From d47af7895477a615b02579722a68c6effb822be0 Mon Sep 17 00:00:00 2001
From: Agrin Hilmkil <agrinh@users.noreply.github.com>
Date: Tue, 24 Oct 2023 21:53:18 +0100
Subject: [PATCH] Release 0.3.5 (#72)

* Release 0.3.5

* Add devcontainer badge to README.md

* Add missing dotfiles

* Delete removed files

* Add missing pre-commit config

* Fix extra argument for pre-commit
---
 .devcontainer/devcontainer.json               |   10 +-
 .github/workflows/ci-build.yml                |   32 +-
 .pre-commit-config.yaml                       |   39 +
 Dockerfile                                    |    5 +-
 README.md                                     |   18 +-
 examples/csuite_example.ipynb                 |   12 +-
 .../multi_investment_sales_attribution.ipynb  |  126 +-
 poetry.lock                                   | 4623 ++++++++++++-----
 pyproject.toml                                |  397 +-
 .../config/lightning/default_data.yaml        |    1 +
 .../config/lightning/default_gaussian.yaml    |    6 +-
 .../config/lightning/default_spline.yaml      |    6 +-
 .../data_generation/samplers/__init__.py      |    0
 .../functional_relationships_sampler.py       |   48 +
 .../samplers/noise_dist_sampler.py            |   68 +
 .../data_generation/samplers/sampler.py       |   16 +
 .../data_generation/samplers/sem_sampler.py   |   30 +
 .../data_generation/synthetic_dataset.py      |  116 +
 .../datasets/causica_dataset_format.py        |    3 +-
 src/causica/datasets/interventional_data.py   |   14 +-
 src/causica/datasets/normalization.py         |   84 +
 src/causica/datasets/standardizer.py          |   75 -
 src/causica/distributions/noise/bernoulli.py  |    4 +-
 .../distributions/noise/categorical.py        |    2 +-
 src/causica/distributions/transforms.py       |  150 -
 .../distributions/transforms/__init__.py      |    3 +
 src/causica/distributions/transforms/base.py  |  132 +
 src/causica/distributions/transforms/joint.py |  100 +
 .../transforms/tensor_to_tensordict.py        |   83 +
 .../functional_relationships/__init__.py      |   12 +-
 .../deci_functional_relationships.py          |   26 +
 .../do_functional_relationships.py            |    4 +-
 .../functional_relationships.py               |    8 -
 src/causica/functional_relationships/icgnn.py |  217 -
 .../linear_functional_relationships.py        |    4 +-
 .../rff_functional_relationships.py           |   76 +
 src/causica/lightning/callbacks.py            |   11 +-
 .../data_modules/basic_data_module.py         |    6 +-
 .../data_modules/synthetic_data_module.py     |  200 +
 .../data_modules/variable_spec_data.py        |   63 +-
 src/causica/lightning/modules/deci_module.py  |  104 +-
 src/causica/nn/__init__.py                    |    1 +
 src/causica/nn/deci_embed_nn.py               |  162 +
 src/causica/training/auglag.py                |  110 +-
 src/causica/training/per_variable_metrics.py  |  245 +-
 .../data_generation/test_synthetic_dataset.py |  111 +
 test/datasets/test_datasets.py                |    1 -
 test/datasets/test_standardizer.py            |    8 +-
 test/distributions/noise/test_joint.py        |    6 +-
 test/distributions/test_sem_distribution.py   |    4 +-
 test/distributions/transforms/test_base.py    |   43 +
 .../test_joint.py}                            |   20 +-
 .../transforms/test_transform_modules.py      |  126 +
 .../test_functional_relationships.py          |   82 +-
 test/integration/decimodule.pt                |  Bin 0 -> 260889 bytes
 test/integration/test_evaluation.py           |   16 -
 test/integration/test_pytorch_lightning.py    |   53 +-
 .../test_deci_embed_nn.py}                    |    4 +-
 test/sem/test_treatment_effects.py            |   24 +-
 test/training/test_per_variable_metrics.py    |   63 -
 60 files changed, 5700 insertions(+), 2313 deletions(-)
 create mode 100644 .pre-commit-config.yaml
 create mode 100644 src/causica/data_generation/samplers/__init__.py
 create mode 100644 src/causica/data_generation/samplers/functional_relationships_sampler.py
 create mode 100644 src/causica/data_generation/samplers/noise_dist_sampler.py
 create mode 100644 src/causica/data_generation/samplers/sampler.py
 create mode 100644 src/causica/data_generation/samplers/sem_sampler.py
 create mode 100644 src/causica/data_generation/synthetic_dataset.py
 create mode 100644 src/causica/datasets/normalization.py
 delete mode 100644 src/causica/datasets/standardizer.py
 delete mode 100644 src/causica/distributions/transforms.py
 create mode 100644 src/causica/distributions/transforms/__init__.py
 create mode 100644 src/causica/distributions/transforms/base.py
 create mode 100644 src/causica/distributions/transforms/joint.py
 create mode 100644 src/causica/distributions/transforms/tensor_to_tensordict.py
 create mode 100644 src/causica/functional_relationships/deci_functional_relationships.py
 delete mode 100644 src/causica/functional_relationships/icgnn.py
 create mode 100644 src/causica/functional_relationships/rff_functional_relationships.py
 create mode 100644 src/causica/lightning/data_modules/synthetic_data_module.py
 create mode 100644 src/causica/nn/__init__.py
 create mode 100644 src/causica/nn/deci_embed_nn.py
 create mode 100644 test/data_generation/test_synthetic_dataset.py
 create mode 100644 test/distributions/transforms/test_base.py
 rename test/distributions/{test_transforms.py => transforms/test_joint.py} (67%)
 create mode 100644 test/distributions/transforms/test_transform_modules.py
 create mode 100644 test/integration/decimodule.pt
 rename test/{functional_relationships/test_fgnni.py => nn/test_deci_embed_nn.py} (84%)
 delete mode 100644 test/training/test_per_variable_metrics.py

diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
index b7fb275..12c13da 100644
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@@ -6,10 +6,7 @@
 		"target": "dev"
 	},
 	"runArgs": [
-		// The default shared memory size of Docker is 64MB which can lead to memory
-		// issues when using Pytorch dataloaders with multiple workers.
-		// See https://github.com/aws/sagemaker-python-sdk/issues/937 and
-		// https://github.com/pytorch/pytorch#docker-image.
+		// give headspace for pytorch dataloaders passing tensors across processes
 		"--shm-size=1gb"
 	],
 	"containerEnv": {
@@ -22,7 +19,8 @@
 	"hostRequirements": {
 		"gpu": "optional"  // Mount GPU(s) if available
 	},
-	"postStartCommand": "git config --global core.editor \"code --wait\"",
+	"postCreateCommand": "pre-commit install",
+	"postStartCommand": "git config --global core.editor \"code --wait\" && poetry install",
 	"shutdownAction": "none",
 	"customizations": {
 		"vscode": {
@@ -33,7 +31,7 @@
 				"python.testing.pytestEnabled": true,
 				"python.testing.pytestArgs": [
 					"--continue-on-collection-errors",
-					"causica/test"
+					"test"
 				],
 				"python.testing.unittestEnabled": false,
 				"vim.textwidth": 120,
diff --git a/.github/workflows/ci-build.yml b/.github/workflows/ci-build.yml
index f94647c..0dc5e83 100644
--- a/.github/workflows/ci-build.yml
+++ b/.github/workflows/ci-build.yml
@@ -14,47 +14,41 @@ jobs:
 
     steps:
     - uses: actions/checkout@v3
-      with:
-        path: "repo"
 
     - uses: actions/setup-python@v2
       with:
-        python-version: "3.9"
+        python-version: "3.10"
 
     - uses: actions/cache@v3
       with:
         path: ~/.cache/pypoetry/virtualenvs
-        key: ${{ hashFiles('repo/poetry.lock') }}
+        key: ${{ hashFiles('poetry.lock') }}
       id: cache
-    
+
     - name: Install poetry
       run: |
         curl -sSL https://install.python-poetry.org | python3 -
 
     - name: Install dependencies
-      shell: bash -l {0}
       run: |
-        cd repo
-        poetry env use 3.9
+        poetry env use 3.10
         poetry install
       if: steps.cache.outputs.cache-hit != 'true'
 
+    - name: Verify pre-commit checks
+      uses: pre-commit/action@v3.0.0
+      with:
+        extra_args: --all-files
+
     - name: Test with pytest
-      shell: bash -l {0}
-      run: |
-        cd repo
-        poetry run python -m pytest ./test
+      run: poetry run python -m pytest ./test
 
     - name: Create build artifact
-      shell: bash -l {0}
-      run: |
-        cd repo
-        poetry build
+      run: poetry build
 
     - name: Upload build artifact
-      # if: github.ref == 'refs/heads/main' # only create artifacts on push to main
       uses: actions/upload-artifact@v3
       with:
         name: Build artifacts
-        path: ./repo/dist
-        retention-days: 90
\ No newline at end of file
+        path: ./dist
+        retention-days: 90
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..deb80ca
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,39 @@
+repos:
+- repo: local
+  hooks:
+  - id: black
+    name: black
+    entry: poetry run black
+    language: system
+    types_or: [python, jupyter]
+    args: [--config=./pyproject.toml]
+- repo: local
+  hooks:
+  - id: isort
+    name: isort
+    entry: poetry run isort
+    language: system
+    types: [python]
+- repo: local
+  hooks:
+  - id: poetry lock
+    name: poetry lock check
+    entry: poetry lock --check
+    pass_filenames: false
+    language: system
+- repo: local
+  hooks:
+  - id: mypy
+    name: mypy
+    entry: poetry run mypy
+    pass_filenames: false
+    language: system
+    types: [python]
+    args: ["--config-file=pyproject.toml", "."]
+- repo: local
+  hooks:
+  - id: pylint
+    name: pylint
+    entry: poetry run pylint
+    language: system
+    types: [python]
diff --git a/Dockerfile b/Dockerfile
index 721f04b..9bb91d6 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -31,8 +31,7 @@ RUN curl -sSL https://install.python-poetry.org | python3 -
 WORKDIR /workspaces/causica
 COPY pyproject.toml poetry.lock ./
 RUN --mount=type=cache,target=/root/.cache/pypoetry,sharing=locked \
-    mkdir -p src/causica && touch README.md src/causica/__init__.py && \
-    poetry install --only main
+    poetry install --only main --no-root --no-directory
 
 FROM base as deploy
 COPY . /workspaces/causica
@@ -74,4 +73,4 @@ RUN <<EOT
 EOT
 
 RUN --mount=type=cache,target=/root/.cache/pypoetry,sharing=locked \
-    poetry install --with dev
+    poetry install --with dev --no-root --no-directory
diff --git a/README.md b/README.md
index aafa14e..803f199 100644
--- a/README.md
+++ b/README.md
@@ -1,15 +1,17 @@
 [![Causica CI Build](https://github.com/microsoft/causica/actions/workflows/ci-build.yml/badge.svg)](https://github.com/microsoft/causica/actions/workflows/ci-build.yml)
+[![Open in Dev Containers](https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/microsoft/causica)
+
 
 # Causica
 
 ## Overview
- 
-Causal machine learning enables individuals and organizations to make better data-driven decisions. In particular, causal ML allows us to answer “what if” questions about the effect of potential actions on outcomes. 
- 
+
+Causal machine learning enables individuals and organizations to make better data-driven decisions. In particular, causal ML allows us to answer “what if” questions about the effect of potential actions on outcomes.
+
 Causal ML is a nascent area, we aim  to enable a **scalable**, **flexible**, **real-world applicable end-to-end** causal inference framework. In perticular, we bridge between causal discovery, causal inference, and deep learning to achieve the goal.  We aim to develop technology can automate causal decision-making using existing observational data alone, output both the discovered causal relationships and estimate the effect of actions simultaneously.
- 
+
 Causica is a deep learning library for end-to-end causal inference, including both causal discovery and inference.  It implements deep end-to-end inference framework [2] and different alternatives.
- 
+
 This project splits the interventional decision making from observational decision making Azua repo found here [Azua](https://github.com/microsoft/project-azua).
 
 This codebase has been heavily refactored, you can find the previous version of the code [here](https://github.com/microsoft/causica/releases/tag/v0.0.0).
@@ -26,7 +28,7 @@ pip install causica
 
 ## About
 
-Real-world data-driven decision making requires causal inference to ensure the validity of drawn conclusions. However, it is very uncommon to have a-priori perfect knowledge of the causal relationships underlying relevant variables. DECI allows the end user to perform causal inference without having complete knowledge of the causal graph. This is done by combining the causal discovery and causal inference steps in a single model. DECI takes in observational data and outputs ATE and CATE estimates. 
+Real-world data-driven decision making requires causal inference to ensure the validity of drawn conclusions. However, it is very uncommon to have a-priori perfect knowledge of the causal relationships underlying relevant variables. DECI allows the end user to perform causal inference without having complete knowledge of the causal graph. This is done by combining the causal discovery and causal inference steps in a single model. DECI takes in observational data and outputs ATE and CATE estimates.
 
 For more information, please refer to the [paper](https://arxiv.org/abs/2202.02195).
 
@@ -37,7 +39,7 @@ DECI is a generative model that employs an additive noise structural equation mo
 
 **Simulation-based Causal Inference**
 
-DECI estimates causal quantities (ATE) by applying the relevant interventions to its learnt causal graph (i.e. mutilating incoming edges to intervened variables) and then sampling from the generative model. This process involves first sampling a vector of exogenous noise from the learnt noise distribution and then forward simulating the SEM until an observation vector is obtained. ATE can be computed via estimating an expectation over the effect variable of interest using MonteCarlo samples of the intervened distribution of observations. 
+DECI estimates causal quantities (ATE) by applying the relevant interventions to its learnt causal graph (i.e. mutilating incoming edges to intervened variables) and then sampling from the generative model. This process involves first sampling a vector of exogenous noise from the learnt noise distribution and then forward simulating the SEM until an observation vector is obtained. ATE can be computed via estimating an expectation over the effect variable of interest using MonteCarlo samples of the intervened distribution of observations.
 
 ## How to run
 
@@ -60,7 +62,7 @@ The Spline model uses a flexible spline flow that is learnt from the data. This
 
 To use DECI to learn the functional relationships, remove the variational distribution terms from the loss and replace the sample with the known graph.
 
-## Further extensions 
+## Further extensions
 
 For now, we have removed Rhino and DDECI from the codebase but they will be added back. You can still access the previously released versions [here](https://github.com/microsoft/causica/releases/tag/v0.0.0).
 
diff --git a/examples/csuite_example.ipynb b/examples/csuite_example.ipynb
index 076499f..f87ad5a 100644
--- a/examples/csuite_example.ipynb
+++ b/examples/csuite_example.ipynb
@@ -36,7 +36,7 @@
     "    JointNoiseModule,\n",
     "    create_noise_modules,\n",
     ")\n",
-    "from causica.functional_relationships import ICGNN\n",
+    "from causica.functional_relationships import DECIEmbedFunctionalRelationships\n",
     "from causica.graph.dag_constraint import calculate_dagness\n",
     "from causica.sem.sem_distribution import SEMDistributionModule\n",
     "from causica.training.auglag import AugLagLossCalculator, AugLagLR, AugLagLRConfig"
@@ -168,12 +168,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "icgnn = ICGNN(\n",
+    "functional_relationships = DECIEmbedFunctionalRelationships(\n",
     "    shapes=tensordict_shapes(dataset_train),\n",
     "    embedding_size=32,\n",
     "    out_dim_g=32,\n",
-    "    norm_layer=torch.nn.LayerNorm,\n",
-    "    res_connection=True,\n",
+    "    num_layers_g=2,\n",
+    "    num_layers_zeta=2,\n",
     ")"
    ]
   },
@@ -213,7 +213,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sem_module: SEMDistributionModule = SEMDistributionModule(adjacency_dist, icgnn, noise_module)\n",
+    "sem_module: SEMDistributionModule = SEMDistributionModule(adjacency_dist, functional_relationships, noise_module)\n",
     "\n",
     "sem_module.to(device);"
    ]
@@ -233,7 +233,7 @@
    "outputs": [],
    "source": [
     "modules = {\n",
-    "    \"icgnn\": sem_module.functional_relationships,\n",
+    "    \"functional_relationships\": sem_module.functional_relationships,\n",
     "    \"vardist\": sem_module.adjacency_module,\n",
     "    \"noise_dist\": sem_module.noise_module,\n",
     "}\n",
diff --git a/examples/multi_investment_sales_attribution.ipynb b/examples/multi_investment_sales_attribution.ipynb
index 630b85d..bcec9b9 100644
--- a/examples/multi_investment_sales_attribution.ipynb
+++ b/examples/multi_investment_sales_attribution.ipynb
@@ -120,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -135,7 +135,7 @@
     "data_module = BasicDECIDataModule(\n",
     "    df.loc[:, \"Global Flag\":\"Revenue\"],  # remove ground truth from dataframe\n",
     "    variables=[Variable.from_dict(d) for d in variables_spec],\n",
-    "    batch_size=1024,\n",
+    "    batch_size=128,\n",
     "    normalize=True,\n",
     ")\n",
     "num_nodes = len(data_module.dataset_train.keys())"
@@ -420,12 +420,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -474,7 +474,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -496,7 +496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -516,7 +516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -543,7 +543,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -580,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -600,10 +600,19 @@
     "\n",
     "lightning_module = DECIModule(\n",
     "    noise_dist=ContinuousNoiseDist.GAUSSIAN,\n",
-    "    prior_sparsity_lambda=1.0,\n",
-    "    init_rho=1.0,\n",
-    "    init_alpha=0.0,\n",
-    "    auglag_config=AugLagLRConfig(lr_init_dict={\"vardist\": 1e-2, \"icgnn\": 3e-4, \"noise_dist\": 3e-3}),\n",
+    "    prior_sparsity_lambda=43.0,\n",
+    "    init_rho=30.0,\n",
+    "    init_alpha=0.20,\n",
+    "    auglag_config=AugLagLRConfig(\n",
+    "        max_inner_steps=3400,\n",
+    "        max_outer_steps=8,\n",
+    "        lr_init_dict={\n",
+    "            \"icgnn\": 0.00076,\n",
+    "            \"vardist\": 0.0098,\n",
+    "            \"functional_relationships\": 3e-4,\n",
+    "            \"noise_dist\": 0.0070,\n",
+    "        },\n",
+    "    ),\n",
     ")\n",
     "\n",
     "lightning_module.constraint_matrix = torch.tensor(constraint_matrix)\n",
@@ -619,7 +628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -627,12 +636,19 @@
      "output_type": "stream",
      "text": [
       "You are using a CUDA device ('NVIDIA A100 80GB PCIe') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
+      "Missing logger folder: /workspaces/causica/pkg/causica/examples/lightning_logs\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
       "\n",
       "  | Name        | Type                  | Params\n",
       "------------------------------------------------------\n",
-      "0 | sem_module  | SEMDistributionModule | 20.1 K\n",
-      "1 | auglag_loss | AugLagLossCalculator  | 0     \n",
+      "0 | auglag_loss | AugLagLossCalculator  | 0     \n",
+      "1 | sem_module  | SEMDistributionModule | 20.1 K\n",
       "------------------------------------------------------\n",
       "19.9 K    Trainable params\n",
       "196       Non-trainable params\n",
@@ -643,7 +659,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7b109af6e544453a9d371310612cd8d4",
+       "model_id": "94ddb8a3368740c0bd3b8ed63007c1ed",
        "version_major": 2,
        "version_minor": 0
       },
@@ -658,22 +674,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Updating alpha to: 4.655742645263672\n",
-      "Updating alpha to: 7.376716613769531\n",
-      "Updating rho, dag penalty prev:  2.7209739685\n",
-      "Updating rho, dag penalty prev:  2.7209739685\n",
-      "Updating rho, dag penalty prev:  2.7209739685\n",
-      "Updating rho, dag penalty prev:  2.7209739685\n",
-      "Updating rho, dag penalty prev:  2.7209739685\n",
-      "Updating rho, dag penalty prev:  2.7209739685\n",
-      "Updating rho, dag penalty prev:  2.7209739685\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`Trainer.fit` stopped: `max_epochs=2000` reached.\n"
+      "Updating alpha to: 71.24188232421875\n",
+      "Updating alpha to: 106.83052062988281\n",
+      "Updating rho, dag penalty prev:  1.1862878799\n",
+      "Updating alpha to: 534.152587890625\n",
+      "Updating rho, dag penalty prev:  0.0000000000\n",
+      "Updating alpha to: 2670.762939453125\n",
+      "Updating alpha to: 13353.814453125\n",
+      "Updating alpha to: 66769.0703125\n"
      ]
     }
    ],
@@ -691,7 +699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -700,7 +708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -712,12 +720,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -769,18 +777,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'Tech Support': (13534.717, 161.19157),\n",
-       " 'Discount': (6590.535, 144.62265),\n",
-       " 'New Engagement Strategy': (3145.7988, 169.46815)}"
+       "{'Tech Support': (8176.4062, 153.94827),\n",
+       " 'Discount': (5063.41, 151.71974),\n",
+       " 'New Engagement Strategy': (182.05078, 161.37265)}"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -789,14 +797,14 @@
     "revenue_estimated_ate = {}\n",
     "num_samples = 10 if test_run else 20000\n",
     "sample_shape = torch.Size([num_samples])\n",
-    "transform = data_module.normalizer.transform_modules[outcome]().inv\n",
+    "normalizer = data_module.normalizer\n",
     "\n",
     "for treatment in treatment_columns:\n",
     "    intervention_a = TensorDict({treatment: torch.tensor([1.0])}, batch_size=tuple())\n",
     "    intervention_b = TensorDict({treatment: torch.tensor([0.0])}, batch_size=tuple())\n",
     "\n",
-    "    rev_a_samples = transform(sem.do(interventions=intervention_a).sample(sample_shape)[outcome])\n",
-    "    rev_b_samples = transform(sem.do(interventions=intervention_b).sample(sample_shape)[outcome])\n",
+    "    rev_a_samples = normalizer.inv(sem.do(interventions=intervention_a).sample(sample_shape))[outcome]\n",
+    "    rev_b_samples = normalizer.inv(sem.do(interventions=intervention_b).sample(sample_shape))[outcome]\n",
     "\n",
     "    ate_mean = rev_a_samples.mean(0) - rev_b_samples.mean(0)\n",
     "    ate_std = np.sqrt((rev_a_samples.var(0) + rev_b_samples.var(0)) / num_samples)\n",
@@ -818,12 +826,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1600x400 with 3 Axes>"
       ]
@@ -855,21 +863,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'Tech Support': array([27994.332 ,  6760.779 , 17379.525 , ...,  7761.703 ,  7245.6387,\n",
-       "         8695.703 ], dtype=float32),\n",
-       " 'Discount': array([22081.3    ,  -525.4912 ,  5963.828  , ...,  8198.828  ,\n",
-       "          520.78613,  8621.551  ], dtype=float32),\n",
-       " 'New Engagement Strategy': array([22081.3    ,  -424.02734, -2353.0273 , ...,     0.     ,\n",
-       "            0.     , 22103.     ], dtype=float32)}"
+       "{'Tech Support': array([ 8218.861 ,  5665.8604,  9793.176 , ...,  7723.617 ,  5633.7676,\n",
+       "        11714.125 ], dtype=float32),\n",
+       " 'Discount': array([10727.676  , -1442.8438 ,  6223.4414 , ...,  6601.885  ,\n",
+       "          423.50586,  7198.4336 ], dtype=float32),\n",
+       " 'New Engagement Strategy': array([ 21.433594, -23.797852,  80.54297 , ..., -39.203125, -32.075195,\n",
+       "        -72.13281 ], dtype=float32)}"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -881,9 +889,9 @@
     "\n",
     "for treatment in treatment_columns:\n",
     "    do_sem = sem.do(interventions=TensorDict({treatment: torch.tensor([1.0])}, batch_size=tuple()))\n",
-    "    do_a_cfs = transform(do_sem.noise_to_sample(base_noise)[outcome]).cpu().detach().numpy()[:, 0]\n",
+    "    do_a_cfs = normalizer.inv(do_sem.noise_to_sample(base_noise))[outcome].cpu().detach().numpy()[:, 0]\n",
     "    do_sem = sem.do(interventions=TensorDict({treatment: torch.tensor([0.0])}, batch_size=tuple()))\n",
-    "    do_b_cfs = transform(do_sem.noise_to_sample(base_noise)[outcome]).cpu().detach().numpy()[:, 0]\n",
+    "    do_b_cfs = normalizer.inv(do_sem.noise_to_sample(base_noise))[outcome].cpu().detach().numpy()[:, 0]\n",
     "    revenue_estimated_ite[treatment] = do_a_cfs - do_b_cfs\n",
     "\n",
     "revenue_estimated_ite"
@@ -891,12 +899,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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VCoD+91axtm/fDqlUinbt2uHp06fKn6CgIDg7O+Po0aMA/suru3fvXqPm7WzTpo3K6HbFvbtHjx4q56rpnp77Pv3q1Ss8ffoU77zzDgRBUEs/oom5z52IiKiwcPo5ERGRCK6urgDUO+za3Lt3D1ZWVmora3t5ecHNzQ337t1T2Z47cAn8t6JyxYoVNW7PmxfNysoKlStXVtmmWF04d762vXv34quvvkJcXJxKbs+8OeEAwM/PT+v55TZv3jyEh4ejYsWKCAoKQseOHTFw4EBlfRTnWr16dbX31qhRAydPnlTZ5uDgoMxhp1C6dGm1c85L2+fY2dmhcuXKatdcDEXwIffK3wq7du1CVlYWLl26hE8++URt/1tvvaW2kMu9e/dQtWpVlaArkDN1OPc56EvMNbt37x68vb3VArSa/l0MZWNjo/c0fX1UqFBB7Xe1dOnSuHz5ssk+U8HJyUllxXRNdP0taNO8eXNkZ2cjJiYGFStWxOPHj9G8eXNcu3ZNJagZEBAAd3d3g8/BlNdP8TeSNzhZoUKFfK+bvvdWsW7duoWUlBR4enpq3K9YLCwkJAQ9evTArFmzsGjRIrRs2RLdunVD3759YW9vb/DnF+Sefv/+fUyfPh27d+9Wu++lpKTo/GxznzsREVFhYVCTiIhIBFdXV5QvXx5Xr17V632agoWa5F5kRsx2wYDFOE6cOIEuXbqgRYsWWLFiBby9vWFra4t169Zh8+bNauVzjxbKT69evdC8eXPs3LkTf/zxB+bPn4+5c+fit99+MyhPn7ZzNocqVarAxsZG4797SEgIAGhd+Vjs9dNE2++NtsWSLOWa2dvbqwVsgfzPR5+6G/PvwRQM/Vto0KABHBwc8Oeff6JSpUrw9PREtWrV0Lx5c6xYsQIZGRk4ceIE3nvvvQLVz5TXT/E3kvdBji41atQAAOUiOsYil8vh6emJTZs2adyveAggkUiwY8cOnDlzBnv27MHBgwcxZMgQLFiwAGfOnNE4SlsMQ+/pMpkM7dq1w7NnzzB58mTUqFEDTk5OePDgAQYNGgS5XK7zs8197kRERIWF08+JiIhE6tSpE+7cuYOYmBidZX18fCCXy3Hr1i2V7Y8ePcKLFy/g4+Nj1LrJ5XK1KaR///03ACinQP76669wcHBQdlzfffddnSPPxPL29sbo0aPx+++/IyEhAWXKlMHXX38NAMpzvXnzptr7bt68abRroe1zMjMzkZCQYNDnODk5oWXLljh+/LjaSu2G1vHWrVtqgYkbN24o9wM5o+cAqK1MbuhITsWxExMT1Uadavp3MbbSpUtrXGU97/mIfQhgyfL7W9BGMQ38xIkTOHHihHKRn+bNmyMjIwObNm3Co0eP0KJFi3yPY67r9/LlS+zcuRMVK1ZUjjoWq1q1aqhevTp27dqlcUS0ofz9/ZGcnIymTZuibdu2aj9169ZVKd+kSRN8/fXXOH/+PDZt2oRr165h69atAAr3ul65cgV///03FixYgMmTJ6Nr165o27Ytypcvr1ZWW72Mee5ERESWjEFNIiIikSZNmgQnJycMGzYMjx49Utt/584dLFmyBADQsWNHAMDixYtVyixcuBAA1FYaN4Zly5Yp/18QBCxbtgy2trZo06YNgJwRQhKJRGW03927d/H7778b/JkymUxtOqSnpyfKly+vnN7eoEEDeHp6YtWqVSpT3g8cOIDr168b7Vq0bdsWdnZ2+O6771RGnv34449ISUkx+HOmT58OmUyG/v37awy66DPKrWPHjkhKSsK2bduU27Kzs7F06VI4OzsrR3/6+PjA2toaf/75p8r7V6xYYdA5KD47OzsbK1euVG6TyWRYunSpwccUy9/fH2fOnEFmZqZy2969e/Hvv/+qlFOsIq4pAGrpxPwt5Kd58+b466+/cPToUWVQ08PDAzVr1sTcuXOVZfJjjuv35s0bDBgwAM+ePcNnn31mUABw1qxZSE5OxrBhw5Cdna22/48//sDevXv1OmavXr0gk8kwe/ZstX3Z2dnKa/T8+XO1v+F69eoBgPLfrVSpUgAK57oqRnLmrpMgCMq2JTdt/97GPHciIiJLxunnREREIvn7+2Pz5s344IMPULNmTQwcOBCBgYHIzMzE6dOnsX37dgwaNAgAULduXYSHh2P16tV48eIFQkJCcPbsWWzYsAHdunVTLpJhLA4ODoiKikJ4eDgaN26MAwcOYN++fZg2bZpyqmFYWBgWLlyIDh06oG/fvnj8+DGWL1+OKlWqGJxTLy0tDRUqVEDPnj1Rt25dODs749ChQzh37hwWLFgAIGchnLlz52Lw4MEICQlBnz598OjRIyxZsgS+vr6YMGGCUa5B2bJlMXXqVMyaNQsdOnRAly5dcPPmTaxYsQINGzZUWSBFH82bN8eyZcswduxYVK1aFf369UONGjWQmZmJv//+G5s2bYKdnR28vLx0HuvDDz/E999/j0GDBuHChQvw9fXFjh07cOrUKSxevFiZj1AqleL999/H0qVLIZFI4O/vj7179ypz4Rmic+fOaNq0KaZMmYK7d+8iICAAv/32m6gcfQU1bNgw7NixAx06dECvXr1w584dbNy4Ef7+/irl/P394ebmhlWrVsHFxQVOTk5o3Lix6PyuppSSkoKNGzdq3Ne/f39Rfwv5ad68Ob7++mv8+++/KsHLFi1a4Pvvv4evr6/OfKWmvn4PHjxQXoOXL18iPj4e27dvR1JSEj7++GOMGDFC7T1///23xutWrlw5tGvXDgDwwQcf4MqVK/j6669x8eJF9OnTBz4+PkhOTkZUVBQOHz6sMUVGfkJCQjBixAhERkYiLi4O7du3h62tLW7duoXt27djyZIl6NmzJzZs2IAVK1bgvffeg7+/P9LS0rBmzRq4uroqH045OjoiICAA27ZtQ7Vq1eDu7o7AwEAEBgbqewl1qlGjBvz9/fHJJ5/gwYMHcHV1xa+//qoxp3BQUBAAYNy4cQgNDYW1tTV69+5t1HMnIiKyaOZZdJ2IiKjo+vvvv4Xhw4cLvr6+gp2dneDi4iI0bdpUWLp0qZCenq4sl5WVJcyaNUvw8/MTbG1thYoVKwpTp05VKSMIguDj4yOEhYWpfQ4AISIiQmVbQkKCAECYP3++clt4eLjg5OQk3LlzR2jfvr1QqlQpoVy5csKMGTMEmUym8v4ff/xRqFq1qmBvby/UqFFDWLdunTBjxgwh71cCTZ+de9+MGTMEQRCEjIwM4dNPPxXq1q0ruLi4CE5OTkLdunWFFStWqL1v27Ztwttvvy3Y29sL7u7uQr9+/YT//e9/KmUU55KXpjpqs2zZMqFGjRqCra2tUK5cOWHUqFHC8+fPNR7vyZMnoo4pCIJw8eJFYeDAgUKlSpUEOzs7wcnJSahTp47w8ccfC7dv31YpGxISItSqVUvjcR49eiQMHjxY8PDwEOzs7ITatWsL69atUyv35MkToUePHkKpUqWE0qVLCyNGjBCuXr0qAFApr881S05OFgYMGCC4uroKUqlUGDBggHDx4kW1Y+oyf/58AYCQkJCgsx4KCxYsEN566y3B3t5eaNq0qXD+/HkhJCRECAkJUSm3a9cuISAgQLCxsVGpl7ZrGh4eLvj4+Oiss7a/M0EQhHPnzuV7DUJCQgQAWn8EQb+/BU1SU1MFa2trwcXFRcjOzlZu37hxowBAGDBggMZ6Feb1U5yvRCIRXF1dhVq1agnDhw8X/vrrL43vye+a5a23IAjC4cOHha5duwqenp6CjY2NULZsWaFz587Crl27lGU03QPzs3r1aiEoKEhwdHQUXFxchNq1awuTJk0SHj58KAiCIMTGxgp9+vQRKlWqJNjb2wuenp5Cp06dhPPnz6sc5/Tp00JQUJBgZ2encg8Ue//UVu+jR48KAITt27crt8XHxwtt27YVnJ2dBQ8PD2H48OHCpUuX1H5Hs7OzhbFjxwply5YVJBKJWj2Mde5ERESWSiIIFpJZnYiIiAwyaNAg7Nixw6j56IiIiIiIiCwZc2oSERERERERERFRkcKgJhERERERERERERUpDGoSERERERERERFRkcKgJlER1rJlS5OsvElERcv69euZT5OKlZkzZ0IikZi7GkRERERkwRjUJDIiiUQi6ufYsWPmrioAIDMzE0uWLMHbb78NV1dXuLm5oVatWvjwww9x48YNc1fPqL755hv8/vvv5q4GEVGJtH79epV20MHBAeXLl0doaCi+++47pKWlmbuKhYJtERFZKsV92sHBAQ8ePFDbbymDKVq2bKm1j1WjRg1zV69E2b9/P2bOnCm6vFwux08//YTGjRvD3d0dLi4uqFatGgYOHIgzZ84oy8XHx2PmzJm4e/eu8SsNYMWKFVi/fr1Jjk2Fz8bcFSAqTn7++WeV1z/99BOio6PVttesWbMwq6VVjx49cODAAfTp0wfDhw9HVlYWbty4gb179+Kdd94pVl8MvvnmG/Ts2RPdunUzd1WIiEqsL7/8En5+fsjKykJSUhKOHTuG8ePHY+HChdi9ezfq1KkDAPj8888xZcoUM9fW+NgWEZGly8jIwJw5c7B06VJzV0WrChUqIDIyUm27VCo1Q21Krv3792P58uWiA5vjxo3D8uXL0bVrV/Tr1w82Nja4efMmDhw4gMqVK6NJkyYAcoKas2bNQsuWLeHr62v0eq9YsQIeHh4YNGiQ0Y9NhY9BTSIj6t+/v8rrM2fOIDo6Wm27JTh37hz27t2Lr7/+GtOmTVPZt2zZMrx48cI8FTMiQRCQnp4OR0dHc1eFiIgAvPvuu2jQoIHy9dSpU3HkyBF06tQJXbp0wfXr1+Ho6AgbGxvY2PBrKhFRYatXrx7WrFmDqVOnonz58uaujkZSqdQi+1ek3aNHj7BixQoMHz4cq1evVtm3ePFiPHnyxKDjsr9HnH5OVMjkcjkWL16MWrVqwcHBAeXKlcOIESPw/PlztbIHDhxASEgIXFxc4OrqioYNG2Lz5s1q5eLj49GqVSuUKlUKb731FubNm6ezHnfu3AEANG3aVG2ftbU1ypQpo3w9aNAgjU/JNOU8k0gkGDNmDDZt2oTq1avDwcEBQUFB+PPPPzW+98aNG+jVqxdcXV1RpkwZfPTRR0hPT1cpm52djdmzZ8Pf3x/29vbw9fXFtGnTkJGRoVLO19cXnTp1wsGDB9GgQQM4Ojri+++/h0QiwatXr7Bhwwbl9BQ+mSMisgytW7fGF198gXv37mHjxo0ANLcv0dHRaNasGdzc3ODs7Izq1aurPZRLT0/HzJkzUa1aNTg4OMDb2xvdu3dXtnkA8OrVK3z88ceoWLEi7O3tUb16dXz77bcQBEFZ5u7du5BIJBqnp0kkEpVRKYq63r59G4MGDYKbmxukUikGDx6M169fq7yPbRERWbpp06ZBJpNhzpw5ospv3LgRQUFBcHR0hLu7O3r37o1///1Xuf+7776DtbW1yoCJBQsWQCKRYOLEicptMpkMLi4umDx5slHOQ+y9GQDevHmDcePGwcPDAy4uLujSpQsePHigdr+/d+8eRo8ejerVq8PR0RFlypTB+++/r3Ga9OXLlxESEgJHR0dUqFABX331FdatWweJRKJW/sCBA2jevDmcnJzg4uKCsLAwXLt2TaXMoEGD4OzsjPv376NTp05wdnbGW2+9heXLlwMArly5gtatW8PJyQk+Pj4a+4wvXrzA+PHjle1flSpVMHfuXMjlcmUZRfv37bffYvXq1cr+V8OGDXHu3DmV+ig+O3cKAG0SEhIgCILGvqdEIoGnpyeAnDQI77//PgCgVatWaunbtPX3AGDdunVo3bo1PD09YW9vj4CAAKxcuVLls3x9fXHt2jUcP35ceeyWLVvqdY0AIDk5GQMGDFCmcAsPD8elS5dUvjso/r0vXryods7ffPMNrK2tNaZ6IP3wEThRIRsxYgTWr1+PwYMHY9y4cUhISMCyZctw8eJFnDp1Cra2tgBybuhDhgxBrVq1MHXqVLi5ueHixYuIiopC3759lcd7/vw5OnTogO7du6NXr17YsWMHJk+ejNq1a+Pdd9/VWg8fHx8AwKZNm9C0aVOjjog5fvw4tm3bhnHjxsHe3h4rVqxAhw4dcPbsWbVcPL169YKvry8iIyNx5swZfPfdd3j+/Dl++uknZZlhw4Zhw4YN6NmzJz7++GP89ddfiIyMxPXr17Fz506V4928eRN9+vTBiBEjMHz4cFSvXh0///wzhg0bhkaNGuHDDz8EAPj7+xvtfImIqGAGDBiAadOm4Y8//sDw4cPV9l+7dg2dOnVCnTp18OWXX8Le3h63b9/GqVOnlGVkMhk6deqEw4cPo3fv3vjoo4+QlpaG6OhoXL16Ff7+/hAEAV26dMHRo0cxdOhQ1KtXDwcPHsSnn36KBw8eYNGiRQafQ69eveDn54fIyEjExsbihx9+gKenJ+bOnQsAbIuIqEjw8/PDwIEDsWbNGkyZMiXf0Zpff/01vvjiC/Tq1QvDhg3DkydPsHTpUrRo0QIXL16Em5sbmjdvDrlcjpMnT6JTp04AgBMnTsDKygonTpxQHuvixYt4+fIlWrRoobOOMpkMT58+Vdvu6OgIJycnlW267s1AToDul19+wYABA9CkSRMcP34cYWFhasc/d+4cTp8+jd69e6NChQq4e/cuVq5ciZYtWyI+Ph6lSpUCADx48EAZkJs6dSqcnJzwww8/wN7eXu2YP//8M8LDwxEaGoq5c+fi9evXWLlyJZo1a4aLFy+qDCyRyWR499130aJFC8ybNw+bNm3CmDFj4OTkhM8++wz9+vVD9+7dsWrVKgwcOBDBwcHw8/MDALx+/RohISF48OABRowYgUqVKuH06dOYOnUqEhMTsXjxYpV6bd68GWlpaRgxYgQkEgnmzZuH7t27459//oGtrS1GjBiBhw8faky1pomi77l9+3a8//77ymuVV4sWLTBu3Dh89913mDZtmjJtW+70bZr6ewCwcuVK1KpVC126dIGNjQ327NmD0aNHQy6XIyIiAkDOqNCxY8fC2dkZn332GQCgXLlyel0juVyOzp074+zZsxg1ahRq1KiBXbt2ITw8XOVcevbsiYiICGzatAlvv/22yr5NmzahZcuWeOutt3ReO9JBICKTiYiIEHL/mZ04cUIAIGzatEmlXFRUlMr2Fy9eCC4uLkLjxo2FN2/eqJSVy+XK/w8JCREACD/99JNyW0ZGhuDl5SX06NEj37rJ5XLl+8uVKyf06dNHWL58uXDv3j21suHh4YKPj4/a9hkzZgh5byMABADC+fPnldvu3bsnODg4CO+9957ae7t06aLy/tGjRwsAhEuXLgmCIAhxcXECAGHYsGEq5T755BMBgHDkyBHlNh8fHwGAEBUVpVZXJycnITw8XPsFISIik1m3bp0AQDh37pzWMlKpVHj77bcFQVBvXxYtWiQAEJ48eaL1/WvXrhUACAsXLlTbp2g7f//9dwGA8NVXX6ns79mzpyCRSITbt28LgiAICQkJAgBh3bp1ascCIMyYMUP5WlHXIUOGqJR77733hDJlyqhsY1tERJYq9336zp07go2NjTBu3Djl/pCQEKFWrVrK13fv3hWsra2Fr7/+WuU4V65cEWxsbJTbZTKZ4OrqKkyaNEkQhJz7cZkyZYT3339fsLa2FtLS0gRBEISFCxcKVlZWwvPnz/Otp6L/oulnxIgRynJi780XLlwQAAjjx49XKTdo0CC1+/3r16/V6hMTE6PWHxs7dqwgkUiEixcvKrclJycL7u7uAgAhISFBEARBSEtLE9zc3IThw4erHDMpKUmQSqUq28PDwwUAwjfffKPc9vz5c8HR0VGQSCTC1q1bldtv3LihVvfZs2cLTk5Owt9//63yWVOmTBGsra2F+/fvC4LwX/tXpkwZ4dmzZ8pyu3btEgAIe/bsUW7L29fVZeDAgQIAoXTp0sJ7770nfPvtt8L169fVym3fvl0AIBw9elRtX379PU3/PqGhoULlypVVttWqVUsICQlRKyv2Gv36668CAGHx4sXKMjKZTGjdurXad4c+ffoI5cuXF2QymXJbbGys1u8YpD9OPycqRNu3b4dUKkW7du3w9OlT5U9QUBCcnZ1x9OhRADlT7NLS0jBlyhQ4ODioHCPvsH5nZ2eVnDJ2dnZo1KgR/vnnn3zrIpFIcPDgQXz11VcoXbo0tmzZgoiICPj4+OCDDz4oUE7N4OBgBAUFKV9XqlQJXbt2xcGDByGTyVTKKp6aKYwdOxZATuLp3P/NPT0FAD7++GMAwL59+1S2+/n5ITQ01OC6ExGReTg7O2tdBd3NzQ0AsGvXLrUpYAq//vorPDw8lO1Iboq2c//+/bC2tsa4ceNU9n/88ccQBAEHDhwwuP4jR45Ued28eXMkJycjNTXV4GMSEZlD5cqVMWDAAKxevRqJiYkay/z222+Qy+Xo1auXSr/Gy8sLVatWVfZrrKys8M477yhTUV2/fh3JycmYMmUKBEFATEwMgJzRm4GBgcr7fX58fX0RHR2t9jN+/Hi1srruzVFRUQCA0aNHq5TT1JbkztuYlZWF5ORkVKlSBW5uboiNjVXui4qKQnBwMOrVq6fc5u7ujn79+qkcLzo6Gi9evECfPn1UrqG1tTUaN26svIa5DRs2TPn/bm5uqF69OpycnNCrVy/l9urVq8PNzU2lP7h9+3Y0b94cpUuXVvmstm3bQiaTqaUK++CDD1C6dGmV6wZAZx8zP+vWrcOyZcvg5+eHnTt34pNPPkHNmjXRpk0bvaZha+vv5f73SUlJwdOnTxESEoJ//vkHKSkpOo8r9hpFRUXB1tZWZWaJlZWVWr8WAAYOHIiHDx+q/Ftu2rQJjo6O6NGjh+hzJu04/ZyoEN26dQspKSnKnCF5PX78GMB/+S7zTtXWpEKFCmqBztKlS+Py5cs632tvb4/PPvsMn332GRITE3H8+HEsWbIEv/zyC2xtbZW5zfRVtWpVtW3VqlXD69ev8eTJE3h5eWkt6+/vDysrK2WumXv37sHKygpVqlRRKefl5QU3Nzfcu3dPZbtiigURERUtL1++1No+fvDBB/jhhx8wbNgwTJkyBW3atEH37t3Rs2dPWFnlPKO/c+cOqlevnm86lXv37qF8+fJwcXFR2a6Y1pa3TdFHpUqVVF4rOoPPnz+Hq6urwcclIjKHzz//HD///DPmzJmDJUuWqO2/desWBEHQ+L0fgDKlFpATEJs5cybevHmDEydOwNvbG/Xr10fdunVx4sQJtGvXDidPnlQJzOXHyckJbdu2FVVW171Z0dfI24fI2/cAcnJvRkZGYt26dXjw4IFKLubcQbN79+4hODhY7f15j3nr1i0AObmlNcnbdjg4OKBs2bIq26RSqcb+oFQqVVmz4datW7h8+bLa+xUU/VCF/K6boRSBv4iICCQnJ+PUqVNYtWoVDhw4gN69e6ukI8iPtv7eqVOnMGPGDMTExKjlTU1JSYFUKs33uGKv0b179+Dt7a02hV7T70y7du3g7e2NTZs2oU2bNpDL5diyZQu6du2q9l2EDMOgJlEhksvl8PT0xKZNmzTu13YDzY+1tbXG7bkbWTG8vb3Ru3dv9OjRA7Vq1cIvv/yC9evXw8bGRmvS57yjLo1B22fll3g6N658R0RU9Pzvf/9DSkqKxg4BkHNv//PPP3H06FHs27cPUVFR2LZtG1q3bo0//vhDa1toKEPaPWO1x0RElqBy5cro378/Vq9ejSlTpqjtl8vlkEgkOHDggMb7n7Ozs/L/mzVrhqysLMTExODEiRPKUX/NmzfHiRMncOPGDTx58kS53ZiMeW8eO3Ys1q1bh/HjxyM4OBhSqRQSiQS9e/fWOosgP4r3/PzzzyqDPhTyPqTTdi5izlEul6Ndu3aYNGmSxrLVqlXT+5gFUaZMGXTp0gVdunRBy5Ytcfz4cdy7d0+ZezM/mvp7d+7cQZs2bVCjRg0sXLgQFStWhJ2dHfbv349FixaJ+vfR9xqJYW1tjb59+2LNmjVYsWIFTp06hYcPH6rMtKSCYVCTqBD5+/vj0KFDaNq0ab7BN8XCAVevXtXawTMVW1tb1KlTB7du3VJOISldurTG6ejaRrQonjrm9vfff6NUqVJqgdtbt26pPG27ffs25HK5Mim2j48P5HI5bt26pZIg+tGjR3jx4oWohg8QHxQlIqLCp1hkIL/0IVZWVmjTpg3atGmDhQsX4ptvvsFnn32Go0ePom3btvD398dff/2FrKwslRFCufn4+ODQoUNIS0tTGSFx48YN5X7gvxEpedu+gozkBNgWEVHR8vnnn2Pjxo0qi+ooKBZf8/Pz0xnsadSoEezs7HDixAmcOHECn376KYCcRWHWrFmDw4cPK18XNkVfIyEhQWXU6e3bt9XK7tixA+Hh4ViwYIFyW3p6ulpb4ePjo/H9ebcp+nyenp6iR54ayt/fHy9fvjTq5xirTWvQoAGOHz+OxMRE+Pj4GHTcPXv2ICMjA7t371YZZappCr+244u9Rj4+Pjh69Chev36tMlpT0785kDMFfcGCBdizZw8OHDiAsmXLMl2aETGnJlEh6tWrF2QyGWbPnq22Lzs7W9kgtm/fHi4uLoiMjER6erpKOWM9Hbt16xbu37+vtv3FixeIiYlB6dKllQFIf39/pKSkqExpT0xMVFt5XCEmJkYlr8y///6LXbt2oX379mpP/ZYvX67yeunSpQCgXLm9Y8eOAKC2It/ChQsBQOPKhJo4OTkVKE8oERGZxpEjRzB79mz4+fmp5RtTePbsmdo2Ra6yjIwMAECPHj3w9OlTLFu2TK2sou3s2LEjZDKZWplFixZBIpEo2x5XV1d4eHio5RhbsWKFfieXB9siIipK/P390b9/f3z//fdISkpS2de9e3dYW1tj1qxZav0TQRCQnJysfO3g4ICGDRtiy5YtuH//vspIzTdv3uC7776Dv78/vL29TX9SeSiCS3nv74o+SW7W1tZq57p06VK1UfyhoaGIiYlBXFycctuzZ8/UZuuFhobC1dUV33zzDbKystQ+78mTJ3qdS3569eqFmJgYHDx4UG3fixcvkJ2drfcxFSvNi2nXkpKSEB8fr7Y9MzMThw8fVkk3ps9xFRR9zLwpAdatW6ex3pqOLfYahYaGIisrC2vWrFHul8vlav1ahTp16qBOnTr44Ycf8Ouvv6J37975psoh/fBKEhWikJAQjBgxApGRkYiLi0P79u1ha2uLW7duYfv27ViyZAl69uwJV1dXLFq0CMOGDUPDhg3Rt29flC5dGpcuXcLr16+xYcOGAtfl0qVL6Nu3L9599100b94c7u7uePDgATZs2ICHDx9i8eLFysahd+/emDx5Mt577z2MGzcOr1+/xsqVK1GtWjWV4KVCYGAgQkNDMW7cONjb2yu/JMyaNUutbEJCArp06YIOHTogJiYGGzduRN++fVG3bl0AQN26dREeHo7Vq1fjxYsXCAkJwdmzZ7FhwwZ069YNrVq1EnW+QUFBOHToEBYuXIjy5cvDz88PjRs3NvTyERGRAQ4cOIAbN24gOzsbjx49wpEjRxAdHQ0fHx/s3r1bbXE8hS+//BJ//vknwsLC4OPjg8ePH2PFihWoUKECmjVrBiBnJMRPP/2EiRMn4uzZs2jevDlevXqFQ4cOYfTo0ejatSs6d+6MVq1a4bPPPsPdu3dRt25d/PHHH9i1axfGjx+vHDUD5CzGMGfOHAwbNgwNGjTAn3/+ib///rtA58+2iIiKms8++ww///wzbt68iVq1aim3+/v746uvvsLUqVNx9+5ddOvWDS4uLkhISMDOnTvx4Ycf4pNPPlGWb968OebMmQOpVIratWsDyBmhWL16ddy8eRODBg0SXaeUlBStuf/1ndYbFBSEHj16YPHixUhOTkaTJk1w/Phx5f0+96i+Tp064eeff4ZUKkVAQABiYmJw6NAhlClTRuWYkyZNwsaNG9GuXTuMHTsWTk5O+OGHH1CpUiU8e/ZMeUxXV1esXLkSAwYMQP369dG7d2+ULVsW9+/fx759+9C0aVOND+oM8emnn2L37t3o1KkTBg0ahKCgILx69QpXrlzBjh07cPfuXXh4eOh1TMXCsOPGjUNoaCisra3Ru3dvjWX/97//oVGjRmjdujXatGkDLy8vPH78GFu2bMGlS5cwfvx45efXq1cP1tbWmDt3LlJSUmBvb4/WrVtrzbsN5AwKsrOzQ+fOnTFixAi8fPkSa9asgaenp9piV0FBQVi5ciW++uorVKlSBZ6enmjdurXoa9StWzc0atQIH3/8MW7fvo0aNWpg9+7dygewmkaCDhw4UPn3wKnnRlb4C64TlRwRERGCpj+z1atXC0FBQYKjo6Pg4uIi1K5dW5g0aZLw8OFDlXK7d+8W3nnnHcHR0VFwdXUVGjVqJGzZskW5PyQkRKhVq5ba8cPDwwUfH5986/bo0SNhzpw5QkhIiODt7S3Y2NgIpUuXFlq3bi3s2LFDrfwff/whBAYGCnZ2dkL16tWFjRs3CjNmzFA7PwBCRESEsHHjRqFq1aqCvb298PbbbwtHjx5VKad4b3x8vNCzZ0/BxcVFKF26tDBmzBjhzZs3KmWzsrKEWbNmCX5+foKtra1QsWJFYerUqUJ6erpKOR8fHyEsLEzj+d64cUNo0aKF4OjoKAAQwsPD870+RERkPOvWrRMAKH/s7OwELy8voV27dsKSJUuE1NRUlfJ525fDhw8LXbt2FcqXLy/Y2dkJ5cuXF/r06SP8/fffKu97/fq18NlnnynbCy8vL6Fnz57CnTt3lGXS0tKECRMmCOXLlxdsbW2FqlWrCvPnzxfkcrnasYYOHSpIpVLBxcVF6NWrl/D48WMBgDBjxgy1uj558kTjOSckJCi3sS0iIkuluGedO3dObV94eLgAQGO/49dffxWaNWsmODk5CU5OTkKNGjWEiIgI4ebNmyrl9u3bJwAQ3n33XZXtw4YNEwAIP/74o6h6hoSEqLQneX8U9Lk3v3r1SoiIiBDc3d0FZ2dnoVu3bsLNmzcFAMKcOXOU5Z4/fy4MHjxY8PDwEJydnYXQ0FDhxo0bgo+Pj9r9/OLFi0Lz5s0Fe3t7oUKFCkJkZKTw3XffCQCEpKQklbJHjx4VQkNDBalUKjg4OAj+/v7CoEGDhPPnzyvLhIeHC05OThqvh6Z/F039orS0NGHq1KlClSpVBDs7O8HDw0N45513hG+//VbIzMwUBEEQEhISBADC/Pnz1Y6Zt/3Lzs4Wxo4dK5QtW1aQSCQa+70KqampwpIlS4TQ0FChQoUKgq2treDi4iIEBwcLa9asUWuD16xZI1SuXFmwtrYWACj7kvn193bv3i3UqVNHcHBwEHx9fYW5c+cKa9euVfv3TkpKEsLCwgQXFxcBgBASEqLXNRIEQXjy5InQt29fwcXFRZBKpcKgQYOEU6dOCQCErVu3qtUtMTFRsLa2FqpVq6b1GpFhJILA7OVEZDwSiQQRERE6nyrOnDkTs2bNwpMnT/R+KkhERERERGQqcXFxePvtt7Fx40atqVH0NX78eHz//fd4+fKl0Re4I/P7/fff8d577+HkyZNo2rSpyr6nT5/C29sb06dPxxdffGGmGhZPzKlJRERERERERCXSmzdv1LYtXrwYVlZWBi9elPeYycnJ+Pnnn9GsWTMGNIuBvP++MpkMS5cuhaurK+rXr69Wfv369ZDJZBgwYEBhVbHEYE5NIiIiIiIiIiqR5s2bhwsXLqBVq1awsbHBgQMHcODAAXz44YeoWLGiQccMDg5Gy5YtUbNmTTx69Ag//vgjUlNTOUqvmBg7dizevHmD4OBgZGRk4LfffsPp06fxzTffwNHRUVnuyJEjiI+Px9dff41u3brB19fXfJUuphjUJCIiIiIiIqIS6Z133kF0dDRmz56Nly9folKlSpg5cyY+++wzg4/ZsWNH7NixA6tXr4ZEIkH9+vXx448/GjzykyxL69atsWDBAuzduxfp6emoUqUKli5dijFjxqiU+/LLL3H69Gk0bdoUS5cuNVNtizfm1CQiIiIiIiIiIqIihTk1iYiIiIiIiIiIqEhhUJOIiIiIiIiIiIiKFObUNBK5XI6HDx/CxcUFEonE3NUhIqJ8CIKAtLQ0lC9fHlZWfL6nCds1IqKig+1a/timEREVHfq0aQxqGsnDhw8NXhmNiIjM499//0WFChXMXQ2LxHaNiKjoYbumGds0IqKiR0ybxqCmkbi4uADIueiurq5mrg0REeUnNTUVFStWVN67SR3bNSKiooPtWv7YphERFR36tGkMahqJYhqDq6srG0oioiKCU9C0Y7tGRFT0sF3TjG0aEVHRI6ZNY8IVIiIiIiIiIiIiKlIY1CQiIiIiIiIiIqIihUFNIiIiIiIiIiIiKlIY1CQiIiIiIiIiIqIihUFNIiIiIiIiIiIiKlIY1CQiIiIiIiIiIqIixcbcFSAiyo9MLuBswjM8TkuHp4sDGvm5w9pKYu5qEVEJwPsPEREREZHlYlCTiCxW1NVEzNoTj8SUdOU2b6kDZnQOQIdAbzPWjIiKO95/iIiIiIgsG6efE5FFirqaiFEbY1UCCgCQlJKOURtjEXU10Uw1I6LijvcfIiIiIiLLx6AmEVkcmVzArD3xEDTsU2ybtSceMrmmEkREhuP9h4iIiIioaDBrUDMyMhINGzaEi4sLPD090a1bN9y8eVO5/9mzZxg7diyqV68OR0dHVKpUCePGjUNKSorKcSQSidrP1q1bVcocO3YM9evXh729PapUqYL169er1Wf58uXw9fWFg4MDGjdujLNnz5rkvIkof2cTnqmNkMpNAJCYko6zCc8Kr1JEVCLw/kNEREREVDSYNah5/PhxRERE4MyZM4iOjkZWVhbat2+PV69eAQAePnyIhw8f4ttvv8XVq1exfv16REVFYejQoWrHWrduHRITE5U/3bp1U+5LSEhAWFgYWrVqhbi4OIwfPx7Dhg3DwYMHlWW2bduGiRMnYsaMGYiNjUXdunURGhqKx48fm/w6EJGqx2naAwqGlCMiEov3HyIiIiKiosGsCwVFRUWpvF6/fj08PT1x4cIFtGjRAoGBgfj111+V+/39/fH111+jf//+yM7Oho3Nf9V3c3ODl5eXxs9ZtWoV/Pz8sGDBAgBAzZo1cfLkSSxatAihoaEAgIULF2L48OEYPHiw8j379u3D2rVrMWXKFKOeNxHlz9PFwajlqPjgatRkavrcf/j7SERERERkPha1+rliWrm7u3u+ZVxdXVUCmgAQERGBYcOGoXLlyhg5ciQGDx4MiSSnYxETE4O2bduqlA8NDcX48eMBAJmZmbhw4QKmTp2q3G9lZYW2bdsiJibGGKdGRHpo5OcOb6kDklLSNea1kwDwkuYEEKjk4GrUVBjE3n+ev8pEs7lH+PtIRERERGQmFrNQkFwux/jx49G0aVMEBgZqLPP06VPMnj0bH374ocr2L7/8Er/88guio6PRo0cPjB49GkuXLlXuT0pKQrly5VTeU65cOaSmpuLNmzd4+vQpZDKZxjJJSUka65KRkYHU1FSVHyIyDmsrCWZ0DgCQE0DITfF6RucAjogqQbgaNRUWMfefLnW9EbGZv49EREREROZkMUHNiIgIXL16VW2BH4XU1FSEhYUhICAAM2fOVNn3xRdfoGnTpnj77bcxefJkTJo0CfPnzzdpfSMjIyGVSpU/FStWNOnnEZU0HQK9sbJ/fXhJVaeCekkdsLJ/fY6EKkG4GjUVtvzuP8v7vo3dlxL5+0hEREREZGYWMf18zJgx2Lt3L/78809UqFBBbX9aWho6dOgAFxcX7Ny5E7a2tvker3Hjxpg9ezYyMjJgb28PLy8vPHr0SKXMo0eP4OrqCkdHR1hbW8Pa2lpjGW15OqdOnYqJEycqX6emphYosMm8XETqOgR6o12AF/82Sjh9VqMO9i9TeBWjYk3b/Ye/j0RkTOwDEBERGc6sQU1BEDB27Fjs3LkTx44dg5+fn1qZ1NRUhIaGwt7eHrt374aDg+4E/nFxcShdujTs7e0BAMHBwdi/f79KmejoaAQHBwMA7OzsEBQUhMOHDytXTZfL5Th8+DDGjBmj8TPs7e2Vxy8o5okj0s7aSsLAQAnH1ajJXDTdf/j7SETGwj4AERFRwZg1qBkREYHNmzdj165dcHFxUeavlEqlcHR0RGpqKtq3b4/Xr19j48aNKrkry5YtC2tra+zZswePHj1CkyZN4ODggOjoaHzzzTf45JNPlJ8zcuRILFu2DJMmTcKQIUNw5MgR/PLLL9i3b5+yzMSJExEeHo4GDRqgUaNGWLx4MV69eqVcDd1UFHni8k5SU+Tl4jRbIirp9FmNmsjU+PtIRMbAPgAREVHBmTWouXLlSgBAy5YtVbavW7cOgwYNQmxsLP766y8AQJUqVVTKJCQkwNfXF7a2tli+fDkmTJgAQRBQpUoVLFy4EMOHD1eW9fPzw759+zBhwgQsWbIEFSpUwA8//IDQ0FBlmQ8++ABPnjzB9OnTkZSUhHr16iEqKkpt8SBj0pUnToKcvFztArw4DYWISiyxq1E38nMv7KpRCcTfRyIqKPYBiIiIjMOsCwUJgqDxZ9CgQQBygp3ayvj6+gIAOnTogIsXLyItLQ0vX75EXFwcRowYASsr1VNr2bIlLl68iIyMDNy5c0f5GbmNGTMG9+7dQ0ZGBv766y80btzYpOevT14uIqKSSsxq1DM6B1hcx2/OnDmQSCQYP368clt6ejoiIiJQpkwZODs7o0ePHmr5nO/fv4+wsDCUKlUKnp6e+PTTT5Gdna1S5tixY6hfvz7s7e1RpUoVrF+/Xu3zly9fDl9fXzg4OKBx48Y4e/asKU6zxCmqv49EZDnYByAiIjIOi1n9vCRiXi4iInHyW43aEqfonTt3Dt9//z3q1Kmjsn3ChAnYs2cPtm/fjuPHj+Phw4fo3r27cr9MJkNYWBgyMzNx+vRpbNiwAevXr8f06dOVZRISEhAWFoZWrVohLi4O48ePx7Bhw3Dw4EFlmW3btmHixImYMWMGYmNjUbduXYSGhuLx48emP/kSoKj9PhKRZWEfgIiIyDgsYvXzkop5uYiIxNO2GrWljYh7+fIl+vXrhzVr1uCrr75Sbk9JScGPP/6IzZs3o3Xr1gBy0q3UrFkTZ86cQZMmTfDHH38gPj4ehw4dQrly5VCvXj3Mnj0bkydPxsyZM2FnZ4dVq1bBz88PCxYsAADUrFkTJ0+exKJFi5RpVRRpWBR5oVetWoV9+/Zh7dq1mDJlSiFfkeKpqPw+EpHlYR+AiIjIODhS04wUebm0dX8kyFkBkXm5iIhyKFaj7lrvLQT7l7HIAFJERATCwsLQtm1ble0XLlxAVlaWyvYaNWqgUqVKiImJAQDExMSgdu3aKvmcQ0NDkZqaimvXrinL5D12aGio8hiZmZm4cOGCShkrKyu0bdtWWUaTjIwM5YJ8uRfmI+2Kwu8jEVke9gGIiIiMg0FNM2JeLiKi4mXr1q2IjY1FZGSk2r6kpCTY2dnBzc1NZXu5cuWQlJSkLJN3gTrFa11lUlNT8ebNGzx9+hQymUxjGcUxNImMjIRUKlX+VKxYUdxJExGRXtgHICIiMg4GNc2MebmIiIqHf//9Fx999BE2bdoEB4eiN2Vw6tSpSElJUf78+++/5q4SEVGxxT4AERFRwTGnpgVgXi4idTK5wL8JKlIuXLiAx48fo379+sptMpkMf/75J5YtW4aDBw8iMzMTL168UBmt+ejRI3h5eQEAvLy81FYpV6yOnrtM3hXTHz16BFdXVzg6OsLa2hrW1tYayyiOoYm9vT3s7e31P/EShvcmIjIW9gGIiIgKhkFNC6HIy0VEQNTVRMzaE4/ElP9W/fSWOmBG5wCOXCCL1aZNG1y5ckVl2+DBg1GjRg1MnjwZFStWhK2tLQ4fPowePXoAAG7evIn79+8jODgYABAcHIyvv/4ajx8/hqenJwAgOjoarq6uCAgIUJbZv3+/yudER0crj2FnZ4egoCAcPnwY3bp1AwDI5XIcPnwYY8aMMdn5lwS8NxGRsbEPQEREZDgGNYnIokRdTcSojbEQ8mxPSknHqI2xnJJFFsvFxQWBgYEq25ycnFCmTBnl9qFDh2LixIlwd3eHq6srxo4di+DgYDRp0gQA0L59ewQEBGDAgAGYN28ekpKS8PnnnyMiIkI5inLkyJFYtmwZJk2ahCFDhuDIkSP45ZdfsG/fPuXnTpw4EeHh4WjQoAEaNWqExYsX49WrV8rV0El/vDcREREREVkWBjWJyGLI5AJm7YlXCxoAgICc5Pmz9sSjXYAXp2ZRkbRo0SJYWVmhR48eyMjIQGhoKFasWKHcb21tjb1792LUqFEIDg6Gk5MTwsPD8eWXXyrL+Pn5Yd++fZgwYQKWLFmCChUq4IcffkBoaKiyzAcffIAnT55g+vTpSEpKQr169RAVFaW2eBCJw3sTEREREZHlkQiCoOk7OukpNTUVUqkUKSkpcHV1NXd1iIqkmDvJ6LPmjM5yW4Y34VQtKhDes3XjNfqPqe5NzM9JRMbCe3b+eH2IiIoOfe7ZHKlJRIVOW0f+cVq67jcDossRERmDKe5NzM9JRERERFQwDGoSUaHKryPv6eIg6hhiyxERGYOx703Mz0lEREREVHBW5q4AEZUcio587oAm8F9H/vmrTHhLHaBt8qUEOQHQRn7uJq9rYZDJBcTcScauuAeIuZMMmZzZQIgsUSM/d6Pdm3Tl5wRy8nPyfkBERERElD+O1CSiQiFmoY3Z++LxRVhNRGy+CAmgUlYRTJjROaBY5Jzj1FOiosPaSoIZnQMwamNsge9NZxOeqT3YyU0AkJiSjrMJz5g7mIiIiIgoHxypSWRmJWW0ntiOfGkne6zsXx9eUtVpnF5Sh2IzJVPXiNWoq4lmqpl+SsrvLhEAdAj0Nsq9ibmDiagkWL58OXx9feHg4IDGjRvj7Nmz+Zbfvn07atSoAQcHB9SuXRv79+/XWnbkyJGQSCRYvHixkWtNRERFDUdqEplRSRqtp09Hvmu9t9AuwKtYrgosZsTqrD3xaBfgZdHnW5J+d4kUOgR6F/jexNzBRFTcbdu2DRMnTsSqVavQuHFjLF68GKGhobh58yY8PT3Vyp8+fRp9+vRBZGQkOnXqhM2bN6Nbt26IjY1FYGCgStmdO3fizJkzKF++fGGdDhERWTCO1CQyk+IyWk8sfTvy1lYSBPuXQdd6byHYv4xFB/j0oc/UU0tV0n53iXIr6L3JmPk5iYgs0cKFCzF8+HAMHjwYAQEBWLVqFUqVKoW1a9dqLL9kyRJ06NABn376KWrWrInZs2ejfv36WLZsmUq5Bw8eYOzYsdi0aRNsbW0L41SIiMjCMahJZAYlcaEIduRzFPWppyXxd5fImBT5OQGo3Q+LW+5gIip5MjMzceHCBbRt21a5zcrKCm3btkVMTIzG98TExKiUB4DQ0FCV8nK5HAMGDMCnn36KWrVq6axHRkYGUlNTVX6IiKj4YVCTyAyKw2g9fbEjn6OoTz0tib+7RMZmrPycRESW5unTp5DJZChXrpzK9nLlyiEpKUnje5KSknSWnzt3LmxsbDBu3DhR9YiMjIRUKlX+VKxYUc8zISKiooA5NYnMoKiP1jOUoiOfNxejVwnKxagYsZqUkq5xtKMEOdfDUkesltTfXSJjM0Z+TiKikuDChQtYsmQJYmNjIZGIu0dOnToVEydOVL5OTU1lYJOIqBhiUJPIDIr6aL2CKOkdecWI1VEbYyEBVAKbRWHEakn+3SUyNkV+TiKi4sLDwwPW1tZ49OiRyvZHjx7By8tL43u8vLzyLX/ixAk8fvwYlSpVUu6XyWT4+OOPsXjxYty9e1ftmPb29rC3ty/g2RARkaXj9HMiMyjp+SX1XWhDJhcQcycZu+IeIOZOcpHP11iUp56W9N9dIiIqHorbdwtLYWdnh6CgIBw+fFi5TS6X4/DhwwgODtb4nuDgYJXyABAdHa0sP2DAAFy+fBlxcXHKn/Lly+PTTz/FwYMHTXcyRERk8ThSk8gMivpovcIUdTVRbbq6dzGYrl5UR6zyd5eIiIq64vrdwlJMnDgR4eHhaNCgARo1aoTFixfj1atXGDx4MABg4MCBeOuttxAZGQkA+OijjxASEoIFCxYgLCwMW7duxfnz57F69WoAQJkyZVCmjOqodltbW3h5eaF69eqFe3JERGRROFKTyEyK8mi9whJ1NRGjNsaqLUyTlJKOURtjEXU10Uw1Mw59R6xaCv7uEhFRUVXcv1tYgg8++ADffvstpk+fjnr16iEuLg5RUVHKxYDu37+PxMT/rvM777yDzZs3Y/Xq1ahbty527NiB33//HYGBgeY6BSIiKiIkgiBwroURpKamQiqVIiUlBa6uruauDhUhMrlQ5EbrFQaZXECzuUe0rrStWFDn5OTWvF5mUpR/d3nP1o3XiIiKm+L83YL37Pzx+hARFR363LM5/ZzIzLhQhGZnE55p7XQAOdOeE1PScTbhGa+fmfB3l4iIihJ+tyAiIipeGNQkIov0OE17p8OQckRElqooj3omKkr43YKIiKh4YVCTiCySp4uD7kJ6lCMiskRcsISo8PC7BRERUfHChYKIyCI18nOHt9QB2sYqSZDT8W/k516Y1SIiUiGTC4i5k4xdcQ8QcycZMrn4VOX7Lz/ESC5YQlRo+N2CiIioeOFITSKySNZWEszoHIBRG2MhQU6eKwVFZ2RG5wBO0SQisynIKMv9lxMxZstFjfsE5NznZu2JR7sAL97niIyE3y2IiIiKF47UJCKL1SHQGyv714eXVHUamJfUASv71+fUTCIym6iriRhl4CjLqKuJGL05FvkN6sy9YAmRoQoykrgoMOT8+N2CiIio+DDrSM3IyEj89ttvuHHjBhwdHfHOO+9g7ty5qF69urJMeno6Pv74Y2zduhUZGRkIDQ3FihUrUK5cOWWZ+/fvY9SoUTh69CicnZ0RHh6OyMhI2Nj8d3rHjh3DxIkTce3aNVSsWBGff/45Bg0apFKf5cuXY/78+UhKSkLdunWxdOlSNGrUyOTXgYi06xDojXYBXjjzTzJi7iQDEBBc2QNNuCqpUXGhEiLxZHIBs/bEQ1P4RNcoS8V7xeKCJWSo4p6vtSDnp/huwXaPiIioaDNrUPP48eOIiIhAw4YNkZ2djWnTpqF9+/aIj4+Hk5MTAGDChAnYt28ftm/fDqlUijFjxqB79+44deoUAEAmkyEsLAxeXl44ffo0EhMTMXDgQNja2uKbb74BACQkJCAsLAwjR47Epk2bcPjwYQwbNgze3t4IDQ0FAGzbtg0TJ07EqlWr0LhxYyxevBihoaG4efMmPD09zXOBqERicElddHySSsdl2dE7xapjZm7FveNLZGxnE56pjdDMLfcoy+A8D2B0vTcvLlhChlCMJM4beFeMJC7qIxKNcX7WVhK1v08iIiIqWiSCIFjMPJQnT57A09MTx48fR4sWLZCSkoKyZcti8+bN6NmzJwDgxo0bqFmzJmJiYtCkSRMcOHAAnTp1wsOHD5WjN1etWoXJkyfjyZMnsLOzw+TJk7Fv3z5cvXpV+Vm9e/fGixcvEBUVBQBo3LgxGjZsiGXLlgEA5HI5KlasiLFjx2LKlCk6656amgqpVIqUlBS4uroa+9JQCcHgkjptHRdFmLeod8zMraReX96zdeM10m5X3AN8tDVOZ7klveuha723DHovkHP/Pzm5dYl/sEX6kckFNJt7RGvwXIKcqdZF9XeruJ+foXjPzh+vDxFR0aHPPduicmqmpKQAANzdc1YcvHDhArKystC2bVtlmRo1aqBSpUqIiYkBAMTExKB27doq09FDQ0ORmpqKa9euKcvkPoaijOIYmZmZuHDhgkoZKysrtG3bVlmGyNQKkp+tuNI1xRPImeJZ3HKEFRZeXyLDiB09qamch5O96M/hgiVkCH1GEhdFxf38iIiISDyLCWrK5XKMHz8eTZs2RWBgIAAgKSkJdnZ2cHNzUylbrlw5JCUlKcvkDmgq9iv25VcmNTUVb968wdOnTyGTyTSWURwjr4yMDKSmpqr8EBmKwSXN2HExLV5fIsM08nOHt9QB2sKNEuSMsmzk5655pwjj21QplqOkyfTE5mEtqvlai/v5ERERkXgWE9SMiIjA1atXsXXrVnNXRZTIyEhIpVLlT8WKFc1dJSrCGFzSjB0X0+L1JTKMtZUEMzoHAFCPUSpeaxtl+fRlhqjP8CvrXIAaUklWkJHERUFxPz8iIiISzyKCmmPGjMHevXtx9OhRVKhQQbndy8sLmZmZePHihUr5R48ewcvLS1nm0aNHavsV+/Ir4+rqCkdHR3h4eMDa2lpjGcUx8po6dSpSUlKUP//++6/+J070/xhc0owdF9Pi9SUyXIdAb6zsXx9eUtW/Dy+pQ765aPl3R6ZWoJHERUBxPz8iIiISz6xBTUEQMGbMGOzcuRNHjhyBn5+fyv6goCDY2tri8OHDym03b97E/fv3ERwcDAAIDg7GlStX8PjxY2WZ6OhouLq6IiAgQFkm9zEUZRTHsLOzQ1BQkEoZuVyOw4cPK8vkZW9vD1dXV5UfIkOxk6sZOy6mxetLVDAdAr1xcnJrbBneBEt618OW4U1wcnLrfKeN8++OTK0gI4mLguJ+fkRERCSeWYOaERER2LhxIzZv3gwXFxckJSUhKSkJb968AQBIpVIMHToUEydOxNGjR3HhwgUMHjwYwcHBaNKkCQCgffv2CAgIwIABA3Dp0iUcPHgQn3/+OSIiImBvn5OMf+TIkfjnn38wadIk3LhxAytWrMAvv/yCCRMmKOsyceJErFmzBhs2bMD169cxatQovHr1CoMHDy78C0MlDju5mrHjYlq8vkQFZ20lQbB/GXSt9xaC/cvo/Hvh3x0VBkNHEhcVxf38iIiISByJIAhmW3lEItH8hX3dunUYNGgQACA9PR0ff/wxtmzZgoyMDISGhmLFihUq08Lv3buHUaNG4dixY3ByckJ4eDjmzJkDGxsbZZljx45hwoQJiI+PR4UKFfDFF18oP0Nh2bJlmD9/PpKSklCvXj189913aNy4sahz0WfJeSJNFKufA1BZMEjxV1KSv6RHXU3ErD3xKnlHvaUOmNE5oMReE2MqideX92zdeI1MqyT+3VHhk8kFnE14hsdp6fB0yXk4WpwC5sX9/PTBe3b+eH2IiIoOfe7ZZg1qFidsKMkY2MnVjh0X0ypp15f3bN14jUyvpP3dEZHp8J6dP14fIqKiQ597tk2+e4moUHUI9Ea7AC92cjVQTPEk0+D1JSp8/LsjIiIiIjIcg5pEFoadXCKi4oOjMYmIiIiITINBTSIqkhgoICJLx5QiRPljW05EREQFwaAmkYnwi7rpMFBARJZOsfhb3sTlSSnpGLUxtkQv/kbmY0nfTdiWExERUUExqElkAvyibhgxnS1tgYLElHSM3BiLFX3fRsc65Quv0kREucjkAs78k4wpv15Ru08BgABAAmDWnni0C/Diwy4qNJb03YRBfyIiIjIGBjWJjKyofVG3lFEbYjpbMrmAWXviNQYKFMZsuYhlkKBjHcu5xkRUMmi6j2kiIOdBzNmEZ8yhTIXCkr6b5NeWM+hPRERE+rAydwWIihNdX9SBnC/qMnl+YbnCE3U1Ec3mHkGfNWfw0dY49FlzBs3mHkHU1cRCr8eojbFqgQBFZ0tRn7MJz3QGC+QCMHpzbKGfAxGVbNruY/l5nCa+LJGhLO27ia62PHfQn4iIiCg/DGoSGVFR+qIuNpBoavp0tvQJAFhS8JiIirfMbDmm7dQ83Tw/ni4OJqkPUW6W9t1EbFvOoD8RERHpwqAmkREVlS/qljRqQ5/Olj4BAEsJHhNR8RZ1NRFNIg/j2ass0e+RICe9RiM/d9NVjOj/Wdp3E7FtOYP+REREpAuDmkRGVFS+qFvSqA19OluN/NzhLRV/7cwdPCai4k0x4v3Zq0y93zujcwDzBVKhsLTvJoq2XNtvP4P+REREJBaDmkRGVFS+qFvSqA19OlvWVhLM6Bxg9GMTEelLzMJlmlhJgA9b+FnUgnFUvFnad5PcbXneOileM+hPREREYjCoSWREReWLuthgn4eTvYlron9nq0OgN1b0fRv5XUJLCR4TUfElZuEyTQQBWP1nAhczo0Jjid9NOgR6Y2X/+vDKM/vCS+pQqCuxExERUdHGoCYVKplcQMydZOyKe4CYO8nFciGXovBFPcindL5BQYWPt18yecfbkM5WxzrlsaxPfY3Hs6TgMREVX4aOZDfHatNElvjdpEOgN05Obo0tw5tgSe962DK8CU5Obm0R35OIiIioaLAxdwWo5Ii6mohZe+JVRrZ4Sx0wo3NAsfsC2yHQG+0CvHA24Rkep6XD0yVn1KClBNku3HsOMX3ppNScldBN3eFRdLby/n545fP70bGON1ZZ6fceIiJjKUh6i9x5i4P9yxivUkT5sMTvJtZWEv4NEBERkcEY1KRCoVhMIW8cLSmlcIJm5mDJX9T1HWE0a0882gV4mbTjY0hnyxI7aERUMihSZySlpOudV1OBi5lRYbPk7yZERERE+mJQk0wuv8UUBORMFy6MoBn9R58RRoU5osiQzhY7aERkDorUGaM2xkICGBTY5GJmJZdMLvCBHBEREVEBMahJJqdrMQVOwyt8howw4ogiIiJV2lJn6CJBTqqMkr6YWUkN7JWkdDxEREREpsSgJpmc2GAYg2a6GasDmHuEkVi3HqUh5k5yiel0EhGJ0SHQG3I5MHqzuPspFzPLUVIDeyUxHQ8RERGRqTCoSSYndnodp+Hlz9gdQMUIo5m7ryEpNUNn+WVH72DZ0TslotNpqJI66oioJJPJBczeFy+6PBczK7mBPabjISIiIjIug4Ka9+/fx7179/D69WuULVsWtWrVgr29vbHrRsWErqnOnIanm6k6gIqFdpYduY1Fh/4W9Z7i3uk0VEkddVRcsF0jQ+lKsaIwplUVNK3iUeIfdpTkwB7T8VBhY9tGRETFnZXYgnfv3sXkyZPh4+MDPz8/hISE4N1330WDBg0glUrRrl07bN++HXK53JT1pSJIMdUZ+G/anQKn4emmqwMI5HQAZXLD1t+1tpLgo7ZVsap/fXhLdY+WNcZnFjeKoHPezqoiABx1NdFMNSNdpk+fznaNCkRs6pSq5ZwR7F+mxLd1+gT2ihum46HCcO/ePfbZiIioxBAV1Bw3bhzq1q2LhIQEfPXVV4iPj0dKSgoyMzORlJSE/fv3o1mzZpg+fTrq1KmDc+fOmbreVMQopjp75QmaeUkdOOJPh8LqAHYI9MbJya2xZXgTjGlVJd+yxbnTqS9TB53JNCZNmgQgp/PHdo0KQmzqlMepGbwPoGQH9piOhwpDs2bN2GcjIqISQ1RQ08nJCf/88w9++eUXDBgwANWrV4eLiwtsbGzg6emJ1q1bY8aMGbh+/Tq+/fZb/Pvvv6auNxVBuYNmS3rXw5bhTXBycmsGNHUozA6gtZUEwf5lULWcc6F9ZlEnNui8/lQCAxoWpFSpUgCADRs2GK1dW7lyJerUqQNXV1e4uroiODgYBw4cUO5PT09HREQEypQpA2dnZ/To0QOPHj1SOcb9+/cRFhaGUqVKwdPTE59++imys7NVyhw7dgz169eHvb09qlSpgvXr16vVZfny5fD19YWDgwMaN26Ms2fPGnCVSAxFihVd4y+/3n8dQV9Fl/iR2yU5sKfrd0WCnLQlTMdDBXHp0iX22YiIqMQQFdSMjIxEmTLicvt06NAB3bt3L1ClqPhSBM261nuL0/BEEtuxU6xObozAWUnudOpLbGB39r7raDb3SIkPaFiKmTNnii4rtl2rUKEC5syZgwsXLuD8+fNo3bo1unbtimvXrgEAJkyYgD179mD79u04fvw4Hj58qHJcmUyGsLAwZGZm4vTp09iwYQPWr1+P6dOnK8skJCQgLCwMrVq1QlxcHMaPH49hw4bh4MGDyjLbtm3DxIkTMWPGDMTGxqJu3boIDQ3F48ePRZ8ziZdfipW8XrzOwsgSnpKiJAf2mI6HCoO7u7i/HfbZiIioOJAIgsChQ0aQmpoKqVSKlJQUuLq6mrs6VIzI5AKazT0iaiEKwDiL0yg+U9fiTicnty7xna+YO8nos+aMqLIS5IzcHNLUF+0CvEr8giHmVFj3bHd3d8yfPx89e/ZE2bJlsXnzZvTs2RMAcOPGDdSsWRMxMTFo0qQJDhw4gE6dOuHhw4coV64cAGDVqlWYPHkynjx5Ajs7O0yePBn79u3D1atXlZ/Ru3dvvHjxAlFRUQCAxo0bo2HDhli2bBkAQC6Xo2LFihg7diymTJkiuu5s1/QTdTURM3fHIylV973azdEGF75oX2L//hV5iAGotDGKq1Hc09JwYTkyBd6z88frQ0RUdOhzzxa9UBAAXL9+HRcvXlS+fvnyJfr37w8fHx+N0+iISH8yuYCYO8nYFfcAMXeSAQBd6orv5BhjcRpLGk2S93pY2hRusVNPgf8672tP3UWfNWc4ctMC3Lx50yTtmkwmw9atW/Hq1SsEBwfjwoULyMrKQtu2bZVlatSogUqVKiEmJgYAEBMTg9q1aysDmgAQGhqK1NRU5WjPmJgYlWMoyiiOkZmZiQsXLqiUsbKyQtu2bZVlyDQ6BHqjV1AFUWVfvMnGsiO3TVwjy1XS82xbUjoeS29jyTDssxERUUlho0/hCRMm4N1338Xbb78NAJg9ezbOnj2LTz/9FJs2bcL48eOxZcsWk1SUqCTQNHrDy9UB6dky0ccQkBN4nLUnHu0CvAwOPCo6nWr1KcTRJKYYzSKTCzib8AyP09Lh6eJQ4NGSigDwqI2xypGYYikC0CWhE2+ppk6dis6dOxutXbty5QqCg4ORnp4OZ2dn7Ny5EwEBAYiLi4OdnR3c3NxUypcrVw5JSUkAgKSkJJWApmK/Yl9+ZVJTU/HmzRs8f/4cMplMY5kbN27kW/eMjAxkZGQoX6empoo+b8q5X313VHygct3pBIxpXaXEjtbsEOiNdgFeRr0fFyWKdDzmxBGjxRf7bEREVFLoFdSMj4/HrFmzlK+3b9+OpUuXIiwsDO3atUPz5s2NXkGikkIxHS9vUEzMVMa8cq9OXpBOkzk7nVqvRwECgabqwGkLAOtirAA0Ge7mzZv4+uuvla8L2q5Vr14dcXFxSElJwY4dOxAeHo7jx48bu9omERkZqdLGk3gyuYCZu6/p9Z4Xr7MKfI8u6iwhsFdSmaKNJcvBPhsREZUUooKagwcPBgA8evQI3377LZydnfHy5Uvcv38f27Ztw6+//gpBEPDs2TMMGTIEALB27VrT1ZqomJHJBczaE6/XKD8xjLkiemHK73oYGgg0dQdOEQBefyoBs/ddF/0+YwWgST+jR48GADx+/Nio7ZqdnR2qVKkCAAgKCsK5c+ewZMkSfPDBB8jMzMSLFy9URms+evQIXl5eAAAvLy+1VcoVUwRzl8k7bfDRo0dwdXWFo6MjrK2tYW1trbGM4hjaTJ06FRMnTlS+Tk1NRcWKFXWeMwFnE54hKTVDd8E8DHloRVRQpmhjyXKMHj3aIvpsy5cvx/z585GUlIS6deti6dKlaNSokdby27dvxxdffIG7d++iatWqmDt3Ljp27AgAyMrKwueff479+/fjn3/+gVQqRdu2bTFnzhyUL1/eJPUnIqKiQVROzXXr1mHdunXw9fVFz549sW7dOoSGhqJ+/fr46aefsHbtWkRGRqJ06dJYu3YtA5pEejqb8EyvEX5iFdXVyXVdj9yBQDF0deCAnA5cQXOJWVtJMKipn+gcm7kZIwBN4q1YsQIAUKlSJZO2a3K5HBkZGQgKCoKtrS0OHz6s3Hfz5k3cv38fwcHBAIDg4GBcuXJFZZXy6OhouLq6IiAgQFkm9zEUZRTHsLOzQ1BQkEoZuVyOw4cPK8toY29vD1dXV5UfEsfQv9/Ze68xry4VOmO3sWRZVqxYYfY+27Zt2zBx4kTMmDEDsbGxqFu3LkJDQ1Xat9xOnz6NPn36YOjQobh48SK6deuGbt26KRfFe/36NWJjY/HFF18gNjYWv/32G27evIkuXbqYpP5ERFR06LVQkKKxCQ0NxUcffYRRo0Yp9504cQL16tUzdv2ISgRjB7QkyJlW3cjP3ajHLSxir4fYcoXZgctvkaX8FNUAdFHXs2dPo7VrU6dOxZ9//om7d+/iypUrmDp1Ko4dO4Z+/fpBKpVi6NChmDhxIo4ePYoLFy5g8ODBCA4ORpMmTQAA7du3R0BAAAYMGIBLly7h4MGD+PzzzxEREQF7e3sAwMiRI/HPP/9g0qRJuHHjBlasWIFffvkFEyZMUNZj4sSJWLNmDTZs2IDr169j1KhRePXqlXLWBRmfoX+/z15lFXhhNyr6CnuxHmO3sWR5zN1nW7hwIYYPH47BgwcjICAAq1atQqlSpbQGUZcsWYIOHTrg008/Rc2aNTF79mzUr18fy5YtAwBIpVJER0ejV69eqF69Opo0aYJly5bhwoULuH//vknPhYiILJteQc2ZM2diyZIlqFatGn788UeVDtLDhw9Vpq2J8eeff6Jz584oX748JBIJfv/9d5X9EolE48/8+fOVZXx9fdX2z5kzR+U4ly9fRvPmzeHg4ICKFSti3rx5anXZvn07atSoAQcHB9SuXRv79+/X61yICsKYAa38VicvKqucir0eHs72os6nsDtw2lb21aSoB6CLuqlTpxqtXXv8+DEGDhyI6tWro02bNjh37hwOHjyIdu3aAQAWLVqETp06oUePHmjRogW8vLzw22+/Kd9vbW2NvXv3wtraGsHBwejfvz8GDhyIL7/8UlnGz88P+/btQ3R0NOrWrYsFCxbghx9+QGhoqLLMBx98gG+//RbTp09HvXr1EBcXh6ioKLXFg8h4Gvm5w8vV3uD3G2OkOBVNUVcT0WzuEfRZcwYfbY1DnzVn0GzuEZMGusW2sbrKFZXvFCWRsfts+sjMzMSFCxfQtm1b5TYrKyu0bdsWMTExGt8TExOjUh4AQkNDtZYHgJSUFEgkErUF+BQyMjKQmpqq8kNERMWPRBAEs30DOXDgAE6dOoWgoCB0794dO3fuRLdu3ZT7Fau95i4/dOhQ3L59G5UrVwaQE9QcOnQohg8frizn4uICJycnADk5wapVq4a2bdti6tSpuHLlCoYMGYLFixfjww8/BJAz5aFFixaIjIxEp06dsHnzZsydOxexsbEIDAwUdS6pqamQSqVISUnhlD3Sm0wuoNncI0hKSdc7r2YpO2u8zvxvdXRtC98UpVVOdV0PCQC3Urawt7FSyWOn7Xxi7iSjz5ozOj93y/AmRs1rqVhp/VB8En48dVdtvyLkzAUZCh/v2brxGukn6moiRm6MNfj9xr7/kOXTluvZ1G2DmDbWS+qAk5Nba82pWZS+U5QUlnLPfvjwId566y2cPn1aJe3JpEmTcPz4cfz1119q77Gzs8OGDRvQp08f5bYVK1Zg1qxZajmiASA9PR1NmzZFjRo1sGnTJo31mDlzpsbF78x9fYiISDd92jRRIzVNFfd899138dVXX+G9997TuN/Ly0vlZ9euXWjVqpUyoKng4uKiUk4R0ASATZs2ITMzE2vXrkWtWrXQu3dvjBs3DgsXLlSW0TXlgcjUDJ2yLAEgdbTFpqGNsaR3PWwZ3gQnJ7dGuwAvldET+y/ndJzyTsFOSknHyI2x+HLPNYsaZWFtJcEXYQFaO1sCgOevs9QW5lAs+pN3hEsjP/d881yaarSkYpGlLzrXwqr+9eGdZ+Sml9SBAU0zMePzPCqmOgR6Y1X/+nArZWvQ+znVt2QprFzPmuT3nSO/2R4KimCspu8UTKdAppaVlYVevXpBEASsXLlSa7mpU6ciJSVF+fPvv/8WYi2JiKiwiApq1qpVC1u3bkVmZma+5W7duoVRo0apTf82hkePHmHfvn0YOnSo2r45c+agTJkyePvttzF//nxkZ2cr98XExKBFixaws7NTbgsNDcXNmzfx/PlzZRl9pzxwSgMZmz5TlhUUuSCtrCToWu8tNPJzx7IjtxA0O1plKtuYLeojQRTvB4C1p+4WypQ3saKuJmL2vniN+7ykDlqDBto6ggXtwBlDh0BvnJzcGluGN1EJQDOgaR6NGzcGALO2a1T8dAj0RsyUNga9l3l1SxZzL9aj7TuHrodt5gzGkjg7duwwa9vm4eEBa2trtRGWjx49gpeXl8b3eHl5iSqvCGjeu3dPuZCeNlz8joioZLARU2jp0qWYPHkyRo8ejXbt2qFBgwYoX748HBwc8Pz5c8THx+PkyZO4du0axowZo5KM2lg2bNgAFxcXdO/eXWX7uHHjUL9+fbi7u+P06dOYOnUqEhMTlSMxk5KS4Ofnp/IeRV6xpKQklC5dGklJSWq5xsqVK6c2/T23yMhIjVMaiAqiQ6A32gV44WzCMzxOS8etR2lYdvSOzvc9TktH1NVETPntCl68zlLbL7ZvoRhlYc7Rg9qm4yn0alABSw7f1vr+3B3B3FM5FR24vNPlvApxupxi5CaZ37x589C1a1dUrVoV7du3N0u7RsXT5r/u6VVeMdWXeXVLFktYrCfvdw5Pl5zfw/we8OkTjGV7Zx5Lly7FJ598YrY+m52dHYKCgnD48GFlWjG5XI7Dhw9jzJgxGt8THByMw4cPY/z48cpt0dHRKtPXFQHNW7du4ejRoyhThr9fREQkMqjZpk0bnD9/HidPnsS2bduwadMm3Lt3D2/evIGHhwfefvttDBw4EP369UPp0qVNUtG1a9eiX79+cHBQfaKcO9F1nTp1YGdnhxEjRiAyMlK5WqwpTJ06VeWzU1NTUbFiRZN9HpUcuQNfMXeSRQU17z59jcWH/tY7H2deAnI62LP2xKNdgJdJRy5qkt8IECCnbhtOiwsYaOoIGtKBo+KpZcuWAICtW7di7969ZmnXqHi6m/xKdNnCGilOlsdYi/UUlL4P2ywhGEv5O378OC5fvmy2PhuQ0z8LDw9HgwYN0KhRIyxevBivXr1SLlg0cOBAvPXWW4iMjAQAfPTRRwgJCcGCBQsQFhaGrVu34vz581i9ejWAnIBmz549ERsbi71790ImkykHn7i7u6vMyCMiopJFVFBToVmzZmjWrJmp6qLViRMncPPmTWzbtk1n2caNGyM7Oxt3795F9erVtU5nAKCc0iB2ykNu9vb2Jg2aUtGiWBDG2IEyRS7I/JL5l3O1x5az9wsc0FQw5ygLMSNAXrxRH4mqibaOYO4OnKn+3ajoCA4OVlk5nKggoq4m4ve4h6LLSx1tMKdHHaahKIHEtO+WOILXUoKxlD9z9dkUPvjgAzx58gTTp09HUlIS6tWrh6ioKOXMuPv378PK6r8saO+88w42b96Mzz//HNOmTUPVqlXx+++/KxdsffDgAXbv3g0AqFevnspnHT16VPmgkoiISh69gprm8uOPPyIoKAh169bVWTYuLg5WVlbw9PQEkNNh/eyzz5CVlQVb25w8fNHR0ahevbryCaWYKQ9E2phyBVBFLshRG2OVC+QoKEJvfRpVwqJDtwr0OZqYY5SF2M90c7RFypssUR3B3IFLDyd7QAI8fZmBu09fY8vZ+0hK5cqtRFRwulJnaJLyJhvXE9PMMjKezEtM+26JI3iLajCWCt+YMWO0Tjc/duyY2rb3338f77//vsbyvr6+XOCPiIg0ErVQkKm8fPkScXFxiIuLAwAkJCQgLi4O9+/fV5ZJTU3F9u3bMWzYMLX3x8TEYPHixbh06RL++ecfbNq0CRMmTED//v2VAcu+ffvCzs4OQ4cOxbVr17Bt2zYsWbJEZer4Rx99hKioKCxYsAA3btzAzJkzcf78ea0NMZGCsVcAlckFlVXLZXJBZzJ/Xw+nAp+HJuYYZSH2Mwc3zcmTq2vRn6iriWg294hy0aR+P/6Ffj/8hY+2xmHRob9VApoAV24lIsPoSp2hjQBgyeFbCPoqmvedEkjRvpdzVZ35U87V3qy5rfNjCQvvERERESmYdaTm+fPn0apVK+VrRaAxPDwc69evB5CT70wQBPTp00ft/fb29ti6dStmzpyJjIwM+Pn5YcKECSoBS6lUij/++AMREREICgqCh4cHpk+fjg8//FBZRteUByJNdK0Aqm9uSl0jPrXlgoy5kyy6zlYS3YsG5R5lUdjTs8WOABnTugqqeznnu+iPIaOmzJ1TlIiKJl2pM3R58ToLIzfGYpWFBrLI1LSFBy2TJSy8R0RERAQAEoFj+Y0iNTUVUqkUKSkpcHV1NXd1qBDE3ElGnzVndJbbMryJztyU2gJwim6NthEbMrmAM/8kY/SmWKSIyDW5rPfbeJSWjj9vPcHxv5+q7c/9eQBMNq0+P4prAWiejpf7WmgLusrkAprNPVKgIIOYf7e8mKOz6OA9WzdeI/F2xT3AR1vjCnwcb6kDTk5uzftGCWFo228p2OZZFt6z88frQ0RUdOhzzy4SOTWJLJGxVgA1dMSnppGd2rg52uCDhhXx9YHrKuXzjtxUjLIAoLGjpZiebcqOlj4jQLSt2lrQUVOA/jlFTZlblYgsm4eTcRYOTExJx/pTCfBwsWeQqJgz9mwPc9B35XQiIiIiYxOdU/OXX35BZmam8vX//vc/yOVy5evXr19j3rx5xq0dkQUz1gqgYlb8VqxGrqAtl6c2WXIB3/+ZoFZeEdAc2tQXW4Y3wcnJrdEuwCvfjhaQ09GS6ZrHXgAdAr1xcnJrbBneBEt611PWTWxw0BiLHOmTU9TYuVWp8LBdI6MwYsxp9r7r+GhrHPqsOYNmc48UmfuHppzQmrZRDkPafiIxfvvtN7ZtRERUYogeqdmnTx8kJiYqVxUPCAhAXFwcKleuDABIS0vD1KlTMWnSJNPUlMhEDJ0+ZawVQPUd8WnIghSvMmRa90kA7L+ahGlhAcocnWI7WqYcoVGQESAFWeRI35Vbi8Nom5IsJSUFHh4eANiukeGevswwyXELY3S8oXK3nXefvsaWs/dVFl9zK2ULICdfqIKljF431bRpfY5rSNvPqd4kxtChQ9GxY0f22YiIqEQQHdTMm3qTqTipOCjIlGHFCqCjNsZCAs35H8WsAKrviE9jTK3OLW+Q0ljT6s1JV8BZG0NWbtVntA2n6Vk2tmtkqII8SMmPpT4YEZP+JHcwU8ESgrSmShWi73H1afuZ3oT0wT4bERGVJKKnnxMVN9qmDCempGPkxljsv6x7yp8i/6OXVLVz4iV1EN1pUwTgtHVVJcjpvChGDpoqmKg4rrGm1ZuTIuAM6DcrVJ9/N4XiEAQmooLRdR8vCEubhqxv+pPcCiuFiTamShViyHHFtv3PX2UyvQkRERGRFgxqUokkZgr3mC2x2H/5oc5jFTT/Y34BOE0jB00VTPRwskfMnWQkpabD3clWa7m8QVZLpS3gnJuXqz0mtK2q/Hc7/mkrSB3t9Mr/VhyCwERUMIY+SNGHJTwYMST9SV7mCtLqShUCGBZszcyWY9rOK3ofV0zb/0VYTczeZ94c10RERESWTK/Vzw8ePAipVAoAkMvlOHz4MK5evQoAePHihdErR2QqYqZwywVg9OaLWGUlETUVvSBTi/VZ8dvQqdX5cStli4+3X1LJh6aJIdOzzalDoDfaBXgp85B5ONkDkpz8d3lzkkVdTUTI/KN6T+8zVm5VMo/Dhw/j2rVrANiuUcG0C/DC+LZVsfzobWTKjB9ksoQHI8ZMf1LYQVpTpAqJupqIaTuv4tkr9an2Yo6rq+2XOtoxvQkZhH02IiIqKfQKaoaHh6u8HjFihMpricTygxxEgH6dqcLKZZY3AKcIugFAzJ1klW2KXJ7GkpP7THunTEFTkNXS5Q04515s4WzCMzTyc0d0fBJGbYxVC0oqUhGs6Ps2OtYpr/X4xsitSuYxcuRIldds18gQYnJMGsqSHowYMxBZ2EFaY6cKUUw5Fxu+1rbgT7sAL41tv7WVBLviHhi1zlRysM9GREQlheigplwuN2U9iAqVPp2pwhwFkTcAl9/iAJpGdxjC2d4GLzOyte4v42SHz8NqwkvqaPLVVk29uqum6+nl6oD0bJmOVAQXsQwSdKyjOZirz0hbsiwvXryAq6uruatBRZi+wS19WNqDEWMEIs0VpDVmqhBDpuHrWvAnd2BT8cCN6U3IEGzXiIioJBEd1BwyZAiWLFkCFxcXU9aHqFAopgyLDQiaYxSEto5y7tVjT05ujbMJz3Dy9hMsP3pH789wtLXKN6AJAMmvMuEldTR5UNfUq7tqvZ46ptwDilQEsVhlpX0RIW0jbS0hEEHapaWlsfNHBjNGjkmF0qVsYW9jhaTUDOU2S3swUtD0J+YM0hozVYi+0/C9XO3x/FUmIjZrbtNHboyFWylblRXjvaUO+CIsgOlNSG8RERFYsWIF+2xERFQiiF4oaMOGDXjz5o0p60JUaHIn6BfDw9ledFmZXEDMnWS9FpvRdAwxCxoAQLB/GUxsVx1SR+2L+2jTq0FFUeWi45P0PrY+TLUirYKxAg+6FmRQjLTtWu8tBPuXYUCzCEhP57RNMpyxckxKAER2r41TU9oYvOhcYdBnQSS3UrZwK6XaLnlJHbCyv/aHQ6ak76J8+dH3QeebLBmm/Z7/YkK5A5pATvsXsTkWXep6G6XOVHJs3ryZfTYiIioxRI/UFASurEjFS4dAb6zoWx9jtsRCV9xx4rY4zOpaS2dHzFijDfVd0CA6Pknv4Km9jRXa1/LChph7OsvuinuIz8L+6zgZc5q4rgCuBAXPa2qswAMXZCCi3Iwxit/N0RZzetRWthGWfn/Rmm7D1R59GlWCr4eTSk5oSxq9bqxUIfpO9055k/+MCE0U7d/uS4lY3vdtzN53nelNSBT22YiIqCTRa6GgtLQ0ODjk/0WO0/ioKOlYxxvL8DZGb76Yb7lHaRkYuTEWq/IZYSJmurjYzoc+CxoYms8tI1sOCIC7k22+K7cCOVPQFcE8Y08TN8WKtHkZM30AF2QoXtLS0pCamppvGbZrpI0xchku71cfTat4GKE2hUefdBuFFaQV+7DNGKlCCjoNXyxF+1fayV6ZbqawA8SmznVNpsE+GxERlRR6BTWrVaumdZ8gCJBIJJDJZAWuFFFh6linPFZZSTBzd7zO/IpTf7uiccSgsUcbiu0oezjb45PtlwzuVMX8k4yudd/CutN3dZbNL4BqSOA293GNWU4TYy6iwAUZipegoCCt+9iukS765mfOy0oCNPQtevkQLS3Qpe/DtryL8ulLMZV91MZYSACTBjaBnPavoHU2hKlzXZPpsM9GREQlhV5BzR07dsDdveh9+SbSpUOgN5zsbDBg7dl8yz1/nYUz/ySrjaox9mjDIJ/SsJIg32nxVhJALhcKNK162dHbcLKzFlU2vwBqQaaJF8bqrmIWiJA62iA1PVvrNeeCDMXTTz/9hAoVKpi7GlREWVtJ0KWuN77/M8Gg98sF4MK95xY15VxXwNLSAl2meNimTd5rs7zv2/hyb7zK4k6mYI6HaYV5Xcn42GcjIqKSQq+gZtOmTeHp6WmquhCZ1V8Jz0SVi7mjHtQ09mjDC/ee68zzKRfE1zk/rzLzf1KvCOZBgEmmiQf5lNY5Bd67gMHE/EbVKLrqc3rUgVyes8p5XlyQofhq0qQJ/P39zV0NKqJkcgG7LxVsITNLSmmhK2BpaYGuwsjJrKDp2uRdCMnYzPUwrTCvK5kG+2xERFRSiF79nKj4EzuBTL2csUcbiu/kmnbSW+5g3tNX4kaiiK27TC5gyaG/0ejrQzpzenap662z46Rt1XnF9oxsOca3rYZyrqr/BrlX4+1Yxxur+teHt1R7GSIiBWMsQmYpKS0UAcu856MIWO6//DDfQBeQE+jSd9G6gtBnlkRBaLs2L15nqa1aro0EOUFQrzxtkCIwakmrmxfWdSUiIiIqKNEjNX18fGBtLW6aKlF+LC0Xl0JwZQ8sO3pHVLm8xExv1me0hdhOrrWVaZ9LSEvZYk73nFV5Y+4ki3qPmLpHXU3ElN+uiO4M7r6UiEkdamr9PdE2uqhLXW/svpSotkLvhLZVVVbozX1cYywiQUUH2zUqiIKOsnR3skWQT2kj1cZwYkbmfb7rar4PoIyxqJu+CiMnc37XRizljIDutTW2L9HxSQVekd2YCuO6kulUrFiRbRsREZUYooOaCQmG5Ysiys3ScnHl1sS/DNxK2eYbaHMrZYsmGjpruhYNEAB0DMzpyIgJkIkJkpZztce2c//qOq0CcbS1RrsAL9F1EhO4NWS1dkVHuZGfu8bOoKbjJaaka8xz9yg1A4sP3cLK/vW1drzNsSADmQdzjlFBFHSU5bNXWQiZf9TsbaCYkXm6RtQrGCPQJfbhp7FmSeT3ecYYjZs3QJm3fbGEh2m5r8HTNHEzMyxllDGpunLlClc2JyKiEkN0ULN+/fqiysXGquejIwIsP+m8tZUEc7rXxsiN2n+H53SvrbWT0SHQGyv711cL2ioW/Pnx1F38eOquqCCurhyQAoBmVTywI/aBfiepp9yjbsTkpdQ1Ta4gI16i45Mw8Zc4tVGX6dlyvY7HfGCUW/PmzXWOaGG7RtoUdPVzwDLaQGOOuCtooEufh5/GeNim6fNcHKwRVKk0mlctCzdHw/NmhlTzwMiQKqIClOZ8mKbpGuS3WCEXzrNsYto1gG0bEREVD6KDml27djVlPaiYKypJ5zsE5uRUnLn7mspqpl6u9pjZpZbODmfu0RbR8UlYe+quWqdAbAdWW5BU+v/5t0wd0FTI3dnVViex0+QKMuJl7am7atsMXXHWHNMkyTKFhYXB3t7e3NWgIkrxsCe/h2G6WEIbKDYQ6e5kh+evMo2SZkUTfR9+FvRhm7bPS0uX4djfT3Hs76eQFOCf4/jfT9GnUSWLfnim7RrkF9AEuHCeJWO7RkREJYnooOaMGTNMWQ8q5vRJOq9PkMkU+Tn1mQam7fMb+blj4i9xGo+vrQOr6Vh563L36WssPvS3iZcHUpW3s6vp+gT5lMaFe8+xK+6Bsu4A1M7H0NFA+Y0YKQjmA6MpU6Zwmh6ZnbkftIgd8fhFWAAiNhs+Wj8/hj78NORhm0wu4Mw/yZjy6xWd7alQwLbHEh7YaiNm9kTef2tz5vokcdiuERFRSSI6qElUEKZIOm/K/JxipoHl9/lSRzu9gri6ziXYvwxkcgHN5h4ptIBmfqNucl+fqKuJCJl/VKXuitVcc+cn9ZY6oHfDigbVxVSL6TIfGBEVhCIoZCzmetAidsRjh0BvrLQSF0DU96FjQR5+6vMwUlN7a0qWPCtAzOwJAUDP+m+hebWyXDiPiIiILA6DmlQojJXMX8Hc+Tl1ff6Qpr6ijvM4LV30uRhjsQJ9CAC61PXOt/Oire6aFltKSknHokO34FbKFimvs0QFZ92dbNG1bnmsO31Pr7rrwnxgRGQMxr4vm/NBi5gRjzK5AKmjHSZ1qIFnLzPg7mQHL6mjWqDLkIeOBX34KfZhpL4L1eXmYGuF9Cy58nUpO2u8zpTpfJ+lzgoQW68dsQ/QNqCcRQZmiYiIqGRjUJMKRZBPaZ1TiK0kOeV0MXd+TjGfvzNOXL5LD2d7fLL9kqhzMUenaPWfCXi7Ummt0/f0WfRHcT6K/xfj2ass7LqUKLK0fpgPjIgKylj3ZXM+aMk7ovL4p61w4d5ztRGP+QUq8wY0DXnoaOyHn5rO09CF6hTSs+SY0LYafD1KwdPFAXK5gH4//qXzfZY6K0CfelnyNHoiIiIquazMXQEqGS7ce65zCrFcyCmni9gpaouibyLmTjJkRp67LObzn73KgruTHbR99ZcgpzMIAaLO5cw/yXiaZtiiOAU1a0+8xmtoyAglATmjOB1txd96nr/K1OszdHF3sjXrKsNEVHzcffrKaMcyx4OWqKuJaDb3CPqsOYOPtsahz5ozCJl/FClvMtG13lsI9i+jDGiO2hirds9XBCqjruY8fNL10A/Q3qYo8nrqajcNDfwaa1Tt1nP30alOeQT7l0ET/zImrbOpKa65GIpp9ERERESWhEFNKhTGzKkp9ljLjt5BnzVn0GzuEey/nIiYO8nYFfegwIFOsZ/frV55AFDr7Che925YCQfjk0Qda/hP5zF733WRNTSe3DnMZHJB5RompRreOXyTa/qemDoY0xeddK9iT0Ski0wuYMvZ+wU+jrfUwSwPWkwRqNQnL2ZeiryegPZ2syCBX2ONqs1df1PX2dRy118MS51GT0RERCWXQdPPDx8+jMOHD+Px48eQy1WDE2vXrjVKxah4Mea0Mn2ncSWmpGP05liVbQVZUEjs57cL8EIjP3e16XpupWwhAFh06G/RnykmZ5cpHYpPwsRf4lTOw93J1ow1MpyXq2VOAyTzYrtG+jqb8AxJqYaPoLe3scLolv4Y07pqoQe99Enjok+gsqAPMPVZyVzXQkR593s424uqm771N2T1dUvSIdAbE9pWxaJDt3SWtdRp9KQd2zYiIiru9A5qzpo1C19++SUaNGgAb29vSCSW+fSZLItiilNSSrrGTpQ++cR0HUuMgiwopM+5WFtJVFZkvfv0laiOg6X58dRdtW3PXqkvBmTJuDgQacN2jQxR0FFrGdlyLD50C9W9XAo98GWqQKXY6fj5BcfErGSuayEiTfu9XO31WqguP0/TMrAr7oGybvqsvm6JxrSuii1n/9U6A4PtZ9HEto2IiEoCvYOaq1atwvr16zFgwABT1IeKKcUUp1EbYyGB6pRifado5XcssQqyoJC+56JYkVUmF9Bs7hEDamteuhZ4KgqKwjRAMh+2a2SIu09fG+U45liARZ9ApdjReWIe2okNjuW3krmuhYg+bOGH1X8mqO8vwKja3KwkUEkHkzuYWlRXB7e2kmBml5zvNUDBvqOR5WDbRkREJYHeOTUzMzPxzjvvmKIuVMwppmh55UlK72VAPjFtx9JHfrm9DP38/M7FWIsU5KXI3WkqlhDQVCy2sKLv2wZNezfkd4xKDrZrpK+oq4lYrEcKEW0K0g4VhD4pYcQu4CM2v2hBgmNi8ntqCmjmVZDYXN42MW8O0qLKmN/RyDKwbSMiopJA76DmsGHDsHnzZqN8+J9//onOnTujfPnykEgk+P3331X2Dxo0CBKJROWnQ4cOKmWePXuGfv36wdXVFW5ubhg6dChevnypUuby5cto3rw5HBwcULFiRcybN0+tLtu3b0eNGjXg4OCA2rVrY//+/UY5R1LVIdAbJye3xpbhTbCkdz1sGd4EJye3NujLcu5jjWlVxeA6GTqFUPH5m4Y1xphWVTCmlT++7VkX7QK8jPo5umRki190Rx9WEmDQOz4mObY+co8S6VinPL7oVEvU+8a0qlLg3zEqGYzZrlHxl19gzVCFvQCLPiuNi1kMp3fDSqJGQo5vW61A92Ix0+bF/LsoApNDmvpi07DG8HK113otFLQFQnWt6l6UGPM7Gpkf2zYiIioJRE0/nzhxovL/5XI5Vq9ejUOHDqFOnTqwtVUdNbVw4ULRH/7q1SvUrVsXQ4YMQffu3TWW6dChA9atW6d8bW+vmui9X79+SExMRHR0NLKysjB48GB8+OGHykY8NTUV7du3R9u2bbFq1SpcuXIFQ4YMgZubGz788EMAwOnTp9GnTx9ERkaiU6dO2Lx5M7p164bY2FgEBgaKPh8SJ79pZYYeq5GfO36N/Z9BeTYLkvg+Oj5JJW/XsqN3tC5CZKoE+3/+/cQkx13Wpz5KO9lh/el7Jjm+WBIJMLy5n/J6il3op2kVjyI7FZBMb9q0acr/2tjYGK1do+LPFKPuC3sBFl1pVAQA7wbm5IhU5IzMbzEcsQ/XfD1KFajexgz+SgAcuJqEz8ICMLNLrXxT2vSs/xZ2xD7QeqzcI26LertjzO9oZB7Tpk2DnZ2dUftsRERElkpUUPPixYsqr+vVqwcAuHr1aoE+/N1338W7776bbxl7e3t4eWke+Xb9+nVERUXh3LlzaNCgAQBg6dKl6NixI7799luUL18emzZtQmZmJtauXQs7OzvUqlULcXFxWLhwoTKouWTJEnTo0AGffvopAGD27NmIjo7GsmXLsGrVqgKdIxUOQ/Js6srtpWtlVV15vfJO1zLGAkeavDLByugT2lZFxzrekMkFk9RZH3IB+P7PBNSt4IaOdcobddEpKrkuX76s/K+1tbXR2jUq/ow9qtLbTPcrbYFKiQQQBGDtqbtYe+quyoM6bYvhxNxJFvWZBQ3eGjP4mzsQqe1aeOcK2uYX1FQo7BG3RJoo2jXAeH02IiIiSyUqqHn06FFT10OrY8eOwdPTE6VLl0br1q3x1VdfoUyZnCfIMTExcHNzUwY0AaBt27awsrLCX3/9hffeew8xMTFo0aIF7OzslGVCQ0Mxd+5cPH/+HKVLl0ZMTIzKaFRFmbzT4XPLyMhARsZ/U61SU1ONdMZkKG2dEk10Jb7XtbKqrrxemhYhMsYCR4XF18MJgPg6a1tMSHHNbialFXjV9zFbLmIZJOhYx9toi05RybV3715IpVLs3bsXrq6u5q4OFSHGHlXZu2El5f1K18M0Y8sdqDwUn4QfT93VmjNS8aBO0yi+wnrYZIqHg4pApCUEbYmMge0aERGVJHrn1BwyZAjS0tLUtr969QpDhgwxSqUUOnTogJ9++gmHDx/G3Llzcfz4cbz77ruQyXJGpiUlJcHT01PlPTY2NnB3d0dSUpKyTLly5VTKKF7rKqPYr0lkZCSkUqnyp2LFigU7WTIKRT6oCW2r5VtOWspWa+J7xQjMvIHR3IsBiMnrpWnxB2MscFQYcnfM2gV4YXzbqpA6al+gR/j/3qWjrVWe7Tk7FEHSgpALwOjNOdefCxqQMRVmu0ZFn658lPpSTMmOupqIZnOPoM+aM/hoaxz6rDmDZnOPKBegkckFxNxJxq64B4i5k2y0/I3WVhI08nPH/quav/MoPuWznVexM/Z/Gj9bTN5NYzxsyu9zDJW7vVNMve5a7y0E+5dR1lefHKREloJtGxERlQR6BzU3bNiAN2/eqG1/8+YNfvrpJ6NUSqF3797o0qULateujW7dumHv3r04d+4cjh07ZtTPMcTUqVORkpKi/Pn333/NXSXKZeu5/FdhdbS11rigj5iVVWftiUdSqrgpZpqmoikXGBraGG75BArNIW/HTNHJXnToFl68ydL6PsW1eZOlmlftUWoGRm2Mxd2nr41WR8ViDFzQgIylMNs1KvpyB9aMwdPFQefDtMj98fkGPA2RO0i6/lSCzgd1ya8yMeGXS1o/u7AeNhnz4aDYQGRhBW31YaogNxUfbNuIiKgkEDX9HMiZXi0IAgRBQFpaGhwc/vsyKZPJsH//frVRk8ZWuXJleHh44Pbt22jTpg28vLzw+PFjlTLZ2dl49uyZMg+nl5cXHj16pFJG8VpXGW25PIGcXJ95Fy0iyyBmEQdtCf3FjsB89lL3Kq+A9qlo1lYSWFlJ8g0UFra8HTNtOUP1oXjvulMJKF3KFs9fF/x8c//bcUEDKqiUlBSztmtUNCkCa1N+vYwXb7INOoZiSnaQT2mEzD+a78O07/9MUNunLX+zGJpSrOhD22fnN4XbmDoEeqN1jXJoEnkYz15lGnycL8JyApUxd5J11lfXYkmF+UBNV4ocKtksoc9GRERUWEQHNd3c3CCRSCCRSFCtmvrUXolEglmzZhm1cnn973//Q3JyMry9c76wBQcH48WLF7hw4QKCgoIAAEeOHIFcLkfjxo2VZT777DNkZWUpV/2Ljo5G9erVUbp0aWWZw4cPY/z48crPio6ORnBwsEnPh0xDbKL+Q/FJagExse91d7IrcP4wsaM9C4tXnpyhU367YrScZcYO3nIxBjIWX19fs7ZrVHR1CPTGgStJ2HXpod7vzf0Q6cK95wYFF7Xlb9bFmA+spu28gjdZcni5/hcMLKyHTRfuPS9QQBMAbj1Ow+y54oODhRW0zY++ixRSyVOpUiWz99mIiIgKi+ig5tGjRyEIAlq3bo1ff/0V7u7/BWvs7Ozg4+OD8uXL6/XhL1++xO3bt5WvExISEBcXB3d3d7i7u2PWrFno0aMHvLy8cOfOHUyaNAlVqlRBaGgoAKBmzZro0KEDhg8fjlWrViErKwtjxoxB7969lXXp27cvZs2ahaFDh2Ly5Mm4evUqlixZgkWLFik/96OPPkJISAgWLFiAsLAwbN26FefPn8fq1av1Oh+yDGIT9f946i4a+rmrfPkX+14vqWOBF6t5miZutKcpuTvZomvdt1ChtCPcnewgdbSDTC5g6eFbeGGEUZWmwsUYyFh2796Nzp07G61do5Ij6moi9hgQ0ARUHyLtitO9qrY2ufM3iwkk5pdixRDPXmVhwrY4AIU/UtAYD7c0LWCnKzhozhkChixSSCXP3r17UapUKaP22YiIiCyV6KBmSEgIgJzAo+IJYEGdP38erVq1Ur5WrEAeHh6OlStX4vLly9iwYQNevHiB8uXLo3379pg9e7bKtO9NmzZhzJgxaNOmDaysrNCjRw989913yv1SqRR//PEHIiIiEBQUBA8PD0yfPh0ffvihssw777yDzZs34/PPP8e0adNQtWpV/P777wgMDCzwOVLha+TnjlJ21nidKdNZNu+Xf31WcLW2khg8FS3qaiKWHinYauCGkDraYFmf+vgr4RkAAdZWVth27l+VUaNSRxukGDid0tSMtYIukULz5s2N2q6Rfgp7tW9jibqaiJEbY/V+n6OtFX4Y2BBNci1CY4yHNGIDfGLSsxiqsEcK3n36yiTHteTgoD6LFDI1S8nVrFkzuLq6sm0jIqISQXRQU+HevXu4d++e1v0tWrQQfayWLVsqV0fW5ODBgzqP4e7ujs2bN+dbpk6dOjhx4kS+Zd5//328//77Oj+PjMdUnVmZXMCbLN0BTUD9y79iMQBNIzDx/687BuZMPWv0/6M89Z2Kpm9n2MYKyJbrLidGi6oemPTr5Xw7ReYOaGrLvWmuxRio+DNmu0biFdW8gDK5gJm7rxn03jdZchy58QhW/7/iuGLl8fwepokhNjBqytQdpg4G5v7O4OFkj81/5b8gYEFYanBQ7L8fU7QQwLbN2BT3oKTUdDxNy0Dyq3QkpWTgrdKOaOJXBnK5gJ1xD/C/529Qwc0BPYIqooGvOzacSsBvFx8gUyaHf1kntKnpiUPxj3H1YQpevM6CnRXg7GiDtNdZeJXnK7iDNWBrY42sLBnSjdQXoMIj+f+f3P909taAs70NZHIZJBJrAALeZMuRmZXzDUDRltrZAI62NsjMFuBoZ4XKHqUgAfC/Fxl4mZGJ9Cw5ZHLAzsYKVcqWgpWVFZ69ysCLNzI42lrB3ckWrg62eP46G8721qjs6YSHz9ORkp6F9EwZUt9kIluQoIaXC0aG+CPY3wN//ZOM32L/h9T0LEgkQDkXB0gkEtjZSHDk+mM8e50JO2srNPZzR92KpVHGyQ7JLzMRn5SK15kyBPm4IcBbimevM5V9YgAqbTckwNOXGfBwtgcE4OmrDK3957yxgnoV3bD5r3u49+w1KpZ2RA0vVzx7nalyXE2fm3dbUmrO+hhupezw7FUmnr/OhJUECK7sofLQOb+6iKmvMR/UG3Jscw4cKOzPlgj5RRU1sLJSXzA99xNAmUxcMKm4SU1NhVQqRUpKClxdXc1dHYtnys7sjyf+wex910WXX9K7HrrWe0tn/awkQO7FRQ2pr0wuIOiraL2mdjf0LY1zd5+LLq+Nk701XmVY7t/nmFZV0LSKBxr5uSM6PqlIBjuo6Mh9z3Zzc1Pbz3bNtO2atryAiqtuyXkBY+4ko8+aMwU+Tu57mqEjPxWj109Obi3qy6Kx6q7LluFNtAYDDfmiW9CFjQyl6fuBOYn998vv+lPxlfeebe4+2/LlyzF//nwkJSWhbt26WLp0KRo1aqS1/Pbt2/HFF1/g7t27qFq1KubOnYuOHTsq9wuCgBkzZmDNmjV48eIFmjZtipUrV6Jq1aqi6lOQNs1c9yCiosytVM56JmL6vXn7eQX5m9P0uWLr4lbKFnO611b5DiombmHK2IYhxzbnwAFjfbY+92z11k6H58+fq/w8fvwYUVFRaNiwIf744w99D0clkKIzm/cmpZi6FnU1sUDHv/fstV7lNY1w6RDojZOTW2PL8CYY0tQXgGpAE9BcX5lcQMydZOyKe4CYO8nIzJarvD59+6neuSob+RZ8qrUEgK213n/uerG3Kdjxq5ZzVq5onvv6L+ldD1uGN8HJya0tNshBRRvbtcKlKy8gkDPaT5b3pmshjLXIm75tXt6wnyGj1xv5ucPdyVZ8JQ2kbaRg1NVENJt7BH3WnMFHW+PQZ80ZNJt7JN9roO07Q2GwtPzNilG92v61JcjpODBFCwHmbdu2bduGiRMnYsaMGYiNjUXdunURGhqKx48fayx/+vRp9OnTB0OHDsXFixfRrVs3dOvWDVevXlWWmTdvHr777jusWrUKf/31F5ycnBAaGor0dNPeG8x5DyIqyl68zhLd7839naigf3OaPldsXV68zsLIXN/NxMQtTBnbMOTYpo61GLu+xqD3SE1tjh8/jokTJ+LChQvGOFyRw5Ga4sjkAprNPaL1JpV71AmgPmw8v46bYvTHtrP38bvIxRu8dYxw0ae+mkYX5h3d6WRnhVeZ+s0f6VavPHbFPTR4WqK31AG9G1bCokN/G3iEwsGRJVSYxNyz2a6Zpl0r6qPN9J0NkB9FGyIIApJSNS8eJwEgLWULBxtrlYCqoU/c919+iNGbLxag1rpp+rczZHSurjbYVPQdAVuYFNcR0LxIoSWPcibTEnvPLoy2rXHjxmjYsCGWLVsGAJDL5ahYsSLGjh2LKVOmqJX/4IMP8OrVK+zdu1e5rUmTJqhXrx5WrVoFQRBQvnx5fPzxx/jkk08AACkpKShXrhzWr1+P3r1766yTIW2aue5BRCWRBEA5V3sAEqM9QDaUl6s9/pzUGiHzj+qMA+j6Dmfo9wl94hCKYxvyHmMx9mfrc8/WO6emNuXKlcPNmzeNdTgqpsQmuV925Da2nrsvetiyoUPUdY1wEV/fW1h86JZaZy3vQCN9A5oA8HucYavr9m9SCWG1y6ORnzv2XjbsGIVB0YDJBQG74h4UqcVCqHhju2YaRT0voJuj8UY6KtoQXWVevM7CpqH1YWUlKXB+otBAb3Sqk4S9l03ztLx0KVu1kYKGrtptyoWNdLHU/M0dAr0NXqSQCDB925aZmYkLFy5g6tSpym1WVlZo27YtYmJiNL4nJiZGuWCsQmhoKH7//XcAOQvVJiUloW3btsr9UqkUjRs3RkxMjMagZkZGBjIy/gs0pKam6n0u5rwHEZU0AqA1OFjYklIz8HPMXVFxgPwUJEe3IYsDmnNBQXN+tt5BzcuXL6u8FgQBiYmJmDNnDurVq2eselExJbaTqmlUoaaVVWVyAcuO3DZoFOKQpr46v/yLre+6U3cNHklpKmG1yytvGJY2hS43AUB6thz9fvhLuY35M6kwGatdi4yMxG+//YYbN27A0dER77zzDubOnYvq1asry6Snp+Pjjz/G1q1bkZGRgdDQUKxYsQLlypVTlrl//z5GjRqFo0ePwtnZGeHh4YiMjISNzX9N9rFjxzBx4kRcu3YNFStWxOeff45Bgwap1EfffGaFRez9yFLvWzH/JJvlc5++yihwfsfCyAunqS009IuuOQLbbo62mNOjtkW3P4YsUkglj7n6bE+fPoVMJlNp14CcYOqNGzc0vicpKUlj+aSkJOV+xTZtZfKKjIzErFmzDDoHBUt9uEZEpqdvSrv8GHIvMWQQgDkHDpjzs/UOatarVw8SiURt1fImTZpg7dq1RqsYFU8F6aTmHc0RHZ+EmbuvGfxEp12Al84yYuv74o1+eTJNzd1JdaRMIz93eLnaW8zTr7zy5jjRFMAmMhVjtWvHjx9HREQEGjZsiOzsbEybNg3t27dHfHw8nJycAAATJvxfe3ceF1W5/wH8MzMww6IMIMJAbriLuHtR0swUAzWXtM00Tb16Xcj15lKaptc9NcvSWzf13qtk+btmqcUNUTMRN5AUUXNBLWUwRUBF1jm/P7gzMjArzM7n/XrxqjnnzDnPmcHz5XzP8zzfmdi/fz927doFuVyO2NhYDBs2DElJSQDKCzcMHDgQCoUCx44dQ1ZWFkaPHg13d3csX74cQHmPlYEDB2LSpEnYsWMHEhMT8ec//xnBwcGIjo4G8GQ+s82bN6Nbt2748MMPER0djUuXLiEwMNASH5tBhorBGKv2rR6e4ojzApapBCRc0D0nnLXVNMmrb/i3peUWlFQ7OanMe4zkq/c0vzcB3jJrNVOvT0Z2Ro/mATY/rrkkYpFDTs9AjqO237PNnz9fq/dnfn4+GjZsaNY+HPXhGhFZX2N/L4vtqzrXkup0ArBnxwF7HtvspGZmZqbWa7FYjPr168PDgxd9Ms7YzawxxoZ7m8KcG+byZKCH3ef1MNeLHZ/S6rEhEYswIqIR1h+4bPS9dWQSuEnEZhc0siRDwxGJLM1ScS0+Pl7r9bZt2xAYGIiUlBT06tULeXl5+OKLLxAXF4c+fcrnDd66dSvatGmD48ePo3v37vjxxx+RkZGBAwcOICgoCB07dsTSpUsxd+5cLF68GFKpFJs3b0ZoaCjWrl0LAGjTpg2OHj2K9evXa5Ka69atw4QJEzB27FgAwObNm7F//35s2bJF53xmlqSrN6CvpzvG9ghFbJ/mkIhFWDQoDJO3p0IE3fMCOurQ35OZOciz8EMsPy93yNzEyM4vslqS19Dwb2uonMS8fte03g7vfXceDwpLNa8VPh7w9XJHXkGJ1duu/py7N7VcorA6ld6JLMVe92wBAQGQSCTIzs7WWp6dnQ2FQneHAoVCYXB79X+zs7MRHBystY2+XqcymQwyWc0ejNT0voWITFdxTs3sfPv+m1P4yPBGZBP842im0YfwgiBY5W+46nQCsGfHAXse26xyxSUlJRg3bhyKi4vRuHFjNG7cGA0bNmRCk0ymvpkF9FdyNUV1h3ube8OsTgY6mygdvVCbBHib9N6XuzRAyoJ+WDiwjaWbZZaKwxGJrMWacS0vLw8A4O9fHrxTUlJQUlKiNSdY69at0ahRI808Y8nJyWjXrp3WELvo6Gjk5+fj/Pnzmm0q7kO9jXof6vnMKm5jbD4zoHz+sfz8fK0fc+mrepj7uATrD/yKLn9LQHx6lmZeQIVc+3NWyD0cuoe2NYbMCADee8FwXKxpktdS88J5S037s7HiU/j49Cx8aOIUMRUTmkB5pflcGyU0Acsm06tT6Z3IUux5zyaVStGlSxckJiZqlqlUKiQmJiIyMlLneyIjI7W2B4CEhATN9qGhoVAoFFrb5Ofn48SJE3r3aQkV71uIyHrUkXfx4LZYPFj330S2tHhwW0jdxEbzFosGhWHx4LZGt6nO3xam5E0q77s677EUex7brKSmu7t7lflZiMxl6GZ2ZlQLk/ZR3eHeQT4ys2+YmwRYruu5LSh8ZOjS2A/JV+/h27RbSL56D2UqweSu3l+n/A4AeCOyCfy9LVcQo7o4nxFZk7XimkqlwowZM9CjRw+Eh4cDKJ8TTCqVwtfXV2vbyvOG6ZozTL3O0Db5+fl4/PixwfnM9M09BpTPPyaXyzU/5g7TM6U3YG5BCSZtT9UkNo/O7YMvJ3THhtc64ssJ3XF0bh+HTWgC1hkyk1tQAj9vmVWTvJa6jhordidC+ZzI6qfwtu4haqrKf09bOpmuL7mvnlqlJonNMpVQJb4TVWbve7ZZs2bh888/xz//+U9cuHABkydPxqNHjzSjB0aPHq1VSGj69OmIj4/H2rVrcfHiRSxevBinT59GbGwsAEAkEmHGjBn429/+hu+++w7nzp3D6NGjERISgqFDh1r1XNT3LcFyduIhMoeflzt8vUy7l6wYh/XlCkzlq+O4upbpe+/mCn8PmPIQ3poP6quzb3t2HLDXsc0efj5q1Ch88cUXWLlypTXaQ7WEvknuAWDnqd8MdluWe7lXe2j02lc6mj1Xljk3sZWHUtpD18b+6LX6kNaQ+WC5B15or4BIBAhGGvioqAwfJf6Kr0//jpxH9p8rlPMZkbVZI65NnToV6enpOHr0qMX2aW01nX/MnN6AFaeWcKZ5ASNC/eHvLUXOo2KL7vfOg0IM6fiU1Yq/2OI6quspvCNWDhYB2DiiE/y8ZVYZFl7dSu+m0DW1AwvrkT72vGd79dVX8ccff+C9996DUqlEx44dER8fr3nYdvPmTYjFT/rWPP3004iLi8OCBQvwzjvvoEWLFtizZ4/moSAAzJkzB48ePcLEiRORm5uLnj17Ij4+3ia9TyvetyjzC3H3QRHuPSqEMq8IT/l5ontoPahUAr5Ju4Xf7z9GA18PDO/SEF2b+OOfSZnYfeYWistUaFbfG33bBOJAxh2k385DbkEJpGKgjqcbHhSU4JF2Z3V4SAB3NwlKSspQaPiZEjkg0f9+Kn51MglQR+aGMlUZRCIJAAGPS1UoLimPGuo4IXUDPN3dUFwqwFMqRtMAL4gA/J5bhIdFxSgsUaFMBUjdxGhe3wtisRg5j4qQ+7gMnu5i+Hu7w8fDHfcLSlFHJkHTQG/cvl+IvMISFBaXIf9xMUoFEVor6mLSs80Q2SwAJ67dw+7U35FfWAKRCAiq6wGRSASpmwgHL9xBTkExpBIxuoX6o0NDP9TzluLew2JkKPNRUFyGLo19ERYsR05Bsda9vfrvmgBvGSAC7j4sQkAdGSCUF0HUFYd15Qo6NvRF3IkbuJFTgIZ+nmit8EFOQbHWfnUdt/IyZX4hch4Wwder/O+5+wXFEIuAyKYB6N6sXpXYbEpxPmsW8KvOvu1ZUNAexzY7qVlaWootW7bgwIED6NKli6b4gdq6dess1jhybfpuZo3NtTb26dBqVTsHyi925jJ1foiFA8OwdL91q8qaYt+5qj1AsvIK8fnP103ex4bEKxZsUfU4crEQci2WjmuxsbHYt28fjhw5ggYNGmiWKxQKFBcXIzc3V6u3ZuV5w06ePKm1P/U8YxW30TX3mI+PDzw9PSGRSMyezwyo+fxj5vQG1FXp2hlIxCL8bUg4psSlWnS/6qSjtZK8tpgXTqEjueZoPe39vNyxYph1K5sfv3avWpXejdFX6ImF9Ugfe9+zxcbGanpaVnb48OEqy15++WW8/PLLevcnEomwZMkSLFmyxFJNNIsp1+dnW1ctxDfpueaY9FxzrWWvd2tiyaYRWcQzLevjmZb1da57b1D191vdv2t0/Zsb/0zTGh3Xkm2pzjbVVZ1927PjgK2PbXJSUyKRICsrC+np6ejcuTMA4NdftRNLIhEnP6eaU3dbrtwbQX3D1C9MgZ2nblYreVid3iqmFrWICQ9GVFgQ3tl9DrvP/A6OCKs+Ry8WQq7jjz/+sFhcEwQBb731Fr755hscPnwYoaGhWuu7dOkCd3d3JCYmYvjw4QCAS5cu4ebNm5o5wSIjI7Fs2TLcuXNHU6U8ISEBPj4+CAsL02zz/fffa+274txjFeczUw/LU89npu8G0xLMvb46WsLLVAPaB+OFc8E6HyCZy1YPbwzFsZqKfa45ejQP0PkU3lF62pcXqmqC2D4trBpT4tOzMO8/50za1pzff2v2/iTX4+fnx3s2IiKqNUxOagr/G7N66NAhqzWGSM1Yt+UX2ivM6nkIaM/zVZ32GEq0xoQHl9/M7D5n16rhzqrysHhdPX6IrMVScW3q1KmIi4vDt99+i7p162rmr5TL5fD09IRcLsf48eMxa9Ys+Pv7w8fHB2+99RYiIyPRvXt3AMDzzz+PsLAwvPHGG1i9ejWUSiUWLFiAqVOnanpRTpo0CRs3bsScOXMwbtw4HDx4EF9//TX279+vacusWbMwZswYdO3aFREREfjwww+15jOzBnVvQFMfODlKwqs6nmtV3yJJTcB2D2/0xTHfGkzpAgAtgurofRpvz8rBdWRueKVrA/QLU9hkyJW+npT6mPP7b2wYf3V7f5Jr4j0bERHVJmYPPyeyFX3dluPTs8xOaALm3TiWqYQqCdV+YQrUlbkj+dpdAOVt6960fN6N+PQsTNpu2eGItcm0Pi3QvWk9m8/5QWRJmzZtAgD07t1ba/nWrVvx5ptvAgDWr18PsViM4cOHo6ioCNHR0fj0008120okEuzbtw+TJ09GZGQkvL29MWbMGK3hdqGhodi/fz9mzpyJDRs2oEGDBvjHP/6B6OhozTbG5jOzhoq9AQ0ldlxhaonUm/drvA+xCNjwWifU9XDHB/+9BEDQO5+TpVR+YBjgLcPsXb8AqH5S01Byzpo9RI35+6gu6NHCvDm0q8ucgkjq33+VSsC3abdMinmm9up01t7PRERERNUlEgRjZUPKicVi/O1vf0OdOnUMbjdt2jSLNMzZ5OfnQy6XIy8vDz4+PvZujssqUwnoueqgWUPPZW5iTOndHLF9mpt0o6hrIn51tbSKvVmCKwyHb7f4vygoLjPjTEjN18sdKQv6MYlJNqW+Zi9cuBABAYYTH4xr5sU1Q73W1f/KnX3+v5c2JeH0jdwa78dbJsGjIu3Y4evljpVWnvdRLfnqPYz4/Hi13qtOzn3wUge9E/2r6Yqr1qJu19G5fcyKK7oeZpr6fnM/x8q9Y40V+zF1/19O6M6emrWY+potEol4z6YD79WIiJyHOddss5KaDRo0gEQi0b8zkQjXrl0zr7UugoHSNmpyA2ZKhVBzho+pb3U6N/ZFigVubl2Vn5c77heU6O2ls9nJkxvknNTX7AYNGsDNTf+gBca16sW1MpWAjQcvY2vSdeQ+Nj154yyGfZqE1Ju5Vj2GLa6N36bdwvSdaWa/T309Nyc5913qLUz72vxjVadt5iTNLfG7aurn6C2V4JGOB6DGkv3qB7rGChaam8gl11Ixqcl7tqp4r0ZE5DzMuWabNfz89OnTmsIFRPZQk6FVxiqEmjN8DHiSoGNCU9uMvi3QrdJQ8oQMZZVeOq6S3CDndvjwYTRr1szezXA5ErEI06NaIrZPi2r3fnNkLYK8rZ7UXPzdeasUfqnYI/Hug6Jq7cP3fw+rKvfG1RVny1QCpn2Ziv3nlDVuu9F2ebpj5XDTe7ka6lVsTlVxU+fHlLqJdSY1jRX7MbVgoSv82yLL4D0bERHVFiYnNVkljxxBTQpLGLtpMDYRvyOx9dxkplL4yPBW36rVZY0VfiIi16RvbmRn5+cps/oxlPlFFi/8omsYuDnxxNfTHR+P6IS3/+8XnevVcfbdb9LxuLgMN3MKsOnwFRSW2iZifTKyM3o01z2dROXh5fcfFWNqnP6RGeZUFTelIJK/tztyHumfu9RYsR9TChYSAbxnIyKi2sXs6udE9hQR6l+jSq2GbhqcaYJ9R/zXKAKweHBbvTd+rprcIKLaR5lvm3iRkKG02HVT3/Qq5sST5S+2w6/ZD6DM19/DUwBw71ExZn6tO/FZXd5SCQqKywwOv+7eVPdnVd1krvpvhm1JmQioK0OAtwwQAXcfas8hWrEnpT6dGvoi8eIfxk7T4N8ifEBIpuA9GxER1SYmJzUXLVpkdMJpqj1qMqF+TSRkKKud0KxI101DTXqB1nb+3u5Y/qJtClsQWZK3t7e9m0BOKETuaZPjbEm6johQ/xpfW82dXkWXQe0VWLrfNsV+dBGJRJrek+YMv7ZEMnfp/gs6l1ecRiUmPBgTe4Xi70cydW5rSkITMP63CB8QkjHz5s3jPRsREdUaZiU1qXZTJzITMpTYk3YbOY+KNetsMT+i+qbMEnTdNJgyfIyqqiNzw/H5UZC6ie3dFCKzeXl52bsJ5GTi07MQd+KGTY5l6vBnY2o6vYqHmxh7z1p/TkxDHhaVYmZUC+w89ZvJw68tkcw1pOK8m/3CFPjulyy924oAiESASk9j1L1NI0L9rdJWqj3mzZvH2EZERLWGWYWCqPbSNXSrInMm1K8uS8x5aeimwdBE/KTfhGeaMqFJRLWCvl5/1mJsnkVT1XR6lcJSVY3ebylNArxxdG4fk0eKWHuu7IrzbtaVuRs8lgBAPSqYxX6IiIiILIOZCDJKfRNn7I91oPwP+zJ93RBqqKY3ZerbhIUD2+BkZg6+TbuF5Kv3tNqrnohfIedQdFP4erkjtk9zezeDiMjqrN3rz5Caxj9Tp1epI3PsZ92BdT00w6+HdHwKkc3qGUwC2mKubHXiOfnaXZO2H9+jSZW/MRRyD6s+FCYiIiJyVY791yvZnTk3cZbqUaJPQJ2aVZtVyD0wuEMwlu6/oJWgrTx0Xj0R/8eJv+LDxCs1OqarWzmsHXuVEFGtYO1ef4bUdM5nY9OriFD+kOq+BeastpbgagzNtu1c2abFwqgwBd4ZGMZiP0REREQWwJ6aZFB1buKs1jOiBt1jYp9rhoUDw/DZkcwq56MeOh+frj0X1r+O22bONGckEgGfvs5eJURUe9ii119lIlQvmVeZenoV9T4rHwMAVA5cMVmE6g3NVidzbSGyWT0Eyz30pjYrfpfm9DYlIiIiIv1M6qnZqVMniESm/cGVmppaowaRY6nOTZy1ekbcfVRU7fdGNg3A1C91z4NWcU4sdTGGk5k5yHnkuD1W7E0QgMt3Htq7GUTV1rNnTwDAM888A4lEYnBbxjXXoi56Z24vOdv2+rP8PIvq6VUqz4+tkHugS2M/7Durv8iNPfl6uWPlsHbVeoimTuZO2m69f8Pqubq7N62HhQPbYErcGZ3bAJwzk2zDlLgGMLYREZFrMCmpOXToUM3/FxYW4tNPP0VYWBgiIyMBAMePH8f58+cxZcoUqzSS7MecmzhrV+4M8K7e8HNvmQQnrt1DroFhdZWHztujR469SURAmRkddbYey0Rsn+a8QSOn9MILL+DcuXMYOHAgBEFgXKsldBW9qzwFiT4Rof7wlkrwqLjM2s0EYLiqd3Wpp1epmNS9/6gYU+IcN7nxyYjO6NEiQOc6UxLUMeHB2DyqM/666yweFpVatG0Vk5UJGUos3X9B53bW+C6J9Bk4cCBkMhnv2YiIqFYQCYJ5443+/Oc/Izg4GEuXLtVavmjRIvz222/YsmWLRRvoLPLz8yGXy5GXlwcfHx97N8diylQCeq46qHceLjX1H/bWnOg+6cpdjPzHiWq919Rq5utf6QCF3BNJV+5i46HaN5+mv7cU3UL98UO60qTtv5zQ3SrzpxJZW8Vr9qxZsxjXdHC1uKavcrk58evlzcdw6vp9q7QPKC9kF1BXZlIP0ur2OK28j3aL/4sCGyVqzaF+UHp0bh+d52Vugrq4VIXOS3/EwyLLnav6eAB0/m6pffp6Zwxoz4QmWVflazbv2bS5WkwjInJl5lyzzU5qyuVynD59Gi1atNBafvnyZXTt2hV5eXnmt9gFuHKgVN8IAvoTg6b2dKmJ3Sm/Y9auX6y2f6A8qZfzqNiqx3B0IgCeUolJN7kbXuuIIR2fsn6jiCys4jW7YcOGjGs6uFJcUz+g0zdHtLEEmtrLm5Jw6kauVdooApCxJAaeUuPDRvUl9BYObAM/b1mVRKeuBCgAjPzHcRy/lmOV86kJfYlm9XkkZCixJem6ye9Ti0/PMjgUvV9YINJv5Zs8l/inr3dCdHiwRX63iGqq8jWb92zaXCmmERG5OnOu2WZXP/f09ERSUlKVAJmUlAQPD9vON0W2oW8eLn9vd7zY8SlEhSmsXrlzxfcZ+PuRTKvtX622JzTVxCbOoWvrOeaIrIFxzfUZK3pXeQoSXeLTs6yW0FS3oceqg1j+YrjBB4T6epxm5RVWmc8xWO6BwR2C8d0vWVrn7+vlDghA7mP7zh09pXczyNzE+PLkTSjzn8ybrWu4tq5EbmW65siuSD0UXdffM38bEo4B7UM0iVNlfiGW7juvd35tEYCl+y9A7iWt8e8WkTUwthERUW1gdlJzxowZmDx5MlJTUxEREQEAOHHiBLZs2YKFCxdavIHkGHTNw2XtRKaarRKaVE4A8LCoFN4yCR7pGaZn7flTiWyJcc0xGBtOXZPh1qbOk6xvuzKVgEXfppu0j5rIeVSMydtT9fY0LFMJeH9vhknTqQDlyTRd8dPQHNO21EpRF0M6PoXYPi30frdlKgEbD17G+gOXTdqnsSSisb9n1JXJk6/eM1gwUH2c5Kv3TGpXbZyrm+yLsY2IiGoDs5Oa8+bNQ9OmTbFhwwZs374dANCmTRts3boVr7zyiln7OnLkCNasWYOUlBRkZWXhm2++0RQlKikpwYIFC/D999/j2rVrkMvliIqKwsqVKxESEqLZR5MmTXDjxg2t/a5YsQLz5s3TvD579iymTp2KU6dOoX79+njrrbcwZ84crffs2rULCxcuxPXr19GiRQusWrUKAwYMMOt8XJ36D31bKi5V4TMmNO3ita4NsSXput7551jFlVyFJeMaVY+x+RFrUuAHML1Xub7tTmbmIPuB7Xry6+tpaKzHqbNRf976/r6IT8/C4u8yoMw3/5wNJRFN+XvG9CSkaSlmjmwgW2NsIyKi2sDspCYAvPLKKxYJho8ePUKHDh0wbtw4DBs2TGtdQUEBUlNTsXDhQnTo0AH379/H9OnTMXjwYJw+fVpr2yVLlmDChAma13Xr1tX8f35+Pp5//nlERUVh8+bNOHfuHMaNGwdfX19MnDgRAHDs2DGMGDECK1aswAsvvIC4uDgMHToUqampCA8Pr/F5kvnUPXK+PnXT5B4pZFlRYQr8KdS/SiKBVVzJFVkqrpH59A2nVuYVYvL2VEzsFYrPjmTqXW9KgZ+IUH8Eyz30Fr0z1vvclr3sDPU0dJXefqb09tf3e2EqXUlEc3r7mpqElIhFNfrdIrImxjYiInJ11UpqAkBxcTHu3LkDlUqltbxRo0Ym76N///7o37+/znVyuRwJCQlayzZu3IiIiAjcvHlT6zh169aFQqHQuZ8dO3aguLgYW7ZsgVQqRdu2bZGWloZ169ZpkpobNmxATEwM3n77bQDA0qVLkZCQgI0bN2Lz5s0mnw9ZhinzZlWXWASomCU1Klj+5GbPXtMOENmaJeIamcfQcGr1/Iif/1w1oVlxvb5ejRVJxCIsGhSGydtTIYJ23zpTep/bo5edrgSmq/T2E2D48zZ3mH1lvl7uVZKIpvT2rZj0DPCWQeHjYbSX6FenfsN7L7TF1Ljq/W4RWRtjGxERuTKzk5qXL1/GuHHjcOzYMa3lgiBAJBKhrMx4xeTqysvLg0gkgq+vr9bylStXYunSpWjUqBFef/11zJw5E25u5aeWnJyMXr16QSqVaraPjo7GqlWrcP/+ffj5+SE5ORmzZs3S2md0dDT27Nmjty1FRUUoKnoyqX1+fn7NT7AWqtxr4v6jYkyNq37PDH3UtxIbR3TGnrTf8WPGHQsfwbW89qdGVeYXI3JV9oxrtZ0pBXwEAwHBnCIs+oremdL7vEtjP4P7tgZdCUxjPU6dhZ+XO/qF6X4YDdR8mP3Yp0O1kojGegNvGtUZAKr8bsjcxEaPpcwvgp+3tNq/W0TWwthGRES1gdlJzTfffBNubm7Yt28fgoODITKxSnJNFRYWYu7cuRgxYoRWSfdp06ahc+fO8Pf3x7FjxzB//nxkZWVh3bp1AAClUonQ0FCtfQUFBWnW+fn5QalUapZV3EapVOptz4oVK/D+++9b6vRqJV29JsQiU2enMk/FG4usvMdMahrRJMDL3k0gshl7xTWy3HBqU/dT3aJ3KTfuW6KZJjE0XNlQj1Nncr+gxGAiuia/F75e7ojt01zz2pTewPN3n8N9HcWTikpVVZbpcudBIYZ0fIojG8ihMLYREVFtYHZSMy0tDSkpKWjdurU12qNTSUkJXnnlFQiCgE2bNmmtq9jDsn379pBKpfjLX/6CFStWQCaTWa1N8+fP1zp2fn4+GjZsaLXjuRp9vSasMTQ89rnmmNmvpebGor639X4vXIWrDHEkMoU94hqVs9S1xpz9VKf3ua3msjRluLK+HqfOxtBnWpPfi5XD2ml9dqb0BtaV0DSHsYJHRPbA2EZERLWB2UnNsLAw3L171xpt0Umd0Lxx4wYOHjyo1UtTl27duqG0tBTXr19Hq1atoFAokJ2drbWN+rV6Hk592+ibpxMAZDKZVZOmrqymc2WZq0fzAM0NTplKwOL95210ZOcUzIIGVMvYOq7REzUdTm2rIiyWSr52b+qPlkF10djfC0F1PbDshwvVGq6s7nEaG5eKH9L1jypxZIY+0+r8XlSeH1PNmglpFgEiR8bYRkREtYHZSc1Vq1Zhzpw5WL58Odq1awd3d3et9caSjuZQJzQvX76MQ4cOoV4940+/09LSIBaLERgYCACIjIzEu+++i5KSEk1bExIS0KpVK/j5+Wm2SUxMxIwZMzT7SUhIQGRkpMXOhZ6o6VxZ5qicoDuZmYOcRzXrkeHqWNCAahtbxjXSZonh1La4ZkWE+sPTXYzHJaYNR9bn+LUcHL+WA6A8Pi0c2AZ+3rJqDVdOyFA6ZULTlESgKb8XMW2D0KWxPwLqlhf00ffZWWvkAYsAkaNjbCMiotrA7KRmVFQUAKBv375ay6sz6fTDhw9x5coVzevMzEykpaXB398fwcHBeOmll5Camop9+/ahrKxMM8elv78/pFIpkpOTceLECTz33HOoW7cukpOTMXPmTIwaNUqTsHz99dfx/vvvY/z48Zg7dy7S09OxYcMGrF+/XnPc6dOn49lnn8XatWsxcOBA7Ny5E6dPn8Znn31m7sdDJrDVMD4AGNwhWOtm40CG890A2ooIwCevd2JBA6p1LBnXyHzVHU7t7+2O5S+2s8k1KyFDWeOEZmXKvEJMjTuDTaM6Y0jHp8x6r3rEg6PTVQ1cANA/vHzuSUNJXH2/F2JR+VQ18eezEX8+W9NDU99+rFVciUWAyNExthERUW0gEgRDdUWr+umnnwyuf/bZZ03e1+HDh/Hcc89VWT5mzBgsXry4SoEftUOHDqF3795ITU3FlClTcPHiRRQVFSE0NBRvvPEGZs2apTU0/OzZs5g6dSpOnTqFgIAAvPXWW5g7d67WPnft2oUFCxbg+vXraNGiBVavXo0BAwaYfC75+fmQy+XIy8vjk08jkq/ew4jPj9vkWH5e7ji9oB8kYhHKVAL+tCyBPTX1mNG3BWb0a2nvZhDZRMVr9pkzZwxua05ccyW2jmtlKgEnM3PwQ3oW/pV8w+j261/tiBc7mZcMrG67eqxMhDK/yOL7VvdaPDq3j9HefurP586DQtx9UISl+y9YvD2W4u/ljiEdQ/DtL1nIeVSsWa5OSKopfDyweHDVxGDFcw2oIwME4ODFbHyRdL3KsdSf2qZRnfUmGNXzeAM1L64U+1wz9Ghen0WAyOFUvmZb8p7NFfBejYjIeZhzzTY7qUm6MVCarkwloOeqgwZ7TVS+8amJmVEtMD2qpU2Tqc5ow2sdze4tROSseM02zl6fUdKVuxj5jxNGt3t3QBuM6xlq9cSSLWLHlxO6GywwE5+e5bSFgfy93dGpoS8SL/6hd5vNFRKSus5V4eOBwtIy5Boo6BNsJDlc08/QnAQ0kT0wrhnGz4eIyHmYc802e/j5kSNHDK7v1auXubukWsbQXFnq24SNIzrBz1uGr07dxJ602zU63tak64jt08Kmw96dESueU23FuOZgTHygtez7C9iSlGn1IcC2iB2GjqHuZeisT6BzHpUYTGgCwPzd59AvTIGEDKXOc1XmG/8OsvIKcTIzR29yWF1cqWIP0NlfpyE7v8joZ8v5M8kZMbYREVFtYHZSs3fv3lWWiURP/sDj/CxkCn1zZVWeo0qZX1jjpGbu4xKczMxh0k4PVm+l2o5xzbEkXsw2eVtlXiEmb081OPS4pmwRO/QdQz13prMmNE11v6AEx67crfG5JmQoDfZ4lYhFWusXD25rUpEqzp9JzoixjYiIagOzk5r379/Xel1SUoIzZ85g4cKFWLZsmcUaRq6j4txYFau7Vu41oavyq8LHMjeTdx4U4oX2IfD1cjc4fK22Ye8TIsY1RxKfnoUtOuZN1EdA+XXs/b0Z6BemsMp1zNoPfIINPFQ6mZnjlEPOq2N36u81Ptdv027j3YGmxzO9D1h9ZBgR0QhNArzNrkpP5CgY24iIqDYwO6kpl8urLOvXrx+kUilmzZqFlJQUizSMXIOuOayCK/R4qNxrorKIUH9IJUBxDR8ms5embux9QsS45iiqW9FbgPGhx47M0EOl2jRtyqOaBnoA9x4Vm/17YMoDViJnxNhGRES1gdlJTX2CgoJw6dIlS+2OXIC+ecD0DRfU16OzoZ8Xrt4tqHY7vGUSRIT64/jVe+ylCcDHww3vD24LhdyTN25EBjCu2VZNeyVaKwF49JLh+SBrYnyPJgYfKl2/+8hqx3Y0f2rihx8zTJ96QJ/q/B4Ye8BK5EoY24iIyJWYndQ8e/as1mtBEJCVlYWVK1eiY8eOlmoXOTlD84DpGi6or0fnwoFtoMwvqlljBOC/6Uq88825mu3HRawc1g4D2ofYuxlEDoNxzTHUNClpjR758elZmLEzzeL7VYsKU+hdV6YS8OXJm1Y7dk081yoA7Rv4YUPiZYvsz9fLHWOeDsWWpOtQ5hXWaF5NjswgKsfYRkREtYHZSc2OHTtCJBJBELT/5OzevTu2bNlisYaRczPW46bicMG8x8V6e3ROiTtT47Y8Ki7DlLjUGu/HFfylVygTmkSVMK45huomo6xV7MzaVcfreUsNtvlkZk7NH+pZwZjIxnhvUFv0XHXQYvtcOawdpG5iLBoUZlLhHl1Y9I5IG2MbERHVBmYnNTMzM7Vei8Vi1K9fHx4efDJOT5ja40aZX4jV8Rf19ugky6jnLcXSIeEY0J5zZxJVxrjmGCJC/REs9zCrp561ip3Zour40iHhBtvsqPNpxoQHW6yAkcJHhsWD22qG4Osr3CMWASojX4YAFr0jqoixjYiIagOzk5qNGze2RjvIxZja4ybnYVGtqexqD1OebYZnWtbn3JlEBjCuOQaJWKS3p576ta+Xu9bcyNYqdmbtquMvtAs2+pDJEYdRqyu1f/fL7Rrtx9fLHZ+M6IzuzepViU2VC/fcfVCEpfsvGN3noPYKFr0jqoCxjYiIagNxdd70008/YdCgQWjevDmaN2+OwYMH4+eff7Z028iJRYT6w9fL3eA2fl7u8PeW2qhFtY+3VILZ0a0QqeOmkYi0Ma45BnVPPYVcO6GnkHtg86jOSFnQD19O6I4Nr3XElxO64+jcPlZJZFm7l2TzwDp615WpBCRfvQdlfiH8vQ3HUVtbNCgMCRlKLN13vlrvF/3vZ+WwdujRIkBvbFIX7hnS8SkE1JWZtO+fL99DmbHunES1DGMbERG5OrN7am7fvh1jx47FsGHDMG3aNABAUlIS+vbti23btuH111+3eCPJNQkAAn0cryeKq1jzUgcmM4lMwLjmWCr31Aus66HV29wWVaqt3Uty67FMvNW3RZVrtK6ieY7A18sdK4e1AwCT5hkV/e89Mjex1ryg1elZa+p3kfu4BCczc1jFnOh/GNuIiKg2MDupuWzZMqxevRozZ87ULJs2bRrWrVuHpUuXMkASgPKhexWHCOqSW1ACCDB7DjUyjR97wRKZhHHN8ah76tnL/UfFVt1/3uPSKgk4axcmqq7+4QpsfL0zAKDnqoMmtU8AsGJYO53JaQBIvnpPZ8Jal4hQf/h6uiP3seG/KQDHnYeUyB4Y24iIqDYwe/j5tWvXMGjQoCrLBw8eXGVCaqq9TL2xSLyYjUWDwgA8Kfigxj6GNcObOyLTMK45J/Uw7W/TbiH5quWGHpepBCyp5vBqc1S8RtuiMFF1dW3sh31nb2NbUqbJPUh9vdzRL0yhNYw8slk9JGQo0XPVQYz4/Dim70zDiM+Po+eqg4hPz9K7L4lYhLE9Qk06riPOQ0pkL4xtRERUG5id1GzYsCESExOrLD9w4AAaNmxokUaR8zP1xmJL0nUA0DuH2pjIRpZuWq3Bmzsi0zCuOZ/49Cyzk2OmKFMJ2JaUqTVk2loqXqOtXZiousQiYOn+C5i+M82kYj1quQUlWJ/wq1ayWd0TtfJ5KvMKMXl7qsHvLrZPc4PzdIvwpIgREZVjbCMiotrA7OHns2fPxrRp05CWloann34aQPn8LNu2bcOGDRss3kByThGh/giWexi9SRMBeH9vBo7O7aNzmNqWo9ds02AXIkJ5Qpg3d0SmYVxzLvqGaauTY5tGda5W8SBbzmep8JFpXaMdtWd9TTq/bjx0BRsPXUGw3AMLB4Zh6X7dPVEFPPlbQN27szKJWISVw9ph0vbUKuvUWy8aFMZ5pIkqsFdsy8nJwVtvvYW9e/dCLBZj+PDh2LBhA+rU0V8grbCwELNnz8bOnTtRVFSE6OhofPrppwgKCgIA/PLLL1i5ciWOHj2Ku3fvokmTJpg0aRKmT59utfMgIiLnYHZSc/LkyVAoFFi7di2+/vprAECbNm3w1VdfYciQIRZvIDkniViERYPCdN6AVCQAyMor1MwtVnkONWPzcpI23twRmY9xzXkYGqZtSnJMH1vPZ7l4cFut9jlaz3qxqGYJzYqUeYWYEmfe3wK6xIQHY/OozlUSz9UpPkRUG9grto0cORJZWVlISEhASUkJxo4di4kTJyIuLk7ve2bOnIn9+/dj165dkMvliI2NxbBhw5CUlAQASElJQWBgILZv346GDRvi2LFjmDhxIiQSCWJjY612LkRE5PjMSmqWlpZi+fLlGDduHI4ePWqtNpGLiAkPxvgeTfDF/4aYG+KovVScTZCPDIsHt+XNHZGJSktLsWTJEsY1J2FsmLYpybHKbDmfZR2ZGz54uX2Va3REqD/8vaXIsXKBIlP0aFYPSVfvWWx/5nyuxv4WiAkP1jmqgw/xiLTZ657twoULiI+Px6lTp9C1a1cAwMcff4wBAwbggw8+QEhISJX35OXl4YsvvkBcXBz69OkDANi6dSvatGmD48ePo3v37hg3bpzWe5o2bYrk5GTs3r2bSU0iolrOrDk13dzcsHr1apSWllqrPeRiosIUJm2nr5eKrxcreJtjfM+mTGgSmYFxzbmY+gDMnAdltpzPcnRkI53XaIlYhI4N5TZpgzEdGvra7dim9FitXHyICU2iquwV25KTk+Hr66tJaAJAVFQUxGIxTpw4ofM9KSkpKCkpQVRUlGZZ69at0ahRIyQnJ+s9Vl5eHvz99U+1VFRUhPz8fK0fIiJyPWYXCurbty9++ukna7SFXJB6bk19txzGJvf//X6B1drmin7j50VkNsY152HqMG1zhnPbcqTAtbuPdC6PT8/CwYt/2Kwd+vh6uaNH8wCbH5eFfogszx6xTalUIjAwUGuZm5sb/P39oVQq9b5HKpXC19dXa3lQUJDe9xw7dgxfffUVJk6cqLctK1asgFwu1/ywOBIRkWsye07N/v37Y968eTh37hy6dOkCb29vrfWDBw+2WOPI+ann1py8PRUiaA9DMzb/Y5lKwFenf7NFM11GY38vezeByOkwrjkP9YMyZV6hzmHN1SmUZsv5LI9dKa8GXjHmFZeq8M436TZrgyErh7VD96b14OvlbrU5rc39W4CIqseSsW3evHlYtWqVwW0uXLhQrXaaKz09HUOGDMGiRYvw/PPP691u/vz5mDVrluZ1fn4+E5tERC7I7KTmlClTAADr1q2rsk4kEqGsrKzmrSKXEhMejE06Jvf395ZiSMcQyD2lVW7yAOD41XsoLFHZurlOSywC3ohsYu9mEDkdxjXnUZMHZfoYS5RaUn5hqdZ8n/HpWXjnm3PIeWT/onifvt4JMeHBiE/PMjuhGSz3gCAIyM4vMphsXjiwDZbuv8BCP0Q2YMnYNnv2bLz55psGt2natCkUCgXu3Lmjtby0tBQ5OTlQKHRPSaVQKFBcXIzc3Fyt3prZ2dlV3pORkYG+ffti4sSJWLBggcH2yGQyyGQyg9sQEZHzMzupqVIxyUTmqzi5/4EMJb5Ju4V7j4qxJek6tiRdR7COm5pjV+/ascXOZ8IzoZC6mT2jBFGtx7jmXKr7oEyfiolSW1APd7d1xXVDZvRtgQHtQzRFk8w1uEMwOjXyM5psjgkPRnR4MAv9ENmAJWNb/fr1Ub9+faPbRUZGIjc3FykpKejSpQsA4ODBg1CpVOjWrZvO93Tp0gXu7u5ITEzE8OHDAQCXLl3CzZs3ERkZqdnu/Pnz6NOnD8aMGYNly5ZZ4KyIiMgVMANCNlGmEjQJzS+SrlfplZKVV4jJ21MRn56lWXaL80OabMIzTTB/QJi9m0FEZBMx4cE4OrcPvpzQHeN7NIG/t7vmQdmIz4+j56qDWvHElP198nonK7b4iYA6MptWXDfGz8sdb/VtAaD6RZO++yUL/cIU2DSqMxRy7eH8CrkHNo3qrHloyUI/RK6rTZs2iImJwYQJE3Dy5EkkJSUhNjYWr732mqby+a1bt9C6dWucPHkSACCXyzF+/HjMmjULhw4dQkpKCsaOHYvIyEh0794dQPmQ8+eeew7PP/88Zs2aBaVSCaVSiT/+sP9cxEREZF8m99R8/PgxEhMT8cILLwAon6ekqKhIs14ikWDp0qXw8LDd3FTkHOLTs7D4uwwo8w3fKAkA/rrrFzwuUUHh4+EQN3vO4I3ujfDuwLb2bgaRU4qPj8crr7wCgHHN2UjEIuQ9Lk9kVo4Xyv89KKuYTDPGz9tGwxQF21ZcN0QEYMWwdprEYnWLJmXlFeJkZo7WqAz2xCSyj8ePH+PIkSN2u2fbsWMHYmNj0bdvX4jFYgwfPhwfffSRZn1JSQkuXbqEgoInnRfWr1+v2baoqAjR0dH49NNPNev/7//+D3/88Qe2b9+O7du3a5Y3btwY169ft8p5EBGRczA5qfnPf/4T+/fv1wTIjRs3om3btvD09AQAXLx4ESEhIZg5c6Z1WkpOKT49C5PMGNL3sKgMM79KAwB4yyRWapVrGdAuxN5NIHJaW7du1SQ1Gdeci6HejgLKE3bv781AvzCFSUk1W1VBv/uoyPhGNqBr2peaFE1Sf37qnphEZB9xcXE4ePCg3e7Z/P39ERcXp3d9kyZNIAjaV24PDw988skn+OSTT3S+Z/HixVi8eLElm0lERC7C5OHnO3bswMSJE7WWxcXF4dChQzh06BDWrFmDr7/+2uINJOdVphIwb/e5ar//URGLcxgTbGaVXyLSVrnwAeOa8zDW21HAkx6EpgioY5uemoF1PWxacV2XhQPb4OjcPlV6saqLJlWnX6W9z4mIyu3atYv3bEREVGuYnNS8cuUK2rVrp3nt4eEBsfjJ2yMiIpCRYf7k8uS6jl+7Z3YFVTKdCOZX+SUibWFhT+aiZVxzLqb2rDS5B6YN5jzx9XJHRKg/IkL9ofCxfRJQhPKHYW/2CAUAJF+9h2/TbiH56j1NcaVFg8I025qzTz5gI3IM165d4z0bERHVGiYnNXNzc7XmY/njjz/QpEkTzWuVSqW1nij56j17N8FlBVcqvEBE1VNcXKz5f0vEtSNHjmDQoEEICQmBSCTCnj17tNYLgoD33nsPwcHB8PT0RFRUFC5fvqy1TU5ODkaOHAkfHx/4+vpi/PjxePjwodY2Z8+exTPPPAMPDw80bNgQq1evrtKWXbt2oXXr1vDw8EC7du3w/fffm3Uujs7UnoGmbmeLYeFjnw6FRCyCRCzCiIhGVj+eLosGhSEhQ4meqw5ixOfHMX1nmlZxJXV1+coFf3SpWNmcD9iIHENeXh7v2YiIqNYwOanZoEEDpKen611/9uxZNGjQwCKNIlfBUj+WJnMTY8f4bjqHDRKR+Qz1VqlOXHv06BE6dOigd16w1atX46OPPsLmzZtx4sQJeHt7Izo6GoWFT3oTjhw5EufPn0dCQgL27duHI0eOaA0lzM/Px/PPP4/GjRsjJSUFa9asweLFi/HZZ59ptjl27BhGjBiB8ePH48yZMxg6dCiGDh1qMI47G2NDpc3tQRhg5UJBvl7uiO3TXPO6SYCXVY+n6/ibRnUGAEzenlpl6L66uJI6samuLr/htY74ckJ3fPp6JwQbqWxORPYXEhLCezYiIqo1RELlmZr1mD59Og4cOICUlJQq1fIeP36Mrl27IioqChs2bLBKQx1dfn4+5HI58vLy4OPjY+/mOISky3cx8osT9m6GS5netzlm9mtl72YQOT31Nbt169Y4c+aMVeKaSCTCN998g6FDhwIo76UZEhKC2bNn469//SuA8h41QUFB2LZtG1577TVcuHABYWFhOHXqFLp27QqgvEL7gAED8PvvvyMkJASbNm3Cu+++C6VSCalUCgCYN28e9uzZg4sXLwIAXn31VTx69Aj79u3TtKd79+7o2LEjNm/ebNZn5MhxLT49C5P/V4yu4h8z6kSnOQm3pCt3MfIf1otZmyu1JfnqPYz4/LjVjlfZjvHd0L1ZPfRcdVDvXKQilCcqj87to7PnZZlKYGVzIgelvmZPmjQJR44c4T1bJc4Q04iIqJw512yTe2q+8847yMnJQatWrbBmzRp8++23+Pbbb7F69Wq0atUK9+/fxzvvvFPjxpPr6N6sHrykrGBuKR5uYkzr29LezSByKffv37dZXMvMzIRSqURUVJRmmVwuR7du3ZCcnAwASE5Ohq+vryahCQBRUVEQi8U4ceKEZptevXppEpoAEB0djUuXLuH+/fuabSoeR72N+ji6FBUVIT8/X+vH0ekbKl2dHoR38q1T/TxY7lEloQk86Wlqbeoeq92b1atxcSV1ZfMhHZ9CZLN6TGgSOaDZs2fzno2IiGoNk5OaQUFBOHbsGNq0aYN58+bhxRdfxIsvvoj58+cjLCwMR48eRVBQkFkH59xjrs9dwhseS5n0bDPeQBJZ2I8//mjRuGaIUqkEgCr7DAoK0qxTKpUIDAzUWu/m5gZ/f3+tbXTto+Ix9G2jXq/LihUrIJfLNT8NGzY09xTtQtdQ6epM0fHb/UcWbVeHBnKDbZGIRXihvfWHbQt4MuelxYsrEZHDCQwMtPg9GxERkaNyM2fj0NBQxMfHIycnB1euXAEANG/eHP7+1at4qZ57bNy4cRg2bFiV9eq5x/75z38iNDQUCxcuRHR0NDIyMjTDKUaOHImsrCwkJCSgpKQEY8eOxcSJExEXFwfgydxjUVFR2Lx5M86dO4dx48bB19dXM0eZeu6xFStW4IUXXkBcXByGDh2K1NRUhIeHV+vcCDiZmYO8x6X2bobLCK3vbe8mELmcJk2aWDSuObP58+dj1qxZmtf5+flOk9hU9yCsiR0nblqoNeWeaRFgsE3x6Vn4/OdMix5Tl3E9mmiSqpYurkREjsnS92xERESOyqykppq/vz8iIiJqfPD+/fujf//+OtcJgoAPP/wQCxYswJAhQwAA//rXvxAUFIQ9e/Zo5h6Lj4/Xmnvs448/xoABA/DBBx8gJCQEO3bsQHFxMbZs2QKpVIq2bdsiLS0N69at0yQ1N2zYgJiYGLz99tsAgKVLlyIhIQEbN240ee4xqkpppaF8tRVvMomsx1JxzRCFQgEAyM7ORnDwkx562dnZ6Nixo2abO3fuaL2vtLQUOTk5mvcrFApkZ2drbaN+bWwb9XpdZDIZZDLrFstxZPmPSyy6v8imAXrXlakEvL9Xf5EqS+oX9uQ7Vw95V+YV6izlp55T09TiSkTk2GwR24iIiOzJ5OHntsa5x5xfzsMiezfBZSh8ZLzJJHJyoaGhUCgUSExM1CzLz8/HiRMnEBkZCQCIjIxEbm4uUlJSNNscPHgQKpUK3bp102xz5MgRlJQ8ScIlJCSgVatW8PPz02xT8TjqbdTHoaqKSkyqm2gSb6kY3Q300jQ2t6Wl1POWasUOiViERYPCAKBK1Xj1a/VQdSIiIiIiR+ewSU3OPeb8/L2lxjcikywe3JY3mURO4OHDh0hLS0NaWhqA8gd0aWlpuHnzJkQiEWbMmIG//e1v+O6773Du3DmMHj0aISEhmgrpbdq0QUxMDCZMmICTJ08iKSkJsbGxeO211xASEgIAeP311yGVSjF+/HicP38eX331FTZs2KA1dHz69OmIj4/H2rVrcfHiRSxevBinT59GbGysrT8Sp/D92SyoLLi/NS91MHjNPpCh/+8LS1o6JLxKOyxZXImIiIiIyJ6qNfycnHvuMVtRyD3t3QSn5y2VYO0rHXiTSeQkTp8+jeeee07zWh0nxowZg23btmHOnDl49OgRJk6ciNzcXPTs2RPx8fGaeaIBYMeOHYiNjUXfvn0hFosxfPhwfPTRR5r1crkcP/74I6ZOnYouXbogICAA7733nmZKFQB4+umnERcXhwULFuCdd95BixYtsGfPHs4TrUOZSsC7e85ZbH9/6RWKAe1D9K6PT8/CF0nXLXY8w+3QHTtiwoPRL0yBk5k5uPOgEIF1y4ec8+EZERERETkTh01qcu4x56eeu8sWQ+xc0fDOIVj9UkfeZBI5kd69e0MQ9A9jFolEWLJkCZYsWaJ3G39/f02xO33at2+Pn3/+2eA2L7/8Ml5++WXDDSaczMzB/QLLzKe5/pWOeLHzU3rXl6kELP7OunNp+nm5YdnQdgYTq4BliisREREREdmTww4/59xjzk89dxdTcubz9XRnQpOIyAbuPLDMg7c6UpHBhCYAbDx42apF9F7q/BROL3jeaEKTiIiIiMgV2DWpybnHXF9MeDBmRLW0dzOcztgeoUxoEhHZQEAdy4y6aN/Qz+D6+PQsrD9w2SLH0sVLKsEqI3N5EhERERG5ErsOP+fcY7VDkwAvezfBqfh6uSO2T3N7N4OIqHawUNHzZvXr6F1XphLw/l7rDjv/S69mTGgSERERUa1i16Qm5x6rHQLrehjfiDRWDmvHG1MiIhu5+6jIIvt5Z0CY3nXHr96z6vzSfBhGRERERLWRw86pSa5DXTCIjJsZ1YKVzomIbMjfS1rjffh5ucFTKtG5Lj49CxP+fbrGxzCED8OIiIiIqDZiUpOsTl0wqLrca8lvabDcA7F9Wti7GUREtcrib8/VeB+vRTSqsqxMJWDDgV8xaXsqCorLanwMfWb05cMwIiIiIqqd7Dr8nGqPmPBgDAgPwvfp2Wa/t0RlhQY5GBGARYPC2NOGiMhGylQC3opLwdV7j2u8r57N6mu9jk/PwuLvMqxa6RwA/Lzc8VZfPgwjIiIiotqJSU2yGQFM2Oni7y3F8hfD2dOGiMhG4tOzMPc/Z5H3uLTG+5J7uqF7s3qa19+fzcKUuNQa79cUKzjsnIiIiIhqMSY1ySbKVAJOZOaY9R4RALmXO3ILSqzTKAdQRybB8fl9IXWrJWPsiYjsLD49C5O2Wy7puGp4e01i8fuztxH75RmL7VsfPgwjIiIiImJSk2zkZGYOch4Vm7y9ut/J2KdDsf7Ar9ZplAP44OUOTGgSEdlImUrA+3szLLa/xn4ypN/KQ/qtfLiJRfgw8bLF9q2PNx+GEREREREBYFKTbOTOA/PmFVPIPbBoUBj6hSnwz+RM5Dxyrd6awf87P/ayISKynZOZOcjKs9w8lzfuF2HjoasW258pJj7TjAlNIiIiIiIwqUk2EljXw6TtYp9rjh7NAxAR6q8ZzjekQwi2HrthzebZzJTeTfFMi0Ct8yMiItsw9wGbo/H1ckdsn+b2bgYRERERkUNgUpNsIiLUH8FyDyjzCiHoWC9Cee/Mmf1aVkn2NfDzskkbbSGyaQAiKxSUICIi2/nXsev2bkKNrGRhICIiIiIiDY5fIpuQiEVYNCgMAKrUQFe/XjQoTOfNmn8dmXUbZ0PmFksiIiLL2PvLbaTczLV3M6rF19MNm0d15pQlREREREQVMKlJNhMTHoxNozpDIdceiq6Qe2CTgZs1hY9pQ9edg65+qkREZE1lKgFv/98v9m5GtbzQPhgpC59nQpOIiIiIqBIOPyebigkPRr8wBU5m5uDOg0IE1vUwOr9kRKg/vNwlKCgps2FLrSOyaYC9m0BEVOscv3YPhSUqezfDLP7e7vjbkHAMaB9i76YQERERETkkJjXJ5iRikVnzSkrEIgxoF4z/S/3diq2yPneJCN05nyYRkU2VqQRM2X7a3s0w2ejIxugfHsyCckRERERERnD4OTmF5cPa2bsJNTb52aa8QSUisqH49CyEvRePvELn6enfPzwYkc3qMV4QERERERnBpCY5BambGAPbBdm7GVrMud2UuYkxPaqV1dpCRETa4tOzMHl7KopKnWPYuQhAsLx8ShYiIiIiIjKOSU1yGutf7WzvJmgxp+TPhtc6stcNEZGNlKkEvL83w2al2UQABoQHwc/LvdrvB4BFg8IYK4iIiIiITMSkJjmNlBv37d2EKjzcDf8TEgH49PVOrFpLRGRDJzNzkJVXaLPj/fmZUHw6qitOvBMFf2/zE5sKuQc2jerMWEFEREREZAYWCiKnceeB7W5QTfVcq0D8kK7Uu/6T1ztjQHvepBIRVVamEnAyMwd3HhQisK6H2YVxDL3/Hz9ftVazddp3Ngvz+reB1E2M5S+2w+TtqUZ7ib7QPhj9woKqde5ERERERMSkJjmRwLoe9m5CFc3qe2PzqM5Y/F0GlPlPkq7Bcg8sGhTGXjdERDrEp2fh/b0ZWr0pzbluGnp/n9ZBSLz4h1XarU9WXiFOZuYgslk9xIQHY9OozlXap1bPW4qlQ8L5wIuIiIiIqIaY1CSnERHqD39vKXIeFdu7KRqRTQPQo0UA+oUpatTjiIiotlAX8Knck1GZV4jJ21ONDsM29n65h8TibTZFxdEEMeHBmrigzHuMnEfF8K8jg8KH8YGIiIiIyFKY1CSnIRGLMLp7Y3yYeNneTQFQPl9m92b1AJS3LfJ//09ERLoZKuAjoPy6+v7eDPQLU+hM/Bl7PwDkFpZZrsFmqDyagHGBiIiIiMi6WCiInEpofW97N0EjWO7B3jZERGYwVsBHwJOh3NV5vz2IUB4PIkL97d0UIiIiIqJahUlNciqONK/m7bxCxKdn2bsZREROw9SCb/q2s3UBIGPUj7UWDQrjQy4iIiIiIhtjUpOcSpfGfnCk+8b392agTGWsxi0REQGmP5jStV1xqcrmBYCMUcg9jM4BSkRERERE1sGkJjmVlBv34Ug5REPDJImISFtEqD+C5R7Q92zK0FDufx7LtGrbqmPhQNOqtRMRERERkeUxqUlORZnvWHOpAaYPpyQiqu0kYhEWDQoDgCqJTWNDufeevW3dxlXD0v3srU9EREREZC9MapLTiE/PwtJ95+3djCocaZ5PIiJHFxMejE2jOkMh1752GhrKXaYSkHE731ZNNBl76xMRERER2Y+bvRtAZIr49CxM3p4KR+oPI0L5TTgr3hIRmScmPBj9whQ4mZmDOw8KEVi3/Fqqr9jOxoNXUKqycSNNxN76RERERET2wZ6a5PDKVALe35vhcAlNgBVviYiqSyIWIbJZPbzQPgQAsO/sbSRfvVdlOHeZSsDWJMebT1ONvfWJiJ7IycnByJEj4ePjA19fX4wfPx4PHz40+J7CwkJMnToV9erVQ506dTB8+HBkZ2fr3PbevXto0KABRCIRcnNzrXAGRETkTNhTkxzeycwcZOXZtyeMCNBKqirkHlg0iAUiiIhqIj49C+/vzdC6xgdXur6ezMxB7uMSm7br4xGdIBGJEPtlqt7idOytT0RU1ciRI5GVlYWEhASUlJRg7NixmDhxIuLi4vS+Z+bMmdi/fz927doFuVyO2NhYDBs2DElJSVW2HT9+PNq3b49bt25Z8zSIiMhJMKlJDs8RhvYJABYObIOAujKjwySJiMg4fdOKKPMKMXl7qmZ+TXvEAIlIhAHtg7ERnTAl7kyV9eytT0RU1YULFxAfH49Tp06ha9euAICPP/4YAwYMwAcffICQkJAq78nLy8MXX3yBuLg49OnTBwCwdetWtGnTBsePH0f37t01227atAm5ubl477338MMPP9jmpIiIyKE5/PDzJk2aQCQSVfmZOnUqAKB3795V1k2aNElrHzdv3sTAgQPh5eWFwMBAvP322ygtLdXa5vDhw+jcuTNkMhmaN2+Obdu22eoUyQhHGdoXUFeGIR2fQmSzeryJJSKqAUPTiqiXvb+3vLK4PWLAlLhUxKdnYUD7EGwe1RnBZhQ1IiKqrZKTk+Hr66tJaAJAVFQUxGIxTpw4ofM9KSkpKCkpQVRUlGZZ69at0ahRIyQnJ2uWZWRkYMmSJfjXv/4Fsdj4LWxRURHy8/O1foiIyPU4fE/NU6dOoaysTPM6PT0d/fr1w8svv6xZNmHCBCxZskTz2svLS/P/ZWVlGDhwIBQKBY4dO4asrCyMHj0a7u7uWL58OQAgMzMTAwcOxKRJk7Bjxw4kJibiz3/+M4KDgxEdHW2DsyRDIkL94evljtwC2w4/rMxRkqtERM7O2LQiAp5UFo8I9Yevp7vNh6C/vzcD/cIUZhc1IiKqrZRKJQIDA7WWubm5wd/fH0qlUu97pFIpfH19tZYHBQVp3lNUVIQRI0ZgzZo1aNSoEa5du2a0LStWrMD7779fvRMhIiKn4fA9NevXrw+FQqH52bdvH5o1a4Znn31Ws42Xl5fWNj4+Ppp1P/74IzIyMrB9+3Z07NgR/fv3x9KlS/HJJ5+guLgYALB582aEhoZi7dq1aNOmDWJjY/HSSy9h/fr1Nj9f0q2opMz4RlZ2/1GRvZtAROQSTB1SfudBISRiEcb2CLVyi6pSJ1WBJ0WN2FufiGqjefPm6Rw5V/Hn4sWLVjv+/Pnz0aZNG4waNcqs9+Tl5Wl+fvvtN6u1j4iI7Mfhk5oVFRcXY/v27Rg3bhxEoic3FDt27EBAQADCw8Mxf/58FBQUaNYlJyejXbt2CAoK0iyLjo5Gfn4+zp8/r9mm4pAH9TYVhzxUxiENtnP86j08LlHZuxlYuv9Claq8RERkPlN7vqu3i+3THL5e7tZskk5JV/7At2m3dFZlJyKqLWbPno0LFy4Y/GnatCkUCgXu3Lmj9d7S0lLk5ORAoVDo3LdCoUBxcXGVSubZ2dma9xw8eBC7du2Cm5sb3Nzc0LdvXwBAQEAAFi1apHO/MpkMPj4+Wj9EROR6HH74eUV79uxBbm4u3nzzTc2y119/HY0bN0ZISAjOnj2LuXPn4tKlS9i9ezeA8iENFROaADSv1UMa9G2Tn5+Px48fw9PTs0pbOKTBdv59PNPeTQDwpNdOZLN69m4KEZFTiwj1R7DcA8q8Qp3zalauLC4Ri7ByWDtM2p5q03ZuPHRV8/8KHw8sHhzGeTSJqNapX78+6tevb3S7yMhI5ObmIiUlBV26dAFQnpBUqVTo1q2bzvd06dIF7u7uSExMxPDhwwEAly5dws2bNxEZGQkA+M9//oPHjx9r3nPq1CmMGzcOP//8M5o1a1bT0yMiIifmVD01v/jiC/Tv31+rct7EiRMRHR2Ndu3aYeTIkfjXv/6Fb775BlevXjWwp5rjkAbbKFMJSLxwx/iGNuIIldiJiJydRCzCokFhAJ5UElfTV1k8JjwYH73SwTYN1EGZX4hJ28sLCBERUVVt2rRBTEwMJkyYgJMnTyIpKQmxsbF47bXXNPdvt27dQuvWrXHy5EkAgFwux/jx4zFr1iwcOnQIKSkpGDt2LCIjIzWVz5s1a4bw8HDNT2hoqOZ4lefwJCKi2sVpkpo3btzAgQMH8Oc//9ngduqngFeuXAFQPqQhOztbaxv1a/WQBn3b+Pj46OylCXBIg60cv3YPDjDyXIPFgoiILCMmPBibRnWGwozK4u9884utmqfX/N3nOBSdiEiPHTt2oHXr1ujbty8GDBiAnj174rPPPtOsLykpwaVLl7SmC1u/fj1eeOEFDB8+HL169YJCodCMuiMiIjLEaYafb926FYGBgRg4cKDB7dLS0gAAwcHlN0ORkZFYtmwZ7ty5o3mSl5CQAB8fH4SFhWm2+f7777X2k5CQoBnyQPaTfPWevZugEVxhKCQREdWcOZXFv0m9hYe2LYCu0/2CEhy/dg89mgfYuylERA7H398fcXFxetc3adIEgqD9YMjDwwOffPIJPvnkE5OO0bt37yr7ICKi2skpkpoqlQpbt27FmDFj4Ob2pMlXr15FXFwcBgwYgHr16uHs2bOYOXMmevXqhfbt2wMAnn/+eYSFheGNN97A6tWroVQqsWDBAkydOhUymQwAMGnSJGzcuBFz5szBuHHjcPDgQXz99dfYv3+/Xc6XnihV2b/quVrloZBERFRz6srihpSpBMz6Os02DTJB8lUmNYmIiIiI7M0php8fOHAAN2/exLhx47SWS6VSHDhwAM8//zxat26N2bNnY/jw4di7d69mG4lEgn379kEikSAyMhKjRo3C6NGjsWTJEs02oaGh2L9/PxISEtChQwesXbsW//jHPxAdHW2zcyTd7j+yf7ccuacbNusZCklERNZ3/No9nQWF7EVwqNYQEREREdVOTtFT8/nnn9c5xKBhw4b46aefjL6/cePGVYaXV9a7d2+cOXOm2m0k6/hP6u/2bgI+HdmFPXKIiOzorztP27sJWnw93e3dBCIiIiKiWs8pempS7ZRXUIJSBygSdPdhkb2bQERUaz0uLkPWQ8eZigQA/L1l9m4CEREREVGtx6QmOawXPz1q7yYAYMVzIiJ7mv9/qfZuQhW5BcX2bgIRERERUa3HpCY5pDKVgMy7BfZuBny93KFSCShTcf40IiJ72HP2jr2bUIW/t9TeTSAiIiIiqvWY1CSHdDIzxyHKMOQWlGDkFyfQc9VBxKdn2bs5RES1SrEjzEGig0Luae8mEBERERHVekxqkkO686DQ3k3QoswrxOTtqUxsEhHZ0Naka/ZuQhXBcg9EhPrbuxlERERERLUek5rkkBxtHkt1r9H392ZwKDoRkY2s/OGSvZtQxaJBYZCIRfZuBhERERFRrcekJjmkiFB/uDvYb6cAICuvECczc+zdFCIil7dozzmHmIZETSIGNo/qjJjwYHs3hYiIiIiIALjZuwFEukjEIvh4uuPeoxJ7N6UKRxsaT0TkSspUAo5duYt/Hr9p76ZoSV3wPORe7vZuBhERERER/Q+TmuSQylQCcgscL6EJON7QeCIiZ1KmEnAyMwd3HhQisG75/JTq4dzx6Vl4f28GsvIc6+FR0/peTGgSERERETkYJjXJIZ3MzEGZHcYdigC9wx1FABQsEEFETuSTTz7BmjVroFQq0aFDB3z88ceIiIiwybF1JS8TMpRVkpbBcg8sGhQGAJi8PdWhhpyrTe/b0t5NICIiIiKiSpjUJIekzLdPLx31zXTl5Ka6JAQLRBCRs/jqq68wa9YsbN68Gd26dcOHH36I6OhoXLp0CYGBgVY9tq4el75e7jp74CvzCjF5eyrkXu4OmdAE2EOfiIiIiMgROVgpFqJy2bmP7Xp8T6lE67VC7oFNLBBBRE5k3bp1mDBhAsaOHYuwsDBs3rwZXl5e2LJli1WPG5+ehcnbU6sMIdc3pYjwvx9HnHJEhPKepOyhT0RERETkeNhTkxzS7rTf7Xr8guIyzOjbAqH1vavM+UZE5OiKi4uRkpKC+fPna5aJxWJERUUhOTlZ53uKiopQVFSkeZ2fn2/2cctUAt7fm+GwPS7NwR76RERERESOjT01yeGUqQRcvfPI3s3Av4/fwAvtQxDZrB5vaInIqdy9exdlZWUICgrSWh4UFASlUqnzPStWrIBcLtf8NGzY0OzjnszMcbgiP9XFHvpERERERI6NPTXJ4dirSFBl9x4V42RmDiKb1bN3U4iIrG7+/PmYNWuW5nV+fr7Zic07Dxw/oRks98DgDsH4Nu02lPlPeqYqfGQYEdEYTQK82EOfiIiIiMgJMKlJDseRboodqS1ERKYKCAiARCJBdna21vLs7GwoFAqd75HJZJDJZDU6riMX1Gn3lA/eGRCmSVbOiWlTpTo7k5hERERERM6DSU1yOI50U+xIbSEiMpVUKkWXLl2QmJiIoUOHAgBUKhUSExMRGxtrteNGhPojWO4BZV6h3efVjAz1h5ubGE3qeeGdAWFVCsBJxCL2xCciIiIicmJMapLDiQj1h6+nO3If27cSLiveEpEzmzVrFsaMGYOuXbsiIiICH374IR49eoSxY8da7ZgSsQiLBoVh8vZUiAC7JjanRbVk0pKIiIiIyIWxUBA5HIlYhLE9mti1DSKw4i0RObdXX30VH3zwAd577z107NgRaWlpiI+Pr1I8yNJiwoOxaVRnKOTaPd39vNz1vkcEwNfLHZa64vKhFBERERGR62NPTXJIsX1a4O8/XUVBicpqx1D4yDCoQwj+k3oLOY+KNcuD5R5YNCiMFW+JyOnFxsZadbi5PjHhwegXpqgyZ2VChhLv783QqpCuvuYCwOTtqRY5Ph9KERERERG5PiY1ySFJxCJ88HJHTImzzA1uZTOjWiK2T3NIxCLM689iEURElqZrzkp9yU71NXfTqM5Vkp7mEImAT0Z05kMpIiIiIqJagElNclgD2gfjL7+H4u9HMi22T129MFksgojIdgxdcysmPRMylNiTdlurJ70xn4zohAHtmdAkIiIiIqoNmNQkhzZ/QBg6NPDFzK/OoKjM8LZeYqBYAEorVKaQe7qhXxsFejSvB4Xck70wiYgcnDrpGdmsHt4dGKbp1enr6Y4fM5Q4cuE2fsvXDgicNoSIiIiIqPZhUpMc3oD2IYgOD8bxa/eQfPUeAAHdmtSDWCLC3YdFWsMXy1QCh5ITEbmIyr06n20VCLzYntd6IiIiIiJiUpOcg0QsQo/mAejRPMDodhxKTkTk2nitJyIiIiIisb0bQERERERERERERGQOJjWJiIiIiIiIiIjIqTCpSURERERERERERE6FSU0iIiIiIiIiIiJyKkxqEhERERERERERkVNhUpOIiIiIiIiIiIicipu9G+AqBEEAAOTn59u5JUREZIz6Wq2+dlNVjGtERM6Dcc0wxjQiIudhTkxjUtNCHjx4AABo2LChnVtCRESmevDgAeRyub2b4ZAY14iInA/jmm6MaUREzseUmCYS+DjPIlQqFW7fvo26detCJBJZ/Xj5+flo2LAhfvvtN/j4+Fj9eNbkSucCuNb5uNK5ADwfR2brcxEEAQ8ePEBISAjEYs7Eoout41p1uNK/AWN4rq6ntpwnwHO1BcY1w6wR01zx99rVzsnVzgfgOTkDVzsfwLHv1dhT00LEYjEaNGhg8+P6+Pi4zD8UVzoXwLXOx5XOBeD5ODJbngt7shhmr7hWHa70b8AYnqvrqS3nCfBcrY1xTT9rxjRX/L12tXNytfMBeE7OwNXOB3DMezU+xiMiIiIiIiIiIiKnwqQmERERERERERERORUmNZ2UTCbDokWLIJPJ7N2UGnOlcwFc63xc6VwAno8jc6VzIdupTb83PFfXU1vOE+C5kmtyxe/a1c7J1c4H4Dk5A1c7H8Cxz4mFgoiIiIiIiIiIiMipsKcmERERERERERERORUmNYmIiIiIiIiIiMipMKlJREREREREREREToVJTSIiIiIiIiIiInIqTGra0fXr1zF+/HiEhobC09MTzZo1w6JFi1BcXKy1jUgkqvJz/PhxrX3t2rULrVu3hoeHB9q1a4fvv/9ea70gCHjvvfcQHBwMT09PREVF4fLly1rb5OTkYOTIkfDx8YGvry/Gjx+Phw8fWu8D+J9PPvkETZo0gYeHB7p164aTJ09a/ZgVrVixAn/6059Qt25dBAYGYujQobh06ZLWNr17967yHUyaNElrm5s3b2LgwIHw8vJCYGAg3n77bZSWlmptc/jwYXTu3BkymQzNmzfHtm3bqrSnpp/H4sWLq7S1devWmvWFhYWYOnUq6tWrhzp16mD48OHIzs52yHNp0qSJzt//qVOnAnD87+XIkSMYNGgQQkJCIBKJsGfPHq31lvp3efbsWTzzzDPw8PBAw4YNsXr16iptqek1wtC5lJSUYO7cuWjXrh28vb0REhKC0aNH4/bt21rH0PV9rly50ubnQq7D3vHDEFNiizNdj82xcuVKiEQizJgxQ7PMVc711q1bGDVqFOrVqwdPT0+0a9cOp0+f1qx3pOt6TZSVlWHhwoVaf6MuXboUFeuLOuu5ulJsppoz5X4MsN33aa/7McAxYmptiJ2uEiNdKR66Qsyr1bFNILv54YcfhDfffFP473//K1y9elX49ttvhcDAQGH27NmabTIzMwUAwoEDB4SsrCzNT3FxsWabpKQkQSKRCKtXrxYyMjKEBQsWCO7u7sK5c+c026xcuVKQy+XCnj17hF9++UUYPHiwEBoaKjx+/FizTUxMjNChQwfh+PHjws8//yw0b95cGDFihFU/g507dwpSqVTYsmWLcP78eWHChAmCr6+vkJ2dbdXjVhQdHS1s3bpVSE9PF9LS0oQBAwYIjRo1Eh4+fKjZ5tlnnxUmTJig9R3k5eVp1peWlgrh4eFCVFSUcObMGeH7778XAgIChPnz52u2uXbtmuDl5SXMmjVLyMjIED7++GNBIpEI8fHxmm0s8XksWrRIaNu2rVZb//jjD836SZMmCQ0bNhQSExOF06dPC927dxeefvpphzyXO3fuaJ1HQkKCAEA4dOiQIAiO/718//33wrvvvivs3r1bACB88803Wust8e8yLy9PCAoKEkaOHCmkp6cLX375peDp6Sn8/e9/12xjiWuEoXPJzc0VoqKihK+++kq4ePGikJycLERERAhdunTROt/GjRsLS5Ys0fq+Kv47s9W5kGtwhPhhiCmxxZmux6Y6efKk0KRJE6F9+/bC9OnTXepcc3JyhMaNGwtvvvmmcOLECeHatWvCf//7X+HKlSuabRzpul4Ty5YtE+rVqyfs27dPyMzMFHbt2iXUqVNH2LBhg9OfqyvFZqo5U+7HbPl92uN+TBAcJ6a6eux0lRjpavHQFWJebY5tTGo6mNWrVwuhoaGa1+qk5pkzZ/S+55VXXhEGDhyotaxbt27CX/7yF0EQBEGlUgkKhUJYs2aNZn1ubq4gk8mEL7/8UhAEQcjIyBAACKdOndJs88MPPwgikUi4deuWJU5Np4iICGHq1Kma12VlZUJISIiwYsUKqx3TmDt37ggAhJ9++kmz7Nlnn9UKPJV9//33glgsFpRKpWbZpk2bBB8fH6GoqEgQBEGYM2eO0LZtW633vfrqq0J0dLTmtSU+j0WLFgkdOnTQuS43N1dwd3cXdu3apVl24cIFAYCQnJzscOdS2fTp04VmzZoJKpVKEATn+l4qBxdL/bv89NNPBT8/P835CIIgzJ07V2jVqpXmtSWuEYbORZeTJ08KAIQbN25oljVu3FhYv3693vfY41zIeTli/DCkcmxx9uuxLg8ePBBatGghJCQkaF2fXeVc586dK/Ts2VPveke6rtfUwIEDhXHjxmktGzZsmDBy5EiXOldXis1kOZXvx2z1fdrrfkwQHDemulLsdKUY6Wrx0NViXm2LbRx+7mDy8vLg7+9fZfngwYMRGBiInj174rvvvtNal5ycjKioKK1l0dHRSE5OBgBkZmZCqVRqbSOXy9GtWzfNNsnJyfD19UXXrl0120RFRUEsFuPEiRMWO7+KiouLkZKSotUusViMqKgoTbvsIS8vDwCqfA87duxAQEAAwsPDMX/+fBQUFGjWJScno127dggKCtIsi46ORn5+Ps6fP6/ZxtD3ZMnP4/LlywgJCUHTpk0xcuRI3Lx5EwCQkpKCkpISrWO0bt0ajRo10vpdcKRzUSsuLsb27dsxbtw4iEQizXJn+l4qstS/y+TkZPTq1QtSqVSr/ZcuXcL9+/dNOkdT2mKuvLw8iEQi+Pr6ai1fuXIl6tWrh06dOmHNmjVaw2Yc9VzI8Thq/DCkcmxx5uuxPlOnTsXAgQOrtMdVzvW7775D165d8fLLLyMwMBCdOnXC559/rlnvSNf1mnr66aeRmJiIX3/9FQDwyy+/4OjRo+jfv7/LnWtFjnRejGf2U/l+zFbfpz3uxwDHjqmuFDtdKUa6Wjx09ZjnSO23Rmxzq9a7yCquXLmCjz/+GB988IFmWZ06dbB27Vr06NEDYrEY//nPfzB06FDs2bMHgwcPBgAolUqtCxwABAUFQalUatarlxnaJjAwUGu9m5sb/P39NdtY2t27d1FWVqazXRcvXrTKMY1RqVSYMWMGevTogfDwcM3y119/HY0bN0ZISAjOnj2LuXPn4tKlS9i9ezcA/d+Bep2hbfLz8/H48WPcv3/fIp9Ht27dsG3bNrRq1QpZWVl4//338cwzzyA9PR1KpRJSqbRKoqny74KjnEtFe/bsQW5uLt58803NMmf6Xiqz1L9LpVKJ0NBQvefo5+dnkWuEOQoLCzF37lyMGDECPj4+muXTpk1D586d4e/vj2PHjmH+/PnIysrCunXrHPZcyDE5YvwwRFdscebrsS47d+5EamoqTp06VWWdq5zrtWvXsGnTJsyaNQvvvPMOTp06hWnTpkEqlWLMmDEOdV2vqXnz5iE/Px+tW7eGRCJBWVkZli1bhpEjR2ra4SrnWpEjnRfjmX3ouh+z1fdpj/sxwHFjqivFTleLka4WD1095jlS+60R25jUtIJ58+Zh1apVBre5cOGCVvGWW7duISYmBi+//DImTJigWR4QEIBZs2ZpXv/pT3/C7du3sWbNGk1Skyxn6tSpSE9Px9GjR7WWT5w4UfP/7dq1Q3BwMPr27YurV6+iWbNmtm6mQeonSgDQvn17dOvWDY0bN8bXX38NT09PO7asZr744gv0798fISEhmmXO9L3UFiUlJXjllVcgCAI2bdqkta7itax9+/aQSqX4y1/+ghUrVkAmk9m6qUQ2oy+2uIrffvsN06dPR0JCAjw8POzdHKtRqVTo2rUrli9fDgDo1KkT0tPTsXnzZowZM8bOrbOsr7/+Gjt27EBcXBzatm2LtLQ0zJgxAyEhIS53ruSaLHk/RvbhKrHTFWOkq8VDxjznxuHnVjB79mxcuHDB4E/Tpk0129++fRvPPfccnn76aXz22WdG99+tWzdcuXJF81qhUFSpjpadnQ2FQqFZr15maJs7d+5orS8tLUVOTo5mG0sLCAiARCIx2C5bio2Nxb59+3Do0CE0aNDA4LbdunUDAM33oO87UK8ztI2Pjw88PT2t9nn4+vqiZcuWuHLlChQKBYqLi5Gbm6v3GI54Ljdu3MCBAwfw5z//2eB2zvS9WOrfZU3O0ZxrhCnUCc0bN24gISFBq5emLt26dUNpaSmuX7/ucOdCjs3R4och+mKLs16PdUlJScGdO3fQuXNnuLm5wc3NDT/99BM++ugjuLm5ISgoyCXONTg4GGFhYVrL2rRpo5nixZGu6zX19ttvY968eXjttdfQrl07vPHGG5g5cyZWrFih1Q5XONeKHOm8GM9qxpL3Y7b6Pu1xPwY4Zkx1pdjpijHS1eKhq8c8R2q/NWIbk5pWUL9+fbRu3drgj3oeglu3bqF3797o0qULtm7dCrHY+FeSlpaG4OBgzevIyEgkJiZqbZOQkIDIyEgAQGhoKBQKhdY2+fn5OHHihGabyMhI5ObmIiUlRbPNwYMHoVKpNIkiS5NKpejSpYtWu1QqFRITEzXtsgVBEBAbG4tvvvkGBw8erNKlWpe0tDQA0HwPkZGROHfunNaFQJ3UUV/wjX1P1vo8Hj58iKtXryI4OBhdunSBu7u71jEuXbqEmzdvav0uONq5bN26FYGBgRg4cKDB7Zzpe7HUv8vIyEgcOXIEJSUlWu1v1aoV/Pz8TDpHU9pijDqhefnyZRw4cAD16tUz+p60tDSIxWLNUAdHORdyfI4SPwwxFluc9XqsS9++fXHu3DmkpaVpfrp27YqRI0dq/t8VzrVHjx64dOmS1rJff/0VjRs3BuBY1/WaKigoqPI3qUQigUqlcrlzrciRzovxrGYseT9mq+/THvdjgGPFVFeMna4YI10tHrp6zHOk9lsltlWrvBBZxO+//y40b95c6Nu3r/D7778LWVlZmh+1bdu2CXFxccKFCxeECxcuCMuWLRPEYrGwZcsWzTZJSUmCm5ub8MEHHwgXLlwQFi1aJLi7uwvnzp3TbLNy5UrB19dX+Pbbb4WzZ88KQ4YMEUJDQ4XHjx9rtomJiRE6deoknDhxQjh69KjQokULYcSIEVb9DHbu3CnIZDJh27ZtQkZGhjBx4kTB19dXq7KbtU2ePFmQy+XC4cOHtb6DgoICQRAE4cqVK8KSJUuE06dPC5mZmcK3334rNG3aVOjVq5dmH6WlpUJ4eLjw/PPPC2lpaUJ8fLxQv359Yf78+Zptrl27Jnh5eQlvv/22cOHCBeGTTz4RJBKJEB8fb9HPY/bs2cLhw4eFzMxMISkpSYiKihICAgKEO3fuCIIgCJMmTRIaNWokHDx4UDh9+rQQGRkpREZGOuS5CEJ5Vb5GjRoJc+fO1VruDN/LgwcPhDNnzghnzpwRAAjr1q0Tzpw5o6kIbol/l7m5uUJQUJDwxhtvCOnp6cLOnTsFLy8v4e9//7tmG0tcIwydS3FxsTB48GChQYMGQlpamta/I3V1vGPHjgnr168X0tLShKtXrwrbt28X6tevL4wePdrm50KuwRHihyHGYosgON/12BwVK7u6yrmePHlScHNzE5YtWyZcvnxZ2LFjh+Dl5SVs375ds40jXddrYsyYMcJTTz0l7Nu3T8jMzBR2794tBAQECHPmzHH6c3Wl2Ew1Z8r9mC2/T3vcjwmC48TU2hI7nT1Gulo8dIWYV5tjG5OadrR161YBgM4ftW3btglt2rQRvLy8BB8fHyEiIkLYtWtXlX19/fXXQsuWLQWpVCq0bdtW2L9/v9Z6lUolLFy4UAgKChJkMpnQt29f4dKlS1rb3Lt3TxgxYoRQp04dwcfHRxg7dqzw4MED65x8BR9//LHQqFEjQSqVChEREcLx48etfsyK9H0HW7duFQRBEG7evCn06tVL8Pf3F2QymdC8eXPh7bffFvLy8rT2c/36daF///6Cp6enEBAQIMyePVsoKSnR2ubQoUNCx44dBalUKjRt2lRzjIpq+nm8+uqrQnBwsCCVSoWnnnpKePXVV4UrV65o1j9+/FiYMmWK4OfnJ3h5eQkvvvii1h9ujnQugiAI//3vfwUAVX5fneF7OXTokM7frTFjxgiCYLl/l7/88ovQs2dPQSaTCU899ZSwcuXKKm2p6TXC0LlkZmbq/Xd06NAhQRAEISUlRejWrZsgl8sFDw8PoU2bNsLy5cuFwsJCm58LuQ57xw9DjMUWQXC+67E5Kt+wucq57t27VwgPDxdkMpnQunVr4bPPPtNa70jX9ZrIz88Xpk+fLjRq1Ejw8PAQmjZtKrz77ruaB1XOfK6uFJup5ky5HxME232f9rofEwTHiKm1JXa6Qox0pXjoCjGvNsc2kSAIQvX6eBIRERERERERERHZHufUJCIiIiIiIiIiIqfCpCYRERERERERERE5FSY1iYiIiIiIiIiIyKkwqUlEREREREREREROhUlNIiIiIiIiIiIicipMahIREREREREREZFTYVKTiIiIiIiIiIiInAqTmkQOZNu2bfD19bV3M8ziaG3u1asX4uLiqiw/fPgwtm3bVmX53bt3ERgYiN9//90GrSMiqj0cLT6YwtHazJhGROQ4HC1GmMLR2sy4RpbGpCZRNb355psQiURVfmJiYkx6f5MmTfDhhx9qLXv11Vfx66+/WqG12mwd3EQiEfbs2YNt27bp/Mwq/ly/fh2LFy/Wua5169YGj/Pdd98hOzsbr732msltCwgIwOjRo7Fo0aKaniYRkdNiTDMdYxoRkeNjXDMd4xo5Mzd7N4DImcXExGDr1q1ay2QyWbX35+npCU9Pz5o2y2G9+uqrWn9IDBs2DOHh4ViyZIlmWf369QEAbdu2xYEDB7Te7+Zm+JL10UcfYezYsRCLnzyvSUtLw9tvv43U1FQUFxdj7dq1GD58OBYvXqzZZuzYsejSpQvWrFkDf3//mpwiEZHTYkwzD2MaEZFjY1wzD+MaOSP21CSqAZlMBoVCofXj5+cHABAEAYsXL0ajRo0gk8kQEhKCadOmAQB69+6NGzduYObMmZonW0DVp3KLFy9Gx44dsWXLFjRq1Ah16tTBlClTUFZWhtWrV0OhUCAwMBDLli3Tate6devQrl07eHt7o2HDhpgyZQoePnwIoLxr/9ixY5GXl6c5tjpoFBUV4a9//SueeuopeHt7o1u3bjh8+LDWvrdt24ZGjRrBy8sLL774Iu7du2fy5+Xp6an1WUmlUnh5eWktk0gkAMqDYuXPNiAgQO++//jjDxw8eBCDBg3SLBMEAUOGDIGnpydWrFiBOXPmYPny5VX+GGnbti1CQkLwzTffmHwuRESuhjGNMY2IyJUwrjGuketjT00iK/nPf/6D9evXY+fOnWjbti2USiV++eUXAMDu3bvRoUMHTJw4ERMmTDC4n6tXr+KHH35AfHw8rl69ipdeegnXrl1Dy5Yt8dNPP+HYsWMYN24coqKi0K1bNwCAWCzGRx99hNDQUFy7dg1TpkzBnDlz8Omnn+Lpp5/Ghx9+iPfeew+XLl0CANSpUwcAEBsbi4yMDOzcuVMTOGJiYnDu3Dm0aNECJ06cwPjx47FixQoMHToU8fHxDjMU4OjRo/Dy8kKbNm00y+7du4ebN28iLi4OJSUlmKeLxAAABkFJREFUkEqlGDRokFYwVYuIiMDPP/+M8ePH27LZREROgTHNthjTiIisi3HNthjXyFqY1CSqgX379mmCjNo777yDd955Bzdv3oRCoUBUVBTc3d3RqFEjREREAAD8/f0hkUhQt25dKBQKg8dQqVTYsmUL6tati7CwMDz33HO4dOkSvv/+e4jFYrRq1QqrVq3CoUOHNIFyxowZmvc3adIEf/vb3zBp0iR8+umnkEqlkMvlEIlEWse+efMmtm7dips3byIkJAQA8Ne//hXx8fHYunUrli9fjg0bNiAmJgZz5swBALRs2RLHjh1DfHx8jT/Lys6dO1flsx01ahQ2b96sc/sbN24gKChIazhDQEAAWrVqhaVLlyImJsbg3DQhISE4c+aMRdpOROSMGNMY04iIXAnjGuMauT4mNYlq4LnnnsOmTZu0lqnn+Xj55Zfx4YcfomnTpoiJicGAAQMwaNAgo3ONVNakSRPUrVtX8zooKAgSiUQrIAQFBeHOnTua1wcOHMCKFStw8eJF5Ofno7S0FIWFhSgoKICXl5fO45w7dw5lZWVo2bKl1vKioiLUq1cPAHDhwgW8+OKLWusjIyOtEihbtWqF7777TmuZj4+P3u0fP34MDw+PKsv/+9//4r333sPy5cuRn5+Pf//733j33XfRp08fre08PT1RUFBgmcYTETkhxjTGNCIiV8K4xrhGro9JTaIa8Pb2RvPmzXWua9iwIS5duoQDBw4gISEBU6ZMwZo1a/DTTz/B3d3d5GNU3lYkEulcplKpAADXr1/HCy+8gMmTJ2PZsmXw9/fH0aNHMX78eBQXF+sNlA8fPoREIkFKSopmrhS1yk/hbEEqler9bHUJCAjA/fv3qyxv3Lgx/vnPf+Lw4cM4dOgQHj58iJiYGJw5cwZt27bVbJeTk6OZ+JqIqDZiTLMexjQiIttjXLMexjVyFCwURGRFnp6eGDRoED766CMcPnwYycnJOHfuHIDyQFBWVmbxY6akpEClUmHt2rXo3r07WrZsidu3b2tto+vYnTp1QllZGe7cuYPmzZtr/aiHPrRp0wYnTpzQet/x48ctfg7V0alTJyiVSp3BUi00NBRr165F3bp1q7Q7PT0dnTp1snYziYicFmOa7TCmERFZH+Oa7TCukbUwqUlUA0VFRVAqlVo/d+/eBVBeee6LL75Aeno6rl27hu3bt8PT0xONGzcGUD5U4ciRI7h165bmPZbQvHlzlJSU4OOPP8a1a9fw73//u8rcJk2aNMHDhw+RmJiIu3fvoqCgAC1btsTIkSMxevRo7N69G5mZmTh58iRWrFiB/fv3AwCmTZuG+Ph4fPDBB7h8+TI2btxoleEMAFBaWlrls83Ozta7fadOnRAQEICkpCTNstu3b2PWrFk4e/YsioqKUFBQgL///e/Izc3VCooFBQVISUnB888/b5VzISJyBoxpjGlERK6EcY1xjVwfk5pENRAfH4/g4GCtn549ewIAfH198fnnn6NHjx5o3749Dhw4gL1792rmPFmyZAmuX7+OZs2aWbQrfYcOHbBu3TqsWrUK4eHh2LFjB1asWKG1zdNPP41Jkybh1VdfRf369bF69WoAwNatWzF69GjMnj0brVq1wtChQ3Hq1Ck0atQIANC9e3d8/vnn2LBhAzp06IAff/wRCxYssFjbKzp//nyVz1b9R4YuEokEY8eOxY4dOzTLfHx8UFpaipdeeglDhgzBzJkz8eGHH2Lr1q3o3LmzZrtvv/0WjRo1wjPPPGOVcyEicgaMaYxpRESuhHGNcY1cn0gQBMHejSAisgSlUom2bdsiNTW1SlA9fPgwrl+/jjfffLPK+7p3745p06bh9ddft1FLiYiIDGNMIyIiV8K4RtbAnppE5DIUCgW++OIL3Lx50+T33L17F8OGDcOIESOs2DIiIiLzMKYREZErYVwja2BPTSIiIiIiIiIiInIq7KlJREREREREREREToVJTSIiIiIiIiIiInIqTGoSERERERERERGRU2FSk4iIiIiIiIiIiJwKk5pERERERERERETkVJjUJCIiIiIiIiIiIqfCpCYRERERERERERE5FSY1iYiIiIiIiIiIyKkwqUlEREREREREREROhUlNIiIiIiIiIiIicir/D/7iOijwKupUAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 1600x400 with 3 Axes>"
       ]
diff --git a/poetry.lock b/poetry.lock
index fcdddff..3f679d3 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,112 +1,110 @@
-# This file is automatically @generated by Poetry 1.4.0 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
-version = "1.4.0"
+version = "2.0.0"
 description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
-category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
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+    {file = "absl-py-2.0.0.tar.gz", hash = "sha256:d9690211c5fcfefcdd1a45470ac2b5c5acd45241c3af71eed96bc5441746c0d5"},
+    {file = "absl_py-2.0.0-py3-none-any.whl", hash = "sha256:9a28abb62774ae4e8edbe2dd4c49ffcd45a6a848952a5eccc6a49f3f0fc1e2f3"},
 ]
 
 [[package]]
 name = "aiohttp"
-version = "3.8.5"
+version = "3.8.6"
 description = "Async http client/server framework (asyncio)"
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
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 [package.dependencies]
@@ -125,7 +123,6 @@ speedups = ["Brotli", "aiodns", "cchardet"]
 name = "aiosignal"
 version = "1.3.1"
 description = "aiosignal: a list of registered asynchronous callbacks"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -138,14 +135,13 @@ frozenlist = ">=1.1.0"
 
 [[package]]
 name = "alembic"
-version = "1.11.1"
+version = "1.12.0"
 description = "A database migration tool for SQLAlchemy."
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
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@@ -160,30 +156,182 @@ tz = ["python-dateutil"]
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 version = "4.9.3"
 description = "ANTLR 4.9.3 runtime for Python 3.7"
-category = "main"
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 ]
 
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+optional = false
+python-versions = ">=3.8"
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+idna = ">=2.8"
+sniffio = ">=1.1"
+
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+trio = ["trio (>=0.22)"]
+
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+version = "0.1.3"
+description = "Disable App Nap on macOS >= 10.9"
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+python-versions = "*"
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+[package.extras]
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+tests = ["pytest"]
+
+[[package]]
+name = "arrow"
+version = "1.3.0"
+description = "Better dates & times for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
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+types-python-dateutil = ">=2.8.10"
+
+[package.extras]
+doc = ["doc8", "sphinx (>=7.0.0)", "sphinx-autobuild", "sphinx-autodoc-typehints", "sphinx_rtd_theme (>=1.3.0)"]
+test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "pytz (==2021.1)", "simplejson (==3.*)"]
+
+[[package]]
+name = "astroid"
+version = "2.15.8"
+description = "An abstract syntax tree for Python with inference support."
+optional = false
+python-versions = ">=3.7.2"
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+
+[[package]]
+name = "asttokens"
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+description = "Annotate AST trees with source code positions"
+optional = false
+python-versions = "*"
+files = [
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+
+[package.extras]
+test = ["astroid", "pytest"]
+
+[[package]]
+name = "async-lru"
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+description = "Simple LRU cache for asyncio"
+optional = false
+python-versions = ">=3.8"
+files = [
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+[package.dependencies]
+typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
+
 [[package]]
 name = "async-timeout"
-version = "4.0.2"
+version = "4.0.3"
 description = "Timeout context manager for asyncio programs"
-category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
-    {file = "async-timeout-4.0.2.tar.gz", hash = "sha256:2163e1640ddb52b7a8c80d0a67a08587e5d245cc9c553a74a847056bc2976b15"},
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+    {file = "async-timeout-4.0.3.tar.gz", hash = "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f"},
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 [[package]]
 name = "attrs"
 version = "23.1.0"
 description = "Classes Without Boilerplate"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -202,7 +350,6 @@ tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pyte
 name = "azure-common"
 version = "1.1.28"
 description = "Microsoft Azure Client Library for Python (Common)"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -212,34 +359,32 @@ files = [
 
 [[package]]
 name = "azure-core"
-version = "1.28.0"
+version = "1.29.5"
 description = "Microsoft Azure Core Library for Python"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "azure-core-1.28.0.zip", hash = "sha256:e9eefc66fc1fde56dab6f04d4e5d12c60754d5a9fa49bdcfd8534fc96ed936bd"},
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+    {file = "azure-core-1.29.5.tar.gz", hash = "sha256:52983c89d394c6f881a121e5101c5fa67278ca3b1f339c8fb2ef39230c70e9ac"},
+    {file = "azure_core-1.29.5-py3-none-any.whl", hash = "sha256:0fa04b7b1f7d44a4fb8468c4093deb2ea01fdf4faddbf802ed9205615f99d68c"},
 ]
 
 [package.dependencies]
 requests = ">=2.18.4"
 six = ">=1.11.0"
-typing-extensions = ">=4.3.0"
+typing-extensions = ">=4.6.0"
 
 [package.extras]
 aio = ["aiohttp (>=3.0)"]
 
 [[package]]
 name = "azure-identity"
-version = "1.13.0"
+version = "1.14.1"
 description = "Microsoft Azure Identity Library for Python"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "azure-identity-1.13.0.zip", hash = "sha256:c931c27301ffa86b07b4dcf574e29da73e3deba9ab5d1fe4f445bb6a3117e260"},
-    {file = "azure_identity-1.13.0-py3-none-any.whl", hash = "sha256:bd700cebb80cd9862098587c29d8677e819beca33c62568ced6d5a8e5e332b82"},
+    {file = "azure-identity-1.14.1.zip", hash = "sha256:48e2a9dbdc59b4f095f841d867d9a8cbe4c1cdbbad8251e055561afd47b4a9b8"},
+    {file = "azure_identity-1.14.1-py3-none-any.whl", hash = "sha256:3a5bef8e9c3281e864e869739be8d67424bff616cddae96b546ca2a5168d863d"},
 ]
 
 [package.dependencies]
@@ -247,13 +392,11 @@ azure-core = ">=1.11.0,<2.0.0"
 cryptography = ">=2.5"
 msal = ">=1.20.0,<2.0.0"
 msal-extensions = ">=0.3.0,<2.0.0"
-six = ">=1.12.0"
 
 [[package]]
 name = "azure-mgmt-core"
 version = "1.4.0"
 description = "Microsoft Azure Management Core Library for Python"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -268,7 +411,6 @@ azure-core = ">=1.26.2,<2.0.0"
 name = "azure-storage-blob"
 version = "12.13.0"
 description = "Microsoft Azure Blob Storage Client Library for Python"
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -283,13 +425,12 @@ msrest = ">=0.6.21"
 
 [[package]]
 name = "azureml-mlflow"
-version = "1.52.0"
+version = "1.53.0"
 description = "Contains the integration code of AzureML with Mlflow."
-category = "main"
 optional = false
 python-versions = ">=3.7,<4.0"
 files = [
-    {file = "azureml_mlflow-1.52.0-py3-none-any.whl", hash = "sha256:ca8b2f197108e2d8ad9661c25dc8220cfb092681824fbd9587d57152d7c238ee"},
+    {file = "azureml_mlflow-1.53.0-py3-none-any.whl", hash = "sha256:b5d8bfcf3cc34c3c1a5ddcc84f3006d857ffcdfdee8c7e7b47eec80416c8c0ed"},
 ]
 
 [package.dependencies]
@@ -307,23 +448,118 @@ python-dateutil = ">=2.7.3,<3.0.0"
 [package.extras]
 deployments = ["flask", "numpy", "pandas"]
 
+[[package]]
+name = "babel"
+version = "2.13.1"
+description = "Internationalization utilities"
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "Babel-2.13.1.tar.gz", hash = "sha256:33e0952d7dd6374af8dbf6768cc4ddf3ccfefc244f9986d4074704f2fbd18900"},
+]
+
+[package.extras]
+dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
+
+[[package]]
+name = "backcall"
+version = "0.2.0"
+description = "Specifications for callback functions passed in to an API"
+optional = false
+python-versions = "*"
+files = [
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+[[package]]
+name = "beautifulsoup4"
+version = "4.12.2"
+description = "Screen-scraping library"
+optional = false
+python-versions = ">=3.6.0"
+files = [
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+
+[package.extras]
+html5lib = ["html5lib"]
+lxml = ["lxml"]
+
+[[package]]
+name = "black"
+version = "22.12.0"
+description = "The uncompromising code formatter."
+optional = false
+python-versions = ">=3.7"
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+tomli = {version = ">=1.1.0", markers = "python_full_version < \"3.11.0a7\""}
+
+[package.extras]
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+d = ["aiohttp (>=3.7.4)"]
+jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
+uvloop = ["uvloop (>=0.15.2)"]
+
+[[package]]
+name = "bleach"
+version = "6.1.0"
+description = "An easy safelist-based HTML-sanitizing tool."
+optional = false
+python-versions = ">=3.8"
+files = [
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+
+[package.extras]
+css = ["tinycss2 (>=1.1.0,<1.3)"]
+
 [[package]]
 name = "blinker"
-version = "1.6.2"
+version = "1.6.3"
 description = "Fast, simple object-to-object and broadcast signaling"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "blinker-1.6.3.tar.gz", hash = "sha256:152090d27c1c5c722ee7e48504b02d76502811ce02e1523553b4cf8c8b3d3a8d"},
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 [[package]]
 name = "cachetools"
 version = "5.3.1"
 description = "Extensible memoizing collections and decorators"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -333,188 +569,198 @@ files = [
 
 [[package]]
 name = "certifi"
-version = "2023.5.7"
+version = "2023.7.22"
 description = "Python package for providing Mozilla's CA Bundle."
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
 [[package]]
 name = "cffi"
-version = "1.15.1"
+version = "1.16.0"
 description = "Foreign Function Interface for Python calling C code."
-category = "main"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 files = [
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 version = "2.2.1"
 description = "Extended pickling support for Python objects"
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
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 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
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+test = ["pytest"]
+typing = ["mypy (>=0.990)"]
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 [package.dependencies]
@@ -711,35 +976,34 @@ toml = ["tomli"]
 
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 [package.dependencies]
@@ -757,26 +1021,28 @@ test-randomorder = ["pytest-randomly"]
 
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+version = "0.12.1"
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 ]
 
+[package.extras]
+docs = ["ipython", "matplotlib", "numpydoc", "sphinx"]
+tests = ["pytest", "pytest-cov", "pytest-xdist"]
+
 [[package]]
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@@ -786,29 +1052,101 @@ pyjwt = ">=1.7.0"
 requests = ">=2.17.3"
 six = ">=1.10.0"
 tabulate = ">=0.7.7"
-urllib3 = ">=1.26.7,<2.0.0"
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 [[package]]
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 ]
 
 [package.dependencies]
 marshmallow = ">=3.18.0,<4.0.0"
 typing-inspect = ">=0.4.0,<1"
 
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+version = "1.8.0"
+description = "An implementation of the Debug Adapter Protocol for Python"
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+python-versions = ">=3.8"
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+description = "Decorators for Humans"
+optional = false
+python-versions = ">=3.5"
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+[[package]]
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+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
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+optional = false
+python-versions = ">=3.7"
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+
+[package.extras]
+graph = ["objgraph (>=1.7.2)"]
+
+[[package]]
+name = "distlib"
+version = "0.3.7"
+description = "Distribution utilities"
+optional = false
+python-versions = "*"
+files = [
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+]
+
 [[package]]
 name = "docker"
 version = "6.1.3"
 description = "A Python library for the Docker Engine API."
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -830,7 +1168,6 @@ ssh = ["paramiko (>=2.4.3)"]
 name = "docstring-parser"
 version = "0.15"
 description = "Parse Python docstrings in reST, Google and Numpydoc format"
-category = "main"
 optional = false
 python-versions = ">=3.6,<4.0"
 files = [
@@ -842,7 +1179,6 @@ files = [
 name = "entrypoints"
 version = "0.4"
 description = "Discover and load entry points from installed packages."
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -852,45 +1188,71 @@ files = [
 
 [[package]]
 name = "exceptiongroup"
-version = "1.1.2"
+version = "1.1.3"
 description = "Backport of PEP 654 (exception groups)"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
 test = ["pytest (>=6)"]
 
+[[package]]
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+version = "2.0.0"
+description = "Get the currently executing AST node of a frame, and other information"
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[package.extras]
+tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"]
+
+[[package]]
+name = "fastjsonschema"
+version = "2.18.1"
+description = "Fastest Python implementation of JSON schema"
+optional = false
+python-versions = "*"
+files = [
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+
+[package.extras]
+devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"]
+
 [[package]]
 name = "filelock"
-version = "3.12.2"
+version = "3.12.4"
 description = "A platform independent file lock."
-category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+typing = ["typing-extensions (>=4.7.1)"]
 
 [[package]]
 name = "flask"
-version = "2.3.2"
+version = "2.3.3"
 description = "A simple framework for building complex web applications."
-category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
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 click = ">=8.1.3"
 itsdangerous = ">=2.1.2"
 Jinja2 = ">=3.1.2"
-Werkzeug = ">=2.3.3"
+Werkzeug = ">=2.3.7"
 
 [package.extras]
 async = ["asgiref (>=3.2)"]
@@ -906,46 +1268,53 @@ dotenv = ["python-dotenv"]
 
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 ]
 
 [package.extras]
@@ -962,11 +1331,21 @@ ufo = ["fs (>=2.2.0,<3)"]
 unicode = ["unicodedata2 (>=15.0.0)"]
 woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
 
+[[package]]
+name = "fqdn"
+version = "1.5.1"
+description = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers"
+optional = false
+python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4"
+files = [
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+]
+
 [[package]]
 name = "frozenlist"
 version = "1.4.0"
 description = "A list-like structure which implements collections.abc.MutableSequence"
-category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -1035,14 +1414,13 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2023.6.0"
+version = "2023.10.0"
 description = "File-system specification"
-category = "main"
 optional = false
 python-versions = ">=3.8"
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 [package.dependencies]
@@ -1075,14 +1453,13 @@ tqdm = ["tqdm"]
 
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+version = "4.0.11"
 description = "Git Object Database"
-category = "main"
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@@ -1090,37 +1467,36 @@ smmap = ">=3.0.1,<6"
 
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+version = "3.1.40"
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 [package.dependencies]
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+
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-urllib3 = "<2.0"
 
 [package.extras]
 aiohttp = ["aiohttp (>=3.6.2,<4.0.0.dev0)", "requests (>=2.20.0,<3.0.0.dev0)"]
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 name = "google-auth-oauthlib"
 version = "1.0.0"
 description = "Google Authentication Library"
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
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+version = "21.2.0"
 description = "WSGI HTTP Server for UNIX"
-category = "main"
 optional = false
 python-versions = ">=3.5"
 files = [
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 ]
 
 [package.dependencies]
-setuptools = ">=3.0"
+packaging = "*"
 
 [package.extras]
 eventlet = ["eventlet (>=0.24.1)"]
@@ -1305,7 +1688,6 @@ tornado = ["tornado (>=0.2)"]
 name = "hydra-core"
 version = "1.3.2"
 description = "A framework for elegantly configuring complex applications"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -1314,15 +1696,28 @@ files = [
 ]
 
 [package.dependencies]
-antlr4-python3-runtime = ">=4.9.0,<4.10.0"
+antlr4-python3-runtime = "==4.9.*"
 omegaconf = ">=2.2,<2.4"
 packaging = "*"
 
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+description = "File identification library for Python"
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+python-versions = ">=3.8"
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+[package.extras]
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+
 [[package]]
 name = "idna"
 version = "3.4"
 description = "Internationalized Domain Names in Applications (IDNA)"
-category = "main"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -1334,7 +1729,6 @@ files = [
 name = "importlib-metadata"
 version = "6.8.0"
 description = "Read metadata from Python packages"
-category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -1352,25 +1746,23 @@ testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs
 
 [[package]]
 name = "importlib-resources"
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+version = "6.1.0"
 description = "Read resources from Python packages"
-category = "main"
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 python-versions = ">=3.8"
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 python-versions = ">=3.7"
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@@ -1378,11 +1770,114 @@ files = [
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 ]
 
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+pyside6 = ["pyside6"]
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+
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+black = ["black"]
+doc = ["docrepr", "exceptiongroup", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
+kernel = ["ipykernel"]
+nbconvert = ["nbconvert"]
+nbformat = ["nbformat"]
+notebook = ["ipywidgets", "notebook"]
+parallel = ["ipyparallel"]
+qtconsole = ["qtconsole"]
+test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
+test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]
+
+[[package]]
+name = "ipython-genutils"
+version = "0.2.0"
+description = "Vestigial utilities from IPython"
+optional = false
+python-versions = "*"
+files = [
+    {file = "ipython_genutils-0.2.0-py2.py3-none-any.whl", hash = "sha256:72dd37233799e619666c9f639a9da83c34013a73e8bbc79a7a6348d93c61fab8"},
+    {file = "ipython_genutils-0.2.0.tar.gz", hash = "sha256:eb2e116e75ecef9d4d228fdc66af54269afa26ab4463042e33785b887c628ba8"},
+]
+
+[[package]]
+name = "ipywidgets"
+version = "8.1.1"
+description = "Jupyter interactive widgets"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "ipywidgets-8.1.1-py3-none-any.whl", hash = "sha256:2b88d728656aea3bbfd05d32c747cfd0078f9d7e159cf982433b58ad717eed7f"},
+    {file = "ipywidgets-8.1.1.tar.gz", hash = "sha256:40211efb556adec6fa450ccc2a77d59ca44a060f4f9f136833df59c9f538e6e8"},
+]
+
+[package.dependencies]
+comm = ">=0.1.3"
+ipython = ">=6.1.0"
+jupyterlab-widgets = ">=3.0.9,<3.1.0"
+traitlets = ">=4.3.1"
+widgetsnbextension = ">=4.0.9,<4.1.0"
+
+[package.extras]
+test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
+
 [[package]]
 name = "isodate"
 version = "0.6.1"
 description = "An ISO 8601 date/time/duration parser and formatter"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -1393,11 +1888,41 @@ files = [
 [package.dependencies]
 six = "*"
 
+[[package]]
+name = "isoduration"
+version = "20.11.0"
+description = "Operations with ISO 8601 durations"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "isoduration-20.11.0-py3-none-any.whl", hash = "sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042"},
+    {file = "isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9"},
+]
+
+[package.dependencies]
+arrow = ">=0.15.0"
+
+[[package]]
+name = "isort"
+version = "5.12.0"
+description = "A Python utility / library to sort Python imports."
+optional = false
+python-versions = ">=3.8.0"
+files = [
+    {file = "isort-5.12.0-py3-none-any.whl", hash = "sha256:f84c2818376e66cf843d497486ea8fed8700b340f308f076c6fb1229dff318b6"},
+    {file = "isort-5.12.0.tar.gz", hash = "sha256:8bef7dde241278824a6d83f44a544709b065191b95b6e50894bdc722fcba0504"},
+]
+
+[package.extras]
+colors = ["colorama (>=0.4.3)"]
+pipfile-deprecated-finder = ["pip-shims (>=0.5.2)", "pipreqs", "requirementslib"]
+plugins = ["setuptools"]
+requirements-deprecated-finder = ["pip-api", "pipreqs"]
+
 [[package]]
 name = "itsdangerous"
 version = "2.1.2"
 description = "Safely pass data to untrusted environments and back."
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1405,11 +1930,29 @@ files = [
     {file = "itsdangerous-2.1.2.tar.gz", hash = "sha256:5dbbc68b317e5e42f327f9021763545dc3fc3bfe22e6deb96aaf1fc38874156a"},
 ]
 
+[[package]]
+name = "jedi"
+version = "0.19.1"
+description = "An autocompletion tool for Python that can be used for text editors."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "jedi-0.19.1-py2.py3-none-any.whl", hash = "sha256:e983c654fe5c02867aef4cdfce5a2fbb4a50adc0af145f70504238f18ef5e7e0"},
+    {file = "jedi-0.19.1.tar.gz", hash = "sha256:cf0496f3651bc65d7174ac1b7d043eff454892c708a87d1b683e57b569927ffd"},
+]
+
+[package.dependencies]
+parso = ">=0.8.3,<0.9.0"
+
+[package.extras]
+docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"]
+qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
+
 [[package]]
 name = "jinja2"
 version = "3.1.2"
 description = "A very fast and expressive template engine."
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1425,21 +1968,33 @@ i18n = ["Babel (>=2.7)"]
 
 [[package]]
 name = "joblib"
-version = "1.3.1"
+version = "1.3.2"
 description = "Lightweight pipelining with Python functions"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "joblib-1.3.1-py3-none-any.whl", hash = "sha256:89cf0529520e01b3de7ac7b74a8102c90d16d54c64b5dd98cafcd14307fdf915"},
-    {file = "joblib-1.3.1.tar.gz", hash = "sha256:1f937906df65329ba98013dc9692fe22a4c5e4a648112de500508b18a21b41e3"},
+    {file = "joblib-1.3.2-py3-none-any.whl", hash = "sha256:ef4331c65f239985f3f2220ecc87db222f08fd22097a3dd5698f693875f8cbb9"},
+    {file = "joblib-1.3.2.tar.gz", hash = "sha256:92f865e621e17784e7955080b6d042489e3b8e294949cc44c6eac304f59772b1"},
 ]
 
+[[package]]
+name = "json5"
+version = "0.9.14"
+description = "A Python implementation of the JSON5 data format."
+optional = false
+python-versions = "*"
+files = [
+    {file = "json5-0.9.14-py2.py3-none-any.whl", hash = "sha256:740c7f1b9e584a468dbb2939d8d458db3427f2c93ae2139d05f47e453eae964f"},
+    {file = "json5-0.9.14.tar.gz", hash = "sha256:9ed66c3a6ca3510a976a9ef9b8c0787de24802724ab1860bc0153c7fdd589b02"},
+]
+
+[package.extras]
+dev = ["hypothesis"]
+
 [[package]]
 name = "jsonargparse"
 version = "4.20.1"
 description = "Implement minimal boilerplate CLIs derived from type hints and parse from command line, config files and environment variables."
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -1448,131 +2003,543 @@ files = [
 ]
 
 [package.dependencies]
-docstring-parser = {version = ">=0.15", optional = true, markers = "extra == \"signatures\""}
-PyYAML = ">=3.13"
-typeshed-client = {version = ">=2.1.0", optional = true, markers = "extra == \"signatures\""}
+docstring-parser = {version = ">=0.15", optional = true, markers = "extra == \"signatures\""}
+PyYAML = ">=3.13"
+typeshed-client = {version = ">=2.1.0", optional = true, markers = "extra == \"signatures\""}
+
+[package.extras]
+all = ["argcomplete (>=2.0.0)", "docstring-parser (>=0.15)", "fsspec (>=0.8.4)", "jsonnet (>=0.13.0)", "jsonnet-binary (>=0.17.0)", "jsonschema (>=3.2.0)", "omegaconf (>=2.1.1)", "reconplogger (>=4.4.0)", "requests (>=2.18.4)", "ruyaml (>=0.20.0)", "typeshed-client (>=2.1.0)", "typing-extensions (>=3.10.0.0)"]
+argcomplete = ["argcomplete (>=2.0.0)"]
+dev = ["Sphinx (>=1.7.9)", "autodocsumm (>=0.1.10)", "coverage (>=4.5.1)", "mypy (>=0.701)", "pre-commit (>=2.19.0)", "pycodestyle (>=2.5.0)", "pylint (>=2.15.6)", "responses (>=0.12.0)", "sphinx-autodoc-typehints (>=1.19.5)", "sphinx-rtd-theme (>=0.4.3)", "tox (>=3.25.0)", "types-PyYAML (>=6.0.11)", "types-requests (>=2.28.9)"]
+doc = ["Sphinx (>=1.7.9)", "autodocsumm (>=0.1.10)", "sphinx-autodoc-typehints (>=1.19.5)", "sphinx-rtd-theme (>=0.4.3)"]
+fsspec = ["fsspec (>=0.8.4)"]
+jsonnet = ["jsonnet (>=0.13.0)", "jsonnet-binary (>=0.17.0)"]
+jsonschema = ["jsonschema (>=3.2.0)"]
+maintainer = ["bump2version (>=0.5.11)"]
+mypy = ["mypy (>=0.701)", "types-PyYAML (>=6.0.11)"]
+omegaconf = ["omegaconf (>=2.1.1)"]
+pylint = ["pylint (>=2.15.6)"]
+reconplogger = ["reconplogger (>=4.4.0)"]
+ruyaml = ["ruyaml (>=0.20.0)"]
+signatures = ["docstring-parser (>=0.15)", "typeshed-client (>=2.1.0)"]
+test = ["coverage (>=4.5.1)", "responses (>=0.12.0)", "types-PyYAML (>=6.0.11)", "types-requests (>=2.28.9)"]
+test-no-urls = ["coverage (>=4.5.1)"]
+types-pyyaml = ["types-PyYAML (>=6.0.11)"]
+typing-extensions = ["typing-extensions (>=3.10.0.0)"]
+urls = ["requests (>=2.18.4)"]
+
+[[package]]
+name = "jsonpickle"
+version = "3.0.2"
+description = "Python library for serializing any arbitrary object graph into JSON"
+optional = false
+python-versions = ">=3.7"
+files = [
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+[package.extras]
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+testing-libs = ["simplejson", "ujson"]
+
+[[package]]
+name = "jsonpointer"
+version = "2.4"
+description = "Identify specific nodes in a JSON document (RFC 6901)"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
+files = [
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+python-versions = ">=3.8"
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+idna = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+isoduration = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+jsonpointer = {version = ">1.13", optional = true, markers = "extra == \"format-nongpl\""}
+jsonschema-specifications = ">=2023.03.6"
+referencing = ">=0.28.4"
+rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""}
+rpds-py = ">=0.7.1"
+uri-template = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+webcolors = {version = ">=1.11", optional = true, markers = "extra == \"format-nongpl\""}
+
+[package.extras]
+format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
+format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"]
+
+[[package]]
+name = "jsonschema-specifications"
+version = "2023.7.1"
+description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
+[[package]]
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+description = "Jupyter metapackage. Install all the Jupyter components in one go."
+optional = false
+python-versions = "*"
+files = [
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+ipywidgets = "*"
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+nbconvert = "*"
+notebook = "*"
+qtconsole = "*"
+
+[[package]]
+name = "jupyter-client"
+version = "8.4.0"
+description = "Jupyter protocol implementation and client libraries"
+optional = false
+python-versions = ">=3.8"
+files = [
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+
+[package.extras]
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+test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
+
+[[package]]
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+version = "6.6.3"
+description = "Jupyter terminal console"
+optional = false
+python-versions = ">=3.7"
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+
+[package.extras]
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+
+[[package]]
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+python-versions = ">=3.8"
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+test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
+
+[[package]]
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+optional = false
+python-versions = ">=3.8"
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+test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "pytest-console-scripts", "rich"]
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+[[package]]
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 version = "0.9.0"
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 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1591,20 +2558,18 @@ typing = ["mypy (>=1.0.0)"]
 
 [[package]]
 name = "lit"
-version = "16.0.6"
+version = "17.0.3"
 description = "A Software Testing Tool"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
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 description = "A super-fast templating language that borrows the best ideas from the existing templating languages."
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1622,24 +2587,23 @@ testing = ["pytest"]
 
 [[package]]
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-version = "3.4.3"
+version = "3.5"
 description = "Python implementation of John Gruber's Markdown."
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 optional = false
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 description = "Python plotting package"
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 [package.dependencies]
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 cycler = ">=0.10"
 fonttools = ">=4.22.0"
 kiwisolver = ">=1.0.1"
-numpy = ">=1.20"
+numpy = ">=1.21,<2"
 packaging = ">=20.0"
 pillow = ">=6.2.0"
-pyparsing = ">=2.3.1,<3.1"
+pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
+setuptools_scm = ">=7"
+
+[[package]]
+name = "matplotlib-inline"
+version = "0.1.6"
+description = "Inline Matplotlib backend for Jupyter"
+optional = false
+python-versions = ">=3.5"
+files = [
+    {file = "matplotlib-inline-0.1.6.tar.gz", hash = "sha256:f887e5f10ba98e8d2b150ddcf4702c1e5f8b3a20005eb0f74bfdbd360ee6f304"},
+    {file = "matplotlib_inline-0.1.6-py3-none-any.whl", hash = "sha256:f1f41aab5328aa5aaea9b16d083b128102f8712542f819fe7e6a420ff581b311"},
+]
+
+[package.dependencies]
+traitlets = "*"
+
+[[package]]
+name = "mccabe"
+version = "0.7.0"
+description = "McCabe checker, plugin for flake8"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "mccabe-0.7.0-py2.py3-none-any.whl", hash = "sha256:6c2d30ab6be0e4a46919781807b4f0d834ebdd6c6e3dca0bda5a15f863427b6e"},
+    {file = "mccabe-0.7.0.tar.gz", hash = "sha256:348e0240c33b60bbdf4e523192ef919f28cb2c3d7d5c7794f74009290f236325"},
+]
+
+[[package]]
+name = "mdit-py-plugins"
+version = "0.4.0"
+description = "Collection of plugins for markdown-it-py"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "mdit_py_plugins-0.4.0-py3-none-any.whl", hash = "sha256:b51b3bb70691f57f974e257e367107857a93b36f322a9e6d44ca5bf28ec2def9"},
+    {file = "mdit_py_plugins-0.4.0.tar.gz", hash = "sha256:d8ab27e9aed6c38aa716819fedfde15ca275715955f8a185a8e1cf90fb1d2c1b"},
+]
+
+[package.dependencies]
+markdown-it-py = ">=1.0.0,<4.0.0"
+
+[package.extras]
+code-style = ["pre-commit"]
+rtd = ["myst-parser", "sphinx-book-theme"]
+testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
 
 [[package]]
 name = "mdurl"
 version = "0.1.2"
 description = "Markdown URL utilities"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -1815,16 +2817,26 @@ files = [
     {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
 ]
 
+[[package]]
+name = "mistune"
+version = "3.0.2"
+description = "A sane and fast Markdown parser with useful plugins and renderers"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "mistune-3.0.2-py3-none-any.whl", hash = "sha256:71481854c30fdbc938963d3605b72501f5c10a9320ecd412c121c163a1c7d205"},
+    {file = "mistune-3.0.2.tar.gz", hash = "sha256:fc7f93ded930c92394ef2cb6f04a8aabab4117a91449e72dcc8dfa646a508be8"},
+]
+
 [[package]]
 name = "mlflow"
-version = "2.5.0"
+version = "2.7.1"
 description = "MLflow: A Platform for ML Development and Productionization"
-category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mlflow-2.5.0-py3-none-any.whl", hash = "sha256:981fcb3480ca7383b47e22b5e4c726d21e2c87fb4035e5a1b57574736c665576"},
-    {file = "mlflow-2.5.0.tar.gz", hash = "sha256:f992ae8ea9c73502344baf48c4ec447aa9efbfa8965bc090868e6163234f4eb0"},
+    {file = "mlflow-2.7.1-py3-none-any.whl", hash = "sha256:d9b9f0f32d495f6abbd4068ce9af4a06d08cfafdbaf575248304af1a4ae070ec"},
+    {file = "mlflow-2.7.1.tar.gz", hash = "sha256:853b2038496fb8f85cad205943dede877f65ab86e856f296587885dafcef643d"},
 ]
 
 [package.dependencies]
@@ -1836,7 +2848,7 @@ docker = ">=4.0.0,<7"
 entrypoints = "<1"
 Flask = "<3"
 gitpython = ">=2.1.0,<4"
-gunicorn = {version = "<21", markers = "platform_system != \"Windows\""}
+gunicorn = {version = "<22", markers = "platform_system != \"Windows\""}
 importlib-metadata = ">=3.7.0,<4.7.0 || >4.7.0,<7"
 Jinja2 = [
     {version = ">=2.11,<4", markers = "platform_system != \"Windows\""},
@@ -1848,7 +2860,7 @@ numpy = "<2"
 packaging = "<24"
 pandas = "<3"
 protobuf = ">=3.12.0,<5"
-pyarrow = ">=4.0.0,<13"
+pyarrow = ">=4.0.0,<14"
 pytz = "<2024"
 pyyaml = ">=5.1,<7"
 querystring-parser = "<2"
@@ -1863,19 +2875,18 @@ waitress = {version = "<3", markers = "platform_system == \"Windows\""}
 aliyun-oss = ["aliyunstoreplugin"]
 databricks = ["azure-storage-file-datalake (>12)", "boto3 (>1)", "google-cloud-storage (>=1.30.0)"]
 extras = ["azureml-core (>=1.2.0)", "boto3", "google-cloud-storage (>=1.30.0)", "kubernetes", "mlserver (>=1.2.0,!=1.3.1)", "mlserver-mlflow (>=1.2.0,!=1.3.1)", "prometheus-flask-exporter", "pyarrow", "pysftp", "requests-auth-aws-sigv4", "virtualenv"]
-gateway = ["aiohttp (<4)", "fastapi (<1)", "psutil (<6)", "pydantic (>=1.0,<2)", "uvicorn[standard] (<1)", "watchfiles (<1)"]
+gateway = ["aiohttp (<4)", "fastapi (<1)", "psutil (<6)", "pydantic (>=1.0,<3)", "uvicorn[standard] (<1)", "watchfiles (<1)"]
 sqlserver = ["mlflow-dbstore"]
 
 [[package]]
 name = "mlflow-skinny"
-version = "2.5.0"
+version = "2.7.1"
 description = "MLflow: A Platform for ML Development and Productionization"
-category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mlflow-skinny-2.5.0.tar.gz", hash = "sha256:cabb5213cdc68e540d5b8828f93d636c1ea5ac33a44288c8c5d05b65b7055747"},
-    {file = "mlflow_skinny-2.5.0-py3-none-any.whl", hash = "sha256:b2bef381fc7e842e1846debdd7261bdbf8f218bb88f2232092dd86dd2b06e9e1"},
+    {file = "mlflow-skinny-2.7.1.tar.gz", hash = "sha256:d23e8716d1a313129353d585e5c5e4b9390128a343e18cb9e8441af97708eef6"},
+    {file = "mlflow_skinny-2.7.1-py3-none-any.whl", hash = "sha256:1bf423915460d60a0a5bdb7c88da5a309d57972d99da4a34993b0971235b21e6"},
 ]
 
 [package.dependencies]
@@ -1896,14 +2907,13 @@ sqlparse = ">=0.4.0,<1"
 aliyun-oss = ["aliyunstoreplugin"]
 databricks = ["azure-storage-file-datalake (>12)", "boto3 (>1)", "google-cloud-storage (>=1.30.0)"]
 extras = ["azureml-core (>=1.2.0)", "boto3", "google-cloud-storage (>=1.30.0)", "kubernetes", "mlserver (>=1.2.0,!=1.3.1)", "mlserver-mlflow (>=1.2.0,!=1.3.1)", "prometheus-flask-exporter", "pyarrow", "pysftp", "requests-auth-aws-sigv4", "virtualenv"]
-gateway = ["aiohttp (<4)", "fastapi (<1)", "psutil (<6)", "pydantic (>=1.0,<2)", "uvicorn[standard] (<1)", "watchfiles (<1)"]
+gateway = ["aiohttp (<4)", "fastapi (<1)", "psutil (<6)", "pydantic (>=1.0,<3)", "uvicorn[standard] (<1)", "watchfiles (<1)"]
 sqlserver = ["mlflow-dbstore"]
 
 [[package]]
 name = "mpmath"
 version = "1.3.0"
 description = "Python library for arbitrary-precision floating-point arithmetic"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -1919,18 +2929,17 @@ tests = ["pytest (>=4.6)"]
 
 [[package]]
 name = "msal"
-version = "1.22.0"
-description = "The Microsoft Authentication Library (MSAL) for Python library enables your app to access the Microsoft Cloud by supporting authentication of users with Microsoft Azure Active Directory accounts (AAD) and Microsoft Accounts (MSA) using industry standard OAuth2 and OpenID Connect."
-category = "main"
+version = "1.24.1"
+description = "The Microsoft Authentication Library (MSAL) for Python library"
 optional = false
-python-versions = "*"
+python-versions = ">=2.7"
 files = [
-    {file = "msal-1.22.0-py2.py3-none-any.whl", hash = "sha256:9120b7eafdf061c92f7b3d744e5f325fca35873445fa8ffebb40b1086a13dd58"},
-    {file = "msal-1.22.0.tar.gz", hash = "sha256:8a82f5375642c1625c89058018430294c109440dce42ea667d466c2cab520acd"},
+    {file = "msal-1.24.1-py2.py3-none-any.whl", hash = "sha256:ce4320688f95c301ee74a4d0e9dbcfe029a63663a8cc61756f40d0d0d36574ad"},
+    {file = "msal-1.24.1.tar.gz", hash = "sha256:aa0972884b3c6fdec53d9a0bd15c12e5bd7b71ac1b66d746f54d128709f3f8f8"},
 ]
 
 [package.dependencies]
-cryptography = ">=0.6,<43"
+cryptography = ">=0.6,<44"
 PyJWT = {version = ">=1.0.0,<3", extras = ["crypto"]}
 requests = ">=2.0.0,<3"
 
@@ -1941,7 +2950,6 @@ broker = ["pymsalruntime (>=0.13.2,<0.14)"]
 name = "msal-extensions"
 version = "1.0.0"
 description = "Microsoft Authentication Library extensions (MSAL EX) provides a persistence API that can save your data on disk, encrypted on Windows, macOS and Linux. Concurrent data access will be coordinated by a file lock mechanism."
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -1960,7 +2968,6 @@ portalocker = [
 name = "msrest"
 version = "0.7.1"
 description = "AutoRest swagger generator Python client runtime."
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -1982,7 +2989,6 @@ async = ["aiodns", "aiohttp (>=3.0)"]
 name = "multidict"
 version = "6.0.4"
 description = "multidict implementation"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2062,11 +3068,56 @@ files = [
     {file = "multidict-6.0.4.tar.gz", hash = "sha256:3666906492efb76453c0e7b97f2cf459b0682e7402c0489a95484965dbc1da49"},
 ]
 
+[[package]]
+name = "mypy"
+version = "1.6.1"
+description = "Optional static typing for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "mypy-1.6.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e5012e5cc2ac628177eaac0e83d622b2dd499e28253d4107a08ecc59ede3fc2c"},
+    {file = "mypy-1.6.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:d8fbb68711905f8912e5af474ca8b78d077447d8f3918997fecbf26943ff3cbb"},
+    {file = "mypy-1.6.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:21a1ad938fee7d2d96ca666c77b7c494c3c5bd88dff792220e1afbebb2925b5e"},
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+    {file = "mypy-1.6.1-cp39-cp39-win_amd64.whl", hash = "sha256:a43ef1c8ddfdb9575691720b6352761f3f53d85f1b57d7745701041053deff30"},
+    {file = "mypy-1.6.1-py3-none-any.whl", hash = "sha256:4cbe68ef919c28ea561165206a2dcb68591c50f3bcf777932323bc208d949cf1"},
+    {file = "mypy-1.6.1.tar.gz", hash = "sha256:4d01c00d09a0be62a4ca3f933e315455bde83f37f892ba4b08ce92f3cf44bcc1"},
+]
+
+[package.dependencies]
+mypy-extensions = ">=1.0.0"
+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
+typing-extensions = ">=4.1.0"
+
+[package.extras]
+dmypy = ["psutil (>=4.0)"]
+install-types = ["pip"]
+reports = ["lxml"]
+
 [[package]]
 name = "mypy-extensions"
 version = "1.0.0"
 description = "Type system extensions for programs checked with the mypy type checker."
-category = "main"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -2074,65 +3125,214 @@ files = [
     {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
 ]
 
+[[package]]
+name = "nbclient"
+version = "0.8.0"
+description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
+optional = false
+python-versions = ">=3.8.0"
+files = [
+    {file = "nbclient-0.8.0-py3-none-any.whl", hash = "sha256:25e861299e5303a0477568557c4045eccc7a34c17fc08e7959558707b9ebe548"},
+    {file = "nbclient-0.8.0.tar.gz", hash = "sha256:f9b179cd4b2d7bca965f900a2ebf0db4a12ebff2f36a711cb66861e4ae158e55"},
+]
+
+[package.dependencies]
+jupyter-client = ">=6.1.12"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+nbformat = ">=5.1"
+traitlets = ">=5.4"
+
+[package.extras]
+dev = ["pre-commit"]
+docs = ["autodoc-traits", "mock", "moto", "myst-parser", "nbclient[test]", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling"]
+test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
+
+[[package]]
+name = "nbconvert"
+version = "7.9.2"
+description = "Converting Jupyter Notebooks"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "nbconvert-7.9.2-py3-none-any.whl", hash = "sha256:39fe4b8bdd1b0104fdd86fc8a43a9077ba64c720bda4c6132690d917a0a154ee"},
+    {file = "nbconvert-7.9.2.tar.gz", hash = "sha256:e56cc7588acc4f93e2bb5a34ec69028e4941797b2bfaf6462f18a41d1cc258c9"},
+]
+
+[package.dependencies]
+beautifulsoup4 = "*"
+bleach = "!=5.0.0"
+defusedxml = "*"
+jinja2 = ">=3.0"
+jupyter-core = ">=4.7"
+jupyterlab-pygments = "*"
+markupsafe = ">=2.0"
+mistune = ">=2.0.3,<4"
+nbclient = ">=0.5.0"
+nbformat = ">=5.7"
+packaging = "*"
+pandocfilters = ">=1.4.1"
+pygments = ">=2.4.1"
+tinycss2 = "*"
+traitlets = ">=5.1"
+
+[package.extras]
+all = ["nbconvert[docs,qtpdf,serve,test,webpdf]"]
+docs = ["ipykernel", "ipython", "myst-parser", "nbsphinx (>=0.2.12)", "pydata-sphinx-theme", "sphinx (==5.0.2)", "sphinxcontrib-spelling"]
+qtpdf = ["nbconvert[qtpng]"]
+qtpng = ["pyqtwebengine (>=5.15)"]
+serve = ["tornado (>=6.1)"]
+test = ["flaky", "ipykernel", "ipywidgets (>=7)", "pytest", "pytest-dependency"]
+webpdf = ["playwright"]
+
+[[package]]
+name = "nbformat"
+version = "5.9.2"
+description = "The Jupyter Notebook format"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.dependencies]
+fastjsonschema = "*"
+jsonschema = ">=2.6"
+jupyter-core = "*"
+traitlets = ">=5.1"
+
+[package.extras]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
+test = ["pep440", "pre-commit", "pytest", "testpath"]
+
+[[package]]
+name = "nest-asyncio"
+version = "1.5.8"
+description = "Patch asyncio to allow nested event loops"
+optional = false
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 version = "11.10.3.66"
 description = "CUBLAS native runtime libraries"
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 optional = false
 python-versions = ">=3"
 files = [
@@ -2148,7 +3348,6 @@ wheel = "*"
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 version = "11.7.101"
 description = "CUDA profiling tools runtime libs."
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 python-versions = ">=3"
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@@ -2164,7 +3363,6 @@ wheel = "*"
 name = "nvidia-cuda-nvrtc-cu11"
 version = "11.7.99"
 description = "NVRTC native runtime libraries"
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 python-versions = ">=3"
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@@ -2181,7 +3379,6 @@ wheel = "*"
 name = "nvidia-cuda-runtime-cu11"
 version = "11.7.99"
 description = "CUDA Runtime native Libraries"
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 optional = false
 python-versions = ">=3"
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@@ -2197,7 +3394,6 @@ wheel = "*"
 name = "nvidia-cudnn-cu11"
 version = "8.5.0.96"
 description = "cuDNN runtime libraries"
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 optional = false
 python-versions = ">=3"
 files = [
@@ -2213,7 +3409,6 @@ wheel = "*"
 name = "nvidia-cufft-cu11"
 version = "10.9.0.58"
 description = "CUFFT native runtime libraries"
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 python-versions = ">=3"
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@@ -2225,7 +3420,6 @@ files = [
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 description = "CURAND native runtime libraries"
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 python-versions = ">=3"
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@@ -2241,7 +3435,6 @@ wheel = "*"
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 description = "CUDA solver native runtime libraries"
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 python-versions = ">=3"
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@@ -2258,7 +3451,6 @@ wheel = "*"
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@@ -2274,7 +3466,6 @@ wheel = "*"
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 description = "NVIDIA Collective Communication Library (NCCL) Runtime"
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 python-versions = ">=3"
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@@ -2285,7 +3476,6 @@ files = [
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 version = "11.7.91"
 description = "NVIDIA Tools Extension"
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 optional = false
 python-versions = ">=3"
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@@ -2301,7 +3491,6 @@ wheel = "*"
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 description = "A generic, spec-compliant, thorough implementation of the OAuth request-signing logic"
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 python-versions = ">=3.6"
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@@ -2318,7 +3507,6 @@ signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
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 version = "2.3.0"
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 python-versions = ">=3.6"
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@@ -2387,86 +3584,159 @@ pytz = ">=2020.1"
 [package.extras]
 test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"]
 
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+
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 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
 
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@@ -2475,14 +3745,13 @@ testing = ["pytest", "pytest-benchmark"]
 
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+
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+
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 [package.dependencies]
@@ -2558,7 +3928,6 @@ numpy = ">=1.16.6"
 name = "pyasn1"
 version = "0.5.0"
 description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs (X.208)"
-category = "main"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
@@ -2570,7 +3939,6 @@ files = [
 name = "pyasn1-modules"
 version = "0.3.0"
 description = "A collection of ASN.1-based protocols modules"
-category = "main"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
@@ -2585,7 +3953,6 @@ pyasn1 = ">=0.4.6,<0.6.0"
 name = "pycparser"
 version = "2.21"
 description = "C parser in Python"
-category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
@@ -2595,14 +3962,13 @@ files = [
 
 [[package]]
 name = "pygments"
-version = "2.15.1"
+version = "2.16.1"
 description = "Pygments is a syntax highlighting package written in Python."
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.extras]
@@ -2612,7 +3978,6 @@ plugins = ["importlib-metadata"]
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 version = "2.8.0"
 description = "JSON Web Token implementation in Python"
-category = "main"
 optional = false
 python-versions = ">=3.7"
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@@ -2629,16 +3994,40 @@ dev = ["coverage[toml] (==5.0.4)", "cryptography (>=3.4.0)", "pre-commit", "pyte
 docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"]
 tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"]
 
+[[package]]
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+python-versions = ">=3.7.2"
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+isort = ">=4.2.5,<6"
+mccabe = ">=0.6,<0.8"
+platformdirs = ">=2.2.0"
+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
+tomlkit = ">=0.10.1"
+
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+spelling = ["pyenchant (>=3.2,<4.0)"]
+testutils = ["gitpython (>3)"]
+
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-version = "3.0.9"
+version = "3.1.1"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
-category = "main"
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+    {file = "pyparsing-3.1.1-py3-none-any.whl", hash = "sha256:32c7c0b711493c72ff18a981d24f28aaf9c1fb7ed5e9667c9e84e3db623bdbfb"},
+    {file = "pyparsing-3.1.1.tar.gz", hash = "sha256:ede28a1a32462f5a9705e07aea48001a08f7cf81a021585011deba701581a0db"},
 ]
 
 [package.extras]
@@ -2646,14 +4035,13 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pytest"
-version = "7.4.0"
+version = "7.4.2"
 description = "pytest: simple powerful testing with Python"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "pytest-7.4.0-py3-none-any.whl", hash = "sha256:78bf16451a2eb8c7a2ea98e32dc119fd2aa758f1d5d66dbf0a59d69a3969df32"},
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+    {file = "pytest-7.4.2-py3-none-any.whl", hash = "sha256:1d881c6124e08ff0a1bb75ba3ec0bfd8b5354a01c194ddd5a0a870a48d99b002"},
+    {file = "pytest-7.4.2.tar.gz", hash = "sha256:a766259cfab564a2ad52cb1aae1b881a75c3eb7e34ca3779697c23ed47c47069"},
 ]
 
 [package.dependencies]
@@ -2671,7 +4059,6 @@ testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "no
 name = "pytest-cov"
 version = "3.0.0"
 description = "Pytest plugin for measuring coverage."
-category = "dev"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -2690,7 +4077,6 @@ testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtuale
 name = "python-dateutil"
 version = "2.8.2"
 description = "Extensions to the standard Python datetime module"
-category = "main"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
 files = [
@@ -2701,65 +4087,69 @@ files = [
 [package.dependencies]
 six = ">=1.5"
 
+[[package]]
+name = "python-json-logger"
+version = "2.0.7"
+description = "A python library adding a json log formatter"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "python-json-logger-2.0.7.tar.gz", hash = "sha256:23e7ec02d34237c5aa1e29a070193a4ea87583bb4e7f8fd06d3de8264c4b2e1c"},
+    {file = "python_json_logger-2.0.7-py3-none-any.whl", hash = "sha256:f380b826a991ebbe3de4d897aeec42760035ac760345e57b812938dc8b35e2bd"},
+]
+
 [[package]]
 name = "pytorch-lightning"
-version = "1.9.5"
+version = "2.1.0"
 description = "PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate."
-category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+    {file = "pytorch-lightning-2.1.0.tar.gz", hash = "sha256:bf9e26b293e1ccda5f8e146fe58716eecfd77e9639ef3ec2210b0dcba51c4593"},
+    {file = "pytorch_lightning-2.1.0-py3-none-any.whl", hash = "sha256:2802d683ef513235dfc211f6bc45d7086e8982feaac1625aafd2886c5e5b96f8"},
 ]
 
 [package.dependencies]
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 hydra-core = {version = ">=1.0.5", optional = true, markers = "extra == \"extra\""}
 jsonargparse = {version = ">=4.18.0", extras = ["signatures"], optional = true, markers = "extra == \"extra\""}
-lightning-utilities = ">=0.6.0.post0"
+lightning-utilities = ">=0.8.0"
 matplotlib = {version = ">3.1", optional = true, markers = "extra == \"extra\""}
 numpy = ">=1.17.2"
 omegaconf = {version = ">=2.0.5", optional = true, markers = "extra == \"extra\""}
-packaging = ">=17.1"
+packaging = ">=20.0"
 PyYAML = ">=5.4"
-rich = {version = ">=10.14.0,<10.15.0.a || >10.15.0.a", optional = true, markers = "extra == \"extra\""}
+rich = {version = ">=12.3.0", optional = true, markers = "extra == \"extra\""}
 tensorboardX = {version = ">=2.2", optional = true, markers = "extra == \"extra\""}
-torch = ">=1.10.0"
+torch = ">=1.12.0"
 torchmetrics = ">=0.7.0"
 tqdm = ">=4.57.0"
 typing-extensions = ">=4.0.0"
 
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 [[package]]
 name = "pytz"
-version = "2023.3"
+version = "2023.3.post1"
 description = "World timezone definitions, modern and historical"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [[package]]
 name = "pywin32"
 version = "306"
 description = "Python for Window Extensions"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -2779,11 +4169,25 @@ files = [
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 ]
 
+[[package]]
+name = "pywinpty"
+version = "2.0.12"
+description = "Pseudo terminal support for Windows from Python."
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
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 name = "pyyaml"
 version = "6.0.1"
 description = "YAML parser and emitter for Python"
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -2792,6 +4196,7 @@ files = [
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@@ -2799,8 +4204,15 @@ files = [
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@@ -2817,6 +4229,7 @@ files = [
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 [package.dependencies]
 joblib = ">=1.1.1"
-numpy = ">=1.17.3"
+numpy = ">=1.17.3,<2.0"
 scipy = ">=1.5.0"
 threadpoolctl = ">=2.0.0"
 
@@ -2964,31 +4673,36 @@ tests = ["black (>=23.3.0)", "matplotlib (>=3.1.3)", "mypy (>=1.3)", "numpydoc (
 
 [[package]]
 name = "scipy"
-version = "1.11.1"
+version = "1.11.3"
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+    {file = "scipy-1.11.3.tar.gz", hash = "sha256:bba4d955f54edd61899776bad459bf7326e14b9fa1c552181f0479cc60a568cd"},
 ]
 
 [package.dependencies]
@@ -2999,28 +4713,64 @@ dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyl
 doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
 test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
+[[package]]
+name = "send2trash"
+version = "1.8.2"
+description = "Send file to trash natively under Mac OS X, Windows and Linux"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
+files = [
+    {file = "Send2Trash-1.8.2-py3-none-any.whl", hash = "sha256:a384719d99c07ce1eefd6905d2decb6f8b7ed054025bb0e618919f945de4f679"},
+    {file = "Send2Trash-1.8.2.tar.gz", hash = "sha256:c132d59fa44b9ca2b1699af5c86f57ce9f4c5eb56629d5d55fbb7a35f84e2312"},
+]
+
+[package.extras]
+nativelib = ["pyobjc-framework-Cocoa", "pywin32"]
+objc = ["pyobjc-framework-Cocoa"]
+win32 = ["pywin32"]
+
 [[package]]
 name = "setuptools"
-version = "68.0.0"
+version = "68.2.2"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
-category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
+files = [
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+    {file = "setuptools-68.2.2.tar.gz", hash = "sha256:4ac1475276d2f1c48684874089fefcd83bd7162ddaafb81fac866ba0db282a87"},
+]
+
+[package.extras]
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+testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.1)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
+
+[[package]]
+name = "setuptools-scm"
+version = "8.0.4"
+description = "the blessed package to manage your versions by scm tags"
+optional = false
+python-versions = ">=3.8"
 files = [
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+    {file = "setuptools-scm-8.0.4.tar.gz", hash = "sha256:b5f43ff6800669595193fd09891564ee9d1d7dcb196cab4b2506d53a2e1c95c7"},
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 ]
 
+[package.dependencies]
+packaging = ">=20"
+setuptools = "*"
+tomli = {version = ">=1", markers = "python_version < \"3.11\""}
+typing-extensions = "*"
+
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
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-testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
+docs = ["entangled-cli[rich]", "mkdocs", "mkdocs-entangled-plugin", "mkdocs-material", "mkdocstrings[python]", "pygments"]
+rich = ["rich"]
+test = ["build", "pytest", "rich", "wheel"]
 
 [[package]]
 name = "six"
 version = "1.16.0"
 description = "Python 2 and 3 compatibility utilities"
-category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
 files = [
@@ -3030,65 +4780,93 @@ files = [
 
 [[package]]
 name = "smmap"
-version = "5.0.0"
+version = "5.0.1"
 description = "A pure Python implementation of a sliding window memory map manager"
-category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
+files = [
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+    {file = "smmap-5.0.1.tar.gz", hash = "sha256:dceeb6c0028fdb6734471eb07c0cd2aae706ccaecab45965ee83f11c8d3b1f62"},
+]
+
+[[package]]
+name = "sniffio"
+version = "1.3.0"
+description = "Sniff out which async library your code is running under"
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "sniffio-1.3.0.tar.gz", hash = "sha256:e60305c5e5d314f5389259b7f22aaa33d8f7dee49763119234af3755c55b9101"},
+]
+
+[[package]]
+name = "soupsieve"
+version = "2.5"
+description = "A modern CSS selector implementation for Beautiful Soup."
+optional = false
+python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
 name = "sqlalchemy"
-version = "2.0.19"
+version = "2.0.22"
 description = "Database Abstraction Library"
-category = "main"
-optional = false
-python-versions = ">=3.7"
-files = [
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 ]
 
 [package.dependencies]
@@ -3096,7 +4874,7 @@ greenlet = {version = "!=0.4.17", markers = "platform_machine == \"aarch64\" or
 typing-extensions = ">=4.2.0"
 
 [package.extras]
-aiomysql = ["aiomysql", "greenlet (!=0.4.17)"]
+aiomysql = ["aiomysql (>=0.2.0)", "greenlet (!=0.4.17)"]
 aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing-extensions (!=3.10.0.1)"]
 asyncio = ["greenlet (!=0.4.17)"]
 asyncmy = ["asyncmy (>=0.2.3,!=0.2.4,!=0.2.6)", "greenlet (!=0.4.17)"]
@@ -3123,7 +4901,6 @@ sqlcipher = ["sqlcipher3-binary"]
 name = "sqlparse"
 version = "0.4.4"
 description = "A non-validating SQL parser."
-category = "main"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -3136,11 +4913,29 @@ dev = ["build", "flake8"]
 doc = ["sphinx"]
 test = ["pytest", "pytest-cov"]
 
+[[package]]
+name = "stack-data"
+version = "0.6.3"
+description = "Extract data from python stack frames and tracebacks for informative displays"
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[package.dependencies]
+asttokens = ">=2.1.0"
+executing = ">=1.2.0"
+pure-eval = "*"
+
+[package.extras]
+tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
+
 [[package]]
 name = "sympy"
 version = "1.12"
 description = "Computer algebra system (CAS) in Python"
-category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -3155,7 +4950,6 @@ mpmath = ">=0.19"
 name = "tabulate"
 version = "0.9.0"
 description = "Pretty-print tabular data"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3168,13 +4962,12 @@ widechars = ["wcwidth"]
 
 [[package]]
 name = "tensorboard"
-version = "2.13.0"
+version = "2.14.1"
 description = "TensorBoard lets you watch Tensors Flow"
-category = "main"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "tensorboard-2.13.0-py3-none-any.whl", hash = "sha256:ab69961ebddbddc83f5fa2ff9233572bdad5b883778c35e4fe94bf1798bd8481"},
+    {file = "tensorboard-2.14.1-py3-none-any.whl", hash = "sha256:3db108fb58f023b6439880e177743c5f1e703e9eeb5fb7d597871f949f85fd58"},
 ]
 
 [package.dependencies]
@@ -3187,45 +4980,42 @@ numpy = ">=1.12.0"
 protobuf = ">=3.19.6"
 requests = ">=2.21.0,<3"
 setuptools = ">=41.0.0"
+six = ">1.9"
 tensorboard-data-server = ">=0.7.0,<0.8.0"
 werkzeug = ">=1.0.1"
-wheel = ">=0.26"
 
 [[package]]
 name = "tensorboard-data-server"
-version = "0.7.1"
+version = "0.7.2"
 description = "Fast data loading for TensorBoard"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "tensorboard_data_server-0.7.1-py3-none-any.whl", hash = "sha256:9938bd39f5041797b33921066fba0eab03a0dd10d1887a05e62ae58841ad4c3f"},
-    {file = "tensorboard_data_server-0.7.1-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:be8d016a1aa394e6198280d4a3dc37898f56467310c5f5e617cac10a783e055a"},
-    {file = "tensorboard_data_server-0.7.1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:255c02b7f5b03dd5c0a88c928e563441ff39e1d4b4a234cdbe09f016e53d9594"},
+    {file = "tensorboard_data_server-0.7.2-py3-none-any.whl", hash = "sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb"},
+    {file = "tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60"},
+    {file = "tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:ef687163c24185ae9754ed5650eb5bc4d84ff257aabdc33f0cc6f74d8ba54530"},
 ]
 
 [[package]]
 name = "tensorboardx"
-version = "2.6.1"
+version = "2.6.2.2"
 description = "TensorBoardX lets you watch Tensors Flow without Tensorflow"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
-    {file = "tensorboardX-2.6.1-py2.py3-none-any.whl", hash = "sha256:4960feb79b1b84fd2b020885b09fd70962caec277d4bc194f338a6c203cd78ca"},
-    {file = "tensorboardX-2.6.1.tar.gz", hash = "sha256:02e2b84d7dc102edb7a052c77041db30fd6ba9b990635178919b8e9cfa157e96"},
+    {file = "tensorboardX-2.6.2.2-py2.py3-none-any.whl", hash = "sha256:160025acbf759ede23fd3526ae9d9bfbfd8b68eb16c38a010ebe326dc6395db8"},
+    {file = "tensorboardX-2.6.2.2.tar.gz", hash = "sha256:c6476d7cd0d529b0b72f4acadb1269f9ed8b22f441e87a84f2a3b940bb87b666"},
 ]
 
 [package.dependencies]
 numpy = "*"
 packaging = "*"
-protobuf = ">=4.22.3"
+protobuf = ">=3.20"
 
 [[package]]
 name = "tensordict"
 version = "0.1.2"
 description = ""
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -3242,11 +5032,30 @@ checkpointing = ["torchsnapshot-nightly"]
 h5 = ["h5py (>=3.8)"]
 tests = ["pytest", "pytest-benchmark", "pytest-instafail", "pytest-rerunfailures", "pyyaml"]
 
+[[package]]
+name = "terminado"
+version = "0.17.1"
+description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "terminado-0.17.1-py3-none-any.whl", hash = "sha256:8650d44334eba354dd591129ca3124a6ba42c3d5b70df5051b6921d506fdaeae"},
+    {file = "terminado-0.17.1.tar.gz", hash = "sha256:6ccbbcd3a4f8a25a5ec04991f39a0b8db52dfcd487ea0e578d977e6752380333"},
+]
+
+[package.dependencies]
+ptyprocess = {version = "*", markers = "os_name != \"nt\""}
+pywinpty = {version = ">=1.1.0", markers = "os_name == \"nt\""}
+tornado = ">=6.1.0"
+
+[package.extras]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
+test = ["pre-commit", "pytest (>=7.0)", "pytest-timeout"]
+
 [[package]]
 name = "threadpoolctl"
 version = "3.2.0"
 description = "threadpoolctl"
-category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -3254,11 +5063,50 @@ files = [
     {file = "threadpoolctl-3.2.0.tar.gz", hash = "sha256:c96a0ba3bdddeaca37dc4cc7344aafad41cdb8c313f74fdfe387a867bba93355"},
 ]
 
+[[package]]
+name = "tinycss2"
+version = "1.2.1"
+description = "A tiny CSS parser"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "tinycss2-1.2.1-py3-none-any.whl", hash = "sha256:2b80a96d41e7c3914b8cda8bc7f705a4d9c49275616e886103dd839dfc847847"},
+    {file = "tinycss2-1.2.1.tar.gz", hash = "sha256:8cff3a8f066c2ec677c06dbc7b45619804a6938478d9d73c284b29d14ecb0627"},
+]
+
+[package.dependencies]
+webencodings = ">=0.4"
+
+[package.extras]
+doc = ["sphinx", "sphinx_rtd_theme"]
+test = ["flake8", "isort", "pytest"]
+
+[[package]]
+name = "tokenize-rt"
+version = "5.2.0"
+description = "A wrapper around the stdlib `tokenize` which roundtrips."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "tokenize_rt-5.2.0-py2.py3-none-any.whl", hash = "sha256:b79d41a65cfec71285433511b50271b05da3584a1da144a0752e9c621a285289"},
+    {file = "tokenize_rt-5.2.0.tar.gz", hash = "sha256:9fe80f8a5c1edad2d3ede0f37481cc0cc1538a2f442c9c2f9e4feacd2792d054"},
+]
+
+[[package]]
+name = "toml"
+version = "0.10.2"
+description = "Python Library for Tom's Obvious, Minimal Language"
+optional = false
+python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
+files = [
+    {file = "toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"},
+    {file = "toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"},
+]
+
 [[package]]
 name = "tomli"
 version = "2.0.1"
 description = "A lil' TOML parser"
-category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3266,11 +5114,21 @@ files = [
     {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
 ]
 
+[[package]]
+name = "tomlkit"
+version = "0.12.1"
+description = "Style preserving TOML library"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "tomlkit-0.12.1-py3-none-any.whl", hash = "sha256:712cbd236609acc6a3e2e97253dfc52d4c2082982a88f61b640ecf0817eab899"},
+    {file = "tomlkit-0.12.1.tar.gz", hash = "sha256:38e1ff8edb991273ec9f6181244a6a391ac30e9f5098e7535640ea6be97a7c86"},
+]
+
 [[package]]
 name = "torch"
 version = "2.0.0"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
-category = "main"
 optional = false
 python-versions = ">=3.8.0"
 files = [
@@ -3324,59 +5182,91 @@ opt-einsum = ["opt-einsum (>=3.3)"]
 
 [[package]]
 name = "torchmetrics"
-version = "1.0.1"
+version = "1.2.0"
 description = "PyTorch native Metrics"
-category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "torchmetrics-1.0.1-py3-none-any.whl", hash = "sha256:5278ebdf4ecc168d88d87d3f02045ceee6a4a4ae24d8bf09d616ab67441dde0a"},
+    {file = "torchmetrics-1.2.0-py3-none-any.whl", hash = "sha256:da2cb18822b285786d082c40efb9e1d861aac425f58230234fe6ce233cf002f8"},
+    {file = "torchmetrics-1.2.0.tar.gz", hash = "sha256:7eb28340bde45e13187a9ad54a4a7010a50417815d8181a5df6131f116ffe1b7"},
 ]
 
 [package.dependencies]
-lightning-utilities = ">=0.7.0"
+lightning-utilities = ">=0.8.0"
 numpy = ">1.20.0"
-packaging = "*"
 torch = ">=1.8.1"
 
 [package.extras]
-all = ["SciencePlots (>=2.0.0)", "lpips (<=0.1.4)", "matplotlib (>=3.2.0)", "mypy (==1.4.1)", "nltk (>=3.6)", "pycocotools (>2.0.0)", "pystoi (>=0.3.0)", "regex (>=2021.9.24)", "scipy (>1.0.0)", "torch-fidelity (<=0.3.0)", "torchaudio (>=0.10.0)", "torchvision (>=0.8)", "tqdm (>=4.41.0)", "transformers (>=4.10.0)", "types-PyYAML", "types-emoji", "types-protobuf", "types-requests", "types-setuptools", "types-six", "types-tabulate"]
+all = ["SciencePlots (>=2.0.0)", "lpips (<=0.1.4)", "matplotlib (>=3.2.0)", "mypy (==1.5.1)", "nltk (>=3.6)", "piq (<=0.8.0)", "pycocotools (>2.0.0)", "pystoi (>=0.3.0)", "regex (>=2021.9.24)", "scipy (>1.0.0)", "torch-fidelity (<=0.4.0)", "torchaudio (>=0.10.0)", "torchvision (>=0.8)", "tqdm (>=4.41.0)", "transformers (>4.4.0)", "transformers (>=4.10.0)", "types-PyYAML", "types-emoji", "types-protobuf", "types-requests", "types-setuptools", "types-six", "types-tabulate"]
 audio = ["pystoi (>=0.3.0)", "torchaudio (>=0.10.0)"]
 detection = ["pycocotools (>2.0.0)", "torchvision (>=0.8)"]
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-typing = ["mypy (==1.4.1)", "types-PyYAML", "types-emoji", "types-protobuf", "types-requests", "types-setuptools", "types-six", "types-tabulate"]
+dev = ["SciencePlots (>=2.0.0)", "bert-score (==0.3.13)", "cloudpickle (>1.3)", "coverage (==7.3.1)", "dython (<=0.7.4)", "fairlearn", "fast-bss-eval (>=0.1.0)", "faster-coco-eval (>=1.3.3)", "fire (<=0.5.0)", "huggingface-hub (<0.18)", "jiwer (>=2.3.0)", "kornia (>=0.6.7)", "lpips (<=0.1.4)", "matplotlib (>=3.2.0)", "mir-eval (>=0.6)", "mypy (==1.5.1)", "netcal (>1.0.0)", "nltk (>=3.6)", "numpy (<1.25.0)", "pandas (>1.0.0)", "pandas (>=1.4.0)", "phmdoctest (==1.4.0)", "piq (<=0.8.0)", "psutil (<=5.9.5)", "pycocotools (>2.0.0)", "pystoi (>=0.3.0)", "pytest (==7.4.2)", "pytest-cov (==4.1.0)", "pytest-doctestplus (==1.0.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "pytorch-msssim (==1.0.0)", "regex (>=2021.9.24)", "requests (<=2.31.0)", "rouge-score (>0.1.0)", "sacrebleu (>=2.0.0)", "scikit-image (>=0.19.0)", "scikit-learn (>=1.1.1)", "scipy (>1.0.0)", "sewar (>=0.4.4)", "statsmodels (>0.13.5)", "torch-complex (<=0.4.3)", "torch-fidelity (<=0.4.0)", "torchaudio (>=0.10.0)", "torchvision (>=0.8)", "tqdm (>=4.41.0)", "transformers (>4.4.0)", "transformers (>=4.10.0)", "types-PyYAML", "types-emoji", "types-protobuf", "types-requests", "types-setuptools", "types-six", "types-tabulate"]
+image = ["lpips (<=0.1.4)", "scipy (>1.0.0)", "torch-fidelity (<=0.4.0)", "torchvision (>=0.8)"]
+multimodal = ["piq (<=0.8.0)", "transformers (>=4.10.0)"]
+test = ["bert-score (==0.3.13)", "cloudpickle (>1.3)", "coverage (==7.3.1)", "dython (<=0.7.4)", "fairlearn", "fast-bss-eval (>=0.1.0)", "faster-coco-eval (>=1.3.3)", "fire (<=0.5.0)", "huggingface-hub (<0.18)", "jiwer (>=2.3.0)", "kornia (>=0.6.7)", "mir-eval (>=0.6)", "netcal (>1.0.0)", "numpy (<1.25.0)", "pandas (>1.0.0)", "pandas (>=1.4.0)", "phmdoctest (==1.4.0)", "psutil (<=5.9.5)", "pytest (==7.4.2)", "pytest-cov (==4.1.0)", "pytest-doctestplus (==1.0.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "pytorch-msssim (==1.0.0)", "requests (<=2.31.0)", "rouge-score (>0.1.0)", "sacrebleu (>=2.0.0)", "scikit-image (>=0.19.0)", "scikit-learn (>=1.1.1)", "scipy (>1.0.0)", "sewar (>=0.4.4)", "statsmodels (>0.13.5)", "torch-complex (<=0.4.3)"]
+text = ["nltk (>=3.6)", "regex (>=2021.9.24)", "tqdm (>=4.41.0)", "transformers (>4.4.0)"]
+typing = ["mypy (==1.5.1)", "types-PyYAML", "types-emoji", "types-protobuf", "types-requests", "types-setuptools", "types-six", "types-tabulate"]
 visual = ["SciencePlots (>=2.0.0)", "matplotlib (>=3.2.0)"]
 
+[[package]]
+name = "tornado"
+version = "6.3.3"
+description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
+optional = false
+python-versions = ">= 3.8"
+files = [
+    {file = "tornado-6.3.3-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:502fba735c84450974fec147340016ad928d29f1e91f49be168c0a4c18181e1d"},
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+    {file = "tornado-6.3.3-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7ac51f42808cca9b3613f51ffe2a965c8525cb1b00b7b2d56828b8045354f76a"},
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+    {file = "tornado-6.3.3-cp38-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:ceb917a50cd35882b57600709dd5421a418c29ddc852da8bcdab1f0db33406b0"},
+    {file = "tornado-6.3.3-cp38-abi3-musllinux_1_1_i686.whl", hash = "sha256:7d01abc57ea0dbb51ddfed477dfe22719d376119844e33c661d873bf9c0e4a16"},
+    {file = "tornado-6.3.3-cp38-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:9dc4444c0defcd3929d5c1eb5706cbe1b116e762ff3e0deca8b715d14bf6ec17"},
+    {file = "tornado-6.3.3-cp38-abi3-win32.whl", hash = "sha256:65ceca9500383fbdf33a98c0087cb975b2ef3bfb874cb35b8de8740cf7f41bd3"},
+    {file = "tornado-6.3.3-cp38-abi3-win_amd64.whl", hash = "sha256:22d3c2fa10b5793da13c807e6fc38ff49a4f6e1e3868b0a6f4164768bb8e20f5"},
+    {file = "tornado-6.3.3.tar.gz", hash = "sha256:e7d8db41c0181c80d76c982aacc442c0783a2c54d6400fe028954201a2e032fe"},
+]
+
 [[package]]
 name = "tqdm"
-version = "4.65.0"
+version = "4.66.1"
 description = "Fast, Extensible Progress Meter"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "tqdm-4.65.0-py3-none-any.whl", hash = "sha256:c4f53a17fe37e132815abceec022631be8ffe1b9381c2e6e30aa70edc99e9671"},
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+    {file = "tqdm-4.66.1-py3-none-any.whl", hash = "sha256:d302b3c5b53d47bce91fea46679d9c3c6508cf6332229aa1e7d8653723793386"},
+    {file = "tqdm-4.66.1.tar.gz", hash = "sha256:d88e651f9db8d8551a62556d3cff9e3034274ca5d66e93197cf2490e2dcb69c7"},
 ]
 
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [package.extras]
-dev = ["py-make (>=0.1.0)", "twine", "wheel"]
+dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
 notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
+[[package]]
+name = "traitlets"
+version = "5.11.2"
+description = "Traitlets Python configuration system"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "traitlets-5.11.2.tar.gz", hash = "sha256:7564b5bf8d38c40fa45498072bf4dc5e8346eb087bbf1e2ae2d8774f6a0f078e"},
+]
+
+[package.extras]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
+test = ["argcomplete (>=3.0.3)", "mypy (>=1.5.1)", "pre-commit", "pytest (>=7.0,<7.5)", "pytest-mock", "pytest-mypy-testing"]
+
 [[package]]
 name = "triton"
 version = "2.0.0"
 description = "A language and compiler for custom Deep Learning operations"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -3410,28 +5300,37 @@ torch = "*"
 tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"]
 tutorials = ["matplotlib", "pandas", "tabulate"]
 
+[[package]]
+name = "types-python-dateutil"
+version = "2.8.19.14"
+description = "Typing stubs for python-dateutil"
+optional = false
+python-versions = "*"
+files = [
+    {file = "types-python-dateutil-2.8.19.14.tar.gz", hash = "sha256:1f4f10ac98bb8b16ade9dbee3518d9ace017821d94b057a425b069f834737f4b"},
+    {file = "types_python_dateutil-2.8.19.14-py3-none-any.whl", hash = "sha256:f977b8de27787639986b4e28963263fd0e5158942b3ecef91b9335c130cb1ce9"},
+]
+
 [[package]]
 name = "types-pyyaml"
-version = "6.0.12.11"
+version = "6.0.12.12"
 description = "Typing stubs for PyYAML"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
-    {file = "types-PyYAML-6.0.12.11.tar.gz", hash = "sha256:7d340b19ca28cddfdba438ee638cd4084bde213e501a3978738543e27094775b"},
-    {file = "types_PyYAML-6.0.12.11-py3-none-any.whl", hash = "sha256:a461508f3096d1d5810ec5ab95d7eeecb651f3a15b71959999988942063bf01d"},
+    {file = "types-PyYAML-6.0.12.12.tar.gz", hash = "sha256:334373d392fde0fdf95af5c3f1661885fa10c52167b14593eb856289e1855062"},
+    {file = "types_PyYAML-6.0.12.12-py3-none-any.whl", hash = "sha256:c05bc6c158facb0676674b7f11fe3960db4f389718e19e62bd2b84d6205cfd24"},
 ]
 
 [[package]]
 name = "typeshed-client"
-version = "2.3.0"
+version = "2.4.0"
 description = "A library for accessing stubs in typeshed."
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "typeshed_client-2.3.0-py3-none-any.whl", hash = "sha256:27976287e0682be3005233f08f6f3cedf0c496413bcb213378f46e08798988c4"},
-    {file = "typeshed_client-2.3.0.tar.gz", hash = "sha256:e2734755f93a0b6c1da8daad11670c75a36583d889d4d7b110eb882765b8bced"},
+    {file = "typeshed_client-2.4.0-py3-none-any.whl", hash = "sha256:5358cab27cf2d7b1cd1e77dd92a3ac3cd9cd31df9eb2e958bd280a38160a3219"},
+    {file = "typeshed_client-2.4.0.tar.gz", hash = "sha256:b4e4e3e40dca91ce1a667d2eb0eb350a0a2c0d80e18a232d18857aa61bed3492"},
 ]
 
 [package.dependencies]
@@ -3439,21 +5338,19 @@ importlib-resources = ">=1.4.0"
 
 [[package]]
 name = "typing-extensions"
-version = "4.7.1"
-description = "Backported and Experimental Type Hints for Python 3.7+"
-category = "main"
+version = "4.8.0"
+description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.7.1-py3-none-any.whl", hash = "sha256:440d5dd3af93b060174bf433bccd69b0babc3b15b1a8dca43789fd7f61514b36"},
-    {file = "typing_extensions-4.7.1.tar.gz", hash = "sha256:b75ddc264f0ba5615db7ba217daeb99701ad295353c45f9e95963337ceeeffb2"},
+    {file = "typing_extensions-4.8.0-py3-none-any.whl", hash = "sha256:8f92fc8806f9a6b641eaa5318da32b44d401efaac0f6678c9bc448ba3605faa0"},
+    {file = "typing_extensions-4.8.0.tar.gz", hash = "sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef"},
 ]
 
 [[package]]
 name = "typing-inspect"
 version = "0.9.0"
 description = "Runtime inspection utilities for typing module."
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -3465,28 +5362,61 @@ files = [
 mypy-extensions = ">=0.3.0"
 typing-extensions = ">=3.7.4"
 
+[[package]]
+name = "uri-template"
+version = "1.3.0"
+description = "RFC 6570 URI Template Processor"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7"},
+    {file = "uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363"},
+]
+
+[package.extras]
+dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake8-commas", "flake8-comprehensions", "flake8-continuation", "flake8-datetimez", "flake8-docstrings", "flake8-import-order", "flake8-literal", "flake8-modern-annotations", "flake8-noqa", "flake8-pyproject", "flake8-requirements", "flake8-typechecking-import", "flake8-use-fstring", "mypy", "pep8-naming", "types-PyYAML"]
+
 [[package]]
 name = "urllib3"
-version = "1.26.16"
+version = "2.0.7"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
-category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
+python-versions = ">=3.7"
+files = [
+    {file = "urllib3-2.0.7-py3-none-any.whl", hash = "sha256:fdb6d215c776278489906c2f8916e6e7d4f5a9b602ccbcfdf7f016fc8da0596e"},
+    {file = "urllib3-2.0.7.tar.gz", hash = "sha256:c97dfde1f7bd43a71c8d2a58e369e9b2bf692d1334ea9f9cae55add7d0dd0f84"},
+]
+
+[package.extras]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.1.0)", "urllib3-secure-extra"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
+
+[[package]]
+name = "virtualenv"
+version = "20.24.6"
+description = "Virtual Python Environment builder"
+optional = false
+python-versions = ">=3.7"
 files = [
-    {file = "urllib3-1.26.16-py2.py3-none-any.whl", hash = "sha256:8d36afa7616d8ab714608411b4a3b13e58f463aee519024578e062e141dce20f"},
-    {file = "urllib3-1.26.16.tar.gz", hash = "sha256:8f135f6502756bde6b2a9b28989df5fbe87c9970cecaa69041edcce7f0589b14"},
+    {file = "virtualenv-20.24.6-py3-none-any.whl", hash = "sha256:520d056652454c5098a00c0f073611ccbea4c79089331f60bf9d7ba247bb7381"},
+    {file = "virtualenv-20.24.6.tar.gz", hash = "sha256:02ece4f56fbf939dbbc33c0715159951d6bf14aaf5457b092e4548e1382455af"},
 ]
 
+[package.dependencies]
+distlib = ">=0.3.7,<1"
+filelock = ">=3.12.2,<4"
+platformdirs = ">=3.9.1,<4"
+
 [package.extras]
-brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)", "brotlipy (>=0.6.0)"]
-secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress", "pyOpenSSL (>=0.14)", "urllib3-secure-extra"]
-socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
+docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
+test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"]
 
 [[package]]
 name = "waitress"
 version = "2.1.2"
 description = "Waitress WSGI server"
-category = "main"
 optional = false
 python-versions = ">=3.7.0"
 files = [
@@ -3498,33 +5428,68 @@ files = [
 docs = ["Sphinx (>=1.8.1)", "docutils", "pylons-sphinx-themes (>=1.0.9)"]
 testing = ["coverage (>=5.0)", "pytest", "pytest-cover"]
 
+[[package]]
+name = "wcwidth"
+version = "0.2.8"
+description = "Measures the displayed width of unicode strings in a terminal"
+optional = false
+python-versions = "*"
+files = [
+    {file = "wcwidth-0.2.8-py2.py3-none-any.whl", hash = "sha256:77f719e01648ed600dfa5402c347481c0992263b81a027344f3e1ba25493a704"},
+    {file = "wcwidth-0.2.8.tar.gz", hash = "sha256:8705c569999ffbb4f6a87c6d1b80f324bd6db952f5eb0b95bc07517f4c1813d4"},
+]
+
+[[package]]
+name = "webcolors"
+version = "1.13"
+description = "A library for working with the color formats defined by HTML and CSS."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "webcolors-1.13-py3-none-any.whl", hash = "sha256:29bc7e8752c0a1bd4a1f03c14d6e6a72e93d82193738fa860cbff59d0fcc11bf"},
+    {file = "webcolors-1.13.tar.gz", hash = "sha256:c225b674c83fa923be93d235330ce0300373d02885cef23238813b0d5668304a"},
+]
+
+[package.extras]
+docs = ["furo", "sphinx", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-notfound-page", "sphinxext-opengraph"]
+tests = ["pytest", "pytest-cov"]
+
+[[package]]
+name = "webencodings"
+version = "0.5.1"
+description = "Character encoding aliases for legacy web content"
+optional = false
+python-versions = "*"
+files = [
+    {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"},
+    {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
+]
+
 [[package]]
 name = "websocket-client"
-version = "1.6.1"
+version = "1.6.4"
 description = "WebSocket client for Python with low level API options"
-category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "websocket-client-1.6.1.tar.gz", hash = "sha256:c951af98631d24f8df89ab1019fc365f2227c0892f12fd150e935607c79dd0dd"},
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+    {file = "websocket-client-1.6.4.tar.gz", hash = "sha256:b3324019b3c28572086c4a319f91d1dcd44e6e11cd340232978c684a7650d0df"},
+    {file = "websocket_client-1.6.4-py3-none-any.whl", hash = "sha256:084072e0a7f5f347ef2ac3d8698a5e0b4ffbfcab607628cadabc650fc9a83a24"},
 ]
 
 [package.extras]
-docs = ["Sphinx (>=3.4)", "sphinx-rtd-theme (>=0.5)"]
+docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"]
 optional = ["python-socks", "wsaccel"]
 test = ["websockets"]
 
 [[package]]
 name = "werkzeug"
-version = "2.3.6"
+version = "3.0.0"
 description = "The comprehensive WSGI web application library."
-category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "Werkzeug-2.3.6-py3-none-any.whl", hash = "sha256:935539fa1413afbb9195b24880778422ed620c0fc09670945185cce4d91a8890"},
-    {file = "Werkzeug-2.3.6.tar.gz", hash = "sha256:98c774df2f91b05550078891dee5f0eb0cb797a522c757a2452b9cee5b202330"},
+    {file = "werkzeug-3.0.0-py3-none-any.whl", hash = "sha256:cbb2600f7eabe51dbc0502f58be0b3e1b96b893b05695ea2b35b43d4de2d9962"},
+    {file = "werkzeug-3.0.0.tar.gz", hash = "sha256:3ffff4dcc32db52ef3cc94dff3000a3c2846890f3a5a51800a27b909c5e770f0"},
 ]
 
 [package.dependencies]
@@ -3535,24 +5500,117 @@ watchdog = ["watchdog (>=2.3)"]
 
 [[package]]
 name = "wheel"
-version = "0.40.0"
+version = "0.41.2"
 description = "A built-package format for Python"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "wheel-0.41.2-py3-none-any.whl", hash = "sha256:75909db2664838d015e3d9139004ee16711748a52c8f336b52882266540215d8"},
+    {file = "wheel-0.41.2.tar.gz", hash = "sha256:0c5ac5ff2afb79ac23ab82bab027a0be7b5dbcf2e54dc50efe4bf507de1f7985"},
 ]
 
 [package.extras]
-test = ["pytest (>=6.0.0)"]
+test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
+
+[[package]]
+name = "widgetsnbextension"
+version = "4.0.9"
+description = "Jupyter interactive widgets for Jupyter Notebook"
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "widgetsnbextension-4.0.9.tar.gz", hash = "sha256:3c1f5e46dc1166dfd40a42d685e6a51396fd34ff878742a3e47c6f0cc4a2a385"},
+]
+
+[[package]]
+name = "wrapt"
+version = "1.15.0"
+description = "Module for decorators, wrappers and monkey patching."
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
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+    {file = "wrapt-1.15.0-py3-none-any.whl", hash = "sha256:64b1df0f83706b4ef4cfb4fb0e4c2669100fd7ecacfb59e091fad300d4e04640"},
+    {file = "wrapt-1.15.0.tar.gz", hash = "sha256:d06730c6aed78cee4126234cf2d071e01b44b915e725a6cb439a879ec9754a3a"},
+]
 
 [[package]]
 name = "yarl"
 version = "1.9.2"
 description = "Yet another URL library"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -3638,21 +5696,20 @@ multidict = ">=4.0"
 
 [[package]]
 name = "zipp"
-version = "3.16.2"
+version = "3.17.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
-category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.16.2-py3-none-any.whl", hash = "sha256:679e51dd4403591b2d6838a48de3d283f3d188412a9782faadf845f298736ba0"},
-    {file = "zipp-3.16.2.tar.gz", hash = "sha256:ebc15946aa78bd63458992fc81ec3b6f7b1e92d51c35e6de1c3804e73b799147"},
+    {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
+    {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
 testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = "~3.10"
-content-hash = "503f04d1e74a42a174e1df1af090808d91cf38c81cba0bf1d7cf06ebf98a42fc"
+content-hash = "e0877eb6530c740b4e5a784d40d005fad4ee1ce96ea49e5c212cc04d93a1dc9d"
diff --git a/pyproject.toml b/pyproject.toml
index a8bd590..477b04e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "causica"
-version = "0.3.4"
+version = "0.3.5"
 description = ""
 readme = "README.md"
 authors = []
@@ -10,23 +10,408 @@ packages = [
 license = "MIT"
 
 [tool.poetry.dependencies]
-python = "~3.10"
 azureml-mlflow = "^1.46.0"
+dataclasses-json = "^0.5.7"
+jsonargparse = "<4.21.0"  # 4.21.0 breaks lightning cli
 mlflow = "^2.0.0"
 numpy = "^1.22.4"
 pandas = "^1.4.2"
+python = "~3.10"
+pytorch-lightning = {version = "^2.1.0", extras= ["extra"]}
 tensorboard = "^2.9.0"
-pytorch-lightning = {version = "^1.9.0", extras= ["extra"]}
-jsonargparse = "<4.21.0"  # 4.21.0 breaks lightning cli
-dataclasses-json = "^0.5.7"
-types-PyYAML = "^6.0.12.2"
 tensordict = "^0.1.0"
 torch = "2.0.0"
+torchmetrics = "^1.0.3"
+types-PyYAML = "^6.0.12.2"
 
 [tool.poetry.dev-dependencies]
+black = {version="^22.6.0", extras=["jupyter"]}
+isort =  "^5.10.1"
+jupyter = "^1.0.0"
+jupytext = "^1.13.8"
+mypy = "^1.0.0"
+pre-commit = "^2.19.0"
+pylint = "^2.14.4"
 pytest = "^7.1.2"
 pytest-cov = "^3.0.0"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"
+
+[tool.black]
+line-length = 120
+
+[tool.isort]
+line_length = 120
+profile = "black"
+py_version = 310
+known_first_party = ["causica"]
+
+# Keep import sorts by code jupytext percent block (https://github.com/PyCQA/isort/issues/1338)
+treat_comments_as_code = ["# %%"]
+
+[tool.pytest.ini_options]
+addopts = "--durations=200"
+junit_family = "xunit1"
+
+[tool.mypy]
+ignore_missing_imports = true
+
+[tool.pylint.main]
+# Specify a score threshold to be exceeded before program exits with error.
+fail-under = 9.94
+# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the
+# number of processors available to use.
+jobs = 2
+
+# Control the amount of potential inferred values when inferring a single object.
+# This can help the performance when dealing with large functions or complex,
+# nested conditions.
+limit-inference-results = 100
+
+# Minimum Python version to use for version dependent checks. Will default to the
+# version used to run pylint.
+py-version = "3.10"
+
+# When enabled, pylint would attempt to guess common misconfiguration and emit
+# user-friendly hints instead of false-positive error messages.
+suggestion-mode = true
+
+[tool.pylint.basic]
+# Naming style matching correct argument names.
+argument-naming-style = "snake_case"
+
+# Regular expression matching correct argument names. Overrides argument-naming-
+# style. If left empty, argument names will be checked with the set naming style.
+argument-rgx = "^[a-z][a-z0-9_]*$"
+
+# Naming style matching correct attribute names.
+attr-naming-style = "snake_case"
+
+# Regular expression matching correct attribute names. Overrides attr-naming-
+# style. If left empty, attribute names will be checked with the set naming
+# style.
+attr-rgx = "^_{0,2}[a-z][a-z0-9_]*$"
+
+# Naming style matching correct class attribute names.
+class-attribute-naming-style = "any"
+
+# Regular expression matching correct class attribute names. Overrides class-
+# attribute-naming-style. If left empty, class attribute names will be checked
+# with the set naming style.
+class-attribute-rgx = "^(_?[A-Z][A-Z0-9_]*|__[a-z0-9_]+__|_?[a-z][a-z0-9_]*)$"
+
+# Naming style matching correct class constant names.
+class-const-naming-style = "UPPER_CASE"
+
+# Naming style matching correct class names.
+class-naming-style = "PascalCase"
+
+# Regular expression matching correct class names. Overrides class-naming-style.
+# If left empty, class names will be checked with the set naming style.
+class-rgx = "^_?[A-Z][a-zA-Z0-9]*$"
+
+# Naming style matching correct constant names.
+const-naming-style = "UPPER_CASE"
+
+# Regular expression matching correct constant names. Overrides const-naming-
+# style. If left empty, constant names will be checked with the set naming style.
+const-rgx = "^(_?[A-Z][A-Z0-9_]*|__[a-z0-9_]+__|_?[a-z][a-z0-9_]*)$"
+
+# Minimum line length for functions/classes that require docstrings, shorter ones
+# are exempt.
+docstring-min-length = 10
+
+# Naming style matching correct function names.
+function-naming-style = "snake_case"
+
+# Regular expression matching correct function names. Overrides function-naming-
+# style. If left empty, function names will be checked with the set naming style.
+function-rgx = "^(?:(?P<exempt>setUp|tearDown|setUpModule|tearDownModule)|(?P<camel_case>_?[A-Z][a-zA-Z0-9]*)|(?P<snake_case>_?[a-z][a-z0-9_]*))$"
+
+# Good variable names which should always be accepted, separated by a comma.
+good-names = ["main", "_"]
+
+# Naming style matching correct inline iteration names.
+inlinevar-naming-style = "any"
+
+# Regular expression matching correct inline iteration names. Overrides
+# inlinevar-naming-style. If left empty, inline iteration names will be checked
+# with the set naming style.
+inlinevar-rgx = "^[a-z][a-z0-9_]*$"
+
+# Naming style matching correct method names.
+method-naming-style = "snake_case"
+
+# Regular expression matching correct method names. Overrides method-naming-
+# style. If left empty, method names will be checked with the set naming style.
+method-rgx = "(?x)^(?:(?P<exempt>_[a-z0-9_]+__|runTest|setUp|tearDown|setUpTestCase|tearDownTestCase|setupSelf|tearDownClass|setUpClass|(test|assert)_*[A-Z0-9][a-zA-Z0-9_]*|next)|(?P<camel_case>_{0,2}[A-Z][a-zA-Z0-9_]*)|(?P<snake_case>_{0,2}[a-z][a-z0-9_]*))$"
+
+# Naming style matching correct module names.
+module-naming-style = "snake_case"
+
+# Regular expression matching correct module names. Overrides module-naming-
+# style. If left empty, module names will be checked with the set naming style.
+module-rgx = "^(_?[a-z][a-z0-9_]*|__init__)$"
+
+# Regular expression which should only match function or class names that do not
+# require a docstring.
+no-docstring-rgx = "(__.*__|main|test.*|.*test|.*Test)$"
+
+# List of decorators that produce properties, such as abc.abstractproperty. Add
+# to this list to register other decorators that produce valid properties. These
+# decorators are taken in consideration only for invalid-name.
+property-classes = ["abc.abstractproperty", "cached_property.cached_property", "cached_property.threaded_cached_property", "cached_property.cached_property_with_ttl", "cached_property.threaded_cached_property_with_ttl"]
+
+# Naming style matching correct variable names.
+variable-naming-style = "snake_case"
+
+# Regular expression matching correct variable names. Overrides variable-naming-
+# style. If left empty, variable names will be checked with the set naming style.
+variable-rgx = "^[a-z][a-z0-9_]*$"
+
+[tool.pylint.classes]
+# Warn about protected attribute access inside special methods
+# check-protected-access-in-special-methods =
+
+# List of method names used to declare (i.e. assign) instance attributes.
+defining-attr-methods = ["__init__", "__new__", "setUp"]
+
+# List of member names, which should be excluded from the protected access
+# warning.
+exclude-protected = ["_asdict", "_fields", "_replace", "_source", "_make"]
+
+# List of valid names for the first argument in a class method.
+valid-classmethod-first-arg = ["cls", "class_"]
+
+# List of valid names for the first argument in a metaclass class method.
+valid-metaclass-classmethod-first-arg = ["mcs"]
+
+[tool.pylint.design]
+# Maximum number of arguments for function / method.
+max-args = 5
+
+# Maximum number of attributes for a class (see R0902).
+max-attributes = 7
+
+# Maximum number of boolean expressions in an if statement (see R0916).
+max-bool-expr = 5
+
+# Maximum number of branch for function / method body.
+max-branches = 12
+
+# Maximum number of locals for function / method body.
+max-locals = 15
+
+# Maximum number of parents for a class (see R0901).
+max-parents = 7
+
+# Maximum number of public methods for a class (see R0904).
+max-public-methods = 20
+
+# Maximum number of return / yield for function / method body.
+max-returns = 6
+
+# Maximum number of statements in function / method body.
+max-statements = 50
+
+# Minimum number of public methods for a class (see R0903).
+min-public-methods = 2
+
+[tool.pylint.exceptions]
+# Exceptions that will emit a warning when caught.
+overgeneral-exceptions = ["builtins.StandardError", "builtins.Exception", "builtins.BaseException"]
+
+[tool.pylint.format]
+# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
+# expected-line-ending-format =
+
+# Regexp for a line that is allowed to be longer than the limit.
+ignore-long-lines = "(?x)(^\\s*(\\#\\ )?<?https?://\\S+>?$|^\\s*(from\\s+\\S+\\s+)?import\\s+.+$)"
+
+# Number of spaces of indent required inside a hanging or continued line.
+indent-after-paren = 4
+
+# String used as indentation unit. This is usually "    " (4 spaces) or "\t" (1
+# tab).
+indent-string = "    "
+
+# Maximum number of characters on a single line.
+max-line-length = 200
+
+# Maximum number of lines in a module.
+max-module-lines = 99999
+
+# Allow the body of an if to be on the same line as the test if there is no else.
+single-line-if-stmt = true
+
+[tool.pylint.imports]
+# Deprecated modules which should not be used, separated by a comma.
+deprecated-modules = ["regsub", "TERMIOS", "Bastion", "rexec", "sets"]
+
+# Force import order to recognize a module as part of a third party library.
+known-third-party = ["enchant", "absl"]
+
+[tool.pylint.logging]
+# The type of string formatting that logging methods do. `old` means using %
+# formatting, `new` is for `{}` formatting.
+logging-format-style = "old"
+
+# Logging modules to check that the string format arguments are in logging
+# function parameter format.
+logging-modules = ["logging", "absl.logging", "tensorflow.io.logging"]
+
+[tool.pylint."messages control"]
+# Only show warnings with the listed confidence levels. Leave empty to show all.
+# Valid levels: HIGH, CONTROL_FLOW, INFERENCE, INFERENCE_FAILURE, UNDEFINED.
+confidence = ["HIGH", "CONTROL_FLOW", "INFERENCE", "INFERENCE_FAILURE", "UNDEFINED"]
+
+# Disable the message, report, category or checker with the given id(s). You can
+# either give multiple identifiers separated by comma (,) or put this option
+# multiple times (only on the command line, not in the configuration file where
+# it should appear only once). You can also use "--disable=all" to disable
+# everything first and then re-enable specific checks. For example, if you want
+# to run only the similarities checker, you can use "--disable=all
+# --enable=similarities". If you want to run only the classes checker, but have
+# no Warning level messages displayed, use "--disable=all --enable=classes
+# --disable=W".
+
+disable = [
+  "abstract-method",
+  "arguments-differ",
+  "attribute-defined-outside-init",
+  "c-extension-no-member",
+  "import-error",
+  "invalid-name",
+  "missing-class-docstring",
+  "missing-function-docstring",
+  "missing-module-docstring",
+  "no-member",
+  "no-name-in-module",
+  "too-few-public-methods",
+  "too-many-ancestors",
+  "too-many-arguments",
+  "too-many-instance-attributes",
+  "too-many-locals",
+  "too-many-statements",
+  "wrong-import-order",  # avoid conflicts with isort
+  ]
+[tool.pylint.miscellaneous]
+# List of note tags to take in consideration, separated by a comma.
+notes = ["TODO"]
+
+[tool.pylint.refactoring]
+# Maximum number of nested blocks for function / method body
+max-nested-blocks = 5
+
+# Complete name of functions that never returns. When checking for inconsistent-
+# return-statements if a never returning function is called then it will be
+# considered as an explicit return statement and no message will be printed.
+never-returning-functions = ["sys.exit", "argparse.parse_error"]
+
+[tool.pylint.reports]
+# Python expression which should return a score less than or equal to 10. You
+# have access to the variables 'fatal', 'error', 'warning', 'refactor',
+# 'convention', and 'info' which contain the number of messages in each category,
+# as well as 'statement' which is the total number of statements analyzed. This
+# score is used by the global evaluation report (RP0004).
+evaluation = "10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)"
+
+# Activate the evaluation score.
+score = true
+
+[tool.pylint.similarities]
+# Comments are removed from the similarity computation
+ignore-comments = true
+
+# Docstrings are removed from the similarity computation
+ignore-docstrings = true
+
+# Signatures are removed from the similarity computation
+ignore-signatures = true
+
+# Minimum lines number of a similarity.
+min-similarity-lines = 4
+
+[tool.pylint.spelling]
+# Limits count of emitted suggestions for spelling mistakes.
+max-spelling-suggestions = 4
+
+# List of comma separated words that should be considered directives if they
+# appear at the beginning of a comment and should not be checked.
+spelling-ignore-comment-directives = "fmt: on,fmt: off,noqa:,noqa,nosec,isort:skip,mypy:"
+
+[tool.pylint.string]
+# This flag controls whether inconsistent-quotes generates a warning when the
+# character used as a quote delimiter is used inconsistently within a module.
+check-quote-consistency = true
+
+[tool.pylint.typecheck]
+# List of decorators that produce context managers, such as
+# contextlib.contextmanager. Add to this list to register other decorators that
+# produce valid context managers.
+contextmanager-decorators = ["contextlib.contextmanager", "contextlib2.contextmanager"]
+
+# Tells whether missing members accessed in mixin class should be ignored. A
+# class is considered mixin if its name matches the mixin-class-rgx option.
+# Tells whether to warn about missing members when the owner of the attribute is
+# inferred to be None.
+ignore-none = true
+
+# This flag controls whether pylint should warn about no-member and similar
+# checks whenever an opaque object is returned when inferring. The inference can
+# return multiple potential results while evaluating a Python object, but some
+# branches might not be evaluated, which results in partial inference. In that
+# case, it might be useful to still emit no-member and other checks for the rest
+# of the inferred objects.
+ignore-on-opaque-inference = true
+
+# List of symbolic message names to ignore for Mixin members.
+ignored-checks-for-mixins = ["no-member", "not-async-context-manager", "not-context-manager", "attribute-defined-outside-init"]
+
+# List of class names for which member attributes should not be checked (useful
+# for classes with dynamically set attributes). This supports the use of
+# qualified names.
+ignored-classes = ["optparse.Values", "thread._local", "_thread._local"]
+
+# Show a hint with possible names when a member name was not found. The aspect of
+# finding the hint is based on edit distance.
+missing-member-hint = true
+
+# The minimum edit distance a name should have in order to be considered a
+# similar match for a missing member name.
+missing-member-hint-distance = 1
+
+# The total number of similar names that should be taken in consideration when
+# showing a hint for a missing member.
+missing-member-max-choices = 1
+
+# Regex pattern to define which classes are considered mixins.
+mixin-class-rgx = ".*[Mm]ixin"
+
+# List of decorators that change the signature of a decorated function.
+# signature-mutators =
+
+[tool.pylint.variables]
+# Tells whether unused global variables should be treated as a violation.
+allow-global-unused-variables = true
+
+# List of names allowed to shadow builtins
+# allowed-redefined-builtins =
+
+# List of strings which can identify a callback function by name. A callback name
+# must start or end with one of those strings.
+callbacks = ["cb_", "_cb"]
+
+# A regular expression matching the name of dummy variables (i.e. expected to not
+# be used).
+dummy-variables-rgx = "^\\*{0,2}(_$|unused_|dummy_)"
+
+# Argument names that match this expression will be ignored. Default to name with
+# leading underscore.
+ignored-argument-names = "_.*|^ignored_|^unused_"
+
+# List of qualified module names which can have objects that can redefine
+# builtins.
+redefining-builtins-modules = ["six", "six.moves", "past.builtins", "future.builtins", "functools"]
diff --git a/src/causica/config/lightning/default_data.yaml b/src/causica/config/lightning/default_data.yaml
index 58a0e9b..e273964 100644
--- a/src/causica/config/lightning/default_data.yaml
+++ b/src/causica/config/lightning/default_data.yaml
@@ -3,3 +3,4 @@ init_args:
   dataset_name: csuite_nonlingauss
   batch_size: 128
   load_counterfactual: false
+  load_interventional: true
diff --git a/src/causica/config/lightning/default_gaussian.yaml b/src/causica/config/lightning/default_gaussian.yaml
index ff2c42e..49fa50c 100644
--- a/src/causica/config/lightning/default_gaussian.yaml
+++ b/src/causica/config/lightning/default_gaussian.yaml
@@ -5,8 +5,8 @@ model:
     noise_dist: "GAUSSIAN"
     embedding_size: 32
     out_dim_g: 32
-    norm_layer: true
-    res_connection: true
+    num_layers_g: 2
+    num_layers_zeta: 2
     init_alpha: 0.0
     init_rho: 1.0
     prior_sparsity_lambda: 5.0
@@ -18,7 +18,7 @@ model:
         lr_update_lag_best: 250
         lr_init_dict:
           vardist: 0.01
-          icgnn: 0.01
+          functional_relationships: 0.01
           noise_dist: 5e-3
         aggregation_period: 20
         lr_factor: 0.1
diff --git a/src/causica/config/lightning/default_spline.yaml b/src/causica/config/lightning/default_spline.yaml
index 73a1ec2..9e71602 100644
--- a/src/causica/config/lightning/default_spline.yaml
+++ b/src/causica/config/lightning/default_spline.yaml
@@ -5,8 +5,8 @@ model:
     noise_dist: "SPLINE"
     embedding_size: 32
     out_dim_g: 32
-    norm_layer: true
-    res_connection: true
+    num_layers_g: 2
+    num_layers_zeta: 2
     init_alpha: 0.0
     init_rho: 1.0
     prior_sparsity_lambda: 5.0
@@ -18,7 +18,7 @@ model:
         lr_update_lag_best: 250
         lr_init_dict:
           vardist: 0.01
-          icgnn: 0.01
+          functional_relationships: 0.01
           noise_dist: 1e-4
         aggregation_period: 20
         lr_factor: 0.1
diff --git a/src/causica/data_generation/samplers/__init__.py b/src/causica/data_generation/samplers/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/causica/data_generation/samplers/functional_relationships_sampler.py b/src/causica/data_generation/samplers/functional_relationships_sampler.py
new file mode 100644
index 0000000..16e71a2
--- /dev/null
+++ b/src/causica/data_generation/samplers/functional_relationships_sampler.py
@@ -0,0 +1,48 @@
+from __future__ import annotations
+
+import torch
+import torch.distributions as td
+
+from causica.data_generation.samplers.sampler import Sampler
+from causica.functional_relationships.functional_relationships import FunctionalRelationships
+from causica.functional_relationships.linear_functional_relationships import LinearFunctionalRelationships
+from causica.functional_relationships.rff_functional_relationships import RFFFunctionalRelationships
+
+
+class FunctionalRelationshipsSampler(Sampler[FunctionalRelationships]):
+    """Abstract class for sampling Functional Relationships."""
+
+    def __init__(self, shapes_dict: dict[str, torch.Size]) -> None:
+        super().__init__()
+
+        self.shapes_dict = shapes_dict
+
+
+class LinearRelationshipsSampler(FunctionalRelationshipsSampler):
+    """Sample a Linear Functional Relationship, by providing a distribution for the coefficient matrix."""
+
+    def __init__(self, scale_dist: td.Distribution, shapes_dict: dict[str, torch.Size]):
+        super().__init__(shapes_dict)
+        self.scale_dist = scale_dist
+
+    def sample(self) -> FunctionalRelationships:
+        return LinearFunctionalRelationships(
+            shapes=self.shapes_dict, initial_linear_coefficient_matrix=self.scale_dist.sample()
+        )
+
+
+class RFFFunctionalRelationshipsSampler(FunctionalRelationshipsSampler):
+    """Sample a Non Linear Functional Relationship, by providing two distributions:
+    a first distribution for the random features, and a second distribution for the linear outer coefficients."""
+
+    def __init__(self, rf_dist: td.Distribution, coeff_dist: td.Distribution, shapes_dict: dict[str, torch.Size]):
+        super().__init__(shapes_dict)
+        self.rf_dist = rf_dist
+        self.coeff_dist = coeff_dist
+
+    def sample(self) -> FunctionalRelationships:
+        return RFFFunctionalRelationships(
+            shapes=self.shapes_dict,
+            initial_random_features=self.rf_dist.sample(),
+            initial_coefficients=self.coeff_dist.sample(),
+        )
diff --git a/src/causica/data_generation/samplers/noise_dist_sampler.py b/src/causica/data_generation/samplers/noise_dist_sampler.py
new file mode 100644
index 0000000..e328085
--- /dev/null
+++ b/src/causica/data_generation/samplers/noise_dist_sampler.py
@@ -0,0 +1,68 @@
+import abc
+from typing import Mapping
+
+import torch
+import torch.distributions as td
+
+from causica.data_generation.samplers.sampler import Sampler
+from causica.distributions import JointNoiseModule
+from causica.distributions.noise import NoiseModule, UnivariateNormalNoiseModule
+from causica.distributions.noise.bernoulli import BernoulliNoiseModule
+
+
+class NoiseModuleSampler(Sampler[NoiseModule]):
+    """
+    An interface of a univariate noise sampler
+    """
+
+    @abc.abstractmethod
+    def sample(
+        self,
+    ) -> NoiseModule:
+        """Sample a sample type with given shape"""
+
+
+class JointNoiseModuleSampler(NoiseModuleSampler):
+    """Sampler for JointNoiseModule, given shapes and types of different variables"""
+
+    def __init__(
+        self,
+        noise_dist_samplers: Mapping[str, NoiseModuleSampler],
+    ):
+        super().__init__()
+        self.noise_dist_samplers = noise_dist_samplers
+
+    def sample(self) -> JointNoiseModule:
+        noise_modules = {}
+        for key, noise_sampler in self.noise_dist_samplers.items():
+            noise_modules[key] = noise_sampler.sample()
+        return JointNoiseModule(independent_noise_modules=noise_modules)
+
+
+class UnivariateNormalNoiseModuleSampler(NoiseModuleSampler):
+    """Sample a UnivariateNormalNoiseModule, with standard deviation given by a distribution."""
+
+    def __init__(self, std_dist: td.Distribution, dim: int = 1):
+        super().__init__()
+        self.std_dist = std_dist
+        self.dim = dim
+
+    def sample(
+        self,
+    ):
+        return UnivariateNormalNoiseModule(dim=self.dim, init_log_scale=torch.log(self.std_dist.sample()).item())
+
+
+class BernoulliNoiseModuleSampler(NoiseModuleSampler):
+    """Sample a BernoulliNoiseModule, with base_logits given by a distribution."""
+
+    def __init__(self, base_logits_dist: td.Distribution, dim: int = 1):
+        super().__init__()
+        self.base_logits_dist = base_logits_dist
+        self.dim = dim
+
+    def sample(
+        self,
+    ) -> NoiseModule:
+        base_logits = self.base_logits_dist.sample().item()
+        return BernoulliNoiseModule(dim=self.dim, init_base_logits=base_logits)
diff --git a/src/causica/data_generation/samplers/sampler.py b/src/causica/data_generation/samplers/sampler.py
new file mode 100644
index 0000000..670addf
--- /dev/null
+++ b/src/causica/data_generation/samplers/sampler.py
@@ -0,0 +1,16 @@
+import abc
+from typing import Generic, TypeVar
+
+SampleType = TypeVar("SampleType")
+
+
+class Sampler(Generic[SampleType], abc.ABC):
+    """
+    An interface of a sampler, useful for generative processes
+
+    The interface only allows sampling one thing at a time.
+    """
+
+    @abc.abstractmethod
+    def sample(self) -> SampleType:
+        """Sample a sample type with given shape"""
diff --git a/src/causica/data_generation/samplers/sem_sampler.py b/src/causica/data_generation/samplers/sem_sampler.py
new file mode 100644
index 0000000..420b11b
--- /dev/null
+++ b/src/causica/data_generation/samplers/sem_sampler.py
@@ -0,0 +1,30 @@
+import torch
+
+from causica.data_generation.samplers.functional_relationships_sampler import FunctionalRelationshipsSampler
+from causica.data_generation.samplers.noise_dist_sampler import JointNoiseModuleSampler
+from causica.data_generation.samplers.sampler import Sampler
+from causica.distributions import AdjacencyDistribution
+from causica.sem.distribution_parameters_sem import DistributionParametersSEM
+
+
+class SEMSampler(Sampler[DistributionParametersSEM]):
+    """Sample a SEM given adjacency, a JointNoiseModuleSampler and functional relationships distributions."""
+
+    def __init__(
+        self,
+        adjacency_dist: AdjacencyDistribution,
+        joint_noise_module_sampler: JointNoiseModuleSampler,
+        functional_relationships_sampler: FunctionalRelationshipsSampler,
+    ):
+        self.adjacency_dist = adjacency_dist
+        self.joint_noise_module_sampler = joint_noise_module_sampler
+        self.functional_relationships_sampler = functional_relationships_sampler
+        self.shapes_dict: dict[str, torch.Size] = functional_relationships_sampler.shapes_dict
+
+    def sample(self):
+        adjacency_matrix = self.adjacency_dist.sample()
+        functional_relationships = self.functional_relationships_sampler.sample()
+        joint_noise_module = self.joint_noise_module_sampler.sample()
+        return DistributionParametersSEM(
+            graph=adjacency_matrix, noise_dist=joint_noise_module, func=functional_relationships
+        )
diff --git a/src/causica/data_generation/synthetic_dataset.py b/src/causica/data_generation/synthetic_dataset.py
new file mode 100644
index 0000000..1170751
--- /dev/null
+++ b/src/causica/data_generation/synthetic_dataset.py
@@ -0,0 +1,116 @@
+from itertools import repeat, starmap
+from typing import Optional
+
+import numpy as np
+import torch
+from tensordict import TensorDict
+from torch.utils.data import IterableDataset
+
+from causica.data_generation.samplers.sem_sampler import SEMSampler
+from causica.datasets.interventional_data import InterventionData
+from causica.distributions.transforms import TensorToTensorDictTransform
+
+
+class CausalDataset(IterableDataset):
+    """A dataset that returns data from SEM samples.
+
+    SEM samples consist of (dataset, noise, graph, interventions)
+
+    The dataset holds samples from a SEM in a TensorDict as {node_name: [num_samples, *node_shape]}
+    The noise holds samples from a SEM in a TensorDict as {node_name: [num_samples, *node_shape]}
+    The graph is the causal graph of the SEM [num_nodes, num_nodes]
+    The interventions are a list of InterventionData objects
+    """
+
+    def __init__(
+        self,
+        sem_sampler: SEMSampler,
+        sample_dataset_size: int,
+        dataset_size: Optional[int] = None,
+        num_interventions: int = 0,
+        num_intervention_samples: int = 1000,
+        num_sems: int = 0,
+    ):
+        """
+        Args:
+            sem_sampler: The sampler for SEMs
+            sample_dataset_size: The size of the dataset to sample from the SEM
+            dataset_size: The size of this dataset, if not supplied it will be infinitely long
+                It is useful to set this value to a finite size so it can be used with `ChainDataset`,
+                which relies on the iterator terminating to chain the next one.
+            num_interventions: The number of interventions to sample per dataset. If 0, no interventions are sampled.
+            num_intervention_samples: The number of samples to use to estimate the mean.
+            num_sems: The number of sems to sample the data from. If 0, each data sample is generated from a new SEM.
+        """
+        self.sem_sampler = sem_sampler
+        self.sample_dataset_size = torch.Size([sample_dataset_size])
+        self.dataset_size = dataset_size
+        self.num_interventions = num_interventions
+        self.num_intervention_samples = num_intervention_samples
+        self.num_sems = num_sems
+
+        self.sems = [sem_sampler.sample() for _ in range(num_sems)]
+        self.td_to_tensor_transform = TensorToTensorDictTransform(self.sem_sampler.shapes_dict)
+        self.cur_iter = 0
+
+    def __iter__(self):
+        """Return an iterator over samples in the dataset.
+
+        See Also:
+            CausalDataset: For a description of the format of the samples.
+
+        Note:
+            We use starmap as in `repeatfunc` https://docs.python.org/3/library/itertools.html#itertools-recipes
+            This creates a generator applying `_sample `dataset_size` times, or an infinite
+            generator if `dataset_size` is `None`
+        """
+        return starmap(self._sample, repeat(tuple(), times=self.dataset_size))
+
+    def _sample_intervention(self, sem, tensordict_data) -> InterventionData:
+        """Sample an intervention and it's sample mean from a given SEM.
+
+        Args:
+            sem: SEM to sample interventional data from.
+            tensordict_data: Base data for sampling an intervention value.
+
+        Returns:
+            an intervention data object
+        """
+        # sample the treatment and effect variable
+        treatment = np.random.choice(sem.node_names, size=1, replace=False).item()
+
+        batch_axes = tuple(range(tensordict_data.batch_dims))
+        treatment_shape = tensordict_data[treatment].shape[tensordict_data.batch_dims :]
+        treatment_max = torch.amax(tensordict_data[treatment], dim=batch_axes)
+        treatment_min = torch.amin(tensordict_data[treatment], dim=batch_axes)
+
+        treatment_a = torch.rand(treatment_shape) * (treatment_max - treatment_min) + treatment_min
+
+        intervention_a = TensorDict({treatment: treatment_a}, batch_size=torch.Size())
+
+        intervention_a_samples = sem.do(intervention_a).sample((self.num_intervention_samples,))
+
+        intervention_data = InterventionData(
+            intervention_a_samples,
+            intervention_a,
+            TensorDict({}, batch_size=torch.Size()),
+        )
+        return intervention_data
+
+    def _sample(
+        self,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, Optional[list[InterventionData]]]:
+        """Sample a new dataset and returns the data, graph and interventions if num_interventions > 0."""
+        if self.num_sems > 0:
+            sem = self.sems[self.cur_iter % self.num_sems]
+            self.cur_iter += 1
+        else:
+            sem = self.sem_sampler.sample()
+        noise = sem.sample_noise(self.sample_dataset_size)
+        observations = sem.noise_to_sample(noise)
+
+        interventions = None
+        if self.num_interventions > 0:
+            interventions = [self._sample_intervention(sem, observations) for _ in range(self.num_interventions)]
+
+        return (observations, noise, sem.graph, interventions)
diff --git a/src/causica/datasets/causica_dataset_format.py b/src/causica/datasets/causica_dataset_format.py
index 613537b..b2ac878 100644
--- a/src/causica/datasets/causica_dataset_format.py
+++ b/src/causica/datasets/causica_dataset_format.py
@@ -296,7 +296,8 @@ def tensordict_from_variables_metadata(data: np.ndarray, variables_list: list[Va
 
     # guaranteed to be ordered correctly in python 3.7+ https://docs.python.org/3/library/collections.html#collections.Counter
     sizes = Counter(d.group_name for d in variables_list)  # get the dimensions of each key from the variables
-    assert sum(sizes.values()) == data.shape[1], "Variable sizes do not match data shape"
+    sum_sizes = sum(sizes.values())
+    assert sum_sizes == data.shape[1], f"Variable sizes do not match data shape, got {sum_sizes} and {data.shape}"
 
     # NOTE: This assumes that variables in the same group will have the same type.
     dtypes = {item.group_name: DTYPE_MAP[item.type] for item in variables_list}
diff --git a/src/causica/datasets/interventional_data.py b/src/causica/datasets/interventional_data.py
index 3684bbc..d19a28e 100644
--- a/src/causica/datasets/interventional_data.py
+++ b/src/causica/datasets/interventional_data.py
@@ -1,7 +1,7 @@
 from dataclasses import dataclass, field
 
 import torch
-from tensordict import TensorDict
+from tensordict import TensorDictBase
 
 
 @dataclass
@@ -19,9 +19,9 @@ class InterventionData:
         condition_values: A dictionary of node names to 1D numpy arrays of the conditioned values
     """
 
-    intervention_data: TensorDict
-    intervention_values: TensorDict
-    condition_values: TensorDict
+    intervention_data: TensorDictBase
+    intervention_values: TensorDictBase
+    condition_values: TensorDictBase
     sampled_nodes: set[str] = field(init=False)  # the nodes that are neither conditioned nor sampled
 
     def __post_init__(self):
@@ -53,9 +53,9 @@ class CounterfactualData:
         intervention_values: A dictionary of node names to 1D numpy arrays of the intervened values
     """
 
-    counterfactual_data: TensorDict
-    intervention_values: TensorDict
-    factual_data: TensorDict
+    counterfactual_data: TensorDictBase
+    intervention_values: TensorDictBase
+    factual_data: TensorDictBase
     sampled_nodes: set[str] = field(init=False)
 
     def __post_init__(self):
diff --git a/src/causica/datasets/normalization.py b/src/causica/datasets/normalization.py
new file mode 100644
index 0000000..9a5f74a
--- /dev/null
+++ b/src/causica/datasets/normalization.py
@@ -0,0 +1,84 @@
+"""Module that provides data normalization functionality."""
+from typing import Callable, Optional
+
+import torch
+import torch.distributions as td
+from tensordict import TensorDictBase
+from torch import nn
+
+from causica.distributions.transforms import JointTransformModule, SequentialTransformModule, TransformModule
+
+Normalizer = TransformModule[TensorDictBase, TensorDictBase]
+FitNormalizerType = Callable[[TensorDictBase], Normalizer]
+
+
+class LoadNoneTensorMixin(nn.Module):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        self._register_load_state_dict_pre_hook(self._update_tensor_size_on_load)
+
+    def _update_tensor_size_on_load(self, state_dict: dict[str, torch.Tensor], prefix: str, *args, **kwargs) -> None:
+        _ = args, kwargs
+        for key, value in state_dict.items():
+            local_key = key.removeprefix(prefix)
+            if hasattr(self, local_key) and getattr(self, local_key) is None:
+                setattr(self, local_key, torch.empty_like(value))
+
+
+class Standardizer(TransformModule[torch.Tensor, torch.Tensor], td.AffineTransform, LoadNoneTensorMixin):
+    """Standardizer module for a single variable, ie a single tensor."""
+
+    def __init__(self, mean: Optional[torch.Tensor], std: Optional[torch.Tensor], *args, **kwargs) -> None:
+        """
+        Args:
+            mean: Mean of the variable
+            std: Standard deviation of the variable
+            *args, **kwargs: Passed to the AffineTransform
+        """
+        loc = scale = None
+        if mean is not None and std is not None:
+            loc = -mean / std
+            scale = 1 / std
+        super().__init__(loc, scale, *args, **kwargs)
+        del self.loc, self.scale  # Unset these temporarily to allow registering as buffers
+
+        self.loc: torch.Tensor
+        self.scale: torch.Tensor
+        self.register_buffer("loc", loc)
+        self.register_buffer("scale", scale)
+
+
+def fit_standardizer(data: TensorDictBase) -> Normalizer:
+    """Return a standardizer that updates data to zero mean and unit standard deviation."""
+    means = data.apply(
+        lambda x: torch.mean(x, dim=0, keepdim=False),
+        batch_size=torch.Size(),
+    )
+    # Filter out std == 0
+    stds = data.apply(
+        lambda x: torch.std(x, dim=0, keepdim=False),
+        batch_size=torch.Size(),
+    ).apply(lambda x: torch.where(x == 0, torch.ones_like(x), x))
+
+    return JointTransformModule({key: Standardizer(means.get(key), stds.get(key)) for key in means.keys()})
+
+
+def chain_normalizers(*fit_functions: FitNormalizerType) -> FitNormalizerType:
+    """Chain a number of normalizers together.
+
+    Args:
+        *fit_functions: Functions that produce normalizers.
+
+    Returns:
+        A function that fits the sequence of normalizers.
+    """
+
+    def sequential_fitting(X: TensorDictBase) -> Normalizer:
+        transform_modules = []
+        for fit_function in fit_functions:
+            transform_module = fit_function(X)
+            X = transform_module(X)
+            transform_modules.append(transform_module)
+        return SequentialTransformModule[TensorDictBase, TensorDictBase](*transform_modules)
+
+    return sequential_fitting
diff --git a/src/causica/datasets/standardizer.py b/src/causica/datasets/standardizer.py
deleted file mode 100644
index 0568ce0..0000000
--- a/src/causica/datasets/standardizer.py
+++ /dev/null
@@ -1,75 +0,0 @@
-"""
-Module that provides data normalisation functionality.
-"""
-import torch
-import torch.distributions as td
-from tensordict import TensorDict
-from torch import nn
-
-from causica.distributions.transforms import JointTransform
-
-
-class SingleVariableStandardizer(nn.Module):
-    """Standardizer module for a single variable, ie a single tensor."""
-
-    def __init__(self, mean: torch.Tensor, std: torch.Tensor):
-        """
-        Args:
-            mean: Mean of the variable
-            std: Standard deviation of the variable
-        """
-        super().__init__()
-
-        if mean.shape != std.shape:
-            raise ValueError("mean and std must have the same shape.")
-
-        self.mean: torch.Tensor
-        self.std: torch.Tensor
-
-        self.register_buffer("mean", mean)
-        self.register_buffer("std", std)
-
-    def __call__(self) -> td.AffineTransform:
-        return super().__call__()
-
-    def forward(self) -> td.AffineTransform:
-        return td.AffineTransform(loc=-self.mean / self.std, scale=1 / self.std)
-
-
-class JointStandardizer(nn.Module):
-    """Standardizer module for TensorDicts."""
-
-    def __init__(self, means: TensorDict, stds: TensorDict):
-        """
-        Args:
-            means: Means of the variables
-            stds: Standard deviations of the variables
-        """
-        super().__init__()
-
-        if set(means.keys()) != set(stds.keys()):
-            raise ValueError("Requires means and stds to have the same keys.")
-
-        if not all(m.shape == s.shape for m, s in zip(means.values(), stds.values())):
-            raise ValueError("Requires means and stds to have the same shapes.")
-
-        self.transform_modules = nn.ModuleDict(
-            {key: SingleVariableStandardizer(means[key], stds[key]) for key in means.keys()}
-        )
-
-    def forward(self) -> JointTransform:
-        return JointTransform({key: module() for key, module in self.transform_modules.items()})
-
-
-def fit_standardizer(data: TensorDict) -> JointStandardizer:
-    """Calculate the mean and standard deviation over the first dimension of each variable in the TensorDict and return a standardizer."""
-    means = data.apply(
-        lambda x: torch.mean(x, dim=0, keepdim=False),
-        batch_size=torch.Size(),
-    )
-    # Filter out std == 0
-    stds = data.apply(
-        lambda x: torch.std(x, dim=0, keepdim=False),
-        batch_size=torch.Size(),
-    ).apply(lambda x: torch.where(x == 0, torch.ones_like(x), x))
-    return JointStandardizer(means=means, stds=stds)
diff --git a/src/causica/distributions/noise/bernoulli.py b/src/causica/distributions/noise/bernoulli.py
index a227c1f..02d00ce 100644
--- a/src/causica/distributions/noise/bernoulli.py
+++ b/src/causica/distributions/noise/bernoulli.py
@@ -72,13 +72,13 @@ def mode(self):
 class BernoulliNoiseModule(NoiseModule[IndependentNoise[BernoulliNoise]]):
     """Represents a BernoulliNoise distribution with learnable logits."""
 
-    def __init__(self, dim: int):
+    def __init__(self, dim: int, init_base_logits: float = 0.0):
         """
         Args:
             dim: Number of dimensions (independent Bernouilli's).
         """
         super().__init__()
-        self.base_logits = nn.Parameter(torch.zeros(dim))
+        self.base_logits = nn.Parameter(torch.full(torch.Size([dim]), init_base_logits))
 
     def forward(self, x: Optional[torch.Tensor] = None) -> IndependentNoise[BernoulliNoise]:
         if x is None:
diff --git a/src/causica/distributions/noise/categorical.py b/src/causica/distributions/noise/categorical.py
index 45b3ce7..d0b128f 100644
--- a/src/causica/distributions/noise/categorical.py
+++ b/src/causica/distributions/noise/categorical.py
@@ -12,7 +12,7 @@
 class CategoricalNoise(OneHotCategorical, Noise):
     def __init__(self, delta_logits: torch.Tensor, base_logits: torch.Tensor):
         """
-        A Categorical distribution with parameters defined by base_logits and self.delta_logits (ICGNN predictions).
+        A Categorical distribution with parameters defined by base_logits and self.delta_logits (predictions from an NN).
 
         Args:
             delta_logits: Tensor with shape [sample_shape, event_shape]
diff --git a/src/causica/distributions/transforms.py b/src/causica/distributions/transforms.py
deleted file mode 100644
index 8e13180..0000000
--- a/src/causica/distributions/transforms.py
+++ /dev/null
@@ -1,150 +0,0 @@
-"""
-Wrapper around torch.distributions.transforms to allow for joint transforms on TensorDicts.
-"""
-from typing import Mapping
-
-import torch
-import torch.distributions as td
-from tensordict import TensorDict
-
-
-class JointTransform(td.Transform):
-    """A joint transform that applies a different transform to each key in the TensorDict.
-
-    Keys in the input that are not found in the transform are left unchanged.
-
-    This is heavily inspired by the `torch.distributions.transforms.StackTransform` class.
-    See https://pytorch.org/docs/stable/distributions.html#torch.distributions.transforms.StackTransform
-    """
-
-    def __init__(self, transformations: Mapping[str, td.Transform], cache_size: int = 0):
-        """
-        Args:
-            transformations: A dictionary of transforms, where the keys are the keys in the TensorDict
-            cache_size: Size of cache. If zero, no caching is done. If one, the latest single value is cached.
-                Only 0 and 1 are supported.
-        """
-        assert all(
-            isinstance(t, td.Transform) for t in transformations.values()
-        ), f"All transformations must be of type torch.distributions.Transform, but are {[type(t) for t in transformations.values()]}."
-        if cache_size:
-            transformations = {key: t.with_cache(cache_size) for key, t in transformations.items()}
-        super().__init__(cache_size=cache_size)
-
-        self.transformations = transformations
-
-    def _call(self, x: TensorDict) -> TensorDict:
-        return x.clone().update(
-            {key: transform(x[key]) for key, transform in self.transformations.items() if key in x.keys()}
-        )
-
-    def _inverse(self, y: TensorDict) -> TensorDict:
-        # We cannot use ._inv as pylint complains with E202: _inv is hidden because of `self._inv = None`
-        # in td.Transform.__init__
-
-        return y.clone().update(
-            {key: transform.inv(y[key]) for key, transform in self.transformations.items() if key in y.keys()}
-        )
-
-    def log_abs_det_jacobian(self, x: TensorDict, y: TensorDict) -> torch.Tensor:
-        if set(x.keys()) != set(y.keys()):
-            raise ValueError("x and y must have the same keys.")
-
-        if not set(self.transformations.keys()).issubset(x.keys()):
-            raise ValueError("All keys in transformations must be in x and y.")
-
-        return x.clone().update(
-            {
-                key: self.transformations[key].log_abs_det_jacobian(x[key], y[key])
-                if key in self.transformations
-                else torch.zeros_like(x[key])
-                for key in x.keys()
-            }
-        )
-
-    @property
-    def bijective(self):
-        return all(t.bijective for t in self.transformations.values())
-
-    @property
-    def domain(self):
-        return {key: t.domain for key, t in self.transformations.items()}
-
-    @property
-    def codomain(self):
-        return {key: t.codomain for key, t in self.transformations.items()}
-
-
-class TensorToTensorDictTransform(td.Transform):
-    """
-    A transform for converting a torch tensor to a TensorDict.
-
-    It extracts the slices from the last dimension of the tensor and assigns them to the correct key.
-    """
-
-    bijective = True
-
-    def __init__(self, shapes: dict[str, torch.Size]):
-        """
-        Args:
-            shapes: the shapes of each of the keys
-        """
-        super().__init__()
-        self.shapes = shapes
-        self.num_keys = len(shapes)
-        self.output_shape, self.slices = shapes_to_slices(self.shapes)
-
-    def _call(self, x: torch.Tensor) -> TensorDict:
-        """Create a Tensordict by retrieving the slice associated with each key."""
-        return TensorDict({name: x[..., slice_] for name, slice_ in self.slices.items()}, batch_size=x.shape[:-1])
-
-    def _inverse(self, y: TensorDict) -> torch.Tensor:
-        """
-        Create a tensor by stacking the slice associated with each key.
-
-        Args:
-            y: Tensordict with batch_shape
-        Returns:
-            A tensor with shape batch_shape + [output_shape]
-        """
-        return torch.cat([y[name] for name in self.slices], dim=-1)
-
-    def log_abs_det_jacobian(self, _: torch.Tensor, y: TensorDict) -> TensorDict:
-        """This transformation doesn't affect the log det jacobian"""
-        return y.apply(torch.zeros_like)
-
-    def stacked_key_masks(self) -> torch.Tensor:
-        """
-        Create a binary of matrix of where each key is in the tensor.
-
-        Returns:
-            A matrix of shape [num_keys, output_shape] with 1 if the index of the tensor
-            belongs to the key corresponding to that row
-        """
-        stacked_key_masks = torch.zeros((self.num_keys, self.output_shape), dtype=torch.float)
-        for i, slice_ in enumerate(self.slices.values()):
-            stacked_key_masks[i, slice_] = 1.0
-        return stacked_key_masks
-
-
-def shapes_to_slices(shapes: dict[str, torch.Size]) -> tuple[int, dict[str, slice]]:
-    """
-    Convert a dictionary of shapes to a dictionary of masks by stacking the shapes
-
-    Each mask corresponds to the embedded location in the tensor
-
-    Args:
-        shapes: A dict of key names to shapes
-    Returns:
-        The shape of the stacked tensor and a dictionary of each key to the mask
-    """
-    assert all(len(shape) == 1 for shape in shapes.values())
-
-    slices: dict[str, slice] = {}
-    idx = 0
-    for name, shape in shapes.items():
-        next_idx = idx + shape[-1]
-        slices[name] = slice(idx, next_idx)
-        idx = next_idx
-
-    return next_idx, slices
diff --git a/src/causica/distributions/transforms/__init__.py b/src/causica/distributions/transforms/__init__.py
new file mode 100644
index 0000000..d513f4c
--- /dev/null
+++ b/src/causica/distributions/transforms/__init__.py
@@ -0,0 +1,3 @@
+from causica.distributions.transforms.base import SequentialTransformModule, TransformModule, TypedTransform
+from causica.distributions.transforms.joint import JointTransform, JointTransformModule
+from causica.distributions.transforms.tensor_to_tensordict import TensorToTensorDictTransform, shapes_to_slices
diff --git a/src/causica/distributions/transforms/base.py b/src/causica/distributions/transforms/base.py
new file mode 100644
index 0000000..0048fa7
--- /dev/null
+++ b/src/causica/distributions/transforms/base.py
@@ -0,0 +1,132 @@
+"""
+Wrapper around torch.distributions.transforms to allow for joint transforms on TensorDicts.
+"""
+import weakref
+from typing import Any, Generic, Optional, TypeVar, Union
+
+import torch
+from torch import nn
+from torch.distributions.transforms import Transform, _InverseTransform
+
+T_co = TypeVar("T_co", covariant=True, bound="TypedTransform")
+
+
+class _TransformRef(Generic[T_co]):
+    """A covariant reference to a transform.
+
+    Particularily used to allow subclasses to refine the inverse type.
+    """
+
+    weak_ref: Optional[weakref.ReferenceType[T_co]]
+
+    def __init__(self, obj: T_co):
+        self.weak_ref = weakref.ref(obj)
+
+    def __call__(self) -> Optional[T_co]:
+        return self.weak_ref() if self.weak_ref is not None else None
+
+
+X = TypeVar("X", covariant=False)
+Y = TypeVar("Y", covariant=False)
+
+
+class TypedTransform(Generic[X, Y], Transform):
+    """Transforms with typed argument and return values.
+
+    Should not be instantiated directly, but rather through the `TypedTransform` class.
+
+    Notes:
+        Special care needs to be taken in overriding the inverse. In particular, the weak reference is of a
+        non-covariant generic type, and therefore isn't compatible with subclasses returning refined inverses. We solve
+        this by using a custom covariant reference class.
+    """
+
+    def __call__(self, x: X) -> Y:  # pylint: disable=useless-parent-delegation
+        return super().__call__(x)  # type: ignore
+
+    def _inv_call(self, y: Y) -> X:  # pylint: disable=useless-parent-delegation
+        return super()._inv_call(y)  # type: ignore
+
+    def _call(self, x: X) -> Y:  # pylint: disable=useless-parent-delegation
+        return super()._call(x)
+
+    def _inverse(self, y: Y) -> X:  # pylint: disable=useless-parent-delegation
+        return super()._inverse(y)
+
+    def log_abs_det_jacobian(self, x: X, y: Y):  # pylint: disable=useless-parent-delegation
+        return super().log_abs_det_jacobian(x, y)
+
+    @property
+    def inv(self) -> "TypedTransform[Y, X]":
+        inv: Optional[TypedTransform[Y, X]] = None
+        match self._inv:
+            case _TransformRef():
+                inv = self._inv()
+            case TypedTransform():
+                inv = self._inv
+        if inv is None:
+            inv = _TypedInverseTransform[Y, X](self)
+            self._inv: Union[_TransformRef[_TypedInverseTransform[Y, X]], TypedTransform[Y, X]] = _TransformRef[
+                _TypedInverseTransform[Y, X]
+            ](inv)
+        return inv
+
+
+class _TypedInverseTransform(Generic[Y, X], TypedTransform[Y, X], _InverseTransform):
+    _inv: TypedTransform[X, Y]
+
+
+class TransformModule(Generic[X, Y], TypedTransform[X, Y], nn.Module):
+    """Transforms with learnable parameters.
+
+    This is similar to the `pyro.distributions.torch_transform.TransformModule` class.
+    """
+
+    def __hash__(self):
+        """Return the nn.Module based hash.
+
+        Notes:
+            The Transformation hash is None.
+        """
+        return super(torch.nn.Module, self).__hash__()
+
+    @property
+    def inv(self) -> "TransformModule[Y, X]":
+        inv: Optional[TransformModule[Y, X]] = None
+        match self._inv:
+            case _TransformRef():
+                inv = self._inv()
+            case TransformModule():
+                inv = self._inv
+        if inv is None:
+            inv = _InverseTransformModule[Y, X](self)
+            self._inv: Union[_TransformRef[_InverseTransformModule[Y, X]], TransformModule[Y, X]] = _TransformRef[
+                _InverseTransformModule[Y, X]
+            ](inv)
+        return inv
+
+
+class _InverseTransformModule(Generic[Y, X], TransformModule[Y, X], _TypedInverseTransform):
+    """Inverse transformation of a TransformModule."""
+
+    _inv: TransformModule[X, Y]
+
+
+class SequentialTransformModule(Generic[X, Y], TransformModule[X, Y], nn.Sequential):
+    """Sequential transform with TransformModule transformations."""
+
+    def __init__(self, *args: TransformModule[X, Y], cache_size: int = 0):
+        super().__init__(cache_size=cache_size)
+        for idx, module in enumerate(args):
+            self.add_module(str(idx), module)
+
+    def _inverse(self, y: Y) -> X:
+        current: Any = y
+        # Create a tuple of submodules before reversing, to avoid a copy of the Sequential module
+        for module in tuple(self)[::-1]:
+            assert isinstance(module, TransformModule)
+            current = module.inv(current)
+        return current
+
+    def _call(self, x: X) -> Y:
+        return nn.Sequential.__call__(self, x)
diff --git a/src/causica/distributions/transforms/joint.py b/src/causica/distributions/transforms/joint.py
new file mode 100644
index 0000000..c97b0a0
--- /dev/null
+++ b/src/causica/distributions/transforms/joint.py
@@ -0,0 +1,100 @@
+from typing import Generic, Mapping, TypeVar
+
+import torch
+import torch.distributions as td
+from tensordict import TensorDictBase
+from torch import nn
+
+from causica.distributions.transforms.base import TransformModule, TypedTransform
+
+
+class JointTransform(TypedTransform[TensorDictBase, TensorDictBase]):
+    """A joint transform that applies a different transform to each key in the TensorDict.
+
+    Keys in the input that are not found in the transform are left unchanged.
+
+    This is heavily inspired by the `torch.distributions.transforms.StackTransform` class.
+    See https://pytorch.org/docs/stable/distributions.html#torch.distributions.transforms.StackTransform
+    """
+
+    def __init__(self, transformations: Mapping[str, td.Transform], cache_size: int = 0):
+        """
+        Args:
+            transformations: A dictionary of transforms, where the keys are the keys in the TensorDict
+            cache_size: Size of cache. If zero, no caching is done. If one, the latest single value is cached.
+                Only 0 and 1 are supported.
+        """
+        bad_transformation_types = {type(t) for t in transformations.values() if not isinstance(t, td.Transform)}
+        if bad_transformation_types:
+            raise TypeError(
+                "All transformations must be subtypes of `torch.distributions.Transform`, but the "
+                f"following are not: {bad_transformation_types} are not."
+            )
+        if cache_size:
+            transformations = {key: t.with_cache(cache_size) for key, t in transformations.items()}
+        super().__init__(cache_size=cache_size)
+        self.transformations = transformations
+
+    def _call(self, x: TensorDictBase) -> TensorDictBase:
+        return x.clone().update(
+            {key: transform(x[key]) for key, transform in self.transformations.items() if key in x.keys()}
+        )
+
+    def _inverse(self, y: TensorDictBase) -> TensorDictBase:
+        # We cannot use ._inv as pylint complains with E202: _inv is hidden because of `self._inv = None`
+        # in td.Transform.__init__
+
+        return y.clone().update(
+            {key: transform.inv(y[key]) for key, transform in self.transformations.items() if key in y.keys()}
+        )
+
+    def log_abs_det_jacobian(self, x: TensorDictBase, y: TensorDictBase) -> TensorDictBase:
+        if set(x.keys()) != set(y.keys()):
+            raise ValueError("x and y must have the same keys.")
+
+        if not set(self.transformations.keys()) <= set(x.keys()):
+            raise ValueError("All keys in transformations must be in x and y.")
+
+        return x.clone().update(
+            {
+                key: self.transformations[key].log_abs_det_jacobian(x[key], y[key])
+                if key in self.transformations
+                else torch.zeros_like(x[key])
+                for key in x.keys()
+            }
+        )
+
+    @property
+    def bijective(self):
+        return all(t.bijective for t in self.transformations.values())
+
+    @property
+    def domain(self):
+        return {key: t.domain for key, t in self.transformations.items()}
+
+    @property
+    def codomain(self):
+        return {key: t.codomain for key, t in self.transformations.items()}
+
+
+T_co = TypeVar("T_co", bound=nn.Module, covariant=True)
+
+
+class _TypedModuleDict(Generic[T_co], nn.ModuleDict, Mapping[str, T_co]):
+    """Allow a ModuleDict to be interpreted as a mapping."""
+
+    def __hash__(self) -> int:
+        return nn.ModuleDict.__hash__(self)
+
+
+class JointTransformModule(JointTransform, TransformModule[TensorDictBase, TensorDictBase]):
+    """Joint transform with TransformModule transformations applied per key to a TensorDict."""
+
+    def __init__(self, transformations: Mapping[str, TransformModule], *args, **kwargs):
+        """
+        Args:
+            transformations: A mapping of transforms, where the keys are the keys in the TensorDict.
+            *args, **kwargs: Passed to the JointTransform.
+        """
+        super().__init__(transformations, *args, **kwargs)
+        self.transformations = _TypedModuleDict[TransformModule](transformations)
diff --git a/src/causica/distributions/transforms/tensor_to_tensordict.py b/src/causica/distributions/transforms/tensor_to_tensordict.py
new file mode 100644
index 0000000..73e70f8
--- /dev/null
+++ b/src/causica/distributions/transforms/tensor_to_tensordict.py
@@ -0,0 +1,83 @@
+import torch
+from tensordict import TensorDict, TensorDictBase
+from torch.distributions.constraints import Constraint
+
+from causica.distributions.transforms.base import TypedTransform
+
+
+class TensorToTensorDictTransform(TypedTransform[torch.Tensor, TensorDictBase]):
+    """
+    A transform for converting a torch tensor to a TensorDict.
+
+    It extracts the slices from the last dimension of the tensor and assigns them to the correct key.
+    """
+
+    arg_constraints: dict[str, Constraint] = {}
+    bijective = True
+
+    def __init__(self, shapes: dict[str, torch.Size]):
+        """
+        Args:
+            shapes: the shapes of each of the keys
+        """
+        super().__init__()
+        self.shapes = shapes
+        self.num_keys = len(shapes)
+        self.output_shape, self.slices = shapes_to_slices(self.shapes)
+
+    def _call(self, x: torch.Tensor) -> TensorDictBase:
+        """Create a Tensordict by retrieving the slice associated with each key."""
+        return TensorDict({name: x[..., slice_] for name, slice_ in self.slices.items()}, batch_size=x.shape[:-1])
+
+    def _inverse(self, y: TensorDictBase) -> torch.Tensor:
+        """
+        Create a tensor by stacking the slice associated with each key.
+
+        Args:
+            y: Tensordict with batch_shape
+        Returns:
+            A tensor with shape batch_shape + [output_shape]
+        """
+        return torch.cat([y[name] for name in self.slices], dim=-1)
+
+    def log_abs_det_jacobian(self, _: torch.Tensor, y: TensorDictBase) -> TensorDictBase:
+        """This transformation doesn't affect the log det jacobian"""
+        return y.apply(torch.zeros_like)
+
+    def stacked_key_masks(self) -> torch.Tensor:
+        """
+        Create a binary of matrix of where each key is in the tensor.
+
+        Returns:
+            A matrix of shape [num_keys, output_shape] with 1 if the index of the tensor
+            belongs to the key corresponding to that row
+        """
+        stacked_key_masks = torch.zeros((self.num_keys, self.output_shape), dtype=torch.float)
+        for i, slice_ in enumerate(self.slices.values()):
+            stacked_key_masks[i, slice_] = 1.0
+        return stacked_key_masks
+
+
+def shapes_to_slices(shapes: dict[str, torch.Size]) -> tuple[int, dict[str, slice]]:
+    """
+    Convert a dictionary of shapes to a dictionary of masks by stacking the shapes
+
+    Each mask corresponds to the embedded location in the tensor
+
+    Args:
+        shapes: A dict of key names to shapes
+
+    Returns:
+        The shape of the stacked tensor and a dictionary of each key to the mask
+    """
+    assert all(len(shape) == 1 for shape in shapes.values())
+
+    slices: dict[str, slice] = {}
+    idx = 0
+    next_idx = 0
+    for name, shape in shapes.items():
+        next_idx = idx + shape[-1]
+        slices[name] = slice(idx, next_idx)
+        idx = next_idx
+
+    return next_idx, slices
diff --git a/src/causica/functional_relationships/__init__.py b/src/causica/functional_relationships/__init__.py
index b55432b..fe1f470 100644
--- a/src/causica/functional_relationships/__init__.py
+++ b/src/causica/functional_relationships/__init__.py
@@ -1,4 +1,8 @@
-from .do_functional_relationships import DoFunctionalRelationships, create_do_functional_relationship
-from .functional_relationships import FunctionalRelationships
-from .icgnn import ICGNN
-from .linear_functional_relationships import LinearFunctionalRelationships
+from causica.functional_relationships.deci_functional_relationships import DECIEmbedFunctionalRelationships
+from causica.functional_relationships.do_functional_relationships import (
+    DoFunctionalRelationships,
+    create_do_functional_relationship,
+)
+from causica.functional_relationships.functional_relationships import FunctionalRelationships
+from causica.functional_relationships.linear_functional_relationships import LinearFunctionalRelationships
+from causica.functional_relationships.rff_functional_relationships import RFFFunctionalRelationships
diff --git a/src/causica/functional_relationships/deci_functional_relationships.py b/src/causica/functional_relationships/deci_functional_relationships.py
new file mode 100644
index 0000000..4ed40a5
--- /dev/null
+++ b/src/causica/functional_relationships/deci_functional_relationships.py
@@ -0,0 +1,26 @@
+import torch
+from tensordict import TensorDict
+
+from causica.functional_relationships.functional_relationships import FunctionalRelationships
+from causica.nn import DECIEmbedNN
+
+
+class DECIEmbedFunctionalRelationships(FunctionalRelationships):
+    """
+    This is a `FunctionalRelationsips` that wraps the `DECIEmbedNN` module.
+    """
+
+    def __init__(
+        self,
+        shapes: dict[str, torch.Size],
+        embedding_size: int,
+        out_dim_g: int,
+        num_layers_g: int,
+        num_layers_zeta: int,
+    ) -> None:
+        super().__init__(shapes=shapes)
+
+        self.nn = DECIEmbedNN(self.stacked_key_masks, embedding_size, out_dim_g, num_layers_g, num_layers_zeta)
+
+    def forward(self, samples: TensorDict, graphs: torch.Tensor) -> TensorDict:
+        return self.tensor_to_td(self.nn(self.tensor_to_td.inv(samples), graphs))
diff --git a/src/causica/functional_relationships/do_functional_relationships.py b/src/causica/functional_relationships/do_functional_relationships.py
index 44eb96d..1d25f71 100644
--- a/src/causica/functional_relationships/do_functional_relationships.py
+++ b/src/causica/functional_relationships/do_functional_relationships.py
@@ -49,10 +49,10 @@ def pad_intervened_graphs(self, graphs: torch.Tensor) -> torch.Tensor:
     def forward(self, samples: TensorDict, graphs: torch.Tensor) -> TensorDict:
         """
         Args:
-            samples: Batched inputs, size batch_size_x + (processed_dim_all).
+            samples: Batched inputs, size batch_size_x + (concatenated_shape).
             graphs: Weighted adjacency matrix, size batch_size_g + (n, n)
         Returns:
-            A tensor of shape batch_shape_x + batch_shape_g + (processed_dim_all)
+            A tensor of shape batch_shape_x + batch_shape_g + (concatenated_shape)
         """
         # add the expanded intervention values to the samples
         samples_with_do = samples.update(self.do.expand(*samples.batch_size))
diff --git a/src/causica/functional_relationships/functional_relationships.py b/src/causica/functional_relationships/functional_relationships.py
index 0227f28..dd6e0e2 100644
--- a/src/causica/functional_relationships/functional_relationships.py
+++ b/src/causica/functional_relationships/functional_relationships.py
@@ -1,5 +1,4 @@
 import abc
-from typing import Any
 
 import torch
 from tensordict import TensorDict
@@ -22,13 +21,6 @@ def __init__(self, shapes: dict[str, torch.Size]) -> None:
         # this needs to be registered to the module, and register buffer doesn't work
         self.stacked_key_masks = torch.nn.Parameter(self.tensor_to_td.stacked_key_masks(), requires_grad=False)
 
-    def set_extra_state(self, state: dict[str, Any]):
-        self.shapes = state.pop("shapes")
-        self.tensor_to_td = TensorToTensorDictTransform(self.shapes)
-
-    def get_extra_state(self) -> dict[str, Any]:
-        return {"shapes": self.shapes}
-
     @abc.abstractmethod
     def forward(self, samples: TensorDict, graphs: torch.Tensor) -> TensorDict:
         """Calculates the predictions of the children from parents.
diff --git a/src/causica/functional_relationships/icgnn.py b/src/causica/functional_relationships/icgnn.py
deleted file mode 100644
index cc927a8..0000000
--- a/src/causica/functional_relationships/icgnn.py
+++ /dev/null
@@ -1,217 +0,0 @@
-from typing import Optional, Type
-
-import torch
-from tensordict import TensorDict
-from torch import nn
-
-from causica.functional_relationships.functional_relationships import FunctionalRelationships
-
-
-class ICGNN(FunctionalRelationships):
-    """
-    This is a `FunctionalRelationsips` that implements the ICGNN.
-
-    Details can be found here: https://openreview.net/forum?id=S2pNPZM-w-f
-
-    This wraps the `FGNNI` in a `TensorDict` interface.
-    """
-
-    def __init__(
-        self,
-        shapes: dict[str, torch.Size],
-        embedding_size: Optional[int] = None,
-        out_dim_g: Optional[int] = None,
-        norm_layer: Optional[Type[nn.LayerNorm]] = None,
-        res_connection: bool = False,
-    ) -> None:
-        super().__init__(shapes=shapes)
-
-        self.nn = FGNNI(self.stacked_key_masks, embedding_size, out_dim_g, norm_layer, res_connection)
-
-    def forward(self, samples: TensorDict, graphs: torch.Tensor) -> TensorDict:
-        return self.tensor_to_td(self.nn(self.tensor_to_td.inv(samples), graphs))
-
-
-class FGNNI(nn.Module):
-    """
-    Defines the function f for the SEM. For each variable x_i we use
-    f_i(x) = f(e_i, sum_{k in pa(i)} g(e_k, x_k)), where e_i is a learned embedding
-    for node i.
-    """
-
-    def __init__(
-        self,
-        group_mask: torch.Tensor,
-        embedding_size: Optional[int] = None,
-        out_dim_g: Optional[int] = None,
-        norm_layer: Optional[Type[nn.LayerNorm]] = None,
-        res_connection: bool = False,
-    ):
-        """
-        Args:
-            group_mask: A mask of shape (num_nodes, num_processed_cols) such that group_mask[i, j] = 1. when col j is in group i.
-            embedding_size: Size of the embeddings used by each node. If none, default is processed_dim_all.
-            out_dim_g: Output dimension of the "inner" NN, g. If none, default is embedding size.
-            layers_g: Size of the layers of NN g. Does not include input not output dim. If none, default
-                      is [a], with a = max(2 * input_dim, embedding_size, 10).
-            layers_f: Size of the layers of NN f. Does not include input nor output dim. If none, default
-                      is [a], with a = max(2 * input_dim, embedding_size, 10)
-        """
-        super().__init__()
-        self.group_mask = group_mask
-        self.num_nodes, self.processed_dim_all = group_mask.shape
-        # Initialize embeddings
-        self.embedding_size = self.processed_dim_all if embedding_size is None else embedding_size
-        aux = torch.randn(self.num_nodes, self.embedding_size) * 0.01
-        self.embeddings = nn.parameter.Parameter(aux, requires_grad=True)  # Shape (input_dim, embedding_size)
-
-        # Set value for out_dim_g
-        out_dim_g = self.embedding_size if out_dim_g is None else out_dim_g
-        # Set NNs sizes
-        a = max(4 * self.processed_dim_all, self.embedding_size, 64)
-        in_dim_g = self.embedding_size + self.processed_dim_all
-        in_dim_f = self.embedding_size + out_dim_g
-        self.g = generate_fully_connected(
-            input_dim=in_dim_g,
-            output_dim=out_dim_g,
-            hidden_dims=[a, a],
-            non_linearity=nn.LeakyReLU,
-            activation=nn.Identity,
-            device=group_mask.device,
-            normalization=norm_layer,
-            res_connection=res_connection,
-        )
-        self.f = generate_fully_connected(
-            input_dim=in_dim_f,
-            output_dim=self.processed_dim_all,
-            hidden_dims=[a, a],
-            non_linearity=nn.LeakyReLU,
-            activation=nn.Identity,
-            device=group_mask.device,
-            normalization=norm_layer,
-            res_connection=res_connection,
-        )
-        self.w = torch.nn.Parameter(torch.zeros((self.num_nodes, self.num_nodes)), requires_grad=True)
-
-    def forward(self, samples: torch.Tensor, graphs: torch.Tensor) -> torch.Tensor:
-        """
-        Computes non-linear function h(X, W) using the given weighted adjacency matrix.
-
-        g takes inputs of size batch_shape + (embedding_size + processed_dim_all) and outputs batch_shape + (out_dim_g)
-        the input will be appropriately masked to correspond to one variable group
-
-        f takes inputs of size batch_shape + (embedding_size + out_dim_g) and outputs batch_shape + (processed_dim_all)
-        the ouptut is then masked to correspond to one variable
-
-        Args:
-            samples: Batched inputs, size batch_size_x + (processed_dim_all).
-            graphs: Weighted adjacency matrix, size batch_size_g + (n, n)
-        Returns:
-            A tensor of shape batch_shape_x + batch_shape_g + (processed_dim_all)
-        """
-        batch_shape_x = samples.shape[:-1]
-        batch_shape_g = graphs.shape[:-2]
-
-        # Generate required input for g (concatenate X and embeddings)
-        # Pointwise multiply X
-        # Shape batch_shape_x + (num_nodes, processed_dim_all)
-        masked_samples = torch.einsum("...i,ji->...ji", samples, self.group_mask)
-        E = self.embeddings.expand(*batch_shape_x, -1, -1)  # Shape batch_shape_x + (num_nodes, embedding_size)
-
-        # Shape batch_shape_x + (num_nodes, embedding_size + processed_dim_all)
-        embedded_samples = self.g(
-            torch.cat([masked_samples, E], dim=-1)
-        )  # Shape batch_shape_x + (num_nodes, out_dim_g)
-
-        target_shape = batch_shape_x + batch_shape_g + embedded_samples.shape[-2:]
-        view_shape = batch_shape_x + (1,) * len(batch_shape_g) + embedded_samples.shape[-2:]
-        # Shape batch_shape_x + batch_shape_g + (num_nodes, out_dim_g)
-        embedded_samples_broad = embedded_samples.view(view_shape).expand(target_shape)
-        # Aggregate sum and generate input for f (concatenate X_aggr and embeddings)
-        # Shape batch_shape_x + batch_shape_g + (num_nodes, out_dim_g)
-        samples_aggr_sum = torch.einsum("...jk,...jl->...lk", embedded_samples_broad, graphs * self.w)
-
-        # expand dimensions of E batch_shape_x + batch_shape_g + (num_nodes, embedding_size)
-        E_broad = E.view(view_shape).expand(target_shape)
-        # Run f Shape batch_shape_x + batch_shape_g + (num_nodes, processed_dim_all)
-        samples_rec = self.f(torch.cat([samples_aggr_sum, E_broad], dim=-1))
-        # Mask and aggregate Shape batch_shape_x + batch_shape_g + (processed_dim_all)
-        return torch.einsum("...ij,ij->...j", samples_rec, self.group_mask)
-
-
-def generate_fully_connected(
-    input_dim: int,
-    output_dim: int,
-    hidden_dims: list[int],
-    non_linearity: Optional[Type[nn.Module]],
-    activation: Optional[Type[nn.Module]],
-    device: torch.device,
-    p_dropout: float = 0.0,
-    normalization: Optional[Type[nn.LayerNorm]] = None,
-    res_connection: bool = False,
-) -> nn.Module:
-    """
-    Generate a fully connected network.
-
-    Args:
-        input_dim: Int. Size of input to network.
-        output_dim: Int. Size of output of network.
-        hidden_dims: List of int. Sizes of internal hidden layers. i.e. [a, b] is three linear layers with shapes (input_dim, a), (a, b), (b, output_dim)
-        non_linearity: Non linear activation function used between Linear layers.
-        activation: Final layer activation to use.
-        device: torch device to load weights to.
-        p_dropout: Float. Dropout probability at the hidden layers.
-        init_method: initialization method
-        normalization: Normalisation layer to use (batchnorm, layer norm, etc). Will be placed before linear layers, excluding the input layer.
-        res_connection : Whether to use residual connections where possible (if previous layer width matches next layer width)
-
-    Returns:
-        Sequential object containing the desired network.
-    """
-    layers: list[nn.Module] = []
-
-    prev_dim = input_dim
-    for idx, hidden_dim in enumerate(hidden_dims):
-
-        block: list[nn.Module] = []
-
-        if normalization is not None and idx > 0:
-            block.append(normalization(prev_dim).to(device))
-        block.append(nn.Linear(prev_dim, hidden_dim).to(device))
-
-        if non_linearity is not None:
-            block.append(non_linearity())
-        if p_dropout != 0:
-            block.append(nn.Dropout(p_dropout))
-
-        if res_connection and (prev_dim == hidden_dim):
-            layers.append(_ResBlock(nn.Sequential(*block)))
-        else:
-            layers.append(nn.Sequential(*block))
-        prev_dim = hidden_dim
-
-    if normalization is not None:
-        layers.append(normalization(prev_dim).to(device))
-    layers.append(nn.Linear(prev_dim, output_dim).to(device))
-
-    if activation is not None:
-        layers.append(activation())
-
-    return nn.Sequential(*layers)
-
-
-class _ResBlock(nn.Module):
-    """
-    Wraps an nn.Module, adding a skip connection to it.
-    """
-
-    def __init__(self, block: nn.Module):
-        """
-        Args:
-            block: module to which skip connection will be added. The input dimension must match the output dimension.
-        """
-        super().__init__()
-        self.block = block
-
-    def forward(self, x):
-        return x + self.block(x)
diff --git a/src/causica/functional_relationships/linear_functional_relationships.py b/src/causica/functional_relationships/linear_functional_relationships.py
index e46b390..7bba008 100644
--- a/src/causica/functional_relationships/linear_functional_relationships.py
+++ b/src/causica/functional_relationships/linear_functional_relationships.py
@@ -31,10 +31,10 @@ def __init__(
     def forward(self, samples: TensorDict, graphs: torch.Tensor) -> TensorDict:
         """
         Args:
-            samples: Batched inputs, size batch_size_x + (processed_dim_all).
+            samples: Batched inputs, size batch_size_x + (concatenated_shape).
             graphs: Weighted adjacency matrix, size batch_size_g + (n, n)
         Returns:
-            A Dict of tensors of shape batch_shape_x + batch_shape_g + (processed_dim_all)
+            A Dict of tensors of shape batch_shape_x + batch_shape_g + (concatenated_shape)
         """
         return self.tensor_to_td(self.linear_map(self.tensor_to_td.inv(samples), graphs))
 
diff --git a/src/causica/functional_relationships/rff_functional_relationships.py b/src/causica/functional_relationships/rff_functional_relationships.py
new file mode 100644
index 0000000..fbce0df
--- /dev/null
+++ b/src/causica/functional_relationships/rff_functional_relationships.py
@@ -0,0 +1,76 @@
+import math
+
+import torch
+from tensordict import TensorDict
+
+from causica.functional_relationships.functional_relationships import FunctionalRelationships
+
+
+class RFFFunctionalRelationships(FunctionalRelationships):
+    """
+    A simple random fourier feature-based functional relationship.
+    The formula implemented here is:
+    x_i = sqrt(2/M) * sum_{i}^{M} alpha_i sin(<w_i, pa(x_i)>)
+    """
+
+    def __init__(
+        self,
+        shapes: dict[str, torch.Size],
+        initial_random_features: torch.Tensor,
+        initial_coefficients: torch.Tensor,
+        trainable: bool = False,
+    ) -> None:
+        """
+        Args:
+            shapes: Dict of node shapes (how many dimensions a variable has)
+                Order corresponds to the order in graph(s).
+            initial_random_features: a tensor containing the random features [num_rf, output_shape]
+            initial_coefficients: a tensor containing the linear outer coefficients [num_rf,]
+            trainable: whether the coefficient matrix should be learnable
+        """
+        super().__init__(shapes=shapes)
+
+        assert initial_random_features.shape[0] == initial_coefficients.shape[0]
+        self.num_rf = initial_random_features.shape[0]
+
+        self.shape = self.tensor_to_td.output_shape
+        assert initial_random_features.shape[1] == self.shape
+
+        self.linear_coefficients_inner = torch.nn.Parameter(initial_random_features, requires_grad=trainable)
+        self.linear_coefficients_outer = torch.nn.Parameter(initial_coefficients, requires_grad=trainable)
+
+    def forward(self, samples: TensorDict, graphs: torch.Tensor) -> TensorDict:
+        """
+        Args:
+            samples: Batched inputs, size batch_size_x + (concatenated_shape).
+            graphs: Weighted adjacency matrix, size batch_size_g + (n, n)
+        Returns:
+            A Dict of tensors of shape batch_shape_x + batch_shape_g + (concatenated_shape)
+        """
+        return self.tensor_to_td(self.non_linear_map(self.tensor_to_td.inv(samples), graphs))
+
+    def non_linear_map(self, samples: torch.Tensor, graph: torch.Tensor) -> torch.Tensor:
+        """
+        Applies the non linear function to a concatenated tensor of samples.
+
+        Args:
+            samples: tensor of shape batch_shape_x + [n_cols]
+            graph: tensor of shape batch_shape_g + [n_nodes, n_nodes]
+        Returns:
+            tensor of shape batch_shape_x + batch_shape_g + [n_cols]
+        """
+        batch_shape_x = samples.shape[:-1]
+        batch_shape_g = graph.shape[:-2]
+
+        masked_graph = torch.einsum("ji,...jk,kl->...il", self.stacked_key_masks, graph, self.stacked_key_masks)
+
+        graph_broad = masked_graph.expand(*(batch_shape_x + tuple([-1] * len(graph.shape))))
+        target_shape = batch_shape_x + batch_shape_g + samples.shape[-1:]
+        view_shape = batch_shape_x + (1,) * len(batch_shape_g) + samples.shape[-1:]
+        # Shape batch_shape_x + batch_shape_g + (num_nodes, out_dim_g)
+        samples_broad = samples.view(view_shape).expand(target_shape)
+
+        inner_prods = torch.einsum("ij,...j,...jk->...ik", self.linear_coefficients_inner, samples_broad, graph_broad)
+        return math.sqrt(2 / self.num_rf) * torch.einsum(
+            "i,...ij->...j", self.linear_coefficients_outer, torch.sin(inner_prods)
+        )
diff --git a/src/causica/lightning/callbacks.py b/src/causica/lightning/callbacks.py
index 654fba0..e16ee26 100644
--- a/src/causica/lightning/callbacks.py
+++ b/src/causica/lightning/callbacks.py
@@ -1,6 +1,6 @@
 from pathlib import Path
 from tempfile import TemporaryDirectory
-from typing import Any
+from typing import Any, Optional
 
 import mlflow
 import pytorch_lightning as pl
@@ -15,7 +15,7 @@
 class AuglagLRCallback(pl.Callback):
     """Wrapper Class to make the Auglag Learning Rate Scheduler compatible with Pytorch Lightning"""
 
-    def __init__(self, scheduler: AugLagLR, log_auglag: bool = False):
+    def __init__(self, scheduler: AugLagLR, log_auglag: bool = False, disabled_epochs: Optional[set[int]] = None):
         """
         Args:
             scheduler: The auglag learning rate scheduler to wrap.
@@ -23,6 +23,7 @@ def __init__(self, scheduler: AugLagLR, log_auglag: bool = False):
         """
         self.scheduler = scheduler
         self._log_auglag = log_auglag
+        self._disabled_epochs = disabled_epochs
 
     def on_train_batch_end(
         self, trainer: pl.Trainer, pl_module: pl.LightningModule, outputs: STEP_OUTPUT, batch: Any, batch_idx: int
@@ -35,6 +36,12 @@ def on_train_batch_end(
         assert isinstance(optimizer, torch.optim.Optimizer)
         auglag_loss: AugLagLossCalculator = pl_module.auglag_loss  # type: ignore
 
+        # Disable if we reached a disabled epoch - disable, otherwise make sure the scheduler is enabled
+        if self._disabled_epochs and trainer.current_epoch in self._disabled_epochs:
+            self.scheduler.disable(auglag_loss)
+        else:
+            self.scheduler.enable(auglag_loss)
+
         is_converged = self.scheduler.step(
             optimizer=optimizer,
             loss=auglag_loss,
diff --git a/src/causica/lightning/data_modules/basic_data_module.py b/src/causica/lightning/data_modules/basic_data_module.py
index 08ae362..d5b39c5 100644
--- a/src/causica/lightning/data_modules/basic_data_module.py
+++ b/src/causica/lightning/data_modules/basic_data_module.py
@@ -6,7 +6,7 @@
 from torch.utils.data import DataLoader
 
 from causica.datasets.causica_dataset_format import Variable, tensordict_from_variables_metadata
-from causica.datasets.standardizer import fit_standardizer
+from causica.datasets.normalization import fit_standardizer
 from causica.datasets.tensordict_utils import identity, tensordict_shapes
 from causica.datasets.variable_types import VariableTypeEnum
 from causica.lightning.data_modules.deci_data_module import DECIDataModule
@@ -40,9 +40,7 @@ def __init__(
         if self.normalize:
             continuous_keys = [k for k, v in self._variable_types.items() if v == VariableTypeEnum.CONTINUOUS]
             self.normalizer = fit_standardizer(self._dataset_train.select(*continuous_keys))
-
-            transform = self.normalizer()
-            self._dataset_train = transform(self._dataset_train)
+            self._dataset_train = self.normalizer(self._dataset_train)
 
     @property
     def dataset_name(self) -> TensorDict:
diff --git a/src/causica/lightning/data_modules/synthetic_data_module.py b/src/causica/lightning/data_modules/synthetic_data_module.py
new file mode 100644
index 0000000..d725e49
--- /dev/null
+++ b/src/causica/lightning/data_modules/synthetic_data_module.py
@@ -0,0 +1,200 @@
+from itertools import product
+from typing import Iterable
+
+import pytorch_lightning as pl
+import torch
+import torch.distributions as td
+from tensordict import TensorDict
+from torch.utils.data import ChainDataset, DataLoader, Dataset
+
+from causica.data_generation.samplers.functional_relationships_sampler import (
+    LinearRelationshipsSampler,
+    RFFFunctionalRelationshipsSampler,
+)
+from causica.data_generation.samplers.noise_dist_sampler import (
+    JointNoiseModuleSampler,
+    UnivariateNormalNoiseModuleSampler,
+)
+from causica.data_generation.samplers.sampler import Sampler
+from causica.data_generation.samplers.sem_sampler import SEMSampler
+from causica.data_generation.synthetic_dataset import CausalDataset
+from causica.distributions import ErdosRenyiDAGDistribution
+from causica.functional_relationships.functional_relationships import FunctionalRelationships
+
+
+class SyntheticDataModule(pl.LightningDataModule):
+    """A datamodule to produce datasets and their underlying causal graphs and interventions."""
+
+    def __init__(
+        self,
+        node_nums: list[int],
+        probs: list[float],
+        train_batch_size: int,
+        test_batch_size: int,
+        dataset_size: int,
+        num_interventions: int = 0,
+        num_sems: int = 0,
+        num_workers: int = 0,
+    ) -> None:
+        """
+        Args:
+            node_nums: List of number of nodes for generated graphs
+            probs: List of the probabilities of edges in each graph when using Erdos Renyi
+            train_batch_size: The training batch size to use
+            test_batch_size: The testing batch size to use
+            dataset_size: The size of dataset to generate
+            num_interventions: The number of interventions to generate (0 for no interventions)
+            num_sems: The number of SEMs to generate (0 for infinite SEMs)
+            num_workers: The number of workers to use for the dataloader
+        """
+        super().__init__()
+        self.node_nums = node_nums
+        self.probs = probs
+        self.dataset_size = dataset_size
+        self.train_batch_size = train_batch_size
+        self.test_batch_size = test_batch_size
+        self.sems_dict: dict[tuple[int, float, bool], SEMSampler] = {}
+        self.num_interventions = num_interventions
+        self.num_workers = num_workers
+        self.num_sems = num_sems
+
+        self.dataloader_args = {
+            "collate_fn": _tuple_collate_fn,
+            "num_workers": self.num_workers,
+            "worker_init_fn": worker_init_fn if self.num_workers > 0 else None,
+            "persistent_workers": self.num_workers > 0,
+            "pin_memory": True,
+            "prefetch_factor": 16 if self.num_workers > 0 else None,
+        }
+
+        self.val_dataloader_args = {
+            "collate_fn": _tuple_collate_fn,
+            "pin_memory": True,
+        }
+
+        self.train_dataset: Dataset
+        self.val_dataset: Dataset
+
+        self.save_hyperparameters()
+
+    def setup(self, stage: str):
+        for num_nodes, prob, is_linear in product(self.node_nums, self.probs, (True, False)):
+            shapes_dict = {f"x_{i}": torch.Size([1]) for i in range(num_nodes)}
+            noise_dist_samplers = {
+                f"x_{i}": UnivariateNormalNoiseModuleSampler(std_dist=td.Uniform(low=0.2, high=2.0), dim=1)
+                for i in range(num_nodes)
+            }
+            joint_noise_module_sampler = JointNoiseModuleSampler(noise_dist_samplers)
+            adjacency_dist = ErdosRenyiDAGDistribution(num_nodes=num_nodes, probs=torch.tensor(prob))
+
+            dim = sum(shape.numel() for shape in shapes_dict.values())
+
+            functional_relationships_sampler: Sampler[FunctionalRelationships]
+
+            if is_linear:
+                ones_matrix = torch.ones((dim, dim), dtype=torch.float32)
+                functional_relationships_sampler = LinearRelationshipsSampler(
+                    scale_dist=td.Uniform(
+                        low=ones_matrix,
+                        high=3.0 * ones_matrix,
+                    ),
+                    shapes_dict=shapes_dict,
+                )
+            else:
+                num_rf = 100
+                ones_matrix = torch.ones((num_rf, dim), dtype=torch.float32)
+                ones = torch.ones((num_rf,), dtype=torch.float32)
+                functional_relationships_sampler = RFFFunctionalRelationshipsSampler(
+                    rf_dist=td.Uniform(
+                        low=7.0 * ones_matrix,
+                        high=10.0 * ones_matrix,
+                    ),
+                    coeff_dist=td.Uniform(
+                        low=10.0 * ones,
+                        high=20.0 * ones,
+                    ),
+                    shapes_dict=shapes_dict,
+                )
+
+            # store each of the sem samplers in a dictionary
+            self.sems_dict[(num_nodes, prob, is_linear)] = SEMSampler(
+                adjacency_dist=adjacency_dist,
+                joint_noise_module_sampler=joint_noise_module_sampler,
+                functional_relationships_sampler=functional_relationships_sampler,
+            )
+
+        self.train_dataset = self._get_dataset(self.train_batch_size)
+        self.val_dataset = self._get_dataset(self.test_batch_size)
+
+    def _get_dataset(self, dataset_size: int) -> Dataset:
+        """Builds causal datasets given the SEM samplers.
+
+        Args:
+            dataset_size: Number of samples of the causal dataset (ie number of datasets generated).
+
+        Returns:
+            dataset object
+        """
+        dataset_fraction = self.num_workers if self.num_workers > 0 else 1
+        factor = self.num_sems if self.num_sems > 0 else 1
+        dataset = ChainDataset(
+            [
+                CausalDataset(
+                    sampler,
+                    sample_dataset_size=self.dataset_size,
+                    dataset_size=dataset_size * factor * 16 // dataset_fraction,
+                    num_interventions=self.num_interventions,
+                    num_sems=self.num_sems,
+                )
+                for sampler in self.sems_dict.values()
+            ]
+        )
+
+        return dataset
+
+    def train_dataloader(self):
+        return DataLoader(dataset=self.train_dataset, batch_size=self.train_batch_size, **self.dataloader_args)
+
+    def val_dataloader(self):
+        datasets = [self.train_dataset, self.val_dataset] if self.num_sems > 0 else [self.val_dataset]
+
+        return [
+            DataLoader(dataset=dataset, batch_size=self.test_batch_size, **self.val_dataloader_args)
+            for dataset in datasets
+        ]
+
+
+def worker_init_fn(_):
+    worker_info = torch.utils.data.get_worker_info()
+
+    dataset = worker_info.dataset  # the dataset copy in this worker process
+    if isinstance(dataset, ChainDataset):
+        # Split chained datasets across multiple workers.
+        dataset.datasets = [dataset.datasets[worker_info.id % len(dataset.datasets)]]
+
+
+def _tuple_collate_fn(data: Iterable[tuple[torch.Tensor, ...]]) -> tuple[torch.Tensor, ...]:
+    """Collates a list of tuples of tensors into a tuple of tensors.
+
+    The dataloader returns batch_shape tuples of (X, y, ...), so we stack them all
+    to get tensors of shapes [batch_shape, *X.shape] and [batch_shape, *y.shape] and so on.
+
+    Args:
+        data: list of tuple of tensors to collate. Assumes the dimensions of the tensors in the tuples match.
+
+    Returns:
+        collated data
+    """
+
+    def _nested_stack(x: list):
+        """Stacks a tuple of tensors, returns None if an element is None, or returns lists of lists."""
+        if isinstance(x[0], (torch.Tensor, TensorDict)):
+            return torch.stack(x, dim=0)
+        if isinstance(x[0], list):
+            return list(x)
+        if x[0] is None:
+            return None
+
+        raise ValueError(f"Unexpected type {type(x[0])}")
+
+    return tuple(_nested_stack(list(x)) for x in zip(*data))
diff --git a/src/causica/lightning/data_modules/variable_spec_data.py b/src/causica/lightning/data_modules/variable_spec_data.py
index 7f94b29..51018cf 100644
--- a/src/causica/lightning/data_modules/variable_spec_data.py
+++ b/src/causica/lightning/data_modules/variable_spec_data.py
@@ -5,13 +5,14 @@
 from typing import Any, Iterable, Optional, Union
 
 import torch
-from tensordict import TensorDict
+from tensordict import TensorDict, TensorDictBase
 from torch.utils.data import DataLoader
 
 from causica.datasets.causica_dataset_format import CAUSICA_DATASETS_PATH, DataEnum, VariablesMetadata, load_data
-from causica.datasets.standardizer import JointStandardizer, fit_standardizer
+from causica.datasets.normalization import FitNormalizerType, Normalizer, fit_standardizer
 from causica.datasets.tensordict_utils import identity, tensordict_shapes
 from causica.datasets.variable_types import VariableTypeEnum
+from causica.distributions.transforms import JointTransformModule
 from causica.lightning.data_modules.deci_data_module import DECIDataModule
 
 
@@ -36,18 +37,21 @@ def __init__(
         dataset_name: str = "anonymous_dataset",
         normalize: Union[bool, Iterable[str]] = False,
         exclude_normalization: Iterable[str] = tuple(),
+        fit_normalizer: FitNormalizerType = fit_standardizer,
         load_counterfactual: bool = False,
+        load_interventional: bool = False,
         **storage_options: Any,
     ):
         """
         Args:
             root_path: Path to directory with causal data
             batch_size: Batch size for training and test data.
-            storage_options: Storage options forwarded to `fsspec` when loading files.
             dataset_name: A name for the dataset
-            load_counterfactual: Whether there is counterfactual data
             normalize: Whether to normalize the data or list of variables to normalize
             exclude_normalization: Which variables to exclude from normalization
+            load_counterfactual: Whether counterfactual data should be loaded
+            load_interventional: Whether interventional data should be loaded
+            **storage_options: Storage options forwarded to `fsspec` when loading files.
         """
         super().__init__()
         self.batch_size = batch_size
@@ -58,8 +62,13 @@ def __init__(
         self.normalize = normalize
         self.exclude_normalization = set(exclude_normalization)
         self.load_counterfactual = load_counterfactual
+        self.load_interventional = load_interventional
 
-        self.normalizer: Optional[JointStandardizer] = None
+        self.fit_normalizer = fit_normalizer
+        self.normalizer: Optional[Normalizer] = None
+        self._dataset_train: TensorDictBase
+        self._dataset_test: TensorDictBase
+        self.true_adj: torch.Tensor
 
     @property
     def variable_shapes(self) -> dict[str, torch.Size]:
@@ -90,12 +99,21 @@ def _load_all_data(self, variables_metadata: VariablesMetadata):
             load_data, root_path=self.root_path, variables_metadata=variables_metadata, **self.storage_options
         )
 
-        self._dataset_train = _load_data(data_enum=DataEnum.TRAIN)
-        self._dataset_test = _load_data(data_enum=DataEnum.TEST)
-        self.true_adj = _load_data(data_enum=DataEnum.TRUE_ADJACENCY)
-        self.interventions = _load_data(data_enum=DataEnum.INTERVENTIONS)
-
-        self.counterfactuals = None
+        dataset_train = _load_data(data_enum=DataEnum.TRAIN)
+        dataset_test = _load_data(data_enum=DataEnum.TEST)
+        true_adj = _load_data(data_enum=DataEnum.TRUE_ADJACENCY)
+        assert isinstance(dataset_train, TensorDict)
+        assert isinstance(dataset_test, TensorDict)
+        assert isinstance(true_adj, torch.Tensor)
+        self._dataset_train = dataset_train
+        self._dataset_test = dataset_test
+        self.true_adj = true_adj
+
+        self.interventions = []
+        if self.load_interventional:
+            self.interventions = _load_data(data_enum=DataEnum.INTERVENTIONS)
+
+        self.counterfactuals = []
         if self.load_counterfactual:
             self.counterfactuals = _load_data(data_enum=DataEnum.COUNTERFACTUALS)
 
@@ -114,8 +132,8 @@ def prepare_data(self):
         #  init_args:
         #    ...
         #    variables_metadata: null
-        _load_data = partial(load_data, root_path=self.root_path, **self.storage_options)
-        variables_metadata: VariablesMetadata = _load_data(data_enum=DataEnum.VARIABLES_JSON)
+        _load_data = partial(load_data, root_path=self.root_path, **self.storage_options)  # type: ignore
+        variables_metadata: VariablesMetadata = _load_data(data_enum=DataEnum.VARIABLES_JSON)  # type: ignore
 
         self._load_all_data(variables_metadata)
 
@@ -140,11 +158,11 @@ def prepare_data(self):
                     if v == VariableTypeEnum.CONTINUOUS and k not in self.exclude_normalization
                 }
 
-            self.normalizer = fit_standardizer(self._dataset_train.select(*normalization_variables))
-
-            transform = self.normalizer()
-            self._dataset_train = transform(self._dataset_train)
-            self._dataset_test = transform(self._dataset_test)
+            self.normalizer = self.fit_normalizer(self._dataset_train.select(*normalization_variables))
+            self._dataset_train = self.normalizer(self._dataset_train)
+            self._dataset_test = self.normalizer(self._dataset_test)
+        else:
+            self.normalizer = JointTransformModule({})
 
     def train_dataloader(self):
         return DataLoader(
@@ -162,11 +180,8 @@ def test_dataloader(self):
             test_dataloader,
             DataLoader(dataset=self.true_adj[None, ...]),
             DataLoader(dataset=self.interventions, collate_fn=identity, batch_size=None),
+            DataLoader(dataset=self.counterfactuals, collate_fn=identity, batch_size=None),
         ]
-
-        if self.counterfactuals is not None:
-            dataloader_list.append(DataLoader(dataset=self.counterfactuals, collate_fn=identity, batch_size=None))
-
         return dataloader_list
 
 
@@ -185,12 +200,16 @@ def __init__(
         batch_size: int = 128,
         dataset_path: str = CAUSICA_DATASETS_PATH,
         load_counterfactual: bool = False,
+        load_interventional: bool = False,
+        normalize: Union[bool, Iterable[str]] = False,
     ):
         super().__init__(
             root_path=os.path.join(dataset_path, dataset_name),
             batch_size=batch_size,
             dataset_name=dataset_name,
             load_counterfactual=load_counterfactual,
+            load_interventional=load_interventional,
+            normalize=normalize,
         )
 
 
diff --git a/src/causica/lightning/modules/deci_module.py b/src/causica/lightning/modules/deci_module.py
index 0aca5c3..c0373b1 100644
--- a/src/causica/lightning/modules/deci_module.py
+++ b/src/causica/lightning/modules/deci_module.py
@@ -1,11 +1,10 @@
 import logging
-from typing import Any, Optional, Sequence, Union
+from typing import Any, Mapping, Optional, Sequence, Union
 
 import fsspec
 import numpy as np
 import pytorch_lightning as pl
 import torch
-from pytorch_lightning.trainer.states import TrainerFn
 from pytorch_lightning.utilities.types import STEP_OUTPUT
 from tensordict import TensorDict
 
@@ -22,7 +21,7 @@
 )
 from causica.distributions.noise.joint import ContinuousNoiseDist
 from causica.fsspec_helpers import get_storage_options_for_path
-from causica.functional_relationships import ICGNN
+from causica.functional_relationships import DECIEmbedFunctionalRelationships
 from causica.graph.dag_constraint import calculate_dagness
 from causica.graph.evaluation_metrics import adjacency_f1, orientation_f1
 from causica.lightning.callbacks import AuglagLRCallback
@@ -53,8 +52,8 @@ def __init__(
         noise_dist: ContinuousNoiseDist = ContinuousNoiseDist.SPLINE,
         embedding_size: int = 32,
         out_dim_g: int = 32,
-        norm_layer: bool = True,
-        res_connection: bool = True,
+        num_layers_g: int = 2,
+        num_layers_zeta: int = 2,
         init_alpha: float = 0.0,
         init_rho: float = 1.0,
         prior_sparsity_lambda: float = 0.05,
@@ -62,31 +61,34 @@ def __init__(
         auglag_config: Optional[AugLagLRConfig] = None,
         expert_graph_container: Optional[ExpertGraphContainer] = None,
         constraint_matrix_path: Optional[str] = None,
+        disable_auglag_epochs: Optional[int] = None,
     ):
         super().__init__()
         self.auglag_config = auglag_config if auglag_config is not None else AugLagLRConfig()
+        self.disable_auglag_epochs = disable_auglag_epochs
+        self.lr_scheduler = AugLagLR(config=self.auglag_config)
         self.constraint_matrix_path = constraint_matrix_path
         self.constraint_matrix: Optional[torch.Tensor] = None
 
         self.embedding_size = embedding_size
+        self.out_dim_g = out_dim_g
+        self.num_layers_g = num_layers_g
+        self.num_layers_zeta = num_layers_zeta
+
         self.expert_graph_container: Optional[ExpertGraphContainer] = expert_graph_container
 
         self.gumbel_temp = gumbel_temp
-        self.init_alpha = init_alpha
-        self.init_rho = init_rho
         self.is_setup = False
         self.noise_dist = noise_dist
-        self.norm_layer = norm_layer
-        self.out_dim_g = out_dim_g
         self.prior_sparsity_lambda = prior_sparsity_lambda
-        self.res_connection = res_connection
+
+        self.auglag_loss: AugLagLossCalculator = AugLagLossCalculator(init_alpha=init_alpha, init_rho=init_rho)
 
         # Inferred once the datamodule is available using `self.infer_missing_state_from_dataset()`
-        self.dataset_name = None
         self.num_samples = None
         self.variable_group_shapes = None
         self.variable_types = None
-        self.variable_names = None
+        self.save_hyperparameters()
 
     def prepare_data(self) -> None:
         """Set the constraint matrix (if necessary)."""
@@ -103,11 +105,9 @@ def prepare_data(self) -> None:
 
     def infer_missing_state_from_dataset(self):
         dataset_defined_members = [
-            self.dataset_name,
             self.num_samples,
             self.variable_group_shapes,
             self.variable_types,
-            self.variable_names,
         ]
         if any(member is None for member in dataset_defined_members):
             datamodule = getattr(self.trainer, "datamodule", None)
@@ -116,49 +116,41 @@ def infer_missing_state_from_dataset(self):
                     f"Incompatible data module {datamodule}, requires a DECIDataModule but is "
                     f"{type(datamodule).mro()}"
                 )
-            if self.dataset_name is None:
-                self.dataset_name = datamodule.dataset_name
             if self.num_samples is None:
                 self.num_samples = len(datamodule.dataset_train)
             if self.variable_group_shapes is None:
                 self.variable_group_shapes = datamodule.variable_shapes
             if self.variable_types is None:
                 self.variable_types = datamodule.variable_types
-            if self.variable_names is None:
-                self.variable_names = datamodule.column_names
 
     def setup(self, stage: Optional[str] = None):
+
+        _ = stage
         if self.is_setup:
             return  # Already setup
-        if stage not in {TrainerFn.TESTING, TrainerFn.FITTING}:
-            raise ValueError(f"Model can only be setup during the {TrainerFn.FITTING} and {TrainerFn.TESTING} stages.")
 
         self.infer_missing_state_from_dataset()
-        assert self.dataset_name is not None
         assert self.num_samples is not None
         assert self.variable_group_shapes is not None
         assert self.variable_types is not None
-        assert self.variable_names is not None
 
-        self.node_names = list(self.variable_group_shapes)
         num_nodes = len(self.variable_group_shapes)
 
         adjacency_dist: DistributionModule[AdjacencyDistribution] = ENCOAdjacencyDistributionModule(num_nodes)
         if self.constraint_matrix is not None:
             adjacency_dist = ConstrainedAdjacency(adjacency_dist, self.constraint_matrix)
 
-        icgnn = ICGNN(
+        functional_relationships = DECIEmbedFunctionalRelationships(
             shapes=self.variable_group_shapes,
             embedding_size=self.embedding_size,
             out_dim_g=self.out_dim_g,
-            norm_layer=None if self.norm_layer is False else torch.nn.LayerNorm,
-            res_connection=self.res_connection,
+            num_layers_g=self.num_layers_g,
+            num_layers_zeta=self.num_layers_zeta,
         )
         noise_submodules = create_noise_modules(self.variable_group_shapes, self.variable_types, self.noise_dist)
         noise_module = JointNoiseModule(noise_submodules)
-        self.sem_module: SEMDistributionModule = SEMDistributionModule(adjacency_dist, icgnn, noise_module)
-        self.auglag_loss: AugLagLossCalculator = AugLagLossCalculator(
-            init_alpha=self.init_alpha, init_rho=self.init_rho
+        self.sem_module: SEMDistributionModule = SEMDistributionModule(
+            adjacency_dist, functional_relationships, noise_module
         )
         self.prior: GibbsDAGPrior = GibbsDAGPrior(
             num_nodes=num_nodes,
@@ -196,12 +188,16 @@ def training_step(self, *args, **kwargs) -> STEP_OUTPUT:
     def configure_optimizers(self):
         """Set the learning rates for different sets of parameters."""
         modules = {
-            "icgnn": self.sem_module.functional_relationships,
+            "functional_relationships": self.sem_module.functional_relationships,
             "vardist": self.sem_module.adjacency_module,
             "noise_dist": self.sem_module.noise_module,
         }
         parameter_list = [
-            {"params": module.parameters(), "lr": self.auglag_config.lr_init_dict[name], "name": name}
+            {
+                "params": module.parameters(),
+                "lr": self.auglag_config.lr_init_dict[name],
+                "name": name,
+            }
             for name, module in modules.items()
         ]
 
@@ -214,11 +210,12 @@ def configure_optimizers(self):
 
     def configure_callbacks(self) -> Union[Sequence[pl.Callback], pl.Callback]:
         """Create a callback for the auglag callback."""
-        lr_scheduler = AugLagLR(config=self.auglag_config)
-        return [AuglagLRCallback(lr_scheduler, log_auglag=True)]
+        disabled_epochs = set(range(self.disable_auglag_epochs)) if self.disable_auglag_epochs else None
+        return [AuglagLRCallback(self.lr_scheduler, log_auglag=True, disabled_epochs=disabled_epochs)]
 
     def test_step_observational(self, batch: TensorDict, *args, **kwargs):
         """Evaluate the log prob of the model on the test set using multiple graph samples."""
+        _, _ = args, kwargs
         batch = batch.apply(lambda t: t.to(torch.float32, non_blocking=True))
         sems = self.sem_module().sample(torch.Size([NUM_GRAPH_SAMPLES]))
         dataset_size = self.trainer.datamodule.dataset_test.batch_size  # type: ignore
@@ -249,17 +246,48 @@ def test_step_interventions(self, interventions: InterventionWithEffects, *args,
         _, _ = args, kwargs
         sems_list: list[SEM] = list(self.sem_module().sample(torch.Size([NUM_ATE_ITE_SEMS])))
         interventional_log_prob = eval_intervention_likelihoods(sems_list, interventions)
-        self.log("eval/Interventional_LL", torch.mean(interventional_log_prob).item(), add_dataloader_idx=False)
+        self.log(
+            "eval/Interventional_LL",
+            torch.mean(interventional_log_prob).item(),
+            add_dataloader_idx=False,
+        )
 
         mean_ate_rmse = eval_ate_rmse(sems_list, interventions)
-        self.log("eval/ATE_RMSE", list_mean(list(mean_ate_rmse.values())).item(), add_dataloader_idx=False)
+        self.log(
+            "eval/ATE_RMSE",
+            list_mean(list(mean_ate_rmse.values())).item(),
+            add_dataloader_idx=False,
+        )
 
     def test_step_counterfactuals(self, counterfactuals: CounterfactualWithEffects, *args, **kwargs):
         """Evaluate the ITE performance of the model"""
         _, _ = args, kwargs
         sems_list = list(self.sem_module().sample(torch.Size([NUM_ATE_ITE_SEMS])))
         ite_rmse = eval_ite_rmse(sems_list, counterfactuals)
-        self.log("eval/ITE_RMSE", list_mean(list(ite_rmse.values())).item(), add_dataloader_idx=False)
+        self.log(
+            "eval/ITE_RMSE",
+            list_mean(list(ite_rmse.values())).item(),
+            add_dataloader_idx=False,
+        )
 
-    def on_save_checkpoint(self, checkpoint: dict[str, Any]) -> None:
-        checkpoint["sem_module"] = self.sem_module
+    def load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True):
+        # initialise all the parameters, we can
+        super().load_state_dict(state_dict, strict=False)
+        # setup the model
+        self.setup()
+        # load the state dict again to fill in the parameters
+        return super().load_state_dict(state_dict, strict=strict)
+
+    def set_extra_state(self, state: Any):
+        self.variable_group_shapes = state["shapes"]
+        self.variable_types = state["types"]
+        self.constraint_matrix = state["constraint_matrix"]
+        self.num_samples = state["num_samples"]
+
+    def get_extra_state(self) -> Any:
+        return {
+            "shapes": self.variable_group_shapes,
+            "types": self.variable_types,
+            "constraint_matrix": self.constraint_matrix,
+            "num_samples": self.num_samples,
+        }
diff --git a/src/causica/nn/__init__.py b/src/causica/nn/__init__.py
new file mode 100644
index 0000000..2230faf
--- /dev/null
+++ b/src/causica/nn/__init__.py
@@ -0,0 +1 @@
+from causica.nn.deci_embed_nn import DECIEmbedNN
diff --git a/src/causica/nn/deci_embed_nn.py b/src/causica/nn/deci_embed_nn.py
new file mode 100644
index 0000000..645ccc8
--- /dev/null
+++ b/src/causica/nn/deci_embed_nn.py
@@ -0,0 +1,162 @@
+import torch
+from torch import nn
+
+
+class DECIEmbedNN(nn.Module):
+    """
+    Defines the function f for the SEM. For each variable x_i we use
+    f_i(x) = f(e_i, sum_{k in pa(i)} g(e_k, x_k)), where e_i is a learned embedding
+    for node i.
+    """
+
+    def __init__(
+        self,
+        group_mask: torch.Tensor,
+        embedding_size: int,
+        out_dim_g: int,
+        num_layers_g: int,
+        num_layers_zeta: int,
+    ):
+        """
+        Args:
+            group_mask: A mask of shape (num_nodes, num_processed_cols) such that group_mask[i, j] = 1. when col j is in group i.
+            embedding_size: Size of the embeddings used by each node. Uses the larger of 4 * concatenated_shape or embedding_size.
+            out_dim_g: Output dimension of the "inner" NN, l. If none, default is embedding size.
+            num_layers_g: Number of layers in the "inner" NN, l.
+            num_layers_zeta: Number of layers in the "outer" NN, ζ.
+        """
+        super().__init__()
+        self.group_mask = group_mask
+        num_nodes, concatenated_shape = group_mask.shape
+        # Initialize embeddings uⱼ
+        self.embeddings = nn.parameter.Parameter(0.01 * torch.randn(num_nodes, embedding_size), requires_grad=True)
+
+        # Set value for out_dim_g
+        # Set NNs sizes
+        a = max(4 * concatenated_shape, embedding_size, 64)
+        in_dim_g = embedding_size + concatenated_shape
+        in_dim_f = embedding_size + out_dim_g
+        self.l = _generate_fully_connected(
+            input_dim=in_dim_g,
+            output_dim=out_dim_g,
+            hidden_dims=[
+                a,
+            ]
+            * num_layers_g,
+        )
+        self.zeta = _generate_fully_connected(
+            input_dim=in_dim_f,
+            output_dim=concatenated_shape,
+            hidden_dims=[
+                a,
+            ]
+            * num_layers_zeta,
+        )
+        self.w = torch.nn.Parameter(torch.zeros((num_nodes, num_nodes)), requires_grad=True)
+
+    def forward(self, samples: torch.Tensor, graphs: torch.Tensor) -> torch.Tensor:
+        """
+        Computes non-linear function hᵢ(X, G) using the given adjacency matrix.
+
+            hᵢ(x, G) =  ζᵢ(Σⱼ Wᵢⱼ Gⱼᵢ lⱼ(xⱼ)
+
+        We also use an embedding u so:
+
+            hᵢ(x, G) =  ζ(uᵢ, Σⱼ Wᵢⱼ Gⱼᵢ l(uⱼ, xⱼ))
+
+        l takes inputs of size batch_shape + (embedding_size + concatenated_shape) and outputs batch_shape + (out_dim_g)
+        the input will be appropriately masked to correspond to one variable group
+
+        ζ takes inputs of size batch_shape + (embedding_size + out_dim_g) and outputs batch_shape + (concatenated_shape)
+        the ouptut is then masked to correspond to one variable
+
+        Args:
+            samples: Batched inputs, size batch_size_x + (concatenated_shape).
+            graphs: Adjacency matrix, size batch_size_g + (num_nodes, num_nodes).
+        Returns:
+            A tensor of shape batch_shape_x + batch_shape_g + (concatenated_shape)
+        """
+        batch_shape_x = samples.shape[:-1]
+        batch_shape_g = graphs.shape[:-2]
+
+        # Shape batch_shape_x + (num_nodes, concatenated_shape)
+        masked_samples = torch.einsum("...i,ji->...ji", samples, self.group_mask)
+        # Shape batch_shape_x + (num_nodes, embedding_size)
+        expanded_embed = self.embeddings.expand(*batch_shape_x, -1, -1)
+
+        # l(uⱼ, xⱼ) Shape batch_shape_x + (num_nodes, embedding_size + concatenated_shape)
+        encoded_samples = self.l(
+            torch.cat([masked_samples, expanded_embed], dim=-1)  # (concatenate xⱼ and embeddings uⱼ)
+        )  # Shape batch_shape_x + (num_nodes, out_dim_g)
+
+        target_shape = batch_shape_x + batch_shape_g + encoded_samples.shape[-2:]
+        view_shape = batch_shape_x + (1,) * len(batch_shape_g) + encoded_samples.shape[-2:]
+        # Shape batch_shape_x + batch_shape_g + (num_nodes, out_dim_g)
+        encoded_samples_broad = encoded_samples.view(view_shape).expand(target_shape)
+        # Aggregate sum and generate input for f (concatenate X_aggr and embeddings)
+        # Σⱼ Wᵢⱼ Gⱼᵢ l(uⱼ, xⱼ) Shape batch_shape_x + batch_shape_g + (num_nodes, out_dim_g)
+        encoded_samples_aggr = torch.einsum("...jk,...jl->...lk", encoded_samples_broad, self.w * graphs)
+
+        # expand dimensions of expanded_embed batch_shape_x + batch_shape_g + (num_nodes, embedding_size)
+        expanded_embed_broad = expanded_embed.view(view_shape).expand(target_shape)
+        # ζ(uᵢ, Σⱼ Wᵢⱼ Gⱼᵢ l(uⱼ, xⱼ)) Shape batch_shape_x + batch_shape_g + (num_nodes, concatenated_shape)
+        decoded_samples = self.zeta(torch.cat([encoded_samples_aggr, expanded_embed_broad], dim=-1))
+
+        # Mask and aggregate Shape batch_shape_x + batch_shape_g + (concatenated_shape)
+        return torch.einsum("...ij,ij->...j", decoded_samples, self.group_mask)
+
+
+def _generate_fully_connected(
+    input_dim: int,
+    output_dim: int,
+    hidden_dims: list[int],
+) -> nn.Module:
+    """
+    Generate a fully connected network.
+
+    Args:
+        input_dim: Int. Size of input to network.
+        output_dim: Int. Size of output of network.
+        hidden_dims: List of int. Sizes of internal hidden layers. i.e. [a, b] is three linear layers with shapes (input_dim, a), (a, b), (b, output_dim)
+
+    Returns:
+        Sequential object containing the desired network.
+    """
+    layers: list[nn.Module] = []
+
+    prev_dim = input_dim
+    for idx, hidden_dim in enumerate(hidden_dims):
+
+        block: list[nn.Module] = []
+
+        if idx > 0:
+            block.append(nn.LayerNorm(prev_dim))
+        block.extend([nn.Linear(prev_dim, hidden_dim), nn.LeakyReLU()])
+
+        seq_block: nn.Module = nn.Sequential(*block)
+        if prev_dim == hidden_dim:
+            seq_block = _ResBlock(seq_block)
+        layers.append(seq_block)
+
+        prev_dim = hidden_dim
+
+    layers.extend([nn.LayerNorm(prev_dim), nn.Linear(prev_dim, output_dim)])
+
+    return nn.Sequential(*layers)
+
+
+class _ResBlock(nn.Module):
+    """
+    Wraps an nn.Module, adding a skip connection to it.
+    """
+
+    def __init__(self, block: nn.Module):
+        """
+        Args:
+            block: module to which skip connection will be added. The input dimension must match the output dimension.
+        """
+        super().__init__()
+        self.block = block
+
+    def forward(self, x):
+        return x + self.block(x)
diff --git a/src/causica/training/auglag.py b/src/causica/training/auglag.py
index a69286c..154e7ee 100644
--- a/src/causica/training/auglag.py
+++ b/src/causica/training/auglag.py
@@ -1,6 +1,6 @@
 from collections import deque
 from dataclasses import dataclass, field
-from typing import Optional, Union
+from typing import Any, Optional, Union
 
 import torch
 from dataclasses_json import dataclass_json
@@ -10,44 +10,48 @@
 class AugLagLossCalculator(torch.nn.Module):
     def __init__(self, init_alpha: float, init_rho: float):
         super().__init__()
+        self.init_alpha = init_alpha
+        self.init_rho = init_rho
+
         self.alpha: torch.Tensor
         self.rho: torch.Tensor
-        self.register_buffer("alpha", torch.tensor(init_alpha, dtype=torch.float))
-        self.register_buffer("rho", torch.tensor(init_rho, dtype=torch.float))
+        self.register_buffer("alpha", torch.tensor(self.init_alpha, dtype=torch.float))
+        self.register_buffer("rho", torch.tensor(self.init_rho, dtype=torch.float))
 
     def forward(self, objective: torch.Tensor, constraint: torch.Tensor) -> torch.Tensor:
         return objective + self.alpha * constraint + self.rho * constraint * constraint / 2
 
 
 @dataclass_json
-@dataclass(frozen=True)
+@dataclass
 class AugLagLRConfig:
-    """
-    Configuration parameters for the AuglagLR scheduler.
-
-    lr_update_lag: Number of iterations to wait before updating the learning rate.
-    lr_update_lag_best: Number of iterations to wait after the best model before updating the learning rate.
-    lr_init_dict: Dictionary of intitialization parameters for every new inner optimization step.
-        This must contain all parameter_groups for all optimizers
-    aggregation_period: Aggregation period to compare the mean of the loss terms across this period.
-    lr_factor: Learning rate update schedule factor (exponential decay).
-    penalty_progress_rate: Number of iterations to wait before updating rho based on the dag penalty.
-    safety_rho: Maximum rho that could be updated to.
-    safety_alpha: Maximum alpha that could be udated to.
-    max_lr_down: Maximum number of lr update times to decide inner loop termination.
-    inner_early_stopping_patience: Maximum number of iterations to run after the best inner loss to terminate inner loop.
-    max_outer_steps: Maximum number of outer update steps.
-    patience_penalty_reached: Maximum number of outer iterations to run after the dag penalty has reached a good value.
-    patience_max_rho: Maximum number of iterations to run once rho threshold is reached.
-    penalty_tolerance: Tolerance of the dag penalty
-    max_inner_steps: Maximum number of inner loop steps to run.
-
+    """Configuration parameters for the AuglagLR scheduler.
+
+    Attributes:
+        lr_update_lag: Number of iterations to wait before updating the learning rate.
+        lr_update_lag_best: Number of iterations to wait after the best model before updating the learning rate.
+        lr_init_dict: Dictionary of intitialization parameters for every new inner optimization step. This must contain
+            all parameter_groups for all optimizers
+        aggregation_period: Aggregation period to compare the mean of the loss terms across this period.
+        lr_factor: Learning rate update schedule factor (exponential decay).
+        penalty_progress_rate: Number of iterations to wait before updating rho based on the dag penalty.
+        safety_rho: Maximum rho that could be updated to.
+        safety_alpha: Maximum alpha that could be udated to.
+        max_lr_down: Maximum number of lr update times to decide inner loop termination.
+        inner_early_stopping_patience: Maximum number of iterations to run after the best inner loss to terminate inner
+            loop.
+        max_outer_steps: Maximum number of outer update steps.
+        patience_penalty_reached: Maximum number of outer iterations to run after the dag penalty has reached a good
+            value.
+        patience_max_rho: Maximum number of iterations to run once rho threshold is reached.
+        penalty_tolerance: Tolerance of the dag penalty
+        max_inner_steps: Maximum number of inner loop steps to run.
     """
 
     lr_update_lag: int = 500
     lr_update_lag_best: int = 250
     lr_init_dict: dict[str, float] = field(
-        default_factory=lambda: {"vardist": 0.1, "icgnn": 0.0003, "noise_dist": 0.003}
+        default_factory=lambda: {"vardist": 0.1, "functional_relationships": 0.0003, "noise_dist": 0.003}
     )
     aggregation_period: int = 20
     lr_factor: float = 0.1
@@ -64,11 +68,14 @@ class AugLagLRConfig:
 
 
 class AugLagLR:
-    def __init__(self, config: AugLagLRConfig) -> None:
-        """A Pytorch like scheduler which performs the Augmented Lagrangian optimization procedure, which consists of
-        an inner loop which optimizes the objective for a fixed set of lagrangian parameters. The lagrangian parameters are
-        annealed in the outer loop, according to a schedule as specified by the hyperparameters.
+    """A Pytorch like scheduler which performs the Augmented Lagrangian optimization procedure.
+
+    It consists of an inner loop which optimizes the objective for a fixed set of lagrangian parameters. The lagrangian
+    parameters are annealed in the outer loop, according to a schedule as specified by the hyperparameters.
+    """
 
+    def __init__(self, config: AugLagLRConfig) -> None:
+        """
         Args:
             config: An `AugLagLRConfig` object containing the configuration parameters.
         """
@@ -83,6 +90,10 @@ def __init__(self, config: AugLagLRConfig) -> None:
         self.loss_tracker: deque[torch.Tensor] = deque([], maxlen=config.aggregation_period)
         self._init_new_inner_optimisation()
 
+        # Track whether auglag is disabled and the state of the loss when it was disabled
+        self._disabled = False
+        self._disabled_loss_state: Optional[dict[str, Any]] = None
+
     def _init_new_inner_optimisation(self) -> None:
         """Init the hyperparameters for a new inner loop optimization."""
         self.best_loss = torch.tensor(torch.inf)
@@ -119,8 +130,7 @@ def _is_outer_converged(self) -> bool:
         )
 
     def _enough_steps_since_last_lr_update(self) -> bool:
-        """Check if enough steps have been taken since the previous learning rate update, based on the previous
-        update step iteration.
+        """Check if enough steps have been taken since the previous learning rate update, based on the previous one.
 
         Returns:
             bool: indicating whether sufficient steps have occurred since the last update
@@ -246,6 +256,42 @@ def _check_best_loss(self):
                 self.best_loss = avg_loss
                 self.last_best_step = self.step_counter
 
+    @property
+    def disabled(self) -> bool:
+        return self._disabled
+
+    def enable(self, loss: AugLagLossCalculator) -> None:
+        """Enable auglag with the given loss calculator.
+
+        If auglag is disabled, this will restore the loss calculator state to the state when it was disabled and will
+        allow `step` to increment auglag iterations again.
+
+        Args:
+            loss: The loss calculator used with this scheduler
+        """
+        if not self._disabled:
+            return
+        if self._disabled_loss_state is not None:
+            loss.load_state_dict(self._disabled_loss_state)
+            self._disabled_loss_state = None
+            self._disabled = False
+
+    def disable(self, loss: AugLagLossCalculator) -> None:
+        """Disable auglag with the given loss calculator.
+
+        If auglag is enabled, this disables auglag iterations when `step` is called, stores the current state of the
+        loss so that it can be re-enabled and sets the constraint factors in the loss calculator to 0.
+
+        Args:
+            loss: The loss calculator used with this scheduler
+        """
+        if self._disabled:
+            return
+        self._disabled_loss_state = loss.state_dict()
+        loss.alpha = torch.zeros_like(loss.alpha)
+        loss.rho = torch.zeros_like(loss.rho)
+        self._disabled = True
+
     def step(
         self,
         optimizer: Union[Optimizer, list[Optimizer]],
@@ -264,6 +310,8 @@ def step(
         Returns:
             bool: if the auglag has converged (False) or not (True)
         """
+        if self.disabled:
+            return False
         assert torch.all(lagrangian_penalty >= 0), "auglag penalty must be non-negative"
         self._update_loss_tracker(loss_value.detach())
         self._cur_lagrangian_penalty = lagrangian_penalty.detach()
diff --git a/src/causica/training/per_variable_metrics.py b/src/causica/training/per_variable_metrics.py
index a960258..6c8e242 100644
--- a/src/causica/training/per_variable_metrics.py
+++ b/src/causica/training/per_variable_metrics.py
@@ -1,247 +1,42 @@
-from typing import Callable, Iterable, Optional
+from typing import Iterable, Optional, Union
 
-import torch
-from tensordict import TensorDict
+from torchmetrics import Metric, MetricCollection
+from torchmetrics.wrappers import MultitaskWrapper
 
-from causica.datasets.causica_dataset_format import CounterfactualWithEffects
-from causica.datasets.tensordict_utils import expand_tensordict_groups
-from causica.distributions.transforms import JointTransform
-from causica.sem.distribution_parameters_sem import DistributionParametersSEM
-from causica.sem.structural_equation_model import SEM, counterfactual
 
-
-def calculate_counterfactual_deci_metrics(
-    sems: Iterable[SEM],
-    counterfactual_data: CounterfactualWithEffects,
-    grouped_variable_names: Optional[dict[str, list[str]]] = None,
-    standardizer: Optional[JointTransform] = None,
-) -> dict[str, TensorDict]:
-    """Evaluate the counterfacual rmses of a model.
-
-    Args:
-        sems: An iterable of structural equation models to evaluate the ITE RMSE of
-        counterfactual_data: Data of true counterfactuals to use for evaluation.
-        grouped_variable_names: Optional dictionary that holds the names of the variables in each group. If given,
-            the variables are evaluated individually. Otherwise, the variables are evaluated groupwise.
-        standardizer: Standardizer that is used to invert the predictions and loaded data to convery the metrics into
-            the space of the original data.
-
-    Returns:
-        Dict of RMSEs and MAPEs for each effect variable we're interested in
+def create_metrics_for_variables(variables: Iterable[str], metrics: MetricCollection) -> MultitaskWrapper:
     """
-    intervention, _, effects = counterfactual_data
-
-    # generate samples from the intervened distribution and the base distribution
-    stacked: TensorDict = torch.stack(
-        [counterfactual(sem, intervention.factual_data, intervention.intervention_values) for sem in sems]
-    )
-    generated_cf_outcomes = stacked.apply(
-        lambda v: v.mean(axis=0), batch_size=intervention.factual_data.batch_size, inplace=False
-    )
-    true_counterfactual_outcome = intervention.counterfactual_data
-
-    if standardizer is not None:
-        generated_cf_outcomes = standardizer.inv(generated_cf_outcomes)
-        true_counterfactual_outcome = standardizer.inv(true_counterfactual_outcome)
-
-    if grouped_variable_names:
-        generated_cf_outcomes = expand_tensordict_groups(generated_cf_outcomes, grouped_variable_names)
-        true_counterfactual_outcome = expand_tensordict_groups(true_counterfactual_outcome, grouped_variable_names)
-        effects = {
-            var for group_name, group in grouped_variable_names.items() for var in group if group_name in effects
-        }
-
-    return {
-        "rmse": eval_per_variable_metric(generated_cf_outcomes, true_counterfactual_outcome, effects, rmse),
-        "mape": eval_per_variable_metric(generated_cf_outcomes, true_counterfactual_outcome, effects, mape),
-        "smape": eval_per_variable_metric(generated_cf_outcomes, true_counterfactual_outcome, effects, smape),
-    }
-
-
-def calculate_observational_deci_metrics(
-    sems: Iterable[DistributionParametersSEM],
-    observations: TensorDict,
-    continuous_variables: list[str],  # keys to variables to be interpreted as categorical
-    binary_variables: list[str],  # keys to variables to be interpreted as binary
-    categorical_variables: list[str],  # keys to variables to be interpreted as categorical
-    grouped_variable_names: Optional[dict[str, list[str]]] = None,
-    standardizer: Optional[JointTransform] = None,
-) -> dict[str, TensorDict]:
-    """Calculates the RMSE and accuracy of the predictions of a model.
+    This function will create a MultiTaskWrapper obj with keys to be the variable names.
 
     Args:
-        sems: An iterable of structural equation models to evaluate the ITE RMSE of
-        observations: Observational data to evaluate
-        continuous_variables: Keys of the continuous variables
-        binary_variables: Keys of the binary variables
-        categorical_variables: Keys of the categorical variables
-        grouped_variable_names: Optional dictionary that holds the names of the variables in each group. If given,
-            the variables are evaluated individually. Otherwise, the variables are evaluated groupwise.
-        standardizer: Standardizer that is used to invert the predictions and loaded data to convery the metrics into
-            the space of the original data.
+        variables: Variables name list to calculate the metrics for.
+        metrics: MetricCollection to store the metrics we want to compute.
 
     Returns:
-        Dict holding the different metrics
+        MultitaskWrapper obj with keys to be the variable names.
     """
-    assert all(
-        set(sem.node_names) == set(observations.keys()) for sem in sems
-    ), f"observations must be compatible with SEMs: got {set(observations.keys())} but expected {set(list(sems)[0].node_names)}"
-
-    assert set(continuous_variables + binary_variables + categorical_variables) == set(
-        observations.keys()
-    ), f"observations must be compatible with variables: got {set(observations.keys())} but expected {set(continuous_variables + binary_variables + categorical_variables)}"
-    assert set(continuous_variables).isdisjoint(
-        set(binary_variables)
-    ), f"continuous and binary variables must be disjoint: got {set(continuous_variables).intersection(set(binary_variables))}"
-    assert set(continuous_variables).isdisjoint(
-        set(categorical_variables)
-    ), f"continuous and categorical variables must be disjoint: got {set(continuous_variables).intersection(set(categorical_variables))}"
-    assert set(binary_variables).isdisjoint(
-        set(categorical_variables)
-    ), f"binary and categorical variables must be disjoint: got {set(binary_variables).intersection(set(categorical_variables))}"
 
-    stacked: TensorDict = torch.stack([sem.func(observations, sem.graph) for sem in sems])
-    mean_predictions = stacked.apply(lambda v: v.mean(axis=0), batch_size=observations.batch_size, inplace=False)
-
-    if standardizer is not None:
-        mean_predictions = standardizer.inv(mean_predictions)
-        observations = standardizer.inv(observations)
-
-    if grouped_variable_names:
-        mean_predictions = expand_tensordict_groups(mean_predictions, grouped_variable_names)
-        observations = expand_tensordict_groups(observations, grouped_variable_names)
-        continuous_variables = [
-            var for group in grouped_variable_names.values() for var in group if var in continuous_variables
-        ]
-        binary_variables = [
-            var for group in grouped_variable_names.values() for var in group if var in binary_variables
-        ]
-        categorical_variables = [
-            var for group in grouped_variable_names.values() for var in group if var in categorical_variables
-        ]
-
-    return {
-        "rmse": eval_per_variable_metric(mean_predictions, observations, continuous_variables, rmse),
-        "mape": eval_per_variable_metric(mean_predictions, observations, continuous_variables, mape),
-        "smape": eval_per_variable_metric(mean_predictions, observations, continuous_variables, smape),
-        "binary_accuracy": eval_per_variable_metric(mean_predictions, observations, binary_variables, binary_accuracy),
-        "categorical_accuracy": eval_per_variable_metric(
-            mean_predictions, observations, categorical_variables, categorical_accuracy
-        ),
+    metrics_dict: dict[str, Union[Metric, MetricCollection]] = {
+        key: metrics.clone(postfix=f".{key}") for key in variables
     }
 
+    return MultitaskWrapper(metrics_dict)
 
-def eval_per_variable_metric(
-    predictions: TensorDict,
-    observations: TensorDict,
-    variables: Iterable[str],
-    metric: Callable[[torch.Tensor, torch.Tensor], torch.Tensor],
-) -> TensorDict:
-    """Calculates a metric for  and accuracy of the predictions of a model.
-
-    Args:
-        predictions: Predictions to evaluate
-        observations: Observational data to evaluate
-        variables: Keys of the variables to evaluate the metric for
-        metric: Metric to evaluate
-        grouped_variable_names: Optional dictionary that holds the names of the variables in each group. If given,
-            the variables are evaluated individually. Otherwise, the variables are evaluated groupwise.
-        standardizer: Standardizer that is used to invert the predictions and loaded data to convery the metrics into
-            the space of the original data.
-
-    Returns:
-        Dict holding the metric for each node
-    """
-    assert set(variables).issubset(
-        predictions.keys()
-    ), f"predictions must contain all variables: got {predictions.keys()} but expected {variables}"
-    assert set(variables).issubset(
-        observations.keys()
-    ), f"observations must contain all variables: got {observations.keys()} but expected {variables}"
-
-    metric_results = predictions.select(*variables).apply(
-        metric, observations.select(*variables), batch_size=torch.Size([])
-    )
-
-    return metric_results
-
-
-def binary_accuracy(logits: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
-    """Calculate the accuracy of a prediction for a binary variable.
-
-    Args:
-        logits: Tensor of logits [batch_size] or [batch_size, num_dims_for_node]
-        target: Tensor of targets [batch_size] or [batch_size, num_dims_for_node]
-
-    Returns:
-        Accuracy of the prediction
-    """
-    return torch.mean(((torch.sigmoid(logits) > 0.5) == target).float())
-
-
-def categorical_accuracy(logits: torch.Tensor, target: torch.Tensor, target_is_onehot: bool = True) -> torch.Tensor:
-    """Calculate the accuracy of a prediction for a categorical variable.
-
-    Args:
-        logits: Tensor of logits [batch_size, num_classes]
-        target: Tensor of targets [batch_size, num_classes] (if target_is_onehot is True) or [batch_size] (otherwise)
-        target_is_onehot: Whether the target is one hot. Defaults to True.
-
-    Returns:
-        Accuracy of the prediction
-    """
-    if target_is_onehot:
-        target = torch.argmax(target, -1)
-
-    prediction = torch.argmax(logits, dim=-1)
-
-    return torch.mean((prediction == target).float())
 
-
-def rmse(prediction: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
-    """Calculate the RMSE of a prediction. This will sum over all dimensions except the batch dimension.
-
-    Args:
-        prediction: Tensor of predictions [batch_size, num_dims_for_node]
-        target: Tensor of targets [batch_size, num_dims_for_node]
-
-    Returns:
-        RMSE of the prediction
+def filter_metrics_wrapper(variable_list: Optional[list[str]], metrics_wrapper: MultitaskWrapper) -> MultitaskWrapper:
     """
-    return torch.sqrt(torch.mean(torch.sum((prediction - target) ** 2, -1)))
-
-
-def mape(prediction: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
-    """Calculate the mean absolute percentage error of a prediction. This will sum over all dimensions except the batch dimension.
+    This will filter the MultiTaskWrapper obj to select only the variables in variable_list. If None, the metrics will not be filtered.
 
     Args:
-        prediction: Tensor of predictions [batch_size, num_dims_for_node]
-        target: Tensor of targets [batch_size, num_dims_for_node]
+        variable_list: List of variables to filter the metrics_dict.
+        metrics_wrapper: MultitaskWrapper obj with keys to be the variable names.
 
     Returns:
-        MAPE of the prediction
+        Filtered MultitaskWrapper obj.
     """
+    if variable_list is None:
+        return metrics_wrapper
 
-    return torch.mean(torch.nansum(torch.abs((prediction - target) / target), -1))
+    filtered_dict = {key: metrics_wrapper.task_metrics[key] for key in variable_list}
 
-
-def smape(prediction: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
-    """Calculate the symmetric mean absolute percentage error of a prediction. This will sum over all dimensions except the batch dimension.
-
-    Args:
-        prediction: Tensor of predictions [batch_size, num_dims_for_node]
-        target: Tensor of targets [batch_size, num_dims_for_node]
-
-    Returns:
-        sMAPE of the prediction
-    """
-    return torch.mean(
-        torch.sum(
-            torch.where(
-                torch.abs(target) + torch.abs(prediction) == 0,
-                torch.zeros_like(target),
-                torch.abs((prediction - target) / (torch.abs(target) + torch.abs(prediction))),
-            ),
-            -1,
-        )
-    )
+    return MultitaskWrapper(filtered_dict)
diff --git a/test/data_generation/test_synthetic_dataset.py b/test/data_generation/test_synthetic_dataset.py
new file mode 100644
index 0000000..4e12088
--- /dev/null
+++ b/test/data_generation/test_synthetic_dataset.py
@@ -0,0 +1,111 @@
+import torch
+import torch.distributions as td
+from torch.utils.data import DataLoader
+
+from causica.data_generation.samplers.functional_relationships_sampler import LinearRelationshipsSampler
+from causica.data_generation.samplers.noise_dist_sampler import (
+    BernoulliNoiseModuleSampler,
+    JointNoiseModuleSampler,
+    UnivariateNormalNoiseModuleSampler,
+)
+from causica.data_generation.samplers.sem_sampler import SEMSampler
+from causica.data_generation.synthetic_dataset import CausalDataset
+from causica.datasets.interventional_data import InterventionData
+from causica.distributions import ErdosRenyiDAGDistribution
+from causica.lightning.data_modules.synthetic_data_module import _tuple_collate_fn
+
+
+def test_mixed_type_causal_dataset():
+
+    shapes_dict = {
+        "x_0": torch.Size([1]),
+        "x_1": torch.Size([5]),
+        "x_2": torch.Size([1]),
+    }
+
+    noise_dist_samplers = {
+        "x_0": UnivariateNormalNoiseModuleSampler(std_dist=td.Uniform(low=0.2, high=2.0), dim=1),
+        "x_1": UnivariateNormalNoiseModuleSampler(std_dist=td.Uniform(low=0.2, high=2.0), dim=5),
+        "x_2": BernoulliNoiseModuleSampler(base_logits_dist=td.Uniform(low=0.2, high=2.0), dim=1),
+    }
+
+    # Create adjacency distribution, joint noise module sampler, and functional relationships sampler
+    adjacency_dist = ErdosRenyiDAGDistribution(num_nodes=3, probs=torch.tensor(0.2))
+    joint_noise_module_sampler = JointNoiseModuleSampler(noise_dist_samplers)
+    initial_linear_coefficient_matrix_shape = sum(shape[0] for shape in shapes_dict.values())
+    functional_relationships_sampler = LinearRelationshipsSampler(
+        td.Uniform(
+            low=torch.ones((initial_linear_coefficient_matrix_shape, initial_linear_coefficient_matrix_shape)),
+            high=3.0 * torch.ones((initial_linear_coefficient_matrix_shape, initial_linear_coefficient_matrix_shape)),
+        ),
+        shapes_dict,
+    )
+
+    sem_sampler = SEMSampler(adjacency_dist, joint_noise_module_sampler, functional_relationships_sampler)
+
+    dataset = CausalDataset(sem_sampler, 5, 7, 1, 2)
+
+    for sample in dataset:
+        assert sample[0]["x_0"].shape == torch.Size([5, 1])
+        assert sample[0].batch_size == torch.Size([5])
+        assert sample[1]["x_0"].shape == torch.Size([5, 1])
+        assert sample[1].batch_size == torch.Size([5])
+        assert sample[0]["x_1"].shape == torch.Size([5, 5])
+        assert sample[1]["x_1"].shape == torch.Size([5, 5])
+        assert sample[2].shape == torch.Size([3, 3])
+        assert len(sample[3]) == 1
+        assert isinstance(sample[3][0], InterventionData)
+        assert all((sample[0]["x_2"].unique()[:, None] == torch.tensor([0.0, 1.0])).any(dim=1))
+
+
+def test_causal_dataset_dataloader():
+    shapes_dict = {
+        "x_0": torch.Size([1]),
+        "x_1": torch.Size([1]),
+        "x_2": torch.Size([1]),
+    }
+
+    noise_dist_samplers = {
+        "x_0": UnivariateNormalNoiseModuleSampler(std_dist=td.Uniform(low=0.2, high=2.0), dim=1),
+        "x_1": UnivariateNormalNoiseModuleSampler(std_dist=td.Uniform(low=0.2, high=2.0), dim=1),
+        "x_2": UnivariateNormalNoiseModuleSampler(std_dist=td.Uniform(low=0.2, high=2.0), dim=1),
+    }
+    # Create adjacency distribution, joint noise module sampler, and functional relationships sampler
+    adjacency_dist = ErdosRenyiDAGDistribution(num_nodes=3, probs=torch.tensor(0.2))
+    joint_noise_module_sampler = JointNoiseModuleSampler(noise_dist_samplers)
+    initial_linear_coefficient_matrix_shape = sum(shape[0] for shape in shapes_dict.values())
+    functional_relationships_sampler = LinearRelationshipsSampler(
+        td.Uniform(
+            low=torch.ones((initial_linear_coefficient_matrix_shape, initial_linear_coefficient_matrix_shape)),
+            high=3.0 * torch.ones((initial_linear_coefficient_matrix_shape, initial_linear_coefficient_matrix_shape)),
+        ),
+        shapes_dict,
+    )
+
+    sem_sampler = SEMSampler(adjacency_dist, joint_noise_module_sampler, functional_relationships_sampler)
+
+    dataset = CausalDataset(sem_sampler, 5, 7, 1, 2)
+
+    loader = DataLoader(dataset, batch_size=2, collate_fn=_tuple_collate_fn, drop_last=True)
+
+    for sample in loader:
+        assert sample[0]["x_0"].shape == torch.Size([2, 5, 1])
+        assert sample[0].batch_size == torch.Size([2, 5])
+        assert sample[1]["x_0"].shape == torch.Size([2, 5, 1])
+        assert sample[1].batch_size == torch.Size([2, 5])
+        assert sample[2].shape == torch.Size([2, 3, 3])
+        assert len(sample[3]) == 2
+        assert len(sample[3][0]) == 1
+        assert isinstance(sample[3][0][0], InterventionData)
+
+    dataset = CausalDataset(sem_sampler, 5, 7, 0)
+
+    loader = DataLoader(dataset, batch_size=2, collate_fn=_tuple_collate_fn, drop_last=True)
+
+    for sample in loader:
+        assert sample[0]["x_0"].shape == torch.Size([2, 5, 1])
+        assert sample[0].batch_size == torch.Size([2, 5])
+        assert sample[1]["x_0"].shape == torch.Size([2, 5, 1])
+        assert sample[1].batch_size == torch.Size([2, 5])
+        assert sample[2].shape == torch.Size([2, 3, 3])
+        assert sample[3] is None
diff --git a/test/datasets/test_datasets.py b/test/datasets/test_datasets.py
index 6e4853d..0aeed6a 100644
--- a/test/datasets/test_datasets.py
+++ b/test/datasets/test_datasets.py
@@ -5,7 +5,6 @@
 
 from causica.datasets.causica_dataset_format import Variable, tensordict_from_variables_metadata
 from causica.datasets.tensordict_utils import convert_one_hot, tensordict_from_pandas
-from causica.datasets.variable_types import VariableTypeEnum
 
 
 def test_dataset_without_groups():
diff --git a/test/datasets/test_standardizer.py b/test/datasets/test_standardizer.py
index 8dd49d3..5b68794 100644
--- a/test/datasets/test_standardizer.py
+++ b/test/datasets/test_standardizer.py
@@ -1,7 +1,7 @@
 import torch
 from tensordict import TensorDict
 
-from causica.datasets.standardizer import fit_standardizer
+from causica.datasets.normalization import fit_standardizer
 
 
 def test_standardizer():
@@ -13,7 +13,7 @@ def test_standardizer():
         batch_size=100,
     )
 
-    standardizer = fit_standardizer(data)()
+    standardizer = fit_standardizer(data)
 
     standardized_data = standardizer(data)
 
@@ -58,7 +58,7 @@ def test_standardizer_subset():
         batch_size=100,
     )
 
-    standardizer = fit_standardizer(data.select("x"))()
+    standardizer = fit_standardizer(data.select("x"))
 
     standardized_data = standardizer(data)
     assert torch.allclose(
@@ -85,7 +85,7 @@ def test_standardizer_with_zero_std():
         batch_size=100,
     )
 
-    standardizer = fit_standardizer(data)()
+    standardizer = fit_standardizer(data)
 
     standardized_data = standardizer(data)
 
diff --git a/test/distributions/noise/test_joint.py b/test/distributions/noise/test_joint.py
index 00c3d9a..3e61729 100644
--- a/test/distributions/noise/test_joint.py
+++ b/test/distributions/noise/test_joint.py
@@ -11,6 +11,8 @@
     UnivariateNormalNoise,
 )
 
+torch.manual_seed(0)
+
 NOISE_DISTRIBUTIONS = [
     IndependentNoise(BernoulliNoise(torch.randn(3), torch.randn(3)), 1),
     IndependentNoise(UnivariateNormalNoise(torch.randn(5), torch.arange(1, 6, dtype=torch.float)), 1),
@@ -90,8 +92,8 @@ def test_joint_noise_empirical(noise_a: Noise, noise_b: Noise):
     mean, std = torch.mean, torch.std
     for key, value in samples.items():
         joint_value = joint_samples.get(key)
-        torch.testing.assert_close(mean(value, sample_dim), mean(joint_value, sample_dim), atol=0.01, rtol=0.01)
-        torch.testing.assert_close(std(value, sample_dim), std(joint_value, sample_dim), atol=0.01, rtol=0.01)
+        torch.testing.assert_close(mean(value, sample_dim), mean(joint_value, sample_dim), atol=0.2, rtol=0.2)
+        torch.testing.assert_close(std(value, sample_dim), std(joint_value, sample_dim), atol=0.2, rtol=0.2)
 
     # Similar log probs
     torch.testing.assert_close(mean(log_probs), mean(joint_log_probs), atol=0.01, rtol=0.01)
diff --git a/test/distributions/test_sem_distribution.py b/test/distributions/test_sem_distribution.py
index b4832bb..cd2b09d 100644
--- a/test/distributions/test_sem_distribution.py
+++ b/test/distributions/test_sem_distribution.py
@@ -6,7 +6,7 @@
 from causica.distributions.noise.joint import JointNoiseModule
 from causica.distributions.noise.noise import Noise, NoiseModule
 from causica.distributions.noise.univariate_normal import UnivariateNormalNoiseModule
-from causica.functional_relationships import ICGNN
+from causica.functional_relationships import DECIEmbedFunctionalRelationships
 from causica.functional_relationships.functional_relationships import FunctionalRelationships
 from causica.sem.sem_distribution import SEMDistribution
 
@@ -19,7 +19,7 @@ def create_sem_params(
         name: UnivariateNormalNoiseModule(shape[-1]) for name, shape in shapes.items()
     }
     noise_dist = JointNoiseModule(independent_noise_modules)
-    func = ICGNN(shapes)
+    func = DECIEmbedFunctionalRelationships(shapes, embedding_size=32, out_dim_g=32, num_layers_g=2, num_layers_zeta=2)
     logits_exist = torch.randn((num_nodes, num_nodes))
     logits_orient = torch.randn(((num_nodes * (num_nodes - 1)) // 2,))
     adjacency_dist = ENCOAdjacencyDistribution(logits_exist=logits_exist, logits_orient=logits_orient)
diff --git a/test/distributions/transforms/test_base.py b/test/distributions/transforms/test_base.py
new file mode 100644
index 0000000..4972678
--- /dev/null
+++ b/test/distributions/transforms/test_base.py
@@ -0,0 +1,43 @@
+import gc
+
+from causica.distributions.transforms.base import TypedTransform
+
+
+class _StrIntTransform(TypedTransform[str, int]):
+    """Dummy transform for testing types."""
+
+    def _call(self, x: str) -> int:
+        return int(x)
+
+    def _inverse(self, y: int) -> str:
+        return str(y)
+
+
+def test_typed_transform() -> None:
+    """Tests the basic functionality of TypedTransform.
+
+    Should be paired with a mypy step to ensure that the types are consistent."""
+
+    transform = _StrIntTransform()
+    x: str = "1"
+    y: int = transform(x)
+    inverse: TypedTransform[int, str] = transform.inv  # Check that the inverse is indeed recognized as a TypedTransform
+    assert isinstance(inverse, TypedTransform)
+    assert transform.inv(y) == x
+
+
+def test_weak_ref_inv_release() -> None:
+    """Test that the weak reference to the inverse is released."""
+    transform = _StrIntTransform()
+    inverse = transform.inv
+    id_ = id(inverse)
+
+    # Check that the inverse is kept while there is an active reference
+    assert id(transform.inv) == id_
+
+    # Release the direct reference to the inverse and force garbage collection
+    del inverse
+    gc.collect()
+
+    # Check that accessing the inverse now yields a new object
+    assert id(transform.inv) != id_
diff --git a/test/distributions/test_transforms.py b/test/distributions/transforms/test_joint.py
similarity index 67%
rename from test/distributions/test_transforms.py
rename to test/distributions/transforms/test_joint.py
index 68a95f1..8e26736 100644
--- a/test/distributions/test_transforms.py
+++ b/test/distributions/transforms/test_joint.py
@@ -3,7 +3,7 @@
 from tensordict import TensorDict
 from torch.distributions import AffineTransform
 
-from causica.distributions.transforms import JointTransform
+from causica.distributions.transforms.joint import JointTransform
 
 
 @pytest.fixture(name="affine_transform")
@@ -16,26 +16,32 @@ def fixture_affine_transform() -> JointTransform:
     return JointTransform(transformation_dict)
 
 
-def test_joint_transform_call_and_inv(affine_transform):  #
+def test_joint_transform_call_and_inv(affine_transform):
     data = TensorDict({"x": torch.randn((100, 1)), "y": torch.randn((100, 1))}, batch_size=100)
 
     transformed_data = affine_transform(data)
 
-    assert torch.allclose(transformed_data["x"], data["x"], atol=1e-6)
-    assert torch.allclose(transformed_data["y"], data["y"] * 2 + 1, atol=1e-6)
+    assert torch.allclose(transformed_data["x"], data.get("x"), atol=1e-6)
+    assert torch.allclose(transformed_data["y"], data.get("y") * 2 + 1, atol=1e-6)
 
-    assert torch.allclose(affine_transform.inv(transformed_data)["x"], data["x"], atol=1e-6)
-    assert torch.allclose(affine_transform.inv(transformed_data)["y"], data["y"], atol=1e-6)
+    assert torch.allclose(affine_transform.inv(transformed_data)["x"], data.get("x"), atol=1e-6)
+    assert torch.allclose(affine_transform.inv(transformed_data)["y"], data.get("y"), atol=1e-6)
 
 
 def test_joint_transform_log_abs_det_jacobian(affine_transform):
     data = TensorDict({"x": torch.randn((100, 1)), "y": torch.randn((100, 1))}, batch_size=100)
 
     transformed_data = affine_transform(data)
-
     log_abs_det_jacobian = affine_transform.log_abs_det_jacobian(data, transformed_data)
 
     assert torch.allclose(log_abs_det_jacobian["x"], torch.zeros_like(log_abs_det_jacobian["x"]), atol=1e-6)
     assert torch.allclose(
         log_abs_det_jacobian["y"], torch.log(torch.ones_like(log_abs_det_jacobian["y"]) * 2), atol=1e-6
     )
+
+
+def test_joint_transform_passthrough(affine_transform):
+    data = TensorDict(
+        {"x": torch.randn((100, 1)), "y": torch.randn((100, 1)), "z": torch.randn((100, 3))}, batch_size=100
+    )
+    assert torch.allclose(affine_transform(data)["z"], data.get("z"))
diff --git a/test/distributions/transforms/test_transform_modules.py b/test/distributions/transforms/test_transform_modules.py
new file mode 100644
index 0000000..f9fbdd6
--- /dev/null
+++ b/test/distributions/transforms/test_transform_modules.py
@@ -0,0 +1,126 @@
+"""Tests for the different TransformModules to make sure tensors are properly registered."""
+import io
+import itertools
+from typing import Any, TypeVar
+
+import pytest
+import torch
+from tensordict import TensorDictBase, make_tensordict
+
+from causica.distributions.transforms import SequentialTransformModule
+from causica.distributions.transforms.base import TransformModule
+from causica.distributions.transforms.joint import JointTransformModule
+
+
+class _OffsetTransformModule(TransformModule[torch.Tensor, torch.Tensor]):
+    """Dummy transform module that adds a constant to the input tensor.
+
+    Used for testing the registration of transform modules."""
+
+    def __init__(self, offset: torch.Tensor):
+        super().__init__(cache_size=0)
+        self.offset: torch.Tensor
+        self.register_buffer("offset", offset)
+
+    def _call(self, x: torch.Tensor) -> torch.Tensor:
+        return x + self.offset
+
+    def _inverse(self, y: torch.Tensor) -> torch.Tensor:
+        return y - self.offset
+
+
+def _test_triplets():
+    """Generate test triplets of (data, transform, expected_result)."""
+    data = torch.randn((3, 1), dtype=torch.float32)
+    offset = torch.full((3, 1), 7.5, dtype=torch.float32)
+    transform = _OffsetTransformModule(offset)
+    return [
+        (data, transform, data + offset),
+        (data, SequentialTransformModule[torch.Tensor, torch.Tensor](transform, transform.inv), data),
+        (data, SequentialTransformModule[torch.Tensor, torch.Tensor](transform, transform), data + 2 * offset),
+        (make_tensordict({"a": data}), JointTransformModule({"a": transform}), make_tensordict({"a": data + offset})),
+    ]
+
+
+X = TypeVar("X", torch.Tensor, TensorDictBase)
+Y = TypeVar("Y", torch.Tensor, TensorDictBase)
+
+
+@pytest.mark.parametrize("data,transform,expected_result", _test_triplets())
+def test_transform_module_output(data: X, transform: TransformModule[X, Y], expected_result: Y) -> None:
+    output = transform(data)
+    torch.testing.assert_close(output, expected_result)
+
+    inverse = transform.inv
+    assert inverse.inv is transform
+    torch.testing.assert_close(inverse(output), data)
+
+
+@pytest.mark.parametrize("data,transform,_", _test_triplets())
+@pytest.mark.parametrize("to_kwargs", [{"dtype": torch.float16}])
+def test_registration(data: X, transform: TransformModule[X, Y], _, to_kwargs: dict[str, Any]) -> None:
+    """Test that registration is working by testing that the state can be moved and loaded."""
+    transform_modified: TransformModule[X, Y] = transform.to(**to_kwargs)
+
+    # Collect parameters and buffers as tensors
+    tensors = dict(itertools.chain(transform.named_buffers(), transform.named_parameters()))
+    tensors_modified = dict(itertools.chain(transform_modified.named_buffers(), transform_modified.named_parameters()))
+
+    # Check that the tensors are equivalent
+    assert set(tensors) == set(tensors_modified)
+    for name in tensors:
+        torch.testing.assert_close(tensors[name].to(**to_kwargs), tensors_modified[name])
+
+    # Check that the state dict is consistent and picklable
+    state_dict = transform_modified.state_dict()
+    with io.BytesIO() as f:
+        torch.save(state_dict, f)
+        f.seek(0)
+        state_dict = torch.load(f)
+    for name in tensors:
+        torch.testing.assert_close(tensors[name].to(**to_kwargs), state_dict[name])
+
+    # Produce the output for x
+    if isinstance(data, TensorDictBase):
+        x_modified = data.apply(lambda x_: x_.to(**to_kwargs))
+    else:
+        x_modified = data.to(**to_kwargs)
+    y_modified = transform_modified(x_modified)
+    y = transform(data)
+
+    # Check that the output remains correct, i.e. the transformation is approx equivariant w.r.t. the `to` operator.
+    if isinstance(y, TensorDictBase):
+        assert isinstance(y_modified, TensorDictBase)  # plays nicer with mypy than checking type equality
+        for key in y.keys():
+            torch.testing.assert_close(y_modified.get(key), y.get(key).to(**to_kwargs), atol=2e-2, rtol=1e-2)
+    else:
+        assert isinstance(y_modified, torch.Tensor)  # plays nicer with mypy than checking type equality
+        torch.testing.assert_close(y_modified, y.to(**to_kwargs), atol=2e-2, rtol=1e-2)
+
+
+def test_transform_module_registration_buffers() -> None:
+    # Check that z is in buffers
+    offset = torch.randn((5, 1))
+    transform = _OffsetTransformModule(offset)
+    buffers = dict(transform.named_buffers())
+    torch.testing.assert_close(buffers["offset"], offset)
+
+
+def test_sequential_transform_module_inner_buffers() -> None:
+    offset = torch.randn((5, 1))
+    transform = _OffsetTransformModule(offset)
+    seq_transform = SequentialTransformModule[torch.Tensor, torch.Tensor](transform, transform.inv)
+    # Check that buffers are stored for the inner transformation
+    seq_buffers = dict(seq_transform.named_buffers())
+    for name, buffer in transform.named_buffers():
+        torch.testing.assert_close(seq_buffers[f"0.{name}"], buffer)
+
+
+def test_joint_transform_module_inner_buffers() -> None:
+    offset = torch.randn((2,))
+    transform = _OffsetTransformModule(offset)
+    joint_transform = JointTransformModule({"a": transform})
+    # Check that buffers are stored for the inner transformation
+    joint_buffers = dict(joint_transform.named_buffers())
+    for name, buffer in transform.named_buffers():
+        torch.testing.assert_close(joint_buffers[f"transformations.a.{name}"], buffer)
diff --git a/test/functional_relationships/test_functional_relationships.py b/test/functional_relationships/test_functional_relationships.py
index 3becaea..bfc9bc6 100644
--- a/test/functional_relationships/test_functional_relationships.py
+++ b/test/functional_relationships/test_functional_relationships.py
@@ -1,7 +1,13 @@
+import math
+
 import pytest
 import torch
 
-from causica.functional_relationships import ICGNN, LinearFunctionalRelationships
+from causica.functional_relationships import (
+    DECIEmbedFunctionalRelationships,
+    LinearFunctionalRelationships,
+    RFFFunctionalRelationships,
+)
 
 
 @pytest.fixture(name="two_variable_dict")
@@ -24,28 +30,28 @@ def fixture_two_variable_graphs():
     return torch.Tensor([[[0.0, 1.0], [0.0, 0.0]], [[0.0, 0.0], [1.0, 0.0]]])
 
 
-def test_ICGNN_init(two_variable_dict):
-    icgnn = ICGNN(two_variable_dict)
+def test_func_rel_init(two_variable_dict):
+    func_rel = DECIEmbedFunctionalRelationships(two_variable_dict, 32, 32, 2, 2)
 
-    assert icgnn.tensor_to_td.output_shape == 3
+    assert func_rel.tensor_to_td.output_shape == 3
 
 
-def test_ICGNN_forward(two_variable_dict, two_variable_graph, two_variable_sample):
-    icgnn = ICGNN(two_variable_dict)
-    icgnn.nn.w = torch.nn.Parameter(torch.ones_like(icgnn.nn.w), requires_grad=False)
-    prediction = icgnn(two_variable_sample, two_variable_graph)
+def test_func_rel_forward(two_variable_dict, two_variable_graph, two_variable_sample):
+    func_rel = DECIEmbedFunctionalRelationships(two_variable_dict, 32, 32, 2, 2)
+    func_rel.nn.w = torch.nn.Parameter(torch.ones_like(func_rel.nn.w), requires_grad=False)
+    prediction = func_rel(two_variable_sample, two_variable_graph)
     assert set(prediction.keys()) == {"x1", "x2"}
     assert prediction["x1"].shape == (3, 1), f"got {prediction['x1'].shape}"
     assert prediction["x2"].shape == (3, 2)
-    assert torch.all(prediction["x1"] == prediction["x1"][0, 0])
-    assert not torch.all(prediction["x2"][..., 0] == prediction["x2"][0, 0])
-    assert not torch.all(prediction["x2"][..., 1] == prediction["x2"][0, 1])
+    assert torch.allclose(prediction["x1"], prediction["x1"][0, 0])
+    assert not torch.allclose(prediction["x2"][..., 0], prediction["x2"][0, 0])
+    assert not torch.allclose(prediction["x2"][..., 1], prediction["x2"][0, 1])
 
 
-def test_ICGNN_forward_multigraph(two_variable_dict, two_variable_graphs, two_variable_sample):
-    icgnn = ICGNN(two_variable_dict)
-    icgnn.nn.w = torch.nn.Parameter(torch.ones_like(icgnn.nn.w), requires_grad=False)
-    prediction = icgnn(two_variable_sample, two_variable_graphs)
+def test_func_rel_forward_multigraph(two_variable_dict, two_variable_graphs, two_variable_sample):
+    func_rel = DECIEmbedFunctionalRelationships(two_variable_dict, 32, 32, 2, 2)
+    func_rel.nn.w = torch.nn.Parameter(torch.ones_like(func_rel.nn.w), requires_grad=False)
+    prediction = func_rel(two_variable_sample, two_variable_graphs)
     assert set(prediction.keys()) == {"x1", "x2"}
     assert prediction["x1"].shape == (3, 2, 1), f"got {prediction['x1'].shape}"
     assert prediction["x2"].shape == (3, 2, 2)
@@ -53,7 +59,7 @@ def test_ICGNN_forward_multigraph(two_variable_dict, two_variable_graphs, two_va
     assert torch.allclose(prediction["x1"][:, 0, :], prediction["x1"][0, 0, :])
     assert torch.allclose(prediction["x2"][:, 1, :], prediction["x2"][0, 1, :])
 
-    prediction2 = icgnn({"x1": torch.randn((3, 1)), "x2": torch.randn((3, 2))}, two_variable_graphs)
+    prediction2 = func_rel({"x1": torch.randn((3, 1)), "x2": torch.randn((3, 2))}, two_variable_graphs)
     assert torch.allclose(prediction2["x1"][:, 0, :], prediction["x1"][:, 0, :])
     assert torch.allclose(prediction2["x2"][:, 1, :], prediction["x2"][:, 1, :])
 
@@ -92,3 +98,47 @@ def test_linear_forward_multigraph(two_variable_dict, two_variable_graphs, two_v
     assert torch.allclose(prediction["x1"][:, 1, :], true_x1_prediction)
     true_x2_prediction = torch.matmul(two_variable_sample["x1"], coef_matrix[:1, 1:])
     assert torch.allclose(prediction["x2"][:, 0, :], true_x2_prediction)
+
+
+def test_non_linear_forward(two_variable_dict, two_variable_graph, two_variable_sample):
+    random_features = torch.rand((5, 3))
+    coeff_alpha = torch.rand((5,))
+    func_rel = RFFFunctionalRelationships(two_variable_dict, random_features, coeff_alpha)
+    prediction = func_rel(two_variable_sample, two_variable_graph)
+    assert set(prediction.keys()) == {"x1", "x2"}
+    assert prediction["x1"].shape == (3, 1), f"got {prediction['x1'].shape}"
+    assert prediction["x2"].shape == (3, 2)
+    assert torch.all(prediction["x1"] == 0.0)
+
+    true_inner_prods = two_variable_sample["x1"] * random_features[:, 0]
+    transformed_inner_prods = torch.sin(true_inner_prods) * coeff_alpha
+    true_x2_prediction = math.sqrt(2 / 5) * torch.sum(transformed_inner_prods, dim=-1)
+    true_x2_prediction = true_x2_prediction.unsqueeze(-1).repeat(1, 2)
+    assert torch.allclose(prediction["x2"], true_x2_prediction)
+
+
+def test_non_linear_forward_multigraph(two_variable_dict, two_variable_graphs, two_variable_sample):
+    random_features = torch.rand((5, 3))
+    coeff_alpha = torch.rand((5,))
+    func_rel = RFFFunctionalRelationships(two_variable_dict, random_features, coeff_alpha)
+    prediction = func_rel(two_variable_sample, two_variable_graphs)
+
+    assert set(prediction.keys()) == {"x1", "x2"}
+    assert prediction["x1"].shape == (3, 2, 1), f"got {prediction['x1'].shape}"
+    assert prediction["x2"].shape == (3, 2, 2)
+
+    # x1 and x2 are initial nodes for graphs 0 and 1 respectively
+    assert torch.all(prediction["x1"][:, 0, :] == 0.0)
+    assert torch.all(prediction["x2"][:, 1, :] == 0.0)
+
+    true_inner_prods = two_variable_sample["x1"] * random_features[:, 0]
+    transformed_inner_prods = torch.sin(true_inner_prods) * coeff_alpha
+    true_x2_prediction = math.sqrt(2 / 5) * torch.sum(transformed_inner_prods, dim=-1)
+    true_x2_prediction = true_x2_prediction.unsqueeze(-1).repeat(1, 2)
+    assert torch.allclose(prediction["x2"][:, 0, :], true_x2_prediction)
+
+    true_inner_prods = torch.matmul(two_variable_sample["x2"], random_features[:, 1:].transpose(-2, -1))
+    transformed_inner_prods = torch.sin(true_inner_prods) * coeff_alpha
+    true_x1_prediction = math.sqrt(2 / 5) * torch.sum(transformed_inner_prods, dim=-1)
+    true_x1_prediction = true_x1_prediction.unsqueeze(-1)
+    assert torch.allclose(prediction["x1"][:, 1, :], true_x1_prediction)
diff --git a/test/integration/decimodule.pt b/test/integration/decimodule.pt
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diff --git a/test/integration/test_evaluation.py b/test/integration/test_evaluation.py
index 6ad5f8c..a6a368b 100644
--- a/test/integration/test_evaluation.py
+++ b/test/integration/test_evaluation.py
@@ -11,7 +11,6 @@
 from causica.functional_relationships.linear_functional_relationships import LinearFunctionalRelationships
 from causica.sem.distribution_parameters_sem import DistributionParametersSEM
 from causica.training.evaluation import eval_ate_rmse, eval_intervention_likelihoods, eval_ite_rmse
-from causica.training.per_variable_metrics import calculate_counterfactual_deci_metrics
 
 
 def sem(data_dir: str) -> DistributionParametersSEM:
@@ -58,18 +57,3 @@ def test_evaluation_ite(dataset):
     root = os.path.join(CAUSICA_DATASETS_PATH, dataset)
     ite = eval_ite_rmse([sem(root)], load_data(root, DataEnum.COUNTERFACTUALS)[0])
     assert ite.shape == tuple()
-
-
-@pytest.mark.parametrize("dataset", ["csuite_linexp_2"])
-def test_calculate_counterfactual_deci_metrics(dataset):
-    root = os.path.join(CAUSICA_DATASETS_PATH, dataset)
-    data = load_data(root, DataEnum.COUNTERFACTUALS)
-    cf = calculate_counterfactual_deci_metrics([sem(root)], data[0])
-    grouped_variable_names = {"x0": ["x0_0", "x0_1"], "x1": ["x1_0", "x1_1"]}
-    cf_metrics = calculate_counterfactual_deci_metrics(
-        [sem(root)], data[0], grouped_variable_names=grouped_variable_names
-    )
-    assert cf["rmse"].shape == tuple()
-    assert cf_metrics["rmse"].shape == tuple()
-    assert len(cf["rmse"].keys()) == 1
-    assert len(cf_metrics["rmse"].keys()) == 2
diff --git a/test/integration/test_pytorch_lightning.py b/test/integration/test_pytorch_lightning.py
index db0de9b..cdbde78 100644
--- a/test/integration/test_pytorch_lightning.py
+++ b/test/integration/test_pytorch_lightning.py
@@ -46,9 +46,58 @@ def test_pytorch_lightning_deterministic(dataset, noise_dist):
     trainer.test(module, data_module)
 
 
-@pytest.mark.parametrize("dataset", ["csuite_mixed_simpson"])
-def test_pytorch_lightning_expert_input(tmp_path, dataset):
+def test_pytorch_lightning_save_checkpoint(tmp_path):
+    """Test we can load a DECI Module"""
+    dataset = "csuite_mixed_simpson"
+    active_run = mlflow.active_run()
+    if active_run is not None:
+        mlflow.end_run()
+
+    trainer = pl.Trainer(fast_dev_run=True)
+    data_module = CSuiteDataModule(dataset_name=dataset)
+    data_module.prepare_data()
+    adj_matrix = data_module.true_adj
+    expert_graph_container = ExpertGraphContainer(
+        dag=adj_matrix, mask=torch.ones_like(adj_matrix), confidence=0.9, scale=1.0
+    )
+
+    constraint_matrix_path = tmp_path / "constraint_graph.npy"
+    with constraint_matrix_path.open("wb") as f:
+        np.save(f, adj_matrix.numpy())
+
+    module = DECIModule(
+        constraint_matrix_path=str(constraint_matrix_path),
+        expert_graph_container=expert_graph_container,
+        noise_dist=ContinuousNoiseDist.SPLINE,
+    )
+
+    trainer.fit(module, data_module)
+    path = tmp_path / "test.ckpt"
+    trainer.save_checkpoint(path)
+
+    module2 = DECIModule.load_from_checkpoint(path)
+
+    module1_params = dict(module.named_parameters())
+    module2_params = dict(module2.named_parameters())
+    assert module1_params.keys() == module2_params.keys()
+    # check that all parameters in the model are equal after loading
+    for key, value in module1_params.items():
+        torch.testing.assert_close(value, module2_params[key])
+    # check that the expert graph container is in the module params
+    # by the above check it is preserved by checkpointing
+    assert "expert_graph_container.dag" in module1_params.keys()
+    # check the constraint matrix is equal
+    torch.testing.assert_close(module.constraint_matrix, module2.constraint_matrix)
+
+
+def test_pytorch_lightning_load_checkpoint():
+    """Check that loading a historical model works"""
+    DECIModule.load_from_checkpoint("test/integration/decimodule.pt")
+
+
+def test_pytorch_lightning_expert_input(tmp_path):
     """Test that the additional expert graph and constraints can be used."""
+    dataset = "csuite_mixed_simpson"
     active_run = mlflow.active_run()
     if active_run is not None:
         mlflow.end_run()
diff --git a/test/functional_relationships/test_fgnni.py b/test/nn/test_deci_embed_nn.py
similarity index 84%
rename from test/functional_relationships/test_fgnni.py
rename to test/nn/test_deci_embed_nn.py
index c182e25..b982292 100644
--- a/test/functional_relationships/test_fgnni.py
+++ b/test/nn/test_deci_embed_nn.py
@@ -1,7 +1,7 @@
 import pytest
 import torch
 
-from causica.functional_relationships.icgnn import FGNNI
+from causica.nn import DECIEmbedNN
 
 PROCESSED_DIM = 6
 NODE_NUM = 4
@@ -27,6 +27,6 @@ def test_fgnni_broadcast(graph_shape, sample_shape):
     graph_tensor = torch.randint(0, 2, (*graph_shape, NODE_NUM, NODE_NUM), dtype=torch.float32)
     sample_tensor = torch.randn((*sample_shape, PROCESSED_DIM))
 
-    fgnni = FGNNI(group_mask=GROUP_MASK)
+    fgnni = DECIEmbedNN(group_mask=GROUP_MASK, embedding_size=32, out_dim_g=32, num_layers_g=2, num_layers_zeta=2)
     out = fgnni(sample_tensor, graph_tensor)
     assert out.shape == sample_shape + graph_shape + (PROCESSED_DIM,)
diff --git a/test/sem/test_treatment_effects.py b/test/sem/test_treatment_effects.py
index 7370048..726d708 100644
--- a/test/sem/test_treatment_effects.py
+++ b/test/sem/test_treatment_effects.py
@@ -26,7 +26,8 @@ def test_ate_ite_cf_two_node(graph, two_variable_dict):
     intervention_values_a = TensorDict({"x2": torch.tensor([1.42, 0.42])}, batch_size=tuple())
     intervention_values_b = TensorDict({"x2": torch.tensor([0.42, 1.42])}, batch_size=tuple())
     average_treatment_effect = ate(sem, intervention_values_a, intervention_values_b)
-    factual_data = sem.sample(torch.Size([100]))
+    sample_size = 100
+    factual_data = sem.sample(torch.Size([sample_size]))
     if graph[0, 1] > 0.0:
         expected_treatment_effect = torch.zeros_like(average_treatment_effect["x1"])
         expected_mean_a = factual_data["x1"]
@@ -34,12 +35,12 @@ def test_ate_ite_cf_two_node(graph, two_variable_dict):
         expected_mean_a = torch.einsum("i,ij->j", intervention_values_a["x2"], coef_matrix[1:, :1])
         expected_mean_b = torch.einsum("i,ij->j", intervention_values_b["x2"], coef_matrix[1:, :1])
         expected_treatment_effect = expected_mean_a - expected_mean_b
-    assert torch.allclose(average_treatment_effect["x1"], expected_treatment_effect, rtol=1e-4)
+    torch.testing.assert_close(average_treatment_effect["x1"], expected_treatment_effect)
 
     individual_treatment_effect = ite(sem, factual_data, intervention_values_a, intervention_values_b)
     cf_effect = counterfactual(sem, factual_data, intervention_values_a)
-    assert torch.allclose(individual_treatment_effect["x1"], expected_treatment_effect)
-    assert torch.allclose(cf_effect["x1"], expected_mean_a)
+    torch.testing.assert_close(individual_treatment_effect["x1"], expected_treatment_effect.expand((sample_size, 1)))
+    torch.testing.assert_close(cf_effect["x1"], expected_mean_a.expand((sample_size, 1)))
 
 
 def test_ate_ite_cf_three_node(three_variable_dict):
@@ -51,16 +52,17 @@ def test_ate_ite_cf_three_node(three_variable_dict):
     intervention_values_a = TensorDict({"x2": torch.tensor([1.42, 0.42])}, batch_size=tuple())
     intervention_values_b = TensorDict({"x2": torch.tensor([0.42, 1.42])}, batch_size=tuple())
     average_treatment_effect = ate(sem, intervention_values_a, intervention_values_b)
-    assert torch.allclose(average_treatment_effect["x1"], torch.zeros_like(average_treatment_effect["x1"]))
+    torch.testing.assert_close(average_treatment_effect["x1"], torch.zeros_like(average_treatment_effect["x1"]))
     expected_mean_a = torch.einsum("i,ij->j", intervention_values_a["x2"], coef_matrix[2:4, 4:])
     expected_mean_b = torch.einsum("i,ij->j", intervention_values_b["x2"], coef_matrix[2:4, 4:])
     expected_treatment_effect = expected_mean_a - expected_mean_b
-    assert torch.allclose(average_treatment_effect["x3"], expected_treatment_effect)
+    torch.testing.assert_close(average_treatment_effect["x3"], expected_treatment_effect)
 
-    factual_data = sem.sample(torch.Size([100]))
+    sample_size = 100
+    factual_data = sem.sample(torch.Size([sample_size]))
     individual_treatment_effect = ite(sem, factual_data, intervention_values_a, intervention_values_b)
     cf_effect = counterfactual(sem, factual_data, intervention_values_a)
-    assert torch.allclose(individual_treatment_effect["x1"], torch.zeros_like(individual_treatment_effect["x1"]))
-    assert torch.allclose(individual_treatment_effect["x3"], expected_treatment_effect)
-    assert torch.allclose(cf_effect["x1"], factual_data["x1"])
-    assert torch.allclose(cf_effect["x3"], expected_mean_a)
+    torch.testing.assert_close(individual_treatment_effect["x1"], torch.zeros_like(individual_treatment_effect["x1"]))
+    torch.testing.assert_close(individual_treatment_effect["x3"], expected_treatment_effect.expand((sample_size, 1)))
+    torch.testing.assert_close(cf_effect["x1"], factual_data["x1"])
+    torch.testing.assert_close(cf_effect["x3"], expected_mean_a.expand((sample_size, 1)))
diff --git a/test/training/test_per_variable_metrics.py b/test/training/test_per_variable_metrics.py
deleted file mode 100644
index 8e0a88d..0000000
--- a/test/training/test_per_variable_metrics.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import torch
-
-from causica.training.per_variable_metrics import binary_accuracy, categorical_accuracy, mape, rmse, smape
-
-
-def test_binary_accuracy():
-    logits = torch.tensor([-1.0, 1.0, 1.0, -1.0])
-
-    target = torch.tensor([0.0, 1.0, 0.0, 0.0])
-
-    assert binary_accuracy(logits, target) == 0.75
-
-
-def test_categorical_accuracy():
-    logits = torch.tensor([[0.1, 0.2, 0.7], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7]])
-
-    target = torch.tensor([2, 1, 0, 1])
-
-    assert categorical_accuracy(logits, target, False) == 0.25
-
-    target = torch.tensor([[0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 0]])
-
-    assert categorical_accuracy(logits, target, True) == 0.25
-
-
-def test_rmse():
-    prediction = torch.tensor([[0.1, 0.2, 0.7], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7]])
-
-    target = torch.tensor([[0.1, 0.2, 0.7], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7]])
-
-    assert rmse(prediction, target) == 0.0
-
-    prediction = torch.tensor([[0.2, 0.3, 0.8], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7]])
-
-    assert torch.allclose(torch.tensor(0.0075) ** 0.5, rmse(prediction, target))
-
-
-def test_mape():
-    prediction = torch.tensor([[0.1, 0.2, 0.7], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7]])
-
-    target = torch.tensor([[0.1, 0.2, 0.7], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7]])
-
-    assert mape(prediction, target) == 0.0
-
-    prediction = torch.tensor([[0.2, 0.3, 0.8], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7]])
-
-    true_mape = torch.sum(torch.ones((3,)) * 0.1 / target[0]) / 4
-
-    assert torch.allclose(true_mape, mape(prediction, target))
-
-
-def test_smape():
-    prediction = torch.tensor([[0.1, 0.2, 0.7], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7]])
-
-    target = torch.tensor([[0.1, 0.2, 0.7], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7]])
-
-    assert smape(prediction, target) == 0.0
-
-    prediction = torch.tensor([[0.2, 0.3, 0.8], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7]])
-
-    true_smape = torch.sum(torch.ones((3,)) * 0.1 / (target[0] + prediction[0])) / 4
-
-    assert torch.allclose(true_smape, smape(prediction, target))