code: def make_dataset(request):
      nrows, ncols, n_info, datatype = request.param
      if (
          nrows == 500000
          and datatype == np.float64
          and pytest.max_gpu_memory < 32
      ):
          if pytest.adapt_stress_test:
              nrows = nrows * pytest.max_gpu_memory // 32
          else:
              pytest.skip(
                  "Insufficient GPU memory for this test."
                  "Re-run with 'CUML_ADAPT_STRESS_TESTS=True'"
              )
      X, y = make_regression(
          n_samples=nrows,
          n_informative=n_info,
          n_features=ncols,
          random_state=0,
      )
      X = cp.array(X).astype(datatype)
      y = cp.array(y).astype(datatype)
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, train_size=0.8, random_state=10
      )
  
      return nrows, datatype, X_train, X_test, y_train, y_test
  
file_path: cuml/python/cuml/tests/test_mbsgd_regressor.py
imports: from sklearn.model_selection import train_test_split
  from cuml.datasets import make_regression
  from sklearn.linear_model import SGDRegressor
  from cuml.testing.utils import unit_param, quality_param, stress_param
  from cuml.metrics import r2_score
  from cuml.linear_model import MBSGDRegressor
  from cuml.internals.safe_imports import gpu_only_import
  import pytest
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 1 for calling a global function with example usage: make_dataset(request) and returns: nrows,  datatype,  X_train,  X_test,  y_train,  y_test