class_docstr: Decollate a batch of data. If input is a dictionary, it also supports to only decollate specified keys.
  Note that unlike most MapTransforms, it will delete the other keys that are not specified.
  if `keys=None`, it will decollate all the data in the input.
  It replicates the scalar values to every item of the decollated list.
  
  Args:
      keys: keys of the corresponding items to decollate, note that it will delete other keys not specified.
          if None, will decollate all the keys. see also: :py:class:`monai.transforms.compose.MapTransform`.
      detach: whether to detach the tensors. Scalars tensors will be detached into number types
          instead of torch tensors.
      pad_batch: when the items in a batch indicate different batch size,
          whether to pad all the sequences to the longest.
          If False, the batch size will be the length of the shortest sequence.
      fill_value: the value to fill the padded sequences when `pad_batch=True`.
      allow_missing_keys: don't raise exception if key is missing.
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code: def __call__(self, data: dict | list):
      d: dict | list
      if len(self.keys) == 1 and self.keys[0] is None:
          # it doesn't support `None` as the key
          d = data
      else:
          if not isinstance(data, dict):
              raise TypeError(
                  "input data is not a dictionary, but specified keys to decollate."
              )
          d = {}
          for key in self.key_iterator(data):
              d[key] = data[key]
  
      return decollate_batch(
          d,
          detach=self.detach,
          pad=self.pad_batch,
          fill_value=self.fill_value,
      )
  
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 Decollated obj's underlying member method code with example usage: obj.__call__(data) and returns: decollate_batch