code: def _align_emission_and_tokens(
      emission: Tensor, tokens: List[int], blank: int = 0
  ):
      device = emission.device
      emission = emission.unsqueeze(0)
      targets = torch.tensor([tokens], dtype=torch.int32, device=device)
  
      aligned_tokens, scores = F.forced_align(
          emission, targets, blank=blank
      )
  
      scores = scores.exp()  # convert back to probability
      aligned_tokens, scores = (
          aligned_tokens[0],
          scores[0],
      )  # remove batch dimension
      return aligned_tokens, scores
  
imports: from abc import ABC, abstractmethod
  from typing import Dict, List
  import torch
  import torchaudio.functional
  from torch import Tensor
  from torchaudio.functional import TokenSpan
label_desc: Use image node_id 1 for calling a global function with example usage: _align_emission_and_tokens(emission, tokens, blank) and returns: aligned_tokens,  scores