class_docstr: E2E module.
  
  :param int idim: dimension of inputs
  :param int odim: dimension of outputs
  :param Namespace args: argument Namespace containing options
class_docstr_tok: ["E2E","module",".",":","param","int","idim",":","dimension","of","inputs",":","param","int","odim",":","dimension","of","outputs",":","param","Namespace","args",":","argument","Namespace","containing","options"]
docstr: Forward decoder and attention loss.
docstr_tok: ["Forward","decoder","and","attention","loss","."]
code: def decoder_and_attention(self, hs_pad, hs_mask, ys_pad, batch_size):
      """Forward decoder and attention loss."""
      # forward decoder
      ys_in_pad, ys_out_pad = add_sos_eos(
          ys_pad, self.sos, self.eos, self.ignore_id
      )
      ys_mask = target_mask(ys_in_pad, self.ignore_id)
      pred_pad, pred_mask = self.decoder(
          ys_in_pad, ys_mask, hs_pad, hs_mask
      )
  
      # compute attention loss
      loss_att = self.criterion(pred_pad, ys_out_pad)
      acc = th_accuracy(
          pred_pad.view(-1, self.odim),
          ys_out_pad,
          ignore_label=self.ignore_id,
      )
      return pred_pad, pred_mask, loss_att, acc
  
imports: import logging
  import math
  from argparse import Namespace
  import numpy
  import torch
  from espnet.nets.asr_interface import ASRInterface
  from espnet.nets.ctc_prefix_score import CTCPrefixScore
  from espnet.nets.e2e_asr_common import end_detect
  from espnet.nets.pytorch_backend.ctc import CTC
  from espnet.nets.pytorch_backend.e2e_asr import CTC_LOSS_THRESHOLD
  from espnet.nets.pytorch_backend.e2e_asr_mix import E2E
  from espnet.nets.pytorch_backend.e2e_asr_mix import PIT
  from espnet.nets.pytorch_backend.e2e_asr_transformer import E2E
  from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask, th_accuracy
  from espnet.nets.pytorch_backend.rnn.decoders import CTC_SCORING_RATIO
  from espnet.nets.pytorch_backend.transformer.add_sos_eos import add_sos_eos
  from espnet.nets.pytorch_backend.transformer.encoder_mix import EncoderMix
  from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask, target_mask
label_desc: Use image node_id 4 for calling the E2E obj's underlying member method code with example usage: obj.decoder_and_attention(hs_pad, hs_mask, ys_pad, batch_size) and returns: pred_pad,  pred_mask,  loss_att,  acc