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 __init__(self, batch_size=1):
      super(BatchedNoopShufflingBuffer, self).__init__(
          batch_size=batch_size
      )
      self._size = 0
      self._buffer = []
      self._done_adding = False
      self._batch_start_idx = 0
  
imports: import abc
  import six
  import torch
label_desc: Use image node_id 1 to create a new BatchedNoopShufflingBuffer object from inherited base classes: BatchedShufflingBufferBase with example: obj = BatchedNoopShufflingBuffer(batch_size)