std::vector<std::vector<pir::OpResult>> MeanOp::Decomp(pir::Operation* op) {
  MeanOp op_obj = op->dyn_cast<MeanOp>();
  (void)op_obj;

  VLOG(4) << "Decomp Prepare inputs of mean";

  Tensor x(std::make_shared<primitive::LazyTensor>(op_obj.x()));

  VLOG(4) << "Decomp prepare attributes of mean";

  IntArray axis = op->attribute("axis").dyn_cast<paddle::dialect::IntArrayAttribute>().data();
  bool keepdim = op->attribute("keepdim").dyn_cast<pir::BoolAttribute>().data();

  VLOG(4) << "Decomp prepare call mean's decomp interface";

  auto org_res = op->results();
  std::vector<std::vector<pir::OpResult>> res(org_res.size());

  Tensor op_res = paddle::primitive::details::mean_decomp<primitive::LazyTensor>(x, axis, keepdim);
  res[0].push_back(
    std::static_pointer_cast<primitive::LazyTensor>(op_res.impl())
        ->value()
        .dyn_cast<pir::OpResult>());
  return res;

}


std::vector<std::vector<pir::OpResult>> LayerNormOp::Decomp(pir::Operation* op) {
  LayerNormOp op_obj = op->dyn_cast<LayerNormOp>();
  (void)op_obj;

  VLOG(4) << "Decomp Prepare inputs of layer_norm";

  Tensor x(std::make_shared<primitive::LazyTensor>(op_obj.x()));
  paddle::optional<Tensor> scale;
  if (!IsEmptyValue(op_obj.scale())){
      scale = paddle::make_optional<Tensor>(Tensor(std::make_shared<primitive::LazyTensor>(op_obj.scale())));
  }
  paddle::optional<Tensor> bias;
  if (!IsEmptyValue(op_obj.bias())){
      bias = paddle::make_optional<Tensor>(Tensor(std::make_shared<primitive::LazyTensor>(op_obj.bias())));
  }

  VLOG(4) << "Decomp prepare attributes of layer_norm";

  float epsilon = op->attribute("epsilon").dyn_cast<pir::FloatAttribute>().data();
  int begin_norm_axis = op->attribute("begin_norm_axis").dyn_cast<pir::Int32Attribute>().data();

  VLOG(4) << "Decomp prepare call layer_norm's decomp interface";

  auto org_res = op->results();
  std::vector<std::vector<pir::OpResult>> res(org_res.size());

  std::tuple<Tensor, Tensor, Tensor> op_res = paddle::primitive::details::layer_norm_decomp<primitive::LazyTensor>(
          x, scale, bias, epsilon, begin_norm_axis);
  for (size_t i = 0; i < org_res.size(); ++i) {
      res[i].push_back(std::static_pointer_cast<primitive::LazyTensor>(std::get<i>(op_res).impl())->value().dyn_cast<pir::OpResult>());
      }

  return res;

}