Full Code of pyg-team/pytorch_geometric for AI

gitextract_yj2ugid6/

├── .github/
│   ├── CODEOWNERS
│   ├── CONTRIBUTING.md
│   ├── ISSUE_TEMPLATE/
│   │   ├── bug-report.yml
│   │   ├── config.yml
│   │   ├── documentation.yml
│   │   ├── feature-request.yml
│   │   ├── installation.yml
│   │   └── refactor.yml
│   ├── actions/
│   │   └── setup/
│   │       └── action.yml
│   ├── dependabot.yml
│   ├── labeler.yml
│   └── workflows/
│       ├── _testing.yml
│       ├── auto-merge.yml
│       ├── building_nightly.yml
│       ├── changelog.yml
│       ├── documentation.yml
│       ├── examples.yml
│       ├── labeler.yml
│       ├── linting.yml
│       ├── testing.yml
│       └── testing_rag.yml
├── .gitignore
├── .pre-commit-config.yaml
├── CHANGELOG.md
├── CITATION.cff
├── LICENSE
├── README.md
├── benchmark/
│   ├── README.md
│   ├── citation/
│   │   ├── README.md
│   │   ├── __init__.py
│   │   ├── appnp.py
│   │   ├── arma.py
│   │   ├── cheb.py
│   │   ├── datasets.py
│   │   ├── gat.py
│   │   ├── gcn.py
│   │   ├── inference.sh
│   │   ├── run.sh
│   │   ├── sgc.py
│   │   ├── statistics.py
│   │   └── train_eval.py
│   ├── inference/
│   │   ├── README.md
│   │   └── inference_benchmark.py
│   ├── kernel/
│   │   ├── README.md
│   │   ├── __init__.py
│   │   ├── asap.py
│   │   ├── datasets.py
│   │   ├── diff_pool.py
│   │   ├── edge_pool.py
│   │   ├── gcn.py
│   │   ├── gin.py
│   │   ├── global_attention.py
│   │   ├── graclus.py
│   │   ├── graph_sage.py
│   │   ├── main.py
│   │   ├── main_performance.py
│   │   ├── sag_pool.py
│   │   ├── set2set.py
│   │   ├── sort_pool.py
│   │   ├── statistics.py
│   │   ├── top_k.py
│   │   └── train_eval.py
│   ├── loader/
│   │   └── neighbor_loader.py
│   ├── multi_gpu/
│   │   └── training/
│   │       ├── README.md
│   │       ├── common.py
│   │       ├── training_benchmark_cuda.py
│   │       └── training_benchmark_xpu.py
│   ├── points/
│   │   ├── README.md
│   │   ├── __init__.py
│   │   ├── datasets.py
│   │   ├── edge_cnn.py
│   │   ├── mpnn.py
│   │   ├── point_cnn.py
│   │   ├── point_net.py
│   │   ├── spline_cnn.py
│   │   ├── statistics.py
│   │   └── train_eval.py
│   ├── runtime/
│   │   ├── README.md
│   │   ├── __init__.py
│   │   ├── dgl/
│   │   │   ├── gat.py
│   │   │   ├── gcn.py
│   │   │   ├── hidden.py
│   │   │   ├── main.py
│   │   │   ├── rgcn.py
│   │   │   └── train.py
│   │   ├── gat.py
│   │   ├── gcn.py
│   │   ├── main.py
│   │   ├── rgcn.py
│   │   └── train.py
│   ├── setup.py
│   ├── training/
│   │   ├── README.md
│   │   └── training_benchmark.py
│   └── utils/
│       ├── __init__.py
│       ├── hetero_gat.py
│       ├── hetero_sage.py
│       └── utils.py
├── codecov.yml
├── docker/
│   ├── Dockerfile
│   ├── Dockerfile.xpu
│   ├── README.md
│   └── singularity
├── docs/
│   ├── Makefile
│   ├── README.md
│   ├── requirements.txt
│   └── source/
│       ├── .gitignore
│       ├── _figures/
│       │   ├── .gitignore
│       │   ├── build.sh
│       │   ├── graph.tex
│       │   ├── hg_example.tex
│       │   ├── to_hetero.tex
│       │   └── to_hetero_with_bases.tex
│       ├── _static/
│       │   └── js/
│       │       └── version_alert.js
│       ├── _templates/
│       │   └── autosummary/
│       │       ├── class.rst
│       │       ├── inherited_class.rst
│       │       ├── metrics.rst
│       │       ├── nn.rst
│       │       └── only_class.rst
│       ├── advanced/
│       │   ├── batching.rst
│       │   ├── compile.rst
│       │   ├── cpu_affinity.rst
│       │   ├── graphgym.rst
│       │   ├── hgam.rst
│       │   ├── jit.rst
│       │   ├── remote.rst
│       │   └── sparse_tensor.rst
│       ├── cheatsheet/
│       │   ├── data_cheatsheet.rst
│       │   └── gnn_cheatsheet.rst
│       ├── conf.py
│       ├── external/
│       │   └── resources.rst
│       ├── get_started/
│       │   ├── colabs.rst
│       │   └── introduction.rst
│       ├── index.rst
│       ├── install/
│       │   ├── installation.rst
│       │   └── quick-start.html
│       ├── modules/
│       │   ├── contrib.rst
│       │   ├── data.rst
│       │   ├── datasets.rst
│       │   ├── distributed.rst
│       │   ├── explain.rst
│       │   ├── graphgym.rst
│       │   ├── llm.rst
│       │   ├── loader.rst
│       │   ├── metrics.rst
│       │   ├── nn.rst
│       │   ├── profile.rst
│       │   ├── root.rst
│       │   ├── sampler.rst
│       │   ├── transforms.rst
│       │   └── utils.rst
│       ├── notes/
│       │   ├── batching.rst
│       │   ├── cheatsheet.rst
│       │   ├── colabs.rst
│       │   ├── create_dataset.rst
│       │   ├── create_gnn.rst
│       │   ├── data_cheatsheet.rst
│       │   ├── explain.rst
│       │   ├── graphgym.rst
│       │   ├── heterogeneous.rst
│       │   ├── installation.rst
│       │   ├── introduction.rst
│       │   ├── jit.rst
│       │   ├── load_csv.rst
│       │   ├── remote.rst
│       │   ├── resources.rst
│       │   └── sparse_tensor.rst
│       └── tutorial/
│           ├── application.rst
│           ├── compile.rst
│           ├── create_dataset.rst
│           ├── create_gnn.rst
│           ├── dataset.rst
│           ├── dataset_splitting.rst
│           ├── distributed.rst
│           ├── distributed_pyg.rst
│           ├── explain.rst
│           ├── gnn_design.rst
│           ├── graph_transformer.rst
│           ├── heterogeneous.rst
│           ├── load_csv.rst
│           ├── multi_gpu_vanilla.rst
│           ├── multi_node_multi_gpu_vanilla.rst
│           ├── neighbor_loader.rst
│           ├── point_cloud.rst
│           └── shallow_node_embeddings.rst
├── examples/
│   ├── README.md
│   ├── agnn.py
│   ├── ar_link_pred.py
│   ├── argva_node_clustering.py
│   ├── arma.py
│   ├── attentive_fp.py
│   ├── autoencoder.py
│   ├── cluster_gcn_ppi.py
│   ├── cluster_gcn_reddit.py
│   ├── colors_topk_pool.py
│   ├── compile/
│   │   ├── gcn.py
│   │   └── gin.py
│   ├── contrib/
│   │   ├── README.md
│   │   ├── pgm_explainer_graph_classification.py
│   │   ├── pgm_explainer_node_classification.py
│   │   ├── rbcd_attack.py
│   │   └── rbcd_attack_poisoning.py
│   ├── cora.py
│   ├── correct_and_smooth.py
│   ├── cpp/
│   │   ├── CMakeLists.txt
│   │   ├── README.md
│   │   ├── main.cpp
│   │   └── save_model.py
│   ├── datapipe.py
│   ├── dgcnn_classification.py
│   ├── dgcnn_segmentation.py
│   ├── dir_gnn.py
│   ├── distributed/
│   │   ├── README.md
│   │   ├── graphlearn_for_pytorch/
│   │   │   ├── README.md
│   │   │   ├── dist_train_sage_sup_config.yml
│   │   │   ├── dist_train_sage_supervised.py
│   │   │   ├── launch.py
│   │   │   └── partition_ogbn_dataset.py
│   │   ├── kuzu/
│   │   │   ├── README.md
│   │   │   └── papers_100M/
│   │   │       ├── README.md
│   │   │       ├── prepare_data.py
│   │   │       └── train.py
│   │   └── pyg/
│   │       └── README.md
│   ├── dna.py
│   ├── egc.py
│   ├── equilibrium_median.py
│   ├── explain/
│   │   ├── README.md
│   │   ├── captum_explainer.py
│   │   ├── captum_explainer_hetero_link.py
│   │   ├── gnn_explainer.py
│   │   ├── gnn_explainer_ba_shapes.py
│   │   ├── gnn_explainer_link_pred.py
│   │   └── graphmask_explainer.py
│   ├── faust.py
│   ├── film.py
│   ├── gat.py
│   ├── gcn.py
│   ├── gcn2_cora.py
│   ├── gcn2_ppi.py
│   ├── geniepath.py
│   ├── glnn.py
│   ├── gpse.py
│   ├── graph_gps.py
│   ├── graph_sage_unsup.py
│   ├── graph_sage_unsup_ppi.py
│   ├── graph_saint.py
│   ├── graph_unet.py
│   ├── hetero/
│   │   ├── README.md
│   │   ├── bipartite_sage.py
│   │   ├── bipartite_sage_unsup.py
│   │   ├── dmgi_unsup.py
│   │   ├── han_imdb.py
│   │   ├── hetero_conv_dblp.py
│   │   ├── hetero_link_pred.py
│   │   ├── hgt_dblp.py
│   │   ├── hierarchical_sage.py
│   │   ├── load_csv.py
│   │   ├── metapath2vec.py
│   │   ├── recommender_system.py
│   │   ├── temporal_link_pred.py
│   │   └── to_hetero_mag.py
│   ├── hierarchical_sampling.py
│   ├── infomax_inductive.py
│   ├── infomax_transductive.py
│   ├── jit/
│   │   ├── README.md
│   │   ├── film.py
│   │   ├── gat.py
│   │   ├── gcn.py
│   │   └── gin.py
│   ├── kge_fb15k_237.py
│   ├── label_prop.py
│   ├── lcm_aggr_2nd_min.py
│   ├── lightgcn.py
│   ├── link_pred.py
│   ├── linkx.py
│   ├── llm/
│   │   ├── README.md
│   │   ├── g_retriever.py
│   │   ├── git_mol.py
│   │   ├── glem.py
│   │   ├── molecule_gpt.py
│   │   ├── protein_mpnn.py
│   │   └── txt2kg_rag.py
│   ├── lpformer.py
│   ├── mem_pool.py
│   ├── mixhop.py
│   ├── mnist_graclus.py
│   ├── mnist_nn_conv.py
│   ├── mnist_voxel_grid.py
│   ├── multi_gpu/
│   │   ├── README.md
│   │   ├── distributed_batching.py
│   │   ├── distributed_sampling.py
│   │   ├── distributed_sampling_multinode.py
│   │   ├── distributed_sampling_multinode.sbatch
│   │   ├── distributed_sampling_xpu.py
│   │   ├── mag240m_graphsage.py
│   │   ├── model_parallel.py
│   │   ├── papers100m_gcn.py
│   │   ├── papers100m_gcn_multinode.py
│   │   ├── pcqm4m_ogb.py
│   │   └── taobao.py
│   ├── mutag_gin.py
│   ├── node2vec.py
│   ├── ogbn_proteins_deepgcn.py
│   ├── ogbn_train.py
│   ├── ogc.py
│   ├── pmlp.py
│   ├── pna.py
│   ├── point_transformer_classification.py
│   ├── point_transformer_segmentation.py
│   ├── pointnet2_classification.py
│   ├── pointnet2_segmentation.py
│   ├── ppi.py
│   ├── proteins_diff_pool.py
│   ├── proteins_dmon_pool.py
│   ├── proteins_gmt.py
│   ├── proteins_mincut_pool.py
│   ├── proteins_topk_pool.py
│   ├── pytorch_ignite/
│   │   ├── README.md
│   │   └── gin.py
│   ├── pytorch_lightning/
│   │   ├── README.md
│   │   ├── gin.py
│   │   ├── graph_sage.py
│   │   └── relational_gnn.py
│   ├── qm9_nn_conv.py
│   ├── qm9_pretrained_dimenet.py
│   ├── qm9_pretrained_schnet.py
│   ├── quiver/
│   │   ├── README.md
│   │   ├── multi_gpu_quiver.py
│   │   └── single_gpu_quiver.py
│   ├── randlanet_classification.py
│   ├── randlanet_segmentation.py
│   ├── rdl.py
│   ├── rect.py
│   ├── reddit.py
│   ├── renet.py
│   ├── rev_gnn.py
│   ├── rgat.py
│   ├── rgcn.py
│   ├── rgcn_link_pred.py
│   ├── seal_link_pred.py
│   ├── sgc.py
│   ├── shadow.py
│   ├── sign.py
│   ├── signed_gcn.py
│   ├── super_gat.py
│   ├── tagcn.py
│   ├── tensorboard_logging.py
│   ├── tgn.py
│   ├── triangles_sag_pool.py
│   ├── unimp_arxiv.py
│   ├── upfd.py
│   └── wl_kernel.py
├── graphgym/
│   ├── agg_batch.py
│   ├── configs/
│   │   ├── example.yaml
│   │   └── pyg/
│   │       ├── example_graph.yaml
│   │       ├── example_link.yaml
│   │       └── example_node.yaml
│   ├── configs_gen.py
│   ├── custom_graphgym/
│   │   ├── __init__.py
│   │   ├── act/
│   │   │   ├── __init__.py
│   │   │   └── example.py
│   │   ├── config/
│   │   │   ├── __init__.py
│   │   │   └── example.py
│   │   ├── encoder/
│   │   │   ├── __init__.py
│   │   │   └── example.py
│   │   ├── head/
│   │   │   ├── __init__.py
│   │   │   └── example.py
│   │   ├── layer/
│   │   │   ├── __init__.py
│   │   │   └── example.py
│   │   ├── loader/
│   │   │   ├── __init__.py
│   │   │   └── example.py
│   │   ├── loss/
│   │   │   ├── __init__.py
│   │   │   └── example.py
│   │   ├── network/
│   │   │   ├── __init__.py
│   │   │   └── example.py
│   │   ├── optimizer/
│   │   │   ├── __init__.py
│   │   │   └── example.py
│   │   ├── pooling/
│   │   │   ├── __init__.py
│   │   │   └── example.py
│   │   ├── stage/
│   │   │   ├── __init__.py
│   │   │   └── example.py
│   │   ├── train/
│   │   │   ├── __init__.py
│   │   │   └── example.py
│   │   └── transform/
│   │       └── __init__.py
│   ├── grids/
│   │   ├── example.txt
│   │   └── pyg/
│   │       └── example.txt
│   ├── main.py
│   ├── parallel.sh
│   ├── run_batch.sh
│   ├── run_single.sh
│   └── sample/
│       ├── dimensions.txt
│       └── dimensionsatt.txt
├── pyproject.toml
├── readthedocs.yml
├── test/
│   ├── conftest.py
│   ├── contrib/
│   │   ├── explain/
│   │   │   └── test_pgm_explainer.py
│   │   └── nn/
│   │       └── models/
│   │           └── test_rbcd_attack.py
│   ├── data/
│   │   ├── lightning/
│   │   │   └── test_datamodule.py
│   │   ├── test_batch.py
│   │   ├── test_data.py
│   │   ├── test_database.py
│   │   ├── test_datapipes.py
│   │   ├── test_dataset.py
│   │   ├── test_dataset_summary.py
│   │   ├── test_feature_store.py
│   │   ├── test_graph_store.py
│   │   ├── test_hetero_data.py
│   │   ├── test_hypergraph_data.py
│   │   ├── test_inherit.py
│   │   ├── test_on_disk_dataset.py
│   │   ├── test_remote_backend_utils.py
│   │   ├── test_storage.py
│   │   ├── test_temporal.py
│   │   └── test_view.py
│   ├── datasets/
│   │   ├── graph_generator/
│   │   │   ├── test_ba_graph.py
│   │   │   ├── test_er_graph.py
│   │   │   ├── test_grid_graph.py
│   │   │   └── test_tree_graph.py
│   │   ├── motif_generator/
│   │   │   ├── test_custom_motif.py
│   │   │   ├── test_cycle_motif.py
│   │   │   ├── test_grid_motif.py
│   │   │   └── test_house_motif.py
│   │   ├── test_ba_shapes.py
│   │   ├── test_bzr.py
│   │   ├── test_elliptic.py
│   │   ├── test_enzymes.py
│   │   ├── test_explainer_dataset.py
│   │   ├── test_fake.py
│   │   ├── test_git_mol_dataset.py
│   │   ├── test_imdb_binary.py
│   │   ├── test_infection_dataset.py
│   │   ├── test_karate.py
│   │   ├── test_medshapenet.py
│   │   ├── test_molecule_gpt_dataset.py
│   │   ├── test_mutag.py
│   │   ├── test_planetoid.py
│   │   ├── test_protein_mpnn_dataset.py
│   │   ├── test_snap_dataset.py
│   │   ├── test_suite_sparse.py
│   │   ├── test_tag_dataset.py
│   │   ├── test_teeth3ds.py
│   │   └── test_web_qsp_dataset.py
│   ├── distributed/
│   │   ├── test_dist_link_neighbor_loader.py
│   │   ├── test_dist_link_neighbor_sampler.py
│   │   ├── test_dist_neighbor_loader.py
│   │   ├── test_dist_neighbor_sampler.py
│   │   ├── test_dist_utils.py
│   │   ├── test_local_feature_store.py
│   │   ├── test_local_graph_store.py
│   │   ├── test_partition.py
│   │   └── test_rpc.py
│   ├── explain/
│   │   ├── algorithm/
│   │   │   ├── test_attention_explainer.py
│   │   │   ├── test_captum.py
│   │   │   ├── test_captum_explainer.py
│   │   │   ├── test_captum_hetero.py
│   │   │   ├── test_explain_algorithm_utils.py
│   │   │   ├── test_gnn_explainer.py
│   │   │   ├── test_graphmask_explainer.py
│   │   │   └── test_pg_explainer.py
│   │   ├── conftest.py
│   │   ├── metric/
│   │   │   ├── test_basic_metric.py
│   │   │   ├── test_faithfulness.py
│   │   │   └── test_fidelity.py
│   │   ├── test_explain_config.py
│   │   ├── test_explainer.py
│   │   ├── test_explanation.py
│   │   ├── test_hetero_explainer.py
│   │   └── test_hetero_explanation.py
│   ├── graphgym/
│   │   ├── example_node.yml
│   │   ├── test_config.py
│   │   ├── test_graphgym.py
│   │   ├── test_logger.py
│   │   └── test_register.py
│   ├── io/
│   │   ├── example1.off
│   │   ├── example2.off
│   │   ├── test_fs.py
│   │   └── test_off.py
│   ├── llm/
│   │   ├── conftest.py
│   │   ├── models/
│   │   │   ├── test_g_retriever.py
│   │   │   ├── test_git_mol.py
│   │   │   ├── test_glem.py
│   │   │   ├── test_llm.py
│   │   │   ├── test_llm_judge.py
│   │   │   ├── test_molecule_gpt.py
│   │   │   ├── test_protein_mpnn.py
│   │   │   ├── test_sentence_transformer.py
│   │   │   ├── test_txt2kg.py
│   │   │   └── test_vision_transformer.py
│   │   ├── test_large_graph_indexer.py
│   │   ├── test_rag_loader.py
│   │   └── utils/
│   │       ├── test_rag_backend_utils.py
│   │       ├── test_rag_feature_store.py
│   │       ├── test_rag_graph_store.py
│   │       └── test_vectorrag.py
│   ├── loader/
│   │   ├── test_cache.py
│   │   ├── test_cluster.py
│   │   ├── test_dataloader.py
│   │   ├── test_dynamic_batch_sampler.py
│   │   ├── test_graph_saint.py
│   │   ├── test_hgt_loader.py
│   │   ├── test_ibmb_loader.py
│   │   ├── test_imbalanced_sampler.py
│   │   ├── test_link_neighbor_loader.py
│   │   ├── test_mixin.py
│   │   ├── test_neighbor_loader.py
│   │   ├── test_neighbor_sampler.py
│   │   ├── test_prefetch.py
│   │   ├── test_random_node_loader.py
│   │   ├── test_shadow.py
│   │   ├── test_temporal_dataloader.py
│   │   ├── test_utils.py
│   │   └── test_zip_loader.py
│   ├── metrics/
│   │   └── test_link_pred_metric.py
│   ├── my_config.yaml
│   ├── nn/
│   │   ├── aggr/
│   │   │   ├── test_aggr_utils.py
│   │   │   ├── test_attention.py
│   │   │   ├── test_basic.py
│   │   │   ├── test_deep_sets.py
│   │   │   ├── test_equilibrium.py
│   │   │   ├── test_fused.py
│   │   │   ├── test_gmt.py
│   │   │   ├── test_gru.py
│   │   │   ├── test_lcm.py
│   │   │   ├── test_lstm.py
│   │   │   ├── test_mlp_aggr.py
│   │   │   ├── test_multi.py
│   │   │   ├── test_patch_transformer.py
│   │   │   ├── test_quantile.py
│   │   │   ├── test_scaler.py
│   │   │   ├── test_set2set.py
│   │   │   ├── test_set_transformer.py
│   │   │   ├── test_sort.py
│   │   │   └── test_variance_preserving.py
│   │   ├── attention/
│   │   │   ├── test_performer_attention.py
│   │   │   ├── test_polynormer_attention.py
│   │   │   └── test_qformer.py
│   │   ├── conv/
│   │   │   ├── cugraph/
│   │   │   │   ├── test_cugraph_gat_conv.py
│   │   │   │   ├── test_cugraph_rgcn_conv.py
│   │   │   │   └── test_cugraph_sage_conv.py
│   │   │   ├── test_agnn_conv.py
│   │   │   ├── test_antisymmetric_conv.py
│   │   │   ├── test_appnp.py
│   │   │   ├── test_arma_conv.py
│   │   │   ├── test_cg_conv.py
│   │   │   ├── test_cheb_conv.py
│   │   │   ├── test_cluster_gcn_conv.py
│   │   │   ├── test_create_gnn.py
│   │   │   ├── test_dir_gnn_conv.py
│   │   │   ├── test_dna_conv.py
│   │   │   ├── test_edge_conv.py
│   │   │   ├── test_eg_conv.py
│   │   │   ├── test_fa_conv.py
│   │   │   ├── test_feast_conv.py
│   │   │   ├── test_film_conv.py
│   │   │   ├── test_fused_gat_conv.py
│   │   │   ├── test_gat_conv.py
│   │   │   ├── test_gated_graph_conv.py
│   │   │   ├── test_gatv2_conv.py
│   │   │   ├── test_gcn2_conv.py
│   │   │   ├── test_gcn_conv.py
│   │   │   ├── test_gen_conv.py
│   │   │   ├── test_general_conv.py
│   │   │   ├── test_gin_conv.py
│   │   │   ├── test_gmm_conv.py
│   │   │   ├── test_gps_conv.py
│   │   │   ├── test_graph_conv.py
│   │   │   ├── test_gravnet_conv.py
│   │   │   ├── test_han_conv.py
│   │   │   ├── test_heat_conv.py
│   │   │   ├── test_hetero_conv.py
│   │   │   ├── test_hgt_conv.py
│   │   │   ├── test_hypergraph_conv.py
│   │   │   ├── test_le_conv.py
│   │   │   ├── test_lg_conv.py
│   │   │   ├── test_meshcnn_conv.py
│   │   │   ├── test_message_passing.py
│   │   │   ├── test_mf_conv.py
│   │   │   ├── test_mixhop_conv.py
│   │   │   ├── test_nn_conv.py
│   │   │   ├── test_pan_conv.py
│   │   │   ├── test_pdn_conv.py
│   │   │   ├── test_pna_conv.py
│   │   │   ├── test_point_conv.py
│   │   │   ├── test_point_gnn_conv.py
│   │   │   ├── test_point_transformer_conv.py
│   │   │   ├── test_ppf_conv.py
│   │   │   ├── test_res_gated_graph_conv.py
│   │   │   ├── test_rgat_conv.py
│   │   │   ├── test_rgcn_conv.py
│   │   │   ├── test_sage_conv.py
│   │   │   ├── test_sg_conv.py
│   │   │   ├── test_signed_conv.py
│   │   │   ├── test_simple_conv.py
│   │   │   ├── test_spline_conv.py
│   │   │   ├── test_ssg_conv.py
│   │   │   ├── test_static_graph.py
│   │   │   ├── test_supergat_conv.py
│   │   │   ├── test_tag_conv.py
│   │   │   ├── test_transformer_conv.py
│   │   │   ├── test_wl_conv.py
│   │   │   ├── test_wl_conv_continuous.py
│   │   │   ├── test_x_conv.py
│   │   │   └── utils/
│   │   │       └── test_gnn_cheatsheet.py
│   │   ├── dense/
│   │   │   ├── test_dense_gat_conv.py
│   │   │   ├── test_dense_gcn_conv.py
│   │   │   ├── test_dense_gin_conv.py
│   │   │   ├── test_dense_graph_conv.py
│   │   │   ├── test_dense_sage_conv.py
│   │   │   ├── test_diff_pool.py
│   │   │   ├── test_dmon_pool.py
│   │   │   ├── test_linear.py
│   │   │   └── test_mincut_pool.py
│   │   ├── functional/
│   │   │   ├── test_bro.py
│   │   │   └── test_gini.py
│   │   ├── kge/
│   │   │   ├── test_complex.py
│   │   │   ├── test_distmult.py
│   │   │   ├── test_rotate.py
│   │   │   └── test_transe.py
│   │   ├── models/
│   │   │   ├── test_attentive_fp.py
│   │   │   ├── test_attract_repel.py
│   │   │   ├── test_autoencoder.py
│   │   │   ├── test_basic_gnn.py
│   │   │   ├── test_correct_and_smooth.py
│   │   │   ├── test_deep_graph_infomax.py
│   │   │   ├── test_deepgcn.py
│   │   │   ├── test_dimenet.py
│   │   │   ├── test_gnnff.py
│   │   │   ├── test_gpse.py
│   │   │   ├── test_graph_mixer.py
│   │   │   ├── test_graph_unet.py
│   │   │   ├── test_jumping_knowledge.py
│   │   │   ├── test_label_prop.py
│   │   │   ├── test_lightgcn.py
│   │   │   ├── test_linkx.py
│   │   │   ├── test_lpformer.py
│   │   │   ├── test_mask_label.py
│   │   │   ├── test_meta.py
│   │   │   ├── test_metapath2vec.py
│   │   │   ├── test_mlp.py
│   │   │   ├── test_neural_fingerprint.py
│   │   │   ├── test_node2vec.py
│   │   │   ├── test_pmlp.py
│   │   │   ├── test_polynormer.py
│   │   │   ├── test_re_net.py
│   │   │   ├── test_rect.py
│   │   │   ├── test_rev_gnn.py
│   │   │   ├── test_schnet.py
│   │   │   ├── test_sgformer.py
│   │   │   ├── test_signed_gcn.py
│   │   │   ├── test_tgn.py
│   │   │   └── test_visnet.py
│   │   ├── norm/
│   │   │   ├── test_batch_norm.py
│   │   │   ├── test_diff_group_norm.py
│   │   │   ├── test_graph_norm.py
│   │   │   ├── test_graph_size_norm.py
│   │   │   ├── test_instance_norm.py
│   │   │   ├── test_layer_norm.py
│   │   │   ├── test_mean_subtraction_norm.py
│   │   │   ├── test_msg_norm.py
│   │   │   └── test_pair_norm.py
│   │   ├── pool/
│   │   │   ├── connect/
│   │   │   │   └── test_filter_edges.py
│   │   │   ├── select/
│   │   │   │   └── test_select_topk.py
│   │   │   ├── test_approx_knn.py
│   │   │   ├── test_asap.py
│   │   │   ├── test_avg_pool.py
│   │   │   ├── test_cluster_pool.py
│   │   │   ├── test_consecutive.py
│   │   │   ├── test_decimation.py
│   │   │   ├── test_edge_pool.py
│   │   │   ├── test_glob.py
│   │   │   ├── test_graclus.py
│   │   │   ├── test_knn.py
│   │   │   ├── test_max_pool.py
│   │   │   ├── test_mem_pool.py
│   │   │   ├── test_pan_pool.py
│   │   │   ├── test_pool.py
│   │   │   ├── test_sag_pool.py
│   │   │   ├── test_topk_pool.py
│   │   │   └── test_voxel_grid.py
│   │   ├── test_compile_basic.py
│   │   ├── test_compile_conv.py
│   │   ├── test_compile_dynamic.py
│   │   ├── test_data_parallel.py
│   │   ├── test_encoding.py
│   │   ├── test_fvcore.py
│   │   ├── test_fx.py
│   │   ├── test_inits.py
│   │   ├── test_model_hub.py
│   │   ├── test_model_summary.py
│   │   ├── test_module_dict.py
│   │   ├── test_parameter_dict.py
│   │   ├── test_reshape.py
│   │   ├── test_resolver.py
│   │   ├── test_sequential.py
│   │   ├── test_to_fixed_size_transformer.py
│   │   ├── test_to_hetero_module.py
│   │   ├── test_to_hetero_transformer.py
│   │   ├── test_to_hetero_with_bases_transformer.py
│   │   └── unpool/
│   │       └── test_knn_interpolate.py
│   ├── profile/
│   │   ├── test_benchmark.py
│   │   ├── test_nvtx.py
│   │   ├── test_profile.py
│   │   ├── test_profile_utils.py
│   │   └── test_profiler.py
│   ├── sampler/
│   │   ├── test_sampler_base.py
│   │   └── test_sampler_neighbor_sampler.py
│   ├── test_config_mixin.py
│   ├── test_config_store.py
│   ├── test_debug.py
│   ├── test_edge_index.py
│   ├── test_experimental.py
│   ├── test_hash_tensor.py
│   ├── test_home.py
│   ├── test_index.py
│   ├── test_inspector.py
│   ├── test_isinstance.py
│   ├── test_onnx.py
│   ├── test_seed.py
│   ├── test_typing.py
│   ├── test_warnings.py
│   ├── testing/
│   │   └── test_decorators.py
│   ├── transforms/
│   │   ├── test_add_gpse.py
│   │   ├── test_add_metapaths.py
│   │   ├── test_add_positional_encoding.py
│   │   ├── test_add_remaining_self_loops.py
│   │   ├── test_add_self_loops.py
│   │   ├── test_cartesian.py
│   │   ├── test_center.py
│   │   ├── test_compose.py
│   │   ├── test_constant.py
│   │   ├── test_delaunay.py
│   │   ├── test_distance.py
│   │   ├── test_face_to_edge.py
│   │   ├── test_feature_propagation.py
│   │   ├── test_fixed_points.py
│   │   ├── test_gcn_norm.py
│   │   ├── test_gdc.py
│   │   ├── test_generate_mesh_normals.py
│   │   ├── test_grid_sampling.py
│   │   ├── test_half_hop.py
│   │   ├── test_knn_graph.py
│   │   ├── test_laplacian_lambda_max.py
│   │   ├── test_largest_connected_components.py
│   │   ├── test_line_graph.py
│   │   ├── test_linear_transformation.py
│   │   ├── test_local_cartesian.py
│   │   ├── test_local_degree_profile.py
│   │   ├── test_mask_transform.py
│   │   ├── test_node_property_split.py
│   │   ├── test_normalize_features.py
│   │   ├── test_normalize_rotation.py
│   │   ├── test_normalize_scale.py
│   │   ├── test_one_hot_degree.py
│   │   ├── test_pad.py
│   │   ├── test_point_pair_features.py
│   │   ├── test_polar.py
│   │   ├── test_radius_graph.py
│   │   ├── test_random_flip.py
│   │   ├── test_random_jitter.py
│   │   ├── test_random_link_split.py
│   │   ├── test_random_node_split.py
│   │   ├── test_random_rotate.py
│   │   ├── test_random_scale.py
│   │   ├── test_random_shear.py
│   │   ├── test_remove_duplicated_edges.py
│   │   ├── test_remove_isolated_nodes.py
│   │   ├── test_remove_self_loops.py
│   │   ├── test_remove_training_classes.py
│   │   ├── test_rooted_subgraph.py
│   │   ├── test_sample_points.py
│   │   ├── test_sign.py
│   │   ├── test_spherical.py
│   │   ├── test_svd_feature_reduction.py
│   │   ├── test_target_indegree.py
│   │   ├── test_to_dense.py
│   │   ├── test_to_device.py
│   │   ├── test_to_sparse_tensor.py
│   │   ├── test_to_superpixels.py
│   │   ├── test_to_undirected.py
│   │   ├── test_two_hop.py
│   │   └── test_virtual_node.py
│   ├── utils/
│   │   ├── conftest.py
│   │   ├── test_assortativity.py
│   │   ├── test_augmentation.py
│   │   ├── test_coalesce.py
│   │   ├── test_convert.py
│   │   ├── test_cross_entropy.py
│   │   ├── test_degree.py
│   │   ├── test_dropout.py
│   │   ├── test_embedding.py
│   │   ├── test_functions.py
│   │   ├── test_geodesic.py
│   │   ├── test_grid.py
│   │   ├── test_hetero.py
│   │   ├── test_homophily.py
│   │   ├── test_index_sort.py
│   │   ├── test_isolated.py
│   │   ├── test_laplacian.py
│   │   ├── test_lexsort.py
│   │   ├── test_loop.py
│   │   ├── test_map.py
│   │   ├── test_mask.py
│   │   ├── test_mesh_laplacian.py
│   │   ├── test_negative_sampling.py
│   │   ├── test_nested.py
│   │   ├── test_noise_scheduler.py
│   │   ├── test_normalize_edge_index.py
│   │   ├── test_normalized_cut.py
│   │   ├── test_num_nodes.py
│   │   ├── test_one_hot.py
│   │   ├── test_ppr.py
│   │   ├── test_random.py
│   │   ├── test_repeat.py
│   │   ├── test_scatter.py
│   │   ├── test_segment.py
│   │   ├── test_select.py
│   │   ├── test_smiles.py
│   │   ├── test_softmax.py
│   │   ├── test_sort_edge_index.py
│   │   ├── test_sparse.py
│   │   ├── test_spmm.py
│   │   ├── test_subgraph.py
│   │   ├── test_to_dense_adj.py
│   │   ├── test_to_dense_batch.py
│   │   ├── test_total_influence.py
│   │   ├── test_train_test_split_edges.py
│   │   ├── test_tree_decomposition.py
│   │   ├── test_trim_to_layer.py
│   │   ├── test_unbatch.py
│   │   └── test_undirected.py
│   └── visualization/
│       ├── test_graph_visualization.py
│       └── test_influence.py
└── torch_geometric/
    ├── __init__.py
    ├── _compile.py
    ├── _onnx.py
    ├── backend.py
    ├── config_mixin.py
    ├── config_store.py
    ├── contrib/
    │   ├── __init__.py
    │   ├── datasets/
    │   │   └── __init__.py
    │   ├── explain/
    │   │   ├── __init__.py
    │   │   └── pgm_explainer.py
    │   ├── nn/
    │   │   ├── __init__.py
    │   │   ├── conv/
    │   │   │   └── __init__.py
    │   │   └── models/
    │   │       ├── __init__.py
    │   │       └── rbcd_attack.py
    │   └── transforms/
    │       └── __init__.py
    ├── data/
    │   ├── __init__.py
    │   ├── batch.py
    │   ├── collate.py
    │   ├── data.py
    │   ├── database.py
    │   ├── datapipes.py
    │   ├── dataset.py
    │   ├── download.py
    │   ├── extract.py
    │   ├── feature_store.py
    │   ├── graph_store.py
    │   ├── hetero_data.py
    │   ├── hypergraph_data.py
    │   ├── in_memory_dataset.py
    │   ├── lightning/
    │   │   ├── __init__.py
    │   │   └── datamodule.py
    │   ├── makedirs.py
    │   ├── on_disk_dataset.py
    │   ├── remote_backend_utils.py
    │   ├── separate.py
    │   ├── storage.py
    │   ├── summary.py
    │   ├── temporal.py
    │   └── view.py
    ├── datasets/
    │   ├── __init__.py
    │   ├── actor.py
    │   ├── airfrans.py
    │   ├── airports.py
    │   ├── amazon.py
    │   ├── amazon_book.py
    │   ├── amazon_products.py
    │   ├── aminer.py
    │   ├── aqsol.py
    │   ├── attributed_graph_dataset.py
    │   ├── ba2motif_dataset.py
    │   ├── ba_multi_shapes.py
    │   ├── ba_shapes.py
    │   ├── bitcoin_otc.py
    │   ├── brca_tgca.py
    │   ├── citation_full.py
    │   ├── city.py
    │   ├── coauthor.py
    │   ├── coma.py
    │   ├── cornell.py
    │   ├── dblp.py
    │   ├── dbp15k.py
    │   ├── deezer_europe.py
    │   ├── dgraph.py
    │   ├── dynamic_faust.py
    │   ├── elliptic.py
    │   ├── elliptic_temporal.py
    │   ├── email_eu_core.py
    │   ├── entities.py
    │   ├── explainer_dataset.py
    │   ├── facebook.py
    │   ├── fake.py
    │   ├── faust.py
    │   ├── flickr.py
    │   ├── freebase.py
    │   ├── gdelt.py
    │   ├── gdelt_lite.py
    │   ├── ged_dataset.py
    │   ├── gemsec.py
    │   ├── geometry.py
    │   ├── git_mol_dataset.py
    │   ├── github.py
    │   ├── gnn_benchmark_dataset.py
    │   ├── graph_generator/
    │   │   ├── __init__.py
    │   │   ├── ba_graph.py
    │   │   ├── base.py
    │   │   ├── er_graph.py
    │   │   ├── grid_graph.py
    │   │   └── tree_graph.py
    │   ├── heterophilous_graph_dataset.py
    │   ├── hgb_dataset.py
    │   ├── hm.py
    │   ├── hydro_net.py
    │   ├── icews.py
    │   ├── igmc_dataset.py
    │   ├── imdb.py
    │   ├── infection_dataset.py
    │   ├── instruct_mol_dataset.py
    │   ├── jodie.py
    │   ├── karate.py
    │   ├── last_fm.py
    │   ├── lastfm_asia.py
    │   ├── linkx_dataset.py
    │   ├── lrgb.py
    │   ├── malnet_tiny.py
    │   ├── md17.py
    │   ├── medshapenet.py
    │   ├── mixhop_synthetic_dataset.py
    │   ├── mnist_superpixels.py
    │   ├── modelnet.py
    │   ├── molecule_gpt_dataset.py
    │   ├── molecule_net.py
    │   ├── motif_generator/
    │   │   ├── __init__.py
    │   │   ├── base.py
    │   │   ├── custom.py
    │   │   ├── cycle.py
    │   │   ├── grid.py
    │   │   └── house.py
    │   ├── movie_lens.py
    │   ├── movie_lens_100k.py
    │   ├── movie_lens_1m.py
    │   ├── myket.py
    │   ├── nell.py
    │   ├── neurograph.py
    │   ├── ogb_mag.py
    │   ├── omdb.py
    │   ├── opf.py
    │   ├── ose_gvcs.py
    │   ├── particle.py
    │   ├── pascal.py
    │   ├── pascal_pf.py
    │   ├── pcpnet_dataset.py
    │   ├── pcqm4m.py
    │   ├── planetoid.py
    │   ├── polblogs.py
    │   ├── ppi.py
    │   ├── protein_mpnn_dataset.py
    │   ├── qm7.py
    │   ├── qm9.py
    │   ├── rcdd.py
    │   ├── reddit.py
    │   ├── reddit2.py
    │   ├── rel_link_pred_dataset.py
    │   ├── s3dis.py
    │   ├── sbm_dataset.py
    │   ├── shapenet.py
    │   ├── shrec2016.py
    │   ├── snap_dataset.py
    │   ├── suite_sparse.py
    │   ├── tag_dataset.py
    │   ├── taobao.py
    │   ├── teeth3ds.py
    │   ├── tosca.py
    │   ├── tu_dataset.py
    │   ├── twitch.py
    │   ├── upfd.py
    │   ├── utils/
    │   │   ├── __init__.py
    │   │   └── cheatsheet.py
    │   ├── web_qsp_dataset.py
    │   ├── webkb.py
    │   ├── wikics.py
    │   ├── wikidata.py
    │   ├── wikipedia_network.py
    │   ├── willow_object_class.py
    │   ├── word_net.py
    │   ├── yelp.py
    │   └── zinc.py
    ├── debug.py
    ├── deprecation.py
    ├── device.py
    ├── distributed/
    │   ├── __init__.py
    │   ├── dist_context.py
    │   ├── dist_link_neighbor_loader.py
    │   ├── dist_loader.py
    │   ├── dist_neighbor_loader.py
    │   ├── dist_neighbor_sampler.py
    │   ├── event_loop.py
    │   ├── local_feature_store.py
    │   ├── local_graph_store.py
    │   ├── partition.py
    │   ├── rpc.py
    │   └── utils.py
    ├── edge_index.py
    ├── experimental.py
    ├── explain/
    │   ├── __init__.py
    │   ├── algorithm/
    │   │   ├── __init__.py
    │   │   ├── attention_explainer.py
    │   │   ├── base.py
    │   │   ├── captum.py
    │   │   ├── captum_explainer.py
    │   │   ├── dummy_explainer.py
    │   │   ├── gnn_explainer.py
    │   │   ├── graphmask_explainer.py
    │   │   ├── pg_explainer.py
    │   │   └── utils.py
    │   ├── config.py
    │   ├── explainer.py
    │   ├── explanation.py
    │   └── metric/
    │       ├── __init__.py
    │       ├── basic.py
    │       ├── faithfulness.py
    │       └── fidelity.py
    ├── graphgym/
    │   ├── __init__.py
    │   ├── benchmark.py
    │   ├── checkpoint.py
    │   ├── cmd_args.py
    │   ├── config.py
    │   ├── contrib/
    │   │   ├── __init__.py
    │   │   ├── act/
    │   │   │   └── __init__.py
    │   │   ├── config/
    │   │   │   └── __init__.py
    │   │   ├── encoder/
    │   │   │   └── __init__.py
    │   │   ├── head/
    │   │   │   └── __init__.py
    │   │   ├── layer/
    │   │   │   ├── __init__.py
    │   │   │   └── generalconv.py
    │   │   ├── loader/
    │   │   │   └── __init__.py
    │   │   ├── loss/
    │   │   │   └── __init__.py
    │   │   ├── network/
    │   │   │   └── __init__.py
    │   │   ├── optimizer/
    │   │   │   └── __init__.py
    │   │   ├── pooling/
    │   │   │   └── __init__.py
    │   │   ├── stage/
    │   │   │   └── __init__.py
    │   │   ├── train/
    │   │   │   └── __init__.py
    │   │   └── transform/
    │   │       └── __init__.py
    │   ├── imports.py
    │   ├── init.py
    │   ├── loader.py
    │   ├── logger.py
    │   ├── loss.py
    │   ├── model_builder.py
    │   ├── models/
    │   │   ├── __init__.py
    │   │   ├── act.py
    │   │   ├── encoder.py
    │   │   ├── gnn.py
    │   │   ├── head.py
    │   │   ├── layer.py
    │   │   ├── pooling.py
    │   │   └── transform.py
    │   ├── optim.py
    │   ├── register.py
    │   ├── train.py
    │   └── utils/
    │       ├── LICENSE
    │       ├── __init__.py
    │       ├── agg_runs.py
    │       ├── comp_budget.py
    │       ├── device.py
    │       ├── epoch.py
    │       ├── io.py
    │       ├── plot.py
    │       └── tools.py
    ├── hash_tensor.py
    ├── home.py
    ├── index.py
    ├── inspector.py
    ├── io/
    │   ├── __init__.py
    │   ├── fs.py
    │   ├── npz.py
    │   ├── obj.py
    │   ├── off.py
    │   ├── planetoid.py
    │   ├── ply.py
    │   ├── sdf.py
    │   ├── tu.py
    │   └── txt_array.py
    ├── isinstance.py
    ├── lazy_loader.py
    ├── llm/
    │   ├── __init__.py
    │   ├── large_graph_indexer.py
    │   ├── models/
    │   │   ├── __init__.py
    │   │   ├── g_retriever.py
    │   │   ├── git_mol.py
    │   │   ├── glem.py
    │   │   ├── llm.py
    │   │   ├── llm_judge.py
    │   │   ├── molecule_gpt.py
    │   │   ├── protein_mpnn.py
    │   │   ├── sentence_transformer.py
    │   │   ├── txt2kg.py
    │   │   └── vision_transformer.py
    │   ├── rag_loader.py
    │   └── utils/
    │       ├── __init__.py
    │       ├── backend_utils.py
    │       ├── feature_store.py
    │       ├── graph_store.py
    │       └── vectorrag.py
    ├── loader/
    │   ├── __init__.py
    │   ├── base.py
    │   ├── cache.py
    │   ├── cluster.py
    │   ├── data_list_loader.py
    │   ├── dataloader.py
    │   ├── dense_data_loader.py
    │   ├── dynamic_batch_sampler.py
    │   ├── graph_saint.py
    │   ├── hgt_loader.py
    │   ├── ibmb_loader.py
    │   ├── imbalanced_sampler.py
    │   ├── link_loader.py
    │   ├── link_neighbor_loader.py
    │   ├── mixin.py
    │   ├── neighbor_loader.py
    │   ├── neighbor_sampler.py
    │   ├── node_loader.py
    │   ├── prefetch.py
    │   ├── random_node_loader.py
    │   ├── shadow.py
    │   ├── temporal_dataloader.py
    │   ├── utils.py
    │   └── zip_loader.py
    ├── logging.py
    ├── metrics/
    │   ├── __init__.py
    │   └── link_pred.py
    ├── nn/
    │   ├── __init__.py
    │   ├── aggr/
    │   │   ├── __init__.py
    │   │   ├── attention.py
    │   │   ├── base.py
    │   │   ├── basic.py
    │   │   ├── deep_sets.py
    │   │   ├── equilibrium.py
    │   │   ├── fused.py
    │   │   ├── gmt.py
    │   │   ├── gru.py
    │   │   ├── lcm.py
    │   │   ├── lstm.py
    │   │   ├── mlp.py
    │   │   ├── multi.py
    │   │   ├── patch_transformer.py
    │   │   ├── quantile.py
    │   │   ├── scaler.py
    │   │   ├── set2set.py
    │   │   ├── set_transformer.py
    │   │   ├── sort.py
    │   │   ├── utils.py
    │   │   └── variance_preserving.py
    │   ├── attention/
    │   │   ├── __init__.py
    │   │   ├── performer.py
    │   │   ├── polynormer.py
    │   │   ├── qformer.py
    │   │   └── sgformer.py
    │   ├── conv/
    │   │   ├── __init__.py
    │   │   ├── agnn_conv.py
    │   │   ├── antisymmetric_conv.py
    │   │   ├── appnp.py
    │   │   ├── arma_conv.py
    │   │   ├── cg_conv.py
    │   │   ├── cheb_conv.py
    │   │   ├── cluster_gcn_conv.py
    │   │   ├── collect.jinja
    │   │   ├── cugraph/
    │   │   │   ├── __init__.py
    │   │   │   ├── base.py
    │   │   │   ├── gat_conv.py
    │   │   │   ├── rgcn_conv.py
    │   │   │   └── sage_conv.py
    │   │   ├── dir_gnn_conv.py
    │   │   ├── dna_conv.py
    │   │   ├── edge_conv.py
    │   │   ├── edge_updater.jinja
    │   │   ├── eg_conv.py
    │   │   ├── fa_conv.py
    │   │   ├── feast_conv.py
    │   │   ├── film_conv.py
    │   │   ├── fused_gat_conv.py
    │   │   ├── gat_conv.py
    │   │   ├── gated_graph_conv.py
    │   │   ├── gatv2_conv.py
    │   │   ├── gcn2_conv.py
    │   │   ├── gcn_conv.py
    │   │   ├── gen_conv.py
    │   │   ├── general_conv.py
    │   │   ├── gin_conv.py
    │   │   ├── gmm_conv.py
    │   │   ├── gps_conv.py
    │   │   ├── graph_conv.py
    │   │   ├── gravnet_conv.py
    │   │   ├── han_conv.py
    │   │   ├── heat_conv.py
    │   │   ├── hetero_conv.py
    │   │   ├── hgt_conv.py
    │   │   ├── hypergraph_conv.py
    │   │   ├── le_conv.py
    │   │   ├── lg_conv.py
    │   │   ├── meshcnn_conv.py
    │   │   ├── message_passing.py
    │   │   ├── mf_conv.py
    │   │   ├── mixhop_conv.py
    │   │   ├── nn_conv.py
    │   │   ├── pan_conv.py
    │   │   ├── pdn_conv.py
    │   │   ├── pna_conv.py
    │   │   ├── point_conv.py
    │   │   ├── point_gnn_conv.py
    │   │   ├── point_transformer_conv.py
    │   │   ├── ppf_conv.py
    │   │   ├── propagate.jinja
    │   │   ├── res_gated_graph_conv.py
    │   │   ├── rgat_conv.py
    │   │   ├── rgcn_conv.py
    │   │   ├── sage_conv.py
    │   │   ├── sg_conv.py
    │   │   ├── signed_conv.py
    │   │   ├── simple_conv.py
    │   │   ├── spline_conv.py
    │   │   ├── ssg_conv.py
    │   │   ├── supergat_conv.py
    │   │   ├── tag_conv.py
    │   │   ├── transformer_conv.py
    │   │   ├── utils/
    │   │   │   ├── __init__.py
    │   │   │   └── cheatsheet.py
    │   │   ├── wl_conv.py
    │   │   ├── wl_conv_continuous.py
    │   │   └── x_conv.py
    │   ├── data_parallel.py
    │   ├── dense/
    │   │   ├── __init__.py
    │   │   ├── dense_gat_conv.py
    │   │   ├── dense_gcn_conv.py
    │   │   ├── dense_gin_conv.py
    │   │   ├── dense_graph_conv.py
    │   │   ├── dense_sage_conv.py
    │   │   ├── diff_pool.py
    │   │   ├── dmon_pool.py
    │   │   ├── linear.py
    │   │   └── mincut_pool.py
    │   ├── encoding.py
    │   ├── functional/
    │   │   ├── __init__.py
    │   │   ├── bro.py
    │   │   └── gini.py
    │   ├── fx.py
    │   ├── glob.py
    │   ├── inits.py
    │   ├── kge/
    │   │   ├── __init__.py
    │   │   ├── base.py
    │   │   ├── complex.py
    │   │   ├── distmult.py
    │   │   ├── loader.py
    │   │   ├── rotate.py
    │   │   └── transe.py
    │   ├── lr_scheduler.py
    │   ├── model_hub.py
    │   ├── models/
    │   │   ├── __init__.py
    │   │   ├── attentive_fp.py
    │   │   ├── attract_repel.py
    │   │   ├── autoencoder.py
    │   │   ├── basic_gnn.py
    │   │   ├── captum.py
    │   │   ├── correct_and_smooth.py
    │   │   ├── deep_graph_infomax.py
    │   │   ├── deepgcn.py
    │   │   ├── dimenet.py
    │   │   ├── dimenet_utils.py
    │   │   ├── gnnff.py
    │   │   ├── gpse.py
    │   │   ├── graph_mixer.py
    │   │   ├── graph_unet.py
    │   │   ├── jumping_knowledge.py
    │   │   ├── label_prop.py
    │   │   ├── lightgcn.py
    │   │   ├── linkx.py
    │   │   ├── lpformer.py
    │   │   ├── mask_label.py
    │   │   ├── meta.py
    │   │   ├── metapath2vec.py
    │   │   ├── mlp.py
    │   │   ├── neural_fingerprint.py
    │   │   ├── node2vec.py
    │   │   ├── pmlp.py
    │   │   ├── polynormer.py
    │   │   ├── re_net.py
    │   │   ├── rect.py
    │   │   ├── rev_gnn.py
    │   │   ├── schnet.py
    │   │   ├── sgformer.py
    │   │   ├── signed_gcn.py
    │   │   ├── tgn.py
    │   │   └── visnet.py
    │   ├── module_dict.py
    │   ├── norm/
    │   │   ├── __init__.py
    │   │   ├── batch_norm.py
    │   │   ├── diff_group_norm.py
    │   │   ├── graph_norm.py
    │   │   ├── graph_size_norm.py
    │   │   ├── instance_norm.py
    │   │   ├── layer_norm.py
    │   │   ├── mean_subtraction_norm.py
    │   │   ├── msg_norm.py
    │   │   └── pair_norm.py
    │   ├── parameter_dict.py
    │   ├── pool/
    │   │   ├── __init__.py
    │   │   ├── approx_knn.py
    │   │   ├── asap.py
    │   │   ├── avg_pool.py
    │   │   ├── cluster_pool.py
    │   │   ├── connect/
    │   │   │   ├── __init__.py
    │   │   │   ├── base.py
    │   │   │   └── filter_edges.py
    │   │   ├── consecutive.py
    │   │   ├── decimation.py
    │   │   ├── edge_pool.py
    │   │   ├── glob.py
    │   │   ├── graclus.py
    │   │   ├── knn.py
    │   │   ├── max_pool.py
    │   │   ├── mem_pool.py
    │   │   ├── pan_pool.py
    │   │   ├── pool.py
    │   │   ├── sag_pool.py
    │   │   ├── select/
    │   │   │   ├── __init__.py
    │   │   │   ├── base.py
    │   │   │   └── topk.py
    │   │   ├── topk_pool.py
    │   │   └── voxel_grid.py
    │   ├── reshape.py
    │   ├── resolver.py
    │   ├── sequential.jinja
    │   ├── sequential.py
    │   ├── summary.py
    │   ├── to_fixed_size_transformer.py
    │   ├── to_hetero_module.py
    │   ├── to_hetero_transformer.py
    │   ├── to_hetero_with_bases_transformer.py
    │   └── unpool/
    │       ├── __init__.py
    │       └── knn_interpolate.py
    ├── profile/
    │   ├── __init__.py
    │   ├── benchmark.py
    │   ├── nvtx.py
    │   ├── profile.py
    │   ├── profiler.py
    │   └── utils.py
    ├── resolver.py
    ├── sampler/
    │   ├── __init__.py
    │   ├── base.py
    │   ├── hgt_sampler.py
    │   ├── neighbor_sampler.py
    │   └── utils.py
    ├── seed.py
    ├── template.py
    ├── testing/
    │   ├── __init__.py
    │   ├── asserts.py
    │   ├── data.py
    │   ├── decorators.py
    │   ├── distributed.py
    │   ├── feature_store.py
    │   └── graph_store.py
    ├── transforms/
    │   ├── __init__.py
    │   ├── add_gpse.py
    │   ├── add_metapaths.py
    │   ├── add_positional_encoding.py
    │   ├── add_remaining_self_loops.py
    │   ├── add_self_loops.py
    │   ├── base_transform.py
    │   ├── cartesian.py
    │   ├── center.py
    │   ├── compose.py
    │   ├── constant.py
    │   ├── delaunay.py
    │   ├── distance.py
    │   ├── face_to_edge.py
    │   ├── feature_propagation.py
    │   ├── fixed_points.py
    │   ├── gcn_norm.py
    │   ├── gdc.py
    │   ├── generate_mesh_normals.py
    │   ├── grid_sampling.py
    │   ├── half_hop.py
    │   ├── knn_graph.py
    │   ├── laplacian_lambda_max.py
    │   ├── largest_connected_components.py
    │   ├── line_graph.py
    │   ├── linear_transformation.py
    │   ├── local_cartesian.py
    │   ├── local_degree_profile.py
    │   ├── mask.py
    │   ├── node_property_split.py
    │   ├── normalize_features.py
    │   ├── normalize_rotation.py
    │   ├── normalize_scale.py
    │   ├── one_hot_degree.py
    │   ├── pad.py
    │   ├── point_pair_features.py
    │   ├── polar.py
    │   ├── radius_graph.py
    │   ├── random_flip.py
    │   ├── random_jitter.py
    │   ├── random_link_split.py
    │   ├── random_node_split.py
    │   ├── random_rotate.py
    │   ├── random_scale.py
    │   ├── random_shear.py
    │   ├── remove_duplicated_edges.py
    │   ├── remove_isolated_nodes.py
    │   ├── remove_self_loops.py
    │   ├── remove_training_classes.py
    │   ├── rooted_subgraph.py
    │   ├── sample_points.py
    │   ├── sign.py
    │   ├── spherical.py
    │   ├── svd_feature_reduction.py
    │   ├── target_indegree.py
    │   ├── to_dense.py
    │   ├── to_device.py
    │   ├── to_sparse_tensor.py
    │   ├── to_superpixels.py
    │   ├── to_undirected.py
    │   ├── two_hop.py
    │   └── virtual_node.py
    ├── typing.py
    ├── utils/
    │   ├── __init__.py
    │   ├── _assortativity.py
    │   ├── _coalesce.py
    │   ├── _degree.py
    │   ├── _grid.py
    │   ├── _homophily.py
    │   ├── _index_sort.py
    │   ├── _lexsort.py
    │   ├── _negative_sampling.py
    │   ├── _normalize_edge_index.py
    │   ├── _normalized_cut.py
    │   ├── _one_hot.py
    │   ├── _scatter.py
    │   ├── _segment.py
    │   ├── _select.py
    │   ├── _softmax.py
    │   ├── _sort_edge_index.py
    │   ├── _spmm.py
    │   ├── _subgraph.py
    │   ├── _to_dense_adj.py
    │   ├── _to_dense_batch.py
    │   ├── _train_test_split_edges.py
    │   ├── _tree_decomposition.py
    │   ├── _trim_to_layer.py
    │   ├── _unbatch.py
    │   ├── augmentation.py
    │   ├── convert.py
    │   ├── cross_entropy.py
    │   ├── dropout.py
    │   ├── embedding.py
    │   ├── functions.py
    │   ├── geodesic.py
    │   ├── hetero.py
    │   ├── influence.py
    │   ├── isolated.py
    │   ├── laplacian.py
    │   ├── loop.py
    │   ├── map.py
    │   ├── mask.py
    │   ├── mesh_laplacian.py
    │   ├── mixin.py
    │   ├── nested.py
    │   ├── noise_scheduler.py
    │   ├── num_nodes.py
    │   ├── ppr.py
    │   ├── random.py
    │   ├── repeat.py
    │   ├── smiles.py
    │   ├── sparse.py
    │   └── undirected.py
    ├── visualization/
    │   ├── __init__.py
    │   ├── graph.py
    │   └── influence.py
    └── warnings.py

SYMBOL INDEX (8209 symbols across 1230 files)

FILE: benchmark/citation/appnp.py
  class Net (line 31) | class Net(torch.nn.Module):
    method __init__ (line 32) | def __init__(self, dataset):
    method reset_parameters (line 38) | def reset_parameters(self):
    method forward (line 42) | def forward(self, data):

FILE: benchmark/citation/arma.py
  class Net (line 32) | class Net(torch.nn.Module):
    method __init__ (line 33) | def __init__(self, dataset):
    method reset_parameters (line 42) | def reset_parameters(self):
    method forward (line 46) | def forward(self, data):

FILE: benchmark/citation/cheb.py
  class Net (line 29) | class Net(torch.nn.Module):
    method __init__ (line 30) | def __init__(self, dataset):
    method reset_parameters (line 35) | def reset_parameters(self):
    method forward (line 39) | def forward(self, data):

FILE: benchmark/citation/datasets.py
  function get_planetoid_dataset (line 7) | def get_planetoid_dataset(name, normalize_features=False, transform=None):

FILE: benchmark/citation/gat.py
  class Net (line 30) | class Net(torch.nn.Module):
    method __init__ (line 31) | def __init__(self, dataset):
    method reset_parameters (line 39) | def reset_parameters(self):
    method forward (line 43) | def forward(self, data):

FILE: benchmark/citation/gcn.py
  class Net (line 28) | class Net(torch.nn.Module):
    method __init__ (line 29) | def __init__(self, dataset):
    method reset_parameters (line 34) | def reset_parameters(self):
    method forward (line 38) | def forward(self, data):

FILE: benchmark/citation/sgc.py
  class Net (line 27) | class Net(torch.nn.Module):
    method __init__ (line 28) | def __init__(self, dataset):
    method reset_parameters (line 33) | def reset_parameters(self):
    method forward (line 36) | def forward(self, data):

FILE: benchmark/citation/statistics.py
  function print_dataset (line 4) | def print_dataset(dataset):

FILE: benchmark/citation/train_eval.py
  function random_planetoid_splits (line 19) | def random_planetoid_splits(data, num_classes):
  function run_train (line 43) | def run_train(dataset, model, runs, epochs, lr, weight_decay, early_stop...
  function run_inference (line 121) | def run_inference(dataset, model, epochs, profiling, bf16, use_compile,
  function run (line 152) | def run(dataset, model, runs, epochs, lr, weight_decay, early_stopping,
  function train (line 164) | def train(model, optimizer, data):
  function evaluate (line 174) | def evaluate(model, data):
  function inference (line 193) | def inference(model, data):

FILE: benchmark/inference/inference_benchmark.py
  function full_batch_inference (line 35) | def full_batch_inference(model, data):
  function run (line 44) | def run(args: argparse.ArgumentParser):

FILE: benchmark/kernel/asap.py
  class ASAP (line 13) | class ASAP(torch.nn.Module):
    method __init__ (line 14) | def __init__(self, dataset, num_layers, hidden, ratio=0.8, dropout=0):
    method reset_parameters (line 31) | def reset_parameters(self):
    method forward (line 40) | def forward(self, data):
    method __repr__ (line 60) | def __repr__(self):

FILE: benchmark/kernel/datasets.py
  class NormalizedDegree (line 10) | class NormalizedDegree:
    method __init__ (line 11) | def __init__(self, mean, std):
    method __call__ (line 15) | def __call__(self, data):
  function get_dataset (line 22) | def get_dataset(name, sparse=True, cleaned=False):

FILE: benchmark/kernel/diff_pool.py
  class Block (line 10) | class Block(torch.nn.Module):
    method __init__ (line 11) | def __init__(self, in_channels, hidden_channels, out_channels, mode='c...
    method reset_parameters (line 22) | def reset_parameters(self):
    method forward (line 27) | def forward(self, x, adj, mask=None):
  class DiffPool (line 33) | class DiffPool(torch.nn.Module):
    method __init__ (line 34) | def __init__(self, dataset, num_layers, hidden, ratio=0.25):
    method reset_parameters (line 52) | def reset_parameters(self):
    method forward (line 63) | def forward(self, data):
    method __repr__ (line 85) | def __repr__(self):

FILE: benchmark/kernel/edge_pool.py
  class EdgePool (line 13) | class EdgePool(torch.nn.Module):
    method __init__ (line 14) | def __init__(self, dataset, num_layers, hidden):
    method reset_parameters (line 29) | def reset_parameters(self):
    method forward (line 38) | def forward(self, data):
    method __repr__ (line 54) | def __repr__(self):

FILE: benchmark/kernel/gcn.py
  class GCN (line 8) | class GCN(torch.nn.Module):
    method __init__ (line 9) | def __init__(self, dataset, num_layers, hidden):
    method reset_parameters (line 18) | def reset_parameters(self):
    method forward (line 25) | def forward(self, data):
    method __repr__ (line 36) | def __repr__(self):
  class GCNWithJK (line 40) | class GCNWithJK(torch.nn.Module):
    method __init__ (line 41) | def __init__(self, dataset, num_layers, hidden, mode='cat'):
    method reset_parameters (line 54) | def reset_parameters(self):
    method forward (line 62) | def forward(self, data):
    method __repr__ (line 76) | def __repr__(self):

FILE: benchmark/kernel/gin.py
  class GIN0 (line 9) | class GIN0(torch.nn.Module):
    method __init__ (line 10) | def __init__(self, dataset, num_layers, hidden):
    method reset_parameters (line 36) | def reset_parameters(self):
    method forward (line 43) | def forward(self, data):
    method __repr__ (line 54) | def __repr__(self):
  class GIN0WithJK (line 58) | class GIN0WithJK(torch.nn.Module):
    method __init__ (line 59) | def __init__(self, dataset, num_layers, hidden, mode='cat'):
    method reset_parameters (line 89) | def reset_parameters(self):
    method forward (line 97) | def forward(self, data):
    method __repr__ (line 111) | def __repr__(self):
  class GIN (line 115) | class GIN(torch.nn.Module):
    method __init__ (line 116) | def __init__(self, dataset, num_layers, hidden):
    method reset_parameters (line 142) | def reset_parameters(self):
    method forward (line 149) | def forward(self, data):
    method __repr__ (line 160) | def __repr__(self):
  class GINWithJK (line 164) | class GINWithJK(torch.nn.Module):
    method __init__ (line 165) | def __init__(self, dataset, num_layers, hidden, mode='cat'):
    method reset_parameters (line 195) | def reset_parameters(self):
    method forward (line 203) | def forward(self, data):
    method __repr__ (line 217) | def __repr__(self):

FILE: benchmark/kernel/global_attention.py
  class GlobalAttentionNet (line 8) | class GlobalAttentionNet(torch.nn.Module):
    method __init__ (line 9) | def __init__(self, dataset, num_layers, hidden):
    method reset_parameters (line 19) | def reset_parameters(self):
    method forward (line 27) | def forward(self, data):
    method __repr__ (line 38) | def __repr__(self):

FILE: benchmark/kernel/graclus.py
  class Graclus (line 15) | class Graclus(torch.nn.Module):
    method __init__ (line 16) | def __init__(self, dataset, num_layers, hidden):
    method reset_parameters (line 26) | def reset_parameters(self):
    method forward (line 34) | def forward(self, data):
    method __repr__ (line 52) | def __repr__(self):

FILE: benchmark/kernel/graph_sage.py
  class GraphSAGE (line 8) | class GraphSAGE(torch.nn.Module):
    method __init__ (line 9) | def __init__(self, dataset, num_layers, hidden):
    method reset_parameters (line 18) | def reset_parameters(self):
    method forward (line 25) | def forward(self, data):
    method __repr__ (line 36) | def __repr__(self):
  class GraphSAGEWithJK (line 40) | class GraphSAGEWithJK(torch.nn.Module):
    method __init__ (line 41) | def __init__(self, dataset, num_layers, hidden, mode='cat'):
    method reset_parameters (line 54) | def reset_parameters(self):
    method forward (line 62) | def forward(self, data):
    method __repr__ (line 76) | def __repr__(self):

FILE: benchmark/kernel/main.py
  function logger (line 51) | def logger(info):

FILE: benchmark/kernel/main_performance.py
  function prepare_dataloader (line 57) | def prepare_dataloader(dataset_name):
  function run_train (line 75) | def run_train():
  function run_inference (line 117) | def run_inference():

FILE: benchmark/kernel/sag_pool.py
  class SAGPool (line 13) | class SAGPool(torch.nn.Module):
    method __init__ (line 14) | def __init__(self, dataset, num_layers, hidden, ratio=0.8):
    method reset_parameters (line 29) | def reset_parameters(self):
    method forward (line 38) | def forward(self, data):
    method __repr__ (line 55) | def __repr__(self):

FILE: benchmark/kernel/set2set.py
  class Set2SetNet (line 8) | class Set2SetNet(torch.nn.Module):
    method __init__ (line 9) | def __init__(self, dataset, num_layers, hidden):
    method reset_parameters (line 19) | def reset_parameters(self):
    method forward (line 27) | def forward(self, data):
    method __repr__ (line 38) | def __repr__(self):

FILE: benchmark/kernel/sort_pool.py
  class SortPool (line 8) | class SortPool(torch.nn.Module):
    method __init__ (line 9) | def __init__(self, dataset, num_layers, hidden):
    method reset_parameters (line 20) | def reset_parameters(self):
    method forward (line 28) | def forward(self, data):
    method __repr__ (line 42) | def __repr__(self):

FILE: benchmark/kernel/statistics.py
  function print_dataset (line 4) | def print_dataset(dataset):

FILE: benchmark/kernel/top_k.py
  class TopK (line 13) | class TopK(torch.nn.Module):
    method __init__ (line 14) | def __init__(self, dataset, num_layers, hidden, ratio=0.8):
    method reset_parameters (line 29) | def reset_parameters(self):
    method forward (line 38) | def forward(self, data):
    method __repr__ (line 55) | def __repr__(self):

FILE: benchmark/kernel/train_eval.py
  function cross_validation_with_val_set (line 20) | def cross_validation_with_val_set(dataset, model, folds, epochs, batch_s...
  function k_fold (line 98) | def k_fold(dataset, folds):
  function num_graphs (line 116) | def num_graphs(data):
  function train (line 123) | def train(model, optimizer, loader):
  function eval_acc (line 138) | def eval_acc(model, loader):
  function eval_loss (line 150) | def eval_loss(model, loader):
  function inference_run (line 163) | def inference_run(model, loader, bf16):

FILE: benchmark/loader/neighbor_loader.py
  function run (line 16) | def run(args: argparse.ArgumentParser):

FILE: benchmark/multi_gpu/training/common.py
  function train_homo (line 28) | def train_homo(model: Any, loader: NeighborLoader, optimizer: torch.opti...
  function train_hetero (line 44) | def train_hetero(model: Any, loader: NeighborLoader,
  function maybe_synchronize (line 61) | def maybe_synchronize(device: str):
  function create_mask_per_rank (line 68) | def create_mask_per_rank(
  function run (line 86) | def run(rank: int, world_size: int, args: argparse.ArgumentParser,
  function get_predefined_args (line 256) | def get_predefined_args() -> argparse.ArgumentParser:

FILE: benchmark/multi_gpu/training/training_benchmark_cuda.py
  function run_cuda (line 18) | def run_cuda(rank: int, world_size: int, args: argparse.ArgumentParser,

FILE: benchmark/multi_gpu/training/training_benchmark_xpu.py
  function get_dist_params (line 16) | def get_dist_params() -> Tuple[int, int, str]:
  function custom_optimizer (line 36) | def custom_optimizer(model: Any, optimizer: Any) -> Tuple[Any, Any]:

FILE: benchmark/points/datasets.py
  function get_dataset (line 7) | def get_dataset(num_points):

FILE: benchmark/points/edge_cnn.py
  class Net (line 28) | class Net(torch.nn.Module):
    method __init__ (line 29) | def __init__(self, num_classes):
    method forward (line 45) | def forward(self, pos, batch):

FILE: benchmark/points/mpnn.py
  class Net (line 28) | class Net(torch.nn.Module):
    method __init__ (line 29) | def __init__(self, num_classes):
    method forward (line 45) | def forward(self, pos, batch):

FILE: benchmark/points/point_cnn.py
  class Net (line 26) | class Net(torch.nn.Module):
    method __init__ (line 27) | def __init__(self, num_classes):
    method forward (line 42) | def forward(self, pos, batch):

FILE: benchmark/points/point_net.py
  class Net (line 28) | class Net(torch.nn.Module):
    method __init__ (line 29) | def __init__(self, num_classes):
    method forward (line 45) | def forward(self, pos, batch):

FILE: benchmark/points/spline_cnn.py
  class Net (line 26) | class Net(torch.nn.Module):
    method __init__ (line 27) | def __init__(self, num_classes):
    method forward (line 38) | def forward(self, pos, batch):

FILE: benchmark/points/statistics.py
  function print_dataset (line 6) | def print_dataset(train_dataset, test_dataset):

FILE: benchmark/points/train_eval.py
  function run_train (line 18) | def run_train(train_dataset, test_dataset, model, epochs, batch_size,
  function run_inference (line 58) | def run_inference(test_dataset, model, epochs, batch_size, profiling, bf16,
  function run (line 84) | def run(train_dataset, test_dataset, model, epochs, batch_size, lr,
  function train (line 96) | def train(model, optimizer, train_loader, device):
  function test (line 109) | def test(model, test_loader, device):
  function inference (line 123) | def inference(model, test_loader, device, bf16):

FILE: benchmark/runtime/dgl/gat.py
  class GATConv (line 10) | class GATConv(torch.nn.Module):
    method __init__ (line 11) | def __init__(self, g, in_channels, out_channels, heads=1,
    method reset_parameters (line 27) | def reset_parameters(self):
    method gat_msg (line 32) | def gat_msg(self, edge):
    method gat_reduce (line 38) | def gat_reduce(self, node):
    method forward (line 44) | def forward(self, x):
  class GAT (line 54) | class GAT(torch.nn.Module):
    method __init__ (line 55) | def __init__(self, g, in_channels, out_channels):
    method forward (line 61) | def forward(self, x):
  class GATSPMVConv (line 69) | class GATSPMVConv(torch.nn.Module):
    method __init__ (line 70) | def __init__(self, g, in_channels, out_channels, heads=1,
    method reset_parameters (line 85) | def reset_parameters(self):
    method forward (line 91) | def forward(self, x):
    method edge_attention (line 104) | def edge_attention(self, edge):
    method edge_softmax (line 108) | def edge_softmax(self):
  class GATSPMV (line 116) | class GATSPMV(torch.nn.Module):
    method __init__ (line 117) | def __init__(self, g, in_channels, out_channels):
    method forward (line 123) | def forward(self, x):

FILE: benchmark/runtime/dgl/gcn.py
  class GCNConv (line 9) | class GCNConv(torch.nn.Module):
    method __init__ (line 10) | def __init__(self, g, in_channels, out_channels):
    method reset_parameters (line 17) | def reset_parameters(self):
    method gcn_msg (line 21) | def gcn_msg(self, edge):
    method gcn_reduce (line 24) | def gcn_reduce(self, node):
    method forward (line 27) | def forward(self, x):
  class GCN (line 35) | class GCN(torch.nn.Module):
    method __init__ (line 36) | def __init__(self, g, in_channels, out_channels):
    method forward (line 41) | def forward(self, x):
  class GCNSPMVConv (line 48) | class GCNSPMVConv(torch.nn.Module):
    method __init__ (line 49) | def __init__(self, g, in_channels, out_channels):
    method reset_parameters (line 56) | def reset_parameters(self):
    method forward (line 60) | def forward(self, x):
  class GCNSPMV (line 70) | class GCNSPMV(torch.nn.Module):
    method __init__ (line 71) | def __init__(self, g, in_channels, out_channels):
    method forward (line 76) | def forward(self, x):

FILE: benchmark/runtime/dgl/hidden.py
  class HiddenPrint (line 8) | class HiddenPrint:
    method __enter__ (line 9) | def __enter__(self):
    method __exit__ (line 13) | def __exit__(self, exc_type, exc_val, exc_tb):

FILE: benchmark/runtime/dgl/rgcn.py
  class RGCNConv (line 9) | class RGCNConv(torch.nn.Module):
    method __init__ (line 10) | def __init__(self, g, in_channels, out_channels, num_relations, num_ba...
    method reset_parameters (line 26) | def reset_parameters(self):
    method rgcn_reduce (line 33) | def rgcn_reduce(self, node):
    method forward (line 36) | def forward(self, x):
  class RGCN (line 69) | class RGCN(torch.nn.Module):
    method __init__ (line 70) | def __init__(self, g, in_channels, out_channels, num_relations):
    method forward (line 75) | def forward(self, x):
  class RGCNSPMVConv (line 81) | class RGCNSPMVConv(torch.nn.Module):
    method __init__ (line 82) | def __init__(self, g, in_channels, out_channels, num_relations, num_ba...
    method reset_parameters (line 98) | def reset_parameters(self):
    method forward (line 105) | def forward(self, x):
  class RGCNSPMV (line 138) | class RGCNSPMV(torch.nn.Module):
    method __init__ (line 139) | def __init__(self, g, in_channels, out_channels, num_relations):
    method forward (line 146) | def forward(self, x):

FILE: benchmark/runtime/dgl/train.py
  function train_runtime (line 7) | def train_runtime(model, data, epochs, device):

FILE: benchmark/runtime/gat.py
  class GAT (line 7) | class GAT(torch.nn.Module):
    method __init__ (line 8) | def __init__(self, in_channels, out_channels):
    method forward (line 13) | def forward(self, data):

FILE: benchmark/runtime/gcn.py
  class GCN (line 7) | class GCN(torch.nn.Module):
    method __init__ (line 8) | def __init__(self, in_channels, out_channels):
    method forward (line 13) | def forward(self, data):

FILE: benchmark/runtime/rgcn.py
  class RGCN (line 7) | class RGCN(torch.nn.Module):
    method __init__ (line 8) | def __init__(self, in_channels, out_channels, num_relations):
    method forward (line 14) | def forward(self, data):

FILE: benchmark/runtime/train.py
  function train_runtime (line 7) | def train_runtime(model, data, epochs, device):

FILE: benchmark/training/training_benchmark.py
  function train_homo (line 46) | def train_homo(model, loader, optimizer, device, progress_bar=True, desc...
  function train_hetero (line 74) | def train_hetero(model, loader, optimizer, device, progress_bar=True, de...
  function run (line 98) | def run(args: argparse.ArgumentParser):

FILE: benchmark/utils/hetero_gat.py
  class HeteroGAT (line 7) | class HeteroGAT(torch.nn.Module):
    method __init__ (line 8) | def __init__(self, metadata, hidden_channels, num_layers, output_chann...
    method forward (line 15) | def forward(self, x_dict, edge_index_dict):
    method inference (line 19) | def inference(self, loader, device, progress_bar=False, **kwargs):
    method test (line 31) | def test(self, x, loader, device, progress_bar=False):

FILE: benchmark/utils/hetero_sage.py
  class HeteroGraphSAGE (line 7) | class HeteroGraphSAGE(torch.nn.Module):
    method __init__ (line 8) | def __init__(self, metadata, hidden_channels, num_layers, output_chann...
    method forward (line 14) | def forward(self, x_dict, edge_index_dict):
    method inference (line 18) | def inference(self, loader, device, progress_bar=False, **kwargs):
    method test (line 30) | def test(self, loader, device, progress_bar=False):

FILE: benchmark/utils/utils.py
  function emit_itt (line 24) | def emit_itt(*args, **kwargs):
  function get_dataset_with_transformation (line 39) | def get_dataset_with_transformation(name, root, use_sparse_tensor=False,
  function get_dataset (line 83) | def get_dataset(name, root, use_sparse_tensor=False, bf16=False):
  function get_model (line 89) | def get_model(name, params, metadata=None):
  function get_split_masks (line 117) | def get_split_masks(data, dataset_name):
  function save_benchmark_data (line 129) | def save_benchmark_data(csv_data, batch_size, layers, num_neighbors,
  function write_to_csv (line 144) | def write_to_csv(csv_data, write_csv='bench', training=False):
  function test (line 163) | def test(model, loader, device, hetero, progress_bar=True,

FILE: docs/source/_static/js/version_alert.js
  function warnOnLatestVersion (line 1) | function warnOnLatestVersion() {

FILE: docs/source/conf.py
  function rst_jinja_render (line 71) | def rst_jinja_render(app, _, source):
  function setup (line 77) | def setup(app):

FILE: examples/agnn.py
  class Net (line 16) | class Net(torch.nn.Module):
    method __init__ (line 17) | def __init__(self):
    method forward (line 24) | def forward(self):
  function train (line 39) | def train():
  function test (line 47) | def test():

FILE: examples/ar_link_pred.py
  class GCNEncoder (line 13) | class GCNEncoder(torch.nn.Module):
    method __init__ (line 14) | def __init__(self, in_channels, hidden_channels, out_channels):
    method forward (line 19) | def forward(self, x, edge_index):
  class LinkPredictor (line 24) | class LinkPredictor(torch.nn.Module):
    method __init__ (line 25) | def __init__(self, in_channels, hidden_channels):
    method forward (line 30) | def forward(self, z_i, z_j):
  class ARLinkPredictor (line 37) | class ARLinkPredictor(torch.nn.Module):
    method __init__ (line 38) | def __init__(self, in_channels):
    method forward (line 44) | def forward(self, z_i, z_j):
  function train (line 59) | def train(encoder, predictor, data, optimizer):
  function test (line 93) | def test(encoder, predictor, data):
  function compute_auc (line 115) | def compute_auc(pos_out, neg_out):
  function main (line 129) | def main():

FILE: examples/argva_node_clustering.py
  class Encoder (line 35) | class Encoder(torch.nn.Module):
    method __init__ (line 36) | def __init__(self, in_channels, hidden_channels, out_channels):
    method forward (line 42) | def forward(self, x, edge_index):
  class Discriminator (line 47) | class Discriminator(torch.nn.Module):
    method __init__ (line 48) | def __init__(self, in_channels, hidden_channels, out_channels):
    method forward (line 54) | def forward(self, x):
  function train (line 70) | def train():
  function test (line 91) | def test(data):
  function plot_points (line 121) | def plot_points(data, colors):

FILE: examples/arma.py
  class Net (line 16) | class Net(torch.nn.Module):
    method __init__ (line 17) | def __init__(self, in_channels, hidden_channels, out_channels):
    method forward (line 27) | def forward(self, x, edge_index):
  function train (line 47) | def train():
  function test (line 56) | def test():

FILE: examples/attentive_fp.py
  class GenFeatures (line 13) | class GenFeatures:
    method __init__ (line 14) | def __init__(self):
    method __call__ (line 36) | def __call__(self, data):
  function train (line 119) | def train():
  function test (line 134) | def test(loader):

FILE: examples/autoencoder.py
  class GCNEncoder (line 37) | class GCNEncoder(torch.nn.Module):
    method __init__ (line 38) | def __init__(self, in_channels, out_channels):
    method forward (line 43) | def forward(self, x, edge_index):
  class VariationalGCNEncoder (line 48) | class VariationalGCNEncoder(torch.nn.Module):
    method __init__ (line 49) | def __init__(self, in_channels, out_channels):
    method forward (line 55) | def forward(self, x, edge_index):
  class LinearEncoder (line 60) | class LinearEncoder(torch.nn.Module):
    method __init__ (line 61) | def __init__(self, in_channels, out_channels):
    method forward (line 65) | def forward(self, x, edge_index):
  class VariationalLinearEncoder (line 69) | class VariationalLinearEncoder(torch.nn.Module):
    method __init__ (line 70) | def __init__(self, in_channels, out_channels):
    method forward (line 75) | def forward(self, x, edge_index):
  function train (line 94) | def train():
  function test (line 107) | def test(data):

FILE: examples/cluster_gcn_ppi.py
  class Net (line 28) | class Net(torch.nn.Module):
    method __init__ (line 29) | def __init__(self, in_channels, hidden_channels, out_channels, num_lay...
    method forward (line 40) | def forward(self, x, edge_index):
  function train (line 56) | def train():
  function test (line 71) | def test(loader):

FILE: examples/cluster_gcn_reddit.py
  class Net (line 24) | class Net(torch.nn.Module):
    method __init__ (line 25) | def __init__(self, in_channels, out_channels):
    method forward (line 31) | def forward(self, x, edge_index):
    method inference (line 39) | def inference(self, x_all):
  function train (line 71) | def train():
  function test (line 91) | def test():  # Inference should be performed on the full graph.

FILE: examples/colors_topk_pool.py
  class HandleNodeAttention (line 16) | class HandleNodeAttention:
    method __call__ (line 17) | def __call__(self, data):
  class Net (line 33) | class Net(torch.nn.Module):
    method __init__ (line 34) | def __init__(self, in_channels):
    method forward (line 43) | def forward(self, data):
  function train (line 71) | def train(epoch):
  function test (line 87) | def test(loader):

FILE: examples/compile/gcn.py
  class GCN (line 28) | class GCN(torch.nn.Module):
    method __init__ (line 29) | def __init__(self, in_channels, hidden_channels, out_channels):
    method forward (line 35) | def forward(self, x, edge_index, edge_weight):
  function train (line 58) | def train():
  function test (line 69) | def test():

FILE: examples/compile/gin.py
  class GIN (line 31) | class GIN(torch.nn.Module):
    method __init__ (line 32) | def __init__(self, in_channels, hidden_channels, out_channels, num_lay...
    method forward (line 44) | def forward(self, x, edge_index, batch, batch_size):
  function train (line 65) | def train():
  function test (line 81) | def test(loader):

FILE: examples/contrib/pgm_explainer_graph_classification.py
  function normalized_cut_2d (line 33) | def normalized_cut_2d(edge_index, pos):
  class Net (line 39) | class Net(torch.nn.Module):
    method __init__ (line 40) | def __init__(self):
    method forward (line 59) | def forward(self, x, edge_index, **kwargs):
  function train (line 81) | def train(model, dataloader):

FILE: examples/contrib/pgm_explainer_node_classification.py
  class Net (line 22) | class Net(torch.nn.Module):
    method __init__ (line 23) | def __init__(self):
    method forward (line 28) | def forward(self, x, edge_index, edge_weight):

FILE: examples/contrib/rbcd_attack.py
  class GCN (line 20) | class GCN(torch.nn.Module):
    method __init__ (line 21) | def __init__(self, in_channels, hidden_channels, out_channels):
    method reset_parameters (line 27) | def reset_parameters(self):
    method forward (line 31) | def forward(self, x, edge_index, edge_weight=None, **kwargs):
  class WeightedGATConv (line 47) | class WeightedGATConv(GATConv):
    method edge_update (line 49) | def edge_update(self, alpha_j: Tensor, alpha_i: Optional[Tensor],
  class GAT (line 69) | class GAT(torch.nn.Module):
    method __init__ (line 70) | def __init__(self, in_channels, hidden_channels, out_channels):
    method reset_parameters (line 78) | def reset_parameters(self):
    method forward (line 82) | def forward(self, x, edge_index, edge_weight=None):
  function train (line 89) | def train(model, data, epochs=200, lr=0.01, weight_decay=5e-4):
  function accuracy (line 100) | def accuracy(pred, y, mask):
  function test (line 105) | def test(model, data):
  function metric (line 112) | def metric(*args, **kwargs):

FILE: examples/contrib/rbcd_attack_poisoning.py
  class PoisoningPRBCDAttack (line 43) | class PoisoningPRBCDAttack(PRBCDAttack):
    method _forward (line 44) | def _forward(self, x: Tensor, edge_index: Tensor, edge_weight: Tensor,
    method _forward_and_gradient (line 57) | def _forward_and_gradient(self, x: Tensor, labels: Tensor,

FILE: examples/cora.py
  class Net (line 24) | class Net(torch.nn.Module):
    method __init__ (line 25) | def __init__(self):
    method forward (line 30) | def forward(self):
  function train (line 44) | def train():
  function test (line 52) | def test():

FILE: examples/correct_and_smooth.py
  function train (line 33) | def train():
  function test (line 44) | def test(out=None):

FILE: examples/cpp/main.cpp
  function main (line 7) | int main(int argc, const char *argv[]) {

FILE: examples/cpp/save_model.py
  class GIN (line 11) | class GIN(torch.nn.Module):
    method __init__ (line 12) | def __init__(self, in_channels: int, hidden_channels: int,
    method forward (line 30) | def forward(

FILE: examples/datapipe.py
  function molecule_datapipe (line 29) | def molecule_datapipe() -> IterDataPipe:
  class MeshOpener (line 48) | class MeshOpener(IterDataPipe):
    method __init__ (line 50) | def __init__(self, dp: IterDataPipe) -> None:
    method __iter__ (line 62) | def __iter__(self):
  function mesh_datapipe (line 80) | def mesh_datapipe() -> IterDataPipe:

FILE: examples/dgcnn_classification.py
  class Net (line 91) | class Net(torch.nn.Module):
    method __init__ (line 92) | def __init__(self, out_channels, k=20, aggr='max'):
    method forward (line 101) | def forward(self, data):
  function train (line 117) | def train():
  function test (line 132) | def test(loader):

FILE: examples/dgcnn_segmentation.py
  class Net (line 32) | class Net(torch.nn.Module):
    method __init__ (line 33) | def __init__(self, out_channels, k=30, aggr='max'):
    method forward (line 43) | def forward(self, data):
  function train (line 59) | def train():
  function test (line 81) | def test(loader):

FILE: examples/dir_gnn.py
  class DirGNN (line 42) | class DirGNN(torch.nn.Module):
    method __init__ (line 43) | def __init__(self, in_channels, hidden_channels, out_channels, alpha):
    method forward (line 51) | def forward(self, x, edge_index):
  function train (line 67) | def train():
  function test (line 78) | def test():

FILE: examples/distributed/graphlearn_for_pytorch/dist_train_sage_supervised.py
  function test (line 18) | def test(model, test_loader, dataset_name):
  function run_training_proc (line 41) | def run_training_proc(

FILE: examples/distributed/graphlearn_for_pytorch/partition_ogbn_dataset.py
  function partition_dataset (line 10) | def partition_dataset(

FILE: examples/distributed/kuzu/papers_100M/train.py
  class GraphSAGE (line 62) | class GraphSAGE(torch.nn.Module):
    method __init__ (line 63) | def __init__(self, in_channels, hidden_channels, out_channels, num_lay...
    method forward (line 87) | def forward(self, x, edge_index):

FILE: examples/dna.py
  function gen_uniform_20_20_60_split (line 17) | def gen_uniform_20_20_60_split(data):
  class Net (line 29) | class Net(torch.nn.Module):
    method __init__ (line 30) | def __init__(self, in_channels, hidden_channels, out_channels, num_lay...
    method reset_parameters (line 41) | def reset_parameters(self):
    method forward (line 47) | def forward(self, x, edge_index):
  function train (line 74) | def train():
  function test (line 84) | def test():

FILE: examples/egc.py
  class Net (line 40) | class Net(torch.nn.Module):
    method __init__ (line 41) | def __init__(self, hidden_channels, num_layers, num_heads, num_bases):
    method forward (line 68) | def forward(self, x, adj_t, batch):
  function train (line 93) | def train():
  function evaluate (line 113) | def evaluate(loader):

FILE: examples/explain/captum_explainer.py
  class GCN (line 16) | class GCN(torch.nn.Module):
    method __init__ (line 17) | def __init__(self):
    method forward (line 22) | def forward(self, x, edge_index):

FILE: examples/explain/captum_explainer_hetero_link.py
  class GNNEncoder (line 38) | class GNNEncoder(torch.nn.Module):
    method __init__ (line 39) | def __init__(self, hidden_channels, out_channels):
    method forward (line 44) | def forward(self, x, edge_index):
  class EdgeDecoder (line 50) | class EdgeDecoder(torch.nn.Module):
    method __init__ (line 51) | def __init__(self, hidden_channels):
    method forward (line 56) | def forward(self, z_dict, edge_label_index):
  class Model (line 65) | class Model(torch.nn.Module):
    method __init__ (line 66) | def __init__(self, hidden_channels):
    method forward (line 72) | def forward(self, x_dict, edge_index_dict, edge_label_index):

FILE: examples/explain/gnn_explainer.py
  class GCN (line 16) | class GCN(torch.nn.Module):
    method __init__ (line 17) | def __init__(self):
    method forward (line 22) | def forward(self, x, edge_index):

FILE: examples/explain/gnn_explainer_ba_shapes.py
  function train (line 32) | def train():
  function test (line 44) | def test():

FILE: examples/explain/gnn_explainer_link_pred.py
  class GCN (line 30) | class GCN(torch.nn.Module):
    method __init__ (line 31) | def __init__(self, in_channels, hidden_channels, out_channels):
    method encode (line 36) | def encode(self, x, edge_index):
    method decode (line 41) | def decode(self, z, edge_label_index):
    method forward (line 45) | def forward(self, x, edge_index, edge_label_index):
  function train (line 54) | def train():
  function test (line 67) | def test(data):

FILE: examples/explain/graphmask_explainer.py
  class GCN (line 19) | class GCN(torch.nn.Module):
    method __init__ (line 20) | def __init__(self):
    method forward (line 25) | def forward(self, x, edge_index):
  class GAT (line 63) | class GAT(torch.nn.Module):
    method __init__ (line 64) | def __init__(self):
    method forward (line 69) | def forward(self, x, edge_index):

FILE: examples/faust.py
  class Net (line 24) | class Net(torch.nn.Module):
    method __init__ (line 25) | def __init__(self):
    method forward (line 36) | def forward(self, data):
  function train (line 56) | def train(epoch):
  function test (line 69) | def test():

FILE: examples/film.py
  class Net (line 21) | class Net(torch.nn.Module):
    method __init__ (line 22) | def __init__(self, in_channels, hidden_channels, out_channels, num_lay...
    method forward (line 37) | def forward(self, x, edge_index):
  function train (line 59) | def train():
  function test (line 74) | def test(loader):

FILE: examples/gat.py
  class GAT (line 38) | class GAT(torch.nn.Module):
    method __init__ (line 39) | def __init__(self, in_channels, hidden_channels, out_channels, heads):
    method forward (line 46) | def forward(self, x, edge_index):
  function train (line 59) | def train():
  function test (line 70) | def test():

FILE: examples/gcn.py
  class GCN (line 49) | class GCN(torch.nn.Module):
    method __init__ (line 50) | def __init__(self, in_channels, hidden_channels, out_channels):
    method forward (line 57) | def forward(self, x, edge_index, edge_weight=None):
  function train (line 77) | def train():
  function test (line 88) | def test():

FILE: examples/gcn2_cora.py
  class Net (line 19) | class Net(torch.nn.Module):
    method __init__ (line 20) | def __init__(self, hidden_channels, num_layers, alpha, theta,
    method forward (line 36) | def forward(self, x, adj_t):
  function train (line 61) | def train():
  function test (line 72) | def test():

FILE: examples/gcn2_ppi.py
  class Net (line 24) | class Net(torch.nn.Module):
    method __init__ (line 25) | def __init__(self, hidden_channels, num_layers, alpha, theta,
    method forward (line 41) | def forward(self, x, adj_t):
  function train (line 64) | def train():
  function test (line 80) | def test(loader):

FILE: examples/geniepath.py
  class Breadth (line 30) | class Breadth(torch.nn.Module):
    method __init__ (line 31) | def __init__(self, in_dim, out_dim):
    method forward (line 35) | def forward(self, x, edge_index):
  class Depth (line 40) | class Depth(torch.nn.Module):
    method __init__ (line 41) | def __init__(self, in_dim, hidden):
    method forward (line 45) | def forward(self, x, h, c):
  class GeniePathLayer (line 50) | class GeniePathLayer(torch.nn.Module):
    method __init__ (line 51) | def __init__(self, in_dim):
    method forward (line 56) | def forward(self, x, edge_index, h, c):
  class GeniePath (line 64) | class GeniePath(torch.nn.Module):
    method __init__ (line 65) | def __init__(self, in_dim, out_dim):
    method forward (line 72) | def forward(self, x, edge_index):
  class GeniePathLazy (line 82) | class GeniePathLazy(torch.nn.Module):
    method __init__ (line 83) | def __init__(self, in_dim, out_dim):
    method forward (line 92) | def forward(self, x, edge_index):
  function train (line 115) | def train():
  function test (line 131) | def test(loader):

FILE: examples/glnn.py
  function train_teacher (line 40) | def train_teacher():
  function test_teacher (line 51) | def test_teacher():
  function train_student (line 77) | def train_student():
  function test_student (line 91) | def test_student():

FILE: examples/gpse.py
  function load_ZINC (line 25) | def load_ZINC(args):
  class IdentityNodeEncoder (line 66) | class IdentityNodeEncoder(torch.nn.Module):
    method __init__ (line 67) | def __init__(self, emb_dim):
    method forward (line 70) | def forward(self, batch):
  class LinearNodeEncoder (line 74) | class LinearNodeEncoder(torch.nn.Module):
    method __init__ (line 75) | def __init__(self, emb_dim, emb_pe_out, bias=True):
    method forward (line 79) | def forward(self, batch):
  class TypeDictNodeEncoder (line 85) | class TypeDictNodeEncoder(torch.nn.Module):
    method __init__ (line 86) | def __init__(self, emb_dim, num_types=28):
    method forward (line 95) | def forward(self, batch):
  class GNNStackStage (line 102) | class GNNStackStage(torch.nn.Module):
    method __init__ (line 115) | def __init__(self, dim_in, dim_out, num_layers, conv_type='gcn',
    method forward (line 131) | def forward(self, batch):
  class GPSEPlusGNN (line 145) | class GPSEPlusGNN(torch.nn.Module):
    method __init__ (line 177) | def __init__(self, dim_emb, dim_conv, conv_type, num_layers, dim_pe_in,
    method forward (line 194) | def forward(self, batch):
  function train (line 205) | def train(loader):
  function test (line 225) | def test(loader):

FILE: examples/graph_gps.py
  class GPS (line 39) | class GPS(torch.nn.Module):
    method __init__ (line 40) | def __init__(self, channels: int, pe_dim: int, num_layers: int,
    method forward (line 71) | def forward(self, x, pe, edge_index, edge_attr, batch):
  class RedrawProjection (line 82) | class RedrawProjection:
    method __init__ (line 83) | def __init__(self, model: torch.nn.Module,
    method redraw_projections (line 89) | def redraw_projections(self):
  function train (line 113) | def train():
  function test (line 131) | def test(loader):

FILE: examples/graph_sage_unsup.py
  function train (line 43) | def train():
  function test (line 63) | def test():

FILE: examples/graph_sage_unsup_ppi.py
  function train (line 48) | def train():
  function encode (line 72) | def encode(loader):
  function test (line 84) | def test():

FILE: examples/graph_saint.py
  class Net (line 32) | class Net(torch.nn.Module):
    method __init__ (line 33) | def __init__(self, hidden_channels):
    method set_aggr (line 42) | def set_aggr(self, aggr):
    method forward (line 47) | def forward(self, x0, edge_index, edge_weight=None):
  function train (line 64) | def train():
  function test (line 90) | def test():

FILE: examples/graph_unet.py
  class Net (line 16) | class Net(torch.nn.Module):
    method __init__ (line 17) | def __init__(self):
    method forward (line 23) | def forward(self):
  function train (line 38) | def train():
  function test (line 46) | def test():

FILE: examples/hetero/bipartite_sage.py
  class MovieGNNEncoder (line 51) | class MovieGNNEncoder(torch.nn.Module):
    method __init__ (line 52) | def __init__(self, hidden_channels, out_channels):
    method forward (line 59) | def forward(self, x, edge_index):
  class UserGNNEncoder (line 65) | class UserGNNEncoder(torch.nn.Module):
    method __init__ (line 66) | def __init__(self, hidden_channels, out_channels):
    method forward (line 73) | def forward(self, x_dict, edge_index_dict):
  class EdgeDecoder (line 92) | class EdgeDecoder(torch.nn.Module):
    method __init__ (line 93) | def __init__(self, hidden_channels):
    method forward (line 98) | def forward(self, z_src, z_dst, edge_label_index):
  class Model (line 107) | class Model(torch.nn.Module):
    method __init__ (line 108) | def __init__(self, num_users, hidden_channels, out_channels):
    method forward (line 115) | def forward(self, x_dict, edge_index_dict, edge_label_index):
  function train (line 131) | def train():
  function test (line 146) | def test(data):

FILE: examples/hetero/bipartite_sage_unsup.py
  class ItemGNNEncoder (line 109) | class ItemGNNEncoder(torch.nn.Module):
    method __init__ (line 110) | def __init__(self, hidden_channels, out_channels):
    method forward (line 116) | def forward(self, x, edge_index):
  class UserGNNEncoder (line 122) | class UserGNNEncoder(torch.nn.Module):
    method __init__ (line 123) | def __init__(self, hidden_channels, out_channels):
    method forward (line 130) | def forward(self, x_dict, edge_index_dict):
  class EdgeDecoder (line 149) | class EdgeDecoder(torch.nn.Module):
    method __init__ (line 150) | def __init__(self, hidden_channels):
    method forward (line 155) | def forward(self, z_src, z_dst, edge_label_index):
  class Model (line 164) | class Model(torch.nn.Module):
    method __init__ (line 165) | def __init__(self, num_users, num_items, hidden_channels, out_channels):
    method forward (line 173) | def forward(self, x_dict, edge_index_dict, edge_label_index):
  function train (line 195) | def train():
  function test (line 220) | def test(loader):

FILE: examples/hetero/dmgi_unsup.py
  class DMGI (line 27) | class DMGI(torch.nn.Module):
    method __init__ (line 28) | def __init__(self, num_nodes, in_channels, out_channels, num_relations):
    method reset_parameters (line 36) | def reset_parameters(self):
    method forward (line 43) | def forward(self, x, edge_indices):
    method loss (line 59) | def loss(self, pos_hs, neg_hs, summaries):
  function train (line 90) | def train():
  function test (line 103) | def test():

FILE: examples/hetero/han_imdb.py
  class HAN (line 22) | class HAN(nn.Module):
    method __init__ (line 23) | def __init__(self, in_channels: Union[int, Dict[str, int]],
    method forward (line 30) | def forward(self, x_dict, edge_index_dict):
  function train (line 51) | def train() -> float:
  function test (line 63) | def test() -> List[float]:

FILE: examples/hetero/hetero_conv_dblp.py
  class HeteroGNN (line 17) | class HeteroGNN(torch.nn.Module):
    method __init__ (line 18) | def __init__(self, metadata, hidden_channels, out_channels, num_layers):
    method forward (line 31) | def forward(self, x_dict, edge_index_dict):
  function train (line 54) | def train():
  function test (line 66) | def test():

FILE: examples/hetero/hetero_link_pred.py
  function weighted_mse_loss (line 50) | def weighted_mse_loss(pred, target, weight=None):
  class GNNEncoder (line 55) | class GNNEncoder(torch.nn.Module):
    method __init__ (line 56) | def __init__(self, hidden_channels, out_channels):
    method forward (line 61) | def forward(self, x, edge_index):
  class EdgeDecoder (line 67) | class EdgeDecoder(torch.nn.Module):
    method __init__ (line 68) | def __init__(self, hidden_channels):
    method forward (line 73) | def forward(self, z_dict, edge_label_index):
  class Model (line 82) | class Model(torch.nn.Module):
    method __init__ (line 83) | def __init__(self, hidden_channels):
    method forward (line 89) | def forward(self, x_dict, edge_index_dict, edge_label_index):
  function train (line 98) | def train():
  function test (line 111) | def test(data):

FILE: examples/hetero/hgt_dblp.py
  class HGT (line 17) | class HGT(torch.nn.Module):
    method __init__ (line 18) | def __init__(self, hidden_channels, out_channels, num_heads, num_layers):
    method forward (line 33) | def forward(self, x_dict, edge_index_dict):
  function train (line 60) | def train():
  function test (line 72) | def test():

FILE: examples/hetero/hierarchical_sage.py
  class HierarchicalHeteroGraphSage (line 33) | class HierarchicalHeteroGraphSage(torch.nn.Module):
    method __init__ (line 34) | def __init__(self, edge_types, hidden_channels, out_channels, num_laye...
    method forward (line 48) | def forward(self, x_dict, edge_index_dict, num_sampled_edges_dict,
  function train (line 93) | def train():
  function test (line 121) | def test(loader):

FILE: examples/hetero/load_csv.py
  function load_node_csv (line 17) | def load_node_csv(path, index_col, encoders=None, **kwargs):
  function load_edge_csv (line 29) | def load_edge_csv(path, src_index_col, src_mapping, dst_index_col, dst_m...
  class SequenceEncoder (line 45) | class SequenceEncoder:
    method __init__ (line 47) | def __init__(self, model_name='all-MiniLM-L6-v2', device=None):
    method __call__ (line 52) | def __call__(self, df):
  class GenresEncoder (line 58) | class GenresEncoder:
    method __init__ (line 61) | def __init__(self, sep='|'):
    method __call__ (line 64) | def __call__(self, df):
  class IdentityEncoder (line 75) | class IdentityEncoder:
    method __init__ (line 78) | def __init__(self, dtype=None):
    method __call__ (line 81) | def __call__(self, df):

FILE: examples/hetero/metapath2vec.py
  function train (line 37) | def train(epoch, log_steps=100, eval_steps=2000):
  function test (line 60) | def test(train_ratio=0.1):

FILE: examples/hetero/recommender_system.py
  class GNN (line 93) | class GNN(torch.nn.Module):
    method __init__ (line 94) | def __init__(self, hidden_channels):
    method forward (line 100) | def forward(self, x, edge_index):
  class InnerProductDecoder (line 107) | class InnerProductDecoder(torch.nn.Module):
    method forward (line 108) | def forward(self, x_dict, edge_label_index):
  class Model (line 114) | class Model(torch.nn.Module):
    method __init__ (line 115) | def __init__(self, hidden_channels):
    method forward (line 121) | def forward(self, x_dict, edge_index_dict, edge_label_index):
  function train (line 130) | def train():
  function test (line 156) | def test(edge_label_index, exclude_links):

FILE: examples/hetero/temporal_link_pred.py
  class GNNEncoder (line 63) | class GNNEncoder(torch.nn.Module):
    method __init__ (line 64) | def __init__(self, hidden_channels, out_channels):
    method forward (line 69) | def forward(self, x, edge_index):
  class EdgeDecoder (line 75) | class EdgeDecoder(torch.nn.Module):
    method __init__ (line 76) | def __init__(self, hidden_channels):
    method forward (line 81) | def forward(self, z_dict, edge_label_index):
  class Model (line 90) | class Model(torch.nn.Module):
    method __init__ (line 91) | def __init__(self, hidden_channels):
    method forward (line 97) | def forward(self, x_dict, edge_index_dict, edge_label_index):
  function train (line 106) | def train():
  function test (line 129) | def test(loader):

FILE: examples/hetero/to_hetero_mag.py
  function init_params (line 53) | def init_params():
  function train (line 60) | def train():
  function test (line 80) | def test(loader):

FILE: examples/hierarchical_sampling.py
  function train (line 32) | def train(trim=False):

FILE: examples/infomax_inductive.py
  class Encoder (line 21) | class Encoder(torch.nn.Module):
    method __init__ (line 22) | def __init__(self, in_channels, hidden_channels):
    method forward (line 37) | def forward(self, x, edge_index, batch_size):
  function corruption (line 44) | def corruption(x, edge_index, batch_size):
  function train (line 57) | def train(epoch):
  function test (line 75) | def test():

FILE: examples/infomax_transductive.py
  class Encoder (line 13) | class Encoder(torch.nn.Module):
    method __init__ (line 14) | def __init__(self, in_channels, hidden_channels):
    method forward (line 19) | def forward(self, x, edge_index):
  function corruption (line 25) | def corruption(x, edge_index):
  function train (line 46) | def train():
  function test (line 56) | def test():

FILE: examples/jit/film.py
  class FiLM (line 22) | class FiLM(torch.nn.Module):
    method __init__ (line 23) | def __init__(self, in_channels: int, hidden_channels: int,
    method forward (line 39) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
  function train (line 55) | def train():
  function test (line 70) | def test(loader):

FILE: examples/jit/gat.py
  class GAT (line 16) | class GAT(torch.nn.Module):
    method __init__ (line 17) | def __init__(self):
    method forward (line 24) | def forward(self, x, edge_index):
  function train (line 38) | def train():
  function test (line 49) | def test():

FILE: examples/jit/gcn.py
  class GCN (line 17) | class GCN(torch.nn.Module):
    method __init__ (line 18) | def __init__(self, in_channels: int, hidden_channels: int,
    method forward (line 24) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
  function train (line 41) | def train():
  function test (line 52) | def test():

FILE: examples/jit/gin.py
  class GIN (line 22) | class GIN(torch.nn.Module):
    method __init__ (line 23) | def __init__(self, in_channels, hidden_channels, out_channels, num_lay...
    method forward (line 47) | def forward(self, x, edge_index, batch):
  function train (line 64) | def train():
  function test (line 80) | def test(loader):

FILE: examples/kge_fb15k_237.py
  function train (line 54) | def train():
  function test (line 68) | def test(data):

FILE: examples/lcm_aggr_2nd_min.py
  class RandomPermutation (line 18) | class RandomPermutation(BaseTransform):
    method forward (line 19) | def forward(self, data: Data) -> Data:
  class Random2ndMinimumDataset (line 24) | class Random2ndMinimumDataset(InMemoryDataset):
    method __init__ (line 29) | def __init__(
    method get_data (line 43) | def get_data(
  class BitwiseEmbedding (line 79) | class BitwiseEmbedding(torch.nn.Module):
    method __init__ (line 80) | def __init__(self, emb_dim: int):
    method forward (line 85) | def forward(self, x: Tensor) -> Tensor:
  class LCM (line 90) | class LCM(torch.nn.Module):
    method __init__ (line 91) | def __init__(self, emb_dim: int, dropout: float = 0.25):
    method forward (line 110) | def forward(self, x: Tensor, batch: Tensor) -> Tensor:
  function train (line 122) | def train():
  function test (line 137) | def test(loader):

FILE: examples/lightgcn.py
  function train (line 36) | def train():
  function test (line 70) | def test(k: int):

FILE: examples/link_pred.py
  class Net (line 32) | class Net(torch.nn.Module):
    method __init__ (line 33) | def __init__(self, in_channels, hidden_channels, out_channels):
    method encode (line 38) | def encode(self, x, edge_index):
    method decode (line 42) | def decode(self, z, edge_label_index):
    method decode_all (line 45) | def decode_all(self, z):
  function train (line 55) | def train():
  function test (line 82) | def test(data):

FILE: examples/linkx.py
  function train (line 26) | def train():
  function test (line 38) | def test():

FILE: examples/llm/g_retriever.py
  function adjust_learning_rate (line 28) | def adjust_learning_rate(param_group: dict, LR: float, epoch: int,
  function save_params_dict (line 52) | def save_params_dict(model, save_path):
  function load_params_dict (line 77) | def load_params_dict(model, save_path):
  function normalize_batch_dtype (line 88) | def normalize_batch_dtype(batch):
  function get_loss (line 94) | def get_loss(model, batch, model_save_name="gnn+llm") -> Tensor:
  function inference_step (line 122) | def inference_step(model, batch, model_save_name="gnn+llm",

FILE: examples/llm/git_mol.py
  function eval (line 20) | def eval(model, data_loader):
  function train (line 32) | def train(

FILE: examples/llm/glem.py
  function get_n_params (line 32) | def get_n_params(model):
  function main (line 42) | def main(args):

FILE: examples/llm/molecule_gpt.py
  function save_params_dict (line 20) | def save_params_dict(model, save_path):
  function eval (line 33) | def eval(model, data_loader):
  function train (line 45) | def train(

FILE: examples/llm/protein_mpnn.py
  function loss_smoothed (line 17) | def loss_smoothed(y, logits, mask, weight=0.1):
  function loss_nll (line 30) | def loss_nll(y, logits, mask):
  class NoamOpt (line 41) | class NoamOpt:
    method __init__ (line 43) | def __init__(self, model_size, factor, warmup, optimizer, step):
    method param_groups (line 52) | def param_groups(self):
    method step (line 56) | def step(self):
    method rate (line 65) | def rate(self, step=None):
    method zero_grad (line 72) | def zero_grad(self):
  function train (line 76) | def train(model, optimizer, data_loader, device, scaler):
  function eval (line 130) | def eval(model, data_loader, device):
  function main (line 156) | def main(args):

FILE: examples/llm/txt2kg_rag.py
  function parse_args (line 71) | def parse_args():
  function _process_and_chunk_text (line 208) | def _process_and_chunk_text(text, chunk_size, doc_parsing_mode):
  function get_data (line 232) | def get_data(args):
  function index_kg (line 306) | def index_kg(args, context_docs):
  function update_data_lists (line 379) | def update_data_lists(args, data_lists):
  function make_dataset (line 428) | def make_dataset(args):
  function train (line 570) | def train(args, train_loader, val_loader):
  function test (line 720) | def test(model, test_loader, args):

FILE: examples/lpformer.py
  function train_epoch (line 74) | def train_epoch():
  function test (line 128) | def test():
  function set_seeds (line 158) | def set_seeds(seed):

FILE: examples/mem_pool.py
  class Net (line 27) | class Net(torch.nn.Module):
    method __init__ (line 28) | def __init__(self, in_channels, hidden_channels, out_channels, dropout):
    method forward (line 45) | def forward(self, x, edge_index, batch):
  function train (line 67) | def train():
  function test (line 93) | def test(loader):

FILE: examples/mixhop.py
  class MixHop (line 21) | class MixHop(torch.nn.Module):
    method __init__ (line 22) | def __init__(self):
    method forward (line 35) | def forward(self, x, edge_index):
  function train (line 59) | def train():
  function test (line 71) | def test():

FILE: examples/mnist_graclus.py
  function normalized_cut_2d (line 33) | def normalized_cut_2d(edge_index, pos):
  class Net (line 39) | class Net(torch.nn.Module):
    method __init__ (line 40) | def __init__(self):
    method forward (line 47) | def forward(self, data):
  function train (line 70) | def train(epoch):
  function test (line 88) | def test():

FILE: examples/mnist_nn_conv.py
  function normalized_cut_2d (line 32) | def normalized_cut_2d(edge_index, pos):
  class Net (line 38) | class Net(torch.nn.Module):
    method __init__ (line 39) | def __init__(self):
    method forward (line 58) | def forward(self, data):
  function train (line 81) | def train(epoch):
  function test (line 99) | def test():

FILE: examples/mnist_voxel_grid.py
  class Net (line 24) | class Net(torch.nn.Module):
    method __init__ (line 25) | def __init__(self):
    method forward (line 33) | def forward(self, data):
  function train (line 63) | def train(epoch):
  function test (line 81) | def test():

FILE: examples/multi_gpu/distributed_batching.py
  class GIN (line 21) | class GIN(torch.nn.Module):
    method __init__ (line 22) | def __init__(
    method forward (line 47) | def forward(
  function run (line 66) | def run(rank: int, world_size: int, dataset_name: str, root: str) -> None:

FILE: examples/multi_gpu/distributed_sampling.py
  class SAGE (line 18) | class SAGE(torch.nn.Module):
    method __init__ (line 19) | def __init__(
    method forward (line 33) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
  function test (line 43) | def test(
  function run (line 61) | def run(rank: int, world_size: int, dataset: Reddit) -> None:

FILE: examples/multi_gpu/distributed_sampling_multinode.py
  class SAGE (line 17) | class SAGE(torch.nn.Module):
    method __init__ (line 18) | def __init__(
    method forward (line 32) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
    method inference (line 41) | def inference(
  function run (line 69) | def run(world_size: int, rank: int, local_rank: int):

FILE: examples/multi_gpu/distributed_sampling_xpu.py
  class GAT (line 37) | class GAT(torch.nn.Module):
    method __init__ (line 38) | def __init__(
    method forward (line 67) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
    method inference (line 75) | def inference(
  function train_step (line 108) | def train_step(model: Any, optimizer: Any, x: Tensor, edge_index: Tensor,
  function run (line 118) | def run(rank: int, world_size: int, dataset: PygNodePropPredDataset):
  function get_dist_params (line 188) | def get_dist_params() -> Tuple[int, int, str]:

FILE: examples/multi_gpu/mag240m_graphsage.py
  function common_step (line 17) | def common_step(batch, model):
  function training_step (line 25) | def training_step(batch, acc, model):
  function validation_step (line 32) | def validation_step(batch, acc, model):
  class HeteroSAGEConv (line 37) | class HeteroSAGEConv(torch.nn.Module):
    method __init__ (line 38) | def __init__(self, in_channels, out_channels, dropout, node_types,
    method forward (line 55) | def forward(self, x_dict, edge_index_dict):
  class HeteroGraphSAGE (line 65) | class HeteroGraphSAGE(torch.nn.Module):
    method __init__ (line 66) | def __init__(self, in_channels, hidden_channels, num_layers, out_chann...
    method forward (line 98) | def forward(self, x_dict, edge_index_dict):
  function run (line 104) | def run(

FILE: examples/multi_gpu/model_parallel.py
  class GCN (line 19) | class GCN(torch.nn.Module):
    method __init__ (line 20) | def __init__(self, in_channels, out_channels, device1, device2):
    method forward (line 28) | def forward(self, x, edge_index):
  function train (line 47) | def train():
  function test (line 58) | def test():

FILE: examples/multi_gpu/papers100m_gcn.py
  function get_num_workers (line 18) | def get_num_workers(world_size):
  function run_train (line 30) | def run_train(rank, data, world_size, model, epochs, batch_size, fan_out,

FILE: examples/multi_gpu/papers100m_gcn_multinode.py
  function get_num_workers (line 24) | def get_num_workers() -> int:
  function run (line 36) | def run(world_size, data, split_idx, model, acc, wall_clock_start):

FILE: examples/multi_gpu/pcqm4m_ogb.py
  function ogb_from_smiles_wrapper (line 41) | def ogb_from_smiles_wrapper(smiles, *args, **kwargs):
  class GINConv (line 54) | class GINConv(MessagePassing):
    method __init__ (line 55) | def __init__(self, emb_dim):
    method forward (line 71) | def forward(self, x, edge_index, edge_attr):
    method message (line 77) | def message(self, x_j, edge_attr):
    method update (line 80) | def update(self, aggr_out):
  class GCNConv (line 84) | class GCNConv(MessagePassing):
    method __init__ (line 85) | def __init__(self, emb_dim):
    method forward (line 91) | def forward(self, x, edge_index, edge_attr):
    method message (line 103) | def message(self, x_j, edge_attr, norm):
    method update (line 106) | def update(self, aggr_out):
  class GNNNode (line 110) | class GNNNode(torch.nn.Module):
    method __init__ (line 111) | def __init__(self, num_layers, emb_dim, drop_ratio=0.5, JK="last",
    method forward (line 145) | def forward(self, batched_data):
  class GNNNodeVirtualNode (line 179) | class GNNNodeVirtualNode(torch.nn.Module):
    method __init__ (line 181) | def __init__(self, num_layers, emb_dim, drop_ratio=0.5, JK="last",
    method forward (line 221) | def forward(self, batched_data):
  class GNN (line 282) | class GNN(torch.nn.Module):
    method __init__ (line 283) | def __init__(
    method forward (line 361) | def forward(self, batched_data):
  function train (line 372) | def train(model, rank, device, loader, optimizer):
  function eval (line 388) | def eval(model, device, loader, evaluator):
  function test (line 406) | def test(model, device, loader):
  function run (line 420) | def run(rank, dataset, args):

FILE: examples/multi_gpu/taobao.py
  class ItemGNNEncoder (line 23) | class ItemGNNEncoder(torch.nn.Module):
    method __init__ (line 24) | def __init__(self, hidden_channels, out_channels):
    method forward (line 30) | def forward(self, x, edge_index):
  class UserGNNEncoder (line 36) | class UserGNNEncoder(torch.nn.Module):
    method __init__ (line 37) | def __init__(self, hidden_channels, out_channels):
    method forward (line 44) | def forward(self, x_dict, edge_index_dict):
  class EdgeDecoder (line 63) | class EdgeDecoder(torch.nn.Module):
    method __init__ (line 64) | def __init__(self, hidden_channels):
    method forward (line 69) | def forward(self, z_src, z_dst, edge_label_index):
  class Model (line 78) | class Model(torch.nn.Module):
    method __init__ (line 79) | def __init__(self, num_users, num_items, hidden_channels, out_channels):
    method forward (line 87) | def forward(self, x_dict, edge_index_dict, edge_label_index):
  function run_train (line 100) | def run_train(rank, data, train_data, val_data, test_data, args, world_s...
  function pre_transform (line 240) | def pre_transform(data):

FILE: examples/mutag_gin.py
  class GIN (line 48) | class GIN(torch.nn.Module):
    method __init__ (line 49) | def __init__(self, in_channels, hidden_channels, out_channels, num_lay...
    method forward (line 61) | def forward(self, x, edge_index, batch):
  function train (line 77) | def train():
  function test (line 93) | def test(loader):

FILE: examples/node2vec.py
  function train (line 33) | def train():
  function test (line 46) | def test():
  function plot_points (line 66) | def plot_points(colors):

FILE: examples/ogbn_proteins_deepgcn.py
  class DeeperGCN (line 32) | class DeeperGCN(torch.nn.Module):
    method __init__ (line 33) | def __init__(self, hidden_channels, num_layers):
    method forward (line 52) | def forward(self, x, edge_index, edge_attr):
  function train (line 74) | def train(epoch):
  function test (line 100) | def test():

FILE: examples/ogbn_train.py
  function get_loader (line 109) | def get_loader(input_nodes: dict[str, Tensor]) -> NeighborLoader:
  function train (line 127) | def train(epoch: int) -> tuple[Tensor, float]:
  function test (line 161) | def test(loader: NeighborLoader) -> float:
  function get_model (line 182) | def get_model(model_name: str) -> torch.nn.Module:

FILE: examples/ogc.py
  class LinearNeuralNetwork (line 57) | class LinearNeuralNetwork(torch.nn.Module):
    method __init__ (line 58) | def __init__(self, num_features: int, num_classes: int, bias: bool = T...
    method forward (line 62) | def forward(self, x: Tensor) -> Tensor:
    method test (line 66) | def test(self, U: Tensor, y_one_hot: Tensor, data: Data):
    method update_W (line 82) | def update_W(self, U: Tensor, y_one_hot: Tensor, data: Data):
  function update_U (line 102) | def update_U(U: Tensor, y_one_hot: Tensor, pred: Tensor, W: Tensor):
  function ogc (line 116) | def ogc() -> float:

FILE: examples/pmlp.py
  function train (line 28) | def train():
  function test (line 39) | def test():

FILE: examples/pna.py
  class Net (line 36) | class Net(torch.nn.Module):
    method __init__ (line 37) | def __init__(self):
    method forward (line 59) | def forward(self, x, edge_index, edge_attr, batch):
  function train (line 82) | def train(epoch):
  function test (line 98) | def test(loader):

FILE: examples/point_transformer_classification.py
  class TransformerBlock (line 32) | class TransformerBlock(torch.nn.Module):
    method __init__ (line 33) | def __init__(self, in_channels, out_channels):
    method forward (line 47) | def forward(self, x, pos, edge_index):
  class TransitionDown (line 54) | class TransitionDown(torch.nn.Module):
    method __init__ (line 58) | def __init__(self, in_channels, out_channels, ratio=0.25, k=16):
    method forward (line 64) | def forward(self, x, pos, batch):
  class Net (line 87) | class Net(torch.nn.Module):
    method __init__ (line 88) | def __init__(self, in_channels, out_channels, dim_model, k=16):
    method forward (line 117) | def forward(self, x, pos, batch=None):
  function train (line 144) | def train():
  function test (line 160) | def test(loader):

FILE: examples/point_transformer_segmentation.py
  class TransitionUp (line 35) | class TransitionUp(torch.nn.Module):
    method __init__ (line 39) | def __init__(self, in_channels, out_channels):
    method forward (line 44) | def forward(self, x, x_sub, pos, pos_sub, batch=None, batch_sub=None):
  class Net (line 57) | class Net(torch.nn.Module):
    method __init__ (line 58) | def __init__(self, in_channels, out_channels, dim_model, k=16):
    method forward (line 111) | def forward(self, x, pos, batch=None):
  function train (line 172) | def train():
  function test (line 193) | def test(loader):

FILE: examples/pointnet2_classification.py
  class SAModule (line 16) | class SAModule(torch.nn.Module):
    method __init__ (line 17) | def __init__(self, ratio, r, nn):
    method forward (line 23) | def forward(self, x, pos, batch):
  class GlobalSAModule (line 34) | class GlobalSAModule(torch.nn.Module):
    method __init__ (line 35) | def __init__(self, nn):
    method forward (line 39) | def forward(self, x, pos, batch):
  class Net (line 47) | class Net(torch.nn.Module):
    method __init__ (line 48) | def __init__(self):
    method forward (line 58) | def forward(self, data):
  function train (line 68) | def train(epoch):
  function test (line 79) | def test(loader):

FILE: examples/pointnet2_segmentation.py
  class FPModule (line 37) | class FPModule(torch.nn.Module):
    method __init__ (line 38) | def __init__(self, k, nn):
    method forward (line 43) | def forward(self, x, pos, batch, x_skip, pos_skip, batch_skip):
  class Net (line 51) | class Net(torch.nn.Module):
    method __init__ (line 52) | def __init__(self, num_classes):
    method forward (line 70) | def forward(self, data):
  function train (line 88) | def train():
  function test (line 110) | def test(loader):

FILE: examples/ppi.py
  class Net (line 21) | class Net(torch.nn.Module):
    method __init__ (line 22) | def __init__(self):
    method forward (line 30) | def forward(self, x, edge_index):
  function train (line 43) | def train():
  function test (line 58) | def test(loader):

FILE: examples/proteins_diff_pool.py
  class GNN (line 33) | class GNN(torch.nn.Module):
    method __init__ (line 34) | def __init__(self, in_channels, hidden_channels, out_channels,
    method bn (line 51) | def bn(self, i, x):
    method forward (line 59) | def forward(self, x, adj, mask=None):
  class Net (line 75) | class Net(torch.nn.Module):
    method __init__ (line 76) | def __init__(self):
    method forward (line 92) | def forward(self, x, adj, mask=None):
  function train (line 122) | def train(epoch):
  function test (line 138) | def test(loader):

FILE: examples/proteins_dmon_pool.py
  class Net (line 26) | class Net(torch.nn.Module):
    method __init__ (line 27) | def __init__(self, in_channels, out_channels, hidden_channels=32):
    method forward (line 43) | def forward(self, x, edge_index, batch):
  function train (line 68) | def train(train_loader):
  function test (line 84) | def test(loader):

FILE: examples/proteins_gmt.py
  class Net (line 25) | class Net(torch.nn.Module):
    method __init__ (line 26) | def __init__(self):
    method forward (line 38) | def forward(self, x0, edge_index, batch):
  function train (line 58) | def train():
  function test (line 74) | def test(loader):

FILE: examples/proteins_mincut_pool.py
  class Net (line 26) | class Net(torch.nn.Module):
    method __init__ (line 27) | def __init__(self, in_channels, out_channels, hidden_channels=32):
    method forward (line 43) | def forward(self, x, edge_index, batch):
  function train (line 70) | def train(epoch):
  function test (line 86) | def test(loader):

FILE: examples/proteins_topk_pool.py
  class Net (line 22) | class Net(torch.nn.Module):
    method __init__ (line 23) | def __init__(self):
    method forward (line 37) | def forward(self, data):
  function train (line 67) | def train(epoch):
  function test (line 82) | def test(loader):

FILE: examples/pytorch_ignite/gin.py
  class Model (line 16) | class Model(torch.nn.Module):
    method __init__ (line 17) | def __init__(self, in_channels: int, out_channels: int,
    method forward (line 28) | def forward(self, data):
  function main (line 35) | def main():

FILE: examples/pytorch_lightning/gin.py
  class Model (line 14) | class Model(pl.LightningModule):
    method __init__ (line 15) | def __init__(self, in_channels: int, out_channels: int,
    method forward (line 29) | def forward(self, x, edge_index, batch):
    method training_step (line 35) | def training_step(self, data, batch_idx):
    method validation_step (line 43) | def validation_step(self, data, batch_idx):
    method test_step (line 49) | def test_step(self, data, batch_idx):
    method configure_optimizers (line 55) | def configure_optimizers(self):

FILE: examples/pytorch_lightning/graph_sage.py
  class Model (line 14) | class Model(pl.LightningModule):
    method __init__ (line 15) | def __init__(self, in_channels: int, out_channels: int,
    method forward (line 27) | def forward(self, x, edge_index):
    method training_step (line 30) | def training_step(self, data, batch_idx):
    method validation_step (line 39) | def validation_step(self, data, batch_idx):
    method test_step (line 46) | def test_step(self, data, batch_idx):
    method configure_optimizers (line 53) | def configure_optimizers(self):

FILE: examples/pytorch_lightning/relational_gnn.py
  class GNN (line 20) | class GNN(torch.nn.Module):
    method __init__ (line 21) | def __init__(self, hidden_channels: int, out_channels: int,
    method forward (line 30) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
  class RelationalGNN (line 38) | class RelationalGNN(LightningModule):
    method __init__ (line 39) | def __init__(
    method forward (line 58) | def forward(
    method common_step (line 65) | def common_step(self, batch: Batch) -> Tuple[Tensor, Tensor]:
    method training_step (line 71) | def training_step(self, batch: Batch, batch_idx: int) -> Tensor:
    method validation_step (line 79) | def validation_step(self, batch: Batch, batch_idx: int):
    method test_step (line 85) | def test_step(self, batch: Batch, batch_idx: int):
    method configure_optimizers (line 91) | def configure_optimizers(self):
  function main (line 95) | def main():

FILE: examples/qm9_nn_conv.py
  class MyTransform (line 18) | class MyTransform:
    method __call__ (line 19) | def __call__(self, data):
  class Complete (line 25) | class Complete:
    method __call__ (line 26) | def __call__(self, data):
  class Net (line 71) | class Net(torch.nn.Module):
    method __init__ (line 72) | def __init__(self):
    method forward (line 84) | def forward(self, data):
  function train (line 107) | def train(epoch):
  function test (line 121) | def test(loader):

FILE: examples/quiver/multi_gpu_quiver.py
  class SAGE (line 19) | class SAGE(torch.nn.Module):
    method __init__ (line 20) | def __init__(self, in_channels, hidden_channels, out_channels,
    method forward (line 31) | def forward(self, x, adjs):
    method inference (line 41) | def inference(self, x_all, device, subgraph_loader):
  function run (line 65) | def run(rank, world_size, dataset, quiver_feature, quiver_sampler):

FILE: examples/quiver/single_gpu_quiver.py
  class SAGE (line 41) | class SAGE(torch.nn.Module):
    method __init__ (line 42) | def __init__(self, in_channels, hidden_channels, out_channels):
    method forward (line 51) | def forward(self, x, adjs):
    method inference (line 66) | def inference(self, x_all):
  function train (line 109) | def train(epoch):
  function test (line 144) | def test():

FILE: examples/randlanet_classification.py
  class SharedMLP (line 32) | class SharedMLP(MLP):
    method __init__ (line 34) | def __init__(self, *args, **kwargs):
  class LocalFeatureAggregation (line 46) | class LocalFeatureAggregation(MessagePassing):
    method __init__ (line 48) | def __init__(self, channels):
    method forward (line 55) | def forward(self, edge_index, x, pos):
    method message (line 60) | def message(self, x_j: Tensor, pos_i: Tensor, pos_j: Tensor,
  class DilatedResidualBlock (line 92) | class DilatedResidualBlock(torch.nn.Module):
    method __init__ (line 93) | def __init__(
    method forward (line 116) | def forward(self, x, pos, batch):
  function decimate (line 129) | def decimate(tensors, ptr: Tensor, decimation_factor: int):
  class Net (line 136) | class Net(torch.nn.Module):
    method __init__ (line 137) | def __init__(
    method forward (line 158) | def forward(self, x, pos, batch, ptr):
  function train (line 175) | def train(epoch):
  function test (line 191) | def test(loader):

FILE: examples/randlanet_segmentation.py
  class FPModule (line 54) | class FPModule(torch.nn.Module):
    method __init__ (line 56) | def __init__(self, k, nn):
    method forward (line 61) | def forward(self, x, pos, batch, x_skip, pos_skip, batch_skip):
  class Net (line 68) | class Net(torch.nn.Module):
    method __init__ (line 69) | def __init__(
    method forward (line 102) | def forward(self, x, pos, batch, ptr):
  function train (line 143) | def train():
  function test (line 165) | def test(loader):

FILE: examples/rdl.py
  class GloveTextEmbedding (line 44) | class GloveTextEmbedding:
    method __init__ (line 46) | def __init__(self, device: Optional[torch.device] = None) -> None:
    method __call__ (line 52) | def __call__(self, sentences: List[str]) -> Tensor:
  class HeteroEncoder (line 56) | class HeteroEncoder(torch.nn.Module):
    method __init__ (line 72) | def __init__(
    method reset_parameters (line 106) | def reset_parameters(self) -> None:
    method forward (line 111) | def forward(
  class HeteroTemporalEncoder (line 131) | class HeteroTemporalEncoder(torch.nn.Module):
    method __init__ (line 155) | def __init__(self, node_types: List[NodeType], channels: int) -> None:
    method reset_parameters (line 168) | def reset_parameters(self) -> None:
    method forward (line 175) | def forward(
  class HeteroGraphSAGE (line 204) | class HeteroGraphSAGE(torch.nn.Module):
    method __init__ (line 226) | def __init__(
    method reset_parameters (line 255) | def reset_parameters(self) -> None:
    method forward (line 263) | def forward(
  class Model (line 285) | class Model(torch.nn.Module):
    method __init__ (line 307) | def __init__(
    method reset_parameters (line 346) | def reset_parameters(self) -> None:
    method forward (line 353) | def forward(
  class AttachTargetTransform (line 391) | class AttachTargetTransform:
    method __init__ (line 400) | def __init__(self, entity: str, target: Tensor) -> None:
    method __call__ (line 404) | def __call__(self, batch: HeteroData) -> HeteroData:
  class TrainingTableInput (line 409) | class TrainingTableInput(NamedTuple):
  function get_task_type_params (line 431) | def get_task_type_params(
  function to_unix_time (line 461) | def to_unix_time(ser: pd.Series) -> np.ndarray:
  function get_train_table_input (line 477) | def get_train_table_input(
  function train (line 523) | def train(
  function test (line 555) | def test(
  function main (line 572) | def main():

FILE: examples/reddit.py
  class SAGE (line 35) | class SAGE(torch.nn.Module):
    method __init__ (line 36) | def __init__(self, in_channels, hidden_channels, out_channels):
    method forward (line 42) | def forward(self, x, edge_index):
    method inference (line 51) | def inference(self, x_all, subgraph_loader):
  function train (line 76) | def train(epoch):
  function test (line 101) | def test():

FILE: examples/renet.py
  function train (line 52) | def train():
  function test (line 68) | def test(loader):

FILE: examples/rev_gnn.py
  class GNNBlock (line 22) | class GNNBlock(torch.nn.Module):
    method __init__ (line 23) | def __init__(self, in_channels, out_channels):
    method reset_parameters (line 28) | def reset_parameters(self):
    method forward (line 32) | def forward(self, x, edge_index, dropout_mask=None):
  class RevGNN (line 39) | class RevGNN(torch.nn.Module):
    method __init__ (line 40) | def __init__(self, in_channels, hidden_channels, out_channels, num_lay...
    method reset_parameters (line 59) | def reset_parameters(self):
    method forward (line 66) | def forward(self, x, edge_index):
  function train (line 112) | def train(epoch):
  function test (line 139) | def test(epoch):

FILE: examples/rgat.py
  class RGAT (line 16) | class RGAT(torch.nn.Module):
    method __init__ (line 17) | def __init__(self, in_channels, hidden_channels, out_channels,
    method forward (line 24) | def forward(self, x, edge_index, edge_type):
  function train (line 37) | def train():
  function test (line 48) | def test():

FILE: examples/rgcn.py
  class Net (line 42) | class Net(torch.nn.Module):
    method __init__ (line 43) | def __init__(self):
    method forward (line 50) | def forward(self, edge_index, edge_type):
  function train (line 68) | def train():
  function test (line 79) | def test():

FILE: examples/rgcn_link_pred.py
  class RGCNEncoder (line 27) | class RGCNEncoder(torch.nn.Module):
    method __init__ (line 28) | def __init__(self, num_nodes, hidden_channels, num_relations):
    method reset_parameters (line 37) | def reset_parameters(self):
    method forward (line 42) | def forward(self, edge_index, edge_type):
  class DistMultDecoder (line 50) | class DistMultDecoder(torch.nn.Module):
    method __init__ (line 51) | def __init__(self, num_relations, hidden_channels):
    method reset_parameters (line 56) | def reset_parameters(self):
    method forward (line 59) | def forward(self, z, edge_index, edge_type):
  function negative_sampling (line 73) | def negative_sampling(edge_index, num_nodes):
  function train (line 86) | def train():
  function test (line 111) | def test():
  function compute_rank (line 122) | def compute_rank(ranks):
  function compute_mrr (line 132) | def compute_mrr(z, edge_index, edge_type):

FILE: examples/seal_link_pred.py
  class SEALDataset (line 21) | class SEALDataset(InMemoryDataset):
    method __init__ (line 22) | def __init__(self, dataset, num_hops, split='train'):
    method processed_file_names (line 30) | def processed_file_names(self):
    method process (line 33) | def process(self):
    method extract_enclosing_subgraphs (line 70) | def extract_enclosing_subgraphs(self, edge_index, edge_label_index, y):
    method drnl_node_labeling (line 92) | def drnl_node_labeling(self, edge_index, src, dst, num_nodes=None):
  class DGCNN (line 139) | class DGCNN(torch.nn.Module):
    method __init__ (line 140) | def __init__(self, hidden_channels, num_layers, GNN=GCNConv, k=0.6):
    method forward (line 167) | def forward(self, x, edge_index, batch):
  function train (line 190) | def train():
  function test (line 207) | def test(loader):

FILE: examples/sgc.py
  class Net (line 15) | class Net(torch.nn.Module):
    method __init__ (line 16) | def __init__(self):
    method forward (line 25) | def forward(self):
  function train (line 42) | def train():
  function test (line 50) | def test():

FILE: examples/shadow.py
  class GNN (line 23) | class GNN(torch.nn.Module):
    method __init__ (line 24) | def __init__(self, in_channels, hidden_channels, out_channels):
    method forward (line 31) | def forward(self, x, edge_index, batch, root_n_id):
  function train (line 51) | def train():
  function test (line 67) | def test(loader):

FILE: examples/sign.py
  class Net (line 26) | class Net(torch.nn.Module):
    method __init__ (line 27) | def __init__(self):
    method forward (line 35) | def forward(self, xs):
  function train (line 51) | def train():
  function test (line 73) | def test(loader):

FILE: examples/signed_gcn.py
  function train (line 37) | def train():
  function test (line 47) | def test():

FILE: examples/super_gat.py
  class Net (line 17) | class Net(torch.nn.Module):
    method __init__ (line 18) | def __init__(self):
    method forward (line 29) | def forward(self, x, edge_index):
  function train (line 44) | def train(data):
  function test (line 55) | def test(data):

FILE: examples/tagcn.py
  class Net (line 16) | class Net(torch.nn.Module):
    method __init__ (line 17) | def __init__(self):
    method forward (line 22) | def forward(self):
  function train (line 41) | def train():
  function test (line 49) | def test():

FILE: examples/tensorboard_logging.py
  class Net (line 17) | class Net(torch.nn.Module):
    method __init__ (line 18) | def __init__(self):
    method forward (line 23) | def forward(self, x, edge_index):
  function train (line 41) | def train():
  function test (line 52) | def test():

FILE: examples/tgn.py
  class GraphAttentionEmbedding (line 60) | class GraphAttentionEmbedding(torch.nn.Module):
    method __init__ (line 61) | def __init__(self, in_channels, out_channels, msg_dim, time_enc):
    method forward (line 68) | def forward(self, x, last_update, edge_index, t, msg):
  class LinkPredictor (line 75) | class LinkPredictor(torch.nn.Module):
    method __init__ (line 76) | def __init__(self, in_channels):
    method forward (line 82) | def forward(self, z_src, z_dst):
  function train (line 117) | def train():
  function test (line 156) | def test(loader):

FILE: examples/triangles_sag_pool.py
  class HandleNodeAttention (line 17) | class HandleNodeAttention:
    method __call__ (line 18) | def __call__(self, data):
  class Net (line 35) | class Net(torch.nn.Module):
    method __init__ (line 36) | def __init__(self, in_channels):
    method forward (line 47) | def forward(self, data):
  function train (line 74) | def train():
  function test (line 90) | def test(loader):

FILE: examples/unimp_arxiv.py
  class UniMP (line 15) | class UniMP(torch.nn.Module):
    method __init__ (line 16) | def __init__(self, in_channels, num_classes, hidden_channels, num_layers,
    method forward (line 39) | def forward(self, x, y, edge_index, label_mask):
  function train (line 59) | def train(label_rate=0.65):  # How many labels to use for propagation.
  function test (line 75) | def test():

FILE: examples/upfd.py
  class Net (line 32) | class Net(torch.nn.Module):
    method __init__ (line 33) | def __init__(self, model, in_channels, hidden_channels, out_channels,
    method forward (line 51) | def forward(self, x, edge_index, batch):
  function train (line 74) | def train():
  function test (line 91) | def test(loader):

FILE: examples/wl_kernel.py
  class WL (line 27) | class WL(torch.nn.Module):
    method __init__ (line 28) | def __init__(self, num_layers):
    method forward (line 32) | def forward(self, x, edge_index, batch=None):

FILE: graphgym/agg_batch.py
  function parse_args (line 6) | def parse_args():

FILE: graphgym/configs_gen.py
  function parse_args (line 19) | def parse_args():
  function get_fname (line 44) | def get_fname(string):
  function grid2list (line 51) | def grid2list(grid):
  function lists_distance (line 62) | def lists_distance(l1, l2):
  function grid2list_sample (line 71) | def grid2list_sample(grid, sample=10):
  function load_config (line 82) | def load_config(fname):
  function load_search_file (line 90) | def load_search_file(fname):
  function load_alias_file (line 112) | def load_alias_file(fname):
  function exclude_list_id (line 120) | def exclude_list_id(list, id):
  function gen_grid (line 124) | def gen_grid(args, config, config_budget=None):
  function gen_grid_sample (line 166) | def gen_grid_sample(args, config, config_budget=None, compare_alias_list...

FILE: graphgym/custom_graphgym/act/example.py
  class SWISH (line 10) | class SWISH(nn.Module):
    method __init__ (line 11) | def __init__(self, inplace=False):
    method forward (line 15) | def forward(self, x):

FILE: graphgym/custom_graphgym/config/example.py
  function set_cfg_example (line 7) | def set_cfg_example(cfg):

FILE: graphgym/custom_graphgym/encoder/example.py
  class ExampleNodeEncoder (line 11) | class ExampleNodeEncoder(torch.nn.Module):
    method __init__ (line 18) | def __init__(self, emb_dim, num_classes=None):
    method forward (line 24) | def forward(self, batch):
  class ExampleEdgeEncoder (line 32) | class ExampleEdgeEncoder(torch.nn.Module):
    method __init__ (line 33) | def __init__(self, emb_dim):
    method forward (line 44) | def forward(self, batch):

FILE: graphgym/custom_graphgym/head/example.py
  class ExampleNodeHead (line 7) | class ExampleNodeHead(nn.Module):
    method __init__ (line 9) | def __init__(self, dim_in, dim_out):
    method _apply_index (line 13) | def _apply_index(self, batch):
    method forward (line 20) | def forward(self, batch):

FILE: graphgym/custom_graphgym/layer/example.py
  class ExampleConv1 (line 17) | class ExampleConv1(MessagePassing):
    method __init__ (line 19) | def __init__(self, in_channels, out_channels, bias=True, **kwargs):
    method reset_parameters (line 34) | def reset_parameters(self):
    method forward (line 38) | def forward(self, batch):
    method message (line 46) | def message(self, x_j):
    method update (line 49) | def update(self, aggr_out):
  class ExampleConv2Layer (line 57) | class ExampleConv2Layer(MessagePassing):
    method __init__ (line 59) | def __init__(self, in_channels, out_channels, bias=True, **kwargs):
    method reset_parameters (line 74) | def reset_parameters(self):
    method forward (line 78) | def forward(self, x, edge_index):
    method message (line 83) | def message(self, x_j):
    method update (line 86) | def update(self, aggr_out):
  class ExampleConv2 (line 93) | class ExampleConv2(nn.Module):
    method __init__ (line 94) | def __init__(self, dim_in, dim_out, bias=False, **kwargs):
    method forward (line 98) | def forward(self, batch):

FILE: graphgym/custom_graphgym/loader/example.py
  function load_dataset_example (line 6) | def load_dataset_example(format, name, dataset_dir):

FILE: graphgym/custom_graphgym/loss/example.py
  function loss_example (line 8) | def loss_example(pred, true):

FILE: graphgym/custom_graphgym/network/example.py
  class ExampleGNN (line 13) | class ExampleGNN(torch.nn.Module):
    method __init__ (line 14) | def __init__(self, dim_in, dim_out, num_layers=2, model_type='GCN'):
    method build_conv_model (line 26) | def build_conv_model(self, model_type):
    method forward (line 36) | def forward(self, batch):

FILE: graphgym/custom_graphgym/optimizer/example.py
  function adagrad_optimizer (line 11) | def adagrad_optimizer(params: Iterator[Parameter], base_lr: float,
  function plateau_scheduler (line 17) | def plateau_scheduler(optimizer: Optimizer, patience: int,

FILE: graphgym/custom_graphgym/pooling/example.py
  function global_example_pool (line 6) | def global_example_pool(x, batch, size=None):

FILE: graphgym/custom_graphgym/stage/example.py
  function GNNLayer (line 9) | def GNNLayer(dim_in, dim_out, has_act=True):
  class GNNStackStage (line 14) | class GNNStackStage(nn.Module):
    method __init__ (line 16) | def __init__(self, dim_in, dim_out, num_layers):
    method forward (line 24) | def forward(self, batch):

FILE: graphgym/custom_graphgym/train/example.py
  function train_epoch (line 17) | def train_epoch(logger, loader, model, optimizer, scheduler):
  function eval_epoch (line 36) | def eval_epoch(logger, loader, model):
  function train_example (line 51) | def train_example(loggers, loaders, model, optimizer, scheduler):

FILE: test/conftest.py
  function load_dataset (line 14) | def load_dataset(root: str, name: str, *args, **kwargs) -> Dataset:
  function get_dataset (line 50) | def get_dataset() -> Callable:
  function enable_extensions (line 64) | def enable_extensions():  # Nothing to do.
  function disable_extensions (line 69) | def disable_extensions():
  function without_extensions (line 89) | def without_extensions(request):
  function spawn_context (line 95) | def spawn_context():

FILE: test/contrib/explain/test_pgm_explainer.py
  class GCN (line 12) | class GCN(torch.nn.Module):
    method __init__ (line 13) | def __init__(self, model_config: ModelConfig):
    method forward (line 25) | def forward(self, x, edge_index, edge_weight=None, batch=None, **kwargs):
  function test_pgm_explainer_classification (line 59) | def test_pgm_explainer_classification(node_idx, task_level, perturbation...
  class DummyModel (line 93) | class DummyModel(torch.nn.Module):
    method forward (line 94) | def forward(self, x, edge_index, **kwargs):
  function test_batch_perturb_features_on_node (line 98) | def test_batch_perturb_features_on_node():

FILE: test/contrib/nn/models/test_rbcd_attack.py
  class GCN (line 10) | class GCN(torch.nn.Module):
    method __init__ (line 11) | def __init__(self):
    method forward (line 16) | def forward(self, x, edge_index, edge_weight):
  class GNN (line 22) | class GNN(torch.nn.Module):
    method __init__ (line 23) | def __init__(self):
    method forward (line 29) | def forward(self, x, edge_index, edge_weight, batch=None):
  function test_prbcd_attack (line 43) | def test_prbcd_attack(model, budget, loss, is_undirected, with_early_sto...
  function test_grbcd_attack (line 82) | def test_grbcd_attack(model, budget, is_undirected):

FILE: test/data/lightning/test_datamodule.py
  class LinearGraphModule (line 35) | class LinearGraphModule(LightningModule):
    method __init__ (line 36) | def __init__(self, in_channels: int, hidden_channels: int,
    method forward (line 48) | def forward(self, x: Tensor, batch: Data) -> Tensor:
    method training_step (line 57) | def training_step(self, data: Data, batch_idx: int):
    method validation_step (line 65) | def validation_step(self, data: Data, batch_idx: int):
    method test_step (line 70) | def test_step(self, data: Data, batch_idx: int):
    method configure_optimizers (line 75) | def configure_optimizers(self):
  function test_lightning_dataset (line 84) | def test_lightning_dataset(get_dataset, strategy_type):
  class LinearNodeModule (line 164) | class LinearNodeModule(LightningModule):
    method __init__ (line 165) | def __init__(self, in_channels: int, out_channels: int):
    method forward (line 175) | def forward(self, x: Tensor) -> Tensor:
    method training_step (line 181) | def training_step(self, data: Data, batch_idx: int):
    method validation_step (line 190) | def validation_step(self, data: Data, batch_idx: int):
    method test_step (line 196) | def test_step(self, data: Data, batch_idx: int):
    method configure_optimizers (line 202) | def configure_optimizers(self):
  function test_lightning_node_data (line 213) | def test_lightning_node_data(get_dataset, strategy_type, loader):
  class LinearHeteroNodeModule (line 273) | class LinearHeteroNodeModule(LightningModule):
    method __init__ (line 274) | def __init__(self, in_channels: int, out_channels: int):
    method forward (line 284) | def forward(self, x: Tensor) -> Tensor:
    method training_step (line 290) | def training_step(self, data: HeteroData, batch_idx: int):
    method validation_step (line 299) | def validation_step(self, data: HeteroData, batch_idx: int):
    method test_step (line 305) | def test_step(self, data: HeteroData, batch_idx: int):
    method configure_optimizers (line 311) | def configure_optimizers(self):
  function preserve_context (line 316) | def preserve_context():
  function test_lightning_hetero_node_data (line 328) | def test_lightning_hetero_node_data(preserve_context, get_dataset):
  function test_lightning_data_custom_sampler (line 353) | def test_lightning_data_custom_sampler():
  function test_lightning_hetero_link_data (line 377) | def test_lightning_hetero_link_data():
  function test_lightning_hetero_link_data_custom_store (line 437) | def test_lightning_hetero_link_data_custom_store():
  function test_eval_loader_kwargs (line 475) | def test_eval_loader_kwargs(get_dataset):

FILE: test/data/test_batch.py
  function test_batch_basic (line 15) | def test_batch_basic():
  function test_index (line 111) | def test_index():
  function test_edge_index (line 136) | def test_edge_index():
  function test_batch_with_sparse_tensor (line 177) | def test_batch_with_sparse_tensor():
  function test_batch_with_torch_coo_tensor (line 237) | def test_batch_with_torch_coo_tensor():
  function test_batching_with_new_dimension (line 279) | def test_batching_with_new_dimension():
  function test_pickling (line 321) | def test_pickling(tmp_path):
  function test_recursive_batch (line 341) | def test_recursive_batch():
  function test_batching_of_batches (line 399) | def test_batching_of_batches():
  function test_hetero_batch (line 412) | def test_hetero_batch():
  function test_pair_data_batching (line 480) | def test_pair_data_batching():
  function test_batch_with_empty_list (line 513) | def test_batch_with_empty_list():
  function test_nested_follow_batch (line 524) | def test_nested_follow_batch():
  function test_torch_sparse_batch (line 566) | def test_torch_sparse_batch(layout):
  function test_torch_nested_batch (line 588) | def test_torch_nested_batch():

FILE: test/data/test_data.py
  function test_data (line 13) | def test_data():
  function test_data_attr_cache (line 151) | def test_data_attr_cache():
  function test_data_attr_cache_not_shared (line 180) | def test_data_attr_cache_not_shared():
  function test_to_heterogeneous_empty_edge_index (line 194) | def test_to_heterogeneous_empty_edge_index():
  function test_data_subgraph (line 216) | def test_data_subgraph():
  function test_data_subgraph_with_list_field (line 269) | def test_data_subgraph_with_list_field():
  function test_data_empty_subgraph (line 285) | def test_data_empty_subgraph():
  function test_copy_data (line 295) | def test_copy_data():
  function test_data_sort (line 320) | def test_data_sort():
  function test_debug_data (line 350) | def test_debug_data():
  function run (line 364) | def run(rank, data_list):
  function test_data_share_memory (line 370) | def test_data_share_memory():
  function test_data_setter_properties (line 384) | def test_data_setter_properties():
  function test_data_update (line 410) | def test_data_update():
  function test_data_connected_components (line 421) | def test_data_connected_components():
  function test_data_find_parent (line 449) | def test_data_find_parent():
  function test_data_union (line 465) | def test_data_union():
  function test_basic_feature_store (line 514) | def test_basic_feature_store():
  function test_basic_graph_store (line 550) | def test_basic_graph_store():
  function test_data_generate_ids (line 587) | def test_data_generate_ids():
  function test_data_with_tensor_frame (line 601) | def test_data_with_tensor_frame():
  function test_data_time_handling (line 628) | def test_data_time_handling(num_nodes, num_edges):
  function test_data_inc (line 677) | def test_data_inc():

FILE: test/data/test_database.py
  function test_database_single_tensor (line 22) | def test_database_single_tensor(tmp_path, Database, batch_size):
  function test_database_schema (line 61) | def test_database_schema(tmp_path, Database):
  function test_index (line 153) | def test_index(tmp_path, Database):
  function test_edge_index (line 183) | def test_edge_index(tmp_path, Database):
  function test_database_syntactic_sugar (line 223) | def test_database_syntactic_sugar(tmp_path):
  function in_memory_get (line 273) | def in_memory_get(data):
  function db_get (line 277) | def db_get(db):

FILE: test/data/test_datapipes.py
  function dataset_adapter (line 12) | def dataset_adapter() -> DatasetAdapter:
  function test_dataset_adapter (line 19) | def test_dataset_adapter(dataset_adapter):
  function test_datapipe_batch_graphs (line 32) | def test_datapipe_batch_graphs(dataset_adapter):
  function test_functional_transform (line 39) | def test_functional_transform(dataset_adapter):
  function test_datapipe_parse_smiles (line 46) | def test_datapipe_parse_smiles():

FILE: test/data/test_dataset.py
  class MyTestDataset (line 14) | class MyTestDataset(InMemoryDataset):
    method __init__ (line 15) | def __init__(self, data_list, transform=None):
  class MyStoredTestDataset (line 20) | class MyStoredTestDataset(InMemoryDataset):
    method __init__ (line 21) | def __init__(self, root, data_list, transform=None):
    method processed_file_names (line 27) | def processed_file_names(self) -> str:
    method process (line 30) | def process(self):
  function test_in_memory_dataset (line 34) | def test_in_memory_dataset():
  function test_stored_in_memory_dataset (line 77) | def test_stored_in_memory_dataset(tmp_path):
  function test_stored_hetero_in_memory_dataset (line 102) | def test_stored_hetero_in_memory_dataset(tmp_path):
  function test_index (line 125) | def test_index(tmp_path):
  function test_edge_index (line 149) | def test_edge_index(tmp_path):
  function test_in_memory_num_classes (line 184) | def test_in_memory_num_classes():
  function test_in_memory_dataset_copy (line 212) | def test_in_memory_dataset_copy():
  function test_to_datapipe (line 229) | def test_to_datapipe():
  function test_in_memory_sparse_tensor_dataset (line 247) | def test_in_memory_sparse_tensor_dataset():
  function test_collate_with_new_dimension (line 264) | def test_collate_with_new_dimension():
  function test_hetero_in_memory_dataset (line 295) | def test_hetero_in_memory_dataset():
  function test_override_behavior (line 323) | def test_override_behavior():
  function test_lists_of_tensors_in_memory_dataset (line 389) | def test_lists_of_tensors_in_memory_dataset():
  function test_lists_of_sparse_tensors (line 410) | def test_lists_of_sparse_tensors():
  function test_file_names_as_property_and_method (line 432) | def test_file_names_as_property_and_method():
  function test_to_on_disk_dataset (line 460) | def test_to_on_disk_dataset(tmp_path):

FILE: test/data/test_dataset_summary.py
  function check_stats (line 9) | def check_stats(stats: Stats, expected: Tensor):
  function test_dataset_summary (line 20) | def test_dataset_summary():
  function test_dataset_summary_representation (line 35) | def test_dataset_summary_representation():
  function test_dataset_summary_hetero (line 45) | def test_dataset_summary_hetero():
  function test_dataset_summary_hetero_representation_length (line 58) | def test_dataset_summary_hetero_representation_length():
  function test_dataset_summary_hetero_per_type_check (line 70) | def test_dataset_summary_hetero_per_type_check():

FILE: test/data/test_feature_store.py
  class MyTensorAttrNoGroupName (line 12) | class MyTensorAttrNoGroupName(TensorAttr):
    method __init__ (line 13) | def __init__(self, attr_name=_FieldStatus.UNSET, index=_FieldStatus.UN...
  class MyFeatureStoreNoGroupName (line 18) | class MyFeatureStoreNoGroupName(MyFeatureStore):
    method __init__ (line 19) | def __init__(self):
  function test_feature_store (line 24) | def test_feature_store():
  function test_feature_store_override (line 94) | def test_feature_store_override():

FILE: test/data/test_graph_store.py
  function test_graph_store (line 13) | def test_graph_store():
  function test_graph_store_conversion (line 43) | def test_graph_store_conversion():

FILE: test/data/test_hetero_data.py
  function test_init_hetero_data (line 34) | def test_init_hetero_data():
  function test_hetero_data_to_from_dict (line 100) | def test_hetero_data_to_from_dict():
  function test_hetero_data_functions (line 114) | def test_hetero_data_functions():
  function test_hetero_data_set_value_dict (line 170) | def test_hetero_data_set_value_dict():
  function test_hetero_data_rename (line 182) | def test_hetero_data_rename():
  function test_dangling_types (line 203) | def test_dangling_types():
  function test_hetero_data_subgraph (line 215) | def test_hetero_data_subgraph():
  function test_hetero_data_empty_subgraph (line 336) | def test_hetero_data_empty_subgraph():
  function test_copy_hetero_data (line 356) | def test_copy_hetero_data():
  function test_to_homogeneous_and_vice_versa (line 388) | def test_to_homogeneous_and_vice_versa():
  function test_to_homogeneous_padding (line 531) | def test_to_homogeneous_padding():
  function test_hetero_data_to_canonical (line 547) | def test_hetero_data_to_canonical():
  function test_hetero_data_invalid_names (line 563) | def test_hetero_data_invalid_names():
  function test_hetero_data_update (line 576) | def test_hetero_data_update():
  function test_hetero_data_connected_components (line 597) | def test_hetero_data_connected_components():
  function test_hetero_data_connected_components_single_component (line 686) | def test_hetero_data_connected_components_single_component():
  function test_hetero_data_find_parent (line 701) | def test_hetero_data_find_parent():
  function test_hetero_data_union (line 716) | def test_hetero_data_union():
  function test_basic_feature_store (line 764) | def test_basic_feature_store():
  function test_hetero_data_with_tensor_frame (line 801) | def test_hetero_data_with_tensor_frame():
  function test_basic_graph_store (line 854) | def test_basic_graph_store():
  function test_generate_ids (line 896) | def test_generate_ids():
  function test_invalid_keys (line 914) | def test_invalid_keys():

FILE: test/data/test_hypergraph_data.py
  function test_hypergraph_data (line 9) | def test_hypergraph_data():
  function test_hypergraphdata_subgraph (line 138) | def test_hypergraphdata_subgraph():

FILE: test/data/test_inherit.py
  class MyData (line 6) | class MyData(Data):
    method __init__ (line 7) | def __init__(self, x=None, edge_index=None, arg=None):
    method random (line 10) | def random(self):
  class MyInMemoryDataset (line 14) | class MyInMemoryDataset(InMemoryDataset):
    method __init__ (line 15) | def __init__(self):
    method _download (line 25) | def _download(self):
    method _process (line 28) | def _process(self):
  class MyDataset (line 32) | class MyDataset(Dataset):
    method __init__ (line 33) | def __init__(self):
    method _download (line 36) | def _download(self):
    method _process (line 39) | def _process(self):
    method len (line 42) | def len(self):
    method get (line 45) | def get(self, idx):
  function test_inherit (line 52) | def test_inherit():

FILE: test/data/test_on_disk_dataset.py
  function test_pickle (line 11) | def test_pickle(tmp_path):
  function test_custom_schema (line 57) | def test_custom_schema(tmp_path):

FILE: test/data/test_remote_backend_utils.py
  function test_num_nodes_size (line 15) | def test_num_nodes_size(FeatureStore, GraphStore):

FILE: test/data/test_storage.py
  function test_base_storage (line 10) | def test_base_storage():
  function test_storage_tensor_methods (line 60) | def test_storage_tensor_methods():
  function test_setter_and_getter (line 93) | def test_setter_and_getter():

FILE: test/data/test_temporal.py
  function get_temporal_data (line 8) | def get_temporal_data(num_events, msg_channels):
  function test_temporal_data (line 18) | def test_temporal_data():
  function test_train_val_test_split (line 84) | def test_train_val_test_split():
  function test_temporal_indexing (line 98) | def test_temporal_indexing():

FILE: test/data/test_view.py
  function test_views (line 4) | def test_views():

FILE: test/datasets/graph_generator/test_ba_graph.py
  function test_ba_graph (line 4) | def test_ba_graph():

FILE: test/datasets/graph_generator/test_er_graph.py
  function test_er_graph (line 4) | def test_er_graph():

FILE: test/datasets/graph_generator/test_grid_graph.py
  function test_grid_graph (line 4) | def test_grid_graph():

FILE: test/datasets/graph_generator/test_tree_graph.py
  function test_tree_graph (line 7) | def test_tree_graph(undirected):

FILE: test/datasets/motif_generator/test_custom_motif.py
  function test_custom_motif_pyg_data (line 9) | def test_custom_motif_pyg_data():
  function test_custom_motif_networkx (line 22) | def test_custom_motif_networkx():
  function test_custom_motif_unknown (line 36) | def test_custom_motif_unknown():

FILE: test/datasets/motif_generator/test_cycle_motif.py
  function test_cycle_motif (line 4) | def test_cycle_motif():

FILE: test/datasets/motif_generator/test_grid_motif.py
  function test_grid_motif (line 4) | def test_grid_motif():

FILE: test/datasets/motif_generator/test_house_motif.py
  function test_house_motif (line 4) | def test_house_motif():

FILE: test/datasets/test_ba_shapes.py
  function test_ba_shapes (line 4) | def test_ba_shapes(get_dataset):

FILE: test/datasets/test_bzr.py
  function test_bzr (line 6) | def test_bzr(get_dataset):
  function test_bzr_with_node_attr (line 19) | def test_bzr_with_node_attr(get_dataset):

FILE: test/datasets/test_elliptic.py
  function test_elliptic_bitcoin_dataset (line 6) | def test_elliptic_bitcoin_dataset(get_dataset):

FILE: test/datasets/test_enzymes.py
  function test_enzymes (line 10) | def test_enzymes(get_dataset):
  function test_enzymes_with_node_attr (line 61) | def test_enzymes_with_node_attr(get_dataset):
  function test_cleaned_enzymes (line 69) | def test_cleaned_enzymes(get_dataset):

FILE: test/datasets/test_explainer_dataset.py
  function test_explainer_dataset_ba_house (line 17) | def test_explainer_dataset_ba_house(graph_generator, motif_generator):
  function test_explainer_dataset_reproducibility (line 38) | def test_explainer_dataset_reproducibility():

FILE: test/datasets/test_fake.py
  function test_fake_dataset (line 9) | def test_fake_dataset(num_graphs, edge_dim, task):
  function test_fake_hetero_dataset (line 47) | def test_fake_hetero_dataset(num_graphs, edge_dim, task):

FILE: test/datasets/test_git_mol_dataset.py
  function test_git_mol_dataset (line 16) | def test_git_mol_dataset(split: Tuple[int, int]) -> None:

FILE: test/datasets/test_imdb_binary.py
  function test_imdb_binary (line 6) | def test_imdb_binary(get_dataset):

FILE: test/datasets/test_infection_dataset.py
  class DummyGraph (line 9) | class DummyGraph(GraphGenerator):
    method __call__ (line 10) | def __call__(self) -> Data:
  function test_infection_dataset (line 18) | def test_infection_dataset():
  function test_infection_dataset_reproducibility (line 45) | def test_infection_dataset_reproducibility():

FILE: test/datasets/test_karate.py
  function test_karate (line 1) | def test_karate(get_dataset):

FILE: test/datasets/test_medshapenet.py
  function test_medshapenet (line 9) | def test_medshapenet():

FILE: test/datasets/test_molecule_gpt_dataset.py
  function test_molecule_gpt_dataset (line 7) | def test_molecule_gpt_dataset():

FILE: test/datasets/test_mutag.py
  function test_mutag (line 5) | def test_mutag(get_dataset):
  function test_mutag_with_node_attr (line 17) | def test_mutag_with_node_attr(get_dataset):

FILE: test/datasets/test_planetoid.py
  function test_citeseer (line 7) | def test_citeseer(get_dataset):
  function test_citeseer_with_full_split (line 36) | def test_citeseer_with_full_split(get_dataset):
  function test_citeseer_with_random_split (line 47) | def test_citeseer_with_random_split(get_dataset):

FILE: test/datasets/test_protein_mpnn_dataset.py
  function test_protein_mpnn_dataset (line 8) | def test_protein_mpnn_dataset():

FILE: test/datasets/test_snap_dataset.py
  function test_ego_facebook_snap_dataset (line 6) | def test_ego_facebook_snap_dataset(get_dataset):
  function test_soc_slashdot_snap_dataset (line 35) | def test_soc_slashdot_snap_dataset(get_dataset):
  function test_wiki_vote_snap_dataset (line 43) | def test_wiki_vote_snap_dataset(get_dataset):

FILE: test/datasets/test_suite_sparse.py
  function test_suite_sparse_dataset (line 6) | def test_suite_sparse_dataset(get_dataset):
  function test_illc1850_suite_sparse_dataset (line 15) | def test_illc1850_suite_sparse_dataset(get_dataset):

FILE: test/datasets/test_tag_dataset.py
  function test_tag_dataset (line 7) | def test_tag_dataset() -> None:

FILE: test/datasets/test_teeth3ds.py
  function test_teeth3ds (line 7) | def test_teeth3ds(tmp_path) -> None:

FILE: test/datasets/test_web_qsp_dataset.py
  function test_web_qsp_dataset (line 16) | def test_web_qsp_dataset(tmp_path):
  class MockSentenceTransformer (line 24) | class MockSentenceTransformer:
    method __init__ (line 25) | def __init__(self, *args, **kwargs):
    method to (line 28) | def to(self, device):
    method eval (line 31) | def eval(self):
    method encode (line 34) | def encode(self, sentences, batch_size=None, output_device=None):
  function create_mock_graphs (line 45) | def create_mock_graphs(tmp_path: str, train_size: int, val_size: int,
  function test_kgqa_base_dataset (line 144) | def test_kgqa_base_dataset(tmp_path, monkeypatch):

FILE: test/distributed/test_dist_link_neighbor_loader.py
  function create_dist_data (line 22) | def create_dist_data(tmp_path: str, rank: int):
  function dist_link_neighbor_loader_homo (line 29) | def dist_link_neighbor_loader_homo(
  function dist_link_neighbor_loader_hetero (line 79) | def dist_link_neighbor_loader_hetero(
  function test_dist_link_neighbor_loader_homo (line 146) | def test_dist_link_neighbor_loader_homo(
  function test_dist_link_neighbor_loader_hetero (line 187) | def test_dist_link_neighbor_loader_hetero(

FILE: test/distributed/test_dist_link_neighbor_sampler.py
  function create_data (line 27) | def create_data(rank, world_size, time_attr: Optional[str] = None):
  function create_hetero_data (line 78) | def create_hetero_data(tmp_path: str, rank: int):
  function dist_link_neighbor_sampler (line 86) | def dist_link_neighbor_sampler(
  function dist_link_neighbor_sampler_temporal (line 164) | def dist_link_neighbor_sampler_temporal(
  function dist_link_neighbor_sampler_hetero (line 252) | def dist_link_neighbor_sampler_hetero(
  function dist_link_neighbor_sampler_temporal_hetero (line 343) | def dist_link_neighbor_sampler_temporal_hetero(
  function test_dist_link_neighbor_sampler (line 441) | def test_dist_link_neighbor_sampler(disjoint):
  function test_dist_link_neighbor_sampler_temporal (line 459) | def test_dist_link_neighbor_sampler_temporal(seed_time, temporal_strategy):
  function test_dist_link_neighbor_sampler_edge_level_temporal (line 483) | def test_dist_link_neighbor_sampler_edge_level_temporal(
  function test_dist_link_neighbor_sampler_hetero (line 512) | def test_dist_link_neighbor_sampler_hetero(tmp_path, disjoint):
  function test_dist_link_neighbor_sampler_temporal_hetero (line 551) | def test_dist_link_neighbor_sampler_temporal_hetero(
  function test_dist_link_neighbor_sampler_edge_level_temporal_hetero (line 603) | def test_dist_link_neighbor_sampler_edge_level_temporal_hetero(

FILE: test/distributed/test_dist_neighbor_loader.py
  function create_dist_data (line 22) | def create_dist_data(tmp_path: str, rank: int):
  function dist_neighbor_loader_homo (line 29) | def dist_neighbor_loader_homo(
  function dist_neighbor_loader_hetero (line 82) | def dist_neighbor_loader_hetero(
  function test_dist_neighbor_loader_homo (line 160) | def test_dist_neighbor_loader_homo(
  function test_dist_neighbor_loader_hetero (line 197) | def test_dist_neighbor_loader_hetero(

FILE: test/distributed/test_dist_neighbor_sampler.py
  function create_data (line 25) | def create_data(rank: int, world_size: int, time_attr: Optional[str] = N...
  function create_hetero_data (line 76) | def create_hetero_data(
  function dist_neighbor_sampler (line 86) | def dist_neighbor_sampler(
  function dist_neighbor_sampler_temporal (line 152) | def dist_neighbor_sampler_temporal(
  function dist_neighbor_sampler_hetero (line 228) | def dist_neighbor_sampler_hetero(
  function dist_neighbor_sampler_temporal_hetero (line 307) | def dist_neighbor_sampler_temporal_hetero(
  function test_dist_neighbor_sampler (line 392) | def test_dist_neighbor_sampler(disjoint):
  function test_dist_neighbor_sampler_temporal (line 410) | def test_dist_neighbor_sampler_temporal(seed_time, temporal_strategy):
  function test_dist_neighbor_sampler_edge_level_temporal (line 434) | def test_dist_neighbor_sampler_edge_level_temporal(
  function test_dist_neighbor_sampler_hetero (line 463) | def test_dist_neighbor_sampler_hetero(tmp_path, disjoint):
  function test_dist_neighbor_sampler_temporal_hetero (line 501) | def test_dist_neighbor_sampler_temporal_hetero(
  function test_dist_neighbor_sampler_edge_level_temporal_hetero (line 553) | def test_dist_neighbor_sampler_edge_level_temporal_hetero(

FILE: test/distributed/test_dist_utils.py
  function test_remove_duplicates (line 9) | def test_remove_duplicates():
  function test_remove_duplicates_disjoint (line 22) | def test_remove_duplicates_disjoint():

FILE: test/distributed/test_local_feature_store.py
  function test_local_feature_store_global_id (line 8) | def test_local_feature_store_global_id():
  function test_local_feature_store_utils (line 35) | def test_local_feature_store_utils():
  function test_homogeneous_feature_store (line 64) | def test_homogeneous_feature_store():
  function test_heterogeneous_feature_store (line 94) | def test_heterogeneous_feature_store():

FILE: test/distributed/test_local_graph_store.py
  function test_local_graph_store (line 8) | def test_local_graph_store():
  function test_homogeneous_graph_store (line 39) | def test_homogeneous_graph_store():
  function test_heterogeneous_graph_store (line 69) | def test_heterogeneous_graph_store():
  function test_sorted_graph_store (line 101) | def test_sorted_graph_store():

FILE: test/distributed/test_partition.py
  function test_partition_data (line 18) | def test_partition_data(tmp_path):
  function test_partition_hetero_data (line 65) | def test_partition_hetero_data(tmp_path):
  function test_partition_data_temporal (line 100) | def test_partition_data_temporal(tmp_path):
  function test_partition_data_edge_level_temporal (line 121) | def test_partition_data_edge_level_temporal(tmp_path):
  function test_partition_hetero_data_temporal (line 144) | def test_partition_hetero_data_temporal(tmp_path):
  function test_partition_hetero_data_edge_level_temporal (line 168) | def test_partition_hetero_data_edge_level_temporal(tmp_path):
  function test_from_partition_data (line 198) | def test_from_partition_data(tmp_path):
  function test_from_partition_hetero_data (line 233) | def test_from_partition_hetero_data(tmp_path):
  function test_from_partition_temporal_data (line 261) | def test_from_partition_temporal_data(tmp_path):
  function test_from_partition_edge_level_temporal_data (line 287) | def test_from_partition_edge_level_temporal_data(tmp_path):
  function test_from_partition_hetero_temporal_data (line 315) | def test_from_partition_hetero_temporal_data(tmp_path):
  function test_from_partition_hetero_edge_level_temporal_data (line 348) | def test_from_partition_hetero_edge_level_temporal_data(tmp_path):

FILE: test/distributed/test_rpc.py
  function run_rpc_feature_test (line 12) | def run_rpc_feature_test(
  function test_dist_feature_lookup (line 84) | def test_dist_feature_lookup():

FILE: test/explain/algorithm/test_attention_explainer.py
  class AttentionGNN (line 25) | class AttentionGNN(torch.nn.Module):
    method __init__ (line 26) | def __init__(self):
    method forward (line 32) | def forward(self, x, edge_index):
  class HeteroAttentionGNN (line 39) | class HeteroAttentionGNN(torch.nn.Module):
    method __init__ (line 40) | def __init__(self, metadata, model_config=None):
    method forward (line 69) | def forward(self, x_dict, edge_index_dict, **kwargs):
  class HeteroConvAttentionGNN (line 90) | class HeteroConvAttentionGNN(torch.nn.Module):
    method __init__ (line 91) | def __init__(self, metadata, model_config=None):
    method _initialize_layers (line 128) | def _initialize_layers(self, x_dict):
    method forward (line 145) | def forward(self, x_dict, edge_index_dict):
  function test_attention_explainer (line 184) | def test_attention_explainer(index, check_explanation):
  function test_attention_explainer_supports (line 203) | def test_attention_explainer_supports(explanation_type, node_mask_type):
  function test_attention_explainer_attentive_fp (line 219) | def test_attention_explainer_attentive_fp(check_explanation):
  function test_attention_explainer_hetero (line 239) | def test_attention_explainer_hetero(index, hetero_data,
  function test_attention_explainer_hetero_conv (line 277) | def test_attention_explainer_hetero_conv(index, hetero_data,

FILE: test/explain/algorithm/test_captum.py
  function test_to_captum (line 36) | def test_to_captum(model, mask_type, output_idx):
  function test_captum_attribution_methods (line 65) | def test_captum_attribution_methods(mask_type, method):
  function test_custom_explain_message (line 109) | def test_custom_explain_message():
  function test_to_captum_input (line 135) | def test_to_captum_input(mask_type):

FILE: test/explain/algorithm/test_captum_explainer.py
  class GCN (line 38) | class GCN(torch.nn.Module):
    method __init__ (line 39) | def __init__(self, model_config: ModelConfig):
    method forward (line 54) | def forward(self, x, edge_index, batch=None, edge_label_index=None):
  function check_explanation (line 82) | def check_explanation(
  function test_unsupported_methods (line 100) | def test_unsupported_methods(method):
  function test_captum_explainer_binary_classification (line 120) | def test_captum_explainer_binary_classification(
  function test_captum_explainer_multiclass_classification (line 165) | def test_captum_explainer_multiclass_classification(
  function test_captum_hetero_data (line 218) | def test_captum_hetero_data(method, node_mask_type, edge_mask_type, index,
  function test_captum_explainer_supports (line 243) | def test_captum_explainer_supports(node_mask_type):

FILE: test/explain/algorithm/test_captum_hetero.py
  function test_captum_attribution_methods_hetero (line 35) | def test_captum_attribution_methods_hetero(mask_type, method, hetero_data,

FILE: test/explain/algorithm/test_explain_algorithm_utils.py
  class HeteroModel (line 10) | class HeteroModel(torch.nn.Module):
    method __init__ (line 11) | def __init__(self):
  class GraphSAGE (line 33) | class GraphSAGE(torch.nn.Module):
    method __init__ (line 34) | def __init__(self):
    method forward (line 39) | def forward(self, x, edge_index):
  function test_set_clear_mask (line 44) | def test_set_clear_mask(hetero_data):

FILE: test/explain/algorithm/test_gnn_explainer.py
  class GNN (line 22) | class GNN(torch.nn.Module):
    method __init__ (line 23) | def __init__(self, Conv, model_config: ModelConfig):
    method forward (line 38) | def forward(self, x, edge_index, batch=None, edge_label_index=None):
  function test_gnn_explainer_binary_classification (line 90) | def test_gnn_explainer_binary_classification(
  function test_gnn_explainer_multiclass_classification (line 152) | def test_gnn_explainer_multiclass_classification(
  function test_gnn_explainer_regression (line 205) | def test_gnn_explainer_regression(
  function test_gnn_explainer_cheb_conv (line 250) | def test_gnn_explainer_cheb_conv(check_explanation):
  function test_gnn_explainer_attentive_fp (line 272) | def test_gnn_explainer_attentive_fp(check_explanation):
  function test_gnn_explainer_hetero (line 306) | def test_gnn_explainer_hetero(

FILE: test/explain/algorithm/test_graphmask_explainer.py
  class GCN (line 15) | class GCN(torch.nn.Module):
    method __init__ (line 16) | def __init__(self, model_config: ModelConfig):
    method forward (line 28) | def forward(self, x, edge_index, batch=None, edge_label_index=None):
  function check_explanation (line 50) | def check_explanation(
  function test_graph_mask_explainer_binary_classification (line 99) | def test_graph_mask_explainer_binary_classification(
  function test_graph_mask_explainer_multiclass_classification (line 151) | def test_graph_mask_explainer_multiclass_classification(
  function test_graph_mask_explainer_regression (line 198) | def test_graph_mask_explainer_regression(

FILE: test/explain/algorithm/test_pg_explainer.py
  class GCN (line 16) | class GCN(torch.nn.Module):
    method __init__ (line 17) | def __init__(self, model_config: ModelConfig):
    method forward (line 29) | def forward(self, x, edge_index, batch=None, edge_label_index=None):
  function test_pg_explainer_node (line 43) | def test_pg_explainer_node(device, check_explanation, mode):
  function test_pg_explainer_graph (line 90) | def test_pg_explainer_graph(device, check_explanation, mode):
  function test_pg_explainer_hetero (line 141) | def test_pg_explainer_hetero(device, hetero_data, hetero_model,
  function test_pg_explainer_supports (line 229) | def test_pg_explainer_supports():
  function test_pg_explainer_native_hetero (line 287) | def test_pg_explainer_native_hetero(device, hetero_data, hetero_model_na...
  function test_pg_explainer_hetero_conv (line 387) | def test_pg_explainer_hetero_conv(device, hetero_data, hetero_model_custom,

FILE: test/explain/conftest.py
  function data (line 27) | def data():
  function hetero_data (line 36) | def hetero_data():
  function hetero_model (line 52) | def hetero_model():
  function hetero_model_custom (line 57) | def hetero_model_custom():
  class GraphSAGE (line 61) | class GraphSAGE(torch.nn.Module):
    method __init__ (line 62) | def __init__(self):
    method forward (line 67) | def forward(self, x, edge_index):
  class HeteroSAGE (line 72) | class HeteroSAGE(torch.nn.Module):
    method __init__ (line 73) | def __init__(self, metadata, model_config: Optional[ModelConfig] = None):
    method forward (line 86) | def forward(self, x_dict, edge_index_dict,
  function check_explanation (line 106) | def check_explanation():
  function check_explanation_hetero (line 138) | def check_explanation_hetero():
  class NativeHeteroGNN (line 178) | class NativeHeteroGNN(torch.nn.Module):
    method __init__ (line 179) | def __init__(self, metadata, model_config: Optional[ModelConfig] = None,
    method _initialize_layers (line 210) | def _initialize_layers(self, x_dict):
    method forward (line 227) | def forward(self, x_dict, edge_index_dict):
  function hetero_model_native (line 259) | def hetero_model_native():
  class HeteroConvModel (line 263) | class HeteroConvModel(torch.nn.Module):
    method __init__ (line 264) | def __init__(self, metadata, model_config: Optional[ModelConfig] = None):
    method forward (line 289) | def forward(self, x_dict, edge_index_dict):

FILE: test/explain/metric/test_basic_metric.py
  function test_groundtruth_metrics (line 10) | def test_groundtruth_metrics():
  function test_perfect_groundtruth_metrics (line 25) | def test_perfect_groundtruth_metrics():
  function test_groundtruth_true_negative (line 39) | def test_groundtruth_true_negative():

FILE: test/explain/metric/test_faithfulness.py
  class DummyModel (line 12) | class DummyModel(torch.nn.Module):
    method __init__ (line 13) | def __init__(self, model_config: ModelConfig):
    method forward (line 17) | def forward(self, x, edge_index):
  function test_unfaithfulness (line 29) | def test_unfaithfulness(top_k, explanation_type, node_mask_type, return_...

FILE: test/explain/metric/test_fidelity.py
  class DummyModel (line 13) | class DummyModel(torch.nn.Module):
    method forward (line 14) | def forward(self, x, edge_index):
  function test_fidelity (line 19) | def test_fidelity(explanation_type):
  function test_characterization_score (line 50) | def test_characterization_score():
  function test_fidelity_curve_auc (line 60) | def test_fidelity_curve_auc():

FILE: test/explain/test_explain_config.py
  function test_threshold_config (line 17) | def test_threshold_config(threshold_pairs):
  function test_configuration_config (line 40) | def test_configuration_config(explanation_type, mask_type):

FILE: test/explain/test_explainer.py
  class DummyModel (line 8) | class DummyModel(torch.nn.Module):
    method forward (line 9) | def forward(self, x, edge_index):
  function test_get_prediction (line 13) | def test_get_prediction(data):
  function test_forward (line 34) | def test_forward(data, target, explanation_type):
  function test_hard_threshold (line 70) | def test_hard_threshold(data, threshold_value, node_mask_type):
  function test_topk_threshold (line 96) | def test_topk_threshold(data, threshold_value, threshold_type, node_mask...

FILE: test/explain/test_explanation.py
  function create_random_explanation (line 13) | def create_random_explanation(
  function test_available_explanations (line 49) | def test_available_explanations(data, node_mask_type, edge_mask_type):
  function test_validate_explanation (line 65) | def test_validate_explanation(data):
  function test_node_mask (line 85) | def test_node_mask(data):
  function test_edge_mask (line 111) | def test_edge_mask(data):
  function test_visualize_feature_importance (line 140) | def test_visualize_feature_importance(tmp_path, data, top_k, node_mask_t...

FILE: test/explain/test_hetero_explainer.py
  class DummyModel (line 12) | class DummyModel(torch.nn.Module):
    method forward (line 13) | def forward(self, x_dict, edge_index_dict, *args) -> torch.Tensor:
  function test_get_prediction (line 17) | def test_get_prediction(hetero_data):
  function test_forward (line 39) | def test_forward(hetero_data, target, explanation_type):
  function test_hard_threshold (line 74) | def test_hard_threshold(hetero_data, threshold_value, node_mask_type):
  function test_topk_threshold (line 100) | def test_topk_threshold(hetero_data, threshold_value, threshold_type,

FILE: test/explain/test_hetero_explanation.py
  function create_random_explanation (line 13) | def create_random_explanation(
  function test_available_explanations (line 46) | def test_available_explanations(hetero_data, node_mask_type, edge_mask_t...
  function test_validate_explanation (line 62) | def test_validate_explanation(hetero_data):
  function test_node_mask (line 82) | def test_node_mask():
  function test_edge_mask (line 98) | def test_edge_mask():
  function test_visualize_feature_importance (line 126) | def test_visualize_feature_importance(
  function test_hetero_visualize_graph (line 148) | def test_hetero_visualize_graph(tmp_path, hetero_data):

FILE: test/graphgym/test_config.py
  class MyConfig (line 7) | class MyConfig:
  function my_func (line 12) | def my_func(a: int, b: int = 2) -> str:
  function test_from_config (line 16) | def test_from_config():

FILE: test/graphgym/test_graphgym.py
  function trivial_metric (line 38) | def trivial_metric(true, pred, task_type):
  function test_run_single_graphgym (line 49) | def test_run_single_graphgym(tmp_path, capfd, auto_resume, skip_train_eval,
  function test_graphgym_module (line 114) | def test_graphgym_module(tmp_path):
  function destroy_process_group (line 167) | def destroy_process_group():
  function test_train (line 176) | def test_train(destroy_process_group, tmp_path, capfd):

FILE: test/graphgym/test_logger.py
  function test_logger_callback (line 8) | def test_logger_callback():

FILE: test/graphgym/test_register.py
  function identity_act (line 8) | def identity_act(x: torch.Tensor) -> torch.Tensor:
  function test_register (line 13) | def test_register():

FILE: test/io/test_fs.py
  function tmp_fs_path (line 20) | def tmp_fs_path(request, tmp_path) -> str:
  function test_get_fs (line 28) | def test_get_fs():
  function test_normpath (line 34) | def test_normpath():
  function test_exists (line 39) | def test_exists(tmp_fs_path):
  function test_makedirs (line 47) | def test_makedirs(tmp_fs_path):
  function test_ls (line 55) | def test_ls(tmp_fs_path, detail):
  function test_cp (line 68) | def test_cp(tmp_fs_path):
  function test_extract (line 95) | def test_extract(tmp_fs_path):
  function test_torch_save_load (line 126) | def test_torch_save_load(tmp_fs_path):

FILE: test/io/test_off.py
  function test_read_off (line 12) | def test_read_off():
  function test_write_off (line 26) | def test_write_off():

FILE: test/llm/conftest.py
  function pytest_collection_modifyitems (line 8) | def pytest_collection_modifyitems(items):

FILE: test/llm/models/test_g_retriever.py
  function test_g_retriever (line 16) | def test_g_retriever(use_lora: bool) -> None:
  function test_g_retriever_many_tokens (line 63) | def test_g_retriever_many_tokens() -> None:
  class DummyHFModel (line 110) | class DummyHFModel(nn.Module):
    method __init__ (line 111) | def __init__(self, vocab_size=10):
    method forward (line 116) | def forward(self, inputs_embeds=None, **kwargs):
  class DummyLLM (line 128) | class DummyLLM:
    method __init__ (line 129) | def __init__(self, hidden_dim):
    method _get_embeds (line 135) | def _get_embeds(self, question, *args):
  class DummyGNN (line 146) | class DummyGNN(nn.Module):
    method __init__ (line 148) | def __init__(self, in_channels=4, out_channels=8):
    method forward (line 154) | def forward(self, *args, **kwargs):
  function test_gretriever_prefix_embedding_injection (line 160) | def test_gretriever_prefix_embedding_injection(batch_size):

FILE: test/llm/models/test_git_mol.py
  function test_git_mol (line 8) | def test_git_mol():

FILE: test/llm/models/test_glem.py
  function test_deal_nan_tensor_replaces_nans (line 7) | def test_deal_nan_tensor_replaces_nans():
  function test_deal_nan_non_tensor_passthrough (line 17) | def test_deal_nan_non_tensor_passthrough():
  function test_deal_nan_tensor_dtypes (line 23) | def test_deal_nan_tensor_dtypes(dtype):
  function test_deal_nan_is_non_mutating (line 43) | def test_deal_nan_is_non_mutating():

FILE: test/llm/models/test_llm.py
  function test_get_llm_kwargs (line 12) | def test_get_llm_kwargs():
  function test_llm (line 22) | def test_llm(sys_prompt, context, use_embedding) -> None:
  class DummyBatch (line 47) | class DummyBatch(dict):
    method to (line 49) | def to(self, device):
  class DummyTokenizer (line 53) | class DummyTokenizer:
    method __call__ (line 57) | def __call__(self, texts, return_tensors=None, padding=True):
  class DummyModel (line 75) | class DummyModel(torch.nn.Module):
    method get_input_embeddings (line 76) | def get_input_embeddings(self):
    method forward (line 79) | def forward(self, inputs_embeds=None, attention_mask=None, **kwargs):
  function dummy_llm (line 91) | def dummy_llm():
  function test_llm_prepare_inputs (line 100) | def test_llm_prepare_inputs(dummy_llm):
  function test_llm_single_prompt (line 120) | def test_llm_single_prompt(dummy_llm):
  function test_llm_variable_lengths (line 126) | def test_llm_variable_lengths(dummy_llm):

FILE: test/llm/models/test_llm_judge.py
  function test_llm_judge (line 6) | def test_llm_judge():

FILE: test/llm/models/test_molecule_gpt.py
  function test_molecule_gpt (line 12) | def test_molecule_gpt() -> None:

FILE: test/llm/models/test_protein_mpnn.py
  function test_protein_mpnn (line 8) | def test_protein_mpnn():

FILE: test/llm/models/test_sentence_transformer.py
  function test_sentence_transformer (line 17) | def test_sentence_transformer(batch_size, pooling_strategy, device, verb...
  function test_mean_pooling (line 48) | def test_mean_pooling():
  function test_last_pooling (line 58) | def test_last_pooling(mask):

FILE: test/llm/models/test_txt2kg.py
  function test_init_local_lm_flag (line 16) | def test_init_local_lm_flag():
  function test_parse_n_check_triples_formats (line 22) | def test_parse_n_check_triples_formats():
  function test_chunk_text_simple_sentence (line 29) | def test_chunk_text_simple_sentence():
  class DummyLLM (line 36) | class DummyLLM:
    method __init__ (line 37) | def __init__(self):
    method inference (line 40) | def inference(self, *args, **kwargs):
  function test_local_lm_integration (line 44) | def test_local_lm_integration(monkeypatch):
  function test_add_doc_empty (line 60) | def test_add_doc_empty(monkeypatch):
  function test_add_doc_to_KG (line 67) | def test_add_doc_to_KG(monkeypatch):
  function test_merge_triples_deterministically_basic (line 83) | def test_merge_triples_deterministically_basic():
  function test_merge_triples_deterministically_unicode_and_nonstring (line 101) | def test_merge_triples_deterministically_unicode_and_nonstring():
  function test_merge_triples_deterministically_empty (line 119) | def test_merge_triples_deterministically_empty():
  function test_merge_triples_deterministically_singleton (line 127) | def test_merge_triples_deterministically_singleton():
  function test_chunk_to_triples_str_cloud (line 135) | def test_chunk_to_triples_str_cloud(monkeypatch):
  function dummy_multiproc_helper (line 177) | def dummy_multiproc_helper(
  function test_extract_relevant_triples_cloud (line 191) | def test_extract_relevant_triples_cloud(monkeypatch):
  function test_multiproc_helper_success (line 202) | def test_multiproc_helper_success(monkeypatch):
  function test_multiproc_helper_retry (line 233) | def test_multiproc_helper_retry(monkeypatch):
  function test_add_doc_empty_text (line 262) | def test_add_doc_empty_text():
  function test_add_doc_empty_text_with_QA_pair (line 275) | def test_add_doc_empty_text_with_QA_pair():
  function kg_cpu (line 287) | def kg_cpu():
  function test_add_doc_empty_text_cpu (line 292) | def test_add_doc_empty_text_cpu(kg_cpu):
  function test_add_doc_empty_text_with_QA_pair_cpu (line 300) | def test_add_doc_empty_text_with_QA_pair_cpu(kg_cpu):
  function test_add_doc_nonempty_text_placeholder (line 308) | def test_add_doc_nonempty_text_placeholder(kg_cpu, monkeypatch):

FILE: test/llm/models/test_vision_transformer.py
  function test_vision_transformer (line 10) | def test_vision_transformer(device):

FILE: test/llm/test_large_graph_indexer.py
  function featurize (line 30) | def featurize(s: str) -> int:
  function sample_triplets (line 34) | def sample_triplets(amount: int = 1) -> List[TripletLike]:
  function test_basic_collate (line 43) | def test_basic_collate():
  function test_large_graph_index (line 70) | def test_large_graph_index():
  function test_save_load (line 155) | def test_save_load(tmp_path):

FILE: test/llm/test_rag_loader.py
  class MockRAGFeatureStore (line 22) | class MockRAGFeatureStore:
    method __init__ (line 24) | def __init__(self):
    method retrieve_seed_nodes (line 28) | def retrieve_seed_nodes(self, query: Any, **kwargs):
    method config (line 35) | def config(self) -> Dict[str, Any]:
    method config (line 39) | def config(self, config: Dict[str, Any]):
    method retrieve_seed_edges (line 46) | def retrieve_seed_edges(self, query: Any, **kwargs):
    method load_subgraph (line 50) | def load_subgraph(self, sample):
  class MockRAGGraphStore (line 58) | class MockRAGGraphStore:
    method __init__ (line 60) | def __init__(self):
    method sample_subgraph (line 64) | def sample_subgraph(self, seed_nodes, seed_edges=None, **kwargs):
    method config (line 71) | def config(self) -> Dict[str, Any]:
    method config (line 75) | def config(self, config: Dict[str, Any]):
    method register_feature_store (line 82) | def register_feature_store(self, feature_store):
  class TestRAGQueryLoader (line 86) | class TestRAGQueryLoader:
    method setup_method (line 88) | def setup_method(self):
    method test_initialization_basic (line 97) | def test_initialization_basic(self):
    method test_initialization_with_all_params (line 108) | def test_initialization_with_all_params(self):
    method test_bad_config (line 126) | def test_bad_config(self):
    method test_config_propagation (line 133) | def test_config_propagation(self):
    method test_basic_query_without_vector_retriever (line 140) | def test_basic_query_without_vector_retriever(self):
    method test_query_with_vector_retriever (line 156) | def test_query_with_vector_retriever(self):
    method test_query_with_subgraph_filter (line 177) | def test_query_with_subgraph_filter(self):
  function test_rag_loader_integration (line 204) | def test_rag_loader_integration(tmp_path):

FILE: test/llm/utils/test_rag_backend_utils.py
  function test_preprocess_triplet (line 19) | def test_preprocess_triplet():
  function test_batch_knn (line 25) | def test_batch_knn():
  function create_mock_data (line 43) | def create_mock_data(num_nodes=3, num_edges=2):
  function test_empty_graph (line 67) | def test_empty_graph():
  function test_topk_zero (line 83) | def test_topk_zero():
  class MockSentenceTransformer (line 96) | class MockSentenceTransformer:
    method encode (line 97) | def encode(self, sentences, **kwargs):
  function create_mock_graph_and_triples (line 101) | def create_mock_graph_and_triples():
  function test_apply_retrieval_via_pcst_isolated_node (line 121) | def test_apply_retrieval_via_pcst_isolated_node():
  class MockEmbeddingModel (line 141) | class MockEmbeddingModel:
    method __init__ (line 143) | def __init__(self, embed_dim: int = 64):
    method __call__ (line 146) | def __call__(self, texts: List[str], **kwargs) -> torch.Tensor:
  class TestCreateGraphFromTriples (line 162) | class TestCreateGraphFromTriples:
    method setup_method (line 164) | def setup_method(self):
    method test_create_graph_basic_functionality (line 171) | def test_create_graph_basic_functionality(self):
    method test_create_graph_empty_triples (line 192) | def test_create_graph_empty_triples(self):
  class TestCreateRemoteBackendFromGraphData (line 205) | class TestCreateRemoteBackendFromGraphData:
    method setup_method (line 207) | def setup_method(self):
    method test_create_backend_data_load (line 219) | def test_create_backend_data_load(self):

FILE: test/llm/utils/test_rag_feature_store.py
  class TestKNNRAGFeatureStore (line 11) | class TestKNNRAGFeatureStore:
    method setup_method (line 13) | def setup_method(self):
    method test_bad_config (line 24) | def test_bad_config(self):
    method create_feature_store (line 30) | def create_feature_store(self):
    method test_retrieve_seed_nodes_single_query (line 43) | def test_retrieve_seed_nodes_single_query(self):
    method test_retrieve_seed_nodes_multiple_queries (line 78) | def test_retrieve_seed_nodes_multiple_queries(self):
    method test_load_subgraph_valid_sample (line 112) | def test_load_subgraph_valid_sample(self, induced):

FILE: test/llm/utils/test_rag_graph_store.py
  function setup_test_fixtures (line 11) | def setup_test_fixtures():
  function test_sample_subgraph_with_valid_tensor_input (line 18) | def test_sample_subgraph_with_valid_tensor_input():
  function test_bad_config (line 65) | def test_bad_config():

FILE: test/llm/utils/test_vectorrag.py
  function sample_documents (line 8) | def sample_documents():
  function sample_model (line 18) | def sample_model():
  function test_save_load (line 34) | def test_save_load(sample_documents, sample_model, tmp_path):
  function test_query (line 47) | def test_query(sample_documents, sample_model):

FILE: test/loader/test_cache.py
  function test_cached_loader (line 11) | def test_cached_loader(device):
  function test_cached_loader_transform (line 46) | def test_cached_loader_transform(device):

FILE: test/loader/test_cluster.py
  function test_cluster_gcn (line 11) | def test_cluster_gcn():
  function test_keep_inter_cluster_edges (line 111) | def test_keep_inter_cluster_edges():
  function test_cluster_gcn_correctness (line 156) | def test_cluster_gcn_correctness(get_dataset, sparse_format):

FILE: test/loader/test_dataloader.py
  function test_dataloader (line 28) | def test_dataloader(num_workers, device):
  function test_dataloader_on_disk_dataset (line 78) | def test_dataloader_on_disk_dataset(tmp_path, num_workers):
  function test_dataloader_fallbacks (line 94) | def test_dataloader_fallbacks():
  function test_multiprocessing (line 144) | def test_multiprocessing():
  function test_pin_memory (line 159) | def test_pin_memory():
  function test_heterogeneous_dataloader (line 171) | def test_heterogeneous_dataloader(num_workers):
  function test_index_dataloader (line 193) | def test_index_dataloader(num_workers):
  function test_edge_index_dataloader (line 216) | def test_edge_index_dataloader(num_workers, sort_order):
  function test_dataloader_tensor_frame (line 247) | def test_dataloader_tensor_frame():
  function test_dataloader_sparse (line 266) | def test_dataloader_sparse():

FILE: test/loader/test_dynamic_batch_sampler.py
  function test_dataloader_with_dynamic_batches (line 10) | def test_dataloader_with_dynamic_batches():

FILE: test/loader/test_graph_saint.py
  function test_graph_saint (line 13) | def test_graph_saint():

FILE: test/loader/test_hgt_loader.py
  function is_subset (line 16) | def is_subset(subedge_index, edge_index, src_idx, dst_idx):
  function test_hgt_loader (line 25) | def test_hgt_loader():
  function test_hgt_loader_on_cora (line 141) | def test_hgt_loader_on_cora(get_dataset):
  function test_hgt_loader_disconnected (line 192) | def test_hgt_loader_disconnected():

FILE: test/loader/test_ibmb_loader.py
  function test_ibmb_batch_loader (line 20) | def test_ibmb_batch_loader(use_sparse_tensor, kwargs):
  function test_ibmb_node_loader (line 49) | def test_ibmb_node_loader(use_sparse_tensor, kwargs):

FILE: test/loader/test_imbalanced_sampler.py
  function test_dataloader_with_imbalanced_sampler (line 15) | def test_dataloader_with_imbalanced_sampler():
  function test_in_memory_dataset_imbalanced_sampler (line 51) | def test_in_memory_dataset_imbalanced_sampler():
  function test_neighbor_loader_with_imbalanced_sampler (line 67) | def test_neighbor_loader_with_imbalanced_sampler():
  function test_hetero_neighbor_loader_with_imbalanced_sampler (line 98) | def test_hetero_neighbor_loader_with_imbalanced_sampler():

FILE: test/loader/test_link_neighbor_loader.py
  function unique_edge_pairs (line 16) | def unique_edge_pairs(edge_index):
  function test_homo_link_neighbor_loader_basic (line 25) | def test_homo_link_neighbor_loader_basic(device, subgraph_type,
  function test_hetero_link_neighbor_loader_basic (line 112) | def test_hetero_link_neighbor_loader_basic(subgraph_type, neg_sampling_r...
  function test_hetero_link_neighbor_loader_loop (line 157) | def test_hetero_link_neighbor_loader_loop(subgraph_type):
  function test_link_neighbor_loader_edge_label (line 187) | def test_link_neighbor_loader_edge_label():
  function test_temporal_homo_link_neighbor_loader (line 219) | def test_temporal_homo_link_neighbor_loader(batch_size):
  function test_temporal_hetero_link_neighbor_loader (line 250) | def test_temporal_hetero_link_neighbor_loader():
  function test_custom_hetero_link_neighbor_loader (line 296) | def test_custom_hetero_link_neighbor_loader():
  function test_homo_link_neighbor_loader_no_edges (line 363) | def test_homo_link_neighbor_loader_no_edges():
  function test_hetero_link_neighbor_loader_no_edges (line 379) | def test_hetero_link_neighbor_loader_no_edges():
  function test_homo_link_neighbor_loader_triplet (line 402) | def test_homo_link_neighbor_loader_triplet(disjoint, temporal, amount):
  function test_hetero_link_neighbor_loader_triplet (line 483) | def test_hetero_link_neighbor_loader_triplet(disjoint, temporal, amount):
  function test_link_neighbor_loader_mapping (line 583) | def test_link_neighbor_loader_mapping():

FILE: test/loader/test_mixin.py
  function test_cpu_affinity_neighbor_loader (line 19) | def test_cpu_affinity_neighbor_loader(loader_cores, spawn_context):
  function init_fn (line 41) | def init_fn(worker_id):
  function test_multithreading_neighbor_loader (line 49) | def test_multithreading_neighbor_loader(spawn_context):

FILE: test/loader/test_neighbor_loader.py
  function is_subset (line 56) | def is_subset(subedge_index, edge_index, src_idx, dst_idx):
  function test_homo_neighbor_loader_basic (line 69) | def test_homo_neighbor_loader_basic(
  function test_hetero_neighbor_loader_basic (line 148) | def test_hetero_neighbor_loader_basic(subgraph_type, dtype):
  function test_homo_neighbor_loader_on_karate (line 316) | def test_homo_neighbor_loader_on_karate(get_dataset, subgraph_type):
  function test_hetero_neighbor_loader_on_karate (line 362) | def test_hetero_neighbor_loader_on_karate(get_dataset, subgraph_type):
  function test_temporal_hetero_neighbor_loader_on_karate (line 408) | def test_temporal_hetero_neighbor_loader_on_karate(get_dataset):
  function test_custom_neighbor_loader (line 426) | def test_custom_neighbor_loader():
  function test_custom_hetero_neighbor_loader (line 471) | def test_custom_hetero_neighbor_loader():
  function test_custom_hetero_neighbor_loader_duplicate (line 552) | def test_custom_hetero_neighbor_loader_duplicate():
  function test_temporal_custom_neighbor_loader_on_karate (line 583) | def test_temporal_custom_neighbor_loader_on_karate(get_dataset):
  function test_pyg_lib_and_torch_sparse_homo_equality (line 644) | def test_pyg_lib_and_torch_sparse_homo_equality():
  function test_pyg_lib_and_torch_sparse_hetero_equality (line 665) | def test_pyg_lib_and_torch_sparse_hetero_equality():
  function test_memmap_neighbor_loader (line 716) | def test_memmap_neighbor_loader(tmp_path):
  function test_homo_neighbor_loader_sampled_info (line 737) | def test_homo_neighbor_loader_sampled_info():
  function test_hetero_neighbor_loader_sampled_info (line 758) | def test_hetero_neighbor_loader_sampled_info():
  function test_neighbor_loader_mapping (line 798) | def test_neighbor_loader_mapping():
  function test_weighted_homo_neighbor_loader (line 823) | def test_weighted_homo_neighbor_loader():
  function test_weighted_hetero_neighbor_loader (line 853) | def test_weighted_hetero_neighbor_loader():
  function test_edge_level_temporal_homo_neighbor_loader (line 887) | def test_edge_level_temporal_homo_neighbor_loader():
  function test_edge_level_temporal_hetero_neighbor_loader (line 914) | def test_edge_level_temporal_hetero_neighbor_loader():
  function test_neighbor_loader_with_tensor_frame (line 944) | def test_neighbor_loader_with_tensor_frame(device):
  function test_neighbor_loader_input_id (line 972) | def test_neighbor_loader_input_id():
  function test_temporal_neighbor_loader_single_link (line 1001) | def test_temporal_neighbor_loader_single_link():

FILE: test/loader/test_neighbor_sampler.py
  function test_neighbor_sampler_basic (line 12) | def test_neighbor_sampler_basic():
  function test_neighbor_sampler_invalid_kwargs (line 44) | def test_neighbor_sampler_invalid_kwargs():
  function test_neighbor_sampler_on_cora (line 52) | def test_neighbor_sampler_on_cora(get_dataset):

FILE: test/loader/test_prefetch.py
  function test_prefetch_loader (line 9) | def test_prefetch_loader(device):

FILE: test/loader/test_random_node_loader.py
  function test_random_node_loader (line 8) | def test_random_node_loader():
  function test_heterogeneous_random_node_loader (line 27) | def test_heterogeneous_random_node_loader():

FILE: test/loader/test_shadow.py
  function test_shadow_k_hop_sampler (line 10) | def test_shadow_k_hop_sampler():

FILE: test/loader/test_temporal_dataloader.py
  function test_temporal_dataloader (line 9) | def test_temporal_dataloader(batch_size, drop_last):

FILE: test/loader/test_utils.py
  function test_index_select (line 7) | def test_index_select():
  function test_index_select_out_of_range (line 14) | def test_index_select_out_of_range():

FILE: test/loader/test_zip_loader.py
  function test_zip_loader (line 11) | def test_zip_loader(filter_per_worker):

FILE: test/metrics/test_link_pred_metric.py
  function test_precision (line 28) | def test_precision(num_src_nodes, num_dst_nodes, num_edges, batch_size, k):
  function test_recall (line 70) | def test_recall():
  function test_f1 (line 108) | def test_f1():
  function test_map (line 124) | def test_map():
  function test_ndcg (line 140) | def test_ndcg():
  function test_mrr (line 177) | def test_mrr():
  function test_hit_ratio (line 194) | def test_hit_ratio():
  function test_coverage (line 211) | def test_coverage():
  function test_diversity (line 234) | def test_diversity():
  function test_personalization (line 249) | def test_personalization(device):
  function test_average_popularity (line 270) | def test_average_popularity():
  function test_metric_collection (line 287) | def test_metric_collection(num_src_nodes, num_dst_nodes, num_edges):
  function test_empty_ground_truth (line 331) | def test_empty_ground_truth():

FILE: test/nn/aggr/test_aggr_utils.py
  function test_multihead_attention_block (line 13) | def test_multihead_attention_block(device: torch.device):
  function test_multihead_attention_block_dropout (line 34) | def test_multihead_attention_block_dropout(device: torch.device):
  function test_set_attention_block (line 41) | def test_set_attention_block():
  function test_induced_set_attention_block (line 57) | def test_induced_set_attention_block():
  function test_pooling_by_multihead_attention (line 72) | def test_pooling_by_multihead_attention():

FILE: test/nn/aggr/test_attention.py
  function test_attentional_aggregation (line 10) | def test_attentional_aggregation(dim):

FILE: test/nn/aggr/test_basic.py
  function test_validate (line 18) | def test_validate():
  function test_basic_aggregation (line 44) | def test_basic_aggregation(Aggregation):
  function test_var_aggregation (line 66) | def test_var_aggregation():
  function test_empty_std_aggregation (line 78) | def test_empty_std_aggregation():
  function test_learnable_aggregation (line 94) | def test_learnable_aggregation(Aggregation, learn):
  function test_learnable_channels_aggregation (line 122) | def test_learnable_channels_aggregation(Aggregation):

FILE: test/nn/aggr/test_deep_sets.py
  function test_deep_sets_aggregation (line 6) | def test_deep_sets_aggregation():

FILE: test/nn/aggr/test_equilibrium.py
  function test_equilibrium (line 9) | def test_equilibrium(iter, alpha):
  function test_equilibrium_batch (line 28) | def test_equilibrium_batch(iter, alpha):

FILE: test/nn/aggr/test_fused.py
  function test_fused_aggregation (line 18) | def test_fused_aggregation(aggrs):
  function test_empty_fused_std_aggregation (line 45) | def test_empty_fused_std_aggregation():
  function naive_aggr (line 75) | def naive_aggr(x, index, dim_size):
  function fused_aggr (line 79) | def fused_aggr(x, index, dim_size):

FILE: test/nn/aggr/test_gmt.py
  function test_graph_multiset_transformer (line 7) | def test_graph_multiset_transformer():

FILE: test/nn/aggr/test_gru.py
  function test_gru_aggregation (line 6) | def test_gru_aggregation():

FILE: test/nn/aggr/test_lcm.py
  function test_lcm_aggregation_with_project (line 10) | def test_lcm_aggregation_with_project():
  function test_lcm_aggregation_without_project (line 21) | def test_lcm_aggregation_without_project():
  function test_lcm_aggregation_error_handling (line 32) | def test_lcm_aggregation_error_handling():

FILE: test/nn/aggr/test_lstm.py
  function test_lstm_aggregation (line 7) | def test_lstm_aggregation():

FILE: test/nn/aggr/test_mlp_aggr.py
  function test_mlp_aggregation (line 6) | def test_mlp_aggregation():

FILE: test/nn/aggr/test_multi.py
  function test_multi_aggr (line 21) | def test_multi_aggr(multi_aggr_tuple):

FILE: test/nn/aggr/test_patch_transformer.py
  function test_patch_transformer_aggregation (line 8) | def test_patch_transformer_aggregation(device: torch.device) -> None:

FILE: test/nn/aggr/test_quantile.py
  function test_quantile_aggregation (line 12) | def test_quantile_aggregation(q, interpolation, dim, dim_size, fill_value):
  function test_median_aggregation (line 41) | def test_median_aggregation():
  function test_quantile_aggregation_multi (line 80) | def test_quantile_aggregation_multi():
  function test_quantile_aggregation_validate (line 103) | def test_quantile_aggregation_validate():

FILE: test/nn/aggr/test_scaler.py
  function test_degree_scaler_aggregation (line 8) | def test_degree_scaler_aggregation(train_norm):

FILE: test/nn/aggr/test_set2set.py
  function test_set2set (line 6) | def test_set2set():

FILE: test/nn/aggr/test_set_transformer.py
  function test_set_transformer_aggregation (line 10) | def test_set_transformer_aggregation():

FILE: test/nn/aggr/test_sort.py
  function test_sort_aggregation (line 6) | def test_sort_aggregation():
  function test_sort_aggregation_smaller_than_k (line 30) | def test_sort_aggregation_smaller_than_k():
  function test_sort_aggregation_dim_size (line 56) | def test_sort_aggregation_dim_size():

FILE: test/nn/aggr/test_variance_preserving.py
  function test_variance_preserving (line 12) | def test_variance_preserving():

FILE: test/nn/attention/test_performer_attention.py
  function test_performer_attention (line 6) | def test_performer_attention():

FILE: test/nn/attention/test_polynormer_attention.py
  function test_performer_attention (line 6) | def test_performer_attention():

FILE: test/nn/attention/test_qformer.py
  function test_qformer (line 6) | def test_qformer():

FILE: test/nn/conv/cugraph/test_cugraph_gat_conv.py
  function test_gat_conv_equality (line 17) | def test_gat_conv_equality(bias, bipartite, concat, edge_attr, heads,

FILE: test/nn/conv/cugraph/test_cugraph_rgcn_conv.py
  function test_rgcn_conv_equality (line 18) | def test_rgcn_conv_equality(aggr, bias, bipartite, max_num_neighbors,

FILE: test/nn/conv/cugraph/test_cugraph_sage_conv.py
  function test_sage_conv_equality (line 17) | def test_sage_conv_equality(aggr, bias, bipartite, max_num_neighbors,

FILE: test/nn/conv/test_agnn_conv.py
  function test_agnn_conv (line 12) | def test_agnn_conv(requires_grad):

FILE: test/nn/conv/test_antisymmetric_conv.py
  function test_antisymmetric_conv (line 9) | def test_antisymmetric_conv():

FILE: test/nn/conv/test_appnp.py
  function test_appnp (line 10) | def test_appnp():
  function test_appnp_dropout (line 45) | def test_appnp_dropout():

FILE: test/nn/conv/test_arma_conv.py
  function test_arma_conv (line 11) | def test_arma_conv():
  function test_lazy_arma_conv (line 35) | def test_lazy_arma_conv():

FILE: test/nn/conv/test_cg_conv.py
  function test_cg_conv (line 12) | def test_cg_conv(batch_norm):
  function test_cg_conv_with_edge_features (line 55) | def test_cg_conv_with_edge_features():

FILE: test/nn/conv/test_cheb_conv.py
  function test_cheb_conv (line 8) | def test_cheb_conv():
  function test_cheb_conv_batch (line 50) | def test_cheb_conv_batch():

FILE: test/nn/conv/test_cluster_gcn_conv.py
  function test_cluster_gcn_conv (line 10) | def test_cluster_gcn_conv():

FILE: test/nn/conv/test_create_gnn.py
  class GCNConv (line 7) | class GCNConv(MessagePassing):
    method __init__ (line 8) | def __init__(self, in_channels, out_channels):
    method forward (line 12) | def forward(self, x, edge_index):
    method message (line 24) | def message(self, x_j, norm):
    method update (line 27) | def update(self, aggr_out):
  function test_create_gnn (line 31) | def test_create_gnn():

FILE: test/nn/conv/test_dir_gnn_conv.py
  function test_dir_gnn_conv (line 6) | def test_dir_gnn_conv():
  function test_static_dir_gnn_conv (line 17) | def test_static_dir_gnn_conv():

FILE: test/nn/conv/test_dna_conv.py
  function test_dna_conv (line 13) | def test_dna_conv(channels, num_layers):
  function test_dna_conv_sparse_tensor (line 48) | def test_dna_conv_sparse_tensor(channels, num_layers):

FILE: test/nn/conv/test_edge_conv.py
  function test_edge_conv_conv (line 13) | def test_edge_conv_conv():
  function test_dynamic_edge_conv (line 66) | def test_dynamic_edge_conv():

FILE: test/nn/conv/test_eg_conv.py
  function test_eg_conv_with_error (line 11) | def test_eg_conv_with_error():
  function test_eg_conv (line 24) | def test_eg_conv(aggregators, add_self_loops):
  function test_eg_conv_with_sparse_input_feature (line 63) | def test_eg_conv_with_sparse_input_feature():

FILE: test/nn/conv/test_fa_conv.py
  function test_fa_conv (line 13) | def test_fa_conv():

FILE: test/nn/conv/test_feast_conv.py
  function test_feast_conv (line 10) | def test_feast_conv():

FILE: test/nn/conv/test_film_conv.py
  function test_film_conv (line 9) | def test_film_conv():

FILE: test/nn/conv/test_fused_gat_conv.py
  function test_to_graph_format (line 7) | def test_to_graph_format() -> None:
  function test_fused_gat_conv (line 26) | def test_fused_gat_conv() -> None:

FILE: test/nn/conv/test_gat_conv.py
  function test_gat_conv (line 15) | def test_gat_conv(residual):
  function test_gat_conv_with_edge_attr (line 157) | def test_gat_conv_with_edge_attr():
  function test_gat_conv_empty_edge_index (line 195) | def test_gat_conv_empty_edge_index(device):

FILE: test/nn/conv/test_gated_graph_conv.py
  function test_gated_graph_conv (line 10) | def test_gated_graph_conv():

FILE: test/nn/conv/test_gatv2_conv.py
  function test_gatv2_conv (line 15) | def test_gatv2_conv(residual):
  function test_gatv2_conv_with_edge_attr (line 143) | def test_gatv2_conv_with_edge_attr():

FILE: test/nn/conv/test_gcn2_conv.py
  function test_gcn2_conv (line 10) | def test_gcn2_conv():

FILE: test/nn/conv/test_gcn_conv.py
  function test_gcn_conv (line 12) | def test_gcn_conv():
  function test_gcn_conv_with_decomposed_layers (line 57) | def test_gcn_conv_with_decomposed_layers():
  function test_gcn_conv_with_sparse_input_feature (line 81) | def test_gcn_conv_with_sparse_input_feature():
  function test_static_gcn_conv (line 93) | def test_static_gcn_conv():
  function test_gcn_conv_error (line 102) | def test_gcn_conv_error():
  function test_gcn_conv_flow (line 107) | def test_gcn_conv_flow():
  function test_gcn_norm_gradient (line 120) | def test_gcn_norm_gradient(requires_grad, layout):

FILE: test/nn/conv/test_gen_conv.py
  function test_gen_conv (line 16) | def test_gen_conv(aggr):

FILE: test/nn/conv/test_general_conv.py
  function test_general_conv (line 19) | def test_general_conv(kwargs):

FILE: test/nn/conv/test_gin_conv.py
  function test_gin_conv (line 13) | def test_gin_conv():
  function test_gine_conv (line 71) | def test_gine_conv():
  function test_gine_conv_edge_dim (line 126) | def test_gine_conv_edge_dim():
  function test_static_gin_conv (line 142) | def test_static_gin_conv():
  function test_static_gine_conv (line 152) | def test_static_gine_conv():

FILE: test/nn/conv/test_gmm_conv.py
  function test_gmm_conv (line 12) | def test_gmm_conv(separate_gaussians):
  function test_lazy_gmm_conv (line 77) | def test_lazy_gmm_conv(separate_gaussians):

FILE: test/nn/conv/test_gps_conv.py
  function test_gps_conv (line 12) | def test_gps_conv(norm, attn_type):

FILE: test/nn/conv/test_graph_conv.py
  function test_graph_conv (line 11) | def test_graph_conv():
  class EdgeGraphConv (line 132) | class EdgeGraphConv(GraphConv):
    method message (line 133) | def message(self, x_j, edge_weight):
  function test_inheritance (line 137) | def test_inheritance():

FILE: test/nn/conv/test_gravnet_conv.py
  function test_gravnet_conv (line 8) | def test_gravnet_conv():

FILE: test/nn/conv/test_han_conv.py
  function test_han_conv (line 9) | def test_han_conv():
  function test_han_conv_lazy (line 71) | def test_han_conv_lazy():
  function test_han_conv_empty_tensor (line 117) | def test_han_conv_empty_tensor():

FILE: test/nn/conv/test_heat_conv.py
  function test_heat_conv (line 11) | def test_heat_conv(concat):

FILE: test/nn/conv/test_hetero_conv.py
  function test_hetero_conv (line 27) | def test_hetero_conv(aggr):
  function test_gcn2_hetero_conv (line 74) | def test_gcn2_hetero_conv():
  class CustomConv (line 99) | class CustomConv(MessagePassing):
    method __init__ (line 100) | def __init__(self, out_channels):
    method forward (line 104) | def forward(self, x, edge_index, y, z):
    method message (line 107) | def message(self, x_j, y_j, z_j):
  function test_hetero_conv_with_custom_conv (line 111) | def test_hetero_conv_with_custom_conv():
  class MessagePassingLoops (line 136) | class MessagePassingLoops(MessagePassing):
    method __init__ (line 137) | def __init__(self):
  function test_hetero_conv_self_loop_error (line 142) | def test_hetero_conv_self_loop_error():
  function test_hetero_conv_with_dot_syntax_node_types (line 148) | def test_hetero_conv_with_dot_syntax_node_types():
  function test_compile_hetero_conv_graph_breaks (line 184) | def test_compile_hetero_conv_graph_breaks(device):
  function gen_args (line 221) | def gen_args():
  class HeteroGNN (line 225) | class HeteroGNN(torch.nn.Module):
    method __init__ (line 226) | def __init__(self, channels: int = 32, num_layers: int = 2):
    method forward (line 253) | def forward(self, x_dict, edge_index_dict):

FILE: test/nn/conv/test_hgt_conv.py
  function test_hgt_conv_same_dimensions (line 12) | def test_hgt_conv_same_dimensions():
  function test_hgt_conv_different_dimensions (line 62) | def test_hgt_conv_different_dimensions():
  function test_hgt_conv_lazy (line 112) | def test_hgt_conv_lazy():
  function test_hgt_conv_out_of_place (line 159) | def test_hgt_conv_out_of_place():
  function test_hgt_conv_missing_dst_node_type (line 181) | def test_hgt_conv_missing_dst_node_type():
  function test_hgt_conv_missing_input_node_type (line 199) | def test_hgt_conv_missing_input_node_type():
  function test_hgt_conv_missing_edge_type (line 217) | def test_hgt_conv_missing_edge_type():

FILE: test/nn/conv/test_hypergraph_conv.py
  function test_hypergraph_conv_with_more_nodes_than_edges (line 6) | def test_hypergraph_conv_with_more_nodes_than_edges():
  function test_hypergraph_conv_with_more_edges_than_nodes (line 35) | def test_hypergraph_conv_with_more_edges_than_nodes():

FILE: test/nn/conv/test_le_conv.py
  function test_le_conv (line 10) | def test_le_conv():

FILE: test/nn/conv/test_lg_conv.py
  function test_lg_conv (line 10) | def test_lg_conv():

FILE: test/nn/conv/test_meshcnn_conv.py
  function test_meshcnn_conv (line 15) | def test_meshcnn_conv(in_channels: int, out_channels: int):

FILE: test/nn/conv/test_message_passing.py
  class MyConv (line 28) | class MyConv(MessagePassing):
    method __init__ (line 29) | def __init__(self, in_channels: Union[int, Tuple[int, int]],
    method forward (line 39) | def forward(
    method message (line 61) | def message(self, x_j: Tensor, edge_weight: Optional[Tensor]) -> Tensor:
    method message_and_aggregate (line 64) | def message_and_aggregate(self, adj_t: Adj, x: OptPairTensor) -> Tensor:
  class MyConvWithSelfLoops (line 68) | class MyConvWithSelfLoops(MessagePassing):
    method __init__ (line 69) | def __init__(self, aggr: str = 'add'):
    method forward (line 72) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
  function test_my_conv_basic (line 79) | def test_my_conv_basic():
  function test_my_conv_save (line 132) | def test_my_conv_save(tmp_path):
  function test_my_conv_edge_index (line 148) | def test_my_conv_edge_index():
  class MyCommentedConv (line 159) | class MyCommentedConv(MessagePassing):
    method __init__ (line 161) | def __init__(self) -> None:
    method forward (line 164) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
  function test_my_commented_conv (line 169) | def test_my_commented_conv():
  class MyKwargsConv (line 182) | class MyKwargsConv(MessagePassing):
    method __init__ (line 183) | def __init__(self) -> None:
    method forward (line 186) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
  function test_my_kwargs_conv (line 190) | def test_my_kwargs_conv():
  function test_my_conv_out_of_bounds (line 201) | def test_my_conv_out_of_bounds():
  function test_my_conv_jit (line 216) | def test_my_conv_jit():
  function test_my_conv_jit_save (line 257) | def test_my_conv_jit_save(tmp_path):
  function test_my_conv_aggr (line 267) | def test_my_conv_aggr(aggr):
  function test_my_static_graph_conv (line 277) | def test_my_static_graph_conv():
  class MyMultipleAggrConv (line 306) | class MyMultipleAggrConv(MessagePassing):
    method __init__ (line 307) | def __init__(self, **kwargs):
    method forward (line 310) | def forward(self, x: Tensor, edge_index: Adj) -> Tensor:
  function test_my_multiple_aggr_conv (line 319) | def test_my_multiple_aggr_conv(multi_aggr_tuple):
  function test_my_multiple_aggr_conv_jit (line 338) | def test_my_multiple_aggr_conv_jit():
  function test_copy (line 353) | def test_copy():
  class MyEdgeConv (line 371) | class MyEdgeConv(MessagePassing):
    method __init__ (line 372) | def __init__(self):
    method forward (line 375) | def forward(self, x: Tensor, edge_index: Adj) -> Tensor:
    method edge_update (line 383) | def edge_update(self, x_j: Tensor, x_i: Tensor) -> Tensor:
    method message (line 386) | def message(self, edge_attr: Tensor) -> Tensor:
  function test_my_edge_conv (line 390) | def test_my_edge_conv():
  function test_my_edge_conv_jit (line 409) | def test_my_edge_conv_jit():
  function test_message_passing_hooks (line 428) | def test_message_passing_hooks():
  function test_modified_message_passing_hook (line 517) | def test_modified_message_passing_hook():
  class MyDefaultArgConv (line 539) | class MyDefaultArgConv(MessagePassing):
    method __init__ (line 540) | def __init__(self):
    method forward (line 544) | def forward(self, x: Tensor, edge_index: Adj) -> Tensor:
    method message (line 547) | def message(self, x_j, zeros: bool = True):
  function test_my_default_arg_conv (line 551) | def test_my_default_arg_conv():
  class MyMultipleOutputConv (line 569) | class MyMultipleOutputConv(MessagePassing):
    method __init__ (line 570) | def __init__(self):
    method forward (line 573) | def forward(self, x: Tensor, edge_index: Tensor) -> Tuple[Tensor, Tens...
    method message (line 577) | def message(self, x_j: Tensor) -> Tuple[Tensor, Tensor]:
    method aggregate (line 580) | def aggregate(self, inputs: Tuple[Tensor, Tensor],
    method update (line 585) | def update(self, inputs: Tuple[Tensor, Tensor]) -> Tuple[Tensor, Tensor]:
  function test_tuple_output (line 589) | def test_tuple_output():
  function test_tuple_output_jit (line 599) | def test_tuple_output_jit():
  class MyExplainConv (line 615) | class MyExplainConv(MessagePassing):
    method __init__ (line 616) | def __init__(self):
    method forward (line 619) | def forward(self, x: Tensor, edge_index: Adj) -> Tensor:
  function test_explain_message (line 623) | def test_explain_message():
  class MyAggregatorConv (line 652) | class MyAggregatorConv(MessagePassing):
    method __init__ (line 653) | def __init__(self, **kwargs):
    method forward (line 656) | def forward(self, x: Tensor, edge_index: Adj) -> Tensor:
  function test_message_passing_with_aggr_module (line 669) | def test_message_passing_with_aggr_module(aggr_module):
  function test_message_passing_int32_edge_index (line 686) | def test_message_passing_int32_edge_index():
  function test_traceable_my_conv_with_self_loops (line 706) | def test_traceable_my_conv_with_self_loops(num_nodes):
  function test_pickle (line 733) | def test_pickle(tmp_path):
  class MyOptionalEdgeAttrConv (line 744) | class MyOptionalEdgeAttrConv(MessagePassing):
    method __init__ (line 745) | def __init__(self):
    method forward (line 748) | def forward(self, x, edge_index, edge_attr=None):
    method message (line 751) | def message(self, x_j, edge_attr=None):
  function test_my_optional_edge_attr_conv (line 755) | def test_my_optional_edge_attr_conv():

FILE: test/nn/conv/test_mf_conv.py
  function test_mf_conv (line 9) | def test_mf_conv():

FILE: test/nn/conv/test_mixhop_conv.py
  function test_mixhop_conv (line 10) | def test_mixhop_conv():

FILE: test/nn/conv/test_nn_conv.py
  function test_nn_conv (line 14) | def test_nn_conv(device):

FILE: test/nn/conv/test_pan_conv.py
  function test_pan_conv (line 11) | def test_pan_conv():

FILE: test/nn/conv/test_pdn_conv.py
  function test_pdn_conv (line 9) | def test_pdn_conv():
  function test_pdn_conv_with_sparse_node_input_feature (line 32) | def test_pdn_conv_with_sparse_node_input_feature():

FILE: test/nn/conv/test_pna_conv.py
  function test_pna_conv (line 18) | def test_pna_conv(divide_input):
  function test_pna_conv_get_degree_histogram_neighbor_loader (line 44) | def test_pna_conv_get_degree_histogram_neighbor_loader():
  function test_pna_conv_get_degree_histogram_dataloader (line 58) | def test_pna_conv_get_degree_histogram_dataloader():

FILE: test/nn/conv/test_point_conv.py
  function test_point_net_conv (line 13) | def test_point_net_conv():

FILE: test/nn/conv/test_point_gnn_conv.py
  function test_point_gnn_conv (line 10) | def test_point_gnn_conv():

FILE: test/nn/conv/test_point_transformer_conv.py
  function test_point_transformer_conv (line 11) | def test_point_transformer_conv():

FILE: test/nn/conv/test_ppf_conv.py
  function test_ppf_conv (line 14) | def test_ppf_conv():

FILE: test/nn/conv/test_res_gated_graph_conv.py
  function test_res_gated_graph_conv (line 12) | def test_res_gated_graph_conv(edge_dim):

FILE: test/nn/conv/test_rgat_conv.py
  function test_rgat_conv (line 27) | def test_rgat_conv(mod, attention_mechanism, attention_mode, concat, edg...
  function test_rgat_conv_jit (line 101) | def test_rgat_conv_jit():

FILE: test/nn/conv/test_rgcn_conv.py
  function test_rgcn_conv_equality (line 15) | def test_rgcn_conv_equality(conf, device):
  function test_rgcn_conv_basic (line 52) | def test_rgcn_conv_basic(cls, conf, device):

FILE: test/nn/conv/test_sage_conv.py
  function test_sage_conv (line 18) | def test_sage_conv(project, aggr):
  function test_lazy_sage_conv (line 60) | def test_lazy_sage_conv(project):
  function test_lstm_aggr_sage_conv (line 75) | def test_lstm_aggr_sage_conv():
  function test_mlp_sage_conv (line 90) | def test_mlp_sage_conv():
  function test_multi_aggr_sage_conv (line 116) | def test_multi_aggr_sage_conv(aggr_kwargs):
  function test_compile_multi_aggr_sage_conv (line 130) | def test_compile_multi_aggr_sage_conv(device):

FILE: test/nn/conv/test_sg_conv.py
  function test_sg_conv (line 10) | def test_sg_conv():

FILE: test/nn/conv/test_signed_conv.py
  function test_signed_conv (line 10) | def test_signed_conv():

FILE: test/nn/conv/test_simple_conv.py
  function test_simple_conv (line 17) | def test_simple_conv(aggr, combine_root):

FILE: test/nn/conv/test_spline_conv.py
  function test_spline_conv (line 12) | def test_spline_conv():
  function test_lazy_spline_conv (line 68) | def test_lazy_spline_conv():

FILE: test/nn/conv/test_ssg_conv.py
  function test_ssg_conv (line 10) | def test_ssg_conv():

FILE: test/nn/conv/test_static_graph.py
  class MyConv (line 7) | class MyConv(MessagePassing):
    method forward (line 8) | def forward(self, x, edge_index):
  function test_static_graph (line 12) | def test_static_graph():

FILE: test/nn/conv/test_supergat_conv.py
  function test_supergat_conv (line 10) | def test_supergat_conv(att_type):

FILE: test/nn/conv/test_tag_conv.py
  function test_tag_conv (line 10) | def test_tag_conv():
  function test_static_tag_conv (line 44) | def test_static_tag_conv():

FILE: test/nn/conv/test_transformer_conv.py
  function test_transformer_conv (line 16) | def test_transformer_conv(edge_dim, concat):

FILE: test/nn/conv/test_wl_conv.py
  function test_wl_conv (line 9) | def test_wl_conv():

FILE: test/nn/conv/test_wl_conv_continuous.py
  function test_wl_conv (line 9) | def test_wl_conv():

FILE: test/nn/conv/test_x_conv.py
  function test_x_conv (line 8) | def test_x_conv():

FILE: test/nn/conv/utils/test_gnn_cheatsheet.py
  function test_gnn_cheatsheet (line 4) | def test_gnn_cheatsheet():

FILE: test/nn/dense/test_dense_gat_conv.py
  function test_dense_gat_conv (line 10) | def test_dense_gat_conv(heads, concat):
  function test_dense_gat_conv_with_broadcasting (line 53) | def test_dense_gat_conv_with_broadcasting():

FILE: test/nn/dense/test_dense_gcn_conv.py
  function test_dense_gcn_conv (line 7) | def test_dense_gcn_conv():
  function test_dense_gcn_conv_with_broadcasting (line 51) | def test_dense_gcn_conv_with_broadcasting():

FILE: test/nn/dense/test_dense_gin_conv.py
  function test_dense_gin_conv (line 10) | def test_dense_gin_conv():
  function test_dense_gin_conv_with_broadcasting (line 57) | def test_dense_gin_conv_with_broadcasting():

FILE: test/nn/dense/test_dense_graph_conv.py
  function test_dense_graph_conv (line 10) | def test_dense_graph_conv(aggr):
  function test_dense_graph_conv_batch (line 41) | def test_dense_graph_conv_batch(aggr):
  function test_dense_graph_conv_with_broadcasting (line 81) | def test_dense_graph_conv_with_broadcasting(aggr):

FILE: test/nn/dense/test_dense_sage_conv.py
  function test_dense_sage_conv (line 7) | def test_dense_sage_conv():
  function test_dense_sage_conv_with_broadcasting (line 51) | def test_dense_sage_conv_with_broadcasting():

FILE: test/nn/dense/test_diff_pool.py
  function test_dense_diff_pool (line 10) | def test_dense_diff_pool():

FILE: test/nn/dense/test_dmon_pool.py
  function test_dmon_pooling (line 8) | def test_dmon_pooling():

FILE: test/nn/dense/test_linear.py
  function test_linear (line 25) | def test_linear(weight, bias, device):
  function test_lazy_linear (line 36) | def test_lazy_linear(weight, bias, device):
  function test_load_lazy_linear (line 57) | def test_load_lazy_linear(dim1, dim2, bias, device):
  function test_identical_linear_default_initialization (line 89) | def test_identical_linear_default_initialization(lazy):
  function test_copy_unintialized_parameter (line 104) | def test_copy_unintialized_parameter():
  function test_copy_linear (line 111) | def test_copy_linear(lazy, device):
  function test_hetero_linear_basic (line 135) | def test_hetero_linear_basic(device):
  function test_hetero_linear_initializer (line 149) | def test_hetero_linear_initializer():
  function test_hetero_linear_amp (line 162) | def test_hetero_linear_amp(device, use_segment_matmul):
  function test_lazy_hetero_linear (line 180) | def test_lazy_hetero_linear(device):
  function test_hetero_dict_linear (line 193) | def test_hetero_dict_linear(bias, device):
  function test_hetero_dict_linear_jit (line 223) | def test_hetero_dict_linear_jit():
  function test_lazy_hetero_dict_linear (line 236) | def test_lazy_hetero_dict_linear(device):
  function test_hetero_linear_sort (line 257) | def test_hetero_linear_sort(type_vec, device):
  function get_xs (line 287) | def get_xs(mean: float, std: float, num_types: int,
  function sequential (line 299) | def sequential(xs: List[Tensor], weights: List[Tensor]) -> List[Tensor]:
  function nested (line 302) | def nested(xs: List[Tensor], weights: List[Tensor]) -> List[Tensor]:
  function grouped (line 307) | def grouped(x: Tensor, ptr: Tensor, weight: Tensor) -> Tensor:
  function padded (line 310) | def padded(x: Tensor, weight: Tensor) -> Tensor:
  function dgl_mm (line 313) | def dgl_mm(x: Tensor, count: Tensor, weight: Tensor) -> Tensor:

FILE: test/nn/dense/test_mincut_pool.py
  function test_dense_mincut_pool (line 9) | def test_dense_mincut_pool():

FILE: test/nn/functional/test_bro.py
  function test_bro (line 6) | def test_bro():

FILE: test/nn/functional/test_gini.py
  function test_gini (line 6) | def test_gini():

FILE: test/nn/kge/test_complex.py
  function test_complex_scoring (line 6) | def test_complex_scoring():
  function test_complex (line 40) | def test_complex():

FILE: test/nn/kge/test_distmult.py
  function test_distmult (line 6) | def test_distmult():

FILE: test/nn/kge/test_rotate.py
  function test_rotate (line 6) | def test_rotate():

FILE: test/nn/kge/test_transe.py
  function test_transe (line 6) | def test_transe():

FILE: test/nn/models/test_attentive_fp.py
  function test_attentive_fp (line 7) | def test_attentive_fp():

FILE: test/nn/models/test_attract_repel.py
  function test_ar_link_predictor (line 6) | def test_ar_link_predictor():
  function test_ar_link_predictor_with_custom_ratio (line 31) | def test_ar_link_predictor_with_custom_ratio():

FILE: test/nn/models/test_autoencoder.py
  function test_gae (line 10) | def test_gae():
  function test_vgae (line 52) | def test_vgae():
  function test_arga (line 68) | def test_arga():
  function test_argva (line 85) | def test_argva():
  function test_init (line 100) | def test_init():

FILE: test/nn/models/test_basic_gnn.py
  function test_gcn (line 36) | def test_gcn(out_dim, dropout, act, norm, jk):
  function test_graph_sage (line 52) | def test_graph_sage(out_dim, dropout, act, norm, jk):
  function test_gin (line 68) | def test_gin(out_dim, dropout, act, norm, jk):
  function test_gat (line 84) | def test_gat(out_dim, dropout, act, norm, jk):
  function test_pna (line 106) | def test_pna(out_dim, dropout, act, norm, jk):
  function test_edge_cnn (line 128) | def test_edge_cnn(out_dim, dropout, act, norm, jk):
  function test_jit (line 139) | def test_jit():
  function test_one_layer_gnn (line 151) | def test_one_layer_gnn(out_dim, jk):
  function test_batch (line 166) | def test_batch(norm):
  function test_basic_gnn_inference (line 185) | def test_basic_gnn_inference(get_dataset, jk):
  function test_compile_basic (line 208) | def test_compile_basic(device):
  function test_packaging (line 220) | def test_packaging():
  function test_onnx (line 254) | def test_onnx(tmp_path: str) -> None:
  function test_trim_to_layer (line 315) | def test_trim_to_layer():
  function test_compile_graph_breaks (line 350) | def test_compile_graph_breaks(Model, device):
  function test_basic_gnn_cache (line 378) | def test_basic_gnn_cache():
  function gen_args (line 416) | def gen_args():

FILE: test/nn/models/test_correct_and_smooth.py
  function test_correct_and_smooth (line 10) | def test_correct_and_smooth():

FILE: test/nn/models/test_deep_graph_infomax.py
  function test_infomax (line 8) | def test_infomax(device):
  function test_infomax_predefined_model (line 47) | def test_infomax_predefined_model(device):

FILE: test/nn/models/test_deepgcn.py
  function test_deepgcn (line 13) | def test_deepgcn(block_tuple, ckpt_grad):

FILE: test/nn/models/test_dimenet.py
  function test_dimenet_modules (line 14) | def test_dimenet_modules():
  function test_dimenet (line 32) | def test_dimenet(Model):

FILE: test/nn/models/test_gnnff.py
  function test_gnnff (line 9) | def test_gnnff():

FILE: test/nn/models/test_gpse.py
  function test_gpse_training (line 16) | def test_gpse_training():
  function test_gpse_from_pretrained (line 49) | def test_gpse_from_pretrained():
  function test_gpse_node_encoder (line 65) | def test_gpse_node_encoder(expand_x):

FILE: test/nn/models/test_graph_mixer.py
  function test_node_encoder (line 10) | def test_node_encoder():
  function test_link_encoder (line 34) | def test_link_encoder():
  function test_latest_k_edge_attr (line 58) | def test_latest_k_edge_attr():

FILE: test/nn/models/test_graph_unet.py
  function test_graph_unet (line 8) | def test_graph_unet():

FILE: test/nn/models/test_jumping_knowledge.py
  function test_jumping_knowledge (line 7) | def test_jumping_knowledge():
  function test_hetero_jumping_knowledge (line 43) | def test_hetero_jumping_knowledge():

FILE: test/nn/models/test_label_prop.py
  function test_label_prop (line 8) | def test_label_prop():

FILE: test/nn/models/test_lightgcn.py
  function test_lightgcn_ranking (line 11) | def test_lightgcn_ranking(embedding_dim, with_edge_weight, lambda_reg, a...
  function test_lightgcn_link_prediction (line 47) | def test_lightgcn_link_prediction(embedding_dim, with_edge_weight, alpha):

FILE: test/nn/models/test_linkx.py
  function test_linkx (line 11) | def test_linkx(num_edge_layers):

FILE: test/nn/models/test_lpformer.py
  function test_lpformer (line 9) | def test_lpformer():

FILE: test/nn/models/test_mask_label.py
  function test_mask_label (line 6) | def test_mask_label():
  function test_ratio_mask (line 24) | def test_ratio_mask():

FILE: test/nn/models/test_meta.py
  function test_meta_layer (line 16) | def test_meta_layer():
  function test_meta_layer_example (line 41) | def test_meta_layer_example():

FILE: test/nn/models/test_metapath2vec.py
  function test_metapath2vec (line 8) | def test_metapath2vec(device):
  function test_metapath2vec_empty_edges (line 45) | def test_metapath2vec_empty_edges():

FILE: test/nn/models/test_mlp.py
  function test_mlp (line 10) | def test_mlp(norm, act_first, plain_last):
  function test_batch (line 46) | def test_batch(norm):
  function test_mlp_return_emb (line 67) | def test_mlp_return_emb():
  function test_fine_grained_mlp (line 82) | def test_fine_grained_mlp(plain_last):

FILE: test/nn/models/test_neural_fingerprint.py
  function test_neural_fingerprint (line 11) | def test_neural_fingerprint(batch):

FILE: test/nn/models/test_node2vec.py
  function test_node2vec (line 18) | def test_node2vec(device, p, q):

FILE: test/nn/models/test_pmlp.py
  function test_pmlp (line 7) | def test_pmlp():

FILE: test/nn/models/test_polynormer.py
  function test_polynormer (line 11) | def test_polynormer(local_attn, qk_shared, pre_ln, post_bn):

FILE: test/nn/models/test_re_net.py
  class MyTestEventDataset (line 9) | class MyTestEventDataset(EventDataset):
    method __init__ (line 10) | def __init__(self, root, seq_len):
    method num_nodes (line 15) | def num_nodes(self):
    method num_rels (line 19) | def num_rels(self):
    method processed_file_names (line 23) | def processed_file_names(self):
    method _download (line 26) | def _download(self):
    method process_events (line 29) | def process_events(self):
    method process (line 36) | def process(self):
  function test_re_net (line 41) | def test_re_net(tmp_path):

FILE: test/nn/models/test_rect.py
  function test_rect (line 9) | def test_rect():

FILE: test/nn/models/test_rev_gnn.py
  function test_revgnn_forward_inverse (line 10) | def test_revgnn_forward_inverse(num_groups):
  function test_revgnn_backward (line 35) | def test_revgnn_backward(num_groups):
  function test_revgnn_multi_backward (line 50) | def test_revgnn_multi_backward(num_groups):
  function test_revgnn_diable (line 69) | def test_revgnn_diable(num_groups):
  function test_revgnn_with_args (line 90) | def test_revgnn_with_args(num_groups):

FILE: test/nn/models/test_schnet.py
  function generate_data (line 10) | def generate_data():
  function test_schnet (line 21) | def test_schnet(use_interaction_graph, use_atomref):
  function test_schnet_batch (line 53) | def test_schnet_batch():

FILE: test/nn/models/test_sgformer.py
  function test_sgformer (line 6) | def test_sgformer():

FILE: test/nn/models/test_signed_gcn.py
  function test_signed_gcn (line 8) | def test_signed_gcn():

FILE: test/nn/models/test_tgn.py
  function test_tgn (line 15) | def test_tgn(neg_sampling_ratio):

FILE: test/nn/models/test_visnet.py
  function test_visnet (line 13) | def test_visnet(kwargs):

FILE: test/nn/norm/test_batch_norm.py
  function test_batch_norm (line 10) | def test_batch_norm(device, conf):
  function test_batch_norm_single_element (line 26) | def test_batch_norm_single_element():
  function test_hetero_batch_norm (line 44) | def test_hetero_batch_norm(device, conf):

FILE: test/nn/norm/test_diff_group_norm.py
  function test_diff_group_norm (line 8) | def test_diff_group_norm(device):
  function test_group_distance_ratio (line 31) | def test_group_distance_ratio():

FILE: test/nn/norm/test_graph_norm.py
  function test_graph_norm (line 8) | def test_graph_norm(device):

FILE: test/nn/norm/test_graph_size_norm.py
  function test_graph_size_norm (line 7) | def test_graph_size_norm():

FILE: test/nn/norm/test_instance_norm.py
  function test_instance_norm (line 10) | def test_instance_norm(conf, device):

FILE: test/nn/norm/test_layer_norm.py
  function test_layer_norm (line 11) | def test_layer_norm(device, affine, mode):
  function test_hetero_layer_norm (line 32) | def test_hetero_layer_norm(device, affine):

FILE: test/nn/norm/test_mean_subtraction_norm.py
  function test_mean_subtraction_norm (line 7) | def test_mean_subtraction_norm():

FILE: test/nn/norm/test_msg_norm.py
  function test_message_norm (line 8) | def test_message_norm(device):

FILE: test/nn/norm/test_pair_norm.py
  function test_pair_norm (line 9) | def test_pair_norm(scale_individually):

FILE: test/nn/pool/connect/test_filter_edges.py
  function test_filter_edges (line 8) | def test_filter_edges():

FILE: test/nn/pool/select/test_select_topk.py
  function test_topk_ratio (line 12) | def test_topk_ratio():
  function test_select_topk (line 39) | def test_select_topk(min_score):

FILE: test/nn/pool/test_approx_knn.py
  function to_set (line 9) | def to_set(edge_index):
  function test_approx_knn (line 15) | def test_approx_knn():
  function test_approx_knn_graph (line 45) | def test_approx_knn_graph():

FILE: test/nn/pool/test_asap.py
  function test_asap (line 10) | def test_asap():
  function test_asap_jit_save (line 42) | def test_asap_jit_save():

FILE: test/nn/pool/test_avg_pool.py
  function test_avg_pool_x (line 8) | def test_avg_pool_x():
  function test_avg_pool (line 46) | def test_avg_pool():
  function test_avg_pool_neighbor_x (line 81) | def test_avg_pool_neighbor_x():

FILE: test/nn/pool/test_cluster_pool.py
  function test_cluster_pooling (line 14) | def test_cluster_pooling(edge_score_method):

FILE: test/nn/pool/test_consecutive.py
  function test_consecutive_cluster (line 6) | def test_consecutive_cluster():

FILE: test/nn/pool/test_decimation.py
  function test_decimation_basic (line 6) | def test_decimation_basic():
  function test_decimation_single_cloud (line 20) | def test_decimation_single_cloud():
  function test_decimation_almost_empty (line 32) | def test_decimation_almost_empty():

FILE: test/nn/pool/test_edge_pool.py
  function test_compute_edge_score_softmax (line 8) | def test_compute_edge_score_softmax():
  function test_compute_edge_score_tanh (line 26) | def test_compute_edge_score_tanh():
  function test_compute_edge_score_sigmoid (line 39) | def test_compute_edge_score_sigmoid():
  function test_edge_pooling (line 52) | def test_edge_pooling():

FILE: test/nn/pool/test_glob.py
  function test_global_pool (line 10) | def test_global_pool():
  function test_permuted_global_pool (line 43) | def test_permuted_global_pool():
  function test_dense_global_pool (line 70) | def test_dense_global_pool():

FILE: test/nn/pool/test_graclus.py
  function test_graclus (line 8) | def test_graclus():

FILE: test/nn/pool/test_knn.py
  function test_l2 (line 16) | def test_l2(device, k):
  function test_mips (line 40) | def test_mips(device, k):
  function test_approx_l2 (line 65) | def test_approx_l2(device, k, reserve):
  function test_approx_mips (line 89) | def test_approx_mips(device, k, reserve):
  function test_mips_exclude (line 112) | def test_mips_exclude(device, k):

FILE: test/nn/pool/test_max_pool.py
  function test_max_pool_x (line 8) | def test_max_pool_x():
  function test_max_pool (line 46) | def test_max_pool():
  function test_max_pool_neighbor_x (line 81) | def test_max_pool_neighbor_x():

FILE: test/nn/pool/test_mem_pool.py
  function test_mem_pool (line 7) | def test_mem_pool():

FILE: test/nn/pool/test_pan_pool.py
  function test_pan_pooling (line 8) | def test_pan_pooling():

FILE: test/nn/pool/test_pool.py
  function test_radius_graph_jit (line 12) | def test_radius_graph_jit():

FILE: test/nn/pool/test_sag_pool.py
  function test_sag_pooling (line 13) | def test_sag_pooling():

FILE: test/nn/pool/test_topk_pool.py
  function test_filter_adj (line 8) | def test_filter_adj():
  function test_topk_pooling (line 26) | def test_topk_pooling():

FILE: test/nn/pool/test_voxel_grid.py
  function test_voxel_grid (line 9) | def test_voxel_grid():
  function test_single_voxel_grid (line 30) | def test_single_voxel_grid():

FILE: test/nn/test_compile_basic.py
  function gather_scatter (line 14) | def gather_scatter(x, edge_index, reduce='sum'):
  function gather_cat_scatter (line 20) | def gather_cat_scatter(x, edge_index, reduce='sum'):
  function gather_weight_scatter (line 26) | def gather_weight_scatter(x, edge_index, edge_weight, reduce='sum'):
  function gather_transform_scatter (line 32) | def gather_transform_scatter(x, edge_index, matrix, reduce='sum'):
  function fused_gather_scatter (line 38) | def fused_gather_scatter(x, edge_index, reduce=('sum', 'mean', 'max')):
  function test_torch_compile (line 49) | def test_torch_compile(device):

FILE: test/nn/test_compile_conv.py
  class MySAGEConv (line 17) | class MySAGEConv(torch.nn.Module):
    method __init__ (line 18) | def __init__(self, in_channels: int, out_channels: int):
    method forward (line 23) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
  function test_compile_conv (line 34) | def test_compile_conv(device, Conv):
  function test_compile_conv_edge_index (line 57) | def test_compile_conv_edge_index(device, Conv):

FILE: test/nn/test_compile_dynamic.py
  class MySAGEConv (line 16) | class MySAGEConv(torch.nn.Module):
    method __init__ (line 17) | def __init__(self, in_channels: int, out_channels: int):
    method forward (line 22) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
  function test_dynamic_torch_compile (line 32) | def test_dynamic_torch_compile(device):

FILE: test/nn/test_data_parallel.py
  function test_data_parallel_single_gpu (line 10) | def test_data_parallel_single_gpu():
  function test_data_parallel_multi_gpu (line 20) | def test_data_parallel_multi_gpu():

FILE: test/nn/test_encoding.py
  function test_positional_encoding (line 8) | def test_positional_encoding(device):
  function test_temporal_encoding (line 17) | def test_temporal_encoding(device):

FILE: test/nn/test_fvcore.py
  function test_fvcore (line 8) | def test_fvcore():

FILE: test/nn/test_fx.py
  function test_dropout (line 6) | def test_dropout():

FILE: test/nn/test_inits.py
  function test_inits (line 16) | def test_inits():
  function test_reset (line 51) | def test_reset():

FILE: test/nn/test_model_hub.py
  class DummyModel (line 18) | class DummyModel(GCN, PyGModelHubMixin):
    method __init__ (line 19) | def __init__(self, model_name, dataset_name, model_kwargs):
  function model (line 25) | def model():
  function test_model_init (line 30) | def test_model_init():
  function test_save_pretrained (line 39) | def test_save_pretrained(model, tmp_path):
  function test_save_pretrained_internal (line 48) | def test_save_pretrained_internal(model, tmp_path):
  function test_save_pretrained_with_push_to_hub (line 56) | def test_save_pretrained_with_push_to_hub(model, tmp_path):
  function test_from_pretrained (line 87) | def test_from_pretrained(model, tmp_path):
  function test_from_pretrained_internal (line 96) | def test_from_pretrained_internal(model, monkeypatch):

FILE: test/nn/test_model_summary.py
  class GraphSAGE (line 14) | class GraphSAGE(torch.nn.Module):
    method __init__ (line 15) | def __init__(self):
    method forward (line 21) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
  class ModuleDictModel (line 28) | class ModuleDictModel(nn.Module):
    method __init__ (line 29) | def __init__(self):
    method forward (line 36) | def forward(self, x: torch.Tensor, act_type: str) -> torch.Tensor:
  function gcn (line 41) | def gcn():
  function test_summary_basic (line 56) | def test_summary_basic(gcn):
  function test_summary_with_sparse_tensor (line 76) | def test_summary_with_sparse_tensor(gcn):
  function test_lazy_gcn (line 96) | def test_lazy_gcn():
  function test_summary_with_max_depth (line 120) | def test_summary_with_max_depth(gcn):
  function test_summary_with_leaf_module (line 141) | def test_summary_with_leaf_module(gcn):
  function test_summary_with_reusing_layers (line 170) | def test_summary_with_reusing_layers():
  function test_summary_with_to_hetero_model (line 186) | def test_summary_with_to_hetero_model():
  function test_summary_with_module_dict_model (line 220) | def test_summary_with_module_dict_model():
  function test_summary_with_jit_model (line 238) | def test_summary_with_jit_model():

FILE: test/nn/test_module_dict.py
  function test_internal_external_key_conversion (line 6) | def test_internal_external_key_conversion():
  function test_dot_syntax_keys (line 21) | def test_dot_syntax_keys():
  function test_tuple_keys (line 39) | def test_tuple_keys():
  function test_reserved_keys (line 56) | def test_reserved_keys():

FILE: test/nn/test_parameter_dict.py
  function test_internal_external_key_conversion (line 6) | def test_internal_external_key_conversion():
  function test_dot_syntax_keys (line 21) | def test_dot_syntax_keys():
  function test_tuple_keys (line 40) | def test_tuple_keys():
  function test_reserved_keys (line 58) | def test_reserved_keys():

FILE: test/nn/test_reshape.py
  function test_reshape (line 6) | def test_reshape():

FILE: test/nn/test_resolver.py
  function test_activation_resolver (line 15) | def test_activation_resolver():
  function test_aggregation_resolver (line 37) | def test_aggregation_resolver(aggr_tuple):
  function test_multi_aggregation_resolver (line 43) | def test_multi_aggregation_resolver():
  function test_normalization_resolver (line 66) | def test_normalization_resolver(norm_tuple):
  function test_optimizer_resolver (line 74) | def test_optimizer_resolver():
  function test_lr_scheduler_resolver (line 99) | def test_lr_scheduler_resolver(scheduler_args):

FILE: test/nn/test_sequential.py
  function test_sequential_basic (line 20) | def test_sequential_basic():
  function test_sequential_jit (line 70) | def test_sequential_jit():
  function symbolic_trace (line 96) | def symbolic_trace(module):
  function test_sequential_tracable (line 105) | def test_sequential_tracable():
  function test_sequential_with_multiple_return_values (line 117) | def test_sequential_with_multiple_return_values():
  function test_sequential_with_ordered_dict (line 132) | def test_sequential_with_ordered_dict():
  function test_sequential_to_hetero (line 149) | def test_sequential_to_hetero():

FILE: test/nn/test_to_fixed_size_transformer.py
  class Model (line 7) | class Model(torch.nn.Module):
    method __init__ (line 8) | def __init__(self):
    method forward (line 12) | def forward(self, x, batch):
  function test_to_fixed_size (line 16) | def test_to_fixed_size():

FILE: test/nn/test_to_hetero_module.py
  function test_to_hetero_linear (line 13) | def test_to_hetero_linear(LinearCls):
  function test_to_hetero_message_passing (line 32) | def test_to_hetero_message_passing():

FILE: test/nn/test_to_hetero_transformer.py
  class Net1 (line 34) | class Net1(torch.nn.Module):
    method __init__ (line 35) | def __init__(self):
    method forward (line 40) | def forward(self, x: Tensor, edge_attr: Tensor) -> Tuple[Tensor, Tensor]:
  class Net2 (line 46) | class Net2(torch.nn.Module):
    method __init__ (line 47) | def __init__(self):
    method forward (line 53) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
  class Net3 (line 60) | class Net3(torch.nn.Module):
    method __init__ (line 61) | def __init__(self):
    method forward (line 66) | def forward(self, x: Tensor, edge_index: Tensor,
  class Net4 (line 72) | class Net4(torch.nn.Module):
    method __init__ (line 73) | def __init__(self):
    method forward (line 79) | def forward(self, x0: Tensor, edge_index: Tensor) -> Tensor:
  class Net5 (line 85) | class Net5(torch.nn.Module):
    method __init__ (line 86) | def __init__(self, num_layers):
    method forward (line 94) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
  class Net6 (line 100) | class Net6(torch.nn.Module):
    method __init__ (line 101) | def __init__(self, num_layers):
    method forward (line 109) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
  class Net7 (line 115) | class Net7(torch.nn.Module):
    method __init__ (line 116) | def __init__(self):
    method forward (line 121) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
  class Net8 (line 127) | class Net8(torch.nn.Module):
    method __init__ (line 128) | def __init__(self):
    method forward (line 132) | def forward(self, x: Tensor) -> Tensor:
  class Net9 (line 137) | class Net9(torch.nn.Module):
    method __init__ (line 138) | def __init__(self):
    method forward (line 142) | def forward(self, x: Tensor) -> Tensor:
  class Net10 (line 146) | class Net10(torch.nn.Module):
    method __init__ (line 147) | def __init__(self):
    method forward (line 151) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
  class Net11 (line 157) | class Net11(torch.nn.Module):
    method __init__ (line 158) | def __init__(self):
    method forward (line 163) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
  class Net12 (line 170) | class Net12(torch.nn.Module):
    method __init__ (line 171) | def __init__(self):
    method forward (line 175) | def forward(self, x: Tensor) -> Tensor:
  function test_to_hetero_basic (line 179) | def test_to_hetero_basic():
  class GCN (line 311) | class GCN(torch.nn.Module):
    method __init__ (line 312) | def __init__(self):
    method forward (line 317) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
  function test_to_hetero_with_gcn (line 323) | def test_to_hetero_with_gcn():
  function test_to_hetero_with_basic_model (line 340) | def test_to_hetero_with_basic_model():
  class GraphConv (line 367) | class GraphConv(MessagePassing):
    method __init__ (line 368) | def __init__(self, in_channels, out_channels):
    method reset_parameters (line 372) | def reset_parameters(self):
    method forward (line 375) | def forward(self, x, edge_index):
  class RGCN (line 381) | class RGCN(torch.nn.Module):
    method __init__ (line 382) | def __init__(self, in_channels, out_channels):
    method forward (line 387) | def forward(self, x, edge_index):
  function test_to_hetero_and_rgcn_equal_output (line 391) | def test_to_hetero_and_rgcn_equal_output():
  class GraphLevelGNN (line 453) | class GraphLevelGNN(torch.nn.Module):
    method __init__ (line 454) | def __init__(self):
    method forward (line 460) | def forward(self, x: Tensor, edge_index: Tensor, batch: Tensor) -> Ten...
  function test_graph_level_to_hetero (line 467) | def test_graph_level_to_hetero():
  class MessagePassingLoops (line 491) | class MessagePassingLoops(MessagePassing):
    method __init__ (line 492) | def __init__(self):
    method forward (line 496) | def forward(self, x):
  class ModelLoops (line 500) | class ModelLoops(torch.nn.Module):
    method __init__ (line 501) | def __init__(self):
    method forward (line 505) | def forward(self, x):
  function test_hetero_transformer_self_loop_error (line 509) | def test_hetero_transformer_self_loop_error():
  function test_to_hetero_validate (line 516) | def test_to_hetero_validate():
  function test_to_hetero_on_static_graphs (line 524) | def test_to_hetero_on_static_graphs():
  function test_to_hetero_lazy_cuda (line 544) | def test_to_hetero_lazy_cuda():

FILE: test/nn/test_to_hetero_with_bases_transformer.py
  class Net1 (line 20) | class Net1(torch.nn.Module):
    method __init__ (line 21) | def __init__(self):
    method forward (line 26) | def forward(self, x: Tensor, edge_attr: Tensor) -> Tuple[Tensor, Tensor]:
  class Net2 (line 32) | class Net2(torch.nn.Module):
    method __init__ (line 33) | def __init__(self):
    method forward (line 39) | def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
  class Net3 (line 46) | class Net3(torch.nn.Module):
    method __init__ (line 47) | def __init__(self):
    method forward (line 52) | def forward(self, x: Tensor, edge_index: Tensor,
  class Net4 (line 58) | class Net4(torch.nn.Module):
    method __init__ (line 59) | def __init__(self):
    method forward (line 65) | def forward(self, x0: Tensor, edge_index: Tensor) -> Tensor:
  class Net5 (line 71) | class Net5(torch.nn.Module):
    metho

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    "path": "CHANGELOG.md",
    "chars": 136484,
    "preview": "# Changelog\n\nAll notable changes to this project will be documented in this file.\nThe format is based on [Keep a Changel"
  },
  {
    "path": "CITATION.cff",
    "chars": 1178,
    "preview": "---\ncff-version: 1.2.0\nmessage: \"Please cite our papers if you use this code in your own work.\"\ntitle: \"Fast Graph Repre"
  },
  {
    "path": "LICENSE",
    "chars": 1067,
    "preview": "Copyright (c) 2023 PyG Team <team@pyg.org>\n\nPermission is hereby granted, free of charge, to any person obtaining a copy"
  },
  {
    "path": "README.md",
    "chars": 60034,
    "preview": "<p align=\"center\">\n  <img height=\"150\" src=\"https://raw.githubusercontent.com/pyg-team/pyg_sphinx_theme/master/pyg_sphin"
  },
  {
    "path": "benchmark/README.md",
    "chars": 773,
    "preview": "# PyG Benchmark Suite\n\nThis benchmark suite provides evaluation scripts for **[semi-supervised node classification](http"
  },
  {
    "path": "benchmark/citation/README.md",
    "chars": 985,
    "preview": "# Semi-supervised Node Classification\n\nEvaluation scripts for various methods on the Cora, CiteSeer and PubMed citation "
  },
  {
    "path": "benchmark/citation/__init__.py",
    "chars": 183,
    "preview": "from .datasets import get_planetoid_dataset\nfrom .train_eval import random_planetoid_splits, run\n\n__all__ = [\n    'get_p"
  },
  {
    "path": "benchmark/citation/appnp.py",
    "chars": 2469,
    "preview": "import argparse\n\nimport torch\nimport torch.nn.functional as F\nfrom citation import get_planetoid_dataset, random_planeto"
  },
  {
    "path": "benchmark/citation/arma.py",
    "chars": 2751,
    "preview": "import argparse\n\nimport torch\nimport torch.nn.functional as F\nfrom citation import get_planetoid_dataset, random_planeto"
  },
  {
    "path": "benchmark/citation/cheb.py",
    "chars": 2311,
    "preview": "import argparse\n\nimport torch\nimport torch.nn.functional as F\nfrom citation import get_planetoid_dataset, random_planeto"
  },
  {
    "path": "benchmark/citation/datasets.py",
    "chars": 596,
    "preview": "import os.path as osp\n\nimport torch_geometric.transforms as T\nfrom torch_geometric.datasets import Planetoid\n\n\ndef get_p"
  },
  {
    "path": "benchmark/citation/gat.py",
    "chars": 2600,
    "preview": "import argparse\n\nimport torch\nimport torch.nn.functional as F\nfrom citation import get_planetoid_dataset, random_planeto"
  },
  {
    "path": "benchmark/citation/gcn.py",
    "chars": 2222,
    "preview": "import argparse\n\nimport torch\nimport torch.nn.functional as F\nfrom citation import get_planetoid_dataset, random_planeto"
  },
  {
    "path": "benchmark/citation/inference.sh",
    "chars": 5840,
    "preview": "#!/bin/sh\n\necho \"Cora\"\necho \"====\"\n\necho \"GCN\"\npython gcn.py --dataset=Cora --inference\npython gcn.py --dataset=Cora --r"
  },
  {
    "path": "benchmark/citation/run.sh",
    "chars": 2452,
    "preview": "#!/bin/sh\n\necho \"Cora\"\necho \"====\"\n\necho \"GCN\"\npython gcn.py --dataset=Cora\npython gcn.py --dataset=Cora --random_splits"
  },
  {
    "path": "benchmark/citation/sgc.py",
    "chars": 2001,
    "preview": "import argparse\n\nimport torch\nimport torch.nn.functional as F\nfrom citation import get_planetoid_dataset, random_planeto"
  },
  {
    "path": "benchmark/citation/statistics.py",
    "chars": 459,
    "preview": "from citation import get_planetoid_dataset\n\n\ndef print_dataset(dataset):\n    data = dataset[0]\n    print('Name', dataset"
  },
  {
    "path": "benchmark/citation/train_eval.py",
    "chars": 5956,
    "preview": "import time\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import tensor\nfrom torch.optim import Adam\n\nfrom to"
  },
  {
    "path": "benchmark/inference/README.md",
    "chars": 1772,
    "preview": "# Inference Benchmark\n\n## Environment setup\n\n1. Confirm that PyG is properly installed.\n1. Install dataset package:\n   `"
  },
  {
    "path": "benchmark/inference/inference_benchmark.py",
    "chars": 15806,
    "preview": "import argparse\nimport warnings\nfrom collections import defaultdict\nfrom contextlib import nullcontext\n\nimport torch\n\nfr"
  },
  {
    "path": "benchmark/kernel/README.md",
    "chars": 1810,
    "preview": "# Graph Classification\n\nEvaluation script for various methods on [common benchmark datasets](https://chrsmrrs.github.io/"
  },
  {
    "path": "benchmark/kernel/__init__.py",
    "chars": 159,
    "preview": "from .datasets import get_dataset\nfrom .train_eval import cross_validation_with_val_set\n\n__all__ = [\n    'get_dataset',\n"
  },
  {
    "path": "benchmark/kernel/asap.py",
    "chars": 2105,
    "preview": "import torch\nimport torch.nn.functional as F\nfrom torch.nn import Linear\n\nfrom torch_geometric.nn import (\n    ASAPoolin"
  },
  {
    "path": "benchmark/kernel/datasets.py",
    "chars": 2090,
    "preview": "import os.path as osp\n\nimport torch\n\nimport torch_geometric.transforms as T\nfrom torch_geometric.datasets import TUDatas"
  },
  {
    "path": "benchmark/kernel/diff_pool.py",
    "chars": 3118,
    "preview": "from math import ceil\n\nimport torch\nimport torch.nn.functional as F\nfrom torch.nn import Linear\n\nfrom torch_geometric.nn"
  },
  {
    "path": "benchmark/kernel/edge_pool.py",
    "chars": 1869,
    "preview": "import torch\nimport torch.nn.functional as F\nfrom torch.nn import Linear\n\nfrom torch_geometric.nn import (\n    EdgePooli"
  },
  {
    "path": "benchmark/kernel/gcn.py",
    "chars": 2606,
    "preview": "import torch\nimport torch.nn.functional as F\nfrom torch.nn import Linear\n\nfrom torch_geometric.nn import GCNConv, Jumpin"
  },
  {
    "path": "benchmark/kernel/gin.py",
    "chars": 7178,
    "preview": "import torch\nimport torch.nn.functional as F\nfrom torch.nn import BatchNorm1d as BN\nfrom torch.nn import Linear, ReLU, S"
  },
  {
    "path": "benchmark/kernel/global_attention.py",
    "chars": 1339,
    "preview": "import torch\nimport torch.nn.functional as F\nfrom torch.nn import Linear\n\nfrom torch_geometric.nn import AttentionalAggr"
  },
  {
    "path": "benchmark/kernel/graclus.py",
    "chars": 1866,
    "preview": "import torch\nimport torch.nn.functional as F\nfrom torch.nn import Linear\n\nfrom torch_geometric.data import Batch\nfrom to"
  },
  {
    "path": "benchmark/kernel/graph_sage.py",
    "chars": 2620,
    "preview": "import torch\nimport torch.nn.functional as F\nfrom torch.nn import Linear\n\nfrom torch_geometric.nn import JumpingKnowledg"
  },
  {
    "path": "benchmark/kernel/main.py",
    "chars": 2528,
    "preview": "import argparse\nfrom itertools import product\n\nfrom asap import ASAP\nfrom datasets import get_dataset\nfrom diff_pool imp"
  },
  {
    "path": "benchmark/kernel/main_performance.py",
    "chars": 5544,
    "preview": "import argparse\nfrom itertools import product\n\nimport torch\nfrom datasets import get_dataset\nfrom gcn import GCN\nfrom gi"
  },
  {
    "path": "benchmark/kernel/sag_pool.py",
    "chars": 1943,
    "preview": "import torch\nimport torch.nn.functional as F\nfrom torch.nn import Linear\n\nfrom torch_geometric.nn import (\n    GraphConv"
  },
  {
    "path": "benchmark/kernel/set2set.py",
    "chars": 1326,
    "preview": "import torch\nimport torch.nn.functional as F\nfrom torch.nn import Linear\n\nfrom torch_geometric.nn import SAGEConv, Set2S"
  },
  {
    "path": "benchmark/kernel/sort_pool.py",
    "chars": 1492,
    "preview": "import torch\nimport torch.nn.functional as F\nfrom torch.nn import Conv1d, Linear\n\nfrom torch_geometric.nn import SAGECon"
  },
  {
    "path": "benchmark/kernel/statistics.py",
    "chars": 570,
    "preview": "from kernel.datasets import get_dataset\n\n\ndef print_dataset(dataset):\n    num_nodes = num_edges = 0\n    for data in data"
  },
  {
    "path": "benchmark/kernel/top_k.py",
    "chars": 1942,
    "preview": "import torch\nimport torch.nn.functional as F\nfrom torch.nn import Linear\n\nfrom torch_geometric.nn import (\n    GraphConv"
  },
  {
    "path": "benchmark/kernel/train_eval.py",
    "chars": 5451,
    "preview": "import time\n\nimport torch\nimport torch.nn.functional as F\nfrom sklearn.model_selection import StratifiedKFold\nfrom torch"
  },
  {
    "path": "benchmark/loader/neighbor_loader.py",
    "chars": 6313,
    "preview": "import argparse\nimport ast\nimport os.path as osp\nfrom contextlib import nullcontext\nfrom timeit import default_timer\n\nim"
  },
  {
    "path": "benchmark/multi_gpu/training/README.md",
    "chars": 2904,
    "preview": "# Training Benchmark\n\n## Running benchmark on CUDA GPU\n\nRun benchmark, e.g. assuming you have `n` NVIDIA GPUs:\n\n```\npyth"
  },
  {
    "path": "benchmark/multi_gpu/training/common.py",
    "chars": 9114,
    "preview": "import argparse\nimport ast\nfrom time import perf_counter\nfrom typing import Any, Callable, Tuple, Union\n\nimport torch\nim"
  },
  {
    "path": "benchmark/multi_gpu/training/training_benchmark_cuda.py",
    "chars": 1537,
    "preview": "import argparse\nimport os\nfrom typing import Union\n\nimport torch\nimport torch.distributed as dist\nimport torch.multiproc"
  },
  {
    "path": "benchmark/multi_gpu/training/training_benchmark_xpu.py",
    "chars": 1959,
    "preview": "import os\nfrom typing import Any, Tuple\n\nimport intel_extension_for_pytorch as ipex\nimport oneccl_bindings_for_pytorch  "
  },
  {
    "path": "benchmark/points/README.md",
    "chars": 730,
    "preview": "# Point Cloud classification\n\nEvaluation scripts for various methods on the ModelNet10 dataset:\n\n- **[MPNN](https://gith"
  },
  {
    "path": "benchmark/points/__init__.py",
    "chars": 107,
    "preview": "from .datasets import get_dataset\nfrom .train_eval import run\n\n__all__ = [\n    'get_dataset',\n    'run',\n]\n"
  },
  {
    "path": "benchmark/points/datasets.py",
    "chars": 636,
    "preview": "import os.path as osp\n\nimport torch_geometric.transforms as T\nfrom torch_geometric.datasets import ModelNet\n\n\ndef get_da"
  },
  {
    "path": "benchmark/points/edge_cnn.py",
    "chars": 2288,
    "preview": "import argparse\n\nimport torch\nimport torch.nn.functional as F\nfrom points.datasets import get_dataset\nfrom points.train_"
  },
  {
    "path": "benchmark/points/mpnn.py",
    "chars": 3031,
    "preview": "import argparse\n\nimport torch\nimport torch.nn.functional as F\nfrom points.datasets import get_dataset\nfrom points.train_"
  },
  {
    "path": "benchmark/points/point_cnn.py",
    "chars": 2588,
    "preview": "import argparse\n\nimport torch\nimport torch.nn.functional as F\nfrom points.datasets import get_dataset\nfrom points.train_"
  },
  {
    "path": "benchmark/points/point_net.py",
    "chars": 2761,
    "preview": "import argparse\n\nimport torch\nimport torch.nn.functional as F\nfrom points.datasets import get_dataset\nfrom points.train_"
  },
  {
    "path": "benchmark/points/spline_cnn.py",
    "chars": 2995,
    "preview": "import argparse\n\nimport torch\nimport torch.nn.functional as F\nfrom points.datasets import get_dataset\nfrom points.train_"
  },
  {
    "path": "benchmark/points/statistics.py",
    "chars": 751,
    "preview": "from points.datasets import get_dataset\n\nfrom torch_geometric.transforms import RadiusGraph\n\n\ndef print_dataset(train_da"
  },
  {
    "path": "benchmark/points/train_eval.py",
    "chars": 4068,
    "preview": "import time\n\nimport torch\nimport torch.nn.functional as F\nfrom torch.optim import Adam\n\nfrom torch_geometric.loader impo"
  },
  {
    "path": "benchmark/runtime/README.md",
    "chars": 148,
    "preview": "# Runtimes\n\nRun the test suite for PyG via\n\n```\npython main.py\n```\n\nInstall `dgl` and run the test suite for DGL via\n\n``"
  },
  {
    "path": "benchmark/runtime/__init__.py",
    "chars": 69,
    "preview": "from .train import train_runtime\n\n__all__ = [\n    'train_runtime',\n]\n"
  },
  {
    "path": "benchmark/runtime/dgl/gat.py",
    "chars": 4635,
    "preview": "import dgl.function as fn\nimport torch\nimport torch.nn.functional as F\nfrom dgl.nn.pytorch import EdgeSoftmax\nfrom torch"
  },
  {
    "path": "benchmark/runtime/dgl/gcn.py",
    "chars": 2439,
    "preview": "import dgl.function as fn\nimport torch\nimport torch.nn.functional as F\nfrom torch.nn import Parameter\n\nfrom torch_geomet"
  },
  {
    "path": "benchmark/runtime/dgl/hidden.py",
    "chars": 326,
    "preview": "import os\nimport sys\nimport warnings\n\nwarnings.filterwarnings('ignore')\n\n\nclass HiddenPrint:\n    def __enter__(self):\n  "
  },
  {
    "path": "benchmark/runtime/dgl/main.py",
    "chars": 2262,
    "preview": "from itertools import product\n\nimport dgl\nimport torch\nfrom dgl import DGLGraph\nfrom dgl.contrib.data import load_data\nf"
  },
  {
    "path": "benchmark/runtime/dgl/rgcn.py",
    "chars": 4986,
    "preview": "import dgl.function as fn\nimport torch\nimport torch.nn.functional as F\nfrom torch.nn import Parameter as Param\n\nfrom tor"
  },
  {
    "path": "benchmark/runtime/dgl/train.py",
    "chars": 1136,
    "preview": "import time\n\nimport torch\nimport torch.nn.functional as F\n\n\ndef train_runtime(model, data, epochs, device):\n    if hasat"
  },
  {
    "path": "benchmark/runtime/gat.py",
    "chars": 635,
    "preview": "import torch\nimport torch.nn.functional as F\n\nfrom torch_geometric.nn import GATConv\n\n\nclass GAT(torch.nn.Module):\n    d"
  },
  {
    "path": "benchmark/runtime/gcn.py",
    "chars": 562,
    "preview": "import torch\nimport torch.nn.functional as F\n\nfrom torch_geometric.nn import GCNConv\n\n\nclass GCN(torch.nn.Module):\n    d"
  },
  {
    "path": "benchmark/runtime/main.py",
    "chars": 1378,
    "preview": "import os.path as osp\nfrom itertools import product\n\nimport torch\nfrom runtime.gat import GAT\nfrom runtime.gcn import GC"
  },
  {
    "path": "benchmark/runtime/rgcn.py",
    "chars": 651,
    "preview": "import torch\nimport torch.nn.functional as F\n\nfrom torch_geometric.nn import FastRGCNConv\n\n\nclass RGCN(torch.nn.Module):"
  },
  {
    "path": "benchmark/runtime/train.py",
    "chars": 1004,
    "preview": "import time\n\nimport torch\nimport torch.nn.functional as F\n\n\ndef train_runtime(model, data, epochs, device):\n    optimize"
  },
  {
    "path": "benchmark/setup.py",
    "chars": 363,
    "preview": "from setuptools import find_packages, setup\n\nsetup(\n    name='torch_geometric_benchmark',\n    version='0.1.0',\n    descr"
  },
  {
    "path": "benchmark/training/README.md",
    "chars": 1755,
    "preview": "# Training Benchmark\n\n## Environment setup\n\n1. Confirm that PyG is properly installed.\n1. Install dataset package:\n   ``"
  },
  {
    "path": "benchmark/training/training_benchmark.py",
    "chars": 16565,
    "preview": "import argparse\nimport ast\nimport warnings\nfrom collections import defaultdict\nfrom contextlib import nullcontext\n\nimpor"
  },
  {
    "path": "benchmark/utils/__init__.py",
    "chars": 421,
    "preview": "from .utils import emit_itt\nfrom .utils import get_dataset, get_dataset_with_transformation\nfrom .utils import get_model"
  },
  {
    "path": "benchmark/utils/hetero_gat.py",
    "chars": 1739,
    "preview": "import torch\nfrom tqdm import tqdm\n\nfrom torch_geometric.nn import GAT, to_hetero\n\n\nclass HeteroGAT(torch.nn.Module):\n  "
  },
  {
    "path": "benchmark/utils/hetero_sage.py",
    "chars": 1683,
    "preview": "import torch\nfrom tqdm import tqdm\n\nfrom torch_geometric.nn import GraphSAGE, to_hetero\n\n\nclass HeteroGraphSAGE(torch.nn"
  },
  {
    "path": "benchmark/utils/utils.py",
    "chars": 6967,
    "preview": "import os\nimport os.path as osp\nfrom datetime import datetime\n\nimport torch\nfrom ogb.nodeproppred import PygNodePropPred"
  },
  {
    "path": "codecov.yml",
    "chars": 253,
    "preview": "# See: https://docs.codecov.io/docs/codecov-yaml\ncoverage:\n  range: 80..100\n  round: down\n  precision: 2\n  status:\n    p"
  },
  {
    "path": "docker/Dockerfile",
    "chars": 788,
    "preview": "FROM nvcr.io/nvidia/cuda-dl-base:24.09-cuda12.6-devel-ubuntu22.04\n\n# Based on NGC PyG 24.09 image:\n# https://docs.nvidia"
  },
  {
    "path": "docker/Dockerfile.xpu",
    "chars": 1412,
    "preview": "ARG BASE_IMAGE=\"intel/intel-extension-for-pytorch\"\nARG BASE_TAG=\"2.1.30-xpu\"\n\nFROM ${BASE_IMAGE}:${BASE_TAG}\n\n# meta inf"
  },
  {
    "path": "docker/README.md",
    "chars": 3707,
    "preview": "# Docker on NVIDIA GPU\n\nThe recommended way to use Docker for NVIDIA hardware is described [here](https://catalog.ngc.nv"
  },
  {
    "path": "docker/singularity",
    "chars": 1938,
    "preview": "Bootstrap: docker\nFrom: nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04\n\n%post\n  CURDIR=$(pwd)\n\n  # Set timezone to Etc/UTC fo"
  },
  {
    "path": "docs/Makefile",
    "chars": 215,
    "preview": "SPHINXBUILD   = sphinx-build\nSPHINXPROJ    = pytorch_geometric\nSOURCEDIR     = source\nBUILDDIR      = build\n\n.PHONY: hel"
  },
  {
    "path": "docs/README.md",
    "chars": 521,
    "preview": "# Building Documentation\n\nTo build the documentation:\n\n1. [Build and install](https://github.com/pyg-team/pytorch_geomet"
  },
  {
    "path": "docs/requirements.txt",
    "chars": 160,
    "preview": "https://download.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp310-cp310-manylinux_2_28_x86_64.whl\nnumpy>=1.19.5\ngit+https://g"
  },
  {
    "path": "docs/source/.gitignore",
    "chars": 11,
    "preview": "generated/\n"
  },
  {
    "path": "docs/source/_figures/.gitignore",
    "chars": 18,
    "preview": "*.aux\n*.log\n*.pdf\n"
  },
  {
    "path": "docs/source/_figures/build.sh",
    "chars": 149,
    "preview": "#!/bin/sh\n\nfor filename in *.tex; do\n  basename=$(basename $filename .tex)\n  pdflatex \"$basename.tex\"\n  pdf2svg \"$basena"
  },
  {
    "path": "docs/source/_figures/graph.tex",
    "chars": 359,
    "preview": "\\documentclass{standalone}\n\n\\usepackage{tikz}\n\n\\begin{document}\n\n\\begin{tikzpicture}\n  \\node[draw,circle,label= left:{$x"
  },
  {
    "path": "docs/source/_figures/hg_example.tex",
    "chars": 962,
    "preview": "\\documentclass{standalone}\n\n\\usepackage{tikz}\n\n\\begin{document}\n\n\\begin{tikzpicture}\n  \\node[draw,rectangle, align=cente"
  },
  {
    "path": "docs/source/_figures/to_hetero.tex",
    "chars": 3068,
    "preview": "\\documentclass{standalone}\n\n\\usepackage{tikz}\n\n\\definecolor{green}{RGB}{159,213,179}\n\\definecolor{blue}{RGB}{10,153,201}"
  },
  {
    "path": "docs/source/_figures/to_hetero_with_bases.tex",
    "chars": 3578,
    "preview": "\\documentclass{standalone}\n\n\\usepackage{tikz}\n\n\\definecolor{green}{RGB}{159,213,179}\n\\definecolor{blue}{RGB}{10,153,201}"
  },
  {
    "path": "docs/source/_static/js/version_alert.js",
    "chars": 749,
    "preview": "function warnOnLatestVersion() {\n  if (!window.READTHEDOCS_DATA || window.READTHEDOCS_DATA.version !== \"latest\") {\n    r"
  },
  {
    "path": "docs/source/_templates/autosummary/class.rst",
    "chars": 133,
    "preview": "{{ fullname | escape | underline}}\n\n.. currentmodule:: {{ module }}\n\n.. autoclass:: {{ objname }}\n   :show-inheritance:\n"
  },
  {
    "path": "docs/source/_templates/autosummary/inherited_class.rst",
    "chars": 198,
    "preview": "{{ fullname | escape | underline}}\n\n.. currentmodule:: {{ module }}\n\n.. autoclass:: {{ objname }}\n   :show-inheritance:\n"
  },
  {
    "path": "docs/source/_templates/autosummary/metrics.rst",
    "chars": 156,
    "preview": "{{ fullname | escape | underline}}\n\n.. currentmodule:: {{ module }}\n\n.. autoclass:: {{ objname }}\n   :show-inheritance:\n"
  },
  {
    "path": "docs/source/_templates/autosummary/nn.rst",
    "chars": 432,
    "preview": "{{ fullname | escape | underline}}\n\n.. currentmodule:: {{ module }}\n\n{% if objname != \"MessagePassing\" %}\n.. autoclass::"
  },
  {
    "path": "docs/source/_templates/autosummary/only_class.rst",
    "chars": 120,
    "preview": "{{ fullname | escape | underline}}\n\n.. currentmodule:: {{ module }}\n\n.. autoclass:: {{ objname }}\n   :show-inheritance:\n"
  },
  {
    "path": "docs/source/advanced/batching.rst",
    "chars": 12408,
    "preview": "Advanced Mini-Batching\n======================\n\nThe creation of mini-batching is crucial for letting the training of a de"
  },
  {
    "path": "docs/source/advanced/compile.rst",
    "chars": 6798,
    "preview": "Compiled Graph Neural Networks\n==============================\n\n:meth:`torch.compile` is the latest method to speed up yo"
  },
  {
    "path": "docs/source/advanced/cpu_affinity.rst",
    "chars": 13181,
    "preview": "CPU Affinity for PyG Workloads\n==============================\n\nThe performance of :pyg:`PyG` workloads using CPU can be "
  },
  {
    "path": "docs/source/advanced/graphgym.rst",
    "chars": 13029,
    "preview": "Managing Experiments with GraphGym\n==================================\n\nGraphGym is a platform for **designing and evalua"
  },
  {
    "path": "docs/source/advanced/hgam.rst",
    "chars": 8736,
    "preview": "Hierarchical Neighborhood Sampling\n==================================\n\nOne of the design principles of :pyg:`PyG` is tha"
  },
  {
    "path": "docs/source/advanced/jit.rst",
    "chars": 4446,
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    "path": "docs/source/advanced/remote.rst",
    "chars": 12448,
    "preview": "Scaling Up GNNs via Remote Backends\n===================================\n\n:pyg:`PyG` (2.2 and beyond) includes numerous p"
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    "path": "docs/source/advanced/sparse_tensor.rst",
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    "preview": "Memory-Efficient Aggregations\n=============================\n\nThe :class:`~torch_geometric.nn.conv.message_passing.Messag"
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    "preview": "Dataset Cheatsheet\n==================\n\n.. note::\n\n    This dataset statistics table is a **work in progress**.\n    Pleas"
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    "path": "docs/source/cheatsheet/gnn_cheatsheet.rst",
    "chars": 5535,
    "preview": "GNN Cheatsheet\n==============\n\n* :class:`~torch_sparse.SparseTensor`: If checked (✓), supports message passing based on "
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    "chars": 2839,
    "preview": "import datetime\nimport os.path as osp\nimport sys\n\nimport pyg_sphinx_theme\n\nimport torch_geometric\n\nauthor = 'PyG Team'\np"
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    "path": "docs/source/external/resources.rst",
    "chars": 6448,
    "preview": "External Resources\n==================\n\n* Fey *et al.*: **PyG 2.0: Scalable Learning on Real World Graphs** [`Paper <http"
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    "path": "docs/source/get_started/colabs.rst",
    "chars": 7778,
    "preview": "Colab Notebooks and Video Tutorials\n===================================\n\nOfficial Examples\n-----------------\n\nWe have pr"
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    "path": "docs/source/get_started/introduction.rst",
    "chars": 16606,
    "preview": "Introduction by Example\n=======================\n\nWe shortly introduce the fundamental concepts of :pyg:`PyG` through sel"
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    "path": "docs/source/index.rst",
    "chars": 2261,
    "preview": ":github_url: https://github.com/pyg-team/pytorch_geometric\n\nPyG Documentation\n=================\n\n:pyg:`null` **PyG** *(P"
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    "path": "docs/source/install/installation.rst",
    "chars": 12768,
    "preview": "Installation\n============\n\n:pyg:`PyG` is available for :python:`Python 3.10` to :python:`Python 3.14`.\n\n.. note::\n   We "
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    "preview": "<style>\n  .quick-start {\n    display: flex;\n    flex-direction: row;\n    flex-wrap: nowrap;\n    margin-bottom: 20px;\n  }"
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    "preview": "torch_geometric.contrib\n=======================\n\n.. currentmodule:: torch_geometric.contrib\n\n:obj:`torch_geometric.contr"
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    "path": "docs/source/modules/data.rst",
    "chars": 1390,
    "preview": "torch_geometric.data\n====================\n\n.. contents:: Contents\n    :local:\n\nData Objects\n------------\n\n.. currentmodu"
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    "path": "docs/source/modules/datasets.rst",
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    "preview": "torch_geometric.datasets\n========================\n\n.. contents:: Contents\n    :local:\n\nHomogeneous Datasets\n------------"
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    "path": "docs/source/modules/distributed.rst",
    "chars": 634,
    "preview": "torch_geometric.distributed\n===========================\n\n.. warning::\n    ``torch_geometric.distributed`` has been depre"
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    "path": "docs/source/modules/explain.rst",
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    "preview": "torch_geometric.explain\n=======================\n\n.. currentmodule:: torch_geometric.explain\n\n.. warning::\n\n    This modu"
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    "path": "docs/source/modules/graphgym.rst",
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    "preview": "torch_geometric.graphgym\n========================\n\n.. contents:: Contents\n    :local:\n\nWorkflow and Register Modules\n---"
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    "path": "docs/source/modules/llm.rst",
    "chars": 693,
    "preview": "torch_geometric.llm\n=======================\n\n.. currentmodule:: torch_geometric.llm\n\n.. autosummary::\n   :nosignatures:\n"
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    "path": "docs/source/modules/loader.rst",
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    "preview": "torch_geometric.loader\n======================\n\n.. currentmodule:: torch_geometric.loader\n\n.. autosummary::\n   :nosignatu"
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    "path": "docs/source/modules/metrics.rst",
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    "preview": "torch_geometric.metrics\n=======================\n\n.. contents:: Contents\n    :local:\n\nLink Prediction Metrics\n-----------"
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    "path": "docs/source/modules/nn.rst",
    "chars": 11236,
    "preview": "torch_geometric.nn\n==================\n\n.. contents:: Contents\n    :local:\n\n.. autoclass:: torch_geometric.nn.sequential."
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    "path": "docs/source/modules/profile.rst",
    "chars": 290,
    "preview": "torch_geometric.profile\n=======================\n\n.. currentmodule:: torch_geometric.profile\n\n.. autosummary::\n    :nosig"
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    "path": "docs/source/modules/root.rst",
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    "preview": "torch_geometric\n===============\n\nTensor Objects\n--------------\n\n.. currentmodule:: torch_geometric\n\n.. autosummary::\n   "
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    "path": "docs/source/modules/sampler.rst",
    "chars": 419,
    "preview": "torch_geometric.sampler\n=======================\n\n.. currentmodule:: torch_geometric.sampler\n\n.. autosummary::\n   :nosign"
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    "preview": "torch_geometric.transforms\n==========================\n\n.. contents:: Contents\n    :local:\n\nTransforms are a general way "
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    "path": "docs/source/modules/utils.rst",
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    "preview": "torch_geometric.utils\n=====================\n\n.. currentmodule:: torch_geometric.utils\n\n.. autosummary::\n    :nosignature"
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    "chars": 48,
    "preview": ":orphan:\n\n.. include:: ../advanced/batching.rst\n"
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    "preview": ":orphan:\n\nGNN Cheatsheet\n==============\n\n* :class:`~torch_sparse.SparseTensor`: If checked (✓), supports message passing"
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  {
    "path": "docs/source/notes/colabs.rst",
    "chars": 49,
    "preview": ":orphan:\n\n.. include:: ../get_started/colabs.rst\n"
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    "preview": ":orphan:\n\n.. include:: ../tutorial/create_dataset.rst\n"
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    "path": "docs/source/notes/create_gnn.rst",
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    "preview": ":orphan:\n\n.. include:: ../tutorial/create_gnn.rst\n"
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    "preview": ":orphan:\n\nDataset Cheatsheet\n==================\n\n.. note::\n\n    This dataset statistics table is a **work in progress**."
  },
  {
    "path": "docs/source/notes/explain.rst",
    "chars": 47,
    "preview": ":orphan:\n\n.. include:: ../tutorial/explain.rst\n"
  },
  {
    "path": "docs/source/notes/graphgym.rst",
    "chars": 48,
    "preview": ":orphan:\n\n.. include:: ../advanced/graphgym.rst\n"
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    "path": "docs/source/notes/heterogeneous.rst",
    "chars": 53,
    "preview": ":orphan:\n\n.. include:: ../tutorial/heterogeneous.rst\n"
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    "path": "docs/source/notes/installation.rst",
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    "preview": ":orphan:\n\n.. meta::\n   :http-equiv=refresh: 0; URL=../install/installation.html\n\nInstallation\n============\n\nThis page ha"
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    "path": "docs/source/notes/introduction.rst",
    "chars": 55,
    "preview": ":orphan:\n\n.. include:: ../get_started/introduction.rst\n"
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    "chars": 43,
    "preview": ":orphan:\n\n.. include:: ../advanced/jit.rst\n"
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    "path": "docs/source/notes/load_csv.rst",
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    "preview": ":orphan:\n\n.. include:: ../tutorial/load_csv.rst\n"
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    "chars": 46,
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    "chars": 53,
    "preview": ":orphan:\n\n.. include:: ../advanced/sparse_tensor.rst\n"
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  {
    "path": "docs/source/tutorial/application.rst",
    "chars": 188,
    "preview": "Use-Cases & Applications\n========================\n\n.. nbgallery::\n    :name: rst-gallery\n\n    neighbor_loader\n    point_"
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  {
    "path": "docs/source/tutorial/compile.rst",
    "chars": 47,
    "preview": ":orphan:\n\n.. include:: ../advanced/compile.rst\n"
  },
  {
    "path": "docs/source/tutorial/create_dataset.rst",
    "chars": 9032,
    "preview": "Creating Graph Datasets\n=======================\n\nAlthough :pyg:`PyG` already contains a lot of useful datasets, you may "
  },
  {
    "path": "docs/source/tutorial/create_gnn.rst",
    "chars": 13226,
    "preview": "Creating Message Passing Networks\n=================================\n\nGeneralizing the convolution operator to irregular "
  },
  {
    "path": "docs/source/tutorial/dataset.rst",
    "chars": 150,
    "preview": "Working with Graph Datasets\n===========================\n\n.. nbgallery::\n    :name: rst-gallery\n\n    create_dataset\n    l"
  },
  {
    "path": "docs/source/tutorial/dataset_splitting.rst",
    "chars": 7330,
    "preview": "Dataset Splitting\n=================\n\nDataset splitting is a critical step in graph machine learning, where we divide our"
  },
  {
    "path": "docs/source/tutorial/distributed.rst",
    "chars": 185,
    "preview": ".. _distributed_tutorials:\n\nDistributed Training\n====================\n\n.. nbgallery::\n    :name: rst-gallery\n\n    multi_"
  },
  {
    "path": "docs/source/tutorial/distributed_pyg.rst",
    "chars": 18285,
    "preview": "Distributed Training in PyG\n===========================\n\n.. warning::\n    ``torch_geometric.distributed`` has been depre"
  },
  {
    "path": "docs/source/tutorial/explain.rst",
    "chars": 9402,
    "preview": "Explaining Graph Neural Networks\n================================\n\nInterpreting GNN models is crucial for many use cases"
  },
  {
    "path": "docs/source/tutorial/gnn_design.rst",
    "chars": 137,
    "preview": "Design of Graph Neural Networks\n===============================\n\n.. nbgallery::\n    :name: rst-gallery\n\n    create_gnn\n "
  },
  {
    "path": "docs/source/tutorial/graph_transformer.rst",
    "chars": 13640,
    "preview": "Graph Transformer\n=================\n\n`Transformer <https://arxiv.org/abs/1706.03762>`_ is an effictive architecture in `"
  },
  {
    "path": "docs/source/tutorial/heterogeneous.rst",
    "chars": 24705,
    "preview": "Heterogeneous Graph Learning\n============================\n\nA large set of real-world datasets are stored as heterogeneou"
  },
  {
    "path": "docs/source/tutorial/load_csv.rst",
    "chars": 10712,
    "preview": "Loading Graphs from CSV\n=======================\n\nIn this example, we will show how to load a set of :obj:`*.csv` files a"
  },
  {
    "path": "docs/source/tutorial/multi_gpu_vanilla.rst",
    "chars": 9559,
    "preview": "Multi-GPU Training in Pure PyTorch\n==================================\n\n.. note::\n    For multi-GPU training with cuGraph"
  },
  {
    "path": "docs/source/tutorial/multi_node_multi_gpu_vanilla.rst",
    "chars": 6582,
    "preview": "Multi-Node Training using SLURM\n===============================\n\n.. note::\n    For multi-GPU training with cuGraph, refe"
  },
  {
    "path": "docs/source/tutorial/neighbor_loader.rst",
    "chars": 12672,
    "preview": "Scaling GNNs via Neighbor Sampling\n==================================\n\nOne of the challenges of Graph Neural Networks is"
  },
  {
    "path": "docs/source/tutorial/point_cloud.rst",
    "chars": 13272,
    "preview": "Point Cloud Processing\n======================\n\nThis tutorial explains how to leverage Graph Neural Networks (GNNs) for o"
  },
  {
    "path": "docs/source/tutorial/shallow_node_embeddings.rst",
    "chars": 8880,
    "preview": "Shallow Node Embeddings\n=======================\n\nIn this tutorial, we will take a closer look at how to learn *shallow n"
  },
  {
    "path": "examples/README.md",
    "chars": 4985,
    "preview": "# Examples\n\nThis folder contains a plethora of examples covering different GNN use-cases.\nThis readme highlights some ke"
  },
  {
    "path": "examples/agnn.py",
    "chars": 1957,
    "preview": "import os.path as osp\n\nimport torch\nimport torch.nn.functional as F\n\nimport torch_geometric.transforms as T\nfrom torch_g"
  },
  {
    "path": "examples/ar_link_pred.py",
    "chars": 7078,
    "preview": "import argparse\nimport os.path as osp\n\nimport torch\nimport torch.nn.functional as F\n\nimport torch_geometric.transforms a"
  },
  {
    "path": "examples/argva_node_clustering.py",
    "chars": 4419,
    "preview": "import os.path as osp\n\nimport matplotlib.pyplot as plt\nimport torch\nfrom sklearn.cluster import KMeans\nfrom sklearn.mani"
  },
  {
    "path": "examples/arma.py",
    "chars": 2347,
    "preview": "import os.path as osp\n\nimport torch\nimport torch.nn.functional as F\n\nimport torch_geometric.transforms as T\nfrom torch_g"
  },
  {
    "path": "examples/attentive_fp.py",
    "chars": 5407,
    "preview": "import os.path as osp\nfrom math import sqrt\n\nimport torch\nimport torch.nn.functional as F\nfrom rdkit import Chem\n\nfrom t"
  },
  {
    "path": "examples/autoencoder.py",
    "chars": 3995,
    "preview": "import argparse\nimport os.path as osp\nimport time\n\nimport torch\n\nimport torch_geometric.transforms as T\nfrom torch_geome"
  },
  {
    "path": "examples/cluster_gcn_ppi.py",
    "chars": 3278,
    "preview": "import os.path as osp\nimport time\n\nimport torch\nimport torch.nn.functional as F\nfrom sklearn.metrics import f1_score\n\nfr"
  },
  {
    "path": "examples/cluster_gcn_reddit.py",
    "chars": 3672,
    "preview": "import time\n\nimport torch\nimport torch.nn.functional as F\nfrom torch.nn import ModuleList\nfrom tqdm import tqdm\n\nfrom to"
  },
  {
    "path": "examples/colors_topk_pool.py",
    "chars": 3825,
    "preview": "import copy\nimport os.path as osp\n\nimport torch\nimport torch.nn.functional as F\nfrom torch.nn import Linear as Lin\nfrom "
  },
  {
    "path": "examples/compile/gcn.py",
    "chars": 2667,
    "preview": "import os.path as osp\nimport time\n\nimport torch\nimport torch.nn.functional as F\n\nimport torch_geometric.transforms as T\n"
  },
  {
    "path": "examples/compile/gin.py",
    "chars": 3147,
    "preview": "import os.path as osp\nimport time\n\nimport torch\nimport torch.nn.functional as F\n\nimport torch_geometric\nfrom torch_geome"
  },
  {
    "path": "examples/contrib/README.md",
    "chars": 1393,
    "preview": "# Examples for External Contributions\n\nThis directory contains examples demonstrating functionality included in the `tor"
  },
  {
    "path": "examples/contrib/pgm_explainer_graph_classification.py",
    "chars": 4040,
    "preview": "\"\"\"This is an example of using the PGM explainer algorithm on a graph\nclassification task.\n\"\"\"\nimport os.path as osp\n\nim"
  },
  {
    "path": "examples/contrib/pgm_explainer_node_classification.py",
    "chars": 2392,
    "preview": "\"\"\"This is an example of using the PGM explainer algorithm on a node\nclassification task.\n\"\"\"\nimport os.path as osp\n\nimp"
  },
  {
    "path": "examples/contrib/rbcd_attack.py",
    "chars": 7759,
    "preview": "import copy\nimport os.path as osp\nfrom typing import Optional\n\nimport torch\nimport torch.nn.functional as F\nfrom torch i"
  }
]

// ... and 1287 more files (download for full content)