def spatial_inter_hemi_connectivity(src, dist, verbose=None):
    """Get vertices on each hemisphere that are close to the other hemisphere

    Parameters
    ----------
    src : instance of SourceSpaces
        The source space. Must be surface type.
    dist : float
        Maximal Euclidean distance (in m) between vertices in one hemisphere
        compared to the other to consider neighbors.
    verbose : bool, str, int, or None
        If not None, override default verbose level (see mne.verbose).

    Returns
    -------
    connectivity : sparse COO matrix
        The connectivity matrix describing the spatial graph structure.
        Typically this should be combined (addititively) with another
        existing intra-hemispheric connectivity matrix, e.g. computed
        using geodesic distances.
    """
    from scipy.spatial.distance import cdist
    src = _ensure_src(src, kind='surf')
    conn = cdist(src[0]['rr'][src[0]['vertno']],
                 src[1]['rr'][src[1]['vertno']])
    conn = sparse.csr_matrix(dist <= conn, dtype=int)
    empties = [sparse.csr_matrix((nv, nv), dtype=int) for nv in conn.shape]
    conn = sparse.vstack([sparse.hstack([empties[0], conn]),
                          sparse.hstack([conn.T, empties[1]])])
    return conn
dataset/ETHPy150Open mne-tools/mne-python/mne/source_estimate.py/spatial_inter_hemi_connectivity/swapped operands of `<=`