code: def forward(self, edge_type: Tensor, *args, **kwargs) -> Tensor:
      out = None
      # Call message passing modules and perform aggregation:
      for conv in self.convs:
          conv._edge_type = edge_type
          res = conv(*args, **kwargs)
          del conv._edge_type
          out = res if out is None else out.add_(res)
      return out
  
imports: import copy
  import warnings
  from typing import Any, Dict, List, Optional, Union
  import torch
  from torch import Tensor
  from torch.nn import Module, Parameter
  from torch_geometric.nn.conv import MessagePassing
  from torch_geometric.nn.dense import Linear
  from torch_geometric.nn.fx import Transformer
  from torch_geometric.typing import EdgeType, Metadata, NodeType, SparseTensor
  from torch_geometric.utils.hetero import get_unused_node_types
label_desc: Use image node_id 3 for calling the HeteroBasisConv obj's underlying member method code with example usage: obj.forward(edge_type) and returns: out