code: def create_multiclass_classification_dataset(
      num_classes=5, num_features=20, num_informative=4
  ):
      """Create a multiclass classification dataset.
  
      param num_classes: Number of classes.
      type num_classes: int
      param num_features: Number of features.
      type num_features: int
      param num_informative: Number of informative features.
      type num_informative: int
      return: Tuple of X_train, y_train, X_test, y_test, classes.
      rtype: Tuple of pandas.DataFrame, pandas.DataFrame, numpy.ndarray,
                numpy.ndarray, list
      """
      X, y = make_classification(
          n_classes=num_classes,
          n_features=num_features,
          n_informative=num_informative,
      )
  
      # Split data into train and test
      x_train, x_test, y_train, y_test = train_test_split(
          X, y, test_size=0.2, random_state=0
      )
      feature_names = [
          "col" + str(i) for i in list(range(x_train.shape[1]))
      ]
      classes = np.unique(y_train).tolist()
  
      return (
          pd.DataFrame(x_train, columns=feature_names),
          y_train,
          pd.DataFrame(x_test, columns=feature_names),
          y_test,
          classes,
      )
  
file_path: responsible-ai-toolbox/rai_test_utils/rai_test_utils/datasets/tabular/classification_data_utils.py
imports: import numpy
  import pandas
  import shap
  from pandas import read_csv
  from sklearn.datasets import load_breast_cancer, load_iris, load_wine, make_classification
  from sklearn.model_selection import train_test_split
  from rai_test_utils.utilities import retrieve_dataset
label_desc: Use image node_id 6 for calling a global function with example usage: create_multiclass_classification_dataset(num_classes, num_features, num_informative) and returns: pd,  y_train,  pd,  y_test,  classes