def load_state_dict(self, state_dict, optimizer_overrides=None):
        """Load an optimizer state dict.

        In general we should prefer the configuration of the existing optimizer
        instance (e.g., learning rate) over that found in the state_dict. This
        allows us to resume training from a checkpoint using a new set of
        optimizer args.
        """
        if "loss_scale" in state_dict and self.scaler is not None:
            self.scaler.loss_scale = state_dict["loss_scale"]

        self.wrapped_optimizer.load_state_dict(state_dict, optimizer_overrides)

        # Hack: PyTorch automatically casts the optimizer state to match the
        # type of the current parameters. But with --memory-efficient-fp16 the
        # params are FP16 while the optimizer state is FP32 and we don't want
        # to cast. A workaround is to manually copy back the original state
        # after the optimizer has been loaded.
        if not getattr(self.optimizer, "disable_mem_eff_fp16_loading_hack", False):
            groups = self.optimizer.param_groups
            saved_groups = state_dict["param_groups"]
            id_map = {
                old_id: p
                for old_id, p in zip(
                    chain(*(g["params"] for g in saved_groups)),
                    chain(*(g["params"] for g in groups)),
                )
            }
            for k, v in state_dict["state"].items():
                if k in id_map:
                    param = id_map[k]
                    self.optimizer.state[param] = v
Load an optimizer state dict.

        In general we should prefer the configuration of the existing optimizer
        instance (e.g., learning rate) over that found in the state_dict. This
        allows us to resume training from a checkpoint using a new set of
        optimizer args.
        
understand_icl/fairseq/fairseq/optim/fp16_optimizer.py