if (TF_GetCode(status) != TF_OK) return;

  // unwrap op_ and set step_id only if valid step id value was set.
  // Currently only required for non-TFRT use cases, e.g., EagerOp.
  if (step_id_.has_value()) {
    tensorflow::unwrap(op_.get())->SetStepId(step_id_.value());
  }

  TFE_Execute(op_.get(), unwrapped_results.data(), &real_num_outputs, status);

  // followed by `TFE_ContextAsyncWait` to clear pending operations. It will be
  // used to cancel all other operations if any fails.
  //
  // Set step_id to configure the step id used for rendezvous creation. step id
  // of value -1 is reserved for global rendezvous and should not be set here.
  void StartExecute(TFE_Context* context,

      CancellationManager* cancellation_manager) = 0;

  // Returns the stack trace set by `SetStackTrace` if exists.
  virtual absl::optional<ManagedStackTrace> GetStackTrace() = 0;

  virtual void SetStepId(int64_t step_id) = 0;

  // For LLVM style RTTI.
  static bool classof(const AbstractOperation* ptr) {
    return ptr->getKind() == kEager || ptr->getKind() == kTfrt;
  }

 protected:

  // no-op cleanup function.
  auto cleanup = [](const std::string& name) {};
  // Use step_id zero as we only have a single context concurrently and
  // concurrently running each of the MLIR functions create a new device.
  step_container_ = std::make_unique<tensorflow::ScopedStepContainer>(
      /*step_id=*/0, cleanup);
  absl::Status status = step_container_->Create(
      device_->resource_manager(),

}

int64_t TF_GetIterId(TF_OpKernelContext* ctx) {
  return reinterpret_cast<::tensorflow::OpKernelContext*>(ctx)
      ->frame_iter()
      .iter_id;
}

int64_t TF_GetStepId(TF_OpKernelContext* ctx) {
  return reinterpret_cast<::tensorflow::OpKernelContext*>(ctx)->step_id();
}

int TF_GetDeviceId(TF_OpKernelContext* ctx) {
  // TensorFlow always sets device in OpKernelContext.

    EXPECT_EQ(TF_OK, TF_GetCode(status));
    TF_DeleteStatus(status);
  }

  {
    OpKernelContext::Params p;
    DummyDevice dummy_device(nullptr);
    p.device = &dummy_device;
    p.step_id = 43;

    Tensor t(tensorflow::uint8(123));

    gtl::InlinedVector<TensorValue, 4> inputs;
    // Simulate 2 inputs
    inputs.emplace_back(&t);
    inputs.emplace_back();
    p.inputs = inputs;

    return "Hello World"


@app.get("/path/{item_id}")
def get_id(item_id):
    return item_id


@app.get("/path/str/{item_id}")
def get_str_id(item_id: str):
    return item_id


@app.get("/path/int/{item_id}")
def get_int_id(item_id: int):
    return item_id


@app.get("/path/float/{item_id}")
def get_float_id(item_id: float):
    return item_id


@app.get("/path/bool/{item_id}")

@router.get("/")
async def read_items():
    return fake_items_db


@router.get("/{item_id}")
async def read_item(item_id: str):
    if item_id not in fake_items_db:
        raise HTTPException(status_code=404, detail="Item not found")
    return {"name": fake_items_db[item_id]["name"], "item_id": item_id}


@router.put(
    "/{item_id}",
    tags=["custom"],
    responses={403: {"description": "Operation forbidden"}},
)

@router.get("/")
async def read_items():
    return fake_items_db


@router.get("/{item_id}")
async def read_item(item_id: str):
    if item_id not in fake_items_db:
        raise HTTPException(status_code=404, detail="Item not found")
    return {"name": fake_items_db[item_id]["name"], "item_id": item_id}


@router.put(
    "/{item_id}",
    tags=["custom"],
    responses={403: {"description": "Operation forbidden"}},
)

@app.api_route("/items/{item_id}", methods=["GET"])
def get_items(item_id: str):
    return {"item_id": item_id}


def get_not_decorated(item_id: str):
    return {"item_id": item_id}


app.add_api_route("/items-not-decorated/{item_id}", get_not_decorated)


@app.delete("/items/{item_id}")
def delete_item(item_id: str, item: Item):
    return {"item_id": item_id, "item": item}