class_docstr: E2E module for chainer backend.
  
  Args:
      idim (int): Dimension of the inputs.
      odim (int): Dimension of the outputs.
      args (parser.args): Training config.
      flag_return (bool): If True, train() would return
          additional metrics in addition to the training
          loss.
docstr: E2E greedy/beam search.
  
  Args:
      x (chainer.Variable): Input tensor for recognition.
      recog_args (parser.args): Arguments of config file.
      char_list (List[str]): List of Characters.
      rnnlm (Module): RNNLM module defined at `espnet.lm.chainer_backend.lm`.
  
  Returns:
      List[Dict[str, Any]]: Result of recognition.
docstr_tok: ["E2E","greedy\/beam","search",".","Args",":","x","(","chainer.Variable",")",":","Input","tensor","for","recognition",".","recog_args","(","parser.args",")",":","Arguments","of","config","file",".","char_list","(","List","[","str","]",")",":","List","of","Characters",".","rnnlm","(","Module",")",":","RNNLM","module","defined","at","`","espnet.lm.chainer_backend.lm","`",".","Returns",":","List","[","Dict","[","str",",","Any","]","]",":","Result","of","recognition","."]
code: def recognize(self, x, recog_args, char_list, rnnlm=None):
      """E2E greedy/beam search.
  
      Args:
          x (chainer.Variable): Input tensor for recognition.
          recog_args (parser.args): Arguments of config file.
          char_list (List[str]): List of Characters.
          rnnlm (Module): RNNLM module defined at `espnet.lm.chainer_backend.lm`.
  
      Returns:
          List[Dict[str, Any]]: Result of recognition.
  
      """
      # subsample frame
      x = x[:: self.subsample[0], :]
      ilen = self.xp.array(x.shape[0], dtype=np.int32)
      h = chainer.Variable(self.xp.array(x, dtype=np.float32))
  
      with chainer.no_backprop_mode(), chainer.using_config(
          "train", False
      ):
          # 1. encoder
          # make a utt list (1) to use the same interface for encoder
          h, _ = self.enc([h], [ilen])
  
          # calculate log P(z_t|X) for CTC scores
          if recog_args.ctc_weight > 0.0:
              lpz = self.ctc.log_softmax(h).data[0]
          else:
              lpz = None
  
          # 2. decoder
          # decode the first utterance
          y = self.dec.recognize_beam(
              h[0], lpz, recog_args, char_list, rnnlm
          )
  
          return y
  
imports: import logging
  import math
  import chainer
  import numpy
  from chainer import reporter
  from espnet.nets.chainer_backend.asr_interface import ChainerASRInterface
  from espnet.nets.chainer_backend.ctc import ctc_for
  from espnet.nets.chainer_backend.rnn.attentions import att_for
  from espnet.nets.chainer_backend.rnn.decoders import decoder_for
  from espnet.nets.chainer_backend.rnn.encoders import encoder_for
  from espnet.nets.e2e_asr_common import label_smoothing_dist
  from espnet.nets.pytorch_backend.e2e_asr import E2E
  from espnet.nets.pytorch_backend.nets_utils import get_subsample
label_desc: Use image node_id 5 for calling the E2E obj's underlying member method code with example usage: obj.recognize(x, recog_args, char_list, rnnlm) and returns: y