diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 00000000..f1677884 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,80 @@ +name: Documentation +on: + push: + branches: ["main"] + paths: + - "neural_lam/**" + - "docs/**" + - "pyproject.toml" + - ".github/workflows/docs.yml" + pull_request: + branches: ["main"] + paths: + - "neural_lam/**" + - "docs/**" + - "pyproject.toml" + - ".github/workflows/docs.yml" + workflow_dispatch: + +concurrency: + group: "docs-${{ github.ref }}" + cancel-in-progress: true + +jobs: + build-docs: + name: Build docs + runs-on: ubuntu-latest + permissions: + contents: read + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Set up uv + uses: astral-sh/setup-uv@v5 + + - name: Set up Python 3.11 + uses: actions/setup-python@v5 + with: + python-version: "3.11" + + - name: Install docs dependencies + run: uv sync --extra docs --group dev --no-cache + + + + - name: Build docs with sphinx + run: uv run sphinx-build -W --keep-going -b html docs/ docs/_build/html/ + + - name: Upload docs artifact + uses: actions/upload-artifact@v4 + with: + name: docs-html + path: docs/_build/html/ + + linkcheck: + name: Link check + runs-on: ubuntu-latest + permissions: + contents: read + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Set up uv + uses: astral-sh/setup-uv@v5 + + - name: Set up Python 3.11 + uses: actions/setup-python@v5 + with: + python-version: "3.11" + + - name: Install docs dependencies + run: uv sync --extra docs --group dev --no-cache + + + + - name: Run linkcheck + run: uv run sphinx-build -W --keep-going -b linkcheck docs/ docs/_build/linkcheck/ diff --git a/.gitignore b/.gitignore index a2c5bb26..c493be3c 100644 --- a/.gitignore +++ b/.gitignore @@ -52,6 +52,11 @@ share/python-wheels/ # mkdocs documentation /site +# Jupyter Book / Sphinx documentation +docs/_build/ +docs/api/ + + ### Vim ### # Swap [._]*.s[a-v][a-z] diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 229326ea..6d35eb16 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -17,6 +17,7 @@ repos: hooks: - id: codespell description: Check for spelling errors + args: ["--ignore-words-list", "astroid"] - repo: https://github.com/psf/black rev: 25.11.0 diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 00000000..b147538a --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,11 @@ +version: 2 + +build: + os: ubuntu-22.04 + tools: + python: "3.11" + commands: + - pip install uv + - uv sync --extra docs --group dev + - uv run sphinx-build -W --keep-going -b html docs/ docs/_build/html/ + - cp -r docs/_build/html/* $READTHEDOCS_OUTPUT/html/ diff --git a/CHANGELOG.md b/CHANGELOG.md index fd123dee..bc98dd78 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,6 +9,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Added +- Add a Sphinx + `sphinx-autoapi` documentation site (intro, install, quickstart, auto-generated API reference) on `myst-nb` and `sphinx-book-theme`, with a `docs` CI build/linkcheck job and a ReadTheDocs config. closes #61 [\#272](https://github.com/mllam/neural-lam/pull/272) @Mohit-Lakra + - Add `PropagationNet` GNN layer that incentivises directional message propagation from sender to receiver nodes, and expose it alongside `InteractionNet` through four new CLI arguments (`--g2m_gnn_type`, diff --git a/docs/_static/logo.png b/docs/_static/logo.png new file mode 100644 index 00000000..8e65f460 Binary files /dev/null and b/docs/_static/logo.png differ diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 00000000..2447234c --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,114 @@ +# Standard library +import os +import sys + +# Add the project root to sys.path so sphinx can find neural_lam +sys.path.insert(0, os.path.abspath("..")) + +project = "Neural-LAM" +copyright = "2024–2026, MLLAM Community" +author = "MLLAM Community" + +# General Sphinx configuration +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] + +extensions = [ + "autoapi.extension", + "myst_nb", + "sphinx.ext.napoleon", + "sphinx.ext.viewcode", + "sphinx.ext.intersphinx", + "sphinx_copybutton", + "sphinxext.opengraph", +] + +# AutoAPI settings +autoapi_dirs = ["../neural_lam"] +autoapi_root = "api" +autoapi_type = "python" +autoapi_options = [ + "members", + "undoc-members", + "show-inheritance", + "show-module-summary", +] +autoapi_python_class_content = "both" +autoapi_member_order = "groupwise" +autoapi_python_use_implicit_namespaces = False +autoapi_keep_files = True +autoapi_add_toctree_entry = True +autoapi_ignore = ["**/tests/**", "**/conftest.py"] + +# Napoleon settings +napoleon_numpy_docstring = True +napoleon_google_docstring = False +napoleon_include_init_with_doc = True +napoleon_include_private_with_doc = False +napoleon_include_special_with_doc = False +napoleon_use_param = True +napoleon_use_rtype = True +napoleon_use_ivar = True + +# Intersphinx settings +intersphinx_mapping = { + "python": ("https://docs.python.org/3", None), + "numpy": ("https://numpy.org/doc/stable", None), + "torch": ("https://pytorch.org/docs/stable", None), + "pytorch_lightning": ("https://lightning.ai/docs/pytorch/stable/", None), + "torch_geometric": ( + "https://pytorch-geometric.readthedocs.io/en/latest/", + None, + ), +} + +# MyST / Notebook settings +myst_enable_extensions = [ + "colon_fence", + "dollarmath", + "linkify", + "substitution", + "tasklist", + "deflist", + "fieldlist", + "html_admonition", + "html_image", + "smartquotes", + "attrs_inline", +] +nb_execution_mode = "off" + +# HTML Theme +html_theme = "sphinx_book_theme" +html_logo = "_static/logo.png" +html_static_path = ["_static"] + +html_theme_options = { + "repository_url": "https://github.com/mllam/neural-lam", + "use_repository_button": True, + "use_issues_button": True, + "use_edit_page_button": True, + "repository_branch": "main", + "path_to_docs": "docs", + "show_navbar_depth": 2, + "show_toc_level": 2, + "extra_footer": ( + '

Built with Sphinx | ' + 'Source

' + ), +} + +# OpenGraph settings +ogp_site_url = "https://neural-lam.readthedocs.io/en/latest/" +ogp_image = "_static/logo.png" +ogp_use_first_image = True + +suppress_warnings = [ + "autoapi.python_import_resolution", + "myst.xref_missing", +] + +# Linkcheck settings +linkcheck_ignore = [ + r"https://kutt\.to/mllam", # Returns 403 Forbidden for bots + r"https://docs\.pytorch\.org/.*", # Flaky anchors in intersphinx +] diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md new file mode 100644 index 00000000..fb88f259 --- /dev/null +++ b/docs/getting-started/installation.md @@ -0,0 +1,29 @@ +# Installation + +## Prerequisites +- Python >=3.10 +- Git +- (Optional) CUDA-capable GPU for training + +```{include} ../../README.md +:start-after: "# Installing Neural-LAM" +:end-before: "# Using Neural-LAM" +``` + +## Building Documentation Locally + +```{code-block} bash +# Install docs dependencies +uv sync --extra docs --group dev + +# Build the documentation +sphinx-build -b html docs/ docs/_build/html/ + +# Open in browser +open docs/_build/html/index.html # macOS +# xdg-open docs/_build/html/index.html # Linux +``` + +```{seealso} +See the {doc}`quickstart` guide to run your first training. +``` diff --git a/docs/getting-started/quickstart.md b/docs/getting-started/quickstart.md new file mode 100644 index 00000000..4dd71f1e --- /dev/null +++ b/docs/getting-started/quickstart.md @@ -0,0 +1,52 @@ +# Quickstart + +## Overview + +This guide walks through the minimum steps to get started with Neural-LAM: +1. Set up example data +2. Create a graph +3. Train a model +4. Evaluate the model + +## Step 1: Download Example Data + +When running tests for the first time, example data is automatically downloaded from S3. You can trigger this download by running a minimal test. + +```{code-block} bash +# Run a minimal test to trigger the data download +pytest tests/test_training.py -vv -s -k "test_training" +``` + +## Step 2: Create a Graph + +Before training, you must construct a graph mesh for your data. + +```{code-block} bash +python -m neural_lam.create_graph --config_path --name +``` + +This script builds the mesh graph required by the GNN models. + +## Step 3: Train a Model + +Now you can train a model using the graph and configuration. + +```{code-block} bash +python -m neural_lam.train_model --config_path --model graph_lam --graph +``` + +Neural-LAM supports several models like `graph_lam`, `hi_lam`, and `hi_lam_parallel`. + +## Step 4: Evaluate + +After training, you can evaluate the model on the test set by loading the saved checkpoint. + +```{code-block} bash +python -m neural_lam.train_model --eval test --config_path --load +``` + +## Configuration + +Neural-LAM uses a YAML configuration system powered by `dataclass-wizard`. The configuration defines the dataset paths, training parameters, and model hyperparameters. For complete details, see the API reference for {py:class}`neural_lam.config.NeuralLAMConfig`. + +- {doc}`../api/index` for complete API reference diff --git a/docs/graph_storage_spec.md b/docs/graph_storage_spec.md index 9259c9fc..48af14f4 100644 --- a/docs/graph_storage_spec.md +++ b/docs/graph_storage_spec.md @@ -1,7 +1,7 @@ -# Neural-LAM Graph Storage Specification +# Graph Storage Specification Version: 0.1.0 diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 00000000..40fefd70 --- /dev/null +++ b/docs/index.md @@ -0,0 +1,44 @@ +# Neural-LAM + +Neural-LAM is a PyTorch and PyTorch Lightning framework for high-resolution weather prediction using Graph Neural Networks. It provides a modular approach to Limited Area Modeling, supporting multiple graph-based architectures such as GraphLAM, HiLAM, and HiLAMParallel to process and predict meteorological data efficiently. + +```{toctree} +:maxdepth: 2 +:hidden: + +getting-started/installation +getting-started/quickstart +``` + +```{toctree} +:maxdepth: 2 +:caption: Tutorials +:hidden: + +notebooks/create_reduced_meps_dataset +``` + +```{toctree} +:maxdepth: 2 +:caption: Reference +:hidden: + +api/index +graph_storage_spec +``` + +- **[🚀 Getting Started](getting-started/installation.md)**: Installation guide and quickstart tutorial to get you up and running. +- **[📚 API Reference](api/index)**: Auto-generated reference for all modules, classes, and functions. + +## Key Features + +- **Modular design**: Swap datastores, models, and graph structures independently +- **Multiple model architectures**: GraphLAM (flat), HiLAM (hierarchical), HiLAMParallel (parallel hierarchical) +- **Flexible data handling**: Abstract datastore interface supporting zarr, numpy, and custom formats via mllam-data-prep +- **Production-ready**: PyTorch Lightning for training, W&B/MLflow logging, checkpoint management + +## Quick Links + +- [GitHub Repository](https://github.com/mllam/neural-lam) +- [Issue Tracker](https://github.com/mllam/neural-lam/issues) +- [MLLAM Community Slack](https://kutt.to/mllam) diff --git a/docs/validate_graph.py b/docs/validate_graph.py index ed6535d4..aefd0d3c 100644 --- a/docs/validate_graph.py +++ b/docs/validate_graph.py @@ -1273,7 +1273,7 @@ def validate_graph_directory( spec_text = textwrap.dedent( f"""\ - # Neural-LAM Graph Storage Specification + # Graph Storage Specification Version: {CURRENT_GRAPH_FORMAT_SPEC_VERSION} diff --git a/neural_lam/create_graph.py b/neural_lam/create_graph.py index 19734d72..2b391c9f 100644 --- a/neural_lam/create_graph.py +++ b/neural_lam/create_graph.py @@ -366,42 +366,39 @@ def create_graph( `graph_dir_path`. Creates the following files for all graphs: + - g2m_edge_index.pt [2, N_g2m_edges] - g2m_features.pt [N_g2m_edges, d_features] - m2g_edge_index.pt [2, N_m2m_edges] - m2g_features.pt [N_m2m_edges, d_features] - m2m_edge_index.pt list of [2, N_m2m_edges_level], length==n_levels - m2m_features.pt list of [N_m2m_edges_level, d_features], - length==n_levels + length==n_levels - mesh_features.pt list of [N_mesh_nodes_level, d_mesh_static], - length==n_levels - - where - d_features: - number of features per edge (currently d_features==3, for - edge-length, x and y) - N_g2m_edges: - number of edges in the graph from grid-to-mesh - N_m2g_edges: - number of edges in the graph from mesh-to-grid - N_m2m_edges_level: - number of edges in the graph from mesh-to-mesh at a given level - (list index corresponds to the level) - d_mesh_static: - number of static features per mesh node (currently - d_mesh_static==2, for x and y) - N_mesh_nodes_level: - number of nodes in the mesh at a given level + length==n_levels + + where: + + * d_features: number of features per edge (currently d_features==3, for + edge-length, x and y) + * N_g2m_edges: number of edges in the graph from grid-to-mesh + * N_m2g_edges: number of edges in the graph from mesh-to-grid + * N_m2m_edges_level: number of edges in the graph from mesh-to-mesh + at a given level (list index corresponds to the level) + * d_mesh_static: number of static features per mesh node (currently + d_mesh_static==2, for x and y) + * N_mesh_nodes_level: number of nodes in the mesh at a given level And in addition for hierarchical graphs: - - mesh_up_edge_index.pt - list of [2, N_mesh_updown_edges_level], length==n_levels-1 - - mesh_up_features.pt - list of [N_mesh_updown_edges_level, d_features], length==n_levels-1 - - mesh_down_edge_index.pt - list of [2, N_mesh_updown_edges_level], length==n_levels-1 - - mesh_down_features.pt - list of [N_mesh_updown_edges_level, d_features], length==n_levels-1 + + - mesh_up_edge_index.pt: list of [2, N_mesh_updown_edges_level], + length==n_levels-1 + - mesh_up_features.pt: list of [N_mesh_updown_edges_level, d_features], + length==n_levels-1 + - mesh_down_edge_index.pt: list of [2, N_mesh_updown_edges_level], + length==n_levels-1 + - mesh_down_features.pt: list of [N_mesh_updown_edges_level, d_features], + length==n_levels-1 where N_mesh_updown_edges_level is the number of edges in the graph from mesh-to-mesh between two consecutive levels (list index corresponds index diff --git a/neural_lam/datastore/base.py b/neural_lam/datastore/base.py index 8376d38d..e5307d9d 100644 --- a/neural_lam/datastore/base.py +++ b/neural_lam/datastore/base.py @@ -421,6 +421,7 @@ def expected_dim_order( ---------- category : str The category of the dataset (state/forcing/static). + Returns ------- List[str] @@ -481,12 +482,13 @@ class BaseRegularGridDatastore(BaseDatastore): The following methods and attributes must be implemented for datastore that represents regular-gridded data: - - `grid_shape_state` (property): 2D shape of the grid for the state + + * `grid_shape_state` (property): 2D shape of the grid for the state variables. - - `get_xy` (method): Return the x, y coordinates of the dataset, with the + * `get_xy` (method): Return the x, y coordinates of the dataset, with the option to not stack the coordinates (so that they are returned as a 2D grid). - - `get_lat_lon` (method): Return the latitude/longitude coordinates of + * `get_lat_lon` (method): Return the latitude/longitude coordinates of the dataset for convenience when plotting. The operation of going from (x,y)-indexed regular grid @@ -526,10 +528,11 @@ def get_xy(self, category: str, stacked: bool) -> np.ndarray: ------- np.ndarray The x, y coordinates of the dataset, returned differently based on - the value of `stacked`: - `stacked==True`: shape `(n_grid_points, - 2)` where - n_grid_points=N_x*N_y. - - `stacked==False`: shape `(N_x, N_y, 2)` + the value of `stacked`: + + * `stacked==True`: shape `(n_grid_points, 2)` where + n_grid_points=N_x*N_y. + * `stacked==False`: shape `(N_x, N_y, 2)` """ def unstack_grid_coords( diff --git a/neural_lam/datastore/mdp.py b/neural_lam/datastore/mdp.py index 7cad45d7..55e91dc2 100644 --- a/neural_lam/datastore/mdp.py +++ b/neural_lam/datastore/mdp.py @@ -500,9 +500,10 @@ def get_xy(self, category: str, stacked: bool) -> ndarray: np.ndarray The x, y coordinates of the dataset, returned differently based on the value of `stacked`: - - `stacked==True`: shape `(n_grid_points, 2)` where - n_grid_points=N_x*N_y. - - `stacked==False`: shape `(N_x, N_y, 2)` + + * `stacked==True`: shape `(n_grid_points, 2)` where + n_grid_points=N_x*N_y. + * `stacked==False`: shape `(N_x, N_y, 2)` """ # assume variables are stored in dimensions [grid_index, ...] diff --git a/neural_lam/datastore/npyfilesmeps/store.py b/neural_lam/datastore/npyfilesmeps/store.py index e14bbc74..029452fb 100644 --- a/neural_lam/datastore/npyfilesmeps/store.py +++ b/neural_lam/datastore/npyfilesmeps/store.py @@ -84,60 +84,60 @@ class NpyFilesDatastoreMEPS(BaseRegularGridDatastore): `[y, x]`. - Folder structure: - - meps_example_reduced - ├── data_config.yaml - ├── samples - │ ├── test - │ │ ├── nwp_2022090100_mbr000.npy - │ │ ├── nwp_2022090100_mbr001.npy - │ │ ├── nwp_2022090112_mbr000.npy - │ │ ├── nwp_2022090112_mbr001.npy - │ │ ├── ... - │ │ ├── nwp_toa_downwelling_shortwave_flux_2022090100.npy - │ │ ├── nwp_toa_downwelling_shortwave_flux_2022090112.npy - │ │ ├── ... - │ │ ├── wtr_2022090100.npy - │ │ ├── wtr_2022090112.npy - │ │ └── ... - │ ├── train - │ │ ├── nwp_2022040100_mbr000.npy - │ │ ├── nwp_2022040100_mbr001.npy - │ │ ├── ... - │ │ ├── nwp_2022040112_mbr000.npy - │ │ ├── nwp_2022040112_mbr001.npy - │ │ ├── ... - │ │ ├── nwp_toa_downwelling_shortwave_flux_2022040100.npy - │ │ ├── nwp_toa_downwelling_shortwave_flux_2022040112.npy - │ │ ├── ... - │ │ ├── wtr_2022040100.npy - │ │ ├── wtr_2022040112.npy - │ │ └── ... - │ └── val - │ ├── nwp_2022060500_mbr000.npy - │ ├── nwp_2022060500_mbr001.npy - │ ├── ... - │ ├── nwp_2022060512_mbr000.npy - │ ├── nwp_2022060512_mbr001.npy - │ ├── ... - │ ├── nwp_toa_downwelling_shortwave_flux_2022060500.npy - │ ├── nwp_toa_downwelling_shortwave_flux_2022060512.npy - │ ├── ... - │ ├── wtr_2022060500.npy - │ ├── wtr_2022060512.npy - │ └── ... - └── static - ├── border_mask.npy - ├── diff_mean.pt - ├── diff_std.pt - ├── flux_stats.pt - ├── grid_features.pt - ├── nwp_xy.npy - ├── parameter_mean.pt - ├── parameter_std.pt - ├── parameter_weights.npy - └── surface_geopotential.npy + Folder structure:: + + meps_example_reduced + ├── data_config.yaml + ├── samples + │ ├── test + │ │ ├── nwp_2022090100_mbr000.npy + │ │ ├── nwp_2022090100_mbr001.npy + │ │ ├── nwp_2022090112_mbr000.npy + │ │ ├── nwp_2022090112_mbr001.npy + │ │ ├── ... + │ │ ├── nwp_toa_downwelling_shortwave_flux_2022090100.npy + │ │ ├── nwp_toa_downwelling_shortwave_flux_2022090112.npy + │ │ ├── ... + │ │ ├── wtr_2022090100.npy + │ │ ├── wtr_2022090112.npy + │ │ └── ... + │ ├── train + │ │ ├── nwp_2022040100_mbr000.npy + │ │ ├── nwp_2022040100_mbr001.npy + │ │ ├── ... + │ │ ├── nwp_2022040112_mbr000.npy + │ │ ├── nwp_2022040112_mbr001.npy + │ │ ├── ... + │ │ ├── nwp_toa_downwelling_shortwave_flux_2022040100.npy + │ │ ├── nwp_toa_downwelling_shortwave_flux_2022040112.npy + │ │ ├── ... + │ │ ├── wtr_2022040100.npy + │ │ ├── wtr_2022040112.npy + │ │ └── ... + │ └── val + │ ├── nwp_2022060500_mbr000.npy + │ ├── nwp_2022060500_mbr001.npy + │ ├── ... + │ ├── nwp_2022060512_mbr000.npy + │ ├── nwp_2022060512_mbr001.npy + │ ├── ... + │ ├── nwp_toa_downwelling_shortwave_flux_2022060500.npy + │ ├── nwp_toa_downwelling_shortwave_flux_2022060512.npy + │ ├── ... + │ ├── wtr_2022060500.npy + │ ├── wtr_2022060512.npy + │ └── ... + └── static + ├── border_mask.npy + ├── diff_mean.pt + ├── diff_std.pt + ├── flux_stats.pt + ├── grid_features.pt + ├── nwp_xy.npy + ├── parameter_mean.pt + ├── parameter_std.pt + ├── parameter_weights.npy + └── surface_geopotential.npy For the MEPS dataset: N_t' = 65 @@ -244,11 +244,12 @@ def get_dataarray( xr.DataArray The data array for the given category and split, with dimensions per category: - state: `[elapsed_forecast_duration, analysis_time, grid_index, - feature, ensemble_member]` - forcing: `[elapsed_forecast_duration, analysis_time, grid_index, - feature]` - static: `[grid_index, feature]` + + * state: ``[elapsed_forecast_duration, analysis_time, grid_index,`` + ``feature, ensemble_member]`` + * forcing: ``[elapsed_forecast_duration, analysis_time,`` + ``grid_index, feature]`` + * static: ``[grid_index, feature]`` """ if category == "state": @@ -669,9 +670,10 @@ def get_xy(self, category: str, stacked: bool) -> np.ndarray: np.ndarray The x, y coordinates of the dataset (with x first then y second), returned differently based on the value of `stacked`: - - `stacked==True`: shape `(n_grid_points, 2)` where - n_grid_points=N_x*N_y. - - `stacked==False`: shape `(N_x, N_y, 2)` + + * `stacked==True`: shape `(n_grid_points, 2)` where + n_grid_points=N_x*N_y. + * `stacked==False`: shape `(N_x, N_y, 2)` """ diff --git a/neural_lam/datastore/plot_example.py b/neural_lam/datastore/plot_example.py index 13f32e57..789282bf 100644 --- a/neural_lam/datastore/plot_example.py +++ b/neural_lam/datastore/plot_example.py @@ -34,7 +34,7 @@ def plot_example_from_datastore( Whether to standardize the data before plotting, by default True. selection : dict, optional Selections to apply to the dataarray, for example - `time="1990-09-03T0:00" would select this single timestep, by default + `time="1990-09-03T0:00"` would select this single timestep, by default {}. index_selection: dict, optional Index-based selection to apply to the dataarray, for example diff --git a/neural_lam/gnn_layers.py b/neural_lam/gnn_layers.py index 7a92ea76..9468cbcb 100644 --- a/neural_lam/gnn_layers.py +++ b/neural_lam/gnn_layers.py @@ -182,6 +182,7 @@ def aggregate( ) -> tuple[torch.Tensor, torch.Tensor]: """ Overridden aggregation function to: + * return both aggregated and per-edge messages, * only aggregate to the number of receiver nodes (``self.num_rec``) rather than to ``dim_size``. @@ -260,9 +261,16 @@ def get_gnn_class(gnn_type: str) -> Type[pyg.nn.MessagePassing]: """ Look up a GNN class by name. - gnn_type: One of the keys in GNN_TYPES - (currently "InteractionNet" or "PropagationNet") - Returns the corresponding GNN class. + Parameters + ---------- + gnn_type : str + One of the keys in GNN_TYPES (currently "InteractionNet" or + "PropagationNet") + + Returns + ------- + Type[pyg.nn.MessagePassing] + The corresponding GNN class. 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