code: def output(self, node: Node, target: Any, name: str):
      # Split the output to dictionaries, holding either node type-wise or
      # edge type-wise data.
      def _recurse(value: Any) -> Any:
          if isinstance(value, Node) and self.is_edge_level(value):
              self.graph.inserting_before(node)
              return self.graph.create_node(
                  "call_function",
                  target=split_output,
                  args=(value, self.find_by_name("edge_offset_dict")),
                  name=f"{value.name}__split",
              )
  
              pass
          elif isinstance(value, Node):
              self.graph.inserting_before(node)
              return self.graph.create_node(
                  "call_function",
                  target=split_output,
                  args=(value, self.find_by_name("node_offset_dict")),
                  name=f"{value.name}__split",
              )
  
          elif isinstance(value, dict):
              return {k: _recurse(v) for k, v in value.items()}
          elif isinstance(value, list):
              return [_recurse(v) for v in value]
          elif isinstance(value, tuple):
              return tuple(_recurse(v) for v in value)
          else:
              return value
  
      if node.type is not None and isinstance(node.args[0], Node):
          output = node.args[0]
          Type = EdgeType if self.is_edge_level(output) else NodeType
          node.type = Dict[Type, node.type]
      else:
          node.type = None
  
      node.args = (_recurse(node.args[0]),)
  
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 6 for calling the ToHeteroWithBasesTransformer obj's underlying member method code with example usage: obj.output(node, target, name) and returns: self, self, unknown, unknown, tuple, value