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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def cluster_sites(mol, tol, give_only_index=False):
 """ Cluster sites based on distance and species type. Args: mol (Molecule):
 Molecule **with origin at center of mass**. tol (float):
 Tolerance to use. Returns: (origin_site, clustered_sites):
 origin_site is a site at the center of mass (None if there are no origin atoms). clustered_sites is a dict of {(avg_dist, species_and_occu):
 [list of sites]} """

    # Cluster works for dim > 2 data. We just add a dummy 0 for second
    # coordinate.
    dists = [[np.linalg.norm(site.coords), 0] for site in mol]
    import scipy.cluster as spcluster
    f = spcluster.hierarchy.fclusterdata(dists, tol, criterion='distance')
    clustered_dists = defaultdict(list)
    for i, site in enumerate(mol):
        clustered_dists[f[i]].append(dists[i])
    avg_dist = {label: np.mean(val) for label, val in clustered_dists.items()}
    clustered_sites = defaultdict(list)
    origin_site = None
    for i, site in enumerate(mol):
        if avg_dist[f[i]] < tol:
            if give_only_index:
                origin_site = i
            else:
                origin_site = site
        else:
            if give_only_index:
                clustered_sites[
                    (avg_dist[f[i]], site.species)].append(i)
            else:
                clustered_sites[
                    (avg_dist[f[i]], site.species)].append(site)
    return origin_site, clustered_sites