How to use the ivis.nn.losses.semi_supervised_loss function in ivis

# Multiclass Linear SVM output
                            supervised_output = Dense(self.n_classes, activation='linear',
                                                      name='supervised',
                                                      kernel_regularizer=regularizers.l2())(anchor_embedding)
                else:
                    # Regression
                    if len(Y.shape) > 1:
                        self.n_classes = Y.shape[-1]
                    else:
                        self.n_classes = 1
                    supervised_output = Dense(self.n_classes, activation='linear',
                                              name='supervised')(anchor_embedding)

                supervised_loss = keras.losses.get(self.supervision_metric)
                if self.supervision_metric == 'sparse_categorical_crossentropy':
                    supervised_loss = semi_supervised_loss(supervised_loss)

                final_network = Model(inputs=self.model_.inputs,
                                      outputs=[self.model_.output,
                                               supervised_output])
                self.model_ = final_network
                self.model_.compile(
                    optimizer='adam',
                    loss={
                        'stacked_triplets': triplet_loss_func,
                        'supervised': supervised_loss
                         },
                    loss_weights={
                        'stacked_triplets': 1 - self.supervision_weight,
                        'supervised': self.supervision_weight})

                # Store dedicated classification model