text: """only needed post torch 1.10 updates patches improvements that allow us to handle tensordicts
  """
  
  import torch
  import re
  import collections
  from torch._six import string_classes
  
  
  np_str_obj_array_pattern = re.compile(r'[SaUO]')
  
  
  default_collate_err_msg_format = (
      "default_collate: batch must contain tensors, numpy arrays, numbers, "
      "dicts or lists; found {}"
  )
  
  
  def default_collate(batch):  # noqa: C901
      """Function that takes in a batch of data and puts the elements within the batch
      into a tensor with an additional outer dimension - batch size. The exact output type can be
      a :class:`torch.Tensor`, a `Sequence` of :class:`torch.Tensor`, a
      Collection of :class:`torch.Tensor`, or left unchanged, depending on the input type.
      This is used as the default function for collation when
      `batch_size` or `batch_sampler` is defined in :class:`~torch.utils.data.DataLoader`.
  
      Args:
          batch: a single batch to be collated
      Examples:
          >>> from collections import namedtuple
          >>> # Example with a batch of `int`s:
          >>> default_collate([0, 1, 2, 3])
          tensor([0, 1, 2, 3])
          >>> # Example with a batch of `str`s:
          >>> default_collate(['a', 'b', 'c'])
          ['a', 'b', 'c']
          >>> # Example with `Map` inside the batch:
          >>> default_collate([{'A': 0, 'B': 1}, {'A': 100, 'B': 100}])
          {'A': tensor([  0, 100]), 'B': tensor([  1, 100])}
          >>> # Example with `NamedTuple` inside the batch:
          >>> Point = namedtuple('Point', ['x', 'y'])
          >>> default_collate([Point(0, 0), Point(1, 1)])
          Point(x=tensor([0, 1]), y=tensor([0, 1]))
          >>> # Example with `Tuple` inside the batch:
          >>> default_collate([(0, 1), (2, 3)])
          [tensor([0, 2]), tensor([1, 3])]
          >>> # Example with `List` inside the batch:
          >>> default_collate([[0, 1], [2, 3]])
          [tensor([0, 2]), tensor([1, 3])]
      """
      elem = batch[0]
      elem_type = type(elem)
      if isinstance(elem, torch.Tensor):
          out = None
          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)
      elif (
          elem_type.__module__ == 'numpy'
          and elem_type.__name__ != 'str_'
          and elem_type.__name__ != 'string_'
      ):
          if elem_type.__name__ in ['ndarray', 'memmap']:
              # array of string classes and object
              if np_str_obj_array_pattern.search(elem.dtype.str) is not None:
                  raise TypeError(default_collate_err_msg_format.format(elem.dtype))
  
              return default_collate([torch.as_tensor(b) for b in batch])
          elif elem.shape == ():  # scalars
              return torch.as_tensor(batch)
      elif isinstance(elem, float):
          return torch.tensor(batch, dtype=torch.float64)
      elif isinstance(elem, int):
          return torch.tensor(batch)
      elif isinstance(elem, string_classes):
          return batch
      elif isinstance(elem, collections.abc.Mapping):
          return elem_type({key: default_collate([d[key] for d in batch]) for key in elem})
  
      elif isinstance(elem, tuple) and hasattr(elem, '_fields'):  # namedtuple
          return elem_type(*(default_collate(samples) for samples in zip(*batch)))
      elif isinstance(elem, collections.abc.Sequence):
          # check to make sure that the elements in batch have consistent size
          it = iter(batch)
          elem_size = len(next(it))
          if any(len(elem) != elem_size for elem in it):
              raise RuntimeError('each element in list of batch should be of equal size')
          transposed = list(zip(*batch))  # It may be accessed twice, so we use a list.
  
          if isinstance(elem, tuple):
              return [default_collate(samples) for samples in transposed]  # Backwards compatibility.
          try:
              return elem_type([default_collate(samples) for samples in transposed])
          except TypeError:
              # The sequence type may not support `__init__(iterable)` (e.g., `range`).
              return [default_collate(samples) for samples in transposed]
  
      raise TypeError(default_collate_err_msg_format.format(elem_type))
  