code: def valid_step(self, batch_itr):
      if (
          self.model.global_steps
          % self.fs_args.validate_interval_updates
          != 0
      ):
          return
      with torch.no_grad():
          self.model.eval()
          for subset in batch_itr.valid_dataset():
              with metrics.aggregate(new_root=True) as agg:
                  for batch, is_dummy_batch in batch_itr.valid_batch():
                      (
                          _,
                          sample_size,
                          logging_output,
                      ) = self.task.valid_step(
                          batch,
                          self.model.module.model,
                          self.model.module.criterion,
                      )
                      logging_outputs = [logging_output]
                      if is_dummy_batch:
                          if torch.is_tensor(sample_size):
                              sample_size.zero_()
                          else:
                              sample_size *= 0.0
                      logging_outputs, (
                          sample_size,
                      ) = torch_reduce_sum(
                          self.model.device,
                          logging_outputs,
                          sample_size,
                          ignore=is_dummy_batch,
                      )
                      logging_output = self.reduce_log(
                          logging_outputs, sample_size
                      )
              log_dist(
                  "Valid on step: {}, dataset: {}. {}".format(
                      self.model.global_steps,
                      subset,
                      view_log(agg.get_smoothed_values()),
                  ),
                  ranks=[0],
              )
  
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 5 for calling the DsFairseqTrainer obj's underlying member method code with example usage: obj.valid_step(batch_itr) without return types