code: def __getitem__(self, index: int):
      data = dict(self.data[index])
      # If pad collation was used, then we need to undo this first
      if self.pad_collation_used:
          data = PadListDataCollate.inverse(data)
  
      if not isinstance(self.invertible_transform, InvertibleTransform):
          warnings.warn(
              "transform is not invertible, can't invert transform for the input data."
          )
          return data
      return self.invertible_transform.inverse(data)
  
imports: from __future__ import annotations
  import warnings
  from collections.abc import Callable, Sequence
  from typing import Any
  from torch.utils.data import Dataset
  from torch.utils.data.dataloader import DataLoader
  from monai.config import KeysCollection
  from monai.data.dataloader import DataLoader
  from monai.data.utils import decollate_batch, no_collation, pad_list_data_collate
  from monai.transforms.croppad.batch import PadListDataCollate
  from monai.transforms.inverse import InvertibleTransform
  from monai.transforms.transform import MapTransform, Transform
  from monai.utils import first
label_desc: Use image node_id 2 for calling the _BatchInverseDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: self, data