code: def minimize(self, model, session=None, var_list=None, feed_dict=None,
                   maxiter=1000, initialize=True, anchor=True, step_callback=None, **kwargs):
          
          raise NotImplementedError()
docs: :param model: GPflow model with objective tensor.
          :param session: Session where optimization will be run.
          :param var_list: List of extra variables which should be trained during optimization.
          :param feed_dict: Feed dictionary of tensors passed to session run method.
          :param maxiter: Number of run interation.
          :param initialize: If `True` model parameters will be re-initialized even if they were
              initialized before in the specified session.
          :param anchor: If `True` trained variable values computed during optimization at
              particular session will be synchronized with internal parameter values.
          :param step_callback: A callback function to execute at each optimization step.
              Callback takes an arbitrary list of arguments. Input arguments depend on
              interface implementation.
          :param kwargs: This is a dictionary of extra parameters for session run method.