}

  TFE_Execute(op_.get(), unwrapped_results.data(), &real_num_outputs, status);
  if (TF_GetCode(status) != TF_OK) {
    cancellation_manager_->StartCancel();
    return;
  }
  unwrapped_results.resize(real_num_outputs);
  outputs->reserve(real_num_outputs);
  for (TFE_TensorHandle* unwrapped_result : unwrapped_results) {
    outputs->emplace_back(unwrapped_result);
  }
}

  AbstractTensorHandlePtr model_out(model_outputs[0]);

  TF_Tensor* model_out_tensor;
  TF_RETURN_IF_ERROR(GetValue(model_out.get(), &model_out_tensor));
  int num_dims_out = TF_NumDims(model_out_tensor);
  TF_DeleteTensor(model_out_tensor);

  // If the output is a scalar, then return the scalar output
  if (num_dims_out == 0) {
    outputs[0] = model_out.release();

//                  absl::Span<AbstractTensorHandle* const> grad_inputs,
//                  absl::Span<AbstractTensorHandle*> grad_outputs) override {
//     grad_outputs[0] = grad_inputs[0];
//     grad_outputs[1] = grad_inputs[0];
//     grad_outputs[0]->Ref();
//     grad_outputs[1]->Ref();
//     return OkStatus();
//   }
//   ~AddGradientFunction() override {}
// };
//

    TF_RETURN_IF_ERROR(tape.ComputeGradient(ctx, /*targets=*/temp_outputs,
                                            /*sources=*/inputs,
                                            /*output_gradients=*/{}, outputs));
    for (auto temp_output : temp_outputs) {
      temp_output->Unref();
    }
    return absl::OkStatus();
  };
}

}  // namespace internal

    sub: Optional[SubItem] = None
    if PYDANTIC_V2:
        model_config = {"json_schema_serialization_defaults_required": True}


def get_app_client(separate_input_output_schemas: bool = True) -> TestClient:
    app = FastAPI(separate_input_output_schemas=separate_input_output_schemas)

    @app.post("/items/")
    def create_item(item: Item):
        return item

    @app.post("/items-list/")
    def create_item_list(item: List[Item]):

zirain <******@****.***> 1657592759 +0800

  }
  ResetOutputValues();
  output_values_.resize(outputs_.size(), nullptr);

  const TF_Output* inputs_ptr = inputs_.empty() ? nullptr : &inputs_[0];
  TF_Tensor* const* input_values_ptr =
      input_values_.empty() ? nullptr : &input_values_[0];

  const TF_Output* outputs_ptr = outputs_.empty() ? nullptr : &outputs_[0];
  TF_Tensor** output_values_ptr =
      output_values_.empty() ? nullptr : &output_values_[0];

zirain <******@****.***> 1657592759 +0800

  std::vector<MaybeParallelTensorOwned> typed_outputs(
      std::move(maybe_typed_outputs.value()));

  if (typed_outputs.size() > *num_outputs) {
    TF_SetStatus(status, TF_INTERNAL,
                 "The allocated output buffer was too small.");
    return;
  }

  for (int i = 0; i < typed_outputs.size(); ++i) {
    MaybeParallelTensorOwned typed_output(std::move(typed_outputs[i]));

  void SetOutputs(std::initializer_list<TF_Operation*> outputs);
  void SetOutputs(const std::vector<TF_Output>& outputs);
  void SetTargets(std::initializer_list<TF_Operation*> targets);

  void Run(TF_Status* s);

  void CloseAndDelete(TF_Status* s);

  TF_Tensor* output_tensor(int i) { return output_values_[i]; }

  TF_Session* mutable_session() { return session_; }

 private: