def METHOD_NAME(outs):
    """Create schedule for tensors"""
    s = te.create_schedule([x.op for x in outs])
    output_op = outs[0].op
    scheduled_ops = []
    def traverse(op):
        """Traverse operators from computation graph"""
        # inline all one-to-one-mapping operators except the last stage (output)
        if tag.is_broadcast(op.tag):
            if op not in s.outputs:
                s[op].compute_inline()
            else:  # inject custom schedule
                if len(op.axis) == 3:  # schedule bias + bn + relu
                    n, c, w = op.axis
                    fused = s[op].fuse(n, c)
                    s[op].parallel(fused)
                    s[op].vectorize(w)
            for tensor in op.input_tensors:
                if isinstance(tensor.op, te.tensor.ComputeOp) and tensor.op not in scheduled_ops:
                    traverse(tensor.op)
        if "conv1d_ncw" in op.tag:
            conv = op.output(0)
            kernel = op.input_tensors[1]
            if isinstance(kernel.op, te.tensor.ComputeOp) and "dilate" in kernel.op.tag:
                s[kernel].compute_inline()
            data = op.input_tensors[0]
            data_pad = None
            if isinstance(data.op, te.tensor.ComputeOp) and "pad" in data.op.tag:
                data_pad = data
                data = data_pad.op.input_tensors[0]
            n_pad, c_pad, w_pad = data_pad.op.axis
            pad_fused = s[data_pad].fuse(n_pad, c_pad)
            s[data_pad].parallel(pad_fused)
            C = conv
            n, c, w = C.op.axis
            rc, rw = C.op.reduce_axis
            n_out, c_out, w_out = output_op.axis
            s[C].vectorize(w)
            if op != output_op:  # fuse bias + bn + relu into conv
                s[C].compute_at(s[output_op], w_out)
            else:
                fused = s[C].fuse(n, c)
                s[C].parallel(fused)
        scheduled_ops.append(op)
    traverse(output_op)
    return s