def METHOD_NAME(params):
    from paddlex.seg import transforms
    seg_list = []
    min_value = max(params.image_shape) * 4 // 5
    max_value = max(params.image_shape) * 6 // 5
    seg_list.extend([
        transforms.ResizeRangeScaling(
            min_value=min_value, max_value=max_value),
        transforms.RandomBlur(prob=params.blur_prob)
    ])
    if params.rotate:
        seg_list.append(
            transforms.RandomRotate(rotate_range=params.max_rotation))
    if params.scale_aspect:
        seg_list.append(
            transforms.RandomScaleAspect(
                min_scale=params.min_ratio, aspect_ratio=params.aspect_ratio))
    seg_list.extend([
        transforms.RandomDistort(
            brightness_range=params.brightness_range,
            brightness_prob=params.brightness_prob,
            contrast_range=params.contrast_range,
            contrast_prob=params.contrast_prob,
            saturation_range=params.saturation_range,
            saturation_prob=params.saturation_prob,
            hue_range=params.hue_range,
            hue_prob=params.hue_prob),
        transforms.RandomVerticalFlip(prob=params.vertical_flip_prob),
        transforms.RandomHorizontalFlip(prob=params.horizontal_flip_prob),
        transforms.RandomPaddingCrop(crop_size=max(params.image_shape)),
        transforms.Normalize(
            mean=params.image_mean, std=params.image_std)
    ])
    train_transforms = transforms.Compose(seg_list)
    eval_transforms = transforms.Compose([
        transforms.ResizeByLong(long_size=max(params.image_shape)),
        transforms.Padding(target_size=max(params.image_shape)),
        transforms.Normalize(
            mean=params.image_mean, std=params.image_std)
    ])
    return train_transforms, eval_transforms