code: def regressionFolderWrapper(inputFolder, model_type, model_name):
      files = "*.wav"
      if os.path.isdir(inputFolder):
          strFilePattern = os.path.join(inputFolder, files)
      else:
          strFilePattern = inputFolder + files
  
      wavFilesList = []
      wavFilesList.extend(glob.glob(strFilePattern))
      wavFilesList = sorted(wavFilesList)
      if len(wavFilesList) == 0:
          print("No WAV files found!")
          return
      Results = []
      for wavFile in wavFilesList:
          R, regressionNames = aT.file_regression(
              wavFile, model_name, model_type
          )
          Results.append(R)
      Results = numpy.array(Results)
  
      for i, r in enumerate(regressionNames):
          [Histogram, bins] = numpy.histogram(Results[:, i])
          centers = (bins[0:-1] + bins[1::]) / 2.0
          plt.subplot(len(regressionNames), 1, i + 1)
          plt.plot(centers, Histogram)
          plt.title(r)
      plt.show()
  
imports: from __future__ import print_function
  import argparse
  import os
  import numpy
  import glob
  import matplotlib.pyplot
  import ShortTermFeatures
  import MidTermFeatures
  import audioTrainTest
  import audioSegmentation
  import audioVisualization
  import audioBasicIO
  import scipy.io.wavfile
  import matplotlib.patches
label_desc: Use image node_id 14 for calling a global function with example usage: regressionFolderWrapper(inputFolder, model_type, model_name) without return types