code: def __init__(
      self,
      module: Module,
      metadata: Metadata,
      num_bases: int,
      in_channels: Optional[Dict[str, int]] = None,
      input_map: Optional[Dict[str, str]] = None,
      debug: bool = False,
  ):
      super().__init__(module, input_map, debug)
  
      self.metadata = metadata
      self.num_bases = num_bases
      self.in_channels = in_channels or {}
      assert len(metadata) == 2
      assert len(metadata[0]) > 0 and len(metadata[1]) > 0
  
      self.validate()
  
      # Compute IDs for each node and edge type:
      self.node_type2id = {k: i for i, k in enumerate(metadata[0])}
      self.edge_type2id = {k: i for i, k in enumerate(metadata[1])}
  
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 1 to create a new ToHeteroWithBasesTransformer object from inherited base classes: Transformer with example: obj = ToHeteroWithBasesTransformer(module, metadata, num_bases, in_channels, input_map, debug)