docstr: Generate list of time-stamped token sequences
  
  Args:
      emission (Tensor): Sequence of token probability distributions in log-domain.
          Shape: `(time, tokens)`.
      tokens (list of integer sequence): Tokenized transcript.
          Output from :py:class:`torchaudio.pipelines.Wav2Vec2FABundle.Tokenizer`.
  
  Returns:
      (list of TokenSpan sequence): Tokens with time stamps and scores.
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code: def __call__(
      self, emission: Tensor, tokens: List[List[int]]
  ) -> List[List[TokenSpan]]:
      """Generate list of time-stamped token sequences
  
      Args:
          emission (Tensor): Sequence of token probability distributions in log-domain.
              Shape: `(time, tokens)`.
          tokens (list of integer sequence): Tokenized transcript.
              Output from :py:class:`torchaudio.pipelines.Wav2Vec2FABundle.Tokenizer`.
  
      Returns:
          (list of TokenSpan sequence): Tokens with time stamps and 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 the IAligner obj's underlying member method code with example usage: obj.__call__(emission, tokens) without return types