code: def list_data_collate(batch: Sequence):
      """
      Enhancement for PyTorch DataLoader default collate.
      If dataset already returns a list of batch data that generated in transforms, need to merge all data to 1 list.
      Then it's same as the default collate behavior.
  
      Note:
          Need to use this collate if apply some transforms that can generate batch data.
  
      """
  
      if pytorch_after(1, 13):
          # needs to go here to avoid circular import
          from monai.data.meta_tensor import MetaTensor
  
          default_collate_fn_map.update(
              {MetaTensor: collate_meta_tensor_fn}
          )
      elem = batch[0]
      data = (
          [i for k in batch for i in k]
          if isinstance(elem, list)
          else batch
      )
      key = None
      collate_fn = (
          default_collate
          if pytorch_after(1, 13)
          else collate_meta_tensor
      )
      try:
          if config.USE_META_DICT:
              data = pickle_operations(data)  # bc 0.9.0
          if isinstance(elem, Mapping):
              ret = {}
              for k in elem:
                  key = k
                  data_for_batch = [d[key] for d in data]
                  ret[key] = collate_fn(data_for_batch)
          else:
              ret = collate_fn(data)
          return ret
      except RuntimeError as re:
          re_str = str(re)
          if "equal size" in re_str:
              if key is not None:
                  re_str += f"\nCollate error on the key '{key}' of dictionary data."
              re_str += (
                  "\n\nMONAI hint: if your transforms intentionally create images of different shapes, creating your "
                  + "`DataLoader` with `collate_fn=pad_list_data_collate` might solve this problem (check its "
                  + "documentation)."
              )
          _ = dev_collate(data)
          raise RuntimeError(re_str) from re
      except TypeError as re:
          re_str = str(re)
          if "numpy" in re_str and "Tensor" in re_str:
              if key is not None:
                  re_str += f"\nCollate error on the key '{key}' of dictionary data."
              re_str += (
                  "\n\nMONAI hint: if your transforms intentionally create mixtures of torch Tensor and numpy ndarray, "
                  + "creating your `DataLoader` with `collate_fn=pad_list_data_collate` might solve this problem "
                  + "(check its documentation)."
              )
          _ = dev_collate(data)
          raise TypeError(re_str) from re
  
imports: from __future__ import annotations
  import hashlib
  import json
  import logging
  import math
  import os
  import pickle
  import sys
  from collections import abc, defaultdict
  from collections.abc import Generator, Iterable, Mapping, Sequence, Sized
  from copy import deepcopy
  from functools import reduce
  from itertools import product, starmap, zip_longest
  from pathlib import PurePath
  from typing import Any
  import numpy
  import torch
  from torch.utils.data._utils.collate import default_collate
  from monai import config
  from monai.config.type_definitions import NdarrayOrTensor, NdarrayTensor, PathLike
  from monai.data.meta_obj import MetaObj
  from monai.utils import MAX_SEED, BlendMode, Method, NumpyPadMode, TraceKeys, convert_data_type, convert_to_dst_type, ensure_tuple, ensure_tuple_rep, ensure_tuple_size, fall_back_tuple, first, get_equivalent_dtype, issequenceiterable, look_up_option, optional_import, pytorch_after
label_desc: Use image node_id 11 for calling a global function with example usage: list_data_collate(batch) and returns: ret