code: def testRegression(self):
      # This test assesses the capability to work in streaming mode
      # and to be reset and process new files.
      model = join(testdata.models_dir, "vgg", "vgg4.pb")
  
      filenames = [
          join(testdata.audio_dir, "recorded", "cat_purrrr.wav"),
          join(testdata.audio_dir, "recorded", "distorted.wav"),
          join(testdata.audio_dir, "recorded", "dubstep.wav"),
      ]
  
      true_probs = [
          [1.0, 0.0, 0.0, 0.0],
          [0.0, 0.0, 0.005, 0.995],
          [0.0, 0.0, 1.0, 0.0],
      ]
  
      frameSize = 1024
      hopSize = frameSize
      shape = [-1, 1, 128, 128]
  
      loader = MonoLoader()
      w = Windowing(type="hann", zeroPadding=frameSize)
      frameCutter = FrameCutter(
          frameSize=frameSize, hopSize=hopSize, startFromZero=True
      )
      spectrum = Spectrum()
      mel = MelBands(numberBands=128, type="magnitude")
      logNorm = UnaryOperator(type="log")
      vtt = VectorRealToTensor(
          shape=shape, lastPatchMode="repeat", patchHopSize=1
      )
      ttp = TensorToPool(
          mode="overwrite", namespace="model/Placeholder"
      )
      tfp = TensorflowPredict(
          graphFilename=model,
          inputs=["model/Placeholder"],
          outputs=["model/Softmax"],
          isTraining=False,
          isTrainingName="model/Placeholder_1",
      )
      ptt = PoolToTensor(namespace="model/Softmax")
      ttv = TensorToVectorReal()
  
      pool = Pool()
  
      # Connecting the algorithms
      loader.audio >> frameCutter.signal
      frameCutter.frame >> w.frame >> spectrum.frame
      spectrum.spectrum >> mel.spectrum
      mel.bands >> logNorm.array >> vtt.frame
      vtt.tensor >> ttp.tensor
      ttp.pool >> tfp.poolIn
      tfp.poolOut >> ptt.pool
      ptt.tensor >> ttv.tensor
      ttv.frame >> (pool, "vgg_probs")
  
      aggrPool = PoolAggregator(defaultStats=["mean"])
  
      # Run Essentia
      for filename, expectedValues in zip(filenames, true_probs):
          loader.configure(filename=filename)
          run(loader)
  
          foundValues = aggrPool(pool)["vgg_probs.mean"]
  
          reset(loader)
          pool.clear()
  
          self.assertAlmostEqualVector(
              foundValues, expectedValues, 1e-2
          )
  
file_path: essentia/test/src/unittests/machinelearning/test_tensorflowpredict_streaming.py
imports: from essentia_test import *
  from essentia.streaming import *
  from essentia.standard import PoolAggregator
label_desc: Use image node_id 2 for calling the TestTensorflowPredict_Streaming obj's underlying member method code with example usage: obj.testRegression() without return types