def quantized_elemwise_add(lhs=None, rhs=None, lhs_min=None, lhs_max=None,
    rhs_min=None, rhs_max=None, out=None, name=None, **kwargs):
    """elemwise_add operator for input dataA and input dataB data type of int8,
    and accumulates in type int32 for the output. For each argument, two more arguments of type
    float32 must be provided representing the thresholds of quantizing argument from data
    type float32 to int8. The final outputs contain result in int32, and min
    and max thresholds representing the threholds for quantizing the float32 output into int32.

    .. Note::
        This operator only supports forward propogation. DO NOT use it in training.



    Parameters
    ----------
    lhs : NDArray
        first input
    rhs : NDArray
        second input
    lhs_min : NDArray
        3rd input
    lhs_max : NDArray
        4th input
    rhs_min : NDArray
        5th input
    rhs_max : NDArray
        6th input

    out : NDArray, optional
        The output NDArray to hold the result.

    Returns
    -------
    out : NDArray or list of NDArrays
        The output of this function.
    """
    return 0,
