code: def train_step(
      self,
      sample,
      model,
      criterion,
      optimizer,
      update_num,
      ignore_grad=False,
  ):
      model.train()
      model.set_num_updates(update_num)
      # task ids
      if self.task_type == "lang":
          encoder_task_ids = sample["src_lang_ids"]
          decoder_task_ids = sample["tgt_lang_ids"]
      elif self.task_type == "domain":
          encoder_task_ids = sample["domain_ids"]
          decoder_task_ids = sample["domain_ids"]
      model.encoder.set_task_ids(encoder_task_ids)
      model.decoder.set_task_ids(decoder_task_ids)
  
      with torch.autograd.profiler.record_function("forward"):
          with torch.cuda.amp.autocast(
              enabled=(isinstance(optimizer, AMPOptimizer))
          ):
              loss, sample_size, logging_output = criterion(
                  model, sample
              )
              # KL loss
              if self.args.encoder_attn_head_select:
                  sample_sizes = self.get_sample_sizes(
                      sample, encoder_task_ids, self.encoder_tasks
                  )
                  loss += self.kl_loss(
                      model.encoder.attn_head_selector.head_samples,
                      sample_sizes,
                      self.encoder_head_prior,
                  )
              if self.args.decoder_self_attn_head_select:
                  sample_sizes = self.get_sample_sizes(
                      sample, decoder_task_ids, self.decoder_tasks
                  )
                  loss += self.kl_loss(
                      model.decoder.self_attn_head_selector.head_samples,
                      sample_sizes,
                      self.decoder_head_prior,
                  )
              if self.args.dec_enc_attn_head_select:
                  sample_sizes = self.get_sample_sizes(
                      sample, decoder_task_ids, self.decoder_tasks
                  )
                  loss += self.kl_loss(
                      model.decoder.enc_attn_head_selector.head_sampes,
                      sample_sizes,
                      self.decoder_head_prior,
                  )
  
      if ignore_grad:
          loss *= 0
      with torch.autograd.profiler.record_function("backward"):
          optimizer.backward(loss)
      return loss, sample_size, logging_output
  
file_path: fairseq/examples/attention_head_selection/src/speech_to_text_head_selection.py
imports: import torch
  from fairseq.optim.amp_optimizer import AMPOptimizer
  from fairseq.tasks import register_task
  from fairseq.tasks.speech_to_text import SpeechToTextTask
  from .data.speech_to_text_dataset_with_domain import SpeechToTextDatasetCreatorWithDomain
  from .loss.attention_head_selection import HeadSelectionLoss
label_desc: Use image node_id 7 for calling the SpeechToTextHeadSelectionTask obj's underlying member method code with example usage: obj.train_step(sample, model, criterion, optimizer, update_num, ignore_grad) and returns: loss,  sample_size,  logging_output