How to use the batchgenerators.dataloading.multi_threaded_augmenter.MultiThreadedAugmenter function in batchgenerators

p_per_sample=self.Config.P_SAMP))

                if self.Config.DAUG_NOISE:
                    tfs.append(GaussianNoiseTransform(noise_variance=self.Config.DAUG_NOISE_VARIANCE,
                                                      p_per_sample=self.Config.P_SAMP))

                if self.Config.DAUG_MIRROR:
                    tfs.append(MirrorTransform())

                if self.Config.DAUG_FLIP_PEAKS:
                    tfs.append(FlipVectorAxisTransform())

        tfs.append(NumpyToTensor(keys=["data", "seg"], cast_to="float"))

        #num_cached_per_queue 1 or 2 does not really make a difference
        batch_gen = MultiThreadedAugmenter(batch_generator, Compose(tfs), num_processes=num_processes,
                                           num_cached_per_queue=1, seeds=None, pin_memory=True)
        return batch_gen  # data: (batch_size, channels, x, y), seg: (batch_size, channels, x, y)

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

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)
    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=data_gen.n_filled_threads,
                                                     seeds=range(data_gen.n_filled_threads))
    return multithreaded_generator

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 self.Config.DAUG_RESAMPLE:
                    tfs.append(SimulateLowResolutionTransform(zoom_range=(0.5, 1), p_per_sample=0.2))

                if self.Config.DAUG_NOISE:
                    tfs.append(GaussianNoiseTransform(noise_variance=(0, 0.05), p_per_sample=0.2))

                if self.Config.DAUG_MIRROR:
                    tfs.append(MirrorTransform())

                if self.Config.DAUG_FLIP_PEAKS:
                    tfs.append(FlipVectorAxisTransform())

        tfs.append(NumpyToTensor(keys=["data", "seg"], cast_to="float"))

        # num_cached_per_queue 1 or 2 does not really make a difference
        batch_gen = MultiThreadedAugmenter(batch_generator, Compose(tfs), num_processes=num_processes,
                                           num_cached_per_queue=1, seeds=None, pin_memory=True)
        return batch_gen  # data: (batch_size, channels, x, y), seg: (batch_size, channels, x, y)

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