code: def mod_default_collate(batch):
      """Makes a tensor from list of batch values.
  
      Note that this doesn't need to zip(*) values together
      as PaddedBatch connects them already (by key).
  
      Here the idea is not to error out.
  
      This is modified from:
      https://github.com/pytorch/pytorch/blob/c0deb231db76dbea8a9d326401417f7d1ce96ed5/torch/utils/data/_utils/collate.py#L42
      """
      elem = batch[0]
      elem_type = type(elem)
      if isinstance(elem, torch.Tensor):
          out = None
          try:
              if torch.utils.data.get_worker_info() is not None:
                  # If we're in a background process, concatenate directly into a
                  # shared memory tensor to avoid an extra copy
                  numel = sum([x.numel() for x in batch])
                  storage = elem.storage()._new_shared(numel)
                  out = elem.new(storage)
              return torch.stack(batch, 0, out=out)
          except RuntimeError:  # Unequal size:
              return batch
      elif (
          elem_type.__module__ == "numpy"
          and elem_type.__name__ != "str_"
          and elem_type.__name__ != "string_"
      ):
          try:
              if (
                  elem_type.__name__ == "ndarray"
                  or elem_type.__name__ == "memmap"
              ):
                  # array of string classes and object
                  if (
                      np_str_obj_array_pattern.search(elem.dtype.str)
                      is not None
                  ):
                      return batch
                  return mod_default_collate(
                      [torch.as_tensor(b) for b in batch]
                  )
              elif elem.shape == ():  # scalars
                  return torch.as_tensor(batch)
          except RuntimeError:  # Unequal size
              return batch
      elif isinstance(elem, float):
          return torch.tensor(batch, dtype=torch.float64)
      elif isinstance(elem, int):
          return torch.tensor(batch)
      else:
          return batch
  
imports: import math
  import os
  import re
  import csv
  import shutil
  import urllib.request
  import collections.abc
  import torch
  import tqdm
  import pathlib
  import speechbrain
  from numbers import Number
label_desc: Use image node_id 13 for calling a global function with example usage: mod_default_collate(batch) and returns: torch, batch, torch, mod_default_collate, batch, torch, batch, batch, torch