def METHOD_NAME(self, issue_manager, embeddings):
    issue_manager.find_issues(features=embeddings["embedding"])
    issues, summary, info = issue_manager.issues, issue_manager.summary, issue_manager.info
    expected_issue_mask = np.array([False] * 3 + [True] * 2)
    assert np.all(
        issues["is_near_duplicate_issue"] == expected_issue_mask
    ), "Issue mask should be correct"
    assert summary["issue_type"][0] == "near_duplicate"
    assert summary["score"][0] == pytest.approx(expected=0.03122489, abs=1e-7)
    assert (
        info.get("near_duplicate_sets", None) is not None
    ), "Should have sets of near duplicates"
    new_issue_manager = NearDuplicateIssueManager(
        datalab=issue_manager.datalab,
        metric="euclidean",
        k=2,
        threshold=0.1,
    )
    new_issue_manager.find_issues(features=embeddings["embedding"])