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,
      ys_prev_pad: torch.Tensor,
      ys_prev_lens: torch.Tensor,
  ):
      # 0. Prepare input and output with sos, eos, sop
      ys = [y[y != self.ignore_id] for y in ys_pad]
      ys_prev = [y[y != self.ignore_id] for y in ys_prev_pad]
  
      _sos = ys_pad.new([self.sos])
      _eos = ys_pad.new([self.eos])
      _sop = ys_pad.new([self.sop])
      ys_in = []
      ys_in_lens = []
      ys_out = []
      for y_prev, y in zip(ys_prev, ys):
          if self.na in y_prev:
              # Prev is not available in this case
              y_in = [_sos, y]
              y_in_len = len(y) + 1
              y_out = [y, _eos]
          else:
              y_in = [_sop, y_prev, _sos, y]
              y_in_len = len(y_prev) + len(y) + 2
              y_out = [
                  self.ignore_id * ys_pad.new_ones(len(y_prev) + 1),
                  y,
                  _eos,
              ]
  
          ys_in.append(torch.cat(y_in))
          ys_in_lens.append(y_in_len)
          ys_out.append(torch.cat(y_out))
  
      ys_in_pad = pad_list(ys_in, self.eos)
      ys_in_lens = torch.tensor(ys_in_lens).to(ys_pad_lens)
      ys_out_pad = pad_list(ys_out, self.ignore_id)
  
      # 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_out_pad.cpu()
          )
  
      return loss_att, acc_att, cer_att, wer_att
  
imports: import logging
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from torch.cuda.amp import autocast
  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.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 pad_list, th_accuracy
  from espnet.nets.pytorch_backend.transformer.label_smoothing_loss import LabelSmoothingLoss
label_desc: Use image node_id 6 for calling the ESPnetS2TModel obj's underlying member method code with example usage: obj._calc_att_loss(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, ys_prev_pad, ys_prev_lens) and returns: loss_att,  acc_att,  cer_att,  wer_att