class_docstr: E2E module.
  
  Args:
      idim (int): Input dimmensions.
      odim (int): Output dimmensions.
      args (Namespace): Training config.
      ignore_id (int, optional): Id for ignoring a character.
      flag_return (bool, optional): If true, return a list with (loss,
      loss_ctc, loss_att, acc) in forward. Otherwise, return loss.
class_docstr_tok: ["E2E","module",".","Args",":","idim","(","int",")",":","Input","dimmensions",".","odim","(","int",")",":","Output","dimmensions",".","args","(","Namespace",")",":","Training","config",".","ignore_id","(","int",",","optional",")",":","Id","for","ignoring","a","character",".","flag_return","(","bool",",","optional",")",":","If","true",",","return","a","list","with","(","loss",",","loss_ctc",",","loss_att",",","acc",")","in","forward",".","Otherwise",",","return","loss","."]
docstr: E2E recognition function.
  
  Args:
      x (ndarray): Input acouctic feature (B, T, D) or (T, D).
      recog_args (Namespace): Argment namespace contraining options.
      char_list (List[str]): List of characters.
      rnnlm (chainer.Chain): Language model module defined at
      `espnet.lm.chainer_backend.lm`.
  
  Returns:
      List: N-best decoding results.
docstr_tok: ["E2E","recognition","function",".","Args",":","x","(","ndarray",")",":","Input","acouctic","feature","(","B",",","T",",","D",")","or","(","T",",","D",")",".","recog_args","(","Namespace",")",":","Argment","namespace","contraining","options",".","char_list","(","List","[","str","]",")",":","List","of","characters",".","rnnlm","(","chainer.Chain",")",":","Language","model","module","defined","at","`","espnet.lm.chainer_backend.lm","`",".","Returns",":","List",":","N-best","decoding","results","."]
code: def recognize(self, x_block, recog_args, char_list=None, rnnlm=None):
      """E2E recognition function.
  
      Args:
          x (ndarray): Input acouctic feature (B, T, D) or (T, D).
          recog_args (Namespace): Argment namespace contraining options.
          char_list (List[str]): List of characters.
          rnnlm (chainer.Chain): Language model module defined at
          `espnet.lm.chainer_backend.lm`.
  
      Returns:
          List: N-best decoding results.
  
      """
      with chainer.no_backprop_mode(), chainer.using_config(
          "train", False
      ):
          # 1. encoder
          ilens = [x_block.shape[0]]
          batch = len(ilens)
          xs, _, _ = self.encoder(x_block[None, :, :], ilens)
  
          # calculate log P(z_t|X) for CTC scores
          if recog_args.ctc_weight > 0.0:
              lpz = self.ctc.log_softmax(
                  xs.reshape(batch, -1, self.dims)
              ).data[0]
          else:
              lpz = None
          # 2. decoder
          if recog_args.lm_weight == 0.0:
              rnnlm = None
          y = self.recognize_beam(xs, lpz, recog_args, char_list, rnnlm)
  
      return y
  
imports: import logging
  import math
  from argparse import Namespace
  from distutils.util import strtobool
  import chainer
  import chainer.functions
  import numpy
  from chainer import reporter
  from espnet.nets.chainer_backend.asr_interface import ChainerASRInterface
  from espnet.nets.chainer_backend.transformer import ctc
  from espnet.nets.chainer_backend.transformer.attention import MultiHeadAttention
  from espnet.nets.chainer_backend.transformer.decoder import Decoder
  from espnet.nets.chainer_backend.transformer.encoder import Encoder
  from espnet.nets.chainer_backend.transformer.label_smoothing_loss import LabelSmoothingLoss
  from espnet.nets.chainer_backend.transformer.training import CustomConverter, CustomParallelUpdater, CustomUpdater
  from espnet.nets.ctc_prefix_score import CTCPrefixScore
  from espnet.nets.e2e_asr_common import ErrorCalculator, end_detect
  from espnet.nets.pytorch_backend.nets_utils import get_subsample
  from espnet.nets.pytorch_backend.transformer.plot import PlotAttentionReport
label_desc: Use image node_id 7 for calling the E2E obj's underlying member method code with example usage: obj.recognize(x_block, recog_args, char_list, rnnlm) and returns: y