code: def make_mlp_sequential_model(hparams):
      """Creates a sequential multi-layer perceptron model."""
      model = tf.keras.Sequential()
      model.add(
          tf.keras.layers.InputLayer(
              input_shape=(hparams.max_seq_length,), name="words"
          )
      )
      # Input is already one-hot encoded in the integer format. We cast it to
      # floating point format here.
      model.add(
          tf.keras.layers.Lambda(
              lambda x: tf.keras.backend.cast(x, tf.float32)
          )
      )
      for num_units in hparams.num_fc_units:
          model.add(tf.keras.layers.Dense(num_units, activation="relu"))
          model.add(tf.keras.layers.Dropout(hparams.dropout_rate))
      model.add(
          tf.keras.layers.Dense(
              hparams.num_classes, activation="softmax"
          )
      )
      return model
  
file_path: neural-structured-learning/neural_structured_learning/examples/graph_keras_mlp_cora.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl import app
  from absl import flags
  from absl import logging
  import attr
  import neural_structured_learning
  import tensorflow
label_desc: Use image node_id 4 for calling a global function with example usage: make_mlp_sequential_model(hparams) and returns: model