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()
      src_tokens = sample["net_input"]["src_tokens"]
      src_lens = sample["net_input"]["src_lengths"]
      tgt_lens = sample["target_lengths"]
  
      feat_out, eos_out, extra = model(
          src_tokens=src_tokens,
          src_lengths=src_lens,
          prev_output_tokens=sample["net_input"]["prev_output_tokens"],
          incremental_state=None,
          target_lengths=tgt_lens,
          speaker=sample["speaker"],
      )
  
      l1_loss, mse_loss, eos_loss = self.compute_loss(
          extra["feature_out"],
          feat_out,
          eos_out,
          feat_tgt,
          eos_tgt,
          tgt_lens,
          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"], src_lens, tgt_lens, reduction
          )
      ctc_loss = torch.tensor(0.0).type_as(l1_loss)
      if self.ctc_weight > 0.0:
          net_output = (feat_out, eos_out, extra)
          lprobs = model.get_normalized_probs(
              net_output, log_probs=True
          )
          lprobs = lprobs.transpose(0, 1)  # T x B x C
          src_mask = lengths_to_mask(src_lens)
          src_tokens_flat = src_tokens.masked_select(src_mask)
          ctc_loss = (
              F.ctc_loss(
                  lprobs,
                  src_tokens_flat,
                  tgt_lens,
                  src_lens,
                  reduction=reduction,
                  zero_infinity=True,
              )
              * self.ctc_weight
          )
      loss = l1_loss + mse_loss + eos_loss + attn_loss + ctc_loss
  
      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),
          "ctc_loss": utils.item(ctc_loss.data),
      }
      return loss, sample_size, logging_output
  
imports: import logging
  from dataclasses import dataclass, field
  from functools import lru_cache
  from typing import Any, Dict, List
  import torch
  import torch.nn.functional
  from omegaconf import II
  from fairseq import utils
  from fairseq.logging import metrics
  from fairseq.criterions import FairseqCriterion, register_criterion
  from fairseq.data.data_utils import lengths_to_mask
  from fairseq.dataclass import FairseqDataclass
label_desc: Use image node_id 2 for calling the Tacotron2Criterion obj's underlying member method code with example usage: obj.forward(model, sample, reduction) and returns: loss,  sample_size,  logging_output