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: E2E beam search.
  
  :param ndarray x: input acoustic feature (T, D)
  :param Namespace trans_args: argument Namespace containing options
  :param list char_list: list of characters
  :param torch.nn.Module rnnlm: language model module
  :return: N-best decoding results
  :rtype: list
code: def translate(self, x, trans_args, char_list, rnnlm=None):
      """E2E beam search.
  
      :param ndarray x: input acoustic feature (T, D)
      :param Namespace trans_args: argument Namespace containing options
      :param list char_list: list of characters
      :param torch.nn.Module rnnlm: language model module
      :return: N-best decoding results
      :rtype: list
      """
      logging.info("input lengths: " + str(x.shape[0]))
      hs = self.encode(x).unsqueeze(0)
      logging.info("encoder output lengths: " + str(hs.size(1)))
  
      # 2. Decoder
      # decode the first utterance
      y = self.dec.recognize_beam(
          hs[0], None, trans_args, char_list, rnnlm
      )
      return y
  
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 13 for calling the E2E obj's underlying member method code with example usage: obj.translate(x, trans_args, char_list, rnnlm) and returns: y