code: def test_dtw_subseq():
      srand()
  
      # query is a linear ramp
      X = np.linspace(0, 1, 100)
  
      # database is query surrounded by noise
      noise_len = 200
      noise = np.random.rand(noise_len)
      Y = np.concatenate((noise, noise, X, noise))
  
      _, mut_wp = librosa.sequence.dtw(X, Y, subseq=True)
  
      # estimated sequence has to match original sequence
      # note the +1 due to python indexing
      mut_X = Y[mut_wp[-1][1] : mut_wp[0][1] + 1]
      assert np.array_equal(X, mut_X)
  
imports: import librosa
  import numpy
  from scipy.spatial.distance import cdist
  import pytest
  from test_core import srand
label_desc: Use image node_id 16 for calling a global function with example usage: test_dtw_subseq() without return types