code: def regression(self, parameters):
      # Test a simple tensorflow model trained on Essentia features.
      # The ground true values were obtained with the following script:
      expectedValues = [
          9.9997020e-01,
          5.3647455e-14,
          2.9801236e-05,
          4.9495230e-12,
      ]
  
      patchSize = 128
      numberBands = 128
      frameSize = 1024
      hopSize = frameSize
  
      filename = join(testdata.audio_dir, "recorded", "cat_purrrr.wav")
  
      audio = MonoLoader(filename=filename)()
  
      w = Windowing(type="hann", zeroPadding=frameSize)
      spectrum = Spectrum()
      mels = MelBands(numberBands=numberBands, type="magnitude")
      logNorm = UnaryOperator(type="log")
  
      bands = []
      for frame in FrameGenerator(
          audio, frameSize=frameSize, hopSize=hopSize
      ):
          melFrame = mels(spectrum(w(frame)))
          bands.append(logNorm(melFrame))
      bands = array(bands)
  
      discard = bands.shape[0] % patchSize
      bands = numpy.reshape(
          bands[:-discard, :], [-1, patchSize, numberBands]
      )
      batch = numpy.expand_dims(bands, 1)
  
      pool = Pool()
      pool.set("model/Placeholder", batch)
  
      tfp = TensorflowPredict(**parameters)
      poolOut = tfp(pool)
  
      foundValues = poolOut["model/Softmax"].mean(axis=0).squeeze()
  
      self.assertAlmostEqualVector(foundValues, expectedValues, 1e-5)
  
file_path: essentia/test/src/unittests/machinelearning/test_tensorflowpredict.py
imports: from essentia_test import *
  import sys
  import os
label_desc: Use image node_id 1 for calling the TestTensorFlowPredict obj's underlying member method code with example usage: obj.regression(parameters) without return types