code: def testRegression(self):
      # Hardcoded analysis parameters
      sampleRate = 16000
      frameSize = 400
      hopSize = 160
  
      audio = MonoLoader(
          filename=join(testdata.audio_dir, "recorded/vignesh.wav"),
          sampleRate=sampleRate,
      )()
  
      expected = [
          9.18252861e-02,
          1.11090327e00,
          1.27663188e00,
          7.67182742e-01,
          -5.08587282e-01,
          -7.35953555e-01,
          -1.67525709e-01,
          3.83205337e-01,
          8.37841018e-01,
          6.22906870e-01,
          3.12534805e-03,
          7.77315942e-01,
          7.48971536e-01,
          8.61180275e-01,
          7.12877376e-02,
          -3.25823188e-01,
          -3.45770341e-01,
          -6.06429663e-01,
          -1.23588742e00,
          -8.00219610e-01,
          -4.48695015e-01,
          -8.86346252e-01,
          -7.01429737e-01,
          -1.92338917e-01,
          -5.24386627e-01,
          -4.86066723e-01,
          2.13553896e-03,
          -4.40487141e-01,
          1.96063574e-01,
          4.46905819e-01,
          -8.60898118e-02,
          -5.08393019e-02,
          -6.52852794e-01,
          -8.62813901e-01,
          -1.25070663e00,
          -1.36270763e00,
          -1.52816302e00,
          -1.39475057e00,
          -1.02900690e00,
          -4.27016926e-01,
          -5.22283756e-01,
          -6.05011834e-01,
          -7.67452240e-01,
          -9.07988201e-01,
          -1.04165164e00,
          -8.13776139e-01,
          -4.83653929e-01,
          -2.00050489e-01,
          -3.94115941e-01,
          -1.64469489e00,
          -2.47999268e00,
          -2.52071269e00,
          -2.31027679e00,
          -2.20797295e00,
          -2.03132993e00,
          -1.53524443e00,
          -1.09347116e00,
          -7.60057691e-01,
          -6.83884745e-01,
          -5.03377082e-01,
          -4.72284172e-01,
          -7.03885687e-01,
          -1.11937559e00,
          -1.25580341e00,
      ]
  
      tfmf = TensorflowInputVGGish()
      frames = [
          tfmf(frame)
          for frame in FrameGenerator(
              audio,
              frameSize=frameSize,
              hopSize=hopSize,
              startFromZero=True,
          )
      ]
      obtained = numpy.mean(frames, axis=0)
  
      # Setting a high tolerance value due to the mismatch between the
      # original and replicated features. However, they are close enough to
      # make valid predictions.
      self.assertAlmostEqualVector(obtained, expected, 1e1)
  
imports: from essentia_test import *
  import numpy
label_desc: Use image node_id 1 for calling the TestTensorflowInputVGGish obj's underlying member method code with example usage: obj.testRegression() without return types