def METHOD_NAME(small_robot_mp4_path, hdf5_file_path):
    # Create some fake data using two different size videos.
    skeleton = sleap.Skeleton.from_names_and_edge_inds(["A"])
    labels = sleap.Labels(
        [
            sleap.LabeledFrame(
                frame_idx=0,
                video=sleap.Video.from_filename(small_robot_mp4_path, grayscale=True),
                instances=[
                    sleap.Instance.from_pointsarray(
                        np.array([[128, 128]]), skeleton=skeleton
                    )
                ],
            ),
            sleap.LabeledFrame(
                frame_idx=0,
                video=sleap.Video.from_filename(
                    hdf5_file_path, dataset="/box", input_format="channels_first"
                ),
                instances=[
                    sleap.Instance.from_pointsarray(
                        np.array([[128, 128]]), skeleton=skeleton
                    )
                ],
            ),
        ]
    )
    # Create a loader for those labels.
    labels_reader = providers.LabelsReader(labels)
    ds = labels_reader.make_dataset()
    ds_iter = iter(ds)
    # Check LabelReader can provide different shapes of individual samples
    assert next(ds_iter)["image"].shape == (320, 560, 1)
    assert next(ds_iter)["image"].shape == (512, 512, 1)
    # Check util functions
    h, w = labels_reader.max_height_and_width
    assert h == 512
    assert w == 560
    assert labels_reader.is_from_multi_size_videos