‘Model’ object has no attribute ‘_name’ tensorflow keras

def NCF(feature_dim_dict,num_users, num_items, mf_dim=10, layers=[10],reg_layers=[0], reg_mf=0,embedding_size=8,l2_reg_linear=0.00001, l2_reg_embedding=0.00001,init_std=0.0001, seed=1024, task=’binary’): assert len(layers) == len(reg_layers) check_feature_config_dict(feature_dim_dict)

deep_emb_list, linear_emb_list, dense_input_dict, inputs_list =preprocess_input_embedding(feature_dim_dict,                                                                                         embedding_size,                                                                                            l2_reg_embedding,                                                                                            l2_reg_linear, init_std,                                                                                            seed,                                                                                            create_linear_weight=True) num_layer = len(layers) movie_emb=deep_emb_list[0] user_emb=deep_emb_list[1] inputs_list=[user_emb,movie_emb] mf_user_latent = tf.keras.layers.Flatten()(user_emb) mf_item_latent = tf.keras.layers.Flatten()(movie_emb) mf_vector =tf.multiply(mf_user_latent, mf_item_latent) mlp_user_latent = tf.keras.layers.Flatten()(movie_emb) mlp_item_latent =tf.keras.layers.Flatten()(user_emb) mlp_vector = tf.keras.layers.Concatenate(axis=-1)([mlp_user_latent, mlp_item_latent]) for idx in range(1, num_layer):     layer = tf.keras.layers.Dense(layers[idx], W_regularizer=l2(reg_layers[idx]), activation='relu', name="layer%d" % idx)     mlp_vector = layer(mlp_vector) predict_vector = tf.keras.layers.Concatenate(axis=-1)([mf_vector, mlp_vector])  prediction =tf.keras.layers.Dense(1, kernel_initializer='lecun_uniform')(predict_vector)  model =tf.keras.models.Model(inputs=inputs_list,outputs=prediction) return model