How to use the batchgenerators.transforms.crop_and_pad_transforms.CenterCropTransform function in batchgenerators

if mode == "train":
        transform_list = [CenterCropTransform(crop_size=target_size),
                          ResizeTransform(target_size=target_size, order=1),
                          MirrorTransform(axes=(2,)),
                          SpatialTransform(patch_size=(target_size, target_size, target_size), random_crop=False,
                                           patch_center_dist_from_border=target_size // 2,
                                           do_elastic_deform=True, alpha=(0., 1000.), sigma=(40., 60.),
                                           do_rotation=True,
                                           angle_x=(-0.1, 0.1), angle_y=(0, 1e-8), angle_z=(0, 1e-8),
                                           scale=(0.9, 1.4),
                                           border_mode_data="nearest", border_mode_seg="nearest"),
                          ]

    elif mode == "val":
        transform_list = [CenterCropTransform(crop_size=target_size),
                          ResizeTransform(target_size=target_size, order=1),
                          ]

    elif mode == "test":
        transform_list = [CenterCropTransform(crop_size=target_size),
                          ResizeTransform(target_size=target_size, order=1),
                          ]

    transform_list.append(NumpyToTensor())

    return Compose(transform_list)

my_transforms = []
    if do_aug:
        mirror_transform = Mirror(axes=np.arange(2, cf.dim+2, 1))
        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'],
                                             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'])

        my_transforms.append(spatial_transform)
    else:
        my_transforms.append(CenterCropTransform(crop_size=cf.patch_size[:cf.dim]))

    my_transforms.append(ConvertSegToBoundingBoxCoordinates(cf.dim, get_rois_from_seg_flag=False, class_specific_seg_flag=cf.class_specific_seg_flag))
    all_transforms = Compose(my_transforms)
    # multithreaded_generator = SingleThreadedAugmenter(data_gen, all_transforms)
    multithreaded_generator = MultiThreadedAugmenter(data_gen, all_transforms, num_processes=cf.n_workers, seeds=range(cf.n_workers))
    return multithreaded_generator

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

if is_training:
        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'],
                                             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'])

        my_transforms.append(spatial_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))
    all_transforms = Compose(my_transforms)

    multithreaded_generator = MultiThreadedAugmenter(data_gen, all_transforms, num_processes=data_gen.n_filled_threads,
                                                     seeds=range(data_gen.n_filled_threads))
    return multithreaded_generator

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'],
                                             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'])

        my_transforms.append(spatial_transform)
    else:
        my_transforms.append(CenterCropTransform(crop_size=cf.patch_size[:cf.dim]))

    my_transforms.append(ConvertSegToBoundingBoxCoordinates(cf.dim, cf.roi_items, False, cf.class_specific_seg))
    all_transforms = Compose(my_transforms)
    # multithreaded_generator = SingleThreadedAugmenter(data_gen, all_transforms)
    multithreaded_generator = MultiThreadedAugmenter(data_gen, all_transforms, num_processes=data_gen.n_filled_threads,
                                                     seeds=range(data_gen.n_filled_threads))
    return multithreaded_generator

my_transforms = []
    if is_training:
        mirror_transform = Mirror(axes=np.arange(cf.dim))
        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'],
                                             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'])

        my_transforms.append(spatial_transform)
    else:
        my_transforms.append(CenterCropTransform(crop_size=cf.patch_size[:cf.dim]))

    my_transforms.append(ConvertSegToBoundingBoxCoordinates(cf.dim, get_rois_from_seg_flag=False, class_specific_seg_flag=cf.class_specific_seg_flag))
    all_transforms = Compose(my_transforms)
    # multithreaded_generator = SingleThreadedAugmenter(data_gen, all_transforms)
    multithreaded_generator = MultiThreadedAugmenter(data_gen, all_transforms, num_processes=cf.n_workers, seeds=range(cf.n_workers))
    return multithreaded_generator