code: def test_message_passing_int32_edge_index():
      # Check that we can dispatch an int32 edge_index up to aggregation
      x = torch.randn(4, 8)
      edge_index = torch.tensor(
          [[0, 1, 2, 3], [0, 0, 1, 1]], dtype=torch.int32
      )
      edge_weight = torch.randn(edge_index.shape[1])
  
      # Use a hook to promote the edge_index to long to workaround PyTorch CPU
      # backend restriction to int64 for the index.
      def cast_index_hook(module, inputs):
          input_dict = inputs[-1]
          input_dict["index"] = input_dict["index"].long()
          return (input_dict,)
  
      conv = MyConv(8, 32)
      conv.register_aggregate_forward_pre_hook(cast_index_hook)
  
      assert conv(x, edge_index, edge_weight).size() == (4, 32)
  
imports: import copy
  from typing import Tuple, Union
  import pytest
  import torch
  from torch import Tensor
  from torch.nn import Linear
  import torch_geometric.typing
  from torch_geometric.nn import MessagePassing, aggr
  from torch_geometric.typing import Adj, OptPairTensor, OptTensor, Size, SparseTensor
  from torch_geometric.utils import add_self_loops, scatter, spmm, to_torch_csc_tensor
label_desc: Use image node_id 18 for calling a global function with example usage: test_message_passing_int32_edge_index() and returns: input_dict