code: def test_predict_base_model(self):
      model = build_linear_functional_model(
          input_shape=(2,), weights=np.array([1.0, -1.0])
      )
      inputs = {FEATURE_NAME: tf.constant([[5.0, 3.0]])}
  
      graph_reg_model = (
          graph_regularization.GraphRegularizationWithCaching(model)
      )
      graph_reg_model.compile(
          optimizer=tf.keras.optimizers.SGD(0.01), loss="MSE"
      )
  
      prediction = model.predict(x=inputs, steps=1, batch_size=1)
  
      self.assertAllEqual([[1 * 5.0 + (-1.0) * 3.0]], prediction)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  from neural_structured_learning import configs
  from research.carls import graph_regularization
  from research.carls import neighbor_cache_client
  import numpy
  import tensorflow
  from google.protobuf import text_format
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 2 for calling the GraphRegularizationWithCachingTest obj's underlying member method code with example usage: obj.test_predict_base_model() without return types