text: Title: bunch of CI failures with latest updates
  Body: ### 🐛 Describe the bug
  
  when updating from 8c849a482c3cf2326c1f493e79d04169b26dfb0b to the latest commit c0c2d5fefddbce412741db68cc7a74af225fa94a
  we now see the following errors (their all pretty much the same, let me know if you want the full log)
  ```
  ______________________________ test_to_undirected ______________________________
      def test_to_undirected():
          row = torch.tensor([0, 1, 1])
          col = torch.tensor([1, 0, 2])
      
          edge_index = to_undirected(torch.stack([row, col], dim=0))
          assert edge_index.tolist() == [[0, 1, 1, 2], [1, 0, 2, 1]]
      
          @torch.jit.script
  >       def jit(edge_index: Tensor) -> Tensor:
  test/utils/test_undirected.py:37: 
  _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
  /usr/local/lib/python3.10/dist-packages/torch/jit/_script.py:1428: in script
      ret = _script_impl(
  /usr/local/lib/python3.10/dist-packages/torch/jit/_script.py:1204: in _script_impl
      fn = torch._C._jit_script_compile(
  /usr/local/lib/python3.10/dist-packages/torch/jit/_script.py:1498: in _get_overloads
      _compile_function_with_overload(overload_fn, qual_name, obj)
  /usr/local/lib/python3.10/dist-packages/torch/jit/_script.py:1471: in _compile_function_with_overload
      fn = torch._C._jit_script_compile_overload(
  /usr/local/lib/python3.10/dist-packages/torch/jit/_script.py:1498: in _get_overloads
      _compile_function_with_overload(overload_fn, qual_name, obj)
  /usr/local/lib/python3.10/dist-packages/torch/jit/_script.py:1471: in _compile_function_with_overload
      fn = torch._C._jit_script_compile_overload(
  /usr/local/lib/python3.10/dist-packages/torch/jit/_recursive.py:1003: in try_compile_fn
      return torch.jit.script(fn, _rcb=rcb)
  /usr/local/lib/python3.10/dist-packages/torch/jit/_script.py:1428: in script
      ret = _script_impl(
  /usr/local/lib/python3.10/dist-packages/torch/jit/_script.py:1204: in _script_impl
      fn = torch._C._jit_script_compile(
  /usr/local/lib/python3.10/dist-packages/torch/jit/_recursive.py:1003: in try_compile_fn
      return torch.jit.script(fn, _rcb=rcb)
  /usr/local/lib/python3.10/dist-packages/torch/jit/_script.py:1428: in script
      ret = _script_impl(
  /usr/local/lib/python3.10/dist-packages/torch/jit/_script.py:1204: in _script_impl
      fn = torch._C._jit_script_compile(
  /usr/local/lib/python3.10/dist-packages/torch/jit/_recursive.py:1003: in try_compile_fn
      return torch.jit.script(fn, _rcb=rcb)
  /usr/local/lib/python3.10/dist-packages/torch/jit/_script.py:1428: in script
      ret = _script_impl(
  _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
  obj = <function is_compiling at 0xf103a8e791b0>, optimize = None, _frames_up = 1
  _rcb = <function createResolutionCallbackFromEnv.<locals>.<lambda> at 0xf10712e6fc70>
  example_inputs = None
      def _script_impl(
          obj,
          optimize=None,
          _frames_up=0,
          _rcb=None,
          example_inputs: Union[List[Tuple], Dict[Callable, List[Tuple]], None] = None,
      ):
          global type_trace_db
      
          if optimize is not None:
              warnings.warn(
                  "`optimize` is deprecated and has no effect. "
                  "Use `with torch.jit.optimized_execution()` instead",
                  FutureWarning,
                  stacklevel=3,
              )
      
          # No-op for modules, functions, class instances that are already scripted
          if isinstance(obj, RecursiveScriptClass):
              return obj
          if isinstance(obj, ScriptModule):
              return obj
          if isinstance(obj, ScriptFunction):
              return obj
      
          if example_inputs:
              # If MonkeyType is installed, enable profile directed type annotation
              # Check if example_inputs are defined and generate call traces
              # for the method by running eager mode version of the method with
              # the provide example inputs. This logs all the traces in type_trace_db
              type_trace_db = JitTypeTraceStore()
              if monkeytype_trace:
                  monkeytype_config = JitTypeTraceConfig(type_trace_db)
                  with monkeytype_trace(monkeytype_config):
                      if isinstance(example_inputs, Dict):
                          # If the obj is an nn.Module or a class, then each method is
                          # executed with the arguments provided in the example inputs.
                          # example inputs here will be of type Dict(class.method, (arguments))
                          # This is used to infer type annotations for those methods
                          # which are not called directly under the hood of monkeytype.
                          for module, example_input in example_inputs.items():
                              for example in example_input:
                                  module(*example)
                      elif isinstance(example_inputs, List):
                          for examples in example_inputs:
                              obj(*examples)
                      else:
                          raise ValueError(
                              "Error: Unable to infer types. Please format the inputs to type `List[Tuple]`"
                              " or `Dict[Callable, List[Tuple]]` to be run with MonkeyType."
                          )
              else:
                  warnings.warn(
                      "Warning: monkeytype is not installed. Please install https://github.com/Instagram/MonkeyType "
                      "to enable Profile-Directed Typing in TorchScript. Refer to "
                      "https://github.com/Instagram/MonkeyType/blob/master/README.rst to install MonkeyType. "
                  )
      
          if isinstance(obj, torch.nn.Module):
              obj = call_prepare_scriptable_func(obj)
              return torch.jit._recursive.create_script_module(
                  obj, torch.jit._recursive.infer_methods_to_compile
              )
          else:
              obj = obj.__prepare_scriptable__() if hasattr(obj, "__prepare_scriptable__") else obj  # type: ignore[operator]
      
          if isinstance(obj, dict):
              return create_script_dict(obj)
          if isinstance(obj, list):
              return create_script_list(obj)
      
          if inspect.isclass(obj):
              qualified_name = _qualified_name(obj)
              # If this type is a `nn.Module` subclass, they probably meant to pass
              # an instance instead of a Module
              if issubclass(obj, torch.nn.Module):
                  raise RuntimeError(
                      f"Type '{obj}' cannot be compiled since it inherits from nn.Module, pass an instance instead"
                  )
      
              # Enums are automatically usable in TorchScript, explicitly scripting
              # is not necessary, but not harmful either.
              if issubclass(obj, enum.Enum):
                  return obj
      
              if not _is_new_style_class(obj):
                  raise RuntimeError(
                      "TorchScript classes must be new-style classes. "
                      "Please inherit from 'object'."
                  )
              if len(obj.mro()) > 2:
                  raise RuntimeError(
                      "TorchScript classes does not support inheritance yet. "
                      "Please directly inherit from 'object'."
                  )
              if _rcb is None:
                  _rcb = _jit_internal.createResolutionCallbackFromFrame(_frames_up + 1)
              _compile_and_register_class(obj, _rcb, qualified_name)
              return obj
          elif inspect.isfunction(obj) or inspect.ismethod(obj):
              qualified_name = _qualified_name(obj)
              # this is a decorated fn, and we need to the underlying fn and its rcb
              if hasattr(obj, "__script_if_tracing_wrapper"):
                  obj = obj.__original_fn  # type: ignore[union-attr]
                  _rcb = _jit_internal.createResolutionCallbackFromClosure(obj)
      
              # some functions are explicitly marked as not supported in script mode
              if hasattr(obj, "__script_unsupported"):
                  raise RuntimeError("TorchScript error: " + obj.__script_unsupported)
      
              _check_directly_compile_overloaded(obj)
              maybe_already_compiled_fn = _try_get_jit_cached_function(obj)
              if maybe_already_compiled_fn:
                  maybe_already_compiled_fn._torchdynamo_inline = obj  # type: ignore[attr-defined]
                  return maybe_already_compiled_fn
              ast = get_jit_def(obj, obj.__name__)
              if _rcb is None:
                  _rcb = _jit_internal.createResolutionCallbackFromClosure(obj)
  >           fn = torch._C._jit_script_compile(
                  qualified_name, ast, _rcb, get_default_args(obj)
              )
  E           RuntimeError: 
  E           undefined value torch:
  E             File "/usr/local/lib/python3.10/dist-packages/typing_extensions.py", line 34
  E               It will depend on the context where to use what.
  E               """
  E               return torch.compiler.is_compiling()
  E                      ~~~~~ <--- HERE
  E           'is_compiling' is being compiled since it was called from 'is_compiling'
  E             File "/usr/local/lib/python3.10/dist-packages/torch_geometric/_compile.py", line 14
  E               """
  E               if torch_geometric.typing.WITH_PT21:
  E                   return torch._dynamo.is_compiling()
  E                          ~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
  E               return False  # pragma: no cover
  E           'is_compiling' is being compiled since it was called from 'index_sort'
  E             File "/usr/local/lib/python3.10/dist-packages/torch_geometric/utils/_index_sort.py", line 30
  E                       (default: :obj:`False`)
  E               """
  E               if stable or not torch_geometric.typing.WITH_INDEX_SORT or is_compiling():
  E                                ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
  E                   return inputs.sort(stable=stable)
  E               return pyg_lib.ops.index_sort(inputs, max_value=max_value)
  E           'index_sort' is being compiled since it was called from 'coalesce'
  E             File "/usr/local/lib/python3.10/dist-packages/torch_geometric/utils/_coalesce.py", line 147
  E           
  E               if not is_sorted:
  E                   idx[1:], perm = index_sort(idx[1:], max_value=num_nodes * num_nodes)
  E                   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
  E                   if isinstance(edge_index, Tensor):
  E                       edge_index = edge_index[:, perm]
  E           'coalesce' is being compiled since it was called from 'to_undirected'
  E             File "/usr/local/lib/python3.10/dist-packages/torch_geometric/utils/undirected.py", line 209
  E                   edge_attr = [torch.cat([e, e], dim=0) for e in edge_attr]
  E           
  E               return coalesce(edge_index, edge_attr, num_nodes, reduce)
  E                      ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
  E           'to_undirected' is being compiled since it was called from 'jit'
  E             File "/opt/pyg/pytorch_geometric/test/utils/test_undirected.py", line 38
  E               @torch.jit.script
  E               def jit(edge_index: Tensor) -> Tensor:
  E                   return to_undirected(edge_index)
  E                          ~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
  /usr/local/lib/python3.10/dist-packages/torch/jit/_script.py:1204: RuntimeError
  =============================== warnings summary ===============================
  ../../../usr/local/lib/python3.10/dist-packages/torch_geometric/_compile.py:14: 2 warnings
  test/contrib/nn/models/test_rbcd_attack.py: 36 warnings
  test/data/test_batch.py: 3 warnings
  test/data/test_data.py: 2 warnings
  test/data/test_datapipes.py: 1 warning
  test/data/test_dataset_summary.py: 5 warnings
  test/data/test_graph_store.py: 1 warning
  test/data/test_hypergraph_data.py: 1 warning
  test/datasets/graph_generator/test_ba_graph.py: 1 warning
  test/datasets/graph_generator/test_er_graph.py: 1 warning
  test/datasets/graph_generator/test_grid_graph.py: 1 warning
  test/datasets/graph_generator/test_tree_graph.py: 1 warning
  test/datasets/test_ba_shapes.py: 1 warning
  test/datasets/test_bzr.py: 1 warning
  test/datasets/test_enzymes.py: 2 warnings
  test/datasets/test_explainer_dataset.py: 3 warnings
  test/datasets/test_fake.py: 36 warnings
  test/datasets/test_imdb_binary.py: 1 warning
  test/datasets/test_infection_dataset.py: 2 warnings
  test/datasets/test_mutag.py: 1 warning
  test/datasets/test_planetoid.py: 1 warning
  test/datasets/test_snap_dataset.py: 12 warnings
  test/distributed/test_local_graph_store.py: 1 warning
  test/explain/algorithm/test_attention_explainer.py: 4 warnings
  test/explain/algorithm/test_captum.py: 13 warnings
  test/explain/algorithm/test_gnn_explainer.py: 866 warnings
  test/explain/algorithm/test_graphmask_explainer.py: 648 warnings
  test/explain/algorithm/test_pg_explainer.py: 12 warnings
  test/loader/test_cache.py: 4 warnings
  test/loader/test_imbalanced_sampler.py: 3 warnings
  test/loader/test_link_neighbor_loader.py: 41 warnings
  test/loader/test_neighbor_loader.py: 44 warnings
  test/loader/test_zip_loader.py: 2 warnings
  test/nn/aggr/test_attention.py: 2 warnings
  test/nn/aggr/test_basic.py: 5 warnings
  test/nn/aggr/test_fused.py: 7 warnings
  test/nn/aggr/test_multi.py: 10 warnings
  test/nn/aggr/test_scaler.py: 2 warnings
  test/nn/aggr/test_set2set.py: 1 warning
  test/nn/conv/cugraph/test_cugraph_gat_conv.py: 48 warnings
  test/nn/conv/cugraph/test_cugraph_rgcn_conv.py: 144 warnings
  test/nn/conv/cugraph/test_cugraph_sage_conv.py: 128 warnings
  test/nn/conv/test_agnn_conv.py: 2 warnings
  test/nn/conv/test_antisymmetric_conv.py: 1 warning
  test/nn/conv/test_appnp.py: 2 warnings
  test/nn/conv/test_arma_conv.py: 2 warnings
  test/nn/conv/test_cg_conv.py: 3 warnings
  test/nn/conv/test_cheb_conv.py: 2 warnings
  test/nn/conv/test_cluster_gcn_conv.py: 1 warning
  test/nn/conv/test_create_gnn.py: 1 warning
  test/nn/conv/test_dir_gnn_conv.py: 2 warnings
  test/nn/conv/test_dna_conv.py: 2 warnings
  test/nn/conv/test_edge_conv.py: 1 warning
  test/nn/conv/test_eg_conv.py: 5 warnings
  test/nn/conv/test_fa_conv.py: 1 warning
  test/nn/conv/test_feast_conv.py: 1 warning
  test/nn/conv/test_film_conv.py: 1 warning
  test/nn/conv/test_fused_gat_conv.py: 1 warning
  test/nn/conv/test_gat_conv.py: 5 warnings
  test/nn/conv/test_gated_graph_conv.py: 1 warning
  test/nn/conv/test_gatv2_conv.py: 3 warnings
  test/nn/conv/test_gcn2_conv.py: 1 warning
  test/nn/conv/test_gcn_conv.py: 9 warnings
  test/nn/conv/test_gen_conv.py: 3 warnings
  test/nn/conv/test_general_conv.py: 8 warnings
  test/nn/conv/test_gin_conv.py: 5 warnings
  test/nn/conv/test_gmm_conv.py: 4 warnings
  test/nn/conv/test_gps_conv.py: 6 warnings
  test/nn/conv/test_graph_conv.py: 2 warnings
  test/nn/conv/test_han_conv.py: 3 warnings
  test/nn/conv/test_heat_conv.py: 2 warnings
  test/nn/conv/test_hetero_conv.py: 11 warnings
  test/nn/conv/test_hgt_conv.py: 7 warnings
  test/nn/conv/test_hypergraph_conv.py: 2 warnings
  test/nn/conv/test_le_conv.py: 1 warning
  test/nn/conv/test_lg_conv.py: 1 warning
  test/nn/conv/test_message_passing.py: 36 warnings
  test/nn/conv/test_mf_conv.py: 1 warning
  test/nn/conv/test_mixhop_conv.py: 1 warning
  test/nn/conv/test_nn_conv.py: 2 warnings
  test/nn/conv/test_pdn_conv.py: 2 warnings
  test/nn/conv/test_pna_conv.py: 3 warnings
  test/nn/conv/test_point_conv.py: 1 warning
  test/nn/conv/test_point_gnn_conv.py: 1 warning
  test/nn/conv/test_point_transformer_conv.py: 1 warning
  test/nn/conv/test_ppf_conv.py: 1 warning
  test/nn/conv/test_res_gated_graph_conv.py: 2 warnings
  test/nn/conv/test_rgat_conv.py: 65 warnings
  test/nn/conv/test_rgcn_conv.py: 18 warnings
  test/nn/conv/test_sage_conv.py: 22 warnings
  test/nn/conv/test_sg_conv.py: 1 warning
  test/nn/conv/test_signed_conv.py: 1 warning
  test/nn/conv/test_simple_conv.py: 4 warnings
  test/nn/conv/test_ssg_conv.py: 1 warning
  test/nn/conv/test_static_graph.py: 1 warning
  test/nn/conv/test_supergat_conv.py: 2 warnings
  test/nn/conv/test_tag_conv.py: 2 warnings
  test/nn/conv/test_transformer_conv.py: 4 warnings
  test/nn/conv/test_wl_conv.py: 1 warning
  test/nn/conv/test_wl_conv_continuous.py: 1 warning
  test/nn/dense/test_dense_gat_conv.py: 4 warnings
  test/nn/dense/test_dense_gcn_conv.py: 1 warning
  test/nn/dense/test_dense_gin_conv.py: 1 warning
  test/nn/dense/test_dense_graph_conv.py: 6 warnings
  test/nn/dense/test_dense_sage_conv.py: 1 warning
  test/nn/dense/test_linear.py: 14 warnings
  test/nn/models/test_attentive_fp.py: 1 warning
  test/nn/models/test_basic_gnn.py: 1821 warnings
  test/nn/models/test_correct_and_smooth.py: 1 warning
  test/nn/models/test_deep_graph_infomax.py: 2 warnings
  test/nn/models/test_deepgcn.py: 8 warnings
  test/nn/models/test_graph_unet.py: 1 warning
  test/nn/models/test_label_prop.py: 1 warning
  test/nn/models/test_lightgcn.py: 36 warnings
  test/nn/models/test_linkx.py: 2 warnings
  test/nn/models/test_metapath2vec.py: 3 warnings
  test/nn/models/test_neural_fingerprint.py: 2 warnings
  test/nn/models/test_node2vec.py: 2 warnings
  test/nn/models/test_pmlp.py: 1 warning
  test/nn/models/test_rect.py: 1 warning
  test/nn/models/test_rev_gnn.py: 20 warnings
  test/nn/models/test_signed_gcn.py: 2 warnings
  test/nn/models/test_tgn.py: 2 warnings
  test/nn/pool/select/test_select_topk.py: 1 warning
  test/nn/pool/test_asap.py: 1 warning
  test/nn/pool/test_avg_pool.py: 1 warning
  test/nn/pool/test_edge_pool.py: 2 warnings
  test/nn/pool/test_glob.py: 2 warnings
  test/nn/pool/test_max_pool.py: 3 warnings
  test/nn/pool/test_sag_pool.py: 1 warning
  test/nn/pool/test_topk_pool.py: 1 warning
  test/nn/test_compile_basic.py: 2 warnings
  test/nn/test_compile_conv.py: 4 warnings
  test/nn/test_model_summary.py: 5 warnings
  test/nn/test_sequential.py: 4 warnings
  test/nn/test_to_hetero_module.py: 3 warnings
  test/nn/test_to_hetero_transformer.py: 10 warnings
  test/nn/test_to_hetero_with_bases_transformer.py: 5 warnings
  test/profile/test_profile.py: 7 warnings
  test/profile/test_profiler.py: 2 warnings
  test/sampler/test_sampler_base.py: 2 warnings
  test/test_edge_index.py: 208 warnings
  test/test_warnings.py: 1 warning
  test/transforms/test_add_metapaths.py: 4 warnings
  test/transforms/test_face_to_edge.py: 1 warning
  test/transforms/test_feature_propagation.py: 1 warning
  test/transforms/test_gdc.py: 2 warnings
  test/transforms/test_line_graph.py: 1 warning
  test/transforms/test_local_cartesian.py: 1 warning
  test/transforms/test_local_degree_profile.py: 1 warning
  test/transforms/test_node_property_split.py: 3 warnings
  test/transforms/test_pad.py: 34 warnings
  test/transforms/test_random_link_split.py: 3 warnings
  test/transforms/test_remove_duplicated_edges.py: 1 warning
  test/transforms/test_rooted_subgraph.py: 2 warnings
  test/transforms/test_sign.py: 1 warning
  test/transforms/test_to_sparse_tensor.py: 8 warnings
  test/transforms/test_to_undirected.py: 3 warnings
  test/transforms/test_two_hop.py: 1 warning
  test/utils/test_assortativity.py: 1 warning
  test/utils/test_augmentation.py: 1 warning
  test/utils/test_coalesce.py: 2 warnings
  test/utils/test_convert.py: 18 warnings
  test/utils/test_embedding.py: 1 warning
  test/utils/test_grid.py: 1 warning
  test/utils/test_loop.py: 3 warnings
  test/utils/test_mesh_laplacian.py: 2 warnings
  test/utils/test_negative_sampling.py: 3 warnings
  test/utils/test_num_nodes.py: 1 warning
  test/utils/test_ppr.py: 2 warnings
  test/utils/test_random.py: 3 warnings
  test/utils/test_scatter.py: 6 warnings
  test/utils/test_softmax.py: 3 warnings
  test/utils/test_sort_edge_index.py: 1 warning
  test/utils/test_sparse.py: 22 warnings
  test/utils/test_spmm.py: 2 warnings
  test/utils/test_train_test_split_edges.py: 1 warning
  test/utils/test_tree_decomposition.py: 2 warnings
  test/utils/test_trim_to_layer.py: 1 warning
  test/utils/test_undirected.py: 2 warnings
  test/visualization/test_influence.py: 1 warning
    /usr/local/lib/python3.10/dist-packages/torch_geometric/_compile.py:14: FutureWarning: `torch._dynamo.external_utils.is_compiling` is deprecated. Use `torch.compiler.is_compiling` instead.
      return torch._dynamo.is_compiling()
  ../../../usr/local/lib/python3.10/dist-packages/torch_geometric/graphgym/imports.py:14
    /usr/local/lib/python3.10/dist-packages/torch_geometric/graphgym/imports.py:14: UserWarning: Please install 'pytorch_lightning' via  'pip install pytorch_lightning' in order to use GraphGym
      warnings.warn("Please install 'pytorch_lightning' via  "
  test/data/test_batch.py::test_pickling
    /opt/pyg/pytorch_geometric/test/data/test_batch.py:333: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
      batch = torch.load(path)
  test/data/test_dataset.py: 4 warnings
  test/datasets/test_bzr.py: 2 warnings
  test/datasets/test_elliptic.py: 1 warning
  test/datasets/test_enzymes.py: 3 warnings
  test/datasets/test_imdb_binary.py: 1 warning
  test/datasets/test_mutag.py: 2 warnings
  test/datasets/test_planetoid.py: 3 warnings
  test/datasets/test_snap_dataset.py: 3 warnings
  test/datasets/test_suite_sparse.py: 2 warnings
  test/io/test_fs.py: 2 warnings
  test/nn/models/test_re_net.py: 1 warning
  test/transforms/test_random_link_split.py: 1 warning
    /usr/local/lib/python3.10/dist-packages/torch_geometric/io/fs.py:215: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
      return torch.load(f, map_location)
  test/loader/test_prefetch.py: 10 warnings
    /usr/local/lib/python3.10/dist-packages/torch_geometric/loader/prefetch.py:76: DeprecationWarning: The argument 'device' of Tensor.pin_memory() is deprecated. Please do not pass this argument. (Triggered internally at /opt/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:46.)
      batch = batch.pin_memory(self.device_helper.device)
  test/loader/test_prefetch.py: 10 warnings
    /usr/local/lib/python3.10/dist-packages/torch_geometric/loader/prefetch.py:76: DeprecationWarning: The argument 'device' of Tensor.is_pinned() is deprecated. Please do not pass this argument. (Triggered internally at /opt/pytorch/pytorch/aten/src/ATen/native/Memory.cpp:31.)
      batch = batch.pin_memory(self.device_helper.device)
  test/nn/conv/cugraph/test_cugraph_gat_conv.py: 24 warnings
  test/nn/conv/cugraph/test_cugraph_rgcn_conv.py: 72 warnings
  test/nn/conv/cugraph/test_cugraph_sage_conv.py: 64 warnings
    /usr/local/lib/python3.10/dist-packages/pylibcugraphops/pytorch/graph.py:71: UserWarning: dst_max_in_degree currently has no effect
      warnings.warn("dst_max_in_degree currently has no effect")
  test/nn/conv/test_message_passing.py::test_my_conv_save
    /opt/pyg/pytorch_geometric/test/nn/conv/test_message_passing.py:142: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
      conv = torch.load(path)
  test/nn/conv/test_message_passing.py::test_pickle
    /opt/pyg/pytorch_geometric/test/nn/conv/test_message_passing.py:741: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
      model = torch.load(path)
  test/nn/conv/test_rgcn_conv.py: 12 warnings
    /usr/local/lib/python3.10/dist-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`.
      warnings.warn(
  test/nn/models/test_basic_gnn.py::test_packaging
    /opt/pyg/pytorch_geometric/test/nn/models/test_basic_gnn.py:238: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
      model = torch.load(path)
  test/nn/nlp/test_sentence_transformer.py: 12 warnings
    /usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884
      warnings.warn(
  test/nn/nlp/test_sentence_transformer.py: 12 warnings
    /usr/local/lib/python3.10/dist-packages/transformers/modeling_attn_mask_utils.py:445: FutureWarning: `torch._dynamo.external_utils.is_compiling` is deprecated. Use `torch.compiler.is_compiling` instead.
      or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
  test/nn/test_model_hub.py::test_from_pretrained
    /usr/local/lib/python3.10/dist-packages/torch_geometric/nn/model_hub.py:178: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
      state_dict = torch.load(model_file, map_location=map_location)
  test/profile/test_profiler.py::test_profiler[cpu]
  test/profile/test_profiler.py::test_profiler[cuda:0]
    /usr/local/lib/python3.10/dist-packages/torch_geometric/profile/profiler.py:342: FutureWarning: `self_cuda_memory_usage` is deprecated. Use `self_device_memory_usage` instead.
      hasattr(e, "self_cuda_memory_usage") for e in events)
  test/profile/test_profiler.py::test_profiler[cpu]
  test/profile/test_profiler.py::test_profiler[cuda:0]
    /usr/local/lib/python3.10/dist-packages/torch_geometric/profile/profiler.py:345: FutureWarning: `self_cuda_memory_usage` is deprecated. Use `self_device_memory_usage` instead.
      [getattr(e, "self_cuda_memory_usage", 0) or 0 for e in events])
  test/profile/test_profiler.py::test_profiler[cpu]
  test/profile/test_profiler.py::test_profiler[cuda:0]
    /usr/local/lib/python3.10/dist-packages/torch_geometric/profile/profiler.py:355: FutureWarning: `self_cuda_time_total` is deprecated. Use `self_device_time_total` instead.
      hasattr(e, "self_cuda_time_total") for e in events)
  test/profile/test_profiler.py::test_profiler[cpu]
  test/profile/test_profiler.py::test_profiler[cuda:0]
    /usr/local/lib/python3.10/dist-packages/torch_geometric/profile/profiler.py:358: FutureWarning: `self_cuda_time_total` is deprecated. Use `self_device_time_total` instead.
      [getattr(e, "self_cuda_time_total", 0) or 0 for e in events])
  test/profile/test_profiler.py::test_profiler[cpu]
  test/profile/test_profiler.py::test_profiler[cuda:0]
    /usr/local/lib/python3.10/dist-packages/torch_geometric/profile/profiler.py:364: FutureWarning: `cuda_time_total` is deprecated. Use `device_time_total` instead.
      cuda_total=sum([e.cuda_time_total or 0 for e in events]),
  test/test_edge_index.py::test_save_and_load[int64-cpu]
  test/test_edge_index.py::test_save_and_load[int64-cuda:0]
  test/test_edge_index.py::test_save_and_load[int32-cpu]
  test/test_edge_index.py::test_save_and_load[int32-cuda:0]
    /opt/pyg/pytorch_geometric/test/test_edge_index.py:1259: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
      out = torch.load(path)
  test/test_index.py::test_save_and_load[int64-cpu]
  test/test_index.py::test_save_and_load[int64-cuda:0]
  test/test_index.py::test_save_and_load[int32-cpu]
  test/test_index.py::test_save_and_load[int32-cuda:0]
    /opt/pyg/pytorch_geometric/test/test_index.py:532: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
      out = torch.load(path)
  test/utils/test_convert.py: 16 warnings
    /usr/local/lib/python3.10/dist-packages/cugraph/structure/symmetrize.py:92: FutureWarning: Multi is deprecated and the removal of multi edges will no longer be supported from 'symmetrize'. Multi edges will be removed upon creation of graph instance.
      warnings.warn(
  test/utils/test_scatter.py::test_scatter_backward[min-cuda:0]
    /usr/local/lib/python3.10/dist-packages/torch_geometric/warnings.py:11: UserWarning: The usage of `scatter(reduce='min')` can be accelerated via the 'torch-scatter' package, but it was not found
      warnings.warn(message)
  test/utils/test_scatter.py::test_scatter_backward[max-cuda:0]
    /usr/local/lib/python3.10/dist-packages/torch_geometric/warnings.py:11: UserWarning: The usage of `scatter(reduce='max')` can be accelerated via the 'torch-scatter' package, but it was not found
      warnings.warn(message)
  test/utils/test_sparse.py::test_to_torch_coo_tensor_save_load
    /opt/pyg/pytorch_geometric/test/utils/test_sparse.py:227: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
      adj = torch.load(path)
  -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
  ---------- coverage: platform linux, python 3.10.12-final-0 ----------
  Coverage XML written to file coverage.xml
  =========================== short test summary info ============================
  FAILED test/nn/aggr/test_fused.py::test_fused_aggregation[aggrs0] - RuntimeError: 
  FAILED test/nn/aggr/test_fused.py::test_fused_aggregation[aggrs1] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/aggr/test_fused.py::test_fused_aggregation[aggrs2] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/aggr/test_fused.py::test_fused_aggregation[aggrs3] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/aggr/test_fused.py::test_fused_aggregation[aggrs4] - RuntimeError: 
  FAILED test/nn/aggr/test_fused.py::test_fused_aggregation[aggrs5] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/aggr/test_fused.py::test_fused_aggregation[aggrs6] - RuntimeError: 
  FAILED test/nn/aggr/test_gmt.py::test_graph_multiset_transformer - RuntimeError: 
  FAILED test/nn/aggr/test_multi.py::test_multi_aggr[multi_aggr_tuple0] - RuntimeError: 
  FAILED test/nn/aggr/test_multi.py::test_multi_aggr[multi_aggr_tuple1] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/aggr/test_multi.py::test_multi_aggr[multi_aggr_tuple2] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/aggr/test_multi.py::test_multi_aggr[multi_aggr_tuple3] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/aggr/test_multi.py::test_multi_aggr[multi_aggr_tuple4] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/aggr/test_multi.py::test_multi_aggr[multi_aggr_tuple5] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/aggr/test_multi.py::test_multi_aggr[multi_aggr_tuple6] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/aggr/test_multi.py::test_multi_aggr[multi_aggr_tuple7] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/aggr/test_multi.py::test_multi_aggr[multi_aggr_tuple8] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/aggr/test_multi.py::test_multi_aggr[multi_aggr_tuple9] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/aggr/test_scaler.py::test_degree_scaler_aggregation[True] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/aggr/test_scaler.py::test_degree_scaler_aggregation[False] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/aggr/test_set_transformer.py::test_set_transformer_aggregation - RuntimeError: 
  FAILED test/nn/conv/test_agnn_conv.py::test_agnn_conv[True] - RuntimeError: 
  FAILED test/nn/conv/test_agnn_conv.py::test_agnn_conv[False] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_appnp.py::test_appnp - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_arma_conv.py::test_arma_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_arma_conv.py::test_lazy_arma_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_cg_conv.py::test_cg_conv[False] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_cg_conv.py::test_cg_conv[True] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_cg_conv.py::test_cg_conv_with_edge_features - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_cheb_conv.py::test_cheb_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_cluster_gcn_conv.py::test_cluster_gcn_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_dna_conv.py::test_dna_conv[3-32] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_dna_conv.py::test_dna_conv_sparse_tensor[3-32] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_edge_conv.py::test_edge_conv_conv - RuntimeError: 
  FAILED test/nn/conv/test_eg_conv.py::test_eg_conv[True-aggregators0] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_eg_conv.py::test_eg_conv[True-aggregators1] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_eg_conv.py::test_eg_conv[False-aggregators0] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_eg_conv.py::test_eg_conv[False-aggregators1] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_fa_conv.py::test_fa_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_feast_conv.py::test_feast_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_film_conv.py::test_film_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_gat_conv.py::test_gat_conv[False] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_gat_conv.py::test_gat_conv[True] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_gated_graph_conv.py::test_gated_graph_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_gatv2_conv.py::test_gatv2_conv[False] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_gatv2_conv.py::test_gatv2_conv[True] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_gcn2_conv.py::test_gcn2_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_gcn_conv.py::test_gcn_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_gcn_conv.py::test_gcn_conv_with_decomposed_layers - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_gen_conv.py::test_gen_conv[softmax] - RuntimeError: 
  FAILED test/nn/conv/test_gen_conv.py::test_gen_conv[powermean] - RuntimeError: 
  FAILED test/nn/conv/test_gen_conv.py::test_gen_conv[aggr2] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_gin_conv.py::test_gin_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_gin_conv.py::test_gine_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_gmm_conv.py::test_gmm_conv[True] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_gmm_conv.py::test_gmm_conv[False] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_graph_conv.py::test_graph_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_heat_conv.py::test_heat_conv[True] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_heat_conv.py::test_heat_conv[False] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_le_conv.py::test_le_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_lg_conv.py::test_lg_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_message_passing.py::test_my_commented_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_message_passing.py::test_my_kwargs_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_message_passing.py::test_my_conv_jit - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_message_passing.py::test_my_conv_jit_save - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_message_passing.py::test_my_multiple_aggr_conv_jit - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_message_passing.py::test_my_edge_conv_jit - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_message_passing.py::test_my_default_arg_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_message_passing.py::test_tuple_output_jit - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_message_passing.py::test_explain_message - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_message_passing.py::test_traceable_my_conv_with_self_loops[4] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_message_passing.py::test_traceable_my_conv_with_self_loops[8] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_message_passing.py::test_traceable_my_conv_with_self_loops[2] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_message_passing.py::test_traceable_my_conv_with_self_loops[0] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_message_passing.py::test_pickle - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_mf_conv.py::test_mf_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_mixhop_conv.py::test_mixhop_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_nn_conv.py::test_nn_conv[cpu] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_nn_conv.py::test_nn_conv[cuda:0] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_pdn_conv.py::test_pdn_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_pdn_conv.py::test_pdn_conv_with_sparse_node_input_feature - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_pna_conv.py::test_pna_conv[True] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_pna_conv.py::test_pna_conv[False] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_point_conv.py::test_point_net_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_point_gnn_conv.py::test_point_gnn_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_point_transformer_conv.py::test_point_transformer_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_ppf_conv.py::test_ppf_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_res_gated_graph_conv.py::test_res_gated_graph_conv[None] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_res_gated_graph_conv.py::test_res_gated_graph_conv[4] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_rgat_conv.py::test_rgat_conv_jit - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_rgcn_conv.py::test_rgcn_conv_basic[conf0-RGCNConv-cpu] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_rgcn_conv.py::test_rgcn_conv_basic[conf0-RGCNConv-cuda:0] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_rgcn_conv.py::test_rgcn_conv_basic[conf0-FastRGCNConv-cpu] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_rgcn_conv.py::test_rgcn_conv_basic[conf0-FastRGCNConv-cuda:0] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_rgcn_conv.py::test_rgcn_conv_basic[conf1-RGCNConv-cpu] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_rgcn_conv.py::test_rgcn_conv_basic[conf1-RGCNConv-cuda:0] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_rgcn_conv.py::test_rgcn_conv_basic[conf1-FastRGCNConv-cpu] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_rgcn_conv.py::test_rgcn_conv_basic[conf1-FastRGCNConv-cuda:0] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_rgcn_conv.py::test_rgcn_conv_basic[conf2-RGCNConv-cpu] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_rgcn_conv.py::test_rgcn_conv_basic[conf2-RGCNConv-cuda:0] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_rgcn_conv.py::test_rgcn_conv_basic[conf2-FastRGCNConv-cpu] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_rgcn_conv.py::test_rgcn_conv_basic[conf2-FastRGCNConv-cuda:0] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_sage_conv.py::test_sage_conv[mean-False] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_sage_conv.py::test_sage_conv[mean-True] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_sage_conv.py::test_sage_conv[sum-False] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_sage_conv.py::test_sage_conv[sum-True] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_sg_conv.py::test_sg_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_signed_conv.py::test_signed_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_simple_conv.py::test_simple_conv[mean-None] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_simple_conv.py::test_simple_conv[sum-sum] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_simple_conv.py::test_simple_conv[aggr2-cat] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_simple_conv.py::test_simple_conv[mean-self_loop] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_ssg_conv.py::test_ssg_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_tag_conv.py::test_tag_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/conv/test_wl_conv_continuous.py::test_wl_conv - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/dense/test_linear.py::test_hetero_linear_basic[cpu] - RuntimeError: 
  FAILED test/nn/dense/test_linear.py::test_hetero_linear_basic[cuda:0] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/dense/test_linear.py::test_hetero_dict_linear_jit - RuntimeError: 
  FAILED test/nn/models/test_attentive_fp.py::test_attentive_fp - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/models/test_basic_gnn.py::test_jit - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/models/test_linkx.py::test_linkx[1] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/models/test_linkx.py::test_linkx[2] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/models/test_meta.py::test_meta_layer_example - RuntimeError: 
  FAILED test/nn/models/test_rect.py::test_rect - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/norm/test_graph_norm.py::test_graph_norm - RuntimeError: 
  FAILED test/nn/norm/test_instance_norm.py::test_instance_norm[True] - RuntimeError: 
  FAILED test/nn/norm/test_instance_norm.py::test_instance_norm[False] - RuntimeError: 
  FAILED test/nn/norm/test_layer_norm.py::test_layer_norm[graph-True-cpu] - RuntimeError: 
  FAILED test/nn/norm/test_layer_norm.py::test_layer_norm[graph-True-cuda:0] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/norm/test_layer_norm.py::test_layer_norm[graph-False-cpu] - RuntimeError: 
  FAILED test/nn/norm/test_layer_norm.py::test_layer_norm[graph-False-cuda:0] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/norm/test_layer_norm.py::test_layer_norm[node-True-cpu] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/norm/test_layer_norm.py::test_layer_norm[node-True-cuda:0] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/norm/test_layer_norm.py::test_layer_norm[node-False-cpu] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/norm/test_layer_norm.py::test_layer_norm[node-False-cuda:0] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/norm/test_mean_subtraction_norm.py::test_mean_subtraction_norm - RuntimeError: 
  FAILED test/nn/norm/test_pair_norm.py::test_pair_norm[False] - RuntimeError: 
  FAILED test/nn/norm/test_pair_norm.py::test_pair_norm[True] - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/pool/select/test_select_topk.py::test_topk_ratio - RuntimeError: 
  FAILED test/nn/pool/test_asap.py::test_asap - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/pool/test_asap.py::test_asap_jit_save - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/pool/test_avg_pool.py::test_avg_pool_x - RuntimeError: 
  FAILED test/nn/pool/test_edge_pool.py::test_compute_edge_score_softmax - RuntimeError: 
  FAILED test/nn/pool/test_edge_pool.py::test_edge_pooling - RuntimeError: 
  FAILED test/nn/pool/test_max_pool.py::test_max_pool_x - RuntimeError: 
  FAILED test/nn/pool/test_sag_pool.py::test_sag_pooling - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/nn/pool/test_topk_pool.py::test_topk_pooling - RuntimeError: 
  FAILED test/nn/test_sequential.py::test_sequential_jit - RuntimeError: Can't redefine method: forward on class: __torch__.torch_geom...
  FAILED test/test_edge_index.py::test_torch_script - AssertionError: Regex pattern did not match.
  FAILED test/utils/test_coalesce.py::test_coalesce_jit - RuntimeError: 
  FAILED test/utils/test_grid.py::test_grid - RuntimeError: 
  FAILED test/utils/test_isolated.py::test_contains_isolated_nodes - RuntimeError: 
  FAILED test/utils/test_laplacian.py::test_get_laplacian - RuntimeError: 
  FAILED test/utils/test_softmax.py::test_softmax - RuntimeError: 
  FAILED test/utils/test_sort_edge_index.py::test_sort_edge_index_jit - RuntimeError: 
  FAILED test/utils/test_sparse.py::test_to_torch_coo_tensor - RuntimeError: 
  FAILED test/utils/test_spmm.py::test_spmm_jit[sum] - RuntimeError: 
  FAILED test/utils/test_spmm.py::test_spmm_jit[mean] - RuntimeError: 
  FAILED test/utils/test_to_dense_adj.py::test_to_dense_adj - RuntimeError: 
  FAILED test/utils/test_to_dense_batch.py::test_to_dense_batch_jit - RuntimeError: 
  FAILED test/utils/test_undirected.py::test_is_undirected - RuntimeError: 
  FAILED test/utils/test_undirected.py::test_to_undirected - RuntimeError: 
  ```
  
  ### Versions
  
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