code: def train_step(self, sample, is_dummy_batch):
      self.model.train()
      self.model.zero_grad()
  
      loss, sample_size, logging_output = self.model(sample)
  
      if is_dummy_batch:
          if torch.is_tensor(sample_size):
              sample_size.zero_()
          else:
              sample_size *= 0.0
          loss *= 0.0
      if torch.is_tensor(sample_size):
          sample_size = sample_size.float()
      else:
          sample_size = float(sample_size)
  
      logging_outputs, (sample_size,) = torch_reduce_sum(
          self.model.device,
          [logging_output],
          sample_size,
          ignore=is_dummy_batch,
      )
  
      final_loss = loss * (dist.get_world_size() / sample_size)
      self.model.backward(final_loss)
      self.model.step()
  
      logging_output = self.reduce_log(logging_outputs, sample_size)
  
      if self.model.global_steps % self.model.steps_per_print() != 0:
          return
  
      log_dist(
          f'Step: {self.model.global_steps}, \
              {view_log(metrics.get_smoothed_values("train_inner"))}',
          [0],
      )
      metrics.reset_meters("train_inner")
  
imports: import math
  import torch
  from torch import nn
  import torch.distributed
  import deepspeed
  from deepspeed.utils import log_dist
  from fairseq import tasks, distributed_utils
  from fairseq.logging import metrics
  from ds_fairseq_data import BatchIterator
  from ds_fairseq_argument import gen_ds_fairseq_arg
label_desc: Use image node_id 4 for calling the DsFairseqTrainer obj's underlying member method code with example usage: obj.train_step(sample, is_dummy_batch) without return types