code: def make_regression_dataset(dataset, n_rows, n_cols):
      np.random.seed(137)
      if dataset == "reg1":
          X, y = make_regression(
              n_rows,
              n_cols,
              n_informative=2,
              n_targets=1,
              random_state=137,
          )
      elif dataset == "reg2":
          X, y = make_regression(
              n_rows,
              n_cols,
              n_informative=2,
              n_targets=1,
              random_state=137,
              noise=10,
          )
      elif dataset == "Friedman":
          X, y = make_friedman1(
              n_samples=n_rows,
              n_features=n_cols,
              noise=0.0,
              random_state=137,
          )
      else:
          raise ValueError("Wrong option for dataste: ", dataset)
      scaler = StandardScaler()
      X = scaler.fit_transform(X)
      dtype = np.float32
      X = X.astype(dtype)
      y = y.astype(dtype)
      X_train, X_test, y_train, y_test = train_test_split(X, y)
      return X_train, X_test, y_train, y_test
  
imports: import platform
  from sklearn.preprocessing import StandardScaler
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import mean_squared_error
  from sklearn.datasets import make_classification, make_gaussian_quantiles
  from sklearn.datasets import make_regression, make_friedman1
  from sklearn.datasets import load_iris, make_blobs
  from sklearn import svm
  from cuml.testing.utils import unit_param, quality_param, stress_param, compare_svm, compare_probabilistic_svm, svm_array_equal
  from cuml.common import input_to_cuml_array
  import cuml.svm
  import cuml
  from cuml.internals.safe_imports import gpu_only_import_from
  from cuml.internals.safe_imports import cpu_only_import
  import pytest
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 15 for calling a global function with example usage: make_regression_dataset(dataset, n_rows, n_cols) and returns: X_train,  X_test,  y_train,  y_test