def make_optimizer(
        self,
        reset_optimizer_state=False,
        remove_params=False,
        verbose_optimizer=False,
        **kwargs
    ):
        """Optimizer initialization.

        Called before each training experience to configure the optimizer.

        :param reset_optimizer_state: bool, whether to reset the
            state of the optimizer, defaults to False
        :param remove_params: bool, whether to remove parameters that
                              are in the optimizer but not found in the current model
        :param verbose_optimizer: bool, print optimized parameters
                                  along with their parameter group

        Warnings:
            - The first time this function is called
              for a given strategy it will reset the
              optimizer to gather the (name, param)
              correspondance of the optimized parameters
              all of the model parameters will be put in the
              optimizer, regardless of what parameters are
              initially put in the optimizer.

        """
        self.optimized_param_id = update_optimizer(
            self.optimizer,
            dict(self.model.named_parameters()),
            self.optimized_param_id,
            reset_state=reset_optimizer_state,
            remove_params=remove_params,
            verbose=verbose_optimizer,
        )
Optimizer initialization.

        Called before each training experience to configure the optimizer.

        :param reset_optimizer_state: bool, whether to reset the
            state of the optimizer, defaults to False
        :param remove_params: bool, whether to remove parameters that
                              are in the optimizer but not found in the current model
        :param verbose_optimizer: bool, print optimized parameters
                                  along with their parameter group

        Warnings:
            - The first time this function is called
              for a given strategy it will reset the
              optimizer to gather the (name, param)
              correspondance of the optimized parameters
              all of the model parameters will be put in the
              optimizer, regardless of what parameters are
              initially put in the optimizer.

        
avalanche/training/templates/observation_type/batch_observation.py