code: def mlp(
      uid,
      iid,
      num_users,
      num_items,
      layers,
      reg_layers,
      act_fn,
      seed=None,
  ):
      with tf.variable_scope("MLP") as scope:
          user_emb, item_emb = emb(
              uid=uid,
              iid=iid,
              num_users=num_users,
              num_items=num_items,
              emb_size=int(layers[0] / 2),
              reg_user=reg_layers[0],
              reg_item=reg_layers[0],
              seed=seed,
              scope=scope,
          )
          interaction = tf.concat([user_emb, item_emb], axis=-1)
          for i, layer in enumerate(layers[1:]):
              interaction = tf.layers.dense(
                  interaction,
                  units=layer,
                  name="layer{}".format(i + 1),
                  activation=act_functions.get(act_fn, tf.nn.relu),
                  kernel_initializer=tf.initializers.lecun_uniform(
                      seed
                  ),
                  kernel_regularizer=tf.keras.regularizers.L2(
                      reg_layers[i + 1]
                  ),
              )
          return interaction
  
imports: import warnings
  import tensorflow.compat.v1
label_desc: Use image node_id 5 for calling a global function with example usage: mlp(uid, iid, num_users, num_items, layers, reg_layers, act_fn, seed) and returns: interaction