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: Scorers.
code: def scorers(self):
      """Scorers."""
      return dict(decoder=self.dec)
  
imports: import argparse
  import copy
  import logging
  import math
  import os
  from itertools import groupby
  import chainer
  import nltk
  import numpy
  import torch
  from chainer import reporter
  from espnet.nets.e2e_asr_common import label_smoothing_dist
  from espnet.nets.pytorch_backend.ctc import CTC
  from espnet.nets.pytorch_backend.initialization import lecun_normal_init_parameters, set_forget_bias_to_one
  from espnet.nets.pytorch_backend.nets_utils import get_subsample, pad_list, to_device, to_torch_tensor
  from espnet.nets.pytorch_backend.rnn.argument import add_arguments_rnn_attention_common, add_arguments_rnn_decoder_common, add_arguments_rnn_encoder_common
  from espnet.nets.pytorch_backend.rnn.attentions import att_for
  from espnet.nets.pytorch_backend.rnn.decoders import decoder_for
  from espnet.nets.pytorch_backend.rnn.encoders import encoder_for
  from espnet.nets.st_interface import STInterface
  from espnet.utils.fill_missing_args import fill_missing_args
label_desc: Use image node_id 11 for calling the E2E obj's underlying member method code with example usage: obj.scorers() and returns: dict