def METHOD_NAME(min_labels):
    labels_reader = providers.LabelsReader(min_labels)
    instance_centroid_finder = instance_centroids.InstanceCentroidFinder(
        center_on_anchor_part=True,
        anchor_part_names="A",
        skeletons=labels_reader.labels.skeletons,
    )
    instance_cropper = instance_cropping.InstanceCropper(
        crop_width=160, crop_height=160, keep_full_image=False
    )
    ds = instance_centroid_finder.transform_dataset(labels_reader.make_dataset())
    ds = instance_cropper.transform_dataset(ds)
    example = next(iter(ds))
    assert example["instance_image"].shape == (160, 160, 1)
    assert example["instance_image"].dtype == tf.uint8
    assert example["bbox"].shape == (4,)
    assert example["bbox"].dtype == tf.float32
    assert example["center_instance"].shape == (2, 2)
    assert example["center_instance"].dtype == tf.float32
    assert example["center_instance_ind"] == 0
    assert example["center_instance_ind"].dtype == tf.int32
    assert example["all_instances"].shape == (2, 2, 2)
    assert example["all_instances"].dtype == tf.float32
    assert example["centroid"].shape == (2,)
    assert example["centroid"].dtype == tf.float32
    assert example["full_image_height"] == 384
    assert example["full_image_height"].dtype == tf.int32
    assert example["full_image_width"] == 384
    assert example["full_image_width"].dtype == tf.int32
    assert example["video_ind"] == 0
    assert example["video_ind"].dtype == tf.int32
    assert example["frame_ind"] == 0
    assert example["frame_ind"].dtype == tf.int64
    np.testing.assert_array_equal(example["scale"], (1.0, 1.0))
    assert example["scale"].dtype == tf.float32
    assert example["skeleton_inds"].shape == (2,)
    assert example["skeleton_inds"].dtype == tf.int32
    assert "image" not in example