How to use the albumentations.HueSaturationValue function in albumentations

def light_augmentations():
    return A.Compose([
        A.HorizontalFlip(),
        A.RandomBrightnessContrast(),
        A.OneOf([
            A.ShiftScaleRotate(scale_limit=0.05, rotate_limit=15, border_mode=cv2.BORDER_CONSTANT),
            A.IAAAffine(),
            A.IAAPerspective(),
            A.NoOp()
        ]),
        A.HueSaturationValue(),
        A.Normalize()
    ])

def get_transforms(size: int, scope: str = 'weak', crop='random'):
    augs = {'strong': albu.Compose([albu.HorizontalFlip(),
                                    albu.ShiftScaleRotate(shift_limit=0.0, scale_limit=0.2, rotate_limit=20, p=.4),
                                    albu.ElasticTransform(),
                                    albu.OpticalDistortion(),
                                    albu.OneOf([
                                        albu.CLAHE(clip_limit=2),
                                        albu.IAASharpen(),
                                        albu.IAAEmboss(),
                                        albu.RandomBrightnessContrast(),
                                        albu.RandomGamma()
                                    ], p=0.5),
                                    albu.OneOf([
                                        albu.RGBShift(),
                                        albu.HueSaturationValue(),
                                    ], p=0.5),
                                    ]),
            'weak': albu.Compose([albu.HorizontalFlip(),
                                  ]),
            }

    aug_fn = augs[scope]
    crop_fn = {'random': albu.RandomCrop(size, size),
               'center': albu.CenterCrop(size, size)}[crop]
    normalize = albu.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])

    pipeline = albu.Compose([aug_fn, crop_fn, normalize], additional_targets={'target': 'image'})

    def process(a, b):
        r = pipeline(image=a, target=b)
        return r['image'], r['target']

A.Transpose(),
            A.RandomGridShuffle(),
            A.ShiftScaleRotate(
                scale_limit=0.1, rotate_limit=45, border_mode=cv2.BORDER_CONSTANT, mask_value=0, value=0
            ),
            A.ElasticTransform(border_mode=cv2.BORDER_CONSTANT, alpha_affine=5, mask_value=0, value=0),
            # Add occasion blur
            A.OneOf([A.GaussianBlur(), A.GaussNoise(), A.IAAAdditiveGaussianNoise(), A.NoOp()]),
            # D4 Augmentations
            A.OneOf([A.CoarseDropout(), A.MaskDropout(max_objects=10), A.NoOp()]),
            # Spatial-preserving augmentations:
            A.OneOf(
                [
                    A.RandomBrightnessContrast(brightness_by_max=True),
                    A.CLAHE(),
                    A.HueSaturationValue(),
                    A.RGBShift(),
                    A.RandomGamma(),
                    A.NoOp(),
                ]
            ),
            # Weather effects
            A.OneOf([A.RandomFog(fog_coef_lower=0.01, fog_coef_upper=0.3, p=0.1), A.NoOp()]),
            A.Normalize(),
        ]

A.ISONoise(p=0.5),

        # Brightness/contrast augmentations
        A.OneOf([
            A.RandomBrightnessContrast(brightness_limit=0.5,
                                       contrast_limit=0.4),
            IndependentRandomBrightnessContrast(brightness_limit=0.25,
                                                contrast_limit=0.24),
            A.RandomGamma(gamma_limit=(50, 150)),
            A.NoOp()
        ]),

        A.OneOf([
            A.RGBShift(r_shift_limit=40, b_shift_limit=30, g_shift_limit=30),
            A.HueSaturationValue(hue_shift_limit=10,
                                 sat_shift_limit=10),
            A.ToGray(p=0.2),
            A.NoOp()
        ]),

        A.ChannelDropout(),
        A.RandomGridShuffle(p=0.3),

        # D4
        A.Compose([
            A.RandomRotate90(),
            A.Transpose()
        ])

def get_transform(train: bool) -> Callable:
    train_initial_size = 2048
    crop_min_max_height = (400, 533)
    crop_width = 512
    crop_height = 384
    if train:
        transforms = [
            A.LongestMaxSize(max_size=train_initial_size),
            A.RandomSizedCrop(
                min_max_height=crop_min_max_height,
                width=crop_width,
                height=crop_height,
                w2h_ratio=crop_width / crop_height,
            ),
            A.HueSaturationValue(
                hue_shift_limit=7,
                sat_shift_limit=10,
                val_shift_limit=10,
            ),
            A.RandomBrightnessContrast(),
            A.RandomGamma(),
        ]
    else:
        test_size = int(train_initial_size *
                        crop_height / np.mean(crop_min_max_height))
        print(f'Test image max size {test_size} px')
        transforms = [
            A.LongestMaxSize(max_size=test_size),
        ]
    transforms.extend([
        ToTensor(),

) -> Callable:
    train_initial_size = 3072  # this value should not matter any more?
    crop_ratio = crop_height / test_height
    crop_min_max_height = tuple(
        int(train_initial_size * crop_ratio * (1 + sign * scale_aug))
        for sign in [-1, 1])
    if train:
        transforms = [
            LongestMaxSizeRandomSizedCrop(
                max_size=train_initial_size,
                min_max_height=crop_min_max_height,
                width=crop_width,
                height=crop_height,
                w2h_ratio=crop_width / crop_height,
            ),
            A.HueSaturationValue(
                hue_shift_limit=color_hue_aug,
                sat_shift_limit=color_sat_aug,
                val_shift_limit=color_val_aug,
            ),
            A.RandomBrightnessContrast(),
            A.RandomGamma(),
        ]
    else:
        transforms = [
            A.LongestMaxSize(max_size=test_height),
        ]
    if normalize:
        transforms.append(A.Normalize())
    transforms.extend([
        ToTensor(),
    ])