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 __init__(
      self,
      model: RNNT,
      blank: int,
      temperature: float = 1.0,
      hypo_sort_key: Optional[Callable[[Hypothesis], float]] = None,
      step_max_tokens: int = 100,
  ) -> None:
      super().__init__()
      self.model = model
      self.blank = blank
      self.temperature = temperature
  
      if hypo_sort_key is None:
          self.hypo_sort_key = _default_hypo_sort_key
      else:
          self.hypo_sort_key = hypo_sort_key
  
      self.step_max_tokens = step_max_tokens
  
imports: from typing import Callable, Dict, List, Optional, Tuple
  import torch
  from torchaudio.models import RNNT
label_desc: Use image node_id 1 to create a new RNNTBeamSearch object from inherited base classes: torch.nn with example: obj = RNNTBeamSearch(model, blank, temperature, hypo_sort_key, step_max_tokens)