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: Recognize input speech of each speaker.
  
  :param ndnarray x: input acoustic feature (B, T, D) or (T, D)
  :param Namespace recog_args: argment Namespace contraining options
  :param list char_list: list of characters
  :param torch.nn.Module rnnlm: language model module
  :return: N-best decoding results
  :rtype: list
docstr_tok: ["Recognize","input","speech","of","each","speaker",".",":","param","ndnarray","x",":","input","acoustic","feature","(","B",",","T",",","D",")","or","(","T",",","D",")",":","param","Namespace","recog_args",":","argment","Namespace","contraining","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, recog_args, char_list=None, rnnlm=None, use_jit=False
  ):
      """Recognize input speech of each speaker.
  
      :param ndnarray x: input acoustic feature (B, T, D) or (T, D)
      :param Namespace recog_args: argment Namespace contraining options
      :param list char_list: list of characters
      :param torch.nn.Module rnnlm: language model module
      :return: N-best decoding results
      :rtype: list
      """
      # Encoder
      enc_output = self.encode(x)
  
      # Decoder
      nbest_hyps = []
      for enc_out in enc_output:
          nbest_hyps.append(
              self.recog(enc_out, recog_args, char_list, rnnlm, use_jit)
          )
      return nbest_hyps
  
imports: import logging
  import math
  from argparse import Namespace
  import numpy
  import torch
  from espnet.nets.asr_interface import ASRInterface
  from espnet.nets.ctc_prefix_score import CTCPrefixScore
  from espnet.nets.e2e_asr_common import end_detect
  from espnet.nets.pytorch_backend.ctc import CTC
  from espnet.nets.pytorch_backend.e2e_asr import CTC_LOSS_THRESHOLD
  from espnet.nets.pytorch_backend.e2e_asr_mix import E2E
  from espnet.nets.pytorch_backend.e2e_asr_mix import PIT
  from espnet.nets.pytorch_backend.e2e_asr_transformer import E2E
  from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask, th_accuracy
  from espnet.nets.pytorch_backend.rnn.decoders import CTC_SCORING_RATIO
  from espnet.nets.pytorch_backend.transformer.add_sos_eos import add_sos_eos
  from espnet.nets.pytorch_backend.transformer.encoder_mix import EncoderMix
  from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask, target_mask
label_desc: Use image node_id 7 for calling the E2E obj's underlying member method code with example usage: obj.recognize(x, recog_args, char_list, rnnlm, use_jit) and returns: nbest_hyps