code: def test_regressor_mg_train_sg_predict(
      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
      )
  
      X_test_local = X_test.compute()
  
      dist_model = LinearRegression(
          fit_intercept=fit_intercept, client=client
      )
      dist_model.fit(X_train, y_train)
  
      expected = dist_model.predict(X_test).compute()
  
      local_model = dist_model.get_combined_model()
      actual = local_model.predict(X_test_local)
  
      assert_equal(expected.get(), actual.get())
  
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 4 for calling a global function with example usage: test_regressor_mg_train_sg_predict(datatype, keys, data_size, fit_intercept, client) without return types