class_docstr: Batch beam search implementation.
code: def _select(self, hyps: BatchHypothesis, i: int) -> Hypothesis:
      return Hypothesis(
          yseq=hyps.yseq[i, : hyps.length[i]],
          score=hyps.score[i],
          scores={k: v[i] for k, v in hyps.scores.items()},
          states={
              k: self.scorers[k].select_state(v, i)
              for k, v in hyps.states.items()
          },
          hs=hyps.hs[i] if self.return_hs else [],
      )
  
imports: import logging
  from typing import Any, Dict, List, NamedTuple, Tuple
  import torch
  from packaging.version import parse
  from torch.nn.utils.rnn import pad_sequence
  from espnet.nets.beam_search import BeamSearch, Hypothesis
label_desc: Use image node_id 3 for calling the BatchBeamSearch obj's underlying member method code with example usage: obj._select(hyps, i) and returns: Hypothesis