code: def test_serialize_mnmg_model(client):
      X, y = make_regression(
          n_samples=1000, n_features=20, random_state=0
      )
      X, y = da.from_array(X), da.from_array(y)
  
      model = LinearRegression(client)
      model.fit(X, y)
  
      pickled_model = pickle.dumps(model)
      unpickled_model = pickle.loads(pickled_model)
  
      assert np.allclose(unpickled_model.coef_, model.coef_)
  
file_path: cuml/python/cuml/tests/dask/test_dask_serialization.py
imports: from distributed.protocol.serialize import serialize
  from cuml.naive_bayes.naive_bayes import MultinomialNB
  from cuml.internals.array_sparse import SparseCumlArray
  from cuml.dask.linear_model import LinearRegression
  from cuml.internals.safe_imports import gpu_only_import
  from dask import array
  from sklearn.datasets import make_regression
  import numpy
  import pickle
label_desc: Use image node_id 3 for calling a global function with example usage: test_serialize_mnmg_model(client) without return types