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

if type(self.model_def) is str:
                input_size = (X.shape[-1],)
                self.model_, anchor_embedding, _, _ = \
                    triplet_network(base_network(self.model_def, input_size),
                                    embedding_dims=self.embedding_dims)
            else:
                self.model_, anchor_embedding, _, _ = \
                    triplet_network(self.model_def,
                                    embedding_dims=self.embedding_dims)

            if Y is None:
                self.model_.compile(optimizer='adam', loss=triplet_loss_func)
            else:
                if is_categorical(self.supervision_metric):
                    if not is_multiclass(self.supervision_metric):
                        if not is_hinge(self.supervision_metric):
                            # Binary logistic classifier
                            if len(Y.shape) > 1:
                                self.n_classes = Y.shape[-1]
                            else:
                                self.n_classes = 1
                            supervised_output = Dense(self.n_classes, activation='sigmoid',
                                                      name='supervised')(anchor_embedding)
                        else:
                            # Binary Linear SVM output
                            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',
                                                      kernel_regularizer=regularizers.l2())(anchor_embedding)

self.n_classes = Y.shape[-1]
                            else:
                                self.n_classes = 1
                            supervised_output = Dense(self.n_classes, activation='sigmoid',
                                                      name='supervised')(anchor_embedding)
                        else:
                            # Binary Linear SVM output
                            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',
                                                      kernel_regularizer=regularizers.l2())(anchor_embedding)
                    else:
                        if not is_hinge(self.supervision_metric):
                            validate_sparse_labels(Y)
                            self.n_classes = len(np.unique(Y[Y != np.array(-1)]))
                            # Softmax classifier
                            supervised_output = Dense(self.n_classes, activation='softmax',
                                                      name='supervised')(anchor_embedding)
                        else:
                            self.n_classes = len(np.unique(Y, axis=0))
                            # 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: