code: def create_msx_data(test_size):
      """Create msx dataset for classification.
  
      param test_size: Size of the test dataset.
      type test_size: float
      return: Tuple of X_train, X_test, y_train, y_test.
      rtype: Tuple of sparse matrix, sparse matrix, sparse matrix, sparse matrix
      """
      sparse_matrix = retrieve_dataset("msx_transformed_2226.npz")
      sparse_matrix_x = sparse_matrix[:, : sparse_matrix.shape[1] - 2]
      sparse_matrix_y = sparse_matrix[
          :, (sparse_matrix.shape[1] - 2) : (sparse_matrix.shape[1] - 1)
      ]
      return train_test_split(
          sparse_matrix_x,
          sparse_matrix_y,
          test_size=test_size,
          random_state=7,
      )
  
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 9 for calling a global function with example usage: create_msx_data(test_size) and returns: train_test_split