class_docstr: Beam search implementation for Transducer.
code: def reset(self):
      beam = min(self.beam_size, self.vocab_size)
      self.beam_state = self.decoder.init_state(beam)
      self.B = [
          Hypothesis(
              yseq=[self.blank_id],
              score=0.0,
              dec_state=self.decoder.select_state(self.beam_state, 0),
          )
      ]
      self.cache = {}
  
imports: import logging
  from dataclasses import dataclass
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from espnet2.asr.decoder.abs_decoder import AbsDecoder
  from espnet2.asr_transducer.joint_network import JointNetwork
  from espnet2.lm.transformer_lm import TransformerLM
  from espnet.nets.beam_search import Hypothesis
  from espnet.nets.pytorch_backend.transducer.utils import is_prefix, recombine_hyps, select_k_expansions, subtract
label_desc: Use image node_id 3 for calling the BeamSearchTransducerStreaming obj's underlying member method code with example usage: obj.reset() without return types