code: def __init__(
      self,
      dp: IterDataPipe,
      batch_size: int,
      drop_last: bool = False,
  ) -> None:
      super().__init__(
          dp,
          batch_size=batch_size,
          drop_last=drop_last,
          wrapper_class=Batch.from_data_list,
      )
  
imports: import copy
  from typing import Any, Callable, Iterator, Optional, Sequence
  import torch
  from torch_geometric.data import Batch
  from torch_geometric.utils import from_smiles
label_desc: Use image node_id 1 to create a new Batcher object from inherited base classes: IterBatcher with example: obj = Batcher(dp, batch_size, drop_last)