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-{"language": "Python", "id": 2, "repo_owner": "pyg-team", "repo_name": "pytorch_geometric", "head_branch": "master", "workflow_name": "Testing minimal PyTorch 2.1", "workflow_filename": "minimal_testing.yml", "workflow_path": ".github/workflows/minimal_testing.yml", "contributor": "pyg-team", "sha_fail": "405ef2cd74212ee061a67cb454d99994f34a95c9", "sha_success": "b17555e5b91e00259a904b902d1774184f1e09f2", "workflow": "name: Testing minimal PyTorch 2.1\n\non:  # yamllint disable-line rule:truthy\n  push:\n    branches:\n      - master\n  pull_request:\n\njobs:\n\n  minimal_pytest:\n    runs-on: ubuntu-latest\n\n    steps:\n      - name: Checkout repository\n        uses: actions/checkout@v4\n        with:\n          fetch-depth: 40\n\n      # Skip workflow if only certain files have been changed.\n      - name: Get changed files\n        id: changed-files-specific\n        uses: tj-actions/changed-files@v34\n        with:\n          files: |\n            benchmark/**\n            conda/**\n            docker/**\n            docs/**\n            examples/**\n            graphgym/**\n            README.md\n            CHANGELOG.md\n\n      - name: Setup packages\n        if: steps.changed-files-specific.outputs.only_changed != 'true'\n        uses: ./.github/actions/setup\n        with:\n          full_install: false\n\n      - name: Install main package\n        if: steps.changed-files-specific.outputs.only_changed != 'true'\n        run: |\n          pip install -e .[test]\n\n      - name: Run tests\n        if: steps.changed-files-specific.outputs.only_changed != 'true'\n        run: |\n          pytest\n", "logs": [{"step_name": "minimal_pytest/6_Run tests.txt", "log": "##[group]Run pytest\n\u001b[36;1mpytest\u001b[0m\nshell: /usr/bin/bash -e {0}\nenv:\n  pythonLocation: /opt/hostedtoolcache/Python/3.8.18/x64\n  PKG_CONFIG_PATH: /opt/hostedtoolcache/Python/3.8.18/x64/lib/pkgconfig\n  Python_ROOT_DIR: /opt/hostedtoolcache/Python/3.8.18/x64\n  Python2_ROOT_DIR: 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\u001b[32mPASSED\u001b[0m\ntest/datasets/test_fake.py::test_fake_hetero_dataset[graph-1-10] \u001b[32mPASSED\u001b[0m\ntest/datasets/test_fake.py::test_fake_hetero_dataset[graph-4-1] \u001b[32mPASSED\u001b[0m\ntest/datasets/test_fake.py::test_fake_hetero_dataset[graph-4-10] \u001b[32mPASSED\u001b[0m\ntest/datasets/test_fake.py::test_fake_hetero_dataset[auto-0-1] \u001b[32mPASSED\u001b[0m\ntest/datasets/test_fake.py::test_fake_hetero_dataset[auto-0-10] \u001b[32mPASSED\u001b[0m\ntest/datasets/test_fake.py::test_fake_hetero_dataset[auto-1-1] \u001b[32mPASSED\u001b[0m\ntest/datasets/test_fake.py::test_fake_hetero_dataset[auto-1-10] \u001b[32mPASSED\u001b[0m\ntest/datasets/test_fake.py::test_fake_hetero_dataset[auto-4-1] \u001b[32mPASSED\u001b[0m\ntest/datasets/test_fake.py::test_fake_hetero_dataset[auto-4-10] \u001b[32mPASSED\u001b[0m\ntest/datasets/test_imdb_binary.py::test_imdb_binary \u001b[33mSKIPPED\u001b[0m (Fast test\nrun)\ntest/datasets/test_infection_dataset.py::test_infection_dataset \u001b[32mPASSED\u001b[0m\ntest/datasets/test_infection_dataset.py::test_infection_dataset_reproducibility \u001b[32mPASSED\u001b[0m\ntest/datasets/test_karate.py::test_karate \u001b[32mPASSED\u001b[0m\ntest/datasets/test_mutag.py::test_mutag \u001b[32mPASSED\u001b[0m\ntest/datasets/test_mutag.py::test_mutag_with_node_attr \u001b[32mPASSED\u001b[0m\ntest/datasets/test_planetoid.py::test_citeseer \u001b[32mPASSED\u001b[0m\ntest/datasets/test_planetoid.py::test_citeseer_with_full_split \u001b[32mPASSED\u001b[0m\ntest/datasets/test_planetoid.py::test_citeseer_with_random_split \u001b[32mPASSED\u001b[0m\ntest/datasets/test_snap_dataset.py::test_ego_facebook_snap_dataset \u001b[33mSKIPPED\u001b[0m\ntest/datasets/test_snap_dataset.py::test_soc_slashdot_snap_dataset \u001b[33mSKIPPED\u001b[0m\ntest/datasets/test_snap_dataset.py::test_wiki_vote_snap_dataset \u001b[33mSKIPPED\u001b[0m\ntest/datasets/test_suite_sparse.py::test_suite_sparse_dataset \u001b[33mSKIPPED\u001b[0m\ntest/datasets/test_suite_sparse.py::test_illc1850_suite_sparse_dataset \u001b[33mSKIPPED\u001b[0m\ntest/datasets/graph_generator/test_ba_graph.py::test_ba_graph \u001b[32mPASSED\u001b[0m\ntest/datasets/graph_generator/test_er_graph.py::test_er_graph \u001b[32mPASSED\u001b[0m\ntest/datasets/graph_generator/test_grid_graph.py::test_grid_graph \u001b[32mPASSED\u001b[0m\ntest/datasets/motif_generator/test_custom_motif.py::test_custom_motif_pyg_data \u001b[32mPASSED\u001b[0m\ntest/datasets/motif_generator/test_custom_motif.py::test_custom_motif_networkx \u001b[32mPASSED\u001b[0m\ntest/datasets/motif_generator/test_custom_motif.py::test_custom_motif_unknown \u001b[32mPASSED\u001b[0m\ntest/datasets/motif_generator/test_cycle_motif.py::test_cycle_motif \u001b[32mPASSED\u001b[0m\ntest/datasets/motif_generator/test_house_motif.py::test_house_motif \u001b[32mPASSED\u001b[0m\ntest/distributed/test_dist_utils.py::test_remove_duplicates \u001b[32mPASSED\u001b[0m\ntest/distributed/test_dist_utils.py::test_remove_duplicates_disjoint \u001b[32mPASSED\u001b[0m\ntest/distributed/test_local_feature_store.py::test_local_feature_store_global_id \u001b[32mPASSED\u001b[0m\ntest/distributed/test_local_feature_store.py::test_local_feature_store_utils \u001b[32mPASSED\u001b[0m\ntest/distributed/test_local_feature_store.py::test_homogeneous_feature_store \u001b[32mPASSED\u001b[0m\ntest/distributed/test_local_feature_store.py::test_heterogeneous_feature_store \u001b[32mPASSED\u001b[0m\ntest/distributed/test_local_graph_store.py::test_local_graph_store \u001b[32mPASSED\u001b[0m\ntest/distributed/test_local_graph_store.py::test_homogeneous_graph_store \u001b[32mPASSED\u001b[0m\ntest/distributed/test_local_graph_store.py::test_heterogeneous_graph_store \u001b[32mPASSED\u001b[0m\ntest/distributed/test_partition.py::test_partition_data \u001b[33mSKIPPED\u001b[0m (Not\ncompiled with METIS support)\ntest/distributed/test_partition.py::test_partition_hetero_data \u001b[33mSKIPPED\u001b[0m\ntest/distributed/test_partition.py::test_from_partition_data \u001b[33mSKIPPED\u001b[0m\ntest/distributed/test_partition.py::test_from_partition_hetero_data \u001b[33mSKIPPED\u001b[0m\nProcess SpawnProcess-6:\nTraceback (most recent call last):\n  File \"/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/multiprocessing/process.py\", line 315, in _bootstrap\n    self.run()\n  File \"/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/multiprocessing/process.py\", line 108, in run\n    self._target(*self._args, **self._kwargs)\n  File \"/home/runner/work/pytorch_geometric/pytorch_geometric/test/distributed/test_rpc.py\", line 79, in run_rpc_feature_test\n    assert torch.allclose(cpu_tensor0, tensor0.wait())\n  File \"/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/torch/futures/__init__.py\", line 76, in wait\n    return super().wait()\n  File \"/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/torch/futures/__init__.py\", line 268, in raise_error\n    raise fut_result\n  File \"/home/runner/work/pytorch_geometric/pytorch_geometric/torch_geometric/distributed/local_feature_store.py\", line 164, in when_finish\n    remote_feature_list = remote_fut.wait()\n  File \"/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/torch/futures/__init__.py\", line 76, in wait\n    return super().wait()\n  File \"/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/torch/futures/__init__.py\", line 268, in raise_error\n    raise fut_result\n  File \"/home/runner/work/pytorch_geometric/pytorch_geometric/torch_geometric/distributed/local_feature_store.py\", line 256, in when_finish\n    fut_list = collect_fut.wait()\nRuntimeError: AttributeError: On WorkerInfo(id=0, name=dist-feature-test-0):\nAttributeError(\"'NoneType' object has no attribute 'rpc_async'\")\nTraceback (most recent call last):\n  File \"/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/torch/distributed/rpc/internal.py\", line 207, in _run_function\n    result = python_udf.func(*python_udf.args, **python_udf.kwargs)\n  File \"/home/runner/work/pytorch_geometric/pytorch_geometric/torch_geometric/distributed/rpc.py\", line 168, in _rpc_async_call\n    return _rpc_call_pool.get(call_id).rpc_async(*args, **kwargs)\nAttributeError: 'NoneType' object has no attribute 'rpc_async'\n\n\nAt:\n  /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/torch/distributed/rpc/internal.py(234): _handle_exception\n\n[W tensorpipe_agent.cpp:725] RPC agent for dist-feature-test-1 encountered error when reading incoming request from dist-feature-test-0: pipe closed (this error originated at tensorpipe/core/pipe_impl.cc:356)\n[W tensorpipe_agent.cpp:725] RPC agent for dist-feature-test-0 encountered error when reading incoming request from dist-feature-test-1: EOF: end of file (this error originated at tensorpipe/transport/uv/connection_impl.cc:132)\n[W tensorpipe_agent.cpp:939] RPC agent for dist-feature-test-0 encountered error when reading incoming response from dist-feature-test-1: EOF: end of file (this error originated at tensorpipe/transport/uv/connection_impl.cc:132)\nOn WorkerInfo(id=0, name=dist-feature-test-0):\nAttributeError(\"'NoneType' object has no attribute 'rpc_async'\")\nTraceback (most recent call last):\n  File \"/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/torch/distributed/rpc/internal.py\", line 207, in _run_function\n    result = python_udf.func(*python_udf.args, **python_udf.kwargs)\n  File \"/home/runner/work/pytorch_geometric/pytorch_geometric/torch_geometric/distributed/rpc.py\", line 168, in _rpc_async_call\n    return _rpc_call_pool.get(call_id).rpc_async(*args, **kwargs)\nAttributeError: 'NoneType' object has no attribute 'rpc_async'\n\nProcess SpawnProcess-5:\nTraceback (most recent call last):\n  File \"/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/multiprocessing/process.py\", line 315, in _bootstrap\n    self.run()\n  File \"/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/multiprocessing/process.py\", line 108, in run\n    self._target(*self._args, **self._kwargs)\n  File \"/home/runner/work/pytorch_geometric/pytorch_geometric/test/distributed/test_rpc.py\", line 80, in run_rpc_feature_test\n    assert torch.allclose(cpu_tensor1, tensor1.wait())\n  File \"/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/torch/futures/__init__.py\", line 76, in wait\n    return super().wait()\n  File \"/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/torch/futures/__init__.py\", line 268, in raise_error\n    raise fut_result\n  File \"/home/runner/work/pytorch_geometric/pytorch_geometric/torch_geometric/distributed/local_feature_store.py\", line 164, in when_finish\n    remote_feature_list = remote_fut.wait()\n  File \"/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/torch/futures/__init__.py\", line 76, in wait\n    return super().wait()\n  File \"/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/torch/futures/__init__.py\", line 268, in raise_error\n    raise fut_result\n  File \"/home/runner/work/pytorch_geometric/pytorch_geometric/torch_geometric/distributed/local_feature_store.py\", line 256, in when_finish\n    fut_list = collect_fut.wait()\nRuntimeError: EOF: end of file (this error originated at tensorpipe/transport/uv/connection_impl.cc:132)\ntest/distributed/test_rpc.py::test_dist_feature_lookup \u001b[32mPASSED\u001b[0m\ntest/explain/test_explain_config.py::test_threshold_config[threshold_pairs0] \u001b[32mPASSED\u001b[0m\ntest/explain/test_explain_config.py::test_threshold_config[threshold_pairs1] \u001b[32mPASSED\u001b[0m\ntest/explain/test_explain_config.py::test_threshold_config[threshold_pairs2] \u001b[32mPASSED\u001b[0m\ntest/explain/test_explain_config.py::test_threshold_config[threshold_pairs3] \u001b[32mPASSED\u001b[0m\ntest/explain/test_explain_config.py::test_threshold_config[threshold_pairs4] \u001b[32mPASSED\u001b[0m\ntest/explain/test_explain_config.py::test_threshold_config[threshold_pairs5] \u001b[32mPASSED\u001b[0m\ntest/explain/test_explain_config.py::test_threshold_config[threshold_pairs6] \u001b[32mPASSED\u001b[0m\ntest/explain/test_explain_config.py::test_threshold_config[threshold_pairs7] \u001b[32mPASSED\u001b[0m\ntest/explain/test_explain_config.py::test_threshold_config[threshold_pairs8] \u001b[32mPASSED\u001b[0m\ntest/explain/test_explain_config.py::test_configuration_config[None-model] \u001b[32mPASSED\u001b[0m\ntest/explain/test_explain_config.py::test_configuration_config[None-phenomenon] \u001b[32mPASSED\u001b[0m\ntest/explain/test_explain_config.py::test_configuration_config[None-invalid] \u001b[32mPASSED\u001b[0m\ntest/explain/test_explain_config.py::test_configuration_config[object-model] \u001b[32mPASSED\u001b[0m\ntest/explain/test_explain_config.py::test_configuration_config[object-phenomenon] \u001b[32mPASSED\u001b[0m\ntest/explain/test_explain_config.py::test_configuration_config[object-invalid] \u001b[32mPASSED\u001b[0m\ntest/explain/test_explain_config.py::test_configuration_config[common_attributes-model] \u001b[32mPASSED\u001b[0m\ntest/explain/test_explain_config.py::test_configuration_config[common_attributes-phenomenon] \u001b[32mPASSED\u001b[0m\ntest/explain/test_explain_config.py::test_configuration_config[common_attributes-invalid] \u001b[32mPASSED\u001b[0m\ntest/explain/test_explain_config.py::test_configuration_config[attributes-model] \u001b[32mPASSED\u001b[0m\ntest/explain/test_explain_config.py::test_configuration_config[attributes-phenomenon] 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\u001b[32mPASSED\u001b[0m\ntest/explain/metric/test_faithfulness.py::test_unfaithfulness[log_probs-attributes-model-None] \u001b[32mPASSED\u001b[0m\ntest/explain/metric/test_faithfulness.py::test_unfaithfulness[log_probs-attributes-model-2] \u001b[32mPASSED\u001b[0m\ntest/explain/metric/test_faithfulness.py::test_unfaithfulness[log_probs-attributes-phenomenon-None] \u001b[32mPASSED\u001b[0m\ntest/explain/metric/test_faithfulness.py::test_unfaithfulness[log_probs-attributes-phenomenon-2] \u001b[32mPASSED\u001b[0m\ntest/explain/metric/test_fidelity.py::test_fidelity[model] \u001b[32mPASSED\u001b[0m\ntest/explain/metric/test_fidelity.py::test_fidelity[phenomenon] \u001b[32mPASSED\u001b[0m\ntest/explain/metric/test_fidelity.py::test_characterization_score \u001b[32mPASSED\u001b[0m\ntest/explain/metric/test_fidelity.py::test_fidelity_curve_auc \u001b[32mPASSED\u001b[0m\ntest/graphgym/test_config.py::test_from_config 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\u001b[32mPASSED\u001b[0m\ntest/loader/test_link_neighbor_loader.py::test_homo_link_neighbor_loader_triplet[1-True-True] \u001b[32mPASSED\u001b[0m\ntest/loader/test_link_neighbor_loader.py::test_homo_link_neighbor_loader_triplet[2-False-False] \u001b[32mPASSED\u001b[0m\ntest/loader/test_link_neighbor_loader.py::test_homo_link_neighbor_loader_triplet[2-False-True] \u001b[32mPASSED\u001b[0m\ntest/loader/test_link_neighbor_loader.py::test_homo_link_neighbor_loader_triplet[2-True-False] \u001b[32mPASSED\u001b[0m\ntest/loader/test_link_neighbor_loader.py::test_homo_link_neighbor_loader_triplet[2-True-True] \u001b[32mPASSED\u001b[0m\ntest/loader/test_link_neighbor_loader.py::test_hetero_link_neighbor_loader_triplet[1-False-False] \u001b[32mPASSED\u001b[0m\ntest/loader/test_link_neighbor_loader.py::test_hetero_link_neighbor_loader_triplet[1-False-True] \u001b[32mPASSED\u001b[0m\ntest/loader/test_link_neighbor_loader.py::test_hetero_link_neighbor_loader_triplet[1-True-False] \u001b[32mPASSED\u001b[0m\ntest/loader/test_link_neighbor_loader.py::test_hetero_link_neighbor_loader_triplet[1-True-True] \u001b[32mPASSED\u001b[0m\ntest/loader/test_link_neighbor_loader.py::test_hetero_link_neighbor_loader_triplet[2-False-False] \u001b[32mPASSED\u001b[0m\ntest/loader/test_link_neighbor_loader.py::test_hetero_link_neighbor_loader_triplet[2-False-True] \u001b[32mPASSED\u001b[0m\ntest/loader/test_link_neighbor_loader.py::test_hetero_link_neighbor_loader_triplet[2-True-False] \u001b[32mPASSED\u001b[0m\ntest/loader/test_link_neighbor_loader.py::test_hetero_link_neighbor_loader_triplet[2-True-True] \u001b[32mPASSED\u001b[0m\ntest/loader/test_link_neighbor_loader.py::test_link_neighbor_loader_mapping \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_homo_neighbor_loader_basic[None-dtype0-SubgraphType.directional-device0] 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\u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_homo_neighbor_loader_basic[True-dtype0-SubgraphType.induced-device0] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_homo_neighbor_loader_basic[True-dtype1-SubgraphType.directional-device0] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_homo_neighbor_loader_basic[True-dtype1-SubgraphType.bidirectional-device0] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_homo_neighbor_loader_basic[True-dtype1-SubgraphType.induced-device0] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_homo_neighbor_loader_basic[False-dtype0-SubgraphType.directional-device0] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_homo_neighbor_loader_basic[False-dtype0-SubgraphType.bidirectional-device0] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_homo_neighbor_loader_basic[False-dtype0-SubgraphType.induced-device0] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_homo_neighbor_loader_basic[False-dtype1-SubgraphType.directional-device0] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_homo_neighbor_loader_basic[False-dtype1-SubgraphType.bidirectional-device0] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_homo_neighbor_loader_basic[False-dtype1-SubgraphType.induced-device0] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_hetero_neighbor_loader_basic[dtype0-SubgraphType.directional] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_hetero_neighbor_loader_basic[dtype0-SubgraphType.bidirectional] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_hetero_neighbor_loader_basic[dtype0-SubgraphType.induced] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_hetero_neighbor_loader_basic[dtype1-SubgraphType.directional] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_hetero_neighbor_loader_basic[dtype1-SubgraphType.bidirectional] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_hetero_neighbor_loader_basic[dtype1-SubgraphType.induced] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_homo_neighbor_loader_on_cora[SubgraphType.directional] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_homo_neighbor_loader_on_cora[SubgraphType.bidirectional] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_homo_neighbor_loader_on_cora[SubgraphType.induced] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_hetero_neighbor_loader_on_cora[SubgraphType.directional] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_hetero_neighbor_loader_on_cora[SubgraphType.bidirectional] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_hetero_neighbor_loader_on_cora[SubgraphType.induced] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_temporal_hetero_neighbor_loader_on_cora \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_custom_neighbor_loader \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_temporal_custom_neighbor_loader_on_cora \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_pyg_lib_and_torch_sparse_homo_equality \u001b[33mSKIPPED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_pyg_lib_and_torch_sparse_hetero_equality \u001b[33mSKIPPED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_memmap_neighbor_loader \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_cpu_affinity_neighbor_loader[None] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_cpu_affinity_neighbor_loader[loader_cores1] \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_homo_neighbor_loader_sampled_info \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_hetero_neighbor_loader_sampled_info \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_neighbor_loader_mapping \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_weighted_homo_neighbor_loader \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_weighted_hetero_neighbor_loader \u001b[32mPASSED\u001b[0m\ntest/loader/test_neighbor_loader.py::test_neighbor_loader_with_tensor_frame[device0] \u001b[33mSKIPPED\u001b[0m\ntest/loader/test_neighbor_sampler.py::test_neighbor_sampler_basic \u001b[33mSKIPPED\u001b[0m\ntest/loader/test_neighbor_sampler.py::test_neighbor_sampler_invalid_kwargs \u001b[33mSKIPPED\u001b[0m\ntest/loader/test_neighbor_sampler.py::test_neighbor_sampler_on_cora \u001b[33mSKIPPED\u001b[0m\ntest/loader/test_prefetch.py::test_prefetch_loader[device0] 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\u001b[33mSKIPPED\u001b[0m\ntest/utils/test_convert.py::test_from_cugraph[True-False-None] \u001b[33mSKIPPED\u001b[0m\ntest/utils/test_convert.py::test_from_cugraph[True-False-edge_weight1] \u001b[33mSKIPPED\u001b[0m\ntest/utils/test_convert.py::test_from_cugraph[False-True-None] \u001b[33mSKIPPED\u001b[0m\ntest/utils/test_convert.py::test_from_cugraph[False-True-edge_weight1] \u001b[33mSKIPPED\u001b[0m\ntest/utils/test_convert.py::test_from_cugraph[False-False-None] \u001b[33mSKIPPED\u001b[0m\ntest/utils/test_convert.py::test_from_cugraph[False-False-edge_weight1] \u001b[33mSKIPPED\u001b[0m\ntest/utils/test_convert.py::test_to_dgl_graph \u001b[33mSKIPPED\u001b[0m (Package(s)\n{'dgl'} are not installed)\ntest/utils/test_convert.py::test_to_dgl_hetero_graph \u001b[33mSKIPPED\u001b[0m (Package(s)\n{'dgl'} are not installed)\ntest/utils/test_convert.py::test_to_dgl_sparse \u001b[33mSKIPPED\u001b[0m (Package(s)\n{'dgl', 'torch_sparse'} are not installed)\ntest/utils/test_convert.py::test_from_dgl_graph \u001b[33mSKIPPED\u001b[0m (Package(s)\n{'dgl'} are not installed)\ntest/utils/test_convert.py::test_from_dgl_hetero_graph \u001b[33mSKIPPED\u001b[0m\ntest/utils/test_cross_entropy.py::test_sparse_cross_entropy_multiclass \u001b[32mPASSED\u001b[0m\ntest/utils/test_cross_entropy.py::test_sparse_cross_entropy_multilabel \u001b[32mPASSED\u001b[0m\ntest/utils/test_degree.py::test_degree \u001b[32mPASSED\u001b[0m\ntest/utils/test_dropout.py::test_dropout_adj \u001b[32mPASSED\u001b[0m\ntest/utils/test_dropout.py::test_dropout_node \u001b[32mPASSED\u001b[0m\ntest/utils/test_dropout.py::test_dropout_edge \u001b[32mPASSED\u001b[0m\ntest/utils/test_dropout.py::test_dropout_path \u001b[33mSKIPPED\u001b[0m (Package(s)\n{'torch_cluster'} are not installed)\ntest/utils/test_embedding.py::test_get_embeddings \u001b[32mPASSED\u001b[0m\ntest/utils/test_functions.py::test_cumsum \u001b[32mPASSED\u001b[0m\ntest/utils/test_geodesic.py::test_geodesic_distance \u001b[33mSKIPPED\u001b[0m (Package(s)\n{'gdist'} are not installed)\ntest/utils/test_get_laplacian.py::test_get_laplacian \u001b[32mPASSED\u001b[0m\ntest/utils/test_get_mesh_laplacian.py::test_get_mesh_laplacian_of_cube \u001b[32mPASSED\u001b[0m\ntest/utils/test_get_mesh_laplacian.py::test_get_mesh_laplacian_of_irregular_triangular_prism \u001b[32mPASSED\u001b[0m\ntest/utils/test_grid.py::test_grid \u001b[32mPASSED\u001b[0m\ntest/utils/test_hetero.py::test_construct_bipartite_edge_index \u001b[32mPASSED\u001b[0m\ntest/utils/test_homophily.py::test_homophily \u001b[32mPASSED\u001b[0m\ntest/utils/test_isolated.py::test_contains_isolated_nodes \u001b[32mPASSED\u001b[0m\ntest/utils/test_isolated.py::test_remove_isolated_nodes \u001b[32mPASSED\u001b[0m\ntest/utils/test_lexsort.py::test_lexsort \u001b[32mPASSED\u001b[0m\ntest/utils/test_loop.py::test_contains_self_loops 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\u001b[33mSKIPPED\u001b[0m\ntest/utils/test_smiles.py::test_from_to_smiles[F/C=C\\\\F0] \u001b[33mSKIPPED\u001b[0m\ntest/utils/test_smiles.py::test_from_to_smiles[F/C=C\\\\F1] \u001b[33mSKIPPED\u001b[0m\ntest/utils/test_smiles.py::test_from_to_smiles[COc1cccc([C@@H]2Oc3ccc(OC)cc3/C(=N/OC[C@@H](C)[C@H](OCc3ccccc3)C(C)C)[C@@H]2O)c1] \u001b[33mSKIPPED\u001b[0m\ntest/utils/test_smiles.py::test_from_to_smiles[C/C(=C\\\\C(=O)c1ccc(C)o1)Nc1ccc2c(c1)OCO2] \u001b[33mSKIPPED\u001b[0m\ntest/utils/test_smiles.py::test_from_to_smiles[F[B-](F)(F)c1cnc2ccccc2c1] \u001b[33mSKIPPED\u001b[0m\ntest/utils/test_smiles.py::test_from_to_smiles[COC(=O)[C@@]1(Cc2ccccc2)[C@H]2C(=O)N(C)C(=O)[C@H]2[C@H]2CN=C(SC)N21] \u001b[33mSKIPPED\u001b[0m\ntest/utils/test_smiles.py::test_from_to_smiles[O=C(O)c1ccc(NS(=O)(=O)c2ccc3c(c2)C(=O)c2cc(S(=O)(=O)Nc4ccc(C(=O)O)cc4)ccc2-3)cc1] \u001b[33mSKIPPED\u001b[0m\ntest/utils/test_softmax.py::test_softmax \u001b[32mPASSED\u001b[0m\ntest/utils/test_softmax.py::test_softmax_backward 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\u001b[32mPASSED\u001b[0m\ntest/utils/test_spmm.py::test_spmm_basic[sum-device0] \u001b[32mPASSED\u001b[0m\ntest/utils/test_spmm.py::test_spmm_basic[mean-device0] \u001b[32mPASSED\u001b[0m\ntest/utils/test_spmm.py::test_spmm_reduce[min-device0] \u001b[32mPASSED\u001b[0m\ntest/utils/test_spmm.py::test_spmm_reduce[max-device0] \u001b[32mPASSED\u001b[0m\ntest/utils/test_spmm.py::test_spmm_layout[sum-layout0-device0] \u001b[32mPASSED\u001b[0m\ntest/utils/test_spmm.py::test_spmm_layout[sum-layout1-device0] \u001b[32mPASSED\u001b[0m\ntest/utils/test_spmm.py::test_spmm_layout[sum-layout2-device0] \u001b[32mPASSED\u001b[0m\ntest/utils/test_spmm.py::test_spmm_layout[mean-layout0-device0] \u001b[32mPASSED\u001b[0m\ntest/utils/test_spmm.py::test_spmm_layout[mean-layout1-device0] \u001b[32mPASSED\u001b[0m\ntest/utils/test_spmm.py::test_spmm_layout[mean-layout2-device0] \u001b[32mPASSED\u001b[0m\ntest/utils/test_spmm.py::test_spmm_layout[min-layout0-device0] \u001b[32mPASSED\u001b[0m\ntest/utils/test_spmm.py::test_spmm_layout[min-layout1-device0] \u001b[32mPASSED\u001b[0m\ntest/utils/test_spmm.py::test_spmm_layout[min-layout2-device0] \u001b[32mPASSED\u001b[0m\ntest/utils/test_spmm.py::test_spmm_layout[max-layout0-device0] \u001b[32mPASSED\u001b[0m\ntest/utils/test_spmm.py::test_spmm_layout[max-layout1-device0] \u001b[32mPASSED\u001b[0m\ntest/utils/test_spmm.py::test_spmm_layout[max-layout2-device0] \u001b[32mPASSED\u001b[0m\ntest/utils/test_spmm.py::test_spmm_jit[sum] \u001b[32mPASSED\u001b[0m\ntest/utils/test_spmm.py::test_spmm_jit[mean] \u001b[32mPASSED\u001b[0m\ntest/utils/test_subgraph.py::test_get_num_hops \u001b[32mPASSED\u001b[0m\ntest/utils/test_subgraph.py::test_subgraph \u001b[32mPASSED\u001b[0m\ntest/utils/test_subgraph.py::test_subgraph_large_index[device0] \u001b[33mSKIPPED\u001b[0m\ntest/utils/test_subgraph.py::test_bipartite_subgraph \u001b[32mPASSED\u001b[0m\ntest/utils/test_subgraph.py::test_bipartite_subgraph_large_index[device0] \u001b[33mSKIPPED\u001b[0m\ntest/utils/test_subgraph.py::test_k_hop_subgraph \u001b[32mPASSED\u001b[0m\ntest/utils/test_to_dense_adj.py::test_to_dense_adj \u001b[32mPASSED\u001b[0m\ntest/utils/test_to_dense_adj.py::test_to_dense_adj_with_empty_edge_index \u001b[32mPASSED\u001b[0m\ntest/utils/test_to_dense_adj.py::test_to_dense_adj_with_duplicate_entries \u001b[32mPASSED\u001b[0m\ntest/utils/test_to_dense_batch.py::test_to_dense_batch[70.0] \u001b[32mPASSED\u001b[0m\ntest/utils/test_to_dense_batch.py::test_to_dense_batch[fill1] \u001b[32mPASSED\u001b[0m\ntest/utils/test_to_dense_batch.py::test_to_dense_batch_disable_dynamic_shapes \u001b[32mPASSED\u001b[0m\ntest/utils/test_to_dense_batch.py::test_to_dense_batch_jit \u001b[33mSKIPPED\u001b[0m (Fast\ntest run)\ntest/utils/test_train_test_split_edges.py::test_train_test_split_edges \u001b[32mPASSED\u001b[0m\ntest/utils/test_trim_to_layer.py::test_trim_sparse_tensor \u001b[33mSKIPPED\u001b[0m\ntest/utils/test_trim_to_layer.py::test_trim_to_layer_basic \u001b[32mPASSED\u001b[0m\ntest/utils/test_trim_to_layer.py::test_trim_to_layer_hetero \u001b[32mPASSED\u001b[0m\ntest/utils/test_trim_to_layer.py::test_trim_to_layer_with_neighbor_loader \u001b[32mPASSED\u001b[0m\ntest/utils/test_trim_to_layer.py::test_trim_to_layer_filtering \u001b[32mPASSED\u001b[0m\ntest/utils/test_unbatch.py::test_unbatch \u001b[32mPASSED\u001b[0m\ntest/utils/test_unbatch.py::test_unbatch_edge_index \u001b[32mPASSED\u001b[0m\ntest/utils/test_undirected.py::test_is_undirected \u001b[32mPASSED\u001b[0m\ntest/utils/test_undirected.py::test_to_undirected \u001b[32mPASSED\u001b[0m\ntest/visualization/test_graph_visualization.py::test_visualize_graph_via_graphviz[None] \u001b[33mSKIPPED\u001b[0m\ntest/visualization/test_graph_visualization.py::test_visualize_graph_via_graphviz[graphviz] \u001b[33mSKIPPED\u001b[0m\ntest/visualization/test_graph_visualization.py::test_visualize_graph_via_networkx[None] \u001b[33mSKIPPED\u001b[0m\ntest/visualization/test_graph_visualization.py::test_visualize_graph_via_networkx[networkx] \u001b[33mSKIPPED\u001b[0m\ntest/visualization/test_influence.py::test_influence \u001b[32mPASSED\u001b[0m\n\n=================================== FAILURES ===================================\n\u001b[31m\u001b[1m__________________ test_load_lazy_linear[True--1--1-device0] ___________________\u001b[0m\n\ndim1 = -1, dim2 = -1, bias = True, device = device(type='cpu')\n\n    @withCUDA\n    @pytest.mark.parametrize('dim1', [-1, 16])\n    @pytest.mark.parametrize('dim2', [-1, 16])\n    @pytest.mark.parametrize('bias', [True, False])\n    def test_load_lazy_linear(dim1, dim2, bias, device):\n        lin1 = Linear(dim1, 32, bias=bias).to(device)\n        lin2 = Linear(dim2, 32, bias=bias).to(device)\n        lin2.load_state_dict(lin1.state_dict())\n    \n        if dim1 != -1:\n            assert isinstance(lin1.weight, torch.nn.Parameter)\n            assert isinstance(lin2.weight, torch.nn.Parameter)\n            assert torch.allclose(lin1.weight, lin2.weight)\n            assert not hasattr(lin1, '_hook')\n            assert not hasattr(lin2, '_hook')\n        else:\n            assert isinstance(lin1.weight, UninitializedParameter)\n            assert isinstance(lin2.weight, UninitializedParameter)\n            assert hasattr(lin1, '_hook')\n            assert hasattr(lin2, '_hook')\n    \n        if bias:\n            assert isinstance(lin1.bias, torch.nn.Parameter)\n            assert isinstance(lin2.bias, torch.nn.Parameter)\n>           assert torch.allclose(lin1.bias, lin2.bias)\n\u001b[1m\u001b[31mE           assert False\u001b[0m\n\u001b[1m\u001b[31mE            +  where False = <built-in method allclose of type object at 0x7fdb7d42d560>(Parameter containing:\\ntensor([       nan, 4.5866e-41, 6.4835e-16, 3.0670e-41, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00], requires_grad=True), Parameter containing:\\ntensor([       nan, 4.5866e-41, 6.4835e-16, 3.0670e-41, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00], requires_grad=True))\u001b[0m\n\u001b[1m\u001b[31mE            +    where <built-in method allclose of type object at 0x7fdb7d42d560> = torch.allclose\u001b[0m\n\u001b[1m\u001b[31mE            +    and   Parameter containing:\\ntensor([       nan, 4.5866e-41, 6.4835e-16, 3.0670e-41, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00], requires_grad=True) = Linear(-1, 32, bias=True).bias\u001b[0m\n\u001b[1m\u001b[31mE            +    and   Parameter containing:\\ntensor([       nan, 4.5866e-41, 6.4835e-16, 3.0670e-41, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00], requires_grad=True) = Linear(-1, 32, bias=True).bias\u001b[0m\n\n\u001b[1m\u001b[31mtest/nn/dense/test_linear.py\u001b[0m:69: AssertionError\n\u001b[33m=============================== warnings summary ===============================\u001b[0m\ntorch_geometric/graphgym/config.py:19\n  /home/runner/work/pytorch_geometric/pytorch_geometric/torch_geometric/graphgym/config.py:19: UserWarning: Could not define global config object. Please install 'yacs' via 'pip install yacs' in order to use GraphGym\n    warnings.warn(\"Could not define global config object. Please install \"\n\ntorch_geometric/graphgym/imports.py:14\n  /home/runner/work/pytorch_geometric/pytorch_geometric/torch_geometric/graphgym/imports.py:14: UserWarning: Please install 'pytorch_lightning' via  'pip install pytorch_lightning' in order to use GraphGym\n    warnings.warn(\"Please install 'pytorch_lightning' via  \"\n\ntest/nn/test_fx.py::test_dropout\n  /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/torch/overrides.py:110: UserWarning: 'has_cuda' is deprecated, please use 'torch.backends.cuda.is_built()'\n    torch.has_cuda,\n\ntest/nn/test_fx.py::test_dropout\n  /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/torch/overrides.py:111: UserWarning: 'has_cudnn' is deprecated, please use 'torch.backends.cudnn.is_available()'\n    torch.has_cudnn,\n\ntest/nn/test_fx.py::test_dropout\n  /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/torch/overrides.py:117: UserWarning: 'has_mps' is deprecated, please use 'torch.backends.mps.is_built()'\n    torch.has_mps,\n\ntest/nn/test_fx.py::test_dropout\n  /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/torch/overrides.py:118: UserWarning: 'has_mkldnn' is deprecated, please use 'torch.backends.mkldnn.is_available()'\n    torch.has_mkldnn,\n\ntest/nn/dense/test_linear.py::test_hetero_linear[device0]\n  /opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/torch/jit/_check.py:178: UserWarning: The TorchScript type system doesn't support instance-level annotations on empty non-base types in `__init__`. Instead, either 1) use a type annotation in the class body, or 2) wrap the type in `torch.jit.Attribute`.\n    warnings.warn(\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n\u001b[36m\u001b[1m=========================== short test summary info ============================\u001b[0m\n\u001b[31mFAILED\u001b[0m test/nn/dense/test_linear.py::\u001b[1mtest_load_lazy_linear[True--1--1-device0]\u001b[0m - assert False\n +  where False = <built-in method allclose of type object at 0x7fdb7d42d560>(Parameter containing:\\ntensor([       nan, 4.5866e-41, 6.4835e-16, 3.0670e-41, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00], requires_grad=True), Parameter containing:\\ntensor([       nan, 4.5866e-41, 6.4835e-16, 3.0670e-41, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00], requires_grad=True))\n +    where <built-in method allclose of type object at 0x7fdb7d42d560> = torch.allclose\n +    and   Parameter containing:\\ntensor([       nan, 4.5866e-41, 6.4835e-16, 3.0670e-41, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00], requires_grad=True) = Linear(-1, 32, bias=True).bias\n +    and   Parameter containing:\\ntensor([       nan, 4.5866e-41, 6.4835e-16, 3.0670e-41, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\\n        0.0000e+00, 0.0000e+00], requires_grad=True) = Linear(-1, 32, bias=True).bias\n\u001b[31m===== \u001b[31m\u001b[1m1 failed\u001b[0m, \u001b[32m4940 passed\u001b[0m, \u001b[33m798 skipped\u001b[0m, \u001b[33m7 warnings\u001b[0m\u001b[31m in 186.09s (0:03:06)\u001b[0m\u001b[31m ======\u001b[0m\n##[error]Process completed with exit code 1.\n"}], "diff": "diff --git a/CHANGELOG.md b/CHANGELOG.md\nindex afea1dfbe..22598fb75 100644\n--- a/CHANGELOG.md\n+++ b/CHANGELOG.md\n@@ -7,6 +7,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).\n \n ### Added\n \n+- Added support for `to_hetero_with_bases` on static graphs ([#8247](https://github.com/pyg-team/pytorch_geometric/pull/8247))\n - Added the `RCDD` dataset ([#8196](https://github.com/pyg-team/pytorch_geometric/pull/8196))\n - Added distributed `GAT + ogbn-products` example targeting XPU device ([#8032](https://github.com/pyg-team/pytorch_geometric/pull/8032))\n - Added the option to skip explanations of certain message passing layers via `conv.explain = False` ([#8216](https://github.com/pyg-team/pytorch_geometric/pull/8216))\ndiff --git a/test/nn/test_to_hetero_transformer.py b/test/nn/test_to_hetero_transformer.py\nindex 6bf1c269b..2ea69c840 100644\n--- a/test/nn/test_to_hetero_transformer.py\n+++ b/test/nn/test_to_hetero_transformer.py\n@@ -520,10 +520,8 @@ def test_to_hetero_on_static_graphs():\n         'author': torch.randn(4, 100, 16),\n     }\n     edge_index_dict = {\n-        ('paper', 'written_by', 'author'):\n-        torch.randint(100, (2, 200), dtype=torch.long),\n-        ('author', 'writes', 'paper'):\n-        torch.randint(100, (2, 200), dtype=torch.long),\n+        ('paper', 'written_by', 'author'): torch.randint(100, (2, 200)),\n+        ('author', 'writes', 'paper'): torch.randint(100, (2, 200)),\n     }\n \n     metadata = list(x_dict.keys()), list(edge_index_dict.keys())\ndiff --git a/test/nn/test_to_hetero_with_bases_transformer.py b/test/nn/test_to_hetero_with_bases_transformer.py\nindex 6f40f0cb2..3736ad6c6 100644\n--- a/test/nn/test_to_hetero_with_bases_transformer.py\n+++ b/test/nn/test_to_hetero_with_bases_transformer.py\n@@ -293,3 +293,24 @@ def test_to_hetero_with_bases_validate():\n \n     with pytest.warns(UserWarning, match=\"letters, numbers and underscores\"):\n         model = to_hetero_with_bases(model, metadata, num_bases=4, debug=False)\n+\n+\n+def test_to_hetero_with_bases_on_static_graphs():\n+    x_dict = {\n+        'paper': torch.randn(4, 100, 16),\n+        'author': torch.randn(4, 100, 16),\n+    }\n+    edge_index_dict = {\n+        ('paper', 'written_by', 'author'): torch.randint(100, (2, 200)),\n+        ('author', 'writes', 'paper'): torch.randint(100, (2, 200)),\n+    }\n+\n+    metadata = list(x_dict.keys()), list(edge_index_dict.keys())\n+    model = to_hetero_with_bases(Net4(), metadata, num_bases=4,\n+                                 in_channels={'x0': 16}, debug=False)\n+\n+    out_dict = model(x_dict, edge_index_dict)\n+\n+    assert len(out_dict) == 2\n+    assert out_dict['paper'].size() == (4, 100, 32)\n+    assert out_dict['author'].size() == (4, 100, 32)\ndiff --git a/torch_geometric/nn/to_hetero_with_bases_transformer.py b/torch_geometric/nn/to_hetero_with_bases_transformer.py\nindex 01410d967..cb787f364 100644\n--- a/torch_geometric/nn/to_hetero_with_bases_transformer.py\n+++ b/torch_geometric/nn/to_hetero_with_bases_transformer.py\n@@ -324,7 +324,7 @@ class HeteroBasisConv(torch.nn.Module):\n         # to a materialization of messages.\n         def hook(module, inputs, output):\n             assert isinstance(module._edge_type, Tensor)\n-            if module._edge_type.size(0) != output.size(0):\n+            if module._edge_type.size(0) != output.size(-2):\n                 raise ValueError(\n                     f\"Number of messages ({output.size(0)}) does not match \"\n                     f\"with the number of original edges \"\n@@ -332,7 +332,7 @@ class HeteroBasisConv(torch.nn.Module):\n                     f\"passing layer create additional self-loops? Try to \"\n                     f\"remove them via 'add_self_loops=False'\")\n             weight = module.edge_type_weight.view(-1)[module._edge_type]\n-            weight = weight.view([-1] + [1] * (output.dim() - 1))\n+            weight = weight.view([1] * (output.dim() - 2) + [-1, 1])\n             return weight * output\n \n         params = list(module.parameters())\n@@ -415,7 +415,7 @@ def get_node_offset_dict(\n     out: Dict[NodeType, int] = {}\n     for key in type2id.keys():\n         out[key] = cumsum\n-        cumsum += input_dict[key].size(0)\n+        cumsum += input_dict[key].size(-2)\n     return out\n \n \n@@ -433,7 +433,7 @@ def get_edge_offset_dict(\n         elif value.dtype == torch.long and value.size(0) == 2:\n             cumsum += value.size(-1)\n         else:\n-            cumsum += value.size(0)\n+            cumsum += value.size(-2)\n     return out\n \n \n@@ -458,7 +458,7 @@ def get_edge_type(\n             out = torch.full((value.nnz(), ), i, dtype=torch.long,\n                              device=value.device())\n         else:\n-            out = value.new_full((value.size(0), ), i, dtype=torch.long)\n+            out = value.new_full((value.size(-2), ), i, dtype=torch.long)\n         outs.append(out)\n \n     return outs[0] if len(outs) == 1 else torch.cat(outs, dim=0)\n@@ -474,7 +474,7 @@ def group_node_placeholder(input_dict: Dict[NodeType, Tensor],\n                            type2id: Dict[NodeType, int]) -> Tensor:\n \n     inputs = [input_dict[key] for key in type2id.keys()]\n-    return inputs[0] if len(inputs) == 1 else torch.cat(inputs, dim=0)\n+    return inputs[0] if len(inputs) == 1 else torch.cat(inputs, dim=-2)\n \n \n def group_edge_placeholder(\n@@ -528,7 +528,7 @@ def group_edge_placeholder(\n         return torch.stack([row, col], dim=0)\n \n     else:\n-        return torch.cat(inputs, dim=0)\n+        return torch.cat(inputs, dim=-2)\n \n \n ###############################################################################\n@@ -542,9 +542,9 @@ def split_output(\n     offset_dict: Union[Dict[NodeType, int], Dict[EdgeType, int]],\n ) -> Union[Dict[NodeType, Tensor], Dict[EdgeType, Tensor]]:\n \n-    cumsums = list(offset_dict.values()) + [output.size(0)]\n+    cumsums = list(offset_dict.values()) + [output.size(-2)]\n     sizes = [cumsums[i + 1] - cumsums[i] for i in range(len(offset_dict))]\n-    outputs = output.split(sizes)\n+    outputs = output.split(sizes, dim=-2)\n     return {key: output for key, output in zip(offset_dict, outputs)}\n \n \n", "difficulty": "3"}
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