code: def __init__(
      self,
      module: Module,
      metadata: Metadata,
      aggr: str = "sum",
      input_map: Optional[Dict[str, str]] = None,
      debug: bool = False,
  ):
      super().__init__(module, input_map, debug)
  
      self.metadata = metadata
      self.aggr = aggr
      assert len(metadata) == 2
      assert len(metadata[0]) > 0 and len(metadata[1]) > 0
      assert aggr in self.aggrs.keys()
  
      self.validate()
  
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 1 to create a new ToHeteroTransformer object from inherited base classes: Transformer with example: obj = ToHeteroTransformer(module, metadata, aggr, input_map, debug)