code: def test_extract_hyps():
      from speechbrain.lobes.models.g2p.homograph import (
          SubsequenceExtractor,
      )
  
      phns = torch.tensor(
          [
              [1, 2, 3, 0, 3, 1, 2, 1, 0, 3, 1, 2, 0, 0],
              [1, 2, 3, 1, 0, 3, 2, 1, 0, 1, 3, 2, 0, 0],
              [1, 2, 3, 1, 2, 3, 0, 1, 3, 1, 3, 2, 0, 1],
          ]
      )
      hyps = [
          [1, 2, 3, 2, 0, 3, 1, 2, 1, 0, 3, 1, 2],
          [1, 2, 3, 0, 3, 2, 1, 0, 1, 3, 2],
          [1, 2, 3, 1, 2, 3, 0, 1, 3, 1, 3, 2, 0, 1],
      ]
      subsequence_phn_start = torch.tensor([4, 0, 7])
      ref_hyps = [[3, 1, 2, 1], [1, 2, 3], [1, 3, 1, 3, 2]]
  
      extractor = SubsequenceExtractor(word_separator=0)
      subsequence_hyps = extractor.extract_hyps(
          ref_seq=phns,
          hyps=hyps,
          subsequence_phn_start=subsequence_phn_start,
      )
      assert subsequence_hyps == ref_hyps
  
imports: import torch
  from torch.nn import functional
label_desc: Use image node_id 4 for calling a global function with example usage: test_extract_hyps() without return types