code: def mt_collate(batch):
      error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
      elem_type = type(batch[0])
      if torch.is_tensor(batch[0]):
          stacked = torch.stack(batch, 0)
          return stacked
      elif (
          elem_type.__module__ == "numpy"
          and elem_type.__name__ != "str_"
          and elem_type.__name__ != "string_"
      ):
          elem = batch[0]
          if elem_type.__name__ == "ndarray":
              # array of string classes and object
              if re.search("[SaUO]", elem.dtype.str) is not None:
                  raise TypeError(error_msg.format(elem.dtype))
              return torch.stack(
                  [torch.from_numpy(b) for b in batch], 0
              )
          if elem.shape == ():  # scalars
              py_type = (
                  float if elem.dtype.name.startswith("float") else int
              )
              return __numpy_type_map[elem.dtype.name](
                  list(map(py_type, batch))
              )
      elif isinstance(batch[0], int_classes):
          return torch.LongTensor(batch)
      elif isinstance(batch[0], float):
          return torch.DoubleTensor(batch)
      elif isinstance(batch[0], string_classes):
          return batch
      elif isinstance(batch[0], collections.Mapping):
          return {
              key: mt_collate([d[key] for d in batch])
              for key in batch[0]
          }
      elif isinstance(batch[0], collections.Sequence):
          transposed = zip(*batch)
          return [mt_collate(samples) for samples in transposed]
  
      return batch
  
imports: import os
  import re
  import collections
  from medicaltorch import transforms
  from tqdm import tqdm
  import numpy
  import nibabel
  from torch.utils.data import Dataset
  import torch
  from torch._six import string_classes, int_classes
  from PIL import Image
label_desc: Use image node_id 1 for calling a global function with example usage: mt_collate(batch) and returns: batch, stacked, torch, __numpy_type_map, torch, torch, batch, unknown, unknown