code: def build_linear_keras_sequential_model(input_shape, weights):
      model = tf.keras.Sequential()
      model.add(tf.keras.Input(shape=input_shape, name="feature"))
      model.add(
          tf.keras.layers.Dense(
              weights.shape[-1],
              use_bias=False,
              kernel_initializer=tf.keras.initializers.Constant(
                  weights
              ),
          )
      )
      return model
  
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 1 for calling a global function with example usage: build_linear_keras_sequential_model(input_shape, weights) and returns: model