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 retrieve(self):
      batch = []
      cur_batch_size = min(self._size, self.batch_size)
      for v in self._buffer:
          v_batch = v[
              self._batch_start_idx : self._batch_start_idx
              + cur_batch_size
          ]
          batch.append(v_batch)
      # Increase batch start idx with current batch size
      self._batch_start_idx += cur_batch_size
      # Decrease size with current batch size
      self._size -= cur_batch_size
      return batch
  
imports: import abc
  import six
  import torch
label_desc: Use image node_id 3 for calling the BatchedNoopShufflingBuffer obj's underlying member method code with example usage: obj.retrieve() and returns: batch