code: def extract_features_and_train(
      paths,
      mid_window,
      mid_step,
      short_window,
      short_step,
      classifier_type,
      model_name,
      compute_beat=False,
      train_percentage=0.90,
      dict_of_ids=None,
      use_smote=False,
  ):
      """
      This function is used as a wrapper to segment-based audio feature extraction
      and classifier training.
      ARGUMENTS:
          paths:                      list of paths of directories. Each directory
                                      contains a signle audio class whose samples
                                      are stored in seperate WAV files.
          mid_window, mid_step:       mid-term window length and step
          short_window, short_step:   short-term window and step
          classifier_type:            "svm" or "knn" or "randomforest" or
                                      "gradientboosting" or "extratrees"
          model_name:                 name of the model to be saved
          dict_of_ids:                a dictionary which has as keys the full path of audio files and as values the respective group ids
      RETURNS:
          None. Resulting classifier along with the respective model
          parameters are saved on files.
      """
  
      # STEP A: Feature Extraction:
      (
          features,
          class_names,
          file_names,
      ) = aF.multiple_directory_feature_extraction(
          paths,
          mid_window,
          mid_step,
          short_window,
          short_step,
          compute_beat=compute_beat,
      )
      file_names = [item for sublist in file_names for item in sublist]
      if dict_of_ids:
          list_of_ids = [dict_of_ids[file] for file in file_names]
      else:
          list_of_ids = None
      if len(features) == 0:
          print(
              "trainSVM_feature ERROR: No data found in any input folder!"
          )
          return
  
      n_feats = features[0].shape[1]
      feature_names = ["features" + str(d + 1) for d in range(n_feats)]
  
      for i, feat in enumerate(features):
          if len(feat) == 0:
              print(
                  "trainSVM_feature ERROR: "
                  + paths[i]
                  + " folder is empty or non-existing!"
              )
              return
  
      # STEP B: classifier Evaluation and Parameter Selection:
      if classifier_type == "svm" or classifier_type == "svm_rbf":
          classifier_par = np.array(
              [0.001, 0.01, 0.5, 1.0, 5.0, 10.0, 20.0]
          )
      elif classifier_type == "randomforest":
          classifier_par = np.array([10, 25, 50, 100, 200, 500])
      elif classifier_type == "knn":
          classifier_par = np.array([1, 3, 5, 7, 9, 11, 13, 15])
      elif classifier_type == "gradientboosting":
          classifier_par = np.array([10, 25, 50, 100, 200, 500])
      elif classifier_type == "extratrees":
          classifier_par = np.array([10, 25, 50, 100, 200, 500])
  
      # get optimal classifier parameter:
      temp_features = []
      for feat in features:
          if feat.ndim == 1:  # this class has only 1 sample
              feat = feat.reshape((1, feat.shape[0]))
          temp = []
          for i in range(feat.shape[0]):
              temp_fv = feat[i, :]
              if (not np.isnan(temp_fv).any()) and (
                  not np.isinf(temp_fv).any()
              ):
                  temp.append(temp_fv.tolist())
              else:
                  print(
                      "NaN Found! Feature vector not used for training"
                  )
          temp_features.append(np.array(temp))
      features = temp_features
  
      best_param = evaluate_classifier(
          features,
          class_names,
          classifier_type,
          classifier_par,
          1,
          list_of_ids,
          n_exp=-1,
          train_percentage=train_percentage,
          smote=use_smote,
      )
  
      print("Selected params: {0:.5f}".format(best_param))
  
      # STEP C: Train and Save the classifier to file
      # Get featues in the X, y format:
      features, labels = features_to_matrix(features)
      # Apply smote if necessary:
      if use_smote:
          sm = SMOTE(random_state=2)
          features, labels = sm.fit_resample(features, labels)
  
      # Use mean/std standard feature scaling:
      scaler = StandardScaler()
      features = scaler.fit_transform(features)
      mean = scaler.mean_.tolist()
      std = scaler.scale_.tolist()
  
      # Then train the final classifier
      if classifier_type == "svm":
          classifier = train_svm(features, labels, best_param)
      elif classifier_type == "svm_rbf":
          classifier = train_svm(
              features, labels, best_param, kernel="rbf"
          )
      elif classifier_type == "randomforest":
          classifier = train_random_forest(features, labels, best_param)
      elif classifier_type == "gradientboosting":
          classifier = train_gradient_boosting(
              features, labels, best_param
          )
      elif classifier_type == "extratrees":
          classifier = train_extra_trees(features, labels, best_param)
  
      # And save the model to a file, along with
      # - the scaling -mean/std- vectors)
      # - the feature extraction parameters
      if classifier_type == "knn":
          feature_matrix = features.tolist()
          labels = labels.tolist()
          save_path = model_name
          save_parameters(
              save_path,
              feature_matrix,
              labels,
              mean,
              std,
              class_names,
              best_param,
              mid_window,
              mid_step,
              short_window,
              short_step,
              compute_beat,
          )
  
      elif (
          classifier_type == "svm"
          or classifier_type == "svm_rbf"
          or classifier_type == "randomforest"
          or classifier_type == "gradientboosting"
          or classifier_type == "extratrees"
      ):
          with open(model_name, "wb") as fid:
              cPickle.dump(classifier, fid)
          save_path = model_name + "MEANS"
          save_parameters(
              save_path,
              mean,
              std,
              class_names,
              mid_window,
              mid_step,
              short_window,
              short_step,
              compute_beat,
          )
  
imports: from __future__ import print_function
  from imblearn.under_sampling import RandomUnderSampler
  from imblearn.over_sampling import SMOTE
  from sklearn.model_selection import GroupShuffleSplit
  from pyAudioAnalysis import audioBasicIO
  from pyAudioAnalysis import MidTermFeatures
  import sys
  import numpy
  import os
  import glob
  import pickle
  import csv
  import ntpath
  from scipy import linalg
  from scipy.spatial import distance
  import sklearn.svm
  import sklearn.decomposition
  import sklearn.ensemble
  import plotly
  import plotly.subplots
  import plotly.graph_objs
  import sklearn.metrics
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import StandardScaler
label_desc: Use image node_id 10 for calling a global function with example usage: extract_features_and_train(paths, mid_window, mid_step, short_window, short_step, classifier_type, model_name, compute_beat, train_percentage, dict_of_ids, use_smote) without return types