code: def engine_apply_transform(
      batch: Any, output: Any, transform: Callable[..., dict]
  ) -> tuple[Any, Any]:
      """
      Apply transform on `batch` and `output`.
      If `batch` and `output` are dictionaries, temporarily combine them for the transform,
      otherwise, apply the transform for `output` data only.
  
      """
      if isinstance(batch, dict) and isinstance(output, dict):
          data = dict(batch)
          data.update(output)
          transformed_data = apply_transform(transform, data)
  
          if not isinstance(transformed_data, dict):
              raise AssertionError(
                  "With a dict supplied to apply_transform a single dict return is expected."
              )
  
          for k, v in transformed_data.items():
              # split the output data of post transforms into `output` and `batch`,
              # `batch` should be read-only, so save the generated key-value into `output`
              if k in output or k not in batch:
                  output[k] = v
              else:
                  batch[k] = v
      else:
          output = apply_transform(transform, output)
  
      return batch, output
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from collections.abc import Callable, Sequence
  from typing import TYPE_CHECKING, Any, cast
  import torch
  from monai.config import IgniteInfo
  from monai.transforms import apply_transform
  from monai.utils import ensure_tuple, min_version, optional_import
  from monai.utils.enums import CommonKeys, GanKeys
label_desc: Use image node_id 4 for calling a global function with example usage: engine_apply_transform(batch, output, transform) and returns: batch,  output