code: def test_rqa_edge(gap_onset, gap_extend, knight, backtrack: bool):
      rec = np.asarray(
          [[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 1, 1], [0, 0, 0, 1]]
      )
      kwargs = dict(
          gap_onset=gap_onset,
          gap_extend=gap_extend,
          knight_moves=knight,
      )
  
      if backtrack:
          out = librosa.sequence.rqa(rec, backtrack=backtrack, **kwargs)
          score, path = out
          __validate_rqa_results(
              rec, score, path, gap_onset, gap_extend, backtrack, knight
          )
          assert len(path) == 3
      else:
          out = librosa.sequence.rqa(rec, backtrack=backtrack, **kwargs)
          # without backtracking, make sure the output is just the score matrix
          assert out.shape == rec.shape
  
imports: import numpy
  import pytest
  from test_core import srand
  import librosa
label_desc: Use image node_id 34 for calling a global function with example usage: test_rqa_edge(gap_onset, gap_extend, knight, backtrack) without return types