def save_state(self, state_dict, itr=None):
        """
        Saves the state of an experiment.

        To be clear: this is about saving *state*, not logging diagnostics.
        All diagnostic logging is separate from this function. This function
        will save whatever is in ``state_dict``---usually just a copy of the
        environment---and the most recent parameters for the model you 
        previously set up saving for with ``setup_tf_saver``. 

        Call with any frequency you prefer. If you only want to maintain a
        single state and overwrite it at each call with the most recent 
        version, leave ``itr=None``. If you want to keep all of the states you
        save, provide unique (increasing) values for 'itr'.

        Args:
            state_dict (dict): Dictionary containing essential elements to
                describe the current state of training.

            itr: An int, or None. Current iteration of training.
        """
        if proc_id()==0:
            fname = 'vars.pkl' if itr is None else 'vars%d.pkl'%itr
            try:
                joblib.dump(state_dict, osp.join(self.output_dir, fname))
            except:
                self.log('Warning: could not pickle state_dict.', color='red')
            if hasattr(self, 'tf_saver_elements'):
                self._tf_simple_save(itr)
            if hasattr(self, 'pytorch_saver_elements'):
                self._pytorch_simple_save(itr)

        Saves the state of an experiment.

        To be clear: this is about saving *state*, not logging diagnostics.
        All diagnostic logging is separate from this function. This function
        will save whatever is in ``state_dict``---usually just a copy of the
        environment---and the most recent parameters for the model you 
        previously set up saving for with ``setup_tf_saver``. 

        Call with any frequency you prefer. If you only want to maintain a
        single state and overwrite it at each call with the most recent 
        version, leave ``itr=None``. If you want to keep all of the states you
        save, provide unique (increasing) values for 'itr'.

        Args:
            state_dict (dict): Dictionary containing essential elements to
                describe the current state of training.

            itr: An int, or None. Current iteration of training.
        
spinup/utils/logx.py