code: def __init__(
      self,
      module: torch.nn.Module,
      node_types: List[NodeType],
      edge_types: List[NodeType],
      aggr: str = "sum",
  ):
      from torch_geometric.nn import HeteroConv, MessagePassing
  
      super().__init__()
  
      self.node_types = node_types
      self.node_type_to_index = {
          key: i for i, key in enumerate(node_types)
      }
      self.edge_types = edge_types
  
      if not isinstance(module, MessagePassing):
          raise ValueError(
              f"Expected 'MessagePassing' module "
              f"(got '{type(module)}'"
          )
  
      if (
          not hasattr(module, "reset_parameters")
          and sum([p.numel() for p in module.parameters()]) > 0
      ):
          warnings.warn(
              f"'{module}' will be duplicated, but its parameters "
              f"cannot be reset. To suppress this warning, add a "
              f"'reset_parameters()' method to '{module}'"
          )
  
      convs = {
          edge_type: copy.deepcopy(module) for edge_type in edge_types
      }
      self.hetero_module = HeteroConv(convs, aggr)
      self.hetero_module.reset_parameters()
  
imports: import copy
  import warnings
  from typing import Dict, List, Optional, Union
  import torch
  import torch.nn.functional
  from torch import Tensor
  import torch_geometric
  from torch_geometric import is_compiling
  from torch_geometric.typing import EdgeType, NodeType, OptTensor
  from torch_geometric.utils import cumsum, scatter
label_desc: Use image node_id 1 to create a new ToHeteroMessagePassing object from inherited base classes: torch.nn with example: obj = ToHeteroMessagePassing(module, node_types, edge_types, aggr)