How to use the igraph.Graph.Read_Pickle function in igraph

import igraph 
import sys 
import scipy.sparse as sp
import numpy as np 
import json
import time

sf_graph_file = '../data/network_graph.pkl'
g = igraph.Graph.Read_Pickle(sf_graph_file)
print(g.summary())

t0 = time.time()
path_collection = g.get_shortest_paths(1, weights='sec_length', output='epath')
t1 = time.time()
print(t1-t0)
sys.exit(0)

# IGRAPH D--- 83335 149318 -- 
# + attr: n_x (v), n_y (v), node_index (v), node_osmid (v), edge_index (e), edge_osmid (e), end_node (e), sec_length (e), speed_limit (e), start_node (e)

# node_osmid_list = g.vs['node_osmid']
# node_osmid2graphid = {node_osmid_list[i]: i for i in range(len(node_osmid_list))}
# with open('../data/node_osmid2graphid.json', 'w') as outfile:
#     json.dump(node_osmid2graphid, outfile, indent=2)
# sys.exit(0)

def parse_obo_graph(path):
    stored_pickles_found = False

    g = {'biological_process': igraph.Graph(directed=True), 
         'cellular_component': igraph.Graph(directed=True),
         'molecular_function': igraph.Graph(directed=True) }

    for ns in g:
        pickle_file_path = "{0}.{1}.graph".format(path, ns)
        if os.path.exists(pickle_file_path):
            print("Using stored ontology graph: {0}".format(pickle_file_path))
            g[ns] = igraph.Graph.Read_Pickle(fname=pickle_file_path)
            stored_pickles_found = True

    # key: GO:ID, value = {'ns': 'biological_process', 'idx': 25}
    terms = dict()

    if stored_pickles_found is True:
        print("Using stored terms data structure: {0}.terms".format(path))
        with open("{0}.terms".format(path), 'rb') as f:
            terms = pickle.load(f)

    # key: namespace, value=int
    next_idx = {'biological_process': 0, 
                'cellular_component': 0,
                'molecular_function': 0 }

    id = None

def load(path):
    with open(path, 'r') as f:
        graph = Graph.Read_Pickle(f)
    return graph

def main():
    absolute_path = os.path.dirname(os.path.abspath(__file__))
    logging.basicConfig(filename=absolute_path+'/sf_abm_mp.log', level=logging.DEBUG)
    logger = logging.getLogger('main')
    logger.info('{} \n\n'.format(datetime.datetime.now()))

    t_start = time.time()

    ### Read initial graph
    global g
    g = igraph.Graph.Read_Pickle(absolute_path+'/../data_repo/data/sf/network_graph.pkl')
    logger.info('graph summary {}'.format(g.summary()))
    g.es['fft'] = np.array(g.es['sec_length'], dtype=np.float)/np.array(g.es['maxmph'], dtype=np.float)*2.23694
    fft_array = np.array(g.es['fft'], dtype=np.float)
    capacity_array = np.array(g.es['capacity'], dtype=np.float)
    ### 2.23694 is to convert mph to m/s;
    ### the free flow time should still be calibrated rather than equal to the time at speed limit, check coefficient 1.2 in defining ['weight']
    logger.info('max/min FFT in seconds: {}/{}'.format(max(g.es['fft']), min(g.es['fft'])))

    g.es['weight'] = fft_array * 1.2 ### According to (Colak, 2015), for SF, even vol=0, t=1.2*fft, maybe traffic light? 1.2 is f_p - k_bay

    for day in [1]:
        for hour in range(9, 10):

            logger.info('*************** DY{} HR{} ***************'.format(day, hour))

            t0 = time.time()

def parse_obo_graph(path):
    stored_pickles_found = False

    g = {'biological_process': igraph.Graph(directed=True), 
         'cellular_component': igraph.Graph(directed=True),
         'molecular_function': igraph.Graph(directed=True) }

    for ns in g:
        pickle_file_path = "{0}.{1}.graph".format(path, ns)
        if os.path.exists(pickle_file_path):
            print("Using stored ontology graph: {0}".format(pickle_file_path))
            g[ns] = igraph.Graph.Read_Pickle(fname=pickle_file_path)
            stored_pickles_found = True

    # key: GO:ID, value = {'ns': 'biological_process', 'idx': 25}
    terms = dict()

    if stored_pickles_found is True:
        print("Using stored terms data structure: {0}.terms".format(path))
        with open("{0}.terms".format(path), 'rb') as f:
            terms = pickle.load(f)

    # key: namespace, value=int
    next_idx = {'biological_process': 0, 
                'cellular_component': 0,
                'molecular_function': 0 }

    id = None

def main():
    absolute_path = os.path.dirname(os.path.abspath(__file__))
    logging.basicConfig(filename=absolute_path+'/sf_abm_mp.log', level=logging.DEBUG)
    logger = logging.getLogger('main')
    logger.info('{} \n\n'.format(datetime.datetime.now()))

    t_start = time.time()

    ### Read initial graph
    global g
    g = igraph.Graph.Read_Pickle(absolute_path+'/../data_repo/data/sf/network_graph.pkl')
    logger.info('graph summary {}'.format(g.summary()))
    g.es['fft'] = np.array(g.es['sec_length'], dtype=np.float)/np.array(g.es['maxmph'], dtype=np.float)*2.23694
    fft_array = np.array(g.es['fft'], dtype=np.float)
    capacity_array = np.array(g.es['capacity'], dtype=np.float)
    ### 2.23694 is to convert mph to m/s;
    ### the free flow time should still be calibrated rather than equal to the time at speed limit, check coefficient 1.2 in defining ['weight']
    logger.info('max/min FFT in seconds: {}/{}'.format(max(g.es['fft']), min(g.es['fft'])))

    g.es['weight'] = fft_array * 1.2 ### According to (Colak, 2015), for SF, even vol=0, t=1.2*fft, maybe traffic light? 1.2 is f_p - k_bay

    for day in [1]:
        for hour in range(9, 10):

            logger.info('*************** DY{} HR{} ***************'.format(day, hour))

            t0 = time.time()