def _prepare_loss(self, loss_name):
        """
        Parse the loss parameters and load the loss layers.

        Args:
            loss_name (str): The root name of loss in the yaml file.
        Returns:
            dict: A dict including the loss parameters and layers.
        """
        args = self.dic.get(loss_name, {}).copy()
        if 'types' in args and 'coef' in args:
            len_types = len(args['types'])
            len_coef = len(args['coef'])
            if len_types != len_coef:
                if len_types == 1:
                    args['types'] = args['types'] * len_coef
                else:
                    raise ValueError(
                        'The length of types should equal to coef or equal to 1 in loss config, but they are {} and {}.'
                        .format(len_types, len_coef))
        else:
            raise ValueError(
                'Loss config should contain keys of "types" and "coef"')

        losses = dict()
        for key, val in args.items():
            if key == 'types':
                losses['types'] = []
                for item in args['types']:
                    if item['type'] != 'MixedLoss':
                        if 'ignore_index' in item:
                            assert item['ignore_index'] == self.train_dataset.ignore_index, 'If ignore_index of loss is set, '\
                            'the ignore_index of loss and train_dataset must be the same. \nCurrently, loss ignore_index = {}, '\
                            'train_dataset ignore_index = {}. \nIt is recommended not to set loss ignore_index, so it is consistent with '\
                            'train_dataset by default.'.format(item['ignore_index'], self.train_dataset.ignore_index)
                        item['ignore_index'] = \
                            self.train_dataset.ignore_index
                    losses['types'].append(self._load_object(item))
            else:
                losses[key] = val
        if len(losses['coef']) != len(losses['types']):
            raise RuntimeError(
                'The length of coef should equal to types in loss config: {} != {}.'
                .format(len(losses['coef']), len(losses['types'])))
        return losses

        Parse the loss parameters and load the loss layers.

        Args:
            loss_name (str): The root name of loss in the yaml file.
        Returns:
            dict: A dict including the loss parameters and layers.
        
contrib/RSSegBenchmark/c2fnet/cvlibs/config.py