class_docstr: Beam search decoder for RNN-T model.
  
  See Also:
      * :class:`torchaudio.pipelines.RNNTBundle`: ASR pipeline with pretrained model.
  
  Args:
      model (RNNT): RNN-T model to use.
      blank (int): index of blank token in vocabulary.
      temperature (float, optional): temperature to apply to joint network output.
          Larger values yield more uniform samples. (Default: 1.0)
      hypo_sort_key (Callable[[Hypothesis], float] or None, optional): callable that computes a score
          for a given hypothesis to rank hypotheses by. If ``None``, defaults to callable that returns
          hypothesis score normalized by token sequence length. (Default: None)
      step_max_tokens (int, optional): maximum number of tokens to emit per input time step. (Default: 100)
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code: def _gen_new_hypos(
      self,
      base_hypos: List[Hypothesis],
      tokens: List[int],
      scores: List[float],
      t: int,
      device: torch.device,
  ) -> List[Hypothesis]:
      tgt_tokens = torch.tensor(
          [[token] for token in tokens], device=device
      )
      states = _batch_state(base_hypos)
      pred_out, _, pred_states = self.model.predict(
          tgt_tokens,
          torch.tensor([1] * len(base_hypos), device=device),
          states,
      )
      new_hypos: List[Hypothesis] = []
      for i, h_a in enumerate(base_hypos):
          new_tokens = _get_hypo_tokens(h_a) + [tokens[i]]
          new_hypos.append(
              (
                  new_tokens,
                  pred_out[i].detach(),
                  _slice_state(pred_states, i, device),
                  scores[i],
              )
          )
      return new_hypos
  
imports: from typing import Callable, Dict, List, Optional, Tuple
  import torch
  from torchaudio.models import RNNT
label_desc: Use image node_id 6 for calling the RNNTBeamSearch obj's underlying member method code with example usage: obj._gen_new_hypos(base_hypos, tokens, scores, t, device) and returns: new_hypos