How to use the movingpandas.trajectory.Trajectory function in movingpandas

def make_traj(nodes, crs=CRS_METRIC, id=1, parent=None):
    nodes = [node.to_dict() for node in nodes]
    df = pd.DataFrame(nodes).set_index('t')
    geo_df = GeoDataFrame(df, crs=crs)
    return Trajectory(id, geo_df, parent=parent)

def copy(self):
        """
        Return a copy of the trajectory.

        Returns
        -------
        Trajectory
        """
        return Trajectory(self.df.copy(), self.id, parent=self.parent)

if __name__ == '__main__':
    xmin, xmax, ymin, ymax = 116.3685035,116.3702945,39.904675,39.907728 
    polygon = Polygon([(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin), (xmin, ymin)])
    
    t_start = datetime.now()
    df = read_file(os.path.join(script_path,'demodata_geolife.gpkg'))
    df['t'] = pd.to_datetime(df['t'])
    df = df.set_index('t')
    print("Finished reading {} rows in {}".format(len(df),datetime.now() - t_start))
    
    t_start = datetime.now()
    trajectories = []
    for key, values in df.groupby(['trajectory_id']):
        trajectory = Trajectory(key, values)
        print(trajectory)
        trajectories.append(trajectory)
    print("Finished creating {} trajectories in {}".format(len(trajectories),datetime.now() - t_start))
    
    t_start = datetime.now()    
    intersections = []
    for key, values in df.groupby(['trajectory_id']):
        traj = Trajectory(key, values)
        for intersection in traj.clip(polygon):
            intersections.append(intersection)
    print("Found {} intersections in {}".format(len(intersections),datetime.now() - t_start))

def _df_to_trajectories(self, df, traj_id_col, obj_id_col):
        trajectories = []
        for traj_id, values in df.groupby([traj_id_col]):
            if len(values) < 2:
                continue
            if obj_id_col in values.columns:
                obj_id = values.iloc[0][obj_id_col]
            else:
                obj_id = None
            trajectory = Trajectory(values, traj_id, obj_id=obj_id)
            if trajectory.get_length() < self.min_length:
                continue
            trajectories.append(trajectory)
        return trajectories

def df_to_trajectories(self, df, trajectory_id):
        trajectories = []
        for key, values in df.groupby([trajectory_id]):
            if len(values) < 2:
                continue
            trajectory = Trajectory(key, values)
            if trajectory.get_length() < self.min_length:
                continue
            trajectories.append(trajectory)
        return trajectories

import pandas as pd 
from geopandas import read_file

script_path = os.path.dirname(__file__)
sys.path.append(os.path.join(script_path,".."))

from movingpandas.trajectory import Trajectory


if __name__ == '__main__':       
    df = read_file(os.path.join(script_path,'demodata_geolife.gpkg'))
    df['t'] = pd.to_datetime(df['t'])
    df = df.set_index('t')       
    trajectories = []
    for key, values in df.groupby(['trajectory_id']):
        trajectories.append(Trajectory(key, values))        
        
    intersections = []            
    polygon_file = fiona.open(os.path.join(script_path,'demodata_grid.gpkg'), 'r')
    for feature in polygon_file:
        for traj in trajectories:
            for intersection in traj.intersection(feature):
                intersections.append(intersection)
        
    for intersection in intersections:
        print(intersection)
        
    polygon_file.close()

Returns
        -------
        list
            List of trajectories
        """
        result = []
        temp_df = self.df.copy()
        temp_df['t'] = temp_df.index
        temp_df['gap'] = temp_df['t'].diff() > gap
        temp_df['gap'] = temp_df['gap'].apply(lambda x: 1 if x else 0).cumsum()
        dfs = [group[1] for group in temp_df.groupby(temp_df['gap'])]
        for i, df in enumerate(dfs):
            df = df.drop(columns=['t', 'gap'])
            if len(df) > 1:
                result.append(Trajectory(df, '{}_{}'.format(self.id, i)))
        return result

"""
        Return Trajectory segment between times t1 and t2.

        Parameters
        ----------
        t1 : datetime.datetime
            Start time for the segment
        t2 : datetime.datetime
            End time for the segment

        Returns
        -------
        Trajectory
            Extracted trajectory segment
        """
        segment = Trajectory(self.df[t1:t2], self.id, parent=self)
        if not segment.is_valid():
            raise RuntimeError("Failed to extract valid trajectory segment between {} and {}".format(t1, t2))
        return segment