class_docstr: E2E module for Transducer models.
  
  Args:
      idim: Dimension of inputs.
      odim: Dimension of outputs.
      args: Namespace containing model options.
      ignore_id: Padding symbol ID.
      blank_id: Blank symbol ID.
      training: Whether the model is initialized in training or inference mode.
class_docstr_tok: ["E2E","module","for","Transducer","models",".","Args",":","idim",":","Dimension","of","inputs",".","odim",":","Dimension","of","outputs",".","args",":","Namespace","containing","model","options",".","ignore_id",":","Padding","symbol","ID",".","blank_id",":","Blank","symbol","ID",".","training",":","Whether","the","model","is","initialized","in","training","or","inference","mode","."]
docstr: Recognize input features.
  
  Args:
      feats: Feature sequence. (F, D_feats)
      beam_search: Beam search class.
  
  Returns:
      nbest_hyps: N-best decoding results.
docstr_tok: ["Recognize","input","features",".","Args",":","feats",":","Feature","sequence",".","(","F",",","D_feats",")","beam_search",":","Beam","search","class",".","Returns",":","nbest_hyps",":","N-best","decoding","results","."]
code: def recognize(
      self, feats: numpy.ndarray, beam_search: BeamSearchTransducer
  ) -> List:
      """Recognize input features.
  
      Args:
          feats: Feature sequence. (F, D_feats)
          beam_search: Beam search class.
  
      Returns:
          nbest_hyps: N-best decoding results.
  
      """
      self.eval()
  
      if self.etype == "custom":
          enc_out = self.encode_custom(feats)
      else:
          enc_out = self.encode_rnn(feats)
  
      nbest_hyps = beam_search(enc_out)
  
      return [asdict(n) for n in nbest_hyps]
  
imports: import logging
  import math
  from argparse import ArgumentParser, Namespace
  from dataclasses import asdict
  from typing import List
  import chainer
  import numpy
  import torch
  from espnet.nets.asr_interface import ASRInterface
  from espnet.nets.beam_search_transducer import BeamSearchTransducer
  from espnet.nets.pytorch_backend.nets_utils import get_subsample, make_non_pad_mask
  from espnet.nets.pytorch_backend.transducer.arguments import add_auxiliary_task_arguments, add_custom_decoder_arguments, add_custom_encoder_arguments, add_custom_training_arguments, add_decoder_general_arguments, add_encoder_general_arguments, add_rnn_decoder_arguments, add_rnn_encoder_arguments, add_transducer_arguments
  from espnet.nets.pytorch_backend.transducer.custom_decoder import CustomDecoder
  from espnet.nets.pytorch_backend.transducer.custom_encoder import CustomEncoder
  from espnet.nets.pytorch_backend.transducer.error_calculator import ErrorCalculator
  from espnet.nets.pytorch_backend.transducer.initializer import initializer
  from espnet.nets.pytorch_backend.transducer.rnn_decoder import RNNDecoder
  from espnet.nets.pytorch_backend.transducer.rnn_encoder import encoder_for
  from espnet.nets.pytorch_backend.transducer.transducer_tasks import TransducerTasks
  from espnet.nets.pytorch_backend.transducer.utils import get_decoder_input, valid_aux_encoder_output_layers
  from espnet.nets.pytorch_backend.transformer.attention import MultiHeadedAttention, RelPositionMultiHeadedAttention
  from espnet.nets.pytorch_backend.transformer.mask import target_mask
  from espnet.nets.pytorch_backend.transformer.plot import PlotAttentionReport
  from espnet.utils.fill_missing_args import fill_missing_args
label_desc: Use image node_id 18 for calling the E2E obj's underlying member method code with example usage: obj.recognize(feats, beam_search) and returns: unknown