code: def regression(self, parameters):
      # The expected values were self-generated with TensorflowPredictTempoCNN
      # from commit deb334ba01f849e74b0e6e8554b2e17d73bc6966
      expected = [
          6.81436131e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          6.43281146e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.93597724e-06,
          5.37369260e-06,
          5.61165098e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.29615545e-06,
          1.54262583e-04,
          3.62297491e-04,
          2.58193875e-04,
          1.07488459e-05,
          7.51755033e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.61612069e-06,
          4.17900737e-05,
          5.96406335e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.32490412e-06,
          1.30493099e-05,
          1.07832220e-05,
          5.18454181e-06,
          1.03671191e-05,
          8.58808835e-06,
          1.15191098e-04,
          1.64165122e-05,
          1.20488658e-05,
          1.52187367e-05,
          3.93088922e-05,
          5.18454181e-06,
          5.18454181e-06,
          9.91457364e-06,
          5.66988228e-06,
          8.09343692e-06,
          1.51625994e-04,
          2.98513245e-04,
          1.89710336e-05,
          5.18454181e-06,
          9.77961099e-06,
          5.19856485e-06,
          1.11890822e-05,
          5.18454181e-06,
          5.18454181e-06,
          7.28983014e-06,
          1.39451868e-05,
          2.78414045e-05,
          3.43431420e-05,
          1.41270539e-05,
          1.25369352e-05,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.27262637e-06,
          7.14806265e-06,
          6.32071078e-06,
          5.98581255e-06,
          2.18235818e-05,
          1.09844186e-05,
          1.34545080e-05,
          5.18621164e-06,
          1.68646511e-04,
          1.09641778e-03,
          1.38453615e-05,
          3.01377531e-05,
          1.21552097e-02,
          9.64710116e-01,
          1.37740122e-02,
          1.28899017e-04,
          5.89064148e-05,
          8.77115599e-05,
          1.34839304e-03,
          4.11161163e-04,
          2.86400136e-05,
          5.18454181e-06,
          1.44103651e-05,
          2.40279369e-05,
          5.18454181e-06,
          5.48158596e-06,
          3.18061357e-04,
          1.57283881e-04,
          1.29225233e-03,
          2.19263566e-05,
          5.95881011e-06,
          5.19396053e-06,
          5.18454181e-06,
          6.97886162e-06,
          5.99685018e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          1.38969726e-05,
          1.19574506e-05,
          5.18521047e-05,
          6.30647337e-05,
          6.18311378e-06,
          5.18454181e-06,
          5.56253462e-06,
          1.13041697e-05,
          2.63892180e-05,
          1.03348422e-04,
          1.33199101e-05,
          1.43749730e-05,
          3.64447420e-04,
          2.36525921e-05,
          9.88977990e-05,
          8.33314698e-06,
          5.58819511e-06,
          1.15462044e-05,
          5.68051291e-06,
          3.33551179e-05,
          1.42920344e-05,
          1.12948746e-05,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.79629932e-06,
          5.41440022e-06,
          5.36711059e-06,
          8.51361256e-06,
          5.18454181e-06,
          1.75346850e-05,
          1.11004565e-05,
          5.48184034e-05,
          5.85014059e-05,
          2.29484838e-04,
          3.69668414e-05,
          1.19517599e-05,
          7.46400983e-05,
          5.18454181e-06,
          5.18454181e-06,
          5.21284055e-05,
          1.26784771e-05,
          5.18454181e-06,
          5.50738332e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.27911197e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          1.73420085e-05,
          9.13620534e-06,
          5.18454181e-06,
          5.92750121e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.19091100e-06,
          5.18454181e-06,
          5.90774516e-06,
          5.18454181e-06,
          6.08426490e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.31059550e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.35843628e-06,
          5.18454181e-06,
          5.18454181e-06,
          9.49293644e-06,
          9.07244521e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          1.40652155e-05,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
          5.18454181e-06,
      ]
  
      filename = join(testdata.audio_dir, "recorded", "techno_loop.wav")
  
      audio = MonoLoader(filename=filename, sampleRate=11025)()
      found = TensorflowPredictTempoCNN(**parameters)(audio)
      found = numpy.mean(found, axis=0)
  
      self.assertAlmostEqualVector(found, expected, 1e-5)
  
file_path: essentia/test/src/unittests/machinelearning/test_tensorflowpredicttempocnn.py
imports: from essentia_test import *
label_desc: Use image node_id 1 for calling the TestTensorflowPredictTempoCNN obj's underlying member method code with example usage: obj.regression(parameters) without return types