code: def get_cars_data():
      
  
      df = pd.read_csv('source_data/cars/car.data.txt')
      X = df.reindex(columns=[x for x in df.columns.values if x != 'class'])
      y = df.reindex(columns=['class'])
      y = preprocessing.LabelEncoder().fit_transform(y.values.reshape(-1, ))
  
      mapping = [
          {'col': 'buying', 'mapping': [('vhigh', 0), ('high', 1), ('med', 2), ('low', 3)]},
          {'col': 'maint', 'mapping': [('vhigh', 0), ('high', 1), ('med', 2), ('low', 3)]},
          {'col': 'doors', 'mapping': [('2', 0), ('3', 1), ('4', 2), ('5more', 3)]},
          {'col': 'persons', 'mapping': [('2', 0), ('4', 1), ('more', 2)]},
          {'col': 'lug_boot', 'mapping': [('small', 0), ('med', 1), ('big', 2)]},
          {'col': 'safety', 'mapping': [('high', 0), ('med', 1), ('low', 2)]},
      ]
  
      return X, y, mapping
docs: Load the cars dataset, split it into X and y, and then call the label encoder to get an integer y column.
  
      :return: