Sure, here you go:
    def configure_optimizers(self) -> th.optim.Optimizer:
        # optimizer
        all_params = list(itertools.chain(
            [x for x in self.rgcn.multi_modal_embed.parameters()],
            [x for x in self.rgcn.parameters()],
        ))

        optimizer = th.optim.AdamW(all_params, betas=(0.9, 0.999), lr=self.learning_rate, weight_decay=self.weight_decay)
        return optimizer