def METHOD_NAME(cfg, data, weight, bias=None, out_dtype=None):
    """Dense operator for int8 on CUDA"""
    if out_dtype is None:
        out_dtype = data.dtype
    batch, in_dim = get_const_tuple(data.shape)
    out_dim, _ = get_const_tuple(weight.shape)
    k = te.reduce_axis((0, in_dim), name="k")
    matmul = te.compute(
        (batch, out_dim),
        lambda i, j: te.sum(
            data[i, k].astype(out_dtype) * weight[j, k].astype(out_dtype), axis=[k]
        ),
        tag="dense_int8",
    )
    cfg.add_flop(batch * in_dim * out_dim * 2)
    if bias is not None:
        matmul = te.compute(
            (batch, out_dim),
            lambda i, j: matmul[i, j] + bias[j].astype(out_dtype),
            tag=tag.BROADCAST,
        )
        cfg.add_flop(batch * out_dim)
    return matmul