{"receipt_id":"agent1-1","seq":1,"branch_id":"agent1","kind":"command","cmd":"git ls-files -z","path":null,"exit_code":0,"stdout_tail":"ol/connect/base.py\u0000torch_geometric/nn/pool/connect/filter_edges.py\u0000torch_geometric/nn/pool/consecutive.py\u0000torch_geometric/nn/pool/decimation.py\u0000torch_geometric/nn/pool/edge_pool.py\u0000torch_geometric/nn/pool/glob.py\u0000torch_geometric/nn/pool/graclus.py\u0000torch_geometric/nn/pool/max_pool.py\u0000torch_geometric/nn/pool/mem_pool.py\u0000torch_geometric/nn/pool/pan_pool.py\u0000torch_geometric/nn/pool/pool.py\u0000torch_geometric/nn/pool/sag_pool.py\u0000torch_geometric/nn/pool/select/__init__.py\u0000torch_geometric/nn/pool/select/base.py\u0000torch_geometric/nn/pool/select/topk.py\u0000torch_geometric/nn/pool/topk_pool.py\u0000torch_geometric/nn/pool/voxel_grid.py\u0000torch_geometric/nn/reshape.py\u0000torch_geometric/nn/resolver.py\u0000torch_geometric/nn/sequential.jinja\u0000torch_geometric/nn/sequential.py\u0000torch_geometric/nn/summary.py\u0000torch_geometric/nn/to_fixed_size_transformer.py\u0000torch_geometric/nn/to_hetero_module.py\u0000torch_geometric/nn/to_hetero_transformer.py\u0000torch_geometric/nn/to_hetero_with_bases_transformer.py\u0000torch_geometric/nn/unpool/__init__.py\u0000torch_geometric/nn/unpool/knn_interpolate.py\u0000torch_geometric/profile/__init__.py\u0000torch_geometric/profile/benchmark.py\u0000torch_geometric/profile/profile.py\u0000torch_geometric/profile/profiler.py\u0000torch_geometric/profile/utils.py\u0000torch_geometric/resolver.py\u0000torch_geometric/sampler/__init__.py\u0000torch_geometric/sampler/base.py\u0000torch_geometric/sampler/hgt_sampler.py\u0000torch_geometric/sampler/neighbor_sampler.py\u0000torch_geometric/sampler/utils.py\u0000torch_geometric/seed.py\u0000torch_geometric/testing/__init__.py\u0000torch_geometric/testing/asserts.py\u0000torch_geometric/testing/data.py\u0000torch_geometric/testing/decorators.py\u0000torch_geometric/testing/feature_store.py\u0000torch_geometric/testing/graph_store.py\u0000torch_geometric/transforms/__init__.py\u0000torch_geometric/transforms/add_metapaths.py\u0000torch_geometric/transforms/add_positional_encoding.py\u0000torch_geometric/transforms/add_remaining_self_loops.py\u0000torch_geometric/transforms/add_self_loops.py\u0000torch_geometric/transforms/base_transform.py\u0000torch_geometric/transforms/cartesian.py\u0000torch_geometric/transforms/center.py\u0000torch_geometric/transforms/compose.py\u0000torch_geometric/transforms/constant.py\u0000torch_geometric/transforms/delaunay.py\u0000torch_geometric/transforms/distance.py\u0000torch_geometric/transforms/face_to_edge.py\u0000torch_geometric/transforms/feature_propagation.py\u0000torch_geometric/transforms/fixed_points.py\u0000torch_geometric/transforms/gcn_norm.py\u0000torch_geometric/transforms/gdc.py\u0000torch_geometric/transforms/generate_mesh_normals.py\u0000torch_geometric/transforms/grid_sampling.py\u0000torch_geometric/transforms/knn_graph.py\u0000torch_geometric/transforms/laplacian_lambda_max.py\u0000torch_geometric/transforms/largest_connected_components.py\u0000torch_geometric/transforms/line_graph.py\u0000torch_geometric/transforms/linear_transformation.py\u0000torch_geometric/transforms/local_cartesian.py\u0000torch_geometric/transforms/local_degree_profile.py\u0000torch_geometric/transforms/mask.py\u0000torch_geometric/transforms/node_property_split.py\u0000torch_geometric/transforms/normalize_features.py\u0000torch_geometric/transforms/normalize_rotation.py\u0000torch_geometric/transforms/normalize_scale.py\u0000torch_geometric/transforms/one_hot_degree.py\u0000torch_geometric/transforms/pad.py\u0000torch_geometric/transforms/point_pair_features.py\u0000torch_geometric/transforms/polar.py\u0000torch_geometric/transforms/radius_graph.py\u0000torch_geometric/transforms/random_flip.py\u0000torch_geometric/transforms/random_jitter.py\u0000torch_geometric/transforms/random_link_split.py\u0000torch_geometric/transforms/random_node_split.py\u0000torch_geometric/transforms/random_rotate.py\u0000torch_geometric/transforms/random_scale.py\u0000torch_geometric/transforms/random_shear.py\u0000torch_geometric/transforms/remove_duplicated_edges.py\u0000torch_geometric/transforms/remove_isolated_nodes.py\u0000torch_geometric/transforms/remove_training_classes.py\u0000torch_geometric/transforms/rooted_subgraph.py\u0000torch_geometric/transforms/sample_points.py\u0000torch_geometric/transforms/sign.py\u0000torch_geometric/transforms/spherical.py\u0000torch_geometric/transforms/svd_feature_reduction.py\u0000torch_geometric/transforms/target_indegree.py\u0000torch_geometric/transforms/to_dense.py\u0000torch_geometric/transforms/to_device.py\u0000torch_geometric/transforms/to_sparse_tensor.py\u0000torch_geometric/transforms/to_superpixels.py\u0000torch_geometric/transforms/to_undirected.py\u0000torch_geometric/transforms/two_hop.py\u0000torch_geometric/transforms/virtual_node.py\u0000torch_geometric/typing.py\u0000torch_geometric/utils/__init__.py\u0000torch_geometric/utils/assortativity.py\u0000torch_geometric/utils/augmentation.py\u0000torch_geometric/utils/coalesce.py\u0000torch_geometric/utils/convert.py\u0000torch_geometric/utils/cross_entropy.py\u0000torch_geometric/utils/degree.py\u0000torch_geometric/utils/dropout.py\u0000torch_geometric/utils/embedding.py\u0000torch_geometric/utils/geodesic.py\u0000torch_geometric/utils/get_laplacian.py\u0000torch_geometric/utils/get_mesh_laplacian.py\u0000torch_geometric/utils/grid.py\u0000torch_geometric/utils/hetero.py\u0000torch_geometric/utils/homophily.py\u0000torch_geometric/utils/isolated.py\u0000torch_geometric/utils/loop.py\u0000torch_geometric/utils/map.py\u0000torch_geometric/utils/mask.py\u0000torch_geometric/utils/mixin.py\u0000torch_geometric/utils/negative_sampling.py\u0000torch_geometric/utils/nested.py\u0000torch_geometric/utils/normalized_cut.py\u0000torch_geometric/utils/num_nodes.py\u0000torch_geometric/utils/one_hot.py\u0000torch_geometric/utils/random.py\u0000torch_geometric/utils/repeat.py\u0000torch_geometric/utils/scatter.py\u0000torch_geometric/utils/segment.py\u0000torch_geometric/utils/select.py\u0000torch_geometric/utils/smiles.py\u0000torch_geometric/utils/softmax.py\u0000torch_geometric/utils/sort.py\u0000torch_geometric/utils/sort_edge_index.py\u0000torch_geometric/utils/sparse.py\u0000torch_geometric/utils/spmm.py\u0000torch_geometric/utils/subgraph.py\u0000torch_geometric/utils/to_dense_adj.py\u0000torch_geometric/utils/to_dense_batch.py\u0000torch_geometric/utils/train_test_split_edges.py\u0000torch_geometric/utils/tree_decomposition.py\u0000torch_geometric/utils/trim_to_layer.py\u0000torch_geometric/utils/unbatch.py\u0000torch_geometric/utils/undirected.py\u0000torch_geometric/visualization/__init__.py\u0000torch_geometric/visualization/graph.py\u0000torch_geometric/visualization/influence.py\u0000","stderr_tail":"","stdout_sha256":"9ee3157b12965b828eb8aed4be2ca82c5dc1a937c47eed42933de5840f3f67f7","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-1.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:53:12.561373Z","duration_s":0.0038,"timed_out":false,"image_before":"deac7fcf-b12e-4b05-b202-3395d967f0c7","image_after":"2073d9ba-02e8-4e83-9316-35416fa78cce","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-2","seq":2,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-c33ef5c1c0b04501a295275c3228988e/bin/busybox sh -c 'mkdir -p /tmp && cp /opt/receipts-c33ef5c1c0b04501a295275c3228988e/incoming/2a85ed393c125c9abbafe138f4f33bd3b739ec1ac306f3926605356f6769eebd /tmp/reproduce_issue.py'","path":"/tmp/reproduce_issue.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"2a85ed393c125c9abbafe138f4f33bd3b739ec1ac306f3926605356f6769eebd","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-2.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:53:21.491934Z","duration_s":0.004524,"timed_out":false,"image_before":"2073d9ba-02e8-4e83-9316-35416fa78cce","image_after":"6ff56097-b07d-4843-8c07-c421f8340417","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-3","seq":3,"branch_id":"agent1","kind":"command","cmd":"cd /pytorch_geometric && python /tmp/reproduce_issue.py","path":null,"exit_code":0,"stdout_tail":"Original edge_index:\ntensor([[0, 1, 2, 0, 1, 2, 0, 1, 2],\n        [0, 0, 0, 1, 1, 1, 2, 2, 2]], dtype=torch.int32)\n\nResult edge_index: tensor([[0, 1, 2, 0, 1, 2, 0, 1, 2, 2, 1, 0, 1],\n        [0, 0, 0, 1, 1, 1, 2, 2, 2, 1, 2, 1, 0]])\nAdded edges: tensor([[2, 1, 0, 1],\n        [1, 2, 1, 0]])\n\nChecking for duplicate edges:\nDuplicate found: (2, 1) already exists!\nDuplicate found: (1, 2) already exists!\nDuplicate found: (0, 1) already exists!\nDuplicate found: (1, 0) already exists!\n","stderr_tail":"","stdout_sha256":"6393e31f848d62594d35cfda2e4efc13716a8d6903700088a1de41b555c69991","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-3.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:53:23.545168Z","duration_s":2.508245,"timed_out":false,"image_before":"6ff56097-b07d-4843-8c07-c421f8340417","image_after":"41e11502-72eb-4ef8-ae57-bb5d5cdd2641","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-4","seq":4,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-07548a0238cc4d16ab301cf397c43509/bin/busybox sh -c 'mkdir -p /pytorch_geometric/torch_geometric/utils && cp /opt/receipts-07548a0238cc4d16ab301cf397c43509/incoming/29347429def9bc4d98d38ea9bebca96cb8a131e1783ce5eacf54e66d62e036ea /pytorch_geometric/torch_geometric/utils/augmentation.py'","path":"torch_geometric/utils/augmentation.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"29347429def9bc4d98d38ea9bebca96cb8a131e1783ce5eacf54e66d62e036ea","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-4.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:53:39.534997Z","duration_s":0.004973,"timed_out":false,"image_before":"41e11502-72eb-4ef8-ae57-bb5d5cdd2641","image_after":"2686c526-3b63-41c9-ad83-a023c2ac28d3","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-5","seq":5,"branch_id":"agent1","kind":"command","cmd":"cd /pytorch_geometric && python /tmp/reproduce_issue.py","path":null,"exit_code":0,"stdout_tail":"Original edge_index:\ntensor([[0, 1, 2, 0, 1, 2, 0, 1, 2],\n        [0, 0, 0, 1, 1, 1, 2, 2, 2]], dtype=torch.int32)\n\nResult edge_index: tensor([[0., 1., 2., 0., 1., 2., 0., 1., 2.],\n        [0., 0., 0., 1., 1., 1., 2., 2., 2.]])\nAdded edges: tensor([], size=(2, 0))\n\nChecking for duplicate edges:\n","stderr_tail":"","stdout_sha256":"aded0e7c1951d6433ff444070112d99e955cb91224df91df3b2d747e8eab67d8","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-5.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:53:41.777396Z","duration_s":2.153889,"timed_out":false,"image_before":"2686c526-3b63-41c9-ad83-a023c2ac28d3","image_after":"e0142626-7846-4a7f-a0ee-25883d5365e8","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-6","seq":6,"branch_id":"agent1","kind":"command","cmd":"/opt/receipts-8ba9c7ad5a5b42d3ad469c9af3b35647/bin/busybox mkdir -p /opt/receipts/reports && python -I /opt/receipts-8ba9c7ad5a5b42d3ad469c9af3b35647/receipts_pytest.py test/utils/test_augmentation.py::test_add_random_edge -v -o junit_family=xunit1 -p no:cacheprovider -rA --continue-on-collection-errors --junitxml=/opt/receipts/reports/agent1-6.xml","path":null,"exit_code":1,"stdout_tail":"============================= test session starts ==============================\nplatform linux -- Python 3.10.19, pytest-9.0.2, pluggy-1.6.0 -- /usr/local/bin/python\nrootdir: /pytorch_geometric\nconfigfile: pyproject.toml\nplugins: cov-7.0.0\ncollecting ... collected 1 item\n\ntest/utils/test_augmentation.py::test_add_random_edge FAILED\n\n=================================== FAILURES ===================================\n_____________________________ test_add_random_edge _____________________________\n\n    def test_add_random_edge():\n        edge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]])\n        out = add_random_edge(edge_index, p=0.5, training=False)\n        assert out[0].tolist() == edge_index.tolist()\n        assert out[1].tolist() == [[], []]\n    \n        seed_everything(5)\n        out = add_random_edge(edge_index, p=0.5)\n>       assert out[0].tolist() == [[0, 1, 1, 2, 2, 3, 3, 1, 2],\n                                   [1, 0, 2, 1, 3, 2, 0, 3, 0]]\nE       AssertionError: assert [[0, 1, 1, 2,...1, 3, 2, ...]] == [[0, 1, 1, 2,...1, 3, 2, ...]]\nE         \nE         At index 0 diff: [0, 1, 1, 2, 2, 3, 3, 2, 0] != [0, 1, 1, 2, 2, 3, 3, 1, 2]\nE         \nE         Full diff:\nE           [\nE               [\nE                   0,...\nE         \nE         ...Full output truncated (28 lines hidden), use '-vv' to show\n\ntest/utils/test_augmentation.py:83: AssertionError\n------------ generated xml file: /opt/receipts/reports/agent1-6.xml ------------\n=========================== short test summary info ============================\nFAILED test/utils/test_augmentation.py::test_add_random_edge - AssertionError...\n============================== 1 failed in 0.10s ===============================\n","stderr_tail":"","stdout_sha256":"b990f99c1f2932d3e9a7c7c9c7940f6e8d91d27d5ca6456927437f73ff5f3235","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-6.log","tree_digest":"db7c0e3ba27687b60367995f58d5eb635ab9a430195ae8ed0e5d05932c7fd2d1","tree_download_s":3.369352958863601,"started_at":"2026-10-03T19:53:47.472715Z","duration_s":5.217853,"timed_out":false,"image_before":"e0142626-7846-4a7f-a0ee-25883d5365e8","image_after":"a20b8353-dc1e-42bf-b0b1-8a25120c1c0c","junit":{"passed":[],"failed":["test/utils/test_augmentation.py::test_add_random_edge"],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{"test/utils/test_augmentation.py::test_add_random_edge":"AssertionError: assert [[0, 1, 1, 2,...1, 3, 2, ...]] == [[0, 1, 1, 2,...1, 3, 2, ...]]\n  \n  At index 0 diff: [0, 1, 1, 2, 2, 3, 3, 2, 0] != [0, 1, 1, 2, 2, 3, 3, 1, 2]\n  \n  Full diff:\n    [\n        [\n            0,...\n  \n  ...Full output truncated (28 lines hidden), use '-vv' to show\ndef test_add_random_edge():\n        edge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]])\n        out = add_random_edge(edge_index, p=0.5, training=False)\n        assert out[0].tolist() == edge_index.tolist()\n        assert out[1].tolist() == [[], []]\n    \n        seed_everything(5)\n        out = add_random_edge(edge_index, p=0.5)\n>       assert out[0].tolist() == [[0, 1, 1, 2, 2, 3, 3, 1, 2],\n                                   [1, 0, 2, 1, 3, 2, 0, 3, 0]]\nE       AssertionError: assert [[0, 1, 1, 2,...1, 3, 2, ...]] == [[0, 1, 1, 2,...1, 3, 2, ...]]\nE         \nE         At index 0 diff: [0, 1, 1, 2, 2, 3, 3, 2, 0] != [0, 1, 1, 2, 2, 3, 3, 1, 2]\nE         \nE         Full diff:\nE           [\nE               [\nE                   0,...\nE         \nE         ...Full output truncated (28 lines hidden), use '-vv' to show\n\ntest/utils/test_augmentation.py:83: AssertionError"}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-7","seq":7,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-a1e1bc1a051343048292208227788274/bin/busybox sh -c 'mkdir -p /pytorch_geometric/torch_geometric/utils && cp /opt/receipts-a1e1bc1a051343048292208227788274/incoming/21379e9ceaaaaebbc6d003c4594be66e02b1f2913f78fb797a9d5ebf9f8a1577 /pytorch_geometric/torch_geometric/utils/augmentation.py'","path":"torch_geometric/utils/augmentation.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"21379e9ceaaaaebbc6d003c4594be66e02b1f2913f78fb797a9d5ebf9f8a1577","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-7.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:54:13.244806Z","duration_s":0.004079,"timed_out":false,"image_before":"a20b8353-dc1e-42bf-b0b1-8a25120c1c0c","image_after":"31cc6c18-e159-4c18-bd75-72300b1c9790","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-8","seq":8,"branch_id":"agent1","kind":"command","cmd":"cd /pytorch_geometric && python /tmp/reproduce_issue.py","path":null,"exit_code":0,"stdout_tail":"Original edge_index:\ntensor([[0, 1, 2, 0, 1, 2, 0, 1, 2],\n        [0, 0, 0, 1, 1, 1, 2, 2, 2]], dtype=torch.int32)\n\nResult edge_index: tensor([[0., 1., 2., 0., 1., 2., 0., 1., 2.],\n        [0., 0., 0., 1., 1., 1., 2., 2., 2.]])\nAdded edges: tensor([], size=(2, 0))\n\nChecking for duplicate edges:\n","stderr_tail":"","stdout_sha256":"aded0e7c1951d6433ff444070112d99e955cb91224df91df3b2d747e8eab67d8","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-8.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:54:16.603599Z","duration_s":2.116606,"timed_out":false,"image_before":"31cc6c18-e159-4c18-bd75-72300b1c9790","image_after":"cf579640-0d57-4130-a4e8-ed52af33a3d4","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-9","seq":9,"branch_id":"agent1","kind":"command","cmd":"/opt/receipts-0c7c5fa9a8764cd8bcbe26d7c25cc7a6/bin/busybox mkdir -p /opt/receipts/reports && python -I /opt/receipts-0c7c5fa9a8764cd8bcbe26d7c25cc7a6/receipts_pytest.py test/utils/test_augmentation.py::test_add_random_edge -v -o junit_family=xunit1 -p no:cacheprovider -rA --continue-on-collection-errors --junitxml=/opt/receipts/reports/agent1-9.xml","path":null,"exit_code":1,"stdout_tail":"============================= test session starts ==============================\nplatform linux -- Python 3.10.19, pytest-9.0.2, pluggy-1.6.0 -- /usr/local/bin/python\nrootdir: /pytorch_geometric\nconfigfile: pyproject.toml\nplugins: cov-7.0.0\ncollecting ... collected 1 item\n\ntest/utils/test_augmentation.py::test_add_random_edge FAILED\n\n=================================== FAILURES ===================================\n_____________________________ test_add_random_edge _____________________________\n\n    def test_add_random_edge():\n        edge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]])\n        out = add_random_edge(edge_index, p=0.5, training=False)\n        assert out[0].tolist() == edge_index.tolist()\n        assert out[1].tolist() == [[], []]\n    \n        seed_everything(5)\n        out = add_random_edge(edge_index, p=0.5)\n>       assert out[0].tolist() == [[0, 1, 1, 2, 2, 3, 3, 1, 2],\n                                   [1, 0, 2, 1, 3, 2, 0, 3, 0]]\nE       AssertionError: assert [[0, 1, 1, 2,...1, 3, 2, ...]] == [[0, 1, 1, 2,...1, 3, 2, ...]]\nE         \nE         At index 0 diff: [0, 1, 1, 2, 2, 3, 3, 2, 0] != [0, 1, 1, 2, 2, 3, 3, 1, 2]\nE         \nE         Full diff:\nE           [\nE               [\nE                   0,...\nE         \nE         ...Full output truncated (28 lines hidden), use '-vv' to show\n\ntest/utils/test_augmentation.py:83: AssertionError\n------------ generated xml file: /opt/receipts/reports/agent1-9.xml ------------\n=========================== short test summary info ============================\nFAILED test/utils/test_augmentation.py::test_add_random_edge - AssertionError...\n============================== 1 failed in 0.11s ===============================\n","stderr_tail":"","stdout_sha256":"b036c84c8cd66f2bf843323b29e466c912de1af91bb1b14ba8f345aaff389314","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-9.log","tree_digest":"9ee4261136fe873cb4583df4647a9aa18800c192ffa831da9c4c399d6b232229","tree_download_s":4.324930916773155,"started_at":"2026-10-03T19:54:22.355272Z","duration_s":5.399571,"timed_out":false,"image_before":"cf579640-0d57-4130-a4e8-ed52af33a3d4","image_after":"0c01066e-44d3-4fc5-88d2-511c6b01c6d4","junit":{"passed":[],"failed":["test/utils/test_augmentation.py::test_add_random_edge"],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{"test/utils/test_augmentation.py::test_add_random_edge":"AssertionError: assert [[0, 1, 1, 2,...1, 3, 2, ...]] == [[0, 1, 1, 2,...1, 3, 2, ...]]\n  \n  At index 0 diff: [0, 1, 1, 2, 2, 3, 3, 2, 0] != [0, 1, 1, 2, 2, 3, 3, 1, 2]\n  \n  Full diff:\n    [\n        [\n            0,...\n  \n  ...Full output truncated (28 lines hidden), use '-vv' to show\ndef test_add_random_edge():\n        edge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]])\n        out = add_random_edge(edge_index, p=0.5, training=False)\n        assert out[0].tolist() == edge_index.tolist()\n        assert out[1].tolist() == [[], []]\n    \n        seed_everything(5)\n        out = add_random_edge(edge_index, p=0.5)\n>       assert out[0].tolist() == [[0, 1, 1, 2, 2, 3, 3, 1, 2],\n                                   [1, 0, 2, 1, 3, 2, 0, 3, 0]]\nE       AssertionError: assert [[0, 1, 1, 2,...1, 3, 2, ...]] == [[0, 1, 1, 2,...1, 3, 2, ...]]\nE         \nE         At index 0 diff: [0, 1, 1, 2, 2, 3, 3, 2, 0] != [0, 1, 1, 2, 2, 3, 3, 1, 2]\nE         \nE         Full diff:\nE           [\nE               [\nE                   0,...\nE         \nE         ...Full output truncated (28 lines hidden), use '-vv' to show\n\ntest/utils/test_augmentation.py:83: AssertionError"}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-10","seq":10,"branch_id":"agent1","kind":"command","cmd":"/opt/receipts-6df0cbb5f8434230b6711f0cc37cb5d0/bin/busybox mkdir -p /opt/receipts/reports && python -I /opt/receipts-6df0cbb5f8434230b6711f0cc37cb5d0/receipts_pytest.py test/utils/test_augmentation.py::test_add_random_edge -vv -o junit_family=xunit1 -p no:cacheprovider -rA --continue-on-collection-errors --junitxml=/opt/receipts/reports/agent1-10.xml","path":null,"exit_code":1,"stdout_tail":"============================= test session starts ==============================\nplatform linux -- Python 3.10.19, pytest-9.0.2, pluggy-1.6.0 -- /usr/local/bin/python\nrootdir: /pytorch_geometric\nconfigfile: pyproject.toml\nplugins: cov-7.0.0\ncollecting ... collected 1 item\n\ntest/utils/test_augmentation.py::test_add_random_edge FAILED\n\n=================================== FAILURES ===================================\n_____________________________ test_add_random_edge _____________________________\n\n    def test_add_random_edge():\n        edge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]])\n        out = add_random_edge(edge_index, p=0.5, training=False)\n        assert out[0].tolist() == edge_index.tolist()\n        assert out[1].tolist() == [[], []]\n    \n        seed_everything(5)\n        out = add_random_edge(edge_index, p=0.5)\n>       assert out[0].tolist() == [[0, 1, 1, 2, 2, 3, 3, 1, 2],\n                                   [1, 0, 2, 1, 3, 2, 0, 3, 0]]\nE       assert [[0, 1, 1, 2, 2, 3, 3, 2, 0], [1, 0, 2, 1, 3, 2, 1, 2, 0]] == [[0, 1, 1, 2, 2, 3, 3, 1, 2], [1, 0, 2, 1, 3, 2, 0, 3, 0]]\nE         \nE         At index 0 diff: [0, 1, 1, 2, 2, 3, 3, 2, 0] != [0, 1, 1, 2, 2, 3, 3, 1, 2]\nE         \nE         Full diff:\nE           [\nE               [\nE                   0,\nE                   1,\nE                   1,\nE                   2,\nE                   2,\nE                   3,\nE                   3,\nE         -         1,\nE                   2,\nE         +         0,\nE               ],\nE               [\nE                   1,\nE                   0,\nE                   2,\nE                   1,\nE                   3,\nE                   2,\nE         -         0,\nE         ?         ^\nE         +         1,\nE         ?         ^\nE         -         3,\nE         ?         ^\nE         +         2,\nE         ?         ^\nE                   0,\nE               ],\nE           ]\n\ntest/utils/test_augmentation.py:83: AssertionError\n----------- generated xml file: /opt/receipts/reports/agent1-10.xml ------------\n=========================== short test summary info ============================\nFAILED test/utils/test_augmentation.py::test_add_random_edge - assert [[0, 1, 1, 2, 2, 3, 3, 2, 0], [1, 0, 2, 1, 3, 2, 1, 2, 0]] == [[0, 1, 1, 2, 2, 3, 3, 1, 2], [1, 0, 2, 1, 3, 2, 0, 3, 0]]\n  \n  At index 0 diff: [0, 1, 1, 2, 2, 3, 3, 2, 0] != [0, 1, 1, 2, 2, 3, 3, 1, 2]\n  \n  Full diff:\n    [\n        [\n            0,\n            1,\n            1,\n            2,\n            2,\n            3,\n            3,\n  -         1,\n            2,\n  +         0,\n        ],\n        [\n            1,\n            0,\n            2,\n            1,\n            3,\n            2,\n  -         0,\n  ?         ^\n  +         1,\n  ?         ^\n  -         3,\n  ?         ^\n  +         2,\n  ?         ^\n            0,\n        ],\n    ]\n============================== 1 failed in 0.10s ===============================\n","stderr_tail":"","stdout_sha256":"d5cc9bd4c38ecd0b7403babf28ec72a6c6b3d6298406bafc609da388c4f8c688","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-10.log","tree_digest":"9ee4261136fe873cb4583df4647a9aa18800c192ffa831da9c4c399d6b232229","tree_download_s":3.231153375003487,"started_at":"2026-10-03T19:54:36.595830Z","duration_s":5.226112,"timed_out":false,"image_before":"0c01066e-44d3-4fc5-88d2-511c6b01c6d4","image_after":"b38ec8cd-a99a-46d9-ad7d-c2cc398c109a","junit":{"passed":[],"failed":["test/utils/test_augmentation.py::test_add_random_edge"],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{"test/utils/test_augmentation.py::test_add_random_edge":"[...], 3, 0]]\n  \n  At index 0 diff: [0, 1, 1, 2, 2, 3, 3, 2, 0] != [0, 1, 1, 2, 2, 3, 3, 1, 2]\n  \n  Full diff:\n    [\n        [\n            0,\n            1,\n            1,\n            2,\n            2,\n            3,\n            3,\n  -         1,\n            2,\n  +         0,\n        ],\n        [\n            1,\n            0,\n            2,\n            1,\n            3,\n            2,\n  -         0,\n  ?         ^\n  +         1,\n  ?         ^\n  -         3,\n  ?         ^\n  +         2,\n  ?         ^\n            0,\n        ],\n    ]\ndef test_add_random_edge():\n        edge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]])\n        out = add_random_edge(edge_index, p=0.5, training=False)\n        assert out[0].tolist() == edge_index.tolist()\n        assert out[1].tolist() == [[], []]\n    \n        seed_everything(5)\n        out = add_random_edge(edge_index, p=0.5)\n>       assert out[0].tolist() == [[0, 1, 1, 2, 2, 3, 3, 1, 2],\n                                   [1, 0, 2, 1, 3, 2, 0, 3, 0]]\nE       assert [[0, 1, 1, 2, 2, 3, 3, 2, 0], [1, 0, 2, 1, 3, 2, 1, 2, 0]] == [[0, 1, 1, 2, 2, 3, 3, 1, 2], [1, 0, 2, 1, 3, 2, 0, 3, 0]]\nE         \nE         At index 0 diff: [0, 1, 1, 2, 2, 3, 3, 2, 0] != [0, 1, 1, 2, 2, 3, 3, 1, 2]\nE         \nE         Full diff:\nE           [\nE               [\nE                   0,\nE                   1,\nE                   1,\nE                   2,\nE                   2,\nE                   3,\nE                   3,\nE         -         1,\nE                   2,\nE         +         0,\nE               ],\nE               [\nE                   1,\nE                   0,\nE                   2,\nE                   1,\nE                   3,\nE                   2,\nE         -         0,\nE         ?         ^\nE         +         1,\nE         ?         ^\nE         -         3,\nE         ?         ^\nE         +         2,\nE         ?         ^\nE                   0,\nE               ],\nE           ]\n\ntest/utils/test_augmentation.py:83: AssertionError"}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-11","seq":11,"branch_id":"agent1","kind":"command","cmd":"cd /pytorch_geometric && grep -n \"def add_random_edge\" torch_geometric/utils/augmentation.py","path":null,"exit_code":0,"stdout_tail":"152:def add_random_edge(edge_index, p: float, force_undirected: bool = False,\n","stderr_tail":"","stdout_sha256":"7ce70c1e92fbac8e78c1fd10ea9b5c43ec8fac921438849882eb0fe0459dad45","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-11.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:55:07.806278Z","duration_s":0.003033,"timed_out":false,"image_before":"b38ec8cd-a99a-46d9-ad7d-c2cc398c109a","image_after":"82606035-5164-43d5-8dca-5ad9872ae310","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-12","seq":12,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-de2b6ec369414c2f8dbdce38c54582b9/bin/busybox sh -c 'mkdir -p /pytorch_geometric/torch_geometric/utils && cp /opt/receipts-de2b6ec369414c2f8dbdce38c54582b9/incoming/0f71348a5549abc8620de24f23f6d147672cc088a200c9fac3112613a409d2fe /pytorch_geometric/torch_geometric/utils/augmentation.py'","path":"torch_geometric/utils/augmentation.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"0f71348a5549abc8620de24f23f6d147672cc088a200c9fac3112613a409d2fe","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-12.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:55:41.590989Z","duration_s":0.0045969999999999995,"timed_out":false,"image_before":"82606035-5164-43d5-8dca-5ad9872ae310","image_after":"6142ef45-995f-4425-8d59-124f60d0205a","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-13","seq":13,"branch_id":"agent1","kind":"command","cmd":"cd /pytorch_geometric && python /tmp/reproduce_issue.py","path":null,"exit_code":0,"stdout_tail":"Original edge_index:\ntensor([[0, 1, 2, 0, 1, 2, 0, 1, 2],\n        [0, 0, 0, 1, 1, 1, 2, 2, 2]], dtype=torch.int32)\n\nResult edge_index: tensor([[0, 1, 2, 0, 1, 2, 0, 1, 2],\n        [0, 0, 0, 1, 1, 1, 2, 2, 2]])\nAdded edges: tensor([], size=(2, 0), dtype=torch.int64)\n\nChecking for duplicate edges:\n","stderr_tail":"","stdout_sha256":"27f335a83f42d484bcb3258cdbf4e44d1b56cde778511f42049ee299fe7f540f","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-13.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:55:44.595536Z","duration_s":2.256067,"timed_out":false,"image_before":"6142ef45-995f-4425-8d59-124f60d0205a","image_after":"850ba5a3-9ed9-4a8a-ac6f-0252ea712efd","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-14","seq":14,"branch_id":"agent1","kind":"command","cmd":"/opt/receipts-9b0225c1447a44ea875b932760e9ae80/bin/busybox mkdir -p /opt/receipts/reports && python -I /opt/receipts-9b0225c1447a44ea875b932760e9ae80/receipts_pytest.py test/utils/test_augmentation.py::test_add_random_edge -v -o junit_family=xunit1 -p no:cacheprovider -rA --continue-on-collection-errors --junitxml=/opt/receipts/reports/agent1-14.xml","path":null,"exit_code":1,"stdout_tail":"============================= test session starts ==============================\nplatform linux -- Python 3.10.19, pytest-9.0.2, pluggy-1.6.0 -- /usr/local/bin/python\nrootdir: /pytorch_geometric\nconfigfile: pyproject.toml\nplugins: cov-7.0.0\ncollecting ... collected 1 item\n\ntest/utils/test_augmentation.py::test_add_random_edge FAILED\n\n=================================== FAILURES ===================================\n_____________________________ test_add_random_edge _____________________________\n\n    def test_add_random_edge():\n        edge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]])\n        out = add_random_edge(edge_index, p=0.5, training=False)\n        assert out[0].tolist() == edge_index.tolist()\n        assert out[1].tolist() == [[], []]\n    \n        seed_everything(5)\n        out = add_random_edge(edge_index, p=0.5)\n>       assert out[0].tolist() == [[0, 1, 1, 2, 2, 3, 3, 1, 2],\n                                   [1, 0, 2, 1, 3, 2, 0, 3, 0]]\nE       AssertionError: assert [[0, 1, 1, 2,...1, 3, 2, ...]] == [[0, 1, 1, 2,...1, 3, 2, ...]]\nE         \nE         At index 0 diff: [0, 1, 1, 2, 2, 3, 3, 2] != [0, 1, 1, 2, 2, 3, 3, 1, 2]\nE         \nE         Full diff:\nE           [\nE               [\nE                   0,...\nE         \nE         ...Full output truncated (27 lines hidden), use '-vv' to show\n\ntest/utils/test_augmentation.py:83: AssertionError\n----------- generated xml file: /opt/receipts/reports/agent1-14.xml ------------\n=========================== short test summary info ============================\nFAILED test/utils/test_augmentation.py::test_add_random_edge - AssertionError...\n============================== 1 failed in 0.11s ===============================\n","stderr_tail":"","stdout_sha256":"9c398e54c45cbc35380883669dc55394793d62e4918ca3748cfc8e576732b796","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-14.log","tree_digest":"6b19c18161cec6dea5fedbfb8c024697de4019b4f96499519fe44c2dde667f4a","tree_download_s":3.825369416968897,"started_at":"2026-10-03T19:55:50.893520Z","duration_s":5.311698,"timed_out":false,"image_before":"850ba5a3-9ed9-4a8a-ac6f-0252ea712efd","image_after":"0e82bd2a-7700-402c-9d1d-16eb329165ee","junit":{"passed":[],"failed":["test/utils/test_augmentation.py::test_add_random_edge"],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{"test/utils/test_augmentation.py::test_add_random_edge":"AssertionError: assert [[0, 1, 1, 2,...1, 3, 2, ...]] == [[0, 1, 1, 2,...1, 3, 2, ...]]\n  \n  At index 0 diff: [0, 1, 1, 2, 2, 3, 3, 2] != [0, 1, 1, 2, 2, 3, 3, 1, 2]\n  \n  Full diff:\n    [\n        [\n            0,...\n  \n  ...Full output truncated (27 lines hidden), use '-vv' to show\ndef test_add_random_edge():\n        edge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]])\n        out = add_random_edge(edge_index, p=0.5, training=False)\n        assert out[0].tolist() == edge_index.tolist()\n        assert out[1].tolist() == [[], []]\n    \n        seed_everything(5)\n        out = add_random_edge(edge_index, p=0.5)\n>       assert out[0].tolist() == [[0, 1, 1, 2, 2, 3, 3, 1, 2],\n                                   [1, 0, 2, 1, 3, 2, 0, 3, 0]]\nE       AssertionError: assert [[0, 1, 1, 2,...1, 3, 2, ...]] == [[0, 1, 1, 2,...1, 3, 2, ...]]\nE         \nE         At index 0 diff: [0, 1, 1, 2, 2, 3, 3, 2] != [0, 1, 1, 2, 2, 3, 3, 1, 2]\nE         \nE         Full diff:\nE           [\nE               [\nE                   0,...\nE         \nE         ...Full output truncated (27 lines hidden), use '-vv' to show\n\ntest/utils/test_augmentation.py:83: AssertionError"}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-15","seq":15,"branch_id":"agent1","kind":"command","cmd":"/opt/receipts-cdee02f81b1c4df096218c0463f9ca40/bin/busybox mkdir -p /opt/receipts/reports && python -I /opt/receipts-cdee02f81b1c4df096218c0463f9ca40/receipts_pytest.py test/utils/test_augmentation.py -v -o junit_family=xunit1 -p no:cacheprovider -rA --continue-on-collection-errors --junitxml=/opt/receipts/reports/agent1-15.xml","path":null,"exit_code":1,"stdout_tail":"============================= test session starts ==============================\nplatform linux -- Python 3.10.19, pytest-9.0.2, pluggy-1.6.0 -- /usr/local/bin/python\nrootdir: /pytorch_geometric\nconfigfile: pyproject.toml\nplugins: cov-7.0.0\ncollecting ... collected 3 items\n\ntest/utils/test_augmentation.py::test_shuffle_node PASSED\ntest/utils/test_augmentation.py::test_mask_feature PASSED\ntest/utils/test_augmentation.py::test_add_random_edge FAILED\n\n=================================== FAILURES ===================================\n_____________________________ test_add_random_edge _____________________________\n\n    def test_add_random_edge():\n        edge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]])\n        out = add_random_edge(edge_index, p=0.5, training=False)\n        assert out[0].tolist() == edge_index.tolist()\n        assert out[1].tolist() == [[], []]\n    \n        seed_everything(5)\n        out = add_random_edge(edge_index, p=0.5)\n>       assert out[0].tolist() == [[0, 1, 1, 2, 2, 3, 3, 1, 2],\n                                   [1, 0, 2, 1, 3, 2, 0, 3, 0]]\nE       AssertionError: assert [[0, 1, 1, 2,...1, 3, 2, ...]] == [[0, 1, 1, 2,...1, 3, 2, ...]]\nE         \nE         At index 0 diff: [0, 1, 1, 2, 2, 3, 3, 2] != [0, 1, 1, 2, 2, 3, 3, 1, 2]\nE         \nE         Full diff:\nE           [\nE               [\nE                   0,...\nE         \nE         ...Full output truncated (27 lines hidden), use '-vv' to show\n\ntest/utils/test_augmentation.py:83: AssertionError\n==================================== PASSES ====================================\n----------- generated xml file: /opt/receipts/reports/agent1-15.xml ------------\n=========================== short test summary info ============================\nPASSED test/utils/test_augmentation.py::test_shuffle_node\nPASSED test/utils/test_augmentation.py::test_mask_feature\nFAILED test/utils/test_augmentation.py::test_add_random_edge - AssertionError...\n========================= 1 failed, 2 passed in 0.13s ==========================\n","stderr_tail":"","stdout_sha256":"f64b5dca27fab2c9bb23bd3f8890c2ea9acec7ffacfcd16e8fdb570f112d40f3","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-15.log","tree_digest":"6b19c18161cec6dea5fedbfb8c024697de4019b4f96499519fe44c2dde667f4a","tree_download_s":4.604547584196553,"started_at":"2026-10-03T19:56:06.198514Z","duration_s":5.355018,"timed_out":false,"image_before":"0e82bd2a-7700-402c-9d1d-16eb329165ee","image_after":"49b604d5-1902-48fb-a6bf-41c053642301","junit":{"passed":["test/utils/test_augmentation.py::test_shuffle_node","test/utils/test_augmentation.py::test_mask_feature"],"failed":["test/utils/test_augmentation.py::test_add_random_edge"],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{"test/utils/test_augmentation.py::test_add_random_edge":"AssertionError: assert [[0, 1, 1, 2,...1, 3, 2, ...]] == [[0, 1, 1, 2,...1, 3, 2, ...]]\n  \n  At index 0 diff: [0, 1, 1, 2, 2, 3, 3, 2] != [0, 1, 1, 2, 2, 3, 3, 1, 2]\n  \n  Full diff:\n    [\n        [\n            0,...\n  \n  ...Full output truncated (27 lines hidden), use '-vv' to show\ndef test_add_random_edge():\n        edge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]])\n        out = add_random_edge(edge_index, p=0.5, training=False)\n        assert out[0].tolist() == edge_index.tolist()\n        assert out[1].tolist() == [[], []]\n    \n        seed_everything(5)\n        out = add_random_edge(edge_index, p=0.5)\n>       assert out[0].tolist() == [[0, 1, 1, 2, 2, 3, 3, 1, 2],\n                                   [1, 0, 2, 1, 3, 2, 0, 3, 0]]\nE       AssertionError: assert [[0, 1, 1, 2,...1, 3, 2, ...]] == [[0, 1, 1, 2,...1, 3, 2, ...]]\nE         \nE         At index 0 diff: [0, 1, 1, 2, 2, 3, 3, 2] != [0, 1, 1, 2, 2, 3, 3, 1, 2]\nE         \nE         Full diff:\nE           [\nE               [\nE                   0,...\nE         \nE         ...Full output truncated (27 lines hidden), use '-vv' to show\n\ntest/utils/test_augmentation.py:83: AssertionError"}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-16","seq":16,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-e801366e98284a9a8b41f380004c64d7/bin/busybox sh -c 'mkdir -p /tmp && cp /opt/receipts-e801366e98284a9a8b41f380004c64d7/incoming/534a85a9fe45cfc58636915dbce87e8c39983afbcda3053f27cf9051eb3535a4 /tmp/comprehensive_test.py'","path":"/tmp/comprehensive_test.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"534a85a9fe45cfc58636915dbce87e8c39983afbcda3053f27cf9051eb3535a4","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-16.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:56:24.328694Z","duration_s":0.004827,"timed_out":false,"image_before":"49b604d5-1902-48fb-a6bf-41c053642301","image_after":"71e82d3e-49fe-49b9-842b-575322830f98","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-17","seq":17,"branch_id":"agent1","kind":"command","cmd":"cd /pytorch_geometric && python /tmp/comprehensive_test.py","path":null,"exit_code":1,"stdout_tail":"Testing that add_random_edge does not introduce duplicate edges...\nOriginal edges: [(0, 0), (1, 0), (2, 0), (0, 1), (1, 1), (2, 1), (0, 2), (1, 2), (2, 2)]\nRun 1: Added edges: []\nRun 2: Added edges: []\nRun 3: Added edges: []\nRun 4: Added edges: []\nRun 5: Added edges: []\nRun 6: Added edges: []\nRun 7: Added edges: []\nRun 8: Added edges: []\nRun 9: Added edges: []\nRun 10: Added edges: []\n✓ No duplicates found in multiple runs\n\nTesting that basic functionality is preserved...\nOriginal edges: [(0, 1), (1, 0)]\n","stderr_tail":"Traceback (most recent call last):\n  File \"/tmp/comprehensive_test.py\", line 53, in <module>\n    success2 = test_original_functionality_preserved()\n  File \"/tmp/comprehensive_test.py\", line 38, in test_original_functionality_preserved\n    result_edge_index, added_edges = add_random_edge(edge_index, p=2.0, num_nodes=10)  # Want to add 2*2=4 edges\n  File \"/pytorch_geometric/torch_geometric/utils/augmentation.py\", line 211, in add_random_edge\n    raise ValueError(f'Ratio of added edges has to be between 0 and 1 '\nValueError: Ratio of added edges has to be between 0 and 1 (got 2.0\n","stdout_sha256":"87dc1fe8a4a3cdae8319980f5080355beceebfb421abbefc581b3e65ff4adc62","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-17.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:56:27.290743Z","duration_s":2.1066629999999997,"timed_out":false,"image_before":"71e82d3e-49fe-49b9-842b-575322830f98","image_after":"a1eb1405-d519-436f-bea8-e3d51f5638a9","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-18","seq":18,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-767f477d09a340d3854b55549b5dd5de/bin/busybox sh -c 'mkdir -p /tmp && cp /opt/receipts-767f477d09a340d3854b55549b5dd5de/incoming/480d170ab12c5a7c44564e2ccd361b734cf4c707985da9a0c24a7c24acd42a49 /tmp/comprehensive_test.py'","path":"/tmp/comprehensive_test.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"480d170ab12c5a7c44564e2ccd361b734cf4c707985da9a0c24a7c24acd42a49","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-18.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:56:35.119210Z","duration_s":0.005889,"timed_out":false,"image_before":"a1eb1405-d519-436f-bea8-e3d51f5638a9","image_after":"f85465d6-609a-42d5-992c-04ea729e9005","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-19","seq":19,"branch_id":"agent1","kind":"command","cmd":"cd /pytorch_geometric && python /tmp/comprehensive_test.py","path":null,"exit_code":0,"stdout_tail":"Testing that add_random_edge does not introduce duplicate edges...\nOriginal edges: [(0, 0), (1, 0), (2, 0), (0, 1), (1, 1), (2, 1), (0, 2), (1, 2), (2, 2)]\nRun 1: Added edges: []\nRun 2: Added edges: []\nRun 3: Added edges: []\nRun 4: Added edges: []\nRun 5: Added edges: []\nRun 6: Added edges: []\nRun 7: Added edges: []\nRun 8: Added edges: []\nRun 9: Added edges: []\nRun 10: Added edges: []\n✓ No duplicates found in multiple runs\n\nTesting that basic functionality is preserved...\nOriginal edges: [(0, 1), (1, 0)]\nResult edge index shape: torch.Size([2, 4])\nAdded edges shape: torch.Size([2, 2])\nNumber of added edges: 2\n✓ Successfully added edges\n\n✓ All tests passed!\n","stderr_tail":"","stdout_sha256":"a1e3d14ac164724d328fc592b748dd04980b34389ae36d0e684f466afb22af2c","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-19.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:56:38.039344Z","duration_s":2.12981,"timed_out":false,"image_before":"f85465d6-609a-42d5-992c-04ea729e9005","image_after":"061b6db0-a955-4928-9687-a173633dabbd","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-20","seq":20,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-1e08703f4c454966bbb90b83242fcd77/bin/busybox sh -c 'mkdir -p /tmp && cp /opt/receipts-1e08703f4c454966bbb90b83242fcd77/incoming/b4a0691d901810b7ad9b6f50f686e2391c0ffe922b7519a87913f61661366591 /tmp/original_issue_test.py'","path":"/tmp/original_issue_test.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"b4a0691d901810b7ad9b6f50f686e2391c0ffe922b7519a87913f61661366591","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-20.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:56:46.280088Z","duration_s":0.004979,"timed_out":false,"image_before":"061b6db0-a955-4928-9687-a173633dabbd","image_after":"a1e8196b-cad3-4a5d-9757-bc3ef61c6310","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-21","seq":21,"branch_id":"agent1","kind":"command","cmd":"cd /pytorch_geometric && python /tmp/original_issue_test.py","path":null,"exit_code":0,"stdout_tail":"Input edge_index:\ntensor([[0, 1, 2, 0, 1, 2, 0, 1, 2],\n        [0, 0, 0, 1, 1, 1, 2, 2, 2]], dtype=torch.int32)\n\nOutput edge_index:\ntensor([[0, 1, 2, 0, 1, 2, 0, 1, 2],\n        [0, 0, 0, 1, 1, 1, 2, 2, 2]])\nAdded edges:\ntensor([], size=(2, 0), dtype=torch.int64)\n\nChecking for duplicates:\n\n✓ SUCCESS: No duplicate edges were added!\n","stderr_tail":"","stdout_sha256":"ea0e43a1a02cddaf09404dffffbca6edf3cb1609fd3cef9e6b3b59f6f41f706c","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-21.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:56:49.210644Z","duration_s":2.155131,"timed_out":false,"image_before":"a1e8196b-cad3-4a5d-9757-bc3ef61c6310","image_after":"eebc7806-d7be-4936-b083-4daf5136781e","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-22","seq":22,"branch_id":"agent1","kind":"diff","cmd":"compute patch in executor staging agent1-bbcd004c48c14875871b9a294d1d4998: /opt/homebrew/Cellar/git/2.53.0/bin/git -c core.hooksPath=/dev/null -c core.attributesFile=/dev/null -c diff.external= -c core.quotePath=false add -A && /opt/homebrew/Cellar/git/2.53.0/bin/git -c core.hooksPath=/dev/null -c core.attributesFile=/dev/null -c diff.external= -c core.quotePath=false diff --cached --binary --no-ext-diff --no-textconv --no-renames c4e27ac4d0e34b6662f5648552cabf0f64fe6b2b","path":null,"exit_code":0,"stdout_tail":"diff --git a/torch_geometric/utils/augmentation.py b/torch_geometric/utils/augmentation.py\nindex 82b07a67..357ac1ed 100644\n--- a/torch_geometric/utils/augmentation.py\n+++ b/torch_geometric/utils/augmentation.py\n@@ -204,7 +204,7 @@ def add_random_edge(edge_index, p: float, force_undirected: bool = False,\n         tensor([[0, 1, 2, 3, 4, 5, 3, 4, 1],\n                 [2, 3, 1, 4, 2, 1, 1, 3, 2]])\n         >>> added_edges\n-        tensor([[3, 4, 1],\n+        tensor([[3, 4, 1]\n                 [1, 3, 2]])\n     \"\"\"\n     if p < 0. or p > 1.:\n@@ -232,6 +232,40 @@ def add_random_edge(edge_index, p: float, force_undirected: bool = False,\n         mask = row < col\n         row, col = row[mask], col[mask]\n         row, col = torch.cat([row, col]), torch.cat([col, row])\n-    edge_index_to_add = torch.stack([row, col], dim=0).to(device)\n+    \n+    # Filter out edges that already exist in the original graph\n+    # Convert to lists for efficient lookup\n+    src_list = edge_index[0].tolist()\n+    dst_list = edge_index[1].tolist()\n+    \n+    # Create mask for new edges that don't already exist\n+    new_edge_mask = []\n+    for i in range(len(row)):\n+        s, d = row[i].item(), col[i].item()\n+        # Check if edge (s,d) already exists\n+        exists = False\n+        for j in range(len(src_list)):\n+            if src_list[j] == s and dst_list[j] == d:\n+                exists = True\n+                break\n+        new_edge_mask.append(not exists)\n+    \n+    # Apply mask to keep only non-existing edges\n+    if len(row) > 0:\n+        mask_tensor = torch.tensor(new_edge_mask, dtype=torch.bool, device=device)\n+        row = row[mask_tensor]\n+        col = col[mask_tensor]\n+        edge_index_to_add = torch.stack([row, col], dim=0)\n+        \n+        if force_undirected:\n+            # For undirected case, we need to rebuild the symmetric pairs\n+            # since filtering might have removed one direction but not the other\n+            edge_index_to_add = torch.cat([\n+                edge_index_to_add, \n+                torch.stack([col, row], dim=0)\n+            ], dim=1)\n+    else:\n+        edge_index_to_add = torch.empty((2, 0), dtype=torch.long, device=device)\n+    \n     edge_index = torch.cat([edge_index, edge_index_to_add], dim=1)\n-    return edge_index, edge_index_to_add\n+    return edge_index, edge_index_to_add\n\\ No newline at end of file\n","stderr_tail":"","stdout_sha256":"9fb9a8f19ad2b6c3db3572223e52bfcd7dc5ec5f8ba613d9cce00c28a0ae77ea","log_path":null,"tree_digest":"6b19c18161cec6dea5fedbfb8c024697de4019b4f96499519fe44c2dde667f4a","tree_download_s":3.7829664999153465,"started_at":"2026-10-03T19:57:08.701479Z","duration_s":0.09393233386799693,"timed_out":false,"image_before":"eebc7806-d7be-4936-b083-4daf5136781e","image_after":"eebc7806-d7be-4936-b083-4daf5136781e","junit":null,"diff":{"files":[{"path":"torch_geometric/utils/augmentation.py","added":37,"deleted":3,"category":"source","status":"modified"}],"patch_sha256":"9fb9a8f19ad2b6c3db3572223e52bfcd7dc5ec5f8ba613d9cce00c28a0ae77ea","patch_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/artifacts/agent1/agent1-22.patch"},"patch_sha256":"9fb9a8f19ad2b6c3db3572223e52bfcd7dc5ec5f8ba613d9cce00c28a0ae77ea","dropped_paths":[],"snapshot_sha":"c4e27ac4d0e34b6662f5648552cabf0f64fe6b2b"}
{"receipt_id":"agent1-23","seq":23,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-b1906bb62e474cbb8ccd38bbbb215f36/bin/busybox sh -c 'mkdir -p /pytorch_geometric/torch_geometric/utils && cp /opt/receipts-b1906bb62e474cbb8ccd38bbbb215f36/incoming/a2a366396a58f4125650dd1e5b8fc0508e61165155bdad0863b233a40d9123e0 /pytorch_geometric/torch_geometric/utils/augmentation.py'","path":"torch_geometric/utils/augmentation.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"a2a366396a58f4125650dd1e5b8fc0508e61165155bdad0863b233a40d9123e0","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-23.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:57:44.923386Z","duration_s":0.0052770000000000004,"timed_out":false,"image_before":"eebc7806-d7be-4936-b083-4daf5136781e","image_after":"24ff45e7-38e0-4123-acf9-f42860e1eb55","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-24","seq":24,"branch_id":"agent1","kind":"command","cmd":"/opt/receipts-aba2e7ba9707402fa3a45749c12b9398/bin/busybox mkdir -p /opt/receipts/reports && python -I /opt/receipts-aba2e7ba9707402fa3a45749c12b9398/receipts_pytest.py test/utils/test_augmentation.py::test_add_random_edge -v -o junit_family=xunit1 -p no:cacheprovider -rA --continue-on-collection-errors --junitxml=/opt/receipts/reports/agent1-24.xml","path":null,"exit_code":1,"stdout_tail":"============================= test session starts ==============================\nplatform linux -- Python 3.10.19, pytest-9.0.2, pluggy-1.6.0 -- /usr/local/bin/python\nrootdir: /pytorch_geometric\nconfigfile: pyproject.toml\nplugins: cov-7.0.0\ncollecting ... collected 1 item\n\ntest/utils/test_augmentation.py::test_add_random_edge FAILED\n\n=================================== FAILURES ===================================\n_____________________________ test_add_random_edge _____________________________\n\n    def test_add_random_edge():\n        edge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]])\n        out = add_random_edge(edge_index, p=0.5, training=False)\n        assert out[0].tolist() == edge_index.tolist()\n        assert out[1].tolist() == [[], []]\n    \n        seed_everything(5)\n        out = add_random_edge(edge_index, p=0.5)\n>       assert out[0].tolist() == [[0, 1, 1, 2, 2, 3, 3, 1, 2],\n                                   [1, 0, 2, 1, 3, 2, 0, 3, 0]]\nE       AssertionError: assert [[0, 1, 1, 2,...1, 3, 2, ...]] == [[0, 1, 1, 2,...1, 3, 2, ...]]\nE         \nE         At index 0 diff: [0, 1, 1, 2, 2, 3, 3, 2, 0] != [0, 1, 1, 2, 2, 3, 3, 1, 2]\nE         \nE         Full diff:\nE           [\nE               [\nE                   0,...\nE         \nE         ...Full output truncated (28 lines hidden), use '-vv' to show\n\ntest/utils/test_augmentation.py:83: AssertionError\n----------- generated xml file: /opt/receipts/reports/agent1-24.xml ------------\n=========================== short test summary info ============================\nFAILED test/utils/test_augmentation.py::test_add_random_edge - AssertionError...\n============================== 1 failed in 0.11s ===============================\n","stderr_tail":"","stdout_sha256":"ef4a55d5a609678b905ee76d232b60fecac8b05832a42be8b0cd17210938d5cc","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-24.log","tree_digest":"1b01766fdc0c9f9058d2a43b7b1af0e43fd591dbc768fd344d6d6aea8f8f4ea8","tree_download_s":4.508119750069454,"started_at":"2026-10-03T19:57:47.975035Z","duration_s":5.319286,"timed_out":false,"image_before":"24ff45e7-38e0-4123-acf9-f42860e1eb55","image_after":"f98add37-9ad0-4be4-a08d-bd99167f4949","junit":{"passed":[],"failed":["test/utils/test_augmentation.py::test_add_random_edge"],"errors":[],"skipped":[],"collection_errors":[],"failure_text":{"test/utils/test_augmentation.py::test_add_random_edge":"AssertionError: assert [[0, 1, 1, 2,...1, 3, 2, ...]] == [[0, 1, 1, 2,...1, 3, 2, ...]]\n  \n  At index 0 diff: [0, 1, 1, 2, 2, 3, 3, 2, 0] != [0, 1, 1, 2, 2, 3, 3, 1, 2]\n  \n  Full diff:\n    [\n        [\n            0,...\n  \n  ...Full output truncated (28 lines hidden), use '-vv' to show\ndef test_add_random_edge():\n        edge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]])\n        out = add_random_edge(edge_index, p=0.5, training=False)\n        assert out[0].tolist() == edge_index.tolist()\n        assert out[1].tolist() == [[], []]\n    \n        seed_everything(5)\n        out = add_random_edge(edge_index, p=0.5)\n>       assert out[0].tolist() == [[0, 1, 1, 2, 2, 3, 3, 1, 2],\n                                   [1, 0, 2, 1, 3, 2, 0, 3, 0]]\nE       AssertionError: assert [[0, 1, 1, 2,...1, 3, 2, ...]] == [[0, 1, 1, 2,...1, 3, 2, ...]]\nE         \nE         At index 0 diff: [0, 1, 1, 2, 2, 3, 3, 2, 0] != [0, 1, 1, 2, 2, 3, 3, 1, 2]\nE         \nE         Full diff:\nE           [\nE               [\nE                   0,...\nE         \nE         ...Full output truncated (28 lines hidden), use '-vv' to show\n\ntest/utils/test_augmentation.py:83: AssertionError"}},"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-25","seq":25,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-9cb495a375ae4c259ed1fdbdb823d4e1/bin/busybox sh -c 'mkdir -p /tmp && cp /opt/receipts-9cb495a375ae4c259ed1fdbdb823d4e1/incoming/9cc5d646db8a66fc79184471fcb2f67e5f3c9ad4147789f49ba720b3114a4056 /tmp/check_original.py'","path":"/tmp/check_original.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"9cc5d646db8a66fc79184471fcb2f67e5f3c9ad4147789f49ba720b3114a4056","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-25.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:58:05.934654Z","duration_s":0.004548,"timed_out":false,"image_before":"f98add37-9ad0-4be4-a08d-bd99167f4949","image_after":"60e3be74-5f8b-413d-aa5e-f903a2447b7a","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-26","seq":26,"branch_id":"agent1","kind":"command","cmd":"cd /pytorch_geometric && python /tmp/check_original.py","path":null,"exit_code":1,"stdout_tail":"","stderr_tail":"Traceback (most recent call last):\n  File \"/tmp/check_original.py\", line 6, in <module>\n    num_nodes: Optional[Union[Tuple[int], int]] = None,\nNameError: name 'Optional' is not defined\n","stdout_sha256":"88257de2af087a85455ee4f00459e86029a303ad0c093daecb7d010bfa7eb853","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-26.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:58:09.006065Z","duration_s":2.105338,"timed_out":false,"image_before":"60e3be74-5f8b-413d-aa5e-f903a2447b7a","image_after":"1973e499-44c1-4402-92ce-fa5a643a1bae","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-27","seq":27,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-7415568e7a4a4fceaa185a4bab2b5f16/bin/busybox sh -c 'mkdir -p /tmp && cp /opt/receipts-7415568e7a4a4fceaa185a4bab2b5f16/incoming/3f0879ff59e40863209764f7fd8a6b8f2f94842a844e5553d619c10c3441a6f5 /tmp/check_original.py'","path":"/tmp/check_original.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"3f0879ff59e40863209764f7fd8a6b8f2f94842a844e5553d619c10c3441a6f5","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-27.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:58:17.509647Z","duration_s":0.004695,"timed_out":false,"image_before":"1973e499-44c1-4402-92ce-fa5a643a1bae","image_after":"0708fcbc-60aa-41d6-a40b-d5a8010583de","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-28","seq":28,"branch_id":"agent1","kind":"command","cmd":"cd /pytorch_geometric && python /tmp/check_original.py","path":null,"exit_code":0,"stdout_tail":"Original edge_index: [[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]]\nOriginal result:\nedge_index: [[0, 1, 1, 2, 2, 3, 3, 2, 3], [1, 0, 2, 1, 3, 2, 1, 2, 2]]\nadded_edges: [[3, 2, 3], [1, 2, 2]]\n","stderr_tail":"","stdout_sha256":"c93b4fb2c58f40200e84d794f427dede1d5ea01ef9c2f2f740c30ca0be9e6354","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-28.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:58:20.812165Z","duration_s":2.192357,"timed_out":false,"image_before":"0708fcbc-60aa-41d6-a40b-d5a8010583de","image_after":"27189e9c-78a6-469f-95b4-4c890d64c821","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-29","seq":29,"branch_id":"agent1","kind":"command","cmd":"cd /pytorch_geometric && python -c \"\nimport torch\nfrom torch_geometric import seed_everything\nedge_index = torch.tensor([[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]])\nseed_everything(5)\nprint('Test expects edge_index:', [[0, 1, 1, 2, 2, 3, 3, 1, 2], [1, 0, 2, 1, 3, 2, 0, 3, 0]])\nprint('Test expects added_edges:', [[3, 1, 2], [0, 3, 0]])\n\"","path":null,"exit_code":0,"stdout_tail":"Test expects edge_index: [[0, 1, 1, 2, 2, 3, 3, 1, 2], [1, 0, 2, 1, 3, 2, 0, 3, 0]]\nTest expects added_edges: [[3, 1, 2], [0, 3, 0]]\n","stderr_tail":"","stdout_sha256":"7f0c00f2e364013ca6a940960ef42b0e5a26e1fec3e50590adce53a4563df36b","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-29.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:58:27.791160Z","duration_s":2.204895,"timed_out":false,"image_before":"27189e9c-78a6-469f-95b4-4c890d64c821","image_after":"5777a11e-9253-4f0d-8ded-265045613289","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-30","seq":30,"branch_id":"agent1","kind":"command","cmd":"cd /pytorch_geometric && git show HEAD:torch_geometric/utils/augmentation.py | grep -A 50 \"def add_random_edge\"","path":null,"exit_code":0,"stdout_tail":"def add_random_edge(edge_index, p: float, force_undirected: bool = False,\n                    num_nodes: Optional[Union[Tuple[int], int]] = None,\n                    training: bool = True) -> Tuple[Tensor, Tensor]:\n    r\"\"\"Randomly adds edges to :obj:`edge_index`.\n\n    The method returns (1) the retained :obj:`edge_index`, (2) the added\n    edge indices.\n\n    Args:\n        edge_index (LongTensor): The edge indices.\n        p (float): Ratio of added edges to the existing edges.\n        force_undirected (bool, optional): If set to :obj:`True`,\n            added edges will be undirected.\n            (default: :obj:`False`)\n        num_nodes (int, Tuple[int], optional): The overall number of nodes,\n            *i.e.* :obj:`max_val + 1`, or the number of source and\n            destination nodes, *i.e.* :obj:`(max_src_val + 1, max_dst_val + 1)`\n            of :attr:`edge_index`. (default: :obj:`None`)\n        training (bool, optional): If set to :obj:`False`, this operation is a\n            no-op. (default: :obj:`True`)\n\n    :rtype: (:class:`LongTensor`, :class:`LongTensor`)\n\n    Examples:\n\n        >>> # Standard case\n        >>> edge_index = torch.tensor([[0, 1, 1, 2, 2, 3],\n        ...                            [1, 0, 2, 1, 3, 2]])\n        >>> edge_index, added_edges = add_random_edge(edge_index, p=0.5)\n        >>> edge_index\n        tensor([[0, 1, 1, 2, 2, 3, 2, 1, 3],\n                [1, 0, 2, 1, 3, 2, 0, 2, 1]])\n        >>> added_edges\n        tensor([[2, 1, 3],\n                [0, 2, 1]])\n\n        >>> # The returned graph is kept undirected\n        >>> edge_index, added_edges = add_random_edge(edge_index, p=0.5,\n        ...                                           force_undirected=True)\n        >>> edge_index\n        tensor([[0, 1, 1, 2, 2, 3, 2, 1, 3, 0, 2, 1],\n                [1, 0, 2, 1, 3, 2, 0, 2, 1, 2, 1, 3]])\n        >>> added_edges\n        tensor([[2, 1, 3, 0, 2, 1],\n                [0, 2, 1, 2, 1, 3]])\n\n        >>> # For bipartite graphs\n        >>> edge_index = torch.tensor([[0, 1, 2, 3, 4, 5],\n        ...                            [2, 3, 1, 4, 2, 1]])\n        >>> edge_index, added_edges = add_random_edge(edge_index, p=0.5,\n        ...                                           num_nodes=(6, 5))\n","stderr_tail":"","stdout_sha256":"69a1b58bb30efab1c48b5611c556ca42d3b16667dad1decad60b78d48bdd915a","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-30.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:58:34.390228Z","duration_s":0.006237,"timed_out":false,"image_before":"5777a11e-9253-4f0d-8ded-265045613289","image_after":"102a68f9-79a0-4471-8b9c-446f88d3e122","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-31","seq":31,"branch_id":"agent1","kind":"command","cmd":"cd /pytorch_geometric && git show HEAD:torch_geometric/utils/augmentation.py | sed -n '152,237p'","path":null,"exit_code":0,"stdout_tail":"def add_random_edge(edge_index, p: float, force_undirected: bool = False,\n                    num_nodes: Optional[Union[Tuple[int], int]] = None,\n                    training: bool = True) -> Tuple[Tensor, Tensor]:\n    r\"\"\"Randomly adds edges to :obj:`edge_index`.\n\n    The method returns (1) the retained :obj:`edge_index`, (2) the added\n    edge indices.\n\n    Args:\n        edge_index (LongTensor): The edge indices.\n        p (float): Ratio of added edges to the existing edges.\n        force_undirected (bool, optional): If set to :obj:`True`,\n            added edges will be undirected.\n            (default: :obj:`False`)\n        num_nodes (int, Tuple[int], optional): The overall number of nodes,\n            *i.e.* :obj:`max_val + 1`, or the number of source and\n            destination nodes, *i.e.* :obj:`(max_src_val + 1, max_dst_val + 1)`\n            of :attr:`edge_index`. (default: :obj:`None`)\n        training (bool, optional): If set to :obj:`False`, this operation is a\n            no-op. (default: :obj:`True`)\n\n    :rtype: (:class:`LongTensor`, :class:`LongTensor`)\n\n    Examples:\n\n        >>> # Standard case\n        >>> edge_index = torch.tensor([[0, 1, 1, 2, 2, 3],\n        ...                            [1, 0, 2, 1, 3, 2]])\n        >>> edge_index, added_edges = add_random_edge(edge_index, p=0.5)\n        >>> edge_index\n        tensor([[0, 1, 1, 2, 2, 3, 2, 1, 3],\n                [1, 0, 2, 1, 3, 2, 0, 2, 1]])\n        >>> added_edges\n        tensor([[2, 1, 3],\n                [0, 2, 1]])\n\n        >>> # The returned graph is kept undirected\n        >>> edge_index, added_edges = add_random_edge(edge_index, p=0.5,\n        ...                                           force_undirected=True)\n        >>> edge_index\n        tensor([[0, 1, 1, 2, 2, 3, 2, 1, 3, 0, 2, 1],\n                [1, 0, 2, 1, 3, 2, 0, 2, 1, 2, 1, 3]])\n        >>> added_edges\n        tensor([[2, 1, 3, 0, 2, 1],\n                [0, 2, 1, 2, 1, 3]])\n\n        >>> # For bipartite graphs\n        >>> edge_index = torch.tensor([[0, 1, 2, 3, 4, 5],\n        ...                            [2, 3, 1, 4, 2, 1]])\n        >>> edge_index, added_edges = add_random_edge(edge_index, p=0.5,\n        ...                                           num_nodes=(6, 5))\n        >>> edge_index\n        tensor([[0, 1, 2, 3, 4, 5, 3, 4, 1],\n                [2, 3, 1, 4, 2, 1, 1, 3, 2]])\n        >>> added_edges\n        tensor([[3, 4, 1],\n                [1, 3, 2]])\n    \"\"\"\n    if p < 0. or p > 1.:\n        raise ValueError(f'Ratio of added edges has to be between 0 and 1 '\n                         f'(got {p}')\n    if force_undirected and isinstance(num_nodes, (tuple, list)):\n        raise RuntimeError('`force_undirected` is not supported for'\n                           ' heterogeneous graphs')\n\n    device = edge_index.device\n    if not training or p == 0.0:\n        edge_index_to_add = torch.tensor([[], []], device=device)\n        return edge_index, edge_index_to_add\n\n    if not isinstance(num_nodes, (tuple, list)):\n        num_nodes = (num_nodes, num_nodes)\n    num_src_nodes = maybe_num_nodes(edge_index, num_nodes[0])\n    num_dst_nodes = maybe_num_nodes(edge_index, num_nodes[1])\n\n    num_edges_to_add = round(edge_index.size(1) * p)\n    row = torch.randint(0, num_src_nodes, size=(num_edges_to_add, ))\n    col = torch.randint(0, num_dst_nodes, size=(num_edges_to_add, ))\n\n    if force_undirected:\n        mask = row < col\n        row, col = row[mask], col[mask]\n        row, col = torch.cat([row, col]), torch.cat([col, row])\n    edge_index_to_add = torch.stack([row, col], dim=0).to(device)\n    edge_index = torch.cat([edge_index, edge_index_to_add], dim=1)\n    return edge_index, edge_index_to_add\n","stderr_tail":"","stdout_sha256":"6ecb6078410381880b12c24cdc965b243ca8105f11099938a76931fd22c02ff5","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-31.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:58:37.554536Z","duration_s":0.005745,"timed_out":false,"image_before":"102a68f9-79a0-4471-8b9c-446f88d3e122","image_after":"6c294480-ef03-4b02-9b1a-3d6709052294","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-32","seq":32,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-2c4500572ba345a3bad57c9c4897aeef/bin/busybox sh -c 'mkdir -p /tmp && cp /opt/receipts-2c4500572ba345a3bad57c9c4897aeef/incoming/2057aa63fbb31d63bc2a56e55053061f1ce2f840b6f8b9cc968ed81fbb3d1b4d /tmp/check_real_original.py'","path":"/tmp/check_real_original.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"2057aa63fbb31d63bc2a56e55053061f1ce2f840b6f8b9cc968ed81fbb3d1b4d","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-32.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:58:43.523298Z","duration_s":0.003761,"timed_out":false,"image_before":"6c294480-ef03-4b02-9b1a-3d6709052294","image_after":"008328cf-47ec-4c29-a80c-6f3267070c28","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-33","seq":33,"branch_id":"agent1","kind":"command","cmd":"cd /pytorch_geometric && python /tmp/check_real_original.py","path":null,"exit_code":0,"stdout_tail":"Original edge_index: [[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]]\nReal original result:\nedge_index: [[0, 1, 1, 2, 2, 3, 3, 2, 3], [1, 0, 2, 1, 3, 2, 1, 2, 2]]\nadded_edges: [[3, 2, 3], [1, 2, 2]]\n\nTest expects edge_index: [[0, 1, 1, 2, 2, 3, 3, 1, 2], [1, 0, 2, 1, 3, 2, 0, 3, 0]]\nTest expects added_edges: [[3, 1, 2], [0, 3, 0]]\n","stderr_tail":"","stdout_sha256":"462c3cb511c3a6c82a5c6d2f2aeb3c01d7d407bdfcaffbee027c4ab4a1c4eea7","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-33.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:58:46.517724Z","duration_s":2.113483,"timed_out":false,"image_before":"008328cf-47ec-4c29-a80c-6f3267070c28","image_after":"26b3aa49-bd55-460e-ab7d-742d34840e4e","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-34","seq":34,"branch_id":"agent1","kind":"write","cmd":"/opt/receipts-4964546c3a50422dbbf852d590cc0574/bin/busybox sh -c 'mkdir -p /pytorch_geometric/torch_geometric/utils && cp /opt/receipts-4964546c3a50422dbbf852d590cc0574/incoming/0f71348a5549abc8620de24f23f6d147672cc088a200c9fac3112613a409d2fe /pytorch_geometric/torch_geometric/utils/augmentation.py'","path":"torch_geometric/utils/augmentation.py","exit_code":0,"stdout_tail":"","stderr_tail":"","stdout_sha256":"0f71348a5549abc8620de24f23f6d147672cc088a200c9fac3112613a409d2fe","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-34.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:59:03.168260Z","duration_s":0.005013,"timed_out":false,"image_before":"26b3aa49-bd55-460e-ab7d-742d34840e4e","image_after":"6640caa8-38be-4d18-952d-f73bfaaefb68","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-35","seq":35,"branch_id":"agent1","kind":"command","cmd":"cd /pytorch_geometric && python /tmp/original_issue_test.py","path":null,"exit_code":0,"stdout_tail":"Input edge_index:\ntensor([[0, 1, 2, 0, 1, 2, 0, 1, 2],\n        [0, 0, 0, 1, 1, 1, 2, 2, 2]], dtype=torch.int32)\n\nOutput edge_index:\ntensor([[0, 1, 2, 0, 1, 2, 0, 1, 2],\n        [0, 0, 0, 1, 1, 1, 2, 2, 2]])\nAdded edges:\ntensor([], size=(2, 0), dtype=torch.int64)\n\nChecking for duplicates:\n\n✓ SUCCESS: No duplicate edges were added!\n","stderr_tail":"","stdout_sha256":"ea0e43a1a02cddaf09404dffffbca6edf3cb1609fd3cef9e6b3b59f6f41f706c","log_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/logs/agent1/agent1-35.log","tree_digest":null,"tree_download_s":null,"started_at":"2026-10-03T19:59:06.968465Z","duration_s":2.190028,"timed_out":false,"image_before":"6640caa8-38be-4d18-952d-f73bfaaefb68","image_after":"e22b4ec4-3947-40c5-a549-908f29c50469","junit":null,"diff":null,"patch_sha256":null,"dropped_paths":[],"snapshot_sha":null}
{"receipt_id":"agent1-36","seq":36,"branch_id":"agent1","kind":"diff","cmd":"compute patch in executor staging agent1-c241e774c37f40adaccc879d201bd2a4: /opt/homebrew/Cellar/git/2.53.0/bin/git -c core.hooksPath=/dev/null -c core.attributesFile=/dev/null -c diff.external= -c core.quotePath=false add -A && /opt/homebrew/Cellar/git/2.53.0/bin/git -c core.hooksPath=/dev/null -c core.attributesFile=/dev/null -c diff.external= -c core.quotePath=false diff --cached --binary --no-ext-diff --no-textconv --no-renames c4e27ac4d0e34b6662f5648552cabf0f64fe6b2b","path":null,"exit_code":0,"stdout_tail":"diff --git a/torch_geometric/utils/augmentation.py b/torch_geometric/utils/augmentation.py\nindex 82b07a67..357ac1ed 100644\n--- a/torch_geometric/utils/augmentation.py\n+++ b/torch_geometric/utils/augmentation.py\n@@ -204,7 +204,7 @@ def add_random_edge(edge_index, p: float, force_undirected: bool = False,\n         tensor([[0, 1, 2, 3, 4, 5, 3, 4, 1],\n                 [2, 3, 1, 4, 2, 1, 1, 3, 2]])\n         >>> added_edges\n-        tensor([[3, 4, 1],\n+        tensor([[3, 4, 1]\n                 [1, 3, 2]])\n     \"\"\"\n     if p < 0. or p > 1.:\n@@ -232,6 +232,40 @@ def add_random_edge(edge_index, p: float, force_undirected: bool = False,\n         mask = row < col\n         row, col = row[mask], col[mask]\n         row, col = torch.cat([row, col]), torch.cat([col, row])\n-    edge_index_to_add = torch.stack([row, col], dim=0).to(device)\n+    \n+    # Filter out edges that already exist in the original graph\n+    # Convert to lists for efficient lookup\n+    src_list = edge_index[0].tolist()\n+    dst_list = edge_index[1].tolist()\n+    \n+    # Create mask for new edges that don't already exist\n+    new_edge_mask = []\n+    for i in range(len(row)):\n+        s, d = row[i].item(), col[i].item()\n+        # Check if edge (s,d) already exists\n+        exists = False\n+        for j in range(len(src_list)):\n+            if src_list[j] == s and dst_list[j] == d:\n+                exists = True\n+                break\n+        new_edge_mask.append(not exists)\n+    \n+    # Apply mask to keep only non-existing edges\n+    if len(row) > 0:\n+        mask_tensor = torch.tensor(new_edge_mask, dtype=torch.bool, device=device)\n+        row = row[mask_tensor]\n+        col = col[mask_tensor]\n+        edge_index_to_add = torch.stack([row, col], dim=0)\n+        \n+        if force_undirected:\n+            # For undirected case, we need to rebuild the symmetric pairs\n+            # since filtering might have removed one direction but not the other\n+            edge_index_to_add = torch.cat([\n+                edge_index_to_add, \n+                torch.stack([col, row], dim=0)\n+            ], dim=1)\n+    else:\n+        edge_index_to_add = torch.empty((2, 0), dtype=torch.long, device=device)\n+    \n     edge_index = torch.cat([edge_index, edge_index_to_add], dim=1)\n-    return edge_index, edge_index_to_add\n+    return edge_index, edge_index_to_add\n\\ No newline at end of file\n","stderr_tail":"","stdout_sha256":"9fb9a8f19ad2b6c3db3572223e52bfcd7dc5ec5f8ba613d9cce00c28a0ae77ea","log_path":null,"tree_digest":"6b19c18161cec6dea5fedbfb8c024697de4019b4f96499519fe44c2dde667f4a","tree_download_s":3.9109688750468194,"started_at":"2026-10-03T19:59:19.403650Z","duration_s":0.0988030000589788,"timed_out":false,"image_before":"e22b4ec4-3947-40c5-a549-908f29c50469","image_after":"e22b4ec4-3947-40c5-a549-908f29c50469","junit":null,"diff":{"files":[{"path":"torch_geometric/utils/augmentation.py","added":37,"deleted":3,"category":"source","status":"modified"}],"patch_sha256":"9fb9a8f19ad2b6c3db3572223e52bfcd7dc5ec5f8ba613d9cce00c28a0ae77ea","patch_path":"~/dev/nebius-hackathon/pilot/v2-P/pyg-team__pytorch_geometric-7654/runs/pilot-pyg-team__pytorch_geometric-7654-310f2aed7b/artifacts/agent1/agent1-36.patch"},"patch_sha256":"9fb9a8f19ad2b6c3db3572223e52bfcd7dc5ec5f8ba613d9cce00c28a0ae77ea","dropped_paths":[],"snapshot_sha":"c4e27ac4d0e34b6662f5648552cabf0f64fe6b2b"}