How to use the pypet.pypetconstants.LOAD_NOTHING function in pypet

self._args = continue_dict['args']
        # Keyword arguments to the user's job function
        self._kwargs = continue_dict['kwargs']
        # Postproc Function
        self._postproc = continue_dict['postproc']
        # Postprog args
        self._postproc_args = continue_dict['postproc_args']
        # Postproc Kwargs
        self._postproc_kwargs = continue_dict['postproc_kwargs']

        old_start_timestamp = continue_dict['start_timestamp']

        # Unpack the trajectory
        self._traj.v_full_copy = continue_dict['full_copy']
        # Load meta data
        self._traj.f_load(load_parameters=pypetconstants.LOAD_NOTHING,
                          load_derived_parameters=pypetconstants.LOAD_NOTHING,
                          load_results=pypetconstants.LOAD_NOTHING,
                          load_other_data=pypetconstants.LOAD_NOTHING)

        # Now we have to reconstruct previous results
        result_list = []
        full_filename_list = []
        for filename in os.listdir(self._continue_path):
            _, ext = os.path.splitext(filename)

            if ext != '.rcnt':
                continue

            full_filename = os.path.join(self._continue_path, filename)
            cnt_file = open(full_filename, 'rb')
            result_list.append(dill.load(cnt_file))

Attribute Error:

                If options 1 and 2 (load skeleton and load data) are applied but the
                objects already exist in your trajectory. This prevents implicitly overriding
                data in RAM. Use -1 and -2 instead to load only items that are currently not
                in your trajectory in RAM. Or remove the items you want to 'reload' first.

        """

        # Do some argument validity checks first
        if name is None and index is None:
            name = self.v_name

        if as_new:
            load_parameters=pypetconstants.LOAD_DATA
            load_derived_parameters = pypetconstants.LOAD_NOTHING
            load_results = pypetconstants.LOAD_NOTHING
            load_other_data = pypetconstants.LOAD_NOTHING

        self._storage_service.load(pypetconstants.TRAJECTORY, self, trajectory_name=name,
                                  trajectory_index=index,
                                  as_new=as_new, load_parameters=load_parameters,
                                  load_derived_parameters=load_derived_parameters,
                                  load_results=load_results,
                                  load_other_data=load_other_data,
                                  force=force)

        # If a trajectory is newly loaded, all parameters are unlocked.
        if as_new:
            for param in self._parameters.itervalues():
                param.f_unlock()
            self._stored=False

the first place) and returns a :class:`~pypet.trajectory.Trajectory` instance loaded from
    the `index` th Trajectory stored in the file. In this function, nothing is loaded
    for the results and derived parameters, whereas the parameters are fully loaded.

    This is recommended when the file size is REALLY LARGE (e.g. > 20GB)

    :param filename: filename of an HDF file created by LTL
    :param name_or_index: The name or index of the trajectory to load from the file,
        if unspecified, the LAST trajectory is loaded.
    """
    traj = Trajectory(filename=filename)

    load_params_dict = {
        'load_parameters':pypetconstants.LOAD_DATA,
        'load_results':pypetconstants.LOAD_NOTHING,
        'load_derived_parameters':pypetconstants.LOAD_NOTHING,
        'force':True
    }
    if isinstance(name_or_index, str):
        load_params_dict['name'] = name_or_index
    else:
        index = int(name_or_index)
        load_params_dict['index'] = index

    # Loading Trajectory from file.
    with timed(logger, "Primary Loading of The HDF File"):
        traj.f_load(**load_params_dict)
    logger.info("Finished Primary Loading")
    return traj

If options 1 and 2 (load skeleton and load data) are applied but the
                objects already exist in your trajectory. This prevents implicitly overriding
                data in RAM. Use -1 and -2 instead to load only items that are currently not
                in your trajectory in RAM. Or remove the items you want to 'reload' first.

        """

        # Do some argument validity checks first
        if name is None and index is None:
            name = self.v_name

        if as_new:
            load_parameters=pypetconstants.LOAD_DATA
            load_derived_parameters = pypetconstants.LOAD_NOTHING
            load_results = pypetconstants.LOAD_NOTHING
            load_other_data = pypetconstants.LOAD_NOTHING

        self._storage_service.load(pypetconstants.TRAJECTORY, self, trajectory_name=name,
                                  trajectory_index=index,
                                  as_new=as_new, load_parameters=load_parameters,
                                  load_derived_parameters=load_derived_parameters,
                                  load_results=load_results,
                                  load_other_data=load_other_data,
                                  force=force)

        # If a trajectory is newly loaded, all parameters are unlocked.
        if as_new:
            for param in self._parameters.itervalues():
                param.f_unlock()
            self._stored=False
        else:
            self.f_lock_parameters()

# Keyword arguments to the user's job function
        self._kwargs = continue_dict['kwargs']
        # Postproc Function
        self._postproc = continue_dict['postproc']
        # Postprog args
        self._postproc_args = continue_dict['postproc_args']
        # Postproc Kwargs
        self._postproc_kwargs = continue_dict['postproc_kwargs']

        old_start_timestamp = continue_dict['start_timestamp']

        # Unpack the trajectory
        self._traj.v_full_copy = continue_dict['full_copy']
        # Load meta data
        self._traj.f_load(load_parameters=pypetconstants.LOAD_NOTHING,
                          load_derived_parameters=pypetconstants.LOAD_NOTHING,
                          load_results=pypetconstants.LOAD_NOTHING,
                          load_other_data=pypetconstants.LOAD_NOTHING)

        # Now we have to reconstruct previous results
        result_list = []
        full_filename_list = []
        for filename in os.listdir(self._continue_path):
            _, ext = os.path.splitext(filename)

            if ext != '.rcnt':
                continue

            full_filename = os.path.join(self._continue_path, filename)
            cnt_file = open(full_filename, 'rb')
            result_list.append(dill.load(cnt_file))
            cnt_file.close()

def _finalize(self):
        """Final rollback initiated by the environment

        Restores the trajectory as root of the tree, and loads meta data from disk.
        This updates the trajectory's information about single runs, i.e. if they've been
        completed, when they were started, etc.

        """
        self.f_restore_default()
        self._nn_interface._change_root(self)
        self.f_load(self.v_name,None, False, pypetconstants.LOAD_NOTHING, pypetconstants.LOAD_NOTHING,
                  pypetconstants.LOAD_NOTHING)

def _finalize(self):
        """Final rollback initiated by the environment

        Restores the trajectory as root of the tree, and loads meta data from disk.
        This updates the trajectory's information about single runs, i.e. if they've been
        completed, when they were started, etc.

        """
        self.f_restore_default()
        self._nn_interface._change_root(self)
        self.f_load(self.v_name,None, False, pypetconstants.LOAD_NOTHING, pypetconstants.LOAD_NOTHING,
                  pypetconstants.LOAD_NOTHING)

# load_results = kwargs.pop('load_results')

        if not as_new:
            # if not traj.f_is_empty():
            #     raise TypeError('You cannot f_load a trajectory from disk into a non-_empty one.')
            traj._stored=True

        self._trj_load_meta_data(traj,as_new,force)
        self._ann_load_annotations(traj,self._trajectory_group)





        if (as_new and (load_derived_params != pypetconstants.LOAD_NOTHING or load_results !=
                        pypetconstants.LOAD_NOTHING)):
            raise ValueError('You cannot load a trajectory as new and load the derived '
                                 'parameters and results. Only parameters are allowed.')


        if as_new and load_params != pypetconstants.LOAD_DATA:
            raise ValueError('You cannot load the trajectory as new and not load the data of '
                                 'the parameters.')




        for what,loading in ( ('config',load_params),('parameters',load_params),
                             ('derived_parameters',load_derived_params),
                             ('results',load_results) ):

            if loading != pypetconstants.LOAD_NOTHING:

# Postproc Function
        self._postproc = continue_dict['postproc']
        # Postprog args
        self._postproc_args = continue_dict['postproc_args']
        # Postproc Kwargs
        self._postproc_kwargs = continue_dict['postproc_kwargs']

        old_start_timestamp = continue_dict['start_timestamp']

        # Unpack the trajectory
        self._traj.v_full_copy = continue_dict['full_copy']
        # Load meta data
        self._traj.f_load(load_parameters=pypetconstants.LOAD_NOTHING,
                          load_derived_parameters=pypetconstants.LOAD_NOTHING,
                          load_results=pypetconstants.LOAD_NOTHING,
                          load_other_data=pypetconstants.LOAD_NOTHING)

        # Now we have to reconstruct previous results
        result_list = []
        full_filename_list = []
        for filename in os.listdir(self._continue_path):
            _, ext = os.path.splitext(filename)

            if ext != '.rcnt':
                continue

            full_filename = os.path.join(self._continue_path, filename)
            cnt_file = open(full_filename, 'rb')
            result_list.append(dill.load(cnt_file))
            cnt_file.close()
            full_filename_list.append(full_filename)