+                "train_loader = DataLoader(\n",
+                "    train_ds, batch_size=batch_size, shuffle=True, num_workers=4, drop_last=True, persistent_workers=True\n",
+                ")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## Preprocessing of the BRATS Dataset in 2D slices for validation\n",
+                "We download the BRATS validation dataset from the Decathlon dataset. We define the dataloader to load 2D slices as well as the corresponding ground truth tumor segmentation for validation."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 65,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Loading dataset: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 96/96 [00:45<00:00,  2.12it/s]"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Length of training data: 96\n",
+                        "Validation Image shape torch.Size([1, 64, 64])\n",
+                        "Validation Label shape torch.Size([1, 64, 64])\n"
+                    ]
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "\n"
+                    ]
+                }
+            ],
+            "source": [
+                "val_ds = DecathlonDataset(\n",
+                "    root_dir=root_dir,\n",
+                "    task=\"Task01_BrainTumour\",\n",
+                "    section=\"validation\",\n",
+                "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
+                "    num_workers=4,\n",
+                "    download=False,  # Set download to True if the dataset hasnt been downloaded yet\n",
+                "    seed=0,\n",
+                "    transform=train_transforms,\n",
+                ")\n",
+                "print(f\"Length of training data: {len(val_ds)}\")\n",
+                "print(f'Validation Image shape {val_ds[0][\"image\"].shape}')\n",
+                "print(f'Validation Label shape {val_ds[0][\"label\"].shape}')\n",
+                "\n",
+                "val_loader = DataLoader(\n",
+                "    val_ds, batch_size=batch_size, shuffle=False, num_workers=4, drop_last=True, persistent_workers=True\n",
+                ")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "\n",
+                "## Define network, scheduler, optimizer, and inferer\n",
+                "\n",
+                "At this step, we instantiate the MONAI components to create a DDPM, the UNET, the noise scheduler, and the inferer used for training and sampling. We are using the DDPM scheduler containing 1000 timesteps, and a 2D UNET with attention mechanisms in the 3rd level (`num_head_channels=64`).<br>\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 66,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "device = torch.device(\"cuda\")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 67,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "DiffusionModelUNet(\n",
+                            "  (conv_in): Convolution(\n",
+                            "    (conv): Conv2d(2, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "  )\n",
+                            "  (time_embed): Sequential(\n",
+                            "    (0): Linear(in_features=64, out_features=256, bias=True)\n",
+                            "    (1): SiLU()\n",
+                            "    (2): Linear(in_features=256, out_features=256, bias=True)\n",
+                            "  )\n",
+                            "  (down_blocks): ModuleList(\n",
+                            "    (0): DownBlock(\n",
+                            "      (resnets): ModuleList(\n",
+                            "        (0): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Identity()\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (downsampler): Downsample(\n",
+                            "        (op): Convolution(\n",
+                            "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+                            "        )\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (1): DownBlock(\n",
+                            "      (resnets): ModuleList(\n",
+                            "        (0): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Identity()\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (downsampler): Downsample(\n",
+                            "        (op): Convolution(\n",
+                            "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+                            "        )\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (2): AttnDownBlock(\n",
+                            "      (attentions): ModuleList(\n",
+                            "        (0): AttentionBlock(\n",
+                            "          (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (resnets): ModuleList(\n",
+                            "        (0): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Identity()\n",
+                            "        )\n",
+                            "      )\n",
+                            "    )\n",
+                            "  )\n",
+                            "  (middle_block): AttnMidBlock(\n",
+                            "    (resnet_1): ResnetBlock(\n",
+                            "      (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "      (nonlinearity): SiLU()\n",
+                            "      (conv1): Convolution(\n",
+                            "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "      )\n",
+                            "      (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "      (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "      (conv2): Convolution(\n",
+                            "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "      )\n",
+                            "      (skip_connection): Identity()\n",
+                            "    )\n",
+                            "    (attention): AttentionBlock(\n",
+                            "      (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "      (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "      (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "      (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "      (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "    )\n",
+                            "    (resnet_2): ResnetBlock(\n",
+                            "      (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "      (nonlinearity): SiLU()\n",
+                            "      (conv1): Convolution(\n",
+                            "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "      )\n",
+                            "      (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "      (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "      (conv2): Convolution(\n",
+                            "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "      )\n",
+                            "      (skip_connection): Identity()\n",
+                            "    )\n",
+                            "  )\n",
+                            "  (up_blocks): ModuleList(\n",
+                            "    (0): AttnUpBlock(\n",
+                            "      (resnets): ModuleList(\n",
+                            "        (0): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
+                            "          )\n",
+                            "        )\n",
+                            "        (1): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
+                            "          )\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (attentions): ModuleList(\n",
+                            "        (0): AttentionBlock(\n",
+                            "          (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "        )\n",
+                            "        (1): AttentionBlock(\n",
+                            "          (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (upsampler): Upsample(\n",
+                            "        (conv): Convolution(\n",
+                            "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "        )\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (1): UpBlock(\n",
+                            "      (resnets): ModuleList(\n",
+                            "        (0): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
+                            "          )\n",
+                            "        )\n",
+                            "        (1): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
+                            "          )\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (upsampler): Upsample(\n",
+                            "        (conv): Convolution(\n",
+                            "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "        )\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (2): UpBlock(\n",
+                            "      (resnets): ModuleList(\n",
+                            "        (0): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
+                            "          )\n",
+                            "        )\n",
+                            "        (1): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
+                            "          )\n",
+                            "        )\n",
+                            "      )\n",
+                            "    )\n",
+                            "  )\n",
+                            "  (out): Sequential(\n",
+                            "    (0): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "    (1): SiLU()\n",
+                            "    (2): Convolution(\n",
+                            "      (conv): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "    )\n",
+                            "  )\n",
+                            ")"
+                        ]
+                    },
+                    "execution_count": 67,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "model = DiffusionModelUNet(\n",
+                "    spatial_dims=2,\n",
+                "    in_channels=2,\n",
+                "    out_channels=1,\n",
+                "    num_channels=(64, 64, 64),\n",
+                "    attention_levels=(False, False, True),\n",
+                "    num_res_blocks=1,\n",
+                "    num_head_channels=64,\n",
+                "    with_conditioning=False,\n",
+                ")\n",
+                "model.to(device)"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 68,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "scheduler = DDPMScheduler(num_train_timesteps=1000)\n",
+                "optimizer = torch.optim.Adam(params=model.parameters(), lr=2.5e-5)\n",
+                "inferer = DiffusionInferer(scheduler)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "\n",
+                "### Model training of the Diffusion Model<br>\n",
+                "We train our diffusion model for 4000 epochs.\\\n",
+                "In every step, we concatenate the original MR image to the noisy segmentation mask, to predict a slightly denoised segmentation mask.\\\n",
+                "This is described in Equation 7 of the paper https://arxiv.org/pdf/2112.03145.pdf."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 71,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "n_epochs = 4000\n",
+                "val_interval = 50\n",
+                "epoch_loss_list = []\n",
+                "val_epoch_loss_list = []"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 72,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Epoch 0 Validation loss 0.95528511206309\n",
+                        "Epoch 50 Validation loss 0.012534369714558125\n",
+                        "Epoch 100 Validation loss 0.006971345127870639\n",
+                        "Epoch 150 Validation loss 0.007212877739220858\n",
+                        "Epoch 200 Validation loss 0.0032773815716306367\n",
+                        "Epoch 250 Validation loss 0.0032613773364573717\n",
+                        "Epoch 300 Validation loss 0.0027105089587469897\n",
+                        "Epoch 350 Validation loss 0.00643360164637367\n",
+                        "Epoch 400 Validation loss 0.004260187192509572\n",
+                        "Epoch 450 Validation loss 0.003338431240990758\n",
+                        "Epoch 500 Validation loss 0.003913956073423226\n",
+                        "Epoch 550 Validation loss 0.002972103344897429\n",
+                        "Epoch 600 Validation loss 0.004632922820746899\n",
+                        "Epoch 650 Validation loss 0.0021505119123806558\n",
+                        "Epoch 700 Validation loss 0.0031663976066435375\n",
+                        "Epoch 750 Validation loss 0.0030567607997606197\n",
+                        "Epoch 800 Validation loss 0.002882685783940057\n",
+                        "Epoch 850 Validation loss 0.0033122211073835692\n",
+                        "Epoch 900 Validation loss 0.002124195219948888\n",
+                        "Epoch 950 Validation loss 0.0046148050266007585\n",
+                        "Epoch 1000 Validation loss 0.0033069681376218796\n",
+                        "Epoch 1050 Validation loss 0.002037846017628908\n",
+                        "Epoch 1100 Validation loss 0.00229898770339787\n",
+                        "Epoch 1150 Validation loss 0.002420713659375906\n",
+                        "Epoch 1200 Validation loss 0.0041328890559573965\n",
+                        "Epoch 1250 Validation loss 0.004329038473467032\n",
+                        "Epoch 1300 Validation loss 0.0023907446302473545\n",
+                        "Epoch 1350 Validation loss 0.0030839802930131555\n",
+                        "Epoch 1400 Validation loss 0.0031827394074449935\n",
+                        "Epoch 1450 Validation loss 0.0028609122770527997\n",
+                        "Epoch 1500 Validation loss 0.0028457901595781245\n",
+                        "Epoch 1550 Validation loss 0.004309413023293018\n",
+                        "Epoch 1600 Validation loss 0.0026823601219803095\n",
+                        "Epoch 1650 Validation loss 0.0026449985646953187\n",
+                        "Epoch 1700 Validation loss 0.0023076763997475305\n",
+                        "Epoch 1750 Validation loss 0.002638093564504137\n",
+                        "Epoch 1800 Validation loss 0.0023806413325170674\n",
+                        "Epoch 1850 Validation loss 0.0018986108091970284\n",
+                        "Epoch 1900 Validation loss 0.0031037907659386597\n",
+                        "Epoch 1950 Validation loss 0.003802627402668198\n",
+                        "Epoch 2000 Validation loss 0.002883425874946018\n",
+                        "Epoch 2050 Validation loss 0.0025882223077739277\n",
+                        "Epoch 2100 Validation loss 0.0024797461228445172\n",
+                        "Epoch 2150 Validation loss 0.002770921913906932\n",
+                        "Epoch 2200 Validation loss 0.0031128532718867064\n",
+                        "Epoch 2250 Validation loss 0.0026554526605953774\n",
+                        "Epoch 2300 Validation loss 0.0006344413559418172\n",
+                        "Epoch 2350 Validation loss 0.003407757030799985\n",
+                        "Epoch 2400 Validation loss 0.003249160130508244\n",
+                        "Epoch 2450 Validation loss 0.0028327014297246933\n",
+                        "Epoch 2500 Validation loss 0.001949470800658067\n",
+                        "Epoch 2550 Validation loss 0.0037267633403340974\n",
+                        "Epoch 2600 Validation loss 0.0025921284686774015\n",
+                        "Epoch 2650 Validation loss 0.0028454426986475787\n",
+                        "Epoch 2700 Validation loss 0.0028132296477754912\n",
+                        "Epoch 2750 Validation loss 0.002190210895302395\n",
+                        "Epoch 2800 Validation loss 0.00311990175396204\n",
+                        "Epoch 2850 Validation loss 0.0022285515442490578\n",
+                        "Epoch 2900 Validation loss 0.0025012800081943474\n",
+                        "Epoch 2950 Validation loss 0.0019996101036667824\n",
+                        "Epoch 3000 Validation loss 0.002304922246063749\n",
+                        "Epoch 3050 Validation loss 0.002658801774183909\n",
+                        "Epoch 3100 Validation loss 0.0020781653001904488\n",
+                        "Epoch 3150 Validation loss 0.002127699709186951\n",
+                        "Epoch 3200 Validation loss 0.0021539490359524884\n",
+                        "Epoch 3250 Validation loss 0.003169172133008639\n",
+                        "Epoch 3300 Validation loss 0.002952027212207516\n",
+                        "Epoch 3350 Validation loss 0.0019326623684416215\n",
+                        "Epoch 3400 Validation loss 0.0023521527958412967\n",
+                        "Epoch 3450 Validation loss 0.0018178095730642478\n",
+                        "Epoch 3500 Validation loss 0.0019376981460178893\n",
+                        "Epoch 3550 Validation loss 0.003424471477046609\n",
+                        "Epoch 3600 Validation loss 0.001281890688308825\n",
+                        "Epoch 3650 Validation loss 0.00280005915556103\n",
+                        "Epoch 3700 Validation loss 0.002113828396735092\n",
+                        "Epoch 3750 Validation loss 0.0026302541761348643\n",
+                        "Epoch 3800 Validation loss 0.003950760544588168\n",
+                        "Epoch 3850 Validation loss 0.0018702246791993578\n",
+                        "Epoch 3900 Validation loss 0.003523522444690267\n",
+                        "Epoch 3950 Validation loss 0.003113662280763189\n",
+                        "train diffusion completed, total time: 11462.321615695953.\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkQAAAHZCAYAAABn8CRaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB0IUlEQVR4nO3dd3iT5foH8O+b0XSntIVCaSl7l+ECVPYUUQFFtkzhh4C44AiowAEEBx4QxYMDqExBQGWDLFEOyBCkUPYqVGhpadPdJnl+f4SEpE3SlCZNWr6f68pFeefz5s24cz9LEkIIEBERET3EZO4uABEREZG7MSAiIiKihx4DIiIiInroMSAiIiKihx4DIiIiInroMSAiIiKihx4DIiIiInroMSAiIiKihx4DIiIiInroMSCicmHo0KGQJAnVq1d3d1GISqR69eqQJAlDhw61uU12djZmzJiBpk2bws/PD5IkQZIkvPHGGxbbXb9+HaNHj0atWrXg7e1t2u6nn35y6TU4avr06aYykecojfvSrl07SJKEdu3auewcxcWAyMPs27fP9EKcPn26u4tDHiI+Ph6ffPIJunTpgho1asDf3x8+Pj6oWrUqunbtilmzZuHKlSvuLuZD5erVq6b3qvlDLpcjKCgIUVFRaNmyJcaOHYvly5cjIyPDKefNz89Hp06dMH36dPz999/Iysqyut3169fx6KOP4uuvv8bly5eRm5vrlPOTdeaf3ZIkISAgwOa9MZednQ21Wm2x7759+1xfYCpE4e4CEJFtubm5mDJlCr788kurX2gJCQlISEjAzp078cEHH6BPnz749NNPERkZ6YbSEgDo9XqkpaUhLS0N169fx+HDh7Fo0SIEBARg5MiRmDlzJvz8/B74+OvWrcPBgwcBGDKjQ4YMQWhoKACY/gWAWbNm4c6dO1AoFJg9ezbatGkDf39/AEBUVFQJrpAckZGRgZ9++gkDBgywu93PP/8MjUZTSqUiexgQUbmwbNkyLFu2zN3FcKrk5GQ8//zzpi+/gIAA9O/fHx07dkRERASUSiVu3bqFP/74Axs2bMCFCxewdu1atGrVqlDVCbnWCy+8gFmzZpn+n5WVhdTUVJw5cwb79+/H5s2bkZ6ejv/85z/YsmULNm/ejDp16lg91tWrV+2e69dffwUAVK5cGd9++y3kcrnd7Xr27IlJkyY9wFW53vTp08tlJtzb2xs5OTlYvnx5kQHR8uXLLfYh92FAROSB9Ho9+vXrZwqGunfvjqVLl6JSpUqFtn3uuefw4YcfYsWKFZg4cWJpF5UABAUFoXHjxoWWd+nSBW+88QauX7+OkSNHYteuXTh//jx69OiBw4cPIygoqNjnunnzJgCgZs2aNoMh8+3q1q1b7HNQyTz//PNYu3Ytdu3ahVu3bqFy5cpWt0tMTMTOnTsBGILqH374oTSLSQWwDRGRB1q4cKHpF36nTp3w888/Ww2GjGQyGV555RUcO3YMTZo0Ka1ikoOqVauGbdu24dlnnwUAnD9//oEzI8aqU6VSaXe7vLw8h7Yj5+vSpQsqV64MnU6H1atX29xu9erV0Gq1CAsLQ+fOnUuxhGQNA6Jy6s8//8Srr76KunXrwt/fH35+fqhfvz7Gjh2LCxcu2N338uXLmDdvHp577jlUr14dPj4+8PHxQVRUFPr27Yvt27fb3X/ZsmWmxoFXr15Fbm4u5s+fj5YtWyI0NNSiwXjBbfV6Pb7++ms8+eSTqFChAvz8/NCkSRPMnj3bbgPFonqZFWyofuTIEfTv3x8RERFQqVSoWrUqBg8ejLi4OLvXBgCZmZn497//jejoaPj5+SEkJARPP/00lixZAiGERePKB2kcmZ+fj08++QSAIY2+dOlSKBSOJXMjIiLQoUMHi2WO9sAreC8KKtj76dixYxg6dChq1KgBlUpl6pFSq1YtSJKEp59+usjy3rp1CwqFApIk4e2337a6jVarxXfffYfu3bsjPDwcKpUKoaGhaNOmDebPn19kNcOxY8cwYsQI1K1bF35+fvD29kZkZCQeffRRjB07Fr/88guEEEWWtaTkcjmWLVsGX19fAMA333yDO3fuFNrOWi8z8wbc+/fvBwDs37/foiFu9erVLe6h0YwZMyy2Mz+uIz3agKJfQzqdDsuWLUPXrl1RuXJleHl5ISgoCHXq1EHHjh3x4Ycf4syZM4X2c7Q309WrV/Hmm2+iUaNGCAgIgK+vL+rUqYPRo0fj1KlTdvd15nvfUXK5HP379wdwv0rMmu+//x4AMGDAALvZPnN5eXlYtGgR2rdvj4oVK8LLywuVK1dG9+7dsWLFCuj1+iKPcePGDYwdOxY1a9aEt7c3wsPD8fzzz5t+hDkqKysL8+fPR/v27REWFgYvLy9UqlQJXbp0wdKlS6HT6Yp1PLcT5FH27t0rAAgAYtq0acXePz8/X4wZM8Z0DGsPpVIpvv76a6v7X7582e6+xsegQYNEfn6+1WMsXbrUtN2RI0dEs2bNCu1vvDbzbWNjY0WHDh1snvOJJ54QGRkZVs85ZMgQAUBERUVZXW9+3oULFwqFQmH1HL6+vmL//v02n9/r16+L2rVr2yxjjx49xM6dO03/37t3r81j2bJp0yaL57mkinpujMzvxZUrVwqtj4qKEgDEkCFDxFdffWX1ORRCiPfee08AEJIkWT2Ouf/85z+mfY8dO1Zo/cWLF0XDhg3tvhbr1Kkjzp8/b/X4n332mZDJZEW+ntPT0+2W05orV66Y9h8yZIjD+40aNcq038qVKwutN3+erZ3L1iMqKsriHtp6mB/X2rmssfcaSk9PF61bty7yvC+++GKhfadNm2bx2rEmJiZGqFQqm8eVy+Xiww8/tLm/s977RTH/7F66dKk4fvy4xWdbQadPnzatP378uMW9s/W5cfXqVdGgQQO7z/PTTz8tkpOTbZZz3759IjAw0Ob+M2bMcOi+/Pnnn6Jq1ap2y/LEE0+IW7duWd2/bdu2AoBo27at3ee1NLENUTkzYsQI06+OZ555BgMHDkTdunUhSRJOnDiB+fPn4/Tp0xg1ahQqV66M5557zmJ/nU4HLy8vdO3aFZ07d0bDhg0RHByMlJQUnD9/Hl9++SVOnz6NFStWoGbNmpgxY0aR5Tl16hReeeUV9O3bF5UrV8b169ehUqkKbTtq1CgcOnQIQ4YMwcsvv2za9uOPP8b//vc//Pnnn5g1axbmzJnzwM/Pjh07cPjwYTRp0gQTJkxAdHQ0srOzsXHjRixYsABZWVkYPHgwLly4AC8vL4t98/Ly0L17d1y8eNH0/I4aNQqRkZG4ceMGvv76a2zevBlJSUkPXD4ApgwAAPTo0aNEx3KFI0eOYMWKFYiMjMQ777yDRx99FDqdDgcOHAAADBw4ELNmzYIQAqtWrcKUKVNsHmvlypUAgPr16+ORRx6xWPfPP//gqaeewu3btxEQEIBRo0ahU6dOCAsLQ1paGnbu3IkFCxbgwoUL6NatG44fPw61Wm3a/++//8Y777wDvV6PGjVqYNy4cWjWrBmCg4ORkZGBCxcuYO/evdi4caMLniXbOnXqhK+//hoAcODAgSIb3QJA1apVTZmQYcOG4ejRo3jsscewdOlS0zbGX+ePPfYYACA6OhoAMGbMGLz22mum7SpUqOC0awEMWR7jve/RowcGDhyIatWqwdvbG0lJSTh58iQ2b978QGPabNmyBUOHDoUQAv7+/nj77bfRqVMnKBQKHDx4EHPmzMGdO3cwZcoUBAUFYcyYMTaPVZL3/oNo3rw5GjdujNjYWCxfvhxz5861WG/MHDVq1AjNmzfHyZMn7R4vIyMDHTp0wOXLlwEYGssPHz4c4eHhuHLlCr744gvs378fv//+O3r06IEDBw4UyjpdvXoVzz33HNLT0yGTyTBq1Ci89NJLUKvV+PvvvzF37lxMmzbN9Bqy5dSpU2jfvj0yMzNRqVIljBkzBq1bt0ZISAgSExPxyy+/YPHixfjzzz/xwgsv4MCBA2Wj6tbdERlZKkmG6McffzTt+80331jdJjs725SFqV69eqEsT0ZGhkhISLB5Dr1eL4YOHSoACD8/P5Gamlpom4K/Ur/77jubxyu47fLlywttk5OTIxo3biwAiJCQEKuZKUczRABE9+7dRW5ubqFtZs2aZdpmw4YNhdZ/9tlnpvXjxo2zep5x48ZZnOtBMkSdO3c27W8r81Eczs4QARDR0dHi7t27No/1yCOPCACiUaNGNrc5f/686XgzZ84stL5Hjx4CgIiMjBSXLl2yeozjx48LPz8/AUC89957Fuvef/990+vU1q9UIYRITU0VOp3O5npbHjRDdPHiRdN+HTp0KLS+qKyNo7+sHfkccUaGKDIyUgAQL730kt1jWMta2MtE5OXlmTIQ/v7+4q+//iq0zdWrV0WVKlVMGZ6kpKRC2zjjve+IghkiIYT46KOPBAARERFh8RrT6/Wm523u3LlCCFFkhuidd94xrS/4Wjcec+DAgaZtFi1aVGib3r17m9avWrWq0HqNRiOaNm1q8ZxZO0+TJk0EANG0aVOrz7kQQmzbts2Unf32228LrffEDBHbEJUjxsxJr169MHLkSKvbeHt744svvgBg+LVQsI2Ln58fqlSpYvMckiRh3rx5kMvlyMzMLLLOuUOHDhg+fLhD5e/duzcGDRpUaLlKpcK4ceMAGLqiW2uL4ChjmxxrvwBff/1103LjL15zixcvBgCEh4eb2vgU9MknnyA8PPyBywfAol1JWFhYiY7lKl9++aXdHlIDBw4EAJw+fdrmL19jdghAoSxJbGwsNm/eDAD44osvULNmTavHaN68OcaOHQsAWLJkicW6W7duATD0srL3PKrVashkpfdRGBISYvr77t27pXZeVzE+z61bt7a7XXBwcLGOu3HjRlNPualTp6JZs2aFtomKijK9F7OysiwyZgWV5L3/oAYOHAiZTIYbN25YZH737duH+Ph4yGQy03vFntzcXHz77bcAgIYNG1ptkC9JEhYtWmR6fRk/543++ecf/PzzzwAMmTxjGydzAQEBpuylLVu2bMHff/8NwNAGynzsK3PdunXDSy+9BAB274snYUBUTty8eRPHjh0DALz88st2t23QoIHpRfy///3P7rb5+fm4ceMG4uLiEBsbi9jYWCQkJJjedEWleR15szuy7aOPPmr625gyfhCdO3e22VsrICDANDZMwXPcvHkT586dA2B4fr29va0ew9vbG3369Hng8gFAenq66e+SDODnKpGRkUV++fXv398UZKxatcrqNsbeN61atSoU8Bg/uH19fU09s2xp06YNAMMglfHx8ablxsD+zJkz+PPPP+0eozQZB0cELO91WWV8nn/44QeHRmZ2lPHHliRJdn9U9enTx1RVau8H2oO+90uiatWqaN++PQDLxtXGv9u1a4eIiIgij3Ps2DGkpqYCMDRwt9UAOzAw0PT5f+bMGfzzzz+mdXv37jU1ch42bJjNcz3xxBNo1KiRzfXG92a9evWK7NFqfG8eOXKkTDSwZkBUThw9etT0d//+/a1OKWD+MGYhjL/uzOXn5+PLL79Ey5Yt4e/vj8jISDRs2BDR0dGmR2JiIgBY7SVjrjhdwOvXr29znfmvy5J8idg7h/l5Cp4jNjbW9Ld5cGZNUfXvRQkICDD9nZmZWaJjuYIj97RKlSqm3m6rV68u1IvryJEjOH/+PADrgbDx9ZyVlWXqhWbrYd7Oyvz13L9/fyiVSuTm5uKpp57Cc889h//+9784ffp0qfQqs8X8tRUYGOi2cjjLkCFDAAAHDx40tdXauHFjidvSGd9z1atXtzvkhJeXF5o3b26xjzUP+t4vqVdeeQUA8OOPPyI7OxvZ2dlYv349AGDw4MEOHcP8ulq0aGF3W/P15vuZ98Z7/PHH7R7jiSeesLnO+N48d+5ckd8zxsx+Xl4eUlJS7J7TEzAgKieMAUpxFfxFl5KSglatWmHcuHE4fPiwaSwTW7Kzs+2uL04DTmN3ZGvMqzRK8kvD3jnMz1PwHOZVG/Y+nAGgYsWKD1g6A/MU9O3bt0t0LFdw9J4aA534+Hj89ttvFuuM1WUKhcJqRtMZr+f69etj9erVqFChArRaLTZv3owxY8agcePGqFSpEgYPHuzU6hFHmf+IKG41kid6//33MXz4cEiShMTERHz55Zfo3bs3wsLCEB0djWnTpj3Q69j4BepItbFx4EN7X7oP+t4vqd69e8PX1xfp6en4+eef8dNPP0Gj0cDHxwcvvviiQ8cwv66ing/zQSDN9yvOZ5i9czjru8YTsZdZOWH+Jl65cqXDmZmCX24TJkwwVb0ZezE0adIElSpVMs2WDRgGmouPjy/yl7ajY2vQfU2bNsWuXbsAAMePH7c5xYO7OHpPe/fujddeew3Z2dlYtWoV2rZtC8DwWjWOyNulSxerAaTx9VyjRg388ssvDpetRo0aFv9/8cUX0alTJ/zwww/YsWMHDhw4gKSkJNy5cwcrVqzAihUrMGTIECxZsqTU2hH99ddfpr/r1atXKud0JaVSie+++w5vv/02Vq9ejT179uDo0aPIy8szVbN/9tlnWLFiBV544YViH9+R3mnuzPgVxd/fH7169cLKlSuxfPlyU1l79uxpkQ12VFHPh63nwnz5gx4DuP/efOqpp/Df//7X7nHMlbRtZWlgQFROmDfUlCTJ6jQCRdFoNKYvqgEDBlg0ei2oPDQGLQ7zwLGoX0glrSpo27YtPv30UwCGBox9+/Yt0fGMX/RFDdjm7Oq5wMBAPPfcc1i7di3WrVuHhQsXwsvLC3v27DFVbdlqN2Z8Pd++fRv169d3eGBKa9RqNUaNGoVRo0YBMLSt+OWXX7Bw4UIkJCQgJiYGzZs3x4QJEx74HMVhDHYBODR4pSs587XRsGFDzJw5EzNnzkR2djb++OMPrFq1Ct9//z0yMjLQv39/XLp0yW6nDXPG7Jm1av2CjBkoT824vfLKK1i5cqVpmg7A8eoywPK6bt26ZXc6FvNsnPl+5n/fvn3b7gTQ9j7jQkJCcPv2bSQlJT3Q94wnY5VZOWGsQwdg8aYrjgsXLiA/Px8A0K9fP5vbnTt3DhkZGQ90jrLKvJGheXsta4paX5QuXbqYfk2tW7fO1NPmQRl/hRobZdpibDTuTMaA5+7du6YRzo2NrP38/GxmDIyv56ysLPzxxx9OLVPDhg3x7rvv4tChQ6ZG62vXrnXqOWxJSkqyuP4uXbqUynltMb42ivqBU9zXho+PDzp16oQlS5aYeoFlZ2ebeg46wvhle/XqVbtf0Pn5+aasm6d+QXfs2BFVqlSBVqs1TdVRnHtvfl2HDx+2u615BwLz/YzjUgGGNnz22FtvfG+eP38e165ds3ucsoYBUTlRu3ZtNGzYEACwZs0aXL9+vdjH0Gq1pr/t1fcWJ01aXkRERJh+la1bt87mdBE5OTlYt25dic7l5eWFd955x3S8ESNGONyu4caNG9izZ4/FMmM1Unp6us0vtry8PFNDT2d65plnTL9MV65ciZycHGzYsAGAocrAVi8680Dp448/dnq5AENvOeM9LapzgDPo9XoMHTrU9N4aNWqU2zMaxtfG8ePHbVaTxMbGFjk9hj0dO3Y0/V2c57lTp04ADNU3BYdUMPfjjz8iLS3NYh9PI5fLMXjwYKhUKqhUKgwaNKhYzQkeffRR0zAXMTExNj8P0tPTTcF9w4YNLbJx7du3N50zJibG5rmOHj1qt3H6888/b/rbVe9Nd2FAVI689957AAxfor1797ZbdZObm4tFixZZfLHXrl3bVLdsHO26oM2bN2PhwoVOLHXZMXr0aACG7t22ZpWfOHEiEhISSnyuCRMmmLrr7tixA7169bJ7P4UQWLlyJR599FHTGCFGxrY7ADBv3jyr+06YMMEp5S5IqVSahiHYtGkTVq1aBY1GA8D+MAuPP/646Rf01q1bMW3aNLvnuXr1aqFJNH/66Se7WbH4+HicPXsWQOG2R852/fp1dOvWDVu3bgVgaPBd1DWVBuNrIyEhweokpOnp6Xa7vKekpBQ5F5x5xro4z3OvXr1MmdIPP/zQ6hAf8fHxph8Pvr6+druTu9tHH32EnJwc5OTkmKrEHaVSqUxjy50+fdrqDAFCCIwbN84UdBp7eBlVqVLF9EPjl19+sZoVzcjIMFUt2/Liiy+iQYMGAICvvvoK3333nd3tY2NjsWnTJrvbeAq2IfJgJ06cwLJly4rc7umnn0bt2rXRv39/7NixAzExMTh27BgaNmyI0aNHo23btqhYsSIyMzNx6dIlHDhwABs2bEBKSoqpSyhgqBvu3r07tmzZgq1bt6Jbt24YPXo0qlWrhsTERKxfvx7Lli1DzZo1kZqaWuK2MmXNuHHjsHTpUsTGxuKLL77A5cuXMXr0aERERJim7tiyZQueeOIJU9r6QaYrAAxtO9auXYsePXrg8OHD2LRpE2rVqoWBAweiQ4cOiIiIgFKpxK1bt3Do0CGsX7/e9OVeUPPmzdGyZUscOnQI33zzDfLy8jBkyBCo1WpcuHAB//3vf7Fv3z60atWqyHGpHsSgQYOwePFiZGdnmyZwrVixYpGzey9duhSPPfYY/vnnH/z73//Gjh07MHz4cERHR8Pb2xvJycn4+++/sX37duzZswc9e/a0GGxu/vz5GDhwIJ599ll06NABDRo0gFqtxt27d3H06FEsXLjQ1EvS3pQPjkhNTbX4VZ2dnY3U1FScOXMG+/btw+bNm00Z2Hr16mHz5s0W04y4y6BBgzB9+nRoNBqMGDECFy9eRNeuXSFJEo4ePYrPPvsMN2/eRPPmzS0agxtpNBq88MILqF69Onr37o0WLVogKioKCoUC//zzDzZt2mQaUDAiIqLQVEH2KJVKfP3116apJp5++mlMnDgRHTt2NE3dMXfuXFN12qeffmpzkMDy4IMPPsCGDRtw+fJlzJw5E7GxsYWm7jAOtNuqVSurgc28efOwa9cupKenY8CAAdi/fz9eeuklBAYGmqbuOH/+PB577DGbVf9yuRw//PADnnzySWRkZGDkyJFYt24dBgwYgHr16kGpVCIxMRF//fUXNm/ejIMHD+Ltt98u1r13G3cMj022mQ//7ujDOEy8EEJotVoxadIkIZfLi9zPz89PZGVlWZz/+vXrolq1ajb3qVatmjh9+rTdIf+LmgLiQbY1nybB/HqNijO5qz1FDSd/7do1UatWLZvPT5cuXcS2bdtM/z906JDd8xUlOztbTJgwQXh5eRV5PyVJEoMGDRI3b94sdJy4uDhRqVIlm/u+9dZbxZrctTj0er3FtB+wM/VJQVevXhWPP/64Q++DYcOGWexrvJf2HkVNDGqPIxOumj8CAwPFW2+9JTIzM+0etzSn7hBCiLVr19r8vPD29hZr1661+f5y9DmoWrWqOH78eKFzOzKJ6LJly5w2uas9JZ1KwtrUHcXhyOSuV65cEfXr17f7XD/11FN2J3fdu3evCAgIsLn/tGnTHLovJ0+eFHXq1HHo/s+YMaPQ/py6g1xOLpfjo48+wpkzZ/D222+jefPmqFChAuRyOQICAtCoUSMMHDgQMTEx+Oeff+Dj42Oxf2RkJI4fP46JEyeibt26UKlUUKvVaNq0KaZNm4YTJ06Y2io9jKpVq4aTJ09ixowZaNy4MXx8fBAUFISWLVti0aJF2LZtm0U1ZEmzAN7e3pg/fz4uXLiAuXPnolOnTqhWrRp8fHzg7e2N8PBwdOnSBbNnz8aVK1ewfPlyq91b69evj+PHj2PMmDGIioqCl5cXKlasiG7dumHLli1Wq9KcRZKkQlNzODKhKWCYmuHw4cPYuHEj+vXrhxo1asDX1xdKpRIVK1bEk08+ibfffhv79+8vlLpfu3YtVq5ciaFDh6JZs2aoXLkyFAoF/P390bhxY7z22mv466+/MHnyZKddK2C43sDAQERERKBFixYYM2YMli9fjoSEBMybN6/I8XBKW58+fXDw4EH06tULFStWhJeXFyIjIzFkyBAcPXrU7sjrUVFROHHiBD755BM888wzqFevHoKCgqBQKBAaGmrqMRkXF2fR8aM4hgwZgrNnz2LChAlo0KAB/Pz84OPjg1q1auHVV191yT30VNWrV8fJkyfxxRdfoG3btggJCYFSqURYWBi6deuG5cuX47fffrPbNq1du3Y4ffq0xWdBWFgYnn32WWzfvt3qtCDWNGnSBGfOnEFMTAx69uyJyMhIeHt7w8vLC1WqVEG7du3w3nvv4dixY/jggw+c9Ay4liSEBw/gQFQGzZo1C++//z4UCgXS09NtTvNBRESegxkiIicSQpjGcmrWrBmDISKiMoIBEVExXL161WJ4goI++OADU+Na4xxPRETk+VhlRlQM06dPx9KlSzFgwAA89dRTCA8PR35+PuLi4hATE2Pq5dGwYUMcP34cKpXKvQUmIiKHsNs9UTFdv34dc+fOtbm+fv362LJlC4MhIqIyhAERUTGMGDECarUaO3bswMWLF5GUlITs7GwEBwejadOm6NWrF4YPHw4vLy93F5WIiIqBVWZERET00GOGyEF6vR4JCQkICAh44NGHiYiIqHQJIZCeno7w8HDIZLb7kjEgclBCQgIiIyPdXQwiIiJ6APHx8YiIiLC5ngGRgwICAgAYntDAwEA3l4aIiIgcodFoEBkZafoet4UBkYOM1WSBgYEMiIiIiMqYopq7cGBGIiIieuh5fECUnp6OSZMmoUuXLqhYsSIkSXJ48jkASExMxNChQxEaGgpfX1+0atUKu3fvdl2BiYiIqMzx+IAoOTkZX3/9NXJzc9GzZ89i7Zubm4uOHTti9+7dWLBgAX7++WfTrMD79+93TYGJiIiozPH4NkRRUVG4e/cuJEnCnTt38O233zq873fffYfY2FgcPHgQrVq1AgC0b98eTZs2xaRJk3D48GFXFZuIiIjKEI/PEEmS9MDj/mzcuBH16tUzBUMAoFAoMGjQIPz555+4efOms4pJREREZZjHB0QlERsbiyZNmhRablx2+vTp0i4SEREReSCPrzIrieTkZAQHBxdablyWnJxsc9/c3Fzk5uaa/q/RaJxfQCKiMig/Px86nc7dxaCHlFwuh1KpdPpxy3VABNgfd8Deujlz5mDGjBmuKBIRUZmk0Whw584dix+LRO6gUqkQGhrq1HEBy3VAFBISYjULlJKSAgBWs0dGkydPxltvvWX6v3GkSyKih5FGo8HNmzfh7++P0NBQKJVKzutIpU4Igfz8fKSlpZnaATsrKCrXAVF0dDROnTpVaLlxWePGjW3uq1KpoFKpXFY2IqKy5M6dO/D390dERAQDIXIrHx8fBAQE4MaNG7hz547TAqJy3ai6V69eOHv2rEX3eq1WixUrVqBFixYIDw93Y+mIiMqG/Px85ObmQq1WMxgijyBJEtRqNXJzc5Gfn++UY5aJgGjbtm348ccfsWnTJgDAmTNn8OOPP+LHH39EVlYWAGDEiBFQKBS4du2aab/hw4ejUaNG6NOnD1atWoVff/0VL7/8Ms6dO4ePPvrILddCRFTWGBtQu6IhK9GDMr4endXAv0xUmY0ZM8Yi0Fm3bh3WrVsHALhy5QqqV68OnU4HnU4HIYRpO5VKhd27d2PSpEkYP348srKy0KxZM2zbtg1t27Yt9esoKDsX+Hw9IARQMxx4ub27S0REZBuzQ+RJnP16lIR5BEE2aTQaqNVqpKWlOa2+MkUDhDxv+Lt7S2DLXKcclojIqXJycnDlyhXUqFED3t7e7i4OEQDHX5eOfn+XiSqz8so8uNXr3VcOIiKihx0DIjeSmQVEzNMREZE5SZLQrl07dxfjoVEm2hCVVxYZIgZEREQep7jtVNgKpexiQORGzBAREXm2adOmFVo2Y8YMqNVqvPHGGy49d1xcHHx9fV16DrqPAZEbMUNEROTZpk+fXmjZjBkzEBQUZHWdM9WvX9+lxydLbEPkRjKzZ58ZIiKisuvq1auQJAlDhw7F2bNn0bt3b4SGhkKSJFy9ehUAsHHjRvTv3x+1a9eGr68v1Go1WrdujfXr11s9prU2REOHDjUdc9GiRWjQoAG8vb0RFRWFGTNmQM8eOg+MGSI3ktnoZXZZdxPx+kTkQ4vH5Q2glvmXfuGIiKjYLl68iJYtW6JRo0YYMmQIUlJS4OXlBcAwR6aXlxeefvppVKlSBUlJSfjll1/w0ksv4fPPP8f48eMdPs/EiROxb98+9OjRA126dMFPP/2E6dOnIy8vD7Nnz3bV5ZVrDIjcyFaV2Zc567Eg9wcAwIGAxWglsz3nGhEReY4//vgD77//Pv79738XWrd161bUrFnTYllGRgaefPJJvP/++xgxYoTDbYaOHTuGv//+G1WqVAEAvP/++6hTpw4WLlyIadOmmYIwchwDIjey1ahaIclNf+dDW4olIiIqvsdGAbdS3F0K2yoHA0e/LqVzVa6M9957z+q6gsEQAPj7+2Po0KF4++23ceTIEYdnUXj//fdNwRAAhIaG4oUXXkBMTAzOnTuH6OjoB7uAhxgDIjeylSFS4H5ApIVz5mghInKVWynAzTvuLoVnaNq0qc3sTGJiIubOnYtt27bh2rVryM7OtlifkJDg8HkeeeSRQssiIiIAAKmpqY4XmEwYELmRrUbVSrPbki+YISIiz1Y52N0lsK80yxcWFmZ1eUpKCh5//HFcv34dTz31FDp16oSgoCDI5XKcOHECP//8M3Jzcx0+j1qtLrRMoTB8dzhrstOHDQMiN7I1dYd5lRkzRETk6UqrOqossDWQ43fffYfr169j1qxZmDp1qsW6uXPn4ueffy6N4pEd7HbvZsb3jnmve/MMkY4BERFRmXfp0iUAwPPPP19o3YEDB0q7OGQFAyI3MwZEFhkisFE1EVF5EhUVBQD4/fffLZavWrUKW7dudUeRqAAGRG5m7Glm0YZIup8h0gpmiIiIyrrBgwdDrVZj/PjxePnllzFx4kR07doVgwcPRu/evd1dPAIDIrczZYhs9DJjhoiIqOyLiIjA/v370bFjR/z6669YvHgxcnNzsXPnTjz33HPuLh4BkASn5nWIRqOBWq1GWloaAgMDnXZc785Abj7QrDbw17eGZd/k/IwxWR8DAL71m4Khqmeddj4iouLKycnBlStXUKNGDXh7e7u7OEQAHH9dOvr9zQyRmxm73uttDczIbvdEREQux4DIzaw1qjbvZcZu90RERK7HgMjNrDWqZhsiIiKi0sWAyM2sNqo2H5iRvcyIiIhcjgGRm1ntdm8+dQczRERERC7HgMjNrGWI5JzclYiIqFQxIHIzYy8zTu5KRETkPgyI3Mw4DSAndyUiInIfBkRuZsoQmS1jt3siIqLSxYDIzYqc3JVVZkRERC7HgMjNiprcVccMERERkcsxIHIzq1N3cGBGIiKiUsWAyM2sNapmGyIiIqLSxYDIzaw1qubkrkRED4fp06dDkiTs27fPYrkkSWjXrl2Jj+NMQ4cOhSRJuHr1qsvO4U4MiNzM+uSu7HZPROQJ+vfvD0mSsGbNGrvbJScnQ6VSITQ0FHl5eaVUOudatmwZJEnCsmXL3F0Ut2BA5GbWJ3fl1B1ERJ5gxIgRAIClS5fa3W7FihXIy8vD4MGD4eXlVeLzxsXF4fvvvy/xcZxpzpw5iIuLQ9WqVd1dFJdQFL0JuRIndyUi8lwdO3ZE9erV8euvvyI+Ph6RkZFWtzMGTMYAqqTq16/vlOM4U5UqVVClShV3F8NlmCFyM07uSkTkuSRJwrBhw6DX6xETE2N1m2PHjuHkyZN44oknEBwcjGnTpqFly5aoVKkSVCoVqlevjtdeew2JiYnFOq+1NkTx8fHo378/goOD4e/vj7Zt2+K3336zeoy8vDwsXLgQXbt2RWRkJFQqFSpVqoTevXvjr7/+sth26NChGDZsGABg2LBhkCTJ9DDfxlYbopiYGLRs2RL+/v7w9/dHy5YtrT5f+/btgyRJmD59Oo4fP46uXbsiICAAarUavXr1cmv7JAZEbmY1Q8Q2REREHmPYsGGQyWRYtmwZhPmv13vMs0O//fYb5s2bh7CwMPTv3x/jx49HrVq18NVXX6FVq1ZIS0t74HL8888/aNWqFdasWYMnnngCr7/+OoKDg9G5c2ccOnSo0PYpKSl44403kJubi+7du+PNN99Eu3btsHXrVjz55JM4cuSIaduePXvihRdeAAC88MILmDZtmulRlDfffBNDhw7FjRs3MGLECIwcORI3b97E0KFD8dZbb1nd5+jRo2jdujUUCgVGjx6Nxx57DD/99BM6deqEnJycB3yGSkiQQ9LS0gQAkZaW5tTj1h0kBNoKUaHH/WUafYaQJz8p5MlPii5przv1fERExZWdnS3OnDkjsrOz3V0Ut+natasAIPbt22exPCcnR1SoUEH4+vqKtLQ0cfv2bZGenl5o/5iYGAFAzJo1y2L5tGnTBACxd+9ei+UARNu2bS2WDRkyxOoxFi9eLGDorGxxnJycHHHjxo1CZYmNjRX+/v6iU6dOFsuXLl0qAIilS5dafQ6M579y5Ypp2W+//SYAiAYNGojU1FTT8tTUVFG/fn0BQBw4cMC0fO/evaayrlmzxuL4gwcPFgDE6tWrrZ6/IEdfl45+f7MNkZtZndyV4xARURnSIm04bulT3F0MmyrLgnFYvaRExxg+fDh27NiBJUuWoG3btqblGzduxN27dzFkyBAEBgYiMDDQ6v6DBw/G+PHj8euvv2Lq1KnFPn9eXh5++OEHVKpUCW+//bbFupEjR2LevHk4f/68xXKVSmW1AXSjRo3Qvn177NixA/n5+VAqlcUuj5GxR9r06dOhVqtNy9VqNaZNm4b+/ftj2bJlePrppy32a9OmDfr27WuxbPjw4Vi+fDmOHDmCfv36PXCZHhQDIjezPrkrq8yIqOy4pU/BTZHk7mLYpi96k6L07NkTISEh+PHHH/HFF18gICAAALBkiSHQGj58uGnbDRs2YPHixTh+/Dju3r0Lne7+53hCQsIDnf/cuXPIyclBhw4d4O3tbbFOJpPhySefLBQQAcCJEyfw8ccf4/fff8etW7eQn59vsf7OnTslaihtbItkrb2TcdmJEycKrXvkkUcKLYuIiAAApKamPnB5SoIBkZtZG4dIzsldiagMqSwLdkrQ4SqVZcElPoaXlxcGDRqEBQsWYO3atRgxYgTi4+Oxe/du1KlTB23atAEAzJs3D++88w4qVqyILl26ICIiAj4+PgCA+fPnIzc394HOb2x7VKlSJavrw8LCCi07ePAgOnToAADo0qUL6tSpA39/f0iShJ9++gknT5584PIYaTQayGQyVKxY0WqZZDKZ1XZT5tkkI4XCEJKYB5CliQGRm8msNKqWJAkKyKGFjpO7EpHHK2l1VFkxYsQILFiwAEuWLMGIESOwbNky6PV6U3ZIq9Vi5syZCA8Px4kTJyyCBCEEPv744wc+tzGAsNVT7fbt24WWzZ49G7m5ufj999/x1FNPWaw7dOgQTp48+cDlMQoMDIRer0dSUlKhYC0xMRF6vd5mNaKnYS8zNzNVmRXouGDsaZbPgIiIyCNER0fj8ccfx8GDB3H27FksW7YMcrkcQ4YMAWCofkpLS0PLli0LZUyOHj2K7OzsBz53vXr14O3tjaNHjxbqhaXX63Hw4MFC+1y6dAnBwcGFgqGsrCwcP3680PZyueF7pzgZmubNmwOA1SlD9u/fDwBo1qyZw8dzJwZEbmatUTVwfywiLcchIiLyGMaBF0eOHInLly+je/fupjY4lSpVgo+PD44fP46srCzTPnfv3sX48eNLdF4vLy+8/PLLSExMxLx58yzWffvtt1bbD0VFReHu3bs4ffq0aZlOp8M777yDpKTCbb6Cgw1Vizdu3HC4XMZgcMaMGdBoNKblGo0GM2bMsNjG07HKzM2sNaoG7o1WLYB8jlRNROQx+vfvj7feegt//PEHAMuRqWUyGV577TXMmzcPTZs2xXPPPQeNRoNt27YhKioK4eHhJTr33LlzsXv3brz33nv4/fff0bx5c8TFxWHr1q3o0qULdu7cabH9+PHjsXPnTjz99NN4+eWX4e3tjX379uHmzZto165doaxOq1at4OPjg/nz50Oj0ZiyXO+++67NMrVp0wbjx4/HwoUL0bhxY7z44osQQmDDhg2Ij4/H66+/bmpf5emYIXIza42qAfMMEQMiIiJPERgYiJdeegmAodHws88+a7F+zpw5mD17NiRJwqJFi7Br1y7069cPO3fuLFH3dsAwdcbBgwfRt29fHDp0CAsWLEBycjJ27dqFVq1aFdq+R48e+PHHH1GzZk2sWLECq1atQv369fHnn38iKiqq0PbBwcH48ccfUadOHXz11VeYPHkyJk+eXGS5Pv/8cyxZsgSVK1fG119/jW+++QaVK1fGkiVLsGDBghJdc2mShLAy7CYVotFooFarkZaW5tQGYo+PBo6eA+QyQLvn/vJqd19AgriDCFklXA3a6LTzEREVV05ODq5cuYIaNWoU6vJN5C6Ovi4d/f5mhsjNrE3dAdyf4JWTuxIREbkeAyI3sza5K3C/yoyTuxIREbkeAyI3M5tI2CIoMna7ZxsiIiIi12NA5GYysztgHhAppXsZIo5UTURE5HIMiNzMLEFUYIJXZoiIiIhKCwMiN7PIEJktV7DbPRERUalhQORmMrMUkUWG6F4vMz300AsPnjWRiB4aHKWFPImzX48MiNzMvFG1edd7pdkg4swSEZE7Gee4ys/Pd3NJiO4zvh6Nr8+SYkDkZrYaVRvbEAHsek9E7qVUKqFSqZCWlsYsEXkEIQTS0tKgUqlKPAK4EecyczNbjaqNvcyAe4Mzmm9IRFTKQkNDcfPmTdy4cQNqtRpKpRKSxA8mKl1CCOTn5yMtLQ0ZGRmoWrWq045dJgKijIwMvPfee1i7di1SUlJQv359vPvuu+jXr1+R++7duxcffvghTp48iaysLNSsWRMjR47E2LFjnZZmKwnzDJGeGSIi8lDGKQ/u3LmDmzdvurk09LBTqVSoWrWqU6fSKhMBUe/evXHkyBHMnTsXdevWxapVq9C/f3/o9XoMGDDA5n6//vorunbtijZt2uCbb76Bn58ffvnlF0yYMAGXLl3yiEnnbA3MyDZERORpAgMDERgYiPz8fOh0/Fwi95DL5U6rJjPn8QHR1q1bsWvXLlMQBADt27fHtWvXMHHiRPTt29dmpmfZsmVQKpXYvHkz/Pz8AACdOnXCuXPnsGzZMo8IiGRFjFQNcHBGIvIsSqXSJV9IRO7k8Y2qN27cCH9/f/Tp08di+bBhw5CQkIDDhw/b3FepVMLLyws+Pj4Wy4OCgjxmxmZbvcyM3e4BZoiIiIhczeMDotjYWDRo0AAKhWUyq0mTJqb1tvzf//0f8vLy8PrrryMhIQGpqalYvnw5Nm7ciEmTJtk9b25uLjQajcXDFWxliMyrzNiGiIiIyLU8PiBKTk5GcHBwoeXGZcnJyTb3bdGiBfbs2YONGzeiatWqqFChAoYNG4bZs2fj7bfftnveOXPmQK1Wmx6RkZEluxAbHGlUzQwRERGRa3l8QATAbtdOe+uOHTuGXr164dFHH8WmTZuwZ88eTJ48Ge+99x5mzpxp95yTJ09GWlqa6REfH//A5bdHsjFStXm3e7YhIiIici2Pb1QdEhJiNQuUkpICAFazR0Zjx45FWFgYNm7caGp43b59e8hkMkyfPh0DBw5EzZo1re6rUqmgUqmccAX2OdKomhkiIiIi1/L4DFF0dDTi4uKg1VpmSU6dOgUAaNy4sc19T5w4gUcffbRQL7THH38cer0ecXFxzi9wMdmeuoMBERERUWnx+ICoV69eyMjIwPr16y2Wx8TEIDw8HC1atLC5b3h4OI4ePVpovIz//e9/AICIiAjnF7iYbGWI5KwyIyIiKjUeX2X2zDPPoHPnzhgzZgw0Gg1q166N1atXY/v27VixYoUp+zNixAjExMTg0qVLiIqKAgC8+eabeP311/Hcc89h9OjR8PX1xe7duzFv3jx06tQJTZs2deelAWCGiIiIyBN4fEAEABs2bMDUqVPxwQcfmKbuWL16tcXUHTqdDjqdzmLiwfHjx6Nq1ar4z3/+g5EjRyI7OxvVq1fHtGnT8Oabb7rjUgrh5K5ERETuJwlOXewQjUYDtVqNtLQ0p86d0v/fwJo9hr+vrAaqVzH8PTN7CWZkfwcA2OT/KZ7xauW0cxIRET0sHP3+9vg2ROWdzZGqLarMmCEiIiJyJQZEbubISNVsQ0RERORaDIjczJG5zNjLjIiIyLUYELmZI42qmSEiIiJyLQZEbmY+8YjF1B2c3JWIiKjUMCByM4sMkdlyiwyRYIaIiIjIlRgQuZnMkcldWWVGRETkUgyI3Izd7omIiNyPAZGb2WpUzW73REREpYcBkZvZalRt2e2eAREREZErMSByM9uNqs0zRKwyIyIiciUGRG4m2WhUbTm5KzNERERErsSAyM1sTt1h1stMy5GqiYiIXIoBkZuZV5nZ6mXGDBEREZFrMSByM5uNqjl1BxERUalhQORmthpVs8qMiIio9DAgcjNHGlUzQ0RERORaDIjczGajak7uSkREVGoYELmZzak7JE7uSkREVFoYELmZzcldmSEiIiIqNQyI3Mz2SNVsQ0RERFRaGBC5ma1G1Za9zBgQERERuRIDIjez1ajacmBGVpkRERG5EgMiN7PVqFppMbkrM0RERESuxIDIzRzKEHFgRiIiIpdiQORmNjNEEjNEREREpYUBkZtZTO5qY6RqtiEiIiJyLQZEbuZIlRkzRERERK7FgMjNbFWZySQZZPduDyd3JSIici0GRG5mK0ME3M8SMUNERETkWgyI3MxWhgi43/WebYiIiIhciwGRm1lM3VEwQ3RvgleOVE1ERORaDIjczCxBZNHLDGCGiIiIqLQwIHIzi273bENERETkFgyI3Mxeo2rj4IysMiMiInItBkRuZq9RtTFDxCozIiIi12JA5Gb2GlUrWWVGRERUKhgQuZm9RtXGXmac3JWIiMi1GBC5mUWGqMA6Yy8zZoiIiIhciwGRm1m0ISqYITK1IWJARERE5EoMiNxMZqdRtfxeQKSDDqJgAyMiIiJyGgZEbma3UfW9bvcAq82IiIhciQGRm9ltVH0vQwQwICIiInIlBkRu5kijaoBjEREREbkSAyI3s9uoWjLLEHG0aiIiIpdhQORmdqfuYIaIiIioVDAgcjNHpu4A2IaIiIjIlRgQuZnMTpWZRS8zVpkRERG5DAMiN7PXqNo8Q8QqMyIiItdhQORm9hpVm7chYpUZERGR6zAgcjN7jarNe5lxglciIiLXYUDkZvYaVTNDREREVDoYELmZ3QwR2xARERGVCgZEbmYvQyQHB2YkIiIqDQyI3My8l5m9bvfMEBEREblOmQiIMjIy8MYbbyA8PBze3t5o1qwZ1qxZ4/D+P//8M9q2bYvAwED4+fmhUaNG+Prrr11YYsc5WmXGNkRERESuoyh6E/fr3bs3jhw5grlz56Ju3bpYtWoV+vfvD71ejwEDBtjdd+7cuZg6dSr+7//+D5MnT4ZSqcTZs2eRl5dXSqW3z9FG1exlRkRE5DoeHxBt3boVu3btMgVBANC+fXtcu3YNEydORN++fSGXy63ue+zYMUydOhVz5szBpEmTTMs7duxYKmV3hKPd7pkhIiIich2PrzLbuHEj/P390adPH4vlw4YNQ0JCAg4fPmxz3y+++AIqlQrjx493dTEfmOPd7pkhIiIichWPD4hiY2PRoEEDKBSWyawmTZqY1tvy22+/oUGDBli/fj3q1asHuVyOiIgIvPvuux5TZWYxdQfbEBEREbmFx1eZJScno2bNmoWWBwcHm9bbcvPmTSQlJeH111/HzJkz0bBhQ+zevRtz585FfHw8Vq5caXPf3Nxc5Obmmv6v0WhKcBW2mSWIOLkrERGRm3h8QAQAknm9UjHW6fV6pKenY/Xq1ejXrx8AQ/ujzMxMzJ8/HzNmzEDt2rWt7jtnzhzMmDGjZAV3gEW3ew7MSERE5BYeX2UWEhJiNQuUkpIC4H6myNa+ANC1a1eL5c888wwA4Pjx4zb3nTx5MtLS0kyP+Pj4YpfdEfYaVXPqDiIiotLh8QFRdHQ04uLioNVaZkhOnToFAGjcuLHNfY3tjAoS9yIPmcz25atUKgQGBlo8XMFeo2pO7kpERFQ6PD4g6tWrFzIyMrB+/XqL5TExMQgPD0eLFi1s7vviiy8CALZt22axfOvWrZDJZHj88cedX+BisteomhkiIiKi0uHxbYieeeYZdO7cGWPGjIFGo0Ht2rWxevVqbN++HStWrDCNQTRixAjExMTg0qVLiIqKAmDomr948WK89tpruHPnDho2bIhff/0VX375JV577TXTdu5kr1E12xARERGVDo8PiABgw4YNmDp1Kj744AOkpKSgfv36Fg2lAUCn00Gn05mqwwBAqVRi165dmDJlCj788EOkpKSgRo0amDt3Lt566y13XEohFhmiAussBmZkLzMiIiKXkYQoWFFD1mg0GqjVaqSlpTm1PdHvfwOtXzf8/U5f4JMx99f9kncAvTPeBQDM8hmNd31ecdp5iYiIHgaOfn97fBui8s5eo2o5B2YkIiIqFQyI3Mxuo2r2MiMiIioVDIjczH6javYyIyIiKg0MiNzMXqNqJXuZERERlQoGRG5m0YbIXoaIvcyIiIhchgGRm9mdusOsDZGOVWZEREQuw4DIzTi5KxERkfsxIHIze42qOXUHERFR6WBA5GaOjlTNbvdERESuw4DIzew1qmaGiIiIqHQwIHIze42qLdsQMSAiIiJyFQZEbmZv6g7LyV1ZZUZEROQqLp3t/vr161i9ejUSEhLwyCOPYPDgwZDJGIOZs9vt3uz2MENERETkOiWOTr766isEBwfj888/t1h+6NAhREdHY8qUKVi4cCGGDx+Orl27Ql+wocxDztFu92xDRERE5DolDoh++eUXaDQa9O7d22L5W2+9hfT0dDz55JN44403UKVKFezZswdr1qwp6SnLFbuNqiWzDBGrzIiIiFymxAHR2bNnUbFiRURERJiWXblyBYcOHUKDBg3w22+/4bPPPsP27dshhMC3335b0lOWK442qmaGiIiIyHVKHBAlJSVZBEMAsHfvXgBAv379IN1LgTRu3Bi1a9fGxYsXS3rKcsVeo2rLNkTMEBEREblKiQMinU6HnJwci2UHDhyAJElo27atxfLg4GAkJSWV9JTlir0Mkczs9nByVyIiItcpcUBUvXp1XLx4EampqQAMAdL27dvh7e2NVq1aWWybkpKC4ODgkp6yXLGXIZIkyZQl4uSuRERErlPigOjZZ59Fbm4uBgwYgM2bN2PUqFG4ffs2nn32WSiVStN2aWlpuHz5MqKiokp6ynLFYuqOgnN34H47IlaZERERuU6JxyGaMmUKfvrpJ2zfvh07duyAEAJqtRozZ8602G79+vXQ6/Vo3759SU9Zrsjs9DIDDD3NskUuq8yIiIhcqMQBUXBwMI4fP45vv/0WFy5cQGRkJIYNG4YqVapYbHf58mW88MILePHFF0t6ynLFXpUZwAwRERFRaXDKSNWBgYF466237G4za9YsZ5yq3LHXqBq4HxCx2z0REZHrcB4NNysqQ2RsVM2BGYmIiFynxAFRQkICfvnlF8TGxlosF0Lgs88+Q4MGDaBWq9GhQwecOHGipKcrd4psVC0xQ0RERORqJQ6IFixYgF69euHMmTMWyz/77DNMnDgR586dQ3p6Ovbt24eOHTsiMTGxpKcsV8wSRNYbVRszRGxDRERE5DIlDoh2794NLy8v9OzZ07RMp9Ph448/hkwmw3//+1+cOHECAwYMwN27dzF//vySnrJcscgQWVnPNkRERESuV+KA6ObNm6hatSq8vLxMyw4dOoSkpCQ8++yzGDVqFJo0aYLFixfD19cX27ZtK+kpyxVHut0DbENERETkSiUOiFJSUhAaGmqxzDh1R48ePUzL/Pz8UKdOHVy7dq2kpyxXHO12zwwRERGR65Q4IPL19cXt27ctlu3btw8A0KZNG4vlSqUS+fn5JT1luVL0SNX32xAJaxsQERFRiZU4IIqOjsb169dx6NAhAEB8fDz27t2LqlWrom7duhbbXrt2DWFhYSU9ZblSVKNqYy8zANDDygZERERUYiUOiEaOHAkhBLp3746XXnoJTz75JLRaLUaOHGmxXVxcHJKSktC4ceOSnrJcKapRtdJs7ExWmxEREblGiQOiV155BW+99RY0Gg02bNiAmzdv4qWXXsK7775rsd3SpUsBAJ07dy7pKcsVqYhG1cY2RAC73hMREbmKU6bu+PTTT/Huu+/i0qVLiIyMRHh4eKFtunXrhqeeegqtW7d2xinLjaKm7jD2MgNgmOBVKrwNERERlYxTAiIACA0NLdTbzFyHDh2cdapyxbzKzF4vM4AZIiIiIldxWkBklJ2djUuXLiE9PR0BAQGoVasWfHx8nH2acqPIRtVmARHbEBEREbmG0yZ33bFjB9q1awe1Wo2mTZvi6aefRtOmTU3zmO3cudNZpypXimxUbVZlxsEZiYiIXMMpAdH06dPRvXt3/Pbbb9BqtVAqlQgPD4dSqYRWq8W+ffvwzDPPYPr06c44XblSnEbVzBARERG5RokDou3bt+Pf//43ZDIZXnvtNZw7dw45OTmIj49HTk4Ozp07h9deew1yuRwzZ87Ejh07nFHuckMqqlE12xARERG5XIkDos8//xySJGHJkiX44osvUKdOHYv1derUwRdffIElS5ZACIEFCxaU9JTljjEost6oukAvMyIiInK6EgdER44cQUREBAYPHmx3u0GDBiEyMhJ//vlnSU9Z7hi73lvvds8MERERkauVOCBKT093eDqOsLAwZGZmlvSU5Y6xYXVR3e7ZhoiIiMg1ShwQhYeH4+zZs0UGOpmZmYiLi0OVKlVKespyx1RlZrVRtXkvMwZERERErlDigKhr167IyMjAq6++iry8PKvb5OXlYeTIkcjKykK3bt1Kespyx16VmfnkrlpWmREREblEiQdmnDJlCn744Qf88MMP2LdvH1599VU0bNgQlSpVQmJiIs6cOYNvvvkGt2/fhlqtxuTJk51R7nLFXqNqTu5KRETkeiUOiCIjI7Ft2za8/PLLiI+Px6xZswptI4RAtWrVsHbtWkRGRpb0lOWO3QwRu90TERG5nFOm7mjRogXOnj2LVatWYefOnTh//jwyMjLg7++PunXromvXrujfvz+uXLmCv//+G02aNHHGacsNuxmigpO7EhERkdM5bS4zHx8fjBgxAiNGjLC5Tdu2bXH37l1otcx0mDP1MuNI1URERG7htLnMHCWs1Qs95GR2B2Zko2oiIiJXK/WAiAqT28kQWU7uygwRERGRKzAg8gDGKjMdq8yIiIjcggGRB7CbITIfmJFVZkRERC7BgMgD2M0QmQ/MyCozIiIil2BA5AHkduYyY4aIiIjI9Yrd7f77779/4JPl5uY+8L7lmbGXmc5KAohtiIiIiFyv2AHR0KFDIRlHEiwmIcQD71ueye/FPEUNzJgvmCEiIiJyhWIHRNWqVSv1oCYjIwPvvfce1q5di5SUFNSvXx/vvvsu+vXrV6zjvPfee5g9ezYaNWqE2NhYF5W2+EwZIvYyIyIicotiB0RXr151QTHs6927N44cOYK5c+eibt26WLVqFfr37w+9Xo8BAwY4dIwTJ07g008/RVhYmItLW3z2epkpOLkrERGRyzlt6g5X2bp1K3bt2mUKggCgffv2uHbtGiZOnIi+fftCLpfbPYZWq8WwYcMwevRonDx5Enfu3CmNojvM0XGIWGVGRETkGh7fy2zjxo3w9/dHnz59LJYPGzYMCQkJOHz4cJHHmDt3LlJSUjB79mxXFbNEHB2pmhkiIiIi1/D4gCg2NhYNGjSAQmGZzGrSpIlpvT1nzpzBrFmz8NVXX8Hf399l5SwJhzNE7HZPRETkEh5fZZacnIyaNWsWWh4cHGxab4ter8fw4cPRu3dvdO/evVjnzc3NtRgmQKPRFGv/4rA3DhEbVRMREbmex2eIANjt1WZv3WeffYYLFy5g/vz5xT7nnDlzoFarTY/IyMhiH8NR9sYhsqgyYxsiIiIil/D4gCgkJMRqFiglJQXA/UxRQdevX8cHH3yAadOmwcvLC6mpqUhNTYVWq4Ver0dqaiqys7Ntnnfy5MlIS0szPeLj451zQVbYG4eIGSIiIiLX8/iAKDo6GnFxcdBqLbMjp06dAgA0btzY6n6XL19GdnY2JkyYgAoVKpgef/zxB+Li4lChQgVMnjzZ5nlVKhUCAwMtHq5izBAJYXiY49QdRERErufxbYh69eqFb775BuvXr0ffvn1Ny2NiYhAeHo4WLVpY3a9Zs2bYu3dvoeVvvPEG0tLSsHTpUkRERLis3MUhMwtL9fr7GSOAk7sSERGVBo8PiJ555hl07twZY8aMgUajQe3atbF69Wps374dK1asMI1BNGLECMTExODSpUuIiopCUFAQ2rVrV+h4QUFB0Gq1Vte5i9wsINIVCIiYISIiInI9jw+IAGDDhg2YOnUqPvjgA9PUHatXr7aYukOn00Gn00EUrHMqA2Rm7cILjkXENkRERESuJ4myGEG4gUajgVqtRlpamtPbE3V8C9hz3PB35nbA1/v+upv6JESl9gQA9FK2xbqAD516biIiovLM0e9vj29U/TAwzxAVHJyRGSIiIiLXY0DkAeQFGlWbU3JyVyIiIpdjQOQBZAUaVZsz72XGyV2JiIhcgwGRB2CGiIiIyL0YEHkAuxkiTu5KRETkcgyIPIBFhqhAnz+LRtUcmJGIiMglGBB5AIteZgViHkmSIL8XFGmZISIiInIJBkQewHxkansTvLINERERkWswIPIA9sYhAgDlvYCIbYiIiIhcgwGRB7DXywwAFJKhpxnbEBEREbkGAyIPYK+XGWCeIWJARERE5AoMiDxAkRkitiEiIiJyKQZEHqDIDNG9KjOOVE1EROQaDIg8gL1xiABmiIiIiFyNAZEHsDcOEXB/+g4GRERERK7BgMgDFDkO0b0JXlllRkRE5BoMiDxAUeMQscqMiIjItRgQeYCiepkZq8w4MCMREZFrMCDyAEX1MjPOZaaHHnphZQMiIiIqEQZEHqDIDNG9bvcAq82IiIhcgQGRBygqQ2RsQwQwICIiInIFBkQeoKhxiIxtiAD2NCMiInIFBkQeoKhxiIzd7gFmiIiIiFyBAZEHKGocIosMEXuaEREROR0DIg/g6DhEADNERERErsCAyAMUp5cZ2xARERE5HwMiD8BeZkRERO7FgMgDmFeZ2RupGmBARERE5AoMiDyAeaNqqxkis15mrDIjIiJyPgZEHsAiQ2RttntWmREREbkUAyIP4OjkrgC73RMREbkCAyIPUOTkruYDMwpmiIiIiJyNAZEHYIaIiIjIvRgQeQB2uyciInIvBkQeoMjJXTkwIxERkUsxIPIARU7uygwRERGRSzEg8gDFmdxVyzZERERETseAyAMUa3JX9jIjIiJyOgZEHqDoyV3NRqpmhoiIiMjpGBB5gKJ7mXEuMyIiIldiQOQBih6HiI2qiYiIXIkBkQcoMkPEyV2JiIhcigGRByhqHCJWmREREbkWAyIPUNQ4ROZVZmxUTURE5HwMiDxAUeMQKTi5KxERkUsxIPIARY1DxMldiYiIXIsBkQcoqpeZnL3MiIiIXIoBkQcoqpcZJ3clIiJyLQZEHkBe5MCMzBARERG5EgMiD6Awa1RtvZeZeRsiBkRERETOxoDIA5gHRFor8Y55LzMdq8yIiIicjgGRBygqIGKGiIiIyLUYEHmAIjNEbENERETkUgyIPECRGSKzXmYcmJGIiMj5GBB5gOJkiDgwIxERkfMxIPIArDIjIiJyrzIREGVkZOCNN95AeHg4vL290axZM6xZs6bI/TZs2ID+/fujdu3a8PHxQfXq1TFw4EBcuHChFErtOPNxiIpsVM1eZkRERE6nKHoT9+vduzeOHDmCuXPnom7duli1ahX69+8PvV6PAQMG2Nzvo48+QuXKlTF16lTUrFkT8fHx+PDDD/HII4/g0KFDaNSoUSlehW3F6XbPDBEREZHzeXxAtHXrVuzatcsUBAFA+/btce3aNUycOBF9+/aF3Hy6eDObNm1CpUqVLJZ16NAB1atXx3/+8x98++23Li+/I4rX7Z4ZIiIiImfz+CqzjRs3wt/fH3369LFYPmzYMCQkJODw4cM29y0YDAFAeHg4IiIiEB8f7/SyPiiLkao5dQcREVGp8/iAKDY2Fg0aNIBCYZnMatKkiWl9cVy+fBnXrl0rsrosNzcXGo3G4uEqxel2zzZEREREzufxAVFycjKCg4MLLTcuS05OdvhYWq0WI0aMgL+/P9588027286ZMwdqtdr0iIyMLF7Bi4G9zIiIiNzL4wMiAJAk6YHWmRNCYMSIEThw4AC+//77IgOcyZMnIy0tzfRwZRUb2xARERG5l8c3qg4JCbGaBUpJSQEAq9mjgoQQGDlyJFasWIGYmBi88MILRe6jUqmgUqmKX+AHUFRAJJNkkCBBQHCkaiIiIhfw+AxRdHQ04uLioNVaZkZOnToFAGjcuLHd/Y3B0NKlS/Htt99i0KBBLivrgyoqIALuZ4m0zBARERE5nccHRL169UJGRgbWr19vsTwmJgbh4eFo0aKFzX2FEHj11VexdOlSLF68GMOGDXN1cR+I3IGAyNiOiG2IiIiInM/jq8yeeeYZdO7cGWPGjIFGo0Ht2rWxevVqbN++HStWrDCNQTRixAjExMTg0qVLiIqKAgC8/vrr+O677zB8+HBER0fj0KFDpuOqVCo0b97cLddUkCQZRqvW6e1kiCQFIDi5KxERkSt4fEAEGKbgmDp1Kj744AOkpKSgfv36WL16Nfr162faRqfTQafTQQhhWrZp0yYAwJIlS7BkyRKLY0ZFReHq1aulUn5HKOT2AyJjhoiNqomIiJxPEuYRBNmk0WigVquRlpaGwMBApx/fvxuQmQM0rgGcWlp4fdW7z+G2SEGUrDIuBa0vvAEREREV4uj3t8e3IXpYGNsR2a0yAwdmJCIicgUGRB5CUURAxEbVRERErsOAyEMUFRAZu92zDREREZHzMSDyEA5niNjLjIiIyOkYEHkIY0BkbbZ7wKwNETNERERETseAyEOwDREREZH7MCDyEMUJiDhSAhERkXMxIPIQRQZE0v0xNHXMEhERETkVAyIPYQyI8m00EVLi/oRn+QyIiIiInIoBkYdQ3ot38mwERAqzgIgz3hMRETkXAyIPofIy/KvXA1or8Y55lRm73hMRETkXAyIPoVLe/zs3v/B6VpkRERG5DgMiD1FUQMQqMyIiItdhQOQhvL3u/201Q2RWZZbPKjMiIiKnYkDkISwyRHmF1zNDRERE5DoMiDyEqqgMEcwyRGxDRERE5FQMiDxEkW2IJLMMEavMiIiInIoBkYcoupeZeYaIVWZERETOxIDIQxSvDREzRERERM7EgMhDFNWGSGGeIRLMEBERETkTAyIPUaw2RMwQERERORUDIg/BNkRERETuw4DIQxSrDRF7mRERETkVAyIPYd6GKMdaQMQqMyIiIpdhQOQhipy6w6zKjCNVExERORcDIg9RvMldmSEiIiJyJgZEHqLIRtUSu90TERG5CgMiD8GBGYmIiNyHAZGHKN7krswQERERORMDIg/ByV2JiIjchwGRh+DAjERERO7DgMhDsA0RERGR+zAg8hBFtiFiLzMiIiKXYUDkITgOERERkfswIPIQRQdEbENERETkKgyIPITF1B2c3JWIiKhUMSDyEMXqds8qMyIiIqdiQOQhzDNE2bmF17PbPRERkeswIPIQCgXgozL8nZ5tZT2rzIiIiFyGAZEHCfA1/KvJLLzOvNu9lhkiIiIip2JA5EECjQFRVuF17HZPRETkOgyIPEign+FfTSYghOU6tiEiIiJyHQZEHsSYIdLqgJwCXe85uSsREZHrMCDyIMY2RACQXqDaTGlWZcYMERERkXMxIPIggWYBUcGG1eYjVbMNERERkXMxIPIgxjZEAJCYarlOaVZlxsldiYiInIsBkQepFX7/76u3LNcxQ0REROQ6DIg8SKUK9/9O0Vius2xDxICIiIjImRgQeZDggPt/p6RbrrPsZcYqMyIiImdiQORBggPv/10wQ8QqMyIiItdhQORB7GWIWGVGRETkOgyIPIi9DJEcrDIjIiJyFQZEHqSC//2/txyyXCdJkmk+M1aZERERORcDIg+iUFj+P79AIogBERERkWswIPJgO/60/L9xglcOzEhERORcZSIgysjIwBtvvIHw8HB4e3ujWbNmWLNmjUP7JiYmYujQoQgNDYWvry9atWqF3bt3u7jED25iv/t/PzfFctZ7Y9f7LOQgT+SXcsmIiIjKrzIREPXu3RsxMTGYNm0atm3bhscffxz9+/fHqlWr7O6Xm5uLjh07Yvfu3ViwYAF+/vlnhIWFoVu3bti/f38plb54Oj1q+X9ZeyApFYi9DCiEEgBwTX8LVVOfw6jMOdiZf5iNrImIiEpIEsI8B+F5tm7dimeffRarVq1C//79Tcu7dOmC06dP4/r165DL5Vb3XbRoEcaOHYuDBw+iVatWAACtVoumTZvC398fhw8fdrgcGo0GarUaaWlpCAwMLHqHB5SdC/h2tb5OGj8TsvbbCy0XaUHAmWYQehkkuRbePjrkaHWATA9xOxytZE1xZntThOSFYfpQYMMBICcPaN8cyM0D6kYCIYGA2s8wWnbFIMBHZchO6XTAnTTDPGu+3sCfN+/iP3/HIqTmLXSqUQk1ZOEI14bDS+uHPC0QqgYkyfa1+ajsX3++FlDIbR+DiIioOBz9/vb4gOjVV1/FmjVrcPfuXSjMWh2vXr0aAwYMwB9//IEnn3zS6r6dO3dGfHw8zp49a7F8zpw5mDJlCm7cuIGqVas6VI7SCogAYMxnwH9/sbJCpgMePQjpqd2QHv8dkk92sY4rbleBiGsKnG8E5JpFJuJe9KGXQ+SqgHwvIE9leAgJUo0LQL1TkOrFQgq/Yf3YaUHA7XDgbghElh+Q7Qdk+hv+zfYF5FpAlQt45QJeOYa/IQzrs/yALH8gyw8iyw+SdzYQfMfwqHAHUoU7QEA6kFwR4mY1ICESIqEacLMaoKkA+GQCfumAX4bh4ZsByPQAJEAvAUJmuEa98V85ICTUqSJHqkaGJI0eUOYjrGI+bqfnQ1LmA4p8IMcHIl0NpAcCGYFoFKqGl0yG1IAbyKxwHUl+14Hw6/CqFo/AAC2SbwYZnod0NaAJQqgsCEnX1UB6EJAeiCrKIITKA6GAAn9dMD1zeLKpDmfidUjN0iE0SCC8okCtqnpAJhBWMR9Va6dgfWwSTt5NAircAUKSIPmlG65RpgdkOkRW0SM7Tw+f/EAo7oahfWQY/jwQBtmdyvjnSjCETIuhPbNxIz0Hq37LQdNG2UjJ0qJmVT3+SRFIyRBo3VQgyF/ARylHdpYC/yTK4adUoEVdBWR6OTSZEm4kAb+fAhpEGXpFRlTLwtHbKRDqu8hW3cVNbQrSpDTIcvxR3SsUObdD4J8TikcqhmDZ2iC0aSJBApCRLaFpLSAkUMLfF2RoWVeBJxsosGGPEhkZcoT4yZGUoYNe0uGV7lqcvKpFVr4Wadl6+HvJ4atU4M/TcjxRVw4/pRx3UhS4m6yCj0KO2lUlJKUCT0cD124LpItMhEWl4UKaBtcyNVAGZuB8Ug58ArIhvHOg9MuGT0AuUpK9cOS4H9rV80MllR+CZH44e94XNSoqkJEhR4CXAimpcjxaSwFNugyxl2V4pI6EykESvBQSfLxkuJGsg1ypRUKqDknpOlSuqEVYqA7HLmkRqM7HLY0WNSO1SM7UIidHgqTzQoZGiQreXkC+EtWDVagkKqJeqB8UcuDERaB6ZSDABwjyB7JygQxk4jyu4EjGNQSoc+HvLUGTIYc2X8KZKzIoJDl0eQo0CFciO1OJto2USExSIkujQoS/H+qG+GHbPl80CvfGlX8khFfSQhF6B7KKSTiRkohMvyTofTTw0fsh4VogKioCECILhF4TCF+5F1TeOoQG63A3Swellw65Oh2ycoGGUYBOJyExFQjyA2SSDGG+3jhxxgsVfbzRqrY3stNVyMjT4UpqJoLCMpAjz0I6MpChz4FM64UAGD4DQr38EOrliyClCr+f0aFpXR0ytVrkCx1upuig9pGhWgUvVFB5IdDLC9duS8jMBgQMz1W1MAE99EjJ0mJ/rBbVI7RIydIiKTMfFUO0CA3W4tYdBUK9VQjyUiHUW4VApRcuJkgIqwAE+AJ5+cDddCA9G9jyP6BXa6BKCJBwxzDXpJeXQLXIbOSrMnFXn44bWRm4q8vApeQc1K0qQQEFMrNkCPaTwc9LDpVQwVcXgBCFPyop/aHL8oFSIcFLAcQnAV5KgWC1DpJXPu5m6LH9oAp1q8jxVCMJqRmAn4/hR6WfN6DyAhLvAoG+wMlLQGK6FiGRd5Hvn4JMVQqu5aQgLVuPx4OroIquCqoiDEKrgJ+P4QenVmf4MaxTZuPQzRTclTQIkKtQI9gbgXIfBHmp4K33hlwmIUurRUpOHjJ1eciF4TM8S5mO27q7SNKn4npOKpJFGjL1uWjoXwmV8qqgGqqgllcVJF2rgHwdEFktC9fz7yDT5w7itXdwR9xFbp4EP4USXlDCW67EzX8UqBHqhSaBldFcUbdY321FKTcBUatWraDT6fDnn5YtjE+fPo3GjRtj8eLFGDVqlNV9q1SpgtatW2Pt2rUWy7ds2YIePXpgx44d6NKli0PlKM2ACABmLwfe+87OBl65wCMHIT21B9Jjf0BS5bq8TOQcIscbkOsAmQ6SXO/u4pQrQi8ZAvp8L0MA7JcBSV72emWKjAAgsTKQVBkiqTKgVUCKvAJUuwKp4m3nnEMnA3J8AJ8sSDKP/hooksjzArSK++8rZfGbEQi9BGgNzRIgCQDi3r8ATBlrs+dJEiV63oRODmT5Gn7UKPIBZX6h4wmdzPDD1Pw1LdMbyiETgKQHFFpI/unWT2J+rjuVDK8phQ4ISgaCUor8US30UsmuMcfb8KO6GD/eu2Z1xZaIDx74nNY4+v2tsLnGQyQnJ6NmzZqFlgcHB5vW29vXuF1x983NzUVu7v0gQ6PR2NzWFaYOBoZ2A15fCGw6CFQKAjKygbTMexvkqYBD7SEOtYfwyjW8wHVywxtGrzD8LemBmuchNTgJqeFJoM5pSKq8By6TyPMCLtaHOBdtyM6EJAFhCZDCEoCwBCA4yWUfrEIrh6Tw3C82oZM5HNxI3jkuLs3DS5IJQ/axjP9AkPzTAf90oOYFuKr2WJLrAb/MojcsAySvPMDrwT/bgHuvnRIeo1jnk+sMmW+72+gBn2zDo6TnCvvH8CjOfiX8PH+Qz7q/ziqBiBKd9oF5fEAEGAYlfJB1Jdl3zpw5mDFjRtGFc6GqFYH1/3ZkSxWAcIslQhja4ej1T0CSnsDpK4ACeUjyO4sTOVehUOqRmg54qwSu3gKS0gCVtxbJ2XnQK3ORnJMLnTwPtaLyEZ4XgQoJ0Th/oC5OX1QiPARIzwKaBAF514DFX9w7qSIP1etocCc/ExnIBHwzoa6YCY0uC4/XUeDPv+9Vw+Ua/lUpJeQqMgHfDEi+hu3hmwHkeRuqx1JC4ZMViuxbIYZfssF3gPDrkMKvA+HxkKpeN1SRZfpDZPoDmQGGarosf0NAWPCXlEx/719hqH6U6Q2/AAUA7b1fmPmGqgvo5IYPIf80ICAdUkAa4K8BlHkQt6oC/0QCN6sZqu4SIg37+WUAgXeBwDQgIA2S+i4QoAECUoHANMMxAtIAnyxAdy9oNT70MlNVHoTsflWfXgaRGgzcDTU9J0ipaKiWM99Pf28fdSpQ8Rak0NtAxdtA6G1IQSkQeSog1xvI8QZyfQz/apX3qxPFvepFAJDrDVWcci2g0Bp+dVvLskjCcC9Tgw1lTAsGUisAGYGGexl8r7qzwh2gQrLhuSi4vyQM90GuhaTINzuf1nBdWsPzJHQKw32BZLh3ch2ke/9Crrv3CzsPUBr/zTMsz/IH0gPvVX3eq/7M8je8nnK9Db9gc30Mr0tlHuCTef+16JNpuFfG8pjOpTWUATC9hqR71yL0MlOZTeXXyw3PtXG58W+zzICp7KpsSKGJQMVbhntX4EeAyPQD4mtCXK8BxNcwPNcy3f3n8V4VquG4WsO/xod3jiEb5GO8vizDNWYGGF5byZWA5IpASkXD8+WTBclfY3jd+6cbXrtyreG1qy/w2oUEU/bEmFWR6+5VkecaMtheOYAqx7BPlj+Eqbrcz/Da9Mq9V6Yswz3wyTQss3ivKO5nSe49Z5IqF0Jx7/kzbmN63i2fc6G9997WKgyZFa9cQxB0r5xQ3guIhGS4JiGZJYUks3X3ZPve+/wJMFxHpr/h9WT8vJHrTK9vqHINn3Xm1fs+mYbrKfj5IyRDWczLp8y793klu1/9LyTD9aSr778Pje9FAKiUACnsH8MP1rAESH4ZhkvICDBsZ9wnI/DeayQbkureffLONpQ9936GShizVRkBgCYI0ARBaNSGpgv5SsNrtpIh8JIq/gNUumUoc3JFiLshhs+xlFDDuQHDe0mZZ/j33nuha4Oa0OsBmRu6fHl8QBQSEmI1k5OSkgIAVjNAzth38uTJeOutt0z/12g0iIyMdLjc7maM9YwvqsY1AcAL9dEErVVNDAt9720c5MABowC0sr7qv28b//ICEHrvYcVAB85jV8V7j0eL2tBNAu893MkPgGPt4lyrAtz2M6+c0AkdEvR3cE1/CznIQ315FKpWqAgpUgKsN5ssd4w/7Epj/9I6V0nOU9Iypukz4AUlfIJVQDXr2+j1gE4PKIsRHdwvV6Nilcf8ei7dBKqFFe+8zubxAVF0dDRWr14NrVZr0aj61KlTAIDGjRvb3de4nTlH9lWpVFCpiugSRUTkInJJjkh5GCLlYe4uituUtLdpcfYvrXOV5DwlLaNa5l/kNjJZ8bMzD1ou8/1qecDvOI8fh6hXr17IyMjA+vXrLZbHxMQgPDwcLVq0sLvv2bNnLbrXa7VarFixAi1atEB4eLjNfYmIiOjh4fEZomeeeQadO3fGmDFjoNFoULt2baxevRrbt2/HihUrTGMQjRgxAjExMbh06RKioqIAAMOHD8eXX36JPn36YO7cuahUqRIWLVqEc+fO4ddff3XnZREREZEH8fiACAA2bNiAqVOn4oMPPkBKSgrq16+P1atXo1+/+/Nc6HQ66HQ6mI8ioFKpsHv3bkyaNAnjx49HVlYWmjVrhm3btqFt27buuBQiIiLyQB4/DpGnKO1xiIiIiKjkHP3+9vg2RERERESuxoCIiIiIHnoMiIiIiOihx4CIiIiIHnoMiIiIiOihx4CIiIiIHnoMiIiIiOihx4CIiIiIHnoMiIiIiOihVyam7vAExgG9NRqNm0tCREREjjJ+bxc1MQcDIgelp6cDACIjI91cEiIiIiqu9PR0qNVqm+s5l5mD9Ho9EhISEBAQAEmSnHZcjUaDyMhIxMfHl9s50sr7NfL6yr7yfo28vrKvvF+jK69PCIH09HSEh4dDJrPdUogZIgfJZDJERES47PiBgYHl8kVurrxfI6+v7Cvv18jrK/vK+zW66vrsZYaM2KiaiIiIHnoMiIiIiOihx4DIzVQqFaZNmwaVSuXuorhMeb9GXl/ZV96vkddX9pX3a/SE62OjaiIiInroMUNEREREDz0GRERERPTQY0BEREREDz0GRG6SkZGBN954A+Hh4fD29kazZs2wZs0adxfLrn379kGSJKuPQ4cOWWx7/PhxdOrUCf7+/ggKCkLv3r1x+fJlq8dduHAh6tevD5VKhRo1amDGjBnIz893+fWkp6dj0qRJ6NKlCypWrAhJkjB9+nSr27riehITEzF06FCEhobC19cXrVq1wu7du0v9+oYOHWr1ntavX9+jr2/Pnj0YPnw46tevDz8/P1StWhUvvPACjh07Vmjbsnj/HL2+snr/AODEiRN49tlnUa1aNfj4+CA4OBitWrXCihUrCm1bFu+ho9dXlu9hQd9++y0kSYK/v3+hdR5/DwW5RefOnUVQUJD473//K/bs2SNGjhwpAIiVK1e6u2g27d27VwAQH374ofjf//5n8UhPTzdtFxcXJwICAkTr1q3Fli1bxPr160WjRo1EeHi4SExMtDjmrFmzhCRJYvLkyWLv3r3i448/Fl5eXuLVV191+fVcuXJFqNVq0aZNG9PzP23atELbueJ6cnJyROPGjUVERIRYsWKF2Llzp3jhhReEQqEQ+/btK9XrGzJkiPDx8Sl0T0+cOFFoW0+6vpdeekm0b99eLFq0SOzbt0+sW7dOtGzZUigUCrF7927TdmX1/jl6fWX1/glh+EwZPXq0WL58udizZ4/YtGmT6NevnwAgZs6cadqurN5DR6+vLN9Dczdu3BBqtVqEh4cLPz8/i3Vl4R4yIHKDLVu2CABi1apVFss7d+4swsPDhVardVPJ7DMGROvWrbO7XZ8+fURoaKhIS0szLbt69apQKpVi0qRJpmV37twR3t7eYtSoURb7z549W0iSJE6fPu3cCyhAr9cLvV4vhBAiKSnJZsDgiuv58ssvBQBx8OBB07L8/HzRsGFD8cQTT5Tq9Q0ZMqTQh5c1nnZ9t2/fLrQsPT1dhIWFiY4dO5qWldX75+j1ldX7Z0+LFi1EZGSk6f9l9R7aUvD6yss97NGjh3juueesXk9ZuIcMiNxg5MiRwt/fX+Tn51ssX7VqlQAg/vjjDzeVzD5HAqL8/Hzh4+MjRo8eXWhdly5dRJ06dUz/X7FihQAg/ve//1lsl5CQIACI2bNnO6/wRbAVMLjqejp16iTq1atX6JgffvihACBu3LhRwiuy5IyAyJOvz1z79u1F3bp1hRDl5/6ZM78+Icrf/RNCiGeffVbUqFFDCFE+76H59QlRPu7h8uXLRUBAgIiPjy90PWXlHrINkRvExsaiQYMGUCgsp5Jr0qSJab0nGzt2LBQKBQIDA9G1a1f8/vvvpnWXLl1Cdna26VrMNWnSBBcvXkROTg6A+9cZHR1tsV2VKlUQGhrqEc+Dq64nNjbW5jEB4PTp0067BkdkZ2ejcuXKkMvliIiIwLhx45CSkmKxTVm4vrS0NBw/fhyNGjUCUP7uX8HrMyrr90+v10Or1SIpKQmLFi3Cjh078K9//QtA+biH9q7PqCzfw8TERLzxxhuYO3eu1Tk/y8o95OSubpCcnIyaNWsWWh4cHGxa74nUajUmTJiAdu3aISQkBBcvXsQnn3yCdu3aYcuWLejataup7MZrMRccHAwhBO7evYsqVaogOTkZKpUKfn5+Vrf1hOfBVdeTnJxs85jm5y0NTZs2RdOmTdG4cWMAwP79+/Gf//wHu3fvxpEjR0yNI8vC9Y0dOxaZmZmYOnWqxXnKy/0reH1A+bh/r732GhYvXgwA8PLywueff47Ro0dbnKss30N71weU/Xv42muvoV69ehgzZozV9WXlHjIgchNJkh5onTs1b94czZs3N/2/devW6NWrF6KjozFp0iR07drVtM7R6ysrz4MrrsdTrv3NN9+0+H/nzp3RvHlzvPTSS/jmm28s1nvy9b3//vtYuXIlFi5ciEcfffSBylIWr6883L8pU6Zg5MiRSExMxKZNmzBu3DhkZmbinXfeKXZ5PPEai7q+snwP169fj02bNuGvv/4q8piefg9ZZeYGISEhViNXY3rUWsTrqYKCgtCjRw/8/fffyM7ORkhICADrkXlKSgokSUJQUBAAw/OQk5ODrKwsq9t6wvPgquvx9NdAr1694OfnZzGcgidf34wZMzBr1izMnj0b48aNsygHUPbvn63rs6Ws3b9q1arhscceQ/fu3fHVV19h1KhRmDx5MpKSksrFPbR3fbaUhXuYkZGBsWPHYvz48QgPD0dqaipSU1ORl5cHAEhNTUVmZmaZuYcMiNwgOjoacXFx0Gq1FstPnToFAKa0aVkh7k2HJ0kSatWqBR8fH9O1mDt16hRq164Nb29vAPfriAtue+vWLdy5c8cjngdXXU90dLTNYwKe8RoQQkAmu/8R4anXN2PGDEyfPh3Tp0/HlClTLNaVh/tn7/rsKSv3z5onnngCWq0Wly9fLhf3sCDz67PH0+/hnTt3cPv2bcybNw8VKlQwPVavXo3MzExUqFABAwcOLDv3sFhNsMkptm7dKgCINWvWWCzv1q2bR3e7tyYlJUVUrVpVNGvWzLTs5ZdfFpUqVRIajca07Nq1a8LLy0v861//Mi1LTk4W3t7e4v/+7/8sjjlnzpxS6XZvzl4vLFdcz6JFiwQAcejQIdOy/Px80ahRI9GiRQsnXpmBveuz5ocffhAAxPz5803LPPH6/v3vfwsA4r333rO5TVm+f45cnzVl5f7ZMnjwYCGTyUzj05Tle2hNweuzpizcw+zsbLF3795Cj65duwpvb2+xd+9ecerUKSFE2biHDIjcpHPnzqJChQri66+/Fnv27BGvvvqqACBWrFjh7qLZ1L9/f/Gvf/1LrFu3Tuzdu1d8/fXXol69ekKhUIhdu3aZtouLixP+/v6iTZs2YuvWrWLDhg2icePGdgfgmjJliti3b5/45JNPhEqlKpWBGYUwBKfr1q0TS5YsEQBEnz59xLp168S6detEZmamy64nJydHNGrUSERGRoqVK1eKXbt2iV69ejl90LSiru/q1aviySefFJ9//rnYunWr2LZtm3j33XeFt7e3aNSokcjIyPDY6/v0008FANGtW7dCA9qZd9ktq/fPkesry/dPCCFeffVV8fbbb4sffvhB7Nu3T/z444+ib9++AoCYOHGiabuyeg8dub6yfg+tsTaMQFm4hwyI3CQ9PV28/vrronLlysLLy0s0adJErF692t3FsmvOnDmiWbNmQq1WC7lcLipWrCh69eol/vzzz0LbHj16VHTs2FH4+vqKwMBA0bNnT3Hx4kWrx12wYIGoW7eu8PLyEtWqVRPTpk0TeXl5rr4cIYQQUVFRAoDVx5UrV1x6Pbdu3RKvvPKKCA4OFt7e3qJly5YWgWVpXF9KSoro1auXqF69uvDx8RFeXl6iTp06YtKkSSI1NdWjr69t27Y2r61g8rss3j9Hrq8s3z8hhFiyZIlo3bq1CA0NFQqFQgQFBYm2bduK5cuXF9q2LN5DR66vrN9Da2yNq+Tp91AS4l4DECIiIqKHFBtVExER0UOPARERERE99BgQERER0UOPARERERE99BgQERER0UOPARERERE99BgQERER0UOPARERUQlJkuSSGe6JqPQwICKiUlW9enVTAGHvsWzZMncXlYgeIgp3F4CIHk516tRBpUqVbK4PCwsrxdIQ0cOOARERucWUKVMwdOhQdxeDiAgAq8yIiIiIGBARkeczb7S8atUqPPHEE/D390dwcDB69uyJ2NhYm/tmZmZi1qxZaNKkCfz8/BAYGIgWLVrgyy+/hFartblfSkoKpk2bhubNmyMwMBD+/v5o0KAB/u///g9//fWXzf22bduGNm3aICAgAGq1Gs8884zN7a9du4bRo0ejZs2aUKlUCAgIQM2aNdGrVy+sWbPGwWeHiJxCEBGVoqioKAFALF261OF9AAgA4qOPPhIAROXKlcVjjz0mAgICBADh4+MjDhw4UGi/xMREER0dLQAImUwmmjRpIho0aGA6XufOnUV2dnah/U6cOCHCw8NN+zVs2FA0a9ZMBAYGCgBiyJAhVsv31VdfCUmSRJUqVcQjjzwi/Pz8BADh7+8v4uLiLPa5cuWKCA0NFQCEr6+viI6OFs2aNRPBwcECgGjatKnDzw8RlRwDIiIqVSUJiJRKpZg3b57Q6XRCCCEyMzPFwIEDBQARFRUlsrKyLPZ78cUXBQDRqFEjcfHiRdPyI0eOiLCwMAFATJo0yWKftLQ0Ua1aNQFAdOvWTcTHx1us/+2338SKFSusls/X19fiujQajejYsaMAIPr27Wuxz7hx40zBVXp6usW6uLg4sXjxYoefHyIqOQZERFSqjAFRUY+7d++a9jEue/755wsdLzc3V1SuXFkAEEuWLDEtP3/+vJAkSQAQx48fL7Tf2rVrBQDh5+cnNBqNafnHH38sAIgGDRqInJwch67JWL7x48cXWvf3338LAEKtVlss79q1qwAgTp486dA5iMi12MuMiNyiqG73CkXhj6exY8cWWubl5YWRI0di1qxZ2LFjB4YNGwYA2LVrF4QQePrpp9G8efNC+7344ouIiIjAjRs38Mcff6Bbt24AgJ9//hkAMGHCBKhUqmJd08iRIwsti46Ohre3N9LS0pCcnIyQkBAAQGRkJADgxx9/RHR0NAd2JHIzBkRE5BYP0u2+QYMGdpefP3/etMz4d8OGDa3uI5PJUL9+fdy4cQPnz583BURxcXEAgJYtWxarbABQq1Ytq8srVqyI+Ph4ZGRkmAKisWPHIiYmBjNnzsT333+Pbt26oXXr1mjfvj3Cw8OLfW4iKhn2MiOiMsNWRsk4iGN6erppWUZGht19bO2n0WgAAEFBQcUun5+fn9XlMpnho1YIYVrWrFkz/Pbbb+jSpQtu3ryJxYsXY9CgQYiIiEDXrl1NgRkRlQ4GRERUZiQlJVldnpiYCAAICAgwLfP397dYZ83t27cL7Wf8OzU1tURldUTLli2xY8cO3L17F9u3b8e//vUvREREYOfOnejcuXOplIGIDBgQEVGZYStrYlxet25d0zLj32fOnLG6j16vx9mzZwvt16hRIwDAoUOHSl5gB/n7+6Nr166YO3cuzp49i1q1auHmzZvYtm1bqZWB6GHHgIiIyoxFixYVWpaXl4fvvvsOANClSxfT8i5dukCSJPz+++9WB0bcsGEDbty4AT8/Pzz11FOm5T179gQALFy4EHl5eU6+gqL5+voiOjoaAJCQkFDq5yd6WDEgIqIyY8uWLViwYIGpLU52djZeffVVJCQkIDIyEv369TNtW7t2bfTu3RsA8Morr+Dy5cumdcePH8frr78OABg3bpxFldmoUaMQFRWF06dPo3fv3rh586ZFGX7//XesXLmyxNcyZswY/PDDD8jKyrJY/ttvv2H37t0AgEceeaTE5yEix0jCvJUfEZGLVa9eHdeuXSuy2/3LL79sClqMXdI/+ugj/Otf/0LlypURGRmJc+fOQaPRwNvbGzt27ECbNm0sjpGUlISOHTvi1KlTkMvlaNy4MfLz803VaJ06dcKmTZvg7e1tsd/JkyfRrVs33Lp1CzKZDA0aNIBSqcSVK1eQlpaGIUOGYNmyZabtjeWz9XFqvOYrV66gevXqAAyNqk+ePAmFQoE6deogICAAt2/fxrVr1wAAgwYNwvLlyx18VomopBgQEVGpMgYHRZkwYQLmz58PwDLgWLVqFebPn4/Tp09DqVSibdu2mDlzJpo0aWL1OJmZmfjss8+wdu1aXLp0CTKZDA0bNsQrr7yC0aNHQ6lUWt0vOTkZ8+bNwy+//IIrV65ALpcjIiIC7dq1w+jRo9G0aVPTtg8SEO3duxc///wzDhw4gPj4eKSlpaFKlSqoX78+xo4dix49enBsIqJSxICIiDxeUQEHEVFJsQ0RERERPfQYEBEREdFDjwERERERPfQYEBEREdFDj5O7EpHHY2NqInI1ZoiIiIjooceAiIiIiB56DIiIiIjooceAiIiIiB56DIiIiIjooceAiIiIiB56DIiIiIjooceAiIiIiB56DIiIiIjooff/UwoJAHzlxYwAAAAASUVORK5CYII=\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "\n",
+                "\n",
+                "scaler = GradScaler()\n",
+                "total_start = time.time()\n",
+                "\n",
+                "for epoch in range(n_epochs):\n",
+                "    model.train()\n",
+                "    epoch_loss = 0\n",
+                "\n",
+                "    for step, data in enumerate(train_loader):\n",
+                "        images = data[\"image\"].to(device)\n",
+                "        seg = data[\"label\"].to(device)  # this is the ground truth segmentation\n",
+                "        optimizer.zero_grad(set_to_none=True)\n",
+                "        timesteps = torch.randint(0, 1000, (len(images),)).to(device)  # pick a random time step t\n",
+                "\n",
+                "        with autocast(enabled=True):\n",
+                "            # Generate random noise\n",
+                "            noise = torch.randn_like(seg).to(device)\n",
+                "            noisy_seg = scheduler.add_noise(\n",
+                "                original_samples=seg, noise=noise, timesteps=timesteps\n",
+                "            )  # we only add noise to the segmentation mask\n",
+                "            combined = torch.cat(\n",
+                "                (images, noisy_seg), dim=1\n",
+                "            )  # we concatenate the brain MR image with the noisy segmenatation mask, to condition the generation process\n",
+                "            prediction = model(x=combined, timesteps=timesteps)\n",
+                "            # Get model prediction\n",
+                "            loss = F.mse_loss(prediction.float(), noise.float())\n",
+                "        scaler.scale(loss).backward()\n",
+                "        scaler.step(optimizer)\n",
+                "        scaler.update()\n",
+                "        epoch_loss += loss.item()\n",
+                "\n",
+                "    epoch_loss_list.append(epoch_loss / (step + 1))\n",
+                "    if (epoch) % val_interval == 0:\n",
+                "        model.eval()\n",
+                "        val_epoch_loss = 0\n",
+                "        for step, data_val in enumerate(val_loader):\n",
+                "            images = data_val[\"image\"].to(device)\n",
+                "            seg = data_val[\"label\"].to(device)  # this is the ground truth segmentation\n",
+                "            timesteps = torch.randint(0, 1000, (len(images),)).to(device)\n",
+                "            with torch.no_grad():\n",
+                "                with autocast(enabled=True):\n",
+                "                    noise = torch.randn_like(seg).to(device)\n",
+                "                    noisy_seg = scheduler.add_noise(original_samples=seg, noise=noise, timesteps=timesteps)\n",
+                "                    combined = torch.cat((images, noisy_seg), dim=1)\n",
+                "                    prediction = model(x=combined, timesteps=timesteps)\n",
+                "                    val_loss = F.mse_loss(prediction.float(), noise.float())\n",
+                "            val_epoch_loss += val_loss.item()\n",
+                "        print(\"Epoch\", epoch, \"Validation loss\", val_epoch_loss / (step + 1))\n",
+                "        val_epoch_loss_list.append(val_epoch_loss / (step + 1))\n",
+                "\n",
+                "torch.save(model.state_dict(), \"./segmodel.pt\")\n",
+                "total_time = time.time() - total_start\n",
+                "print(f\"train diffusion completed, total time: {total_time}.\")\n",
+                "plt.style.use(\"seaborn-bright\")\n",
+                "plt.title(\"Learning Curves Diffusion Model\", fontsize=20)\n",
+                "plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n",
+                "plt.plot(\n",
+                "    np.linspace(val_interval, n_epochs, int(n_epochs / val_interval)),\n",
+                "    val_epoch_loss_list,\n",
+                "    color=\"C1\",\n",
+                "    linewidth=2.0,\n",
+                "    label=\"Validation\",\n",
+                ")\n",
+                "plt.yticks(fontsize=12)\n",
+                "plt.xticks(fontsize=12)\n",
+                "plt.xlabel(\"Epochs\", fontsize=16)\n",
+                "plt.ylabel(\"Loss\", fontsize=16)\n",
+                "plt.legend(prop={\"size\": 14})\n",
+                "plt.show()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "\n",
+                "# Sampling of a new segmentation mask for an input image of the validation set<br>\n",
+                "\n",
+                "Starting from random noise, we want to generate a segmentation mask for a brain MR image of our validation set.\\\n",
+                "Due to the stochastic generation process, we can sample an ensemble of n different segmentation masks per MR image.\\\n",
+                "First, we pick an image of our validation set, and check the ground truth segmentation mask."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 120,
+            "metadata": {
+                "lines_to_end_of_cell_marker": 2
+            },
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/plain": [
+                            "DiffusionModelUNet(\n",
+                            "  (conv_in): Convolution(\n",
+                            "    (conv): Conv2d(2, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "  )\n",
+                            "  (time_embed): Sequential(\n",
+                            "    (0): Linear(in_features=64, out_features=256, bias=True)\n",
+                            "    (1): SiLU()\n",
+                            "    (2): Linear(in_features=256, out_features=256, bias=True)\n",
+                            "  )\n",
+                            "  (down_blocks): ModuleList(\n",
+                            "    (0): DownBlock(\n",
+                            "      (resnets): ModuleList(\n",
+                            "        (0): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Identity()\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (downsampler): Downsample(\n",
+                            "        (op): Convolution(\n",
+                            "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+                            "        )\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (1): DownBlock(\n",
+                            "      (resnets): ModuleList(\n",
+                            "        (0): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Identity()\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (downsampler): Downsample(\n",
+                            "        (op): Convolution(\n",
+                            "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+                            "        )\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (2): AttnDownBlock(\n",
+                            "      (attentions): ModuleList(\n",
+                            "        (0): AttentionBlock(\n",
+                            "          (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (resnets): ModuleList(\n",
+                            "        (0): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Identity()\n",
+                            "        )\n",
+                            "      )\n",
+                            "    )\n",
+                            "  )\n",
+                            "  (middle_block): AttnMidBlock(\n",
+                            "    (resnet_1): ResnetBlock(\n",
+                            "      (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "      (nonlinearity): SiLU()\n",
+                            "      (conv1): Convolution(\n",
+                            "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "      )\n",
+                            "      (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "      (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "      (conv2): Convolution(\n",
+                            "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "      )\n",
+                            "      (skip_connection): Identity()\n",
+                            "    )\n",
+                            "    (attention): AttentionBlock(\n",
+                            "      (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "      (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "      (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "      (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "      (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "    )\n",
+                            "    (resnet_2): ResnetBlock(\n",
+                            "      (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "      (nonlinearity): SiLU()\n",
+                            "      (conv1): Convolution(\n",
+                            "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "      )\n",
+                            "      (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "      (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "      (conv2): Convolution(\n",
+                            "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "      )\n",
+                            "      (skip_connection): Identity()\n",
+                            "    )\n",
+                            "  )\n",
+                            "  (up_blocks): ModuleList(\n",
+                            "    (0): AttnUpBlock(\n",
+                            "      (resnets): ModuleList(\n",
+                            "        (0): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
+                            "          )\n",
+                            "        )\n",
+                            "        (1): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
+                            "          )\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (attentions): ModuleList(\n",
+                            "        (0): AttentionBlock(\n",
+                            "          (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "        )\n",
+                            "        (1): AttentionBlock(\n",
+                            "          (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "          (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (upsampler): Upsample(\n",
+                            "        (conv): Convolution(\n",
+                            "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "        )\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (1): UpBlock(\n",
+                            "      (resnets): ModuleList(\n",
+                            "        (0): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
+                            "          )\n",
+                            "        )\n",
+                            "        (1): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
+                            "          )\n",
+                            "        )\n",
+                            "      )\n",
+                            "      (upsampler): Upsample(\n",
+                            "        (conv): Convolution(\n",
+                            "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "        )\n",
+                            "      )\n",
+                            "    )\n",
+                            "    (2): UpBlock(\n",
+                            "      (resnets): ModuleList(\n",
+                            "        (0): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
+                            "          )\n",
+                            "        )\n",
+                            "        (1): ResnetBlock(\n",
+                            "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
+                            "          (nonlinearity): SiLU()\n",
+                            "          (conv1): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
+                            "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "          (conv2): Convolution(\n",
+                            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "          )\n",
+                            "          (skip_connection): Convolution(\n",
+                            "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
+                            "          )\n",
+                            "        )\n",
+                            "      )\n",
+                            "    )\n",
+                            "  )\n",
+                            "  (out): Sequential(\n",
+                            "    (0): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
+                            "    (1): SiLU()\n",
+                            "    (2): Convolution(\n",
+                            "      (conv): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+                            "    )\n",
+                            "  )\n",
+                            ")"
+                        ]
+                    },
+                    "execution_count": 120,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "idx = 0\n",
+                "data = val_ds[idx]\n",
+                "inputimg = data[\"image\"][0, ...]  # Pick an input slice of the validation set to be segmented\n",
+                "inputlabel = data[\"label\"][0, ...]  # Check out the ground truth label mask. If it is empty, pick another input slice.\n",
+                "\n",
+                "\n",
+                "plt.figure(\"input\" + str(inputlabel))\n",
+                "plt.imshow(inputimg, vmin=0, vmax=1, cmap=\"gray\")\n",
+                "plt.axis(\"off\")\n",
+                "plt.tight_layout()\n",
+                "plt.show()\n",
+                "\n",
+                "plt.figure(\"input\" + str(inputlabel))\n",
+                "plt.imshow(inputlabel, vmin=0, vmax=1, cmap=\"gray\")\n",
+                "plt.axis(\"off\")\n",
+                "plt.tight_layout()\n",
+                "plt.show()\n",
+                "\n",
+                "\n",
+                "model.eval()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Then we set the number of samples in the ensemble n. \\\n",
+                "Starting from the input image (which ist the brain MR image), we follow Algorithm 1 of the paper \"Diffusion Models for Implicit Image Segmentation Ensembles\" (https://arxiv.org/pdf/2112.03145.pdf) n times.\\\n",
+                "This gives us an ensemble of n different predicted segmentation masks."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 122,
+            "metadata": {
+                "lines_to_next_cell": 2
+            },
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:19<00:00, 52.45it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:18<00:00, 52.95it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:18<00:00, 53.05it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:18<00:00, 52.93it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:18<00:00, 53.06it/s]\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "n = 5\n",
+                "input_img = inputimg[None, None, ...].to(device)\n",
+                "ensemble = []\n",
+                "for k in range(5):\n",
+                "    noise = torch.randn_like(input_img).to(device)\n",
+                "    current_img = noise  # for the segmentation mask, we start from random noise.\n",
+                "    combined = torch.cat(\n",
+                "        (input_img, noise), dim=1\n",
+                "    )  # We concatenate the input brain MR image to add anatomical information.\n",
+                "\n",
+                "    scheduler.set_timesteps(num_inference_steps=1000)\n",
+                "    progress_bar = tqdm(scheduler.timesteps)\n",
+                "    chain = torch.zeros(current_img.shape)\n",
+                "    for t in progress_bar:  # go through the noising process\n",
+                "        with autocast(enabled=False):\n",
+                "            with torch.no_grad():\n",
+                "                model_output = model(combined, timesteps=torch.Tensor((t,)).to(current_img.device))\n",
+                "                current_img, _ = scheduler.step(\n",
+                "                    model_output, t, current_img\n",
+                "                )  # this is the prediction x_t at the time step t\n",
+                "                if t % 100 == 0:\n",
+                "                    chain = torch.cat((chain, current_img.cpu()), dim=-1)\n",
+                "                combined = torch.cat(\n",
+                "                    (input_img, current_img), dim=1\n",
+                "                )  # in every step during the denoising process, the brain MR image is concatenated to add anatomical information\n",
+                "\n",
+                "    plt.style.use(\"default\")\n",
+                "    plt.imshow(chain[0, 0, ..., 64:].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
+                "    plt.tight_layout()\n",
+                "    plt.axis(\"off\")\n",
+                "    plt.show()\n",
+                "    ensemble.append(current_img)  # this is the output of the diffusion model after T=1000 denoising steps"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "lines_to_next_cell": 2
+            },
+            "source": [
+                "\n",
+                "## Segmentation prediction\n",
+                "The predicted segmentation mask is obtained from the output of the diffusion model by thresholding.\\\n",
+                "We compute the Dice score for all predicted segmentations of the ensemble, as well as the pixel-wise mean and the variance map over the ensemble.\\\n",
+                "As shown in the paper \"Diffusion Models for Implicit Image Segmentation Ensembles\" (https://arxiv.org/abs/2112.03145), we see that taking the mean over n=5 samples improves the segmentation performance.\\\n",
+                "The variance maps highlights pixels where the model is unsure about it's own prediction.\n",
+                "\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 123,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "def dice_coeff(im1, im2, empty_score=1.0):\n",
+                "    im1 = np.asarray(im1).astype(bool)\n",
+                "    im2 = np.asarray(im2).astype(bool)\n",
+                "\n",
+                "    im_sum = im1.sum() + im2.sum()\n",
+                "    if im_sum == 0:\n",
+                "        return empty_score\n",
+                "\n",
+                "    # Compute Dice coefficient\n",
+                "    intersection = np.logical_and(im1, im2)\n",
+                "\n",
+                "    return 2.0 * intersection.sum() / im_sum"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 128,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Dice score of sample0 0.8455882352941176\n",
+                        "Dice score of sample1 0.860655737704918\n",
+                        "Dice score of sample2 0.8475836431226765\n",
+                        "Dice score of sample3 0.8820960698689956\n",
+                        "Dice score of sample4 0.8627450980392157\n",
+                        "Dice score on the mean map 0.889763779527559\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "image/png": "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\n",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "for i in range(len(ensemble)):\n",
+                "    prediction = torch.where(ensemble[i] > 0.5, 1, 0).float()  # a binary mask is obtained via thresholding\n",
+                "    score = dice_coeff(\n",
+                "        prediction[0, 0].cpu(), inputlabel.cpu()\n",
+                "    )  # we compute the dice scores for all samples separately\n",
+                "    print(\"Dice score of sample\" + str(i), score)\n",
+                "\n",
+                "\n",
+                "E = torch.where(torch.cat(ensemble) > 0.5, 1, 0).float()\n",
+                "var = torch.var(E, dim=0)  # pixel-wise variance map over the ensemble\n",
+                "mean = torch.mean(E, dim=0)  # pixel-wise mean map over the ensemble\n",
+                "mean_prediction = torch.where(mean > 0.5, 1, 0).float()\n",
+                "\n",
+                "score = dice_coeff(mean_prediction[0, ...].cpu(), inputlabel.cpu())  # Here we predict the Dice score for the mean map\n",
+                "print(\"Dice score on the mean map\", score)\n",
+                "\n",
+                "plt.style.use(\"default\")\n",
+                "plt.imshow(mean[0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")  # We plot the mean map\n",
+                "plt.tight_layout()\n",
+                "plt.axis(\"off\")\n",
+                "plt.show()\n",
+                "plt.style.use(\"default\")\n",
+                "plt.imshow(var[0, ...].cpu(), vmin=0, vmax=1, cmap=\"jet\")  # We plot the variance map\n",
+                "plt.tight_layout()\n",
+                "plt.axis(\"off\")\n",
+                "plt.show()"
+            ]
+        }
+    ],
+    "metadata": {
+        "jupytext": {
+            "formats": "ipynb,py:light"
+        },
+        "kernelspec": {
+            "display_name": "Python 3 (ipykernel)",
+            "language": "python",
+            "name": "python3"
+        },
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.8.13"
+        }
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Length of training data: 388\n",
-      "Train image shape torch.Size([1, 64, 64])\n",
-      "Train label shape torch.Size([1, 64, 64])\n"
-     ]
-    }
-   ],
-   "source": [
-    "batch_size = 32\n",
-    "\n",
-    "train_ds = DecathlonDataset(\n",
-    "    root_dir=root_dir,\n",
-    "    task=\"Task01_BrainTumour\",\n",
-    "    section=\"training\",  # validation\n",
-    "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
-    "    num_workers=4,\n",
-    "    download=False,  # Set download to True if the dataset hasnt been downloaded yet\n",
-    "    seed=0,\n",
-    "    transform=train_transforms,\n",
-    ")\n",
-    "\n",
-    "print(f\"Length of training data: {len(train_ds)}\")  # this gives the number of patients in the training set\n",
-    "print(f'Train image shape {train_ds[0][\"image\"].shape}')\n",
-    "print(f'Train label shape {train_ds[0][\"label\"].shape}')\n",
-    "\n",
-    "\n",
-    "train_loader = DataLoader(\n",
-    "    train_ds, batch_size=batch_size, shuffle=True, num_workers=4, drop_last=True, persistent_workers=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Preprocessing of the BRATS Dataset in 2D slices for validation\n",
-    "We download the BRATS validation dataset from the Decathlon dataset. We define the dataloader to load 2D slices as well as the corresponding ground truth tumor segmentation for validation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████| 96/96 [00:45<00:00,  2.12it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Length of training data: 96\n",
-      "Validation Image shape torch.Size([1, 64, 64])\n",
-      "Validation Label shape torch.Size([1, 64, 64])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "val_ds = DecathlonDataset(\n",
-    "    root_dir=root_dir,\n",
-    "    task=\"Task01_BrainTumour\",\n",
-    "    section=\"validation\",\n",
-    "    cache_rate=1.0,  # you may need a few Gb of RAM... Set to 0 otherwise\n",
-    "    num_workers=4,\n",
-    "    download=False,  # Set download to True if the dataset hasnt been downloaded yet\n",
-    "    seed=0,\n",
-    "    transform=train_transforms,\n",
-    ")\n",
-    "print(f\"Length of training data: {len(val_ds)}\")\n",
-    "print(f'Validation Image shape {val_ds[0][\"image\"].shape}')\n",
-    "print(f'Validation Label shape {val_ds[0][\"label\"].shape}')\n",
-    "\n",
-    "val_loader = DataLoader(\n",
-    "    val_ds, batch_size=batch_size, shuffle=False, num_workers=4, drop_last=True, persistent_workers=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Define network, scheduler, optimizer, and inferer\n",
-    "\n",
-    "At this step, we instantiate the MONAI components to create a DDPM, the UNET, the noise scheduler, and the inferer used for training and sampling. We are using the DDPM scheduler containing 1000 timesteps, and a 2D UNET with attention mechanisms in the 3rd level (`num_head_channels=64`).<br>\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "device = torch.device(\"cuda\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DiffusionModelUNet(\n",
-       "  (conv_in): Convolution(\n",
-       "    (conv): Conv2d(2, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "  )\n",
-       "  (time_embed): Sequential(\n",
-       "    (0): Linear(in_features=64, out_features=256, bias=True)\n",
-       "    (1): SiLU()\n",
-       "    (2): Linear(in_features=256, out_features=256, bias=True)\n",
-       "  )\n",
-       "  (down_blocks): ModuleList(\n",
-       "    (0): DownBlock(\n",
-       "      (resnets): ModuleList(\n",
-       "        (0): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Identity()\n",
-       "        )\n",
-       "      )\n",
-       "      (downsampler): Downsample(\n",
-       "        (op): Convolution(\n",
-       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (1): DownBlock(\n",
-       "      (resnets): ModuleList(\n",
-       "        (0): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Identity()\n",
-       "        )\n",
-       "      )\n",
-       "      (downsampler): Downsample(\n",
-       "        (op): Convolution(\n",
-       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (2): AttnDownBlock(\n",
-       "      (attentions): ModuleList(\n",
-       "        (0): AttentionBlock(\n",
-       "          (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
-       "        )\n",
-       "      )\n",
-       "      (resnets): ModuleList(\n",
-       "        (0): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Identity()\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       "  (middle_block): AttnMidBlock(\n",
-       "    (resnet_1): ResnetBlock(\n",
-       "      (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "      (nonlinearity): SiLU()\n",
-       "      (conv1): Convolution(\n",
-       "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "      )\n",
-       "      (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "      (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "      (conv2): Convolution(\n",
-       "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "      )\n",
-       "      (skip_connection): Identity()\n",
-       "    )\n",
-       "    (attention): AttentionBlock(\n",
-       "      (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "      (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
-       "      (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
-       "      (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
-       "      (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
-       "    )\n",
-       "    (resnet_2): ResnetBlock(\n",
-       "      (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "      (nonlinearity): SiLU()\n",
-       "      (conv1): Convolution(\n",
-       "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "      )\n",
-       "      (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "      (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "      (conv2): Convolution(\n",
-       "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "      )\n",
-       "      (skip_connection): Identity()\n",
-       "    )\n",
-       "  )\n",
-       "  (up_blocks): ModuleList(\n",
-       "    (0): AttnUpBlock(\n",
-       "      (resnets): ModuleList(\n",
-       "        (0): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
-       "          )\n",
-       "        )\n",
-       "        (1): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "      (attentions): ModuleList(\n",
-       "        (0): AttentionBlock(\n",
-       "          (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
-       "        )\n",
-       "        (1): AttentionBlock(\n",
-       "          (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
-       "        )\n",
-       "      )\n",
-       "      (upsampler): Upsample(\n",
-       "        (conv): Convolution(\n",
-       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (1): UpBlock(\n",
-       "      (resnets): ModuleList(\n",
-       "        (0): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
-       "          )\n",
-       "        )\n",
-       "        (1): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "      (upsampler): Upsample(\n",
-       "        (conv): Convolution(\n",
-       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (2): UpBlock(\n",
-       "      (resnets): ModuleList(\n",
-       "        (0): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
-       "          )\n",
-       "        )\n",
-       "        (1): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       "  (out): Sequential(\n",
-       "    (0): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "    (1): SiLU()\n",
-       "    (2): Convolution(\n",
-       "      (conv): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "    )\n",
-       "  )\n",
-       ")"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model = DiffusionModelUNet(\n",
-    "    spatial_dims=2,\n",
-    "    in_channels=2,\n",
-    "    out_channels=1,\n",
-    "    num_channels=(64, 64, 64),\n",
-    "    attention_levels=(False, False, True),\n",
-    "    num_res_blocks=1,\n",
-    "    num_head_channels=64,\n",
-    "    with_conditioning=False,\n",
-    ")\n",
-    "model.to(device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scheduler = DDPMScheduler(num_train_timesteps=1000)\n",
-    "optimizer = torch.optim.Adam(params=model.parameters(), lr=2.5e-5)\n",
-    "inferer = DiffusionInferer(scheduler)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "### Model training of the Diffusion Model<br>\n",
-    "We train our diffusion model for 4000 epochs.\\\n",
-    "In every step, we concatenate the original MR image to the noisy segmentation mask, to predict a slightly denoised segmentation mask.\\\n",
-    "This is described in Equation 7 of the paper https://arxiv.org/pdf/2112.03145.pdf."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_epochs = 4000\n",
-    "val_interval = 50\n",
-    "epoch_loss_list = []\n",
-    "val_epoch_loss_list = []"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0 Validation loss 0.95528511206309\n",
-      "Epoch 50 Validation loss 0.012534369714558125\n",
-      "Epoch 100 Validation loss 0.006971345127870639\n",
-      "Epoch 150 Validation loss 0.007212877739220858\n",
-      "Epoch 200 Validation loss 0.0032773815716306367\n",
-      "Epoch 250 Validation loss 0.0032613773364573717\n",
-      "Epoch 300 Validation loss 0.0027105089587469897\n",
-      "Epoch 350 Validation loss 0.00643360164637367\n",
-      "Epoch 400 Validation loss 0.004260187192509572\n",
-      "Epoch 450 Validation loss 0.003338431240990758\n",
-      "Epoch 500 Validation loss 0.003913956073423226\n",
-      "Epoch 550 Validation loss 0.002972103344897429\n",
-      "Epoch 600 Validation loss 0.004632922820746899\n",
-      "Epoch 650 Validation loss 0.0021505119123806558\n",
-      "Epoch 700 Validation loss 0.0031663976066435375\n",
-      "Epoch 750 Validation loss 0.0030567607997606197\n",
-      "Epoch 800 Validation loss 0.002882685783940057\n",
-      "Epoch 850 Validation loss 0.0033122211073835692\n",
-      "Epoch 900 Validation loss 0.002124195219948888\n",
-      "Epoch 950 Validation loss 0.0046148050266007585\n",
-      "Epoch 1000 Validation loss 0.0033069681376218796\n",
-      "Epoch 1050 Validation loss 0.002037846017628908\n",
-      "Epoch 1100 Validation loss 0.00229898770339787\n",
-      "Epoch 1150 Validation loss 0.002420713659375906\n",
-      "Epoch 1200 Validation loss 0.0041328890559573965\n",
-      "Epoch 1250 Validation loss 0.004329038473467032\n",
-      "Epoch 1300 Validation loss 0.0023907446302473545\n",
-      "Epoch 1350 Validation loss 0.0030839802930131555\n",
-      "Epoch 1400 Validation loss 0.0031827394074449935\n",
-      "Epoch 1450 Validation loss 0.0028609122770527997\n",
-      "Epoch 1500 Validation loss 0.0028457901595781245\n",
-      "Epoch 1550 Validation loss 0.004309413023293018\n",
-      "Epoch 1600 Validation loss 0.0026823601219803095\n",
-      "Epoch 1650 Validation loss 0.0026449985646953187\n",
-      "Epoch 1700 Validation loss 0.0023076763997475305\n",
-      "Epoch 1750 Validation loss 0.002638093564504137\n",
-      "Epoch 1800 Validation loss 0.0023806413325170674\n",
-      "Epoch 1850 Validation loss 0.0018986108091970284\n",
-      "Epoch 1900 Validation loss 0.0031037907659386597\n",
-      "Epoch 1950 Validation loss 0.003802627402668198\n",
-      "Epoch 2000 Validation loss 0.002883425874946018\n",
-      "Epoch 2050 Validation loss 0.0025882223077739277\n",
-      "Epoch 2100 Validation loss 0.0024797461228445172\n",
-      "Epoch 2150 Validation loss 0.002770921913906932\n",
-      "Epoch 2200 Validation loss 0.0031128532718867064\n",
-      "Epoch 2250 Validation loss 0.0026554526605953774\n",
-      "Epoch 2300 Validation loss 0.0006344413559418172\n",
-      "Epoch 2350 Validation loss 0.003407757030799985\n",
-      "Epoch 2400 Validation loss 0.003249160130508244\n",
-      "Epoch 2450 Validation loss 0.0028327014297246933\n",
-      "Epoch 2500 Validation loss 0.001949470800658067\n",
-      "Epoch 2550 Validation loss 0.0037267633403340974\n",
-      "Epoch 2600 Validation loss 0.0025921284686774015\n",
-      "Epoch 2650 Validation loss 0.0028454426986475787\n",
-      "Epoch 2700 Validation loss 0.0028132296477754912\n",
-      "Epoch 2750 Validation loss 0.002190210895302395\n",
-      "Epoch 2800 Validation loss 0.00311990175396204\n",
-      "Epoch 2850 Validation loss 0.0022285515442490578\n",
-      "Epoch 2900 Validation loss 0.0025012800081943474\n",
-      "Epoch 2950 Validation loss 0.0019996101036667824\n",
-      "Epoch 3000 Validation loss 0.002304922246063749\n",
-      "Epoch 3050 Validation loss 0.002658801774183909\n",
-      "Epoch 3100 Validation loss 0.0020781653001904488\n",
-      "Epoch 3150 Validation loss 0.002127699709186951\n",
-      "Epoch 3200 Validation loss 0.0021539490359524884\n",
-      "Epoch 3250 Validation loss 0.003169172133008639\n",
-      "Epoch 3300 Validation loss 0.002952027212207516\n",
-      "Epoch 3350 Validation loss 0.0019326623684416215\n",
-      "Epoch 3400 Validation loss 0.0023521527958412967\n",
-      "Epoch 3450 Validation loss 0.0018178095730642478\n",
-      "Epoch 3500 Validation loss 0.0019376981460178893\n",
-      "Epoch 3550 Validation loss 0.003424471477046609\n",
-      "Epoch 3600 Validation loss 0.001281890688308825\n",
-      "Epoch 3650 Validation loss 0.00280005915556103\n",
-      "Epoch 3700 Validation loss 0.002113828396735092\n",
-      "Epoch 3750 Validation loss 0.0026302541761348643\n",
-      "Epoch 3800 Validation loss 0.003950760544588168\n",
-      "Epoch 3850 Validation loss 0.0018702246791993578\n",
-      "Epoch 3900 Validation loss 0.003523522444690267\n",
-      "Epoch 3950 Validation loss 0.003113662280763189\n",
-      "train diffusion completed, total time: 11462.321615695953.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "scaler = GradScaler()\n",
-    "total_start = time.time()\n",
-    "\n",
-    "for epoch in range(n_epochs):\n",
-    "    model.train()\n",
-    "    epoch_loss = 0\n",
-    "\n",
-    "    for step, data in enumerate(train_loader):\n",
-    "        images = data[\"image\"].to(device)\n",
-    "        seg = data[\"label\"].to(device)  # this is the ground truth segmentation\n",
-    "        optimizer.zero_grad(set_to_none=True)\n",
-    "        timesteps = torch.randint(0, 1000, (len(images),)).to(device)  # pick a random time step t\n",
-    "\n",
-    "        with autocast(enabled=True):\n",
-    "            # Generate random noise\n",
-    "            noise = torch.randn_like(seg).to(device)\n",
-    "            noisy_seg = scheduler.add_noise(\n",
-    "                original_samples=seg, noise=noise, timesteps=timesteps\n",
-    "            )  # we only add noise to the segmentation mask\n",
-    "            combined = torch.cat(\n",
-    "                (images, noisy_seg), dim=1\n",
-    "            )  # we concatenate the brain MR image with the noisy segmenatation mask, to condition the generation process\n",
-    "            prediction = model(x=combined, timesteps=timesteps)\n",
-    "            # Get model prediction\n",
-    "            loss = F.mse_loss(prediction.float(), noise.float())\n",
-    "        scaler.scale(loss).backward()\n",
-    "        scaler.step(optimizer)\n",
-    "        scaler.update()\n",
-    "        epoch_loss += loss.item()\n",
-    "\n",
-    "    epoch_loss_list.append(epoch_loss / (step + 1))\n",
-    "    if (epoch) % val_interval == 0:\n",
-    "        model.eval()\n",
-    "        val_epoch_loss = 0\n",
-    "        for step, data_val in enumerate(val_loader):\n",
-    "            images = data_val[\"image\"].to(device)\n",
-    "            seg = data_val[\"label\"].to(device)  # this is the ground truth segmentation\n",
-    "            timesteps = torch.randint(0, 1000, (len(images),)).to(device)\n",
-    "            with torch.no_grad():\n",
-    "                with autocast(enabled=True):\n",
-    "                    noise = torch.randn_like(seg).to(device)\n",
-    "                    noisy_seg = scheduler.add_noise(original_samples=seg, noise=noise, timesteps=timesteps)\n",
-    "                    combined = torch.cat((images, noisy_seg), dim=1)\n",
-    "                    prediction = model(x=combined, timesteps=timesteps)\n",
-    "                    val_loss = F.mse_loss(prediction.float(), noise.float())\n",
-    "            val_epoch_loss += val_loss.item()\n",
-    "        print(\"Epoch\", epoch, \"Validation loss\", val_epoch_loss / (step + 1))\n",
-    "        val_epoch_loss_list.append(val_epoch_loss / (step + 1))\n",
-    "\n",
-    "torch.save(model.state_dict(), \"./segmodel.pt\")\n",
-    "total_time = time.time() - total_start\n",
-    "print(f\"train diffusion completed, total time: {total_time}.\")\n",
-    "plt.style.use(\"seaborn-bright\")\n",
-    "plt.title(\"Learning Curves Diffusion Model\", fontsize=20)\n",
-    "plt.plot(np.linspace(1, n_epochs, n_epochs), epoch_loss_list, color=\"C0\", linewidth=2.0, label=\"Train\")\n",
-    "plt.plot(\n",
-    "    np.linspace(val_interval, n_epochs, int(n_epochs / val_interval)),\n",
-    "    val_epoch_loss_list,\n",
-    "    color=\"C1\",\n",
-    "    linewidth=2.0,\n",
-    "    label=\"Validation\",\n",
-    ")\n",
-    "plt.yticks(fontsize=12)\n",
-    "plt.xticks(fontsize=12)\n",
-    "plt.xlabel(\"Epochs\", fontsize=16)\n",
-    "plt.ylabel(\"Loss\", fontsize=16)\n",
-    "plt.legend(prop={\"size\": 14})\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Sampling of a new segmentation mask for an input image of the validation set<br>\n",
-    "\n",
-    "Starting from random noise, we want to generate a segmentation mask for a brain MR image of our validation set.\\\n",
-    "Due to the stochastic generation process, we can sample an ensemble of n different segmentation masks per MR image.\\\n",
-    "First, we pick an image of our validation set, and check the ground truth segmentation mask."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {
-    "lines_to_end_of_cell_marker": 2
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "DiffusionModelUNet(\n",
-       "  (conv_in): Convolution(\n",
-       "    (conv): Conv2d(2, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "  )\n",
-       "  (time_embed): Sequential(\n",
-       "    (0): Linear(in_features=64, out_features=256, bias=True)\n",
-       "    (1): SiLU()\n",
-       "    (2): Linear(in_features=256, out_features=256, bias=True)\n",
-       "  )\n",
-       "  (down_blocks): ModuleList(\n",
-       "    (0): DownBlock(\n",
-       "      (resnets): ModuleList(\n",
-       "        (0): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Identity()\n",
-       "        )\n",
-       "      )\n",
-       "      (downsampler): Downsample(\n",
-       "        (op): Convolution(\n",
-       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (1): DownBlock(\n",
-       "      (resnets): ModuleList(\n",
-       "        (0): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Identity()\n",
-       "        )\n",
-       "      )\n",
-       "      (downsampler): Downsample(\n",
-       "        (op): Convolution(\n",
-       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (2): AttnDownBlock(\n",
-       "      (attentions): ModuleList(\n",
-       "        (0): AttentionBlock(\n",
-       "          (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
-       "        )\n",
-       "      )\n",
-       "      (resnets): ModuleList(\n",
-       "        (0): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Identity()\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       "  (middle_block): AttnMidBlock(\n",
-       "    (resnet_1): ResnetBlock(\n",
-       "      (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "      (nonlinearity): SiLU()\n",
-       "      (conv1): Convolution(\n",
-       "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "      )\n",
-       "      (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "      (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "      (conv2): Convolution(\n",
-       "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "      )\n",
-       "      (skip_connection): Identity()\n",
-       "    )\n",
-       "    (attention): AttentionBlock(\n",
-       "      (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "      (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
-       "      (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
-       "      (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
-       "      (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
-       "    )\n",
-       "    (resnet_2): ResnetBlock(\n",
-       "      (norm1): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "      (nonlinearity): SiLU()\n",
-       "      (conv1): Convolution(\n",
-       "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "      )\n",
-       "      (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "      (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "      (conv2): Convolution(\n",
-       "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "      )\n",
-       "      (skip_connection): Identity()\n",
-       "    )\n",
-       "  )\n",
-       "  (up_blocks): ModuleList(\n",
-       "    (0): AttnUpBlock(\n",
-       "      (resnets): ModuleList(\n",
-       "        (0): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
-       "          )\n",
-       "        )\n",
-       "        (1): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "      (attentions): ModuleList(\n",
-       "        (0): AttentionBlock(\n",
-       "          (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
-       "        )\n",
-       "        (1): AttentionBlock(\n",
-       "          (norm): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (to_q): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (to_k): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (to_v): Linear(in_features=64, out_features=64, bias=True)\n",
-       "          (proj_attn): Linear(in_features=64, out_features=64, bias=True)\n",
-       "        )\n",
-       "      )\n",
-       "      (upsampler): Upsample(\n",
-       "        (conv): Convolution(\n",
-       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (1): UpBlock(\n",
-       "      (resnets): ModuleList(\n",
-       "        (0): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
-       "          )\n",
-       "        )\n",
-       "        (1): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "      (upsampler): Upsample(\n",
-       "        (conv): Convolution(\n",
-       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (2): UpBlock(\n",
-       "      (resnets): ModuleList(\n",
-       "        (0): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
-       "          )\n",
-       "        )\n",
-       "        (1): ResnetBlock(\n",
-       "          (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)\n",
-       "          (nonlinearity): SiLU()\n",
-       "          (conv1): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (time_emb_proj): Linear(in_features=256, out_features=64, bias=True)\n",
-       "          (norm2): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "          (conv2): Convolution(\n",
-       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          )\n",
-       "          (skip_connection): Convolution(\n",
-       "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
-       "          )\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       "  (out): Sequential(\n",
-       "    (0): GroupNorm(32, 64, eps=1e-06, affine=True)\n",
-       "    (1): SiLU()\n",
-       "    (2): Convolution(\n",
-       "      (conv): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "    )\n",
-       "  )\n",
-       ")"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idx = 0\n",
-    "data = val_ds[idx]\n",
-    "inputimg = data[\"image\"][0, ...]  # Pick an input slice of the validation set to be segmented\n",
-    "inputlabel = data[\"label\"][0, ...]  # Check out the ground truth label mask. If it is empty, pick another input slice.\n",
-    "\n",
-    "\n",
-    "plt.figure(\"input\" + str(inputlabel))\n",
-    "plt.imshow(inputimg, vmin=0, vmax=1, cmap=\"gray\")\n",
-    "plt.axis(\"off\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "plt.figure(\"input\" + str(inputlabel))\n",
-    "plt.imshow(inputlabel, vmin=0, vmax=1, cmap=\"gray\")\n",
-    "plt.axis(\"off\")\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "\n",
-    "model.eval()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then we set the number of samples in the ensemble n. \\\n",
-    "Starting from the input image (which ist the brain MR image), we follow Algorithm 1 of the paper \"Diffusion Models for Implicit Image Segmentation Ensembles\" (https://arxiv.org/pdf/2112.03145.pdf) n times.\\\n",
-    "This gives us an ensemble of n different predicted segmentation masks."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:19<00:00, 52.45it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:18<00:00, 52.95it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:18<00:00, 53.05it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:18<00:00, 52.93it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:18<00:00, 53.06it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "n = 5\n",
-    "input_img = inputimg[None, None, ...].to(device)\n",
-    "ensemble = []\n",
-    "for k in range(5):\n",
-    "    noise = torch.randn_like(input_img).to(device)\n",
-    "    current_img = noise  # for the segmentation mask, we start from random noise.\n",
-    "    combined = torch.cat(\n",
-    "        (input_img, noise), dim=1\n",
-    "    )  # We concatenate the input brain MR image to add anatomical information.\n",
-    "\n",
-    "    scheduler.set_timesteps(num_inference_steps=1000)\n",
-    "    progress_bar = tqdm(scheduler.timesteps)\n",
-    "    chain = torch.zeros(current_img.shape)\n",
-    "    for t in progress_bar:  # go through the noising process\n",
-    "        with autocast(enabled=False):\n",
-    "            with torch.no_grad():\n",
-    "                model_output = model(combined, timesteps=torch.Tensor((t,)).to(current_img.device))\n",
-    "                current_img, _ = scheduler.step(\n",
-    "                    model_output, t, current_img\n",
-    "                )  # this is the prediction x_t at the time step t\n",
-    "                if t % 100 == 0:\n",
-    "                    chain = torch.cat((chain, current_img.cpu()), dim=-1)\n",
-    "                combined = torch.cat(\n",
-    "                    (input_img, current_img), dim=1\n",
-    "                )  # in every step during the denoising process, the brain MR image is concatenated to add anatomical information\n",
-    "\n",
-    "    plt.style.use(\"default\")\n",
-    "    plt.imshow(chain[0, 0, ..., 64:].cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
-    "    plt.tight_layout()\n",
-    "    plt.axis(\"off\")\n",
-    "    plt.show()\n",
-    "    ensemble.append(current_img)  # this is the output of the diffusion model after T=1000 denoising steps"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "source": [
-    "\n",
-    "## Segmentation prediction\n",
-    "The predicted segmentation mask is obtained from the output of the diffusion model by thresholding.\\\n",
-    "We compute the Dice score for all predicted segmentations of the ensemble, as well as the pixel-wise mean and the variance map over the ensemble.\\\n",
-    "As shown in the paper \"Diffusion Models for Implicit Image Segmentation Ensembles\" (https://arxiv.org/abs/2112.03145), we see that taking the mean over n=5 samples improves the segmentation performance.\\\n",
-    "The variance maps highlights pixels where the model is unsure about it's own prediction.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def dice_coeff(im1, im2, empty_score=1.0):\n",
-    "    im1 = np.asarray(im1).astype(bool)\n",
-    "    im2 = np.asarray(im2).astype(bool)\n",
-    "\n",
-    "    im_sum = im1.sum() + im2.sum()\n",
-    "    if im_sum == 0:\n",
-    "        return empty_score\n",
-    "\n",
-    "    # Compute Dice coefficient\n",
-    "    intersection = np.logical_and(im1, im2)\n",
-    "\n",
-    "    return 2.0 * intersection.sum() / im_sum"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dice score of sample0 0.8455882352941176\n",
-      "Dice score of sample1 0.860655737704918\n",
-      "Dice score of sample2 0.8475836431226765\n",
-      "Dice score of sample3 0.8820960698689956\n",
-      "Dice score of sample4 0.8627450980392157\n",
-      "Dice score on the mean map 0.889763779527559\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbsAAAG7CAYAAABaaTseAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAIHUlEQVR4nO3dS3LiSBRAUdHhHZk1wZpgTbAm9aBraGXLKj721TlDBK60B3UjI14mh3me5wkAwv559wIA4NnEDoA8sQMgT+wAyBM7APLEDoA8sQMgT+wAyPtY+8bD4fDMdQDAJmvuRrGzAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8sQOgDyxAyBP7ADIEzsA8j7evQBgvz4/Pxef3e/3F66EOjs7APLEDoA8sQMgT+wAyBM7APLEDoA8Rw9gp06n07efjY4DjI4RXK/Xb/88eCQ7OwDyxA6APLEDIE/sAMgTOwDyDvM8z6veeDg8ey3AtDwJeblcXryS9zqfz1++vjTZyX6tyZidHQB5YgdAntgBkCd2AOSJHQB5YgdAnoug4S8tHRUYXbQ8ujSZ//gb8Uh2dgDkiR0AeWIHQJ7YAZAndgDkuQgaVhhNVu7tguZ3Ox6Pi8/u9/sLV8JP4SJoAJjEDoAdEDsA8sQOgDyxAyBP7ADIcxE0rDAaab9er1++Pjqu8Og1LF2avOUzr7Tl7+p4AVvY2QGQJ3YA5IkdAHliB0Ce2AGQJ3YA5Dl6AH9sHcVfGpH/CaP9I48e4R/9vKVnWz4DW9jZAZAndgDkiR0AeWIHQJ7YAZB3mOd5XvXGw+HZa4GHud1ui8+2TEkuTVyOjP6d4qTm6Hfaclm2/3NYa03G7OwAyBM7APLEDoA8sQMgT+wAyBM7APIcPeDH2zLCf7lcnrUcHmh0xOF4PL5wJfxmjh4AwCR2AOyA2AGQJ3YA5IkdAHliB0Dex7sXAP9nNJ7+07894N22fHvB6HNb/95L33qw5dskYAs7OwDyxA6APLEDIE/sAMgTOwDyXARN0u12W3xWnOBcmp4cTTtumdTcOt0Jz+QiaACYxA6AHRA7APLEDoA8sQMgT+wAyHMRNEnH43Hx2dLRg8vl8u3P/Gaj33fpyIKjB/xWdnYA5IkdAHliB0Ce2AGQJ3YA5LkIGv7S6XT61uvT9PjpzvP5/OXro4ugR2swdclv4iJoAJjEDoAdEDsA8sQOgDyxAyBP7ADIc/QAnmTr8YKlC5pHxwGWjh7AHjh6AACT2AGwA2IHQJ7YAZAndgDkiR0AeY4eAPCrOXoAAJPYAbADYgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5H2vfOM/zM9cBAE9jZwdAntgBkCd2AOSJHQB5YgdAntgBkCd2AOSJHQB5YgdA3r8AHsUqzMqIOwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i in range(len(ensemble)):\n",
-    "\n",
-    "    prediction = torch.where(ensemble[i] > 0.5, 1, 0).float()  # a binary mask is obtained via thresholding\n",
-    "    score = dice_coeff(\n",
-    "        prediction[0, 0].cpu(), inputlabel.cpu()\n",
-    "    )  # we compute the dice scores for all samples separately\n",
-    "    print(\"Dice score of sample\" + str(i), score)\n",
-    "\n",
-    "\n",
-    "E = torch.where(torch.cat(ensemble) > 0.5, 1, 0).float()\n",
-    "var = torch.var(E, dim=0)  # pixel-wise variance map over the ensemble\n",
-    "mean = torch.mean(E, dim=0)  # pixel-wise mean map over the ensemble\n",
-    "mean_prediction = torch.where(mean > 0.5, 1, 0).float()\n",
-    "\n",
-    "score = dice_coeff(mean_prediction[0, ...].cpu(), inputlabel.cpu())  # Here we predict the Dice score for the mean map\n",
-    "print(\"Dice score on the mean map\", score)\n",
-    "\n",
-    "plt.style.use(\"default\")\n",
-    "plt.imshow(mean[0, ...].cpu(), vmin=0, vmax=1, cmap=\"gray\")  # We plot the mean map\n",
-    "plt.tight_layout()\n",
-    "plt.axis(\"off\")\n",
-    "plt.show()\n",
-    "plt.style.use(\"default\")\n",
-    "plt.imshow(var[0, ...].cpu(), vmin=0, vmax=1, cmap=\"jet\")  # We plot the variance map\n",
-    "plt.tight_layout()\n",
-    "plt.axis(\"off\")\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "formats": "ipynb,py:light"
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+    "nbformat": 4,
+    "nbformat_minor": 4
 }
diff --git a/tutorials/generative/image_to_image_translation/tutorial_segmentation_with_ddpm.py b/tutorials/generative/image_to_image_translation/tutorial_segmentation_with_ddpm.py
index eaa08f5b..4b612c94 100644
--- a/tutorials/generative/image_to_image_translation/tutorial_segmentation_with_ddpm.py
+++ b/tutorials/generative/image_to_image_translation/tutorial_segmentation_with_ddpm.py
@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: light
 #       format_version: '1.5'
-#       jupytext_version: 1.14.4
+#       jupytext_version: 1.14.1
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
@@ -105,7 +105,7 @@
         transforms.LoadImaged(keys=["image", "label"]),
         transforms.EnsureChannelFirstd(keys=["image", "label"]),
         transforms.Lambdad(keys=["image"], func=lambda x: x[channel, :, :, :]),
-        transforms.AddChanneld(keys=["image"]),
+        transforms.EnsureChannelFirstd(keys=["image"], channel_dim="no_channel"),
         transforms.EnsureTyped(keys=["image", "label"]),
         transforms.Orientationd(keys=["image", "label"], axcodes="RAS"),
         transforms.Spacingd(keys=["image", "label"], pixdim=(3.0, 3.0, 2.0), mode=("bilinear", "nearest")),