def load_state_dict(self, state_dict, move_to_device=True):
        """Load an optimizer state.

        Arguments:
            state_dict (`dict`):
                An optimizer state (should be returned from a call to `state_dict`) to load.
            move_to_device (`bool`, defaults to `True`):
                Whether to move the optimizer's state to the device.
        """
        # deepcopy, to be consistent with module API
        state_dict = deepcopy(state_dict)
        # Validate the state_dict
        groups = self.param_groups
        saved_groups = state_dict["param_groups"]

        if len(groups) != len(saved_groups):
            raise ValueError("loaded state dict has a different number of parameter groups")
        param_lens = (len(g["params"]) for g in groups)
        saved_lens = (len(g["params"]) for g in saved_groups)
        if any(p_len != s_len for p_len, s_len in zip(param_lens, saved_lens)):
            raise ValueError(
                "loaded state dict contains a parameter group that doesn't match the size of optimizer's group",
            )

        # Update the state
        id_map = {
            old_id: p
            for old_id, p in zip(
                chain.from_iterable(g["params"] for g in saved_groups),
                chain.from_iterable(g["params"] for g in groups),
            )
        }

        def cast(param, value):
            r"""Make a deep copy of value, casting all tensors to device of param."""
            if isinstance(value, torch.Tensor):
                # Floating-point types are a bit special here. They are the only ones
                # that are assumed to always match the type of params.
                if param.is_floating_point() and value.dtype != torch.uint8:
                    value = value.to(param.dtype)
                return value
            elif isinstance(value, dict):
                for k, v in value.items():
                    if k in self.non_castable_tensor_keys:
                        if move_to_device:
                            value[k] = v.to(param.device)
                    else:
                        value[k] = cast(param, v)

                return value
            elif isinstance(value, container_abcs.Iterable):
                return type(value)(cast(param, v) for v in value)
            else:
                return value

        # Copy state assigned to params (and cast tensors to appropriate types).
        # State that is not assigned to params is copied as is (needed for
        # backward compatibility).
        state = defaultdict(dict)
        for k, v in state_dict["state"].items():
            if k in id_map:
                param = id_map[k]
                state[param] = cast(param, v)
            else:
                state[k] = v

        # Update parameter groups, setting their 'params' value
        def update_group(group, new_group):
            new_group["params"] = group["params"]
            return new_group

        param_groups = [update_group(g, ng) for g, ng in zip(groups, saved_groups)]
        self.__setstate__({"state": state, "param_groups": param_groups})
Load an optimizer state.

        Arguments:
            state_dict (`dict`):
                An optimizer state (should be returned from a call to `state_dict`) to load.
            move_to_device (`bool`, defaults to `True`):
                Whether to move the optimizer's state to the device.
        
bitsandbytes/optim/optimizer.py