code: def forward(self, model, sample, reduction="mean"):
      bsz, max_len, _ = sample["target"].size()
      feat_tgt = sample["target"]
      feat_len = (
          sample["target_lengths"].view(bsz, 1).expand(-1, max_len)
      )
      eos_tgt = torch.arange(max_len).to(sample["target"].device)
      eos_tgt = eos_tgt.view(1, max_len).expand(bsz, -1)
      eos_tgt = (eos_tgt == (feat_len - 1)).float()
  
      feat_out, eos_out, extra = model(
          src_tokens=sample["net_input"]["src_tokens"],
          src_lengths=sample["net_input"]["src_lengths"],
          prev_output_tokens=sample["net_input"]["prev_output_tokens"],
          prev_output_tokens_mt=sample["multitask"][model.mt_task_name][
              "net_input"
          ]["prev_output_tokens"],
          tgt_speaker=sample["net_input"]["tgt_speaker"],
          target_lengths=sample["target_lengths"],
          return_all_hiddens=True,
      )
  
      l1_loss, mse_loss, eos_loss = self.compute_loss(
          extra["feature_out"],
          feat_out,
          eos_out,
          feat_tgt,
          eos_tgt,
          sample["target_lengths"],
          reduction,
      )
      attn_loss = torch.tensor(0.0).type_as(l1_loss)
      if self.guided_attn is not None:
          attn_loss = self.guided_attn(
              extra["attn"],
              sample["net_input"]["src_lengths"],
              sample["target_lengths"],
              reduction,
          )
      loss = (
          l1_loss + mse_loss + eos_loss + attn_loss
      )  # do not include ctc loss as there's no text target
  
      sample_size = (
          sample["nsentences"]
          if self.sentence_avg
          else sample["ntokens"]
      )
      logging_output = {
          "loss": utils.item(loss.data),
          "ntokens": sample["ntokens"],
          "nsentences": sample["nsentences"],
          "sample_size": sample_size,
          "l1_loss": utils.item(l1_loss.data),
          "mse_loss": utils.item(mse_loss.data),
          "eos_loss": utils.item(eos_loss.data),
          "attn_loss": utils.item(attn_loss.data),
      }
  
      if len(self.multitask_criterion) == 0:
          return loss, sample_size, logging_output
  
      # multitask
      multitask_loss, multitask_log = self.get_multitask_loss(
          model, sample, extra
      )
      loss += multitask_loss
      logging_output["multitask"] = multitask_log
      return loss, sample_size, logging_output
  
imports: import logging
  import math
  from collections import OrderedDict
  import torch
  from fairseq import utils
  from fairseq.logging import metrics
  from fairseq.criterions import register_criterion
  from fairseq.criterions.ctc import CtcCriterion
  from fairseq.criterions.label_smoothed_cross_entropy_with_rdrop import RdropLabelSmoothedCrossEntropyCriterion, RdropLabelSmoothedCrossEntropyCriterionConfig, duplicate_input
  from fairseq.criterions.tacotron2_loss import Tacotron2Criterion, Tacotron2CriterionConfig
label_desc: Use image node_id 2 for calling the SpeechToSpectrogram2passMultitaskTaskCriterion obj's underlying member method code with example usage: obj.forward(model, sample, reduction) and returns: loss,  sample_size,  logging_output, loss,  sample_size,  logging_output