code: def forward(self, model, sample, reduce=True):
      net_output = model(**sample["net_input"])
      loss, nll_loss = self.compute_loss(
          model, net_output, sample, reduce=reduce
      )
  
      ctc_loss = torch.tensor(0.0).type_as(loss)
      if self.ctc_weight > 0.0:
          ctc_lprobs, ctc_lens = model.get_ctc_output(
              net_output, sample
          )
          ctc_tgt, ctc_tgt_lens = model.get_ctc_target(sample)
          ctc_tgt_mask = lengths_to_mask(ctc_tgt_lens)
          ctc_tgt_flat = ctc_tgt.masked_select(ctc_tgt_mask)
          reduction = "sum" if reduce else "none"
          ctc_loss = (
              F.ctc_loss(
                  ctc_lprobs,
                  ctc_tgt_flat,
                  ctc_lens,
                  ctc_tgt_lens,
                  reduction=reduction,
                  zero_infinity=True,
              )
              * self.ctc_weight
          )
      loss += ctc_loss
  
      sample_size = (
          sample["target"].size(0)
          if self.sentence_avg
          else sample["ntokens"]
      )
      logging_output = {
          "loss": utils.item(loss.data),
          "nll_loss": utils.item(nll_loss.data),
          "ctc_loss": utils.item(ctc_loss.data),
          "ntokens": sample["ntokens"],
          "nsentences": sample["target"].size(0),
          "sample_size": sample_size,
      }
      if self.report_accuracy:
          n_correct, total = self.compute_accuracy(
              model, net_output, sample
          )
          logging_output["n_correct"] = utils.item(n_correct.data)
          logging_output["total"] = utils.item(total.data)
      return loss, sample_size, logging_output
  
file_path: fairseq/fairseq/criterions/label_smoothed_cross_entropy_with_ctc.py
imports: import math
  from dataclasses import dataclass, field
  import torch
  import torch.nn.functional
  from fairseq import utils
  from fairseq.logging import metrics
  from fairseq.criterions import register_criterion
  from fairseq.criterions.label_smoothed_cross_entropy import LabelSmoothedCrossEntropyCriterion, LabelSmoothedCrossEntropyCriterionConfig
  from fairseq.data.data_utils import lengths_to_mask
label_desc: Use image node_id 2 for calling the LabelSmoothedCrossEntropyWithCtcCriterion obj's underlying member method code with example usage: obj.forward(model, sample, reduce) and returns: loss,  sample_size,  logging_output