diff --git a/.gitignore b/.gitignore
index 84af625..f64a748 100644
--- a/.gitignore
+++ b/.gitignore
@@ -61,10 +61,7 @@ venv.bak/
 
 # Data
 /data
-/data081
-/SAM-Med3D
-/models
-*.pth
+/assets
 
 #WandB
 /notebooks/wandb
@@ -72,6 +69,5 @@ venv.bak/
 /wandb
 
 # Medical files
-*.dcm
 *.nii
 *.nii.gz
diff --git a/Demo-notebooks/DRR.ipynb b/Demo-notebooks/DRR.ipynb
new file mode 100644
index 0000000..e8b4a11
--- /dev/null
+++ b/Demo-notebooks/DRR.ipynb
@@ -0,0 +1,395 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Digitally Reconstructed Radiograph (DDR) generation\n",
+    "---\n",
+    "\n",
+    "This notebook demonstrates the creation of a DRR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Importing libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import torchio as tio\n",
+    "\n",
+    "sys.path.append(\"..\")\n",
+    "\n",
+    "from src.preprocessing import get_transforms, get_datasets, get_dataloaders, add_vessel_contrast\n",
+    "# from src.config import config\n",
+    "from src.drr import create_drr\n",
+    "from src.vizualization import plot_results, plot_drr_enhancement\n",
+    "\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "from diffdrr.visualization import plot_drr, plot_mask\n",
+    "\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from diffdrr.drr import DRR\n",
+    "from diffdrr.data import read, transform_hu_to_density\n",
+    "\n",
+    "from diffdrr.pose import convert\n",
+    "from diffdrr.visualization import drr_to_mesh, img_to_mesh\n",
+    "import pyvista\n",
+    "\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "from src.model import TACEnet\n",
+    "from monai.losses import DiceCELoss\n",
+    "\n",
+    "from torchvision import transforms\n",
+    "\n",
+    "# Set the device\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Data preprocessing\n",
+    "In this pipeline we make use of HCC-TACE-Seg dataset. This retrospectively acquired data collection includes pre- and post-procedure CT imaging studies of 105 confirmed HCC patients who underwent TACE between 2002 and 2012 with an available treatment outcome, in the form of time-to-progression and overall survival. Baseline imaging includes multiphasic contrast-enhanced CT with no image artifacts (e.g. surgical clip) and was obtained 1-12 weeks (average 3 weeks) prior to the first TACE session. Semiautomatic segmentation of liver, tumor, and blood vessels created using AMIRA was manually clinically curated. These segmentations of each pre-procedural CT study were done for the purpose of algorithm training for prediction and automatic liver tumor segmentation, and are provided here (NIfTI converted to DICOM-SEG format).\n",
+    "\n",
+    "\n",
+    "The volumes and segmnetations have the following shapes when loaded from a batch:\n",
+    "\n",
+    "- volume: (1, 1, 512, 512, D) -> (B, C, H, W, D) where the depth (D) is not constant\n",
+    "- segmentation (1, 5, 512, 512, D) -> (B, C, H, W, D) where the depth (D) is not constant and correpsonds to volume\n",
+    "\n",
+    "\n",
+    "The following tranformations are then performed:\n",
+    "1. Load volume and segmentation to MONAI meta tensor.\n",
+    "2. Ensure the channel dimension is placed before the spatial dimensions\n",
+    "3. Resize the volume and segmnetation to a new desired dimension, using padding and cropping (optional).\n",
+    "4. Isolate the vessel channel of the multi class segmentation mask. This essentially turns the task in a binary segmentation problem where the segmentation will have a shape equal to the volume: (1, 1, 512, 512, D)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0] []\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get transforms\n",
+    "transform = get_transforms(resize_shape= [512, 512, 96], contrast_value=1000)\n",
+    "\n",
+    "# Get datasets\n",
+    "train_ds, val_ds = get_datasets(\n",
+    "    root_dir=\"../data081\",\n",
+    "    collection=\"HCC-TACE-Seg\",\n",
+    "    transform=transform,\n",
+    "    download=False,\n",
+    "    val_frac=0.0,\n",
+    "    download_len=1,\n",
+    "    seed=42\n",
+    ")\n",
+    "\n",
+    "# Get dataloaders\n",
+    "train_loader, val_loader = get_dataloaders(train_ds, val_ds, batch_size=1)\n",
+    "\n",
+    "# Check length of datasets to make sure data split is correct\n",
+    "print(train_ds.get_indices(), val_ds.get_indices())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 1, 512, 512, 96]) torch.Size([1, 1, 512, 512, 96])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sample a batch of data from the dataloader\n",
+    "batch = next(iter(train_loader))\n",
+    "\n",
+    "# Separate the image and segmentation from the batch\n",
+    "volumes, targets = batch[\"image\"], batch[\"seg\"]\n",
+    "\n",
+    "print(volumes.shape, targets.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From these volumes and segmentation we can take a slice and visualize the slice to evaulate the inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "slice_idx = 40\n",
+    "\n",
+    "# Sample a slice from the image\n",
+    "CT_slice = volumes[0, 0, :, :, slice_idx].cpu()\n",
+    "\n",
+    "# Get the maximum segmentation class for each pixel in the slice\n",
+    "CT_seg_slice = targets[0, 0, :, :, slice_idx].cpu()\n",
+    "\n",
+    "# Plot the image and segmentation slice as a subplot\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "ax[0].imshow(CT_slice, cmap=\"gray\")\n",
+    "ax[0].set_title(\"CT Slice\")\n",
+    "ax[0].axis(\"off\")\n",
+    "ax[1].imshow(CT_seg_slice, cmap=\"tab20\")\n",
+    "ax[1].set_title(\"CT Segmentation Slice\")\n",
+    "ax[1].axis(\"off\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Creating DRR Images\n",
+    "DRR images are essentially simulated X-ray images reconsturcted by raytracing through a given 3D CT volume. The DiffDRR package comes with most of the functionality needed out-of-the-box. We will demonstrate below how to create DRR images and control output rotation, zoom and more. Currently, this does not works vectorized over a batch. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract first image and segmentation from the batch\n",
+    "img = volumes[0]\n",
+    "seg = targets[0]\n",
+    "\n",
+    "# Read in CT volume and label map into a subject object\n",
+    "subject = read(\n",
+    "    tensor=img,\n",
+    "    label_tensor=seg,\n",
+    "    orientation=\"AP\",\n",
+    "    bone_attenuation_multiplier=5,\n",
+    ")\n",
+    "\n",
+    "# Create a DRR object, this will project the CT to the DRR\n",
+    "drr = DRR(\n",
+    "    subject,  # A torchio.Subject object storing the CT volume, origin, and voxel spacing\n",
+    "    sdd=1020,  # Source-to-detector distance (i.e., the C-arm's focal length)\n",
+    "    height=200,  # Height of the DRR (if width is not seperately provided, the generated image is square)\n",
+    "    width=200,  # Width of the DRR\n",
+    "    delx=2.0,  # Pixel spacing (in mm)\n",
+    "    dely=2.0,  # Pixel spacing (in mm)\n",
+    "    x0=0,  # # Principal point X-offset\n",
+    "    y0=0,  # Principal point Y-offset\n",
+    "    p_subsample=None,  # Proportion of pixels to randomly subsample\n",
+    "    reshape=True,  # Return DRR with shape (b, 1, h, w)\n",
+    "    reverse_x_axis=True,  # If True, obey radiologic convention (e.g., heart on right)\n",
+    "    patch_size=None,  # Render patches of the DRR in series\n",
+    "    renderer=\"siddon\",  # Rendering backend, either \"siddon\" or \"trilinear\"\n",
+    ").to(device)\n",
+    "\n",
+    "# Set the camera pose with rotations (yaw, pitch, roll) and translations (x, y, z)\n",
+    "zero = torch.tensor([[0.0, 0.0, 0.0]], device=device)\n",
+    "translations = torch.tensor([[0.0, 800.0, 0.0]], device=device)\n",
+    "\n",
+    "# Define the camera pose with rotations and translations in degrees, later converted to radians\n",
+    "rotations = torch.tensor([[0.0, 0.0, 0.0]], device=device)\n",
+    "rotations = torch.deg2rad(rotations)\n",
+    "\n",
+    "# Convert the rotations and translations to a pose matrix\n",
+    "pose1 = convert(zero, translations, parameterization=\"euler_angles\", convention=\"ZXY\")\n",
+    "pose2 = convert(rotations, zero, parameterization=\"euler_angles\", convention=\"ZXY\")\n",
+    "pose = pose1.compose(pose2)\n",
+    "\n",
+    "# Generate the DRR\n",
+    "drr_image_raw = drr(pose, mask_to_channels=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Enhance the vessel contrast in the CT using the segmentation\n",
+    "img_enhanced = add_vessel_contrast(img, seg, contrast_value=2000)\n",
+    "\n",
+    "# Read in CT volume and label map into a subject object\n",
+    "subject_enhanced = read(\n",
+    "    tensor=img_enhanced,\n",
+    "    label_tensor=seg,\n",
+    "    orientation=\"AP\",\n",
+    "    bone_attenuation_multiplier=5,\n",
+    ")\n",
+    "\n",
+    "# Create a DRR object, this will project the CT to the DRR\n",
+    "drr_enhanced = DRR(\n",
+    "    subject_enhanced,  # A torchio.Subject object storing the CT volume, origin, and voxel spacing\n",
+    "    sdd=1020,  # Source-to-detector distance (i.e., the C-arm's focal length)\n",
+    "    height=200,  # Height of the DRR (if width is not seperately provided, the generated image is square)\n",
+    "    width=200,  # Width of the DRR\n",
+    "    delx=2.0,  # Pixel spacing (in mm)\n",
+    "    dely=2.0,  # Pixel spacing (in mm)\n",
+    "    x0=0,  # # Principal point X-offset\n",
+    "    y0=0,  # Principal point Y-offset\n",
+    "    p_subsample=None,  # Proportion of pixels to randomly subsample\n",
+    "    reshape=True,  # Return DRR with shape (b, 1, h, w)\n",
+    "    reverse_x_axis=True,  # If True, obey radiologic convention (e.g., heart on right)\n",
+    "    patch_size=None,  # Render patches of the DRR in series\n",
+    "    renderer=\"siddon\",  # Rendering backend, either \"siddon\" or \"trilinear\"\n",
+    ").to(device)\n",
+    "\n",
+    "# Set the camera pose with rotations (yaw, pitch, roll) and translations (x, y, z)\n",
+    "zero = torch.tensor([[0.0, 0.0, 0.0]], device=device)\n",
+    "translations = torch.tensor([[0.0, 800.0, 0.0]], device=device)\n",
+    "\n",
+    "# Define the camera pose with rotations and translations in degrees, later converted to radians\n",
+    "rotations = torch.tensor([[0.0, 0.0, 0.0]], device=device)\n",
+    "rotations = torch.deg2rad(rotations)\n",
+    "\n",
+    "# Convert the rotations and translations to a pose matrix\n",
+    "pose1 = convert(zero, translations, parameterization=\"euler_angles\", convention=\"ZXY\")\n",
+    "pose2 = convert(rotations, zero, parameterization=\"euler_angles\", convention=\"ZXY\")\n",
+    "pose = pose1.compose(pose2)\n",
+    "\n",
+    "# Generate the DRR\n",
+    "drr_image_enhanced = drr_enhanced(pose, mask_to_channels=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The implementation of the DRR generation allows us to visualize both the entire image and only certain structures of interest, in this case the vessels. We can now visualize the raw DRR, the vessels only and the enhanced DRR. Note that the output of the DRR has the following shape:\n",
+    "\n",
+    "$$(B, C, H, W) -> (1, 2, 200, 200)$$\n",
+    "\n",
+    "Where the B does not correspond to the dataloaders batch, but the number of different poses that are provided. This means that, although DRR image generation currently only works with 1 volume segmentation pair at once, per volume multiple DRR orientations can be created in parallel. Since we are interested in changing the angle this makes it perfectly sufficient to only uses batches of size 1 and create multiple DRR images for training per batch iteration.\n",
+    "\n",
+    "C is the number of channels per generated DRR (this by setting `mask_to_channel = True`) and allows for conditional rendering of different structures. Summing over the channel dimension will give the expected image. Note that the `plot_drr` function takes in a shape of (B, C, H, W), which explains the added logic below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<Axes: xlabel='Vessel Overlay'>]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 4200x2325 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the segmentation masks\n",
+    "fig, axs = plt.subplots(\n",
+    "    nrows=1,\n",
+    "    ncols=3,\n",
+    "    figsize=(14, 7.75),\n",
+    "    tight_layout=True,\n",
+    "    dpi=300,\n",
+    ")\n",
+    "\n",
+    "# Plot the DRR, DRR with vessel contrast, and the enhanced DRR\n",
+    "plot_drr(drr_image_raw.sum(dim=1, keepdim=True), axs=axs[0], ticks=False, title=\"DRR\")\n",
+    "plot_drr(\n",
+    "    drr_image_enhanced[:, 1, :, :].unsqueeze(dim=1),\n",
+    "    axs=axs[1],\n",
+    "    ticks=False,\n",
+    "    title=\"DRR Vessel Only\",\n",
+    ")\n",
+    "plot_drr(\n",
+    "    drr_image_enhanced.sum(dim=1, keepdim=True),\n",
+    "    axs=axs[2],\n",
+    "    ticks=False,\n",
+    "    title=\"Vessel Overlay\",\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "torch",
+   "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Demo-notebooks/Evaluation.ipynb b/Demo-notebooks/Evaluation.ipynb
new file mode 100644
index 0000000..f755b8a
--- /dev/null
+++ b/Demo-notebooks/Evaluation.ipynb
@@ -0,0 +1,431 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model Evaluation\n",
+    "---\n",
+    "\n",
+    "This notebook will demonstrate the evaluation of the models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Importing libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import libraries\n",
+    "import sys\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import torchio as tio\n",
+    "\n",
+    "sys.path.append(\"..\")\n",
+    "\n",
+    "from src.preprocessing import get_transforms, get_datasets, get_dataloaders, add_vessel_contrast\n",
+    "# from src.config import config\n",
+    "from src.drr import create_drr\n",
+    "from src.vizualization import plot_results, plot_drr_enhancement\n",
+    "import src.training\n",
+    "\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "from diffdrr.visualization import plot_drr, plot_mask\n",
+    "\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from diffdrr.drr import DRR\n",
+    "from diffdrr.data import read, transform_hu_to_density\n",
+    "\n",
+    "from diffdrr.pose import convert\n",
+    "from diffdrr.visualization import drr_to_mesh, img_to_mesh\n",
+    "import pyvista\n",
+    "\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "from src.model import TACEnet\n",
+    "from monai.losses import DiceCELoss\n",
+    "from timeit import default_timer as timer\n",
+    "import numpy as np\n",
+    "import torchvision.transforms as tf\n",
+    "from torchvision import transforms\n",
+    "\n",
+    "# Set the device\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Evaluate the non-deformation model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x800 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.6111773961490939, 0.5983437550987105, 0.5211740984848688, 0.33544345249300705, 0.15924259249931083, 0.0754908252774726, 0.05951922110160694, 0.08734356488911317, 0.14783748195385443, 0.2348085825598787]\n"
+     ]
+    }
+   ],
+   "source": [
+    "loss = src.training.fetchResults('../models/TACEnet_vessel_enhancement_29052024.pth')\n",
+    "print(loss)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Evaluate the deformation model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0] []\n",
+      "torch.Size([1, 1, 512, 512, 96]) torch.Size([1, 1, 512, 512, 96])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get transforms\n",
+    "transform = get_transforms(resize_shape= [512, 512, 96], contrast_value=1000)\n",
+    "\n",
+    "# Get datasets\n",
+    "train_ds, val_ds = get_datasets(\n",
+    "    root_dir=\"../data081\",\n",
+    "    collection=\"HCC-TACE-Seg\",\n",
+    "    transform=transform,\n",
+    "    download=False,\n",
+    "    val_frac=0.0,\n",
+    "    download_len=1,\n",
+    "    seed=42\n",
+    ")\n",
+    "\n",
+    "# Get dataloaders\n",
+    "train_loader, val_loader = get_dataloaders(train_ds, val_ds, batch_size=1)\n",
+    "\n",
+    "# Check length of datasets to make sure data split is correct\n",
+    "print(train_ds.get_indices(), val_ds.get_indices())\n",
+    "\n",
+    "# Sample a batch of data from the dataloader\n",
+    "batch = next(iter(train_loader))\n",
+    "\n",
+    "# Separate the image and segmentation from the batch\n",
+    "volumes, targets = batch[\"image\"], batch[\"seg\"]\n",
+    "\n",
+    "print(volumes.shape, targets.shape)\n",
+    "\n",
+    "volumes = add_vessel_contrast(volumes, targets, contrast_value=4000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<All keys matched successfully>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define model\n",
+    "model = TACEnet()\n",
+    "\n",
+    "# Move model to device\n",
+    "model.to(device)\n",
+    "\n",
+    "# Initialize the criterion, DICE loss with cross entropy uses binary cross entropy when there are only two classes\n",
+    "criterion = torch.nn.MSELoss()\n",
+    "\n",
+    "# Initialize the optimizer, use config to set hyperparameters\n",
+    "optimizer = torch.optim.Adam(\n",
+    "    model.parameters(),\n",
+    "    lr=1e-3,\n",
+    "    weight_decay=1e-5,\n",
+    ")\n",
+    "\n",
+    "model.load_state_dict(torch.load(\"../models/TACEnet_vessel_enhancement_deformations_06062024.pth\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Sven\\anaconda3\\envs\\torch\\Lib\\site-packages\\torchio\\transforms\\augmentation\\spatial\\random_elastic_deformation.py:303: RuntimeWarning: The maximum displacement is larger than the coarse grid spacing for dimensions: [2], so folding may occur. Choose fewer control points or a smaller maximum displacement\n",
+      "  self.parse_free_form_transform(\n",
+      "c:\\Users\\Sven\\anaconda3\\envs\\torch\\Lib\\site-packages\\torch\\_tensor.py:1418: UserWarning: Using a target size (torch.Size([1, 256, 256])) that is different to the input size (torch.Size([1, 1, 256, 256])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
+      "  ret = func(*args, **kwargs)\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.eval()\n",
+    "\n",
+    "losses = []\n",
+    "rotation = 0\n",
+    "\n",
+    "for i in range(20):\n",
+    "                # Read the image and segmentation subject\n",
+    "        subject = read(\n",
+    "                tensor=volumes[0],\n",
+    "                label_tensor=targets[0],\n",
+    "                orientation=\"AP\",\n",
+    "                bone_attenuation_multiplier=5.0,\n",
+    "                )\n",
+    "\n",
+    "                # Create a RandomElasticDeformation transform\n",
+    "        deform = tio.RandomElasticDeformation(p=1.0, num_control_points=7, max_displacement=50)\n",
+    "\n",
+    "                # Apply the transform to the subject\n",
+    "        deformed_subject = deform(subject)\n",
+    "\n",
+    "        deformed_subject.density = transform_hu_to_density(\n",
+    "                deformed_subject.volume.data, 5.0\n",
+    "                )\n",
+    "                \n",
+    "                # Create raw DRR\n",
+    "        drr_raw = create_drr(\n",
+    "                deformed_subject,\n",
+    "                sdd=1020,\n",
+    "                height=256,\n",
+    "                width=256,\n",
+    "                rotations=torch.tensor([[rotation, 0.0, 0.0]]),\n",
+    "                translations=torch.tensor([[0.0, 850.0, 0.0]]),\n",
+    "                mask_to_channels=True,\n",
+    "                device=\"cpu\",\n",
+    "                )\n",
+    "\n",
+    "                # Extract DRR body and vessels\n",
+    "        drr_body = drr_raw[:, 0]\n",
+    "        drr_vessels = drr_raw[:, 1]\n",
+    "\n",
+    "                # Combine DRR body and vessels with enhancement factors\n",
+    "        ef = 0.6\n",
+    "        drr_combined_low_enhancement = (drr_body + ef * drr_vessels).unsqueeze(0)\n",
+    "        \n",
+    "                # Move tensors to the appropriate device\n",
+    "        drr_combined_low_enhancement = drr_combined_low_enhancement.to(device)\n",
+    "        drr_combined_target = drr_vessels.to(device)\n",
+    "\n",
+    "                # Forward pass\n",
+    "        prediction, latent_representation = model(targets.to(device), drr_combined_low_enhancement)\n",
+    "\n",
+    "                # Compute loss\n",
+    "        loss = criterion(prediction, drr_combined_target)\n",
+    "\n",
+    "                \n",
+    "\n",
+    "        # Plot the images (optional)\n",
+    "        #plot_results(drr_combined_low_enhancement, drr_combined_target, prediction, latent_representation, vmax=25)\n",
+    "\n",
+    "        losses.append(loss.item())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x17dda520140>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.boxplot(losses)\n",
+    "plt.ylabel('MSE loss')\n",
+    "# Set custom x-axis labels\n",
+    "plt.xticks([1], [''])\n",
+    "plt.axhline(y=0.08690, color='r', linestyle='--')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0896688874810934"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(losses)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Evaluate the inference speed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.1 3D speed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.001203799998620525\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = src.model.TACEnet().to('cuda')\n",
+    "input1 = torch.rand([1,1,512,512,96]).to('cuda')\n",
+    "input2 = torch.rand([1,1,256,256]).to('cuda')\n",
+    "\n",
+    "start = timer()\n",
+    "output, _ = model(input1,input2)\n",
+    "end = timer()\n",
+    "\n",
+    "print(end-start)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.2 2D speed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.00141789999906905\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = src.model.ConvNet().to('cuda')\n",
+    "array = np.random.randint(0, 1, size=(256,256,2))\n",
+    "transform = tf.ToTensor()\n",
+    "\n",
+    "\n",
+    "start = timer()\n",
+    "input = transform(array).to('cuda').float().unsqueeze(0)\n",
+    "output = model(input)\n",
+    "end = timer()\n",
+    "\n",
+    "print(end-start)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "torch",
+   "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Demo-notebooks/Training.ipynb b/Demo-notebooks/Training.ipynb
new file mode 100644
index 0000000..f7c300e
--- /dev/null
+++ b/Demo-notebooks/Training.ipynb
@@ -0,0 +1,499 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Training the Models\n",
+    "---\n",
+    "\n",
+    "This notebook is used for training the models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Importing libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import libraries\n",
+    "import sys\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import torchio as tio\n",
+    "\n",
+    "sys.path.append(\"..\")\n",
+    "\n",
+    "from src.preprocessing import get_transforms, get_datasets, get_dataloaders, add_vessel_contrast\n",
+    "# from src.config import config\n",
+    "from src.drr import create_drr\n",
+    "from src.vizualization import plot_results, plot_drr_enhancement\n",
+    "import src.training\n",
+    "\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "from diffdrr.visualization import plot_drr, plot_mask\n",
+    "\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from diffdrr.drr import DRR\n",
+    "from diffdrr.data import read, transform_hu_to_density\n",
+    "\n",
+    "from diffdrr.pose import convert\n",
+    "from diffdrr.visualization import drr_to_mesh, img_to_mesh\n",
+    "import pyvista\n",
+    "\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "from src.model import TACEnet\n",
+    "from monai.losses import DiceCELoss\n",
+    "\n",
+    "from torchvision import transforms\n",
+    "\n",
+    "# Set the device\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Non deformation model\n",
+    "\n",
+    "Below is a short demo of training the non deformation model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1/10] - Loss: 1.5928856134414673\n",
+      "[2/10] - Loss: 1.5512844324111938\n",
+      "[3/10] - Loss: 1.5332229137420654\n",
+      "[4/10] - Loss: 1.5243804454803467\n",
+      "[5/10] - Loss: 1.5101368427276611\n",
+      "[6/10] - Loss: 1.5192246437072754\n",
+      "[7/10] - Loss: 1.5048763751983643\n",
+      "[8/10] - Loss: 1.495262622833252\n",
+      "[9/10] - Loss: 1.4919564723968506\n",
+      "[10/10] - Loss: 1.482800006866455\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1/10] - Loss: 1.495617389678955\n",
+      "[2/10] - Loss: 1.4696060419082642\n",
+      "[3/10] - Loss: 1.466841459274292\n",
+      "[4/10] - Loss: 1.4637898206710815\n",
+      "[5/10] - Loss: 1.4220823049545288\n",
+      "[6/10] - Loss: 1.464769959449768\n",
+      "[7/10] - Loss: 1.4444843530654907\n",
+      "[8/10] - Loss: 1.4435250759124756\n",
+      "[9/10] - Loss: 1.4187465906143188\n",
+      "[10/10] - Loss: 1.3912562131881714\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1/10] - Loss: 1.3607779741287231\n",
+      "[2/10] - Loss: 1.3327232599258423\n",
+      "[3/10] - Loss: 1.3637094497680664\n",
+      "[4/10] - Loss: 1.30686354637146\n",
+      "[5/10] - Loss: 1.2870440483093262\n",
+      "[6/10] - Loss: 1.3375027179718018\n",
+      "[7/10] - Loss: 1.2994071245193481\n",
+      "[8/10] - Loss: 1.3228328227996826\n",
+      "[9/10] - Loss: 1.3623496294021606\n",
+      "[10/10] - Loss: 1.28748619556427\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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zzzyzoc3169cHHR0dwfvf//6G7XfeeWeQy+XC7XfccUcAIPjmN7/pPb+kbQVB8meb22+/PQAQfOUrX5nWn+t+dPnllweZTKbhueWCCy4IfNLQfl5OOeWUoFAoBA8++GC47fHHHw8WLVoUHHXUUeG2NJ+TVsBU5DlOT0/PtOrI++23H/r7+7Fy5Uqcc8452HvvvXHjjTeiq6tr2vE33XQT+vv70d/fj0MOOQRXXXUVzj77bHzkIx/ZXqdASANBEOC6667DSSedhCAIsGnTpvBnzZo1GBoawh//+MeGY84++2wUCoXwtXgjH3rooYb9enp6GubkFAoFPOc5z2nYr6OjI2xramoKAwMDqNVqePaznz2tXwB4xSte0ZDhYPveuHEjfv7zn+Occ87BM57xjIZjJSqT5px/8IMfYJdddsHLXvaysJ2uri6cd955kdc1CTpCNTQ0hE2bNuHoo4/GQw89NC1FatWqVdPmMf3whz/EbrvthpNPPjncViqVpmWU/OlPf8L999+PV77yldi8eXN4rmNjYzj22GPx85//HFNTUzM+HzJ/WLduHXbeeWccc8wxALZ+d17xilfgmmuu8aYDJ+Hwww/HzTffPO3nzDPPnLbvv/zLvzS8PvLII6fdY4D4ewLQ+F2bnJzEpk2b8NznPhcAnPcZV9+bN2/G8PAwgK1pw1NTU3jPe96DbLbx0U3uMzfffDMGBwdx5plnNtxjOjo6cPjhh4eZFU888QT+9Kc/4ayzzgqjR8DWYj0HHnjgtLGR9iGtfbMMDAzglltuwemnn46RkZHwM7R582asWbMG999/PzZs2NBwzHnnndeQgXDkkUeiXq/jkUcemdG5/Od//if6+/uxfPlyPPvZz8ZPfvITvP3tb8eb3/zmcJ9vfvObOOCAA7D//vs3fOZf+MIXAsC0bKL/9b/+Fw477LDw9TOe8Qz88z//M370ox9Nu8/Y7+T111+PqakpnH766Q19rVixAvvss0/Yl3ynfvSjHzmnE6RpS0jybBOFvh+NjY1h06ZNeN7znocgCHDHHXckakNTr9dx00034ZRTTsFee+0Vbt9ll13wyle+Er/85S/De5cwW58TC1OR5zijo6NYvnx5w7brrrsOvb292LhxIz796U/j4Ycf9hZ1Ofzww3HZZZehXq/jr3/9Ky677DJs2bKlQSQQsj3ZuHEjBgcH8aUvfQlf+tKXnPs8/fTTDa+tYJSHyi1btjRs33333ael+C1ZsgR/+ctfGrZ97Wtfw8c+9jHce++9qFar4fZVq1ZNG0tc32JYotbQTXPOjzzyCPbee+9p57Hffvt520/Kr371K1x88cX49a9/Pc3gDg0NNTzkuq7FI488gtWrV08bm61ce//99wMAzjrrLO9YhoaGOCWCANj6kHTNNdfgmGOOwcMPPxxuP/zww/Gxj30MP/nJT8JU27QsW7YML3rRi2L3K5VK01LglyxZMu0eAyS7Hw0MDODSSy/FNddcM+1+Zp1IcW329vbiwQcfRDabjRSe8r2Th3pLb28vAIQPkvvss8+0ffbbb79EAojMXdLYN8sDDzyAIAjw7ne/G+9+97ud+zz99NPYbbfdwtdJ7XNa/vmf/xkXXnghKpUKbr/9dnzgAx/A+Ph4g2Pn/vvvxz333OOdvmK/e67P/L777ovx8XFs3LgRK1asCLfb63X//fcjCAJnGwDCaQirVq3Cm9/8Znz84x/HunXrcOSRR+Lkk0/Gq1/96tDGJm1LSPps4+PRRx/Fe97zHtxwww3T/i+u+1EcGzduxPj4uPO55IADDsDU1BT+/ve/46CDDgq3z9bnxEJhO4d57LHHMDQ0NO2h8aijjgrnop100kk45JBD8KpXvQp/+MMfpnlytVFfs2YN9t9/f5x44on41Kc+1eD1ImR7IZG6V7/61V7hY5e46ujocO4X/P95L2n2u/rqq7F27VqccsopeNvb3obly5ejo6MDl19+ubOoWtK+o2jmnFvNgw8+iGOPPRb7778/Pv7xj2OPPfZAoVDAD37wA3ziE5+YFkGdSQVkaesjH/mId9mBnp6eptsn84tbbrkFTzzxBK655hpcc801095ft25d08I2Kb7veZp99T3h9NNPx2233Ya3ve1tOPTQQ9HT04OpqSkcd9xxzmyFVt5nrrrqqoYHdKEdluogMyOtfbPIZ+itb32rt/KwfSZtxWfXxe677x4+v/7TP/0Tli1bhgsvvBDHHHNMOC94amoKhxxyCD7+8Y8729hjjz2a7t/awKmpKWQyGdx4443Oc9Y27WMf+xjWrl2L7373u7jpppvwxje+EZdffjl+85vfYPfdd0/VFjCza1yv1/HiF78YAwMDeMc73oH9998f3d3d2LBhA9auXbvdsqdm63Ni4V1uDiOTt6PKmvf09ODiiy/G2WefjWuvvRZnnHFGZJsnnHACjj76aHzgAx/A+eefv+DLl5PtT39/PxYtWoR6vZ4oktJqvvWtb2GvvfbC9ddf3+ABbXZZEUnD+etf/+rdJ80577nnnvjrX/+KIAgaxve3v/0t0XisV1f43ve+h3K5jBtuuKHBc+oq/BQ1trvvvnva2GyFainu0dvbG3u+vvGShcO6deuwfPnysCKn5vrrr8e3v/1tfOELX2ib5aa2bNmCn/zkJ7j00kvxnve8J9wuEdVmWL16NaampnD33Xd7nUXyvVu+fHnk927PPff0jifpfYbMTZLaN999V+xZPp9vqX1uxX3+/PPPxyc+8Qn8+7//O0499VRkMhmsXr0af/7zn3Hssccm6sP1mb/vvvvQ1dUVW7Rw9erVCIIAq1atwr777hvb1yGHHIJDDjkE//7v/47bbrsNz3/+8/GFL3wBl112Weq2kuA7/zvvvBP33Xcfvva1r+G1r31tuP3mm29O3Ialv78fXV1dzvvFvffei2w2OyOnwkzgHNs5yi233IL3ve99WLVqFV71qldF7vuqV70Ku+++e0P1xCje8Y53YPPmzfiP//iPVgyVkFR0dHTgpS99Ka677jqnGJyN8u+2f6DRS/jb3/4Wv/71r5tqr7+/H0cddRSuvPJKPProow3vSR9pzvmf/umf8Pjjj+Nb3/pWuG18fNybwmwRZ9Xg4GDDdtd5Dw0N4Stf+UqidoGtTrYNGzY0LPswOTk57V5y2GGHYfXq1fjoRz+K0dHRae3o8/WNlywMJiYmcP311+PEE0/Ey172smk/F154IUZGRqYtNTKXcX3XgK3VYZvllFNOQTabxXvf+95pERbpZ82aNejt7cUHPvCBhhRUQb53u+yyCw499FB87Wtfa0hDvPnmm3H33Xc3PUay40lq36Qmi73vLl++HC94wQvwxS9+EU888cS09pu1z93d3TO+x+dyObzlLW/BPffcg+9+97sAtmZGbNiwwfk8OzExgbGxsYZtv/71rxtS7f/+97/ju9/9Ll7ykpfEZm2cdtpp6OjowKWXXjrtux0EQbiU1/DwMGq1WsP7hxxyCLLZbLgEUdK20pDG9gdBgE996lOJ27B0dHTgJS95Cb773e82LM/31FNP4etf/zqOOOKIcOrD9oYR2znAjTfeiHvvvRe1Wg1PPfUUbrnlFtx8883Yc889ccMNN8QuipzP53HRRRfhbW97G374wx/iuOOOi9z/+OOPx8EHH4yPf/zjuOCCC6bl8hMy23zwgx/ErbfeisMPPxznnnsuDjzwQAwMDOCPf/wjfvzjH2NgYGDW+j7xxBNx/fXX49RTT8UJJ5yAhx9+GF/4whdw4IEHOkVYEj796U/jiCOOwD/8wz/gvPPOw6pVq7B+/Xp8//vfx5/+9CcAyc/53HPPxWc/+1m89rWvxR/+8AfssssuuOqqq5zF4VxIYYx3vetdOOOMM5DP53HSSSfhJS95CQqFAk466SScf/75GB0dxX/8x39g+fLlzgcYF+effz4++9nP4swzz8RFF12EXXbZBevWrQvvUeLtzWaz+PKXv4zjjz8eBx10EM4++2zstttu2LBhA2699Vb09vbie9/7XuR4mU2yMLjhhhswMjLSUJBM89znPhf9/f1Yt24dXvGKV6Ruf8OGDc614Ht6enDKKaekbi8Jvb29OOqoo/DhD38Y1WoVu+22G2666aaG+cNp2XvvvfGud70L73vf+3DkkUfitNNOQ7FYxO23345dd90Vl19+OXp7e3HFFVfgNa95Df7hH/4BZ5xxBvr7+/Hoo4/i+9//Pp7//Ofjs5/9LADg8ssvxwknnIAjjjgC55xzDgYGBvCZz3wGBx10UNP3QbJ9uPLKK/HDH/5w2vaLLroosX3r7OzEgQceiG984xvYd999sXTpUhx88ME4+OCD8bnPfQ5HHHEEDjnkEJx77rnYa6+98NRTT+HXv/41HnvsMfz5z39OPebDDjsMV1xxBS677DLsvffeWL58uXcueBRr167Fe97zHnzoQx/CKaecgte85jW49tpr8S//8i+49dZb8fznPx/1eh333nsvrr322nAtduHggw/GmjVrGpb7AbYupRXH6tWrcdlll+Gd73wn1q9fj1NOOQWLFi3Cww8/jG9/+9s477zz8Na3vhW33HILLrzwQrz85S/Hvvvui1qthquuuip0cKdpKw2rV6/G4sWL8YUvfAGLFi1Cd3c3Dj/8cOy///5YvXo13vrWt2LDhg3o7e3Fdddd55zbKvb4jW98I9asWYOOjg5vJuhll12Gm2++GUcccQT+9V//FblcDl/84hdRLpcb1tjd7rS0xjJJhZTAlp9CoRCsWLEiePGLXxx86lOfCoaHhxv2lxLkGzdunNbW0NBQ0NfX11BOfc899wxOOOEEZ99f/epXvWXBCdkePPXUU8EFF1wQ7LHHHkE+nw9WrFgRHHvsscGXvvSlcB9Z7seWzZdlN/Tn9+ijjw4OOuigaf2cddZZDctXTE1NBR/4wAeCPffcMygWi8GznvWs4H/+53+m7Sd96KUFBDiWSfjrX/8anHrqqcHixYuDUqkU7LfffsG73/3u1OccBFuXIDj55JODrq6uYNmyZcFFF10U/PCHP0y03E8QBMH73ve+YLfddguy2WzDUjo33HBD8MxnPjMolUrBypUrgw996EPBlVdeOW25nah7x0MPPRSccMIJ4TJjb3nLW4LrrrsuABD85je/adj3jjvuCE477bRgp512CorFYrDnnnsGp59+evCTn/wk0XjJ/Oekk04KSqXStOXqNGvXrg3y+Xy4VBZasNyP/q6fddZZQXd397TjxeYKae4Jjz32WHg/6OvrC17+8pcHjz/++LT9fHZdng/sd+HKK68MnvWsZwXFYjFYsmRJcPTRR4dLJAm33nprsGbNmqCvry8olUrB6tWrg7Vr1zYscxIEQXDdddcFBxxwQFAsFoMDDzwwuP7666fdB8ncwT4z2p+///3vie1bEATBbbfdFhx22GFBoVCY9rl88MEHg9e+9rXBihUrgnw+H+y2227BiSeeGHzrW9+aNh67/JDYbW2rnnzyyeCEE04IFi1aFACIXfon6jt+ySWXNLRfqVSCD33oQ8FBBx0Ufi8OO+yw4NJLLw2GhoamtXn11VcH++yzT3h9rE2NetYOgq3fmyOOOCLo7u4Ouru7g/333z+44IILgr/97W9BEGy1keecc06wevXqoFQqBUuXLg2OOeaY4Mc//nHqtoIg+bNNEGxdeuzAAw8McrlcwzPS3XffHbzoRS8Kenp6gmXLlgXnnntu8Oc//3nac1StVgve8IY3BP39/UEmk2m4/7mee/74xz8Ga9asCXp6eoKurq7gmGOOCW677baGfdJ8TlpB5v8PlhBCCGmKT37yk3jTm96Exx57rKFaJiGEEDIXyGQyuOCCC8KsBTI/4RxbQgghiZmYmGh4PTk5iS9+8YvYZ599KGoJIYQQssPgHFtCCCGJOe200/CMZzwDhx56KIaGhnD11Vfj3nvvxbp163b00AghhBCygKGwJYQQkpg1a9bgy1/+MtatW4d6vY4DDzwQ11xzTVOFfQghhBBCWgXn2BJCCCGEEEIIaWs4x5YQQgghhBBCSFtDYUsIIYQQQgghpK2hsCWEEEIIIYQQ0tYkLh6VyWRmcxyEzBvm+rR1fpcJScZc/i7ncqz9SEgcQRCgXq/v6GFE8s53vhMf/OAHd/QwCJnTvPzlL8e1114bux8jtoQQQgghhBBC2hoKW0IIIYQQQgghbQ2FLSGEEEIIIYSQtobClhBCCCGEEEJIW0NhSwghhBBCCCGkraGwJYQQQgghhBDS1lDYEkIIIYQQQghpayhsCSGEEEIIIYS0NRS2hBBCCCGEEELaGgpbQgghhBBCCCFtDYUtIYQQQgghhJC2hsKWEEIIIYQQQkhbQ2FLCCGEEEIIIaStobAlhBBCCCGEENLWUNgSQgghhBBCCGlrKGwJIYQQQgghhLQ1FLaEEEIIIYQQQtoaCltCCCGEEEIIIW0NhS0hhBBCCCGEkLaGwpYQQgghhBBCSFtDYUsIIYQQQgghpK2hsCWEEEIIIYQQ0tbkdvQACCGEENIcQRC0vM1MJtPyNgkhhJDZJrGwtYZuNowpIYQQQnYstO+zAx0GhBAyuyQWttmsP2t5rhvBuT4+S7uNlxBCCCHR0La3FjoKCCGWlgjbOObyzXwuj03gGFtHu4yTEEIIIX5c9pxil5CFzXYRti7mqsCYq+PStMMYgfk9ziAI2ub8CCHzj0wmw3sQIYQQothhwjaKuWqs5+q4hLk+PqFdxinY8crrdjsPQsj8otnoFO9dZL4SBAGjtoQsYJouHjWb7KibUpyxny83y3Z9qNnR45Yo7Y4eByGEzIT5YsvmIrQPOxZ+tglZ2HC5H8VCuSHOh/Pc3g8PU1NTO6RfQggh7cN8sK/tgrXHvPaEEApb0pZsLwMWBAGmpqYafsvfhBBCCNkxUMgSQiwUtoQ40ALWilpGbQkhhBBCCJlbUNgSYrBiVotaQgiZC7AqMiGEENLInCweRWNNWk3c59cXoZXfhBCykJnt+yDTSgkhhMyUORmxnQ8Gbj6Jofnw//Ah/yctZJl2TAhZCMyl+9tcGoswn20fIYTMR3aIsPUZi9k2IjM1nGmOp0Gc+7iitEw7JoTMR1pxT1to6+bO5rj5jEAIIa1nVoStvmH7/na9Tvpe0r6BbYYprYFKs38rjd9sjpNswyVmmXZMCJkvzCVH7Ezan6/35Ln0/yGEkPlC08LWJ1J9v13HRNHsTb+joyPcJsU1ZipY4o7d3sK22fGk3T4f0VFaK2oJIaTd0ffzuSCIXGPYEaJO+pT2oq5TM+ObTTsa1fZc+B8TQshcoaniUS7xmkTYutA37GYMjd1PC5QogxMVPfaNudVRXN/44o6119YKM9fxcdvixjAfxK8Vskw7JoTMN+aa0LH31x01vplkjCVxFujt29OmJHm+IYSQhUKqiK0IWP2jt8vfen+NS0jpJQvSCNEgCGLFqM+g6vHascQJ82YMVpy3Vc5Fn1NUBDGTySCbzTbsW6/XE/Xp+h9EiVjXPlGCeHuQNvLvSjlm2jEhhGx/2vG+m2RppblUI4RilxCyUEksbDs6OmKFrdz8o6KrPkFrPaJpjEgaz6uIwmw2O22sWjBa8dtKXN7fbDaLqampBrEadQ0k5Vq3k0ag6v2bFbb691x8WLEpxxS0hJD5wvZIgZ1t5uo52HHNxjQq65xPOqa0/fj2p9AlhMxHZixsRQwK9Xo9UYQy6m95rWmF4ctms8jn82FftVoNU1NTodDNZrMN5+kai2uMzY5NG7aOjo4wSisiN0qEyTWfmppqELk+Aarbcgl6199R7dl24n5vT1zzaOWHEELmM9YuNSPOtjczEZIuWiFIXU7xVtuztPOh9XWa6Xja4XNBCCFpaVrY6shmoVAIBa1EHZOK2yTb447x7W9f6/RdET+ZTAYdHR3I5XLo6Oho2CeqfWCbAJX2ZoJcX2Crc0C37RJkImyTClH539Tr9Wlpy0JUdFu3J8dbYRslbrdXVFcEfL1e5zxaQsi8RWdM+UTZXL/vRUUtmzkHO6UnDa5j4hzugi/jLMn0qqTnl8QB3UxAoNnrRQghc5HEwjaXy3mFbS63tRmfYHJh55bq7Zo4g+Fq1+4n4lXGrEVtNptFLpdDPp9viNgmJZfLOYVnEsNmx60rOgdBgFwu5xRluj8dLXf1L4JWH+eKBPuMplwf6ymWv61ojXo9m2nATDsmhCwkkthDzVwUMK5sKN9UoDQirRX3fWkr7dSoJK9tH/qZJA7XM0VSBz/tISFkvtMSYStRPUnrTZqGar2rSSKyLo+syyBow6ijjrqAkIhJEb25XC4U6Wnwicu0uNKggfhiTT5hrc+zXq83/L90pDlK1ObzeeRyuYb0aPkfuNKbXWJXH9dqfIKWaceEkPlOlKCxdiOtEJ4rNBOFTXKurjbjIqutjoSnFeHaOd/MONrp/04IIc2QKhVZfrtSkYUgCJDNZsNU0LgbcBpjm0TURglanVJrRa38rc91RxiBbDaLYrEYW+VYi0Z9XlHp4PJ/s9v0b709l8uhp6cn/LtcLqNarU5zYqT5aRX6XCXFmoKWELJQ8KUi2+0uOzbb0du07Sd1gqchyfNEkm0+x3kSZiNC6rK7hBBCtpIqYgtsE4s2Yqvfl3mc8uMTHFrQauPhE1v6GNdrK3BFtGqBrUW5vG/PxdWvFoRR42sVcj2tY0Cuo1wzLdyjxqOjtHKeUd5pEfs6ki3zqHW0VgtwvV1+arVa+BloFdKP/mwx7ZgQspDwZdrYfXyRydmM3salxkY5ul3MhXt7q1KcfW3H4fp/2cDAbDgICCGknUhdPMolbG36bqVSmWY4bYqyK03K/sixUTf9KKGr58vqPvT4tbgFGgWsPkf9WmNTd62Ai0tT1inBItj0cSLY9Bh0H1rgirh1/W0dAK5oruyvI9cibKXAlhW2upqzTVOu1+th5elWoQWtzQoghJCFhitiG+cUnispqVFjalZ424yuuPei9tfb5bomsTdJ9ndNMfJFka2D2xcEiEqtniv/c0IImU0SC1vXDdQKoVwuF6aq6qq9rlRZ/dq2mSYiqoWrbU8LUSuaXXOErcgVYWzXjLV9W8Er5+irXCz9ijjTkU+7nq093mWoZJs91npzbVta3GsngAhY2VYsFgE0LuVko7fAVtEpEVqJ1tZqNec1SIP0YwUt044JIWQrUU7XKIHnen+mY4gTiHHo/aKOiTuvqPGIfXRli6W5Hj7xGCVuk0RWdbRdnhdkvPrZIup6Rl03Cl1CyHwkVaUkEUJWMAJbRWCtVguFkk2Z1dE8Hb21QlO36aJZL66NvsqYJeIsBZJ0NFe2ibDVkUoZgxa/WuBpoarnxNhoq6RuW2TJHy04fdFJfY31uPQ46/U6crkcarUa8vl82L52Tsjf+Xw+PF/9PykUCg0PAroolE1JrtfrqFarqf5HLnSqsaS2c14RIYQkJ85etlrkNNOf7xjrnBaSpDP70nc1SUR4lN2NasuXJuwSwK73tPNc9hWnub0eSSPPFLWEkPlMKmFrhae9uerImsUVTbRCUretj3G1JfvaG7RvLqxO7ZX9stksCoXCtHmqIgxlGSARfWIsxLDU6/WG4lNSebharTakZ+vlkHRkWV8vnVZrDY82ZnbJIkEXc9Lv25ThbDYbOiC0MJdx6uV9dHRWCoTJebqubytTkKUtLWiZdkwIIdtw2UB9P05zv5wtsZOmXdfzhBVv9n3527ePfV/vo53u9v0kaby+iKxv3yh0dNYeI7ZaP0PooECSlOuk4yCEkHYm/do2Ci1wdZqojUwC09de1T+yzXdD17/18RpX2q8enzb0Osqcz+fD6KOOYOr3JTVXkDZ0JWUAYTVjm4osUVCJYuqUXolgW6Ml43dFeuU4K7Rlu/zWYlcitLVabZr4t6/1+ekotLyOMuJa2DYrQrXY11FaQgghjbhEmbWbaaJ0URFNn4iLS4f1RU5doss6oe0xdmqTHasrsutqywrCqCiqpZX2yF4f1/9Tfruy3eJEeJS4TwNtMCGkHUgsbCuVSsN6prZAUaVSQbVaRblcbpj/aIWZFqCyn440Cq6iS9ao6f3jooNagGpjL4LNzoeRyKtO6dX7STsSpdWRVBF1Ohoq802r1WpDQSi7XI0LK8h1VNwVuZXfUX/L8bJdhHWtVguj1dKfLgYmkVhbjVjeq1ar4Y8+nzRG0UaxCSGENJJEfOkfl+BxHR91r06Stht1jOt4a9tsZpfrnCyuvpNss6JPi0SfGLfj1vvPNBrq6tMlWqNSiqPasNc6CUkj0oQQMhdILGxFqOg5pTpqWavVwnVObcotMH2+pp6DqrGRRx11BRrnmSY1Ii7PrJ4TLFFMV3pupVIJ29CiV5+TjXrK+cscU3ECSMRWXxu9bI7PQ23PVxdPckV0fe9pASrnrv8fci5SAMrO75V+rbjV45e2pQ0hrbBl2jEhhPixwjXuPZ+oBJKlyQq+KPBMMnTsa1/U0toz+76rDd9ziLaRdr9mzyfpMfa6u/qOc1pEOY6t4E7Ttm+shBDSDiQWthKF1F5KLf7kxzVfFGgsaKQLF1kxZ1OW5b24ubaucQmuJXj0uWgxK+/JvNipqamGObLWmEhfOsKpz79eryOfz09bnsYlDKWCsG3fpl7blG+XgNURXRvdFdHpenjQ/xeZbyvXbHJyMvws2PHbZXissNXnRAghZGa4hIsVSlEZL82K0ijxFxUNtE7aNCIyCIKG6Uo+sWrHYffVdTTse77ztJFZV7qw71jXdldU2HVdfE50O/Yox4VrTGkisDONQBNCyPYmsbDVN1GbMiwixkYK9f668JFdasf1t0anA+uUZhvZtOlLUZ7JqBu7vvHLHFxXcSf7t0uIu4SlFoOy3TUnWadp65RpG820UW19DlINWfdtjaPLUMqP7n9qaiqsmGyvpcvoU8QSQsj2w9qm2aKZe7wrMmvb0+/5tkWJW5/QdolJ3xjj0o1947P9+sR91PsuB4Udi332SXoevrEQQsh8IrWwdSHFo3w3T1ssyrVOrBXLrnakfZ8Alv1cf9u24zy8OiqqRbmk8FqjrK+PLaClj9EVlXVfNhKs58QKWuzaOcu2sqNr3rKOsrpEsjWi+jroqLXPIGqj6/ofEkIImT1akRqs22m2jShxaMVdmuinjdwmnbLiE7Vxx7rG5hLRUcLROo+j2vb1pV8njbzqY9NEaQkhpJ1JXRVZ3xylSq6unmtxCVud9uuqihxlCKwIlmOT4hK+WrjatmX+bTabDefH2nHp62GjuNKmNkhRYtKit4kY1uJSR7tdx9rKxrVaDblcbpowttFk2SZp40EQhMf5HAqu6DyNKSGEzC2inJMup60+xvV+XPTS12faqKF2DrsiurZt7QR2RXx9fejfPnHr2pbU2e7r277veq0zuGhjCSGkkZZEbF1Lu2jRKAWmtKjV4taXGivt+KK5cVFXnxGx7UVFgK0Y1AJQi0KdXqwFoRa2Nvqqo7U2aivt2ocI1zXTUVs7F1f219FuLWy1EddOCi1w5XWShxAZF40uIYTsGKLsdZSodb2f9nUriGpTbJW2ra59ooRpq7BR0aT4nlVc7+v9tL3WQj2NrU0SJSeEkHZlRhFbwRY9EkTgiHjM5XLhsjuuuSJRqcz6tWufqG1aALpEtCxjlMlMr9asl8XRhtLlMZa5xjpa6YrM+ubTanRfNrKsx6eNqq/CtIwNQPj/sJWS9bhEmMt7OiXZ1bdFOwsIIYTMDlrgAMlTiF3RTpfg2REiKGlqcpR4l/f1cny+rLK4TClX6nHSNOpWYZ+TtNM6iqRpy4QQMl9ILWw1OhroSwOSKK2IWokIapGmBZQca8WobI/CF7n03fxtSrQVn1IROZPJTFtbz24Dts2tdUWfReS7KgW7sJFZWxnZd95R2LWH7bnaatHyYCBIOnIQBA3zhF1jT3u+hBBCmsM3F7MV0dXtJeBcdt4+V1iR6Xtfv3Zti4ty2vdadS0tvmvrc/LbfdKIVYpbQshCIJWw9d0UXUv7AAgFrYhaLdLkOEGLWVe6bRxaACcpRqWFa5TwteNwpUlb762roIXuQ4ShFvg6Cuo6D53aGxUpdUVgfePQSLTad06ubT7s/5kQQsiOJYmD2EYlW9FnM+LRFSG1Y3JFUl34BKC2aXEC2Td+2TfNe67xuURzmlRyZkcRQshWUglbe7OOuvFK6rH8ANOX6LFC2CUaLVHCTNKcbVuCb11ViWLqeajSRqlUmjanR6K1Or3Jpjtpoyt/6wi167y0kLURWv3aClbpx8470pWPXddLj1vGaOfT6tRxPZY4mIpMCCFzgzhR6xJSrYrUWodoVLtRItHa36SZXNYO636SilffGF3nlGaua9S1SNteWjjXlhAyH2k6FdklPLUw1KJWC844LywwPeIahZ1/60vXsYJTo1OX9e9SqRQaRZsSLSnVWlRKBFjms1pvtF3rV/qWok2S9uxKOxbhG5Vu7Fr6R18TXeTL5SGWNGM9Tlcfdskie67SHisjE0LIjsWVdSP4op6tFjy+ZwUfaaKVSbe7xK110utrYbOXkhJ17aIEvXXs6+2+ecBRUW5f33GCmhBC2pnEwtZ3E7TpxEBjCnKayJ0tSmTFnUuMxaUs24hl1Hno/kVIShvaaNjopqBFvC02BWDaWr8uw+paCilNWhQw/brplGdf2rhuR0dprVPA1advbExFJoSQ7UMzgirp+60ax0zbddlh+Vv/1uPwpe76xKL+7RKNaYRumn1t+/p8o8Yb9fxDCCELjaarIkd5VHO5HPL5fJiCHIWruq/ertvVv2UfV2RTL7kT17evP6AxAu3z3Nr0YxGvehyufYHp4temLbs8uL6x2iit7Vf/thFjaxhF3OoqyPK+jeJK5Nbn9Y8qMkUIIaS1RKXzRh3TKnHka6vVmTu+CKd9T9tVK3yTRo61jfQ5hX3nHRVNjhKrrihunLhldhQhZKHTVEjNJe6AbYJIRzsjO48ozKTRKUGC9KPbchWNcolAuxyNbzkhOZ8oj6gIPYnWSjoxMN0JEDUeLRx1RNWOSY896lxdRt9VYVpHZW2fOjVa+pPor6uwlkWOJYQQMrtEZdRYm2T/bpWone3ooU2nTTKWqHN3jdcKSp89ttuSOA985+Pb15ei7GuTkVtCyEKn6Tm2rvkdwFbxVCgUYm+wvrVQoyKV8r4tpCR/21RfEW1xgs/1dzabRT6fD7fZYlF2rmytVkOhUPCem8ub64pK63mpQRB4HQSuysu6TR3B1dfAnkcUMldaLweki2UlcUp0dHQ0zO0lhBDSOlxpszadNe74ZgVR0uNadf8XG2nFpm1fO5PtOH0RUd2Hb/wue+cSur6xp8H1fGXHY/eLw0Z1OdeWEDLfaErY6gifjRrq5XOsgIor9OQToPKeRAqtAYmaQ5vmpm2NVaVSQaFQmFb8Kkp4ixCU9Fv5rQWxPs61zI/r3Hwp1zImPR6fF1f+P1LcyopjfW4isF1Vke05yP/E9z9lOjJxoT+LhJDWkGb+Z7PMpqD1pdZqZ3pSMQlsWwZPfs9kfFH7N5MKbAV3VH9W5Lpsd5I2tOOcopYQMt9IlSfqS9sJG/v/wknQokynrcalr/rEkfRnRWIrEE+wK4ra0dERijfp30aHZWySrutKlbIiVfepo7W2TenTRkvtNbXnYs9Dtts0bt2Ga+6tK81b0q618HWlJjMdmfigs4OQmRMnTrSQmc1+BOmrFVFam3qsbWjcc0TU6yR9RhFVYyQtaZ0ELoHrm/drtzFzihAy30kVsXV5+bT40dFaS1x6se5D76PFpohm31I0afDNddXjqNfrqFarYWRJUmrlfGVZHG1gqtUqqtVqKHyt8JRraEWsLxKcxOjptCrraHBFs7VY1Q8LFl1NWY7Tgleui04Pc6WSc9kfYpHpCuVyeUcPhZB5gSvSOVOh5Zpy5IoiNmOPo1J+ffvZtdp9NkXbQ1cNiTRj8o3RZbOjIqdJ+007Tl9ElhBCFiJNz7EFtgkfXyQQiPesinASkaSFoy962yy2TSsCgUYhNjU1hVqthqmpKVSr1Wn72XYkkqujuS5Do50Bcpw+T9e562iwHoduQ+/rQj8I6HnJPmzUVq/bK2ORObT63OLmSZP5SzabRVdXFyYmJiI/W3buOyGkdUTNP01ybNR3My4aOhNhlVRY6h+fmLP7JRHoac9B2/m4KUFJscfGif2k5+YbH1OSCSHziRk9WYrxk1RdXxpM3E3TN4/EHici0zWGJGN1eXq1cHNFSa1XWMSrLojkMkJRHlVJ4dVRadc4Nfa1RK1d6c1Jrrlu11WgSs7bzhG27eooreta6jGR+U9UBoBGHEVJqqcTQvzECR/fPmnbTNpfVJpumkwkvZ/LJicV73E2Oa7vJPiuSdI27PG+4+y5u/qgUCWELGRmFLHVRscXrY1Cv6+r7rrmmmqkQJMWhr4iSzK+qAii6xiX0bQFm2zU1B6nhbQuXOEaS9S1ijNUujK07Vfet/3J33JOUWnJun1XgS+XCHfN8SXznyAIEheEymQyKBQKKJfLLZ0vT8hCwhUdnE1noo0AxqW/xjlpk/TnciK7xuLClbLsGkOc2E36fhoxG7Vv3LnpaHVUe3p7VJSaEELmA00JW3sjdVXO9eEramR/ZF+d7iqCqaOjI0z59bWr247qH2iMRuoiVVHL8fjmrvoMhzZ6Mv4oQ+Qian+fMLDpznEPHzbl2PYt45f/hz1P3V6UM4HMX6Ie2AqFAiqVCoCtDqpisQgAmJiY2G7jI2S+4rq/N5MSa0VTFK5MKP1es1jhrJ87XP3YtOAoYWhTd21/rnE0M25X+3bMcdfIZbflOFcqdJSdJ4SQ+c6MJ7nZG3NUmoxdU84n/HQVXinSpI/J5XLI5/OxQs32HSVqdSRW2uzu7g4LVrmi0a5tUi3YGmEd9Y2LZPsMeFQ6VlQqVFyfdn6xz1Gh+3RVhXb1q1+ThUG1WvVGbUXUCvl8HsVikfNtCWkhrbjnugSkD7tygYyhFeg5pFKgMkmkVohK65X3o2qBNHseSaLoSUWoz6HdrNOCEELmK00v96PTcqMitj4PrvbCShqzNY6+SGQ2m0U+n29YWki36Utl1vNSXZWVffNXZHzai63Fq1wDGX8QBOE10UKwVquFlZalonKUkfEJ2aiIrut9GbcrndiH/X9IO3a5haix688LhS2xiMOqp6cHpVJpRw+HkLZjNu6radrUotPW2WiFgNKOW+0cdmUE6cixtTuujDDdbpwIbZW4nUlbPuGd9jprMU2RSwiZb7Q0TGI9tfqm6ZoDKtWQ7fxcn/CU6K1Eg/TyQtqASdRRi80kc/j0+rNBEGB8fLyhUrM1knpcWpDryshaDMr8Qy1sdREqF0mNoH0AsALdnqfFt66tPj7J+sGuhw37NyEAUC6XMTo6iiAI0N3djUKhsKOHREjb08y9Nokj0udUFRunxW1UBlFapC0p3Kir9dv2o177tsn448bczNijsq9a6fRNKlCtqE8T+SaEkHagKWGbyWQa5qXKXNe4m6O+mWvRaY2UjcQCjRFCSXXUhlS3oY1UUlEr+2r0HF4dedU/+tyCIEClUgnXsq1Wqw0iXQyoNtAidqOity4jbgWs9pzrNm27OnKrsdfIGl3fA438dhlMG9VluunCQ+bQuqjX6xgfH0e5XA6zIKJgBWVCktFKoeKKcurt2nkbJdJakaLsctz6spm07dL215XR5NrmwhX1TTt+2972gMKVELJQmFFV5Gaxgkd7ZIGtBWbsUjbaiEoKY6FQQDab9RaeqdfrDcWm7BjiKgDr1Kqoqsuu6LLgmmtoz1enOdulczRWZNp2ZMxxkVXpR0eSffvZMdvzkPWHXfvFnQ+Z/3R1dTWsA22R71tPTw8ymQwmJiYiU+1d32VCyFbSiMck+2i7FDcNBthmf3xz7HX2UzMptPI7KiPJNzaX6PXtm2R8SdOufXbbleqc9ppYp7K0RSFLCFmoJA6hRaUm1Wq1RNE4GwXUokeLsCCIXkpHpz7pQlMuAeUat25b/+gx6H2suPVdCz0e1xxUHa2ViK1+rT3fLqyXWgtq31whu83n1Y4iypsdJcBt1JbCdv5j59lNTU1Fphjrh2ApChcFH9gIiaeV3xOd/RS1jy9TZ3viiyy73nP92PdnSpwzYKYRXCtik4yZdpgQMp9puniU7/2wYY8RdEVrbUqRRHdsG3pfifTIHF3XPnFe5ihEbLvEsohS3af0J1WR9XYtWPVxNmIqv+38XJ0+ZR8g9HH2WNu/FK+yJKnUrBHHgxX6LhHLSO3CwjpmxsbGGqqau0gqbF2ZEfx8EdJIq0SZq70kU1PivpPNjM+X9RMXQRY7qdeRj8JlO31tR72eiUjeHvcz3jMJIfOVpic9uqrluryPUTdQHbHUx8ratXbZHN2uVBeWsWjPZdxNO4lAz2azKBQKKBQK06JQMkaNGK+Ojo4wQhXnvY66hkk8yrrYVdQxVhD4BELaObBJHhZ8kXQyP3F9TkulEjo7O72fr+Hh4bCIlGsJrSj4uSKkkVZ9H+z32Noze+8Xe5R2OZ4kY9bOY59NdYlvO+4ktt+XOmz7SiqU49537ZP2f9isI4MZMISQ+caMUpG1oYgTcK7lYwB/MYVqtRou6+PrU7ftG6NrHFrE2WNErEmUSXt8Xe3pMenIKICGdGl5bXE9LOgIr75OPgOo+45a0sgebysh62sUhU01tWOJErMUIQuLer2O0dFRFAoF9PT0RO7r+r7HPXhJ1gYhC5lW32ejRGsS+2DrYrics67j4sakxxB1zrZve15JnhX0/lGRYd/0pyTHph1XEihWCSELmRmXqfUZuiTpSFHzSXX0UgSaFpq6HfldKBRQLBadfbsEtW98sr1Wq01bjscX+RQjqufORl0LbfilCJbu214L+W2j275jotDiWf//0syN0tcv7jPAiO3CRr4TSTICZH76okWL0NfXF3uMfOcIIVvxpesmuQdHiU+xyb52xKZoYWuXwtO/02KzmpLaFNc5RR0XFRVutv0kEV4dYW6FvdxeUV9CCJlLzDjUoT2jOh1YpwBZ0RZVrVdTrVaRz+eRy+Ua2pP2pYJqJrN1qZBcLodCoYBKpRIud2OFqS+1WdKINVIUq6Ojw+ldtdFQV0q2Ts3SFV3lQV+2u6pIxj2w+yo7R0V27XhtW1qsJl0mybYfJWj1Z4QsDIIgwOjoKLq7u5HP550VkgcGBgAA+XwenZ2d6OnpQVdXF0qlEsbHx1GtVjE5OTntuDSfUUIWCq77bJQolfej0M5Q3/uS6SSZFNbhG9d+FDozyRWNtfvqNnXEWNsp1zXSUWFr49PYMl/7UbhEe9Lr0ixRTn5CCGk3ZixsrbH0pQdls9kGoZYkeqPTn1xGsVarYXx8HPl8Hr29veju7kYul8OWLVsiRaHLYOgld1zGxaYKy1JC1tCJeM3n86hUKpEGR85JHvRzuVyDuLTIdXClKcvDhG89Wh8uo2YNpe991z7yf9aiVkenycJEsg3EiWO/F7VaDRs3bkRPTw9WrVqF/v5+dHV14f777w8dVa4HuFwu58xiIITEEycM7b6yv9gaaz/EmSxFHV22NkqQxomsKBEXlc4bZ+NsOzYTyz4bJGkn6n37nGGvT1Rqs68fV/vNjJkQQtqZ7TY5zQou8fz6DJkYzlqthmKxGEZibVsi9KrVKkZGRlCr1TA5ORnO0bWCWtrW23yGVNIiZR8RrTYd2lYDlvMql8vONq0g1FVhXeMF4B2vbUvG6PMU24cQGb/9P0TNC9bHu4ymSxgzHXlhoT8Dsrbl+Pg4+vr6UCwWMTIy4nTQyPd4cnISlUoFAwMDGB0dDbMx9HdKokKM2hIynWYFjCuNVt+7pbhjJpMJbY21K7IPsNVxK84s16oILpvhG4/d1yUA485F92vRkV05Pk5Ix5Hk3Ow+Lgd3kv9nlLhP2xYhhLQjiYWt70YpN2B5eI3bPylaUMoDrMx5tVSrVQRBgM7OToyPjzeMLZvNolgsNqTaStt6aRxguqGSyszywC3nqdOh9dxfMeQitOV6aKGpI5v2fH1RV9/49Db94KG96UlSoXR6mX1QiEpJ1v1YfBFbCtuFgY7cS/qxfA+6urowMTEROqo0k5OTyGQy2HnnnfHUU0+F28Wx09fXh46OjvDYfD7vXcaKkIWCFmpRUc8kEVG7v/x22Szf/V/bWhG1Ykc6OjoabKX0G2VrtAB2CT4t1FyZREmioz5hHCduk9g0V79xJMmm8qVHU7QSQhYqqSK2vht4lKfUld4rRM3XqdVq4VI7ktoUhRjSQqGAarUaCkttYKMiO9aTK2OWebYydi3UbCELiZZWKpUwvViMjO3bFmySiJRLuNvz1NdMPywI+nx9Rk6Pxxb30Oc6E2yklsJ24aA/g+VyGV1dXQCASqWCUqkU+eBVrVbR0dGBnp4e9PX1YXx8PMzekPR++X5NTEwwYksWNEnmYNr9XVk1vr/19BexMy6HqY3ETk1NIZ/Ph85gsUd2/XPdnivt1op2bY/1fpJ5ZJ292g66xK9P3CZ1yLrO3bblGq8+thnhnEa8uvqwzgDaZkLIfCB1KrK9yes5NULUkj5yA9VCs6urK/TiFotFPP300wC2FW8qFotOkabHIYVlisUi6vV6Q6GZer3eMO9Txq3PQSMGslKpIJPJoKurq8HzHARBmFIs8wYl7blSqTQUyhBDbo1KrVZriHKLgK/X62F02oXLa26vsY7c6mJVSbBGWf+vrHMgSSSXwnb+o4uirVixArlcDsViEYsWLcJf//pX1Go1jI2NobOzE729vbHOm8HBQQDAkiVLUKvVMDAwgImJCQAIfwONaf+ELFSaiQa6jtPHi4AV+yIOJUkx1t87lyiS54JCoRDaM7lHWJFp+46yEdpeaiEbFW3VffqwEV1rq+JSd5uN3Oq+k4zNjjFpW1FiO8kYCCGkXUi13I/LqxsVrQX8S9QAW41TPp8PDZTsWywWQ6Es23Sk1IXM4RPjqYW2S2TadV614dPviXjVxthV/Ea3odOV9XsisKU9vTSQFcNRzgEXLo+x3e4iqsJlXBs+ke0amxbbFLfziyAIkMvl0NXV1ZC6X6vV0NPTg0KhAADh/PeOjo4wG8NFpVLB008/jUqlgmw2i0Kh4L3PJClCR8hCI4mQswJOOx/18WKnZbtd09aV/iqRUtcUHunfNaa493znlkSY+dKsfWOwDmlrY319JrVvMxGTPns/m30SQkg7MOPiURJ11EZQexQFu7xNJpNBqVQKq5qWy2VkMhlMTEygVCoB2GZcyuVyWEBKPMEaMZiTk5Ph8jxSbMpGLPP5/LRIo6vgkk6XclVk1WJXorbyMO9a8scX3ZTrIu351oR1EWVAfallSbEeXlf6c1IYrZ2/yLrRvb29KJVKqNfr2Lx5M4IgwJYtW7Bo0aIwhVi+Hx0dHejt7UUQBA1z4gGES4Rs2bIlTM/v7e3FyMjItIeyYrHYcC/IZrOJqpETMh/wpZcCjcI1STvyWx+vs27Erop9k/1dBRq10NVOad/yOb606Kjoo6sOhU/sRglmu799ZvFdv6hoaZytazYF2HW9rFMhLrIcNX5CCJkPJA53+MSJa53auJu1K/par9fD4lDyQKvb00viCK6buzzoxs1bkd86Iqvfk75sxWMbJZLXMn67n0uk6jZdVSLtkj6udF7f9bVpXs1GSF2RdtfyRvq3/B0lfClw5xdSYK1QKCCbzYbrzZbLZYyOjqJUKqGnp6dhbt74+Hg4xUBjv0tSSEovG6Kx8+6npqYoasmCxwrUKGzk1pdSbL+HOpPKLuuj7b928rqmzfjGFIfOqJJztuI2LmqdBG1PXW0ltccumnU4W6dzK+0p752EkHYnlbC1yMOqFIiwqYW+m3qtVkNnZyey2SxGR0cxNjbW8L6kK+oH11qthomJiTAqo/uQNF6ZV6sFpe3fGsQowyPnZFOobOqwFoG5XC5Mr3alSuqx6eIUOq3LhU/cWsNqjaz87Ur7TBIZ1kY0TiS7ort2TBS184tyuYxly5aho6MDGzZsCOfHA1s/O5s2bUKhUGhwVJXLZWzatAn5fD4sKiX7S5bF4OAggiBAPp8Po8IWWdZLwwczstBJ4gB1oSsV21oKrmwiO01Io8Wg6327X9w2OS9tg3wi1r4f9TwwE+y1dV3vKHse9X5SXNeVNpYQspBp2QS1uBu4RtaeLJfLoeHTBkfSnWwFRruMjmVqaipcGN5H1Bqv9rXMLZI2taF0pRvLuZVKJXR2dk5bAsklDJMIRkvUXFaLFaaaJCnF8vAiuOYPJxkHmZ9IsbbR0VHnkjuVSiWshGwdVWNjY9OEqVCv19Hd3Y3Ozs6Gz5P+fvuOJWQhYwVeFFpguSKTOvIKNEZddXaRL0oclZ5ro7x6u0/cikjVc3vtudpzEdtnI8suZsMx1mp76Eo/jzove+1914wQQuYDqYWtvUHqeTTANu9ulOGQ6sGSNmhvtGIsdYEZMVYibKXaor0hy8OuNWTStxWkYiTteMWAa2Hrugb6YV3SsmTerE7R1sZYIsEzMSyuaLJux/U/8IlbK3Cj1hKs1+vTHips31Fp2GnmFJG5Tz6fx/DwMLZs2eL83JTLZdRqNfT19YVFpICtwnVsbAxBEGDRokUN78l3Y2xsLHRW6ciuD110jpCFiLWncfdanbmks5AsruV+7LQZ11QV2cc3Hp9Iixq3K7vJlbHksum+COdMbVIzx7fCDrqes5KkPrciTZsQQuYiM47YpvF86mIMuiKwy5hKNVRXNDObzU4TtWKwJCIrAjIKSRt2VUYWrKHV+8r7el8x5Hp9WpmrK+nVus00xaIsSYyaHa+PqP9FGux8a5cAJvMD+R9HZVFkMhnk83n09vY2pOjr922FZNlHqo/raQm+z1JXV9e0YlKELGSSRmzlRzt+fZE/65QVQazn6WrEHriWvBNc83yjbJtL/NqIb9y5p0kbTkrS41s199f2nSZCTwgh85UZhTeilsvRKUv6ZprP55HP5xvWmXXtK393dXWhVCo1VBy2+8oSI9lsFl1dXSgUCuH+UoxGHpylEIbuV7Be32q1Goprebgul8uoVCoNIl3EsU1RljHI2PT5WqOcxLPuM+Q+fP+bZtBrELqwFS+1Y2GmYpnMTXp7e9Hd3Y2BgYFwm30ole9SLpfDsmXL0NfXh/HxcYyNjYUiVKYdZDJbK6XLd2vZsmVYtGgRxsfHMTk5iXw+37A2ZrlcDvvhsj+EbCWNaNJTYXwpzDoyK45a2W7TkeV4/bcsFVStVp1TY6zdkN+u9GT9bOGqtCzYdXZdbfquRzM0I1RbKW7TjNtmdVHsEkLmE00Xj7LpPjaNyeehleV9dGTFehunpqYwMTERPrhms9mwumqpVJqWgpvJZML+s9lsGB3SxS1KpRK6uroSeXJl+R/5EawhFkNpvdeyj01Fdl1DX9qUxheFjdtPb59pNFbPWZJ05KToJaCYAjV/EIeTy0kl1Ot1bNmyBcPDw2HhtyVLlmD33XfH0qVLUSgUwgdfeQiu1+uoVCro6OjAM57xDHR1daFWq6FaraKjowNLly5Ff39/Qz98OCOkkbh7rRWRcTZIR2a1s9euHqDbFsErBSZ1PzaDKW6cMg47dn2urqlNrnNJ0lc74YuYu4hLByeEkHYmVZjDF+kUfMZRttn5b9qjar2r8iBr57Dm83mUSqWGFF4R0foBWYo3CdVqFRMTE9PGpufkClqciojTnmHdryxRZOcSyfu2bVe0NomonQ2DO9MoV9I0Y+2dl9ekvREHk8U3z3Z0dBRdXV0N0Zbu7m4sW7YMxWIxbEuqIvf09IQPzDvttFO4trXMzd28eXNDH9lsNqyuzugtWegkFSuue7GvdoI4em1EVgtUl32TY2QqgthKcUZbkarXz5U27LOCz1Gs06k1rVxqaDbZHrbRJYJ39HkTQkiraEmlFZtK5EopBratCzs5OdmwXUc39TGVSiU0lhMTEw1Fqmzb4hEul8vYsmVL6FGW/SqVSsOYZRkRiQr7UnUlfUrEru4vl8s1pEfLORQKhbA/lwG2It6ef1wU1y654EPEuFxDX8pWVBt6DPrvqMXrLZKKSsExfxBhu2XLlobt9rsPbP2sjIyMhHPjN27c2JBGLPvKZ6SzsxPFYhGDg4O4//77p82xHR4ebji2p6cHixYtwtDQUNgfIQsdbWd892oROK50YFd7tVotXG5PIrgu263bEic10GhvxJ7LOMVWuqqr2/Ox2+y5yDhkfLpf2S/OoTxbIjOu76QO41aMgxBC5hstVxpW/GhEBMY9eMoNt1KpoFwuN8wVdRU50oZKPMF6Xq3eR792LRcSdbP3ebLtNh29leOSRCxdEezZpBkB4Domaqw6HZuGdP5QrVYb5sz5kO/g2NgYhoeHUa1Ww4dM/aPne8lyYLVaLVyrVqomZzKNxabkoXV8fHx2T5iQNiROvLnEon2tM25s1Facvva7bKfZ2OistOuKxjZzTtKfz8645pPG2aRW2ODtGQlt5ZxdQghpV2YsbJOKFfH2yhI/Fp0+JNEgWTqnVqs1rIMpD7/Srn4Ylv3r9TqKxWLD/vl8vsG4xa2DKd5jKRalPcvWS63PSYrj6DRiVyVne/7SlqtNoRVRTz0XyUaL7X6uv13bkjgrXMsckfZkbGwMIyMjzirE+iFy8eLFKBaLYUG3arWKxYsXo7OzE8A2oSrfNZlPLyn+k5OTqFQqWLx4cdh+oVDAokWLAGxb11bP8yVkIdKMMLHrlAv2Hq2X2JH9XcvN6VRgK2xtCrHYRWuP4s7RFqtyjd3OH7b1MpoliXM7zfu+9reXjaQtJoTMN2acipw2ZUfPW41qU0StRGY6OjrQ3d2NiYmJcL7O5OQk6vU6qtVqg9dYV1uVtGBpSwvgJIZuamoK1Wo1FMXyU6vVGopb6QJJUYWxbMpzGqGq5/a6Uoht+nfSB51mUzftA4o+J9fYZD/53zJltL0R8elDHkKLxWKYQSHz33feeWds3rw5rGS+efNmVCqVsAKyfD/lO1atVtHT04PR0dGwsFRnZ2f4/bSpzYSQ+LRWbdN8x9soJ7CtkrldLk/eswUW7WoGul0Rt9oe+7KhdPtaJEsfUY5jl03S5+6KJieh1anD+hrtiCjqbKZhE0LIbNOSObZpiHoQzmS2Lu8DICz0JMv1DA8Ph4a0u7u7wTDquToSwRGBW61WUSqVUCqVwnmxlUoFg4ODsd5RXcFZV2/UHmgt2FztyTa93JBGi1RbSdliq05HXcekBjGNEdNRa5nLZPvy9a09/rIfaW+ixGQul0N/f39YFbmjowNdXV0YHx/HwMAACoUCuru7sfPOOzekOkqbU1NT4dJg4kSamJjAkiVLsHTpUvT09KCzsxOjo6N48MEHWxKNIWQhEidsxZ7qjCWdWqzrWch70q7tw4pScTaLnfWNw4o96dvnOLap0L5z0+/77LdL2Nv+0tBqsdqMEI0bA8UtIaRdSSxs4+ajREUgdQRRjKRsl7blt454SnRV9pUoztjYWEOhJjGU8iMpT52dnQ3GVFc7TiIMq9UqMplMmNqs37dRSR1NleWCbLGKJNFZ64nW11Bfq7To6+ibo2T78r3W4jXKQFqPvDwA6f8To7btSz6f96bzy8Pq5ORk6JCSOfbyHZ+cnMSTTz4ZTivo7u4O5+DKWrVBsLXa+dKlS8PsDBG6mUwmzNYghEwnTsAkiQxa26OjvDrqKW3Jsj66D/mts3Vkjr6dnhIVrdVRYfvc4DrGCl3XuOYiaaK2zZ6Hq30KWkJIu9PyiK2NOto5nBIx1Yhxk7Ther0ezonVkdupqamGasPZbBa1Wq3BWywPxLI+Znd3NzZu3NggTpMUvXGdl02nEsTbLGPUhtoaZBG9tm2LT8i60oxnaojkwcTnlPCNUfpOci0lfVyuk8s5QNoLWcfWIt+74eFhTE5OolQqobu7G1u2bAnvB5JNMTo6GkZtyuVyOA87m82ir68PXV1d6OnpQU9PDzZv3oy//OUvGB8fD4VtuVyOnStPCInGdR+X+7RrzqxgU5nt/FZdK0PsPLCtorLY5WbSbl19x0Vn7bFW3LnSkaPE3mwKwWZSktNewx2Z8kwIIbPBdktFlpu0r1iRrHEnc/KArVWR9ZI1cjywLS1WRHAms3V5oJ6eHhSLRRSLxTAdeddddw0ju4899tg0YR2Hncsj5yKiWjzUWrDqJQb0nOJmjKDLq6ppxrjaaLOdE+Xa15VynMYg6oitjmJT3LYnrv+bTCcoFAoYHBxEX18fAGB0dLQhgi9LcwHbKq1WKhUUi0Vks1lUq1Xsvvvu6O3txeLFi1Gr1TA+Po4jjzwS3d3dqNfr+MUvfhG2ywczQvw0YyO06ImLomr7ro+VoolA45x8+Z5LQUkrnpOMy/Zt0WMBttk5G3HWzDQzqtUkva816xiIOpbRW0JIO5IqFRloFHm+1FpX1BbYGq3Uc0z1PkEQhEVjJE3Jl76rKwyLsZLIbrlcxrJly9DT04NyuYzx8fFwju3IyEhkZWJB3/A7OjpQKpVQKBRQq9UwOjqKIAhQKBQaBJpEIgUt1HUxnGaJErczfbDX83x9a+Ta9nVKuYwhDonaSnXcuPR2MnfxLbEjn8XBwcHQsTQ2NgZg2zxznR2Qy+XCefWSzbFx40ZkMhkMDg7i4IMPxm677YbBwUE89dRT6Ovrw9KlS7FhwwZ0dXVhy5YtqT5DXV1dKJVKKJfL4bgIWQhECTnBTkmxKcf2GC18reCUqGwul0NnZ2foANZTjcTuyLakmUh6OoscY5fUc52bToXWYjpu+o0P3xSeqLHPliNO1+pIek/U0XVdM4N2mRDSrsxqxNZ6P/Xasjp6qx94ATQYORFP9Xo9jLoKusKuLj6jPcCSzjw5ORlWVPWNU9BzhVxpSq65oXqOsTb02WwW+Xwe5XI5fKCPi1L6otq6f/ug4Xpg8UVlXa/tedi2LOKk0ONIUhlZnBI2Es+IW3vh+/zKerNSCEanCYtjR1IP9XdfCkXp7/nIyAhGRkbC7/OiRYuwefNmDA0NYcOGDRgaGop9AMtkMg1jkvm5UUXsCFlIuOZY2oio3U9j03dtZpZe+1bsn2RoWOHsEoou0axtTto0YV0PI0kac1KStjGTfqKi5804iuPEOEUuIaTdaJmwtXNHxbDFpfaIuLHrzPkMkghOQa+p19HRgZ6eHuRyOUxOTqJWq6GzsxOVSgVDQ0PYsmVLmLpcKBQaDKoUtgEab+ZipKUysghl2V+Mo45KiwEXAyzpty7Psr0WUe/ra9OKdGTbl47a2vRj3X5UcauosUobuVwuLAzm85iTuUvU/6ujoyMs8ATA6UgSZ0+lUmlYm1rm28ma1TvvvDM6OzsxODiIiYkJLFu2DCMjI3j44Ydx3333oVqthnNwJd0/CAKMj497syMqlUrqqQiEzFWaETJx0UTZz/U7SVvWsStF4MTWi+NKisHpfXWbvikwYrO0o1gXa3SNw7ZpHaq+v5Net6TXphX4xKh+ZkmDvhYUsoSQdid1KnLU+zZKp8WtK61Yl/q3N1RXfxK5kfd11EWnJtdqNQwPD4dzbmX/fD4f/s7lcg0RR92WFqwyRkmt0sUvADQIVleRDRulltQsV8Qram1a1/WWPoQob67rwUQEqn5POw98jgYR+r4HAddrvV2q5DIduT3RS3oI+Xw+TNe3ywBJCqI4sICtc271d2hycjJ8sBJhWygUMDY2hkcffRTDw8PYY489wuJsPT09GB8fR2dnJ4rFIsbHx8PsDVnfGtj6neAat2Sh4bIPQpR4cUVFZbsrs8mXqmzFbSazrYK5LSAlbSVJCdZiVpD7kbW9rrH4zt8ldG3/SURfkkyqtCT5X7qyyJKir0/U2AkhpB1oWcRWhKKvoIwPV0S3UCg0pDCKl9euj6eXEKlUKg3zcuVBuVQqhcuOVKvVBi+w7kOnJwGYNh9IBK5EHGW7HrcWimKABVvIwnWN7DEyzriHEH1NkhrOKCeCnKcYS5dIteOyojwuKiCpp3KtbD9k7uKKhur/o3aK9PX1YWRkpCEboaurC5OTkwAQFmDr6uoKMyFGR0cxOjoaRnvESbVkyRJMTk5i8+bNGB0dxdTUFMbGxsLCUvKZs99dQuYjcZFFvaRaUpFiRY7YJCuuXEJUH6N/ZAkvaUem5kiUVb6v4gjW0VWd5WRttM2C8oliLW71fla0xl3jpNdwNgSiTuv22WP9Own2vPT/VP8mhJB2YsbCtpmbn+wv6cfacOpIrI5u2vkwIn6tkK5WqxgbG0NnZyd6e3ux7777YmhoCCMjIygUCiiVSmFf1uAODg5ieHi4IRopfUt/kvIs47Nr8GnjLCmXWgjI+9I2ML2IVpprbA2Rqx1f9NS+p/d3LVmkEQNrKyprXHNuddRWHAR2/KT90BVP9edA5tEBjY4oIZfLYdGiRaGo1YyOjuLJJ5/EsmXLcOCBB+K0007D+vXrsWHDBvT09GDZsmVhX6Ojo2EmRi6Xw0MPPYS///3vYeE2QhYKWpy6MmKSOJqjIpVW9PgioII4nHO5HEqlEjo7O5HNZlEul5HNZlEsFpHJZMKikfr4uHWqoxzp1i6L/YkSwVHnm4Yo+5oW60yI20/2Tdq3/bxEOUsIIWSu07KIrRSCcUVJdDqS6zig8eapKwzr5X1EhMpyIPYGLKmIIyMjyGS2LjsifUvEt7u7G11dXejs7Gxot6OjA0888UQojO26tTp9SkeG8vl8uF2Mp56rqte41RFn7VWPYiZGRYtJ12vA7V3WVSqjCl25jGBcH/JaRL8WtqS9sVVKAYTVyoFtkZNKpRJ+dzKZTENaslAsFlGv1/H4448jm81i5513DiO4ixYtQrlcxooVK9Df349ly5YBQDh/vlgs4re//S3GxsawefPmMHsjCp2FQch8IE4IpcFWyHdF+XxjkEJRwNbvtdSq0DZR5tpL+9rWa+eYtStir/RrK1x9IlZHmJNcp2btlL1ezYhdX9TdN6ZmU55t1JYQQtqNVMLWJ8Jmkkoa59nUKUSSwiSi05b3l/m19Xod4+PjKBaLGBsbw8TEBLq6upDNZjE8PIyOjg4sXrw4jL7WajVs3ry5Yd1cm1arU6F0RFPmiur0S+0BrVQqoRjWKVatNBquqG1atCjXaWSu/aLGAfgjtXoficaLuKURbX/iPn96mY9FixZhYGDAe9/I5/OYnJzE5OQkNm3ahCeffBJPPvkktmzZgv7+fhQKBTz66KMoFovYZ599UCqVwikI9957L8bGxlAqlaaJWu1MKRQKYWE3HXEmpN1JI9g0zaQsu7bZ77Q4d+V7Jt8/vcY70LiMnNhTl3iVv211ffu+L23XFd2c6zYoyiZbwS/b07Zv/3/tcF0IIUSTOmLr8wJXKhXn9qgUJZlbY7HL7GjEOAq5XC58OJX3giAIi9hs3LgR5XIZExMT6O7uxh577IFdd90VpVIJY2NjqNfr4ZqbmUxjxWVpX9KhRfRJAQx5LX3qlGLr/XSlU+n99fWJ8jDHkcYI6YcCnR6ti13p/ex4bDt2/HYsdj9xDNRqtYb5zqT90EXg7HaJugDb5qFLdFUXdpL1piVNXURpuVzG0NAQ7rzzToyMjGBgYAC77LILjj76aDz3uc/FkiVL8OSTT6JSqeDBBx8MHUnSt3y2i8UiSqUShoeHAWyr2FwqlZiuTOYV2uZE3ZOTYsWSTUN2RU0FcV5J5FavKiAZFmJzKpVKw4oDvur8Lkcp0JgB5jpfuRa+6TNxzrm019H3TBQV4ZZ+fFOLfONKey6+4xm1JYS0M9F5sCmQ8v0NjZvKhfYm6au6K4ZSbsqStiiGULbJvmKo9Pwc6fupp57C+Pg4yuVyWHBm48aNGBoaArA1VbJer6OzszOcoyfpxVq4SjqU3PSr1SoqlUq4Pq01lHpOjzX0cQbHl1qU9BjZL83DuoxPX1fdZlzKtB1L1P/etitpaaR90fPSNR0dHQ3L6xQKBXR1daFWq4VTBcSRJeJYMi5kbrs4kf785z/jqaeewvDwMDZs2IBarYa//OUvWL9+PQCETqylS5eip6cHXV1d6O7uDscxNTUVfl81UliOkPlEVOaNz0EdZWdslNP143PKyne4Xq+H60jbJf60LZD7gk5J1n3oFQj0tCX9o88/TQaST4y6bGwzJD02rSM77hkhrn0tpF2RW0IIaQdmrCZ8BlK/rwsuhR07hIy0owvMyPESEZXfsq8UkBJhKymusgyIzPErlUoIggBbtmwJ55BKxLBUKqGrqwuFQiG8mesqhDqiaKOyEl2S+YIyLqn46JtbrM837j3ttfYZam2AbCq1zyjZectSDdmKUOshl7+tVz0Ol0dZBI1E10j7EfV/04Kxo6MjXMNSL7cln7vR0dHwuyZOqEKhgP7+fvT29mLTpk3IZrNYunQppqam8MADD6Ber2NiYgLZbBaLFi3CkiVLsHz5cvT29oZVt3WUWKoxEzLfiXOEJo3GuVJ3fW3qfq2d0jZVBKmuQSG1LsQeSDaWtUHSto5IWyHmOj+dnh2VSZbWgZyENJFT3U8zWVo22tsMrRLxhBCyvWm6eFTaCF6SG6wr9SWXyzWkMepiUnocYixlPc1qtYonn3wSmUxjBWWp0CjLg0xOTmJiYgLlchmbNm3C4OBguASJ9iZLarNOfZbIrjyAS+qk9iJL4Sp5oLZGIkmamL7WrrlB9r2ogk8WLW7jikT5ilFFYc/JVkeWfUTckPYi7n+mPx+lUgkjIyMAEH6/JNVY2spktlZFLxQK6O7uRmdnJ8bHx/GHP/whnJs7NjaGarUaHvf0008jm81iYGAAmzdvxsDAAO655x488sgj4XdWR40JWSi4IqjN3Ge1bZF7ty866xLCkoWl14eX77rYYnF6iVNY2tHRWd2u7OuLGkuWhhWz4iDXfVhnbqvTcK3oj0KPN010N2oKU9qU5LT9E0LIXGFGVZGtsXC9L9jUIV38yWdA7DI/vtL+Ml9PV1GUPsSALl26FKOjoxgYGMDo6CiWLVuGUqmE8fFxAFuF8ZIlS5DJZDAyMuJMTZTlgST6LIZYClpJISrtTXYZTrleMkY7v8VnkOz5J3UuxBkoX7t2u/7fxRl9vW/UPvJjq2SS9kAeWJOg5+Ba5waw9UG0XC6H0wIymQzGxsbCB1RxgBxwwAF4/PHHcc899+DRRx/FwQcfjCVLluDpp58OhfI+++yDXC6Hxx57DGNjY7Ny7oTMVeIibjMVuHFtWDGp95XvsmRRlcvlcCUCEbtCLpebJpi1sLZZUZLdZecXJ42EJhVzruvbSiHczP/HN/ZmxSkdzYSQdqSp4lFpsZVP08z9tBVztWAFMC19VgpU2OUCJicnkc1m0dfXh2q1isnJyYbo7Pj4OIIgaKjaaNFpUUEQhOJXjLMYZZ9QFZIaV3ucFYqu6zjTdF47N9YWhbKebb1PlAHVIl63J9h1bUl7kOa7rNOA9Xx5+cxJERl5OK1WqxgeHg7TEguFAkZGRrBlyxbkcjmsXLkyLCy1fv16jI2NIZ/PhwJXCtXQWUIWElE2Ok3k0IUvAhyV2gs0PgOIndWFoySbylWE0N5jrLj2nUuUg9h3jL4+acVlq6O8UdlZPnz7pdke9fxCsUsImeu0bB1bIN5oNiuKdfqTFlFavIpR1FURZe7O4OAgBgYGkMvlsHz5cnR3d4cPvRMTE+GNvFQqNay7CaBhbrB4gvX8WRFskuKs9wUQjs0VqZbzsmmSrRB2VgTb9OEoI+xyJPjatlHcNEbV7iNOCgrbhYE4raampsK57tlsNqxWriM85XIZhUIB9913H+699150dXXhmc98JnbddVcMDg7iqaeewubNm8Pv15IlS/DEE0/s4DMkZPuSVog1g7UdOooalcaqhW25XEalUgkdVpJ5VS6XQ0eU2FedmmzPUwtb+VtnHEVFlH1EXcO4a+azf65xJyXKSZ6UZo6jiCWEtCOphO1s3+iSpreKIQS23rBFjOookPUOSxXGgYGBUGxKlLBcLk+rwOhCG0opeqHHIWvyaeOeyWwrfiXjy2Qy04Sw0My8GDvGuKjtTIVjEjHrOkb/lr+1t1/mL3PplfmPfIYWLVoULvlUq9UwPDwcfp6KxWLD50YErjwU33fffcjlchgZGQmrLY+MjCCbzTZUOZdMCpmTR8cJIc3jyzjSr60DWtsJEaBiM2UfPVVJtov9lO+/a16sjey6UpftOPV4ZhrFboY09j1tJLgVzmHtpOD9khDSTiQWtlYsudKPfMRVB05CEAQolUrhmrLVarVhLo7uSy8fo9fJrNVqGBoaapiPm8/nwwfoqakp7LTTTujq6grFb7lcDgtMAY3XQfr3CTFZpkiqs+qxSAVnfV2sF9yK1DQFoVy40ok19n8cF3nX4tZXFMoVOXZFgSViq+cpk/nL4sWLUSwWUalUMDQ05CzuVKlU0N3djUKhgHq9Hi7RBWxNa16/fn34/crn8+HyPjJX98ADD8Ty5cvR0dGB4eFhDA4OYtOmTdi0aROdJ4Q0gbVV1mZpUam3a5Fk95GpQ9KetolBEITV1GV6gkRwfVNgXGPQTlRfMcZm0o99WCEv22y/SbOZZpOoMaR5ziOEkLlAU6nIVgDFRQjTLHmj04j0Mj+yHqVEXl0PpjI/T27ULuErnmF5T9bTlL4kPaqvry+M/kp7UqRKGyVdGVkLMi2eJd1YRLkWtNZwaAPTzHzZqIJNOpXbhQhpOx9JiPofJhGiPkGrH5L0/4+0N/L90VXGC4UCdt5553Ct2snJSeccWD3/TiqSuwiCAJOTk2EUd6eddkKpVAqdWJ2dnVi5ciVKpVJDNGhoaMh5fyCENOJKpbVRVI0vZdjXrtglPX1HpgCJ2JSUZamZIRFd1xxb6wz2RW1952b/jroeSVOaXeNMS9r05VbbUJdAJ4SQuUbL5timvdm5IsBAo5ASAVgqlcICT1EPo9obq9fC1f3pwlPyvo4WydIg1WoVpVIprNJaLBbDiOTExAQmJydDIZ3P5zE1NYWxsbFpolAqKYunWb8n5+jCbo+LMPmKPs2ENG3YhwI7flfql92mhS1pf+znXRxG+XweAwMDmJiY8H7GpGDU1NQUJicnnVXKbV+VSgUjIyPhkkJdXV0YHBzE6Ogo+vr6MDg4iP7+fpRKJWzatAmTk5MYGBjgUkBkwTDTiKSubSGvk0QcXVFU135aKOvjqtXqtPm44vRyFYPSwlsL4LgI7Ww5VGcqNGfb0esa30ynRRFCyI6g5XNsm/Hq+faVCGKxWES5XJ72cKuPKxaLDdFYMZCuVF6JvEq0VUeU5EF6dHQUO++8M0qlErq7u9Hd3Y2JiQls2rQp7Fv6k8iUCGK5DvJgL6nINiKq1/J0XcNWp0vaNGCXAHb1mfRhKEkVZde+tg0u+9O++B5iJaVwyZIlGBoa8i7Bk81m0dPTE6Ypx4naWq0WzqcNgq3z7YvFYhgJHh4exuOPP45nPetZWLJkCXbZZRcsXrwYDz/8MO655x6Mj4+HBeEIaWeS3KPT7KO/x66Ip12OT7bbaGgSUSuCWa89C2yrayFTd8TJXSgUAGydajQxMdHgyBbntbSnl96T9lyRZDnPuJTkVoi8NM9HzfZHQUoIWYi0tCpys0RFGDs6OsIHXIsYHxG1ErXRuCr6isGTSKq0JenEwNYH5qeffhq9vb0IggD5fB49PT3I5XLhfL2RkZGGpQrE2IrxlL6LxWIoovP5fJhWmWQOq+88fCQxZmlTnK0RFsOfdEyuebY2Uqsfhma6ZBHZceRyOa8QzefzGB0dxfDwsDctvbe3N8zOiFt/VgrJSXRXIsCSHTE6Ohru++c//xkrV65EJrO1+vnuu++O3t5eLF++HA8++CAeeeQRilsy75gtYaOnA7lEot7mEo+auIiptCHiVupiFIvFcMrP5ORkmKIMoGFs2pksdksv3edyyEaNlWKREELmLrMqbJN6Je0DpRxXKBRCYWuNphgdXWU4idCSglEyn1YqGYug1aJKCttIOrFUXhUBPT4+HhpFicbq5YFkDm4mkwnPxbeGnvYWu9J2k4rcpIUo9HymmURpLXFp0FHv6wcNPkC0Jz5RK0WghoeHp32fgW1zajOZTDhnNo5cLofu7m5MTU1hdHQ0dGq5pisMDQ3h4YcfxujoKLLZLPbaay8sXboUY2NjGBsbw+OPP05hSwj8937tdNTRWtd8VWkn6T3cFd112Xydeiw2ubOzc9oxUbUcZF9dsMra3Cj7FzVvWLcfd3y7QFtMCGkndnjENipdVbyx5XLZOT9TlusRIxsnbAuFAqamplCpVBpSlPRxtg15wN68eTMmJyexdOnS0Gu8aNGiMOWxWCw2pDSKOJN5uAAaUqJcfbk8xa6HhTjxGFU8yiWeXUQZ31anYumorX0AIfMDyVoYHBz0Fn4rlUqRFc81ixYtQr1ex8jICIrFYuwc3CAIMDQ0hKmpKdx111144IEH0NPTEzrOuO4tIdvw2QhdC8NlB1xpvjY7x+6bVADLvmLD5bU8J+gpP/ocbBtRjtM0U6mSCNwo2nEOazNTzQghZHuyXYStLtjkQgyQFbWZzNZCTTq6KO/pObE6AuS74eo5N5VKJVXBGL12ZldXV0MlV1laSC97oz3bYnSr1WoY+bXXxod9CNCG2pWumyaS2+w83jgxnNZAu45hxHZ+ISmDepkd+fxKRXJxCklKoQ/JgpAfXSwqCSMjI7j33nvR0dGBpUuXhvPj07RByELDzj/1OaSTzM/V+wHbsniS3u+lfsXk5GRDJFfm8etpQC7kWSNNn1H4UrF9bc9Vu2bHPFPhTgghO4Kmha3LWKW9AWrjY4VvEGxdxkOLWllqR7y1Mg4RlXoMViQDCCsZJ5mDaqszA8D4+Dg2bdqEWq2GfD4fRoxlfm8mkwmXI5JxSYp0uVwOo8pxBiMqhWmmXt6082PjxpGkvzTtuuY9kfamXq9jYGAgdAblcjl0dnaiVqs1zINN8j+XJT8GBwfD1P5mx7Rx40bk8/lweSCdxUEIme5Q1VWGtQPSClnXNle7du3zNIhTG0BYhFE7lZNkRkWJ0bRRSXut5oJztlVjmAvnQgghSWhZlZ6otB5v5yrq6DJ8WuyKV1an89q0I2tMXf371sDVuN6XG7ukSWqBqdfalLm2er1dPW4ZmzwUpH0wnwspQM0auKix64i9fjgh7YdeDgRAQwVyYOt3cGJiIrbQmwsdabFLaDVDrVZDsVhET0/PnPhuETJXsXNRtc11/Wji5qPafZKmJ2v7KvvL9B8tnF2RZJfzekfcA2ZbMPK+RghZSGzXObZRBRVc2GIPSY9P209UOxaZc1sul0OBGwRblxkpl8vTFpf3taPR6Zlpr09SozgXPa46hds+WNAYty+u/52kCMr3JW4Ora9d+3A9UyQzRJxThMwXZvt+32yqratuRDPIfcAumacjtnaKUJzje6bQbhFCyI5lxsI2aWTNik2JbOoqiza9WGPXpE2KFIyqVqtNGR07j0iqKJfL5Yb5rrlcLvQcy/ICel0+nQKto8bNpNy6Cm64ljVyVYaU39qDLdfWJRh8Y3N5u5s5B/3a/jAduT2xUdQ089mjWLRoEWq1GsbHx1vSnqDToQlpR5LatjROX72vdizPxLGkj0/Shk5t1m1obNaW3c8ncH3TmVqNddD73p8tR0Ta6UBp/i+EEDLXSCxsZ+MmljRS6RJRtgiNCxGiOn252fPQxadEcElkViI9hUKhIU0Z2FpcqlKpNAj4JLiimHIOGl+FWWD6EkH2OmtRqx9a9PsucRlVdXmmuAqSkPZmpp+LTGbrsl6VSoVRVUK2IzY92BVtTXufTns/8O3vy+7RjjW7hq0VytbeRb1OM16fQIwS6S6B20x0m6KTELKQSSxsZ3qTt1FLINqoiAfVJWDTRPFEaM4kWisRWftblhqanJwMxysFrqTAlIhJ+ZHCUnLOSfA9ULjQ4lUf43MA6HTgqPPX13w2I6k2akvaH5kP26zAlQdSvewXISQan7iaifDxtdfM8UmO89lKax90XQa99J/dbq/JjoiSxl37qKhp3P9Ui/hmI+tzcdoSIYQkZU7MsbWiCdgWbY3a13es9NUqoy5iUYSsnkcrgl0bg3w+HxbMEaErD/cyBjlHl8G2f0fNLxaRKWndSR0Aur0ooaofIGZL3OrUMP0gQmE7f2hFkSdCSDJaLUysLWoGm9rcTP++dvU+rnRp62DW83Bn41q5IsLN4LL7vsiuPY9WnReFLiGknWi5sI27kVvj5trftT2JiPKJ3Jkga+bKb1kIXoRtoVCYtuRAEATI5/MoFAoAtgpdLQ7lId0laqOEtzXCegkkSSuOu042Qhv3/3KJ2ShD16zYtQ8Denw0qoQQ4me2009nEgG07aR5P0rY2b99+wg2m0nqX+j1bKPSgOPGFXdMGlzRVzu+qGNajW88hBAy12ha2Lbi5pbGKKXFpjA3G6kVMStGUaKzsualrFErESlZ6kd7hQUpLOUSbbLdFXG1jgCZ+ypLqGhB64vYNiv0dd+6PbkGst31QGLnQyc1ukxFJoSQdKRNCU7jhE7bfiuxYjPqucHaHNmmi09q2xR1TnFTgOz7rkhxq0hjO9PsH9XH9kjZJoSQVtOUsHUZl9kUqa2g2Tm2IjbFKEoxCrnRl8vlcD1Nu8yPpC7r66UNRC6XQ7VabRC7utiFTffVglhEbZJ1ee16wXqus4xPi1R7DXQ7djxyfWzRLH2+vkivHOtqkxFbQgiZPVz3VZeQmys2XIjK9AIQOlrF9mQyjbU6ouyl2JuoiGycGI6K/kZFhGeS4m3b0+fh6qvZfpNk3BFCyI4ksbCVdFo9V8XOhYy7ycUVNkqSYpQGK+ii2rQRTTk/SSeWfeRHRymloBSwrSKxpCvrfiWlGdgqiHWKsp4rK/gKbkkUWUSx9GlxVUfW6PbF2MdVPJZ0a3stZbkjPRdSG3gdtdVVqm3fIuzttSOEELKNVtvLqPZng5k4m11ECUl7nN3uei5J46xPIlyj2ox7TxNVl0Ne60h1nHBNKnqtYKdtJoTMRVIJWytqkwiPKNHqE7r6OBdxS/34bvRxY7TH6khtoVBAqVQK+xYRl8lk0NnZ2bBIvERURTSKmJ2amgqrIpdKJeTzeeTz+bBirI346tQpaVcLa4naxkU3o9LJRFz6UrOsYZR5Sfb6dXR0oFqthudil2jS23K5nFPcioc9CIJw6aSZFh0ihJD5SJKaB0nYkQJFxp4kLdqi16TVP1rY+eyhFqLWvoo9SnpdZvv6pRGS+nztEoPNXGNXFNgeQ4FLCJlLJBa2UjxJRx9dUVcXPlErr9NG5nwRSjsOn/c1zitrxbaueqxThTOZrZWSC4VCOM9V5tHKMbo6cq1WC4VpEATT5shGGTDtTEh6rV3XRPoTrGG3Hlo7L0kixnoffZ7j4+PTiljZyK2IeB3F1Q8TEtWt1Wrh/hS4hBDSyGxmtTQTQbRO1CQRyiT9JHHaSp/aSezLQPKJPt9vfT7WTrcyNdd1LdNeU9lHBw5aEWXV5+56ZqDAJYTMBRIL20Kh0GBEbcQ2idiyv6241X+7ooyum6rsF7Vcjetv13g0URHpqampMApr59/qSsWyTVKHdb+ZTCYUwlYI+saZBtf/w16jqAcFlwHVKcIiNvU+HR0dKJVKmJycdP7/dKqxK6orTgFga8pzoVAIr08mk2mIihNCyEImjTCcSfvN7hcXjbWiK67PuPG4nL42Y0zPu7Vt6kivq7+oY1pBEmEcd61tG9qeRh0fh7XnFLiEkLlKYmErosR307LzQwXf/nbNVbuvvXn6BKpNQ4oqDBHVnxXWrvQkea0LLgHA5ORkQzRWvy9CsFqthvvpQlOVSiUsvJTEINj/gcuwyhhd18513VzvSTs6yqrTvIBtacqZTCZMyw6CYNqaoz4hq3/bCs4SCZeorVxbEboUuIQQ0ojvXt/McWnbTCtobNpw2vFpmy3H20r91j6KPRebrp2ldupPsynSemxWtMdFt217vmNcKcH2tRXxzZyHxka57fGtjA4TQkizpBK2WpzoVOQkczlE6GmxZIWObi9q+RotsqzgjEo9kgij7isqOiriM5PJNBSIkj7ECyriS1dAluWA5D3ZX88blTVtZV6q9bS6nAWulFwb8ba4DI1NOXY5C6ywtW3bKLMIdv1/sGnItg/blkSxxSjn8/mG66rFLQUuIWShEnfvSyoWk95DXXbTZUfSCJs4gSbtWGerS9Sm7UPbk5mIMJ9z2JXx5BuH4Ht2so7gpOOyc4VnYi91inOUkzoq64sQQmabxMJWInI6Wqdfu+a26vfk76jopOynl7DR2wC/IIojKlrrQvcpEUmpQqyj177oqJ5vW6lUQiNTrVbD61Cv18MfaxSivKMa6wDwGfq4glu+/YU4Qy391ut1VCqVaftoYesy0joqbB0AEsGVhxAtbuXaUeASQhYSUdk2aUgTrXVF5lz9z+SeHCcIbT+u8dksK/2+FXzagevqxxVZtX2ndRLEnV/S49I4D9IKzSinQ5QD3TohfPsTQshs0LSwjZpjq7frCJ5e6zTqRmeNYpThSYJvPVhXv76bsRhLWZs26nh9DtZ4ikjWaVC6P9/1jDN+rjE1e72sF946GVzpUhKltkv+SBsuYav/1hFbVzEtHfGXhxL9OypaTwgh8w0rIpOKhyT2xPZh/47aNlPSjM0lNF2/bdTTbgOmF1UE3Ncq6XXYHs7WJH34MunSRn99xyTJBKPAJYRsL1ombGUbsC3tWCJrAKYJj7iF0jVp9k1yrCtVJs1NPspbadvX47DjkeitS9ja44Dp81BnKuR8xjsuIu7qW9KGJyYmnNHeJBFbu79rPxspt8KWApcQshDwCTnAPzczTQqvr7+4bWmIigjr175+XOmvrrFFOWT1+74IcKtIer1mEgHWbVhbOlN819Hiit66XhNCSKuZNWFr54LGefpmA5/4a4XRiMPlxdTbbIVfMaDb84bve1BpVhhKISxXmzYy63oAsR51n0HW0VstbGWulGzbHh5zQgjZUfjSTH0CLS4Cm6bPVpBELCcZc5IxpRGtURHb2Sbq/NMIxKRrHKc9P9fnyPfsQoFLCNnezIqwBZLNYU2Kax1W31xR/V7cEkC+G3pcmnQcvhu8S9RFHeuL1raSJGlWUX1rwxWVom1Fq+0rTtwmEbi+6C0FLiFkvhEXsbX7RbXhI85BG/VeVGQvrq1m5oJGiSbfedo03WbEr8UXLU8z9qQCvlnR2mqbaB0BvrRnX0SeEEJaRWJhqysH6jTQqNRZ1w2rmZtYkjVZXe8lNaQuku6fNBJthV9c+zba3Aphm+TBxP4d13fUw5XvvTS/kwhT7WRx/ejCVIQQMh9wRc1c70VtS9NHknbSvjcT4RU3ZzROXCaNXqe9bmnFZzN9NDOO7dGOS+Dqz6a2w4zeEkJmg6Yjtlo4yGtg+hIwrr+TMJNoZSu9wHHt+va3N+005+BLoZ6NqG0SIxwlVqP2ce0fJ4TjPNY+JHor1y7qhxBC5gNx90wdPZxvwqEZwZXkvTQRxWaubZpni+3ljJ2tCC7gr2lCgUsImQ1SR2ytsJW/9W/fNtdr3zZfwac0pE15ifLgRs1/TeJ1jmtb/92KasYzea+Vr5MK2STjTEpcBNdGzwkhpN1IGwlkxkoyfDYpbg5zK/tsNXPhf28FblR6st6fEELSMCNhK7/jBG0ScRu1Pen7M91fk8SrmjSNKsnDxWzcxOOMWTPjT7JP2r9ng0wm4xW4Ykg5/5YQ0s64nIMUBLPDXLIVc2ksaUmSnizb7TZCCIkjsbC1BrQdhG2zx0TR7HyZdhK3aYVsktc7gqiILeffEkLamaSOSQuFwlasuLLsCHu8UEianmz3IYSQOBILW4srtTSNmG03cTvT+Txxwm82b9zNpCg3K3i3B81E7iV6K3NwRdjqObkUuISQ+c72vMfNdXHY7PQd0hqiBC6jt4SQZmha2ALRYi3qBpR0nm2a99Pu1yrSGsaZGMukqdBRNBtxnks0Oz5X9FaLXBG3nH9LCCEzZ67bEjI3cBU5Y3oyIaQZUqUip6EZT+hcSDWeKXMlqkn82Pm3Wtxy/i0hhBCyffEJWlcqMgUuIcTHjCK2rWa2IpCEWHyRWytyKXAJIYSQ7UNUxNZCgUsIscwpYdtKKEbmD7NptHT0Vkdr7W/OvyWEEEK2D1GFvSwsMEUIEVoubPnwT1rN9vhM2aWsosQtP+OEEELI7KPn30bB6C0hBJjFObaEtCPZbDZR5JYFpgghOxraZbIQcBWQIoQQF/M2FZnMb2bbuOmlgOxvAA1/E0IIIWT2SRrBJYQsTNpK2PJmto12uxbtNl4fNKqEkLkC70dkIRI1/5bzbQlZ2Mx6KvJcMbpzZRyWuTouS7uMU9OOYyaEkKTwHkcWMnTsEEIsiYWtpF3OpZvIXBqLZq6OS5jr47PMlfHOlXEQQgjvR4RQ3BJCGkksbOv1+qwNYq7elObquIS5Pj6hXcYJtNdYCSELG96vCGElZELINlJHbNMwV43uXB2XMNfHJ7TDONthjIQQkhbe2wghhJBGWiJs57KBnctjEzhGQgghhBBCCGmetp5j66MdxkgIIYQQQgghpDXMaioyIYQQQgghhBAy22R39AAIIYQQQgghhJCZQGFLCCGEEEIIIaStobAlhBBCCCGEENLWUNgSQgghhBBCCGlrKGwJIYQQQgghhLQ1FLaEEEIIIYQQQtoaCltCCCGEEEIIIW0NhS0hhBBCCCGEkLaGwpYQQgghhBBCSFtDYUsIIYQQQgghpK2hsCWEEEIIIYQQ0tZQ2BJCCCGEEEIIaWsobAkhhBBCCCGEtDUUtoQQQgghhBBC2hoKW0IIIYQQQgghbQ2FLSGEEEIIIYSQtobClhBCCCGEEEJIW0NhSwghhBBCCCGkraGwJYQQQgghhBDS1lDYEkIIIYQQQghpayhsCSGEEEIIIYS0NRS2hBBCCCGEEELaGgpbQgghhBBCCCFtDYUtIYQQQgghhJC2hsKWEEIIIYQQQkhbk9vRAyCEEEIIIaTVZDKZHT2EWFatWoXnPve5O3oYhMxpnvnMZybaLxMEQTDLYyGEEEIIIYQQQmYNpiITQgghhBBCCGlrKGwJIYQQQgghhLQ1FLaEEEIIIYQQQtoaCltCCCGEEEIIIW0NhS0hhBBCCCGEkLaGwpYQQgghhBBCSFtDYUsIIYQQQgghpK2hsCWEEEIIIYQQ0tZQ2BJCCCGEEEIIaWv+H+oPi0VEbAmVAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1/10] - Loss: 1.2199254035949707\n",
+      "[2/10] - Loss: 1.255885362625122\n",
+      "[3/10] - Loss: 1.252474069595337\n",
+      "[4/10] - Loss: 1.1571918725967407\n",
+      "[5/10] - Loss: 1.1066519021987915\n",
+      "[6/10] - Loss: 1.2302470207214355\n",
+      "[7/10] - Loss: 1.0621248483657837\n",
+      "[8/10] - Loss: 1.0320061445236206\n",
+      "[9/10] - Loss: 1.0852118730545044\n",
+      "[10/10] - Loss: 1.02060866355896\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1/10] - Loss: 1.0982850790023804\n",
+      "[2/10] - Loss: 0.9964359998703003\n",
+      "[3/10] - Loss: 1.0771063566207886\n",
+      "[4/10] - Loss: 0.9588796496391296\n",
+      "[5/10] - Loss: 0.8119112849235535\n",
+      "[6/10] - Loss: 1.0366179943084717\n",
+      "[7/10] - Loss: 1.005511999130249\n",
+      "[8/10] - Loss: 0.9402894377708435\n",
+      "[9/10] - Loss: 0.7103167772293091\n",
+      "[10/10] - Loss: 0.8441991209983826\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "src.training.trainModel(5,10, save=False, load=False, path=\"../models/TACEnet_vessel_enhancement_2_0.pth\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Deformation model\n",
+    "\n",
+    "Below follows a demo of training the deformation model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0] []\n",
+      "torch.Size([1, 1, 512, 512, 96]) torch.Size([1, 1, 512, 512, 96])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get transforms\n",
+    "transform = get_transforms(resize_shape= [512, 512, 96], contrast_value=1000)\n",
+    "\n",
+    "# Get datasets\n",
+    "train_ds, val_ds = get_datasets(\n",
+    "    root_dir=\"../data081\",\n",
+    "    collection=\"HCC-TACE-Seg\",\n",
+    "    transform=transform,\n",
+    "    download=False,\n",
+    "    val_frac=0.0,\n",
+    "    download_len=1,\n",
+    "    seed=42\n",
+    ")\n",
+    "\n",
+    "# Get dataloaders\n",
+    "train_loader, val_loader = get_dataloaders(train_ds, val_ds, batch_size=1)\n",
+    "\n",
+    "# Check length of datasets to make sure data split is correct\n",
+    "print(train_ds.get_indices(), val_ds.get_indices())\n",
+    "\n",
+    "# Sample a batch of data from the dataloader\n",
+    "batch = next(iter(train_loader))\n",
+    "\n",
+    "# Separate the image and segmentation from the batch\n",
+    "volumes, targets = batch[\"image\"], batch[\"seg\"]\n",
+    "\n",
+    "print(volumes.shape, targets.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define model\n",
+    "model = TACEnet()\n",
+    "\n",
+    "# Move model to device\n",
+    "model.to(device)\n",
+    "\n",
+    "# Initialize the criterion, DICE loss with cross entropy uses binary cross entropy when there are only two classes\n",
+    "criterion = torch.nn.MSELoss()\n",
+    "\n",
+    "# Initialize the optimizer, use config to set hyperparameters\n",
+    "optimizer = torch.optim.Adam(\n",
+    "    model.parameters(),\n",
+    "    lr=1e-3,\n",
+    "    weight_decay=1e-5,\n",
+    ")\n",
+    "\n",
+    "volumes = add_vessel_contrast(volumes, targets, contrast_value=4000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Sven\\anaconda3\\envs\\torch\\Lib\\site-packages\\torchio\\transforms\\augmentation\\spatial\\random_elastic_deformation.py:303: RuntimeWarning: The maximum displacement is larger than the coarse grid spacing for dimensions: [2], so folding may occur. Choose fewer control points or a smaller maximum displacement\n",
+      "  self.parse_free_form_transform(\n",
+      "c:\\Users\\Sven\\anaconda3\\envs\\torch\\Lib\\site-packages\\torch\\_tensor.py:1418: UserWarning: Using a target size (torch.Size([1, 256, 256])) that is different to the input size (torch.Size([1, 1, 256, 256])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
+      "  ret = func(*args, **kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch [1/5], Step [1/1], Loss: 0.9728062152862549\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch [2/5], Step [1/1], Loss: 1.2726126909255981\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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X23/961/bAOyvfvWrjufnti7bdv97+MADD9gA7C984QstxzP9Xtxyyy22x+Np+q274YYbbCdpqF8vF198sR0MBu2nn35abTt48KAdj8ftc845R23r5jqZD8QVuceJxWIt2ZG3b9+OkZERbN68Gddccw2OO+44fOc730EkEmnZ/+6778bIyAhGRkawY8cO3Hbbbbj66qvxwQ9+cLFOQRAWjF27dmH16tV4yUteAmDGVeYP//APcfvttzu6A7vhjDPOwD333NPyd8UVV7SU/eM//uOm92effTaeeeaZlnJ/+Id/2OQpQTOqvCy3BpdKJUxMTODMM88EAPzqV79ydezJyUlkMhkAM27DjUYD73rXu+D1Nv/ck/X3nnvuQSqVwhVXXIGJiQn15/P5cMYZZygLztjYGB588EFceeWVasYZmEnwcdJJJ7W0TVj+2LaNO+64A6985Sth23bT9bNz506k0+mW6/bqq69GMBhU7033ATDz7OPxdcFgEC984Qubyvl8PlVXo9HA1NQUarUaXvCCFxjvl0734Pj4OH70ox/hmmuuwTHHHNO0L90v3Zzzt7/9baxduxavfvWrVT2RSATXXXdd2351A/+tSKfTmJiYwLnnnotnnnmmxd1xy5YtLTGJ3/3ud7F+/XpcdNFFaptlWS3eYQ8++CB2796N1772tZicnFTnms/ncd555+FHP/oRGo3GnM9HmB+6vSd0pqamcO+99+Kyyy5DNptV3/fk5CR27tyJ3bt348CBA037XHfddU3eRGeffTbq9Tr27t07p3P553/+Z4yMjGB0dBQveMEL8P3vfx9/+Zd/iT//8z9XZb761a/ixBNPxAknnNB0L770pS8FgBYPhN/5nd/B6aefrt4fc8wx+IM/+AN873vfaxkz6M/XO++8E41GA5dddlnTsdasWYNt27apY9Hz8Xvf+54xNKibugg3v4ft4L8X+XweExMTeNGLXgTbtvHrX//aVR2cer2Ou+++GxdffDGOPfZYtX3t2rV47Wtfix//+MdqHEIs1HWiI67IPU4ul8Po6GjTtjvuuAMDAwMYHx/Hxz/+cTz77LOOiSDOOOMMvOc970G9XsdvfvMbvOc978H09HTTwEIQ+pF6vY7bb78dL3nJS/Dss8+q7WeccQY+9KEP4fvf/75yte2W4eFh/N7v/V7HcpZltbjarVq1CtPT0y1l9YEyDbB52ampKdx88824/fbbceTIkaby+mC1U50DAwN4+umn4fV62wrP3bt3A4AaCOgMDAwAgHr4bNu2raXM9u3bXQ2ahOXF+Pg4UqkUPvvZz+Kzn/2ssYx+Hbu5DwBgw4YNLa73q1atwkMPPdS07V//9V/xoQ99CI8//jiq1aravmXLlpa2dDo2DRLbraHbzTnv3bsXxx13XMt5bN++3bF+t/zkJz/BjTfeiJ/+9Kctg+d0Ot00+WTqi71792Lr1q0tbdMzytPvw5VXXunYlnQ6LeFNPUQ394TOU089Bdu28c53vhPvfOc7jWWOHDmC9evXq/du7+lu+YM/+AO86U1vQqVSwQMPPID3ve99KBQKTZO0u3fvxmOPPebo8q7//pieX8cffzwKhQLGx8exZs0atV3vr927d8O2bWMdAFQ4w5YtW/Dnf/7n+PCHP4xdu3bh7LPPxkUXXYTXv/716r50Wxfh9vfQiX379uFd73oX7rrrrpbvxTS26MT4+DgKhYLxt+zEE09Eo9HAc889h5NPPlltX6jrREeEbQ+zf/9+pNPplgfNOeeco+JXXvnKV2LHjh143eteh//5n/9pscrwAfrOnTtxwgkn4BWveAU+9rGPNc16CUK/ce+992JsbAy33347br/99pbPd+3aNWth6xafzzfnsvb/H7sDzMTM3n///XjHO96BU089FbFYDI1GA+eff77RKuKmzk5QvbfddlvTQ53oh/T+wtJA187rX/96R+GjL1fn9pp1U+6LX/wirrrqKlx88cV4xzvegdHRUfh8Ptxyyy3GBInzeb90c87zzdNPP43zzjsPJ5xwAj784Q9j48aNCAaD+Pa3v42PfOQjLb8Vc8mATHV98IMfdFxCJBaLzbp+YX7p9p7Qoe/77W9/u2PmYX1MOh/3lYkNGzao8evv//7vY3h4GG9605vwkpe8RMUFNxoN7NixAx/+8IeNdWzcuHHWx9fvm0ajAY/Hg+985zvGc+b3wYc+9CFcddVV+MY3voG7774bb3nLW3DLLbfgZz/7GTZs2NBVXcDc+rher+NlL3sZpqam8Fd/9Vc44YQTEI1GceDAAVx11VWL5nGxUNeJjoxYehgK3m6X1jwWi+HGG2/E1Vdfja985Su4/PLL29Z54YUX4txzz8X73vc+XH/99Ss+fbnQv+zatQujo6Mqix/nzjvvxH/8x3/g05/+dN8sazE9PY3vf//7uPnmm/Gud71LbSeLyWzYunUrGo0GHn30UcdBKSXiGB0dbWul3rRpk2N7nnjiiVm3UehfRkZGEI/HUa/XXXk4zDdf+9rXcOyxx+LOO+9ssmbMdrkvcqn7zW9+41imm3PetGkTfvOb38C27ab2ub1fdAsN8c1vfhPlchl33XVXkxXElPipXdseffTRlrbpGarp92FgYKDj+Tq1V1g83N4TTt8V3QOBQGBe7+n5uDauv/56fOQjH8Hf/u3f4pJLLoHH48HWrVvxv//7vzjvvPNcHcP0/HryyScRiUQ6JjrbunUrbNvGli1bcPzxx3c81o4dO7Bjxw787d/+Le6//368+MUvxqc//Wm85z3v6bouNzid/8MPP4wnn3wS//qv/4o3vvGNavs999zjug6dkZERRCIR42/Z448/Dq/XO6dJhbkgMbY9yr333ou/+7u/w5YtW/C6172ubdnXve512LBhQ1Mm1Hb81V/9FSYnJ/FP//RP89FUQVh0isUi7rzzTrziFa/Aq1/96pa/N73pTchmsy3LE/QyNJupz15+9KMfnXWdF198MbxeL9797ne3zMrScXbu3ImBgQG8733va3JbIygd/9q1a3HqqafiX//1X5tcl+655x48+uijs26j0L/4fD686lWvwh133GEUgwuxlIN+fKD5nvn5z3+On/70p7Oqb2RkBOeccw4+//nPY9++fU2f0TG6Oeff//3fx8GDB/G1r31NbSsUCo4uzDo08ZxKpZq2m847nU7jC1/4gqt6gZn7/sCBA02/kaVSqWVccPrpp2Pr1q34h3/4B+RyuZZ6+Pk6tVdYPNzeE5STRf+uRkdH8bu/+7v4zGc+g7GxsZb6Z3tPR6PROV8Xfr8ff/EXf4HHHnsM3/jGNwDMeDkdOHDAOJ4tFovI5/NN23760582hc0899xz+MY3voGXv/zlHT2wLr30Uvh8Ptx8880tz2nbttWyfJlMBrVarenzHTt2wOv1qiWI3NbVDd38Xti2jY997GOu69Dx+Xx4+ctfjm984xtNS3odPnwYX/rSl3DWWWepMKbFRiy2PcB3vvMdPP7446jVajh8+DDuvfde3HPPPdi0aRPuuuuujosiBwIBvPWtb8U73vEOfPe738X555/ftvwFF1yAU045BR/+8Idxww03tPjyC0Kvc9dddyGbzTYlPuGceeaZGBkZwa5du/CHf/iHXdd/4MAB4/rRsVgMF198cdf1uWFgYADnnHMOPvCBD6BarWL9+vW4++67m+KHu+W4447D3/zN3+Dv/u7vcPbZZ+PSSy9FKBTCAw88gHXr1uGWW27BwMAAbr31VrzhDW/Ab/3Wb+Hyyy/HyMgI9u3bh29961t48YtfjE9+8pMAgFtuuQUXXnghzjrrLFxzzTWYmprCJz7xCZx88snGQa+w/Pn7v/973HfffTjjjDNw7bXX4qSTTsLU1BR+9atf4b/+678wNTW1YMd+xStegTvvvBOXXHIJLrzwQjz77LP49Kc/jZNOOmnW1+PHP/5xnHXWWfit3/otXHfdddiyZQv27NmDb33rW3jwwQcBuD/na6+9Fp/85Cfxxje+Ef/zP/+DtWvX4rbbbjMmejRBSW7+5m/+BpdffjkCgQBe+cpX4uUvfzmCwSBe+cpX4vrrr0cul8M//dM/YXR01ChGTFx//fX45Cc/iSuuuAJvfetbsXbtWuzatUuNN8hy4/V68bnPfQ4XXHABTj75ZFx99dVYv349Dhw4gPvuuw8DAwP45je/2ba94hk2v3z+85/Hd7/73Zbtb33rW13fE+FwGCeddBK+/OUv4/jjj8fg4CBOOeUUnHLKKfjHf/xHnHXWWdixYweuvfZaHHvssTh8+DB++tOfYv/+/fjf//3frtt8+umn49Zbb8V73vMeHHfccRgdHXXM69COq666Cu9617vw/ve/HxdffDHe8IY34Ctf+Qr++I//GPfddx9e/OIXo16v4/HHH8dXvvIVtX4zccopp2Dnzp1Ny/0AM8vidWLr1q14z3veg7/+67/Gnj17cPHFFyMej+PZZ5/Ff/zHf+C6667D29/+dtx7771405vehNe85jU4/vjjUavVcNttt6lJsW7q6oatW7cimUzi05/+NOLxOKLRKM444wyccMIJ2Lp1K97+9rfjwIEDGBgYwB133GGMbaV7+C1veQt27twJn8/n6An6nve8B/fccw/OOuss/Omf/in8fj8+85nPoFwut6w5vqjMa45loSsoBTb9BYNBe82aNfbLXvYy+2Mf+5idyWSaylMK8vHx8Za60um0nUgkmtKpb9q0yb7wwguNx/6Xf/kXx7TggtDrvPKVr7Qty2pZ4opz1VVX2YFAQC3JgXlY7ocvaXPllVfa0Wi0ZX+6TwlaaoQvUUBAS5+/f/9++5JLLrGTyaSdSCTs17zmNfbBgwdbyjn9FtBvir7cxuc//3n7tNNOs0OhkL1q1Sr73HPPVUskEffdd5+9c+dOO5FI2JZl2Vu3brWvuuqqpqURbNu277jjDvvEE0+0Q6GQfdJJJ9l33nmnfeWVV8pyPyuYw4cP2zfccIO9ceNGOxAI2GvWrLHPO+88+7Of/awqQ8v96Etg0P3Bn0XnnnuuffLJJ7ccR7/OGo2G/b73vc/etGmTHQqF7NNOO83+z//8z5Zy3dyDtm3bv/nNb9R9aFmWvX37dvud73xn1+ds2zPLiVx00UV2JBKxh4eH7be+9a32d7/7XVfL/di2bf/d3/2dvX79etvr9Tbd23fddZf9vOc9z7Ysy968ebP9/ve/3/785z/fcv+3Gwc888wz9oUXXqiWDPyLv/gL+4477rAB2D/72c+ayv7617+2L730UntoaMgOhUL2pk2b7Msuu8z+/ve/76q9wtzRx4z633PPPef6nrBt277//vvt008/3Q4Ggy33wdNPP22/8Y1vtNesWWMHAgF7/fr19ite8Qr7a1/7Wkt79OWH6F7n1/ehQ4fsCy+80I7H4zaAjkv/tHte33TTTU31VyoV+/3vf7998sknq2fc6aefbt988812Op1uqfOLX/yivW3bNtU/+n3Ybqxt2zPPwLPOOsuORqN2NBq1TzjhBPuGG26wn3jiCdu2Z+6ra665xt66dattWZY9ODhov+QlL7H/67/+q+u6bNv976FtzyxhdtJJJ9l+v7/pd/XRRx+1f+/3fs+OxWL28PCwfe2119r/+7//2/LbW6vV7De/+c32yMiI7fF4msYypt/KX/3qV/bOnTvtWCxmRyIR+yUveYl9//33N5Xp5jqZDzz/f2MFQRAEQRCEFcxHP/pRvO1tb8P+/fubMt8KQr/j8Xhwww03KA8kYXkiMbaCIAiCIAgrjGKx2PS+VCrhM5/5DLZt2yaiVhCEvkRibAVBEARBEFYYl156KY455hiceuqpSKfT+OIXv4jHH38cu3btWuqmCYIgzAoRtoIgCIIgCCuMnTt34nOf+xx27dqFer2Ok046CbfffvusEu4JgiD0AhJjKwiCIAiCIAiCIPQ1EmMrCIIgCIIgCIIg9DUibAVBEARBEARBEIS+RoStIAiCIAiCIAiC0Ne4Th7l8XgWsh2CsGzo9bD1QCAAoPfbKQhLTa1WW+omOCLPZEFwR68/6/72b/8WJ598MlKpFOr1+lI3RxB6jkKhgFWrVuHaa6/tWFayIguCIAiCIAjCElCr1ZDJZDA9PS3CVhAYtm3D6/WiUqkgFAq52keErSAIgiAIgiAsAfV6HaVSCblcToStIGj4fD7UajVUq1VX5UXYCoIgCIIgCMISYNu2cpfudbdpQVhs6P5we29I8ihBEARBEARBEAShrxFhKwiCIAiCIAiCIPQ1ImwFQRAEQRAEQRCEvkaErSAIgiAIgiAIgtDXiLAVBEEQBEEQBEEQ+hoRtoIgCIIgCIIgCEJfI8JWEARBEARBEARB6GtE2AqCIAiCIAiCIAh9jQhbQRAEQRAEQRAEoa8RYSsIgiAIgiAIgiD0NSJsBUEQBEEQBEEQhL5GhK0gCIIgCIIgCILQ14iwFQRBEARBEARBEPoaEbaCIAiCIAiCIAhCXyPCVhAEQRAEQRAEQehrRNgKgiAIgiAIgiAIfY0IW0EQBEEQBEEQBKGvEWErCIIgCIIgCIIg9DUibAVBEARBEARBEIS+RoStIKxAbNte6iYIgiAIgiAIwrwhwlYQBEEQBEEQBEHoa/xuC3o8noVsh7CAiHVOrl9BEARBEARBWM64FrZ+v+uiggPzITD1Okx16tu4qFtpItdJ0Orb+ft2Ing5COSVdg0IQjcsh3tcEARBEFYirtWqz+dbyHasSNqJVKfXukg1DcJkYDY7OvUlvXYrgnuZfm33coPfwzLhMH84Xd9OfSz3gyAIgiD0P11ZbGXgNXeoD2lA6/R+OYin5YST6NXFbj98V/3QxpWC3OeLi/SxIAiCICxfZmWxXUqB208DEy5adQssidjZCqJ+6gdBEARBEARBEISFZFaBs70kqnqpLUCrmNVFLdBf1j1BEARBEARBEIRepy8zQvWaIHQjZk0s1XmYXJ4XI9ZPXNkFQRAEQRAEQVgIZr3cz0KLlLmIvm727XQepozCJiE7lzbMZZ/Z1uf2M7ff83xdD7Opp9cmOgRBEARBEARBWFxmbbFdTDGxkMfi1kuOKT5W/++m7m7bMhfa7W/KsjybpYO6YbEstJ0mFkT4CoIgCIIgCMLyZtYW24XGabmb2ezfqZ5utxNu+2S+y7Urr/ebKXGVXn6h1m3tdsmN2dLpu24nfEX0CoIgCIIgCEL/Mydh206gdBIvbqyFbuqZbdlO8KVcOpVzW9981eVU1hTjO1/ifD5ZDJfrTteTqX9My/cIgiAIgiAIgtD7dOWK3MmdtZ2YWorEQU5rj8523/lqw0Ids16vu05ctVgshUBsZ4XuFCveaDSalmLSJzh6qW8FQRAEQRCEVpxCDYXljWthW6vVAHQfZzobZisGO8VVzlZkuY1dnS3zIf5s24bX60Wj0ZhzXfPJQlwnnfprLpMJJlduLnC9Xq9jWUEQBEEQBGHx4UKWxsJer1eN2xZDvwhLj2th20kwzVZMdHOB6cfoVsjOR5Zg/l53Y23HfMUJO0Giy7ZtNBqNec3Y7NblvFOSrbn+mMz1++/2GE7XKf1IUn/zz+UHUxAEQRAEYXEhQ0S9Xm8ap9O4jBsm6vX6krRRWHjmnBXZjcvnbJL7mOrjLqZO/9u9dnMMp+0+n6/lWPV6Xbmu6nW4idVcSBFEApdE7nzU1y5+t5OA7pSkyk0buxHKbq8Pt/AfyUajAa/X2+SmLMJWEARBEARhceAWWhqnNxoNlMtl+P1+hMNhAEA+n4fP5wMA+Hw+NZ6X8drypCthy0VCJ+E2l2VyTMdyOnYnIenW2thO1Hq9XnUjkKDppt2mWM12bZlvGo2Gir91Op4u0HQhxy32+iSDU51O22frwjubZGVukkTNRuhSf+g/kOSyLwiCIAiCIMwPpnGnPh71er0IhULKqLN9+3bs27cP2WwWqVQKlmUhEoksRfOFRcK1sK3X62qmg5vzdauc7gbbjaXMScS2s7rpx5+PBFZ63bZtO7otOAkjk1DkcZrtJgTmayaJn0etVlPi1m399F1yN2d+Tqa2O21z47Lcqf75cKE2HcsUP9uuDqLRaKhZQOCoBV8QBEEQBEGYP2isRp6SXNR6vV4Eg0FUKhU0Gg1Uq1XEYjGk02msWbMGIyMjKBaLGB8fRzqdRiAQUEYr09hP6F+6Sh7FB//6hUCDerdxp1z4OQlYXci6jankgtqtQNSFl8n1md9M7dpqSjCki0ndvXmh8fv9qNVqTeekW2L1NnNrpEm0OVli9QkGU7yv3sem/50mKty4oZv619RumpTRJx6crMv6+dXrdTV5IAiCIAiCIMwNPoaj2NlqtQq/3w+Px4NKpaJckCuVikqialkWLMtCNptFrVZT4914PI58Pt8USijCdnnRlcVWFwlcAHBRa3IV4K91kaOX7fSn19vOGui0zQ28rSTqdEu0bomlGSDqE9qPypBY1P/rN5bp3GYjgnnb/X6/ig/1er0tQowsuvV6vSmTHI8j1vuV6tYt9SZXZzffl0kQ6+9NAlrHyfpPr/Wyep+ZxK1u5aXz56JWhK1gotuJNkEQBEFYyegGIRKuxWIRkUhExdOGQiGUy2VYlqVia+mzyclJBINBbNmyBQCQy+XU/tVqVY11aXws9D9dZUUmix8N6LlrMs2YEO0smjzekwtCnpCHv24nMDpt4zdGO+upDj++LthNQghoDkrnbrwejwd+v9/oyu10zsCMENXdoDvNLOkCnIQs1cfL0MwW/dG5UjnC5/O1iFheB/WJbml16mPTd+JG4Jr+TPvw70Vvg97HTvs1Gg0EAgFjXfz4tVoN1WrV6LEgCMR8XRem3yO55hYG6VdBEISlhcap5XIZtVoNXq9XjblCoRCi0Sh8Ph+q1Sqq1SpSqZQav1G87cTEBKampnDcccfBtm1VHoDSLovlPSksPF0lj+IiyOfzqRmOQCCgxJBusdJFBE+6QyKDi1r+nyChwUUbr1//r1tVeVl9u0kgkTA1nYeT+yv1B9VDkwA0ExQMBuH3++H3+1vEqclaS3Xqx+JluWjlQtPJbZdEN7WNxBhZaHV4f1MZXdDyfqPvniyXXOiZrLR6f5omIpx+bPTJiXaDUKdyPD7WSRybris6x1qtpoStDIJXNu0eiqbfK6frrBucJuoEQRAEod+hMVe1WlXjXdIcfExOrsahUAjJZBLFYhHpdBq2bSMej8Pn82Fqagr79++HZVkYHR3FxMQEcrkcACAajTqG5Yng7T9mtdwPiZZGo6GsiiRunaxcwFFxFQgEmqyZJmHL9zdZTDtdbO0ElZv3JpdpHmMLHBV3JNLppjO1mQQu3ZD8PJ3OxWRd1D8ndCtrO5cKn8+nkoHR98ctsvy90ySBaR8+wKZ9nSYI9Pe6dVwXxbwtToJZf29yX+dluHVan+Cg79RUP4lZErYiKpYvbkWjfr/rdbjZ1k2b6Jj6xMtCsBKF80o7X0EQhMWAnlkkVJ08kExGKhq7WpalBC2F1VmWhVAopPavVquoVCqYnp5GqVRCsVhEJpPBhg0bAEBZei3LQiAQaBtGZppEFsHbu8x6HVuy4nHxya233BrGrXxcxOrweFPuJkvxi1wktZtJ0V1G+TZTOUIXXPTftJ23gVtX6Saj8yBrbTAYVH3Dxa6T+OJJurgLcDvLqr6/0zlTW7lVVbeKm45F3zl9zt036vW6Esm873S3c6f+5RZvbvnl15d+rZnievVtPFbYBO973k7qI14vWWlJ1OpLKAnLj3bf71I/2PSHrFurcbestGt8pZ2vIAjCYkFjKsrbo2c4rtVqLd6KXq8XlmW15LChMDmv14tAIKAMR5FIBIFAAOVyGc8++yxSqZSKzS0UCojFYgCAcDiMaDSqLMIknOm4lUpFjf9IBJdKpY7LfgpLy6yFLXBU6NCgn7sqk4jjQpbccQEYLwz+ntx2a7UaCoUCKpVKywyOKVaSW9A6iVrT57rgMrk689dk2SORwwUfiTX6oxvP6fw5dMPSTcsFqW7ddJPRjZ8vdwenG5lv5/voQlaP0+WJqej8+Ywc7W+y3vNz4RZ7inlwErWm2TMnwUtQ+3hZU7wzz1RN9dTr9SYL7XJJEiUzj3Oj3eTaYuHWc6VT2X4Rc3Ppcyc3cL1+QRAEYWGgcR6JyUajoZbpIc1AYY4ej0dZU23bRi6XQyQSUUYTXp9t2yiXywBmBCv93sfjcTz++OMYGRlBPB7H9PQ0kskk4vE4bNvG0NAQMpkMCoWCOl6hUFAxvYFAQBl6SqVS0zjXyYAkLC1zErYECUqyrtIfCTlupSQLpi4igKMWXRq8BAIBVKtV+Hw+lEqlJsstF2F6nKkuYPiF1+m1aZvuLkvbuGW6XWyxLsD5uTtZg53qpf1190Aef8AxCXiTQOZWST6jxl2ceT18EkMXldwyb+oLvU+pP3jyLX7eeuZlaqduMeXZnrnA5hZYvSzvE94fdJ1RQoJKpaLqXW4/ZHMVaKZ9eZ2miYi5oNe9lOJyqY/fCTditpO1Vy+/1HTT53o5EbWCIAhLh23bKhsxCVoai/n9/ibrKR+HkguyyUORxKdt2ygWixgdHUUwGITH44FlWdi0aROy2SxyuRxSqRRisRhCoRCCwSAKhQKq1Sry+TwAIBgMtoxFaczp9/thWZZygZakU73JvAhbgoQNT6xTq9WUtZK714ZCoaYswdwFmYtgAAiFQiiVSspq5iSqdIGlx3nqQlcfyOgWLF3k6m2k2SWT9ZJmnUjUmwZYTgN9XdgResytSRTrLhz6uZrcubl45Uv+6Am7TNmQ6QeJ9zP9p5tf73PTeXOXE34eept53TxJFS/D42O5VZ2LaNNkA/8BIzFLwna5x9J2Y711I1CdJmzcWM3cHH++6ptvQd9PdGPBXYpzNV2Tndrh9LkIWkEQhKWFxl3cczMej6NcLqttHo8HpVIJkUhECdpcLodgMKgEb6VSgWVZTWFrtPyPbdvIZDIAoGJxBwYGEIlE1Lbp6WmsWbMGg4ODKJVKCIVCaqxHx/B4PAiFQkpz0Hhezysj9B7zKmwJLnDJ4hoIBJTQAdBkoeNxuTwRE90AfFaF0nzrYsr0nos03i59G/9P6JZeoPWmDIVC8Hg8TWKPlyGBS+fHb2ZdpJpuEt3iyV12TZgGaLqoNLlO8HhSek0WSi5MSSRyiy7/rvWJBb1duijnll3qH94WXUBR+6h/+UQHjxfm1xAXsXqMN+97qocELRe17RJxLTcWyz15vuufL2uwMIPp++m2j926/jr9Bs7WKms6xmw+EwRBEOYXMkgMDg6iVquhXC4jn8+rPDTk9Vev11EsFpFIJFCv1xEMBpWeiMfjsCwL2WwW5XIZgUAAqVQKwWAQkUgEpVIJ5XIZXq8XyWQS4+PjGB0dVYY2v9+PQqGAfD6PNWvWYHJyEpZlqaWCwuEwgJlxcKPRULG1sVgMtm2rBLgibnuTBRG2BE/+VKlUUC6Xmyy6eoptEhxc9HKxS1YzEhy0DpXJ1dFpwKLHo3Krri58neI1aT/gqFU5GAwCOOrSqifC4i7ZPNEURxeuJksvF356e3QLpI5T3GqtVlPHo74md3HeP9ySbBLH5KpB50HHJNFrcr2m/qEfkkQigVKp1OISrU9akAcAxePyeF6aLNEnSThUX7VaRblcVn8kaknorlTa/WDz777bH/XZClCnfWYjhITOzFXwud3f9P3Nx/fY6fgiaAVBEBYHeu7zMX6pVFLjt2q1ikAggFKppCymNOZMp9NYtWoVyuUyVq9erQRuIBBAKBTC2NiYciPWjS6ZTAbRaBTbtm1DPp/H1q1bEQqF8OMf/xgnnHAC6vU6stksYrGYisudnJzEmjVrlGEnHA4rF2TyHKU4X3mO9CYLKmwJutiq1SoKhQJs21YColwuIxQKqYuUEkyRsCKBQuKLiyxKLtVp5oQEEM+oxl1TqV4dkzWXi2B9f5MQ5W7V3N1WF6HcKuvkiuxksTWdNwlNLmCpXt5XdDzuas3Pn7t5U5/TDwevQxe83IpN353ez2TtDgQCCAaDalaOvlMntw9ybaf1Yz0ej5pZ0/uHt4ev31ur1VAqldRfuVxu8gYQ3Flvu52xbOeC3465WOW6qWe+MV2Lvch8tc3N9bCQ34EIWkEQhN6C/+7SOLxYLAIACoWCynhMWY3z+bwao/n9fkQiEaxduxYTExPw+XyoVquYnJxEoVBoEsaBQAD5fB6hUEgt/eP3+1VWZKqPxpvZbBbZbBbVahVTU1OYnJxEIpGAZVmwbVvplXA4jHq9rsR4tVpVGkU/P2HpWRRhS5BIIvcDel+pVNRsDV103DWZxBS5h3JXYl43oYtG3XrJ42LbxZLp8Z7ccsvdW+k9F69O/3URy2ewuCjTXYjpP08cReegD55JdJJbB//jWZD15X70fuDr3epWXt5Ges/7mdyweV/xNnKRalmWst6HQiHlZhwKhZr6h64J6rdqtarSt+vWdi5g9aRmtGQPWWhJ0ApmOlloZ+OOM9+znb1qrdUnkRai/rnsy9s2H9+HHm7QzX5OuGmXCFpBEISlhxtKaMwKQLkBA0CpVFLjNtu2USqV1IohFFdbqVSU0YvGoJT0aWJiAplMBlNTUwCgRDHtHwgEmjIor1u3Tnl5HjhwACeccAICgQCKxSK8Xq/KphyLxeDxzCSc8vl8SKVSynBCYW75fF59LlmRe5NFFbZAsztsvV5vcU+mWFwuYuhmKBaLKgW3SYwRXDSSUKa1segPMCdj4n9Uhrdd34/KmZaO0V9Tm7iLLI8F5QmT+DmQJZLXARzNmMyPRf1KIptiRCmugeJSATT1PfUlX86HxDFP2EXnyl2A9e+UbyPLqmlygVyeQ6EQhoaGEAqFEIvFlGjl58QFP9VPFv9cLte0HA+Pj9WX6eH762sjC51xErFurLsrFafJM1Nfmvqx3cTBbKzpTt+V2++uW6usLppN+5om9Nod3207BUEQhMWFjBHAzFiSsh9TwidaZzaTycDv96NSqbSsflKtVhGPx7F69WpEo1FkMhlMTk4imUwim80qYWvbNoLBYJP7sMdzNGFUsVhEKBRCKpVCOBxuMmTQerZjY2OIxWIoFAoAoLTDwMBAUz4eyvdD8cG0XcY9vcWSCVs9izFZGfXkRFzs8WzLpgRF3IrJExLx9bF4QiEOF6vcKkj/eRZgEnVkSaZAd90dmV6TpdRJzPp8PpV9jc6F9xedi+4urItv6gN+fpTyXE/oRe4VdAzel1Se6tRFIR988jVd9c9p30qlAgDKzYTaS5MOwWAQlmUhmUyq2blcLmcUo2R9LRQKSKVSGB8fx/T0NEqlkhK0+rI8MsBdPNp5QDiVcVNnLz04uAhr97nbemazjeMkGp0E5VzdumdjiXV7TNOxZ3O9CIIgCIsPDw8DoAxLtEyOx+NBJpNRn5fLZVXesiwEg0Fks1kkk0n1/qmnnkI8Hkc0GgUAlWMlFAqhXq9jaGgIExMTGBkZQaPRwODgIKLRKCKRCMrlMp577jkMDAwog0kikcCePXuQTCbVuHFychK5XA6hUAhTU1MYHh5GJBJBNptVVuB8Pq/qobG5WG17j0UXtlx86q6sQLNFlws33bKmWzR0N2E9464uVvlAyuNpXuxZF3k8IRIJbx73CzQvMaMLV+5KQdZmagdlgguFQurGL5VK6rxJ2JPlk86bBB/d3OReqwtP3tfccsvdh/nsGu8/6hvKAMfhyaR0d2cuommNMuojLmz5d1GtVlUQP/UtX+KJXIcpJrZYLGJychKpVEplzJYfl8WhG6GpW9udLJRu6uEspdB1Y1F0at9CiHTu+mX6jB97oTF9127dnOc6ESX3vyAIwtJBY0IaswcCgSYDVaFQQDKZhMfjUe6/q1atQr1eRzqdVmvVkqXU7/erBFDxeByVSkUJy1wuh9HRURw6dAjxeLxpfEnjTrIKAzOrmFD87dTUFGKxGPx+P8bHxzEyMoLx8XFs2LBBxfGmUik1bs5kMojFYmg0GohEIqjX602hfkJvsWTClotRvg04akE0uQFzeNZeLmopHpMvIaNbYnm9XGjp9euJp5wyGpM441ZVn8+nAszJ2kztoP0p4J1cMcgiTfGtPFEVCTwS37ZtqyWHyMLNrZV631KQvZ6Jmk8kmCzZpv7XBbPJqkrWWmp/uwkBj8ejrK40CZBOp5W4pWRjlLGY3JB5XUJvshBirlesuG7cbPXyTsxGDLo9rtsynXA7wdDJqu1U33y5IQuCIAiLCxlbaFkeYMYIk8/nVVjb/v374fV6lVAcGBhQY1taizYWiyEYDKJcLqNQKCASiagyk5OT8Pl8GBkZQaFQUCF35EkZDAaxefNmZLNZDA4OotFoKAMI1eX3+zE9Pa3Gm6FQCPF4HLlcDvF4vGnZIUpuCgDDw8NqHM3HzPJc6i0WXdgCrUmL9D9Ctzy6uXicRC3FjupuyHxRaD2mlcfRkusDlaU20Xv+mlsiycWaW3J1uNjk1lPa37ZtFbzOlzji7toknildOm8Xr4fiC3imY963vO2mG5d+PPRJCF1EU4IwOhafHNCPT3i9XuTzeeWiXCwWm6y2eqwst8zLj0vv0s56ORd6xYo7X9deO8vrUqC3xY2bsKm8brVvd7x27wVBEITehca5PMcLrW7h8XgQCoXUGJZEazgcVuPWSqWi4mDJ05Gsr4VCAfF4HPl8HqVSCYODgypbcSqVQjQaVfl5bHsm7ta2beTzeRV2R4lJM5kMbHsm6zElrtq4cSOmp6dVneFwuEkLkLcgid25ehgJC8eSCFtdDHFRRO4HfJtuZSURp8fYckupvgau7ooMNMeo8u3cLZc+521v51pJ9XLxSxZY7hYNQN3wVD9PmMTdhekHgvqDnzOlLI9EIuo971/aX4+v5cKY3KOBo3GwelIp3WJrEra8XLVaVYkAqA9JtHPLLvU3fTfVahWWZalFt3l/cpFNgp4y6Jnc1YXeoN19MpeHQi8IwPmetXVr6XTTLl6fm7LdftZN+fk8hiAIgtB7cOMGuevW63WEQqGmfDKUPKper6uVLUqlElavXq1C2HK5HBqNhnIh5ksyEgMDAwCAwcFBZRlOJBKYmprC2rVrAQDRaBT1eh1TU1Mq1w6NvePxuKpvcnIS0WjUaOCicwCajWzy7OpNlkzYtrPakmjhFxbQbCnlFx8XPSRiucVWT9iku9xyC6kOr5OXJXRroW6V5ftzIU0Ckma2KpWKKkt9Q+fH43e5dZjfXO0SalE53uf6Oerr0HIrL6+Lf3/6Mfix+bI6vB+4COXtor6lGT5aZ8yyLBQKhSZrOe9HPoEha9AuHrO1LM7VItkLYlbHyY1/rvV1U56z2HG1C4V4YQiCIPQPfGym52GhbMgURkZjWVpHNhaLqTwzg4ODyOVyCAQCyGazKklUJpNBoVDAunXrVCxuLpeDz+dTGY19Ph8GBwfVahy0Ri6Vy2azsCxLrZpBYrvRaKh1awcGBlo0hhhO+oclEbZAq8gyWW9JzFFsqu7GywUvF5Bc8NAMDY9r1d2MuestH0jxrMgU96lba03WXyeLsJ5lmQQc1adbMoGjrtUk3Mglmfah/xTLyvclazH1s+7yzGfXqB6ewlwXwXrf8OPQNupDmljgLs28f+gzLkbJbbtWq6nFtanveSwtWX7J7YRSvJNwFpYnvShqCTdi1K0V1e15zsY6Ol+4dQPvJddqQRAEYeHQw/wo5pUsrzQeJOtnKBTC2NgY6vU6RkdHUS6XEY/HVcgZAOWG7PF4UCwWYVmWOs7g4CDy+bzyXCQ3YgrLCwQCGBgYwMTEBILBIDKZjBoTAzNjV8uykM/nYdsz6+RS6Bu10cngJZOuvcuSCdt27sj6Nu7KS3ALKYlgErO0hIxusdWFsG5l4WKVC1C+L5Xj4pgPWHU3Bj0rs37jkzsGwV+TtRZAS2wqby9346Y+4a7aJHArlYoSj52SQjllezNNLujfG7XbJIq5Ozj98WPR901rGnOrPbe60zWhx1ILi0e3omUuD4N++G479Yfp/Dvt06vC0O13SeVM59Cr5yYIgiDMD2SIoLEeJWfK5/PIZrNqfVm+wgcAjIyMqJC0QqGg1qytVCo4+eSTlfWVMi1T3KvX60W5XEYymUS1WkU+n0elUkGxWES1WkUoFFLjyEajoULeSqVSk0GsXC7DsiwARw1v/LnHNYiI3N5iSYUtiRfu4kqikKcIpwuKBIyeCZeED2Uvoz+ecdcEt9DqgksXU3rsG6+Tv+YWSQBKeHFXWrpx+L4mV0Y94ZVuQeXluXu3/pr/8WWNdBHP+1/vN93iytvB44T5hASPgeVxvFQXfUd6QiiyLuvimLeFx+wGg0FVXtyRexOTtwPQvzOf+rm4PQf9Hncj7Ph90+3xFhLTQ77biQ5BEARheaGPTb1erxoDFotF2LatkjPRGLRSqagleLLZLDwej4qrTafTKmNyMplEKpXCxo0bEY1GccoppyAcDuO5555TS/ns3r0bxxxzDJLJJPL5vFpOslqttiSFomcXGVJoHEn/6Rxo23IaxyxXlkzYAmhaU9UplpPEHE80RBZJurBIQAUCgRYhyUWXju5K7GSldRJ4prp0UUztMSVf4svh6DcFzRzRDBR39+XH5P2mW8EJLmx5eX6OevyxSYjo2/QZK/690Xb6rshlmFt1yXrMhbtuxTVZ2Xl76YeIxK2sZyssNp2uN7dWWTfl3BxvMdEn/NqVMdFLQl0QBEHoHtNvvD5uJrfkYDCIcDiM6elpDAwMYHJyEpFIBMcff7zKpxIIBJBMJmHbNg4ePKgMNTReLpVKKoPx1q1bUa1WsW7dOpRKJRw+fBijo6OoVqvYs2cPGo2GyrJMCa10KyuN0wkeCsn1B4VF0vm0M5wJS8eSC1tuyQOOJi3iSZpI5JBQ5AHdXIiSsOXWQ/3m0i2PtJ22cXHnZKXUt+vH0ON86Sbg50jxodyCqseIcrdgvm6WHreqn59pbVfdlYILUT3Blu5mrQ+o+Y2uu42TpZb3PwlQOl/uJk3H4+7ItK3dQN8pXrlcLkuQ/yLSjTvpbMRLLz402nlv6HSaIOL7uxG3vYppFnu+6hXRKwiC0PvQOJ1cfil5KI0BgaNjxuHhYQwMDMDv96NUKmHdunXKPdmyLASDQVQqFUxNTaFQKODAgQNYv349yuWySho1OTmJQCCgrLAkjIPBILLZrFrL1rZnlv+hhKaU44XDnzW6ByLBx8y8LBlphN5gSYUtABWorV9ophhPbgXlcEGmx7I6WV91i6vJUqvHgpo+I3QXYfLjN1ljdddqj8ejBB8XbLwv9CzS3BrK28bdignKNkxt4+fA1/x1sojqbeeWVG6lpc/4+ZLI5d8PuaFTP1DbK5VKUxtNfU+vqQ/498eTbMlguLdxI4B6/UHhpn1OApbQxbHTb8pyRwSsIAhCf0LGGqB5bE2hhjTmI0NHOBxGOBzG+vXrEY1GkcvlMDExgWKxCI/Hg5GRERVetmnTJuzZswderxf5fF7F0G7cuBEez8xSmLt371bPztWrV+OBBx6A3+/H+Pg4KpWKynpMy0lSoikSuXxlDoLG5cDR0MlarYZkMolgMKgSWlFZoXdYcmFLa6tSBjLg6CwJF0nkcmpyGTAJH12oESZBa3Ihpm3cp14X1HR8k4WQC0l+XI/Hg3A4jGQyqcQdT37FrdVc6JIg5H3j8XjUsjgk9HgcAIle2p/ftNzSqSdf4sKbfx80+KTETbrbM31HtF23cpMg5cs5cRdzinHQBa+TdZonkqL3gUBA3JEXmbla6ZzEXK9aL03us7Ntpz5BxLf1o9jrth/4/e10vv3YD4IgCCsFErWhUAjlchn1eh25XA4AVHKmfD4PANi6dStOOOEETE5OYs2aNYhGo5iensaqVauwe/duHD58GLFYTAnZsbExrF69GqVSCdVqFaVSCQcPHkQ8Hke5XEa5XEatVkMqlUKlUoHP50O5XEYqlUIgEFBitFQqoVAoIBwOY9WqVcjlck2CnI+duQdiIBBAqVRSY/harYZwOKzOS8Jpeo8lF7YA1KLLXDjSRUIXG4/V5JY5nrqbWzp1qycfcOmzMlzYkljigpLXZRJzhJ5kiurjx6Ey5XJZue1yS6a+RA63JNPx9IB23eWX32Dcoktr51JdXNia3CudBpskULmVlq9Py79D7rbM96dsdFzI0g+FPikAzEwq0LpkvD5uodYnAYT+Q58M6WUWyuW2VwX9QtLpnGXgIAiC0JvwsRyN1UhUAjNj7lgshlAopLIcNxoNpFIpFQtLr+PxOFKpFOr1OqanpzE8PIxKpYLBwUGMjIw0jWknJycRDAYxMTGBsbExxONxjI+P48knn4TH48GaNWswMjKCQqGAQCCAUCiEUqmEUCjUlKOFrLYUzlgqlZoMWqVSCZZlwbIsZW2mxFbc5VroDXpC2JJApTVdCX3pGRJkVK5SqTS5P5gsrlz0ENwiwv/oAidrJf8MQJOFlR9Tr9N0DiRgA4FAU5pxvowPj0HQ4xFoDVreVuo33dpMNyv9kYsvt8pyKy4X+iZRYbKWknAnCyydK2+D7rLMLTO0L8Uq8LL0I8MnB/TvlaN/3zxuV+hdTNfaYogXbiXs9nh8Nleva77atdLQ+1QQ3CC/8YLQG9AzkCdXKpVKylDj9/sRj8cRjUaRTCZhWRZ+9atfAQCy2Sy8Xi/27t2LgYEB1Go1TE5OolAoYHx8HIlEAkNDQ8pSWigUEIlEkE6nAQCHDh1CLBbDwMAAxsfHUS6XEQwG1fI9U1NTyjAWjUYxNDSEbDaLYDAIy7LUOLxWq8GyLBQKBQBQrtCVSgXBYBChUAj1eh2hUEiNqeU3qDfpCWFLws0UY8sHPPSa+8QDaBE0uvgxWWgBNFn4ePIpKsf/8331+vWypmPwtugWT9rH5IZJwpFbankbuVik+nkgOwXSk1DkIl+3BvNjO713Ole6wamtfBvVoVuT9Tr4Orx6fLJuSSb4xAKPr5YfnMWlW3E3n4KwW/Rrqlv0e3W25+DGMr0QYlf3zhCEfmSleTUIQi9C41mT8cXjmQm9A4BkMolMJoNMJoNwOIzR0VE899xzaDQaOHLkCEqlEqLRKDKZDCYmJrB//34Ui0VMTEzA7/cjFoshHo/Dtm08+eSTWL16tXo+1mo1jIyMYGpqCgCwfv16pFIpHD58GJOTkwiHw8jlcti8eTN8Ph/i8bgaG1M+HLL+ksGMDCuFQgGxWAyNRgOWZSGfzzeNs015dISlpSeELXA0iRS5oxI8HpNuIH7jkMWPBCNgXsZH/+Pur9wayN2agaNiup1ltpN1UxeS3DefzouLQg4X5cDROFZdBOvH5sHwtBSS7hbslADLlLSLyuv769mpqQ/12GM9fbp+TKqP/utldKs7L8ezMPMMzyvVAtZPLKa45dfDXMXdfF1bpt+LhToW0a/3hNzPKwO+fmQ73JQRBGFh0b0SaVmfQCCgxp1erxdTU1NYt24darUa9u/fj8OHD6vx7KFDh1Cv17Fx40ZUq1Xs27dPLQu0Zs0arF+/HqFQCGNjY9i/fz+Gh4dVSGI8HldLPcZiMaRSKQwNDWFwcBDlchmlUgmlUklZdaemplCv17FmzRoEg0EcPnwYIyMjagmhyclJDAwMqHMgy26pVGoKr5NnUe/SM8K2Xq+jUqnAsqwmMUgXEf8jqy0lMQKOrvvKl/vRRSGAJhHr9/sRDoeVW4HuYqi77XIXW16f6TXQLNT0z+imrFQqyirLl/7hdZKFlrs/6Ev6UNu5ZZa3Q08O5UZgmuDi1tQP3CJNrtJOZU2ihotYOm/+I6K3zel74hMYQm9i+n7m+p2129/kEdHt8fg1O1ur83wK+bm4cYvlVug1PB4PIpEIstmsq7IUzycIwtJAz1EyzpCg9Xq9iMViyOfzalx66NAheL1elQU5Foth3759yOfzCAaDSKVSSCQSWLt2LcbHx9WYuFgsIpvNolwuw7IsZQSjvDv1eh1jY2PweDw48cQTEYlEUCqVMDk5iUqlgkgkgkQiAcuyVMLayclJ9TsTjUaVOzJNrJHrcSQSUUlJyd3aaews9AY9I2xt21aLL7cbmJLwIRcC4GgSJD3Wkj7jFkcqxxNF+f3+JtFocjemWSfy3ecCmMfx8iRGvD6ebY3crrlI5EvU8ME2fU5+/XSj633E3ZVNIpL3C3dxnk1sWzs3X92izeORybrL421Nx9fdWXgSAm4dNol30zrEwuIwW+urvt9cvzcnsToXIdeuzm7OezbHdgpXoM/0e262AncubZwrCzHJIfQf9Hx0Y7WlZwcNOgVBWFx0gUdL+dB9nMlk1BI9Xq8XmUwGPp8PpVIJAJRb7/r162HbNsbGxpSApBjZRqOBSCSCyclJFItFFItFlTSW9rEsS42Rq9WqGs9zi7HHMxPuVigUYFkWyuUyvF4votEoUqkU1q9fj2KxqPLrRCKRJk9B0gn8uSTPp96kZ4QtMJOciQK1CW6x5dY6eqDxrMm6KNX/CP6ajseTHXG3Y758DN24fIkZKsfXa3U6Lrdkkn8+T7ykZxTmr3nsKm3TLbA8SzHNZvOZtGAw2CJwTe7P/LhOA3ae9ElvMx/scwsqT3blVF4/b26p1QfwJhdp+n7ETW1l4sYSq0+uOIkq036LjVO72t2bndqpP5Sd3KGX+qG91McXlgZaWsPpvrQsC8ViEQDUJLduRREEYeGh5yetWOHz+ZBOp1VcLR+HTUxMKG9L8sYjwwXFvwaDQezZswdTU1M4fPgwTjvtNEQiERQKBdRqNcRiMUxOTiKXyymvR3InJk1A9U9MTMCyLEQiEUSjUQAzlt94PI5SqaSWHyLhTGI2mUxifHwc0WgUkUgEtVoNhUJBiWYSy0Lv0lPClmZ5dBHL3ZC564FlWUq8Udyqnv2YxBUXWLxeLqr0xFN6XC3dlIlEAtls1nFwyR/IThZMcmng8ERLPFEU9Qlto6V1qKxu9eSilly2a7Wasljr4pYGBbol1TTAdTNTpVuMucilz51EPB2Tu5nrn5le83PiVluht1kIq1w7j492YtBtO+ZiYZ6va3Iu9ch9IfQ67TyJ9GdHKBRCJBJBKpWShIGCsIjwsTKNzYPBoBq7pVIp+P1+ZDIZDAwMoFQqoV6vIxwOIxqNqmRQ5XIZjz32mMpQPD4+jlNOOQXDw8MYHh5GJpNRy+7Q+N/r9WJ6elqJ6Gw2q+rbt2+fSghF4X6JRALRaBTpdBr5fB6xWAzFYhHj4+NYvXo1pqamMDg4iImJCaxevRqxWAwejwepVEqJdz2/jdCb9JSwBZrjKTn8QcetnfwiJ/jasHzdW93CSPWWy2UlvPTkUTzDMInLWCyGYDCIfD6Pcrms6uF16q95XCoJTmoTHyib4oP17MlUjvbjApnOgwtE6h8SsNxll47F43tNAqDTzcwFb7tBiT5h0c6qRtb4TpYlvV/axQgLvYF+jc3WjZloZ3XV70dupeXv+Tana85p22yZzb22GDhNbC0k4nosEO0mp+iZS+9LpRKSySRWrVqFbDar3BAFQVgcaDxLY69SqYRyuYxwOKyW5alUKmr9WMuysGHDBjUenpycVOvX5vN5bN26VVlXae3bfD6PdDqN3bt3IxaLYWhoSNU/PT2tMhjv27cPIyMjyroajUaRSCSajEXBYBDT09MqOzIAJBIJhEIhxGIxxGIxAEdD33K5nDIOCb1PTwlb7k6rWzrJismFDgWQ64NDErXBYFDdcOSSC6DJIswHtRQfwGNoqT4qU6vVkM/nkUgkEIlE1M2mW1/1wSo/Nrcgk9ikG0jPHEzt5v3BP+Pr2eqDdhKHPH6XW775DxFfXqfTgFYX607ollm9P6ku3WLL20IxDXq/thMXkhG5tzFNUMz1u2q3v1P9XMjSezeuvnNtp+l6dvrNc5poWkj4JJXeJ4tJr7hEC0tLKBRqErM61WoV2WwWIyMjKvFUNpuVcBRBWARo3E7W1nK5rAxOuVwOmUwGxWJRLbmzY8cOeL1eZc2l3/doNIpsNot169ZhYGAAY2NjavyezWYRiUQQi8UQjUZV1uJSqYRgMKjWnh0dHYXH48HU1JRaz7ZWqyGZTKJWq6FUKiEUCmFoaAjVahWDg4Pq9f79+xEOh3H88cer8WcqlUKlUkGxWBRR20f0lLAFWoWOPqjjcaT0moQQd0fm2ZG5C287F0LK6BYKhZDL5VQZ/fi03pZlWcrtwpTRmNqiW4r1dVq50OVWTO6a3M46SXGyPOkSd7PmfUUTA1SGXJP12NROVnPdHcyNRVcv48Z6SxMGXIA7wZOCCb2LSbDNp3DTrbF8m6msU5ZzfZLH1MZ2ll2ntnWzfanp9vzmCv+e9Ik6YWXi8/mwevVqTExMGMVqo9FAsVhUy33U63WViVUQhIWBfptpXE0JochwUq1WlctxOBzG2NgYTj31VAAzIrZUKiGXyzUZlCYmJhCJRODz+XDMMceo5ThrtRpSqRSy2SwGBwdhWRa8Xq+y8iaTSeTzeRSLRQQCAbXubLVaxcDAAPx+v1rnNp/Pw+PxYGhoCH6/H9FoFMViEdFoFMcddxwAqMRXjUYDuVyuKfRP6H16TtgSurWAtunL/pCwJfQ4WQBNok+HW2yBo+vmUkpxXWRTGyYnJxEKhdSNqyd3Ikgs0n70Q0DB9lyE6uKVr89Kolu3gPKlcbiFlAtq7l5J4pZbhrnIcMp47GQ94QNfp5uet8tkrdOttCbLlc/na1oAvJ0Ls1h6lo5urIuzEUvdihyT1VG/xtxYkJdCkDpZa922zS1L4Xbshl500RYWHxKowWBQJY3Sse2Z7KjPe97zkEwmUalUMDExsZjNFIQVBV+Fg0SkbduwLAuWZSEajaqEpV6vF/l8HoVCAYFAAMViUf2eF4tFVCoV5HI5bNiwAQMDA4hEIhgeHkY2m0UqlcL27dtx+PBhJJNJeL1eTExMKFGbSCRQKpUQDocRCARQLpdV/clkEolEAqeddhqq1SqGh4eVmAaO5qux7ZkY3OnpaTWeL5fLTXG18vzpH3pW2LaDu/VyQUYil1truSXQjdWF4m0pIJ1Si1MZ7uaay+UwODiIaDSKTCbTVCc/TjAYVK5UJGSpPrppeHZikzDXlzOgc6QlkkyYRCifxSaRzQUvd2/mdThZa3XXaH5MLqp5HboVyGS51ZcHcoIs1rw9vWr9EuaO24dLO1d3k0eIk4XXjdV2ruj18jY5nZt+DrTdqc5u22D6fLFZbBdsoTep1+vIZDIqu+n09LTRGmvbNg4fPowNGzZgcHDQsVwvTuIIQr9B40nbtpXLLy25E4lEVGwqAOXlSEvx5PN5hEIhxONxADMWUsuyAEB5XJAll/ZLJpMq9LDRaCAUCqkEUYlEQsXANhoNlMtlDA0NAQAGBwfV+Nq2bQwNDaFcLiObzcLr9SIQCGBoaAj1el1lT87n801LeAr9Rc8KWz0zMtCcWZg/lOjiJFHG99NdhLkbLnfLpTKURdi2bYTDYZTLZeNsDQnbUCiEgYEBNdujQwmu+ACNJ7Yicam7SfMbyrSUQa1WUynT+XJEtC+vh9qhJ7DSz4f6gLtKc3dmN+7G+mvTf25V5tAsGvUJvebviXYDXnFf7H2cLJHzIWT4tdxOtJo8Azq5vy6E2DJZjHk7Z3Mdd9tGU18sNSJqBWDmOVEsFmFZFoaGhpDJZBzdjCcnJ+H3+zE6OorR0VGMjY21lKGJ0164xgWhX/F4PIhGo2pMR8tv0n1VLpeV52OxWFSil5I3UdwqidXDhw8jFouhVqvBsqymZXU8Hg9yuZwyOvE1ZteuXYsTTjgBU1NTKrY+GAxidHRUWW0LhQKOOeYYbN26FYFAAAcPHkQmk0GpVMKRI0cQjUbVuraZTEaNuXk4oNA/9JywdRrI6YMcEoymRFDcsujG7ZAEMWVFppuI3BDohuVtpDaQJTYYDDY9LLnVk8QxHZtu7FAohFKppNrABSfFlnKXZC5+KVCfBC6Vp5tRt6Tq7pjUNrJ48qRcc5mh4lZZXdTqdevH0ZdjItoNcGU2bfmwUELGJG4Bs2XUSRS3a6NbsWs6frt6edvdMBvR7eTNslQTQzKAEHQoy/H09DSy2Syi0SgajUZLwkZgZpKXrC3kdWVCnhuCMHdoWcZKpYJwOKxEbD6fVy7Hfr8flmXB4/Go/6FQCIlEQpU9cuSIym1DbsH0miaiKDFVPp/HwMAARkZG1Bq4kUgEmzZtUhmVx8bGkMlkUKlUUC6XsX79eqRSKQSDQRw5cgRHjhxBIpFQa92SniCxzQW6PJP6j57MsqO7D7eDRBqJIkogpcfhUll9EGuyxJI4LBQKKJVKKphd38e2bWQyGRVvS8HoZPU0tRU4ahkGmpf10dvArUd8eR46P7r5+CCU6q3Vak39SFZYfWCt942pT7jwNyVn0vflMbV6Xe0SO3FXbG69NVls3SA/SCsbfi3rlkinSS5TGT2Lt34MtzhNqrltuxu68apo14alunfESiu0g1YliMfj6lmnk0qlMDY2hmAwiHg8rq4p07NWEITZQWM98jicnp5W4+VQKIRqtYpgMIh6vY5QKAS/349AIIBAIIBqtYpyuawmqCguF5i5xyuVivoj12RyHa5Wq0ilUjh8+DDq9TqGh4cRDocxPj6uElLR+raRSATVahUHDx5UVthQKIR169bB6/WqBLA0eVYsFtVSRVzcCv1Fz1lsCTeWW5plIXFHootEHS9vsmZQVmAutvhxSSQDQCwWQ7lcVmnFeb3ZbFaVoZs0mUxi7969KlMc7UMuvuQ+TA9b3Zef3pOYpLTntVqtyU1Ct0Rxqy4X/E6Dc7LukuXbyXXTJEhNiam4+4YTPP6Z97tuJaK+aVcfj7HV3SmF3mYh3HqJTvU6WXF1Ky2fXKL3puPM1mrr1Fa9fd1abjvVa2pDLzzEe6ENQm9CE570rBocHESpVGrJb0FujbZtY926dXj66acRiUSwdu1aPP3000ZLLz+GXIOC0BmeoJRciskbEYBKdkpjdFOCUCIWi6FUKsHn8yEUCsHj8SAYDKJarSpjUaPRQKVSQSwWU/llpqenlQil/DgAmoxc8XhcjSWLxSImJyeVGI/H4ygUCko88+WHZBzZv/ScsOUPFidhqwtREnuhUKhlrdp2A05ufQwGg2pRZprByWazylIZiUSa1orlFz+t1+Xz+ZBIJNTC0RMTEyq5E7/hKLjdtm01Y0TnwM9TX2eWuxzz/jFZkfgyQFzc0mcAmrI1m4LkdaHIjz9bUdsuhs8kdPXzalensPyY7UDTZJnVraBOotTJjd8pznyuA2F+fKe62l37vK1zaUsvDOp7oQ1C70Dr0QcCATUApSU6aFJaF7bE1NQUisUiRkZGEI1GkUgkEI1GkU6nHY8n158guIeejWSN5c9RGi86xbOTtZYspwBUQij6jNbD5WNhEr8kdBuNBlKpFIaHh1GpVFSiKoqZDQaDTUmoaAxMddK4nmJ6ucFJ6E96TtgSJsuIk9WQ/shyRzeUnoCK10kDVV43zfBYlgWfz4d8Pg8Aam0sspqaElKRi5TP50M0GsX69esBAI8++qjy8+euDeSmQWKQblAuSnVh7jYuiH4Y+LnTDwitu0vxw3rfcBHOX+t9R3W3m/12206Obi3jZd0eS4Tu8mG2g0zdysm9Jahe3S2fv6eyfP3nxcA0sNbvPdM2k7h1mtRzstoKQq9AE7s08UuDZlrGI5PJqAErfyZzKN7PsiwkEgm8+MUvRjAYxO7du9XSI6bJXMmEKgju0L36uKcRN5g4eQhRrplisYhgMKgsvHoSWJ43plarqRVJSNT6/X6kUikAQDgcRjqdRiQSQb1eRzqdVrG1dLxDhw5hw4YNar1r0gv0uyOTW/1NTwlbbkXhr3lyKPqvi1wSbvS5yQqi31xksY1Go4jFYsr1oVQqIZFIwLZtNetbq9UwODiIRCKBdDrdsvYs+f5T4opisagWja9UKk0JqMjFmOINPB6PWs6HxC931yWhyx/eNOOku2abXhOUIp1EM1mBKb7A6WHOXUgIes0XrubiWMc0mObuKbr1zJT52gTvfz3Bl8y+r0z0a4yuMz5o1X8TnLxDuCuwyT14Lm3j6K7/Tu0z3UNOdXTTDhG6Qq/g8XgQj8cxOjqKZDKJbDaLQqGA1atXo1Kp4Omnn0YqlYLP58OGDRswNDSEdDqtEjlyarUa0uk0wuEwUqkUSqUSpqenVbIbChWi6z8UChnrEQShFX1C1TTB2s5jku47WoO2VqthenpaxdzqRiRgxvAUj8dVgtdCoQDLstRY1rZtjIyMKOttvV7H/v37EQ6HMTAwgGq1ig0bNqixt23baozOz0HoX3pK2BImCwXhlDmXypI10g3k0kB/wIx1Np1Oo1KpqPWuUqkUGo0GstksEolEU6ytLqDz+TzGxsaQTCYxODiIgYEBHD58WMUKUBwu0JxJmQtN3TJJxyAhSwN0njxDdwV2cgvmD20ev8sFpZ74iS/PQ67X9J7+c3dk3p5236VJeOpigp+LLl71mXWnAb+wMmhnndSzp+sPYaf69P31fZ2O1227ne6DbuqYK71yz8iE1MrG6/UiEokgmUzC7/djenoaR44cQaFQwNatWxGPxzE1NYV6vY7x8XGsW7euaYUCnUqlgomJCWzbtg0nn3wy9u3bhyNHjqjB7MDAgPL4osFtsVhc5LMWhOWBaYIWaJ2E5ZmOacxO9z4fhwJHx6DhcFjdnySCAWBgYEDVT7loQqEQBgcHkc/nVWgheWlYlqXWuaYxpTxzlg89J2zbDTh1i67uZkzbOl2gXCgODAwgFouhUCgglUqp2FdyX4jFYigWi2pR6Wq1ikAggEgkgmKx2JR8iaBF4uPxuLpJuQ8/uT3QTBO5OdLattVqtSmelwffU8wB9RMteaQLQaeBbrVabbJU+/1+ZLPZFgunSRTr23iiKtpXj9/V4cKVf2cmdxX92E5WW/6dc7ErA+T+p51FVcd03XdyzXWqV5+w4r9JuuXWTX2dcGPJNR3D1BY3IreTqBeEpcDr9WLVqlUYHR1FLpfDc889p6yqqVQKTzzxBNavX49cLodKpYJCoYB9+/bB7/djcHAQ6XTa6K104MABFAoFrFmzBqOjoyqBDD1PySVR3JAFYXboRg3AOes+jZnJuAQcNZxYlqViYXndtj2zAkq1WlV5agqFAkKhEPL5PLxeL8LhsMotQ0aYcrmMcrmMWCyGTCaDWCyGdDqt2kAxvMLyoSeX+3HjFuf0Od0oZM00xXACR8UQzfoeOnRICVUAyu+eXIZpX0pBbhr40l8+n8e+ffuQyWQwODioFrEul8uYmppCqVRCrVZTN2apVFL71mq1pqROJMBJwJZKJRWPS+2k/nBaYkh3x6TBMGWY5BZS6jeT9dM0+2ZKqENt0ZcYMokOve302pQRmVuInerlsdYyaO9/5mK15IKv3cO23TFMrvbtjjefuHV3NpXR26L/Rs32WIuF02BIWP5YloWnnnoKjz/+uBKcwMyzrlKpwO/3q8EwAPVMpvhbE9lsFg888ACOHDmCbdu2IZlMApjxYCLXZB5rJwiCO+jZQaKTj786PRPJiEMJWgndQOHxeFQ4H1lzyV2ZVhnJ5/PI5/PweGZi8ePxOGzbxujoqFo/lxLE5nI5tc71bJaSFHqbnhK2JOZ0AaW7xjpZ7nT3BZPw5GUpjTjdjPpNWCwW4fP5MDQ0hGQyqZI9kbCNRqNqG8fj8SCbzaJUKqlscbwNPAMyn4HiZfTEVpQljm5s3lc8LpfiBkyWKqqLXpNI1vuKl9EtVG7FgZO4dTv4178v3SrO26y/loHJ8qLdNdPO0mkqZ7p+9WtNn8jh91I7EdlJeHUjfGcjkt32U7tzN5VfasTrYuUQDofV2pcmaKL52GOPxerVq9XSIpQoKhqNNi3Tx/fLZrOYnp5GMBhU++lQkkhBENxBv8903/CxKI3/TBOtNEas1+sIh8PIZrPKiktjc14/t8SSoSgUCikjCBmVUqkUkskkQqEQhoeHEY1GUa/XsWrVKjXu9vl8mJiYQD6fR61W66nnnTB3ekrYAs5ZcTvtA0C5J5BQNK2XystHo1E142OyPJL/Pa1LSynJAahZJn4zc1E6NTWFQqGAVatWYWhoSJXRBSS/+XUBS6K3UqkoMcwFuKl/eOytSZBy+Cw3t9zSEke6uHWyovD9deHAk/bQ9rmg18MnOUyxvsLKxXQN6Ndou5h9p/qcPA860e21r0/suK17LqK42wkoQZhPRkZGMDAw4JgBn+JiBwcHsXbtWmV1IQYHB5ue04Rt25icnFQxuYlEouUZQh5L/NrX17kXBKEVLm5pXEw5XJy8nWjcSElbq9WqsvwCzgYs8jakrOmRSAS2bStDVDKZRDweV8KWEsWRtwcA5TUpbsjLk56amuSWStNnOiYXVBqo8lkc3SWC32ylUqkl2RTVWyqVcPjwYQwPDyMYDGJwcBBDQ0MqEN3JEsnPw7KsJrcp7j5Ls8b0Q0APc11IklWVL0FCa3RxAcoFPj9XbvHmVm993Vi9r3R3EF7O9H1wN2B+fKqH0qjrdXQSILx+3han45u+c2Flo3swcC8Gnt6fTz7p+5osu/p1Nl/i1a2bs2n/2VqOeZ90KisIC0Uul3McB1QqFTz22GPYuHEjkskkjjvuOJUnYmxsDKFQyGix5fd3IpFoEr/8mtfvnbksZycIKwH+HKTkrXRPDgwMNIXOccgLj7wOKbaWrLWdxoX6SgcDAwMqL87o6KgK/Tty5AhyuZxauYT2pTaItXb50XNTkbqbrS6+nKCytVpNWTm5i6/pxmrn8kTk83kcOnRICeB6vY5oNIqRkRFYlqUsrnpbaKHpfD4P27aVy7Jt2+rBS7NGQGsmOd0CDBwVifqx9P10t+p2Fleqw/TXrq9NOA2IdYutqS0mkd2t5Z7vb7LACysbfo/xiRo314rJdVevbzaWXL0t802n+9kkZHv1vunVdgnzBy3hEY1GHcuk02n85je/QSaTgW3PhPasXr0aO3bsQCQSUWu1c2zbRiQSwapVq5BOp1XMHXA00aFt28o1URAEd5BApTGoZVkolUoIBAKoVCpN4z99Pw4ZPwD3ySJt+6jnI43F6/U6HnnkEWXFLZVKKoEU0JyjRbwxlic9ZbElTJYRjpNLLIeLQV4P3XwUY0oJlHg9NPPEEzTRDUoPP3qgmmaj/H6/cpEi6yq5OZHrs9frVWvvUsKrcDisbkSyKtOgmzK+UXsoe7O+LqeTxYlcqngCJj1+lvcbYVpSh8+06dAPCx9Q8/bwDMhUD1/I2wk31wC1XwbAAqFff/z+18u5ge5LfUKq3TFNn/N9F3LGWBfN+kw4t9KatgvCYkLXnUlc6s+IqakpWJaFWq2GVCqlnou0lrwOLevj9/uVBYeWF+FJqgRB6A5+z/Kxc6PRaFovlmNagq/bSV4aY5KLcTAYRC6XQygUwvj4OACoMD5agUQ8kpY/PSFs9UEUuZTyC1+/APUZFydhx8UVF1S2bSOTyTStmcXL0I1JN2U6nW5avJ1ibKmdhMczk72tWCzC45nJwrZmzRpMTk6iXC6rssFgUPn812o1lEoltUYuPXB5hkbuYh0Oh+H3+9UyCPSDoluTuAsw/emWU10I8h8YHoNElmBejr4r3v9O8cz8u+V1OMVB69v0QbcuDvR9ROCuHPjkjJPQpHL8P9+3k0uxXoaHMzgtMdbp+jMd16mcE926HXfTzl64f3qhDcLiQW7IpjVp9WfEwYMHkUwmkU6n1edO18vAwICyBluWhXXr1mHPnj0oFostS4sQtCQerZkpCIIZvsyjbrhx8gIyTbrOBR7qRmNtqpM8KGWydmXQE8K2E7pocuvyRzcYH/Tym4xSjZsEFJXXl52hrIwUqG4SZF6vF+VyWa3zSr7/tm2r5Bfc0koWoGq1qtbM5efGhTaJQe7mwcvx8yNrMP2nP36upoG1U8wR/XB0Eq8m2okGOp4+I2+yRrc7trgg9x9ztQy6EWqdPDw6WW/59adP+rh9aLdrjxtx60Sn/tMn+boVwoKwmJTL5aaYdx2Td0EoFIJlWchkMo4CNBAIKNHcaDSQSCQQDAaRz+dRLBZV3fpxaYwgCIIzdN/QCiAAlHXU6Rk539A4mRtL6Ni6pVZY3vSUsNUteFzE8AtWp91Nw2dtSChxN1+ajeVWFxK0tHRAuVxWLk+hUAgDAwOIRCJqgXjuLuzz+ZRYJhGZyWSQSCRg2zampqZQrVbVDwC5Q9MSBMFgEIFAQGVsA9AkRsnlIhAIqO2mJE9Obo5Orsr6e5O4pR8JythMn3FrLreKOx3f5B7Kf3i4lbqbpXu4sJUlf1YeTg8u04ywaekoPrBtN9mjX+NOrs1uH6Rzeei7cXk2bTO5Y8mDX+gF2onT4eFheDwe5PN5ZRWKRqNIJBIqBIassITX61VLicRiMaxevRqPPPIItmzZgng8jgMHDqBer2NgYEBZf2m/cDisPKMEQXCGj/lm41Y8X22gY3fKriwsX3pK2AJYEHcB3QrL3weDQdRqNSWGSCzGYjFleeWpwYvFovLhpyUDaFkeEp006xwOh+H1epHL5VAoFBCJRJDNZlGpVNTDstGYWVw6Ho8rUaYLVi4eqa0ej0etp+skaukceTxtpz7i1lMd3TWZTxrQuegWLdMx9Ne6VdgkTp3q1Cc/9AkRYfGZqxV2vulksW3n2m5ylzJd+7wu0+tO7evkbuzG8tvpGG4+W4rBiCAQXq8XiURCxcdxEokEAODw4cNq0Or3+5HJZDA+Pg6v16uyoVKYEDDzXKCwn1AohHQ6jf3792NsbAzDw8NIpVJIp9NNohaA43NQEAQzvfLc6JV2CEtDzwlbbi10iolz2gY4D6r1GSRufdH3CYVC8Pv9yOfzTZmVKQETWReLxSICgQDi8Th8Ph8ikYhydxocHEQsFkM6nUaxWMTU1BSi0ag6Dq2fRTEJPp8PlUpFuS+Hw2GVnIpuUhK2POZVF7bUTr7dyQplEpedBDCVcYp9dkM7V0/qX6dEHnTuJovTYrm8CO1ZKlHr9rvnky96iAJ34+eYvBfovW69nS/4da6LWzd93EuTC7NB7uWVh5O3DS2bNzEx0fRsqNfrCAQCCIVCqNVqmJ6eRjQaRTgcVp5AlDxm/fr12Lx5M6ampuD1epFKpVqW2aMQn3A4jMHBQRQKhUU7d0EQBGHu9JSwJcGkDxY77UOQMOLvye+el+dCMJ/PNw1w/X6/mgWmbMZELBZDIBDA9PS0Sg4VCATg9/uRSCQQCoVQr9cRCoWwdu1a9dAMh8MoFAqoVCqwLAvDw8OIRCIAgEKhgGq1qlySyYWKFq02iXESuNxCSf1Gg0G/369Es0kE0ms38Yi8PO87bt3lgw3d+mWyYpncPrlLcyAQaDkWtxR7vV5lIac+4+Kk3wf1/Uw3/b/Q31Mn92Sncrp3B5XRr2099l0PC3AjztqV0cVtp/Ld0s39LwgLjc/nw9TUVMu2eDyO6enplqRSgUAAq1evxpEjR9RnlUoFqVQKPp8PGzduRCAQQCwWw/bt25W7Ma1Hf+TIESSTSfU8pYnjaDSKdDqNfD6/aOcuCIIgzJ2eE7am14B5Ddd2+zuhCyt9EE5uwJT1GJgZvAaDQbXAMxeTJMSq1SrK5TIqlQqCwSD8fj+i0SgajQYymQwsy1Ki2bKspgXi0+m02o/aR8mkTNZNagM/H/01WZXpQe3Gqt2pH50som5EQTsrrV53u3hq4KjVVj8+7xOx9iwdvSCO9ImTdr8tHKe4VN1FX/f0ME3ELdQ1OJ/9q1utBWEpMf12kzW2VqvB7/er5y89UylRI68DgErSWCwWEYlEYFkWhoaGYFkWxsfH1UR1MBhU+5GI9ng8jtmSBUEQhN6l54Rtp0FbO4GrC1U9/pPX4fHMxMbSa1rU3ev1olQqoV6vIxwOq1ncSCSiklCQWASgEikdOXJEudEGg0FEIhFs3bpVid+DBw8qi2wkElFuyfF4HFNTUygWi4jFYqqd5XK5KQkViex6vY5CoaCslRyeuZlcm8kt2WR5chrEm4Sv0wNeT8hFf6YYxHbilluDefIoPmAxLRVE/aCLWhmQrGxM7uomi6uO/tvBlwKjbfy6pN8PU7mFPJ+FqF8Qlhr+e09LdAAzCRjr9Tri8ThqtRqKxSJWrVqFRqOB8fFxY7b8QqGAp556StUVi8Vw5plnIhaLoVqt4uGHH4ZlWcrduNFoIBKJ4Nhjj8W+ffuMSw4JgiAIvU3PCFvuyuc0OOw2/tMUq8NFFF/n1e/3K+FJsaulUkkds1QqoVKpoFqtGoW1ZVmIRqMAZlyhpqamUCgUkEgkMDw8DMuyMDExgS1btmD79u3wer148MEHkclkAEDFleruvvy89LhT3WrE9+NuVfpauJ0GyU6DXJMbc7vldZyEczu6EaZcaND3txDumkJ39JoruFtrrWk/fckCbr0lMUvXHl9ewBSL222/6O1eqH7tpe+qXVyxsLzRv+9QKASPx9OU1KlQKKhrZHJy0nHJPdo/mUyiXC5jcnISTzzxBKanp7Fu3Tokk0kMDAxg3759eP7zn4/rrrsOtVoNX/rSl9SzWxAEQeg/eirlHx8MunF5dYKEqg7PtkvHIEtjsVhEsVhUCzsDM3GqPO62VCq1PET9fj+CwSBisZhyc0okEtiwYQMKhQJSqRRqtRpKpRIymQzi8ThGR0eVKxUdj8SyaTAXjUaVuxRBopVbjsmVip+/njWY969uxeX9T4N204DeDW4HpLq1lgsBU2IvEzSZsdjrpglmekUo6e7u+mu+zXRPmCaZ+L1A9x/fj+4R/n62md5N92i781jOrLTzXYno33Emk2nJVEzPpWAwiGg0qtag5Xg8M8kXaVI5mUxi48aNeNnLXoZKpYJnn30WQ0NDyGazOHLkCDZv3oznP//5GB4exsaNG9USgIIgCEL/0RMWW7KMkDDRB4Hc7dTNAJFbbYH2FiRyHyRhyK24ZEX1er2oVCot+9ExyIU5l8uhXC4jkUjA7/djenoauVxOPZyTySQ8Hg+mpqYwPj6OfD4Pv9+PSCSCQqEAn8+HQCCAarUKy7JUYitugSWhTeekJ1iq1+tGUU/ldRdK3aLE+5D24+9N9eoCoJMFV/+cuyDTZ7SskVMdJiheqlgsykBYAOA+OZJTnDh9Rr8pfGKMr19NtIsNd5sDwI3FspPHRa9MLnSL6bdf7mWBiEQiKqFTOp1ustjq10kgEEA6nUa5XMbq1asxPj6ukkwdPnwY1WoV69atQzabxW9+8xs8/fTTGBsba0pcSNCqB7VarWnyWxAEQegtekLYAmhxQdYfUnpsbTuh5fEcTdtvWjaGjqMLYIIENE/SZFmWyoRM7aG4XEpgMTk5CQBKnG7atAl+vx/PPvssIpEIksmkyqh86NAhTE9PI5FIqGWCaO1bfs4+n0/F21arVSW4eQZWml2m91SOb3PqK8JkmTL1G0cfUOji1snaZGqDKQbYRLvP6LvQRbvQ/8zX92lyEzYdix+Tx+Lz/fjSUyaXfD2u102b3ArWdvfWXEXtUt83/SzMhYWDTzCTx1IwGFSxsDyjvs/nQ6lUUs9kn8+HaDSK5z//+SiXy/jgBz+o4mkffPBB7NmzB7t378bExARGRkYcJ9dlXVtBEITepieErR5baxoMunXn426CTrRzqaW2kAUXOGqdpSyJZGEOBoOqrlwup6w4qVQKzz77LIaHhzE4OIhgMIhgMIiNGzdiYGAA9XodyWRSLSo/NTWFRqOBer2uxHgqlVJLB1FGSGo7CVtaXoivbcuT2Ph8vhbh2Mki0k7Qmlx8dRfOTkLV6Vi6iyd3hdaPp8PPjYQ/nftSD9JXGrONIe3EbL9H0zF0t3qna0z/PSJPDvot8Pv9KvMqJZGj/XkiqXZtny+x7nSu/YjpPDpNRgjLH3om0OtoNIpCoWAMD/L5fJienlbbJicn8eSTT2LHjh3Yvn07hoeHkclksH37dpx22mmYnp7GoUOHUKlUkEgk0Gg0cOTIEbV/rVZrcYsWBEEQeo8lF7ZcqPGBJHB0EEMxrp2sGlyIUkZhTqfkVPRA1LPxlstl5fLk8XgQCoWUcCoWiy1W4VKphKmpKUxPT6NWq2F4eBilUgmDg4M4++yzm9r7s5/9DI8++ij27t2rklXR+ZLbE61HS+dOlmLbthGNRlGv15uWJuDWaGpbJws3lXESrk59xl0yqY52FmKTG7LJ+jQbKxSPtaX+4RMUwsrDZP3jvy/tJrn0a1oPUSDvCBK3XADziRx9Ysck0txYKamedsLPbV29TLv2iyfGyoNPJhE8bMjj8ShPp0KhgGKx2JL8qVqt4n/+539w5plnolqtYuPGjTh8+DDK5TI+9KEPKQvvt7/9bdx///3Ys2cPvv71r6Ner7c8SwVBEITepSf8arig1cUtCRWywOqWWJMQIusnrzsUChljT3W3ZB4vysvwhFLBYBCVSgXZbFYdh88mAzPi9sknn0ShUMCOHTuwY8cOxGKxlnqf//zn49RTT4VlWbBtW8Xckrgll2KCi7VKpdIU+8uX2KFz0mNwTeff7j8/Lh+cd4qj1evX3Z55f3Hx7JT4y82xgKPr97YTLMLC0Kv97cbqb9pHnygCjk7E0KCaJpW4xVY/jmniRhdvnVyQu2Eu34OIRqGXoOe96bokLybKnkxi1ukaHhsbw7333os9e/bgbW97G37nd34HU1NTeO655wDMhBtdeumluP766/H7v//7SiyHQiEMDg4C6N3fOEEQBGGGJRe2PGZUFyPcAmd6oJji3oCZ2dlSqdRUjix4umim43g8HuXyyzMictfYQCCAQCCAUqnU5BrM2+b1ehGNRlGpVFAqleD1elEoFBCJRDAyMtIimmlN2zVr1sDr9ar4XGDmgRoKhRAMBhEIBIxCl5Yb4f3BLTtOcUG83Z2so7zNpkGD7ibeyaLE0QUEnaeb45igc+ZuyULvsdgDRNNkjGnyhv+nMk4uydzDQ7/u9GvV5N7M35vaa2pjp/26wRQKMN/Mpp3t+kNYeVC4jU6hUFDeS3zNWf3eCwQCCIfDKJVKePbZZzEwMIBMJoPh4WGsX78en/rUp5rq3bZtm1rZAJi5HnO5nLrH2zHbDOiCIAjC/NATrsj6H4kxPRmQLn65QNXdaXWxacpgqh/f5AJH+9TrdZWUgiyrfDBIImr9+vVIJBKo1Wqo1+t4+umnkclksGHDBtRqNRQKhRbL7cjICI4//nhMTEyg0WioZYUoOcbAwADS6TSKxWKTuCUBXalUWgap3Hprgg+c3SSvocG8G0yDcuor3UrLIZcyfVklt4Nabt3XBYAMjAWgffZj/p5fr3xiTI/ndprEMU3StfNyaDe5NJuBsslK7PQ5L9Or94nbvhSWB3wSyQR5V1GIkmkdeGBm4vh5z3seEokE0uk0Go0GfvjDH2Lfvn049thjMTIygo0bN7bUv2XLFlxwwQV45plnmpJQcq8M8tyi0KRqtYpoNNrkySUIgiAsLksqbHUhwoUrWS+DwaBxltQ0M8rr4di23WKFpf9k7dRdZbk1l1tF8/m8iqnjsXSRSEQtCF8qlVAsFpVAozVrTz/99CZLMjE0NKTaXalUlCi2LAvhcBjlcrkpGyRf0ofawC3SfP1ZOgen7Mhu4vH4uZoG4PqfLqpN4kEXtnxgPZdBAX2nFBMtGZJ7j6W0aOgTOrTNJET5f379cG8N0wSRadKm0/XnZKE1lXPTf277WI8Bnm9mW2+/xwkLs4fyS3QqwyeBLcuC3+9vyqkQiUSQSCQwNDSEdDqN6elp2LaN/fv3w+PxYHh4GJdeeikee+yxlvqj0aiqmz+PyKOoWCyqZzJZi2mZIHnWCIIgLB1LKmw7iVpurSXavW7n1qd/rq9z2w5aU5UEG7klezweJbxp7dp0Oo1CoYBCoYBMJoNDhw4hmUyiUqngzDPPRKPRQC6XU4mh6O/hhx9GqVSCZVmqnTRTTA9rajvNTtNSBrSPLs7bWWFI9OkiXu9X075O/aX3rZM7sZM11uv1qgkBXqcp1tHUXrp2+HJIPB5SWDj6XYQ4CSl98oVfS3Sd6fH1fGJJ9yQx0Y21tFvB6raeTve8ICwWbn6r9XXla7WayksRCoUAAGvWrIFlWdi/fz/GxsYwOTmJRqOBZ555Bs888wyq1Sre8IY3YMuWLbjzzjuRTqfxwAMP4JFHHkEqlUIgEGgR2Hx5PZ1qtdqU4EoQBEFYfJZM2Opux7orMglbfR/9tWl/2s6XBjCJXhKpet0mdBdaEoSWZcHr9WJ6elrNGBcKhabkVaVSCUeOHEGlUsHIyAieeuopPPnkk5iamlJC7tChQ2qg7PP5UKvVUKlU4PV6W+J5SbDVajV4PB4Vl8uXyDEJ13bnpotbOlc3/cH7k/63S5zD4fs0Gg1UKpW2ybs6QRZb+i7Eattb9IoAdmMRNAlOvn60U7Io/TW35nbbhk7tnu/rul/uEbmfly+zmYSk3BjBYBCJRAIejwcHDx5ErVZTGY8pwzEtp/fAAw/Atm2cdNJJeOKJJ7B792489NBDGB8fR6PRwOHDh1t+A0xJ4gRBEITeYcmELU8KpYtanhHY5F5sckN2ErWmMnrSKdPgkmJKdUupZVkqoRMltaBEUdRmEqIUI0vld+/ejUwmg7179+KZZ57BoUOHMDAwoMRtNBpVCao8Ho+yFFN7+DmRyzIX9vpsMhf2pv7nfdVOAPN69AkDN0ln+Hej16FbmU2xf7q7ZCdLFH0PfBJABsILR6+I1dnixlrL70PdbZ67H7u9J4Du+k1vS6fr2UlM9xPc00buXcEEXd+Dg4NIJBIq50Qul0Mul2tJyEjXET2Xv/nNb2LdunV49NFH8dRTT+Ghhx5SIUDZbFZZgOkZyZNUCYIgCL3HkgpbJ2utvm6tDhdK7VyQnfZpV4biZOr1unL59fl8CIfDiEQiauCaSqVakjaRaCMxBczEAlGc7O23346hoSGsXr0aoVAIa9asQa1WUw/QarWKfD6vBL1lWcjlcuo9uV+RKKS+I4HtdqabC1jdvZILXNNgmpfjf50G0bxO3bKrz4qbJi06DeI5Pp8PwWBQre3bKZGWsDgsxXfgZmKknajV3d11DwAuYrk1lyexaXdvdLpvnMSdHtvrFP/ert5OZdrRjfv0bOsXMSt0Yv369Sor8tjYGEqlkvG5pV9PgUAAlUoFN9xwA7Zs2YITTzwRwWAQW7duxfT0NMrlMkKhEGq1GvL5/FKcmiAIgjALlkTYOllrubB1M+AyLRHUye2WcKqfrLS87nA4jLVr16JcLiOdTqNarbaIWhKY/NxotpisuV6vFwcPHsTevXuxfv16bNu2DSMjI5icnEQymcTExAQKhQKq1aqyDvN28gEzt2KXy2XldktQ1mbdbUoXtZx21lT+ng/oOyXLoIEFrf+by+VUO3TBqQ8+2lne6T93t+bu35TBmr4TEbaCLm7dCCcnF2JTOSpLE1u65dat67Ob9rvZ7gYRj0KvEwgE1PONrK+RSATHHnusWk8+n8+jXC63XM/kxUTPB3qu0uR1PB7H008/jccffxybN2/GMcccg5e97GVIp9N49tln8dhjj+HAgQNynwiCIPQJS7KOLbfUEiQi+RI/tJ2X0f/r24BW11unZYJM7aJYVQAq+7BlWUgmk2g0Gsjn8ygWi46CjtyHydUpFAqphy+3uB45cgT5fB7btm3DmWeeie3bt2PVqlXquDyrMiWjqtVqqFaryv2R3KQofojw+XwIhUIIBAItQpHjlFSKBDG3xuoDdTfulqbYRD6DPhvByV2odbgrOln9TdeaMH/0er+aPAL013wbv66dvDz0a5/H3PIwBO4S73RMU31u2krtM71eDNy6W8/HcYSVi776QKPRQDgcxtDQEE499VQEAgFMTU0hm806eizRMxOYeTauWrUK1WoV4+PjKgYXAA4fPoxVq1bhkksuwetf/3qcddZZyiIsCIIg9AeLbrHlgkYXnBST6rQ+JJUx1enmuPr+urXY9GD0+XyIRqOYmJjA2NhYW3dfn8+HWCymrIWlUgm5XK4pNsfv9yORSMDr9eLhhx+Gx+PBCSecgHg8jt/5nd/B7t27ceTIEezbtw/5fB4ej0e51NI50Jq6dEyg2Q3aZPE29ZtuoeXb21mGnNwYdRHB6+BrAfK63MC/IzdxgyT6eVItYWXTzSRMO/deDvecIKsSAOVxQfciTRDp1z+nnTu0iXb3nptz7DfENXnlwu8bmmweGhrCQw89hCeeeKLj0kBerxfhcFjF4E5OTiqX5UqlgnA4jNHRUaRSKfzoRz9COBzGi1/8YoyMjOC1r30tRkdHsW/fPuzevbtljXVBEASht1h0YdvJWss/4691K63JDRkwJ0pqN+Cj45r2IzemYrGoMh07DSjD4bCyklJ230wmo9a8pYEyxX8GAgGk02nl5pRMJnHSSSfht3/7t1EsFvHkk0/i0UcfxaFDh1oGrZRgijJH8+RPTkvtcMGpL03Cz4Ofl76/k+XJFP+nxx7atq0SeJgEarcWICfrO7mckagwWf6FxWcp+79TjKvTNn6NmSyq+v2ge43we5K7KJuOq7ex2/5yEsYLHQu7kLidYBCWF3yi2fRcrlQqmJqawsTEREdRG4lEsGbNGkxMTODQoUNqMorfh4FAAKFQCJFIBNlsFg8++CCmp6dxzDHH4IwzzsAf/MEfwOfz4aGHHsK3vvUt7N69uy/vJ0EQhJXAklpsdWstuY9SOaJdXGgnnEQ0xyn5USgUUtZVPasiH/SSdZCsq9z1iQQdxe2Gw2FlzbVtG+l0Gh7PTHzu+Pg4RkdHMTo6ikKhgPHx8SY3ZqrD5/OpWCFaLJ73G50LF5b8XJ3QXY15f5ncv9u5c5r6n++nDy7m07WRjqNn1harz/zST2LDbawsYLaGOpXRPRj4ta1bgUnYLqbFZzlf73I/L0/aPQsikQiGh4cBwFHUejwz68tTjoqpqamm+FuayG40GvD7/YjFYmg0GiiVSqjVanjiiSdQrVbh8XgQiUSwadMmnHnmmQgGg3j44YcxNjaGbDa7MCcvCIIgzIlFFbZ6FmTg6IBP/6PPnNDdiLvBafCptzUYDKpZXIqPNVkrSdgCQKlUQqFQUMegB2ggEEA0GoXP50Mmk0GpVAIAFAoFlXV53759qFQqOPPMM7Fx40bkcjkEAgE8+eSTLQ9mciGmtW5JHFNf0HHbWWH5dpOg5f2sn7ObAaU+oaALAFM7nNpqEth6wigdmizh+8tAePFZagHs5nt300aTZdbkfcAtTiRk9WvX7XXo5PZvKtNt3f3Gcj43wR2RSAQDAwPqWWnC4/GovBg0Qcyh+9Lr9WJkZATFYhHpdFoJZdu2kc/ncejQIdTrdVQqFVx00UWIRCI4/fTT8dxzz+HJJ5+Ua1EQBKEHWTRhywWsLkrJHdhkreXMJgmQk/jV3fT0z4PBIFatWqUspvwhRhmHY7GYKttoNJDNZlUyinq9Dtu21cMyHo/D6/Uin88ray19ns1m1XJCkUgE+/fvBwBYloVoNArLslAsFlXdZAUm8Upr2gJH42bp+Hpf6OfvZC11cv/TB9FO/c1dCE0WWxP6ZMNcLVtk3abrhvpOBiRzZ6nFqhvaubJ2mmTpFv33hCdLM03YmDwrnM6hE7OdeOoHOrkjL6dzFdoTCoUQi8Vw8OBBZDKZps+CwSBqtRpWrVqlnjnlchkTExMt9dBzJZlMwrZt5HK5Fuvv4cOH4fV6EYlE8NOf/hRf//rXMTo6Ctu2EQqF5LoTBEHoUXpK2LYTrvMxkHZaCkivm9aQrdfrmJqaask4THG0wEwyC7LQVioVdR6UHZn28Xg8SKVSRmHVaDRQrVZRq9Vw8OBBpFIpJJNJWJaFcrkMj2cmhrdYLCrRSlYgvu4sCV96z4WhG4uVPjh3ErG6ddfJ0krfLV/307SUkEnwuhW1pkE9cDSul+Js+0GICb0Jn6Bpdx+18zigbZ0mhkz1OV3j7Y61XBFBsXKh1QTI24kIhUIIh8MoFAqYnJxU2/m1oidJJKvu2NiY8Vi2baNQKODQoUOYnp7GrbfeiuOPPx4AjLk2BEEQhN5g0YWtSdSaXJSd6uCvOz1cTELWaWDJBRHBl++gwS0XkYVCoaleqpv2IRFcrVaRy+Va1pQlSNjSOrmBQACFQgHJZFItLeTxeJS7M9VN7tJkweXt5Ik3nERiO4suP6dOawM71aP/N8EHHPrSDia4C7KbwQV9B/x6k4HxyqbTdW8qp3sf6OWcrIn0uVMZ/fhuLLjdiGO9HZ3Kyb0h9Cper9eYlZgml3Wrq2miiRJFFYtFTE1NtT1eKpVCKpUCAExPT2Pv3r0YGBho+ywXBEEQlpZFFba6uCV4XK0b3AitdlYObuU0tZMsizwBlH5sEr36cfgDLxAIIBgMolKpOMYD8TZRUigSzcFgEOVyuWV5JBK04XAYfr+/qW4e1+cGNwNZk5uyaRvQavnt9F21izt0O8DnsbZ6GZoQcDNxIrijn/rQjZW1XZhCpzo6xYrzOty0xw28jtncv3Mtt9h0ckcWVgZO906nzMhAs7CNRCIoFApqCT63LEXyN0EQBKE73KvJOcBFGb3n2+dLdOiCq9sHkNfrRSgUAoAm0crXoSwUCmrpH35MHhdq2zYsy0IoFEI2mzXGu/I2NxoNFItF5PN55cLMXYop/pj6igQwbaMMjgS5KrdDn2hwKuMGbmV1itmlclRGbwOAjtdBOwuw0zUGQGXbFmErEE7eCW6Erukab3d/87KdXJTdWpMX4jrul3ujk2u2sHwgl2Fg5vlAGZFnw8DAAMLhMI4cOTIrcWrbNuLxuIrjFQRBEHqPRRe2+h99DrgbBDrRqSyfaeUCSBfCNIvL41h1Orn2kiitVCptXWv59nq9jlwuh1KphEql0iRWuVC1bVtlaqxUKkin0y3W4LnMKrsdWPPPdMHIv0c6f9N3qk9w8Lr078gUb+gkzPW1kH0+n0r4IeJ2ZdLOG6AbT4G5HBNon3Ctm/oWwrraqxZbTj+0UZg/bNtWMbU06Tub32/y2unWSqu3pVarubIQC4IgCEvDorgim8Qs0Y0LMuBuYDMX4UKWWIp57bYuevDWarWmJXr4Z/Rah2dLLpVKyGQyyOVyal1cErVErVZT23k/djv4M7WJu305DcZNn+vn6PE0J7jSLV0m64ubPuexuZ3Kezwz6xr6/X6JjZoj/TQpMBfXVadrdK7Cqpv9+6mvBWGhqVQqKJVKKlynGzweD4rF4pyEbb1ex+TkZFNiSEEQBKG3WBRha1q7lv/pokyPgaX3sxmotnMx5GX0z8j12O0DTE+AxJM3EZ3azpfsqdfrKJfLyOfzKhsy9QEw85DVlxTqBj1zspO41be1w2S15dmbTd+FHr/sJFY7iW7eN/o2j+foWsNzGdgI/U8317LuZdDp/nWaxNI/N7k2m+p2uhfd/AbORyxvL6F/H93GGQvLAycvqk50ynHhBtu25fkhCILQ4yy4K7KT2zH/TN8OHBW4+uBuNg813TWWu/W2GxwVCgWVbVhHF97AjAsyAKO7UrtYOV3cezweVKtVTE1NKdfkWq3WkhjK7/erpYTo+Hq/dRr06RZzGjDqllX9uzB9L6bvh/e523aZJkHcYIqz5SI3FArJ0j8rjNm675pc8mcroExeCW5coxfCbV6ufaGfafdMNkHPZEEQBGFlsOAWWz2+lrbNR71u4aKWr6mq1+N2G+AcZ2varotHXie1h5ak4QmiaF1ay7Ja4ky9Xi8CgQDq9bpysXWKHXaySPOkTfpMOBegTmK0kyWLW1baCVrapvedbsF1ilnk1lq+L7kdU5/qEwGC4Jb5tgqaxK7TZ7M9ficB3e8sN6u0MP/I9SEIgrCyWHBh62RBm43Q7STWuqlDX6zd1I5uj9cuflN3n6PjkegKBoMAji4RVK1WVVlav7ZSqah+sywLwWBQWUJJ3JGgaxfHaoK7JncSsk7v+Xa9DlNfmlySTdYq0wC2nTDg27g7MmWSFmaHTAh0h5M7stMEUjuvDieLLn9vOo6IP2ElI0vzCIIgrCwW3BWZL+fjBn1JmHYCieg0cOvWPVYXZqbX3WKKL6Zle0igUixoMBhUllgamJLgJesuCTaKtW1n5elWkHQS+Py/m+Rf7URpNzGPnc7Dabkf3t+0RJKwOKw0MezmOnVy43dTXt/u5v5ZLt9BO+EvCIIgCIKwoOYrk4W2XSwmt+TxpEPzKUSc3PNM1sP5hp8zCViPZyZjr9frVWvf0ueVSgW2PZMcij4nl+VwOIxgMOgqc7OTNYgnvKL2cbdkLoxNbsW8Dqfj6q+d+tgU90xt04/RzhrdycUzEAjA5/N1FaclzJ7Zelb0G05eBfr17vT7M1vr6ny5LQuCIAiCIPQ7C2q60q21JjdkE04WVBI4ulVX34/j5KLq1AYn0dsp1tRpMGkS5XRcipPlVkS+5iqtg8uPSVZdn8+nRK5+Xp3O0Q18oO12oOxkTXf7vXRyN3Yq69bVmmKXyRIuCAtFp3unnedJp5h0fR/Tb5T+2XJkuZ6XIAiCIAizY1Estk7xtNyawa20OvSZviyQkzjRra96PKcbl7ZuxZypTm4J1fuAkhnRex772Wg0UKlUlIuxbdsqvpYEWr1eb7K4mmJT250rR7fcOmHqH5NFtR2mfjXtrw/I3Viz+IQBXS8U90z9Qv0usYfdIRMB7nCKq3X6zM1+Qiv8d10QBEEQBAFYZGHbTZwtuSGbBnu6wHJTry50gVbh2Y5uBp760j36ZxTvSTGyZH0FoJb14dZpGsDVajVYlqXWuA2FQsqluVqtNlmJnFx9qQ3txCjPHE3LFtE5mSYhvF4varVaywSC03fDre9Ofelk5e02plB3ryaLLbl6CwvPSnFH1nFyi2838TSXfjJ5MawU4SciVxAEQRCEBRO2JChM292IXT4YNsXZ6nGxVHc7TAPs2Vhp9bK8Dl3U8nMkwUhlKEFUIBBQS/oAM+I2FArB5/M1CTJKNEVWx0gkgmAwCJ/P11RntVptcmHWk2TRIJC2eTweFe9LluBGo4FarQa/349Go6GWySEBqw+cfT6fEundZFZu17dOZWcjcAnqRzofGQwLc0H//en2euKTUHO9Hp3CLkzH7Edm81siCIIgCMLKoecstp3c9ebaHlP7Oh3XbZ18vV7u+qr/UYwsWWpDoRBisZiyxFqWpdxoeZIjvlwNLTzv8cws/UNiMhQKKWFL2+g1ueXyRetJ1Hq9XhXTS5ZkLn6pj/x+f5PVlluWqW2mY5riB2k/opOl2c13YbJG6xML1PfcVVlYWNq57PcTC9H+uYrjuRyP04tCsRfbJAiCIAhCb7KgFluTmF2ogW27AeFs3Zd53ab/XMjy5EQ8aRYlhuLlyFrIRS4XooFAAIFAAKVSqcktmItDbnHkMbr0n1ttnc6Jju/1elGtVptEHolfva+oDLWV/3ErMQljXieV4VZhXUDr36EuVE3fs0nQ8jhbbvHXJxZE2HZmPu9Z03fX68z2t8JkiXUbNrEUMbfdWncXw91Z4o4FQRAEQXDLgglbPRtyuz83dRFuBlr6tk7H5KLTBIlIKqP/55ZAfbtTP9B5kdWWxCWJVIq19Xq9CAaDqNVqqNVqqNfrar96va7KkUCj41F5ipHVRShZXsnlmARmp37jiZdMLsdctHKLLvUtF7YkfLnIpXaYxDLfpoth3dJcq9VaLMvkUu31elEsFlGpVLpOfiXMnuUmYjvtb7LE6jHopv07ueIvJm6su4shcN0cS0ILBEEQBGFlsygWW11MkvCjpW74H4lBAE0uo/SfrG26kHQSyboQ5W3TLammOkxCltrW7hz1z+m4pn4CgEKhgEKhAK/Xi3K53JQV2efzoVaroVqtKlFaLBYBAOVyGcBRQUmCNZ/Pt2ROBppjYvkyQ53Olc5H/35poK4nxeLiU4faZRLBurAFjroj60JWtxpzgc/FM6+brrNSqaQEvSSRWjh6TcxyYbQQbXMSrrrHiFO76LVu4e01wdbutwxYfOvyfMUpC4IgCILQvyyosDVtI0EbDAYdhS2JJi5qTa91AcbFK6GLV2qHbm2l93yApItdp/+EaVDrtPYulS2VSvB4Ztas5QKLW0DJqkiijbIgk2WS2khlfD6fipfVXW1J7PG+pMEg708ew6tbn6n/uWjVhS/1Jbfu8vPmMa5cgJIw112YdQHr5Apt+uOil44bjUZRqVSUuBWX5PllMQXtbI41n+0zCVF9eyf4vWL6rN/oVUEuCIIgCMLyZUGErS4KuRsrCdpQKNQibLmVUxevnYRtOyssDRr17TzWFWhdx5W7z5riP7l4pT9+TN1VlwQUPw5ZOMl1lvqP2qaLLhKMlUpFla/Vamq5ILJ40ue6SzC1hY5Blls+IKekVbrF2SR89b7iBAKBFvGpC379utH3pfby/iRRrZ+XyfrLv38qQ5MrNMHCk2YJZhbKwtkNvSqWeHuc2uamzctF1AqCIAiCICwFCyZs+R8tU0PL2tBr3cqnWzz1mDR9O33GBaXH42mqW7fu6VZeflyTWAJaM/fyNlKcq16er/NKmFxeeRkev2rbdsvSOQCa4lDpHKrVqlqah0QfF8q6wNb7lNqquyvrkwxOFlienIlbs3VXYF3Umqxc/HNaakgXtvprfo5Ox9MFgt/vVxmkSdjyPhOO4kbULrTo1S2hK41urste6p+5tKXbcxZ3ZEEQBEFY2SyIsOXL2gSDQYRCIQSDwSZBCxwVMJQ4yWSxJYsmtzKSqONWWxJXJMBIcJqEse4yy615ugWwHfpyPLrLMMHFKhdfOmS5BmassNwqSm60FG9r23aTlZaOT9t1gacLUSrP45pNfUXuwqaJBpMLJp84KJVKqh6TGDXF5PH9uZuwyfKr96O+XT8mH+xSUq5gMIhwONx0rnoirZXMYgraXhJkc0W/xt28bxfa0K1bsxPLqY/bYepLQRAEQRCWN/MqbEmQkIsnuRuTy7HuWqsndCJRx4Uxue6SqOVZgfUYWd2tlAsUpwGd7jrMt3N0q6TuDszR1241wS2o9J8SGVH76bx00cff81hXvryNLmpNYpqLcCexzd219T7hwpuEI3e11sUkHVOH2mzbtrJ+698XLUdkEre83k4CmC8vROcQDAZVu0lMU7bplT4gbnf/rBSRpNPpmuC/afo2t+/578J8WiA7/R72EvN53mLFFQRBEITlz7wJWx6vSUKWJycioQA4rwdLLrEkYrkVlqxpPMZTj7M1uf8C5sGcXk4XbXobdffcRqOhrLXtXGrbWWidcBqAcXdhmiTgAzbqL54ZmAtXJ2spnT/VzwfT3M2aJh90wa23WxfcuougLhj1PuKWYXItB45mgDaVp/Pl702xxZSsii+fxL0BqJ18Eqab7265sZgCyI3L82LCr1HTb4fTfapPWnVCjwPn25zez4al7tt2nhrzQac+6tUYbUEQBEEQ5oc5C1sSmeRirGc41sUR7QO0ikfdOsjdZPVsybwcveb18T+OaXDn1J5OVqrZLBOjCzrTZ05lTOfFB2v1er3pe6B+12Ns9f80qNatsqbBNYlnPT6a19nOCqxbcZ0sq7xMqVRCMBhUr/Vzovf6f/1cuSWfT7Rwi7aTwF1u1ls359JJCOl1zIdwcpp4WSjRq9fbSawuBJ2EsMl7o1uc+m4xLbg8JGOhjuemj8R6KwiCIAjLk1kLW+52TFZaLmy50NQHEWRp5OgxnpQ4iAtbACrG1CkTMtWli1z9WO3cBZ0suyaLbLu4UdN7E+0sCaYBvpMlicMTPunutwR3bSYLud4eXWzqkxC83fp7k8XJKSaWn5dejlzU6TNyS9br0S2+JlHN19Cl5X5M7dXXIaZ99P5fjsxWdMyn0DX18UL1ez98n3MVt6bfjMUQ6/yY+oTWXMV6p2O2Q8StIAiCICw/ZiVsyWLH3Y7pPxeNTpg+5wmNAChro26dpaRKuojt9F4/rm6l5a/bWXX1sty11mSBbdcXsxHBJnjbSIDRtnbuyNx1m/pVP0+TNVXv43aDZiexq1tmSXTqkJAlrwCPx6PciE0WW15PJ4std0fm36FpwoT6yjSZsZyYT0vaQlrmViILKQDdWq453YjDXrxXxDVZEARBEJYXXQlbGuxzd2NKDuUkirod2PIESCRidCuhbrEF0GLBpfbqYtZJvOouuKYyprJUziSy2kHWVH6c2boFOiWraici9URZ1Ff6+rS8vKl9PJZW38eNK6tueTUdl45DSxpRNmqT9dckcPXjcXFLopZbZPXrh3sNUDknK3i/slAC1I13gdAbdCvw5ioIe2Xio1tBLwiCIAhCb+Ja2JKg1eNoeYIo00DF7TYOiVsuxHSRaxKpbrfxczJtn81gyySM9Xbo5zjXQZ1pEOYkMul/JyHWLnaW9m8Xh+tG6Okuie3WteXbSFTS2rNOVmCeNMp0LJPrMxfG3GrL/7jAJddt0zrGvc5Sic1eETLzwXych4iouTPffSguyoIgCILQv7gWtsFgsCnzMYlaEqCmQWs7S2Qn66RuUdQHG2SNc2tV5WW72acd3bgZ6/XP1poMzE48dtqPTx7MFd1NV//edUurvp3OV28vuSNXq1VUq1VHV+R24pW/1i3RvD30up3AJZHv8Xj6Kv52KcXlbL0Seo25fM9O93k/XDvzzWzOeaH7SVyUBUEQBKE/cS1syd2YhK2+hqn+2mS1nI3ABVpdaTvt62bQ7GSt1dGP6SQO9T5wEks6fJDr5FrNrag85lgfIDv1T7u2L8TgzY1lWHcd1tvCLSf8ey6VSsYsxVys6uKW12ESt07fl5OLotN3xOOZhfZ0uu76Xfi2QwRTdyxVfy3na1AQBEEQliOzErbcgtjO2gU0WyZNA4V2yYZMAkWvx/S60zYAxmy47QSNG2tcO0HVCTfWX91iSN8FdwfnQtiN1dapfU7bnQSrbmXlfWDqTydXYVM7nMSoXka3/jqJWdM2fc1bvQ36OfDvhK+p3K3VXzDjZjJoIY8pomZxEbEvCIIgCMJc6UrYusl4rGMSpVwUmEQDWeTa1WkSxLqQMok7kzByOga5l5owWYl5AiKncgsBF7pc+NJrPW7WSXw5iV4nUazjdhLAqZyT1dtJmLr5vFN5/bN2kyPt4AJXWFgWQwQthdBajN8Kt79n3Z6/xKYKgiAIgrDUdJU8CpifTJhAqxWSPiNh2ElY6O1oJ3CpXnrtpm4nce02/sqNeJ4veCZpHVPSIy569f6n/+0su6bXJiFp2n8x/rsRs/TaybqvC1w3A389bloQusHpmppr2Xb7uPnMDSJqBUEQBEFYama1ju18oAsNSsBDgqqdVbebY9i2bUyI5FY469bXTgM4EjedBJjT8drVa3rPY4VNdegilbJb0z5OIldvk0modmtJ7cba2u6/mzKd+luPaXb73wm35QShG7oRjCIuBUEQBEFYybgWtgs9aCIBxkWYG5HRyZJmsj6a9jPV285V1U19Tm3Wcdu33dThJHJpksD0OXdb1v/0ejsJR6f26+LUJH5na611Oqb+ejYx2p3qF2utIAiCIAiCICwdS2axdcJklZuNOOFQxlqn47TbZhLOsxH58yF63LbZCb40jQnK6stjdN0K3HZtMVmbnUQtf2967XSsbi3e7ba3+67cui4LgiAIgiAIgrB4LLkrMofiRDsljiLciFsSHTwGdS7CdDZuxvTZQlq9nQSaLro6tYGvJcut3dz12Y27sn4c7lJO//VtfD+9v7oRtZ3eu00GZXo/VwuvIAiCIAiCIAjzT8+4IhM+n6+tqHFqkxvLm5OAcutuqr9vF//bzvI8X33pRnyZ3ndqAxe0+nnwiQIngeum3W7KO7mGt3M/N30f3Qhbt9eRExLnKAiCIAiCIAiLT8+5Ins8Hvj9/o5L/uh066ZriqGdLxfjxRI33brjdoJbUXkWaaey+jJCc2Uu7sSdYqDdIhZXQRAEQRAEQeg/XAvbbkTmfEAiay7uw7M5ZqfYzuWEU2yo2++60WjMWty2E+VLFcMsCIIgCIIgCEJ/4lrYmpbMWSzINXauQtOt+HET2+lWiC2mKHaTIMkp3na27SRxO5c63OImuZcgCIIgCIIgCCsP18K2Xq8vueXSaTmYpWqL2+1u2+l22Rqnz51iSU0xqN30XTt35G7rclNvryCiWRAEQRAEQRD6g56y2HYrtnpJ6C40nc5tsc+dEkg5LR0kCIIgCIIgCIKwWPSUsJ0N7ZaJWc5Cd6ngSwh1WppJEARBEARBEARhMeh7YesGEbjzA3c77tZS2+/fgbglC4IgCIIgCELv0rNZkYXeYyVbwlfiOfcy8n3MDZmo6YxcY4IgCILQX/TcOraCIAg6IjLmF0oiJ5iR600QBEEQ+g8RtoKwQpHB+8pGvn9BEARBEJYT3qVugCAIgiAIgiAIgiDMBRG2giAIgiAIgiAIQl8jwlYQBEEQBEEQBEHoa0TYCoIgCIIgCIIgCH2NCFtBEARBEARBEAShrxFhKwiCIAiCIAiCIPQ1ImwFQRAEQRAEQRCEvkaErSAIgiAIgiAIgtDXiLAVBEEQBEEQBEEQ+hoRtoIgCIIgCIIgCEJfI8JWEARBEARBEARB6GtE2AqCIAiCIAiCIAh9jQhbQRAEQRAEQRAEoa8RYSsIgiAIgiAIgiD0NSJsBUEQBEEQBEEQhL5GhK0gCIIgCIIgCILQ14iwFQRBEARBEARBEPoaEbaCIAiCIAiCIAhCXyPCVhAEQRAEQRAEQehrRNgKgiAIgiAIgiAIfY0IW0EQBEEQBEEQBKGvEWErCIIgCIIgCIIg9DX+pW6AIAiCIAiCIKxEvF6v+vP5fEvdHEHoKTweD7xe93ZYEbaCIAiCIAiCsARkMhmUy2VUq1XU6/Wlbo4g9BR+vx8+nw/hcNhd+QVujyAIPYrH41nqJghCz2Lb9lI3QRCEFcA3vvENnHjiicjn82g0GkvdHEHoKfx+PyzLwhlnnOGqvMeWp7cgCIIgCIIgCILQx0jyKEEQBEEQBEEQBKGvEWErCIIgCIIgCIIg9DUibAVBEARBEARBEIS+RoStIAiCIAiCIAiC0NeIsBUEQRAEQRAEQRD6GhG2giAIgiAIgiAIQl8jwlYQBEEQBEEQBEHoa0TYCoIgCIIgCIIgCH2NCFtBEARBEARBEAShr/n/AISaNLyw3iWpAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch [3/5], Step [1/1], Loss: 1.6677085161209106\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch [4/5], Step [1/1], Loss: 1.368072271347046\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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jcX/7fffd5wHwfvCDHzjrFzYvzwv/e3j33Xd7ALxvfetbk85n+7246qqrvEgkUvVbd9FFF3kuaWjeL6eddpqXTCa9J5980t/27LPPer29vd6xxx7rb6vnPmkFdEVuc3p6eiZFRz7wwAOxcOFCrFy5Eueddx72228/3HzzzchkMpOOv/XWW7Fw4UIsXLgQhx12GK699lqce+65+NSnPjVdVSBkyli/fj322msvHH/88QB2u8q86U1vwvXXX+90Bw7DUUcdhdtuu23S39lnnz0p7T/+4z9WfT/mmGPw1FNPTUr3pje9qcpTQkZUdVptDc7lchgcHMRLX/pSAMC9994b6tw7duzAyMgIgN1uw5VKBZdeeimi0eqfe7H+3nbbbRgaGsLZZ5+NwcFB/y8Wi+Goo47yLThbt27FX//6V5xzzjn+iDOwO8DHmjVrJpWNzH48z8MNN9yAU045BZ7nVd0/a9euxfDw8KT79txzz0UymfS/254DYPe7T8+vSyaTeMlLXlKVLhaL+XlVKhXs3LkTpVIJL3rRi6zPS61ncGBgAL/97W9x3nnnYZ999qk6Vp6Xeur885//HEuWLMEb3vAGP59MJoMLLrggsF3DoH8rhoeHMTg4iOOOOw5PPfXUJHfHVatWTZqTeMstt2Dp0qU49dRT/W3pdHqSd9hf//pXPP7443jzm9+MHTt2+HUdHx/HCSecgN/+9reoVCpN14e0hnqfCZOdO3fi9ttvx5lnnonR0VH/eu/YsQNr167F448/ji1btlQdc8EFF1R5Ex1zzDEol8vYtGlTU3X5z//8TyxcuBCLFi3Ci170IvzqV7/CBz/4Qbz3ve/10/zgBz/AwQcfjIMOOqjqWXzlK18JAJM8EP73//7fOPLII/3v++yzD/7hH/4Bv/jFLyb1Gcz364033ohKpYIzzzyz6lyLFy/G/vvv759L3o+/+MUvrFOD6slLCPN7GIT+vRgfH8fg4CBe9rKXwfM83HfffaHy0JTLZdx666047bTTsO+++/rblyxZgje/+c34/e9/7/dDhKm6T0zoitzmjI2NYdGiRVXbbrjhBvT19WFgYABf/OIXsWHDBmcgiKOOOgpXXnklyuUy/va3v+HKK6/Erl27qjoWhHQi5XIZ119/PY4//nhs2LDB337UUUfhM5/5DH71q1/5rrb1smDBArzqVa+qmS6dTk9ytZs7dy527do1Ka3ZUZYOtk67c+dOXHHFFbj++uuxffv2qvRmZ7VWnn19fXjyyScRjUYDhefjjz8OAH5HwKSvrw8A/JfP/vvvPynNgQceGKrTRGYXAwMDGBoawje+8Q184xvfsKYx7+MwzwEALFu2bJLr/dy5c/HAAw9UbfvOd76Dz3zmM3jkkUdQLBb97atWrZpUllrnlk5i0Bq69dR506ZN2G+//SbV48ADD3TmH5Y//OEPuOyyy/DHP/5xUud5eHi4avDJ1habNm3C6tWrJ5XNjCgvvw/nnHOOsyzDw8Oc3tRG1PNMmDzxxBPwPA8f/vCH8eEPf9iaZvv27Vi6dKn/PewzXS//8A//gIsvvhiFQgF33303Pv7xj2NiYqJqkPbxxx/H3//+d6fLu/n7Y3t/HXDAAZiYmMDAwAAWL17sbzfb6/HHH4fnedY8APjTGVatWoX3vve9+OxnP4v169fjmGOOwamnnoq3vvWt/nMZNi8h7O+hi6effhqXXnopbrrppknXxda3qMXAwAAmJiasv2UHH3wwKpUKnnnmGRxyyCH+9qm6T0wobNuYzZs3Y3h4eNKL5thjj/Xnr5xyyik47LDD8Ja3vAX33HPPJKuM7qCvXbsWBx10EF73utfhC1/4QtWoFyGdxu23346tW7fi+uuvx/XXXz9p//r16xsWtmGJxWJNp/X+39wdYPec2TvvvBMf+MAHcPjhh6OnpweVSgUnnXSS1SoSJs9aSL7XXntt1Utd6ITw/mRmkHvnrW99q1P4mMvVhb1nw6S77rrrsG7dOpx22mn4wAc+gEWLFiEWi+Gqq66yBkhs5fNST51bzZNPPokTTjgBBx10ED772c9i+fLlSCaT+PnPf47Pfe5zk34rmomALHl96lOfci4h0tPT03D+pLXU+0yYyPV+//vf74w8bPZJW/Fc2Vi2bJnff33ta1+LBQsW4OKLL8bxxx/vzwuuVCo47LDD8NnPftaax/Llyxs+v/ncVCoVRCIR3HzzzdY66+fgM5/5DNatW4ef/OQnuPXWW/Hud78bV111Ff70pz9h2bJldeUFNNfG5XIZr371q7Fz50586EMfwkEHHYTu7m5s2bIF69atmzaPi6m6T0zYY2ljZPJ2UFjznp4eXHbZZTj33HPx/e9/H2eddVZgnieffDKOO+44fPzjH8eFF164x4cvJ53L+vXrsWjRIj+Kn+bGG2/Ej370I3zta1/rmGUtdu3ahV/96le44oorcOmll/rbxWLSCKtXr0alUsHDDz/s7JRKII5FixYFWqlXrFjhLM+jjz7acBlJ57Jw4UL09vaiXC6H8nBoNT/84Q+x77774sYbb6yyZjS63Je41P3tb39zpqmnzitWrMDf/vY3eJ5XVb6wz4tpoRF++tOfIp/P46abbqqygtgCPwWV7eGHH55UNjNCtfw+9PX11ayvq7xk+gj7TLiulTwDiUSipc90K+6NCy+8EJ/73Ofwb//2bzj99NMRiUSwevVq3H///TjhhBNCncP2/nrssceQyWRqBjpbvXo1PM/DqlWrcMABB9Q812GHHYbDDjsM//Zv/4Y777wTL3/5y/G1r30NV155Zd15hcFV/wcffBCPPfYYvvOd7+Dtb3+7v/22224LnYfJwoULkclkrL9ljzzyCKLRaFODCs3AObZtyu23346PfvSjWLVqFd7ylrcEpn3LW96CZcuWVUVCDeJDH/oQduzYgf/4j/9oRVEJmXay2SxuvPFGvO51r8Mb3vCGSX8XX3wxRkdHJy1P0M7IaKY5evn5z3++4TxPO+00RKNRfOQjH5k0KivnWbt2Lfr6+vDxj3+8ym1NkHD8S5YsweGHH47vfOc7Va5Lt912Gx5++OGGy0g6l1gshte//vW44YYbrGJwKpZyMM8PVD8zd911F/74xz82lN/ChQtx7LHH4pprrsHTTz9dtU/OUU+dX/va1+LZZ5/FD3/4Q3/bxMSE04XZRAaeh4aGqrbb6j08PIxvfetbofIFdj/3W7ZsqfqNzOVyk/oFRx55JFavXo1Pf/rTGBsbm5SPrq+rvGT6CPtMSEwW81otWrQIr3jFK/D1r38dW7dunZR/o890d3d30/dFPB7H+973Pvz973/HT37yEwC7vZy2bNli7c9ms1mMj49XbfvjH/9YNW3mmWeewU9+8hOceOKJNT2wzjjjDMRiMVxxxRWT3tOe5/nL8o2MjKBUKlXtP+ywwxCNRv0liMLmVQ/1/F54nocvfOELofMwicViOPHEE/GTn/ykakmvbdu24bvf/S6OPvpofxrTdEOLbRtw880345FHHkGpVMK2bdtw++2347bbbsOKFStw00031VwUOZFI4JJLLsEHPvAB3HLLLTjppJMC07/mNa/BoYceis9+9rO46KKLJvnyE9Lu3HTTTRgdHa0KfKJ56UtfioULF2L9+vV405veVHf+W7Zssa4f3dPTg9NOO63u/MLQ19eHY489Fp/85CdRLBaxdOlS3HrrrVXzh+tlv/32w7/+67/iox/9KI455hicccYZSKVSuPvuu7H33nvjqquuQl9fH66++mq87W1vwxFHHIGzzjoLCxcuxNNPP42f/exnePnLX44vf/nLAICrrroKJ598Mo4++micd9552LlzJ770pS/hkEMOsXZ6yezn3//933HHHXfgqKOOwvnnn481a9Zg586duPfee/HLX/4SO3funLJzv+51r8ONN96I008/HSeffDI2bNiAr33ta1izZk3D9+MXv/hFHH300TjiiCNwwQUXYNWqVdi4cSN+9rOf4a9//SuA8HU+//zz8eUvfxlvf/vbcc8992DJkiW49tprrYEebUiQm3/913/FWWedhUQigVNOOQUnnngikskkTjnlFFx44YUYGxvDf/zHf2DRokVWMWLjwgsvxJe//GWcffbZuOSSS7BkyRKsX7/e72+I5SYajeKb3/wmXvOa1+CQQw7Bueeei6VLl2LLli2444470NfXh5/+9KeB5aVnWGu55pprcMstt0zafskll4R+Jrq6urBmzRp873vfwwEHHIB58+bh0EMPxaGHHoqvfOUrOProo3HYYYfh/PPPx7777ott27bhj3/8IzZv3oz777+/7jIfeeSRuPrqq3HllVdiv/32w6JFi5xxHYJYt24dLr30UnziE5/Aaaedhre97W34/ve/j3/8x3/EHXfcgZe//OUol8t45JFH8P3vf99fv1k49NBDsXbt2qrlfoDdy+LVYvXq1bjyyivxL//yL9i4cSNOO+009Pb2YsOGDfjRj36ECy64AO9///tx++234+KLL8Yb3/hGHHDAASiVSrj22mv9QbF68qqH1atXY86cOfja176G3t5edHd346ijjsJBBx2E1atX4/3vfz+2bNmCvr4+3HDDDda5rfIMv/vd78batWsRi8WcnqBXXnklbrvtNhx99NH4p3/6J8TjcXz9619HPp+ftOb4tNLSGMukLiQEtvwlk0lv8eLF3qtf/WrvC1/4gjcyMlKVXkKQDwwMTMpreHjY6+/vrwqnvmLFCu/kk0+2nvvb3/62Myw4Ie3OKaec4qXT6UlLXGnWrVvnJRIJf0kOtGC5H72kzTnnnON1d3dPOl6eU0GWGtFLFAgwwudv3rzZO/300705c+Z4/f393hvf+Ebv2WefnZTO9VsgvynmchvXXHON98IXvtBLpVLe3LlzveOOO85fIkm44447vLVr13r9/f1eOp32Vq9e7a1bt65qaQTP87wbbrjBO/jgg71UKuWtWbPGu/HGG71zzjmHy/3swWzbts276KKLvOXLl3uJRMJbvHixd8IJJ3jf+MY3/DSy3I+5BIY8H/pddNxxx3mHHHLIpPOY91mlUvE+/vGPeytWrPBSqZT3whe+0Puf//mfSenqeQY9z/P+9re/+c9hOp32DjzwQO/DH/5w3XX2vN3LiZx66qleJpPxFixY4F1yySXeLbfcEmq5H8/zvI9+9KPe0qVLvWg0WvVs33TTTd4LXvACL51OeytXrvQ+8YlPeNdcc82k5z+oH/DUU095J598sr9k4Pve9z7vhhtu8AB4f/rTn6rS3nfffd4ZZ5zhzZ8/30ulUt6KFSu8M8880/vVr34Vqrykecw+o/n3zDPPhH4mPM/z7rzzTu/II4/0ksnkpOfgySef9N7+9rd7ixcv9hKJhLd06VLvda97nffDH/5wUnnM5YfkWdf393PPPeedfPLJXm9vrweg5tI/Qe/ryy+/vCr/QqHgfeITn/AOOeQQ/x135JFHeldccYU3PDw8Kc/rrrvO23///f32MZ/DoL625+1+Bx599NFed3e3193d7R100EHeRRdd5D366KOe5+1+rs477zxv9erVXjqd9ubNm+cdf/zx3i9/+cu68/K88L+Hnrd7CbM1a9Z48Xi86nf14Ycf9l71qld5PT093oIFC7zzzz/fu//++yf99pZKJe9d73qXt3DhQi8SiVT1ZWy/lffee6+3du1ar6enx8tkMt7xxx/v3XnnnVVp6rlPWkHk/xWWEEIIIYTswXz+85/He97zHmzevLkq8i0hnU4kEsFFF13keyCR2Qnn2BJCCCGE7GFks9mq77lcDl//+tex//77U9QSQjoSzrElhBBCCNnDOOOMM7DPPvvg8MMPx/DwMK677jo88sgjWL9+/UwXjRBCGoLClhBCCCFkD2Pt2rX45je/ifXr16NcLmPNmjW4/vrrGwq4Rwgh7QDn2BJCCCGEEEII6Wg4x5YQQgghhBBCSEdDYUsIIYQQQgghpKOhsCWEEEIIIYQQ0tGEDh4ViUSmshyEzBrafdo6n2VCwtHOzzKfY0LC0c7PMQC8+93vxoknngjP89q+rIRMN/Ku6+npwfHHH18zPaMiE0IIIYQQMgOIoKWoJWQy8lyEfT7oikwIIYQQQgghpC0J66VEYUsIIYQQQgghpKOhsCWEEEIIIYQQ0tFQ2BJCCCGEEEII6WgobAkhhBBCCCGEdDQUtoQQQgghhBBCOhoKW0IIIYQQQgghHQ2FLSGEEEIIIYSQjobClhBCCCGEEEJIR0NhSwghhBBCCCGko6GwJYQQQgghhBDS0VDYEkIIIYQQQgjpaChsCSGEEEIIIYR0NBS2hBBCCCGEEEI6GgpbQgghhBBCCCEdDYUtIYQQQgghhJCOhsKWEEIIIYQQQkhHQ2FLCCGEEEIIIaSjobAlhBBCCCGEENLRUNgSQgghhBBCCOloKGwJIYQQQgghhHQ0FLaEEEIIIYQQQjoaCltCCCGEEEIIIR1NPGzCSCTif/Y8b0oKQ0gr0fcsIYQQQgghZPYSWtjGYrGWnZTCeOqhqCOEEEIIIYTsKYQWtvH480mnQpg2k+d0CuWpFoydLEhbfR1mqi06+RqEpZ3rOF3Pczu3ASGEEEIIqY/QwjYarX86rquDam73PM/ZyQzq5E635ddVxjAd5KnqRDeS71S1WyQSmRFrfFAb1Gofipv2g9eEEEIIIYTUS0NzbAG7ONLbXJ/DHFvPcTNB2I53q9PZjmllm0x1+05F/mHEtK19zfYz0zQjlkkw7fIc1wOvOSGEEEJIexNa2AJ2sWr+Nz/bvofNs9ax9TBVomq6j99TOtitrKft2oexvstnc5vOrxOvx3SUuRPFaxCzrT7TRSc+H4QQQgjpTEILW8/znGK2XiEaJp+g49qFZsvTbvVpllZ0YoMs0bb8XW7stcoS5CbvsuaKqDW/hznfnsJsu6dJc4S9H/j8EEIIIaRZGha2YcVorXw0LrdSdpY7g1ZcpyBraNBcbE2zgtM1B9z131XediVsOzaSLyGN4Lp3OuWZIoQQQsjMU5crMtCcqBVcljfZNxWuyGHKMBOdqEbqt6cIiFoDHy7Lqml1bcV1dVmQp+Jcncieck+S6WVPfqYIIYQQUh91C1uXeKj3ePlcz7GNnMf13/wctC3MeRop41SlbTaveiNRz3Qk5KBr2i6BuWY7ndRmUyWUOqHunQQFLSGEEELqoa6oyKaLp1hv6+2A2KxuLqtXraBUtQRskLANUz6NSzSZbdMK6pmrHBazjbX1PUze9UaubjSgmFnesJ9t38NQ7zJOcr3N60+mXuC2czu3c9lstLMQ77S2JIQQQsjMU9c6tjYrbT1zZsPMe2wk6qwpMlzbzHK4hFfQXN8gN2pXuSKRiL8OcKVSqWkprkds1oOUwfM8RKNRVCoVVCqVwPTRaNRvj1Kp5KcPI2BrfQ5yaW9GvDYrbsPk38iAyZ5EM54MbMv6aWRgrdF23hMHLQghhBDS/tTliuzq/IcVYmEteLUi45riNUjMBgnRIHEeVFZXnrb2EVGrBwZkWxjx7Sp7I0QiESQSCZRKJUQiEcRiMRQKBavlVpdbzyXVQth1DcOKWNe+WkK3UYIEcliLcCvLM1PMRNk7ub00rt+9dqhfI2Vo5PekGQ+dZvIhhBBCCAmi7jm2Nmp1/sNYamu5udYSs7btZj5hxFWYz2GtI7FYDIlEwreOlstlXzDG4/FJ4taFpKlUKk0L23g8jng8XtVOrnxF/Hqeh0qlgng8Htg2QcLX1c6uQYZGBxjCitewx5OZodnrMB1uts2UsZVTF5px/54Oqy2fKUIIIYRMBw0L21oW1maDDJnzZ2uJWXGzbdT9uB6xW6tTKvtisViViKxUKojFYlWCN4wrq9SvlutwLaStYrGYL0JjsZgvbIMspaYLctD/Rts66LNNRNvK6Sp/q6hlWW/m+uzptPJahc2r1m+S/m1rtQv8VN+bYWhUXLfKaksIIYQQ0ipCC9tKpeK70wLVHb0gQRTGXS/oeJuIdVlpdV71Cuta7qdBrtOu40TEynZx7Y3H4/4+1/lMxFoq1tNmkPOXSiWUy2V/e1DbAbBaY00RKuLbLKc58FArH70t6C9s2U3CCOEgV3Htni1lKZfLVe1JwjGTFr2w56418BQ2j1oDgmFxCe5682rVAECY87SruA07DYQQQggh7U1oYStutGFcZzWuTkKYzkO9otY8Nui8YeZ1BpUnDCIgtWVWXIHFFVnnWwux2rZiDmo8HkcikaiyAmux6BLPZnuZYlcErYg8l/uw1NmWn00Ym+d3lUe7i7twdbJt222eA3LdBAmq1axFfU+jE11Um/UMaLX7e1jPhTCEGfhrRvSFGTCcbjrxHiSEEEKIndDCtlgswvO8KitkUEfFZokz07iox1rryi/IddAmZvU5g6y9uj5a6JiWPbHMatdfAEgmk5OEraSvZXmR84mAMgVWWMxyawFaS1DWErTyvVwu+xZhMz997XS0aLlmsk08BMJabm33WrMdV9vxusxSPxHynSxsm7H8NXqeTiGsoO2Eurmuba2yh3HHbuac0yVyg+rgmjJBCCGEkPanLlfkYrHod9x18COdxhQXYa2RpmVM/usAS6agreXe5hLe2o3Udn69zczL9d+sr55fKy7HQLWwrccVGagWVEFpaokr01Islludt2swwiZozeNE5JZKJRSLRX9QRJ9TPptl1mJb8tX3lbbm1ityNWFdS83yymcRsiLeRdR2QmfYdl+bTJXraCcIP5Na4rXT6tSox00zVls9jaVW/vWWp1VQ1BJCCCGdTV3Bo0RQyF8kEvEFru7U13K3s82RNNO5LLRBls16LMNhrMa1LL42wQPs7sSJC7J8l3KnUqkqF2V9jIgJLfJ0eUwLpqudg66DeZzurAZZbV0u27agUnJ/aEGv557aokHrQQDdXiIe9X1nCl1T5OpyhLXi1uok62sjYlaL2tnQEbbdG83Wq9NEnybMfWJ7ltqVRq6l7be23vvCNghYa8pAo1blRnH9vhFCCCGkc6g7KrJ07LWQEHFrjsrbOskiAqQTbROukr6WsK1lnQvrcmbb5hJDptjUn6VsWpzppX2i0SgSicSkJXcE0yKrXZhlv0vUBlmb9bVyWXPlXCJA9blkEENbu13n1PlLW9hckc066/96HnKpVPLvN33vuESutJ8peoMIs1/cjaU8LjfrTqOW50IY91STesRHM1bAmWQqBW29lstm8m3k2WjEE6KWd4yZd72Ct1FopSWEEEJmDw0t9yMder22qbZCaqGpOyja0msKVdt8UZuwTSaTfhlEhLk6JGE7KjYRG9SxslmVBdPNGHjeYituyHqpH5erq3zXllzZpv/rtPq/6SYswkyuj2lBlTm7euDAFuXXFK9acOr7Qv5KpRIKhQJKpVJVnbTFWgt/ab90Ou1/N4Wt7TxmECx9b7gs/Gbb2Sz+kpcpaGeLlRaoLW6DqOUhEHSMy3NgJqnXmt+I9b8VtMKa3sjxYYSw7bqGHWSsV/CGqYOtrShoCSGEkNlHU+vY2pY20RZKbSn0PK/KqmuKWr1Oq7aO6nSRyPOuz4VCwS+H/Ld1YMJ0qMJYMnQZ9HGmSzGwex6tbJN6i2ATi63UGcCkYFw24a8Fpc1qrfe5ohybllLT2ijuw6aLtLksUFAbmtZSObZYLFbVS4S0BCOrVCp+u8XjcWQyGb8sExMTKBaLvsiNRCK+UHZZlMrlMgqFQt2iy8zDFLWzNfKx2U5Bltt6njGTIKHTDgIjjKhvRsw2ch+6RGyz4jbMYIJ5jlrnDLJku+6hWsKzVhlrESRi2+GeI4QQQkhraFjYAtXWOu0SarrbSsdFd57NubI6T+2mbJuPabqh6mNt+YXFJi7N40WEarFuBocScSaIkNXWXG3h1iJXn1sLX2lrKZ+0rQ5apMtkuudKGeW7zl8EuuRvugdLGh2NWZ9Ln0PyEbGq62Yb0JBySXuJB0AsFkM6nQYA/x4TUSu43IOLxSKy2ayfXt9DtgjW5v0l5zLF7J7QCdbCzvV5OiyS00kYoRqUJkjMuc4XNDhgs2C6fisbvRa2c7ju73rFbS1sdQ3KU787XPkEQTFLCCGE7Bk0JWyB58WtaaGTSLuJRGKS663MNRVBZcvTZRUrlUqTrL6tFh3SYUokEn55tNjW0Yy1cNPutNpqC+wWfSL4U6lUleXWJqj1ckHaJVYQYSzCTredKWrNJWmAaoEr/3U95b+cI5/PT7rW5nUS13Qd8Em3j1w7m7g0y5zL5dDd3e2L5FqWdymTiNqJiYlJ9bN5AJhot22XlXq2YxMb5mdNGBEYdB753KxgqkVQuRq1uLqEZ6NWX5dYbXW72MRsLat8rTIE1amefMz7whwUbbSMFLSEEELI7KZpYSuIlcu0oInAESEXiUSqXHLlT7BZcrU7rYirbDaLfD5fNf9S0mr3ZKB2QBO93+b2LGlE6JkdUL1N0sqcWhHhInbT6TTS6bS/zxTF8j2RSFRZanUdtbt3pVKpcrk159PqebVm3c120YGedN3keHFV1tdBuwSbHUstZOPxuDOIlLZGi2AvFotV9dXXRyy9prA2y6TLZQp42aePc0VR3lMxLbbAZPHVqNXSTNdKz4uwZajX2mmro2uAJIzIC1OmIEEXxtJqHhM2Ta1jbYLVRr0eM0Hpa10vWmYJIYSQPZuWCVtBRILMcUwkEigUCkgmk0ilUkgmk75YEutlKpWqstzKZ5tlT84h8y5dS8HoCLqAfe1XLSZN4Wq6B+t02kKo0YGxRHyJQJWASOl0Gt3d3b4VWwtGaRtx8U0kEr5rrbRpJBLxXXQBVIk4sZjL+UUYRiLPz2nV23R7utpI9pluyeVy2R+QMNvCHCjQLteyX9JoN2UR5FL+QqHgD4boedj6PLoN9X/bvGVbvU1RS6qxiSgtbk0Lo8uCae53uaC6LJbNiNBWYgpZ2z1mE6OugZVa1tkgy2rQwJ32vjDLb+YXZl+t/a662NI1Qj3Xk88xIYQQsmfScmGrqVQqyOfzKBaLyOfzyGazvtWyXC6jp6cHAHwRp+eiasuc2UEUoZjP550RamuN/NvEbK1OpmkZFjEknVWzcyfCVsR7T08PEokEenp6qpbXERdeXddYLIauri6Uy2WMjY2hUCj4ZUwkElWdY1OAS9trIanT689moCldFzMolmmRNteqNcWhLVCYiRn0SoS6XFdzXVtdR/ku7s9S5iDXZVNQUdSGwyVENWEEr/ld7mnz/muURgVtkNALOldYi3WQ2G2kbDN5v5rXWbaZmPeAaemvVQe5J8JYhvUxhBBCCNkzmVJhK+j5j/l8Hrlczl8GJp1Oo6uryxe92kVZ5udq0SeiNplMVkX6tbna2jAFsy3asc7PJfzExVen1wGiRKBJnbQQ1FZa4Hm3YxHNOvgSAN9iK8cUi0XfKinnFhdcHWhJt712E9fiV9fNrLu2TJtWUROXq6TZxnJeV0dYyl0qlZDP5/066nnNul6C1E3uMVtnWMqg89hT59E2ShiBa+4PclttxHJoy8e1PazQCSsyzXupXqZbeNncxqf6PPXsd90brnaqNahCCCGEkD2XaRG2GpkTms1mAewWbYVCAfl8vmrOrQhYLW7F+ul5XpVANoUZYLcQ6eVlbJZEU9yZgs5mldUdKim7CDSZV6oDNnmeh0Kh4JcBQJUoFdEnwl+O0/nIfxH3cj4tVM366O/mvFSdTs5vE/ZaBJrWlCCrim4/06VVp5FrIUK8WCxag1yZmO7kOv8gSyA7wo3TiMA1v5sCtN7r4bIWNpqPSRjrYKMCdypx3edhLKQ2XIN+tvPYroXLalvr/PWIVT7LhBBCCJl2YSuIBVcLJLFcivupfBbX5HQ6jXg87gvhXC5XNZfWFUlZBzAS0WzO+9SYlj0t8rR1VwsnyS8SifiCTAtSz/N8a6JEWxYBK9ZcEbZSZl13XU/5EzFby1XP7IiaFludzhSrUk+bddcWbEkPIGghrMtZy7Ijwlb+dPRoGy7XdVtnnLQW22BFGIEUVix1AmGslYJtYKcT6my7dkHPWRjX5HrPSwghhBASxIwJW+B5kaTdXbVA1YIPeF4MT0xM+NZabbE1Lau6k6WXz9FCyAyKpANbucos5THXqpUyauujLeiUIFZJObe44ZpRjHX9xNKrjzM7zjariSlIXdfCtN5qEWwKWTO9lE0PTAjJZNKvmxb9Gn0+aRsJICV1kzzMQFIyz7bW0kBkatCi1jYHM8zx+n9YagnpZnAJMZsFMky5m7UsN3tcs/m56llLxDYiaAkhhBBC6mVGhS1QbcGoZXkUISNiVqglasVC64p4LGJQLKSSRgtUbU0FMMk6KCJOxKfkJX9aWJsBqLQIlry0xVbqDMCPGizH23BZwkwXZG1xdglfW2All1uwtnSLRVkPFoi4lcEBHdHavP5S13w+7wccE/FqE+Yyr9llhSdTh8vdtJbbqb7/bPuDsD0DLuHY7JxdW9nMZ6Bey2LY9PUODDRCGNdg11xY1zGN7HdZ9Gm1JYQQQkgYZlTYmp3hILQlr1AoWMVQLVFrrhmrBa225JpzPyVvEaH6eP1ZR1c289Fu0HqNWm15daEtttpqKd+12DMtqqZ11LS2uqynLsuMtghrS7S0lbmkjywLZBMdWtzq6NZm+UXUmwGkdJ7add1cV5i0B2Esd41es7CuwM1gG8hplFZaa6fqPg+yVrfCSs5nlBBCCCGtZMYtthrXHFmNnr8KoMoCCmCSqDUjKtv+i1VURxk2Ax6Ji6220GoXWfmsha+5Dq9YLSUAllg05Ry6U6jPaQpXOY9ZX5dLthavpsunKWptlnMt1G3CVq8dq4/V7sg2K6q0gVjgtcu5lEmXPZFITCqDRq5pIpGYFBmZtB+tErEuMRXWUhsmT/PZdH2ezntuuu9v04rqIkj0ttLVnBBCCCFEMyPC1uV+CFRb/QTdiTKFred5vvXSJWq1a6+2zJrrv0onynQ5ljx1eslb0piWStscXhG2yWQS+XzeT++aX6iFoxZ6riWKzPbQHXvbUj3md11+Ob8OiqXLpK+LFtD6O/D8YIE+p2nJLpfLKBaLfkRpW6ApvaatuUSPdhvX7uSkvbBdE9e9HzaPZq9zvcc3ej5buWvVeSoFXT1u143mbXNdtrmo12oLiltCCCGEhKFtLLZBYhfYLZAKhYJvvdTWVPmuxZctArJpqTXdhbU1VIShFmY6fy0uzaVmzG1moCPJQ9BCTgSaFvh6zVwzcrKer6uts7o+traWc5llMt2rZRBA199sD9M6rN2mzXYxvwPPu2mbgxYaCRyWSCRQLBatFm5zQIFr1LYvjVrvbJ9d22qJpXq2uwjjXl1PuqmkkfObv48uK7hpzQ2qb639hBBCCCGNMGPCtpaQBaqFpAhYLeT0f0FErQ7eZC4F4wowpDtmtjRa+On5tFpk6WWETEEo80WlXIVCwbew6vPq9Pl83g+KBKDKXVfKJLiEnOm+LNtMIW4GrzIDWEn+umOqr6OIcS1y5VjTAquvrS6DXsJIl9vzPN+iG4vFAucEm9Z10n7o6xX2d8D8HLStkTRTcawrj7Du0c1YKhsR72Etp7WuQ1g3cLOONoFsS0cIIYQQYtI2FlvAvuSMCD0RPJLOJuzEwphKpSZZbbWlVo4z3XJ1Ploc6c82t2NTgNusw9IxK5VKyGazSCaT/txSmT8KwA+gJEJeymbr2JkRlU33Y0mj66VdiyVf2/xZPQggc1t1NGr93+x86vnPiURikpuyeW3lfDoCtW2ubywW812WxTVZLLy6U21a4snMYVr5bB4SQQSJ2lrXuJ57oF5xbEsfVJ+pEGitrp+ZzlbOestvu/Y2a60tXwpZQgghhNTDjFpsgefFoU3UCrJfuyELWkTp5XWSyWRVlGNTqIr10RSEWsCagtX8rPOVz+bcXrOuItBNYWnmJ9tEvEqQJRti0ZRzmcGizHm55lqvMifVrJfkLeJUroGkMa+BpJHyaOutXDtzKSFdVxkQEMu2eS/IUkemK7hpOaawnRla7V5qWvfNz63Kv94yAMFuua58mhFqjYjJZnHVr5G6TNU1JIQQQggRZlzYCtr91kSEnWsNUwC+mJU/M0CU7qSJ6NLWQp1GW13N7TbLoE3YipVTl1fckE0xLRZKnact6JINveSPLJ8jn8V1W8pgzjvWddJL5GixaFqDzcjH2v1Y8tOfRRRLGW2dWi1s5XrIOc1IyWKt1SLY5oKtrdKkfXENZNX63Cj1WmaDqCXkXfvC3petErPNWMzl+FaI27DwuSWEEEJII7SNsHXtE3HoErTicixiNp1OT1qP1rToyTbbPExbACVzn+Sj3Wd1sCURtRqph4hqEYqmKNZlk/mkEghJC2Cdr14L1lzqR44x3bBN12ozAJbZJlqI646yCF2bG7R0pGWerW4HnU6Eqq6vbm9ZFqlSqUyyNutrpa33It5dVm4y/dQrTF3W0uk4d6PH6gGhMOlc1CM6w263/SZNl7h1/TbUmw8hhBBCiIu2Cx5lupjaLAxaoImoTafTVYGjdHrBlqfko/ebws90mdXiSx8veYiwNdfX1RZdcx6q5G/OjRVXYGD3Ukdm+5mi3ayjLrOs8aottlrUmu1guocHdaJ1fc10WtjqtXtt83R1m8ZiMeTz+SqBqq3t+nqZmNeItD8262yzoraVFtqg40xPhUax/SY24/YbNm09853NAcJ6LcuNuKzXU05CCCGE7JnMaPAo7fZaq6NjWmzFcieWWu1ia+s86Y6y6Z5sCkHTYquXDDJFn7YmizAV8Wb+JRIJZDIZPyIyUC36dB5mRy7INVmXXbtaa0GuhTmAqjnGZhvrgFdmWtPiosuhrbdmuUxxa6uHWH3F5dgWkTkajSKVSqFYLFYJfT2YoQcRaA2afhoRdq0UtWGPa1Y069+vMPk1Ux/X1I1W0Yz11vytcu1v5FyEEEIIIWFpC4ttkLjVQZR0erFkmnNqdX42bMGfbFZOLW5NoSfHaousFsJ6DVpdXhGb4lJrs87aovzqObISVMnWWTQFrilqRezpayB1MIWz5Cn7dNlqzYs03YJ1G0i9zXmxpmDWlma9tq+kMcsr59HnbXXnn7QOl3VzqkVtK+6JZi2yjQr/sGKwmXPI8Y2Ux/TEaOT8FLyEEEIIaZQZt9gKQUJEp9NiM51OI5FIVC3pY4pTE1Oc2iIeiwDU0YJNoWlaanV+usxyXDKZ9N2AbQGazA6h/lwsFqsEqjmf1eYaaIpY+S511EGZ9PxWLUDNa2CKR9c1cqHbFng+yrEZKVnaSIvcWCxWtQyQy43dbHt2lmeWegRWPaK2UVfWVtCocAxTjiBxOV0DNc08M2YZ+ewRQgghZLpoi3VsbVZTjU3kJBKJqrmpcqw5/1LnL59tc3Xlu+RvswDLMjRaiNWyAJtWT6mPrMMqaCuvWT4dnEkspjYREGSl1u7UAKoiFEvekcjz831d18kU1rbyyjldAb8kH10vU6jreppu1K7OMi20nctUidpm74kwg2MzjU2Iurw6mgkW1WjZGs2fg1KEEEIIqYcZF7Y2EWqiRY5ep9ac++oKPGUKZpuFVo431741BZdYHG3i2CZw9T5zPVlJqwWcWIJNV2axHufz+ap5tNo92eXeLcJWn9sMYKWtz7YBBlOk67m0QZhBonR+Osq06Wpus+ZrS6/t3NpCyw5xe2MbYApK2+g52pkg4RZW1AUN2DXDVD0/zVi7CSGEEEKCmFFhawo/l1VOi1oJLCSRh22i1hSZ+hz63DqNzaqpMS2lZh1cllqxNpZKJV+MS0AkHelXo0WouObKMSJkJZ3LoqTLZauXtg6blmvTWuoKxCVCXAvXRubduQY1dMAo3R56SSOhlZ16Mj24nqVW59tK2sVa28i56xGrtJYSQgghpNOYcWFby9VPvovFUoSt3qYFniksbeeyCU8dJEqwWUFdZbRhim2xsOpOozlfV/IWMVsqlXxhqt2HdRn1Z3O/nn8sZTKPtQlyM7CV2Zbmd31u87Ngrl+r87HVT9LJ9kjk+TV7dV42N0x2yjuLVojcoN+SqbwfbPesptVWSldenTKoY3tebdsJETjQQgghJAwzLmyDvgPPC08RK3ppH9NSaxO0en6mmU6smHq/CMpisehbRoM6jDZroxayUga9LRKZvMat/NfzTrW1tFKp+C64WvC52kvQSyCZ59P5mwMEAKrm0ppWbO32q+f/mufQ9XK1nS6P5KH/S3vptradyxYwrFM6+7ORsG1f7zWqp5M7Xa6vYTwSmslvJu7jqRATYfKkgCEaWeItn8+Hmv5CCCFkz2XGhK0WRqZLsHwGnhc7YqGVuaXmMjb6WEGssKlUqirokxbE5jGmuNTlbaR+WryJKI1GoygWi/53ifar04qITSQSftpCoYBSqeSLPC3ktEDV4lZHi9YdRt2uuh11G8hn/V/Xz3XtbO7JtnzMebS6XBp9nlgsNmkdXNsSROwcty+1nqUwz9pUiWbXMbZtcl82eq+FEcMzPSe11eKWzyWJRCLo6ury32eEEEJIq5gxYasFqYg9wG7FFaujGUG3lqXWFH9awJnWPdPl2CawTPGng0GZwlJcjnU9PM9DPp/3LcIiUEXI6mVuzOBQ5XK5ykKry6HXenUF0jKPsdXJbAeNKUzF2qvLY7ZDUH4aV+fZFMXmNrkGAKzuyexETw82r4Vm82p0f6PnrPdeCSqHbcDGFKhhz9euHgfT/WzxWZ49RCKRSasL1EIGfwkhhJAgZlTYahdjc5+gha+O3GtGQ9bH6c6gjmBsui+b1r9IJIJMJoNEIlG13XRlNuug62G69Wq3WXGpFUEmQl0LNKmrFm46CrBNyJsC37RkC2aH27TmBgla+WzOkzU7JzZRq49rBD3gIBGgdRApM605SEH2TGz3v+t+qPc+sd1fYefXdloH3RT+U2GhJnsO4n1kYr4vBQkWKb/9hBBCiIsZE7ZamAq2YEt6jimAquV4bJjCz3Rbtlko5XtXVxf6+voAAMViEblcrkoI26y6ppi2ufbKfy0KzZe3tkbrc9nOKXlIe+i6a7FqE8HaSqzPpfPUbaPLbZZXt5/Nwm0S1DkOEtZ6QMMU19oV2XZ+doTag1a6F4fFFJX6vq+HMOnruc9a7d471bimITSTR63tZM/EfNYSiQTmz5+PUqmEXC43Q6UihBDSKcyIsNUCzWWBFDdcAJMssjqdFrA2F1wRPyKIBc/zUCwWq/LSVldzeRyb+66tw2tGPTbXajUtuLJPjotEJq8x67KoSvuYc26D3B9dbsm2/MMIVS1+TSuz7bpqkW7r1Aa5E+vgWaa1NqgepP0JEo/mviBhZbv3Xc9Do+UKGgyabUxX/WZ7O5LaeJ43KSCieDAlEgkkEglrwERCCCFEmDFhqy22NjdaYLco1NFwbfNWtaDTy/Zot2Nz3VghFotVfRdBlkwmkUwmUSgUJgV2MiMHS97a4qpf0Mlk0q+P66XsEugmpnVU5t9qwWwLhmU7n87PFIm24E1aqEtdTHdMmxjVdbFFMg7q0NqsuqYrt7lUUq08yNTiEoJB++vNT7YHXdtWWX9dYriWe7FNjHcCYZ+XVlida019ILMXeZfrwWUbso57Op32vahq4ZriRAghZPYzI8LWdOG1WW9FdEpa7Y7qEoJ67qwWzqlUCvF4HMPDw865lzptrSjKcqwIVS2uJAiUXtZHypbJZBCJRKrmCmnBrNtFRFw0GkVPTw8mJiZQKpV8sS/nlcBSMrKtxa0tgJQpOvV/22i52UEwrbJBmC7U0h5mOVzHavdibRF2WXpdVlwy9TQj5ILEa63jZuIaBz1DNiFseoPobe1CI2Wx/aa0Km8yu+nq6kIikcCuXbtC3R9dXV3+1KBa6eV9R+suIYTsecyIsNXCUwtYYLJrslgU9dxRMziSFsPyB6BKbJoBqQRzeyKR8C2hpsDWx8lL07U8jRZgqVTKH22OxWJIJBKT3JP1HGBZ1iiXy1WtpavFm+wXgSl5SZtK/lIHl3XE1jnV5TddfLWotYlMydO8jrrdxQpvBqIyz29uE+qZy0umlqmyRLqEsuueDZtPI8g5bcLUFLJBIr1WpzxMh71V8BkhM0ktTxshEomgt7cX5XIZQ0NDoe7beDxOUUsIIXsoMyZsTQurKe5cS/PYxJKIQZ2vOVcznU77Vk+NrVOqXZ/1OQQRkGIxDQpaJALUzEcsr7Z5vPJfz/OVMul6yRJA5lxXc86tiHSznqZl19ZuNsupKyKxDfNcWhCY7eWaX2sTECba2m3mS6aHMBbWRrdpGpkr6ypPPfewPmc991UYL4Iw+dksxfXQyc9Cu1m4SXMUi0Ukk8lQ17VcLiOdToe+/oVCgfcKIYTsocyYsHVZXrXI0pZNcx/gDhhlCqRSqYRCoYBEIgHAPtfV1lEUa20ikfCtxp63ey1aOb/+r9e1lbVlZbukKxaLk/ZLmbQVV2/LZrO+gDatplpgatdfaQeb2JOyiHVXC3XzeO32rNtUxK0tQrVpiXa1cZhIypowYlpbjMnMEMaCWS8uS62tYxzUWTbLptMGic5W1cNWtkY64bZjmimja4pGs7RKyJPZhbzPxJvJHHAW5P2dTCat90NXV5cfC0MfY4ODI4QQMvuZdmGrXWW1mAWqLSNmFGNzCR3bnFrTqimUSiXk83mkUinfymmWSbsd6/wB+GvopVIp/zwiKnVwK9v8OXM9W6mvzYpjLiuk20fPTbUFTnK5BJvukzYRattmWmvNpYrkOJu4NcW5uFbrMmhR4XKDNtvHNT/XFMgUtdNLkPttvceFOSbM4IYtT/1bY/MacIlkSe8qW73C1+W10CyNilPzWav3+GYFQ62BCjL7kHdnX18fBgcHnenknTwyMjJpzXlgt7DVz3Q2m21qMIUQQkhnM+3CVq9Da0ZANvE8D6lUyreQ6g6XnkurRaM5x1PQYkzOp9PIsblcDvF4HOl0GqVSCXvttRcWL16M4eFhjI2N+ZZfsb7KeTX6RWsKVTOqsiliTRGsRbuO0Gx2jG3ukpK/ic5fRzc2xazu1JuuwmZ+QWhLsBCLxfxAW1IOfS6zDqZVLagMtNhOD61u41r5mVZb89m1PQOmaJP7rF4BZZbNVtYwVtSwwryVbduqjv50CAOKjz0H23vBpFgsYsOGDVi8eDHS6TTy+TyWL1+OF7/4xXj88ccxMTGBXC6HfD7vx7CwWX/F26oeDyFCCCGdx4xYbG2Bn0yhK5iupZJOC9QwboeFQgHA8yO8OiiTWJElaJS4C/f09KBYLGLLli0YHh72ozKKlTKZTE6yFEpehUKhqsMtQaP0GrVSJklTKBT8dNqK3NXVhXK5jFwuh2KxaBW1nuf5dTDFqm4LXR6dRlux9VqxYTAFhymAZY6vvmZhsLlZm59dUNhOL41Y+Ro9XotDU9QCk4Wh53lVvy1BLu3moEg97slmWWzH2qyq9Q4U1aJZt+JadWgWitc9F3lGotEoJiYmfKvt+Pi4c26sxLJIJpPo7+9HpVLBPffcg8HBQYyPj1d5NHV1dTmFrbxvCSGEzF6mVdhqi6XNqqYFrnw395kCWIsv0xoqJJPJqjk9QPU8UO0Wm8/n4Xkeuru7sWTJEsRiMWzbtg35fN4/t6whW6lUkEwmq6zQkUjEX5ZH5v5IvVOplL89yBUXeN6FV4S7zDn2PK9quSDdPiKczajDOl9pM3NOrTmAoPM1A2eZItkWFdr8HyREAFivp3msKZrZQW4/WiGmbPmZls5GrfJhjqn3vjLLVq+rtCtNqwSuiyDrMZ8tMhXI+ywej6NQKKBQKGDJkiUA4C9dZ+J5HkZHRzF37lzsv//+8DwPjzzyCMbHx/33XrlcRldXF4rFIrq6upBKpaoGmovFItLpNEZGRqa7yoQQQqaRaRW2en6toDuuZie2UqmgWCxWuS67xKts7+7uRjKZRDKZRCqVwrZt2/yoivF43F+jVsqjhZuIJhkdlmOAauujCM1isYhyuYxSqVS1LJAeGdauw/l83rfG6v3ikit1LxaLiMfjviCXZX/0S98mivWyP1IvmwVVu/7qz0HXTWMKZi2KbW7DGpulWT7re0C7RZvWY1P82ta6dZ2ftJYw4qtRa67t/rANaNTrVqxpRkDqZ1u+18o/rCV0ujwOptIyK/k3kzef4c5E37/9/f3o6elBOp1GX18fHnjgARQKBXR3d/tL1gH2oI5CqVRCPB7H/PnzMTExUeW5JFOVEokEstms328Adk99isfjyGazU1hbQggh7cK0C1ubG6rNMptMJq1pzI5YJBJBOp1GJpNBX18fYrGYP9cmnU5j/vz52LlzZ5X4TCQSvvVT0OJJjpVIjHourRyjl/DRllyx7GrBLNZdcXEWsQxUi2ltjdbivlKp+GJZXIjNToC85EXcRqNRpFIpFAqFSXOZanWaddtql2Vx+dJiWItPVyfWZtGyWctdVmOzreRzLSsZrbqdhb6HtNu67d6QbbblsnReQd+ByeI5qFyCSwza7v9WCtRmRWI70OnlJ7WJRCL+lJ9FixYhnU5jfHwcsVgMyWQSK1euxBNPPOFbXDOZjC9OXch7R97J6XQao6OjAJ6/p8Qaq+NpFAoF/11NCCFk9jPtrsjm/Fprof7fmrTA8y80LRS1CBUhKEK1UCggm836LkqJRMIPVFQsFlEoFHyrrWwDJlshJyYmMDY2Nmn9VZ1Gyi/nkTm4pquuRGOWbbJdogWLUEwkEkgmk1Xzd8VKLWg3ZC0KxSVY7xO3ZD3nVvbp/2Zn02bZFbRrtGkhtWG7xpJei1upgxbuWjSbx9b6TlE7tdQSbEFuxbXSm/el7d4VTG+BestUS9QG7au3DYLOH4apvJ+nUjTT5XnPIxqNoqenB9lsFhMTE/57OZ1OI51OVwWFHBsbQ1dXFxKJhP8+NpF3+8DAQKgYDbKCwfj4eEvrRQghpL2ZdoutS9CKQNUWSy0qXR1asa7m83mMjY35FlYRZXPmzPE7VhIYqru7G+l0GmNjY4hEIpOCTZTLZYyPj/tzVsXNWVsupVzRaNQXkLLN7CzLfFkpty6/1FVErVlX2TY6Olrlaiz5isjUx4kgti1tZApv/d9El1GQ9Xzls010hrEM2yy9IppdAsAU0jYLrq4TI2BOD62wSrqs9ebzYjtOlyNIKLm8CcKWzcynlni3DYbVey7yPBTBnUEikcDcuXNRKBQwODhY9X4tFApYtGiR/w7xPA8DAwNYvnw55s6di4GBAet1rlQq2LVrl/8ukjm0trTShwiyAjNKMiGEzE6mPSqyiZ43awo01xJAAKrSRKNRP+S/Pl5eqD09PRgZGfGtmiMjI7611BZBUV52iUTCd2fS1kVtQY3H44hEIlVzfsygTLlczq9nKpXyoy+LVVVcrFKplJ9exHChUPAFu6z9J+WWz+bSR1pQC642db3YdafBTKMt6EHiNsiaasu31lq0tgjYppg1v5POwCVa9XYZsNHHmIMjrnybEYthXZTDHuu6P2uJ+Kmm1Vbbds2LTC0SH2JoaGjS+3V4eBhz5sxBKpXCxMQEgN3v6WeffRaZTCbwHiyXy0gmk8hms36QRxu2958tL0IIIbOPGRe2pluyWFpF+Ok5ntKx1Z2+VCqFXC7nuwFrZG5sX18f8vm8H8XY5Z4kIjkej2Pp0qXIZDLYsWOHvwRPuVz2Baq8GMUKrF+UZhm12DVFoKTVrtVCV1dX1RxZLdy0i7Hkr+cwa5cu3fm3CVmXy6RLXOqoymaAqrDYOvW6Q6LbUAeHCkJbbYOWdCHTh/kchHH5NZ8R2/3psqAGudXr+1qLYls5beW3EVQnl+C23ZdB5Sakk4jH49ixY4c/B1bjeR4GBwcxd+5cjI2NoVQqoaenB8PDw77Q1Yg3UywWwxFHHIEFCxZg48aNAJ5fC12eZ+3JZMsrFovxvUAIIbOcaRe2Yec+StAkU2RpV9hUKuWP/JqiV7vRxmIxZDIZf7md8fFxP1JyIpHwxaO4HksZS6USuru7sWDBAhSLRYyNjflBoLTINIVdJpPxlxjQ81tF/Ir4tLkKy3GxWAz5fN6fnyTn0H+2AFLyope8TMu2tKXNOlTLdVjnJdfBJdo1YS1BpnuyiQ5eJddVpzWvCZk92O4h3aHVrsyudIDb5VkIGnBpJWHvT5er9VQJ3lZZbRvNo9VWYzK9RKNRdHd3B85t7erqQnd3N7q7u5HP57F48WIMDw8D2O1dValUfGGaSCTQ1dWF0dFRPwjjggULUCqVkM/nMTIyUvP3Xu4pCVLF9WwJIWT2MiPCViMixYyYrDut8l/vj8fj6Onp8YM26fx1xzWXy6Gvrw/9/f2+q6+49/b09PhiUI4VF2B58Yo7lSxNoAM+ZbPZSaJWlhfI5/O+CJM6i9AUi6SJuG1JGXW72NZv1VYnl+XJtFiJ67OUW6fX0ZgFM6KytgyLG7QIC5d7V1DHw+ZGbLPMm5hu4VIG/Z90FrVEjTl/Vf7btptpdP5BFl4b5uCJeZ6gY2q5F9cr4ma7BZeitnNJJBJYtGgREokEBgYGqvbp56dYLKKnpwcrV65ENBpFJpPB+Pg4du7ciUwmA2D3XFyZblMul/0AjMuXL8eGDRtQLpcxb948f7BZYm3YBLWI44mJCevUI0IIIbOH2uEFW4gpcMJENzQFihwTi8X8EVztqmxSKpVQKpWq3IzFUivbkslk1fI/siyB5C9LBcnxWoTbzpnP5303aI0IMVeHWqI26/ayuTSbEaHNc8g+7eYdZKWypdfb9fmlzuVyeVIUZtlnXmebFczWbs26EZsimUwdQdc37HGAPZhZvecyB0FqeR6EwXwWbINELpdoWxnqeRZd5ZkOpttaW8vNnHQO0WgUXV1d/jtXo6+rTAtKp9Po7+9HJpNBf38/0um087kTbyoZjK5UKn4AKclfL6NnnrtQKPieVoQQQmYv02qx1YIoTAdGXo4u610kErHO4xGLXzqdBgA899xzWLBggT9Ptqenxw/gpAM7yUtUoiaXSiXs3LnTj8YolmM9f1W75aZSKX8Or54XGovF0NXVhf7+ft/Cmcvl/JdsMpmsapdyuezP6+3q6kIkEkEul/NFr9nB1lZV3VY2q5ZNULqiVdvSatFpG6hwdfTDdChE8IvLsU0oSB21Bd+cX6zLR6aPRgWJad10pbFdX1MUu+45M7K3LY2tTIDbsusS5Lb05gBQPS63nST0pkIY8znuDKLRKEqlEnbt2jVpnzxzIkYffPBBLF68GMuXL8fExAQWLlzoW1yHh4f9QWQRpcuXL8d+++2HwcFB/P3vf8fmzZur1rovlUqT+gKylvvcuXOxY8eOqa08IYSQtmBaha12NxUhZbofhyWfz2N8fNwXmbpzrEWciGMRsUNDQ0in0ygWi/48nlQq5acHdrtBjYyM+EEocrmcHyE5m82iWCz6C8ULslyPy1rZ19eHVCqFRCKB8fHxKlEmx2qXah1AKZlMolQqTVrnz9WGWqSan3WZzPYOYzHT22wWaVt687t5nM3CahO3LvQSUWTmaYU10vRWMD+bx+iI2Ta333qEaVA5TQEdZLW1ESTKbWWbTiggSTNks1k/foULvQTP1q1bEYlEMDw8jLlz5yKfz2Pbtm2oVCp+nAp5R2ezWTz99NNYunQp8vk8stms70Wll9rTJBIJZDIZaywLQgghs5MZmWPrWj4GCF7iB6gOHiXzcEwikYi/ELy4LOtOpFhpxYIq28RqKnmLJVeO6+7uRjwex8TEBMrlsi80ZbmdSqVSFcVYiMViyOVyqFQq6O3tRTKZ9EW3BMpIp9OIRqNVQr1QKPjLG4i1VtbMNdtLC9x63R3N+csubB1flwXX1Uk2rWXmIIC+P+rtaMu1lmBbpP1xDbKYgzHmMaYw1PmYQeTkv0sQy3F64E2fI0gw62PNOrnqZd739Vhvp5J2LUe7lIvURoIdupC4GPLunDNnDrZv345du3YhmUxi7733RiqVQj6fx+DgoH+cvBuHhoaQTCZRLBYxOjqKefPmIZvNYmRkBJVKBV1dXb7olRUTzFULCCGEzF5mZLkf6aS4XGBdIsu0ypqf5dg5c+YgHo/7L89SqYSxsTGMjo4iEon4o8aZTAaJRAI9PT2YP3++7+IqoliWCioUCsjn84jFYujv78fixYuRyWT8yIw7d+7E6OioP3fIRNykurq6AADz588HsNsqKy/o0dFRfwTa8zzfFTmbzfouWoVCwbfkutZ8NefEuixXLkFRC1un3SZozc6ozUprIyjok0v8yOdIJOJbvxn5sn1xCURbOmCy10GQi7vpEaKPsd2btYRvkEi1uSC7ymTWO0isdarnAcUnyWaz1oHdnp4epNNp9Pb2Ynh4GLlczn/viTdVJBJBJpNBPB7HwoULsWbNGj/OxZw5c9Db2wsA2GuvvTA2NuYPYC5YsADLli1DX1+fH7xx8+bNeOaZZzjISQghexjTGjzKRtgObq35d7bOrlhgxaW1VCpVpRWLabFYRD6f94NMyQLz8ifWVHnxiiCWvMVqawteIXNgddAns6zm/Fc5RqJC6mV7bO7HpgCoZbG1WVjNpYSChKh5vP4z0wTl5Tq+Vp6u79IGdE3uPFzX0hw80X+29Z1teQDPu6uLd4V+jvTzFfRbE8bNOEwal9W3Hi+LqaBRARCm3o3CZ7izEA8njX4+JbCiCE7tfeR5nh90sVAoYGJiAvl8HtFoFIlEwp8ylMlk0NfX578fe3t7/fm9IpJl+pDE2SCEELJnMCMWW8DewbPNB9XptVgSq6XN8uh5nj9fVV6ecpwEaPK83a6+8Xgc4+PjvoU0k8mgt7cXfX19iEajKBQK6OnpwRNPPAFgt7VV5sOOj49jYmLCH6VOJBKB9dVRGcvlsi+0RRCL+1SpVPKts/Lilzm+0hkXq24k8vzyQTZBWM9cQxcud1D9Wbezmc68NjZrmRzvErJBHWfdLtJWEvyLkTA7hzCW3Fr3r00M63tDPus1pXVgOH1Mo2W0lS/svUzs1DsfmrQP8t7N5XJ+zAq9tJ2eH18oFNDV1YWRkRH/vTxv3jx/ub5cLoeVK1diyZIl+MEPfoBKpYK9994bW7Zs8d2RC4UCBgYG/ClJ2Wx2JqtPCCFkGpkxYWtaJ4I6i1qMSlothLRLsuyXF5zNeifuTbKEgKxbm81mq1yGe3t7kU6nsWvXLn8kuLe3F/PmzfMDWOg5tbU6vBKMKpPJ+GXWllcRqul02he2YpmSz2Y9pVOgLcOAO+CTrZyma3OrCBLYZpnCEOSKLN+1FU7mI3N+VfvhEoi17hdXehPbIJn2dBDLkE0UB507yL05CJ2+k62Q9V6fZtC/6xS0nYEObqiR99PExMSkd425fF0ksjtgYl9fH8bGxrBlyxYUi0UsWLAAq1atQm9vLyqVih9/YvHixTjyyCMRiURw1113YePGjf6Ar7xjua45IYTsGcxIVORGj9WWWsHsvKbTaZTLZRQKBV/caOEnLzqZHyvr4SWTSeTzeUQiEf/Yvr4+zJ8/H+l0GvPnz8eTTz6JrVu3+tEazaAU0ik2hZQEi5IX+65du9DT02PtJEtQqlgs5rs5S0AOLaAjkd1r4koZzA55LXdKbQGR0fNacwz1ceY20wobdE5bOv1dCxBd51qd6ng87gsWGbDQgwOkMzAHqsx7w5bexOYpIP9luoHpBmnzdqhlbQ36PbPdr50oam3tMZVCs5Z3BkVuZyFrv4+NjfmDSRLQUTxq5HkslUoYGhpCd3c3YrEYtm/f7g9UFgoF9Pf34+ijj8bTTz+N/fffH3/5y19w9913Y8eOHRgdHa0S1p0+iEQIIaR+ZkzYyuisGShKv4i0iJVoyNrKaVovAfjr08p8WTnW7CSLcBSxK0JVz9XR1j4JfhGNRjExMYHR0VE/4qLUS+YCmULKDGQ0NDSEcrmMrq6uqmVtJKhVsVisevlLPbWlWs8T1PWytXnQ9iCriE3UBgnYoGNNF2QzH9vaomE7JaZFTkS/3AumGziZflz3l+saNyMGbfm6rPumoA3TGTbLZv4O2QaPgsrW7h3wqRaz5r3hEq8UtZ1JOp32Vx0w14zX/QHg+bXrK5UKhoeH/fdCNptFPB5HJpMBsHuJvoULF6Knpwee52F0dBQ7duxAPp/H2NiY/+6WCMx6mSFCCCGzl2kVtmK1DNORSyQSfoh/7V4rx2n3YhE0WlTqtCJy5eWpLTUiHicmJlAqlRCLxZDNZpHP51EoFDA0NIRcLoclS5Ygn8/7838GBwf9dftEZEajUX8pApNkMolIJOILZrEqmsGVROBqi6MW9fIn7smpVGqSkA4Sn+ZAgClqTcGr05r514tLONsEdCQSQSqVmrRurwx22P6LmJXrL1GmbZY30hrMZzlIkDZyDWp5DIRxddcDQHoerR5U0/P1zXOFEeRhxalNwJniOGwe0yWGZ/K5sQ288VnuPOT9JIEZx8bGqvaL6AXge1RInIxKpeLPme3u7saWLVtQqVRwwAEHYHBw0B+k3rp1KwYGBgA8H8RKphyZwtZ2D/X29vrvX5dLNSGEkPZm2ufY2tyIbcv71Ipqq9ezlXTimqstn9otWF5kpVLJDy4kolT2lctljI+PI5/P+xZZsfjqwFLDw8MYGxvzozVmMhlfHNvKLQGipAOdSqX8DrfNTbZcLlcJ23g8jq6uLt/6K8fo8rssHzp/vW6n/m9is14FuWSax5qfXRa0IOES1u1UI2IW2D04IpGvtaAh7Yvt+gdZPG1eC+ZzoOfw6ePlsw5e43Ifdt03YcWs+RyZFt56RGonC7tmy97Jdd8TEQ8neQZl+T0THThQhK0ml8thcHAQg4ODGB0d9aMd9/T0YMGCBejr68OOHTuQTCYxMTGBWCyGdDqNsbExP+iU7byaTCbju0NT2BJCSGcy7cJWhKZ2pXUJGLHYaeFrpjddl7Ug05/FqidW1a6urqp5PVoA53I5bNq0yZ8XJOJW1rRdvnw5EokEnn76aWzevBkjIyMolUq+q1S5XMbo6GhVfYrFor90kAhwHdVYu0XrjrhYZqPRqL/GXyQSwfj4uB+MSpYeMuseRK3OtM2aarokiuB3XcMgC4u2oNVau9YW/MMU5lIG7YIsVlz5L21JZo56hEmQG73rN0Pfn/q+1PeHfNfzr2UgzXwOgwZ+apU9qIzmNjnGdZztWWql5bZZsegaqKrn+KDBA1ubUeC2P/KuE2yDi5FIxH+HRSIRdHV1+YPKmg0bNiAej/uxMfL5PCYmJpBMJrF8+XL09vZicHAQpVIJIyMjmDdvHjKZDObOnYtYLIaBgYGqZyeZTKKnpwcAMDIygm3btk1xaxBCCJlqZsRiqzuMLqutGfFYp7URiUR816WgTlYikUB/fz9isZgf6ViTTCZ9Qbt9+3YsXLgQfX196OnpwbJly7Bjxw6Uy2VkMhnMmzcPAPyoyWNjY/6C8xp5mYuAlvVxBR0EypwXLB3sUqmEZDLpLykk85YqlYo//0i7YQe1v8tq63IVljS6UxLk7mzDPIcEyLJFyQSedxc3xYm4kAaJYVmrVNzQ5F7SooVML/VaJYOwWWzN+1OfU/bp6x+Px30XRi0obUv/1CqDbZ+tLPo402qrCWqren4Tp5qpFJdBYtbloULaC72sVhASqFHeX7t27aran0qlkEgkMDo6io0bN+Loo4/GsmXL/CWA7rvvPoyOjmLVqlVYsGABstksBgcH8fTTT2NoaAjbtm2zPsu2wIyEEEI6mxlZ7kcsJ6aY1d/FUluPdcfW2TO3idU0Go0im836nVsRS+l02rfm7tq1y3f9lTm/CxcuxL333ouenh7stddemDdvHkZHRzE0NIRdu3ZheHjYX4A+m81WWahFjBaLRT9wVCSye3mffD7vuzHLS3Z8fNyvh7jUplIp3zVZ5gNLh127b+t2cwlBm1un2WbSrvV2IMMIGWlTV/mkPbTVVndm9fxaaWdJL4I2Eon4lnlabdsLl2WyHrFWy/qpnwvzvpbtElhM7hU9oKTn92uXdlv5bS7Gtcprq3NYS2870inlJNNDGMEo0ewjkcikQIuRSAQ9PT3+0nvj4+O47777UCqVsHr1auy111445JBDcPPNN2PRokVYtWoVNm3ahLGxMfT392Ovvfbyl86bmJjA8PCwf45IJIK+vj7s2rWLwpYQQmYJbbGOrdmREwEjEW11Glen15xHarOMyHcJDqGtNGLlGxsbg+d5vjvU8PAwstmsH/ypt7cXCxYswJYtW9DV1YV58+bhBS94AXp6ejA8PIxt27ahUCigWCxiw4YNeOyxx7B9+3Z4nodsNuu7LJsux9IhFBdavcyNbBMBK0I8kUggl8tV1TWZTKJQKEzqXJoRpl1zmM3Ov+n6GCQ65BxhOrbFYtEXtmb5zLJIeczP5jYtcEUwi5iVoFJc13bmmCrBY/M00PeGbdBHnj8dcAyodlk2xazcX+b9o89n+70Rgp6dMG1js9SGzb+Z84Yp01RYUG2DEeY+Cun2JaxYlPu2UCigq6vLf8ZE8A4NDfnxLMrlMp577jl/EHnx4sV42ctehp07d+Kaa67BC1/4QpxwwgnYf//9/ak727Ztw9atW/Hoo4/iT3/6E/7617+iVCohlUr5HlgyfYUQQkhnM+3CVqwiGlfHRAScrcOm3Wm1Rc+GdIAymQw8z8PAwIDvEiziUDqr0skdHh72hbUEnshms0ilUujv78f27dsxb948HHTQQejt7QUAzJkzBytWrPBfxLlcDgMDAxgZGfHzF7finTt3+mvZSvCpcrmMfD5f1XHWHW6ZDyxu1FJvbdnW817N+pviVrejnMO8VjZxWwtXh9T8PzExMSlNPflJncwOr2zTa5XqqMl6wIS0hlZYX8OiXYxt5wojAMU1WecjAk0Pisg2LWj1oJSrfPrc5nltZXaV1ZZvkBvzVBDW+hz2mXLlF0akBold0j6Y65DbEG+leDyO4eFhALuX9Umn0ygWi/40oVgs5sfbyGazAHbPic1ms76ld7/99sO+++6LAw88EO94xzv8ubOS9oknnsC8efMwNDSE7du3Y+vWrVW/AYQQQmYHMyJsg9auBewRkW2dZC3swnQ0ZQRYRmYLhQISiQTi8TgKhYLfyZV5d+KWGI1G8eyzz6K7uxuLFi1CIpHA8uXLMTw8jMcffxz77rsv5syZ459vzpw5mDNnjh+huVKpYMOGDZOsijJCLS/nZDJZtXRNT0+Pv+wQ8PwafzIPWJZOyOVyfh3FIuxavsRsK9PaZLseZj5hRuJtedhcmrWw0GULKrtZjyALrp5va3NLJc3Ryk5hvXnVstTa0piWW3FRTyaTvmDV/22WQu2ZIPevKbbNe132RSL2KOiu+pn39J5839JK2znIevBByCCSDrSo37sy8CmeTFqEjo6O4rHHHsODDz6IwcFBzJ8/H2eccQbuuecefPWrX8VZZ52FffbZBwDQ19eHI444AkcccQT22WcfLF68GLfccgsefvhhf0qUGahKpigRQgjpLKZd2IogCxK32gobZJmIx+O+e1IYxPKqO4zFYrEqn0ql4geT6Onp8cVjuVzGk08+iaGhIfT39+OAAw7AokWLMD4+jrGxMaTTaX8JAqGnpwfd3d2+6/LExESVtadQKGDevHl+pMdCoeALsa6uLvT39yOfz/vzdrUrrbSfjGZrcSsdgFqdP3MdWPN62MSlK0/dqa9l4TGtVrZyBe03zwXAKnTFQqvbTCJRk9ZRb3tOZfsHWUvN/SJsRYRKJ1encd3T2kXZdEN2Deroe9U2yON63mbb/VqPKKWI7UzqeSebyIoB+p0s7zd5FmTpvXvvvReRSASrV6/GmWeeiYMPPhhPPfUUHnjgAYyNjWHNmjVVea9YsQLz5s3D6tWr8eijj/qxKzTxeNyPhyHiXN6zhUKhakk/3puEENJeTLuwTSQSSCQSoTtrrk6jKXxrIdGDTYuxuPZqS6qsGavnwmazWWzduhWbNm3C3Llz/SWDCoUChoeHEY1GkU6n/UjJwG7L7b777uuv5Xf//fdXWVIlKvPSpUsxOjqKXC7nu2nLiHcqlUIqlfK/i7iVdXhFDGuCXPV0/V2WW9d1COM+aIuEqa+ZdBTk2rk67y7rrs1qq8WIGVBKuyDLnMqw0TpJfbjEZFjqEXE2ARvmeJtLsgx26eWhXK7MOp32QrBZdnWZ9P3s8jBwPV/md9egUSMCeLqFYz2uxkH7ab1tb8JeE9vvcC6XmzT3XaYNZTIZJBIJpNNplMtlPPDAAyiXy3jooYew77774tWvfjXmzZuHhx9+GIlEAosXL8a8efOQy+WQTqexfPlynHLKKUgmkxgfH8cvf/nLSefXvwNCJBLx10WXJfsk4CMhhJD2YdqFbTwe912NgMnL/ACTO342V2UAVRZMV35mh0+EqnZJLRQKiEajSKVS/gtVltXxPA/j4+O+KCuVSnjuuedw1113YWxsDEcccQTmzp07ycVXlpqRF2R3dzdSqRSy2WyVhSiXy6FQKPijxGIxzmazyOfzfluJO628YE2hq92Ppb6uQE5hOoM2AWBr/6DrZROmIgi0sNX5m9Ys879uY1MYmP9F/ErbaNdk0/WM1EejVsRWWx9dFlpbOv1c6HtP7jm9xJgur16erJabsun9YBu4MT0WdD5hBIGtrrPNqlsPFLezD7HUdnV1IZVKIR6Po7u723/2du3a5Xs6AbsHib/3ve8hl8vhxBNPRCaTwZIlSzA0NIR58+Zh27ZtePLJJ7HffvuhWCwilUph8eLFvmVWkEFiWYFATwOSecDJZNL3/OG9Rwgh7cW0CluJ5GsTs7Wwddzk5af32dxqbWiBJCPB4gJVLpdRLBb9JYckuJTM/xGL88jICAYGBnwRXCgUkEwmMWfOHFQqFWQyGTz77LN4+umnMTw8jP7+/qq1dqPRKAqFArZu3eq/tEV8SbmKxSIymUzVfCMRZeIWpdvI5XKp0wS5CbssqK6OuQvX8SIitLAw05kdhlriXLse66WbZJ+IW5mnJZ0Sdkjaj0Ytj/o4l/twLRd4ecZNbwJNGAuqTWyHtcjOBGGehXqf/1YS1luEzA4ikUiVp5IMKudyOX8VAL1efCwWQyaTAQBs3LgRTz75JOLxOJ588kkMDg5i7ty5GB0dxaOPPorDDjsM6XQajz32GAYGBpDJZKqErdzn8q6wIcsHUdgSQkj7Me3CViy2QGOdOrGYmO63jSAvxEQige7ubn+N2VKp5K9Fm0ql/PNGIrujOHZ3dyORSGBoaAi//e1v0dPTg76+PvT39yOZTOK5555Df38/5s+f77szZ7NZRKNRP1iUvDTL5TJGRkaQy+XQ3d3tW2alTPICT6fT/hxdMzqrzAHWFidz7p/Z3tolWbet7GtkHpwWr+aAg5nWVh6zXKY7sjkXynRHNjsiphVdrOcUtq2hWQHaTJp6y2Ib3JHz6MjiMndWLPsAJllozfvSJmBtA0RmBG+XINb5tCNTXbaw7shk9hGLxfy4E+Vy2V9qr1AooFAoYGJiAl1dXVXHRKNRZDIZZDIZPPPMM/jUpz6FeDyOlStXIplMYt68eUgkEpgzZw6WL1+OeDyORx99FDt27EA8Hkdvb68/SCwDx7IcoAtOZSGEkPZkWoWtuPkKYdxhgzqsrs6liU1AicAR66sEjBJRKy+ufD6PVCrld3pLpRLGxsYwMjLiWwHj8Tj6+/tx2GGHYe+998bGjRuxfft2ZLNZFAoFjI2NAXg+YrEIUOkwy8tUliDSbseZTAbj4+MA4M/x0Z1y2S5iUMrtEpB6n80Ko62pYdHHNupKqfeZAa3CrI1rupVq9Hq2IljkO2mOeu+VVp/bLINNONruHT2oI8+izlMs+zbXePPPLI+kC2M51mnN57KdBZx5zW2u2a7jGqlT0HHt3E4kPBKdXObPypI/smqBuAWLuBVLbqlUwuDgIHK5HJLJJAYGBhCJRDA2NoYXvOAFWLFiBQYGBpBOpzEwMIAdO3Zgx44d8DzPX0ZIAkKFXYedEEJIezJtwjYSiUwKyNAMNmFm62CL+7N2JS6VSliyZAnmzZuH7du3+y9Q7SZs5j1v3jxUKhU/OnGxWPQtqJ63OwDNY489hpGREWzevBkjIyPYe++9MTo6ivHxcd+lOZvNVo32atdccYOUEeqJiQnfgiTux+IiJdbvVCqFcrnsW5wlf5vIdHWa5b9tbVtJa2KKwnoEba0BCWkH25q85vlcrsy26LZ6fVJZC5idmPqZafdZ271rE7dhrq05oGMKULOjqwejXOdxudGb5zLLMRXt6npObGkazV+3ve0ZN2mFALZZxvksdwaRyO6l7eRdKtdu//33x5IlS/DUU09hx44dfjyKXbt2VR0vv+177703yuUydu3ahVQqhbGxMX8erOftjo2xdetW3Hvvvfj73/+O7du348ADD8S2bdv8teVl0FkQDyveS4QQ0plMq7CV+TJaOJmWuTDCt5GXjgiZSqXiz5OVUd9CoVAlanVnMJ1O+1bdRCLhuyyZc1vHx8exceNG7Ny506+HiNpCoYDu7m7fgiv5a6EViexePzefz8PzPP+//OkAWdplUizgnuf5+dhcdm1t5uoUmseba382ik0A2NBCVJdJrx9qlk/vD8pfR7xkdOTOJMhKavusCZNengfzmTN/G0wBW4/ACkozFSLX9eyxA0+mG3m/6e+ynM7OnTvx3HPPBboCp1Ip9PX1+e+ITCaD4eFh36IrDA0N4f7778cTTzwBz/OQyWTw9NNPI5vNIhaLYcWKFXjkkUeq3gGMckwIIZ3NjAhbG/V25sKIYL0kh7y8tBgcGRnBrl27/CBUwPPWSnGLisfjfmh/EcFanOoyl8tljI+Po1wuI5FIIJlMIhqNIp/Po7+/H3vvvTcKhQLy+TxGR0f9EWs5r3YzljKJhVkEnY7mLMd6nucvkWC2pxwj5QtqZ1MYmpZcG3p0u5bLZT1ol/GgMgeJdjlet7G23FLYzj5sllTTmqj36c82DwdbPhp55mxWXH3fugSxeb5WWx5dArbRc4SxAMv+Zq22ZPZhvmMEif6fy+WqRK+ZpqurC+l0GtFoFNlsFgAwPj7ujHIv3lTyTu7v70c6nca2bduwceNGvgMIIWSWMe3C1iZGm+lk1ZqvKW68unMrbsHmS1SEsKCFuBbGeh6rWZ5CoYBIJOKvmdvd3Y358+f76+L29PSgu7vbd3+emJhANpvF2NhY1Si1nhNaKpX8F7BeqkiXWwtyiQ4sZZQ/be10dfJd4jbIjdi0apntb2urMIMYOn/T3TGoHGZUZI12RQ4SGaR5psq9VmNzPzbdY3U6s2wugWnbpiOhmr9jtg67KW7NcoexKjd6f071PR1G4E6FSK/VZnyW2xuZL2vbrl2JXcfKYLO8hz3PqxnoSd7/IyMjiEQi6O/vx6pVqxCLxTA2NoZIJILDDjsMXV1dGB4exo4dOzA0NISNGzfSgksIIR3GtAnbIGvtVGG6B0qHtLu7G6VSCRMTE1UWF4mALG7HyWQSlUrFt8Lql5yrYycRjcV6WywW/eV/JCJ0X18f4vE4RkZGfIEleZtBoACgt7cXAPx5QWbHXQdHEguvWT6xUuuRbRHNJjZxG8aN2NbxlGPDXn+bhcuMkqwt1boNXAMOsl+3mwwGUNhOL638DbBZRPU5ap0rrMuwKZKByespyz1l3qM6TZDQdZW70XuzHe7rWudvlVW3HepKwmGz1iaTScydOxe7du3Czp07J+2TII+ZTMZfu3ZsbAyVSsVp3dWItbZSqWDXrl0YGhpCd3c3MpkMjjrqKESjUey7777o7e3F008/7QeJTKVSFLaEENJhTIuwFUutzdLRDEEdRe1+rNPIi1JcgqVDKgGZurq6fPclsThKxGTJyzwPUD1fVdyqAPjzckdGRvyAU7JofG9vLxYvXoyJiQn09PRgcHAQhULBL4+e69fT04N8Po9cLuefU1sfpdz5fL6qvObSO812moPEgik0m7XW2lxJg6xZcn/JdTfLoK1tMqBAppdWWnH1M1fPuZoRi3J80FJZks7mIWGms3X0bZ4P7Sjcav0WTMf527FdiBubUOzu7kYkEvGDMWrS6TT6+/urBn9LpZK/lmxY5Hnr7u5GPB73vaUeffRRjI2NYfPmzVi1apW/TB+we73aTZs2+asSEEIIaX+mRdiKmNAdPrNjGDZwlI2gzq25rIuIRokeLCI0Ho/7Yf8TiYT/gtXzXs3zuebQJZPJKitlPp9HNptFLpfzX8ie56GnpwddXV3o6enxlxTK5XK+e1Q6nQaw+wUrQTIkPzmXbV6fWI60OPY8r2qpIWlzE5ebry2dmd7W0bSJUpfbZhjkXtJri5rH29yPdSdI348Ut40x1e1Wy7JZyxXWfDbM48IS5Jlg3nv6mZf/EuTNrI/rmTHz1d/rKb/r2SBkppH3k7wDZLBW3mvyrpNt27ZtA/D886Wn2oQlkUggFov5a9ZKfItnn33WXwN+48aNOProo7HffvthyZIlmDt3Lrq6urBt2zY899xztN4SQkgHMC3C1mUdC7KoBFlhgOfnb4o4Mbfr5V70PnmJ6Tmzgry4RBBKp1Sst0EdS3FlTiQSvstxLpfz58UWi0XE43FfZA0NDcHzPCxZsgT9/f2IxWJYsmQJxsbG/Pqbo9IS0EpeytoqLevgimA3XW+l/rZyh3X3awUyeKHFrS6DKTZNYeISo2YdzfVsbWKXorZ+2qHNzOfa3O56ThsReEEC0eatoZ85HaBMe03ofG3WWf3ZHLhqtOytYiryq6detNJ2PuY9VCwWqwag9fOmAyzKvkQiEfpc4oLc29uLRCKB8fFxX0gXi0V0dXX5wSFHRkbw5z//GQCwzz77YOHChVi0aBE2bNiAOXPmYPv27RgcHOT654QQ0sZMm8VWW2tbgdnhdHW4RKxKmmKx6FtHpTyy1I8gwrBcLvsvXD3P00YkEvHn646Pj/vzc3K5nF9ncWmS9hgeHsbOnTuxdOlSrFixAsuWLUMymcSmTZswPDyM4eFhf66uiGxZ51aLd6m7XvpHt7mMjutOoa5HPevXCq7olmZnXf93LRsknfd65ry6lv6xlV/XQwvrdhBps4mpEFIuXPdJWLfkMGV1DQS59st9rKckyJ94fshzHLadGrW62izWhLQDpuVTvJPy+bw/QGuuLyvUc09Ho1Gk02mUy2UMDw8jk8n4QRslHzNY1cDAAP7yl7+gXC7jf/2v/4U1a9bgyCOPxD333IN7770X4+PjVpdpQggh7cGUC1tTYAlB7ncmtk6oafFwuSZqlycRq1q41kLSBpVZ8pYleyqVih8V2dURlnKNjo5i06ZNSKVS6O7uxoEHHojly5dj+/bt/vyeJ5980l/XVkastUAVN6toNIpUKlUVgEp3qF3Y3MLDpHOJW422oNqsprZ5uDYXTzNP231iCyBlzrkVAW2ei7QfYTw3arnqBnkrBGH7bXGVwTyXIINP4j6py+p6bsw8wpSjVh1ala4RptrC2kz7kPZA1m8POxWpHjfkdDrtTwEaGRkBEDy4XiqVsH37dtx1113+fXXIIYfgta99LebMmYNcLodnnnkGg4ODvN8IIaQNmRZha6KtpfplZr4otKuxjSARbJuzK6672WwWiUTCufadTm++BM11UYHdllJZwkdGo22Re4Fq0SblHB8fx1NPPYV4PI79998fixcvhud5yGazGBoaqppvJMeJ4I7H40ilUr61Np1Ow/O8qrnB2nqjrbn1uGtKXYPchHW99D49MKD3mcF1XHlrXBbhoGNsedRyLSezA5tnRy3X3nrvC9dzYP5+mK70YQbxploU6s+tPFer8uYzOvuRgWC9Dnut9GHo6uryl9HTsSVqUSwWsWPHDvz+97/Hli1bkEwmcfDBB2PZsmU44IADkEgkMDw8bF22iBBCyMwyra7I5nyxVsyFc7n12dxP5fwS/l+XQxM0cqxfqrLObCQS8V2OzU6zy9Ksy+N5HkZGRvD000/jgQce8K2+wO6IkUuWLMH999/vL0gvbs86n3K5jEKh4M8HtrWNS2Tr/UEEWcZsBK1ZbOvwm+eyuZ2FKaMtWJQ+T1AbkPYijMtwWBfooPvWJcTqFbg2rwzzPLV+r/TxjdLIsRSRZKZoJnikjUhkd5BFHUCx3vKMjo5icHAQ99xzDzZv3oyXvOQlOPDAA5FMJrFr1y489thjLSsvIYSQ1jDtFlsRf618kbmsHLpTqcWtzH2tVe5aL8NEIuEHixJxabrCBlmMdJkrlQqGh4fx0EMPYfv27Vi2bBkikUhVcAtx15Koy5KnnDefz1cJ26C6ybH1duDrEbcuy5QZNKqe+Yb6vGFcoQWb67lNOJPOJOg61nOP1cor7LFyn9o8Plp9ThuNdOb5LJCZQpbVawWybF8sFsPIyEjD93WlUsGWLVtQKBTQ29vrx9yQoFOEEELaj2mbY6utiGYwJpfbcFjLhqTXaWxiSp9TRnLNOVouN2YbMrcVALLZ7KROrS5v0MtV0hSLRWzfvh0TExPYtWsXent7sWDBAl+E60EBLaBFrLus12agJVtb6XJK3YNEo8v9MmibK9iUpDWFai3rWpg2JZ2Py/uhkTxMWm2ltOWl55HL759OH1SvekW5Jsgjo52eD1qKSSuJx+NIJpOTAkM1QiQSwY4dOzAwMIBsNotMJoN4PI7BwcEWlJQQQkirmdbgUUB14Idm3Pw0YYWonhurj3EJ4Vrn1ssBlUolZ1Aic66rTZhFo1HfPVqiNsbjcWSzWYyOjiIS2R1lVZYA0mtjSv6u+cq1rJquObB6v6v+Lsx8agXLsQ1stAKb23HQvG5SPzavhKk6j9BOwswkaO6s+VyYvwkmraxnrXMR0ukkk0kkEgmMjIy0ZAUG/bv27LPPore3F3PmzPHX3CWEENJeTKmw1aJWPkukXr1IexhRapuD6cIl0ET8ydzYWh1lvd9mNRLXKXmBmlZhm5ANEo6RSMR3bU4kEohGo76wjcViSKfTVfno9o3H476blFl2XUYT8/poq64rYJRr3msj82FNbO7F0yGUyJ6J65kUWnXv6Wd1Jl3gw3i8tDuuwQGyZ6KfK/FcqidychBmPvl8HhMTE0in0/6a84QQQtqHKRe2Nmtf0PqjYV4UWozpPM11Us10puVYluLQ+8yObpBLsTmf1kUtl1kRpVKeeDyOrq4uP3JzJBLx586m02kUCgV4nueL31KphGQy6Z+rUCj47aFf8mY5TGu63m7bZrPAuqxPen89I+e2e6PezkPQ/aXdsjnHtnlmgwVwuu6BoKkKYY+dynIR0omY76VWWGqDziVrU8disUlr8hJCCJlZpt0VWYSOOdc2bF61MDvaptW4VnrZpv/rz6bAbbZTLy7IstalzA+SAFHZbBa5XK5q7m9PTw+KxaIvZoHdLlgyF1fK5prTWutzkFhxCVVzrrIuh2mFtX23nSeIegWzra7s0NdHpwvY6aLW740rfZi0rWa2nIOQ6cDzPBSLReRyuZZZhQkhhLSOKbfY2j7XEiRB69e2Qki6yhDGpa2ZTpprvqdYa9PptG+19TwP4+PjGBwc9AcB4vG4HxVZlvuJx+NIp9O+C7K2lOpIwC4Ltm3Or02kCmHchLUlVPIw5zO3cmmHRqHFtjOZToFt83IIg23qhMtl33Ys70tC2o9SqYShoSG+OwghpE2ZUnXhEqf1Wtskr6DvgN091pVXUOCoRssk2Dqw5lxWKUM8HkcksntdWlk+SMqmXYx1/fTx2uJrWkK1y62rzGGD1oR9kQfNVdRziV33hpmHvldsluCw59floBty66mnLZtt95m4dkH3qyuNOa9W/xdswrlZV/ywtMv934py8Hkm00XQVBdCCCEzy5S7IgN2kWQTXTZMS2OY+aymxbGW5Tconc3l2OZqKCLLFG+2dLIvFoshlUr5lldZPkgiLUtaM5pzqVTy2667uxuRSMS32JrnM0Vtrfmzut1cAlPKZROd5rkkP5crsP5sLkkUdG/Y9tXrmszOydQT9Fw1a3lt9fzeevNqxKMk7G/TTAh3Pg+EEEII6WSmLSqy/u6ymGlLZD1i1lxmxyacXGULEqEiMvV5bMe5rJE6XzNIlRzT1dXlR2mORCJIJpO+wEulUv5SQpJeu2mLtbZcLvtzcmOxGMrl8iR3YLNutvZwiUr9WQfcku+6jWyDGOY+WxCdsLjS15PPVAcYme006o5bz7HNHtdqd+Vm8wszuGbb55oS0Aps52z1eVphnSeEEEIICcO0WGwBu+ARYSZrz8ViMf9PBJRYNk3x6MIUxfK/HhGq8zKtlGGOMYWyS1iKNVbK5Hm7Iy6WSiVkMhkAu9e0LRaLfuAoHUE5nU776fv7+5HNZv28xeLbqqiNcm6gWphq92hdLxHHtiWDBN1pD+PeFXaOYtg0pHlcgsy1PUwezZZhuvOZKoHo8myYSoIGpqaCVrkhE0IIIYRMmbB1ib9I5Hm3W4n+m06nJwlbEXta4NrcfE20kAojbG1C2MRm+TXndwYJ2aD8RdjqtXDlnOVy2W8X04oqgwKFQgHxeNy30przdkWM6vNpN3DTIm1afXX9bXNa5Tw20SnnkHpLICt9TtOia3P91ulsnVjbNm3h1v9lmSfbOUjj2KyPrbDYzrSlttFz2QZ6dLvYvClsc2ttXi0uOlHg1RoEcO23WbSnyrJNCCGEkM5gWqMix2IxX8xKoCT5SyaTvjXSFLW1xK1LeJodbO1GawrgIFFrBiuyzSutt0OlxV4sFvPzLxaLKJfLvuAVIRaPx31xJpGUZb+USR8Xj8etHeVyuVwVKVkLXB2ROaiTaFuqR9La1swNEyCn3nbT5zfnIIsw15ZwKZdYsOVeI80TZlDHdUwrzjtTBA3C2ARrrXnjplW2XuvpVIjeZi24YZ71MOK21rkpagkhhBAyLXNsRYhpK20qlfIXOjeFrRaxpqAFJrv7mufT3/V/V2AnWydcOklmJGLpQIkAddXZVh4AviA1hbkWqoVCAaVSCbFYDBMTE774lcEB+S9itlwuTxJqImx1GfX1EDEr1lu5BpVKBYVCoco12FYH09prs0qFmcuqr41gWpWDjtPXRNdN9uulkkT0Stsmk0m/rUn9uJ6dWoKzWUE6VYI2yDJYz3G235GgY01R1s4CrREBGXQMpxUQQgghpFVMiyuyiAgRTzI3VFsctVCSzp5YEYFqQegSXC73YBHLghZcIgZNq6Vs0wJXhKLMeXV1hF3CVtdF11PqViwWfaGVSCRQqVQwPj4Oz/P8NW49z0OpVEI8HkexWITnecjn81Vlk7YzxbfpAqwt0fF4HIVCwa+bdkWu1c5mfmY6cwDA5lZstpOenyttbUZN1veLTQyb9ZV6lUqlKouuDBKwA+3GZolt1Cpr87CotxytJOi6h7knXFZNl6XRVf+pcKlt9T3drOW3ld4bhBBCCCGaKRW2YqVNpVJIpVK+tVYLNBFm2u3WtNCKlU1bHAVXRGRzHq1YNkXcSL5aHImYks6WdtsFqgWWy+XW1dl3WZzlXLlczheXIlBF5EajUeTzeX++bFdXFwBUidhSqYR8Pu/Py5XjTVFpWlDj8biffz6fr2pnWz3MfLT4lLqYf+Yx0k7aqmp2bkX8i2B1WX61K7VcM3ObpJN20tv1/GCbBZ5Mppa4tImzWi6tYQVrGPffMMe3Els9a1lgdT10e4U9zkU91uJGCOMWbKLr2krrtM3Fm0KZEEII2XOZEmErgY10xGMRUCIuJZ2OtmtGQ5Ztek1XUxwKWsSa/01xoy3CYvU0xZYpYAH3Oqk6f5cg1Mv0aGEt4jqXy1W1jXbl9TwPhUIBAHz37YmJCb/cExMTKJVKvoXW8zxks1n/GGkvXRZz2Z5isVgVfdkWhEuXy2wTU8iabac777rzqUWomVbaSCI/26y1uv1daFFbKpV8wa/dl/W1Yce4mlqDHPp6mddZpzHzM/Oodf5a6RsRW80SJLBtvwu1rLn1bLMxVfduM/lq0W4Tt60+byPimxBCCCGdT0uFrbh1yrxZPWdW5m6aAjUWi1VZHk3XY3PdVgCTPuvzm8sEyfxJLbxc1kKz42UKKEkjBFmIXBGE5b+2Cku9tYVVW0J1xORkMonh4eGqubayJJBe71bEruRl61hHIpFJAw7aau2qn21NXrO9TKFrs9JIelPM6/LJ+WyiNoyFy0QPWOhrr+8dm+vznorNBVn/DyLIuhp0Llc+7UijAq2WwK31vRnqcedu5LytEq3N1nmqrdeEEEIIaS+aErZacIolUdyP9Z8IMEmro/qKsBJEXGqBp912TXGo0RGP5c+cK2pbd1Wf29znErgmNqHtstKYaaQ9dDskk0kA8N2hRXCK5VJbo8XaK0GRisUiJiYmqgYKbPXV18+0NruEua3dTRdk7Q5s5mMuNWRGWNbXS8+1Nec1m+I2aH6t3qfbzowCLfep5CvlkzbfUzrHNk8Ic7vtu2tb2MGgeq229TBV1y7M74hpvQ5Kq7fZvBvCliGIVrdFvfnVepbCDlCZaYMswRS5hBBCyOynbmGrBYAIWbHQ2pbv0QICQJXANd2HbbgEpTm3ttbnoG22NKYlsJZl0HZe17lsAkHWqxWLoRacphiUYFwiIrX10fN2uyHncrma7eISqcBkV2+d3lY/07qpRaTeZlqgzY6p7JfBErE6S7RoyUfnJ59N12edRlvHzXm75sCJadGX5YNqzfftZILu3zCCNixhRW7Qce1EK6yTtt8b/Xk6LZutzNfMwyU8p9vC67KSE0IIIaSzCS1stVVRC1i9ZI98lwBRLpdWmxVP7wvTka4n0E+z1h6TIAtKPa6amkKhUCVYgefXZdXzgiORiB8AKZVKoVAo+EGmxFU5l8shn8/7eZjlqeVmbKY1O6e2/WYH3GwHvd+cS6yP05Z6bYWWiM1BFlvzPKbo9TzPn0sskZF1u9qEu9zzIm51ALJO7xiHGfiw7Q+TXxiCLJn15DHThBGG5gBPLQtio67IjbRns21YjzANY4EOm2/YfFxQ4BJCCCGzi9DCtqury7fOyp9YZbULslhkg7BZvMzoukBrBanZeWlV3jZLS628XZ1asVh63vORorUo08dGo1Ekk8mqdW9lu6xDa4pQV0AojW1gIaheppu2S6yYndCgQFzaii+Bw7R4l3S1BK2ZTtyKzfm1LnS9dWAzEd+dLG6DrLJhBO1UPD+dShgX6nqFaqPt0m7t6RLxtTxgauXZSoJcmAkhhBDSOdQtbLW4NQNEAahassdcEgawdyJsIieMUKmFGflXtgUJvmYIqpfZwdNCXm/TkYxroYWWdpXV+10i1hSw2krsqptZfpfIdOFKp/MSZK6r3Ee5XG6SS7Zgcw/W94+2KJvzbeUcYTq2InC1WOnEAFMud/J6BpRaVed68gn7fIZNN1XXrZY1NiytKF+r3MaboVH341alCQPFLSGEENL5hBa2Yo01rbZ6vVFBrIcuTCuaOXfRFB76vw5MpPOzWaAikd3BqUwXU3N+r/7TgkrSNoIrmrIWUy7Lps3914aUWYIbaddaXU8btSy2LpGuBwFcAw+2z2GW59FtI5GyAfjL9ListK57xSZsbX/6WpiuyWaecs/IfaJdmjsNl2XWZnWfivo1aqmrJdbCPDtTSavbKmy9p6MsU5F/I3nUawEPA12TCSGEkM6mLmGrRZ64Y+rldMw5jXp+qClGtEAN0xE153KGcf9zCRQzrRa5Uk8RL3qOpaQB3K60QZgCybU/jNVVz1GVa2EKNZdI1XU3z11LcLvK7Cq/3he038zDtIa6RGyjwtYWKVnX37RqmveQXidZ1g+eTUGlanXum90/XbjuW2E6rdNB559K62TQHOBWWZfroZ7zzMR9ROstIYQQ0pnUFTzKJgqAyZY5U8yaacR11kUtcVsLW5owx8mcTtOSJUJXi16J2lsPtSyVgHv+sc2CZl4HOV67ywYF6pL9tm1AuE63S8Tq/2FclOV/GLHqEri2tK7jTCurOehgE7bmfWEOhoj1thNppiM/3SKgnvPV+s2oJXynmulou1r1n4p8pyOfqR5woLglhBBCOovQykwi0mrRanMH0x1715w9EQSmKLMJ5anGtNCZVkL9X1tuxc1ZrLp6u1kfG+Y+l0AM6nRrK7i53WV5DcrbzEc6d0Gu0bZ7wNxe63OQGNWfgwRvrXxcf7b5pi4rrkYPNujBjlpu+O3OTIuVRqh1n9c6LkyaMFAIuZlqAS1pWjlIMZMDHoQQQgipn9DC1nQtdqWxWaxcc/nk+1RYFMK6H5pixiXUzHTA8+7YprDVSx25cJ0n6LO5TYthmzXRJlxt4jVM+YKoZSEPI0Rr/bcJ1TDHuASu4LLGmnnp+1QLfn2dw0QEbyeaFWLtIuRc5ahnLq7ruHapY7syUwMhrvRTIXAJIYQQ0hmEFrZhovVKR98Uv7UsIjPpylirbK75qlogmVF5dYAhPUfWdqyrHPUKN9N9uJawDdvxa2TQIayFNuy2eoWt63NQPfS1Ni25tvLZLLp6DvZspZOEnm0gop7jNFMhlOpty1oeFp1AqwRs2OMocAkhhJA9h9DCVlwsTbdNl4umS4CY1Oq4THfHxDWvMqg+sVhskvARsaujLNdy4a3Xcqn/l8vlQMt4s9S6To1YoMMK4CCx6mqPsNjKbfMqcB1bqVRatlzUdFKP0NPUE+yoXTDvDVsd6rHumtZ7V7pW0+5t3kz5pqpuFLiEEELInkNoYSvzT4PcX8305v4womYmcIl115I9+rigyMMy11Uf6zpGC+cwFsmgQYOwbthhaUbUmt8bteY2K2DDIGKvHsFjDlZ0Sgd6T3a3red5qXV8K9vOldd03lOzVZh30rNJCCGEkMaoKyqyDq6kxQbQXKclrBWoWde9WoQRTdqS63L3lf8SIEu+689iyQ0qsym0XO0+lWLPhu08tbY1K26nmiCx47Li1roX2hldp9ng4lov7Xatgtq8U65Hp5STEEIIIbOTutariUQivtttLcviVDBVnVFbmWsFY7JZdU1Ln3bJ1vvL5XJV0KGg85sCxGx32zlaIXbDHBdG4AaJ1HbrCLuEquteMPe3W32Im3qs8tPBTMQacNEu5Wgl7XCNCSGEEDK11BUV2YarQ9bugsYmUm2dH3OOrRDkNijuxuYxGj03M2w59TbXoIJN2Nq+m8dpGrEetcM1rRfbfGqXW3qQ2LV97wQ68ZpNBbUGtqaLqRog2ZOvcyc+l4QQQghpjLqDR9VDLSE4lSIpSKTKOYI6kmFFrP6sIyDb1qU1j3WdP+i7iFptETbzCfofRvSa7s9hyjWThOm8hhWqYf+7PrMjPTuYSYtuqwVuUPnb5RluBa24TvS+IIQQQjqXuiy2MzmHsx7CWJGbyVswo+HWErM6jyDXw0bLaQrdWsI5aH5r2P9B5W3UndnE1WGttd3mQuxKU0uw2s4XtI/MHmrdo5187cOUfaZE3ky0KwUtIYQQ0rmEFraueZtTOYdzOgkjWkzhKFGP66mLWHZdrt3ThVnmoPpPlXBtRtTW2l/LYt/Idpvl3Lav3dH3cL3HtSszKS4bbU9bPu1IJwt3QgghhOw5hBa2QUvf1ONK2w7YRJxLtLjcchshGo0iGo1OWjap3Wil62JQXo12mBs9bjbND26W2VbnRuoT9j4KI1xnW3sSQgghhHQaTQePCqJdO3tatOrv5v5WodetbWdBS8ieRD3PeS0PBzL7aNf3FyGEEELs1OWKPNuYjjqZEYzbGXbWCQlHJzzPhBBCCCF7EnWtY0vqp1NELSGEEEIIIYR0KrUXUiWEEEIIIYQQQtoYCltCCCGEEEIIIR0NhS0hhBBCCCGEkI6GwpYQQgghhBBCSEdDYUsIIYQQQgghpKOhsCWEEEIIIYQQ0tFQ2BJCCCGEEEII6WgobAkhhBBCCCGEdDQUtoQQQgghhBBCOhoKW0IIIYQQQgghHQ2FLSGEEEIIIYSQjobClhBCCCGEEEJIR0NhSwghhBBCCCGko6GwJYQQQgghhBDS0VDYEkIIIYQQQgjpaChsCSGEEEIIIYR0NBS2hBBCCCGEEEI6GgpbQgghhBBCCCEdDYUtIYQQQgghhJCOhsKWEEIIIYQQQkhHQ2FLCCGEEEIIIaSjobAlhBBCCCGEENLRUNgSQgghhBBCCOloKGwJIYQQQgghhHQ08ZkuACGEEEIIIXsinuf5f4QQO2GfDwpbQgghhBBCZoBIJAIgfMedkD2RSqUSKh2FLSGEEEIIITPAT37yEwwODlLYEuKgUqng0EMPxate9aqaaSMenyRCCCGEEEIIIR0Mg0cRQgghhBBCCOloKGwJIYQQQgghhHQ0FLaEEEIIIYQQQjoaCltCCCGEEEIIIR0NhS0hhBBCCCGEkI6GwpYQQgghhBBCSEdDYUsIIYQQQgghpKOhsCWEEEIIIYQQ0tFQ2BJCCCGEEEII6Wj+f44SsEln67sjAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch [5/5], Step [1/1], Loss: 1.2527068853378296\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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3v+15fGHrcpzwv4d33HGHA8D58pe/3LA/2+/FVVdd5UQikbrfuksvvdTxkobm/XLGGWc4yWTSeeihh9xl27Ztc3p7e53jjjvOXdbMfdIO6Irc4fT09DRkRz7ooIMwNDSEdevW4cILL8T++++PG2+8EdlstmH7m266CUNDQxgaGsLhhx+Oa6+9FhdccAE+9KEPzdchEDJnbNq0Cfvssw9OOOEEADOuMi996UvxjW98w9MdOAxHH300br755oa/c889t6Hsv/7rv9Z9P/bYY/Hwww83lHvpS19a5ykhI6q6rLYGFwoF7Nq1C8961rMAAH/+859D7Xv37t0YHx8HMOM2XKvV8O53vxvRaP3PvVh/b775ZoyOjuLcc8/Frl273L9YLIajjz7ateBs374df/3rX3Heeee5I87ATIKPQw45pKFtZPHjOA6uu+46nHbaaXAcp+7+2bhxI8bGxhru2wsuuADJZNL9bnsOgJl3n46vSyaTeOYzn1lXLhaLuXXVajWMjIygUqngGc94hvV5CXoGh4eH8atf/QoXXnghnvKUp9RtK89LM8f84x//GCtXrsSLX/xit55sNouLL77Y97yGQf9WjI2NYdeuXTj++OPx8MMPN7g77rfffg0xiT/5yU+wevVqnH766e6ydDrd4B3217/+FQ888ABe9rKXYffu3e6xTk1N4cQTT8SvfvUr1Gq1WR8PaQ/NPhMmIyMjuOWWW3D22WdjYmLCvd67d+/Gxo0b8cADD2Dr1q1121x88cV13kTHHnssqtUqNm/ePKtj+a//+i8MDQ1h+fLleMYznoGf//znePvb3443v/nNbplvf/vbOPjgg7Fhw4a6Z/F5z3seADR4IPyv//W/cNRRR7nfn/KUp+Bf/uVf8NOf/rShz2C+X6+//nrUajWcffbZdftasWIFDjjgAHdf8n786U9/ag0NaqYuIczvoR/692Jqagq7du3Cs5/9bDiOg7/85S+h6tBUq1XcdNNNOOOMM/DUpz7VXb5y5Uq87GUvw29+8xu3HyLM1X1iQlfkDmdychLLly+vW3bdddehr68Pw8PD+OQnP4lHHnnEMxHE0UcfjSuvvBLVahV///vfceWVV2LPnj11HQtCupFqtYpvfOMbOOGEE/DII4+4y48++mh85CMfwc9//nPX1bZZli1bhuc///mB5dLpdIOr3eDgIPbs2dNQ1uwoSwdblx0ZGcEVV1yBb3zjG9i5c2ddebOzGlRnX18fHnroIUSjUV/h+cADDwCA2xEw6evrAwD35XPAAQc0lDnooINCdZrI4mJ4eBijo6P4whe+gC984QvWMuZ9HOY5AIA1a9Y0uN4PDg7ib3/7W92yr371q/jIRz6Ce++9F+Vy2V2+3377NbQlaN/SSfSbQ7eZY968eTP233//huM46KCDPOsPy29/+1tcdtll+N3vftfQeR4bG6sbfLKdi82bN2P9+vUNbTMzysvvw3nnnefZlrGxMYY3dRDNPBMmDz74IBzHwbve9S68613vspbZuXMnVq9e7X4P+0w3y7/8y7/gda97HUqlEu644w68//3vRz6frxukfeCBB/CPf/zD0+Xd/P2xvb8OPPBA5PN5DA8PY8WKFe5y83w98MADcBzHWgcAN5xhv/32w5vf/GZ89KMfxaZNm3Dsscfi9NNPxyte8Qr3uQxblxD299CLxx57DO9+97txww03NFwXW98iiOHhYeTzeetv2cEHH4xarYbHH38chx56qLt8ru4TEwrbDmbLli0YGxtreNEcd9xxbvzKaaedhsMPPxwvf/nL8ac//anBKqM76Bs3bsSGDRvwwhe+EJ/4xCfqRr0I6TZuueUWbN++Hd/4xjfwjW98o2H9pk2bWha2YYnFYrMu6/z/2B1gJmb2tttuw9ve9jYcccQR6OnpQa1Ww8knn2y1ioSpMwip99prr617qQvdkN6fLAxy77ziFa/wFD7mdHVh79kw5b72ta/h/PPPxxlnnIG3ve1tWL58OWKxGK666iprgsR2Pi/NHHO7eeihh3DiiSdiw4YN+OhHP4p9990XyWQSP/7xj/Gxj32s4bdiNhmQpa4PfehDnlOI9PT0tFw/aS/NPhMmcr3f+ta3emYeNvuk7XiubKxZs8btv/7zP/8zli1bhte97nU44YQT3LjgWq2Gww8/HB/96Eetdey7774t7998bmq1GiKRCG688UbrMevn4CMf+QjOP/98fP/738dNN92EN7zhDbjqqqvw+9//HmvWrGmqLmB257hareIFL3gBRkZG8I53vAMbNmxALpfD1q1bcf7558+bx8Vc3Scm7LF0MBK87ZfWvKenB5dddhkuuOACfOtb38I555zjW+epp56K448/Hu9///txySWX7PXpy0n3smnTJixfvtzN4qe5/vrr8d3vfhef+9znumZaiz179uDnP/85rrjiCrz73e92l4vFpBXWr1+PWq2Ge+65x7NTKok4li9f7mulXrt2rWd77rvvvpbbSLqXoaEh9Pb2olqthvJwaDff+c538NSnPhXXX399nTWj1em+xKXu73//u2eZZo557dq1+Pvf/w7HceraF/Z5MS00wg9+8AMUi0XccMMNdVYQW+Inv7bdc889DW0zM1TL70NfX1/g8Xq1l8wfYZ8Jr2slz0AikWjrM92Oe+OSSy7Bxz72MfzHf/wHzjzzTEQiEaxfvx533nknTjzxxFD7sL2/7r//fmSz2cBEZ+vXr4fjONhvv/1w4IEHBu7r8MMPx+GHH47/+I//wG233YbnPOc5+NznPocrr7yy6brC4HX8d911F+6//3589atfxate9Sp3+c033xy6DpOhoSFks1nrb9m9996LaDQ6q0GF2cAY2w7llltuwXvf+17st99+ePnLX+5b9uUvfznWrFlTlwnVj3e84x3YvXs3vvjFL7ajqYTMO9PT07j++uvxwhe+EC9+8Ysb/l73utdhYmKiYXqCTkZGM83Ry49//OMt13nGGWcgGo3iPe95T8OorOxn48aN6Ovrw/vf//46tzVB0vGvXLkSRxxxBL761a/WuS7dfPPNuOeee1puI+leYrEYXvSiF+G6666zisG5mMrB3D9Q/8z84Q9/wO9+97uW6hsaGsJxxx2Ha665Bo899ljdOtlHM8f8z//8z9i2bRu+853vuMvy+bynC7OJDDyPjo7WLbcd99jYGL785S+HqheYee63bt1a9xtZKBQa+gVHHXUU1q9fjw9/+MOYnJxsqEcfr1d7yfwR9pmQnCzmtVq+fDme+9zn4vOf/zy2b9/eUH+rz3Qul5v1fRGPx/GWt7wF//jHP/D9738fwIyX09atW6392enpaUxNTdUt+93vflcXNvP444/j+9//Pk466aRAD6yzzjoLsVgMV1xxRcN72nEcd1q+8fFxVCqVuvWHH344otGoOwVR2LqaoZnfC8dx8IlPfCJ0HSaxWAwnnXQSvv/979dN6fXEE0/g61//Oo455hg3jGm+ocW2A7jxxhtx7733olKp4IknnsAtt9yCm2++GWvXrsUNN9wQOClyIpHAG9/4RrztbW/DT37yE5x88sm+5U855RQcdthh+OhHP4pLL720wZefkE7nhhtuwMTERF3iE82znvUsDA0NYdOmTXjpS1/adP1bt261zh/d09ODM844o+n6wtDX14fjjjsOH/zgB1Eul7F69WrcdNNNdfHDzbL//vvjne98J9773vfi2GOPxVlnnYVUKoU77rgDq1atwlVXXYW+vj5cffXVeOUrX4mnP/3pOOecczA0NITHHnsMP/rRj/Cc5zwHn/70pwEAV111FU499VQcc8wxuPDCCzEyMoJPfepTOPTQQ62dXrL4+c///E/ceuutOProo3HRRRfhkEMOwcjICP785z/jZz/7GUZGRuZs3y984Qtx/fXX48wzz8Spp56KRx55BJ/73OdwyCGHtHw/fvKTn8QxxxyDpz/96bj44oux33774dFHH8WPfvQj/PWvfwUQ/pgvuugifPrTn8arXvUq/OlPf8LKlStx7bXXWhM92pAkN+985ztxzjnnIJFI4LTTTsNJJ52EZDKJ0047DZdccgkmJyfxxS9+EcuXL7eKERuXXHIJPv3pT+Pcc8/FG9/4RqxcuRKbNm1y+xtiuYlGo/jSl76EU045BYceeiguuOACrF69Glu3bsWtt96Kvr4+/OAHP/BtLz3D2ss111yDn/zkJw3L3/jGN4Z+JjKZDA455BB885vfxIEHHoglS5bgsMMOw2GHHYbPfOYzOOaYY3D44YfjoosuwlOf+lQ88cQT+N3vfoctW7bgzjvvbLrNRx11FK6++mpceeWV2H///bF8+XLPvA5+nH/++Xj3u9+ND3zgAzjjjDPwyle+Et/61rfwr//6r7j11lvxnOc8B9VqFffeey++9a1vufM3C4cddhg2btxYN90PMDMtXhDr16/HlVdeiX//93/Ho48+ijPOOAO9vb145JFH8N3vfhcXX3wx3vrWt+KWW27B6173OrzkJS/BgQceiEqlgmuvvdYdFGumrmZYv349BgYG8LnPfQ69vb3I5XI4+uijsWHDBqxfvx5vfetbsXXrVvT19eG6666zxrbKM/yGN7wBGzduRCwW8/QEvfLKK3HzzTfjmGOOwWtf+1rE43F8/vOfR7FYbJhzfF5pa45l0hSSAlv+ksmks2LFCucFL3iB84lPfMIZHx+vKy8pyIeHhxvqGhsbc/r7++vSqa9du9Y59dRTrfv+yle+4pkWnJBO57TTTnPS6XTDFFea888/30kkEu6UHGjDdD96SpvzzjvPyeVyDdvLcyrIVCN6igIBRvr8LVu2OGeeeaYzMDDg9Pf3Oy95yUucbdu2NZTz+i2Q3xRzuo1rrrnGOfLII51UKuUMDg46xx9/vDtFknDrrbc6GzdudPr7+510Ou2sX7/eOf/88+umRnAcx7nuuuucgw8+2EmlUs4hhxziXH/99c55553H6X72Yp544gnn0ksvdfbdd18nkUg4K1ascE488UTnC1/4gltGpvsxp8CQ50O/i44//njn0EMPbdiPeZ/VajXn/e9/v7N27VonlUo5Rx55pPPDH/6woVwzz6DjOM7f//539zlMp9POQQcd5LzrXe9q+pgdZ2Y6kdNPP93JZrPOsmXLnDe+8Y3OT37yk1DT/TiO47z3ve91Vq9e7USj0bpn+4YbbnCe9rSnOel02lm3bp3zgQ98wLnmmmsann+/fsDDDz/snHrqqe6UgW95y1uc6667zgHg/P73v68r+5e//MU566yznKVLlzqpVMpZu3atc/bZZzs///nPQ7WXzB6zz2j+Pf7446GfCcdxnNtuu8056qijnGQy2fAcPPTQQ86rXvUqZ8WKFU4ikXBWr17tvPCFL3S+853vNLTHnH5InnV9f+/YscM59dRTnd7eXgdA4NQ/fu/ryy+/vK7+UqnkfOADH3AOPfRQ9x131FFHOVdccYUzNjbWUOfXvvY154ADDnDPj/kc+vW1HWfmHXjMMcc4uVzOyeVyzoYNG5xLL73Uue+++xzHmXmuLrzwQmf9+vVOOp12lixZ4pxwwgnOz372s6brcpzwv4eOMzOF2SGHHOLE4/G639V77rnHef7zn+/09PQ4y5Ytcy666CLnzjvvbPjtrVQqzutf/3pnaGjIiUQidX0Z22/ln//8Z2fjxo1OT0+Pk81mnRNOOMG57bbb6so0c5+0g8j/bywhhBBCCNmL+fjHP443velN2LJlS13mW0K6nUgkgksvvdT1QCKLE8bYEkIIIYTsZUxPT9d9LxQK+PznP48DDjiAopYQ0pUwxpYQQgghZC/jrLPOwlOe8hQcccQRGBsbw9e+9jXce++92LRp00I3jRBCWoLClhBCCCFkL2Pjxo340pe+hE2bNqFareKQQw7BN77xjZYS7hFCSCfAGFtCCCGEEEIIIV0NY2wJIYQQQgghhHQ1FLaEEEIIIYQQQroaCltCCCGEEEIIIV1N6ORRkUhkLttByKKh08PW+SwTEo5Ofpb5HBMSjk5+jgHgta99LU466aSFbgYhHYm86/r6+nDCCScElmdWZEIIIYQQQgghHUWzA1N0RSaEEEIIIYQQ0tVQ2BJCCCGEEEII6WoobAkhhBBCCCGEdDUUtoQQQgghhBBCuhoKW0IIIYQQQgghXQ2FLSGEEEIIIYSQrobClhBCCCGEEEJIV0NhSwghhBBCCCGkq6GwJYQQQgghhBDS1VDYEkIIIYQQQgjpaihsCSGEEEIIIYR0NRS2hBBCCCGEEEK6GgpbQgghhBBCCCFdDYUtIYQQQgghhJCuhsKWEEIIIYQQQkhXQ2FLCCGEEEIIIaSrobAlhBBCCCGEENLVUNgSQgghhBBCCOlqKGwJIYQQQgghhHQ1FLaEEEIIIYQQQroaCltCCCGEEEIIIV0NhS0hhBBCCCGEkK6GwpYQQgghhBBCSFcTX+gGzBeRSGShm0AIIYQQQgghZA4ILWwjkQgcx5nLtswZImq9/gdtR7qPbr1XyezgM0sIIYQQsnfSlLBdDJ1Gm7jVx7YYjpGQvQE+q4QQQgghRAgtbGOx2Fy2Y86RTrBY8mydYi1y56vTPJ+WRdu+FpM4CHONyQx787npZu8TQgghhBBiJ7Swjcf3jnBcmyXXJgJsHWO/zrLXOlOMhd0ubMc8qJxe36rYmQ+RFGYfc+VivthE4GI7nlZo9hxQCHc/vO8JIYSQxU1otRqNLu4EymbHtVar1QnbaDRa1zEKY/UxRaNZfrbfvZY1s95Gs8IwjDiej3jmMJbaVte1Uo50Hu0evKHgXRj4DBJCCCHEpC1m2HZaP9rVUWyXVc5xHPfPFLh+4lYv9/psLvMr14ro9VseFi+Xba/vXuJSzl+z27WCecxh90MXZv/z0u3szdeVEEIIIWSx01TyKNtn23ebQAsSd7MlyDrq136/uvQyLXC1yA0r1P3EqN95mi9Lbdh6wpxbm5ANQp/LoHvMr31eZUyBu7cLnWYs1N0gboPukXZd8244F4sdDkIRQgghxKRpV2Sb4NCiz0+M6W3ajc0qp9GdWi1Og7CJUbOj3KwFV7c5KJlVUGe8HZ3sdlp79fH4DTbYjt22bZBQlvuylWNoVXzvjQTFgncCzQ6KzGYfZG4JO/jIASpCCCGECLMStrVaDbVarUHULjRhOjoSQ2tu00yyqFqtVrdNM1bhZsvNBlsMbBgLehgLs9/2zbTPz9rWTOIov+WtxgLPR3zwfNNMmzvlufZDD4b4eRRovAabgvYTlqDnjjwJB5gIIYQQMltamsfWcZw6UavLeNGODl07O+Ne7tPa6uNljfUSfNqCG9YiHKZttn3OBtN6GtbKLtfdLNtsXWa9ts+2737bhtmmmevRrnq6mU4TYWE9Irzub7OusL9X7XyOO+3eWehr3GnngxBCCCHdS0sxto7joFqtNrWjsB2YMPGqs7Hghd2ftkA3K1Z1GTPZVDNtayfmuTNdfM1BCkFiicViXyqVXHGr6w37X28XRsiGWdaMgFgo61030YzY8Rogmgv8LLHNehAE/ZYs1mtr0ko4xnzumxBCCCEkLC1lRdbCJixesblA81a+sG7PsxFAtn1qUWib/kf/t3XCbVZgvW4+0bGpsVgM1WrVHawwrVV6DmNTANuuoZ+49bvmYUXGbK9rOyxpi7FT3qyXQ5gyrcbaNyNOm607iHZ4THTC/WGzYrcyEDFbT5lOOBeEEEIIWfw0bbF1HAfRaNRXXHpZKHUHx+bSOltX0SA3RS+rZZBF1a9d0WgU8Xgc0WgUsVjMXV6pVOpEoNmplPNnE8thLbw28RwUkyrrtRVW2huLxRpEayQScY/LcRwkEgmrOJV9m+LVS8zaypn4uSk3syzMunax0K6d7WQ252u2wnQhsf0m2dygvSzG+vt8ijpzUK0dA3vdKOgJIYQQsnfStMVWhI7EW9rEotdnoVKphLa6Nts2v+VBFh6bsAwSlY7joFKpIBKJuP91GbM+fe5Ml95area2QUSnzTIsy/zcwbVoBRqt7CLIpZ5oNFqXCMwsK+7nXsJW9iFuzbLcS+gGiWDb+Z4rt9dmBjK8kOvZrIt+J6HFW6sCZSHE6lyKqbAWyLloQ6t1hvm9I4QQQghZbLTkigwAsVisTlyZrrZ+1kabgGlnx9DLCmzL7BxkFfRap49Rjse0QosITSQSSCQSrnAol8tue/T5EiuqKbBtbREX4SAhYgpcc51cRxGuXuKyVqu5+9NCVo5XPtvcmc22m3hZu2yulO3CS5y04iZeq9VQqVTq3Lm7ldkI2jDnqtMJIwqDvCK86pxvbINs7bDA7i2CeW85TkIIIWSxEFrYiquqaX0UC10YQSuIdVDTDquZbdsgoeInvPWfub353S92VBCLXrVabRCyYkHV59gmSs32t+rKrZEY23g8brUkSxlT0GoRYAp9jXkcXlZbc+qoIFfl2R63iZ9LtLaU6/IiaheLsG2Vbhe0QjMDMrZt2vk71k7C/C4LQYIu7LnoVhbDMRBCCCF7I6GFbblcRq1WQywWq4vPlMRC2mqnsXWC4vE4qtVqg0CylQ9rRbV13LwscIJfXJyf+6xgWlxt+4vH424Mbrlcdt2wZR8yKCDWU3EP9rMu2wYGTMKUEWS/2tqqj9Osx2ahlu/lctm1/Gps1jwv1+Qgl+V20UxdIrrlOmlBK6LWK7N0J9Ks1ZF44+dZ0eq27WQ2VtpWt+2W58BEt7tbj4EQQgjZW2la2Iq41dZFU3AFdQhEEJrWQSCcm7CfFdZmPfGyJJviVQta2/5s4laLU9OtWM6RFsA6hlYvE3dlM6mTec70uTbFb6vWGNlO4mdtgxT6XNkErXZlNq+rl2XXPK82i62fqA1yFw0ibCdci0A5ThG0XkK+G1hM4tY2aLJQdJrVdiEsrK24PS80czF4RgghhJD5oylhW61WXVErMZdijRTBInGYXp19LWq066spSL3Eq5+FVAsqvTzIpdWr42d2/L1Esbm9iE45D9ImSQ4lYheYsZamUikkk8m6aXV0XV5tNUWuLtuK5URbkfVxakuuJJqSz7IfnfhK79dvsCCRSNTF5ur926zCsp12//aysNiuqZ+bqdc6sdIKYqkV63u3ux7bro3JXIuUsAMyQeW8nu3Ztr0ZYdrMIFO79hmGZlyszeeold8QW92dKhhppSWEEEIWB00ljxLhKFZbiRnVAhd4UrRqkeKFCD1d1rR8+ola01qsxa1NXMlyc3Re16OXm5+lrFfHUydkMsW96WocjUaRTCaRzWaRTCZ96zX3Ie3Q2ZP9RLCJ7Zj0NE5aVOrBB1N06nUiTv3EqLRJzoUeCJEpkmz7MgcStAjWCcya7Zj6CTvdXmmftta2Mp9zp2Aet995MC3Ws6UV0daq0Gu17bNpo9dg2ELQ7P7NAYRWzpmXoG3Vo2QuoZWWEEIIWTy0lBVZrJBa4IoFV7sniyg13Uptbrs2QWmWARqnv9Gdplgs5iZCAuqTEfmhRZyt3X6YVuNkMumKbBG2Ukbap63bmUwG6XTadUP2cmPU60wR7ieIm7VyaauxFppS1hS+epqbSqWCaDTaYH21tTMejyOVSiGdTjeIWi1uvSy4ul22aadsx2gTcaYIMT0ATEErCaK6uSNsG7TR5ybIemurx6vMfGITVOb1DXvdmnHTtgnZThO3QeLe6xmRZUGeLmGeO3P5QkIrLSGEELL4aHm6H8dx6jr68idWuFgs5pbVolU6EWZGYNM6ayIxq1rMaDdQU+Dq/dosgHobLzdXwWbxNTuwsp20T+JmdZtMUZtMJpHJZJBKpZBIJNwytsRRprC1fdbtbRVzkKFaraJcLje4BesMzyL65Fyb0wLpdsmxDwwMIJFIIJ1Oo1AooFQquQMmWjza/st+JO47CK9z5HXetKDVcbTdLmj9CHIdtwm9VoRf0P7biZcVPmifYdrrdy6CPC86RfgDdvFps9qGOZ6gbVoZfLLtK8xgo61uv8+EEEII6X5aFraCCFxT6Njck7Xl0pzaRj6bSZXEPRaYSW6kxYagxaRNdIrQ1eJMt1//l89eHSgt2PR+TKEOoO745XjlvMTjcTdhlHyOxWINlltT7Ns60EEd5aAMyTYBKm2u1WooFAoNllIRsra69Py6tnrT6TR6e3tdS3U8Hsf09HSdsDWtweayYrFYd9299mV+9uvMapdoiaGVwZu9pRPsJ3CbseY2U8bveZtLWhXmQYJ5tgNNrQrfMJ4ps61D4/Vb1IwoDTO4ECRG/c6X1+87IYQQQhYfsxa2gumSmkgk6gSuKcK0wNMCF2i05oq4EzfZyclJt6y4v2rXVL9YU1Pk2QSjxhTmunNvE7PSVhGxYmmWc6CtsrLeNtWPFvf6s26LzcJtihGbqNViUb7Ltnp7yfassx3LOTddkW1JwKQeOVa5hul0GqlUCj09Pe450DHbWsTK91Kp5ArNQqHgWpFtotY8F2E602ZSqG7OdtwO9H0eZM210arI1fubC5oRjc2I+GbFb9h6w1oypUwz671Eou0Ztl13Lyu1n8U0zHHY2tQKptfI3vosE0IIIXsLbRO2gnb7rVQqdZZJL8upWCpF/OjlZnIk+Z5KpVAsFlEsFq2xoDbMTpvfFDrmei2+vYSniFY916/+0xZcmyA2xa/OQK0TbJniWESICE4RfPF4vCFzr7a8yn+dmVgLGrlephuwxox9lfZLbK0cs8Qep1Ip95hlWTKZrLME64EKfc1E1ObzeVfYtoLZ2RW3Zi1suzkxVLvwElXmAI8uYxIkDrW11lavV5v8aJd4ta0Pa41txWrrN0Aln/08Sbz2Y3qxBMW+mmW83NC9hK1pgfcTurbvs8Ur1IQQQgghi5u2C1vgSZdOM1ZRz9WqOztaFOm5X7WAk46TCOViseiWLZVKDQmHzLrN5aZgNf9rtAA13af1ci1oRfTajkVbdUXE6RhcsWrKesdx6pJ06czLImxlEEGm49Hr9BzEEgMr7RCrOvBk5l8tjKW81OtllY3FYqhUKq6FV45f6kmlUu7URnK+ZN5cOXbpEJvWHzm+QqGA6elplEolqxVIrnMz96mIWT19DzvC9Xi5JAeJ2yCrpZ9Qs+3fq06/fbRSrlXx6lemWatjmAEBW/lmXXWb2YetPq9zFca93PxNni10OyaEEEL2buZE2AoicHXcYiKRcJMlpVKpOndk02VZW0UlGVM2m20Qjslk0t2Xtiyan4EnRZhpDTYtEHqZiERtZRUxqkWrbq8W5LJfOYZUKtUwNZGuW6YAEhEoYlYSOdkszDKIIFZQLULl2LUlVKyhImojkUhd4ifdZtN1W6ycsl4Eoh4sMC2eMgeyCFntCm22y+zwVqtVFAoFFAqFutha3XkO6yIriJVWi1p2gv2xWfRMbEIuSODqOm3WvWatqmFoxaJqEnTPmQMurezDb79By1qtr9nBodkMLDW7zULUSQghhJDuYE6FraZWq6FUKrlCQjqyyWQSqVTKdf9MJpMAvDMPa5EsQlaEkYgyLawkMzHwZFyvKVQ1+rtpPTTjYr3c/rSg11bIaDSKcrmMYrFYV6+23AIzQi6fz6NUKrllxLop1lepV0S9WBuj0ShKpZLr3iuu2mbGYbFym8eqXZTlGpmZkc1EYWbdsk85tkql4v6X6xSLxTA5Oeles2Kx6F5Tcx/5fB75fB6Tk5MoFouh7zk/IaHdj20u1sQbm5uqn0uxTQD6Cb12i5IwgtLLAtqOupspZxJGEIc5X2HFcBiRG3TNgyyn8hs5F4LWq82EEEIIWfzMm7AVRJxNT08DQJ0lV/6bGYS1W6zEWObz+ToRJB0lW2yk1KdddkVsaTdo3TmzCVdtFTbjUmW9afkF7LG82nIsIk6LcDlPZtyr/i5iOBKJuAMGWmRGIhFXwGpxKwMMsq224Mq50J1UnanYS9yKaDWzVkvMbDQaddclk0mk02nk83n3uooY1u2U+qS9xWIRhULBbeNskPazE9w6XoLGhl5vuqvKZ/0/LO2ygrZaVxg36Vbr9TtPYdrTCl7X0WaZDSNqvdrTbmHrty9CCCGE7B3Mu7AFnhSIIn5Mq5+OzdQit1QqIRqNYmJiAoVCoU7MardjjdSRTCZdd1gRtdrN2ct6qjt04mJs60BJeT1/r2xj6/zqRFliJZZyEpOqp1EyXW5Nt2gtLAGgVCrV/ddCVESw1OUn8EyXcGkfgLp9intxJBJx2yDxu1oUy+dIJIJEIlF3XOa8yNpVW+rzmmaoWdgJbg82AeZltbVZcL2sd0Ei2bb/MG0MKheEXz1B4i9oH7aBNL+2+YlH8zcnjAA319vab+7Dr+4wVlSbNT9oG0IIIYQQGwsibIF6dzRtBdQWVP1dhFO5XHane9HT1WgLnOM4dXGuYqUUUSpiC3hSpOl92jprYmEV8abX6f96O6+paMRSq7eT4wDgWq61wBM3bDP5VCKRcMWr7F/HrGrxqqe00cLW5pJsE/vmMcr50FmNi8WitQ160KFSqaBYLCKRSLiu59oCbTtfknTMPMekM/ATdYBdXGlh1orwabZ9C1FPkDi0lQ2zf/N70DNhqzfIFTyMNd5mhQ+D9pBpZjtCCCGEEC8WTNjakgSJwNXztWrEqmd2iM1MvTqplLg22+aRBeqzHJtokWeKO9mPLqfbrC2dury2RNvKO46DUqnkCluz42fGvk5OTrrts9Wn3Yh1lmrtemwmvjLjj/2sKTomV9orScH0OtOiLG7HUkbcw73OZTKZRDKZxPT0NGNiOww/y6zts7ltM9badrQVaK8Ls9CsKG21zGzbEXb7sKJcX0NTsBJCCCGEzBcLbrE1v3t1iPS0M17ltFATUauFrel+LNvYOuAigs3pe7wErNkO3RZZpuemFRdls6Mt2ZYLhUKdBVnEnJ4CSASrbCftNzM663Os2yL/bQm1RCyLYLVdJy1udRytZH2WQQcpI6LajMu1JeSSLNDyOR6PI5PJYHJy0jrlEFkc+D3Xfp/DMhfb+A2MzRezbUOQi3QrLtRhtqMIJoQQQki7WHBh6yduTQuuIGLP5t4YjUYbphMyLZJSt2mR1IJQi18voWjWp8Wv3hZ40p1WhK4+Bikn0wFJEiktirW4FGEn4lOsn7Jvs6wWvQDcdmgrtJ62SNqjMzHb3MbNDqwsM63R2mIrYlniq7Vbsz7v+nw6juPOgStJxkjnEcaVNmh7k4UQtc3U3aygbKeQM+sJK8CDzrNNmPp5bVCcEkIIIaQTWFBhq92RTbfioI6S7lTpeWX1PLna5Rd4UiialkZBW2q1SDWFr5SVOm3WXNOFWLsG29qfSCTQ29sLAG4yJtNaLNuJO7ZYWUXoyfFoS6s+Vh2vLAJaEjiZbtVa2OsYWRHUcq20W7ScI/PYdIIrLcLlWFKplJtoynRH1m2Sazs9Pc2OdJfjJ4Jty1sVzGG2m4u6bcc3XxbcMAI3jAU2KL7WfEbNQcYwcb8UxYQQQghpFx0hbPUybRG0baO3BZ7sHOlpgcS91sxQLMmX9HY2K63NQmuu94rNNRNgyX8tBPU6qSuTybiCT8Sm3r+OldXtAWDN1CyuvLqNOrZYi3h9LFqY6umE5FzqDM2mWAdQF1trJroyY5SlDhmUAODO8SuYn9PpdN00TaTzCBNbayOM0G2HNbXZMs0SJGj9LKd+NGuh9cNmobWVaaaNfu3xqovilhBCCCHtYsGELYAGl1agPvZWW1RtiZqA+uRPyWSywdJos0aaFlgtbLXbsmyjBa1pxZV26vab7dQutWbnT8cDm5ZOLfS1qJXtJMGUiENbwiezTkGOQ0Sl2V6Neb71wILpJi5WV2m7tE+LcEG7TNus4jrOVr6LJV4nESOLk9lYPINE1myZrWj2E6nzfV+HseC22iaKVkIIIYTMFx0pbMUt1YYWb8CTgk3+i/uxTeTZrLSmqDXXSV26HIAGoanbo/cFwFM46rham5u0WJjL5XJDcijZLpVKYWRkpO78mZ1JM1bYvAblcrnOyq0HBnR7zetgiltJBCVxv3ofepDCtKRLGTNplTkQINcjnU670wqRzmC28bVBtEPUzmX7zH20Kmrbtf9WtvOyqOrflKByze6PFltCCCGEtIsFF7Y6y7EpcM2yOp7VTDYkwkwL2yALLdBoWTVFrk6mJGJOiy4/q5Jp3bW1JR6Pu1ZIOWaZrxdAXeImU3Cbc9iKONTz+8p+JfOxxhbfbB6HFtw2d2fTNVosydrVXDqupriNRqMoFot1olYyX9tEuGyXTqcxNTVVd+ykM/ATuK26Jc/WOjofgjbs/mwCzm8Ayax3rkVx2NwGYbb3+24up7glhBBCyGxZUGELwJ36RTIBA/b4W6A+LlQSLGkBJtZC00JqYrPW6qzApnCWerSoNV1wNTZ3Zdmv1K9Fns6QrN2OtTuvTtoky/L5PAqFguuWqy2hphu3ab0WF2Gb6NeWWDkH2mJsOy5tzU4mk3CcmWzKOoGVub2IbbHQO46DZDKJcrncEM+sz1EymUQqlXJFP+lsggStTQDZPjfLXAjaMNZgr+MNm7Bptm2ZDV7iOSiRlJ/1NcwyCltCCCGEzJYFF7Zm9l7BZsUFnky2lEqlEIlE3IRLWrhp0anFnmk51VZGPces7jCKQPSz0kq7bPuwrZc4VVlfLpcxNTXlxo5q1105RgDufxGfxWKxYWofjdRni0+2xR2bFhWbK7BOLiX1aNEpZXTMsJn9Wp8nEbT6eywWc622un59/jKZDKamptgZ7lDCWGfbIWzCCM12MBuLcJjjCnvs82199tp/GJEun4OucxjhTIiG9wohhBAbCy5sxfqoxYssF0Fkzpkqgk9EnRaoEptrurKaAs4UtebUQDb8BK1tH3oZgLp5YvW+RMiWSiV3uhs5XhG65n7MwQA/C5HEvppJtWzt1dt5Wcy1BdvLfVxckoEnrfKmcK5Wq+45qVarSKfTqNVqSCQS7nkwreki1JPJJJLJJAqFQsNxk4Wl1XhbmydAM+Wb2c9cW0a7udMd5vy0W1hQqJBm4P1CCCHERkcJWxMRfNripy2eNmFrWmxNMaZjPKUu+aznZZV9acGsYzrNfdj2ZbroyjI9dY6egki7HGuXZDObsVhNZb22mNrKmedCPttchG2Wc73cywptCnJ9nDoDszlPsbhQ2wYdxJprxlFLvHUymWQSqQ6iVUHbCrO11AZ1jOfbMmoO+ixEG7za41dGaEUIU5yQVpEBUMlFQQghhAAdIGyBRndkm0VQyoirsAgnnYnXnI7Hyy1YxGw6na7rWJkur1pQytQ65ly4un5tJfaKRRVRZxPG0jb5r5MqyTbaZTcSiaBQKDS4bOt26Vha0+ptCkz9X6PXmefSr6weHJCkUHKe9TmXa6fdqqW8ttpKPRJfLVZovR1Z3LTT9bjThJXpkhvkkTGfhBkI8BrYCOOOHGYfZPEjuROC5ilf6OeBEEJIZ9IRwla7G5svLFusrZmN1zadjs3FVlto4/E4stkspqenG+ahNd2YZX0qlXIthGa95vyrslyOTVtZ9baJRAKZTMZ1Ofaq12ZdFZddPTAgQrFWq9XVqZNemXHHevDAD93hNs+BFspmPV7TDGlXc51QS6yyOsZWJ5GS9TKgYUs0RjoTvwEfc5nf91YwvTG8yrRrP83g5dLvtawTO/azGVww/5O9k2w2i4mJicB3kePMJBqMxWIMRyGEEOLSMcLWzx1ZCybdKRWhak7xI+tsmYu1ezEAN75T1sl6sdDqfcbj8YZMvNpCbFpybccpIlO+i5ATN2dtFRWXa52lWFulZZ1Yks1BAN12EYBSpwwG2KxCXp0K05Ik4tQso8+ped7lGAU9X69uo5flXbskS5ytZIUm3YeXa3yz27e673YIKZswm2uBNtfuyq1aVIMGDcJYfcneS7FYrJsDXWO+V+SdT2FLCCFE6Chha8uOrONsRQSl02k3+ZLEdnpZaYH6aWt0TK0IQ9PSCsAqzDKZDDKZDAqFgjuVjdlWOR6bhVJnV5b9FovFuv2LpVWEtxlfLMehY0tNAS7l9WCBnF/pHJhz/dquiT4mfS6l42GeP5tl3bTwaoustjKXy2VX9Os4aFsnR9eTSqVQKBQCR/jJ/BJWeJkDVTbaKd68rMPtFlTzJdDaLXBbiZU117eynbk9Be7eiX5PmtjeyUuXLkU8HsfU1JTroUQIIWTvpSOELfCk5U4sduY6LZqk86OtroIpbkXM6v/ATMdJRnq9BLFsH4lE3LifZDKJbDaLQqGAfD7f0E5tbY1Go57JLbQoLZfLDdMMaUGoLcry2RSepju1tnDLev1XqVRcV17tzq3Pr+keqRNE2abw0dvqaXpsrsLmIIIWwXoQwuu6AKiz2toGRUhn4mfVM/Er16qY83Jzbsf908yxtVI34J8BvdV9N3PssxkICLJo02q7d1Mul13PnSCKxSIAYOnSpcjlchgfH8f4+LjvNkw4RQghi5uOEbYilrzckcXaqMWPFoOmG7JeLtY9LYC1tdHMHiz7NMXX9PQ0kskkMpmMm8SoVCrVWVN1G5LJpPsStbkpyz4dx3HjZfUyEbU6S7JYXMWNGXjSsqlFpGnplTr1qLdYQ6UeWyysnAtT5HpdKy1MdXysFsB6ud7OdNXW109/F8QlWWKfmURq4ZlLYadpZR9m20xr8WwE1Xy4Hdu+m+ehGUv5bGinALW5cQO02u6NiGdRGEqlEnbu3IlcLoelS5cilUphcnLSN5RGv5MJIYQsPjpG2AJPCi9bh8Ym1rxiaLVF14y/NV1j5bP5wtNCVIs0qdcUxGan2RTjtuPRMa3mZ9PyqbeTaXAEnflYrLpenWE5x1K/uD1r0SntsJ17WefV6dRlxOpqWnZFlGtxa+5P0O7XtkRSMu2PxESTxU0zlt5mrZDzbS20DQI0u3+vgYT5GmAgZK6QwWMvqtUq8vm8+56XMCMvYes4ToOXFSGEkMVFozJcIEzxY3PL1XG2WojZLLVhRK3ej1h1BVuGYgCYnJxEpVJBMplEOp32dWO2dVLNKYEAuK60Oka3Wq26MUOmhVR3wv0Epk0gitVa1pvZjM12mJ91e2R03XQVN12hw2RbNpNFyXWzrdcx0/F4HIlEAqlUih35DqYVwTjb6zkXbsydhP4d8Ro8M7/7/Wa0uv924xd+QPYeJNQkiGKx6E57t3TpUt+yXu7vhBBCFgcdI2xNbK7BpputV1ngyey8NlHrVX8ul0MqlapbbsbmVqtVN3OjtkiaSEKksJ1Icw5eG1pc6vhYvU+bq7N8TiQSSCQSDXXpqYFEqJrZqE2BbZbxclXWYtw8Pi1cvcS6meRKH58IWz2AQbqD+RC6QQLJtNC2s4Mbpi5bmWYtuH4Da7J9WDE734MPtoFDigyi6evra3gnm5RKJUxMTACYmZJPD1CHgd4NhBCyeOgqJSCWTFumXkELKBMtxkwkaUUymayL6zTFl8Talkol5HI55HK5BrGl3WvNWFRTLMo2WqjaRJ7ZedXuufLdPFf6PwDXVUsnZrKdD1Pc6jq11TzIJdlcr8W7H+Y2+ljlGHTSq1gshkQi0XSHhnQGc+H+axOsXkJSe3HMdXxnUL0LFVfqNeDX7vqbgWJj70ay3afT6cCyU1NTKJVK6O3txcDAQNP3zkI9d4QQQtpLVwlboD6pknwHGqfFMV2Ug4jFYpicnEQikXBfpOb0QLKfSqXiWmy1pTAajbpTEWkrryk+E4mEdWoh29yyXu13HKch/kjH49qO3WZBtdWrRal2WZa26VhWPzdjKdusVcxmYTIt2qbbsnmdyN6LTZz6DfzYQhRkm9m4xc7HvdiODvls6whrnQ76PbaFlJC9E/ldHxsbQyaTQU9Pj2/5crmMYrHozkGv7x/9Lva6r+jtQwghi4OO+DXXnUy/Do0WtTZroNkh0h1W08po/lWrVZRKJXde2UQi4SmUisUi9uzZg2Kx6E43A8y4zQ4ODqK/vx89PT1IJpN1ySxEINpiefWUPqb1SJ8b7RIt4tZ8KZvCVJaVSiX3HNgs23qQQM6XbqecZ5to98KMiTYtzV7ba8Hs5YasLc/ijswO8eKkleuq77lm6jUHTvQyv7Jh2tmKO7UXc2VlnSvrapj4Rpt7Mtn70O/5fD7f8N40KRaL2LFjB6amptDX1+cK4Xg8jrVr12JwcBCRyMxc9DbMKQYJIYR0Jx0hbIHwnSkzLlTws7Ro9+UgIVYul5FIJNDb22sVjFK/uCQDQE9PD+LxOLLZLJYtW4ZcLufuS4s4cZlNpVKuVVgEqMTjijgzp7wxRb8WrvJS1kJP2qvFrYhMKWe6eJnWWPPcmcdkJqLyin02k1KZ4lajr5s+Lr1/nZVa3JH1f9IdBMXJz6ZeP8HktX/5rr0DwtynQXXPNe2wLNN1k3Qa8kxOTk4iEomgr6/Pt3y5XMbIyAgikQjS6TSi0SgGBgawfPlyDA4OIpfLufO3m/d7NBpFNpudy8MhhBAyD3RUUGLYJA5m4iKzg2laI0V02fan64xEIm4SJe0WbHPBBWbieuQl2tPTg76+Puyzzz5wHAd79uxxO8Uy7Y/MfyufZeoBmX5HW3Z1G2W5nh7HPFemq5XNvVL2IaPfuVwO5XK5bq5c2/kxz69YoW3Jo0zxbdZntsfretvcsaWNejsz7lbm+SV7N6Y1U+4Z876y/eaY24Z1pdf1+93X7Rp8sXl02I6xmfqasQLbfh9atfZ6bTeb4yGLA0nEKC7GfvfD1NQUJiYmkM1mEYvFsGrVKuy///6o1WoYHx/H9PQ04vG4+24aHByE48wkVtyzZ898HRIhhJA5oqOEbRBhRJNtvfnZz2pbq9UwPT2NaDSKTCbjTiNge5mK1VgsraVSCdu2bcOuXbswOTnpCtp4PI6BgQFUKhVUq1U3UZVQLBZdcSvJq2ROXbHmitVWBKUIPaknEomgVCq525idVG1p1UJdz4mrxa0Wobozrq2ocuxeQlJvZ3Mtls+yX/Mce20j+9eWbH0+2BkmgF0UeYlbs4z+MxPASRlzW1O0et2DfkLZrNfvPjafz3bc87Opx68dYa4DIV6Uy2V3mr1SqeR571QqFRSLRTeZ4MjICO6//373vaxDbIaGhpBKpdxkkHoee0IIId1JxwpbEW9iPbUhsZ5aqJqJnoBwVhJThBUKBXf6HxGetjbKvLY9PT3IZrPu3HtiOZT40vHxcZRKJd+On45/1ctF0Gpxq91wbZ1rWa5jd/X/YrEI4ElrsZeVWLbxsoz7CVOv86zrBZ60AOv1XkmpzH3Ln9wL4mbGTjMxhVaQtVVvZ1tvq8d8Nsx9m8v82tkqC+1+H+ZZa+aZDHveyN5DuVzG6Ogo+vv7EYlEUCgUrOVqtRp27dqF6elpLFu2DIlEAsVi0fo+GRkZqetnaPSgFiGEkO6hY2JsNWEsHTbx1Upd2hqrO66O47hCNKhTJkmnxIIbi8Xq5t6zzROrxbqIcbGAyrGIKLa5/MoyU9jb3JltLr9iCQ5zzkyLtT5PrcS12tyk9VRHZtkwGSu1sGUikO7G6z4wn4Fm6pM69Hezfq/vYdpi+z1ptn3NrpsrZrPPuRChCy3cSedQLBaRzWbdhI1eTE1NudMFTU9PW98J8l6Xd6bM8Q7A9bQihBDSXXSksBWCOkl6vVgz9TpTFAYRidRn1xXhJ5mP/VySp6enUSgUEIlEkMvlMDAwUFdGkkPZxKgWxmJBlXgi0zok+5PjEpcrmUJILKjavdjMIGzW6zVvrQ3doffLRB2EvjZybLa69H8THWdruiOTxU1YAdWM0PIb0Jlr8UmrJCF25B0XiURc76nBwcHAKYD27NmDSqWC1atXY99998U+++xTt96cLk8L20qlQtdkQgjpQjpK2MoIalAMnGC6B+vtxQ3XS4j6tUEQgajjNm0WVMeZmXZHxK2ez9Z2PLFYzB1xtq0zha/flDjilqxdjs05fPUyLR69jsXLOibldTIraZ8tG7Otw6+zWpuDDnLsrbgiapdkZkcmQPvmQzWfqdnUE3adbUCrmfo6gTDxxoSEQXtOOY6DdDpdJ0RtVCoV5PN55PN59PX1YfXq1b7eP2H7HYQQQjqXjhK2QjPWGNMl2Wah9XJhFPElwkziY/XLE4Dr4mq6Hpp/+XwelUoFqVQK/f39ntMHmFPfmOg5XmWKIJtYs7kgiyiOx+MNU5Zoy61OIuVlZTXjWE1XaLOc7bwEoQcfRPBL+7zaFASFbedgc2FvZpt2lGuGML8bQe3wC4sIe18udKe6U6zQ7RqcIN2Lfl/KND56cNiPqakpFItFDA4OYt999/W08uo8D4QQQrqXjhK2YRK4aLSo1Z3GZtyPgSeFayqVckeBtWXStKB6WTOr1Sr27NmDqakpVKtVpFKpBnEoCSnEwip1iKDWIlLEp8SNmutE/MmUPVosm0m0gtyMzWMyLb42V2GdZdm8dmFdm4GZjotkjDaTZ2n8lut2h4nJJfNDM6KkFSEUZkDGa30rdc4G2zPV7LbdSre3nyws8m7o6+tDIpFAqVRqcCW2USwWsWPHDgwPD2N6erph7nbB9q6S9zchhJDuoWMUgGn1C4MkZdJWPz/3WnN/wJMTs8s8tEuWLEEikYDjzCSPkilxJJGEXxweABQKBTfp1JIlS3xfpLoObaHUHWBpo8Tc6vbLsYqwNbEJz7Dn1uZ+qWNXbeJRH0MzAwuCzZJtWuTN/UgZffztcBslC8tsrH1621ZErLleJ2oLe1+FcSVuhoW24M41QeeIz/PiR94v8XgcS5cuxZo1a7BhwwY87WlPQ29vL4CZTMbT09OIRCLIZDKh6h0ZGcGOHTtQLBaxatUq63bmOwRAwzuXEEJI59Mxaf+8rLGmgJLvIrzEMlitVuvW2TqCWjg6zkziJYmFlW0lY7FYVs0ER7oe3XaJs63VahgeHkY8Hkcul0Nvby9KpVLdFD3VarVuztlEIoFEIoFyuYxSqVRn6dVuwzqrsnYJtiW5MEegdQyu6RIZZCE143NtYlPH8koCLDm+IGGh3Zy9rLxBYsWcf5fsPeh7TN8btgEd81luFq/fFr92NXs/8v4leyPVahXZbNYN44lEZqb1mZ6eRqVSQSQSqfPsCZu1uFQqYfPmzRgYGMDq1asxMjKCxx9/vOE5M4Wtbf5qQgghnU1HWmybRUSpFkZBLyVxP04mk677seM4iMfjGBgYQCaTcRNCjY2NIRqNoq+vD8lkskFsmy/IYrHoTt1jlhexasbrAk9OLi9TBglyHFrU2iy+Nsz96IEAbdn2cjnWcbphrpF2BTbFqmk9t1l3pbweqPDajz4/ZqeEmZG7nzACz0vIyvatClnz3tTPSCv1hC3TjKjdGwTw3nCM5En0QC8wY70dHBzE2rVr3Sn0pqensWfPHkQiMzMQhPmtL5VKKJfLKJfLbpwu8OT7ynzXxGKxwKzLhBBCOo+OEbaaZjszYtWUbcNY7WSu2Uwmg0QigWq1imKxiEKhgEQigWQy6VpD8/m8GzMbJCBrtZrrjpxIJDAwMOAmuTDjR7XVVbaXeFNTzJpCVvYlI9gSb2S+5E2XXhGBXm7aXjGJft/N7bXLptd18BK1Ya+frT59XuhCtvjxsoaa957tfg0reG3P60LcW177nM0zM1+0+jyTvYdIZGaKu1qt5iYQlCl3JCGjfrcVi0WUy2U35jaIYrGIBx98EMViEb29vXXb2BIV0lpLCCHdSccIW5sQ0qOq+r9fHUHurLJc3I/FnXl6ehqjo6PYs2ePa2mV/dVqNUxOTrquy7qtfsJNz6mqLa1+McHVahXT09N1dYnQlfXy0rW5IHu5D+s26M9aDMr2poXZPFabBdgmbm1lvCy3Zlvkv5/VVmd4NjsnjLPtDFoVKNrtPWx52z61tdVcH/b+sFls2yW8zGdqMcLnkAQhMbMrVqxAT08PHMfB7t27sWXLFtdteMmSJe69VK1WsWPHDmQyGfT394e6x8bGxlxvqLDvcEIIId1FxwhbP3c8vxeMnqrHFJHmn+4si0idnp7G+Pg4isWiKxyr1Wqdi7II33w+j1Qq1ZDt2NamiYkJ92Xd09NTFyObSCRcISZt0cdqugnrP5sLsczdKlMSaQu2PmY/y47XsbRyLWx1hO0kmII3TIdFD3wspFWNtJ8gcWuLFff7LdF1Bt2TtntfeyT4bR/UBr/6CZ/fvY1arYYlS5Zg6dKl2LVrF+677z5MTEygWq1ibGwM27Ztc12RhWq1iomJCcTjcfT29voOgor78apVq/CUpzwFS5curavH3DasizMhhJDOomOELdD8CKmtU2t2bm1JjpLJpGuRrVQqbiInYOYlVygUrC87mWLAZg3U+5ekFtFoFKlUCkuXLnU7whILKtP7SDltrazVam5GZimjMwbLcUkbxXVLrwuydup2B8UO2ty0bGXMcl7xjrY2eNXn52qq267FvF+9ZPHh51VgftbLmvm9sd1zrRK0b1v93Ww96ua2k/khEomgWCxiZGQEExMTvu9tTbFYRLVa9RWhMuidSqUQj8exZMkSHHDAAXXbmJmS9buaEEJI99BRwlbw6ziay83YWnEtlmW2//ISGxsbw/j4OEqlUp2AHB8fdy2rGrHa6vIabXGRuKBcLodcLoelS5d6Ck2ZHN50ldbnwiZsJYNzLpdDT0+PG4ckMcO6vO2cmp11nUTDdF/W8bn6WM2Ovk2QmqLdZom2lRErdxCSCEws16QzafdAQxjB6uXq24x118sjIMzx2NztwzJbAd0JUNSSMEis7BNPPIGxsbGG+2ZiYgKlUgl9fX11yycnJzE2NoapqSnPe61araJcLqNWq6G3txeDg4MYGBjAypUr3TKlUqlO3MZiMQpbQgjpQjpmup9mCGOJ9HPzE6FYKBRQLpfdLMXAk5ZQSUCkpxSSdZLx2LY/PW2QzD8bjUbd2FzTzVbH+QpmjKqX26NsIxZkPaevl4j1s6rajscUl0Hul7Zpd/T2zQjPIGEr58lxHDdmei7iIMn8YFr4mxF1fs+7DHjJZy93dXnObPG4zdxLumy7rK9h4o07WQQ3Ey+ttwH4HC92xBoroTQ2JFmUOcWPWFa115WJvJcqlYqbGDIejyObzbplZCBaMN/JhBBCuoOOFLZBYstEWxF1rK0WVrJM/k9PT6NUKtW5FWvBHI1Gkc1mMT09jWKxWFe/fJaXpdn2UqmEnp4eN9PyyMgIHGcmIZW8oKWsZIIsFAoA4Mb2yn7khW2KPPlcKpUQiUSQTqfd46xUKnXt1Mcoc8x6CQfby9y0gOtzpTufZkdUi31T8No6rdpaK27c4pIt+5J5cs2ORywWQzKZdGOk/To6ZHEQxqJpu89My2sYwWgTu62ItaDtvIR1M3V0KmHipWd7bkn3IQPLO3fudN+DJvIOy2Qydb/9kUjEFaQyYG2+kzOZjDvvbSQSwa5du3DnnXeiUqkgnU6jUCjAcRzk83l3oDkSiTQkcSSEENL5dIywbdbFT8rKtlrcmvXqZDHpdNoVmLoOQRIgicVVi95oNIqenh53rlnJTKzrENGVyWQQiUQwNTWFyclJNxujjBZPT0+7LrSO47gvY211FJdibWUSFyn5LhPXR6NRV6ib1mmJQbUJVF3WS8CabtLNdPLlPOpBA1lmimjtgiydmHK5HMrKLLHG5XLZtZKThWe2saj6c7N1eXkueNUbdh/ms2A+F14uzV7rwuI3INVpBB1nsy7ZzW5DugdJyDg+Pu5ZJpvNoqenx/V+yufzAOC+k1OpFBKJBPL5PMbGxuq2FQ+rWCyG6elpxGIxNzZXBoDlPRyPx12XZNusA4QQQjqbjur9t9Jx0XGZ5lQ6NtLpdMOorrYwitVTXJZ6e3vrMi6KeJTRX5uASiQS7stT5sAtFAqIxWLIZrN1L9FsNusKPJ0ASSapl/3IetPyZLoyixVZj2hLObH6mtZTr/g/GTCwiVqzvLkfQbtm6+Wme7M5ZZAp+L3cR/UyuS7xeLwhoRZZeOZKmPhZNc3vXrHktm2DBJU5GOc3KDcXx94OF+dm6p9vzPO50O0hc0NPT0/dwKuNZDKJVCqFbDaL5cuXY2hoCPF4HNVqFXv27HEHlE1XZSGXy2HlypWYmprC8PAwarUaRkdHEYvF0NfXh4GBAXdwPJvNUtQSQkiX0jEWWy3E9LIgy5t20RWBp7cRC4dM/B6PxzExMdHgriRl5U/iY3O5HDKZDHp7e10LbTabRTQadUeG4/E40uk0gJnR4WKxiKmpKXcKoXK57M6DKwmO+vv7MTQ0hJ6eHtx///11LlDiTixlxX1ZYmi1kJPOn7g6l0qlBssnMCOUI5GZzJM63lD+i/A0LUJ6f7YYSNPa5RXLaLp5a0uXzVJcLpcxOjrqtsu0tHlZbROJhBsvlUwm64Q8WVy0InRsAyQ2bwN9f9sEo82LQD8nfgMxs8Gvvnbvq1WhbDs3YZaRvY9IJILBwUHs2rXL+k4WRkZGkMvlsGLFCixbtgzxeBz5fN5NCiXvTPmtTyQS6O3tRaVSQT6fx/j4uDuoXK1W67yoyuWyOz1fX18fVq5cidHR0Xk6A4QQQtpJxwjbZvCylAah56U13XU1lUrFTVShBaeeYkd3zKSMtm7qjq3sT7spZzIZZLPZhnpkG5lWSFyj/TrYenuvY7LtI8it0SYkm3UT1+LAjK8NijX0iov0aocMCmirHJOAdA/NuNm2KuK87uMgAavvR7Ocfu7CiLWgONOgdnk9Q+0W062KT5u792zboaEgXjykUqnQv9GSPwGYGUzu7+9HuVzG9PS0W4/ExUYiEZTL5YZ3sn4fSbZkABgaGkI2m8XAwABSqRTvMUII6VI6Ttj6ufP5rbOhO3yRSAS5XK4u4ZMZjyqfS6USdu/ejcHBQXeidnEJjsVirjuzJhqNupZVcTMGZlyXdQKkbDaLZcuWYcmSJYhGo+6cfZFIxLWOJpNJNwGUXi6uyqZo1tZuLWBln7VaDcVi0a3DdAvWx25anrzOexgRYg4+2NyotQu1l2VHt8HcTluv9fkrlUpIpVLuKD7FLTGxeSCEjQ01n5HZiHKbcJVyrcSidgJh2t6uMqR7Wb58OQqFQmCiJvGQyuVy6OvrQ39/PwYGBgDMTAXU29uLiYkJ7N69G8DMMzQ5OVl372SzWRQKBRSLxbr8GL29vTjyyCOxatUqJJNJPPTQQ7znCCGkS+lIYSv4xcGFsdqagkwyEJudULOzKkJQXFu1lVaPMEv5SqXS8BKNx+N1876mUikMDg5icHDQrXNqagq7du1qiAuOxWIoFAquW7XEivb19aFWq7mZmnX87eTkpGthtp1Ls64w7pVe59GGHhXX8bJeliy5ftqF2RbnC6DufOu2mHMNSj3xeNyNT6bVdvESxuPAS3iGtazaxK+ILe0VoO//oJjcsCK4mdjhuWAhOvdeQpYCd3EiiaCCYlrFfbhUKqG3t9cVwtFo1I2PlcFbANYp+ZLJJAqFgjsPfSQSwZIlS7BixQrs2rXLfXcMDw+HnsM2Eokgm82iVCq5Xlac/5YQQhaOjhG2NkuhX1kvvDo/iUQC5XLZdU8KU3elUsH09DRGR0frrIGSNdFxHNedGIA7VYHE8kiMbX9/v7vt/vvvj/7+fsRiMdx9993unLlex6ZFXzQaRaVScWODZL24VQFPDgZIeX1eIpFIXUZJ23mzuTZ6nSfT3VDPAWyzzmp37Fgs5nZQpKMh2ISxfLbdJ9qKLcvT6bQb7ywubOwYdzatCLSw19Sr7rAxt7q8zS0+SNT6taPbRZuXUG/3cTU7MEE6m0hkZkodeaeFYXh4GACwY8cOd0q3dDqNoaGhhoEsjWTNl+nx9tlnH6xduxalUgnHHnssDjzwQORyOVx77bUN1uOgQddCoVCXYZkQQsjC0TG/xNqaCDR2NM0pY4KsiOb2YukUgSPLbHWKpU/cgPXcqpKMQrLvmlbQWCyGnp4e5HI5JBIJOM7MFEPFYtEVuuImNT4+Xjdvn7gKSwZlEYqSRGpyctIVael02n3RSr26nRpJrAHY56m10UzH0ezUe8XCyvFJh3d6erpuTl8vl2eg0WqrE4WZVl8pE4vFXHdkSbxFFo75sjTaCPIg0Ott96Febgo5Pf+ybb9+Ls9ez0o3EfQ73MwAWatlSPfhOE4oN2SNeDrJe0MSMso7OZPJuLk0SqUSpqamAABLlixx57Kt1WrubATiGRWPx7F582bs3r3b6sIsU/w5juNm3JfZFeSZzmaz1kFjQggh80dHCVuzQ6ndjbV1zratF7oja5u8XcrIXyqVcoVquVx2Y2plflSx+koMbDQaRS6XQzweR09PjxuzI27D4s68efNmVCoVN8Zn8+bNGB4eduvP5/OIRCKuSO3p6WkYgZaJ5E13YhH9MmJsdgL1HL/aXUpbmmwddz/XSb3OtNzqbbXLtik8zSl/9PU05841Bbm22pr3jNQh89oWi8UGt2XS3YRx57Wtb8YF2eYtYKvXdENuVYi1U7yFdXeej7Y0i587soZit/splUrunPJepNNpd5BYPJ9yuVyd2/HExASAmZCfnp4edzqgPXv2IBKJYMWKFchms6hWq0gmk1i9ejX+8Y9/YHx8HDt37sSvfvUr3HbbbRgeHkZ/fz/S6TQmJycBwP0v7xA9sKrfS4VCIbTlmRBCyNzQEcJWrGs2cWuWM8VuM9mRxeXVto3Mkec4juu+qqfXEUuvTsgk1lyJmU0mk8hms0in00ilUgCeFHObN292hevExATGx8fdjpm4zWqX3Eql4h6ftCWRSLiuVNpKJFbjZDJZ565sCkRTTIbtQPq5GZoWMFud0kazHi16bZZZP1FrExmmgBZLt8xJrKeDIHPLfFgfzfvMdi+E2c6rDr1M/2bIgJWUNQdhZBvTKmve62GEmZeFea5pp5WUFlfihSRPtJHJZDA4OOhaXuXdF4lE3EFnsbgCcC2wMgjd09ODTCaDpUuXYtWqVa6X0JIlSxCLxXDXXXfBcRzs3LkTW7duxc6dO90wH7H6auTdL95X8s6Vue059y0hhCw8HSNstag1vwcJ3jB4ZUCW/YtrsXY9liREYvUzraWRSMSdN1XcmWSbpUuXuommtm/fjkqlgmc/+9lIJpPYsWOH265kMulOVyBz1sqLWl6YwEziqJGREQBwha60Px6Pu/O2VioVpNNp9yUvx6c759qCK+tt371cJnWdXt9tZc0BBVN0m3UEWZ1E9Ovplsz9plIplEolFAqFrnX17Cbm6hw3U28rbfATxV73L9AoUs3fF6+BoyDLrs2bohlx2E33ejPHZv4+kO7FL6mfvFu1kJW56DOZDOLxOMbGxhqSB0pIzvj4OKamplzLbaVSweGHH47e3l4AwD/+8Q9MTEzglFNOQS6Xc+e1HRkZQblcRqlUQiaTqZsX3mzf8uXL3SzMHDAlhJDOoPnJYOeAZsSrXyfTb/tUKmVdJ67EIgrF1Visr5VKBVNTU+6LS2JcRUjG43FXBBeLRTf+Z2BgAPvuuy/WrFmDZDKJAw88EBs2bHBjZwG4InTJkiVIp9Oua7N2u5L9mMcsnV45bzo7q1hzxYVLW8NtiZ3Ma2DiZxnT593LiqrnArYJWH0MZj02oWurx2sQRAYtJLaXLAxhnuvZ1Gm795rBLy7c3KdtEE4nbTNjzmW7sBZb8542RfPeyN587IuVbDbr6XGVy+WQSqWwc+dON741nU5j5cqVKBaLGBkZaRjIlHdoLpdDOp1GLpfD+Pg4RkdHUavVsGrVKuy///444ogj3Hf0c57zHGQyGTiO4777e3t7sWbNGuyzzz51uSwEeS7z+TzDWwghpMPoGIutKaxsgkWX98LWWdT1mp1EnWBIWzgl26GXGNbu0OIiXC6Xkc1m3cQUMvJbrVaxbt06JJNJ7Nq1C0uWLEF/fz8mJyeRTqcxOjqK6elp1+or9fb29iKXy2FsbAxjY2NuLJI5OizxpHrEWhJMRSIRt245fi2ATREYthMtx++VNMe0wpjn31xus9roa2/OvetnLZbPcp1E3NPCM7/43UPNChW/OO9m6gjC1i4tYrVg1VZX28AT8OTzYd7nfm3x81Ro1nLbKbRice7G4yTh8cpUL4ORU1NT7swEEm4zMjLiaR2V96e8i2WAt7+/300eBQBbtmxBqVTCgQceiEQigTvuuANLlixxXZ97enqwY8cOjI6ONsT/isdWrVbD2NhYm88IIYSQ2dIxwtbmeqzFYzPi1ozdFFcmiZcVdGIoEbW1Wg2xWAzlchmO47ixsrou/V3iWmXaAmAmA2O1WkU+n3ezH+dyOfz+97/H9PQ0Vq5c6c59m06nkUwmsW7dOhQKBYyPj2PHjh2oVCpIJpMAZjoA0h4vi6XE/8p36TTIXLeyrVhvAdTN+yd16BhkWwdcypuJn8wOv14nZYPmkzVFgim8zbrNQRA97Y92PZdYW0L8sMWG6+dLJ4vxssoC9cns9FzWYdkbrZPNCFmK3u5HckHY3i/9/f2usBVLKgA343A6nbbWWalUkEqlEI/Hkc/nXVFaLBYxODiIZDKJyclJbN26FePj46hUKvjiF7+IVCqFww47DMPDw9i2bRuWLFkCANhvv/0AzEwx9PDDDyOfz1ufe0IIIZ1Dxwhb02Iry7UbqzklkFmHF+I+rOuPRqPIZDKIRCJuen9TKIqw1KO0sm56etoVTcDMyzORSKC3txfVatUVqY8//jh27tyJ8fFx5PN5pNNpd9qhnp4eHHTQQYhGo+jr60OxWMS9996LarWKsbEx1+orL9FEIlEnDMW9VspJ2+SFruNPpa3mgIH893KhDDrHYay1UqfX/LRmXV4uxmbiK6/lZlvkvtkbBcPehNcAiLku7Lbm4Jgs05YmGTTRz5muo1aroVQq1U0L4uW94LVPW5vmmk7vuPNZ7n5syZYkfnb37t0N96CepsdGtVp1rbv6nbnPPvugVqvhgQcewOOPP45HH30U27ZtAzCTybi/vx+bN2/G9u3bUS6XsW7dOmzYsAH77rsvli1bht/+9re48cYbcf/99yMejzNJFCGEdDALLmxFdNjiO02rpLnOzwpnc+fTFkCxZOp5baVesbDI9AC2TmUkEnHXi5AU4ek4M/PUFotFJJNJLFu2zO3cptNpHHLIIVi/fj0GBgYa2p1KpZBKpfDLX/4ShUIB6XQasVgMfX19ruVWklXJsch+xSotaIusxJnKetO90rRG2a6D7RybFizzs9d3s359jv2stl4uzl7WWkHckTkdw9xhWtDN5V7f27VfP6EYFu2xYPv90b87+t4UgSsDTXLPyzo9qBNWNNrKhRlI6lRm207bs98Nx00asV03yVkhiRptSDZiL++fbDaLUqmEUqkEYOa9Nzk5iS1btmBgYAATExPIZDJYtWoVdu7cienpaSxbtgxHHnkkrrzyShxyyCENda5ZswbZbBYf+chH6mYuIIQQ0nl0hLC1uR57uSGbn20iB3jSpVh3MGU/yWQSqVTKjcMxt08kEm66f+0upTvuEtcqc9z19fW5U/1ImdWrV6O3txelUgnbtm3D6OgolixZgiOPPNKaNKNarSIej7tTCDiO4yaSkizHIky1FVYEm+nyqOP7RMjLtn7ZKAHvZDq2+oMERZB11mu/entT6AaJb/1Z7jERHuyYdB9e92CQcPWKzQ2qz7yf9KCJ/Pd6RmRwTAbFvOZrbte92O77uVOeDz6rixfzuqZSKaTTaRQKBVeUmuvlnaen+NFEo1H09PRg165dAIDly5cjm82ip6cHhUIBhUIBL3jBC7B06VJs27YNP/nJTzA+Po59990Xr3jFK1zXY9u+ZUo+Zj8mhJDOZkGFrbbWarSQMf80enoer7rNjqQWuDJljrb8SrKhSGQm6ZIpwKLRqBsXK8ktgJnRZrEKTk5OIplMolwuI5VKYWBgAMlkEqOjoygUCrjvvvuwYcMGt85SqYR77rkHTzzxBB577DFs3boVpVLJnWooEolgYmKibooffU68Ejhpy6Ut7tW0VJvJb/xErtfcnbb/XvG3fmLUFB82q7GXJdk8B5JUJJFIoFAosLO8gNiuYZjr4eU10ez+/Or3GjAxy8lvlvz+APVx/dIBlj8RuF7PlK0dCyXquunZoPDtPrwGVSVpop5rXr835H3oJS57e3uRTqexZ8+eOo+rnp4epNNpZDIZpFIpVKtVLFmyBMlkEkcffTQymQxqtRq++c1v4qUvfakrbh9//HHccsstuO+++/D73/8ef/vb3+jtQwghXcCCC1tTrJqWWVumZCGRSCAajdZlLtRlKpVKnQCMx+PIZDJu4iYRtGZnOZFINGRsjEaj6O/vR6VSQbFYRKFQcCdor1arGB0dxfj4OMbHx5FMJt0pfCSeVlyPh4eHsWfPHjfr4u7du5HP57FlyxY8/PDD7tQEkgRDLD/alVg60dJpluOT+WtlmT4O01orL2mveEI/wWn+2cRDGPdTLaa9rFpSLmiZn+CR/ciABZk7ZuN2HCRUmrW8BsWK2+5rfe/LAJDpkixl9T1lDtzIc2j+jvi55NuW67oXuytuM8ez2I59b8FLlJbLZUxNTXluJ4PUZo6JwcFBFItFTExMYGJiom5Q6NFHH0UqlcJDDz2EpUuXYv369chkMli2bBlyuRwuuugirFy5Er/73e/whz/8Ab/4xS9w7bXXYnR0FKOjo3jkkUewZcsWbNmyxU0uSQghpLNZMGFrWmR159HLQqu3FbySApmdUXEPnJqaanB9rtVqdR3UarXqCj+x6IqIFoEslhspk0wm3SkAxFqby+UAAP39/VizZg0ikZm43P7+fuzevRulUgmpVAqjo6MA4M6bOz09DeBJi6uMKmsXLVtssExEr90lbQmbpFNudjJsHX7TwqQFrT7Pch7N7b0w96Hb6tdhtbU7Go3WuWh7ZaE144xJ+wg7kBG0bbOCxWbJDdqfuc7cn5dgNq26tVqtbuosbaH1SpQWRpj6ncu5FHQUimQhmJiYcD/LO6xWq7nJGyXbsRaXEgok73Kxxsrvu+M4bszuxMSEK04HBgaQTqcxODiINWvWYM2aNXj605+OdevW4eGHH657NyxfvhyRSATFYhFbt26lGzIhhHQBCyZsbYLWywVZuw+bYteWQMomlIFG11UtcnSnTsShvDC167LuwIr4TSQSSKfTmJiYQDabRV9fHyqVCrZs2YLdu3ejr6/Pneanr68P/f392LFjB4aHh5HP57F9+3Y89thjrqW1v78f6XQavb29AICxsTHXagsAk5OTrsiVGNt4PO66X+uOtY4F1B32MKLWXD/bjm8zbqQ2N2ebxcqvHi2C9T1Ea0/3EUYUm/iJ3qBtvDATrGmXSdPDwy8MoJl92uLRgwaA9gb4LC8+RNgCTz5r8q6V6ywDlMBMVmOx9OoQm1QqhWw2i4mJCaTTaSxfvhzFYhF/+tOfcOedd2KfffbBnj178Nvf/hYDAwM49NBDMTIyglKphB07duDee+/FXXfdhWq1ilQqhac85Skol8sYGxtDpVJBLpdDJpPBxMQEqtVqXcIqQgghC8eCCVtzmgxtZdTiNUhw2axvpqA1t5d9azdDeYnKC9Nm1ZUXro6ri8fjqNVqyOfz7ihyPB5HoVDA5OQkotEoRkZGcN9997nxtvF4HGNjY+58uf39/VixYgVyuRwGBwddUZtKpbB9+3ZEIhEMDAy4L1Zxg5aOs5xHEbUiwCVhlD4+sUjb3Hj1sYoo1MtM663NKq47+jYLrpclLGgqoGbw2p+fFwBpnaBnNCytCBQ/66q0ySZ+gtyUbeW0q7LMGe03n7M+L/pPzzetB8psA1Be7ZoL/M5jp0Ahu7gpFAruNTZjWvV7St4Z4u6vicViKBaLbl6FeDyObDaL8fFxPPHEE0ilUtizZw/uvPNOd1BansF0Oo2pqSksWbIEhx56KKampvCMZzwDq1evdmc6ePzxx/Hggw9iYGAAY2Nj2L17tzsvLq26hBCysCyIsLXN/aiz1ppTAHnF2ZZKpYasxtoqp5ebZSRrcjKZrHuBipUznU4jGo1a96FfsPF4HPF43HWFAoCRkRFXfErnV17CIyMjKJfLKBaLyGQybpnp6em6qQwkWdTq1avR19fn7m9iYgLbt2/H9u3bsXPnTrdtIlbL5TLK5bLrPq3XaRdK81j0eRJxL/+93CXNwQObi7DtvOk6tQA2hUEz8XZaVJvHYwoLMj80Y6EPW0/YdX6uxkF12Kys5mev+uX3w8S05prnRgSvGV7gF0c8H1BIkvlABogjkYgrLjXVatUVoJVKxZ1L1syvEYlEkMlkAMCdTm9iYgL33Xdf3Tp5H8fjcQwPD7vbp9NplMtlrFy5EsPDw9i8eTP23Xdf7LPPPohEIsjlcjjxxBOx//77Y82aNYjFYti5cycefvhh3H777XjwwQeZZIoQQhaQeRe2kUjEdZuVF5GeA9JrTlugPrEUgDorh2Bub7ohi9CUdpRKpQbBI9bcUqlUN++r/BfRW6vV0Nvb68YCjY+Po1wuuxkce3t73XhZmfNWXKrErbharWLnzp1uoqc9e/Zg2bJlGBgYQF9fnyuak8kkBgYGsGfPHpRKJdcFa2pqyn1ByzHJyLNkVQ5K9KTPUxC22FpdhxbNNsuVrsNWr9d3G35CVWJtbRZbdtbbh+38hxViQeVms342glBv6zXIYhtAkc82F2SzXJBYtu1zobC1e77aymd170De5YlEwppESiyvhULBFbWaaDTqvieHhoYAzNyTe/bsqZu5YJ999sHu3bsBzEzRVygUkMlkEIlE3MFoANiyZYubmPJPf/oTHnnkEQwODqKvrw/j4+Po7+/H1NQUDj/8cDcHxvDwsJsUkhBCyMIw78JWrLFagEqMqBa8gH8yIxFRWtzEYjGkUqm6EVOx5Ml+xXoqbsBeLn96zligvrMriaQAuLE14gYci8WQy+UQiUQwPj7uCj3JnixtT6VS6Ovrc8WpvFgnJyfdl7jUX6vV0NfXh1KphNHRUYyNjdVNNSSWY+0aKetkiiA5bjkWfW5s7lN+IlRbak2Lre1amVYofU6ljnYkdPKz1NvWk/bRilV1LmlGXDcjgk3RahvAkXK6vBnrbpbrFvws5O04FgrZvQt5/8g7yCtOVdyL/dbHYjFks1nk83lMTU3VhcQMDg4iFothx44drjCuVCqoVCpIp9Ou6/KKFSuwe/duFItF95302GOPYdu2bVi3bh16enrwxBNPIJPJYMmSJcjn85iYmMDdd9+N+++/353XnhBCyMIwr8JWRKwWOWbiI8GWNEqLWptrqWnptVlrNX7WB+0WKPuXidprtRqmp6frXrLpdBpLly5FJBLB6Ogopqam6mJUq9UqyuWyW48IWRGDMn8fAOzatQuFQgGDg4PIZDJIp9PYsmULRkZGXCtxLBZDJpNxE2VEIjPuzDL3rghpsRB7DQ7INZDjtolR8xrqbWSZuGDaEuPYRK25re7QmjG6Jl5i3KxXl5f/Xt4ApHn8rLULfY69LKphvsv2cq/bEjV5/ZbYnjXTY8J2r5PmMc8l6X78rqVOIKUHV8ViKoPEup6lS5di7dq1eOSRR5DP5+vq1x5ZMg2geEAB9f2JSqWCzZs3u4kdly1bhj/96U/4wQ9+4HpKDQ0NYWBgALt37+Y9SQghC8S8ClsdO6v/tCuyLf5Mf/YStYK43uoyWlxKO4BGN0PpJOXz+br6M5mM+/KsVqsN7lAizkUQSxIJnXgJmLHaSke2XC5jYmKiIa5UJ4UqFovufLuyz0gk4u5b1yXt0nP3irXX61zZOob6HJkxubKNXEOvzr7NjdNmGQvqlPoJWNPt2VxnHod2dV9o0bXYaMVaO5/XwLzvbN9t7fK6N22DPn7bmfe+17RWYTrD7DDTqrsYMN+NQZiW0L6+PgwODmJ6ehq7d++2xrX29vbW5asw75lEIuE+l/l8Hj09Pdi+fbtnG8rlMnbt2gVgxlXZVpdtP4QQQuaPeRO2piiVZeKCbIoiL0usn6gVEaez30YiEU9xF9S5FpfiaDTqxs7ql5fUmU6nkcvlUCgUkM/n3fn29EtbXKRFeOukVKawzWazyOVySKVSSCQSqFQqrhs1MOMmLSJWTz2kBXQymXTje8222K6NPtfaNUxjxjhL2/V3EeZyzr0sWeZx29rTbAfBNgjiNchBZsd8CFM/F+Fm9u9lkW2mHbNZr8t4/ffbrpnBIEK6gVazB/f19SGZTCISidTlmNBEo1EkEgmsWrUKo6Oj2Lx5s7UuGWiW9/nY2FhLbQJmBoR3795tjQ8mhBAyfyyIsNXfxQ3ZzJDsVV4+A/VWO5uLaZjOr01Ii/uvWP6KxaIrHM1OZk9Pjzt5fKFQQLFY9HXjFQurFrXa8ijZjM04vEKh4JY1hbUg8/vpGOBWhJzNUupnKbd1tHUMtK2t2lLt1ckxp1IBwoleL4u/Pg7SHmznOuj8tuv8B8XGmvdrkJhsRgCb7vPNtLMZ9+Mwzxohix2Z/71UKrkuw9qVWIhGoxgcHEQ2m3Wn4vFC5qi1TRnULHoQmRBCyMIxb8LWnMJHRJw57Q/Q2EE2rbXNorczBZopkmOxGJYtW4Zly5Zh+/bt7gvU7Bxns1lkMhnE43FMTk668bLmfqXu8fFxd7l2RdSd3EhkZsqP6elpRCIzcbe9vb0YHx/H9PS0+/KNx+NuoirZTs/Lm06nkc/n3WzJEv+q96+PxXa+tPu2eb50h97mOi7HJBZTaXdQp9zs/Pu5IocZ8Tct0eKGTGE7O8Kev9me59kIZK8BkCBRa3oZ2MqFtbaa+Flrdb3zfX+26iExX9gGEMwBs05tOwmP130oSZ02bNiAO++8E9u3b/dMIrVq1SrE43Hs3LkT+Xzec1/JZBL5fN59PxJCCFkczIuw1TG0Ipp00ijBZqX1wssiGeTSLMsk67C2GkajUdcFeOvWrRgeHq4TaNJ+sYwCcOeNFbFqCmadmEmLWL9Os7gqx2IxV9Sa+9AxrqY4j0ajbqytPq9eliK/mEFzbmGvayHuyzY3ZjOeSs6PlLUJWJuA0G0PSiBli7OV46A7cvuZb2ttEOazaD57Xu1txprqtc+gZV7b2pgP0WYTjUHtskGBSWaDhCRp0RqJRLBkyRJkMhncfvvt2LFjh+f26XQasVgMpVLJOiWQ3J8yvzsAazlCCCHdy7wIW9PVWAslvc7cRv7b3JC1C7Je7rVMlktH08x67DiOK9ympqbqXIukXCKRQCKRcKf2mZqachM3maJLELdgeVl7iTV9XJIkKh6Po1KpoFqt1s1Jq60V+hwBqItXFldvLTZtWYv1f2mL2fHX10hbfEWgmufXNsewiVxXsZSb7mBeHWw/92WpzxQvWtRS2LaOzfW4E7G5IOt1XrQaT+sXSx5Ur25nkOtxO62rc+HmTHFLWkEGY20eFvl8Hrt27bLGwEajUfT29rpT5u3Zs8c6163kfnAcB5lMBolEAiMjI3N6TIQQQuafORe22gqrBYZYPU3XUC/LoZcLqVj8wriYaiuhbV9iRZ6amnKnBtAdyVgshmQyiVKphGKx6L48TeuifM9ms6jVasjn81YhpkWtzhCtPxeLRXef0v54PO4moxLLrFiWU6kUSqVSXfxytVr1TAhlO0f6mumOhpcoNi3G+rNXLKLtmuvEU7I8bOyTttB6LafFdv6YazfkZrcNEqSzIWy8t22dOaAUlrkQkNqybXPNDkOYwSwKXyLI4K3fu6larbqeSyaxWAx9fX0oFovudHc25L7r7+93Y3R5HxJCyOJjzoWtzVobiUQaRK2XoG0WP5dImUfWtIxEo1Fks1n09fUhn88jn8+7gjGRSCCVSjXMk6exvSAzmYybudgWO6eXaQt2IpFAMpl056bVlkw5j8lkEul02rXmOo5TJ7K1MNTxuNrt14yN1VZpLTB1RzdoEEIvNwcFbOfMnPZEC09ZlkgkPOOpwqLvCR3nzY5N69jug06x4HoNpoSJYTXLtGKBtdGqe2+7sO3fLwRhNvtZSLdq0l3YpunR7LvvvtixY0edYE0mk+jp6UGlUsH4+Dgef/zxwP3E43HkcjlMTEzM+n1CCCGkc5kXi60pYE2rmbb4tboP+W/rtIl40i7Kuk3SntHRUXeqHgBuBmL5bIsdNS2Usj/J2GibF9Zsu3a3FjEtAjaVSrkxtoJYYEWoFYtFd3vHmZm+QB+fCFW/8yciWKzDfjHBfkJGZ0MWdHxrkNVXW2zj8TjS6TQAuAm1TJdk8zhsn6Vdci4obLsfv9hVuZ/97pVWlwetC+M1ElRHu5gLV+Nm929eg3Yfv+1ak8VBKpWyilaZqaCZTMaVSqXpbQghhHQfcypsRczo7+ayMNt6xcwGbS+Y7rTa5VdedLbESCISvUaVtUjTnyWhlFdZQbtkA6hLpmWKUi34RDibdUniDG1tln3YkmSYLoeJRMIq4LWLom6b1znRZc19eYlb3RbTVTybzaJcLgeO7tswLfPaatvqXIp7KwvpXtwsfqK2WRHUimjyGmRpJt7Xq94w7Qkrav0GCNqBXxgCxSgx0R5Kvb29AIBisVhXRvJbNEOlUsHk5GR7GkkIIaRjmRdha8bANpPEJ0ynK2znzEx2JNRqNVSrVbc9Ya2CtvrDltVWWhG2OlO0JM2IROqzRGqRK/9FrIsQ1+vkvIvLsk4ipc+/uCqLtdZsf5jza5ubV3+3WenDWMYSiQTS6XRgXJSXRVz/14MJrQhl0jkux17MtVjzImifXi75c93WThSQ7bay0mq7ODCz2cfj8QZhSwghhHgxp1l0zLhawDsJlOBl0fXaxlbWFrtps2CIsKlWq5iYmHCzD+sypvVRLwv753escgy6fbVaDcViEYVCARMTE9ZEVqYVV0SxCONkMlkn1E2Lrz5PgohivTxMDJ4+36YV2+u7jtuVevW1l8EGmUe3t7fXHQDQdfidX5vFXwYSJMabzI5OO4cL4X7r95vmNYAzm3aFPedBz4dXXWF/v8Ls39yHLaTBtk2YdvqFRJDuRHsW7dy5E47jIJlMLmCLCCGEdBNzJmx17Kh8N/+A1tyMbdhiQrVlU5cD4FowdXmJi7V17IKWebng2tBWbBmh1vG4YqWVxFOm5VQPGEjCKW311dbbYrFY5+7s1R59bvw66uZ50NbhsJ1gs3OrXZ1tbspiRc7lcnXi16+z64cMArBDHJ6wsaPziZcFVP53ggXPyy0/DLM5p814sfh9ny3zdR34LC9OZDo9QgghJAxz5opsE7FhrH9eddkwrbW6M6ctmV7rtaCLRCJu4ihTePl1oM3vQcdkin3tMqxdiSVBlMS9yrb6uGS7eDxeZ4XV1k/TWikWULMtXhZUm4VFiwfbuQg6d7KNl8DV+xLB7CfOvVyQTaGuz4lYthln25k0K8y6RdiY7fX6TfR7bvzW28q2q1wrmL8v8tzPxfWiO/Liw5YbghBCCPFiToWtzRVZ00wnpJWOkFfMrLStVqu5YsmrwziXHWcRsiJOtfDSFlgtRE133Hg87k6Jo5MuOY6DarWKcrnsTl9kxpT6idcgq22Y/7qdstxPhMo6PQdutVp15zoMmqMwDNrSzTjb5ukWATmf2ISal8ut3+Ce+Xw0I2I7gaABQK/PhBBCCCHtYM6ErZkwCmh0OwWetJx6CR6hmVF+KauFa5h6w7rlNdO516LUZh00LRlirY3FYshms4hGo27yKJ3N13GcuszOpVLJdduyJcfSAl3OiS32LUjgm59t1mRdLowI9rKayvZybKVSKTCe0XEc65RDuoy22upkWcRON4jZubICzgYvQduswJvtoOB8sNDtobWWEEIIIfPmimzr2JmZcvU6W32asHPeitDxSzTitU8t2rQwNMvb6jbjiEVQmi7COkO0JH8CZjIB53K5hjllRbjKZy0eJdY2yL3WK+GWPh9+lhZ9DkREhhW15jIzEZQpeM12tYLt3hHrNzvE7WEuhGWzdc6nuPUbXLGh2+bXxmY8D9pNs89Bq89NK9vxGSWEEEJIEHMibLUbsm2dn9teGExh5mUNMS2xtja1IgLN+k33XRGxpiu2LNOiykwkpbMCSzZkM1ZUC21TIJrnwOs8eR2Xl0A1/8ypfcx1NjdnXafXOr9tNNoq63f/SDk9gKAzIzPONjyzFVOtCM9mQwG8Bp1axW/7sHUHxZrPdnCl1XPTSj1eLtZh9+G3fjYDV+04j4QQQgjpbuZM2Hr91e38/ycEMkWeKfa05dO2L7/vuj1mGZ1Yybbeq92mYNV/Ipq0lVYfi96vzWKtrZQ6G6Q5PY3uzJl1BnVQg0StaTm1fdbtMBN0mWjxbRPGepn+r0W83kYfu7YY6z+93BSvyWQSlUoF5XKZcbZzgNczONs6ZlvnbOtpJQQhaNswVtxmaUbctVtQm4OJ5r7CWLRt7fMbvPT7TgghhJC9hzkRtrZpfgQRI+J2K/Otisg1LZta5Gqha3aETHEp7bCJUHOZl3VZH4vXvrSw1PXZxKO5zCZq9fJmOr1yTrSIswlIcV+uVqvudEKm1bUZ919b2/T519fbdu5taDdkW9tMN2ybsLX9STItcfeW+YLL5TKtti3gNRhkW++3LEz97SjX7npa3W+rVmuv781s2078PEO8rr3fNl4eHOa+gsQzIYQQQvZO5lTYmq6vMn9oIpFAMplEOp12ha3Eh2oxpAWuCF5dr03M6jr0aH+QmJX9eHWMzKmBhDCdd9kmSDzJvkW06eWm1VSLPj09kJ4TV/5kv7VarU7Mmp1FLyHtJ/ptZczrowcuZLlp0U4kEnXLEolEw/Hr82eKXfN82ARvpVJx5weW/YnALxQKDeedhIsl9bp3WhW2cyF+20G79mXzfJDlts9eZfyYy/MSVLefu3JYq6y53k8oy3/zN5LPMiGEELJ30XZha1oztVBJJpPuXzabRTqdRiKRcOcUNa22XpZb2Q+AhnUyp6u2ftosrHrUP2wcn20bU2B5WWEFW6yqTaTZhKrMcVsqldxMyFqwyWddn2BOfaQtyzp5lT7/esDANlBhXm9zuXnNvLaLRqPuAIcWwnJv6PtBH4PXObVZoeW7nMtcLodsNotkMolEIoGJiQkUi0U3A7POJE3qMZ+hdojaVoWYn3tru2n2fghjUWxV4DbDbC3n5nbNeHH4WWHDlg/bLuDJRH9BIRSEEEIIWXzMibDVAiWVStUJWv1nWmi9rKmCLb5TRLEgwk5iTWOxmGdsqK3ttv3ZLAFeWYT9rIgSM6sFlrjVyjKxqGohq//r49P70/PYenWMvSzJ5rXzErxyvYBGq7yXld7LCmwmcrKVj8ViSKVS7nXWf173h5fANdfHYjHkcjnE43Gk02lMT09jenoa+XwexWIRhUIB5XIZ5XK5brBgbyWMmDXLtrIsbDvmknYIWHN5KwJ2tvebn8uuWSbsOr/fE7NMq9fX67c6zPnQ96VN2Jr/CSGEELJ4aLuwFaGiBaxY3kSE1mo1lMtlOI6DcrnsrjettNri62X90262tk6LX4faLG8Tq34iyRYHahOxIlR1OS1qvf6b0914xYKabre2jpzt+P3OkbnOHHTw+g/Yk3LpuGObKLaVl/tFrxfLv47T1oMbplVdjl+uh+1aZrNZxONxpFIpZDIZFAoFFItF14Ir/yuVSt10S4udIDEbZKENs65Z2nne50LA+i1vVby2esxhBKbf70Mzltlm1oex2Ib53ZZlehsZrPOy5NuE897wLBNCCCF7A20RtqbLsRaq2gqoXWxFJIhFVdx4bVmEzalz9H41XoLO/OxlxdPfvYSQLuuX5de23hRZko1XLzfnptXt1vGxfscWdD5shBUfXp1S7VpsLjPX6229rL62BFw6Ftt0YxaBq13btdi2WdRNC64IZokFl+RmyWTSTTK1t1hxw1hmNVpImOLEa6Bpvs5dM/uZrdCcjeV1rs5HM/WGcTUOU29QPWFEb9gBOC9xK2007zmv74v1WSaEEEL2FmYtbE0hIOJCW1kB1MWJynaVSqXOMmfGd0o5UyAJNvcym9XVFueq/5uiNCjJk80yaq7X4ljqjkQidQmcbNuVy2XP/ZpWYt1mfTzmcdrwcrv2KxO0D6/9+XVQva6txpYdW28viaCkrL4XtceAvsfMdss1F4Er+5F6YrEYyuUySqUS4vF4g8BdLNiEhZfYCBKw5v3QjGhqhVbqbpfVtpU6O1FILcRgg9c9FfY3ya980HZ6v3N9fxJCCCFkbmlZ2IpIMEWtjoGMRCKuoDXR7qs6QZC21DbjMmtaSsNiuvsG4SVKpX3aKm1rs1iqg9oTRjB6WZu9trEJjWZoh7upTVR61SX3kO2cyPpoNGodDEgmk3XlZKBFBK52Zwbq43ulXbLOrEPu+1gsVuei3M2dYi/xGua6hhWwphButt52Mx9CuNVtup0wx+xnKQ0jfJvdn7lvLWZtll5CCCGEdBctCVuxYJliVrsMA/a4V9PKaVrjzClkzA6MabUzLaMmflPSlMtld1vT7dXW+TZFepgyZlntbuyFFqymeLWV1fWb/706iLOlmbr8hKxNROmOpq286cJsit5YLFY3AOE4juvGXCqVEIlEMD09XTe4IgMssr15fJLgSrdBn3uZOsjvXuxUmhG1YVzag8oECdz5FhV+A1ULYX3tJFHVzt8Mk7AW1lbPR1AdfqKW1ltCCCGk+2hK2EoHXieDEoGrxYAWY37JjMQaZ7PMyp85XQ9Qn2xIklAFuVAKtgzK5nZe38MIW8dxPK26urPkdV68LBE2S6dNMLajM9aKlc5r+2at7lLGvE9sgt28dnK/2AY79DI5dzrLMzCTnEsss3pfukwsFkO1WkU8HnfjwsWtvlKpzKkQmAuC3I2bJYzwDVt2LvY/m7rmS+g0Y9H2cvluJ7Nx8W3nPsMI1aA6vDAHqsx3CiGEEEI6n9DCVjr8OkGPl6D1ch01kfLiuuslZm3iVj5rt1/dVtt/8zNQ7/obRoSZ+/MSt2FcnE3XaT9xatuP13HNdQe8mY63lzjwOx7zWvhZboF6N2JzsEB7AZjJuqQe+V6tVhssxWLtlftfbytuzfo+NS3/nUozVlpNGJHjJ7Q61QrWKVbidtQzl8eykGIvyPtkthZgU9wSQgghpLsILWwlIZS20konXgvaVlwxvaxvQX9SVu9fE7bzLuI6SFCa+wprNdB/foLOdI2T7W3/TRbawuC172YEucZPyAZZb3V5OZfmfSnb6izUpiAW9JRSsl7KigeDuCJ3g7Btl5ANWzbsQE27WWix6neMzQwOzQXtOv+ddG3DeIOEwWYFJ4QQQkjnE1rYJpPJuuQ5Iia0eDCT+IR1QxUxoOeC1eW9BK3NImsTnc0I3DCC1SZ6wmATrH77CfNdmI+OWLMd1jDlg8SAzdLitY3tPrG5ess6Wwy4bTv5q1QqdQml9P5tcbmdTjtFbbMsRuEQ5GXQSbSzbZ1y3+tBwVbb1CnHQgghhJDmacpia3P51MLWtEraxIKXBU/q1uLSJmRsIsa2XLYLEqpe/4NcqU0rq9nOMNsJpogKW1ezzLbT1myb/FwHgyy5QXXYrrfexsuCq8vYRK3Mt6zLmBZaP+t0N1hsgYUdIOlmgs6P7fluto5OodmBqYX2GpE26P+AtxvyQreVEEIIIe0ltLDVbsca3ZnRwtTsTHiJXY2ID0kAZFpw9fZmPV4iV1uX/YSjWY+0wWsbWyfOS+SGdZvzEkW2cx6mXLPrvWjGpXI2nUWveyOMld3WLpsFV7bxcnX2mmpJD76Y+7cJYLI48RJJfsu6lbDHogc4O5HFdE0IIYQQ4k3L89hqbBYvm/XWT+ya4kVEKVCfQTiM0LSJXVs8sK7DJqq8rC9hrKpmPWZbw1pmm+ksLrS10EvY+Vk4w9Zr+2zWYxOxso1tcMQs7+VWLve1DHZIHRS0ixev6+k3yBJm+8VCp4rY2bLYrxshhBCymGmLsNXYrJ1e4tFL3HoJXVnnVbdsZwoXvU8tZrzqEMJYEFsRqEGu2a1aguajUxbUoW3GuuuFlzXd77Pe1i/u2c+Sa+5fW++1N4LX/QLUz3dLuhfTw0Sg8JkhjPDvdHgtCSGEkMVFU8K2mY6AKUZNq6suJ/+1oLCJW1u9fpYzLyuursesSx9nWNGq6/XqCDdTh5ebdrfTjDXXa51t4CHI2m4b4PCqw9wWaJyOyba9tuqSxUPYgScyQ1gvFkIIIYSQdhNa2M6mM6djXYF61+Kwc9CGEaZm3V4uvzaRY4of0/ob9vi9rLtB7rTS9oUmyJrcan3NrpM2BG1rE65+9dpcks36bGX9kPubonZxMNvfutnWsRigiCWEEELIfNN2V2Q/pLMjItdLjAL+osPrP1AfZ+rntmzuw9yX/m6KvNm6J4aNOV3IzvFsXI5nW7etnJ87uMbLFdnrfgoriL3aJ27KFLWLh9mI071N0FLAEkIIIaRTmDdh6ycitTU3rBANI3J1LK1uh5eAtrlJ+3VUg6yas+3kzlcnuZXOabtj7Jq13triW73qssXUBsXW+i3Tglbwc1cm3clsPAoWGxSwhBBCCOl05sUVOQym4PASuPqzKTy9xK3537TIzCa2djbnxS9mcy72F4ZWLbVeVq6wMYqzdfXWn23W12Y65l7HYNbr5dZMOh9er+Zoh3s2IYQQQshcMq+uyM1giggvkesVu9rOBExe1t1W6uh0goSp13G04/jCiFZbQjDdBhnsaLU9fuLYr95uub5khiBvDEIIIYQQ0l2EFrazFQyadoz+m+3xE7ztbF9QfG2Q+J2rzrTNZXYuaSVONui7eQz6+2wGFbzaFBRf22ziMDI/8Hp0F+16XgkhhBBC/GjKFdkv/nWhCYqZnW3Cp7D7b3Z9uy2Ac2E5ta0L43Icpj3tug5+Fnqbm3u7xTJpL7weRAi6F+ZC+PL+I4QQQrqP0MK2Wq26n4OspJ3WKbAJGC9R0ylip937nk2Madh17SJs1uggd/O5HszoZoISn5ll9wb2luOcLZ1mQeV1I4QQQgjQhLCtVCq+670SPHWLuAgrbju1/UF0aru9pleylTHFWKcMQnRaRz8snXpPzBV72/HOFc2ex259PgghhBDSXbRN2AbhJXy7hW5qazfhFVdrK9NJ2KaVMpfvrXTi9SILRyffD3xeCSGEkMVDS67IrcKMssSLbroHxKpsTk21N2TaXezHR/YueD8TQgghi4d5FbaELAa60ePApJvbTgghhBBCiEl0oRtACJlfKGoJIYQQQshig8KWEEIIIYQQQkhXQ2FLCCGEEEIIIaSrobAlhBBCCCGEENLVUNgSQgghhBBCCOlqKGwJIYQQQgghhHQ1FLaEEEIIIYQQQroaCltCCCGEEEIIIV0NhS0hhBBCCCGEkK6GwpYQQgghhBBCSFdDYUsIIYQQQgghpKuhsCWEEEIIIYQQ0tVQ2BJCCCGEEEII6WoobAkhhBBCCCGEdDUUtoQQQgghhBBCuhoKW0IIIYQQQgghXQ2FLSGEEEIIIYSQrobClhBCCCGEEEJIV0NhSwghhBBCCCGkq6GwJYQQQgghhBDS1VDYEkIIIYQQQgjpaihsCSGEEEIIIYR0NRS2hBBCCCGEEEK6GgpbQgghhBBCCCFdDYUtIYQQQgghhJCuJr7QDSCEEEIIIWRvxXGcuv+EkBkikQgAoFarhSpPYUsIIYQQQsgC8MMf/hAjIyMUtYR44DgODj/8cJx44omBZSMOnyRCCCGEEEIIIV0MY2wJIYQQQgghhHQ1FLaEEEIIIYQQQroaCltCCCGEEEIIIV0NhS0hhBBCCCGEkK6GwpYQQgghhBBCSFdDYUsIIYQQQgghpKuhsCWEEEIIIYQQ0tVQ2BJCCCGEEEII6WoobAkhhBBCCCGEdDX/Dyag/jmItHV/AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_epochs = 5\n",
+    "\n",
+    "\n",
+    "# Training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    for i in range(len(volumes)): \n",
+    "        rotation = 0\n",
+    "        \n",
+    "        # Read the image and segmentation subject\n",
+    "        subject = read(\n",
+    "            tensor=volumes[i],\n",
+    "            label_tensor=targets[i],\n",
+    "            orientation=\"AP\",\n",
+    "            bone_attenuation_multiplier=5.0,\n",
+    "        )\n",
+    "\n",
+    "        # Create a RandomElasticDeformation transform\n",
+    "        deform = tio.RandomElasticDeformation(p=1.0, num_control_points=7, max_displacement=50)\n",
+    "\n",
+    "        # Apply the transform to the subject\n",
+    "        deformed_subject = deform(subject)\n",
+    "\n",
+    "        deformed_subject.density = transform_hu_to_density(\n",
+    "            deformed_subject.volume.data, 5.0\n",
+    "        )\n",
+    "        \n",
+    "        # Create raw DRR\n",
+    "        drr_raw = create_drr(\n",
+    "            deformed_subject,\n",
+    "            sdd=1020,\n",
+    "            height=256,\n",
+    "            width=256,\n",
+    "            rotations=torch.tensor([[rotation, 0.0, 0.0]]),\n",
+    "            translations=torch.tensor([[0.0, 850.0, 0.0]]),\n",
+    "            mask_to_channels=True,\n",
+    "            device=\"cpu\",\n",
+    "        )\n",
+    "\n",
+    "        # Extract DRR body and vessels\n",
+    "        drr_body = drr_raw[:, 0]\n",
+    "        drr_vessels = drr_raw[:, 1]\n",
+    "\n",
+    "        # Combine DRR body and vessels with enhancement factors\n",
+    "        ef = 0.6\n",
+    "        drr_combined_low_enhancement = (drr_body + ef * drr_vessels).unsqueeze(0)\n",
+    "    \n",
+    "        # Move tensors to the appropriate device\n",
+    "        drr_combined_low_enhancement = drr_combined_low_enhancement.to(device)\n",
+    "        drr_combined_target = drr_vessels.to(device)\n",
+    "\n",
+    "        # Forward pass\n",
+    "        prediction, latent_representation = model(targets.to(device), drr_combined_low_enhancement)\n",
+    "\n",
+    "        # Compute loss\n",
+    "        loss = criterion(prediction, drr_combined_target)\n",
+    "\n",
+    "        print(\n",
+    "            f\"Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{len(volumes)}], Loss: {loss.item()}\"\n",
+    "        )\n",
+    "\n",
+    "        # Plot the images (optional)\n",
+    "        plot_results(\n",
+    "            drr_combined_low_enhancement, drr_combined_target, prediction,latent_representation, vmax=25\n",
+    "        )\n",
+    "\n",
+    "        # Backward pass and optimization\n",
+    "        optimizer.zero_grad()\n",
+    "        loss.backward()\n",
+    "        optimizer.step()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "torch",
+   "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Deprecated-notebooks/diffdrr.ipynb b/Deprecated-notebooks/diffdrr.ipynb
new file mode 100644
index 0000000..4c99ef8
--- /dev/null
+++ b/Deprecated-notebooks/diffdrr.ipynb
@@ -0,0 +1,497 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Creating Digitally Reconstructed Radiographs (DRRs) using `DiffDRR`:\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating DRR and rendering 2D visualizations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import libraries\n",
+    "import sys\n",
+    "\n",
+    "sys.path.append(\"..\")\n",
+    "\n",
+    "from src.preprocessing import get_transforms, get_datasets, get_dataloaders\n",
+    "\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from diffdrr.drr import DRR\n",
+    "from diffdrr.visualization import plot_drr, plot_mask\n",
+    "from diffdrr.data import read\n",
+    "\n",
+    "from diffdrr.pose import convert\n",
+    "from diffdrr.visualization import drr_to_mesh, img_to_mesh\n",
+    "import pyvista\n",
+    "\n",
+    "# Set the device\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0] []\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get transforms\n",
+    "transform = get_transforms(resize_shape=[512, 512, 96],contrast_value=1000)\n",
+    "\n",
+    "# Get datasets\n",
+    "train_ds, val_ds = get_datasets(\n",
+    "    root_dir=\"../data\",\n",
+    "    collection=\"HCC-TACE-Seg\",\n",
+    "    transform=transform,\n",
+    "    download_len=1,\n",
+    "    val_frac=0.0,\n",
+    "    download=False\n",
+    ")\n",
+    "\n",
+    "# Get dataloaders\n",
+    "train_loader, val_loader = get_dataloaders(train_ds, val_ds, batch_size=1)\n",
+    "\n",
+    "# Get datalists\n",
+    "train_dl = train_ds.datalist\n",
+    "val_dl = val_ds.datalist\n",
+    "\n",
+    "# Check length of datasets, dataloaders and datalists\n",
+    "print(train_ds.get_indices(), val_ds.get_indices())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 1, 512, 512, 96]) torch.Size([1, 1, 512, 512, 96])\n",
+      "torch.float32 torch.float32\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sample a batch of data from the dataloader\n",
+    "batch = next(iter(train_loader))\n",
+    "\n",
+    "# Separate the image and segmentation from the batch\n",
+    "image, seg = batch[\"image\"], batch[\"seg\"]\n",
+    "\n",
+    "print(image.shape, seg.shape)\n",
+    "print(image.dtype, seg.dtype)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = [0, 1, 2, 3, 4]\n",
+    "\n",
+    "# structures = [\"Liver\", \"Mass\", \"Portal vein\", \"Abdominal aorta\", \"Background\"]\n",
+    "# labels = subject.structures.query(f\"group in {structures}\")[\"id\"].tolist()\n",
+    "\n",
+    "# Read in CT volume and label map\n",
+    "subject = read(tensor=image[0], label_tensor=seg[0], orientation=\"AP\", bone_attenuation_multiplier=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.drr import create_drr\n",
+    "\n",
+    "img = create_drr(\n",
+    "    volume=image[0],\n",
+    "    segmentation=seg[0],\n",
+    "    sdd=1020,\n",
+    "    height=200,\n",
+    "    width=200,\n",
+    "    delx=2.0,\n",
+    "    dely=2.0,\n",
+    "    x0=0,\n",
+    "    y0=0,\n",
+    "    reverse_x_axis=True,\n",
+    "    patch_size=None,\n",
+    "    renderer=\"siddon\",\n",
+    "    rotations=torch.tensor([[0.0, 0.0, 0.0]]),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 2, 200, 200])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(img.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "im = img.sum(dim=1, keepdim=True)\n",
+    "drr_image = plot_drr(im, ticks=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Or read in CT volume and label map from datalist path\n",
+    "subject_path = read(\n",
+    "    filename=train_dl[0][\"image\"],\n",
+    "    orientation=\"AP\",\n",
+    "    bone_attenuation_multiplier=5,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a DRR object\n",
+    "drr = DRR(\n",
+    "    subject,  # A torchio.Subject object storing the CT volume, origin, and voxel spacing\n",
+    "    sdd=1020,  # Source-to-detector distance (i.e., the C-arm's focal length)\n",
+    "    height=200,  # Height of the DRR (if width is not seperately provided, the generated image is square)\n",
+    "    width=200,  # Width of the DRR\n",
+    "    delx=2.0,  # Pixel spacing (in mm)\n",
+    "    dely=2.0,  # Pixel spacing (in mm)\n",
+    "    x0=0,  # # Principal point X-offset\n",
+    "    y0=0,  # Principal point Y-offset\n",
+    "    p_subsample = None,  # Proportion of pixels to randomly subsample\n",
+    "    reshape = True,  # Return DRR with shape (b, 1, h, w)\n",
+    "    reverse_x_axis = True,  # If True, obey radiologic convention (e.g., heart on right)\n",
+    "    patch_size = None,  # Render patches of the DRR in series\n",
+    "    renderer = \"siddon\",  # Rendering backend, either \"siddon\" or \"trilinear\"\n",
+    ").to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[0.3491, 0.0000, 0.0000]], device='cuda:0')\n",
+      "RigidTransform()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set the camera pose with rotations (yaw, pitch, roll) and translations (x, y, z)\n",
+    "\n",
+    "zero = torch.tensor([[0.0, 0.0, 0.0]], device=device)\n",
+    "translations = torch.tensor([[0.0, 800.0, 0.0]], device=device)\n",
+    "#rotations = torch.tensor([[torch.pi / 2, torch.pi / 6, 0.0]], device=device)\n",
+    "rotations = torch.tensor([[20.0, 0.0, 0.0]], device=device)\n",
+    "rotations = torch.deg2rad(rotations)\n",
+    "print(rotations)\n",
+    "pose1 = convert(zero, translations, parameterization=\"euler_angles\", convention=\"ZXY\")\n",
+    "pose2 = convert(rotations, zero, parameterization=\"euler_angles\", convention=\"ZXY\")\n",
+    "pose = pose1.compose(pose2)\n",
+    "print(pose)\n",
+    "\n",
+    "img = drr(pose, mask_to_channels = True)\n",
+    "\n",
+    "im = img.sum(dim=1, keepdim=True)\n",
+    "plot_drr(im, ticks=False)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 4200x2325 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "labels = [\"Liver\"]\n",
+    "\n",
+    "# Plot the segmentation masks\n",
+    "fig, axs = plt.subplots(\n",
+    "    nrows=1,\n",
+    "    ncols=3,\n",
+    "    figsize=(14, 7.75),\n",
+    "    tight_layout=True,\n",
+    "    dpi=300,\n",
+    ")\n",
+    "\n",
+    "im = img.sum(dim=1, keepdim=True)\n",
+    "plot_drr(im, axs=axs[0], ticks=False, title=\"DRR\")\n",
+    "plot_drr(\n",
+    "    img[:, 1, :, :].unsqueeze(dim=1), axs=axs[1], ticks=False, title=\"DRR Vessel Only\"\n",
+    ")\n",
+    "plot_drr(im, axs=axs[2], ticks=False, title=\"Vessel Overlay\")\n",
+    "\n",
+    "for idx, label in enumerate(labels):\n",
+    "    mask = img[:, 1, :, :].unsqueeze(dim=1)\n",
+    "    vessel_mask = plot_mask(mask, axs=axs[2], alpha=0.5, return_masks=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating 3D mesh renders and 3D to 2D projections:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Performing Labeled Surface Extraction: 100%|██████████[00:00<00:00]\n",
+      "Finding and Labeling Connected Regions.: 100%|██████████[00:00<00:00]\n",
+      "Smoothing Mesh using Taubin Smoothing: 100%|██████████[00:00<00:00]\n",
+      "Filling Holes: 100%|██████████[00:00<00:00]\n",
+      "Cleaning: 100%|██████████[00:00<00:00]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Make a mesh from the CT volume\n",
+    "ct = drr_to_mesh(subject, \"surface_nets\", threshold=225, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[1.5708, 0.5236, 0.0000]], device='cuda:0')\n",
+      "RigidTransform()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Initialize the DRR module for generating synthetic X-rays\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "drr = DRR(subject, sdd=1020.0, height=200, delx=2.0).to(device)\n",
+    "\n",
+    "# Make a pose\n",
+    "zero = torch.tensor([[0.0, 0.0, 0.0]], device=device)\n",
+    "xyz = torch.tensor([[0.0, 800.0, 0.0]], device=device)\n",
+    "rot = torch.tensor([[torch.pi / 2, torch.pi / 6, 0.0]], device=device)\n",
+    "print(rot)\n",
+    "pose1 = convert(zero, xyz, parameterization=\"euler_angles\", convention=\"ZXY\")\n",
+    "pose2 = convert(rot, zero, parameterization=\"euler_angles\", convention=\"ZXY\")\n",
+    "pose = pose1.compose(pose2)\n",
+    "print(pose)\n",
+    "\n",
+    "plot_drr(drr(pose))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fcdd99bc787b4ec9a3d450b712d2231e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:59896/index.html?ui=P_0x167afba4650_0&reconnect=auto\" class=\"pyvis…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Make a mesh from the camera and detector plane\n",
+    "camera, detector, texture, principal_ray = img_to_mesh(drr, pose)\n",
+    "\n",
+    "# Make the plot\n",
+    "plotter = pyvista.Plotter()\n",
+    "plotter.add_mesh(ct)\n",
+    "plotter.add_mesh(camera, show_edges=True, line_width=1.5)\n",
+    "plotter.add_mesh(principal_ray, color=\"lime\", line_width=3)\n",
+    "plotter.add_mesh(detector, texture=texture)\n",
+    "\n",
+    "# Render the plot\n",
+    "plotter.add_axes()\n",
+    "plotter.add_bounding_box()\n",
+    "\n",
+    "plotter.show()  # If running Jupyter locally\n",
+    "# plotter.show(jupyter_backend=\"server\")  # If running Jupyter remotely\n",
+    "# plotter.export_html(\"render.html\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Registrating random X-ray pose to reference:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from diffdrr.pose import convert\n",
+    "\n",
+    "\n",
+    "def pose_from_carm(sid, tx, ty, alpha, beta, gamma):\n",
+    "    rot = torch.tensor([[alpha, beta, gamma]])\n",
+    "    xyz = torch.tensor([[tx, sid, ty]])\n",
+    "    return _pose_from_carm(rot, xyz)\n",
+    "\n",
+    "\n",
+    "def _pose_from_carm(rot, xyz):\n",
+    "    zero = torch.tensor([[0.0, 0.0, 0.0]]).to(rot)\n",
+    "    R = convert(\n",
+    "        rot,\n",
+    "        zero,\n",
+    "        parameterization=\"euler_angles\",\n",
+    "        convention=\"ZXY\",\n",
+    "    )\n",
+    "    t = convert(\n",
+    "        zero,\n",
+    "        xyz,\n",
+    "        parameterization=\"euler_angles\",\n",
+    "        convention=\"ZXY\",\n",
+    "    )\n",
+    "    return t.compose(R)\n",
+    "\n",
+    "\n",
+    "gt_pose = _pose_from_carm(rotations, translations)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "torch-gpu",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/evaluate.ipynb b/Deprecated-notebooks/evaluate.ipynb
similarity index 94%
rename from notebooks/evaluate.ipynb
rename to Deprecated-notebooks/evaluate.ipynb
index 040e173..617431a 100644
--- a/notebooks/evaluate.ipynb
+++ b/Deprecated-notebooks/evaluate.ipynb
@@ -164,6 +164,31 @@
     "new_image = nib.Nifti1Image(seg_numpy, np.eye(4))\n",
     "nib.save(new_image, \"target.nii\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 512, 512, 133])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from diffdrr.data import load_example_ct\n",
+    "\n",
+    "# Load the example CT data\n",
+    "subject = load_example_ct()\n",
+    "\n",
+    "subject.volume.data.shape"
+   ]
   }
  ],
  "metadata": {
diff --git a/Deprecated-notebooks/pipeline.ipynb b/Deprecated-notebooks/pipeline.ipynb
new file mode 100644
index 0000000..83c616b
--- /dev/null
+++ b/Deprecated-notebooks/pipeline.ipynb
@@ -0,0 +1,806 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TACE procedure methodology walkthrough\n",
+    "---\n",
+    "\n",
+    "This notebook demonstrates the end-to-end workflow for training a model that can reconstruct vessel enhanced DRR images from raw DRR images making use of the 3D spatial information extracted from medical CT volumes of the corresponding patient."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Importing libraries\n",
+    "This pipeline is mainly based on Pytorch and its medical imaging extension MONAI for data processing and model training and DiffDRR for DRR generation. Additionally, custom functionality is imported."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import libraries\n",
+    "import sys\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import torchio as tio\n",
+    "\n",
+    "sys.path.append(\"..\")\n",
+    "\n",
+    "from src.preprocessing import get_transforms, get_datasets, get_dataloaders, add_vessel_contrast\n",
+    "# from src.config import config\n",
+    "from src.drr import create_drr\n",
+    "from src.vizualization import plot_results, plot_drr_enhancement\n",
+    "\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "from diffdrr.visualization import plot_drr, plot_mask\n",
+    "\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from diffdrr.drr import DRR\n",
+    "from diffdrr.data import read, transform_hu_to_density\n",
+    "\n",
+    "from diffdrr.pose import convert\n",
+    "from diffdrr.visualization import drr_to_mesh, img_to_mesh\n",
+    "import pyvista\n",
+    "\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "from src.model import TACEnet\n",
+    "from monai.losses import DiceCELoss\n",
+    "\n",
+    "from torchvision import transforms\n",
+    "\n",
+    "# Set the device\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Data preprocessing\n",
+    "In this pipeline we make use of HCC-TACE-Seg dataset. This retrospectively acquired data collection includes pre- and post-procedure CT imaging studies of 105 confirmed HCC patients who underwent TACE between 2002 and 2012 with an available treatment outcome, in the form of time-to-progression and overall survival. Baseline imaging includes multiphasic contrast-enhanced CT with no image artifacts (e.g. surgical clip) and was obtained 1-12 weeks (average 3 weeks) prior to the first TACE session. Semiautomatic segmentation of liver, tumor, and blood vessels created using AMIRA was manually clinically curated. These segmentations of each pre-procedural CT study were done for the purpose of algorithm training for prediction and automatic liver tumor segmentation, and are provided here (NIfTI converted to DICOM-SEG format)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The volumes and segmnetations have the following shapes when loaded from a batch:\n",
+    "\n",
+    "- volume: (1, 1, 512, 512, D) -> (B, C, H, W, D) where the depth (D) is variable\n",
+    "- segmentation (1, 5, 512, 512, D) -> (B, C, H, W, D) where the depth (D) is variable and correpsonds to volume\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following tranformations are then performed:\n",
+    "1. Load volume and segmentation to MONAI meta tensor.\n",
+    "2. Ensure the channel dimension is placed before the spatial dimensions\n",
+    "3. Resize the volume and segmnetation to a new desired dimension, using padding and cropping (optional).\n",
+    "4. Isolate the vessel channel of the multi class segmentation mask. This essentially turns the task in a binary segmentation problem where the segmentation will have a shape equal to the volume: (1, 1, 512, 512, D)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+
+      "[0] []\n"
+
+     ]
+    }
+   ],
+   "source": [
+    "# Get transforms\n",
+    "transform = get_transforms(resize_shape= [512, 512, 96], contrast_value=1000)\n",
+    "\n",
+    "# Get datasets\n",
+    "train_ds, val_ds = get_datasets(\n",
+    "    root_dir=\"../data081\",\n",
+    "    collection=\"HCC-TACE-Seg\",\n",
+    "    transform=transform,\n",
+    "    download=False,\n",
+    "    val_frac=0.0,\n",
+    "    download_len=1,\n",
+    "    seed=42\n",
+    ")\n",
+    "\n",
+    "# Get dataloaders\n",
+    "train_loader, val_loader = get_dataloaders(train_ds, val_ds, batch_size=1)\n",
+    "\n",
+    "# Check length of datasets to make sure data split is correct\n",
+    "print(train_ds.get_indices(), val_ds.get_indices())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+
+
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([1, 1, 512, 512, 96]) torch.Size([1, 1, 512, 512, 96])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sample a batch of data from the dataloader\n",
+    "batch = next(iter(train_loader))\n",
+    "\n",
+    "# Separate the image and segmentation from the batch\n",
+    "volumes, targets = batch[\"image\"], batch[\"seg\"]\n",
+    "\n",
+    "print(volumes.shape, targets.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(metatensor(0.0243), metatensor(0.0006))"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.std_mean(seg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From these volumes and segmentation we can take a slice and visualize the slice to evaulate the inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "slice_idx = 40\n",
+    "\n",
+    "# Sample a slice from the image\n",
+    "CT_slice = volumes[0, 0, :, :, slice_idx].cpu()\n",
+    "\n",
+    "# Get the maximum segmentation class for each pixel in the slice\n",
+    "CT_seg_slice = targets[0, 0, :, :, slice_idx].cpu()\n",
+    "\n",
+    "# Plot the image and segmentation slice as a subplot\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "ax[0].imshow(CT_slice, cmap=\"gray\")\n",
+    "ax[0].set_title(\"CT Image\")\n",
+    "ax[0].axis(\"off\")\n",
+    "ax[1].imshow(CT_seg_slice, cmap=\"tab20\")\n",
+    "ax[1].set_title(\"CT Segmentation\")\n",
+    "ax[1].axis(\"off\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Creating DRR images\n",
+    "DRR images are essentially simulated X-ray images reconsturcted by raytracing through a given 3D CT volume. The DiffDRR package comes with most of the functionality needed out-of-the-box. We will demonstrate below how to create DRR images and control output rotation, zoom and more. Currently, this does not works vectorized over a batch. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extract first image and segmentation from the batch\n",
+    "img = volumes[0]\n",
+    "seg = targets[0]\n",
+    "\n",
+    "# Read in CT volume and label map into a subject object\n",
+    "subject = read(\n",
+    "    tensor=img,\n",
+    "    label_tensor=seg,\n",
+    "    orientation=\"AP\",\n",
+    "    bone_attenuation_multiplier=5,\n",
+    ")\n",
+    "\n",
+    "# Create a DRR object, this will project the CT to the DRR\n",
+    "drr = DRR(\n",
+    "    subject,  # A torchio.Subject object storing the CT volume, origin, and voxel spacing\n",
+    "    sdd=1020,  # Source-to-detector distance (i.e., the C-arm's focal length)\n",
+    "    height=200,  # Height of the DRR (if width is not seperately provided, the generated image is square)\n",
+    "    width=200,  # Width of the DRR\n",
+    "    delx=2.0,  # Pixel spacing (in mm)\n",
+    "    dely=2.0,  # Pixel spacing (in mm)\n",
+    "    x0=0,  # # Principal point X-offset\n",
+    "    y0=0,  # Principal point Y-offset\n",
+    "    p_subsample=None,  # Proportion of pixels to randomly subsample\n",
+    "    reshape=True,  # Return DRR with shape (b, 1, h, w)\n",
+    "    reverse_x_axis=True,  # If True, obey radiologic convention (e.g., heart on right)\n",
+    "    patch_size=None,  # Render patches of the DRR in series\n",
+    "    renderer=\"siddon\",  # Rendering backend, either \"siddon\" or \"trilinear\"\n",
+    ").to(device)\n",
+    "\n",
+    "# Set the camera pose with rotations (yaw, pitch, roll) and translations (x, y, z)\n",
+    "zero = torch.tensor([[0.0, 0.0, 0.0]], device=device)\n",
+    "translations = torch.tensor([[0.0, 800.0, 0.0]], device=device)\n",
+    "\n",
+    "# Define the camera pose with rotations and translations in degrees, later converted to radians\n",
+    "rotations = torch.tensor([[0.0, 0.0, 0.0]], device=device)\n",
+    "rotations = torch.deg2rad(rotations)\n",
+    "\n",
+    "# Convert the rotations and translations to a pose matrix\n",
+    "pose1 = convert(zero, translations, parameterization=\"euler_angles\", convention=\"ZXY\")\n",
+    "pose2 = convert(rotations, zero, parameterization=\"euler_angles\", convention=\"ZXY\")\n",
+    "pose = pose1.compose(pose2)\n",
+    "\n",
+    "# Generate the DRR\n",
+    "drr_image_raw = drr(pose, mask_to_channels=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For our task we would need to also simulate DRR images with an enhanced vessel network corresponding to the same CT volume. Since DRR images are directly created from a CT volume the enhancement needs to be applied to the CT itself first"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Enhance the vessel contrast in the CT using the segmentation\n",
+    "img_enhanced = add_vessel_contrast(img, seg, contrast_value=2000)\n",
+    "\n",
+    "# Read in CT volume and label map into a subject object\n",
+    "subject_enhanced = read(\n",
+    "    tensor=img_enhanced,\n",
+    "    label_tensor=seg,\n",
+    "    orientation=\"AP\",\n",
+    "    bone_attenuation_multiplier=5,\n",
+    ")\n",
+    "\n",
+    "# Create a DRR object, this will project the CT to the DRR\n",
+    "drr_enhanced = DRR(\n",
+    "    subject_enhanced,  # A torchio.Subject object storing the CT volume, origin, and voxel spacing\n",
+    "    sdd=1020,  # Source-to-detector distance (i.e., the C-arm's focal length)\n",
+    "    height=200,  # Height of the DRR (if width is not seperately provided, the generated image is square)\n",
+    "    width=200,  # Width of the DRR\n",
+    "    delx=2.0,  # Pixel spacing (in mm)\n",
+    "    dely=2.0,  # Pixel spacing (in mm)\n",
+    "    x0=0,  # # Principal point X-offset\n",
+    "    y0=0,  # Principal point Y-offset\n",
+    "    p_subsample=None,  # Proportion of pixels to randomly subsample\n",
+    "    reshape=True,  # Return DRR with shape (b, 1, h, w)\n",
+    "    reverse_x_axis=True,  # If True, obey radiologic convention (e.g., heart on right)\n",
+    "    patch_size=None,  # Render patches of the DRR in series\n",
+    "    renderer=\"siddon\",  # Rendering backend, either \"siddon\" or \"trilinear\"\n",
+    ").to(device)\n",
+    "\n",
+    "# Set the camera pose with rotations (yaw, pitch, roll) and translations (x, y, z)\n",
+    "zero = torch.tensor([[0.0, 0.0, 0.0]], device=device)\n",
+    "translations = torch.tensor([[0.0, 800.0, 0.0]], device=device)\n",
+    "\n",
+    "# Define the camera pose with rotations and translations in degrees, later converted to radians\n",
+    "rotations = torch.tensor([[0.0, 0.0, 0.0]], device=device)\n",
+    "rotations = torch.deg2rad(rotations)\n",
+    "\n",
+    "# Convert the rotations and translations to a pose matrix\n",
+    "pose1 = convert(zero, translations, parameterization=\"euler_angles\", convention=\"ZXY\")\n",
+    "pose2 = convert(rotations, zero, parameterization=\"euler_angles\", convention=\"ZXY\")\n",
+    "pose = pose1.compose(pose2)\n",
+    "\n",
+    "# Generate the DRR\n",
+    "drr_image_enhanced = drr_enhanced(pose, mask_to_channels=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The implementation of the DRR generation allows us to visualize both the entire image and only certain structures of interest, in this case the vessels. We can now visualize the raw DRR, the vessels only and the enhanced DRR. Note that the output of the DRR has the following shape:\n",
+    "\n",
+    "$$(B, C, H, W) -> (1, 2, 200, 200)$$\n",
+    "\n",
+    "Where the B does not correspond to the dataloaders batch, but the number of different poses that are provided. This means that, although DRR image generation currently only works with 1 volume segmentation pair at once, per volume multiple DRR orientations can be created in parallel. Since we are interested in changing the angle this makes it perfectly sufficient to only uses batches of size 1 and create multiple DRR images for training per batch iteration.\n",
+    "\n",
+    "C is the number of channels per generated DRR (this by setting `mask_to_channel = True`) and allows for conditional rendering of different structures. Summing over the channel dimension will give the expected image. Note that the `plot_drr` function takes in a shape of (B, C, H, W), which explains the added logic below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the segmentation masks\n",
+    "fig, axs = plt.subplots(\n",
+    "    nrows=1,\n",
+    "    ncols=3,\n",
+    "    figsize=(14, 7.75),\n",
+    "    tight_layout=True,\n",
+    "    dpi=300,\n",
+    ")\n",
+    "\n",
+    "# Plot the DRR, DRR with vessel contrast, and the enhanced DRR\n",
+    "plot_drr(drr_image_raw.sum(dim=1, keepdim=True), axs=axs[0], ticks=False, title=\"DRR\")\n",
+    "plot_drr(\n",
+    "    drr_image_enhanced[:, 1, :, :].unsqueeze(dim=1),\n",
+    "    axs=axs[1],\n",
+    "    ticks=False,\n",
+    "    title=\"DRR Vessel Only\",\n",
+    ")\n",
+    "plot_drr(\n",
+    "    drr_image_enhanced.sum(dim=1, keepdim=True),\n",
+    "    axs=axs[2],\n",
+    "    ticks=False,\n",
+    "    title=\"Vessel Overlay\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Model definition\n",
+    "The model used for this task, consists of 2 parts:\n",
+    "1. 3D CT volume embedding module: This part takes in a CT volume and embeds it to a 2D plane essentially compressing all 3D information in a 2D projection.\n",
+    "2. The second part uses this CT embedding in combination with a rawm unenhanced X-ray (DRR) image to reconstruct a vessel enhanced DRR image that can be usefull for a surgeon during image guided TACE procedures.\n",
+    "\n",
+    "**IMAGE OF FINAL MODEL STRUCTURE**"
+   ]
+  },
+  {
+   "cell_type": "code",
+
+   "execution_count": 4,
+
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<All keys matched successfully>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define model\n",
+    "model = TACEnet()\n",
+    "\n",
+    "# Move model to device\n",
+    "model.to(device)\n",
+    "\n",
+    "# Initialize the criterion, DICE loss with cross entropy uses binary cross entropy when there are only two classes\n",
+    "criterion = torch.nn.MSELoss()\n",
+    "\n",
+    "# Initialize the optimizer, use config to set hyperparameters\n",
+    "optimizer = torch.optim.Adam(\n",
+    "    model.parameters(),\n",
+
+    "    lr=1e-3,\n",
+    "    weight_decay=1e-5,\n",
+    ")\n",
+    "\n",
+    "model.load_state_dict(torch.load(\"../models/TACEnet_vessel_enhancement_deformations_06062024.pth\"))"
+
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Model Training\n",
+    "The training of the model is not as straight forward as compared to simple feedforward networks for classification. We need to execute the following steps for each image in a given batch:\n",
+    "\n",
+    "1. Use the raw 3D CT volume to create the unenhanced DRR image.\n",
+    "2. Add vessel enhancement to the CT volume making use of the corresponding vessel segmentation map.\n",
+    "3. Use the enhanced 3D CT volume to create the vessel enhanced DRR image.\n",
+    "4. Embed the CT volume in latent space using the models encoder part\n",
+    "\n",
+    "Then in terms of bayesian notation the problem boils down to the following where the exact model architecture is arbitrary:\n",
+    "\n",
+    "$$p(DRR_e | DRR_r, CT_{embed})$$\n",
+    "\n",
+    "with for clarity:\n",
+    "- $DRR_e$ = The vessel enhanced DRR image\n",
+    "- $DRR_r$ = The raw DRR image\n",
+    "- $CT_{embed}$ = The CT latent embedding\n",
+    "\n",
+    "This expression can be read as \"the probability of $DRR_e$  given $DRR_r$ and $CT_{embed}$.\n",
+    "\n",
+    "We are trying to model the probability distribution of a vessel enhanced DRR image given a raw DRR image and a CT latent embedding. This is a bayesian interference task, where we are trying to infer the vessel enhanced DRR image from the raw DRR image and the CT latent embedding 3D spatial data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At a high level, the task can be explained as a conditional image impainting or object recovery task. You might be familiar with text-based image impainting, where a model is prompted with text to change an image accordingly. This works by embedding the text prompt in latent space, to stir the image impainting/generation. In this case, the text prompt is replaced with a CT latent embedding, which guides the transformation of the image or helps in recovering the vessel object.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Sven\\anaconda3\\envs\\torch\\Lib\\site-packages\\monai\\transforms\\io\\array.py:259: UserWarning: Failed to read ..\\data081\\HCC-TACE-Seg\\HCC_081\\300\\image\\LICENSE with exception: \n",
+      "File is missing DICOM File Meta Information header or the 'DICM' prefix is missing from the header. Use force=True to force reading..\n",
+      "  img = reader.read(filename)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Input volume and target shape: (torch.Size([1, 1, 512, 512, 96]), torch.Size([1, 1, 512, 512, 96]))\n"
+     ]
+    }
+   ],
+   "source": [
+
+    "drr_test = create_drr(\n",
+    "    subject,\n",
+    "    sdd=1020,\n",
+    "    height=256,\n",
+    "    width=256,\n",
+    "    rotations=torch.tensor([[0.0, 0.0, 0.0]]),\n",
+    "    translations=torch.tensor([[0.0, 850.0, 0.0]]),\n",
+    "    mask_to_channels=True,\n",
+    "    device=device,\n",
+    ")\n",
+
+    "\n",
+    "drr_test_body = drr_test[:, 0]\n",
+    "drr_test_vessels = drr_test[:, 1]\n",
+    "\n",
+    "enhancement_factors = [1, 0.5, 0.25, 0.0]\n",
+    "plot_drr_enhancement(drr_test_body, drr_test_vessels, enhancement_factors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "volumes = add_vessel_contrast(volumes, targets, contrast_value=4000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+
+    "num_epochs = 10000\n",
+    "\n",
+    "\n",
+    "# Training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    for i in range(len(volumes)): \n",
+    "        rotation = 0\n",
+    "        \n",
+    "        # Read the image and segmentation subject\n",
+    "        subject = read(\n",
+    "            tensor=volumes[i],\n",
+    "            label_tensor=targets[i],\n",
+    "            orientation=\"AP\",\n",
+    "            bone_attenuation_multiplier=5.0,\n",
+    "        )\n",
+
+    "\n",
+    "        # Create a RandomElasticDeformation transform\n",
+    "        deform = tio.RandomElasticDeformation(p=1.0, num_control_points=7, max_displacement=50)\n",
+    "\n",
+    "        # Apply the transform to the subject\n",
+    "        deformed_subject = deform(subject)\n",
+    "\n",
+    "        deformed_subject.density = transform_hu_to_density(\n",
+    "            deformed_subject.volume.data, 5.0\n",
+    "        )\n",
+    "        \n",
+    "        # Create raw DRR\n",
+    "        drr_raw = create_drr(\n",
+    "            deformed_subject,\n",
+    "            sdd=1020,\n",
+    "            height=256,\n",
+    "            width=256,\n",
+    "            rotations=torch.tensor([[rotation, 0.0, 0.0]]),\n",
+    "            translations=torch.tensor([[0.0, 850.0, 0.0]]),\n",
+    "            mask_to_channels=True,\n",
+    "            device=\"cpu\",\n",
+    "        )\n",
+    "\n",
+
+    "        # Extract DRR body and vessels\n",
+    "        drr_body = drr_raw[:, 0]\n",
+    "        drr_vessels = drr_raw[:, 1]\n",
+
+    "\n",
+    "        # Combine DRR body and vessels with enhancement factors\n",
+    "        ef = 0.6\n",
+    "        drr_combined_low_enhancement = (drr_body + ef * drr_vessels).unsqueeze(0)\n",
+    "    \n",
+    "        # Move tensors to the appropriate device\n",
+    "        drr_combined_low_enhancement = drr_combined_low_enhancement.to(device)\n",
+    "        drr_combined_target = drr_vessels.to(device)\n",
+    "\n",
+
+    "        # Forward pass\n",
+    "        prediction = model(targets.to(device), drr_combined_low_enhancement)\n",
+
+    "\n",
+    "        # Compute loss\n",
+    "        loss = criterion(prediction, drr_combined_target)\n",
+    "\n",
+
+    "        print(\n",
+    "            f\"Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{len(volumes)}], Loss: {loss.item()}\"\n",
+    "        )\n",
+
+    "\n",
+    "        # Plot the images (optional)\n",
+    "        plot_results(\n",
+    "            drr_combined_low_enhancement, drr_combined_target, prediction, vmax=25\n",
+    "        )\n",
+    "\n",
+    "        # Backward pass and optimization\n",
+    "        optimizer.zero_grad()\n",
+    "        loss.backward()\n",
+    "        optimizer.step()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save(model.state_dict(), \"../models/TACEnet_vessel_enhancement_deformations_2024.pth\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Sven\\anaconda3\\envs\\torch\\Lib\\site-packages\\torchio\\transforms\\augmentation\\spatial\\random_elastic_deformation.py:303: RuntimeWarning: The maximum displacement is larger than the coarse grid spacing for dimensions: [2], so folding may occur. Choose fewer control points or a smaller maximum displacement\n",
+      "  self.parse_free_form_transform(\n",
+      "c:\\Users\\Sven\\anaconda3\\envs\\torch\\Lib\\site-packages\\torch\\_tensor.py:1418: UserWarning: Using a target size (torch.Size([1, 256, 256])) that is different to the input size (torch.Size([1, 1, 256, 256])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
+      "  ret = func(*args, **kwargs)\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.eval()\n",
+    "\n",
+    "losses = []\n",
+    "rotation = 0\n",
+    "\n",
+    "for i in range(20):\n",
+    "                # Read the image and segmentation subject\n",
+    "        subject = read(\n",
+    "                tensor=volumes[0],\n",
+    "                label_tensor=targets[0],\n",
+    "                orientation=\"AP\",\n",
+    "                bone_attenuation_multiplier=5.0,\n",
+    "                )\n",
+    "\n",
+    "                # Create a RandomElasticDeformation transform\n",
+    "        deform = tio.RandomElasticDeformation(p=1.0, num_control_points=7, max_displacement=50)\n",
+    "\n",
+    "                # Apply the transform to the subject\n",
+    "        deformed_subject = deform(subject)\n",
+    "\n",
+    "        deformed_subject.density = transform_hu_to_density(\n",
+    "                deformed_subject.volume.data, 5.0\n",
+    "                )\n",
+    "                \n",
+    "                # Create raw DRR\n",
+    "        drr_raw = create_drr(\n",
+    "                deformed_subject,\n",
+    "                sdd=1020,\n",
+    "                height=256,\n",
+    "                width=256,\n",
+    "                rotations=torch.tensor([[rotation, 0.0, 0.0]]),\n",
+    "                translations=torch.tensor([[0.0, 850.0, 0.0]]),\n",
+    "                mask_to_channels=True,\n",
+    "                device=\"cpu\",\n",
+    "                )\n",
+    "\n",
+    "                # Extract DRR body and vessels\n",
+    "        drr_body = drr_raw[:, 0]\n",
+    "        drr_vessels = drr_raw[:, 1]\n",
+    "\n",
+    "                # Combine DRR body and vessels with enhancement factors\n",
+    "        ef = 0.6\n",
+    "        drr_combined_low_enhancement = (drr_body + ef * drr_vessels).unsqueeze(0)\n",
+    "        \n",
+    "                # Move tensors to the appropriate device\n",
+    "        drr_combined_low_enhancement = drr_combined_low_enhancement.to(device)\n",
+    "        drr_combined_target = drr_vessels.to(device)\n",
+    "\n",
+    "                # Forward pass\n",
+    "        prediction, latent_representation = model(targets.to(device), drr_combined_low_enhancement)\n",
+    "\n",
+    "                # Compute loss\n",
+    "        loss = criterion(prediction, drr_combined_target)\n",
+    "\n",
+    "                \n",
+    "\n",
+    "        # Plot the images (optional)\n",
+    "        #plot_results(drr_combined_low_enhancement, drr_combined_target, prediction, latent_representation, vmax=25)\n",
+    "\n",
+    "        losses.append(loss.item())\n",
+    "\n"
+
+   ]
+  },
+  {
+   "cell_type": "code",
+
+   "execution_count": 19,
+
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+
+       "<matplotlib.lines.Line2D at 0x1a8fb1f96d0>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.boxplot(losses)\n",
+    "plt.ylabel('MSE loss')\n",
+    "# Set custom x-axis labels\n",
+    "plt.xticks([1], [''])\n",
+    "plt.axhline(y=0.08690, color='r', linestyle='--')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.08696612492203712"
+
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(losses)"
+
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "torch-gpu",
+   "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/preprocessing.ipynb b/Deprecated-notebooks/preprocessing.ipynb
similarity index 100%
rename from notebooks/preprocessing.ipynb
rename to Deprecated-notebooks/preprocessing.ipynb
diff --git a/notebooks/speed.ipynb b/Deprecated-notebooks/speed.ipynb
similarity index 100%
rename from notebooks/speed.ipynb
rename to Deprecated-notebooks/speed.ipynb
diff --git a/DiffDRR b/DiffDRR
deleted file mode 160000
index 8d32481..0000000
--- a/DiffDRR
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit 8d324817fe922ee34e74654cb3cc67fc5ce611fc
diff --git a/Functions/tace_net_input.py b/Functions/tace_net_input.py
deleted file mode 100644
index e69de29..0000000
diff --git a/Functions/vessel-registration.py b/Functions/vessel-registration.py
deleted file mode 100644
index 90f6b3c..0000000
--- a/Functions/vessel-registration.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import numpy as np
-import warnings
-
-
-
-
-def simpleVesselRegistration(DRR: np.ndarray, Segmentation: np.ndarray)->np.ndarray:
-    """
-    The most basic version of vessel registration where the vessels are directly mapped to its corresponding location in the 2D front view and the third axis is mapped to intensity.
-
-    Params: 
-    - DRR: np.ndarray
-        The Digitally Reconstructed Radiograph in 2D with shape (512,96)
-    - Segmentation: np.ndarray
-        The vessel segmentation in 3D with shape (512,512,96)
-
-    Returns:
-    - A 2D segmentation overlay with shape (512,96)
-    """
-
-    #Check the input shapes
-    if not (np.shape(DRR) == (512,96)):
-        warnings.warn("DRR shape does not match expectation")
-
-    if not (np.shape(Segmentation) == (512,512,96)):
-        warnings.warn("Segmentation shape does not match expectation")
-
-    #Sum over depth axis
-    flatSegmentation = np.sum(Segmentation, axis=1)
-
-    #MinMax Normalization
-    normalizedFlatSegmentation = flatSegmentation / (np.max(flatSegmentation))
-
-    #Assert shape
-    assert (np.shape(normalizedFlatSegmentation)) == (np.shape(DRR)), "Shape of the registration does not match the shape of the DRR"
-
-    return normalizedFlatSegmentation
-
-
-
-
-
-
-    
\ No newline at end of file
diff --git a/README.md b/README.md
index b67e1cc..eb4061b 100644
--- a/README.md
+++ b/README.md
@@ -1,21 +1,33 @@
-# 5ARIP10 Interdisciplinary team project
+# 5ARIP10 Interdisciplinary Team Project Track 3 Group 3
 
 ## Introduction
-This project, conducted in collaboration between Eindhoven University of Technology (TU/e) and Philips, is focused on .......... for the Image Guided Therapy Challenge on Transarterial Chemoembolization (TACE) procedures. The goal of this project ..........
+
+This project, conducted in collaboration between Eindhoven University of Technology (TU/e) and Philips, is focused on developing an AI solution for the Image Guided Therapy Challenge on Transarterial Chemoembolization (TACE) procedures. The goal of this project is to enhance the efficiency and accuracy of TACE procedures using advanced AI techniques.
 
 ## Getting Started
 
 ### Prerequisites
 
-List what software and libraries you need to install the software and how to install them.
+This project is developed using Python and pip for package management. Ensure you have the following installed on your system:
+
+- Python 3.7 or higher
 
 ### Installation
 
-Provide step by step series of examples that tell you how to get a development environment running.
+To set up a development environment for this project, follow these steps:
+
+1. Clone the repository to your local machine: `git clone https://github.com/LucianoDeben/5ARIP10-ITP-T3G3.git`
+2. Navigate to the project directory.
+3. Create a virtual environment: `python -m venv env` or `conda create --name env`.
+4. Activate the virtual environment: `source env/bin/activate` (Linux/macOS) or `.\env\Scripts\activate` (Windows) or `conda activate myenv` (Conda).
+5. Install the required packages: `pip install -r requirements.txt`
 
-## Usage
+### Usage
 
-Explain how to use your project. Provide examples.
+After setting up the project, you can run the Streamlit demo application in `src`:
+
+1. `cd src`
+2. `streamlit run app.py`
 
 ## License
 
@@ -30,8 +42,9 @@ We would like to thank the following resources and individuals:
 - Gopalakrishnan, Vivek, and Golland, Polina for their work on [fast auto-differentiable digitally reconstructed radiographs](https://link.springer.com/chapter/10.1007/978-3-031-23179-7_1). We utilized their [`DiffDRR`](https://github.com/eigenvivek/DiffDRR?tab=readme-ov-file#user-content-fn-1-aa759ff9097582506ce05933e125ab0a): Auto-differentiable DRR rendering and optimization in PyTorch in our project.
 
 ## Authors
+
 This project was developed by:
 
-- Author 1: Crapels, Dhani - d.r.m.crapels@student.tue.nl
-- Author 2: Deben, Luciano - l.m.deben@student.tue.nl
-- Author 3: Bierenbroodspot, Sven - s.a.k.bierenbroodspot@student.tue.nl
+- Crapels, Dhani - <d.r.m.crapels@student.tue.nl>
+- Deben, Luciano - <l.m.deben@student.tue.nl>
+- Bierenbroodspot, Sven - <s.a.k.bierenbroodspot@student.tue.nl>
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+++ /dev/null
@@ -1,1011 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Creating Digitally Reconstructed Radiographs (DRRs) using `DiffDRR`:\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating DRR and rendering 2D visualizations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cuda:0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Import libraries\n",
-    "import sys\n",
-    "\n",
-    "sys.path.append(\"..\")\n",
-    "\n",
-    "from src.preprocessing import get_transforms, get_datasets, get_dataloaders\n",
-    "\n",
-    "import torch\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from diffdrr.drr import DRR\n",
-    "from diffdrr.visualization import plot_drr, plot_mask\n",
-    "from diffdrr.data import read\n",
-    "\n",
-    "from diffdrr.pose import convert\n",
-    "from diffdrr.visualization import drr_to_mesh, img_to_mesh\n",
-    "import pyvista\n",
-    "\n",
-    "# Set the device\n",
-    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
-    "print(device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2024-05-15 11:06:33,816 - INFO - Expected md5 is None, skip md5 check for file ..\\data\\HCC-TACE-Seg\\1.2.276.0.7230010.3.1.3.8323329.41.1604860085.518229.zip.\n",
-      "2024-05-15 11:06:33,817 - INFO - File exists: ..\\data\\HCC-TACE-Seg\\1.2.276.0.7230010.3.1.3.8323329.41.1604860085.518229.zip, skipped downloading.\n",
-      "2024-05-15 11:06:33,818 - INFO - Writing into directory: ..\\data\\HCC-TACE-Seg\\raw\\1.2.276.0.7230010.3.1.3.8323329.41.1604860085.518229.\n",
-      "2024-05-15 11:06:34,310 - INFO - Expected md5 is None, skip md5 check for file ..\\data\\HCC-TACE-Seg\\1.3.6.1.4.1.14519.5.2.1.1706.8374.172517341095680731665822868712.zip.\n",
-      "2024-05-15 11:06:34,310 - INFO - File exists: ..\\data\\HCC-TACE-Seg\\1.3.6.1.4.1.14519.5.2.1.1706.8374.172517341095680731665822868712.zip, skipped downloading.\n",
-      "2024-05-15 11:06:34,310 - INFO - Writing into directory: ..\\data\\HCC-TACE-Seg\\HCC_017\\300\\image.\n",
-      "2024-05-15 11:06:34,589 - INFO - Expected md5 is None, skip md5 check for file ..\\data\\HCC-TACE-Seg\\1.2.276.0.7230010.3.1.3.8323329.2087.1600929091.391108.zip.\n",
-      "2024-05-15 11:06:34,589 - INFO - File exists: ..\\data\\HCC-TACE-Seg\\1.2.276.0.7230010.3.1.3.8323329.2087.1600929091.391108.zip, skipped downloading.\n",
-      "2024-05-15 11:06:34,599 - INFO - Writing into directory: ..\\data\\HCC-TACE-Seg\\raw\\1.2.276.0.7230010.3.1.3.8323329.2087.1600929091.391108.\n",
-      "2024-05-15 11:06:35,129 - INFO - Expected md5 is None, skip md5 check for file ..\\data\\HCC-TACE-Seg\\1.3.6.1.4.1.14519.5.2.1.1706.8374.231513034103627633230071228105.zip.\n",
-      "2024-05-15 11:06:35,129 - INFO - File exists: ..\\data\\HCC-TACE-Seg\\1.3.6.1.4.1.14519.5.2.1.1706.8374.231513034103627633230071228105.zip, skipped downloading.\n",
-      "2024-05-15 11:06:35,129 - INFO - Writing into directory: ..\\data\\HCC-TACE-Seg\\HCC_077\\300\\image.\n",
-      "2024-05-15 11:06:37,602 - INFO - Expected md5 is None, skip md5 check for file ..\\data\\HCC-TACE-Seg\\1.2.276.0.7230010.3.1.3.8323329.41.1604860085.518229.zip.\n",
-      "2024-05-15 11:06:37,602 - INFO - File exists: ..\\data\\HCC-TACE-Seg\\1.2.276.0.7230010.3.1.3.8323329.41.1604860085.518229.zip, skipped downloading.\n",
-      "2024-05-15 11:06:37,602 - INFO - Writing into directory: ..\\data\\HCC-TACE-Seg\\raw\\1.2.276.0.7230010.3.1.3.8323329.41.1604860085.518229.\n",
-      "2024-05-15 11:06:38,251 - INFO - Expected md5 is None, skip md5 check for file ..\\data\\HCC-TACE-Seg\\1.3.6.1.4.1.14519.5.2.1.1706.8374.172517341095680731665822868712.zip.\n",
-      "2024-05-15 11:06:38,251 - INFO - File exists: ..\\data\\HCC-TACE-Seg\\1.3.6.1.4.1.14519.5.2.1.1706.8374.172517341095680731665822868712.zip, skipped downloading.\n",
-      "2024-05-15 11:06:38,251 - INFO - Writing into directory: ..\\data\\HCC-TACE-Seg\\HCC_017\\300\\image.\n",
-      "2024-05-15 11:06:38,519 - INFO - Expected md5 is None, skip md5 check for file ..\\data\\HCC-TACE-Seg\\1.2.276.0.7230010.3.1.3.8323329.2087.1600929091.391108.zip.\n",
-      "2024-05-15 11:06:38,519 - INFO - File exists: ..\\data\\HCC-TACE-Seg\\1.2.276.0.7230010.3.1.3.8323329.2087.1600929091.391108.zip, skipped downloading.\n",
-      "2024-05-15 11:06:38,519 - INFO - Writing into directory: ..\\data\\HCC-TACE-Seg\\raw\\1.2.276.0.7230010.3.1.3.8323329.2087.1600929091.391108.\n",
-      "2024-05-15 11:06:39,034 - INFO - Expected md5 is None, skip md5 check for file ..\\data\\HCC-TACE-Seg\\1.3.6.1.4.1.14519.5.2.1.1706.8374.231513034103627633230071228105.zip.\n",
-      "2024-05-15 11:06:39,034 - INFO - File exists: ..\\data\\HCC-TACE-Seg\\1.3.6.1.4.1.14519.5.2.1.1706.8374.231513034103627633230071228105.zip, skipped downloading.\n",
-      "2024-05-15 11:06:39,034 - INFO - Writing into directory: ..\\data\\HCC-TACE-Seg\\HCC_077\\300\\image.\n",
-      "[2 1 3 0 5 4] [6]\n",
-      "6 1\n",
-      "6 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get transforms\n",
-    "transform = get_transforms(contrast_value=1000)\n",
-    "\n",
-    "# Get datasets\n",
-    "train_ds, val_ds = get_datasets(\n",
-    "    root_dir=\"../data\",\n",
-    "    collection=\"HCC-TACE-Seg\",\n",
-    "    transform=transform,\n",
-    "    download_len=2,\n",
-    "    val_frac=0.2,\n",
-    ")\n",
-    "\n",
-    "# Get dataloaders\n",
-    "train_loader, val_loader = get_dataloaders(train_ds, val_ds, batch_size=1)\n",
-    "\n",
-    "# Get datalists\n",
-    "train_dl = train_ds.datalist\n",
-    "val_dl = val_ds.datalist\n",
-    "\n",
-    "# Check length of datasets, dataloaders and datalists\n",
-    "print(train_ds.get_indices(), val_ds.get_indices())\n",
-    "print(len(train_loader), len(val_loader))\n",
-    "print(len(train_dl), len(val_dl))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([1, 1, 256, 256, 48]) torch.Size([1, 1, 256, 256, 48])\n",
-      "torch.float32 torch.float32\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Sample a batch of data from the dataloader\n",
-    "batch = next(iter(train_loader))\n",
-    "\n",
-    "# Separate the image and segmentation from the batch\n",
-    "image, seg = batch[\"image\"], batch[\"seg\"]\n",
-    "\n",
-    "print(image.shape, seg.shape)\n",
-    "print(image.dtype, seg.dtype)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([1, 1, 256, 256, 48])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "image.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labels = [0, 1, 2, 3, 4]\n",
-    "\n",
-    "# structures = [\"Liver\", \"Mass\", \"Portal vein\", \"Abdominal aorta\", \"Background\"]\n",
-    "# labels = subject.structures.query(f\"group in {structures}\")[\"id\"].tolist()\n",
-    "\n",
-    "# Read in CT volume and label map\n",
-    "subject = read(tensor=image[0], label_tensor=seg[0], orientation=\"AP\", bone_attenuation_multiplier=5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from src.drr import create_drr\n",
-    "\n",
-    "img = create_drr(\n",
-    "    volume=image[0],\n",
-    "    segmentation=seg[0],\n",
-    "    sdd=1020,\n",
-    "    height=200,\n",
-    "    width=200,\n",
-    "    delx=2.0,\n",
-    "    dely=2.0,\n",
-    "    x0=0,\n",
-    "    y0=0,\n",
-    "    reverse_x_axis=True,\n",
-    "    patch_size=None,\n",
-    "    renderer=\"siddon\",\n",
-    "    rotations=torch.tensor([[-20.0, 0.0, 0.0]]),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([1, 2, 200, 200])\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(img.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "im = img.sum(dim=1, keepdim=True)\n",
-    "drr_image = plot_drr(im, ticks=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Or read in CT volume and label map from datalist path\n",
-    "subject_path = read(\n",
-    "    filename=train_dl[0][\"image\"],\n",
-    "    orientation=\"AP\",\n",
-    "    bone_attenuation_multiplier=5,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a DRR object\n",
-    "drr = DRR(\n",
-    "    subject,  # A torchio.Subject object storing the CT volume, origin, and voxel spacing\n",
-    "    sdd=1020,  # Source-to-detector distance (i.e., the C-arm's focal length)\n",
-    "    height=200,  # Height of the DRR (if width is not seperately provided, the generated image is square)\n",
-    "    width=200,  # Width of the DRR\n",
-    "    delx=2.0,  # Pixel spacing (in mm)\n",
-    "    dely=2.0,  # Pixel spacing (in mm)\n",
-    "    x0=0,  # # Principal point X-offset\n",
-    "    y0=0,  # Principal point Y-offset\n",
-    "    p_subsample = None,  # Proportion of pixels to randomly subsample\n",
-    "    reshape = True,  # Return DRR with shape (b, 1, h, w)\n",
-    "    reverse_x_axis = True,  # If True, obey radiologic convention (e.g., heart on right)\n",
-    "    patch_size = None,  # Render patches of the DRR in series\n",
-    "    renderer = \"siddon\",  # Rendering backend, either \"siddon\" or \"trilinear\"\n",
-    ").to(device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([[0.3491, 0.0000, 0.0000]], device='cuda:0')\n",
-      "RigidTransform()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set the camera pose with rotations (yaw, pitch, roll) and translations (x, y, z)\n",
-    "\n",
-    "zero = torch.tensor([[0.0, 0.0, 0.0]], device=device)\n",
-    "translations = torch.tensor([[0.0, 800.0, 0.0]], device=device)\n",
-    "#rotations = torch.tensor([[torch.pi / 2, torch.pi / 6, 0.0]], device=device)\n",
-    "rotations = torch.tensor([[20.0, 0.0, 0.0]], device=device)\n",
-    "rotations = torch.deg2rad(rotations)\n",
-    "print(rotations)\n",
-    "pose1 = convert(zero, translations, parameterization=\"euler_angles\", convention=\"ZXY\")\n",
-    "pose2 = convert(rotations, zero, parameterization=\"euler_angles\", convention=\"ZXY\")\n",
-    "pose = pose1.compose(pose2)\n",
-    "print(pose)\n",
-    "\n",
-    "img = drr(pose, mask_to_channels = True)\n",
-    "\n",
-    "im = img.sum(dim=1, keepdim=True)\n",
-    "plot_drr(im, ticks=False)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 4200x2325 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "labels = [\"Liver\"]\n",
-    "\n",
-    "# Plot the segmentation masks\n",
-    "fig, axs = plt.subplots(\n",
-    "    nrows=1,\n",
-    "    ncols=3,\n",
-    "    figsize=(14, 7.75),\n",
-    "    tight_layout=True,\n",
-    "    dpi=300,\n",
-    ")\n",
-    "\n",
-    "im = img.sum(dim=1, keepdim=True)\n",
-    "plot_drr(im, axs=axs[0], ticks=False, title=\"DRR\")\n",
-    "plot_drr(\n",
-    "    img[:, 1, :, :].unsqueeze(dim=1), axs=axs[1], ticks=False, title=\"DRR Vessel Only\"\n",
-    ")\n",
-    "plot_drr(im, axs=axs[2], ticks=False, title=\"Vessel Overlay\")\n",
-    "\n",
-    "for idx, label in enumerate(labels):\n",
-    "    mask = img[:, 1, :, :].unsqueeze(dim=1)\n",
-    "    vessel_mask = plot_mask(mask, axs=axs[2], alpha=0.5, return_masks=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([  0, 255], dtype=torch.uint8)\n",
-      "torch.Size([1, 1, 200, 200, 4])\n",
-      "torch.Size([1, 2, 200, 200])\n",
-      "tensor([0.0000e+00, 6.7832e-05, 5.0792e-04,  ..., 2.9336e+01, 2.9516e+01,\n",
-      "        2.9758e+01], device='cuda:0')\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(vessel_mask[:,:,:,:,3].unique())\n",
-    "print(vessel_mask.shape)\n",
-    "\n",
-    "print(img.shape)\n",
-    "print(img.unique()) \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating 3D mesh renders and 3D to 2D projections:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Performing Labeled Surface Extraction: 100%|██████████[00:00<00:00]\n",
-      "Finding and Labeling Connected Regions.: 100%|██████████[00:00<00:00]\n",
-      "Smoothing Mesh using Taubin Smoothing: 100%|██████████[00:00<00:00]\n",
-      "Filling Holes: 100%|██████████[00:00<00:00]\n",
-      "Cleaning: 100%|██████████[00:00<00:00]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Make a mesh from the CT volume\n",
-    "ct = drr_to_mesh(subject, \"surface_nets\", threshold=225, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([[1.5708, 0.5236, 0.0000]], device='cuda:0')\n",
-      "RigidTransform()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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tU2JsBpOi0esuAjh2CLcpPmpMMKlfy9jEcFx1OsKmqAzkGOWJtH1Gs5VnpZyq/beJVtnXpRR1pZpsG/9nxcijVCPiktrk0atDOhv8na2vajx8mBSNmEwma8u3PX36dO39iHt7e92gGcQUsxVuIi4G4WSkOmR043XivinMTZG11VBS7Gvn+0KugI4qjbg88pPvr2zwzWKxWOuosbsT+atq1NhkVi8dicqvSTO2ByZFo3vI8UYMLPQdEd3C4BHrb7jANxYIR9qIfKBK1vvfBC03YzWEfxNclSCue87hdeSjr/Eams9VBvFkMb4q30wNVturbbqfB39Vx0b0q/EWqrSOKT5MmBSNtekTu7u7cXh42PW08e7EiFgb2MDLuC2Xy05NwmWKvPCbv69a1032RdyPVXBuAmPKuK1RqlV5rfIr5ZeRow6yQd6ZiszyxBQhjQtyXbBN4+XYroqxLy/jYcCk+MiRxUr4HYkcs2FFeXp62qkAXUlEh8czKndqH65KaJssL3cV3IUhzJTXTZEgk4YOkskGzVTIlCvPKcxU1tDYIrv0eSUl1FHv3aHufSbI7B6/6hxI425hUjQurV26XC7XlnFjFxQ+mLMIlytijWdnZ3F+fn5pasZNuk81v6vO4xszPWLIsRmG5HdVtZkNkmJs8kLnq7pSFdmIUj7fbE3TbFQqxxoxeIYH2XCsm19thnz0GCVhJVVOx4vg67F8PnajPgyYFI1LRoeNBbbDqOzt7a0ZFibAzJ2V/c6IUgc8VBgy8pPTtPKqSOEuYpEZqQ/NC8f2qSoFj4ztw3UoXx2l3IohosxqWb+MeDI3q45KzUajqkIcMgo1m5PYChPYhfpwYFI0LrmA+F2I3IPGB/EYKEmsgIP4Y2uNUzXgQ3AdrtMMQ2KQ12nMhpLVpmpsqPu0z/C3BsNcBeoOVWLqU4g4TomM77flcn1SP47HPYo2QYiAXaG8+lJ2rSqyy0IQPN0D+dl9+jBgUnzkyFxT5+fnsbe316lCpFN3KE/bQMwxW5xZiXAs0bRiNlleaniGKqK+kZnZ6E3el+XRyo+Puwqq9tSBTtkxWZuMmTM5pOOgbkzNZ4xCzOoyZJQqz13Uby4zm5ZRnWNG2rwvO87EeP9hUnzE0Fiivg7q4OAgItZH2kEhoqeNUacMLCjeUoo3BTY6Y2Nm6t5To4aOAPZhG6cfe37XqUQr0u6r05gVerJj+1bpYQKurk9LOarLla+BukrZjc/xRq6HfnOMkePo2ahTdP70vPhb68NtuEkc17hdmBSN1EXFBMAuJ/yeTqfdfEY2IPP5/NJk5r54IsrMtiuGxHsAVTxDXIZKgkPL6kvfwlAi5Xl4VdljVF7ExbsvN0V1LI8gzcqvBqNErCu8zOuQuXf1+mXzESvFiPPAPa8r1bAi1PzUO5KpQ+5UOrZ4/2FSfMTg+YlMbBEfPfD7+/tdDHFvby9ms1mX/uDgoFOSEdGNPMWIVH6TRmYMNiVFReZKrciv5RZjotl09OlYtxh3EPrqHjGe8Ko0Y6dNjIGqQB30xISWzTXkfYgDZueAMvT6wf2v6bA+L9IifogPk+1i8dFi+OpKVWJj1YgyuY31w65au1DvL0yKjxzaC+YPJu1DJWL7/v7+JWOHD9ZC5UELXE6rHpvWf8i2qgevx4wZXJOp66zcql6Zau7Lo5WuIrq+DklGyLptSL361FKWN6s33V/FabOODCtFEKqSEFyvlTs14kIx6sR+pKs+TMpIm9WbPSvG/YRJ8RFD5x7iA1V4cHDQPcR4+fD+/n4cHBx0Dz0+p6ennSHa39/vjoEx4l71GFyHAamOb8V3NlWLmx7fmkeox1Xu1qHuyQp9qjvLX8lMY3Baj2pyfbaovELbQ92qHE9kZRgR3b3JaTQvqDm4/7ORrkzkfA7VOXOHEc/MkEFZxt3BpPiIkfV2QYq8BirvQ0+ajUsrxqMGcFNi3AR9KidzR/YN0lFCysijb/WcodNShsb6KtWYDRSqUKXtI6oqptfKO1OGLYWeKcNMiVaT90GSfD48oIzdp3y83ttMbqr6VCWDYJkUuXO46T1t3DxMio8USoZwe4IMoQgBLAoecWGA+NVQMCD8KimM5ssM3lijMEZ59RnyITFHHTk4pP4cs+Jtfa7OIa7KFlrtMMa9XCHbx6qI42WVAqsUI9JkJNcaRKNgBaZKjcmIiZLJCjFErM4ETwff4xpzx0cVIhMhl8VK0e7T+wuT4iMGK0AYDX1PIvds8VDz6FIYE82X356xyTQBxZg8+txSQ9SMGroxpNgqs9VBaJHwJqjyaLlq+/JRwtH92TVv1aMqlxWbKubMZanzDfFbR0KjPN4OAuZOHdyoXMflcnlp6cNMKXLdMnI3Kd5vmBQfIdRdym5TJkT+r7FFJknMS2RyRUyHSWCooc+McDY6dIzR12Or/xGbLx4+xGXamtc3ZH9fOVetX1Yf/h5atg6C2YSgKzdj5X7Gfaf3KxMgx7kzFy726+jXzOuBzl9LKWYDdUyK9xsmxUcIHSgAIwJjww8/XKvVg86uV/znPLhnXdWlhbHGg3vrQ/PMXHuZyw/595Wf5dty/WnZfRhah1acrg/a8eiLHQ5p9+y4bF9LlbPXgjt2fE+jg7ezsxMnJyeXXhOFDzp+OF+QN9JqR0xdqSibXaNKjJqe45tD28y4XZgUHyEypchGhedu4cM9ZQa7STlvjEblFxO30Df8fuz5tTAkppj18rNyrhIH3ITwrzNdhWywCKCxwL58+JihHaPW6FqkBaFNJpNuQBi7WXmlpZ2dnUvr9WLfdDqNiOje7sLH62IJJycnHfHxvb23t3fJHau/2WuiI2ON+wWT4iNE5jplgwJCw3zEbLQg8mBVyUaLDVJVh+y3pm8N8mjl2YIenxn5q5YxBJvmlcWo8H9srFbB5IUOEr6z8luxVb2u6hYd2qFgdykrwel0GrPZrFtUQhcDQP5woUKlLRaLOD4+Xisbg8tAXkpyi8UiptNpd7y6YVEnns6hg3z0nHn6h3F/YFJ8hMhcTxzrwGo0cIvyA8xxFDY+Sog8vL2qQ/Zb0UeKQ4yr7s8GBmn6oVMyFNdJmn1l9H0PhSrBiHoUKW8bEjdkQs06V5oP78vOC/cfRkcfHBzE06dP11yZiHvjfmVSRF35/2Qy6RQjxxb5mUCeu7u73cpN+HDIQI9HfVkhQrUqURr3AybFR4hMKergBZBjNhgBpMEqkadkZKqiFScaipsyHjpQIiIfITiEyIcSc19druKaHYOqTbljo4NLqhGWSoxMejoSM0OL3FnRHR4edkpRlxvkuF9EdB07JUW4S/EsYMEJEJfOYwQB7u/vd79PT0/j5OSkS49nAMsiascRI7Xx3CBvk+L9gknxEUIHK2QAKSJ9lYZ/40EfGiPK9sHQZq7BmyIHVZB9ZFQpouq/Dnip9rXSbQI9r6ycyiiDLPqIUeOP6n7N6tNX3ywdTxnCSkvT6bRzaXIMWDs3uM81HshpECrAIJhsygaOwVxGHuEKBanTNxDPnEwma+up6qIWxv2BSfGRIVOJ7D7l3vLx8XG3tBurSXxrfATb2NBWq7L0ufqULK8aK8uQGVE26mNHb16lji032qau3BbYdZipYpw7iK6aRsCjL5GfQtu3qn9Fonx/4u0scJ/u7Ox0rlKoNVZt2na4PzleziOoEVfE9QCR8chRkBy7bHk0KlQoPy9MyKif44r3EybFRwhVipkx4tiikmGfKuJee9UbHqOsGJlLs4oZVjFEPZbz6FOmQ2KkfWW3FGiWPntJ8lASqpCN9h3iSkU6bXdVk1V9MiXcp8iZFEFgWJ8Xq84g5sdlZiqYrzM6gFwOdwI0dJB1JDlc0BcbZZWocXjj/sCk+IjAD7MSIr55cM35+Xm37Bt60TAA7GpSg9lSDkOMwG0ZCnX/8vdQ8uvbvknaTMFuWlY1UInzYBeplt2KBfatHBSxPrimFaPNCFU7Ybx/b28vnjx50nkz9FpCJbLHAvnxvZvlD/WHEaUcB0Q6pOGpHDy/UdcI5rZgch3ShsbtwqT4yNBSiTr0Hb+1hxxxYWSWy+Xaqh5Dlvfi8oYqHVVR2fSDPmXYmgOpqrGlfDIMVYpjybNSWfp/08EaV4nZZq9hyjpEOA9t/6ps7rxxPryPXZ5I00cyfM9rCAD1ru6RqhPZ56XIiL7lpTHuHibFR4TM/cPb2WBAGfJ7FCPWF1jmAQ7ZKiAR/SM2K+PRZ+QrQs1Qqb/KtRax2Wt9OP+W0RzqGs7IooWszkNUKcfFWNlUrtUhLmElbM43258dm5EGLwaBGN9rr722NkKUY3Ts2ZhMJt0oUy5ruVx2E/NZEfK1w4L46AggZggXqp4L6oP2ZcLNYvqbdmqM64dJ8ZEiMzwgRCVLNcYgQAx15/xa5WTbW/9bhqJFsEMNTKuMPgKqSE/3b+I+BvQdg5VK4fSM7NyGqGV8eD5qX+dGwaoN9xi71jNSzOLQfD9qhwL/9/f3uzmE6uLEOatC5PzZ68CEyt4CVqacns8ti0fqs8V1UbVoYrwfMCk+ImSuUDUWPJBhMvloUvOzZ8+6eVmnp6cxn8+7dGoIMtcZl19h05iV5rGJGzCL4Q0l5D532SZ1qFRUlvcYQ8pu4crNnbkVs1gjjmt1hrL4ZLa/yldJA8oQ9yamQmDi/XK5XFvebbFYdPMIlXjwDCD9dDqNxWIR8/l8zePBJI4YO9LMZrNONeK+044M6sxtiHgm0oJoPQr1fsCk+IhQxUUA7f2C9PihzYxwRYhZGVl5VbpKKbXidGPe4K7Hs6FWo91n/FsEMRasPvoUdKZ2K/JUF15EvsSdKp0sHshKqzoHJVZtq6ojkBEiylJllZ07T5Vgbwa3RVbfrJOoypPfIIOPnnN2/2dKtTpP425hUnxEwEOp3/zR9ykitgiVGLH+wuHKOLZGDmqd+LuP0Fok0HfskNhi32AeBVbxyc6vUnxVXfuWxsvSazmMyo3HZKLGX2NcPH0gQzYPtSKtPuWfERDnBdXFo6OxD3MVtXx2W2q8FHE/Ll/jqRw/x/keHBx0cyX39/e7EbC8HiqfM7cdzgF5gbiN+wOT4iOBKsTsP6fFN9Qi94pVAQAtIswUT/a/RSRVmkzpjYEaUeQxxFjx6McsT/6vaimLCWZKqsKQuCenyVan0foyKcKNmBGbviosK1vdrn0xT1ax2T2auZYxGV7jkZnqa5FyNsmfz4XJlcMGIEGk5+Xh9HngtkPbemrG/YNJ8RFADYSONMVvfjkrSBArh+A/FKOuHoJyWi7HlotIjXdf+k3K6EPmjhsKrn9GOkOUYnVslmbI/qzzMJRoM4WjeY9x+/XFa5U0M8UMlYj6YH4iYonotDEZ8XzFSp3rfnb38ou2lWx5hPZ0Oo1Xr17FfD5fm7+rShxvn0HeEReel/l8PqgtjZuFSfERIItdaKxG3akR6y41rCCCdBj+zgZEy8yQKQtNq6MIqxhaH/Eo1B2axckq4hhDttq2rcE/QzoS1XkNdRtn+SqyUZkR6+pNVTmn0zLUDVl5DbLrPKStcS0rcubBM3CT6go3SvDYxnWBGsX5Z+um4nVSGvPk9yZyWew2ZcVo3A+YFB8BVCFy/DCLLeKhhgpEj/jw8DBOTk5iPp93Hy4jop6ikG3rM5RDz61SIZmhrP6r8crK6atHBXZBqltxE7SmVUTUSgxQN2ZGKlXMUcusrmnLBV219ZC2n0wuRp3ivgW5sBo7ODiI6XTa3cNHR0fdCGpOy+TOa7wiT341FEhRB5VhbeDj4+O1tVGza47O5GQyidls1p3PUCVv3DxMio8IWa9a1SN/0MvO5iKyC0lJSZVj9bvaNlQBDslraL4wSH3H99WlUm9j6jrUxalljXE7ax30vsgUTnbMVZB1pFrnnpUHpQbCQn64b9nNqfe1uk1BUEpoIN9qcQRcD46zZnM7h4z8Ne4eJsVHADy42beqRHbl8LxEGBUswAy3z/7+/tobx2FsWu5C/s72twxjRVwtsmrF77I0LbVZYUi6ynWXpanikmj37Ji+tsvWIFVXr6bNwN4ErQOOHWPoM+U8JG6KcjBfEHFGnlP75MmTbp3U5fJi5Rp86zsOmaixHcQakd+/IF08C1CC6nrmFx/v7Oysrb7DxGu1eLcwKT4CqAJUMuT/rBjY+MHoRMTaKFQ21HAPXcXV2Le/j2zH7BsaqxyrvIYQdF+nYJPzbB3PLkEmPJ03l5Wj7dRyt+p5RgxbhEHLG6Ias+uknT6QDtytOmAmW7dX1aPGTZksM5c8LwWHc+H68uA0LavvvI2bh0nxESAjwz6SVDXARoAH3Ggvf2dnZ22+lhoN3TZWUWTH9Q2M6TMyY9xYmZpqDZLoi/8NQV+8M3NZa1l8nbKFvHV+YlU23yuahv+z4lE3aV+btJQ7A/crxz9V0UE18ghqxMlxDJOwxhz5nNn9enZ21q2sw20I7wmnzToJPEhN29a4W5gUHwEqpZipwkwBnJ6ednEbkBobII7N6JB+NYjVvD6ua4YsTjlEdXHZVb7Z+wqH1Ktv4nqVZowSqFyRFeG10mS/1R1YtRnvb6lKPraqV9ZRatW9gsYOcQyrQXQAQIonJydr29G+fH8jv4hYm16h6hEr2+BdjjxvkduBY514RriOVafGuBuYFLcYmSHLiE+VI/9HHAbp0LOGYuTBODyxWUfp9dXxKkahMuDV/iF56L5W3bJ9Q2JiQ45Fe7PKykgly2dMjLBSb1k9dQpBphShkrhuQ9yoVZ0zZYvtTIqYU6h1RGxxMpnE6elpR0wcEuBOH0+x4Ckd/Iwglgg1yh4SXhFHFSHOg/NnN6txtzApbjmqmKFOydA07E7DUlggQzY++vADSDMUVzEI94EUK1fhUFRxQHyzi1CREZSq9GpwjqbVfeoirpQi179ywbKrk1HdJzyKU8vgspjAuE6al65Tym5gJi4mMig6uF05Zs5kVk1d4fbRclhtmhTvD0yKW4xK/WX/Ob0qRR7Vx4uEM6nyw54Z4SF1zdDnIuU0WV4tQlNjtQkqRVXVqRUXHOuGHFKvqiyts8a2Mne33idZnkxefM68P1Ol2fnhPtKYr66JqlOGMpLXObh8rzMZsteDzwVuUi4v4vL7GvU8mNg5fybF/f19k+I9gklxi5G5TfWhg2HQkagZYWrcsXIJVWiR1xBy6SPO7PyH4CrEOJYUs3Jb9YKx7OscVHHCyrWZkXClFrFP7wfNMysf+zUm1+ocKDLVh+1YYk2VHxQYx/d0+oMSHOff16lDZwEDc5DvELWMY6fTaed6NSHeH5gUtxgtlVgRYYsYW6SoaTOSaCm2IYZa8+NtQ46tUCmnMRhT7yGqkY+rSIfTV/tb16N1jlrfqlPFdczqFrH+xonqHPR8Wv+5Lqy0eB+262Ld6obGKFDNX92hnIYHk+kCAC03KucP0j49PY2jo6NL57Wp58K4OkyKW4wWIbZ+qyLgfFqKsqVQWkYwUx9Dzm0s2JhtmtdY5TrG+Ou+zCAPVZytumXE3Ke2K1Js1ZHvHY3VVXWLuDxFRl2LHINEXJvJUAfbYDvfv1qfLCbOdeHBM1n7RHw0WAdLy2UqFBP7Obap59ZSm8btwKS45WgRV5UWv7MYTKYq9dgq/xYqwlI3Vka41XzBSq1Vhr0Cj/jsq182EjRzxQ0dmdtSwEPJUNtMlV2LdLXNdFCPkoceD9Jit60SM+fH29XdyR0zPa66L9kjgryy9gMxcr112bi++4QJmVUktyUvfDGZTLpBbJUKN24fJsUtxFBVyPMNs1Gqur9yuwJ9pBSRD5xp5ZGdm+bFBm/IcXp8VS+AJ7tX59hyn2k7YZBFVQegjzgrcq+IMUvbp0ZbpIg6ZvkzAXKHok8p8n5MoUB+nCfS4/5kssTAMHaXTqfTtUE7PDcV8wxRJvKYTC4GBfH11XbG84FtPEUEv6EQdWk5dSmbGO8eJsUtREaCmdLL3DatbVk+ETkpVQ93pUSGntdV8lICU8U09tiq7MqVWKWtSELVadVhqIipUrdar754L/LKjDaOr8roU+VZp0DbWNWpvlRY64a6MOmAPDX+p8qRXbOsbrU9+Le+c5HLAbGyYkYdMfcxaze7Tu8OJsUtxP7+fkyn024VDzaa2SdTf5WqzNL0Gf4M2TEV+tQlb1ODkg1C6YujjamLkmVfbK7Kk4/XPPreWN+nroeMCG6RfXWNub5DOihVXK6qx3Q67Y7DQvQ4nxZZczxPpz+cnJx0ZMnTL1gBMvEy0bLynUwm3QLgeBZAjvyyY+Rxfn7elYV8Tk5OLrlPtbNi3D5MilsIPKBYuR89Uh1qrrEWfGcPaDVgh49hVKQxRO2pMejLO9veIlKtk/4eAs2LFVc2kKVFijimivP11WHIObSUbEspZtc4az9t5yGeAlZNmjfu4YhYc0Fin96j2K4LbfNAGuSFj7prh7QnD+rJOoZ6DhHrnRpuO9QjayMT4t3BpLiFQG8VpDibzSLiorfLrp5MFVYPPR5ujUECQxXjGEUZUauzVj4tMmq58PrKruoyJK++MtRIt9KOKbuvjVsqt/IGaJ0zNarlshobcix/FovF2qjNiLg0ipMH5qhHAMTIKo3dpfouRMT9dLAMq1S8+gnPEr9q7fz8vEu7t7cXOzs7MZ1OuyUToVKZnFudCeP2YFLcQoDMQI780PJix5U6VCPI/6u4ouan27R+1b7qfFrH9xFvtW+MMssU4XUZMC0/U2K6b0zeWb76u1KLLW8A172a5tLX1qySq/sOafTtHpyey2fFCLQ6E1ymnu+QTggIkEkX2yIuVrvhUEZVNy7buBuYFLcQMFJ7e3txcHAQs9ksdnd34/j4eI0UIy4MQRYv1LldOvcrUxAVIVaGKNs3FJnrF+ef/a/A+4csWs1lVxiaj9Yjc+n2uZM1fStdSzlnx1cje6tYZJ961/gpf6uyirg8yEgJFHXkVzG1Bv3s7+9HxMVUEXZfTibrg2ROTk5iPp+XHZaMlFEfnMPu7m4cHh52z83x8XF873vfS9vFuB8wKW4h9CEDme3v76+5sJjgAP3dpyavgxRbYOPYN8o1U3OtsjLyzF46y3XRQUuVMeO69hm8avQon5cqOc63j/wzhThUKbbqpHXLFE5WN97G5eJ+5KkYIDyQjZ4jjyrl8AAIjwe78L2ObdUbKlrn0vrPRI3fOB94bTJUXgGT5e3DpLiFgPvm5OSkU4sYjTqZTOLo6CjtbatCZMOkinLTkad9cwqr7bwIdJW2IodKQfL+LK6XqUFs65uv2Oea1ThVptaGvu4Jv7O2GxLrQ7nqgkTalrrk35myxPlnCk47bxx7wz22WCzi1atXsVgsuuXc+IPYOUaqog7wirA7k68pv+aJ64H68uC06rzRTvqcTKfTtYFtUKD8rHF+OsUkaz/j9mBS3DKoAdOh5LxWJLbpw6okyXn3KcUMmkcrfUutVAZ607wzImfjnamxTcrKDFtl7Hh7Ns9QjTCOYYXbKr+lcDVmx2X0QdVRRdDV+qc4Bz4/vS9bixloBw9uUgCvZ4pY9whwLBCo2rGvs4Vz1OOQRuuo+437AZPiFgEPG3rPAEbDHRwcdCNSETNRYuSHlj/ZqjaZ4azIYuz/bJ+6eqt0QGXEWoSWzVkbUlZLKXJ92F2oeet/GOuWKm11FFptlZWZkUFFinpc1VlSZddSQPv7+zGZTC69NQIvs4Z602kMPA2D70/M02WX7Hw+716Bdn5+Hh9++GHnVeFnASoUnSTE4nm1GpTDi4orieNb92eKuuXBMG4PJsUtgholXmmD3Y94IDEaLuuhc55VOchryHGbkqLmOYYUW2VVSgqEyEY8I7IW+bfic0o8LaOXqbqsjVvquso3c++OyUfrpW7lPkJu7WeXZ0a2LXcxSIzJjctkbwkryir2yJ0CrU+GbOTrmEXFjbuHSXELgQd/Op3G4eFhpxDx8KHXDIPAvVcdcVopR1WLYxRLRhxDjuUedgsZ2Wg+VXpd6zJTRK06ctoMfeqwGpDSKu+qBMTfmrZPKfJ3RtJZrFbTa9tDWYHcZrNZd3/xm+o5HfZh7h/uFRAZRmBHfOQmPjg46EiKV7mZTCZr+YHksDaqtgu72nkQDdcNSvL4+Libp8h5ZJ00fFsp3j5MilsGNhxq0Fg58j4e9aeDbJT0+v5n9cn+DzHsmrbPfdqXX5ZHRgLZCitjCDHbX5FORrqqWjW/q5Ag9o9xC2fkzNsqQhxbB1wbXmKN8+VOGscDuZ68jeulC3wz+JngFWu0bshTVSjXJ1uhRgfTcH6btJNxszApbhHwME+n03jy5EnXc0WPeDabdXEWfuhBoPv7+51riV2qOoJO5ytGXI4r9g1Qqf7rdiWwPmPSMsoZ0TN0gAtv6yMRJQ4l0er41mTuKh43pA0ql2tWVuvc+srS41sqUX+rKuc3WWAwDOcLlydPwcAbLrRzpvXS0aZIjzIODw/j/Pw8jo+PL50bu4bRJnt7e2trmeJ8QIC8uk5F/q370ER4dzApbhm4x8zfERfrPoL0kJ5X3ECvGYN1sE3zyz7Ij7+1bkO3ZdsrldeKiWXlZHXtI73KzZm5vrIBJRlRqeFnVyCnU+U6RJVpPcaCj2NizFauaXVsMqXLeWQxWrhHq3uK73FVbegAKvmhLCVNfX2TEiATI/Lc29tbW2KO1WhEdO5SXuy7ait1oXL9jLuBSXGLgAcKag8KEA8Z3jTAo/EiLpTibDbrRu0hHgkDgDRZTHEMIQ410kzafXn1DX5ptRV+9ylCLaNyGw6NJWb7YHxVTUEZZURVnWvl6sxUSaZWlRx0e3Ve6lJkQq9cxUpwWKsXpKL1Qnr1hPCH1RqrbRwD5cgLhEdcjHRFev0N0tYYPep/enp6aWANnkl+Kwe3k3Y0uf2M24dJcYugKnE6ncZ0Ou2Gsqu6YXcUiJLjWFCM0+m0I8Y+lZgpsiFQ92ur19xSEJymTylpvTNFl50LK4hNSbEVn+My2FWYDczIzk8JJzsHRraKj44mzfLg/DO1zu0KctDz5g6anhPPp+XyOAaO7TxVYzKZdIt1Zy/y5fqqqxxlgMS4Q6kEph03fsZQP3Rq2H2rnUluK/zOvArG7cCkuCVgEsEDiRji0dFR2uuezWZrahKjUfEAgxAPDw879cm9Xy6X68EYOkSf0+AceFtfjEwJeigqUutzpyJNpryydPp7KEniu4rfZumr+md10m/krQa/dWzrvJloeEoD0kIZMnGwGoyIjux4EAzuR+QDDwg6ciBFvLOQ8wJRRUQXG4R7FGWB0HTFG7QRFCOOZ9JD3UHq6GhmscmMGFsdQuPmYVLcAuiDhGHgmJbBw8nR60XchnvfrCCn0+ma8YKxqeZaAX2jO1vKIjuXvnz491BSbCmoPoW5Sf59pMjKrkVsLWXcKrN1HhVJVyN9s+Oq9mKViDy5Q8Wva9JrjnsO92a1PizHBUG6XB68IQAUIKZG8KhSHMudy4joFtfXDiGra56CwS5cdC75nDQcwe2o7WxivH2YFLcEFSnOZrNutY6I9VF4/M437oEjlqjqk5ViZgwrsmkZ15YLib/7iCyiHpU5hiyQT5/SGgslO3UJVp2EbFuWpqU8W+dcnVO1cHWlBDOoK5jz4GumJIS0+gZ7LZPvY56fyAQKAtI2VzcxPzeoC3cYI2KN6LL6aFwTA274/JgUEbbIOj9jXPPG9cKkuAXIXC5wGSEeyG4dfsjYOGCS/9OnT7sHmF+lw/EWHN/nVmupveocOC0biBbhspFtDRyJGPZap6yuQ+eWZaMZNa5VuS8rMs4MZUWEnK5PbVb7++aEtq6xkgafA+fb6lBFXLynUNctRYdvf39/jaSYtAAdmMNzICPW45bwoLAi5IFP7MblbyZQ1FGPwflkYQxuu6t0vIzrgUlxS5ApRag7xFiykYMcS8K7F58+fRoR6+uAwujoxH81bDzYIEtTbctcoJWa1G3ZsPaqjbKyW+7SvvPISCxTWdyRiLj8uqiM0FrlZtsrglUlym2kir+vDbX8bLK7zj/Ujk12rbPz4ZGb2A9XKQ+0YRJbLpdr4QJ+bRoPnomItTx4bVM8KyibO4/L5XJN3ekAHL4W7EZFPXmdVD7vvg6jVeLtwaS4BVCliIcPBMUPf8TlIezAbDbrPmq4sJA4D53PVJmO9utzQ7aMcWUodBCO5lEZkEodDSHFav9YY5W5SZkotRw+Vz1Wy86Its8AMylqR6PVCWCVlJE4ux55WomeO/+vFCS/GJtHS3MZPD1D1Sqfp5IkyItfFcXzcrP24jaC+kTcESGGiOjiljpNhD0wfE2yD8qsrrlx/TApbgn4QdL4BvdkdRBARKwRJ0/YR74R0W1HXmoQWka05RLMfitaJJh9aznVNjXSmeGpDHWVV1VmpZ5h8GCwM1TnqW3LajRTcIC6kplwWm2Z5aGxuYrYWzGyvjJ5LVPkp94K9YLo5H2dCsEL5Ov7DDl/bhs8J/jPnU64XqEy8cnmTnJ9q46Corr/jOuHSXELkPUuIz7qqR4dHcX+/n4cHBzEfD7veu0cK9TAPxsUjETVF7miHI73cH3YSPYplswoZi7aijwrozrUwGdoEWaL9HRfpe5aqqnlfs5iV1l5+n5F5NNSdX3XJCJ3jWrZXNYmbuEqDe4rjNzk5QYRCzw9Pe3cpyA/VZLcUcT5YGoFgHsdaU5PT5vqER0Rdq3ysnVnZ2fdNBHtWGqbYT+HNtB+2dqqxvXCpLglyFwvMBBYoeb09PSSW4v/s5pk1QDCZBdsRG6guUfN+7LFA/Bb0SLEFilmx/Cxfb1yNeqZWzEjrVaeQ8F17VOVuq9POWTtocdUU2kq1ZeVX5Gw1rtSjEMwmUzWPBastKHY+JqBeLAtm8jP6pHjmFUnAWVr20yn0849imcG9UJdeYGBLAxRtVnW3sbNwKT4wMFklj2weAh3dnbi8PCwe/iwHT3YxWLRjVRlEoOhYXdTxOVRhbwvW8qKlSm28Tefj+7vUzCVcc0MWbZfj20ptSxNy1BVSi7DUGJVElI1pnHCqq373MSt80YZGmPEcZUrGOB7VkdzVmnxrWUjD77XUXd9bRrUlipGjGTFNiYpJlaNqe/u7nbPEDwwBwcH3XZ1G+/s7MTJyUmcnp527lSoXD2nrBMzZOS0cTWYFB8w1IDxh91F2M+Eh4efCYwfRHYJtRSWkjDHSjgGk0167iOBMYSI3y01OkRdViqw5e6sUPX2M1xVabby1U6LkniLOLP8MqJnV18GeB+4PE6LmGor/swqCsfo/Yo6sFuUPRxahhJW1XlidyePTIUCZIJFGfrybrQDL0HH+Wfnzb+tEG8HJsUHjkwpVoYbDx4vinx+fh7z+Tz29vbi+Ph4zTWF0ab89gwYAx0aj54v14sNlk7tyM5DlV32m7/1/DR93wAdTgNkI2czg6R5ZUozU6IVaVRoGUJuU05buTFVebCbG6Mx4TXQgSpcH76GOuK17/5j9yu269xClKsdO96naljzx3aOlUdEFxvkUAF3FFEvnr+I/BGbZ4WLGD3K2tn5aGrT+fl5vHr1qlsgHEvOnZycdC8bhsLkToJOg9J2v6nOk3EBk+KWQEkxc71k2yIuYn54QCNirXfNeerIP147VdNXqAaB8LfmM0QlVnm1yFDrqfEdzbMiKd6fpR2qxICMkMeo1Kzc7L7AteOVX7QOQ1CVw9t4RCYfkxEAkyLXpdWOGouungcmoOo8tJ48oAd5gMx5wA+OZeJF/UGi6ECqy1k/2i7VeRvXC5PiAwY/rBVx4WE8PT1d69FOJpMuhnh4eNi9TSPiYn1IAA8z4iZIw6+fgqrEA889doBjQZmq6VOHLbKtjHAGnm7SMq4ZQfTFdCqirsrI8tM4VIuIVTVl+Wjbcdn6gmkooJOTk0v3AOfL55HFl7kumM6jLvmMCDV/rq9eV3ZZVi7d5fJiBRxWdyiLXZx8DBOYvoqN3aX8DOggMyV9KEbugPKzqmu98mo9OJ7VsN2pNwOT4pYg6xHr6jP6EMElylM08OBpDJAfTM5bBwdkdeL/3Pvuix9pnmpoeLt+ZwqwqtuY3neLzMbkkRGUoiLGbHurs8BkwumySeoVsfWdT1YePnp/4XxR/76y9Lqi08YLinP74X9rEQnkw1MkuKOmxJ8pP66btoe2tz5DfD6sMDnvbPoLd4SMm4FJ8QGjpRSxsPfHPvaxboFvqDhOw8fxyDvuGaPXit4tv1GcX5tTKRb+r8YnUzd8TJaXHlcRaeYizYx+Ztiqnri6NVsjJrXeWg+OjzFpaHtlE+J1X0aanE7vEa0XKxlWO3yt1JhXnRA19FgNiQd1sWLkWHSmIPk37lsoNOSTdQChyCLWFxnQN8Qw+YBMj4+P19qP4+o4jmPyGDyDBTB2d3fj2bNnsbu7G//3//7fLn6I+iBP/I74KN6JmC63pd4bYzpxxniYFLcEmYFiRccPkrovtTcbEZeMF7udYBDYHTTGnaPE1lI5/DtL10duLUKslFh2LOMqsTbdx8QYsa56VEFkUGLMCLFPBar6GXJ+rWvDnZIWGWsHSck/i6tqHTIVhWOxn0m3uo/0XgfUdct1ZzLVN2JEXHhi9Ppl7l6tC7cjn48J8eZhUtwiZAYxIrqYCF4hxbEWdeFEXMQQT05O1t5enrmQhpBhi3AqVZkZ2ey/GsMh6rCPPAHuBKhrrVKJGZFlBhvfatihhrJjs7hZdg76FpNqhDLyBHGw4dXOkZ5rSyWiDhwvyzpPqBMTCpebTbRfLpfdCFJ9ewYUo64Sw94O7dRxvJw7eVm5EXHplWqsdrmtcB339/e7l33ztUA8d7FYrCnNbApUdp9UHQXj6jApbjnwIKrxYmPC76PjHjwrEDZSvL8yzEPq1do3RD3qNiVENdxjetrqtlNSzOI9WR0rlaSGbsi5qoHOMHQkMNyBTAZKztW5cl36FGKFLC8mZSacLB3v0+uux2SjWAEmaiVsHF+NVEa67H7juCK7XyuVqufYSjPGK2OMh0lxS5CpNsReWkPEz8/P4/j4OBaLRbdMFR5mXiHk9PS0G2HKSiQbacfoI6Ghqg3bK4Wpxqn1m41+VucspsUvZ+btGSny/6y+lUHrc6lxXkrSVR2q63J4eBhPnz6NFy9edNdfVRcryOzcqjryPQLwvEdWVBWRRFxucx6IArWHNXnZ+3FyctLlixHWUJUvX7681C5Z5w7Pi8bdlTh5AX1Wyfv7+zGfz2MymcTBwUGcnp6uLf3Gzx+OQbrj4+OU+EyGtwOT4hZDh3UDatQwiAYLKWNZNyBzHSrYaG+KTHUoGWT1z9yNWTqua/Zbt2XqsCLSPmTElSmUiPq1TJla2GQULNSTTmznMpgQ1W3H51GpRx1tiu2ttq/OFeXq+xC5DtmHSUzdsVoeq3dVvFwnXaCC1wXm9JnyBDkjf61PFTs2Ed4uTIoPHFWPMiJiPp/Hq1ev4uDgYG19RY5rIKahI+d4uoU+4Ojt60NcKRbd1zKu7KrKjDEjGwQBVMcMUYgZGWo8bAzxVnVgks3IDW2cufwULcXNBMFTcGCk+WW9qJuqtMo9nG3b29vrVA/eKaik02ofjavxOxCVmJbLi5cKZ+SH89AJ860BNHyP8nPAo1kjopvfO5vN1kah4hywID8UJ1a6Adgbw8g6SlUHyrh+mBS3CBXxALySDLu6dEIwu3b6DNgYaJ309xhXahXLYVSk1/dbVVtlkMYQIiveShlm4M5Hlo7zrZRGpsBg4EFcIA9+S/zQc23t0/0to66dkSwvrkvWuWld84jLLlgejJadS6VGI/Il7kB0p6enMZ/PO6JE3jwaPBvEVJ2TyfD2YFJ8wGgZ34wYERPE4Aqowul0uuYu5XmJOE6hxgWoiKkv5taCGv2MDDPXXBV743xVeXCdIy6TIvbrCEdOryN5OZ0a2b4J5ppnn2Hsa0+UeXJyEq9eveo+OvqxmpPYB3SueNQyyCJzYVb143QYVZqRYkaWXBcFL1/Ib3/heKbWgVW9eg2UEHd2duLFixdxcnIS3//+92M+n3drnaJOer9wniBQlJu1j3GzMCk+cFQPznK57BYf5lX5mTQ57XK5XFONfYSSuU8VlcqsDGxL+bUIsWoHJRzt4fNcM07T6qUPNUqtDkvL2LWUV8TlNu1za1Z1g5HGgtVqnDlPbush5w9SzBTcpp4HeC2QB+ebkRPHISeTydoLspnc2E0asU6kTORMmFB5fJy2oU754PPXWCXAHZC+e8S4OZgUtxAwSkdHRxER8bGPfSz29/e7XiiC/SAErFXK8aasZ18ZbiXaiH4l2HKjVlBl2Dp/fCsRskGBQtT0QwevVPXIXJ5DlF5L9fTFEbNpA5mKx/nN5/PuvX4w4tUcvRaUVCKicx+q0uL0Wf6tDt5k8tFLs5X0tM2wn+dJghT57S7wljAZKjnqtA8leXhaoPB4QQv+jX18rTISbrV5n1vYuD6YFLcAWdwEAyiw0DcGI0Rc9LqzgTJZ775y641FRiJ9hFgpxExRaX0zQuwzLqo8qroNJfKs/i2i3gStNsrA7cAGXAkma5u+dgEyBa4eCq2/bq/qr+5k/FeXK5epk/oxRYNJM+Ij5ayqDp1MbMOHl37TGLeSrLYtD3ZDG0wmk7WBPBlMhjcPk+IWAqQ4m81iNpvFcrns3pnII97whvCIPJaD7fyt+/rcfUCmDlrEkimfjATVzat17XOPZufEeWfngN9K8lmvP2ubinBax2TpqnplvxXs5mNXn7pOh7h1s4n67Jpk93ym7rSuQ5S6EmF23zL54JlAuYgtQukdHh52bmXUFeeWjYbl0bs6B5jnOGYegqy9ON/qHrD79HZgUnzgqIiA4xro1QI6BxEPNyYi86jTPhLZBBmhKKopH2yo1cBmeWxSt5Y7uJoGMtQNhvPIXLxDjlNUUwuy84LLHMqHiZCVS9amVaelAl9jPldWpdX1Y9czk3HW3qoQsY3bAwN1ptNp1wZQhJj4jzmQmLrErmY+F0wNwQfkx+8V1Q/qzp1IrSNcsKhLRFxSp8btwKS4hWDjw/MPeYUaNlZ4qGE0sqkYGUFWZDRErQx9yFWhVOUPUawZmbYIJKtni3iGoNXBGJunGtY+4LwzdagTz9m123fOmVs0a0ONMaqSzK5JpqpZaWVuWCVkdPz29/e7+xuqESOreWoGfutC4kyIWClH4/Bad1aRGcnjw9cjG2kLWCXePEyKWwBVi6wM0QPFMm14ACOim9SPh5cfbF6OauiDyL3/imw0bQs6SGVI+XosgwdLVG48/q2qFN+qGitkZFH97muP1nXQumckBjLUuBb2KSkOdY1XnQY+p6w9eV6sKiyAFaOO2MzatbqG7ELFwBfU7eDgYK0TycfD5Yq6cJxwZ2cnptNpPHnypHuuWDGibjrClX8rUD6v3JO1v4nxZmFS3BKwcdX3zfFbLiaTSTccXx9inZJRlVGVz4SY9fj5m4/pQ9/0DyXPym2aufNaRj2rdx8h9tUVZfdNxOe0+l/VU5/axbGsVOB2BDnxPTD0umhHoVI3mgbfVadH7x+eS5hBOztaJtcT3hMcx2uiakxV7yfODx3P2Wy2Vr9seUQ9Dtv0fECE/PJjPieT4e3ApPiAUbkzQYR4aKEOMdgGhImRbgcHB7Gz89Gi31jMWJVUpapUUbRcbS2iHaoar5IGRNCqY4Wst89gBaRQAgMJVaNPM+On1xoGnt19ev58DTFNAsYcnSFMF9BBMEpcrXbJyuN9SjTcOeN5gEoifQOXWsqZpxWdnp7Gy5cv1xQiuysjLkbhcqeF68EftCc8MNzRgIclW3O4z+tSkTAPjjMx3jxMilsAfdjYDcMuU7wqCAME0GvGw8eEoflnv4dM4B+ixjjfzOiNIa8K7PpiozMUqoqqc9hE6WVphv7mumV1xr3B8eWIi3uDXZiaV2bU+zoUVd3UbcsknA2U4W/Ni5Whls31gxsS8zIXi0XXSeRy8Vv/s8tWrzu/bq1Sl+yGVrTcqJym5bkxbgYmxQcOfvBU2cEQTqfT7sNkqC9MZQWJwQOcH5fJxq5Fjpm7sk9FZqooU2ZD1GhmWFUxZu7UjJD7BrZk58p54bfOm6zaWNHqOGj92OBiVKW+tggqERPZ+fr0GeHMXQ6FxPXh88tGE2dovXeQXZR8jyt4hCkTC16Thfsd675iHi/agQcgaXuwusZ/HpyjebTued6HtLhOugCBVeLtwKS4ZagMMIwJFKG6qpAGqrLPTcXGsEqjRlOPrdK33EytMrk8nA/vY2RD+avz4DzG9NhbJJd1ZIbUocorI0MldHaXqssSqocHWg09V74emccCv5UAMjAJVe2iA4LU7cvlaTugAwDFzGqPP9rR47qDcLHoNz8vLe8An2MLmdvWSvH2YFJ84MgMElxjeHgxIRnDyLUHzbGQzGXG+au7i40SP7yZQap+Z/83if2xMQH5Axqvyc6ryjMiusEPVX2VqFuLIHA7cgel6lhwHiAs7uTwKihqUFF3HM8KMeKyG7BPLWJ/1qFQ1ZYprIh88jrngWsPpaltpF4K5JupcLQBpl5wJ2C5XMbx8fHayFMdbIPzwfOElxjjuZpMJt2i33g9Fc6RV9HJOnBZO7AS1jmSJsbbgUlxC5CpMB4FBwXAhkWD+VmelVsvI03NL1OilUsxS8fl9BFopiA0vRK5nl+lhtl1BRVd9fS1w6D7MjWiRlzrnSkPqBy0GU/yVmXO5MLl8AATdvdpG2bu5Gxfq0OFtOpOBKnrvVKpaq4zAELPrgOTvF43EDh3ELL643gmaOxDfF6XyuNz0Y5gi9y0E9inKI2bgUnxAYNJBv8BGBc8uIvFYm20H0PXhczcXGz01AhHXB7Kn7nwFH0u1Faalophw8t5aN59KlHroCTUOka36Ut7lRSreZXaBlwXxLJ4YBVfH4wmZhJlIshUKivBrJ2ztmS3IpORtom2vZ4fYtoYHMbHg8R4FC3StBQ8Rpzy6OqIiFevXjUXLkf7Y8oFpm7g/Hh0K6tPzk/rxOpd68ptgakiqD+HQKwWbx4mxQeOlutNt1eDEgA1/pnBZkOov3ku5JDpEUrqnD/PoRt6bHbO2fYhKjFLx98VKetvVkiZQs2mZTDJZHPw2Djqtck6PWxwOY26CjMVn+3ncrP7oWpfJR0lIBBCde01vQ7kQRncWUOZcLHiHM7OztZUYqXY8RvLHyIPVX19Hayh0I4IT8ewC/V2YFLcAqgB4niTGjI2qDAs/OooGCbsZ+D4bNAE6sDlVm5ONY4RuUs0m7/G9cA2dc+qUuQyMqKr2pN/a3tmLr4qH1aKmVpU4uHrw+2E1x/pKGJ+T2ZWD8SQOX6oHSQmmIz4obBQhsYgcR+dnJysDerJiEIJHR0HXFcousPDwzU1eHZ2tlaWTifi88mmRGBaEsrgWKxeG5TLSyAulxeviDo/P++We0N9sg4JylUFjXozueMZhFJmlch1NG4WJsUtQWVYMzeUKg19WNWVCkLIXKxDyIG345sH0nA9NU8+js8rUxPVsZUrmNO1yA5GV8trGSgtKyPEPnWhahhGVNu+lY+mydybnH+mjKuymBS5vEoNYn82epTJVn8jHx2Ag7w0Dpq1bRaP1Hq1roUSmI7QrvLU/LN8Of/JZNItSt53rHEzMCk+cFQGHgsg83qmGB2HOAmOQw98sfhohZTZbBYR0fWGDw4OuoWQs/hIZoQyg8DbMhdfS4mp8aimVLRimZnLUVHlmanbljrD/hYRZsosI0LUAdcURhkKMBtRq22g21iZcdury1w7MeoF4MEzSrqVp0BHemrZXE8lTKzEwx083Mvcfpw3n+98Pu/OeW9vb22Opl4LrfvOzs7aKFTOlxcFB6Bes5ilnl9EdCtQHRwcRMRHcc+Iy++8NG4WJsUtgqom3acPpxpNHM8DM3gCMZNgn3sUx2dD3bUuSnit3nqrR6+GlcH5tkgxM+jZSN2K/JUk9bwrQlRizLZl10vz5nrgd6U6mLR1yoqiUkW6vUWKiuwa8rZsviy7SVudjOy8lstltzAF7m3tBGT3dHbPZaED3T4GXCcm1lZH0rgZmBS3BPzw8ILgEbEWcwLx4N2JeF0UE8prr73WKTHEOKA6OW6Sxb5UOcA4aXxEY1IgYq43l8EKRQ195ipW46bxI3xXxq1VvhrqzPirStRys/KUDJE3u+3QdnqOlZLVc+A4JMcjq5gpuzMnk8maUmWXOvKFeuW6A7w4BLebzm/FPaSjTFEul60xUl12DXFNfOOefvLkyVq7wouSdWo4b3Qm0Q64r7Vcvs56Dbg9sA0eGh7Ryh0qVZ3GzcGkuGVgY6wEoiv460Os8SZdTYPz1hFxfCyrUJ6KwMZeSRHltdxLXPfsPx/TUiGbkCIPhsjKzxRklWfrHKrzYRci/+ZysE9H53Kd1FVYERgbZpw/d5yQN0/I1/pwPkBfHFjPP2tDJWquK+5Nde+ibhwT5DpkSlnz1In57HpFem1T5N0a9a2hA24D/W3cPEyKW4DMaMzn804NcswCQXwdLYdeL5avWi6X3cg6NhjYD6AMVhZsKHTkHT7cc2e3ERtEddsCTPiKaru2V2aYM9XHxlXdmJoHb8uMY1U/rg8TEY5DvErnF+poR1YxXCfOF8YdeanxVcJlDwCMO5TW2dlZvHjxoiuTSSJTNCAlAPemxi+5TTN3sl43/OZRqdxBY+hSdlBn0+m0Gz3L5eE/8odXY29vL169etUpTNzT3AY8olvrjW8OT6BuyIM7tY4n3h5MilsCNay8XWOJcIXCOLIBAfGpgsrUXsSFKw//kR/ABipiveeuAy2wPyJXjXq++r9SFlU7AZXhbZXbSsdlVCo3q2NLLXIHInOv4X/VbrgO7Abk47WeSorYz29YYVUGt6jeD9q22Xbdx+2a3dfshcB2JnPuLOiAG4An9fM0CIQIUJbG9zgfdatyvbN7pwoLIHTA+ep1bd03xvXCpLiFyJQZuzwPDw+7NVAXi0X39vGIj+a0vXr1KhaLRUyn04i4eAsAG1w2sMBkMll7Zx3XhQ0ZiJeV1HK5HtdSVaEupszVlBEl6jAWOg+yUntaV64LjKwadq1vtY9d0zxoBPtVPeggl2wfx9/U5Y26VqSL0a9QNVBGEbH2lgmuv3aQUA7StkhTrxvagOdochomaw0FADhHKDwoX9x7s9ksFov1Uara7jg39qzoexXRTtk9xPcRVKpeCyZbddMaNwuT4pZBDQyMEIxMRiowbHAl8QRluMbY0GSkizL14cV+uEbxgLNC1SkBPOSeXUhsZDJVpUajcstpjz/r1bNi1WMz5aHb0OZwV/P5ZsSoYJJlhcjlcYeE02XtogY3GxySXUdtB53eox0lJvOsbfnclfS0Ltr26tpVlzUTYkYirDwxChXnyIsOcH6YrhQRl0hYvSD6G9M+svsL9dH7UJ+vityNm4NJcUugD466tuDe0p51RMTJyUlERPfOPcRW1I2q6g7lsKFqxYcionvVDo8G5cE8PH+SDS5+ZyTF55y1i5IJ1z8jRzW4HE/SY/hYzhttieXBMmVTESzKU6LjjgMbTCaLjDyVFFkxZuTP6XmkJ9Ticrns3kuoI0q5vRiq4oaAry23iQ4o4hh3pRLVDa2EDzcqvCY4ljuMZ2dncXx83O3DWsJcXy6bY7t6n/H8Sr3HeWSwdkCNm4dJcQuRGcGIi8A+JkHjP46BcYHhQ2+a89VeOL6VFLUXDcCQ8OhV7lHDnaVkhrRANXm/Uj6ZetW8ldjU3aV1yMrl9oi4iMGxy5DTZfEnjZnxOXMdua6sHLNrBjAh6GjRzJWp9xLIcD6fd2WpGz1DKz6sbcgdLt2fkVA2Qjq7xlmb45s7fNjO+eBlxBEXS87pPawLIej5MLijpfeyyfBuYVLcAqjBZ9cnP5wc54Dri/8fHR11MUZ2KSFfGCFeSUXjXniYdf4WPtmoU6gpVTxq6HhaREWIakj0t7YX/1dS1DgmymUlhLRaH33HIa9nqWVnhIw81PAyMoWR3Q/8jWuUxRS1vbkM/H758mVHipPJpIuHwc2ohKztm7l3s2uSgVeRQXtBlQN6XgDfp6q8QXqLxSKePHmyRlQ4N87n7OwsXr16dYkEs2uUnRen3dvb654zfo70uTEx3h5MilsCfmjwIlR2wamhA+nt7e11E5lBSHCnAlnPVRUjD0vHJG4e3crHqwHhWAp6/hjcAxIE2BjyNpwX2iL7tNqM8waY9DNXrtZHSQXKF8YvItamxHDePM9PXZxc34xUMrdkpY71+vW5MjWeOZ/PO6JgLwNfL1Vi2jZZm7NbuKXG+bzYq6Av9K06QNyOWpbO08SzMJlM1ly08/m8284xSC2Ll59TZcrb0JnBPcL3mV5vk+PNw6S4RWBjwfMU8YDzO+GY/LCeJEiMFSLyY7cbf3SoP94+MJ/Pu9F5MJoRFwNQ8MYHJpxqgA2P4NQRffgdsb6mJr6RB6My0JqGiVvrB2T1QXtERPc+SyZFdByUFJnUuX053xZhqFJk4m51alodBlXm6OSAJPh+wrXSNuSly7jjkXWKWFXqtVJSxDnwexg5LZ8Hn5d2fvg89RriGWElf3p6utZx1JclIz3qlQ0Y4uvI6lXn6ur5GDcPk+IWAsoE5AjSA9lhQfCI6FxhPNyeJ3Zz4B8kx3FBJkOUhwEJrBQ1xhYRaxOXmWiVkHSUZdbrxj4mLRzHxpbT97Vh1qvn/5nblOt5enoaR0dHa/EqNnhwHaNddUCOLo2n9eBtTCpMrtw2uj+LW3HcUttUXecR64NNeDSrTmdQ0svOR9tS43PckeBz5PuD26zqvOA4vZZQ9tw2+M3qHc+AumS184j7P4uncptyp07bcMi9alwvTIpbCBhczCM7ODjoJiVzcB8P9Hw+j+VyGQcHB922jBSZGNlwQ12enp7G8fHxGilmw9jRs8aaqhkpsnpko58pRICNGBvLzHWrx1cGWlVoxPp0CT6et+H7+Pi4G4mK7Wz0QIownhrz03mg6vZjF56SjrZN5l5Wtchl6LHL5foKLxGXF2vgN66wcotYJ82s3fka8T7+r9dQyV47EpwmG6jE74mMyF82zJ4QkOLp6emleCKTJ1/D1vVQUqw6DVaLtweT4paAjRrihSAfGCqsZAOABEFAOAYkyi6k09PTNZeokuF8Pl8jRSwMoMYWdWX3Iht1JT5Vf63es5KifleuOVWmvI2NlroU+5Qi6hIR3cAUKAomwZOTk05xoW2Wy4v4bAVcS54DyshGQmaEqG3G7YJ7iGOJvF3Lxn9Vdly2Iqsjvnk7d5gmk0m3uEREdPdpRSx83/M+fE+n07W3w8D1qfcnL7WH88UxqrpBoBn4HtYYNdBybxs3B5PiFoENOYwUj+xkt2lEdCQJlxjmJfIkZrjMQHwcd0TscD6fd2QIclTjxAqVY2o8X05ji5mCUGPE0F5+RYraXmzceR/y1AndbOj7SBHQt8ajjSKi62Rwx4DVutYLbcFTBLQuaKsx0xVUDTM5MOEoKWZtgHaq2qfVXhk54puvl4465QUS0AYRFzE/hnaIoOb5nmAlyQuAq4LnjgCfB5Sl1keh7Wh1eLcwKW4BmDTwcPNLT9nY8kOK76dPn0ZEXHLVcUxvPp/Hy5cvO4KEujk+Pu7UIvei1RiiLmdnZx1Rs9sUJMmLi7OhYUOhxjZLn5GiKlc+Ty6HFSK7xSLWF7FWN6NeD+TPpM9lsfuZ41E6ChWfrCPA56KqXI18pr7VXcvXC2QYEWskjleO8WotWbuqgmSy4fPRDlTmqs7IlQeyID6r9x4P6OJ8Mhft7u5uHB4erp0viPHZs2exXC7j+9//fpeHEh63JS8wwOD/OBbPg7qXqw6McbMwKW4R1ACqolEji4cNbqijo6NL+UVc9MLZRQrlCHKEca8UGasZ/mTGXw1DRT7YnymK7JNNhtd8M+KpyJLbKDNmfP4gxez1XdyR0HZBm2l9uGxF5i7ENh6hmpUJoNPCCpn3o5OTnT/XjRWjxofxP5u7CfC5c16sWjP3N59zn+uUj+f3JCLNzs5O1wnQa8uehMorkV0nTqPHZ0rRxHh7MCluEdBLZdcSpmJwjxuDBBAvwUg6AEbh1atXndF++fJlfP/73+9cpVCMPJgmMwgwajDIKAeu2tPT024fT45X5RZx2ZBlRMrfTCpZGjXYGIxUdS6YFBn6PzOKFYFVqlYVr5bD6jkz8OyqRVr8zxQi0jFxqXpEvmdnZ3F0dHTp1U/Ih4koczXjG2Q41Phr5wTXjcmKnwEeEMVKnPPCS7ZxHLu5mWihHHGPcp0zrwE6gtnAH24X3HfL5cUgJh2RbNwuTIpbAjUqGuvhHjobQXZdcl5w7YFEj4+PO0JE/JBjLGrAOS8lqkwJjVWIOI6/K9dTRjbcJkzG7E7NVIjWqyI7Jk91XWbtUxGiKlxdREDzz9odxrxql6xt+DyWy+UltcVvxdDry6TC58wEWqnB7Hpz/ln8LrtGvAh+Roqs8Lhd8Czo9cWxOvIY56yvnOJ9QHavcFuhDjxdKrsmxs3CpLhFmEwmXS/z1atXMZvNuuXFGCBCqDVWiuhlYzj96elpvHjxolOKPE9RyU0NHRRnxIW7jZc7wzcGMbCbNRsJqr8zAuHtWq/KyLCBVIPOBks7Dtr2vF2NauVm1fqqIeXtnA/qze3Bo1G1EwRFwrFfnRep5KVlwo0YcaHyjo+P164rypvP52uvZMoIKGtH7ONjtD1QH9wzOseT1THOl5UeX09WajwALCM/7ihl58EdmMylm90zVUex6qQYNw+T4hYiU2y6n9OxIeBtPFGZ3x3Hq9ggn4xo1NjydrjZVLFkriiUURGLGpBsNKbWDahUQcT6mzkqMuN8sn1jSFFXpFGy1LLY8PJ5tEhW44ct1zKXBXckLxbPddZz5OkYWeyW65khayftlKADV8Vk0enS+bJZxwKhBFzvIYsPZJ0yPafKk9DXDlk5xu3ApLgFYIMGsjk8PFzr8cNIwM3Dc6iyOWechskRcRKUVxkDpIXRxMhATP9APlCKMKLYzwYp4vJEewYMIeqkZJApy6ztWAlw/BBt0+r1c9mKatRopQiVmPQb+fGCAJn7VEmWY4VanrYNvrFE3dOnT7tRp7g+nAcrJNxDqDMPyuH2zNymyJNdrZyGO234vbOzs9Z544E77PbER2Ovi8Wii+chP0xX2t/fX5uwz+TKnTkeQcydwRah4VlEvbkts+k4xu3ApLhFGPIQqWrjXjUbVzz43MtWo9kiRTYOnF/Wo+ePqhiGxvW4rKpOes5VrzyblsGfDBkBZkpRY7ZZWj337FxU4bKq5m9Vt6p8s/yr9sH56xSfbMAJ0mu5mbJttUNVP4674f7hlWa4o6V5a/uri5nj49iOTgcWBsg8H31t2fLY8LPG16UaxW3cDkyKWwQ8XBgY89prr8V0Ou0MABvnbDAEVNtsNuv+Y/1OTNzPesH88OM/YjmA1gGjYpGelSLSqzprkUtGrNXIvcz4sirktsFHDVxmGLUuaGfOA9s0bRWX5evKZWVxTi5Pz129BnoN+ThtC75+HKNUcsquB6fHR+9DPXd84/5RUkUdeYk5TA3StuT2QdmY07izs9Ot0vTy5csuL4zI3t3d7eLnPNK6gt5zLWKbTCZrrmi0EUg9G5lr3A5MilsCNgB4OKteLaDKg7+RVmNQ2F4pGJTdmoisxi8jqCz+o6jieFmvPWsDLk9HnCL/ym1bqQX9zaTCaZloM0XRIszMLQwia4GVVkXe7HJllciExvdDNhpU2yYjdC5Tj81iyq22ZqVYnZeSP49O5ZhkVram0bbkzssYAmP3LV8/E+HdwqS4RcDDjgcVvc2Dg4O1dR0jLg/uiLh4gwWrxKxnnKkyfHRhawDqoHKfarwMPenMfdlSXPpfDYwSiZavnQOO3+mIRP7d6nyo6tI6aFvpKi7qpsR+VhM6elbPNzO4TMxY+QWxMeQ3nU5jNpvFwcFBRESnqKC0ZrPZWgya68RtiHuNiYk9FYysg8btBwKB0oJKZKVYdWym02ns7+/HkydPIiK6EdVKeIhzI0/kmylBJjc+bzyLjD53Kk/tqO5h42ZhUtxCsCHIVvMAMqWE4zlmVCF7wFvHZA94pfJ0RZVMFWo+2f9MhWk8LKsPl8ftWZFfn7JhsuftqsTZnajnoWWxsmEiaKlGNuQoDyqQ7xXcExrvqjok3KnJ2oLPQdNkbaNtznXGNj5frgeffzZ6lDsOWadLVSC3u14btBNfB+089V2Lqp2Mu4FJcYugRgu9fDykvEoHP8Qcm2G1qUYB7jOUxWWywchct1nPV8vIFBPIkeuUEWmrTmpUuTyud2aIWAVko0jVgCl5alxO13bltspUHddXzzNTn5XBV4LhKRYRsbYgNp8bYmpPnz5dyw/rdR4dHXWuS4zazGKg2uZM/iiXR0Hrsdk1ibgYBFOtu6ujTlFXlKOr77B6ZWXIrtaqbvocZM8AzjciunpzmrEuWOP6YVLcQrBhgTHPDF6lQli9qIqpXF4tYuF8lah4P/JRN2ulnvTYjGyzumkcrE/tZYSl5QGVSuDzwDE8DL+lrpS89RqgfhlptvaxIuIpDgx2JYJU8K3GX92W2fXR8+P688INTOK8yox22qpOFteft4PY1JWb1bu6DvrBc5HdlxoWwG+dcoH7obqXjduDSXGLkPV0F4tFN9pODTQvehxx8RBjpKmSCve6M5da5k7lfTw5WuOKnBfHH2Eo+DVB6tLT78xY83mrwcnm82Vty0ZUVRynQV4w4NwWiF9Va3FmeWdKMXMP8n/uSLDBVwKHwkI7Y54o6oKFsBFbm8/n3bFMCFkcj/9X5M/XgImK9+N+4FGjvI/zUKXFeeL8JpNJt+IOOoxQvlwutxkP5sF9qe2vip/js0yKvIIOx2CR3iR4tzApbhHYSDBgVEAsMJZ4I4DGo1rkgO2Voc3SRlyewF7FzXgbK0V1Yykpcn4VKWZlcHux8dRzV/dpRsZ83HJ5+VVPXJ/W5GzOgzsOSnxQ/6qSquum7c2kqVNt+AXVSMP589qn3IECCYAsdVQv54Hz4RWIlLT5G2lA3KrW+BVMWbuDeDimCUJE56WaClEp0ayjxe3fdy2ya6PlmyRvFybFLQJ6/GzgYETOzs7itdde6wygPuRsvIAWUbJiZGPH6DMOmTFnYmLSgrFFfXA8f2furSx9Fu/SercIizsFVR5Zx4TrqK9Liri83qu6+wAoHKh/voYoiz0G2TcTItx5GlueTCbduzK5/hHRKUYQJ4gK9eB4nc75xHlxfbkTgU4c3mChc1V5lCmXsbe315G1KmzUm1dUwoo1UMxYFSe79nwtslij3hPcttz2fH9Uqlm38bdx8zApbhHYEERcvDYKBgAv+K0URtb7rsBuM4W6WDn9mPPQOI0qPjUYMFTVBGpVWlnPPyM8PbesrpUh1bRaHrczE0VG7qpEuaPAddUYrA68YQLSN50gDdyUHOdD22buTW4HPe9MTbXaG8eze5/rzkvIMdnivHiwD3sb1AXLihYEfHR0dOl6qjKsOkzVM4P7GB0FdDLUI6DPr3E3MCluEWCgWCmCEM/PL9Yx1fgijIa6tVpExu4xHMdTHdg4VHlkRl8NO3/z9uw4JXbNk1WoHsfI3J48alTdefytBj/r+bP7l9NqPkw2mj/PR0WdMtepup6RFm2BNT3RFug0QUHN5/NL15QJjolHFT9ib+iEMQkgnyw+iP8nJydr5wGyQ1yQ3bt87dARRPlMnPv7+92IbKhRlHlychIvXrxIO4uoE5+zPiv8jd/8XOA5ZHczd2CqkIJJ8nZhUtwi8EPLBgZGEWSocwAj1mNGSk5qaFkFsvtN96vrCGm195+5AFUtqppQQsvqrvv5XLW91OBru7JxV8WWGTB1wbL7DdsrRcr15+uo1wLKR8+Jr42eixKWLtCAPFE22hSvF1MvA0+DyAw4k3IGvne0Hgpegg5tCtcnrg+WNcR9xm2h7bOzs9MtSoCwAxQj8s/OSTtSfI8qSfJ9CaXIpMjX2rgfMCluGdjgcQ8U2yLW3VJqQDIjlhGjbmcXERsdQNVCVm/UgY1MZuAzN2qmwLLefkauXN+WyzgjJyU9QKd9aGywIiuuF6dF2zJ0JKYSQEaKCq43DzpRlyHuHZAGv/UEabidqk5Xdu6ZambCRzuoKtROw+npaec+1fgz8tXrhLfJHB8fx2KxiNlslnYKUJ52urL687OA+uO+zjqfSrxZR8ukeXswKW4h4EJaLBZdD5/dnRiYoIY6W5YK+WWEwMZLyUXzYpXBRJyppYrMMqOkaiwzJpVS4XyYgDN3L9S1diiqHj/yhNHGIJYsBqt1UoWrefL1Y4Wi6bJ8Aa23pueOFN8j6DjBtalla/5aBwbHBgH1HvB9oHXOrhfqxZPydQoQuzCPj48jIrpF7/E2DK1Lhex54c4J7qtsDibXJ/NoGHcDk+IWQklxb29vbe1THiqfxUeUAKEcsrlp7CKFa4iPRXouXxUmp+Ny1LhnJJjtUzJkcuXz4//s2q2IK3ONVfmy0lC3KUMVJ+pbue64zZQUVKlq3TPVA+hygKgzz51jlcMjRLX+2lZcJism9mboubHHQqdJqLsb/0HW5+fn3ZtetAPBc2DPz8+7mOmrV6+60aeaf0aKWeeMryUfo+o0y4PbrFLUxu3ApLhFaKkBNmYwNnCFMZFkJMnD65EvG2KQMFQUGxVgf3+/+0Z+OuAHZVVGJDvPjCRZWbTUGZ8H8qqgrtw+NzDckKwUeT+gLluud+ZSQ72VFKq6cLtU+bMxx6AbbiMmIh78EhHdFAYdlVrVJeLyCFaOuem56L3JeSlx4T9P8cDLgvll2Xt7e/HkyZO1kbcffvhhnJycxHw+XyuL4524duo+r0iMn5/lchnHx8eXlnXL2of/Vx0j4+ZgUtxi4OHGyEl2Iemyb3ocg5Uif6tiBDHyYAwYfX79kLpQua4R+Ui+lutPDaa6UXlfRkARlxeyzgij5WJlg4wOBNKrgmXDqh0MlJUZW3xX8xCBaqoMn3O1H+Sti4Nr/XmyO5Nm3z2VEQu7gTUt/87c4NlI4YjowgbYBlLEoJyjo6O1Y16+fNnN563KapFTto9VMZSvvgC51fkxGd4NTIpbBqgSXsYq4vIb79FrXi6Xa5PA1cCxy4tf5aQuPihQji+yoeNFn3UwBOrXMuY4N6St/rNBy4yO9vKxjf9X5MgGvUU6y+VyjTQ4poQ0TAzZvMqWYeRzzM6HOxdcNl9Pvs5cJ3SA9vf311zcrBArRcdt2YKmZYWvbZspREA7FZpmMvloOTet73K5jA8//DCOjo7i8PCwI0W4irku7DHh+J+6w7VctJMOhNI89N7V8zAx3j5MilsIEB6TIkMJUg1LxOWHktUBG3VVW+q6BFgdsvuUy9B69BGPfjKlNdSoQD3j3FgVZehTRMhDVw/Kjs1cZ1V6JjOcL18XfHP5PFcvK4MNPytFVaJKukOQjUau/mvHLGsTQN2v2X2eKfOIj56Po6OjtXcXYr3f7L7Hf503mtWTz4vPqfJecFn627gbmBS3EHiw9vf3u3lYQObO4+NaBonVhJKkHqtTD2BcMI2AX2iM9GycOV89rz4SrAxLn4rh/Ctjp+SidVYFXY061TmcXIeKdDKXLUbEcvnIR0mS66edIXRWMJn/5OSk8w5ErM8RzNqxalsmvGqloWy/5qGdBCY71K+qg5IUvvla8XzN7B7CdckGTGm57FnR0aate7Vynxu3C5PiFkEfpEyNDTlOjU+m+DIyQJnL5XJNBbAB0yW2tNxMzfB/7WFXRkbVwxC3Xp8CQp5cz4zEmYyqeYlVbDKLl7JRzVyBGpvlTkNGuhmJ8mhkLks7Ptm56HWs3NNch9b+LH1FRH0qq3J186AxdmVq3bjTxopPy8iIER3HivyM+wmT4hZCyQNGk2MZ+J9NMufedJ8C45ghyub/agB0gE+loLBP1aH+r/LROrLhGqMY9Ty5HCVw7N/Z2bm0iLeSHL6rAR1ZHTDNRle5QZ24A8Tz4nTQDBM12gVvj9Bz5bR9CraPqCry1o4B58fH6wAjpMnyxP9sdC//1hghdwo4HfZpp4HrAeAe5xGvPPoYx2f5tpSkcTswKW4ZMoOaxVuqYysXZmUUMyPF6kYNpg5eyHrYXHblUhra2+5TIRXUGGVKr4p/ZgNr8D3EyLVUGfLX9mMFqd/8LsrsOuJ6tZRwS91Vqpf3ATooprqOLY9BK87bajd2NfM5c3wwi/WxQtTrzHmr6uZBO1ls2bifMCluIRaLi5VIFotFN3mf56CpoVaDEJGv35iRRdYTb5FPNZ0gy7sanaf5Zf8rYm6h5Y7jc9XOg9YRK6XwK5W4fkPUanY9oC6rds9UBs+106Xm2B2uhh0KksExQv7mcvQ8uF4aV9PjUI+snXiQVkT+Fg3tdGG+KHcQQVLn5+dr8ywr0uLnIyPxKl6qLw3W88/y6+sQGTcPk+KWAg+duto0TaaAgGxEaqYY1IXVArv7eLh6Vj6XmRkKNoAt9ZD9Rl5jlaOSZuUyhPHLiKIaLdkqF7+1PZgAtOOiKj8z7KoQsU3LzK7LmPojj0rNoS3xnd0nqBvc06hbyy2O9NV9rm2iMV3UqeoUZuUx2WdlczlZW+lv43ZhUtwiqDHEBGasJqNp2eDxhHygb1ALo3KLajpWbWyMW+SXGYiM7NR4VoTY1xPv294iU520XymrzE2H/XodASy+nZ2fTgxHGq4HDybRc0FMESSibk58dECO1rt1/TKCyjo1Svo8iGuxWFyaPsFxbbQLLxrBz8B8Pl+Ln/JSfPifoVJ2XL7G4Jlw+xRgdc1NjrcPk+IWA4YQLxeulhTD7+oBHKIcVPWxeuE0aowzVZPln5XVh00MSt8xmWHLlF+m6IaUkSk9PQ7tpvP8VAEy4bHayQhZt/FKRdV5V/Wv8taRs1XHgu8N1EVdlExQ3EHgaTMt97kqwtY919cZVJLXdtCysvzHbDduFibFLQM/vCBEvBbn4OCgi7FkSgCjUlXVqAspI9JMPakh5WN1zp+6ejMyUWJRddI3+EbfPdhqQ/7Wc81IhefJLZcX8xOr9lGDV6kHbTvehmuobykZg2wUJQgICkvbmdOxctT20vihqmYeANQiLD6v6o0STICIoeOlxDq6MyIurUHK955ClV8Fvg48iCdTi5w3l6HbjduHSXGLwQ9ztiwYwO7TSoVlD3P2ILOyyPbrouItZZm5Y3lfVTcmJyajbEi/5t+niPpia5ny7VOJrTyqPIfm3Qd2TwLsRs0WVcBxrMB0XmpWjv5WRafnWsXdmGCyDhzco1jVCduzTgZ+Z4TFZWbEr+ej10M7BVpOX+fLuBuYFLcQ2jsHGWDkHpSiPsB4iCu3E/eWK+OsCq56wNm9x/mymsD+IW42nAO2R6zHh3DuTNyaF3+3fmt63sdxP0CXxxujCvjc1DjrQJRKVWcuvaqTAZJEPXVFHn4nJrwOWCQC9eP4JitoVrbZiFfUsVJX2WCYrIMXER0xZgu044MRqEpUmceD21w7UrhHUTZWa+J3M/apTL0+Jse7g0lxi6EPO95gwcYJ+/g7Ih8lmuUNQ8FKQWOFDB3M01paa+g5ZiSsBkgH9fB5VoQ3pGzNj0lAXZEZObXy5rrrPt6WXSftqFT5axqNxSkBgWRQLkhQY4/sXuVyVB22CE47XlnsFND8cDymxLDixX8m3qxttH213bM6q5cii13quWkZxt3CpLilUKO6WCzWRifu7u6ujUplhZbF+zhfNizYxu7ZIdBl3lrGkQ1slj8rw8w9hWOgHngenJZXGaW+Xj7PBdT245G9mautKrOvLqpauMMx9DqgfsiPr0tGGHpOeBtL9jorfgentjN3nliFcp2gUjk+m3VqsnNmEr8qmOA0Px3Yg+uAV1RlZNciXS7PavFuYFLcQlSuPXYbZWpO43yKTJFVirACE22mRCrSaxn5PmOC4zP1GHF5abkhqNqYz43TtlQ356fKtVWfrO1ZOQ8hRm0XrWelMpXU1WOQlcNouXz13qxU11WgHaa+tFU8kTsSvI0JP/NGIF1fjNq4fZgUHwFY2WkMT1UMP5RsuDIDmJFQZWQyhZQN8FCXbuucAFVOmZuNDZe6cFUJDymzpeIyAtayMxflWKjKztpeSWcIGWi8N1N5XCbfS5mK00E4qiD7OiW6LqmWkbloK4Vbbc/aCODnJbuufE0nk8naOzSZFPsU4NgOpnEzMCluKVqKLuuFM3QEqObbKqdVH+SXqRn95joykfOwdzWmbLwUfOxQ4qu26+8qboRz0Tgjn9cQJcj/WwpdSTIif7cfflcKBoYdbtGIiwE2SM9TI1AOtwWACfdKsEq0fD4gFH1Bc0UaWT367tsKWXtqJxDb+JpyXJUJdGiHK6uHyfFuYFJ8BNCHW92oHGeDwcrWwdSBKurW4rIqqHJRl2nmvuX/1bJpGSGqouD6qvqtysu2ZypV24SNM0hGY26Zu7hl0FuGMjtvvjaZm7IPGh8FiTO5tZQvSFXPFddwb29vLU9W+FiXlN2WQ7wSQ1R3a/Sx3hOVF6I6Dtc6m/fYUoiVijQx3j5MiluOPpeMEhv3+GEQ2c2qveZMnQyBkgY+2UtcUU91k2UDQ/icFH0xUy0v+18ZxcyIsoFVN2m28kqr7n37Wmkzos2UIqfTtqpc0pyX7tdyq9gb13e5XF5agUf3D1X6mbs4U+ctF3ZWvnYWuY0wDaQVK6zik5rehHg3MCluIdR46EObpQWgtnjSc2ZIMjUy9CGuDJMer3WFEdX4IPLE/6oeGv9qIVODvD3rbGRExO2XxTGrEbVVfdSNl6VT452poJZS1uuTuTu1LXH9dG4okwbScWyR444oE27TTDVl26r2085G5j5Gm2btyGVmSlFj8hqz1/wqd3VWnnF3MCluOSqlyA8fu8nw8PIDzoar5eYZo2LYILEaZQXFxMFgUqsMohrLzLhVxFjFJJEPf+u2jOy57YDsHIeo7T7FlMVkq3w5BqbbcJzeA1oOE3728mM+L25z7rxoPXjCe19bVNuVgDJi78uL21nrxNePnx0QekW06o7NOq9ctnH7MCluKfrUBKCGC8abe8CctupBb/IQq7Lg+W76tnitW6ZusnNn46jGsm+QyxCVonVUZNuVaDLlrHXR7yHt3afiMwXMbQ1XoLq6tQykw/FMmFwWd7YiLtYfVcWmylbrmNUfdc4IHscqMbbaL2vrzBOQdSIqUmyhz31r3B5Mio8AlarJXEr6cLK7UuM/GsMbawjQs0Y5EesjNRWqHDNiUIOcEZsODqnqpt8tEsyUONepb9I317nKJ+uQZOmHuOi0PFZt7ALMFGy2/B+O4bw1RtxHctW2bH9LzVf/UXeuS1UnLSdz52adyYjLi4trB611Ha0Q7x4mxS1Ey5Dq/ux/xHqPP3Njaq8+Ux0Z2JDyW+D52EqN4fjsXJSENJ+MADJDrXn1EVB1vlpGFo+LWI8pDnHttUih1W6t82Djrm5BJTTtOLFKzPIFKgVXnUuWrko/hkQql7zmpR2+TJ2PdcH3KfzsvjNuHybFLUVFgvzQs7tHwQ87XvGT9WgzxQhURgPHqJsJ/8/Pz9cWmGaD3DJSgLokkQ514TllSK/tw3XKtlcKMssnI1/UsSLmPlRlVteFz0WPhwqqBomgvtnvqv25PE0zdOm1oTHFVucE9dF2HpI3t2NW5ypemo0+5XpwvhnBmwzvFiZFI3WnsYuxZYAzw6dEx4AhaZGiLv+Guqlhywy4Gu/KwAx19fap65YCb+Wng5eyumfHtcrJOgtZ56FFJqwQWT1W1yGrOxNsVucqppe5Y8e4uKu2qVR7y0vA93dFVDr6FPlz7D3Lu+98hqQ1bg4mxS1HZry1V5utc4l4ECuqyn2auZwq8OjPzFiDGDNDxEpGy2qpsb6YzVACqohlbCyVgVV2uB4aa8zKzFApd27vvjrv7e2lrnMgU9hKmlx2Vt8+UsS+VqeG02leQ65vNV9V24jv1yzPVmhB8+VjtL7Zc2rcDUyKW4rKKClB9LlPWTFWrp7MLdcy3Hxsi3SUpNVQ9hndSm1kqq8y1FonTdfqCKgq1HTZAJyIy+9f1PxaZVWEmKXJUJFRRpLcmepTXUPKGKIMh3RgWkSeHZdtZ09GRoraOWjF1E1yDwsmxS1En3EB+N14iqwHrEvBZWptiGpaLtuvmuIYjqrYiiiyfdimRmmMsmsRSdbxqDoYQ9QRj8BtxTz7SI3jXIC6qlsEW11zrUs2UrhPMVV11ryrPKsOXpUn0vG9MWQFG45va1nZQCkd3MXemCFqkjsxVot3C5PilqNyy/QpOqB6uFvuUy2jqo8icxnycRmxZGqyhSHn3TKY6h4cor40TsrIVEw2wrNVtxYxV4o2I5eWd6BvBaDK3VrVNfuv5Dg0z1ZeWd5DRo0qQWXXQ/PSdFrvFhEa9wcmxS1F5T7VNH0GZ2dn59Ji1jwPq3IbqdFVImUlmMXQNH+NAbUMG869Uo1DyFvRMnBq2HgflHg14jJTW+p21TRVBwdlV9e0tY/rzZP2hxBTtlRd3whVbM+uo7pX+9zfmUs9S498q5AB0uD+5G8G1zkb5VzVrerUcQzdJHn3MCk+UrQevmwUqhqvPjKsSGToQ99yAWfuwYh84nlfGVp/3ReRL2rA6VqdizEGLhu8UZE7f2dlZ8Tfintx+Sgza8PK3T1mLVmtY3YdW+3Wd736CLyvnkNcmRWZq6LkbeoGbqlm4+5gUtxS6AOdKcchxiMbhZr1dFv10P/qYmIyg0HW1xZlk/w1HpcR8RAX7tA4aMvwVsazRe4VNI7ad3x1ParOSuXG5fZH21cDTDLjP/Y8K0VfxZuHrg+LvDO03MHqyWgpa7wnUsvk89F7NvOUVPU1Qd4dTIpbCDU0lXFs9ZqrOJPm1+eKZMM+xF3L9Vc3VZZ3NYClVSdsG+NGrerOBDs0ZhiRn081zL8VZ9V6c0eA26fqIPBxXKa2vabTY4coSq1rpej1mmq7VO02hFj63MGs/Ks2U5eo3gNDVXNGunaf3j1MiluOSimCdFqxm4hhy2JlMZc+6GCOyhi3jDWfX6ucFrj+V0mr7dAyvupWy/Yjz8qN2WfUNQ2rk8rwanuzauRz0vKyuKeeS6veQ+LaKD9rtzEKsiJ7VdiIJWb5skrMFppord/L5el/k+H9gElxSzH0IctUpIIJFP/7FFJfmew2VSwWi25Se0YuY8tr1WOo+7ePDCs3Y8RlMqkUZ0spjo1ZViq4dc7ZqEr+nZXVpxKHdDT0mOpcKvIbWgbKUdcv58EKUZWixlz5uLGklj13Jsb7AZPiI0BFfGPIBa91irhsnFpxOwUbH6TRATwcQ6xUSmt04xDD0nKd9rmHlVTYmPW55/pUD6fJjhlSR03DBr7VCeDtrIb6SLnVIQC4IzXGzYxj+bjMbdyCkqDOteTzQBtlSrGKs27aSWsRo8nx7mBS3HJkvVBWfS1kRliNQusBbsXgqtiLKshMpWg87jpcqFo33tdyDyvR9MXWKtWTkVylHPvOKYvbZb8zaHyuL0aWxfc2IYnWMdkoz75j+sqp3LpKkpqmIsasHVqoCNG4e5gUtxjZw8ZxjwrZWqgcX+rr9WfgQQUZ0WRqsVJd2QjIFiolWZ2HdiAqJZyR2tD4WBX7Q5pMLQ9V5K3OSF8dcZ2RJhtl2ULfIBMuf+jgnDHl93kNqpGhuJ6tATZcF+48jFW+yIfvAbtQ7w9Mio8QHBPJUD3kHF/qc59qeWoEQHya3+7ubnNkZlW/IXXoO0ekU0XVpxSz/PsMfGtwiA4GqUikdR5Z/YbGZasRv62BNkPrwxjidkUZVflV2lZ9KpXYp6TVe6H3wSbtkBFjVb5xOzApbjk4RsHxkmrN0woVEbXUDrajTBwDnJ+fx/7+fiwWizg7O+vilpk6yVQFjNjQkZlZ/fU/G8WsznrsULdfNacyI92MCKsRnkOUhRpvLnvsiONKpQ9RiHpctZIN/wfGzoHU4xncjuo5YY9GBjw3VYy2iqdmdao6libEu4VJ8ZFikwdvzMg/Nfo6gADuUSY2GMW+OJVOaRgbW6rUXaUAWwSYxTozt2+lvqrYnboxkcdQd2mGyl2XgctuubLH1qF1TGslmz7vRjZXsXWP6zXNPARZGdqpU4xtB3XVmhDvHibFLYYawcqVNhR9gwyynj33wFXVqGpll2pVPtxWugKLlt9Cy3WKb44vZXlnCoG3MQlW0wC0Ln2Ko29wR4VMnfTdAy2VmHVcxuA6Yodar6ExU4WS4dnZWZlWPRh6P2+iZrUu/G3cDUyKjwBKRGMfOo2lZPm33GmZUtTtQPXaJFUu5+fnzcFCQ9AiOh5w0SKQTHHw/4gLMgeGGO0s9jhk8AYji1sOcfvy/qz9bwJD3a+MIavT4DtzO/OxrQ4Qd/D4pdCbDLDJ6jhGwRs3D5PiI8TYB45dnmqgN3Gf8oOfvQFC177MBp203GyAKshK4WS9/76RiEPVNtQFk0tW16zOON+hRJa5gytXraZvEUFE/5JzV8Wmnour5ovrjfPU80UaKH7uiPW5+YcguwYmxLuFSXHLoe7TTZQi54VpGZkCqQipIkWOKYI4NMZWjb68TtWSuZXVfdqnRrJtMKKodxYz03NrkdgmKqRV7z6yve72rhYTR32yjkMLm9RJ21cHglV5c8yb8+Frg/18/Yecw1WeSeP6YVLcYlQ9zzE90tboRFU9FUFkHyZEJT4Yiixmp0Ypc40NQUVO+K2kqMcNdUnjPPvW5xyiaDZVIa3BPS1UA4VaGDvV5zpckFeZqoEYYnUN+1y0mt9VcNVOq3E9MCk+ImSxiyHHMOBG6ltVporXqZpEnfAiYyCbuD6G/MYsEs110qH5faTY156os76oOTummgeodeub9J6pmNbI2da9MJQUgIrQqjpm+24ydomy+X7UzhrS6Gjiqv6AptNrXXUwMyI0Md4dTIpbjkrZXEe+lYHPDIDWg0kVpLi3t3fJxcgxRx3RqW4q/l/FArM6tWKKarBaCrNVBo+aHTLqs0ViLXdnnxobqnA5zRCX4BDy7HPHqgt5aCdojFLkNoTbtLq+GTGOGXk7hOjGXg/j5mFSfARgg3DVXjjPWcsMRUZ++J8ZHHbFsiuLYzy87BkT5HUpCq4bu24zYt2kR69qpIWMwKo2BXRQTKtuLQKooAsPbJKuNZVnrBrNMHRkbnad+1zf2ino8wqMQXZfmBjvFibFLYa6SzOjvMmcsz6VOLZ+SkpqqLLBDEOUYKtchsYTmRT7zmmIqkF+LTdca65ln9HMCGjINRl63foIQV2OVR5D7pns+KEDVvrA9xoUorpNszrz/r52GkuM2TNq3C1Mio8I2UO3CSFGXFaMqmaAbBSq5sdqMcsnc49el1JUFc3ffeeU1a1Vjp5TH7lV5fP+VhxL82ltG3of9LlP+XdFcH1lDVGjfW2nZaH9dRBXXxuPGRG7aQfTZHi/YFJ8BLiNnugmbrns+CqWpj32PqWYKbgWubDRbA2T74uvDVFl/D9TIdkI26HEmBHlEAIcc51aqmrItiFlRIxv31ZZWYdnrFejjxg3VYr4NjneD5gUtxxZT/k6wD3orIc81PjBrVj1yKv/LTfbELLU/5mKyPIf4kpsqeGqPi0C6COxvjbqU0N9+4Yguy5DY5FjyxiTRhXiJuQz5jzGnKcJ8X7CpPgIMNRF1Eqj6VUNZoQ0tLzlcjl46kWmGrM0Q7ZXrrXKNVedp55ThiymWLUhb9tE1Y29HkPzZFSqUMvetL2qOoxRa9x+2im8DwR0H+pgXIZJcYuRGYhMYYx9OLM8qrxaxJjFJHl0aR+p9qnKPnLEd9+qNUOwiVIemmdfXkPifEPyuaqS7IsrXlUtDok1MvieZ3f7Jtdm6HkMOcfKk2C1eD9gUtxytNTITTyAV4lX6TFjjPiQ+FP2v8+lVo0+HDPApm9/38hT/b0puYy5Dpu6w1UlXqda7PMOZPd2y216089Cheo+NO4HTIqPANxbvo7YDqPqQfcZVVUSrBwjLqu2q/bOtS6qIvryrpRtXznVtk3jsJquVZdNBrm0yhp7/HXFF8fG8dRlqm5UHDPm3Dapf9aRqq75VaYYGdcLk+Ijw009eEOMzBjVNAZDFZTWrzWwpoqLbTKQItt2FXfrVRTOVdOPVZut+OLQtuxr96qdMzJsHdNXhyEY0jHMnpOruHaN64VJcYuR9ZgZQweqtIbat1yYfUop+87yaY00HYLKXXV2djaoXaASJ5NJ7O7ujh7g09p31fjWdWAIyV7VTazlXPWa9tXjKgq3D9fV/lpHE+L9gEnxEWMTd911HJttHxMvGopW2a35iFlZWGpuMpmUS4rdFileJyr366YxxaHIrulYBdkq/yZJsYWsPYe08V3fB8YFTIpbjpuKJWYYMtil77g+lXgd7ku0CdZabc1LBPgNF60Y0ZC6jD1mbBsMGQTUl2YTgh+aZuho2aEYew5jrlt1/HUgU4omx7uHSfGR4C4ftj4DqcZB3Xmt/2PK0kENaoTUFVzFfjaJ/2waM7rKdbvqNb+qN2CTfDYZNDUWVyGfqxKq1sO4fzApPgJAGQFDBjxUscKxMcQhdaswVCFVqqeKJbL7dGi99GXIfbhOd+nQWDDv32RgyFXqNCbdQyaDm6r7Q26TbYNJ8REgU1u6v+/4MduvC1W9W7HHVl1VKW5ap6u4Fm/L+N22kb0v533XuIqSfCxtdN9hUnwEUKV4n3AdqqZv8ru6LscaHx59ulwuL723cAyuI5bVipXx/rHx3OvGVeOS9xlDOmZDF3jQ4427hUnxEeAhPGhDB29Urt7WxOhNyXBM3e4ir+vIu49gNznuqgNxHgL4Xqxi0dV92ZevcbcwKT4C3OcH7TYGnlznwIj7OmDJuH0MiZfe5sAq43pgUnwk8MN2Nbj9DONxIJ+FbBiGYRiPECZFwzAMw1jBpGgYhmEYK5gUDcMwDGMFk6JhGIZhrGBSNAzDMIwVTIqGYRiGsYJJ0TAMwzBWMCkahmEYxgomRcMwDMNYwaRoGIZhGCuYFA3DMAxjBZOiYRiGYaxgUjQMwzCMFUyKhmEYhrGCSdEwDMMwVjApGoZhGMYKJkXDMAzDWMGkaBiGYRgrmBQNwzAMYwWTomEYhmGsYFI0DMMwjBVMioZhGIaxgknRMAzDMFYwKRqGYRjGCiZFwzAMw1jBpGgYhmEYK5gUDcMwDGMFk6JhGIZhrGBSNAzDMIwVTIqGYRiGsYJJ0TAMwzBWMCkahmEYxgomRcMwDMNYwaRoGIZhGCuYFA3DMAxjBZOiYRiGYaxgUjQMwzCMFUyKhmEYhrGCSdEwDMMwVjApGoZhGMYKJkXDMAzDWMGkaBiGYRgrmBQNwzAMYwWTomEYhmGsYFI0DMMwjBVMioZhGIaxgknRMAzDMFYwKRqGYRjGCiZFwzAMw1jBpGgYhmEYK5gUDcMwDGMFk6JhGIZhrGBSNAzDMIwVTIqGYRiGsYJJ0TAMwzBWMCkahmEYxgomRcMwDMNYwaRoGIZhGCuYFA3DMAxjBZOiYRiGYaxgUjQMwzCMFUaR4le/+tX4qZ/6qXj27Fn80A/9UPzKr/xK/N3f/d1amuVyGV/+8pfjzTffjMPDw/i5n/u5+Nu//du1NPP5PN566634wR/8wXjttdfil3/5l+Mf//Efr342hmEYhnEFjCLFd955Jz7/+c/Hn//5n8fbb78dZ2dn8dnPfjZevnzZpfna174Wv/u7vxtf//rX4y/+4i/i+fPn8Qu/8Avx4Ycfdmm++MUvxre+9a345je/Gd/5znfixYsX8bnPfS7Oz8+v78wMwzAMYyyWV8D777+/jIjlO++8s1wul8vFYrF8/vz58rd/+7e7NMfHx8vXX399+Xu/93vL5XK5/Od//ufl/v7+8pvf/GaX5t13313u7Ows//iP/3hQuR988MEyIvzxxx9//HnEnw8++OAqFJbiSjHFDz74ICIi3njjjYiI+O53vxvvvfdefPazn+3SzGaz+Nmf/dn4sz/7s4iI+Mu//Ms4PT1dS/Pmm2/GZz7zmS6NYRiGYdwF9jY9cLlcxpe+9KX4mZ/5mfjMZz4TERHvvfdeRER84hOfWEv7iU98Iv73//7fXZrpdBo/8AM/cCkNjlfM5/OYz+fdf5CxYRiG8XixXC6vPc+NleIXvvCF+Ou//uv47//9v1/aN5lM1v4vl8tL2xStNF/96lfj9ddf7z6f/OQnN622YRiGsSXgsSrXhY2U4ltvvRV/9Ed/FN/+9rfjh3/4h7vtz58/j4iP1OC//Jf/stv+/vvvd+rx+fPncXJyEt/73vfW1OL7778fP/3TP52W91u/9VvxpS99qfu/WCzi//2//xcf//jHe8nWMAzD2C4sl8v48MMP480337z2vEeR4nK5jLfeeiu+9a1vxZ/+6Z/Gpz71qbX9n/rUp+L58+fx9ttvx0/8xE9ERMTJyUm888478Tu/8zsREfGTP/mTsb+/H2+//Xb8u3/37yIi4p/+6Z/ib/7mb+JrX/taWu5sNovZbLa27V/8i38xpuqGYRjGFuH111+/kXxHkeLnP//5+MY3vhF/+Id/GM+ePetigK+//nocHh7GZDKJL37xi/GVr3wlPv3pT8enP/3p+MpXvhJPnjyJX/3VX+3S/vqv/3r8xm/8Rnz84x+PN954I37zN38zfuzHfix+/ud//vrP0DAMwzAGYrIcEamsXJW///u/H//+3//7iPhITf6n//Sf4r/+1/8a3/ve9+Jf/at/Ff/5P//nbjBORMTx8XH8h//wH+Ib3/hGHB0dxb/9t/82/st/+S/xIz/yI1c7G8MwDMO4AkaRomEYhmFsM7z2qWEYhmGsYFI0DMMwjBVMioZhGIaxgknRMAzDMFYwKRqGYRjGCiZFwzAMw1jBpGgYhmEYK5gUDcMwDGMFk6JhGIZhrGBSNAzDMIwVTIqGYRiGsYJJ0TAMwzBW+P+fct3gdp8/SQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Initialize the DRR module for generating synthetic X-rays\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "drr = DRR(subject, sdd=1020.0, height=200, delx=2.0).to(device)\n",
-    "\n",
-    "# Make a pose\n",
-    "zero = torch.tensor([[0.0, 0.0, 0.0]], device=device)\n",
-    "xyz = torch.tensor([[0.0, 800.0, 0.0]], device=device)\n",
-    "rot = torch.tensor([[torch.pi / 2, torch.pi / 6, 0.0]], device=device)\n",
-    "print(rot)\n",
-    "pose1 = convert(zero, xyz, parameterization=\"euler_angles\", convention=\"ZXY\")\n",
-    "pose2 = convert(rot, zero, parameterization=\"euler_angles\", convention=\"ZXY\")\n",
-    "pose = pose1.compose(pose2)\n",
-    "print(pose)\n",
-    "\n",
-    "plot_drr(drr(pose))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cadec773e82e4ec087b72c607c8de7d8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:60766/index.html?ui=P_0x24271225f10_0&reconnect=auto\" class=\"pyvis…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Make a mesh from the camera and detector plane\n",
-    "camera, detector, texture, principal_ray = img_to_mesh(drr, pose)\n",
-    "\n",
-    "# Make the plot\n",
-    "plotter = pyvista.Plotter()\n",
-    "plotter.add_mesh(ct)\n",
-    "plotter.add_mesh(camera, show_edges=True, line_width=1.5)\n",
-    "plotter.add_mesh(principal_ray, color=\"lime\", line_width=3)\n",
-    "plotter.add_mesh(detector, texture=texture)\n",
-    "\n",
-    "# Render the plot\n",
-    "plotter.add_axes()\n",
-    "plotter.add_bounding_box()\n",
-    "\n",
-    "plotter.show()  # If running Jupyter locally\n",
-    "# plotter.show(jupyter_backend=\"server\")  # If running Jupyter remotely\n",
-    "# plotter.export_html(\"render.html\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Registrating random X-ray pose to reference:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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g1IQc9NzxHufvM1ertLxcn20lTmvsTBct0O1ZG6toqsCpKHJHhds0G6vSFV7QkeHVU0pTJRgVxa72fhBBxH9O+IqIlTDq3t5e7O/vrwgiXxvqyN6ddgAgijp8wPWBh4cyILB7e3txeHgYu7u7MZ/Pm+0IrQ4Gg5hOp+m12lM8H1gUtwgYx9u3b8disYhLly41QoXxmfF4HKPRKIbDYWM4lstljEajuHTpUmOYWdxQNosi0B55SRTXSULos19pnC4LJZbKzcbYdFvmPfX1KrPX6nWzGKJteTpMqe14bE3bW0URnRJ4Qjw+lsF14nClhkG5Lbr+c9kI23MIk0URniKLIv5Y9JAsg+uD9xcRMR6PG9GLWI1gYCoGdygw9o0pGshEXS6XcXBwELPZrBFnTgTCOewhnh8silsGfuTw9BAe0hBUZtg5LIVMPnzOnqGGw2DgsF8WMlxnQeYuodFtmYfG20vjdyVRVFQESoLUp77alhGxIoaZl9MliuqVcvgU9eO5eNnaolxHFkOdTpHNXdXrKQkjysZkfnzGWakqiuoB4xh4ioCzojEuifHCiGiWH1TPnT1QnavIWaiLxSLtGPUNOZvNwaK4hfCPf7FYxN27dxtvET3dyWQSo9Go6eViHAWJMjCKMA7j8bjxCnWpLu55Z0t2ZaLUx9tqMzjZ8dimnhOPjaq3xudiLy0LE2P7aDSKd73rXVFVVUyn02bsqe1c2nHAtpKHyJ6ihsA5hMgGXMe5+O/u3bsxn8/j4OBgZRyOrx3XyXXJ5haWBDLzCrPvDe0IjxEiDTHDYhMRcSwDlK+fvVfc21xPnIPvhcViETs7O7G/v994iGgDZJziPkdnkhfK504jv9eORls42pwtFsUthkOXyAItTaWIWPUYI1aNmgpFJi44JjMOSJdvEzgNLWb/M/QzTf5RkdL0ejZgWH2FrzdidU7deDxuDDeMKToQKop6ruz69Hy6YoqKIntxKoqlsUWETdngt4Vy4SGik6PRAQ4flox/2za9Ti6XPUCObGT3Qjatg9uIRT67Tr7/US46kGjbTPzbOgNms7EobjltYUEFhlZXvkHKO4frMIbDY4gcWlVPpGtKBB+T1StDxSY7HudW45eF/hBuw3gVG0N4BGgfhOUuXbrUeBPwRHAMeyboSHB9VXDVwGtyD9qSnzqhYVOdcM6JUyiTy1PvE3XliAF/rqJYWpRbPVD9TtA+HOLUThOfS79zrS86f9gX3yOPX3LWNdqOs7PxPbK3iPbnay6JYBYFsXBuHhbFLSQb02PDCTQExtvwGiFR7lmzIcbrkseAMvoYBTV+mZHB9WXbYez1nOzd6nGZkVUxHAwGTagZgshigM/bnu4AzyMTIg2fZu3CnraKIntTMOzcVplXl3niXAcWAv5chTEz+n1C39l3ADQRqDRWqfXKEoI0O5g7Uno/dF1X23WoWFoINxeL4hYCbwVeCt7PZrMVYYNhRciQf8g6fsKhOU7cicjXq8yMbh9KHoYmyOC8eM/bOOFCvUcNaapR5Wvm95cvX47hcBiXL19eMYAQJkz6xioneOQQPA4IFofjOFSJ8TR8b9gPdYYXz//H43GTMcnHqFhym7I3z22Ka4aHjPuhLaTN2/mzPlEJBfcYohBcpnqtEcejAireqP+bb76ZjiFrBwbHq1cI71IFlzuKPKaY3XNms7AobimZiIBSGEiNHnuDPE7FoqfGqDTW2JdSD7urbDXG6ungevQ6S38siginjkajlYnnOlaZicg63k1pfz1Ox0ojYqVTo/UreWa8rfRUidI9Umo/3med+4C9Xe20ZNei9VDPHcfwHEZ8pveRepRd30/2eUQ0HUKz2VgUTYOOibGRhWFhg4psPB1/QlkM9sFYI8qIyA18FlLNjBd/xuWqYcwEU41UFkLD9XIiDUQQWbrD4TAuXboUw+GwyWbE8VhMGvWFh5iFk9WLVY9D662Ci7pmT3THYtb47M6dO8eumYWDOzPsfWrEQDM+uY44TgVCw8QlWAThKeO+g6ApvNAEt7EmELEgsjC23Yt8Th5r1GhKFo7W7/BBO4fm4WFR3HJKoRwVLlDy1PBZ5o1l4zNt44hZeDUbA9T9dSwu80i6pn/o9XB5EMPhcBj7+/uxu7sbFy9ejOFwGBcvXlxJQsLyX3r+tvAZdzwyo9p1jHpjGvLDUmTszZc8Kw43lzw+bSPsr99B5mH2oY8H23UstxsnEGmnITs+4vh9mo29tl2n3qdm87EobiEqeNk0DIwPZk/KUAFiQw8joNMz9POsTK0fn0sNE/ZT7xDHoF4lryQLwWWGEEYOodELFy7EeDyOt7/97bG/vx9ve9vbYjQaxXg8bjyGxWIRN27cOLYoOtoF5+fPcE54ZGxoNXSp3hl7YxGrzyeEtzadTps5iLokmgpPJqhq7Hlckr+XTCBKCUJd4sbn47FO9qgZePaZt6ZlYsK9JunouCRvx3td8pDPpZ600tdLNmeHRdE0wMjxSjf8KCgdN+yTMKDjWyXvrNQzz4xLZgz1+MyzYQHXhBIOpbJhzDwBDpVhKgM+U7Fgoaiq+0+A16xHDd1GrAookjr4WKAJMxxOZZHVxd+5/Upedmk6iHZS2jxFpiRo+n1qm7Z5XPoZh1i1LfGZ3sNos75JMG33ZeYlltrDIrl5WBS3kOwHjfE+rFwzGAxiMpk0Cx5j7FDXm8SxmsiB19m4jdajzXuEkc88wUxQ+Pw8HpiFDFkIebt6qvgPkWFRnE6nMRjcy9wdjUYxGo2ajkVVVSueNl5jnUx99iRgwea6d32XnJWJ+mI5M2SjgmwhAe0UDAb35yNqxEBFM+uA6PcLgc7GbrnzhPsKHuZkMomIaMZntWOl/7m98F2hnvxEC87gRbklLy+bxF8SP36dhVv5fGbzsCiaIvgRIywIg4YVPbJEgiyhoDRpXv9nYlR6j7L5MzUyXeOipeMy72Y4HMZkMonJZNJM+MZkeBhTDqkhkYXFRMf0svOxIdU5gXpdQMPfXIa2Azwl9frxPWmCTdZ5yOrQBnuwpU6Idmz4XHr/qAhnZYNsabyS16b17ULL4N9CSQj5HGYzsShuMX1++OwdwTjO5/OVJ2mwEdAMSA5VsXHsW6cuwcoMoXogbeEpNlwIfWafTyaTuHDhQuzv7688ugjjdPDS7ty5s9Jx0NV/OLSnwsiGGx2RzIhr6DUrD6LK++M1xhVZoDVkCTHnsGPb95C1GcPfgwoGys7mjKLzwWhmK5evaAZvlyhmczczNMSejafyNu7gcD0tjpuHRdFExHHDySFP7W3zX0bJ8GThJv2sDfWWSsewsc08D36diVNmtObz+UpIE6KhyRqTyaQx5Dp2xWOzmZDxdfGk8JIHrPXHNek4Jp7zB4bD4crUGA0Ncjmlduv6vtoEBWh2cum+OMn9wt8vOgEYl80SoJi2e6uP11kSXRbMrsQzc3ZYFLeUttARjzdhX/5B86LYJWOg2zOPrm+d9LPMw2JDlo0T8XYN57V5kjBeeBIDOgkXLlyInZ2dRnBQJqZqRNwTzFu3bq2MH2rYUtHxNe6c9IE7Lqg/vFh8X8PhcOV6uH1Z+LPvlt+31aEkCG3eWtsxJdo6NOhccGIPBFLLyM6h27MkIv1tKOoxtrWR2QwsiiYionmMDp7wMJlMmv8wNovF4tgjcjKDgH3bkhPWQY2JZi9qWLZkXEu989LYG0J9EdGMH968ebMxtFjyDJPKl8v7j8iCR8LP6dNzqgBm15OJtta/dN18rHquWb04fFyKAmgdtN1VADLjr9esnSt8T9miECqUJTHTduGOCzzGLBO21M4aLs0ojZ3z7yNrN7NZWBRNRNzPNByPx7G/vx+TyST29/eb9TZ57CoTRZTBRhghur4eRkTea89ErORlZfuzF5SNjakYZuXC4N2+fTuOjo5iNBrFZDJpxlTh/cEDU09M6w9jrMvila6pbZtmUfK1cIgU3wnPVeTQZSZm2TWUvJw+HiCXxVNjBoP7j2/S0LLWT0U0mzOZ3QtcRvaMwz4iVRLhTLg1xIpELA6fWhg3D4uiiYj1x/ky49cVily3Hm1C0Sae+N+W9Vo6t3pnfB54wLPZrHl8EC/WzWFRPB2eOwkoA+diccoEKqtbBj9vEf/htfI5UU5WNifmlNqlDRWw0rZS+W3j0/w/O447PbpkW5ug6/56PRopwDHq1XeBaS0XL15sOlSHh4cxnU57z4s0bx0WRVM0SF0eFMiMT9f+Xftl3mEfA1IK1WWfZefvEiNkbeIPaHYhZ37Cy9ZzqFeGfUoComIDOBuTxxMhin1C12zs1zX6Sp+OUR/vk0OeXeLY9j5Dw77Z/avbOQTfB/YYkZi1XN57kPNsNjvm8ZrNwKK4heDHijHES5cuNRPPMVl/Pp/HnTt3mkn7WGgZc/Z4AntEPgUD5+L/WV1KsNHoEje+rrbr1rKwPesAsFFmT2+5XMabb74ZBwcHcXBwsPLIKHhpCJ+y0OFY7KceFHuUfO42uEODjgPGNds6NW3hUq6Ptp2+5vddgqVjfAy8ssFg0Exj0VVpSoKEYyE+6qFzBjB7pbwwApKRONyMsvm+0WgCtnM98VxNfnYllpXj/YbDoT3FDcSiuKUMBvcXuMbanTw+hufwRdxfFQTonMTMCHWhYans8zaRW1cs9X3mJWTeTUkwZ7NZk0w0Go1if39/ZV4hdxJ4PAmGXsUIlDyW0vUtl8tjYU/2YNVg9/X4S16UiqMKXSa0XeC+wXVkYVxuB3Q0uNOkIVjtmOF7QJ058SULc2cdBN6Xt/NxqH9V3V/NaD6fp/d5aU1Yc7ZYFLeMTCAwNqZGDaAHDg8x4v74GuBVUmA0+vzgs33YwLEhgmCX5nnhNYxSKYyXjXtlBo+9NRYLeBwwtvP5PG7cuNEsGB5xryOBxBYch2P5eiCWev38usub5uvibah36WkdEatjZVxONjaXhTG7PMi2+uM8PNeTBRHHaXbwcDhs6o7rw38cw+O9fM9kXj/fD5qRiu8q6zCUPMbsPXugmddoNgeL4hbBP/y+PXreB8KZiU3b3LusvIju8cTM4MJAloSxbcL5upTKUE8BIUt4jfy0DDWueM2ey2mOK6lnyQY9WxmmdL3KOl5413HZvYP6aLvwX+a9suCzcGvHio/Te1g7Wl3j1233sHa2snL6RlPM2WBR3CIw5eLy5cvNGp5Y3STifiIBFo7WRICSAcDrLKSp9Okds+HqUybIjGBmlPWa1BsohQ5hPFXM2AuEB4BwNDzGbAUbPncpyYbbIIO9Gn34roYFM2HUtsrmBpbaryR464h8FrrW7y1rEw116p+uusTtjChHVd1/CDTGIrkN8V9FLFvdaXd3t5nCdOvWrYiIZiF9ZBnj9Xw+j/l83rw3m4VFcYuABzMejxuPJiJfOaUt5NXHaJ40NJQJ2TplnYZXmG3PQof8WtuPPdrM8PPxbZ2NLtjo63iYlpuFhlFGX8+lTRDXIet4aJiy7zn0O9J7iEOibWFQeKn8XWKSP3v9GP9kQeTEKSSgcQeDf2O63WwWFsUtYTAYxKVLl+LSpUvNSiz6+WQyabJQ+UG1LKBHR0cxnU4bL0gNjBrNktelBhr/s+NLXoOWeZKkBRUQ9RSZUgo9DCC/n0wmsVwum0dLoS257MyzzZJMYGS7ws0w/PBO+Vp0zFAzO3UJM11xR4WGQ5Ul2AMvfY56c5nwyJGFmt0D2fek4emqqpoxW21XREPYI+Tj9D+PDWOOKgQRbX7nzp3iFJzTDJGbh4tFcYtAj1Zhw1kak8s8okxI+BilzQtQMVQRKIlCth9fU1+6PJTs/CWRx7SVbCm1zJspCbsKbdc1lQSodGyfNsq+F72WrmO7UA+Rvag+ZWTeZte14HegoWeUwWVpYg57iPyfOyd9sVhuHhZFc6IfZub5rBv24n3bQpNtx7IRhGHCNp6z1nZ8RLdX01UfXfh5b2+vyUzV80M0eW3UNi836yi0eV88PqnCy4+M4nBv11iitrMuGI59sg6Avj9pCJavJeuEoY485pfdp7he9Ti1znxPoIzBYHAso1hDoxhbxlhy6TFqWGrObBYWxS0iSxgo9XYVHl/hsGnEcXHCtqyMPh4i9tX3mRHOztlXoPt4PZnx5RAjG16e1qKdBS6bV77JVropnbPrerQcjgpAeNkzijj+bMa+ZZc80qz+XIc+9WeRiYgVQdLyub31O8zK4XpkUQYNM2fJZtm2rvsEHmb2udksLIpbBGdHRkTs7+83T5THJH6ehwgDgHGZu3fvNk+d5ycqtBlIoCEy7FMysiVxVGPEZWdz9krnZdRoZWFS9pL29vaacVas9DOZTFZCz9PpNBaLRYzH42OdkeFwGPv7+80CALqeJgSLx62ya8N1qWeIxRZ4TE5DvQcHBzGbzeLu3bvpPMYSfUOmbcLI+2WfIakFnTBOamEx0QXpWQR1SCBLJip1brCdfy/ceYiIxsvXkOlgMGiiABi3xP2DxfXn83mz2k1pvVlzdlgUtxAWkMyo4HXEce9SQ0ZtnmVXHfj/g6AeX3buzGPrU56KKQyZjitpen5mxLk8GNrMu1UyUSu9ZkOv4eO27GINI/epR4nSd8tTWkrH6LlwDEKPEfcTj7L2jcgf86T3NdqbM7C1Hho2BXoP8PVqfUodP69ks7lYFLcIGEp4IADGZzabrfSI0eOdz+eNIdB08pKAgL7hSz6+zdvoMsp9DXdXGewZcrnD4XBl7Vf8x9+FCxeaaS+z2Sxu37694q3CA7p79+6x8Sr2SCKi8SK1A1NqJza+qBuHS7GIOc+d0w5PlkQF4D0NBvfH1SAKnLii18FoRyDzPNn7GgwGzVxCFn32FHlahHZGdMyPPUg+j0YJspArjuFnZSITdTgcxnw+T8VOO5Jms7EobjnwBBFewg9eRXMdD6HPZ5kH1Wbws/LaQqLZfm3eaWmFHC1LvUL1fHi8EIY7Ez2uk/5l4dysPbIy1FNFndracR3Uk+oim/qTeVRZeW0h1rZz872UCSKXo0u/abJWySvX8/FvJ+swaqgWHVR9pqM5eyyKW4RmHGIsiScec+JH12R+NW7reIptIcos9JQJYJeXWhKDkvHlcVKga2PCK4Dw6GLp2I7jJpNJDAb35w7CW8OycFn7lJIyMjKPCd6sPl9RxRsRg5JIlcS4Tdi4/Ez0srC9fg/Z+Uvn1v0hMppdCrFCvfhz7YzgPHrN/LvhTFN4jbPZrBknRj04+Qrj9VjtSNvVbAYWxS2Ce7ODwb0Bfxh1GAzsF3HfECDxoSspoCScEf16+H29kEwMS8eyF7BOndQg8sOER6PRMTFB7//w8LDxtDXdnj1ubmsuQ+uWdQbUg9QkET4XCwJEmQ02Zx23ee5ch76ejV4Dh1i7PMVMEHU/7KOT/1l8s1VlNDyO17psnIIQLXfKsqkpLJjsgUIg8dBhffya2QwsilsEnhqPMTFONOCEg/F4vGIUZ7NZDAb3n3OXUfIW1atTA5R9pq9L70vZhJpMUcoqxT4qMmw08X40GsVwOIyLFy/GeDxeeXxRxP2nq0+n04iIxhMAPFcNn+t0DPaisnmD7HHw9XMWJj8hhCMDGEfEPYDHhU2n02a+XHbd2h5Z++n3w6LF15hleLIA6feXhZqBjlty1AP3NnuK2Bfjk4CfuIHPtaOB88EDR7nwytmr544nOiD4bU0mk6Y++D6ccLN5WBS3GB1ryZIBHkZ4pyuk2dVzftDPS3XJRJ3LgzHj4zjjdDAYrIwRcXkw0iyWOI5XviklZWQGXs+B8/KycOw5QYh5jU++lsxz6yOMfVHxywSR92ubn9hGqa59VprR76t0Xm2nNo+3DYvi5mFRNBFxfKJzaYyuFPpa5zx9KAmU7tP2XsspGassKUKPQdtMp9OYz+crnhl7FbwMmJaF8GvEcQOtj0AqhVoj7nsz8EA4BBtx30uFl4L38C51vc/SeG2p/UuiUwo5cluWpqowGlrm82o9NBya1bXtPlrn2Ez8+vwWHnYn05wuFsUtQo0hDCumaSBEiPEzeEUIMWVkPX79PKLfmKIeowbxQT2VtnNl2zMDplmhJaOrHiNe66OJImJl2TX+47bLXnNdIXg8zYLHyLLxsj4GOvOAsu+Fxamts4Lr0ixPwGN82Ri3nlfhe1uPzzocpTZgAcd7tO90Oo2Dg4M4ODiI6XTaePpcf+6A4DhM2j84OGimxszn80LLm7PCorhF6JQLiCKEECvaYJwEHlEbMB4aglvXy9PtOi6oxjbz5Epl6/u20Fifunf1/DGWxmFV3s6p+xHRjD3p2phq+LO6crvw1BoV3j7X2Hbd6hVpvXh8s83D1L9SaDbraPF5smtgsRsM7oeyuVOgz7VEEpneD5oli7/pdBqz2awRRawMxGPM+p0gO/X27dvNuC7GdnWM05w9FsUtA4aZe7IRuReQzcVT8eM/Nm6lXn3Jc8xCmNlrps8++KzNgJb2zwyztgfCp1nKP5ePtsayYZx4w6FrzVrEOTNx5LApe18lrxDn5aXLWED5+kshwdKKNFkb6ussdFoqq3Qv8XiphoCzzkp2X3EblTxHPT+/xqR9JMvge9eVdvh6S2OHfdrSvLVYFLcINqYaastQY1v6PDNefM51BK7kwZX2b0O9x3XGc9QglkRRRZAfMMvXAu9APcTDw8PGoLKh5/pnE8uxnY2tjsXpyi74znn9W0204rK4Y6AeFLdxWweIX2snq0sQ27w33o/Xfs0ELhNh7SxknSbeDwwGgxVvj0OnWQelS/zN5mFR3DL0x14KsTEqnH0Eq2sMUbevI1ilcrOQa58wYUmIM2PPafiDwf35i/icl1bDcWx0dZkzFjo1zuxhlDolgJN4uJ7qlUbcS8SBl6MLOmj5mcBpO7Z919qh6BIJCL2GSrmsLFzPK9PgWBXJUr1LtN1j/H3xggzz+XzFg8Q18dQUtPu697x5a7AobiEqAtm0jIyulPa+AgjaRKjPefQ61CM9LaPD5SAxCR4P5q2pNweyrFLer80z1gfhZvXhtkCWKYRlOBw23oxOJGex7DNVgQUJ58jqgnOrl5T9oS6AE4R0NaKS556hU06ydsuur+/9ovshLM7ryrLwZaF0P0dxc7EobhEwJFh1ZTwer2SZYtIzEmzY6+nqXWdhpj71aQtTPaiotXmJfcaf9Dgev0IYkhMlBoNB06Zd19/W+eB91Otsu562+sNwq2cIoSt5Ll1eoNImiH3K5Ppyedn1al1YOEtjjVxOdg9wedyeWdtyuJsn62dizFN1NHTLWa7m7LEobiE8LQDGEskiMCj4EWdLuz3M8ZE2w17ap4+AlgygGlkdz9JzsqGFOKIO8M4ijj9do4u2a9b6cj3avEiupyb1lMKlWdtoW+P6slWJsjJPe1xtHY9Or5eHDUrlljoipfPiPii1azZNhKMLHnPcLCyKW0xXOEn3zYwM73sS7y4btyvVoS+ZMOh2PkfpvFq35fLeg5bn83lcunQpRqNRsx3o6jToXOChxNpGHGLjrFAW5zYvHJ7GYDBoHl00Go3i6Ogobt68ubLkGE/94GvSSfz6PXMd9AkqWSgeoU++/qzufdCEpa57Vpf0K90LWdi6b9266lESUgvf+cCiuEXoDzQz/n1+uA8a1izRJarrGpW+9czOW0qy4AQKGGCsHcoZvSx2vKJM9p/ryseqKOr3pOLG41RHR0fN8zF5fiovCF4KWXN7qAeNOnLIj+uL91pGyfNW0czgOpXuEW4jFmUNf5bmOZbKXVfUtGORfWZx3GwsilsCQjyLxSL29/djOBzGZDJpJuzzI4fwaBsc05aMoca1bxgQ+5bEaB0DkhnwbJ8uwVWvSEWLvcLbt2/HdDqNCxcuxM7OTjNeN5vNIiKa8VgYaCS7LBaL5iHEmOumSStt43fa3lgpZTC4N1UAj4nCdj5Wp2owyKiFQN29e3dFdPFfhZHrx6FVbrfTEIG2sC6Hk3FuhP51PqKKdZYwlJ2DQRuW5vAi2QbfL7bj96fDEsgSNpuBRXEL4aXdkEnJE4/x1yU2D/JD7hNyfRBj2tZj76pT17lZ5Mbj8cp5SkaWkzGyjEsck43rdbUDr1AEDxKdGWwreVf6nZcyXjPYY+TEmOx4vbYHDY23eY4qztwOKGOdjNvs3NnnvA+HwfUe1OOcaLNZWBS3hMFgEBcuXIgLFy7ExYsXm6Xd+EG0bDQ4s3I4HBZXAGmja5/S55kIl5Ifsv3X9Rjbwpkl4w2jd3BwELu7uytZp8vlsvHeuF25DZGYgdBrRKx4ORH3HzeVnZvrxV5pxL15cBH3PRBdrGG5XDbPBoQI6rm1TXQ8TttFp5iUBINFDGKA+q5DacxQvVdcY9t9m4VZuZPI471Zx2dnZ2dlmg5+U9puem6E1ff29k7UBubhYFHcItg7zAyi0jaxu4u3Khz0sM/TVj4MZCaqOtdO25EFJvO0ujwZGO9shZiSZ9TltbR1ANo8vT5laL217do84mzeIs7F27g83a+NbAgAbcorFGVJSW1l6nXra3uHm4lFcUvAD51FkT3EzCM4iTe4jkh1JU2gvNNMTGgru4+hy7whbTtOstGkD/Ug+obyslAhzsuPr0KINuL+tInSJHIth7977RCx0HJ4kr1UDtW2ebj4z55wVxi9TydBhZavDeC6OHQ8GAxWxtBZDLFK0P7+fsxms7h9+/axNoMnyUvowWvV1XlQB6yAg4iMJ/NvDhbFLYQfa5TBP2I2qOty2oKGMrteP2yysaaS54fteLZhVycC4oB5o33qwmNSLDpAs0NL5+6zveSJqWD2pc890rZP5v2Wwu4RcczT08UG+HvkMGjpO886kev8Vto6K+ZssChuCVV1LzlkOp1GxP1QKj5jw4P5brxQNe/XZ9J2n/r03c4eRmb8Ssdkn+n2vt6ieocIPWNsEI8gGo1GsVwum94/Z3Dye2RGclvy+BeXXaoPvkMk+6jnNRqNmqxY1IHHPTNjzGKB8U4WCvaQSvXSNtXQatf/NtTD7gpl6ner0120rl2LVaAMnf+JLFMOf2vImvfXBKuTdDrNw8GiuEVkobHMKK7Ta33QHm5fb/Ikdep7TN/92rwhHcfjZBkVYPVU1CCySLCR1npqpjAn1KiHU8pw1Prg3LyOKm/Pjtf20WkKbeN86woi/meeWra/Tg/Rz/W70DpmnbAsbK77rOstms3BomhORF/xPIlAlcb91vEIS7B30HaMhuBYWPQpDTo/EavKIDs1uw54CzqehzDrzs5OTCaTlc/m8/mKSI3H42Z6DR5ki7Et1BEeJfaDpwIgYPyg3IiIixcvxnK5jJs3b67sr22n4UddqCDi+KLo3KZ9UNHpI4q6XT19Hh6IWB2X5XPqAgm4xslk0swDxedtc3otfOcHi+IWwsk2MBA8h049FEV71A/bWyyFONcVxHX3jVjPk2GypzyoYeZ25FVmVHi76o1QHs9N5DpzhqomV+nzH2H8VewYFkZNwIGoZ2NwXV6nop4hl9MVPmWPLQuT6r0Dj15FUafRaB0Urhsn4OD3pZ87bLp5WBS3EKxkMx6PYzC4/yTxo6OjYyubRHSn6mfbNXEh24fL52Pa9u8jiFkZWte+glMSBd2H6w/x0ZAke5q8RBzmDWI/jC3CgGZz2GCkcV06rqWixlmWPCWHBUwfecTXX/qe8ZBkCCK85P39/ZXrRj1ZtNu8Kv7Lxub4szZKiwnw9wiPnjuDpXPjOtWzxjEYT0Z26eHh4bEsUyz8wCsaWRw3B4viFoEfLXqtmGAecT9xo29YtM+5stel91lvfl3WOZYNfSk8m42XZWShOt2fQ3NaDgsPG3otl6dz8P4wtllHI7su3a7jgPiMPUbdv6vzwvXgz9b5jk7jPtQ5jpknqKs39Rk/bauftlkW6tXXZnOwKG4R8ATQy0WWKbwHzMl6ELoMcPZZH4+ytE8mMCW6DHlJrNTrQriRDWhWNnc0uDycU0WX57lpeJXLgncWcX/tU3gbPHdQRVUXLkc9svpxhiSSgbJOgoaET9qpKSUcnaYwRuT3oXrGfP7S91q6F7N20In/mnl6Wh1RczpYFLcILPiNsQ6E6JAc0Ycuo1cKU6kh6QpfZp6chinbxIzLLfX+OSSW1aMtdNrVy8d2XRhcDWLE6jgUh0uzlWqwD0/S12kG6vlhTCv7DjBFh6eQcDQhKxtkiz/gHKgb6pJ5TzqhXccIda5sFlbl/do6SCq4Oi4O4cySnzLaBDNrJz4Pvj+HTTcTi+IWMRqNYjKZNMYv81aUzMh2hZc0jMfbHjSUqgY4+6/llcRVw5QqtKW20TEm3odD1IPBICaTSeO9YSyKBZANPn8vLEQ6J5I/w3auI88xhfHVcCiu/eDgoBnX4utCNAHn5IXjuX14ris4OjpqxBZtMBwOm/bRhx3j2OwhyNxGfJ62aUVd3juO57FdXKuuzNPmESraSShNg8G5eCWbk3rY5vSxKG4RBwcHjUHlJAuQGYHMo+sK9WiZJbHS913huaz8LkrHaRgTxle9US2r5CmqIGE7JvWrAWfB0/AaDCV7QmykVdS5LF6cWrezx8YJHzwZHXWYz+fHBAv78r2jK+mod45rPzw8TD1KFsLs6fXcHgp733pMV+i1dA9pPbl9UXZ2TnxPVVWtdEp4OcUSDp9uFhbFLaGqqrhz507MZrPY39+Pvb29JtM021eNDHtVWcin1HMuZRdmRkvDdF2hqAweP8J7Fixsw3v2PNrEVr2HUucBYoF24nNxOSxSECUWR2xTr5u9dtSZxRDPxeRzVlW1YqB3dnbi7t27TXYkwp2cwarn5vl8KI/rgu04jqdqLJf3nhoyHA6PtQ97o6VwooZH8R+eMNow68Dp+9IEfRZm7Mfn4Sdt6Pek7TQYDJprxXdSegpG6X4yZ4eXaTfGGGNqLIpmY/C4ijHmrHH4dIs4rTCNThUoiVkWctU6cEgsC5lq6FTHK9vOW0p0YHQMURNodDxJx5Y44QQhaYzZceZmKTTMZe3s7DShT4QmSyFMPl7HsiKiWYqMQ5ScTYqJ49rGbe2qbYzQKD9NQie/c73QDjzuqmOZfe5RtCUvrs5toaFwbXu+Bg3x833K9wSHVTV0qn+YA4zPkYyEELUfKLzZWBS3iHXHL7oy+9bx7Noy+bi8UuagZgViHy2TjZyOL5Zg0cquV7ezmOE9xp30ySO8X3bdug1jg7gWLoPHxbheOnUh4p5QjMfjxqDPZrNjiTXaPm2JRbqPJv+gXpxQgvNANHkuJYsNJxhxeaV7gQVLVwjSJ1Bk5agwMqXxS04qUjHU97wyEDoNERHj8bj5frmDZTYLi6JpRbPwStsijifWaA86K6Nk+PrUKcv+Qz1K15CJG9c9S6jR49mAIwFD5/NB0NRIQzgwfxEej063wHxSlJHVn9uUjTaOOTg4WBFZ3h+JIJrxinqXOgLaecFxWFCApxiw+OI9Cwi8qdIUjew6s3uJ50RyJ6Lr3s3I5pNyGYPBoJlKwYlB+h1zAhLfV5yc1Kc+5q3Homh60baGJH8OYJyy1PU2r/C0svBOYmy4Xl2iqJ+zIWVjqBmKmYfG+yyXyyYrOPOmuurNxyDjM5tLyCE9ZHBqeLp0HkYFFdfFniHaiEVew7ltUQz1uFWweCH7LEx6knuhzcvPwqfaRqU6tEVDzGZgUTQNXSFMRXvtMHwR+dJZpdBrKUSX1e9BgWBl3oCGKtsMI8pir5WXzoM47e3tHZuaofNDeX8WNSz5VnqCQ2Z44Yns7u7GaDRqPJ+dnZ1mAXj2TvnpHFzOum3KdWOhxPXpdJjSsaXy+3xees8efPZ5aZuWsc5+aAPu0OC+w0IOKuRmM7AomlNDDSMb8gftHT+I0ciMZhYei+hecacEe4JsADVcli15xh0KiBiPV7VdB1BPXZNb0BlAfbIwdxtdhlvDuiy2XZ0LPY+GSx+U0xactjppZ0+9SQ2hWhA3D4uiSWERawtNMaXQl/bS1zUCmgyi4Sq8L4V4S/UqjVVFrD7WSEOofDy8MGSN8iLrXEaX96veEIusXgf25UxP9bhKY3XYPp1OYzqdroQyeZysFBbkumTjaTxGyQ/u1fBpn2zT7NFMpY4CC362ekzWYdP25w4Dn4v31ZWCOOt4OBw2j1/D/4h798TR0VHcvXu3yUxF++Bh1Ds7O82Dqs3ZYlE0EdG+UkwpTFdCBaPvcQ9K39BW333aPBw1zniPhdZ1OgXqpx0LFrTME2SvLgu5ttUvExEIH1avYVEshYyz+qlXi2NUiHmtVIicZpl2dVqya0KZfI+dxPPKOjz8GQsziy2LKIRY/+NztBdnpSJ8ijYqLWVn3nosiqZBjTaH4DJjzgZKDZ6W12dqRMTqUmx8bpSH/33Hl1BXlNl27bqWpgpKVib2u3v3bgwG9+crIpGF24vH+XAN3F4wnDiGF4yOuO8NYayK5weyAUe2Iwsnyo64n5gyHo8bA6306cyoxwbB5fbJ5uRxWFXFl8steZEaxSgJEc7BiU1aP32t9xsnDul9zBmmXA+MGSJaYM4XFkVTZB3PS0UCx2cGVTMvdVv2vjSehp5+1uPXcN9pkoU5+Xp5Hp/WCYZUx90gbGzA2ajqxHhkuEasPt1e68Zir0k/XW3W5Xnp96f3AbeBdm74nF3jdG1eN3fesmvKOmTrnDPrLOIcLIz8vs1r1TZqCyGbtx6Lomko/TDbwljZGJQaBTYgStvk8WxbyZhpaFHrDS+OPSo22hr609BXdu38HteJMOSdO3eaYzGRfrlcXRhbvQ6MNelzElV4ub587Tz1YrlcNmNUg8Eg5vN5zGaz5hw6NobzoSxuI0avm0O7uBfahIDrizqUppuoiGahUr4OjSzoubEPzxtsq6fWhevDXq1eT1YvbjNEAPB4rtls5lVuNgiL4pbTJUqgrSfLQqGGQwWxzWDhf+YV9KXvvmzc+Zz6vnRcV5kaysT2zCvQ8cHM69ZzlzoQXA57cZx4o5PJuzwbPbd2irLtXMcsTKptrufI3uu1ZyKV1VXR7NsuuGw9Z59hAa2bzkttm4Nq3nosiialLVzZFu5RYYxYNfpcDr/XcGipzJIw8Gsd64IBZg8sO5caa61D2389P5+Dx+3YM+Lszcy4ajtDzPC8RIDwIVbJwXmyMJ+2IdedjTzCuyx+Gu5TUSy1R3aethCi1rM0t5FDp3p+PUbHBnWuahbtKHl+/Biu7Dp5fDEri+vKwmg2A4uiOTWynnvfBBumSxizc3aVVfJGeFtJqEv79a2LhtrwgN/S+FeX55B535kYZl57dk3reCmZmPH2LvBdoHOA/1kZKlJ9PduSwGibcH2y+mmd2s7Zx4s35wOLoomI9pVGGIQG1YNQg8FeRx8jsW7orM+1tHl+fJ7SeBaua2dnZ2WMLfNq+FxcR/XQ5vN5zOfzODg4iP39/bh48eJKiv5oNGrGnLLy+DWPIbJHMx6PV/ZnTzKb+6deLLaj/nq9WZi4zUNEOYqGENFWWkdsx/J3mmXKCUhdZB23LKTM9VPPlI/jjNuSR9inbg6fbg4WxS2nlP0JSuLIn+t+fby8zMhmdPXAS2Xq9tI4U2n8TmmrZ3btLKg4VsONnA0KUeCM0sxT0vPyZ5yFycej/OwJEqVwuE7Mz8KmGtrVduL6ZSHirE2yNkayUtYZ6UupY1Hal5Os2u5trmN2r7Z1DIEFcbOwKJoVTmJsSr1jpvTDL4nVOnXMvDcWopKXp8fr6+y8mXEseQaDwf2M18PDw6Zs7M+LYi+Xy9jb24vRaNR4dNPpdMV7y0RxMLi/fBuPden0i9lsFnfv3l0RYN2POy+clJP9Z28OgqsCh3PwuJ9mYjKl8UnURwU/C3F2hU7VQ+S6srfKY6ql49elNAZpNguLojkV9Mde8jxLY4PrnodFL/MesjGvUog226fN0LI31HadXR6wLhLAYTyEO3l/XB+Ej8Oi8DJRX50qk4UBS1mvbe2XtVGpjfl61CvkDNCuDlPpfsE1lRKVcJ7Scdm+msDE5+fryMiiJub8YVE0x+ibHJN5TRwuLLGuMK5rbNhIa1Zhm1CWREDrwgJTaqvS5H2UzeNWGJfCWCI8vslkkh7PY2k7OzsxmUxWPLzpdHpMaCNiZUxxZ2enmS9ZarusPXiqSRZGVaEsebfshbV1VnQcj/fh70G9365jFPa6ef+2sCnXLVs8QO8NZ5ieDyyK5oHIjMVJxki6jukjhuopnvScfeqihrvPudgol7yuPvXlMliUSp2AzGuEwFVV1SQS8XGlKRh969oWbtQ2K403Z5+z91nap084PztXdt6SGPLnpU6bRfB8YlE0EdG9LmhmhDLjUyonS7JAOVmoMktQ6JMgkZ2j65wKi0CWQMGixP/bylPx0pBi5pmqAGReHIchdSI4e4/8FIyqqlYWAOekEhXMLs9Z2wznLYVotf3bREfLYu9a7zu0lc5HzOrHdeHP9LuE58jettbnQccZzeZhUdxSTuLNRZz+fKt1vI6uY9WAZ2NW6vmoAWbjyNt1uoAuAKBlZUuY4VFDfBzEistEUgmObctgrKqqWc6NxVHrggQePjemWyC7M+LeIuTqWWrZWadBRbPNk2vb1scb1e+Gr6lUru6bbc88v1I4thQWZk4yR9ecPRZFExHtUzKyfR9mD1knpZ/UQ2wL/2UGHOdioYNRRDILvCg+j9aBEzb4qRe7u7uxv7/feGoR0WSlRtz39A4PD1eEmbNLVYCxEADXA/vxeXV+H0RuPp/HYHB/lRyIIjxJbi+cD3UtebldXrO2OWhLvFHxyzojPHbadnzW8dF7Gt+9rpebzWdkr1h/R11tcdLOqXl4WBTNMfqIUF8xbOsta3iL9+/jGepnarhLIT+UXTqXGlueRoDP1aircdelxzjspmE+XeYrS16B1wmvj6+Tzw1vlEOX7BFC8BBKxWOossSZzBPPvGhuM1yTikTJi8s8PO2kcPtn3rwKbSkCoNek93HmSfI9AKHUsCkf15VRazYfi6IpUgp5dYWpIrof/dR1rtL+mbFhg6fTHLJj2uqigtgmAn3K07LRLm3z7nSVFJ6mkYkitzVEUf/jie/z+bzJcoWXqc9BbBPGUltpe2RLzfH/Nm8wE9KSp9injiC7D9gzbCtDowjmzxeLojlTSgYmM3hdnmK2Ekw2fthm8DPvCOGzwWBwzDvT+kO4eOkxGNTd3d0YDocrIsTH4BpKnjjECiFPJMOwRwWxawvtQWixH49fIsTKYpmFoTMvvOQ9ZcK0jrCoOPL16vlwDv2e9Lxd432ZmPMCB1kI9SQdJbN5WBTNCut4PSXY4PQpr+SRsniVjCy2Za+BJsWUjsV7LU/Dslm9NesRosjeHCfa8LJlutpL1hZcV4w5akYkj4FyXVVIUJZm+kIUIdCDwb3nMJY6GaVxxOz7LHlY69xvJXFUgcq+b9QTop+1a+lcXV5pdh+c5BrNZmBRNCt0/eh5v2zf0hhSl0fYdn4+X+m8pWkNWTml68nOw+KR1VHFYDQarQgWtsOLu337djOuh/Am/jjbVKdX8Hk5s5XRxxlxmBQPGcZ44mw2i+l02jx0WL1nXlR8sVisJN5oe2kbsFDqMVkYtC3Unn1W8khL0QCth3qZXeOM6oV23bMl79HZqOcDi+IW8rCTALoMi+7L9YKRKnlwfSdEZ8fr+fSzzCNqE8TsWobDYURE42FhO0QMq83gM16iDX8R9wwo1kVtC1PyOThEWlWrz4+ECKKsw8PDmM1mMZ/PY7FYrKyZOhjcz0adzWbFNlchKrUrt3smIOuGNnnfrD5Z+fpdcOeB66BJVVxO1sHTzoS9wvOPRdH0IhO5bEmrLIzWFlrLPstEqkTJe+Hj2/bh7SVBzI5XDwDtgfAoCxpfH8SRk1z29/fT6RJ6DRqa5e9hd3c3xuNxE6JdLpeNME+n0zg8PGwEEJ4ixhRRZ77eu3fvNtcRsRruVYFpe51ROqZtP51Sgu19RQnbu6ZWaBltn/W5Nx92B9ScPhZF00rJwKnBavuc/7e9ZkohOP2863XbsaXP+pStsEDx+xIQ3cViEaPR6JjHp8LIn0EcIVjqafK4Iv4jBHp4eBiHh4eNaHNol883n89Xpo5knZnTMvhdAloKmWr79PXWVNiz/12fmT9fLIqmla5xumx7n1Bpab9sTI89tzaRKglimwiW0HOVzqmeAybS89xDTMiHJ8kJMgiT3r17d+U6IVgQuYj7oVYNZ7IXhHAmRJCFEeUul8sYDocxHo9XJvHz0m9c97brxzb2vLJOUtu4HV8Di74uWpDNF+XyS0LH5+Bx12wZuuwBx33uny7BRoQgaw+zWVgUt4zT+jFyOTpZXekjhigz89R0bO+kIdU2QcP7kshnApwZ5dLcyOzaYJh5/I/31fqV1hEFvEwcT8rn1yz2eGpG3w5PW7tnnn1XqLGt86ShYS27hIZms7FEHU/MwrBo/66IRVZv0DX+bWHcTCyKppVSz5dRQVRDwgaG/6uhycb12EMsCVsmfOuEPUvH9hkX43MgkYaNOhJveJ+dnZ0Yj8eNgUYoU68H79UT1M9xDs40XSwWMZ1OV8Yu2RPUP3iWasj5GG0n9QpVsLV98B/HcPgWx2erAelarpmg8T2VeYrwsvf29lb2KV2bnkuvgwWbw9ZctrNNzycWRXMi+oyvqDjyf/68dFyfnnSXAS6FUEt16BJQFSQ9lsOeKhpZWJTH/9jA63lxfCaKKg4cLmWx40n96tWyYONcvF+pM3KaY4sR7Y99KnVW+o4Llib7o+x1r6MkwBn2Cs8PFsUt4iQ/zJKHwOjqLKBL/LLPVLz6TsHQMtiY84LZXE8dLyolt/A29QJYRNgTUePOXt5gMFjxWFj0tC1ZQHE9vFoLln3j60Q9WLSWy2Xj0fD3wq/hTQ0Gg5jNZs0iAZnwlbwt/kz30evia9jd3W3mePJqQHos1zsTP+2E6Dk5jMr7rBMl0HbI6mjOLxZFc2IyQ8DeBhtdft/mIZZCWV1GpxR6zcpAmI4Fo3Rdeg19UO8hq0tm1HGubEWWtjq0hZZZBFAelprj9VdxTh6/y8LifT2jUptovXBOHuvLBKvre9DOGIdpszpktEUd2q6rFCY9SYfOnD0WxS2mK3xZ2p6FJSPyuYqlcKWGVvFeH8ukAlUyXPzXtl4n11MzDUvX3sdLxnl56TRdaDsrk0ULwoUxQNAWPsUqOdgPfxyqZREcDocxGo2a1W14PdZS2LbUwegb4tQxQLQ/h5h1HDGD2yv7HrKJ9yy0baHT0jXz9qxTUOpUZW3ZlZVrNgOL4paiP8a2Xu06P9x1PYiSwS2FzkrH6+vSe/7fdzxsnevvOieHDiNiRUSz70CFPqsTJ6zAQ9J66LJmpTriNcYg+3hKXE5pPJCFSQVq3XumhK7lyuduE8I2ThIpMOcbi6JZm5KRQG+9NAYVkXuALAbZky6ULu+x5DVqWJL36woLqgdUyrjl/Xg8cT6fx3A4jOFwuLJY+GKxaDXiVVV+4oOKGWdpsqgtl8sYjUYxHo9jOp3G3bt3G08SDxXmxBwsC4f36jGx18MCrN6vCiH/Ze2dHZ99r1yX7Pvh+zDzWLO2y+4hRSMGKvKl47Lzt20zZ4tF0ZyIrjGuNjJBzAx9W1klYWAhVEHkaQ9ZEoiGyLrOnxlt9lb4HHjN6ftVVTUCiWNKnYI2b6etUzAY3EvqwWR9CDQfqxP7NUxbGg/kPy5PRTEijk1ZKIVe+4hLVxu0dWzUUy5FSDLR7htZWAeHTzcPi6LpRfbj7Rv6ykSmtL5nqQdfEisVQ87yhHGHt7NYLFIv5qSGLxP3+Xy+4kVB9OAl8oN/sboNPwNxMFh9liGPlWUZr9ouOtY1Go3iwoULceHChbh8+XLs7u42a59yO/HqN23ZxNxmmhiDOmVeoY4ZZmLWx2vPQs9Zlmm2RB13WkrCn2VP47WKdmnVI3O+sSiatXnQkE829aEtJNrlRZa8RqZ0HiYTxD4eiO4HkeGMz4hY8Qq1nqXkETbkXI8u8YaHCC+Rp2JwuFQ7H6Vr1O0qIvy+5AW2ldN2HW203TeZV9oVieAQdBYN6KpflydsNh+LoilSGo/B++yHny351iZIJQ9RP+sTVi1lAXaFYLuOx/VG5EkcOJbLwzggxIiFTD1b9qRgiHVaAl7zZ20Ge2dnpwmZTiaTGA6HjbeMp2ZgPLGqqpXs18FgcGwsEefLvvcshKp111By9l/btK9AZ/cG5lzydbVFIjS03na9+Kwroccr2pxPLIrm1GkTxq45gW296UwcSz1y3k89Ls7S5GMyr0LDt20Cm4kIv14ul03CzWAwODZXEH+YeI868kLf7MWoVwQBGI/HK56iLqs2HA6PrXAD8YYHyYuPox4sBFnSDM7B7cXtk7VNKXydCeM6332XV7bOfZcd13f/PlEVe5CbhUXRFGkLR/LnfVHPoat8PVefMZzM2GXrd/K4EwtSHyPcJcR4n60gg+cY4np4DA7JKKPRqBl/5DVMFWzjlW9Go1Fcvnx5RexYFHd3d2MymazMY4RQ7uzsNOdC++g5eMUetIWOs+m4ZCbgXKa2LXttGdxBycLdqEdp7qN2mNA2gDOoedUj7ZBk02QscOcfi6Ip0maU2n78pRR1za7Uc7UZuGx8TcfssnKza+ka7+kar8P+beE9FVmehrGzs9MIta4k03UdGdkUhFKYEAk17ImWxgGzUGKpLfrUWT3A0nn7kIVM9TMdGyzVT6MXugBDFh4uRSK4vZ2Icz6xKJpO2sZWlLZVbRQ2XmwwdayHPRberxSKVdGGGGv9dQUUDlGWRDQzvtk18iot7C3CyCILlMOnbW2VXaduy8baNCN3Pp83DxnuCvfiOvB/nfugT9voSjaZiJTCxVxu1h46RttWv9I2nR+KclkENYs1e2/OFxZF0wvuAbf1hOG1rDP2ku3LQpkt35UJlYYYecpCacWYdVGvQeub1S/injAilKvZn5g7yEk5CKei3KOjo9jb21u5TryHuCDkWvqustAmj0VyZwPHZUknpTAyf2elTkVWRts90uYRtpXP4dPSMW3CrXXQtsnu/XXu+VL9zdljUdxCSj/m7LMMDWeyF6YGCfuX6tBmsNh4as+fjVopTMXbWVxLtHlC2dhXW1tpOXigL9oKDxWGUEL4IG74jxAn5hxGRLPaDM6BcUM8V1HHySDEeIwUtwmHMXmcTkOQJU8xE8au7XpetEOJTBjbRJePawtzl86j6HfN3ndbHUuRC7PZWBS3mK5ed7afvi4ZqjavLBPEPuGs7NwQE/6vYzslQ106r9Jm6LvGrdR7RKKK7sOCCXFkDw6T/geD1QcXR6xmfOKhwtpG7DHu7u6udBgguOyZZl57X/R74k5O2/fcJh5tgtXlpev+/BqdDn6eZde5tb7qhev9x/uZzceiuOX07aGrwcoM3zrn6zpvn3O3GaWI4wtEZ15JlyFsS0LR913Cod4RH5eJ+Hg8bjJKOTt1Mpk0IWpM81gul01mK1arwXWUwqXs7eNpGdr+2mYscNn2Ulty+/D3loVsuzpDXGaJ7HtTL187TG33Zlb/TPSy1/YSzxcWRVOkZFT6CGnJK2sLOfWtB47REO18Pj9WNpeRjUNm5augcMhPPZ8SOA77HR4exs7OThNGRfg0OzfOjyXjsDoOloerqmrlNcKiBwcHzfgkysY5te11GggScHRqRl/axKDNQ2/7HjIPs9T54JBsSRCzzgjqwEJZ8uiyjoIm1nAnzWJ4PrEomgemLTRW2n8dQVQBwnsNnbGxyoxttm9Wt+x/aX8NnwIWU17blLNOI8peLCcJ4VgIKgSRk3cODw8bT5HFDEu8VVW1sh11gShivdXRaBR7e3sxn89PJIr6p+3Uh5J4dR2jGa0shCqKfB4uv5QoptnL2b3WVs8+wwlmM7AomiKZZ1fyBrpCkl1GMTNQbZ6lbtekGvznkKWGKrPz9amvehZd14YJ+ajHeDyO8XjcHK8JMNmE8ux8uqh3KfTIAgtBRb2m02kjgBBOCDB7XDytI/suslAih6+5E4NtfcbYujxJbuNsmgeXc1Lx4TFbXBsvnl7qCHA9veTb+cGiaDrpMiiZp8bvdT9FvcC23ncWqtL9tEevK6foUmTreK7ZNZeOYY+WH50EYUJGKHtxWVmlzsDh4WGvunNdMXbIYdMsMYnrW6qfiiBv00SeiOOP0yp55RklLz/zBkse3TrRDOybzYVdp3O4jodsNgOLojnGaf2Q1YvKxoj0M1DyIjRkquOKumoODLxSyhDk49jYtl1j17WrweRVbJDcAk+MM0NVBDg7lcdQ0QbD4XBlziMeV4XEHHiso9EoZrNZzOfz2Nvbi/39/ZXzw5vltsJKOG20ea3ojPDi3G3fcVvbZufFcW1h7jZB1nMDTkjK7pVMiLle7FWuc03m7LAomofOuiLbJ9SVeQ4wYLz8mxqskhfLnz1I3Ut1zUASDMb2uG6lRA0YWvZ4ubyqur90G8rl7NXRaBTj8bjxMlEHtJ0uIBBxTxQRcs3EpO37KnnwLIx9Oh9t6PGlTlffyIWWhSkbXWFSLVfbyJwPLIrmGCf9AZfCW7pNPUim1PPODGpmDOFpZceWwpDYh70YrWdfg60h0/F43AgfGA6Hsb+/35S5v7+/Mn8Ry7Cpd8ZeMrcRyobxxms8S1GXnMOcxKwNUNeLFy/G3t5e4zE+8sgjMZ/P48aNGzGfz+PNN988NqbG7Z6JJockEUrl0Dauo+QpZt8Bz+PMFhjXRCbtTPUJg2dTLZTSEAB7i306e+bssShuOX3GPUqhJv3x6/hNxOrUhLbjOLTISRqZOPbtsWf1V2Ol21GHjMzjLJ1PvdVMkNkgs4fGj3TK2kBT/rPHT3WFCXH92s489QPhVx4LvXXr1spi5lxGqZ21DdmjVY9e26oLvn/0PtTP2+pY+lyfaKIdgaw+KqDrXI85eyyKphUVCrxfLBbN3La28TddyksFR70LNkJcHhvNLmPDgqzvqypffzQTSz6vJolk5+Vj4LlgriGWaIu4t+TbzZs3m/cQH3hps9ksDg8Pm6xQCCbmNt69e3flfPp4Kr6G8Xgci8WiebYi9sM5cE34DAsGzGazmM1m8ac//alZGGCxWMSNGzfi8PAwbt++fcwDyu4V3q7eG3ux3DFoW7NU25nnWmaeogqtdhZ4AQMtG/ej3iucecrLvbGQYprMdDpt2rpLUM1mYFHcYtYJCfJr/OgzIVSBzMZx2Ih0iWLE8Sc1lAyZ7p9lDiJppC2tXqd3lEQxax/et6qqRhS5Lru7u02STFVVMR6Pm/mBu7u7MZ1OG1HEeB5EseR5ZXXitkbiDa734OAgZrNZU1deWo7bCOusHh4eNkYedePnVGZCqN+1tiXCqJko4vMS6tnq/ET+7trKyiba83fMoqjenwqcepOYM8qJNmbzsSiaIlnIjT0M7ZmraLSF8TKj2WacImLFgHOdMgGLyJ+Tp6KoQoz/uuCzXm/WVvC4RqNRvOtd71oRFYRE2XDjXFxf1Ic9EyTKoE7a1m1tjnNy4g3GEzPvDt4b14XbBnW7detW0zliUdROjrYhi5mOA3IIme8Vfc3Xzw9yzsL3+t2XwrN6HyO0m4khjuVOhNYT/z0/8fxhUTStsFDw0mSZUS5ti2gXQ96ehU0BjFVmoGCkIo4/J1HL57mBHA7D51lihJZdAqvOIMSJsFm2gk3WKdC/iGgSY1gUUVY29tj2PWTCzu0OrxT1Qr35e1ksFjGdTlceWFwSRYbrAJHnupREMSuHRRGZsVxvfV96qoWWyd8z7g3sryKPjgZfu8Oj5x+LoikC44SxQxj6TLCAikY2hsNk4zGZ14my2cMCfbJNS+dmEcxECUauJIaZ+Ozs7MT//d//rZyb9+fMS814RT15G+YZYnvW7lmYVx9uzONaHPrMyulCQ4g6ub/kIUbEioeonYPsPKVrhWcOUWxjHVEsRRtKIs/n0HCphpXN5mNRNClVVa0Y0Yw2cQRqXLJMRT4njKum56MsjA+piPB/FbCs/tzzZ1GEUVOjzwZc61UKZZbagwUr2z/znnlSf+kc6gXC60PImb9PXSOVr6XtOrKwJHcuuP7ZMfAQNQEr+6/tweVBuLpEkT1FhE+xnUPX2pHocw8paFOE5y2C5xOL4hbSZbiRWFF6ksNJ0ISGksfYFpLVsTc9VsFxWWiPj2VjxkLIdS55rVo3Pi/XrTQmpmVloc22JJ8SmZBgabk+Yb4uUeS2LYVPlbYOQSmsnl0Lzo/xxNIzKiPiWCdHP9fvbzgcrnjx8ORxDISUxRVhZBZDjWaY84NF0TQgrDafz09FCIEaTTUYKhil/2yISl4Fk43boQ4ssJqAo/VVQ8j14kn52efZWFRJ8EvjfSXB7AOPiXIItuTVMaUwben6ukRWQ5RKW6hR33PyEI8p6v6aKKPeKR/H45y82AHvz/cCtvFCC3xf48kjmdeY3StmM7Aomoi4n1k6n8+PzYM7DXTsSUN3mZHK0Gy/PnSNBXE4sRQuzkSRPTh41ewxtmUlogz2Svh4Nrp9w8IaCtQ2KHnp69DHa+1qb64ro15WW1ITPEXuTJQ89NL3iXMy2snpI1i4bxBpwDHoZDmcer6wKG4xMBiYS7fO8/PWRUN2mxRa0mzUrH4I+XHYjYWyT9uh7DZx1QQUHNeXNkN+GskefcdOs7HXEtm0hux4bjsNcWbn6nOfdY1/qjBnoXR0iLhDtbOz04gh3/sP8zdmTgeL4hYDD+nOnTsR8fDCOCo06i2Vxoywb58xuj6UQnZ9Ht+kYTf17HS6REbJAKsg6go1qGfJuymNZ2ZjdpvEOm2VHZuFsJVSRELHm7VO2Xhv1zgnC+JgMCh2sjbtezCrWBS3EBiK6XS69uTidYWzlFlYErs+52gTrezzLuOrE9DbylCjqmNr67RPmwHWBI8+5bRt33RDfJK6sjD29Y71+yt9b9m4b1fdSmXrMTyuu4mdlW3HorhlVFXVLNk1m83OujqnwoN4uGqc1jFQ64pgRDmRqLQtOyf2Ocn5zelwWkJmQdw8LIpbAGfJYY5YxjYa2Cw79WHTx7MrhZVPUu6fG11e/HkCYftNGmPfdiyKWwI8RGOMMWW8Wq0xxhhTY1E0xhhjaiyKxhhjTI1F0RhjjKmxKBpjjDE1FkVjjDGmxqJojDHG1FgUjTHGmBqLojHGGFNjUTTGGGNqLIrGGGNMjUXRGGOMqbEoGmOMMTUWRWOMMabGomiMMcbUWBSNMcaYGouiMcYYU2NRNMYYY2osisYYY0yNRdEYY4ypsSgaY4wxNRZFY4wxpsaiaIwxxtRYFI0xxpgai6IxxhhTY1E0xhhjaiyKxhhjTI1F0RhjjKmxKBpjjDE1FkVjjDGmxqJojDHG1FgUjTHGmBqLojHGGFNjUTTGGGNqLIrGGGNMjUXRGGOMqbEoGmOMMTUWRWOMMabGomiMMcbUWBSNMcaYGouiMcYYU2NRNMYYY2osisYYY0yNRdEYY4ypsSgaY4wxNRZFY4wxpsaiaIwxxtRYFI0xxpgai6IxxhhTY1E0xhhjaiyKxhhjTI1F0RhjjKmxKBpjjDE1FkVjjDGmxqJojDHG1FgUjTHGmBqLojHGGFNjUTTGGGNqLIrGGGNMjUXRGGOMqbEoGmOMMTUWRWOMMabGomiMMcbUWBSNMcaYGouiMcYYU2NRNMYYY2osisYYY0yNRdEYY4ypsSgaY4wxNRZFY4wxpsaiaIwxxtRYFI0xxpgai6IxxhhTY1E0xhhjaiyKxhhjTI1F0RhjjKmxKBpjjDE1FkVjjDGmxqJojDHG1FgUjTHGmBqLojHGGFNjUTTGGGNqLIrGGGNMjUXRGGOMqbEoGmOMMTUWRWOMMabGomiMMcbUWBSNMcaYGouiMcYYU2NRNMYYY2osisYYY0yNRdEYY4ypsSgaY4wxNRZFY4wxpsaiaIwxxtRYFI0xxpgai6IxxhhTY1E0xhhjaiyKxhhjTI1F0RhjjKmxKBpjjDE1FkVjjDGmxqJojDHG1FgUjTHGmBqLojHGGFNjUTTGGGNqLIrGGGNMjUXRGGOMqbEoGmOMMTUWRWOMMabGomiMMcbUrCWKzz//fHz4wx+Oy5cvx7vf/e743Oc+F7/97W9X9qmqKr72ta/F9evXY39/Pz7+8Y/Hb37zm5V9ZrNZPPPMM/HOd74zLl68GJ/97GfjD3/4w4NfjTHGGPMArCWKr7zySjz11FPxs5/9LF5++eVYLBbx5JNPxp07d5p9vvGNb8Q3v/nNeOGFF+LnP/95XLt2LT75yU/GrVu3mn2effbZeOmll+LFF1+Mn/zkJ3H79u349Kc/HUdHR6d3ZcYYY8y6VA/AG2+8UUVE9corr1RVVVXL5bK6du1a9fWvf73ZZzqdVleuXKm+9a1vVVVVVX/605+q4XBYvfjii80+r732WrWzs1P94Ac/6HXemzdvVhHhP//5z3/+2+K/mzdvPoiEpTzQmOLNmzcjIuLq1asREfHqq6/G66+/Hk8++WSzz3g8jo997GPx05/+NCIifvGLX8Th4eHKPtevX48nnnii2ccYY4w5C/ZOemBVVfGlL30pPvrRj8YTTzwRERGvv/56REQ8+uijK/s++uij8T//8z/NPqPRKN7xjncc2wfHK7PZLGazWfMeYmyMMWZ7qarq1Ms8saf49NNPx69+9av4j//4j2OfDQaDlfdVVR3bprTt8/zzz8eVK1eav/e9730nrbYxxpg/EzhX5bQ4kaf4zDPPxPe///348Y9/HO95z3ua7deuXYuIe97gX/zFXzTb33jjjcZ7vHbtWszn87hx48aKt/jGG2/ERz7ykfR8X/3qV+NLX/pS8365XMb/+3//Lx555JFOsTXGGPPnRVVVcevWrbh+/fqpl72WKFZVFc8880y89NJL8aMf/Sgee+yxlc8fe+yxuHbtWrz88svxl3/5lxERMZ/P45VXXol//ud/joiID33oQzEcDuPll1+Ov/u7v4uIiD/+8Y/x61//Or7xjW+k5x2PxzEej1e2vf3tb1+n6sYYY/6MuHLlykMpdy1RfOqpp+I73/lOfO9734vLly83Y4BXrlyJ/f39GAwG8eyzz8Zzzz0Xjz/+eDz++OPx3HPPxYULF+Lzn/98s+8Xv/jF+PKXvxyPPPJIXL16Nb7yla/EBz7wgfjEJz5x+ldojDHG9GRQrTFSWQpVfvvb346///u/j4h73uQ//dM/xb/927/FjRs34q/+6q/iX/7lX5pknIiI6XQa//AP/xDf+c534uDgIP72b/82/vVf/zXe+973PtjVGGOMMQ/AWqJojDHG/DnjtU+NMcaYGouiMcYYU2NRNMYYY2osisYYY0yNRdEYY4ypsSgaY4wxNRZFY4wxpsaiaIwxxtRYFI0xxpgai6IxxhhTY1E0xhhjaiyKxhhjTM3/B8s13/b5mInVAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import torch\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "from diffdrr.metrics import (\n",
-    "    MultiscaleNormalizedCrossCorrelation2d,\n",
-    "    NormalizedCrossCorrelation2d,\n",
-    ")\n",
-    "from diffdrr.registration import Registration\n",
-    "\n",
-    "# Make the ground truth X-ray\n",
-    "SDD = 1020.0\n",
-    "HEIGHT = 200\n",
-    "DELX = 2.0\n",
-    "\n",
-    "true_params = {\n",
-    "    \"sdr\": SDD,\n",
-    "    \"alpha\": 0.0,\n",
-    "    \"beta\": 0.0,\n",
-    "    \"gamma\": 0.0,\n",
-    "    \"bx\": 0.0,\n",
-    "    \"by\": 850.0,\n",
-    "    \"bz\": 0.0,\n",
-    "}\n",
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "\n",
-    "drr = DRR(subject, sdd=SDD, height=HEIGHT, delx=DELX).to(device)\n",
-    "rotations = torch.tensor(\n",
-    "    [\n",
-    "        [\n",
-    "            true_params[\"alpha\"],\n",
-    "            true_params[\"beta\"],\n",
-    "            true_params[\"gamma\"],\n",
-    "        ]\n",
-    "    ]\n",
-    ").to(device)\n",
-    "translations = torch.tensor(\n",
-    "    [\n",
-    "        [\n",
-    "            true_params[\"bx\"],\n",
-    "            true_params[\"by\"],\n",
-    "            true_params[\"bz\"],\n",
-    "        ]\n",
-    "    ]\n",
-    ").to(device)\n",
-    "ground_truth = drr(\n",
-    "    rotations,\n",
-    "    translations,\n",
-    "    parameterization=\"euler_angles\",\n",
-    "    convention=\"ZXY\",\n",
-    ")\n",
-    "\n",
-    "plot_drr(ground_truth)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from diffdrr.pose import convert\n",
-    "\n",
-    "\n",
-    "def pose_from_carm(sid, tx, ty, alpha, beta, gamma):\n",
-    "    rot = torch.tensor([[alpha, beta, gamma]])\n",
-    "    xyz = torch.tensor([[tx, sid, ty]])\n",
-    "    return _pose_from_carm(rot, xyz)\n",
-    "\n",
-    "\n",
-    "def _pose_from_carm(rot, xyz):\n",
-    "    zero = torch.tensor([[0.0, 0.0, 0.0]]).to(rot)\n",
-    "    R = convert(\n",
-    "        rot,\n",
-    "        zero,\n",
-    "        parameterization=\"euler_angles\",\n",
-    "        convention=\"ZXY\",\n",
-    "    )\n",
-    "    t = convert(\n",
-    "        zero,\n",
-    "        xyz,\n",
-    "        parameterization=\"euler_angles\",\n",
-    "        convention=\"ZXY\",\n",
-    "    )\n",
-    "    return t.compose(R)\n",
-    "\n",
-    "\n",
-    "gt_pose = _pose_from_carm(rotations, translations)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Make a random DRR\n",
-    "np.random.seed(5)\n",
-    "\n",
-    "\n",
-    "def get_initial_parameters(true_params):\n",
-    "    alpha = true_params[\"alpha\"] + np.random.uniform(-np.pi / 4, np.pi / 4)\n",
-    "    beta = true_params[\"beta\"] + np.random.uniform(-np.pi / 4, np.pi / 4)\n",
-    "    gamma = true_params[\"gamma\"] + np.random.uniform(-np.pi / 4, np.pi / 4)\n",
-    "    bx = true_params[\"bx\"] + np.random.uniform(-30.0, 30.0)\n",
-    "    by = true_params[\"by\"] + np.random.uniform(-30.0, 30.0)\n",
-    "    bz = true_params[\"bz\"] + np.random.uniform(-30.0, 30.0)\n",
-    "    pose = pose_from_carm(by, bx, bz, alpha, beta, gamma).cuda()\n",
-    "    # rotations, translations = pose.convert(\"euler_angles\", \"ZXY\")\n",
-    "    rotations = torch.tensor([[alpha, beta, gamma]], device=device)\n",
-    "    translations = torch.tensor([[bx, by, bz]], device=device)\n",
-    "    return rotations, translations, pose\n",
-    "\n",
-    "\n",
-    "rotations, translations, pose = get_initial_parameters(true_params)\n",
-    "drr = DRR(subject, sdd=SDD, height=HEIGHT, delx=DELX).to(device)\n",
-    "with torch.no_grad():\n",
-    "    est = drr(pose)\n",
-    "plot_drr(est)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.7771040201187134"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "criterion = NormalizedCrossCorrelation2d()\n",
-    "criterion(ground_truth, est).item()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def optimize(\n",
-    "    reg: Registration,\n",
-    "    ground_truth,\n",
-    "    lr_rotations=5e-2,\n",
-    "    lr_translations=1e2,\n",
-    "    momentum=0,\n",
-    "    dampening=0,\n",
-    "    n_itrs=1,\n",
-    "    optimizer=\"sgd\",  # 'sgd' or `adam`\n",
-    "):\n",
-    "    if optimizer == \"sgd\":\n",
-    "        optimizer = torch.optim.SGD(\n",
-    "            [\n",
-    "                {\"params\": [reg._rotation], \"lr\": lr_rotations},\n",
-    "                {\"params\": [reg._translation], \"lr\": lr_translations},\n",
-    "            ],\n",
-    "            momentum=momentum,\n",
-    "            dampening=dampening,\n",
-    "            maximize=True,\n",
-    "        )\n",
-    "    else:\n",
-    "        optimizer = torch.optim.Adam(\n",
-    "            [\n",
-    "                {\"params\": [reg._rotation], \"lr\": lr_rotations},\n",
-    "                {\"params\": [reg._translation], \"lr\": lr_translations},\n",
-    "            ],\n",
-    "            maximize=True,\n",
-    "        )\n",
-    "\n",
-    "    params = []\n",
-    "    losses = []\n",
-    "    for itr in tqdm(range(n_itrs), ncols=50):\n",
-    "        # Save the current set of parameters\n",
-    "        alpha, beta, gamma = reg.rotation.squeeze().tolist()\n",
-    "        bx, by, bz = reg.translation.squeeze().tolist()\n",
-    "        params.append([i for i in [alpha, beta, gamma, bx, by, bz]])\n",
-    "\n",
-    "        # Run the optimization loop\n",
-    "        optimizer.zero_grad()\n",
-    "        estimate = reg()\n",
-    "        loss = criterion(ground_truth, estimate)\n",
-    "        loss.backward(retain_graph=True)\n",
-    "        optimizer.step()\n",
-    "        losses.append(loss.item())\n",
-    "\n",
-    "        if loss > 0.999:\n",
-    "            tqdm.write(f\"Converged in {itr} iterations\")\n",
-    "            break\n",
-    "\n",
-    "    plt.show()\n",
-    "    df = pd.DataFrame(params, columns=[\"alpha\", \"beta\", \"gamma\", \"bx\", \"by\", \"bz\"])\n",
-    "    df[\"loss\"] = losses\n",
-    "    return df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████| 1/1 [00:00<00:00,  1.58it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.7767581343650818\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████| 1/1 [00:00<00:00,  2.13it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.7767581343650818\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████| 1/1 [00:00<00:00,  2.14it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.7767581343650818\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████| 1/1 [00:00<00:00,  1.98it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.7767581343650818\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Base SGD\n",
-    "drr = DRR(subject, sdd=SDD, height=HEIGHT, delx=DELX).to(device)\n",
-    "reg = Registration(\n",
-    "    drr,\n",
-    "    rotations.clone(),\n",
-    "    translations.clone(),\n",
-    "    parameterization=\"euler_angles\",\n",
-    "    convention=\"ZXY\",\n",
-    ")\n",
-    "params_base = optimize(reg, ground_truth)\n",
-    "print(params_base[\"loss\"].iloc[-1])\n",
-    "del drr\n",
-    "\n",
-    "# SGD + momentum\n",
-    "drr = DRR(subject, sdd=SDD, height=HEIGHT, delx=DELX).to(device)\n",
-    "reg = Registration(\n",
-    "    drr,\n",
-    "    rotations.clone(),\n",
-    "    translations.clone(),\n",
-    "    parameterization=\"euler_angles\",\n",
-    "    convention=\"ZXY\",\n",
-    ")\n",
-    "params_momentum = optimize(reg, ground_truth, momentum=1e-1)\n",
-    "print(params_momentum[\"loss\"].iloc[-1])\n",
-    "del drr\n",
-    "\n",
-    "# SGD + momentum + dampening\n",
-    "drr = DRR(subject, sdd=SDD, height=HEIGHT, delx=DELX).to(device)\n",
-    "reg = Registration(\n",
-    "    drr,\n",
-    "    rotations.clone(),\n",
-    "    translations.clone(),\n",
-    "    parameterization=\"euler_angles\",\n",
-    "    convention=\"ZXY\",\n",
-    ")\n",
-    "params_momentum_dampen = optimize(reg, ground_truth, momentum=1e-1, dampening=1e-2)\n",
-    "print(params_momentum_dampen[\"loss\"].iloc[-1])\n",
-    "del drr\n",
-    "\n",
-    "# Adam\n",
-    "drr = DRR(subject, sdd=SDD, height=HEIGHT, delx=DELX).to(device)\n",
-    "reg = Registration(\n",
-    "    drr,\n",
-    "    rotations.clone(),\n",
-    "    translations.clone(),\n",
-    "    parameterization=\"euler_angles\",\n",
-    "    convention=\"ZXY\",\n",
-    ")\n",
-    "params_adam = optimize(reg, ground_truth, 1e-1, 1e1, optimizer=\"adam\")\n",
-    "print(params_adam[\"loss\"].iloc[-1])\n",
-    "del drr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from base64 import b64encode\n",
-    "\n",
-    "from IPython.display import HTML, display\n",
-    "\n",
-    "from diffdrr.visualization import animate\n",
-    "\n",
-    "\n",
-    "def animate_in_browser(df):\n",
-    "    n = MAX_LENGTH - len(df)\n",
-    "    df = pd.concat([df, df.iloc[[-1] * n]])\n",
-    "\n",
-    "    out = animate(\n",
-    "        \"<bytes>\",\n",
-    "        df,\n",
-    "        drr,\n",
-    "        ground_truth=ground_truth,\n",
-    "        verbose=True,\n",
-    "        device=device,\n",
-    "        extension=\".webp\",\n",
-    "        duration=30,\n",
-    "        parameterization=\"euler_angles\",\n",
-    "        convention=\"ZXY\",\n",
-    "    )\n",
-    "    display(HTML(f\"\"\"<img src='{\"data:img/gif;base64,\" + b64encode(out).decode()}'>\"\"\"))\n",
-    "\n",
-    "\n",
-    "MAX_LENGTH = max(\n",
-    "    map(\n",
-    "        len,\n",
-    "        [\n",
-    "            params_base,\n",
-    "            params_momentum,\n",
-    "            params_momentum_dampen,\n",
-    "            params_adam,\n",
-    "        ],\n",
-    "    )\n",
-    ")\n",
-    "drr = DRR(subject, sdd=SDD, height=HEIGHT, delx=DELX).to(device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Precomputing DRRs: 100%|█████████████████████| 1/1 [00:00<00:00,  2.36it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<img src='data:img/gif;base64,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'>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Will only run in browser based Jupyter notebooks\n",
-    "animate_in_browser(params_base)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from src.drr import create_drr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "16 16 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "from fractions import Fraction\n",
-    "import math\n",
-    "\n",
-    "\n",
-    "def get_dimensions(n, ratio=Fraction(16, 3)):\n",
-    "    product = n * ratio\n",
-    "    root = round(math.pow(product, (1 / 3)))\n",
-    "\n",
-    "    reduced_root = round(root / ratio)\n",
-    "\n",
-    "    # Check if root is an uneven number\n",
-    "    if root % 2 != 0:\n",
-    "        root -= 1\n",
-    "\n",
-    "    return root, root, reduced_root\n",
-    "\n",
-    "\n",
-    "h, w, d = get_dimensions(758)\n",
-    "print(h, w, d)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "torch-gpu",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/pipeline.ipynb b/notebooks/pipeline.ipynb
deleted file mode 100644
index 0b3ead0..0000000
--- a/notebooks/pipeline.ipynb
+++ /dev/null
@@ -1,1013 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# TACE procedure methodology walkthrough\n",
-    "---\n",
-    "\n",
-    "This notebook demonstrates the end-to-end workflow for training a model that can reconstruct vessel enhanced DRR images from raw DRR images making use of the 3D spatial information extracted from medical CT volumes of the corresponding patient."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Importing libraries\n",
-    "This pipeline is mainly based on Pytorch and its medical imaging extension MONAI for data processing and model training and DiffDRR for DRR generation. Additionally, custom functionality is imported."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cuda:0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Import libraries\n",
-    "import sys\n",
-    "import random\n",
-    "import numpy as np\n",
-    "\n",
-    "sys.path.append(\"..\")\n",
-    "\n",
-    "from src.preprocessing import get_transforms, get_datasets, get_dataloaders, add_vessel_contrast\n",
-    "#from src.config import config\n",
-    "from src.drr import create_drr\n",
-    "\n",
-    "import torch\n",
-    "import matplotlib.pyplot as plt\n",
-    "from diffdrr.visualization import plot_drr, plot_mask\n",
-    "\n",
-    "import torch\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "from diffdrr.drr import DRR\n",
-    "from diffdrr.visualization import plot_drr, plot_mask\n",
-    "from diffdrr.data import read\n",
-    "\n",
-    "from diffdrr.pose import convert\n",
-    "from diffdrr.visualization import drr_to_mesh, img_to_mesh\n",
-    "import pyvista\n",
-    "\n",
-    "from src.model import TACEnet\n",
-    "from monai.losses import DiceCELoss\n",
-    "\n",
-    "from torchvision import transforms\n",
-    "\n",
-    "# Set the device\n",
-    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
-    "print(device)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Data preprocessing\n",
-    "In this pipeline we make use of HCC-TACE-Seg dataset. This retrospectively acquired data collection includes pre- and post-procedure CT imaging studies of 105 confirmed HCC patients who underwent TACE between 2002 and 2012 with an available treatment outcome, in the form of time-to-progression and overall survival. Baseline imaging includes multiphasic contrast-enhanced CT with no image artifacts (e.g. surgical clip) and was obtained 1-12 weeks (average 3 weeks) prior to the first TACE session. Semiautomatic segmentation of liver, tumor, and blood vessels created using AMIRA was manually clinically curated. These segmentations of each pre-procedural CT study were done for the purpose of algorithm training for prediction and automatic liver tumor segmentation, and are provided here (NIfTI converted to DICOM-SEG format)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The volumes and segmnetations have the following shapes when loaded from a batch:\n",
-    "\n",
-    "- volume: (1, 1, 512, 512, D) -> (B, C, H, W, D) where the depth (D) is variable\n",
-    "- segmentation (1, 5, 512, 512, D) -> (B, C, H, W, D) where the depth (D) is variable and correpsonds to volume\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following tranformations are then performed:\n",
-    "1. Load volume and segmentation to MONAI meta tensor.\n",
-    "2. Ensure the channel dimension is placed before the spatial dimensions\n",
-    "3. Resize the volume and segmnetation to a new desired dimension, using padding and cropping (optional).\n",
-    "4. Isolate the vessel channel of the multi class segmentation mask. This essentially turns the task in a binary segmentation problem where the segmentation will have a shape equal to the volume: (1, 1, 512, 512, D)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0] [26 61  2 62 85 48 16 99 56 45 66 96 51 13  7 30 22 24 33  8]\n",
-      "1 20\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get transforms\n",
-    "transform = get_transforms(resize_shape= [512, 512, 96],contrast_value=1000)\n",
-    "\n",
-    "# Get datasets\n",
-    "train_ds, val_ds = get_datasets(\n",
-    "    root_dir=\"../data081\",\n",
-    "    collection=\"HCC-TACE-Seg\",\n",
-    "    transform=transform,\n",
-    "    download=False,\n",
-    "    val_frac=0.2,\n",
-    "    download_len=2\n",
-    ")\n",
-    "\n",
-    "# Get dataloaders\n",
-    "train_loader, val_loader = get_dataloaders(train_ds, val_ds, batch_size=1)\n",
-    "\n",
-    "# Check length of datasets and dataloaders to make sure data split is correct\n",
-    "print(train_ds.get_indices(), val_ds.get_indices())\n",
-    "print(len(train_loader), len(val_loader))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\Sven\\anaconda3\\envs\\torch\\Lib\\site-packages\\monai\\transforms\\io\\array.py:259: UserWarning: Failed to read ..\\data081\\HCC-TACE-Seg\\HCC_081\\300\\image\\LICENSE with exception: \n",
-      "File is missing DICOM File Meta Information header or the 'DICM' prefix is missing from the header. Use force=True to force reading..\n",
-      "  img = reader.read(filename)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([1, 1, 512, 512, 96]) torch.Size([1, 1, 512, 512, 96])\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Sample a batch of data from the dataloader\n",
-    "batch = next(iter(train_loader))\n",
-    "\n",
-    "# Separate the image and segmentation from the batch\n",
-    "image, seg = batch[\"image\"], batch[\"seg\"]\n",
-    "\n",
-    "print(image.shape, seg.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(metatensor(0.0243), metatensor(0.0006))"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "torch.std_mean(seg)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From these volumes and segmentation we can take a slice and visualize the slice to evaulate the inputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "slice_idx = 40\n",
-    "\n",
-    "# Sample a slice from the image\n",
-    "CT_slice = image[0, 0, :, :, slice_idx].cpu()\n",
-    "\n",
-    "# Get the maximum segmentation class for each pixel in the slice\n",
-    "CT_seg_slice = seg[0, 0, :, :, slice_idx].cpu()\n",
-    "\n",
-    "# Plot the image and segmentation slice as a subplot\n",
-    "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
-    "ax[0].imshow(CT_slice, cmap=\"gray\")\n",
-    "ax[0].set_title(\"CT Image\")\n",
-    "ax[0].axis(\"off\")\n",
-    "ax[1].imshow(CT_seg_slice, cmap=\"tab20\")\n",
-    "ax[1].set_title(\"CT Segmentation\")\n",
-    "ax[1].axis(\"off\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Creating DRR images\n",
-    "DRR images are essentially simulated X-ray images reconsturcted by raytracing through a given 3D CT volume. The DiffDRR package comes with most of the functionality needed out-of-the-box. We will demonstrate below how to create DRR images and control output rotation, zoom and more. Currently, this does not works vectorized over a batch. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Extract first image and segmentation from the batch\n",
-    "img = image[0]\n",
-    "seg = seg[0]\n",
-    "\n",
-    "# Read in CT volume and label map into a subject object\n",
-    "subject = read(\n",
-    "    tensor=img,\n",
-    "    label_tensor=seg,\n",
-    "    orientation=\"AP\",\n",
-    "    bone_attenuation_multiplier=5,\n",
-    ")\n",
-    "\n",
-    "# Create a DRR object, this will project the CT to the DRR\n",
-    "drr = DRR(\n",
-    "    subject,  # A torchio.Subject object storing the CT volume, origin, and voxel spacing\n",
-    "    sdd=1020,  # Source-to-detector distance (i.e., the C-arm's focal length)\n",
-    "    height=200,  # Height of the DRR (if width is not seperately provided, the generated image is square)\n",
-    "    width=200,  # Width of the DRR\n",
-    "    delx=2.0,  # Pixel spacing (in mm)\n",
-    "    dely=2.0,  # Pixel spacing (in mm)\n",
-    "    x0=0,  # # Principal point X-offset\n",
-    "    y0=0,  # Principal point Y-offset\n",
-    "    p_subsample=None,  # Proportion of pixels to randomly subsample\n",
-    "    reshape=True,  # Return DRR with shape (b, 1, h, w)\n",
-    "    reverse_x_axis=True,  # If True, obey radiologic convention (e.g., heart on right)\n",
-    "    patch_size=None,  # Render patches of the DRR in series\n",
-    "    renderer=\"siddon\",  # Rendering backend, either \"siddon\" or \"trilinear\"\n",
-    ").to(device)\n",
-    "\n",
-    "# Set the camera pose with rotations (yaw, pitch, roll) and translations (x, y, z)\n",
-    "zero = torch.tensor([[0.0, 0.0, 0.0]], device=device)\n",
-    "translations = torch.tensor([[0.0, 800.0, 0.0]], device=device)\n",
-    "\n",
-    "# Define the camera pose with rotations and translations in degrees, later converted to radians\n",
-    "rotations = torch.tensor([[0.0, 0.0, 0.0]], device=device)\n",
-    "rotations = torch.deg2rad(rotations)\n",
-    "\n",
-    "# Convert the rotations and translations to a pose matrix\n",
-    "pose1 = convert(zero, translations, parameterization=\"euler_angles\", convention=\"ZXY\")\n",
-    "pose2 = convert(rotations, zero, parameterization=\"euler_angles\", convention=\"ZXY\")\n",
-    "pose = pose1.compose(pose2)\n",
-    "\n",
-    "# Generate the DRR\n",
-    "drr_image_raw = drr(pose, mask_to_channels=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For our task we would need to also simulate DRR images with an enhanced vessel network corresponding to the same CT volume. Since DRR images are directly created from a CT volume the enhancement needs to be applied to the CT itself first"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Enhance the vessel contrast in the CT using the segmentation\n",
-    "img_enhanced = add_vessel_contrast(img, seg, contrast_value=2000)\n",
-    "\n",
-    "# Read in CT volume and label map into a subject object\n",
-    "subject_enhanced = read(\n",
-    "    tensor=img_enhanced,\n",
-    "    label_tensor=seg,\n",
-    "    orientation=\"AP\",\n",
-    "    bone_attenuation_multiplier=5,\n",
-    ")\n",
-    "\n",
-    "# Create a DRR object, this will project the CT to the DRR\n",
-    "drr_enhanced = DRR(\n",
-    "    subject_enhanced,  # A torchio.Subject object storing the CT volume, origin, and voxel spacing\n",
-    "    sdd=1020,  # Source-to-detector distance (i.e., the C-arm's focal length)\n",
-    "    height=200,  # Height of the DRR (if width is not seperately provided, the generated image is square)\n",
-    "    width=200,  # Width of the DRR\n",
-    "    delx=2.0,  # Pixel spacing (in mm)\n",
-    "    dely=2.0,  # Pixel spacing (in mm)\n",
-    "    x0=0,  # # Principal point X-offset\n",
-    "    y0=0,  # Principal point Y-offset\n",
-    "    p_subsample=None,  # Proportion of pixels to randomly subsample\n",
-    "    reshape=True,  # Return DRR with shape (b, 1, h, w)\n",
-    "    reverse_x_axis=True,  # If True, obey radiologic convention (e.g., heart on right)\n",
-    "    patch_size=None,  # Render patches of the DRR in series\n",
-    "    renderer=\"siddon\",  # Rendering backend, either \"siddon\" or \"trilinear\"\n",
-    ").to(device)\n",
-    "\n",
-    "# Set the camera pose with rotations (yaw, pitch, roll) and translations (x, y, z)\n",
-    "zero = torch.tensor([[0.0, 0.0, 0.0]], device=device)\n",
-    "translations = torch.tensor([[0.0, 800.0, 0.0]], device=device)\n",
-    "\n",
-    "# Define the camera pose with rotations and translations in degrees, later converted to radians\n",
-    "rotations = torch.tensor([[0.0, 0.0, 0.0]], device=device)\n",
-    "rotations = torch.deg2rad(rotations)\n",
-    "\n",
-    "# Convert the rotations and translations to a pose matrix\n",
-    "pose1 = convert(zero, translations, parameterization=\"euler_angles\", convention=\"ZXY\")\n",
-    "pose2 = convert(rotations, zero, parameterization=\"euler_angles\", convention=\"ZXY\")\n",
-    "pose = pose1.compose(pose2)\n",
-    "\n",
-    "# Generate the DRR\n",
-    "drr_image_enhanced = drr_enhanced(pose, mask_to_channels=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The implementation of the DRR generation allows us to visualize both the entire image and only certain structures of interest, in this case the vessels. We can now visualize the raw DRR, the vessels only and the enhanced DRR. Note that the output of the DRR has the following shape:\n",
-    "\n",
-    "$$(B, C, H, W) -> (1, 2, 200, 200)$$\n",
-    "\n",
-    "Where the B does not correspond to the dataloaders batch, but the number of different poses that are provided. This means that, although DRR image generation currently only works with 1 volume segmentation pair at once, per volume multiple DRR orientations can be created in parallel. Since we are interested in changing the angle this makes it perfectly sufficient to only uses batches of size 1 and create multiple DRR images for training per batch iteration.\n",
-    "\n",
-    "C is the number of channels per generated DRR (this by setting `mask_to_channel = True`) and allows for conditional rendering of different structures. Summing over the channel dimension will give the expected image. Note that the `plot_drr` function takes in a shape of (B, C, H, W), which explains the added logic below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Plot the segmentation masks\n",
-    "fig, axs = plt.subplots(\n",
-    "    nrows=1,\n",
-    "    ncols=3,\n",
-    "    figsize=(14, 7.75),\n",
-    "    tight_layout=True,\n",
-    "    dpi=300,\n",
-    ")\n",
-    "\n",
-    "# Plot the DRR, DRR with vessel contrast, and the enhanced DRR\n",
-    "plot_drr(drr_image_raw.sum(dim=1, keepdim=True), axs=axs[0], ticks=False, title=\"DRR\")\n",
-    "plot_drr(\n",
-    "    drr_image_enhanced[:, 1, :, :].unsqueeze(dim=1),\n",
-    "    axs=axs[1],\n",
-    "    ticks=False,\n",
-    "    title=\"DRR Vessel Only\",\n",
-    ")\n",
-    "plot_drr(\n",
-    "    drr_image_enhanced.sum(dim=1, keepdim=True),\n",
-    "    axs=axs[2],\n",
-    "    ticks=False,\n",
-    "    title=\"Vessel Overlay\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. Model definition\n",
-    "The model used for this task, consists of 2 parts:\n",
-    "1. 3D CT volume embedding module: This part takes in a CT volume and embeds it to a 2D plane essentially compressing all 3D information in a 2D projection.\n",
-    "2. The second part uses this CT embedding in combination with a rawm unenhanced X-ray (DRR) image to reconstruct a vessel enhanced DRR image that can be usefull for a surgeon during image guided TACE procedures.\n",
-    "\n",
-    "IMAGE OF FINAL MODEL STRUCTURE "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define model\n",
-    "model = TACEnet()\n",
-    "\n",
-    "# Move model to device\n",
-    "model.to(device)\n",
-    "\n",
-    "# Initialize the criterion, DICE loss with cross entropy uses binary cross entropy when there are only two classes\n",
-    "criterion = torch.nn.MSELoss()\n",
-    "\n",
-    "# Initialize the optimizer, use config to set hyperparameters\n",
-    "optimizer = torch.optim.AdamW(\n",
-    "    model.parameters(),\n",
-    "    lr=2e-3,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5. Model Training\n",
-    "The training of the model is not as straight forward as compared to simple feedforward networks for classification. We need to execute the following steps for each image in a given batch:\n",
-    "\n",
-    "1. Use the raw 3D CT volume to create the unenhanced DRR image.\n",
-    "2. Add vessel enhancement to the CT volume making use of the corresponding vessel segmentation map.\n",
-    "3. Use the enhanced 3D CT volume to create the vessel enhanced DRR image.\n",
-    "4. Embed the CT volume in latent space using the models encoder part\n",
-    "\n",
-    "Then in terms of bayesian notation the problem boils down to the following where the exact model architecture is arbitrary:\n",
-    "\n",
-    "$$p(DRR_e | DRR_r, CT_{embed})$$\n",
-    "\n",
-    "with for clarity:\n",
-    "- $DRR_e$ = The vessel enhanced DRR image\n",
-    "- $DRR_r$ = The raw DRR image\n",
-    "- $CT_{embed}$ = The CT latent embedding\n",
-    "\n",
-    "This expression can be read as \"the probability of $DRR_e$  given $DRR_r$ and $CT_{embed}$.\n",
-    "\n",
-    "We are trying to model the probability distribution of a vessel enhanced DRR image given a raw DRR image and a CT latent embedding. This is a bayesian interference task, where we are trying to infer the vessel enhanced DRR image from the raw DRR image and the CT latent embedding 3D spatial data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At a high level, the task can be explained as a conditional image impainting or object recovery task. You might be familiar with text-based image impainting, where a model is prompted with text to change an image accordingly. This works by embedding the text prompt in latent space, to stir the image impainting/generation. In this case, the text prompt is replaced with a CT latent embedding, which guides the transformation of the image or helps in recovering the vessel object.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\Sven\\anaconda3\\envs\\torch\\Lib\\site-packages\\monai\\transforms\\io\\array.py:259: UserWarning: Failed to read ..\\data081\\HCC-TACE-Seg\\HCC_081\\300\\image\\LICENSE with exception: \n",
-      "File is missing DICOM File Meta Information header or the 'DICM' prefix is missing from the header. Use force=True to force reading..\n",
-      "  img = reader.read(filename)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Input volume and target shape: (torch.Size([1, 1, 512, 512, 96]), torch.Size([1, 1, 512, 512, 96]))\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Sample a batch of data from the dataloader\n",
-    "batch = next(iter(train_loader))\n",
-    "\n",
-    "# Separate the image and segmentation from the batch\n",
-    "volumes, targets = batch[\"image\"], batch[\"seg\"]\n",
-    "volumes_enhanced = add_vessel_contrast(volumes, targets, contrast_value=4000)\n",
-    "\n",
-    "\n",
-    "print(f\"Input volume and target shape: {volumes.shape, targets.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 1200x400 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "drr_raw = create_drr(\n",
-    "        volumes_enhanced[0],\n",
-    "        targets[0],\n",
-    "        bone_attenuation_multiplier=5.0,\n",
-    "        sdd=1020,\n",
-    "        height=256,\n",
-    "        width=256,\n",
-    "        rotations=torch.tensor([[0.0, 0.0, 0.0]]),\n",
-    "        translations=torch.tensor([[0.0, 850.0, 0.0]]),\n",
-    "        mask_to_channels=True,\n",
-    "        device=\"cpu\",\n",
-    "        )\n",
-    "\n",
-    "\n",
-    "drr_body = drr_raw[:,0,:,:]\n",
-    "drr_vessels = drr_raw[:,1,:,:]\n",
-    "\n",
-    "drr_body = drr_body/(torch.max(drr_body.flatten()))\n",
-    "drr_vessels = (drr_vessels/(torch.max(drr_vessels.flatten())))*2\n",
-    "\n",
-    "ef = 1\n",
-    "drr_combined = drr_body + ef*drr_vessels\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Create a figure and a set of subplots\n",
-    "fig, axs = plt.subplots(1, 3, figsize=(12, 4))\n",
-    "\n",
-    "# Plot each image in a subplot\n",
-    "axs[0].imshow(drr_combined.squeeze(), cmap='gray',vmax = 3)\n",
-    "axs[0].set_title(ef)\n",
-    "\n",
-    "ef = 0.5\n",
-    "drr_combined = drr_body + ef*drr_vessels\n",
-    "\n",
-    "axs[1].imshow(drr_combined.squeeze(), cmap='gray',vmax = 3)\n",
-    "axs[1].set_title(ef)\n",
-    "\n",
-    "ef = 0.3\n",
-    "drr_combined = drr_body + ef*drr_vessels\n",
-    "\n",
-    "axs[2].imshow(drr_combined.squeeze(), cmap='gray',vmax = 3)\n",
-    "axs[2].set_title(ef)\n",
-    "\n",
-    "# Hide the axes labels\n",
-    "for ax in axs:\n",
-    "        ax.axis('off')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'whiskers': [<matplotlib.lines.Line2D at 0x1e4bbd803b0>,\n",
-       "  <matplotlib.lines.Line2D at 0x1e4bbd81d30>],\n",
-       " 'caps': [<matplotlib.lines.Line2D at 0x1e4bbd82f00>,\n",
-       "  <matplotlib.lines.Line2D at 0x1e4afb39ca0>],\n",
-       " 'boxes': [<matplotlib.lines.Line2D at 0x1e4bbd0e1b0>],\n",
-       " 'medians': [<matplotlib.lines.Line2D at 0x1e4afb381a0>],\n",
-       " 'fliers': [<matplotlib.lines.Line2D at 0x1e4afb381d0>],\n",
-       " 'means': []}"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "plt.boxplot(drr_vessels.flatten())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<All keys matched successfully>"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.load_state_dict(torch.load('../models/TACEnet_vessel_enhancement_2_0.pth'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loss: 0.3224516212940216\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\Sven\\anaconda3\\envs\\torch\\Lib\\site-packages\\torch\\_tensor.py:1418: UserWarning: Using a target size (torch.Size([1, 256, 256])) that is different to the input size (torch.Size([1, 1, 256, 256])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
-      "  ret = func(*args, **kwargs)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1200x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loss: 1.1897306442260742\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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wf/AHf2D27NljHnzwQfO9733PXHDBBQaAedOb3mTPu+6660wYhuZrX/ta2/Xvete7DADz9a9/va1MAOZ73/uePXbXXXeZQqFgLr74YnuM/Pqc5zynrcw777zTRFFkXve617Ud//GPf2wymYw9/v3vf98AMB/5yEdS29dvWcb0Px5+97vfNQDM+9///o77ucaLa665xgRB0DbWvfjFLzZppqF+Xy666CKTy+XMr371K3ts165dZmhoyJx77rn22FLek+WAD0Ve4xgcHOzIjnzSSSdhbGwM27dvxwtf+EIcf/zx+NznPodSqdRx/Y033oixsTGMjY3hjDPOwHXXXYfLLrsMb3rTm1arCR4eK4brr78eW7ZsweMe9zgArVCZ3/3d38UHP/jB1HDgfnDWWWfhpptu6vh7znOe03HuH/7hH7Z9P+ecc/DrX/+647zf/d3fbYuU4IyqPFd6g6vVKvbu3YtHPepRAIBbbrmlr3tPTExgZmYGQCtsOEkSvPrVr0YYtg/39P7edNNNmJqawnOe8xzs3bvX/kVRhLPOOst6cO6//3784Ac/wAte8AI74wy0EnyceuqpHXXzOPRhjMHHPvYxPPWpT4Uxpu392blzJ6anpzve28suuwy5XM5+d8kB0OI+ub4ul8vhkY98ZNt5URTZspIkweTkJJrNJh7xiEc45aWXDO7Zswdf/epX8cIXvhDHHHNM27WUl6W0+bOf/SyOOOIIPOMZz7DllEolvOhFL+rar/1AjhXT09PYu3cvzjvvPPz617/uCHc87rjjOtYkfv7zn8e2bdvwtKc9zR4rFAod0WE/+MEPcPvtt+P3fu/3MDExYds6Pz+P888/H1/96leRJMkBt8djebBUmdCYnJzEzTffjGc961mYnZ21z3tiYgI7d+7E7bffjvvuu6/tmhe96EVt0UTnnHMO4jjGXXfddUBt+Zd/+ReMjY1hfHwcj3jEI/DFL34Rf/EXf4E//dM/ted85CMfwSmnnIKTTz65TRYf//jHA0BHBML/+T//B2eeeab9fswxx+B3fud38IUvfKFDZ9D8+vGPfxxJkuBZz3pW2722bt2KE044wd6L/PiFL3zBuTRoKWUR/YyH3SDHi/n5eezduxePfvSjYYzB97///b7KkIjjGDfeeCMuuugiPOQhD7HHjzjiCPze7/0e/ud//sfqIcRKvScaPhR5jWNubg7j4+Ntxz72sY9heHgYe/bswT/8wz/gjjvuSE0EcdZZZ+G1r30t4jjGT37yE7z2ta/Fvn372hQLD4/1iDiO8cEPfhCPe9zjcMcdd9jjZ511Ft785jfji1/8og21XSo2b96M3/7t3+55XqFQ6Ai127BhA/bt29dxrlaUqWDLcycnJ3H11Vfjgx/8IB588MG287Wy2qvM4eFh/OpXv0IYhl0Nz9tvvx0ArCKgMTw8DACWfE444YSOc0466aS+lCaPQwt79uzB1NQU3vOe9+A973mP8xz9HvcjBwBw1FFHdYTeb9iwAT/60Y/ajv3bv/0b3vzmN+PWW29Fo9Gwx4877riOuvS6N5XEbnvoLqXNd911F44//viOdpx00kmp5feLr3/967jyyivxzW9+s0N5np6ebpt8cvXFXXfdhR07dnTUTWeU5/jwghe8ILUu09PTfnnTGsJSZELjl7/8JYwxeNWrXoVXvepVznMefPBBbNu2zX7vV6aXit/5nd/BS17yEtTrdXz3u9/F61//epTL5bZJ2ttvvx0///nPU0Pe9fjj4q8TTzwR5XIZe/bswdatW+1x3V+33347jDHOMgDY5QzHHXcc/vRP/xRvectbcP311+Occ87B0572NPz+7/++lct+yyL6HQ/TcPfdd+PVr341brjhho7n4tItemHPnj0ol8vOseyUU05BkiS45557cNppp9njK/WeaHjDdg3j3nvvxfT0dAfRnHvuuXb9ylOf+lScccYZeO5zn4v//d//7fDKSAV9586dOPnkk/GUpzwFb3/729tmvTw81htuvvlm3H///fjgBz+ID37wgx2/X3/99ftt2PaLKIoO+FyzsHYHaK2Z/cY3voE///M/x8Me9jAMDg4iSRJccMEFTq9IP2X2Asu97rrr2kidWA/p/T0ODvju/P7v/36q4aO3q+v3ne3nvA984AO49NJLcdFFF+HP//zPMT4+jiiKcM011zgTJC6nvCylzcuNX/3qVzj//PNx8skn4y1veQuOPvpo5HI5fPazn8Vb3/rWjrHiQDIgs6w3velNqVuIDA4O7nf5HsuLpcqEBp/3K1/5ytTMw1onXQ65cuGoo46y+uuTn/xkbN68GS95yUvwuMc9zq4LTpIEZ5xxBt7ylrc4yzj66KP3+/5abpIkQRAE+NznPudss5SDN7/5zbj00kvxqU99CjfeeCNe9rKX4ZprrsG3vvUtHHXUUUsqCziwPo7jGE94whMwOTmJv/zLv8TJJ5+MgYEB3Hfffbj00ktXLeJipd4TDa+xrGFw8Xa3tOaDg4O48sorcdlll+HDH/4wnv3sZ3ct88ILL8R5552H17/+9bjiiisO+/TlHusX119/PcbHx20WP4mPf/zj+MQnPoF3vetd62Zbi3379uGLX/wirr76arz61a+2x+kx2R/s2LEDSZLgZz/7WapSykQc4+PjXb3Uxx57bGp9brvttv2uo8f6xdjYGIaGhhDHcV8RDsuNj370o3jIQx6Cj3/8423ejP3d7oshdT/5yU9Sz1lKm4899lj85Cc/gTGmrX79yov20BCf/vSnUavVcMMNN7R5QVyJn7rV7Wc/+1lH3XSGao4Pw8PDPdubVl+P1UO/MpH2rCgD2Wx2WWV6Od6NK664Am9961vxN3/zN7j44osRBAF27NiBH/7whzj//PP7uoeLv37xi1+gVCr1THS2Y8cOGGNw3HHH4cQTT+x5rzPOOANnnHEG/uZv/gbf+MY38JjHPAbvete78NrXvnbJZfWDtPb/+Mc/xi9+8Qv827/9G57//Ofb4zfddFPfZWiMjY2hVCo5x7Jbb70VYRge0KTCgcCvsV2juPnmm/G3f/u3OO644/Dc5z6367nPfe5zcdRRR7VlQu2Gv/zLv8TExAT++Z//eTmq6uGx6qhUKvj4xz+OpzzlKXjGM57R8feSl7wEs7OzHdsTrGVwNlPPXr7tbW/b7zIvuugihGGI17zmNR2zsrzPzp07MTw8jNe//vVtYWsE0/EfccQReNjDHoZ/+7d/awtduummm/Czn/1sv+vosX4RRRGe/vSn42Mf+5jTGFyJrRz0/YF2mfn2t7+Nb37zm/tV3tjYGM4991y8733vw9133932G++xlDY/+clPxq5du/DRj37UHiuXy6khzBqceJ6ammo77mr39PQ03v/+9/dVLtCS+/vuu69tjKxWqx16wZlnnokdO3bg7//+7zE3N9dRjmxvWn09Vg/9ygRzsuhnNT4+jsc+9rF497vfjfvvv7+j/P2V6YGBgQN+LzKZDP7sz/4MP//5z/GpT30KQCvK6b777nPqs5VKBfPz823HvvnNb7Ytm7nnnnvwqU99Ck984hN7RmBdcskliKIIV199dQdPG2PstnwzMzNoNpttv59xxhkIw9BuQdRvWUvBUsYLYwze/va3912GRhRFeOITn4hPfepTbVt67d69G//xH/+Bs88+2y5jWm14j+0awOc+9znceuutaDab2L17N26++WbcdNNNOPbYY3HDDTf03BQ5m83i5S9/Of78z/8cn//853HBBRd0Pf9JT3oSTj/9dLzlLW/Bi1/84o5Yfg+PtY4bbrgBs7OzbYlPJB71qEdhbGwM119/PX73d393yeXfd999zv2jBwcHcdFFFy25vH4wPDyMc889F2984xvRaDSwbds23HjjjW3rh5eK448/Hn/913+Nv/3bv8U555yDSy65BPl8Ht/97ndx5JFH4pprrsHw8DCuvfZaPO95z8Nv/uZv4tnPfjbGxsZw99134zOf+Qwe85jH4B//8R8BANdccw0uvPBCnH322XjhC1+IyclJvOMd78Bpp53mVHo9Dn383d/9Hb70pS/hrLPOwuWXX45TTz0Vk5OTuOWWW/Df//3fmJycXLF7P+UpT8HHP/5xXHzxxbjwwgtxxx134F3vehdOPfXU/X4f/+Ef/gFnn302fvM3fxMvetGLcNxxx+HOO+/EZz7zGfzgBz8A0H+bL7/8cvzjP/4jnv/85+N///d/ccQRR+C6665zJnp0gUlu/vqv/xrPfvazkc1m8dSnPhVPfOITkcvl8NSnPhVXXHEF5ubm8M///M8YHx93GiMuXHHFFfjHf/xHPOc5z8HLX/5yHHHEEbj++uutvkHPTRiGeO9734snPelJOO2003DZZZdh27ZtuO+++/ClL30Jw8PD+PSnP921vj4ybHnxvve9D5///Oc7jr/85S/vWyaKxSJOPfVUfOhDH8KJJ56IjRs34vTTT8fpp5+Of/qnf8LZZ5+NM844A5dffjke8pCHYPfu3fjmN7+Je++9Fz/84Q+XXOczzzwT1157LV772tfi+OOPx/j4eGpeh2649NJL8epXvxpveMMbcNFFF+F5z3sePvzhD+MP//AP8aUvfQmPecxjEMcxbr31Vnz4wx+2+zcTp59+Onbu3Nm23Q/Q2havF3bs2IHXvva1+Ku/+ivceeeduOiiizA0NIQ77rgDn/jEJ/CiF70Ir3zlK3HzzTfjJS95CZ75zGfixBNPRLPZxHXXXWcnxZZS1lKwY8cOjI6O4l3veheGhoYwMDCAs846CyeffDJ27NiBV77ylbjvvvswPDyMj33sY861rZThl73sZdi5cyeiKEqNBH3ta1+Lm266CWeffTb++I//GJlMBu9+97tRq9U69hxfVSxrjmWPJYEpsPmXy+XM1q1bzROe8ATz9re/3czMzLSdzxTke/bs6ShrenrajIyMtKVTP/bYY82FF17ovPe//uu/pqYF9/BY63jqU59qCoVCxxZXEpdeeqnJZrN2Sw4sw3Y/ckubF7zgBWZgYKDjesopwa1G5BYFBFT6/HvvvddcfPHFZnR01IyMjJhnPvOZZteuXR3npY0FHFP0dhvve9/7zMMf/nCTz+fNhg0bzHnnnWe3SCK+9KUvmZ07d5qRkRFTKBTMjh07zKWXXtq2NYIxxnzsYx8zp5xyisnn8+bUU081H//4x80LXvACv93PYYzdu3ebF7/4xeboo4822WzWbN261Zx//vnmPe95jz2H2/3oLTAoH5KLzjvvPHPaaad13Ee/Z0mSmNe//vXm2GOPNfl83jz84Q83//Vf/9Vx3lJk0BhjfvKTn1g5LBQK5qSTTjKvetWrltxmY1rbiTztaU8zpVLJbN682bz85S83n//85/va7scYY/72b//WbNu2zYRh2CbbN9xwg3noQx9qCoWC2b59u3nDG95g3ve+93XIfzc94Ne//rW58MIL7ZaBf/Znf2Y+9rGPGQDmW9/6Vtu53//+980ll1xiNm3aZPL5vDn22GPNs571LPPFL36xr/p6HDi0zqj/7rnnnr5lwhhjvvGNb5gzzzzT5HK5Djn41a9+ZZ7//OebrVu3mmw2a7Zt22ae8pSnmI9+9KMd9dHbD1HW5fv9wAMPmAsvvNAMDQ0ZAD23/unG11dddVVb+fV63bzhDW8wp512muW4M88801x99dVmenq6o8wPfOAD5oQTTrD9o+Wwm65tTIsDzz77bDMwMGAGBgbMySefbF784heb2267zRjTkqsXvvCFZseOHaZQKJiNGzeaxz3ucea///u/l1yWMf2Ph8a0tjA79dRTTSaTaRtXf/azn5nf/u3fNoODg2bz5s3m8ssvNz/84Q87xt5ms2le+tKXmrGxMRMEQZsu4xorb7nlFrNz504zODhoSqWSedzjHme+8Y1vtJ2zlPdkORAsVNbDw8PDw8PDw+Mwxtve9ja84hWvwL333tuW+dbDY70jCAK8+MUvthFIHocm/BpbDw8PDw8PD4/DDJVKpe17tVrFu9/9bpxwwgneqPXw8FiX8GtsPTw8PDw8PDwOM1xyySU45phj8LCHPQzT09P4wAc+gFtvvRXXX3/9wa6ah4eHx37BG7YeHh4eHh4eHocZdu7cife+9724/vrrEccxTj31VHzwgx/cr4R7Hh4eHmsBfo2th4eHh4eHh4eHh4eHx7qGX2Pr4eHh4eHh4eHh4eHhsa7hDVsPDw8PDw8PDw8PDw+PdQ1v2Hp4eHh4eHh4eHh4eHisa/SdPCoIgpWsh4fHIYO1vmw9k8nAGLPm6+nhcTARBAHiOD7Y1UiF52QPj/6w1rlu50NfhQfO3YjyEQZJ9mDXxsNjbSEAEFUCDN1l8L33/WnP831WZA8PDw8PDw8PD4+DgUyIZgloDhok+eRg18bDY00hMAGSKERjoL/JXG/Yenh4eHh4eHh4eBwEmCAAQsCE8AsEPTwUDAxMZGDC/gxbL0IeHh4eHh4CPszXw8PDw8Nj/cEbth4eHh4eHh4eHh4eHh7rGt6w9fDw8PDw8PDw8PDw8FjX8Iath4eHh4eHh4eHh4eHx7qGN2w9PDw8PDw8PDw8PDw81jW8Yevh4eHh4eHh4eHh4eGxruENWw8PDw8PDw8PDw8PD491DW/Yenh4eHh4eHh4eHh4eKxreMPWw8PDw8PDw8PDw8PDY13DG7YeHh4eHh4eHh4eHh4e6xresPXw8PDw8PDw8PDw8PBY1/CGrYeHh4eHh4eHh4eHh8e6hjdsPTw8PDw8PDw8PDw8PNY1vGHr4eHh4eHh4eHh4eHhsa7hDVsPDw8PDw8PDw8PDw+PdY3MwbhpEAQdx4wxB6EmHh4eB4IwDJ2y6+XZw2P9wHOyh4eHh8ehgFU3bIMgQBRFbcf6VYy9Au3hsXYQhiHCsD3oYyky6uXZw+Pgw3Oyh4eHh8ehglU1bEmgkkSXm0CXSrSehD08lo4gCBCG4X7J8lLO9fLs4bFy8Jzs4eHh4XEoYVUNWyrCa4FED5SAPfl6HM44WLK8mmV6eBzq8Jzs4eHh4XEoYdUMW5KnJlFgbRHkch7zYVoehyKiKEImk0Emk+kIRQaWVzbW4zEPj/UAz8npxzw8PDw81idWxbDlrHAmk0EURX0rw/38vlYIs99jXon2WM+QHh7XGluin3d1vcma9zJ5HCrwnLxy5Xt4eHh4HDysuGEr1/CkESjP61WOC0shlrVCfCt5z2794UnY40DQryzz3H7Kc6Hf93QtyNbB9CZ5efbYH3hOPvjjRj+/eXh4eHgsHStq2C5FET6Qeyz3ucs9E71WydXPSnv0C8pytxDk5bzXcp4HrG95W+n7eRw+8Jx8YNctZ1mekz08PDyWHytq2Opwp/WCNLI9WCTc77H1cI7H+kQYhl3DFtc6XLK7EvLsOr6aivByle2V6UMTnpN7H/ec7OHh4bF+sWKGLRVhEqgmoEN5gD0QEu7VLwdT2e3nnOW6pttxj9WFVIQpy4fTs1kppdr128FUclfaAD6c3pm1CM/J/R+X8Jzc+7iHh4fHWsGKGLZBEHQowq5z1grW0mC9v+uaXFjvZHogM9Br6ZmuZ0gPTyaTse/fSsrvofbsuvXVcodTrxXZ3Z9rvCyvHDwn7z88Jx943bod9/Dw8FhOLLthSwJdjbV4y4W1ROjLCVe7lkIua8l7tBJl+HDL7pDratOU4ZW676GGbu/VWlpT7Dq2FmV1pWXZGHPIvIeek9cOPCd7Tvbw8FhZLLth22sLAY+Di5VI7AEsbZZ2LSrCK0HI6x2rlSzqcMBqK+oH6pFbTnleCwpyt+9pbT1UjCvPyWsbnpNX57uHh8fhgWU1bKUivJ4SU3gcOA7mGqbVJuXDgUC1UXu4ras93HEw1xWvhe+HmlHrOfnwg+dkDw+PwxHLZtj2SkyxkvCD2PrGcq5hAg6+YrzekSbLy2HUeBweWK51xcDB8d4eCoat52SP/YXnZA8Pj/WKZTFs9RqetRB253H44kBJ13XscCHS5ZDltSKPh8ozWUmslWfVDQdDnvfnvmsJnpM91hI8J3t4eKwWlsWwPZgEutawHAO4x+pipdYjrjdQGc5ms4eELLP++vkc7HYt5X052HVdj1iujL/rue89Jy/Cc/L6g+dkDw+P/cUBG7brLdtiGvpdj3Kga8+6oZ9ZyrRj/fzmcWBYyWe/FhBFkTVqD4V1ta4Q6n7k+UCf54F4EtZzfx8qCIJgXcu05+Tux5cCz8lrG4c6J3t4eCwdB2TYMjFFNptd9TU8/UInAjmQ//v5Lu8rz1lqQpK0xAj9JlDY38yHaxFr8b061JCWZOZA+n4l369eRutSjdrV2pf3QP7v5/vhAj8muOE52XPyamAtvlceHh4ewAEYtkxMQQ/PWoJUarWC20353R+C7FYHTaTdyuTxMAzttgycbU+SBPV6vePeS/l/f8l3vRCtx/4jDENks9lll+UDyb6p5dQlq2nnuK5fyr37/T2tvgT7lTINAHEco16vwxizJJldSeN4uXCoKbvrrT2ek3vXwXOyh8dhgMAAWqwpNmZ9jeseS8d+sZ9ei7ea6BY6SNLsl0S7lZmGbrO8y7GWzxiDJEk6ypAhZVoR0ES5P0ry/sxA9/ot7TyPtYMgCNqM2tUOP05Tbl1yvFyyzPbJc7slD+qnPzguyPHBGINms4kwDJEkCcIwtL93M7wPVGZXS6bl7+vNCOyG9dgWz8mek/v5Le08D49DBqFZ+AMC/g8ABjBJAMQLf97APWSxXwwoCXQl1vCkhSr1461JU4Jl2fLTBRf59aMMp91Ln9MPoSRJ0kamsnxJrLL/tRdIlpVWr5WYVV5Okl4urEdldaWhleGVXo/XTY7TfkuT+V7vZ7fn7SqnX5nvBdcYkCQJ4jju6qFyybKrLkCnPOvfl0v+9P8HW4aXE72e6XobLzwntx9Lu85zsudkj0MYwYIhGxmE2QRRlCCTjZHNxAgDg2YSolbNolHNAM2wZeB6HHJYsmHLBDNcw7PcCILAJq7h935mfHkd1xUZYxDHsSWWpQzOrhngfpThXqR6IAO5bKvcnzAIAsRx7Ly/rpdLIdbndDuXnifX+f2S6P5+eiw/dLIoYn/fU/ms+vG6djNmaXTznWs2m0uS4zRFXB7X/8tz9fUu2eZ53TxG3bxT/D+XyyGbzSIIAjSbzdR7ymPdZFmel2YQR1HU5onaXznWx9J+71ee15Kyu5bq0g2ekz0nu873nOxxWCFoeWkRAEFkEIQGUSZBIdfAhlIFI7kKmibCRKWEiZkB1Ms5GBMCyfoY5z36x5IM2zAMrRK2XAQqSbObMuwiUSrj8hjrxcGXBCOP8ZpudeJnvzO83ZTgbpBlSSXbZWhEUdSW5IchZ1QWXJBk2G0WtpsXqFfZaX0kQ+KazWaHcdJNIdbHtDLkyfXAEEURcrkccrkcMpnMsvSnS45df0Cnh0dey+9SvgFYo89lhHaDNEK1YelSiNOMXV2m6z4uz4erzZRlTizwGdDgdOFAZFme5+q/tLJ5jkwq1mg0OtYXpsmw/K2XPK8XQ3ItwXMyUo97Tu5sl+dkj3WNQLxjMpQ4NECw8Ls4nCQB4iREYgJkwgSDUQ0AUG1k0GxGiOmx9cbtIYW+DdsgCCyBLtfeeHI2t5sS7FJ6NdFROSNpynN0O4D+BmEXUabNDLvu0es+/cxysy2cES4UCsjlcnZWmGv4+mmHVB61MivP7VdR5n2bzabzHCrDrj7QhO6ade5GpEmSHBCprpQSvR7I3ZUsqh8DUUKfTwWvHznm9dpw1bLMdamu+6bJYrdj/bTDVUba+6vlV9ePf0w8w36iglkoFJDP5xEEgVUy+5VlfT9pLMhze8myvl8cx86yqLRT7lxlyXv2K88sT5a7mkgbB9aDke052XOyhufkTqwHTvboAzRa+ZokpmXc8jiNWwBIApgkgAmBOAkxU80jTkLkM000k5aMhGGCJJPAmNBe43FooG/DVieYWQ7QY5FGmC5FWJ5LyPA91z1cM8S6jDRFVaMbQfaaNdbHdNv0nyRS9hWVYba7W6IQTTA0FBi+lBbC1MvjI40SAJbMddtzuZxVbGRYqSw/DMO2dsg6aaJ3kSjL6xWauZJwzV6vZWSzWasQSyxVruX5UpaBTsWXXpu03wE4DTT5zmYyGTSbzTbjkde56tRN9jT6UXbT7sPv8k/LspRT9lWpVEIul4Mxxnp9ukG+X0mS2MkEqcQuVZb1ljDSiyPbls1mrcyxPVKRZbuTJOlQ1HW9df3keTJUNa2vVxLrRYYBz8mE52TPyRrrjZM9+gQNWH6hcWsMYBZ+N4BJAuvcNQaoNzOoNxflMo4XJtUjAxgD02xd5xNKHRro27ClIrxciSnoyUhThnUSBkkouVzO/j4/P59KoiRQDt7a0yProj0t3eoNdBJot1lj18DK9rNd8pMeHt1mGiRUKun56QZXaBLL7OV9cdWfii3rRkWYoZSynHw+bw2RKIrsWkn9vKTnTrYnTSFOI9E4jleFTDVpuhT4tQy5phM48BltKcuA26jVRjC/5/N5+77PzMw4vYVaydbvdC951e3sZQSk9Uc3756UZb7D/chyLpez8iAVST3OpoUvStlaDllm/aUs08NPT5SW5TSDRI7hvWSZxzlmdwvlXE6kyTF/W8vwnLx4LuA52XPy+uVkjz7B5FBAyxCli9ZgweO66MU1cQgTGRiTADAwJkAch63wY8PxzSCJEph4+XMTeBw8LMmwXc41PHIj+bT1dWmzw8Vi0danVquh2Wx2zACnhTHKT3m+Pp7mpZEDvvY66PK6EavsA4YHSXLiMbkWjwpmJpOxhKGJi/2XlnVRKgryWlf99XedfIb1osfIVRe5hQzr7DJOJKIosgTM++oZYH7X5aaFUu4PZN20Z0kT+XqaHea71MvQ69YWKUt8FxiO5zJiZQIZKcv0WmazWVQqFacsS0VY3rebp6abQqzrIK93yXKvfjgQWc7lcmg0GlYx7gWXN3M5ZJljipRlfR7bQWVYKrGu+7LthUIBjUbDKT88l8+cyjDfg/0NUU57N9IU37R6rWV4Tvac7Dn50OBkjz7QbT7atBu1PDdJAiRJgEzGIBvFMFGAejOyHltjAhgTLYY4+1flkEDfhu1yZ1uU3hy5pkfOCrvWtABoI5hsNtuWgVAOoNLjIL/zXBKKrhN/l8d1SJKGPF/OUqZ5AGQGRZbPdvG3MAyRz+fbZtCpPNCjsb/KsOwjF2nqY677cK0Old00UuEsMtD+HKWhwnqxHzZu3IiZmRkbFiWTXGiFmETeaDRS29QP5HPqRpRp7VwvJCqTRfVjvPaClA8dqtfL00PlMQgC+xxZrzTlMC2UUdfJJc9pddWKHY/r966bLMv2SlnmWCVl2RiDfD7fpmSulGHL9milUParrLM0WPU4Jo9Jo0LXXT7fTZs2YWpqqs2bpGVZepd6ZYbuB0uVZf3/eoDnZM/JGp6T1ycne/QA44oDIFiIPO48B0BoWhmRMwmC0CBcuC4AkM82kQ0TxCZAtZFBI44WEkilRLyIe1rwvj5keU1jScmjVgokEplVUHp6jDFtM8WZTAa5XA7A4poRWQ4HdDlYayLVg52cOZbkKpVgKuFyNlvPugJom61Oy+QqiVISqLxPPp9HsVhsWwvJWWI9W+qCJgVC9kUagbpml2U5/GT95Uyu7E95HtcD8rtMXESiZb8UCgVUq1WrVNTr9bZnxzpSIWYdlhr2pAlQE2Q3EpXtW6/QBg+PEWnt0+++yysg32/t6eH/2WzWrk+jt4jKFJ9nmtGk66hlN00RXoosS4VVhsfK97qbLHMM41o8yjIN224GuKxL2vsmZVkqpVr2u8kzz5WhjNrA0PeUhkQQBHY8dskyDYFesszvct3wUuRrOWV5JfluueA52XOy5+RDj5MPG8gMxvKRBerTtH83Bi3DMs24DA2iKEEYJZBDZD6KMZyvIoRBJZvFTD2PMnJoNsNWOPJCUqm2tbZc0xuo41zb67EmseR9bLtBhxV1G2i6DT6SSOVMMcmLoYvGmLbZYQ7Uen2W/q5DorSCIJVg7Y3hfSSJamWU5A+gQ+Fj+1yz4Gwj78kQJ529tpfh4SIEF6iguK5lqKoMWXJ5hjQ581pNplJRYh+USqW2DJKSNKlESO+cJvgkSWyYUxiGaDQa9lyX0se69iLKNOI81KAVRv0bsKhw9av862PasNWfDMdNksQmKKIcM1mUrCOfOf/n+VJZ0+gmy/xdvtvSEynv7VpDrMvTsizDtKVcs68Yyqj7luj3PZRGhryWf3K8krIsz5WeNT3OSLDecowrlUpoNBr2WLlctn3eTZaB9rBGPl8py7ynrCuxVBnWxsChDs/JnpM9J3usOchMxoQ1Js1iaHCwYEBK45bfpaGJhe9JACQBkiQEAoMgAILAIDEBEhOgEDUwlKmhls0gG8WIgpZXtxoZNOoZJI0QphkAOnKe9WFFArTO8cbtmsSyGbaaQCXJ9JpZlooksDhrr49LAuH/JE49CykHPa5/4SfQPotJApMkp9cGkhRlW3hMko5UxmTdqLTzOD91v0llOJ/P2wQzUvlNIwg54JP02G7WTRIc79vLaGH7JUFppTgMQ1s++5R14PlsF/tX7r3I/+V5xWIRwOK+hwwn0+GKwGI2V90nfNbsFxdRpoUxHarQ3kD+r3/fH1l2GZYswyXLUv6kEimPuzIly7A/qcRK5VR7XvTvElKW2cY0Wc7lcm2KO99Z3TaWK72W/GQ7WBfWTXp9pCzz3ZehwmnQ769+3noM6Pa+y7FV9g2fVxzHbd5pAHatNfslk8mgWCxaDxD7Vhqzsr1SlqWh4jIO+jVmD0d4Tvac7DnZY01Cb9MDAK0cTy0sGLVBaGxIcBC0EkAhCWC4/6xNfbzgwU0MTDNAbEIkYbBwPWBMgEo2i7lGHrkwRjaMMZytIhc2Uc1nUWlmMV/PYb6aQ7WSQ1KNgDhAW1KqEK3yABgE7fX1WFPo27DVs30acnDXgyyhB2Z+urwN/E2Tpr6Hq0wd7sTwGCqGbItUyIwxdlaSM7Jy2wNNNJyllt4iJkghqNAmSYLBwUE0Gg3UarU2oiRIsiQMhv6wTjJMUJKxKwSKhObqZwlem9Y+9qE+n/9rg0KWQaLTpEQlndfQiKFXq1arWYKVz9W1pYnrXv2QpDx2OJKmbLsLS5VlQhuV+lxpxLm8HGljAJUrWRdtkEsZoSzTUKZXWCvq8l3O5/Nt73e9Xk+V5eHhYdRqNVSrVQRB0KYMAouyLLMdU5alh6TRaFj5023WoXX80/3m6i8tH7KvXIqy9nhrWZaGte4/ZliVnmeGpVar1Q4vNdvtmlDhd712z8uyG56TPSd7TvZYt6DXte2YiDde8JIGC6HFUSaBMUCzGSGpRzZfFICWkZksXB+jZfwGgAlaBmnTAPNRDkEwgEYSYSBTRyaMkYtaf4PZGoZyWcxkC5gIDcomj6S2YNzaei0a2a26eW/tWkXfhm2tVnMSlvzuIjp9jj5f7pemZ3U1wfaaaZbeCEkaevZY3p/HJHnqsCRNPiy7VCpZAqLngiDxkWg3bdqEZrOJcrmMJEksQbJN/J9rgUiibA/7QZKFhJw15flLIYa0Zyb7jn3mIm3Zf81m066/oWKhZ675nf0kQ9ZkPwLpIXIsq1arodFo2BniXspwtzIPB9Rqta7td8my/E1+EmEY2hA2KS/S8+Yy4vibro+UZX0frUDLMuW7KLcSkrLu8nwMDAxYj4YMxeM1NNbCMMT4+DgajQZmZ2eRJInN7My6MiSzXq+j2Wwim822eSypEOtwW2DR+KSyKT0s3cY+2Q+UfWkIU9bYz9LbRLlgn4dhaEOnKctMBKMVZWnwyvWONFr0OOQyrAHYsYuyzHDkXoqxLvNwgufkzvt4Tl5sq+dkjzUNs+D1BODKSBwshBEHoUGUSVAq1BCFBtVGBtUgBxtujIXsx40QaIaLnlSzUAgMTBKgUc9gDkCtkUEx10Ax20A+aiIXxQhhEMJgMFdDYgIYE6CCHJJ61ConxKJRy7r713XNYkmGLZFGppK4ehEoAJs9D2hPCEFSIcHqMEIOvhxAa7VaW0gM0KlAMdxGE7FU5DhjK+/nUgxc4YMsQ57HELwoilAoFAAshumRIFkHY1rhPlQigyCws9NsFz1LOkwPaCdRbh8ShiGq1aolXu2xAjrX9EjCTJsxdp3DPmYdC4UC6vU66vW6VVT5bOSnXkdHo4DX0NiQCj+3kyBZV6tVVKtVNBoNS96SOD3aUa1W+/Le9CPL/E4PDL/zfYiiyBpzchuRNFmu1+sd2UVdsqwVW6B9uws5nmh5lTIqZVgqz0xoxXNzuZxN/sT3m+cPDAzYtvD8KIrsOym9HVTyOCbQW6Rlk3JMry5lmUqpDMOURqxLSewly9L41c+/UCigWCxaD7XOhsp20xDlOEtlmAa+lGXZRi3LvA/HDqn8ei9OOzwne072nOyx7tBmIKIV6qsN20CcZwIEgUEuE6OQaSIKExgToNmMEEUJslGMxASoNzJo1DOI6+FCGHGwmCU5apXVbIaI4xCNZoRaNoNCroF8FCNYWGubj5oYLVQAAPsCg2olB7MQNh2gFdKcMETZY82ib8O2Xq93HHMRpRyotWdFX0eFF3CTKIlPEigVZQ7KHEA1iUrIWWeSESGJClgMvWM9XLPe/I0JUghZR5ZVr9cRBAHm5ubs+hQqdWyXJinZjzI7KL1QVBS1ci89YtK4kGV0Uwwl6bi8ZTzHVQaVAOmRokIQx7Gts3xOMjSLxgj7Ts7yyplfKgWSoPX/Xvntjn76SBuAGvqY9vLw3ZOyLN95/kkFq1qtOhViXac0xYiyLJUuel+1Usw2SO+plCfpsaBXhvUDgGKxaN/narVq68cy6LEk+L5ToWWWZBoBchykHLqUfZblMmalkssy5DVapqWxqBVkPhv2g1Rcq9WqbY+chOA9GJotZZnPlGWwHBrvUn4p73xPXe+fh+dkz8mL53hO9ljToJFKz+dCmLEFDVwZWhwGMEkrKrnZiFBvRshHsd3Cx5iWR7eQa6CQaSJOQszXs6jUcqjXM3YdbhiZhQzJBkkSwCQBmiZEEETWoAWAxARoxBEGczXruQ0Dg2YcIljwHidJgHoti3ihHI+1iQNKHqUHKznAumYS+Z3kmiQJarVam4eFCjLJjoOyJFAOzMYYJ4nqGWoZWihJQnqQZAgOlWOp3MuZXJKVVg5kPankU4mtVCptGV7lvWW4JJU/lieJTZJapVLpMDxIlDQWeK0MB+oW9pNGkJpY9XnSa0Sy5+w+70uCYx/ohCG1Ws16xMIwtLO8MkMqSZSkKZVkPwu8dHRTNtLChtPKoSxL2dWflCn+0WDkO0VvnZZl7VmioiUTOEl54nWUZQBWGZeQyVf0faRCzPc5SVqZm+fn59vCiLmWVBrPPJ8GMxV5aQSyn+fn59vuyeNSdtnPVCil0q/HWPlMXM9KnivHBAleS28z20s5pFIsPTG8J0M2pSzLa12yzGPyffAG7dLhOdlzMu/vOdljTUAmiQqBIEoQRAahTcQEmAVD0cSBNXBbhuPCeAGgUmtlMzcmQL2eQbMRIYlDZMIEhUwTpWwd2ShGNkowH+XQaEQIAiAMW9v+JAuGaLLgzW02W79HoUEUJoiTEPVmhNgEyIYJDIAoTJAsrPsNA4MwMkiyMZI4aGVPDgCfGXntYVm3+1kqZJgZv8uZWkm2hCRIXssBlug2oPIeJDl5PskTWMyuKJVzqTTrWXBZtkw2QZCUZfiPXMPDNW1JktiZeBIry5MkTIVBz4izP+RMLI9pJVQrjy4SdSlGrnWB8n/tmeH92VYZyqi9WoVCwYZ/SWVX/kkvnyZz6cHyWB2wz/mMgcX3gO+qy9NKuaISJI0fwiXL+p3vR5ZlmVI5pidIXifPkSGI+jq+x1R2eY9CoWDbS++Q9PxS0dUGhVxTyr6R/aJlWY6fLEt7bbUs6LHUpVjLT2mgy7WEUlmXz4PHlyrL8t2R/b+UCRaPA4fnZM/JnpM9lgV6254ACBZCg6NMjEym5UU1C2takyRAHIcwcYCkGS56cE0AgxD1OiemAsTNCEktQhJEmFswLIcKNYSBsV5Ylh8ubPcDBNa4ZUixSVrHspmWJzg2AZpxtPh/M0KzGQImQBglCEODOG7V12Pt4qAbthwMdXIC/q69PjwuPR/yWk0yBEmQiihnMHW4FYC2/3VokbwP4E7wIAdyenK4fkeTlgwN08qDJhkqjVKBkP3o8gaRsAhXOBN/7+W9SesDqQxpxVp6m9gfUkGWoCeIz0aTtWyDhCs0zWN1oQ3bpUJ6IKTyyd/kOCDXaTLjMcNfpREJLMqyVnpd74k8zv+lvHC8kIoevUqsO+vp8orJMD8A1mjmMSrZ0vCVBqO8XhoTUtGVCrK83iX3+rscNynLbIu8P3/j2jtZjmwbZVnua6rhZXltwXOy52QJz8ke+wXppeXj56cBTBIiSQzCENZIbHlwE5ig5Qk1zXDBa7uw7Y9pGb5AKwwZBkAzQNNkMIsCGnGEbBSjmYRoNFrjTxQlQAjEcdjy8CZiTXsCNJMQcTNEnAuRyzVbhjaAejNqrddthi0jm+tsI9Oqfxx6T+0axqobtnqQk7N8vQZADrz0kFCp5Nofzoi6wAGbWRFlxlSSGz9lKJKsl1zjQ0hilEq0TO4SRYv73zGTKtujy5XeL1kuz9Uz4dpLI2fUJaHqOstZYlc/uaBnxSXRu4hffsrnwi0EZJ11mfQWybbKNuvwM0+iBxdSIe4F6YWTClgYhja7MBVAF6T3hXKlZViGFEoDzaUoA53GIuVXJ59i0pl8Po/p6WmrvNL7o8MZ5W8cF/id4ZsE6yjDo9kHUoa0wi/XFLOeLs+LhBzr+By0MasVcl0PPgNt8BLdZFmPu/Kd8PK8uvCc7DnZc7LHsoL71EokLSPWmLD1BRES7k2L1hpW+39ogEzSso/DVjhxlElahuoCGnEAY1pb8sS1CFUAjSiCSQLEzZZMxAve4SRuGbAmDheN7oXQ5wX7GGFokMs2W+tqTYAkCZE0otaPBgBaWwjZRFcL3mSPtYeD7rHVs7j9giTAGV6pJMqBXM6a0ssg97iUGRe1Ygy0J5jRg7T8LstxKcMsM5PJtGX7lGGMrBv3gGS5mUwGjUbDtlv2nyRQ2XZ+ai+RJE1JWBpSOUgjKu0dAtA2GyzJTkIqC1qRkKGhXNvHMvm8ZNvSnofH6qObLEtPjzxfrj1l2J9eQ8r/pQwEQWC9CJQ9KnnSg6plWSrN8j3WyrC8Vips0pPEcUd7rijLTBDFtXdcr0qZlAapVIClLMs+YB1cnnGpFLMcCd0Hsl94D31vLcvyXJbFT+mpY/9RluM47pBlXsfnr8cpL8sHB56TPSd7TvZYFljDzywagAYtb6wJYAzakgubJARf+yBsrcHldj9haJDJxMhGMaLQIJ9toJrJolbLtva0NYCJA8RoeVLNgmc3WTCgzcKxVkKpYNE4XchwnAQR4ihBkglsOHNnOwCfDXl9YMUNW5eSpX9LC7dJA89NC0mSa7bk7ChJSm76rr0fslydHVKfx3JdCq8maKB9T0+SjJ7N5jYjBMuQnhvZB7pf5O+cZdaKMtskv7OO0lDQxKsJS3qX5IyunN1meXotHY/J5yM9RbI+cv2PPFfPEMtn53Hg6Mdjo8/n89XXae+Evod+btIbkibLXAMnFWKpCMt7aUOV5UjPi5R7qThLYy0Igg6PhfSKSKNAe4VlOWn9JyHbrfs2LemVVHTlGCRDil3PhPeT7ZGyzLZwEkCGSsvfZHg10O6d536a0gCX9XQp2x7LD8/JnpM9J3usOEwAJAaQ43jYMlSD0LR5dAMAJhAGMIBgIelTy5OLhWRPBrlMA8O5OlCsYr6ewzyzIDM5FL3FQcvb21mvhb8kaP0BQBNo1iMgMDC5GMaWsVB/b9CuKxxUjy0hiSSNHFxKDgdsWY5MhKDPlWGLLkVYz/bKGV3u7afDiDhoS0KWyjoJpVwuY3Bw0DnI69BDEj3vIz1CVKzlrKyeMdaeF0l0BK/RHi+XYaCJmX/SS8Vz2GZeR4+NJFKSp1yjl9YG1o2kLOslj7Nv5Myzx+pBKlVL7XtjTNs+r0C6LFMx4z6y8hgApyzL95qeVN5Xji29ZDlJEpsZlLIsz2GZ9FzIfV1ZT4Yh8ztlQSvVLuNCHuNxqUTyXlKWpcIvZdkV9pgmT1KWKZNUbum9SZNlCZfM6vBs2R7Zvx6rC8/JnpM9J3scMMyCUWjX3AJB1AopDqMEre13QgTcJza0Tl1r1CZJKyy4tcY2QABgIFfHhnwZY8U5lJs5zNQKmK3lUGtkUa9HACIwfBlAa61sGCx6XxMsGqsLRq6pRWgCSJIQYWD8dj7rGKtu2OrBTQ6i2vvgulYOmCxLppfXa0u0QivDFeUsoxyc9Z9rBplweWGkt4ftkkkxZBuZyEK2hZAKnp6hZVvleiit+LqyjbIMuSch6y+JW3/K8mWZbCPrJK+T93PNMLPPGNJF0OvDLSFcz4NETU+P9+4sP/anT/uRZSpXhPSaumQZQIcsu+RJr7fTsk5DTyq6WnHWsqwNLMqOq328ToZOS0+Wa7wBFj2z2tPC/6Wca6NRerlkaLZUumVbCC3Lrnu6FH6XQUM51G3md37qcHGOa7pfdPlpRpTH8sBzsudkwnOyx4qAIckL62yD0CCKEgRBiCRIQHEIFs4NmN04NAgCAMYgTgIkcYRqDZiKCsiGMUbzFYzmysiFTWSjIqYqrW19mgAymQTZTNyKOI5bk2BBABg9v8Kw5CSAqUeI4xDJwlZE8MbtusSyGrb7O5CREJYyo8f1eDoMSA/YQbAYtkjCk6QnyVSGNLpmSllX2dY4jjuyuLJcElMul0OpVGojYhK8VvBZplToJVkYY9rantZn2hvDe7EsSeokcrZXt0O2XdZF9j+v1yGSUmlmu6QinM/n2xJSsE1yn0P+Lg0RWV/X7L7HwYF+L7VRwu/SsKSSKK8BOteghWFok8xIrwbfJVmelPNesizvJesijWHKST6fR7FY7JAX+b98v7UHlt5mqfBL2Xb1g0uWKb9RFFnjQI5bLnnQsiz7Xm9bovuHY5aWZdYniiLrRdcJr6ScumSZ5UvDQD4Tj/2H5+RFeE72nOxxELHgKTVxK7txJhMjm22C2/3EcYg4XjhvYbuefLaBbJSgEYeoNbJoNFrraSu1HPYAqMUZDOVqyAQx8lET+UwT9UwrZDkTJchnm4iTAPVwYasgsYVQG/g1XqhjuHCAYcse6wrLatj2O7MuiY7XkYxcpJDmJQjD0Ln/GgdTYwxyuVzHdiB68E3zdPAc1xoaGfYj16DJ+wOwCl+xWESj0bDKuVyLJolH3l8SiiRqWacgCGx5nGXVs8+8RifpcCkJ2lul/5cKMb/LBDqsG8+Ra5hyuVzbFil8ZlwrKffFY9nSSJF9Jslc9pvHwYX2UPQDKpValvU4kc1mkc/nrSxreQZgZZmKIdApy1Kx09enhTVynCgWiyiVSqjX6zaMUnp00mSZXpx6vW4zP/M81ikMQ+TzebvFhh7zpPJLmelmuGuZ0F4fXTaNShkWLA0SbmsCoE2WmSSI9ZeyrJVtjj9altl+lwfNY//hOdlzsudkjzWDJACaIZLIIMmGKOQaKGabMACqjQwqtRyaTSCKDEr5OjYWy9iYLyMMDCZrJUxWSpiv5RDHIar1LJpxhGozg5F8FaVMHZuL8yhmG2jEEaIwQT5qopG0kkclcYjmQibkrsaqCYAYi2uAO4xg036ux5rDQVlj6wp9kqE0MgzOda4cxOV2AloZ4zoczgxzJlR6O6QylaZQuWZaZR1IBnr2ld6UJElQLpctiVFhkOvJOHvL+tXr9TYFgeQj7wcszijLkK5arWbrIZUEKvuSQNPIRyo60giQyoIkMCruVCjY//V63W5urw0Nlq/Dxlg2lQfpseOffA/0eh+tpHmsHrQsy+NpCrb0LMhsmzKcjV4WadRSnrVxJ70FGi6PhpYDbfBSjpnReH5+3n7nO9psNu17z3pR3qncUm4rlUpb/0gjV/41Gg3UarU2OdOy3KvNut85PrpkWa6d01u+cHxxGdxSIXaNKVSMc7lcW7u1LMv+TtvqyWNl4DnZc7LnZI9VgWl5bZMkQC4TY3NxHqVMHTONAnYHQyjXsghDg1wUY3NhHjsG9mBDZh4P1ofx69xm7C4PYa6WR725EE2RhMhFMY4Z2IcjctPIhw3UkiwaJkJsQpSTHO4pbMDduQ3YN19EtZJDUlvYxqdrPR0GbYDW3rz83RgfrrwGcdD2sdUzrf2GPmmSozLMAVUqiXotng534jly70lZPmdmeV8XweoZVvkp1/M1Gg2rlJP05SyqJF55vVQwdN+QuGTdpRIqZ4FdbZf1ll4s18w9r9NEyLLZLj4DqaDKNpMUpQIhCVGSKYmUfSNDn/QzkOd4LA/69fbI8/m+6LWg/ZRJhbibLGulk79TSexHlqUHRntAtHdIG45UFKUsy3brta4AUK/X25R+1xiXJAkajUZHG+ndYaKctLBNOda4ZDkIOrM5y35yybL03nKbD/lcZHgyy9GhkbJ99XrdGhYy9Fk/A+mN91h5eE72nOw52WNFoPeyDVp/fF3CwGAwW8MRhWlszOXQTEI04yE04gj1OEK5mUVsQgyFVSAHzDSLqMcRMmGCSiOL2ATIRzGGs1XsKOzBWaVf4siohoYB9iR5TMUlTCUlbM7OYihbxa+zm7E7GsQ8CkgQAU0szesaAIhaWZ1NEtjtgrzndm3hoHts5YywVIS6kakmOtf5VHbl4KxniElqcvCWxMV7yftppdhFonJ2Ul4rw5dIIqwjZ7B5LcuUhCPbKe8vyYX10xkj2TZNuLoftTLAuujnI/tLz/bqMrVHR6/X07O/8ndChpbK+knSTpvd99g/7G9f9qMQu5Qx7c2T/8vwPZfnQ4b0sexesizL13Ihz9PhyQDaPBc0xpndVXpijFnMIqplWd6fiiQ9O/yjp5r10AqnlnsgXZYlWG+9xk62nXUG0NaHlDmpOOssunrclXXSsqz7gX3mZXn14DnZc7LnZI8DRooha39b8HgGUYIgMIiTEE0TohTVMZKpYKpRxFS1iGo9i7lKHrujIYzkqhiKqgiDZOHPIIRBJkxgkhCJCVCNM5iNC2iYCIUgQC4AppMECUIkJkQprGMsN4dKKYvEtLIsV0wOiYlaYce9DFNZd7tdEXzm5DWKg2LY6oGOA2y/W4XIGX1XohESpYs09GypnpXU2QVlmTzm+tOzy1IJlfeT63JkWJNcE8PQPRJKFEV2c3iX0cBZYs4MUxFm38qQIO3pSVO2+ZtL4WE9ZDm8hrPbOkxMGyqud0D3o7y/Vh50XamQ8P4eBwdpnp40I0s+M71/JM+XHlApr2leFSnL8t12GZT81MqwVMrYJnkNPY9895gNWHp+wnAxnJiGd6lUQqVSsefo0GhjjPXwZrNZFAoFW1+OkayHfNepTGr50Mo3/9fjrPRU6b6Qz1IqrXx+/C7XPcoxT69r1qHgslz9zD1WB56TW/CcvAjPyR77BWnMAotGIYAgMggyre1+AKBSz2JftYR9+RK25mawMTePofwQZsoF1OsZ7Jsr4e7MBhSjBkYzZdSSDMrNHOYaOVQbGSTJQhbvBQO3nORwe2E3skGMyeYgJpsDqCUZNEyESpxDFBgMZWuYL+TQbIaoJwEMQrRt/aM9sDIEWdux3q5dk1hTmoOegewGl3JG6JlhDu5yXYserNPCm6TnQBMn78Xy5ayuPE4FmB4e7h2niZB1Yft4jiR8/iZD/eR6FknULItriGR9tYIg+z3tNxKd61nI6+RssvRGyd+lB4vPUhI9SVcqGTq8UZdNY0C3x2P1Ibenkc/JBfl+6WOUW638SnmW5UrjzyWrUpnT52glU3uNpPeUMs19XHXbWQbbRcNXjkvsExnSx1BByjjPp4eUhiShjXEqxK7+luGHLkNB94u8RzdvmQxllMaD7APpjWXItu43/Rz43HWZHquH9czJ+Xzehs97Tvac7LHKoEGojNwgNAgXDNt6I4M98wMoZjaiGDWwKTuPowamMFfPYaIxiEY9g71zA8iGm3BEqRV6nJigFarczCCOQxgToN6MUK7l8GB5CLfkj0YhaqBpIjQXDN5QeJLjJEQmTJDLNZHEIeLIWMPWJAFMHC6EKLPOaBm1Yav+JgkWPbX+lV6TWBOGrZ7Nl//L2T45gEvCYRlAe0p/zqy61t/JMvRG44QkRJ6vyVPWy0UCTErBLIO1Wq1jfZlWvFnPubm5tvAomWWUBMv/pQdHJ7KRhrcmQirNLuKRpC37Syutkvh12doTI5NYJEnS1i7OiOtnIe/LMlmODhONogi5XK5vT4PH8sIVzqafg8u4dClo0oCVRi3lSb4n/NReCy1bUpZ1HVyfQHsoZJIkKBQKyOVyqFQqqNVq9hw5VjGkknI/Nzdn32/9zrPecn2elGXZFul90e++7APWWcuwvI7f9cSA67gcQ2S/AO3bvEilV449OpmQHCelLFMxZrnMIO2V4tXFocDJMoLBc/Li9Z6TPVYNARbCdhfe19AgDBNkMgnCMEEch2gmIabrBVTiLI7JT2AkU0YmSPDzYAtmqnkAwGw9j1w0gFzYRC6KUci0tvFpBJE1bmv1DGr1DCbDEqKFUOcAQCaKkcvIpTIB4iRs/ZaNEWUWzg0Mms0IzXq0EKIcdGZPNgHatgBKAr++dg1iTRi2hCZTrQDrc+WgL2f19QxwWviTDt1zkaRLeXaVpSFJlMSXtm6M9WDymEKh0KEg6BllqWgD7aGR/E3OnrI+LgVRKpOaUOVMt0ztL/tDPzuW6Qqnkn2TzWY7rpPPQRKuzD6bNtPM43z+VFY8Vh9SPvl/r3eH4HsunyVlR4Y8alkG3ImhtLy6lGV9nqwjPymflC1djmwHPbTDw8MAgLm5OXsOtwFhnXUWYiqCQRB09IVum1ZcWS+576a8RnuZ+JvsN5chqT0xbGs3WZbja7+yrI0LKctp44nHymE9c7JLNgDPyZ6TPVYcQcvDGURJK/w4aB0LA4NsNkY+20AuEyNOWkapMQGaJsJAWMPR2Qkcmd2HLfkZ3FnehLlGy7jNhDEyYQKgiVK23go/TkIkARDHAZrNCCZe8KYuGNRRJkahAOQyMcLAoBFHaDRbnlwACMOWlRqGiXUsJ3GIJDQLRuvCQWZAZpiyNG491hzWhGEriUEPqGkgabq8P9xfUg6qWhl0eXZ4rVQmZbk8X85OArCErcOeZNIYptfn+dz7kaTHso0xqFar1hPk8oDp9kqvlryGs68uctekpclWkrGceZXKsFYutGIj68dnJtdRciac95LPhDO8cuZerqNkfViurA8VChoRHqsHLcv9KMM6dJeggZfP5+2zpazp6+W7K403l7eB0F5cQitpUqbjOG7brof7VcoQX7kuL4oi1Go1W0ep+Lo8MloZBtrXHtNzJPtLjwt6rJNlyN9cmVV5juxfKedy3NNtYb11nbQsy3u4ZFkep1dPjoHeuF15rBVOjqIIw8PDKJfLzskanq85uVgsIpvNYnZ21nOy52SP1cCCMdsK2xVJlhZ+C4NWCLIcvqMwgVkIL95bG8C9+Y3YGM1hW2YfRgfLOKU4jNm4gHKSx3RcxL5GCZP1AZSbrSVBzSREo9Hy2iaNsBVGnAAIACPunw0TZKPWe15ttEKYbbUDA2MCyMRXwcL1LQuZccpYNHa9l3ZNY9UMW62QyO96Vlieowd7CZfCyvAiSWIMg3IpRGlKr54ZBtxJZoDF0Cbuj6e9RiQDY4xVhDm4M9SHBEOClTObegsU7WmRyreeVdazvADaZlN1wh5dhiRY/cd7yE8XJAFyplv2KQ0Y9geVIxoF7KtGo2HX9NDI0PfhJ9dOScPJY3WgZbmbcasVNwmZ6Vi+5y5vaRq6eWVc77JL5vkuUsnl+0tZ1nu7yrVsjUbD7ndJWWI5eg2d3BKEsim9OkmS2HvrkEx5jm6jDPGVcqqNi37DfaUirtfXuRLxSFlm/dNkWU9acOygUuxlefmwHjhZG7L9cnKj0UA+n0ehULATzrIMz8mekz2WEW0Zj1uGLdAK+6V3MwkXIp9MgDgOUQ0ThGHL4G1EIXaZEdTjDPY2BnFCcTe2ZqYxFFYwENYQmxCzSQH5YAMSE6LczCIKCgtGaWttrDU2w5a3NohaXlhjAsQmQFZU15gADD8OgpZxGwCIogRRJkaSBADClnG84KxFvFC+N2rXPFbNsO2miOoZ0F4zxHLmXpYbRREKhUJbQgoe53UuUiTBcI2Jy1vbTamW6+uMWdzeQxIQ4FY8XQq/njVmuZIQSUysk4tYSUgkaF4n+04muXD1kWxXmgEilXZdX/08WT9XpksSpK4DZ7l5riv0SYdqyVlzqXh4rCzSPD1p/S+NwDRZlgaTlmX9Xur3QkKWr5PVyN/ldWkyz3fKFdqn32d5vTT2ZVtliKaWl0wmY71L0mMjjVbKlWvs1ONkmidIy7Bsq5Rl3U79rKkAp8kytz3hGOUax+V9WJ5cy9vvxIZHOtYDJ2ezWdTrdae3thcnZ7NZu6eu52TPyR6rA8MltQFsuG4r3BgwJkSQtHbYoRc3k41tVO+ueBhTtSJ2lUZwZHEaI5kKskGMcOGMmskgDBIUoiYGsnUbUtxoGMRRKzQZWDBUFwznOA5RruUQLySSsr8HBpkoQRQm9tOYwBrDTQgnbrxQA//KrgusiVBkCTnguQY+OUOqCZF/cl0e0HsGmJAzilpJljOOWgEmaco1ha4tD6RSFkXtWUFJumyfS1HVpCGPSQLpRmosX4d7SZJ29bvuK1mu6/nI3+XsrJwpljPgLi+QVDpkv2gCdU1EyLbpvvZYOcj3tZfyor0d+jeZZKaXLOt1t4Tru17fx/vxk7Ij28FrtSeL7598x12yLJVUWS8dSggshgjLNstzpFzwu1yDLI1uPZZKhdWlDOtnI+8ty2T9aTxwTTHPZbuk90vKctqWMGlGuZTlfj3LHsuDg8nJpVIJcRzbqAgts8VisS1iQsoxPbGst+fkxd89J3ssK8yCFbuwvhUwCDOtPWe5hpavpTGtUN8AQBi1tv7JRjGi0CBOAuwrFzFby2FPZRAD2brdtzYXNZELY4RB6/3LLiSRCgKDONtEMw7RiCMbZsx7xnGISpJFvRkhDFv1CQKDMDTWqM1GMbLhoozECwZyEocL3luP9YQ1ZdjKwV8Opi6QCCSZkDwZ+sSBVM/w65lV/iZnHeWfXNfnIjK5DYgruyKJgqGGVOyokOoZZJYv76c9JZpI5L558k97iDTBSjLlcT2LreuY9mw0cbsgvUV6lpj14TmS9CVcM+q6r6US4kl09aHfF5dBKM+Vx6RBm81m29ZuSlmWRpqUR8CdMIoKtss7LMt01Vu+XxwTpIHM90zWSxqo8vo0xVjWQYcrSoOWnh9ZrryPDvmTRodWonuNs73AMl0yxuNalrW8a+Xc9V3LslacPVYGB5uTZ2dnOyatJCc3Gg0nJw8MDGB0dBRBEGBubs5zch/P2HOyxwHBBEDSCkkOsgb5fANDxRqCwKDWyKBazyKOQ4QLmZGjKGltu5NpJYUKA4N6HKERR6g1sphKQlQWontoaOYyTZSyDeSjJhrxYhIoZkquxxFqjQxqjQySJGgzTJsBEIaJNaZZbitLcoBGaJCNYiQmQBQmSKIFYzyOWqHOPgPyusFBM2y7KSV6INaKsISLRKXCKcuUIXsuj40kP56jlUdZJ4YLccsA6eFIksWtPKIosut9aDxzcJfKpiSLIFhMZKUVOVk/1kXOghrTWjfEzKvS8yTbIpVn+clyXTPc+jn1G04kSVwSuCZ5WT9moNWZJ9PqI6/VBo2LjD1WHnw3AbfBIqFlmYaoVIiA9hA8lzIsy+dxGYqo5VkqWLyGoCxLI7VYLNq1e1LZk4lTWCbrViwWrSzLrMrS4KYhylA93jOXy6FWq7UZF1KxlYa0Vr5dY5fLE9OvLMs6aOWXBrceVwC3LOtzdL31ca18eywv1iIn61D7fjnZGIPp6em2NaCek9vhOdlj2bGwptaYAFGUYLRQwXCuinIzh33VIqqNDKIFAzIK2t9T7lEbBQZm4beG3ac2s2AUZ1HNZTCQayAxASqNDBpxZA3kOAkRL6zhjeNwMUuyCVq5n8IAoTVOEyQIkCQt4zcMDTIL2ZNttuYkXDBq4cOQ1xHWhMdWz2jKgdZFoDosigqlXMfDwdO1ybtLedKDbpoSrOtN5HI5RFGEcrlsEzHoUDxXmBPPkyQvFVVNLlrhlbO5kiSk91l6nbTHSF6j+7mf2d60c/R9eC+t1KaRmzQMdBgVj3cLedL38CGMqwMtR/IdcHlrNSgDuVzOJo/icW2A9irDJcsa2nMq26DfXZbN0Ftp0EqFNkmSNs8wDVadhZiyLNceSqWZSq9M7CLrrSfSgiBoM8Blf+jzlwItx9oDzz5zKatBEHSMwVqJl7Isv2tOkM/TG7Yri7XEyeTGpXBykiQYHR21HDk9PW3r5znZc7LHCiIBTCNEtZrFXCGPUqaOXNhshQ0DyISt9ayJCazXtRG31sfGpmVoGgM0ESJcMEjjOESzESEIFrcHikKDejODRiNCIwDqzda71GwKo9YsGLULr3iQtAxdIzyvcRwgbrTGqzgTIowWMjXHIZI4WMi07L216wkHzbB1zfLJATBtYHZ958xwPp+3hCrXrsmZYpdnR97flalRz0zK33UIk/b2kPiSJGnbDkieI0O4WB49OzL7ovSUsFxd/7SZYJeXRV8jjQHZV3Lj+aXCtUZI14HPSm6zxH4IgsUQNb0uSD8nHQanjSyPlYeenQfSE0fJ3+Q7mM1m7UQR0G6kud5PVx2kwanlVyumaeOBa7yhbPJ6WZYcBxg+zazIVH6l8iflWyuzMlRT7n9JGdahnLrOVN5dsqzlWPa9NlSlzKVdJ/+Xa2v179LLricpXFzQTZalseuxPFiLnByGIQqFQgd39eLkarWKRqPRtl2YNCbTOJnvHI97Tvac7NEHmBUZaHk24wCNShZ7wwHUmhnkMs2WNzUJ0VwwbBsLYcNNeldpgC4YrkGYIIpaSZ74esbxYgKoKEpa4cYLRmeStK5PkhBJc8EYZfX470KWY3ksCFoeZiQBYsAa1oZGbRx4b+06w5rw2MpQozRlzUWqJAC5Jo/HXAocr+GnJDMO1DorqfZKuMjYGGOV8W7eFf7PmUoZuig9PtVqtSNkj6Qold56vY5MJtPmPdJ1zOVydruRtFl36TmRM+Quxb8bGernpY2WtFlp+VxczzrNeOGnnil2/cm2eawcdH9LaE+PVAYZiitlmedoL0naPYF2WZb34u9pRq1LuXXdi9fTcGTYsPaKUuErl8ttRpwMQ5bvbL1eRxRFaDQa1osr789wTmaI1YatlmUJKctSWe014UCl2zUe6rFJQhtCVIC18u8Ku3TJMX/zsrx6WCuczHX2lAt97zQZbjabKBaLGBkZwdzcXMf7ksbJMprCc7LnZI8+wP1rI9P63yyE7jZD1OZzaNQzrTWtAfmgZVzqNbAAbBhzy5AMkWQMwiheuMa0QoPjALUkiygTIwgWDNSFLMfAgmd1wTOLYNGQ5d66i+cuVD9oHeeRJF64fuGzFYbsJ1DXEw7aPrYSrsF6KeVxZliSgitxi74f0B72Foah3YpCbt7Oc6XnSJclZzXld6ClzHKP2yAIrDeqUqnY2U+2KW2Ql6TBcwHYNW1SqdbbZcg+Y/+QvOV5cvuBIFhMkCOVXD2DK8vWyoJLEeH99XGp1GovERUHl6dHP4duf3qG2WP5IftaK1FE2ndjTIeXRyrDet9Vl3Eq/3iOvEeaIavHHtd9ZH1ZXqFQQBiGKJfLANDmJZZyosNz5T1dMiMNZ63Ea9mVcsnvNK55LxoJ8hyOHToJlvYEaSNXIk2WZT1lXfuV5bRnsD9c4eHGeuDkIAgwNDSEOI4xNTXVIcfkVVfdpXEofwPSObleryOXy7UlXfOc7DnZoweC1r6xYWbhfY4DmEYINFve04QeXWuEti4zZtEwlR5fJmuKEwOTBbAQHowkgGkGMM3WOWHUMnwzmdbWPsmCQZsECzdLgpZNGhi7dY+9jeFn0KpDaMA9d00cLHpqvVG77nBQPbZ6Nq9bSBLBQZXEBMCuxZNrZOI4dm6loUHSkUoYZ1TljCxJZX8y+ZGoZXZlls82SZKUxOVSvllXGZ4kSUyHMsp6a2KTpKJDhmTmVV5DyPppAtVKdzfFQM4kS+UmbWafbdNtT1OA9TGPlQWfUS9Z1kYZQ3fpodHvgjSUe8kyr9Fy3W2trayXfJ9c/0tFOEkS1Ov1tokweZ4Oo5bjDY9J+ZBJpKRyLMN4eQ+2i0asri/Bazn+aKNby7JWcrUsd/Ow8Hc5MSF/k3VKk2X+z/6S13mFeGWx1jg5jmNUKhWMjo5iZmbGJmxjvUqlEubm5rrWKy2KwcXJcRyjVqt1GGuekz0ne/SBwCxs4wPEsVn0eEI856C1bpZGrolkeLCBQcvQNAZAo+XRNdmF953ZiQGYIGylfwpCAAlaW/hgwSu7cC/7mtMLbBZCnJOFUGYRDs9zFwxob9SuX6yaYasHMD3AaZLQcA3EVFalwsbBX14jiUiCngz9u2sgBtqTJ6TVS663kW0NglZGRWZQrtfrlqQkmdF7IsOVZH1diTf4Xc8YszxZZz17qxVXWR6PaW+OPld7dHqVSSLspiDzWv0MexGhq1/4qZ+/x/KimwKjw5A1KDM0avnctfGyFFnW9057d3QIoC5bXsf7FItFG3VRrVbRbDatUUqDk4p9FEUdWY0ZXqnLZ53ZboY0SmWfddDyljb2SK8QDQ/trWGb5blSMZa/aWNTl5fWd646dYN+n7Qse6X4wLBeOFkb27IujJTQnCwnbVyh+2mcLOVXcqjn5MVyPSd7tGEh9Ng0w9Y2tvR8AgueWGUkylcsRJsnF1g0bplhGc0AJolgw5wBG1YcMETZLCaECgKxLpdlhCy7FQqdiRJkohhNAA1EMAkWsyd7T+26x5pYYytJ1DWLlzZwci2azJ7q2kc2rSz5O+/PGWL9O9A9dKvRaNg6aHCvPH5q4uRvMpGF9HrVarWOUCA5EywVB9aBHiTZFzK0UYaGyfZJ0mJ/yHWBmjz5HDSRuspzkaarPEl8cuZYX582Gy/rr8vrV6n2WDrYv3J7n36Nj2az2ZEJ+UBlWXs7iV4eozTPZBAsbkOUy+Ws0SzDbXmOTj6jZZkyIzMl84+yy72xGd0hvc7SG+2SY/m/7Bsa01qpll4l2R6p6Lv6TMqgS4nW50tZ1hMGLrnsZXx5WV4ZrCVOrlQq2Lt3b8fvvF+a0T03N4fh4WFnPdM4WSZd85zsOdmjTyx4OI0JF0J/IYzbhU/XI04C0Ia1Bi0Ww4RbXwIgFuUsrOcNs8nCOlu+86E1chev7bxlEABhYBAufAKAWciibA1a/zquaxx0w9Y1Gy8/JYwxHaF9TNpExYgp9Xm+S8GiYigVXqnYFQqFtjWwsp6yjG711EobyZDKqoRUviWp8zdXshepTEoyoReYyrVcZ0elW84My5lkSX7yuZCkXUqJfB6yjmmKsLwPy3OtkWJomyS+XuQnFTFZlryXx8rC1ff9nE9Z5vpamX0Y6AwRJujhke+hXgPmege0nMny5LkuxRIA5ubmUCgU2uql2669SNwmSJaljUkZqlmv15HNZm1/6LV0OklWo9HokBOXLGuDX/drmmGvn1uaN0n+Jtuoy+8G/axc31119zhwrDVONsZgcHAQuVwODzzwQEc95Xf5qd85eS7X6y+VkxlC7DnZc7KHAg1QAFjKY00Wzg+DBa8s3N5S6/FdeOcWPLNhSI9ty2htJYPS14rbJQGaScsIjs3C9j9J0J792Htr1zXWRBxI2sAHuL0raf/LwdgVRqU/9aAsB34XsbvuSTDUUG8xwrqQ1ACgVCp1JKLSa3po0Lr2gNTkn7b2RbaBYYj5fB7ZbNbOqMs1RrovqEzr2V/2Hf+0At+P0qrrKRUPSfryWD8enV739US6suhmhPRzLdD+Tst3wXUuoWVAek5c71tavV33cL3zVFClvLPe2sCkskpvjJRjrSDqzKuyD6Sc5XI5lEol6+WmPLvaIbcqkZ4lHaqY9hz0b65xUY/ZabIs5f1A4GV5ZbGWONk1sdQPJ09NTaFWq6FQKCCbzbaVnc/nMTg4iEajgWw2iyOPPBLFYrHtehcnN5vNjskpz8npZfWCl+NDDAv7xoJrYQ0W/9JAD6/KVpx+D9isxcyqTKM2DA0ymQVPbiZJNbCNCVrbDy3sn9vKfCzq77Gusewe2/1RWtIUzV7Q63EAWO9Gt3JdM46SnOr1OvL5vLM+kjBk+VQaNXnLbYRIQtlsFoVCAcCi0iu9TLyHJDAeo0dHrtHT4VhpoUCsP9sttx0icbvW1kkvEtcqpc3s6vvrZ8ByXCFLLuVa1luuk/KEuDbhUlD7eU70aOhjrtA81zuQZpy5lHD93fWOamWa4wPlFVjcugeAVXq1bMh1fjyP7WSZ2Wy2bfsP2S45lrk8LyyfXmttuEoZk9dpr62Ux7Tn1Wucdo2r8jjhZXl1cShw8szMTEdYcS9OrlQqdokQ60BOzufz2Lhxo510LhaL2LJlC0qlEprNJubm5lCr1do4mfVjaLXnZC/HHj3Qy0iUqYr5B/Q2hk1rTWxiAgRqbW02ihGGCYwBmmit/eW96OFthT0v7FnL7Xy8QXvIYNU9tt1m+PSsfrdrpAFJo5Lre1weHkKGLUoilbOfDBnUM7hA+wyyrH8QBCiXy5Y0g2Axw+vg4CA2bNiAUqlkrydxacVbhw6xviRgEok8LvtEZk3V/V6tVtuyPdOYoALhmiVmf8g/GR6t+6BXMgiXJ8BFiOw/1iFtRl/2XTdjxXVvj+VHmizLsFd9vvzNJcu6XIIyIJViKZ8uj6QO8UvzSEr5kHtySkNVKpTA4kQUoceWKIqQy+VsWXyvWa6WZSbScnmjmbTKNR5oOZR1kDJMY1xuTyL7odv2ZrLdWuaXQ5ZZbjcj28vz8mA9cHKtVkO1WsXWrVs7rk/jZAC46667kMlkMDAwYN/DoaEhnHTSSXjoQx+KY445xnph9aS0HJdoyMpJJM/JnpM9lhlG/a+NTe6Zu5D9OMCCgRuHaDYiNJstWSvlGhgaqKI4WENUbCLIJnavWr6uxgQtb28c+jW1hxjWxD62hB4M+xmQSQhyRlPOJLpmJ3WoTrdySVxp9dezzQxPpOLKfS7vv/9+1Ot1DAwMAGjNJpOQeH6tVmvrK9ZTzujynnIWulqt2nbrvpCkY4yx2yWQRPnJursUBD3by+/85PVyXaOrDpIISbjdZrJ5jfZkybU+Gmnhbp48Vw+yv/vpd56vZVm/I2myrN8r+T/fR32d9vL0qq/0gDLBDLC4no2hjlJedb3luCAjMeQ62mq12vauU3a0/CdJYpPRSIWa77/M3irb5FpuQS+UVILlcgrKWjcFVo65ad5BKcu6Xl4+Vx/rhZPJUfS+Dg4Oolwu98XJTAZZKBSQz+fRbDaxZcsWZLNZfP3rX8cDDzyAgYEBVKvVNk7O5/MYGRnB9PS0rWM+n8f8/LxNOAW0y5PnZM/JHkuECQCYVlblOABCLGQn7uNSEyBZ2EqI1xgAUZRBPtvEUL6OYraJ+UyMcjWHuBnBxAGaJoPW3rUGcRz2fT+P9YN1lTwKWAzl07PHUgFMGzTlHpeu2UiJMAxtUgmSFI9zINd1T5IEtVoN+XzeZk0tFAqoVqtt6/FIXEw+w9AmJs7hujkZBsXtgbguh+0hodLzIokpbRZbkhI3WNdkponb1a+StPV1PN+lFHeb6dUgYcrzu4VV9YIn05WDluNufZ327LV3Na0MvV9tN2NKfsr78P3XsizrIOtE2YrjGMVi0e5lyy1DANi1rEwYUywWO4wC6fGloSsV2kaj0ZagRtbBZWDIEEvKs4ZrXJB9oz28+vlpb7j+Tf8vveWyDt32ntVGcto5S5k08Vg61honc8KmUChgeHgYYRii0WjYiZ1unNxoNDAxMYGNGzdi48aNiKII4+PjmJycRL1e7+DkwcFBJEliZXB4eBj5fB6FQsFu8cUxZ3Z2FpVKxXMyPCd7HCAWMhG3hQWnJZAyxm4vZLgF0MJrmwCoBxlUoyyyhQT5TLO1Y1ASohKHMHGEpAkkcdDy4MbBwvpa/w4eSlh2w7afgVFCEpFOTsDfXeXyfCqJve7Pc+V95P11G6hw8rscwNOStMhzGaaUzWZtyKCctY6iyJJhELSyM9IjROWWCWr0ejlJInpv3bQ1MrKu8pxCoYBSqYSpqam2UGw5u8ty+DvDq2Q/ukgSaF9f5VKA06Cfj/TgdVN8dchb2v8eyw/5fNKU47Tr9H7OQP+y3A0sUypyrjFA14fXUhGU8qUNbynL0hiVmVPjOLYhh/yNnxxTXPtky0kzWSdd52azicHBQYyOjmLPnj0dW5FoWZbjlPyTv8s6yHrIumhDlnDJ6IHKssf+YT1ysg6pj+PY5r2QuSeA3pxMGcvlchgYGMDo6CgmJiasvEk5LhQKdhK6XC6j0WjYxJAzMzOo1WrW6JRLDzwne072ANrWzPJ16sdoDMzitb3W19LDuuCtldsKGYRoBkC52nJK5bNNJOL+zH5sEtOqVrJQnschhVXz2KYpkXqdXL/KsFy3IsP6uoUn6dlFZiEcGhqy2QlnZ2cxPT2NarVqw4h5vcuzIf+nEszyaRxnMhm7dRDX11HRpbeWoYP0/DCcSXs4tILJWWFgMftyGIZtx2U9NQHTGy3XDPYLPTusw5NIfpyBlgq19gDJtmnik8pNmvIiZ5F7GVEeKwO5viztOaVdJ41B7YV1GZ5alnUoLhUtORmkFWftyZDvppQzel+BRY/s/Pw88vk8SqWS9dBSnqSxyjbJyS0J3qter7eFD8ukc3KJgrxGKrBxHGNubg4yAiRtHHQZ+WmyLEOrteEi5dh1H/3s+L8MW+z1jrh4QR732H+sRU4eGBhAPp/H9u3bMTQ0hNHRUfzyl7/E7bffjomJCYyPj6NQKLSFNffiZMmDo6OjCMMQw8PDuO222+waYHLy7Owsms2mTejG3+M4Rq1Ww/z8vOdkz8keaQgAhAvrYA0WsiOb3satCRa3/ell2PJ8fY61i0M0kMF8EqCeixCGBs1mtBhybNDa2kdd53Ho4KBnRe5nZpjQiqH8nkY28n9dfjabteFFBEndGNMWVsxsh0D7TKmuozGtNTMyqyHXr3FdXC6XsyFKJDvtYZEJLQB0kLcOOZRrmfid9+Q18pPPieSdFlrUzbjopjjL75JY+303eI32Une7Pu05e6wOpOdPKlI6VDDtGWpPKNAZ8uoynLVspJWv6yANaL0+TBq7/E7FlcYjs6dzjRw9slRIue6WYY0ySZSUCY4D/JRKu6stur5ybJFbhOlrXZ5Y3Ze6v+T/+nxX3VzHpSx3C8vUkJMPHvuP9cTJAwMD2LJlC4rFon1vuF1PkiTYt28fhoeHMTIygtnZ2bZ3KY2TaZCWSiWbuZiyOzc35+RkOanMMcJzsudkjz4QAkG08G7EodsIlTALxi9TIu+PocnLF7yvBmErI7IJEEZJa69a7lMry/chyIckVn2NrSYeuTWAPC7JwzUgSg+pS+HtNWAz7CiXy1kPTBC0r1OrVCoIw9BmR240GqnrXlhHemd4jSRcKr0MZ5KGrewDGe5EMqQiK8N+ZGIrlweLIZTasyL7k0a09uDIukv0O4uvFWi9JlD+1s0QSSu/G/olfI+loR+ZWsozk15N+R5rxbHXfaWB6vJQuCA9xP3U03U9xwjKMpcPSOO1Wq1aWZZ7UgNom1CjdxZA2z640lCQ3hT9G8cI6a2VxonLc9Ltf9nn2tB2GTK9lNz9leX9vcajf6wVTs7lcti6dStGRkZQrVaxa9cua9gWCgXMz8/j/vvvBwAcccQRAIDp6emenNxoNLB37157DY/JkOI0Tg6CALlczsp5N04GgGKxaEOXPSd3Xqfr7HGIgSHCYbAYXtzPc7bGrfi+FNDTC7QZt7EBEiaWsnvr+vfuUMdByYrMQVN7d4ADH/BkSCLJQYZVSSWN63KogJZKpY76NJtNu32PHuzl7KUke51+n580mhneRKVQ711HxVaGMElCo/eH99XKLtCZ/VR6nOQz4bnaGGCddChUWtgX76XL1dBrsHToWFrZRNo6Hg1PnMuPXjPzaUqP67g2iFyQ68Y4qaRlmed1q6O8rzR8pfKp3xcZyqwnpwDYbXsoi5Rv3kMblDyf6+aBdmWSYwOVYinHcryUCrHLOy37k8or2yu9SVoZ7uYll3XVBlCah1yOSQciy16GVw5riZPDsJWssVQq2RwTU1NTqFQqbdsFGWNQLpexcePGtq1niDROptw1Gg1UKhXrFe3FySyjH04GFifZmNjKc3J7Xbw8H+JYCCk2TbSM237CiuW1BwIZxkxj1oQwNJjpsfU45LGqHls9MEri0YNerwGQSpsMu9NKFJVTTdL5fB6Dg4M2o2GtVkMQtPahLZVKbUov95nL5XIoFApt62TS2ijX2RSLRTvTWywW7dYeuVwOSZLYbQt4LRV4KgIkb6B95pdZk6UXmW3UIVma+HspLXpmnka6fm6SeHuBz4vPIy1cTdY37bOfe3msLqTXQr9jva6j0lar1dqMN4KGon7fpFeInhCXcQosrn9bqleZ99H3ZFZkKuSM+pCGp1Sw5VY/VFCpKDMbeq1Wa8twzPv1K8symkO2T3t1XVuISMj+kWXJ58L7yIm1Xn3o+uwHXp5XDmuBkzOZDIaHh3H00Udj9+7dqFQq2Lt3L4IgwAMPPICxsTErO0mSYHJyElNTUxgZGcHmzZsxNzeHSqXSs41MBDU6OmqNW4Y5M4ILAGZmZtrec+nN9ZzsOdmjB+Ra1qUYtssBadwiAGIsGszeqD1scNC2++lX+e31u2uNj1YgJXK5HPL5PKrVqjVo+cd9Zl37XtbrdbvOVpKTrB8JgYostwPhfeQsMMlRbiVEhZMkk8lkUCqV7H0ZPtUrlEnXvZtnivXSHhnZp9p40Ep3Wmia9hJpcpaes7Q2aENG1rVfLEWJ9tg/dJPlbjLcbbafHpU0WQbc68263U8bzPr9ksqeNk4pb/Ta1Ov1tj0tdXv4njIJ3eDgoE0E1Wg0UK1WrbGpjci0vtLn6HbrNanawyoVbQA2akUrt/L6Xt4e2V55PqENGx7rV5a9/K4ODgYnB0GA4eFhjI2NYWJiAnv27GmTy4mJCWzduhXZbLaDM6anpzEzM9NWpzROzmazyOVy2LBhA4aGhuz+tJKTS6USAGBubs5eX6/XLX97Tl4sw3OyR1fI0OLVDv2VtzvQEGePdYmDatjq2eF+IAdE6Y0hQcmkCxzwOVhTMSQZyboEQWAVUIZOyXtxNpezsbq+Ls/Q4OCgzYbK5Bcsn/tbzs/Pt7VL3pNhUtxvT5Yn65CmWLv6zKW4UOF1Ea301PA4FX9Z57Tnp71m+jeZVVGHZOlQLB22lYZeoZEeywtpEKUpxtpgAtzhqlqWqSjKpG48j+VKOZDjisvrkaa8y3dYGrUyekJuG0KFMgxbGVaNMahUKm1J5mTZHFPYlkKhYBVCtlEqpNLz5YI0wuU7TQNCjoHSmOQ50sOtFW193PXMtHeI/dPtGcv+70eW5Rgjy0r77nFgOBiczN9mZ2ft9jZSRjdt2oRMJtMmL8TY2JiNeuIkkYSsS71eR6lUwrHHHosHH3wQtVqtg5OZHVkaibp9kpMzmQwGBwcxPz/flvSN7dT1SOszz8lejg9JrBUjcq3Uw2PVcFBTTvZDnGmKTdpgKEPt5P6TukyZnEErdFpRBxYJTSuJ/GQGVB4LwxDZbLbNCyuVcYY/6ZlaSWC8F8+T4VBaSeef7qNuyqAkTRJ62kwvZ6tlnaWRoZVjV5+7ZrRdfZ4GaQD0uke3ungsP/Q7sRzgmjjKsjRstbfVBV0Xl6Ek3xPXd2nYyvdWjh9AeyIoniPllO92s9m0Xts0OU6Tg251J6iky/HFZbi6fu9Wrgb7Mk2WtXGky+wlyx4HBweDkylP1Wq1g1dp0GazWeu1ZR2LxSIGBgacnBxFkZ1wAlrvaz6fx/DwcNvkNN/fbDbblmND1s3FyQMDAyiVSjYE2nOy52QPD4+1gWX12PY7w9vvoOkqUw7E8jcquvJ3qWzS2CyVSjaxg7wHr8tkMnZPW4YkcdbZFaIsryfCMMTIyIjNtsi1tSw/k8lgbm7Oelu4fyXJjH0jE2zIpDPDw8N2axFeQ6VB18UF17pj6f2RpChntGX4k5zx5cx7N08Pj/M6niNJmwoJy+J9eF23mWFZZ9cz6qcMj/2D9vT0C/1s5HEty/K4C3IiSp4n14IC7VsJyfNcMuMyzgqFQpvSGccx9u7da/ez1ftVy4kx9hEzJYdhiA0bNlhZDsPWel05vvEzTZ70uKNlWnrLCCnL3KpIh1x3k2X9/LT3XN5PZ4GVmWXTIL3l2tCR74Ret+jRibXOycViEUceeST27t3bcQ9jWjknBgYGMDg4iA0bNuDOO+9EkrTyYnD3AVlnycnyHYuiCCeeeKJdvzs8PIyBgQHMzs4ijmMMDQ3ZbYFYV8qGi5O5cwKXEnlOdrfJc7KHh8dqY1k9tr0Gb9fArX9Pg2vw0wqbS3nSil+SJKkhfgwlJKGMjo5idHTUJpiQXhsJHTYYBIHdh69cLqNer6NYLFoSHh4eRj6ft1sKMMxZz6BKopFtrdfrbYTn6r80QtHly+PymUgCJZHrGWnXbK38v5c3zaWY6hnjpcD1zGWd/GzxykAqYEtFmnKjZVkmPNLy4bpOlimVUSqp/Xh7KXsy+6+Uu2w225YlVa51o0Ep30mXrMoM59Jr4qqLPsZrtNKrZVlCR570a8j08iSnGZk68iUtBFJDt0u2mf97ee6Ntc7JNBDL5bLzHrt378bs7Kz1Tp566qk49dRTsWXLFgwODrZlIJbg5DGRy+Vs6PI999yDyclJbNu2DccccwwKhQJOPPFEbNq0yXLy6OhoW/31mJPNZtvu6znZDc/JHh4eq41VCUVOC6ORIURpioo+pg0+DU0OcjaS93R5Xnnu7OysDRUEWsQ7MDBgM6BK7wMJgB5ZtpUhTcymWK/XMTk5iWq1aj23rAOTWmiFm7PVcuaZs8D0+NCLK8lIenll/8mZbN1uaQhI5TqtLP4uw6/4uzYaXM9eliO3QnIpVWnXdyPEpbxPHv2jm7dUvgP99HE345RIU9Dku5xWJ/le8x3r9k6klcn/aajWajVUKhWUy2W7Z22StNbw5fN55PN5a+xKL3GtVrOKMBPLAUC5XLbeJynP0lOk6+mqq5Zl6cGR455LluWY6Gp/N3mWBraUZYl+npPH6mKtcDK5j5O78h40iO+++25MTU3Z30qlEo4++miMjo4in8/bKAdZh/HxcQwPD9u2jo+PI5vNYt++fajVaqjVavjZz36G6elpDA8Po9ls2vBilpnL5ZycPDAwgDiObfI4z8mekz08PNYOVj15lDQwXcqwnB1MA5Uoni/LlmE/VBBluE43z5IxrbBgbsbO2dtu0PUNggADAwN2vRDryhnpMAwxNzdnDdZCoYBGo2FDEhnOxPoDsGt1wzC0WRqZpIYKt/TA0MOrjQJZR9lHsi0819U38nr5DPXMsiwrzeDRRCqT6xByllgqQ90Mol7k6rG8kMoW0Pmce3kEKR8ug0m+pzL5jAwtZh3SymYZejsgl8dSv2suz65M9jQ3N2fHiYGBAWzatAlxHCObzVpDVu53SXnn+tZ6vY75+fk2L69sG8OZ6e1inXTIp6vfea5L5uT17E+XEivLoMzrkHF9X7k3qPxNrjXm2NXP5IZXhlceB5OTa7Wa3T82rW6Tk5PI5/Mol8uYmZlxrqmVYEi/lKOjjz4a+/btQ6PRsHL54IMP2giqqakpm9Axn89jZmYGIyMjHZwMLGZSlu+552TPyR4eHmsDq2bY6kE1bea/GzRJdjtPz3wC7eSbBjlLKpVJDuiadDhbK7Ohag8RyZJK3fT0dJunVm8ZwrKZpblSqbSFNfIeaUaDa3bWNfOa1p88JonT5SXSBHcg6LYPqfR86bq6iDmNrD1WB653hYqg/l8+s26eAJcsu95/l4eCSCuf77srFJBGsQxrlkab3LMzSRK79jbNMGBCLFfIXzfjgcaHRto4qr06sr9cniLe32U0y/7Q10vDVY9N3WRZltPvd9fxXhOPHulYC5zMiKZuHMKICLmbQRAEljdlSDD5aO/evXYyWRqe1AHy+TyGhoaQy+VQq9Vw2223YWRkpG3CWbcpm81idHQU2WwWExMTnpPhOdnDw2Pt4aBkRe6W6a+botKLeOXvroE9SZKObKYuyHAiolAodJCd9MRwnZwxLa+vJL1cLoehoSEEQStxTLlctgZ2rVbD4OCgvY5EQQWZm8qXy2W7rYD2hOiEHFqhluvcNClqJVUbBFqhdRkOulz954Ksv+xLqRjoMHIJF6F6rBzS5LKbLEvI59fvus5e5aV5eeV3liXDcRkqzHNcxiwVYLlWVtePijblr1KpoFgsotFo2PX6es1rpVLBzMwMZmZmbMZnPYFG6ONybSzLc3mAXF4alucaM3W5srxu8it/k/3DcS9NljV6KccaaR4nj/3HweLkWq2GmZmZrhPOcRxjfn4etVqtTSbGxsYwPj7eIQOUWYY3J0mCmZkZe32z2cTw8DCOP/54ZDIZzM7OYnp6GpVKxS4h2rBhQwcnl0olZLNZTE1NWR73nIy237w8enh4HGysWlZkqXS6vClLgR5w5UDcTTEzpuVd1V4DWS96UOlVkuE9TGAhkz7xu5xJltuSDA0NYW5uDrOzs1bRpcE6MDAAoEUI8/PzbV5fhi5xhtTlSdFKg05eoT02kmy1UtzP85DlsT90GOlSlU2tEMt3Qz4nhqsxg7QLrnt7j87+Q74jWrbkOS7FaymQylma0SuVMnokpCLO99ul5OlyCJfXt5f3iYqzlDOul43jGM1mEzMzM1ZJDsPFrXV4T30/AG0ebNlGDWk0aoWY/3dbbuFSpF31kXXR/+vzXTLXTZbZX71kOa2O3e7t0Y61zslJkmDfvn3OCRrWa2BgAAMDA3aSmGHGXKs+MDCA+fl5BEGAzZs3o1QqoVwuY3Z2FkDL08pJ6TiO8ZCHPAT33HMP7rzzTszNzdlM5vPz89i8ebO9v+Rk6gZywspzsudkDw+PtYdVXWMrB1+X8rmUAU8qeFrRc50r19lKxVEqw4ODgwBgk0fJWWRZNrOh1mo1GGOwceNGDA0N2e0+ZPuy2Wzb3pXsh1qthjiOcdRRR9mQ5ImJiTYiBdDmYWbfyfUsMhW/Bo1suUbPpcizTmmKoouYZF3ked2UZl2GJvFuJMi2dFMkXPfo91yPTvTzDNO8fd2MVNd9tCzL++vQ5bTn6PJuSHQLz5MTV1zTzrV4UoHV680YWjwwMGCznc/OztpraGhKQ1WOgcDimORSSuW9OFZxXGIyOXmO9va4+ki2mde5fu/2XY8bLqQZ8f3Issvg2l+u8EjHWuDkcrncJiOSk7dv3w4A2Lt3L4IgsF5SAJicnLRtGBoaQqFQwOTkJJrNJh760IfiuOOOs9v7MBFUFEUYGRnBL37xC0xOTrZ5KZlU6txzz0WpVML999+PW2+9te08JoiS76TnZM/JHh4eawernjxKJ6lYTriULB1+o0mECiWVQp3ARYKztsCi0hbHMarVqg1xpHcml8vZbQxkUg0ZhtRoNDA/P4+5uTm77od1lfvfDQwM2Ays8t6cwZZGsCQ11imKIlSr1dQZZj1bLmeYibT1jJpIdTlyvbGGDGuShoOujzyHEwWuc3W7+Kn/vJdn6XDJ62obHVKW05IdaS8jkB76LL0l8l3i/1TU9Xk6W3Gj0UC1WsXMzAySpJUBWSr3Um6ZLE5PXDWbTZtNHUDHukHp+Z2fn29b28dztFfbJcdSmZbnuQxY+Rvv18v7o+99ILKsley0983jwHCwOTmXy7VNIlO+me2YGchlJJSs++DgoM1fYUwrfHjv3r0oFAp2D9xCoYDR0VHMz89j165ddv0t70Purlar2LNnj+WuQqGAYrFoE73xPd20aRN2797dNmbw03Oy52QPD4+Dh2U1bLvN3gOdngrX70s9Ju+ZRqAElVK9j20URcjn8zYDYtqsdRC0tvLhzG4QtJLBTE9Po1wuY2hoCCMjIygUChgeHoYxBpVKxXpwSKLZbNZ6fefm5lAul1EsFu09qtVq2+wrs6OS2KWCybBoWWf2CY1jqYh3mwXm72khkK5JAT07rH93fbqglfQ0Y0SGg2t0e69c3giPA4M02rTsL9XT0ysxklaG0zxC+j3Xv6etUeX/NCh5HscGKr1URmlYy/rwd17PdbpBEFgDNpvNIggCq7RToTdmcXsv9qvc+gdY3PNWGspSiXWteevW5/KaNJln3foxal333V9Z5rVp13lZ7g9rnZMBYHBw0HIkrymVShgZGcHU1BSmpqbsMX2/KIpwxBFHYGJiApOTkwiC1jr2W2+9Fbt27cIJJ5yAE044AZs3b8YJJ5yAJEmwe/du3HnnnajX68jlctiwYQNKpRIymQxKpRLuuece7N69G0cccYSV1b1797bJmzEG+Xy+bVmC52TPyR4eHgcfB9Vjq2fwXOd2QzePjBzgJWlIRZL3ZH1c2RU5w8rZWyam0Gg2m21rerg3H7cQILgVSCaTwaZNmzA+Po5yuYxKpWL36mN9SZBUohl6GASBDYNm8hqSC5VotpHKsOyTbusY2R/diFTPUusy5Ayzi0i1V00rO9xOIU2Rlmufdbi4673yXp6VgSt88UAVFVdYbJosaw+lvKbbM5fnyndeTibJhFGUZ8qufscHBwcxOjpqFXFpAFNmpbJLWU2SBMVi0W4NRlmmF4ReKGAx8R1lXHqIuvWlywOTBt2PLll1PRvX82BZMkdAWh1dSJNjlucV4uXDweRkKWvSwONk7vT0dMe9M5kMisUiKpUKxsbGUKlU2pb6EHNzc7jttttgjMHg4CAGBwetIcwojEwmg6GhITz44IMolUp4zGMeg4c97GF48MEH8eCDD1oDWHMyvcyekxd/95zs4eGxFrBqWZGlYpL2ezd0G/h1OI/8Xw/GemsBEnq5XG4b9F2eniRZTPKkiYAELfe4y+VyGBsbsxmRqazynlRum80mBgcHMTAw0KZYS4Khgc0Z5tHRUUuqWjGQ/WBMK2EWidTV51qJ6SfkSfa/Ni54XPdjL4+Pnmxw1YW/UWFI89hp9Hr/PJYGOeEiIZ9h2hpLFyhDUpZd76U2nuQ7pX9nuRKULZ3tWJ6nlS/XZBbbz7BD+V4Vi0Wb4EYr7hwbGFZMo5jr8nS9ZD2SJEGlUunwFgOdkwzSiEiTOSljWvnVfca6yPL0+a5nIuvmClNkH+p3yeWJkuMM+9wrxvuPtcDJURRh3759bfLF0OD777+/7Z2RxiDQmnBqNBp44IEHMDc31yH3lKV8Pt+2pOBRj3oUduzYgTAMbfg/78MkVdVqFePj49i8ebP1tKZx8qZNm3DMMcdg69atnpM9J3t4eBxkrKrHVm4p0M+MnSQj+b9WnmV4YVo5UtmT68tIWq6ZZGBRqWUoYjclkQN7sVhsW2NHAtNbAHBdHhPDMBGNVAy4byYVaGOM9eLwGhJpmndKrvGVx10k5Gqf7mOX8iuv1WVohdh1nSZYGgb6XiyLCX6YpEOGxcq2pc0UexwYuiWNSoNWtLpBvjNawZPvoD5Pvqtp72Fa3TRk+5j5XL+P9MDMzs7aiI+RkRG7tQjrxHKkV4pZlOmhkp5NniM9NgCsnGsjMO3d7mXU9jpfy3QvWXaVR8OiXC63GeQuWWbd+Bw5Bugx28vy8uBgc7LkJhqLXLPJ9al89jIb8fT0NEqlkk0W5QI9u5lMBmNjY/Z4Npu1HJ3P560MBkGA3bt3o16vIwxDNJtN7Nu3D/v27WubdMvn8xgcHLQGsPbUek72nOzh4XHwsCKGbdpgnjbD3s/gdqCzetJbwgFbhvtJ5cnVBiZvkmvTXF4JGqEALOHl83lLiLwnN3mfmprCUUcdZYmxWq3a+rAejUbDGs1MVtVsNq1BzP+l0igVDX6SdAnZ72ntluiHcPV5cjY8jUx1veWMeLdQSxr9MjRRrkPW9fEkujTI56KPp62J1X3cy5hNU4hlma5rZL1cxq/MrNqrDjqcjwjDsE2Zk22XfdBoNDA7O2u9PIODg5ienna+93xHWTYzL9P7I9fZSs+WHDtZhk5KpyHL6NZ21s0FWRd+12NItzBJ2b8ask+lLGsDVt5L143leLnujrXKyYxy4ns+ODiIbDaLmZkZy2t8vtrDyWzKaZzM7fjy+Tw2btxojydJgs2bNyOKImzYsMHmtygWi/jpT38KYwzOOussNBoNzMzMYN++fW1b2nA9KeWXiaU8J3tO9vDwOPhYNY8tB7xuM8McPF1rqOTscBpoMLrW0bruJz0nkqTl2jYJDswMNyKBsVySX71et6HNzWYThUIBpVIJcRzbGWRiZGQEmzZtsgZwqVTC1NSUJRXOXMukMXEco1arIZ/P23a4woQ0MbFvXH0pCU6uk2HZaYaMvJc2hDj7HoYhqtVqx/6fLqWE32W/8l7a28Pn7ZqtTpsN1sqGx9LRrywvVWHRMqhlmfdxGUryfi7juJ+6sFzpOZVeVe0llkZqPp+3mVeZ3ZzKrawjjVjWM0laWZQLhUJblIb0+LDt0muUy+VsoirZTt1X8russ2yDy7OkJwWYRCsIWsl5ZJ/ofpbyyDa76inB58126vP1e5ZmzHv0j7XAyXofV7nVlny+zAauwXelUChYrmVkAADLpVNTU/acubk5bNq0CUcddRSq1So2bdqEfD6PcrmMubk57NixA6eccgomJiYwPj6OSqWC22+/3eoEtVoNs7OzCILFtbOekz0ne3h4rA0sq2Gbpjx2m33XM5Sa+PpVkOXgymRPeqDkd+kt6QaXJ8K1vo0KmTGtLMgTExNWOZ2fn7cJMDKZDAYHBxHHMWZnZzE8PIw4jjE5OYn5+XnMzMzYrYOkp4UKbZIk9l781DPu8jpNOiRIl5LiUlJds7iuvpFkLc+TBKzbpMuVSjTXJcr7pJGiTFohzyEps0xZjsf+Q3p5XMaGlmUZjttLCXbdi2GJae+ia8zQ6MfzIeWcSiQVThq30nCmscekNbzHgw8+aDMeuzKfSqOVywsY4aHD9lxtcxmrrramybJ+dq5y5Fig7y1lTV6rvbp85myrHntcHnLt6dKynBaO7BXidKx1TqbHNpvNYnh4GBMTEx3lkD8ajUYHb3OpD98dYmRkBEHQSgi5e/dufP/730exWESj0cB9992H22+/HUnS2ppn27ZtqFar+MUvfoHx8XHUajXcdddduOOOO/CLX/zCJnWU7ywnqJrNZgcn03CU/O052XOyh4fH6mDFPbZU5uTMcNrssES/5ClnLTlIM/mEK1MigLYZYdaP92Jd9f5zvBfDkKiwyYQWVIQ3bNiAoaEhVKtV5HI5NBoN1Go1SyQk6M2bN+OYY47B5s2b0Wg0cNttt6Fer9vsq9JrxPaxnvSeAC1PsSzfpbDQc+I6vhSkXeOanTXG2P0HpdKuCY79J9snCVcTqVyDR4OJRM1ytQIs782QT4+lQ67JA9CXLO8v6NHIZDJt3slenjpt9Gjjsh9Q2S6VSnbPSdcyAY4fW7duxfbt2zE+Po5ms4k9e/bg7rvvxvz8fIdnlV5a1o+Z0yuVio3yoHHNd1a2jdfLNklFOk2mu7VdGtLaQOXxer3e9vylR0cbQ5RL6f12KfFsp5ZlRsVI40A/W6mce/SPtcjJ9Xod+/bts+8WJ304AcT9n11J3AqFgs1CXq1WMTAwAKDlWWWo8Omnn47jjz8e999/PzZs2IDZ2VlMTEwgn8+jVqthfn4eGzduxNlnn43HPvaxAIDf+q3fwic/+UnccsstCMOwjZPn5+etkS05mZPXs7OzmJ6e7snJOlrCc7KHh4fHgWFFDFuXl0ETYrfZ4QMBiY8hPmmJJWQ9SfA67NEFDr4MDaYiyrrPz8/jwQcftLPIpVLJhhlu2bIFW7duRaPRwMDAAI477jhs27YNw8PD2Ldvn/XyPPjgg7Y8aUQHQdDhvarX67ZOOqRaz/ZqAkybqXUdS5vdTytHkplUbF114m98Bvq4qy6yDvQIcFY5bZaYirUPYdx/dJNlQisyaeXoT61Q8X3mRNNS9ztdCnQIMg05AG0eVSreDDseGBjAxo0bccIJJ2D79u0YHh7G/Pw8tm7dipGREdxxxx144IEH2hLUsXxOnrG98rsxpi06RNaL7dXrcF1yKEMY0/rN5enR4BgkJ9l0fbTMSsW3m1dIjyuUZfmbNtwpy3w3ljJpcThivXEyQ3qZsJETSvpdNsZgbm4OADAwMIB8Po9qtYpyuWx58d5778U3v/lNDAwMIJvN4ogjjsDY2BiazSYe8YhH4JxzzrEyq7nhoosuAgD86Ec/AgA7ycZcFwA63j/uTc+2p3GyDvXVvxOekz08PDz6x4onj+JAJge2A1VAus0uU2F0hSellSPXwkmvQjfI8ChtaD744IOIoggbN27E0NAQduzYgQcffBDz8/PYs2cPNm3ahGw2a/eunZ6ext69ezEyMoKHPvSh+MEPfoByudzm8eD2IJlMxnqB+BvryzbLEEAa7vQC1+v1NsWR/eAiRT47eczV391+AxaVYBnKpMneRZYuZayXscSy9Qy0/KPXPU3h92jB9Uy6yfJSZbuXMiO9C1qWu91Hvr+6/ml1cMl7HMcol8t2D+t8Po8wbGVLbTQamJ+ft+GHjUYD+/btwwMPPIAoinDCCSfgqKOOsqGQfCcpr/REsxzKMscf1lcml5Gecso3f9fKJWXFJS+95FX3lZZjaWC4Qpu7Kefa2502lktZlpl3tSFNz5CX5e5Yb5ws18kyZNjlsZXXZbNZ5HI5O0lMzM7O4pvf/CYKhQJ+4zd+Aw95yEPwvOc9D1/96ldxxx134Dvf+Q4e/vCHp45HF110EfL5PL7yla8gl8tZ+R8cHATQmvianJxEpVKx72gul7Nr57l+V3IyQ6/L5TJmZ2c9J3tO9vDwWCasaChyNwJdKqHqtaTyHhJ6RlKeJ70fHMRJKHJwZihN2iBLD2ylUnHWJ45jVCoVlMtlFAoFDA8P4+ijj8Y999yDubk5O8ifccYZOOaYY+zan0KhYEMY77777jaDmxvWy/W23AeTa3hJDJztlqFDkqRkEhoqjbJ/eDzNu5s2G+x6Ptqzo98BSdj6f1knV+gTgLZnpElUr+eTs8NeGV46eskykTaz74J8lhrSc6uvkb9rpaybwaTb061eUt4ajQby+TxGRkYQRRFmZ2cxOzuLRqOBrVu34qSTTsKpp57aUQ6zrzLbMdsrE6xomZPyyT1eOZGl1ynLvTVd4ynvJ8e8bs9F/+Yyal2Kadr1rAM/tTcX6MxmK+vtWrsrZZqTHl6W+8Na5+QgCDAwMGC5lc+fS3RczzkIAmzatAn1eh0PPPCA8x6ccN61axfGx8exdetWXHDBBfjsZz+Lu+66C9lsFr/xG7+BDRs2ONv6iEc8Al/72tdsJuYgaEVthOHirgpJkmBgYABHHnkkbrvtNgCw2wG5OFlHW3hO9pzs4eFx4FjxNbYyfE8rm2mfvcrrpgzTsORWO72w1JlqDs5pBMvBeXJyEvV6HblcDhs2bMCWLVswOjqK22+/Hbt378bk5CS2bNmCwcFBjI6OAgCmp6cxNTWF8fFxZDIZO2stleG5uTmbfTkIgra9ddkW7rvLNUBck+cKI2Q5rplYjW4zwfw9DZKwXb/J/pNKb69n2C2MyTVDLL1ArqREHouQig3QW5bTDNwDgQz56weusL9u9egVmWGMsev0wjDE5s2bMTY2hvn5edx7772YmJjAbbfdhi1btmDTpk3YuHGj3fpjfn4e8/PzbYYXJ6iA1kQUxynpOZbvPjO9ch1hmgzKtXquZ+BSitOUSJfCLA1Ml8zJ8/QkWi8FnPXsR5b1GEFZdo1hHm6sdU6WkO9VGpiYyrVGMwxbiSIrlQp++MMfYnp6Gps2bcLWrVsBAE9+8pPxne98B1/72tfwgx/8AH/yJ3/ivMfY2BhGRkawe/futrKLxSKmp6ft2uE4jrF3796enMw9cj0ne0728PBYXqzoGtt+/4B0Ik0jVjnzrCE3kO9Vjh5E9QCuiYUhM7Js/i8TJgCL3uByuYw9e/YgDENs3LgR9Xodc3Nz+NWvfoUtW7Ygm82iUChYBTqbzeLII4/EXXfdZdfoZDIZu55WEh+VfjmTSq8tM8q6slHKtrIfdaif7B/tuZG/p3l5JCnKe3QzJlwzx/q4/JSzvRJSEXb9+RDGpUG+J/3IcDeFOO0ZumSfRiA9nvK6NPRj4PQKgyZksiROJHGP2mw2i3w+j8nJSdxyyy3IZrPYvn07CoWCzYZer9cxODiIer3eZnwynFHWVcsNDdsgCOz4oPMAaCPS1S4tS671sVqOexmgLlnudo28n5brbrIsQxj1u+ZluX+sZ07m2m7X842iCENDQ3bCl5CTMJlMBqOjo9YQrdVqqFarKBQKAICHPvSh2LdvH+666y7ccsst+M3f/E1nvR796EfjhhtusEsAisViW0K5bDaLJEmwZ88e2z7PyYvwnOzh4bEaWNE1tnpmWCvH+nxXGfp7P2TLjJpy/YiE/C7DfaVXIi2kzuWRIDhoFwoFGyL3wAMP4O6778aGDRtwxBFHIJPJYHh4GMViERs3bkQ2m8W+ffuQJAkmJyexb98+VKvVjjBnKrnsP3opGNIo68PQSbZHhjxpyBCoXopGWsIJ17nyf71FEusly5HESw+XfB7yOhf4bKQirH93vYce/cFl1OpjQPpaPtd3CTlmyPP12rNeoMKWZjDqc11wTWgZY2z4caFQQLFYBNBaX1csFjE6Oopms4l7770X1WoVExMT1lvLPa1Zrgyt5URYWhZgZnAvFov297Rsp/30k75Wj3ey7fpc/ez02OeSOentkcaGq3x9nQ515G+u99ArxN2xXjjZGGPDfXkdJ5JdScJcz16/l/l8Hps2bUImk0GxWMTXvvY1fOpTn8LJJ5+Mxz/+8SiVSjjhhBMwNjaGk08+uaPds7Oz2Lt3L3bt2tXx2/z8vOUsYNEz6znZc7KHh8fBwbIatpJ0eimxacqx6zoZ6sRPluP6vVf4jau+/N4NXOsmy5Lb/TCRDGdjGWrYbDYxOztrty4YGhqydeXavWazifvvvx979+7F7OwsZmZm2mZ1mWiGGVqDILAhyTKBFM9n9kk9w2qMseGQXLfXj/fKRaDyf21E8Jgk57QZZgmeK5PipJ3D/11hdfJd0qFPcoY4bebco1OxSfOq9vpzQU7QsBxZpnxu+jp9HuvaSynqZrTpNunr5LXNZhPlchmZTAbZbBYDAwPYtGkTRkdHUalUUK/XMT8/b2V+enq6bczivenxZSilVtwpz41Gw3qDdB31dkhUPtMmGnSb9P/d+oPPcyneXdkW1xihz9fvmCyjm8enV1KiwxXrlZP12JF2faFQwMTERBtP0pBqNpsolUrWWGbCxn379mFmZgb5fB6f+cxnMDo6imOPPdbZLz/5yU/w7W9/G/fffz+mp6ctX3CCulartW1HxAlpz8mekz08PA4ODkooMtAZrugiM5eSw0+X8iYVT00MaUq2nll1kSmPyfUwDHEikTYaDYyMjNgwI2Y65fXcB5MEmMlkkM/nMTU1hXq9jomJCczMzNh1SDI8mcqwvD8NDRIElTpJojpjM/uUCjmVYenlchFq2uyyVFhcs7zyz0WgmmBdyrL8XT8/6dHhzH43Y0rXq99wVI8WXEqK/C3NiALQYcBqOZfXSdmml0PXw2WkpU1U9TJ4NeT9pYeUk1gArBeJ79/09LTdh3Z2dha1Ws16khjBwbGB18n70SCVMss26fWJUtHMZDLI5XJ2/S3HCpdBIWWwmyzrc+U+3y50ex4uJb7be5Imy73CGNnepXj3DyesN07mb1yCII1VWS7lgu98qVRCqVRCNpvF7OwsTjzxRLuLwH333YdGo2HfkcnJSUxMTGB0dBT79u1DqVTCwMAAHv7wh9uyf/7zn2Nqagr5fB7Dw8N2vS4TSFar1bYt9zwne0728PA4uFixUGRXmKL8Xf7vIljXp+t6PWMMoENxTINLEQQWDVh9XxlKx9AoqWQyaQy3BeFxuUUAPTpB0NqTdnh4GNu3b0e9XkelUsH8/DwAWFLkDObk5KSd2U2S1tY+vDc9PrqeMuyLM60yFE23zRUu5FJMu/0mPzVJdoNWjFlXV0gW/9fPWHtttDLs+u5DGHtDhy9quAxerfCmQRsv+jn3I8tp77J+97opV1JJk14Jtl3WVU+Glctl7Nq1C4ODg9iyZYt9D2dnZ+12Qdls1rZH1km2m0akrBPrQi+G9JowjJl1SjPsXW3X7dB9IOvmut6lNHe7F2VZyrTrXKKbB1bKrxwfuoVFHu5Yb5wsl90ASE0gx3W1fPaVSgW5XA5BEKBYLOKee+5BtVrFyMiInZgqFAo28/LMzAyq1arNgTE1NYVjjz0WGzduRLlcxvz8PMrlMjZv3gxjjDV+y+UybrvtNtRqNbvFHu8LeE4mPCd7eHisNlYsK7KLSNMGQ9e1/ZSvz+2leEvoAZ9KZTclWIYQFotFa1yWy2VLxJyplWuKms0mcrmcnZml12Z+fh533HEH6vW69eSSwJlgSoIDvvSgBEFgvcYywQbX+EridfWPJCx5zNWH/YYHaSNIK8FpkG1KI1KtGOvr2A5JmPIceZ5f09MfuslyWv8C6Z7YtPBFl4HcD7Qsu+rfq6x+DCPdbrm/7tzcHO6++26Uy2XUajXMzc2hUqlYDxEns+hlpQErDVnuN03PDr0yYRja4/L910qvS75c7VmKLMvnJb2wvZIApb0PWpZ1aGMvWXbJNft1Ke/M4Yj1zMlp/MX3cGBgABs2bMDAwAAAYNeuXXatrjEGMzMzKJVKNiMxAAwPDyOKIuTzeczMzKDRaODuu+/GJz7xCZxzzjkolUptk1Tz8/PYtWtXW/JIOYHtOdlzsoeHx8HHihm2aWss9P/8zk85CPajDOsy+oVrcHUpxvo7DdQgWPSiuLwwVEhp3OoBm+2Ynp7G9PQ0CoUCjjnmGIyNjdn1tHfffTf27dtnPcP01JIAuEZP7nPJOrOeMpOqVCbp7dGhXf30mYsMXeFOPO5ShtMgy3GFbMnftSEi3xFJtC7FWBoHHt3RK4Swn//19Wll90I3JTntflKu04xfAG3y4xpnXIqd9MTs2bMH09PTiKIIw8PDGB8fRzabxczMDPbt29c2USWTSVG5ptFLLy/ltFQqIQxDVKvVVFnWfarlR8qKq01pY58uW4YndutLXQ5ljtdqT40sX7ZBK8Msz/XnZbk7DkVOpnwwSoqJ2uTesgRzXDQaDZTLZYyOjtqyaHQGQYCf//zn+NGPfoTNmzfjyU9+Ms4991xs2rQJcRzj05/+NH74wx/ayCrPyZ6TPTw81hZWPBSZ37ud6zpHH+9FtIT2uPYKg3TBNZDzPvl83iq05XI5tSyGJjFUSq6t4cbuTCyVy+VgjEGtVsOmTZswPj5u1+sBLe8tFV4A1uPDNSw8rhVa1kl6RahcyjBqV79IhUKHlLn6St5Pl6vX6rj6WZ4nwyr1s3CRp667S0lLO+5JtDukLOv+4++u/13n9FJQ02S5W9lpv0tvj1bIOOmk7yPf937axVBBlsV1cpTNwcFBjIyMAID1FMnIDl4TBAHy+TwajQZmZ2cBLC5B0O+5VA4Z3skyXYarbKc+Rxrm7KNuIYtSGe7m8ZFlSdnn/XV/p8m0ft+0Eu0N2/5xqHJyFEUYHx9HELTCl3ft2uWUA6Alg1xPa4zB3Nyc3YZnfHwcURRhz549KJfLdi3t7t27ccIJJ2BwcBAAcN5556Fer2N6ehqlUgn33XcfKpWKNYw9J7vr7jnZw8NjtbAihq0Oy+k2iMlP/f/BANewSo8r0Ao/3rRpE5IksdkRCVc7jDFte28C7Wt0CSaBAmDX5E1MTCCOY5RKJWzcuNEmnWKm5CAIrMHMPflYFu/D7Is6yYQmLBfhyeeklds0uIyeXtdJBVjWKY10db1cdXN5r/Saaa0Ma8PHowUqjq5+T3veLmNE9rMOT14u6Hc5TTlyve/yeLdQOLbN5WkEYJPJUf7oGWJSOJYhwxd5bhzHdpsvaSwzcRRDkeW6O9ZdK/Jp77K8t04g5Roj5HUuA9V1Ly3DLlnWCnGa59f1LLopx2xbv57kwwnrlZODIMDg4KCdBObesQAwOjqK3/iN30CtVsOtt97a4SHVdTemFZIsDUouEcjlckiSxO5sEAStxFU/+tGPUCqVcPTRR2PLli3YsmULTjvtNExPT2Pz5s0YGhrC5OQk6vU69u7dC8Bzsr6H52QPD4/VxLIbtlqJ1eTp+q4/eynD/RCtJAjXNS4SYQgTjzWbTURRZDdy37dvH4IgsEoq0K5A63tKBbDRaGB0dNQSHLM5UrHjup3p6Wnk8/m2bQWKxSIGBwexadMmW49du3Y5lUreW2ZklGTDPk1beyc/9XGtCOtzXUTdC7Iu3QhUX9PNsNL16kcZ9nBDrnvqJsv6WbjCHgnZ70tRpHWZhH7H+U5pQ0c/a75r2tskkVY/Vx1kkpRarYZarYZCoWDXzzMEMZPJoFAoWO+OTpKkvZLSk6PHQiqJLm+nhpZLrTx2k2WJtPW1WhnuBj1B0K8su+6r30OvELdjPXNyHMdtIfzz8/PI5XJ2kvknP/mJDfd3TRzre+bzebslT7VaxYknnmjzWTz44IMAWut1s9ksGo0GoijCr3/9a2QyGeulNcZgdHQU27dvx0Mf+lBUq1X86Ec/wuc//3k74Szvz3t7Tvac7OHhsfJYdsOWinC39Tz6u0tpk8f5f7ffiW6z9f2QryyDgzmNzMHBQZvivxtcJMT9Z5lMisRBQ5d72XJGmQQqPcMjIyOWnMMwxAMPPABjWhvQs2xJ7rod3fpHK9P8v9tMrb42bbaZkN4i+b9LwU7z9KQZSxJpGVVdz8Urw+lwGbVEt750/Z52XTfsjyzL49q4le+p9ET3Gke61VnLFu9Zq9Wwe/futkQxYRiiWCxieHgYGzduRBAEmJ+fR61Ws55a1ieOY+tNkkagvKdWWvkps0LL9qVNhOk2agNDX+cKO9Rhxrpc9o087nqf9PWuNYc05vX75hViN9YbJ7NcHRUgx+larYYkSbBt27a2ieY08D7k01wuh1KpBAAol8t2XTsNUGMMisUiGo0GJicnceutt2Lr1q0YHBxEvV63W/Ns3LgRg4ODePSjH404jvGVr3zF7pHL+3lO9pzs4eGxelhWw5aEpIkzjTz5Xf/vGuxWAtKT41qnwvtqBbPbehIJEiUTSiRJYkOqgEVlMEkSDA0NWQWYSq68LwmSiWaGhoYwMzNjvcquTI1U4F0bnlM5TGuLS+HvRjQu0nMRYy+kKfHdwHNca6t6KVRpho1Hure2lzIsoceEXsf7QZoS2M3LqK/vZuy53nvp2XVdy2PNZtPucQmgbekAy+B6PADI5/M2g3q1Wm0bE6ThQFnuZjx2a28vyDb1kmWg07h1eYz7MYjTnmXaOsludU+7x+GM9cjJYdhKwqQNVimDU1NTllur1Wpf9TGmlcciCFoTx5s2bUK1WsXExIS9F3kXAI488khEUYRyudy2HdDg4CDCMMTk5CSOPPJIW/74+Dg2bdqEubk5z8mekz08PA4SVsSwTVNY08jRda5r9r3XYNdrwJVhinLtixy05cylHNCLxSIAtK3j6XZ/lpfJZOz6HbnVAM8JwxCFQgHFYhFJkqBUKiEIAlQqFWSzWRjTWhe0Z88eTExMYMOGDSgWizDGIJvNol6vo16vd1Xm0gx3jW5KT9q+clIJTTMSXDPuOtRJ16Pf2Vq5Xkcfd53nMsTSyjic0cvw7KYMu8IVebwfLGWmXnone3mGdJ21Z1Ie1+NAL2M3DEO7vpZZWplwhmvkmXBufn7eZkLnucYYVCoV6y1imbqPu61J7dVeXpvmCaNBLfvVVa6snxxXuhnc2mO7FNCgd+U16NZej/XJydIgBDrfRYbmH3HEETYbeTd+knWlUTsyMoJarYapqak2A5r9NT4+jpGREezdu9cuRZqcnMT4+DjiOMbdd9+NW265BbfccgtOPfVUjIyMoFwuY2hoyK6X95zcedx1nudkDw+P5cSyG7bd/vQ5/A50JpmRv0ksZ7hZWvlaIZNKK0mXygLPkeeTPBnuyNlZ7k0p+4CbxkdRhLGxMYRhiLm5OWQyGbsXJmdxucUA9+fj/rnaixMEi3tsNhoNJ0HocKS0Z6X7Ki1MUX7ubwjRUmaEpWIgSVcStibHNE+ED2HshJTFNFk+WEgLwdVIM0x5XCYwoXKdBsoylUapPMoxIwxDDA0NYdu2bQiCABMTEygUClZ55tYg9NowBJIhybo+9F5FUWQnxtIMRsqEHJtc8qyvT5M7liUnD5aCpSjDLnlN88B34xIvy+1Yr5ws30sXJ2ezWRQKBZtcrR9O3rZtm92BgOvd5+fnOziZIcqVSgWbNm1CEASYm5tDkiS444477LZbQRDg17/+NWq1ms1+PjEx0bYHtWyX52TPyR4eHiuPFfHY8n/X792+Hwi6hSHqAZ6ei348H7IMPZOsZ01liCEVUKkIy+tIXCRkEiVneplYhvtZBkFgvbP0As/NzVkPj4t40sKOdNim/D/NcOlm0PTbj2kz0rpOEvvr5elWn26KvkcLaWtrJdKUrgPt3/2RZVc9ZQZhrRjLY/0o67xG3ksr3PQ68Xcqw4VCAVNTUzYMkWGKjN6oVqtW7rlFkbwnJ7/6iRSR6Pb85JpYeb02EHiuq4xu4cfdvLfd0I/cdvtNGjZerg9dTib3zs/Po9Fo2ONM+sR3M5PJ2IgIbsHDCSLN5XzvybkzMzMoFAptnMzIgWazabfg++Uvf4nBwUEMDAxg7969dtmB52R3ud1+85zs4eFxoFhWw7ZbcopeA9VKD2Q6BEfWrxfhM2EUjVb+5pp1pRLKGWFN7lJhlOF/zWYTs7OzSJLEbiLPmWFgcR9czvzqTKh6xpZKtPaAuGZge/W9K5xJXic/0whV9r/sA86Wu+7TDXJmWN/PpVB1M7pkKKtH9/A3+d2F1Zhpl+9SNwXQ5emRn7zOBWkU87urP+RYQNms1+uYnJxErVbDwMAAms0mqtWqfffjOLbKOJVloN0rrBVeGsTyOO8tQzVl/7vkIk3Zdhk4abIs5bjbmlntNXOh33Go2+SK/N/LcjsOVU5uNBqYmJiw3lX+pg3WMAxt0sfp6WmUy2Urey5OZtm1Wg1hGDo5mRNS5GRGdOVyuQ5+95zsrl9aXfnp5djDw2N/sWyGrRyM+lWCXb/tD5nqNTgupU8PrK41ZN28VAwf0uE2EsYsJqcIgqDtPHl/kqDcGiBJEuzduxfNZhMjIyOoVCo29LhQKCAIFrcLoMEslUtXv6UZGS5lIg3SU8VztfHgyj6bdg+t+FJZ18+Jv8v+dnmBdN33ZzaZ5XgSXUQ3WeildPYjw70MSqKXLFPOtKdOG3jd6iTlR09oaW+XVPi0HEhv7L333otarYbx8XFUKhVMT08jiiKbeIZKsXz3Wa6Eqz3d+lHLl1asu8mQNo5dhqlUlFkvaWyzL9Mm3eSxbnVhWf3IpGvCwcvyoc3JxhjMz88jjmMUi0W7hY+ebG42m5icnLTvBDlXc3KpVMLQ0BD27dvXtuRIc/Ls7CyAdk5mBnMmc/Sc7DnZw8Pj4GHZDFu9DqffmVdeu1zoNjvp8uCkkYNWCmmQhmFovS+utrgGete50khlNtQoipDL5VCr1TAzM9PmrSXZBEFgs0CGYWhnkvW9OXuslRkS1lL6TveN9lxr4tNrEF2zyS5Fp1siGt2Xcq1OWj36Qbf39HDFcqyt7SXTac+nm8fDZcDIc+U5+h3rVo9+z00D312u9eP9BwYGUK/XMTU1ZY9rWWY/UPYZxijrx+29+F3+xsgNaYBLpVTeQ7aRa3cJKYv8n/LlMjJl/2tjVyaEkffWcBnHByqHLM9nRj70ORloGZi5XK4rJ7sSFMlz+b7QaN0fTp6YmPCc7DnZw8NjDWDZDVvXrLCLVJdjVnipSPMckJB6ncf1rXoWUyOtLdI4lmvZMpmMPc61tcBiFlNJzFIZBFqhjPTgShLK5XIdWwrI8tKUh7S2SMU2bSZVK76udYqaSF0Gi1SSe5Gh633qVre08jyJLkKvsXQhTVE+0H7slo2XcL03PC7fO9ekkut9S4NrTHMpz1KuM5kMCoWCNRwbjYb93xjjDIPkfSj7er0h97h2eUqpSPbTb9qDkwapqEpDXY+R3fqN44WWz35kWxvEvcYAl5LucXhw8tTUFIrFopW1NHTj5FKphDAMUalUEASt5QSekz0ne3h4rE8si2HLAbMfstTXrST0AK0HbZfSpIlAK00y2VM3pLWNa2yo0ObzeRQKBbulBcvP5XL2Gn6vVCptM8VhGNpQKElymUzGlsf78DdXeDRnprvNGruUR9esrE7IIdsglV1XP8sQrn48aNI4YLZJ1qEfD5CrLYc7pFGbJs/99tX+yH6aQc33QSZY6leW9fuWZtzuzzsgjdJcLodisWjDkWnUcnsuKs35fB5zc3NtHl7Kssyoakwr+6s0eKVHleOFBMOh08J4Xd4hV7tdnh0pb7J8/akV5X5BHpFrjXtBn+NluYXDhZNpFFJGljrhzD3mZ2dnbZ95Tvac7OHhsX6xLIZtr9DFtTBAaQ+ArpPL4yHP7TW7KMtIG8CNaWVWZghgJpNBEATI5XIoFAp26w8qpywriiIMDQ3Za1zr81h3Zm9k5kZZF0lwnJFO6yv9p+Ey+vuB3D+YZcj/XffR312klzab3K9ivRbe0bWANIU4rd97oZuh6kKa51GWpxVCfX5aGVohlkpgmnHH99VVlgzn5f1yuRxKpRKmp6fbZJP1zmaz2LBhg+1nKaNUkCmXcRxbL48MUaYsyzqkybL06EqjNs27q43Xbkav3gs8bbxweYD1/fREgzTel2Ic67IOVxxOnNxNTntxcqPRwPT0tJUfz8mekz08PNY3Vsyw1ThQBZnoRmISaV4ZV+InelJcZbiu72fm0gXeRyrBAwMDKBaLbXtd8hyZOZXbFjSbTeTzeesNImHKOjPsUWdOJqQi7CJJPevrUobYB/qY/I3/S8+SK+mEXKvEc/TsddrMtXyfeoVipl3fz3mHC/qRZWD5+quXLLkUNf1uacjtcVxrU9Pu76pLmqJIg5JelVwuh6GhIQwPD6NYLNrsq9lsFrVaDdVqFQDsNVwXmM1mUa/XrTLNkEcarTRs6QXS9crlcm3t1YYjFXFpsLrkVsuQNBqkgi77Q67Blffjn2v8ccmy9tLIeu8PpEJ9uMr14cTJjUYDjUZjSfXX/OI52XOyh4fHoYEDzhCRNht8oGQpkeYt7aassh6uusrzkiSxZLOUhCNLUbrkIE9llUTGkJ25uTmrDAdBgGw221Yn/sYZXxJLNpu1yS64P1+hUHDOCnOmmUksuJemq+2yvr0MHFefdps5Tpt1DoJWSGc2m+14hyRBptVFniO9SGn312043ImU/exSeLV8y2vkp+5v+V7pRCTdEpO4jDRdV12WNACl0tbNy6Hv3+2d0feXsiwnrCYnJzE1NWUN1mw22yZrctsRyrsxrf1q6/U65ubmUKlUUK/XUSwWO/qJ7aUsB0GARqOBer3ubK82OHtNVsh7aC8v66rv47ovlX9poPQ7xsp6LsVr62X58OPktLp0g+fkzj70nOzh4XEo4IA9tktRfg8E/XhcAHf4UhrJy2vSrtPY39lEEiOADiWzUqlgamrKzuxydpdKJDeil0QnN6LnWhap7BpjrPeH7YuiyCrZVB5kX2gSkmv1ZH3S+oez7LofXSGIhCyPJM81dtw6SZ+n77kc7xrL2V8v0aGAA1F+XXKo+1T+L0MEqejxfG0Ma+8By5J1Zh1k2f2+F/Ld1udJbxR/lyHIwKK3xRiDmZkZ7Nmzx16Xz+fte82QYrnHNY0/KsCUHa4bzOfzbXVgyCPvyeuazWabAekaEzmWdOsHCblvbj+QyinDL2l4c6xxnd9NtvX44NEbnpP7q7vnZM/JHh4ehx6WxbDtlqSi2/96wJPKqB4Y5SyhnLmU34HF9SIczPVvuhySA9e2aULpRaSuesq2BEHQNgvM36j0ZTIZzM3N2WOcFSahyjYxtJFkSEIsFAp2lphrg4CWckyizGQybbPALCNt1lWSplY+XbOr+rdeM+2yDF12ELQyVZZKJVSrVavMk/hlCKRU1F0E3m8dDne4jLqlKCQuWXb9LuWP58lQQCmjsix5nPKqx55espxWZ9eYo+Wf96HM0Oij0kdDc3Z2FlEUoVQq2W0/4ji2SWpyuRySJEG9XrcGMr1FzWYTg4ODKBQKVq4HBgYQBIEth4awlGW21dXnfMdp2Gq5TPPS8LdeSfJ0Gbq8MAxRKBQwPDyM+fl561UGWuMT2+UKWe52v27f92di5lCC52TPyZ6TPTw8Dlf0bdimKQocuPSnDnuTBEMyA9oVW/3nUmJJNnIm0kXKsh7yvppYZR20sqzBAV4rBLp9LFOTKOvNYwzTYwZGkiZDFLnmhmRHrxAVapYjiaBQKCCfzwMAarUajDG2fBKQbCfgzsoo2ybJRiaj0X0gZ4GloeTyurgUYXk+w8Py+bxN4CFJlF4q/lH5pxdLtqsfHE7KcJpi2E2OpfEpz3fJtf5Mk2kpj2myyXLTZFnLaj+yTIVMtqkfhRhYXCMr942kHDIrMo05AFYJpOxzLKDSy3ddyj/f21KphEKhgCRp7bEpPbXSS8t2yJBDrdiynVqWpedHGxAMq9SyLMcJrYjq9ZK8NxX9+fl5u06RBj8N3GazaQ1+AG2Zbl3hyIe7Auw52XMy4DnZw8PDQ6Jvw1amupeDJz0OnF2VBKcJyUWiLuU3jUQlIblmh10KbjcyJlxkLMtL8whoRVj+Jushy6NSLMN7GK7HmWGeT8KShgIVPK69I7GQtMIwRLVabVNwXTPDrKckQ9kO2RY5Q0wyBtrDTfk9rb8kJMGyDnq2mGTJrVLYJv1spQLE55gkiU3WI7dJ8WiB4bFaYaIsU7nTsuZSmrS8dDNsAXTIcjfjl+hXlqWsaeVZQiptaXLrqoMOcaZiy09gcZxk0ifWlcacVH5ptA0MDNj3nbJFuWN2ZcoaxwA9NvB8lyIo6yu9N5RlGfZJeSS0oSo/pVEs7yXHAdY1iiIMDAzAGINyuWyfgXxWUn61pycIAhuuKZ+hy8N0uCjEnpMX4TnZc7KHh4cHsATDNp/PdwyGnMGjF0GG0rjIKE0hTiNQoHNGV85AS2XIRWxp5ChnglkfrbDKcvTsZRpIzJpQeV9ZHomCSSNIdsysSHKRIT5SQc9kMqhWq22/B0FgvR98DjIBjMuYcZFgmkIgoWeUl9pfUkHRygrLpiJbKpVsCCOVC/ks9RYJ8no5eywzzvYTYnmogoatBJU5eiSl96JfOdbKqg4zTFMo+1GIeT6/y3PlcflOa7lLgz7HZcQB7eMJIcNqOUZGUWtNHdejUZb5/sl9rCn31Wq1w+NKL2ahULCKMOWtVzt6eW9YDhVwHtPn6uegy5d9LBV5XUcm9RocHEStVrPjnDQajDHW4NfeaynLTObDuuvszIeLJ9dzsudkCc/JHh4eHkswbBlap0mKHh75J2dfZcge4SJZeb5LydVEqmd+ea2LwLTinaac6sFcfvYiUPm7JGNdLx5niJ8kzXK5jHK53Jb6n3WWM+/NZtNmTtUz3bVazc4yUzHmTGmaEkt0I1jp6ZFbj7iUF9lOCdc6Oh7j7LUkehIgw7iYjMMVmkXFmEZZPp+3/zNkU4dvyoy03RSGQw0yQyewqKBJjy2VE22kSmj503KrZVQbyVIJ1kqsfHe6ybJLjlkGP11hd2nKn0uOXfeX92JiGJkdeWZmBrOzs3ZtrWxbPp+3WUYbjQampqYwMzNjFWpZH4679XrdrmsDYBVC3V5XO+Wzkn3FMUJ6XWSbZRkuWZb9xd9Zrvzj7zTS2R6XASqNXL6TSZK0hV/L/+n9oSzrbV/6NYDWIzwne072nOzh4eHRjiV5bCVIRlTS6H2hwgZ0kodGt1lZDmycEZSfvL8eTOkJ0fdkOSR0Y4ydMeQflSdg0buwVA+AJnDZTtkfNBiYWZHEKNehcLDnTKZWEKSSx/OpPNbrdWSzWbsdAYCO2VDWUbZRk5xuB/uNIZTdlEat4Mh+dJGWnsGW7xBng2u1WhuJ8hodbsXnyJlkvpt6tlh7GuTs9KHs/WEIo2xbmkIMdBqaGi5D0/VOUD75O58PIWVRhsZKTyDQnqmYHhRtFPN3KpK9wt54Pw39nmqvkjRG5+bmrCxScWVbZAixDLNlHxizuA5W7m/LtWtSlmVYs6yjDhvUkGMjQ5D76RtZX94nLbRRPj+2n2CiqDAM7VpD1lcq6TzGiY98Pm/7jUpxo9HoCJeXEQCusolDxdj1nNwbnpMX4TnZw8PjcEDfhq1L6dMz/HIgkqRGaKVXEiKJTM/a8jjQmRVQKrty5lcSuSRlejk0aUtlj+XL7J1LGUhdRKoV4yiKUKlUOhQGAB1JLVgvEkIQtEJG6cHhLLP0XJA4qLwyeUOaR0oqf64ZYoKErklUf7o8bq6y9bUuEg/D0CoFrn06+SfDvyQJynBY/ceZeW1A6VAx13qj9Qwqo/r9k8+N7ea50vBLk2PpweE7KGVVG6381LLMsmUYJY/xOXFmX8syy3fJsn5uac9Ry4iWA3m8VqthdnbWtkMa5Fxnyz6SxiT7p1AooFar2bDlJEnsFj71et16fekZoZHHd941zrlkSdaZ50ljQcsnr6fyqceBNAU67f5Swef45ZJjbdxqr68eS2VYN41dKcuyTC3LLnl2jclrFZ6T+4PnZM/JHh4ehw/6Nmy7DRwuZRhYnJXV52qilaSmPScuopZKuD7G9PxUfnmdPFcOsHrw1oMnIa/rBU2eWkFyEYzMzkiilYO3DP2jskklQw7w2tsFAENDQzYMKg0uxd+l2FExdxGvVGw0AUsFVT4Hec9uJM53RSu98t7SiNAKsnzmuo3SE8h6dSNSF5GvJ/SrHLqUXKBd3vm7PJ9Kn0uWZXlSboF2DySPMcyXYbj6XF229ILoZ0dIWe7HgEkzbAlpuAOL2+9QSZPeWp5PpZfKbhzHNvOxfsfZ1ihqbSMkxzZXm1xelv2R5f+/vXvZbVw5AjBc9FiIMYuzyfu/U54iQBYBksV4POfYYhaTtkul6itv3dT/AYORKZKiKBarmpemPQCip4vFsm7ohHFF7vfj+t66VOM2Fct2v6+HlcSyjWO9jx0BOZmcHMYjJ4+dkwGsp7lha3eQIWGmzux4vHFt4gzj2WRqj/xdLhd5eXn53NmGHZ4udGwytDvbMH9bqNmjmR79PWPfOXVJZ2AbEF4hkptW5OvRJD9//vx8XIFefls86ISkv4ddX/oh83p4GN9bPlsI6PVpGxpecanf9wpSPW3J33rZQ/EVListSaCxpOqdKetZbHsOBYk9S1YSy2G8mli2RfHlcpHv37/fFG3hd9PFmN2eY0VNLJZLGjFewzbHxpLep8TWZ6xoDetDROT19fXzUtzwvv1cbx3YOAuvvTM94bVXDIfPDOs6tS9KFZg2Lm1MtsRyOFAwz/Mqsdw7cjI5ObxHTj5PTgawTHHDVj+LUeR2p6aPVOaOhGpeorHFVCqh2kT6/Pz8ee+WPuJvj0TmOmzQSotf73tYqfnYRKLZI+axBGPHD2eFXl9f3QI4/B8rju0wfeTU64m0ZF3q9Z4bR38f+34sUZaMowtz/Sy+kEx1gj5jEtXPY9Su1+vnc0RTsez9dmvHsr7HVP9ugT4ToPc9sW0wt93l1MZz+Lzc9lBaEIdY/vHjh9uw92JAv/aKf11Qxgro2Lq043nseLH9zJJYDjGsYzlsD/p5ol7spmLZrosekZPrkJPvkZMBnE1xwzZ0oqAv+bK98In4Sc+yCSaVJO10+gzPNE2fiSL0Ovj29nZzT4s+am13zvrz7DCvwIsVOl5Si3330oRcU1SlElOu0LdHxvWw2LKE5BNex9Zr6vNybEERO5KdSp7eMF0I6ySaKuhtUs0l1t79+vVLRL4uAdPrJMRCLo5jMWxfp2LZfpaO5Y+PD3l9ff28h0vHsi6Cw+fZz9Tv6d9f34MZa5ylYt0b3xumi04ttW9I7V/sPcWx7dPOx4tv+5m5TnliRfsesWy/Q6w41tuF97uGnJFq6NrXvSMnk5P13+TkcXMygPUUN2xDMRyOyOodkD1T4p3ZSSUP7xJGO59Ygg2JMjwrTssVqLEEYy83spdjevMJcp8XYxNhqsDU75UUorlC2juq6X2GbSS0HhH1lrkkiccScW0RHMYrLWD1dmKXc9SCeJqmmx59w32edltfcily+D92GaRXFOtYtvHXGsv2TG9tLMeK4lSxqMcpiWW9bXqNDL0MsYZmKj7s66NjOba83vqIDWstYG2+0vO2xXLPyMnk5KPjmJwMoDdVZ2zDzkQXxLEjg14RVrpz9y59CuPbZBqODqeSeMkR2drE702reYkldtmhHTd1H08swXhH0muSWy5R2XG9wtO+9pQsU674TS1vqgheu2C1v+coR4fDmR4R+YzlsNyxR1Dov+37Md5ZnthljfpvvV5rGtZ6GjvuI8ZyeF0Ty3b61DCRbeK59IxPayzHGh62E5veY5mcnJ9WGzWOycnlRs3JANZT3LDVz5gTkZtEqtmdW0rsLEjY+bUUxd4R39qkmrrMsmT6rXlHQ0Xi3z9ME/vbS2yx3zFXdLZoWbbY2R693DXb4hpKOiHpgRfLubOAVsnZknB2tDWO7eecLY5F9o3l1Pix5WgVK6xb4tlui1vFs21A9bB9pJCT08u9J3IyORlAH6p6RbYPUNfPpmuR2rl5RYa+xCpInRWy8wnvxT4jprbTiVJhHt4R9tyO33tfFx+xcfR4ufnGktnS5FlyRqgksXsJMzZ/fJnn28duTNP0+azG0nWXGi92Rky/tg3eWBzr8bXaOF4zhtcqolPr0D47NjXN0fEcW7ZcTPcWx7Eznb0iJ8eXrxU5OT6MnAxgBMUNW5Gv56XpxKV7ZVxTaqeoE3nsSLJI/qhwS0K109XYoyDWajuF8P4uuWRwSdJKfbb3mgS5juv1etchUUsstzaEdUyWxHHqzM5ahbG3fHvZMqZz8bx1QVwSx1vE9dLfcIR9DTn5froa5OS6zyYnA+hdVcNW5PflhfreHhFxHx2yJb0jtQlS/287imkpjmPj11jrcpg1LhEMas+u6HFilwi2fnZueK96P6OTEzp30XGhew3eUmzbycVx7tLF1sI4No+jtMT6kjOm9gyLN33rchxl6XKMEt/k5Hrk5PRn54b3apSYBbCdpuwXK4iPkNqRp5KhTaheb6lrJ9MetJ5hCa+5vOjLGb6/vZRRRHaJ5dJCqiSG9Wtb/LU2bq09GrtLiuRcUZw609LzmZcz7HP3QE4eFzl5PY/+/QE0NmxFbu8Bay2I9zorZKWS4ZJE2kOSTa3TkgSamscZk0YPv9nRbCc00/T1iJ6jf/Oas4jeb1kSvyXbQO4xQWsoWddLYvjo37LFXsus74HM3Q/ZK3Jy3Xz3Qk6u08NvBmBczQ3beb7vuEKkj8v4cmoTTakedshnTHRbWnt9jbj+vVhe2gnNEbZuzPXQsN1iPqlLz0fbBlqd4awXOfleD9vvqNvTUcjJAJZYdCNO6IBG5CuBhN5Vj3bEzowd6DbWKE6W3nNU+/dodGdSQa5xO/p3rnXW77tVo6IHrXHcQ4OoBTn5+M98BORkAL1a3MOE1yujyLiFAfqTS1BLL/Va++9R2Z6Sp+mrh9WzfEf0qeUy7DXi9GwNWxFyMrZHTgbQq1W6TgwdV9zMeOdeGTGe2vuGlhSqR/09mo+Pj7vLGY/qhKbF6Ou/NyWNoaVx7A07OtZHbwSSk9GCnAxgdKtlutC5iv6nnz+5JXZcfWjtCGOEQrd1mUb0/v5+N8w2bs/yXeELv2/q3tMzx7bIORq35OTHRk5mOwQezWoN23me73pXvVwuXdzbg1stl/2VDnuUBHnmBDrP811RLDJGJzSPomZ726pBWjLdXkU0Ddt75ORxkJPJyQDWseq1STaRhnv0SKTraE1+3vDRjuS2TLNVYnyEhBnu09NF8aixPOLvVbPtbRW3W867h3hPvTd6ozYgJ2+LnExOBtCX1W+60QXx54ccmEhH2eHV7rBHK1z3moajuOuxjVt7Bldjvf5W03gqHdY6Xeu8jhxnjX0EbpGT25CTyckAxrNJbxLhMkYReYheGY84u7L3dD0l0tgwrE9fkhycuROaNRuRS4adpTFLPPeBnBwffnRubZ2OnAwA9zarUL2C+Nu3b4cn0tKd797F55rz7/0ocG44+uIVxXt1QuOp2W56aah6w3qY15rzTg3H8cjJy4f1OB05GQC+bNawneffHdCIyM0ljEsK4pKd7tpHakuH9Tr/LcdJDcd5hFi299u2Pgao5zjufdjW+6KS9zAmcnIf8ycnA8B2Nr2m8Hq9iogU39vzaMVsD/MiCaKEd7+tiN9Tcm2DiWHEM/ZBTk4P62FexDAAtNv8Zrnr9Xp3tsc709NLYttj2B6fCawtNG6to7bvXuLZG7bFdwPWQE4mJwPAWe3SC4x3b4/WS3I7clhqONALWxS3Fog14444rOQ94Cjk5Pyw1HAAQJ9269403Nvj6SmZcZQWiJvn+aYzKXum50wN25L3gFGRkwEAZ7Nbw9YWxHp4bPxehgH4EmI59l7pcM66AMchJwMAzmbXB1J6BTGFLDCe0AmNtVXsEtPA+sjJAIAz2bVhK3JfEHMJIDCmmsZtyXsA9kdOBgCcxe4NW5F4QQxgLMQyMD7iGABwBv7D6wAAAAAAGAQNWwAAAADA0GjYAgAAAACGRsMWAAAAADA0GrYAAAAAgKHRsAUAAAAADI2GLQAAAABgaDRsAQAAAABDo2ELAAAAABgaDVsAAAAAwNBo2AIAAAAAhkbDFgAAAAAwNBq2AAAAAICh0bAFAAAAAAyNhi0AAAAAYGg0bAEAAAAAQ6NhCzygaZpkmqajFwPo0jzPRy8CAACoTMfP2ywFgF5N00ThDkTM88xBHwC7ma5Xmd5Fnv4SuT6x7wFuzCJPf03y9F5Wt9KwBQDg/2jUAtjT9ONNXv49y/z8JB+Xo5cG6M+3P0X+9t9r0bg0bIEHRhEP+J6euFMHwPbmf/5L/v6PF3n/40XmZ3IyoM3TJNN1lst/3orGn2auSQQAAAAADIxD0gAAAACAodGwBQAAAAAMjYYtAAAAAGBoNGwBAAAAAEOjYQsAAAAAGBoNWwAAAADA0GjYAgAAAACGRsMWAAAAADA0GrYAAAAAgKH9D8bdOpo8NUthAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1200x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loss: 1.258635401725769\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loss: 0.8533031344413757\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loss: 0.7845696210861206\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loss: 1.4120641946792603\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loss: 0.7309361696243286\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loss: 0.4287447929382324\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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wgx+Ypz3taQaAeetb32rT3XjjjSYMQ/M///M/ieff+973GgDmm9/8ZiJPAOYHP/iBvXb//febQqFgLrnkEnuN/PXyyy9P5Llz504TRZF54xvfmLj+s5/9zGQyGXv9Rz/6kQFgPvGJT6S2r9e8jOl9Pvz+979vAJgPfehDc8pzzRfXX3+9CYIgMde99KUvNWmqoR4vF198scnlcuaee+6x1x566CHT399vnvCEJ9hrCxkniwHvirzC0dfXNyc68qmnnoqhoSGceOKJePGLX4yTTz4ZX/rSl1AqleY8f/PNN2NoaAhDQ0M466yzcOONN+LKK6/EW9/61qVqgofHEcNNN92ETZs24UlPehKAjqvM7/3e7+GjH/1oqjtwLzj33HNxyy23zPm7/PLL56T9oz/6o8Tv888/H/fee++cdL/3e7+X8JTgiqpMK63BtVoNBw4cwGMf+1gAwA9/+MOeyj548CAmJiYAdNyG4zjG6173OoRhcrqn9feWW27B2NgYLr/8chw4cMD+RVGEc88911pw9uzZgx//+Md40YteZFecgU6Aj9NPP31O3TyOfhhj8KlPfQrPfOYzYYxJjJ8dO3ZgfHx8zri98sorkcvl7G8XHQAd3if31+VyOZxzzjmJdFEU2bziOMbIyAharRYe85jHOOllPhocHh7G17/+dbz4xS/Gwx/+8MSzpJeFtPmLX/wijjvuODznOc+x+ZRKJbzkJS/p2q+9QM4V4+PjOHDgAC644ALce++9c9wdt23bNmdP4pe//GUcf/zxeNaznmWvFQqFOd5hP/7xj3HXXXfh93//93Hw4EHb1unpaVx44YX4+te/jjiOD7s9HouDhdKExsjICG677TZcdtllmJyctO/74MGD2LFjB+666y7s3r078cxLXvKShDfR+eefj3a7jfvvv/+w2vLP//zPGBoawsaNG/GYxzwGt956K/7iL/4Cf/Znf2bTfOITn8AjH/lInHbaaQlafPKTnwwAczwQHve4x+Hss8+2vx/+8Ifjd3/3d/GVr3xljsyg+eunP/1pxHGMyy67LFHW5s2bccopp9iyyB+/8pWvOLcGLSQvopf5sBvkfDE9PY0DBw7gt37rt2CMwY9+9KOe8pBot9u4+eabcfHFF+Okk06y14877jj8/u//Pr7xjW9YOYQ4UuNEw7sir3BMTU1h48aNiWuf+tSnMDAwgOHhYfz93/897rvvvtRAEOeeey7e8IY3oN1u4+c//zne8IY3YHR0NCFYeHisRrTbbXz0ox/Fk570JNx33332+rnnnou3ve1tuPXWW62r7UKxYcMG/PZv//a86QqFwhxXu7Vr12J0dHROWi0oU8CWaUdGRnDdddfhox/9KPbv359Ir4XV+fIcGBjAPffcgzAMuyqed911FwBYQUBjYGAAACzzOeWUU+akOfXUU3sSmjyOLgwPD2NsbAzvf//78f73v9+ZRo/jXugAALZu3TrH9X7t2rX46U9/mrj2r//6r3jb296GO++8E81m017ftm3bnLrMVzaFxG5n6C6kzffffz9OPvnkOe049dRTU/PvFd/85jdxzTXX4Nvf/vYc4Xl8fDyx+OTqi/vvvx/bt2+fUzcdUZ7zw4te9KLUuoyPj/vtTSsIC6EJjbvvvhvGGLz2ta/Fa1/7Wmea/fv34/jjj7e/e6XpheJ3f/d38bKXvQyNRgPf//738aY3vQmVSiWxSHvXXXfhF7/4RarLu55/XPzrEY94BCqVCoaHh7F582Z7XffXXXfdBWOMMw8AdjvDtm3b8Gd/9md4+9vfjptuugnnn38+nvWsZ+EP/uAPLF32mhfR63yYhgceeACve93r8LnPfW7Oe3HJFvNheHgYlUrFOZc98pGPRBzHePDBB3HGGWfY60dqnGh4xXYFY9euXRgfH5/DaJ7whCfY/SvPfOYzcdZZZ+H5z38+/vd//3eOVUYK6Dt27MBpp52GZzzjGXjXu96VWPXy8FhtuO2227Bnzx589KMfxUc/+tE592+66aZDVmx7RRRFh53WzOzdATp7Zr/1rW/hNa95DR71qEehr68PcRzjaU97mtMq0kue84H53njjjQmmTqyG8P4eywOOnT/4gz9IVXz0cXW9jtle0n34wx/GFVdcgYsvvhivec1rsHHjRkRRhOuvv94ZIHEx6WUhbV5s3HPPPbjwwgtx2mmn4e1vfzse9rCHIZfL4Ytf/CLe8Y53zJkrDicCMvN661vfmnqESF9f3yHn77G4WChNaPB9v/rVr06NPKxl0sWgKxe2bt1q5dff+Z3fwYYNG/Cyl70MT3rSk+y+4DiOcdZZZ+Htb3+7M4+HPexhh1y+pps4jhEEAb70pS852yzp4G1vexuuuOIKfPazn8XNN9+MV7ziFbj++uvxne98B1u3bl1QXsDh9XG73cZTnvIUjIyM4C//8i9x2mmnoVwuY/fu3bjiiiuWzOPiSI0TDS+xrGBw83a3sOZ9fX245pprcOWVV+LjH/84nve853XN86KLLsIFF1yAN73pTbj66quP+fDlHqsXN910EzZu3Gij+El8+tOfxn/8x3/gve9976o51mJ0dBS33norrrvuOrzuda+z12kxORRs374dcRzjjjvuSBVKGYhj48aNXa3UJ5xwQmp9fvnLXx5yHT1WL4aGhtDf3492u92Th8Ni45Of/CROOukkfPrTn05YMw71uC+61P385z9PTbOQNp9wwgn4+c9/DmNMon690ou20BCf//znUa/X8bnPfS5hBXEFfupWtzvuuGNO3XSEas4PAwMD87Y3rb4eS4deaSLtXZEGstnsotL0YoyNq6++Gu94xzvw//7f/8Mll1yCIAiwfft2/OQnP8GFF17YUxku/vWrX/0KpVJp3kBn27dvhzEG27ZtwyMe8Yh5yzrrrLNw1lln4f/9v/+Hb33rW3j84x+P9773vXjDG96w4Lx6QVr7f/azn+FXv/oV/vVf/xUvfOEL7fVbbrml5zw0hoaGUCqVnHPZnXfeiTAMD2tR4XDg99iuUNx22234m7/5G2zbtg3Pf/7zu6Z9/vOfj61btyYioXbDX/7lX+LgwYP4p3/6p8WoqofHkqNareLTn/40nvGMZ+A5z3nOnL+XvexlmJycnHM8wUoGVzP16uU73/nOQ87z4osvRhiGeP3rXz9nVZbl7NixAwMDA3jTm96UcFsjGI7/uOOOw6Me9Sj867/+a8J16ZZbbsEdd9xxyHX0WL2IogjPfvaz8alPfcqpDB6Joxx0+UCSZr773e/i29/+9iHlNzQ0hCc84Qn44Ac/iAceeCBxj2UspM2/8zu/g4ceegif/OQn7bVKpZLqwqzBheexsbHEdVe7x8fH8aEPfainfIEO3e/evTsxR9ZqtTlywdlnn43t27fj7/7u7zA1NTUnH9netPp6LB16pQnGZNHvauPGjXjiE5+I973vfdizZ8+c/A+Vpsvl8mGPi0wmgz//8z/HL37xC3z2s58F0PFy2r17t1OerVarmJ6eTlz79re/ndg28+CDD+Kzn/0snvrUp87rgXXppZciiiJcd911c/i0McYeyzcxMYFWq5W4f9ZZZyEMQ3sEUa95LQQLmS+MMXjXu97Vcx4aURThqU99Kj772c8mjvTat28fPvKRj+C8886z25iWGt5iuwLwpS99CXfeeSdarRb27duH2267DbfccgtOOOEEfO5zn5v3UORsNotXvvKVeM1rXoMvf/nLeNrTntY1/dOf/nSceeaZePvb346XvvSlc3z5PTxWOj73uc9hcnIyEfhE4rGPfSyGhoZw00034fd+7/cWnP/u3bud50f39fXh4osvXnB+vWBgYABPeMIT8Ja3vAXNZhPHH388br755sT+4YXi5JNPxl//9V/jb/7mb3D++efj0ksvRT6fx/e//31s2bIF119/PQYGBnDDDTfgBS94AX7jN34Dz3ve8zA0NIQHHngAX/jCF/D4xz8e//AP/wAAuP7663HRRRfhvPPOw4tf/GKMjIzg3e9+N8444wyn0Otx9ONv//Zv8dWvfhXnnnsurrrqKpx++ukYGRnBD3/4Q/zXf/0XRkZGjljZz3jGM/DpT38al1xyCS666CLcd999eO9734vTTz/9kMfj3//93+O8887Db/zGb+AlL3kJtm3bhp07d+ILX/gCfvzjHwPovc1XXXUV/uEf/gEvfOEL8b//+7847rjjcOONNzoDPbrAIDd//dd/jec973nIZrN45jOfiac+9anI5XJ45jOfiauvvhpTU1P4p3/6J2zcuNGpjLhw9dVX4x/+4R9w+eWX45WvfCWOO+443HTTTVbeoOUmDEN84AMfwNOf/nScccYZuPLKK3H88cdj9+7d+OpXv4qBgQF8/vOf71pf7xm2uPjgBz+IL3/5y3Ouv/KVr+yZJorFIk4//XR87GMfwyMe8QisW7cOZ555Js4880z84z/+I8477zycddZZuOqqq3DSSSdh3759+Pa3v41du3bhJz/5yYLrfPbZZ+OGG27AG97wBpx88snYuHFjalyHbrjiiivwute9Dm9+85tx8cUX4wUveAE+/vGP44/+6I/w1a9+FY9//OPRbrdx55134uMf/7g9v5k488wzsWPHjsRxP0DnWLz5sH37drzhDW/AX/3VX2Hnzp24+OKL0d/fj/vuuw//8R//gZe85CV49atfjdtuuw0ve9nL8NznPhePeMQj0Gq1cOONN9pFsYXktRBs374da9aswXvf+1709/ejXC7j3HPPxWmnnYbt27fj1a9+NXbv3o2BgQF86lOfcu5tJQ2/4hWvwI4dOxBFUaon6Bve8AbccsstOO+88/Anf/InyGQyeN/73od6vT7nzPElxaLGWPZYEBgCm3+5XM5s3rzZPOUpTzHvete7zMTERCI9Q5APDw/PyWt8fNwMDg4mwqmfcMIJ5qKLLnKW/S//8i+pYcE9PFY6nvnMZ5pCoTDniCuJK664wmSzWXskBxbhuB95pM2LXvQiUy6X5zxPOiV41Ig8ooCACp+/a9cuc8kll5g1a9aYwcFB89znPtc89NBDc9KlzQWcU/RxGx/84AfNox/9aJPP583atWvNBRdcYI9IIr761a+aHTt2mMHBQVMoFMz27dvNFVdckTgawRhjPvWpT5lHPvKRJp/Pm9NPP918+tOfNi960Yv8cT/HMPbt22de+tKXmoc97GEmm82azZs3mwsvvNC8//3vt2l43I8+AoP0IXnRBRdcYM4444w55ehxFsexedOb3mROOOEEk8/nzaMf/Wjzn//5n3PSLYQGjTHm5z//uaXDQqFgTj31VPPa1752wW02pnOcyLOe9SxTKpXMhg0bzCtf+Urz5S9/uafjfowx5m/+5m/M8ccfb8IwTND25z73OfNrv/ZrplAomBNPPNG8+c1vNh/84Afn0H83OeDee+81F110kT0y8M///M/Npz71KQPAfOc730mk/dGPfmQuvfRSs379epPP580JJ5xgLrvsMnPrrbf2VF+Pw4eWGfXfgw8+2DNNGGPMt771LXP22WebXC43hw7uuece88IXvtBs3rzZZLNZc/zxx5tnPOMZ5pOf/OSc+ujjh0jrcnzv3bvXXHTRRaa/v98AmPfon278+tprr03k32g0zJvf/GZzxhlnWB539tlnm+uuu86Mj4/PyfPDH/6wOeWUU2z/aDrsJmsb0+GB5513nimXy6ZcLpvTTjvNvPSlLzW//OUvjTEdunrxi19stm/fbgqFglm3bp150pOeZP7rv/5rwXkZ0/t8aEznCLPTTz/dZDKZxLx6xx13mN/+7d82fX19ZsOGDeaqq64yP/nJT+bMva1Wy7z85S83Q0NDJgiChCzjmit/+MMfmh07dpi+vj5TKpXMk570JPOtb30rkWYh42QxEMxU1sPDw8PDw8PD4xjGO9/5TrzqVa/Crl27EpFvPTxWO4IgwEtf+lLrgeRxdMLvsfXw8PDw8PDwOMZQrVYTv2u1Gt73vvfhlFNO8Uqth4fHqoTfY+vh4eHh4eHhcYzh0ksvxcMf/nA86lGPwvj4OD784Q/jzjvvxE033bTcVfPw8PA4JHjF1sPDw8PDw8PjGMOOHTvwgQ98ADfddBPa7TZOP/10fPSjHz2kgHseHh4eKwF+j62Hh4eHh4eHh4eHh4fHqobfY+vh4eHh4eHh4eHh4eGxquEVWw8PDw8PDw8PDw8PD49VDa/Yenh4eHh4eHh4eHh4eKxq9Bw8KgiCI1kPD4+jBit923oQBJ6ePTy6gDS8kmnZ07CHR29YyXQMAE879S8xcu5GVDaGiLPLXRsPj5WFwABhHeh7qI1vf+zV86b3UZE9PDw8PDw8PDw8lgOZCK1CgFYRiHPoSPIeHh4dmABRBLSKvTkZe8XWw8PDw8NDIAiCFW/l8fDwOEoQBDAhgBAwoZ93PDwsAgDGwIQBTI9OSl6x9fA4huEFeA8PDw8PjxUEv8vAw+OQ4YNHeXgc4/B79Tw8PDw8PDw8PFY7vGLr4eHh4eHh4eHh4eHhsarhFVsPDw8PDw8PDw8PDw+PVQ2v2Hp4eHh4eHh4eHh4eHisanjF1sPDw8PDw8PDw8PDw2NVwyu2Hh4eHh4eHh4eHh4eHqsaXrH18PDw8PDw8PDw8PDwWNXwiq2Hh4eHh4eHh4eHh4fHqoZXbD08PDw8PDw8PDw8PDxWNbxi6+Hh4eHh4eHh4eHh4bGq4RVbDw8PDw8PDw8PDw8Pj1UNr9h6eHh4eHh4eHh4eHh4rGp4xdbDw8PDw8PDw8PDw8NjVcMrth4eHh4eHh4eHh4eHh6rGl6x9fDw8PDw8PDw8PDw8FjV8Iqth4eHh4eHh4eHh4eHx6pGZrkr4OHhsXwwxix3FTw8PDw8PDw8PDwOGz0rtkEQzJvGC8keHisfYRh2peeF0LGneQ+P5YHnyR4eHh4eHkn0rNhGUTRvml6Y6FKm8fDwmIsoig5bsV0NdOznCI+jGZ4ne3h4eHh4JNGzYpvJdJLOx7xWGiP1TNvDI4n5FFtgZdHWoeah27jYNOznBI/lhOfJnv48PDw8PJI4JIvtcgqrRyLNSqrLoab18OgVFIh7wUqhnZVEo7x/JF1BPe17zAfPkz1P9vDw8PBI4rBdkVcTE/PlHH46j9UP0vKhWDQPd0webbSzUsrpNc1C0nmsbHiefPSWs5B0Hh4eHh6zOOzgUb1YLXrFYq+criRGtRTlrJY8PMNeXgRB4KTb1UzLK2XcHi15LJcrqJ8beofnySu/nNWSh6c7Dw+PowWLGhV5IXBNpAsp43DrcygTuWeoi5/HUjJkz7yXDiudlnt5bjXQx9FSBu8vVrTuI5nHSoHnyZ4nH4k8PE/28PBYzVi2c2wXmykfTvm9Tq5Hos4rhdktRjkroYzlvr/acKQViaXAodLlctDz0UAjK6EOS3F/uXnUUmO52+t58uKXsxLKWO77Hh4exxaWTbFdSVhOht5r2cvl9rcSGONSlHEsMVeOufnG3nILuqupT4nl6tPVQsur6f5qHH9HCzxPPrJpPE/28PA4WuEV21WCI8Hol8KVbSnSrvT7Kw3GGIRhuNzVmBfLrVivJiwnLS8k/XLTYrf7+t5qo2uPpYXnyav3voeHx9GLnhXbOI67HgjvJxKPpdyPpXGo4+9YZKBxHK8KxdZj+bBQ+lyugEULeXahtLzSadvzZI/54Hmyh4fHsYaeFdt2u92ViS6ndcVPZMce9Hg71PF3LI6ddruNTCZzSHvaPDyONA6Hl6wUBXsp4Hmyx0qC58keHh4rAT0rtq1WC9lsdkW6B67EOnmsDhyLY6fVaiGXyyXavtT94IUXj8XEsUrHnid7HG3wY8fDw+NwsCDFlpYeDw+P1YtWq7Xs7sjLFc10tcMLfR6E58keHh4eHh5JLGiPbRzHAI6OI0I0liKa6ZEK5OCxNDhalIo4jhPuyEfLWHO9n17f2aHOaYfSd708c7SMtdWMlf4OPE/2PPlYx0qnUQ8Pj6VHz4qtMaYn16eVPtF02wfSyx6Rxd434rq+kLTdrs93b7VgpY+p1QbSMt2R2b+raay46HYhtHy4Y6pboKHFot/V9D6OBshFntWw4ON5cvq1XuB58qFjpY8pDw+PYxcL8mFqtVorflLWQu58n/q767dEmuCT9gyv0zpG9884jq1LqM47rcxefy/k2kLL9jg64KLlpRRW5htXC6HjNFo+HCtWmgAdBAEymYyl4zAMEccxGo2GpeXFouPDpeuVTtMrUTheiXXqBs+TPU/28PBQmCF9M/MZkGQ86RwTWJBiy4m/WyTGpYJLsO32XX7q7xrdGNBCAu64yuXexjAMEUVRzyvQ3SxCh8JUF0MYPpR69HLP48hjuWnZJbj2SssuunKh23jUzy7kHFbp/snvYRj2ZDVjOb3Q2aHS8eHQ9kLmmV7yX22K4mrDctOxhOfJ6fX1PNnDY4kQACbqKLUmBBDOjGsDBHGAIAaCNrySexRjQYotXZ/y+fwRFVjSLEmaQeo/eU+m19/TynIxSn3tUPfUyVXgdrs9p66udgJAFEW2TClEy/y6tWkh12QbFyJg98rgXd97zedw4QXsJFy0vJSCTRoNu2icv/XzEml07FJmD1UBM8bYPFut1pxnu+UhBeZehctuNO5KL8Gy5rM+pdHrodLxkaThpcZqmDM8T/Y82XXN82SPYxIzSm0cASZjYLIGJmOAXNwx27ZCBLUQUTVE0AKC+VmsxyrEgsMpttvteQ+GP1ykrezKP+kKqNPKCdolEM/nrpR235WfTM+6uZjTfOUyjY5w6Ypcq/PqRQCW9Uz7zTowv17y5Uo3wUiduoxuTNP16frusXigQCxp+XAEjW7vqJvS2k0g7iVvWYbrmW4CMa+lCcj6umuecdWXcxKVWdf8sBChXqNXeqew3atwKi1WHB9U4HsVgOej4cWm56VU5lYqPE/2PFnXz/Nkj2MOMxZaEwAm6ii0JmMQlFooluso5JpotSNMV/JojecQTUUIm165PRqxYMW21Wqh2WwuKhNdyKqvZKL84ySrJ+/5GKgsuxvjTLvmSp9WTpqViM9SGJBMSTOo+bCYliAKxfyUETh1OgYvYXq+B72q7apPNwFYXnP9eRwe2u02Wq0WMpnMYfdnmlDIcd0LLUdRlBg70ooyn+CbVpduz+k5Io2OdZq0SLSyrXJ+4j2ZTj5/KAKxfi7tmhSMOVe2222nMBuGod0/HMdxT4sMtHxpJdpF167fafuSV4I1Z7XMMZ4ne56s03me7HFUQpK1SV6nUgvNOgwQhgbFbAuZfANhYDDeDBE3AwTtsLP/1g/dowqHdAAeV/EXQ/jQgq/+LZmoFhyz2awVjlqtVs/uQK569yoMy2sLsfB0E/yz2SwKhYJNQ2VDCyrzCZlMo5nMfEzT1Q6pZJAxaoGYaaViJAPraOHVVZ6L0fbCSClEuxhvL1gswdlVp9UAjpNms5kYe4da/zT6lb8Jl6CYy+Ws1UlbGA6nfWlCMu+ltbfb+EgTbKkYlsvlRP/y+kLhsrgS+loajWtFIwgCa6nXz7CeVGplW6XFSJ6BLL/LfnHRvqZVScPy+3JC1mchiwzLDc+TPU+WaT1PXn082WMeBEnFNYjRUUilUjvDggID8LWbOEC9lsWYCRCFMVrtsPPMjEXXIABib7k9mnDIiu3huj5FUZRYEZVMBZhr/XCtpEZRlNjj4hJeDwW9CvndJuK0e5KxRlGEbDaLfD6PQqFgy8xkMgnLibbwEK76SauMRi8r1oQUxCmkuCx7ZKKtVivRb5I5uqJN6v5wCf46H3mNDMslHC8FdLndVtBXMprNJtrt9mG5I6fRMu9pupXpWKakfflbW0cXKih1E5zny3c+K5BsfxAEyOVyKBQKyOfzADpjhLTcS130/EW6kmNqoftuOc+wTM7ben6goi/L1IoF5xW6vnZTpvWzLvrlXsVuNN3L+z4cgViXL+twuIsrSwnPk5PpFnLP82R3f3ie7LHiQAXWdL5b5XZGwbWW15kgUWbmQqsZod0KYUwA0w6A1sxCTwaIzYw+7C23Rw0OSbFtt9uH5frEiZcCFxklmYUWhuV1Pg/MXaWWn92sNbou8ns3Rix/d5usdZ7yO5lnFEVWECbTBGYtINlsNjV/F1xCc7fAMfNN9nw/FPDIQF2rvRRquFqvBVltIXL1q8wnn89bJhUEAZrNZqpwzHRUzo4kM9PMW/+5+malI45jNJtNO/4WKoRIWk5TarV11mXB7TUY03xKjLQaauWM7UubF3pVZAm2L5PJWEE4m81aWuGY1LTtytdlJeGzely5rCtp+UkLlBRmuwmc2kor6Y3Hy0iXyDQEQYBCoWD7IgxDNBqNOXmyLE1LfO5ICMYuRdb1t1rgebLnyRKeJ69enuwxD2i1Fa91jjtxGzDRzDUTAMbAxCFMMwTagX3WZAxgOlbd5d/84rFYOCTFFuhYevL5/IL2mxCScUpmCiSFYcl0NFMNwxC1Ws0ydHmfAm2ahWQhq7ra8iCvp1l5XHnItmQyGevqRCYqg7ZI16FuSGPoLKubxaEXYZGMnRaaIAgSCohLwNZ7JMlcpLCfBtY7l8shk8nY9ypdMtOEYVqhXPuJDgWyb7sJvloQX20wJumO3ItVxSUESUHQtdeU17Vyy9/VajVhQeB71EqWRjc60Uou6yo/ZT/Mp8hqZVy2m4qtdFfkuJdRVNPK7iZ8pVlYu0GXx/lFnnvqUhq14O1S9KSL6XzKbS6XQzabRb1et/OankPYNmlJIj3pOh8OdHu6CcOrkZ49T/Y82fPk1c+TPdwwwayeCiCpiArFNuGS3J6x0GaAIOpERjaNyD5tAjOj5Hq19mjCISu23Ad3KEyUiKLIHlOQJgyTuUhGms/nkc1mE4yUQpZLgOxFaCV0e+RKc7vdTlig5GolP3UdmAeFYK785vN5K4Twnlw5lvXoZuFxlcX6ugTOhQjE7I98Pm+FYLlKrP9YnmSU0jonGaJsUy6Xs6vCAKyiIK1bUrB2fSdzZz0PlaHp9hwLjJPuyFzd74VWXEKkFohdiqwWkvP5PHK5XIKWgVma7da/LuunS3CWNCvTsQyWk6Zg6jzZpkwmg1wuZ/9YDhVbqezLOst6yfxdVhdeYz5pQr0LLlouFApWUaRbse4TOcZdCwHSDVOWEYadvdIArKWWfcT0uVzOKvqybbJcCsJaoe11DtftWQgtr2Z69jzZ82TPk1c3DXukIFCfKTB27yw6kZEjYxXeKNNGmDNoZ+OOW3IrBFpB5y/oPBvooRMIRZr3/PBa8ThkxZbuMIy+t1ArD0GG4fqTwrBkqsViEQCQzWbtPhNgdp+RtiL0ykBdK9MS2hVJMjIpCGoBmUIwhV65744rmyyf6Vx7nDTS+rPbBK+vaQUizfrCoDIArDChrTvArHVIrtTyGi09LIeCcF9fH+I4RqlUsnnoFXIKSnxWMjpp6UvbNzQfullwjnbGyT7slZYJrZhpq638A5K0znFdLpcBdGir0Wggl8vZvWFa2eiVDiTdaBpKE0jlmJFCmrRosO5S0KXrMemO8xH/pPAv69QLdJRo2T6XZUb/dgnapEWOZVf7ZT+xzlIwZj9I63oQdNwe16xZg1arZd+rnL+ZpzxCSFt7SL+NRiMRRGwhSu1i0fJqo3PPkz1P9jzZY9UgZQowWok16jccrsciTxOhc35tZDoJAyAIDDKZGMV8A2EA1JoZ1GtZNGsZmFYAExoEIU3CIq8Z668taua+j6S8snHIiq0xBo1Gw67u9mrl0XnEcWwZsRYcNYPlZFwsFm20UU7EMr20RHSzxBB6BVrvDZTtk5NoN2FYC5NSyOV3+ZnL5ZDP5xN7nIBZBizL0wqF7mPJ3KVgruvlsli53Ddl3q4VXuYj3ZN0v8n7UqjgSjD7PZfL2fykC6dkomyTXhVuNBo9C8LzrQBLl61DYcqrCZKWe1W8tBVTK6CSnrPZrJO2oihCuVxGo9Gw12i9pXIrrYu6HFkPQgrdLoE8rR3yt54/5D5CbcGR5VE4LhQK1nVPPgfMWl51/7mg6UmOy7TnJC2zHH1N0qRLueE1qYzo8lzzI612fN+FQmGORU1bmNg+WmgB2PNzFxK8KY2WZaCro52WPU/2PNnzZI/VAukynAgANaNf2r206l5g0HEfVsolIyEHMWBi5h0AsYGJA/DIn3K2gXoug/GwgOnAoImZbbeNEGEjQNhCIkIy62LrBMxGUfbK7YrEISu2wOzElrZ62euqrJxo+aktI5ph6smYgjBXLfUKopyMJVxWGL1KTOgJtZvFg5AWG2nJ4d4zChAUCrUrJ/OgcEDmpRmpZP5SGZArqrKPXQxSrxTrfpLlMQ8+o+tC4ZX9KCNKyr6h+xpdF/mp35c82kAyU9ZDMtA0pDFOKfzKsXMsQUZVTWu7pAXXdwphLqFa01gQdAILaesnA47wj1YJlwVVC8hSUJUCq6tOacI1FS9pZeLYl59yLuKePP7O5/OJvbZyfmS7pNuovE+a4bzQaDRsPaWAqPcfd1NAtULLMpvNpi2zG1hn9nEcx6jX64k6SBovFouWlqUVUdIrBWDO3US73Ua9Xp/3+BoXLbvoeCG0fDTQvOfJnid7nuyxWmCAWeU2FkbZADAhgNDMWnB5qx0k0lnlckapRTNAaDBjiZ3JIwQajQiNfIRiNkA+amGwWEM200at0ESjkUGznkG7koGpRIjqAYL2rGWW9TShNOc6XJc9VgQOW7Gl21E3hukSdLW1QFp5CG1l4OSs9+HxeQpfLiZqGyz2vHESl3tppAWGn8ynGxOVK6esDxkZGbVcCeVKsFz1prVKli3dhshIZN9JRqmFEVkXVxpZf3nNxUil8kDBmoKxTCP7gvnwfdRqtcQzcoWcCgJdyygIsKxuq/ytVsvmq4UeWS/JLD3jTIK0TKGuG1yKGIVXSQdSaUujZR0oSFoeSI9SmQWSezLlNdKPVGq1ZUaPCU0DFM75Z4yxQrtsXy6Xs+6XxWLRlksljsK+VK45Rtlubc2UNCXnHN132vIkwTrqseyiHQqwVCZlH0taZjskrTAglOx/+VcsFpHP52GMQb1ehzEda6JcOGHZErTWuuCV2fnhebLnyTqN58keKx0mAAKppIrrCIVrMYCgESBsBEA7AIdHMKPU0pobtAKYFjr6ZwaIwxDNahYToUGrHSKfbSEKDHJRG7mojXa+gXohg+lcHo0wD5gIUaOj3MJ06paw2C4s3IPHEuOwFFsKKzzvbaHQTJSfLgsLf3MvnssKoVeqOeFqKxSZsdwfJ6/JOrCdtEbJ33pSZxAZliUZgRSGM5kMyuWyLbtWq8EYY90XyTSjKEKz2bTtkhYbWb6ObqoFQDIZyXBlWpclSws5TEPmns/n0Ww251h9tOBD0BInGZ58z8aYxOq4ZHJpoFAhx4Or/cxLM1FZ12MVciw1Gg0Ui8VUCwHTz/dbWlekoilpmvQgx4+kZQqhkq6lICgFVlpa5F44Wl6kkiqtCq4xL/NnnoQ875dWqv7+fmSzWQwODlpFdmJiAq1WywbFomKcyWSsJVJaeqRrsrSAEIxWzfSNRgONRsPWJ0150XMe+1O+M7mvkO9C07GkP9IrFSfOAWwj78dxbK3Wrr1/Lsh5So4l1kEKwVoAXigtH80073my58meJ3usBtDd16CjOCb2rQYA98eayMBkYgTFNoLAIM5EiMMIaMOaTIN2gLApFNE280DHz7gFoB6hEeTQamaQybaQzbaRy7SQjWIEgUEu0waKncXahskDU1FHgZ6pj7UcKxdoj5WHw1JsAdiJVK9MSmiBlp/SFU0KRTofacVhGlo85ESbtnqoBTwyKzJRGURCM3D5rFxB1CvB/K1XnyXzz2azKJfLibPyeJ2WHikM8D6ZMYUBMliu0EoLh3QPq1arMMagVqtZJsy0rhVRtrmb+5B0r6TVJo2JuwRv9pO2ysRxbJmrtBDp/uf7Zdvr9bq9poXdtNVgDzeoNGnhVULTMt8PrZDyvcqjKSSkAsr3KIUeKRx2g7TuyL2dMn9gbtApKQRTYJQ0LGmW6aUVh256AwMDKBaL6O/vR19fn61DrVazdWm1Wtbyk8/nrfVSun4zUnGz2bT9T6Gels/p6Wk0m01MT0+jWq3a/gZmlULXO9LWLD1/8TOXy6HRaCSssITe16utrtrK3G63Ua1W7SIJ85DzhFRYqYxR0Jf5aAFYK7W94FgTlD1P9jzZ82SPFQ0xBIMYSZfimXsmFOkCIMrEyGTbaEYGbQBohUBkgEwMY4LOHtlaiKAZIJwhFzOjGGPGhdjUQ7SbIdqZEK1cjHY+RDPTtsVkojbKxTqMAZpBHqYaIog7waU6Z+d29uB6xXZl47AV23a7jVqtlspEtaXGFizcCSXjkC5C2qVQCovVatVaQHQEPmBu4Ajmo1ehmVamI1OUQlYvliqgI1SQ0fA5/mZ/cTWzr68PQdBxgZJ7cPgcGQiZkUzT399v6yajjAJIMFgKHLIuFEi6CR2u1V7Zv3yHshxZBym8E5LJ8Rn2M4Wicrls+6tWqyXerxSc6vU6pqamLCMlMz0cAfhYx3y0zGtaUZRBfyjAAbN7M0nPUlDNZrMYHx9Hu93G1NSUPcdWujTyvWtLjHZz1u6JrI8UYqVFQLeL9EKakJ+SdmW7OQexznKOYr2lKx7HI+vG8R/HsY1AyrlBtrPZbCKXy9m5juOcfSkVTin8SsVWzrGantlORqOu1Wr2OvOhpVhfo6Aq3TGluyb7BYB1eeQiBunaGOOk5W4W2l5wrCmzEp4nJ397nux5sscKQDCrrCbceoGOlXXm0yaPZ6y5CGDiAGbGZBoGBm0+Gxpk8m1ksm20WyFahQziWoS4HnastiFmjv+Z2RBrgo6l14RoA2gGDPBoEMch2nGIXKaFYqEJmADNKAszcxwQAgPEAeJ6iDAOEgGmPFYWDluxBYB6ve4UgoF0JsqVTumq1o2JSsEsk8mgWq2iUqnMsRJJhiIFPQrb0nWJDE+v8vJ+2uTLdNKSw7Y2m82ExUi6ZUrmwT+eFyfdm8hYdJmSIchVY8lo2EdSsJXMTDJFCp+uPW1pbkzSUsO8KYy7GK60vMn7UsjXexC5R5FWHDJP+dloNOz7bzabqNVqiX71jPPQcCi0TPqRx2Xwt6RneY3CYiaTQaVSSQhMAOa8R6mg0YoiFTrSorQ0pc0Fuu7S7Vm2T84PVNJ5X1oZG40GqtWqdf+kG6O0ckih1UXLpMV6vW7piXWW7soMyKKtV7R26nbK+cBFy6wn6ZF0pJVfOdfI6+z/NFqmKyjbRRrmM1RsK5WKFcjZf3yvXpldODxP9jzZ82SPFQG68YazFlSeMwugo9TGM8Ga2oGNeBzMKI8mAIIwQLs2s1+/HQDNEEEjgDEh2tkQ2VwLhWIDzUyMRiaDOMrAUPvNxLMW4TY6GcYB0ArRDg2CMEQYGsRxgHYrg1ar8xuBQZCJYczMgnZogKgTYdk0TcfF2bslr0gsimI734TlYqLtdnsO85JMU1slJBOlyxwtAGmrkCxbRjVMYyasR1p7WD4AZ368DsC6H2pBi6u2FOx5LQxDy4iYrxSyaTHR7eLzer+OFghdTEULjuwPyeRkWpbJOmazWSsUSBdCvSrP69o1ivWnACAVDFqP2G6mkavEbDPfP1fcvXC7cEjhaKG0TEGSY58BgqRgrGlY0nIYhnZ1X1rxWBeOKVmmVGhpZdJ71aSg67pOOpT35djRAjXvNRoNALOWESqdrDsFYj2/yT6RiisVS5mnFo679Ye2EElBmvfT3inTcA9ir7QslR+JOI6tiyXbns1mkc/nrVVM0jGFeEnHpGuvzB4ePE+GvQ54nux5sseSgwptMPvdKrXZzr5ZKpzGBDboU9AKOpGPqeACCBsB2pkQbQMgDjoBpOohTMsgRgb1wMAUZhazwo57MsIACA2CsKOMokXNeqZ+rQAmjtAyAcJsJ/KUaQdot2YW2tohTDOcVWCt1XaJ+9FjwVgUxXY+6MlTrzzKSVxbNqQgx++cRLVri2TUkgnpiJ1aaCU0o5F5SAuMZnRsl14RlteYns+USiUrYMs9OZL5co+f3ntGKwcFDGmp0UKwtvKwT6VlTb4b13vT70YL1y6lQwvCQHKvHvdCcq8gr7daLRuMRO6Nku1ivdkWlutXhJcGss/5WypdUuGT910WJLqrybGdRoe01nLcMECRK185NjXd8jrz5G9puaWFSqaTVlvpYt3X12etG6RlmV8URTaCcq1WS7gbsw8ogEuBXubB9BQe9Twk28U5RL4nbaHRlmReo4Av+8alKEv3TO51poLNtK1Wy7p0cn+xaywxDxk4Slq40uAF5kOH58meJwOeJ3scIUgr7czvOZGETYDEeTkz0Y8RohPsqdWx2gIdF+YgDmBmlE5ac8N2gCAO0UYWtVaIIDKdNM1gtkyDmQBSHYWZ9etYhoOOMpsPEGQ7QaSMCToKbSNE0Ahn99TSldoAYUsoyB4rDkui2KZBr17KfS9AksHNt/InmZuc1OX5dPJIEA1p4ZH5acYiBT6ZTzcGKi1MUpDmyi4FBs2cKWTreknhshdmyD6WAq5kdrLfpMAg00omyvzomsY6SwYr6yYZn6wrXb6A2WiVchWZ9aCiIPuc5UsrYJq7qcehQdObHAc6nRbees27m0VGKntUKmkBlNFS5biUecp667JIn5LOJd3KvX96XqEgT2sx6UBGCGbdJZ0xnbQy0UJLF0zWTdZZjmttxdH9r9vK98U+ksK0ngMY5EoqxmwraVq7ULJu+XzezuGkZd5nflKh0ONA0jL7RY61NEXMY3HhebLnyZ4nexw2qAgCswptDASmo2maoHMWrbHX1OOhATIzizYhgIyBycadYFEGiHNA0O4osEErQFgNEZtZxTOYUWxNbDp5tQMEjTAZLTmecX1umplttwZhzgDGwJiO9ThszASiisVzti3wyu0KxZIotq5JXQtovazsaVccLeyQaUkXJVekRS2cS4FLM2LNxFzCMDDrviSvcwWUzzFfMgvWkyvDOg0FTS20kxnKVVfZj9o6080aJqFXu6U1TDJY3e961Zh1p2ulrA8ZKlfF+d7kqniz2bSWHh6TIsuT9ZWuZB6HDy1UaYXJRct8To5HeVROWnp+SmsvkAwO5bLWysjHcjxroVVe14qrvq7LlWldyi7TA0i4IMtotLIujL7qUsBJ/6QVab3RfaOtp1K5dL0nORcFwWykZ913Mq0UiGl9l4ot3bFpYZXXOA/K9Nx7mM1m5xznI5UFLTDruunfXrk9dHie7Hky6+N5sseRwBzFj1tqTYAgNh2lVppxDWDP1AnMTDTjGSU3MkCms+c1CDtKbjuKENTDzjE/M4ooIgDtme8zWZmoo8CGLViLLY3FQQyY9sx++8ggjjquy6xPEKOjxEoSpRXYY8ViyVyR01Ys5Xfp6jSfAC0nbU7+0krCazxGQEdFJFxuikCSoaX98Xkp/MrnJFMn+KwMqCEtIJJ5y2iOcjVXWrIk4+J92U/ynmvfjlRcpGVMBtiQ9ZbvivlIJurKR7ukSUFcCjtyhZhCLhUKOVZknmnvz+PQMR/9daNll0XRlQeAOQKWtC66juiQZ9XKMtMUVz0uXDSsrTKaljWdu4Q1BjuStCxpwJiOm2Iul7PzFdtMQVXue5P9pC0reu5z0TLbStph5FoJ2YdS8OY9/Q6l9Uv2vcyXz8ijiKio6nmaz0shXfaxV1yPLDxP9jzZ82SPIwoqjhwG1oJrZo/yMbP37DMzllcqtryOdgCEAcJMjEy+BVNsotnIdIJKNcOZZ4WrcZDM134azB6TO7PNF40AJgo7W3CzcSe4lMt9WublsWKxbBZbAHMm814EGS3waOuLTMcV4TQmqZ/RFhy9MqoFXgp62oojhXPNQMl8Wq2WPedRCpJMQ+GAwVhyuVxCEeBzWqGQFh4yJMn09L4f2U6u6GqXRJmGkOXoPF2CMetL5UTmScFA5keBmNYc5kt3KrkCLxmpF4YXF7pPe6FlvkOtTOk8pIIn36cug7QkXXz5jKRHWZ4UAGWZWomVQq4WtuV9qWjzedKVPNZHKoyS3pvNJiYnJxGGnTNw0yKf8lmp1LJvZSAamU6PedaXf1Qk9OIB60cLO/OXgr2k5TAME/uH9eIDn9MBvZrNpu0vumzL9yDfvfwt29VNsfJYODxPnq2T58meJ3scIcwoqlapjTATPCruKK0xOvtorSLacQNGiIRSG7RnZIUYiKMYUSFGKd9A2GdQbWRRq2fRrGdg6pHdX2syM5ZeoLOXttXJkwqtjGgctIGoFqJtgs7xPkBHufVYlVjWPbauld40yAmXTIGMitel+w+ZAQUwKTDrPTdyhdklAPM7maIMdqGZi7xHZkLQxYl7BQHYSIZSyGWdJBN0lSUZEwN3SMbKfpIBXchopHVErwjrsqTArq1lFGSl5UcLBWyzy+Il3ZaCYPaIhWazaRUAKbikCTb89Ht6lgfSCgHM3Z+b9gyfC4LA7u/S1kup1ErhVI9Ljm0XHcvvzIu0o4VpppWWTklXQPIoIO4RlGdIsg+k8i7d/KTFku1inzEwDZ/jHEAa15D9k9YmqZzKcqUSoAVZfjIfKqbSnZNzJ8tzWYKazSaKxaJ9Xr8fXSfXXMc2eOX2yMLzZM+TPU/2WHTMKLcJC23UMZ3KLbY2WFQwoxHPPGNMx404aIWIaxnUI4NcpoW+fAOFUgu1XBNT2TyqQQ6xyczsy40RZOPO8TytIOn2DMzGraKi2wIiBo5iXX3ss1WJZVdsJePUK4rzCceSCTItJ11pqZAuPJpJaGFKT9IAEsKcZgQuxqatOzKdtKZIJk8LF5/XgR5kPlJA1KvDUgjVfci2ArBCu6tNElL4kAIFn9FWHrkfT65ys12u1Wa5Wkymy/dI1ycJzVRdK/0ei4de+lXSshSKOUbTxpceIwQtBByjhULB0kuaVVcLVhLaokMa1Gk0rdG6pOcqlsMgODJv6fZHdz4q7FLxY75UjnlP04iLlllfCuSkZ9k3mva1MCz7SJ6lKxUVCuFSaJbzSJqLM+tTr9et0C6PEGFaHYHZJRDrdnsL0JGD58meJwOeJ3ssPoKZAExBCwgyQceimp3RWg2AOOgosGbGgpsxCHNtBGHn7FrTCDtH9sRAqx5hKiigFYco5ZoIAoNs1EYj2+4c09MOEGRjRJkYcTtAHM5sH6CyqqcxuiUbdI4G4rBlkCiPVYUVodjqVWIJLRTL+5ywNSOVQWakVUJPsnKvTBpzlauaktlQANWBW7jKKxmWLDebzWJgYMAySX2kiGwz/+QqsewjvVdNrnRLlyv2s+wHbQXTcAkTesU4l8sBQOIweGmB4yqvFmBc4wCYFTDorqgjL6YxSdeKtsfh4VCsY5IG0yw9Ok9podAr+7R8kEY4PrTiKn+7xoGkB453qZCRbmhF4fM8h5HP6TLz+TzWrl1r6y0tPtKlmbRH5VEeFULlXfafbI8xxrog8rekX0kz8ym1sr3ynWUynaOT2GYqslLJYf9IoVVDvnP5rhhUS9aF85pLcellrvA4MvA82fNk+U49T/ZYFMxYbAN7Vi1gckCYayPKdOi13YwQVzMdl+HIICq0US7X0F/oBCqbqOVRreTRbkQwcYBWPYNKHKLVilDMN5DPthCGMeq5NuI4QCbTRiaM0WhlUIkDmFa2E7AqDhJuyAlLssFs5GRgrgKMee55rAgsq2ILJKMHSktB2molIVdHpcsQhTQKl3KClRNx2kqwi3myPD5Py4ZmLsxH7i/Uq5e07pAByZVRKQhoAVKvEsujN6RLJ/tOuxhKxizbKYVMucLsEipcYD5k4GwHy2K96OaoV//lM3rlmc/pe1L4l9dpFfLC7+JgocqtpuVe3oMe5/L9hWFnPyqP95HWvG60TEirjlZk+VtGAnUJY65xybrSdZo0Lctg++X+O1qF9P7aZrOZOM+X/UilWraZFhaWxzRaSNW0rBV/Sae6LEnPABL7c6kIy/dLhYJzn0vRkZYj15iS873sY0/LSw/Pkz1P9jzZY1Eh3Y2pPBogyrSxpr+KQqaFyVoeE0ERcTUDRAbZXAvryhWc2D+C/kwN++v9eHByDUanSmg0MjDtACYG2u0QuUwbx/eNY2NhCrmwhdgEiE2Ipgkx3izi/uJaHMj2ozWZRViJENUdyq1EynA1eu+vy/rrsexYdsVWMo5ukx8nS8kkOPly5RSAZaDSQqMFPy28yt9yf5oWKqXQqevlylOD+ckVVO5fkW0AZoVyyUSksCkZHhmUPG9S7j2SRwTwWVlvma+svxRkpMXIdV+uUlOId1mb5Ptmn0gXTL7fbDY7p5+1lcf157G4WIhyq2lDjkMNqQyRlimcUhksFAoJy2QvtExIhVYLW8Dc/b06HylsusB8pQBPIU4qrlK5zuVy1uLRbX4wxljLKWlBuv7KNsj3w76RVhV5T39nWdK6qhVWlqlpWQr1sg60vJOmtcKvy3f1tZx7dV1YlheUjxw8T/Y82fNkj0WDGAImmP0DgDA06M/Xsak4iYl8AW0TYKpdhGmHaLcDtOMQubCFjblJhIFBpZVDOw4xHeXQakUwBsjlWhjM13DGwB48vvwrPCwzjhgBhttl7G/346HmWmzIbcIvcxvxUH4AldEigAyiemAV7J7bEXaUWxMaG/RqQXl4LAmWXbGV7m7yezfIlUcyDWNM4mgLTvBp1h096bpcnOQzUpBzrZpqQU3+lkKeZFjSQiJ/S+ZIYVkyLRcj13lTsJb7dtjHaf0sGbP8LsvR312WOdZHrvIzIAfPu9QCeRpz5XX5PuQ7cwlDXuhdXMh31U3RlRZK17iQllj+pqCrBUlaeXq11Ep6J+1It2WZTroQ6rlgvrlCWjfluGW+tP7IPX3aQgwg0WYpmPJZuY+NrrzZbNbu6QXmHvGhLS/yN2lRvzvZLmld1gql7BtGg6UCIOcsppPzi7TayrmQaTl3uxRyPXY8fR95eJ7sebLnyR6HDKXI2t80kmYMTDR7PQwM+rM1lDMNTNQLqFbyaDVDtGpZjFaK2FcYwECmhggxMkEbQWAQBAZhGMPMaMht0xnTA2END8twcWca0yaH/rCGodwkpss5tOIQ+0yAalwETISo0aNyS4UcgImMVW7DZuA8EchjebGs59jynpzQXZMyIVcjM5mM3bclr7kEYfmcXtWk0Mk/puGnFBw189SMVDNPV/15j25PhOwDyWS1y1Amk0G1Wp3Th3KPohZo9Z4/+ZxenZf9r1eRdbtkMAqZp0thICOXCoyL+crvsk0uBUQqGC4m67H00ONLBwfSoCWgXq8nhClpbQXmugHKa/zOMUMLD6/L+lC41JaG+axHWrmXymo+n0/ck8K2FIApVFKgzWazqFQqc/pOtkvPC7JcKYCzr+M4TswXzFML7roPuCdPKjQU1nWf0PosLVISTEcrFesp+0W/NyDpWqrfncfiwvNkJO4Dnid7nuxx2BDWWHkOrOG9EIizmDnux6DVijBZz2O6mMeabBXrChWMFEpoVTIwtQiVKI+HcgMoZRooZxpomQitdoRGI4NWKwJMgFYzwn704SeZrcgHLewvPohc0Mbe1iAeaqzFeLuIyVYB9XYG+aiFcqGBRl8G7XaAIA4RWo17ptrSRVm0iW7Ixg/pFY9lt9gCSZcZl/VBg4Ihg7vwWRlN0CWsMh0w9xxLCsN6hZL1k8xYTtaacbrKcbWTVhcZSIbtZzp5PpyslxQSWWcyFIJMSAsV8jm2SwqzLsh9UTKdtMDpd6hduABYYVj3h8xDPkvBxhU5lf1FxtzNxdNjaeASiF1pJCggAki8d0nLel+eyyLAtFKQ1mPQdd6ktvS68pfP8LukuXw+jziOrWVVt5H3pILHCKNyfEtBVwv60n1RCvtyfgjDWTdkOZ/oNsh5TdO0nGtcVhu5eMD+lMGmNPSxIjLSLH/z/QCze571QoieAz2OLFYTT9ZzvufJnid7LDNmlFoqtPZaaDrH/MycL9uuRxibKmJ3dhD5/hbW56cxOZDHA7UsWpNZxNUMxiZKuD9ai/XFCuqtDNpxgHYrQtzouCLHQYjpOMTd8QaM1Er4QfHhKEQtNOLIui63TIh2HKIZh2jHAaIoRjvXiZhsuBY3s2c2aAWdzxlS0Eotr8MEPmLyCsWKUGw10+zF9UnuXQFgjxGQrkQuARiYZQryM22lVD+7EKXW9TwAu1fF5UYFJCNLksHTZUgK19K1T/YF60arF60vut5SmGQd2K+ulXAtMLvgYraSAVJ4CILAqQQQWvjVQTX4Xe5fpPLgsXyQ1gH+lp+EHFsy4ArHu9ybqwViIHksDD+p+EmhUP5JJViOfxf0OJJjmb+DIECxWLQCIIU6Pe7jOLYumYVCAa1WK6EAAMk5TwahAWAVSQrPtVotEbmVZZHeSc90FZZtci3Ozdd22adyDs3n87Yv5dm7euFAKiCuNBJ0Qfe0vLxYLTyZaVmm58lz4Xmyx3LAADN7Us2s5TbquPIiaxCEnXHWamYwUcujXs7gxNJB9GdrCAODBzNr0KxnEJsAE9UCACAKDKLQIJNtw5gAcWtma0ErQHU6hz21LIazfQjDGBx2YRgjmtFAWzNRlON2CIQGJhvDZGYSBgZoBwjrIcJ6YC9JdI4L6mjsAeA+Oshj2bEiFVtCWlQkyGQIWgTk+XiEazVXujql1UOvBLuEan1dMgv5nCyHDK1QKMAYY895JFOVz0urDstgUBktdOi9eq4IlHo1VioF0mVKMktXf2pBYz6hRzI9LZAz2I7On+nZfnnNJaxz/6FnossLKYh2E7bk+JIBmKRgpMe+fE4KUZqWOdblmNFKnRZcXdekBVUK0XIfYRzHKBQKaLfbqFarqNVqCVqm0Me03INLBVUqsOwrvdeYCoIE+0zOeTKaK2lTzgu6/S7FUr8vqbTr+VHvYW40GglLF8eDtDRLy5YuQyoN+Xwe09PTiXTdxpPH4mK18GRglk96nux5sscKQYCOUht19tN29tqambNr484xP9HMmAkM4hmr6rrMNB5R2IPjcuP4WfF47KkMoN7q8LVMOMMTozZy2RbiOIAxEUwrhIkDmEaAOAZayFrrcJCLkc23UMh3zLLtdohmM0LcmjG/RjN+x2GnHmiGMC0DhMo9GYCZ+W0VWsxVfD1WBpZdsdWrsnJlUU+ILqYqXWqk8Ml0Oh8pCMsJm/m7BF5+ypVa18qvi+FL1z+W0Wg05uyvISMjg2BdwjBErVZDpVKxfaVdo3V53YQJ14qvbB/rlbZyzWuulV9t1ZGCuoQUKrgyTbctKeRLdzTXGaf6Ny19/niB5UM3l0WO3Tie3Qcq70nFxhU0RtOyFIZdwh2QPn7TlFvm67JyMI10tZuenrZjUe6n5RhklGd5juvExIQVfOUxJLqOWqklTUjalnOefp5KtZxLeE32oRZKJWRal/XbtcdXu0MaY6wrNa1rUkDm+5cKdzabRTabnUPLXrk98lhunqzz78aToyhCX18f6vV64rxaz5M9T/ZYYsy47JqgY5lFaOxvhDPK48xfMKPoBgEQBAZtE2CsXsS+5gA2ZCZwamEPjsuOYmSwD1PtAibbBYw1ixhtlDBaL6HRigBkO0f+NEOgFXRciNuBVUZNxsDEAeJMjCiMEYUG7XhGCW6FVjntVNjAmpWDGStzHMwovJ0sufd21hV5abrVY+FYdsUWmLs3j6AQ5nJvlEKVZIxy5VYzEnmPeeTzebtCqwXe+RiPzlNaHpie36WLIq07ZBDS+iNXoZmHS2CUfcN07A99lAChV6+1wCj708VEpeLAe2luUDqtriuZHgB78DvbTIFfBgHSio5rvxBdNpmfx9KDQh4wV5jSkBYLKm9SAOxGd/KaHFfSUpsmAOrver5wzTNSOJf00Ww2MTk5mTijVp6pKQXAdruNSqWSEJDZZmlpkbRIGtF9qNtPZRGYFaSltZjXmV7PuS6F1vXepCWYeco9v2EYWrdk2UdU0GW057QFB2m1p0U/bZ7xODJYLp4chiEGBgYwNja2IJ4sr3me7HmyxxIjwOxRPpmOQmv1xBiACWBoGW0GaMcR2mGEIDQIMp3oxvun+vBjsxVj/UWcXNqPLdkxPCw7gnY2QCXOYyTbh93RGgBAtZlFNYrRBIAYCNqB3RsLzOyJBQADxHGAVhwiCsVCDEljRsFG2FGyTTRT/1ZHm7XJDGbdjj0LWvFYUYqttuC4LDvSBQaYKwhL4QhICoBSMCVcx22kMUuCwup8DFTuQZHWC94nI5B76tg+bUnWfaQFT8lsZPvlofWEZIoUJrRQoBmVfFcyD3lN5i/bp5+XZcr9PVKYokWMgXnS3ov8LoViffyCx+IjTfHRtJz2LOlQCprd9tbyj+mksKgVQ9cCFPN0CZjaSqvTclylWWWk5YYuudo9s1tf6P127A+21xgzJ+Ksri/nD3lfC9zyzwW9UCDrqBVi1lu6nfK5er2eoOVGo4F8Pm9pn/OLnj9luXIRoZcx5bF4WC6eHIbhnABU8/FkYwzy+Tyq1eocmvU8eTZ/z5M9jigCYM7ZN2b2M7CabgDMrHGYyMDkArRDg1ojiz1xP8brBewpD+LE8kGszVaQDdpomxBNE6FpIuTCNsrZBiq5LFrNCC0TdPTNsKPcAgC4lzfqWG3r9Sza7RBxPMO7Z/b3BmFnr28QGYSBQWwCtIMMYgOEQThb/zaAOOBjHiscK0qxBeau4ss0enImo5L7z6QwLN0YpSsU00lBjJ+aEZMxygmcEz8wuzKpV15lHdKEabZLuvzIFVcpZLpWayUzdAnzZLqaKbvqID9dArEs38WcNOOVAoCrLMnc+R5ozWG99VEidE1Ma4dkoi5XKY/FhWssuGhZjj9tkdACMceCFAZdnzIPlqHp10V/mtb1XCHTEK48tLWD7aaix3lDCoBUBNk/skypkMp5i3ODtB7JPtT9qs8R5jzH66QtF23I96EFdJdimUbLsp3SwivzIC1ryEVDWvxkn3ksDZaLJxeLxUSZkpa5P51Rs+U9BhyTizueJ3ue7LFEoJuuARAHMKGZUSxn7yWsnQYzbskGQRSjE+zJoNnMYKyRwXQ9h5FqCQP5mlU4M2GMUJhLc5k2cvkWEBjE2Qhxa8bFuB0kFGzTDNE0GbSiCEEwU8+ZgFZBaBCGBmEUI4pmzsU1AdpxxxOZlmCYwJ9Xu4qwYhTbNKFJw7UKy0lVWyv4KVeQ9X48l0XIJUAzbalUsoKrXlWW4G99lId0NeRzzE8r1OwHV310cAf9nGTuEq49UbLPtSIv+0nukdSrxFrY7mYR0m3jO+I5gvJ5CjHaGqXBNrHutJb5FeLFQTchTKeTljhg1tIjLSxA8txD/tbjVpZLRUcfISLTzCe0Mm/SnWtvKvNh3VkP7sOTtCx/05WPwqCsH9NrATUIgsT+MykEajdtKZjyedmfuixphaHCKedP15mXLuXWZUllW7Q7JstilFmOB1p7giCwLo367E/9fuR78taepcVy8eQ0fix5nEQURVi3bh1yuRyq1apzn60u3/PkJDxP9lgMBDFmj/cJAVOIEZZaCEKDuBnC1KMZbXHGJTkTI8zGyGTbyGbbCMOOYtlqds6obbdDTNbyCAOD1oylNRu1kc+2EAUGzXYIYzoG4Ey2BWSBditCuxV29t22A6A9I3M0w86+2WjG7ZimV4POkT8mgmHdgpl0sensraWLs4+AvGqwIhRbQjMjKVhxcpeTJT8lw5AMJQxD5HK5OUIm8221WomIgLJ8lieZHd3tpADLPOXZi9plKAgCKxSHYWcPURAEib29wNz9RxK0EstjAGghkoKrTu9aIZZ1kwKyZPaaKco+l/mmQTPfbpAWLArEFBKazaYN2KXflQbfs0up8Fg6yHfjek9pY0PSt/xNZZbBaPhupTXFpdB2E5KlAsn6UJjlfjJdR45LHvGRyWSwbt06ZDIZTE9PJ57hPOGyUOTzeQRB0vIk9+VKF2bOYVSAZV30AoLsb9mPLqFW0jrgPs5FK64uSBqT5XA+koJ3vV6381KaIK/rJ+nZW3uWHkvNkycmJlAul508mfQieXImk0GhUEAmk0GxWMT09LTnySnwPNnjSCOI0VFaDYBsjHJfDaVcE/VmBtOVPNqtEEEIhGGMMIoRhgZBQI+BEO12xyZr4gCtZgRjAsRx0LneDhBEBtnsTFRkE6DZyHTyjIy1+gIz5cfBbDApAyAIZt2TszGC0MDEYWc/bdxxhw4yM4qtmVmEi4POHl5aoz1WBVaEYqsnRmnZ4V41QlsYXMIwBWHp2qgFXnnNNSlLoZjfJZOj65N0W5LPsf4E20KmwpVQCgdkFNLlUJarr0dRlDheQ++/Y+AH7a4kV2Rl+ayjZjzyu1ZYemGiLsi6aEsglQLucZJ5uVwX9TvUCxHehXHx0e3dy/chId+3VHa0FdNFy4ySrC0efE6X57IcuQRs1guYnVckfTCdFhi1YCr34LG+jIQshVyOTVovgY7gnMvlUKvVLA3rMasDuaT1P6/rd8A+7wXz0W1aGtc8IfuCYD20ktpNKJa07IXipcFy8mQuGPfCk6X3wLp16zA1NeV5cgo8T/ZYEsQdKyfqERqNDEq5JrIzbsPNMEIYGmQybau08lxZYwLE7Y6SCQBtA8Qm6EQ2boZAKwQCA5PvPAcA7VaIuBkBTSDOzCz6Mq0M9kRX6SDo6KydfzAGQCtE0JyZH7Idiy4MgHaAoBkgbAXeWrvKsGSK7XyTrmakFAC15YGgG4xe+SdTlQfD81npwudiIhpSsGIe9XodhULnsOhsNotarQZgNkKgXoGWZfNetVpNnPdIJkyrhGTaLFtGBiXzZz8BySBYGpKRyvbLVWbdbinw85rMg/3mYlKy/Voo1X3jep5CAtOyT+aLqkgmqvvPC8NLC71CT+j3wHer99PKfKgQykBRUgF1CWH6nWvFWY9D0oCkQ+keqestFcyJiQkrzLJucl6SVkfWs9FoJNwLpZLL31LApBWJ+bPvZLul66Rsl2ybzFMv1gVBMGd/Luct3Vdp71PSsrQOSVpmHaW7tssay3evFyI8Fg8rlSczirZWmGVeki+OjY1h48aNiKIIg4ODGBkZAeB5soTnyR5HFIE1cgIAwjZgqiHqE3kcbEWIMm2rvAJx5yzZOECbZ8pSoTWwGZkg7LgEB+jca3cKieMATRN0LK5tKrABjJkJ9tQKZ/bFJuvXqaOZNb8GHTdmAyQtu1FglfPOEULeWrvasGIstlqolZCToBaAJDOlZUSGpHdZKyQD4cQr08mVYV2nZrOJQqGAYrGIOO5E+5xP+NJ7cunOSNekVqs1R5iVba7VanOYEtvuUhZ0m2W7ZP4uYVWm19AukpKRuoRg2f+9MDL5rIyGKl0uoyiyzDRNIPZMdPmQpqACc/fa8j1KhYp5kJap2HIc8U9HXZXlS4VX39PpKHCzHpr2tQKp82m1WqjX69ba02w2reJKcE9pHMeoVqv2Hq3QUvmUID00Go3Eb0kj7EvpBu2qq5wTaAHXxwDpd+GaB6SyIfuHddLCdxAEtu2all1zvlSi5JyapgB7HBmsBJ4chqEd+9148tTUFDZt2oRNmzah2WxibGxsXp5cLBYTkbs9T3bD82SPeRGgc16tOOIHMRA2AmAyg3Y9Qiuaed/hjEU0wMweWOUuDHQCNdGjOGNgMmY2ivHMMyYOYDLxbHnRjLI6Y8lF3HEjNgHsnl4jztLV05oJZrIyAdACwlanrKA9417th+uqwopRbKWbrxR8tMCkJ3498eoVYzmBy2fkb81EpdAsIa0lXMGUDNdl3SF4hqMUBnmGroS0KPWyD0YyGZcFRQqYafmlXZd92a1tsq9kmbIN3Sw+8lMrN8xTur7NZ2HX78Vj6UBaPpTnqFhKK63LwiOhx5UUMrW1QI+HVqtlI63KSMYLVaAymQzK5TIqlcociyjrTLdEaVGR1hpaQaQirecCve9X07QxJmFB0YL3fLTMdKyHdM+W+cn08nra+9EWol6sr3wnMpCWp+Wlw3LzZO6b5Xm28/Fkjo9eeXIQBCgUCvaIIM+TkbjmebJHr5Dn18YzQyJoA2Er6Ci3zVkF0yq/HcOt3M4KGXa4o1AGMC0gzgEm2/lNC6ppBTCZoKP0ZmIEEffMBjDtWVNsYAAEnTN0g5lPiH24horwzLO2bK/UrmosmWLbbYWOE18vz0qLj17llUIwhSHJILpNqDJin85T5kEmx4k9zS1LQ1pCoihCoVCYs9+G9yVTDMMwwWxlxFFpLeazUph2MSeXBUqnl3XRLplSsOmVQbkEYhfD1+l137vKcwnN2kXLW3mWDvPRsgSDt/E5vbJPUADUip8cQy5rrHaBk+NJu/RKC2YvkM8Vi0W7f84lUDItLdDcvsDAN3HcCawko7Rq2nVZyFxKsLQyyX6R1mF5LIpLYZRt0JaebnNct2ekot/rvCHndG/tWXysdJ5Mq2q9Xu/Kk2kxbLVaViHWypuGXCjyPNnzZI9DR2CE7kfrKAKY2NiIwjMqI+QHMKPUzhz7Y6jwMrMYCOOO1mzatNjOWG2pcAaACWdyDwHAdCyzM/lQcQ5M0HFDjmbKmAlaZWJFB6ajzPLPK7WrE8tusZUCaNp9YK6LG606hBSKmU4rqryuBSRpRZATsPzU9dGKsAuaqQHJFVcZXEK6DDG9dOWSVh8GqWB9Zf4uV0FpEdLWK5nGxdDlOyJzd+0d0qu63RhnmoDs6kuXwqIh35VUYvzq8JFBN4E4jZa7KT1agJYr/KQDl1IrFSX5Ka2ELFsra/IZTfN6ISvNekLrsjEGtVptjqWTeUnBn3sHJf02m805yrw891ULisyT+1T1s2m0LBfjSMvyqCGXhUdaqxaC+WhZQ7ZR9nk3F1SPI4OVwJPb7bZ17++VJ+fzeev2ngaWrfmp5MlSAfc82fNkj3lAI2g7QBACxlApDGbce5G+R3VmijGOqSYAOsqtwYwVdiavmTw7lmIqsR1F2oiNvoGZUYJnFFk7vEODcMZ1OZg50qcTQVk845XaVY1lV2yBdGF4vvS0OvBPBpiZD3rFGUjuV2E5emWRkQFl8BPX+XWSees8C4UC8vl8YtXYZS2WeZZKJTSbTTSbTdTrdWvtovWFyoDL5VFCMmcXpEDJ39zjyPaTIaf1qUuJkdclpJAtGbl8Rq5+y2suRiz7zlt5lh7aiwHoTaHhJ+lY0njau5MKqrzGenDcsny5kCQFOp3nfHVmmmw2i3K5nNhvT4VTKqTMi4rwwMAAarUaGo0GqtWqPT6IR/4wmjDnGT2HuPpA92naM7LdUhHXiwYuWk6z2sh0WgF3pZH5dBN0Zd95gXhpsdw8mUdbkSfLhWHNexqNBqamplCtVpHP5xGGyaBG2noaBEFiLtA8mfA82fNkj94QzAyRMA6ssgoOm2DGuOpgYUHcuY/2jEUVgAwgFZhOPkEgXJZn9st23JCNVVpNa6ZgHtHTDmyZhpWgthoYhIGxW3LDFmzAKX+0z+rHsiu2XHnU11wTPSdZTpQM9CDT6ZXHbgIR7zFwRKlUQrvdRr1et8xRPysFt7S80yZsOfnX63XLtF1t1cwAmGX2UpmWK7/A7PmRui7SMiZXjGXfS1dIaWWSq9V6QUALxPJT1n8+JqZXql0Cba+M0Ft4lgeall20o8cCr7ncYjm+pGAon2EZ+l1TgaRyRxqXdZL1kOXqucMliMo84jhGrVZDJpOxboi67a75qdFooNVqWTdKaXWhUK4X2uTzOuiUdgeVEUxdlhlNt92EYtnP3WhQWmcAJKzs+l43pFmEeqmDx+FjJfDkWq2GAwcOYMOGDajVahgbG+vKk3kkllwUkuVruBQyyZNdCrTnyZ4ne6SAVtoAswotgIRrccpzmHlu1m14RrE0yXQBXY+tC/KMtXbGYussxziqEM8cIRTCnnVrlWBX2R6rDitCsZ1P2HEJmLQMGWPmuEC5rC5pCqhmUIRLoGU9GQmV0UXlcR0UqKMospETtSDM9NxfJ12eJLjqTWGXFl69/1C3W68M8zpXs7k3SFtmXMKnTKP7R1vTZB6yXr3sp5Hlyr05cnW6mzKj89J18sLwkYdrJV8roYR8HwzgRFp2jT+9VUAqbC7Fh8I2xyzzkAIZ01Ig1m3h/l/WweWizP2ypVLJSccsh/MCBWi6GkvlTwvHshxdt1KphEwmg0qlkrBoyfq55gHp2sj6SMutLEPOB7zX7QgTDSoHsn2ybbKsNLiEaY8ji5XCk0lbki9o8F6lUsHY2BjK5bINCgV0eF6hUEAQBCgWixgeHk4sZGmeXCwWE+fTe57sebJHj9CWzvmUWv24HDppz1HpnFFITWRmLcQBwGN8TNbANA2COEitFwNNBW3MRFFeWH09Vi6WRbGVk1o3Fxx5XQu2aavH8nO+VWFO3BRga7Ua8vm8zYPPa6ZFpsZAFTKaMoNI5HI5u3Is99TRIkymLwNfSEhXI2BW8ZYCKAVHubrc7Vw5vc9FHjAv+0srDLpfJdOX9dH5S4G2m0upzFM/K/tdQr4bvWLuheDFh34XrnfJ97/QfLsJhWljRgtSVB6lIMYxDsyNdO5SiulGCHQWnbLZrP0t98uxbq1WC9VqFWEYolgsIgxDe6atyyLE55rNpqU96bLI9HRldu1/1RZb9rtWWvUeWQmXFVjSp14EkPmkvXt5XS8AMKiWTCv7Jq2evD6fMuRx+FipPPngwYPo7++fM470nBHHMSqVCtasWYNSqYTx8XEYY5DNZtHX14d2u43BwUGUy2W7DYDn3UqeHASdRWm6Fkt4nux5sscC0IuSOGNxTURL5rNdLKfBjHILE8xYfGfml8ggCGPEAGJGOW6JY38CgyA0nb3AMWYttt5Ke1RhyRRbLRDJibLbhCeZpXbHAZCwqmh3w7T8CBkplMxRCq7MSwuJQRCgWq3CGINCoWAZ3ubNm9Hf3499+/ah2WxaCy7348q8pSCbxvwloyLDBWYtXGSEmgm6GCMAG6SGZZLxdAtC41I0tMLBtrFcl3LTCyPVkKvKeq8ioVeM9TXdBx5HDq73Tnp1WW71+GKwGCp13fLV4B6/NOtOmiuyLIv3oihCuVxGNpvF9PS0DShDaIuJXAyT+9303CVpmS6PjK6qhUBtmZb1pqWY7tvyOmnQZflOWzTQViLZfzICq65bmgu3CzKtpl85r0qhWNfZY3GxGngy963TDZl5uRZuDhw4gHa7jaGhIRQKBbRaLZx33nl4+MMfju985zuYmppCNpu1tEfezLrqo3Tkp+fJHXie7HEkkIisLK7NTYjZM2mZIOYNwCBEkGsjKrQQZ2K0sxGCethRghmoynSUWmOCTsArb6k96rCsrshpE24vaaUQKkP081ov+RFkFnHciXKYz+ftnh2ZVgtcFGJlpNF9+/bZs/fks7lcLmG1JQOnAszyydz4rBRc+YyM/EiLL4UJ7XoprSxSwZbCNeskBRRakVyCuVwRlmB5mrHLlW79qeGy0mlrAtsmlQGd3jPQpUeaq6krTdo9qdC65gctRDOdVDSlkKhd6VygOyHHO+vRaDQwOTmZULpcYyuXy9mjfEijmj5IA3TjI70yYA0Fa2OMVfAp4Mo82D5afUnTuVzOWoOBpEJMWmZf6COB5L48rdho67N2QZTzjaRRbcnSSpScm+ajUZeg7HFksFJ4MjBr7a/Vali7di0mJydRqVQSafU+00ajgWaziVKphDAMUavV8L3vfQ933XWX5b8cx6VSCZOTkwA645zH9lSrVc+TBTxP9jhiEJbZoI2OfhoH6HWEBO2ONZYux8YYmMggyjaRK7XQyoVo1bIw9RBodxRZ1CPE7Y61t3MubuCDRR1lWPZzbNPcnrpNgtqFL20ida34U7CU16WlAIDdD5d2FAbr3Wq10Gw2USgUUC6XrUIsJ3cKrGSq+XweURShv78fxWLRMrNarZZYZZVMki6R2sVJWohYR7n66+p7KcRLwT+tv3XeOj/2hX6WjFrnJy1laYKvq/6u+knBXb4XV5keRx5S8XOt5Eto4UwLyaRTwkXLUjGSiqu2DsrrafMNBbNCoYBisWiFXtKbS5mmy+TAwADy+XxiS0Emk7FRjlkfRlGu1Wp2QYsRVEkL3O8rrVYuyyrTS4GYgrLLyqsFUZmHttppWte0rD+lgO16zy6LkotGtSXYtZjgsXhYqTyZilc+n8f09DTiOEapVEIURdZTStZDzv3T09OYmJjA4OAgtm7dijVr1mBiYmJOu8lzs9ks+vv7kclksHbtWgwODiKXy6HZbOLgwYOeJ3ue7LEUMAADQFkLapd9tkE7QNgETBsI0LG6mqBzz4Qh4myEINtGLtehy6bJArUIQXPGfbk5M3ab3mJ7NGJZLLbamiAnSC1AuhihTitdg1xWB52PS0hrNptW0dSrn5qRyutMVywWE8xZCpFUgoMgsHt5GIGRgWRodaFw7CqTgishBWeWpS0qsr8kMpkMSqWS3UskV4k1I9JMzQVdT34yP10n5ikZYFo5ckVYvlv9DmVeMtqsx+Ii7R25xk0voCALYA7duWhZWjHku6dFU9ePdCrdXHX5/MxmsxgYGECxWEStVnMKbawjy2o2m9atkUJ6Pp+3e/dYH9Imo7BLpY0WYnnkDy0+aeOdfZPP57FmzRpUKhXruinva2uqSwh20ZUWZDUtuwRbrbzq39I6laYoa7dsLgB4BffIYSXwZD7LuWB6ehobNmywaRnUib/5nK4b023cuNEqi7I+3D8/MTGBMAwxPj6eUCrHx8c9T/Y82eNQMaM7WitoD0pjIJPNp9Ta6MVBIlkQznglN0O0axHqQRaZDPlIxwWZFlq6LjNwlMfRhWULHgUkz8pLE1pcyqi0bOiw/t0mTU6yhUIBfX19yOVyKBaLGB0dxcTEBKrVKorFovOYDZm/FEhlEJhSqWTrx/QAEsIm0HEFHBsbs0xDH/KuV3CBTmArukuRmUi3S2n1cR36zvrwj1Gd5RElhGRq8v2w/7SwINsqn9e/pUuVfJcSaYw07d26XJ9kvzFPjyMHjg0XLad98jvdaSUtz0fH8nm6D+ZyORuJnO59si4uxVjXmzTEfXjAbOAXl8JtTOfYnv3792PTpk2JPBg5nVYdRlAHYPft0oVZL6RJxZk0TiuutrbSVXNqasq6aco66nekFxHksUCall2fdGmUgq2818s7k59padIUK0/LRwbLxZO5RWfdunXYsmUL+vv7MTQ0hDvvvBP33nsvDhw4gPXr16NYLDqDMLl4MheTGo0GTjrpJOzcuTMxhuM4xtTUlKUvOe9wPmKUYs+TPU/26B0mRGcPLBVJaYFNAffX8rigAOjuGszttQazsaZmvgTtAGgAJgjRNhnE2bCjaNPluB0gbM/mYxVlj6MKy37cD4A5jCnNFQqYOwHLSbyXibNQKCCXyyWYgDyagKuzct+srqdEHMd2Py6FPrmKK5VcKVCTAfMeMLunhquakkGSUfB5LbTK/iAT1a6d2pLDszQXAinUSMad5nJKaOGdAraLYXZ792l10u9KBufyOHJwKa567Gm46JRjX1t6XDQn85FKqUwnBUaONRctS2sSLakyMJOkG6nEyXziOHbSMrcqSFrmPBGGIbLZrBUCtUWVC14UpqUQKemOn7TWut6N/K0VXplO9pcLrnSybbJOrmf0u9OfrsVEYDaAlcfSYal4chzHWLNmDQYGBqz7PheJ6d5fq9VQqVSQy+USsSpkPSXa7TampqasxwS9qXgEkHQbljw5n89bJdbzZHcZGp4neyQwY6010cwYagezimgaDDrH7YSz33uCyJPKcQggRoAwCGFM57xahAZohTOK7UxZC7Ame6w+LJtiK4WitJXh+aDd6lzuTkQURSgUCujv77cBIrgySybKvW/T09Po6+vD4OAgRkdH51h7ZPn83Wg0UKlUEkEndBo+7wr0wjzkge+SwekosZKhkGFIhVoK9ppR8lkKCDKvbsxQMnb5uxcGyk9tseOz3aw9LmF8Pmh3Oo/Fg2sFvxs99zI+5LvSeWsXwDDs7G3lAhUFWFpFqZRyoYnW3G7H5xC1Wg3j4+N2gUsqvkznmgN4ZjXvcS7Rx13QMiOvybHqEiyZr7Qu6ed0nq4+lvlyDpEKaZoiLN+RbKOmL5d7o4bLVVzCZe2Zz4LosThYap6cz+etpbZUKmHPnj2Ynp7GyMgItmzZgjVr1uDAgQOYmppCq9XC8ccfj5NPPhl33HFHV57caDQwNTWF8fFx7Nu3D/l8PhHcycWT+/v7AWDOAhV5Ml2KPU+efd7zZI85kG7EgfibB7TqWhfmhQwtk8wjBGBMgNgAiEOYyHRckFvCguxxVGPJj/vRQiyQPuHpyVkzXCqQZFTdLA9hGNpzZ4OgE1WULkuMoEhwnwzPpkzLU7oMyujGhA6iod2mdDspSOu6AEhESmVZMrgOGaUUDCUjZT91i06pmal8Zy7Liu6LXpipFnRcTNTFbHm9G/TY8DiycAmtLppNAy0EAJwueNrqKsuT7n/SCkqXN70dgGXROqrnC4KuvXQRlu2U9ZFtc1moGUQum83aaxRai8UiANh60/WZ+ZGW5fwh99uxPrT4uuop+5i0LPe6sd5yftB90e0dSsWAdU/bJ63Hx3wWKZeC7bH4WG6enMlkMDg4iEKhgCAIMDU1ZU8U2LJli+V5QEfhbDQa2Lhxo90qNB9PrlarNgAc660tvpKWJDRPlsqo58meJ3u4QXditGd8ihcSmGk+y24PzwPoWGUNEJoApg2YsBMgKmgFXqk9RrDsrsjzrQxrRY+/yRDSVoQ1sy4UCigUCpienkatVrOMgK5OUrAEYNP19fVZpqrzZ96tVgvj4+NYs2bNnAAXWtikWxIAlMtlBEHnTFzJKLRLntxfR8GQ7XftadKr5lK4TetvLby4XM80I5WrxdKqIt1DdRlplj7NnF0Mcz6hO+27x5GDprM0Ibjbe5JjM80VTj4n97/KfaguayDdhKkIU8iULogyLduUy+XsOJT75nRa5sn99ZOTkzZPHR0VmI2oymBTVOblgpucK7RgSjqRUZNdc59cRHMF0JH0ynz1XKXnkTSLl4um5Ryh85VKdq/w9Lx0WAqenMlksGbNGqxduxa7du3CwYMHrUdFpVLB6OgoSqVSYtzs3r0b+/fvx9atWzEyMmKP6pH5sz6VSgX33HMPHvGIR2B6ejoR2Zz0TZ5ujLEyweDgoOfJnid7HAoMgGDWckpL7RE/SifArPKslNsgnjkOCFiYku2xqrEiFNtu1+TKpryv97hIS4oMihIEHVfEQqGAqakp62pE0L2P+2z1gfIMAKMFVFk/MgEe90OmQmYgGR2F93w+b+vOa/rsPylgMkAOMKuMy/ZKAVK780kGJc/rZH1cDJdptDuSFNBldEXJ/NNWiXV9mR/bz/rpa2luU656eSwP5hOGJbRyJceOFMz0uAZm91tSidVCthaqZX5yH6+2OEhLTyaTQbFYnKMou5SxYrFoLbAUdFknWm3ZFmM6gal4nBAFaB3IRiqbQNJyos+x1G66cp4iDXEhQJ5lKftJ5sc8ulm1NeQ8HIah3SvpqqOk5W7Qc8h86T0WD0vBk/v6+rB+/Xrs2rULw8PDNl+gM56mp6dRLpdRKBTs3liWMTY2log2nnaGcyaTwYYNG6zSKr08JG9hvp4ne57scRgQCmYgFNslUSa1cksFO0DPZ+J6HD1Y9uN+urnhAHNXh+WkrZVQbSGVEzX306a57GSzWeTzebtSKxl1oVCwzFELZiyLZ+HRvVAKkaw3mSswK6Bms1kUCoVEvaUQLvuL+TOIRqVSSVW6teVF9olcLWb+0k1Rum7N58rEdqUpAGkCqRR6pCCvFQ+pxMhn0iCFdI+lAd/JfLQsId136con89L5808raWlCuLRAArMKFt0YZeAWCe7Fz+VymJiYsPQlBXY+R4GakZlJx9LdGZh1OZYKLo8RyefzyOfzmJycRLVaTYx/V9s0LbMPqTRoF2HpiaL7RCvDUvGQtCaF5DSFU869up+0cC/7vhc61WV4LD6WgydXq1WMj4/PGTtxHKPRaGDDhg1Ys2aNPXaLvKBer2PNmjVWwdVzB8vatm0bSqUSqtUqms3mHBdkLjin8WSgE2xycnJyzjE+nid7nuzhgGRZSz1VS0WairVnF8ckus+QSwCX8CYnSi2saYbZLR+m06vJ+hkK2ZKxSDfCXC5n9+DocmUa+Zxk/i5rlCw3iiIUi8U5DKKb5YlCq66Htl6xLrTc8FO7QbEevKffA9sh9wpJRjvfe9TXXH0o281PWc9u0HlIpuuF4SMP1zvqJkxpIUfe12NYfsqxqS0b0mIo5wkplEqFOq0dFOZ4xrQsV89FtHQw32w2O6cv9HimkNpoNNBoNKxinPYcy2VZTM85gDQrLcakZZfFVfeJFK71O5QWHVkPF9LmZJlvmktlN7hczD2OHI40TwZmj9GRllc+Q3d9ybNoqaRCunbt2sTxeq65hm7FDOKWyWSswqrHvIsnb9y4cY7F1vNkz5M9VjgCeDPtMY4lt9hqK+ZCBBwJY4w9SoN5udLw0HOe8aiZKAVhuh8Wi8U5EU0lw3C1h89XKpXECjCvkZHpYC88B69YLNoVZQrTZOZy304+n0cYhqhWqzZf1o8MVu61kQzZtd8GSLogMS8X05Er1a6opdKq42KUaZCCM5k9rXFk2tqipPcvudBLGo/Fw3yCjr6nx2Cj0Zj3OAgKnXrPqxS6AFhhkQKvHjPaAqFBt8J6vW7plpYZ7qWj4E3Bd2JiAvV6Hf39/ZienkalUkmcc0kBldag/v5+RFGEgwcPYmJiIjHXyDlNWph5X/a3yw1bKuGufpLWNb1Xl3MXaXkhVhZt8WE/NZvNxLxAOpdziasclyu2t/osPpaaJ+fzeWzatAkPPfSQc88nF3pLpRK2bNmCoaEhjIyMYHx83Hpd0cvKhTDs7HkvlUrYu3cvpqambDyLQqGAffv2Wdqm+7HmyeVyOTEPeJ7sebKHh8fqwLJZbLutDvbyHPejcaLVgp9kIHpVU5YXx53oiRSsM5kM+vr6sHbtWhtFOYoi6+6kIaOwUjDP5/MYHBzEhg0bbMRHXicTkm6K+Xx+jvuyTCOZIlez6d4o+4UMX67uyj+92iotTfyt3TRdQnXa+9LMOC2tvse6sz1SuNb5z2dxk/nJPvBYfLisi65VehckfXLsU3F05ZEmxMn3S5ohHXF7AQPH0fOiW4AqCq0sj1YezgXSMivrzvZzS4JUUOX2BLav1WrZ/ba8x37Rc4Tcpystt9LThJBuiy6hVtND2r1elJs0S4/ea6nTyL7WirlLcU2buz0WF0vFk2l9lS7EsrxGo4H9+/djfHzcLkxv3boVZ5xxBjZv3mz33k5NTTnrw+N9yFvDMMTg4CC2b9+OX//1X7eW3CiKMDAw4OTJ/f391qPC82TPkz08PFYPlk2xlYKwa/JNg8uKoK0BEhRkdTrJXJrNZmI/HVdZy+UySqUSyuUy+vr6nPVjnnIvCq0UOpKyFLy5b0hGayQjc7kfNRoNa0lmWbQqS8aiV3v1XhgpLEvm6WJMrtX0NMvcobo9aWFWunLrPPhdWhVcgrB3XVwauBRQ13tLex+SdmQe3ZRO+azr3XMvfaPRSAiiVFCLxaIVdtOg6YfRiym0c84g/ZGWAVhlmgoyf3MOarVamJ6extjYGKanpxGGoa1zs9lM0L3LyiPpWFpOND3rvtRKq3bZdvVB2n5a1zV5T7pn8v2mvddu5TAfT8tLg6XiyaVSyboEp/Hk6elp3HvvvZicnLTXMpkMNm/ejC1btmDLli044YQTnPVjnrlcztLowMAApqenMT09nVDS+Kd5Ms+17+YS7Hmy58keHh4rD8seFbndbifOqwPmMoJuQqhkDtISI62kdCXihKuDY1B4HR8fTwSKIsis9MRMhiddFOmySFdEWc9Go2EneB4xlMlkbPtpbaFiLC0VtPbEcWwDz7BNmgnSgiTbKVfL5Sfzd7k66rbqPtOQbkuufMgcuzE4tlXWMwzDOdfootktH1c7PBYf0qKhabkbqJxJYZFCslyMki7I8sxaWbYE09AFVi4Y5fP5rq7IsnxuHZBHf7CdrKsxBpOTk3YrgzHGPpvL5VAsFlGv1xOB66QliLTMOnMOkPtf5byihWHpPsw+lH8ako61IMp+ktZWSc8LseKyn2S+pGXtjpnNZq1SIN+DbE83Ad5jcXEkeXIQBDbwE+kTmMuT6dJ8zz33YMOGDRgeHk5YTycmJpznIdPdv1KpoFKpYPPmzXYr0p49ezAyMpJYiGY+5Mm5XA6lUgnr1q3Drl27LD3yeCDPkz1P9vDwWNlYVovtfBOqniTT0kohU0Myz26QFgYpYMdxjMnJSau4ynrwGTJJrlpKBqjbo+uYzWatAkzmIEP/6/TtdjuhIEsBVlpGyNBkQBnd3l5XUOdzM2J+vazKulZ709DNYkNh3+Vy6XH4kIpEr4LI4fS/FrbSLLzd9mSm5cv8uGeu0Wg4y2CaOO7s35MB44C5dOByIaQFB+gElKNbspwzmE+9XsfU1FRC4NNCMWlZBouSddKKbDe6dgnQ8reuZ6/v02XxYf9oenflqwV+CS3cexw5LAVPpgLp2nLgKou8jota9JS4//77MTk5OYdGmY4LQeQRo6OjqFaricUWyfNlH5RKJYyOjlpalIGdXG32PHm2Tp4ne3h4LDeWRLFNc5OZj4l2y6eXiR2AZWYudynJhMgQqcBKxkSBM5vNJoQ3CtrZbDbhfjQ1NWXrp61RBC0UURRhfHzcrn4WCgW7EqytzK1WC5OTkxgdHbV5yj6RSoiMuCjPnnMJxLJeacxbl6H732X96UXY1m6W7GsqABQ+XHVKc6d0Cc4evaPX/mI60shChRi9R/RQXE/1eJV0LscqhUqpiMoFLL3HTu5nA2aPAtELX0zPwHFjY2Oo1Wp2KwO3JDBP1qder2P//v146KGH5uynle0CYJVseXyP7CPWRdOa7hu9SKF/d1NsXb8lutE3LdGkZZf3i3T11Aqyx+JjOXny/v37EwqrhBzD3AJQrVYTvFcGc+vr60uMl3a7jWq1ioGBATQaDbu4tHv3buRyOWt9ZXodVI7WatKl58meJ3t4eKwuLJsrsrQeHK4AoydrLaxpq61OQ9dByYDI/HK5nNN9j9+5Z88YY6OhxnHnqBBGXOQh85pBkQnTFS+OY+vCzLbIlV0yVwDWEgQkV4XZNlmWFFg0s3P1ifyu264FGTI87aqm+/hQ0O153uOKPRcmFpqPR3doa4YLLqtuL32elkYqadpqnKZQSdqVdZLjXiqnLEfugyMqlQpGRkYwPT1t989x4UpacOR+2+npaRucint8JyYmMDU1laBZCqAU2vlb059rz2Kr1Up4c8zXz0GQ3MMvIRcjZB7STVu/e/0Ous3bsm91ebLvJB/gHCwt87qdvSpPHgvHUvFk6QUl08s03A/PccPgbQzsxG0CpAmpbG7cuBFr165Fs9nErl27sHfvXrvoBADlctlGZebCGl2fgyCw1loqsZ4nz8LzZA8Pj5WOZVNstRvfQiY6l+DH65qRUvAE3C54/f39CILAKqR0E6IlNggCewRAqVTCmjVrUC6XEUUR6vU6ANh9srT2kuHKoDJbtmxBLpezAtvBgwetWxWf1W2R0PtxaCWWK6+yT8jsXSuzWhmQ1/U1LaDKvgNmGakWWplG13s+yBVilwVBKgLydzcLhBeGe8N8q+wafFfaO6HX/qZFxCW8yfevy+M403Wn4uiyCuptBNxLx32xlUrFBo+L406kdHl0CRee5LFgQGe/7PT0tA0Ux/JpodR9KgVPjtt8Pm+fl663cq8v5w2t8EkBtluQJtl2l0UpTQmR6VxWF/lOupWbtv9Plp82bqTCdajeAR7zY6l4MgOoAe59oyeccALCMMSePXtQr9ft4nKz2USpVEIYhjhw4ABqtRo2b96MU089FVu3bkUURRgbG7OK8ODgICqVivV4qNVqGBkZwcjICIIgwAUXXIDBwUG7//0Xv/gFxsfHMT09jVKp5HmyqoPnyR4eHqsBS6LY6slZTmx6tVF+puWVBj3hc1LXgQ7kPbrVuATKIAgSZzDyd6PRsKuzFGK5942KsfwdBIFlkrL+FMApUGsGpK1DvE4GkybwatdLrehL6D6RDJl1cLlJut5D2jEsulwX09cCkK6Ly0ok9zazL7RCpIVnDzcOZwVdCsSLCfnO0xQf+U61a6O2CEolkAtezJN7/ni8SKFQQBRFNpCcLFvOCaRfuiMzOJW2IpGWXcGYqNzyutx/yABOsi4SMtCT7gv93aW4uN6ZtrbNZ6HVtM3yOOfqPLrtm81kMrYPpWJxKMqWRzqWkydzsYZnycp7pAPuwwVmg7VRQa1UKtaVOQxDTExMYHR01CqjVIbL5TLiOEaxWLSL1OSbYRjac2c5JrlXlhGbPU/2PNnDw2P1YVkstvMJVhQM54NmmGlWBR0VEOisrhYKBWuplQyDk3IURahUKjafZrNpj+WgmzGtPgMDA8jlcpiamsLk5KRlKPl8HkEQ2L2+8vzLVqtlXZ6lwM36yuusl14JdVlLyKTJzDUDYl+lCYm6H+WKLfvdtbIsP/V1DZ2/qzzXs66xw3dMVzXWj8I+FYM0tyiPJBYibOj3cDjQAp2+7hrL8r48XkaOAS0McqGJx3VwrsnlclizZg02btyI/v5+jI6Ool6vY3p62tIt9/cxCBWAhCLKffqEdkmkoEeF1aVEcvxTSKSwLYVpYK7FSPcRy5f7haXl2qXIyP6XgXbSXJSZVioIGt1oWfcRr7Nucl+0VPI9LS8ulpInh2FoTyuQ93h+PK2xrBfzGhwcRDabxZ49e+y9qakpe3QWz6cdGhrC0NAQTjzxRAwMDODBBx/Evffei0ajgTAM7VnT+/bts1ZgAKjVatbCywi/nicny/M82cPDY6VjWRRbyQz0ZH64wrFrVVFbSCSTlmH4WadyuYxWq4VqtZrIm2lpoeFzfX19yGQy6Ovrs5EYoyhCo9HA5OQkTjjhBGzatAmNRgPDw8OoVCqYnJxMCHCsF9sg9w5JAVauiErXpiDoHB1ijLFRmeWRDfNZXXS/pzFJvcrsEq5dwq0uW7bVZR1i3+r9dvPVn2NLMk1v6ekdC11B1wGZelFy9bjnNU2jWimULnYyvbYwSSGKyqOkE20x4FYF0m2pVML69esRBAGGh4ftXttCoYBt27bh+OOPx4EDB/DQQw/ZfbS6TdJqQ4FaK6asJwPj8Bggae1glOVcLpdqyZE0KZV5l0LBdPJT5sn80hYZXNYbrdRqWqYHi5zLpAKvwffGOUDTsndjXHwsJU8G5p6NCsB6SVQqFQRBJ34F+fLWrVsxNTWFAwcOJPLmPvWJiQlMT09benr4wx+OYrGIzZs3o1qtolQqIZ/PY3JyEjt37sRFF12Ec889F9PT0/jBD36AvXv34oEHHkjwWFlfz5M9T/bw8FgdWBYJQTPNhUxw3dJSuOIfJ2WuDHMC5qpptVq113mUAJlQtVqdcyQBf8dxJ0JyqVSywme1WkUmk8GaNWuwbds264pMobmvrw+NRgP5fN4KsozSqK1LYRiiWCxaxlgoFGyAqSAI7Fl9ZNbaehUEgQ1Mpa0yacKx7se0vtZ9DMw9xkgK1S4BKc3dSddFC7Gu+xJ0EXP98Z7H4kOOLS1k9YK08SbHmYuu9fYB3qPgxT9GM5VlyPrJs2MnJycxMTGBdruNwcFBbNy4EeVyGUBHQOvr68P69evR19dn6TeTyaBQKFjlUy6UZTIZ9Pf323sy+BMV2Fwuh4GBgYQLJOvFvYXckyj7XPZ3mlLpckOWfSuVftmv3Sys81l2XH/dFFLXoggjQEsaZh6elhcfS8mTAWB8fDwxdhgcat++ffZ6tVrF9PQ04jjG2NgY9u3bZ4M4EXQnbrfb6O/vx/HHH28Dt1EJ3rZtGy655BJLx/TCGhoawtjYGNatW2cXpUnTpFMXT16zZg2GhoYwODjoebLnyR4eHisMy2axTWOG3Vbx09K4VjK5gupydaEVx3WPltg0RkImxUA1PMaD+8IorHKPWBAEqNfr1vJTrVYtY5VCHwNQkTkFwaxlGEgKqOwLCuvZbDZxRAlXkOM4TuwH0lauNAGm2zWXlSeNAeoVadd7SmOibCOPWpCBfGi9Y8RXuc9Qnl/IvZBSQPFYPEhFrde+Xeg7IC2T1nQeWnlyWSD4jMtKzDYwDwqoep9Ys9lEpVLB/fffj2q1itHRUYyMjNjAcqwflVwuZPHoH3m2taRLtoELYPl8PuERItNwXz/v8TqQvgfSJYTqNuu+ls+y72Wfut6hVo61UE33Uy4yuCzKGvSqkbQs80uzQnksHEvJk+nCL8F5XrsnA53znqnAuvLmeC6XywjDENVqFY1GAxs2bLBp+vv7EcedoHAMMvWLX/wCu3btwoEDB7B3715bT2B2y4FckIrjGFNTU3YbgtxW4Hmy58keHh4rA0ui2GqGp60HaWnnAwUfLeBIZsKJVgtktM5qActlDXLVP5fLWTeocrlsfwOzblaFQgGlUgmVSgX33XcfwjC0AWDy+XzCZZCuzJVKxVpn5H49Mgh5Dh5dIGW0ZdkX7J9sNpsQJnq1prmEzzSlwnVflpVmYUoD3cW7KU16xVtaelzPSYbr4YZ+51Lw4m9571CFE61oymBshN7LJusjBTTteqdplmMpTUCXi1UDAwMoFot2jx/nCXpgHDx4EGNjY2g2m3ZuYdRWCq6lUglDQ0M4ePCgPd+yUqnYtjEvemJEUYTJyUl7TZ67zX6SSp68J9vNdqTRZDda1tZw3pMKpBaGeU9aw/W7Yd4UfLtZaPT7o2Ihj2LhX5q7tUdvWC6eTOVG8mTJz/RYA5DwuOA9WacwDO1i0sTEBHK5HLZs2ZI447Zer6PVamH9+vXYvHkzhoeH8dnPfha5XA4HDhxAo9HAmjVrEkfVkCdzgYoLUFNTU7Zcz5Pn1k9/ep7s4eGxlFhSiy0ZQdrKpNw/J5E2ic63aq8ZAJVJCq3aJYigMindf8lMKaQ1Gg0baKLRaGBsbMwGoeCZtTzXMpPJWLdIolAo2DyCIEi4IUoXSikcavcirgjTRYr1030ggzikCffsh25MUwu5vVh4dD9rYVq/Q21x40KAFnq7uWZRIOaquhSqvTA8P3oRZCmwuGiZAtBC8ud+Wrk/Lc3aqMubz1qkoReySBfGdNwfDx48aN2Ax8fH0Ww2E5ZcYPZMWulaTForl8uJfKkcyzL1GGw0Gmi1Wujr60sImrpt3Magz+bW7UqjZVffyX5P60uXS7PesysVcancsi8YQK9bHeWzHGNU/qVCfyiLKR5zsdQ8WWPt2rUIggAjIyOJs6L1GKRiWi6X7T50ei7QElupVLB371709/djfHwcd911FzZt2oQoirBv3z6EYYjBwUH09fVZz4hqtYoNGzYgDEMMDAxgbGwMu3btQl9fn+XJ9NbQFmXPkz1P9vDwWHlYMsVWWh60YNWLsJImbBUKBRvhVENaevhbK6nSisA68MgBugdKqxSPKuAKLvfYttttTE9PI4oiTExMYGJiAhs2bLDXBwcH7fc4jq1VlhaPyclJFAoFqxDXajXLfFlP1l26NHFvsHSFkhYs1+qtZETd+tylYEimJ5k6P9MYKe/J4xxYHwqzmjFrwYHvx7UaLVd++Y7kcQOyLL9CPBdpY8ElcLhoWY7NQ9k3xXOiGUDJhTSFLM1qLPfH6bbocU06nJqaQqvVshYg1o2fjJwMYI7llgr9/v377X6+crls999LF2P2F4U8ujwyQA2/S2ihVWK+xQSXxVTOg1pAZx/JOUDSdRRFKBaLiTNJtXulpmUqBTKgF/uCe5xdAjiVW3nuMdviafnQsRw8WfIqpudYlzyZHhwArCcFI5QDSZ6cz+fR19eH/fv3W+VzeHgYzWYTu3fvRj6fxz333IN7770Xj370o1GtVvHggw/ilFNOQa1Ww0MPPYRarWattdVqFQMDA5iamrKnGlAp4xwlI/16nux5soeHx8rBklps5aqwZJrdhGeZTgvM0nqiAz25IIVQXQ6f1RMvQcEwn88n9svREtHX14fBwUFs3rwZk5OTuOuuuyzDLhaLOP3005HJZOwB8FSG9+7da+sVBLOW21wuh/7+fkxPT2NqaiohhPKsPlqVKEBIZi/bpPcmauuRi+GlWcD0qqyLQWtGKpm5FNzlc2mrv/IdSWFLMkS2VeZJJsrVfRnN0TNRNxayar4QWmb6bq6oFJa0i96hQtaHtEzFTwvscuzQcnPiiSfi4Q9/OMbGxmxQmzAMcdppp+HpT3861q9fj7vvvhv33XcfDhw4gPHxcezfvx+1Wg3j4+MAYM/RzGazWLNmjaVl1odCOBW7MAxRr9cT/SUVRWA2CqzL5XM+y4/87hKk0ywusm9k3ZvNpq2361nXNhD+1oKxLEuXn8vlrGLBMUTXT5dC7tE7lpsnj4+Pz5kX6N4vAy/ymD0JY4wN2Dg+Pm4Vs0qlguHhYZxwwgk45ZRTcNppp2FiYgIf+chHEASdoJFDQ0O4/PLLAQCTk5PYvXs3Dhw4gHq9jm9+85uoVqs2P8mTt2zZgpGRERw8eDCxVSKNJ+tgcZ4ne57s4eFxZLHkFlsXE9VIcxHWoHBVLBa7litXOLW7nZywmVbuv+V1BodyBbeoVCoYGxtDoVDA+Pg48vk8Nm3ahD179iAMQ2zatAlnnXUW+vv77Zl7Bw8eRL1eR6FQwK5du+wKLwVWHiovXZZYf+02yNVjl+WK6clwtVVkvhXitDTSHUuWqxmpfA9xHFurFdNISFdGLfx2syZpQUsKV9yTSGbLIwe8MNw7XIqOpmXiUPqVVgBaRNLcGWW5LMs1duUiEKFdaF3KnDEdV+Tx8XEcPHjQuhOuW7fO0udZZ52F7du3AwAe85jH4Nd//dfxi1/8Ag888ADuvvtu7Nmzxx7llc1mEYYhyuUy+vv70Wg0ElZVBlSR9aDiSguuFlrlPjdXP8p3Iml/PhrX/ZlG75I2a7UagiBI7LGVdZZWWJ23S9Fm3i5rrHQFZxpPy4eHlciT+V7lcUBhGGJ6ejoxhqIowsDAANrttvWckNi/fz/uvvtubNiwAaOjoxgcHMS5556Lb3zjG8hkMnjc4x5n0/b39+O0006zv9euXYsvfOELGB0dteOuVquhr68vYYGUllEXT2aQKM+TPU/28PBYOixZrPU0YViuHPJ3ooLzuDWSKaS5I3YDn5FMSVt0WSd5JIgWupvNJkZHR61APDo6io0bN+Kss85CFEW477778Itf/AKTk5PWRRHoHAhfLBaxadMmux+XZZfLZXsGZhRFNkAN0Fk5LpVKCIIA09PTaLVadi+vVHC18CDbI6HvLbQfXUpI2v4/rhAvlIl1E8IkA5V1p/uTtvJQmfDoDtdYkAqO6x248pBCcbexJYUoXd6hwDV3aKVP5s/9Y2NjY9i9ezfuvvtujI6OYsuWLdi2bRtarRb+93//1waPAWDPzNy4cSPWrFmDUqmEXC5n9/NHUYShoSEAHXpn1GQKtUxrjMHExIS12OrjbtIUD91G2R65UKbvuxYreN/1zqXlW17XtCzpXL87LfR2o2fpRs5PfXQI6dnj0LASeTLr0Wg0Egso8vxX1oHB1+j9JDE1NYXbb78d99xzD37605/ijjvuwKMf/Wi8/OUvR6FQwGc+85nUOmzcuBGPe9zj7BFcdFHO5XI2uCNd8fP5PICOVXf9+vWIoshuN1qzZk3iLF7Pkz1P9vDwOPJYstkkjYG6oCfMtFVPWgvoMuTKR4MMO5PJIJ/PW6sD0W637YoiAGs15Zm30p1IC8XNZjOxWh3HMdatW4d8Po98Po+hoSFs2LAB69atw8TEBA4ePIj77rsPzWbTKqrZbBYDAwPIZDIYGRmxlqJSqYSpqSnLJHnkAOtAYYJ7k6TQLldM9UqrXlllvSXS9k32usrsEnxlebzustS58pUrw9LKo11KpQWA/UJGqlf4PWbRTYjiIk83YYaQCq2mYVcZDObiqks3WnbVBUgPhKItjLIsuhVyr28QBOjv70cURTbC8ejoqN1/CwBr1qzBOeecY4/rIqIowrp165DNZvHggw9axZZRVAHYZ/RxQFyskkKnVMrT+lvOm2lKZpqbqbyfNgZcQWXk+9VWLAk9b+q+d5UpeYB2P+Y87q09h4aVxpNLpRIGBgYwPDycWGihZxPde7l4tH///jl8Q44D7pmfnJy0i0vtdhtnnXUW1q1bl9ovW7duxdjYGDZu3IhKpWLPmZY8OZvNYtOmTdi1a5el5dHRURt7gzTMfbd6b6rnyZ4ne3h4HBks2XE/LsuDZqouy858K5VUKAmplLqe528yHhf0fh8yKLZBB9agMjw1NYV6vW6DPoVhiA0bNqBUKuGnP/0pgA7TBGaPMOjv70dfXx+2bt2KvXv32oiDMngVV7BZPsuUwjAD1PT19SEMO3v1XKu2CxEApQDqYqQui4z+rRm2zEMKLxqaQfdSbzmOpABBVye5Sizduz26Q7uRumg5zTrTzbLrEoz18VZpwZLk865xJr+7XI91vfRYp1VmYGDAHgVyyimn4ODBg/jGN76Bc845x7okExs2bMC2bduQy+Xw0EMPWffJSqVirbxBMBsoh+6MtVotETGYCvXg4CCy2Symp6fnHIMk66z7UPe17h8pdLoW6Vz56vfA+mtQwE17H/PRMusl6ZZ5sEwZRIpCsaflhWEl8mRGH3aBHklxHNvgiu12G1EU2bgXMt92u41isYhdu3ZhbGwMa9asQaPRQDabxaMf/Whs2rQJt956Ky688EJneSeddBLOP/983HrrrZicnLT7QBkhPQxDTExMdOXJw8PDlicHQeB5sufJHh4eS4AlUWwlA01zXZH3gbkujPJTQ07wcu+LTiODx3C/rBTOpPUTmLWmaCuMtIoyv0wmYwPBjI2NIZfLoVAooFgsYuPGjdiyZQsqlQomJiZQKBSs5cgYg/7+/oSVNYoiy4QZsZB15XPS6qTrn8vlrDLeS1AGCsNpq8DyPn+7hOU0dLMC8FPv33HBtZdPjw258KAtDNr9yTPRuXCNJ36Xq+0uGpbPd6PtNFdGWnuYhpGE01xaCTlupWDGMSL3mXZz/+M1YzrHeU1OTmL//v0olUrYsmULjjvuOJx55pmYnp7Gnj17UK1Wcdxxx9lzb3mGZblcRj6fR7Vatd4V0ouDi1RU5OX+NllnuiozCqsrUJLLmiL7Q/eDtgDJeUjmofPU0JYcma9+9zJojay3pHcXLfO7y1LIccj50tPywrDSeDItdgxIRvA50sDg4KCNOkzkcjkboZtnHgMdy+74+Diy2SzuuusurFmzBuvWrcPGjRuxffv2OQtTEqVSCZs3b7blkidzG0Ecx5iYmPA82fNkDw+PFYYjrthyVT1tVdi1YiiZroae5HVgFEY11sKva+XZVVdg7qqodGmk8sp0URShVCrZfTj1eh179+7Fvn37kM/nsXHjRpxyyik4/vjjMTY2hpGREQRBJzLj9PQ0RkdHsXv37kQE1Xw+b48cYflaMKfgT5CpNZtN5HI5y2x1eyQz5D3JkFzBJXQ6FxNlnXpZmU9bDea7l8KxLJ/3tECsy3IJZto6Ia3vHm5oAc0l5GqFIy2Nzk9DRw8Ngo5bvkvQcSnPmt75XY91Oc61tZJzFcdFs9nEQw89hJGREezcuROnnXYazj//fPzar/0a7rvvPnuESKPRwPj4OIaHhzExMYFcLodMJoNisYj+/n6Mjo5aD5FarZYog2VyTy4Aq+hSIeYcoGmLAjYhLbFamZT9Ia0n7If56FbPu5JWZT9rIZZzl6uM+axC8rvcpyjPtOU9WnI95sdK5Mnd6grMRhGmAiwjbo+PjyfmgUKhgM2bN9sF49HRUXz729/Gd77zHaxZswaPecxj8NznPtdZ3uTkJG6//XbcdtttNhpzNptFLpezVmBuU/A82fNkDw+PlYclUWxdk5ie9Fz35HXNVDVzJjgBp03UUhh0pZOWDbn/VltBAFiGF4ahZYB0MaY1ZufOnQiCjlvhunXrcPDgQUxPT6Ner9tD5avVqg2OsWbNGqvc1uv1xMH1ZCB0c6ZLFM+6pTWISrZsNzBrba5Wq05hIo0haaapV52lgCKFWNnPMn9XeVqg0u+om/AThu5z8NKsElwh9kx0Llx9zWsLoWXpojefgutSimW5LitA2nhIGytSqdWfUqHlmJB/lUrFuhiWy2Vs2rQJJ5xwAsbGxnDgwAGMjIzY75OTk+jv78e6deswNDRkrUQjIyN48MEHrfsk5whab+lqyajrVIKr1arTcpLJZBJHomjlQrZXC6haGdZ7/2Q5mpaZxjW/yvvacqvTu8ZNN2u6ixfMp3B5uLHSeHK5XEalUrHP6XSc2wuFAkZGRhL5ap7MY/dyuRxGR0dRKpVQrVZRr9fRbrdRqVTwn//5n4iiCJdeemminFarhb1792Lv3r3Yv3+/tRxv3LgRURRhcHAQtVoNExMTnid7nuzh4bFCccQVWzl5AckJU//1YlXlfZd7DjB7GHo3wVdCukPxfhAEqav/knn39fXZleRGo4FarZawMLGe1WrVBroYHR3FwYMHMTU1hampKbTbbWvxZdh99lulUrFBOLhS7bJMSQbH8/R0/clQ5PEivSgH0tKiLVy6X1zMUgrXvVpopBAuv3dbIdbjS7bbJdxLxtpNSD8WoRUM9qdWInSf63suOtVCjZ4fJBgZVT7brc4u5YlwufTJsmXEcykkMg1dD++991788Ic/xMaNG7F371488MADdm+8pH0qrtxXNzk5ienpaQCwbo3cK5rNZu2cQ3dFoEPLlUpljosjLZQ6UqyrnVKJJ93IvpLfXTTkouVu0G7hVIK1cCzvS4F5PmGZ+UjFjEKxdqn2cGOl8WR9brXmyXKh2TUGGQgym83aLT9xHGNsbAzDw8M2IB3pPJvNYv/+/ahUKiiVSojjGD/5yU+we/du7Ny5E/V6HZs2bUK5XLbn47L9k5OTdl7yPNnzZA8Pj5WHI6rYulbl9P1ujLTbp3xeQgZc0uldLnq8LqFXNSVoXSHjnZiYcLpaUdjK5XKYmprCHXfcYffcUoAtlUp2XxEDxARBYINkTExMoFar2TKlgCsZIhkDr0mhnGB9XWH106x0+tN1FJIWkhbCkNKEJdmeNKHXxfxcq8TShdG1QuwjqvaONAsOP9mvacpvGiTNyHQycJBGN8tyN2jrM8HAL3Ks6LKr1SruuecejIyM2DM0GVimUCjYaMpckGq1Wvb864MHD2JycjKx1UAqfOw3RkImvbrOlyQNaytQtzZrwVbOg2kKTprFRSrK+rr8zTKkmzIw6/7cjf6lBVk+77IyUmHxAvH8WIk8We6Lldclus0FmUwGpVIJhUIBALBz506USiUbiJHPZLNZlEollMtl7Nq1C+9///vtnlt6UG3cuBFTU1MYHx/Hvn37MDU1Zc+k9TzZ82QPD4+VjyVTbOU1/afvdbPMyLQuyD1kOs18Qm838IgBoLPCLI8labfb9ugglkN3wjDsnLcnozjSzZDXuSLMFWDu5SUz52+XZRmYVaKpFNMdmnXhnxSYZT6u7/Ia/9JWUyUD1QKvfsfaRZL114sJrnuSuUpmqQVzXTft/pTmSueRhFZUtELrUnLno+eF0GSaoqrLke/R5bqnn5Vux6QVpicdaXc4jrtGo4GDBw/aY0BI06QtGQiOAv3U1JQVhuM4tt+lgsg9eKRhCs6cD1h3l4Ir6+j6LvtT0hLb6bKuSAtMWr+7+pZtkpYceZ+Qiu98cM1zWvHytNwbjiaevGnTJhtxmGdPA51xXq1WsXbtWuu6HASdaOSTk5MIgsBuC6rVajbQ2759+7B+/XocPHgQ+/bts3TJZ6vVqufJ8DzZw8NjZeOIK7aSMaYJqdLFTCJNIO6FyepnuiHNHRmAPXcSgFVO6arIesiVUylYM7AU3Q5p4eEK8NTUFDZt2oRTTjnFMohWq4Xx8fHUM/rIxGSfUsCWAoRMK4VYuTeI9dSuiVqw0Uyv19VU+Z4kM9ZlpQnhLF9+ynpphq7HCO8xuq7LBcq1ouwxF3K8uWhZCzLdaNQlWBP6vXez7HWDHhdUZjn+tNsv65wmIPLsWVqY6D5JeiqXyzjppJNwxhlnoFgs2r307XYbO3futHvoXHRGIZieIBSCs9ms3WMr0wVBYI8ekcffaEVS9rfe8yo/5Xzh6j/mIe9Jt2bSsHxnsh66ray/zNsl9LveB9uvrbfsG0/L3bHaeXIQdPbkFotFBEGA8fFxuwdejzmeNyutvTylYHx83C5Q33///ahUKigWi9i9ezfOOeccXHbZZQCA6elpTE9P4yc/+Ql+9rOfeZ4Mz5M9PDxWNo6YYisn+flWh10Cl4R01+uVMfa6MkyBSLu+hWGINWvWIJPJoFKpJIJCuawQPN+OZ9rS6kL3RMkU5J4aWnm51ycMQ2zcuNEeBt9oNKz1luUyfzJQWpLiOLaRlLV7FxmWPCYhTaFwMVLdl2nMnX1IaGFDr0brvKXQzHtSYNVjJW28aMbsUrzShDePJNIU1W40nEZvWtBJSyfH40JoPm08MOqw3Ovmqrssm2lkPfV+QekCSzrfsGED1q9fj7Vr12LDhg34zne+g2984xsYGxuz++0obHN/YC6Xs7TMPYLlcjkRSZl1Y/0YaErXNc3anGYhlYJ0NyuttP640sq+Y3ky3Xy8wNX38lO2UddRKrm9CvnHGlYLT2aUcPIqei7kcjmcfPLJKBaL2LNnDyqVCur1esKrQdZnamrKuv339fVZ+pqenrbxLXj+O92Y4zjGyMgIwjDE+vXrMTg4iEajgU2bNiGXy2HXrl3WVVmW63my58keHh4rA0um2PbyxwnNxXTTyliMesrz0yQz6Ovrs/tqdIALaa2VQi/QYcTZbNauxmrGIA8jHxkZQa1Ww4knnmgZ6fr161EoFHDPPffYowXGx8cxNjY2p+0sg9Zk2Z/GdNyh9TVX33UTCNOEzjQG5HovUnBOUyJkOn1N5s2ytdUnzeVNCr1aaPPC8PzQioOLJplGnhWdZt1Jo+le3sNCaF7mxWjoVEz1kST6OdZRWnql8KzrWavVsHPnThvw6eSTT8bWrVsBAE984hNRKBRw9913W5fJgwcPWs8NYHYPoTEmITTzPj0+0vpfIk25lf2XNg+4oBVb3U+E9GLRwW10OS4rMtP1IsDL75wLPC13x2rhyWEYJiyemUwGjUYDmUwGD3vYw9But3HvvfdiYmJiznNAUqmr1+sAOgvHXDyWUc7JI4vFIvL5PCqVCu68804MDw/jwgsvxGmnnYYNGzYgk8ng0ksvxa233mqtuHv27MGuXbvm0JbnyR14nuzh4bEcOCKKLQVCGSTANWm7rs2X72LVTyqjctKlsMmgT7Sw6LJ19FEA1urC+5VKBcDsijMZBIPKMOri1NQU7r33Xhw4cACbNm1Cq9XC+vXrcdZZZ6HRaGBqagp33303arWaXfVl0Clahrk/l3kHQWCtvN1cR/U12Rdp3wkK+1KZ0fe7ldkLA02DZoZp7SHSBDTPROdHGi1LgUYLJrwv4bK0yXuud+AaM2lCmutZuvdTkOW+97RxLV0KWV+tVBJUzOim3Gg0sHPnTkxNTWHPnj2oVqt43OMeBwB47GMfi8c+9rHYtWsXvva1r+HnP/85xsbGrNeGMcYe+SNpmXv8KCzL/cFso7Qa8zPNLdnVr0HgDhQl29vLu2GfyO/SxZGfcpFAjiGZPm088J5unxyHHnOxmnhys9nExMSE/U034b6+PuzZswdTU1MYGxvriScPDAxgYGDA0tTevXthjEF/f7+12rbbbUxNTaG/vx+bNm3Czp07sXv3bnzxi1/Ej3/8YzziEY/AE57wBAwMDOCSSy4BAAwPD+MrX/kKxsbG7FFFnid7nuzh4bH8OGKKba8rw2S28+XX7Xcvz/R6zxhjrSWlUslG6JuvTMlQ5X4f1wSt9+xS2a1Wq5icnMSuXbtgjEE+n8eGDRvsPt18Po+HHnrIrkKzXK5wA8lIqSw3jZHKNLJvtAtUWj/pdndb9XXl78pTMtJe8tD5aeZKSMuOHoPzKQHHKqggybHTTXhJ6/uFCL8u4XShwrMU/Dg2pAtx2nyj6bzbflU9/uVWhdHRUavwVioVnH766TjuuOMAAFu3bsW5556LZrOJu+66C5OTk3PcGfv7+230ZPa/pgnZ12n00su4ptKsr+m9ePKe/Eyj0W579Fx10+MrDRyTafzE789zY6XzZAZPk0dmEcYYVCoVNJtNDA0NWZf/+crMZrOWfsbGxqzrPseIpOFSqYQwDDE6OmqjKTebTRw8eBAPPvggbrvtNjz2sY/Fxo0bAQBDQ0N48pOfjMHBQXz7299OeFN5nux5soeHx/LhiCm2+o/XXZ/A3PMtjwRck3NaWXR/ajQaqWnInHisTxiGCfcjliOVY0ZO5bEgRL1eRxiGmJqaQrFYxOjoqD1ShEpuuVy2x4oAs3uRuBJszGwQK7o6a5fAtD6RQrJWVjRz05jvfXVztUpbfZZludwoF8L8pMtjmjDsMQvZz2nuYjLdYvehS7l1geOAY8AljMu66QBHMh+Zn3Y9lm7LcRwnLLU6bRB0LDeTk5PYv38/isUims0mKpUKtm/fDqAzt/T19VnXRwCWvsNwdh9wq9WyQaV4Hq6spxY4XfsMpVWEabrRca+0zPQa0sXY1cdpZabNDdI6nRZhuVel+FjGSufJ3GfaTWEtl8uYmJjAwYMH5+XJhUIBg4ODNlZFtVpN0At/B0EnoNSWLVswMTGBffv22bxGRkasMpzJZHD33Xej3W7bRar169fjuOOOw+DgICYnJwF4nux5soeHx3LjiCi2aSvDgJuRHgrT1AKqa+JPs/y49nbJNJlMBuVyGc1m067+pzECtpcCrlwJZlkEg1Q0m017xI/MNwxDFItFDA4OIpvNYnh4GLt370a5XIYxnf1CFIaDoBOkgnt5pfWXLo1SKCRcq8FS4F+IYCv3z7jS9cLgXK5PvaZ1oVv9vTC8MLhoN42ugXQXZIk0gcU1VtIErDTIdFKxk8HhmE6XIe9JZVHuD5P36NIJzG5nkO3v7+/Htm3bcMopp6Ber+MnP/kJ7r77bkRRhImJCQwPD9vFrEwmg2KxaM+4PnjwoPXKyOfz9jgSaYnkXONqg0sglgqKntN037hospsQLbFQK4u24mprq+v9MK1rbHqB2I2VzpNdrv4yTalUwnHHHYfJyUkbcLGbcpbNZpHL5dBsNi3tMJ20CpdKJaxfvx6VSgUjIyNzeDLQofW+vj5UKhV885vfRKPRwCmnnIJsNouJiQmsX7/eujh7njwXnid7eHgsJRZdsZUChovh6DSHAy2QaUFYX5OrinLylG5BURShUCjAGJM4q1aWx+f6+voSwqHcr+tyd8rn8wA6e4a0lYiKal9fn71Ohjw2NmatRvrsPO694/4eCvKyTWy7y1oz34q8XoFlXhRAe7WuaaFHo5vgLOvgSiOVGC0YayUlTRiWAo3HXDfktH15QFIQmW8s6GM8DrVuWkjWrrOsE+mGwqy2/snjY2RbKFjyulTu2BcyOJzsl2w2i/7+fmzZsgVbtmxBrVbD3r17MTY2hn379tmtDnEcW3dJADbgTLVatcFystlsokzSsY7OzLa4lHvZLraJ7U+LcHy4tCDz1LTfTcBOG196XGl3Szn+PC0nsdp5MrfktFqthEVVlsfnHvawhyXog94PdK+WJxREUYTBwUHEcYy9e/cmFnw0T+axQgz6tnv3bvT39yOKIhw8eNDyXNbd82TPkz08PJYHR0yx1b97XX3sFb1acQgpyOpntVBMhqgtOxSUOanrMP1kUlwp5nV+GjMbKMZlLeEB8vl83kaDpCsVhWAK6GTcrVYrcfg726qDuEgBXaOXfpRMJi2PXve2pZWXxkhdLohpDI9MsVfGLv88E52F7htek/fSrh1JaKGI0FYK0jhpgTQgx5KkCX10hc5fCtBSwXTRRLPZxPT0NB544AHrXtzX14dsNosHH3wQlUrF1tcYY4//aTQadn8fQddF1xhNo0MpOMt3IgV2Ce1WKK/P915d9/SiXTf0Qnda4UkrXwrF8n0e63S92nlyu9120gbbkM/n7dah6elpS5eNRsMGbezr67PuwlrpGh4eTixCd+PJVJD37NmDkZERDAwM2AVwz5M9T/bw8Fh+LLpiK608aavD/O6aTHsRplwrwa40CwUnUhkIRk/aclWW7sQSYRhaZsrfhDxD07VKS4tNrVZDvV5HLpdDtVq1+eVyOWQyGVSr1cR+OkZ6ldYwKaxKBnooARm0QqCVAyko9BKsxiVwy3sSelU4jdHqZ13WhzRhjml9sIoOpALkWlEnuglDLoVqvnRp99OsiS7lTdZLW3M0uim7VIol5HiSe221dTIMQ1SrVQwPD2NwcBBhGCKbzaJarVp6LhQKCMPQBsXJZDJoNptoNBrWwkPXRS6SSeVd00GvgrCsoxTmtbLTLU/Xntdu0Mp4r8/J9rrmCwnXQoy8dywLyKudJzNqsQ4axfy42BvHsXP/bRiGVuEFOluCOA7Hx8cTR225ynbxZO6fzefziKLIHtHF8jxP9jzZw8NjebCoiq0WLlzChmaQLhfChaLX57qVxXrRZahQKNgAEzKNXHlNK1PuF6JQIQ9z12A+7XYb9XrdrhBXKhVMTk7aekiXJimUsxwKwkC6FcbVJ2n1cq2wyv7Q+7ak8i/r0U2Ycr0HF5M8FKFIu29qIVnW3e/Nm4s0pbYbDafhUBUL1/hw1cGloEnhTqch5AKRK1+XUK8FOZeC2Wg0MDo6igcffNAGfBseHsbIyIjNl2d1yjwzmUxCAKcFqBcBTyvZ3cA5JK3f9EKeLgdICtAyvUtQlW3U1w4VVM71mFgMnnK04GjgycViEXEcY926ddi/f/8ceqOFlL9dYBDIIJi18E5MTCROGJCYjyfT64JWYPJ3z5O7w/NkDw+PI41FV2zn24snP3uFtNz08ixXSV0WHdcELoUz7sfpNmmnTfQsmxMyoyVrFypdF1mnZrOJqamphJDLleOpqanENdZfHpVQKBRsPWTb0xil6zqfc+3jkZZoGS02rT16P6VecZfMXrtcdhOC53s/vY4xLeR5dKCtPK53e6j91gsd631iHCtpe8+48CN/s45yH6lrr6amAZcCzLlN721Ns3i2221MTEwgjmOsXbsW2WwWjUYDURSh1WrZLQmynRS6jekcOVYqlWxf6LHv2rem68RnXcqnpC/X8S6uuTyNHiW9yvZrC3MaehGQpSvjfBZb1/68tDYc7TgaeHK1WkUul0vwPVc7euHJxWIR/f39OHDgwGHz5Gazid27d3ue7Piu4Xmyh4fHUuGIKbb8PZ9gfCQgBSAtePK6q3zJKCgEt1ot5yQ7X935TDdrC/Pgyi5Xknn+HaM/sl8lAyNk8Jsw7BxzwOd0f8xn9XEJofK9SYU9bUXVJczKvtJHIen+cD3fzUI2H6IoSuyFdsG1wn0sQysI8rq87/p+pCAFOy28aQVRjpuF1q+b0K33ycmyWE4mk0EURSgWi8jlclaonZ6ehjGd7QQcZ9xHz99SAcnlciiVSolAc3Jfbi+07HJPlHQr55+0PpXfmYb15dzIvtGWIwrFOp+F0JicE1wLF670npZncbTw5Hq9jvHxcRSLRUxNTR0STyZtVioVz5M9T/bw8DhKsaiKrbbwuP4WG90mPs1EuzFPmR/Qmezz+bzzwHidNg1xHHedwMk0GCSqUChYRsoVYaATfbnRaNh9O1Jplvt3pPDQbQ9ON8g+kiu7ZH4y0IfecyifT1MQuL9JCryuY0sWi5lpxSQtjWeiSXQThtNoeSF9dyhppfeCroMW6FyWpDT67+by5rKUaJAu+JfP59Hf349SqYRsNosgCOx+/MHBQTSbTXvUD+vC6KntdhvtdhuZTCbRZtIdlWttSek2drlfl2l0P+i+ZDnSKuyydLOO7B8dMKcXdFNQ9b1elIH5eM2xRuNHE0+u1+sol8vzlt0N0lsi7XnPkz1P9vDwWL1YNMVWrgwvFsPU7ogLgV5hdO2lSyuHz+jIyPq5bvWW+bgQBIHdf8OVZF7L5/OJVVQKxQweJYNf8AgDBqAhg5LHD+h2pbVBHo0i0+p8+Lwr+I62AvA+PynYyHGStqeol4WDbpDutPMxxyMl5K1GaFrmNfnJ7wsROrTb7UIglTc5JuX40XlqRc5VrmuO0WVJgdBVb0nL0vKZz+fR19eHOI4xPT2NarWKer1uBeZisYhCoYB6vW7rUavVUKlUbHApAIl99awz6yk/ZR31MUe8p10NpQVYpmGbZBt1/7ssudqKtVDhvRvmc0fWdT3WcTTy5NHR0UPmyc1mc96Fas+TPU/28PBY3VhUxbbXPxfIEIC5ez5c1pde6qOh3esAzFld1e41WtjT+elrZAguAUALnvLA9mw2i3w+j0KhYN2e6ILFVWKea1ksFm0wDCKTyaBUKiGOY1QqlTlBaXS9dR+xPozOKiNDp/Up665dJLutHjMfXS+5r1m6MEqh+1BXbiloSOjx6JnoLLSA022PHpCkgzQ6dj0jlalufa+VaZbD37SGaAHZpdTOJ0imleuyirJPSJfsr0KhgHK5jIGBAfT19VlLLAPBBUGAQqGAUqmE/v7+OUJkNpu152vyqC9acqVym+ZiyLO4M5kM6vW6FbK1BVZajLXSKK3DUqll2VJ5BeC07korlZyPFmrVle+Cbe7Fanus42jkyTJYWC88meOaW4I8T/Y82cPD4+jGoim2WgDuZVKSE2Svq64UlHQABBe0QKxXhKXwvhBoJi/Lmm/FkunIsNhvZCJkwJpxUCCs1+sJF2kGaclkMqjVajY6IwVuuQ8uLeAMhfEwnI3eLFdyXf2plR0t6Oq0unzmrYVU9oVMr61J8nM+yLGoFy10u45lJir7R++t7RUuCyffr1yll7/T8gDQlc5de/Z6tSZ1o9k0i2A3pTibzSKbzdo2UZCenp5GvV63Sq/sHwakyefz9vguujXmcjlMTU2hUqnY43+y2SyiKHK6YcuFtCiKUC6XEUURarWadZeUyoOuv2v+lVYm13uSZVLYlgqspDu6ccozeSX9uxTmNMFfjps0q5Us/1AF76MBnid330Kg6+R5sufJHh4eqx+LarF1/XZNTGmTlctC6pos01Zd56uPfE4z1UN1kXTV3QW9CkkmKgXSKIpQr9fRarVQLBYTwiWZWqvVwtTUFAAkhM5MJoN2u23PvCVzzuVyVjiWfUsBmWlo3ZHPS0GTz8oy9eqq7Iu0tsv8JBOXAhLrGkWR7Ru9L2qh72m+9Mf66rDLEtmrMJyWnxR2pVB4ODQm66fr6/qun52vfOke2S2tVtwosOZyOTtux8fH0Wg0sG7dOvtcNpu11lcGhiK9MT9GgJX1pUsjBUvSobawFItFe9Z1tVq1bWHbSG9SWGV7pOW4m1KYNiaYh0vQDcPQ7lWs1WpzXELTykqbS9LgGrP6PR4rCq/nyd33x3ue7Hmyh4fH0YdFUWyjKLJWCzID6RIjhSgdMZC/9XUp6Mn9LszbteonJ0KWx2va7U5+lwJemquOyx2z23c9IUtrDgPMMDogV4mz2Szq9bptc7VaRTabtUIzg8pks1nLgCQjpNWI5eXz+YQblF5pZj8VCgV7zJGEXB2W+brapYVZueorBWvZt9LSkwb2F49LkSvX+pN9RLdL+Y6la5ZLMHONp2MRHF8U8iQtk/70nspe6ZhWDD0/pAkwskx5TT7Ha8DsHjmXAD0fLety+Zz+lH90Fczn89ZKS7orl8sYHx+3ZU1PT9s+5RjN5/PIZrMJ60c2m0WpVEK5XEY2mwUA5HI59PX1od1uY3x83Aq6HKukIz7LQFSlUsnmq12F0+YoSSfA3P28Wlgm/XWzCvM621+tVq1yK/PgJ+eGtIjy80VV7UUgPtrp3PNkz5M9T/bw8DgWsSiKLQOhkNm5mKhmjK50wFwmm5ZWMkIJzSjln2SYeoIl0piofm6hDFTeo0BM5hBFEUqlEorFIsKw43okV2n5DC1gtOhod8EoitDX12eFe56hSyFRCrcUmiXjlCvWABKMWrZbr7ITmvl0Y46S+bncn7QlqlQq2cA6UgDWzLTZbNrxwj6ka5h+Lwu1Bh0LYEAj6ZaXJvgCs/TKQEP81MKxpmOtMGthDEDiuhyD8nn5zrSAzTyA+QXCNLp25UVQUad1pVAoIJvNYv369RgcHEQURZicnLTCH4XcOI6Ry+WQy+WsCyPHPpWRbDZrz7/NZrNWUdbHhkjIADZyTyDv6flQ0rJsJ/tKvg+XpYiQQrlUtAk5V2WzWQwMDKBSqWBycjIhSGvFlgoG9wjTrVrXs5tQfKwKxJ4ne57M9BKeJ3t4eBztOGzFlu5vxWIxwRz05C4FXD7nEm55T6+myue1kAYkrQQyL/mpy9IugN2QZsVJ+54mDAOYs49HMrIoitBqtaygXKvVEgxD9qlUPIrForXUUKjVK+RRFCWE0yAIbN68xj7hp2yDXhnWK6q6rfq6S3AmpOAs07AdDOQRBIFVFORzXB1mf+rxRQYrV401jnVhOJPJzKFlTcdSie0mKOtrQJLm9SeQHB9pwjCvSbqRNK7Tauix3E1xnS8vWQ8qrewb0ioDywwODmJ6ejphvaAgq5XjgYEBNBoNa7mRfcj3wDJku6kQyjpLC43uQ1ebpRVF9610ZSakME9Q4CctSUGWZ/syqBbP93VZexhBWtM0AKsUuBYuXO1y4Wildc+TPU/2PNnDw+NYxWErtrlcDsVi0e6fkoKInLyllYfXXIxNC8WScbquzyeM6vR6FZrPaAYhJ3FXGa7vEmnWICmYyvtkXEBntb1QKFi3J71qSkuGjMRKgZnlSgFR11MKm/zNz7S2aotaGsPUTNO1AqvLca0qy3RkkOwXfaB8Wp2koKL7o5u151gD+4BKWKFQSAjEmma0YqvpuZtiq4VieV/TIK9J2tX5S2Fdp2fbXLTcq+CsFV89Z1CJo9BJC6sxBrlczroVj4+P///tnety27iyRpvUJN6TSva8/3vtl0iduVQuU5YdWTw/XE1/agO8iJQEUGtVpWKRIAGCbPTXAAja77//fiL+fOEZD/S8jg6Hgz0+Pp4ErDp6ErfraImiwtnLrQFIqk5ccKZsOZ47bovHebk0nb9f/OnTJ/v8+bM9PT31o7Z+TV4GDwa8XP486rXqyqpDNj036K0ZfDI+2X/jkwHg3lgU2DZN00+n8/dTXIBEQatOMiUWU84o1ajnypH6nXKg0Xl6I6o9pCoc45L0Y/ixsSxaBp+OY/a2mEzXdf3nAh4eHuzbt2/94i9en9477NMY27btV110h+AjR+409vt9f56UA4mCON4LvZ7UvdD9Kr51m9Z13J5aREbTadBwPB7fLcih16Hn9fQuhD9+/NjXsz8H/gkFz2uo53jr+HOnYjgnXONIT2qERYWQBysqfvz9Mb3fni6eIyeUYzni86eB7+FwOPku9Zx2RUdJ1I71GW3btp+26N+rbZrXqbdfv361P//807qu69tJs9cA5I8//uhXMf7nn3/6Eczj8WifPn3qg+T9fm8/fvywHz9+vLMhr8840hPLrfdKr8//V3Hv59RjU6NIWu9uX7Gd8eN9VMenYrZte3JfUiI33oe2fX1PUZ8r7Sxp27bPJ3eP5zwDtYFPfg8+GZ8MAPfDosDWp+YoqVGX6Kg8nYtds1MnkxJQPqUlNrJ+rlTjr8LLzN4t1qDlVWeg37w7Ho/ZRUxS5IRjFA7ekPuCM8fjsXd2j4+PvbBUoab17qsqqpPXESDvQfZPhvg16/W7A42COCd89Rx6v7TnODpNJ54r5QAVPVYDn93u9ft3z8/PJ+eIjjSOHrm48+mfXv9ejyqczN5Pw9o6bsvxXjte92ZvCxHpfff9OtVObVvt2I/1PDQIic+f3j8/RqcB+m8NdM3epsPqc3E4HE7El+cR0X0aHOo1R2Hu4s7z+/79ux0OB/v27VsfdLqA9tVEd7vXBWv2+7217eu0XM/r169f9v37d/vy5Us/Jffnz5/277//nox26P2J37qNAa1eRzxeR+j9fKlRnBigeDvl++MzozblefrnS3zVV6+PVGCbskev84eHh/6331vtiNFPk+h9jfd5S+CT34NPxicDwP2wOLD1nktHnY+ZnTRMjvZe+t854agOJy44ENP6ex9mdiLwnOhoVSCmvufm1zPVgaby0fKpg1HR7AGB14PXqV6XO9Wu604EhQYK7mz1e5HPz8/29PSUvC49Z2qkR++XH+Pn8bS6X9OkiOfV86kTjj3Jjq9AqU40J4ajI/VnzXvVVQC4IPZn1cWdBlBRtG8NFRgRvxca0Pp2s7fnQO3I61NH/bzOXTi6XTuaRsuTGqHRd/68/GZvAjjeey/fnPuYEr9+bX5Ov4a2ff28z99//90v9uLvzsap12630V73+32fxoW0jlA9Pj72I0hRfOa+JZsT9V4P8X5pW5USso7astqft9FjbebLy0sf1PuCWFEUR3vWf97WeydAfAdUbTkGC1u2ZXxyGnxyGnwyAGyNswPbtm37z1w43nDrVJY4UhD/1h72OMIQG7uYVtO58/QGWD+K7j3Mnt4bShWIsTF25jScuV7V6AxS9eC4MImoQFOnpuXzT2C44PP36XyBBr+elLOJvcNax7491ZubGg3QHmAvZ+panOgEU/Xizu7Dhw+23+/f9XiPieFY5jg1T4Mzd6a5f1tyqG4b0ZZdYKVWr4z3R+swNU1Zj9f3+OLCUf7c+RRKDxD9+VXb9Xz0edEy++8l9ykV1OaI12Fm/aeAooA2e+sE8Do4HA4n7abb8l9//dWLP7O3wF2D0vi8+3lS90OvI9pFtFs9Ltpoqk5iJ0JMo3/71EW35dgepERwyr7dlvX58brzTpVovy6QowivHXzye/DJp9eFTwaArbMosNXPV3gPm4/GmL3vjdVjdb82Zr4/Olb9HReL0W9XukD0xjDFWG9vqiHPNe6R2Cs8h1TDrNc51vPq+5+fn08+P+CiIiWE/bd/U1J7pDV/LaM6peiEohjOObGcE01du/8dBY/2Lsd8hn5rUKC98WZvTlR7h3OONIqvWnFBbGb96ILbceo5Tt1zJ7eQk2/X0bWUjesKrr6w0K9fv7K2pPcxh58/J36miNwoFjWojvagz0QMvlW8D+XpgbxPYzR7a7eenp5ORrSigFRb1sA2Xqe2D3F/PCYlvFVoRxsfsmUz66cf63u5erzWoYp8tedUB4YGIKmRnrF/NdsyPjkNPhmfDAD3w9mBrX+ewh2o9iimBGGup1QdaMppxjT+LpsKL3fevvCACk5tSLVsqfLEkYghAZpjyHGmelKjIJ5y3FjvrJ4rJx5TPaqpaYkRFZGpuoz1qfkNMSQQYu9xFLkxjzFnGgWxOkIXD7r6as55bqXX2EdIu647eb/JbNx+PY2KWBW5Zm8r/Xq6OBVZhZuPAvi3S3O2PLVsmnZMPOeOy5071Xbkfg+VTwV6qn08Hl/f9fPRaLVZM0s+6/5PA4xUvilbTrWTaid6bak6iPcplW8sb+7YMXGsQW9K4Ps3SbVeokAesumawCenwSfjkwHgfjgrsG2apn8vwhulpmmSzkrJOdTYO5z7FIiLYv3ciPfkadmcVG9j7HEcQnsM47YpzBHDOYetwnJKPv537jMYUQzrfq3HnKDXNEPpcumHyAUHY0LX/0+l0+P1GnOCWEWNP985x5nbXhNuy7oIjI6sqI3qMfq/EwNXt7fj8TgolHWbT92LeY3Zciyj5jk00jPUXsQyjG1TcRmFZo5UkOjH58oYbdm36/OsQVzufsVndayDINpHqmyp8+q+scBVt8VzzRHEuiiRPntd100WyHParluDT54GPhmfDADb5qzA1t85SQmuIWJjr85Uxa2O+kQhnBLOZul33HKkRHr8e+rxZmkhN8fZat5Lexdz4jQlIHW72en0v6l1McTYtej+uWI4tT23L9czrj26uedXRyHiyE/OodZEXDRK689RgaykbCcniHNTlFNTHv18qVGmKWVIkTp+KDiNaebYp6adEjjHY6MN5mw5JzL9/3jPprRb5xKD7FweU8qd2pcSxNGG4whNqr516uyYLdcU2OKT8clTwCcDwNY5K7D98OFD/33FOeTSa0M1Nspjln6Pz8/jnOMoo+MbShvF65rORxnqMV5yriHHNJbPUJoxxzhWptxxY8J3aF/KgaYc8BTcmcbPyejfNeHTF83y9jlne5yS6IJYpx+bvbfp3FS7nH1Hxmx3bbscIjWqNCW9M/cZygWLvs9svTYjkivrOXY9RRD775QInns9astRHHddV1Vgi0/GJ+fS4JPr8skAsIzZga2/tzS393OIKExib7Dnq2J57N2hlANITdGZ4pCWTnWac+yQ0HPiIhLR0U4VoFPyWkqqLHHbUN5DDjLuzznWsVGcc4lTHGtzomrLc4KDMaItu9A0GxfAY6L4EoI4cgkhnKvbKW3LmH2nnnv9WwMSs/wUyxRTn+ehMl5CFK9pzzr6o7awpo+7JPjkYfDJ6TyGtuGTAaBWZge2PnVxzcYoEhtMXXghNcrj253ce0NK7j2UJU5wibCYM+1pqKGe0yObE59r39vc+ZaM9sRriE5zSv5rosKvBrquO5mGfKk60pEvH/WZasM5+11j5Ccy1+6nph+rV30HNpd27kjp0CjLmswJuGOac+z6UsRz6yhlDeCT8+CTh/OL4JMBoHZmBbZN02S/6XYJvPGLC1Goc0z97cxxpnr+FGuLnHMa2yVCNedYht7vuQRj15By8LcQu1vHbXlIEF9STE2x4TH7XfpeXuo8Oa4R5IzV9xqjKEuuY+pzMiaOx+z4WqI3xRr1dE3wyeuBT06DTwaAmpjlDX3KkzqUazdkqd51/Vsdpo6GaNq5IztD6UsVQGPTjXKjKSlne+41zgmYUk5z7Dy3ptR7P4XdbmcfP340s9vUb04IRfuN23J1ngtyU7+ncque/jlieWwk5ZIj8WPbhmz61oyVp7Ty5sAnv6fUdhmffHlKvfcAcD1mBba73W50oZlrMyaeciM5OfEbHe/Q+c7Zf0mG7knOMcX6u6YQrp2ar2m3252shryUtepijg3r75xgzp3nXC4V7J4z8pNr+24xilKCLazd9pZwTWPgk/HJlyhbrWzxmgBgHpMDW5/y5IskrMGtBNdUR7dEHF/bmZ7jQOecY6vcYw9v0zQni82Uft/njvydK4KnPAtxFHkNzh09rGUU5Vrc2/Xjk5cfe0nwyedxjz4ZANZjcmCrU55qb2ynlr/264Rx7vEet23bf/OyxutfexppiUKqxvsyRon1PETp9wCfDFuEewwAS5gc2KY+AF8yNI7boZZnrhbGbPnebOfervdWlFLPW2lP8MlwK2p55gDg/pgV2J7zzUWApdTyTNVSTrflWsoLsCZTp7aXbh/4ZLgVtTxTtZQTANZjdmBLQwG35pxncM4xW1kxNcdUQXwtaq9PGGbpir1zj1lqv7V8ugSfDKWATwaAUpgc2K65QMXa0KiVx5rvTK3h1NYSu0vTlEDKlmspO1yPtZ75a6VZ+xyl2wQ+GeaATwaAe2BxYEvDcV0u+VmVufls5RzXcLAl4bZcW7lr45L1W4ptXSufW7QDpdsHPrkM8Mnrn+PefDIArMesz/3ce2Ox9PrXnHozJc09naP0/SXh0xdrWACkhHo9twxbCiyvlc+t24qaRmzxyfjkks9R+n4A2CaTA9tSWdsxzUlbi4MpKZ8Sph+tJXzP2V8Cc4Lakq7nErZeil3UVpZb2+Gl93ddV9SzXxP45G2VtfQybMEnA8B6TA5s12wctiYESznHtfKpIY9r1VXNLLm+mp7XkvJZK00p+ZSQx1p1sST9LcAnl3+Oa+VTQx74ZAC4BpMD2+PxmN1XS4N3b/nUco4tis6SeXl5saZp+lHbUp6TkvJZK01N+VxbeK5l92tOqa+pbcEn15dPLefAJwNArUwObF9eXvq/S2h4r3WOkspSUlnnpIOy8MB2iK3ZzjXT1JbPnHS35NrvhJdeJ/jk25elpLLOSQcAsFUmB7aHw2Fw/9acxBbLAmD2JohTgUJtz/TW7BQ7Xod7qEd8cv1lAQCAdTlrxNasLOdQYo8oTg1K5XA49EHtlJVVS7LBW4hObHka1NN1wSdfJh0AANTLrMC2JKc3Nc2S9ABbZMpUZLNt2nI8roZPHq0F7d+2wCcDAACcstpUZDN6TgFq4Jrv2JZGqYFsjXUJtwWfDAAAcMoqqyIDQD24LZca5N0ChD3UBj4ZAADglMmBLQBsg67r+ndrSwpuCS4BAAAA4FwIbAFgEgSeAAAAAFAqBLYAQNAKAAAAAFXT3roAAAAAAAAAAEsgsAUAAAAAAICqIbAFAAAAAACAqiGwBQAAAAAAgKohsAUAAAAAAICqIbAFAAAAAACAqiGwBQAAAAAAgKohsAUAAAAAAICqIbAFAAAAAACAqiGwBQAAAAAAgKohsAUAAAAAAICqIbAFAAAAAACAqiGwBQAAAAAAgKohsAUAAAAAAICqIbAFAGua5tZFAAAAAAA4m99uXQAAKAOCW7h3uq67dREAAADgTAhsAe4UAlmAU5qmIbgFgOsS2xyaIICzIbAFuDOapiGoBQAAKIFjZ+3BrDmYtU1jhnsGeKMza1/M2pdpPT4EtgB3iI5KEeQCvAe7AIBr0Oyf7OHb0Y6/tXb8QLsDENk9d/bxx3FSWgJbgDuDqZYAebquI6gFgKtx/Pp/9t//PdjnL/+x7jfWdAVQuqax5uVou59Pk9I3HSoXAAAAAAAAKoauIQAAAAAAAKgaAlsAAAAAAACoGgJbAAAAAAAAqBoCWwAAAAAAAKgaAlsAAAAAAACoGgJbAAAAAAAAqBoCWwAAAAAAAKgaAlsAAAAAAACoGgJbAAAAAAAAqJr/B/zlPopcdhKpAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1200x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[44], line 5\u001b[0m\n\u001b[0;32m      3\u001b[0m rotation \u001b[38;5;241m=\u001b[39m random\u001b[38;5;241m.\u001b[39muniform(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m40\u001b[39m,\u001b[38;5;241m40\u001b[39m)\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# Create raw DRR\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m drr_raw \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_drr\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvolumes\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtargets\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbone_attenuation_multiplier\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5.0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      9\u001b[0m \u001b[43m    \u001b[49m\u001b[43msdd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1020\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     10\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m256\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     11\u001b[0m \u001b[43m    \u001b[49m\u001b[43mwidth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m256\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     12\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrotations\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[43mrotation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     13\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtranslations\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0.0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m850.0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0.0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     14\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmask_to_channels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     15\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcpu\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     16\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     17\u001b[0m drr_body \u001b[38;5;241m=\u001b[39m drr_raw[:,\u001b[38;5;241m0\u001b[39m,:,:]\n\u001b[0;32m     18\u001b[0m drr_vessels \u001b[38;5;241m=\u001b[39m drr_raw[:,\u001b[38;5;241m1\u001b[39m,:,:]\n",
-      "File \u001b[1;32mc:\\Users\\Sven\\Documents\\GitHub\\5ARIP10-ITP-T3G3\\notebooks\\..\\src\\drr.py:100\u001b[0m, in \u001b[0;36mcreate_drr\u001b[1;34m(volume, segmentation, bone_attenuation_multiplier, sdd, height, width, delx, dely, x0, y0, p_subsample, reshape, reverse_x_axis, patch_size, renderer, rotations, rotations_degrees, translations, mask_to_channels, device)\u001b[0m\n\u001b[0;32m     97\u001b[0m pose2 \u001b[38;5;241m=\u001b[39m convert(rotations, zero, parameterization\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meuler_angles\u001b[39m\u001b[38;5;124m\"\u001b[39m, convention\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mZXY\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     98\u001b[0m pose \u001b[38;5;241m=\u001b[39m pose1\u001b[38;5;241m.\u001b[39mcompose(pose2)\n\u001b[1;32m--> 100\u001b[0m img \u001b[38;5;241m=\u001b[39m \u001b[43mdrr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpose\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask_to_channels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask_to_channels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    101\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m img\n",
-      "File \u001b[1;32mc:\\Users\\Sven\\anaconda3\\envs\\torch\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\Sven\\anaconda3\\envs\\torch\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\users\\sven\\diffdrr\\diffdrr\\drr.py:130\u001b[0m, in \u001b[0;36mforward\u001b[1;34m(self, parameterization, convention, mask_to_channels, *args, **kwargs)\u001b[0m\n\u001b[0;32m    128\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmask\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmask \u001b[38;5;28;01mif\u001b[39;00m mask_to_channels \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 130\u001b[0m     img \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrenderer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    131\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdensity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    132\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43morigin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    133\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mspacing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    134\u001b[0m \u001b[43m        \u001b[49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    135\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    136\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    137\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    138\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    139\u001b[0m     n_points \u001b[38;5;241m=\u001b[39m target\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_patches\n",
-      "File \u001b[1;32mc:\\Users\\Sven\\anaconda3\\envs\\torch\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\Sven\\anaconda3\\envs\\torch\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\users\\sven\\diffdrr\\diffdrr\\renderers.py:30\u001b[0m, in \u001b[0;36mSiddon.forward\u001b[1;34m(self, volume, origin, spacing, source, target, mask)\u001b[0m\n\u001b[0;32m     28\u001b[0m alphas \u001b[38;5;241m=\u001b[39m _get_alphas(source, target, origin, spacing, dims, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39meps)\n\u001b[0;32m     29\u001b[0m alphamid \u001b[38;5;241m=\u001b[39m (alphas[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, \u001b[38;5;241m0\u001b[39m:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m+\u001b[39m alphas[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, \u001b[38;5;241m1\u001b[39m:]) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m---> 30\u001b[0m voxels, idxs \u001b[38;5;241m=\u001b[39m \u001b[43m_get_voxel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m     31\u001b[0m \u001b[43m    \u001b[49m\u001b[43malphamid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvolume\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morigin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mspacing\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmaxidx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meps\u001b[49m\n\u001b[0;32m     32\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     34\u001b[0m \u001b[38;5;66;03m# Step length for alphas out of range will be nan\u001b[39;00m\n\u001b[0;32m     35\u001b[0m \u001b[38;5;66;03m# These nans cancel out voxels convereted to 0 index\u001b[39;00m\n\u001b[0;32m     36\u001b[0m step_length \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mdiff(alphas, dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
-      "File \u001b[1;32mc:\\users\\sven\\diffdrr\\diffdrr\\renderers.py:112\u001b[0m, in \u001b[0;36m_get_voxel\u001b[1;34m(alpha, source, target, volume, origin, spacing, dims, maxidx, eps)\u001b[0m\n\u001b[0;32m    111\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_voxel\u001b[39m(alpha, source, target, volume, origin, spacing, dims, maxidx, eps):\n\u001b[1;32m--> 112\u001b[0m     idxs \u001b[38;5;241m=\u001b[39m \u001b[43m_get_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morigin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mspacing\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmaxidx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meps\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    113\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mtake(volume, idxs), idxs\n",
-      "File \u001b[1;32mc:\\users\\sven\\diffdrr\\diffdrr\\renderers.py:120\u001b[0m, in \u001b[0;36m_get_index\u001b[1;34m(alpha, source, target, origin, spacing, dims, maxidx, eps)\u001b[0m\n\u001b[0;32m    118\u001b[0m idxs \u001b[38;5;241m=\u001b[39m source\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m+\u001b[39m alpha\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m sdd\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m    119\u001b[0m idxs \u001b[38;5;241m=\u001b[39m (idxs \u001b[38;5;241m-\u001b[39m origin) \u001b[38;5;241m/\u001b[39m spacing\n\u001b[1;32m--> 120\u001b[0m idxs \u001b[38;5;241m=\u001b[39m \u001b[43midxs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloor\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    121\u001b[0m \u001b[38;5;66;03m# Conversion to long makes nan->-inf, so temporarily replace them with 0\u001b[39;00m\n\u001b[0;32m    122\u001b[0m \u001b[38;5;66;03m# This is cancelled out later by multiplication by nan step_length\u001b[39;00m\n\u001b[0;32m    123\u001b[0m idxs \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    124\u001b[0m     idxs[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, \u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m*\u001b[39m (dims[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m*\u001b[39m (dims[\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m    125\u001b[0m     \u001b[38;5;241m+\u001b[39m idxs[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, \u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m*\u001b[39m (dims[\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m    126\u001b[0m     \u001b[38;5;241m+\u001b[39m idxs[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, \u001b[38;5;241m2\u001b[39m]\n\u001b[0;32m    127\u001b[0m )\u001b[38;5;241m.\u001b[39mlong()\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "\n",
-    "for i in range(1000):\n",
-    "\n",
-    "    rotation = random.uniform(-40,40)\n",
-    "    # Create raw DRR\n",
-    "    drr_raw = create_drr(\n",
-    "        volumes[0],\n",
-    "        targets[0],\n",
-    "        bone_attenuation_multiplier=5.0,\n",
-    "        sdd=1020,\n",
-    "        height=256,\n",
-    "        width=256,\n",
-    "        rotations=torch.tensor([[rotation, 0.0, 0.0]]),\n",
-    "        translations=torch.tensor([[0.0, 850.0, 0.0]]),\n",
-    "        mask_to_channels=True,\n",
-    "        device=\"cpu\",\n",
-    "        )\n",
-    "    drr_body = drr_raw[:,0,:,:]\n",
-    "    drr_vessels = drr_raw[:,1,:,:]\n",
-    "\n",
-    "    ef = random.uniform(0.3,0.5)\n",
-    "    drr_combined_low_enhancement = (drr_body + ef*drr_vessels).unsqueeze(0)\n",
-    "    #drr_combined_target = (drr_body + tf*drr_vessels).unsqueeze(0)\n",
-    "\n",
-    "\n",
-    "\n",
-    "    prediction, latent_representation = model(targets.to(device), drr_combined_low_enhancement.to(device))\n",
-    "\n",
-    "    #print(f\"Prediction shape: {prediction.shape}\")\n",
-    "        \n",
-    "    loss = criterion(prediction, drr_vessels.to(device))\n",
-    "\n",
-    "    print(f\"Loss: {loss.item()}\")\n",
-    "    # Create a figure and a set of subplots\n",
-    "    fig, axs = plt.subplots(1, 4, figsize=(12, 4))\n",
-    "\n",
-    "    # Plot each image in a subplot\n",
-    "    axs[0].imshow(drr_combined_low_enhancement.squeeze(), cmap='gray', vmax = 25)\n",
-    "    axs[0].set_title('DRR')\n",
-    "\n",
-    "    axs[2].imshow((drr_vessels+drr_body).squeeze(), cmap='gray', vmax = 25)\n",
-    "    axs[2].set_title('enhanced target')\n",
-    "\n",
-    "    axs[1].imshow((prediction+drr_body.to(device)).cpu().detach().numpy().squeeze(), cmap='gray', vmax = 25)\n",
-    "    axs[1].set_title('AI Enhanced')\n",
-    "\n",
-    "    axs[3].imshow(latent_representation.cpu().detach().numpy().squeeze())\n",
-    "    axs[3].set_title('Latent Representation')\n",
-    "\n",
-    "    # Hide the axes labels\n",
-    "    for ax in axs:\n",
-    "        ax.axis('off')\n",
-    "\n",
-    "    plt.show()\n",
-    "    loss.backward()\n",
-    "    optimizer.step()\n",
-    "    optimizer.zero_grad()\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "torch.save(model.state_dict(), \"TACEnet_vessel_enhancement_2_0.pth\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Testing the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Sample a batch of data from the dataloader\n",
-    "batch = next(iter(train_loader))\n",
-    "\n",
-    "# Separate the image and segmentation from the batch\n",
-    "volumes, targets = batch[\"image\"], batch[\"seg\"]\n",
-    "volumes_enhanced = add_vessel_contrast(volumes, targets, contrast_value=4000)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define model\n",
-    "model = TACEnet()\n",
-    "criterion = torch.nn.MSELoss()\n",
-    "\n",
-    "# Move model to device\n",
-    "model.to(device)\n",
-    "model.eval()\n",
-    "model.load_state_dict(torch.load('../models/TACEnet_vessel_enhancement_2_0.pth'))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-40\n",
-      "-20\n",
-      "0\n",
-      "20\n",
-      "40\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in range(5):\n",
-    "    print(i*20-40)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "total_loss = []\n",
-    "#EF sweep\n",
-    "for j in range(10):\n",
-    "    \n",
-    "    ef = j/10\n",
-    "    loss = []\n",
-    "    #Rotation sweep\n",
-    "    for i in range(80):\n",
-    "        drr_raw = create_drr(\n",
-    "            volumes[0],\n",
-    "            targets[0],\n",
-    "            bone_attenuation_multiplier=5.0,\n",
-    "            sdd=1020,\n",
-    "            height=256,\n",
-    "            width=256,\n",
-    "            rotations=torch.tensor([[i-40, 0.0, 0.0]]),\n",
-    "            translations=torch.tensor([[0.0, 850.0, 0.0]]),\n",
-    "            mask_to_channels=True,\n",
-    "            device=\"cpu\",\n",
-    "            )\n",
-    "\n",
-    "\n",
-    "        drr_body = drr_raw[:,0,:,:]\n",
-    "        drr_vessels = drr_raw[:,1,:,:]\n",
-    "        drr_combined_low_enhancement = (drr_body+ef*drr_vessels).unsqueeze(0)\n",
-    "        drr_combined = drr_body+drr_vessels\n",
-    "        \n",
-    "        prediction, latent_representation = model(targets.to(device), drr_combined_low_enhancement.to(device))\n",
-    "        Ai_enhanced = drr_body.to(device)+prediction\n",
-    "\n",
-    "        # Create a figure and a set of subplots\n",
-    "        fig, axs = plt.subplots(1, 4, figsize=(12, 4))\n",
-    "        \n",
-    "        # Plot each image in a subplot\n",
-    "        axs[0].imshow(drr_combined_low_enhancement.squeeze(), cmap='gray', vmax = 25)\n",
-    "        axs[0].set_title('DRR')\n",
-    "\n",
-    "        axs[2].imshow(drr_combined.squeeze(), cmap='gray', vmax = 25)\n",
-    "        axs[2].set_title('enhanced target')\n",
-    "\n",
-    "        axs[1].imshow(Ai_enhanced.cpu().detach().numpy().squeeze(), cmap='gray', vmax = 25)\n",
-    "        axs[1].set_title('AI Enhanced')\n",
-    "\n",
-    "        axs[3].imshow(latent_representation.cpu().detach().numpy().squeeze())\n",
-    "        axs[3].set_title('Latent Representation')\n",
-    "\n",
-    "        # Hide the axes labels\n",
-    "        for ax in axs:\n",
-    "            ax.axis('off')\n",
-    "        plt.show()\n",
-    "        \n",
-    "        loss.append(criterion(Ai_enhanced, drr_combined.to(device)).item())\n",
-    "        print(f\"loop: {j}, angle: {i}\")\n",
-    "        \n",
-    "\n",
-    "\n",
-    "    print(f\"loss of loop {j}: {np.mean(loss)}\")\n",
-    "    total_loss.append(np.mean(loss))\n",
-    "\n",
-    "    "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "torch-gpu",
-   "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": "3.12.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/training.ipynb b/notebooks/training.ipynb
deleted file mode 100644
index 679880b..0000000
--- a/notebooks/training.ipynb
+++ /dev/null
@@ -1,10235 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "sys.path.append(\"..\")\n",
-    "import src.training\n",
-    "import torch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([1, 1, 512, 512, 96])\n",
-      "torch.Size([1, 1, 256, 256])\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0.3800475999014452"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "src.training.testInferenceSpeed(\"../models/TACEnet_vessel_enhancement_29052024.pth\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
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-      "[996/1000] - Loss: 0.13441753387451172\n",
-      "[997/1000] - Loss: 0.13057169318199158\n",
-      "[998/1000] - Loss: 0.34326621890068054\n",
-      "[999/1000] - Loss: 0.2842179536819458\n",
-      "[1000/1000] - Loss: 0.13982725143432617\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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",
-      "text/plain": [
-       "<Figure size 1200x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
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",
-      "text/plain": [
-       "<Figure size 1200x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1/1000] - Loss: 0.12268438935279846\n",
-      "[2/1000] - Loss: 0.18261411786079407\n",
-      "[3/1000] - Loss: 0.18429413437843323\n",
-      "[4/1000] - Loss: 0.11129972338676453\n",
-      "[5/1000] - Loss: 0.16432057321071625\n",
-      "[6/1000] - Loss: 0.12261365354061127\n",
-      "[7/1000] - Loss: 0.09517688304185867\n",
-      "[8/1000] - Loss: 0.12280730903148651\n",
-      "[9/1000] - Loss: 0.12533888220787048\n",
-      "[10/1000] - Loss: 0.09840265661478043\n",
-      "[11/1000] - Loss: 0.1337628960609436\n",
-      "[12/1000] - Loss: 0.10091517865657806\n",
-      "[13/1000] - Loss: 0.1414794623851776\n",
-      "[14/1000] - Loss: 0.11210687458515167\n",
-      "[15/1000] - Loss: 0.1724071353673935\n",
-      "[16/1000] - Loss: 0.14973388612270355\n",
-      "[17/1000] - Loss: 0.1384049654006958\n",
-      "[18/1000] - Loss: 0.19583944976329803\n",
-      "[19/1000] - Loss: 0.16989092528820038\n",
-      "[20/1000] - Loss: 0.17291684448719025\n",
-      "[21/1000] - Loss: 0.17907977104187012\n",
-      "[22/1000] - Loss: 0.19902026653289795\n",
-      "[23/1000] - Loss: 0.08373758941888809\n",
-      "[24/1000] - Loss: 0.08873999118804932\n",
-      "[25/1000] - Loss: 0.17734289169311523\n",
-      "[26/1000] - Loss: 0.19893403351306915\n",
-      "[27/1000] - Loss: 0.0996849313378334\n",
-      "[28/1000] - Loss: 0.1697695404291153\n",
-      "[29/1000] - Loss: 0.1453663408756256\n",
-      "[30/1000] - Loss: 0.16101685166358948\n",
-      "[31/1000] - Loss: 0.12402522563934326\n",
-      "[32/1000] - Loss: 0.142772376537323\n",
-      "[33/1000] - Loss: 0.1167043000459671\n",
-      "[34/1000] - Loss: 0.12328040599822998\n",
-      "[35/1000] - Loss: 0.09377271682024002\n",
-      "[36/1000] - Loss: 0.16469180583953857\n",
-      "[37/1000] - Loss: 0.13046620786190033\n",
-      "[38/1000] - Loss: 0.18666797876358032\n",
-      "[39/1000] - Loss: 0.17609903216362\n",
-      "[40/1000] - Loss: 0.11612245440483093\n",
-      "[41/1000] - Loss: 0.20084555447101593\n",
-      "[42/1000] - Loss: 0.1668366938829422\n",
-      "[43/1000] - Loss: 0.12525799870491028\n",
-      "[44/1000] - Loss: 0.14719295501708984\n",
-      "[45/1000] - Loss: 0.13972517848014832\n",
-      "[46/1000] - Loss: 0.11791549623012543\n",
-      "[47/1000] - Loss: 0.20981761813163757\n",
-      "[48/1000] - Loss: 0.2394314855337143\n",
-      "[49/1000] - Loss: 0.23909223079681396\n",
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",
-      "text/plain": [
-       "<Figure size 1200x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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",
-      "text/plain": [
-       "<Figure size 1200x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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",
-      "text/plain": [
-       "<Figure size 1200x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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",
-      "text/plain": [
-       "<Figure size 1200x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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",
-      "text/plain": [
-       "<Figure size 1200x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1/1000] - Loss: 0.22323521971702576\n",
-      "[2/1000] - Loss: 0.08793055266141891\n",
-      "[3/1000] - Loss: 0.15249814093112946\n",
-      "[4/1000] - Loss: 0.18098391592502594\n",
-      "[5/1000] - Loss: 0.2179928570985794\n",
-      "[6/1000] - Loss: 0.12825733423233032\n",
-      "[7/1000] - Loss: 0.10403570532798767\n",
-      "[8/1000] - Loss: 0.1266414076089859\n",
-      "[9/1000] - Loss: 0.1750904619693756\n",
-      "[10/1000] - Loss: 0.07933184504508972\n",
-      "[11/1000] - Loss: 0.1022505983710289\n",
-      "[12/1000] - Loss: 0.07164502143859863\n",
-      "[13/1000] - Loss: 0.17184728384017944\n",
-      "[14/1000] - Loss: 0.07344882935285568\n",
-      "[15/1000] - Loss: 0.13366982340812683\n",
-      "[16/1000] - Loss: 0.12361009418964386\n",
-      "[17/1000] - Loss: 0.08911700546741486\n",
-      "[18/1000] - Loss: 0.1332547962665558\n",
-      "[19/1000] - Loss: 0.140146404504776\n",
-      "[20/1000] - Loss: 0.10922017693519592\n",
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",
-      "text/plain": [
-       "<Figure size 1200x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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",
-      "text/plain": [
-       "<Figure size 1200x400 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "src.training.trainModel(10,1000, save=True, load=True, path=\"../models/TACEnet_vessel_enhancement_2_0.pth\", verbose=True)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "torch",
-   "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": "3.12.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..fad40d1
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,18 @@
+einops==0.8.0
+fastcore==1.5.29
+imageio==2.34.0
+matplotlib==3.8.3
+monai==1.3.0
+numpy==1.26.4
+pandas==2.2.2
+pyvista==0.43.4
+scipy==1.13.1
+setuptools==68.2.2
+streamlit==1.35.0
+timm==1.0.3
+torch==2.2.1
+torchio==0.19.6
+torchmetrics==1.3.2
+torchvision==0.17.1
+tqdm==4.66.2
+vtk==9.3.0
diff --git a/src/app.py b/src/app.py
new file mode 100644
index 0000000..e4e7b67
--- /dev/null
+++ b/src/app.py
@@ -0,0 +1,170 @@
+import os
+import sys
+
+sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
+
+import streamlit as st
+import torch
+
+# Now you can import from the submodule
+from diffdrr.data import read
+from diffdrr.visualization import plot_drr
+from drr import create_drr
+from model import TACEnet
+from training import loadData, sampleVolume
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Set the page configuration
+st.set_page_config(page_title="DRR Enhancement Model Demo", layout="wide")
+
+
+# Caching the model loading to avoid reloading the model on each interaction
+@st.cache_resource(show_spinner="Loading the model...")
+def load_model():
+    model = TACEnet()
+    model.load_state_dict(
+        torch.load("../models/TACEnet_vessel_enhancement_deformations_30052024.pth")
+    )
+    model.eval()
+    return model
+
+
+# Caching the DRR generation to avoid redundant computations
+@st.cache_data(show_spinner="Generating DRR...")
+def cached_create_drr(_train_loader, contrast_value, height=256, width=256, rotation=0):
+    _volume, _target = sampleVolume(_train_loader, contrast_value=contrast_value)
+    subject = read(
+        tensor=_volume[0], label_tensor=_target[0], bone_attenuation_multiplier=5.0
+    )
+    return (
+        create_drr(
+            subject,
+            device="cpu",
+            height=height,
+            width=width,
+            mask_to_channels=False,
+            rotations=torch.tensor([[rotation, 0.0, 0.0]]),
+        ),
+        _volume,
+        _target,
+    )
+
+
+@st.cache_data
+def cached_loadData():
+    return loadData()
+
+
+@st.cache_data
+def cached_sampleVolume(_train_loader, contrast_value):
+    return sampleVolume(_train_loader, contrast_value=contrast_value)
+
+
+# Show dialog
+@st.experimental_dialog("Accept terms and conditions")
+def terms_conditions():
+    st.write(
+        """
+        By using this application, you agree to the following terms and conditions:
+
+        - This application is for demonstration purposes only.
+        - The results generated by this application are not intended for clinical use.
+        - The model used in this application is trained on synthetic data and may not generalize well to real-world data.
+        - The authors of this application are not responsible for any damages or losses resulting from the use of this application.
+        """
+    )
+    st.write("Do you agree to the terms and conditions?")
+    if st.button("I agree"):
+        st.session_state.terms_conditions = True
+        st.rerun()
+
+
+if "terms_conditions" not in st.session_state:
+    terms_conditions()
+
+st.logo("https://avatars.githubusercontent.com/u/8323854?s=200&v=4")
+
+# Display the DRR and enhanced DRR in the main layout
+st.title("DRR Enhancement Model Demo")
+
+st.divider()
+
+with st.sidebar:
+    st.header("Settings")
+
+    "---"
+
+    st.slider(
+        label="Select rotation",
+        min_value=-45,
+        max_value=45,
+        value=0,
+        step=5,
+        key="rotation_slider",
+        help="Select the rotation angle in degrees",
+        on_change=None,
+        label_visibility="visible",
+    )
+
+    st.slider(
+        label="Select initial contrast",
+        min_value=0,
+        max_value=4000,
+        value=0,
+        step=500,
+        key="contrast_slider",
+        help="Select the intital contrast",
+        on_change=None,
+        label_visibility="visible",
+    )
+
+    "---"
+
+# Load your model
+model = load_model()
+model.to(device)
+
+# Load CT data
+train_loader, val_loader = cached_loadData()
+# volume, target = cached_sampleVolume(
+#     train_loader, contrast_value=st.session_state.contrast_slider
+# )
+
+# Initialize the DRR module for generating synthetic X-rays
+drr, volume, target = cached_create_drr(
+    train_loader,
+    contrast_value=st.session_state.contrast_slider,
+    rotation=st.session_state.rotation_slider,
+)
+
+
+col1, col2, col3 = st.columns(spec=[0.4, 0.2, 0.4])
+
+with col1:
+    st.header("Original DRR")
+    axs = plot_drr(drr, ticks=False)
+    fig = axs[0].figure
+    fig.set_size_inches(2, 2)  # Adjust the figure size
+    st.pyplot(fig)
+
+with col3:
+    st.header("Enhanced DRR")
+    enhanced_placeholder = st.empty()  # Placeholder for enhanced image
+
+with col2:
+    # Add an "Enhance" button
+    if st.button("Enhance"):
+        with st.spinner("Enhancing DRR..."):
+            prediction, latent = model(
+                volume[0].unsqueeze(0).to(device), drr.to(device)
+            )
+            axs = plot_drr(prediction, ticks=False)
+            fig = axs[0].figure
+            fig.set_size_inches(2, 2)  # Adjust the figure size
+            enhanced_placeholder.pyplot(fig)
+
+col4, col5, col6 = st.columns(3)
+col4.metric("Temperature", "70 °F", "1.2 °F")
+col5.metric("Wind", "9 mph", "-8%")
+col6.metric("Humidity", "86%", "4%")
diff --git a/src/config.py b/src/config.py
index fa49d66..43d8f30 100644
--- a/src/config.py
+++ b/src/config.py
@@ -7,16 +7,16 @@
         "learning_rate": 3e-5,
         "batch_size": 1,
         "num_workers": 0,
-        "num_epochs": 150,
+        "num_epochs": 2,
         "num_classes": 1,
         "seg_task": "binary",
         "grad_accum_steps": 1,
-        "val_frac": 0.2,
+        "val_frac": 0.0,
         "seed": 42,
         "weight_decay": 0.01,
         "warmup_scheduler": {"warmup_epochs": 0},
         "contrast_value": 1000,
-        "resize_shape": [256, 256, 48],
+        "resize_shape": [512, 512, 96],
     },
 )
 
diff --git a/src/diffdrr/__init__.py b/src/diffdrr/__init__.py
new file mode 100644
index 0000000..6a9beea
--- /dev/null
+++ b/src/diffdrr/__init__.py
@@ -0,0 +1 @@
+__version__ = "0.4.0"
diff --git a/src/diffdrr/_modidx.py b/src/diffdrr/_modidx.py
new file mode 100644
index 0000000..086f47d
--- /dev/null
+++ b/src/diffdrr/_modidx.py
@@ -0,0 +1,170 @@
+# Autogenerated by nbdev
+
+d = { 'settings': { 'branch': 'main',
+                'doc_baseurl': '/DiffDRR',
+                'doc_host': 'https://vivekg.dev',
+                'git_url': 'https://github.com/eigenvivek/DiffDRR',
+                'lib_path': 'diffdrr'},
+  'syms': { 'diffdrr.data': { 'diffdrr.data.canonicalize': ('api/data.html#canonicalize', 'diffdrr/data.py'),
+                              'diffdrr.data.load_example_ct': ('api/data.html#load_example_ct', 'diffdrr/data.py'),
+                              'diffdrr.data.read': ('api/data.html#read', 'diffdrr/data.py'),
+                              'diffdrr.data.transform_hu_to_density': ('api/data.html#transform_hu_to_density', 'diffdrr/data.py')},
+            'diffdrr.detector': { 'diffdrr.detector.Detector': ('api/detector.html#detector', 'diffdrr/detector.py'),
+                                  'diffdrr.detector.Detector.__init__': ('api/detector.html#detector.__init__', 'diffdrr/detector.py'),
+                                  'diffdrr.detector.Detector._initialize_carm': ( 'api/detector.html#detector._initialize_carm',
+                                                                                  'diffdrr/detector.py'),
+                                  'diffdrr.detector.Detector.forward': ('api/detector.html#detector.forward', 'diffdrr/detector.py'),
+                                  'diffdrr.detector.Detector.intrinsic': ('api/detector.html#detector.intrinsic', 'diffdrr/detector.py'),
+                                  'diffdrr.detector.Detector.reorient': ('api/detector.html#detector.reorient', 'diffdrr/detector.py')},
+            'diffdrr.drr': { 'diffdrr.drr.DRR': ('api/drr.html#drr', 'diffdrr/drr.py'),
+                             'diffdrr.drr.DRR.__init__': ('api/drr.html#drr.__init__', 'diffdrr/drr.py'),
+                             'diffdrr.drr.DRR.forward': ('api/drr.html#drr.forward', 'diffdrr/drr.py'),
+                             'diffdrr.drr.DRR.inverse_projection': ('api/drr.html#drr.inverse_projection', 'diffdrr/drr.py'),
+                             'diffdrr.drr.DRR.perspective_projection': ('api/drr.html#drr.perspective_projection', 'diffdrr/drr.py'),
+                             'diffdrr.drr.DRR.reshape_transform': ('api/drr.html#drr.reshape_transform', 'diffdrr/drr.py'),
+                             'diffdrr.drr.DRR.set_intrinsics': ('api/drr.html#drr.set_intrinsics', 'diffdrr/drr.py'),
+                             'diffdrr.drr.reshape_subsampled_drr': ('api/drr.html#reshape_subsampled_drr', 'diffdrr/drr.py')},
+            'diffdrr.metrics': { 'diffdrr.metrics.DoubleGeodesicSE3': ('api/metrics.html#doublegeodesicse3', 'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.DoubleGeodesicSE3.__init__': ( 'api/metrics.html#doublegeodesicse3.__init__',
+                                                                                 'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.DoubleGeodesicSE3.forward': ( 'api/metrics.html#doublegeodesicse3.forward',
+                                                                                'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.GeodesicSO3': ('api/metrics.html#geodesicso3', 'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.GeodesicSO3.__init__': ('api/metrics.html#geodesicso3.__init__', 'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.GeodesicSO3.forward': ('api/metrics.html#geodesicso3.forward', 'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.GeodesicTranslation': ('api/metrics.html#geodesictranslation', 'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.GeodesicTranslation.__init__': ( 'api/metrics.html#geodesictranslation.__init__',
+                                                                                   'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.GeodesicTranslation.forward': ( 'api/metrics.html#geodesictranslation.forward',
+                                                                                  'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.GradientNormalizedCrossCorrelation2d': ( 'api/metrics.html#gradientnormalizedcrosscorrelation2d',
+                                                                                           'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.GradientNormalizedCrossCorrelation2d.__init__': ( 'api/metrics.html#gradientnormalizedcrosscorrelation2d.__init__',
+                                                                                                    'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.GradientNormalizedCrossCorrelation2d.forward': ( 'api/metrics.html#gradientnormalizedcrosscorrelation2d.forward',
+                                                                                                   'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.LogGeodesicSE3': ('api/metrics.html#loggeodesicse3', 'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.LogGeodesicSE3.__init__': ( 'api/metrics.html#loggeodesicse3.__init__',
+                                                                              'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.LogGeodesicSE3.forward': ( 'api/metrics.html#loggeodesicse3.forward',
+                                                                             'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.MultiscaleNormalizedCrossCorrelation2d': ( 'api/metrics.html#multiscalenormalizedcrosscorrelation2d',
+                                                                                             'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.MultiscaleNormalizedCrossCorrelation2d.__init__': ( 'api/metrics.html#multiscalenormalizedcrosscorrelation2d.__init__',
+                                                                                                      'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.MultiscaleNormalizedCrossCorrelation2d.forward': ( 'api/metrics.html#multiscalenormalizedcrosscorrelation2d.forward',
+                                                                                                     'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.NormalizedCrossCorrelation2d': ( 'api/metrics.html#normalizedcrosscorrelation2d',
+                                                                                   'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.NormalizedCrossCorrelation2d.__init__': ( 'api/metrics.html#normalizedcrosscorrelation2d.__init__',
+                                                                                            'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.NormalizedCrossCorrelation2d.forward': ( 'api/metrics.html#normalizedcrosscorrelation2d.forward',
+                                                                                           'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.NormalizedCrossCorrelation2d.norm': ( 'api/metrics.html#normalizedcrosscorrelation2d.norm',
+                                                                                        'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.Sobel': ('api/metrics.html#sobel', 'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.Sobel.__init__': ('api/metrics.html#sobel.__init__', 'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.Sobel.forward': ('api/metrics.html#sobel.forward', 'diffdrr/metrics.py'),
+                                 'diffdrr.metrics.to_patches': ('api/metrics.html#to_patches', 'diffdrr/metrics.py')},
+            'diffdrr.pose': { 'diffdrr.pose.RigidTransform': ('api/pose.html#rigidtransform', 'diffdrr/pose.py'),
+                              'diffdrr.pose.RigidTransform.__getitem__': ('api/pose.html#rigidtransform.__getitem__', 'diffdrr/pose.py'),
+                              'diffdrr.pose.RigidTransform.__init__': ('api/pose.html#rigidtransform.__init__', 'diffdrr/pose.py'),
+                              'diffdrr.pose.RigidTransform.__len__': ('api/pose.html#rigidtransform.__len__', 'diffdrr/pose.py'),
+                              'diffdrr.pose.RigidTransform.compose': ('api/pose.html#rigidtransform.compose', 'diffdrr/pose.py'),
+                              'diffdrr.pose.RigidTransform.convert': ('api/pose.html#rigidtransform.convert', 'diffdrr/pose.py'),
+                              'diffdrr.pose.RigidTransform.forward': ('api/pose.html#rigidtransform.forward', 'diffdrr/pose.py'),
+                              'diffdrr.pose.RigidTransform.get_se3_log': ('api/pose.html#rigidtransform.get_se3_log', 'diffdrr/pose.py'),
+                              'diffdrr.pose.RigidTransform.inverse': ('api/pose.html#rigidtransform.inverse', 'diffdrr/pose.py'),
+                              'diffdrr.pose._10vec_to_4x4symmetric': ('api/pose.html#_10vec_to_4x4symmetric', 'diffdrr/pose.py'),
+                              'diffdrr.pose._acos_linear_approximation': ('api/pose.html#_acos_linear_approximation', 'diffdrr/pose.py'),
+                              'diffdrr.pose._angle_from_tan': ('api/pose.html#_angle_from_tan', 'diffdrr/pose.py'),
+                              'diffdrr.pose._axis_angle_rotation': ('api/pose.html#_axis_angle_rotation', 'diffdrr/pose.py'),
+                              'diffdrr.pose._copysign': ('api/pose.html#_copysign', 'diffdrr/pose.py'),
+                              'diffdrr.pose._dacos_dx': ('api/pose.html#_dacos_dx', 'diffdrr/pose.py'),
+                              'diffdrr.pose._get_se3_V_input': ('api/pose.html#_get_se3_v_input', 'diffdrr/pose.py'),
+                              'diffdrr.pose._index_from_letter': ('api/pose.html#_index_from_letter', 'diffdrr/pose.py'),
+                              'diffdrr.pose._se3_V_matrix': ('api/pose.html#_se3_v_matrix', 'diffdrr/pose.py'),
+                              'diffdrr.pose._so3_exp_map': ('api/pose.html#_so3_exp_map', 'diffdrr/pose.py'),
+                              'diffdrr.pose._sqrt_positive_part': ('api/pose.html#_sqrt_positive_part', 'diffdrr/pose.py'),
+                              'diffdrr.pose.acos_linear_extrapolation': ('api/pose.html#acos_linear_extrapolation', 'diffdrr/pose.py'),
+                              'diffdrr.pose.axis_angle_to_matrix': ('api/pose.html#axis_angle_to_matrix', 'diffdrr/pose.py'),
+                              'diffdrr.pose.axis_angle_to_quaternion': ('api/pose.html#axis_angle_to_quaternion', 'diffdrr/pose.py'),
+                              'diffdrr.pose.convert': ('api/pose.html#convert', 'diffdrr/pose.py'),
+                              'diffdrr.pose.euler_angles_to_matrix': ('api/pose.html#euler_angles_to_matrix', 'diffdrr/pose.py'),
+                              'diffdrr.pose.hat': ('api/pose.html#hat', 'diffdrr/pose.py'),
+                              'diffdrr.pose.hat_inv': ('api/pose.html#hat_inv', 'diffdrr/pose.py'),
+                              'diffdrr.pose.make_matrix': ('api/pose.html#make_matrix', 'diffdrr/pose.py'),
+                              'diffdrr.pose.matrix_to_axis_angle': ('api/pose.html#matrix_to_axis_angle', 'diffdrr/pose.py'),
+                              'diffdrr.pose.matrix_to_euler_angles': ('api/pose.html#matrix_to_euler_angles', 'diffdrr/pose.py'),
+                              'diffdrr.pose.matrix_to_quaternion': ('api/pose.html#matrix_to_quaternion', 'diffdrr/pose.py'),
+                              'diffdrr.pose.matrix_to_rotation_6d': ('api/pose.html#matrix_to_rotation_6d', 'diffdrr/pose.py'),
+                              'diffdrr.pose.quaternion_adjugate_to_quaternion': ( 'api/pose.html#quaternion_adjugate_to_quaternion',
+                                                                                  'diffdrr/pose.py'),
+                              'diffdrr.pose.quaternion_apply': ('api/pose.html#quaternion_apply', 'diffdrr/pose.py'),
+                              'diffdrr.pose.quaternion_invert': ('api/pose.html#quaternion_invert', 'diffdrr/pose.py'),
+                              'diffdrr.pose.quaternion_multiply': ('api/pose.html#quaternion_multiply', 'diffdrr/pose.py'),
+                              'diffdrr.pose.quaternion_raw_multiply': ('api/pose.html#quaternion_raw_multiply', 'diffdrr/pose.py'),
+                              'diffdrr.pose.quaternion_to_axis_angle': ('api/pose.html#quaternion_to_axis_angle', 'diffdrr/pose.py'),
+                              'diffdrr.pose.quaternion_to_matrix': ('api/pose.html#quaternion_to_matrix', 'diffdrr/pose.py'),
+                              'diffdrr.pose.quaternion_to_quaternion_adjugate': ( 'api/pose.html#quaternion_to_quaternion_adjugate',
+                                                                                  'diffdrr/pose.py'),
+                              'diffdrr.pose.quaternion_to_rotation_10d': ('api/pose.html#quaternion_to_rotation_10d', 'diffdrr/pose.py'),
+                              'diffdrr.pose.random_rigid_transform': ('api/pose.html#random_rigid_transform', 'diffdrr/pose.py'),
+                              'diffdrr.pose.rotation_10d_to_quaternion': ('api/pose.html#rotation_10d_to_quaternion', 'diffdrr/pose.py'),
+                              'diffdrr.pose.rotation_6d_to_matrix': ('api/pose.html#rotation_6d_to_matrix', 'diffdrr/pose.py'),
+                              'diffdrr.pose.se3_exp_map': ('api/pose.html#se3_exp_map', 'diffdrr/pose.py'),
+                              'diffdrr.pose.se3_log_map': ('api/pose.html#se3_log_map', 'diffdrr/pose.py'),
+                              'diffdrr.pose.so3_exp_map': ('api/pose.html#so3_exp_map', 'diffdrr/pose.py'),
+                              'diffdrr.pose.so3_exponential_map': ('api/pose.html#so3_exponential_map', 'diffdrr/pose.py'),
+                              'diffdrr.pose.so3_log_map': ('api/pose.html#so3_log_map', 'diffdrr/pose.py'),
+                              'diffdrr.pose.so3_relative_angle': ('api/pose.html#so3_relative_angle', 'diffdrr/pose.py'),
+                              'diffdrr.pose.so3_rotation_angle': ('api/pose.html#so3_rotation_angle', 'diffdrr/pose.py'),
+                              'diffdrr.pose.standardize_quaternion': ('api/pose.html#standardize_quaternion', 'diffdrr/pose.py')},
+            'diffdrr.registration': { 'diffdrr.registration.PoseRegressor': ( 'api/registration.html#poseregressor',
+                                                                              'diffdrr/registration.py'),
+                                      'diffdrr.registration.PoseRegressor.__init__': ( 'api/registration.html#poseregressor.__init__',
+                                                                                       'diffdrr/registration.py'),
+                                      'diffdrr.registration.PoseRegressor.forward': ( 'api/registration.html#poseregressor.forward',
+                                                                                      'diffdrr/registration.py'),
+                                      'diffdrr.registration.Registration': ( 'api/registration.html#registration',
+                                                                             'diffdrr/registration.py'),
+                                      'diffdrr.registration.Registration.__init__': ( 'api/registration.html#registration.__init__',
+                                                                                      'diffdrr/registration.py'),
+                                      'diffdrr.registration.Registration.forward': ( 'api/registration.html#registration.forward',
+                                                                                     'diffdrr/registration.py'),
+                                      'diffdrr.registration.Registration.pose': ( 'api/registration.html#registration.pose',
+                                                                                  'diffdrr/registration.py'),
+                                      'diffdrr.registration.Registration.rotation': ( 'api/registration.html#registration.rotation',
+                                                                                      'diffdrr/registration.py'),
+                                      'diffdrr.registration.Registration.translation': ( 'api/registration.html#registration.translation',
+                                                                                         'diffdrr/registration.py')},
+            'diffdrr.renderers': { 'diffdrr.renderers.Siddon': ('api/renderers.html#siddon', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers.Siddon.__init__': ('api/renderers.html#siddon.__init__', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers.Siddon.dims': ('api/renderers.html#siddon.dims', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers.Siddon.forward': ('api/renderers.html#siddon.forward', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers.Siddon.maxidx': ('api/renderers.html#siddon.maxidx', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers.Trilinear': ('api/renderers.html#trilinear', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers.Trilinear.__init__': ( 'api/renderers.html#trilinear.__init__',
+                                                                             'diffdrr/renderers.py'),
+                                   'diffdrr.renderers.Trilinear.dims': ('api/renderers.html#trilinear.dims', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers.Trilinear.forward': ('api/renderers.html#trilinear.forward', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers._get_alpha_minmax': ('api/renderers.html#_get_alpha_minmax', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers._get_alphas': ('api/renderers.html#_get_alphas', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers._get_index': ('api/renderers.html#_get_index', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers._get_voxel': ('api/renderers.html#_get_voxel', 'diffdrr/renderers.py')},
+            'diffdrr.utils': { 'diffdrr.utils.get_focal_length': ('api/utils.html#get_focal_length', 'diffdrr/utils.py'),
+                               'diffdrr.utils.get_principal_point': ('api/utils.html#get_principal_point', 'diffdrr/utils.py'),
+                               'diffdrr.utils.make_intrinsic_matrix': ('api/utils.html#make_intrinsic_matrix', 'diffdrr/utils.py'),
+                               'diffdrr.utils.parse_intrinsic_matrix': ('api/utils.html#parse_intrinsic_matrix', 'diffdrr/utils.py')},
+            'diffdrr.visualization': { 'diffdrr.visualization._make_camera_frustum_mesh': ( 'api/visualization.html#_make_camera_frustum_mesh',
+                                                                                            'diffdrr/visualization.py'),
+                                       'diffdrr.visualization.animate': ('api/visualization.html#animate', 'diffdrr/visualization.py'),
+                                       'diffdrr.visualization.drr_to_mesh': ( 'api/visualization.html#drr_to_mesh',
+                                                                              'diffdrr/visualization.py'),
+                                       'diffdrr.visualization.img_to_mesh': ( 'api/visualization.html#img_to_mesh',
+                                                                              'diffdrr/visualization.py'),
+                                       'diffdrr.visualization.labelmap_to_mesh': ( 'api/visualization.html#labelmap_to_mesh',
+                                                                                   'diffdrr/visualization.py'),
+                                       'diffdrr.visualization.plot_drr': ('api/visualization.html#plot_drr', 'diffdrr/visualization.py'),
+                                       'diffdrr.visualization.plot_mask': ( 'api/visualization.html#plot_mask',
+                                                                            'diffdrr/visualization.py')}}}
diff --git a/src/diffdrr/data.py b/src/diffdrr/data.py
new file mode 100644
index 0000000..031202d
--- /dev/null
+++ b/src/diffdrr/data.py
@@ -0,0 +1,181 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../notebooks/api/03_data.ipynb.
+
+# %% ../notebooks/api/03_data.ipynb 3
+from __future__ import annotations
+
+from pathlib import Path
+
+import monai.data.meta_tensor
+import numpy as np
+import pandas as pd
+import torch
+import torchio
+from torchio import LabelMap, ScalarImage, Subject
+from torchio.transforms.preprocessing import ToCanonical
+
+# %% auto 0
+__all__ = ["load_example_ct", "read"]
+
+
+# %% ../notebooks/api/03_data.ipynb 4
+def load_example_ct(
+    labels=None, orientation="AP", bone_attenuation_multiplier=1.0
+) -> Subject:
+    """Load an example chest CT for demonstration purposes."""
+    datadir = Path(__file__).resolve().parent / "data"
+    filename = datadir / "cxr.nii.gz"
+    labelmap = datadir / "mask.nii.gz"
+    structures = pd.read_csv(datadir / "structures.csv")
+    return read(
+        filename,
+        labelmap,
+        labels,
+        orientation=orientation,
+        bone_attenuation_multiplier=bone_attenuation_multiplier,
+        structures=structures,
+    )
+
+
+# %% ../notebooks/api/03_data.ipynb 5
+def read(
+    filename: str | Path = None,  # Path to CT volume
+    labelmap: str | Path = None,  # Path to a labelmap for the CT volume
+    tensor: (
+        monai.data.meta_tensor.MetaTensor | np.ndarray
+    ) = None,  # Optional tensor data for the volume
+    label_tensor: (
+        torch.Tensor | np.ndarray
+    ) = None,  # Optional tensor data for the labelmap
+    labels: int | list = None,  # Labels from the mask of structures to render
+    orientation: str = "AP",  # Frame-of-reference change
+    bone_attenuation_multiplier: float = 1.0,  # Scalar multiplier on density of high attenuation voxels
+    **kwargs,  # Any additional information to be stored in the torchio.Subject
+) -> Subject:
+    """
+    Read an image volume from a variety of formats, and optionally, any
+    given labelmap for the volume. Converts volume to a RAS+ coordinate
+    system and moves the volume isocenter to the world origin.
+    """
+    # Read the volume from a filename
+    if tensor is not None:
+        volume = ScalarImage(
+            path=filename, tensor=tensor, type=torchio.INTENSITY, affine=tensor.affine
+        )
+    else:
+        volume = ScalarImage(path=filename, type=torchio.INTENSITY)
+
+    # If a labelmap is passed, read the mask
+    if labelmap is not None or label_tensor is not None:
+        mask = LabelMap(
+            tensor=label_tensor,
+            path=labelmap,
+            type=torchio.LABEL,
+            affine=tensor.affine if tensor is not None else None,
+        )
+    else:
+        mask = None
+
+    # Frame-of-reference change
+    if orientation == "AP":
+        # Rotates the C-arm about the x-axis by 90 degrees
+        # Rotates the C-arm about the z-axis by -90 degrees
+        reorient = torch.tensor(
+            [
+                [0.0, 1.0, 0.0, 0.0],
+                [0.0, 0.0, -1.0, 0.0],
+                [-1.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 1.0],
+            ]
+        )
+    elif orientation == "PA":
+        # Rotates the C-arm about the x-axis by 90 degrees
+        # Rotates the C-arm about the z-axis by 90 degrees
+        reorient = torch.tensor(
+            [
+                [0.0, 1.0, 0.0, 0.0],
+                [0.0, 0.0, 1.0, 0.0],
+                [-1.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 1.0],
+            ]
+        )
+    elif orientation is None:
+        # Identity transform
+        reorient = torch.tensor(
+            [
+                [1.0, 0.0, 0.0, 0.0],
+                [0.0, 1.0, 0.0, 0.0],
+                [0.0, 0.0, 1.0, 0.0],
+                [0.0, 0.0, 0.0, 1.0],
+            ]
+        )
+    else:
+        raise ValueError(f"Unrecognized orientation {orientation}")
+
+    # Package the subject
+    subject = Subject(
+        volume=volume,
+        mask=mask,
+        reorient=reorient,
+        density=None,  # Temporarily set to None, calculate after reorientation
+        **kwargs,
+    )
+
+    # Canonicalize the images by converting to RAS+ and moving the
+    # Subject's isocenter to the origin in world coordinates
+    subject = canonicalize(subject)
+    subject.density = transform_hu_to_density(
+        subject.volume.data, bone_attenuation_multiplier
+    )
+
+    # Apply mask
+    if labels is not None:
+        if isinstance(labels, int):
+            labels = [labels]
+        mask = torch.any(
+            torch.stack(
+                [subject.mask.data.squeeze() == torch.tensor(idx) for idx in labels]
+            ),
+            dim=0,
+        )
+        subject.density = subject.density * mask
+
+    return subject
+
+
+# %% ../notebooks/api/03_data.ipynb 6
+def canonicalize(subject):
+    # Convert to RAS+ coordinate system
+    subject = ToCanonical()(subject)
+
+    # Move the Subject's isocenter to the origin in world coordinates
+    for image in subject.get_images(intensity_only=False):
+        isocenter = image.get_center()
+        Tinv = np.array(
+            [
+                [1.0, 0.0, 0.0, -isocenter[0]],
+                [0.0, 1.0, 0.0, -isocenter[1]],
+                [0.0, 0.0, 1.0, -isocenter[2]],
+                [0.0, 0.0, 0.0, 1.0],
+            ]
+        )
+        image.affine = Tinv.dot(image.affine)
+
+    return subject
+
+
+# %% ../notebooks/api/03_data.ipynb 7
+def transform_hu_to_density(volume, bone_attenuation_multiplier):
+    volume = volume.to(torch.float32)
+
+    air = torch.where(volume <= -800)
+    soft_tissue = torch.where((-800 < volume) & (volume <= 350))
+    bone = torch.where(350 < volume)
+
+    density = torch.empty_like(volume)
+    density[air] = volume[soft_tissue].min()
+    density[soft_tissue] = volume[soft_tissue]
+    density[bone] = volume[bone] * bone_attenuation_multiplier
+    density -= density.min()
+    density /= density.max()
+
+    return density.squeeze()
diff --git a/src/diffdrr/data/structures.csv b/src/diffdrr/data/structures.csv
new file mode 100644
index 0000000..ec0024d
--- /dev/null
+++ b/src/diffdrr/data/structures.csv
@@ -0,0 +1,119 @@
+group,original_idx,structure,id
+organs,1,spleen,1
+organs,2,kidney_right,2
+organs,3,kidney_left,3
+organs,4,gallbladder,4
+organs,5,liver,5
+organs,6,stomach,6
+organs,7,pancreas,7
+organs,8,adrenal_gland_right,8
+organs,9,adrenal_gland_left,9
+organs,10,lung_upper_lobe_left,10
+organs,11,lung_lower_lobe_left,11
+organs,12,lung_upper_lobe_right,12
+organs,13,lung_middle_lobe_right,13
+organs,14,lung_lower_lobe_right,14
+organs,15,esophagus,15
+organs,16,trachea,16
+organs,17,thyroid_gland,17
+organs,18,small_bowel,18
+organs,19,duodenum,19
+organs,20,colon,20
+organs,21,urinary_bladder,21
+organs,22,prostate,22
+organs,23,kidney_cyst_left,23
+organs,24,kidney_cyst_right,24
+vertebrae,1,sacrum,25
+vertebrae,2,vertebrae_S1,26
+vertebrae,3,vertebrae_L5,27
+vertebrae,4,vertebrae_L4,28
+vertebrae,5,vertebrae_L3,29
+vertebrae,6,vertebrae_L2,30
+vertebrae,7,vertebrae_L1,31
+vertebrae,8,vertebrae_T12,32
+vertebrae,9,vertebrae_T11,33
+vertebrae,10,vertebrae_T10,34
+vertebrae,11,vertebrae_T9,35
+vertebrae,12,vertebrae_T8,36
+vertebrae,13,vertebrae_T7,37
+vertebrae,14,vertebrae_T6,38
+vertebrae,15,vertebrae_T5,39
+vertebrae,16,vertebrae_T4,40
+vertebrae,17,vertebrae_T3,41
+vertebrae,18,vertebrae_T2,42
+vertebrae,19,vertebrae_T1,43
+vertebrae,20,vertebrae_C7,44
+vertebrae,21,vertebrae_C6,45
+vertebrae,22,vertebrae_C5,46
+vertebrae,23,vertebrae_C4,47
+vertebrae,24,vertebrae_C3,48
+vertebrae,25,vertebrae_C2,49
+vertebrae,26,vertebrae_C1,50
+cardiac,1,heart,51
+cardiac,2,aorta,52
+cardiac,3,pulmonary_vein,53
+cardiac,4,brachiocephalic_trunk,54
+cardiac,5,subclavian_artery_right,55
+cardiac,6,subclavian_artery_left,56
+cardiac,7,common_carotid_artery_right,57
+cardiac,8,common_carotid_artery_left,58
+cardiac,9,brachiocephalic_vein_left,59
+cardiac,10,brachiocephalic_vein_right,60
+cardiac,11,atrial_appendage_left,61
+cardiac,12,superior_vena_cava,62
+cardiac,13,inferior_vena_cava,63
+cardiac,14,portal_vein_and_splenic_vein,64
+cardiac,15,iliac_artery_left,65
+cardiac,16,iliac_artery_right,66
+cardiac,17,iliac_vena_left,67
+cardiac,18,iliac_vena_right,68
+skeleton,1,humerus_left,69
+skeleton,2,humerus_right,70
+skeleton,3,scapula_left,71
+skeleton,4,scapula_right,72
+skeleton,5,clavicula_left,73
+skeleton,6,clavicula_right,74
+skeleton,7,femur_left,75
+skeleton,8,femur_right,76
+skeleton,9,hip_left,77
+skeleton,10,hip_right,78
+muscles,11,spinal_cord,79
+muscles,12,gluteus_maximus_left,80
+muscles,13,gluteus_maximus_right,81
+muscles,14,gluteus_medius_left,82
+muscles,15,gluteus_medius_right,83
+muscles,16,gluteus_minimus_left,84
+muscles,17,gluteus_minimus_right,85
+muscles,18,autochthon_left,86
+muscles,19,autochthon_right,87
+muscles,20,iliopsoas_left,88
+muscles,21,iliopsoas_right,89
+muscles,22,brain,90
+skeleton,23,skull,91
+ribs,1,rib_left_1,92
+ribs,2,rib_left_2,93
+ribs,3,rib_left_3,94
+ribs,4,rib_left_4,95
+ribs,5,rib_left_5,96
+ribs,6,rib_left_6,97
+ribs,7,rib_left_7,98
+ribs,8,rib_left_8,99
+ribs,9,rib_left_9,100
+ribs,10,rib_left_10,101
+ribs,11,rib_left_11,102
+ribs,12,rib_left_12,103
+ribs,13,rib_right_1,104
+ribs,14,rib_right_2,105
+ribs,15,rib_right_3,106
+ribs,16,rib_right_4,107
+ribs,17,rib_right_5,108
+ribs,18,rib_right_6,109
+ribs,19,rib_right_7,110
+ribs,20,rib_right_8,111
+ribs,21,rib_right_9,112
+ribs,22,rib_right_10,113
+ribs,23,rib_right_11,114
+ribs,24,rib_right_12,115
+ribs,25,sternum,116
+test,1,carpal,117
+ribs,26,costal_cartilages,118
\ No newline at end of file
diff --git a/src/diffdrr/detector.py b/src/diffdrr/detector.py
new file mode 100644
index 0000000..9c61547
--- /dev/null
+++ b/src/diffdrr/detector.py
@@ -0,0 +1,128 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../notebooks/api/02_detector.ipynb.
+
+# %% ../notebooks/api/02_detector.ipynb 3
+from __future__ import annotations
+
+import torch
+from fastcore.basics import patch
+from torch.nn.functional import normalize
+
+# %% auto 0
+__all__ = ['Detector']
+
+# %% ../notebooks/api/02_detector.ipynb 5
+from .pose import RigidTransform
+from .utils import make_intrinsic_matrix
+
+
+class Detector(torch.nn.Module):
+    """Construct a 6 DoF X-ray detector system. This model is based on a C-Arm."""
+
+    def __init__(
+        self,
+        sdd: float,  # Source-to-detector distance (i.e., focal length)
+        height: int,  # Height of the X-ray detector
+        width: int,  # Width of the X-ray detector
+        delx: float,  # Pixel spacing in the X-direction
+        dely: float,  # Pixel spacing in the Y-direction
+        x0: float,  # Principal point X-offset
+        y0: float,  # Principal point Y-offset
+        reorient: torch.tensor,  # Frame-of-reference change matrix
+        n_subsample: int | None = None,  # Number of target points to randomly sample
+        reverse_x_axis: bool = False,  # If pose includes reflection (in E(3) not SE(3)), reverse x-axis
+    ):
+        super().__init__()
+        self.sdd = sdd
+        self.height = height
+        self.width = width
+        self.delx = delx
+        self.dely = dely
+        self.x0 = x0
+        self.y0 = y0
+        self.n_subsample = n_subsample
+        if self.n_subsample is not None:
+            self.subsamples = []
+        self.reverse_x_axis = reverse_x_axis
+
+        # Initialize the source and detector plane in default positions (along the x-axis)
+        source, target = self._initialize_carm()
+        self.register_buffer("source", source)
+        self.register_buffer("target", target)
+
+        # Create a pose to reorient the scanner
+        self.register_buffer("_reorient", reorient)
+
+    @property
+    def reorient(self):
+        return RigidTransform(self._reorient)
+
+    @property
+    def intrinsic(self):
+        return make_intrinsic_matrix(
+            self.sdd,
+            self.delx,
+            self.dely,
+            self.height,
+            self.width,
+            self.x0,
+            self.y0,
+        ).to(self.source)
+
+# %% ../notebooks/api/02_detector.ipynb 6
+@patch
+def _initialize_carm(self: Detector):
+    """Initialize the default position for the source and detector plane."""
+    try:
+        device = self.sdd.device
+    except AttributeError:
+        device = torch.device("cpu")
+
+    # Initialize the source at the origin and the center of the detector plane on the positive z-axis
+    source = torch.tensor([[0.0, 0.0, 0.0]], device=device)
+    center = torch.tensor([[0.0, 0.0, 1.0]], device=device) * self.sdd
+
+    # Use the standard basis for the detector plane
+    basis = torch.tensor([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]], device=device)
+
+    # Construct the detector plane with different offsets for even or odd heights
+    h_off = 1.0 if self.height % 2 else 0.5
+    w_off = 1.0 if self.width % 2 else 0.5
+
+    # Construct equally spaced points along the basis vectors
+    t = (
+        torch.arange(-self.height // 2, self.height // 2, device=device) + h_off
+    ) * self.delx
+    s = (
+        torch.arange(-self.width // 2, self.width // 2, device=device) + w_off
+    ) * self.dely
+    if self.reverse_x_axis:
+        s = -s
+    coefs = torch.cartesian_prod(t, s).reshape(-1, 2)
+    target = torch.einsum("cd,nc->nd", basis, coefs)
+    target += center
+
+    # Batch source and target
+    source = source.unsqueeze(0)
+    target = target.unsqueeze(0)
+
+    # Apply principal point offset
+    target[..., 1] -= self.x0
+    target[..., 0] -= self.y0
+
+    if self.n_subsample is not None:
+        sample = torch.randperm(self.height * self.width)[: int(self.n_subsample)]
+        target = target[:, sample, :]
+        self.subsamples.append(sample.tolist())
+    return source, target
+
+# %% ../notebooks/api/02_detector.ipynb 7
+from .pose import RigidTransform
+
+
+@patch
+def forward(self: Detector, pose: RigidTransform):
+    """Create source and target points for X-rays to trace through the volume."""
+    pose = self.reorient.compose(pose)
+    source = pose(self.source)
+    target = pose(self.target)
+    return source, target
diff --git a/src/diffdrr/drr.py b/src/diffdrr/drr.py
new file mode 100644
index 0000000..8909e68
--- /dev/null
+++ b/src/diffdrr/drr.py
@@ -0,0 +1,215 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../notebooks/api/00_drr.ipynb.
+
+# %% ../notebooks/api/00_drr.ipynb 3
+from __future__ import annotations
+
+import numpy as np
+import torch
+import torch.nn as nn
+from fastcore.basics import patch
+
+from .detector import Detector
+from .renderers import Siddon, Trilinear
+
+# %% auto 0
+__all__ = ["DRR"]
+
+# %% ../notebooks/api/00_drr.ipynb 7
+from torchio import Subject
+
+
+class DRR(nn.Module):
+    """PyTorch module that computes differentiable digitally reconstructed radiographs."""
+
+    def __init__(
+        self,
+        subject: Subject,  # TorchIO wrapper for the CT volume
+        sdd: float,  # Source-to-detector distance (i.e., the C-arm's focal length)
+        height: int,  # Height of the rendered DRR
+        delx: float,  # X-axis pixel size
+        width: int | None = None,  # Width of the rendered DRR (default to `height`)
+        dely: float | None = None,  # Y-axis pixel size (if not provided, set to `delx`)
+        x0: float = 0.0,  # Principal point X-offset
+        y0: float = 0.0,  # Principal point Y-offset
+        p_subsample: float | None = None,  # Proportion of pixels to randomly subsample
+        reshape: bool = True,  # Return DRR with shape (b, 1, h, w)
+        reverse_x_axis: bool = True,  # If True, obey radiologic convention (e.g., heart on right)
+        patch_size: int | None = None,  # Render patches of the DRR in series
+        renderer: str = "siddon",  # Rendering backend, either "siddon" or "trilinear"
+        **renderer_kwargs,  # Kwargs for the renderer
+    ):
+        super().__init__()
+
+        # Initialize the X-ray detector
+        width = height if width is None else width
+        dely = delx if dely is None else dely
+        if p_subsample is not None:
+            n_subsample = int(height * width * p_subsample)
+        else:
+            n_subsample = None
+        self.detector = Detector(
+            sdd,
+            height,
+            width,
+            delx,
+            dely,
+            x0,
+            y0,
+            subject.reorient,
+            reverse_x_axis=reverse_x_axis,
+            n_subsample=n_subsample,
+        )
+
+        # Initialize the volume
+        self.subject = subject
+        self.volume = subject.volume.data.squeeze()
+        self.register_buffer("density", subject.density)
+        self.register_buffer(
+            "spacing", torch.tensor(subject.volume.spacing, dtype=torch.float32)
+        )
+        self.register_buffer(
+            "origin", torch.tensor(subject.volume.origin, dtype=torch.float32)
+        )
+        if subject.mask is not None:
+            self.register_buffer("mask", subject.mask.data[0].to(torch.int64))
+
+        # Initialize the renderer
+        if renderer == "siddon":
+            self.renderer = Siddon(**renderer_kwargs)
+        elif renderer == "trilinear":
+            self.renderer = Trilinear(**renderer_kwargs)
+        else:
+            raise ValueError(f"renderer must be 'siddon', not {renderer}")
+        self.reshape = reshape
+        self.patch_size = patch_size
+        if self.patch_size is not None:
+            self.n_patches = (height * width) // (self.patch_size**2)
+
+    def reshape_transform(self, img, batch_size):
+        if self.reshape:
+            if self.detector.n_subsample is None:
+                img = img.view(
+                    batch_size, -1, self.detector.height, self.detector.width
+                )
+            else:
+                img = reshape_subsampled_drr(img, self.detector, batch_size)
+        return img
+
+
+# %% ../notebooks/api/00_drr.ipynb 8
+def reshape_subsampled_drr(img: torch.Tensor, detector: Detector, batch_size: int):
+    n_points = detector.height * detector.width
+    drr = torch.zeros(batch_size, n_points).to(img)
+    drr[:, detector.subsamples[-1]] = img
+    drr = drr.view(batch_size, 1, detector.height, detector.width)
+    return drr
+
+
+# %% ../notebooks/api/00_drr.ipynb 10
+from .pose import convert
+
+
+@patch
+def forward(
+    self: DRR,
+    *args,  # Some batched representation of SE(3)
+    parameterization: str = None,  # Specifies the representation of the rotation
+    convention: str = None,  # If parameterization is Euler angles, specify convention
+    mask_to_channels: bool = False,  # If True, structures from the CT mask are rendered in separate channels
+    **kwargs,  # Passed to the renderer
+):
+    """Generate DRR with rotational and translational parameters."""
+    # Initialize the camera pose
+    if parameterization is None:
+        pose = args[0]
+    else:
+        pose = convert(*args, parameterization=parameterization, convention=convention)
+    source, target = self.detector(pose)
+
+    # Render the DRR
+    kwargs["mask"] = self.mask if mask_to_channels else None
+    if self.patch_size is None:
+        img = self.renderer(
+            self.density,
+            self.origin,
+            self.spacing,
+            source,
+            target,
+            **kwargs,
+        )
+    else:
+        n_points = target.shape[1] // self.n_patches
+        img = []
+        for idx in range(self.n_patches):
+            t = target[:, idx * n_points : (idx + 1) * n_points]
+            partial = self.renderer(
+                self.density,
+                self.origin,
+                self.spacing,
+                source,
+                t,
+                **kwargs,
+            )
+            img.append(partial)
+        img = torch.cat(img, dim=1)
+    return self.reshape_transform(img, batch_size=len(pose))
+
+
+# %% ../notebooks/api/00_drr.ipynb 11
+@patch
+def set_intrinsics(
+    self: DRR,
+    sdd: float = None,
+    delx: float = None,
+    dely: float = None,
+    x0: float = None,
+    y0: float = None,
+):
+    self.detector = Detector(
+        sdd if sdd is not None else self.detector.sdd,
+        self.detector.height,
+        self.detector.width,
+        delx if delx is not None else self.detector.delx,
+        dely if dely is not None else self.detector.dely,
+        x0 if x0 is not None else self.detector.x0,
+        y0 if y0 is not None else self.detector.y0,
+        n_subsample=self.detector.n_subsample,
+        reverse_x_axis=self.detector.reverse_x_axis,
+    ).to(self.volume)
+
+
+# %% ../notebooks/api/00_drr.ipynb 12
+from .pose import RigidTransform
+
+
+@patch
+def perspective_projection(
+    self: DRR,
+    pose: RigidTransform,
+    pts: torch.Tensor,
+):
+    extrinsic = (self.detector.reorient.compose(pose)).inverse()
+    x = extrinsic(pts)
+    x = torch.einsum("ij, bnj -> bni", self.detector.intrinsic, x)
+    z = x[..., -1].unsqueeze(-1).clone()
+    x = x / z
+    return x[..., :2]
+
+
+# %% ../notebooks/api/00_drr.ipynb 13
+from torch.nn.functional import pad
+
+
+@patch
+def inverse_projection(
+    self: DRR,
+    pose: RigidTransform,
+    pts: torch.Tensor,
+):
+    extrinsic = self.detector.reorient.compose(pose)
+    x = self.detector.sdd * torch.einsum(
+        "ij, bnj -> bni",
+        self.detector.intrinsic.inverse(),
+        pad(pts, (0, 1), value=1),  # Convert to homogenous coordinates
+    )
+    return extrinsic(x)
diff --git a/src/diffdrr/metrics.py b/src/diffdrr/metrics.py
new file mode 100644
index 0000000..82f08d4
--- /dev/null
+++ b/src/diffdrr/metrics.py
@@ -0,0 +1,179 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../notebooks/api/05_metrics.ipynb.
+
+# %% ../notebooks/api/05_metrics.ipynb 3
+from __future__ import annotations
+
+import torch
+
+# %% auto 0
+__all__ = ['NormalizedCrossCorrelation2d', 'MultiscaleNormalizedCrossCorrelation2d', 'GradientNormalizedCrossCorrelation2d',
+           'LogGeodesicSE3', 'DoubleGeodesicSE3']
+
+# %% ../notebooks/api/05_metrics.ipynb 6
+from einops import rearrange
+
+
+def to_patches(x, patch_size):
+    x = x.unfold(2, patch_size, step=1).unfold(3, patch_size, step=1).contiguous()
+    return rearrange(x, "b c p1 p2 h w -> b (c p1 p2) h w")
+
+# %% ../notebooks/api/05_metrics.ipynb 7
+class NormalizedCrossCorrelation2d(torch.nn.Module):
+    """Compute Normalized Cross Correlation between two batches of images."""
+
+    def __init__(self, patch_size=None, eps=1e-5):
+        super().__init__()
+        self.patch_size = patch_size
+        self.eps = eps
+
+    def forward(self, x1, x2):
+        if self.patch_size is not None:
+            x1 = to_patches(x1, self.patch_size)
+            x2 = to_patches(x2, self.patch_size)
+        assert x1.shape == x2.shape, "Input images must be the same size"
+        _, c, h, w = x1.shape
+        x1, x2 = self.norm(x1), self.norm(x2)
+        score = torch.einsum("b...,b...->b", x1, x2)
+        score /= c * h * w
+        return score
+
+    def norm(self, x):
+        mu = x.mean(dim=[-1, -2], keepdim=True)
+        var = x.var(dim=[-1, -2], keepdim=True, correction=0) + self.eps
+        std = var.sqrt()
+        return (x - mu) / std
+
+# %% ../notebooks/api/05_metrics.ipynb 8
+class MultiscaleNormalizedCrossCorrelation2d(torch.nn.Module):
+    """Compute Normalized Cross Correlation between two batches of images at multiple scales."""
+
+    def __init__(self, patch_sizes=[None], patch_weights=[1.0], eps=1e-5):
+        super().__init__()
+
+        assert len(patch_sizes) == len(patch_weights), "Each scale must have a weight"
+        self.nccs = [
+            NormalizedCrossCorrelation2d(patch_size) for patch_size in patch_sizes
+        ]
+        self.patch_weights = patch_weights
+
+    def forward(self, x1, x2):
+        scores = []
+        for weight, ncc in zip(self.patch_weights, self.nccs):
+            scores.append(weight * ncc(x1, x2))
+        return torch.stack(scores, dim=0).sum(dim=0)
+
+# %% ../notebooks/api/05_metrics.ipynb 9
+from torchvision.transforms.functional import gaussian_blur
+
+
+class Sobel(torch.nn.Module):
+    def __init__(self, sigma):
+        super().__init__()
+        self.sigma = sigma
+        self.filter = torch.nn.Conv2d(
+            in_channels=1,
+            out_channels=2,  # X- and Y-gradients
+            kernel_size=3,
+            stride=1,
+            padding=1,  # Return images of the same size as inputs
+            bias=False,
+        )
+
+        Gx = torch.tensor([[1, 0, -1], [2, 0, -2], [1, 0, -1]]).to(torch.float32)
+        Gy = torch.tensor([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]).to(torch.float32)
+        G = torch.stack([Gx, Gy]).unsqueeze(1)
+        self.filter.weight = torch.nn.Parameter(G, requires_grad=False)
+
+    def forward(self, img):
+        x = gaussian_blur(img, 5, self.sigma)
+        x = self.filter(img)
+        return x
+
+# %% ../notebooks/api/05_metrics.ipynb 10
+class GradientNormalizedCrossCorrelation2d(NormalizedCrossCorrelation2d):
+    """Compute Normalized Cross Correlation between the image gradients of two batches of images."""
+
+    def __init__(self, patch_size=None, sigma=1.0, **kwargs):
+        super().__init__(patch_size, **kwargs)
+        self.sobel = Sobel(sigma)
+
+    def forward(self, x1, x2):
+        return super().forward(self.sobel(x1), self.sobel(x2))
+
+# %% ../notebooks/api/05_metrics.ipynb 14
+from .pose import RigidTransform, convert, so3_log_map
+
+
+class LogGeodesicSE3(torch.nn.Module):
+    """
+    Calculate the distance between transforms in the log-space of SE(3).
+    """
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(
+        self,
+        pose_1: RigidTransform,
+        pose_2: RigidTransform,
+    ) -> Float[torch.Tensor, "b"]:
+        return pose_2.compose(pose_1.inverse()).get_se3_log().norm(dim=1)
+
+# %% ../notebooks/api/05_metrics.ipynb 16
+class GeodesicSO3(torch.nn.Module):
+    """Calculate the angular distance between two rotations in SO(3)."""
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(
+        self,
+        pose_1: RigidTransform,
+        pose_2: RigidTransform,
+    ) -> Float[torch.Tensor, "b"]:
+        r1 = pose_1.matrix[..., :3, :3]
+        r2 = pose_2.matrix[..., :3, :3]
+        rdiff = r1.transpose(-1, -2) @ r2
+        return so3_log_map(rdiff).norm(dim=-1)
+
+
+class GeodesicTranslation(torch.nn.Module):
+    """Calculate the angular distance between two translations in R^3."""
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(
+        self,
+        pose_1: RigidTransform,
+        pose_2: RigidTransform,
+    ) -> Float[torch.Tensor, "b"]:
+        t1 = pose_1.matrix[..., :3, 3]
+        t2 = pose_2.matrix[..., :3, 3]
+        return (t1 - t2).norm(dim=1)
+
+# %% ../notebooks/api/05_metrics.ipynb 18
+class DoubleGeodesicSE3(torch.nn.Module):
+    """
+    Calculate the angular and translational geodesics between two SE(3) transformation matrices.
+    """
+
+    def __init__(
+        self,
+        sdd: float,  # Source-to-detector distance
+        eps: float = 1e-4,  # Avoid overflows in sqrt
+    ):
+        super().__init__()
+        self.sdr = sdd / 2
+        self.eps = eps
+
+        self.rotation = GeodesicSO3()
+        self.translation = GeodesicTranslation()
+
+    def forward(self, pose_1: RigidTransform, pose_2: RigidTransform):
+        angular_geodesic = self.sdr * self.rotation(pose_1, pose_2)
+        translation_geodesic = self.translation(pose_1, pose_2)
+        double_geodesic = (
+            (angular_geodesic).square() + translation_geodesic.square() + self.eps
+        ).sqrt()
+        return angular_geodesic, translation_geodesic, double_geodesic
diff --git a/src/diffdrr/pose.py b/src/diffdrr/pose.py
new file mode 100644
index 0000000..cd63145
--- /dev/null
+++ b/src/diffdrr/pose.py
@@ -0,0 +1,1275 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../notebooks/api/06_pose.ipynb.
+
+# %% auto 0
+__all__ = ['RigidTransform', 'convert', 'rotation_10d_to_quaternion', 'quaternion_to_rotation_10d',
+           'quaternion_adjugate_to_quaternion', 'quaternion_to_quaternion_adjugate']
+
+# %% ../notebooks/api/06_pose.ipynb 4
+import torch
+
+
+class RigidTransform(torch.nn.Module):
+    """
+    Applies rigid transforms in SE(3) to point clouds. Can handle
+    batched rigid transforms, composition of transforms, closed-form
+    inversion, and conversions to various representations of SE(3).
+    """
+
+    def __init__(self, matrix):
+        super().__init__()
+        if matrix.dim() == 2:
+            matrix = matrix.unsqueeze(0)
+        self.register_buffer("matrix", matrix)
+
+    def __len__(self):
+        return len(self.matrix)
+
+    def __getitem__(self, idx):
+        return self.matrix[idx]
+
+    def forward(self, x):
+        """Apply (a batch) of rigid transforms to a pointcloud."""
+        x_pad = torch.nn.functional.pad(x, (0, 1), value=1.0)
+        return torch.einsum("bij, knj -> bni", self.matrix[:, :3], x_pad)
+
+    def inverse(self):
+        R = self.matrix[..., :3, :3]
+        t = self.matrix[..., :3, 3]
+        Rinv = R.mT
+        tinv = -torch.einsum("bij, bj -> bi", Rinv, t)
+        matrix = make_matrix(Rinv, tinv)
+        return RigidTransform(matrix)
+
+    def compose(self, T):
+        matrix = torch.einsum("bij, bjk -> bik", T.matrix, self.matrix)
+        return RigidTransform(matrix)
+
+    def convert(self, parameterization, convention=None):
+        translation = self.matrix[..., :3, 3]
+        if parameterization == "axis_angle":
+            rotation = matrix_to_axis_angle(self.matrix[..., :3, :3])
+        elif parameterization == "euler_angles":
+            rotation = matrix_to_euler_angles(self.matrix[..., :3, :3], convention)
+        elif parameterization == "matrix":
+            rotation = self.matrix[..., :3, :3]
+        elif parameterization == "quaternion":
+            rotation = matrix_to_quaternion(self.matrix[..., :3, :3])
+            rotation = standardize_quaternion(rotation)
+        elif parameterization == "quaternion_adjugate":
+            quaternion = matrix_to_quaternion(self.matrix[..., :3, :3])
+            rotation = quaternion_to_quaternion_adjugate(quaternion)
+        elif parameterization == "rotation_6d":
+            rotation = matrix_to_rotation_6d(self.matrix[..., :3, :3])
+        elif parameterization == "rotation_10d":
+            quaternion = matrix_to_quaternion(self.matrix[..., :3, :3])
+            rotation = quaternion_to_rotation_10d(quaternion)
+        elif parameterization == "se3_log_map":
+            params = self.get_se3_log()
+            rotation = params[..., 3:]
+            translation = params[..., :3]
+        else:
+            raise ValueError(f"Must be in {PARAMETERIZATIONS}, not {parameterization}")
+        return rotation, translation
+
+    def get_se3_log(self):
+        return se3_log_map(self.matrix.mT)
+
+# %% ../notebooks/api/06_pose.ipynb 5
+def make_matrix(R, t):
+    assert (batch_size := len(R)) == len(t)
+    matrix = torch.zeros(batch_size, 4, 4).to(R)
+    matrix[..., :3, :3] = R
+    matrix[..., :3, 3] = t
+    matrix[..., -1, -1] = 1.0
+    return matrix
+
+# %% ../notebooks/api/06_pose.ipynb 6
+from scipy.spatial.transform import Rotation
+
+
+def random_rigid_transform(batch_size=1):
+    """Helper function for testing implementations."""
+    R = torch.from_numpy(Rotation.random(batch_size).as_matrix()).to(torch.float32)
+    t = 100 * torch.randn((batch_size, 3))
+    return RigidTransform(make_matrix(R, t))
+
+# %% ../notebooks/api/06_pose.ipynb 8
+PARAMETERIZATIONS = [
+    "axis_angle",
+    "euler_angles",
+    "matrix",
+    "quaternion",
+    "quaternion_adjugate",
+    "rotation_6d",
+    "rotation_10d",
+    "se3_log_map",
+]
+
+# %% ../notebooks/api/06_pose.ipynb 9
+def convert(*args, parameterization, convention=None) -> RigidTransform:
+    if parameterization == "euler_angles" and convention is None:
+        raise ValueError(
+            "convention for Euler angles must be specified as a 3 letter combination of [X, Y, Z]"
+        )
+
+    if parameterization == "axis_angle":
+        rotation, translation = args
+        rotmat = axis_angle_to_matrix(rotation)
+        matrix = make_matrix(rotmat, translation)
+    elif parameterization == "euler_angles":
+        rotation, translation = args
+        rotmat = euler_angles_to_matrix(rotation, convention)
+        matrix = make_matrix(rotmat, translation)
+    elif parameterization == "matrix":
+        return RigidTransform(args[0])
+    elif parameterization == "quaternion":
+        rotation, translation = args
+        rotmat = quaternion_to_matrix(rotation)
+    elif parameterization == "quaternion_adjugate":
+        rotation, translation = args
+        quaternion = quaternion_adjugate_to_quaternion(rotation)
+        return convert(quaternion, translation, parameterization="quaternion")
+    elif parameterization == "rotation_6d":
+        rotation, translation = args
+        rotmat = rotation_6d_to_matrix(rotation)
+        matrix = make_matrix(rotmat, translation)
+    elif parameterization in ["rotation_10d"]:
+        rotation, translation = args
+        quaternion = rotation_10d_to_quaternion(rotation)
+        return convert(quaternion, translation, parameterization="quaternion")
+    elif parameterization == "se3_log_map":
+        rotation, translation = args
+        params = torch.concat([translation, rotation], axis=-1)
+        matrix = se3_exp_map(params).mT
+    else:
+        raise ValueError(f"Must be in {PARAMETERIZATIONS}, not {parameterization}")
+
+    return convert(matrix, parameterization="matrix")
+
+# %% ../notebooks/api/06_pose.ipynb 11
+def _10vec_to_4x4symmetric(vec):
+    """Convert a 10-vector to a symmetric 4x4 matrix."""
+    b = len(vec)
+    A = torch.zeros(b, 4, 4, device=vec.device)
+    idx, jdx = torch.triu_indices(4, 4)
+    A[..., idx, jdx] = vec
+    A[..., jdx, idx] = vec
+    return A
+
+# %% ../notebooks/api/06_pose.ipynb 12
+def rotation_10d_to_quaternion(rotation: torch.Tensor) -> torch.Tensor:
+    """
+    Convert a 10-vector into a symmetric matrix, whose eigenvector corresponding
+    to the eigenvalue of minimum modulus is the resulting quaternion.
+
+    Source: https://arxiv.org/abs/2006.01031
+    """
+    A = _10vec_to_4x4symmetric(rotation)  # A is a symmetric data matrix
+    return torch.linalg.eigh(A).eigenvectors[..., 0]
+
+
+def quaternion_to_rotation_10d(q: torch.Tensor) -> torch.Tensor:
+    A = -torch.einsum("bi, bj -> bij", q, q)
+    idx, jdx = torch.triu_indices(4, 4)
+    return A[..., idx, jdx]
+
+# %% ../notebooks/api/06_pose.ipynb 13
+def quaternion_adjugate_to_quaternion(rotation: torch.Tensor) -> torch.Tensor:
+    """
+    Convert a 10-vector in the quaternion adjugate, a symmetric matrix whose
+    eigenvector corresponding to the eigenvalue of maximum modulus is the
+    (unnormalized) quaternion. Uses a fast method to solve for the eigenvector
+    without explicity computing the eigendecomposition.
+
+    Source: https://arxiv.org/abs/2205.09116
+    """
+    A = _10vec_to_4x4symmetric(rotation)  # A is the quaternion adjugate
+    norms = A.norm(dim=1).amax(dim=1, keepdim=True)
+    max_eigenvectors = torch.argmax(A.norm(dim=1), dim=1)
+    return A[range(len(A)), max_eigenvectors] / norms
+
+
+def quaternion_to_quaternion_adjugate(q: torch.Tensor) -> torch.Tensor:
+    A = torch.einsum("bi, bj -> bij", q, q)
+    idx, jdx = torch.triu_indices(4, 4)
+    return A[..., idx, jdx]
+
+# %% ../notebooks/api/06_pose.ipynb 16
+# pytorch3d/transforms/rotation_conversions.py
+
+from typing import Optional, Union
+
+import torch.nn.functional as F
+
+Device = Union[str, torch.device]
+
+
+"""
+The transformation matrices returned from the functions in this file assume
+the points on which the transformation will be applied are column vectors.
+i.e. the R matrix is structured as
+
+    R = [
+            [Rxx, Rxy, Rxz],
+            [Ryx, Ryy, Ryz],
+            [Rzx, Rzy, Rzz],
+        ]  # (3, 3)
+
+This matrix can be applied to column vectors by post multiplication
+by the points e.g.
+
+    points = [[0], [1], [2]]  # (3 x 1) xyz coordinates of a point
+    transformed_points = R * points
+
+To apply the same matrix to points which are row vectors, the R matrix
+can be transposed and pre multiplied by the points:
+
+e.g.
+    points = [[0, 1, 2]]  # (1 x 3) xyz coordinates of a point
+    transformed_points = points * R.transpose(1, 0)
+"""
+
+
+def quaternion_to_matrix(quaternions: torch.Tensor) -> torch.Tensor:
+    """
+    Convert rotations given as quaternions to rotation matrices.
+
+    Args:
+        quaternions: quaternions with real part first,
+            as tensor of shape (..., 4).
+
+    Returns:
+        Rotation matrices as tensor of shape (..., 3, 3).
+    """
+    r, i, j, k = torch.unbind(quaternions, -1)
+    # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
+    two_s = 2.0 / (quaternions * quaternions).sum(-1)
+
+    o = torch.stack(
+        (
+            1 - two_s * (j * j + k * k),
+            two_s * (i * j - k * r),
+            two_s * (i * k + j * r),
+            two_s * (i * j + k * r),
+            1 - two_s * (i * i + k * k),
+            two_s * (j * k - i * r),
+            two_s * (i * k - j * r),
+            two_s * (j * k + i * r),
+            1 - two_s * (i * i + j * j),
+        ),
+        -1,
+    )
+    return o.reshape(quaternions.shape[:-1] + (3, 3))
+
+
+def _copysign(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
+    """
+    Return a tensor where each element has the absolute value taken from the,
+    corresponding element of a, with sign taken from the corresponding
+    element of b. This is like the standard copysign floating-point operation,
+    but is not careful about negative 0 and NaN.
+
+    Args:
+        a: source tensor.
+        b: tensor whose signs will be used, of the same shape as a.
+
+    Returns:
+        Tensor of the same shape as a with the signs of b.
+    """
+    signs_differ = (a < 0) != (b < 0)
+    return torch.where(signs_differ, -a, a)
+
+
+def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor:
+    """
+    Returns torch.sqrt(torch.max(0, x))
+    but with a zero subgradient where x is 0.
+    """
+    ret = torch.zeros_like(x)
+    positive_mask = x > 0
+    ret[positive_mask] = torch.sqrt(x[positive_mask])
+    return ret
+
+
+def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
+    """
+    Convert rotations given as rotation matrices to quaternions.
+
+    Args:
+        matrix: Rotation matrices as tensor of shape (..., 3, 3).
+
+    Returns:
+        quaternions with real part first, as tensor of shape (..., 4).
+    """
+    if matrix.size(-1) != 3 or matrix.size(-2) != 3:
+        raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.")
+
+    batch_dim = matrix.shape[:-2]
+    m00, m01, m02, m10, m11, m12, m20, m21, m22 = torch.unbind(
+        matrix.reshape(batch_dim + (9,)), dim=-1
+    )
+
+    q_abs = _sqrt_positive_part(
+        torch.stack(
+            [
+                1.0 + m00 + m11 + m22,
+                1.0 + m00 - m11 - m22,
+                1.0 - m00 + m11 - m22,
+                1.0 - m00 - m11 + m22,
+            ],
+            dim=-1,
+        )
+    )
+
+    # we produce the desired quaternion multiplied by each of r, i, j, k
+    quat_by_rijk = torch.stack(
+        [
+            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
+            #  `int`.
+            torch.stack([q_abs[..., 0] ** 2, m21 - m12, m02 - m20, m10 - m01], dim=-1),
+            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
+            #  `int`.
+            torch.stack([m21 - m12, q_abs[..., 1] ** 2, m10 + m01, m02 + m20], dim=-1),
+            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
+            #  `int`.
+            torch.stack([m02 - m20, m10 + m01, q_abs[..., 2] ** 2, m12 + m21], dim=-1),
+            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
+            #  `int`.
+            torch.stack([m10 - m01, m20 + m02, m21 + m12, q_abs[..., 3] ** 2], dim=-1),
+        ],
+        dim=-2,
+    )
+
+    # We floor here at 0.1 but the exact level is not important; if q_abs is small,
+    # the candidate won't be picked.
+    flr = torch.tensor(0.1).to(dtype=q_abs.dtype, device=q_abs.device)
+    quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(flr))
+
+    # if not for numerical problems, quat_candidates[i] should be same (up to a sign),
+    # forall i; we pick the best-conditioned one (with the largest denominator)
+    out = quat_candidates[
+        F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, :
+    ].reshape(batch_dim + (4,))
+    return standardize_quaternion(out)
+
+
+def _axis_angle_rotation(axis: str, angle: torch.Tensor) -> torch.Tensor:
+    """
+    Return the rotation matrices for one of the rotations about an axis
+    of which Euler angles describe, for each value of the angle given.
+
+    Args:
+        axis: Axis label "X" or "Y or "Z".
+        angle: any shape tensor of Euler angles in radians
+
+    Returns:
+        Rotation matrices as tensor of shape (..., 3, 3).
+    """
+
+    cos = torch.cos(angle)
+    sin = torch.sin(angle)
+    one = torch.ones_like(angle)
+    zero = torch.zeros_like(angle)
+
+    if axis == "X":
+        R_flat = (one, zero, zero, zero, cos, -sin, zero, sin, cos)
+    elif axis == "Y":
+        R_flat = (cos, zero, sin, zero, one, zero, -sin, zero, cos)
+    elif axis == "Z":
+        R_flat = (cos, -sin, zero, sin, cos, zero, zero, zero, one)
+    else:
+        raise ValueError("letter must be either X, Y or Z.")
+
+    return torch.stack(R_flat, -1).reshape(angle.shape + (3, 3))
+
+
+def euler_angles_to_matrix(euler_angles: torch.Tensor, convention: str) -> torch.Tensor:
+    """
+    Convert rotations given as Euler angles in radians to rotation matrices.
+
+    Args:
+        euler_angles: Euler angles in radians as tensor of shape (..., 3).
+        convention: Convention string of three uppercase letters from
+            {"X", "Y", and "Z"}.
+
+    Returns:
+        Rotation matrices as tensor of shape (..., 3, 3).
+    """
+    if euler_angles.dim() == 0 or euler_angles.shape[-1] != 3:
+        raise ValueError("Invalid input euler angles.")
+    if len(convention) != 3:
+        raise ValueError("Convention must have 3 letters.")
+    if convention[1] in (convention[0], convention[2]):
+        raise ValueError(f"Invalid convention {convention}.")
+    for letter in convention:
+        if letter not in ("X", "Y", "Z"):
+            raise ValueError(f"Invalid letter {letter} in convention string.")
+    matrices = [
+        _axis_angle_rotation(c, e)
+        for c, e in zip(convention, torch.unbind(euler_angles, -1))
+    ]
+    # return functools.reduce(torch.matmul, matrices)
+    return torch.matmul(torch.matmul(matrices[0], matrices[1]), matrices[2])
+
+
+def _angle_from_tan(
+    axis: str, other_axis: str, data, horizontal: bool, tait_bryan: bool
+) -> torch.Tensor:
+    """
+    Extract the first or third Euler angle from the two members of
+    the matrix which are positive constant times its sine and cosine.
+
+    Args:
+        axis: Axis label "X" or "Y or "Z" for the angle we are finding.
+        other_axis: Axis label "X" or "Y or "Z" for the middle axis in the
+            convention.
+        data: Rotation matrices as tensor of shape (..., 3, 3).
+        horizontal: Whether we are looking for the angle for the third axis,
+            which means the relevant entries are in the same row of the
+            rotation matrix. If not, they are in the same column.
+        tait_bryan: Whether the first and third axes in the convention differ.
+
+    Returns:
+        Euler Angles in radians for each matrix in data as a tensor
+        of shape (...).
+    """
+
+    i1, i2 = {"X": (2, 1), "Y": (0, 2), "Z": (1, 0)}[axis]
+    if horizontal:
+        i2, i1 = i1, i2
+    even = (axis + other_axis) in ["XY", "YZ", "ZX"]
+    if horizontal == even:
+        return torch.atan2(data[..., i1], data[..., i2])
+    if tait_bryan:
+        return torch.atan2(-data[..., i2], data[..., i1])
+    return torch.atan2(data[..., i2], -data[..., i1])
+
+
+def _index_from_letter(letter: str) -> int:
+    if letter == "X":
+        return 0
+    if letter == "Y":
+        return 1
+    if letter == "Z":
+        return 2
+    raise ValueError("letter must be either X, Y or Z.")
+
+
+def matrix_to_euler_angles(matrix: torch.Tensor, convention: str) -> torch.Tensor:
+    """
+    Convert rotations given as rotation matrices to Euler angles in radians.
+
+    Args:
+        matrix: Rotation matrices as tensor of shape (..., 3, 3).
+        convention: Convention string of three uppercase letters.
+
+    Returns:
+        Euler angles in radians as tensor of shape (..., 3).
+    """
+    if len(convention) != 3:
+        raise ValueError("Convention must have 3 letters.")
+    if convention[1] in (convention[0], convention[2]):
+        raise ValueError(f"Invalid convention {convention}.")
+    for letter in convention:
+        if letter not in ("X", "Y", "Z"):
+            raise ValueError(f"Invalid letter {letter} in convention string.")
+    if matrix.size(-1) != 3 or matrix.size(-2) != 3:
+        raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.")
+    i0 = _index_from_letter(convention[0])
+    i2 = _index_from_letter(convention[2])
+    tait_bryan = i0 != i2
+    if tait_bryan:
+        central_angle = torch.asin(
+            matrix[..., i0, i2] * (-1.0 if i0 - i2 in [-1, 2] else 1.0)
+        )
+    else:
+        central_angle = torch.acos(matrix[..., i0, i0])
+
+    o = (
+        _angle_from_tan(
+            convention[0], convention[1], matrix[..., i2], False, tait_bryan
+        ),
+        central_angle,
+        _angle_from_tan(
+            convention[2], convention[1], matrix[..., i0, :], True, tait_bryan
+        ),
+    )
+    return torch.stack(o, -1)
+
+
+def standardize_quaternion(quaternions: torch.Tensor) -> torch.Tensor:
+    """
+    Convert a unit quaternion to a standard form: one in which the real
+    part is non negative.
+
+    Args:
+        quaternions: Quaternions with real part first,
+            as tensor of shape (..., 4).
+
+    Returns:
+        Standardized quaternions as tensor of shape (..., 4).
+    """
+    return torch.where(quaternions[..., 0:1] < 0, -quaternions, quaternions)
+
+
+def quaternion_raw_multiply(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
+    """
+    Multiply two quaternions.
+    Usual torch rules for broadcasting apply.
+
+    Args:
+        a: Quaternions as tensor of shape (..., 4), real part first.
+        b: Quaternions as tensor of shape (..., 4), real part first.
+
+    Returns:
+        The product of a and b, a tensor of quaternions shape (..., 4).
+    """
+    aw, ax, ay, az = torch.unbind(a, -1)
+    bw, bx, by, bz = torch.unbind(b, -1)
+    ow = aw * bw - ax * bx - ay * by - az * bz
+    ox = aw * bx + ax * bw + ay * bz - az * by
+    oy = aw * by - ax * bz + ay * bw + az * bx
+    oz = aw * bz + ax * by - ay * bx + az * bw
+    return torch.stack((ow, ox, oy, oz), -1)
+
+
+def quaternion_multiply(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
+    """
+    Multiply two quaternions representing rotations, returning the quaternion
+    representing their composition, i.e. the versor with nonnegative real part.
+    Usual torch rules for broadcasting apply.
+
+    Args:
+        a: Quaternions as tensor of shape (..., 4), real part first.
+        b: Quaternions as tensor of shape (..., 4), real part first.
+
+    Returns:
+        The product of a and b, a tensor of quaternions of shape (..., 4).
+    """
+    ab = quaternion_raw_multiply(a, b)
+    return standardize_quaternion(ab)
+
+
+def quaternion_invert(quaternion: torch.Tensor) -> torch.Tensor:
+    """
+    Given a quaternion representing rotation, get the quaternion representing
+    its inverse.
+
+    Args:
+        quaternion: Quaternions as tensor of shape (..., 4), with real part
+            first, which must be versors (unit quaternions).
+
+    Returns:
+        The inverse, a tensor of quaternions of shape (..., 4).
+    """
+
+    scaling = torch.tensor([1, -1, -1, -1], device=quaternion.device)
+    return quaternion * scaling
+
+
+def quaternion_apply(quaternion: torch.Tensor, point: torch.Tensor) -> torch.Tensor:
+    """
+    Apply the rotation given by a quaternion to a 3D point.
+    Usual torch rules for broadcasting apply.
+
+    Args:
+        quaternion: Tensor of quaternions, real part first, of shape (..., 4).
+        point: Tensor of 3D points of shape (..., 3).
+
+    Returns:
+        Tensor of rotated points of shape (..., 3).
+    """
+    if point.size(-1) != 3:
+        raise ValueError(f"Points are not in 3D, {point.shape}.")
+    real_parts = point.new_zeros(point.shape[:-1] + (1,))
+    point_as_quaternion = torch.cat((real_parts, point), -1)
+    out = quaternion_raw_multiply(
+        quaternion_raw_multiply(quaternion, point_as_quaternion),
+        quaternion_invert(quaternion),
+    )
+    return out[..., 1:]
+
+
+def axis_angle_to_matrix(axis_angle: torch.Tensor) -> torch.Tensor:
+    """
+    Convert rotations given as axis/angle to rotation matrices.
+
+    Args:
+        axis_angle: Rotations given as a vector in axis angle form,
+            as a tensor of shape (..., 3), where the magnitude is
+            the angle turned anticlockwise in radians around the
+            vector's direction.
+
+    Returns:
+        Rotation matrices as tensor of shape (..., 3, 3).
+    """
+    return quaternion_to_matrix(axis_angle_to_quaternion(axis_angle))
+
+
+def matrix_to_axis_angle(matrix: torch.Tensor) -> torch.Tensor:
+    """
+    Convert rotations given as rotation matrices to axis/angle.
+
+    Args:
+        matrix: Rotation matrices as tensor of shape (..., 3, 3).
+
+    Returns:
+        Rotations given as a vector in axis angle form, as a tensor
+            of shape (..., 3), where the magnitude is the angle
+            turned anticlockwise in radians around the vector's
+            direction.
+    """
+    return quaternion_to_axis_angle(matrix_to_quaternion(matrix))
+
+
+def axis_angle_to_quaternion(axis_angle: torch.Tensor) -> torch.Tensor:
+    """
+    Convert rotations given as axis/angle to quaternions.
+
+    Args:
+        axis_angle: Rotations given as a vector in axis angle form,
+            as a tensor of shape (..., 3), where the magnitude is
+            the angle turned anticlockwise in radians around the
+            vector's direction.
+
+    Returns:
+        quaternions with real part first, as tensor of shape (..., 4).
+    """
+    angles = torch.norm(axis_angle, p=2, dim=-1, keepdim=True)
+    half_angles = angles * 0.5
+    eps = 1e-6
+    small_angles = angles.abs() < eps
+    large = torch.sin(half_angles) / angles
+    small = 0.5 - (angles * angles) / 48
+    sin_half_angles_over_angles = torch.where(small_angles, small, large)
+    quaternions = torch.cat(
+        [torch.cos(half_angles), axis_angle * sin_half_angles_over_angles], dim=-1
+    )
+    return quaternions
+
+
+def quaternion_to_axis_angle(quaternions: torch.Tensor) -> torch.Tensor:
+    """
+    Convert rotations given as quaternions to axis/angle.
+
+    Args:
+        quaternions: quaternions with real part first,
+            as tensor of shape (..., 4).
+
+    Returns:
+        Rotations given as a vector in axis angle form, as a tensor
+            of shape (..., 3), where the magnitude is the angle
+            turned anticlockwise in radians around the vector's
+            direction.
+    """
+    norms = torch.norm(quaternions[..., 1:], p=2, dim=-1, keepdim=True)
+    half_angles = torch.atan2(norms, quaternions[..., :1])
+    angles = 2 * half_angles
+    eps = 1e-6
+    small_angles = angles.abs() < eps
+    sin_half_angles_over_angles = torch.empty_like(angles)
+    sin_half_angles_over_angles[~small_angles] = (
+        torch.sin(half_angles[~small_angles]) / angles[~small_angles]
+    )
+    # for x small, sin(x/2) is about x/2 - (x/2)^3/6
+    # so sin(x/2)/x is about 1/2 - (x*x)/48
+    sin_half_angles_over_angles[small_angles] = (
+        0.5 - (angles[small_angles] * angles[small_angles]) / 48
+    )
+    return quaternions[..., 1:] / sin_half_angles_over_angles
+
+
+def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor:
+    """
+    Converts 6D rotation representation by Zhou et al. [1] to rotation matrix
+    using Gram--Schmidt orthogonalization per Section B of [1].
+    Args:
+        d6: 6D rotation representation, of size (*, 6)
+
+    Returns:
+        batch of rotation matrices of size (*, 3, 3)
+
+    [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
+    On the Continuity of Rotation Representations in Neural Networks.
+    IEEE Conference on Computer Vision and Pattern Recognition, 2019.
+    Retrieved from http://arxiv.org/abs/1812.07035
+    """
+
+    a1, a2 = d6[..., :3], d6[..., 3:]
+    b1 = F.normalize(a1, dim=-1)
+    b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1
+    b2 = F.normalize(b2, dim=-1)
+    b3 = torch.cross(b1, b2, dim=-1)
+    return torch.stack((b1, b2, b3), dim=-2)
+
+
+def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor:
+    """
+    Converts rotation matrices to 6D rotation representation by Zhou et al. [1]
+    by dropping the last row. Note that 6D representation is not unique.
+    Args:
+        matrix: batch of rotation matrices of size (*, 3, 3)
+
+    Returns:
+        6D rotation representation, of size (*, 6)
+
+    [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
+    On the Continuity of Rotation Representations in Neural Networks.
+    IEEE Conference on Computer Vision and Pattern Recognition, 2019.
+    Retrieved from http://arxiv.org/abs/1812.07035
+    """
+    batch_dim = matrix.size()[:-2]
+    return matrix[..., :2, :].clone().reshape(batch_dim + (6,))
+
+# %% ../notebooks/api/06_pose.ipynb 17
+# pytorch3d/transforms/math.py
+from typing import Tuple
+
+DEFAULT_ACOS_BOUND: float = 1.0 - 1e-4
+
+
+def acos_linear_extrapolation(
+    x: torch.Tensor,
+    bounds: Tuple[float, float] = (-DEFAULT_ACOS_BOUND, DEFAULT_ACOS_BOUND),
+) -> torch.Tensor:
+    """
+    Implements `arccos(x)` which is linearly extrapolated outside `x`'s original
+    domain of `(-1, 1)`. This allows for stable backpropagation in case `x`
+    is not guaranteed to be strictly within `(-1, 1)`.
+
+    More specifically::
+
+        bounds=(lower_bound, upper_bound)
+        if lower_bound <= x <= upper_bound:
+            acos_linear_extrapolation(x) = acos(x)
+        elif x <= lower_bound: # 1st order Taylor approximation
+            acos_linear_extrapolation(x)
+                = acos(lower_bound) + dacos/dx(lower_bound) * (x - lower_bound)
+        else:  # x >= upper_bound
+            acos_linear_extrapolation(x)
+                = acos(upper_bound) + dacos/dx(upper_bound) * (x - upper_bound)
+
+    Args:
+        x: Input `Tensor`.
+        bounds: A float 2-tuple defining the region for the
+            linear extrapolation of `acos`.
+            The first/second element of `bound`
+            describes the lower/upper bound that defines the lower/upper
+            extrapolation region, i.e. the region where
+            `x <= bound[0]`/`bound[1] <= x`.
+            Note that all elements of `bound` have to be within (-1, 1).
+    Returns:
+        acos_linear_extrapolation: `Tensor` containing the extrapolated `arccos(x)`.
+    """
+
+    lower_bound, upper_bound = bounds
+
+    if lower_bound > upper_bound:
+        raise ValueError("lower bound has to be smaller or equal to upper bound.")
+
+    if lower_bound <= -1.0 or upper_bound >= 1.0:
+        raise ValueError("Both lower bound and upper bound have to be within (-1, 1).")
+
+    # init an empty tensor and define the domain sets
+    acos_extrap = torch.empty_like(x)
+    x_upper = x >= upper_bound
+    x_lower = x <= lower_bound
+    x_mid = (~x_upper) & (~x_lower)
+
+    # acos calculation for upper_bound < x < lower_bound
+    acos_extrap[x_mid] = torch.acos(x[x_mid])
+    # the linear extrapolation for x >= upper_bound
+    acos_extrap[x_upper] = _acos_linear_approximation(x[x_upper], upper_bound)
+    # the linear extrapolation for x <= lower_bound
+    acos_extrap[x_lower] = _acos_linear_approximation(x[x_lower], lower_bound)
+
+    return acos_extrap
+
+
+def _acos_linear_approximation(x: torch.Tensor, x0: float) -> torch.Tensor:
+    """
+    Calculates the 1st order Taylor expansion of `arccos(x)` around `x0`.
+    """
+    return (x - x0) * _dacos_dx(x0) + math.acos(x0)
+
+
+def _dacos_dx(x: float) -> float:
+    """
+    Calculates the derivative of `arccos(x)` w.r.t. `x`.
+    """
+    return (-1.0) / math.sqrt(1.0 - x * x)
+
+# %% ../notebooks/api/06_pose.ipynb 18
+# pytorch3d/transforms/so3.py
+
+import warnings
+
+
+def so3_relative_angle(
+    R1: torch.Tensor,
+    R2: torch.Tensor,
+    cos_angle: bool = False,
+    cos_bound: float = 1e-4,
+    eps: float = 1e-4,
+) -> torch.Tensor:
+    """
+    Calculates the relative angle (in radians) between pairs of
+    rotation matrices `R1` and `R2` with `angle = acos(0.5 * (Trace(R1 R2^T)-1))`
+
+    .. note::
+        This corresponds to a geodesic distance on the 3D manifold of rotation
+        matrices.
+
+    Args:
+        R1: Batch of rotation matrices of shape `(minibatch, 3, 3)`.
+        R2: Batch of rotation matrices of shape `(minibatch, 3, 3)`.
+        cos_angle: If==True return cosine of the relative angle rather than
+            the angle itself. This can avoid the unstable calculation of `acos`.
+        cos_bound: Clamps the cosine of the relative rotation angle to
+            [-1 + cos_bound, 1 - cos_bound] to avoid non-finite outputs/gradients
+            of the `acos` call. Note that the non-finite outputs/gradients
+            are returned when the angle is requested (i.e. `cos_angle==False`)
+            and the rotation angle is close to 0 or π.
+        eps: Tolerance for the valid trace check of the relative rotation matrix
+            in `so3_rotation_angle`.
+    Returns:
+        Corresponding rotation angles of shape `(minibatch,)`.
+        If `cos_angle==True`, returns the cosine of the angles.
+
+    Raises:
+        ValueError if `R1` or `R2` is of incorrect shape.
+        ValueError if `R1` or `R2` has an unexpected trace.
+    """
+    R12 = torch.bmm(R1, R2.permute(0, 2, 1))
+    return so3_rotation_angle(R12, cos_angle=cos_angle, cos_bound=cos_bound, eps=eps)
+
+
+def so3_rotation_angle(
+    R: torch.Tensor,
+    eps: float = 1e-4,
+    cos_angle: bool = False,
+    cos_bound: float = 1e-4,
+) -> torch.Tensor:
+    """
+    Calculates angles (in radians) of a batch of rotation matrices `R` with
+    `angle = acos(0.5 * (Trace(R)-1))`. The trace of the
+    input matrices is checked to be in the valid range `[-1-eps,3+eps]`.
+    The `eps` argument is a small constant that allows for small errors
+    caused by limited machine precision.
+
+    Args:
+        R: Batch of rotation matrices of shape `(minibatch, 3, 3)`.
+        eps: Tolerance for the valid trace check.
+        cos_angle: If==True return cosine of the rotation angles rather than
+            the angle itself. This can avoid the unstable
+            calculation of `acos`.
+        cos_bound: Clamps the cosine of the rotation angle to
+            [-1 + cos_bound, 1 - cos_bound] to avoid non-finite outputs/gradients
+            of the `acos` call. Note that the non-finite outputs/gradients
+            are returned when the angle is requested (i.e. `cos_angle==False`)
+            and the rotation angle is close to 0 or π.
+
+    Returns:
+        Corresponding rotation angles of shape `(minibatch,)`.
+        If `cos_angle==True`, returns the cosine of the angles.
+
+    Raises:
+        ValueError if `R` is of incorrect shape.
+        ValueError if `R` has an unexpected trace.
+    """
+
+    N, dim1, dim2 = R.shape
+    if dim1 != 3 or dim2 != 3:
+        raise ValueError("Input has to be a batch of 3x3 Tensors.")
+
+    rot_trace = R[:, 0, 0] + R[:, 1, 1] + R[:, 2, 2]
+
+    if ((rot_trace < -1.0 - eps) + (rot_trace > 3.0 + eps)).any():
+        raise ValueError("A matrix has trace outside valid range [-1-eps,3+eps].")
+
+    # phi ... rotation angle
+    phi_cos = (rot_trace - 1.0) * 0.5
+
+    if cos_angle:
+        return phi_cos
+    else:
+        if cos_bound > 0.0:
+            bound = 1.0 - cos_bound
+            return acos_linear_extrapolation(phi_cos, (-bound, bound))
+        else:
+            return torch.acos(phi_cos)
+
+
+def so3_exp_map(log_rot: torch.Tensor, eps: float = 0.0001) -> torch.Tensor:
+    """
+    Convert a batch of logarithmic representations of rotation matrices `log_rot`
+    to a batch of 3x3 rotation matrices using Rodrigues formula [1].
+
+    In the logarithmic representation, each rotation matrix is represented as
+    a 3-dimensional vector (`log_rot`) who's l2-norm and direction correspond
+    to the magnitude of the rotation angle and the axis of rotation respectively.
+
+    The conversion has a singularity around `log(R) = 0`
+    which is handled by clamping controlled with the `eps` argument.
+
+    Args:
+        log_rot: Batch of vectors of shape `(minibatch, 3)`.
+        eps: A float constant handling the conversion singularity.
+
+    Returns:
+        Batch of rotation matrices of shape `(minibatch, 3, 3)`.
+
+    Raises:
+        ValueError if `log_rot` is of incorrect shape.
+
+    [1] https://en.wikipedia.org/wiki/Rodrigues%27_rotation_formula
+    """
+    return _so3_exp_map(log_rot, eps=eps)[0]
+
+
+def so3_exponential_map(log_rot: torch.Tensor, eps: float = 0.0001) -> torch.Tensor:
+    warnings.warn(
+        """so3_exponential_map is deprecated,
+        Use so3_exp_map instead.
+        so3_exponential_map will be removed in future releases.""",
+        PendingDeprecationWarning,
+    )
+
+    return so3_exp_map(log_rot, eps)
+
+
+def _so3_exp_map(
+    log_rot: torch.Tensor, eps: float = 0.0001
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    """
+    A helper function that computes the so3 exponential map and,
+    apart from the rotation matrix, also returns intermediate variables
+    that can be re-used in other functions.
+    """
+    _, dim = log_rot.shape
+    if dim != 3:
+        raise ValueError("Input tensor shape has to be Nx3.")
+
+    nrms = (log_rot * log_rot).sum(1)
+    # phis ... rotation angles
+    rot_angles = torch.clamp(nrms, eps).sqrt()
+    skews = hat(log_rot)
+    skews_square = torch.bmm(skews, skews)
+
+    R = axis_angle_to_matrix(log_rot)
+
+    return R, rot_angles, skews, skews_square
+
+
+def so3_log_map(
+    R: torch.Tensor, eps: float = 0.0001, cos_bound: float = 1e-4
+) -> torch.Tensor:
+    """
+    Convert a batch of 3x3 rotation matrices `R`
+    to a batch of 3-dimensional matrix logarithms of rotation matrices
+    The conversion has a singularity around `(R=I)`.
+
+    Args:
+        R: batch of rotation matrices of shape `(minibatch, 3, 3)`.
+        eps: (unused, for backward compatibility)
+        cos_bound: (unused, for backward compatibility)
+
+    Returns:
+        Batch of logarithms of input rotation matrices
+        of shape `(minibatch, 3)`.
+    """
+
+    N, dim1, dim2 = R.shape
+    if dim1 != 3 or dim2 != 3:
+        raise ValueError("Input has to be a batch of 3x3 Tensors.")
+
+    return matrix_to_axis_angle(R)
+
+
+def hat_inv(h: torch.Tensor) -> torch.Tensor:
+    """
+    Compute the inverse Hat operator [1] of a batch of 3x3 matrices.
+
+    Args:
+        h: Batch of skew-symmetric matrices of shape `(minibatch, 3, 3)`.
+
+    Returns:
+        Batch of 3d vectors of shape `(minibatch, 3, 3)`.
+
+    Raises:
+        ValueError if `h` is of incorrect shape.
+        ValueError if `h` not skew-symmetric.
+
+    [1] https://en.wikipedia.org/wiki/Hat_operator
+    """
+
+    N, dim1, dim2 = h.shape
+    if dim1 != 3 or dim2 != 3:
+        raise ValueError("Input has to be a batch of 3x3 Tensors.")
+
+    ss_diff = torch.abs(h + h.permute(0, 2, 1)).max()
+
+    HAT_INV_SKEW_SYMMETRIC_TOL = 1e-5
+    if float(ss_diff) > HAT_INV_SKEW_SYMMETRIC_TOL:
+        raise ValueError("One of input matrices is not skew-symmetric.")
+
+    x = h[:, 2, 1]
+    y = h[:, 0, 2]
+    z = h[:, 1, 0]
+
+    v = torch.stack((x, y, z), dim=1)
+
+    return v
+
+
+def hat(v: torch.Tensor) -> torch.Tensor:
+    """
+    Compute the Hat operator [1] of a batch of 3D vectors.
+
+    Args:
+        v: Batch of vectors of shape `(minibatch , 3)`.
+
+    Returns:
+        Batch of skew-symmetric matrices of shape
+        `(minibatch, 3 , 3)` where each matrix is of the form:
+            `[    0  -v_z   v_y ]
+             [  v_z     0  -v_x ]
+             [ -v_y   v_x     0 ]`
+
+    Raises:
+        ValueError if `v` is of incorrect shape.
+
+    [1] https://en.wikipedia.org/wiki/Hat_operator
+    """
+
+    N, dim = v.shape
+    if dim != 3:
+        raise ValueError("Input vectors have to be 3-dimensional.")
+
+    h = torch.zeros((N, 3, 3), dtype=v.dtype, device=v.device)
+
+    x, y, z = v.unbind(1)
+
+    h[:, 0, 1] = -z
+    h[:, 0, 2] = y
+    h[:, 1, 0] = z
+    h[:, 1, 2] = -x
+    h[:, 2, 0] = -y
+    h[:, 2, 1] = x
+
+    return h
+
+# %% ../notebooks/api/06_pose.ipynb 19
+# pytorch3d/transforms/se3.py
+
+
+def se3_exp_map(log_transform: torch.Tensor, eps: float = 1e-4) -> torch.Tensor:
+    """
+    Convert a batch of logarithmic representations of SE(3) matrices `log_transform`
+    to a batch of 4x4 SE(3) matrices using the exponential map.
+    See e.g. [1], Sec 9.4.2. for more detailed description.
+
+    A SE(3) matrix has the following form:
+        ```
+        [ R 0 ]
+        [ T 1 ] ,
+        ```
+    where `R` is a 3x3 rotation matrix and `T` is a 3-D translation vector.
+    SE(3) matrices are commonly used to represent rigid motions or camera extrinsics.
+
+    In the SE(3) logarithmic representation SE(3) matrices are
+    represented as 6-dimensional vectors `[log_translation | log_rotation]`,
+    i.e. a concatenation of two 3D vectors `log_translation` and `log_rotation`.
+
+    The conversion from the 6D representation to a 4x4 SE(3) matrix `transform`
+    is done as follows:
+        ```
+        transform = exp( [ hat(log_rotation) 0 ]
+                         [   log_translation 1 ] ) ,
+        ```
+    where `exp` is the matrix exponential and `hat` is the Hat operator [2].
+
+    Note that for any `log_transform` with `0 <= ||log_rotation|| < 2pi`
+    (i.e. the rotation angle is between 0 and 2pi), the following identity holds:
+    ```
+    se3_log_map(se3_exponential_map(log_transform)) == log_transform
+    ```
+
+    The conversion has a singularity around `||log(transform)|| = 0`
+    which is handled by clamping controlled with the `eps` argument.
+
+    Args:
+        log_transform: Batch of vectors of shape `(minibatch, 6)`.
+        eps: A threshold for clipping the squared norm of the rotation logarithm
+            to avoid unstable gradients in the singular case.
+
+    Returns:
+        Batch of transformation matrices of shape `(minibatch, 4, 4)`.
+
+    Raises:
+        ValueError if `log_transform` is of incorrect shape.
+
+    [1] https://jinyongjeong.github.io/Download/SE3/jlblanco2010geometry3d_techrep.pdf
+    [2] https://en.wikipedia.org/wiki/Hat_operator
+    """
+
+    if log_transform.ndim != 2 or log_transform.shape[1] != 6:
+        raise ValueError("Expected input to be of shape (N, 6).")
+
+    N, _ = log_transform.shape
+
+    log_translation = log_transform[..., :3]
+    log_rotation = log_transform[..., 3:]
+
+    # rotation is an exponential map of log_rotation
+    (
+        R,
+        rotation_angles,
+        log_rotation_hat,
+        log_rotation_hat_square,
+    ) = _so3_exp_map(log_rotation, eps=eps)
+
+    # translation is V @ T
+    V = _se3_V_matrix(
+        log_rotation,
+        log_rotation_hat,
+        log_rotation_hat_square,
+        rotation_angles,
+        eps=eps,
+    )
+    T = torch.bmm(V, log_translation[:, :, None])[:, :, 0]
+
+    transform = torch.zeros(
+        N, 4, 4, dtype=log_transform.dtype, device=log_transform.device
+    )
+
+    transform[:, :3, :3] = R
+    transform[:, :3, 3] = T
+    transform[:, 3, 3] = 1.0
+
+    return transform.permute(0, 2, 1)
+
+
+def se3_log_map(
+    transform: torch.Tensor, eps: float = 1e-4, cos_bound: float = 1e-4
+) -> torch.Tensor:
+    """
+    Convert a batch of 4x4 transformation matrices `transform`
+    to a batch of 6-dimensional SE(3) logarithms of the SE(3) matrices.
+    See e.g. [1], Sec 9.4.2. for more detailed description.
+
+    A SE(3) matrix has the following form:
+        ```
+        [ R 0 ]
+        [ T 1 ] ,
+        ```
+    where `R` is an orthonormal 3x3 rotation matrix and `T` is a 3-D translation vector.
+    SE(3) matrices are commonly used to represent rigid motions or camera extrinsics.
+
+    In the SE(3) logarithmic representation SE(3) matrices are
+    represented as 6-dimensional vectors `[log_translation | log_rotation]`,
+    i.e. a concatenation of two 3D vectors `log_translation` and `log_rotation`.
+
+    The conversion from the 4x4 SE(3) matrix `transform` to the
+    6D representation `log_transform = [log_translation | log_rotation]`
+    is done as follows:
+        ```
+        log_transform = log(transform)
+        log_translation = log_transform[3, :3]
+        log_rotation = inv_hat(log_transform[:3, :3])
+        ```
+    where `log` is the matrix logarithm
+    and `inv_hat` is the inverse of the Hat operator [2].
+
+    Note that for any valid 4x4 `transform` matrix, the following identity holds:
+    ```
+    se3_exp_map(se3_log_map(transform)) == transform
+    ```
+
+    The conversion has a singularity around `(transform=I)` which is handled
+    by clamping controlled with the `eps` and `cos_bound` arguments.
+
+    Args:
+        transform: batch of SE(3) matrices of shape `(minibatch, 4, 4)`.
+        eps: A threshold for clipping the squared norm of the rotation logarithm
+            to avoid division by zero in the singular case.
+        cos_bound: Clamps the cosine of the rotation angle to
+            [-1 + cos_bound, 3 - cos_bound] to avoid non-finite outputs.
+            The non-finite outputs can be caused by passing small rotation angles
+            to the `acos` function in `so3_rotation_angle` of `so3_log_map`.
+
+    Returns:
+        Batch of logarithms of input SE(3) matrices
+        of shape `(minibatch, 6)`.
+
+    Raises:
+        ValueError if `transform` is of incorrect shape.
+        ValueError if `R` has an unexpected trace.
+
+    [1] https://jinyongjeong.github.io/Download/SE3/jlblanco2010geometry3d_techrep.pdf
+    [2] https://en.wikipedia.org/wiki/Hat_operator
+    """
+
+    if transform.ndim != 3:
+        raise ValueError("Input tensor shape has to be (N, 4, 4).")
+
+    N, dim1, dim2 = transform.shape
+    if dim1 != 4 or dim2 != 4:
+        raise ValueError("Input tensor shape has to be (N, 4, 4).")
+
+    if not torch.allclose(transform[:, :3, 3], torch.zeros_like(transform[:, :3, 3])):
+        raise ValueError("All elements of `transform[:, :3, 3]` should be 0.")
+
+    # log_rot is just so3_log_map of the upper left 3x3 block
+    R = transform[:, :3, :3].permute(0, 2, 1)
+    log_rotation = so3_log_map(R, eps=eps, cos_bound=cos_bound)
+
+    # log_translation is V^-1 @ T
+    T = transform[:, 3, :3]
+    V = _se3_V_matrix(*_get_se3_V_input(log_rotation), eps=eps)
+    log_translation = torch.linalg.solve(V, T[:, :, None])[:, :, 0]
+
+    return torch.cat((log_translation, log_rotation), dim=1)
+
+
+def _se3_V_matrix(
+    log_rotation: torch.Tensor,
+    log_rotation_hat: torch.Tensor,
+    log_rotation_hat_square: torch.Tensor,
+    rotation_angles: torch.Tensor,
+    eps: float = 1e-4,
+) -> torch.Tensor:
+    """
+    A helper function that computes the "V" matrix from [1], Sec 9.4.2.
+    [1] https://jinyongjeong.github.io/Download/SE3/jlblanco2010geometry3d_techrep.pdf
+    """
+
+    V = (
+        torch.eye(3, dtype=log_rotation.dtype, device=log_rotation.device)[None]
+        + log_rotation_hat
+        # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`.
+        * ((1 - torch.cos(rotation_angles)) / (rotation_angles**2))[:, None, None]
+        + (
+            log_rotation_hat_square
+            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
+            #  `int`.
+            * ((rotation_angles - torch.sin(rotation_angles)) / (rotation_angles**3))[
+                :, None, None
+            ]
+        )
+    )
+
+    return V
+
+
+def _get_se3_V_input(log_rotation: torch.Tensor, eps: float = 1e-4):
+    """
+    A helper function that computes the input variables to the `_se3_V_matrix`
+    function.
+    """
+    # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`.
+    nrms = (log_rotation**2).sum(-1)
+    rotation_angles = torch.clamp(nrms, eps).sqrt()
+    log_rotation_hat = hat(log_rotation)
+    log_rotation_hat_square = torch.bmm(log_rotation_hat, log_rotation_hat)
+    return log_rotation, log_rotation_hat, log_rotation_hat_square, rotation_angles
diff --git a/src/diffdrr/registration.py b/src/diffdrr/registration.py
new file mode 100644
index 0000000..1e4adcd
--- /dev/null
+++ b/src/diffdrr/registration.py
@@ -0,0 +1,127 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../notebooks/api/08_registration.ipynb.
+
+# %% auto 0
+__all__ = ['Registration', 'PoseRegressor']
+
+# %% ../notebooks/api/08_registration.ipynb 4
+import torch
+import torch.nn as nn
+
+from .drr import DRR
+
+
+class Registration(nn.Module):
+    """Perform automatic 2D-to-3D registration using differentiable rendering."""
+
+    def __init__(
+        self,
+        drr: DRR,  # Preinitialized DRR module
+        rotation: torch.Tensor,  # Initial guess for rotations
+        translation: torch.Tensor,  # Initial guess for translations
+        parameterization: str,  # Specifies the representation of the rotation
+        convention: str = None,  # If `parameterization` is `euler_angles`, specify convention
+    ):
+        super().__init__()
+        self.drr = drr
+        self._rotation = nn.Parameter(rotation)
+        self._translation = nn.Parameter(translation)
+        self.parameterization = parameterization
+        self.convention = convention
+
+    def forward(self, **kwargs):
+        return self.drr(self.pose, **kwargs)
+
+    @property
+    def pose(self):
+        R = convert(
+            self._rotation,
+            torch.tensor([[0.0, 0.0, 0.0]]).to(self._rotation),
+            parameterization=self.parameterization,
+            convention=self.convention,
+        )
+        t = convert(
+            torch.tensor([[0.0, 0.0, 0.0]]).to(self._translation),
+            self._translation,
+            parameterization=self.parameterization,
+            convention=self.convention,
+        )
+        return t.compose(R)
+
+    @property
+    def rotation(self):
+        return (
+            self.pose.convert(self.parameterization, self.convention)[0]
+            .clone()
+            .detach()
+            .cpu()
+        )
+
+    @property
+    def translation(self):
+        return (
+            self.pose.convert(self.parameterization, self.convention)[1]
+            .clone()
+            .detach()
+            .cpu()
+        )
+
+# %% ../notebooks/api/08_registration.ipynb 6
+import timm
+
+from .pose import RigidTransform, convert
+
+
+class PoseRegressor(torch.nn.Module):
+    """
+    A PoseRegressor is comprised of a pretrained backbone model that extracts features
+    from an input X-ray and two linear layers that decode these features into rotational
+    and translational camera pose parameters, respectively.
+    """
+
+    def __init__(
+        self,
+        model_name,
+        parameterization,
+        convention=None,
+        pretrained=False,
+        **kwargs,
+    ):
+        super().__init__()
+
+        self.parameterization = parameterization
+        self.convention = convention
+        n_angular_components = N_ANGULAR_COMPONENTS[parameterization]
+
+        # Get the size of the output from the backbone
+        self.backbone = timm.create_model(
+            model_name,
+            pretrained,
+            num_classes=0,
+            in_chans=1,
+            **kwargs,
+        )
+        output = self.backbone(torch.randn(1, 1, 256, 256)).shape[-1]
+        self.xyz_regression = torch.nn.Linear(output, 3)
+        self.rot_regression = torch.nn.Linear(output, n_angular_components)
+
+    def forward(self, x):
+        x = self.backbone(x)
+        rot = self.rot_regression(x)
+        xyz = self.xyz_regression(x)
+        return convert(
+            rot,
+            xyz,
+            parameterization=self.parameterization,
+            convention=self.convention,
+        )
+
+# %% ../notebooks/api/08_registration.ipynb 7
+N_ANGULAR_COMPONENTS = {
+    "axis_angle": 3,
+    "euler_angles": 3,
+    "se3_log_map": 3,
+    "quaternion": 4,
+    "rotation_6d": 6,
+    "rotation_10d": 10,
+    "quaternion_adjugate": 10,
+}
diff --git a/src/diffdrr/renderers.py b/src/diffdrr/renderers.py
new file mode 100644
index 0000000..508837a
--- /dev/null
+++ b/src/diffdrr/renderers.py
@@ -0,0 +1,193 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../notebooks/api/01_renderers.ipynb.
+
+# %% auto 0
+__all__ = ["Siddon", "Trilinear"]
+
+# %% ../notebooks/api/01_renderers.ipynb 3
+import torch
+
+
+# %% ../notebooks/api/01_renderers.ipynb 7
+class Siddon(torch.nn.Module):
+    """Differentiable X-ray renderer implemented with Siddon's method for exact raytracing."""
+
+    def __init__(self, eps=1e-8):
+        super().__init__()
+        self.eps = eps
+
+    def dims(self, volume):
+        return torch.tensor(volume.shape).to(volume) + 1
+
+    def maxidx(self, volume):
+        return volume.numel() - 1
+
+    def forward(self, volume, origin, spacing, source, target, mask=None):
+        dims = self.dims(volume)
+        maxidx = self.maxidx(volume)
+        origin = origin.to(torch.float64)
+
+        alphas = _get_alphas(source, target, origin, spacing, dims, self.eps)
+        alphamid = (alphas[..., 0:-1] + alphas[..., 1:]) / 2
+        voxels, idxs = _get_voxel(
+            alphamid, source, target, volume, origin, spacing, dims, maxidx, self.eps
+        )
+
+        # Step length for alphas out of range will be nan
+        # These nans cancel out voxels convereted to 0 index
+        step_length = torch.diff(alphas, dim=-1)
+        weighted_voxels = voxels * step_length
+
+        # Handle optional mapping
+        if mask is None:
+            img = torch.nansum(weighted_voxels, dim=-1)
+            img = img.unsqueeze(1)
+        else:
+            # Thanks to @Ivan for the clutch assist w/ pytorch tensor ops
+            # https://stackoverflow.com/questions/78323859/broadcast-pytorch-array-across-channels-based-on-another-array/78324614#78324614
+            channels = torch.take(mask, idxs)  # B D N
+            weighted_voxels = weighted_voxels.nan_to_num()
+            B, D, N = weighted_voxels.shape
+            C = mask.max().item() + 1
+            img = (
+                torch.zeros(B, C, D)
+                .to(volume)
+                .scatter_add_(
+                    1, channels.transpose(-1, -2), weighted_voxels.transpose(-1, -2)
+                )
+            )
+
+        # Finish rendering the DRR
+        raylength = (target - source + self.eps).norm(dim=-1)
+        img *= raylength.unsqueeze(1)
+        return img
+
+
+# %% ../notebooks/api/01_renderers.ipynb 8
+def _get_alphas(source, target, origin, spacing, dims, eps):
+    # Get the CT sizing and spacing parameters
+    alphax = torch.arange(dims[0]).to(source) * spacing[0] + origin[0]
+    alphay = torch.arange(dims[1]).to(source) * spacing[1] + origin[1]
+    alphaz = torch.arange(dims[2]).to(source) * spacing[2] + origin[2]
+
+    # Get the alpha at each plane intersection
+    sx, sy, sz = source[..., 0], source[..., 1], source[..., 2]
+    alphax = alphax.expand(len(source), 1, -1) - sx.unsqueeze(-1)
+    alphay = alphay.expand(len(source), 1, -1) - sy.unsqueeze(-1)
+    alphaz = alphaz.expand(len(source), 1, -1) - sz.unsqueeze(-1)
+
+    sdd = target - source + eps
+    alphax = alphax / sdd[..., 0].unsqueeze(-1)
+    alphay = alphay / sdd[..., 1].unsqueeze(-1)
+    alphaz = alphaz / sdd[..., 2].unsqueeze(-1)
+    alphas = torch.cat([alphax, alphay, alphaz], dim=-1)
+
+    # Get the alphas within the range [alphamin, alphamax]
+    alphamin, alphamax = _get_alpha_minmax(sdd, source, target, origin, spacing, dims)
+    good_idxs = torch.logical_and(alphas >= alphamin, alphas <= alphamax)
+    alphas[~good_idxs] = torch.nan
+
+    # Sort the alphas by ray, putting nans at the end of the list
+    alphas = torch.sort(alphas, dim=-1).values
+
+    # Drop indices where alphas for all rays are nan
+    alphas = alphas[..., ~alphas.isnan().all(dim=0).all(dim=0)]
+
+    return alphas
+
+
+def _get_alpha_minmax(sdd, source, target, origin, spacing, dims):
+    planes = torch.zeros(3).to(source)
+    alpha0 = (planes * spacing + origin - source) / sdd
+    planes = (dims - 1).to(source)
+    alpha1 = (planes * spacing + origin - source) / sdd
+    alphas = torch.stack([alpha0, alpha1]).to(source)
+
+    alphamin = alphas.min(dim=0).values.max(dim=-1).values.unsqueeze(-1)
+    alphamax = alphas.max(dim=0).values.min(dim=-1).values.unsqueeze(-1)
+
+    alphamin = torch.where(alphamin < 0.0, 0.0, alphamin)
+    alphamax = torch.where(alphamax > 1.0, 1.0, alphamax)
+    return alphamin, alphamax
+
+
+def _get_voxel(alpha, source, target, volume, origin, spacing, dims, maxidx, eps):
+    idxs = _get_index(alpha, source, target, origin, spacing, dims, maxidx, eps)
+    return torch.take(volume, idxs), idxs
+
+
+def _get_index(alpha, source, target, origin, spacing, dims, maxidx, eps):
+    sdd = target - source + eps
+    idxs = source.unsqueeze(1) + alpha.unsqueeze(-1) * sdd.unsqueeze(2)
+    idxs = (idxs - origin) / spacing
+    idxs = idxs.floor()
+    # Conversion to long makes nan->-inf, so temporarily replace them with 0
+    # This is cancelled out later by multiplication by nan step_length
+    idxs = (
+        idxs[..., 0] * (dims[1] - 1) * (dims[2] - 1)
+        + idxs[..., 1] * (dims[2] - 1)
+        + idxs[..., 2]
+    ).long()
+    idxs[idxs < 0] = 0
+    idxs[idxs > maxidx] = maxidx
+    return idxs
+
+
+# %% ../notebooks/api/01_renderers.ipynb 10
+from torch.nn.functional import grid_sample
+
+
+class Trilinear(torch.nn.Module):
+    """Differentiable X-ray renderer implemented with trilinear interpolation."""
+
+    def __init__(
+        self,
+        near=0.0,
+        far=1.0,
+        mode="bilinear",
+        eps=1e-8,
+    ):
+        super().__init__()
+        self.near = near
+        self.far = far
+        self.mode = mode
+        self.eps = eps
+
+    def dims(self, volume):
+        return torch.tensor(volume.shape).to(volume) - 1
+
+    def forward(
+        self,
+        volume,
+        origin,
+        spacing,
+        source,
+        target,
+        n_points=250,
+        align_corners=True,
+        mask=None,
+    ):
+        # Ensure not using mask_to_channels
+        if mask is not None:
+            raise ValueError("mask_to_channels can only be True if renderer=='Siddon'")
+
+        # Get the raylength and reshape sources
+        raylength = (source - target + self.eps).norm(dim=-1)
+        source = source[:, None, :, None, :] - origin
+        target = target[:, None, :, None, :] - origin
+
+        # Sample points along the rays and rescale to [-1, 1]
+        alphas = torch.linspace(self.near, self.far, n_points).to(volume)
+        alphas = alphas[None, None, None, :, None]
+        rays = source + alphas * (target - source)
+        rays = 2 * rays / (spacing * self.dims(volume)) - 1
+
+        # Reorder array to match torch conventions
+        volume = volume.permute(2, 1, 0)
+
+        # Render the DRR
+        batch_size = len(rays)
+        vol = volume[None, None, :, :, :].expand(batch_size, -1, -1, -1, -1)
+        img = grid_sample(vol, rays, mode=self.mode, align_corners=align_corners)
+        img = img[:, 0, 0].sum(dim=-1)
+        img *= raylength / n_points
+        return img
diff --git a/src/diffdrr/setup.py b/src/diffdrr/setup.py
new file mode 100644
index 0000000..7f2994d
--- /dev/null
+++ b/src/diffdrr/setup.py
@@ -0,0 +1,7 @@
+from setuptools import find_packages, setup
+
+setup(
+    name="diffdrr",
+    version="0.4.0",
+    packages=find_packages(),
+)
diff --git a/src/diffdrr/utils.py b/src/diffdrr/utils.py
new file mode 100644
index 0000000..7c30684
--- /dev/null
+++ b/src/diffdrr/utils.py
@@ -0,0 +1,64 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../notebooks/api/07_utils.ipynb.
+
+# %% auto 0
+__all__ = ['get_focal_length', 'get_principal_point', 'parse_intrinsic_matrix', 'make_intrinsic_matrix']
+
+# %% ../notebooks/api/07_utils.ipynb 4
+def get_focal_length(
+    intrinsic,  # Intrinsic matrix (3 x 3 tensor)
+    delx: float,  # X-direction spacing (in units length)
+    dely: float,  # Y-direction spacing (in units length)
+) -> float:  # Focal length (in units length)
+    fx = intrinsic[0, 0]
+    fy = intrinsic[1, 1]
+    return abs((fx * delx) + (fy * dely)).item() / 2.0
+
+# %% ../notebooks/api/07_utils.ipynb 5
+def get_principal_point(
+    intrinsic,  # Intrinsic matrix (3 x 3 tensor)
+    height: int,  # Y-direction length (in units pixels)
+    width: int,  # X-direction length (in units pixels)
+    delx: float,  # X-direction spacing (in units length)
+    dely: float,  # Y-direction spacing (in units length)
+):
+    x0 = delx * (intrinsic[0, 2] - width / 2)
+    y0 = dely * (intrinsic[1, 2] - height / 2)
+    return x0.item(), y0.item()
+
+# %% ../notebooks/api/07_utils.ipynb 6
+def parse_intrinsic_matrix(
+    intrinsic,  # Intrinsic matrix (3 x 3 tensor)
+    height: int,  # Y-direction length (in units pixels)
+    width: int,  # X-direction length (in units pixels)
+    delx: float,  # X-direction spacing (in units length)
+    dely: float,  # Y-direction spacing (in units length)
+):
+    focal_length = get_focal_length(intrinsic, delx, dely)
+    x0, y0 = get_principal_point(intrinsic, height, width, delx, dely)
+    return focal_length, x0, y0
+
+# %% ../notebooks/api/07_utils.ipynb 7
+import torch
+
+
+def make_intrinsic_matrix(
+    sdd: float,  # Source-to-detector distance (in units length)
+    delx: float,  # X-direction spacing (in units length / pixel)
+    dely: float,  # Y-direction spacing (in units length / pixel)
+    height: int,  # Y-direction length (in units pixels)
+    width: int,  # X-direction length (in units pixels)
+    x0: float = 0.0,  # Principal point x-coordinate (in units length)
+    y0: float = 0.0,  # Principal point y-coordinate (in units length)
+):
+    return torch.tensor(
+        [
+            [sdd / delx, 0.0, x0 / delx + width / 2],
+            [0.0, sdd / dely, y0 / dely + height / 2],
+            [0.0, 0.0, 1.0],
+        ]
+        # [
+        #     [sdd / delx, 0.0, -x0 / delx + width / 2],
+        #     [0.0, sdd / dely, -y0 / dely + height / 2],
+        #     [0.0, 0.0, 1.0],
+        # ]
+    )
diff --git a/src/diffdrr/visualization.py b/src/diffdrr/visualization.py
new file mode 100644
index 0000000..1ccfdb4
--- /dev/null
+++ b/src/diffdrr/visualization.py
@@ -0,0 +1,344 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../notebooks/api/04_visualization.ipynb.
+
+# %% ../notebooks/api/04_visualization.ipynb 3
+from __future__ import annotations
+
+import tempfile
+
+import imageio.v3 as iio
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+from tqdm import tqdm
+
+# %% auto 0
+__all__ = ['plot_drr', 'plot_mask', 'animate', 'drr_to_mesh', 'labelmap_to_mesh', 'img_to_mesh']
+
+# %% ../notebooks/api/04_visualization.ipynb 5
+import torch
+
+
+def plot_drr(
+    img: torch.Tensor,
+    title: str | None = None,
+    ticks: bool | None = True,
+    axs: matplotlib.axes._axes.Axes | None = None,
+    cmap: str = "gray",
+    **imshow_kwargs,
+):
+    """Plot an image generated by a DRR module."""
+
+    n_imgs = len(img)
+    if axs is None:
+        fig, axs = plt.subplots(ncols=n_imgs, figsize=(10, 5))
+    if n_imgs == 1:
+        axs = [axs]
+    if title is None or isinstance(title, str):
+        title = [title] * n_imgs
+    for img, ax, title in zip(img, axs, title):
+        ax.imshow(img.squeeze().cpu().detach(), cmap=cmap, **imshow_kwargs)
+        _, height, width = img.shape
+        ax.xaxis.tick_top()
+        ax.set(
+            xlabel=title,
+            xticks=[0, width - 1],
+            xticklabels=[1, width],
+            yticks=[0, height - 1],
+            yticklabels=[1, height],
+        )
+        if ticks is False:
+            ax.set_xticks([])
+            ax.set_yticks([])
+    return axs
+
+# %% ../notebooks/api/04_visualization.ipynb 6
+def plot_mask(
+    img: torch.Tensor,
+    axs: matplotlib.axes._axes.Axes,
+    colors=[
+        "rgb(102,194,165)",
+        "rgb(252,141,98)",
+        "rgb(141,160,203)",
+        "rgb(231,138,195)",
+        "rgb(166,216,84)",
+        "rgb(255,217,47)",
+        "rgb(229,196,148)",
+    ],
+    alpha=0.625,
+    return_masks=False,
+):
+    """Plot a 2D rendered segmentation mask. Meant to be called after plot_drr."""
+
+    if len(img) == 1:
+        axs = [axs]
+
+    colors = [[int(c) for c in color[4:][:-1].split(",")] for color in colors]
+    masks = (img > 0).unsqueeze(-1).expand(-1, -1, -1, -1, 4)
+    masks = masks.to(torch.uint8).cpu().detach()
+    masks[..., 3] *= 255
+    for idx, color in enumerate(colors):
+        masks[:, idx :: len(colors), ..., :3] *= torch.tensor(color)
+
+    for idx, ax in enumerate(axs):
+        for jdx in range(masks.shape[1]):
+            ax.imshow(masks[idx, jdx], alpha=alpha)
+
+    if return_masks:
+        return masks
+
+# %% ../notebooks/api/04_visualization.ipynb 7
+import pathlib
+
+import pandas
+
+from .drr import DRR
+
+
+def animate(
+    out: str | pathlib.Path,  # Savepath
+    df: pandas.DataFrame,
+    drr: DRR,
+    parameterization: str,
+    convention: str = None,
+    ground_truth: torch.Tensor | None = None,
+    verbose: bool = True,
+    dtype=torch.float32,
+    device="cpu",
+    **kwargs,  # To pass to imageio.v3.imwrite
+):
+    """Animate the optimization of a DRR."""
+    # Make the axes
+    if ground_truth is None:
+
+        def make_fig():
+            fig, ax_opt = plt.subplots(
+                figsize=(3, 3),
+                constrained_layout=True,
+            )
+            return fig, ax_opt
+
+    else:
+
+        def make_fig(ground_truth):
+            fig, (ax_fix, ax_opt) = plt.subplots(
+                ncols=2,
+                figsize=(6, 3),
+                constrained_layout=True,
+            )
+            plot_drr(ground_truth, axs=ax_fix)
+            ax_fix.set(xlabel="Fixed DRR")
+            return fig, ax_opt
+
+    # Compute DRRs, plot, and save to temporary folder
+    if verbose:
+        itr = tqdm(df.iterrows(), desc="Precomputing DRRs", total=len(df), ncols=75)
+    else:
+        itr = df.iterrows()
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        idxs = []
+        for idx, row in itr:
+            fig, ax_opt = make_fig() if ground_truth is None else make_fig(ground_truth)
+            params = row[["alpha", "beta", "gamma", "bx", "by", "bz"]].values
+            rotations = (
+                torch.tensor(row[["alpha", "beta", "gamma"]].values)
+                .unsqueeze(0)
+                .to(device=device, dtype=dtype)
+            )
+            translations = (
+                torch.tensor(row[["bx", "by", "bz"]].values)
+                .unsqueeze(0)
+                .to(device=device, dtype=dtype)
+            )
+            itr = drr(
+                rotations,
+                translations,
+                parameterization=parameterization,
+                convention=convention,
+            )
+            _ = plot_drr(itr, axs=ax_opt)
+            ax_opt.set(xlabel=f"Moving DRR (loss = {row['loss']:.3f})")
+            fig.savefig(f"{tmpdir}/{idx}.png")
+            plt.close(fig)
+            idxs.append(idx)
+        frames = np.stack(
+            [iio.imread(f"{tmpdir}/{idx}.png") for idx in idxs],
+            axis=0,
+        )
+
+    # Make the animation
+    return iio.imwrite(out, frames, **kwargs)
+
+# %% ../notebooks/api/04_visualization.ipynb 10
+import pyvista
+import vtk
+from torchio import Subject
+
+vtk.vtkLogger.SetStderrVerbosity(vtk.vtkLogger.ConvertToVerbosity(-1))
+
+# %% ../notebooks/api/04_visualization.ipynb 11
+def drr_to_mesh(
+    subject: Subject,  # torchio.Subject with a `volume` attribute
+    method: str,  # Either `surface_nets` or `marching_cubes`
+    threshold: float = 300,  # Min value for marching cubes (Hounsfield units)
+    verbose: bool = True,  # Display progress bars for mesh processing steps
+):
+    """
+    Convert the CT in a DRR object into a mesh.
+
+    If using `method=="surface_nets"`, ensure you have `pyvista>=0.43` and `vtk>=9.3` installed.
+
+    The mesh processing steps are:
+
+    1. Keep only largest connected components
+    2. Smooth
+    3. Decimate (if `method=="marching_cubes"`)
+    4. Fill any holes
+    5. Clean (remove any redundant vertices/edges)
+    """
+    # Turn the CT into a PyVista object and run surface extraction
+    grid = pyvista.ImageData(
+        dimensions=subject.volume.shape[1:],
+        spacing=subject.volume.spacing,
+        origin=subject.volume.origin,
+    )
+
+    if method == "marching_cubes":
+        mesh = grid.contour(
+            isosurfaces=1,
+            scalars=subject.volume.data[0].cpu().numpy().flatten(order="F"),
+            rng=[threshold, torch.inf],
+            method="marching_cubes",
+            progress_bar=verbose,
+        )
+    elif method == "surface_nets":
+        grid.point_data["values"] = (
+            subject.volume.data[0].cpu().numpy().flatten(order="F") > threshold
+        )
+        try:
+            mesh = grid.contour_labeled(smoothing=True, progress_bar=verbose)
+        except AttributeError as e:
+            raise AttributeError(
+                f"{e}, ensure you are using pyvista>=0.43 and vtk>=9.3"
+            )
+    else:
+        raise ValueError(
+            f"method must be `marching_cubes` or `surface_nets`, not {method}"
+        )
+
+    # Process the mesh
+    mesh.extract_largest(inplace=True, progress_bar=verbose)
+    mesh.point_data.clear()
+    mesh.cell_data.clear()
+    mesh.smooth_taubin(
+        n_iter=100,
+        feature_angle=120.0,
+        boundary_smoothing=False,
+        feature_smoothing=False,
+        non_manifold_smoothing=True,
+        normalize_coordinates=True,
+        inplace=True,
+        progress_bar=verbose,
+    )
+    if method == "marching_cubes":
+        mesh.decimate_pro(0.25, inplace=True, progress_bar=verbose)
+    mesh.fill_holes(100, inplace=True, progress_bar=verbose)
+    mesh.clean(inplace=True, progress_bar=verbose)
+    return mesh
+
+# %% ../notebooks/api/04_visualization.ipynb 12
+def labelmap_to_mesh(
+    subject: Subject,  # torchio.Subject with  a `mask` attribute
+    verbose: bool = True,  # Display progress bars for mesh processing steps
+):
+    # Turn the 3D labelmap into a PyVista object and run SurfaceNets
+    grid = pyvista.ImageData(
+        dimensions=subject.mask.shape[1:],
+        spacing=subject.mask.spacing,
+        origin=subject.mask.origin,
+    )
+    grid.point_data["values"] = subject.mask.data[0].numpy().flatten(order="F")
+    mesh = grid.contour_labeled(smoothing=True, progress_bar=verbose)
+    mesh.smooth_taubin(
+        n_iter=100,
+        feature_angle=120.0,
+        boundary_smoothing=False,
+        feature_smoothing=False,
+        non_manifold_smoothing=True,
+        normalize_coordinates=True,
+        inplace=True,
+        progress_bar=verbose,
+    )
+    mesh.clean(inplace=True, progress_bar=verbose)
+    return mesh
+
+# %% ../notebooks/api/04_visualization.ipynb 13
+from .pose import RigidTransform
+
+
+def img_to_mesh(drr: DRR, pose: RigidTransform, **kwargs):
+    """
+    For a given pose (not batched), turn the camera and detector into a mesh.
+    Additionally, render the DRR for the pose. Convert into a texture that
+    can be applied to the detector mesh.
+    """
+    # Turn DRR img into a texture that can be applied to a mesh
+    img = drr(pose)
+    img = img.cpu().squeeze().detach().numpy()
+    img = (img - img.min()) / (img.max() - img.min())
+    img = (255.0 * img).astype(np.uint8)
+    texture = pyvista.numpy_to_texture(img)
+
+    # Make a mesh for the camera and the principal ray
+    source, target = drr.detector(pose)
+    source = source.squeeze().cpu().detach().numpy()
+    target = (
+        target.reshape(drr.detector.height, drr.detector.width, 3)
+        .cpu()
+        .detach()
+        .numpy()
+    )
+    principal_ray = pyvista.Line(source, target.mean(axis=0).mean(axis=0))
+    camera = _make_camera_frustum_mesh(source, target, size=0.125)
+
+    # Make a mesh for the detector plane
+    detector = pyvista.StructuredGrid(
+        target[..., 0],
+        target[..., 1],
+        target[..., 2],
+    )
+    detector.add_field_data([drr.detector.height], "height")
+    detector.add_field_data([drr.detector.width], "width")
+    detector.texture_map_to_plane(
+        origin=target[-1, 0],
+        point_u=target[-1, -1],
+        point_v=target[0, 0],
+        inplace=True,
+    )
+
+    return camera, detector, texture, principal_ray
+
+# %% ../notebooks/api/04_visualization.ipynb 14
+import numpy as np
+
+
+def _make_camera_frustum_mesh(source, target, size=0.125):
+    vertices = np.stack(
+        [
+            source + size * (target[0, 0] - source),
+            source + size * (target[-1, 0] - source),
+            source + size * (target[-1, -1] - source),
+            source + size * (target[0, -1] - source),
+            source,
+        ]
+    )
+    faces = np.hstack(
+        [
+            [4, 0, 1, 2, 3],
+            [3, 0, 1, 4],
+            [3, 1, 2, 4],
+            [3, 0, 3, 4],
+            [3, 2, 3, 4],
+        ]
+    )
+    return pyvista.PolyData(vertices, faces)
diff --git a/src/drr.py b/src/drr.py
index e53eb65..7abfaa1 100644
--- a/src/drr.py
+++ b/src/drr.py
@@ -11,9 +11,7 @@
 
 
 def create_drr(
-    volume,
-    segmentation,
-    bone_attenuation_multiplier=5.0,
+    subject,
     sdd=1020,
     height=200,
     width=200,
@@ -61,14 +59,6 @@ def create_drr(
         img (torch.Tensor): The DRR
     """
 
-    # Read the image and segmentation subject
-    subject = read(
-        tensor=volume,
-        label_tensor=segmentation,
-        orientation="AP",
-        bone_attenuation_multiplier=bone_attenuation_multiplier,
-    )
-
     # Create a DRR object
     drr = DRR(
         subject,  # A torchio.Subject object storing the CT volume, origin, and voxel spacing
@@ -90,12 +80,20 @@ def create_drr(
     if rotations_degrees:
         rotations = torch.deg2rad(rotations)
 
-    zero = torch.tensor([[0.0, 0.0, 0.0]], device=device)
-    pose1 = convert(
-        zero, translations, parameterization="euler_angles", convention="ZXY"
-    )
-    pose2 = convert(rotations, zero, parameterization="euler_angles", convention="ZXY")
-    pose = pose1.compose(pose2)
+    # zero = torch.tensor([[0.0, 0.0, 0.0]], device=device)
+    # pose1 = convert(
+    #     zero, translations.to(device), parameterization="euler_angles", convention="ZXY"
+    # )
+    # pose2 = convert(
+    #     rotations.to(device), zero, parameterization="euler_angles", convention="ZXY"
+    # )
+    # pose = pose1.compose(pose2)
 
-    img = drr(pose, mask_to_channels=mask_to_channels)
+    img = drr(
+        rotations.to(device),
+        translations.to(device),
+        mask_to_channels=mask_to_channels,
+        parameterization="euler_angles",
+        convention="ZXY",
+    )
     return img
diff --git a/src/preprocessing.py b/src/preprocessing.py
index e5660fd..adc839e 100644
--- a/src/preprocessing.py
+++ b/src/preprocessing.py
@@ -5,6 +5,7 @@
 from monai.transforms import (
     Compose,
     DataStatsd,
+    EnsureChannelFirst,
     EnsureChannelFirstd,
     LoadImaged,
     Rand3DElasticd,
@@ -14,6 +15,7 @@
     RandRotated,
     RandScaleIntensityd,
     Resized,
+    ResizeWithPadOrCrop,
     ResizeWithPadOrCropd,
     ScaleIntensityRanged,
 )
@@ -47,39 +49,6 @@ def get_transforms(resize_shape=[256, 256, 48], contrast_value=1000, is_train=Tr
                 LoadImaged(reader="PydicomReader", keys=["image", "seg"]),
                 EnsureChannelFirstd(keys=["image", "seg"]),
                 ResizeWithPadOrCropd(keys=["image", "seg"], spatial_size=resize_shape),
-                # RemoveNecrosisChannel(keys=["seg"]),
-                # Resized(keys=["image", "seg"], spatial_size=resize_shape),
-                # AddBackgroundChannel(keys=["seg"]),
-                # ScaleIntensityRanged(
-                #     keys=["image"],
-                #     a_min=-170,
-                #     a_max=230,
-                #     b_min=-170,
-                #     b_max=230,
-                #     clip=True,
-                # ),
-                # AddVesselContrast(keys=["image", "seg"], contrast_value=contrast_value),
-                # RemoveDualImage(keys=["image", "seg"]),
-                # Resized(keys=["image", "seg"], spatial_size=resize_shape),
-                # ConvertToSingleChannel(keys=["seg"]),
-                # DataStatsd(keys=["image", "seg"], data_shape=True),
-                # RandGridDistortiond(keys=["image", "seg"], prob=0.5),
-                # RandFlipd(keys=["image", "seg"], prob=0.25, spatial_axis=1),
-                # RandRotated(
-                #     keys=["image", "seg"],
-                #     prob=0.50,
-                #     range_x=0.36,
-                #     range_y=0.0,
-                #     range_z=0.0,
-                # ),
-                # RandGaussianNoised(keys=["image"], prob=0.25),
-                # RandScaleIntensityd(keys=["image"], prob=0.25, factors=0.15),
-                # Rand3DElasticd(
-                #     keys=["image", "seg"],
-                #     prob=0.15,
-                #     sigma_range=(5, 8),
-                #     magnitude_range=(0.1, 0.2),
-                # ),
                 ClassIsolation(keys=["seg"], class_index=2),
             ],
             lazy=False,
@@ -91,9 +60,6 @@ def get_transforms(resize_shape=[256, 256, 48], contrast_value=1000, is_train=Tr
                 LoadImaged(reader="PydicomReader", keys=["image", "seg"]),
                 EnsureChannelFirstd(keys=["image", "seg"]),
                 ResizeWithPadOrCropd(keys=["image", "seg"], spatial_size=resize_shape),
-                RemoveNecrosisChannel(keys=["seg"]),
-                AddBackgroundChannel(keys=["seg"]),
-                # AddVesselContrast(keys=["image", "seg"], contrast_value=contrast_value),
                 ClassIsolation(keys=["seg"], class_index=2),
             ],
             lazy=False,
@@ -101,6 +67,11 @@ def get_transforms(resize_shape=[256, 256, 48], contrast_value=1000, is_train=Tr
         return val_transform
 
 
+def get_eval_transforms(resize_shape=[256, 256, 48], contrast_value=1000):
+    transform = ResizeWithPadOrCrop(resize_shape, lazy=False)
+    return transform
+
+
 def get_datasets(
     root_dir="../data",
     collection="HCC-TACE-Seg",
@@ -146,7 +117,7 @@ def get_datasets(
 
     # Create the corresponding validation dataset
     val_dataset = TciaDataset(
-        root_dir="../data",
+        root_dir="../data081",
         collection=collection,
         section="validation",
         transform=transform,
diff --git a/src/training.py b/src/training.py
index b899c82..15eda3c 100644
--- a/src/training.py
+++ b/src/training.py
@@ -1,23 +1,31 @@
-from src.preprocessing import get_transforms, get_datasets, get_dataloaders, add_vessel_contrast
-import torch
-from src.model import TACEnet
 import random
-from src.drr import create_drr
-import numpy as np
-import matplotlib.pyplot as plt
 from timeit import default_timer as timer
 
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+from diffdrr.data import read
+from src.drr import create_drr
+from src.model import TACEnet
+from src.preprocessing import (
+    add_vessel_contrast,
+    get_dataloaders,
+    get_datasets,
+    get_transforms,
+)
+
 
 def loadData():
-    transform = get_transforms(resize_shape= [512, 512, 96],contrast_value=1000)
-    
+    transform = get_transforms(resize_shape=[512, 512, 96], contrast_value=1000)
+
     train_ds, val_ds = get_datasets(
-    root_dir="../data081",
-    collection="HCC-TACE-Seg",
-    transform=transform,
-    download=False,
-    val_frac=0.2,
-    download_len=2
+        root_dir="../data081",
+        collection="HCC-TACE-Seg",
+        transform=transform,
+        download=False,
+        val_frac=0.2,
+        download_len=2,
     )
 
     train_loader, val_loader = get_dataloaders(train_ds, val_ds, batch_size=1)
@@ -27,7 +35,7 @@ def loadData():
 
 def buildModel():
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    
+
     # Define model
     model = TACEnet()
 
@@ -45,26 +53,35 @@ def buildModel():
 
     return model, criterion, optimizer, device
 
-def sampleVolume(train_loader):
+
+def sampleVolume(train_loader, contrast_value=4000):
     # Sample a batch of data from the dataloader
     batch = next(iter(train_loader))
 
     # Separate the image and segmentation from the batch
     volumes, target = batch["image"], batch["seg"]
-    volume = add_vessel_contrast(volumes, target, contrast_value=4000)
+    volume = add_vessel_contrast(volumes, target, contrast_value=contrast_value)
 
     return volume, target
 
-def trainSingleEpoch(model, criterion, optimizer, Volume, Target, device, length, verbose=False):
-    
+
+def trainSingleEpoch(
+    model, criterion, optimizer, Volume, Target, device, length, verbose=False
+):
+
     model.train()
     for i in range(length):
-        rotation = random.uniform(-40,40)
+        rotation = random.uniform(-5, 5)
         # Create raw DRR
-        drr_raw = create_drr(
-            Volume[0],
-            Target[0],
+        subject = read(
+            tensor=Volume[0],
+            label_tensor=Target[0],
+            orientation="AP",
             bone_attenuation_multiplier=5.0,
+        )
+
+        drr_raw = create_drr(
+            subject,
             sdd=1020,
             height=256,
             width=256,
@@ -72,10 +89,9 @@ def trainSingleEpoch(model, criterion, optimizer, Volume, Target, device, length
             translations=torch.tensor([[0.0, 850.0, 0.0]]),
             mask_to_channels=True,
             device="cpu",
-            )
-        
+        )
 
-        enhancement_factor = random.uniform(0.4,0.6)
+        enhancement_factor = random.uniform(0.4, 0.6)
         drr_input, drr_vessels = drr_to_input(drr_raw, enhancement_factor, device)
 
         prediction, _ = model(Target.to(device), drr_input)
@@ -87,16 +103,22 @@ def trainSingleEpoch(model, criterion, optimizer, Volume, Target, device, length
         if verbose:
             print(f"[{i+1}/{length}] - Loss: {loss.item()}")
 
+
 def testModel(model, criterion, Volume, Target, device):
     model.eval()
     loss = []
-    
+
     for i in range(5):
-        rotation = (i*20-40)
-        drr_raw = create_drr(
-            Volume[0],
-            Target[0],
+        rotation = random.uniform(-5, 5)
+        subject = read(
+            tensor=Volume[0],
+            label_tensor=Target[0],
+            orientation="AP",
             bone_attenuation_multiplier=5.0,
+        )
+
+        drr_raw = create_drr(
+            subject,
             sdd=1020,
             height=256,
             width=256,
@@ -104,26 +126,27 @@ def testModel(model, criterion, Volume, Target, device):
             translations=torch.tensor([[0.0, 850.0, 0.0]]),
             mask_to_channels=True,
             device="cpu",
-            )
-        drr_body = drr_raw[:,0,:,:]
-        drr_vessels = drr_raw[:,1,:,:]
-
-        drr_body = (drr_body/(torch.max(drr_body.flatten()))).to(device)
-        drr_vessels = ((drr_vessels/(torch.max(drr_vessels.flatten())))*2).to(device)
+        )
+        drr_body = drr_raw[:, 0, :, :]
+        drr_vessels = drr_raw[:, 1, :, :]
+
+        drr_body = (drr_body / (torch.max(drr_body.flatten()))).to(device)
+        drr_vessels = ((drr_vessels / (torch.max(drr_vessels.flatten()))) * 2).to(
+            device
+        )
         enhancement_factor = 0.5
-        drr_combined = (drr_body + enhancement_factor*drr_vessels).unsqueeze(0)
+        drr_combined = (drr_body + enhancement_factor * drr_vessels).unsqueeze(0)
 
         prediction, _ = model(Target.to(device), drr_combined.to(device))
 
-
-        loss.append(criterion(prediction+drr_body, drr_vessels+drr_body).item())
+        loss.append(criterion(prediction + drr_body, drr_vessels + drr_body).item())
 
     print("-----------------------------------")
     print(f"[TEST] - Loss: {np.mean(loss)}")
     print("-----------------------------------")
 
 
-def trainModel(epochs,length, save, load, path, verbose=False):
+def trainModel(epochs, length, save, load, path, verbose=False):
     train_loader, _ = loadData()
     model, criterion, optimizer, device = buildModel()
     volume, target = sampleVolume(train_loader)
@@ -132,10 +155,12 @@ def trainModel(epochs,length, save, load, path, verbose=False):
         model.load_state_dict(torch.load(path))
 
     for i in range(epochs):
-        trainSingleEpoch(model,criterion,optimizer,volume,target,device,length,verbose)
-        #testModel(model,criterion,volume,target,device)
-        visualizeModel(model,volume,target,device)
-    
+        trainSingleEpoch(
+            model, criterion, optimizer, volume, target, device, length, verbose
+        )
+        # testModel(model,criterion,volume,target,device)
+        visualizeModel(model, volume, target, device)
+
     if save:
         torch.save(model.state_dict(), path)
 
@@ -152,6 +177,7 @@ def fetchResults(path):
 
     return loss
 
+
 def testInferenceSpeed(path):
     train_loader, _ = loadData()
     model, criterion, optimizer, device = buildModel()
@@ -159,173 +185,204 @@ def testInferenceSpeed(path):
 
     model.load_state_dict(torch.load(path))
 
+    subject = read(
+        tensor=volume[0],
+        label_tensor=target[0],
+        orientation="AP",
+        bone_attenuation_multiplier=5.0,
+    )
+
     drr_raw = create_drr(
-            volume[0],
-            target[0],
-            bone_attenuation_multiplier=5.0,
-            sdd=1020,
-            height=256,
-            width=256,
-            rotations=torch.tensor([[0.0, 0.0, 0.0]]),
-            translations=torch.tensor([[0.0, 850.0, 0.0]]),
-            mask_to_channels=True,
-            device="cpu",
-            )
-    
-    drr_input, drr_vessels = drr_to_input(drr_raw, enhancement_factor=0.6, device=device)
+        subject,
+        sdd=1020,
+        height=256,
+        width=256,
+        rotations=torch.tensor([[0, 0.0, 0.0]]),
+        translations=torch.tensor([[0.0, 850.0, 0.0]]),
+        mask_to_channels=True,
+        device="cpu",
+    )
+
+    drr_input, drr_vessels = drr_to_input(
+        drr_raw, enhancement_factor=0.6, device=device
+    )
 
     start = timer()
 
     print(target.size())
     print(drr_input.size())
-    
+
     prediction, latent_representation = model(target.to(device), drr_input)
     end = timer()
 
-    return (end-start)
-
+    return end - start
 
 
 def visualizeModel(model, Volume, Target, device):
-    rotation = random.uniform(-40,40)
+    rotation = random.uniform(-5, 5)
+    subject = read(
+        tensor=Volume[0],
+        label_tensor=Target[0],
+        orientation="AP",
+        bone_attenuation_multiplier=5.0,
+    )
+
     drr_raw = create_drr(
-            Volume[0],
-            Target[0],
-            bone_attenuation_multiplier=5.0,
-            sdd=1020,
-            height=256,
-            width=256,
-            rotations=torch.tensor([[rotation, 0.0, 0.0]]),
-            translations=torch.tensor([[0.0, 850.0, 0.0]]),
-            mask_to_channels=True,
-            device="cpu",
-            )
-    
-    drr_input, drr_vessels = drr_to_input(drr_raw, enhancement_factor=0.5, device=device)
-    
+        subject,
+        sdd=1020,
+        height=256,
+        width=256,
+        rotations=torch.tensor([[rotation, 0.0, 0.0]]),
+        translations=torch.tensor([[0.0, 850.0, 0.0]]),
+        mask_to_channels=True,
+        device="cpu",
+    )
+
+    drr_input, drr_vessels = drr_to_input(
+        drr_raw, enhancement_factor=0.5, device=device
+    )
+
     prediction, latent_representation = model(Target.to(device), drr_input)
 
-    
     # Create a figure and a set of subplots
     fig, axs = plt.subplots(1, 4, figsize=(12, 4))
 
     # Plot each image in a subplot
-    axs[0].imshow(drr_input.cpu().detach().numpy().squeeze(), cmap='gray', vmax = 40)
-    axs[0].set_title('DRR')
+    axs[0].imshow(drr_input.cpu().detach().numpy().squeeze(), cmap="gray", vmax=40)
+    axs[0].set_title("DRR")
 
-    axs[1].imshow(drr_vessels.cpu().detach().numpy().squeeze(), cmap='gray', vmax = 40)
-    axs[1].set_title('enhanced target')
+    axs[1].imshow(drr_vessels.cpu().detach().numpy().squeeze(), cmap="gray", vmax=40)
+    axs[1].set_title("enhanced target")
 
-    axs[2].imshow(prediction.cpu().detach().numpy().squeeze(), cmap='gray', vmax = 40)
-    axs[2].set_title('AI Enhanced')
+    axs[2].imshow(prediction.cpu().detach().numpy().squeeze(), cmap="gray", vmax=40)
+    axs[2].set_title("AI Enhanced")
 
-    axs[3].imshow(latent_representation.cpu().detach().numpy().squeeze(), cmap = 'gray')
-    axs[3].set_title('Latent Representation')
+    axs[3].imshow(latent_representation.cpu().detach().numpy().squeeze(), cmap="gray")
+    axs[3].set_title("Latent Representation")
 
     # Hide the axes labels
     for ax in axs:
-        ax.axis('off')
+        ax.axis("off")
     plt.show()
 
+
 def drr_to_input(drr_raw, enhancement_factor, device):
-    drr_body = drr_raw[:,0,:,:]
-    drr_vessels = drr_raw[:,1,:,:]
+    drr_body = drr_raw[:, 0, :, :]
+    drr_vessels = drr_raw[:, 1, :, :]
+
+    # drr_body = (drr_body/(torch.max(drr_body.flatten()))).to(device)
+    # drr_vessels = ((drr_vessels/(torch.max(drr_vessels.flatten())))*2).to(device)
+    drr_input = (drr_body + enhancement_factor * drr_vessels).unsqueeze(0)
 
-    #drr_body = (drr_body/(torch.max(drr_body.flatten()))).to(device)
-    #drr_vessels = ((drr_vessels/(torch.max(drr_vessels.flatten())))*2).to(device)
-    drr_input = (drr_body + enhancement_factor*drr_vessels).unsqueeze(0)
-    
     drr_vessels = drr_vessels.unsqueeze(0)
-    #print(f"input size: {drr_input.size()}")
-    #print(f"target size: {drr_vessels.size()}")
+    # print(f"input size: {drr_input.size()}")
+    # print(f"target size: {drr_vessels.size()}")
 
     return drr_input.to(device), drr_vessels.to(device)
 
+
 def visualizationResult(model, Volume, Target, device):
     model.eval()
 
-    rotation = random.uniform(-40,40)
+    rotation = random.uniform(-5, 5)
+    subject = read(
+        tensor=Volume[0],
+        label_tensor=Target[0],
+        orientation="AP",
+        bone_attenuation_multiplier=5.0,
+    )
+
     drr_raw = create_drr(
-            Volume[0],
-            Target[0],
-            bone_attenuation_multiplier=5.0,
-            sdd=1020,
-            height=256,
-            width=256,
-            rotations=torch.tensor([[rotation, 0.0, 0.0]]),
-            translations=torch.tensor([[0.0, 850.0, 0.0]]),
-            mask_to_channels=True,
-            device="cpu",
-            )
-    drr_body = drr_raw[:,0,:,:].to(device)
+        subject,
+        sdd=1020,
+        height=256,
+        width=256,
+        rotations=torch.tensor([[rotation, 0.0, 0.0]]),
+        translations=torch.tensor([[0.0, 850.0, 0.0]]),
+        mask_to_channels=True,
+        device="cpu",
+    )
+    drr_body = drr_raw[:, 0, :, :].to(device)
     fig, axs = plt.subplots(2, 4, figsize=(16, 8))
     axs = axs.flatten()
 
-
-    drr_input, drr_vessels = drr_to_input(drr_raw, enhancement_factor=0.6, device=device)
+    drr_input, drr_vessels = drr_to_input(
+        drr_raw, enhancement_factor=0.6, device=device
+    )
     prediction, latent_representation = model(Target.to(device), drr_input)
-    AI_enhanced = drr_body+prediction
+    AI_enhanced = drr_body + prediction
 
-    axs[0].imshow(drr_input.cpu().detach().numpy().squeeze(), cmap='gray', vmax = 40)
-    axs[4].imshow(AI_enhanced.cpu().detach().numpy().squeeze(), cmap='gray', vmax = 40)
+    axs[0].imshow(drr_input.cpu().detach().numpy().squeeze(), cmap="gray", vmax=40)
+    axs[4].imshow(AI_enhanced.cpu().detach().numpy().squeeze(), cmap="gray", vmax=40)
 
-    drr_input, drr_vessels = drr_to_input(drr_raw, enhancement_factor=0.5, device=device)
+    drr_input, drr_vessels = drr_to_input(
+        drr_raw, enhancement_factor=0.5, device=device
+    )
     prediction, latent_representation = model(Target.to(device), drr_input)
-    AI_enhanced = drr_body+prediction
+    AI_enhanced = drr_body + prediction
 
-    axs[1].imshow(drr_input.cpu().detach().numpy().squeeze(), cmap='gray', vmax = 40)
-    axs[5].imshow(AI_enhanced.cpu().detach().numpy().squeeze(), cmap='gray', vmax = 40)
+    axs[1].imshow(drr_input.cpu().detach().numpy().squeeze(), cmap="gray", vmax=40)
+    axs[5].imshow(AI_enhanced.cpu().detach().numpy().squeeze(), cmap="gray", vmax=40)
 
-    drr_input, drr_vessels = drr_to_input(drr_raw, enhancement_factor=0.4, device=device)
+    drr_input, drr_vessels = drr_to_input(
+        drr_raw, enhancement_factor=0.4, device=device
+    )
     prediction, latent_representation = model(Target.to(device), drr_input)
-    AI_enhanced = drr_body+prediction
+    AI_enhanced = drr_body + prediction
 
-    axs[2].imshow(drr_input.cpu().detach().numpy().squeeze(), cmap='gray', vmax = 40)
-    axs[6].imshow(AI_enhanced.cpu().detach().numpy().squeeze(), cmap='gray', vmax = 40)
+    axs[2].imshow(drr_input.cpu().detach().numpy().squeeze(), cmap="gray", vmax=40)
+    axs[6].imshow(AI_enhanced.cpu().detach().numpy().squeeze(), cmap="gray", vmax=40)
 
-    drr_input, drr_vessels = drr_to_input(drr_raw, enhancement_factor=0.1, device=device)
+    drr_input, drr_vessels = drr_to_input(
+        drr_raw, enhancement_factor=0.1, device=device
+    )
     prediction, latent_representation = model(Target.to(device), drr_input)
-    AI_enhanced = drr_body+prediction
+    AI_enhanced = drr_body + prediction
 
-    axs[3].imshow(drr_input.cpu().detach().numpy().squeeze(), cmap='gray', vmax = 40)
-    axs[7].imshow(AI_enhanced.cpu().detach().numpy().squeeze(), cmap='gray', vmax = 40)
+    axs[3].imshow(drr_input.cpu().detach().numpy().squeeze(), cmap="gray", vmax=40)
+    axs[7].imshow(AI_enhanced.cpu().detach().numpy().squeeze(), cmap="gray", vmax=40)
 
     for i in range(8):
-        axs[i].axis('off')
-
+        axs[i].axis("off")
 
     plt.show()
 
 
 def rotationSweep(model, Volume, Target, device, criterion):
     total_loss = []
-    #EF sweep
+    # EF sweep
     for j in range(10):
-        
-        ef = j/10
+
+        ef = j / 10
         loss = []
-        #Rotation sweep
+        # Rotation sweep
         for i in range(80):
-            drr_raw = create_drr(
-                Volume[0],
-                Target[0],
+            subject = read(
+                tensor=Volume[0],
+                label_tensor=Target[0],
+                orientation="AP",
                 bone_attenuation_multiplier=5.0,
+            )
+
+            drr_raw = create_drr(
+                subject,
                 sdd=1020,
                 height=256,
                 width=256,
-                rotations=torch.tensor([[i-40, 0.0, 0.0]]),
+                rotations=torch.tensor([[i - 40, 0.0, 0.0]]),
                 translations=torch.tensor([[0.0, 850.0, 0.0]]),
                 mask_to_channels=True,
                 device="cpu",
-                )
-            
-            drr_input, drr_vessels = drr_to_input(drr_raw, enhancement_factor=ef, device=device)
+            )
+
+            drr_input, drr_vessels = drr_to_input(
+                drr_raw, enhancement_factor=ef, device=device
+            )
             prediction, latent_representation = model(Target.to(device), drr_input)
             loss.append(criterion(prediction, drr_vessels).item())
-            print(f"loop: {j}, angle: {i}")
-        
-        print(f"loss of loop {j}: {np.mean(loss)}")
+            # print(f"loop: {j}, angle: {i}")
+
+        # print(f"loss of loop {j}: {np.mean(loss)}")
         total_loss.append(np.mean(loss))
 
-    
-    return total_loss
\ No newline at end of file
+    return total_loss
diff --git a/src/vizualization.py b/src/vizualization.py
new file mode 100644
index 0000000..4e8467f
--- /dev/null
+++ b/src/vizualization.py
@@ -0,0 +1,52 @@
+import matplotlib.pyplot as plt
+
+
+def plot_drr_enhancement(
+    drr_body, drr_vessels, enhancement_factors, cmap="gray", vmax=20
+):
+    # Move tensors to CPU
+    drr_body = drr_body.cpu()
+    drr_vessels = drr_vessels.cpu()
+
+    fig, axs = plt.subplots(1, len(enhancement_factors), figsize=(12, 4))
+
+    for i, ef in enumerate(enhancement_factors):
+        drr_combined = drr_body + ef * drr_vessels
+        axs[i].imshow(drr_combined.squeeze(), cmap=cmap, vmax=vmax)
+        axs[i].set_title(ef)
+        axs[i].axis("off")
+    plt.show()
+
+
+def plot_results(
+    drr_combined_low_enhancement, drr_combined_target, prediction, latent_representation, vmax=25
+):
+
+    # Move tensors to CPU
+    drr_combined_low_enhancement = drr_combined_low_enhancement.cpu()
+    drr_combined_target = drr_combined_target.cpu()
+    prediction = prediction.cpu()
+
+    # Create a figure and a set of subplots
+    _, axs = plt.subplots(1, 4, figsize=(12, 4))
+
+    # Plot each image in a subplot
+    axs[0].imshow(
+        drr_combined_low_enhancement.squeeze().numpy(), cmap="gray", vmax=vmax
+    )
+    axs[0].set_title("DRR")
+
+    axs[2].imshow(drr_combined_target.squeeze().numpy(), cmap="gray", vmax=vmax)
+    axs[2].set_title("enhanced target")
+
+    axs[1].imshow(prediction.detach().numpy().squeeze(), cmap="gray", vmax=vmax)
+    axs[1].set_title("AI Enhanced")
+    
+    axs[3].imshow(latent_representation.detach().cpu().numpy().squeeze(), cmap="gray")
+    axs[3].set_title("Latent Representation")
+
+    # Hide the axes labels
+    for ax in axs:
+        ax.axis("off")
+
+    plt.show()