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])
code: def _check_and_load_tuple(self, samples):
      if isinstance(samples[0], (tuple, list)) and isinstance(
          samples[0][0], str
      ):
          for kv_pair in samples:
              self.add_field(kv_pair[0], kv_pair[1])
          return True
      else:
          return False
  
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 2 for calling the SampleList obj's underlying member method code with example usage: obj._check_and_load_tuple(samples) and returns: True, False