class_docstr: A wrapper of fairseq data iterator, usage:
  batch_itr = BatchIterator(fairseq_args, fairseq_task)
  for epoch in batch_itr.train_epoch():
      for batch in batch_itr.train_batch():
          train(batch)
          if should_valid():
              for sub in batch_itr.valid_dataset():
                  for sample in batch_itr.valid_batch():
                      valid(sample)
                  pirnt(f"Finish valid {sub}")
      print(f"Finish train epoch {epoch}")
code: def _post_process_batch(self, batch):
      if self._empty_batch(batch) and self._empty_batch(
          self.dummy_batch
      ):
          raise Exception("First batch is empty!")
      if self._empty_batch(batch):
          batch = self.dummy_batch
          is_dummy_batch = True
      else:
          if self._empty_batch(self.dummy_batch):
              self.dummy_batch = batch
          is_dummy_batch = False
      batch = utils.move_to_cuda(batch)
      return batch, is_dummy_batch
  
file_path: lightseq/examples/training/deepspeed/ds_fairseq_data.py
imports: import math
  import logging
  import torch.distributed
  from fairseq import utils
label_desc: Use image node_id 4 for calling the BatchIterator obj's underlying member method code with example usage: obj._post_process_batch(batch) and returns: batch,  is_dummy_batch