class_docstr: Perform inverse on a batch of data. This is useful if you have inferred a batch of images and want to invert
  them all.
class_docstr_tok: ["Perform","inverse","on","a","batch","of","data",".","This","is","useful","if","you","have","inferred","a","batch","of","images","and","want","to","invert","them","all","."]
code: def __call__(self, data: dict[str, Any]) -> Any:
      decollated_data = decollate_batch(
          data,
          detach=self.detach,
          pad=self.pad_batch,
          fill_value=self.fill_value,
      )
      inv_ds = _BatchInverseDataset(
          decollated_data, self.transform, self.pad_collation_used
      )
      inv_loader = DataLoader(
          inv_ds,
          batch_size=self.batch_size,
          num_workers=self.num_workers,
          collate_fn=self.collate_fn,
      )
      try:
          return first(inv_loader)
      except RuntimeError as re:
          re_str = str(re)
          if "equal size" in re_str:
              re_str += "\nMONAI hint: try creating `BatchInverseTransform` with `collate_fn=lambda x: x`."
          raise RuntimeError(re_str) from re
  
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 BatchInverseTransform obj's underlying member method code with example usage: obj.__call__(data) and returns: first