optimum/quanto/tensor/weights/marlin/int4/packed.py [115:133]:
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    @property
    def dtype(self):
        return torch.uint8

    def __tensor_flatten__(self):
        inner_tensors = ["_data"]
        meta = {
            "size": str(list(self.size())),
            "stride": str(self.stride()),
        }
        return inner_tensors, meta

    @staticmethod
    def __tensor_unflatten__(inner_tensors, meta, outer_size, outer_stride):
        assert len(inner_tensors) == 1
        assert len(meta) == 2
        data = inner_tensors["_data"]
        size = ast.literal_eval(meta["size"])
        stride = ast.literal_eval(meta["stride"])
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optimum/quanto/tensor/weights/tinygemm/packed.py [97:117]:
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    @property
    def dtype(self):
        return torch.uint8

    def __tensor_flatten__(self):
        inner_tensors = ["_data"]
        # Since meta can be used for serialization, use only AST compatible strings
        meta = {
            "size": str(list(self.size())),
            "stride": str(self.stride()),
        }
        return inner_tensors, meta

    @staticmethod
    def __tensor_unflatten__(inner_tensors, meta, outer_size, outer_stride):
        assert len(inner_tensors) == 1
        assert len(meta) == 2
        data = inner_tensors["_data"]
        # Meta should contain only AST compatible strings
        size = ast.literal_eval(meta["size"])
        stride = ast.literal_eval(meta["stride"])
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