code: def valid_step(self, sample, model, criterion):
      loss, sample_size, logging_output = super().valid_step(
          sample, model, criterion
      )
  
      if getattr(self.args, "eval_inference", False):
          hypos, inference_losses = self.valid_step_with_inference(
              sample, model, self.generator
          )
          for k, v in inference_losses.items():
              assert k not in logging_output
              logging_output[k] = v
  
          picked_id = 0
          if self.tensorboard_dir and (sample["id"] == picked_id).any():
              self.log_tensorboard(
                  sample,
                  hypos[: self.args.eval_tb_nsample],
                  model._num_updates,
                  is_na_model=getattr(
                      model, "NON_AUTOREGRESSIVE", False
                  ),
              )
      return loss, sample_size, logging_output
  
imports: import logging
  import os
  import os.path
  import torch
  import torch.nn.functional
  import numpy
  from fairseq.data.audio.text_to_speech_dataset import TextToSpeechDatasetCreator
  from fairseq.tasks import register_task
  from fairseq.tasks.speech_to_text import SpeechToTextTask
  from fairseq.speech_generator import AutoRegressiveSpeechGenerator, NonAutoregressiveSpeechGenerator, TeacherForcingAutoRegressiveSpeechGenerator
label_desc: Use image node_id 11 for calling the TextToSpeechTask obj's underlying member method code with example usage: obj.valid_step(sample, model, criterion) and returns: loss,  sample_size,  logging_output