code: def test_get_combined_model(
      datatype, keys, data_size, fit_intercept, client
  ):
      nrows, ncols, n_info = data_size
      X_train, y_train, X_test = make_dataset(
          datatype, nrows, ncols, n_info
      )
      model = LinearRegression(
          fit_intercept=fit_intercept, client=client, verbose=True
      )
      model.fit(X_train, y_train)
      print("Fit done")
  
      combined_model = model.get_combined_model()
      assert combined_model.coef_ is not None
      assert combined_model.intercept_ is not None
  
      y_hat = combined_model.predict(X_train.compute())
  
      np.testing.assert_allclose(
          y_hat.get(), y_train.compute().get(), atol=1e-3, rtol=1e-3
      )
  
file_path: cuml/python/cuml/tests/dask/test_dask_base.py
imports: from sklearn.model_selection import train_test_split
  from cuml.dask.datasets import make_regression
  from cuml.dask.linear_model import LinearRegression
  from cuml.internals.safe_imports import cpu_only_import_from
  import pytest
  from cuml.internals.safe_imports import cpu_only_import
  import cuml
  from cuml.dask.datasets import make_blobs
  from cuml.testing.dask.utils import load_text_corpus
  from cuml.dask.naive_bayes.naive_bayes import MultinomialNB
  from cuml.dask.cluster import KMeans
  from dask_ml.wrappers import ParallelPostFit
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 2 for calling a global function with example usage: test_get_combined_model(datatype, keys, data_size, fit_intercept, client) without return types