code: def temporal_network(gtfs,
                       start_time_ut=None,
                       end_time_ut=None,
                       route_type=None):
      
      events_df = gtfs.get_transit_events(start_time_ut=start_time_ut,
                                          end_time_ut=end_time_ut,
                                          route_type=route_type)
      events_df.drop('to_seq', 1, inplace=True)
      events_df.drop('shape_id', 1, inplace=True)
      events_df.drop('duration', 1, inplace=True)
      events_df.drop('route_id', 1, inplace=True)
      events_df.rename(
          columns={
              'from_seq': "seq"
          },
          inplace=True
      )
      return events_df
docs: Compute the temporal network of the data, and return it as a pandas.DataFrame
  
      Parameters
      ----------
      gtfs : gtfspy.GTFS
      start_time_ut: int | None
          start time of the time span (in unix time)
      end_time_ut: int | None
          end time of the time span (in unix time)
      route_type: int | None
          Specifies which mode of public transport are included, or whether all modes should be included.
          The int should be one of the standard GTFS route_types:
          (see also gtfspy.route_types.TRANSIT_ROUTE_TYPES )
          If route_type is not specified, all modes are included.
  
      Returns
      -------
      events_df: pandas.DataFrame
          Columns: departure_stop, arrival_stop, departure_time_ut, arrival_time_ut, route_type, route_I, trip_I