How to use the batchgenerators.transforms.color_transforms.GammaTransform function in batchgenerators

my_transforms = []
    if do_aug:
        if cf.da_kwargs["mirror"]:
            mirror_transform = Mirror(axes=cf.da_kwargs['mirror_axes'])
            my_transforms.append(mirror_transform)
        spatial_transform = SpatialTransform(patch_size=cf.patch_size[:cf.dim],
                                         patch_center_dist_from_border=cf.da_kwargs['rand_crop_dist'][:2],
                                         do_elastic_deform=cf.da_kwargs['do_elastic_deform'],
                                         alpha=cf.da_kwargs['alpha'], sigma=cf.da_kwargs['sigma'],
                                         do_rotation=cf.da_kwargs['do_rotation'], angle_x=cf.da_kwargs['angle_x'],
                                         angle_y=cf.da_kwargs['angle_y'], angle_z=cf.da_kwargs['angle_z'],
                                         do_scale=cf.da_kwargs['do_scale'], scale=cf.da_kwargs['scale'],
                                         random_crop=cf.da_kwargs['random_crop'],
                                         border_mode_data=cf.da_kwargs['border_mode_data'])
        my_transforms.append(spatial_transform)
        gamma_transform = GammaTransform(gamma_range=cf.da_kwargs["gamma_range"], invert_image=False,
                                         per_channel=False, retain_stats=False)
        my_transforms.append(gamma_transform)
    
    else:
        my_transforms.append(CenterCropTransform(crop_size=cf.patch_size[:cf.dim]))

    if cf.create_bounding_box_targets:
        my_transforms.append(ConvertSegToBoundingBoxCoordinates(cf.dim, cf.roi_items, False, cf.class_specific_seg))
        #batch receives entry 'bb_target' w bbox coordinates as [y1,x1,y2,x2,z1,z2].
    #my_transforms.append(ConvertSegToOnehotTransform(classes=range(cf.num_seg_classes)))
    all_transforms = Compose(my_transforms)
    #MTAugmenter creates iterator from data iterator data_gen after applying the composed transform all_transforms
    multithreaded_generator = MultiThreadedAugmenter(data_gen, all_transforms, num_processes=cf.n_workers,
                                                     seeds=np.random.randint(0,cf.n_workers*2,size=cf.n_workers))
    return multithreaded_generator

"""
    create mutli-threaded train/val/test batch generation and augmentation pipeline.
    :param patient_data: dictionary containing one dictionary per patient in the train/test subset
    :param test_pids: (optional) list of test patient ids, calls the test generator.
    :param do_aug: (optional) whether to perform data augmentation (training) or not (validation/testing)
    :return: multithreaded_generator
    """
    data_gen = BatchGenerator(cf, patient_data, sample_pids_w_replace=sample_pids_w_replace)
    
    my_transforms = []
    if do_aug:
        if cf.da_kwargs["mirror"]:
            mirror_transform = Mirror(axes=cf.da_kwargs['mirror_axes'])
            my_transforms.append(mirror_transform)
        if cf.da_kwargs["gamma_transform"]:
            gamma_transform = GammaTransform(gamma_range=cf.da_kwargs["gamma_range"], invert_image=False,
                                             per_channel=False, retain_stats=True)
            my_transforms.append(gamma_transform)
        if cf.dim == 3:
            # augmentations with desired effect on z-dimension
            spatial_transform = SpatialTransform(patch_size=cf.patch_size,
                                             patch_center_dist_from_border=cf.da_kwargs['rand_crop_dist'],
                                             do_elastic_deform=False,
                                             do_rotation=cf.da_kwargs['do_rotation'], angle_x=cf.da_kwargs['angle_x'],
                                             angle_y=cf.da_kwargs['angle_y'], angle_z=cf.da_kwargs['angle_z'],
                                             do_scale=cf.da_kwargs['do_scale'], scale=cf.da_kwargs['scale'],
                                             random_crop=cf.da_kwargs['random_crop'],
                                             border_mode_data=cf.da_kwargs['border_mode_data'])
            my_transforms.append(spatial_transform)
            # augmentations that are only meant to affect x-y
            my_transforms.append(Convert3DTo2DTransform())
            spatial_transform = SpatialTransform(patch_size=cf.patch_size[:2],