def METHOD_NAME(self, idx) -> List[dict]:
    """Get data processed by ``self.pipeline``.
    Reduce the video loading and decompressing.
    Args:
        idx (int): The index of ``data_info``.
    Returns:
        List[dict]: A list of length num_repeats.
    """
    transforms = self.pipeline.transforms
    data_info = self.get_data_info(idx)
    data_info = transforms[0](data_info)  # DecordInit
    frame_inds_list, frame_inds_length = [], [0]
    fake_data_info = dict(
        total_frames=data_info['total_frames'],
        start_index=data_info['start_index'])
    if not self.sample_once:
        for repeat in range(self.num_repeats):
            data_info_ = transforms[1](fake_data_info)  # SampleFrames
            frame_inds = data_info_['frame_inds']
            frame_inds_list.append(frame_inds.reshape(-1))
            frame_inds_length.append(frame_inds.size +
                                     frame_inds_length[-1])
    else:
        data_info_ = transforms[1](fake_data_info)  # SampleFrames
        frame_inds = data_info_['frame_inds']
        for repeat in range(self.num_repeats):
            frame_inds_list.append(frame_inds.reshape(-1))
            frame_inds_length.append(frame_inds.size +
                                     frame_inds_length[-1])
    for key in data_info_:
        data_info[key] = data_info_[key]
    data_info['frame_inds'] = np.concatenate(frame_inds_list)
    data_info = transforms[2](data_info)  # DecordDecode
    imgs = data_info.pop('imgs')
    data_info_list = []
    for repeat in range(self.num_repeats):
        data_info_ = deepcopy(data_info)
        start = frame_inds_length[repeat]
        end = frame_inds_length[repeat + 1]
        data_info_['imgs'] = imgs[start:end]
        for transform in transforms[3:]:
            data_info_ = transform(data_info_)
        data_info_list.append(data_info_)
    del imgs
    return data_info_list