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

save_checkpoint: (bool) whether to save checkpoint
            automatic_correction: (bool) correct nan errors
            **kwargs:
        """
        # load from saved model
        if prev_model:
            self.load_weights(prev_model)
        is_classification = 'entropy' in self.model.loss
        monitor = 'val_acc' if is_classification else 'val_mae'
        mode = 'max' if is_classification else 'min'
        dirname = kwargs.pop('dirname', 'callback')
        if not os.path.isdir(dirname):
            os.makedirs(dirname)
        if callbacks is 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,

save_checkpoint: (bool) whether to save checkpoint
            automatic_correction: (bool) correct nan errors
            **kwargs:
        """
        # load from saved model
        if prev_model:
            self.load_weights(prev_model)
        is_classification = 'entropy' in self.model.loss
        monitor = 'val_acc' if is_classification else 'val_mae'
        mode = 'max' if is_classification else 'min'
        dirname = kwargs.pop('dirname', 'callback')
        if not os.path.isdir(dirname):
            os.makedirs(dirname)
        if callbacks is 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,