code: def build_linear_keras_functional_model(
      input_shape, weights, input_name="feature"
  ):
      inputs = tf.keras.Input(shape=input_shape, name=input_name)
      layer = tf.keras.layers.Dense(
          weights.shape[-1],
          use_bias=False,
          kernel_initializer=tf.keras.initializers.Constant(weights),
      )
      outputs = layer(inputs)
      return tf.keras.Model(
          inputs={input_name: inputs}, outputs=outputs
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import os
  from absl.testing import parameterized
  import neural_structured_learning.configs
  from neural_structured_learning.keras import adversarial_regularization
  import numpy
  import tensorflow
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 3 for calling a global function with example usage: build_linear_keras_functional_model(input_shape, weights, input_name) and returns: tf