code: def create_cancer_data(return_dataframe=False):
      """Create Breast Cancer dataset for classification.
  
      param return_dataframe: Whether to return data as pandas DataFrame.
      type return_dataframe: bool
      return: Tuple of X_train, X_test, y_train, y_test, feature_names, classes.
      rtype: Tuple of pandas.DataFrame, pandas.DataFrame, numpy.ndarray,
             numpy.ndarray, list, list
      """
      breast_cancer_data = load_breast_cancer()
      classes = breast_cancer_data.target_names.tolist()
  
      # Split data into train and test
      X_train, X_test, y_train, y_test = train_test_split(
          breast_cancer_data.data,
          breast_cancer_data.target,
          test_size=0.2,
          random_state=0,
      )
      feature_names = breast_cancer_data.feature_names
      classes = breast_cancer_data.target_names.tolist()
  
      if return_dataframe:
          X_train = pd.DataFrame(X_train, columns=feature_names)
          X_test = pd.DataFrame(X_test, columns=feature_names)
  
      return X_train, X_test, y_train, y_test, feature_names, 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 4 for calling a global function with example usage: create_cancer_data(return_dataframe) and returns: X_train,  X_test,  y_train,  y_test,  feature_names,  classes