class_docstr: CTC-attention hybrid Encoder-Decoder model
class_docstr_tok: ["CTC-attention","hybrid","Encoder-Decoder","model"]
code: def _calc_att_loss(
      self,
      encoder_out: torch.Tensor,
      encoder_out_lens: torch.Tensor,
      ys_pad: torch.Tensor,
      ys_pad_lens: torch.Tensor,
  ):
      if (
          hasattr(self, "lang_token_id")
          and self.lang_token_id is not None
      ):
          ys_pad = torch.cat(
              [
                  self.lang_token_id.repeat(ys_pad.size(0), 1).to(
                      ys_pad.device
                  ),
                  ys_pad,
              ],
              dim=1,
          )
          ys_pad_lens += 1
  
      ys_in_pad, ys_out_pad = add_sos_eos(
          ys_pad, self.sos, self.eos, self.ignore_id
      )
      ys_in_lens = ys_pad_lens + 1
  
      # 1. Forward decoder
      decoder_out, _ = self.decoder(
          encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens
      )
  
      # 2. Compute attention loss
      loss_att = self.criterion_att(decoder_out, ys_out_pad)
      acc_att = th_accuracy(
          decoder_out.view(-1, self.vocab_size),
          ys_out_pad,
          ignore_label=self.ignore_id,
      )
  
      # Compute cer/wer using attention-decoder
      if self.training or self.error_calculator is None:
          cer_att, wer_att = None, None
      else:
          ys_hat = decoder_out.argmax(dim=-1)
          cer_att, wer_att = self.error_calculator(
              ys_hat.cpu(), ys_pad.cpu()
          )
  
      return loss_att, acc_att, cer_att, wer_att
  
imports: import logging
  from contextlib import contextmanager
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from packaging.version import parse
  from typeguard import check_argument_types
  from espnet2.asr.ctc import CTC
  from espnet2.asr.decoder.abs_decoder import AbsDecoder
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet2.asr.frontend.abs_frontend import AbsFrontend
  from espnet2.asr.postencoder.abs_postencoder import AbsPostEncoder
  from espnet2.asr.preencoder.abs_preencoder import AbsPreEncoder
  from espnet2.asr.specaug.abs_specaug import AbsSpecAug
  from espnet2.asr.transducer.error_calculator import ErrorCalculatorTransducer
  from espnet2.asr_transducer.utils import get_transducer_task_io
  from espnet2.layers.abs_normalize import AbsNormalize
  from espnet2.torch_utils.device_funcs import force_gatherable
  from espnet2.train.abs_espnet_model import AbsESPnetModel
  from espnet.nets.e2e_asr_common import ErrorCalculator
  from espnet.nets.pytorch_backend.nets_utils import th_accuracy
  from espnet.nets.pytorch_backend.transformer.add_sos_eos import add_sos_eos
  from espnet.nets.pytorch_backend.transformer.label_smoothing_loss import LabelSmoothingLoss
label_desc: Use image node_id 8 for calling the ESPnetASRModel obj's underlying member method code with example usage: obj._calc_att_loss(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens) and returns: loss_att,  acc_att,  cer_att,  wer_att