code: def build_model(x, y):
      """Build a keras model."""
      input_shape = x.shape[1:]
      num_classes = y.shape[1]
      l2 = 0
      if FLAGS.model == "lr":
          model = tf.keras.Sequential(
              [
                  tf.keras.layers.Flatten(input_shape=input_shape),
                  tf.keras.layers.Dense(
                      num_classes,
                      kernel_initializer="glorot_normal",
                      kernel_regularizer=tf.keras.regularizers.l2(l2),
                  ),
              ]
          )
      elif FLAGS.model == "nn":
          model = tf.keras.Sequential(
              [
                  tf.keras.layers.Flatten(input_shape=input_shape),
                  tf.keras.layers.Dense(
                      32,
                      activation="relu",
                      kernel_initializer="glorot_normal",
                      kernel_regularizer=tf.keras.regularizers.l2(l2),
                  ),
                  tf.keras.layers.Dense(
                      num_classes,
                      kernel_initializer="glorot_normal",
                      kernel_regularizer=tf.keras.regularizers.l2(l2),
                  ),
              ]
          )
      else:
          raise NotImplementedError
      return model
  
file_path: privacy/research/audit_2020/fmnist_audit.py
imports: import numpy
  import tensorflow.compat.v1
  from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp
  from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent
  from tensorflow_privacy.privacy.optimizers import dp_optimizer_vectorized
  from absl import app
  from absl import flags
  import audit
label_desc: Use image node_id 2 for calling a global function with example usage: build_model(x, y) and returns: model