code: def forward(self, sample):
      loss, sample_size, logging_output = self.criterion(
          self.model, sample
      )
      return loss, sample_size, logging_output
  
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 2 for calling the DsFairseqModel obj's underlying member method code with example usage: obj.forward(sample) and returns: loss,  sample_size,  logging_output