diff --git a/Dockerfile b/Dockerfile
index c949ebd..392efda 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -35,7 +35,10 @@ RUN python -m pip install --no-cache-dir nflows\
     imageio-ffmpeg\
     brax\
     wandb\
-    neuralpredictors
+    neuralpredictors\
+    yacs
+
+RUN pip install --upgrade pillow
 
 RUN pip install git+https://github.com/sinzlab/neuralpredictors.git
 RUN pip install torch-scatter -f https://data.pyg.org/whl/torch-1.9.0+cu111.html
diff --git a/README.md b/README.md
index 786f1e9..afca3f7 100644
--- a/README.md
+++ b/README.md
@@ -20,8 +20,22 @@ from propose.models.flows import CondGraphFlow
 flow = CondGraphFlow.from_pretrained('ppierzc/cgnf/cgnf_human36m:best')
 ```
 
-## Reproducing results
+#### HRNet Loading
+You can also load a pretrained HRNet model.
+```python
+from propose.models.detectors import HRNet
+
+hrnet = HRNet.from_pretrained('ppierzc/cgnf/hrnet:v0')
+```
+This will load the HRNet model provided in the [repo](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch).
+The model loaded here is the `pose_hrnet_w32_256x256` trained on the MPII dataset.
+
 ### Requirements
+#### Requirements for the package
+The requirements for the package can be found in the [requirements.txt](/requirements.txt).
+
+#### Docker
+Alternatively, you can use [Docker](https://www.docker.com/) to run the package.
 This project requires that you have the following installed:
 - `docker`
 - `docker-compose`
@@ -40,15 +54,10 @@ docker pull sinzlab/pytorch:v3.9-torch1.9.0-cuda11.1-dj0.12.7
 5. You can now open JupyterLab in your browser at [`http://localhost:10101`](http://localhost:10101).
 
 #### Available Models
-| Model Name | description                                                        | Artifact path                   |
-| --- |--------------------------------------------------------------------|---------------------------------|
-| cGNF Human 3.6m | Model trained on the Human 3.6M dataset with MPII input keypoints. | ```ppierzc/cgnf/cgnf_human36m:best``` |
-
-### Run Evaluation
-You can run the evaluation script with the following command:
-```
-docker-compose run eval --human36m --experiment=cgnf_human36m 
-```
+| Model Name | description                                                                                                                                             | Artifact path                   | Import Code                      |
+| --- |---------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------|----------------------------------|
+| cGNF Human 3.6m | Model trained on the Human 3.6M dataset with MPII input keypoints.                                                                                      | ```ppierzc/cgnf/cgnf_human36m:best``` | ```from propose.models.flows import CondGraphFlow``` |
+ | HRNet | Instance of the [official](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch) HRNet model trained on the MPII dataset with w32 and 256x256 | ```ppierzc/cgnf/hrnet:v0``` | ```from propose.models.detectors import HRNet``` |
 
 ### Run Tests
 To run the tests, from the root directory call:
@@ -61,17 +70,7 @@ docker-compose run pytest tests
 ## Data
 ### Rat7m
 You can download the Rat 7M dataset from [here](https://figshare.com/collections/Rat_7M/5295370).
-To preprocess the dataset run the following command.
-```
-docker-compose run preprocess --rat7m
-```
 
 ### Human3.6M dataset
 Due to license restrictions, the dataset is not included in the repository.
 You can download it from the official [website](http://vision.imar.ro/human3.6m).
-
-Download the *D3 Positions mono* by subject and place them into the `data/human36m/raw` directory.
-Then run the following command.
-```
-docker-compose run preprocess --human36m
-```
diff --git a/data/human36m/README.md b/data/human36m/README.md
deleted file mode 100644
index 933df97..0000000
--- a/data/human36m/README.md
+++ /dev/null
@@ -1,2 +0,0 @@
-## Human36m data
-Place here the training under `raw/` and test data under `test/`.
\ No newline at end of file
diff --git a/data/human36m/raw/README.md b/data/human36m/raw/README.md
deleted file mode 100644
index cf3d3da..0000000
--- a/data/human36m/raw/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-## Human36m Raw data
-
-Place here the training 2D keypoints under `2D/` of the Human 3.6M dataset and the 3D monocular keypoints of the Human36m dataset under `3D/`.
\ No newline at end of file
diff --git a/data/human36m/test/README.md b/data/human36m/test/README.md
deleted file mode 100644
index cc98c93..0000000
--- a/data/human36m/test/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-## Human36m Test data
-
-Place here the training 2D keypoints under `2D/` of the Human 3.6M dataset and the 3D monocular keypoints of the Human36m dataset under `3D/`.
\ No newline at end of file
diff --git a/docker-compose.yml b/docker-compose.yml
index a025076..5d647ef 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -27,52 +27,6 @@ services:
       - ./scripts:/scripts
       - ./data:/data
 
-  python:
-    &python
-    image: propose
-    entrypoint: [ "python" ]
-
-  train:
-    image: propose
-    volumes:
-      - .:/src/propose
-      - ./scripts:/scripts
-      - ./data:/data
-      - ./experiments:/experiments
-    env_file:
-      - .env
-    entrypoint: [ "python", "/scripts/train.py" ]
-
-  eval:
-    image: propose
-    volumes:
-      - .:/src/propose
-      - ./scripts:/scripts
-      - ./data:/data
-      - ./experiments:/experiments
-    env_file:
-      - .env
-    entrypoint: [ "python", "/scripts/eval.py" ]
-
-  sweep:
-    image: propose
-    volumes:
-      - .:/src/propose
-      - ./scripts:/scripts
-      - ./data:/data
-      - ./sweeps:/sweeps
-    env_file:
-      - .env
-    entrypoint: [ "python", "/scripts/sweep.py" ]
-
-  preprocess:
-    image: propose
-    volumes:
-      - .:/src/propose
-      - ./scripts:/scripts
-      - ./data:/data
-    entrypoint: [ "python", "/scripts/preprocess.py" ]
-
   pytest:
     <<: *common
     volumes:
diff --git a/experiments/human36m/mpii-dev.yaml b/experiments/human36m/mpii-dev.yaml
deleted file mode 100644
index 1e44196..0000000
--- a/experiments/human36m/mpii-dev.yaml
+++ /dev/null
@@ -1,47 +0,0 @@
-seed: 0
-checkpoint_every: 10
-
-tags:
-  - mpii
-  - human36m
-  - dev
-group: dev
-
-dataset:
-  dirname: "/data/human36m/processed"
-  mpii: true
-  num_samples: 1000
-  use_variance: true
-
-train:
-  optimizer:
-    lr: 1.0e-3
-    weight_decay: 0
-  lr_scheduler:
-    patience: 10
-    mode: "min"
-    factor: 0.1
-    threshold: 5.0e-2
-    min_lr: 1.0e-6
-  batch_size: 1000
-  epochs: 100
-
-model:
-  num_layers: 10
-  context_features: 10
-  hidden_features: 50
-  relations:
-      - x
-      - c
-      - r
-      - x->x
-      - x<-x
-      - c->x
-      - r->x
-
-embedding:
-    name: "sage"
-    config:
-      input_dim: 2
-      hidden_dim: 128
-      output_dim: 10
\ No newline at end of file
diff --git a/experiments/human36m/mpii-prod-large.yaml b/experiments/human36m/mpii-prod-large.yaml
deleted file mode 100644
index cc45a78..0000000
--- a/experiments/human36m/mpii-prod-large.yaml
+++ /dev/null
@@ -1,45 +0,0 @@
-seed: 0
-checkpoint_every: 10
-
-tags:
-  - mpii
-  - human36m
-group: prod
-
-dataset:
-  dirname: "/data/human36m/processed"
-  mpii: true
-
-train:
-  optimizer:
-    lr: 1.0e-3
-    weight_decay: 0
-  lr_scheduler:
-    patience: 10
-    cooldown: 5
-    mode: "min"
-    factor: 0.1
-    threshold: 1.0e-2
-    min_lr: 1.0e-6
-  batch_size: 200
-  epochs: 200
-
-model:
-  num_layers: 10
-  context_features: 10
-  hidden_features: 200
-  relations:
-      - x
-      - c
-      - r
-      - x->x
-      - x<-x
-      - c->x
-      - r->x
-
-embedding:
-    name: "sage"
-    config:
-      input_dim: 2
-      hidden_dim: 128
-      output_dim: 10
\ No newline at end of file
diff --git a/experiments/human36m/mpii-prod-multi-sample.yaml b/experiments/human36m/mpii-prod-multi-sample.yaml
deleted file mode 100644
index 49a2a6f..0000000
--- a/experiments/human36m/mpii-prod-multi-sample.yaml
+++ /dev/null
@@ -1,46 +0,0 @@
-seed: 0
-checkpoint_every: 10
-
-tags:
-  - mpii
-  - human36m
-group: prod
-
-dataset:
-  dirname: "/data/human36m/processed"
-  mpii: true
-  num_context_samples: 5
-
-train:
-  optimizer:
-    lr: 1.0e-3
-    weight_decay: 0
-  lr_scheduler:
-    patience: 10
-    cooldown: 5
-    mode: "min"
-    factor: 0.1
-    threshold: 1.0e-2
-    min_lr: 1.0e-6
-  batch_size: 1000
-  epochs: 200
-
-model:
-  num_layers: 10
-  context_features: 10
-  hidden_features: 100
-  relations:
-      - x
-      - c
-      - r
-      - x->x
-      - x<-x
-      - c->x
-      - r->x
-
-embedding:
-    name: "sage"
-    config:
-      input_dim: 2
-      hidden_dim: 128
-      output_dim: 10
\ No newline at end of file
diff --git a/experiments/human36m/mpii-prod-var.yaml b/experiments/human36m/mpii-prod-var.yaml
deleted file mode 100644
index b73c947..0000000
--- a/experiments/human36m/mpii-prod-var.yaml
+++ /dev/null
@@ -1,45 +0,0 @@
-seed: 0
-checkpoint_every: 10
-
-tags:
-  - mpii
-  - human36m
-group: prod
-
-dataset:
-  dirname: "/data/human36m/processed"
-  mpii: true
-  use_variance: true
-
-train:
-  optimizer:
-    lr: 1.0e-3
-    weight_decay: 0
-  lr_scheduler:
-    patience: 10
-    mode: "min"
-    factor: 0.1
-    threshold: 1.0e-2
-    min_lr: 1.0e-6
-  batch_size: 200
-  epochs: 200
-
-model:
-  num_layers: 10
-  context_features: 10
-  hidden_features: 100
-  relations:
-      - x
-      - c
-      - r
-      - x->x
-      - x<-x
-      - c->x
-      - r->x
-
-embedding:
-    name: "sage"
-    config:
-      input_dim: 4
-      hidden_dim: 128
-      output_dim: 10
\ No newline at end of file
diff --git a/experiments/human36m/mpii-prod-xlarge.yaml b/experiments/human36m/mpii-prod-xlarge.yaml
deleted file mode 100644
index 7ad30f0..0000000
--- a/experiments/human36m/mpii-prod-xlarge.yaml
+++ /dev/null
@@ -1,45 +0,0 @@
-seed: 0
-checkpoint_every: 10
-
-tags:
-  - mpii
-  - human36m
-group: prod
-
-dataset:
-  dirname: "/data/human36m/processed"
-  mpii: true
-
-train:
-  optimizer:
-    lr: 1.0e-3
-    weight_decay: 0
-  lr_scheduler:
-    patience: 10
-    cooldown: 5
-    mode: "min"
-    factor: 0.1
-    threshold: 1.0e-2
-    min_lr: 1.0e-6
-  batch_size: 200
-  epochs: 200
-
-model:
-  num_layers: 14
-  context_features: 68
-  hidden_features: 262
-  relations:
-      - x
-      - c
-      - r
-      - x->x
-      - x<-x
-      - c->x
-      - r->x
-
-embedding:
-    name: "sage"
-    config:
-      input_dim: 2
-      hidden_dim: 177
-      output_dim: 68
\ No newline at end of file
diff --git a/experiments/human36m/mpii-prod-xlarge_lr_decr.yaml b/experiments/human36m/mpii-prod-xlarge_lr_decr.yaml
deleted file mode 100644
index a59a296..0000000
--- a/experiments/human36m/mpii-prod-xlarge_lr_decr.yaml
+++ /dev/null
@@ -1,46 +0,0 @@
-seed: 0
-checkpoint_every: 10
-use_pretrained: mpii-prod-xlarge:latest
-
-tags:
-  - mpii
-  - human36m
-group: prod
-
-dataset:
-  dirname: "/data/human36m/processed"
-  mpii: true
-
-train:
-  optimizer:
-    lr: 1.0e-5
-    weight_decay: 0
-  lr_scheduler:
-    patience: 10
-    cooldown: 5
-    mode: "min"
-    factor: 0.1
-    threshold: 1.0e-2
-    min_lr: 1.0e-6
-  batch_size: 200
-  epochs: 200
-
-model:
-  num_layers: 14
-  context_features: 68
-  hidden_features: 262
-  relations:
-      - x
-      - c
-      - r
-      - x->x
-      - x<-x
-      - c->x
-      - r->x
-
-embedding:
-    name: "sage"
-    config:
-      input_dim: 2
-      hidden_dim: 177
-      output_dim: 68
\ No newline at end of file
diff --git a/experiments/human36m/mpii-prod.yaml b/experiments/human36m/mpii-prod.yaml
deleted file mode 100644
index 2d2e5f1..0000000
--- a/experiments/human36m/mpii-prod.yaml
+++ /dev/null
@@ -1,45 +0,0 @@
-seed: 0
-checkpoint_every: 10
-
-tags:
-  - mpii
-  - human36m
-group: prod
-
-dataset:
-  dirname: "/data/human36m/processed"
-  mpii: true
-
-train:
-  optimizer:
-    lr: 1.0e-3
-    weight_decay: 0
-  lr_scheduler:
-    patience: 10
-    cooldown: 5
-    mode: "min"
-    factor: 0.1
-    threshold: 1.0e-2
-    min_lr: 1.0e-6
-  batch_size: 200
-  epochs: 200
-
-model:
-  num_layers: 10
-  context_features: 10
-  hidden_features: 100
-  relations:
-      - x
-      - c
-      - r
-      - x->x
-      - x<-x
-      - c->x
-      - r->x
-
-embedding:
-    name: "sage"
-    config:
-      input_dim: 2
-      hidden_dim: 128
-      output_dim: 10
\ No newline at end of file
diff --git a/experiments/human36m/mpii-prod_man_lr_decr.yaml b/experiments/human36m/mpii-prod_man_lr_decr.yaml
deleted file mode 100644
index e4f6928..0000000
--- a/experiments/human36m/mpii-prod_man_lr_decr.yaml
+++ /dev/null
@@ -1,46 +0,0 @@
-seed: 0
-checkpoint_every: 10
-use_pretrained: mpii-prod:latest
-
-tags:
-  - mpii
-  - human36m
-group: prod
-
-dataset:
-  dirname: "/data/human36m/processed"
-  mpii: true
-
-train:
-  optimizer:
-    lr: 1.0e-4
-    weight_decay: 0
-  lr_scheduler:
-    patience: 10
-    cooldown: 5
-    mode: "min"
-    factor: 0.1
-    threshold: 1.0e-2
-    min_lr: 1.0e-6
-  batch_size: 200
-  epochs: 200
-
-model:
-  num_layers: 10
-  context_features: 10
-  hidden_features: 100
-  relations:
-      - x
-      - c
-      - r
-      - x->x
-      - x<-x
-      - c->x
-      - r->x
-
-embedding:
-    name: "sage"
-    config:
-      input_dim: 2
-      hidden_dim: 128
-      output_dim: 10
\ No newline at end of file
diff --git a/notebooks/demo/load_model.ipynb b/notebooks/demo/load_model.ipynb
deleted file mode 100644
index 3023b8b..0000000
--- a/notebooks/demo/load_model.ipynb
+++ /dev/null
@@ -1,193 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import wandb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "outputs": [],
-   "source": [
-    "api = wandb.Api()\n",
-    "artifact = api.artifact('ppierzc/propose_human36m/mpii-prod:latest')"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "['QUERY',\n '__class__',\n '__delattr__',\n '__dict__',\n '__dir__',\n '__doc__',\n '__eq__',\n '__format__',\n '__ge__',\n '__getattribute__',\n '__getitem__',\n '__gt__',\n '__hash__',\n '__init__',\n '__init_subclass__',\n '__le__',\n '__lt__',\n '__module__',\n '__ne__',\n '__new__',\n '__reduce__',\n '__reduce_ex__',\n '__repr__',\n '__setattr__',\n '__setitem__',\n '__sizeof__',\n '__str__',\n '__subclasshook__',\n '__weakref__',\n '_add_download_root',\n '_aliases',\n '_artifact_name',\n '_attrs',\n '_default_root',\n '_dependent_artifacts',\n '_description',\n '_download_file',\n '_download_roots',\n '_entity',\n '_files',\n '_get_obj_entry',\n '_get_ref_artifact_from_entry',\n '_is_download_root',\n '_is_downloaded',\n '_list',\n '_load',\n '_load_dependent_manifests',\n '_load_manifest',\n '_local_path_to_name',\n '_manifest',\n '_manifest_entry_is_artifact_reference',\n '_metadata',\n '_project',\n '_sequence_name',\n '_use_as',\n '_version_index',\n 'add',\n 'add_dir',\n 'add_file',\n 'add_reference',\n 'aliases',\n 'checkout',\n 'client',\n 'commit_hash',\n 'created_at',\n 'delete',\n 'description',\n 'digest',\n 'download',\n 'entity',\n 'expected_type',\n 'file',\n 'from_id',\n 'get',\n 'get_path',\n 'id',\n 'json_encode',\n 'link',\n 'logged_by',\n 'manifest',\n 'metadata',\n 'name',\n 'new_file',\n 'project',\n 'save',\n 'size',\n 'state',\n 'type',\n 'updated_at',\n 'used_by',\n 'verify',\n 'version',\n 'wait']"
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir(artifact)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "{}"
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "artifact.metadata"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "outputs": [],
-   "source": [
-    "import yaml\n",
-    "from pathlib import Path\n",
-    "\n",
-    "config_file = Path(\"mpii-prod\" + \".yaml\")\n",
-    "config_file = Path(\"../../configs\") / \"human36m\" / config_file\n",
-    "\n",
-    "with open(config_file, \"r\") as f:\n",
-    "    config = yaml.load(f, Loader=yaml.FullLoader)\n",
-    "\n",
-    "    if \"experiment_name\" not in config:\n",
-    "        config[\"experiment_name\"] = \"mpii-prod\""
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "outputs": [],
-   "source": [
-    "artifact.metadata = config"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "True"
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "artifact.save()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "{'seed': 0,\n 'checkpoint_every': 10,\n 'tags': ['mpii', 'human36m'],\n 'group': 'prod',\n 'dataset': {'dirname': '/data/human36m/processed', 'mpii': True},\n 'train': {'optimizer': {'lr': 0.001, 'weight_decay': 0},\n  'lr_scheduler': {'patience': 10,\n   'cooldown': 5,\n   'mode': 'min',\n   'factor': 0.1,\n   'threshold': 0.01,\n   'min_lr': 1e-06},\n  'batch_size': 200,\n  'epochs': 200},\n 'model': {'num_layers': 10,\n  'context_features': 10,\n  'hidden_features': 100,\n  'relations': ['x', 'c', 'r', 'x->x', 'x<-x', 'c->x', 'r->x']},\n 'embedding': {'name': 'sage',\n  'config': {'input_dim': 2, 'hidden_dim': 128, 'output_dim': 10}},\n 'experiment_name': 'mpii-prod'}"
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "artifact.metadata"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/notebooks/toy_problems/CondGNN_demo.ipynb b/notebooks/toy_problems/CondGNN_demo.ipynb
deleted file mode 100644
index 2e59054..0000000
--- a/notebooks/toy_problems/CondGNN_demo.ipynb
+++ /dev/null
@@ -1,1091 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "from torch_geometric.data import HeteroData\n",
-    "from torch_geometric.loader import DataLoader\n",
-    "\n",
-    "import torch\n",
-    "from torch.utils.data import Dataset\n",
-    "import torch.distributions as D\n",
-    "\n",
-    "from propose.models.flows import CondGraphFlow\n",
-    "\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "from propose.training.trainers import supervised_trainer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# Single Point"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "outputs": [],
-   "source": [
-    "class SinglePointDataset(Dataset):\n",
-    "    def __init__(self, length=100, prior=None):\n",
-    "        if prior is None:\n",
-    "            prior = D.MultivariateNormal(torch.zeros(3), torch.eye(3))\n",
-    "\n",
-    "        data_list = []\n",
-    "\n",
-    "        for i in range(length):\n",
-    "            data = HeteroData()\n",
-    "            data['x'].x = prior.sample((1, ))\n",
-    "            data['c'].x = data['x'].x[..., :2]\n",
-    "\n",
-    "            data['c', '->', 'x'].edge_index = torch.LongTensor([[0, 0]]).T\n",
-    "            data_list.append(data)\n",
-    "\n",
-    "        self.data = data_list\n",
-    "\n",
-    "    def __len__(self):\n",
-    "        return len(self.data)\n",
-    "\n",
-    "    def __getitem__(self, idx):\n",
-    "        return self.data[idx]\n",
-    "\n",
-    "    def metadata(self):\n",
-    "        return self.data[0].metadata()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Simple Prior\n",
-    "Standard Normal prior"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "outputs": [],
-   "source": [
-    "dataset = SinglePointDataset(length=1000)\n",
-    "data_loader = DataLoader(dataset, batch_size=100, shuffle=True)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "outputs": [],
-   "source": [
-    "epochs = 100\n",
-    "lr = 0.001\n",
-    "weight_decay = 1e-5\n",
-    "\n",
-    "flow = CondGraphFlow(num_layers=10)\n",
-    "optimizer = torch.optim.Adam(flow.parameters(), lr=lr, weight_decay=weight_decay)\n"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 1/100 | RegPriorLoss 4.5970 | RegPosteriorLoss 3.1274 | Batch: 100%|██████████| 10/10 [00:01<00:00,  8.48it/s]\n",
-      "Epoch: 2/100 | RegPriorLoss 4.5776 | RegPosteriorLoss 2.2565 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.83it/s]\n",
-      "Epoch: 3/100 | RegPriorLoss 4.5108 | RegPosteriorLoss 1.8061 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.47it/s]\n",
-      "Epoch: 4/100 | RegPriorLoss 4.2839 | RegPosteriorLoss 1.5834 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.37it/s]\n",
-      "Epoch: 5/100 | RegPriorLoss 4.4436 | RegPosteriorLoss 1.2534 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.38it/s]\n",
-      "Epoch: 6/100 | RegPriorLoss 4.2493 | RegPosteriorLoss 0.9885 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.21it/s]\n",
-      "Epoch: 7/100 | RegPriorLoss 4.4056 | RegPosteriorLoss 0.8821 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.37it/s]\n",
-      "Epoch: 8/100 | RegPriorLoss 4.3577 | RegPosteriorLoss 0.4797 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.75it/s]\n",
-      "Epoch: 9/100 | RegPriorLoss 4.4496 | RegPosteriorLoss 0.0956 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.87it/s]\n",
-      "Epoch: 10/100 | RegPriorLoss 4.1816 | RegPosteriorLoss 0.0781 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.39it/s]\n",
-      "Epoch: 11/100 | RegPriorLoss 4.2837 | RegPosteriorLoss -0.4580 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.43it/s]\n",
-      "Epoch: 12/100 | RegPriorLoss 4.3107 | RegPosteriorLoss 0.2498 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.31it/s]\n",
-      "Epoch: 13/100 | RegPriorLoss 4.2505 | RegPosteriorLoss -0.8066 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.25it/s]\n",
-      "Epoch: 14/100 | RegPriorLoss 4.1560 | RegPosteriorLoss -1.2952 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.20it/s]\n",
-      "Epoch: 15/100 | RegPriorLoss 4.1568 | RegPosteriorLoss -1.3459 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.65it/s]\n",
-      "Epoch: 16/100 | RegPriorLoss 4.3668 | RegPosteriorLoss -0.8118 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.65it/s]\n",
-      "Epoch: 17/100 | RegPriorLoss 4.2773 | RegPosteriorLoss -1.8980 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.91it/s]\n",
-      "Epoch: 18/100 | RegPriorLoss 4.3613 | RegPosteriorLoss -1.7353 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.77it/s]\n",
-      "Epoch: 19/100 | RegPriorLoss 4.2636 | RegPosteriorLoss -2.1946 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.74it/s]\n",
-      "Epoch: 20/100 | RegPriorLoss 4.1367 | RegPosteriorLoss -2.4915 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.50it/s]\n",
-      "Epoch: 21/100 | RegPriorLoss 4.3649 | RegPosteriorLoss -1.6291 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.27it/s]\n",
-      "Epoch: 22/100 | RegPriorLoss 4.1530 | RegPosteriorLoss -1.7318 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.86it/s]\n",
-      "Epoch: 23/100 | RegPriorLoss 4.1957 | RegPosteriorLoss -2.0297 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.53it/s]\n",
-      "Epoch: 24/100 | RegPriorLoss 4.3765 | RegPosteriorLoss -2.3787 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.67it/s]\n",
-      "Epoch: 25/100 | RegPriorLoss 4.3541 | RegPosteriorLoss -2.9399 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.43it/s]\n",
-      "Epoch: 26/100 | RegPriorLoss 4.1944 | RegPosteriorLoss -2.6573 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.63it/s]\n",
-      "Epoch: 27/100 | RegPriorLoss 4.4574 | RegPosteriorLoss -1.4378 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.23it/s]\n",
-      "Epoch: 28/100 | RegPriorLoss 4.6667 | RegPosteriorLoss -1.8625 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.22it/s]\n",
-      "Epoch: 29/100 | RegPriorLoss 4.5641 | RegPosteriorLoss -2.4468 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.52it/s]\n",
-      "Epoch: 30/100 | RegPriorLoss 4.2891 | RegPosteriorLoss -2.8770 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.52it/s]\n",
-      "Epoch: 31/100 | RegPriorLoss 4.3524 | RegPosteriorLoss -2.5645 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.33it/s]\n",
-      "Epoch: 32/100 | RegPriorLoss 4.2123 | RegPosteriorLoss -3.0054 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.02it/s]\n",
-      "Epoch: 33/100 | RegPriorLoss 4.5098 | RegPosteriorLoss -2.6845 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.38it/s]\n",
-      "Epoch: 34/100 | RegPriorLoss 4.2534 | RegPosteriorLoss -3.1780 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.27it/s]\n",
-      "Epoch: 35/100 | RegPriorLoss 4.1814 | RegPosteriorLoss -2.6203 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.76it/s]\n",
-      "Epoch: 36/100 | RegPriorLoss 4.3357 | RegPosteriorLoss -3.3408 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.64it/s]\n",
-      "Epoch: 37/100 | RegPriorLoss 4.2149 | RegPosteriorLoss -3.7036 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.07it/s]\n",
-      "Epoch: 38/100 | RegPriorLoss 4.2032 | RegPosteriorLoss -3.3757 | Batch: 100%|██████████| 10/10 [00:00<00:00, 10.75it/s]\n",
-      "Epoch: 39/100 | RegPriorLoss 4.3726 | RegPosteriorLoss -3.6787 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.73it/s]\n",
-      "Epoch: 40/100 | RegPriorLoss 4.3182 | RegPosteriorLoss -3.7951 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.28it/s]\n",
-      "Epoch: 41/100 | RegPriorLoss 4.4602 | RegPosteriorLoss -1.9150 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.56it/s]\n",
-      "Epoch: 42/100 | RegPriorLoss 4.3779 | RegPosteriorLoss -3.2424 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.71it/s]\n",
-      "Epoch: 43/100 | RegPriorLoss 4.6814 | RegPosteriorLoss -2.6635 | Batch: 100%|██████████| 10/10 [00:00<00:00, 10.96it/s]\n",
-      "Epoch: 44/100 | RegPriorLoss 4.7552 | RegPosteriorLoss -3.4453 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.72it/s]\n",
-      "Epoch: 45/100 | RegPriorLoss 4.1777 | RegPosteriorLoss -2.5268 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.34it/s]\n",
-      "Epoch: 46/100 | RegPriorLoss 4.2181 | RegPosteriorLoss -3.7598 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.44it/s]\n",
-      "Epoch: 47/100 | RegPriorLoss 4.3143 | RegPosteriorLoss -3.8414 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.37it/s]\n",
-      "Epoch: 48/100 | RegPriorLoss 4.2827 | RegPosteriorLoss -2.3077 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.28it/s]\n",
-      "Epoch: 49/100 | RegPriorLoss 4.3171 | RegPosteriorLoss -3.6855 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.57it/s]\n",
-      "Epoch: 50/100 | RegPriorLoss 4.3994 | RegPosteriorLoss -3.9178 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.27it/s]\n",
-      "Epoch: 51/100 | RegPriorLoss 4.2218 | RegPosteriorLoss -4.1454 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.66it/s]\n",
-      "Epoch: 52/100 | RegPriorLoss 4.2782 | RegPosteriorLoss -4.4437 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.42it/s]\n",
-      "Epoch: 53/100 | RegPriorLoss 4.1859 | RegPosteriorLoss -4.0863 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.13it/s]\n",
-      "Epoch: 54/100 | RegPriorLoss 4.3426 | RegPosteriorLoss -4.3263 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.31it/s]\n",
-      "Epoch: 55/100 | RegPriorLoss 4.2562 | RegPosteriorLoss -4.2612 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.57it/s]\n",
-      "Epoch: 56/100 | RegPriorLoss 4.1923 | RegPosteriorLoss -2.6858 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.58it/s]\n",
-      "Epoch: 57/100 | RegPriorLoss 4.2627 | RegPosteriorLoss -3.6475 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.04it/s]\n",
-      "Epoch: 58/100 | RegPriorLoss 4.1788 | RegPosteriorLoss -3.8972 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.46it/s]\n",
-      "Epoch: 59/100 | RegPriorLoss 4.1730 | RegPosteriorLoss -2.9268 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.40it/s]\n",
-      "Epoch: 60/100 | RegPriorLoss 4.3310 | RegPosteriorLoss -3.5139 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.53it/s]\n",
-      "Epoch: 61/100 | RegPriorLoss 4.4683 | RegPosteriorLoss -4.0177 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.60it/s]\n",
-      "Epoch: 62/100 | RegPriorLoss 4.3266 | RegPosteriorLoss -4.4455 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.87it/s]\n",
-      "Epoch: 63/100 | RegPriorLoss 4.2867 | RegPosteriorLoss -4.5496 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.30it/s]\n",
-      "Epoch: 64/100 | RegPriorLoss 4.1077 | RegPosteriorLoss -4.2195 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.39it/s]\n",
-      "Epoch: 65/100 | RegPriorLoss 4.3249 | RegPosteriorLoss -4.0159 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.72it/s]\n",
-      "Epoch: 66/100 | RegPriorLoss 4.2204 | RegPosteriorLoss -4.3479 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.65it/s]\n",
-      "Epoch: 67/100 | RegPriorLoss 4.3219 | RegPosteriorLoss -4.3029 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.45it/s]\n",
-      "Epoch: 68/100 | RegPriorLoss 4.3912 | RegPosteriorLoss -4.5881 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.40it/s]\n",
-      "Epoch: 69/100 | RegPriorLoss 4.0815 | RegPosteriorLoss -4.8388 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.33it/s]\n",
-      "Epoch: 70/100 | RegPriorLoss 4.2683 | RegPosteriorLoss -4.7758 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.52it/s]\n",
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-      "Epoch: 72/100 | RegPriorLoss 4.1683 | RegPosteriorLoss -4.4804 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.41it/s]\n",
-      "Epoch: 73/100 | RegPriorLoss 4.1510 | RegPosteriorLoss -5.1118 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.98it/s]\n",
-      "Epoch: 74/100 | RegPriorLoss 4.1350 | RegPosteriorLoss -2.0220 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.47it/s]\n",
-      "Epoch: 75/100 | RegPriorLoss 4.2306 | RegPosteriorLoss -2.2679 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.70it/s]\n",
-      "Epoch: 76/100 | RegPriorLoss 4.2887 | RegPosteriorLoss -4.2228 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.69it/s]\n",
-      "Epoch: 77/100 | RegPriorLoss 4.2828 | RegPosteriorLoss -4.2111 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.56it/s]\n",
-      "Epoch: 78/100 | RegPriorLoss 4.0508 | RegPosteriorLoss -4.8679 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.37it/s]\n",
-      "Epoch: 79/100 | RegPriorLoss 4.3322 | RegPosteriorLoss -4.8231 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.47it/s]\n",
-      "Epoch: 80/100 | RegPriorLoss 4.5178 | RegPosteriorLoss -2.8236 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.48it/s]\n",
-      "Epoch: 81/100 | RegPriorLoss 4.4662 | RegPosteriorLoss -4.1457 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.29it/s]\n",
-      "Epoch: 82/100 | RegPriorLoss 4.2241 | RegPosteriorLoss -4.5232 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.64it/s]\n",
-      "Epoch: 83/100 | RegPriorLoss 4.3202 | RegPosteriorLoss -4.8400 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.13it/s]\n",
-      "Epoch: 84/100 | RegPriorLoss 4.2345 | RegPosteriorLoss -5.1442 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.63it/s]\n",
-      "Epoch: 85/100 | RegPriorLoss 4.2025 | RegPosteriorLoss -5.2310 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.95it/s]\n",
-      "Epoch: 86/100 | RegPriorLoss 4.3584 | RegPosteriorLoss -5.1399 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.69it/s]\n",
-      "Epoch: 87/100 | RegPriorLoss 4.1592 | RegPosteriorLoss -5.0522 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.51it/s]\n",
-      "Epoch: 88/100 | RegPriorLoss 4.2510 | RegPosteriorLoss 0.1190 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.39it/s]\n",
-      "Epoch: 89/100 | RegPriorLoss 4.3867 | RegPosteriorLoss -4.4381 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.60it/s]\n",
-      "Epoch: 90/100 | RegPriorLoss 4.2885 | RegPosteriorLoss -4.4346 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.65it/s]\n",
-      "Epoch: 91/100 | RegPriorLoss 4.0596 | RegPosteriorLoss -5.3174 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.39it/s]\n",
-      "Epoch: 92/100 | RegPriorLoss 4.2277 | RegPosteriorLoss -4.8591 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.65it/s]\n",
-      "Epoch: 93/100 | RegPriorLoss 3.9861 | RegPosteriorLoss -5.0286 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.16it/s]\n",
-      "Epoch: 94/100 | RegPriorLoss 4.2380 | RegPosteriorLoss -5.0452 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.12it/s]\n",
-      "Epoch: 95/100 | RegPriorLoss 4.0991 | RegPosteriorLoss -5.3340 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.45it/s]\n",
-      "Epoch: 96/100 | RegPriorLoss 4.4544 | RegPosteriorLoss -4.6271 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.70it/s]\n",
-      "Epoch: 97/100 | RegPriorLoss 4.2774 | RegPosteriorLoss -4.6652 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.46it/s]\n",
-      "Epoch: 98/100 | RegPriorLoss 4.3458 | RegPosteriorLoss -4.3620 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.51it/s]\n",
-      "Epoch: 99/100 | RegPriorLoss 4.2146 | RegPosteriorLoss -3.0101 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.11it/s]\n",
-      "Epoch: 100/100 | RegPriorLoss 4.2941 | RegPosteriorLoss -4.5695 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.43it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "supervised_trainer(data_loader, flow, optimizer, epochs=epochs)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "outputs": [],
-   "source": [
-    "posterior_data = HeteroData({\n",
-    "    'x': {'x': torch.Tensor([[0, 0, 0]]), 'batch': torch.Tensor([0])},\n",
-    "    'c': {'x': torch.Tensor([[1, 1]])},\n",
-    "    ('c', '->', 'x'): {'edge_index': torch.LongTensor([[0, 0]]).T}\n",
-    "})\n",
-    "\n",
-    "prior_data = HeteroData({\n",
-    "    'x': {'x': torch.Tensor([[0, 0, 0]]), 'batch': torch.Tensor([0])},\n",
-    "})"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 1500x1000 with 6 Axes>",
-      "image/png": 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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "M_prior = flow.sample(1000, prior_data)['x']['x'].detach()\n",
-    "M_posterior = flow.sample(1000, posterior_data)['x']['x'].detach()\n",
-    "\n",
-    "plt.style.use('default')\n",
-    "fig, axs = plt.subplots(2, 3, figsize=(15, 10))\n",
-    "\n",
-    "axs[0, 0].scatter(M_prior[..., 0], M_prior[..., 1], c='tab:gray', alpha=0.1, edgecolor='none')\n",
-    "axs[0, 0].set_xlim(-15, 15)\n",
-    "axs[0, 0].set_ylim(-15, 15)\n",
-    "axs[0, 0].set_title('Prior')\n",
-    "axs[0, 0].set_xlabel('X')\n",
-    "axs[0, 0].set_ylabel('Y')\n",
-    "\n",
-    "axs[0, 1].scatter(M_prior[..., 0], M_prior[..., 2], c='tab:gray', alpha=0.1, edgecolor='none')\n",
-    "axs[0, 1].set_xlim(-15, 15)\n",
-    "axs[0, 1].set_ylim(-15, 15)\n",
-    "axs[0, 1].set_xlabel('X')\n",
-    "axs[0, 1].set_ylabel('Z')\n",
-    "\n",
-    "axs[1, 0].scatter(M_posterior[..., 0], M_posterior[..., 1], c='tab:gray', alpha=0.1, edgecolor='none')\n",
-    "axs[1, 0].scatter([1], [1], c='tab:orange')\n",
-    "axs[1, 0].set_xlim(-15, 15)\n",
-    "axs[1, 0].set_ylim(-15, 15)\n",
-    "axs[1, 0].set_title('Posterior')\n",
-    "axs[1, 0].set_xlabel('X')\n",
-    "axs[1, 0].set_ylabel('Y')\n",
-    "\n",
-    "axs[1, 1].scatter(M_posterior[..., 0], M_posterior[..., 2], c='tab:gray', alpha=0.1, edgecolor='none')\n",
-    "axs[1, 1].axvline(1, c='tab:orange')\n",
-    "axs[1, 1].set_xlim(-15, 15)\n",
-    "axs[1, 1].set_xlabel('X')\n",
-    "axs[1, 1].set_ylabel('Z')\n",
-    "\n",
-    "axs[1, 2].hist(M_posterior[..., 2].detach(), bins=np.linspace(-25, 25, 50), orientation='horizontal', density=True, color='tab:gray')\n",
-    "axs[1, 2].set_ylim(-15, 15)\n",
-    "axs[1, 2].set_xlabel('Density')\n",
-    "sns.despine(ax=axs[1, 2])\n",
-    "\n",
-    "axs[0, 2].hist(M_prior[..., 2].detach(), bins=np.linspace(-15, 15, 50), orientation='horizontal', density=True, color='tab:gray')\n",
-    "axs[0, 2].set_ylim(-15, 15)\n",
-    "axs[0, 2].set_xlabel('Density')\n",
-    "sns.despine(ax=axs[0, 2])"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Bimodal Prior"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "outputs": [],
-   "source": [
-    "prior = D.mixture_same_family.MixtureSameFamily(\n",
-    "            D.Categorical(torch.ones(2)),\n",
-    "            D.MultivariateNormal(torch.Tensor([[0, 0, 10], [0, 0, -10]]), covariance_matrix=torch.stack((torch.eye(3), torch.eye(3))))\n",
-    "        )\n",
-    "dataset = SinglePointDataset(length=1000, prior=prior)\n",
-    "data_loader = DataLoader(dataset, batch_size=100, shuffle=True)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "outputs": [],
-   "source": [
-    "epochs = 100\n",
-    "lr = 0.001\n",
-    "weight_decay = 1e-5\n",
-    "\n",
-    "flow = CondGraphFlow(num_layers=10)\n",
-    "optimizer = torch.optim.Adam(flow.parameters(), lr=lr, weight_decay=weight_decay)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 1/100 | RegPriorLoss 6.5244 | RegPosteriorLoss 5.0929 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.23it/s]\n",
-      "Epoch: 2/100 | RegPriorLoss 6.9207 | RegPosteriorLoss 4.4570 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.94it/s]\n",
-      "Epoch: 3/100 | RegPriorLoss 6.6599 | RegPosteriorLoss 4.0469 | Batch: 100%|██████████| 10/10 [00:00<00:00, 10.18it/s]\n",
-      "Epoch: 4/100 | RegPriorLoss 6.2622 | RegPosteriorLoss 3.6760 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.26it/s]\n",
-      "Epoch: 5/100 | RegPriorLoss 6.0012 | RegPosteriorLoss 3.3507 | Batch: 100%|██████████| 10/10 [00:00<00:00, 10.98it/s]\n",
-      "Epoch: 6/100 | RegPriorLoss 5.9623 | RegPosteriorLoss 2.9394 | Batch: 100%|██████████| 10/10 [00:00<00:00, 10.07it/s]\n",
-      "Epoch: 7/100 | RegPriorLoss 5.6509 | RegPosteriorLoss 3.0451 | Batch: 100%|██████████| 10/10 [00:01<00:00,  9.11it/s]\n",
-      "Epoch: 8/100 | RegPriorLoss 5.9067 | RegPosteriorLoss 2.5404 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.90it/s]\n",
-      "Epoch: 9/100 | RegPriorLoss 5.8186 | RegPosteriorLoss 2.7124 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.68it/s]\n",
-      "Epoch: 10/100 | RegPriorLoss 5.4988 | RegPosteriorLoss 2.1835 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.88it/s]\n",
-      "Epoch: 11/100 | RegPriorLoss 5.5503 | RegPosteriorLoss 2.6766 | Batch: 100%|██████████| 10/10 [00:01<00:00,  9.00it/s]\n",
-      "Epoch: 12/100 | RegPriorLoss 5.4748 | RegPosteriorLoss 2.0376 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.32it/s]\n",
-      "Epoch: 13/100 | RegPriorLoss 5.1254 | RegPosteriorLoss 1.5188 | Batch: 100%|██████████| 10/10 [00:00<00:00, 10.04it/s]\n",
-      "Epoch: 14/100 | RegPriorLoss 5.4594 | RegPosteriorLoss 1.5083 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.08it/s]\n",
-      "Epoch: 15/100 | RegPriorLoss 5.3920 | RegPosteriorLoss 1.3640 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.25it/s]\n",
-      "Epoch: 16/100 | RegPriorLoss 5.1108 | RegPosteriorLoss 1.1189 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.00it/s]\n",
-      "Epoch: 17/100 | RegPriorLoss 5.2425 | RegPosteriorLoss 0.6784 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.44it/s]\n",
-      "Epoch: 18/100 | RegPriorLoss 5.0452 | RegPosteriorLoss 0.1666 | Batch: 100%|██████████| 10/10 [00:01<00:00,  9.47it/s]\n",
-      "Epoch: 19/100 | RegPriorLoss 5.1431 | RegPosteriorLoss 0.6521 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.48it/s]\n",
-      "Epoch: 20/100 | RegPriorLoss 5.2990 | RegPosteriorLoss 0.1006 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.83it/s]\n",
-      "Epoch: 21/100 | RegPriorLoss 5.1678 | RegPosteriorLoss -0.0299 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.39it/s]\n",
-      "Epoch: 22/100 | RegPriorLoss 5.0614 | RegPosteriorLoss -0.5452 | Batch: 100%|██████████| 10/10 [00:00<00:00, 10.11it/s]\n",
-      "Epoch: 23/100 | RegPriorLoss 5.2117 | RegPosteriorLoss -0.4967 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.08it/s]\n",
-      "Epoch: 24/100 | RegPriorLoss 5.0587 | RegPosteriorLoss -0.1787 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.88it/s]\n",
-      "Epoch: 25/100 | RegPriorLoss 5.2107 | RegPosteriorLoss -0.0374 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.24it/s]\n",
-      "Epoch: 26/100 | RegPriorLoss 5.2046 | RegPosteriorLoss -0.7510 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.40it/s]\n",
-      "Epoch: 27/100 | RegPriorLoss 5.0809 | RegPosteriorLoss -1.1207 | Batch: 100%|██████████| 10/10 [00:01<00:00,  9.67it/s]\n",
-      "Epoch: 28/100 | RegPriorLoss 5.3225 | RegPosteriorLoss -0.9027 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.68it/s]\n",
-      "Epoch: 29/100 | RegPriorLoss 5.2060 | RegPosteriorLoss -0.4074 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.26it/s]\n",
-      "Epoch: 30/100 | RegPriorLoss 5.1977 | RegPosteriorLoss -0.8102 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.57it/s]\n",
-      "Epoch: 31/100 | RegPriorLoss 5.0185 | RegPosteriorLoss -1.3413 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.03it/s]\n",
-      "Epoch: 32/100 | RegPriorLoss 5.1977 | RegPosteriorLoss -1.3155 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.66it/s]\n",
-      "Epoch: 33/100 | RegPriorLoss 5.1236 | RegPosteriorLoss -0.5209 | Batch: 100%|██████████| 10/10 [00:00<00:00, 10.40it/s]\n",
-      "Epoch: 34/100 | RegPriorLoss 5.1650 | RegPosteriorLoss -1.3524 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.53it/s]\n",
-      "Epoch: 35/100 | RegPriorLoss 5.0338 | RegPosteriorLoss -1.6691 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.61it/s]\n",
-      "Epoch: 36/100 | RegPriorLoss 5.0503 | RegPosteriorLoss -1.7782 | Batch: 100%|██████████| 10/10 [00:01<00:00,  9.90it/s]\n",
-      "Epoch: 37/100 | RegPriorLoss 4.9608 | RegPosteriorLoss -1.8882 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.58it/s]\n",
-      "Epoch: 38/100 | RegPriorLoss 5.2801 | RegPosteriorLoss 0.7491 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.34it/s]\n",
-      "Epoch: 39/100 | RegPriorLoss 5.0832 | RegPosteriorLoss -1.6134 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.75it/s]\n",
-      "Epoch: 40/100 | RegPriorLoss 5.0537 | RegPosteriorLoss -1.7028 | Batch: 100%|██████████| 10/10 [00:01<00:00,  9.84it/s]\n",
-      "Epoch: 41/100 | RegPriorLoss 5.2818 | RegPosteriorLoss -1.9633 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.75it/s]\n",
-      "Epoch: 42/100 | RegPriorLoss 5.0127 | RegPosteriorLoss -2.3892 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.31it/s]\n",
-      "Epoch: 43/100 | RegPriorLoss 4.9780 | RegPosteriorLoss -2.0833 | Batch: 100%|██████████| 10/10 [00:00<00:00, 10.57it/s]\n",
-      "Epoch: 44/100 | RegPriorLoss 4.9907 | RegPosteriorLoss -2.2944 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.94it/s]\n",
-      "Epoch: 45/100 | RegPriorLoss 4.8412 | RegPosteriorLoss -2.3037 | Batch: 100%|██████████| 10/10 [00:01<00:00,  9.70it/s]\n",
-      "Epoch: 46/100 | RegPriorLoss 4.8254 | RegPosteriorLoss -2.7492 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.28it/s]\n",
-      "Epoch: 47/100 | RegPriorLoss 4.9973 | RegPosteriorLoss -2.7051 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.71it/s]\n",
-      "Epoch: 48/100 | RegPriorLoss 5.1764 | RegPosteriorLoss -2.5167 | Batch: 100%|██████████| 10/10 [00:00<00:00, 10.95it/s]\n",
-      "Epoch: 49/100 | RegPriorLoss 4.9834 | RegPosteriorLoss -2.6847 | Batch: 100%|██████████| 10/10 [00:01<00:00,  9.44it/s]\n",
-      "Epoch: 50/100 | RegPriorLoss 5.3009 | RegPosteriorLoss -2.8883 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.40it/s]\n",
-      "Epoch: 51/100 | RegPriorLoss 4.8399 | RegPosteriorLoss -1.7544 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.78it/s]\n",
-      "Epoch: 52/100 | RegPriorLoss 5.1380 | RegPosteriorLoss -2.1063 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.01it/s]\n",
-      "Epoch: 53/100 | RegPriorLoss 5.0755 | RegPosteriorLoss -2.9519 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.68it/s]\n",
-      "Epoch: 54/100 | RegPriorLoss 5.3000 | RegPosteriorLoss -0.2968 | Batch: 100%|██████████| 10/10 [00:01<00:00,  9.91it/s]\n",
-      "Epoch: 55/100 | RegPriorLoss 4.8943 | RegPosteriorLoss -2.6938 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.42it/s]\n",
-      "Epoch: 56/100 | RegPriorLoss 5.0290 | RegPosteriorLoss -2.9708 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.96it/s]\n",
-      "Epoch: 57/100 | RegPriorLoss 5.1306 | RegPosteriorLoss -2.7363 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.97it/s]\n",
-      "Epoch: 58/100 | RegPriorLoss 4.9840 | RegPosteriorLoss -3.1565 | Batch: 100%|██████████| 10/10 [00:01<00:00,  9.97it/s]\n",
-      "Epoch: 59/100 | RegPriorLoss 5.0518 | RegPosteriorLoss -2.7848 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.09it/s]\n",
-      "Epoch: 60/100 | RegPriorLoss 5.2341 | RegPosteriorLoss -3.2551 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.25it/s]\n",
-      "Epoch: 61/100 | RegPriorLoss 5.2830 | RegPosteriorLoss -2.9508 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.15it/s]\n",
-      "Epoch: 62/100 | RegPriorLoss 5.2692 | RegPosteriorLoss -2.9673 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.97it/s]\n",
-      "Epoch: 63/100 | RegPriorLoss 4.9669 | RegPosteriorLoss -2.6646 | Batch: 100%|██████████| 10/10 [00:01<00:00,  9.80it/s]\n",
-      "Epoch: 64/100 | RegPriorLoss 5.2342 | RegPosteriorLoss -2.1537 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.86it/s]\n",
-      "Epoch: 65/100 | RegPriorLoss 5.3447 | RegPosteriorLoss -1.3172 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.81it/s]\n",
-      "Epoch: 66/100 | RegPriorLoss 5.0788 | RegPosteriorLoss -2.8203 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.54it/s]\n",
-      "Epoch: 67/100 | RegPriorLoss 5.0247 | RegPosteriorLoss -3.3309 | Batch: 100%|██████████| 10/10 [00:00<00:00, 10.86it/s]\n",
-      "Epoch: 68/100 | RegPriorLoss 4.8988 | RegPosteriorLoss -2.5898 | Batch: 100%|██████████| 10/10 [00:00<00:00, 10.23it/s]\n",
-      "Epoch: 69/100 | RegPriorLoss 4.9900 | RegPosteriorLoss -2.3094 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.97it/s]\n",
-      "Epoch: 70/100 | RegPriorLoss 5.0561 | RegPosteriorLoss -3.3294 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.12it/s]\n",
-      "Epoch: 71/100 | RegPriorLoss 5.0293 | RegPosteriorLoss -3.1455 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.49it/s]\n",
-      "Epoch: 72/100 | RegPriorLoss 5.1858 | RegPosteriorLoss -3.0141 | Batch: 100%|██████████| 10/10 [00:01<00:00,  8.95it/s]\n",
-      "Epoch: 73/100 | RegPriorLoss 4.9663 | RegPosteriorLoss -3.2878 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.48it/s]\n",
-      "Epoch: 74/100 | RegPriorLoss 4.9243 | RegPosteriorLoss -3.6076 | Batch: 100%|██████████| 10/10 [00:01<00:00,  9.93it/s]\n",
-      "Epoch: 75/100 | RegPriorLoss 5.2362 | RegPosteriorLoss -3.3084 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.33it/s]\n",
-      "Epoch: 76/100 | RegPriorLoss 5.1946 | RegPosteriorLoss -2.3291 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.52it/s]\n",
-      "Epoch: 77/100 | RegPriorLoss 4.9456 | RegPosteriorLoss -3.4906 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.05it/s]\n",
-      "Epoch: 78/100 | RegPriorLoss 4.9334 | RegPosteriorLoss -3.0979 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.17it/s]\n",
-      "Epoch: 79/100 | RegPriorLoss 4.9399 | RegPosteriorLoss -2.7484 | Batch: 100%|██████████| 10/10 [00:01<00:00, 10.00it/s]\n",
-      "Epoch: 80/100 | RegPriorLoss 5.0385 | RegPosteriorLoss -2.3070 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.67it/s]\n",
-      "Epoch: 81/100 | RegPriorLoss 4.9334 | RegPosteriorLoss -1.6025 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.53it/s]\n",
-      "Epoch: 82/100 | RegPriorLoss 4.8287 | RegPosteriorLoss -2.3361 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.19it/s]\n",
-      "Epoch: 83/100 | RegPriorLoss 5.1022 | RegPosteriorLoss -3.4825 | Batch: 100%|██████████| 10/10 [00:00<00:00, 10.28it/s]\n",
-      "Epoch: 84/100 | RegPriorLoss 5.1213 | RegPosteriorLoss -3.2838 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.73it/s]\n",
-      "Epoch: 85/100 | RegPriorLoss 4.8171 | RegPosteriorLoss -3.2660 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.35it/s]\n",
-      "Epoch: 86/100 | RegPriorLoss 5.0760 | RegPosteriorLoss -3.5055 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.76it/s]\n",
-      "Epoch: 87/100 | RegPriorLoss 4.9309 | RegPosteriorLoss -3.9772 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.31it/s]\n",
-      "Epoch: 88/100 | RegPriorLoss 4.9448 | RegPosteriorLoss -2.7976 | Batch: 100%|██████████| 10/10 [00:01<00:00,  9.87it/s]\n",
-      "Epoch: 89/100 | RegPriorLoss 5.1973 | RegPosteriorLoss -3.4582 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.53it/s]\n",
-      "Epoch: 90/100 | RegPriorLoss 5.1121 | RegPosteriorLoss -3.1242 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.93it/s]\n",
-      "Epoch: 91/100 | RegPriorLoss 5.0528 | RegPosteriorLoss -3.5681 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.91it/s]\n",
-      "Epoch: 92/100 | RegPriorLoss 5.0966 | RegPosteriorLoss -3.6631 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.41it/s]\n",
-      "Epoch: 93/100 | RegPriorLoss 4.8900 | RegPosteriorLoss -3.7400 | Batch: 100%|██████████| 10/10 [00:00<00:00, 10.20it/s]\n",
-      "Epoch: 94/100 | RegPriorLoss 5.2390 | RegPosteriorLoss -3.7577 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.03it/s]\n",
-      "Epoch: 95/100 | RegPriorLoss 5.0442 | RegPosteriorLoss -3.1672 | Batch: 100%|██████████| 10/10 [00:00<00:00, 11.25it/s]\n",
-      "Epoch: 96/100 | RegPriorLoss 4.8682 | RegPosteriorLoss -3.1461 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.13it/s]\n",
-      "Epoch: 97/100 | RegPriorLoss 4.9868 | RegPosteriorLoss -3.3163 | Batch: 100%|██████████| 10/10 [00:01<00:00,  9.95it/s]\n",
-      "Epoch: 98/100 | RegPriorLoss 5.1314 | RegPosteriorLoss -3.6799 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.50it/s]\n",
-      "Epoch: 99/100 | RegPriorLoss 4.8637 | RegPosteriorLoss -3.3656 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.48it/s]\n",
-      "Epoch: 100/100 | RegPriorLoss 4.9355 | RegPosteriorLoss -2.9563 | Batch: 100%|██████████| 10/10 [00:00<00:00, 12.05it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "supervised_trainer(data_loader, flow, optimizer, epochs=epochs)\n"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 1500x1000 with 6 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "M_prior = flow.sample(1000, prior_data)['x']['x'].detach()\n",
-    "M_posterior = flow.sample(1000, posterior_data)['x']['x'].detach()\n",
-    "\n",
-    "plt.style.use('default')\n",
-    "fig, axs = plt.subplots(2, 3, figsize=(15, 10))\n",
-    "\n",
-    "axs[0, 0].scatter(M_prior[..., 0], M_prior[..., 1], c='tab:gray', alpha=0.1, edgecolor='none')\n",
-    "axs[0, 0].set_xlim(-15, 15)\n",
-    "axs[0, 0].set_ylim(-15, 15)\n",
-    "axs[0, 0].set_title('Prior')\n",
-    "axs[0, 0].set_xlabel('X')\n",
-    "axs[0, 0].set_ylabel('Y')\n",
-    "\n",
-    "axs[0, 1].scatter(M_prior[..., 0], M_prior[..., 2], c='tab:gray', alpha=0.1, edgecolor='none')\n",
-    "axs[0, 1].set_xlim(-15, 15)\n",
-    "axs[0, 1].set_ylim(-15, 15)\n",
-    "axs[0, 1].set_xlabel('X')\n",
-    "axs[0, 1].set_ylabel('Z')\n",
-    "\n",
-    "axs[1, 0].scatter(M_posterior[..., 0], M_posterior[..., 1], c='tab:gray', alpha=0.1, edgecolor='none')\n",
-    "axs[1, 0].scatter([1], [1], c='tab:orange')\n",
-    "axs[1, 0].set_xlim(-15, 15)\n",
-    "axs[1, 0].set_ylim(-15, 15)\n",
-    "axs[1, 0].set_title('Posterior')\n",
-    "axs[1, 0].set_xlabel('X')\n",
-    "axs[1, 0].set_ylabel('Y')\n",
-    "\n",
-    "axs[1, 1].scatter(M_posterior[..., 0], M_posterior[..., 2], c='tab:gray', alpha=0.1, edgecolor='none')\n",
-    "axs[1, 1].axvline(1, c='tab:orange')\n",
-    "axs[1, 1].set_xlim(-15, 15)\n",
-    "axs[1, 1].set_xlabel('X')\n",
-    "axs[1, 1].set_ylabel('Z')\n",
-    "\n",
-    "axs[1, 2].hist(M_posterior[..., 2].detach(), bins=np.linspace(-25, 25, 50), orientation='horizontal', density=True, color='tab:gray')\n",
-    "axs[1, 2].set_ylim(-15, 15)\n",
-    "axs[1, 2].set_xlabel('Density')\n",
-    "sns.despine(ax=axs[1, 2])\n",
-    "\n",
-    "axs[0, 2].hist(M_prior[..., 2].detach(), bins=np.linspace(-15, 15, 50), orientation='horizontal', density=True, color='tab:gray')\n",
-    "axs[0, 2].set_ylim(-15, 15)\n",
-    "axs[0, 2].set_xlabel('Density')\n",
-    "sns.despine(ax=axs[0, 2])\n",
-    "\n"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# Two Points"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "outputs": [],
-   "source": [
-    "class TwoPointDataset(Dataset):\n",
-    "    def __init__(self, n_items_full=100, n_items_subset=100, prior=None):\n",
-    "        if prior is None:\n",
-    "            prior = D.MultivariateNormal(torch.zeros(3), torch.eye(3) * 4)\n",
-    "\n",
-    "        data_list = []\n",
-    "\n",
-    "        for i in range(n_items_full):\n",
-    "            data = HeteroData()\n",
-    "\n",
-    "            direction = torch.randn(1, 1, 3)\n",
-    "            direction[..., 2] = torch.randn(1, 1) / 5\n",
-    "            direction = direction / torch.norm(direction, dim=-1).unsqueeze(-1)\n",
-    "\n",
-    "            M1 = prior.sample((1,))\n",
-    "            M2 = (M1 + direction).squeeze(0)\n",
-    "\n",
-    "            data['x'].x = torch.stack([M1, M2]).squeeze()\n",
-    "            data['c'].x = data['x'].x[..., :2]\n",
-    "\n",
-    "            data['c', '->', 'x'].edge_index = torch.LongTensor([[0, 0], [1, 1]]).T\n",
-    "            data['x', '->', 'x'].edge_index = torch.LongTensor([[0, 1]]).T\n",
-    "            data['x', '<-', 'x'].edge_index = torch.LongTensor([[0, 1]]).T\n",
-    "            data_list.append(data)\n",
-    "\n",
-    "        for i in range(n_items_subset // 2):\n",
-    "            data = HeteroData()\n",
-    "\n",
-    "            direction = torch.randn(1, 1, 3)\n",
-    "            direction[..., 2] = torch.randn(1, 1) / 5\n",
-    "            direction = direction / torch.norm(direction, dim=-1).unsqueeze(-1)\n",
-    "\n",
-    "            M1 = prior.sample((1,))\n",
-    "            M2 = (M1 + direction).squeeze(0)\n",
-    "\n",
-    "            data['x'].x = torch.stack([M1, M2]).squeeze()\n",
-    "            data['c'].x = data['x'].x[..., :2][0].unsqueeze(0)\n",
-    "\n",
-    "            data['c', '->', 'x'].edge_index = torch.LongTensor([[0, 0]]).T\n",
-    "            data['x', '->', 'x'].edge_index = torch.LongTensor([[0, 1]]).T\n",
-    "            data['x', '<-', 'x'].edge_index = torch.LongTensor([[0, 1]]).T\n",
-    "            data_list.append(data)\n",
-    "\n",
-    "            data = HeteroData()\n",
-    "\n",
-    "            direction = torch.randn(1, 1, 3)\n",
-    "            direction[..., 2] = torch.randn(1, 1) / 5\n",
-    "            direction = direction / torch.norm(direction, dim=-1).unsqueeze(-1)\n",
-    "\n",
-    "            M1 = prior.sample((1,))\n",
-    "            M2 = (M1 + direction).squeeze(0)\n",
-    "\n",
-    "            data['x'].x = torch.stack([M1, M2]).squeeze()\n",
-    "            data['c'].x = data['x'].x[..., :2][1].unsqueeze(0)\n",
-    "\n",
-    "            data['c', '->', 'x'].edge_index = torch.LongTensor([[0, 1]]).T\n",
-    "            data['x', '->', 'x'].edge_index = torch.LongTensor([[0, 1]]).T\n",
-    "            data['x', '<-', 'x'].edge_index = torch.LongTensor([[0, 1]]).T\n",
-    "            data_list.append(data)\n",
-    "\n",
-    "        self.data = data_list\n",
-    "\n",
-    "    def __len__(self):\n",
-    "        return len(self.data)\n",
-    "\n",
-    "    def __getitem__(self, idx):\n",
-    "        return self.data[idx]\n",
-    "\n",
-    "    def metadata(self):\n",
-    "        return self.data[0].metadata()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "outputs": [],
-   "source": [
-    "dataset = TwoPointDataset(n_items_full=1000, n_items_subset=2000)\n",
-    "data_loader = DataLoader(dataset, batch_size=100, shuffle=True)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "outputs": [],
-   "source": [
-    "epochs = 200\n",
-    "lr = 0.001\n",
-    "weight_decay = 1e-5\n",
-    "\n",
-    "flow = CondGraphFlow(num_layers=10)\n",
-    "optimizer = torch.optim.Adam(flow.parameters(), lr=lr, weight_decay=weight_decay)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 1/200 | RegPriorLoss 3.4645 | RegPosteriorLoss -1.6931 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.07it/s]\n",
-      "Epoch: 2/200 | RegPriorLoss 3.4212 | RegPosteriorLoss -3.5099 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.67it/s]\n",
-      "Epoch: 3/200 | RegPriorLoss 3.5003 | RegPosteriorLoss -3.4738 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.63it/s]\n",
-      "Epoch: 4/200 | RegPriorLoss 3.5841 | RegPosteriorLoss -3.9276 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.77it/s]\n",
-      "Epoch: 5/200 | RegPriorLoss 3.6974 | RegPosteriorLoss -2.8440 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.51it/s]\n",
-      "Epoch: 6/200 | RegPriorLoss 3.5986 | RegPosteriorLoss -2.8001 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.68it/s]\n",
-      "Epoch: 7/200 | RegPriorLoss 3.4956 | RegPosteriorLoss -2.8211 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.43it/s]\n",
-      "Epoch: 8/200 | RegPriorLoss 3.7239 | RegPosteriorLoss -3.4899 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.52it/s]\n",
-      "Epoch: 9/200 | RegPriorLoss 3.4703 | RegPosteriorLoss -3.4910 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.72it/s]\n",
-      "Epoch: 10/200 | RegPriorLoss 3.4601 | RegPosteriorLoss -3.1212 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.53it/s]\n",
-      "Epoch: 11/200 | RegPriorLoss 4.1788 | RegPosteriorLoss -3.6358 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.39it/s]\n",
-      "Epoch: 12/200 | RegPriorLoss 3.7598 | RegPosteriorLoss -2.4349 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.73it/s]\n",
-      "Epoch: 13/200 | RegPriorLoss 3.6048 | RegPosteriorLoss -2.6644 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.68it/s]\n",
-      "Epoch: 14/200 | RegPriorLoss 3.6282 | RegPosteriorLoss -3.1546 | Batch: 100%|██████████| 30/30 [00:08<00:00,  3.58it/s]\n",
-      "Epoch: 15/200 | RegPriorLoss 3.8471 | RegPosteriorLoss -2.8924 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.70it/s]\n",
-      "Epoch: 16/200 | RegPriorLoss 3.5502 | RegPosteriorLoss -3.2293 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.39it/s]\n",
-      "Epoch: 17/200 | RegPriorLoss 3.3758 | RegPosteriorLoss -3.5782 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.71it/s]\n",
-      "Epoch: 18/200 | RegPriorLoss 3.8230 | RegPosteriorLoss -3.5608 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.74it/s]\n",
-      "Epoch: 19/200 | RegPriorLoss 3.6100 | RegPosteriorLoss -2.7544 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.77it/s]\n",
-      "Epoch: 20/200 | RegPriorLoss 3.6019 | RegPosteriorLoss -2.1397 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.31it/s]\n",
-      "Epoch: 21/200 | RegPriorLoss 3.6353 | RegPosteriorLoss -3.2445 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.48it/s]\n",
-      "Epoch: 22/200 | RegPriorLoss 3.5949 | RegPosteriorLoss -3.4976 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.70it/s]\n",
-      "Epoch: 23/200 | RegPriorLoss 3.3930 | RegPosteriorLoss -3.8874 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.04it/s]\n",
-      "Epoch: 24/200 | RegPriorLoss 3.5685 | RegPosteriorLoss -2.4724 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.68it/s]\n",
-      "Epoch: 25/200 | RegPriorLoss 3.8115 | RegPosteriorLoss -3.6000 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.77it/s]\n",
-      "Epoch: 26/200 | RegPriorLoss 3.4356 | RegPosteriorLoss -2.5268 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.11it/s]\n",
-      "Epoch: 27/200 | RegPriorLoss 3.4846 | RegPosteriorLoss -3.4214 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.21it/s]\n",
-      "Epoch: 28/200 | RegPriorLoss 3.6104 | RegPosteriorLoss -4.0736 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.98it/s]\n",
-      "Epoch: 29/200 | RegPriorLoss 3.6323 | RegPosteriorLoss -3.6432 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.76it/s]\n",
-      "Epoch: 30/200 | RegPriorLoss 3.6238 | RegPosteriorLoss -3.4276 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.25it/s]\n",
-      "Epoch: 31/200 | RegPriorLoss 3.7551 | RegPosteriorLoss -2.8757 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.42it/s]\n",
-      "Epoch: 32/200 | RegPriorLoss 3.6917 | RegPosteriorLoss -3.6884 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.19it/s]\n",
-      "Epoch: 33/200 | RegPriorLoss 3.6635 | RegPosteriorLoss -2.5259 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.51it/s]\n",
-      "Epoch: 34/200 | RegPriorLoss 3.7067 | RegPosteriorLoss -3.7829 | Batch: 100%|██████████| 30/30 [00:08<00:00,  3.48it/s]\n",
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-      "Epoch: 37/200 | RegPriorLoss 3.6256 | RegPosteriorLoss -3.0696 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.49it/s]\n",
-      "Epoch: 38/200 | RegPriorLoss 3.6025 | RegPosteriorLoss -3.4568 | Batch: 100%|██████████| 30/30 [00:09<00:00,  3.31it/s]\n",
-      "Epoch: 39/200 | RegPriorLoss 3.6619 | RegPosteriorLoss -3.7102 | Batch: 100%|██████████| 30/30 [00:09<00:00,  3.16it/s]\n",
-      "Epoch: 40/200 | RegPriorLoss 3.3449 | RegPosteriorLoss -3.4051 | Batch: 100%|██████████| 30/30 [00:07<00:00,  4.21it/s]\n",
-      "Epoch: 41/200 | RegPriorLoss 3.6434 | RegPosteriorLoss -3.4534 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.07it/s]\n",
-      "Epoch: 42/200 | RegPriorLoss 4.0499 | RegPosteriorLoss -3.8350 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.31it/s]\n",
-      "Epoch: 43/200 | RegPriorLoss 3.5547 | RegPosteriorLoss -2.9338 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.28it/s]\n",
-      "Epoch: 44/200 | RegPriorLoss 3.4333 | RegPosteriorLoss -3.7211 | Batch: 100%|██████████| 30/30 [00:07<00:00,  3.97it/s]\n",
-      "Epoch: 45/200 | RegPriorLoss 3.7536 | RegPosteriorLoss -3.5410 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.88it/s]\n",
-      "Epoch: 46/200 | RegPriorLoss 3.3197 | RegPosteriorLoss -3.7233 | Batch: 100%|██████████| 30/30 [00:07<00:00,  4.23it/s]\n",
-      "Epoch: 47/200 | RegPriorLoss 3.6187 | RegPosteriorLoss -3.2768 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.50it/s]\n",
-      "Epoch: 48/200 | RegPriorLoss 3.5601 | RegPosteriorLoss -3.6765 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.99it/s]\n",
-      "Epoch: 49/200 | RegPriorLoss 3.5188 | RegPosteriorLoss -3.6945 | Batch: 100%|██████████| 30/30 [00:08<00:00,  3.59it/s]\n",
-      "Epoch: 50/200 | RegPriorLoss 3.7474 | RegPosteriorLoss -3.6440 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.06it/s]\n",
-      "Epoch: 51/200 | RegPriorLoss 3.5631 | RegPosteriorLoss -3.6910 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.30it/s]\n",
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-      "Epoch: 54/200 | RegPriorLoss 3.6055 | RegPosteriorLoss -3.0590 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.40it/s]\n",
-      "Epoch: 55/200 | RegPriorLoss 3.6448 | RegPosteriorLoss -3.1023 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.42it/s]\n",
-      "Epoch: 56/200 | RegPriorLoss 3.3658 | RegPosteriorLoss -3.5795 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.14it/s]\n",
-      "Epoch: 57/200 | RegPriorLoss 3.4642 | RegPosteriorLoss -3.0224 | Batch: 100%|██████████| 30/30 [00:08<00:00,  3.74it/s]\n",
-      "Epoch: 58/200 | RegPriorLoss 3.5864 | RegPosteriorLoss -3.4066 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.32it/s]\n",
-      "Epoch: 59/200 | RegPriorLoss 3.6180 | RegPosteriorLoss -3.8189 | Batch: 100%|██████████| 30/30 [00:08<00:00,  3.57it/s]\n",
-      "Epoch: 60/200 | RegPriorLoss 3.4029 | RegPosteriorLoss -0.5228 | Batch: 100%|██████████| 30/30 [00:08<00:00,  3.70it/s]\n",
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-      "Epoch: 62/200 | RegPriorLoss 3.7014 | RegPosteriorLoss -3.7441 | Batch: 100%|██████████| 30/30 [00:08<00:00,  3.65it/s]\n",
-      "Epoch: 63/200 | RegPriorLoss 3.5739 | RegPosteriorLoss -3.3238 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.38it/s]\n",
-      "Epoch: 64/200 | RegPriorLoss 3.4508 | RegPosteriorLoss -3.6886 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.58it/s]\n",
-      "Epoch: 65/200 | RegPriorLoss 3.3610 | RegPosteriorLoss -3.6979 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.29it/s]\n",
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-      "Epoch: 79/200 | RegPriorLoss 3.4446 | RegPosteriorLoss -2.5269 | Batch: 100%|██████████| 30/30 [00:07<00:00,  4.27it/s]\n",
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-      "Epoch: 82/200 | RegPriorLoss 3.3132 | RegPosteriorLoss -3.8903 | Batch: 100%|██████████| 30/30 [00:07<00:00,  4.19it/s]\n",
-      "Epoch: 83/200 | RegPriorLoss 3.6473 | RegPosteriorLoss -3.4008 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.12it/s]\n",
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-      "Epoch: 90/200 | RegPriorLoss 3.4424 | RegPosteriorLoss -3.9635 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.45it/s]\n",
-      "Epoch: 91/200 | RegPriorLoss 3.7042 | RegPosteriorLoss -2.6991 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.43it/s]\n",
-      "Epoch: 92/200 | RegPriorLoss 3.4211 | RegPosteriorLoss -3.5860 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.48it/s]\n",
-      "Epoch: 93/200 | RegPriorLoss 3.6357 | RegPosteriorLoss -3.3488 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.25it/s]\n",
-      "Epoch: 94/200 | RegPriorLoss 3.6493 | RegPosteriorLoss -3.5201 | Batch: 100%|██████████| 30/30 [00:07<00:00,  4.24it/s]\n",
-      "Epoch: 95/200 | RegPriorLoss 3.4983 | RegPosteriorLoss -3.9172 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.94it/s]\n",
-      "Epoch: 96/200 | RegPriorLoss 3.5700 | RegPosteriorLoss -3.0434 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.52it/s]\n",
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-      "Epoch: 98/200 | RegPriorLoss 3.7188 | RegPosteriorLoss -3.7031 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.33it/s]\n",
-      "Epoch: 99/200 | RegPriorLoss 3.8285 | RegPosteriorLoss 0.0427 | Batch: 100%|██████████| 30/30 [00:07<00:00,  4.24it/s] \n",
-      "Epoch: 100/200 | RegPriorLoss 3.6863 | RegPosteriorLoss -4.0318 | Batch: 100%|██████████| 30/30 [00:09<00:00,  3.02it/s]\n",
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-      "Epoch: 110/200 | RegPriorLoss 3.6167 | RegPosteriorLoss -3.6394 | Batch: 100%|██████████| 30/30 [00:09<00:00,  3.15it/s]\n",
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-      "Epoch: 112/200 | RegPriorLoss 3.5217 | RegPosteriorLoss -3.3439 | Batch: 100%|██████████| 30/30 [00:11<00:00,  2.50it/s]\n",
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-      "Epoch: 115/200 | RegPriorLoss 3.6864 | RegPosteriorLoss -3.8154 | Batch: 100%|██████████| 30/30 [00:11<00:00,  2.69it/s]\n",
-      "Epoch: 116/200 | RegPriorLoss 3.3546 | RegPosteriorLoss -3.4256 | Batch: 100%|██████████| 30/30 [00:08<00:00,  3.68it/s]\n",
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-      "Epoch: 120/200 | RegPriorLoss 3.6460 | RegPosteriorLoss -3.6906 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.53it/s]\n",
-      "Epoch: 121/200 | RegPriorLoss 3.4089 | RegPosteriorLoss -2.7079 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.96it/s]\n",
-      "Epoch: 122/200 | RegPriorLoss 3.5427 | RegPosteriorLoss -3.8057 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.95it/s]\n",
-      "Epoch: 123/200 | RegPriorLoss 3.6732 | RegPosteriorLoss -3.6475 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.88it/s]\n",
-      "Epoch: 124/200 | RegPriorLoss 3.3693 | RegPosteriorLoss -3.8832 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.55it/s]\n",
-      "Epoch: 125/200 | RegPriorLoss 3.4127 | RegPosteriorLoss -3.7630 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.42it/s]\n",
-      "Epoch: 126/200 | RegPriorLoss 3.5705 | RegPosteriorLoss -3.3752 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.81it/s]\n",
-      "Epoch: 127/200 | RegPriorLoss 3.3183 | RegPosteriorLoss -3.5861 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.83it/s]\n",
-      "Epoch: 128/200 | RegPriorLoss 3.3626 | RegPosteriorLoss -4.3041 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.59it/s]\n",
-      "Epoch: 129/200 | RegPriorLoss 3.5483 | RegPosteriorLoss -2.8836 | Batch: 100%|██████████| 30/30 [00:06<00:00,  5.00it/s]\n",
-      "Epoch: 130/200 | RegPriorLoss 3.5567 | RegPosteriorLoss -3.9199 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.50it/s]\n",
-      "Epoch: 131/200 | RegPriorLoss 3.5756 | RegPosteriorLoss -3.3646 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.21it/s]\n",
-      "Epoch: 132/200 | RegPriorLoss 3.6122 | RegPosteriorLoss -4.1133 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.23it/s]\n",
-      "Epoch: 133/200 | RegPriorLoss 3.4689 | RegPosteriorLoss -4.1508 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.25it/s]\n",
-      "Epoch: 134/200 | RegPriorLoss 3.2664 | RegPosteriorLoss -3.9683 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.30it/s]\n",
-      "Epoch: 135/200 | RegPriorLoss 3.3489 | RegPosteriorLoss -3.4371 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.55it/s]\n",
-      "Epoch: 136/200 | RegPriorLoss 3.5251 | RegPosteriorLoss -3.8020 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.08it/s]\n",
-      "Epoch: 137/200 | RegPriorLoss 3.3598 | RegPosteriorLoss -3.5026 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.06it/s]\n",
-      "Epoch: 138/200 | RegPriorLoss 3.6905 | RegPosteriorLoss -3.8189 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.20it/s]\n",
-      "Epoch: 139/200 | RegPriorLoss 3.4123 | RegPosteriorLoss -3.2858 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.73it/s]\n",
-      "Epoch: 140/200 | RegPriorLoss 3.3680 | RegPosteriorLoss -3.8900 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.96it/s]\n",
-      "Epoch: 141/200 | RegPriorLoss 3.2488 | RegPosteriorLoss -3.2493 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.17it/s]\n",
-      "Epoch: 142/200 | RegPriorLoss 3.4048 | RegPosteriorLoss -3.5766 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.48it/s]\n",
-      "Epoch: 143/200 | RegPriorLoss 3.2821 | RegPosteriorLoss -3.8201 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.19it/s]\n",
-      "Epoch: 144/200 | RegPriorLoss 3.4479 | RegPosteriorLoss -2.8786 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.39it/s]\n",
-      "Epoch: 145/200 | RegPriorLoss 3.4445 | RegPosteriorLoss -3.7173 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.20it/s]\n",
-      "Epoch: 146/200 | RegPriorLoss 3.3464 | RegPosteriorLoss -3.2587 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.99it/s]\n",
-      "Epoch: 147/200 | RegPriorLoss 3.3734 | RegPosteriorLoss -4.0757 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.07it/s]\n",
-      "Epoch: 148/200 | RegPriorLoss 3.6230 | RegPosteriorLoss -3.0621 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.28it/s]\n",
-      "Epoch: 149/200 | RegPriorLoss 3.5305 | RegPosteriorLoss -3.8547 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.38it/s]\n",
-      "Epoch: 150/200 | RegPriorLoss 3.4532 | RegPosteriorLoss -4.0102 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.94it/s]\n",
-      "Epoch: 151/200 | RegPriorLoss 3.5179 | RegPosteriorLoss -3.1130 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.34it/s]\n",
-      "Epoch: 152/200 | RegPriorLoss 3.4930 | RegPosteriorLoss -3.7731 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.67it/s]\n",
-      "Epoch: 153/200 | RegPriorLoss 3.3603 | RegPosteriorLoss -4.1417 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.50it/s]\n",
-      "Epoch: 154/200 | RegPriorLoss 3.5769 | RegPosteriorLoss -2.9091 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.20it/s]\n",
-      "Epoch: 155/200 | RegPriorLoss 3.4399 | RegPosteriorLoss -3.5216 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.42it/s]\n",
-      "Epoch: 156/200 | RegPriorLoss 3.5481 | RegPosteriorLoss -3.3499 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.90it/s]\n",
-      "Epoch: 157/200 | RegPriorLoss 3.4896 | RegPosteriorLoss -3.8055 | Batch: 100%|██████████| 30/30 [00:07<00:00,  4.26it/s]\n",
-      "Epoch: 158/200 | RegPriorLoss 3.4624 | RegPosteriorLoss -3.2096 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.61it/s]\n",
-      "Epoch: 159/200 | RegPriorLoss 3.3123 | RegPosteriorLoss -3.5298 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.13it/s]\n",
-      "Epoch: 160/200 | RegPriorLoss 3.4794 | RegPosteriorLoss -4.2270 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.36it/s]\n",
-      "Epoch: 161/200 | RegPriorLoss 3.1662 | RegPosteriorLoss -3.8457 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.30it/s]\n",
-      "Epoch: 162/200 | RegPriorLoss 3.3934 | RegPosteriorLoss -3.7767 | Batch: 100%|██████████| 30/30 [00:07<00:00,  3.88it/s]\n",
-      "Epoch: 163/200 | RegPriorLoss 3.7452 | RegPosteriorLoss -2.0929 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.41it/s]\n",
-      "Epoch: 164/200 | RegPriorLoss 3.2583 | RegPosteriorLoss -3.8872 | Batch: 100%|██████████| 30/30 [00:07<00:00,  4.19it/s]\n",
-      "Epoch: 165/200 | RegPriorLoss 3.5118 | RegPosteriorLoss -2.3319 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.15it/s]\n",
-      "Epoch: 166/200 | RegPriorLoss 3.5373 | RegPosteriorLoss -3.7435 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.48it/s]\n",
-      "Epoch: 167/200 | RegPriorLoss 3.4925 | RegPosteriorLoss -3.1217 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.58it/s]\n",
-      "Epoch: 168/200 | RegPriorLoss 3.2918 | RegPosteriorLoss -3.8884 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.05it/s]\n",
-      "Epoch: 169/200 | RegPriorLoss 3.3040 | RegPosteriorLoss -4.3357 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.88it/s]\n",
-      "Epoch: 170/200 | RegPriorLoss 3.5763 | RegPosteriorLoss -3.4680 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.36it/s]\n",
-      "Epoch: 171/200 | RegPriorLoss 3.9645 | RegPosteriorLoss -3.0953 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.72it/s]\n",
-      "Epoch: 172/200 | RegPriorLoss 3.5941 | RegPosteriorLoss -3.6585 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.97it/s]\n",
-      "Epoch: 173/200 | RegPriorLoss 3.7132 | RegPosteriorLoss -2.8053 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.39it/s]\n",
-      "Epoch: 174/200 | RegPriorLoss 3.4746 | RegPosteriorLoss -4.0200 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.13it/s]\n",
-      "Epoch: 175/200 | RegPriorLoss 3.4160 | RegPosteriorLoss -4.1689 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.51it/s]\n",
-      "Epoch: 176/200 | RegPriorLoss 3.5566 | RegPosteriorLoss -4.2008 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.52it/s]\n",
-      "Epoch: 177/200 | RegPriorLoss 3.2705 | RegPosteriorLoss -3.5843 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.62it/s]\n",
-      "Epoch: 178/200 | RegPriorLoss 3.3352 | RegPosteriorLoss -2.9292 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.70it/s]\n",
-      "Epoch: 179/200 | RegPriorLoss 3.3107 | RegPosteriorLoss -4.5642 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.67it/s]\n",
-      "Epoch: 180/200 | RegPriorLoss 3.3370 | RegPosteriorLoss -3.0132 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.63it/s]\n",
-      "Epoch: 181/200 | RegPriorLoss 3.3479 | RegPosteriorLoss -3.8130 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.36it/s]\n",
-      "Epoch: 182/200 | RegPriorLoss 3.5122 | RegPosteriorLoss -3.1810 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.69it/s]\n",
-      "Epoch: 183/200 | RegPriorLoss 3.3298 | RegPosteriorLoss -4.3078 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.19it/s]\n",
-      "Epoch: 184/200 | RegPriorLoss 3.5367 | RegPosteriorLoss -3.9738 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.47it/s]\n",
-      "Epoch: 185/200 | RegPriorLoss 3.6005 | RegPosteriorLoss -2.9738 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.56it/s]\n",
-      "Epoch: 186/200 | RegPriorLoss 3.3725 | RegPosteriorLoss -3.5578 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.18it/s]\n",
-      "Epoch: 187/200 | RegPriorLoss 3.4661 | RegPosteriorLoss -4.2303 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.42it/s]\n",
-      "Epoch: 188/200 | RegPriorLoss 3.5427 | RegPosteriorLoss -3.8078 | Batch: 100%|██████████| 30/30 [00:07<00:00,  4.15it/s]\n",
-      "Epoch: 189/200 | RegPriorLoss 3.3981 | RegPosteriorLoss -3.9446 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.80it/s]\n",
-      "Epoch: 190/200 | RegPriorLoss 3.2735 | RegPosteriorLoss -4.3630 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.67it/s]\n",
-      "Epoch: 191/200 | RegPriorLoss 3.5475 | RegPosteriorLoss -3.8626 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.34it/s]\n",
-      "Epoch: 192/200 | RegPriorLoss 3.4477 | RegPosteriorLoss -4.1110 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.68it/s]\n",
-      "Epoch: 193/200 | RegPriorLoss 3.5629 | RegPosteriorLoss -3.9625 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.62it/s]\n",
-      "Epoch: 194/200 | RegPriorLoss 3.4332 | RegPosteriorLoss -3.9067 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.68it/s]\n",
-      "Epoch: 195/200 | RegPriorLoss 3.2881 | RegPosteriorLoss -4.0957 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.54it/s]\n",
-      "Epoch: 196/200 | RegPriorLoss 3.6119 | RegPosteriorLoss -4.0043 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.30it/s]\n",
-      "Epoch: 197/200 | RegPriorLoss 3.3379 | RegPosteriorLoss -3.6315 | Batch: 100%|██████████| 30/30 [00:06<00:00,  4.99it/s]\n",
-      "Epoch: 198/200 | RegPriorLoss 3.3479 | RegPosteriorLoss -3.5066 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.44it/s]\n",
-      "Epoch: 199/200 | RegPriorLoss 3.4155 | RegPosteriorLoss -4.4695 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.55it/s]\n",
-      "Epoch: 200/200 | RegPriorLoss 3.4443 | RegPosteriorLoss -3.6102 | Batch: 100%|██████████| 30/30 [00:05<00:00,  5.18it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "supervised_trainer(data_loader, flow, optimizer, epochs=epochs)\n"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "outputs": [],
-   "source": [
-    "posterior_data = HeteroData({\n",
-    "    'x': {'x': torch.zeros(2, 1, 3), 'batch': torch.Tensor([0, 1])},\n",
-    "    'c': {'x': torch.Tensor([[2, 2], [1, 2]]), 'batch': torch.Tensor([0, 1])},\n",
-    "    ('x', '->', 'x'): { 'edge_index' : torch.LongTensor([[0, 1]]).T },\n",
-    "    ('x', '<-', 'x'): { 'edge_index' : torch.LongTensor([[0, 1]]).T },\n",
-    "    ('c',  '->', 'x'): { 'edge_index': torch.LongTensor([[0, 0], [1, 1]]).T }\n",
-    "})\n",
-    "\n",
-    "part_data = HeteroData({\n",
-    "    'x': {'x': torch.zeros(2, 1, 3), 'batch': torch.Tensor([0, 1])},\n",
-    "    'c': {'x': torch.Tensor([[1, 1]]), 'batch': torch.Tensor([0])},\n",
-    "    ('x', '->', 'x'): { 'edge_index' : torch.LongTensor([[0, 1]]).T },\n",
-    "    ('x', '<-', 'x'): { 'edge_index' : torch.LongTensor([[0, 1]]).T },\n",
-    "    ('c',  '->', 'x'): { 'edge_index': torch.LongTensor([[0, 0]]).T }\n",
-    "})\n",
-    "\n",
-    "prior_data = HeteroData({\n",
-    "    'x': {'x': torch.zeros(2, 1, 3), 'batch': torch.Tensor([0, 1])},\n",
-    "    ('x', '->', 'x'): { 'edge_index' : torch.LongTensor([[0, 1]]).T },\n",
-    "    ('x', '<-', 'x'): { 'edge_index' : torch.LongTensor([[0, 1]]).T },\n",
-    "})"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "outputs": [],
-   "source": [
-    "M_prior = flow.sample(1000, prior_data)['x']['x'].detach()\n",
-    "M_posterior = flow.sample(1000, posterior_data)['x']['x'].detach()\n",
-    "M_part = flow.sample(1000, part_data)['x']['x'].detach().squeeze()\n",
-    "\n",
-    "prior_dist = torch.norm(M_prior[0] - M_prior[1], dim=-1)\n",
-    "posterior_dist = torch.norm(M_posterior[0] - M_posterior[1], dim=-1)\n",
-    "part_dist = torch.norm(M_part[0] - M_part[1], dim=-1)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 1500x1500 with 9 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.style.use('default')\n",
-    "fig, axs = plt.subplots(3, 3, figsize=(15, 15))\n",
-    "\n",
-    "axs[0, 0].scatter(M_prior[0, :, 0], M_prior[0, :, 1], c='tab:pink', alpha=0.1, edgecolor='none')\n",
-    "axs[0, 0].scatter(M_prior[1, :, 0], M_prior[1, :, 1], c='tab:cyan', alpha=0.1, edgecolor='none')\n",
-    "axs[0, 0].set_xlim(-5, 5)\n",
-    "axs[0, 0].set_ylim(-5, 5)\n",
-    "axs[0, 0].set_title('Prior')\n",
-    "axs[0, 0].set_xlabel('X')\n",
-    "axs[0, 0].set_ylabel('Y')\n",
-    "\n",
-    "axs[0, 1].scatter(M_prior[0, :, 0], M_prior[0, :, 2], c='tab:pink', alpha=0.1, edgecolor='none')\n",
-    "axs[0, 1].scatter(M_prior[1, :, 0], M_prior[1, :, 2], c='tab:cyan', alpha=0.1, edgecolor='none')\n",
-    "axs[0, 1].set_xlim(-5, 5)\n",
-    "axs[0, 1].set_ylim(-5, 5)\n",
-    "axs[0, 1].set_xlabel('X')\n",
-    "axs[0, 1].set_ylabel('Z')\n",
-    "\n",
-    "axs[1, 0].scatter(M_posterior[0, :, 0], M_posterior[0, :, 1], c='tab:pink', alpha=0.1, edgecolor='none')\n",
-    "axs[1, 0].scatter(M_posterior[1, :, 0], M_posterior[1, :, 1], c='tab:cyan', alpha=0.1, edgecolor='none')\n",
-    "axs[1, 0].set_xlim(-5, 5)\n",
-    "axs[1, 0].set_ylim(-5, 5)\n",
-    "axs[1, 0].set_title('Posterior')\n",
-    "axs[1, 0].set_xlabel('X')\n",
-    "axs[1, 0].set_ylabel('Y')\n",
-    "\n",
-    "axs[1, 1].scatter(M_posterior[0, :, 0], M_posterior[0, :, 2], c='tab:pink', alpha=0.1, edgecolor='none')\n",
-    "axs[1, 1].scatter(M_posterior[1, :, 0], M_posterior[1, :, 2], c='tab:cyan', alpha=0.1, edgecolor='none')\n",
-    "axs[1, 1].set_xlim(-5, 5)\n",
-    "axs[1, 1].set_ylim(-5, 5)\n",
-    "axs[1, 1].set_xlabel('X')\n",
-    "axs[1, 1].set_ylabel('Z')\n",
-    "\n",
-    "axs[2, 0].scatter(M_part[0, :, 0], M_part[0, :, 1], c='tab:pink', alpha=0.1, edgecolor='none', zorder=10)\n",
-    "axs[2, 0].scatter(M_part[1, :, 0], M_part[1, :, 1], c='tab:cyan', alpha=0.1, edgecolor='none')\n",
-    "axs[2, 0].set_xlim(-5, 5)\n",
-    "axs[2, 0].set_ylim(-5, 5)\n",
-    "axs[2, 0].set_title('Partial')\n",
-    "axs[2, 0].set_xlabel('X')\n",
-    "axs[2, 0].set_ylabel('Y')\n",
-    "\n",
-    "\n",
-    "axs[2, 1].scatter(M_part[0, :, 0], M_part[0, :, 2], c='tab:pink', alpha=0.1, edgecolor='none')\n",
-    "axs[2, 1].scatter(M_part[1, :, 0], M_part[1, :, 2], c='tab:cyan', alpha=0.1, edgecolor='none')\n",
-    "axs[2, 1].set_xlim(-5, 5)\n",
-    "axs[2, 1].set_ylim(-5, 5)\n",
-    "axs[2, 1].set_xlabel('X')\n",
-    "axs[2, 1].set_ylabel('Z')\n",
-    "\n",
-    "axs[0, 2].hist(prior_dist.unsqueeze(0), bins=np.linspace(0, 5, 50), density=True, color='tab:gray')\n",
-    "axs[0, 2].set_xlabel('$||M_1 - M_2||_2$')\n",
-    "\n",
-    "axs[1, 2].hist(posterior_dist.unsqueeze(0), bins=np.linspace(0, 5, 50), density=True, color='tab:gray')\n",
-    "axs[1, 2].set_xlabel('$||M_1 - M_2||_2$')\n",
-    "\n",
-    "axs[2, 2].hist(part_dist.unsqueeze(0), bins=np.linspace(0, 5, 50), density=True, color='tab:gray')\n",
-    "axs[2, 2].set_xlabel('$||M_1 - M_2||_2$')\n",
-    "\n",
-    "sns.despine(ax=axs[0, 2])\n",
-    "sns.despine(ax=axs[1, 2])\n",
-    "sns.despine(ax=axs[2, 2])\n"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/propose/datasets/human36m/Human36mDataset.py b/propose/datasets/human36m/Human36mDataset.py
index b167444..9deb1d0 100644
--- a/propose/datasets/human36m/Human36mDataset.py
+++ b/propose/datasets/human36m/Human36mDataset.py
@@ -16,54 +16,6 @@
 from tqdm import tqdm
 
 
-def tensor_to_graph(inputs, context, root, edges, context_edges, root_edges):
-    """
-    It takes in the inputs, context, root, and edges, and returns a HeteroData object
-
-    :param inputs: the input tensor
-    :param context: the context nodes
-    :param root: the root node
-    :param edges: the edges between the nodes in the graph
-    :param context_edges: the edges from the context to the inputs
-    :param root_edges: the edges from the root node to the other nodes
-    :return: A hetero data object.
-    """
-    data = HeteroData()
-
-    data["x"].x = inputs
-    data["x", "->", "x"].edge_index = edges
-    data["x", "<-", "x"].edge_index = edges
-
-    data["c"].x = context
-    data["c", "->", "x"].edge_index = context_edges
-
-    data["r"].x = root
-    data["r", "->", "x"].edge_index = root_edges
-    data["r", "<-", "x"].edge_index = root_edges
-
-    return data
-
-
-def tensor_to_human36m_graph(inputs, context, context_edges):
-    """
-    It takes the input tensors, and converts them to a graph
-
-    :param inputs: the input tensor, which is a tensor of shape (num_frames, num_joints, 3)
-    :param context: the context of the graph, which is the same as the input to the model
-    :param context_edges: the edges that are used to compute the context
-    """
-    pose = Human36mPose(np.zeros((1, 17, 3)))
-    edges = torch.LongTensor(pose.edges).T
-
-    edges, root_edges, context_edges = Human36mDataset.remove_root_edges(
-        edges, context_edges, 1
-    )
-
-    return tensor_to_graph(
-        inputs[1:], context, inputs[:1], edges, context_edges, root_edges
-    )
-
-
 class Human36mDataset(Dataset):
     """
     Dataset class for the Human36M dataset
diff --git a/propose/datasets/human36m/preprocess.py b/propose/datasets/human36m/preprocess.py
index 448df37..838fff6 100644
--- a/propose/datasets/human36m/preprocess.py
+++ b/propose/datasets/human36m/preprocess.py
@@ -7,28 +7,10 @@
 from typing import Union
 
 from propose.datasets.human36m.loaders import load_poses, load_cameras
+from propose.poses.human36m import MPII_2_H36M
 
 PathType = Union[str, Path]
 
-MPII_2_H36M = [
-    6,
-    2,
-    1,
-    0,
-    3,
-    4,
-    5,
-    7,
-    8,
-    9,
-    13,
-    14,
-    15,
-    12,
-    11,
-    10,
-]  # Tranform MPII to H36M
-
 
 def process_pose(pose):
     """
diff --git a/propose/models/detectors/__init__.py b/propose/models/detectors/__init__.py
new file mode 100644
index 0000000..e926841
--- /dev/null
+++ b/propose/models/detectors/__init__.py
@@ -0,0 +1 @@
+from .hrnet import HRNet
diff --git a/propose/models/detectors/hrnet/__init__.py b/propose/models/detectors/hrnet/__init__.py
new file mode 100644
index 0000000..e926841
--- /dev/null
+++ b/propose/models/detectors/hrnet/__init__.py
@@ -0,0 +1 @@
+from .hrnet import HRNet
diff --git a/propose/models/detectors/hrnet/config/__init__.py b/propose/models/detectors/hrnet/config/__init__.py
new file mode 100644
index 0000000..937e9a9
--- /dev/null
+++ b/propose/models/detectors/hrnet/config/__init__.py
@@ -0,0 +1,7 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Microsoft
+# Licensed under the MIT License.
+# Written by Bin Xiao (Bin.Xiao@microsoft.com)
+# ------------------------------------------------------------------------------
+
+from .default import _C as config
diff --git a/propose/models/detectors/hrnet/config/default.py b/propose/models/detectors/hrnet/config/default.py
new file mode 100644
index 0000000..36aca27
--- /dev/null
+++ b/propose/models/detectors/hrnet/config/default.py
@@ -0,0 +1,158 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Microsoft
+# Licensed under the MIT License.
+# Written by Bin Xiao (Bin.Xiao@microsoft.com)
+# ------------------------------------------------------------------------------
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+from yacs.config import CfgNode as CN
+
+
+_C = CN()
+
+_C.OUTPUT_DIR = ""
+_C.LOG_DIR = ""
+_C.DATA_DIR = ""
+_C.GPUS = (0,)
+_C.WORKERS = 4
+_C.PRINT_FREQ = 20
+_C.AUTO_RESUME = False
+_C.PIN_MEMORY = True
+_C.RANK = 0
+
+# Cudnn related params
+_C.CUDNN = CN()
+_C.CUDNN.BENCHMARK = True
+_C.CUDNN.DETERMINISTIC = False
+_C.CUDNN.ENABLED = True
+
+# common params for NETWORK
+_C.MODEL = CN()
+_C.MODEL.NAME = "pose_hrnet"
+_C.MODEL.INIT_WEIGHTS = True
+_C.MODEL.PRETRAINED = ""
+_C.MODEL.NUM_JOINTS = 17
+_C.MODEL.TAG_PER_JOINT = True
+_C.MODEL.TARGET_TYPE = "gaussian"
+_C.MODEL.IMAGE_SIZE = [256, 256]  # width * height, ex: 192 * 256
+_C.MODEL.HEATMAP_SIZE = [64, 64]  # width * height, ex: 24 * 32
+_C.MODEL.SIGMA = 2
+_C.MODEL.EXTRA = CN(new_allowed=True)
+
+# Default Stages without special meaning only for the purpose of making it possible to initialize the network
+_C.MODEL.EXTRA.STAGE2 = CN()
+_C.MODEL.EXTRA.STAGE2.NUM_CHANNELS = [2]
+_C.MODEL.EXTRA.STAGE2.NUM_MODULES = 1
+_C.MODEL.EXTRA.STAGE2.BLOCK = "BASIC"
+_C.MODEL.EXTRA.STAGE2.NUM_BRANCHES = 1
+_C.MODEL.EXTRA.STAGE2.NUM_BLOCKS = [1]
+_C.MODEL.EXTRA.STAGE2.FUSE_METHOD = "SUM"
+
+
+_C.MODEL.EXTRA.STAGE3 = CN()
+_C.MODEL.EXTRA.STAGE3.NUM_CHANNELS = [2]
+_C.MODEL.EXTRA.STAGE3.NUM_MODULES = 1
+_C.MODEL.EXTRA.STAGE3.BLOCK = "BASIC"
+_C.MODEL.EXTRA.STAGE3.NUM_BRANCHES = 1
+_C.MODEL.EXTRA.STAGE3.NUM_BLOCKS = [1]
+_C.MODEL.EXTRA.STAGE3.FUSE_METHOD = "SUM"
+
+
+_C.MODEL.EXTRA.STAGE4 = CN()
+_C.MODEL.EXTRA.STAGE4.NUM_CHANNELS = [2]
+_C.MODEL.EXTRA.STAGE4.NUM_MODULES = 1
+_C.MODEL.EXTRA.STAGE4.BLOCK = "BASIC"
+_C.MODEL.EXTRA.STAGE4.NUM_BRANCHES = 1
+_C.MODEL.EXTRA.STAGE4.NUM_BLOCKS = [1]
+_C.MODEL.EXTRA.STAGE4.FUSE_METHOD = "SUM"
+
+_C.MODEL.EXTRA.FINAL_CONV_KERNEL = 1
+_C.MODEL.EXTRA.PRETRAINED_LAYERS = ["conv1"]
+
+_C.LOSS = CN()
+_C.LOSS.USE_OHKM = False
+_C.LOSS.TOPK = 8
+_C.LOSS.USE_TARGET_WEIGHT = True
+_C.LOSS.USE_DIFFERENT_JOINTS_WEIGHT = False
+
+# DATASET related params
+_C.DATASET = CN()
+_C.DATASET.ROOT = ""
+_C.DATASET.DATASET = "mpii"
+_C.DATASET.TRAIN_SET = "train"
+_C.DATASET.TEST_SET = "valid"
+_C.DATASET.DATA_FORMAT = "jpg"
+_C.DATASET.HYBRID_JOINTS_TYPE = ""
+_C.DATASET.SELECT_DATA = False
+
+# training data augmentation
+_C.DATASET.FLIP = True
+_C.DATASET.SCALE_FACTOR = 0.25
+_C.DATASET.ROT_FACTOR = 30
+_C.DATASET.PROB_HALF_BODY = 0.0
+_C.DATASET.NUM_JOINTS_HALF_BODY = 8
+_C.DATASET.COLOR_RGB = False
+
+# train
+_C.TRAIN = CN()
+
+_C.TRAIN.LR_FACTOR = 0.1
+_C.TRAIN.LR_STEP = [90, 110]
+_C.TRAIN.LR = 0.001
+
+_C.TRAIN.OPTIMIZER = "adam"
+_C.TRAIN.MOMENTUM = 0.9
+_C.TRAIN.WD = 0.0001
+_C.TRAIN.NESTEROV = False
+_C.TRAIN.GAMMA1 = 0.99
+_C.TRAIN.GAMMA2 = 0.0
+
+_C.TRAIN.BEGIN_EPOCH = 0
+_C.TRAIN.END_EPOCH = 140
+
+_C.TRAIN.RESUME = False
+_C.TRAIN.CHECKPOINT = ""
+
+_C.TRAIN.BATCH_SIZE_PER_GPU = 32
+_C.TRAIN.SHUFFLE = True
+
+# testing
+_C.TEST = CN()
+
+# size of images for each device
+_C.TEST.BATCH_SIZE_PER_GPU = 32
+# Test Model Epoch
+_C.TEST.FLIP_TEST = False
+_C.TEST.POST_PROCESS = False
+_C.TEST.SHIFT_HEATMAP = False
+
+_C.TEST.USE_GT_BBOX = False
+
+# nms
+_C.TEST.IMAGE_THRE = 0.1
+_C.TEST.NMS_THRE = 0.6
+_C.TEST.SOFT_NMS = False
+_C.TEST.OKS_THRE = 0.5
+_C.TEST.IN_VIS_THRE = 0.0
+_C.TEST.COCO_BBOX_FILE = ""
+_C.TEST.BBOX_THRE = 1.0
+_C.TEST.MODEL_FILE = ""
+
+# debug
+_C.DEBUG = CN()
+_C.DEBUG.DEBUG = False
+_C.DEBUG.SAVE_BATCH_IMAGES_GT = False
+_C.DEBUG.SAVE_BATCH_IMAGES_PRED = False
+_C.DEBUG.SAVE_HEATMAPS_GT = False
+_C.DEBUG.SAVE_HEATMAPS_PRED = False
+
+
+if __name__ == "__main__":
+    import sys
+
+    with open(sys.argv[1], "w") as f:
+        print(_C, file=f)
diff --git a/scripts/__init__.py b/propose/models/detectors/hrnet/experiments/__init__.py
similarity index 100%
rename from scripts/__init__.py
rename to propose/models/detectors/hrnet/experiments/__init__.py
diff --git a/propose/models/detectors/hrnet/experiments/w32_256x256_adam_lr1e-3.yaml b/propose/models/detectors/hrnet/experiments/w32_256x256_adam_lr1e-3.yaml
new file mode 100644
index 0000000..c6ee59d
--- /dev/null
+++ b/propose/models/detectors/hrnet/experiments/w32_256x256_adam_lr1e-3.yaml
@@ -0,0 +1,120 @@
+AUTO_RESUME: true
+CUDNN:
+  BENCHMARK: true
+  DETERMINISTIC: false
+  ENABLED: true
+DATA_DIR: ''
+GPUS: (0,1,2,3)
+OUTPUT_DIR: 'output'
+LOG_DIR: 'log'
+WORKERS: 24
+PRINT_FREQ: 100
+
+DATASET:
+  COLOR_RGB: true
+  DATASET: mpii
+  DATA_FORMAT: jpg
+  FLIP: true
+  NUM_JOINTS_HALF_BODY: 8
+  PROB_HALF_BODY: -1.0
+  ROOT: 'data/mpii/'
+  ROT_FACTOR: 30
+  SCALE_FACTOR: 0.25
+  TEST_SET: valid
+  TRAIN_SET: train
+MODEL:
+  INIT_WEIGHTS: true
+  NAME: pose_hrnet
+  NUM_JOINTS: 16
+  PRETRAINED: 'models/pytorch/imagenet/hrnet_w32-36af842e.pth'
+  TARGET_TYPE: gaussian
+  IMAGE_SIZE:
+  - 256
+  - 256
+  HEATMAP_SIZE:
+  - 64
+  - 64
+  SIGMA: 2
+  EXTRA:
+    PRETRAINED_LAYERS:
+    - 'conv1'
+    - 'bn1'
+    - 'conv2'
+    - 'bn2'
+    - 'layer1'
+    - 'transition1'
+    - 'stage2'
+    - 'transition2'
+    - 'stage3'
+    - 'transition3'
+    - 'stage4'
+    FINAL_CONV_KERNEL: 1
+    STAGE2:
+      NUM_MODULES: 1
+      NUM_BRANCHES: 2
+      BLOCK: BASIC
+      NUM_BLOCKS:
+      - 4
+      - 4
+      NUM_CHANNELS:
+      - 32
+      - 64
+      FUSE_METHOD: SUM
+    STAGE3:
+      NUM_MODULES: 4
+      NUM_BRANCHES: 3
+      BLOCK: BASIC
+      NUM_BLOCKS:
+      - 4
+      - 4
+      - 4
+      NUM_CHANNELS:
+      - 32
+      - 64
+      - 128
+      FUSE_METHOD: SUM
+    STAGE4:
+      NUM_MODULES: 3
+      NUM_BRANCHES: 4
+      BLOCK: BASIC
+      NUM_BLOCKS:
+      - 4
+      - 4
+      - 4
+      - 4
+      NUM_CHANNELS:
+      - 32
+      - 64
+      - 128
+      - 256
+      FUSE_METHOD: SUM
+LOSS:
+  USE_TARGET_WEIGHT: true
+TRAIN:
+  BATCH_SIZE_PER_GPU: 32
+  SHUFFLE: true
+  BEGIN_EPOCH: 0
+  END_EPOCH: 210
+  OPTIMIZER: adam
+  LR: 0.001
+  LR_FACTOR: 0.1
+  LR_STEP:
+  - 170
+  - 200
+  WD: 0.0001
+  GAMMA1: 0.99
+  GAMMA2: 0.0
+  MOMENTUM: 0.9
+  NESTEROV: false
+TEST:
+  BATCH_SIZE_PER_GPU: 32
+  MODEL_FILE: ''
+  FLIP_TEST: true
+  POST_PROCESS: true
+  SHIFT_HEATMAP: true
+DEBUG:
+  DEBUG: true
+  SAVE_BATCH_IMAGES_GT: true
+  SAVE_BATCH_IMAGES_PRED: true
+  SAVE_HEATMAPS_GT: true
+  SAVE_HEATMAPS_PRED: true
\ No newline at end of file
diff --git a/propose/models/detectors/hrnet/hrnet.py b/propose/models/detectors/hrnet/hrnet.py
new file mode 100644
index 0000000..0def771
--- /dev/null
+++ b/propose/models/detectors/hrnet/hrnet.py
@@ -0,0 +1,116 @@
+import torch
+import torch.backends.cudnn as cudnn
+
+from collections import OrderedDict
+
+import os
+
+from .models.pose_hrnet import PoseHighResolutionNet
+from .config import config
+
+import numpy as np
+
+import wandb
+
+
+class HRNet(PoseHighResolutionNet):
+    @classmethod
+    def from_pretrained(cls, artifact_name=None, config_file=None, **kwargs) -> "HRNet":
+        if not config_file:
+            dirname = os.path.dirname(__file__)
+            config_file = os.path.join(
+                dirname, "experiments/w32_256x256_adam_lr1e-3.yaml"
+            )
+
+            config.defrost()
+            config.merge_from_file(config_file)
+            config.freeze()
+
+        model = cls(config, **kwargs)
+
+        api = wandb.Api()
+        artifact = api.artifact(artifact_name, type="model")
+
+        if wandb.run:
+            wandb.run.use_artifact(artifact, type="model")
+
+        artifact_dir = artifact.download()
+
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        state_dict = torch.load(
+            artifact_dir + "/pose_hrnet_w32_256x256.pth",
+            map_location=torch.device(device),
+        )
+
+        new_state_dict = OrderedDict()
+        for k, v in state_dict.items():
+            name = k  # remove module.
+            #  print(name,'\t')
+            new_state_dict[name] = v
+
+        model.load_state_dict(new_state_dict, strict=False)
+
+        return model
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    @staticmethod
+    def get_max_preds(batch_heatmaps: np.array) -> tuple[np.array, np.array]:
+        """
+        get predictions from score maps
+        heatmaps: numpy.ndarray([batch_size, num_joints, height, width])
+        """
+        assert isinstance(
+            batch_heatmaps, np.ndarray
+        ), "batch_heatmaps should be numpy.ndarray"
+        assert batch_heatmaps.ndim == 4, "batch_images should be 4-ndim"
+
+        batch_size = batch_heatmaps.shape[0]
+        num_joints = batch_heatmaps.shape[1]
+        width = batch_heatmaps.shape[3]
+        heatmaps_reshaped = batch_heatmaps.reshape((batch_size, num_joints, -1))
+        idx = np.argmax(heatmaps_reshaped, 2)
+        maxvals = np.amax(heatmaps_reshaped, 2)
+
+        maxvals = maxvals.reshape((batch_size, num_joints, 1))
+        idx = idx.reshape((batch_size, num_joints, 1))
+
+        preds = np.tile(idx, (1, 1, 2)).astype(np.float32)
+
+        preds[:, :, 0] = (preds[:, :, 0]) % width
+        preds[:, :, 1] = np.floor((preds[:, :, 1]) / width)
+
+        pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2))
+        pred_mask = pred_mask.astype(np.float32)
+
+        preds *= pred_mask
+        return preds, maxvals
+
+    def pose_estimate(self, input: torch.Tensor) -> np.array:
+        batch_heatmaps = self.forward(input)
+
+        coords, maxvals = self.get_max_preds(batch_heatmaps.detach().numpy())
+
+        heatmap_height = batch_heatmaps.shape[2]
+        heatmap_width = batch_heatmaps.shape[3]
+
+        # post-processing
+        for n in range(coords.shape[0]):
+            for p in range(coords.shape[1]):
+                hm = batch_heatmaps[n][p]
+                px = int(np.floor(coords[n][p][0] + 0.5))
+                py = int(np.floor(coords[n][p][1] + 0.5))
+                if 1 < px < heatmap_width - 1 and 1 < py < heatmap_height - 1:
+                    diff = np.array(
+                        [
+                            hm[py][px + 1] - hm[py][px - 1],
+                            hm[py + 1][px] - hm[py - 1][px],
+                        ]
+                    )
+                    coords[n][p] += np.sign(diff) * 0.25
+
+        preds = coords.copy() * 4
+
+        return preds, maxvals
diff --git a/scripts/eval/__init__.py b/propose/models/detectors/hrnet/models/__init__.py
similarity index 100%
rename from scripts/eval/__init__.py
rename to propose/models/detectors/hrnet/models/__init__.py
diff --git a/propose/models/detectors/hrnet/models/pose_hrnet.py b/propose/models/detectors/hrnet/models/pose_hrnet.py
new file mode 100644
index 0000000..237db9a
--- /dev/null
+++ b/propose/models/detectors/hrnet/models/pose_hrnet.py
@@ -0,0 +1,525 @@
+# ------------------------------------------------------------------------------
+# Copyright (c) Microsoft
+# Licensed under the MIT License.
+# Written by Bin Xiao (Bin.Xiao@microsoft.com)
+# ------------------------------------------------------------------------------
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import logging
+
+import torch
+import torch.nn as nn
+
+
+BN_MOMENTUM = 0.1
+logger = logging.getLogger(__name__)
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(
+        in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False
+    )
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
+        self.conv2 = nn.Conv2d(
+            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
+        )
+        self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
+        self.conv3 = nn.Conv2d(
+            planes, planes * self.expansion, kernel_size=1, bias=False
+        )
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion, momentum=BN_MOMENTUM)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class HighResolutionModule(nn.Module):
+    def __init__(
+        self,
+        num_branches,
+        blocks,
+        num_blocks,
+        num_inchannels,
+        num_channels,
+        fuse_method,
+        multi_scale_output=True,
+    ):
+        super(HighResolutionModule, self).__init__()
+        self._check_branches(
+            num_branches, blocks, num_blocks, num_inchannels, num_channels
+        )
+
+        self.num_inchannels = num_inchannels
+        self.fuse_method = fuse_method
+        self.num_branches = num_branches
+
+        self.multi_scale_output = multi_scale_output
+
+        self.branches = self._make_branches(
+            num_branches, blocks, num_blocks, num_channels
+        )
+        self.fuse_layers = self._make_fuse_layers()
+        self.relu = nn.ReLU(True)
+
+    def _check_branches(
+        self, num_branches, blocks, num_blocks, num_inchannels, num_channels
+    ):
+        if num_branches != len(num_blocks):
+            error_msg = "NUM_BRANCHES({}) <> NUM_BLOCKS({})".format(
+                num_branches, len(num_blocks)
+            )
+            logger.error(error_msg)
+            raise ValueError(error_msg)
+
+        if num_branches != len(num_channels):
+            error_msg = "NUM_BRANCHES({}) <> NUM_CHANNELS({})".format(
+                num_branches, len(num_channels)
+            )
+            logger.error(error_msg)
+            raise ValueError(error_msg)
+
+        if num_branches != len(num_inchannels):
+            error_msg = "NUM_BRANCHES({}) <> NUM_INCHANNELS({})".format(
+                num_branches, len(num_inchannels)
+            )
+            logger.error(error_msg)
+            raise ValueError(error_msg)
+
+    def _make_one_branch(self, branch_index, block, num_blocks, num_channels, stride=1):
+        downsample = None
+        if (
+            stride != 1
+            or self.num_inchannels[branch_index]
+            != num_channels[branch_index] * block.expansion
+        ):
+            downsample = nn.Sequential(
+                nn.Conv2d(
+                    self.num_inchannels[branch_index],
+                    num_channels[branch_index] * block.expansion,
+                    kernel_size=1,
+                    stride=stride,
+                    bias=False,
+                ),
+                nn.BatchNorm2d(
+                    num_channels[branch_index] * block.expansion, momentum=BN_MOMENTUM
+                ),
+            )
+
+        layers = []
+        layers.append(
+            block(
+                self.num_inchannels[branch_index],
+                num_channels[branch_index],
+                stride,
+                downsample,
+            )
+        )
+        self.num_inchannels[branch_index] = num_channels[branch_index] * block.expansion
+        for i in range(1, num_blocks[branch_index]):
+            layers.append(
+                block(self.num_inchannels[branch_index], num_channels[branch_index])
+            )
+
+        return nn.Sequential(*layers)
+
+    def _make_branches(self, num_branches, block, num_blocks, num_channels):
+        branches = []
+
+        for i in range(num_branches):
+            branches.append(self._make_one_branch(i, block, num_blocks, num_channels))
+
+        return nn.ModuleList(branches)
+
+    def _make_fuse_layers(self):
+        if self.num_branches == 1:
+            return None
+
+        num_branches = self.num_branches
+        num_inchannels = self.num_inchannels
+        fuse_layers = []
+        for i in range(num_branches if self.multi_scale_output else 1):
+            fuse_layer = []
+            for j in range(num_branches):
+                if j > i:
+                    fuse_layer.append(
+                        nn.Sequential(
+                            nn.Conv2d(
+                                num_inchannels[j],
+                                num_inchannels[i],
+                                1,
+                                1,
+                                0,
+                                bias=False,
+                            ),
+                            nn.BatchNorm2d(num_inchannels[i]),
+                            nn.Upsample(scale_factor=2 ** (j - i), mode="nearest"),
+                        )
+                    )
+                elif j == i:
+                    fuse_layer.append(None)
+                else:
+                    conv3x3s = []
+                    for k in range(i - j):
+                        if k == i - j - 1:
+                            num_outchannels_conv3x3 = num_inchannels[i]
+                            conv3x3s.append(
+                                nn.Sequential(
+                                    nn.Conv2d(
+                                        num_inchannels[j],
+                                        num_outchannels_conv3x3,
+                                        3,
+                                        2,
+                                        1,
+                                        bias=False,
+                                    ),
+                                    nn.BatchNorm2d(num_outchannels_conv3x3),
+                                )
+                            )
+                        else:
+                            num_outchannels_conv3x3 = num_inchannels[j]
+                            conv3x3s.append(
+                                nn.Sequential(
+                                    nn.Conv2d(
+                                        num_inchannels[j],
+                                        num_outchannels_conv3x3,
+                                        3,
+                                        2,
+                                        1,
+                                        bias=False,
+                                    ),
+                                    nn.BatchNorm2d(num_outchannels_conv3x3),
+                                    nn.ReLU(True),
+                                )
+                            )
+                    fuse_layer.append(nn.Sequential(*conv3x3s))
+            fuse_layers.append(nn.ModuleList(fuse_layer))
+
+        return nn.ModuleList(fuse_layers)
+
+    def get_num_inchannels(self):
+        return self.num_inchannels
+
+    def forward(self, x):
+        if self.num_branches == 1:
+            return [self.branches[0](x[0])]
+
+        for i in range(self.num_branches):
+            x[i] = self.branches[i](x[i])
+
+        x_fuse = []
+
+        for i in range(len(self.fuse_layers)):
+            y = x[0] if i == 0 else self.fuse_layers[i][0](x[0])
+            for j in range(1, self.num_branches):
+                if i == j:
+                    y = y + x[j]
+                else:
+                    y = y + self.fuse_layers[i][j](x[j])
+            x_fuse.append(self.relu(y))
+
+        return x_fuse
+
+
+blocks_dict = {"BASIC": BasicBlock, "BOTTLENECK": Bottleneck}
+
+
+class PoseHighResolutionNet(nn.Module):
+    def __init__(self, cfg, **kwargs):
+        self.inplanes = 64
+        extra = cfg["MODEL"]["EXTRA"]
+        super(PoseHighResolutionNet, self).__init__()
+
+        # stem net
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
+        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
+        self.relu = nn.ReLU(inplace=True)
+        self.layer1 = self._make_layer(Bottleneck, 64, 4)
+
+        self.stage2_cfg = extra["STAGE2"]
+        num_channels = self.stage2_cfg["NUM_CHANNELS"]
+        block = blocks_dict[self.stage2_cfg["BLOCK"]]
+        num_channels = [
+            num_channels[i] * block.expansion for i in range(len(num_channels))
+        ]
+        self.transition1 = self._make_transition_layer([256], num_channels)
+        self.stage2, pre_stage_channels = self._make_stage(
+            self.stage2_cfg, num_channels
+        )
+
+        self.stage3_cfg = extra["STAGE3"]
+        num_channels = self.stage3_cfg["NUM_CHANNELS"]
+        block = blocks_dict[self.stage3_cfg["BLOCK"]]
+        num_channels = [
+            num_channels[i] * block.expansion for i in range(len(num_channels))
+        ]
+        self.transition2 = self._make_transition_layer(pre_stage_channels, num_channels)
+        self.stage3, pre_stage_channels = self._make_stage(
+            self.stage3_cfg, num_channels
+        )
+
+        self.stage4_cfg = extra["STAGE4"]
+        num_channels = self.stage4_cfg["NUM_CHANNELS"]
+        block = blocks_dict[self.stage4_cfg["BLOCK"]]
+        num_channels = [
+            num_channels[i] * block.expansion for i in range(len(num_channels))
+        ]
+        self.transition3 = self._make_transition_layer(pre_stage_channels, num_channels)
+        self.stage4, pre_stage_channels = self._make_stage(
+            self.stage4_cfg, num_channels, multi_scale_output=False
+        )
+
+        self.final_layer = nn.Conv2d(
+            in_channels=pre_stage_channels[0],
+            out_channels=cfg["MODEL"]["NUM_JOINTS"],
+            kernel_size=extra["FINAL_CONV_KERNEL"],
+            stride=1,
+            padding=1 if extra["FINAL_CONV_KERNEL"] == 3 else 0,
+        )
+
+        self.pretrained_layers = extra["PRETRAINED_LAYERS"]
+
+    def _make_transition_layer(self, num_channels_pre_layer, num_channels_cur_layer):
+        num_branches_cur = len(num_channels_cur_layer)
+        num_branches_pre = len(num_channels_pre_layer)
+
+        transition_layers = []
+        for i in range(num_branches_cur):
+            if i < num_branches_pre:
+                if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
+                    transition_layers.append(
+                        nn.Sequential(
+                            nn.Conv2d(
+                                num_channels_pre_layer[i],
+                                num_channels_cur_layer[i],
+                                3,
+                                1,
+                                1,
+                                bias=False,
+                            ),
+                            nn.BatchNorm2d(num_channels_cur_layer[i]),
+                            nn.ReLU(inplace=True),
+                        )
+                    )
+                else:
+                    transition_layers.append(None)
+            else:
+                conv3x3s = []
+                for j in range(i + 1 - num_branches_pre):
+                    inchannels = num_channels_pre_layer[-1]
+                    outchannels = (
+                        num_channels_cur_layer[i]
+                        if j == i - num_branches_pre
+                        else inchannels
+                    )
+                    conv3x3s.append(
+                        nn.Sequential(
+                            nn.Conv2d(inchannels, outchannels, 3, 2, 1, bias=False),
+                            nn.BatchNorm2d(outchannels),
+                            nn.ReLU(inplace=True),
+                        )
+                    )
+                transition_layers.append(nn.Sequential(*conv3x3s))
+
+        return nn.ModuleList(transition_layers)
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(
+                    self.inplanes,
+                    planes * block.expansion,
+                    kernel_size=1,
+                    stride=stride,
+                    bias=False,
+                ),
+                nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def _make_stage(self, layer_config, num_inchannels, multi_scale_output=True):
+        num_modules = layer_config["NUM_MODULES"]
+        num_branches = layer_config["NUM_BRANCHES"]
+        num_blocks = layer_config["NUM_BLOCKS"]
+        num_channels = layer_config["NUM_CHANNELS"]
+        block = blocks_dict[layer_config["BLOCK"]]
+        fuse_method = layer_config["FUSE_METHOD"]
+
+        modules = []
+        for i in range(num_modules):
+            # multi_scale_output is only used last module
+            if not multi_scale_output and i == num_modules - 1:
+                reset_multi_scale_output = False
+            else:
+                reset_multi_scale_output = True
+
+            modules.append(
+                HighResolutionModule(
+                    num_branches,
+                    block,
+                    num_blocks,
+                    num_inchannels,
+                    num_channels,
+                    fuse_method,
+                    reset_multi_scale_output,
+                )
+            )
+            num_inchannels = modules[-1].get_num_inchannels()
+
+        return nn.Sequential(*modules), num_inchannels
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.conv2(x)
+        x = self.bn2(x)
+        x = self.relu(x)
+        x = self.layer1(x)
+
+        x_list = []
+        for i in range(self.stage2_cfg["NUM_BRANCHES"]):
+            if self.transition1[i] is not None:
+                x_list.append(self.transition1[i](x))
+            else:
+                x_list.append(x)
+        y_list = self.stage2(x_list)
+
+        x_list = []
+        for i in range(self.stage3_cfg["NUM_BRANCHES"]):
+            if self.transition2[i] is not None:
+                x_list.append(self.transition2[i](y_list[-1]))
+            else:
+                x_list.append(y_list[i])
+        y_list = self.stage3(x_list)
+
+        x_list = []
+        for i in range(self.stage4_cfg["NUM_BRANCHES"]):
+            if self.transition3[i] is not None:
+                x_list.append(self.transition3[i](y_list[-1]))
+            else:
+                x_list.append(y_list[i])
+        y_list = self.stage4(x_list)
+
+        x = self.final_layer(y_list[0])
+
+        return x
+
+    def init_weights(self, pretrained=""):
+        logger.info("=> init weights from normal distribution")
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+                nn.init.normal_(m.weight, std=0.001)
+                for name, _ in m.named_parameters():
+                    if name in ["bias"]:
+                        nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.ConvTranspose2d):
+                nn.init.normal_(m.weight, std=0.001)
+                for name, _ in m.named_parameters():
+                    if name in ["bias"]:
+                        nn.init.constant_(m.bias, 0)
+
+        if os.path.isfile(pretrained):
+            pretrained_state_dict = torch.load(pretrained)
+            logger.info("=> loading pretrained model {}".format(pretrained))
+
+            need_init_state_dict = {}
+            for name, m in pretrained_state_dict.items():
+                if (
+                    name.split(".")[0] in self.pretrained_layers
+                    or self.pretrained_layers[0] is "*"
+                ):
+                    need_init_state_dict[name] = m
+            self.load_state_dict(need_init_state_dict, strict=False)
+        elif pretrained:
+            logger.error("=> please download pre-trained models first!")
+            raise ValueError("{} is not exist!".format(pretrained))
+
+
+def get_pose_net(cfg, is_train, **kwargs):
+    model = PoseHighResolutionNet(cfg, **kwargs)
+
+    if is_train and cfg["MODEL"]["INIT_WEIGHTS"]:
+        model.init_weights(cfg["MODEL"]["PRETRAINED"])
+
+    return model
diff --git a/propose/models/flows/CondGraphFlow.py b/propose/models/flows/CondGraphFlow.py
index ffb40e9..b5f483b 100644
--- a/propose/models/flows/CondGraphFlow.py
+++ b/propose/models/flows/CondGraphFlow.py
@@ -5,6 +5,8 @@
 from propose.models.nn.CondGNN import CondGNN
 from propose.models.nn.embedding import embeddings
 
+from torch_geometric.data import HeteroData
+
 from propose.models.transforms.transform import (
     GraphAffineCouplingTransform,
     GraphCompositeTransform,
@@ -22,6 +24,7 @@ def __init__(
         hidden_features=100,
         embedding_net=None,
         relations=None,
+        use_attention=False,
         # mask_idx=[0, 2, 5, 8, 10, 12, 15]
     ):
         """
@@ -41,6 +44,7 @@ def create_net(in_features, out_features):
                 out_features=out_features,
                 hidden_features=hidden_features,
                 relations=relations,
+                use_attention=use_attention,
             )
 
         coupling_constructor = GraphAffineCouplingTransform
@@ -63,11 +67,17 @@ def create_net(in_features, out_features):
             embedding_net=embedding_net,
         )
 
-    def forward(self, inputs):
+    def forward(self, inputs: HeteroData) -> torch.Tensor:
+        """
+        The function takes in a tensor of inputs and returns the log probability of the inputs
+
+        :param inputs: the input data, a tensor of size [batch_size, input_size]
+        :return: The log probability of the inputs.
+        """
         return self.log_prob(inputs)
 
     @classmethod
-    def build_model(cls, config):
+    def build_model(cls, config: dict) -> GraphFlow:
         """
         Builds a CondGraphFlow model from config
         :param config: Config dictionary
@@ -82,7 +92,7 @@ def build_model(cls, config):
         return cls(**config["model"], embedding_net=embedding_net)
 
     @classmethod
-    def from_pretrained(cls, artifact_name):
+    def from_pretrained(cls, artifact_name: str) -> GraphFlow:
         """
         Constructs a pretrained model from the wandb model registry.
         :param artifact_name: Name of the artifact to load.
@@ -100,12 +110,17 @@ def from_pretrained(cls, artifact_name):
 
         device = "cuda" if torch.cuda.is_available() else "cpu"
         flow.load_state_dict(
-            torch.load(artifact_dir + "/model.pt", map_location=torch.device(device))
+            torch.load(artifact_dir + "/model.pt", map_location=torch.device(device)),
+            strict=False,
         )
 
         return flow
 
-    def set_device(self):
+    def set_device(self) -> bool:
+        """
+        If a GPU is available, move the model to the GPU
+        :return: a boolean value.
+        """
         if torch.cuda.is_available():
             self.to("cuda:0")
             return True
diff --git a/propose/models/layers/CondGCN.py b/propose/models/layers/CondGCN.py
index b8d9311..5db5584 100644
--- a/propose/models/layers/CondGCN.py
+++ b/propose/models/layers/CondGCN.py
@@ -5,6 +5,8 @@
 
 from typing import Literal
 
+import itertools
+
 
 class CondGCN(nn.Module):
     """
@@ -20,6 +22,7 @@ def __init__(
         root_features: int = 3,
         aggr: Literal["add", "mean", "max"] = "add",
         relations: list[str] = None,
+        use_attention: bool = False,
     ) -> None:
         super().__init__()
 
@@ -48,9 +51,22 @@ def __init__(
         self.pool = nn.Linear(hidden_features, out_features)
         self.act = nn.ReLU()
 
+        self.attention = nn.Linear(in_features * 2, 1)
+        self.use_attention = use_attention
+
         self.aggr = aggr
 
     def forward(self, x_dict: dict, edge_index_dict: dict) -> tuple[dict, dict]:
+        """
+        The function takes in a dictionary of node features and a dictionary of edge features, and returns a dictionary of
+        node features and a dictionary of edge features.
+
+        :param x_dict: a dictionary of node features
+        :type x_dict: dict
+        :param edge_index_dict: a dictionary of edge indices for each edge type
+        :type edge_index_dict: dict
+        :return: The output of the pooling layer.
+        """
         x = x_dict["x"]
 
         self_x = self.act(self.layers["x"](x))  # self loop values
@@ -90,10 +106,76 @@ def message(
             if dst_name != target:
                 continue
 
+            # attention mechanism
+            if self.use_attention and src_name == "x":
+                yield self.attention_mechanism(x_dict, src_name, dst_name, layer_name)
+                continue
+
             message = self.act(self.layers[layer_name](x_dict[src_name][src]))
 
             yield message, dst
 
+    def attention_mechanism(
+        self, x_dict: dict, src_name: str, dst_name: str, layer_name: str
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        We create a fully connected graph, then we use the attention mechanism to compute the attention
+        between each pair of nodes, which controls the computed message from each node to each other node
+
+        :param x_dict: a dictionary of node features
+        :type x_dict: dict
+        :param src_name: the name of the source node
+        :type src_name: str
+        :param dst_name: the name of the node that the message is being sent to
+        :type dst_name: str
+        :param layer_name: The name of the layer to use for the message
+        :type layer_name: str
+        :return: The message and the destination node.
+        """
+        n_nodes = x_dict[src_name].shape[0]
+
+        indexs = (
+            torch.Tensor(
+                list(itertools.product(list(range(n_nodes)), list(range(n_nodes))))
+            )
+            .long()
+            .t()
+            .to(self.device)
+        )
+
+        src = indexs[1]
+        dst = indexs[0]
+
+        src_x = x_dict[src_name][src]
+        dst_x = x_dict[dst_name][dst]
+
+        attention = self.attention(
+            torch.cat(
+                [
+                    src_x,
+                    dst_x,
+                ],
+                dim=-1,
+            )
+        )
+
+        attention = torch.softmax(attention, dim=0)
+
+        i = torch.arange(n_nodes).long().to(self.device)
+
+        message = self.act(self.layers[layer_name](x_dict[src_name][i]))
+        message = message.repeat(n_nodes, *[1] * (message.dim() - 1))
+
+        message = torch.multiply(message, attention)
+
+        message = message.reshape(
+            -1, n_nodes, message.shape[-2], message.shape[-1]
+        ).sum(0)
+
+        dst = dst.reshape(-1, n_nodes)[:, 0]
+
+        return message, dst
+
     def aggregate(
         self, message: tuple[torch.Tensor, torch.Tensor], self_x: torch.Tensor
     ) -> torch.Tensor:
@@ -160,10 +242,19 @@ def aggregate(
         return aggr_message
 
     @property
-    def device(self):
+    def device(self) -> Literal["cpu", "cuda"]:
+        """
+        It returns the device of the module
+        :return: The device of the first parameter of the model.
+        """
         return next(self.parameters()).device
 
-    def _build_layers(self):
+    def _build_layers(self) -> nn.ModuleDict:
+        """
+        For each relation in the relations list, create a linear layer with the number of features of the first node in
+        the relation as the input size and the number of features of the hidden layer as the output size
+        :return: A dictionary of linear layers.
+        """
         layers_dict = {}
         for relation in self.relations:
             n_features: int = self.features[relation[0]]
diff --git a/propose/models/nn/CondGNN.py b/propose/models/nn/CondGNN.py
index d1543dd..e780146 100644
--- a/propose/models/nn/CondGNN.py
+++ b/propose/models/nn/CondGNN.py
@@ -21,6 +21,7 @@ def __init__(
         hidden_features: int = 10,
         root_features: int = 3,
         relations: list[str] = None,
+        use_attention: bool = False,
     ):
         super().__init__()
 
@@ -35,6 +36,7 @@ def __init__(
                     context_features=context_features,
                     root_features=root_features,
                     relations=relations,
+                    use_attention=use_attention,
                 ),
                 self.gcn(
                     in_features=hidden_features,
@@ -43,6 +45,7 @@ def __init__(
                     context_features=hidden_features,
                     root_features=hidden_features,
                     relations=relations,
+                    use_attention=use_attention,
                 ),
             ]
         )
diff --git a/propose/poses/base.py b/propose/poses/base.py
index 8cd456f..b770474 100644
--- a/propose/poses/base.py
+++ b/propose/poses/base.py
@@ -7,6 +7,9 @@
 
 from propose.cameras import Camera
 
+import torch
+from torch_geometric.data import HeteroData
+
 
 class BasePose(ABC):
     """
@@ -16,12 +19,19 @@ class BasePose(ABC):
     marker_names = []
     adjacency_matrix = None
 
-    def __init__(self, pose_matrix: np.ndarray):
+    def __init__(self, pose_matrix: np.ndarray, occluded_markers: list[bool] = None):
         """
         :param pose_matrix: A ndarray (frames, markers, positions), where frames and markers are optional dimensions.
+        :param occluded_markers: A list of booleans indicating which markers are occluded.
         """
         self.pose_matrix = pose_matrix
 
+        if occluded_markers is None:
+            if len(self.pose_matrix.shape) == 2:
+                self.occluded_markers = [False] * self.pose_matrix.shape[0]
+            if len(self.pose_matrix.shape) == 3:
+                self.occluded_markers = [False] * self.pose_matrix.shape[1]
+
         self.__array_struct__ = self.pose_matrix.__array_struct__
 
         self.set_adjacency_matrix()
@@ -208,6 +218,46 @@ def transform_to_camera(
             camera.world_to_camera_view(self.pose_matrix, translate=translate)
         )
 
+    def to_graph(self) -> tuple[torch.FloatTensor, torch.LongTensor]:
+        """
+        This function takes in a list of edges and a pose matrix and returns a tuple of a node features and an edge_index.
+        These can be used to construct a graph from torch_geometric.
+        :return: node features and edge_index
+        """
+        edge_index = torch.LongTensor(self.edges).T
+
+        return torch.FloatTensor(self.pose_matrix), edge_index
+
+    def _construct_conditional_graph_dict(self, context: "BasePose") -> dict:
+        """
+        > It takes a context pose and returns a dictionary of the data required to construct a conditional graph
+
+        :param context: "BasePose"
+        :type context: "BasePose"
+        :return: A dictionary of dictionaries.
+        """
+        context_node_features = context.pose_matrix
+        context_edge_index = (
+            torch.arange(0, context.pose_matrix.shape[1])[
+                ~torch.BoolTensor(context.occluded_markers)
+            ]
+            .unsqueeze(0)
+            .repeat(2, 1)
+        )
+
+        data = {
+            "x": dict(x=torch.FloatTensor(self.pose_matrix).squeeze()),
+            "c": dict(x=torch.FloatTensor(context_node_features).squeeze()),
+            ("x", "->", "x"): dict(edge_index=torch.LongTensor(self.edges).T),
+            ("x", "<-", "x"): dict(edge_index=torch.LongTensor(self.edges).T),
+            ("c", "->", "x"): dict(edge_index=torch.LongTensor(context_edge_index)),
+        }
+
+        return data
+
+    def conditional_graph(self, context: "BasePose") -> HeteroData:
+        return HeteroData(self._construct_conditional_graph_dict(context))
+
 
 class YamlPose(BasePose):
     def __init__(self, pose_matrix, path):
@@ -270,7 +320,7 @@ def __getitem__(self, item):
     @property
     def bone_lengths(self):
         diff = torch.diff(self.pose_matrix[..., self.edges, :], dim=-2).squeeze()
-        dist = torm.norm(diff, dim=-1)
+        dist = torch.norm(diff, dim=-1)
         return dist
 
     @property
diff --git a/propose/poses/human36m.py b/propose/poses/human36m.py
index 41f33e9..782e8de 100644
--- a/propose/poses/human36m.py
+++ b/propose/poses/human36m.py
@@ -1,6 +1,31 @@
 from propose.poses.base import YamlPose
+
 import os
 
+import numpy as np
+
+from torch_geometric.data import HeteroData
+import torch
+
+MPII_2_H36M = [
+    6,
+    2,
+    1,
+    0,
+    3,
+    4,
+    5,
+    7,
+    8,
+    9,
+    13,
+    14,
+    15,
+    12,
+    11,
+    10,
+]
+
 
 class Human36mPose(YamlPose):
     """
@@ -12,3 +37,74 @@ def __init__(self, pose_matrix, **kwargs):
         path = os.path.join(dirname, "metadata/human36m.yaml")
 
         super().__init__(pose_matrix, path)
+
+    def conditional_graph(self, context: "BasePose") -> HeteroData:
+        graph_dict = self._construct_conditional_graph_dict(context)
+
+        edges = graph_dict[("x", "->", "x")]["edge_index"]
+        context_edges = graph_dict[("c", "->", "x")]["edge_index"]
+
+        edges, root_edges, context_edges = self.remove_root_edges(
+            edges, context_edges, num_context_samples=1
+        )
+
+        graph_dict[("x", "->", "x")]["edge_index"] = edges
+        graph_dict[("x", "<-", "x")]["edge_index"] = edges
+        graph_dict[("c", "->", "x")]["edge_index"] = context_edges
+        graph_dict[("r", "->", "x")] = dict(edge_index=root_edges)
+        graph_dict[("r", "<-", "x")] = dict(edge_index=root_edges)
+
+        graph_dict["r"] = dict(x=graph_dict["x"]["x"][..., :1, :])
+        graph_dict["x"]["x"] = graph_dict["x"]["x"][..., 1:, :]
+        graph_dict["c"]["x"] = graph_dict["c"]["x"][..., 1:, :]
+
+        return HeteroData(graph_dict)
+
+    @classmethod
+    def remove_root_edges(cls, edges, context_edges, num_context_samples):
+        """
+        We remove the root edges from the full edges, and then we subtract 1 from the full edges and context edges to
+        make them zero-indexed
+
+        :param cls: the class of the object
+        :param edges: the edges of the full graph
+        :param context_edges: the edges that are in the context graph
+        :param num_context_samples: The number of samples in the context
+        :return: The edges are being returned with the root edges removed.
+        """
+        full_edges = edges[:, torch.where(edges[0] != 0)[0]]
+        context_edges = context_edges[:, torch.where(context_edges[1] != 0)[0]]
+        root_edges = edges[:, torch.where(edges[0] == 0)[0]]
+
+        full_edges -= 1
+        context_edges[0] -= num_context_samples
+        context_edges[1] -= 1
+        root_edges[1] -= 1
+
+        return full_edges, root_edges, context_edges
+
+
+class MPIIPose(YamlPose):
+    """
+    Pose Class for the Human3.6M dataset.
+    """
+
+    def __init__(self, pose_matrix, **kwargs):
+        dirname = os.path.dirname(__file__)
+        path = os.path.join(dirname, "metadata/mpii.yaml")
+
+        super().__init__(pose_matrix, path)
+
+    def to_human36m(self):
+        """
+        Convert the pose to the Human3.6M format.
+        :return: A Human3.6M pose.
+        """
+
+        pose_matrix = self.pose_matrix.copy()
+        pose_matrix = pose_matrix[:, MPII_2_H36M]
+        pose_matrix = np.insert(pose_matrix, 9, 0, axis=1)
+        pose = Human36mPose(pose_matrix)
+        pose.occluded_markers = self.occluded_markers[0, MPII_2_H36M, 0]
+        pose.occluded_markers = np.insert(pose.occluded_markers, 9, True, axis=0)
+        return pose
diff --git a/propose/poses/metadata/mpii.yaml b/propose/poses/metadata/mpii.yaml
new file mode 100644
index 0000000..3577134
--- /dev/null
+++ b/propose/poses/metadata/mpii.yaml
@@ -0,0 +1,73 @@
+spine:
+    Hip:
+      id: 6
+      parent_id: -1
+
+    Spine:
+      id: 7
+      parent_id: 6
+
+    Thorax:
+      id: 8
+      parent_id: 7
+
+head:
+    Head:
+      id: 9
+      parent_id: 8
+
+leg_r:
+    RHip:
+      id: 2
+      parent_id: 6
+
+    RKnee:
+      id: 1
+      parent_id: 2
+
+    RFoot:
+      id: 0
+      parent_id: 1
+
+leg_l:
+    LHip:
+      id: 3
+      parent_id: 6
+
+    LKnee:
+      id: 4
+      parent_id: 3
+
+    LFoot:
+      id: 5
+      parent_id: 4
+
+
+arm_l:
+    LShoulder:
+      id: 13
+      parent_id: 8
+
+    LElbow:
+      id: 14
+      parent_id: 13
+
+    LWrist:
+      id: 15
+      parent_id: 14
+
+
+arm_r:
+    RShoulder:
+      id: 12
+      parent_id: 8
+
+    RElbow:
+      id: 11
+      parent_id: 12
+
+    RWrist:
+      id: 10
+      parent_id: 11
+
+
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..087ea35
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,18 @@
+nflows==0.14
+imageio==2.19.2
+tqdm==4.64.0
+torch-geometric==2.0.4
+ffmpeg-python==0.2.0
+scikit-image==0.19.3
+cdflib==0.4.4
+imageio-ffmpeg==0.4.7
+brax==0.0.13
+wandb==0.12.21
+yacs==0.1.8
+neuralpredictors==0.3.0
+torch==1.9.0
+
+# Torch Geometric
+--find-links https://data.pyg.org/whl/torch-1.9.0+cu111.html
+torch-scatter==2.0.9
+torch-sparse==0.6.14
diff --git a/scripts/eval.py b/scripts/eval.py
deleted file mode 100644
index 6c3ec0f..0000000
--- a/scripts/eval.py
+++ /dev/null
@@ -1,72 +0,0 @@
-from propose.utils.imports import dynamic_import
-
-import argparse
-
-import os
-import yaml
-
-from pathlib import Path
-
-
-parser = argparse.ArgumentParser(description="Arguments for running the scripts")
-
-parser.add_argument(
-    "--human36m",
-    default=False,
-    action="store_true",
-    help="Run the training script for the Human 3.6m dataset",
-)
-
-parser.add_argument(
-    "--wandb",
-    default=False,
-    action="store_true",
-    help="Whether to use wandb for logging",
-)
-
-parser.add_argument(
-    "--experiment",
-    default="mpii-prod.yaml",
-    type=str,
-    help="Experiment config file",
-)
-
-parser.add_argument(
-    "--script",
-    default="eval.human36m.human36m",
-    type=str,
-    help="Experiment script",
-)
-
-if __name__ == "__main__":
-    args = parser.parse_args()
-
-    if args.wandb:
-        if not os.environ["WANDB_API_KEY"]:
-            raise ValueError(
-                "Wandb API key not set. Please set the WANDB_API_KEY environment variable."
-            )
-        if not os.environ["WANDB_USER"]:
-            raise ValueError(
-                "Wandb user not set. Please set the WANDB_USER environment variable."
-            )
-
-    dataset = Path("")
-    if args.human36m:
-        dataset = Path("human36m")
-
-    config_file = Path(args.experiment + ".yaml")
-    config_file = Path("/experiments") / dataset / config_file
-
-    with open(config_file, "r") as f:
-        config = yaml.load(f, Loader=yaml.FullLoader)
-
-        if "experiment_name" not in config:
-            config["experiment_name"] = args.experiment
-
-    if args.human36m:
-        dynamic_import(args.script, "run")(use_wandb=args.wandb, config=config)
-    else:
-        print(
-            "Not running any scripts as no arguments were passed. Run with --help for more information."
-        )
diff --git a/scripts/eval/human36m/calibration.py b/scripts/eval/human36m/calibration.py
deleted file mode 100644
index 9d45c30..0000000
--- a/scripts/eval/human36m/calibration.py
+++ /dev/null
@@ -1,154 +0,0 @@
-from propose.datasets.human36m.Human36mDataset import Human36mDataset
-from torch_geometric.loader import DataLoader
-
-from propose.utils.reproducibility import set_random_seed
-
-from propose.models.flows import CondGraphFlow
-
-import torch
-
-import os
-
-import time
-from tqdm import tqdm
-import numpy as np
-
-import wandb
-
-import seaborn as sns
-import matplotlib.pyplot as plt
-
-
-def calibration(flow, test_dataloader):
-    total = 0
-    iter_dataloader = iter(test_dataloader)
-    pbar = tqdm(range(len(test_dataloader)))
-
-    quantiles = np.arange(0, 1.05, 0.05)
-    quantile_counts = np.zeros((len(quantiles), 1))
-    q_val = []
-
-    for _ in pbar:
-        batch, _, action = next(iter_dataloader)
-        batch.cuda()
-        samples = flow.sample(200, batch)
-
-        true_pose = (
-            batch["x"]
-            .x.cpu()
-            .numpy()
-            .reshape(-1, 16, 1, 3)[
-                :, np.insert(action["occlusion"].bool().numpy(), 9, False)
-            ]
-        )
-        sample_poses = (
-            samples["x"]
-            .x.detach()
-            .cpu()
-            .numpy()
-            .reshape(-1, 16, 200, 3)[
-                :, np.insert(action["occlusion"].bool().numpy(), 9, False)
-            ]
-        )
-
-        sample_mean = (
-            torch.Tensor(sample_poses).median(-2).values.numpy()[..., np.newaxis, :]
-        )
-        errors = ((sample_mean / 0.0036 - sample_poses / 0.0036) ** 2).sum(-1) ** 0.5
-        true_error = ((sample_mean / 0.0036 - true_pose / 0.0036) ** 2).sum(-1) ** 0.5
-
-        q_vals = np.quantile(errors, quantiles, 2).squeeze(1)
-        q_val.append(q_vals)
-
-        v = np.nanmean((q_vals > true_error.squeeze()).astype(int), axis=1)[
-            :, np.newaxis
-        ]
-        if not np.isnan(v).any():
-            total += 1
-            quantile_counts += v
-
-    quantile_freqs = quantile_counts / total
-
-    return quantiles, quantile_freqs, q_val
-
-
-def calibration_experiment(flow, config, **kwargs):
-    test_dataset = Human36mDataset(
-        **config["dataset"],
-        **kwargs,
-    )
-    test_dataloader = DataLoader(
-        test_dataset, batch_size=1, shuffle=True, pin_memory=False, num_workers=0
-    )
-
-    return calibration(flow, test_dataloader)
-
-
-def run(use_wandb, config):
-    set_random_seed(config["seed"])
-
-    config["dataset"]["dirname"] = config["dataset"]["dirname"] + "/test"
-
-    if use_wandb:
-        wandb.init(
-            project="propose_human36m",
-            entity=os.environ["WANDB_USER"],
-            config=config,
-            job_type="evaluation",
-            name=f"{config['experiment_name']}_calibration_{time.strftime('%d/%m/%Y::%H:%M:%S')}",
-            tags=config["tags"] if "tags" in config else None,
-            group=config["group"] if "group" in config else None,
-        )
-
-    flow = CondGraphFlow.from_pretrained(
-        f'ppierzc/propose_human36m/{config["experiment_name"]}:latest'
-    )
-
-    config["cuda_accelerated"] = flow.set_device()
-    flow.eval()
-
-    # Test
-    quantiles, quantile_freqs, q_val = calibration_experiment(
-        flow,
-        config,
-        occlusion_fractions=[],
-        test=True,
-    )
-
-    sns.set_context("talk")
-    with sns.axes_style("whitegrid"):
-        plt.figure(figsize=(5, 5), dpi=150)
-        plt.fill_between(
-            quantiles,
-            np.mean(quantile_freqs, axis=1) + np.std(quantile_freqs, axis=1),
-            np.mean(quantile_freqs, axis=1) - np.std(quantile_freqs, axis=1),
-            color="#1E88E5",
-            alpha=0.5,
-            zorder=-5,
-            rasterized=True,
-        )
-        plt.plot([0, 1], [0, 1], ls="--", c="tab:gray")
-        plt.plot(
-            quantiles,
-            np.median(quantile_freqs, axis=1),
-            c="#1E88E5",
-            alpha=1,
-            label="cGNF all",
-        )
-        plt.xticks(np.arange(0, 1.2, 0.2))
-        plt.yticks(np.arange(0, 1.2, 0.2))
-        plt.xlabel("Quantile")
-        plt.ylabel("Frequency")
-        plt.text(0.03, 0.07, "reference line", rotation=45, c="k", fontsize=15)
-        plt.xlim(0, 1)
-        plt.ylim(0, 1)
-        plt.title("Calibration")
-        plt.legend(frameon=False)
-
-        plt.gca().set_rasterization_zorder(-1)
-
-    if use_wandb:
-        img = wandb.Image(plt)
-        wandb.log({"calibration": img})
-
-    plt.close()
diff --git a/scripts/eval/human36m/human36m.py b/scripts/eval/human36m/human36m.py
deleted file mode 100644
index 9aefe94..0000000
--- a/scripts/eval/human36m/human36m.py
+++ /dev/null
@@ -1,213 +0,0 @@
-from propose.datasets.human36m.Human36mDataset import Human36mDataset
-from torch_geometric.loader import DataLoader
-
-from propose.utils.reproducibility import set_random_seed
-from propose.evaluation.mpjpe import mpjpe, pa_mpjpe
-from propose.evaluation.pck import pck, human36m_joints_to_use
-
-from propose.models.flows import CondGraphFlow
-
-import os
-
-import time
-from tqdm import tqdm
-import numpy as np
-
-import wandb
-
-
-def evaluate(flow, test_dataloader, temperature=1.0):
-    mpjpes = []
-    pa_mpjpes = []
-    single_mpjpes = []
-    single_pa_mpjpes = []
-    pck_scores = []
-    mean_pck_scores = []
-
-    iter_dataloader = iter(test_dataloader)
-
-    pbar = tqdm(range(len(test_dataloader)))
-
-    for _ in pbar:
-        batch, _, action = next(iter_dataloader)
-        batch.to(flow.device)
-
-        samples = flow.sample(200, batch, temperature=temperature)
-
-        true_pose = batch["x"].x.cpu().numpy().reshape(-1, 16, 1, 3)
-        sample_poses = samples["x"].x.detach().cpu().numpy().reshape(-1, 16, 200, 3)
-
-        true_pose = np.insert(true_pose, 0, 0, axis=1)
-        sample_poses = np.insert(sample_poses, 0, 0, axis=1)
-
-        pck_score = pck(
-            true_pose[:, human36m_joints_to_use] / 0.0036,
-            sample_poses[:, human36m_joints_to_use] / 0.0036,
-        )
-
-        has_correct_pose = pck_score.max().unsqueeze(0).numpy()
-        mean_correct_pose = pck_score.mean().unsqueeze(0).numpy()
-
-        m = mpjpe(true_pose / 0.0036, sample_poses / 0.0036, dim=1)
-        m_single = m[..., 0]
-        m = np.min(m, axis=-1)
-
-        pa_m = (
-            pa_mpjpe(true_pose[0] / 0.0036, sample_poses[0] / 0.0036, dim=0)
-            .unsqueeze(0)
-            .numpy()
-        )
-
-        pa_m_single = pa_m[..., 0]
-        pa_m = np.min(pa_m, axis=-1)
-
-        m = m.tolist()
-        pa_m = pa_m.tolist()
-        m_single = m_single.tolist()
-
-        mpjpes += [m]
-        pa_mpjpes += [pa_m]
-        single_mpjpes += [m_single]
-        single_pa_mpjpes += [pa_m_single]
-
-        pck_scores += [has_correct_pose]
-        mean_pck_scores += [mean_correct_pose]
-
-        pbar.set_description(
-            f"MPJPE: {np.concatenate(mpjpes).mean():.4f}, "
-            f"PA MPJPE: {np.concatenate(pa_mpjpes).mean():.4f}, "
-            f"Single MPJPE: {np.concatenate(single_mpjpes).mean():.4f} "
-            f"Single PA MPJPE: {np.concatenate(single_pa_mpjpes).mean():.4f} "
-            f"PCK: {np.concatenate(pck_scores).mean():.4f} "
-            f"Mean PCK: {np.concatenate(mean_pck_scores).mean():.4f} "
-        )
-
-    return (
-        mpjpes,
-        pa_mpjpes,
-        single_mpjpes,
-        single_pa_mpjpes,
-        pck_scores,
-        mean_pck_scores,
-    )
-
-
-def mpjpe_experiment(flow, config, name="test", **kwargs):
-    test_dataset = Human36mDataset(
-        **config["dataset"],
-        **kwargs,
-    )
-    test_dataloader = DataLoader(
-        test_dataset, batch_size=1, shuffle=True, pin_memory=False, num_workers=0
-    )
-    (
-        test_res,
-        test_res_pa,
-        test_res_single,
-        test_res_pa_single,
-        test_res_pck,
-        test_res_mean_pck,
-    ) = evaluate(flow, test_dataloader)
-
-    res = {
-        f"{name}/test_res": np.concatenate(test_res).mean(),
-        f"{name}/test_res_pa": np.concatenate(test_res_pa).mean(),
-        f"{name}/test_res_single": np.concatenate(test_res_single).mean(),
-        f"{name}/test_res_pa_single": np.concatenate(test_res_pa_single).mean(),
-        f"{name}/test_res_pck": np.concatenate(test_res_pck).mean(),
-        f"{name}/test_res_mean_pck": np.concatenate(test_res_mean_pck).mean(),
-    }
-
-    return res, test_dataset, test_dataloader
-
-
-def run(use_wandb: bool = False, config: dict = None):
-    """
-    Train a CondGraphFlow on the Human36m dataset.
-    :param use_wandb: Whether to use wandb for logging.
-    :param config: A dictionary of configuration parameters.
-    """
-    set_random_seed(config["seed"])
-
-    config["dataset"]["dirname"] = config["dataset"]["dirname"] + "/test"
-
-    if use_wandb:
-        wandb.init(
-            project="propose_human36m",
-            entity=os.environ["WANDB_USER"],
-            config=config,
-            job_type="evaluation",
-            name=f"{config['experiment_name']}_human36m_{time.strftime('%d/%m/%Y::%H:%M:%S')}",
-            tags=config["tags"] if "tags" in config else None,
-            group=config["group"] if "group" in config else None,
-        )
-
-    flow = CondGraphFlow.from_pretrained(
-        f'ppierzc/propose_human36m/{config["experiment_name"]}:v20'
-    )
-
-    config["cuda_accelerated"] = flow.set_device()
-    flow.eval()
-
-    # Test
-    test_res, test_dataset, test_dataloader = mpjpe_experiment(
-        flow,
-        config,
-        occlusion_fractions=[],
-        test=True,
-        name="test",
-    )
-
-    if use_wandb:
-        wandb.log(test_res)
-
-    # Hard
-    hard_res, hard_dataset, hard_dataloader = mpjpe_experiment(
-        flow,
-        config,
-        occlusion_fractions=[],
-        hardsubset=True,
-        name="hard",
-    )
-
-    if use_wandb:
-        wandb.log(hard_res)
-
-    # Occlusion Only
-    mpjpes = []
-    for i in tqdm(range(len(hard_dataset))):
-        batch = hard_dataset[i][0]
-        batch.cuda()
-        samples = flow.sample(200, batch.cuda())
-
-        true_pose = (
-            batch["x"]
-            .x.cpu()
-            .numpy()
-            .reshape(-1, 16, 1, 3)[:, np.insert(hard_dataset.occlusions[i], 9, False)]
-        )
-        sample_poses = (
-            samples["x"]
-            .x.detach()
-            .cpu()
-            .numpy()
-            .reshape(-1, 16, 200, 3)[:, np.insert(hard_dataset.occlusions[i], 9, False)]
-        )
-
-        m = mpjpe(true_pose / 0.0036, sample_poses / 0.0036, dim=1)
-        m = np.min(m, axis=-1)
-
-        m = m.tolist()
-
-        mpjpes += [m]
-
-    occl_res = np.nanmean(mpjpes)
-    if use_wandb:
-        wandb.log({"occl/best_mpjpe": occl_res})
-
-    print("MPJPE for best")
-    print("---")
-    print(f"H36M: {test_res}")
-    print(f"H36MA: {hard_res}")
-    print(f"Occl.: {occl_res}")
-    print("---")
diff --git a/scripts/eval/human36m/per_joint_error.py b/scripts/eval/human36m/per_joint_error.py
deleted file mode 100644
index 35ffa3d..0000000
--- a/scripts/eval/human36m/per_joint_error.py
+++ /dev/null
@@ -1,151 +0,0 @@
-from propose.datasets.human36m.Human36mDataset import Human36mDataset
-from torch_geometric.loader import DataLoader
-from propose.poses.human36m import Human36mPose
-
-from propose.utils.reproducibility import set_random_seed
-from propose.evaluation.mpjpe import mpjpe
-
-from propose.models.flows import CondGraphFlow
-
-import os
-
-import time
-from tqdm import tqdm
-import numpy as np
-
-import wandb
-
-import pandas as pd
-import seaborn as sns
-import matplotlib.pyplot as plt
-
-
-def evaluate(flow, test_dataloader, temperature=1.0):
-    mpjpes_not_occuled = []
-    mpjpes_occuled = []
-
-    iter_dataloader = iter(test_dataloader)
-    for _ in tqdm(range(len(test_dataloader))):
-        batch, _, action = next(iter_dataloader)
-        occluded_joints = action["occlusion"].bool().numpy()
-
-        batch = batch.to(flow.device)
-        samples = flow.sample(200, batch, temperature=temperature)
-
-        true_pose = batch["x"].x.cpu().numpy().reshape(-1, 16, 1, 3)
-        sample_poses = samples["x"].x.detach().cpu().numpy().reshape(-1, 16, 200, 3)
-
-        true_pose = np.insert(true_pose, 0, 0, axis=1)
-        sample_poses = np.insert(sample_poses, 0, 0, axis=1)
-
-        m = mpjpe(true_pose / 0.0036, sample_poses / 0.0036, mean=False)
-        m = np.min(m, axis=-1)
-
-        m = np.delete(m, 0, axis=1)
-        m = np.delete(m, 8, axis=1)
-
-        # if occluded add values to mpjpes_occuled with the unoclluded as nan
-        m_occlued = m.copy()
-        m_occlued[~occluded_joints] = np.nan
-        mpjpes_occuled.append(m_occlued)
-
-        # if not occluded add values to mpjpes_not_occuled with the occluded as nan
-        m_not_occlued = m.copy()
-        m_not_occlued[occluded_joints] = np.nan
-        mpjpes_not_occuled.append(m_not_occlued)
-
-    return mpjpes_not_occuled, mpjpes_occuled
-
-
-def mpjpe_experiment(flow, config, **kwargs):
-    test_dataset = Human36mDataset(**config["dataset"], **kwargs)
-    test_dataloader = DataLoader(
-        test_dataset, batch_size=1, shuffle=True, pin_memory=False, num_workers=0
-    )
-    mpjpes_not_occuled, mpjpes_occuled = evaluate(flow, test_dataloader)
-
-    return np.concatenate(mpjpes_not_occuled).T, np.concatenate(mpjpes_occuled).T
-
-
-def run(use_wandb: bool = False, config: dict = None):
-    """
-    Train a CondGraphFlow on the Human36m dataset.
-    :param use_wandb: Whether to use wandb for logging.
-    :param config: A dictionary of configuration parameters.
-    """
-    set_random_seed(config["seed"])
-
-    config["dataset"]["dirname"] = config["dataset"]["dirname"] + "/test"
-
-    if use_wandb:
-        wandb.init(
-            project="propose_human36m",
-            entity=os.environ["WANDB_USER"],
-            config=config,
-            job_type="evaluation",
-            name=f"{config['experiment_name']}_pje_{time.strftime('%d/%m/%Y::%H:%M:%S')}",
-            tags=config["tags"] if "tags" in config else None,
-            group=config["group"] if "group" in config else None,
-        )
-
-    flow = CondGraphFlow.from_pretrained(
-        f'ppierzc/propose_human36m/{config["experiment_name"]}:latest'
-    )
-
-    config["cuda_accelerated"] = flow.set_device()
-    flow.eval()
-
-    pose = Human36mPose(np.zeros((16, 2)))
-    marker_names = pose.marker_names[1:]
-    del marker_names[8]
-
-    # Test
-    mpjpes_not_occuled, mpjpes_occuled = mpjpe_experiment(
-        flow,
-        config,
-        occlusion_fractions=[],
-        test=True,
-    )
-
-    df_occluded = pd.DataFrame(
-        {key: value for key, value in zip(marker_names, mpjpes_occuled)}
-    )
-
-    df_not_occluded = pd.DataFrame(
-        {key: value for key, value in zip(marker_names, mpjpes_not_occuled)}
-    )
-
-    df = (
-        pd.concat(
-            [df_not_occluded, df_occluded], keys=["not_occluded", "occluded"], axis=1
-        )
-        .stack()
-        .stack()
-        .to_frame()
-        .reset_index()
-    )
-
-    plt.figure(figsize=(15, 5))
-    sns.barplot(data=df, x="level_1", y=0, hue="level_2")
-    plt.xticks(rotation=90)
-    plt.ylabel("MPJPE")
-    plt.xlabel("Joint")
-    plt.legend(title="Occluded?")
-    plt.tight_layout()
-
-    output = {
-        "img": wandb.Image(plt.gcf(), caption="MPJPE"),
-        "occluded": {
-            key: list(filter(lambda x: x, value))
-            for key, value in zip(marker_names, mpjpes_occuled)
-        },
-        "not_occluded": {
-            key: list(filter(lambda x: x, value))
-            for key, value in zip(marker_names, mpjpes_not_occuled)
-        },
-    }
-
-    if use_wandb:
-        wandb.log(output)
-
-    plt.close()
diff --git a/scripts/eval/human36m/single.py b/scripts/eval/human36m/single.py
deleted file mode 100644
index 85f0f5f..0000000
--- a/scripts/eval/human36m/single.py
+++ /dev/null
@@ -1,203 +0,0 @@
-from propose.datasets.human36m.Human36mDataset import Human36mDataset
-from torch_geometric.loader import DataLoader
-
-from propose.utils.reproducibility import set_random_seed
-from propose.evaluation.mpjpe import mpjpe, pa_mpjpe
-from propose.evaluation.pck import pck, human36m_joints_to_use
-
-from propose.models.flows import CondGraphFlow
-
-import os
-
-import time
-from tqdm import tqdm
-import numpy as np
-
-import wandb
-
-
-def evaluate(flow, test_dataloader, temperature=1.0):
-    single_mpjpes = []
-    single_pa_mpjpes = []
-    pck_scores = []
-    mean_pck_scores = []
-
-    iter_dataloader = iter(test_dataloader)
-
-    pbar = tqdm(range(len(test_dataloader)))
-
-    for _ in pbar:
-        batch, _, action = next(iter_dataloader)
-        batch.to(flow.device)
-
-        samples = flow.mode_sample(batch)
-
-        true_pose = batch["x"].x.cpu().numpy().reshape(-1, 16, 1, 3)
-        sample_poses = samples["x"].x.detach().cpu().numpy().reshape(-1, 16, 1, 3)
-
-        true_pose = np.insert(true_pose, 0, 0, axis=1)
-        sample_poses = np.insert(sample_poses, 0, 0, axis=1)
-
-        pck_score = pck(
-            true_pose[:, human36m_joints_to_use] / 0.0036,
-            sample_poses[:, human36m_joints_to_use] / 0.0036,
-        )
-
-        has_correct_pose = pck_score.max().unsqueeze(0).numpy()
-        mean_correct_pose = pck_score.mean().unsqueeze(0).numpy()
-
-        m = mpjpe(true_pose / 0.0036, sample_poses / 0.0036, dim=1)
-        m_single = m[..., 0]
-
-        pa_m = (
-            pa_mpjpe(true_pose[0] / 0.0036, sample_poses[0] / 0.0036, dim=0)
-            .unsqueeze(0)
-            .numpy()
-        )
-
-        pa_m_single = pa_m[..., 0]
-
-        m_single = m_single.tolist()
-
-        single_mpjpes += [m_single]
-        single_pa_mpjpes += [pa_m_single]
-
-        pck_scores += [has_correct_pose]
-        mean_pck_scores += [mean_correct_pose]
-
-        pbar.set_description(
-            f"Single MPJPE: {np.concatenate(single_mpjpes).mean():.4f} "
-            f"Single PA MPJPE: {np.concatenate(single_pa_mpjpes).mean():.4f} "
-            f"PCK: {np.concatenate(pck_scores).mean():.4f} "
-            f"Mean PCK: {np.concatenate(mean_pck_scores).mean():.4f} "
-        )
-
-    return (
-        single_mpjpes,
-        single_pa_mpjpes,
-        pck_scores,
-        mean_pck_scores,
-    )
-
-
-def mpjpe_experiment(flow, config, name="test", **kwargs):
-    test_dataset = Human36mDataset(
-        **config["dataset"],
-        **kwargs,
-    )
-    test_dataloader = DataLoader(
-        test_dataset, batch_size=1, shuffle=True, pin_memory=False, num_workers=0
-    )
-    (
-        test_res_single,
-        test_res_pa_single,
-        test_res_pck,
-        test_res_mean_pck,
-    ) = evaluate(flow, test_dataloader)
-
-    res = {
-        f"{name}/test_res_single": np.concatenate(test_res_single).mean(),
-        f"{name}/test_res_pa_single": np.concatenate(test_res_pa_single).mean(),
-        f"{name}/test_res_pck": np.concatenate(test_res_pck).mean(),
-        f"{name}/test_res_mean_pck": np.concatenate(test_res_mean_pck).mean(),
-    }
-
-    return res, test_dataset, test_dataloader
-
-
-def run(use_wandb: bool = False, config: dict = None):
-    """
-    Train a CondGraphFlow on the Human36m dataset.
-    :param use_wandb: Whether to use wandb for logging.
-    :param config: A dictionary of configuration parameters.
-    """
-    set_random_seed(config["seed"])
-
-    config["dataset"]["dirname"] = config["dataset"]["dirname"] + "/test"
-
-    if use_wandb:
-        wandb.init(
-            project="propose_human36m",
-            entity=os.environ["WANDB_USER"],
-            config=config,
-            job_type="evaluation",
-            name=f"{config['experiment_name']}_single_{time.strftime('%d/%m/%Y::%H:%M:%S')}",
-            tags=config["tags"] if "tags" in config else None,
-            group=config["group"] if "group" in config else None,
-        )
-
-    flow = CondGraphFlow.from_pretrained(
-        f'ppierzc/propose_human36m/{config["experiment_name"]}:v20'
-    )
-
-    config["cuda_accelerated"] = flow.set_device()
-    flow.eval()
-
-    # Test
-    test_res, test_dataset, test_dataloader = mpjpe_experiment(
-        flow,
-        config,
-        occlusion_fractions=[],
-        test=True,
-        name="test",
-    )
-
-    if use_wandb:
-        wandb.log(test_res)
-
-    # Hard
-    hard_res, hard_dataset, hard_dataloader = mpjpe_experiment(
-        flow,
-        config,
-        occlusion_fractions=[],
-        hardsubset=True,
-        name="hard",
-    )
-
-    if use_wandb:
-        wandb.log(hard_res)
-
-    hard_dataset = Human36mDataset(
-        **config["dataset"],
-        occlusion_fractions=[],
-        hardsubset=True,
-    )
-
-    # Occlusion Only
-    mpjpes = []
-    for i in tqdm(range(len(hard_dataset))):
-        batch = hard_dataset[i][0]
-        batch.cuda()
-        samples = flow.mode_sample(batch.cuda())
-
-        true_pose = (
-            batch["x"]
-            .x.cpu()
-            .numpy()
-            .reshape(-1, 16, 1, 3)[:, np.insert(hard_dataset.occlusions[i], 9, False)]
-        )
-        sample_poses = (
-            samples["x"]
-            .x.detach()
-            .cpu()
-            .numpy()
-            .reshape(-1, 16, 1, 3)[:, np.insert(hard_dataset.occlusions[i], 9, False)]
-        )
-
-        m = mpjpe(true_pose / 0.0036, sample_poses / 0.0036, dim=1)
-        m = np.min(m, axis=-1)
-
-        m = m.tolist()
-
-        mpjpes += [m]
-
-    occl_res = np.nanmean(mpjpes)
-    if use_wandb:
-        wandb.log({"occl/best_mpjpe": occl_res})
-
-    print("MPJPE for best")
-    print("---")
-    # print(f"H36M: {test_res}")
-    # print(f"H36MA: {hard_res}")
-    print(f"Occl.: {occl_res}")
-    print("---")
diff --git a/scripts/eval/human36m/temperature.py b/scripts/eval/human36m/temperature.py
deleted file mode 100644
index 26c6ee1..0000000
--- a/scripts/eval/human36m/temperature.py
+++ /dev/null
@@ -1,171 +0,0 @@
-from propose.datasets.human36m.Human36mDataset import Human36mDataset
-from torch_geometric.loader import DataLoader
-
-from propose.utils.reproducibility import set_random_seed
-from propose.evaluation.mpjpe import mpjpe, pa_mpjpe
-from propose.evaluation.pck import pck, human36m_joints_to_use
-
-from propose.models.flows import CondGraphFlow
-
-import os
-
-import time
-from tqdm import tqdm
-import numpy as np
-
-import wandb
-
-
-def evaluate(flow, test_dataloader, temperature=1.0, limit=1000):
-    mpjpes = []
-    pa_mpjpes = []
-    single_mpjpes = []
-    single_pa_mpjpes = []
-    pck_scores = []
-    mean_pck_scores = []
-
-    iter_dataloader = iter(test_dataloader)
-
-    if limit is None:
-        pbar = tqdm(range(len(test_dataloader)))
-    else:
-        pbar = tqdm(range(limit))
-
-    for _ in pbar:
-        batch, _, action = next(iter_dataloader)
-        batch.to(flow.device)
-
-        samples = flow.sample(200, batch, temperature=temperature)
-
-        true_pose = batch["x"].x.cpu().numpy().reshape(-1, 16, 1, 3)
-        sample_poses = samples["x"].x.detach().cpu().numpy().reshape(-1, 16, 200, 3)
-
-        true_pose = np.insert(true_pose, 0, 0, axis=1)
-        sample_poses = np.insert(sample_poses, 0, 0, axis=1)
-
-        pck_score = pck(
-            true_pose[:, human36m_joints_to_use] / 0.0036,
-            sample_poses[:, human36m_joints_to_use] / 0.0036,
-        )
-
-        has_correct_pose = pck_score.max().unsqueeze(0).numpy()
-        mean_correct_pose = pck_score.mean().unsqueeze(0).numpy()
-
-        m = mpjpe(true_pose / 0.0036, sample_poses / 0.0036, dim=1)
-        m_single = m[..., 0]
-        m = np.min(m, axis=-1)
-
-        pa_m = (
-            pa_mpjpe(true_pose[0] / 0.0036, sample_poses[0] / 0.0036, dim=0)
-            .unsqueeze(0)
-            .numpy()
-        )
-
-        pa_m_single = pa_m[..., 0]
-        pa_m = np.min(pa_m, axis=-1)
-
-        m = m.tolist()
-        pa_m = pa_m.tolist()
-        m_single = m_single.tolist()
-
-        mpjpes += [m]
-        pa_mpjpes += [pa_m]
-        single_mpjpes += [m_single]
-        single_pa_mpjpes += [pa_m_single]
-
-        pck_scores += [has_correct_pose]
-        mean_pck_scores += [mean_correct_pose]
-
-        pbar.set_description(
-            f"MPJPE: {np.concatenate(mpjpes).mean():.4f}, "
-            f"PA MPJPE: {np.concatenate(pa_mpjpes).mean():.4f}, "
-            f"Single MPJPE: {np.concatenate(single_mpjpes).mean():.4f} "
-            f"Single PA MPJPE: {np.concatenate(single_pa_mpjpes).mean():.4f} "
-            f"PCK: {np.concatenate(pck_scores).mean():.4f} "
-            f"Mean PCK: {np.concatenate(mean_pck_scores).mean():.4f} "
-        )
-
-    return (
-        mpjpes,
-        pa_mpjpes,
-        single_mpjpes,
-        single_pa_mpjpes,
-        pck_scores,
-        mean_pck_scores,
-    )
-
-
-def mpjpe_experiment(flow, config, name="test", temperature=1.0, **kwargs):
-    test_dataset = Human36mDataset(
-        **config["dataset"],
-        **kwargs,
-    )
-    test_dataloader = DataLoader(
-        test_dataset, batch_size=1, shuffle=True, pin_memory=False, num_workers=0
-    )
-    (
-        test_res,
-        test_res_pa,
-        test_res_single,
-        test_res_pa_single,
-        test_res_pck,
-        test_res_mean_pck,
-    ) = evaluate(flow, test_dataloader, temperature=temperature)
-
-    res = {
-        f"{name}/test_res": np.concatenate(test_res).mean(),
-        f"{name}/test_res_pa": np.concatenate(test_res_pa).mean(),
-        f"{name}/test_res_single": np.concatenate(test_res_single).mean(),
-        f"{name}/test_res_pa_single": np.concatenate(test_res_pa_single).mean(),
-        f"{name}/test_res_pck": np.concatenate(test_res_pck).mean(),
-        f"{name}/test_res_mean_pck": np.concatenate(test_res_mean_pck).mean(),
-    }
-
-    return res, test_dataset, test_dataloader
-
-
-def run(use_wandb: bool = False, config: dict = None):
-    """
-    Train a CondGraphFlow on the Human36m dataset.
-    :param use_wandb: Whether to use wandb for logging.
-    :param config: A dictionary of configuration parameters.
-    """
-    set_random_seed(config["seed"])
-
-    config["dataset"]["dirname"] = config["dataset"]["dirname"] + "/test"
-
-    if use_wandb:
-        wandb.init(
-            project="propose_human36m",
-            entity=os.environ["WANDB_USER"],
-            config=config,
-            job_type="evaluation",
-            name=f"{config['experiment_name']}_temperature_{time.strftime('%d/%m/%Y::%H:%M:%S')}",
-            tags=config["tags"] if "tags" in config else None,
-            group=config["group"] if "group" in config else None,
-        )
-
-    flow = CondGraphFlow.from_pretrained(
-        f'ppierzc/propose_human36m/{config["experiment_name"]}:latest'
-    )
-
-    config["cuda_accelerated"] = flow.set_device()
-    flow.eval()
-
-    temperatures = np.arange(0.1, 1.1, 0.1)
-
-    for temperature in temperatures:
-        # Test
-        test_res, test_dataset, test_dataloader = mpjpe_experiment(
-            flow,
-            config,
-            occlusion_fractions=[],
-            test=True,
-            name="test",
-            temperature=temperature,
-        )
-
-        test_res["temperature"] = temperature
-
-        if use_wandb:
-            wandb.log(test_res)
diff --git a/scripts/preprocess.py b/scripts/preprocess.py
deleted file mode 100644
index 5acdcc1..0000000
--- a/scripts/preprocess.py
+++ /dev/null
@@ -1,66 +0,0 @@
-from pathlib import Path
-from propose.datasets.human36m.preprocess import pickle_poses, pickle_cameras
-
-import argparse
-
-parser = argparse.ArgumentParser(description="Arguments for running the scripts")
-
-parser.add_argument(
-    "--human36m",
-    default=False,
-    action="store_true",
-    help="Run the preprocess script for the Human 3.6m dataset",
-)
-
-parser.add_argument(
-    "--rat7m",
-    default=False,
-    action="store_true",
-    help="Run the preprocess script for the Rat 7m dataset",
-)
-
-parser.add_argument(
-    "--test",
-    default=False,
-    action="store_true",
-    help="Whether the test dataset should be processed",
-)
-
-parser.add_argument(
-    "--universal",
-    default=False,
-    action="store_true",
-    help="Whether the universal dataset should be processed",
-)
-
-
-def human36m(test=False, universal=False):
-    input_dir = Path("/data/human36m/test/") if test else Path("/data/human36m/raw/")
-    output_dir = (
-        Path("/data/human36m/processed/test/")
-        if test
-        else Path("/data/human36m/processed/")
-    )
-
-    print(" 🥒 Pickling Human3.6M cameras")
-    pickle_cameras(input_dir, output_dir)
-    print(" 🥒 Pickling Human3.6M poses")
-    pickle_poses(input_dir, output_dir, test=test, universal=universal)
-    print("Done! 🎉")
-
-
-if __name__ == "__main__":
-    args = parser.parse_args()
-
-    if args.human36m:
-        human36m(args.test)
-
-    if args.rat7m:
-        raise NotImplementedError(
-            "Rat7m data preprocessing is not yet implemented. Look at the notebook preprocess_rat7m.ipynb for more information."
-        )
-
-    if not args.human36m and not args.rat7m:
-        raise ValueError(
-            "No dataset specified. Please use --human36m or --rat7m to specify a dataset to preprocess."
-        )
diff --git a/scripts/sweep.py b/scripts/sweep.py
deleted file mode 100644
index 35ca4c6..0000000
--- a/scripts/sweep.py
+++ /dev/null
@@ -1,106 +0,0 @@
-from pathlib import Path
-from propose.datasets.human36m.preprocess import pickle_poses, pickle_cameras
-
-import argparse
-
-from sweep.human36m import human36m
-
-import os
-import yaml
-
-from pathlib import Path
-
-import wandb
-import torch
-
-from functools import partial
-
-parser = argparse.ArgumentParser(description="Arguments for running the scripts")
-
-parser.add_argument(
-    "--human36m",
-    default=False,
-    action="store_true",
-    help="Run the training script for the Human 3.6m dataset",
-)
-
-parser.add_argument(
-    "--wandb",
-    default=True,
-    action="store_true",
-    help="Whether to use wandb for logging (required for sweeping)",
-)
-
-parser.add_argument(
-    "--sweep",
-    default="sweep",
-    type=str,
-    help="Sweep config file",
-)
-
-parser.add_argument(
-    "--sweep_id",
-    type=str,
-    help="Sweep ID to use if sweep is already running",
-)
-
-if __name__ == "__main__":
-    args = parser.parse_args()
-
-    if args.wandb:
-        if not os.environ["WANDB_API_KEY"]:
-            raise ValueError(
-                "Wandb API key not set. Please set the WANDB_API_KEY environment variable."
-            )
-        if not os.environ["WANDB_USER"]:
-            raise ValueError(
-                "Wandb user not set. Please set the WANDB_USER environment variable."
-            )
-
-    if not args.wandb:
-        raise ValueError("Wandb is required for sweeping.")
-
-    dataset = Path("")
-    if args.human36m:
-        dataset = Path("human36m")
-
-    config_file = Path(args.sweep + ".yaml")
-    config_file = Path("/sweeps") / dataset / config_file
-
-    train_config_file = (
-        Path("/sweeps") / dataset / Path(args.sweep + "_train_config.yaml")
-    )
-
-    with open(config_file, "r") as f:
-        sweep_config = yaml.load(f, Loader=yaml.FullLoader)
-
-    with open(train_config_file, "r") as f:
-        config = yaml.load(f, Loader=yaml.FullLoader)
-        if "name" in sweep_config:
-            config["experiment_name"] = sweep_config["name"]
-
-    if args.human36m:
-        if "cuda_accelerated" not in config:
-            config["cuda_accelerated"] = torch.cuda.is_available()
-
-        sweep_id = args.sweep_id
-        if not sweep_id:
-            sweep_id = wandb.sweep(
-                sweep_config,
-                project="propose_human36m",
-                entity=os.environ["WANDB_USER"],
-            )
-
-        run_func = partial(human36m, use_wandb=args.wandb, config=config)
-
-        wandb.agent(
-            sweep_id,
-            function=run_func,
-            count=config["sweep"]["count"],
-            project="propose_human36m",
-            entity=os.environ["WANDB_USER"],
-        )
-    else:
-        print(
-            "Not running any scripts as no arguments were passed. Run with --help for more information."
-        )
diff --git a/scripts/sweep/__init__.py b/scripts/sweep/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/scripts/sweep/human36m.py b/scripts/sweep/human36m.py
deleted file mode 100644
index 9a98050..0000000
--- a/scripts/sweep/human36m.py
+++ /dev/null
@@ -1,94 +0,0 @@
-from propose.datasets.human36m.Human36mDataset import Human36mDataset
-
-from torch_geometric.loader import DataLoader
-
-from propose.models.flows import CondGraphFlow
-from propose.models.nn.embedding import embeddings
-from propose.training import supervised_trainer
-from propose.utils.reproducibility import set_random_seed
-
-import torch
-
-import wandb
-
-
-def build_config(config, sweep_config):
-    # model config
-    config["model"]["num_layers"] = sweep_config["num_layers"]
-    config["model"]["context_features"] = sweep_config["embedding_out_features"]
-    config["model"]["hidden_features"] = sweep_config["hidden_features"]
-
-    config["embedding"]["config"]["hidden_dim"] = sweep_config[
-        "embedding_hidden_features"
-    ]
-    config["embedding"]["config"]["output_dim"] = sweep_config["embedding_out_features"]
-
-    return config
-
-
-def human36m(
-    use_wandb: bool = False,
-    config: dict = None,
-):
-    """
-    Train a CondGraphFlow on the Human36m dataset.
-    :param use_wandb: Whether to use wandb for logging.
-    :param config: A dictionary of configuration parameters.
-    :param train_config_file: A dictionary of training configuration parameters.
-    """
-    wandb.init()
-
-    sweep_config = wandb.config
-    config = build_config(config, sweep_config)
-    wandb.config.update(config)
-
-    set_random_seed(config["seed"])
-
-    dataset = Human36mDataset(**config["dataset"])
-
-    dataloader = DataLoader(
-        dataset, batch_size=config["train"]["batch_size"], shuffle=True
-    )
-
-    embedding_net = None
-    if config["embedding"]:
-        embedding_net = embeddings[config["embedding"]["name"]](
-            **config["embedding"]["config"]
-        )
-
-    flow = CondGraphFlow(**config["model"], embedding_net=embedding_net)
-
-    num_params = sum(p.numel() for p in flow.parameters())
-    print(f"Number of parameters: {num_params}")
-
-    # set number of parameters in wandb config
-    if use_wandb:
-        wandb.config.num_params = num_params
-
-    if "use_pretrained" in config:
-        artifact = wandb.run.use_artifact(
-            f'ppierzc/propose_human36m/{config["use_pretrained"]}', type="model"
-        )
-        artifact_dir = artifact.download()
-        flow.load_state_dict(torch.load(artifact_dir + "/model.pt"))
-
-    if config["cuda_accelerated"]:
-        flow.to("cuda:0")
-
-    optimizer = torch.optim.Adam(flow.parameters(), **config["train"]["optimizer"])
-
-    lr_scheduler = None
-    if config["train"]["lr_scheduler"]:
-        lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
-            optimizer, **config["train"]["lr_scheduler"], verbose=True
-        )
-
-    supervised_trainer(
-        dataloader,
-        flow,
-        optimizer=optimizer,
-        lr_scheduler=lr_scheduler,
-        epochs=config["train"]["epochs"],
-        device=flow.device,
-        use_wandb=use_wandb,
-    )
diff --git a/scripts/train.py b/scripts/train.py
deleted file mode 100644
index c10a99b..0000000
--- a/scripts/train.py
+++ /dev/null
@@ -1,103 +0,0 @@
-from pathlib import Path
-from propose.datasets.human36m.preprocess import pickle_poses, pickle_cameras
-
-import argparse
-
-from train.human36m import human36m
-
-import os
-import yaml
-
-from pathlib import Path
-
-import wandb
-import torch
-import time
-
-parser = argparse.ArgumentParser(description="Arguments for running the scripts")
-
-parser.add_argument(
-    "--human36m",
-    default=False,
-    action="store_true",
-    help="Run the training script for the Human 3.6m dataset",
-)
-
-parser.add_argument(
-    "--wandb",
-    default=False,
-    action="store_true",
-    help="Whether to use wandb for logging",
-)
-
-parser.add_argument(
-    "--resume",
-    default="",
-    type=str,
-    help="Which run to resume",
-)
-
-parser.add_argument(
-    "--resume_id",
-    default="",
-    type=str,
-    help="Id of run which to resume",
-)
-
-parser.add_argument(
-    "--experiment",
-    default="mpii-prod.yaml",
-    type=str,
-    help="Experiment config file",
-)
-
-if __name__ == "__main__":
-    args = parser.parse_args()
-
-    if args.wandb:
-        if not os.environ["WANDB_API_KEY"]:
-            raise ValueError(
-                "Wandb API key not set. Please set the WANDB_API_KEY environment variable."
-            )
-        if not os.environ["WANDB_USER"]:
-            raise ValueError(
-                "Wandb user not set. Please set the WANDB_USER environment variable."
-            )
-
-    dataset = Path("")
-    if args.human36m:
-        dataset = Path("human36m")
-
-    config_file = Path(args.experiment + ".yaml")
-    config_file = Path("/experiments") / dataset / config_file
-
-    with open(config_file, "r") as f:
-        config = yaml.load(f, Loader=yaml.FullLoader)
-
-        if "experiment_name" not in config:
-            config["experiment_name"] = args.experiment
-
-    if args.human36m:
-        if "cuda_accelerated" not in config:
-            config["cuda_accelerated"] = torch.cuda.is_available()
-
-        if args.wandb:
-            wandb.init(
-                id=args.resume_id if args.resume_id else None,
-                project="propose_human36m",
-                entity=os.environ["WANDB_USER"],
-                config=config,
-                job_type="training",
-                name=args.resume
-                if args.resume
-                else f"{config['experiment_name']}_{time.strftime('%d/%m/%Y::%H:%M:%S')}",
-                tags=config["tags"] if "tags" in config else None,
-                group=config["group"] if "group" in config else None,
-                resume=bool(args.resume),
-            )
-
-        human36m(use_wandb=args.wandb, config=config)
-    else:
-        print(
-            "Not running any scripts as no arguments were passed. Run with --help for more information."
-        )
diff --git a/scripts/train/__init__.py b/scripts/train/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/scripts/train/human36m.py b/scripts/train/human36m.py
deleted file mode 100644
index 0617433..0000000
--- a/scripts/train/human36m.py
+++ /dev/null
@@ -1,81 +0,0 @@
-from propose.datasets.human36m.Human36mDataset import Human36mDataset
-
-from torch_geometric.loader import DataLoader
-
-from propose.models.flows import CondGraphFlow
-from propose.models.nn.embedding import embeddings
-from propose.training import supervised_trainer
-from propose.utils.reproducibility import set_random_seed
-
-import torch
-
-import wandb
-
-
-def human36m(use_wandb: bool = False, config: dict = None):
-    """
-    Train a CondGraphFlow on the Human36m dataset.
-    :param use_wandb: Whether to use wandb for logging.
-    :param config: A dictionary of configuration parameters.
-    """
-    config = wandb.config if use_wandb else config
-
-    set_random_seed(config["seed"])
-
-    dataset = Human36mDataset(**config["dataset"])
-
-    dataloader = DataLoader(
-        dataset, batch_size=config["train"]["batch_size"], shuffle=True
-    )
-
-    embedding_net = None
-    if config["embedding"]:
-        embedding_net = embeddings[config["embedding"]["name"]](
-            **config["embedding"]["config"]
-        )
-
-    flow = CondGraphFlow(**config["model"], embedding_net=embedding_net)
-
-    num_params = sum(p.numel() for p in flow.parameters())
-    print(f"Number of parameters: {num_params}")
-
-    # set number of parameters in wandb config
-    if use_wandb:
-        wandb.config.num_params = num_params
-
-    if "use_pretrained" in config:
-        artifact = wandb.run.use_artifact(
-            f'ppierzc/propose_human36m/{config["use_pretrained"]}', type="model"
-        )
-        artifact_dir = artifact.download()
-        flow.load_state_dict(torch.load(artifact_dir + "/model.pt"))
-
-    if config["cuda_accelerated"]:
-        flow.to("cuda:0")
-
-    optimizer = torch.optim.Adam(flow.parameters(), **config["train"]["optimizer"])
-
-    lr_scheduler = None
-    if config["train"]["lr_scheduler"]:
-        lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
-            optimizer, **config["train"]["lr_scheduler"], verbose=True
-        )
-
-    if use_wandb and wandb.run.resumed:
-        wandb.restore("checkpoint.pt", root="/tmp")
-        checkpoint = torch.load("/tmp/checkpoint.pt")
-
-        flow.load_state_dict(checkpoint["model"])
-        optimizer.load_state_dict(checkpoint["optimizer"])
-        if lr_scheduler:
-            lr_scheduler.load_state_dict(checkpoint["lr_scheduler"])
-
-    supervised_trainer(
-        dataloader,
-        flow,
-        optimizer=optimizer,
-        lr_scheduler=lr_scheduler,
-        epochs=config["train"]["epochs"],
-        device=flow.device,
-        use_wandb=use_wandb,
-    )
diff --git a/sweeps/human36m/sweep.yaml b/sweeps/human36m/sweep.yaml
deleted file mode 100644
index 63848e6..0000000
--- a/sweeps/human36m/sweep.yaml
+++ /dev/null
@@ -1,20 +0,0 @@
-name: "Architecture sweep"
-method: bayes
-
-metric:
-  goal: minimize
-  name: Loss
-
-parameters:
-  num_layers:
-    min: 5
-    max: 20
-  hidden_features:
-    min: 100
-    max: 1024
-  embedding_hidden_features:
-    min: 64
-    max: 1024
-  embedding_out_features:
-    min: 2
-    max: 100
\ No newline at end of file
diff --git a/sweeps/human36m/sweep_train_config.yaml b/sweeps/human36m/sweep_train_config.yaml
deleted file mode 100644
index 26e555d..0000000
--- a/sweeps/human36m/sweep_train_config.yaml
+++ /dev/null
@@ -1,49 +0,0 @@
-seed: 0
-save_best: false
-
-tags:
-  - mpii
-  - human36m
-group: prod
-
-dataset:
-  dirname: "/data/human36m/processed"
-  mpii: true
-  use_variance: true
-
-train:
-  optimizer:
-    lr: 1.0e-3
-    weight_decay: 0
-  lr_scheduler:
-    patience: 10
-    cooldown: 5
-    mode: "min"
-    factor: 0.1
-    threshold: 1.0e-2
-    min_lr: 1.0e-6
-  batch_size: 200
-  epochs: 5
-
-model:
-  num_layers: 10
-  context_features: 10
-  hidden_features: 200
-  relations:
-      - x
-      - c
-      - r
-      - x->x
-      - x<-x
-      - c->x
-      - r->x
-
-embedding:
-    name: "sage"
-    config:
-      input_dim: 2
-      hidden_dim: 128
-      output_dim: 10
-
-sweep:
-  count: 5
\ No newline at end of file
diff --git a/scripts/eval/human36m/__init__.py b/tests/models/detectors/__init__.py
similarity index 100%
rename from scripts/eval/human36m/__init__.py
rename to tests/models/detectors/__init__.py
diff --git a/tests/models/detectors/hrnet_test.py b/tests/models/detectors/hrnet_test.py
new file mode 100644
index 0000000..c1be4ea
--- /dev/null
+++ b/tests/models/detectors/hrnet_test.py
@@ -0,0 +1,19 @@
+import unittest
+
+from propose.models.detectors import HRNet
+
+from unittest.mock import MagicMock, patch
+
+
+class HRNetTests(unittest.TestCase):
+    @patch("propose.models.detectors.hrnet.hrnet.wandb")
+    @patch("propose.models.detectors.hrnet.hrnet.torch.load")
+    def test_has_pretrained_option(self, wandb_mock, load_mock):
+        load_mock.return_value = {}
+        model = HRNet.from_pretrained("artifact", MagicMock())
+
+        self.assertIsNotNone(model)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/models/nn/CondGNN_test.py b/tests/models/nn/CondGNN_test.py
index 2fd02a3..67c8752 100644
--- a/tests/models/nn/CondGNN_test.py
+++ b/tests/models/nn/CondGNN_test.py
@@ -138,6 +138,7 @@ def test_forward(cond_gcn_mock, module_list_mock):
         root_features=in_features,
         hidden_features=hidden_features,
         relations=None,
+        use_attention=False,
     )
     assert cond_gcn_mock.mock_calls[1] == call(
         in_features=hidden_features,
@@ -146,4 +147,5 @@ def test_forward(cond_gcn_mock, module_list_mock):
         root_features=hidden_features,
         hidden_features=hidden_features,
         relations=None,
+        use_attention=False,
     )