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_next_token_probs(
      self,
      enc_out: torch.Tensor,
      hypos: List[Hypothesis],
      device: torch.device,
  ) -> torch.Tensor:
      one_tensor = torch.tensor([1], device=device)
      predictor_out = torch.stack(
          [_get_hypo_predictor_out(h) for h in hypos], dim=0
      )
      joined_out, _, _ = self.model.join(
          enc_out,
          one_tensor,
          predictor_out,
          torch.tensor([1] * len(hypos), device=device),
      )  # [beam_width, 1, 1, num_tokens]
      joined_out = torch.nn.functional.log_softmax(
          joined_out / self.temperature, dim=3
      )
      return joined_out[:, 0, 0]
  
imports: from typing import Callable, Dict, List, Optional, Tuple
  import torch
  from torchaudio.models import RNNT
label_desc: Use image node_id 3 for calling the RNNTBeamSearch obj's underlying member method code with example usage: obj._gen_next_token_probs(enc_out, hypos, device) and returns: joined_out