code: def trainClassifierWrapper(
      method, beat_feats, directories, model_name
  ):
      if len(directories) < 2:
          raise Exception("At least 2 directories are needed")
      aT.extract_features_and_train(
          directories,
          1,
          1,
          aT.shortTermWindow,
          aT.shortTermStep,
          method.lower(),
          model_name,
          compute_beat=beat_feats,
          train_percentage=0.90,
          dict_of_ids=None,
          use_smote=False,
      )
  
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 9 for calling a global function with example usage: trainClassifierWrapper(method, beat_feats, directories, model_name) without return types