class_docstr: E2E module.
  
  :param List idims: List of dimensions of inputs
  :param int odim: dimension of outputs
  :param Namespace args: argument Namespace containing options
docstr: E2E beam search.
  
  :param list of ndarray x: list of input acoustic feature [(T1, D), (T2,D),...]
  :param Namespace recog_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 recognize(self, x_list, recog_args, char_list, rnnlm=None):
      """E2E beam search.
  
      :param list of ndarray x: list of input acoustic feature [(T1, D), (T2,D),...]
      :param Namespace recog_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
      """
      hs_list = self.encode(x_list)
      # calculate log P(z_t|X) for CTC scores
      if recog_args.ctc_weight > 0.0:
          if self.share_ctc:
              lpz_list = [
                  self.ctc[0].log_softmax(hs_list[idx].unsqueeze(0))[0]
                  for idx in range(self.num_encs)
              ]
          else:
              lpz_list = [
                  self.ctc[idx].log_softmax(hs_list[idx].unsqueeze(0))[
                      0
                  ]
                  for idx in range(self.num_encs)
              ]
      else:
          lpz_list = None
  
      # 2. Decoder
      # decode the first utterance
      y = self.dec.recognize_beam(
          hs_list, lpz_list, recog_args, char_list, rnnlm
      )
      return y
  
imports: import argparse
  import logging
  import math
  import os
  from itertools import groupby
  import chainer
  import numpy
  import torch
  from chainer import reporter
  from espnet.nets.asr_interface import ASRInterface
  from espnet.nets.e2e_asr_common import label_smoothing_dist
  from espnet.nets.pytorch_backend.ctc import ctc_for
  from espnet.nets.pytorch_backend.nets_utils import get_subsample, pad_list, to_device, to_torch_tensor
  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, encoder_for
  from espnet.nets.scorers.ctc import CTCPrefixScorer
  from espnet.utils.cli_utils import strtobool
label_desc: Use image node_id 12 for calling the E2E obj's underlying member method code with example usage: obj.recognize(x_list, recog_args, char_list, rnnlm) and returns: y