code: def minimize(self, model, session=None, var_list=None, feed_dict=None, maxiter=1000,
                   disp=False, initialize=False, anchor=True, step_callback=None, **kwargs):
          
          if model is None or not isinstance(model, Model):
              raise ValueError('Unknown type passed for optimization.')
  
          if model.is_built_coherence() is Build.NO:
              raise GPflowError('Model is not built.')
  
          session = model.enquire_session(session)
          self._model = model
          optimizer = self.make_optimize_tensor(model, session,
              var_list=var_list, maxiter=maxiter, disp=disp)
          self._optimizer = optimizer
          feed_dict = self._gen_feed_dict(model, feed_dict)
          optimizer.minimize(session=session, feed_dict=feed_dict, step_callback=step_callback,
                             **kwargs)
          if anchor:
              model.anchor(session)
docs: Minimizes objective function of the model.
  
          :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. Note: scipy optimizer can do early stopping
              if model converged.
          :param disp: ScipyOptimizer option. Set to True to print convergence messages.
          :param initialize: If `True` model parameters will be re-initialized even if they were
              initialized before for gotten session.
          :param anchor: If `True` trained parameters computed during optimization at
              particular session will be synchronized with internal parameter values.
          :param step_callback: A function to be called at each optimization step;
              arguments are the current values of all optimization variables
              flattened into a single vector.
          :type step_callback: Callable[[np.ndarray], None]
          :param kwargs: This is a dictionary of extra parameters for session run method.