code: def test_model_serialization(tmp_path: Path) -> None:
      rng = np.random.default_rng(1234)
      X = rng.random((5, 1))
      Y = rng.standard_normal(X.shape)
      kernel = gpflow.kernels.SquaredExponential()
      model = gpflow.models.GPR((X, Y), kernel=kernel)
  
      frozen_model = gpflow.utilities.freeze(model)
      module_out = tf.Module()
      module_out.predict = tf.function(
          frozen_model.predict_y,
          input_signature=[
              tf.TensorSpec(shape=[None, 1], dtype=tf.float64)
          ],
      )
  
      tf.saved_model.save(module_out, str(tmp_path))
  
      module_in = tf.saved_model.load(str(tmp_path))
  
      Xnew = rng.random((3, 1))
      np.testing.assert_allclose(
          model.predict_y(Xnew),
          module_in.predict(Xnew),
      )
  
imports: from pathlib import Path
  import numpy
  import tensorflow
  import gpflow
label_desc: Use image node_id 1 for calling a global function with example usage: test_model_serialization(tmp_path) without return types