diff --git a/README.md b/README.md
index 0456342f..92c98c41 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,13 @@
-Deeplay is a deep learning library in Python that extends PyTorch with additional functionalities focused on modularity and reusability. It facilitates the definition, training, and adjustment of neural networks by introducing dynamic modification capabilities for model components after their initial creation. Deeplay seeks to address the common issue of rigid and non-reusable modules in PyTorch projects by offering a system that allows for easy customization and optimization of neural network components.
+Deeplay is a deep learning library in Python that extends PyTorch with additional functionalities focused on modularity and reusability.  Deeplay seeks to address the common issue of rigid and non-reusable modules in PyTorch projects by offering a system that allows for easy customization and optimization of neural network components. Specifically, it facilitates the definition, training, and adjustment of neural networks by introducing dynamic modification capabilities for model components after their initial creation.
 
 # Core Philosophy
 
-The core philosophy of Deeplay is to enhance flexibility in the construction and adaptation of neural networks. It is built on the observation that PyTorch modules often lack reusability across projects, leading to redundant implementations. Deeplay enables properties of neural network submodules to be changed post-creation, supporting seamless integration of these modifications. Its design is based on a hierarchy of abstractions from models down to layers, emphasizing compatibility and easy transformation of components. This can be summarized aqs follows:
+The core philosophy of Deeplay is to enhance flexibility in the construction and adaptation of neural networks. It is built on the observation that PyTorch modules often lack reusability across projects, leading to redundant implementations. Deeplay enables properties of neural network submodules to be changed post-creation, supporting seamless integration of these modifications. Its design is based on a hierarchy of abstractions from models down to layers, emphasizing compatibility and easy transformation of components. This can be summarized as follows:
 
 - **Enhance Flexibility:** Neural networks defined using Deeplay should be fully adaptable by the user, allowing dynamic modifications to model components. This should be possible without the author of the model having to anticipate all potential changes in advance.
 - **Promote Reusability:** Deeplay components should be immediately reusable across different projects and models. This reusability should extend to both the components themselves and the modifications made to them.
 - **Support Seamless Integration:** Modifications to model blocks and components should be possible without the user worrying about breaking the model's compatibility with other parts of the network. Deeplay should handle these integrations automatically as far as possible.
-- **Hierarchy of Abstractions:** Neural networks and deep learning are fundamentally hierarchical, with each level of abstraction being mostly agnostic to the details of the levels below it. An application should be agnostic to which model it uses, a model should be agnostic to the specifics of the components it uses, a component should be agnostic to the specifics of the blocks it uses, and so on. Deeplay reflects this hierarchy in its design.
+- **Hierarchy of Abstractions:** Neural networks and deep learning are fundamentally hierarchical, with each level of abstraction being mostly agnostic to the details of the levels below it. An *application* should be agnostic to which model it uses, a *model* should be agnostic to the specifics of the components it uses, a *component* should be agnostic to the specifics of the blocks it uses, and a *block* should be agnostic to the specifics of the *layers* it uses . Deeplay reflects this hierarchy in its design.
 
 # Deeplay Compared to Torch
 
@@ -89,8 +89,16 @@ Here you find a series of notebooks tailored for Deeplay's developers:
 
 - DT111 **[Style Guide](https://github.com/DeepTrackAI/deeplay/blob/develop/tutorials/developers/DT111_style.ipynb)**
 
-- DT121 **[Deeplay Classes Overview](https://github.com/DeepTrackAI/deeplay/blob/develop/tutorials/developers/DT121_overview.ipynb)**
+- DT121 **[Overview of Deeplay Classes](https://github.com/DeepTrackAI/deeplay/blob/develop/tutorials/developers/DT121_overview.ipynb)**
 
 - DT131 **[Deeplay Applications](https://github.com/DeepTrackAI/deeplay/blob/develop/tutorials/developers/DT131_applications.ipynb)**
 
-- DT141 **[Deeplay Models](https://github.com/DeepTrackAI/deeplay/blob/develop/tutorials/developers/DT141_models.ipynb)**
\ No newline at end of file
+- DT141 **[Deeplay Models](https://github.com/DeepTrackAI/deeplay/blob/develop/tutorials/developers/DT141_models.ipynb)**
+
+- DT151 **[Deeplay Components](https://github.com/DeepTrackAI/deeplay/blob/develop/tutorials/developers/DT151_components.ipynb)**
+
+- DT161 **[Deeplay Operations](https://github.com/DeepTrackAI/deeplay/blob/develop/tutorials/developers/DT151_operations.ipynb)**
+
+- DT171 **[Deeplay Blocks](https://github.com/DeepTrackAI/deeplay/blob/develop/tutorials/developers/DT171_vlocks.ipynb)**
+
+- DT181 **[Overview of Deeplay Internal Structure](https://github.com/DeepTrackAI/deeplay/blob/develop/tutorials/developers/DT181_internals.ipynb)**
\ No newline at end of file
diff --git a/setup.py b/setup.py
index b2536714..2b28dbbd 100644
--- a/setup.py
+++ b/setup.py
@@ -8,7 +8,7 @@
 
 setup(
     name="deeplay",
-    version="0.0.7",
+    version="0.1.0",
     license="MIT",
     packages=find_packages(),
     author=(
diff --git a/tutorials/developers/DT101_files.ipynb b/tutorials/developers/DT101_files.ipynb
index f048b266..444a101e 100644
--- a/tutorials/developers/DT101_files.ipynb
+++ b/tutorials/developers/DT101_files.ipynb
@@ -16,13 +16,13 @@
     "Deeplay contains the following files at the root level:\n",
     "- `.gitignore`: Contains the files to be ingnored by GIT.\n",
     "- `.pylintrc`: Configuration file for the pylint tool. It contains the rules for code formatting and style.\n",
-    "- `LICENSE.txt`: License file for the project.\n",
-    "- `README.md`: Project's README file\n",
+    "- `LICENSE.txt`: Deeplay's project license.\n",
+    "- `README.md`: Deeplay's project README file\n",
     "- `requirements.txt`: File containing the dependencies for the project.\n",
-    "- `setup.cfg`: Configuration file for the setup tool. It contains the metadata for the project.\n",
-    "- `setup.py`: Setup file for the project. It contains the instructions for installing the project.\n",
-    "especially the warning to be ignored.\n",
-    "- `stylestubgen.py`: Script to generate the style stubs for the project. These are type hints for the style system. It creates .pyi files for select classes in the project, and adds overrides to the `.style()` method to enforce the type hints. It also handles the doc strings for the styles in the same way."
+    "- `setup.cfg`: Configuration file for the setup tool. It contains the metadata for the Deeplay's project.\n",
+    "- `setup.py`: Setup file for the Deeplay's project. It contains the instructions for installing the Deeplay's project.\n",
+    "especially the warnings to be ignored.\n",
+    "- `stylestubgen.py`: Script to generate the style stubs for the Deeplay's project. These are type hints for the style system. It creates .pyi files for select classes in the project, and adds overrides to the `.style()` method to enforce the type hints. It also handles the doc strings for the styles in the same way."
    ]
   },
   {
@@ -93,7 +93,11 @@
     "\n",
     "- `trainer.py`\n",
     "\n",
-    "  This file contains the `Trainer` class, which is used to train models in the Deeplay library. It extends the Lightning `Trainer` class."
+    "  This file contains the `Trainer` class, which is used to train models in the Deeplay library. It extends the Lightning `Trainer` class.\n",
+    "\n",
+    "- `shapes.py`\n",
+    "\n",
+    "  This files contains the `Variable`class. ### TO BE COMPLETED"
    ]
   },
   {
@@ -110,31 +114,31 @@
     "\n",
     "  Contains the reusable components of the library. These are generally built as a combination of blocks. They are more flexible than full models, but less flexible than blocks.\n",
     "\n",
-    "- `applications`\n",
+    "- `models`\n",
     "\n",
-    "  This directory contains the classes and functions related to applications in the Deeplay library. Applications are classes that contain the training logic for specific tasks, such as classification, regression, segmentation, etc. They handle all the details of training a model for a specific task, except for the model architecture.\n",
+    "  This directory contains the models of the library. These are the full models that are used for training and inference. They are built from blocks and components, and are less flexible than both. They generally represent a specific architecture, such as `ResNet`, `UNet`, etc. \n",
     "\n",
-    "  Generally, the individual applications will be placed in further subdirectories, such as `classification`, `regression`, `segmentation`, etc. However, this is less strict than the root level file structure.\n",
+    "- `applications`\n",
     "\n",
-    "- `models`\n",
+    "  This directory contains the classes and functions related to applications in the Deeplay library. Applications are classes that contain the training logic for specific tasks, such as classification, regression, segmentation. They handle all the details of training a model for a specific task, except for the model architecture, which is typically provided as a model.\n",
     "\n",
-    "  Contains the models of the library. These are the full models that are used for training and inference. They are built from blocks and components, and are less flexible than both. They generally represent a specific architecture, such as `ResNet`, `UNet`, etc. \n",
+    "  Generally, the individual applications will be placed in further subdirectories, such as `classification`, `regression`, `segmentation`. However, this is less strict than the root level file structure.\n",
     "\n",
     "- `initializers`\n",
     "\n",
-    "  Contains the classes for initializing the weights of the models.\n",
+    "  This directory contains the classes for initializing the weights of the models.\n",
     "\n",
     "- `callbacks`\n",
     "\n",
-    "  Contains deeplay specific callbacks. Mainly the logging of the training history and the custom progress bar.\n",
+    "  This directory contains deeplay specific callbacks. Mainly the logging of the training history and the custom progress bar.\n",
     "\n",
     "- `external`\n",
     "\n",
-    "  Contains logic for interacting with external classes and object, such as from `torch`. Most important objects are `Layer` and `Optimizer`.\n",
+    "  This directory contains logic for interacting with external classes and object, such as from `torch`. Most important objects are `Layer` and `Optimizer`.\n",
     "\n",
     "- `ops`\n",
     "\n",
-    "  Contains individual operations that are used in the blocks and components. These are generally low-level, non-trainable operations, such as `Reshape`, `Cat`, etc. They act like individual layers.\n",
+    "  This directory contains individual operations that are used in the blocks and components. These are generally low-level, non-trainable operations, such as `Reshape` and `Cat`. They act like individual layers.\n",
     "\n",
     "- `activelearning`\n",
     "\n",
@@ -142,7 +146,7 @@
     "\n",
     "- `tests`\n",
     "\n",
-    "  Contains the tests for the library. These are used to ensure that the library is working correctly and to catch any bugs that may arise."
+    "  This directory contains the unit tests for the library. These are used to ensure that the library is working correctly and to catch any bugs that may arise."
    ]
   }
  ],
diff --git a/tutorials/developers/DT111_style.ipynb b/tutorials/developers/DT111_style.ipynb
index d95fac04..ed56f84f 100644
--- a/tutorials/developers/DT111_style.ipynb
+++ b/tutorials/developers/DT111_style.ipynb
@@ -22,7 +22,7 @@
     "\n",
     "Beyond what is defined in the PEP 8 guidelines, we have the following naming conventions:\n",
     "\n",
-    "- **Minimize the use of abbreviations.** If an abbreviation is used, it should be well-known and not ambiguous.\n",
+    "- **Minimize the use of abbreviations.** If an abbreviation is used, it should be well-known and non-ambiguous.\n",
     "\n",
     "- **Use standard names for classes.** Use the following names:\n",
     "  - `layer` for a class that represents a single layer in a neural network, typically the learnable part of a block.\n",
@@ -120,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,21 +167,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'torch'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Union, Tuple\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(\n\u001b[1;32m      5\u001b[0m         \u001b[38;5;28mself\u001b[39m, x, \u001b[38;5;241m*\u001b[39margs, batch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m32\u001b[39m, device\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, output_device\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m      6\u001b[0m     ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[torch\u001b[38;5;241m.\u001b[39mTensor, Tuple[torch\u001b[38;5;241m.\u001b[39mTensor, \u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m]]:\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'torch'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import torch\n",
     "from typing import Union, Tuple\n",
@@ -219,8 +207,9 @@
     "        -------\n",
     "        Tensor or tuple of Tensor\n",
     "            The output of the module for the given input data.\n",
-    "            Will match the output of the `forward` method. If the output is a single tensor, \n",
-    "            it is returned as is. If the output is a tuple of tensors, it is returned as a tuple.\n",
+    "            Will match the output of the `forward` method. If the output is a \n",
+    "            single tensor, it is returned as is. If the output is a tuple of \n",
+    "            tensors, it is returned as a tuple.\n",
     "\n",
     "        Examples\n",
     "        --------\n",
@@ -246,9 +235,9 @@
    "source": [
     "### Writing Documentation for Classes\n",
     "\n",
-    "Beyond following the numpydoc style guide, Deeplay requires also the following sections:\n",
+    "Beyond following the NumpyDoc style guide, Deeplay requires also the following sections:\n",
     "\n",
-    "- **Input**:This section should describe the input to the forward method. It should include the type of the input, the shape of the input, and any constraints on the input.\n",
+    "- **Input**: This section should describe the input to the forward method. It should include the type of the input, the shape of the input, and any constraints on the input.\n",
     "\n",
     "- **Output**: This section should describe the output of the forward method. It should include the type of the output, the shape of the output, and any constraints on the output.\n",
     "\n",
@@ -259,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -268,31 +257,39 @@
     "class ConvolutionalNeuralNetwork(DeeplayModule):\n",
     "    \"\"\"Convolutional Neural Network (CNN) component.\n",
     "\n",
-    "    This component is a convolutional neural network (CNN) that consists of multiple convolutional blocks.\n",
-    "    Per default, there is no pooling applied between the blocks. The output of the last block is not flattened.\n",
+    "    This component is a convolutional neural network (CNN) that consists of \n",
+    "    multiple convolutional blocks.\n",
+    "    Per default, there is no pooling applied between the blocks. The output of \n",
+    "    the last block is not flattened.\n",
     "    \n",
     "    The default structure of the CNN is as follows:\n",
     "\n",
-    "    1. Conv2D(in_channels, hidden_channels[0], kernel_size=3, stride=1, padding=1)\n",
+    "    1. Conv2D(in_channels, hidden_channels[0], kernel_size=3, stride=1, \n",
+    "              padding=1)\n",
     "       ReLU\n",
-    "    2. Conv2D(hidden_channels[0], hidden_channels[1], kernel_size=3, stride=1, padding=1)\n",
+    "    2. Conv2D(hidden_channels[0], hidden_channels[1], kernel_size=3, stride=1, \n",
+    "              padding=1)\n",
     "       ReLU \n",
     "    ...\n",
-    "    n. Conv2D(hidden_channels[n-1], out_channels, kernel_size=3, stride=1, padding=1)\n",
+    "    n. Conv2D(hidden_channels[n-1], out_channels, kernel_size=3, stride=1, \n",
+    "              padding=1)\n",
     "       out_activation\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
     "    in_channels: int or None\n",
-    "        Number of input features. If None, the input shape is inferred from the first forward pass\n",
+    "        Number of input features. If None, the input shape is inferred from the \n",
+    "        first forward pass.\n",
     "    hidden_channels: list[int]\n",
     "        Number of hidden units in each layer except the last.\n",
     "    out_channels: int\n",
     "        Number of output features in the last layer.\n",
     "    out_activation: Layer or type[nn.Module], optional\n",
-    "        Specification for the output activation of the last block. (Default: nn.Identity)\n",
+    "        Specification for the output activation of the last block. \n",
+    "        (Default: nn.Identity)\n",
     "    pool: template-like\n",
-    "        Specification for the pooling of the block. Is not applied to the first block. (Default: nn.Identity)\n",
+    "        Specification for the pooling of the block. Is not applied to the first \n",
+    "        block. (Default: nn.Identity)\n",
     "        The pooling will be applied before the layer.\n",
     "    \n",
     "    Attributes\n",
@@ -342,12 +339,15 @@
     "    ConvolutionalNeuralNetwork(\n",
     "      (blocks): LayerList(\n",
     "        (0): Conv2dBlock(\n",
-    "            (layer): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+    "            (layer): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), \n",
+    "                            padding=(1, 1))\n",
     "            (activation): ReLU()\n",
-    "            (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+    "            (normalization): BatchNorm2d(16, eps=1e-05, momentum=0.1, \n",
+    "                                         affine=True, track_running_stats=True)\n",
     "        )\n",
     "        (1): Conv2dBlock(\n",
-    "            (layer): Conv2d(16, 1, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
+    "            (layer): Conv2d(16, 1, kernel_size=(3, 3), stride=(2, 2), \n",
+    "                            padding=(1, 1))\n",
     "            (activation): Sigmoid()\n",
     "        )\n",
     "    )\n",
@@ -367,7 +367,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -376,7 +376,7 @@
        "Ellipsis"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -510,11 +510,6 @@
     "\n",
     "- Specifically test the `.multi()` method, ensure that the subblocks have the correct input/output features/channels."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {
diff --git a/tutorials/developers/DT121_overview.ipynb b/tutorials/developers/DT121_overview.ipynb
index 83c511b7..b53b6ce5 100644
--- a/tutorials/developers/DT121_overview.ipynb
+++ b/tutorials/developers/DT121_overview.ipynb
@@ -44,11 +44,6 @@
         "\n",
         "As a general rule of thumb, for objects derived from `Component`, the number of features in each layer should be defineable by the input arguments. For objects derived from `Model`, only the input and output features must be defineable by the input arguments. In both cases, it is recommended to subclass an existing model or component if possible. This will make it easier to implement the required methods and attributes, and will ensure that the new model or component is compatible with the rest of the library."
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": []
     }
   ],
   "metadata": {
diff --git a/tutorials/developers/DT131_applications.ipynb b/tutorials/developers/DT131_applications.ipynb
index 01f063a3..14e5785f 100644
--- a/tutorials/developers/DT131_applications.ipynb
+++ b/tutorials/developers/DT131_applications.ipynb
@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Woring with Deeplay Applications\n",
+        "# Implementing an Application\n",
         "\n",
         "Applications are broadly defined as classes that represent a task, such as classification or regression, without depending heavily on the exact architecture. They are the highest level of abstraction in the Deeplay library. Applications are designed to be easy to use and require minimal configuration to get started. They are also designed to be easily extensible, so that you can add new features without having to modify the existing code."
       ]
@@ -13,9 +13,27 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## What is There in an Application?\n",
+        "## What Should Be Implemented as an Application?\n",
         "\n",
-        "As a general rule of thumb, try to minimize the number of models in an application. Best is if there is a single model, accessed as `app.model`. \n",
+        "The first step is to ensure that what you want to implement is actually an application.\n",
+        "\n",
+        "Applications should generally not define the model, but rather receive it as an argument. \n",
+        "(In some cases, it may be useful to have a default model, but this should be the exception.)\n",
+        "Therefore, applications should strive to be as model agnostic as possible, so that they can be used with any model that fits the input and output shapes.\n",
+        "\n",
+        "Applications define the training and inference loops, and the loss function.\n",
+        "Applications may also define custom metrics, or specialmethods used for inference. Forexample, a classifier application may define a method to predict a hard label from the input, instead of the probabilities.\n",
+        "\n",
+        "Examples of applications are `Classifier`, `Regressor`, `Segmentor`, and `VanillaGAN`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## What Model(s) Should Be in an Application?\n",
+        "\n",
+        "Most applications are composed of a single model, which is provided in the input arguments. Therefore, as a general rule of thumb, try to minimize the number of models in an application. Best is if there is a single model, accessed as `app.model`. \n",
         "\n",
         "Some applications require more,\n",
         "such as `gan.generator` and `gan.discriminator`. This is fine, but try to keep it to a minimum. \n",
@@ -27,7 +45,450 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Defining How to Train an Application\n",
+        "## Implementing an Application\n",
+        "\n",
+        "Here you'll see the steps you should follow to implement an application in Deeplay. You'll do this through the concrete example of a binary classifier."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 1. Create a New File\n",
+        "\n",
+        "The first step is to create a new file in the `deeplay/applications` directory. It should generally be in a subdirectory, named after the type of application. For example, a binary classifier application should be in  `deeplay/applications/classificaiton/binary.py`.\n",
+        "\n",
+        "**The base class: `Application`.** \n",
+        "Applications should inherit from the `Application` class. This class is a subclass of both `DeeplayModule` and `lightning.LightningModule`. This is to ensure that the \n",
+        "application is trainable with PyTorch Lightning."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This is an example for a binary classifier:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from deeplay.applications import Application\n",
+        "from deeplay.external import  Adam\n",
+        "\n",
+        "import torch\n",
+        "import torch.nn as nn   \n",
+        "import torchmetrics as tm\n",
+        "\n",
+        "class BinaryClassifier(Application):\n",
+        "    def __init__(self, model, **kwargs):\n",
+        "        # If no metrics are provided, add binary accuracy.\n",
+        "        # Note: Users can still set metrics=[] to disable all metrics.\n",
+        "        if kwargs.get(\"metrics\", False) is False:\n",
+        "            kwargs[\"metrics\"] = [tm.Accuracy(\"binary\")]\n",
+        "\n",
+        "        # Note: It's good practice to allow loss and optimizer to be passed in\n",
+        "        # as arguments, so that users can customize them if they want to.\n",
+        "        # Here, this is skipped for simplicity.\n",
+        "        super().__init__(loss=nn.BCELoss(), \n",
+        "                         optimizer=Adam(lr=1e-3),\n",
+        "                         **kwargs)\n",
+        "\n",
+        "        self.model = model\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        # Here the forward pass is defined.\n",
+        "        return self.model(x)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 2. Implement the `.compute_loss()`method\n",
+        "\n",
+        "The first method to implement is `.compute_loss()`. This method should receive\n",
+        "the predictions and the targets, and return the loss. In most cases, the default\n",
+        "behavior is sufficient, but you may want to implement a custom evaluation.\n",
+        "\n",
+        "In this case, you'll convert the targets to float, since the loss function expects floats."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "class BinaryClassifier(Application):\n",
+        "    def __init__(self, model, **kwargs):\n",
+        "        if kwargs.get(\"metrics\", False) is False:\n",
+        "            kwargs[\"metrics\"] = [tm.Accuracy(\"binary\")]\n",
+        "\n",
+        "        super().__init__(loss=nn.BCELoss(), \n",
+        "                         optimizer=Adam(lr=1e-3),\n",
+        "                         **kwargs)\n",
+        "\n",
+        "        self.model = model\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        return self.model(x)\n",
+        "    \n",
+        "    def compute_loss(self, y_hat, y):\n",
+        "        # This method computes the loss.\n",
+        "        # The targets are casted to float to match the expected type of the \n",
+        "        # loss function.\n",
+        "        return super().compute_loss(y_hat, y.float())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 3. Add Annotations\n",
+        "\n",
+        "It's important to add annotations to the class and methods to ensure that the\n",
+        "user knows what to expect. This is also useful for the IDE to provide \n",
+        "autocomplete."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "class BinaryClassifier(Application):\n",
+        "\n",
+        "    # Here only the attributes that are different from the parent class need to \n",
+        "    # be defined.\n",
+        "    model: nn.Module\n",
+        "\n",
+        "\n",
+        "    def __init__(\n",
+        "        self,\n",
+        "        model: nn.Module,\n",
+        "        **kwargs,\n",
+        "    ) -> None:\n",
+        "        if kwargs.get(\"metrics\", False) is False:\n",
+        "            kwargs[\"metrics\"] = [tm.Accuracy(\"binary\")]\n",
+        "        \n",
+        "        super().__init__(loss=nn.BCELoss(), \n",
+        "                         optimizer=Adam(lr=1e-3),\n",
+        "                         **kwargs)\n",
+        "\n",
+        "        self.model = model\n",
+        "\n",
+        "    def forward(\n",
+        "        self, \n",
+        "        x: torch.Tensor,\n",
+        "    ) -> torch.Tensor:\n",
+        "        return self.model(x)\n",
+        "    \n",
+        "    def compute_loss(\n",
+        "        self, \n",
+        "        y_hat: torch.Tensor,\n",
+        "        y: torch.Tensor,\n",
+        "    ) -> torch.Tensor:\n",
+        "        return super().compute_loss(y_hat, y.float())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 4. Document the Application\n",
+        "\n",
+        "The next step is to document the application. This should include a description of \n",
+        "the application, the input and output shapes, and the arguments that can be passed to the application.\n",
+        "Ideally, also all non-trivial methods should be documented."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "class BinaryClassifier(Application):\n",
+        "    \"\"\"Binary Classifier Application\n",
+        "\n",
+        "    This class is a binary classifier application that can be used to train a \n",
+        "    model to classify data into two classes.\n",
+        "\n",
+        "    If no metrics are provided, the binary accuracy is used.\n",
+        "    Set `metrics=[]` to disable all metrics.\n",
+        "\n",
+        "\n",
+        "    Parameters\n",
+        "    ----------\n",
+        "    model : nn.Module\n",
+        "        The model to use as the backbone.\n",
+        "    kwargs : dict\n",
+        "        Additional arguments to pass to the Application class\n",
+        "        See `deeplay.applications.Application` for more details.\n",
+        "\n",
+        "    Input\n",
+        "    -----\n",
+        "    x : torch.Tensor\n",
+        "        The input tensor of shape (N, ...)\n",
+        "        Where N is the batch size and ... is the input shape.\n",
+        "    \n",
+        "    Output\n",
+        "    ------\n",
+        "    y : torch.Tensor\n",
+        "        The output tensor of shape (N, 1)\n",
+        "        Where N is the batch size and 1 is a single value between 0 and 1.\n",
+        "\n",
+        "    Target\n",
+        "    ------\n",
+        "    target : torch.Tensor (float, long, bool)\n",
+        "        The target tensor of shape (N, num_classes).\n",
+        "        Where N is the batch size and num_classes is the number of classes.\n",
+        "        The values of the target should be between 0 and 1 (inclusive).\n",
+        "        Values like 0.1 and 0.9 are allowed.\n",
+        "\n",
+        "    Evaluation\n",
+        "    ----------\n",
+        "    ```python\n",
+        "    x = model(x)\n",
+        "    ```\n",
+        "\n",
+        "    Examples\n",
+        "    --------\n",
+        "    >>> net = MultiLayerPerceptron(3, [64], 1)\n",
+        "    >>> application = BinaryClassifier(net).build()\n",
+        "    >>> x = torch.rand(1000, 3)\n",
+        "    >>> target = x.sum(dim=1, keepdim=True) > 1.5\n",
+        "    >>> hist = application.fit((x, target), epochs=10)\n",
+        "\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    model: nn.Module\n",
+        "\n",
+        "\n",
+        "    def __init__(\n",
+        "        self,\n",
+        "        model: nn.Module,\n",
+        "        **kwargs,\n",
+        "    ) -> None:\n",
+        "        if kwargs.get(\"metrics\", False) is False:\n",
+        "            kwargs[\"metrics\"] = [tm.Accuracy(\"binary\")]\n",
+        "        \n",
+        "        super().__init__(loss=nn.BCELoss(), \n",
+        "                         optimizer=Adam(lr=1e-3),\n",
+        "                         **kwargs)\n",
+        "\n",
+        "        self.model = model\n",
+        "\n",
+        "    def forward(\n",
+        "        self, \n",
+        "        x: torch.Tensor,\n",
+        "    ) -> torch.Tensor:\n",
+        "        \"\"\"Forward pass of the model.\n",
+        "\n",
+        "        Evaluates the model on the input tensor `x`.\n",
+        "\n",
+        "        Parameters\n",
+        "        ----------\n",
+        "        x : torch.Tensor\n",
+        "            The input tensor of shape (N, ...)\n",
+        "            Where N is the batch size and ... is the input shape.\n",
+        "\n",
+        "        Returns\n",
+        "        -------\n",
+        "        torch.Tensor\n",
+        "            The output tensor of shape (N, 1)\n",
+        "            Where N is the batch size and 1 is a single value between 0 and 1.\n",
+        "        \"\"\"\n",
+        "        return self.model(x)\n",
+        "    \n",
+        "    def compute_loss(  ### Should we add docs for this method?\n",
+        "        self, \n",
+        "        y_hat: torch.Tensor,\n",
+        "        y: torch.Tensor,\n",
+        "    ) -> torch.Tensor:\n",
+        "        \"\"\" Compute the loss.\n",
+        "\n",
+        "        Parameters\n",
+        "        ----------\n",
+        "        y_hat : torch.Tensor\n",
+        "            The predicted tensor of shape (N, 1)\n",
+        "            Where N is the batch size and 1 is a single value between 0 and 1.\n",
+        "        y : torch.Tensor\n",
+        "            The target tensor of shape (N, 1)\n",
+        "            Where N is the batch size and 1 is a single value between 0 and 1.\n",
+        "        \n",
+        "        Returns\n",
+        "        -------\n",
+        "        torch.Tensor\n",
+        "            The computed loss.\n",
+        "        \"\"\"\n",
+        "        return super().compute_loss(y_hat, y.float())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can now use the application:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n",
+            "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/configuration_validator.py:74: You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.\n",
+            "Missing logger folder: /Users/giovannivolpe/Documents/GitHub/deeplay/tutorials/developers/lightning_logs\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
+              "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">   </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name          </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Type                 </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Params </span>┃\n",
+              "┡━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
+              "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 0 </span>│ loss          │ BCELoss              │      0 │\n",
+              "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1 </span>│ train_metrics │ MetricCollection     │      0 │\n",
+              "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 2 </span>│ val_metrics   │ MetricCollection     │      0 │\n",
+              "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 3 </span>│ test_metrics  │ MetricCollection     │      0 │\n",
+              "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 4 </span>│ model         │ MultiLayerPerceptron │    321 │\n",
+              "│<span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 5 </span>│ optimizer     │ Adam                 │      0 │\n",
+              "└───┴───────────────┴──────────────────────┴────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
+              "┃\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mName         \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mType                \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mParams\u001b[0m\u001b[1;35m \u001b[0m┃\n",
+              "┡━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n",
+              "│\u001b[2m \u001b[0m\u001b[2m0\u001b[0m\u001b[2m \u001b[0m│ loss          │ BCELoss              │      0 │\n",
+              "│\u001b[2m \u001b[0m\u001b[2m1\u001b[0m\u001b[2m \u001b[0m│ train_metrics │ MetricCollection     │      0 │\n",
+              "│\u001b[2m \u001b[0m\u001b[2m2\u001b[0m\u001b[2m \u001b[0m│ val_metrics   │ MetricCollection     │      0 │\n",
+              "│\u001b[2m \u001b[0m\u001b[2m3\u001b[0m\u001b[2m \u001b[0m│ test_metrics  │ MetricCollection     │      0 │\n",
+              "│\u001b[2m \u001b[0m\u001b[2m4\u001b[0m\u001b[2m \u001b[0m│ model         │ MultiLayerPerceptron │    321 │\n",
+              "│\u001b[2m \u001b[0m\u001b[2m5\u001b[0m\u001b[2m \u001b[0m│ optimizer     │ Adam                 │      0 │\n",
+              "└───┴───────────────┴──────────────────────┴────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Trainable params</span>: 321                                                                                              \n",
+              "<span style=\"font-weight: bold\">Non-trainable params</span>: 0                                                                                            \n",
+              "<span style=\"font-weight: bold\">Total params</span>: 321                                                                                                  \n",
+              "<span style=\"font-weight: bold\">Total estimated model params size (MB)</span>: 0                                                                          \n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mTrainable params\u001b[0m: 321                                                                                              \n",
+              "\u001b[1mNon-trainable params\u001b[0m: 0                                                                                            \n",
+              "\u001b[1mTotal params\u001b[0m: 321                                                                                                  \n",
+              "\u001b[1mTotal estimated model params size (MB)\u001b[0m: 0                                                                          \n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "1fea4d3000b24f719a1372300c4fcd6d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve performance.\n",
+            "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (32) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "(<Figure size 1500x500 with 2 Axes>,\n",
+              " array([<Axes: title={'center': 'Loss'}, xlabel='Step'>,\n",
+              "        <Axes: title={'center': 'Binaryaccuracy'}, xlabel='Step'>],\n",
+              "       dtype=object))"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1500x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from deeplay.components import MultiLayerPerceptron\n",
+        "\n",
+        "net = MultiLayerPerceptron(3, [64], 1, out_activation=nn.Sigmoid)\n",
+        "application = BinaryClassifier(net).build()\n",
+        "\n",
+        "x = torch.rand(1000, 3)\n",
+        "target = x.sum(dim=1, keepdim=True) > 1.5\n",
+        "\n",
+        "hist = application.fit((x, target), max_epochs=15)\n",
+        "\n",
+        "hist.plot()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## NOTE: Defining How to Train an Application\n",
         "\n",
         "The primary function of an application is to define how it is trained. This includes the loss function, the optimizer, and the metrics that are used to evaluate the model. Applications also define how the model is trained, including the training loop, the validation loop, and the testing loop. Applications are designed to be easy to use, so that you can get started quickly without having to worry about the details of the training process."
       ]
@@ -60,15 +521,10 @@
       "source": [
         "### Defining a More Complex Training\n",
         "\n",
-        "If the training process is more complex and you need to define a custom training loop, you can override the `training_step` method entirely. This method is called for each batch of data during training. It should return the loss for the batch. \n",
+        "If the training process is more complex and you need to define a custom training loop, you can override the `.training_step()` method entirely. This method is called for each batch of data during training. It should return the loss for the batch. \n",
         "\n",
         "Note that if you override the `.training_step()` method, you will have to handle the logging of the loss yourself. This is done by calling `self.log(\"train_loss\", loss, ...)` where `...` is any setting you want to pass to the logger (see `lightning.LightningModule.log()` for more information)."
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": []
     }
   ],
   "metadata": {
@@ -87,7 +543,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.7"
+      "version": "3.12.2"
     }
   },
   "nbformat": 4,
diff --git a/tutorials/developers/DT141_models.ipynb b/tutorials/developers/DT141_models.ipynb
index ca2cfe09..e65b4064 100644
--- a/tutorials/developers/DT141_models.ipynb
+++ b/tutorials/developers/DT141_models.ipynb
@@ -4,16 +4,33 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Working with Deeplay Models\n",
+        "# Implementing a Model\n",
         "\n",
-        "Models are broadly defined as classes that represent a specific architecture, such as a ResNet18. Unlike components, they are generally not as flexible in terms of input arguments, and it should be possible to pass them directly to applications. Models are designed to be easy to use and require minimal configuration to get started. They are also designed to be easily extensible, so that you can add new features without having to modify the existing code."
+        "Models are broadly defined as classes that represent a specific architecture, such as `ResNet18`. Unlike components, they are generally not as flexible in terms of input arguments, and it should be possible to pass them directly to applications. Models are designed to be easy to use and require minimal configuration to get started. They are also designed to be easily extensible, so that you can add new features without having to modify the existing code."
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## What is There in a Model?\n",
+        "## What Should Be Implemented as a Model?\n",
+        "\n",
+        "The first step is to ensure that what you want to implement is actually a model.\n",
+        "Most models are composed of a few named components (for example, `ConvolutionalNeuralNetwork`), and generally intended as a complete transformation from input to output for a given task.\n",
+        "\n",
+        "Most models are standard neural networks with exact architectures (like `ResNet50`), but models can also be more general architectures (like a `RecurrentModel`). \n",
+        "\n",
+        "Unlike components, models generally have a rigid structure. It is not expected that\n",
+        "the number of blocks or the sizes of the layers can be defined in the input arguments. However, if possible, the input and output shapes should be flexible.\n",
+        "\n",
+        "Examples of models are `ViT`, `CycleGANGenerator`, `ResNet`, `RecurrentModel`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## What Should a Model Contain?\n",
         "\n",
         "Generally, a model should define an `.__init__()` method that takes all the necessary arguments to define the model and a `.forward()` method that defines the forward pass of the model.\n",
         "\n",
@@ -27,181 +44,521 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Managing Unknown Tensor Sizes\n",
+        "## Implementing a Model\n",
+        "\n",
+        "Here, you'll see the steps you should follow to implement a model in Deeplay. You'll do this implementing the `ResNet18` model."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 1. Create a New File\n",
+        "\n",
+        "The first step is to create a new file in the `deeplay/models` directory. It\n",
+        "can be in a deeper subdirectory if it makes sense.\n",
+        "\n",
+        "**The base class.** \n",
+        "Models generally don't have a fixed base class. Sometimes it makes sense to subclass an existing model, but it is not necessary. It is in some cases possible to subclass a component, if the model is simply that component with some additional layers or with an exact architecture. If neither are applicable, use `DeeplayModule` as the base class.\n",
+        "\n",
+        "**Styled components and blocks.**\n",
+        "Special for the implementation of models is the expectation to used styled components\n",
+        "and blocks where possible. This is to ensure that the modules can be reused in other\n",
+        "models."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 2a. Implement the ResNet18 Block\n",
+        "\n",
+        "First, implement the ResNet block as a styled block. It should be implemented\n",
+        "in the same file as the model.\n",
+        "\n",
+        "**NOTE:** The style should have a small docstring, just like in the case of a method. The first argument should not be documented (just as `self` is not documented in methods)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from deeplay.blocks import Conv2dBlock\n",
+        "\n",
+        "@Conv2dBlock.register_style\n",
+        "def resnet(block: Conv2dBlock, stride: int = 1) -> None:\n",
+        "    \"\"\"ResNet style block composed of two residual blocks.\n",
+        "\n",
+        "    Parameters\n",
+        "    ----------\n",
+        "    stride : int\n",
+        "        Stride of the first block, by default 1\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # 1. Create two blocks.\n",
+        "    block.multi(2)\n",
         "\n",
-        "Tensorflow, and by extension Keras, allows for unknown tensor sizes thanks to the graph structure. This is not possible in PyTorch.\n",
+        "    # 2. Make the two blocks.\n",
+        "    block.blocks[0].style(\"residual\", order=\"lnaln|a\")\n",
+        "    block.blocks[1].style(\"residual\", order=\"lnaln|a\")\n",
         "\n",
-        "If you need to support unknown tensor sizes, you can use the `lazy` module. This module allows for unknown tensor sizes by delaying the\n",
-        "construction of the model until the first forward pass. This is not optimal, so use it sparingly. Examples are `nn.LazyConv2d` and `nn.LazyLinear`.\n",
+        "    # 3. If stride > 1, stride first block and add normalization to shortcut.\n",
+        "    if stride > 1:\n",
+        "        block.blocks[0].strided(stride)\n",
+        "        block.blocks[0].shortcut_start.normalized()\n",
         "\n",
-        "If a model requires unknown tensor sizes, it is heavily encouraged to define the `.validate_after_build()` method, which should call the forward pass with a small input to validate that the model can be built. This will instantiate the lazy modules directly, allowing for a more user-friendly experience."
+        "    # 4. Remove the pooling layer if it exists.\n",
+        "    block[...].isinstance(Conv2dBlock).all.remove(\"pool\", allow_missing=True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "**TODO** where do the next cells belong?"
+        "You can now instatiate this block and verify its structure."
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 14,
+      "execution_count": 2,
       "metadata": {},
       "outputs": [
         {
-          "data": {
-            "text/plain": [
-              "ImageClassifier(\n",
-              "  (model): Sequential(\n",
-              "    (0): ConvolutionalEncoder2d(\n",
-              "      (blocks): LayerList(\n",
-              "        (0): Conv2dBlock(\n",
-              "          (layer): Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-              "          (activation): ReLU()\n",
-              "        )\n",
-              "        (1): Conv2dBlock(\n",
-              "          (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-              "          (layer): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-              "          (activation): ReLU()\n",
-              "        )\n",
-              "        (2): Conv2dBlock(\n",
-              "          (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-              "          (layer): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-              "          (activation): ReLU()\n",
-              "        )\n",
-              "        (3): Conv2dBlock(\n",
-              "          (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-              "          (layer): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-              "          (activation): Identity()\n",
-              "        )\n",
-              "      )\n",
-              "      (postprocess): Identity()\n",
-              "    )\n",
-              "    (1): AdaptiveAvgPool2d(output_size=1)\n",
-              "    (2): MultiLayerPerceptron(\n",
-              "      (blocks): LayerList(\n",
-              "        (0): LinearBlock(\n",
-              "          (layer): Linear(in_features=128, out_features=10, bias=True)\n",
-              "          (activation): Identity()\n",
-              "        )\n",
-              "      )\n",
-              "    )\n",
-              "  )\n",
-              ")"
-            ]
-          },
-          "execution_count": 14,
-          "metadata": {},
-          "output_type": "execute_result"
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Conv2dBlock(\n",
+            "  (blocks): Sequential(\n",
+            "    (0-1): 2 x Conv2dBlock(\n",
+            "      (shortcut_start): Conv2dBlock(\n",
+            "        (layer): Layer[Identity](in_channels=16, out_channels=16, kernel_size=1, stride=1, padding=0)\n",
+            "      )\n",
+            "      (blocks): Sequential(\n",
+            "        (0): Conv2dBlock(\n",
+            "          (layer): Layer[Conv2d](in_channels=16, out_channels=16, kernel_size=3, stride=1, padding=1)\n",
+            "          (normalization): Layer[BatchNorm2d](num_features=16)\n",
+            "          (activation): Layer[ReLU]()\n",
+            "        )\n",
+            "        (1): Conv2dBlock(\n",
+            "          (layer): Layer[Conv2d](in_channels=16, out_channels=16, kernel_size=3, stride=1, padding=1)\n",
+            "          (normalization): Layer[BatchNorm2d](num_features=16)\n",
+            "        )\n",
+            "      )\n",
+            "      (shortcut_end): Add()\n",
+            "      (activation): Layer[ReLU]()\n",
+            "    )\n",
+            "  )\n",
+            ")\n"
+          ]
         }
       ],
       "source": [
-        "import deeplay as dl\n",
+        "block = Conv2dBlock(16, 16).style(\"resnet\")\n",
+        "\n",
+        "print(block)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 2b. Implement the ResNet18 Input Block\n",
+        "\n",
+        "The input block is slightly different from the normal block. You can implement also this block as a styled block."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from deeplay.external.layer import Layer\n",
         "import torch.nn as nn\n",
         "\n",
-        "net = dl.Sequential(\n",
-        "    dl.ConvolutionalEncoder2d(1, [16, 32, 64], 128),\n",
-        "    dl.Layer(nn.AdaptiveAvgPool2d, 1),\n",
-        "    dl.MultiLayerPerceptron(128, [], 10)\n",
-        ")\n",
+        "@Conv2dBlock.register_style\n",
+        "def resnet18_input(block: Conv2dBlock) -> None:\n",
+        "    \"\"\"ResNet18 input block.\n",
         "\n",
-        "class ImageClassifier(dl.Application):\n",
+        "    The block used on the input of the ResNet18 architecture.\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    block.configure(kernel_size=7, stride=2, padding=3, bias=False)\n",
+        "    block.normalized(mode=\"insert\", after=\"layer\")\n",
+        "    block.activated(\n",
+        "        Layer(nn.ReLU, inplace=True), mode=\"insert\", after=\"normalization\",\n",
+        "    )\n",
+        "    pool = Layer(\n",
+        "        nn.MaxPool2d, kernel_size=3, stride=2, padding=1, ceil_mode=False, \n",
+        "        dilation=1,\n",
+        "    )\n",
+        "    block.pooled(pool, mode=\"append\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Also in this case, you can instantiate this block and verify its architecture."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Conv2dBlock(\n",
+            "  (layer): Layer[Conv2d](in_channels=3, out_channels=64, kernel_size=7, stride=2, padding=3)\n",
+            "  (normalization): Layer[BatchNorm2d](num_features=64)\n",
+            "  (activation): Layer[ReLU](inplace=True)\n",
+            "  (pool): Layer[MaxPool2d](kernel_size=3, stride=2, padding=1, ceil_mode=False, dilation=1)\n",
+            ")\n"
+          ]
+        }
+      ],
+      "source": [
         "\n",
-        "    model: nn.Module\n",
+        "block = Conv2dBlock(3, 64).style(\"resnet18_input\")\n",
         "\n",
-        "    def __init__(self, model: nn.Module):\n",
-        "        super().__init__()\n",
-        "        self.model = model\n",
+        "print(block)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 2c. Implement the ResNet18 Backbone\n",
         "\n",
-        "    def forward(self, x):\n",
-        "        return self.model(x)\n",
+        "The backbone is a styled component, and should be implemented in the same file as the\n",
+        "model. As it is a convolutional encoder, you can style a `ConvolutionalEncoder2d` component."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from deeplay.components import ConvolutionalEncoder2d\n",
+        "from deeplay.initializers import Kaiming, Constant\n",
+        "\n",
+        "@ConvolutionalEncoder2d.register_style\n",
+        "def resnet18(\n",
+        "    encoder: ConvolutionalEncoder2d, \n",
+        "    pool_output: bool = True,\n",
+        "    set_hidden_channels: bool = False,\n",
+        ") -> None: \n",
+        "    \"\"\"ResNet18 backbone.\n",
         "\n",
-        "classifier = ImageClassifier(net).create()\n",
-        "classifier"
+        "    Styles a ConvolutionalEncoder2d to have the ResNet18 architecture.\n",
+        "\n",
+        "    Parameters\n",
+        "    ----------\n",
+        "    pool_output : bool\n",
+        "        Whether to append a pooling layer at the end of the encoder, by default \n",
+        "        True.\n",
+        "    set_hidden_channels : bool\n",
+        "        Whether to set the hidden channels to the default ResNet18 values, by \n",
+        "        default False.\n",
+        "    \"\"\"\n",
+        "\n",
+        "    if set_hidden_channels:\n",
+        "        encoder.configure(hidden_channels=[64, 64, 128, 256])\n",
+        "\n",
+        "    # 1. Style the first block.\n",
+        "    encoder.blocks[0].style(\"resnet18_input\")\n",
+        "\n",
+        "    # 2. The second block does not have a stride.\n",
+        "    encoder.blocks[1].style(\"resnet\", stride=1)\n",
+        "\n",
+        "    # 3. The rest of the blocks have a stride of 2.\n",
+        "    encoder[\"blocks\", 2:].hasattr(\"style\").all.style(\"resnet\", stride=2)\n",
+        "\n",
+        "    # 4. Initialize the weights.\n",
+        "    encoder.initialize(Kaiming(targets=(nn.Conv2d,)))\n",
+        "    encoder.initialize(Constant(targets=(nn.BatchNorm2d,)))\n",
+        "\n",
+        "    # 5. Set postprocess to pool the output if needed.\n",
+        "    if pool_output:\n",
+        "        encoder.postprocess.configure(nn.AdaptiveAvgPool2d, output_size=(1, 1))"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Next, we allow the user to set the optimizer. Note that we are using the `create_optimizer_with_params` method to create the optimizer. We also use Adam as the default optimizer. It is better to set the default value of the optimizer to `None` and then set it to Adam in the `__init__` method. This is because the optimizer is a mutable object, and setting it to a default value of `Adam()` will cause all instances of the class to share the same optimizer object."
+        "You can now instantiate the backbone and print out its architecture."
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 15,
+      "execution_count": 6,
       "metadata": {},
       "outputs": [
         {
-          "data": {
-            "text/plain": [
-              "ImageClassifier(\n",
-              "  (model): Sequential(\n",
-              "    (0): ConvolutionalEncoder2d(\n",
-              "      (blocks): LayerList(\n",
-              "        (0): Conv2dBlock(\n",
-              "          (layer): Conv2d(1, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-              "          (activation): ReLU()\n",
-              "        )\n",
-              "        (1): Conv2dBlock(\n",
-              "          (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-              "          (layer): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-              "          (activation): ReLU()\n",
-              "        )\n",
-              "        (2): Conv2dBlock(\n",
-              "          (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-              "          (layer): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-              "          (activation): ReLU()\n",
-              "        )\n",
-              "        (3): Conv2dBlock(\n",
-              "          (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-              "          (layer): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-              "          (activation): Identity()\n",
-              "        )\n",
-              "      )\n",
-              "      (postprocess): Identity()\n",
-              "    )\n",
-              "    (1): AdaptiveAvgPool2d(output_size=1)\n",
-              "    (2): MultiLayerPerceptron(\n",
-              "      (blocks): LayerList(\n",
-              "        (0): LinearBlock(\n",
-              "          (layer): Linear(in_features=128, out_features=10, bias=True)\n",
-              "          (activation): Identity()\n",
-              "        )\n",
-              "      )\n",
-              "    )\n",
-              "  )\n",
-              "  (optimizer): Adam[Adam](lr=0.001)\n",
-              ")"
-            ]
-          },
-          "execution_count": 15,
-          "metadata": {},
-          "output_type": "execute_result"
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "ConvolutionalEncoder2d(\n",
+            "  (blocks): LayerList(\n",
+            "    (0): Conv2dBlock(\n",
+            "      (layer): Layer[Conv2d](in_channels=3, out_channels=16, kernel_size=7, stride=2, padding=3)\n",
+            "      (normalization): Layer[BatchNorm2d](num_features=16)\n",
+            "      (activation): Layer[ReLU](inplace=True)\n",
+            "      (pool): Layer[MaxPool2d](kernel_size=3, stride=2, padding=1, ceil_mode=False, dilation=1)\n",
+            "    )\n",
+            "    (1): Conv2dBlock(\n",
+            "      (blocks): Sequential(\n",
+            "        (0): Conv2dBlock(\n",
+            "          (shortcut_start): Conv2dBlock(\n",
+            "            (layer): Layer[Conv2d](in_channels=16, out_channels=32, kernel_size=1, stride=1, padding=0)\n",
+            "          )\n",
+            "          (blocks): Sequential(\n",
+            "            (0): Conv2dBlock(\n",
+            "              (layer): Layer[Conv2d](in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)\n",
+            "              (normalization): Layer[BatchNorm2d](num_features=32)\n",
+            "              (activation): Layer[ReLU]()\n",
+            "            )\n",
+            "            (1): Conv2dBlock(\n",
+            "              (layer): Layer[Conv2d](in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1)\n",
+            "              (normalization): Layer[BatchNorm2d](num_features=32)\n",
+            "            )\n",
+            "          )\n",
+            "          (shortcut_end): Add()\n",
+            "          (activation): Layer[ReLU]()\n",
+            "        )\n",
+            "        (1): Conv2dBlock(\n",
+            "          (shortcut_start): Conv2dBlock(\n",
+            "            (layer): Layer[Identity](in_channels=32, out_channels=32, kernel_size=1, stride=1, padding=0)\n",
+            "          )\n",
+            "          (blocks): Sequential(\n",
+            "            (0): Conv2dBlock(\n",
+            "              (layer): Layer[Conv2d](in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1)\n",
+            "              (normalization): Layer[BatchNorm2d](num_features=32)\n",
+            "              (activation): Layer[ReLU]()\n",
+            "            )\n",
+            "            (1): Conv2dBlock(\n",
+            "              (layer): Layer[Conv2d](in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1)\n",
+            "              (normalization): Layer[BatchNorm2d](num_features=32)\n",
+            "            )\n",
+            "          )\n",
+            "          (shortcut_end): Add()\n",
+            "          (activation): Layer[ReLU]()\n",
+            "        )\n",
+            "      )\n",
+            "    )\n",
+            "  )\n",
+            "  (postprocess): Layer[AdaptiveAvgPool2d](output_size=(1, 1))\n",
+            ")\n"
+          ]
         }
       ],
       "source": [
-        "from typing import Optional\n",
+        "backbone = ConvolutionalEncoder2d(3, [16], 32).style(\"resnet18\", pool_output=True)\n",
+        "\n",
+        "print(backbone)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 2d. Implement the ResNet18 Model\n",
+        "\n",
+        "You can now finally implement the `ResNet18` model by subclassing `DeeplayModule`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from deeplay.module import DeeplayModule\n",
+        "from deeplay.components import MultiLayerPerceptron\n",
         "\n",
-        "class ImageClassifier(dl.Application):\n",
+        "class ResNet18(DeeplayModule):\n",
         "\n",
-        "    model: nn.Module\n",
-        "    optimizer: dl.Optimizer\n",
+        "    def __init__(self, in_channels=3, latent_channels=512, num_classes=1000):\n",
+        "        self.backbone = ConvolutionalEncoder2d(\n",
+        "            in_channels, \n",
+        "            [64, 64, 128, 256, 512], \n",
+        "            latent_channels\n",
+        "        )\n",
+        "        self.backbone.style(\"resnet18\", pool_output=True)\n",
         "\n",
-        "    def __init__(self, model: nn.Module, optimizer: Optional[dl.Optimizer] = None):\n",
-        "        super().__init__()\n",
-        "        self.model = model\n",
-        "        self.optimizer = optimizer or dl.Adam(lr=0.001)\n",
+        "        self.head = MultiLayerPerceptron(latent_channels, [], num_classes)\n",
         "\n",
         "    def forward(self, x):\n",
-        "        return self.model(x)\n",
+        "        x = self.backbone(x)\n",
+        "        x = self.head(x)\n",
+        "        return x"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 3. Add Annotations\n",
+        "\n",
+        "It's important to add annotations to the class and methods to ensure that the\n",
+        "user knows what to expect. This is also useful for the IDE to provide \n",
+        "autocomplete."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "\n",
+        "class ResNet18(DeeplayModule):\n",
+        "\n",
+        "    def __init__(\n",
+        "        self, \n",
+        "        in_channels: int = 3, \n",
+        "        latent_channels: int = 512, \n",
+        "        num_classes: int = 1000,\n",
+        "    ) -> None: \n",
+        "        self.backbone = ConvolutionalEncoder2d(\n",
+        "            in_channels, \n",
+        "            [64, 64, 128, 256, 512], \n",
+        "            latent_channels\n",
+        "        )\n",
+        "        self.backbone.style(\"resnet18\", pool_output=True)\n",
+        "\n",
+        "        self.head = MultiLayerPerceptron(latent_channels, [], num_classes)\n",
+        "\n",
+        "    def forward(\n",
+        "        self, \n",
+        "        x: torch.Tensor,  \n",
+        "    ) -> torch.Tensor: \n",
+        "        x = self.backbone(x)\n",
+        "        x = self.head(x)\n",
+        "        return x"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### 4. Document the Model\n",
+        "\n",
+        "The next step is to document the model. This should include a description of \n",
+        "the model, the input and output shapes, and the arguments that can be passed to\n",
+        "the model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "class ResNet18(DeeplayModule):\n",
+        "    \"\"\"A ResNet18 model.\n",
+        "\n",
+        "    A ResNet18 model composed of a ConvolutionalEncoder2d backbone and a \n",
+        "    MultiLayerPerceptron head.\n",
+        "\n",
+        "    Parameters\n",
+        "    ----------\n",
+        "    in_channels : int\n",
+        "        The number of input channels, by default 3.\n",
+        "    latent_channels : int\n",
+        "        The number of latent channels (at the end of the backbone), by default \n",
+        "        512.\n",
+        "    num_classes : int\n",
+        "        The number of classes, by default 1000.\n",
+        "    \n",
+        "    Attributes\n",
+        "    ----------\n",
+        "    backbone : ConvolutionalEncoder2d\n",
+        "        The backbone of the model.\n",
+        "    head : MultiLayerPerceptron\n",
+        "        The head of the model. By default a simple linear layer.\n",
+        "    \n",
+        "    Input\n",
+        "    -----\n",
+        "    x : torch.Tensor\n",
+        "        The input tensor of shape (N, in_channels, H, W).\n",
+        "        Where N is the batch size, in_channels is the number of input channels,\n",
+        "        H is the height, and W is the width.\n",
+        "        H and W should be at least 33, but ideally 224.\n",
         "\n",
-        "    def configure_optimizers(self):\n",
-        "        return self.create_optimizer_with_params(self.optimizer, self.parameters())\n",
         "    \n",
-        "classifier = ImageClassifier(net).create()\n",
-        "classifier"
+        "    Output\n",
+        "    ------\n",
+        "    y : torch.Tensor\n",
+        "        The output tensor of shape (N, num_classes).\n",
+        "        Where N is the batch size and num_classes is the number of classes.\n",
+        "\n",
+        "    Evaluation\n",
+        "    ----------\n",
+        "    ```python\n",
+        "    x = backbone(x)\n",
+        "    x = head(x)\n",
+        "    ```\n",
+        "\n",
+        "    Examples\n",
+        "    --------\n",
+        "    >>> model = ResNet18(3, 512, 1000).build()\n",
+        "    >>> x = torch.randn(4, 3, 224, 224)\n",
+        "    >>> y = model(x)\n",
+        "    >>> y.shape\n",
+        "    torch.Size([4, 1000])\n",
+        "\n",
+        "    \"\"\"\n",
+        "\n",
+        "    def __init__( \n",
+        "        self, \n",
+        "        in_channels: int = 3, \n",
+        "        latent_channels: int = 512, \n",
+        "        num_classes: int = 1000,\n",
+        "    ) -> None: \n",
+        "        self.backbone = ConvolutionalEncoder2d(\n",
+        "            in_channels, \n",
+        "            [64, 64, 128, 256, 512], \n",
+        "            latent_channels\n",
+        "        )\n",
+        "        self.backbone.style(\"resnet18\", pool_output=True)\n",
+        "\n",
+        "        self.head = MultiLayerPerceptron(latent_channels, [], num_classes)\n",
+        "\n",
+        "    def forward( \n",
+        "        self, \n",
+        "        x: torch.Tensor,  \n",
+        "    ) -> torch.Tensor:   \n",
+        "        \"\"\"Forward pass of the model.\n",
+        "        \n",
+        "        Evaluates `backbone` and `head` sequentially.\n",
+        "\n",
+        "        Parameters\n",
+        "        ----------\n",
+        "        x : torch.Tensor\n",
+        "            The input tensor of shape (N, in_channels, H, W).\n",
+        "            Where N is the batch size, in_channels is the number of input channels,\n",
+        "            H is the height, and W is the width.\n",
+        "            H and W should be at least 33, but ideally 224.\n",
+        "        \n",
+        "        Returns\n",
+        "        -------\n",
+        "        torch.Tensor\n",
+        "            The output tensor of shape (N, num_classes).\n",
+        "            Where N is the batch size and num_classes is the number of classes.\n",
+        "        \"\"\"\n",
+        "\n",
+        "        x = self.backbone(x)\n",
+        "        x = self.head(x)\n",
+        "        return x"
       ]
     }
   ],
@@ -221,7 +578,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.7"
+      "version": "3.12.2"
     }
   },
   "nbformat": 4,
diff --git a/tutorials/developers/DT151_components.ipynb b/tutorials/developers/DT151_components.ipynb
new file mode 100644
index 00000000..40c934b9
--- /dev/null
+++ b/tutorials/developers/DT151_components.ipynb
@@ -0,0 +1,627 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Implementing a Component"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What Should Be Implemented as a Component?\n",
+    "\n",
+    "The first step is to ensure that what you want to implement is actually a component.\n",
+    "Most components are composed of several blocks, with a mostly sequential forward pass.\n",
+    "They are intended to be used as parts of a model, and are not models themselves.\n",
+    "\n",
+    "A component should have the flexibility in the input arguments to the constructor to\n",
+    "define the architecture of the component, including the number of layers, the number of units in each layer, and some important hyperparameters.\n",
+    "\n",
+    "Examples of components are `ConvolutionalNeuralNetwork` and `MultiLayerPerceptron`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Implementing a Component\n",
+    "\n",
+    "Here you'll see the steps you should follow to implement a component in deeplay. As an example, you'll implement the `ConvolutionalNeuralNetwork`component."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Create a New File\n",
+    "\n",
+    "The first step is to create a new file in the `deeplay/components` directory. It\n",
+    "can be in a deeper subdirectory if it makes sense.\n",
+    "\n",
+    "**The base class.**\n",
+    "Components generally don't have a specific base class. Sometimes it makes sense to subclass an existing component, but it is not necessary. If not, use `DeeplayModule` as the base class."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example implements the `ConvolutionalNeuralNetwork` component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from deeplay.blocks import Conv2dBlock\n",
+    "from deeplay.list import Sequential\n",
+    "from deeplay.module import DeeplayModule\n",
+    "import torch.nn as nn\n",
+    "\n",
+    "class ConvolutionalNeuralNetwork(DeeplayModule):\n",
+    "    def __init__(\n",
+    "        self, \n",
+    "        in_channels,\n",
+    "        hidden_channels,\n",
+    "        out_channels,\n",
+    "        out_activation=nn.ReLU,\n",
+    "    ):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        blocks = Sequential[Conv2dBlock]()\n",
+    "        for in_ch, out_ch in zip([in_channels] + hidden_channels, \n",
+    "                                 hidden_channels + [out_channels]):\n",
+    "            block = Conv2dBlock(in_ch, out_ch, kernel_size=3, padding=0, \n",
+    "                                activation=nn.ReLU)\n",
+    "            blocks.append(block)\n",
+    "        \n",
+    "        # Set the activation function of the last block.\n",
+    "        blocks[-1].activated(out_activation)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        for block in self.blocks:\n",
+    "            x = block(x)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Add Annotations\n",
+    "\n",
+    "It is important to add annotations to the class and methods to ensure that the\n",
+    "user knows what to expect. This is also useful for the IDE to provide \n",
+    "autocomplete."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List, Type\n",
+    "from deeplay.list import Sequential\n",
+    "import torch\n",
+    "\n",
+    "class ConvolutionalNeuralNetwork(DeeplayModule):\n",
+    "\n",
+    "    # Arguments.\n",
+    "    in_channels: int\n",
+    "    hidden_channels: List[int]\n",
+    "    out_channels: int\n",
+    "    out_activation: Type[nn.Module]\n",
+    "\n",
+    "    # Attributes.\n",
+    "    blocks: Sequential[Conv2dBlock]\n",
+    "\n",
+    "    def __init__(\n",
+    "        self, \n",
+    "        in_channels: int,\n",
+    "        hidden_channels: List[int],\n",
+    "        out_channels: int,\n",
+    "        out_activation: Type[nn.Module] = nn.ReLU,\n",
+    "    ) -> None:  \n",
+    "        super().__init__()\n",
+    "\n",
+    "        blocks = Sequential[Conv2dBlock]()\n",
+    "        for in_ch, out_ch in zip([in_channels] + hidden_channels, \n",
+    "                                 hidden_channels + [out_channels]):\n",
+    "            block = Conv2dBlock(in_ch, out_ch, kernel_size=3, padding=0, \n",
+    "                                activation=nn.ReLU)\n",
+    "            blocks.append(block)\n",
+    "        \n",
+    "        # Set the activation function of the last block.\n",
+    "        blocks[-1].activated(out_activation)\n",
+    "\n",
+    "    def forward(\n",
+    "        self, \n",
+    "        x: torch.Tensor,\n",
+    "    ) -> torch.Tensor: \n",
+    "        for block in self.blocks:\n",
+    "            x = block(x)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Document the Component\n",
+    "\n",
+    "The next step is to document the component. This should include a description of \n",
+    "the component, the input and output shapes, and the arguments that can be passed to\n",
+    "the component."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ConvolutionalNeuralNetwork(DeeplayModule):\n",
+    "    \"\"\"A fully convolutional neural network.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    in_channels : int\n",
+    "        The number of input channels.\n",
+    "    hidden_channels : List[int]\n",
+    "        The number of hidden channels.\n",
+    "    out_channels : int\n",
+    "        The number of output channels.\n",
+    "    out_activation : Type[nn.Module]\n",
+    "        The type of activation function of the output layer.\n",
+    "    \n",
+    "    Attributes\n",
+    "    ----------\n",
+    "    blocks : Sequential[Conv2dBlock]\n",
+    "        The list of convolutional blocks.\n",
+    "    \n",
+    "    Input\n",
+    "    -----\n",
+    "    x : torch.Tensor\n",
+    "        The input tensor of shape (N, C, H, W).\n",
+    "        Where N is the batch size, C is the number of channels, H is the \n",
+    "        height, and W is the width.\n",
+    "        Additial dimensions before C are allowed.\n",
+    "    \n",
+    "    Output\n",
+    "    ------\n",
+    "    y : torch.Tensor\n",
+    "        The output tensor of shape (N, out_channels, H', W').\n",
+    "        Where N is the batch size, out_channels is the number of output \n",
+    "        channels, H is the height, and W is the width.\n",
+    "        Additial dimensions before out_channels will be preserved.\n",
+    "\n",
+    "    Evaluation\n",
+    "    ----------\n",
+    "    ```python\n",
+    "    for block in blocks:\n",
+    "        x = block(x)\n",
+    "    ```\n",
+    "\n",
+    "    Examples\n",
+    "    --------\n",
+    "    >>> cnn = ConvolutionalNeuralNetwork(3, [6, 6], 12).build()\n",
+    "    >>> x = torch.randn(3, 3, 32, 32)\n",
+    "    >>> y = cnn(x)\n",
+    "    >>> y.shape\n",
+    "    torch.Size([3, 12, 26, 26])\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Arguments.\n",
+    "    in_channels: int\n",
+    "    hidden_channels: List[int]\n",
+    "    out_channels: int\n",
+    "    out_activation: Type[nn.Module]\n",
+    "\n",
+    "    # Attributes.\n",
+    "    blocks: Sequential[Conv2dBlock]\n",
+    "\n",
+    "    def __init__( \n",
+    "        self, \n",
+    "        in_channels: int,\n",
+    "        hidden_channels: List[int],\n",
+    "        out_channels: int,\n",
+    "        out_activation: Type[nn.Module] = nn.ReLU,\n",
+    "    ) -> None:  \n",
+    "        super().__init__()\n",
+    "\n",
+    "        blocks = Sequential[Conv2dBlock]()\n",
+    "        for in_ch, out_ch in zip([in_channels] + hidden_channels, \n",
+    "                                 hidden_channels + [out_channels]):\n",
+    "            block = Conv2dBlock(in_ch, out_ch, kernel_size=3, padding=0, \n",
+    "                                activation=nn.ReLU)\n",
+    "            blocks.append(block)\n",
+    "        \n",
+    "        # Set the activation function of the last block.\n",
+    "        blocks[-1].activated(out_activation)\n",
+    "\n",
+    "    def forward(\n",
+    "        self, \n",
+    "        x: torch.Tensor,  \n",
+    "    ) -> torch.Tensor:\n",
+    "        \"\"\"Forward pass of the convolutional neural network.\n",
+    "\n",
+    "        Evaluates the convolutional blocks sequentially.\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        x : torch.Tensor\n",
+    "            The input tensor of shape (N, C, H, W).\n",
+    "            Where N is the batch size, C is the number of channels, H is the \n",
+    "            height, and W is the width.\n",
+    "        \n",
+    "        Returns\n",
+    "        -------\n",
+    "        torch.Tensor\n",
+    "            The output tensor of shape (N, out_channels, H', W').\n",
+    "            Where N is the batch size, out_channels is the number of output \n",
+    "            channels, H is the height, and W is the width.\n",
+    "        \n",
+    "        \"\"\"\n",
+    "        \n",
+    "        for block in self.blocks:\n",
+    "            x = block(x)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Define Properties\n",
+    "\n",
+    "There are some properties that should be defined in a component:\n",
+    "- `input`: The input block of the component.\n",
+    "- `output`: The output block of the component.\n",
+    "- `hidden`: The hidden blocks of the component (all except output).\n",
+    "These should generally be defined before the constructor, but after the annotations.\n",
+    "\n",
+    "In the current example, this corresponds to the code:\n",
+    "```python\n",
+    "    @property\n",
+    "    def input(self) -> Conv2dBlock:\n",
+    "        return self.blocks[0]\n",
+    "    \n",
+    "    @property\n",
+    "    def output(self) -> Conv2dBlock:\n",
+    "        return self.blocks[-1]\n",
+    "    \n",
+    "    @property\n",
+    "    def hidden(self) -> ReferringLayerList[Conv2dBlock]:\n",
+    "        return self.blocks[:-1]\n",
+    "```\n",
+    "\n",
+    "**NOTE:** If you subclass another component, it is likely that these will already be defined."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from deeplay.list import ReferringLayerList\n",
+    "\n",
+    "class ConvolutionalNeuralNetwork(DeeplayModule):\n",
+    "    \"\"\"A fully convolutional neural network.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    in_channels : int\n",
+    "        The number of input channels.\n",
+    "    hidden_channels : List[int]\n",
+    "        The number of hidden channels.\n",
+    "    out_channels : int\n",
+    "        The number of output channels.\n",
+    "    out_activation : Type[nn.Module]\n",
+    "        The type of activation function of the output layer.\n",
+    "    \n",
+    "    Attributes\n",
+    "    ----------\n",
+    "    blocks : Sequential[Conv2dBlock]\n",
+    "        The list of convolutional blocks.\n",
+    "    \n",
+    "    Input\n",
+    "    -----\n",
+    "    x : torch.Tensor\n",
+    "        The input tensor of shape (N, C, H, W).\n",
+    "        Where N is the batch size, C is the number of channels, H is the \n",
+    "        height, and W is the width.\n",
+    "        Additial dimensions before C are allowed.\n",
+    "    \n",
+    "    Output\n",
+    "    ------\n",
+    "    y : torch.Tensor\n",
+    "        The output tensor of shape (N, out_channels, H', W').\n",
+    "        Where N is the batch size, out_channels is the number of output \n",
+    "        channels, H is the height, and W is the width.\n",
+    "        Additial dimensions before out_channels will be preserved.\n",
+    "\n",
+    "    Evaluation\n",
+    "    ----------\n",
+    "    ```python\n",
+    "    for block in blocks:\n",
+    "        x = block(x)\n",
+    "    ```\n",
+    "\n",
+    "    Examples\n",
+    "    --------\n",
+    "    >>> cnn = ConvolutionalNeuralNetwork(3, [6, 6], 12).build()\n",
+    "    >>> x = torch.randn(3, 3, 32, 32)\n",
+    "    >>> y = cnn(x)\n",
+    "    >>> y.shape\n",
+    "    torch.Size([3, 12, 26, 26])\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Arguments.\n",
+    "    in_channels: int\n",
+    "    hidden_channels: List[int]\n",
+    "    out_channels: int\n",
+    "    out_activation: Type[nn.Module]\n",
+    "\n",
+    "    # Attributes.\n",
+    "    blocks: Sequential[Conv2dBlock]\n",
+    "    \n",
+    "    @property\n",
+    "    def input(self) -> Conv2dBlock:\n",
+    "        return self.blocks[0]\n",
+    "    \n",
+    "    @property\n",
+    "    def output(self) -> Conv2dBlock:\n",
+    "        return self.blocks[-1]\n",
+    "    \n",
+    "    @property\n",
+    "    def hidden(self) -> ReferringLayerList[Conv2dBlock]:\n",
+    "        return self.blocks[:-1]\n",
+    "\n",
+    "    def __init__( \n",
+    "        self, \n",
+    "        in_channels: int,\n",
+    "        hidden_channels: List[int],\n",
+    "        out_channels: int,\n",
+    "        out_activation: Type[nn.Module] = nn.ReLU,\n",
+    "    ) -> None: \n",
+    "        super().__init__()\n",
+    "\n",
+    "        blocks = Sequential[Conv2dBlock]()\n",
+    "        for in_ch, out_ch in zip([in_channels] + hidden_channels, \n",
+    "                                 hidden_channels + [out_channels]):\n",
+    "            block = Conv2dBlock(in_ch, out_ch, kernel_size=3, padding=0, \n",
+    "                                activation=nn.ReLU)\n",
+    "            blocks.append(block)\n",
+    "        \n",
+    "        # Set the activation function of the last block.\n",
+    "        blocks[-1].activated(out_activation)\n",
+    "\n",
+    "    def forward(\n",
+    "        self, \n",
+    "        x: torch.Tensor,  \n",
+    "    ) -> torch.Tensor:\n",
+    "        \"\"\"Forward pass of the convolutional neural network.\n",
+    "\n",
+    "        Evaluates the convolutional blocks sequentially.\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        x : torch.Tensor\n",
+    "            The input tensor of shape (N, C, H, W).\n",
+    "            Where N is the batch size, C is the number of channels, H is the \n",
+    "            height, and W is the width.\n",
+    "        \n",
+    "        Returns\n",
+    "        -------\n",
+    "        torch.Tensor\n",
+    "            The output tensor of shape (N, out_channels, H', W').\n",
+    "            Where N is the batch size, out_channels is the number of output \n",
+    "            channels, H is the height, and W is the width.\n",
+    "        \n",
+    "        \"\"\"\n",
+    "        \n",
+    "        for block in self.blocks:\n",
+    "            x = block(x)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. Implement Auxiliary Methods\n",
+    "\n",
+    "It might make sense to add additional auxiliary methods to the component. These should generally be convenience methods for complex configurations. For example, a \n",
+    "`ConvolutionalNeuralNetwork` could have a `pooled` method that adds pooling layers\n",
+    "to each block. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from deeplay.external.layer import Layer\n",
+    "from typing_extensions import Self\n",
+    "\n",
+    "class ConvolutionalNeuralNetwork(DeeplayModule):\n",
+    "    \"\"\"A fully convolutional neural network.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    in_channels : int\n",
+    "        The number of input channels.\n",
+    "    hidden_channels : List[int]\n",
+    "        The number of hidden channels.\n",
+    "    out_channels : int\n",
+    "        The number of output channels.\n",
+    "    out_activation : Type[nn.Module]\n",
+    "        The type of activation function of the output layer.\n",
+    "    \n",
+    "    Attributes\n",
+    "    ----------\n",
+    "    blocks : Sequential[Conv2dBlock]\n",
+    "        The list of convolutional blocks.\n",
+    "    \n",
+    "    Input\n",
+    "    -----\n",
+    "    x : torch.Tensor\n",
+    "        The input tensor of shape (N, C, H, W).\n",
+    "        Where N is the batch size, C is the number of channels, H is the \n",
+    "        height, and W is the width.\n",
+    "        Additial dimensions before C are allowed.\n",
+    "    \n",
+    "    Output\n",
+    "    ------\n",
+    "    y : torch.Tensor\n",
+    "        The output tensor of shape (N, out_channels, H', W').\n",
+    "        Where N is the batch size, out_channels is the number of output \n",
+    "        channels, H is the height, and W is the width.\n",
+    "        Additial dimensions before out_channels will be preserved.\n",
+    "\n",
+    "    Evaluation\n",
+    "    ----------\n",
+    "    ```python\n",
+    "    for block in blocks:\n",
+    "        x = block(x)\n",
+    "    ```\n",
+    "\n",
+    "    Examples\n",
+    "    --------\n",
+    "    >>> cnn = ConvolutionalNeuralNetwork(3, [6, 6], 12).build()\n",
+    "    >>> x = torch.randn(3, 3, 32, 32)\n",
+    "    >>> y = cnn(x)\n",
+    "    >>> y.shape\n",
+    "    torch.Size([3, 12, 26, 26])\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Arguments.\n",
+    "    in_channels: int\n",
+    "    hidden_channels: List[int]\n",
+    "    out_channels: int\n",
+    "    out_activation: Type[nn.Module]\n",
+    "\n",
+    "    # Attributes.\n",
+    "    blocks: Sequential[Conv2dBlock]\n",
+    "    \n",
+    "    @property\n",
+    "    def input(self) -> Conv2dBlock:\n",
+    "        return self.blocks[0]\n",
+    "    \n",
+    "    @property\n",
+    "    def output(self) -> Conv2dBlock:\n",
+    "        return self.blocks[-1]\n",
+    "    \n",
+    "    @property\n",
+    "    def hidden(self) -> ReferringLayerList[Conv2dBlock]:\n",
+    "        return self.blocks[:-1]\n",
+    "\n",
+    "    def __init__( \n",
+    "        self, \n",
+    "        in_channels: int,\n",
+    "        hidden_channels: List[int],\n",
+    "        out_channels: int,\n",
+    "        out_activation: Type[nn.Module] = nn.ReLU,\n",
+    "    ) -> None:  \n",
+    "        super().__init__()\n",
+    "\n",
+    "        blocks = Sequential[Conv2dBlock]()\n",
+    "        for in_ch, out_ch in zip([in_channels] + hidden_channels, \n",
+    "                                 hidden_channels + [out_channels]):\n",
+    "            block = Conv2dBlock(in_ch, out_ch, kernel_size=3, padding=0, \n",
+    "                                activation=nn.ReLU)\n",
+    "            blocks.append(block)\n",
+    "        \n",
+    "        # Set the activation function of the last block.\n",
+    "        blocks[-1].activated(out_activation)\n",
+    "\n",
+    "    def forward(\n",
+    "        self, \n",
+    "        x: torch.Tensor,  \n",
+    "    ) -> torch.Tensor:\n",
+    "        \"\"\"Forward pass of the convolutional neural network.\n",
+    "\n",
+    "        Evaluates the convolutional blocks sequentially.\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        x : torch.Tensor\n",
+    "            The input tensor of shape (N, C, H, W).\n",
+    "            Where N is the batch size, C is the number of channels, H is the \n",
+    "            height, and W is the width.\n",
+    "        \n",
+    "        Returns\n",
+    "        -------\n",
+    "        torch.Tensor\n",
+    "            The output tensor of shape (N, out_channels, H', W').\n",
+    "            Where N is the batch size, out_channels is the number of output \n",
+    "            channels, H is the height, and W is the width.\n",
+    "        \n",
+    "        \"\"\"\n",
+    "        \n",
+    "        for block in self.blocks:\n",
+    "            x = block(x)\n",
+    "        return x\n",
+    "\n",
+    "    def pooled(self, \n",
+    "               pool: Layer = Layer(nn.MaxPool2d, 2),\n",
+    "               apply_to_first: bool = False,\n",
+    "               apply_to_last: bool = True) -> Self:\n",
+    "        \"\"\"Add pooling layers after each block.\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        pool : Layer\n",
+    "            The pooling layer.\n",
+    "        apply_to_first : bool\n",
+    "            Whether to apply pooling to the first block.\n",
+    "        apply_to_last : bool\n",
+    "            Whether to apply pooling to the last block.\n",
+    "        \n",
+    "        \"\"\"\n",
+    "        \n",
+    "        if apply_to_first:\n",
+    "            self.input.pooled(pool)\n",
+    "        if apply_to_last:\n",
+    "            self.output.pooled(pool)\n",
+    "\n",
+    "        for block in self.hidden[1:]:\n",
+    "            block.pooled(pool)\n",
+    "\n",
+    "        return self"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py_env_dlcc",
+   "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/tutorials/developers/DT161_operations.ipynb b/tutorials/developers/DT161_operations.ipynb
new file mode 100644
index 00000000..eb8f706c
--- /dev/null
+++ b/tutorials/developers/DT161_operations.ipynb
@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Implementing an Operation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What Should Be Implemented as an Operation?\n",
+    "\n",
+    "The first step is to ensure that what you want to implement is actually an operation.\n",
+    "Most operations are non-trainable, but this is not a strict requirement.\n",
+    "\n",
+    "Examples of operations are `Reshape`, `Concatenate`, `Dropout`.\n",
+    "\n",
+    "**NOTE:** Some operations are trainable. This is useful if the standard constructor of a trainable layer is not well suited for Deeplay, or if a layer needs a custom forward pass. This is the case for attention layers, for example. In this case it's important to ensure that the operation is not actually a operation. If the module contains several layers, it should instead be implemented as a operation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Implementing an Operation\n",
+    "\n",
+    "Here you'll see the steps you should follow to implement an operation in Deeplay. You'll do this by implementing the `Reshape` operation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Create a New File\n",
+    "\n",
+    "The first step is to create a new file in the `deeplay/ops` directory. It\n",
+    "can be in a deeper subdirectory if it makes sense.\n",
+    "\n",
+    "**The base class.**\n",
+    "Some operations have a common base class. These include `ShapeOp` and `MergeOp`.\n",
+    "If your operation fits into one of these categories, you should inherit from the\n",
+    "base class. If not, you should inherit from `DeeplayModule`.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example implements the `Reshape` operation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from deeplay.ops.shape import ShapeOp\n",
+    "\n",
+    "class Reshape(ShapeOp):\n",
+    "    def __init__(self, *shape, copy=False):\n",
+    "        self.shape = shape\n",
+    "        self.copy = copy\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = x.view(*self.shape)\n",
+    "        if self.copy:\n",
+    "            x = x.clone()\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Add Annotations\n",
+    "\n",
+    "It is important to add annotations to the class and methods to ensure that the\n",
+    "user knows what to expect. This is also useful for the IDE to provide \n",
+    "autocomplete."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from deeplay.ops.shape import ShapeOp\n",
+    "import torch\n",
+    "\n",
+    "class Reshape(ShapeOp):\n",
+    "    \n",
+    "    shape: Tuple[int, ...]\n",
+    "    copy: bool\n",
+    "    \n",
+    "    def __init__(\n",
+    "        self, \n",
+    "        *shape: int, \n",
+    "        copy: bool = False,\n",
+    "    ) -> None: \n",
+    "        self.shape = shape\n",
+    "        self.copy = copy\n",
+    "\n",
+    "    def forward(\n",
+    "        self, \n",
+    "        x: torch.Tensor, \n",
+    "    ) -> torch.Tensor:\n",
+    "        x = x.view(*self.shape)\n",
+    "        if self.copy:\n",
+    "            x = x.clone()\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Document the Operation\n",
+    "\n",
+    "The next step is to document the operation. This should include a description of \n",
+    "the operation, the input and output shapes, and the arguments that can be passed to\n",
+    "the operation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Reshape(ShapeOp):\n",
+    "    \"\"\"A operation for reshaping a tensor.\n",
+    "\n",
+    "    This operation reshapes a tensor to a new shape. The new shape is specified \n",
+    "    as a tuple of integers. The `copy` parameter controls whether the reshaped \n",
+    "    tensor is a view of the original tensor or a copy.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    *shape : int\n",
+    "        The new shape of the tensor.\n",
+    "    copy : bool\n",
+    "        Whether to return a copy of the reshaped tensor.\n",
+    "\n",
+    "    Attributes\n",
+    "    ----------\n",
+    "    shape : Tuple[int, ...]\n",
+    "        The new shape of the tensor.\n",
+    "    copy : bool\n",
+    "        Whether to return a copy of the reshaped tensor.\n",
+    "    \n",
+    "    Input\n",
+    "    -----\n",
+    "    x : torch.Tensor (Any, ...)\n",
+    "        The input tensor to reshape.\n",
+    "    \n",
+    "    Output\n",
+    "    ------\n",
+    "    y : torch.Tensor\n",
+    "        The reshaped tensor (*shape).\n",
+    "\n",
+    "    Evaluation\n",
+    "    ----------\n",
+    "    y = x.view(*shape) if not copy else x.view(*shape).clone()\n",
+    "\n",
+    "    Examples\n",
+    "    --------\n",
+    "    >>> operation = Reshape(3, 6, copy=True).build()\n",
+    "    >>> x = torch.randn(2, 9)\n",
+    "    >>> y = operation(x)\n",
+    "    >>> y.shape\n",
+    "    torch.Size([3, 6])\n",
+    "\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    def __init__( \n",
+    "        self, \n",
+    "        *shape: int, \n",
+    "        copy: bool = False,\n",
+    "    ) -> None: \n",
+    "        self.shape = shape\n",
+    "        self.copy = copy\n",
+    "\n",
+    "    def forward(  \n",
+    "        self, \n",
+    "        x: torch.Tensor,  \n",
+    "    ) -> torch.Tensor:\n",
+    "        \"\"\"Forward pass of the reshape operation.\n",
+    "        \n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        x : torch.Tensor\n",
+    "            The input tensor to reshape.\n",
+    "        \n",
+    "        Returns\n",
+    "        -------\n",
+    "        torch.Tensor\n",
+    "            The reshaped tensor.\n",
+    "        \"\"\"\n",
+    "        x = x.view(*self.shape)\n",
+    "        if self.copy:\n",
+    "            x = x.clone()\n",
+    "        return x"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py_env_dlcc",
+   "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/tutorials/developers/DT171_blocks.ipynb b/tutorials/developers/DT171_blocks.ipynb
new file mode 100644
index 00000000..31a4f447
--- /dev/null
+++ b/tutorials/developers/DT171_blocks.ipynb
@@ -0,0 +1,606 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Implementing a Block"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What Should Be Implemented as a Block?\n",
+    "\n",
+    "The first step is to ensure that what you want to implement is actually a block. \n",
+    "The main utility of a block is that the order of operations can be changed.\n",
+    "If this is useful for your module, then you should implement a block.\n",
+    "\n",
+    "Another reason to implement a block is if you want users to be able to add or\n",
+    "remove steps from the block. For example, adding an activation function, or\n",
+    "removing a dropout layer. \n",
+    "\n",
+    "Also, remember that blocks shouls be small and modular. If you are implementing\n",
+    "a block that is too big, you should consider breaking it down into smaller blocks.\n",
+    "\n",
+    "Finally, blocks should be pretty strict in terms of input and output. This is \n",
+    "important to ensure that the user has the flexibility to change the order of\n",
+    "operations without breaking the model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Implementing a Block\n",
+    "\n",
+    "Here you'll see the steps you should follow to implement a block in deeplay. You'd do this by implementing `MyConv1dBlock`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Create a New File\n",
+    "\n",
+    "The first step is to create a new file in the `deeplay/blocks` directory. It\n",
+    "can be in a deeper subdirectory if it makes sense.\n",
+    "\n",
+    "**The base class: `BaseBlock`.**\n",
+    "The file should contain a class that inherits from `BaseBlock`.\n",
+    "\n",
+    "**The arguments.**\n",
+    "The first arguments should specify the input (for example, `in_channels`) and output (for example, `out_channels`) shapes of the block. This is\n",
+    "important to ensure that the block is used correctly.\n",
+    "\n",
+    "The following arguments should specify the arguments for the default layer class.\n",
+    "In the example here, you'll use `torch.nn.Conv1d`, so you should specify the kernel\n",
+    "size, stride, padding, etc.\n",
+    "\n",
+    "Finally, the class should accept `**kwargs` that will be passed to the super class."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example implements the `MyConv1dBlock`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from deeplay.blocks.base import BaseBlock\n",
+    "from deeplay.external.layer import Layer\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "\n",
+    "class MyConv1dBlock(BaseBlock):\n",
+    "    def __init__(\n",
+    "        self, \n",
+    "        in_channels, \n",
+    "        out_channels, \n",
+    "        kernel_size=3, \n",
+    "        stride=1, \n",
+    "        padding=0, \n",
+    "        dilation=1, \n",
+    "        groups=1, \n",
+    "        bias=True,\n",
+    "        order=None,\n",
+    "        **kwargs,\n",
+    "    ):\n",
+    "        \n",
+    "        # Save the input parameters.\n",
+    "        self.in_channels = in_channels\n",
+    "        self.out_channels = out_channels\n",
+    "        self.kernel_size = kernel_size\n",
+    "        self.stride = stride\n",
+    "        self.padding = padding\n",
+    "        self.dilation = dilation\n",
+    "        self.groups = groups\n",
+    "        self.bias = bias\n",
+    "\n",
+    "        # Create the layer.\n",
+    "        layer = Layer(\n",
+    "            nn.Conv1d, \n",
+    "            in_channels=in_channels, \n",
+    "            out_channels=out_channels, \n",
+    "            kernel_size=kernel_size, \n",
+    "            stride=stride, \n",
+    "            padding=padding, \n",
+    "            dilation=dilation, \n",
+    "            groups=groups, \n",
+    "            bias=bias,\n",
+    "        )\n",
+    "        \n",
+    "        # Send the layers and modules to the parent class.\n",
+    "        super(MyConv1dBlock, self).__init__(order=order, layer=layer, **kwargs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can now instantiate this block and print its architecture."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MyConv1dBlock(\n",
+      "  (layer): Layer[Conv1d](in_channels=10, out_channels=4, kernel_size=3, stride=1, padding=0, dilation=1, groups=1, bias=True)\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "block = MyConv1dBlock(in_channels=10, out_channels=4)\n",
+    "\n",
+    "print(block)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Add Annotations\n",
+    "\n",
+    "It is important to add annotations to the class and methods to ensure that the\n",
+    "user knows what to expect. This is also useful for the IDE to provide \n",
+    "autocomplete."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List, Optional\n",
+    "from torch.nn.common_types import _size_1_t\n",
+    "\n",
+    "from deeplay.module import DeeplayModule\n",
+    "\n",
+    "class MyConv1dBlock(BaseBlock):\n",
+    "\n",
+    "    # Annotate the attributes.\n",
+    "    in_channels: int\n",
+    "    out_channels: int\n",
+    "    kernel_size: _size_1_t\n",
+    "    stride: _size_1_t\n",
+    "    padding: _size_1_t\n",
+    "    dilation: _size_1_t\n",
+    "    groups: int\n",
+    "    bias: bool\n",
+    "\n",
+    "    # Also annotate layer.\n",
+    "    layer: Layer \n",
+    "\n",
+    "    def __init__(\n",
+    "        self, \n",
+    "        in_channels: int,\n",
+    "        out_channels: int,\n",
+    "        kernel_size: _size_1_t = 3,\n",
+    "        stride: _size_1_t = 1,\n",
+    "        padding: _size_1_t = 0,\n",
+    "        dilation: _size_1_t = 1, \n",
+    "        groups: int = 1,\n",
+    "        bias: bool = True,\n",
+    "        order: Optional[List[str]] = None,\n",
+    "        **kwargs: DeeplayModule,\n",
+    "    ) -> None:\n",
+    "        \n",
+    "        # Save the input parameters.\n",
+    "        self.in_channels = in_channels\n",
+    "        self.out_channels = out_channels\n",
+    "        self.kernel_size = kernel_size\n",
+    "        self.stride = stride\n",
+    "        self.padding = padding\n",
+    "        self.dilation = dilation\n",
+    "        self.groups = groups\n",
+    "        self.bias = bias\n",
+    "\n",
+    "        # Create the layer.\n",
+    "        layer = Layer(\n",
+    "            nn.Conv1d, \n",
+    "            in_channels=in_channels, \n",
+    "            out_channels=out_channels, \n",
+    "            kernel_size=kernel_size, \n",
+    "            stride=stride, \n",
+    "            padding=padding, \n",
+    "            dilation=dilation, \n",
+    "            groups=groups, \n",
+    "            bias=bias,\n",
+    "        )\n",
+    "        \n",
+    "        # Send the layers and modules to the parent class.\n",
+    "        super().__init__(order=order, layer=layer, **kwargs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Document the Block\n",
+    "\n",
+    "The next step is to document the block. This should include a description of \n",
+    "the block, the input and output shapes, and the arguments that can be passed to\n",
+    "the block."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class MyConv1dBlock(BaseBlock):\n",
+    "    \"\"\"A block for 1D convolutional operations.\n",
+    "\n",
+    "    This block performs a 1D convolutional operation on the input tensor.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    in_channels : int\n",
+    "        The number of input channels.\n",
+    "    out_channels : int\n",
+    "        The number of output channels.\n",
+    "    kernel_size : int\n",
+    "        The size of the convolutional kernel.\n",
+    "    stride : int\n",
+    "        The stride of the convolutional operation.\n",
+    "    padding : int\n",
+    "        The padding of the convolutional operation.\n",
+    "    dilation : int\n",
+    "        The dilation of the convolutional operation.\n",
+    "    groups : int    \n",
+    "        The number of groups for the convolutional operation.\n",
+    "    bias : bool\n",
+    "        Whether to include a bias term in the convolutional operation.\n",
+    "    order : List[str]\n",
+    "        The order of the layers in the block. If None, the order is inferred \n",
+    "        from the order of keyword arguments, with `layer` always being the \n",
+    "        first layer.\n",
+    "    **kwargs\n",
+    "        Additional modules to include in the block. The keys should be the \n",
+    "        names of the modules and the values should be the modules.\n",
+    "    \n",
+    "    Attributes\n",
+    "    ----------\n",
+    "    in_channels : int\n",
+    "        The number of input channels.\n",
+    "    out_channels : int\n",
+    "        The number of output channels.\n",
+    "    kernel_size : int\n",
+    "        The size of the convolutional kernel.\n",
+    "    stride : int\n",
+    "        The stride of the convolutional operation.\n",
+    "    padding : int\n",
+    "        The padding of the convolutional operation.\n",
+    "    dilation : int\n",
+    "        The dilation of the convolutional operation.\n",
+    "    groups : int    \n",
+    "        The number of groups for the convolutional operation.\n",
+    "    bias : bool\n",
+    "        Whether to include a bias term in the convolutional operation.\n",
+    "    order : List[str]\n",
+    "        The order of the layers in the block.\n",
+    "    layer : Layer\n",
+    "        The layer that performs the convolutional operation.\n",
+    "    \n",
+    "    Input\n",
+    "    -----\n",
+    "    x : torch.Tensor (batch_size, in_channels, Any)\n",
+    "        The input tensor to the block.\n",
+    "    \n",
+    "    Output\n",
+    "    ------\n",
+    "    y : torch.Tensor (batch_size, out_channels, Any)\n",
+    "        The output tensor from the block.\n",
+    "\n",
+    "    Evaluation\n",
+    "    ----------\n",
+    "    See :func:`~SequentialBlock.forward` for details.\n",
+    "\n",
+    "    Examples\n",
+    "    --------\n",
+    "    >>> block = MyConv1dBlock(in_channels=3, out_channels=6, kernel_size=3)\n",
+    "    >>> block.build()\n",
+    "    MyConv1dBlock(\n",
+    "      (layer):  Conv1d(3, 6, kernel_size=(3,), stride=(1,))\n",
+    "    )\n",
+    "\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Annotate the attributes.\n",
+    "    in_channels: int\n",
+    "    out_channels: int\n",
+    "    kernel_size: _size_1_t\n",
+    "    stride: _size_1_t\n",
+    "    padding: _size_1_t\n",
+    "    dilation: _size_1_t\n",
+    "    groups: int\n",
+    "    bias: bool\n",
+    "\n",
+    "    # Also annotate layer.\n",
+    "    layer: Layer \n",
+    "\n",
+    "    def __init__(\n",
+    "        self, \n",
+    "        in_channels: int,\n",
+    "        out_channels: int,\n",
+    "        kernel_size: _size_1_t = 3,\n",
+    "        stride: _size_1_t = 1,\n",
+    "        padding: _size_1_t = 0,\n",
+    "        dilation: _size_1_t = 1, \n",
+    "        groups: int = 1,\n",
+    "        bias: bool = True,\n",
+    "        order: Optional[List[str]] = None,\n",
+    "        **kwargs: DeeplayModule,\n",
+    "    ) -> None:\n",
+    "        \n",
+    "        # Save the input parameters.\n",
+    "        self.in_channels = in_channels\n",
+    "        self.out_channels = out_channels\n",
+    "        self.kernel_size = kernel_size\n",
+    "        self.stride = stride\n",
+    "        self.padding = padding\n",
+    "        self.dilation = dilation\n",
+    "        self.groups = groups\n",
+    "        self.bias = bias\n",
+    "\n",
+    "        # Create the layer.\n",
+    "        layer = Layer(\n",
+    "            nn.Conv1d, \n",
+    "            in_channels=in_channels, \n",
+    "            out_channels=out_channels, \n",
+    "            kernel_size=kernel_size, \n",
+    "            stride=stride, \n",
+    "            padding=padding, \n",
+    "            dilation=dilation, \n",
+    "            groups=groups, \n",
+    "            bias=bias,\n",
+    "        )\n",
+    "        \n",
+    "        # Send the layers and modules to the parent class.\n",
+    "        super().__init__(order=order, layer=layer, **kwargs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Add Auxiliary Methods\n",
+    "\n",
+    "There are several special methods that improve the usability of the block.\n",
+    "These don't need to be documented extensibly since they are not meant to be used directly by the user.\n",
+    "\n",
+    "These include:\n",
+    "\n",
+    "- `.call_with_dummy_data()`\n",
+    "\n",
+    "    This method creates some dummy data and calls the forward method. This is used to create any lazy layers, and run all callbacks that are defined to run on forward.\n",
+    "    \n",
+    "    This method will be called immediately before the build phase, and only if the user doesn't provide a dummy input.\n",
+    "    \n",
+    "    It is recommended to use a batch size of 2, to ensure that the batch normalization works correctly. If there are any spatial or temporal dimensions, they should be set to at least 12. This is to reduce the risk of stride or padding errors for small inputs.\n",
+    "\n",
+    "- `.get_default_activation()` (optional)\n",
+    "\n",
+    "    This method should return the default activation function for the block. This will be used if the user calls `.activated()` without specifying an activation function. Default is `nn.ReLU`.\n",
+    "\n",
+    "- `.get_default_normalization()`\n",
+    "\n",
+    "    This method should return the default normalization function for the block. This will be used if the user calls `.normalized()` without specifying a normalization function. \n",
+    "\n",
+    "- `.get_default_merge()` (optional)\n",
+    "\n",
+    "    This method should return the default merge function for the block. This will be used if the user calls `.shortcut()` without specifying a merge function. Default is `ops.Add`.\n",
+    "\n",
+    "- `.get_default_shortcut()` (optional)\n",
+    "\n",
+    "    This method should return the default shortcut function for the block. This will be used if the user calls `.shortcut()` without specifying a shortcut function. Default is `nn.Identity`. \n",
+    "\n",
+    "    This is used if there is a need for a projection in the shortcut connection. For example, if the input and output shapes are different such that the merge function cannot be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from re import L\n",
+    "from deeplay.module import DeeplayModule\n",
+    "from deeplay.ops.merge import MergeOp\n",
+    "\n",
+    "\n",
+    "class MyConv1dBlock(BaseBlock):\n",
+    "    \"\"\"A block for 1D convolutional operations.\n",
+    "\n",
+    "    This block performs a 1D convolutional operation on the input tensor.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    in_channels : int\n",
+    "        The number of input channels.\n",
+    "    out_channels : int\n",
+    "        The number of output channels.\n",
+    "    kernel_size : int\n",
+    "        The size of the convolutional kernel.\n",
+    "    stride : int\n",
+    "        The stride of the convolutional operation.\n",
+    "    padding : int\n",
+    "        The padding of the convolutional operation.\n",
+    "    dilation : int\n",
+    "        The dilation of the convolutional operation.\n",
+    "    groups : int    \n",
+    "        The number of groups for the convolutional operation.\n",
+    "    bias : bool\n",
+    "        Whether to include a bias term in the convolutional operation.\n",
+    "    order : List[str]\n",
+    "        The order of the layers in the block. If None, the order is\n",
+    "        inferred from the order of keyword arguments, with `layer`\n",
+    "        always being the first layer.\n",
+    "    **kwargs\n",
+    "        Additional modules to include in the block. The keys should be\n",
+    "        the names of the modules and the values should be the modules.\n",
+    "    \n",
+    "    Attributes\n",
+    "    ----------\n",
+    "    in_channels : int\n",
+    "        The number of input channels.\n",
+    "    out_channels : int\n",
+    "        The number of output channels.\n",
+    "    kernel_size : int\n",
+    "        The size of the convolutional kernel.\n",
+    "    stride : int\n",
+    "        The stride of the convolutional operation.\n",
+    "    padding : int\n",
+    "        The padding of the convolutional operation.\n",
+    "    dilation : int\n",
+    "        The dilation of the convolutional operation.\n",
+    "    groups : int    \n",
+    "        The number of groups for the convolutional operation.\n",
+    "    bias : bool\n",
+    "        Whether to include a bias term in the convolutional operation.\n",
+    "    order : List[str]\n",
+    "        The order of the layers in the block.\n",
+    "    layer : Layer\n",
+    "        The layer that performs the convolutional operation.\n",
+    "    \n",
+    "    Input\n",
+    "    -----\n",
+    "    x : torch.Tensor (batch_size, in_channels, Any)\n",
+    "        The input tensor to the block.\n",
+    "    \n",
+    "    Output\n",
+    "    ------\n",
+    "    y : torch.Tensor (batch_size, out_channels, Any)\n",
+    "        The output tensor from the block.\n",
+    "\n",
+    "    Evaluation\n",
+    "    ----------\n",
+    "    See :func:`~SequentialBlock.forward` for details.\n",
+    "\n",
+    "    Examples\n",
+    "    --------\n",
+    "    >>> block = MyConv1dBlock(in_channels=3, out_channels=6, kernel_size=3)\n",
+    "    >>> block.build()\n",
+    "    MyConv1dBlock(\n",
+    "      (layer):  Conv1d(3, 6, kernel_size=(3,), stride=(1,))\n",
+    "    )\n",
+    "\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # Annotate the attributes.\n",
+    "    in_channels: int\n",
+    "    out_channels: int\n",
+    "    kernel_size: _size_1_t\n",
+    "    stride: _size_1_t\n",
+    "    padding: _size_1_t\n",
+    "    dilation: _size_1_t\n",
+    "    groups: int\n",
+    "    bias: bool\n",
+    "\n",
+    "    # Also annotate layer.\n",
+    "    layer: Layer \n",
+    "\n",
+    "    def __init__(\n",
+    "        self, \n",
+    "        in_channels: int,\n",
+    "        out_channels: int,\n",
+    "        kernel_size: _size_1_t = 3,\n",
+    "        stride: _size_1_t = 1,\n",
+    "        padding: _size_1_t = 0,\n",
+    "        dilation: _size_1_t = 1, \n",
+    "        groups: int = 1,\n",
+    "        bias: bool = True,\n",
+    "        order: Optional[List[str]] = None,\n",
+    "        **kwargs: DeeplayModule,\n",
+    "    ) -> None:\n",
+    "        \n",
+    "        # Save the input parameters.\n",
+    "        self.in_channels = in_channels\n",
+    "        self.out_channels = out_channels\n",
+    "        self.kernel_size = kernel_size\n",
+    "        self.stride = stride\n",
+    "        self.padding = padding\n",
+    "        self.dilation = dilation\n",
+    "        self.groups = groups\n",
+    "        self.bias = bias\n",
+    "\n",
+    "        # Create the layer.\n",
+    "        layer = Layer(\n",
+    "            nn.Conv1d, \n",
+    "            in_channels=in_channels, \n",
+    "            out_channels=out_channels, \n",
+    "            kernel_size=kernel_size, \n",
+    "            stride=stride, \n",
+    "            padding=padding, \n",
+    "            dilation=dilation, \n",
+    "            groups=groups, \n",
+    "            bias=bias,\n",
+    "        )\n",
+    "        \n",
+    "        # Send the layers and modules to the parent class.\n",
+    "        super().__init__(order=order, layer=layer, **kwargs)\n",
+    "\n",
+    "    def call_with_dummy_data(self) -> None:\n",
+    "        x = torch.randn(2, self.in_channels, 16)\n",
+    "        self(x)\n",
+    "\n",
+    "    def get_default_activation(self) -> DeeplayModule:\n",
+    "        return Layer(nn.ReLU)\n",
+    "\n",
+    "    def get_default_normalization(self) -> DeeplayModule:\n",
+    "        # This assumes that the normalization is applied after Layer.\n",
+    "        # If it is before, num_features should be in_channels.\n",
+    "        # This will be automatically handled during the build process.\n",
+    "        return Layer(nn.BatchNorm1d, num_features=self.out_channels)\n",
+    "    \n",
+    "    def get_default_merge(self) -> MergeOp:\n",
+    "        from deeplay.ops.merge import Add\n",
+    "        return Add()\n",
+    "    \n",
+    "    def get_default_shortcut(self) -> DeeplayModule:\n",
+    "        return MyConv1dBlock(\n",
+    "            self.in_channels, self.out_channels, kernel_size=1, \n",
+    "            stride=self.stride, padding=0,\n",
+    "        )"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py_env_dlcc",
+   "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/tutorials/developers/DT181_internals.ipynb b/tutorials/developers/DT181_internals.ipynb
new file mode 100644
index 00000000..1741af51
--- /dev/null
+++ b/tutorials/developers/DT181_internals.ipynb
@@ -0,0 +1,402 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Overview of Deeplay Internal Structure\n",
+    "\n",
+    "This notebook is a deep dive into the internals of the Deeplay library. It is intended for developers who want to understand how the library works and how to extend it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The `DeeplayModule` Class\n",
+    "\n",
+    "At the core of deeplay is the `DeeplayModule` class. This class is a subclass of `torch.nn.Module` and is responsible to manage the configurations applied by the user, and to build the model based on these configurations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The Lifecycle of a `DeeplayModule` Object\n",
+    "\n",
+    "Let's start by understanding the lifecycle of a `DeeplayModule` object. This is managed by the Deeplay metaclass `ExtendedConstructorMeta`. This metaclass is responsible to create the `DeeplayModule` class and managing its configuration. Let's look at the `.call()` method of the `ExtendedConstructorMeta` metaclass.\n",
+    "\n",
+    "```python\n",
+    "class ExtendedConstructorMeta(type):\n",
+    "\n",
+    "    ...\n",
+    "\n",
+    "    def __call__(cls: Type[T], *args, **kwargs) -> T:\n",
+    "        \"\"\"Construct an instance of a class whose metaclass is Meta.\"\"\"\n",
+    "\n",
+    "        # If the object is being constructed from a checkpoint, we instead\n",
+    "        # load the class from the pickled state and build it using the checkpoint.\n",
+    "        if \"__from_ckpt_application\" in kwargs:\n",
+    "            assert \"__build_args\" in kwargs, \"Missing __build_args in kwargs\"\n",
+    "            assert \"__build_kwargs\" in kwargs, \"Missing __build_kwargs in kwargs\"\n",
+    "\n",
+    "            _args = kwargs.pop(\"__build_args\")\n",
+    "            _kwargs = kwargs.pop(\"__build_kwargs\")\n",
+    "\n",
+    "            app = dill.loads(kwargs[\"__from_ckpt_application\"])\n",
+    "            app.build(*_args, **_kwargs)\n",
+    "            return app\n",
+    "\n",
+    "        # Otherwise, we construct the object as usual.\n",
+    "        obj = cls.__new__(cls, *args, **kwargs)\n",
+    "\n",
+    "        # We store the actual arguments used to construct the object.\n",
+    "        object.__setattr__(\n",
+    "            obj,\n",
+    "            \"_actual_init_args\",\n",
+    "            {\n",
+    "                \"args\": args,\n",
+    "                \"kwargs\": kwargs,\n",
+    "            },\n",
+    "        )\n",
+    "        object.__setattr__(obj, \"_config_tape\", [])\n",
+    "        object.__setattr__(obj, \"_is_calling_stateful_method\", False)\n",
+    "\n",
+    "        # First, we call the __pre_init__ method of the class.\n",
+    "        cls.__pre_init__(obj, *args, **kwargs)\n",
+    "\n",
+    "        # Next, we construct the class. The not_top_level context manager is used to\n",
+    "        # keep track of where in the object hierarchy we currently are.\n",
+    "        with not_top_level(cls, obj):\n",
+    "            obj.__construct__()\n",
+    "            obj.__post_init__()\n",
+    "\n",
+    "        return obj\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Breaking down the lifecycle\n",
+    "\n",
+    "The method is pretty long, so let's break it down into smaller parts."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1. If the Object Is Being Constructed from a Checkpoint, Load and Return It.\n",
+    "\n",
+    "The method first checks if the object is being constructed from a checkpoint. If it is, it loads the object from the checkpoint and returns it.\n",
+    "\n",
+    "```python\n",
+    "if \"__from_ckpt_application\" in kwargs:\n",
+    "    assert \"__build_args\" in kwargs, \"Missing __build_args in kwargs\"\n",
+    "    assert \"__build_kwargs\" in kwargs, \"Missing __build_kwargs in kwargs\"\n",
+    "\n",
+    "    _args = kwargs.pop(\"__build_args\")\n",
+    "    _kwargs = kwargs.pop(\"__build_kwargs\")\n",
+    "\n",
+    "    app = dill.loads(kwargs[\"__from_ckpt_application\"])\n",
+    "    app.build(*_args, **_kwargs)\n",
+    "    return app\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2. Construct the Object with the `.__new__()` Method\n",
+    "\n",
+    "Next, it constructs the object as usual. It creates the object using the `.__new__()` method of the class and sets some internal attributes.\n",
+    "\n",
+    "```python\n",
+    "obj = cls.__new__(cls, *args, **kwargs)\n",
+    "object.__setattr__(\n",
+    "    obj,\n",
+    "    \"_actual_init_args\",\n",
+    "    {\n",
+    "        \"args\": args,\n",
+    "        \"kwargs\": kwargs,\n",
+    "    },\n",
+    ")\n",
+    "object.__setattr__(obj, \"_config_tape\", [])\n",
+    "object.__setattr__(obj, \"_is_calling_stateful_method\", False)\n",
+    "```\n",
+    "\n",
+    "These attributes are \n",
+    "- `_actual_init_args`: The actual arguments used to construct the object. This is used to create new copies of the object.\n",
+    "- `_config_tape`: A list of configurations applied to the object by the user (more on this later). This is also used to create new copies of the object.\n",
+    "- `_is_calling_stateful_method`: A flag that is used to check if the object is currently calling a stateful method. This is used to check if something should be added to the `_config_tape` or not."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3. Call `.__pre_init__()` Method\n",
+    "\n",
+    "Next, it calls the `.__pre_init__()` method of the class. This method is used to perform any pre-initialization steps. For most cases, subclasses do not need to override this method.\n",
+    "\n",
+    "```python\n",
+    "cls.__pre_init__(obj, *args, **kwargs)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4. Construction the Object.\n",
+    "\n",
+    "Next, it constructs the object. This is done by calling the `.__construct__()` method of the object. This method actually calls the `.__init__()` method of the object and sets up the model. More on the `.__construct__()` method later; suffice for now to say that this is where deeper initialization of the object happens, recursively constructing the children of the object.\n",
+    "\n",
+    "After constructing the object, it calls the `.__post_init__()` method of the object. This method is used to perform any post-initialization steps. This does nothing by default.\n",
+    "\n",
+    "**NOTE:** Both the `.__pre_init__()` and `.__post_init__()` methods are called within the `not_top_level` context manager. This context manager is used to keep track of where in the object hierarchy we currently are. We'll cover this more later. But, the primary function of this is to help decide the priority of configurations applied to the object. Configurations applied while currently at the top level (as in, called directly by the user) are given higher priority than configurations applied while constructing the object. And the deeper we go, the lower the priority of the configurations.\n",
+    "\n",
+    "```python\n",
+    "with not_top_level(cls, obj):\n",
+    "    obj.__construct__()\n",
+    "    obj.__post_init__()\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 5. Return the Object\n",
+    "\n",
+    "Finally, it returns the object."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NOTE:** The main reason this is implemented as a meta class instead of using the `.__new__()` and `.__init__()` methods is to guarantee to store the exact arguments used to construct the object, not just the arguments passed up through `.__super__()` calls. This is important for creating new copies of the object. \n",
+    "\n",
+    "Moreover, the arguments passed to the `.__init__()` method may not be the same as the arguments passed to the `.__new__()` method. This is because the configurations applied by the user may change the arguments passed to the `.__init__()` method between the `.__pre_init__()` and `.__construct__()` calls."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The `.__construct__()` method\n",
+    "\n",
+    "The `.__construct__()` method of the `DeeplayModule` class is where the actual initialization of the object happens. This is where the `.__init__()` method of the object is called and the model is set up. The core idea is that the `.__construct__()` method should restore the state of the object to how it was immediately after the `.__pre_init__()` method was called, then find the correct arguments to pass to the `.__init__()` method based on the actual arguments passed to the `.__new__()` method and the configurations applied by the user.\n",
+    "\n",
+    "```python\n",
+    "def __construct__(self):\n",
+    "    with not_top_level(ExtendedConstructorMeta, self):  # (1)\n",
+    "        # Reset construction.\n",
+    "        self._modules.clear()  # (2)\n",
+    "        self._user_config.remove_derived_configurations(self.tags)  # (3)\n",
+    "\n",
+    "        self.is_constructing = True  # (4)\n",
+    "\n",
+    "        args, kwargs = self.get_init_args()  # (5)\n",
+    "        getattr(self, self._init_method)(*(args + self._args), **kwargs)  # (6)\n",
+    "\n",
+    "        self._run_hooks(\"after_init\")  # (7)\n",
+    "        self.is_constructing = False  # (8)\n",
+    "        self.__post_init__()  # (9)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**(1)** This is the same `not_top_level` context manager we saw earlier. This is used to keep track of where in the object hierarchy we currently are."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**(2)** This removes any children of the object. These will only be added during the `.__init__()` method, so they should always be removed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**(3)** Here we encounter two new terms: _derived configurations_ and _tags_.\n",
+    "\n",
+    "#### Tags\n",
+    "\n",
+    "Tags are tuples of strings used to identify a module in the hierarchy. These generally correspond to the names of the modules in the hierarchy. For example, (\"block\", \"layer\") would correspond to a module named `block.layer`. A module can have multiple tags if it exists in multiple places. Tags are used to identify the module in the hierarchy and to apply configurations to the module. It's important to refer to modules by their tags instead of them as objects, since the module may be cleared and re-initialized multiple times during the lifecycle of the object. \n",
+    "\n",
+    "Tags are always relative to the root module (which we have yet to encounter). The root module is the base of the hierarchy and is the only module that is not a child of any other module. A module may exist in multiple places in the hierarchy, but must always have the same root module. Every `DeeplayModule` object keeps track of the current root.\n",
+    "\n",
+    "#### Derived configurations\n",
+    "\n",
+    "Derived configurations are configurations not explicitly applied by the user. \n",
+    "For example, if the `.__init__()` method of a module calls `self.child.configure(\"foo\", 1)`, then the configuration `\"foo\"` is derived. This is because the user did not explicitly apply the configuration, but it was applied by the module itself. Since the configuration is applied during the `.__init__()` method, it should be removed before the `.__init__()` method is called again.\n",
+    "\n",
+    "Deeplay uses the `not_top_level` context manager to decide if a configuration is derived or not. The `not_top_level` context manager stores the tags of the currently constructing module in the `ExtendedConstructorMeta` class. Every time a configuration is added, it also stores these tags as the `source` of the configuration. \n",
+    "\n",
+    "When deciding if a configuration is derived or not, Deeplay checks if the `source` of the configuration is a parent of the the target of the configuration. If it is, then the configuration is NOT derived. If the source is a child of the target, or the target is the same as the source, then the configuration is derived and should be removed before the `.__init__()` method is called.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**(4)** Next, we set the `is_constructing` flag to `True`. This is used to check if the object is currently being constructed. This is used to prevent certain configurations from being applied while the object is being constructed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**(5)** This is where the actual arguments to pass to the `.__init__()` method are determined. This is done by calling the `.get_init_args()` method. This method is responsible for finding the correct arguments to pass to the `.__init__()` method based on the actual arguments passed to the `.__new__()` method and the configurations applied by the user. Each class can override this method to customize how the arguments are determined."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**(6)** Finally, we call the `.__init__()` method of the object with the correct arguments. The `_init_method` attribute is used to determine the name of the `.__init__()` method to call. Most of the time, this is just `\"__init__\"`, but it can be overridden by subclasses to call a different method. The reason for this is to make Deeplay play nicer with editors. It allows the class to define a dummy `.__init__()` method that gives the types and names of the arguments, while the actual initialization logic is in a different method. This allows the editor to provide better autocompletion and type checking."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**(7)** After the `.__init__()` method is called, we run the `after_init` hooks. Hooks are used to run code at specific points in the lifecycle of the object. The `after_init` hook is run after the `.__init__()` method is called. We'll cover hooks in more detail later."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**(8)** We set the `is_constructing` flag to `False` to indicate that the object is no longer being constructed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**(9)** Finally, we call the `__post_init__` method of the object. This method is used to perform any post-initialization steps. This does nothing by default."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The `Config` Object\n",
+    "\n",
+    "For each hierarchy of modules, there is a corresponding `Config` object, which lives on the root module. \n",
+    "\n",
+    "It is a dictionary-like object that stores the configurations applied to the modules in the hierarchy. The keys are tags and the name of the configurable (for example, `(\"block\", \"layer\", \"foo\")`). The values are lists of `ConfigItem` or `DetachedConfigItem` objects. \n",
+    "\n",
+    "`ConfigItem` objects store the `source` of the configuration and the `value` of the configuration. The `source` is the tags of the module that was constructing when the configuration was applied. The `value` is the value of the configuration.\n",
+    "\n",
+    "`DetachedConfigItem` objects are in practice very similar to `ConfigItem`s, and should be ephemeral. They are used to store configurations that are applied by an object that is not part of the same hierarchy. As such, the `tags` of the `source` do not make sense. Instead, the `source` is temporarily set to the object itself. This is okay, because all `DetachedConfigItem`s become `ConfigItem`s after the `.__construct__()` method is called.\n",
+    "\n",
+    "**NOTE:** No `DetachedConfigItem` should exist after the `.__construct__()` method is called."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following is an example where a `DetachedConfigItem` is created:\n",
+    "\n",
+    "```python\t\n",
+    "class Module(DeeplayModule):\n",
+    "    def __init__(self):\n",
+    "        child = LinearBlock(10, 10)\n",
+    "\n",
+    "        # Here, child is not attached to the hierarchy yet, so we don't have tags for it.\n",
+    "        child.configure(\"activation\", nn.ReLU())\n",
+    "\n",
+    "        # Here, the child is attached. This changes the root_module of `child` and we\n",
+    "        # can now get the tags of the child. The DetachedConfigItem is converted to a ConfigItem.\n",
+    "        self.child = child\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Taking the example of `(\"block\", \"layer\", \"foo\")`, the `Config` object would look something like this:\n",
+    "\n",
+    "```python\n",
+    "{\n",
+    "    (\"block\", \"layer\", \"foo\"): [\n",
+    "        ConfigItem(source=None, value=1),\n",
+    "        ConfigItem(source=(\"block\", \"layer\"), value=2),\n",
+    "    ]\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "A `None` source means that the configuration was applied by the user. When deciding which item to use as the actual value, the item with the highest priority is used. The priority is determined by the source of the item. The source closest to the root module has the highest priority. If two items have the same source, the item applied later has higher priority. A `None` source has the highest priority."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Hooks\n",
+    "\n",
+    "Since modules may be reconstructed at any point, it is important that any state-altering methods are re-run after the module is reconstructed. This is where hooks come in. Hooks are used to run code at specific points in the lifecycle of the object.\n",
+    "\n",
+    "To register a method as a hook, you can use the any of the following decorators:\n",
+    "\n",
+    "```python\n",
+    "# Does not create a hook, but adds the method to the config tape, which is replayed\n",
+    "# when model.new() is called.\n",
+    "@stateful \n",
+    "\n",
+    "# Runs the method after the __init__ method is called (and adds to config tape).\n",
+    "@after_init\n",
+    "\n",
+    "# Runs the method before the build method is called (and adds to config tape).\n",
+    "@before_build\n",
+    "\n",
+    "# Runs the method after the build method is called (and adds to config tape).\n",
+    "@after_build\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Config Tape\n",
+    "\n",
+    "The config tape is a list of methods that are run when the `.__new__()` method is called. This method should create a new, identical but detached object. To do so we first create the object with the same exact input arguments (as stored in the metaclass) and then run the same stateful methods, in the same order, with the same arguments. \n",
+    "\n",
+    "**NOTE:** One may imagine that one could simply pass the same configuration object to the new object, but this is far from simple. It is not guaranteed that the configuration object is serializable, and even if it is, it may contain cyclic references and other issues that are hard to resolve."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Checkpointing\n",
+    "\n",
+    "Since Deeplay modules requires an additional `build` step before the weights are created, so the default checkpointing system of `lightning` does not work.\n",
+    "\n",
+    "We have solved this by storing the state of the `Application` object immediately before building as a a hyperparameter in the checkpoint. This is then loaded when the model is loaded from the checkpoint, and the `build` method is called with the same arguments as before before the weights are loaded."
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorials/getting-started/GS101_core_objects.ipynb b/tutorials/getting-started/GS101_core_objects.ipynb
index 1a3fe38d..6dfaa3e6 100644
--- a/tutorials/getting-started/GS101_core_objects.ipynb
+++ b/tutorials/getting-started/GS101_core_objects.ipynb
@@ -8,15 +8,15 @@
     "\n",
     "Before starting to implement applications in Deeplay, let's define the most important objects for building a model in Deeplay:\n",
     "\n",
-    "- **Application:** The higher-level object that defines the neural network architecture and training process. This includes the loss, the optimizer, and the training logic. Applications are typically task-oriented, such as `ImageClassifier`, `ObjectDetector`.\n",
+    "- **Application:** The highest-level object that defines the neural network architecture and training process. This includes the loss, the optimizer, and the training logic. Applications are typically task-oriented, such as `ImageClassifier` and `ObjectDetector`.\n",
     "\n",
-    "- **Model:** A model is a specific neural network architecture that is typically part of an application. Examples include `ResNet`, `VGG`.\n",
+    "- **Model:** A model is a specific neural network architecture that is typically part of an application. Examples include `ResNet` and `VGG`.\n",
     "\n",
-    "- **Component:** A model is usually made by combining multiple components. Components are much more flexible than models. Examples include `MultiLayerPerceptron`, `ConvolutionalEncoder2d`.\n",
+    "- **Component:** A model is usually made by combining multiple components. Components are much more flexible than models. Examples include `MultiLayerPerceptron` and `ConvolutionalEncoder2d`.\n",
     "\n",
-    "- **Block:** A block is a specific combination of layers that performs a small unit of calculation (for example layer plus activation). Blocks are the building blocks of components, and are the most flexible objects in Deeplay. Examples include `LinearBlock`, `Conv2dBlock`.\n",
+    "- **Block:** A block is a specific combination of layers that performs a small unit of calculation (for example layer plus activation). Blocks are the building blocks of components. They are the most flexible objects in Deeplay. Examples include `LinearBlock` and `Conv2dBlock`.\n",
     "\n",
-    "- **Layer:** A layer consists of a single torch layer, such as `torch.nn.Linear`, `torch.nn.Conv2d`. Layers are the most basic building blocks in Deeplay.\n",
+    "- **Layer:** A layer consists of a single torch layer, such as `torch.nn.Linear` and `torch.nn.Conv2d`. Layers are the most basic building blocks in Deeplay.\n",
     "\n",
     "In the following sections, you'll create some examples of these obejcts."
    ]
@@ -27,7 +27,7 @@
    "source": [
     "## Importing Deeplay\n",
     "\n",
-    "Import `deeplay` (shortened to `dl`, as an abbreaviation of both `deeplay` and `deeplearning`) ... "
+    "Import `deeplay` (shortened to `dl`, as an abbreaviation of both *deeplay* and *deep learning*) ... "
    ]
   },
   {
@@ -113,9 +113,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To make the layer into a pure PyTorch module, you simply need to build it.\n",
-    "\n",
-    "**NOTE:** Most Deeplay objects are modified in place when you build them. If you want to keep the original object, either call `.create()` or `.new().build()` instead; in this way, layers will return torch objects, while most other deeplay objects will return Deeplay objects."
+    "To make the layer into a pure PyTorch module, you simply need to build it."
    ]
   },
   {
@@ -137,6 +135,13 @@
     "print(torch_layer)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NOTE:** Most Deeplay objects are modified in place when you build them. If you want to keep the original object, either call `.create()` or `.new().build()` instead; in this way, layers will return torch objects, while most other deeplay objects will return Deeplay objects."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -208,7 +213,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "... you can also add an activation to an existing block using the `activated()` method ..."
+    "... you can also add an activation to an existing block using the `.activated()` method ..."
    ]
   },
   {
@@ -239,7 +244,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "... you can use the `configure()` method to add an activation to a block (this way is rarely needed) ..."
+    "... you can use the `.configure()` method to add an activation to a block (this way is rarely needed) ..."
    ]
   },
   {
@@ -270,7 +275,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "... or you can use the `append()` method (also this way is rarely used)."
+    "... or you can use the `.append()` method (also this way is rarely used)."
    ]
   },
   {
@@ -303,7 +308,7 @@
    "source": [
     "## Components\n",
     "\n",
-    "A `Component` is a collection of blocks that are combined to form a more complex neural network component. \n",
+    "A `Component` is a collection of blocks combined to form a more complex neural network component. \n",
     "\n",
     "In this example, you'll create a simple feedforward neural network component with two linear blocks, each followed by a ReLU activation function."
    ]
@@ -347,7 +352,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Since the component is made of blocks, you can access the blocks using the `blocks` attribute. This allows you to modify the blocks after the component has been created. For example, you can change the activation function of the first block to a Sigmoid function ..."
+    "Since the component is made of blocks, you can access the blocks using the `.blocks` attribute. This allows you to modify the blocks after the component has been created. For example, you can change the activation function of the first block to a Sigmoid function ..."
    ]
   },
   {
@@ -441,12 +446,12 @@
       "    (0): LinearBlock(\n",
       "      (layer): Layer[Linear](in_features=10, out_features=32, bias=True)\n",
       "      (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "      (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "      (normalization): Layer[BatchNorm1d]()\n",
       "    )\n",
       "    (1): LinearBlock(\n",
       "      (layer): Layer[Linear](in_features=32, out_features=32, bias=True)\n",
       "      (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "      (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "      (normalization): Layer[BatchNorm1d]()\n",
       "    )\n",
       "    (2): LinearBlock(\n",
       "      (layer): Layer[Linear](in_features=32, out_features=5, bias=True)\n",
@@ -485,6 +490,7 @@
      "text": [
       "Classifier(\n",
       "  (loss): CrossEntropyLoss()\n",
+      "  (optimizer): Adam[Adam](lr=0.001)\n",
       "  (train_metrics): MetricCollection,\n",
       "    prefix=train\n",
       "  )\n",
@@ -499,12 +505,12 @@
       "      (0): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=10, out_features=32, bias=True)\n",
       "        (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "        (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "        (normalization): Layer[BatchNorm1d]()\n",
       "      )\n",
       "      (1): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=32, out_features=32, bias=True)\n",
       "        (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "        (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "        (normalization): Layer[BatchNorm1d]()\n",
       "      )\n",
       "      (2): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=32, out_features=5, bias=True)\n",
@@ -512,7 +518,6 @@
       "      )\n",
       "    )\n",
       "  )\n",
-      "  (optimizer): Adam[Adam](lr=0.001)\n",
       ")\n"
      ]
     }
@@ -527,7 +532,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The Deeplay optimizer is a wrapper around the torch optimizer and is used to delay the attribution of parameters from the models until after the model is actually created."
+    "**NOTE:** The Deeplay optimizer is a wrapper around the torch optimizer and is used to delay the attribution of parameters from the models until after the model is actually created."
    ]
   }
  ],
diff --git a/tutorials/getting-started/GS111_first_model.ipynb b/tutorials/getting-started/GS111_first_model.ipynb
index 593abeda..858ad6ce 100644
--- a/tutorials/getting-started/GS111_first_model.ipynb
+++ b/tutorials/getting-started/GS111_first_model.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Training Your First Model\n",
     "\n",
-    "In this section, you'll train a simple feedforward neural network on the MNIST task using Deeplay. You'll define the model, loss function, optimizer, and training loop, train the model on the dataset, save the trained model, and finally employ the trained model for inference."
+    "In this section, you'll train a simple feedforward neural network on the MNIST task using Deeplay. You'll define the model, loss function, optimizer, and training loop, train the model on the dataset, save the trained model, and finally employ the trained model for inference on previously unseen data."
    ]
   },
   {
@@ -63,12 +63,12 @@
       "    (0): LinearBlock(\n",
       "      (layer): Layer[Linear](in_features=784, out_features=32, bias=True)\n",
       "      (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "      (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "      (normalization): Layer[BatchNorm1d]()\n",
       "    )\n",
       "    (1): LinearBlock(\n",
       "      (layer): Layer[Linear](in_features=32, out_features=32, bias=True)\n",
       "      (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "      (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "      (normalization): Layer[BatchNorm1d]()\n",
       "    )\n",
       "    (2): LinearBlock(\n",
       "      (layer): Layer[Linear](in_features=32, out_features=10, bias=True)\n",
@@ -124,12 +124,12 @@
       "      (0): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=784, out_features=32, bias=True)\n",
       "        (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "        (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "        (normalization): Layer[BatchNorm1d]()\n",
       "      )\n",
       "      (1): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=32, out_features=32, bias=True)\n",
       "        (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "        (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "        (normalization): Layer[BatchNorm1d]()\n",
       "      )\n",
       "      (2): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=32, out_features=10, bias=True)\n",
@@ -158,7 +158,7 @@
    "source": [
     "## Training the Model\n",
     "\n",
-    "Finally, train the model using the `.fit()` method of the application. You just need to provide the data and the number of epochs. The `.fit()` method will handle the training loop, validation, and logging. You'll pass both the training and validation data. The model will log the loss and accuracy on both datasets, but it'll only update the weights based on the training data. Of course, this automatically selectes the best device on your machine (for example, the GPU if available)."
+    "Finally, train the model using the `.fit()` method of the application. You just need to provide the data and the number of epochs. The `.fit()` method will handle the training loop, validation, and logging, as well as the selection of the best device on your machine (for example, the GPU if available). You'll pass both the training and validation data. The model will log the loss and accuracy on both datasets, but it'll only update the weights based on the training data."
    ]
   },
   {
@@ -170,7 +170,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n"
+      "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n"
      ]
     },
     {
@@ -226,7 +226,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3493185eca3e472fa474f208b1afe557",
+       "model_id": "b1f0de9eed69453abf2e8f0b8558264f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -240,17 +240,15 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/py\n",
-       "torch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a \n",
-       "bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to\n",
-       "improve performance.\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/co\n",
+       "nnectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider\n",
+       "increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve performance.\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/py\n",
-       "torch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a \n",
-       "bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to\n",
-       "improve performance.\n"
+       "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/co\n",
+       "nnectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider\n",
+       "increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve performance.\n"
       ]
      },
      "metadata": {},
@@ -259,17 +257,17 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/py\n",
-       "torch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a \n",
-       "bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to\n",
-       "improve performance.\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/co\n",
+       "nnectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. \n",
+       "Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve \n",
+       "performance.\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/py\n",
-       "torch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a \n",
-       "bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to\n",
-       "improve performance.\n"
+       "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/co\n",
+       "nnectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. \n",
+       "Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve \n",
+       "performance.\n"
       ]
      },
      "metadata": {},
@@ -335,7 +333,7 @@
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+mh07djite9NNNzn62D311FONjkeIs463ptUUQgh32acfr+vn+PHj2q+//qpNmTJFM5lMWlBQkDZhwgTt559/dtrO3/72N2348OFaWFiYFhgYqPXp00dbsGCBVl5ermmapp0+fVqbM2eO1qdPH81oNGqhoaHaiBEjtA8//NAbD1sIIYQQZyH7556tW7fWu15SUpJ2ySWXOK5nZ2drV199tRYUFKSFh4drf/zjH7U9e/ZogPbmm2861nv88ce1mqeDBw4c0MaNG6cFBgZqgDZr1iynWFJSUpzW/+KLL7Tzzz9fCwwM1EJCQrThw4dry5YtcyyfNWuWlpSU5HSf//73v1rPnj01f39/rU+fPtqbb75ZK5bVq1drl19+uZaQkKD5+flpCQkJ2vXXX68dOnTIsc6///1vbdy4cVpkZKTm7++vde/eXbv//vu1/Px8xzq5ubnazTffrEVFRWkmk0mbMmWKduDAAS0pKcnx2OxycnK0uXPnaomJiZqfn5/WqVMnbdasWdrp06cd6xQXF2sPP/yw1rVrV83X11eLi4vTrrnmGu3o0aNuH/9Zs2ZpRqNRc2Xfvn3axIkTNZPJpEVFRWl/+MMftJ07d9bahqZp2p49e7Qrr7xSCwsL0wICArTevXtrjz76aK1tlpWVaeHh4VpoaKhWUlLicr9CCE3TaVo99bBCCCGEEEIIIYRo1yoqKkhISGD69On897//9XY4QrRZ0pNMCCGEEEIIIYTowD777DOys7O56aabvB2KEG2aVJIJIYQQQgghhBAd0ObNm9m1axdPPfUUUVFR/Prrr94OSYg2TSrJhBBCCCGEEEKIDujVV1/ljjvuICYmhnfeecfb4QjR5kklmRBCCCGEEEIIIYQ460klmRBCCCGEEEIIIYQ460mSTAghhBBCCCGEEEKc9Xy8HYCn2Ww20tPTCQ4ORqfTeTscIYQQQrQDmqZRWFhIQkICer18h9hWyec8IYQQQrjLnc95HS5Jlp6eTufOnb0dhhBCCCHaoePHj9OpUydvhyHqIJ/zhBBCCNFUjfmc1+GSZMHBwYB68CEhIV6ORgghhBDtQUFBAZ07d3Z8jhBtk3zOE0IIIYS73Pmc1+GSZPbS+5CQEPnwJIQQQgi3yBC+tk0+5wkhhBCiqRrzOa/DNN1YsmQJ/fr1Y9iwYd4ORQghhBBCCCGEEEK0Mx0mSTZnzhz27dvH1q1bvR2KEEIIIYQQQgghhGhnOkySTAghhBBCCCGEEEKIpupwPcmEEEK0LZqmUVFRgdVq9XYo4izn6+uLwWDwdhhCCCGEEKKNkiSZEEKIFlNeXk5GRgbFxcXeDkUIdDodnTp1wmQyeTsUIYQQQgjRBkmSTAghRIuw2WykpKRgMBhISEjAz89PZg4UXqNpGtnZ2Zw4cYKePXtKRZkQQgghhKhFkmRCCCFaRHl5OTabjc6dOxMUFOTtcIQgOjqa1NRULBaLJMmEEEIIIUQt0rjfTeZNmzBv2uTtMIQQot3Q6+W/GtE2SCWjEEIIIYSoj1SSucFaZCbjrw+DTke3Lz5HbzR6OyQhhBBCCCGEEEII4QHy9b4bshY+hyU9HcvJk2QuXOjtcIQQQggh2p2vvvqK3r1707NnT15//XVvhyOEEEII4SBJskYyb9xI3vsfOK7nLXtfhl0KIYRoUHJyMosXL/bIttasWYNOpyMvL88j2xOitVVUVDBv3jx++OEHtm/fznPPPUdOTo63wxJCCCGEACRJ1ijWIjMZDz8CQNj1MwmbOQOAjIcfwWY2ezM0IYQQLWD8+PHcc889HtnW1q1buf322z2yrfZg/vz5DB482NthiDZqy5Yt9O/fn8TEREwmE9OmTWPlypXeDksIIbyuvfe+lvi9S+L3HEmSNULWooVY0tPxTUwk9r77iLnvfnwTErCcPEnWokXeDk8IIUQr0zSNioqKRq0bHR0ts3uKDmPdunVMnz6dhIQEdDodn332Wa11lixZQnJyMgEBAYwYMYItW7Y4lqWnp5OYmOi4npiYyMmTJ1sjdCFaXFs6yWsKid977L2v22sRhsRfD02DlHXw+Vx4b6b6nbJO3e4hEr9nSeP+Bpg3bSJv2fsAxC9Y4GjWH//0AtJm30zue8sInjwZ48iR3gxTCCHaD5u19fepNzR61dmzZ7N27VrWrl3Liy++CMCbb77JzTffzDfffMMjjzzC7t27WblyJZ07d2bevHls2rQJs9lM3759eeaZZ5g4caJje8nJydxzzz2OyjSdTsdrr73G119/zYoVK0hMTGTRokVcdtllTXpoH3/8MY899hhHjhwhPj6eu+66i3vvvdex/JVXXuGFF17g+PHjhIaGMnbsWJYvXw7A8uXLeeKJJzhy5AhBQUGce+65fP755xgbmJhmzZo1/OUvf2Hv3r34+vrSv39/3nvvPX788UeeeOIJx+O0H7vZs2eTl5fHfffdx+eff05ZWRnnnXceL7zwAoMGDQJUBdpnn33GHXfcwd/+9jdycnK49NJLee211wgNDW3SsRGeZzabGTRoELfccgtXXXVVreUffPAB8+bN41//+hcjRoxg8eLFTJkyhYMHDxITE+P2/srKyigrK3NcLygoaFb8om2zJziMw4dB9gEwRoPJ/deNNzhN8LX03+gphpK8qhVCO0G5GQJCoLQAonqBj5/nAqgoh5zDgA7KCsFaDiEJENndvfhtFrq98ij6wAAwxUJYZ8g5quIuyQPNCgFhENG17o2V5MKpPRAcB1E9nZeVF8OZ36CsQH0e6HQe+AbWfizp29VjMPhBwrmg08HpQyqm8iKwVkBUD+f4H3oINGtV/IlDwK/y/7PTR8Cc5fwZxBgN4cmQc0S9zoqyqp6fkly1TtL5oNND6nr49R0oyFD36X0x+AeruAAie0BIfN3HpDATclPBNwB0BvALAoO/eq4CQsia/ySW9HQAMp99lvgH/wyndlfdPygSovuAqxnDbVbIPghUJi0ie4K1DDL3qes6AxzfDMc3qccVGAadR0HvaRDZTa1TfAYK0tXj8A1w3v7xLVBRpo5Fzc9TGbugNJ+s/3xWFf8zfyP+b8+o7ZXmq7irzy5dkgdZ+8Hm4svG+EHqOSjMhOIc9Xxn7lXHImMHFGWq11/nERA3EEzRYD6t7qs3QKdh6nrOEXVbTF8wRoHVAqd2qdcfqGMQEAZBEXAmhawly6rif+xe4ufeWDu2mnwD1fu6IF29t6uL7qNep1kHYM0C5+cSYPu7Kv7xD0P38VXvgYIMKM2DqN7qubaUqOe2rFAtD+sMZUVVr9NyM1jLyXrty6r4H7+f+DnXq/UNvuqxg3o/Fp6qikFvcH4/+Aaq91t4soqhvFg9f9/+RR37uuL3N6nb7O+34jOQtU9tLyhKxRjRDTJ2qvc0qPdORZnjetabK6viX7iQ+Mcfb/j4tyBJktXD8Z8FEH7D9RhHjnAsM44cSdj1M8lb9j4ZDz8is10KIURj2Kxw7OfW36+rD3Z1ePHFFzl06BADBgzgySefBGDv3r0APPjggyxcuJBu3boRHh7O8ePHufjii1mwYAH+/v688847TJ8+nYMHD9KlS5c69/HEE0/w7LPP8txzz/HSSy9x4403cuzYMSIiItx6WL/88gvXXXcd8+fPZ8aMGfz888/ceeedREZGMnv2bLZt28bdd9/Nu+++y/nnn8+ZM2dYv349ABkZGVx//fU8++yzXHnllRQWFrJ+/Xq0Br4ZrKio4IorruAPf/gDy5Yto7y8nC1btqDT6ZgxYwZ79uzhu+++4/vvvwdwJLiuvfZaAgMD+fbbbwkNDeXf//43F110EYcOHXI87iNHjvDhhx/y5ZdfUlBQwK233sqdd97J0qVL3TououVMmzaNadOm1bn8+eef5w9/+AM333wzAP/617/4+uuveeONN3jwwQdJSEhwqhw7efIkw4cPr3N7zzzzjCPxKtoIS4k6CQqOd33CDirJYc5WiQeDr7rNWqFOcI1R4OPvvH5ZEdYzmVVJpndeRl9+Ru3HP1glEwoz1AltzYSKK0XZar+BYeqkU6dTVQ8BIWqbOp1KkOh91XZtFZCXpuINCIPgWOft2azqJNd+0meMVLcVnlLJC98AxwRfAJnPPVf7JDv/hPptT74c36xOpMsK1MliQKg6NqUFYPCBwHCVVCnMhJBEyNwN295QJ62hndX/a4N/BxazemzFp9Vjq64gXT0PEd3VYwyOU4+9+Iy6jzESLKVQnEPW8/+piv+/H6v4iyoTFbaKqrgrny/yT0B0b5UIKMlVrwufAPU47CfihafUyX1oF0jfBrs+Usc5MAy6jlcn2Se2QUw/sFnAUgzmHECrOpm2lsPpg1UJx+qPsaJU/d8eHEfW/EewZJxyjv/kryqZV5LrnLC0M2ern+rPS/XHCXBoJaz9u3OS4NhPsON/zkmCnCPqMZjiKpMj+9XrRe+jjoFmq3o8NfZj/mkded+sdVzP++BDQgZ3wji4T9U+i3Pg2AZ1OTBcvb6LstRrtqJMvS4c6252fq5cJWn2fwm7lsHVb6hEU6b6nMOJrVXrJAxW262o/KLi1G71es9NVdcNfmAtx7x9P3mfflMV//LPCBnarSp+8080WsZO5+u5x+qI//PaSRqbFdJqVBJm7Xe9n5I89ZObquL/6JOq+L9eS8iYoc7H3xVLCZw+7HpZ9gH1HrPH7hMIg2aqY5q+A3a+r25fs0AllsKTIP94VXWW+bR6L1V/XgHyjlfGX+31s30/eZ98WbXKVz8SMvpcFb89QQbOCTKo/aW1pUT92J8DTYOVf204/slPq78rJ3913l55MZSnqcvFNXqP2pN+9vg/XF4V/7L3CZkyxatFSJIkq4dOh3PWWwghRIcXGhqKn58fQUFBxMXFAXDgwAEAnnzySSZNmuRYNyIiwlEJBfDUU0/x6aef8sUXXzB37tw69zF79myuv159y/f000/zz3/+ky1btjB16lS3Yn3++ee56KKLePTRRwHo1asX+/bt47nnnmP27NmkpaVhNBq59NJLCQ4OJikpiXPPPRdQSbKKigquuuoqkpKSABg4cGCD+ywoKCA/P59LL72U7t1VhULfvn0dy00mEz4+Po5jB/DTTz+xZcsWsrKy8PdXJ8cLFy7ks88+Y/ny5Y6ebaWlpbzzzjuO4XgvvfQSl1xyCYsWLXLanmibysvL+eWXX3jooYcct+n1eiZOnMjGjRsBGD58OHv27OHkyZOEhoby7bffOl6/rjz00EPMmzfPcb2goIDOnTu33INoLk1TJ+6NSeS4YrOqhETNJFIj13dUYg0bqpbpdOq3X+WQb6tFxVhepJJBdSW5XLE/tpO/qhP+0nwITVRVKr5BalsVZep65h5VPVCaB7H91f1PH1QJjjO/qcogg5/ajk4H6dvJemlpVZLmpX8T/8fKSsUT21RSoCRX3Teyuzq+/pUVpkWnwD9EJR/s62Wrv9mEJKhEkaapE7qUNerE2J6k6TJSJWbsirLUj29lssdSoqqITmxV2ygrVMtj+6vkWuXjMaeVOU/w1diTbHucoE4iz/xWdT2sizohLiuCb+51ThIc3wx7lsMvbzonCVyxWqr2U1GqEo32hEh0H8g+oE5SP/jQdfyuqn0c8R90vm4/ya6uMBO+vLt2kuPwyqokR9a+uvcBrhNc4EgUmH9aT95XP7iO/0xK/duuT2OTHPYkQe4xlZS1J8JqVhe5YC0uJWPxO4DqfY2mkff+B2Qsfpturz6uKuLqeNyAej02J/6Pb6mKv6b0Hc7XywqdkhtYy5sWf2O5e/yboEXjP7W7KvbbVqnXO8BQYNit8PoktTxzt+v4aybI2nL8cec0aRd1xu/lIiRJktVDbzQSv+BvLodV1jUMUwghRD30BvXttzf26wHnnXee0/WioiLmz5/P119/7Ug6lZSUkJaWVu92zjmn6sOE0WgkJCSErKx6PujWYf/+/Vx++eVOt40ePZrFixdjtVqZNGkSSUlJdOvWjalTpzJ16lSuvPJKgoKCGDRoEBdddBEDBw5kypQpTJ48mWuuuYbw8PB69xkREcHs2bOZMmUKkyZNYuLEiVx33XXEx9c9zGTnzp0UFRURGRnpdHtJSQlHjx51XO/SpYtTv6pRo0Zhs9k4ePCgJMnagdOnT2O1WomNda7CiY2NdSSafXx8WLRoERMmTMBms/GXv/yl1uuiOn9/f0ditV04fUidtDY09KouJ7apE+zEoVWJrXrX36qSIJ3Ow1pmq6rEeukB55Oj8GQ1TKdmlUXXsY2P7fRhVVlkV5xTVR3gb4LYAWpYlr1qC6qqfmw25wqg9O1OmzZv3+9cSfPZd4SM6FeVZKqeFMip/JtRWcVSr4L0uitpqidpaiaZ8o6Dqax2EsjOnmSi8iTvUVXt6NGT1Lw0zycJ8k9UVbQBZB+QJEd9mpIkaOg16ajW1kDTyPrvcixZOY7e15oG5nXrsaSnk/X6h8T933VV66NVjqisvGy/WLktx232/WTtbVz8R3+E6J7qfYqmktf27Wm2quuO26tuy3p3bd3xL36ZuJnDqt2/chuutotWuf9q6xamNy7+tX9XFZ642Ac475/qyzWyvsvEklXgOv4nHyLuopAa26ixzRrbc1rH/vdy0Myq2O3iBsKgGfDLW/DDU+pLi5qvj/peO5WyNuixZBlcx//XO4kbVeF8n1qjBeq53VLauPh/W9PkJFnWGx+7fv1U9n6Pe+yxJm23uSRJ1gBXwyo1jTqHYQohhGiAhxJW3lCzV9d9993HqlWrWLhwIT169CAwMJBrrrmG8vL6PyT7+vo6XdfpdNhsNo/HGxwczK+//sqaNWtYuXIljz32GPPnz2fr1q2EhYWxatUqfv75Z1auXMlLL73Eww8/zObNm+natZ5eM6g+Y3fffTffffcdH3zwAY888girVq1iZB2l8UVFRcTHx7NmzZpay8LCwjzwSEV7ctlllzW5B1+bZ6/qyD/etCSZ/QT75C/qd1CkSpblHVdJrvBk1VvJUlyZJKocSlOSR9bz/649XM4uN7VqiFR1J35Rw7YaqlioqxIrbqBKbpQVVSXgap5spayv/yE3NcnRUDLCHktTkjQlubWH3dWhzpO89HSy3viEuDk3NGo7LnmqksOekLBZKy9bVUJCs5L1n0/qjv+VN4m76cJq97XW3k6t7dpvs0F+WuPiX/esSnJolUkSp2RNzSRN9WSFjawVp7FkFbmOf/79xI0PasJ2NfU4yotUvA0lCVY+AjoDmqahVejQrFq1XegqD5OG3lfDz2R1PCWn9xvJ260qIl32vv7mJ4JLPsMYW05BWuX7QGcf7aSpy4AhwEZgRNWwuuLT6jOGekn7Qr+L0eX6QP4h9MYg/Dp1csRvWfcOrHyxanu6yn2ggR4MvlXvZ82mltm3bc70I29NVN3xrztEsOFnjLH1v1cdOSao/EdXLR+kwzC06vhb8/PRysvR9LGQfDns+hD2qb89mobj+AJYzHpsFXrHMhzbB03TYS3Xkbe9nvh3mPHVpRMYYSEwshxdZeFteaGBijK9/emoUnklIMyCzmCPwUBFSSzs3u28tk4HpXH4W0GPqsC0FOuxltVd3esXXIG+MntTUaqn8KQ/eQfC647/gA/B8XkERFiwWXRVzy+gq3y+0YHeoDkem/04OeWsEwa7Dii+8va6Kj0bYN5xgLyv19Ydvxd7v0uSrBFi7r0P89p1WE6eJHPhQtA0LOnp+MTHE1OtObIQQoiOwc/PD6u14QkGNmzYwOzZs7nyyisBlQxKTU1t4eiq9O3blw0bNtSKqVevXhgM6hOaj48PEydOZOLEiTz++OOEhYXxww8/cNVVV6HT6Rg9ejSjR4/mscceIykpiU8//dRpeFtdzj33XM4991weeughRo0axXvvvcfIkSNdHrshQ4Zw6tQpfHx8SE5OrnObaWlppKenk5CQAMCmTZvQ6/X07t3bzSMjvCEqKgqDwUBmZqbT7ZmZmWdfJWBFmaqcCqrRZ9BqUcmkyO5qKKCdOQfKC6mlerVW3nE1rLAwo9Zq5h++btpwP0sxWOpfpUmVWG5oE0mm41sgpnJooc1alRCyVVQmfKrfXnXZvP94/Sd5X60hOKEAY7dQrMVl2IrL0GxWsKr7Oy5bK/ANM6DXq4STpbCcigILFJxEK/WFXlPgRBla2hawaQT074ehMslRvvZdyt5+GvT+oNnQbDawaZWXNYzRpfgEqr/JZQUGSk77oWk60KAs34e8w6a64/9+O6Suxy/YWu38Xp1FG+PK8A9R2y0vMmA+VaPi07F+EEHjL8G/8thbMrMw2//fsoyB1J/QpWyrSjCEW/APVdUvFWV6zKfU5AZOJ+2Vl20WHXk76kkS7CqDkhz8TFYCIssJilIv9opSPTkHTDWSWLrKHwhOLCWsmxo2ainRc+LVnWivX4FmsVT9VFSglRQRlhhC7JAC0GxYS/Uc/qzuv3WhycUkjMwDwFqm43Rlgqze3tdbwug2NZuTG8NBc11tZ4wrpcv4qkrN42siHckhAFZthBfV8OXAoUNJXvo/dXv8YFJWfYu11PWXl/6RGt2uqBy2rdNz9H0dFhd/puqLP+3HKNBXKzDVwDfYQI8/JlZuV0fqOxmUZpTV3jBg8LPS6+rBjusn7rqb4mozJkPV8db56OnzxGjHdk+9vYeiAzV6YVXjEx4IlNQbf9Z29Rz1WngNhkBfQM/p/20if+NR1xsFei76HT6hRtj3BTlfHib3y3eAd1yu2/1SA359hsLAa8n9YD05K7bVud2uC24ioIuayCTnw/Wc2ap6z9X7+tnTndDx53D6i7q/rOjy1xswDugKOh15q37h1BvfOhJoOjT4+Dl0vi+BXk/i84swjRsHQMGK78j8PBZ8M9G98xDodaDXo9Or10vsbddgGq7e9+adB8h68xN0evuLAUqPHGs4fi8Nu5QkWSMYTEbHH1v7EEuAkIsvlmGWQgjRASUnJ7N582ZSU1MxmUx1Vnn17NmTTz75hOnTp6PT6Xj00UdbpCKsLvfeey/Dhg3jqaeeYsaMGWzcuJGXX36ZV155BYCvvvqK3377jXHjxhEeHs4333yDzWajd+/ebN68mdWrVzN58mRiYmLYvHkz2dnZTv3FXElJSeE///kPl112GQkJCRw8eJDDhw9z0003AerYpaSksGPHDjp16kRwcDATJ05k1KhRXHHFFTz77LP06tWL9PR0vv76a6688krHMNaAgABmzZrFwoULKSgo4O677+a66647+xIs7ZSfnx9Dhw5l9erVXHHFFQDYbDZWr15db4++ds9Sqpqv15yFMXOvGl5uKVbN5222qmqrnKNgjFHN1n0DG+7JVH2bNViLS8l4/i2g/Q2Xa7CS4Ks1BETrCOgcjlZeRmAnk5oUwFpOSWo2FblFaJbyaokLC1QmMMIHGtAVqKGFBYyndOlKNMs3VQmOCgtaSj84/RvxFX9D76OSPzkHjBSeDIDKhIlm01UWGKnrXSefxifAhtWi48TnsYC+/pO8pVvoNjWbnP0mcvYF13ksuk7JIiBcJYfy95rI3h0CBKif77fAK793rJv03lKChgyB+MEUnfyIzO0hdW638wUVmCqTZMXZ/pzaGlZrnfrizz3iOgGaMMkf/2h/0BkoK6rg1DYX2ZNKceeYsKfQyo4cJuOvf62+d6d1Yy7uhv85nUCnpzytgPRPazQDr0Yf6AtY6o+/MgkYOW0QQRcOA50ea2Y+Zz77oI6tgm//MXDpRNj1PhzcTOlvp4BTLte1VuggeSwMvRldcRl89pRjmc7HUPnjAz4G9H3PhxnqSzVdUQm6r59Aq2jgCzljDMx4gaDDr4FNJT7RNJUM1TSw2vDr2w1+P9NRBua75SlsJWVQkodWXgK+RvALQbPZMIRUe61k7EBv0LD56NDQq79RtqrKMV1EMlzzcNX6XzwEhXUnnepkcx48qPmFwIWPVN3w2TOQ4bp3nIZO/b0ZWnmDPcmi14NmQ4dN9Q308UHv5wdDqt4n+hWvY0gvVcsre43rKhNoGur5gZLaO63OoMcnOgJdt3FQ+bfUJ/E0vnE1qm+rV9AmnAthwaD3xbDiGXyMVjVLp8G36kBYy6EoWz1l/a+AmL7oo37DJ/KI620CuqjuEKUqlPURR1RSyua8Ti16n8rJUgy1nl+HkDg1cQCgBVb2RXSM7tWp111l70bN/hn31G5sRzZSUWKEEgsU1H5d2EpKHZetBUWUHkytP9Y2RJJkjWQcOZKwmTOcvqUrP3KknnsIIYRor+677z5mzZpFv379KCkp4c0333S53vPPP88tt9zC+eefT1RUFA888AAFBQWtFueQIUP48MMPeeyxx3jqqaeIj4/nySefZPbs2YAayvjJJ58wf/58SktL6dmzJ8uWLaN///7s37+fdevWsXjxYgoKCkhKSmLRokX1zlwIEBQUxIEDB3j77bfJyckhPj6eOXPm8Mc//hGAq6++mk8++YQJEyaQl5fHm2++yezZs/nmm294+OGHufnmm8nOziYuLo5x48Y59a/q0aMHV111FRdffDFnzpzh0ksvdST8RNtQVFTEkWqff+wJ0YiICLp06cK8efOYNWsW5513HsOHD2fx4sWYzWbHbJcdkn02uAoXlRBpG9WJTkxf1R+r5jIP8EYllrVXIbaky7G9eyO2fQew5S/CpjOhlZaB1UJIv2BHMuvMpizKskuxlVsrf2zYLDZsZRrlheqkvt4k05s/qht1Gn1nVFXRnV4fTtHJuidICItOdwx5KkyxULDh9TrWDCR2aJ4axqTTU17kR0l23X3wNGMnCPVBZ9GDvrjO9Rx8AqHnRHSnc9D5ZFaeqOOodlFVF8CAq6FTOOh90Ff8hs+JQ+ispWArRxcQombcrKzS0PlVxpexA0OAlYA4PzBGqSoNezVHZSLBcPFc6JUEegO+2w9i0n4CvR5N0yjedQitrP6hcLoAP0xD+6MzGCqHw6mTbN/JF8KAnuohHviN4DMrKmdHrtYrK+c3MGfjS7Zje4awMIzjxqrlmfug4KQaahmqJuTwHXIBnDsEAH3YSYIGVc7e6Rgup6nzd6sNy8lMdYJeX/z+fpjO64//OUOhs5pJ1xBSRMQ1ueh8fNSPrwGdweC47J/cSVV79pmOIWUjnScUorvkHxDTE52vLzpfP3QFqeg+/QMGQwn0ngamGPRGjd6fvqSSLwaDeg7qYAgIpfNTfyLtoefr7309bzb64FCS/nFfvY+zum5LKvs4ZexSsxP6BMJt7zoPGT21G3Z+QI/pJTDlaafhuo4EXI2EStclj6JZbY5ki3n3IdL//lq98Sc8cCtB/XpUJar0OnQ12m50WXCP2l/19wSonmrfP6YS8sNuhbiBJL31ZlX8r0+Citrx2yU+cFu9x8m840CDx7/LgnswDnKuyI259Wpibr263m0DEDeQ6EnJRJ/aDT4Fanhu/GA1U+rOD1TscQMhVj0vUddfQtT1lzS8XSB65sUE9enW8Ovnz7MwDupD9IzKz3bRfdD8Q+DYRrCpZKvOpyolFDbpfELGDK1MyNpgzTNomQfBEAC9L8bH5yB8+Sns/IDguFICbkhAG34nQFUVa+V9/btUVUsH9utBp8fnVCV4bRplvx3n9PvftMne7zqtobne25mCggJCQ0PJz88nJKTub1WawlpkJuWSqWgVFVTk5IGvL702/OSckRdCCAGomQpTUlLo2rUrAQHNqGQQZ4X58+fz2WefsWPHjhbbR32vyZb8/NCRrFmzhgkTJtS6fdasWbz11lsAvPzyyzz33HOcOnWKwYMH889//pMRIzzTv7VNPk8N9NxqSfaTPIAub73lSDSZN20ibbZKTMbffwsBXTuhlZZjKy3FVlqOraQMna8PIWOHOraV/d5XWE6dxlZSiq24GM1cgO3MKWylZRgiokleUZXUS7n6Gkr31q5qAzD4W+l1ZdWQ22M/RFKcVVfSScOeJKvZoDnjiSfUiZIO9AGgN+jo8YcYdD7+4ONP1tpczMeKq6p1DAZ0vj6O3/Gzx6FPWQXp28nXTaHUbwg6Xx/w9a1MjviiO/w1uvSthE0agf6Ce0Cnp+TwMSynTlerAqpMePj4oPMx4J+ciN5P9Xwq3LiDE0++Unn833Q6ybMf/y5/n1frJLvRnJIcq2onOepKEgTHQaHryqfqnF8/jYw/OE713rM3J2+J+F3R+9SaabNJ8bsjJAE+mlWtkrKOJMfkp8HfCOWNSJrWkPHyUvK+XotvYqKj93XKZZdhSU8n/KpLiPvD5WofgWFqCHdRlppVtubMk9WFdlLH60yKOv6Nib+JEyfUG/+l410n6eMGwKk9tW+P6auGl/uHqB6KVovq93ZqZ93xJ54HV70GBh9Vsetqu82Jf94f1fEszlETshz7ufZG4s9Rs5oGhqvJW6qra7g6OA9XD453OZSe5DGq0rj6xCeNjf/yScT937Wqn2XecXUH+2QtpQWqd6Z9u35Brl+/9cUfPxgueFDFb4wC82mXMQLq70Z4sqqiNsWonpqZe8h48a2643fx/0JzuPP5QSrJ3GAwGYm//3awlJK19Dv8uiRhKyyUJJkQQgghzgrjx4+noe9X586d27GHV4I6sTBnqw/6XmItLiXjhbeBBnq6PPeGy/v7xkcTMnqwqnDLTaHw+9WUZZhdrYlB51xtpA8MBF9f9H4G9DYzej8d+sAg9H4GDEZfGDgODP7g40eoLhNjgQVdgD/6wAD0gYHogwLRBwZRlplH5psr6q/keMZ1kiNmXCMOkjEM0rcTalhH6M2PukjSPAamEugzEXvn6sCeSQT2TGrExiF41GDCLrmAvK/Xup7g69LxTU/QgIo3bmBVQqmBShQH30bMjAoYB/dxP/7QzhDVU00gUZRZf7KsqfH7BEBFqfNt0b3VxD8Zuxof/1WXVMUf21/1kss+UHe8Or2acCP/pBoy52+CWV/C29PVY/jlrdqPb/zDkDxaxWZPmBt8VVLAnpioc386Yv40B/PWPbV6X/smJhLz8BNQvYomKKJ2n0NQiY9yM5z8VSX2Iion37EUq/jsSY6a8XcZBde8CTlHVIKprO4hs3WJueVq1/HHRhIz5zag2PFY8TOq4wrOiRs7Y5T6gaqJT2Z9Dh/8HlLX144/eSzMeFclp6ofi9xjanbYmqrPvBuSAJHdiXkwue7j/8TCquNvj8vOFFs1e6XBX/U0BFXxWVoAGTshrItKVk5+Wk2u8dsa1eQ+ujecM6PqdR4cD1E9wMdPxV4z5tj+kH0IzFlV8fsGQlBE3cc/MZGYx55R8VeUq9d0ULWZpANCwL8fpP6krkf1Vq93+2s4JEH93+BvUvEXnIRjG1QizBil4k8eo1oAlBVCZE+V5HT1pVHcgKrnKKba35Po3vXH78Xe7x0mSbZkyRKWLFnSqEbLzWEcMhDKCkmedCU6U1TDdxBCCCEa6f/+7//43//+53LZ7373O/71r3+1ShwmU91NuL/99lvGjh3bKnEI0aaUm1U1g17vsjdYa7PPQtcY+hAThkA/9D6g97Gh01nwDTgDS68BmxquFt4pCGuMHr3Bht5HQ28KQW/Q0Fvz0J8z3Wl7Xd5+C53BAF/+SZ249pwM59/tct9h/euOywiUZZW2vSSTG+pNEtxzF8T3UlUuDVUcJo9RSZzqw3B1uvqTHPYkTc0qIDcmUqj3JPX2yiqgsM4Q2kVd1lc2hI/qARHd1IlzXRobf6fzqmZ0BVWZc3yL87qaDXxqDLtKOp+Y+xPrTzIFBjrHnZtaOwFXXUQ3CEuuWj8wHC59EY5vUkmO0nwICIUB16jkhqs3YkR3MEWrY1bv8dFj6DKQ+EfmkXb3w069r+MX/K1xw8zsyQc/IySNroobVDLGP7h2kiYwDLqNh5F3qvhNaigv9n5Ter16LbqqmqrBEBpO/J9nkfbQ887x/+1v6JMGV22jyyhHIhpQVUUNHR/745v1pUrm7PqgdpLGVQWcv4vef35G9d6vsT9DdGfin3iEtDv+7N7xDwipSpLVjCEgxPm5CI6HoHBVLWmKheheVevarFWzvod1gZBOql+ZfRi/XXQvVcmWvl0lP4MiAQ1DUIDr4189fh8/dfyrvzZqxl3zC5+AUDXk2GZFlfPqYfD1tY9D3AD1uqm57Zi+6nlytczOJ6Bx8XuBDLd0V8ZOlR22P/FCCCFckuGW7svKyqqzp1lISAgxMTEul3nakXp6biYmJhJoP+loZ2S4ZfvnteepKFtVoASEQPwgrw6xrK5Rw82uCccYnA4lua434hOgTljDkyG8a+XvJPAzeXa4XB2sxaWk3PEElqwcp4kHfGMjmz7xgMFPnWiCGi604QU4vrn2eu7MzmnwVcO/XKj+PNh1efUFjBdMrjpBbOg1Yx8GVX290ERVAaJpKsmRtR+yD1YmOSaoREHNSR9i+6tqo4qy2ommOriM/603MZ43RCWFgiLrPtEtylIxVRfVE04frrquaWq20B1LVdWJfwj0nASmOHWi3nm4SoLln4CQRFV9U/P5sj+unKNV/f0qj5n5xxWk3XFP7fgr3w9OTu2p+73gFwSJQ2vfnrm3alha5+HqMfgZ4UTlTIT2ZI2lFMqLnM8RUzdUVdv5GVWy3c4/WE2EYbOR8ehfyfv4c0BNwBH/+OOuY7Szv06CItSxqYv5tHrdgEoAAvj4q/e3bwPvLVev2c7D1bmwvSKvcphexktLyftGTcIRdtlE4p99SS0vzQd06m9nffuomTxqLnMOWMvU6wXUazi2X9X+wpNUUqpSxsMPkffxZyr++o6/pUQNSwwIqZqIpfMIlYiqj7VCve6CIqqSYvUpyVPvg5p/m6wWtSwoUlV35aaq+N9e7eid3qjXj11ZoUqEBYZVPr5S9Ro1RtZ7tzrZj09j71+SC3pfMp5Z2LT43SDDLVuSPQNe+ceu7MgRNKuVAJmeXgghRDPFxMS0WiKsPj169PB2CEK0HdVPcksLas041iiappJKKWuqKjm6jldJmib2AkLTMPaKJmx8P/LW7HNdidXDjNEnvXICN11lX5jKRFhEV3XZFONc4VFdK1Ri1VlJcM+s2gkye9KopvAklayxVM5U12mYOvnK2qeqkm5ZAYdXwdbX1PGP7F4Vd13Hv3pSTKdTw8TqSDoZB/ch7OILqpIE18/EOGFq4w+CKdb17RHdwD9UPY4+l8KoOeoENjdVneD7B6ukhT2ukISq4Xg+NfrABUWok+6irNrxj51A2MyTTiepjgSTKbr+2I3RKhHiG6R6htkq1OustMC50qbbBOheo59hbmplhVhlrFE9q5aFJgI6tayssKpiKqKb2ke1aiHjhCmEXXkJeZ9+XTv+mqJ6qsRJcY3Z+IIiqpJINUX2AI6qIYCVPfEAdbz1PlWvId+A2omn+EGQd0y953wCVM8qva9K4ER0V+vo9cQ89CjmjVtBpyP2vsY36W+wnDQoUr3H/YNVvM3l469eE45hq2r/MbdejXnbHtAbiH1kQdX6AaH1by+mr3pN1nXsm8qepPEJUP35Iis/10T3UbMKhyQ6h/HQI5g3bmn4+PsGqp+KakPQG/M33ODT8HupOnvSqtZ2fKu2E5KgkrJBkcTcNwTzuvXuv35qVt25eg27w358GqvyfR1z3/1Ni7+FSJLMXY4kmUbOm2+R9Y9/YJp4EZ1fftm7cQkhhBBCCM/StNo9l8785t426mp8fHhl4yuZKspUj53cFJVYsP+UFRATqcMcFF17uFmInphrz4e47ioZFtbFvZMXaPpwP1cCw1XiqmaVkcG3dpLpkgswnttXJSDsiSpjlDqRjuhW1X/JXh0CaphS2s/qPnq9Okm2V2eBqvaxP/6k81VPudOHVfKivMi56XTiEFX1k5emegRF9qysvqlRCVRNzCNPYN5xQ90nedUTfJX9kDixTSX26ksQ1HwcfkbnyiF9tdO54Pi6txPaSSUsoiu/2K/eeyiye9NPUnU65+SWXUh8VZIsqI6qkvDkurdb1zHR6aoeQzUxc27FvGET6Kg/fh9/54oiUM9FfQkk+31qiuxe933s/E3Oz1dMX5erGUxG4p9WyaVGDTMzxVQ28e9U/3p1HK9GieyuEor2/mF+1eKy96wKT4ayQgykEf/EIxAYht6dKt/qfchaQs0+bqZol8kqt49/W6A3OF5PBmh/8VfT1o6/JMncVvkhQLNhHFVZ0r5uPdYiMwaT959QIYQQQgjhIa6qxuxDvRp7f3uCyScQBs1Uw6vSd8DO99XtaxZUzS6naao585lU54RYYbrrBuk6PYaoTsRfFkPa++nOlVjPLUY/ehzoDM59rtxlb9ysWWHPx2roKTbV0yh2oEri1DHzmpO4AWrIkcEHgqKqYvIzQXQfYv4UrCpRdBA7/zkwBjn3qaqeXPAzVvbYqTZsSa9XtzWqUVtlVV1QlIon/2RVkqzLKHUbqMRicELV9YRzK3v0oJJsOUcc9zEYfOo/yYvoVtXXy769xKHqeW3M8Kv6HktDwrrUXdHjpxK0Hj9J9Q+ufD5o5uNrHEN4NPF/nqV250784cmeqbDygDqr31yJ7q0q0QwteDofkqAqBQ2+Kgld/XmM7A5hSZXv5wgIScTYtX2nFtw6/tWrb3Ut//puDLfib4PaUvzt+5XsDdWGW/r37o1vUhcsx9Iwr1tLyMUXezc2IYQQQgjRdpzaXZUgq97Taygw7FY1hPHUbvhxAZQVqISYpdj1tvxDKodIJlcNmQzrAgY/jIDhu3ux5qnZ6cIuuQDjiBFVw8ISBkPmvqo+XY0VHA+FGbWHy5XkVlZUVSabGmrw7VM5fMd+Ql/9xN7gBwYfDMEhVUmOYBeNt2tylRxobDLGXvlm30ZIgrotILT2dqtf1+mq3aeyabtvkOO2Bk/yam5bp6t9gq03VCXimiuxsqeYqwqzhHPVa85UNcTf4yepLZnAqckYhfGiSxs/aUF9x6a9aI3ja/Cte19O7+OzLK1g8IGYfuo9XFe/PtFunWWvZk+o/EaxrACdLpGQyVPIee01ClaslCSZEEIIIUSH0sz5rVLWqN+DZjo3vQd1fdAMNYTx+Kaq2/U+ENq5KhnWaahKkAWE1TusUbOqSjOfiFBib73aOXZ776rUn2rfsebwR8d9TGoGQ2tZ7ZnPAsOrekRV5xtU2bMpQM2aWZKrrke5GO4V2V31CnIMudNhHNyMmSwb4hdU2ZfLt/Zx1OmaVk0UHOeZ2KqLHQA5h9Vz31x+RuchctX5m9yaBbNdCHEj4VXfsRGiMZra3F60eZIkc1d55bd7leX3wVNVkqxo7VpsxcXog4K8GJwQQgghhGgzSvLU74TBrpfHV94eHA+Db1QJo9BOzn2muoxqcLikrbQMva8PNiDu7t+7nhHS3puo5kyEAWGuN2qqTADVN3NerX2ghlU2RkiCc2IqKELNblizx1ZuquuEXFO481i8JSDE9QyLdame8KuZzBRCCOE2qQ10lz1jXPkfeEC/fvh26oRWWkrRurYxHbgQQnQYmgYp6+DzufDeTPU7ZV3TZpfzkuTkZBYvXuyRba1ZswadTkdeXp5HtteepaamotPp2LFjh7dDEa1gyZIl9OvXj2HDhrXujpv7t8Y+Q1n6DtfLMypvjxuoenyFJzsniRKHuh7GlHS+mjUvaTRE9kDfZTA9V39H762bCB5xjlrHVZ8cp20PUdVl1XvrxA1UiaSYfk2rkvJxc2KA6gJCVTKx03lVt4V2UrNTxrhomi4UnU7N5lnXa0UIIYRb5C+pu/TOh0yn0xEydQo5r/+Xoh9/IGTqFC8FJoQQHUxJLnzwe0it8QXE9ncheSzMeNdz1QU1jB8/nsGDB3skubV161aMbWCmHiHaszlz5jBnzhwKCgoIDa2jAbmn5R1XVUzN0XW8msVy5/uqB1n1IZendsPOD9TlbuNr39fgp4YI1hTbX/WtCqicQa7aEDM9gNZdzYbpaihdYLiq3vIzOQ81C+uikmX2pJ674s+pHDrZzCGC/jV6kdn7hIn6+bqoHBRCCNEkkiRzW9XslnZh11xD4NChGEeP9lJMQgjRwWhaVYLM1YxwqevV8llf1tujp+XC07Barfj4NPzfaHR07anGhRDtQHMSZAY/1SQ/bqD6ObVbNekfNEMNsczYoRJkFSWqcqr7RDWrZXUR3aouB4RAaYG6bG/GX5f6emvpdKoXWE3hSY15VHULCJVklhBCiA5Bhlu6q6JU/TZnO27yS04meMIE9H7SB0AIIeqlaWpGtIZ+Dq+qSpDdtgqmL4ahs9Xv21ap21PXw5HvG7c9N4ZMzZ49m7Vr1/Liiy+i0+nQ6XS89dZb6HQ6vv32W4YOHYq/vz8//fQTR48e5fLLLyc2NhaTycSwYcP4/vvvnbZXc7ilTqfj9ddf58orryQoKIiePXvyxRdfNPmQfvzxx/Tv3x9/f3+Sk5NZtGiR0/JXXnmFnj17EhAQQGxsLNdcc41j2fLlyxk4cCCBgYFERkYyceJEzGZzo/b7+uuv07dvXwICAujTpw+vvPKKY5l9KOT777/P+eefT0BAAAMGDGDt2rVO21i7di3Dhw/H39+f+Ph4HnzwQSoqKhzLbTYbzz77LD169MDf358uXbqwYMECp2389ttvTJgwgaCgIAYNGsTGjfX3bhKixZli1fA3Q2WD+PEPq0RZRYlq0v/VPep3RYkaLnnjcoip0dg+MBxM1RLscedUXdbXTs5n/uNZjs2+maJ161riEQkhhBBnDakkc5d9GlwhhBDusxTD027MINbQjHBLr3F5t1r+mt7oWaxefPFFDh06xIABA3jyyScB2Lt3LwAPPvggCxcupFu3boSHh3P8+HEuvvhiFixYgL+/P++88w7Tp0/n4MGDdOnSpc59PPHEEzz77LM899xzvPTSS9x4440cO3aMiIiIxj2eSr/88gvXXXcd8+fPZ8aMGfz888/ceeedREZGMnv2bLZt28bdd9/Nu+++y/nnn8+ZM2dYv14NX83IyOD666/n2Wef5corr6SwsJD169ejNSKhuHTpUh577DFefvllzj33XLZv384f/vAHjEYjs2bNcqx3//33s3jxYvr168fzzz/P9OnTSUlJITIykpMnT3LxxRcze/Zs3nnnHQ4cOMAf/vAHAgICmD9/PgAPPfQQr732Gi+88AJjxowhIyODAwcOOMXy8MMPs3DhQnr27MnDDz/M9ddfz5EjRxpV5SdEi4joCvpq30P7m2Dy05C5G07tgeLT6u/RkFlqmKW9GjZxCJz8VV2u+XlTp4PoPqBZXVaSlfz6KyU7dxI+c0bLPCYhhBDiLCGfIN0VEAYcU1NbV2MrKeH0K69StOEnkpctQ+/fQCm8EEKIhjU0I1wLCA0Nxc/Pj6CgIOLiVONqe2LmySefZNKkSY51IyIiGDRokOP6U089xaeffsoXX3zB3Llz69zH7Nmzuf766wF4+umn+ec//8mWLVuYOnWqW7E+//zzXHTRRTz66KMA9OrVi3379vHcc88xe/Zs0tLSMBqNXHrppQQHB5OUlMS5554LqCRZRUUFV111FUlJaqjVwIED69xXdY8//jiLFi3iqquuAqBr167s27ePf//7305Jsrlz53L11VcD8Oqrr/Ldd9/x3//+l7/85S+88sordO7cmZdffhmdTkefPn1IT0/ngQce4LHHHsNsNvPiiy/y8ssvO7bZvXt3xowZ4xTLfffdxyWXXAKo5GP//v05cuQIffr0cetYCuEx9kb40X3UMEtQSa64c+C8W8C3jub2DSXyTXUP3S5PS1ObSGrmsEkhhBDiLCdJMnfpK2cK0qxON+sCAsj/6isqMjIw//QTwRdd5IXghBCijfMNUlVdDfn6Xti5TPUgG+piuX1GuEHXwyWLXKzgYr8ecN555zldLyoqYv78+Xz99deOpFNJSQlplSesdTnnnKqhU0ajkZCQELKysuq5h2v79+/n8ssvd7pt9OjRLF68GKvVyqRJk0hKSqJbt25MnTqVqVOnOoZ5Dho0iIsuuoiBAwcyZcoUJk+ezDXXXEN4eP2TIZjNZo4ePcqtt97KH/7wB8ftFRUVtRqqjxo1ynHZx8eH8847j/379ztiHzVqFLpqPeVGjx5NUVERJ06c4NSpU5SVlXFRA/+fVj+W8fGqgXlWVpYkyUTr8QlQzfRP/lJ5Q+VruqlN8KF2A/t6WAsKsObmAuDbue4KViGEEEI0THqSucs+nbbV4tTjRqfTETJZVRcUrFjhjciEEKLt0+lUtURDP4NvUOvvfL+qEsOu+oxwg29s3PY81Ny/5iyV9913H59++ilPP/0069evZ8eOHQwcOJDy8vJ6t+Pr6zyUSqfTYbPZ6li76YKDg/n1119ZtmwZ8fHxPPbYYwwaNIi8vDwMBgOrVq3i22+/pV+/frz00kv07t2blJSUerdZVFQEwGuvvcaOHTscP3v27GHTpk0eiz0wsI5qmxqqH0t7wq0ljqUQdYo/p+5lCYMhskfjt5U4VK0fHN/wupXKj6mkvCE6CoNJZtIVQgghmkOSZO4yVGvOb3U+CQqeMgWAoh9+xNbACZIQQoh6JI9VPxUlaka4L/8E295Uv1+fpG5PHgvJYxreVhP4+flhtVobXG/Dhg3Mnj2bK6+8koEDBxIXF0dqamqLxORK37592bBhQ62YevXqhcGgvtTx8fFh4sSJPPvss+zatYvU1FR++OEHQCWVRo8ezRNPPMH27dvx8/Pj008/rXefsbGxJCQk8Ntvv9GjRw+nn65duzqtWz1pVlFRwS+//ELfvn0dsW/cuNGpB9qGDRsIDg6mU6dO9OzZk8DAQFavXt30AyREUzU20dp5hOoR5huoZqAMiqjRjywYguOqrjfU888vCELi3Urslx87pu7aRYZaCiGEEM0lwy3dpderZqpWC9gqgKreY4GDB+MTE0NFVhbmDRsInjDBe3EKIUR7ptPBjHfhg9+rWSx/ect5efJYtdxDFWI1JScns3nzZlJTUzGZTHVWJvXs2ZNPPvmE6dOno9PpePTRR1u1iunee+9l2LBhPPXUU8yYMYONGzfy8ssvO2aa/Oqrr/jtt98YN24c4eHhfPPNN9hsNnr37s3mzZtZvXo1kydPJiYmhs2bN5Odne1IYtXniSee4O677yY0NJSpU6dSVlbGtm3byM3NZd68eY71lixZQs+ePenbty8vvPACubm53HLLLQDceeedLF68mLvuuou5c+dy8OBBHn/8cebNm4derycgIIAHHniAv/zlL/j5+TF69Giys7PZu3cvt956a8scUCHssvbWvzyiK4R2qrqu00H8oLrXt2uBCaDK0yqTZNKPTAghhGg2SZI1hc4AWMBWoy+ZXk/w5Mnk/u9/FK5YKUkyIYRojsBwmPUlpP4Euz4A82kwRsE5M1QFWQslyEANo5w1axb9+vWjpKSEN9980+V6zz//PLfccgvnn38+UVFRPPDAAxQUFLRYXDUNGTKEDz/8kMcee4ynnnqK+Ph4nnzySWbPng1AWFgYn3zyCfPnz6e0tJSePXuybNky+vfvz/79+1m3bh2LFy+moKCApKQkFi1axLRp0xrc72233UZQUBDPPfcc999/P0ajkYEDB3LPPfc4rff3v/+dv//97+zYsYMePXrwxRdfEBUVBUBiYiLffPMN999/P4MGDSIiIoJbb72VRx55xHH/Rx99FB8fHx577DHS09OJj4/n//7v/zx2/ISoU0le/ctDEhu/LZ1Ozcqr2VpslnRDVJQkyYQQQggP0GmNmeu9HSkoKCA0NJT8/HxCQkJaZicnf4HyYogboE7iqineto1jv/s9+pAQev20Hp2fXx0bEUKIjq20tJSUlBS6du1KQEBAw3cQHUZqaipdu3Zl+/btDB482NvhONT3mmyVzw+i2VrteUpZX//yrmNbbt9NpNls6PTSSUUIIYSoyZ3PD1JJ1hT6ym8BrRW1FgWeey7+vXoROGgQVrMZH0mSCSGEEEKIFiYJMiGEEKL55H/TprCXytsstRbpDAa6fv4Z8U89iU94eK3lQgghRF3+7//+D5PJ5PKnNYcZ1hWDyWRi/foGKmyEEEIIIYRop6SSrCnsvchKciEkodZiXQv2yRFCCNFxPfnkk9x3330ul7XmEMAdO3bUuSwxseFeTMnJyXSwbg5CQEw/OH0Iont7OxKHkj17OfmnPxE4eDCJixZ6OxwhhBCi3ZMkWVOU5KrfxWfqXEXTNEp37kQfHIx/9+6tFJgQQoj2LCYmhpiYGG+HQY8ePbwdghDeY609UgAAYyQEjWzRSUPcVZ6aiuXkSXzi47wdihBCCNEhyHDLFpK1cCGpM6/nzFtvezsUIYQQQgjRWNbyupe1oQQZQPmxVACZ2VIIIYTwEEmStRDTmDEAFH7/PVpF7Qb/QgghhBCinfA3eTsCl8qPHQPAr4skyYQQQghPkCRZCwkaNgxDeDjW3FyKt271djhCCCGEEKKp4gd7OwKXLMfSAKkkE0IIITxFkmTNVUdjYp2PD8ETJwJQsGJFa0YkhBAdjnnTJsybNnk7DCHE2aqNDbO0c1SSJUuSTAghhPAESZI1Vz2zdwVPmQJA4arv0azW1opICCE6FGuRmYy/PkzGw49gM5u9HY4QwguWLFlCv379GDZsmLdDaTOs+flY8/IA8Ovc2bvBCCGEEB2EJMmaIqJb1WVbHTMgAcYRwzGEhmLNyaF42y+tEJgQQnQ8WQufw5KejuXkSTIXLvR2OI2SnJzM4sWLG7WuTqfjs88+a9F42gt3jps4u8yZM4d9+/axtVVaWLTNqrGarIWFBA4ejH+vXuiNRm+HI4QQQnQIkiRrClNM1eWizDpX0/n6Ypp4kVpt7dqWjkoIIToc88aN5L3/geN63rL3ZdilEEIAfp06kfz+Mrp98bm3QxFCCCE6DEmSNYXBt+qyZqt31YhZs0h69x1i7p3XwkEJIUTHYi0yk/HwIwCEXT+TsJkzAGTYpRBCCCGEEKJFSJKsuerpSQYQ0KsXQcOGoTMYWikgIYToGLIWLcSSno5vYiKx991HzH3345uQgOXkSbIWLWqx/f7nP/8hISEBm835S5DLL7+cW265haNHj3L55ZcTGxuLyWRi2LBhfP/99x7b/+7du7nwwgsJDAwkMjKS22+/naKiIsfyNWvWMHz4cIxGI2FhYYwePZpjlc27d+7cyYQJEwgODiYkJIShQ4eybdu2Ru33p59+YuzYsQQGBtK5c2fuvvtuzNWSkcnJyTz11FNcf/31GI1GEhMTWbJkidM20tLSuPzyyzGZTISEhHDdddeRmelccf3ll18ybNgwAgICiIqK4sorr3RaXlxczC233EJwcDBdunThP//5j1vHT4hmqyj1dgSNojXwGVQIIYQQ7pMkWXOVFXo7AiGEaHdsxcV1/5SVYd60ibxl7wMQv2ABeqMRg8lI/NMLAMh9bxmFa9ZgKy1tcLvuuvbaa8nJyeHHH3903HbmzBm+++47brzxRoqKirj44otZvXo127dvZ+rUqUyfPp20tLRmHBHFbDYzZcoUwsPD2bp1Kx999BHff/89c+fOBaCiooIrrriCCy64gF27drFx40Zuv/12dJUz791444106tSJrVu38ssvv/Dggw/i6+tb3y4BOHr0KFOnTuXqq69m165dfPDBB/z000+O/do999xzDBo0iO3bt/Pggw/ypz/9iVWrVgFgs9m4/PLLOXPmDGvXrmXVqlX89ttvzJgxw3H/r7/+miuvvJKLL76Y7du3s3r1aoYPH+60j0WLFnHeeeexfft27rzzTu644w4OHjzYrOMqhFtyjjpf92ub/b6O/e73HJk8heJGJsKFEEII0TAfbwfQ7pXmN7hKRW4up196iZKdu0j+6EN0eslNCiHObgeHDK1zWdDo87GkpAIQfsP1GEeOcCwzjhxJ2PUzyVv2Pif+7w4Chwwh+b2ljuVHLpqINTfXaXt9D+x3K7bw8HCmTZvGe++9x0UXqb6Sy5cvJyoqigkTJqDX6xk0aJBj/aeeeopPP/2UL774olZSyV3vvfcepaWlvPPOOxgrG3G//PLLTJ8+nX/84x/4+vqSn5/PpZdeSvfu3dXj69vXcf+0tDTuv/9++vTpA0DPnj0btd9nnnmGG2+8kXvuucdxv3/+859ccMEFvPrqqwQEBAAwevRoHnzwQQB69erFhg0beOGFF5g0aRKrV69m9+7dpKSk0Llypr133nmH/v37s3XrVoYNG8aCBQuYOXMmTzzxhGPf1Y8lwMUXX8ydd94JwAMPPMALL7zAjz/+SO/evd06lkI0WfVJmfQ+kHCu92KpR/nRo1jz8tCbTN4ORQghhOgw2mS25quvvqJ379707NmT119/3dvhNJveaCT/iy8p3buXkh07vR2OEEK0bTqd+vGiG2+8kY8//piysjIAli5dysyZM9Hr9RQVFXHffffRt29fwsLCMJlM7N+/3yOVZPv372fQoEGOBBmoxJTNZuPgwYNEREQwe/ZspkyZwvTp03nxxRfJyMhwrDtv3jxuu+02Jk6cyN///neOHj3qaje17Ny5k7feeguTyeT4mTJlCjabjZSUFMd6o0aNcrrfqFGj2L9/vyP2zp07OxJkAP369SMsLMyxzo4dOxyJx7qcc845jss6nY64uDiysrIa9TiE8IjqwxiDIrz+98gVa34+1rw8APy6dPFuMEIIIUQH0uYqySoqKpg3bx4//vgjoaGhDB06lCuvvJLIyEhvh9Zkej8/TBdOoOCLLylc8R1BQ9rmN5JCCNFaev/6S90LDQZKtm8nbfbN5L63jODJkzGOHAngNAyz87//RdCIEU537bHaM73Bpk+fjqZpfP311wwbNoz169fzwgsvAHDfffexatUqFi5cSI8ePQgMDOSaa66hvLzcI/tuyJtvvsndd9/Nd999xwcffMAjjzzCqlWrGDlyJPPnz+eGG27g66+/5ttvv+Xxxx/n/fffr9X3q6aioiL++Mc/cvfdd9da1sWDJ+CBgYENrlNzeKhOp6vVH06IltX2e32VVyblfaKj0QcFeTkaIYQQouNoc5VkW7ZsoX///iQmJmIymZg2bRorV670dli16d1rxB8ydSoABStWosmHfSHEWU4fFFT3j7+/Y1glVM1maS0yk/HXhwE1DNN0wQXoK4cB1rfdpggICOCqq65i6dKlLFu2jN69ezNkyBAANmzYwOzZs7nyyisZOHAgcXFxpKamNv1gVNO3b1927tzp1DB/w4YN6PV6p+GG5557Lg899BA///wzAwYM4L333nMs69WrF3/+859ZuXIlV111FW+++WaD+x0yZAj79u2jR48etX78/Pwc623atMnpfps2bXIM9+zbty/Hjx/n+PHjjuX79u0jLy+Pfv36AapKbPXq1W4eFSFaWTtoiF+eqibr8EtK8nIkQgghRMfi8STZunXrmD59OgkJCeh0Oj777LNa6yxZsoTk5GQCAgIYMWIEW7ZscSxLT08nMTHRcT0xMZGTJ096Oszmi3KvN4px9Gj0RiMVp05RumtXCwUlhBAdR8y99zlms8xcuJCshc85ZruMuffeFt//jTfeyNdff80bb7zBjTfe6Li9Z8+efPLJJ+zYsYOdO3dyww03eKzS6cYbbyQgIIBZs2axZ88efvzxR+666y5+//vfExsbS0pKCg899BAbN27k2LFjrFy5ksOHD9O3b19KSkqYO3cua9as4dixY2zYsIGtW7c69SyrywMPPMDPP//M3Llz2bFjB4cPH+bzzz+v1WNtw4YNPPvssxw6dIglS5bw0Ucf8ac//QmAiRMnMnDgQG688UZ+/fVXtmzZwk033cQFF1zAeeedB8Djjz/OsmXLePzxx9m/fz+7d+/mH//4h0eOnRAto+0NtQQor5zR1jdZkmRCCCGEJ3k8SWY2mxk0aFCtaeHtPvjgA+bNm8fjjz/Or7/+yqBBg5gyZUr76zdS1nDD/ur0/v6YJkwAVDWZEEKI+lWfzTJv2fvkvf8BAPEL/obe2PKzzV144YVERERw8OBBbrjhBsftzz//POHh4Zx//vlMnz6dKVOmOKrMmisoKIgVK1Zw5swZhg0bxjXXXMNFF13Eyy+/7Fh+4MABrr76anr16sXtt9/OnDlz+OMf/4jBYCAnJ4ebbrqJXr16cd111zFt2jSnJvl1Oeecc1i7di2HDh1i7NixnHvuuTz22GMkJCQ4rXfvvfeybds2zj33XP72t7/x/PPPM2XKFEANi/z8888JDw9n3LhxTJw4kW7duvHBBx847j9+/Hg++ugjvvjiCwYPHsyFF17o9EWZEKJxytMqK8m6SJJMCCGE8CSdprVcTblOp+PTTz/liiuucNw2YsQIhg0b5vjAb7PZ6Ny5M3fddRcPPvggP//8M8899xyffvopAPfccw/Dhw93OkGprqyszNFYGaCgoIDOnTuTn59PSEhISz00KDwFpw+ry0nnN2r4ZcGqVZy86258ExLovvp7dG2wEawQQnhKaWkpKSkpdO3a1TE7YlNkzJ/vSJCFXT+T+Mcf91SIwg3Jycncc889jhkw26P6XpMFBQWEhoa2/OcH0Syt8jylrK+6bIqF6F4ts59myH55CUVr1xJ15x0EV34JK4QQQgjX3Pn80Ko9ycrLy/nll1+YOHFiVQB6PRMnTmTjxo0ADB8+nD179nDy5EmKior49ttvHd9Su/LMM88QGhrq+Kk+q1aLMsVWXS5qXBWcaexY/Hv2IHjKFLTS0hYKTAghOpaY++7HNyEB38REYu+7z9vhCCHOJr4NTzbhDdFz59D1ow8lQSaEEEJ4WKsmyU6fPo3VaiU2Ntbp9tjYWE6dOgWAj48PixYtYsKECQwePJh777233pktH3roIfLz8x0/1RsGt6jqVWA5Rxp1F31AAN2+/JLYB/6CvhEzfAkhhKgadtlawyw9aenSpZhMJpc//fv3b7U4pk2bVmccTz/9dKvFIUS7E5LY8DpCCCGE6DB8vB2AK5dddhmXXXZZo9b19/fH39+/hSMSQgjhTcaRI70dQpNcdtlljBgxwuUyX1/fVovj9ddfp6SkxOWyiIiIRm3DUzN4CtGu6NvcRPBo5eWg16PzaZMf44UQQoh2rVX/d42KisJgMJCZmel0e2ZmJnFxca0ZildpFgvmzVvwS+qCX2sNDxVCCNHqgoODCQ4O9nYYTrNGCyHat4IVK0j/68OETJ1K4nPPejscIYQQokNp1a/H/Pz8GDp0KKtXr3bcZrPZWL16NaNGjWrNULwq45FHOX7bbeR9tNzboQghRItrwflhhHCLvBZFR1B+LA0sFnT+ft4ORQghhOhwPJ4kKyoqYseOHezYsQOAlJQUduzYQVpaGgDz5s3jtdde4+2332b//v3ccccdmM1mbr75Zk+H0vLCmzbttumCcQAUrPhOPrALITos+3DC4uJiL0cihFJeXg6AwdDwjNRCtFXlx44B4NelaZ9DhRBCCFE3jw+33LZtGxOqzbQzb948AGbNmsVbb73FjBkzyM7O5rHHHuPUqVMMHjyY7777rlYzf3ctWbKEJUuWYLVam7Udt5hiIVd9UMFmBX3jPnSbLrgAnb8/lmNplB08SECfPi0YpBBCeIfBYCAsLIysLDUDcFBQELrqk54I0YpsNhvZ2dkEBQXhI72cRDtWnlaZJEuSJJkQQgjhaR7/lDh+/PgGq6Pmzp3L3LlzPbrfOXPmMGfOHAoKCggNDfXotuukq1aIl3+i0ZVleqMR07ixFK76noIVKyRJJoTosOz9Ju2JMiG8Sa/X06VLF0nWinbNklqZJEuWJJkQQgjhafJVanNUT5Llpbk1/DJ48hQKV31P4XcriL77bvnALoTokHQ6HfHx8cTExGCxWLwdjjjL+fn5oW+DsxWKhnllxEAbZM3Lw5qfDyCTPwkhhBAtQJJkzaFr+gdt04Tx6Hx9KU9JoezwYQJ69fJcXEII0cYYDAbpAyWEaDKvjBhog8ore/z6xMSgDwrycjRCCCFExyNfpzZHM6q/DCYTxjFjADD/tMFTEQkhhBBCiA5K5+9PyMUXYxo/3tuhCCGEEB2SVJJ5UfRdc4n+09349+7t7VCEEEIIIUR1zRgx0FICevcm8flF3g5DCCGE6LA6TJKsPfaqCOjXz9shCCGEEEKI6vyDoawQIrp5OxIhhBBCtLK29xVZE82ZM4d9+/axdetWb4fSJA3NCCqEEEIIIVqB3uD8uw2pyM5Ga0dfCAshhBDtTYdJknmNj3/V5SYkuizp6Zy8/y8c+93vPRiUEEIIIYToaH67dDoHB59L2W+/eTsUIYQQokOSJFlzxfStupyx0+27641GCr79lpJffqHstxQPBiaEEEIIIZqu6RM0tQRrXh7W/Hw0iwXfuDhvhyOEEEJ0SJIka7ZqH6DKCt2uJjOEhmIcNQqAwpUrPBmYEEIIIYRwVxttgVF+7BgAPjEx6IOCvByNEEII0TFJkszTbO73iQiZOgWAghUrPR2NEEIIIYToAMrT0gDwS0ryciRCCCFExyVJMk/TbG7fxXThhWAwULZ/v+NbQiGEEEII4QWl+eq31rYa5Jenqs+IfsmSJBNCCCFaiiTJmsvf5Hy95Izbm/AJD8c4YgQg1WRCCCGEEG1Csfuf6VqS/YtU3y5dvByJEEII0XF1mCTZkiVL6NevH8OGDfNuIKcPN+luwVPUkMvCFdKXTAghhBDC63Rtq3G/PUkmwy2FEEKIltNhkmRz5sxh3759bN261duhNEnwpIn49+mD6cIJaDb3h2wKIYQQQggP0rWtj8nBEycSPHUq/j17ejsUIYQQosPy8XYAHVLxGQiKcOsuPhERdPvs0xYKSAghhBBCuKdtVZJF/fF2b4cghBBCdHht6yuy9qpmOX55kXfiEEIIIYQQTadpVZfb2HBLIYQQQrQ8SZJ5QqcafdCqf8Byk624mILvVmDJzGpmUEIIIYQQoiOoOH0aS0aGtOQQQgghWpgkyTzBx9/5elFmkzd14q67OXnPPRR8800zgxJCCCGEEG5pxhedLSn3vWUcmXAhp5540tuhCCGEEB2aJMk8pXpJfkVZkzdjmjABgMLvvmtuREIIIYQQwi1tM0lWnpYGgF/nTl6ORAghhOjYJEnmKR7qYRE8aRLodJTs3IklI8MDgQkhhBBCCPe1nZ5k5ceOAeDbpYuXIxFCCCE6NkmStQRNUzNcNoFvbAyBQ4YAULhypSejEkIIIYQQ9Wmjwy0dlWRJyd4NRAghhOjgOkySbMmSJfTr149hw4Y1vHJryNwLNmuT7hoyZQoABSskSSaEEEII4RWhbWNoY0VuLrb8fAD8unT2cjRCCCFEx9ZhkmRz5sxh3759bN261TsBxPStfZv5dJM2FTx5EgAlv/6KJbPpkwAIIYQQQgh3VKskqzkxk5dYKoda+sTGog8M9HI0QgghRMfWYZJkXucfXPu204egJNftTfnGxRE4eDAAxZs3NzMwIYQQQgjhtmb0mPWkqqGWSV6ORAghhOj4fLwdQMdRxwepU3ug61i3txb78MMYwkLx6yxl9UIIIYQQraIN9iTz69adyD/chm9iordDEUIIITo8SZK1UYEDB3g7BCGEEEKIs1MbqSIDCBzQn8AB/b0dhhBCCHFWkOGW7YDWBr/VFEIIIYRoLa03QZN85hJCCCHOZpIkaw3WiibdrezwYY7fOYfjf/yjhwMSQgghhGg/Wn+CprZRSaZpGsXbtmHJzJIvTYUQQohWIMMtW8PpgxDrfpm8LjCQoh9+AL2eipwcfCIjWyA4IYQQQggBtLmeZNa8PI797vcA9N7+KzqZ3VIIIYRoUVJJ1hqKzzTpbn6dOhEwYADYbBSu+t7DQQkhhBBCCGeVSbI20pPMcuwYAD6xseglQSaEEEK0OEmSeYqPX/3LT/wCZYVubzZ4ymQACleuaEpUQgghhBDCXTartyMAoDwtDQC/pCQvRyKEEEKcHSRJ5km+9XzDZymGzL1ubzJkyhQAzJu3UJGb29TIhBBCCCFEQ8ynvR2Bk/JUVUkmSTIhhBCidXSYJFnrzXpUj9j+EBhe9/ImfCvp16UL/v36gtVK4fcy5FIIIYQQosWUNK1FRkspP2ZPknXxciRCCCHE2aHDJMlaf9YjF3wDIapXPSs0rRlsyGRVTVb4nQy5FEIIIYQ4W9iHW/pKJZkQQgjRKjpMkqzNaGyjV0sJnNgGhacaXDVk6hQCzz0X04QJzQxOCCGEEEK0B5qmVVWSdZEkmRBCCNEafLwdQMdTT5Ks+rTiOUdVouz0YQiOq3eLfsnJJC97z0PxCSGEEEKINs9mI2bePMrT0vDr0tnb0QghhBBnBUmSeVpjK8k0W9Vlmw30UtQnhBBCCCEUncFA+MwZ3g5DCCGEOKtIZsbTdA0c0qLs2rel/dyopv4VubnkLV+ONT+/icEJIYQQQoi6NfLLTiGEEEJ0SFJJ5mkNVZJlHwBTtPN6mgal+RAUUe9d026+hbIDB8DgQ9iVVzQ/ViGEEEIIUcXPqD6TtQGl+/djKy7Gv0cPDKGh3g5HCCGEOCtIJVlL6HRe/ctdVY1pGqSsg8/nwnsz1e+UdU59zIInTgSgcIXMcimEEEII4XH+Ieq3wde7cQA5r/+XYzf+jrzly70dihBCCHHWkEqyluAbWP/yYz87Xy8rgmUz4MRW59u3vwvJY2HGuxAYTsiUyZx++WXMGzZgLSzEEBzsvL7VAujAIE+rEEIIIUST+QZ5OwLHzJa+Xbp4ORIhhBDi7CGVZN6mabBmgUqQ+QTC0Jth+ovqt08gpK6HD34PmoZ/z574de+OZrFQtGaN83ZsVkjbBGkbvfIwhBBCCCGEZ2ia5kiS+SUleTkaIYQQ4uwhSbKW4m9q3Hqndqsfn0C4bRVMXwxDZ6vft62qSpSl/gRAyJTJABR8V2PIpaXEY6ELIYQQQgjvseblYSssBMBPKsmEEEKIViNJspYS2aNx66WsUb8HzYS4gc7L4gbCoMqpv3d9AEDwheMAMK9fj7XI7IFAhRBCCCGEk4YmYmph5ampAPjExaEPCPBqLEIIIcTZpMMkyZYsWUK/fv0YNmyYt0NRdI08tCV56nfCYNfL4ytvN58GwD+oAL/EWLSKCkp37WxOhEIIIYQQwonW8CqtwJKWBshQSyGEEKK1dZgk2Zw5c9i3bx9bt25teOVW0chvIAPD1O/0Ha6XZ1TeboxSW9XpSLj/Fnp+uQzj+ec3J0AhhBBCCNEGST8yIYQQwjtkGsSW0tgy/a7j4fBK2Pk+DLvVecjlqd2wUw2z5JwZjpsDe3eF8HCPhSqEEEIIIdqO4ClTMERF4d+tm7dDEUIIIc4qkiRrMY1MksUNVD+ndsPrk1QPsvjBqoJs5wdQUQIJ50LyGOf75aZCcByazoBOX6MgUNO83ktDCCGEEEI0TUDv3gT07u3tMIQQQoizjiTJWkpjk1Q6HYx/GNYsUImyX96qvU5BBhRmUD3xVrz/KNl//Sc+8V1IfH6RR0IWQgghhBDQ6C87hRBCCNGhSJKsLfA3weSnIXM3ZO2H7IOqV1m38bDtLcg5BEuvgwsfBb8gAHQGA8Xb96A7cBRbSQk1i8lcslqgtACCIqTSTAghhBCiJs37jfutRUUUrV6NX1ISgYMHezscIYQQ4qzSYRr3tz1uJqF0Oog7By57CS56DM6/G7qMgt8tB2OMSqCt/TvYKgAI6JmEb1w0WkkJRevWN24f6Tsgax8UnHQvNiGEEEII0SrKDh8m/YEHOfHned4ORQghhDjrSJKspfj4gTHa/ftV/waz3AwBIXDDB+AbBOm/wqZXQNPQ6XQEXzASgMIVK+reRnUVpep3cY77cQkhhBBCiBYnM1sKIYQQ3iNJspYU2cP9+xzb4Hw9c69q5H/ZP0GnVzNh7v4IgJDxowAoXLMGW2lpM4MVQgghhBDeZklLA8CvSxcvRyKEEEKcfSRJ1pIMHmr5duY3MMXB8D+q69vfgd9+JKB3N3wS4tGKizH/vMn1fTUN8tKgNN8zsQghhBBCdHRe7N1aniqVZEIIIYS3SJKspZlimnd/Tauc2RLocwn0v0pd3vAiuowdhEyeAkDBqtWu72/OhtxjkLGr6rZyc/NiEkIIIYQQLaJquKVUkgkhhBCtTZJk7c3Q2ZA0RjXwX/FXQkb2wzhuLKZxY6rWObYBbDZ12VJcexs2a6uEKoQQQgjhCUuWLKFfv34MGzashffk3dktNU2j3D7cUirJhBBCiFYnSbKWZor17PZ0ehg7D6L7QnkRgdv/SpdFTxE6bYrzeuYsz+63OpsNyl0k34QQQgghWsCcOXPYt28fW7du9XYoLcqam4utsBAA386dvRyNEEIIcfaRJFlLCwzz/DYNfnDhIxDaCfLT4L3raietGqoWq2sGzMbI2A4nf4GS3KZvQwghhBBCONEHBdH5tf8Q/7en0AcEeDscIYQQ4qwjSbL2KiAUpv4DgiIhYweW/97AmU9XYSu3VK5QmQRzNxmmaZB3HEoL6l7HnpArynY7bCGEEEKIts87jfv1AQGYxo4l7JprvLJ/IYQQ4mwnSbL2LCAUpj2Lpvcj9e00Mv/zEcU79jfuvnnHXN9elAm5qZCx02NhCiGEEEIIIYQQQrR1kiRr70yx6MbdS3CnUgAKvvpU3d5QBVnecddDMmXmSyGEEEKcrZrTjsIDCr5bQf6XX2LJbMHeskIIIYSoU4dJkrXerEdNoGvhw5w0muCJEwAo3Hkc7ei6lt2fEEIIIYTwuJzXXiP9/r9Qume3t0MRQgghzkodJknWpmc96jwcOp3XorsIuvhmDEYfbOV6zMtfhvTtaoG13HM7KS2A/JN1Ly83Q0WZ5/YnhBBCCHGW0DSN8rQ0APy6dPFyNEIIIcTZqcMkydo0gy/4BrboLnQ+BoIvOB+AwjRf+OIuyDkKRR4s18/YCWd+c72sogxO/grHt7i3TU2TIZ5CCCGEaFt0rd+435qbi62wEHQ6fCVJJoQQQniFJMk6kJCxqlqtMN2IVpwH71wBpfmts/OmJrpOH1LJtfwTno1HCCGEEKIdKU9Vkyr5xMWh9/f3cjRCCCHE2UmSZB1I0MCeGEKDsVl9KKtIgPw0+OHJpg+BbKh5rWaDwlOut2+zqVkyywrr34a90k2SZEIIIYTwOu817i9PU0kyv6Qkr8UghBBCnO0kSdaB6AwGOj8xl57LFhHwu3+AnwmyD8JPi1RCqzEK0qsup21U96+LORtOH4b0HbWX5aepGTRdLTubVJR5faYsIYQQQrR95ccqk2Qy1FIIIYTwGkmSdTCBvbtiMAZCYDhMeAT0PnDsZ9j2hou1a/TbKMl1vm6zVlV6WS1179RaXrt3R1OGX5bkwukjar+ObVfUf5/iM5C2Sf1ua0rzVY+2jJ3ejkQIIYQQbZzlWGXTfqkkE0IIIbxGkmQdmBbdF0b/WV3Z9xns/7LmGs5XLaWuN2S1qERUSzu1BwozIE99SCTrgKpmq2/IZuZeFV/mXvf2pWmQsUvto/ptOUfVENLmqihT24eGh5wKIURjFKTDyV+gwoOzFgsh2ozoefPo9K9XCZ400duhCCGEEGctH28HIDyvaOtust7+jMA+3YifeyOYM+HXd2Dra2CMgS4j3NtgaYH7QTRmiKGlxPXtFZXJOnO2+p1/EmL6uLf/inI1E2dwHASG1V5ebq42qUHltktyq4abBse5t7+aTv7SvPt7Wkku6AwQEOLtSERjWSvAIH+iRTU5R9Xv3FSI7uXVUITo+Fp/dku/Ton4dUps9f0KIYQQoopUknVEej1lR49T+PN2NKsNBlwLPaeovmTrnlUzSrYFp/Y0br2mTMN+5jeVZDu1u/H3sTUwtNMd1YeMNkVpvucq0CrK1bGWYZ/tR/ZBVUXZWrPTeoLNqoY92xrZ/1A0g/Q5FKLFSB9RIYQQ4qwmSbIOyDioN3pTENbcAor3HlZJppF3QuJQsJbB6ifdG1KYf7xlAq2oY3hnTZoNirJV5Vl9vdGasu22yFqhhmqm7/DMh3VrtdlHZRbRtk/T2uesr9kH1bDnnCPejkQIIdqdspQUsl95hcIff/R2KEIIIcRZTZJkHZDOx4fgUYMBKPzpV3Wj3gAXPAAR3aA0D1bPV4mY/JMNb7A1emrVN/um+TRkH4AT21RvtBb7lrcFh1YUZTe+wsbWyERgU5xJablte4umdZxv/suK4NgGb0fRNMU56ndRpnfjEEKIdqhkx05O//Mlzrz9jrdDEUIIIc5qkiTroELGDAWgcMOvaPbkjG8QXPQ4BEWpCpUv5qrqj9agaXVPDADuDU/0dEKkuUMjGyP7AOSnuX+/jpL8aUknf4XjmzvGML8zv8lzLoQQZ6HytGOAzGwphBBCeJskyVpTSEKr7cp4bl/0xkAqzuRTsu9o1YKgSJg4XyXMMvfAhsWQtllVazVHYT3VIynrIfUnOLEVSvKatx/A4/14Gjs8LPeYeixNnVnOXmkjPMtSrIbhVtQxEYQQQgjhrqb0Q20GyzFJkgkhhBBtgSTJWlNEN0gYDMHxLb4rna8PwSMHA1BgH3JpF54ME/6qZjtMWQtbX4es/c3boX0mSoc6Eln2oVhlRU3fl6crbez9n5xuy1YzYFaXV1kJdnyzZ/dfH1cf0m1WNVulVBw5k+MhGstSqoZv22ezFUIIB+/8X1Keak+SdfHK/oUQQgihSJKsNel04B/carsLmTCckHHnYTqvf+2F8YPh/LvV5d0fwqHvPLfjxiTAKsoaXqdOLfQBtnpCKvuAGsZXF2uFalJeVDM52AyFmZC+veFjk31AzVaZ2wH7iwlnkvhrGbmpaiKQnKMNriqEEC1N0zTKpZJMCCGEaBN8vB2AaDmmof0xDXWRILPrcREUnYKdy2DTKxB3jmeGhKZvb/426pN9AOIGtuw+GpJ3DIrPqB9TtGe2efqQ+l1f8stmU/sEKMhQ1YlCCPfUN1GIEEK0MuuZM9jMZtDp8O3c2dvhCCGEEGc1qSQ72w26AbpfpE4av7pHNQ5vSfYqs1rDM93Q1L5mTanKyT7oul+braLu+1jrmp2ykf1NqjegrxnzGS9XvmTuhawD3o1BtKyKMjXzrVl66AkhRGuwV5H5xMeh9/f3cjRCCCHE2U2SZGeBsmPp5CxfieYqSaTTwai5qorMUgzfz29eAqshlmIoK2zZfbhSfAbSNro/PLIoy3W/Nld9zOxKC9zbhzsKT7XcthtiKVHH0ZztuZkkLaWQvsOzw1ZF8+QcgdJ8yNrn7UiEEMKLWq9xf+CAAXT7+isSFy5qtX0KIYQQwjVJknlDQEir7cpWbiH1z8+Q9d/llB5Kdb2SwVc18g/rAiVnVKKsZtN6T2owidTED6bWeqq7MveqhvfZ9VVB1bNf+xDHZmlCJVtHGRZWUeY6sXbmqEqa1vu81EN6dnme1Y3ZWyvKIP+kem8JIYRoEp2fH/7duxM05FxvhyKEEEKc9SRJ5g3GaIjuA53Oa/Fd6f18MQ0/B4CCn37BvOMA5h0uEhJ+JrhoPgRGqH5ba56pf0hhs7RQYuPUzsav6+7U7s2aaKBSebGqmLKUNrBiteOTd6z5+/W2cjMc3wInf6m9rM6hqaJdSN+hhmjXbICfc1RVHgohRHsjX74IIYQQZzVJknmDTqeavfsGtsrugscMAaBg7TbSX3ibjMVvYytxkagxxcBFj4NPAGTsgJ9fbpkPiy31AbS8uHHrZR9U1UvuaMzw0PJiyNwH5fXM7pl9AE5srX871ZMLHqlga4ClRM3211IJq+LK3lYVDSUHRbtjrzorzXO+vSBd9TUTQgjRoOyXXub0v/6NJbOeVg5CCCGEaBWSJPM2fctPMGo6bwA6fz8qss9QkZWDJTOHzP9+7HrlyO5wwQOg08PR72HX+54PKDe1/uXmbPeTWA2pXjlWlAWnD9e/Tk2l+Q3vI3OvSgjlpbkfX13crXhrivQdkHfc9TFpSdYKzz/PoIZ1Nlit116044oGd4ZtCiHEWUrTNM689RbZixdjK2qB/xOFEEII4RZJknlbwrkQngx+QS22C32APwE9k5xuy/t6rethlwCdhsGIO9TlHUvh6OoWi61OGW4MnfS27EOqL5M3K6XKi5uWGKoorxpWW+bGhANaE3tQZR+CU3vU5dyUpm2jIRnbVbWeJyZQ0LS2kXCz2aAk13MTJniDDGHynOKcxiXuQYY0C9FUrfElFWDNycFmNoNOh2+nTq2yTyGEEELUrcMkyZYsWUK/fv0YNmyYt0Nxj28AhHVu0Yoya3Ep5cczAAi7fiZhM2cA1D3sEqD3NBhwjbq84Z+tn7Rq6IS6rkbh3kgiFGWqvkzeYq1Q/b4aGsbpyvHNrm9v6Pg3ZV+gjlVJLpQVtUwVGVQNu/XEDKqnD6vH6s1ZRUFNcHBqD+S0crWfp+SfgLRNLTshyNnEZm3ccNaSPHXcsw+1eEgtpjCz5f5WCNEGlKep6nPf+Hj0/v5ejkYIIYQQHSZJNmfOHPbt28fWrU08eW+LQhM9spmsNz7Gml+Eb2IisffdR8x99+ObkIAlM4esNz6p+45DboLkcapq6MenIbcNNZGva9bHtJ/rqJxovancW53VA5MKVFeQDqk/qWRWXc6G2QytFSqpB2o4qjfZk3RFjexXU1oAZ1KaljRuiYqvMymqYjHniOe3LeqWX/m6tb+O25viM3D6kBoSLkSrad2q1/JU9dnKN6lLq+5XCCGEEK51mCRZu+fjool/RLdmb9a84wB5X68FIH7BAvRGIwaTkfinFwCQ+9Wauodd6vQw5s8Q0x8sZlg9v3UayTeWq5N5TatdQdSYhE57HQpWbnaesMATj8M+U2F7rj7xBKcqu0YeV5u19V9LrhJhGTtV9Va+l5N7bVUrDaOql6ZB1v629eVDW2Np5GQsQrRj5Wnqb4BfUlIDawohhBCiNUiSrK2I6ArBcWDw9dgmrcWlZLzwNgDhN1yPceQIxzLjyJGEXT8TaGDYpcEXLnwEQjqp5NPqJ5xnX/QaXd2NwWvOcnns57orz+xSf/LM8LxWpcHJX9WMmS21/bauJRNSDb1marJa1Gst/deWiccV82k4tkFV/7nSJt6rwqWSXPX8eXKiDyFEu1N+rDJJ1kWSZEIIIURbIEmytsLgC1E9wT/EY5vU6WjUKENbWTmatZ5qK/9gmDgfAkJVb6S1//D+cLucIypB5EphRtO22dihbJ7giR47rhJEFWXto7l7RVnj+lPlpkJBE5/P1laSp37XTNI2lqaBzc0m6/YEqb36ryb7pAyieSwlcHxr3cnIpnA3CSuE6JAsx1Si3C9ZkmRCCCFEWyBJsg5MHxhA/D2zAMh9bxnmTZscy8ybNpG37H0ArHmFHL39cTJf+4jSlBOuNxYcBxc+BgZ/OLkNNr/q3SGK5uz2nQBI39EyTcxPbFXDBE/84n4frQZnwfPgELWsfQ2vU25Wj+Fs6WN1alfTE2x1qa+vnGi8M7+p2WvrSka2lMJTqvF+U5PqNf9GWy0q6Wxtx387hWg1rTMsO+m9pXT78guChg9vlf0JIYQQon6SJOvgjIP7EHbJBQBkPPwINrMZa5GZjL8+DIB/10QMocFYcws488kqUu58kjOfr3a9sejeMO5+QAeHvoM9H7fSo+igWiqBYatQvXxyU1tm+62lKdWK7aGKri6lBbVva2u98goy1EyqljqGZ7dHZUWqf1t9x9pbz8Ppwyqx5an+gJl7VNL59Fneb1CI+rTy+10fEIB/z54YTKZW3a8QQgghXPPxdgCi5cXccjXmrXuwnDxJ5sKFoGlY0tPxjY0kedED6Hx9KNq2h/zvN1K0eTem8wY47lty4DcsOfkEDx+IztcHuoyE4X+ALf+BX98CUzR0vcB7D649O5OihrD6B3s7ksZpjRNrmw309tx9E05U6uorZ7UAOjB0sD95nj6ZM+fUX+For+rLTYGYvp7dt7ekb1e/dXoISfBuLHXy0PNcVqR+F+d4ZntCtKIlS5awZMkSrPW1hxBCCCGEaKYOdsYoXDEEBRD/51mkPfS8Y4glQPw9s9AHBgAQPHIwwSMHYzUXYzAGOdbJWb6Cwg3bMYQYCRk/gtCJowjoMx1dYSbs/xx+egGCIiF2QK39ikbIPwkxfbwdReOU5rfs9s/8po5HwuCmJw41FydPNqsasgbQdWzjtlPXcDRNg8y94BsIkd2bFmNb1phhsNAx+2mZT7fhJJkQYs6cOcyZM4eCggJCQ0O9HY5HFK3/iYIV32EaPZqQadO8HY4QQgghkOGWbV/cQI9sxji4D2EXV1V8hV1yAcbBtZMz1RNkAH6d4/GJCMVaYCb3ix9IvXsBKXc8Qc6xTlgihqmhfT/8TfWOytgFP/8TVj+pfmfs8t4wpYoy7+zX0ywlkLJeVYB441iW5KoJDTw13Ks++SfV79xjnt1uhZtDA0tyIW2j62Wl+Wq5vYG7zaaqr6THU8uxlELxmZbfT0sngb1B1zo9lYQQTVO8bRv5yz/GvHmzt0MRQgghRCWpJGtrap7UBIap6peU9c3edMytV2Petgd0EHvr1Y27z6wriP7ddMzb95O/6mcKN+6g7Fg6WW98SkGPLnSd2htOH4Sv7q7d+P3wSpXkG/8w+Ldyrw1reevur6nM2WDtrmY3rU/6dlW95K7mzEJqtcCpPY1btyRPPZbwrp4b0lgzKahp7p30N3VihHofc42Y8o6pflb+Jgjt3LT95Z/ArQbRpQUQ4LlZcFuUtcJ1dZ87TmxVv+MGqr+HovHaWk87t0mST3hRKySZy9PUl0J+Scktvi8hhBBCNI4kydqLLqNUw+xmJH/swy4BxzDLxtAZDJjOG4DpvAFYi4opWLeN/NUbCRl3Hlw4FD6+BWuphezdEYRMuoDAURegy9gJO9+HU7thzQKY/LRUNdQlc68aYtgQS4n7287aD+GtMK38qd2VF3QQ1aOZG9NUtVb1ZJWlFDJ2gG8QxJ/TuM2U5qvZAasP3XQ30dYQew80e68nV3KOqrhD4iEvTVXKdRmpEqMV5ao3nTsydjZ+2GhLOn244XVqVuSVFjT9OSgrhNI89bzGDqzWu66VnfwF4ga13f52ecdVpWP8IG9HIkQ71XrJ5fJj9iRZl1bbpxBCCCHq10Y/5YtaDD4eqY5yNcTSrTBMQYRfPI7wi8ehaZpKjljLKTwZSu6hAHIPbcbv60xCr7iC0MuW4fvF9WqdzN0Q18jkxtmmrLDltl2SC2Gt+OG7ogmJvJpKC8BSIwFjryay5quhtOVm8DOCvoE/YQUnIdqLPd9K8qqGZobEVw0lPbEVks5vnd5eFWWqKX1D1YruKMltepVSYUbTe3/lHVe/zdkQHNu0bTRXebF6XbVG8tldNmvVrLa5biZfhRCtStM0LMfSAPBLaoN/T4QQQoizlCTJRJPpdDpIWQOA//CLCI2NoWDlKspTU8levJjsF3UEde9OWORRgg/+gF6SZN5x5mjj121KtZqnNZQ4Or6l6nLS+Q1vrzlDTqsrza9KQLhSlOW8z9L8uh+Lp2JqDPvx8mT1Wa2hsG4k+5o6DLap+2sRrTyMsbEJSfvso0KINs+ak4PNbAadDt9OnbwdjhBCCCEqSeN+0TwleQAEjriAhH/8g57r1xO/YAFBw4aBplF85Azpm8OwnsnxyO7MOw5g3nHAI9s6a9Q3FLCm7HZ8bDWr6yRffYktd2Tsqv9YFld7jWfuVT/uDqVsSe68DtxVsx9hayjJc93Qv6IMCjJaNxHZVrTGBAdCCI8oT1NVZL7x8ej9/b0cjRBCCCHsJEnW1tiHxjV1OFJrszfSTt8BgMFkJOzqq0h69x26f7+KqMndCetWjG/hTvjxaTj6IxnPv0H2/76gPCPbrV1Zi0vJeOFtMha/ja3EzRkLReO0ZCLFHU2pFMrYVTW00bEdnGctbK0JHez7rG9mTZutEZU/HqxYSt/uuW15XeVQ78y9tRN06dvVcW1qcvTMb2obthasVtO0xvV0E0J0WJaMDAD8kmWopRBCCNGWyHDLtsbPCEmjvdeU2l1dx6tZLHe+D8NuVTPQVfLzySU6ZhtElKhz/bSfsRzYRN73saDpOL30K4L6JRM6eRzBY8/DEFT/ZAJZ//0YS5aq1sn878fEz72x5R5Xa2vN5FRpPgSEtt7+mqKlKpOOb4HgOIjq2TLbd0f2AdXbSzSPrcK535r9tVNZ5eq8rq3hSQPyT6rf5mwwxagKQU8nV0tyVdye1tqTo5QVgY+/Z/vdCXGWCL3kEoInTMBa2Ea+nBJCCCEEIEmytqm9JMhAJcXiBqqqjtcnwaAZED9YzUS48wPVyD12IJx3CxzfhOG3jSSMyCY/JRBzpj/F+1Ip3pfKqSX/I3h4PyKunU5g7661dmPevp+8b9Y6rud9vZaQMUObPRFBm9GaVT4Zu6DTMPBt/AyndSrJrX9Ym83q3KurtVmKa99WeMozSbLmDm1rTv+3nKOuH1tTlZvB0MrDffJPqC8FAsNbZ382GxzfDD5+arbRBmlqNtK8tBaIpREJsppVkW2F+bSabMQYXfV3qy3MtiqEpzR1UpIm0AcFoQ9qzN8jIYQQQrQWSZK1J8ZoVd3Qluh0MP5hWLNAJcp+ect5edxAtdzfBFE90Z/7e0InpROatgnLvp/J/+UE+SmBlBf6UrBhD0GGvQROHgVdRqJF9kSnN6hhlovfASDs+pmgaeS9/wEZi9+m26uPow/0QLLnbJN9AEI7N387p/bUv/zMby07GUD1oZQtydVJU1mBd/YLnk2glOSp967Bz3PbbHCfuVX92lorwVJepJJT5RWNTJIBxadbNqb65Lgx4UZrytqvfrfkrLxCCCGEEEJ4iSTJ2pPQxLaXJAOVAJv8NGTuht/WqJPuwDDoNl5VkdUcAhSSAAOuwnfAVURNzyUybROlW9aRv/U4IVH5sGc57FlObko0BSdCwRiKJSsH38REYu+7D00D87r1WNLTyXrjE+Lm3ND6j7m9KyuErH3N20Zjej619Os1c2/Lbt/bygpbvhm7fcKB1urXBqq5vnBPQ0OyzaebNky5+Izq4RbV072qvtaYrKG1h48K0Uo0TeP4//0fvrFxxNw7D0NoG2+BIIQQQpxFJEnWnvgHQ+JQ1QPm2M/ejsaZTgdx56gfdwSGo+s9jcDe0wicUQzpv8CxjXByK/mHNUrPFANqWFn8ggXojUZ1+ekFpM2+mdyv1hA8ekjHGXbZnuQd93YEnteKw2wa3ZS/JWYctVpAZ/DO0O6m9uKqL2GSl6YqFqN6NW3b7UHGjvqX2yu83GVPNJ/a47qqrzATbBYI7eR8uzfyV0VZ6v+/tt5TUXQMLZiktebkYF67DvR6Yh95uMX2I4QQQgj3taPmVwIAvyDQG6qu6zrQU+gXBMlj4YK/wIz3SLz3JvQB6rGG33A9xpEjHKsaR45UQy9BZrtsS0ryIG1z+2tIb0+OZR9s/H0sJe5VRNXXu80TrI1MPlWUQ9omOLG1ZeOpi/l004aL1pfAzD2mEiitNfzWG9xN4Hoq4Xv6kBoaW96MHniNfe2XF6tkX7m59rKyIvX+zNjV9DiEaCPKjx0DwDc+Hr1fKw51F0IIIUSDOlCG5SxVs3IiNNE7cXiawRefvudjCA1reN2SM5x8+CmyXvsfRb/sxVosCTOvspY33KusLSnKUkmjkjz3hoea3exX1dLVn7kpjVuvNE/9bs3hlTW5SoJ4gmZrme22Va1Z+djUCsD8k+q1X1TjvWUpqR3/qd3qfeUqESZDdEWrafn3VXmqSpL5JXVp8X0JIYQQwj0y3LKjCYpUJyUdgD4wgPh7ZpH20PPkvreM4MmTMY4cCYB50ybylr0PQHS/HNI3aRTtzybnk3Wgg4CkWAIH9SdoQE+CBvTEJyzEmw/l7GQ+3fLVU55grx5rbo82d3l6QoPWmEjAm1qjP1VbnVHS5oH+X1ktMGy3Mc78pn6fPgimaHXZnKPeb4FhanIXO3vitqkJOSHaifI0NWuub1KSlyMRQgghRE1SSdbR6DtW3tM4uA9hl1wAQMbDj2Azm7EWmcn4q+rhET5lBKZLryfuIhOhySX4GitAg9LUTHI//4GTC/5NzpvvOpI1tnIL5RnZaDUrGDRNVS/8/E9Y/aT6nbGrlXtUdTBN7ZEkhDua9B6tI+FWc0bJtvL290Sy2dOTaFQ/No0d6mtXWJmMLMmDk79KlZg469iHW/pJkkwIIYRoczpWRuVsFdtfNV8OSehwSTKAmFuuxrx1D5aTJ8lcuBA0DUt6Or6xkcT88Ub0gQGED72a8LJCOLEFy96fKN51gJJMPcXZfgQV/wgfboBOwykxJ5G2+Ft8IsMI6t+DwAE9CeqViH/KG+gyawwRPLxSVTmMf1jN4OkB5h2qmkMmGvAym807Teuboj1U43lTXpq3I2h/PFKVVy1LZrOAoYn/95SbVbVZTN/61yvMaNr2hWiylqtedSTJukiSTAghhGhrOl5G5Wxj8IHAcEgeo0586vtGvutYSFnferF5iCEogPg/q2GX9iGWAPH3zEIfGFC1on8wdL8I3+4XEXpxKaHpOyoblG9WQ9GOfk/50SDQhVKRk0fBum0UrNsGgN7XRlBMFFHXjCNw+AWQvgN2vq965KxZAJOfbvaJpbW4lIwX3gYddHv1cefYRatwJClj+0NQhPPC9p6MaitVT63N0xVSHYaHXxA5R1pu9tDG9JJrb5OBCFEPW7Hqy+iXLEkyIYQQoq2RJFl7Fd1bfQMfGK6ut0a/Hi8yDu5D2MUXkPfNWgDCLrmg/mosnwDoMlL92KyQtRfSNhJu3ERo0ilKcnwpzvajJNuf4hxfbBY9RSf9iL7gfujXD4ZCkW0Ixe8+RlDGIQJ7/YKh63nNegxZ//0YS1YOAJn//Zj4uTc2a3vCPU5JyjFT0QdFqIqyjsbdoW9tkc0KmXvA4O/FINz4m1pWqHrbhXcFY6S6zWaDjB3gZ1R/r1tb8RkICHXvPjlHwT+kqndY9cRxuRnSt3souEYcW5u11vBXR5K761gPxSGEC63QZqHHihVYi4rQBwa2+L6EEEII4R5JkrVXphhvR9DqYm69GvO2PaCD2Fuvbvwd9QaIO0f9DLsd/ZmjGNM2YTy+CXJT0WxQGncVJaYJ+PeuOpkt2Lif/D2B5BAI6/5DQI8VVUM0+/dwazIA8/b9jgQfQN7XawkZM7TdDbtsz8NFnZKUL75C/N+eAXOWl6NqAXnHGlihDSbULaVQlKmGjOcecz20rtX7Vrlxopy5TzWdz9qnKnZBzSJablY/tZJkbfA5gMqJC9JVkqwoq2pSi4a4k1SwWqCiETMQ13gNOCW5J9+A3mhs/D6FaIMMJs+0cRBCCCGEZ7WTpjyi0fS+3o6gxdiHXdYaZukOnQ4ie8C5v4PLXoa4c9DpIXDEBUTcdBM6g8Gxqmn8eELH9K+aDODwMc58tpqTf/sXh2+4H2tRsWNda3Fp7ckAqi3LWPwOAGHXzyRs5gwAMha/ja2kESeLbYT9JLW9xQ0ukpTLP8O8aVP7H2JZnaVYzRpoKW543cbSNFXNk3fcc9t05cRW1Vvs9KG6e081d+bJlqy2tc/KWJeSPJX883SFSktWvDQ2QQZQdKrhdeyxpm1q3MyuNWa4tCe5LZk5qjelEEIIIYQQLUCSZB2NXg+JQ2pXLoR19k48HmYc3MezVUzBcep3+o5ai0KmTiHh8kR6TM+ix2WnSBiVS1jPEvwj9fjHBGA4/avqh6RpnJj/Mkd+/wAn//4aZ75aQ+n/t3fn8U3U+f/AX5OzadP0vk/KXU45BJTLBeRQREQ5BJeKsquWn7qILt4oiiwei/gtuiJQ1gNcVkEUZUW0XMopRbBYEGnL0XK1pfRMm8zvj2nTpmfaJp0kfT0fj3m0zUxm3jNpYfLO+/N5nzkHsXIo36U10ps7dUQEQhYsQPCCJ6EOD0f5xau4tOZz+52Lg1m9SV39mdzh2KzBJOWzz8FcbMObdaDpJIizuJRm3/1l7JGSU3kZUkWUI9Qc8lqc65hjtIgAm6rJbJkrK+eYlASsSvSJtZKzeZnV5154qd5/j5yarVV+zep4W53UrJPkXr9BSnITuaCra5ORNecBXNu6Ve5QiIiIqB4cbumONF51Kwyq5i4jax1GSl0sj24ABj4gdbOsknMMOPopAEAd1RE+vhfgE5MHIE8qQNq5FAAgav1Rmq6D2SiiYOdBFOw8CABQ6D2hiQxB6W9nAABhr75qGSIUtuRVZCXcj7yvUuB9cz+nH77oysNFaycpRREo2rUb5efP49L/rULog7fLHaJ9leQ7Zr/nf3bMfu05wXyhjcNn7TnZf3MSe1UVVHm1hsRWdejsMKx5FVzXswGfCNu3l1vRlWY/pXaSG6KI/A2fIvvZ5xC35QsOuyTHcVD1aUlqKop+/BFewzm3HhERkTNiJRm1b6G9pKWiBPhgDPDlY8ChtdLXD8ZIj4f2Am57C5i+HrjzPeDmv0ERPwHw7wgICghlueh8xwVE33IFgT0L4BVSBkEFmAuLLQkyv3tnwGvwIMthvQYPlt7wATj/2vvI3/ETCg8eQ1lW9VAzURRhLi1rcBhnW3Hl4aJFqb8hf6uU3KtKUir1Xghb8ioAIO+zLZZ51sgN1Jf8cuSQWlN5y4Y82qsyMfcP++zHiTVYiXv+PC69+abc4RE1mzFLSoprYtjZkoiIyBmxkowAtc62OWLckSAAI58FUl6VKscOJ1uvD+0lrRcEAALgEyktnUZJ6ytKgau/Q3E5HV5X0uF1OR0ovgrRDBRfVuPc3gCYjY3nok0Fhch+Yy0AwGfMTQifnwAAEEvLkH7Xo4AgQOGhgeChhULnAYWHFgqdFvr+PRA44zZpW1HE5XWbK9d5QKGTtqn6WeXvA014dbMH0WyGoLAtR95gJdaFC7i05nOEJt5r035sIZpMEI3lEM1mKL08LY+XnjkHc3EpxPIKiMZymMvLpe3KK6DQecAwrL9l26sbt6H86jWYS0pQsOswgIaTlPnrNyB7+TrEvftiy+e5I5u1eeOHa+eA3DNSQ4DmdnosbqLqqbwEOHeo7uMXUgH/Ds07Vs192lOtCfIt1/+G7q3fd33JwQojICik5hEaPeAd0qpD1JfkBmpU4n6yHt633gqvwYNbdRyitiKKIoyZUiUpk2RERETOySmTZJMnT0ZKSgpGjRqF//73v3KH46IaqG5QqOpMiIygrtKE2UY7TvjtSrR64NYlwMVjwB8p0nA1nS8QNxII6dX4kAuVBxDSU1qqFF2BUJkwi/Q9jqxNhXXezBXt24f89RsAAB7RvhB0epgrBGjCgiy7MZdUzvMjitL3JWUw5RVY1tfcViwz4uqn3zQYpvfNNyDyuYcrdyfitzsegaBWVybTqhNvCp0Wuu4dEXSvNASxyTeptYaLXkreBHOpEWKNJJbZWA6xvBza6HCEzL3HEtMfiYthyi+w2gZm6ffWo2sHdFj+tGXbsy++g4rL9c/9pI0Jt0qS5W//EcazNkwkXqn80lVkPv0WPDrFwCMuCh4do6CNCYfCQ2vzPlxaG1UqWnUntEpKOnBC/VypkhMFF5o/8X9xrvT33ZCGhg6WXQeyfwFCejTveACQc7z5z2lMjbnCrK7/e4taX0Zeu0OlqQI4u9/6sVYkySqKinHhTenDg0aT3Bx2SS7EdOUKxOJiQKGAJjJS7nCIiIioHk6ZJHvssccwZ84crFu3Tu5Q2gdBCag9m06SaTylJFtpQePbuSJBAEJ7S0treQVKS8zN8BoA+JZ9iPyvd1vezIkikP3MswAAv05FCB1Q+eZdqQF8CoCD2UBQNygDOqPr5+/AXFYGc3EpzKVlMJeUVX4thTqwxjxzogi/0X1gvpIFsaQUZrMKZsFb+lpSBlWgf/WmxnLAZIZoKoOp1DrxBgCCWuqQanlTDdsrsfK2fF+d3KvFXGz9proi71qdY1tiLC+3+lkdEigl9TQqCGo1BLUKgkYNhUYNdUiA1ba+Y4fCdL0IglqNiiu5yN+2p9EkJUSgND0DpekZ1TtRCPDq2x3Rrz5uechUWAylvrq6TQ5tXollR1WNHwDg4urPEDZvpswR2aChSqyRNjRCaUnysXbiyY7qXP83Rtv3AMbCuo9VNDysVDSbUZFfgPKLV1GenwZNH0BX+edV+nsWziyYB5S5SMMMIhtVVZGpw8MhaDQyR0NERET1ccok2ciRI5GSkiJ3GO2HUgMEdJKSZOWNJMpCegGl+e6ZJHOg4AfuQdGhNJSfP4+Lb7wBiCLKL1yAOtAbwbOGAtdPS5N1GwuByyekBVJ9jaDzhyKwCxDUTar4i+oMqGtVt5QVQrHrVYQGHgMCax28arioVm95SNCo0Xn9G9UJN0sCrlRKqAX4SttVjjBtDr87RwNmc3VCS6O2JLVU/tbD3aJfloaSClWJL40KiqoEWGWirkrs60/aHEPAlFutH1Aqkb91Z71JSt/bhsP/jlEo/eMsyk6fRekf0mLKv25VSSaKIn6//xkIapVUbRYXBW1cJDw6RkETHgJB6fjpHRuuxHJ+rtz4oYrV9b9xKBRe+safYO9Oo61Q5/p/lQLD3fvg1c8OHwpUqVVxK5rMELOOQFH5cPmVPFz+6EtUXLpa2SU3F2JFdVWz//3XoJs6BACg9PW2SpA1luSuWeFKZF/2r3I1ZlbORxYdbfd9ExERkX00O0m2a9cuvP766zh8+DCys7OxadMm3HnnnVbbJCUl4fXXX0dOTg769OmDd955BzfeeKO9YiZbKG0cKhZ+A6Cs/DUI6gpcONL49hq+GWkupacHwv42G1lPv1VdvQQg7Im5UFQlCURRGg52JV1KmF1Jl4aKleQCZ/dJCyDN9+MbI71WgZXL/neloaIqHdBnOhDeV5oX6egGaZ61lFel4aSVb2IFQYDK1wD4Nh63QueBsMeluBt9k/q32ZaETfCfJ9l8XTw6tc2bhOA5U1B08HjdJGVIAEIeuBsKnQe00WHAyOp/oypyr8FcWl0RV5F3DeaiEkAUUXT4VxQd/tWyTtCq4XvrUIQ+MsPymLm0zO7DNV2yEguNdCesqkB01IEbS/i3gNX1T1qNsL8/btf9O0qj3SHf+Xurkq3mMiNKfvtDqgbLK0b5uXOVCbCrKL+SB//JoxHywN2W7a/9b4/1DhQCVIF+UIeGQB1WPTRT5e+DjhvXQKUpwcX3/9Ngkrt2hSuRXThwCLpoLIPSzw+aWM5HRkRE5KyanSQrKipCnz59MGfOHNx111111n/66aeYP38+3nvvPQwaNAjLly/H2LFjkZ6ejuBgaeLwvn37oqKios5zv/32W4SHh7fgNKgOlQYI7dn0HDfaJqohahIEKUkW2EWaw4xs5tW3G3wnjLBUc/jeNsK6ikYQAJ8Iaen4J+mxilLg6ukaibOTUve+vDPScnJb9fNVOuDB7VLlGAD0BzDwAalDZ84xKYnWgqGkXn27wfe2EQ2/Sb19JLz6OHc1UINJysdnN5ggqF31pvb3RdfPV6As47xUbXb6HMr+OIvSM+cglhkhaKor30zXi3By2nxowoOgraw684iLgrZjFFT+PhAam+OuAa5cidVk44dlo+QOsUl1rv/nW2EYNQJecb7yBVVTcW6Dqxq8/ufPN9p4w1xmRPnlXCnhdbGy+utSLjx7dILfbSMAAKaCImQtfKvBY1dcqo5L5e+DwFl3QB3sD3VIgLQE+EJQqQBPf+n/lSzpwwBBoYAmKgLIy2w4yR0RgeAnnmjJ1SKSjd+MGfCbMcOqipKIiIicS7OTZOPHj8f48eMbXP/WW29h7ty5uP/++wEA7733HrZu3Yo1a9Zg4cKFAIDU1NSWRVuPsrIylJVVV3wUFHAooIXOr+ltWsI7xP5JMr9YIC/Dvvt0MsEPTEHRoeOAAIQ8MKXpJ6g8pMm/a04AXnRFuvZVibNLaYBolirIqhJkVUJ7AX2mSR07f90MBHSWOpk2N+5GKrGC59RNlDujOknKu25vdoJJ4aGFrlscdN3iLI+JJjOM2ZehUFf/U1qWeQEQRRjPX4Lx/CVc333Ysk7p443AGRPgP2mU5fkQzVKioAFNVmI1pxKoOBe4/Jvt27eSTY0f7toHr7A2C6nZGrz+r7yFuKRn5B/2ajYDF3+td5Ut118Z4AuvPl3h2b0jAKAivwB/DB0G05UGGhOYTJYkmcrfB9qYcKgCfKWkV3DlUvl9zWSzoFAgaObtDZxEfYlj6bEGk9yvvsJhluSyGvs3n4iIiORl1/+ljUYjDh8+jKefru5Kp1AoMHr0aPz000/2PJTFa6+9hpdeeskh+6Y2pJZ3QvS2UPVmD0DL31hbmgLcJP284yXg3EFpiGV9wiofP3cA+OQeQB8iJSR9YwG/GGnopk+E1JChibibU4nljKySlLNubfoJNhCUCmgjrTv4efbsjM7r30TZGanirPSPsyj74yzKzubAdO06hBoJtdLfM5H55OvQxkZIVWcdpHnOtB0ioPSS/iaarMRqoBKoXg0kU2wlllfAVFQMc5kRYqkR5lIjzGVlEMuMMJcZoesaB3WQlJwvPnEa5xavBNBE44fnX0DEwgQU7DwozWWnVlXOS6eyzE+n69YB2igpk2YqKkbZ2WMQii9VzmdXvSjUaig8tFbXuLXsev0BwFjU4CrRbIbpehFEY4XUIba8AubKr6KxHCpfA7QxUrW1WF6B/O9+guidATHnJMTycpjLq5+niQhB7n+/BdD49b+ybjPKbr4BnpXdb5XeepjypU6ygocWmpDqpJc6JAAeHauHSQtKBeLeW9S882+BOknuGdMtQ7+JiIiIiOzJrkmyK1euwGQyISTE+k1jSEgIfvvN9uqF0aNH4+jRoygqKkJkZCQ2btyIIUOG1Lvt008/jfnz51t+LigoQFSUDZ3HCPCPa3obK/afxLa9sfvwuKpqwQup0hDL2rJTpa9KDWAyAoUXpeXs/uptFCrAJ1JKmPnFVibRYgCvIMs8Zk0OF3UBdklS2kjl6w3VDfHwuiHe8pi5zIiyzAtQB1V3Gi394xzE8gqUnspE6alMXKuxD3VoIHxuvanpSqyb+8GzdxcAUrUOAFTkX0f5xatWCSxzqdHyvWFoP6iDA4CzB1B08Ajyv/2xMvFVJm1blQQrMyJ8fgK8bugOALj2/T5LVVV9Ip75C9RBAwAAxguX6nQ0bYjxwmVLQqc+IY/MsCTJSk+fRdbf32xw2+A5dyHgnnEAgJLfM5H19D8tCbSqrqhVSTXfsUPhO0ZKOJdfycPldZstiTlBrUJFbj4KfjggXfMGrr+puBRqf5/KBJWUpPLqFw+fyjnuynPzcf7Vf0nrzALEchPMpUWWxJfvmJsQ8tdpAABTQSFOzVjQ4Ln5jBqC8AVSlbZoMiFnxYcNbqsf3Nu2f7JVSqvfSUGpQIcVz0IV4Ault1eLhge3TOPHsUpyL2j4GhHZjZ1/9ysuX0bGzFnQdIhF1HvvteHfFhERETWHU9Z7f/fddzZvq9VqodXad4LsdkPTiuqtkB7SsD9Tuf3ioebrMBI49a00Sf/AB6yHXOYcA45+Kn0/epGU+MrPBPIyK79mSF/LS6Tv8zKAM9XzLkHtWZk4kyrOgm/vhqKDvwAKhW3DRZ2QnIk9hVYDXZdYq8d8x94Mrz5dUXrmnFV3zYrLeSjPuYK8LSkAGq8EynpamhMq9q2F0HWXEt/Xvt+HS6s2NhiLNjZCSpJVlMGYcxUFKQca3NZUVD0BvqDVVH5VQ6HVQPDQQqHVSN9rNVB4Vf+b4hEXBe+bb8D1vUcab/zwymKoyrLgf9eYyiRTdbKpqjJKE1LdtlVQKqGJioC5tLjW9hWAKFoNYxJLjTAXSvGb6jk3ff/qocyma4W49l39Fc+NXf+C7/fV2V7hqbMkyWAWUZJ2ut79AoCppHq6AEsFnEpZo6KuOrlnNXxRrYZ+cB8IPqFQVFyz3latgjY2AqpJhiYbb0S/8lideQU9OkQ2GK9DlBc3OQzYKsnNYZbkgoyZmSjPypI6QDNBRkRE5LTsmiQLDAyEUqnExYsXrR6/ePEiQkND7XkosgehFX3lPP2B6MFA9i9A6bWmt2+MynWG7Dmd0F7SknNMmqS/zzRpiGV2qpQgqyiR1of0kj4VD+1tPYG/KFY2A8gA8jOqE2jXzlW+cT0hLQCUAMJ6agCNAYo9r1YO2YyVkmg+UYCqFclqUZTO4UwKUJIP6HylBGBoL7t/mu9MBIUCmvBgaMKDgZv7WR6vKChEyYk/kLPyE5v3VbMbp9LbC6ogPygqk1hCZSJL4aGFoNVA5eNt2VbXPQ7Bf7nHar0l8eWhhSYsyLKtYdgAGIYPsFSsNcajQyQin3sY2f/3ceONHwbdCGRWIGTuPTadp2ePTuj42b+lphY1iKIImKxTYR6dYxD3/kuWqi1p+GJ1Ek4bXd0oRuVnQND9d1Un6EpKcW37TzCXNF4Np9B5wGf0YCg8dZYEla5zrGW90qBHxLN/haCpHEoaFg/hWkblUFE1lPrqxKLCU4duW9+z6foKSgWiXkwEDOFSZ9wGuETjjfISaanJXHdic6+a3YDd+N8FkptjulsaMzMBAJrotunsTERERC1j1ySZRqNB//79sWPHDtx5550AALPZjB07dmDevHn2PBQ1Wz1vKLSGZu6inn2E9QbO7G5ZSFYc13LdrQkCMPJZIOVVKcl0ONl6fWgvaX1DbygFAdAHS0vUjdWPm8qBgvPVlWeVVWdeuAjgCnDhCnDhSI39KADvsOrKs6p5z7xDAYWy8XMoK6yOv6ZT31bH35wurG5AZdDDe1BvKLSaJiuBwv/+ILz6dofSuzrZ4jvmJsswwqZ4xEbAIzbCpm0FZfMT623V+EEQBKDWZNgKrcYyTLMpKn8fBE4dZ/WY95Abmrz+kS8+0miiSaFRwzC0xljo8L5AAzktQRCan/xpJEEGNHL9IyKcu/HGtXNyR0BkV8bMLACAJjZG5kiIiIioMc1OkhUWFuL333+3/HzmzBmkpqbC398f0dHRmD9/PmbPno0BAwbgxhtvxPLly1FUVGTpdklOhJ/EuwetHrh1CXDxGPBHSnUlVtzI6gqy5lKqq+cn61Dj8fJiID+reqhmVQKtrEBKqhWcB7J+rLEfjVRlVjXPWVUCTecvxSWK1QkylU7q0hneV5pj7egG6fGUV6Xza4e/r159uzVZCWQZ1uekmmz8cPX3Rp7dgKsND1+0J1uuv1NUYjWiwev/4rNQ6JxyxgUit2SpJIthkoyIiMiZNfsO+dChQ7jlllssP1dNmj979mwkJydj2rRpuHz5Ml544QXk5OSgb9++2LZtW53J/O0tKSkJSUlJMJnqm3mGHCqgY+vetHoFNtpdkWxQ31BKR1B7AkHdpKWKKAKl+TUqzjKk769lARVlQO5paalJ6y0lzTRe1QmyB7dXz6nWH9Icax+MkdZfPOb4c3NSbVWJ5UiNNn4ovCRjZE2z//Vv+2Rvnet/+0h4DRpoXQ1KRLXY92/VmCVVkqk53JKIiMipNTszMXLkSGnul0bMmzevzYdXJiYmIjExEQUFBfDx8Wn6CdQ4nR9QklfrwQZuGJWalh0jcgBgLJKqimyYg4eclCBIvy86P6kKrIpoBq7nVDcJqJrvrOA8UHYduHi8ets+062bDgDSz32mSUNIT3/fbpNkTVZiuQir7oQu1PjBPa//3XDpIe6ck4xcjCiKNSrJYuUNhoiIiBrF8h2qpvGSFqUGCO0pzRV19XcpoWErrV56XlPCbwDUOmkh9yQopEnFDeFA9JDqx01Gab6hvAzg6HrgerZ1cq2msMrHf98hdb/zrtyfIaJ6354BrWtC4QIarcRyEVbdCV0ouQTw+hO1K018ENwS5qIiaDt1QvnZs1BH2jYHJBEREcmDSbL2xjcayP1DmlC9NkEAIqo77EGrBwK7AOcPN77PmgmK4Hjg7IGm42hnE7FTDUoN4B8nLRePS0myC6nSEMvaslMrvxGlxFp9k3krtYAhrEYCrUYizcPXbSpOXLUSqyZXSyzVZLfrX6dCt+248vUncmVKvR4d/vOp3GEQERGRDZgka298IgBPf0DVgkqChpINOj9pXjGNF6DSti4+al86jJS6WB7dIM1BVnPIZc4x4Gjlm4rhT0ndWAsuSMM1r1+Qvr+eA5jKKodzZtTdv1pXK3lWWYHmHQ54NLO7a2NEUYr3TEp144QOI6XzsVOSjpVA8rLb9S8vtlNErSCa5Y6AiIiIiMgpMUnWHjVniGPNN/gNzQMjCEBw99bHRe1PaC9pyTkmTdLfZ5o0xDI7VUqQVZRI62OHSb9ntYdlmk1A4cXKhNkF4FqNBFrRZaC8pP7GAQCg0VsP2/QOl5LI3uGAxtP2cygrrO7QWdOpb6XYRz5rt8pJVgLJyy7X3wFDuVqkJF/uCFrBSa4huTc7ViGLogjBTaqaiYiI3B2TZNQ4tU6qPFOoOLk+2Z8gSEmkqiTT4WTr9VVJpobeXCiU1Umu2kzlUqVZwQXg+vnKKrTKpfgKYCwErqRLS20evtZDN72rqtDCrKswRbE6dpVOakAQ3lcaPnp0g/R4yqvArUvcZtgnuYn6Ki+JyCEuLHgSpcePI/jJBfAePVrucIiIiKgRbpMkS0pKQlJSEkwmk9yhOKfWvEEP6WG/OIhq0+qlJNLFY8AfKdXDFeNGAiGtGK6oVAO+UdJSW0UpUJBdXXVWUCOJVppfvVxKq/tcz4DqCjQI1QmyB7dXDxftD2n46AdjpPUXj7Xb7pxUG6ugiJyb/f9Gy06flrpbKpV23zcRERHZl9skyRITE5GYmIiCggL4+PjIHY7z8IkAKsoArbfckRA1TBCkJFJbJZJUHoB/B2mpzVhcmTyrVX12/YLU6bX4qrTk/FL9nD7TredTA6Sf+0yTquOObQTUnlKFmoePlMCj9slZhlsSuRhX/TBUFEUYs7IAAJqYWHmDISIioia5TZKMGuAfJ3cERK5F4wkEdJKW2koLalSfXZDmHSvJrTtXWpWwyscvHJGWKlpvKWGm86386mf9veVrGyXU2qDxAFVhkoyoJVz1w9CKy5chFhcDCgU0kRFyh0NERERNYJKMiMhWHgZpCaqcwL0kV0qUXUiVhljWlp0qfdV6Awq1NIRTNEsVaWXXgWtnmz6mRl9PMs2v/gRbSxJqbdh4gMBKMnvgNSQXUp6ZCQBQR0RA0GhkjoaIiIiawiQZuT+lWprEncjeOoyUkklHN0hzkNUccplzTOrQCQAjn5aGklYlyErypYRZSV6N7/OB0sqfS/KA0muAaJIaDBgLgWvnmo5H41WdQLNUpPlWf61ZpaZUs/GALJjgIXIN9vk3z1iZJNNER9tlf0RERORYTJKRe4voD1w6Yf8kmX8HwFgEFF6y737JtYT2kpacY9Ik/X2mSUMss1OlBFlFibQ+pDJ5JiikIZQePgBiGt+3aJaqvKoSaCV51Ym12o+VXgPMFdLvpLEIKLAhoab2krrXFl9h44G2xCqo1isvBpQGuaMgd2Xnv1FjZtV8ZE38m09EREROgUkycm8aT8fs18MX8Ilkkqy9EwRpOGJVNdbhZOv1VcMVW1KFJSiqh3f6NlGBIIpStZklcVa7Qi3fumrNXAGUF0kL0HTjgd1vAlGDAe8QQB8KeIdKXx319+XWmCRrNWOR9HdB5AKUAf7w6NED2q5d5Q6FiIiIbOA2STJX7XrULuj8pDfoTdHqpcoZVyIopIofar+0emk44sVjwB8p1RPfx42UKsjaYpiiIEjznmm9Ad+oxrcVRSnJUJoH7H0buPxb040Hiq8C6VvrrtcaqhNm3qHW33sGAApla87KPbGSjKhdCUhIQEBCgtxhEBERkY3cJknmql2P2gWtNxDcXeoGmJfR8HbBPYCrv0tvyO3JN1pKBDhCeF/g/M+O2Te5DkGQhiO6wpBEQZASe1p99d9GU40HguOBkJ7A9RygMEf6WlZQvVw5Wfe5ChXgFdRwEk3jZb9zcqXunCaj3BEQEREREVED3CZJRk7OlooSlQYIiQfO7LbPMT0qk6X6IGloztkD9tlvTYLC/vskaisdRtrWeOCGWXUTgMbi6oRZYQ5w/WKNny9KQzqvZ0tLfbTeNYZuhlgn0byCbK9Cc7XunBVlckdARLawQ4JdrBzdIChZVUtEROQqmCSjtmPrDacgtGxIUkQ/66ouQ0T19ypt8/dH5O6a23igJo0n4B8nLbWZTUBJrpQwq1l9VpVMK82XunyWXQeunqr7fEEJ6IMrk2ghdavRNJVJL3bnJCK7s9+Q6OLDh3H2gQfhOWgQoj9YZbf9EhERkeMwSUZtR+tt23YR/YFr5xuuQGmIPYdv2Uqhrv9x71ApKUDkzBzVeEChlKrBvILqNgQAgPISqdqsdhKt6vuaVWj1/TOg0Ut/YyoP4OJxduckIqdUnpUFsdzO3bWJiIjIoZgko7bj4QOE9ADUusa3U+uAwE7S9o6aS6wlOgyrHgpaVZmmbOhPiFUr5CLkaDyg1gF+sdJSm2gGinPrqUCrXErzpU6eV3+vfk5T3TmPfAT0vRfwCpYSd8oGkttERHZkzMwEAGiim+hQTERERE6DSTJyvJpvsj39bX+evsa8RIJCqj6p+cZYDpEDpKFkTb3J9vBpfiUckVycqfGAoAC8AqUFPeuuLy+tHrZ5ZB2Qn9V0d85LacC3z1UdQOq4qw+pHNIZLH3vFVT9lcOzicgOjJlZAABNbIzMkRAREZGtmCQjx1LrAO/wlj+/ZlJN59t0ksw3WnrT7Cj1VcF5+kuVLzV5BQKXHRcGUbul9qiuQju3X/p7b6o7p2cAoPYECi8BpjJpvrSSXODyifqP4eFrnUTzCpGS9voQqRpN7WHfc3Kl7pxE7Ubr//YslWQxTJIRERG5CibJyHH8YqSkVVsf05FJMlvxjS2R43UYaVt3zmFPSFVyogiUXgOKLkkJs8JL0txoNX+uKJGGdJbmA1fS6z+u1lArgVb1fWVVmsbT9nNwte6cTsF+E6sTOYooijBmSfcjag63JCIichlukyRLSkpCUlISTJXttklGIT2A4qvW3SXl0NAbS4VSGjLZHF6BDa/TeNWtJCMix2tud05BkKq0dL5AYJe6+xNFab6zwovVSbOii0Dh5erHyouAsgJpaaiyVaOvkTQLrq5KsyTRvKq7+LpDd05WwpE7aUl37XpUXLoMsaQEUCqhiZD5foiIiIhs5jZJssTERCQmJqKgoAA+Pj5yh9O+efo3b+4xe4seLCXBas8rFNoTyP0DCOgMZB+1fX9abyCoW8PrfaKB/LMti5WIWs7e3TkFQfp713oDAZ3q38ZYWJlAu1yZQKtZkXZZSp4ZC4HcQunfm/qodVKyTOkBXPnNtbtzshKOqF5ieTm8x42DWFoKQaOROxwiIiKykdskyYgslOr6J9bX+QER9U1c1AStd+NvshWK5u+TiOyjrbtzavSAvx7wj6t/fXlJZQXapVoVaZVfS/OlbfIyqp/TVHfO716SPnhQaaVFWflV5VH9s9qjxuO11lV9X9+2Vc1RWsJdKuGIHEATGYHI5f+UOwwiIiJqJibJiGoK6CR/B00iah5n6s6p1klzI/o1MFF3RalUcVZ4CTi8VkqWNdWd01TmuG65ClX9ibf6EmxWiTetVE1XlSBz1Uo4ooYwsUtERNQuMUlGVJPWu/X7qD3Mk4ioisoD8ImSlsy9UpKsqe6c0YOBHlOkBFtFWeVSKiXPav5cUVbjsZrbltXdtmrye3MFYKwAUNTyc2qqEu6PFCbJqN0x5edDYTBAYLU5ERGRS2GSjFyTzlcaVuUdCgiKxifWb2uRA+WOgIhcQYeRtnXn7H4HENzdfscVRcBcXjeZZioDykvrJtrqS7yZyoCLv0rdQpuqhCvJt1/sRA5nn4n7M2cnwJiRgahV78Prxhvtsk8iIiJyPCbJyLVoPAFjMeDfsfXz6dSnvuEVGq/W74OIqLbmdue0F0EAlBppaU317I8rpCRfU5VwOt+WH4PIBYmiCGNWFsSyMqiCguQOh4iIiJqBNeDkWsL7AVGDpGRZaxJkDSa+aiW4/OOkLnTOqr4GBa3R0GTkRGR/Vd05Q3tJCbHDycBXj0tfqxJkzenO2dY6jJS+Vk3SX1PNSri4kW0YFJH8Ki5dhlhSAiiV0ERGyh0OERERNQMryci1CAKgaqNW6koN4BPR8udH9ANy/3CtoUYqLRDWB8g+KnckRO1DW3fntCe5KuGInJwxMwMAoI6IgKC284dZRERE5FBMkhHVVPMNaWvfnGq8AH2oayXJRNH+1WlE1Dhn6s7ZHFWVcCmvSomyw8nW6529Eo6oUS3/vTVmZgIANNHR9gqGiIiI2giTZESO5Bng2P2LZsfun4ioMa5cCUdUH7H1E/eXZ2UBADQxMa3eFxEREbUtt0mSJSUlISkpCSaTSe5QyBUEdGpgSKEAqD2B8mL7JLgc3frdDjfzLkXnB5TkyR0FEdXU1pVw7e3fPXI5xozKSrIYVpIRERG5GrdJkiUmJiIxMREFBQXw8fGROxxydh4GIHYocPFX66SLUiMNESrJA7xcoSOVA94s8g0oERFRi3kOHAgIAjx69JA7FCIiImomt0mSETVb7WFA0YOlyi+FBvC2Y0dLfTBQUQoEdQPOHrDffgEHJbSYJCMiImop/z/fB/8/3yd3GERERNQCDh4LRuTkdH7SV0HhuAnrg7pKHSNVWvvtU6mWqt4CO9tvnwAAse48Z7FD7XwMIiIiJ8f59IiIiNolVpJR+2YIlxJOHq0cols1V1Zr92MrfQjg3wGoMNr+HIUKMFc0vV3t6jS+USAiIrKJ6fp1mItLoAoOgsD/P4mIiFwOK8mofRMEaThka6u8grpJzQCCu9snLlupNLZva8vNulLjuIo6IiIip9e6KQcKtm3D7yNG4NwjiXaKh4iIiNoSk2RE9qBUAYYwxyaYogbVOF4zkmO28osFdL6AWidV2BEREVGzlGdKnS3V4fx/lIiIyBUxSUbkKlQaaX4zryDAO8z++/eNqv5eX6txgcbL/scjIiJyM8bMLACAJiZG5kiIiIioJZgkI3Il+mAguJvUhdNWnv6trwwLjndMYo6IiMiNGCsryTSxTJIRERG5IibJiNxdSA8goGPznqPysP5Z7QEEdrJfTERERG5GNJthzKqsJIuOljkaIiIiagl2tyRqS36xQF6G3FE0TakCom4EwM5cREREtqi4fBliaSmgVEIdESF3OERERNQCTJIRtSXfKGnS/Ssn5Y6kaa3t+ElE5BCt6z5IZJvmf0hkzKictD8yAoKanaKJiIhcEYdbErU175Cmt6kS4IZDHP3j5I6AiIjI7lRBgfCfMwc+d9whdyhERETUQm5TSZaUlISkpCSYTCa5QyGyH+9QoKIU0Hq37PmCEw6X9IkAcv+QOwqyB60eKCuUOwoiIqegjYtDyFNPyh0GERERtYLbVJIlJiYiLS0NBw8elDsUIvsRBMC/A+AVKHckjucT0founEREREREREQt5DZJMiJycf5xze/CWZPa036xEBFR+yS2fM67kl9/RcWVKxBbsQ8iIiKSF5NkRM7IEA6E9Wn9fmreqHuHtX5/REREVIdoNiNz5iycGjoM5VlZcodDRERELcQkGZGzUSiliioPg3336xsNhPW27z4BILCz/fdJRETkQiouXYJYWgqoVFBHRMgdDhEREbUQk2REcgjp0fbzbwkC4OFT/XNQV/vs17OB+dJUHtXft0lHSxcZ3qLSyh2B43DIKxG1U8ZMqXpMHREOQeU2fbGIiIjaHSbJiOTg6Q94BTWw0gk7UlZRqm3f1jfaAQG4SCKsIV5BQHC83FEQEZGdGTMzAACamBh5AyEiIqJWYZKMyNWF9wWCu7fgiXZKxgn17EfhoE/RXX0y5OBu1hV2RNR8rv7vALklY2YmAEATzSQZERGRK2OSjMjVab0Br1pDHquq1HxkmhclahBcvuqLiIjar/o+AGpE1WT9rCQjIiJybUySEbmjwC7SJP1+HeQ5vkIBaPQO2HEDiTe5koFEcoroL3cE8mhm8oKoLRgzKivJYhwx1QARERG1Fc4sSuRs7PEGUKGwnqS/IaG9gKunpW6aLVV76FPVxPRaPRDaUxpeWJzb+D5snfC9wWFWLvSmWaGUOwLH0QcBeZlyR9F+aNppowQOtyQn5J8wG2UnT0HbpYvcoRAREVErMElG1J7pfIFIB1aj6Pya3iZyIKDUNLzeJwK4dr7yh0beHLvKG2dHVsEo1YCp3HH7r6LzA0ryAEMEcDm9xvG1gFoHlJc4PgYiIodo2f8lvlOm2DkOIiIikgOHWxKRvNQeUuVbQ2pOdO8d7vh4qHEqLRDSA4gcAOiDa60UORSOiIiIiIhcFpNkRORcdL4Nr/MOASL6SRVTtXkYHBaSQ2m9AX+Z5o5rCYVSSoSpdfWsZIKM2oCr/q2T2zJmZKD4yBGY8vPlDoWIiIhaiUkyIqfTzhMNQd0B/7iG12u8UO818u8I+NaaMLmqy6czC+8L+ETaZ18NNUuo3f20PdM6oqEEtSlb5lskakN5Gzcic8a9uJy0Uu5QiIiIqJWYJCNyNlUT37cH9VUjKVXWw/iq3hDXHHZZH6UK8IuRhgJWCewCBMcD3mGtj9UVKBqYZrKhx9sjD1+5I7CPwM5yR0BElcqzsgAAmmh2tiQiInJ1bpMkS0pKQnx8PAYOHCh3KEQ2qmdyYM8AIKhb24ciF1sm21eogegh0hxYtqiZTBMEwCvA+TpKVlV21Te01NDEvGta7+Yfz1WaGpDtGmt2QURtypghdfXVxMbIHAkRERG1ltskyRITE5GWloaDBw/KHQpRy4XESxPZkzWlynETwnsGAH6x9tufLcmLwC7SEtS9efsO7t7E0MwaybCAjtXfO1uSkIjI6dn2f45oNsPISjIiIiK34TZJMiKiFgmJb3ooZ3PYMrRRoZSaECjbaBikoAAiB0oLuRd2EyWSVcWlSxDLygCVCuqICLnDISIiolZikozIXVTN4+UdKm8cbcGpEwNOOrRR7WGfKkUnPb12K7S33BEQtWtVQy3VEeEQVJz/kYiIyNXxf3MidxHQWUqUaW3s/ObUiaYmBHUDLp0ATMa669Q6qYOhQu3a5whIXT7NFUB+VuUDApilIiseBql60VwhdyRE7ZIxq3I+shjOR0ZEROQOmCQjchcKBaDza/vjKlWApz9QnNuCJ7cw4eNhAKIHAWd2110nCED4DS3br7PxqRy6Y0mSNYdMCUIPX8BYLM+xiYhaq5mNTjwHDETIc89BFRzkoICIiIioLTFJRkStF9Kj/oQV2ZcgOH8hmV8soNICOn8g5xfAVC53RO5BHwwUXpI7Cvvw8AFKr8kdBZFdaOM6QBvXQe4wiIiIyE44JxkR2ZmLD3FsKd8oIMxZ54dqRWYtcoA07NNWCqXUgVPj2fJjUl3+HYHoIXJH0Tpab+l3KaSn3JEQEREREdWLSTIiInvwi5UqZJqTUHKEquSUp7999qfWVQ/7JHk11A3VVebeU2qk3yUFbz3IPYhmM659+SVKjh6FaDLJHQ4RERHZAYdbEslFUModQfuh9W67Y3n6AzE3ARePA6UFbXfcKuH9ALOp4YSKs+Mk9O7LVZJ5RDaquHgRF558ClCp0C31iNzhEBERkR3w41wiuWj1gCFc7ijkpfJom+N4BQJBXdvmWIA05LCl6ps0Wh8i/b7Y0phBEGxLkBnCAbWntDhKMyfABiDNb0duikkyciE2JHWNmVJTFU1EBASVi34wQURERFaYJCOSU0DH1iVUXFVYH2ki8rZMXOmD2+5Y9hbURerY2dSbNs/A+h+vr2pR4wVE9pcWZ+JhkDsCJ9VYwpHJJ3J/SUlJiI+Px8CBA+UOxcKYmQkAUMdEyxwJERER2QuTZETU9jwMUoJMpW3b47pz5V5Yb0AfZP1YQCdpnjSfSHliUrCywmGUarkjaD4Ot6RWSExMRFpaGg4ePCh3KBZVSTJNTKy8gRAREZHdMElGRO1HQEdpvjB7c4aJ7T186j5mCJOSZ+2xWrE5vMPkjsB2QV0B3+i2nWfPXto6KU7kYMasqiRZjMyREBERkb3wY34iahn/uPoTM/XR6h0bS3M4ImGkD7X/Phui8bL/PgM7A8VXgeJc++/bFQR2AkqvAeXFckfStPqGDbtKhZZPVMPrBAUgmtsuFqIG2T6XYnkmk2RERETuhpVkRO1WK99Y+0TYnvzy8JEmZI8cYOPO2+hNvy0Js6aGaEbdCGgcOPl9bWoPqRGBPXmHNmNIZgsm46e25RkgdwT1a+zvzS+2zcIgsgfRbIYx6ywAQMM5yYiIiNwGK8mI2hutHjAW2V4FVpNfLHDlVMuGp3n6N/85jmZLd03/OCkplXO8/koXOYaQ2aUjpQ2JyNBedjiOHak9XaPaS26BnYGsq3JH0TwCP7MjF2M2I3LF2zBmZkId7sbzXRIREbUzTJIRtTdhfQFRBBQteFPqHQro/NrX3EKCICUU22w4mBNVaul8m97GLxbIy3BwIJXC+wIXf5WSlldPt80xnUWjE/XXSngy4UTkcIJKBf2IEXKHQURERHbGO2mi9kYQWpYgq9LmCbI2SBq5ypxOVZwpXlsSafaiUEqNCNy5S2l9mv0350S/H0RERERELoRJMiJyPkoV4GGQOvi1RVJO5y8dj1yfzXOrkVOxNfFr7/n4iFqo6McfcW3rVpSfPy93KERERGRHTJIRkX3Za6hXWB9peF1bUCik47UHVfOZ6fya9zylpgXPq1EF2NhwQf8O0tfmJLgCOgL6kPr31dxzq62+DpKOFtKj4XWKJmZGqJlgasvKPjlwKCk5idyPP8GFJxbgekqK3KEQERGRHXFOMiK56UOAggvuU8nUmqGc5HgR/QCzSarWa47wvkBJHuAV1PA2jXUv1FQ2jDAZ667ziZT225yqwaohl4UXbX+OrWxp6GBvjTW2aE6jBpWu9bE4E69AILg7cGa33JEQWTFmZgAANNEx8gZCREREdsUkGZHc/DpIE8N7+ModSev4RALXzknn4+48/QGNV+srllqsFXNOCULzE2SAlMDyDm18G8+AlsVUtX97caY526h1hNqJV762JD/RbEZ51lkAgCaWSTIiIiJ3wiQZkdwUCveYZ8e/A+Ab3Xg1kbtobGicHOobdthWalYuOUtySmxGs4f2MsyWiGxjw78fFTk5EI1GQKWCOiysDYIiIiKituI246KSkpIQHx+PgQMHyh0KUfvVHhJkjmZLgqd2UjWoS9sctz5KFRA5EIga1PoY2kLtRF7VMOea86I1Nn+aU3KS5CRRO2HMygIAaCIjIaj4eTMREZE7cZskWWJiItLS0nDw4EG5QyEiciy1rrqpQVt0/2yK2gNQaeSOolpjE903lAz09AdibpKSZfpQqTovqFv1emepkrNFfbG6Q7UqkZMwZmQCANQx0TJHQkRERPbmNkkyIqJmUzpRYqe5tN5S9VbEALkjkZc9h0tWVUIqFFJ1nr5Gk4KWVtrZg1+s7dsKQv1JssAuTXfJJCKbWCrJYjgfGRERkbvhHTMRtT8hPYCC80BAJ7kjaR1nqt6Sizt0hW2qIYK6iW6btha5uVI1HJET8581E54DBkAd1sTfLhEREbkcJsmIqP3x9JeWtqJQAoFd2+54riSws9wR2EYQHFdNpmoiCWbL843F9omFiJqkDg+HOjxc7jCIiIjIATjckohcn+Dk/5QFdQO8AuSOwjk1VUVFTXP1ikgiZ8TKSyIionbJyd9ZEhE1IqCjNNzOECF3JOTuHFFFVtVFs7VVjc7QvKHVmJAg11CRm4sr772Hgu3b5Q6FiIiIHIDDLYnIdRnCpYWck18H4PJv0vfeocDV09XrBAHwDmv7mOSuDtF4AcYi6fvIgYDJKHUrdQaOHFKq1Ttmv0RtrCw9HZeXvw1NbCwMY8bIHQ4RERHZGZNkRETOROcLlBcDGjdIKuiDpEo/c4WUHKqZJIu5Wf6EVXPYK4FUlSz0MEhz1SmcJEFWH88AoPiqffaldYMGC0QAjJnsbElEROTOmCQjInI0W5JBSjVgKgc8fAB9sONjqsNRk9JrAdQzHNCVEmT25B0mJQxdIQkaEg+c2S13FERtrPF/C42ZmQAAdUx0WwRDREREbYxzkhEROYPIG4Howc4xv1RgZyCiv9xROIeqSfGDuttnf4IgJUIVyrrrDOGAzg8IqtEJ1dOODR+aqoQTFG2XvAzqJp0rkYsxZrGSjIiIyJ2xkoyIyBkoFHCazy3YcbKaIQzQh1S+Pg4W0LH6e50fUHQF8Aqy085tSH55+EjzpVWUNX/3HgagtMD27fVB0mJrpZpWD5QVylRlSVTNmJkBANBEM0lGRETkjpgkIyKi9qMl84q1RYKsNqVaStDZQ3B3x5+Dzr95SbLmCu1T2eTAw3HHIGqCaDajPOssAEATyyQZERGRO3KSsgUiIiI34GzdVj0MgFeg7ds7qrtlaykUTJCR7CpyciAajYBaDXWYDN15iYiIyOFYSUZE1JCaw9+aS6j5GUQ7naS+Nq9AoOCCNHG9PYX0AC7/BgR2aXpbd20YoPaUuqJWJcT844BrZ6vnVHM0d72uRDWogoIQ9+UWlF+8BEHFW2giIiJ3xP/hiYjqo/NtXVVQQCfg3CG7heMW/DoAWoN0be3J0x+IHtK+EzXhNwCmMkCtk372iZAWV9GeXztyUnV/JwW1GtrOnaHt3FmGeIiIiKgtMElGRFQffYj1zx4GoDgXUNj4z2ZVsoKqKRTSZO2OYGuSRe3pmOPLTaEAFK39nWtFospZh2kSEZFbM5lMKC8vlzsMInICGo0GCjvMw8skGRFRbX6xdbvoBXYBCs7XTZ65DSet5NEHA4WX7Lc/ryDgcrr99udSXCSRpdK2rMMmkYPlfvQxzCXFMIwdC010tNzhELVroigiJycH+fn5codCRE5CoVCgQ4cO0Gg0rdoPk2RERLXVN2eWUi0lz9yVhw+g83OuCjgPQ+srv/w7ALlnqn921LA+32jpGhZfdcz+iahtNJJLztuwHsbfT8OjezyTZEQyq0qQBQcHw9PTEwKH7RO1a2azGRcuXEB2djaio6Nb9W8Ck2RERI7mCjduggCE9pQ7CvvzibROkjmKX4z0lUmyutQ6wCdK7iiIWkU0m1GedRYAoImNkTkaovbNZDJZEmQBAQFyh0NETiIoKAgXLlxARUUF1Gp1i/fT+gGbREREDuHEyUVfJn2sNJYIjhwAqFpX9k4kt4rsbIhGI6BWQx0aKnc4RO1a1Rxknp5uOs8oEbVI1TBLk8nUqv0wSUZEVMUrUBre5+ErdyTk7NT1DMl1dcp6PnFzpQ6ZRA5kzMoCAGgiIyGoOBCDyBlwiCUR1WSvfxP4vzwRUZXg7nJHQC7DRSbBb4raEygvlr73i5U6uDZFoQLMFQ4Ni8jZGDMzAQCaGA61JCIicmesJCMiovalqmJK2UZDAKuGZgZ0bJvjNYdSDXQYJi1KNWxK/tWXTBbdJGlI1ABjRlWSjBP2ExERuTMmyYiIqH0J7QV4BUlf24JfLBA1CDCEt83xGuIVJH3V6lu+j+D41nccJXIFtYZsVA23VLOSjIicRGxsLJYvXy5rDIsWLULfvn0b3SYjIwOCICA1NdWmfSYkJODOO+9sdWxELcUkGRGRo6l0ckdANWm8gOBugKYNkz3OMHG9fxwQ1BUIcaUupqxQI+cQ8cbr6PDFZhhuvVXuUIjIhY0cORKPP/64XfZ18OBB/OUvf7F5+6pklVKpxPnz563WZWdnQ6VSQRAEZGRktDim+hJcUVFRyM7ORs+eznv/sX79eiiVSiQmJsoditNLSUmBIAjIz8+XOxSHYZKMiMhRIgcA4Tc4R4LEVXmHASoP+auwACCgk9wRtI5CCeiD65+gv7VaPVEqJ18m56bw9IRH165QBQbKHQoRuTFRFFFRYdu8n0FBQS3q8BkREYF///vfVo+tW7cOERGOadajVCoRGhoKlRM3PVm9ejWeeuoprF+/HqWlpbLGYjQaZT0+MUlGROQ4al3rhrYRoFQBUQOdYz4vQ5jcEbQPKq3cERARkasxm+RZmiEhIQE7d+7E22+/DUEQIAgCkpOTIQgCvvnmG/Tv3x9arRZ79uzB6dOnMWnSJISEhECv12PgwIH47rvvrPZXe7ilIAj44IMPMHnyZHh6eqJz587YsmVLnThmz56NtWvXWj22du1azJ492+qx5ORk+Pr6Wj22efPmBjsILlq0COvWrcMXX3xhOb+UlJR6h1v++uuvuP3222EwGODt7Y1hw4bh9OnT9e5327ZtGDp0KHx9fREQEIDbb7/daluj0Yh58+YhLCwMHh4eiImJwWuvvQZASjouWrQI0dHR0Gq1CA8Px6OPPmq1/zNnzuDHH3/EwoUL0aVLF3z++ed1YlizZg169OgBrVaLsLAwzJs3z7IuPz8ff/3rXxESEgIPDw/07NkTX331leWa1B6Ounz5csTGxlp+rqq+e/XVVxEeHo6uXbsCAD788EMMGDAA3t7eCA0Nxb333otLly5Z7auh67hr1y6o1Wrk5ORYbf/4449j2LBh9V7nmjIzMzFx4kT4+fnBy8sLPXr0wNdff42MjAzccsstAAA/Pz8IgoCEhAQAgNlsxmuvvYYOHTpAp9OhT58++O9//2vZZ1UF2tatW9G7d294eHhg8ODBOH78eJPxtDXnTecSERGRzNqwwiusN1BwAfDrAJw72HbHJbJSd4hvyfFfkf+f/0DXty9875osQ0xE1CizCcj8UZ5jx9wkVWrb4O2338bJkyfRs2dPvPzyywCkJAcALFy4EG+88Qbi4uLg5+eHs2fPYsKECXj11Veh1Wrx73//GxMnTkR6ejqioxtuIPLSSy9h2bJleP311/HOO+9g5syZyMzMhL+/v2WbO+64A++99x727NmDoUOHYs+ePcjLy8PEiROxePHiFl+KBQsW4MSJEygoKLAk4fz9/XHhwgWr7c6fP4/hw4dj5MiR+P7772EwGLB3794GK+iKioowf/589O7dG4WFhXjhhRcwefJkpKamQqFQYMWKFdiyZQv+85//IDo6GmfPnsXZs2cBAJ999hn++c9/YsOGDejRowdycnJw9OhRq/2vXbsWt912G3x8fDBr1iysXr0a9957r2X9u+++i/nz52Pp0qUYP348rl27hr179wKQEkPjx4/H9evX8dFHH6Fjx45IS0uDUmnb70SVHTt2wGAwYPv27ZbHysvLsXjxYnTt2hWXLl3C/PnzkZCQgK+//rrJ6zh8+HDExcXhww8/xJNPPmnZ38cff4xly5Y1GU9iYiKMRiN27doFLy8vpKWlQa/XIyoqCp999hmmTJmC9PR0GAwG6HTStDKvvfYaPvroI7z33nvo3Lkzdu3ahVmzZiEoKAgjRoyw7PvJJ5/E22+/jdDQUDzzzDOYOHEiTp48CbXaASMdWohJMiIiImode3S39PCRFiInU3rsF+T/5z+ouHSJSTIiajEfHx9oNBp4enoiNDQUAPDbb78BAF5++WWMGTPGsq2/vz/69Olj+Xnx4sXYtGkTtmzZYlXFVFtCQgJmzJgBAFiyZAlWrFiBAwcOYNy4cZZt1Go1Zs2ahTVr1mDo0KFYs2YNZs2a1eokhV6vh06nQ1lZmeX86pOUlAQfHx9s2LDBcswuXbo0uP2UKVOsfl6zZg2CgoKQlpaGnj17IisrC507d8bQoUMhCAJiajRYycrKQmhoKEaPHg21Wo3o6GjceOONlvVmsxnJycl45513AADTp0/HE088gTNnzqBDhw4AgFdeeQVPPPEEHnvsMcvzBg4cCAD47rvvcODAAZw4ccJyDnFxcTZdr5q8vLzwwQcfQKOpnqJlzpw5lu/j4uKwYsUKDBw4EIWFhdDr9U1exwceeABr1661JMm+/PJLlJaWYurUqU3Gk5WVhSlTpqBXr151zqkq4RocHGypNCwrK8OSJUvw3XffYciQIZbn7NmzB//617+skmQvvvii5Xd93bp1iIyMxKZNm2yKq60wSUZERES20XjJHQFRmzNmSp0tNexsSeScFEqpokuuY9vBgAEDrH4uLCzEokWLsHXrVmRnZ6OiogIlJSXIquy025DevXtbvvfy8oLBYKgzRA+QEjA33XQTlixZgo0bN+Knn36yeS601kpNTcWwYcNsTsqdOnUKL7zwAvbv348rV67AbDYDkBI5PXv2REJCAsaMGYOuXbti3LhxuP3223FrZZOVe+65B8uXL0dcXBzGjRuHCRMmYOLEiZb50bZv346ioiJMmDABABAYGIgxY8ZgzZo1WLx4MS5duoQLFy5g1KhRDZ5LZGRko0k+W/Tq1csqQQYAhw8fxqJFi3D06FHk5eVZnXd8fHyT1zEhIQHPPfcc9u3bh8GDByM5ORlTp06Fl1fT93KPPvooHn74YXz77bcYPXo0pkyZYvW7Vdvvv/+O4uJiq0QvIA2FveGGG6weq0qiAVLCrWvXrjhx4kSTMbUlzklGRERETYvoB6g9HLNvJt/IiRkzMwEA6piGhzgRkcwUSnkWO6mduFiwYAE2bdqEJUuWYPfu3UhNTUWvXr2anNS9dsJEEARLcqWmXr16oVu3bpgxYwa6d+9eb+dJhUIBsValeHl5ua2n1KCq4Xm2mjhxInJzc7Fq1Srs378f+/fvB1A9wX2/fv1w5swZLF68GCUlJZg6dSruvvtuAFJnzfT0dKxcuRI6nQ6PPPIIhg8fbjmP1atXIzc3FzqdDiqVCiqVCl9//TXWrVsHs9ncZKxNrbf1GtZ+/YuKijB27FgYDAZ8/PHHOHjwIDZt2mR13k0dOzg4GBMnTsTatWtx8eJFfPPNN1bVaY158MEH8ccff+C+++7DsWPHMGDAAEu1XX0KCwsBAFu3bkVqaqplSUtLs5qXzFUwSUZEREQSdSNdshpLZLW0u2XkAGkuMk3zu3MRtRVjFivJiMg+NBoNTKamJ/zfu3cvEhISMHnyZPTq1QuhoaHIyMiwayxz5sxBSkpKg4mToKAgXL9+HUVFRZbHak6+Xx9bzq93797YvXu3TQm3q1evIj09Hc899xxGjRqF7t27Iy8vr852BoMB06ZNw6pVq/Dpp5/is88+Q25uLgApmTRx4kSsWLECKSkp+Omnn3Ds2DFcvXoVX3zxBTZs2GCV2Dly5Ajy8vLw7bffwtvbG7GxsdixY0eD53Lu3DmcPHmy3vVBQUHIycmxSpQ1dQ0BaRju1atXsXTpUgwbNgzdunWrUxFoy3V88MEH8emnn+L9999Hx44dcfPNNzd57CpRUVF46KGH8Pnnn+OJJ57AqlWrAMBS8VbzdY6Pj4dWq0VWVhY6depktURFRVntd9++fZbv8/LycPLkSXTv3t3muNoCh1sSEZFzamnihVouqCuQnwV4t1EnT7VOWoicjvTvj2gyodySJIuVMR4icgexsbHYv38/MjIyoNfr663yAoDOnTvj888/x8SJEyEIAp5//vkGt22puXPn4p577qnTwbLKoEGD4OnpiWeeeQaPPvoo9u/fj+Tk5Eb3GRsbi//9739IT09HQEAAfHzqzjU6b948vPPOO5g+fTqefvpp+Pj4YN++fbjxxhstnR2r+Pn5ISAgAO+//z7CwsKQlZWFhQsXWm3z1ltvISwsDDfccAMUCgU2btyI0NBQ+Pr6Ijk5GSaTyXIuH330EXQ6HWJiYvDhhx8iICAAU6dOrdOxc8KECVi9ejXGjRuHRYsW4aGHHkJwcLBlkv69e/fi//2//4cRI0Zg+PDhmDJlCt566y106tQJv/32GwRBwLhx4zBy5EhcvnwZy5Ytw913341t27bhm2++gcFgaPQ6RkdHQ6PR4J133sFDDz2E48eP12mqYMt1rKpGe+WVVyzNImzx+OOPY/z48ejSpQvy8vLwww8/WBJZMTExEAQBX331FSZMmACdTgdvb28sWLAAf/vb32A2mzF06FBLgwODwWDVOfXll19GQEAAQkJC8OyzzyIwMBB33nmnzbG1BaerJDt79ixGjhyJ+Ph49O7dGxs3bpQ7JCIiovZBpQUCOwNavdyREDmFipwciOXlENRqqMManoiaiMgWCxYsgFKpRHx8PIKCghqcY+ytt96Cn58fbrrpJkycOBFjx45Fv3797BqLSqVCYGCgZX6u2vz9/fHRRx/h66+/Rq9evbB+/XosWrSo0X3OnTsXXbt2xYABAxAUFGTpAllTQEAAvv/+exQWFmLEiBHo378/Vq1aVe/cWgqFAhs2bMDhw4fRs2dP/O1vf8Prr79utY23tzeWLVuGAQMGYODAgcjIyMDXX38NhUIBX19frFq1CjfffDN69+6N7777Dl9++SUCAgKwZs0aTJ48uU6CDJCaBWzZsgVXrlzB7NmzsXz5cqxcuRI9evTA7bffjlOnTlm2/eyzzzBw4EDMmDED8fHxeOqppyxVVt27d8fKlSuRlJSEPn364MCBA1iwYEGj1xCQKtCSk5OxceNGxMfHY+nSpXjjjTeafR0VCgUSEhJgMpnw5z//ucnjVjGZTEhMTET37t0xbtw4dOnSBStXrgQARERE4KWXXsLChQsREhJiaSSxePFiPP/883jttdcsz9u6daulAUKVpUuX4rHHHkP//v2Rk5ODL7/8ss58bHITxNqDZGWWnZ2Nixcvom/fvsjJyUH//v1x8uRJmyaYA4CCggL4+Pjg2rVrTWZoiYjICZ3ZLX3V+QKhvZr//Eu/AUWXpe87DKveX9XP9ogtqCtwOb3ufq+eBgqsW523+piOUHUeHj7ScMeG5J4Brp2Tvq86D1M5kLXPeju/WCAvo/59tOT8q+LTB0vXug3w/sE1OPx1OncIKC8BwvoAHgYU7T+ArNmzoYmLQ8evt9r/eETUbKWlpZbugx4eDpork8hNPPDAA7h8+TK2bNkiaxwpKSm45ZZbkJeX12D1Yms19m9Dc+4fnG64ZVhYGMLCpGEeoaGhCAwMRG5urs1JMiIiImqCb7Q0rNK/Q9Pb1qZQSRVnFWX2j4vIyXgNuhFdDh2CKS9X7lCIiIhsdu3aNRw7dgyffPKJ7AkyV9Ps4Za7du3CxIkTER4eDkEQsHnz5jrbJCUlITY2Fh4eHhg0aBAOHDjQouAOHz4Mk8lUZ7I3IiKiBvlESF+9guSNw5n5xQCxQwGtd+PbCfXcJggCEDkQ0Ic4JjYiOdUzwEKp94KG96JERORCJk2ahFtvvRUPPfQQxowZY7Vu/Pjx0Ov19S5LliyRKWLn0exKsqKiIvTp0wdz5szBXXfdVWf9p59+ivnz5+O9997DoEGDsHz5cowdOxbp6ekIDg4GAPTt2xcVFRV1nvvtt98iPDwcAJCbm4s///nPli4KRERENtF6A9FDAGXlf3HeocD1HOkrVbOlMYIhAijJBTwD6z635vMd1mSBzRuIiIiImislJaXBdR988AFKSkrqXefv7++QeEaOHAknm+mrQc1Oko0fPx7jx49vcP1bb72FuXPn4v777wcAvPfee9i6dSvWrFlj6UTRVNvTsrIy3HnnnVi4cCFuuummJrctK6se8lFQUGDjmRARkVNSagCTEfAMaMU+avz35t9RqirTcp6pZlOqgPAbmvec0F5AzrHWHde/A1CQLVW8EcnowrPPQtBoEDh3LtSVH+QSERG5soiICLlDcGp27W5pNBpx+PBhjB49uvoACgVGjx6Nn376yaZ9iKKIhIQE/OlPf8J9993X5PavvfYafHx8LAuHZhIRubiIfkBID8A7zD77UyikJgAKp2vo7J50vq3fh08kEDVQmvuMSCaiyYSCLV8if/0GiGbX+PSbiIiIWseu7xiuXLkCk8mEkBDreUpCQkKQk5Nj0z727t2LTz/9FJs3b0bfvn3Rt29fHDvW8CfSTz/9NK5du2ZZzp4926pzICIimSnVgKe/A4fwkcNVzXXW1JxnRE6sPDsHYnk5BLUa6jAO1yYiImoPnK675dChQ2E2m23eXqvVQqvlJ81ERNQGvEOB0mutGwrqjmrPMREcDxRe5OT+5NLKszIBAOqoKAhKpczREBERUVuwa5IsMDAQSqUSFy9etHr84sWLCA3lJ3BEROTiAjvLHYFrUGkAX05/QC5MEGDMlJJkmuhomYMhIiKitmLX4ZYajQb9+/fHjh07LI+ZzWbs2LEDQ4YMseehiIiInI+LdO2xOw6NJTdkzMwCAGhi2ECCiIiovWh2kqywsBCpqamWDpVnzpxBamoqsrKkG4n58+dj1apVWLduHU6cOIGHH34YRUVFlm6XRERE7YLWGwjsIncURNRClkqyWCbJiNyOKAJndgFfzAM+mS59PbPLpT7sio2NxfLly2WNYdGiRejbt2+j22RkZEAQBEv+oCkJCQm48847Wx0buT65fsebPdzy0KFDuOWWWyw/z58/HwAwe/ZsJCcnY9q0abh8+TJeeOEF5OTkoG/fvti2bVudyfztLSkpCUlJSTCZTA49DhERUYNqVlSF95UtDCJqPdO1awAANYdbErmXkjzg0/uAjN3Wjx/5EIgdBkz7END5OeTQI0eORN++fe3yxv/gwYPw8vKyefuMjAx06NABCoUCWVlZiIiIsKzLzs5GVFQUTCYTzpw5g9jY2BbFlJCQgPz8fGzevNnyWFRUFLKzsxEYGNiifbaF9evXY9asWXjooYeQlJQkdzgks2ZXko0cORKiKNZZkpOTLdvMmzcPmZmZKCsrw/79+zFo0CB7xlyvxMREpKWl4eDBgw4/FhERUb1c6BNoIqpP9d9w7Ccfo8uB/fAcOFDGeIjIrkSxOkGm0gH97wcmvi19Vemkxz+9T7b/z0VRREVFhU3bBgUFwdPTs9nHiIiIwL///W+rx9atW2eVNLMnpVKJ0NBQqFRO1zPQYvXq1Xjqqaewfv16lJaWyhqL0WiU9fhk5znJiIiIqB1icpDclNJggEKjkTsMImqMKALGItuWU9urE2QPbgcmLgf6J0hfH9xenSj7/Tvb9teM//8SEhKwc+dOvP322xAEAYIgIDk5GYIg4JtvvkH//v2h1WqxZ88enD59GpMmTUJISAj0ej0GDhyI7777zmp/tYeiCYKADz74AJMnT4anpyc6d+6MLVu21Ilj9uzZWLt2rdVja9euxezZs60eS05Ohq+vr9VjmzdvhtDAPKSLFi3CunXr8MUXX1jOLyUlpd7hlr/++ituv/12GAwGeHt7Y9iwYTh9+nS9+922bRuGDh0KX19fBAQE4Pbbb7fa1mg0Yt68eQgLC4OHhwdiYmLw2muvAZCSjosWLUJ0dDS0Wi3Cw8Px6KOPWu3/zJkz+PHHH7Fw4UJ06dIFn3/+eZ0Y1qxZgx49ekCr1SIsLAzz5s2zrMvPz8df//pXhISEwMPDAz179sRXX31luSa1h6MuX77cqlKvanjpq6++ivDwcHTt2hUA8OGHH2LAgAHw9vZGaGgo7r33Xly6dMlqXw1dx127dkGtViMnJ8dq+8cffxzDhg2r9zrXtmfPHgwbNgw6nQ5RUVF49NFHUVRUZFkfGxuLxYsXY8aMGfDy8kJERESdKrysrCxMmjQJer0eBoMBU6dOrdPk8csvv8TAgQPh4eGBwMBATJ482Wp9cXEx5syZA29vb0RHR+P999+3Kf7WYJKMiIiIiIiIXFN5MbAk3Lblk3uk5/SZDoT2st5PaC+gzzTp+4/vtm1/5cU2h/n2229jyJAhmDt3LrKzsy1DHAFg4cKFWLp0KU6cOIHevXujsLAQEyZMwI4dO3DkyBGMGzcOEydOtMwD3pCXXnoJU6dOxS+//IIJEyZg5syZyM3NtdrmjjvuQF5eHvbs2QNASobk5eVh4sSJNp9LfRYsWICpU6di3LhxlvO76aab6mx3/vx5DB8+HFqtFt9//z0OHz6MOXPmNFhBV1RUhPnz5+PQoUPYsWMHFAoFJk+eDLPZDABYsWIFtmzZgv/85z9IT0/Hxx9/bElCffbZZ/jnP/+Jf/3rXzh16hQ2b96MXr2sX/e1a9fitttug4+PD2bNmoXVq1dbrX/33XeRmJiIv/zlLzh27Bi2bNmCTp06AZCaFI4fPx579+7FRx99hLS0NCxduhRKpbJZ127Hjh1IT0/H9u3bLQm28vJyLF68GEePHsXmzZuRkZGBhIQEm67j8OHDERcXhw8//NCyfXl5OT7++GPMmTOnyXhOnz6NcePGYcqUKfjll1/w6aefYs+ePVbJQQB4/fXX0adPHxw5cgQLFy7EY489hu3bt1uuzaRJk5Cbm4udO3di+/bt+OOPPzBt2jTL87du3YrJkydjwoQJOHLkCHbs2IEbb7zR6hhvvvkmBgwYgCNHjuCRRx7Bww8/jPT09GZd3+Zy3ppHIiIicmI1Pklmd0tyM9e++RbXvtoG7zFj4DdtqtzhEJG9NTRvaFgDj9uBj48PNBoNPD09ERoaCgD47bffAAAvv/wyxowZY9nW398fffr0sfy8ePFibNq0CVu2bKmTqKgpISEBM2bMAAAsWbIEK1aswIEDBzBu3DjLNmq1GrNmzcKaNWswdOhQrFmzBrNmzYJarW7V+en1euh0OpSVlVnOrz5JSUnw8fHBhg0bLMfs0qXhRkdTpkyx+nnNmjUICgpCWloaevbsiaysLHTu3BlDhw6FIAiIqdGROCsrC6GhoRg9ejTUajWio6OtkjBmsxnJycl45513AADTp0/HE088gTNnzqBDhw4AgFdeeQVPPPEEHnvsMcvzBlYOw//uu+9w4MABnDhxwnIOcXFxNl2vmry8vPDBBx9AU6NyuWYyKy4uDitWrMDAgQNRWFgIvV7f5HV84IEHsHbtWjz55JMApIqt0tJSTJ3a9P9pr732GmbOnInHH38cANC5c2esWLECI0aMwLvvvgsPDw8AwM0334yFCxdajr13717885//xJgxY7Bjxw4cO3YMZ86csSSD//3vf6NHjx44ePAgBg4ciFdffRXTp0/HSy+9ZDl2zd97AJgwYQIeeeQRAMDf//53/POf/8QPP/xgqbhzBFaSERERUfMxMUZurPT4ryjaswfGP/6QOxQiaoraE3jmgm1LHymBhAup9e8ru/LxPjNs25+6+XOC1WfAgAFWPxcWFmLBggXo3r07fH19odfrceLEiSYryXr37m353svLCwaDoc4QPUBKwGzcuBE5OTnYuHGjTdVF9pKamophw4bZnJQ7deoUZsyYgbi4OBgMBkuVWNW1SEhIQGpqKrp27YpHH30U3377reW599xzD0pKShAXF4e5c+di06ZNVhVr27dvR1FRESZMmAAACAwMxJgxY7BmzRoAwKVLl3DhwgWMGjWqwXOJjIxsNMlni169elklyADg8OHDmDhxIqKjo+Ht7Y0RI0ZYnXdT1zEhIQG///479u3bB0AaPjt16lSbmj0cPXoUycnJ0Ov1lmXs2LEwm804c+aMZbshQ4ZYPW/IkCE4ceIEAODEiROIioqyJMgAID4+Hr6+vpZtUlNTG7y2VWr+TguCgNDQ0Hp/p+2JSTIiIiJqPp8o6c2Bfwe5IyGyO2PWWQCAJjamiS2JSHaCAGi8bFv63is95+gGIOeY9X5yjgFHP5W+7zvTtv3Z6QOj2omLBQsWYNOmTViyZAl2796N1NRU9OrVq8lJ3WsnTARBsAxLrKlXr17o1q0bZsyYge7du6Nnz551tlEoFBBrzblWXl5u6yk1SKfTNWv7iRMnIjc3F6tWrcL+/fuxf/9+ANUT3Pfr1w9nzpzB4sWLUVJSgqlTp+Luu+8GIHXWTE9Px8qVK6HT6fDII49g+PDhlvNYvXo1cnNzodPpoFKpoFKp8PXXX2PdunUwm81NxtrUeluvYe3Xv6ioCGPHjoXBYMDHH3+MgwcPYtOmTVbn3dSxg4ODMXHiRKxduxYXL17EN998Y3MytLCwEH/961+RmppqWY4ePYpTp06hY8eONu3DFrb8Ltj6O21PbjPcMikpCUlJSTCZTHKHQkRE5P5UGiCyv/T9tXPyxkJkZ8az0u+0JoZJMiK3EjtMWjJ2Ax+MkeYgC+srVZAd/RSoKKncZqhDDq/RaGx6v7p3714kJCRYJjEvLCxERkaGXWOZM2cOHnnkEbz77rv1rg8KCsL169dRVFRkSeLUnHy/PracX+/evbFu3TqUl5c3WU129epVpKenY9WqVZYJ56vmUqvJYDBg2rRpmDZtGu6++26MGzcOubm58Pf3h06nw8SJEzFx4kQkJiaiW7duOHbsGGJiYvDFF19gw4YN6NGjh2VfJpMJQ4cOxbfffotx48YhNjYWO3bswC233FLvuZw7dw4nT56st5osKCgIOTk5EEXR0vCgqWsISMNwr169iqVLl1oqsQ4dOlTn2E1dxwcffBAzZsxAZGQkOnbsiJtvvrnJYwNS4jEtLc0y91pDqqrUav7cvXt3AED37t1x9uxZnD171nIOaWlpyM/PR3x8vOUcduzYgfvvv9+muNqK21SSJSYmIi0tDQcPHpQ7FCIiIiJyUaLJjPJz5wEwSUbkdgQBmPahlAirKAEOJwNfPS59rUqQTfvQYVMKxMbGYv/+/cjIyMCVK1carIjp3LkzPv/8c0sFz7333mv36pm5c+fi8uXLePDBB+tdP2jQIHh6euKZZ57B6dOn8cknnyA5ObnRfcbGxuKXX35Beno6rly5Um/V1Lx581BQUIDp06fj0KFDOHXqFD788MN6J2P38/NDQEAA3n//ffz+++/4/vvvMX/+fKtt3nrrLaxfvx6//fYbTp48iY0bNyI0NBS+vr5ITk7G6tWrcfz4cfzxxx/46KOPoNPpEBMTgw8//BABAQGYOnUqevbsaVn69OmDCRMmWCbwX7RoEd58802sWLECp06dws8//2yZw2zEiBEYPnw4pkyZgu3bt+PMmTP45ptvsG3bNgDAyJEjcfnyZSxbtgynT59GUlISvvnmmyZfm+joaGg0Grzzzjv4448/sGXLFixevLjZ17GqGu2VV15pViLq73//O3788UfMmzcPqampOHXqFL744os68+Ht3bsXy5Ytw8mTJ5GUlISNGzda5m4bPXo0evXqhZkzZ+Lnn3/GgQMH8Oc//xkjRoywDC9+8cUXsX79erz44os4ceIEjh07hn/84x82x+kobpMkIyIiIiJqrfLLuRDLyyGo1VA1Mvk0EbkonR8w+0tg9lfADfcBXcZLX2d/JT2u83PYoRcsWAClUon4+HgEBQU1OMfYW2+9BT8/P9x0002YOHEixo4di379+tk1FpVKhcDAQKhU9Q8u8/f3x0cffYSvv/4avXr1wvr167Fo0aJG9zl37lx07doVAwYMQFBQEPbu3Vtnm4CAAHz//fcoLCzEiBEj0L9/f6xatareaiiFQoENGzbg8OHD6NmzJ/72t7/h9ddft9rG29sby5Ytw4ABAzBw4EBkZGTg66+/hkKhgK+vL1atWoWbb74ZvXv3xnfffYcvv/wSAQEBWLNmDSZPnmyp8KppypQp2LJlC65cuYLZs2dj+fLlWLlyJXr06IHbb78dp06dsmz72WefYeDAgZgxYwbi4+Px1FNPWarpunfvjpUrVyIpKQl9+vTBgQMHsGDBgkavISBVoCUnJ2Pjxo2Ij4/H0qVL8cYbbzT7OioUCiQkJMBkMuHPf/5zk8et0rt3b+zcuRMnT57EsGHDcMMNN+CFF15AeHi41XZPPPEEDh06hBtuuAGvvPIK3nrrLYwdOxaANCzyiy++gJ+fH4YPH47Ro0cjLi4On376qeX5I0eOxMaNG7Flyxb07dsXf/rTn3DgwAGb43QUQaw9SNbFFRQUwMfHB9euXYPBYJA7HCIicldndld/30EaAoArvwPXs60faw+unQNyKydyddHz5v2Da3D465S1H4UHU3H22eXQdOyIjlu/sv8xiKhVSktLLd0Hq7rsEVH9HnjgAVy+fBlbtmyx635jY2Px+OOPWzpgOoPG/m1ozv2D28xJRkRERETUWuaiYih9fDjUkoiIXNa1a9dw7NgxfPLJJ3ZPkLk7DrckIiIiIqpkGDYAXXZ9h4jl/5Q7FCIiohaZNGkSbr31Vjz00EMYM2aM1brx48dDr9fXuyxZskSmiJ0HK8mIiIjsxTtUGm6p85U7EiJqJYVGI3cIRERELZKSktLgug8++AAlJSX1rvP397dp//butupM3CZJlpSUhKSkJJta6hIRETmEVg9EDwYUbvPfKxERERG5kYiICLlDcGpuM9wyMTERaWlpOHjwoNyhEBFRe6ZUO6x1vNNyrx5A1I4VHj6OU7MXImve4zBdvy53OERERNTG+FE3EREREbV7psIiZL+5GhVX8lCRdwAKLy+5QyIiIqI25jaVZERERG3Kp7JU3TtU3jicQXurnCO3dOmN11FxJQ8AoNB5QFDwNpnI3RXt24eiffvkDoOInAgryYiIiFrCrwPgFQRo9HJHQkStVPTTT8jf8KnlZ3PBdRTt2wevwYNljIqIHMlUWITsZ54FBAFxW75g9SgRAWAlGRERUcsIAqD1ZhUVkYszFRYh+9nnAAC+M6bDd/o0AED2s8/BXFQkZ2hE5ECX3ngd5RcuoPz8eVx84w25wyEiJ8EkGRERERG1W5fefAPlFy5AHRGBkAULELzgSajDw1F+/jwuvfmm3OERkQPUrh7NX7/BJYZdxsbGYvny5Q7bf0pKCgRBQH5+vt3iSE5Ohq+vb6tjI9eXkJCAO++8U+4wmsQkGREREbUOu1uSiyratw/56zcAAMJefRUKLy8o9V4IW/IqACDvk/Uu8caZiGznTtWjsbGxEAQBGzZsqLOuR48eEAQBycnJLd5/QwmugwcP4i9/+UuL9+to586dg0ajQc+ePeUOhVwQk2RERERE1O5Y5iMC4HfvDHgNHmRZ5zV4MHxnTAfgmm+ciahh7lY9GhUVhbVr11o9tm/fPuTk5MDLQfOsBQUFwdPT0yH7tofk5GRMnToVBQUF2L9/v6yxmEwmmM1mWWOg5nGbJFlSUhLi4+MxcOBAuUMhIiJqXzgvG7kgQQB/d4nciLm4uOGlrAyAbdWj13ftsmm/zfX+++8jPDy8TsJk0qRJmDNnDk6fPo1JkyYhJCQEer0eAwcOxHfffdfkfmfOnImdO3fi7NmzlsfWrFmDmTNnQqWq7tOXkZEBQRCQmppqeSw/Px+CICAlJaXOflNSUnD//ffj2rVrEAQBgiBg0aJFAOoOt8zPz8df//pXhISEwMPDAz179sRXX31Vb7y2nOfKlSvRuXNneHh4ICQkBHfffbdl3X//+1/06tULOp0OAQEBGD16NIpqfJAhiiLWrl2L++67D/feey9Wr15dJ4a9e/di5MiR8PT0hJ+fH8aOHYu8PKm7sdlsxrJly9CpUydotVpER0fj1VdftVyT2sNRU1NTIQgCMjIyAFRX323ZsgXx8fHQarXIysrCwYMHMWbMGAQGBsLHxwcjRozAzz//bBVXQ9exqKgIBoMB//3vf62237x5M7y8vHD9+vV6r3VNZ8+exdSpU+Hr6wt/f39MmjTJEjNQPRTypZdeQlBQEAwGAx566CEYjUbLNmVlZXj00UcRHBwMDw8PDB06FAcPHrQ6zq+//orbb78dBoMB3t7eGDZsGE6fPm21zRtvvIGwsDAEBAQgMTER5eXlTcbfltwmSZaYmIi0tLQ6LxIRERERUW0KLy+EvfoKgLrDKut7I01Ezi29X/8Gl3OPPmpz9ej5ef/Pqnr091Gj691nc91zzz24evUqfvjhB8tjubm52LZtG2bOnInCwkJMmDABO3bswJEjRzBu3DhMnDgRWVlZje43JCQEY8eOxbp16wAAxcXF+PTTTzFnzpxmx1jTTTfdhOXLl8NgMCA7OxvZ2dlYsGBBne3MZjPGjx+PvXv34qOPPkJaWhqWLl0KpVJZ736bOs9Dhw7h0Ucfxcsvv4z09HRs27YNw4cPBwBkZ2djxowZmDNnDk6cOIGUlBTcddddEGtM+/DDDz+guLgYo0ePxqxZs7BhwwarJFpqaipGjRqF+Ph4/PTTT9izZw8mTpwIk8kEAHj66aexdOlSPP/880hLS8Mnn3yCkJCQZl274uJi/OMf/8AHH3yAX3/9FcHBwbh+/Tpmz56NPXv2YN++fejcuTMmTJhgSXA1dh29vLwwffr0OhWDa9euxd133w1vb+9G4ykvL8fYsWPh7e2N3bt3Y+/evdDr9Rg3bpxVEmzHjh2W67p+/Xp8/vnneOmllyzrn3rqKXz22WdYt24dfv75Z3Tq1Aljx45Fbm4uAOD8+fMYPnw4tFotvv/+exw+fBhz5sxBRUWF1etz+vRp/PDDD1i3bh2Sk5NbNSTYEVRNb0JERERE5H6q3hjnr9+A7GefQ9yWLyCKaPCNNBG5LrmrR/38/DB+/Hh88sknGDVqFACpKiowMBC33HILFAoF+vTpY9l+8eLF2LRpE7Zs2YJ58+Y1uu85c+bgiSeewLPPPov//ve/6NixI/r27duqeDUaDXx8fCAIAkJDQxvc7rvvvsOBAwdw4sQJdOnSBQAQFxfX4PZ9+vRp9DyzsrLg5eWF22+/Hd7e3oiJicENN9wAQEqSVVRU4K677kJMTAwAoFevXlb7X716NaZPnw6lUomePXsiLi4OGzduREJCAgBg2bJlGDBgAFauXGl5To8ePQAA169fx9tvv43/+7//w+zZswEAHTt2xNChQ229bACkpNTKlSutzvNPf/qT1Tbvv/8+fH19sXPnTtx+++1NXscHH3wQN910E7KzsxEWFoZLly7h66+/tqna8NNPP4XZbMYHH3wAofJvYO3atfD19UVKSgpuvfVWANJrvmbNGnh6eqJHjx54+eWX8eSTT2Lx4sUoKSnBu+++i+TkZIwfPx4AsGrVKmzfvh2rV6/Gk08+iaSkJPj4+GDDhg1Qq9UAYDmXKn5+fvi///s/KJVKdOvWDbfddht27NiBuXPnNusaO5LbVJIRERERETVX8BMLLPMRXXzjDVx643XLfEXBTzwhd3hEZKOuPx9ucIlcscLm6tHIpP+zqh7ttOO7evfZEjNnzsRnn32Gssrhnx9//DGmT58OhUKBwsJCLFiwAN27d4evry/0ej1OnDjRZCUZANx2220oLCzErl27sGbNmlZXkTVHamoqIiMj6yRDGtLUeY4ZMwYxMTGIi4vDfffdh48//hjFlcNb+/Tpg1GjRqFXr1645557sGrVKsswSUAarvj5559j1qxZlsdmzZplNeSyqpKsPidOnEBZWVmD622l0WjQu3dvq8cuXryIuXPnonPnzvDx8YHBYEBhYaHlvJu6jjfeeCN69OhhqRj86KOPEBMTY6mya8zRo0fx+++/w9vbG3q9Hnq9Hv7+/igtLbUaCtmnTx+rueaGDBmCwsJCnD17FqdPn0Z5eTluvvlmy3q1Wo0bb7wRJ06csJzDsGHDLAmy+vTo0cOqyrAq4edMmCQjIiIionar5nxE+es3IH/DpwCAsFdf4TBLIhei8PRseNFqAdTflKP2MEz9sGE27bclJk6cCFEUsXXrVpw9exa7d+/GzJkzAQALFizApk2bsGTJEuzevRupqano1auX1XC4hqhUKtx333148cUXsX//fss+rc5DIb31rzk00R5zQel0umZt39R5ent74+eff8b69esRFhaGF154AX369EF+fj6USiW2b9+Ob775BvHx8XjnnXfQtWtXnDlzBgDwySefoLS0FIMGDYJKpYJKpcLf//537NmzBydPnmwy3qbOxdZrqNPpLBVbVWbPno3U1FS8/fbb+PHHH5GamoqAgADLedtyHR988EHL0MS1a9fi/vvvr3Oc+hQWFqJ///5ITU21Wk6ePIl77723yefbypZzqJ1AEwTB6RobMElGREREraPzk74qG/7kkMiZeQ0eDN/p0yw/+069G16DB8sYERE5ipzVox4eHrjrrrvw8ccfY/369ejatSv69esHQJpMPiEhAZMnT0avXr0QGhpqNbF6U+bMmYOdO3di0qRJ8PPzq7M+KCgIgDRksUrNSfzro9FoLHN1NaR37944d+6cJQnVFFvOU6VSYfTo0Vi2bBl++eUXZGRk4PvvvwcgJVVuvvlmvPTSSzhy5Ag0Gg02bdoEQBpq+cQTT1glgo4ePYphw4ZhzZo1lnh37NhRb2ydO3eGTqdrcH1LrmHN83700UcxYcIE9OjRA1qtFleuXLGst+U6zpo1C5mZmVixYgXS0tIsQ0Kb0q9fP5w6dQrBwcHo1KmT1eLj42PZ7ujRoygpKbH8vG/fPuj1ekRFRaFjx47QaDTYu3evZX15eTkOHjyI+Ph4yzns3r3b6Sbiby4myYiIiKh1NF5A5EBpIXJRwQuehDosFOrwMIQ8+ZTc4RCRg8hdPTpz5kxs3brV0oGySufOnfH5559bEjv33ntvsypsunfvjitXrtSZ3L2KTqfD4MGDsXTpUpw4cQI7d+7Ec8891+g+Y2NjUVhYiB07duDKlSuWYY81jRgxAsOHD8eUKVOwfft2nDlzBt988w22bdtW7z6bOs+vvvoKK1asQGpqKjIzM/Hvf/8bZrMZXbt2xf79+7FkyRIcOnQIWVlZ+Pzzz3H58mV0794dqamp+Pnnn/Hggw+iZ8+eVsuMGTOwbt06VFRU4Omnn8bBgwfxyCOP4JdffsFvv/2Gd999F1euXIGHhwf+/ve/46mnnsK///1vnD59Gvv27bMM1+zUqROioqKwaNEinDp1Clu3bsWbb75p0+vTuXNnfPjhhzhx4oSl2q9m5ZUt19HPzw933XUXnnzySdx6662IjIy06dgzZ85EYGAgJk2ahN27d+PMmTNISUnBo48+inPnzlm2MxqNeOCBB5CWloavv/4aL774IubNmweFQgEvLy88/PDDePLJJ7Ft2zakpaVh7ty5KC4uxgMPPAAAmDdvHgoKCjB9+nQcOnQIp06dwocffoj09HSb4nQWTJIRERFR66k9AEX9nayIXIFS74Ww115D2JIlUDTRKYyIXFud6tEZ09usevRPf/oT/P39kZ6ebjXU7a233oKfnx9uuukmTJw4EWPHjrVUmdkqICCg0SFva9asQUVFBfr374/HH38cr7zySqP7u+mmm/DQQw9h2rRpCAoKwrJly+rd7rPPPsPAgQMxY8YMxMfH46mnnmqwAq2p8/T19cXnn3+OP/3pT+jevTvee+89rF+/Hj169IDBYMCuXbswYcIEdOnSBc899xzefPNNjB8/HqtXr0Z8fDy6detW55iTJ0+2THTfpUsXfPvttzh69ChuvPFGDBkyBF988QVUKqmn4fPPP48nnngCL7zwArp3745p06ZZ5sxSq9VYv349fvvtN/Tu3Rv/+Mc/mryGVVavXo28vDz069cP9913Hx599FEEBwc3+zo+8MADMBqNzZp3ztPTE7t27UJ0dDTuuusudO/eHQ888ABKS0thMBgs240aNQqdO3fG8OHDMW3aNNxxxx1YtGiRZf3SpUsxZcoU3HfffejXrx9+//13/O9//7NULgYEBOD7779HYWEhRowYgf79+2PVqlWNzlHmjASx5oBaN1BQUAAfHx9cu3bN6gUnIiIiagjvH1wDXyciKi0txZkzZ9ChQwd4eHi0eD+mwiKcueMOQBAQt+ULzkFILuHDDz/E3/72N1y4cAEajcZu+01ISEB+fj42b95st322tcb+bWjO/YPKkUESEREREREROZuawy6ZICNnV1xcjOzsbCxduhR//etf7ZogI2scbklERERERETtjtfgwWzSQS5h2bJl6NatG0JDQ/H0009brVuyZAn0en29y/jx42WK2HW5zXDLpKQkJCUlwWQy4eTJkyzDJyIiIptxGJ9r4OtERPYabknkLnJzc5Gbm1vvOp1Oh4iIiDaOSB4cbllLYmIiEhMTLSdPREREREREROTO/P394e/vL3cYboPDLYmIiIiIiMiluMmAKCKyE3v9m8AkGREREREREbkEtVoNQJrInIioitFoBAAolcpW7cdthlsSERERERGRe1MqlfD19cWlS5cAAJ6enhAEQeaoiEhOZrMZly9fhqenJ1Sq1qW5mCQjIiIiIiIilxEaGgoAlkQZEZFCoUB0dHSrk+ZMkhEREREREZHLEAQBYWFhCA4ORnl5udzhEJET0Gg0UChaP6MYk2RERERERETkcpRKZavnHyIiqokT9xMRERERERERUbvHJBkREREREREREbV7TJIREREREREREVG753ZzkomiCAAoKCiQORIiIiJyFVX3DVX3EeSceJ9HREREzdWc+zy3S5Jdv34dABAVFSVzJERERORqrl+/Dh8fH7nDoAbwPo+IiIhaypb7PEF0s49MzWYzLly4AG9vbwiCYPf9FxQUICoqCmfPnoXBYLD7/qll+Lo4L742zomvi/PiayMPURRx/fp1hIeH26V9ODkG7/PaJ74uzouvjXPi6+K8+NrIozn3eW5XSaZQKBAZGenw4xgMBv5SOyG+Ls6Lr41z4uvivPjatD1WkDk/3ue1b3xdnBdfG+fE18V58bVpe7be5/GjUiIiIiIiIiIiaveYJCMiIiIiIiIionaPSbJm0mq1ePHFF6HVauUOhWrg6+K8+No4J74uzouvDZF8+PfnnPi6OC++Ns6Jr4vz4mvj/Nxu4n4iIiIiIiIiIqLmYiUZERERERERERG1e0ySERERERERERFRu8ckGRERERERERERtXtMkhERERERERERUbvHJFkzJCUlITY2Fh4eHhg0aBAOHDggd0huZdeucRUlUAAADStJREFUXZg4cSLCw8MhCAI2b95stV4URbzwwgsICwuDTqfD6NGjcerUKattcnNzMXPmTBgMBvj6+uKBBx5AYWGh1Ta//PILhg0bBg8PD0RFRWHZsmWOPjWX9tprr2HgwIHw9vZGcHAw7rzzTqSnp1ttU1paisTERAQEBECv12PKlCm4ePGi1TZZWVm47bbb4OnpieDgYDz55JOoqKiw2iYlJQX9+vWDVqtFp06dkJyc7OjTc2nvvvsuevfuDYPBAIPBgCFDhuCbb76xrOfr4hyWLl0KQRDw+OOPWx7ja0PknHiv5zi8z3NOvM9zXrzPcw28z3NDItlkw4YNokajEdesWSP++uuv4ty5c0VfX1/x4sWLcofmNr7++mvx2WefFT///HMRgLhp0yar9UuXLhV9fHzEzZs3i0ePHhXvuOMOsUOHDmJJSYllm3Hjxol9+vQR9+3bJ+7evVvs1KmTOGPGDMv6a9euiSEhIeLMmTPF48ePi+vXrxd1Op34r3/9q61O0+WMHTtWXLt2rXj8+HExNTVVnDBhghgdHS0WFhZatnnooYfEqKgocceOHeKhQ4fEwYMHizfddJNlfUVFhdizZ09x9OjR4pEjR8Svv/5aDAwMFJ9++mnLNn/88Yfo6ekpzp8/X0xLSxPfeecdUalUitu2bWvT83UlW7ZsEbdu3SqePHlSTE9PF5955hlRrVaLx48fF0WRr4szOHDggBgbGyv27t1bfOyxxyyP87Uhcj6813Ms3uc5J97nOS/e5zk/3ue5JybJbHTjjTeKiYmJlp9NJpMYHh4uvvbaazJG5b5q3zyZzWYxNDRUfP311y2P5efni1qtVly/fr0oiqKYlpYmAhAPHjxo2eabb74RBUEQz58/L4qiKK5cuVL08/MTy8rKLNv8/e9/F7t27ergM3Ifly5dEgGIO3fuFEVReh3UarW4ceNGyzYnTpwQAYg//fSTKIrSjbFCoRBzcnIs27z77ruiwWCwvBZPPfWU2KNHD6tjTZs2TRw7dqyjT8mt+Pn5iR988AFfFydw/fp1sXPnzuL27dvFESNGWG6e+NoQOSfe67Ud3uc5L97nOTfe5zkP3ue5Lw63tIHRaMThw4cxevRoy2MKhQKjR4/GTz/9JGNk7ceZM2eQk5Nj9Rr4+Phg0KBBltfgp59+gq+vLwYMGGDZZvTo0VAoFNi/f79lm+HDh0Oj0Vi2GTt2LNLT05GXl9dGZ+Parl27BgDw9/cHABw+fBjl5eVWr023bt0QHR1t9dr06tULISEhlm3Gjh2LgoIC/Prrr5Ztau6jahv+jdnGZDJhw4YNKCoqwpAhQ/i6OIHExETcdtttda4fXxsi58N7PXnxPs958D7POfE+z/nwPs99qeQOwBVcuXIFJpPJ6pcYAEJCQvDbb7/JFFX7kpOTAwD1vgZV63JychAcHGy1XqVSwd/f32qbDh061NlH1To/Pz+HxO8uzGYzHn/8cdx8883o2bMnAOm6aTQa+Pr6Wm1b+7Wp77WrWtfYNgUFBSgpKYFOp3PEKbm8Y8eOYciQISgtLYVer8emTZsQHx+P1NRUvi4y2rBhA37++WccPHiwzjr+zRA5H97ryYv3ec6B93nOh/d5zon3ee6NSTIislliYiKOHz+OPXv2yB0KVeratStSU1Nx7do1/Pe//8Xs2bOxc+dOucNq186ePYvHHnsM27dvh4eHh9zhEBER2YT3ec6H93nOh/d57o/DLW0QGBgIpVJZpyPFxYsXERoaKlNU7UvVdW7sNQgNDcWlS5es1ldUVCA3N9dqm/r2UfMYVL958+bhq6++wg8//IDIyEjL46GhoTAajcjPz7favvZr09R1b2gbg8HAT0oaodFo0KlTJ/Tv3x+vvfYa+vTpg7fffpuvi4wOHz6MS5cuoV+/flCpVFCpVNi5cydWrFgBlUqFkJAQvjZETob3evLifZ78eJ/nnHif53x4n+f+mCSzgUajQf/+/bFjxw7LY2azGTt27MCQIUNkjKz96NChA0JDQ61eg4KCAuzfv9/yGgwZMgT5+fk4fPiwZZvvv/8eZrMZgwYNsmyza9culJeXW7bZvn07unbtyhL8BoiiiHnz5mHTpk34/vvv6wxj6N+/P9RqtdVrk56ejqysLKvX5tixY1Y3t9u3b4fBYEB8fLxlm5r7qNqGf2PNYzabUVZWxtdFRqNGjcKxY8eQmppqWQYMGICZM2davudrQ+RceK8nL97nyYf3ea6F93ny431eOyB35wBXsWHDBlGr1YrJycliWlqa+Je//EX09fW16khBrXP9+nXxyJEj4pEjR0QA4ltvvSUeOXJEzMzMFEVRag3u6+srfvHFF+Ivv/wiTpo0qd7W4DfccIO4f/9+cc+ePWLnzp2tWoPn5+eLISEh4n333SceP35c3LBhg+jp6cnW4I14+OGHRR8fHzElJUXMzs62LMXFxZZtHnroITE6Olr8/vvvxUOHDolDhgwRhwwZYllf1eb41ltvFVNTU8Vt27aJQUFB9bY5fvLJJ8UTJ06ISUlJbHPchIULF4o7d+4Uz5w5I/7yyy/iwoULRUEQxG+//VYURb4uzqRm1yNR5GtD5Ix4r+dYvM9zTrzPc168z3MdvM9zL0ySNcM777wjRkdHixqNRrzxxhvFffv2yR2SW/nhhx9EAHWW2bNni6IotQd//vnnxZCQEFGr1YqjRo0S09PTrfZx9epVccaMGaJerxcNBoN4//33i9evX7fa5ujRo+LQoUNFrVYrRkREiEuXLm2rU3RJ9b0mAMS1a9datikpKREfeeQR0c/PT/T09BQnT54sZmdnW+0nIyNDHD9+vKjT6cTAwEDxiSeeEMvLy622+eGHH8S+ffuKGo1GjIuLszoG1TVnzhwxJiZG1Gg0YlBQkDhq1CjLjZMo8nVxJrVvnvjaEDkn3us5Du/znBPv85wX7/NcB+/z3IsgiqLYdnVrREREREREREREzodzkhERERERERERUbvHJBkREREREREREbV7TJIREREREREREVG7xyQZERERERERERG1e0ySERERERERERFRu8ckGRERERERERERtXtMkhERERERERERUbvHJBkREREREREREbV7TJIREREREREREVG7xyQZEbmky5cv4+GHH0Z0dDS0Wi1CQ0MxduxY7N27FwAgCAI2b94sb5BERERE1Gy8zyMiuajkDoCIqCWmTJkCo9GIdevWIS4uDhcvXsSOHTtw9epVuUMjIiIiolbgfR4RyUUQRVGUOwgioubIz8+Hn58fUlJSMGLEiDrrY2NjkZmZafk5JiYGGRkZAIAvvvgCL730EtLS0hAeHo7Zs2fj2WefhUolfWYgCAJWrlyJLVu2ICUlBWFhYVi2bBnuvvvuNjk3IiIiovaM93lEJCcOtyQil6PX66HX67F582aUlZXVWX/w4EEAwNq1a5GdnW35effu3fjzn/+Mxx57DGlpafjXv/6F5ORkvPrqq1bPf/755zFlyhQcPXoUM2fOxPTp03HixAnHnxgRERFRO8f7PCKSEyvJiMglffbZZ5g7dy5KSkrQr18/jBgxAtOnT0fv3r0BSJ8Ubtq0CXfeeaflOaNHj8aoUaPw9NNPWx776KOP8NRTT+HChQuW5z300EN49913LdsMHjwY/fr1w8qVK9vm5IiIiIjaMd7nEZFcWElGRC5pypQpuHDhArZs2YJx48YhJSUF/fr1Q3JycoPPOXr0KF5++WXLJ5R6vR5z585FdnY2iouLLdsNGTLE6nlDhgzhJ4xEREREbYT3eUQkF07cT0Quy8PDA2PGjMGYMWPw/PPP48EHH8SLL76IhISEercvLCzESy+9hLvuuqvefRERERGRc+B9HhHJgZVkROQ24uPjUVRUBABQq9UwmUxW6/v164f09HR06tSpzqJQVP9zuG/fPqvn7du3D927d3f8CRARERFRvXifR0RtgZVkRORyrl69invuuQdz5sxB79694e3tjUOHDmHZsmWYNGkSAKnz0Y4dO3DzzTdDq9XCz88PL7zwAm6//XZER0fj7rvvhkKhwNGjR3H8+HG88sorlv1v3LgRAwYMwNChQ/Hxxx/jwIEDWL16tVynS0RERNRu8D6PiOTEifuJyOWUlZVh0aJF+Pbbb3H69GmUl5cjKioK99xzD5555hnodDp8+eWXmD9/PjIyMhAREWFpDf6///0PL7/8Mo4cOQK1Wo1u3brhwQcfxNy5cwFIE7omJSVh8+bN2LVrF8LCwvCPf/wDU6dOlfGMiYiIiNoH3ucRkZyYJCMiqqG+bklERERE5Pp4n0dETeGcZERERERERERE1O4xSUZERERERERERO0eh1sSEREREREREVG7x0oyIiIiIiIiIiJq95gkIyIiIiIiIiKido9JMiIiIiIiIiIiaveYJCMiIiIiIiIionaPSTIiIiIiIiIiImr3mCQjIiIiIiIiIqJ2j0kyIiIiIiIiIiJq95gkIyIiIiIiIiKidu//A02+8zbDTK1BAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 1500x500 with 2 Axes>"
       ]
@@ -352,7 +350,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Tip:** If you forgot to store the output of the fit method, you can access the training history using the `.trainer.history` attribute of the application (here `classifier.trainer.history`)."
+    "**TIP:** If you forgot to store the output of the fit method, you can access the training history using the `.trainer.history` attribute of the application (here `classifier.trainer.history`)."
    ]
   },
   {
@@ -394,7 +392,7 @@
    "source": [
     "## Using the Trained Model to Obtain Predictions\n",
     "\n",
-    "Of course, you'll want to use the model for inference. You can do this by calling the model directly with input data. Deeplay provides the `.predict()` method as a convenience, which handles moving the data to the correct device and batching it if necessary."
+    "You'll want to use the model for inference. You can do this by calling the model directly with input data. Deeplay provides the `.predict()` method as a convenience, which handles moving the data to the correct device and batching it if necessary."
    ]
   },
   {
diff --git a/tutorials/getting-started/GS121_modules.ipynb b/tutorials/getting-started/GS121_modules.ipynb
index 92730bf1..7f25df72 100644
--- a/tutorials/getting-started/GS121_modules.ipynb
+++ b/tutorials/getting-started/GS121_modules.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Working with Deeplay Modules\n",
     "\n",
-    "In this section, you'll explore the difference between Deeplay and PyTorch modules. You'll learn how to create and build Deeplay modules as well as how to configure their properties."
+    "In this section, you'll learn how to create and build Deeplay modules as well as how to configure their properties. You'll also understand the difference between Deeplay and PyTorch modules. You'll "
    ]
   },
   {
@@ -47,12 +47,12 @@
       "    (0): LinearBlock(\n",
       "      (layer): Layer[Linear](in_features=784, out_features=32, bias=True)\n",
       "      (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "      (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "      (normalization): Layer[BatchNorm1d]()\n",
       "    )\n",
       "    (1): LinearBlock(\n",
       "      (layer): Layer[Linear](in_features=32, out_features=32, bias=True)\n",
       "      (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "      (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "      (normalization): Layer[BatchNorm1d]()\n",
       "    )\n",
       "    (2): LinearBlock(\n",
       "      (layer): Layer[Linear](in_features=32, out_features=10, bias=True)\n",
@@ -75,7 +75,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "... this module is not built yet. This can be seen in the summary by the existence of Deeplay `Layer` objects. Once the module is built, the `Layer` objects are replaced by the actual PyTorch layers. \n",
+    "... this module is not built yet. For example, this can be seen in the summary by the existence of Deeplay `Layer` objects followed by the underlying PyTorch layer in square brackets. Once the module is built, the `Layer` objects are replaced by the actual PyTorch layers. \n",
     "\n",
     "Start by creating the `mlp` module ..."
    ]
@@ -95,12 +95,12 @@
       "    (0): LinearBlock(\n",
       "      (layer): Layer[Linear](in_features=784, out_features=32, bias=True)\n",
       "      (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "      (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "      (normalization): Layer[BatchNorm1d]()\n",
       "    )\n",
       "    (1): LinearBlock(\n",
       "      (layer): Layer[Linear](in_features=32, out_features=32, bias=True)\n",
       "      (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "      (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "      (normalization): Layer[BatchNorm1d]()\n",
       "    )\n",
       "    (2): LinearBlock(\n",
       "      (layer): Layer[Linear](in_features=32, out_features=10, bias=True)\n",
@@ -236,7 +236,7 @@
    "source": [
     "### Deciding Whether to Use `.build()` or `.create()`\n",
     "\n",
-    "In general, you'll want to use `.build()` when you are sure you won't need the original module anymore, and `.create()` when you want to keep the original template (for example, when you want to create multiple similar modules). Most of the time, you'll want to use `.build()`.\n",
+    "In general, you'll want to use `.build()` when you are sure you won't need the original module anymore, and `.create()` when you want to keep the original template (for example, when you want to create multiple similar modules). Most of the time, you'll probably want to use `.build()`.\n",
     "\n",
     "The `.create()` method is actually equivalent to `.new().build()`. This is because `.new()` clones the object, and `.build()` builds the object in-place. The `.new()` method can also be used by itself to create a clone of the object without building it."
    ]
@@ -249,7 +249,7 @@
     "\n",
     "Deeplay is compatible with both NumPy arrays and PyTorch tensors. However, internally, when a NumPy tensor is passed to the model, it is converted to a PyTorch tensor. This is because PyTorch only works with PyTorch tensors. This conversion also moves the channel dimension of the tensor from the last dimension to the first non-batch dimension (as is expected by PyTorch). \n",
     "\n",
-    "**Note:** While Deeplay takes all possible care to to ensure that this is done correctly, it is generally recommend directly providing PyTorch tensors to avoid any automatic permuting of your data."
+    "**NOTE:** While Deeplay takes all possible care to to ensure that this is done correctly, it is generally recommend directly providing PyTorch tensors to avoid any automatic permuting of your data."
    ]
   },
   {
@@ -260,7 +260,7 @@
     "\n",
     "Deeplay modules have a configuration system that allows you to easily change the properties of a module. At its core, this is done using the `.configure()` method. However, most modules also have specific configuration methods that allow you to change specific properties. For example, the `LinearBlock` has the `.normalized()` and `.activated()` methods that allow you to add normalization and activation to the block.\n",
     "\n",
-    "Importantly, most configurations are applied to many layers at once. For example, you may want all blocks in a component to have the same activation function. There are a few ways to do this, but the most powerful of all is the selection system. This will be more thoroughly explained in a [subsequent notebook](link), but the basic idea is that you can select a subset of layers in a module and apply a configuration to them. This is done using the `.__getitem__()` method. For example, to apply an activation function to all blocks in a component, you can use the following code."
+    "Importantly, most configurations are applied to many layers at once. For example, you may want all blocks in a component to have the same activation function. There are a few ways to do this, but the most powerful of all is the selection system. This will be more thoroughly explained in [GS181 Configuring Deeplay Objects](GS181_configure.ipynb), but the basic idea is that you can select a subset of layers in a module and apply a configuration to them. This is done using the `.__getitem__()` method. For example, to apply an activation function to all blocks in a component, you can use the following code."
    ]
   },
   {
@@ -505,18 +505,18 @@
       "MultiLayerPerceptron(\n",
       "  (blocks): LayerList(\n",
       "    (0): LinearBlock(\n",
-      "      (shortcut_start): Layer[Identity]()\n",
+      "      (shortcut_start): Layer[Linear](in_features=784, out_features=64)\n",
       "      (layer): Layer[Linear](in_features=784, out_features=64, bias=True)\n",
       "      (activation): Layer[ReLU]()\n",
       "      (shortcut_end): Add()\n",
-      "      (normalization): Layer[LayerNorm](normalized_shape=64)\n",
+      "      (normalization): Layer[LayerNorm]()\n",
       "    )\n",
       "    (1): LinearBlock(\n",
       "      (shortcut_start): Layer[Identity]()\n",
       "      (layer): Layer[Linear](in_features=64, out_features=64, bias=True)\n",
       "      (activation): Layer[ReLU]()\n",
       "      (shortcut_end): Add()\n",
-      "      (normalization): Layer[LayerNorm](normalized_shape=64)\n",
+      "      (normalization): Layer[LayerNorm]()\n",
       "    )\n",
       "    (2): LinearBlock(\n",
       "      (layer): Layer[Linear](in_features=64, out_features=10, bias=True)\n",
@@ -561,16 +561,15 @@
       "    (0-1): 2 x Conv2dBlock(\n",
       "      (shortcut_start): Conv2dBlock(\n",
       "        (layer): Identity()\n",
-      "        (activation): Identity()\n",
       "      )\n",
       "      (blocks): Sequential(\n",
       "        (0): Conv2dBlock(\n",
-      "          (layer): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))\n",
+      "          (layer): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (activation): ReLU()\n",
       "        )\n",
       "        (1): Conv2dBlock(\n",
-      "          (layer): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))\n",
+      "          (layer): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (normalization): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
diff --git a/tutorials/getting-started/GS131_methods.ipynb b/tutorials/getting-started/GS131_methods.ipynb
index bd539426..a1687cef 100644
--- a/tutorials/getting-started/GS131_methods.ipynb
+++ b/tutorials/getting-started/GS131_methods.ipynb
@@ -18,7 +18,7 @@
     "import deeplay as dl\n",
     "\n",
     "net = dl.models.SmallMLP(in_features=10, out_features=1)\n",
-    "model = dl.Regressor(net)"
+    "app = dl.Regressor(net)"
    ]
   },
   {
@@ -46,8 +46,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n",
-      "/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/configuration_validator.py:74: You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.\n"
+      "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n",
+      "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/configuration_validator.py:74: You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.\n"
      ]
     },
     {
@@ -103,7 +103,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "99472d233b5e4cc8bd72a2a0060731d3",
+       "model_id": "e916ec3c11eb4e318862ecb24ab5e687",
        "version_major": 2,
        "version_minor": 0
       },
@@ -118,7 +118,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve performance.\n"
+      "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve performance.\n"
      ]
     },
     {
@@ -157,7 +157,7 @@
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1500x500 with 1 Axes>"
       ]
@@ -172,9 +172,9 @@
     "x_numpy = numpy.random.randn(1000, 10)  # Input data.\n",
     "y_numpy = x_numpy.max(axis=1, keepdims=True)  # Target data.\n",
     "\n",
-    "numpy_model = model.create()  # Create new model instance.\n",
+    "numpy_app = app.create()  # Create new application instance.\n",
     "\n",
-    "h = numpy_model.fit(\n",
+    "h = numpy_app.fit(\n",
     "    (x_numpy, y_numpy), \n",
     "    max_epochs=20, \n",
     "    batch_size=10,\n",
@@ -195,6 +195,13 @@
    "execution_count": 3,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/configuration_validator.py:74: You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -248,7 +255,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f033eec018e34a6980b52ef3bc998628",
+       "model_id": "9084f6057c4d456797039848558dca20",
        "version_major": 2,
        "version_minor": 0
       },
@@ -259,6 +266,13 @@
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve performance.\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -295,7 +309,7 @@
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1500x500 with 1 Axes>"
       ]
@@ -310,9 +324,9 @@
     "x_torch = torch.from_numpy(x_numpy).float()\n",
     "y_torch = torch.from_numpy(y_numpy).float()\n",
     "\n",
-    "torch_model = model.create()\n",
+    "torch_app = app.create()\n",
     "\n",
-    "h = torch_model.fit(\n",
+    "h = torch_app.fit(\n",
     "    (x_torch, y_torch),\n",
     "    max_epochs=20, \n",
     "    batch_size=10,\n",
@@ -333,6 +347,13 @@
    "execution_count": 4,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/configuration_validator.py:74: You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -386,7 +407,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "10a313f14db04fda8227376955908910",
+       "model_id": "2deae5b886114631bd55ea2ca5025516",
        "version_major": 2,
        "version_minor": 0
       },
@@ -397,6 +418,13 @@
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve performance.\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -433,7 +461,7 @@
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1500x500 with 1 Axes>"
       ]
@@ -445,9 +473,9 @@
    "source": [
     "dataset = torch.utils.data.TensorDataset(x_torch, y_torch)\n",
     "\n",
-    "dataset_model = model.create()\n",
+    "dataset_app = app.create()\n",
     "\n",
-    "h = dataset_model.fit(\n",
+    "h = dataset_app.fit(\n",
     "    dataset, \n",
     "    max_epochs=20, \n",
     "    batch_size=10,\n",
@@ -523,7 +551,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7d82abe56acf4d66b12e837e9c6a4bb4",
+       "model_id": "cc0c119d2dde484c9896aa6791121f12",
        "version_major": 2,
        "version_minor": 0
       },
@@ -537,17 +565,34 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/py\n",
-       "torch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a \n",
-       "bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to\n",
-       "improve performance.\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/co\n",
+       "nnectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider\n",
+       "increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve performance.\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/co\n",
+       "nnectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider\n",
+       "increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve performance.\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/co\n",
+       "nnectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. \n",
+       "Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve \n",
+       "performance.\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/py\n",
-       "torch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a \n",
-       "bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to\n",
-       "improve performance.\n"
+       "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/co\n",
+       "nnectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. \n",
+       "Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve \n",
+       "performance.\n"
       ]
      },
      "metadata": {},
@@ -589,7 +634,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1500x500 with 1 Axes>"
       ]
@@ -602,9 +647,9 @@
     "x_numpy_val = numpy.random.randn(100, 10)\n",
     "y_numpy_val = x_numpy_val.max(axis=1, keepdims=True)\n",
     "\n",
-    "validated_model = model.create()\n",
+    "validated_app = app.create()\n",
     "\n",
-    "h = validated_model.fit(\n",
+    "h = validated_app.fit(\n",
     "    dataset, \n",
     "    max_epochs=20, \n",
     "    batch_size=10, \n",
@@ -623,7 +668,7 @@
     "\n",
     "You can also pass DeepTrack pipelines to the `Application.fit()` method. For example, this is useful when you want to generate data on-the-fly.\n",
     "\n",
-    "**Note:** Since the data is generated on the fly, you need to indicate the number of batches per epoch of training (`steps_per_epoch`) and whether to generate new data for each batch (`replace`, which can also be a number between 0 and 1, indicating the fraction of data to replace)."
+    "**NOTE:** Since the data is generated on the fly, you need to indicate the number of batches per epoch of training (`steps_per_epoch`) and whether to generate new data for each batch (`replace`, which can also be a number between 0 and 1, indicating the fraction of data to replace)."
    ]
   },
   {
@@ -684,7 +729,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2ec5d960e4804222a25de9def034c596",
+       "model_id": "7b20235890c94113833eb35660e78493",
        "version_major": 2,
        "version_minor": 0
       },
@@ -698,17 +743,15 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/py\n",
-       "torch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a \n",
-       "bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to\n",
-       "improve performance.\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/co\n",
+       "nnectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider\n",
+       "increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve performance.\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/py\n",
-       "torch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a \n",
-       "bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to\n",
-       "improve performance.\n"
+       "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/co\n",
+       "nnectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider\n",
+       "increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve performance.\n"
       ]
      },
      "metadata": {},
@@ -717,17 +760,17 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/py\n",
-       "torch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a \n",
-       "bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to\n",
-       "improve performance.\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/co\n",
+       "nnectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. \n",
+       "Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve \n",
+       "performance.\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/py\n",
-       "torch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a \n",
-       "bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to\n",
-       "improve performance.\n"
+       "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/co\n",
+       "nnectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. \n",
+       "Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve \n",
+       "performance.\n"
       ]
      },
      "metadata": {},
@@ -769,7 +812,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 1500x500 with 1 Axes>"
       ]
@@ -786,9 +829,9 @@
     "\n",
     "pipeline = x_dt & y_dt  # Combine pipelines.\n",
     "\n",
-    "pipeline_model = model.create()\n",
+    "pipeline_app = app.create()\n",
     "\n",
-    "h = pipeline_model.fit(\n",
+    "h = pipeline_app.fit(\n",
     "    pipeline, \n",
     "    max_epochs=20, \n",
     "    batch_size=10, \n",
@@ -819,14 +862,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 4/4 [00:00<00:00, 14.42it/s]\n"
+      "100%|██████████| 4/4 [00:00<00:00,  8.85it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pipeline test result (dataset_model): {'MeanSquaredError': tensor(0.1411, device='mps:0')}\n",
+      "Pipeline test result (dataset_app): {'MeanSquaredError': tensor(0.1519, device='mps:0')}\n",
       "\n"
      ]
     }
@@ -836,11 +879,11 @@
     "\n",
     "mse_metric = torchmetrics.regression.MeanSquaredError()\n",
     "\n",
-    "numpy_res = numpy_model.test(\n",
+    "numpy_res = numpy_app.test(\n",
     "    (x_numpy_val, y_numpy_val), \n",
     "    mse_metric,\n",
     ")\n",
-    "print(f\"Pipeline test result (dataset_model): {numpy_res}\\n\")"
+    "print(f\"Pipeline test result (dataset_app): {numpy_res}\\n\")"
    ]
   },
   {
@@ -852,14 +895,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 4/4 [00:00<00:00, 534.82it/s]"
+      "100%|██████████| 4/4 [00:00<00:00, 418.58it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pipeline test result (torch_model): {'MeanSquaredError': tensor(0.1518, device='mps:0')}\n",
+      "Pipeline test result (torch_app): {'MeanSquaredError': tensor(0.1232, device='mps:0')}\n",
       "\n"
      ]
     },
@@ -875,11 +918,11 @@
     "x_torch_val = torch.from_numpy(x_numpy_val).float()\n",
     "y_torch_val = torch.from_numpy(y_numpy_val).float()\n",
     "\n",
-    "torch_res = torch_model.test(\n",
+    "torch_res = torch_app.test(\n",
     "    (x_torch_val, y_torch_val), \n",
     "    mse_metric,\n",
     ")\n",
-    "print(f\"Pipeline test result (torch_model): {torch_res}\\n\")"
+    "print(f\"Pipeline test result (torch_app): {torch_res}\\n\")"
    ]
   },
   {
@@ -891,14 +934,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 4/4 [00:00<00:00, 727.93it/s]"
+      "100%|██████████| 4/4 [00:00<00:00, 535.57it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pipeline test result (dataset_model): {'MeanSquaredError': tensor(0.1426, device='mps:0')}\n",
+      "Pipeline test result (dataset_app): {'MeanSquaredError': tensor(0.1642, device='mps:0')}\n",
       "\n"
      ]
     },
@@ -913,11 +956,11 @@
    "source": [
     "dataset_val = torch.utils.data.TensorDataset(x_torch_val, y_torch_val)\n",
     "\n",
-    "dataset_res = dataset_model.test(\n",
+    "dataset_res = dataset_app.test(\n",
     "    dataset_val, \n",
     "    mse_metric,\n",
     ")\n",
-    "print(f\"Pipeline test result (dataset_model): {dataset_res}\\n\")"
+    "print(f\"Pipeline test result (dataset_app): {dataset_res}\\n\")"
    ]
   },
   {
@@ -929,14 +972,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 100/100 [00:00<00:00, 164.86it/s]"
+      "100%|██████████| 100/100 [00:00<00:00, 174.21it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pipeline test result (pipeline_model): {'MeanSquaredError': tensor(0.1277, device='mps:0')}\n",
+      "Pipeline test result (pipeline_app): {'MeanSquaredError': tensor(0.1265, device='mps:0')}\n",
       "\n"
      ]
     },
@@ -949,11 +992,11 @@
     }
    ],
    "source": [
-    "pipeline_res = pipeline_model.test(\n",
+    "pipeline_res = pipeline_app.test(\n",
     "    pipeline,\n",
     "    mse_metric,\n",
     ")\n",
-    "print(f\"Pipeline test result (pipeline_model): {pipeline_res}\\n\")"
+    "print(f\"Pipeline test result (pipeline_app): {pipeline_res}\\n\")"
    ]
   },
   {
@@ -974,14 +1017,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 4/4 [00:00<00:00, 143.38it/s]"
+      "100%|██████████| 4/4 [00:00<00:00, 141.95it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pipeline test result: {'MeanSquaredError': tensor(0.1235, device='mps:0'), 'ExplainedVariance': tensor(0.6506, device='mps:0')}\n"
+      "Pipeline test result: {'MeanSquaredError': tensor(0.1177, device='mps:0'), 'ExplainedVariance': tensor(0.6635, device='mps:0')}\n"
      ]
     },
     {
@@ -995,7 +1038,7 @@
    "source": [
     "var_metric = torchmetrics.regression.ExplainedVariance()\n",
     "\n",
-    "pipeline_res = pipeline_model.test(\n",
+    "pipeline_res = pipeline_app.test(\n",
     "    (x_numpy_val, y_numpy_val), \n",
     "    [mse_metric, var_metric],\n",
     ")\n",
@@ -1020,14 +1063,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 4/4 [00:00<00:00, 341.59it/s]"
+      "100%|██████████| 4/4 [00:00<00:00, 384.39it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pipeline dict test result:   {'MSE': tensor(0.1235, device='mps:0'), 'VAR': tensor(0.6506, device='mps:0')}\n"
+      "Pipeline dict test result:   {'MSE': tensor(0.1177, device='mps:0'), 'VAR': tensor(0.6635, device='mps:0')}\n"
      ]
     },
     {
@@ -1039,7 +1082,7 @@
     }
    ],
    "source": [
-    "pipeline_res_dict = pipeline_model.test(\n",
+    "pipeline_res_dict = pipeline_app.test(\n",
     "    (x_numpy_val, y_numpy_val), \n",
     "    {\"MSE\": mse_metric, \"VAR\": var_metric},\n",
     ")\n",
@@ -1062,14 +1105,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 4/4 [00:00<00:00, 436.59it/s]"
+      "100%|██████████| 4/4 [00:00<00:00, 419.46it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pipeline tuples test result: {'mse': tensor(0.1235, device='mps:0'), 'var': tensor(0.6506, device='mps:0')}\n"
+      "Pipeline tuples test result: {'MSE': tensor(0.1177, device='mps:0'), 'VAR': tensor(0.6635, device='mps:0')}\n"
      ]
     },
     {
@@ -1081,9 +1124,9 @@
     }
    ],
    "source": [
-    "pipeline_res_tuples = pipeline_model.test(\n",
+    "pipeline_res_tuples = pipeline_app.test(\n",
     "    (x_numpy_val, y_numpy_val), \n",
-    "    [(\"mse\", mse_metric), (\"var\", var_metric)], \n",
+    "    [(\"MSE\", mse_metric), (\"VAR\", var_metric)], \n",
     ")\n",
     "print(\"Pipeline tuples test result:\", pipeline_res_tuples)"
    ]
@@ -1114,7 +1157,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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iP6/rxGNbjuBEuzfs+PmTs3HXJWXIs1HLqW4UCBFCCCHDlCoJ0S6/jGd2HsMbBxrCjmdb9PjxJeW4sDwnSSNLXRQIEUIIIUOkqsEK0clOiNY0De9+1YI/ba9Ch/fUWBgAV51VhFsvmASzQG/5kdCrQgghhAyBX1bQ4kp+QnSDw4c1W4/g4+MdYcdLc834yaIKTC2k5uEDoUCIEEIIiVGnV0KHV05qQnRAUfF/e+uwYfcJiIFTwZigY7H0vAn49jnF0HFs0saXLigQIoQQQqIUUFQ0u0T45eQmRB9qcOIPWypxtMUTdnz2xEzcs7AchXZjkkaWfigQIoQQQqLgFgNodYlQkzgL5BEDeHbXMfxjfz16jiLTxOOO+WW45LRcMGnQHyyVtu1TIEQIIYQMQFU1tHpEuHtUZB5pmqbh/apW/M+7VWhzS2G3fW16AX5wYSlsRj5Jo4ueUc8hy6yHoEudKtYUCBFCCCH9SIWE6GanH398twofVLeFHZ+QZcK9i8pxZnFGcgYWA55jkW3Rw6RPvbAj9UZECCGEpIAOj4QOrzT4iQmiqBo27zuJ5/99DH75VCDGcwxumDsB351dAr0utZOhdSyLDDMPmyF1Z6soECKEEEJ6kLsSosUkJkQfaXLhD1sqUdnkDjt+VkkG7l1YjpIsU5JGFh2OZZBh1MNm1KV8zhIFQoQQQkgXl19Gm1tKWkK0T1Kw/oPjePnTOvQsUm0z6PCjiydj8bT8lA4sWIaBzcgjw8iDTaGE6IFQIEQIIWTMU1UNrW4RbjF5CdEfHm3Dmq1H0OwSw44vOj0ft19cigyTPkkjGxzDMLAadMgw8mlXu4gCIUIIIWNashOi2z0Snni3CtsrW8KOj8sw4t6F5Th7QmZSxhUti6BDplkPPs0CoG4UCBFCCBmzklkhWtU0vP55A/78/lF4xFP5SBzL4NrZJbhh7ngIfOpsM+/NpNch08yn1Fb4oaBAiBBCyJijqBpaXCK8UnKWwo61erB6SyUO1jvDjk8rsmHFogpMyjEnZVzRMPDBWkCGFA7SYkGBECGEkDHFJwWXwgLqyC+FibKCv35Ug00f10LpkQ1tFjj84MJSfP3MQrApmgzNcyyyzPpR18V+dD0bQgghZADJrA2090QHHttaifpOf9jx+RW5WL5gMrItQlLGNRieY5Fh4mFN4VpAw0GBECGEkFEvoKhocYvwSSNfG6jTK+FP26ux9VBz2PF8m4C7Ly3HuaXZIz6maKRTLaDhoECIEELIqOaTFDS7/GFLUSNB0zS8fbAJT+2ohrNHnzKWAb51djGWnT8RxhTMs2EZBnYjD3sa1QIaDgqECCGEjFrJWgqraffisS2V+KzOEXZ8Sr4VKxaVozzfOuJjGkx3LaBMkz6lusMnGgVChBBCRp1AV5sM/wi3yZACKjZ9XIP//agGsnJqBsrIc7jlgolYcta4lAwyLF0BULrWAhoOCoQIIYSMKl4pgBaXOOJLYZ/XdWL1liOoafeGHZ83ORs/vqQMeTbDiI4nGqOlFtBwJDX0W7VqFWbPng2r1Yq8vDwsWbIEhw8fHvA+f/7zn3HhhRciMzMTmZmZWLhwIfbs2RN2zrJly8AwTNjX5ZdfnsinQgghJMk0LVgbqNExsvlALr+M379zGPe8+FlYEJRt0ePBb0zDb5ackXJBkIHnUJRhRIHdMKaDICDJM0I7duzA8uXLMXv2bAQCAdx///247LLL8OWXX8JsjlxMavv27bjuuuswb948GAwG/O53v8Nll12GgwcPYty4caHzLr/8cqxbty70vSCk5rZEQsjYoaoaDtY70e6VkGXSY1qRbUwko46EZLTJ0DQN737Vgj9tr0KHVw4dZwBcdVYRbr1gUsrV3NHrgrWATPrUGlcyMVoy6or3o6WlBXl5edixYwcuuuiiqO6jKAoyMzPxxBNP4KabbgIQnBHq7OzEq6++GtU1RFGEKJ5qcud0OlFSUgKHwwGbzRbz8yCEkN4+qGrF2h3VqG52Q1Y08ByDyXkW3H7xZMwry0n28NKWpmno9Mro9I1sm4wGhw9rth7Bx8c7wo6X5prxk0UVmFqYWu8do70W0HCkVFaUwxHMrs/Kyor6Pl6vF7Is97nP9u3bkZeXhylTpuD2229HW1tbv9dYtWoV7HZ76KukpGRoT4AQQiL4oKoV928+gEMNTpgFHfKsAsyCDocaXLh/8wF8UNWa7CGmJSmgot7hR4dXGrEgKKCo+NueGtyy/pOwIEjQsfjBhZPw1PfOTqkgiGMZZFsEFGcaKQjqR8rMCKmqim984xvo7OzErl27or7fHXfcgbfffhsHDx6EwRBcg920aRNMJhMmTZqE6upq3H///bBYLNi9ezc4ru9aKM0IEUISRVU1LF23B4canCiwGcIK02mahkaniKmFVmy4eQ4tk8XA4ZXRPoIBEAAcanDiD1sqcbTFE3Z89sRM3LOwHIV244iNZTAswyDDxMNmGBu1gIYjZRYJly9fji+++CKmIOjhhx/Gpk2bsH379lAQBADXXntt6L+nT5+OM888E5MnT8b27dtx6aWX9rmOIAiUQ0QISYiD9U5UN7uRadL3qc7LdL1ZVTe7cbDeienF9iSNMn0ko0K0WwzguV3H8I/99egZdmWaeCxfUIYFU3JTpvIywzCwGXTIGGO1gIYjJQKhO++8E6+//jp27tyJ4uLiqO7z+9//Hg8//DC2bt2KM888c8BzS0tLkZOTg6qqqoiBECGEJEq7V4KsaND3U59F4Fg4VA3tSep/lU5cfhltbglqP7NAqqahqskDh1+C3aBHWb55WA1MNU3D+0da8T/vVaHNHf7z+fr0QvzgokkptdxkNfDINPHQjcFaQMOR1EBI0zTcdddd2Lx5M7Zv345JkyZFdb9HHnkEv/3tb/H2229j1qxZg55fV1eHtrY2FBYWDnfIhBASkyyTHjzHQFJUGNgIS/OKCp5lkGXSJ2F06UFRNbS5RbjFQL/n7KvpwMY9taht80BWNfAsg5JsM66fU4KZ4zNjfsxmpx9/fLcKH1SH55dOyDLh3kXlOLM4I+ZrJopZCBZD1OsoABqKpAZCy5cvx8aNG/Haa6/BarWisbERAGC322E0Btdab7rpJowbNw6rVq0CAPzud7/DAw88gI0bN2LixImh+1gsFlgsFrjdbjz44IP41re+hYKCAlRXV+O+++5DWVkZFi9enJwnSggZs6YV2TA5z4JDDS4U2Ng+OUKdXhlTC62YVkT5iJF4pQBaXRICav/b4vfVdGD1lkp4JQU2Aw8bx0BWNBxtcWP1lkqsWFQRdTCkqBo27zuJ5/99DH751GPyHIMb5k7Ad2eXpEzAYdRzyDTpYUjBfmXpJKk/zbVr18LhcGD+/PkoLCwMfb344ouhc2pqatDQ0BB2H0mS8O1vfzvsPr///e8BABzH4fPPP8c3vvENVFRU4NZbb8U555yD999/n/KACCEjjmUZ3H7xZFgEDo1OET5Zgapq8MkKGp0iLAKH2y+eTAmtvWhacBao0eEfMAhSNQ0b99TCKynIsegh6FiwDANBxyLHoodXUrBxT22/y2k9HWlyYfnGT/Gn7dVhQdBZJRl49qZZuPG8CSkRBOl1LArtRhTajRQExUHK7BpLJU6nE3a7nXaNEULiJqyOUNfSDdURikzu6hMmRtEnrLLRjQdeOwCjXgchQpDiD6jwSwH891XTUVFgiXgNn6Rg/QfH8fKndehZkNpm0OFHF0/G4mn5KZEMzXMsMs16WFKsSGO6o1eTEEJGwLyyHJxbmk2VpQfhEYN9wqKZwQEAh1+CrGqwcZFfRz3HwKVpcPgjJ6N/eLQNa7YeQbNLDDt+2en5+NHFpchIgdwtjmWQYdLDZtClREA22lAgRAghI4RlGdoi3w9N09DmkeD0yYOf3IPdoAfPBnOCBF3fIEFSNPAMA7shPKBpc4t44r1q7KhsCTs+LsOIexeW4+wJsSdYxxvLMLAbediNVAsokSgQIoQQklRSQEWzyw8pEHufsLJ8M0qyzTja4kaORQ8GPZLRocHll1Gaa0FZfrB/pappeP3zBvz5/aPwiKeW3jiWwbWzS3DD3PEQkpx3wzAMrIbgTjCqBZR4FAgRQsY0aoSaXIPVBhoMyzC4fk4JVm+pRKtbgtXAQ88xkJRgEGTSc7h+TglYhsGxVg9Wb6nEwXpn2DWmFdmwYlEFJuVEbvY9kmgr/MijZOkIKFmakLGBGqEmj6pqaPWIcPv7rw0Ui7A6QlpwOay7jtDphTb85cMTePGTOig9sqHNAocfXFiKr59ZOKzCi/Eg8ByyzbQVPhkoEIqAAiFCRr/uRqhuMRD8BM6xkBQVHV4ZFoHDQ1dPp2AoQfyyghaXCFmJfSlsIJEqS++r6cRjWytR3+kPO3d+RS6WL5iMbEtyy6rQTrDko1eeEDLmqKqGtTuq4RYDYY1QDSyHAhuLRqeItTuqcW5pNi2TxZGmaejwyuhMUDsRlmFCW+Q7vRIe/tdX2HqoOeycfJuAuy8tx7ml2QkZQ7Q4lkGGUQ+bkXaCJRsFQoSQMYcaoY48KRBslhpNbaDh0DQNbx1swtM7quHssezGMsC3zynG0nkTYUzi8hPTtRMsg3aCpQwKhAghYw41Qh1Zzq6E6ERnYtS0e7FmayX21zrCjk/Jt+Inl1WgLC9yQcWRYjHokGXSU1PUFEOBECFkzKFGqCNDUTW0ukV4BmiWGg9SQMXf9tRg454ayMqpYMvIc7j1gom46qxxSd2GbtRzyDLrIegoEToVUSBECBlzqBHq0MRSasAnBROiB+oTFg+f1XXisS1HUNPuDTt+/uRs3HVJGfJshoQ+/kD0OhZZZj1MenqrTWX00yGEjDndjVDv33wAjU4RGSYeAsdCVFR0du0aS2Yj1FSsbRRtqQFN09DukeCIsUJ0rJw+Gc/sPIo3v2gMO55j0eOuS8pxYXnydvzpWBYZZh42A5+0MZDo0fb5CGj7PCFjQyo2Qk3F2kbRlhoQA8FZoKFUiI6Wpml496sW/Gl7FTq8p4ItBsBVZxXh1gsmwZykrehsV6K93cjTTrA0QoFQBBQIETJ2pNLsSyrWNlJVDUvX7cGhBmdYqQEgGJQ0OkVMLbTij9fORKdPTmhCdH2nD49vO4KPj3eEHS/NNeMniyowtTA5f6+pJUZ6o6UxQsiYliqNUFO1ttFgpQbsRh0qG1346Gh7qIZPvAUUFS/trcMLu09A7DHbJOhYLJ03Ed8+e1zSdmJRS4z0R4EQIYSkgFStbTRQqQFF1cCCgaSqcPgTU2rgUIMTf9hSiaMtnrDjcyZm4u6F5Si0GxPyuIMx6jlkmqglxmhAgRAhhMQoEctpqVrbKFKpAU3TEFA1qKoWLDXAMLAb4ltqwC0G8Nz7x/CPz+rRc7Et08Rj+YIyLJiSm5Q8HIHnkGXSw6inAGi0oECIEEJikKhk5lStbdS71ICmAQFVg6Zp0BDs8F6aa0FZfnw6t2uahvePtOJ/3qtCmzs86PuPMwvx/QsnwZqE3VjUE2z0okVNQgiJUncy86EGJ8yCDnlWAWZBh0MNLty/+QA+qGod8rW7A44Ob9+E4+7aRpPzLCNe26i71IBZz6Le4YdLDEBRVfgDKlrdEkx6DtfPKYlL9/Ympx//+epB/OqfX4YFQROyTXj8u2dhxaKKEQ+COJZBtllAcaaRgqBRigIhQgiJQu9kZgPPgWUZGHgOBTYBblHB2h3VUNWh7ZrqDjgsAodGpwifrEBVNfhkBY1OMam1jWZNzMKKRVMwKccMvxRAm1eCXwqgNNeCFYsqMHN85rCur6ga/m9vHW5e/zF2H20LHec5BjefPxHP3HjOiCe0B7fC61GSaYLdRNvhRzPaPh8BbZ8nhPR2oM6BH/7lE5gFXcQEWZ+swCsG8PSNs4b1pp1qtY0cPhntnmCfMFXTUNXkgcMvwW7QoyzfPOyZoMomF1ZvqURlkzvs+FklGbh3YTlKskzDun6supui2o08bYUfI2iej5BRIJVq4YxWI5XMPK8sB+eWZo/ozzPS748G9OkTxjJM3LbI+yQF6z44hlc+PYmek2g2gw63z5+My07PH9FZGIZhYDPokEG1gMYcCoQISXOpWIl4NBrJZOaRrG0U6fdnYo4Z3zmnGDNKMhLymLur2/D4tiNodolhxy87PR+3XzwZdtPI5QF1F0PMMPLUFX6MoqWxCGhpjKSLVKxEPFqdqrDsQoFN6LfC8oab56TNbFzv3x+eZeAPqOjwBpOg+8v/GeoSWatbxBPvVWFnZXhS+bgMI+5dWI6zJwwv1ygWDMPAIuiQYeLBUwA0ptGMECFpKlUrEY9Wqd6oNVa9f38AQFY06FgGORY9Wt0SNu6pxYySjLAgZ19NBzbuqUVtmyeUw1SSbcb1c0r6TZpWNQ3//KwBz75/FB5JCR3XsQy+O7sEN8wdD2EECxNautphUABEAAqECElbqVqJeDSbV5aDh66eHlpKcnQFAlMLrWm3FNnz90dRNXgkBYqqgmNYCDwDq4FHbZsHVU2eUF7QvpoOrN5SCa+kwGbgYeMYyIqGoy1urN5SGXEGKXjbEXzZ4Aw7fkaRDfcuqsCknPjUH4pGcAaI2mGQcBQIEZKmUrUS8WiXjGTmRGj3SpACKlhGQbtHhKSo0DSAYQA9xyLDpIesaaHWGaqmYeOeWnglBTkWPRgEn6+gizyDJMoKXvjwBP7+SR2UHtnQZoHDDy8qxdemF8al9lA0THodMs08BB1VgyZ9USBESJpK1UrEY0GqNGodDoteh4CqotEpQdWChQNZBtAA+AMqml1+WARdqHVGVZMHtW0e2Ax8KAjqxiB8Bsnpl/DY1iNocPjDzptfkYs7LylDlnlkficFnkO2OT36gdHOz+ShQIiQNNW79UHv5N1Or4yphdYRr0RMUl+nV4LdoIOiAQEV4DmEZmcYAGA1yAqgaMDk3ODSlcMvQVY12LjIb856joFDVfH0zmrsq+0Muy3fJuDuS8txbml2Ap/VKTzHIsushzlNKkHTzs/kGvJCqSRJOHz4MAKBwOAnE0LiLpUrEacaVdVwoM6BHZUtOFDnGHL153QXUFQ0OHxo90ioavGAYxhwDKCqwaUvDcGiiaoKcAzAMQyqu7q+2w3BXWWy0ve10zQN7V4ZTm8gLAhiGeCaWcV4ftnsEQmCdCyLXKuAkixTWgVBiWrbQqIT82+K1+vFXXfdhQ0bNgAAKisrUVpairvuugvjxo3DL37xi7gPkhAS2WhK3k0U+rQd5JUCaHGJoXwdh18CwwD5dgM6PTIkRYGqBnOEBB2HDDMPn6yEcoTK8s0oyTbjaIsbOZbg0pYoa/AHAnD4AhADatjjTcm34ieXVaAsLz4FGAfCsQwyjHrYjLq0aoVBOz9TQ8wzQitXrsRnn32G7du3w2AwhI4vXLgQL774YkzXWrVqFWbPng2r1Yq8vDwsWbIEhw8fHvR+L730Ek477TQYDAZMnz4db775ZtjtmqbhgQceQGFhIYxGIxYuXIgjR47ENDZC0sW8shxsuHkOnr5xFn7/nRl4+sZZ2HDznDH1Jt8f+rQd/HvY6hbR6PCHJS13z/DwLItxmQYU2U0osAf/f1ymATqWBc8woRwhlmFw/ZwSmPQcGhx+nGjzorbDi2aXFBYEGXkOdy6YjCeun5nwIIhlGGSmcT+wWHZ+ksSJORB69dVX8cQTT+CCCy4I+8FNmzYN1dXVMV1rx44dWL58OT788ENs2bIFsizjsssug8fj6fc+H3zwAa677jrceuut2LdvH5YsWYIlS5bgiy++CJ3zyCOP4I9//COeeuopfPTRRzCbzVi8eDH8fn+/1yUknXUn715ckYvpxXb69IjEN0lNB1JAxclOH5w+uc9t3TM8Tn/wNgPPwqzXwcAH3xZcfhkl2WaU5Z/a3j5zfCauPLMI/oACSdHQ+5Uz8Cx+elk5vnl2cULbVDAMA5uRR0mWCZlmfdr+vkez81OmnZ8JF3Mg1NLSgry8vD7HPR5PzNH4W2+9hWXLlmHatGmYMWMG1q9fj5qaGuzdu7ff+zz++OO4/PLL8bOf/QxTp07Fr3/9a5x99tl44oknAAQ//axZswb/+Z//iauuugpnnnkmXnjhBdTX1+PVV1+NeE1RFOF0OsO+CCHpbax/2nb6ZZzs9EHqtWTVrecMT6tbgj+gQtU0+AMqWt3BytLXzykJ2+Le6ZXw0qd1CCjh19KxDArtAow8hze/aIKawIYFFoMOxZlG5FiEtO8J1nPnZyS083NkxBwIzZo1C2+88Ubo++4/MM8++yzOO++8YQ3G4XAAALKysvo9Z/fu3Vi4cGHYscWLF2P37t0AgGPHjqGxsTHsHLvdjrlz54bO6W3VqlWw2+2hr5KSkmE9D0JI8o3VT9uKqqHJ6UerS8RgHZRmjs/EikUVKM21wC8F0OaV4JcCKM21hBVH1DQNWw81YenzH6PNHf56ZRh5TMgywSrwYVvo482o5zAu04g8q2HUVITu3vnZ4ZX7/Ky6d35OzrPQzs8EizlZ+qGHHsIVV1yBL7/8EoFAAI8//ji+/PJLfPDBB9ixY8eQB6KqKu655x6cf/75OOOMM/o9r7GxEfn5+WHH8vPz0djYGLq9+1h/5/S2cuVKrFixIvS90+mkYIiQNDcW6yz5ZQXNThEBNfIMQyQzx2diRklGv73DTnb68PjWI/jkREfY/QQdizxrcBaom55j4OpRhDEe9LrgVniTPj12gcVitLVtSVcx/2ZdcMEF2L9/Px5++GFMnz4d77zzDs4++2zs3r0b06dPH/JAli9fji+++AK7du0a8jWGShAECIIw4o9LCEmcsVZnqcMjoWOIs1ssw4TaaHQLKCr+/kkdXvjwRJ/ltQwjj1xL3yVHSdHCEqyHQ8eyyDQHZ5lGM9r5mXxDCrEnT56MP//5z3EbxJ133onXX38dO3fuRHFx8YDnFhQUoKmpKexYU1MTCgoKQrd3HyssLAw756yzzorbmAkhqW2sfNqWFRUtLhF+WRn85Ch9We/E6i2VONoavsQ1Z2ImvLKCkx0+9CouDQ0aXH4ZpbmWsATrWLFd+Vt24/B3gaVLtebR0rYlXcUcCNXU1Ax4+/jx46O+lqZpuOuuu7B582Zs374dkyZNGvQ+5513HrZt24Z77rkndGzLli2h/KRJkyahoKAA27ZtCwU+TqcTH330EW6//faox0YISX+j/dO2yy+jzS3FLTnZLQbw3PvH8I/P6sN2hGWaeNy5oAzzp+Rif20nVm+pRKtbgtXAQ88xkJRgEBQpwTpaDMPAZgg2RY1HEnS61Y8aDW1b0hWjDZZN1wvLsgNG6YoS/aeSO+64Axs3bsRrr72GKVOmhI7b7XYYjUYAwE033YRx48Zh1apVAILb5y+++GI8/PDD+PrXv45NmzbhoYcewqeffhrKLfrd736Hhx9+GBs2bMCkSZPwX//1X/j888/x5ZdfhtU+6o/T6YTdbofD4YDNNjqmzQkZy9JlZiBaqhqsDeQW41PZX9M0vH+kFf/zbhXaPOHLa/9xZiG+f+GksCWqfTUd2LinFrVtHshacDmsJNuM6+eU9Ok+Hw2rgUemiYcuTknQ3fWj3GIAmSY99BwLSVHR0TUT+NDV01MyGCLJEfOM0L59+8K+l2UZ+/btw+rVq/Hb3/42pmutXbsWADB//vyw4+vWrcOyZcsABGegWPbUP4558+Zh48aN+M///E/cf//9KC8vx6uvvhqWYH3ffffB4/HgBz/4ATo7O3HBBRfgrbfeiioIIoSMPqPp07ZfVtDiEiH3s+U6Vk1OP/64rQq7j7aFHZ+QZcKKRRURX7fBEqyjZRGCM0B6Xfx2gVG1ZhKrmGeE+vPGG2/g0Ucfxfbt2+NxuaSiGSFCSKrRNA0dXhmdcdrur6gaNu87ief/fQx++VRQxXMMbjh3Aq6dXZKwbeomvQ6ZZh6CLv5d4Q/UOfDDv3wCs6CL2HXeJyvwigE8feOsURMck+GJ237EKVOm4OOPP47X5QghhHSRFRXNLhFinBKiK5tcWL2lEpVN7rDjZ5Vk4N6F5SjJMsXlcXoz6XXIMPERA5R4iaZ+lGMU1o9KF6m4TB1zINS76rKmaWhoaMCvfvUrlJeXx21ghBBC4psQ7ZMUrPvgGF759CR6dhaxGXS4ff5kXHZ6fkL6dQk8h2yzPqEBULexWD8qXaRqAnvMgVBGRkaffyiapqGkpASbNm2K28AIIWQoUvET51DEOyF6d3UbHt92BM0uMez4Zafn4/aLJ8Nuin+9nmTUAhpr9aPSRX8J7N0NkJOZwB5zIPTee++Ffc+yLHJzc1FWVgadbvRV/iSEJF68gpdU/cQZK58UTIiOpUJ0f1rdIp54rwo7K1vDjhdnGnHPwnKcPYRdXoNhGAYZRh4ZSegIP1bqR6WTVE9gj1uy9GhCydKEjJx4BS+jYct0PBOiVU3DPz9rwLPvH4VHOpVbpGMZXDunBDfMnRDX3VpAMACyGnTIMMZvK/xQhf1eddWPSsegeDRI9QT2qKZw/vGPf0R9wW984xtDHgwhZGyJ13R5qn/ijIYUUNHs8vfbLV7VtKi3qx9tcWP1lkp82eAKO35GkQ0rLqvAxOyhV37uj9UQnAFKlYaoVK05daR6AntUgdCSJUuiuhjDMDEVVCSEjF3xDF4O1jtR3exGpqlv/yumq2VDdbMbB+udKbll2tmVEN3fBH1YAcOu2Y1IBQxFWcELH57A3z+pg9IjG9oi6PCDi0rxtekFQ6r6PJBE1AKKl9FUPyqdpXoCe1SBkBqHdWpCCOkpnsFLqn/i7I/SlRDtGSAhel9NB1ZvqYRXUmAz8LBxDGRFC836rFhUgZnjM/HJ8XY8tvUIGhz+sPsvmJKL5QvKkGWO75tMImsBkdEl1RPYKbuZEDLiVFXD3poOeEQFBp6Dpml9gqFYgpdU/8QZSTQJ0aqmYeOeWnglBTkWPZiuTqeCjkGORY9Wt4QNu0/gzQON2PZVc9h9C2wG3L2wDHMnZcd13AaeQ9YIbYUno0OqJ7APKRDyeDzYsWMHampqIEnhf6R+/OMfx2VghJDRqTuJ9asGF1yiDI8UgIFnkWs1wCKc+pMUS/CS6p84e4olIbqqyYPaNg9sBj4UBJ26UHDm7ECdI6xBKssA3zmnGDfNmwhjHIMVnmORZdbDLNDnZxK7VG6APKReY1/72tfg9Xrh8XiQlZWF1tZWmEwm5OXlUSBECOlXz+ToDBMPrxSAX1bgkxWc7PBhXKYRFkEXc/CS6p84u8VaIdrhlyCrGmxc+LilgIomlx8+OXw2aUqBFT9ZVIGyPEvcxsyxDDKMetiMuhHfCk9Gl1RNYI85ELr33ntx5ZVX4qmnnoLdbseHH34Inudxww034O67707EGAkho0Ck5Og8mwEnO3xQVBWKpqLZ6QdrN8DhC8QcvKTyJ05g4ArR/e0Isxv04NlgTpCgY6B2zSa1e6SwWSBBx+L7F5biqrOKwMXpTYVhGNiNPDKMfNLfqMjokYoJ7DEHQvv378fTTz8NlmXBcRxEUURpaSkeeeQRLF26FN/85jcTMU5CSJqLlBxtEXQYl2lEi0uEXw7AJytw+GRMLbQNKXhJxU+cqqqh1SPC7Y+cED3QjrAZJRkoyTbjaIsbJj2HZpcIWQkPpDKMPNbecDbybYa4jdlq4JFpSn4tIEJGQsyBEM/zYNngP468vDzU1NRg6tSpsNvtqK2tjfsACSGjQ387uyyCDmaBg1dU0OaRcNeCctx43oQhBy+p9IlTDChodoqQlcgJ0dHsCFtyViFW/eswOrxy2H0ZBsg08vh/X58atyDILOiC9ZwSsBV+tLQ+IaNPzIHQzJkz8fHHH6O8vBwXX3wxHnjgAbS2tuIvf/kLzjjjjESMkRAyCgy0s4sBA5ZlYNZzOHtC5qh4g3T4upaw+qkNNNiOsBaXiP95twqdXhleKTynyMizmFJgw43njg+rIzRUiW6KOlpan5DRKepASFEUcByHhx56CC5XsFrpb3/7W9x00024/fbbUV5ejueffz5hAyWEpLdE7exKtZmGaGoDAQPvCJMDGvyyiuNt3rDjxZlGLDmrCGcUZQxYWTpaI7ETLJWbbRICxBAIjRs3DsuWLcMtt9yCWbNmAQgujb311lsJGxwhZPQYys6uwYKcZM009DeuWJqlRtoR1r21vq1XMrRBx2LpvIn49jnFw06GVjUNR1s8CKgaijOMGJdhHNb1BnysUdD6hIx+UQdCy5cvx4YNG/Doo49i3rx5uPXWW3HNNdfAZDIlcnyEkFEklp1dgwU5yZppiDSu0lwzbjh3AiryrVFfp/eOMJ+soMkpQuqVTzStyIb/97WpKLAPPw9of00n/r63FidaPQioSHjgmO6tT8jYEHP3+e3bt2PdunV4+eWXwXEcrrnmGtx2222YO3duosY44qj7PCGJFc1Mz0Cd5H+z5Aw8vfMoDjU4w2YagOCsSqNTxNRCKzbcPCeuMw2RxiUGFLR5ZJj0bKjdRVSvgabh5y8fQHWzCxoAhy98KY0BMDHHjGduPBscO/zk5cMNLjz81iF4JCXia5qIwHFHZQt++vfPkGcVIv4cVFVDs1vE778zAxdX5Mb1sQmJVsz/uubPn48NGzagsbERf/jDH3Do0CGcd955mDZtGlavXp2IMRJCRpnunV0XV+RierG9z3JY93KK3cBDVlSIARUCz6LAJsAtKvj9O5WoanJFNdMQL72XeQw8B63rueRYeHglBRv31EasExQJA2BakRVOf6BPEKTXBZ/rnQsmDzsIMgs6FNmN2PhxDTySEho7yzIw8FzoNV27oxqqGtPn4kH1TJCPJBVbn5CxZ8j/wiwWC2677Tbs2rUL//znP9HY2Iif/exn8RwbIWQMOljvxJf1TnhEBTUdXtR1+HCi3YPjrV54JAUZJh617V74ZHXAJqtyHJqsqqqGA3UO7KhswWv760PBFxCsEh1Q1GCrCzCwGnjUtnlQ1eQZ9LpNTj/+36tf4IXdNegZe7AMkGHUYfo4O366eMqwdoQJPIeiDCPybQYcaXZHvUQVT90J8h1euc/uue4E+cl5lpRofULGriFvFfB6vfj73/+OdevWYdeuXZg8eTIFQoSkmVTbcQUAu6paQn24dBwLhgU0DfB3teEozDBA1TSwTLBOD8AgoKrQsSwMfHA3WjxmGnrnAqmaBqdfBs+xYBmmzxu7nmPg0jQ4/P0HX4qq4ZV9J7Hu38fg79Eeg+cYXHFGIeaWZiLbZBjWjjCeY5Fp1of1beuvhlO3WBrcxiJdWp+QsS3mQOiDDz7A888/j5deegmBQADf/va38etf/xoXXXRRIsZHCEmQVKztoqoa3j7YBA2AjmNCwQDDAAwHBBQNzV1vqGY9hxPtwe3lmhY8R9CxyLEEl3qG02Q1Ui6Q0x+sC1Tv8KHAboSpV80dSdHAd7XFiKSyyYU/vFOJI83usOMzx2fg3oXlKM4c3sYTjmWQYdLDZujbE2ygGk5AYpeoUr31CSFRB0KPPPII1q1bh8rKSsyaNQuPPvoorrvuOlit0e+SIISkhlSt7XKw3okmhw8GHQdRUcGyWuhNnQEDltEgBhSY9AY4/TIUVQODYBAABvBJCmravci1CkOeaYi05VvTNBj1HAw8C5+sos0twphpDNX/0aDB5ZdRmmtBWb457Ho+ScHz/z6GzftOhi2D2Qw63DF/Mhadnj+sZqYsw8A2SE+wRNVwilYqtj4hpFvUgdCjjz6KG264AS+99BJVkCYkjaVybZd2r4SACuRaBTQ4/JBVDTo2mFisAVC6lqMUVYOqAeOzTGh1SxADCjQ1OCvEMgzyrALOLc0e0hh6b/lWVC1YF0gDsi0CGjp98MsqnP4ArIIOkhIMgkx6DtfPKQlb0tpd3YbHtx1Bs0sMe4zF0/Lxo4smw27ih/pSgWEYWA3BlhiD1RZKhSWqVGp9QkhPUQdC9fX14Pmh/6MlhKSGVK7t0r2Eo9exXc1Y/RADamjpS89x0HNsaCbLwHOwGHTwS2ooTwiMhja3NOTxd+fTBGv8qGE7qUw8h3y7Ac1OP/ySAklRwTMMSnMtuH5OSSi5udUt4on3qrCzsjXs2sWZRtyzsBxnD7MthqUrAOJjaIpKS1SERBZ1IERBECGjQ7ISZ6MRvoQjwJxthl8OBjkcw6DTJ6PAbkCTwx8aPwMGRj0HIJj7oqoaHGqgz/ijTQzPMunBsUCHVwLLMuAYFgLPhJbBdCyLLJMet15YigwTD7tBH0puVlQNr39ej2ffPwZPj/5gOpbBdXNK8L25E4bV0NSk1yHTzEPQDa0nGC1REdJX4hrMEEJSUjITZwcTcQlHxwIK0OmVYTXocN2c8fjTe1UxjT/axHBN01DT7oFHUuD2B8AAYNngTFSWRQ8jz4ZygS6dmhe2DHa0xY0/bKnEoQZX2HjOKLJhxWUVmJgdnjsUCwPPIStOTVFpiYqQcBQIETLGJDtxdjCDLeGcW5qNtw82Rj3+aBPD/bKCt79oxCNvfwVV1cCxCCU3+2UFDZ0+GPUc7EY+LBfILyt4YfcJvLS3DkqPZTSLoMMPLirF16YXDGsrfKKbohIy1tG/LkLGmFRInB3MYEs40Y4/2sTwKQVWdHplvPDhCXglBUUZBvhkFe3uYO8vhgkGRQzD4J6F5aFcoI+Pt2PN1iNocPjDxr9gSi6WLyhDlnlos2oswyDTpIfN2HcrPCEkvqIKhJzO6KuNUm8uQlJfOiTODrSEE+34B0sMtxt1ONzowsfHOgAAtW0e2Aw8GDAw8RyMmUaIsgZFUxFQNaiKCqugR4dXwp/eq8a2r5rDrllgM+DuhWWYO2loO9YAwGrgkWUefCcYISQ+ogqEMjIyov5UoijK4Cd12blzJx599FHs3bsXDQ0N2Lx5M5YsWdLv+cuWLcOGDRv6HD/99NNx8OBBAMCvfvUrPPjgg2G3T5kyBV999VXU4yJkLEj3xNloxj9QYniwBhEDWVVD1aBlVYONO3V/BgwMPAOAhappaPWI2HKoEe982QSX/1R/MJYBvnNOMW6aNxHGIebxGPXBPKChJkITQoYmqkDovffeC/338ePH8Ytf/ALLli3DeeedBwDYvXs3NmzYgFWrVsX04B6PBzNmzMAtt9yCb37zm4Oe//jjj+Phhx8OfR8IBDBjxgx85zvfCTtv2rRp2Lp1a+h7nY5WAAmJJN0TZwcbf6TEcE3TgrM7qhba/t5dDTq4ZV6DoOsbDHokBR6/gpc/PRl2fEqBFT9ZVIGyPMuQngPPsci26GHS098pQpIhqn95F198cei///u//xurV6/GddddFzr2jW98A9OnT8czzzyDpUuXRv3gV1xxBa644oqoz7fb7bDbT/3Re/XVV9HR0YGbb7457DydToeCgoKoryuKIkTxVMGzWJYCCSGpq3diuKohVBwxUjXokmwzjra4kWPRh7bLq5qGdo+Edq8cdm2TnsOtF0zCN2YUDWkZa6CWGISQkRNzQYvdu3dj1qxZfY7PmjULe/bsicugovXcc89h4cKFmDBhQtjxI0eOoKioCKWlpfje976HmpqaAa+zatWqUJBlt9tRUlKSyGETMir07Mx+oM4RVngwVXQnhpv1LOo7/XCLAaiqBn9ARatbCqsGzTIMrp9TApOeQ6tbgj+gwiMGcLzN2ycIuqAsB+uWzcbVM8cNKQiyGXkUZ5pgN/IUBBGSZIzWu4XyIKZMmYKrrroKjzzySNjx++67D6+99hoOHz48tIEwzKA5Qj3V19dj/Pjx2LhxI6655prQ8X/9619wu92YMmUKGhoa8OCDD+LkyZP44osv+u2LFmlGqKSkBA6Hg5K/CYmgd10eAMizCbhuznhcP2d8SuUZ+WUFb33RgL98WIPaNg9kLdgctSTbHFYNutu+mg68sPsEDje64A+oYbflWgT8+NIynD/EZPJE5QFFWyySENJXzIvSjz32GL71rW/hX//6F+bOnQsA2LNnD44cOYKXX3457gPsz4YNG5CRkdEncOq51HbmmWdi7ty5mDBhAv7+97/j1ltvjXgtQRAgCEIih0vIqNGzLo+g4+CVZIgBBa1uEb/8x0G8+HENVl4xNSV2nnV6JXR4ZZxZnIHffcuOqiYPHH4prBp0T5qmodUt4USbNywIYgBcPXMcbrlgIkx6HVRNG/RaPel1LDJNiakHFG2xSEJIZDH/q/za176GyspKrF27NrQT68orr8SPfvSjEVtS0jQNzz//PG688Ubo9QPX6cjIyEBFRQWqqqpGZGyEjGY96/JYBB3qO/1QNQ0cy4JjNQQUDV81urDylc+x6ptnJu2NOKCoaHGL8PVoc8EyDCoK+k9oPtnpw5qtR7D3REfY8bJcC1ZcVo7TCoKzw/tqOrBxT21wdqlr235/s0s8xyLTrIclQQURoy0WSQjp35D+dZaUlOChhx6K91iitmPHDlRVVfU7w9OT2+1GdXU1brzxxhEYGSGjW3ddngwTj0aHCFXToOO6+3Ax0HEaVE2DwxdIWgd7txhAm1sMq/I8kICi4u+f1OGFD09A6jELZNCxWDpvIr59TnEoD2hfTQdWb6mEV1JgM/CwccFdZkdb3Fi9pRIrFlVg5vhM6FgWmWYeVkPiejRGWywyGT8DQtLJkLr/vf/++7jhhhswb948nDwZ3Er6l7/8Bbt27YrpOm63G/v378f+/fsBAMeOHcP+/ftDyc0rV67ETTfd1Od+zz33HObOnYszzjijz20//elPsWPHDhw/fhwffPABrr76anAcF7bLjZCRkA7JxLHqrsujqoAYUMCxp5qRAsElJE0L5sJ0d7AfKaqqodnlR7PTH3UQdLDegR/+9VM8u+tYWBA0Z1IWnl82G9+dXRIKglRNw8Y9tfBKCnIsegg6FizDQNCxyLHo4ZUU/G1PLTKMPEqyjAkNgoJjH7hYZIaJH/GfASHpKOYZoZdffhk33ngjvve97+HTTz8NJRk7HA489NBDePPNN6O+1ieffIIFCxaEvl+xYgUAYOnSpVi/fj0aGhr67PhyOBx4+eWX8fjjj0e8Zl1dHa677jq0tbUhNzcXF1xwAT788EPk5ubG+lQJGbLRmrfRXZfHLyvQNIDp9VFKA8AwgJHn4BL7doBPFL+soNkpBrfGR8HtD+DZXcfwz8/q0TNkyjTxuOuSMlxckdsnuKhq8oRVnu6JYRjYTTxOdnhxstOPLEvicw4HKhYJAALHwqFqI/YzICRdxRwI/eY3v8FTTz2Fm266CZs2bQodP//88/Gb3/wmpmvNnz8fA21aW79+fZ9jdrsdXq+33/v0HBMhyTCa8za66/IcOOkAoEHTGHTHC92FCo08C4bBiHSw17pq/Dh88uAnd52/80grnni3Cm2e8ADhyjML8f0LS2ExRP6z6PBLfSpPA8Et+hzLgGdZeERlxAKPSMUiexIVdUR+BoSku5iXxg4fPoyLLrqoz3G73Y7Ozs54jImQtNU7b8PAc2BZBgaeQ4FNgFtUsHZHddouk3XX5emufxNQVKiaClXTIKsaOIZBjkWAwxfA5DxLQjvYiwEFJzt9UQdBjU4//t+rX+DBf34ZFgRNyDbhj9eehXsXVfQbBAGA3aAPVZ4GgrNAPMeC54JLZCMdeHQHpR1euc8HSk3T0OmVE/4zIGQ0iDkQKigoiLgDa9euXSgtLY3LoAhJV2Mhb2NeWQ5WXT0dpxVYAYaBpGhQVA0GHYscqx5uUUl4B/tOr4T6Tn9YXk9/FFXDS5/U4pb1H+PDo+2h4zzH4JbzJ+KZG8/BGeMGbzNSlm9GSbYZTn8ALMuA55jQ80tG4NEdlFoEDo1OET5Zgapq8MkKGp1iwn8GhIwWMS+Nff/738fdd9+N559/HgzDoL6+Hrt378ZPf/pT/Nd//VcixkhI2kilvI1EFtmbV5aD15ZfgI17arBpTw0anf7gDRoS2sFeVlS0uET45eiaO1c2ufCHdypxpNkddnzm+Azcu7AcxZmmqB+bZRjcdv4kPPzWIbS6JWSYeAgcC1FR0emVkxJ4zCvLwUNXTw/lozm6tvMn8mdAyGgTcyD0i1/8Aqqq4tJLL4XX68VFF10EQRDw05/+FHfddVcixkhI2kiVvI2RqPzMsgxuOHcCrp8zPi4B12CBm9Mvo90tQY2iGL5PUvDcv49h876T6Hm6zaDDHfMnY9Hp+TG1tjDpdcgy61Gaa4HdxKdU4DGvLAfnlmZTZWlChijmFhvdJElCVVUV3G43Tj/9dFgsQ+u8nIqcTifsdju12CAxU1UNS9ft6WryKYS92WqahkaniKmFVmy4eU7C3qh6V37u9EoQAwoUNRi8nF5oTZnKz90G2mU3Z1IWWt0SvFIgumtVt+L3b1eis1fuULZZjx9fWoYLy6PfQarXscg2CzDqw4NaamlByOgRc47QLbfcApfLBb1ej9NPPx1z5syBxWKBx+PBLbfckogxEpI2kp230bvyc4tLhBhQwbEs9DoG0E5Vfv6gqjUhY4hVd+B2qMEJs6BDnlWAWdDhUIMLv3j5c/zzs/qogqAWV7DFx3++ejAsCOI5BnlWAaqm4ZmdR7GvpmOAqwRxLIMcq4DiTFOfIAgI/pynF9txcUUuphfbKQgiJI3FHAht2LABPp+vz3Gfz4cXXnghLoMiJJ11521MLbTCKwbQ7BbhFQOYWmhN+Nb5npWfW7uWkXQc09VdnYWuK3epu/Jzsnev9bfLTtCxyDbzcIkB/PWjmgGXwxRVw6v7TuLm9R/j/SPhwV2WiceELBMyjHyo6OHGPbX9Xi+Y0K5HSaYJtgQXRCSEpIaoc4ScTic0TYOmaXC5XDAYDKHbFEXBm2++iby8vIQMkpB0k6y8jaFUfp5ePPiOqUSJtMtOUbVgYUQNsBp41LZ5UNXkidgnrLu1xZcNrrDjgo5Fgc0AQXfqsx4DZsDrWQw6ZJr04PtJdCeEjE5RB0IZGRlgGAYMw6CioqLP7QzD4MEHH4zr4AhJZ93LJyMpVSs/96fnLrvugow9Z6n0HAOXpsHhDx+nX1bwwu4TeGlvXVg7jWAxRwaFNgEc2zegiXQ9o55DllkPQdd3CYwQMvpFHQi999570DQNl1xyCV5++WVkZWWFbtPr9ZgwYQKKiooSMkhCSHRSrfLzYHoGbhzHAL1WrCRFA88wsBtOjfPj4+1Ys/UIGhz+sHMXTMnF5dMK8Yd3vkJABSJN7PS8nl7HIsush0mfmM7whJD0EPVfgIsvvhhAsDHq+PHjY9p6SggZGd3J2is3H4BHDCCgqNBxwc7wgV6Vn6cWWvsU/xvp3VCnFVhRkmVCZZMLORZ92DKeBg0uv4zSXAvK8s1o90hYu70a275qDrtGod2Auy8tx5xJWVA1DSXZZhxtcfd7vcl5Fpw3OQt2aj1BCMEQ6gi9++67sFgs+M53vhN2/KWXXoLX68XSpUvjNjhCSOy6Kz+v+tchfNnggqRo4BjAoGNhN/H9Vn4e6UaxPklBi0vENbOKsXpLJVrdEqwGHnouWK3a5Zdh0nO4dnYx/nWgEc+8fxQu/6ndYywDXDOrBDedNwEGnus6xuD6OSX9XC+4k+7eS8spCCKEhMRcR6iiogJPP/10WNd4ANixYwd+8IMf4PDhw3EdYDJQHSEyGqiq1qfys55jIwY3/TWK7eiqmBzP3W6RGqXuq+nAxj21qG3zQNaCy1cl2WZcelou/vVFU9dS3ymnFVjxk0UVmJwXuX5Z7+vpWRbl+RbcMb8speonEUKSL+ZAyGAw4KuvvsLEiRPDjh8/fhxTp06NuLU+3VAgREaTwZa7ThWBdKLAZkhoEUgxEJwFitQjTNU0VDV54PBLMPE67Dnehr/tqUWgRzK0Sc/hlvMn4aqzisANMhZV03Cyw4+ApiLPYqCih4SQiGJeGsvLy8Pnn3/eJxD67LPPkJ2dHa9xEULipL/da90B0t6aDnzV4EKGiR+0Uexgu+AGCrocPhntHqlPp/TQOBkGFQUW7K/txCNvf4XajvAPVReU5eCuS8qQaxUGfc4GPrgTrCzPOui5hJCxLeZA6LrrrsOPf/xjWK1WXHTRRQCCy2J33303rr322rgPkBASfz3zgTyiApcowysFkGczwCKE/1mItlFsfzlGP7ywFGX51kGrQzt8Mp7ecRRvHWwMO55r0eNbZ5dgUq4JHR4Z2RY92H42a/Aci2wL7QQjhEQv5qUxSZJw44034qWXXoJOF/xjo6oqbrrpJjz11FPQ69M/CZGWxsho1jsfSFE11LR7oWkaOJbFuExjWDDkkxV4xQCevnFWvzNC/eUYtXskGHgWKxZVYOb4zIj31TQNWw8140/bq8PyhlgGOL8sBy5/APUdXshdDU5Lss24fk5J2PV0LIsMM0/VoAkhMRty09XKykp89tlnMBqNmD59OiZMmBDvsSUNBUJktIqUD6RpGo63eeCTFUADjHodJuaYwICJKkeov2t2V4hudUsozbXgd9+a3mcm52SnD2u2HsHeE+H9v8pyLfiPMwvw0t46eCUFNgMPnmMgKxqcXbvJViyqwNkTsmA38sgw8pT/QwgZkiHPH1dUVESsME0ISV2RWlowDINcqwEnO3xQNBV+OQCvqIBlGXR27RobqFFs72uqmoaAEmzH019bi4Ci4u+f1OGFD0+EJU4bdCyWnT8RV88ch/s3fwGvpITVAxJ0DHIserS6Jbz4SS3+48wiCDxVhCaEDF1UgdCKFSvw61//GmazGStWrBjw3NWrV8dlYISQ+OvZ0qIni6DDuEwjmp1++GQFbR4JZj2HqYXWQesIdV+TZxkEFDWs5QXQt63FwXoHVm85gmOtnrDz5k7Kwt2XlqPAbkBloxu1bR7YDHxYUUQAYBkWmWY96tp9qGxyJ7VXGiEk/UUVCO3btw+yLIf+uz9UbZqQ1NS9m+t4iwcaNIgBBcZeCcUWQQfWboDDJ+OuBeU4e0JmVFvOs0x66FjAKyt9AizgVFsLnmXx2NZKvP5ZQ1gnjSyzHncumIyLK3JDf0McfgmyqsHG9XhsJpgLxDKAjuXg8ie/VxohJP1FFQi99957Ef+bEJL6eu/mcvkDcPhkjMswwtojuVjTtK7WGzbceN6EqHJuNE1DgU1AUaap37YWTp+ELLOA37x5CO2e8MDlyjML8f0LS2ExhP8pshv04NlgTpCgY8CyDHQsEwqU/AElJXqlEULSX4S2hISQ0aJ7N9ehBifMgg55VgE5FiG0U6zNI0JVNfhkBY1OcdB8oJ7EgIKTnT64xACun1MCk55Dq1uCP6BC1TT4AyqanCJ8soqjrZ6wIGhCtgl/vPYs3Luook8QBABl+WaUZJvh9AegYxnwHBsKgjRNQ6c32DOsd680QgiJVVQzQt/85jejvuArr7wy5MEQQuJHVTWs3VENtxgIqxidZQ52fD/Z6UOrS4IUUKHn2KjygYDumSMZHV45VBxx5vhMrFhUEWpr4VRVBAIavLKCnilDPMfgpvMm4JpZJeAjtYcPncfiRxeV4rdvHkKzW0KGiYfAsRAVNaoEbkIIiVZUgZDdfioZUdM0bN68GXa7HbNmzQIA7N27F52dnTEFTISMRSPZ3T3SDrFuVgOPCdkMHN4All9ShnPGR5cPNFCLjJnjMzGjJAPbvmzGhg+Po97rD7v97PEZuHdhBcZlGgd8DJuRR6ZJjwnZZpgFXWhZz9FVRyjagI0QQqIRVSC0bt260H///Oc/xzXXXIOnnnoKHBfctqooCu644w6quUPIAEa6u3t/O8S6CRwHMAFMyjEPuvNK0zR0eGV0DpCc7JUCeP7fx/HqvpNhs0B2I4/b50/Goql5A26oMOqDbTEE3ant8PPKcnBuafaIBY+EkLEn5oKKubm52LVrF6ZMmRJ2/PDhw5g3bx7a2triOsBkoIKKJN5Gsrt7twN1DvzwL5/ALOhgiFBrJ5qK0QDgl4OzQLLSdxao27+rWvHHbVVocYthxxdPy8ePLpoMu6n/is88xyLLrIdZSG5bjJGcrSOEpI6Y//IEAgF89dVXfQKhr776Cqra/x9KQsaq/nJ1DCyHAhuLRqeItTuqcW5pdlzfeKcV2TA5z4JDDS4U2Ng+XeU7vTKmFlr7TThWu/qLOXu0veitxSXiifeq8P6R1rDjxZlG3LuwvN+2GkCwyWqGiYfd2LfZ60gb6dk6QkjqiDkQuvnmm3Hrrbeiuroac+bMAQB89NFHePjhh3HzzTfHfYCEpLuBcnVi7e4eC5ZlcPvFk3H/5gNodIoxJRx7pQBaXRIC/Xy4UVQN//ysHs/uOgavpISO61gG180pwffmToBe138ytEXQIcush26AhOmR0t9s3aEGF+7ffCAhs3WEkNQRcyD0+9//HgUFBfjDH/6AhoYGAEBhYSF+9rOf4Sc/+UncB0hIuhs8Vye67u5DMa8sBw9dPT3qhGNF1dDmEeH2998pvrrFjdVbKnGowRV2fPo4O1YsKseEbHO/99XrWORYhIhLdcmQrNk6QkjqiDkQYlkW9913H+677z44nU4AoDwaQgaQZQpuV5cUFQa2bwAgKmpCiwNGm3DsFgNoc4t9WmR088sKXth9Ai/trQs7x6TncMUZBbj0tHyUZJki3pdjGWSY9LAbU6s7fLJm6wghqWNI89KBQABbt27F3/72t9Afj/r6erjd7pius3PnTlx55ZUoKioCwzB49dVXBzx/+/btYBimz1djY2PYeU8++SQmTpwIg8GAuXPnYs+ePTGNi5B46s7V6Vl3p9tIFQdkWQbTi+24uCIX04vtYUFQQFHR6PCj2envNwj6+Hg7bt3wCTZ9XBt2TpZJD5OOxbtfNeNX//gCP3/5APbVnOokzzAM7EYexZmmlAuCgOhm6+QEzdYRQlJDzIHQiRMnMH36dFx11VVYvnw5WlpaAAC/+93v8NOf/jSma3k8HsyYMQNPPvlkTPc7fPgwGhoaQl95eXmh21588UWsWLECv/zlL/Hpp59ixowZWLx4MZqbm2N6DELipTtXxyJwaHSK8MnKkKs5x5vDJ6OuwwevFHkprN0j4TdvHMLPXz6ABsepukBZZj2yzTw0aDAbeGSb9TDqdTjatWy2r6YDJr0O4zKMyLYI4FJ0WannbF0kiZ6tI4QkX8yB0N13341Zs2aho6MDRuOpwmhXX301tm3bFtO1rrjiCvzmN7/B1VdfHdP98vLyUFBQEPpi2VNPY/Xq1fj+97+Pm2++GaeffjqeeuopmEwmPP/88zE9BiHx1J2rM7XQCq8YQLNbhFcMYGqhNSnJuFJAxclOH9rcItQIFTRUTcMbnzfg5vUf492vTn2IYBngu7NLMCHLBFUDcix6CDoWLMNA0LHIsejhlVS8/OlJ5FmFAROmU0EqzNYRQpIr5hyh999/Hx988AH0+vBPSBMnTsTJkyfjNrCBnHXWWRBFEWeccQZ+9atf4fzzzwcASJKEvXv3YuXKlaFzWZbFwoULsXv37n6vJ4oiRPFU/ZPu3CdC4ilRxQFjqX/T/ebe6ev7xt/tRJsHq7ccwYGTjrDjJZlGLD1vIooyjXjvUBNsBj6swSoYQMewyDLzON7qSYu8muHsrCOEjA4xB0KqqkJRlD7H6+rqYLVa4zKo/hQWFuKpp57CrFmzIIoinn32WcyfPx8fffQRzj77bLS2tkJRFOTn54fdLz8/H1999VW/1121ahUefPDBhI6dEOBUrk68xFL/ZrDCiFJAxcaParBxTw0CPfKAWAYw8RxcfhnP7KyGzcTDK6mwGU8FBz27w7MMA6c/kDZ5NbHurCOEjC4xB0KXXXYZ1qxZg2eeeQZAMBnS7Xbjl7/8Jb72ta/FfYA9TZkyJayQ47x581BdXY3HHnsMf/nLX4Z83ZUrV2LFihWh751OJ0pKSoY1VkISLdr6N9EURtxf24nVWypR1+ELO27gWQgci4yuXBpZ0dDkFOGVA3D6WWSagvk/PXOA0jGvhlp5EDJ2DamO0OWXX47TTz8dfr8f119/PY4cOYKcnBz87W9/S8QYBzRnzhzs2rULAJCTkwOO49DU1BR2TlNTEwoKCvq9hiAIEAQhoeMkJJ6irX8zozija2dU5Fkgh0/G0zuO4q2D4Tsvcy162Iw82j0Sciz60BKYoGOQbxNwvDUQvM2sB9cjRy+aitWpKt6zdYSQ9BBzIFRSUoLPPvsML774Ij777DO43W7ceuut+N73vheWPD1S9u/fj8LCQgCAXq/HOeecg23btmHJkiUAgkt527Ztw5133jniYyMkUQarf2M36lDZ6ML7R1pRlm9GVZMHDr8Eu0GPsnwzGABbDzXjT9ur4egxU8QywJKZ43BRWS4eevPLvnlAADiGRZ7VgCaXiHqHiByrQHk1hJC0FVMgJMsyTjvtNLz++uv43ve+h+9973vDenC3242qqqrQ98eOHcP+/fuRlZWF8ePHY+XKlTh58iReeOEFAMCaNWswadIkTJs2DX6/H88++yzeffddvPPOO6FrrFixAkuXLsWsWbMwZ84crFmzBh6Ph9p/kFFloPo3iqqBBQNJVbG3ph1/3nUUtW0eyF25L7k2A6SAiiPN4XW/ynIt+MllFZhSYMXHx9shqxpsXK9kaJYFywCZJj08koLiTCPaPRLl1RBC0lZMgRDP8/D7/YOfGKVPPvkECxYsCH3fnaezdOlSrF+/Hg0NDaipqQndLkkSfvKTn+DkyZMwmUw488wzsXXr1rBrfPe730VLSwseeOABNDY24qyzzsJbb73VJ4GakHQWqVq1pmkIqBpUVYOoqNBU4NV9JxFQNdgMPKws0OaRcbA+fFekQcfi5vMn4ptnF4dyfewGPXg2mBMk6JiwZGgA8AcUmPUcfnv1dLAM029eDXV0J4SkOkbrbw9tPx566CFUVlbi2WefhU4X88paWnA6nbDb7XA4HNQ+hKQkVdWwdN0eHGpwId+qh4rgTBA0QIOGVreEgKqBY4BcqwC/rKLJKfYpHDhnUhbuubQcBXZD+PU1DT9/+QCOtnhQYBPCmqNqmoZGp4iphVZsuHlOv4ENdXQnhKSDmAOh7sKJFosF06dPh9kc3mDxlVdeiesAk4ECIZIOPqhqxS9e+RwuvwKrQQc9x0BSNLj8Mng2OFtkFXg4RRkOX3jlaJYBLHoOv/vWDEwp7Fv2gmEYVDY68dCbh+CR1Ij1dQYqBNnfjraOKO5LCCEjKeYpnYyMDHzrW99KxFgIGXUStTSkqhoqCqy4+9JybNxTi9o2D1yaBp5hUJprwcySDPxtTw3qI/QPsxt1yDLp0emTcbDRCacoh5KoWYaBSa9DtkWPSTlmWA18zPV1otnR9qftVTALOnT6ZFoyI4QkVcwzQmMBzQiReEjU0pBXCqDVJSGgBpe5VE0L2xVmNXJ4+M3DOFAfXhlaz7HItwkw8hw6fRI6PDIshuBnIZ5lMD7HjNsvmoxLTw/Pp4s1mDtQ58AP//IJzIIOBp7rc3ubR0SrSwo1YaUlM0JIMkUdCKmqikcffRT/+Mc/IEkSLr30Uvzyl79Mypb5RKNAiAxXIpaGFFVDm1uEW4zcIFVRNbzyaR3W/fs4/IFTuUAMgk1Ss0w8GIaBRwqgvtMPjgWKMozQcywUTYPTF4DVoBv2stWOyhb89O+fIc8q9AmY3GIAJzu8kBUNhXYDMk16WjIjhCRV1B0Rf/vb3+L++++HxWLBuHHj8Pjjj2P58uWJHBshaan30pCB58CyDAw8hwKbALeoYO2Oaqhq9JOxTr+Mug5vv0FQZZMLd/zvp1i742hYECToWFgNOpgFHTQAPllBkzO48zPfZoCR10HgOVgEHoV2w5DG1lt/Hd01TUOLK7hUx7GASa8b9utCCCHDFXUg9MILL+BPf/oT3n77bbz66qv45z//if/93/+FqkauWEvIWDVYscMME4/qZnefbeyRiAEF9Z0+tLrEPrk+QHCZ7In3qnDH/34aVhfIbuTxiytOw2+XTEN5vhV+KYA2rwS3GADDMMizCrAZePBcsDfYUMbWn/46uvtlFWJXkGbgdTDoT/35iddjE0JIrKJOlq6pqQnrJbZw4UIwDIP6+noUFxcnZHCEpKOBih0CgMCxcHT1/+qPqmro8Epw+gP9don/d1Ur/ritCi1uMez44mn5+NFFk2E3BXNwzhqfGcohqmv34X8/qkG2WQ8uwviiGdtg+uvo7pECCKgadAyDXKvQp2J1PB6bEEJiFXUgFAgEYDCE1xrheR6y3H8jR0LGokjFDnsarCmpWwyg3X0qGbq3FpeIJ96rwvtHWsOOF2casWJRBc4qyQg7zjIMKgos0LEsGrP9ePnTOsiqBq7v0OLWMDVSR3dNCwaHORYBFqHvn550bNZKCEl/UQdCmqZh2bJlYc1J/X4/fvSjH4XVEhoNdYQIGY7upaFDDS4U2Niw5bGBmpJKARVtHhE+SYl4XUXV8M/P6vHsrmPw9jhHxzK4fs54XD93PPS6vrM8wd5jPDJNPIozjUMa21D07uieYeTx6NuH8VWjC5qmJfSxCSEkWlEHQkuXLu1z7IYbbojrYAhJtnjU/elvaai/pqSapqHDK8Phk/tdBqtucWP1lkocanCFHZ8+zo57FpZDDmj4rK4zrB4QABj1HLLNQihAYhjENLbh6t3R/Y75I/fY8UJtQggZ3aiOUAS0fX5sinfdn7DrdRUj7H09rxRAm1uCrEReBvPLCl7YfQJ//6QWPXOlLYIOP7yoFAV2AZs+rgtrqlqSbcYNc8dj0bSCiEtQ0Y4tUZL52LGiNiGEjH4UCEVAgdDY0POTfm27F39+/yg8cW4J0d9sghRQ0e6R4JUib4cHgI+Pt2PN1iNocIQ3Or7ktDzcMX8yTrR5sHpLJbySEtoBJisaXP4ArAYOq7555oBjHu5Mx3Dunw6zLNQmhJCxgQKhCCgQGv16ftKXAmpwdxY0jMswwmrgQ+dF22A0WkrXbjDXALvB2j0S/rS9Gu9+1Rx2vNBuwD0LyzF7YlaPpqhu5Fj0YBDsDK/jgnux4jnmSEb7TMmpprbOsDYhQPx/JwghyRV1HSFCRovuT/qHGpwwCzrYjDxUTYOqaqjv9IcVLYxXfRtN0+DwBYsiOvvJBVI1DW983oBl6z4OC4JYBrh2dgmeWzoLsydmAQAqm9w42uIGzzIQZQ0cx0CvY8EyTMJr8vR+/fKsAsyCDocaXLh/8wF8UNU6+EVSXDxrQRFCUlvMTVcJSWeRGoK6/MESEDqOgaJqaHGJMAtcqM7NcOvb+CQFrW6x3zwgAF3LXEdw4GR4f7DTCqz4yaIKTM6zhI7tq+nAUzuq0emVwQBgWQkGrw651lPb0hNVkyeahqprd1Tj3NLstJ4piUctKEJIeqBAiIwpkT7p61gWwf9kwLHBas5+SYVRHyy0M9T6NrISzAPy9NMWAwhumd/4UQ027qlBoEc2tFnP4bYLJ+E/ziwC1yOg2FfTgdVbKuH0yWAZgOMYMGDglxWc7PBhXKYRFkGXsJo8scyU9Nwtlm6GWwuKEJI+KBAiY0qkT/oGnoWgY+GTVehYQNPQVcyQG1J9m+77dA6wHR4IBjWPbT2Cug5f2PGLynOwfEEZcq1C2HFV07BxTy28koLiTCNqO3zwySp4NjibFVCCs1kmPZuwmjxjZaZkqLWgCCHphwIhMqZE+qTPMAxyrQac7PAhoKhgmGA1Zp+sxFzfJpplMIdPxlM7qvH2waaw47lWAd85uxgTckzo8MjItuhD9YAA4FiLFyc7vMixCNBxXGjMsqpBxzJgGcAvB3Cy048MI99nzPHYqTVWZkpirQVFCElfFAiRMaW/T/oWQYeiDANOdvrAMgxc/gB4jsHUQmtUO6ECXctg/XWHB4IzCVsONWPt9mo4fKda07AMcH5ZDlz+AP5vb21YPaDr55Tg7AlZsBl00OkYKCpCszEWQYdxmUa0uPwQAyo0DVA1YFyGEf/va1PDxhyvXV5jaaYkUpsQno3+d4IQkh5o+3wEtH1+dDtVH0bp80nfrGfx/YsmoyTLFNWsiaZpcPoC6PBKUAf4p3Syw4c1Wyuxt6Yz7HhZngX/Mb0AL+2t61MPyOmXYdZz+O3V03HxlDwcqHPgh3/5BGZBBwN/ajZG0zT4ZRUeKQBZUfHc0tmY0aPfWLzr4Qz0+o3G+jrpUPOIEDJ0NCNE0ka83pDOLc3GbReWYtOeGjQ6g8UK9Rzb7yf9/h7XLweXwaRA/8tgsqLi75/U4i8f1oSdZ9CxuPn8iVgycxzu3/wFvJISqgcEAALPoEBvQItbwrO7juHC8tx+Z2MYhoGBZ9Hp0zC10Ibp404lKSdil9dYmynp3SaEEDK6UCBE0kK8lnZ6X4cBgzybgOvmjMf1c8b3CQYiPe6kXDO+N2c8TisceLbwi5MOPLb1CI61esKOn1uahR9fWo4CmwGVjW7UtnlgM/ChIIhlGejYvvWAphfbY85bSdQur94NVWmmhBCSrigQIimvv6Wd7gJ+0S7F9HedJqeIZ98/itIcc5+8mp7n8ywDX0DFl/VO/PbNQ1ixqAIzx2f2eRy3P4A/7zqKf37WEHY8y6zHnQvKcHFFTigocfglyKoGG8cATHArf8/t8r13YcU6G5PIXV40U0IIGQ0oECJJE81SV/fSjssvI8Ooh6wEk4INPIsCmxD10k6sS0Q9z8+3ClABBLqCjhyLHq1uCRv31GJGSUZoZ5emadhR2YIn3qtGu+dUYMEAuHJGEW67cFKfJqh2QzDAUlQNBp7rM2sTaRdWz9mYVo+ITo+MTBMPq4GHqmphr8NY2eVFCCFDRYEQSYpol7oO1jvxZb0DPkmF0++FpgEMAwg6FrlWQ9RLO7EuEXWfbzPwCKgIqwfEgIHVwKO2zYOqJg8qCixodPjx+LYj+OhYe9i1J+WYsWJROaYVRR7baYVWlOVbUdnkhrlXkDTQLiyWDVbEfn7XsQFfw7G0y4sQQoaCeo2RERdLr6pdVa3o8MoQFQUsw3TVy2Hgk9VgDZ2ACjmKpZ1oloh6XqfB6YNfVsEyiFgUUc8xkDUN7V4RL35ci1vWfxwWBOl1LG67YBKeuuHsfoMgi0GHkiwT7lxQBovAodEpwicrUFUNPllBo1Pst15NtK9hdz2cWK9PCCFjBQVCZET1XqIy8BxYloGB51BgE+AWFazdUQ1VDTZBfftgIwCAY5hQQ1GWYYLLSZqGZpcIHYNBl3Z6LhFF0r1EZBV0aHL6oSqAjgVkJfKWeEkJFu1Zu70aT+88Cn+PHWFnj8/AczfNwvVzx4OPEHjxHItCuxF5VgM4lgnl/UwttMIrBtDsFuEVA5haaI2Y/xTLawgg5usTQshYQktjZETF2tW72emHoOMgKypYaKGdVQzDgGM1iAEF+fbBl3YGWyLq8Egoy7PAZtTBIwZQlm9GSbYZR1vcYdvaAUBRVTQ5g0UMO3ynCijajTzumD8ZC6fmQQNQ2eiGwy/BbtCjLN8MlmFgM/LIMun7zMDEsgtrKDvBaJcXIYRERoEQGVGx7mKSFQ15NgENnX4EFA0cG8wR0jRAUTQwABZPyx/0Db2/lgn+gIIOrwwjz+KaWcVgGQaqpqGqyYOzx2egps2DFpcEm5GHnmPQ6ZPR5pHQe7Xs8mkF+OHFpbAbeeyr6cDGPbWobfOEqkRPyDFj+fwyzD8tb8AxRrMLa6g7wWiXFyGE9EWBEBlRse5i4jkGeo7taiUhQgwo0NRgMKTXcTDpOVxQlhvVY/fcel7V5EKHqoFDMKH5+jklmDk+s08Qo2mAomno9IrwyWqfpbLiTCNWLKrAWV2VnLu7w3dXibZxDFQNONriwa/+eRAP6dhhL0XRTjBCCIkfCoTIiIp1F9OpcwWYc0zwSyoCqgqOYeDwy5haaItpx9O5pdk4rcCKT050oMMbvmwVKYiRAipa3CL8soqeIZCOZXD9nPG4fu546HXBmZme3eFzLHqwDAsdF8xpMum5IVVxjsdrSAghpH9JTZbeuXMnrrzyShQVFYFhGLz66qsDnv/KK69g0aJFyM3Nhc1mw3nnnYe333477Jxf/epXYLqSaru/TjvttAQ+CxKLWHYx9T7XL6sQdCx0HAuHPwCLoItpx5PTL6O2w4tOn4yyPAtmT8xCRYEltBzWM4gRdCxkRUWzKzgT1DMImj7Ojj/fdA6WnT8xFAQBQFWTJ1QlWsey4LuCICBy/tNIvIaEEEIGltRAyOPxYMaMGXjyySejOn/nzp1YtGgR3nzzTezduxcLFizAlVdeiX379oWdN23aNDQ0NIS+du3alYjhkyGKZRdTPHY8+WUFdR1etLpEKGrkXWA9gxhNA1rcIk60+8J2gzEAbpw7AY99dwYmZJv7XMPhlxBQAZOeg45j+yQy996iPxy0E4wQQuIjqUtjV1xxBa644oqoz1+zZk3Y9w899BBee+01/POf/8TMmTNDx3U6HQoKCuI1TJIAsexiGuqOp4Ciot0jwS0GBjwPONXqglMU1HcG/7sni8CB4xicUWwLzfL0xDAMijNMEHQMAqoGXd/Unbjn7tBOMEIIGb60zhFSVRUulwtZWVlhx48cOYKioiIYDAacd955WLVqFcaPH9/vdURRhCiKoe+dzuEtXZDoxLKLKZZzVVVDp0+GwydHLIYYCaMx8ErBHWQ98SyDPKsAjmPhlwKwG/oGMTzHItcqYEKWCWX51hHN3aGdYIQQMjxpXVDx97//PdxuN6655prQsblz52L9+vV46623sHbtWhw7dgwXXnghXC5Xv9dZtWoV7HZ76KukpGQkhk8SIJQH5JWiCoJUTcPrnzfgv9/4EmIgvNhiponHhGwTTAIHl19GSbYZk3PNqGx04+Pj7ahsdMOs12FchjFU1HC05u6oqoYDdQ7sqGzBgTpHqFgjIYSkO0aL9iNzgjEMg82bN2PJkiVRnb9x40Z8//vfx2uvvYaFCxf2e15nZycmTJiA1atX49Zbb414TqQZoZKSEjgcDthstPMmHfgkBa1uEXI/laMjOd7mwWNbKnHgZPgMIMcyyLUIsAgcJEWDyy/DpOdw5ZlF+PhEB2rbPAiogF7HoDy/b9f3sD5qXXWEIvVRSxfR9oUjhJB0lJZLY5s2bcJtt92Gl156acAgCAAyMjJQUVGBqqqqfs8RBAGCIMR7mGQEyIqKVpeIz2odfao490cKqPjfj07gb3tqEegxs2HWc1g8LR/H27yoa/eizauAZxiU5lowe0Im/vl5PbySggwjDyPPQVa1UG+vngnKoyl3p7unmVsMINOkh55jISlqxOdNCCHpKO0Cob/97W+45ZZbsGnTJnz9618f9Hy3243q6mrceOONIzA6MlK684De+6oZG/fUhFVxzrIIuLA8F+eMz+wTFO2r6cBjW4+grsMXdr0Ly3Nw1yVlyLEIocrS3YHV5FwzfrH5ALySggKbAbquis4cBxTY2Ij1gUZD7k7vnmbdOU8Gluv3eRNCSLpJaiDkdrvDZmqOHTuG/fv3IysrC+PHj8fKlStx8uRJvPDCCwCCy2FLly7F448/jrlz56KxMdiQ02g0wm4Pvun89Kc/xZVXXokJEyagvr4ev/zlL8FxHK677rqRf4Ik7jRNg9MXQKdPwifH28MKIOpUFW1uCS1uCYcbXXjJqMPkPCuun1OC0lwLntpRjbcPNoVdL88q4MeXlmHe5FOzGizDoKLAEvq+stGN2nYvss36UBDUrb/eXqNBz55mYILLjwFVhY5lYdCzo/Z5E0LGlqQGQp988gkWLFgQ+n7FihUAgKVLl2L9+vVoaGhATU1N6PZnnnkGgUAAy5cvx/Lly0PHu88HgLq6Olx33XVoa2tDbm4uLrjgAnz44YfIzY2uDQNJDlXV+iwlAQgdyzDyGJ9lgtMvQ1G1PgUQfbKKFqcIVdOgYwFFA/yyiqomF37zxiFIARUeSQk9HssAV88ch1vOnwSjPsJe9x4CmgpV1SBE2hOP/nt7pbvunmaSoqLB4Q+2N9GC7U0EHYdsiz5udZFGm0i/zzRrRkhqSmogNH/+/AF39nQHN922b98+6DU3bdo0zFGRkdYzGVcKqAADWAQdOJaBVwxAUjRwDFCSfaonWM8CiADQ7pagaho4jgl2itc0yEqwInTv3WDleRb85LIKVORbBxxX97Z4j6hAr2MH7e3V7pawo7Jl1LzxZZn0UDUNJ7uWETmWAcMGG976ZQUnO3ywG3nqadYLJZcTkl7SLkeIjC49k3EFHQufrMAnK2h1B2cZ9ByQYzWAZ1kcbXFj9ZZKrFhUgYCqQVY12DgGoqxBUhSwbDAI0jQNqqpB0QClRxAk6FjccsEkfHPmOHCDBCkWQYcciwCWZQbt7dXi8oNhGDzy1iEEVIyaN76pBVYomgZF1aDX9WwXAoDVIAU0KJqGqQUDB5RjCSWXE5J+0rqOEElvPZNxLYIOLS4pWHunxyShpAAtThGKpiHHoodXUrBxTy2sBh48y0BWNCiaGlqyUTWt61j4Y/Ecg3sXluM75xQPGASxDINcq4A8myEs8bm/+kC1HV54xOD3FgOPPKsAs6ALvfF9UNWaiJduRBxqdIFjAB3HIKAGX1tNCy5LBtTgcY4Jnkf6Jpd315Yy8BwKbALcooK1O6qpBhMhKYYCIZI03cm4GUYerW6xq6t83/MUTUN71wyR1cCjts0DILhU5vTLoZmKgBIMgnq+zXAsgxyLHhkGHSZmW8Kuq2paWHFEQceiONMIa9dyW0/99fZiujrLj88yjbo3vnavBJZhUWQ3wsizXQFQMBAy8sHjLMtSjlCXnsnlvfvMxbPpLiEkvmhpjCRNdzKuomrwy0pwpkYDgPDAgWUASVEgysElGpcWLHJ4/ZwS/OGdw2j3SH1mgADALHDItwro8MoozbWgLP9Uo9R9NR3YuKc2tO1e0LERiyP21Ls+ULtbwiNvHYLFwA/6xpeOu6qyTHrwHAO9jsXEbDP8snpq1xjPwh9QwSsq5Qh16f591nORP1+O1qR6QtIdzQiRpMk08uBYwCMGgktbQNf/hGMYBpoGKJoKSdHAMwzsBj0K7UbYjfqw3WDdl8gx65FtDgZBJj2H6+eUhGaO9tV0YPWWShxtccMk6FBgM8Bq4KNazuquD3RxRS6yLPpghekB3vjSeVdVd25Ud/81o56D1cCHdtl1emVMzrPEtXdaOusOHKV+qpvHu+kuISQ+KBAiSeEWA7AbeYzLNMEnq2C6JoMYhMdC3f/NMMH8HZdfRnGWCftqO3DL+o/D8lOCrTH0yDbpoECDXwqgNNeCFYsqMHN8JgCEbbvPtwqwCDroOHZIy1mj/Y1vNPdOS4SegWPv3bDdTXcpcCQk9dDSGBlRPklBu1eCKAdncbqXt3xyAIqigeMAjgECXe8jLAMoigZex8ItBqBjGTQ5/Xh659Gw654zIRP3LCxHod0QVhW6d2XpqiYPats9yDLrwfeqCxTrctZgu8kS0W1+pHXnRnVvB3d0Ve+eWjjwMuJY1B043r/5ABqdIjJMPASOhaio6PTKFDgSkqIoECIjUvxNDCjo8MjwSoGw4zPHZ+Inl03B0zuqUdXigawEAyE9x0BDMAGaAWDQseA5Fi0uMSyDKMPI444Fk3HpaXmhQKRnVeg+41AUqCpgiENxxLHyxjeaeqclGgWOhKSflOk+n0qcTifsdvuY6D6f6OJvAUVFu1eC2x8Y8DxV0/D65w14/fN6tHXtENMxQLbVgJIsIz453hHKVel2xRkF+MFFpbAb++7y6o1lGORYBRxr8eCHf/kEZkEHA983GPLJCrxiAE/fOCvqBOfR1m2eDB9VliYkfVAgFMFYCYT6K/7W0TWbMZzib4qqodMrwekPDFg9vLeeDU9VFXj983r8u7ot7JySTCNWLKrAjJKMqK5p4DnkWQXoOBaqqmHpuj1dy1lCn+WsRqeIqYVWbLh5TuiNK5o3NXrjI4SQ9ERLY2NUojqLa5oGh09Gp1eGOoQYm2UYTM4z47X9Djz/72Pw9tgRxnMMrpszHtfPGQ+9bvA8f4ZhkGnikdEjWTnW5axoZ8xGQ7d5QggZi2hGKIKxMCN0oM4R9yUip19Gp0dGQI28iyoa1c1u/GFLJb7qVa34zGI7ViyswPhsU1TX6e4TFum5AdEtZyVyxowQQkhqoBmhMapnMUOXXw4WydOzwYaliC1p2CMG0O6RIPezjTwaPlnBCx8cx0t768JabFgNOvzoolIsPqMgbPfXQHr2CevPYAnAiZoxI4QQklooEBqjatu9cPpldHhFAAwYBhB0HHK7autEUwPHLyto85zaCj9Ue461Y83WI2h0+sOOX3paHu5YMBmZUdbhYRkG2RZ9xBYZEc8fYDkrlnYJiVoSo7wjQghJPAqExqAPqlrx553VXU00geBOcgZ+WcHJDh+KMgxwi0q/NXCkgIp2j9RnK3ys2j0SnnyvCu8dbgk7Xmg34J6F5Zg9MSvqa+l1LPKshqhyh6IaW5LbJSR6Nx8hhJAgCoTGmO4lH4+kYFyGEfWdfgRUDToW4NjgdveTnT6MyzD2qYET6MqPcfnlAR4hijFoGt480Ihndh6FWzwVTLEM8N3ZJbjx3An95vZEYjXwyLH0nbkZjp5Vow1s37Eksmp0f7lJ3S1AKDeJEELihwKhMabnko+B5zAuk0GLyw8xoAb7fTEMGDC47cLS0JutqgZ3gjl8Q9sJ1tPxNg8e21KJAyfDO3CfXmjFikUVKM3tvxhib7EuhcViWpENpbkWfFHvgN2gA89xoRyqRFaNptwkQggZWRQIjTG9l3wsgg5m/anO4izDwOmXUZJlgqZpcPoDcHiHtxMMCC6n/fWjE9i0pxaBHtnQZj2H2y6chP84syjYfT5KPMci3xa/pbDePjzaBodPgqvr+XNsMIcqw6SHGFATVjU6FXKTCCFkLKFAaIyJtOTDMExXR3EOPlmBnmNh0LGo6/ANaydYt09rOrBm6xHUdfjCjl9UnoM7LylDjkWI6XqJWArrqefSVIFNQKdXhhhQ4ZUV+J1+nF5oxcorpiZkeSra3KQ2t4gDdQ5KpCaEkGGiQGiMGaxRaIdHQmmuGdkW/bCDoA6vhEffOowPj7WHHc+zCvjxpWWYNzm2QKK7TYZFSNyvbaSlqUyTHn5ZhayocPhk2I08zi3NTsjjR5ObpKoa1mw7gmannxKpCSFkmBKzrkBSVndlZYvAodEpwicrUFUNXimAk51+GHgW184uibpmTySapuGZndW45ukP+wRBF5XnYN2y2TEHQQLPYVymMaFBEBB5aap7xsxm5JFjFXC0xYOD9c5BrjQ03YFqh1fu05pE0zS0uPzwSAHUtntgFnTIswowC7pQIvUHVa0JGRchhIxWFAiNQd0dsqcWWuHxy2hw+uHyySjNNWPFogrMHJ855GvXdXjxo79+ik0f10HpkQuk5xhYDTpUt7jxVWNsQUSmSY8iuwF8P8tF8RTN0pScwG3z/QWqPllBozOY1K7nWBTajTDwHFiWgYHnUGAT4BYVrN1RDVWlYvGEEBItWhobo+aWZqM8z4JPazrR6ZNgN+hRlm8e8kyQrKjY9HEt/vrhCcjKqTdihgFyzHpkGHmAAVrdEjbuqcWMkoxBH2uwNhmJkMxt8926A9XuOkKOrhYgxZkm1LZ7kGUWKJGaEELihAKhMUbTNDh9AXT6JCiqhvL86Ler9+eLkw6s3lKJ423esONmfbDre8+ZHKuBR22bB1VNHlQU9P/YFoMOOeaB22QkwmA5VInaNt9bpBYgrR4R9730edKKPBJCyGhEgdAY4hYD6BhmT7CeXH4Zf37/GF7/vCHsOMMA+VYBVkHXZ+ZCzzFwaRoc/shv1hzLINuS2ITogcTanT7RY+me2VFVDa/tr4eqaXD6ZdhNfKgvXLeRmK0ihJDRhgKhMSBePcG6aZqG7Ydb8MR7VejwnqoyzQC4sDwXX9Z3QtBxEbe3S4oGnmFgN/R9szbpdcix6KEbgVyggfS3NDW10JqUnVnd7Taqmlxd/eEktHsk5NkMoYBxJGerCCFkNKFAaBSTAio6vBI84vB6gvXU6PBjzbYj2NNrN9ikHDNWLCrH1EIbfv7yARxtcQdr/fSYtdAQ7HRfmmtBWb45dJxhGGSZ9bAb418heqgG604/Uvq029BxqHf44JUU1LZ7MS7TCJ5jR3y2ihBCRgsKhEahePUE60lRNfzf3jps+OA4/IFTS2t6HYubzp2Aa2YVh2Zyrp9TgtVbKtHqlmA18NBzDCQlGASZ9Byun3Nqe368m6XG00Dd6UdCpJpG3TvFmp1++GQF9Z0+5FqEpM1WEUJIuqNAaBRRVQ2dXT3BetegGY6vGp1Y/c4RVLW4w46fMyET9ywsx7gMY9jxmeMzsWJRBTbuqUVtmwcuLbgcVpprwfVzSkLb821GHtnmxFWITnf9tduwCDqYc8zo9MnwigH8bPFpuOqsIpoJIoSQIaBAaBTQtFNNUZU41pDxSgE8v+s4Xt1/Ej0vm2HkcceCybj0tLx+g5iZ4zMxoyQDVU0eOPzh2/M5lkGOJVgIkPRvoJpGDMPAbuAhBlRkWfQUBBFCyBDRO1Eai2dT1N52HWnFH989glZ3+O6ur51RgB9cVApbFPk8LMP02SJv4INb6pOdEJ0OUqGmESGEjHZJfTfauXMnrrzyShQVFYFhGLz66quD3mf79u04++yzIQgCysrKsH79+j7nPPnkk5g4cSIMBgPmzp2LPXv2xH/wSebyy6jr8KHNLcY1CGpxifiv177AA/84GBYElWQa8dh3Z+Cni6dEFQRFkmnSoyjDSEFQlAZrt9HplTE5z0K7xAghZBiS+o7k8XgwY8YMPPnkk1Gdf+zYMXz961/HggULsH//ftxzzz247bbb8Pbbb4fOefHFF7FixQr88pe/xKeffooZM2Zg8eLFaG5uTtTTGFEeMYC6Di9aXGLc6gEBwWToVz49iZvXf4x/V7WFjvMcg6XnTcCfb5qFGcUZQ7o2z7EoyjAi00wzF7EYuN2GSLvECCEkDhgtnlm1w8AwDDZv3owlS5b0e87Pf/5zvPHGG/jiiy9Cx6699lp0dnbirbfeAgDMnTsXs2fPxhNPPAEAUFUVJSUluOuuu/CLX/wiqrE4nU7Y7XY4HA7YbKnxadsnKWj3xq8WUE/VzW78YUslvmp0hR0/s9iOFQsrMD7bNORrJ6tC9GjSXUeoutkNuaumEXWbJ4SQ+EirHKHdu3dj4cKFYccWL16Me+65BwAgSRL27t2LlStXhm5nWRYLFy7E7t27+72uKIoQRTH0vdOZmM7iQ+GXFXR4Jfik+AdAPlnBCx8cx0t768KSoa0GHX50USkuP6NgyDu6kl0hejRJlZpGhBAyGqXVu1RjYyPy8/PDjuXn58PpdMLn86GjowOKokQ856uvvur3uqtWrcKDDz6YkDEPVSKKIfa051g71mw9gkanP+z4wql5uH3+ZGQOIwHXLOiQYxHA0Rt13CS7phEhhIxWaRUIJcrKlSuxYsWK0PdOpxMlJSVJGUsiiiH21O6R8OR7VXjvcEvY8UK7AfcuLMesiVlDvjbHBitEWw2pUyGaEEIIGUhaBUIFBQVoamoKO9bU1ASbzQaj0QiO48BxXMRzCgoK+r2uIAgQBCEhY46Womro9Epw+gNxLYbYTdU0vHmgAc/sPAZ3j1kmjmVwzaxi3HjuBBj4vlu0o2XUc8i10LZ4Qggh6SWtAqHzzjsPb775ZtixLVu24LzzzgMA6PV6nHPOOdi2bVso6VpVVWzbtg133nnnSA83Kqp6qhiimqC89eNtHqx+pxJf1IfnPp1eaMWKRRUozbX0c8/BMUywjo3dRLNAhBBC0k9SAyG3242qqqrQ98eOHcP+/fuRlZWF8ePHY+XKlTh58iReeOEFAMCPfvQjPPHEE7jvvvtwyy234N1338Xf//53vPHGG6FrrFixAkuXLsWsWbMwZ84crFmzBh6PBzfffPOIP7+BdBdD7PRKca0G3ZMUUPHXj05g055aBHo8hlnP4bYLJ+HKGUWhnl9DwXMs8mwCBN3QZ5IIIYSQZEpqIPTJJ59gwYIFoe+783SWLl2K9evXo6GhATU1NaHbJ02ahDfeeAP33nsvHn/8cRQXF+PZZ5/F4sWLQ+d897vfRUtLCx544AE0NjbirLPOwltvvdUngTqZ3GIAHR4prnWAevu0pgNrth5BXYcv7PhF5Tm485Iy5FiGtxRoNfDB7vLUJ4wQQkgaS5k6QqkkkXWEnH4ZrS5x8BOHyOGVsXZHNd75MjxPKs8q4MeXlmHe5OHVnaE+YYQQQkYTejcbYYkKOzVNwztfNmHt9mo4/aeSoVkG+ObZ43DzvEkw6oe3hGXS65Bj0VNCNCGEkFGDAqFRoK7Di8e2HsG+ms6w4+V5FvzksgpU5FuHdf1US4hWVY2KCxJCCIkLCoTSmKyo2PRxLf764QnIyqmpJgPP4ubzJ+GbM8cNu6hhqiVEh7WbUDTwHLWbIIQQMnSUIxRBInOEHD4Zbe7h5wgdqHNg9dZKnGjzhh0/tzQLd19ajnybYdiPkWoJ0R9UteL+zQfgFgPINOmh51hIXQUoLQKHh66eTsEQIYSQmNCMUJpx+WX8+f1jeP3zhrDj2WY97rqkDBeW5ww7cEnFhGhV1bB2RzXcYgAFNkPoORpYDgU2Fo1OEWt3VOPc0mxaJiOEEBK11HmnIwPSNA3bD7fgifeq0OE91X6DAfCNGUW49cJJcWlwauA55FlTr0L0wXonqpvdyDT1naFiGAYZJh7VzW4crHdSTy5CCCFRo0AoDTQ4fHh86xHsOd4RdnxSjhkrFpVjWlF83vgzTXpkmofebDWR2r0SZEWDvp8ATeBYOFQN7V5phEdGCCEknVEglMICior/+/QkNnxwHGLgVPFFvY7F0vMm4DvnFEecuVE1DVVNHjj8EuwGPcryzQNWkOY5FrlWYVi9xhIty6QHzzGQFBUGtu84RUUFzwZ3txFCCCHRokAoRR1qcGL1lkpUt3jCjp8zIRP3LCzHuAxjxPvtq+nAxj21qG3zQFY18CyDkmwzrp9TgpnjM/ucbxF0yLEIKZ9XM63Ihsl5FhxqcKHAxoYtj2mahk6vjKmFVkwrim9yOyGEkNGNAqEU4xEDeP7fx/HqvpPouZ0vw8jjjgWTcelpef0mQ++r6cDqLZXwSgpsBh42joGsaDja4sbqLZVYsagiFAyxDINsix5WQ2rUBhoMyzK4/eLJuH/zATQ6RWSYeAgcC1FR0dm1a+z2iyenfEBHCCEktVAglEJ2HWnFH989glZ3eJ7L184owA8uKoXN2H/QomoaNu6phVdSglveEQwIBB2DHIserW4JG/fUYkZJBox6HXItAvS6+CZEJ7rQ4byyHDx09fRQHSFH14zX1EIr1REihBAyJBQIpYAWl4g/bjuCf1e3hR0fn2XCvYvKMaM4Y9BrVDV5UNvmgc3Ah4KgbgwYWA08ats8aHSIOL/MEvfaQCNV6HBeWQ7OLc2mytKEEELiggKhJFJUDa/tP4nndh2HT1ZCx3mOwffmjse1s8dHPWvj8EuQVQ02LnJAIHAsvAAUTUtIEBSp0OGhBhfu33wg7oUOWZahLfKEEELiggKhJKlqduMPWypxuNEVdnxGsR33LqrA+CxTTNezG/Tg2WBOkKALD3RYloGsqNBzbNx3VVGhQ0IIIemMAqER5pUCeGpHNf5vbx3UHtnQVoMOP7qoFJefUTCkGZuyfDNKss042uI+lSPEADqWBcsAre5AQnZVUaFDQggh6YwCoRH03uFm/L9XDqDe4Q87vnBqHm6fPxmZw5itYRkG188pweotlWh1S7AZeZh4DlKCd1VRocPBJTqJnBBCyNBRIDRCXtt/Endv2h92rCjDgHsuLcesiVlxeYyZ4zOxYlEF/v5JHWraPPBKSsJ3VVGhw4GNVBI5IYSQoaFAaIQsnlaACdkmnGjzgmMZfHdWMW48dwKEOFZzZhkGi88oxNUzi0dsBoIKHfZvpJPICSGExC61OmuOYgaew2+XTMeMYjuevuFs3HZhaVyDIIHnMC7TCIugC+2qurgiF9OL7QldhukudGgRODQ6RfhkBaqqwScraHSKY7bQYe8kcgPPgWUZGHgOBTYBblHB2h3VUHsmihFCCBlxjKZp9Je4F6fTCbvdDofDAZstvjMZnV4J7Z745svYjDyyzX2TlUdS2BJQV6HDsbwEdKDOgR/+5ROYBV3EHm4+WYFXDODpG2dREjkhhCQRLY2NsHgGKyzDINcqwCwk/8dIhQ7DURI5IYSkh+S/g5IhEXgOeVYBfD9vtMlAhQ5PoSRyQghJD6nzLkqilmHSo8huSKkgiITrTiLv8MrovfrcnUQ+Oc8yJpPICSEkldA7aRrRsSwK7UZkRZkPpKoaDtQ5sKOyBQfqHJSYO4IoiZwQQtIDJUtHkMhkaYdPRptbjPl+Jr0OuVYBXJRvnKlQv6a7kGCbW0SHV0aGmUeOWRhTuUOURE4IIamNAqEIUikQYphgHondxEd9n/7q13R0VZgeifo13QHAl/VOOP0yVFUDyzKwGXQ4vcg+pgIBqixNCCGpiwKhCFIlEOI5FrlWIeL26/6oqoal6/bgUIMzrAkqEMxNaXSKmFpoxYab5yTszbg7EOvwSvCICjQtGASpmgaWYWDkdcgy81RQkBBCSNJRjlCKshh0GJdhjCkIAmJrgpoIPQsJBpRgjM3rWOhYFjzLQtUARVXh8geooCAhhJCko0AoxXTXBsqzGoY0YxNN/Ro5gfVrugMxY1fDV45lwCD4PBiGgY4Nbik36rmEBmSEEEJINCgQSiF6HYuiDCOshujzgXrrWb8mkkTXr+kOxBgG0DSg9+Y2BsHjHMMkNCAjhBBCokGB0AhSVQ1f1jvx8fF2VDa6ofZIz7IbeYzLMEKvG96PJNn1a7oDse4gqHcGmobgcUXTqKAgIYSQpKPK0iOkexfVkSYXxEBwVqYk24wb5o7H4jMKYNLH50fRXb/m/s0H0OgUkWHiIXAsREVFZ9eusUTWrznVjd4JPcdCDKhgOIABA03TEFA1GHQsfJKC04tsVFCQEEJIUqXEjNCTTz6JiRMnwmAwYO7cudizZ0+/586fPx8Mw/T5+vrXvx46Z9myZX1uv/zyy0fiqUTUvYvqUIMTJr0O2WY9jHodjrZ4sGZrJfbXdMb18eaV5eChq6djaqEVXjGAZrcIrxjA1EJrwndqnSokqIOOCwZbckBFQFUhqypYBuBYFlaDjgoKEkIISbqkzwi9+OKLWLFiBZ566inMnTsXa9asweLFi3H48GHk5eX1Of+VV16BJJ3KK2lra8OMGTPwne98J+y8yy+/HOvWrQt9LwhC4p7EAHruoiqwGaBqQEBVYdIFg4FGp4i1O6pxbml2XIOCZDZB7Q7EqI4QIYSQVJf0QGj16tX4/ve/j5tvvhkA8NRTT+GNN97A888/j1/84hd9zs/Kygr7ftOmTTCZTH0CIUEQUFBQENUYRFGEKJ6q7eN0xm8nU5/t7JoGnmPBdmUR99zOHu+GpclsgtozEBupytJUuJAQQkiskhoISZKEvXv3YuXKlaFjLMti4cKF2L17d1TXeO6553DttdfCbDaHHd++fTvy8vKQmZmJSy65BL/5zW+QnZ0d8RqrVq3Cgw8+OPQnMoDe29l7t8gQOBaOUbp7aiQDsVRoKUIIIST9JDVHqLW1FYqiID8/P+x4fn4+GhsbB73/nj178MUXX+C2224LO3755ZfjhRdewLZt2/C73/0OO3bswBVXXAFFUSJeZ+XKlXA4HKGvzs5ONDc3w2q1Dv3JdUn2dvaxoGcOllnQIc8qwCzocKjBhfs3H8AHVa3JHiIhhJAUlfSlseF47rnnMH36dMyZMyfs+LXXXhv67+nTp+PMM8/E5MmTsX37dlx66aV9riMIQsJyiE7tonKhwMb2aXnR6ZUxtdBKu6eGqHcOVvfra2A5FNjYhOVgEUIIGR2SOiOUk5MDjuPQ1NQUdrypqWnQ/B6Px4NNmzbh1ltvHfRxSktLkZOTg6qqqmGNdyhO7aLi0OgU4ZMVqKoGn6yg0SkmfDv7aJfsliKEEELSW1IDIb1ej3POOQfbtm0LHVNVFdu2bcN555034H1feukliKKIG264YdDHqaurQ1tbGwoLC4c95qFI5nb20S7ZLUUIIYSkt6Qvja1YsQJLly7FrFmzMGfOHKxZswYejye0i+ymm27CuHHjsGrVqrD7Pffcc1iyZEmfBGi3240HH3wQ3/rWt1BQUIDq6mrcd999KCsrw+LFi0fsefWWzO3so1nPHCwD27dBLeVgEUIIGUjSA6Hvfve7aGlpwQMPPIDGxkacddZZeOutt0IJ1DU1NWDZ8E/7hw8fxq5du/DOO+/0uR7Hcfj888+xYcMGdHZ2oqioCJdddhl+/etfJ62WULdkbmcfrSgHixBCyHAwWu+GVISkme5dY25RidhShJYfCSGE9IcCITIqhNURUoMNXamOECGEkMFQIERGDaosTQghJFYUCBFCCCFkzEqJ7vOEEEIIIclAgRAhhBBCxiwKhAghhBAyZlEgRAghhJAxiwIhQgghhIxZFAgRQgghZMyiQIgQQgghYxYFQoQQQggZsygQIoQQQsiYlfTu8yR6mqbB5XIlexiEEEJI0litVjBM/NonUSCURlpbW5GXl5fsYRBCCCFJ09zcjNzc3LhdjwKhNKLX6wEAtbW1sNlsSR5NanM6nSgpKaHXKgr0WkWPXqvo0OsUPXqtotf9WnW/F8YLBUJppHsq0Gaz0T+YKNFrFT16raJHr1V06HWKHr1W0YvnshhAydKEEEIIGcMoECKEEELImEWBUBoRBAG//OUvIQhCsoeS8ui1ih69VtGj1yo69DpFj16r6CXqtWI0TdPiekVCCCGEkDRBM0KEEEIIGbMoECKEEELImEWBECGEEELGLAqECCGEEDJmUSCUYp588klMnDgRBoMBc+fOxZ49e/o9d/369WAYJuzLYDCM4GiTY+fOnbjyyitRVFT0/9u7+5gq6/cP4O+Dcg4UTzoMMEghEtEQgaCgP0AS8GEEtWbpJCCa2WDr2MjxRw4LJ7o5tZlLNseT/UElYi1SApTTIN3k4RgQmZ5Q1gY4l5og8+Gc6/uH8/w8ykEgOOf4u9+v7d68P/fnc3Oda9e5ubjvg0ClUuHo0aOPXdPU1ITIyEhoNBoEBwejvLx82uN0BBPNVVNT0yM1pVKpMDAwYJuA7ai4uBjR0dFwd3fHM888g/T0dJw7d+6x67777jssXLgQLi4uCAsLw08//WSDaO1rMrlS6vXqq6++wpIlS8z/YWJsbCyOHTs25hol1hQw8VxNVU2xEXIg33zzDT7++GMUFhaivb0d4eHhSElJweXLl62u8fDwQH9/v3m7dOmSDSO2j+HhYYSHh2P//v3jmt/b24vVq1dj2bJl0Ov10Gq1eP/991FXVzfNkdrfRHN137lz5yzqSgl/406n0yE3NxenT59GfX097ty5g+TkZAwPD1td8+uvv2Lt2rXIyclBR0cH0tPTkZ6ejq6uLhtGbnuTyRWgzOuVv78/duzYgba2NrS2tiIxMRFpaWno7u4edb5SawqYeK6AKaopIYcRExMjubm55n2j0Shz586V4uLiUeeXlZWJp6enjaJzTACkpqZmzDmbN2+WxYsXW4y9/fbbkpKSMo2ROZ7x5OrkyZMCQK5evWqTmBzZ5cuXBYDodDqrc9asWSOrV6+2GHv55Zflgw8+mO7wHMp4csXr1f+ZNWuWHDx4cNRjrClLY+VqqmqKd4QcxO3bt9HW1obly5ebx5ycnLB8+XKcOnXK6rqhoSHMmzcPAQEBj+2clerUqVMWeQWAlJSUMfOqdEuXLoWfnx+SkpLQ0tJi73Ds4vr16wCA2bNnW53D2rpnPLkCeL0yGo2oqqrC8PAwYmNjR53DmrpnPLkCpqam2Ag5iCtXrsBoNMLHx8di3MfHx+rnM0JCQlBaWorvv/8eX3/9NUwmE+Li4vD333/bIuQnxsDAwKh5/ffffzEyMmKnqByTn58fDhw4gOrqalRXVyMgIAAJCQlob2+3d2g2ZTKZoNVq8eqrr+LFF1+0Os9abSnhM1X3jTdXSr5edXZ2ws3NDRqNBhs3bkRNTQ0WLVo06lyl19REcjVVNcW/Pv8Ei42NteiU4+LiEBoaipKSEhQVFdkxMnpShYSEICQkxLwfFxcHg8GAPXv24NChQ3aMzLZyc3PR1dWF5uZme4fi8MabKyVfr0JCQqDX63H9+nUcPnwYmZmZ0Ol0Vr/BK9lEcjVVNcVGyEF4e3tjxowZGBwctBgfHByEr6/vuM7h7OyMiIgIXLhwYTpCfGL5+vqOmlcPDw+4urraKaonR0xMjKIagry8PPz444/45Zdf4O/vP+Zca7U13vfsk24iuXqYkq5XarUawcHBAICoqCicOXMGX3zxBUpKSh6Zq/SamkiuHjbZmuKjMQehVqsRFRWFxsZG85jJZEJjY+OYz0cfZDQa0dnZCT8/v+kK84kUGxtrkVcAqK+vH3delU6v1yuipkQEeXl5qKmpwYkTJxAYGPjYNUqtrcnk6mFKvl6ZTCbcunVr1GNKrSlrxsrVwyZdU//549Y0ZaqqqkSj0Uh5ebn8/vvvsmHDBvHy8pKBgQEREcnIyJCCggLz/M8++0zq6urEYDBIW1ubvPPOO+Li4iLd3d32egk2cePGDeno6JCOjg4BILt375aOjg65dOmSiIgUFBRIRkaGef5ff/0lTz31lHzyySfS09Mj+/fvlxkzZsjx48ft9RJsZqK52rNnjxw9elTOnz8vnZ2d8tFHH4mTk5M0NDTY6yXYzIcffiienp7S1NQk/f395u3mzZvmOQ+/B1taWmTmzJmya9cu6enpkcLCQnF2dpbOzk57vASbmUyulHq9KigoEJ1OJ729vfLbb79JQUGBqFQq+fnnn0WENfWgieZqqmqKjZCD2bdvnzz33HOiVqslJiZGTp8+bT4WHx8vmZmZ5n2tVmue6+PjI6tWrZL29nY7RG1b93/F++Htfm4yMzMlPj7+kTVLly4VtVotQUFBUlZWZvO47WGiudq5c6c8//zz4uLiIrNnz5aEhAQ5ceKEfYK3sdHyBMCiVh5+D4qIfPvtt7JgwQJRq9WyePFiqa2ttW3gdjCZXCn1evXee+/JvHnzRK1Wy5w5c+S1114zf2MXYU09aKK5mqqaUomITOweEhEREdH/D/yMEBERESkWGyEiIiJSLDZCREREpFhshIiIiEix2AgRERGRYrERIiIiIsViI0RERESKxUaIiIiIFIuNEBHRBM2fPx979+61dxhENAXYCBHRtFOpVGNuW7dutUkcYWFh2Lhx46jHDh06BI1GgytXrtgkFiJyDGyEiGja9ff3m7e9e/fCw8PDYiw/P988V0Rw9+7daYkjJycHVVVVGBkZeeRYWVkZXn/9dXh7e0/L1yYix8RGiIimna+vr3nz9PSESqUy7//xxx9wd3fHsWPHEBUVBY1Gg+bmZmRlZSE9Pd3iPFqtFgkJCeZ9k8mE4uJiBAYGwtXVFeHh4Th8+LDVONavX4+RkRFUV1dbjPf29qKpqQk5OTkwGAxIS0uDj48P3NzcEB0djYaGBqvnvHjxIlQqFfR6vXns2rVrUKlUaGpqMo91dXVh5cqVcHNzg4+PDzIyMnj3icgBsBEiIodQUFCAHTt2oKenB0uWLBnXmuLiYlRWVuLAgQPo7u7Gpk2bsH79euh0ulHne3t7Iy0tDaWlpRbj5eXl8Pf3R3JyMoaGhrBq1So0Njaio6MDK1asQGpqKvr6+ib92q5du4bExERERESgtbUVx48fx+DgINasWTPpcxLR1Jhp7wCIiADg888/R1JS0rjn37p1C9u3b0dDQwNiY2MBAEFBQWhubkZJSQni4+NHXZeTk4OVK1eit7cXgYGBEBFUVFQgMzMTTk5OCA8PR3h4uHl+UVERampq8MMPPyAvL29Sr+3LL79EREQEtm/fbh4rLS1FQEAA/vzzTyxYsGBS5yWi/453hIjIIbz00ksTmn/hwgXcvHkTSUlJcHNzM2+VlZUwGAxW1yUlJcHf3x9lZWUAgMbGRvT19SE7OxsAMDQ0hPz8fISGhsLLywtubm7o6en5T3eEzp49i5MnT1rEuXDhQgAYM1Yimn68I0REDuHpp5+22HdycoKIWIzduXPH/O+hoSEAQG1tLZ599lmLeRqNxurXcXJyQlZWFioqKrB161aUlZVh2bJlCAoKAgDk5+ejvr4eu3btQnBwMFxdXfHWW2/h9u3bVs8HwCLWB+O8H2tqaip27tz5yHo/Pz+rsRLR9GMjREQOac6cOejq6rIY0+v1cHZ2BgAsWrQIGo0GfX19Vh+DWZOdnY1t27bhyJEjqKmpwcGDB83HWlpakJWVhTfeeAPAvSbm4sWLY8YJ3PvNuIiICHOcD4qMjER1dTXmz5+PmTN52SVyJHw0RkQOKTExEa2traisrMT58+dRWFho0Ri5u7sjPz8fmzZtQkVFBQwGA9rb27Fv3z5UVFSMee7AwEAkJiZiw4YN0Gg0ePPNN83HXnjhBRw5cgR6vR5nz57FunXrYDKZrJ7L1dUVr7zyivmD3jqdDp9++qnFnNzcXPzzzz9Yu3Ytzpw5A4PBgLq6OmRnZ8NoNE4yQ0Q0FdgIEZFDSklJwZYtW7B582ZER0fjxo0bePfddy3mFBUVYcuWLSguLkZoaChWrFiB2tpaBAYGPvb8OTk5uHr1KtatWwcXFxfz+O7duzFr1izExcUhNTUVKSkpiIyMHPNcpaWluHv3LqKioqDVarFt2zaL43PnzkVLSwuMRiOSk5MRFhYGrVYLLy8v86M1IrIPlTz8EJ6IiIhIIfijCBERESkWGyEiIiJSLDZCREREpFhshIiIiEix2AgRERGRYrERIiIiIsViI0RERESKxUaIiIiIFIuNEBERESkWGyEiIiJSLDZCREREpFj/A1Y79DQ/OcfdAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1126,7 +1169,7 @@
    "source": [
     "import seaborn as sns\n",
     "\n",
-    "predicted_y = pipeline_model.predict(\n",
+    "predicted_y = pipeline_app.predict(\n",
     "    x_torch_val, \n",
     "    batch_size=10,\n",
     ").cpu().numpy()\n",
@@ -1155,7 +1198,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/configuration_validator.py:74: You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.\n"
+      "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/configuration_validator.py:74: You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.\n"
      ]
     },
     {
@@ -1211,7 +1254,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "45c76462ef5145ca9d29f019e1970411",
+       "model_id": "3d6d810801444882908309ace5f86e45",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1222,6 +1265,13 @@
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve performance.\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -1258,7 +1308,7 @@
     },
     {
      "data": {
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LRqHguBC9ItmRp4CZcWDVdhGrLJUBxosX39hBoP/0OBU7PlIkI4QQQgghhBDSuKxbtw4PPfQQ9uzZg87OTl8rr40bN+Jb3/oWrr76aliWhY9//OOJWIT58cEPfhDbt2/Hpz71KVx77bV48MEH8aUvfQlf/vKXAQDf/e538fLLL+PVr341+vr68L3vfQ/5fB5nnHEGHnroIfz4xz/G6173OgwODuKhhx7C8PCwK76YjN27d+N//+//jTe96U1YsWIFnn/+ebz44ov47//9vwMQx2737t144oknsGrVKnR1deHyyy/HxRdfjLe85S249dZbsWnTJhw6dAj/+Z//iWuuuabkxtra2op3vetduP322zE+Po73v//9eMc73lFXrpYALckah4mj5c9eq7GZcfH/+EFg/8PAgR3u308dAY6/FE3koiUZIYQQQgghhJAFwo033oh0Oo0tW7Zg6dKlvjHGPv/5z6Ovrw+XXHIJrr76alx55ZU4//zzq9bO888/H//6r/+Kf/7nf8bZZ5+Nm266Cbfccguuu+46AEBvby++9a1v4Td/8zexefNm/O3f/i2+8Y1v4KyzzkJ3dzd+/vOf4w1veAM2bdqEv/iLv8Add9yB17/+9YF1tre347nnnsPb3vY2bNq0CX/0R3+EG264Af/jf/wPAMDb3vY2XHXVVXjta1+LpUuX4hvf+AYsy8L3vvc9vPrVr8b111+PTZs24Xd+53ewd+9eLFu2rFT2hg0b8Na3vhVveMMb8LrXvQ7nnHNOSfCrJ6xCWA7QBmN8fBw9PT0YGxtDd3d3rZtjholhYPi58veBDUC3I3DebpHmFa09wMyYfznLzgLa9dLOYm4SOPiY+LzyAqC5XW97QgghhBBCCCELipmZGezevRunnXYaWltba90cUufcfPPN+M53voMnnngisTqCrkkdnYiWZI3AxBH3d5frpcPcM9MSXI7XTVMFZyw0WpIRQgghhBBCCCFkgUKRrBGYn3Z/zztFMkemy1QCIeachoaF6vlfE0IIIYQQQgghC4U//uM/Rmdnp/Tvj//4j6vWDr82dHZ24v77769aO+oVBu5vBPKezBuFvBCvZsYAy6FzxvGctctr7gTSzsuCgfsJIYQQQgghhJA43HLLLbjxxhulv1UzVFSQy+PKlSsTqfPmm2/GzTffnEjZpqFIVu/kc5VukoU8MLoXGN3vth4Ls/QqFICR3UBbL9DW5/7t1BHgxEtASyew4jz3NuUvUfaAEEIIIYQQQghZ1AwODmJwcLDWzcCGDRtq3YS6hu6W9U52pnJZoQCMHxafnQJamEg2OQyMHQCOPC357Zj4f3bCW5m7XkIIIYQQQgghhJAFCEWyeic7W7mskANSafnyIHJz5c9zk8CJXUC2uMzyuRRoSUYIIYQQQgghhJBFAEWyeifdDHSvcC8r5OWiVpglWbq5/PngY8D4IeD48+K7ikhGSzJCCCGEEEIIIYQsUBiTrN5p6RR/facBJ3cLYSuqSCbDdq/0E8mCrMdmxoBMK5Bp0a+XEEIIIYQQQgghpI6gJVmjkEoBLcWMF4W83N3SmwXTS5A7ppIlmUOEmxkHDj8F7H84uE5CCCGEEEIIIYSQBoAiWSNhC1n5nI8lWYg7ZJCI5iwv77RI83G3nBkNrosQQgghhBBCCFnArFu3DnfeeaeRsn72s5/BsiyMjo4aKa+R2bNnDyzLwhNPPFH1uulu2UhYlvh/9pT89zB3S+nvReHLKZIVcijpp36B+6O4dhJCCCGEEEIIITXkNa95Dc4991wj4taOHTvQ0dERv1GkbqBI1kjIXCydhAlXQZZkTjEsnwXSTZVl+rleEkIIIYQQQgghC4BCoYBcLodMJlwuWbp0aRVaRKoJ3S0bCd/g+kWCYo6F/e4UvfI5h8ulnyVZzEyXkyeA0X3xyiCEEEIIIYQQUh8UCsDcZPX/NMam1113He677z584QtfgGVZsCwLd999NyzLwve//31ccMEFaGlpwS9+8Qvs2rULb37zm7Fs2TJ0dnZi+/bt+NGPfuQqz+tuaVkWvvrVr+Kaa65Be3s7Nm7ciHvuuSfyIf23f/s3nHXWWWhpacG6detwxx13uH7/8pe/jI0bN6K1tRXLli3D29/+9tJv3/zmN7F161a0tbVhYGAAl19+OSYnJ5Xq/epXv4rNmzejtbUVZ555Jr785S+XfrNdIf/5n/8Zl1xyCVpbW3H22Wfjvvvuc5Vx33334aKLLkJLSwuGhobwkY98BNlstvR7Pp/Hrbfeig0bNqClpQVr1qzBpz/9aVcZL7/8Ml772teivb0d27Ztw4MPPqh87KJCS7JGIlQki2BJVnqgOB4s89PAkV8Dbb3lZAGudRXqCuPYTvF/SxfQ1hevLEIIIYQQQgghtWV+CvjMiurX++eHgGY1l8cvfOELeOGFF3D22WfjlltuAQA888wzAICPfOQjuP3223H66aejr68P+/fvxxve8AZ8+tOfRktLC77+9a/j6quvxvPPP481a9b41vHJT34St956K2677TZ88YtfxO/93u9h79696O/v19qtRx99FO94xztw880349prr8Uvf/lLvPe978XAwACuu+46PPLII3j/+9+Pf/iHf8All1yCkZER3H///QCAw4cP453vfCduvfVWXHPNNTh16hTuv/9+FBQExX/8x3/ETTfdhC996Us477zz8Pjjj+M973kPOjo68K53vau03oc+9CHceeed2LJlCz7/+c/j6quvxu7duzEwMICDBw/iDW94A6677jp8/etfx3PPPYf3vOc9aG1txc033wwA+OhHP4q/+7u/w1//9V/jla98JQ4fPoznnnvO1ZaPfexjuP3227Fx40Z87GMfwzvf+U689NJLSlZ+UaFI1khYYe6WMay7nKLX+AHhcjl5XIhYsnXiWpLZ5ObNlEMIIYQQQgghhATQ09OD5uZmtLe3Y/ny5QBQEmZuueUWXHHFFaV1+/v7sW3bttL3T33qU/j2t7+Ne+65B+973/t867juuuvwzne+EwDwmc98Bn/zN3+Dhx9+GFdddZVWWz//+c/jsssuw8c//nEAwKZNm7Bz507cdtttuO6667Bv3z50dHTgjW98I7q6urB27Vqcd955AIRIls1m8da3vhVr164FAGzdulWp3k984hO444478Na3vhUAcNppp2Hnzp34yle+4hLJ3ve+9+Ftb3sbAOB//a//hXvvvRf/5//8H/zZn/0ZvvzlL2P16tX40pe+BMuycOaZZ+LQoUP48Ic/jJtuugmTk5P4whe+gC996UulMtevX49XvvKVrrbceOON+K3f+i0AQnw866yz8NJLL+HMM8/UOpY6UCRrJOw4YUngFMByWcdyBu4nhBBCCCGEEBJCU7uw6qpFvQa48MILXd8nJiZw88034z//8z9LotP09DT27QsOG3TOOeeUPnd0dKC7uxvHjh3Tbs+zzz6LN7/5za5ll156Ke68807kcjlcccUVWLt2LU4//XRcddVVuOqqq0puntu2bcNll12GrVu34sorr8TrXvc6vP3tb0dfX7AX1+TkJHbt2oV3v/vdeM973lNans1m0dPT41r34osvLn3OZDK48MIL8eyzz5bafvHFF8Oykw8W2z4xMYEDBw7gyJEjmJ2dxWWXXRbYHuexHBoaAgAcO3aMIhkpEha4Pw7ewP02J/fI11Fx7czNAU1tRppHCCGEEEIIIaSOsSxlt8d6xJul8sYbb8QPf/hD3H777diwYQPa2trw9re/HXNzc4HlNDW5jVssy0I+b97IpKurC4899hh+9rOf4Qc/+AFuuukm3HzzzdixYwd6e3vxwx/+EL/85S/xgx/8AF/84hfxsY99DA899BBOO+003zInJiYAAH/3d3+HV7ziFa7f0mlzekRbm5pO4DyWtuCWxLF0wsD9i56i8OUK3J+Vr6pjSXbwMeDAI8DsRKzWEUIIIYQQQgghpmhubkYuF5L0DsADDzyA6667Dtdccw22bt2K5cuXY8+ePck3sMjmzZvxwAMPVLRp06ZNJcEqk8ng8ssvx6233oqnnnoKe/bswU9+8hMAQlS69NJL8clPfhKPP/44mpub8e1vfzuwzmXLlmHFihV4+eWXsWHDBtefV1z71a9+VfqczWbx6KOPYvPmzaW2P/jgg64YaA888AC6urqwatUqbNy4EW1tbfjxj38c/QAlBC3JSBGFGGM6lmTZGfH/1HGgpTNgRSvgN0IIIYQQQgghxBzr1q3DQw89hD179qCzs9PXMmnjxo341re+hauvvhqWZeHjH/944lZMTj74wQ9i+/bt+NSnPoVrr70WDz74IL70pS+VMk1+97vfxcsvv4xXv/rV6Ovrw/e+9z3k83mcccYZeOihh/DjH/8Yr3vd6zA4OIiHHnoIw8PDJREriE9+8pN4//vfj56eHlx11VWYnZ3FI488gpMnT+IDH/hAab277roLGzduxObNm/HXf/3XOHnyJP7gD/4AAPDe974Xd955J/7kT/4E73vf+/D888/jE5/4BD7wgQ8glUqhtbUVH/7wh/Fnf/ZnaG5uxqWXXorh4WE888wzePe7353MAVWEIhkRqMQYG90HtPaIrJcqohpgLsA/IYQQQgghhBASkxtvvBHvete7sGXLFkxPT+Pv//7vpet9/vOfxx/8wR/gkksuwZIlS/DhD38Y4+PjVWvn+eefj3/913/FTTfdhE996lMYGhrCLbfcguuuuw4A0Nvbi29961u4+eabMTMzg40bN+Ib3/gGzjrrLDz77LP4+c9/jjvvvBPj4+NYu3Yt7rjjDrz+9a8PrfcP//AP0d7ejttuuw0f+tCH0NHRga1bt+JP//RPXet97nOfw+c+9zk88cQT2LBhA+655x4sWbIEALBy5Up873vfw4c+9CFs27YN/f39ePe7342/+Iu/KG3/8Y9/HJlMBjfddBMOHTqEoaEh/PEf/7Gx4xcVq6CSA7SBGB8fR09PD8bGxtDd3V3r5phnagQ4+ozZMlecCxx6Qn39014FHHq87Ep52qsq19ktUs+iZyXQf7r/70vPBDqX6rSWEEIIIYQQQkgNmZmZwe7du3HaaaehtbW11s0hVWTPnj047bTT8Pjjj+Pcc8+tdXNKBF2TOjoRY5I1Gu39wNC28PV0GH5BfxvV7JYLS4MlhBBCCCGEEELIAoUiWSOSMuwlm5/X30ZV/JqtnjkqIYQQQgghhBBSj/zxH/8xOjs7pX/VdDP0a0NnZyfuv//+qrWjXmFMskbEtEhmpQFoCmXKItmEcBFt7/epm4H7CSGEEEIIIYQsbG655RbceOON0t+qGSrqiSee8P1t5cqVoduvW7cOCyxqlwuKZI2IaZHMdp2cnwGaVP3JNW6KqRNukWwB31CEEEIIIYQQQoiXwcFBDA4O1roZ2LBhQ62bUNfQ3bIRSaWA3jVA+4CZ8nJzwImXgHtuAF76UfRy8nlg8gSQy3p+oLUYIYQQQgghhCw0FrJFEWksTF2LFMkalb61wMB6c+UdeASYOAo8eJcQzMJwXoD259E9wLGd4s+J16WSD1JCCCGEEEIIaViampoAAFNTUzVuCSGCubk5AEA6nY5VDt0tGxlLonG29gAzY/plnfMOYOwAsPtnwE8/A7zxTqDVxy/6xC5hfebl1FHxv7f+irhjFMkIIYQQQgghpFFJp9Po7e3FsWPHAADt7e2wGG+a1Ih8Po/h4WG0t7cjk4knc1Eka2RkIllbbzSRzEoBr78V+Ma1wMndwP23AZfdDKQkKuz4Iff3QkEIYbL2iMKDKtZvKyGEEEIIIYSQmrJ8+XIAKAllhNSSVCqFNWvWxBZrKZI1MjJRKtMWvby2XuDa/wv83W8Chx4Hnvwn4LzfV9iwaBnmdzHay20xje6WhBBCCCGEENLQWJaFoaEhDA4OYn5+vtbNIYuc5uZmpFLxI4pRJGtkZKKUrzVXkUwrkJ3xKS8FLD8buOR9wP13AE/9CzCwCVjzCsX2BFiSFQpCeEulgcEtauURQgghhBBCCKlr0ul07DhQhNQLDNy/kOhZJRfOOpaWPzvdJ1MejdQWuU5/LXDm1eLzL+4Axg8G11sIsSQDgPlpYG4SmBkHCvng8gghhBBCCCGEEEKqDEWyhcLSM4D+09zL2vqAVduB/tPLy5zCmDfemFPkuvAPhMXX/JQI5D/vY30GALlZuwD5715RjO6WhBBCCCGEEEIIqTMokjU6XUNAUzvQPiC+O4WupjagqdXtBtnc4f7dib1e5zIg3QT8t48IoW10L/DgF/3FrYOPubevwLMdLckIIYQQQgghhBBSZ1Aka3SWbABWXeCwCnOIZJmW4iLHabZSwOqLgCUbgY5Bd1n2eks3AWsuBtr7gf/2YcBKA7vvA569J7gtfu6WXnHNJZLRqowQQgghhBBCCCG1hyLZQsMpVKWaKpdZlhDPupZL3C0dl0O66Ja57Gzgoj8Snx/5GnD06YC6fS6nQsEtjOlYko0dAI78GsjT+owQQgghhBBCCCHJQZFsweEUyYpCl5+FV1gmzNYe8f8r/gdw5huBQg742eeAqRN65RXycFmMuQSzEEuykd3A9CgwcTR4PUIIIYQQQgghhJAYUCRbaDgFsXRGtoJ8XQAVro+DW4BlZwF964A33wX0rgVmRoVQlpsPLttJdia6JVlpm5z+NoQQQgghhBBCCCGKUCRbcEgsyXxX9Zz+pnb393RGxCWzLKC5E3jtn4t1hp8Vrpdh5dnMjAHTJ8vfI2W39BHgCCGEEEIIIYQQQgxAkWwhY8ckc+KKT+Y4/cvOqoxR5touBXSvBF71QfH9uf8Adv20/HuY8DW637GuJ3D/3KSIPRYUd8zPZdRbx76HgPmZ8HUJIYQQQgghhBBCHFAkW2g4BSgdS7J0c8i6RZFq9SuAc64Vnx/8EjDycrHeApQzVXrdLQ8+JmKPje2Xr6/KyT1Abk78TwghhBBCCCGEEKIBRbKFhlP4SklOb7pFvm5YEH+nJde23wVWnA/kZoGffgaYnQAOPy7+V8EvcP/sqaAGqJVNCCGEEEIIIYQQEgGKZAuNlk6gby2w9Az38sHNQPcKoHOwvExHJHOSSgOvuhHoXAZMHAF+cYcQyLKKbo6+gfsDLNFskS43D0wMx3fNJIQQQgghhBBCCHFAkWwh0rvGLYYBQMcSYGC9R0AKynQZQms38JqPCjfNAzuAp/5FfVtvTDIdjvwaGH4OOLlbbztCCCGEEEIIIYSQACiSLWZ0hbHuFe7vAxuA33iv+PzEPwEHHlErx8+SzBv83/W92Na5SfH/5PGACmhJRgghhBBCCCGEED0oki1mUpniXzo8cD8gLNFae9zLNlwObHo9gAJw/23AqSPh5fi6WxaZGQdmxtwimVfQo0slIYQQQgghhBBCDEKRbDFjWSJb5erfiCc6XfRHwJIzhJXXTz9djk3W1F7p9gn4B+5HQcQaO/wkcPgpIJ8NanzATxTQCCGEEEIIIYQQogdFssVOKiXPgumHTIBKN4n4ZK09IlbYr74sxC8LQM8qvfYUcuXP+XlnxXrlEEIIIYQQQgghhGhAkYyYoWMJ8OoPiyyZu34CPP89ABbQ3FFpTeYXuL9QCLAy8xBoLUZBjRBCCCGEEEIIIXpQJCPmGDoHOP868XnH3wHHdorPbX3u9YJikrlEspz/ehTCCCGEEEIIIYQQYhCKZMQsZ10DrL1UxBP74SeAU0cBK+1ex1ckKwB5p7tlkEgWAGOSEUIIIYQQQgghRBOKZESTEAHKsoBL/yfQsxqYOg588/rKAPxBLpWu33zcMhWaQQghhBBCCCGEEKIDRTJinqZ24LV/Lv7f+wDw89vcv/tZkhUKnsD9Ie6W8zPAseeAmfHYTSaEEEIIIYQQQsjihiIZSYae1cBrPyY+7/g7YPfPy78FWYg5LcucgpnX4syygOPPA5PDwOEnjTSZEEIIIYQQQgghixeKZCQ5Tn8NcOmfis+//Bvg5F7x2VRMsvlp+XLGJCOEEEIIIYQQQogmFMmIHjoCVCoN/ObHgXWvArIzwM8+A8xNArMT5XWCYpKd3OP8wdsQ9XYQQgghhBBCCCGEhECRjCSHlQLSGeC37wa6VwLjB4Ff/HVATDIEWJl5yw4SySigEUIIIYQQQgghRA+KZCQ5rOLl1bEEuPYfgHQzsP9XwNPfLK9z6ohjA0/gfideizPd3wkhhBBCCCGEEEICoEhGksNyXF4rLwDeUMxy+fj/BQ49Lj5nZ9zbqFqSea3F5hwunIxJRgghhBBCCCGEEE0okhE9WnvU17U8l9f57wI2XSWEsJ/fBkwcc/9eKAQE6/dYiuXmgNx8+fuhJ9TbRQghhBBCCCGEEOKhLkWy7373uzjjjDOwceNGfPWrX611c4iT7pXAwAZg1YXh63pFMssCLv1/xfaz4yKQf27OvY6qJZlfZktRkVoZhBBCCCGEEEIIIUXqTiTLZrP4wAc+gJ/85Cd4/PHHcdttt+HEiRO1bhaxsSygewhoalNYV3J5NbcBr/lzoKUbOPES8NDfOn4sBAT1Z8wxQgghhBBCCCGEJEfdiWQPP/wwzjrrLKxcuRKdnZ14/etfjx/84Ae1bhaJQiotWZYBOgeBV39IiGgv/gB44d7y776WZBFFslwWyM7Jf5saAWbGo5VLCCGEEEIIIYSQBYVxkeznP/85rr76aqxYsQKWZeE73/lOxTp33XUX1q1bh9bWVrziFa/Aww8/XPrt0KFDWLlyZen7ypUrcfDgQdPNJKaRWY1JlxWFsxXnAef9/8Tnh/4WGH4+JCYZgLxqUH8H+x4E9j9UWe78DHD0GeDwk/plEkIIIYQQQgghZMFhXCSbnJzEtm3bcNddd0l//5d/+Rd84AMfwCc+8Qk89thj2LZtG6688kocO3ZMun4Ys7OzGB8fd/2RGtCzqnKZTCRLZcqfz347sPo3gHwWuO9zwPRoiLtlDJdLbxZN73dCCCGEEEIIIYQsaoyLZK9//evxl3/5l7jmmmukv3/+85/He97zHlx//fXYsmUL/vZv/xbt7e342te+BgBYsWKFy3Ls4MGDWLFihW99n/3sZ9HT01P6W716tdkdImr0rgEGNwN968rLpJZklvv3V/6/IhnA5HAxkP9s+feWLve2ynHJCprrE0IIIYQQQgghZLFT1Zhkc3NzePTRR3H55ZeXG5BK4fLLL8eDDz4IALjooovw9NNP4+DBg5iYmMD3v/99XHnllb5lfvSjH8XY2Fjpb//+/YnvB/HQ2i3Er44lQvCykYlUmVb39+YOEcg/0wocfgJ4+O/E8sEtwNA2oKndLgzKlmSj+4GTe0NEMgpohBBCCCGEEEIIKVNVkez48ePI5XJYtmyZa/myZctw5MgRAEAmk8Edd9yB1772tTj33HPxwQ9+EAMDA75ltrS0oLu72/VHqkS6Sfzf1l9elnJeUhIhqmt55bK+tcAl7xefn/wGsPeXIui/ZQkBrlScRkyy0X2e+i2/NQkhhBBCCCGEEEKQCV+l+rzpTW/Cm970plo3g4Sx4jyRHbJjifo2qbSwEPMGzD/t1cDx54Gd/w488NfAxiuAleejJG4VCvruk6qiWqHgdgMlhBBCCCGEEELIoqOqlmRLlixBOp3G0aNHXcuPHj2K5cslFkakvsm0AJ1LKwWmnpVASyfQ7iOetXbLA/1fcD2w7Gxgfhr41h8Bs6c8K2iKZLl5vfUJIYQQQgghhBCyaKmqSNbc3IwLLrgAP/7xj0vL8vk8fvzjH+Piiy+uZlNIkvSfLqzMUpqXVyoD/LcPC/fNEy8C/36D48eCnrslAOQVRTIG+CeEEEIIIYQQQhY9xt0tJyYm8NJLL5W+7969G0888QT6+/uxZs0afOADH8C73vUuXHjhhbjoootw5513YnJyEtdff73pppB6xs+9sa0PeM1Hgf/6c+F62XcasKGY6CE3p1dHLqu4IkUyQgghhBBCCCFksWNcJHvkkUfw2te+tvT9Ax/4AADgXe96F+6++25ce+21GB4exk033YQjR47g3HPPxb333lsRzJ8sYgY3A6/7S+DeDwO//BugY1C4Z+q6T3pFtXxeWJdlWmg9RgghhBBCCCGEEBdWobCw1ILx8XH09PRgbGyMmS7rmZHdwNgB/9/XXgp85/8BnvpnoLUHeOe/Ar2rxHaq9K0FTu4Vn1eeDww/B8xNCVfQ3Bxw9Bnx25qLgXRd5rAghBBCCCGEEEJIDHR0oqrGJCNEmVQKeONfA0s2ATNjwPduBGYn9crwWp7NTYn/p457VlxQOjEhhBBCCCGEEEIiQJGM1IgAYWrpGeL/5nbgtz4PNHcAR38N3PdXmlU4Av27DCZ94qHZ6ynHMiOEEEIIIYQQQshCgSIZqQ1BXr6W47LsWQW86kYAFvDMvwEv/Ui9jnzOWaFaW448Bex7EJifVq+HEEIIIYQQQgghDc+CEcnuuusubNmyBdu3b691U0hsHJZelgWs2g5se6f4/uBdwImX5Jt5cVqSuYq3PCKd4/PMuPh/4phyawkhhBBCCCGEENL4LBiR7IYbbsDOnTuxY8eOWjeFxMWSuENu+x0hluXngZ99tixmBVFwWJJVuFsyDhkhhBBCCCGEEELKLBiRjDQYyklVi4KZlQJe+UGgczkwcRS4/3aPO6WsDqclmaK7pcr6hBBCCCGEEEIIWXBQJCM1QjEmmZOWTuBNXwTSLcChx4An/ym4CqeI5hTMgtwtSW3JzgJjB5k8gRBCCCGEEEJI1aFIRuoPy5J/BoDlZwMXv098fupfgP0P+ZfjFMby3vhkFMbqksNPASMvAyO7at0SQgghhBBCCCGLDIpkpDYEultKYpLZpDLA+tcCZ75RfL//88D4IZ868vLP3vqVXT9J4mRnxP9TI7VtByGEEEIIIYSQRQdFMlIjgtwtnSKZx6oslRGfL3w3sHQzMD8J/PTTwPxMZTlOd8v8vKPqgrv+w09WJgKoV+GsXttFCCGEEEIIIYQ0OBTJSG2IYklmpcuf003Aaz4CtPYCo3uBB79YWabTeuzELvdy57r5rBDK6p18DjiwAxh+vtYtIYQQQgghhBBCFhwUyUj94Qzc77QqSze512sfAP7bR8T6u+8Dnr3H/bvXxbL8g5FmVp3J4yKw/cSxWreEEEIIIYQQQghZcFAkIzVC1d3SuTwlD+R/4bvF50e+Bhx9WqHqfHD9tWBmDDj+IpCb91/H77gQQgghhBBCCCEkNhTJSG1Qdrd0fE6lIXXF3PwmYN2rgUIOuO+vwoO+Fwr1F9vr8FPAqSPAyO5at4QQQgghhBBCCFmUUCQj9YevJVnaZ7kFXPJ+oHctMH0SuO9zwRZZ3sD98pVUWmqe7HTAj4vIkoxWc4QQQgghhBBCqgxFMlIjFC3JLI8lmZ940tQKvPbPgaZ24NhO4XoZVHe9WZKpQOGIEEIIIYQQQghJjAUjkt11113YsmULtm/fXuumEBWcIlVzuxC3bCwfd0s/SzKb7pXAKz8oPj/3H8DLPw2ouwFFMkIIIYQQQgghhCTGghHJbrjhBuzcuRM7duyodVOIEg6RauUFQO+a8nfL57L0i0nmZM0rgK3Xis+//JI8xpdv1st6x7Hv+UbdB0IIIYQQQgghpD5ZMCIZaXB8rcecnxUv13N/F1hxHpCbBX72GWB2wrOCgrtlrdwxleulJRwhhBBCCCGEEGISimSkNnQsFf873Sxt/GJvBcUk8673qg8BHYPAqcPAjz7hFsoKeUhFpnqPU+bc93pvKyGEEEIIIYQQ0mBQJCO1oXMZsHwrMLStuMDHekwnJpmT1m7gN/8CaOkCjj8P/NdHgelR8VvBx5Isn1Uvv+ZQJCOEEEIIIYQQQkxCkYzUBssC2nqBdEZ9G29MspZO/3U7lwH9pwNXfhZo7QVO7gb+6yPA5HEIgakBLcmcNFJbCSGEEEIIIYSQBoAiGakP/NworSBLsgDXy1Rx3b51wFV/BbQvAcYOAPd+WPwvE5kaKaB/I7WVEEIIIYQQQghpACiSkfog0xq+jjcmWVB8MudvPSuB1/8V0DUETBwF7vkT4OQeyUZ1bp3lEvbqvK2kcTj+EnByb61bQQghhBBCCCE1hyIZqQ+a24HBzcDQOf7rZFrgth4LCuLv+a1zGXDV54Ce1cDkMPBvfwiM7Hav00jWWXS3JCaYmxTJLUb31bolhBBCCCGEEFJzKJKR+qFjCdDa416Wmyt/lmXC9ENmZdY+IISy/vXA9IgI5n/8hfLvYcJTdhaYm1Jvg3EWkyWZQhZTEp9GEoYJaSTmpjiZQQghhBDSgFAkI/WNM+OkZam7W/qJLK09IkbZsq3A3ATwg48BR58WvzkFA9ngZv/DwMFHgexc5W+mmD0FjB2U/+ZsEwdfhBBSn5zcK94VIy/XuiWEEEIIIUQTimSkvulaAbR0AQMbJD8qxiTz0twGvOlLwPJzgPlp4IefAA4+BmXrrJFdwPyM2rpRUBlY0QKImIbCKyFmsN2Xxw/Vth2EEEIIIUQbimSkvsk0AyvOBbqHigtULckCKBSAphbgsk8AKy8EcrPAT24BXrhXbfvJ48Chx6LVHYvF5G5JCCGEEEIIIYRUF4pkpIGxAoSyEAEtNy8SAbz2Y8DaS4Rb57f/H+Dl+9Sqzue0WqqNzKqH7paEEEIIIYQQQkhiLBiR7K677sKWLVuwffv2WjeFJIlXFFtzMdDaHb6eFzvWWboJePWHgdNfCxRywP23Ay/+ADW31AoVwepAJJufASaGKdg1EoWCiHk3N1lcYLl/I4QQQgghhJBFzIIRyW644Qbs3LkTO3bsqHVTSLWwLCCVBtItsh9FNks/nJZgqTTwyv8X2PwmAAXgl38DPPnP7vXDBITJE8D0SdWWhyONOWbQkqxQAI78Ol5g6QM7gOHngImj8dpCqsf4QXHOD9bCXZgQQgghhBBC6psFI5KRRYKlccku2eT/W36+stzt7wG2XCO+3387cP8davXkssCxncCRp4FDTwCHnzRglROyfdzyp08C06P+mTR1mBmLXwapDrOnAn6kJRkhhBBCCCFkcUORjDQWVrr8OSjDo2UB6QzQ1Cb/XRZTzLKAC/8A2PZO8f3Ht4i/QiFYlCo4ypo9BcyMA3MT/uurEBaTLK6gQdc6QgghhBBCCCHEBUUy0likHJdsSSSTCT4RM19aFnDu7wEXv098v/8O4N6PBAtySZC0uyUhXnhNEUIIIYQQQhY5FMlI4xJmSRaH834feMPt4vNDfwt890/9M1pKxYWY9YdaiiUkaEQRSiiuEEIIIcmRz4tEObn58HUJIYQQEguKZKRxCRLJUpm4hQMXvQd4y/8S8coe/wfgF58vZ8X0ruslrkgX5m6ZhGXb7ASw71fAsedEnDUdpkaAg4+GxLwKgWJbjeHxJ4SQumR0r0iUc/ipWrekOrA/QAghpIZQJCONS5BQlG4urhOxo2Vvd+7vAm//mhDddt8H/OxzlTO5SViS1cLdcmSXEAEnh0XyAR2OPgPMTYn/ozL8fPlzXJGREELI4mN+Gtj3EDB2oNYtMcvUCfH//FRt21ENTu4F9j0o+hSEEEJIDaBIRhqXIKHIFsmiF17+eNY1wDu+DqSagP2/An5yC5CdiVm+Rv2Rfo+CQ5ianwKyc/pFSC3tHMyeEllAZyWJDSaH9esj5uDMPSGk0RnZDeTmxP+kMRndJ8JbjO6tdUsIIYQsUiiSkcZFxZIsctkewWDDFcDlNwOZVuDQ48APPyGyWIqVZQWYrd+7LK6gIbPUsjyPg7gZOmUcfgqYPgkcWSQuI4QQQgjRhxM3hBBCagRFMtK42CKZrCNVyoJpsJM1tA244lNAUztw7Bng628WsbiS6MiFxhwzWKfdfq9IFokQN0l7v/ySIAQxO6EfK42EUPD5HBOeJ0JILaCrPiGEEEJiQpGMNC4yIcmygNYe/22UO9BewaD4fXAzcOVngJZu4NBjwN1vlLsJxhbOQqzTkgjcb2pwMTMOTJ4wU1apzDFhwXfwUbPlEvNMDIt4MqP7a90SQsiigyLZwoGWZIQQQmoDRTLSuMiEojUXA8u3+m9jpRXLLvh/H9gAXPlZoHOZsCj7v28FJo97C1CrR7V+7zKT1mt+lmTKdXjWO/wkcGynO+ju7ES0GGc29vHNxSiDVIfjL4j/T+5Jtp6pESGeEkKIDS3JCCGEEBITimSkcbFFnM5B8X9TO5BKB3eSU4oiWZjI1bcWuP77QM9qYORl4N4PA6eOKJatUn0NAvdXuFvGrMNObjA3JazA9j8Ur7xasxDiowSJv420f9lZkUn1MGPbEUIcGAkbQAghhJDFDHsTpPFYeqYQu5adJb53LAFWnCv+vHgH/lEtyWQulQPrhVDWtw6YOAp8/8+Asf3y7YOYOCZrQPAyo4KGyZhkEmbHw9epdyaGgb0PSCwGSU3Izta6BYSQuoSWZAuGRpq4IYQQsqCgSEYaj86lwq2yvb+8rKVLzUpMxxVj8gRw6AlgblLuOnbkaSDdBPze/x/oXQNMjwD3fkRYlhVywPyMWj3Dz1cuO/assL4qFEQ69IOPeYLdJ2FJ5jk2kTqoC7RTO/ycOB7Hnq11SwyTROD+BXoNEJI0s6eY9CIuC9bdcqHuFyGEEFJ/UCQjjUnUjrBXSGvt9lmxIOJqzZ4SAomM6ZPA8ReBjkERo6x/vYiR9F8fBX79b8CBHe64XLrMTgDzU8DJvUKoGz/kaF4CMclMdMI580tI8tgC/vx0rVsSjSjZbRc6UyPinDI5CSFFFnh/Ijun9yzMzoqJOsbiJISQxFkwItldd92FLVu2YPv27bVuCqlnvO6WAxuB7hWV6znFHpWA8609Iuvl0s1C0PrBXwBHfi2sy2LhI1wl4W7pl9GzHoi6vzPjwiKv0TqVo/uqnx2SAmfjYAv4dpKERmJ0H7D3l8KFuZ4Y3e+eiKg2U8V3BZOTxGPBWpIRI4wfqo/My7msiNO695fq2xx/QYR8YCxOQghJnAUjkt1www3YuXMnduzYUeumkLrCG5PMc8n7dqid8b/CZvqK6zZ3AFfcAiw/B8hOAz/6BLD7fvHbxDFg30NCtDFCFQQNVdEkEXHF0EDnyK+FRV4jdSqzc8J68OQeIC/J4GoSCmONTb4BXfNO7hX/n3ixdm3I54QVsC1MZWfF/XZiF++JhociGfEhnxf3+Mk98bJtm2BuQn8b1RAehBBCYrNgRDJClKiIW2bJhTJXqKaQQZPz96Y24PKbgVXbhUXAPe8Dnv0PEXcsNxchppVP3Um4W5oeHCZi7aa7WcIiUxK42izZ7+xcQuJZg4oDFDWILmMHRDbio8+I73T/XDjQkmzhYPzZ7pz8bMC+AakeuaywOqy1mEoIqRkUycjiwutuaVmQzzzH6Jylm4HX/Dmw9pXC0uNf3wW8/FPxmzFXmpidR6XOZ9wOakLi22ImOytcNA7QYjYyUyPAyO4Fcl1SEIhEUHbUerwuTuwSiWLqsW11h+Oe4PEihEThxIviuXvsmVq3hBBSIyiSkYWNt5MssyRLwoIm3QS8+kPA5quFu+b9nwdeuNdc+XFmQXNZSLMaeo+V6gAjPy9fXmojByrGmB4V/ychtpoaUNb7wPToM8KSaOJYMuXPzwhXvjhJO0gNqcPrd/yQSBQza8pdfwHjDKkQ5Vk0fRLY+2D9xcxblCRo3U6LQxLE5HHx/2wEt1hCyIKAIhlZXKQy7u+WJR/M6nSu/dZNpYXr5Za3ACgAD34JeOY76uVGqTOMuSlg34Nut89SWTGC48soFITFhglXpnoXXqqFN6YeiU42ofgux54RrnyHn0ymfCcc6C0cVM5lvbmIzc+UhXsV8jng4GPAyMuJNcl1HKMcr6PPCAtwv6zWpIFx9iPq/NmZz4nkLKQ28N1KyKKHIy6ysOlY4v6ebvKsYFUKZwD0BKOgdS3gwncDZ71VfH3kq8B9txkQfRS3z866g71OHA0o0nB2y5kxYP/DIkguiYb3nCTacWtQITLqvRT1WB57TmRM9avXFt0bMah+NalX4bte21WPHNhRTo6iwuSwyP48djDBRjnva4VzyfMdn6SOYV3HSTWIrF2HnwQOPZGcxTMJwfEcmZusXTMIITWDIhlZ2PSfDvStK3+XWZI5f7cJilmji2UBF1wPnPt74vtP/xL40c1qHbaDj8mXq2xbKAiR6sCOYGuuQr5YXkR3Sz+CBDltIrTF5DmsGzSEnbGDwOSJ4HXqddCgy6mjIs6YFhFFsslhIQosxln+6ZN6lkNkcaCaqa/alnBhz7fcvHhHnthVnfbEoV4NW+YmgX2/Akb317olCtTp+052ndrCjNF+lGFMBLWfPSXiLdazEHXwMWB+utatIIRUGYpkZGGTSgNdQwErWEBTa7w6gjri9m+WBWx7p7AqA4AH7gS+9yERuyhSnQqDDec6OZ+4YQBw8FHxt1AEEwA4/pIY/Cw0nNZPQRkuZ08Jl6ZjO83Wn50Fhl+ovzgdx18Qccb8XH8XLFUcOeeyYjBz5NcJZVetJkHPugX0HFzshL0nxw+J+I7jh6rTnoXIyG5hNdsIFuOuPs4Cus/np4GTe4P7eaYZ3S+SCMUVRw89ISZfjvzaSLOM4bU0r7c+DyEkcSiSkYWPN5Cv87sR9zWNAddZ1wC/9dcALGDH3wH3fiRizC7dDp5DrJMxPy0JBl9HnUhdAe/U4WTaEUZuHhjdZ9CKLUiADRgARuksqxzj4efEzPahx/XLV2V+pphcQhVHu3VcHKPc+4s58HPB8ZyqVWysXFYMqoy76yWQwILUiAUqhNQtSR7jKpy/urjfY7bh8JOi3xF10jUKtihqShytpsCnxCJ7vxNCKqBIRhY+3sGsM8Nl0gNdWQfsguuAa/5WiHUv/Qi4/3b9+EWq7pbOz4UCMDUStIF+HcTN8HNiRvfI08mU7xJ8owgVMc9p0i4R2TnhHrzvwWjbayU2WACd4IYW6iJci+MHylaSi5FGfyZXu/31luiAVJ9cVriIy0JKzM+Id82eB4TleaO61NkCE7Pf6lEoCCs2nUm5uUnRxzORkIoQUtdQJCMLH8sTyNd0hkC749/WK/lN1kkvANt+B7jysyJG2p77gZ9+RmLJFVip5jqFctBk39UbfABWK2bGhAtidq4cr2lekjFVmSCrFkUriUjiSR0EuY4S58t5j1VV9G4wgSr2TL1mQPQkSGpg0qhuWJMngP07au9mXK/vjmq1KzcPHHhEDJ6JOUycv8NPCle+iaOVE4ej+8QzpZAX1t+2+F5tq6Z6vX8WOqeOiAnNw09U/lbhbjkuxLSDj4nrphHci4k56s7SkVQDimRk8eG0JDOC7cqYqhTKZBZidodow+XAaz8GpJuBAw8DP77FnYkysEpPp2piWMThcsYK8nYIp0+GlFncttQ5qKeOWz21xcPhp0QHXLfTND8Tr3OsaiWhEjNPvVLN9auASzgJEa5iu0vW2/4r7sOpI8Xg2vvMVLvgBnUNuj/HdgLZGeDYs7VuSf3geu9VyZJsbL+wQjJ1f9WKQkFMVES9v+vxuWBPWE0cQ8V9LnsHjB0Qz8pEM7B6qcPjthiYHBb/Sy0IPdfG+CHgkCOR1mJM3LNYmRoRz4RqujNXk8njYoKgHp/fNYYiGVlcFApAuqVyuRELFAsVL9a8bPah6PqYmwVWbQcuuxnItIrZrB/dpObS5n2YDT8n4nCNH3DXU/qYr2ybrF2l/agxQeej1g9yvwCu0nPtw6kjwq1QllVNVdQKHAA6jp/q8ar1cY2KjiVZXEuwuCLb7Cl1IdwkdufOmKVLja6VariXNuJ9kLQYZOyYVPvYhtRn6nqSHZ98vvESXJx4ScT8G434nKhnawvLUruO7SzJ1XTpnhkLCYWhSh303RYKsmfDgsyYTkKxn4enjtS2HUlx7FkxKWDkGbSwoEhGFhkFoKnNcJEBHS+Ze1ChILJJ2g+koXOA1/0l0NQhLAN+8DEF9xmfOp0POV0XopJGZkm2rzHOtpw6HG4VlyRHnpIvTzWpl2FbHIQmGIgYuF+1DF3q6ZqwKfhYT8pXLn90doJnxsXgUOu+0xyQzE+LOg7s0NsuiKrGJFtAwe297W9Ud8tqMPIysPeXwFwcF/IaUavrtFAQ1uEHdjTWvWIPAnUyFjqfv6azKRt/d3nL8z4/ayQyHXsWOPqMXGTks6k+aeh4oAuE7Gw5xEmSNNIzPA65WdEHPrFLM3nWwoUiGVl8mBbJnJkjvS9O6cxqodK8e+mZwJWfAVq6xWzuf300WAhyPrSds9Wu+jxWR6EvdYfbqHd7QAyWhl8IKaMKnNiVXGB8FZzCp/M8pDVEskCXX89xz86JmCqTxz2rBYhklqol2QLogBc04lT5HbMjTwkrLz8BtLS95jHK58v3ZNJJD5LGxGDNzx28lkyNxIwhKMHkAKrWx2nsoLhvxjSEk5oS9zo1cO5y88W/uYUf4NsZUqLeXdC8z7CaCh2Sa7OeLfEWHWHXxiIUyXLZ2se/dLL/YdE3jjNpPjcF7HtIuNP6Uid9lWpw+ElxLKJaEy8wKJKRxUWhAHQuA1o6ge4Vjh9ivPBKHS9JGUExybwMrAeu+hzQ1i8eUPd+uBhHQ1ppuayJo47FPh3yQkHePtc69gDWR2AZOyjqakSLgqRwnl8dkcxSjItXKIhrYXpUzDZHcbdc6C9413EI2Ve/e69QcP+fywJHd4pYf05cYorCcT38uIhlMT+DBdWpjiLcnNgF7H3AfFvicvQZd6yRWotSjcj4QX83dCc8tjGo0+dHki6lxi8XXn9EEVqKVXLocSGi1JtbXhyRbGSXmMyQhT6pNlmd5G0J0+iTuoagSEYWB83t4v/2fmHFs+I8IUoZQdcFK6Cj1rsGuOqvgI5Boebf+2H5DIc92BjdJyzPbHLz5U6rN3C/arwm25IsnxMDfK/VVhLxb3Tc5OoJV0ZSjU5VKuP+Pn2ymHhBInL6WSI4j1l2VnReStZmiq5x3mvENHZ8F7v8w0+pd0a8wlXYeirrql5HY/uAqRMi1p+z7CO/Dt7Oe65sQXnqREKd7oQ68vmcsBydGXMsdBy78UP6ncrAmdp6ok6fNfXM/LQYQOlQDcGsLkS5emhDgjj7A80dtWuHCt7rwftMrqYwksS1SWGnetTDsZ6fDphIT4BsMZ7q5HDwetUmVgIsjVA0STK6H9j/kJ6ru2nq4n1ZX1AkI4uDFecDay4GMpKg/XEpiUsSd0sZYebK3UPA6/9KWLpNDgP3fqQya5bdMZXFtCoJN6pWR5517H2YGhGWUtMnk394VvvhbMoFxlWOxj54RbIjT4tzOba/8li41vURhEZ2i+tKmuWuhi++sQPlWceZUSG6qIolhYKwoNv7QPA2OudSNX6Z0+1lfkbUH+YKM7pfxG6SV6zhAlsHjO0XlqOHfdxPTx0Wx8QlolWDOhiYJEFuvj4GXYlThbh2WuU20D1ZtyR53EyX7b3+6iQmWYkCYwHVDbW+FhQ48Agw/Hx1hTKg/p6ViWcxrsL+ntzj/t8E8zOaLvB1dl7rAIpkZHFgWUA6E/x7ZALcLWUcV4jr1bFUWJT1rgWmR4RQNnUcGNzsrlM2cM/NCQsWZ3YmrZhkqlkwTRLRTS4KJ3YVA1GbMCfWaJdzH5zCl1PkmZ/2WKcV3PHL/NwtvW69SVuI6WBfo9rtKIgOYKEQbLXkdbccP+Tv+hXlWBx6TNRfIVR7ygrq3HgHZFHPyeRxd3bMpIQVWQZOaQa/BOMtFQq1ce2u9v1ip5cfOxivPRPHhCjdMNRZh7zWz8lGhYHl9ZmfcYfJsDn0OLDvwfpyu1qshL5a60hEq3acMJkoNbpfhKgw9RydnRCJRJQsvRbYJL4pDuwQyaK8MbCJMgtGJLvrrruwZcsWbN++vdZNIYsNlwWWwRdnWx9w5WeBgY3A7Dhwz/8sC2z5nP8ANZ8FDj/hyfqi8qLxxiSrotASpfyobbKtkk5GDEzpJ0wEtWd0v7CIsjszzjKcQmduXsRIcuIUyZximLOjEiiWBB2nKrz8k7aQcR6HU0eFoOXr+hVhQGffZzOjERrnqMtlSRZh5nPimLAUPPhIjHYoYtVB1+DkHpEFOBH3gzrq9JoKkDv8vLAobZRYInU38Ki39lSZyEHjC9KPdUmYu2W1OPRYZSIeoNy+aWfMp4iWkSQmIceynix/C3nxrhx+vloVVi46uUeElZg6YaaKQ4+LWKEqrp0LwZIsSVRFsrp7J9eeOugJm+GGG27Azp07sWPHjlo3hSw2knywtHYDr/tLYHALMDsG/N+3A8eKIorfg69QqBTQCnmEB+73xCTzY/xwFV/GNpJjfPiJeEXmIw4KnMdH9dyf3CPWdVr3lcpwnKs5jwWU1wLJ2WZXx8B7bn0EzkZ6CRYKcO2Hb+bWgOPn5PiLYlbNVX5Q3V7iJveImUzBDk5bjXMofQZo1GvCwsy2ijLpfqBEle8R1SQequTq2AqlKs+fiHXEaVsjuVL7ERZv0Y8k9zdK2bMTGoHFaxSTbKFnPLXJ54DJE4tnf3XIZc3dO1MnhNV1FLfL+RkhsI1LQrf4ESRKmT7XQX06m6RFskZ9ptvUk6DbYCwYkYyQeBhyt/R7GLUPRC++uQO4/Bbg9NcC85PADz4O3Hcr8OQ3fGICSR7oSu6WRez1/NwoJo4mEAMhgrulSja1IKLG/ohjZWO/zP2EK+nLXkEoCmqTagcisY6Azr0VYOlVyhLp3UQxztipI/E6UxX3j87xMmBJJt0moc6P7rPCyakjwp1Zp9MdWEfx2p6b8lhYJES1O8Te+IQVaLYnqP1TI9WPX+NLvQ08DLVn/0PAUUeym0YZYEW2QKwzd8tDjwtrbJmrtrdPI3t+cUBpjuMvAMd2urMHNwqh10GM62R+RrjVRhWmvXjDbehwcre4V5wJwPJ5MbYIywZeN9Rbe+qAfNAkuh88jl7CemeEkDCcgfsLPg+juO5LTa3AO/8Z+Ld3A899F9jzc/EHC1iyEVh5IbDyAvFZ9gJTsSQLamvcl2K6KdidIzSDoYGZotlTQEtX+XtUSzJfiyCVY1Tw/O/5LD13jmUud0unSOY5t6pxYuqps+ON0yJr2/QI0LTCvcwbk0wZzX2PM3gqFPQs+vL58vVpJxtJ3KXAQZxngD0gOvGSSEISF9vd+OCj8cuqR1KGLcmCrhPblbulW7xTtDAhHhi0as1lxbGz78vcvHjPRG6aoWdhbj6G62IDkug7JEbZc5PlrOa+5ckC91vx6l3sODOp2y6lk8MAzqxZk6KRoFg6WZyoqHriGwmy98XxF8Q561kJ9J+utk35R2NNU2ahuVvu3wH0rQU6B9W3yWXdcbdd4VhoDxUVimSExEUlW1/QQ6p9QM2Pv6kVuPb/Ak9/C3jhv4S74fHnxQvt+AvAk/8EtPaITJ6rLhT/26KQs+MS3tji/2EWThpYaQBBA4ewzmvAjJbqfh16AjjtVeXvYQOZXBaYnxIur66mxXCtKWlkqsfW43Lou27AMVBuYww3JV0BSbbNAYmrfEUMmXTlcqd5v9++npIESQ48LobdLb3lBZ3zsQMitpTNulcWBXjJNklZPdRTp6rqbQm4Lkb3C8uvUPHPMdA++BjQsxroXOqzqoZIphTEWOFZnZuLIJIZwFSQ9/kZ8bxo6QRWnCfewSd2Af2n6YnRpqygquICncC9npsXYRu87zhAHOMTLwLdK4H2fvn23v2up0kXmXVNvcQkS4pa709uXjzvOpYAA+tr25akiXWs6+i6k92zdhywsYNykazeRGTZO29+WoyLelb7P7+Uy6/y/mZnREgbVZHMznA6tK38LFeOWeygnp7fdQJFMkIAc50Lv3JMlr/i3OLD8/eFuHbwURHM+9DjYmbq5Z+KPysFLD1TWJid8VvAsi2KdcSMR6RcprP4CO6WJpidEAMtGUeeFGboS8/w/BAhcH95Jc//Ctv5CUKuJoUE7i8UROD5gnf7uNYc8+L6a18CLNmg3janSJadBdLNkg0lbQu1uvHZH2lGWV1LMs81rHNNVqwbsK1TIAPEOU9nkpstPbELaGp3Cz/Ofc3ngVQKNesY14tgNz9Tjo/WtVz9mT43CQw/5y+Sya5pbeHZoIVWokhElblJEVJAZ3+nihYqtsu9nf12ZDfQs0qjOQpWvKeOiMmnCqsk5Uoqy8zNAxnZM6/K7H9YPFeWn13524kXRfKf6VH35JITnedaFOJcy1JL8ZDzbRVDZtTzLVQN7FAUQRnhZZw6LAT48UNmRbJTR8XkREeMsCW6JOlu6Sw7n3dcd8W+WqpO3nl+aMdzTRg/a7iZcWDmGf/nl3oFMbdPGDuEwvhBh0jGOIAmoEhGiCmCOldBAz2VwYFTqHGW1T4AbHyd+Mtny9nvDjwisqYd2yn+Hv8HoGMpMHSusDIbOlcMTILao2ztpEDQPk4MK7jJGLAks2lqKyc9mJssi2S5LHDqkDhOTW3lmCbe7DomXHpUj22h4P7dz7UwMCZZARjbH57NM0rn5tRhMeA7ddhfJAtiakS4gHWvqPxNOogJcQMMc1eNg8lZep37qRTHLoGYZNMny5awfiLZ1Al/gSeMqRFxr/Ws1NuuoHBtR7n3det2LffEvjNVd5jwq3v9jh0QHeWmtuhtSgrvvTq6T/x1DUV7fhil2Dbne/zU4bIAFzTQ0rkWjr8gBjXLzopv4RAX+5q2k4I4UUoAUceDRxVLMunzs46sfGpBPi/iZQFlK2ZlEjh2tkUQYEDs0EFzX0ZeFoLy0Dbxzhp5uXLySca+B4VV78oLgCNPiUnDlRfWt1BW64mYINfC0joJu7yP7Bbnt2tZsvVEJdKYrY6f5zWCIhkhRoliSabwMnaa3foNGlMZYPlW8XfB9aIjbluZHX5SiD0v/VD8WWlhWbbyAvFC7l1bbmMSMcn82my7yZgeKOqUdezZssvTxFHhUrXuUscKARmwtNslsSTT2lzhxXf8RaC1112nzN3QBJHPS3E7O36V1GXZ42oKOK4jPzcpDZFMu+0xBgDjhypFFlUCRbKYqFgm2lZQqm22UuW22jGwdK1wnAPcJCzJVFxt8zlhOdbeD7T1udtRyCPRvEe6Ipxzf+anxCSJc0A5fVLPrTMqWu0uimRAdJHdr1zp55B1pTEQR/03HT8k3GJ614VU4SnXnvUf2197kSwuibtbxrEkkz3bFCzfau2yGAuNts/PyF2unRZ4thWzcvUJPBPrNVuv9zoZOyj+nzgm4ojKJp/KG5c/FvJiQrZQEJZPADB3SlivRiGpySN3JRF/82F6VIwBnDGD/Zg4JtwLe1f7Vzk3WZ4IN4H3uTYzXs6+XVcimU9yqFqLmg0MRTJCAJiZAQsoI64lmWpZTjoHgTNeL/5y82LAenAHcOBRYPyAyKxz5NfAo3cL66mVFwArtwMbLhfb+1owRcBPeLOtm8LSRvs+5CXL82FtdWyTnS4Hl1UNkG635cQuj7ij8CKSWpJpuFv6mVA7r6FTR9xps4M6TbFjBEUV+4rbmegAa8Ugcm2oVqZNxTWsue+njjg21bEkK55zWba2uCSRvUpmUSvLShpE1rm+QStSHcYOiPt7/FClBUPdBQoOWD87Bxx52rMwpPx8Tux/h6YFYdg5iXyvejF03sOef0EuK7aFWVUGpR5M1DnhsJCOO9nh+72GyCxJQvfTDtyvyNSIuFeiWtqqkMQA99QRMUEVZsGp+5xLZEKjiglrnES9vwo5YQ2mW7ZzYig043FQ/brPhgjXl8lzkp0rZ/lUsRS0n7uj++XtKRREXDyjBBwjr1VbLXG58UZwtzQVM3QBUSdnlpAFQCEf8HKKaUnmWj3CyzvdJGKZrTgX2P4eYPywsDA7+Ih4QU0OAy/cK/7u+5xwBVl1obAy615p5qXotDABxANZOXW1IfHDrlf2WfmlUtxGJWEDAEw6kzJILMm0Avf7fPZ2TitEiZiZGZVEtoDtw0ilK4+/dLuwZarbRMDoQFijTfmcGIzJmBwGhi1J3Lwk0LAkg/dcanbYnIMM3/sjoU6cfd0FzUSbHCTIrvPcHJCy3SVj7mcuZMAmY2S3sO6yLb2U0bDcqgtCRDvZO2FmzB03cH46xFIyQOTNzonj3LW8PElTLYafi19GvVgo5PPA7LjI2mojjb/peVf4xSVTpZQttstsIoykj6s9OSmz4FSZlPPDZW1raB/q5RqrwBNXzKZQAAqq/VoHrgnDOrRmVJ3g0D1fWc0JNOlkhuP4J2F5WJHww3Gd52brRyTzsySru/du41AvZ5aQ2uLXMepa7rYACSI37z8DVAtLsiC6h4Duq4HNV4sB6ZFfF2OZ7RAuh4efEH87vgp0Lgc2XSVSEi/fCmQidAYtq1Ik03lw+w1KdWNQHXrC/RJ1iXYxA11KswSdEDHhvOuoztiMHXC7SfkJeUEBlPM5tWtM1v6Te8VAecW5labwUyMxOiTeTkcaFcKK3yDGr61+y+vB3TJO3YW8PGaQzcQxYMkmcY7zeSE8KwcF17DQVEaWqEFTVHJ2nP0sQ6s+eFIVtg1w4BERTL2tz78NrsWGj8XsmHx52Kx5IQ8gwK1T5blXKIh4Pi3dARlBDWXrimJJdvgptbpVOLYTmD0lEhGsvEB9O9nExdGd4r5ZemaMJAO6eI6Z6etQtbzjzwuLcGdsyyht0emLOV1x8/MAapAtNiqB1p4xPAi8Lul2MHpT6FpJ5eajx5ANa7fLaschihXyCH9/SvbBZX0W55glPHkEGH7/KbY3Nx8w9nGUodsnjTv5mZ31j+9cbWKFggEoplVCkYwQP9p6RUddVSTLBzzEA4WtGohkTjItwmps1YXAJf9T7MfT3xLxzI7+Gpg4Ajx2t1g33SyEspUXAqu2CxExaru1hAKdwXxAubOnPKsq+O1PnXB/12n3zKhngaYl2eRxd2wK1c6Js43Dz/ln8JQdq+ys+GvtLluSjLwsAtI6sWfRwxsT3D7fzRSPszFXIdXf4tYLvU5mIS8CxKpw+AkRj2PVheYDt0v3V/LskrpWa3aqnZaQUTrkRlzggmbLqzBIGDsgEclqyMRR8Te4xT/LXNA9MXlcbB+27uTxopXuIX+RLNDKL+J9KbUkUzjPYddZ0LvLzs4Z15U6nyu/pw4+Cqz5jXgJZgAo9UuCJmd0mJsSz6uo96wdMsFp3e2XVdm9QFKYQhvyubKLmLTcGqF6+AIT/TiueV2XrQp3LwuxB95RE6ZMj4pz1DmYvKW1K46bghWZbB9MBZrXvRaV11ec2HWSz4lrTdUF349cFtj3q2I5MldVx3WarUYMO0ebw1xrqwpjkpmGIhkhvmh22PJZISJJizJohZJE3Aeb5naRMfOst4i/+Wkxcz6ySwT8nxwuJgN4FHj4K0D3KmBVMfj/srMDOuaW5BgoPLhP7BLClo6Vl91hViGK376O9Y1fYGOd2DwVs5Sy+kwMVorb7H9Y/L/iXL3NJ0/IB8+y/ZsZFcKbLsoumDq/K1duqBzoW5Kp1j03Kf6fOgH0rIreDudyXTcwabwVzfvM95p34tP27Cxw6HGgcxnQf5retoAYrJzcIwLguzaJ4YYkI59PNoNZPheeEMWPoMvt5G5xn8tcgIPEw2PPqlUSZgkwM6bu6h5GmHWE0nmuhWuU973iaXt2xoBIFqEdURjdL+63pjYx6eZ6bsUoPzcn+ikuFN67SlbXNYqTZYygfTRkMVSyJItehCgnYgFjxZhVE8cqRbKcijukhjWY832VzyLSM8Hl4VBjSzKZBaByn7X4Wy5bzNzZLsYIYesHMT9ZrlcqfifkbqkiuJnoC5jCZUkWNyYZASiSEeKPJRF2gszHc1kg49OpqDd3Sz/Sze72NLUBa14BXHg9sO2dwOjeYiyzR4V7x/gBYOcBYOe/CzfMoW1CMFt5gTsjp6zdQTNu81NCoAsbDMmyzpx4KXw/S9v7vEiCLAi03iMK4pVqogC/dWfGKtsb1MFRWQ6UMy2pcmynT+BViTA4/LxCgQUoHWy/fTjwqMjg6ucCbW9nZ6WKWk8UdAYf+Vx43d7OYyLPCMX9V7LgCKtKwQ3ar8zR/ULoGjsQIJIFMLJLIZFIzEGyndV3cIvauY1CPusvkpkImi+1JK1CJztsEsRlLawjoMssyRIcAMVx9654vnuux6BMpvPTQNpQ/DPV90wQJ/eI/+enRaw3Z1yxOMis85Tam7DoeVyjf5IUgRpZDHfLiomEGMcyny+63UewYPK2xbt834Pis0o2RRWcz4ncvMLkhMySzJC4Y6KfYqUq37u618Vssf/onWzy4hXfQsdDIftn8jjuf8j/N9lnLzPj4l3ff1q0mJNxJtLszPEAIr+XR3aLa6FvbbTtFwAUyQgBIH+Ze0x7U2lg+TnCSkFG9wohWMiIY0rdsUTSroTwfUEVX15968Tf2W8XViuHnxDxcw4+IuIm7X+o/GLpXSvEslUXAmsurSw7zCz90BP67ddN++wb4yssmL4E7zmePFHpqlt6oep0/AJ+r8iwGYOwl31QXCylcqvhBuBgfkoIpktCXC1kFm1KFmtVmu1VtSQLSuSg0o7snHCt7lzmXm4idpvurKZz/UJB8XzolB+wra8wEueYeLAzdA0/J+JDqmIfi3w23FIoN59MMHjVAXYYSc1ae13qVUmqPSd3A32nJRsnTLXto/tEnEmdMAnBFYd8V8A78WhbwwIJnJOQ8iwrecPAU4c1N9A5BoqNT8rd0ltOHA+KI0+Je7mi/6tRvwznPgX1QSPHJMsBLoN/FeEHwZnSCwXg6NNiwm9wc3hZqowd9BmzyNrraZPTUlnHM2L8kHCFXXqmugCk8hywBSXlZGBh5RmYIDn8pPi/kBPJ0HQp5ACEHCPnBLlJjyV7ohEAelYna/Vex1AkI8QPryXZ0jP9YzvZQZa9QoIdrN43JhTCH2xLz/Rfv6lNXxhKN4WIdoqBt5s7gLWXij872PLBR4VgNvycsDob3Qs88y2x7soLgKFzxf/tA+HCodLLzvPy1HUx8bVSCXhB+r2wJ465vzsD9geVEdWSpFDQEMhkFmyaA5AjT+ut761be8CjuH5Qubl59XJs8jm5ICh1Gx0XrqPdCq6NrrJ0xAQFSzJvmUGWJH4MPyv2Z3IY6HDGgtI8fyZiknnXl7rC1dA1wJS7lZ+VYxDHdgpXxxXnBa8Xyw0k6NjGjC+jVIdOMabFUqdFpsNdzHewG1L/1Ii4r9ZerLddICGWZH6Ushr6Td7oNsPEOfTErdKxBNdFNyaZsiVHlZ5Fxp55ivew9nPOsW0+piWZLXY7LUdNPF9cz0VDokLeM6lTkYRIE+9+zk+VE0WEim4ax8gv5IX0HS575kToZ9gTRFPHhbdJnBhqzqzo+Xkg1WJOJAtuhN7qumO0UjUq95+zLQZFsiStqBsIimSE+KL4wGlqdwRY9jw8V18kXvZxAjBXuHw6XmBRRLJMa4BAJYsdhvCHtWUBA+vF3znvEPt86LGildmjwvR698/FHwD0rwc2XgH0ny4sfSLHzonZafTNnJd0zBGNjqhuTCatVavQuT+xyyO6mCZgH4KuD7/fbPcflXrsmUJdUUo7JlkI2Wl3zKd8Vtx7rT3Ako1q9djutXNTQLuqiCv5zYRI5u2gVduiRLqJxj2rGosnlVHYN8fvublyLLCwhDJJiYiqmfHCGNe1qLHrkFhZqK5buULwulYKpQGvbzwcFSuHhAduFce9ygKybQ0W5ZozEbdKmTALbsW4qSbcTGuJaiD1OEJ7XEsyeaEaq/qsqzz4jyjIFfKocI2OEo8XEO/imVG97Imm3C0rC3Z/zefKk9KFsPsqCM8zWCcjqnO9fBaAIZFsakS4Gqo1QmGdiPeByrWq8s6NcjyZ6RIARTJCBLKXuV8mlSC8D6x0E9DeH1a5Xh3OF1gqQnDeIOsFv06N7ou3pQs47b+Jv0JeWFnt/SWw9xfCV35kF/BQcUapuVNYmC3ZAPRvEMJZq6G4JGH4vWCiuFtq1avzMvLrqJsW8kzvl4PxQ/rZFrNz/uUpv/TzAev6LHe6+3jL8muD3zY2LV1uNzDdmGRh7pPHX3SL5WP7RQD7+elgkUzpOGpeF1FE9or1VSw+JAPVqRF3pjHl8jUxZkmmIK46A+S7Zv7DLFCLbQxzVdUO8G/IimFyWKPOIExZkoWIJoU8Ql1ftOqG3oDQtW2IJVnVRJtiPc4YRiMvi3e4MoauJxVUxC3LCv59ZkxigVMDEf/oM0DHoDsDrKoo5XyfTBwTiV6WbBLPAWMxyTxiUdVRsSSTCDTO70H4rlvwVB3l2ihuYyee0JpkNyGSKUwGOK+NaUkSl7AEPvY4RMlKXGGf7Il/EyKZcuZ2RaLeBkoCWEKZtimYAaBIRkgAHqsqvw6Ic3E1OqfOdqQj3MKhLj4GBrmu4lLCH39wM3D2W4XZ+KHHxIto7wPA3IQQz/b+orxNx6DoaA+sBwaKwllbf/iMnLbptl/g/gjulnoVOz5qBO6P3A7JumHbG8s+iRCxyofh59TKD7Qk0cgMaeMXVzC0Hh/STUIIVgkoLt0/hRl5bxry2GnJo8w8FpEJerod14qgwT7nfWJYDOza+4UwaLuThVeg1x7vNoW8SNSSzwJNrRHKKhL2LM7nk3U/G34BmDgqMtk6g1gHXeuqVigmyOfE/dja6+/2plvn3KRIOtO3Fv6DgiKWVySTNkCvfncFGtsHDFpklmT5PHDiRfHe7EzIkrd0zBzHaeygnkhm3NooiBhWLjaHn5KsVoNB5NSI+As6t3OT4r3TucxznB2f7SQ6zZ1A72pUCOhe7PhYYSRhSaZlge2zvMI1UoJS9kufylSEWN3rxdknUY1xFkZQciqVMYDzHe20Yg8Nf2FXkYK/9WmE+6nkdik5dyf3Ar1rzF2P2vd7ZJVMYRVJxnsj7vOLVxhzQpGMEEA+WKmII1Z80C3ZJEygSzGoVDrSBknaksyEJYi07GK723qB9b8JbPsd4XJz/HnR6TrxsrAwO3UYmDwm/vb/qrx9a68QzfrXl/+PMuPkxGTgfh1c7yJFixDV5dJ1FUQZEx39RAaSoZUG/BRwjE4d0bR48NTjcnMO2j/JPaV7TkPd+6JauGhcF77uK0Uhp623MqOtzexEeFNmxsVgoGeVpEMu2f/sTFlIPe1VQjBTYWZMP3Orl0JePJsKBeFSHzVAfphIFsvdyT5ffhaTU0IgA0S8waFzFN16NN0t52eiH5/h54WVS/cK8cyXVxpQgOS34eeK187zIjamc93sXICga2KywlOeKVdDmSXZqUOijzJxTEEki7EPQIznD2Auxp0CFc81T/kVz+kYkwVJoDvIP/hYcbt0uMWZfd2HZRaeHS/Hx6qgjizJlN6lEkuyk3tFgovI9XqvqwgiUJx+rd+1mJ0VsVY7BosTDpL1xg+LpB5eKzlLIubnHW7oqjitvPM54MAOhWyUQda2kslmWZ9+dJ/wZPDrn8QhqF9kYx/P+RnRV+peoeBhFFC2ax3n/Rr3OWR4PLAAoEhGCCCEr+HnxGxDplWIYF1D7gDe9oOua5n48wZqBxD4MmtqD0+HrIKzQ6obqB6IFv8rTqcBkLuu5uZEWwa3iD+buUlhxTTysnihjOwSWVZmRouJAR4tr9vSBQyeJTJoDp0rsrDkc8LCTvcF4/4hYBvTlmRh5SVkSZYIQW012QbF4xdmwTaqankkqcdpaRfbnTBguUpm3EStMULO28TR8l+cTqgd3y2VkXR0JW3IzoSvAwhri1Sm7MItswRRwetKZH+fndATgbwuJ0HXZxzXkaBr0puEI58VA+rTXmVv7L+tjiXZxDEhRnWvKJ5Xzf2ZOiH+Hz/kEMkULDWC8JstLxRE8gq/dROZANO4b4MmNGTHILY1qQJ2vXFEsqpakukSxcrPdBMMDFznJgA4hVJFoTvOvVUoJKCRGejzOJ+/rnukuL5qX9fvvBixoAu6vyP2Fw8+Jp6/2RmRoV72PDvxkrxumVu4ryjlYzlnWe6+zMRRyTMqjiVUwac9RSr6C3FQaJ8s7MOJl8SYcvqk410btylViEm2iAUzimSEACI9+8rzy99tKzI/E3UnqnEMlp8N7H84uB3LtwJHfh28TpKWZPCJw2bSkszGb/Df3CGOw/Kt5WXZGTHDd2IXMPKSEChO7hGuBPt/5bY4y7QIK7P+04sumxuEeKYjKAa+FArxXxo6nZ4gF0bl+mTrqsx4auI7e2vgmImCFJeFtMcmLJaYcj0hooL32texGFOK3ZRggOQwiyRvZzDueZ6bqDwOfm6oNuOH5aLA/Ew5voipTingEbo0BQKnUBQWkypWfJU4A44gAtqcnxfnYXIY6FxezmQ2fiimkBK0bRyRzFNOkIWhkQQqSQ08Ylie+KE00LdFsjjPH8VtZ4vPBZPxSsNc4erNkixq3UHJn7xlhb4TVc91oXLd7KyYpOgeEhbDumi5W/o9o3yeqbHOoacfFXYNGbHa15w8tfd7ehTog//zzBUWAijvm49I5mvtHGCtCIQ8MxT7pd7jXCgEvDOTEuMVrjPbqkwlXqpK2a5VJKK29xqYOgF0DEAKhbFAKJIREohCTDJvJhs/VKwNVNxdnHVEiUkWNOBIbFZXQyiQkWkFlp4h/mxy8yIO0YmXxMD66DOiA5adBo7tFH82qYyYPes/XSQHGFgvYtJkfOIJhbpAxn2BGHgZ6QZ/rwaBZvEJvXSjWuIBalZaCsUE/+jjwiwtJqIYHXm7mIPA0b1qrpQ6yK7Vk7slbXLss1/crmzE1OtSHMfA2cbYmU2DRLIYsRHDxM2oBF3L2VkxyTM/LQZbrgFLjHYEWT/HeRaHCiIKlmRxBu5a79uAgaNMVJ6N61LsqOPUEaClW0wmytaJ029Q3fbQ4+L/1a8AMs369ahM1FTjXWwaV5v9JnI9fS+/Yz78vMKkTMgEorNd3npG94sJz5HdZZEsavKKUPxEpAT6Qn5Wxr7tSLJ/qbDt+CF/yypZ3XNTlRbYpRhgPnHrnDFlS9ZozvvEtJt1wRMCw4PJsY2Sl4qzv1B8D+pOFIXVM3HME4fVZ33byr+tN6S+Gj7H6hSKZISYpGu5sHDSQcVazUlTq8h2k0rrD9Aq6qv4McJshyJei4u4WWjSTWVrsZUXiA782EFg933iHAy/IFw1R3aJ2dETLxUH0z8Q21sp0VHrd8Y5O10IlU6BzYsJ10Hny8+OCxRGptXdsYltSRbQpqhEHkgq1j1xTMSs88YSCizalKVODEsy731tIs6cE5179uhO4ercNeS/jsydS7aPUoEs5nXkmm1OiWPizPBYqqZK17QM5RlxGZJj61tPnJhkQdktw7YNEX39yM2VByrecxbnXDgHF7GtSRzLnOEUpG5CJuO9eMqTWdroMn0SSDdX3gtTJ/TE67DjdPxF8b+vNaZnP6ZGRB9F5d5QWcfZvtxsNJFMFOTzWXedoG2qjYolmVckkwzWLatSIAuz4A1tl0a2SOUyi2VNHAVae8QE6OypyuvN16LK75lqSJjyimSm30F+1kKq5HNlC1/V+qRJnOx2SJLsTA5XTkDmc0KcswnMoBnh+NnXhC81dOsu9Rdi9hW82Ek3SqsHrD83qSCSqTwTFxcLRiS76667cNdddyGXq5LFBFkc6Lpb9qwS1hVRX2CqA67lZ4v/tdzFFOtPamDpFfSMWjfZM9op4VrZvx5YV+zQ2y/PkV3F5AAviU7CzKiIPzG6D3j5p+WiuobcyQEG1ovOmLMuk5ZkqsSxxJM2wcR+eMsMEH+C6lJthx2jyJSbZG6+LMSEFhPU/pBMUWEBoe1g8lqp3h3Y91Jbn2fgL8E+hrOngP7T5OtUWNjFFSYC1rUst5ub3em2UsGBzas56+m9V2LFCtM4PibcLU1jWRKXnCJJxcGqhrtlqOudiaQkAZZk89PCCq97pQj3kGlzi0Eu7aZoNWHHlvPGAlRy0TZAwfHedXL0GWGp3btGoRDNgeP8tHg+qQS+dqHyvtMQsKOslwS+YpAzcLjBxC6q73FvbC6T2S7HD5WFGzvW78B6EfvQWZ+0jX7JmgydQxVLYZW6smEB7TWeY67fI7w3Ze+hoJhkFesXhPWgq98WMR6j/0rBcceS8pJRsiorHivTlmSVG3j+l/0WVMfiFcP8WDAi2Q033IAbbrgB4+Pj6OnpCd+AECU03S0tS1g1HdspxBrt6uJkiVKuJPjnzkGRGWvOQJKBUpVWpcuM7ss6KPFByWrCfhl5zknvamHlt/bS8vKpkaJwVhTNRl4WGTVPHRZ/e39RXrd9SVk0618vzq3MnUCF7Jyem59NpsWz/6ZfaAX3TF/UMnyXB7VXc1+cxy9s00NPBFSbF9elUvUR22+V/nHX68R2ZYg7uA3LlujFrxMmtShIoANlJ9mwg/YD5Q52Km1GWHWuH7Wj7N1/p+XeoSeEK3ev6vPeO0sesB/HX1BuYmU1CXV4J4+LPxk5H5EsStB+J2HJAoxM7oQMLAr5+O/EChHB8U4ceVmIjE6rDV/LrQKQcxzPuNlafapQXkl2fiaOqYlkYffkvofcYRZs64mhbXrxyWT3WsVkxbjnHKuKZIYF+yj9A6BSlCr/EKs5LlQzf8uskZ19wNLzWJI50bf44npOkd7uE00cc4tk2iJSjOdHZDEjoDxngiqdbcN+j2L5LDvnJYs2iau37NxPeyyLdRLAKDWzoJAps5p49iGfT358F/cdOK/hgrtIWDAiGSGJoOsKCYhUwysvMFCf0gbubVUeZmHulqk0sEwhyYAuUVxDXdurZGSy99+x7uqLiiKEp+PZ3i/+Vm0vL5sZL2bW3FUUznYB4weBqePib/9DYr2ffkpYl/WfXrQ22yD+96bPlmGXYe+Tyjlraq+MaWe6Y57PeeIbRCCqu6XuS9h13Kr0Ag9qYy5AAMjOqVsBxrUM9QrRxmZPY1jLBK6aQ0U3JO+YdQ2KH6Nr/Wcyzot39vzkHnWRTLUdUQfKpXpywuomimVf1HuqWlbI7ko1f/MbPAcMBIFyAoigdcLw1uErakg39i8rCatKlUzcBcn7VpuQbXNzZXdPJ3OTmkH8C+HPA6+FZCEvxJfWnpCYsrZgULSisZME2eW1dGm0E5rZxCX7kZ1zLw+zZBYrSYqWWUHpuFt6q3C8B53HSZlimdIYhRGtAKOiaoET2S0yZFJBxyI2Ln7XQXbOx3pYxXpOMXC/8v4Ugt+ZKtft6P7g97hliefO2H7FNrkaYFYky8v2J+a597PEy83FcHFvbCiSEaKK34CzlinMK0S8mA9JuzxdixSlsmO+IFRmnkruH451083qGehau4EV54o/m/kpYSruFM5G9wsXuUOPlwMKA0LM6j9dWJ31rBbBjls6i0GPO0Vn2dXZVjxnze2o6FTEDpjumVmPG+gZCBDJQtwtqzqL60UnW5cPQWb++azk2k2oA19hrRkmTioOeAoFcb2HIe24Ba0vEcFKQW7TgBVQnlLbPQOZqNeKd3Y8loBVpcHcyb3ir2u5+jZxrO0AT+BkxUGkCq53h8bxO3VEZD5dtkVMXgUxdiBi4xT3bWJYTLiUNov7zPNYpVX8LsF3UiZqWwIsyVT3L+r1pu26VPrHu9CfsYPCqjbdBKz5jYCyi+WMvCyssYfOEe9724p57SXCDb4lYmbO2QkhxMssZbzHeWpECLpN7f7rKKMpkgWKkJJnqLZIVkR27p1lR+kbFQrRJ6l0+za6br9a24YRcr9NDlfGdpOd83zWPeHrRMXlVHrfe/rxpWUK+1zIl++PdFPlO9q5DyMvi/5F31r3OiqTXQcfk7e5oj2SSQ2T7payCUTvOEitkvBVDj0unn9R79cGhiIZIYFEsCSLVUecYgLi93jra+32cdGwRbKYVl8y4pYZ9IIJsiSzrHhWbE3twLKzxJ/Nsi3AM98pi2YndokX7PwUcPRp8edHulmIZfafLZ61dAHNjuUtneXvrd1iuyQxEd/Od3YzB7MdP6e4GLfTqLh9rM6pYuD+uMiuc92OlozsjBAcwggVQxXqL4lkkoQHrvUU2i5z+4iE15IszHVQ1ZUkoD2h96PivqictyQwZsUH8ezPZYHxA5LjonAMT7wELN8a3JakMwA7s72JhkArsHnQtZxExr4g8nlgdsxxzBKeKJRNckVyG9O8Dm0XsTBR3H6e2+EKTrwkrPFtRl4W96FfNu3gwoWA6xtr0nOfzRUFosDQDHEE2hiWZM5t/Z6huazoU0mLDLAkc55baaD5MAoiJqBpjIrSCmUMPy8sHwfPjFG0RODxEpgdU2GfTU+a5rPl51G62V8km58R4jdQdAfXeY7EsbKO4m4ZUJf0/tHoY2dnQ6xjPZw6ohFSYuFAkYyQILyxreQrVaUp4XUrtsOyRAdubtIdC8j+LSn8XhCyWR95Af4/VViSKdatg3MWPjsHLNkk/mzyWdGZPbFLJAc4dUQc49lxMbM5e0oMZnJzIni6HUBdlXSLyLzZ2u0W1kriWmfZcs0ptvm9CI8+7R4YmhgkyrIQApWWOCaJW26cTr8qQe4uQWnLdevQvc6D3ESdBFnKuVCc9bXJ5/z3P5WO7z6hE/8riJHdQMeSaNuGktB9EYkoM9EqxTpiRUaxorBSYhLi1GHJ6gptjeu6GkRk3bUQo+vgeZ5WO2bMyC63+BpkFRKGStullu1RDl5SVpyecuam3O9T+1gpP0e9xcfMZK20nyEWT7MTQuSLE5PMue3EMdE/8U7uju4VvwWVL530jHm9FQqazwnV+y+K4BPQFzv+gkjc5Vdnbk5Yg82tFmJQ17LKxFM6ZGfkFoxBWUKVJqd82jF7CpgedW+r84y3UvLnRSlucY2S+1UksiiEj7dGXhaZ3GXryY6/6vNsZLcYq/Sfrm5pPnWcIhkhJAJJuCZGQWeAnEprxvMwgJ8lWaZFrXMS+ELxWBV5j4URyzjHLNKJlyp/TmVEAO++dQAukzSxAGSnRSdg9pTodGZnhLA0d8qx/JSYDXZ+L+REUOzp2coAqGGkmytFtQpxzWvJ1hlt1ls2iBU7r19WtUhl1K6/pCzJDjwSo1xnFSl9t6e84qBAdd91LeROHfYPAu+3P6W6dC3JCvrts5k4alAkq6G4EUbi7Yk4E2+lAtzBbYss1Vl00/sYxzpRR+jRsQZK+DxWWCf6PHdmJ0RbAmNyKbRVJoroTgjkcx5ro93iPRdcsVrZ0phNBgfjykHO/Z5xCqLFqCzOUnG96ZMik2qmVYguKsjEDef3U4fFpJ8XlQy5YZZk9dInB4R1oTPxBBD+nA2yVJ4+Kayhwq7/kZeF2DRxNCABiAJTPu/nwCyhIYKr7Lu97PBTnmtYcXLLGWolqB80cTS8LD9k3jq+51JijVeR7TVkXJKdFYKx7J4LtCQLOV52aIGRl9VFsiVnhK+zAKmjJwkh9UiApdaSTSILZP9pVW2RCyVLt4qNIv4WE7+XekrVz10hcL/9wmrpFvXZQk/cpAGAhjtrwPZN7eKvs/jSa2oLtyQqFIC2XjFIPP6iGHjMnQJmTimKa3NCWDMhrmVaiuKF8y9dFjS8y1PFz+kWMSCZHfds493OsTyVAmBVllWaKSwUy24uzsqlPGV6yqpod/GvOWygVDoResfPSTViktn7U0FAXcrZoDREMh2hxU8gA4r3bEx3S68gFTsOlAGMuYAmQcJtsSygf4N8kqHUBJklWcxMaM4BXbUCW6ts49ovzTJMumzHdof1sSSz43UGxbNRqVv2XLPrHNmt1ETk5oBZx3UwPxXdssuLX8wmUwQ+6xTFh6BtwpgsWr1nZ4Kvu0AxWiLg+bqQhpUfYrkYGDtJUdRQIWsHNA/YduJYpUgWVlfY5FV+Pjz8hl+mQl384oz6XgcFEQfSu0y2XsWiXGW5qu/tsOehHXNuzFBcSF287paFPACFcYlfxujIFqMOrJT6NjqumQsIimSEqOLtCHYtU59VS4woMdMCHopJulv6WSapBoNUGig5ZpOGtjm2TTj1cmQUjrdlleOSacV7Kopr+XkxOycV1yaEcGVSXGtEmtod8eE63RZ2zT7/t3QK8U/lnlHNbhkHqygq6qAaaF/ZksxgpzPsnlU5hs4OsdgocnPMWYYkadUUE+9z1DRWCuhYGiySaddtYAAVB2PiqY5LXVTBN2ZyH18XIckyp3Xuvl8Bay4WbmCtPcVENKVCVSqWL54+qZdwwSs2hR1z577m8+oJgOz1TRGY5dc7ERBgSabi4uVXdmmZjrulxJqmAm9cvojXpz1BEyf4vm+sXgmnjoi//tPD70Xd4x4WBiGfC9dXTPXl/cRev+t7crjyeEyPVpYjS64gFa1VLcns9vjEMi3k1awUTREW1031eZ/PiT651xo3yJJM1b21lknnGgSKZIQoU+cPFNUHXuALPcF9TDeJWArzM+VBkm0lpISGJZl33SQSEZhA9ZyFBTH326alU4g6c1Ph69uU3EInyvHUbHEtN1PuiDv/8nmUgvPnPb/Zs4Oy7Qq5yuX5CNuUtrVnIguOsiTt8HYY5qeKgY41TfFTTW5xreSy2uF2XZ08AUyfKC+LFMAZCI7p5BOTLOh+Nx2PzT72Jkilg+8PFUsyb/DqOIJGoLDjJeiYx7TqSJSk2xLx/eIbowhq59R1rZjexxjlOQeKKpYQru8JCn9BbZDdkyrvseMvlONwrnuliN3Z1KZ2+GT3umWpx1Q0QX4eSPlYU8iuwVpYks1NykMHFApiwmB0n0g6pPwcVLFK89tUYhEUdrKjxCt0bntyT6Vo6rW4UnWPU2XkZaBzMHgd733jbUP7gHB7G90rnglh146uxfbBR/3rDsMr0Fqpcn9Kur6k/OMvVC6bl/RJ/USsqDH1XD8ruCGHVhFzgsGJagzgsQPib+mZQOdSx/aSa0S7fXU+pq0DKJIRokpSqnuccl3bGmifs7zBLcCxnfHLdNLWB6QdL8ewgbBf27x4X4BesaBeLcmU9z1G+3W3dbmFhnT+aoUz2YPdadOhJLIVA8fPTpSt6eZsCzuH+6rrf0cShvx8RGs7S1hUtPUJ11FXptNOt8DmdHlt7fYXlX1jeBkQyXQGRqY0iDDLOF3LLl8rC0WSyH4YxwW0XtwGdfCLF5N0G2ohKOmivduSDVIZMXjSPYaqbm+FPICI76KZUXd9dvzKpjbFer3LqiAuOwei+SwAP5FM1j5Dz4sCAKi6OELuQp+bFwIZoJ8wqKI+xbZMDuttC4hkQjNjweXbcbYqf5RbFepMkCb1LA67b1p7gPb+8j0Rdu3ks9B6YOhMknrxJmOy+1tJvA9lIpmuKGv5WMvW+h3gFTZ12zN5LFwk032JWCmNbRanoEaRjBBl6vwhodw8RUuyjgGgZ5WeO4MKTtEmldEQcQJ2cH7aPeD0DsRMiWTt/f4ZHKOg2i6d2AHuDetXIDRGhONixyxDWsw0u7I/qVTpsLZzuqp6RTXbCi+fFfG3Zk8VXQoKYtDoHDiqkmmRu4O29QE9q8W9kGkW91YqIwYVI7uK39PC+i2VFt8zLQCs8rqlv+Lv9n2kHOzW4MDVhLulk/kpddfuuARa79WT5ZiXIHcNE6i8pCLM7uvEizS9b8bcLcMGnQX3Z9n1b4tkusdQ2eopwIo1dFOnxdNE5fIgK1m/e93UxGW6WS4uOdsTdIykMckMighBZak8B51xjVJNABQzKmu7W4aVF7JtkEBmc+TXemWrWu7GcfUMJaTc0nVsOdoSQJxrK24cvs5BkYwgCdHJV/hRcbcsyD+XliUktCsH7jdsQR5oSaZoAUp3y1AokhGiSl0+UCK0SefFEGWfOweD3WNcyQY8wcaDAtkHteXUYdHJLQ2CE3K37Bg0K5Ipnz8rukZbl9etQaJ0dPpPE+KRbuBgG6e1HUKs7ToHxd+Rp8X33LzoqA6sF8LZvgfDxTbbgg0FMduanfXPOmUSKw2kM4CVKf6fFveYLaJ5/1p7xP5ZXuHNI87JRLl0U7m+VAboGhLCVj4nvjd3lC3q0hGCyB59RsToqzkesaOe3C2TFvAKeYQ+yLTbUAfHz8Rx87Oi8Ht+y+pMZ4CsofZI64whVjm3dQUVt9saEC+tGhaOYQQKVZLfjLpbBpWlKSBo3S8aIlk+zNXeR9jV6p8kOfkQw6pX19Wvoh7NxD5RrEXj0n+6cAu1hcwkRDK/pBNagfuTtCTTOOZe44LQa0ATqat5THfLwc3AsWd9Vl3g4wgfKJIREkQ9pZKWoeJumWkVg8OKtO1h5QWUGUT7khCRzGlJlnZ/b+4IyPYY0papE+WskRWWZIZEslpZZUVxKQRQyhzZaPStBU7uDVjBsGtxkizZ5J4hTzcJMXjpGcWgv6ouj0VXxsljDjHtFDA7Kf7PzYl1RveLz/ms+Mu0iGDE+Xkx0LOXu/5yPjOTOSCbAzALhCTcqip2LDjvX3OnyGzb4nBRbXb8Pj1a65bXOQEz0UaKT8hlVYUTu4SlpfkGJFCmXbRDJKuwlPBxt5SRz5mZKDI1MHdaszjdpHyNMvyekaae4Yoin1+wcumz05SIEOImHiXzX6zYSpK2jO4LeV8HbJu4h0aYRU+R3HzluspVhIlkilZEsntdhkkBVpV0E9DUKvodiaFolRW2ThwLSBPPuMnjlX2NinitSViSaZbh7QN3LAF6VkoSHi1e6lwBIKTGZJqFuu4b76eO8Gtf7xqRhbMkkim6WwaVGQuvJZnnu+9mOrPVnnWdcU/sz75iXACmj0eSgftL2zagSNbUAXSvEJ/HD1X+LnON0SLG8dQh0yoXKkuJJjQGUlZKJARIpUSQXy9tfcI67cAj7uWqLtOFghAxclmgkBX/e0W0CmHNI7x1LgNOHRL3lv07LDEodm6by4bUNS865XNT4lzn5oH5SdFBz2ejx4JLNwshrbU7XFCzRbfW7srgz+EHM/g4u455QtYLUTj0hDgezjYFJovQROl6T0h0Gj/kdvMzRS3ctKZOyJ+LfiLZ3l8Cy7fquaVKm2NIrMo7FPeShZZGzFHA7HFXtSTzi9FqJIh2QL2BaIpkOsdNFr5C1h4lgUwi9un2p068rLe+Ct0rxL00dSLG5aRhSVYoVF4vXnfLUEuyXPg6prEnmpMcB/kF11cSgiXXqt86zrKV2uC3vaSM7JzcGsu0JZmp7JbedbtWyEWyeh//JgRFMkLC6FiScAUBD5+2vmjbOmcDvA+3oIezCUuysIepUzDwulsG1qcRz8bbhuaO8ueB9cXU8bqzJQkLhoGrLbKYZJYlzhMgHwyaKL8aL31fV6m8fNAQRpA1iJUqxpvxMDepVrZlCbfKONazS88ETu42k2p9ySYhuLkyABaE4OaypDtVjv9mZ2OdHXe7r9qJFnJzEcW1lgAxrSi2zYwCbf3imT1VzGQqjYFWB+6BfhTywurQiZVSswBTEdOC3Aed64Rh3zv5rDjXM+MO68kcKsTdQs7zm+P/gs9yeztAWGNm2oQlRab419RW/h8QrsGZVrPP28NPiOto6Sa4rhvZM7EUZ9GHE7vit6eQl7v56D5LnUKLK+B2QL3S5abuJZUJuJy/e77UwsyUsBxy72ln/ovrbhnDGjTucZJlRNTGU19bn7if5qf02uIqUkMkO/pMeJgHlfKq7W6ZKj7XkuxPyvZpelQtZqzrGaEoEqm2QYex/T7lxjxn+ZwI9dLaK85F3ieLrRaLU/jSgSIZIfXM4GaNlZ0WWWn5cl2SEBKcZTa1eUSzgPqULMmc8U0cOAerUUWA9v4azqYEiDpBcdyA2opkncs0gr5XmxqKZECx06QpkoVZWqYl17bdmepbK2Y57QxaSeDcn0yrELRauqK5aMiyW1qWuN6b2vQyr5YSLZzy/NmCmldkcwhwhbwIfD01q58ZrpRkoUtY9LX1ibbnc8JKLfR5EtDpbeoQ1nWh/WLdQbQHKy0GxmHFWFZRdAoRpB75P8Kq2ftbKi3E1ULeY3noI2jVK+kWj5hmf5aJbEXxzbWeQ4Czl/efplBxyLVk5JgVzGS8lrZFUyTLzVb33RZk0WXvj1Morpb1pco7xKQVS+Sg8REmhUwQtq/2xFM+SeHJUa5UICte+yV3SwPn3DTVuNf89mtCkim1YltH3995Hlt7/BNCmE5MEURFuZrX2syY+OtZJd4H0vtQs0yXF49VuYxQJCOkrgmLIyJ7yAEhwpPOgzRpkaxD3d1SiQCT66FtQkxq6RIxA3TpX+921+kaii84KLtb+sQka+0W1kNByQ5qKZJFfeEm/qKukiWZt/PrJEgk8zvfgSKZz292Z0rbZTAKDteIwTOFFVtTO3D4Sf2iTCXbAOBKtGDHLVShUBAWBk5hTWa9Zj8Xpk+W/wp5d5KFk7vN7Q+RYyeBsBwZWr3/WyrLHUkmULRenJ8R/2dnxPM26/xezFgLCPEmNwvAZ2AWlVDxrU1M5ORz5e/O9ZrbxbJ0k3gW2FbcVkrsu/2uqPhzPLsKebUMhGHIBni6lmQmLONKdSusE2RBJfvN1GBbx0rJdx2N8sLqjiO2So9TlS2ivPXZE8r5bIw+QcxzpNtHL9TA3bLkzphkf9Jnn1QsCF0hOBzl9KwSzyxfN2PPskkFQc7J3IRIytS3Vowt/DAlVI8fEiJZYLZOxbKtlGRdimROKJIR0sj4vdRlL7LuFcXg9pJ4Rn7lRe00rDhXDCSGn5f/3tIlBhftA8JNSYU4lmSAEJRau9Xq8tLWW8zu5yjXyCBeIyaZbP87l4UEI6+1SFanrp5xYrxp1ROw//mcf+d5xbnA+GGJCBtkaeknkhUHNalMMThxgrhiWRVjp3ldL20LszCq5RIb1obmDvEniwPnZN0ry+19+b6iuGZbp50Sx6apTcSHG92j7gbrm5ClxXFsw1zcY9STSpdFjUBL36Kw6ydUdS4H2vuEAHVyr4aI5fPZu14t44YWCkIYy866BbSSsDbtFtlKv037CHCz5W2SFt9CcYhnJUvVlHD5sZenmkQ7/UQ2Ky1Z5hDoUilxDgvFMlIpxzYy8U4m6unEIvKQbvJ/NtrbtnS6Xb+Lh6b0+3P/KZ7X9vemtvDBfdhYNt1UtDZ0iKP2d1v8TDcJNyz7N1m/pMIVTXWAblvFObaPKpIVCpXiaHY2OavQ5nYR0zJoX/M58RyePC4srrOz4nk9P12+P+37ed7z2f4tNycmX9LN5YmYpjbxf3MbkGkHTrwk3h/NHULcda7jmryyLclC9q0WlmR2O6vtbqmLN+5i4DtBUt+IZtw7+5kwfVL0AfwwdZ2X4tlK2p6bA0Z2y/sq9vpZh5g4NwUcfNS9Xq37XHUGRTJCao3X394e/Ejj2QThZ5FVXD6wvhznSaUM6XdF7Hg9fiLZ0Dbx0LY72qXqEopJZgzTIplqtX6ijoKQUK9CVSCOfVp9kZipMxKPpMpUBOR1UMj5dwozbWJm0iuSqViSLdkkZkNnxlCK2wRUZpJNBNkAzLPvy7aIDuVIiGWVla7c1iR964CTe/x/710tBlGqMfGyM2Kw2jUkjnNzp/hzdlj7TxO/mbCCaesLj21jgqZ2tXvPJdpJGNxcju+5+34zbTOBicQEllUWMVTi56hSKIiBT8cSMdCWCnDFQbudJGPyeFmYk4p200IQUnb3LhSfVXYiDlLXpJocVoa2oNYmRI5Mq5iYtIVJ5zpeES7TKiwTO5cDM6fKLtFR7xWZFZmOG74tRKsKWJlWMdGUmxX7PjcprH6mR4Q4MD9dFJ1rjJUq9pd7ylaeLZ3CcrTZIaY5/29fIlzWxw95BDfHmMGkhZ5tiQokK6KYzAgrWyZ1rzR4nI6/4B/OJTsjzqkxfNo9dsA/jnYuCxzYUf6u6/a+CKFIRkitae0WolGmVXxfdrZIqd0bI129svDk3c5rSZbQoNrPSiToJRLXkky3LOl2KfnnqOi4W/oGgQ+KJ9SglmTOfc0Ug6YbFckk15/JLH6lMgP2P9Dd0gIKkvMdVJ7dOetaJv72PSQG2CU3iWqIZKi8B6XXrcJ1n2Rb2/tFRzJIJGtfIq47QE0os7OKBonnuXlxPRuhSu42yo/KgBU7lorBuVIxCdyHjYplieulrTfcTTjTIo5x0LWaylQOjOznkJ3d1v6et59PhbLVa98akWHQu37H0qIVVVg5efdvzrpTGSFueJerlhV8IIOPcbo5PGNyyTIJ5WdapgXIzgPICzHJGX+zqbVoCSir0+NOL3XHL5Stm0oC52zZ+tBebn+2y8vPA7Pz0eJAhpHKBFi1OeLreYW3phbRp504JtpXIXRNh4tf2ZnkLKisVFlo8iblKH1uL++rc710i9h+6gSQmwFmJ0VfZX66/L9lifZPHCsumypbidouzCbcmFNNDiu2jrK45RXZgv633bKdfc7Ope5jlRgmnvuW5zpx3lsJv1cmjpWzsnvJzrrHN0m+46TeJUWROSxDMS3JXFAkI6QecLoBNreLeD66GIntZcDdUndQ3NItXuitPWIwoNo2GbbLhGkrL2l6aRMvE9UyAtwDteNdVJHIdSfcZqllngXznaiAjkfex5LMFpBl26iIZM5yvL8nfS243C1DBqZhpNLVsQj1XSVi3V6XLC/GAnpXSyRTfJf4Ha+uIWDJBp0KUTUBsFFQCpYeY+KndI5DrNfTzfJBYO/q4Hg8KjhFqGrT2l2Z1dVL+0BZ+LBp6xPfs7PAivNENlL7vmzr9Q+FEPRbFGxBzevSawtouTlxbLMzQsCaGRWiTIUA59nOK07ls8Iaay7kGZc0dhy+prbiZ4+o1bVcZBqeHhXX1fJzRB+zpRM4VRTq7G02XgHs34FYzxzbFdfPbXdgA9A95LagtROUzE2JeyqVAYafE5ZA+XlxPTqFtpK4VhQN5ybKy20L3pJAGnItq1Jyp8444jSmRdvtSTfbQt22TiwtT8nX8dsmlRYiUiHv2L7YZ3GtF1Jupg0lN2grJSzuxvaL59P8jDhPzR2OyRjD7xpvfGB7YiI7C7S4ggPGqyfo/S+b/Cv4WNJVQJHMCUUyQmpJHGsxL954QKXPcR56nm1be8TLZmY0fDCoSjoDrDxffHbG6RncIl56R36tXla2+IJq7jDTtgqcQqTiQL+lO75blJWSuytYlnx5eQXx37KzxIvaz/01MaIO3Ay5/erU4Y1lYaSOKJZkAS6aQddchZm/RCTTPY4yy5NAFNwtfZd5V5FktzRFSsWVM+LMauD9CDSeAKQj5MsW657DRjs+VUBlcKMUwy/msc36WVsZuE9rGhZAxUo9B2SaKy2a7XZ77/ugc9bUblYks63h0s0AJHFXndaZ3SvKWXxVsGOJzU9JLNhmPC69AUIbLCHMpZvKwpadYKJJInRlnCKYw3or0xJ+ray6ULT74KPiHbb24vJvR55298cyLaIPaiJep1+77Jhk3nXtGGYDG4COAdG/7l0TbtnodW3P50Tfd84hpOWz4nh7rdqc/89NSbZzHIeSBWcWgI9VZCOSbhYCd+8acfxbuoR1eVu/WN7WL763dOk/l7x9/UwLMOfI1GwTZ5IrCXHPhpZkLiiSEVIr1l4qZkWM4WfFEcfdUjLQ7j8NOP6iOZHMXWH5Y1Orvg+/7bah69K0ZKPYJ/+Ci83TPK69a8TL0U8k03G39LMmUHFBaO8X/1dbJDNl0WiciMKNdjUBgpefSGYvk1qSaYhk3uD4USyzdEUyV8fPry6VwTxQyjqWBCpWdaWU6JrXcFBcLsCgBViDWJJpi4wx9ks1flqjofKMV0lGEvvaC9i+a7mw2ohMnQ/OCnlUtrFQvr4nht3HN+icVVsQdLVL1aKkiGUBmSYg3Q2pAKdK/+n6QdGjYqUA2Mffu68y6+00gBgimd0XDpsUC6P0rAw5P96yUulyDEwb1QQ5XnLzYjvbxbqQE9bAnYNiWW4eOPhIUTxzrFNyh85V/laxXLKObZ2WnSmvb6XKrsRB2zvLgVUUpLJiWaZFnJ/crBCmZ8eFADlx1O0eLSOVKYpmDuHM+7l9QEyE+3mv2Aln8tlo50OG7j1c3tD/J4pjUiiSEVJNnObYRgUyuB+azge21sMvxIJHqSxTD1vFwbSXpla1sl1fVQfkmpZkuu3wXc3PYswKdsVRaWPnsvDOQlQix36rwgs7ah06A/Go2S2B8Pat2i7ce1RdjJWsp2Tb6CDpvKlaXHqv4ySzFfoF13U3wPO/IkEWAGMHxIDDBFVzt9R4RkmXV1EQiHK5NEL8M6VYTFWwJAuKobhkY9FFLIJIWesBmmq8U+96tksXUJlkJSgmmCujYQNg4h4JtbA1iDfmWximwnMEXUdeN7yg7cKOt8mMoCvOBY4+U+5HpJsqk4b1rHJ7vUyPmKvfSVOb+zjJMsqG0dwh3sH2/gxtAw4/WXTDHQKOPSsmradPimzLh58S9+7USbFf0yMiAc/suDjOk8PiLwgrBbT2FkWzfreI1rMaaOsRWU5d78I4lmQR7iXVe7jWz+I6gyIZIdVk2dliNq1vXQKFq1hxhBBmSRa3fB2U21KxYfy6vESOSWboOFnpcmKHCjRetkvPFHEvnAysjy6SrbygMoW0E1MDZJVz3zUEoCA6N2GxbWRWFyp1ZFo1O9QBZTozT0Yh3ew+virCTxRLMh2cbgBBVnQVxz4FwNPxSxlyt5QFgk83hZddsiTTbEPQYAioHEzXO8r3cB2IZEm9l2qdTEA24F++VVhu2QO4aliShQV9jjzxV+uBmUr9Phnzolzf7QPCqmR6tAaWjwm6agWhFFfPFAH3gjQOqKl+SkA5hx7XKCjMkszg+Wvp0rekk01qmaBigi3iefHNYlkQlmVdy8XfqguFq6tMiMvNi7AyU7Zw5hHRpovf7Szi9m8jQZmrLRH/sK2/KDyuFUmWWrqFxZxTZPMKla59iZLAQsP6rNbvuzqCIhkh1aSlExg6p8qVxrEk8/k9qZhfJmJRRUo2oOpSFCXWW8B6qmWk0qJj3X+6CNg6caz828AGEWdD9uL07lfn0kqRLM4AxU4L7kvUsiNs1zkoOiAHH0umfED/2rKPvzR7WS48o1oQXhEpSNAKFKyC6tDtIigK9XHu87Y+vRh/snh+SVqSVY1qdWIbyZIspK0D64ETQYMY34JR01hpsmd7Ki0GuCUrhypcp0GWZHHaUNN4ZNCwJCtULotihZTKiGtx7AAwslt/+zgUCrW5lJPKTCkjlS5PQCnF80tAJOteoZYV2bV98TqsN4GiYsI6jYpJLSN4RTLFe8vpDeGNzeh0YfUe16DjnG4SWXs7lvqvAwixcGZU9EmmnCJaUUibGReZT6dOiH6Incn05G4A9/uX29JVdu203TptES03D0weF5ZwvpPnHrSuqRq/7+oIimSELBT8MsvFMZ/1zVi2XDzwE+3gRXS3VOnwRA4mrWlJFlqPhkgGAD0rRcfLFsksSwR7XXsJcPwFt3gGqHUykjSvrqa7pa7lT2ShRjOeix/5XDl+lXaAfElbAoWfiFZRUSzJwtwtVSxe/LYFhECvlQhDUoalEJ8takyyalF32S391qumyBhSV5NE1G/pCnaNA5BIUg8dAhN82F8TdE8utSPkINSze31wAxTW8bMkiyKS2dvUer+rSLUsyfrWhlxPMksyUxb/Kfnn0O289Zt42Bjqp4gV3F9T6YQ0Ms9zLuq7d3AzcOw5MansrkBSZ8xjbU9gtw8AA5Lf23pFH31ktxDMbPHMskQilImjwt11+kTZWi2fFe+k2VPA6L7g+t9wu/AQCSXMgtQzZqRGBoAiGSELCL+nWpwOgI9LmmUJc2GZSGaswxFRJItkfRYzOHVVkQ36fdqlMsudqEhmyv3GcBt1hBrVdnQOCis/WOUsraX9l21XKFuS2RmQdPETxk2hayXh7OQGiZZJCPe+60uuwarGVpTUbUTgqrOYZPXgbhnWVpkLS3OHmJ0/uTeo4FjNik2oBRdiPNNM0OCWZCoUILdE0W17NcTMwGdMrdwtDcbRCsIOXh8rFm9EvJ4GoQmhfNphJAacruVQ0M8+4wDTyBISqOANg9LaA6x5hfhqu1LKJvCGn0Xyz0wLSDWJa6OtV/zZtPUKt/ndDouyQkH0I6dOlF06bfHMtlCbmxSu9rmii6arOp973+T1sIigSEbIQqG5yxGLKaol2QKYPkjC+qz0gvGU3d4vXlz+BQe3J3Znw3meJR0KUwFpI1NNywJd8cR0R8ACVpwvBgP7fhVeR8ErkhWFtaVnBG8TqWkR9zWx6yegPYNbRJBd3/UMnOdMa3g5UWOShbZH4v7ppHOw0iK0lsTObllHMcmsFLDsLDFz78SZFU66XcKDhtZu4TLkN6CWutJLJhJqNYkT+16ptQipYBYjjR0WQSSLnFRJke4VwmrLL85orVz5qhm4312x52u1YpJZwuNCSSRL4l2T4HlOKvO0LHmPEqp9bM8xmZuKEFIiAn6JOqZHK5dZlrBubunyj1297CwRYmV+ytFXKpLKlJMWuNCMSUYAAA0wfUMICWTNb4gA6r5ZHTUeeKGZ6aow6+JbV0yLhqB1orroDW4BlmxS2zaMlk5hnReV3jXixdqxpLwsqc6MKsYsyZIgwjUQtErJ8tEpXAbEJHNakqVbyoud8f66loe3Sa1xAe0IIBUQPFaGazAfINQHtaO9vxzrLswKTSlZgecabO0W5ZtyhTaNaqe9aoNd1Wejz71ezQ63yjnNtFQuCy84YoMUWbIp+F73Pdeee0zled/ao9U0Pao5KWIQ53OrfUAITUrbIYIlmfMcJbDfKhb4tRDKZIJAEkSJj5eEu2XQsqTqd6ITAy60es8KVZt8NSwaSi2sEo6VZ1nBQfjzOf3zX8iLbZo7Kq8vv3fA5HFgNMBautbP4DqFIhkhjU66SQz6TMQk885KRMJUh6OKHW5ZR6ZvbUjZBfE9E5DOXdfkv/+08E6V37nNNIt03s7BVjVmyWR0LBEvcFMDMiUBS7NTHPX6UhmwBi4rkp0ruut4OlHO89+/PkoLKxnYEN4eGUGdOxlK7p8yyy6vcOGDN8aI0gDVU19YIN7SZlVyKYmKdLY4ARrJ3VLFkixSsXUk4pTwCs8ptedtIucjpiVM1O2M9FfgPr6pjIYIUAhv+/KzgRXnOcqPkvxHg3QLgu+DBeAtEIhn3yve17L9T0Aki2RdqbiuM7ai3z1vUgitmOSqkkgWZfI6aGJf9hytRkIJP0syQGS51T2eQfH9go7ZzLhiBRTMbBaMSHbXXXdhy5Yt2L59e62bQkhtsH3do3aC11wc3jn0jX1lSowx8WKP8oAP20Yx4OXgZtEpVq5W8nJfca769mFU093SdhnqXiGOw8rzazQoVbUMSeI68a4eMKuds4P2N6HCTcMmlRLH1aZrSPzfbkeIVYxJ1rk0fB0Zzvu6Y4k7noYM3w5nQL09q9wimTe+iM3QOSJxhcxSLwjfTnOYoJKUSGYo+Hm1YvyokoS7ZXu/ZhtUJhiqcN+bLl8lJhksMVkW9i5O4p0QxXrHXUC0zVq65JNZukSN7agSkyzV5B4ku85PAtdV2CRAwRGTrNbibxIkEW4jUjkJiGOy1b0xqUokGIOqWhMfJuupZcbQoMnGuckIsV8Tdl1eiM+FiCwYkeyGG27Azp07sWPHjlo3hZDa0NojBJbVF8H90lN84KUjCF1LzxBuggOGLF5cJDgrXTEzFuVRKLOIaQUybZ51NN2VnO52UXB1+GMMiLRflBawdJP7WqhqjJqY1gy+ljCas5ku4TPgurIzW2aa1bfJtIhMpsu2qLUJ8HTAYnSGU03+MTJKODuiAbO7zu9B1i+y46J6rFTqrktq2L5BjeuqAsPt7l0N9KzWbEIESzLTVqpRRKhQ9zjJoKjCtVvx+VOPQfLjPLPj7E/fWtF/cbm66gipBbWJRb9zYvpZNHRO5ftEht1HqMW1UDWBxecYSGOSGTgPze1uC69aun0CeqJQvYYeMBGb1llG0DFpao/f9/ZrT9AzYvj5ctgNVQKt30z0f+u9j1Q96vBtSQiJTEuXmLWI6m5ZQYDpMiCCTK84L8ASJE7VknIGNii4ZkWZSfTZpmel+L/vtGjlaHU+DB03pyl2SsWywodeAzP0VXWZ1eyMJl2H2MD/J1sk85riB9YR0OFScW/Ubb+rg5mDdH86l5U7l8quC1HaJBl0KAkc3nsgrpiqSMnFVZNaiXjt/XL3NWWR2C8mWcRuZvfKBAbUPvd9WOB+rYFuAt1qpftKdn9USySLeU/FsjiOIXL2rhH9F8sTTF91P5SyW4b0oUyhHN7AZyKjWiQukkn2yU8csa20TbSppQdSSzItEV73XVglavVOMpp1PiSr65IN0a1sA8NSKFht6lq5Bbpb6hUlL4MimQ1FMkIWPNV44BmqoxDSgeseEpZytktBS5ekKVHED59HYf/pwg01yO3H6/KiK9aEtaG53bMggtWBfwM01q0mJoXdaq0nWT8wcH+RVJPH+s/wazmyC4gHv45ZU2vZEsM5mPdzm6wYhIaIgt7ytC3JvIFtExyk2qTSDpdYXSK2L67bmdKAPwDT7papTARRN2K4gExzdFFTtw3yjYJ/lg6iAiyUTCzToVbulq66g9bRnCxSPh4K90yFxZ+h57GXzmWeOoOI6W4Zx2U36RAQoddicd87l8W0nPWQSnnuR51jG2PyKqwepfdCjPrj0t4fHLMrzsSeWFD+GChGWdGfg3ESbkUh8Uyx9TouqD4UyQhZ6MR5oWlZuSSMs+4lm0TsL28gb0DxRacxu+t1Q7VN6mXxmWQv6CjBR50s26q3jT04d8ay0q2z1sRpl9K2kvOiup1OvcoDOMXZ/bjWb3Es4Qp+WZgcg0Dl2D6KbTLiOuZzr5u+9p2uqFYqevm622VaxWCvdw2wfGs813ff8xuDKAOPKALZ8q3h2wT97psdWpMw692kkInIQeuFLdOrPF45UdxkS/XGfG5L26JjSRYm+njFzDjPYwmtPaIvpJyR08CETCwxPek4qYqWZJ2DZQsgE+fBSvsIoBEnTYNXVC/TjGmRgTJ8qIjJ6q3X0P1dCLEkA2Jc1yoCm8FjmE8i2YDzuZBA8Q0KRTJCFjwxnnhNrSI2TLXqUw1EnkqJgKWmZiV1OkkrzxfWZSUTa69VTMQOjN9LNih7pox0k4hbtfwc9fqXnSXq71srLGKWnqlXZ9KYnpXVGQjFmslU6Ch7yw8aZMeO0RFhe1sM7hoK314pwLj3u+7AQOGeUa7bECvPl1ibVsmqpnsI6CgK42296oPlCpKyJItwHErBzXUGmKpt9ysz6B5Vb0akYxhZ/A4RNaWCmOydaeqeiCqSpfytMZo7gHWXAqskSblUXSN13x/K1qqF8AzAQZYtJo57uln0hVSfqdMnHZlxo56vKiYE0kV2rucmyp+lRpkGhsJWCtJ7z/j1WbFxjG0V66+XyXGldQLe90Fu65aK0BW0bRWPURIZOWuZ2KCOoUhGyEIkarYmGU4zfj+SeEFEtexJ+mVlWf5JDsIG5F7XyShigco2qbRe56K9XwhrvWvEgL8zJEOWjNBYcRpEOYdagWollj4q21sW/GcjZdei4is2agdFuc2Szyqk0sDgWeKa6FjiX36pXD83nrDvPjj3zxbEnWWriOR+MclMEifgb2tPOWspANf1pWQNaqgbF9fd0u+4RrIkk5zr0Op1Jyic2yHafdLaU2ltrRQXqqIR4r+hc4RFoso7F1Bos+IzKWh/VfZHRxCQFyCeL6suDIi5GGRtolC+6u8V7pEB2xYKCpm9A8rzKzssi7Cr+IjJKHTWU6lTmaQH45Jr8fCTwNSIZzWfflRk93Bvn0thgizSupCHGfEtOon+sMEygwSmqM90GYU8MD0a0pY4k0Qx4v/KWL7Vk1DEQRLuli7hrYaiaJ1BkYyQBY/hF1qSpCVCiz0AbZcM0qO6B4UO4qMieeE7X54rLwjYtBaP44AOu+pgb9lZYoAjc32tKopxVmxrucRikjk3VRg8BgpviD/DF8lqy149JSzb7HvQdz+Ky1UDjKsKEvn58meZ5WZQLBNv28Lq6/ARhqMKYKrPyp5VImiwjFSIlYqoSLlJ4UUlGNRdaxPFbfwGESbKVqFnlXB1s2np0s/I6aS1R1huR7JGU4h/6FxPhbY+MVhb8xthlXv+18Ruc1Obf/t8l6u87zWPp5YlWcgzqEIEUGhvRpJAww/phF0V3m0y2vrCYzFGtf5Xjbnodz1MnSh+kGW3TMk/62Cl3FaFka1DFehbV57QrAp1bkkWKGo7vuez/kUU8jHef1U+PkGB+yNj0LBiAUGRjBASgtNyQ2HQFucBm86IF/+qC8vLVpwnOunSmDEGLMkyLeZeCrKXbOcyMXjqXSPbwPGxzl5Mrd1uC5eWTo/FS5H2fmBwc7jbSSwUjk1K5v4qQXoeDLZD1yLFSiVj6m7X3dobowyVAY1jEFhwLJO1xW97P8JSo6tccyod385BYNDHxbi1J0KA5xj3svNaUBEdjT03CvKykrZKkRfm+V/C0DaP9WqARYJWnbJ6A8p1vhN7ViveM97ide4XZ5tCxBfp+ZTdDz71DW4WZag+241YJgVZlciWRZkUC1zZXWbQ86Og4m4ZJ6B7CK3dQE+M95mfdXBUOgeBZQHPSsuKaGkJ8d5uCrLELy30/O/bmJByfJb7iaJWGmhyTKjY2at1LPozinERU2nHZF9o4WplBhZRq76pJb//+k/zrGagfflcDIFU5TmkO0EZVGZIAgKlsr1F1jjrbZ1CkYyQBYnBWQGXe1OYa4EBmjvEjLKzfr+OaFRLMif962MeI++x9pSVSgMrzi3PhNoiTc/KiJ1ngx3usO2dHdolm9wWL6Y6Tibiyi07qxy7LcqgvnMZfDseyvvpk4HO9b+0AsXyNVlxvphxdgWUj2BJ5l7gt6L4zxZ1QutRvIazISKZimjvfXZEuW47omSqjCguNXc6vlQxTojdSV55vrAe0saxHy2OfVCy9vMWZbn/913PaQES1XpGoa6gQbTz+spn1drRu8ZtgVZRbpy4ZhrHDBBCmB+qz+awfQ4VY1SeBxpChu463vewsiUZFI5RgOCq8yxq66tcNrRNbkmmWm7nsoiWso7nkmsCIaTe/vXh6zhp7RGTcHafyW8iw0nYvvtlirVxWYNJzq1f+am0O55o6brQ2F8dN0qjRBFwTNat2T/KtAWIrVEmHCCJ66dJEkKiX5nK/SzfgqPVuwihSEbIQsTptmjygecrktXRQ1XXPNts5eGr9K0VbpcV7omObQeKYlTiLox18LKUdf51cQVNV2yzs7PsPc5+A8fYIpNfmVHFkIDtmtuF65ZrEKfZfu8A0M+ao7RcMV6bqvuR1JLMUYeKhUuFSFOlZ5XutbLyfCFEOwU5JQtDQ/tji6nNHUCLQiy0IJZvE8lN1vxGxGyPqsKH4nWkXX2AsOFd7lw3P68o2qSCXepUnxuhYo7sfnWs13+6ELBU2jy0TZKcQtImLwPrw2OsBe1voGCqYsFhr6eKp0xZvbaYoeICGGglqNGugfXivdSk4oqpIdDrxD+zccWK1BCqdV1mLUtMgNnHubnD866OMUnqd++km0RswBXn6j3D7XWHzhEToPY1r1SEVa47Y7jv3miCx7Kzyp/TzfL2V8S/jbmPy84qPk8TdLfUPg8B65fuv4j7rTOJucihSEbIQiSdES6Lqy8yUJhzlq0KlmRaxOgklVaP+ULwJklQKa8UwN+no9Y9JAaZPStDCorZdm03lBoRpbMap3yX5UME0c3Gdp8IczesVmYhbZFPdf3ieiX3wIDtvOJGUB324MgvY2MUkSzJQYPdedcSfovtae4AujxiQhJZrLx0rxBJO6IMlp14rXHSmegu2DKrqFXbJe5WESzJfEWLoGvWdAIOT8B3b/mxYpI5l0nWcwrfOvdCa7cYTMqyXUd1p5ZuHyCYyJaZsCQLKlPq7nW6mOiyLZyCJrOCRDIdN79URryXlJKVaD63tXH2eRRcZUs/+/zutHYOQ9X63lfolsUk85zv1p4Aq64AwRwQ2/afrmch76o/xqRWUph8Z3rjSFZYUncIMbh7RbEfJhPJPG6pgdeE4qSF838vQ9uCLaKTcLcMomRJlpCo12jCaoLU24iXEGIKpRlHBbTdLav4gPXrOFcVj0imo3cEvdxVM2jGImHxqVFQjgumeH5XnCcyEGUkHSvLqhRWq4akruYOYG4y+vaumGQ+ok6gNUUAPauE4OQUR1xWDArPo4r7SFK3KZFy+TnA1HGgc3l00caFZvbSKDS1hQzoDIjO+gVIFoVl8VMUTHyrDBBqwixg+k8HJoflMRtlFAriuhw6B9J2Kw1+omyD+OemrR8Y3e9XeLyytVGsr6kdmJ1QLMcr4vtcd85M1e0DwMjLam2JbP1ht8PgMfa7FnrXAKP7/Lcr+IhkkS3JHM+f9gFHkH3pyiF1RBAITQh9um7CKuXr9icTI4Lw5If0Xeu8ntJCHLMnKv2E/iQ8ZGTXZ6ZVTBDouGybwrdMxSRV2uUSL7QkI4SoEyUocaIoDqYq1lF4SXitFvyoeOnH7QTrbBPzZRe6eSO+TFXbrChURTnGLZ3qMTNqLSpHij/lg511ScucP2Td5g7/8lQCHXvjlkW9Z5ZsFPdokOtYU6sQ9rQsbgPak7SFYSotF3YiHaMEBu8V4pF3vSgxyaK0x+/ZXKyzZ6Vwz1I+78Xz2tojBmDK9XnXkw30Q+4tncD9YXWqbK9i7RXnPAatn24S58ZpvVLhqiUpT9cl0q8NMmukqO6WWv2DmPeCX6ZfG6dAaKJdzskOl9Vw2CRowLXod07CYpJFfo7oimc+28YJjxBWdsVPIceqWgROGqpMhAdNcCjsm33MZdeyasZu09ZZQev7JUhSLzzidosPimSEEHX8LDdq9ZI1GbPBKRY0t5vP/CSv3OdzgrjMxnXqTKh9UawmAlc13U7FDnSQoBFkMWilzCQvUELS/nSTcMu2U8oHicPKLk9B58AKPh6hOC3J0uGuOhX7E/H66Fpuxi3RS1xLsjj3Zd+62g+QpCi2qcICRHNA6ldXJMsug0SZ6Cm5DBkUqEO397RFV0Dzbh+G1M0qYFvb9a2lu5itebl8/SCLxKj7lGkVblpB2+q6gRpfV0HYcV6Lg5uF6Ni7Vv571Ha5XIDDygs5flEseMPca/3qd+IrvuqKI1WciJZenxJUjunSM2I0JCCzYjXeT/ZEmvTcx7TmjjXR5fOMj+tuWZfv/PqEIhkhJJh0kwjY2z4gdx/zUmv3sag4B7/dYbHAAojamVV+4cXc55UXyOuXVmVihjUE4wPQgHZ6U4fXBEknsGe1uMekHU2D1kS+Flkt5ZTygXGk/I6tzsBcZX0NwrKzpVIinlWp6hh1V7tzWSvL3Sj3vcljo2x9UYXnE+D/jFKt05ntEwgfOEV5F4RtM7C+HKA6DpEsIgxNxqy8oNLSKDAjoEO8G9omrEHDnqdWytOGKMJrsRypiKPh2heZuPesY3mTQ5hs7qyMtxV03bV0upM9KIlkOiJYFAFTFpMsxjlZsqmYXTvEQjEIv36gyUlgL+0DGtk0FYSrFgWXRKWqIkxQBF4zCm1KB7hbqsRZVapG99hIJh9L7YvrbknpRxUeKUJIOMvOApZtCV+v2sheEtoZ4WK+2O2OhlK8tgCq5eLkcgmqgxkl5wu7qd0taJTWcbRzMOQ69DuOSzYJVzibiuvE57pRdo+JKmZZYpCw7Cygc1BvU91ZSqVrTHc/ZJ25Kl5XOu4UZio0WJZPeYObhfi3ZJPC5jHao+KuqkJzB8weF1vccJw32fM1yALIt+gIokecY9w1JGm7CZHMr40+be0Y9M9OqbV/EYQJLes2D0534JTE6jaVFhaeS88MqUMRr7ilYq0UeWJMYbumdiFoJEKAJZmdndbyEbBKyxzXaqgrY0o+AakVrD7ixJ6qeBI6cej53todnPW15u6WVcJPDM+0BPeN4zwbtNZRKUYmkqmOJxI4V+0eb5YKS7KodTbQdVVjGLifENK4SF8SVY50mm5ydyajDjhqMbsTe3Bkog2O/W7tcc9cy/BaZagSV8QEiscr5vVVS0FJBVW30VoRyX0hgnWBb1GGj4GsPGfg4vAC9OtcvlUkawga3OkwtA04dSReGW19wPRJ8dk+JqlU2fI15bXw8RB5Vl1FWAuJSRbEwHrg6NPuZaGWZFEsgYqf/bKwxXFNU26Hn4CmI2w4lg9tk8RskwgfqbT8HEXZv4rBvulBumabVp6vvx8mrD9L1jUhoqjruHuua1m81kyzsOg7/mJ5sSvTa0QRTHV72b3nst6K6G5pav0wUTKIwc3iuOaz4fUrT/6ptsNnnXRTOVapCsr9I8XlWgJ2gCVZUDnGJmc86zd3uO+VVAbIzTmu4ajvvGibLUZoSUYIMYBupzJBErGuCSHdVBzEaaLTUZFtE7vtMbY3FVRcWxxMcDAcpz7l46E4o10zgvbDZ1CSywav57rMLc+gpAouhUZdBk2frwTOf+8aMYPvck1z0NYrYguZwkS2Mb9nYXO7O1B47PIjDGj89i1sn5va/S0sAutXdTGSiBUD68Vkw+Bmvw312iKt1/T6PuvIkhr4bWdM8FOwJFO6hvyWa77zo0yqxM50qrPcsSyqG7HTeir0Papz/BSFDR13y4rJNgMCk+/1pnlNdywB1l4sJhycqGbd9SXu+zPknAaec8MCVFObmXJMbhuE09q7FO7GdreU3E9hSTfEhsE/e5MdLWIokhFCFhh1kTNbEb+OWh2SVCdAd791ZpqDUI7H4So84DfF665eLMlSaWD52ZXLu4suqTqJK3RFjFRKWIkMnaOZCRKSmGm6g/A6EyWNiktF+taKZAxx4uSUKzC8nt/mmrF4LEsyoAzaLs4AKOqz2eeZYCwmmWSbTIu4ryru3yCBw9BkS1Qx0bhVi2JZvuXHFLJk2BbQTtfJxJ79JsvVKUtmpSURgLzXv2uiJCCIu195Wkja6BcTzVn/ko1CfHKGbIjcBi+OMnzbolOc51k6sF7iuq9jYaV6D6iUqZkNPu7ElXd7r6Wtsz8he+6W3qExBFml333Wd7Y/XWyLzLqta0iEIvFen6EZdiWExXldRNDdkhCysFCx6AntlFdpIB00q6VENa0C6kQkCy/QZ7FneXM7sOK8cqfJ77oxvttekSzO/scQhPtOq5xxBoDOpWJAJ4tX5WdV0dQmAjqPvOyznmQ7l5WIxkHuWArMjAlrGVVMDkZ1ymrtBmbGzdWtTZ0JgoFEETI1BsyWZNDhW5fKOkHLi/i+iyKKZJZVLtMrEoY9R3z33wCBZXoGz229wlKhqQ0Y3ZdgvT5I3e081562EKOwztC5wu2sGjFBdY5L6PGIWb5ru4DrumMJkJ0VyQGi0NIJZEL6U/mcw/XPeY4VhKmu5eJvZkyzYZrHykR/yFuGZYUk5DFAUpO8SU+iOjN8Outq7hB9Q1myJyvlCOgPyYSNQZz3Z8bbV3UKaBmgQxK7sHcNcPQZz8KQtrb1Aic127lAqXPTBUJIotgWIDoDThlGXQDjUgN3y6g4Z2wKGnEbjFHrcwW1TlAhZHbZj6Fzgrdr6VTL2FoqIqBuVXfLmt8fNgHtbWrTb6dLcEtwHy1LzOoHJTkIstaTxWwy5TrsZdlW/zaVf1Avr3NZrOZExpn514/YFnExs7qFDlRCyrQH2LIkD5GzW0bMQuaXUCEwAHY9dOdDLH8A8ZwYPNMzaNfpP/iIqVHiU0mL98S+ixzrTPIM8lrNysoxkmQkYfHNG0OstZjZUDbpoiJ+W5ZwD15xrp7I4fy84jxxXfmtOzcJHPm1vEytYPmqArqkDSrraCUx8CtPwX1Uq8+ueL37lRPn/aqb3TL0d8+6zolqZ10tXcLSXmZVtfqi8ARSFW3QW72EM76ct++ie22pbtfSVcwETGhJRshiZtnZwMRRoHN5rVsSnZ5VogNkB31OG3Ax0hFO4mAP+KdGEsxgFVJ/EutqtSFVnplTCSSu0yFKhZjSK+GpL7agEiDgyEhKwImCPSByWkeVLFRUj28VREIrVSk6r7pQHEtd985Y7fB2ziNYbRirPwaDZwHZGeDgowF1xRzcRxkMRRXTZN9TKZElEVblfkZN+mHfu953Utg97XeNVhwjDTGn9FmyLO5l4rSqkBFmuR3ZMklBgNEu0xuTzMAgXYcV5wHjh4IqCy8j6YmYdBOw9IzysVp+jrimK+Kyet0qU/LlfvhZLtt0DQFjB9X6DV4LMN9zXOP3rcvdMmIZps+/ynPXL7GKUv8lKFmQz/3X3FHu84cKsT7v3sC6QiZcKoRVE0Kjc/Xi+q4JE02B1rdshWda13J3co1FCkUyQhYzmRZhjpsUqbQwbzeVRU2GbQ49NSJeml0qgp/PS3XZWUJwk86IJoRtxh8Fk9Yb4SvHqyuoDau2A/NTahYrUTsjqlYfkeuO2rmugmhkkqFtwMQwMPyce7lr0AH/30wjnSlNAfCIZLFdmwPqU91OeonEvYcTvn4sSwyAwuLOdSwBJvoUgq0H1CP77L9B2TXLL6OjX/l++FnxdA4CE0eAuSmPIKT4TOlbC+Tngcnj7uUquLJ+BogQUpEsYmyzINFLNgkVmr0uROzQcbX1O49hgkpwoY6PnsG+9JpQEA10rc26losJvzAr3oH1wOGnKmMPadet2C6/5U5LXlV3M+nAXxPnNZ3KAKu3Ry9LVqbxxE+6lmQaSQR8y1Oxyk3gvZGIK7fPM6x3jfitvR+Yn9EoL0T88lsvcDPF6z8KLZ1iXJJpDRZ6ta7bButz1pB6sM8mhCwoHA/gVduB5Vv1goBHpb1fdCDjvKza+4He1ebalAQmXsZ968TAsm9d/LLiYqWE5V6gQOYcDGoMokxYGhiflU24fFWMuUBILFRqTVTLHyUiT+/7LE5qQBv2m2EsS7inRJ10iXKvptLC+mtVcaAcaNGkcSyc+7DqQlHPivNE3D0dSpZkTcKdrHeN+KxyjAa3CMGx/3SHuOC1SHPGxVEUycKO8/KtYmDmclV3kEoBay4Wx72pXdRhB4f2E4vCBNDI90CU53tYPDhve5PMvutoS99pakJ+cwew5jdC+il19CyWCbnt/YoTYhJ0nhOqWRN1xIaKazWiqOu/gebqJu8n30pibu84phXHN2I/JJUWkw8tXZrPl4B9iZUVNsHroL1fTFJ5y4jah6tVn7MBoSUZIcQAPg/ddFP0zlC1aOgXRsS2965WEwMLGuKUH6svAo49C8yeEt/TzUBurvx7hYtGXAyLEc1dwOxEeBkdATGy3A0J+a5BrVwx43REq0HXcmB6tLoWoTK0raMWMVFjkjktfUItmqQVVy7qWiYEqkxrgJWHCp77s29t0QJCYf86BsqBmNdcLO714897ineUL3uORnGBbu0WgmAQtjvoyvOFUBcljpaWRZhrBfV1I7njpjzWSrIYdYbuZWc4AJ1jGLrfMd+pfs+tKPvtF0+0a4V4Rgc3RHGZQcJch7XfuTFEL5XjLU28IDn/FRlF41hvGrZOCzqmSbuOun7TsChUPn5RhSzZPeypZ2ZUpSDFZUQGRTJCyOKjngb0jULUzkqmRVgS2iLZ0Dkijsipw8XfFVwvdMS6jiXAyT1F97AYs84rzxfm7S1d5bbK6F0j4sm1KGblqjDjr5FBd1S3OMBH0IjoxhAbmWCXElZNymh0ZI1YvagsVy02qNPv+K1vLTC6H1iyKV59iaFyHMLu4YABru55k1n1yLLGBbbHUAw6XwEl7LpN+N1mWQqWVhbkz2HD72BTzxWVwP1qBYWvkkoBq19Rab0Wl1oL8ukmIDcvJinmp8vLnUJuW59498sCowehJSJFEV9qnPjJSH+gRv3bRK47A+8F1WOgHJvO24dTuX91xVLdhFY6ZXPMowpFMkIISYo4sTd8aXDrlKY2YMkGEdMknzUXK8pZ/upXFDvqDos13WPV3CH+bCsyUUjlelZKXSCTUe1zuPoiEYTddpGKQqglWR0lG9ChqU3EnQok4vmq9b3a1g/0rI7Qjiq128TgMNCSrBZCTML3Qdj7Jcwawd4fY0K9z3NBy+JL5xibEgM81k5e60FvshKT90QiSYKSumcVy11xnoij1zEIHNspXyeVEq7M9fY8ChUeqimiKWzrG5PTQNmmCDqmoRZbphNnBKyb0rAk0yk3CpGz6ioVbqichQ9FMkKIWWo9GNQlifYOnQNkZ+OJJwsdLUsmzQ6LdODRYNdlUmRaKmMb6VIr67ekGdwCjOxOKC5hLQaukhnvekU3CLFsXwIDzvtljNQ4JrrXfdLu0E1tIqhzukn+u2p7o7hLRqUkzEUUuEy6Atq4LJW91oIpkb3x+AvAxDG18lIZYGBD9PbEJfbzOeZzItNSTkZk0rpTdxvVmGSubXTdLU1bEJkQ8zVd12XrZFpEHxaotBat52QAYRN4yuWFnWfN55fufkjDkHjK6F0DjO4rftGxhl+g/bcEoEhGCFmEJDxYbO1Jtvyq4eMuVOtZtij1hnVSUmkgJ+kgV3SwGtRKyiQq8TJcPyV4/k2W3dQGLNuSTH2yY9bUVj2Bop4FMsBMx70gsSQb3AKM7QOWnBG/fNVjuPRM4MSL4v+kCcocrWqNYGrQ5BvQ2m+5ZDuT7sxSIVXy/A505y9alvkJkRV1pkRg/aQtiAI3r6d7PU6CmJDrJol+hTNTrrT+oNheEuJYypl81+jSNSSeLYW8/wRDFKT3UQQxMzKK5enGSTPqLq14/vrWOkQyv7Ik7bKTHsxNAZPD+u1bRFAkI4QscuqpQ6lLLdpeA6HI22HpWBrh5R5yrAa3CGsB3Qx22jOETZXJCxoN2Uy1yxIoaNtGvt+AyPecd7PVF4nrQOd4dCwBJo56ylUVJ+v9uBuYjZe5WzoD4EetV3fdzqXiXNX6WleNk5NkBkfT8bb8RJJYdQSIZLKYi4F1FWp/3pMS2WIH7jeAsdhznnIGt4hna9/a4O2Stg41IXDFdVFcvlVM9GpbcfksX3aWSN40sEHEb83OAlMnFMv0FCqzgg99z0W5ZjQC9ytNHBuwWHbWGXdyzc6wvJsiWRC0uSOEGKbWHcQFTq074Em7SKiwNIJVSFgbWrtFjJQgywwA6PV0opva9duxarvGBvVouZbwTGq10Rn4mLJ0sVL6ZbX3i3g/7UGij2FkbUw3mY+3aOL6iZOMQgXv4CUVYF1UD/eDqsWnMWtGDTcs34GtzmDSkKtboNVIAw6TnG2udYZf4+8vU8Koh44BYUWsajGo3IY47oJRJ2RiHpe23pAyIry31l4isganM+HW2n51tfUJwS0OqsdGy03Y8HlSEjljPJdMuakvAmhJRghZfCyUl0Qjtz0OkeJKGBI3upaJTmRuHpifChfVZKSKwf5zWX2RrR7Q7qCFHPu+dSKTaPdQlMZE2MYQHUtE21Uw1bFu6RQxj5TKadTnnMrAWrI/vWuBdAswN6EeP0oHpyvW6lf4xI1JEF0rlrD71HjgfmklCquYEMZiCPeyAXFLVzHBiUx4DbLerANRzbnfXUMiiD4gnh2uRDQK26ssDyKW5VWSkzERy9G+B2O4W0ZFNyaZkyUb41Qc8JPPb4WCumujb8bqkPdcFOHR26a4152v67BOBmTnftbBc2YRQJGMELLIaaTBY5WRdl6iHK8aHePAWDMRsMuwg99HScxgt2noXHPtqjamO2hNrcCaV5grr7nDXFkVOM5X9wqN7Kx+blxRmlAH4lc9XrepNNCzEji5J5nym1rFIDKVSSgroWFUY5KZsiTzG6C6XLHtZ7JBIUZrO9mgVHKchraJtuoKoUm6rirjPBYFYXk6dQLoXgUMP+detXsFMH4owbYkaEmWtOujDNPZuL2YDtxfQjHul51woV4wnd0yznPCXZCnzAjulpYl30zF3dK7jta94BUN69FboT6gSEYIMUs9Dp4WLDU41rU4v1E7w5mWYtynVHRhJ6n9beT7xHkspefG8CysKn1rgaaOZBNnRHWHiTP4186oJqszav2SQX8ig1OV9hlwn4tLvQ0ig1B95pkSdlJ+Q4oQ6w6te8OxbqhFpWK57QPClcv53PCKe6rtrWamUD+8z+fBzcIqrqmtUiRragPWXgpMHQeGnzffFuMamaF3iUo56eZyhkeb5nbh8pedEdaGUVhxLpBpEwlFmj0TbXGswKTbGRajg7ZN5D1vwhIvaQvIgPKjZFhVOY7Kx7qB+5l1AEUyQsjippHFiqRp6xOdfmdHrpHMvC1LBEi3P0cqw7m/vFYA+FiG1AhnW5o6QoK0e2juAOYmgc5B8+2qwJC7ZWi5hug/DZgeFUkySGPSOQiM7Q+PS2VK2GkfENdLKg2cOlJcaNrdzPG8ccbFi/MeT6UCXLk0qYf3o+tYFBMJBFlAJeo2nGR2y4TfPUvPAE68BPSsci/XCbEg2wdbXJMmCTLxPF9A/RSl+zqB/Q2KSSZdXyPQf9AyQPEZYtASnYZkvlAkI4QQIiedAVb/hqcTHeHlnI7rmmS6o62B01pBt+O0UPEOwuoF3XM9tE3EldOyCIhoAeObNS8CUWaRdevrWVU5OEwSo5MVOtdkHV2/pkk3idhpYZYlpizJLAsYPFMIz7ZI5rXIkm+oXofTsifM5bUWE2AmBEeT8Y9qPYmRZHbLqGV3Dqod46Y2kekxFprn0oj1r4o1Wr0IaQUEPoO9lnZhhIlRptwtTYQ88BXDFCzM6kGMXwRQJCOELHLqpbOgiOk4W2F4Z5mj1NneL2KfRHVPqCXOQU8+V7t21Csy4TCqe2C1SaX1r0lTHeJquA2ZGHDVApXBLy2A1VA5TonG8PO2ISRQdVh7ve5v5Q3V2mJEtAm65+rA3bKuSFAki0qS7vhevBOEvi7JNj77Z8eOU5m4kAkoQfe4seQCCTyTm9uBoXOCJ1qjumsH1usV54K2i2rtFsOSLJZI1qD9ghpAkYwQsviottC0oIhwvCwLGFgfvUrZwKZ3NTC6X7iFVYtCDUSyejd0qbWlQk07eRp1OwdHiWTKqiY1Ouem9jvxYOV1jNT9q1PEWcq0mKrE57NfOzTOa9dyYOKocO30LS9CuaEollUPMclcLDBLMnfhCZZtiGZH9ure1UBnSExDl/jh2L/+04HOZWqCtkxAaWoTSUeOv1hcp9bvkCIq10eoqBn2LNHY1xXniedL71r1bQBEuha13S0jWJLVy3luUCiSEUJIw1KDF2A6A8xXv9oK+taJ9PbGBnYK5LMGCmmAjr0Oi87sP6olmXPwHPe+jSAw1H1n2WD7wgZe/acvXpHMD504SzqouHvpCGat3cKFNN3kX17SBFpv1plIVvNJjDj1J3VOq3itOC2SVPorfu6kliXPpi0TkPzuJ13XRV1qlmlZow8S1saWTv2s5ZH3W1MkM+VuWQ/ZsRsEimSEkPg0dHDzRmtvjVmySWTB6lldxUp9OtrVFMgAuls66T8NmBkHOpaEr7uQOmJR98VlYVLrgWudk/RgblFThXhVSd/vslhkOoGxk8SIJVmt4vMlQJIiXc0FQAXSTcJyNZ9V7K9oxvts7hDWT86ytQO/x7zeemP2BeOeR+e+y8I/WL5fDKFSpszN3K84hfLsc5zKiGsrLDkLiQRFMkJIfDLN4kVppRLOlERqTlObSGG+GKFIVqZnFeDrBVHFwUujzIo6B89xBwWR9rmOjw0g9mPFecDsuGK20Rj7U0+BzRc8nvNkH28/a5fIMf9qbEnWsxKYOFblySMFan5917B+y5Lvf7XfEzqhJqL0n72WTyqWSHHEXGc5AxuA7iH7hwiFGbg+nPuSmwteN6nnS9h9Jv3dp0zfSSKJJdmqC4H5aWFhqwxjkqlCkYwQYoa+dbVugQYLJCZZI7ddh5p39IvUIiYZqTMMu1YobReUCGEBPQNCXV0W0L4uFpSsIgyd16VnCquKEy/ZBVeu07FUxBzSdakKo/90oO+0+nsn19odPs67O/RYhpTd1AbMTUWvv9ZEPXZ+cc2chCYQUKSpzUw5ppCKZAm8L03ENfZul24W7e9cprBt8RynmySu564Vw+slvlAkI4QQQoJo6wWmRxUtXEgl1eqUVaGeerB0iVKWyfrrRbT2o97bV0vqZoCUwMC1c6n4vySSSRhYL+I46cZgq6bYZ4K+dcD0SbUBd6LU0N2yubOxRbLIx87PUtZpiVRnsfNMEWZJlgiyiSwvCpZktoeGzJ0cSMZqvp6eWXXIgvGLuuuuu7BlyxZs37691k0hhDQSjfySaOS2a1HjQe/gFpH9rWdNbdvRKFRVpKjlPVAHYsxCcbes+/YRZfwG5k58B3wJi8mpNNC1LMT6osbYmTszrdHL6F0NDJ1T+/AXSzeL/wc2mC877NnXd5rfhsabkghR36POcy6L0QW4XRS1+5EK97QqpvoKXcWsoTJ353oIyaDi9ptuDolbZ2o/GuT6rwMWjEh2ww03YOfOndixY0etm0IIIcQk3SvF/ypB4pMglRaWB7UYcFQ7OQEJwUAHM7YLVD10cmvchqBBQr25ABnDwICy2oPEivrqQFiud1o6gVXbgZUXVLHShK6LjgFg7aWOuFUG6F4BNLcL19kgMs3A0jPM1Vt1DN8rpmKS+ZVZSwY2AKsv8ukjJhyeQOkYKFiS6RxLY27UdXL+6hS6WxJCFh90x2ksWruBNb9R37P/quhee0s2AiO7zQ4yqk21OtLVqMdEuned67ilU2QR9R3UKLanXgYz1aBrCMjPK2b84rugNhgO3B9aR4PRFMOKLAqmY7Q5MT25pBMIv5GvAROPJr/+RiLulrJjXaXnq2X5TygasSQLEPv9EkR41wtql1ITJIH7dbaJWu8ihiIZIYQ0FIt0ULcQBLIoZFqAwTNr3Yr6peaWMRFoaldfd+mZwNgBIfzUDQk8g0yex1SqwRLJLGRqGMvLlLXMYqCpDVh5PpBaBO/ZhhEJYjxnWzqBuUkRf6+E05IsxvDf6PGrQn82XQfW+DrZLX2JIJKFFtko90JtoEhGCCGEEIMEZWNcQOhaBQ5uBsYP6sXnybQEW06odnLZGSa1wO+6841DZuA67T8dmB0Pd8fTYhHcP80dtW6Bm9beWregcRk6V7yfUj6ZLl0C8gKPb+UKhG8q4L2uSKUQkyzR+h3ks2rrdQ0Bpw6X470tQiiSEUIIIWQBUMcddssS8VKMx9Wr4j53DgITx4CeVcDkierVSxqLIKtflQDWcelZCWCl2TJJ9Vi1HZif0s9AKsMvcH0jEEcIsazK+8p5LJJwt6zXiRinJZmpNjZ3iOdM2hbgQibMTFiSRXG3lDE/o9aGgfUiY3BzV/S6GhyKZISQRcgidVkkZMFRw465jtukzrr/X3t3Hhxlffhx/LObY5OQm5ALSLgsilwGIc3Pu0SOoYqKiJapeIDVYovFKsUOoExbKMxop52IdkbFjo5ap4pntYBEPMJNsIjNCA1gSwKKzcGZkHx/f+xkzZI72c2z+zzv18xOkud59tnvs/vN93mez36f79MRq05G+l7g/XbZkxACIVmA3gPGpwy8iCgpc5T3RK61O7i1J1RPtNF7omICOCZbIC5z62V9h0mnv/WOqXj8QODW2/x/r0fjxAXw/euN9tfvLrHdLHtr7VLqkO6tq711tv+EZr/2MEDt7HJ+l+s6DyEZAAAIHjsN3C95byJhGqWIThxCZV8i1Z+WYpODU5beDBXcbu9NNICOnF/f00dIZ6q8vRFbCPHQwk4SMqXaSimur9Ul6R3h2JMsMcv7qK0M7HojoqTssYHtRdbmZdMdaN4r+VSQv3BpflOKYNWHtsK+6KbXbmV+87KkDu7iC3b2vT5vHLroPlLSwC6+lnMRkgFAOPEkeb8Zi4q1uiToFgf0XLF775yu3p0ymHeOs0pUrPeSqO5ouhtYdJxU1811ILz06et9tCYceo+FQxk7I3WoNyBzSg+R3ri8N5x4Wrl0zpPgHXuqN/Ub7q2LnfmiqaciPVLa96QTR711/+TX0pnqwH5xFRnjvVFCk9gUbyCVnOP9u7VDouYhWdKAjl+jp5db9knz3i3db51dX42TEJIBQDhxu6UBlzr7QA+Al2WXXg7zfjPdnUF9sy+Raiq8vSX+u8s7jfbMwZqf/FEPgsrt7uJYX+H+edj8C5tAiE/3Bjad7SXcqZtxdEJvBGRNEjK8D8l7A52T3wR2fNCmm+s09YqL7tNx77DGhu6/XsDGVovni6p2BOgeogAQRsK9pwsnEggrQayv3b3cI6yFwHZGRkv9vte9yy+j+0hpwzoen6o9tIHhrfk+uPlnmTjA2/OjMz0r0AvC/FgpHC+3bOLuQo/lnnC5vF9YhNrdTYMlIsq7vV3pEd6RSI+UMaKdBVr5P2rqzRnpaTmvVZ3sSdZ0V9/O9BZNHSIlZnu/uEIL9CQD4DyxKd6fnd45Aei8MD+xQmgKSjAWRnU13L/c6YzIaGngBKtLAbto7fLCcBGX6g0wQnob+LKiVeeHWK213RHR3vFNOztGXGcvt0y7wFt3YlOlxvrmK2ilDFHf9YJDC4RkAJwnsos7JwAIN00nVz3psRVUnGBZwhPvHT/HynGpmp/khUWvwHAoI1qITfHeNOLbA9K5s1aXpmtcrhANMNr6X7Dz/0gnty11sHfMs8T+581o4wuO7vZma6/NdEd8d4MUv5AMXUVIBsCZAtnVGugstwN3u0E9CbbzgXkPRURJOQU9u1087CdrrLdng9vCehEdJyVk9e64ROgGG7SvffpKtUfCLySzg6arNpwiaUAQLxV34tAS1mLvBABAsKVfJNX813tHJ6AnuhI62jaE4CSh21yu0Oi9lTbM6hLAMbg5RFC42nhfs0Z7B4SPz+j9MoWiQFwq39Z7jaCx69ETAACho09aYO+mFMpaHBByQIdWWHmgn5jtvRNZQpZ1ZQDQOwgVAqcz72VkjLWXc4ec1kKyHgRnne4dTr3vCfrgAwAQjpoOQmOTLS2GT1Ss92dvXmLByQ+6o+9Q7wDx4XTZPTeaAbqJS9WCz8bvcU+PMwJ+05VulIdjpS6jJxkAAOEo/SLvILFNt/y2WuYo6cQxKSHT6pIA9pMyWGps4P/LCvHpUtUhKSbZ6pKgOwgIgoT3tde4I7zHeqZRioqxujSOQEgGAEA4iojyXjYWKiI9UvLAltM5QUGHqCMdioyWMkZYXQpnioiSBn7f2psdAAhPcaneLxCjYqX6091fT/qFgSsTOkRrDwAAwpQTwxW7bHMPtoPgFb2NgCyMMeh54LTx/jGwfNtSh3ov8c8c1bu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",
       "text/plain": [
        "<Figure size 1500x500 with 1 Axes>"
       ]
@@ -1270,9 +1320,9 @@
    "source": [
     "train_dataloader = dl.DataLoader(dataset, batch_size=10, shuffle=True)\n",
     "\n",
-    "trainer_model = model.create()\n",
+    "trainer_app = app.create()\n",
     "trainer = dl.Trainer(max_epochs=20)\n",
-    "trainer.fit(trainer_model, train_dataloader)\n",
+    "trainer.fit(trainer_app, train_dataloader)\n",
     "\n",
     "trainer.history.plot()"
    ]
diff --git a/tutorials/getting-started/GS141_applications.ipynb b/tutorials/getting-started/GS141_applications.ipynb
index 04fa7fe6..2795d10c 100644
--- a/tutorials/getting-started/GS141_applications.ipynb
+++ b/tutorials/getting-started/GS141_applications.ipynb
@@ -45,6 +45,7 @@
      "text": [
       "Regressor(\n",
       "  (loss): L1Loss()\n",
+      "  (optimizer): Adam[Adam](lr=0.001)\n",
       "  (train_metrics): MetricCollection,\n",
       "    prefix=train\n",
       "  )\n",
@@ -59,12 +60,12 @@
       "      (0): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=10, out_features=32, bias=True)\n",
       "        (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "        (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "        (normalization): Layer[BatchNorm1d]()\n",
       "      )\n",
       "      (1): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=32, out_features=32, bias=True)\n",
       "        (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "        (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "        (normalization): Layer[BatchNorm1d]()\n",
       "      )\n",
       "      (2): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=32, out_features=1, bias=True)\n",
@@ -72,7 +73,6 @@
       "      )\n",
       "    )\n",
       "  )\n",
-      "  (optimizer): Adam[Adam](lr=0.001)\n",
       ")\n"
      ]
     }
@@ -80,9 +80,9 @@
    "source": [
     "net = dl.models.SmallMLP(in_features=10, out_features=1)\n",
     "\n",
-    "model = dl.Regressor(net)\n",
+    "regressor = dl.Regressor(net)\n",
     "\n",
-    "print(model)"
+    "print(regressor)"
    ]
   },
   {
@@ -129,12 +129,12 @@
       "      (0): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=10, out_features=32, bias=True)\n",
       "        (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "        (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "        (normalization): Layer[BatchNorm1d]()\n",
       "      )\n",
       "      (1): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=32, out_features=32, bias=True)\n",
       "        (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "        (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "        (normalization): Layer[BatchNorm1d]()\n",
       "      )\n",
       "      (2): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=32, out_features=1, bias=True)\n",
@@ -150,9 +150,9 @@
    "source": [
     "net = dl.models.SmallMLP(in_features=10, out_features=1)  # 0 or 1 as output.\n",
     "\n",
-    "model = dl.BinaryClassifier(net)\n",
+    "classifier = dl.BinaryClassifier(net)\n",
     "\n",
-    "print(model)"
+    "print(classifier)"
    ]
   },
   {
@@ -190,12 +190,12 @@
       "      (0): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=10, out_features=32, bias=True)\n",
       "        (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "        (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "        (normalization): Layer[BatchNorm1d]()\n",
       "      )\n",
       "      (1): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=32, out_features=32, bias=True)\n",
       "        (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "        (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "        (normalization): Layer[BatchNorm1d]()\n",
       "      )\n",
       "      (2): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=32, out_features=3, bias=True)\n",
@@ -211,11 +211,12 @@
    "source": [
     "net = dl.models.SmallMLP(in_features=10, out_features=3)\n",
     "\n",
-    "model = dl.CategoricalClassifier(net, num_classes=3)  # Number of classes \n",
-    "                                                      # matching the number of \n",
-    "                                                      # output features.\n",
+    "classifier = dl.CategoricalClassifier(net, num_classes=3)  # Number of classes \n",
+    "                                                           # matching the \n",
+    "                                                           # number of output \n",
+    "                                                           # features.\n",
     "\n",
-    "print(model)"
+    "print(classifier)"
    ]
   },
   {
@@ -253,12 +254,12 @@
       "      (0): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=10, out_features=32, bias=True)\n",
       "        (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "        (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "        (normalization): Layer[BatchNorm1d]()\n",
       "      )\n",
       "      (1): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=32, out_features=32, bias=True)\n",
       "        (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "        (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "        (normalization): Layer[BatchNorm1d]()\n",
       "      )\n",
       "      (2): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=32, out_features=3, bias=True)\n",
@@ -274,9 +275,9 @@
    "source": [
     "net = dl.models.SmallMLP(in_features=10, out_features=3)\n",
     "\n",
-    "model = dl.MultiLabelClassifier(net)\n",
+    "classifier = dl.MultiLabelClassifier(net)\n",
     "\n",
-    "print(model)"
+    "print(classifier)"
    ]
   },
   {
@@ -392,7 +393,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Custom loss function: <function my_loss_fn at 0x2b83b5940>\n"
+      "Custom loss function: <function my_loss_fn at 0x2ba5b76a0>\n"
      ]
     }
    ],
@@ -433,7 +434,7 @@
    ],
    "source": [
     "class MyRegressor(dl.Regressor):\n",
-    "    \"\"\"My regressor with custom loss function.\"\"\"\n",
+    "    \"\"\"My regressor application with custom loss function.\"\"\"\n",
     "    \n",
     "    def compute_loss(self, y_hat, y):\n",
     "        \"\"\"Compute my custom loss.\"\"\"\n",
@@ -448,7 +449,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note:** In this case, however, the `.loss` atttribute will still be set to the default loss."
+    "**NOTE:** In this case, however, the `.loss` atttribute will still be set to the default loss, so you won't see the correct loss when you print it."
    ]
   },
   {
@@ -561,6 +562,7 @@
      "text": [
       "Regressor(\n",
       "  (loss): L1Loss()\n",
+      "  (optimizer): Adam[Adam](lr=0.01)\n",
       "  (train_metrics): MetricCollection,\n",
       "    prefix=train\n",
       "  )\n",
@@ -588,15 +590,14 @@
       "      )\n",
       "    )\n",
       "  )\n",
-      "  (optimizer): Adam[Adam](lr=0.01)\n",
       ")\n"
      ]
     }
    ],
    "source": [
-    "model = dl.Regressor(net, optimizer=adam).create()\n",
+    "regressor = dl.Regressor(net, optimizer=adam).create()\n",
     "\n",
-    "print(model)"
+    "print(regressor)"
    ]
   },
   {
@@ -619,6 +620,7 @@
      "text": [
       "MyRegressor(\n",
       "  (loss): L1Loss()\n",
+      "  (optimizer): Adam[Adam](lr=0.001)\n",
       "  (train_metrics): MetricCollection,\n",
       "    prefix=train\n",
       "  )\n",
@@ -646,7 +648,6 @@
       "      )\n",
       "    )\n",
       "  )\n",
-      "  (optimizer): Adam[Adam](lr=0.001)\n",
       ")\n"
      ]
     }
@@ -661,9 +662,9 @@
     "\n",
     "net = dl.models.SmallMLP(in_features=10, out_features=1)\n",
     "\n",
-    "model = MyRegressor(net).create()\n",
+    "regressor = MyRegressor(net).create()\n",
     "\n",
-    "print(model)"
+    "print(regressor)"
    ]
   },
   {
@@ -692,7 +693,7 @@
     "\n",
     "net = dl.models.SmallMLP(in_features=10, out_features=1)\n",
     "\n",
-    "model = dl.Regressor(\n",
+    "regressor = dl.Regressor(\n",
     "    net,\n",
     "    metrics=[mse_metric],  # On train, val and test.\n",
     "    train_metrics=[mape_metric],  # On train only.\n",
@@ -717,7 +718,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n"
+      "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n"
      ]
     },
     {
@@ -773,7 +774,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5b56583ff412477cbbe5a4072aebc4be",
+       "model_id": "f3eecbd1906d47d18eb2586b46b2aca0",
        "version_major": 2,
        "version_minor": 0
       },
@@ -787,17 +788,15 @@
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      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/py\n",
-       "torch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a \n",
-       "bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to\n",
-       "improve performance.\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/co\n",
+       "nnectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider\n",
+       "increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve performance.\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/py\n",
-       "torch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a \n",
-       "bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to\n",
-       "improve performance.\n"
+       "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/co\n",
+       "nnectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider\n",
+       "increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve performance.\n"
       ]
      },
      "metadata": {},
@@ -806,17 +805,17 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/py\n",
-       "torch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a \n",
-       "bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to\n",
-       "improve performance.\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/co\n",
+       "nnectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. \n",
+       "Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve \n",
+       "performance.\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/py\n",
-       "torch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a \n",
-       "bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to\n",
-       "improve performance.\n"
+       "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/co\n",
+       "nnectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. \n",
+       "Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve \n",
+       "performance.\n"
       ]
      },
      "metadata": {},
@@ -862,7 +861,7 @@
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",
       "text/plain": [
        "<Figure size 1500x1000 with 4 Axes>"
       ]
@@ -880,14 +879,14 @@
     "x_numpy_val = numpy.random.randn(100, 10)  # Input val data.\n",
     "y_numpy_val = x_numpy_val.max(axis=1, keepdims=True)  # Target val data.\n",
     "\n",
-    "model.fit(\n",
+    "regressor.fit(\n",
     "    (x_numpy, y_numpy), \n",
     "    max_epochs=20, \n",
     "    batch_size=10, \n",
     "    val_data=(x_numpy_val, y_numpy_val), \n",
     "    val_batch_size=50,\n",
     ")\n",
-    "model.trainer.history.plot()"
+    "regressor.trainer.history.plot()"
    ]
   },
   {
@@ -905,7 +904,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "88eea2782f41465082bd220fbb9e05b9",
+       "model_id": "db902de3f105457381f128aa23910360",
        "version_major": 2,
        "version_minor": 0
       },
@@ -916,23 +915,15 @@
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:492: Your `test_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test dataloaders.\n",
-      "/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve performance.\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
        "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
        "┃<span style=\"font-weight: bold\">         Test metric         </span>┃<span style=\"font-weight: bold\">        DataLoader 0         </span>┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\"> testExplainedVariance_epoch </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     -0.3344266414642334     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\"> testMeanSquaredError_epoch  </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.4313027262687683      </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       test_loss_epoch       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.5178554058074951      </span>│\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\"> testExplainedVariance_epoch </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    -0.37505555152893066     </span>│\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\"> testMeanSquaredError_epoch  </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.5574653744697571      </span>│\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\">       test_loss_epoch       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.5969628691673279      </span>│\n",
        "└─────────────────────────────┴─────────────────────────────┘\n",
        "</pre>\n"
       ],
@@ -940,15 +931,23 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1m        Test metric        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m       DataLoader 0        \u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│\u001b[36m \u001b[0m\u001b[36mtestExplainedVariance_epoch\u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    -0.3344266414642334    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36mtestMeanSquaredError_epoch \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    0.4313027262687683     \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      test_loss_epoch      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    0.5178554058074951     \u001b[0m\u001b[35m \u001b[0m│\n",
+       "│\u001b[36m \u001b[0m\u001b[36mtestExplainedVariance_epoch\u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   -0.37505555152893066    \u001b[0m\u001b[35m \u001b[0m│\n",
+       "│\u001b[36m \u001b[0m\u001b[36mtestMeanSquaredError_epoch \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    0.5574653744697571     \u001b[0m\u001b[35m \u001b[0m│\n",
+       "│\u001b[36m \u001b[0m\u001b[36m      test_loss_epoch      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    0.5969628691673279     \u001b[0m\u001b[35m \u001b[0m│\n",
        "└─────────────────────────────┴─────────────────────────────┘\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:492: Your `test_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test dataloaders.\n",
+      "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve performance.\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -976,7 +975,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test results: {'test_loss_epoch': 0.5178554058074951, 'testMeanSquaredError_epoch': 0.4313027262687683, 'testExplainedVariance_epoch': -0.3344266414642334}\n",
+      "Test results: {'test_loss_epoch': 0.5969628691673279, 'testMeanSquaredError_epoch': 0.5574653744697571, 'testExplainedVariance_epoch': -0.37505555152893066}\n",
       "\n"
      ]
     }
@@ -985,7 +984,7 @@
     "x_numpy_test = numpy.random.randn(100, 10)  # Input test data.\n",
     "y_numpy_test = x_numpy_val.max(axis=1, keepdims=True)  # Target test data.\n",
     "\n",
-    "test_results = model.test((x_numpy_test, y_numpy_test))\n",
+    "test_results = regressor.test((x_numpy_test, y_numpy_test))\n",
     "print(f\"Test results: {test_results}\\n\")                                        # Benjamin: Should this also work? If not, why not?"
    ]
   },
@@ -1004,7 +1003,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "03692b17a77b41c9816c29c0b5e39c34",
+       "model_id": "4e79f06a0fdb48cfb71cd4f1336d666e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1015,15 +1014,22 @@
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/checkpoint_connector.py:145: `.test(ckpt_path=None)` was called without a model. The best model of the previous `fit` call will be used. You can pass `.test(ckpt_path='best')` to use the best model or `.test(ckpt_path='last')` to use the last model. If you pass a value, this warning will be silenced.\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
        "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
        "┃<span style=\"font-weight: bold\">         Test metric         </span>┃<span style=\"font-weight: bold\">        DataLoader 0         </span>┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\"> testExplainedVariance_epoch </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     -0.4170423746109009     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\"> testMeanSquaredError_epoch  </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.4578703045845032      </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       test_loss_epoch       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.5276792645454407      </span>│\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\"> testExplainedVariance_epoch </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    -0.35361969470977783     </span>│\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\"> testMeanSquaredError_epoch  </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.5501408576965332      </span>│\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\">       test_loss_epoch       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.5715290307998657      </span>│\n",
        "└─────────────────────────────┴─────────────────────────────┘\n",
        "</pre>\n"
       ],
@@ -1031,22 +1037,15 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1m        Test metric        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m       DataLoader 0        \u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│\u001b[36m \u001b[0m\u001b[36mtestExplainedVariance_epoch\u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    -0.4170423746109009    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36mtestMeanSquaredError_epoch \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    0.4578703045845032     \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      test_loss_epoch      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    0.5276792645454407     \u001b[0m\u001b[35m \u001b[0m│\n",
+       "│\u001b[36m \u001b[0m\u001b[36mtestExplainedVariance_epoch\u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   -0.35361969470977783    \u001b[0m\u001b[35m \u001b[0m│\n",
+       "│\u001b[36m \u001b[0m\u001b[36mtestMeanSquaredError_epoch \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    0.5501408576965332     \u001b[0m\u001b[35m \u001b[0m│\n",
+       "│\u001b[36m \u001b[0m\u001b[36m      test_loss_epoch      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    0.5715290307998657     \u001b[0m\u001b[35m \u001b[0m│\n",
        "└─────────────────────────────┴─────────────────────────────┘\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/giovannivolpe/Documents/GitHub/DeepLearningCrashCourse/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/checkpoint_connector.py:145: `.test(ckpt_path=None)` was called without a model. The best model of the previous `fit` call will be used. You can pass `.test(ckpt_path='best')` to use the best model or `.test(ckpt_path='last')` to use the last model. If you pass a value, this warning will be silenced.\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -1073,9 +1072,9 @@
     {
      "data": {
       "text/plain": [
-       "[{'test_loss_epoch': 0.5276792645454407,\n",
-       "  'testMeanSquaredError_epoch': 0.4578703045845032,\n",
-       "  'testExplainedVariance_epoch': -0.4170423746109009}]"
+       "[{'test_loss_epoch': 0.5715290307998657,\n",
+       "  'testMeanSquaredError_epoch': 0.5501408576965332,\n",
+       "  'testExplainedVariance_epoch': -0.35361969470977783}]"
       ]
      },
      "execution_count": 20,
@@ -1094,7 +1093,7 @@
     "\n",
     "test_dataloader = dl.DataLoader(test_dataset, batch_size=10)\n",
     "\n",
-    "model.trainer.test(dataloaders=test_dataloader)"
+    "regressor.trainer.test(dataloaders=test_dataloader)"
    ]
   },
   {
@@ -1121,7 +1120,7 @@
     }
    ],
    "source": [
-    "h = model.trainer.history\n",
+    "h = regressor.trainer.history\n",
     "\n",
     "print(h.history.keys())  # Training and validation metrics at each epoch.\n",
     "\n",
@@ -1144,7 +1143,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[2.1414897441864014, 0.8103458285331726, 0.3336629867553711, 0.27316048741340637, 0.2614992558956146, 0.2471957504749298, 0.20912817120552063, 0.21034778654575348, 0.2041531801223755, 0.19698187708854675, 0.2087334245443344, 0.1882399618625641, 0.18545161187648773, 0.17339453101158142, 0.18441957235336304, 0.17726264894008636, 0.15804675221443176, 0.17017671465873718, 0.16743065416812897, 0.16892564296722412]\n",
+      "[2.3782708644866943, 0.78560471534729, 0.38036105036735535, 0.2889227569103241, 0.27238044142723083, 0.24795836210250854, 0.24466198682785034, 0.22361309826374054, 0.2379547506570816, 0.20508940517902374, 0.20281915366649628, 0.20150528848171234, 0.19368459284305573, 0.1938815712928772, 0.18229889869689941, 0.17406213283538818, 0.16982495784759521, 0.17380273342132568, 0.17055784165859222, 0.1723780333995819]\n",
       "[100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000]\n",
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]\n"
      ]
@@ -1160,11 +1159,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Saving and loading models\n",
+    "## Saving and Loading Applications\n",
     "\n",
     "Currently, the best way to save a model is to save the state dict.\n",
     "\n",
-    "**Note:** In the future, Deeplay might have a way to save the entire architecture through the config file, but that is not yet implemented."
+    "**NOTE:** In the future, Deeplay might have a way to save the entire architecture through the config file, but that is not yet implemented."
    ]
   },
   {
@@ -1184,8 +1183,8 @@
     }
    ],
    "source": [
-    "torch.save(model.state_dict(), \"model.pth\")  # Saving.\n",
-    "model.load_state_dict(torch.load(\"model.pth\"))  # Loading."
+    "torch.save(regressor.state_dict(), \"model.pth\")  # Saving.\n",
+    "regressor.load_state_dict(torch.load(\"model.pth\"))  # Loading."
    ]
   },
   {
@@ -1196,7 +1195,7 @@
     "\n",
     "Deeplay supports PyTorch Lightning callbacks. These can be used to implement custom behavior during training. As an example, you can use a `EarlyStopping` callback and a `ModelCheckpoint` callback as shown below.\n",
     "\n",
-    "**Note:** Refer to the Lightning documentation for more information on the available callbacks and how to implement your own."
+    "**NOTE:** Refer to the Lightning documentation for more information on the available callbacks and how to implement your own."
    ]
   },
   {
@@ -1257,7 +1256,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "78509e7c6d1d4399bae0e88d7514f712",
+       "model_id": "53e7740f6adf4cf38cb9621f3d17d7ef",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1268,6 +1267,42 @@
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/co\n",
+       "nnectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider\n",
+       "increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve performance.\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/co\n",
+       "nnectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider\n",
+       "increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve performance.\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/co\n",
+       "nnectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. \n",
+       "Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve \n",
+       "performance.\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "/Users/giovannivolpe/Documents/GitHub/deeplay/py_env_dlcc/lib/python3.12/site-packages/lightning/pytorch/trainer/co\n",
+       "nnectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. \n",
+       "Consider increasing the value of the `num_workers` argument` to `num_workers=10` in the `DataLoader` to improve \n",
+       "performance.\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
       "text/html": [
@@ -1294,7 +1329,7 @@
     {
      "data": {
       "text/plain": [
-       "<deeplay.callbacks.history.LogHistory at 0x2d1a824e0>"
+       "<deeplay.callbacks.history.LogHistory at 0x2bf9a2db0>"
       ]
      },
      "execution_count": 24,
@@ -1310,9 +1345,9 @@
     "\n",
     "net = dl.models.SmallMLP(in_features=10, out_features=1)\n",
     "\n",
-    "model = dl.Regressor(net).create()\n",
+    "regressor = dl.Regressor(net).create()\n",
     "\n",
-    "model.fit(\n",
+    "regressor.fit(\n",
     "    (x_numpy, y_numpy), \n",
     "    max_epochs=20, \n",
     "    batch_size=10, \n",
diff --git a/tutorials/getting-started/GS151_models.ipynb b/tutorials/getting-started/GS151_models.ipynb
index ec8d5b8d..95996903 100644
--- a/tutorials/getting-started/GS151_models.ipynb
+++ b/tutorials/getting-started/GS151_models.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,7 +57,6 @@
       "        (0-1): 2 x Conv2dBlock(\n",
       "          (shortcut_start): Conv2dBlock(\n",
       "            (layer): Layer[Identity](in_channels=64, out_channels=64, kernel_size=1, stride=1, padding=0)\n",
-      "            (activation): Layer[Identity]()\n",
       "          )\n",
       "          (blocks): Sequential(\n",
       "            (0): Conv2dBlock(\n",
@@ -80,7 +79,6 @@
       "        (0): Conv2dBlock(\n",
       "          (shortcut_start): Conv2dBlock(\n",
       "            (layer): Layer[Conv2d](in_channels=64, out_channels=128, kernel_size=1, stride=2, padding=0)\n",
-      "            (activation): Layer[Identity]()\n",
       "            (normalization): Layer[BatchNorm2d](num_features=128)\n",
       "          )\n",
       "          (blocks): Sequential(\n",
@@ -99,12 +97,11 @@
       "        )\n",
       "        (1): Conv2dBlock(\n",
       "          (shortcut_start): Conv2dBlock(\n",
-      "            (layer): Layer[Conv2d](in_channels=64, out_channels=128, kernel_size=1, stride=1, padding=0)\n",
-      "            (activation): Layer[Identity]()\n",
+      "            (layer): Layer[Identity](in_channels=128, out_channels=128, kernel_size=1, stride=1, padding=0)\n",
       "          )\n",
       "          (blocks): Sequential(\n",
       "            (0): Conv2dBlock(\n",
-      "              (layer): Layer[Conv2d](in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1)\n",
+      "              (layer): Layer[Conv2d](in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1)\n",
       "              (normalization): Layer[BatchNorm2d](num_features=128)\n",
       "              (activation): Layer[ReLU]()\n",
       "            )\n",
@@ -123,7 +120,6 @@
       "        (0): Conv2dBlock(\n",
       "          (shortcut_start): Conv2dBlock(\n",
       "            (layer): Layer[Conv2d](in_channels=128, out_channels=256, kernel_size=1, stride=2, padding=0)\n",
-      "            (activation): Layer[Identity]()\n",
       "            (normalization): Layer[BatchNorm2d](num_features=256)\n",
       "          )\n",
       "          (blocks): Sequential(\n",
@@ -142,12 +138,11 @@
       "        )\n",
       "        (1): Conv2dBlock(\n",
       "          (shortcut_start): Conv2dBlock(\n",
-      "            (layer): Layer[Conv2d](in_channels=128, out_channels=256, kernel_size=1, stride=1, padding=0)\n",
-      "            (activation): Layer[Identity]()\n",
+      "            (layer): Layer[Identity](in_channels=256, out_channels=256, kernel_size=1, stride=1, padding=0)\n",
       "          )\n",
       "          (blocks): Sequential(\n",
       "            (0): Conv2dBlock(\n",
-      "              (layer): Layer[Conv2d](in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1)\n",
+      "              (layer): Layer[Conv2d](in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1)\n",
       "              (normalization): Layer[BatchNorm2d](num_features=256)\n",
       "              (activation): Layer[ReLU]()\n",
       "            )\n",
@@ -166,7 +161,6 @@
       "        (0): Conv2dBlock(\n",
       "          (shortcut_start): Conv2dBlock(\n",
       "            (layer): Layer[Conv2d](in_channels=256, out_channels=512, kernel_size=1, stride=2, padding=0)\n",
-      "            (activation): Layer[Identity]()\n",
       "            (normalization): Layer[BatchNorm2d](num_features=512)\n",
       "          )\n",
       "          (blocks): Sequential(\n",
@@ -185,12 +179,11 @@
       "        )\n",
       "        (1): Conv2dBlock(\n",
       "          (shortcut_start): Conv2dBlock(\n",
-      "            (layer): Layer[Conv2d](in_channels=256, out_channels=512, kernel_size=1, stride=1, padding=0)\n",
-      "            (activation): Layer[Identity]()\n",
+      "            (layer): Layer[Identity](in_channels=512, out_channels=512, kernel_size=1, stride=1, padding=0)\n",
       "          )\n",
       "          (blocks): Sequential(\n",
       "            (0): Conv2dBlock(\n",
-      "              (layer): Layer[Conv2d](in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1)\n",
+      "              (layer): Layer[Conv2d](in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1)\n",
       "              (normalization): Layer[BatchNorm2d](num_features=512)\n",
       "              (activation): Layer[ReLU]()\n",
       "            )\n",
@@ -267,21 +260,21 @@
       "  (blocks): LayerList(\n",
       "    (0): Conv2dBlock(\n",
       "      (layer): Layer[Conv2d](in_channels=3, out_channels=64, kernel_size=4, stride=2, padding=1)\n",
-      "      (activation): Layer[ReLU]()\n",
+      "      (activation): Layer[LeakyReLU](negative_slope=0.2)\n",
       "    )\n",
       "    (1): Conv2dBlock(\n",
       "      (layer): Layer[Conv2d](in_channels=64, out_channels=128, kernel_size=4, stride=2, padding=1)\n",
-      "      (activation): Layer[ReLU]()\n",
+      "      (activation): Layer[LeakyReLU](negative_slope=0.2)\n",
       "      (normalization): Layer[BatchNorm2d](num_features=128)\n",
       "    )\n",
       "    (2): Conv2dBlock(\n",
       "      (layer): Layer[Conv2d](in_channels=128, out_channels=256, kernel_size=4, stride=2, padding=1)\n",
-      "      (activation): Layer[ReLU]()\n",
+      "      (activation): Layer[LeakyReLU](negative_slope=0.2)\n",
       "      (normalization): Layer[BatchNorm2d](num_features=256)\n",
       "    )\n",
       "    (3): Conv2dBlock(\n",
       "      (layer): Layer[Conv2d](in_channels=256, out_channels=512, kernel_size=4, stride=2, padding=1)\n",
-      "      (activation): Layer[ReLU]()\n",
+      "      (activation): Layer[LeakyReLU](negative_slope=0.2)\n",
       "      (normalization): Layer[BatchNorm2d](num_features=512)\n",
       "    )\n",
       "    (4): Conv2dBlock(\n",
@@ -323,8 +316,7 @@
       "  (encoder): ConvolutionalEncoder2d(\n",
       "    (blocks): LayerList(\n",
       "      (0): Conv2dBlock(\n",
-      "        (reflectionpad2d): Layer[ReflectionPad2d](padding=3)\n",
-      "        (layer): Layer[Conv2d](in_channels=3, out_channels=64, kernel_size=7, stride=1, padding=0)\n",
+      "        (layer): Layer[Conv2d](in_channels=3, out_channels=64, kernel_size=7, stride=1, padding=3, padding_mode=reflect)\n",
       "        (normalization): Layer[InstanceNorm2d](num_features=64)\n",
       "        (activation): Layer[ReLU]()\n",
       "      )\n",
@@ -346,7 +338,6 @@
       "      (0-8): 9 x Conv2dBlock(\n",
       "        (shortcut_start): Conv2dBlock(\n",
       "          (layer): Layer[Identity](in_channels=256, out_channels=256, kernel_size=1, stride=1, padding=0)\n",
-      "          (activation): Layer[Identity]()\n",
       "        )\n",
       "        (blocks): Sequential(\n",
       "          (0-1): 2 x Conv2dBlock(\n",
@@ -372,9 +363,7 @@
       "        (activation): Layer[ReLU]()\n",
       "      )\n",
       "      (2): Conv2dBlock(\n",
-      "        (reflectionpad2d): Layer[ReflectionPad2d](padding=3)\n",
-      "        (layer): Layer[Conv2d](in_channels=64, out_channels=3, kernel_size=7, stride=1, padding=0)\n",
-      "        (normalization): Layer[InstanceNorm2d](num_features=3)\n",
+      "        (layer): Layer[Conv2d](in_channels=64, out_channels=3, kernel_size=7, stride=1, padding=3, padding_mode=reflect)\n",
       "        (activation): Layer[Tanh]()\n",
       "      )\n",
       "    )\n",
@@ -388,25 +377,21 @@
       "      (activation): Layer[LeakyReLU](negative_slope=0.2)\n",
       "    )\n",
       "    (1): Conv2dBlock(\n",
-      "      (pool): Layer[MaxPool2d](kernel_size=2, stride=2)\n",
       "      (layer): Layer[Conv2d](in_channels=64, out_channels=128, kernel_size=4, stride=2, padding=1)\n",
       "      (normalization): Layer[InstanceNorm2d](num_features=128)\n",
       "      (activation): Layer[LeakyReLU](negative_slope=0.2)\n",
       "    )\n",
       "    (2): Conv2dBlock(\n",
-      "      (pool): Layer[MaxPool2d](kernel_size=2, stride=2)\n",
       "      (layer): Layer[Conv2d](in_channels=128, out_channels=256, kernel_size=4, stride=2, padding=1)\n",
       "      (normalization): Layer[InstanceNorm2d](num_features=256)\n",
       "      (activation): Layer[LeakyReLU](negative_slope=0.2)\n",
       "    )\n",
       "    (3): Conv2dBlock(\n",
-      "      (pool): Layer[MaxPool2d](kernel_size=2, stride=2)\n",
       "      (layer): Layer[Conv2d](in_channels=256, out_channels=512, kernel_size=4, stride=1, padding=1)\n",
       "      (normalization): Layer[InstanceNorm2d](num_features=512)\n",
       "      (activation): Layer[LeakyReLU](negative_slope=0.2)\n",
       "    )\n",
       "    (4): Conv2dBlock(\n",
-      "      (pool): Layer[MaxPool2d](kernel_size=2, stride=2)\n",
       "      (layer): Layer[Conv2d](in_channels=512, out_channels=1, kernel_size=4, stride=1, padding=1)\n",
       "      (activation): Layer[Sigmoid]()\n",
       "    )\n",
@@ -743,9 +728,9 @@
    "source": [
     "## Making Your Own Model from Deeplay Components\n",
     "\n",
-    "Typically, you'll make your own model using `Sequential`.\n",
+    "Typically, you'll make your own model using the `Sequential` object.\n",
     "\n",
-    "For example, you might combine both models and components ..."
+    "For example, you might combine both models and other components ..."
    ]
   },
   {
@@ -771,7 +756,6 @@
       "          (0-1): 2 x Conv2dBlock(\n",
       "            (shortcut_start): Conv2dBlock(\n",
       "              (layer): Layer[Identity](in_channels=64, out_channels=64, kernel_size=1, stride=1, padding=0)\n",
-      "              (activation): Layer[Identity]()\n",
       "            )\n",
       "            (blocks): Sequential(\n",
       "              (0): Conv2dBlock(\n",
@@ -794,7 +778,6 @@
       "          (0): Conv2dBlock(\n",
       "            (shortcut_start): Conv2dBlock(\n",
       "              (layer): Layer[Conv2d](in_channels=64, out_channels=128, kernel_size=1, stride=2, padding=0)\n",
-      "              (activation): Layer[Identity]()\n",
       "              (normalization): Layer[BatchNorm2d](num_features=128)\n",
       "            )\n",
       "            (blocks): Sequential(\n",
@@ -813,12 +796,11 @@
       "          )\n",
       "          (1): Conv2dBlock(\n",
       "            (shortcut_start): Conv2dBlock(\n",
-      "              (layer): Layer[Conv2d](in_channels=64, out_channels=128, kernel_size=1, stride=1, padding=0)\n",
-      "              (activation): Layer[Identity]()\n",
+      "              (layer): Layer[Identity](in_channels=128, out_channels=128, kernel_size=1, stride=1, padding=0)\n",
       "            )\n",
       "            (blocks): Sequential(\n",
       "              (0): Conv2dBlock(\n",
-      "                (layer): Layer[Conv2d](in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1)\n",
+      "                (layer): Layer[Conv2d](in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1)\n",
       "                (normalization): Layer[BatchNorm2d](num_features=128)\n",
       "                (activation): Layer[ReLU]()\n",
       "              )\n",
@@ -837,7 +819,6 @@
       "          (0): Conv2dBlock(\n",
       "            (shortcut_start): Conv2dBlock(\n",
       "              (layer): Layer[Conv2d](in_channels=128, out_channels=256, kernel_size=1, stride=2, padding=0)\n",
-      "              (activation): Layer[Identity]()\n",
       "              (normalization): Layer[BatchNorm2d](num_features=256)\n",
       "            )\n",
       "            (blocks): Sequential(\n",
@@ -856,12 +837,11 @@
       "          )\n",
       "          (1): Conv2dBlock(\n",
       "            (shortcut_start): Conv2dBlock(\n",
-      "              (layer): Layer[Conv2d](in_channels=128, out_channels=256, kernel_size=1, stride=1, padding=0)\n",
-      "              (activation): Layer[Identity]()\n",
+      "              (layer): Layer[Identity](in_channels=256, out_channels=256, kernel_size=1, stride=1, padding=0)\n",
       "            )\n",
       "            (blocks): Sequential(\n",
       "              (0): Conv2dBlock(\n",
-      "                (layer): Layer[Conv2d](in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1)\n",
+      "                (layer): Layer[Conv2d](in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1)\n",
       "                (normalization): Layer[BatchNorm2d](num_features=256)\n",
       "                (activation): Layer[ReLU]()\n",
       "              )\n",
@@ -880,7 +860,6 @@
       "          (0): Conv2dBlock(\n",
       "            (shortcut_start): Conv2dBlock(\n",
       "              (layer): Layer[Conv2d](in_channels=256, out_channels=512, kernel_size=1, stride=2, padding=0)\n",
-      "              (activation): Layer[Identity]()\n",
       "              (normalization): Layer[BatchNorm2d](num_features=512)\n",
       "            )\n",
       "            (blocks): Sequential(\n",
@@ -899,12 +878,11 @@
       "          )\n",
       "          (1): Conv2dBlock(\n",
       "            (shortcut_start): Conv2dBlock(\n",
-      "              (layer): Layer[Conv2d](in_channels=256, out_channels=512, kernel_size=1, stride=1, padding=0)\n",
-      "              (activation): Layer[Identity]()\n",
+      "              (layer): Layer[Identity](in_channels=512, out_channels=512, kernel_size=1, stride=1, padding=0)\n",
       "            )\n",
       "            (blocks): Sequential(\n",
       "              (0): Conv2dBlock(\n",
-      "                (layer): Layer[Conv2d](in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1)\n",
+      "                (layer): Layer[Conv2d](in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1)\n",
       "                (normalization): Layer[BatchNorm2d](num_features=512)\n",
       "                (activation): Layer[ReLU]()\n",
       "              )\n",
@@ -927,12 +905,12 @@
       "      (0): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=512, out_features=32, bias=True)\n",
       "        (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "        (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "        (normalization): Layer[BatchNorm1d]()\n",
       "      )\n",
       "      (1): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=32, out_features=32, bias=True)\n",
       "        (activation): Layer[LeakyReLU](negative_slope=0.05)\n",
-      "        (normalization): Layer[BatchNorm1d](num_features=32)\n",
+      "        (normalization): Layer[BatchNorm1d]()\n",
       "      )\n",
       "      (2): LinearBlock(\n",
       "        (layer): Layer[Linear](in_features=32, out_features=1, bias=True)\n",
@@ -1145,18 +1123,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'dl' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m backbone \u001b[38;5;241m=\u001b[39m \u001b[43mdl\u001b[49m\u001b[38;5;241m.\u001b[39mConvolutionalEncoder2d(\n\u001b[1;32m      2\u001b[0m     in_channels\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m, \n\u001b[1;32m      3\u001b[0m     hidden_channels\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m16\u001b[39m, \u001b[38;5;241m32\u001b[39m, \u001b[38;5;241m64\u001b[39m], \n\u001b[1;32m      4\u001b[0m     out_channels\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m128\u001b[39m,\n\u001b[1;32m      5\u001b[0m )\n\u001b[1;32m      7\u001b[0m initializer \u001b[38;5;241m=\u001b[39m dl\u001b[38;5;241m.\u001b[39minitializers\u001b[38;5;241m.\u001b[39mNormal(mean\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, std\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.0\u001b[39m)\n\u001b[1;32m      8\u001b[0m backbone\u001b[38;5;241m.\u001b[39minitialize(initializer)  \u001b[38;5;66;03m# Before building the model.\u001b[39;00m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'dl' is not defined"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean weights first layer: 1.0270298719406128\n",
+      "STD weights first layer: 0.9969273209571838\n"
      ]
     }
    ],
@@ -1184,15 +1159,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean weights first layer: 1.0810060501098633\n",
-      "STD weights first layer: 1.0629596710205078\n"
+      "Mean weights first layer: 0.9408934712409973\n",
+      "STD weights first layer: 1.0349736213684082\n"
      ]
     }
    ],
@@ -1224,7 +1199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -1232,8 +1207,8 @@
      "output_type": "stream",
      "text": [
       "Conv2d \n",
-      "Mean 0.0 \n",
-      "STD 0.0 \n",
+      "Mean 0.014395988546311855 \n",
+      "STD 1.0669336318969727 \n",
       "\n",
       "BatchNorm2d \n",
       "Mean 1.0 \n",
@@ -1291,8 +1266,8 @@
      "output_type": "stream",
      "text": [
       "Conv2d \n",
-      "Mean 0.022924458608031273 \n",
-      "STD 0.9779603481292725 \n",
+      "Mean 0.030025742948055267 \n",
+      "STD 0.9641354084014893 \n",
       "\n",
       "BatchNorm2d \n",
       "Mean 1.0 \n",
@@ -1329,7 +1304,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note:** You can specify the tensors to be initialized during weight initialization. This is done with the `tensors` parameter. For example:\n",
+    "**NOTE:** You can specify the tensors to be initialized during weight initialization. This is done with the `tensors` parameter. For example:\n",
     "\n",
     "```python\n",
     "initializer = Normal()\n",
diff --git a/tutorials/getting-started/GS161_components.ipynb b/tutorials/getting-started/GS161_components.ipynb
index b354da68..0a4d16b9 100644
--- a/tutorials/getting-started/GS161_components.ipynb
+++ b/tutorials/getting-started/GS161_components.ipynb
@@ -70,7 +70,7 @@
     "    in_features=10,\n",
     "    hidden_features=[20, 30, 40],\n",
     "    out_features=5,\n",
-    "    out_activation=dl.torch.nn.Tanh,  ### Why dl.torch and not just torch?\n",
+    "    out_activation=dl.Layer(torch.nn.Tanh),\n",
     ")\n",
     "\n",
     "print(mlp)"
@@ -83,6 +83,11 @@
     "### `ConvolutionalNeuralNetwork`"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -128,7 +133,7 @@
     "    in_channels=3,\n",
     "    hidden_channels=[16, 32, 64],\n",
     "    out_channels=10,\n",
-    "    out_activation=dl.torch.nn.Tanh,\n",
+    "    out_activation=dl.Layer(torch.nn.Tanh),\n",
     "    pool=dl.Layer(torch.nn.MaxPool2d, kernel_size=2),\n",
     ")\n",
     "cnn.normalized(\n",
@@ -198,7 +203,7 @@
     "    in_channels=3,\n",
     "    hidden_channels=[16, 32, 64],\n",
     "    out_channels=10,\n",
-    "    out_activation=dl.torch.nn.Tanh,\n",
+    "    out_activation=dl.Layer(torch.nn.Tanh),\n",
     ")\n",
     "enc.strided(stride=2, apply_to_first_layer=True, apply_to_last_layer=True) \\\n",
     "    .pooled(dl.Layer(torch.nn.AvgPool2d, kernel_size=2)) \\\n",
@@ -257,7 +262,7 @@
     "    in_channels=10,\n",
     "    hidden_channels=[64, 32, 16],\n",
     "    out_channels=3,\n",
-    "    out_activation=dl.torch.nn.Tanh,\n",
+    "    out_activation=dl.Layer(torch.nn.Tanh),\n",
     ")\n",
     "dec.upsampled(\n",
     "    dl.Layer(torch.nn.Upsample, scale_factor=2),\n",
@@ -345,7 +350,7 @@
     "    encoder_channels=[16, 32, 64],\n",
     "    decoder_channels=None,\n",
     "    out_channels=3,\n",
-    "    out_activation=dl.torch.nn.Tanh,\n",
+    "    out_activation=dl.Layer(torch.nn.Tanh),\n",
     ")\n",
     "\n",
     "encdec"
@@ -411,7 +416,7 @@
     "    decoder_channels=[32, 16],\n",
     "    bottleneck_channels=[],\n",
     "    out_channels=3,\n",
-    "    out_activation=dl.torch.nn.Tanh,\n",
+    "    out_activation=dl.Layer(torch.nn.Tanh),\n",
     ")\n",
     "\n",
     "print(encdec)"
@@ -485,7 +490,7 @@
     "    encoder_channels=[16, 32, 64],\n",
     "    bottleneck_channels=None,\n",
     "    out_channels=3,\n",
-    "    out_activation=dl.torch.nn.Tanh,\n",
+    "    out_activation=dl.Layer(torch.nn.Tanh),\n",
     ")\n",
     "\n",
     "print(encdec)"
@@ -568,7 +573,7 @@
     "    bottleneck_channels=None,\n",
     "    decoder_channels=[32, 16],\n",
     "    out_channels=3,\n",
-    "    out_activation=dl.torch.nn.Tanh,\n",
+    "    out_activation=dl.Layer(torch.nn.Tanh),\n",
     "    skip=dl.ops.Add(),\n",
     ")\n",
     "\n",
@@ -645,7 +650,7 @@
     "    bottleneck_channels=None,\n",
     "    decoder_channels=[32, 16],\n",
     "    out_channels=3,\n",
-    "    out_activation=dl.torch.nn.Tanh,\n",
+    "    out_activation=dl.Layer(torch.nn.Tanh),\n",
     ")\n",
     "\n",
     "print(unet)"
@@ -748,7 +753,7 @@
     "    in_features=10,\n",
     "    hidden_features=[20, 30, 40],\n",
     "    out_features=5,\n",
-    "    out_activation=dl.torch.nn.Tanh,\n",
+    "    out_activation=dl.Layer(torch.nn.Tanh),\n",
     ")\n",
     "\n",
     "print(gcnn)"
@@ -833,7 +838,7 @@
     "mpgnn = dl.MessagePassingNeuralNetwork(\n",
     "    hidden_features=[20, 30, 40],\n",
     "    out_features=5,\n",
-    "    out_activation=dl.torch.nn.Tanh,\n",
+    "    out_activation=dl.Layer(torch.nn.Tanh),\n",
     ")\n",
     "\n",
     "print(mpgnn)"
diff --git a/tutorials/getting-started/GS171_blocks.ipynb b/tutorials/getting-started/GS171_blocks.ipynb
index f958b412..ee93f75f 100644
--- a/tutorials/getting-started/GS171_blocks.ipynb
+++ b/tutorials/getting-started/GS171_blocks.ipynb
@@ -6,12 +6,12 @@
    "source": [
     "# Using Deeplay Blocks\n",
     "\n",
-    "Blocks are the most versatile part of Deeplay. They are designed to be transformed from a base block to any other block that accepts the same input tensor shape and returns the same output tensor shape."
+    "Blocks are the most versatile part of Deeplay. They are designed to make it possible to substitute a base block with any other block that accepts the same input tensor shape and returns the same output tensor shape."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -52,12 +52,13 @@
    ],
    "source": [
     "block = dl.LinearBlock(4, 10)\n",
+    "\n",
     "print(block)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -73,12 +74,13 @@
    ],
    "source": [
     "block.activated(torch.nn.ReLU, mode=\"prepend\")\n",
+    "\n",
     "print(block)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -88,19 +90,20 @@
       "LinearBlock(\n",
       "  (activation): Layer[ReLU]()\n",
       "  (layer): Layer[Linear](in_features=4, out_features=10, bias=True)\n",
-      "  (normalization): Layer[LayerNorm](normalized_shape=10)\n",
+      "  (normalization): Layer[LayerNorm]()\n",
       ")\n"
      ]
     }
    ],
    "source": [
     "block.normalized(torch.nn.LayerNorm, mode=\"insert\", after=\"layer\")\n",
+    "\n",
     "print(block)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -111,7 +114,7 @@
       "  (shortcut_start): Layer[Linear](in_features=4, out_features=10)\n",
       "  (activation): Layer[ReLU]()\n",
       "  (layer): Layer[Linear](in_features=4, out_features=10, bias=True)\n",
-      "  (normalization): Layer[LayerNorm](normalized_shape=10)\n",
+      "  (normalization): Layer[LayerNorm]()\n",
       "  (shortcut_end): Add()\n",
       ")\n"
      ]
@@ -119,12 +122,13 @@
    ],
    "source": [
     "block.shortcut(merge=dl.ops.Add(), shortcut=dl.Layer(torch.nn.Linear, 4, 10))\n",
+    "\n",
     "print(block)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -135,7 +139,7 @@
       "  (shortcut_start): Layer[Linear](in_features=4, out_features=10)\n",
       "  (activation): Layer[ReLU]()\n",
       "  (layer): Layer[Linear](in_features=4, out_features=10, bias=True)\n",
-      "  (normalization): Layer[LayerNorm](normalized_shape=10)\n",
+      "  (normalization): Layer[LayerNorm]()\n",
       "  (shortcut_end): Add()\n",
       "  (dropout): Layer[Dropout](p=0.2)\n",
       ")\n"
@@ -144,6 +148,7 @@
    ],
    "source": [
     "block.set_dropout(0.2)\n",
+    "\n",
     "print(block)"
    ]
   },
@@ -156,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -171,12 +176,13 @@
    ],
    "source": [
     "block = dl.LinearBlock(4, 10)\n",
+    "\n",
     "print(block)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -185,9 +191,12 @@
      "text": [
       "LinearBlock(\n",
       "  (blocks): Sequential(\n",
-      "    (0-1): 2 x LinearBlock(\n",
+      "    (0): LinearBlock(\n",
       "      (layer): Layer[Linear](in_features=4, out_features=10, bias=True)\n",
       "    )\n",
+      "    (1): LinearBlock(\n",
+      "      (layer): Layer[Linear](in_features=10, out_features=10, bias=True)\n",
+      "    )\n",
       "  )\n",
       ")\n"
      ]
@@ -200,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -209,10 +218,17 @@
      "text": [
       "LinearBlock(\n",
       "  (blocks): Sequential(\n",
-      "    (0-1): 2 x LinearBlock(\n",
-      "      (shortcut_start): Layer[Identity]()\n",
+      "    (0): LinearBlock(\n",
+      "      (shortcut_start): Layer[Linear](in_features=4, out_features=10)\n",
       "      (layer): Layer[Linear](in_features=4, out_features=10, bias=True)\n",
-      "      (normalization): Layer[LayerNorm](normalized_shape=10)\n",
+      "      (normalization): Layer[LayerNorm]()\n",
+      "      (activation): Layer[ReLU]()\n",
+      "      (shortcut_end): Add()\n",
+      "    )\n",
+      "    (1): LinearBlock(\n",
+      "      (shortcut_start): Layer[Identity]()\n",
+      "      (layer): Layer[Linear](in_features=10, out_features=10, bias=True)\n",
+      "      (normalization): Layer[LayerNorm]()\n",
       "      (activation): Layer[ReLU]()\n",
       "      (shortcut_end): Add()\n",
       "    )\n",
@@ -226,12 +242,13 @@
     "    .activated(torch.nn.ReLU) \\\n",
     "    .normalized(torch.nn.LayerNorm, mode=\"insert\", after=\"layer\") \\\n",
     "    .shortcut()\n",
+    "\n",
     "print(block)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -240,10 +257,17 @@
      "text": [
       "LinearBlock(\n",
       "  (blocks): Sequential(\n",
-      "    (0-1): 2 x LinearBlock(\n",
-      "      (shortcut_start): Layer[Identity]()\n",
+      "    (0): LinearBlock(\n",
+      "      (shortcut_start): Layer[Linear](in_features=4, out_features=10)\n",
       "      (layer): Layer[Linear](in_features=4, out_features=10, bias=True)\n",
-      "      (normalization): Layer[LayerNorm](normalized_shape=10)\n",
+      "      (normalization): Layer[LayerNorm]()\n",
+      "      (activation): Layer[ReLU]()\n",
+      "      (shortcut_end): Add()\n",
+      "    )\n",
+      "    (1): LinearBlock(\n",
+      "      (shortcut_start): Layer[Identity]()\n",
+      "      (layer): Layer[Linear](in_features=10, out_features=10, bias=True)\n",
+      "      (normalization): Layer[LayerNorm]()\n",
       "      (activation): Layer[ReLU]()\n",
       "      (shortcut_end): Add()\n",
       "    )\n",
@@ -255,6 +279,7 @@
    ],
    "source": [
     "block.set_dropout(0.2)\n",
+    "\n",
     "print(block)"
    ]
   },
@@ -267,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -282,12 +307,13 @@
    ],
    "source": [
     "block = dl.Conv2dBlock(3, 10, kernel_size=1)\n",
+    "\n",
     "print(block)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -297,7 +323,6 @@
       "Conv2dBlock(\n",
       "  (shortcut_start): Conv2dBlock(\n",
       "    (layer): Layer[Conv2d](in_channels=3, out_channels=10, kernel_size=1, stride=2, padding=0)\n",
-      "    (activation): Layer[Identity]()\n",
       "  )\n",
       "  (layer): Layer[Conv2d](in_channels=3, out_channels=10, kernel_size=1, stride=2, padding=0)\n",
       "  (activation): Layer[ReLU]()\n",
@@ -318,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -334,12 +359,13 @@
    ],
    "source": [
     "block = dl.Conv2dBlock(3, 10, kernel_size=1).upsampled()\n",
+    "\n",
     "print(block)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -355,6 +381,7 @@
    ],
    "source": [
     "block = dl.Conv2dBlock(3, 10, kernel_size=1).pooled()\n",
+    "\n",
     "print(block)"
    ]
   },
@@ -367,7 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -382,12 +409,13 @@
    ],
    "source": [
     "block = dl.Sequence1dBlock(4, 10).LSTM()\n",
+    "\n",
     "print(block)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -402,12 +430,13 @@
    ],
    "source": [
     "block = dl.Sequence1dBlock(4, 10).GRU()\n",
+    "\n",
     "print(block)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -422,6 +451,7 @@
    ],
    "source": [
     "block = dl.Sequence1dBlock(4, 10).RNN()\n",
+    "\n",
     "print(block)"
    ]
   },
@@ -443,7 +473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -462,6 +492,7 @@
     "block = dl.LinearBlock(4, 10) \\\n",
     "    .activated(torch.nn.ReLU) \\\n",
     "    .normalized()\n",
+    "\n",
     "print(block)"
    ]
   },
@@ -474,7 +505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -491,6 +522,7 @@
    ],
    "source": [
     "block.configure(order=[\"layer\", \"normalization\", \"activation\"])\n",
+    "\n",
     "print(block)"
    ]
   },
@@ -512,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -527,12 +559,13 @@
    ],
    "source": [
     "block = dl.Conv2dBlock(3, 10, kernel_size=1)\n",
+    "\n",
     "print(block)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -548,12 +581,13 @@
    ],
    "source": [
     "block.append(dl.Layer(torch.nn.ReLU))\n",
+    "\n",
     "print(block)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -570,6 +604,7 @@
    ],
    "source": [
     "block.append(dl.Layer(torch.nn.LayerNorm), name=\"normalization\")\n",
+    "\n",
     "print(block)"
    ]
   },
@@ -589,7 +624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -604,12 +639,13 @@
    ],
    "source": [
     "block = dl.Conv2dBlock(3, 10, kernel_size=1)\n",
+    "\n",
     "print(block)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -625,6 +661,7 @@
    ],
    "source": [
     "block.prepend(dl.Layer(torch.nn.MaxPool2d, kernel_size=2), name=\"pool\")\n",
+    "\n",
     "print(block)"
    ]
   },
@@ -637,7 +674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -653,12 +690,13 @@
    ],
    "source": [
     "block = dl.Conv2dBlock(3, 10, kernel_size=1).activated(torch.nn.ReLU)\n",
+    "\n",
     "print(block)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -689,7 +727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -705,13 +743,14 @@
    ],
    "source": [
     "block_with_activation = dl.Conv2dBlock(3, 10, kernel_size=1) \\\n",
-    "    .activated(torch.nn.ReLU) \n",
+    "    .activated(torch.nn.ReLU)\n",
+    "\n",
     "print(block_with_activation)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -727,6 +766,7 @@
    ],
    "source": [
     "block_with_activation.set(\"activation\", torch.nn.ReLU)\n",
+    "\n",
     "print(block_with_activation)"
    ]
   },
@@ -739,7 +779,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -754,12 +794,13 @@
    ],
    "source": [
     "block_without_activation = dl.Conv2dBlock(3, 10, kernel_size=1)\n",
+    "\n",
     "print(block_without_activation)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -775,6 +816,7 @@
    ],
    "source": [
     "block_without_activation.set(\"activation\", torch.nn.ReLU)\n",
+    "\n",
     "print(block_without_activation)"
    ]
   },
@@ -784,12 +826,12 @@
    "source": [
     "### Removing Layers with the `.remove()` Method\n",
     "\n",
-    "Layers can be removed using the `.remove()` method, which removes based on the layer name."
+    "Layers can be removed using the `.remove()` method, which removes a layer based on its name."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -805,12 +847,13 @@
    ],
    "source": [
     "block = dl.Conv2dBlock(3, 10, kernel_size=1).activated(torch.nn.ReLU)\n",
+    "\n",
     "print(block)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -825,6 +868,7 @@
    ],
    "source": [
     "block.remove(\"activation\", allow_missing=True)\n",
+    "\n",
     "print(block)"
    ]
   },
@@ -846,7 +890,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -868,7 +912,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -880,6 +924,9 @@
     }
    ],
    "source": [
+    "x = torch.randn(2, 3, 4, 5)\n",
+    "y = torch.randn(2, 3, 4, 5)\n",
+    "\n",
     "merge_cat = dl.ops.Cat(dim=1).build()\n",
     "\n",
     "print(merge_cat(x, y).shape)"
@@ -887,7 +934,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -899,6 +946,9 @@
     }
    ],
    "source": [
+    "x = torch.randn(2, 3, 4, 5)\n",
+    "y = torch.randn(2, 3, 4, 5)\n",
+    "\n",
     "merge_lambda = dl.ops.Lambda(lambda x: x[0] + x[1]).build()\n",
     "\n",
     "print(merge_lambda(x, y).shape)"
@@ -906,7 +956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -916,7 +966,6 @@
       "Conv2dBlock(\n",
       "  (shortcut_start): Conv2dBlock(\n",
       "    (layer): Layer[Conv2d](in_channels=3, out_channels=10, kernel_size=1, stride=1, padding=0)\n",
-      "    (activation): Layer[Identity]()\n",
       "  )\n",
       "  (layer): Layer[Conv2d](in_channels=3, out_channels=10, kernel_size=1, stride=1, padding=0)\n",
       "  (activation): Layer[ReLU]()\n",
@@ -926,6 +975,9 @@
     }
    ],
    "source": [
+    "x = torch.randn(2, 3, 4, 5)\n",
+    "y = torch.randn(2, 3, 4, 5)\n",
+    "\n",
     "block = dl.Conv2dBlock(3, 10, kernel_size=1).activated(torch.nn.ReLU)\n",
     "block.shortcut(merge=merge_cat)\n",
     "\n",
@@ -941,7 +993,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -962,7 +1014,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -983,9 +1035,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([2, 3, 1])\n"
+     ]
+    }
+   ],
    "source": [
     "x = torch.randn(2, 1, 3, 1)\n",
     "\n",
@@ -996,7 +1056,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1017,7 +1077,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -1038,7 +1098,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -1059,7 +1119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1077,13 +1137,6 @@
     "\n",
     "print(permute(x).shape)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/tutorials/getting-started/GS181_configure.ipynb b/tutorials/getting-started/GS181_configure.ipynb
index c407c3f7..69b59e8c 100644
--- a/tutorials/getting-started/GS181_configure.ipynb
+++ b/tutorials/getting-started/GS181_configure.ipynb
@@ -6,12 +6,12 @@
    "source": [
     "# Configuring Deeplay Objects\n",
     "\n",
-    "The `.configure()` method permits to configure Deeplay objects. It can be combined with selectors to be applied to targeted objects."
+    "The `.configure()` method permits you to configure Deeplay objects. You can combine it with selectors to target specific objects."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -117,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -141,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -179,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -207,7 +207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -304,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -330,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -356,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -382,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -402,7 +402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -422,7 +422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -442,7 +442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -471,7 +471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -490,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -509,7 +509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -535,7 +535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -561,7 +561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -629,7 +629,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -693,7 +693,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -712,7 +712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -731,7 +731,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -750,7 +750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -778,7 +778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -842,7 +842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -901,7 +901,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -960,7 +960,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1024,7 +1024,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1086,13 +1086,6 @@
     "    \n",
     "print(model)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/tutorials/getting-started/GS191_styles.ipynb b/tutorials/getting-started/GS191_styles.ipynb
index d79b28fb..c9ec32e5 100644
--- a/tutorials/getting-started/GS191_styles.ipynb
+++ b/tutorials/getting-started/GS191_styles.ipynb
@@ -25,7 +25,7 @@
    "source": [
     "## Checking Available Styles\n",
     "\n",
-    "You can check the available styles using the `.available_styles()` method. This method can be called either on the class or an instance of the class."
+    "You can check the available styles using the `.available_styles()` method. This method can be called either on the class or on an instance of the class."
    ]
   },
   {
@@ -95,16 +95,15 @@
       "Conv2dBlock(\n",
       "  (shortcut_start): Conv2dBlock(\n",
       "    (layer): Layer[Conv2d](in_channels=3, out_channels=64, kernel_size=1, stride=1, padding=0)\n",
-      "    (activation): Layer[Identity]()\n",
       "  )\n",
       "  (blocks): Sequential(\n",
       "    (0): Conv2dBlock(\n",
-      "      (layer): Layer[Conv2d](in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=0)\n",
+      "      (layer): Layer[Conv2d](in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1)\n",
       "      (activation): Layer[ReLU]()\n",
       "      (normalization): Layer[BatchNorm2d](num_features=64)\n",
       "    )\n",
       "    (1): Conv2dBlock(\n",
-      "      (layer): Layer[Conv2d](in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=0)\n",
+      "      (layer): Layer[Conv2d](in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1)\n",
       "      (activation): Layer[ReLU]()\n",
       "      (normalization): Layer[BatchNorm2d](num_features=64)\n",
       "    )\n",
@@ -139,16 +138,15 @@
       "Conv2dBlock(\n",
       "  (shortcut_start): Conv2dBlock(\n",
       "    (layer): Layer[Conv2d](in_channels=3, out_channels=64, kernel_size=1, stride=2, padding=0)\n",
-      "    (activation): Layer[Identity]()\n",
       "  )\n",
       "  (blocks): Sequential(\n",
       "    (0): Conv2dBlock(\n",
-      "      (layer): Layer[Conv2d](in_channels=3, out_channels=64, kernel_size=3, stride=2, padding=0)\n",
+      "      (layer): Layer[Conv2d](in_channels=3, out_channels=64, kernel_size=3, stride=2, padding=1)\n",
       "      (activation): Layer[ReLU]()\n",
       "      (normalization): Layer[BatchNorm2d](num_features=64)\n",
       "    )\n",
       "    (1): Conv2dBlock(\n",
-      "      (layer): Layer[Conv2d](in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=0)\n",
+      "      (layer): Layer[Conv2d](in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1)\n",
       "      (activation): Layer[ReLU]()\n",
       "      (normalization): Layer[BatchNorm2d](num_features=64)\n",
       "    )\n",
@@ -177,43 +175,40 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "Conv2dBlock(\n",
-       "  (shortcut_start): Conv2dBlock(\n",
-       "    (layer): Layer[Conv2d](in_channels=3, out_channels=64, kernel_size=1, stride=1, padding=0)\n",
-       "    (activation): Layer[Identity]()\n",
-       "  )\n",
-       "  (blocks): Sequential(\n",
-       "    (0): Conv2dBlock(\n",
-       "      (activation): Layer[ReLU]()\n",
-       "      (layer): Layer[Conv2d](in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=0)\n",
-       "    )\n",
-       "    (1): Conv2dBlock(\n",
-       "      (layer): Layer[Conv2d](in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=0)\n",
-       "      (activation): Layer[ReLU]()\n",
-       "    )\n",
-       "    (2): Conv2dBlock(\n",
-       "      (activation): Layer[ReLU]()\n",
-       "      (layer): Layer[Conv2d](in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=0)\n",
-       "    )\n",
-       "    (3): Conv2dBlock(\n",
-       "      (layer): Layer[Conv2d](in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=0)\n",
-       "      (activation): Layer[ReLU]()\n",
-       "    )\n",
-       "  )\n",
-       "  (shortcut_end): Add()\n",
-       ")"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Conv2dBlock(\n",
+      "  (shortcut_start): Conv2dBlock(\n",
+      "    (layer): Layer[Conv2d](in_channels=3, out_channels=64, kernel_size=1, stride=1, padding=0)\n",
+      "  )\n",
+      "  (blocks): Sequential(\n",
+      "    (0): Conv2dBlock(\n",
+      "      (activation): Layer[ReLU]()\n",
+      "      (layer): Layer[Conv2d](in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1)\n",
+      "    )\n",
+      "    (1): Conv2dBlock(\n",
+      "      (layer): Layer[Conv2d](in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1)\n",
+      "      (activation): Layer[ReLU]()\n",
+      "    )\n",
+      "    (2): Conv2dBlock(\n",
+      "      (activation): Layer[ReLU]()\n",
+      "      (layer): Layer[Conv2d](in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1)\n",
+      "    )\n",
+      "    (3): Conv2dBlock(\n",
+      "      (layer): Layer[Conv2d](in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1)\n",
+      "      (activation): Layer[ReLU]()\n",
+      "    )\n",
+      "  )\n",
+      "  (shortcut_end): Add()\n",
+      ")\n"
+     ]
     }
    ],
    "source": [
     "block = dl.Conv2dBlock(3, 64).style(\"residual\", order=\"allaalla\")\n",
-    "block"
+    "\n",
+    "print(block)"
    ]
   },
   {
@@ -268,7 +263,7 @@
       "  (shortcut_start): Layer[Linear](in_features=3, out_features=64, bias=False)\n",
       "  (layer): Layer[Linear](in_features=3, out_features=64, bias=True)\n",
       "  (activation): Layer[GELU]()\n",
-      "  (normalization): Layer[BatchNorm1d](num_features=64)\n",
+      "  (normalization): Layer[BatchNorm1d]()\n",
       "  (shortcut_end): Add()\n",
       ")\n"
      ]
@@ -293,14 +288,15 @@
       "  (shortcut_start): Layer[Linear](in_features=3, out_features=64, bias=True)\n",
       "  (layer): Layer[Linear](in_features=3, out_features=64, bias=True)\n",
       "  (activation): Layer[GELU]()\n",
-      "  (normalization): Layer[BatchNorm1d](num_features=64)\n",
+      "  (normalization): Layer[BatchNorm1d]()\n",
       "  (shortcut_end): Add()\n",
       ")\n"
      ]
     }
    ],
    "source": [
-    "block_with_bias = dl.LinearBlock(3, 64).style(\"my_linear\", bias_in_shortcut=True)\n",
+    "block_with_bias = dl.LinearBlock(3, 64) \\\n",
+    "    .style(\"my_linear\", bias_in_shortcut=True)\n",
     "\n",
     "print(block_with_bias)"
    ]