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

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:
                        self.n_classes = 1