code: def classifyFolderWrapper(
      inputFolder, model_type, model_name, outputMode=False
  ):
      if not os.path.isfile(model_name):
          raise Exception("Input model_name not found!")
      types = ("*.wav", "*.aif", "*.aiff", "*.mp3")
      wavFilesList = []
      for files in types:
          wavFilesList.extend(glob.glob((inputFolder + files)))
      wavFilesList = sorted(wavFilesList)
      if len(wavFilesList) == 0:
          print("No WAV files found!")
          return
      Results = []
      for wavFile in wavFilesList:
          [Result, P, classNames] = aT.file_classification(
              wavFile, model_name, model_type
          )
          Result = int(Result)
          Results.append(Result)
          if outputMode:
              print("{0:s}\t{1:s}".format(wavFile, classNames[Result]))
      Results = numpy.array(Results)
  
      # print distribution of classes:
      [Histogram, _] = numpy.histogram(
          Results, bins=numpy.arange(len(classNames) + 1)
      )
      for i, h in enumerate(Histogram):
          print("{0:20s}\t\t{1:d}".format(classNames[i], h))
  
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 13 for calling a global function with example usage: classifyFolderWrapper(inputFolder, model_type, model_name, outputMode) without return types