class_docstr: A 'no-operation' (noop) implementation of a shuffling buffer. Useful in cases where no shuffling is desired, such
  as test scenarios or iterating over a dataset in a predeterministic order.
code: def _add_many(self, items):
      self._size += len(items[0])
      if len(self._buffer) == 0:
          self._buffer = items
      else:
          # Merge with previous rowgroup leftover
          for i, v in enumerate(items):
              self._buffer[i] = torch.cat(
                  [self._buffer[i][self._batch_start_idx :], v], 0
              )
      # Batch start idx starts from 0 for a fresh rowgroup.
      self._batch_start_idx = 0
  
imports: import abc
  import six
  import torch
label_desc: Use image node_id 2 for calling the BatchedNoopShufflingBuffer obj's underlying member method code with example usage: obj._add_many(items) without return types