code: def build_linear_functional_model(input_shape, weights, num_output=1):
      inputs = tf.keras.Input(shape=input_shape, name=FEATURE_NAME)
      outputs = tf.keras.layers.Dense(
          num_output,
          use_bias=False,
          kernel_initializer=tf.keras.initializers.Constant(weights),
      )(inputs)
      return tf.keras.Model(inputs=inputs, outputs=outputs)
  
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 3 for calling a global function with example usage: build_linear_functional_model(input_shape, weights, num_output) and returns: tf