from tensorflow.python.keras.layers import Dense from tensorflow.python.keras.models import Model # Define model architecture inputs = Input(shape=(784,)) hidden = Dense(128, activation='relu')(inputs) outputs = Dense(10, activation='softmax')(hidden) # Create model object model = Model(inputs=inputs, outputs=outputs) # Add L2 regularization loss to the model regularization_loss = tf.reduce_sum(tf.square(inputs)) model.add_loss(regularization_loss) # Compile and train the model model.compile(optimizer='adam', loss='categorical_crossentropy') model.fit(x_train, y_train, epochs=10, validation_data=(x_val, y_val))In this example, after defining the model architecture, we create a `Model` object with the inputs and outputs. We then define the L2 regularization loss as the sum of the squares of the input values, and use `add_loss` to add this loss to the model. Finally, we compile the model with a categorical cross-entropy loss function and train the model using the `fit` method. During training, the regularization loss will be included in the overall loss function and optimized alongside the primary loss. Overall, `add_loss` is a useful method for customizing the training process in TensorFlow models.