<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def temporal_efficiency(tnet=None, paths=None, calc='global'):
 r""" Returns temporal efficiency estimate. BU networks only. Parameters Input should be *either* tnet or paths. data : array or dict Temporal network input (graphlet or contact). nettype: 'bu', 'bd'. paths : pandas dataframe Output of TenetoBIDS.networkmeasure.shortest_temporal_paths calc : str Options: 'global' (default) - measure averages over time and nodes; 'node' or 'node_from' average over nodes (i) and time. Giving average efficiency for i to j; 'node_to' measure average over nodes j and time; Giving average efficiency using paths to j from i; Returns ------- E : array Global temporal efficiency """


    if tnet is not None and paths is not None:
        raise ValueError('Only network or path input allowed.')
    if tnet is None and paths is None:
        raise ValueError('No input.')
    # if shortest paths are not calculated, calculate them
    if tnet is not None:
        paths = shortest_temporal_path(tnet)

    pathmat = np.zeros([paths[['from', 'to']].max().max(
    )+1, paths[['from', 'to']].max().max()+1, paths[['t_start']].max().max()+1]) * np.nan
    pathmat[paths['from'].values, paths['to'].values,
            paths['t_start'].values] = paths['temporal-distance']

    # Calculate efficiency which is 1 over the mean path.
    if calc == 'global':
        eff = 1 / np.nanmean(pathmat)
    elif calc == 'node' or calc == 'node_from':
        eff = 1 / np.nanmean(np.nanmean(pathmat, axis=2), axis=1)
    elif calc == 'node_to':
        eff = 1 / np.nanmean(np.nanmean(pathmat, axis=2), axis=0)

    return eff