## Использовать `async` или не `async`

Для зависимостей, вызванных **FastAPI** (то же самое, что и ваши *функции обработки пути*), те же правила, что приняты для определения ваших функций.

Вы можете использовать `async def` или обычное `def`.

Вы также можете объявить зависимости с `async def` внутри обычной `def` *функции обработки пути*, или `def` зависимости внутри `async def` *функции обработки пути*, и так далее.

  if (forward_input) {
    TFE_DeleteTensorHandle(n);
  }

  int num_retvals = 1;
  if (async) {
    // Enqueue dummy ops so we backlog async execution & actually test async.
    // This is usually unnecessary, but we've experienced the occasional test
    // failure when testing async mode with no explicit forwarding.
    for (int i = 0; i < 100000; ++i) {
      TFE_Op* add_op_dummy = AddOp(ctx, m, m);

    * This is especially useful if you run `await myfile.read()` once and then need to read the contents again.
* `close()`: Closes the file.

As all these methods are `async` methods, you need to "await" them.

For example, inside of an `async` *path operation function* you can get the contents with:

```Python
contents = await myfile.read()
```

    name: str
    description: Union[str, None] = None
    price: float
    tax: Union[float, None] = None
    tags: List[str] = []


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


@app.get("/items/")
async def read_items() -> List[Item]:
    return [
        Item(name="Portal Gun", price=42.0),
        Item(name="Plumbus", price=32.0),

from fastapi import FastAPI

app = FastAPI()

items = {}


@app.on_event("startup")
async def startup_event():
    items["foo"] = {"name": "Fighters"}
    items["bar"] = {"name": "Tenders"}


@app.get("/items/{item_id}")
async def read_items(item_id: str):

        defaultProfiler,
        display = ParameterDisplay.PROMPT,
        allowEmpty = false,
        description = "Command line option for the performance test task to enable profiling. For example `async-profiler`, `async-profiler-heap`, `async-profiler-all` or `jfr`. Use `none` for benchmarking only."
    )
}

object AdHocPerformanceScenarioLinux : AdHocPerformanceScenario(Os.LINUX)

    name: str
    owner: UserDB


class PetOut(BaseModel):
    name: str
    owner: User


@app.post("/users/", response_model=User)
async def create_user(user: UserCreate):
    return user


@app.get("/pets/{pet_id}", response_model=PetOut)
async def read_pet(pet_id: int):
    user = UserDB(
        email="******@****.***",
        hashed_password="secrethashed",
    )

    price: float
    tax: Union[float, None] = None
    tags: list[str] = []


@app.post("/items/", response_model=Item)
async def create_item(item: Item) -> Any:
    return item


@app.get("/items/", response_model=list[Item])
async def read_items() -> Any:
    return [
        {"name": "Portal Gun", "price": 42.0},
        {"name": "Plumbus", "price": 32.0},

///

##  `async` ⚖️ 🚫 `async`

🔗 🔜 🤙 **FastAPI** (🎏 👆 *➡ 🛠️ 🔢*), 🎏 🚫 ✔ ⏪ 🔬 👆 🔢.

👆 💪 ⚙️ `async def` ⚖️ 😐 `def`.

& 👆 💪 📣 🔗 ⏮️ `async def` 🔘 😐 `def` *➡ 🛠️ 🔢*, ⚖️ `def` 🔗 🔘 `async def` *➡ 🛠️ 🔢*, ♒️.

⚫️ 🚫 🤔. **FastAPI** 🔜 💭 ⚫️❔.

/// note

🚥 👆 🚫 💭, ✅ [🔁: *"🏃 ❓" *](../../async.md){.internal-link target=_blank} 📄 🔃 `async` & `await` 🩺.

  int num_retvals = 1;
  TFE_Execute(matmul, &retvals[0], &num_retvals, status);
  EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);

  // TODO(gjn): Add support for waiting on async local mirrors
  if (!remote && !async && !remote_func_outputs) {
    auto remote_arg =
        tensorflow::TensorHandleFromInterface(tensorflow::unwrap(h1_task2));
    // The input handles should never change since they have been mirrored.