class_docstr: ``SampleList`` is used to collate a list of ``Sample`` into a batch during batch
  preparation. It can be thought of as a merger of list of Dicts into a single Dict.
  
  If ``Sample`` contains an attribute 'text' of size (2) and there are 10 samples in
  list, the returned ``SampleList`` will have an attribute 'text' which is a tensor
  of size (10, 2).
  
  Args:
      samples (type): List of ``Sample`` from which the ``SampleList``
                      will be created.
  
  Usage::
  
      >>> sample_list = [
              Sample({"text": torch.tensor(2)}),
              Sample({"text": torch.tensor(2)})
          ]
      >>> sample_list.text
      torch.tensor([2, 2])
docstr: Get batch size of the current ``SampleList``. There must be a tensor
  be a tensor present inside sample list to use this function.
  Returns:
      int: Size of the batch in ``SampleList``.
code: def get_batch_size(self):
      """Get batch size of the current ``SampleList``. There must be a tensor
      be a tensor present inside sample list to use this function.
      Returns:
          int: Size of the batch in ``SampleList``.
  
      """
      tensor_field = self._get_tensor_field()
      assert (
          tensor_field is not None
      ), "There is no tensor yet in SampleList"
  
      return self[tensor_field].size(0)
  
imports: import collections
  import collections.abc
  import warnings
  from collections import OrderedDict
  from typing import Any, Dict, Union
  import torch
label_desc: Use image node_id 16 for calling the SampleList obj's underlying member method code with example usage: obj.get_batch_size() and returns: self