def METHOD_NAME(cfg, outs):
    """Schedule for binary_dense.
    Parameters
    ----------
    outs: Array of Tensor
        The computation graph description of bitserial dense operator.
        in the format of an array of tensors.
    Returns
    -------
    s: Schedule
        The computation schedule for bitserial_dense.
    """
    outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
    s = te.create_schedule([x.op for x in outs])
    def _schedule(cfg, s, data_vec, weight_vec, output, unipolar):
        z, k, _, y, x = s[weight_vec].op.axis
        s[weight_vec].parallel(z)
        s[weight_vec].vectorize(x)
        x, y = s[output].op.axis
        wb, db, k = s[output].op.reduce_axis
        _, DB, _ = get_const_tuple(data_vec.shape)
        _, _, WB, _, _ = get_const_tuple(weight_vec.shape)
        yo, yi = cfg["tile_y"].apply(s, output, y)
        xo, xi = cfg["tile_x"].apply(s, output, x)
        ko, ki = cfg["tile_k"].apply(s, output, k)
        cfg["reorder_0"].apply(s, output, [yo, xo, ko, xi, wb, db, yi, ki])
        fused = s[output].fuse(xo, yo)
        s[output].parallel(fused)
        nfactor = cfg["tile_y"].size[-1]
        kfactor = cfg["tile_k"].size[-1]
        if nfactor % 8 == 0:
            pc = _intrin_popcount(nfactor, kfactor, WB, DB, unipolar)
            s[output].tensorize(wb, pc)
        return s
    def traverse(op):
        """Internal traverse function"""
        # inline all one-to-one-mapping operators except the last stage (output)
        if tag.is_broadcast(op.tag) or "elemwise" in op.tag:
            if op not in s.outputs:
                s[op].compute_inline()
            for tensor in op.input_tensors:
                if isinstance(tensor.op, tvm.te.ComputeOp):
                    traverse(tensor.op)
        elif op.tag == "bitserial_dense" or "bitserial_dense_unipolar":
            output = op.output(0)
            weight_vec = op.input_tensors[0]
            data_vec = op.input_tensors[1]
            data = data_vec.op.input_tensors[0]
            if "QuantizeInput" in data.op.name:
                data = data.op.input_tensors[0]
            unipolar = output.op.tag == "bitserial_dense_unipolar"
            _schedule(cfg, s, data_vec, weight_vec, output, unipolar)
        else:
            raise RuntimeError(f"Unsupported operator: {op.tag}")
    traverse(outs[0].op)
    return s