code: def output(self, node: Node, target: Any, name: str):
      # Replace the output by dictionaries, holding either node type-wise or
      # edge type-wise data.
      def _recurse(value: Any) -> Any:
          if isinstance(value, Node):
              if self.is_graph_level(value):
                  return value
              return {
                  key: self.find_by_name(
                      f"{value.name}__{key2str(key)}"
                  )
                  for key in self.metadata[
                      int(self.is_edge_level(value))
                  ]
              }
          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]
          if self.is_node_level(output):
              node.type = Dict[NodeType, node.type]
          elif self.is_edge_level(output):
              node.type = Dict[EdgeType, node.type]
      else:
          node.type = None
  
      node.args = (_recurse(node.args[0]),)
  
imports: import copy
  import warnings
  from collections import defaultdict, deque
  from typing import Any, Dict, Optional, Tuple, Union
  import torch
  from torch.nn import Module
  from torch_geometric.nn.dense.linear import is_uninitialized_parameter
  from torch_geometric.nn.fx import Transformer, get_submodule
  from torch_geometric.typing import EdgeType, Metadata, NodeType
  from torch_geometric.utils.hetero import check_add_self_loops, get_unused_node_types
label_desc: Use image node_id 10 for calling the ToHeteroTransformer obj's underlying member method code with example usage: obj.output(node, target, name) and returns: unknown, value, unknown, unknown, tuple, value