def METHOD_NAME(indexes: tp.List[int], arity: int = 2) -> np.ndarray:
    indexes_arr = np.array(indexes, copy=True)
    assert not np.any(np.isnan(indexes_arr))
    pdf_bin_size = 1 / arity
    # We take the center of each bin (in the pdf space)
    x = scipy.stats.norm.ppf(indexes_arr * pdf_bin_size + (pdf_bin_size / 2))  # type: ignore
    nan_indices = np.where(np.isnan(x))
    x[nan_indices] = np.sign(indexes_arr[nan_indices] - (arity / 2.0)) * np.finfo(np.dtype("float")).max
    return x