(self, layer_list, epoch)
Determine which optimizer in the container should go with which layers,
        then apply their optimize functions to those layers.

        Notes:

        We can recalculate ``map_list`` in case ``optimizer_mapping`` changes
        during training.
Determine which optimizer in the container should go with which layers,
        then apply their optimize functions to those layers.
Determine which optimizer in the container should go with which layers then apply their optimize functions to those layers .
def optimize(self, layer_list, epoch):
        """
        Determine which optimizer in the container should go with which layers,
        then apply their optimize functions to those layers.

        Notes:

        We can recalculate ``map_list`` in case ``optimizer_mapping`` changes
        during training.
        """

        if not id(layer_list) in self.map_list_cache:
            self.map_list = self._map_optimizers(layer_list)
            self.map_list_cache[id(layer_list)] = self.map_list
        else:
            self.map_list = self.map_list_cache[id(layer_list)]

        for opt in self.map_list:
            opt.optimize(self.map_list[opt], epoch)
def optimize ( self , layer_list , epoch ) : if not id ( layer_list ) in self . map_list_cache : self . map_list = self . _map_optimizers ( layer_list ) self . map_list_cache [ id ( layer_list ) ] = self . map_list else : self . map_list = self . map_list_cache [ id ( layer_list ) ] for opt in self . map_list : opt . optimize ( self . map_list [ opt ] , epoch )