How to use the megnet.callbacks.ReduceLRUponNan function in megnet

# with this call back you can stop the model training by `touch STOP`
            callbacks = [ManualStop()]
        train_nb_atoms = [len(i['atom']) for i in train_graphs]
        train_targets = [self.target_scaler.transform(i, j) for i, j in zip(train_targets, train_nb_atoms)]

        if validation_graphs is not None:
            filepath = os.path.join(dirname, '%s_{epoch:05d}_{%s:.6f}.hdf5' % (monitor, monitor))
            val_nb_atoms = [len(i['atom']) for i in validation_graphs]
            validation_targets = [self.target_scaler.transform(i, j) for i, j in zip(validation_targets, val_nb_atoms)]
            val_inputs = self.graph_converter.get_flat_data(validation_graphs, validation_targets)

            val_generator = self._create_generator(*val_inputs,
                                                   batch_size=batch_size)
            steps_per_val = int(np.ceil(len(validation_graphs) / batch_size))
            if automatic_correction:
                callbacks.extend([ReduceLRUponNan(filepath=filepath,
                                                  monitor=monitor,
                                                  mode=mode,
                                                  factor=lr_scaling_factor,
                                                  patience=patience,
                                                  )])
            if save_checkpoint:
                callbacks.extend([ModelCheckpointMAE(filepath=filepath,
                                                     monitor=monitor,
                                                     mode=mode,
                                                     save_best_only=True,
                                                     save_weights_only=False,
                                                     val_gen=val_generator,
                                                     steps_per_val=steps_per_val,
                                                     target_scaler=self.target_scaler)])
        else:
            val_generator = None

# with this call back you can stop the model training by `touch STOP`
            callbacks = [ManualStop()]
        train_nb_atoms = [len(i['atom']) for i in train_graphs]
        train_targets = [self.target_scaler.transform(i, j) for i, j in zip(train_targets, train_nb_atoms)]

        if validation_graphs is not None:
            filepath = os.path.join(dirname, '%s_{epoch:05d}_{%s:.6f}.hdf5' % (monitor, monitor))
            val_nb_atoms = [len(i['atom']) for i in validation_graphs]
            validation_targets = [self.target_scaler.transform(i, j) for i, j in zip(validation_targets, val_nb_atoms)]
            val_inputs = self.graph_converter.get_flat_data(validation_graphs, validation_targets)

            val_generator = self._create_generator(*val_inputs,
                                                   batch_size=batch_size)
            steps_per_val = int(np.ceil(len(validation_graphs) / batch_size))
            if automatic_correction:
                callbacks.extend([ReduceLRUponNan(filepath=filepath,
                                                  monitor=monitor,
                                                  mode=mode,
                                                  factor=lr_scaling_factor,
                                                  patience=patience,
                                                  )])
            if save_checkpoint:
                callbacks.extend([ModelCheckpointMAE(filepath=filepath,
                                                     monitor=monitor,
                                                     mode=mode,
                                                     save_best_only=True,
                                                     save_weights_only=False,
                                                     val_gen=val_generator,
                                                     steps_per_val=steps_per_val,
                                                     target_scaler=self.target_scaler)])
        else:
            val_generator = None