code: def decollate_batch(
      batch, detach: bool = True, pad=True, fill_value=None
  ):
      """De-collate a batch of data (for example, as produced by a `DataLoader`).
  
      Returns a list of structures with the original tensor's 0-th dimension sliced into elements using `torch.unbind`.
  
      Images originally stored as (B,C,H,W,[D]) will be returned as (C,H,W,[D]). Other information,
      such as metadata, may have been stored in a list (or a list inside nested dictionaries). In
      this case we return the element of the list corresponding to the batch idx.
  
      Return types aren't guaranteed to be the same as the original, since numpy arrays will have been
      converted to torch.Tensor, sequences may be converted to lists of tensors,
      mappings may be converted into dictionaries.
  
      For example:
  
      .. code-block:: python
  
          batch_data = {
              "image": torch.rand((2,1,10,10)),
              DictPostFix.meta("image"): {"scl_slope": torch.Tensor([0.0, 0.0])}
          }
          out = decollate_batch(batch_data)
          print(len(out))
          >>> 2
  
          print(out[0])
          >>> {'image': tensor([[[4.3549e-01...43e-01]]]), DictPostFix.meta("image"): {'scl_slope': 0.0}}
  
          batch_data = [torch.rand((2,1,10,10)), torch.rand((2,3,5,5))]
          out = decollate_batch(batch_data)
          print(out[0])
          >>> [tensor([[[4.3549e-01...43e-01]]], tensor([[[5.3435e-01...45e-01]]])]
  
          batch_data = torch.rand((2,1,10,10))
          out = decollate_batch(batch_data)
          print(out[0])
          >>> tensor([[[4.3549e-01...43e-01]]])
  
          batch_data = {
              "image": [1, 2, 3], "meta": [4, 5],  # undetermined batch size
          }
          out = decollate_batch(batch_data, pad=True, fill_value=0)
          print(out)
          >>> [{'image': 1, 'meta': 4}, {'image': 2, 'meta': 5}, {'image': 3, 'meta': 0}]
          out = decollate_batch(batch_data, pad=False)
          print(out)
          >>> [{'image': 1, 'meta': 4}, {'image': 2, 'meta': 5}]
  
      Args:
          batch: data to be de-collated.
          detach: whether to detach the tensors. Scalars tensors will be detached into number types
              instead of torch tensors.
          pad: 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: when `pad` is True, the `fillvalue` to use when padding, defaults to `None`.
      """
      if batch is None:
          return batch
      if isinstance(batch, (float, int, str, bytes)) or (
          type(batch).__module__ == "numpy"
          and not isinstance(batch, Iterable)
      ):
          return batch
      if isinstance(batch, torch.Tensor):
          if detach:
              batch = batch.detach()
          if batch.ndim == 0:
              return batch.item() if detach else batch
          out_list = torch.unbind(batch, dim=0)
          # if of type MetaObj, decollate the metadata
          if isinstance(batch, MetaObj):
              for t, m in zip(out_list, decollate_batch(batch.meta)):
                  if isinstance(t, MetaObj):
                      t.meta = m
                      t.is_batch = False
              for t, m in zip(out_list, batch.applied_operations):
                  if isinstance(t, MetaObj):
                      t.applied_operations = m
                      t.is_batch = False
          if out_list[0].ndim == 0 and detach:
              return [t.item() for t in out_list]
          return list(out_list)
  
      b, non_iterable, deco = _non_zipping_check(
          batch, detach, pad, fill_value
      )
      if (
          b <= 0
      ):  # all non-iterable, single item "batch"? {"image": 1, "label": 1}
          return deco
      if pad:  # duplicate non-iterable items to the longest batch
          for k in non_iterable:
              deco[k] = [deepcopy(deco[k]) for _ in range(b)]
      if isinstance(deco, Mapping):
          _gen = (
              zip_longest(*deco.values(), fillvalue=fill_value)
              if pad
              else zip(*deco.values())
          )
          ret = [dict(zip(deco, item)) for item in _gen]
          if not config.USE_META_DICT:
              return ret
          return pickle_operations(ret, is_encode=False)  # bc 0.9.0
      if isinstance(deco, Iterable):
          _gen = (
              zip_longest(*deco, fillvalue=fill_value)
              if pad
              else zip(*deco)
          )
          ret_list = [list(item) for item in _gen]
          if not config.USE_META_DICT:
              return ret_list
          return pickle_operations(
              ret_list, is_encode=False
          )  # bc 0.9.0
      raise NotImplementedError(
          f"Unable to de-collate: {batch}, type: {type(batch)}."
      )
  
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 13 for calling a global function with example usage: decollate_batch(batch, detach, pad, fill_value) and returns: batch, batch, list, deco, pickle_operations, pickle_operations, unknown, unknown, ret, ret_list