def from_dict(cls, qs_dict: dict[str, Any], device: torch.device) -> "QuantState":
        """
        unpacks components of state_dict into QuantState
        where necessary, convert into strings, torch.dtype, ints, etc.

        qs_dict: based on state_dict, with only relevant keys, striped of prefixes.

        item with key `quant_state.bitsandbytes__[nf4/fp4]` may contain minor and non-tensor quant state items.
        """

        # unpacking tensor with non-tensor components
        qs_key = [k for k, v in qs_dict.items() if "quant_state" in k and isinstance(v, torch.Tensor)]
        if not len(qs_key) and "quant_type" not in qs_dict:
            raise ValueError("Expected packed or unpacked quant_state items, found neither")
        elif len(qs_key) != 1 or qs_key[0].split(".")[-1] not in cls.valid_qs_type_keys:
            raise ValueError(
                f"There should be exactly one `quant_state` item with ending from {cls.valid_qs_type_keys}.\nDetected {qs_key}.",
            )

        # unpacking minor and non-tensor quant state items if necessary
        if len(qs_key) == 1:
            first_qs_key = qs_key[0]
            qs_dict.update(unpack_tensor_to_dict(qs_dict.pop(first_qs_key)))

        qs_dict = {k.split(".")[-1]: v for k, v in qs_dict.items()}  # strip prefixes
        assert set(qs_dict.keys()).issubset(cls.valid_qs_keys)

        if "nested_absmax" in qs_dict:
            offset = torch.tensor(float(qs_dict["nested_offset"])).to(device)
            state2 = cls(
                absmax=qs_dict["nested_absmax"].to(device),
                blocksize=qs_dict["nested_blocksize"],
                code=qs_dict["nested_quant_map"].to(device),
                dtype=getattr(torch, qs_dict["nested_dtype"]),
            )
        else:
            offset, state2 = None, None

        quant_state = cls(
            quant_type=qs_dict["quant_type"],
            absmax=qs_dict["absmax"].to(device),
            blocksize=qs_dict["blocksize"],
            code=qs_dict["quant_map"].to(device),
            dtype=getattr(torch, qs_dict["dtype"]),
            shape=torch.Size(qs_dict["shape"]) if qs_dict["shape"] is not None else None,
            offset=offset,
            state2=state2,
        )
        return quant_state

        unpacks components of state_dict into QuantState
        where necessary, convert into strings, torch.dtype, ints, etc.

        qs_dict: based on state_dict, with only relevant keys, striped of prefixes.

        item with key `quant_state.bitsandbytes__[nf4/fp4]` may contain minor and non-tensor quant state items.
        
bitsandbytes/functional.py