code: def make_regression_dataset(datatype, nrows, ncols):
      ninformative = max(min(ncols, 5), int(math.ceil(ncols / 5)))
      X, y = data.make_regression(
          dtype=datatype,
          n_samples=nrows + 1000,
          n_features=ncols,
          random_state=SEED,
          n_informative=ninformative,
      )
      return dsel.train_test_split(
          X, y, random_state=SEED, train_size=nrows
      )
  
imports: import cuml.internals.logger
  import cuml
  import cuml.svm
  import sklearn.svm
  from cuml.testing.utils import unit_param, quality_param, stress_param
  from queue import Empty
  import cuml.model_selection
  import cuml.datasets
  import pytest
  from cuml.internals.safe_imports import cpu_only_import
  import gc
  import multiprocessing
  import time
  import math
  from cuml.internals.safe_imports import gpu_only_import
  from cuml.common import input_to_cuml_array
label_desc: Use image node_id 3 for calling a global function with example usage: make_regression_dataset(datatype, nrows, ncols) and returns: dsel