code: def build_linear_sequential_model(input_shape, weights, num_output=1):
      model = tf.keras.Sequential()
      model.add(
          tf.keras.layers.InputLayer(
              input_shape=input_shape, name=FEATURE_NAME
          )
      )
      model.add(
          tf.keras.layers.Dense(
              num_output,
              input_shape=input_shape,
              use_bias=False,
              name="dense",
              kernel_initializer=tf.keras.initializers.Constant(
                  weights
              ),
          )
      )
      return model
  
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 a global function with example usage: build_linear_sequential_model(input_shape, weights, num_output) and returns: model