code: def test_sklearnex_import_linear(dataframe, queue, macro_block):
      from sklearnex.linear_model import LinearRegression
  
      X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
      y = np.dot(X, np.array([1, 2])) + 3
      X = _convert_to_dataframe(
          X, sycl_queue=queue, target_df=dataframe
      )
      y = _convert_to_dataframe(
          y, sycl_queue=queue, target_df=dataframe
      )
      linreg = LinearRegression()
      if daal_check_version((2024, "P", 0)) and macro_block is not None:
          hparams = linreg.get_hyperparameters("fit")
          hparams.cpu_macro_block = macro_block
          hparams.gpu_macro_block = macro_block
      linreg.fit(X, y)
      if daal_check_version((2023, "P", 100)):
          assert hasattr(linreg, "_onedal_estimator")
      assert "sklearnex" in linreg.__module__
      assert linreg.n_features_in_ == 2
      assert_allclose(_as_numpy(linreg.intercept_), 3.0)
      assert_allclose(_as_numpy(linreg.coef_), [1.0, 2.0])
  
file_path: scikit-learn-intelex/sklearnex/linear_model/tests/test_linear.py
imports: import numpy
  import pytest
  from numpy.testing import assert_allclose
  from sklearn.datasets import make_regression
  from daal4py.sklearn._utils import daal_check_version
  from onedal.tests.utils._dataframes_support import _as_numpy, _convert_to_dataframe, get_dataframes_and_queues
label_desc: Use image node_id 1 for calling a global function with example usage: test_sklearnex_import_linear(dataframe, queue, macro_block) without return types