def METHOD_NAME(cfg, outs):
    """Schedule for bitserial_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):
        s[data_vec].parallel(s[data_vec].op.axis[0])
        s[weight_vec].parallel(s[weight_vec].op.axis[0])
        y, x = s[output].op.axis
        wb, db, k = s[output].op.reduce_axis
        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, yi, wb, db, ki, xi])
        cfg["ann_reduce"].apply(
            s,
            output,
            [db, wb],
            axis_lens=[get_const_int(db.dom.extent), get_const_int(wb.dom.extent)],
            max_unroll=8,
            cfg=cfg,
        )
        cfg["ann_spatial"].apply(
            s,
            output,
            [yi, xi],
            axis_lens=[cfg["tile_y"].size[-1], cfg["tile_x"].size[-1]],
            max_unroll=8,
            cfg=cfg,
        )
        s[output].vectorize(xi)
        s[output].parallel(yo)
        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]
            _schedule(cfg, s, data_vec, weight_vec, output)
        else:
            raise RuntimeError(f"Unsupported operator: {op.tag}")
    traverse(outs[0].op)
    return s