code: def make_classification_dataset(
      datatype, nrows, ncols, n_info, n_classes
  ):
      X, y = make_classification(
          n_samples=nrows,
          n_features=ncols,
          n_informative=n_info,
          n_classes=n_classes,
          random_state=0,
      )
      X = X.astype(datatype)
      y = y.astype(np.int32)
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, train_size=0.8
      )
      return X_train, y_train, X_test
  
imports: from sklearn.model_selection import train_test_split
  from sklearn.manifold import trustworthiness
  from sklearn.datasets import load_iris, make_classification, make_regression
  from sklearn.base import clone
  from cuml.testing.utils import array_equal, unit_param, stress_param, ClassEnumerator, get_classes_from_package, compare_svm, compare_probabilistic_svm
  from cuml.tsa.arima import ARIMA
  import pytest
  import pickle
  import cuml
  from cuml.internals.safe_imports import cpu_only_import, cpu_only_import_from
label_desc: Use image node_id 2 for calling a global function with example usage: make_classification_dataset(datatype, nrows, ncols, n_info, n_classes) and returns: X_train,  y_train,  X_test