def METHOD_NAME(
    testdata_ibma,
    testdata_cbma_full,
    estimator,
    meta_type,
    n_samples,
    target_image,
    voxel_thresh,
):
    """Smoke test the Jackknife method."""
    dset1 = testdata_cbma_full.slice(testdata_cbma_full.ids[:10])
    dset2 = testdata_cbma_full.slice(testdata_cbma_full.ids[10:])
    meta = estimator()
    testdata = testdata_ibma if meta_type == "ibma" else testdata_cbma_full
    res = meta.fit(dset1, dset2) if n_samples == "twosample" else meta.fit(testdata)
    jackknife = diagnostics.Jackknife(target_image=target_image, voxel_thresh=voxel_thresh)
    results = jackknife.transform(res)
    image_name = "_".join(target_image.split("_")[1:])
    image_name = f"_{image_name}" if image_name else image_name
    # For ibma.WeightedLeastSquares we have both positive and negative tail combined.
    contribution_table = (
        results.tables[f"{target_image}_diag-Jackknife_tab-counts"]
        if estimator == ibma.WeightedLeastSquares
        else results.tables[f"{target_image}_diag-Jackknife_tab-counts_tail-positive"]
    )
    clusters_table = results.tables[f"{target_image}_tab-clust"]
    label_maps = results.maps[f"label{image_name}_tail-positive"]
    ids_ = meta.inputs_["id"] if n_samples == "onesample" else meta.inputs_["id1"]
    assert contribution_table.shape[0] == len(ids_)
    assert clusters_table.shape[0] >= contribution_table.shape[1] - 1
    assert len(label_maps) > 0