isinstance(field_info, params.Param)
            and getattr(field_info, "in_", None) is None
        ):
            field_info.in_ = params.ParamTypes.query
        use_annotation_from_field_info = get_annotation_from_field_info(
            use_annotation,
            field_info,
            param_name,
        )
        if not field_info.alias and getattr(field_info, "convert_underscores", None):

                                      absl::MakeSpan(in_gradients));
      if (!persistent_) {
        trace.backward_function_deleter(trace.backward_function);
      }
      if (!s.ok()) {
        return s;
      }
    } else {
      if (!persistent_) {
        trace.backward_function_deleter(trace.backward_function);
      }
      for (Gradient* grad : out_gradients) {
        if (grad != nullptr) {

  }
  return valid;
}
}  // namespace

void TFE_CallDLManagedTensorDeleter(void* dlm_ptr) {
  DLManagedTensor* dlMTensor = static_cast<DLManagedTensor*>(dlm_ptr);
  if (dlMTensor->deleter != nullptr) {
    dlMTensor->deleter(dlMTensor);
  }
}

void* TFE_HandleToDLPack(TFE_TensorHandle* h, TF_Status* status) {
  auto tf_dlm_context = GetDlContext(h, status);
  if (!status->status.ok()) {
    return nullptr;

### Hamburguesas Concurrentes

Vas con la persona que te gusta 😍 a pedir comida rápida 🍔, haces cola mientras el cajero 💁 recoge los pedidos de las personas de delante tuyo.

<img src="https://fastapi.tiangolo.com/img/async/concurrent-burgers/concurrent-burgers-01.png" alt="illustration">

Llega tu turno, haces tu pedido de 2 hamburguesas impresionantes para esa persona 😍 y para ti.

    if operation_id in operation_ids:
        message = (
            f"Duplicate Operation ID {operation_id} for function "
            + f"{route.endpoint.__name__}"
        )
        file_name = getattr(route.endpoint, "__globals__", {}).get("__file__")
        if file_name:
            message += f" at {file_name}"
        warnings.warn(message, stacklevel=1)
    operation_ids.add(operation_id)

        exclude = set(exclude)
    if isinstance(obj, BaseModel):
        # TODO: remove when deprecating Pydantic v1
        encoders: Dict[Any, Any] = {}
        if not PYDANTIC_V2:
            encoders = getattr(obj.__config__, "json_encoders", {})  # type: ignore[attr-defined]
            if custom_encoder:
                encoders.update(custom_encoder)
        obj_dict = _model_dump(
            obj,
            mode="json",

So, we need to do some extra tricks to make it work as if it was just using `threading.local`. The `__init__`, `__setattr__`, and `__getattr__` implement all the required tricks for this to be used by Peewee without knowing that it is now compatible with FastAPI.

!!! tip

    of unacknowledged data per stream and no per-connection limit.

 *  Fix: Don't close a `Deflater` while we're still using it to compress a web socket message. We
    had a severe bug where web sockets were closed on the wrong thread, which caused
    `NullPointerException` crashes in `Deflater`.

 *  Fix: Don't crash after a web socket fails its connection upgrade. We incorrectly released

⚫️ ✔️ 🌐 ⚛ ⚒ 🏒 ⚙️ `contextvars` ↩️ `threading.local`.

`contextvars` 👷 🍖 🎏 🌘 `threading.local`. ✋️ 🎂 🏒 🔗 📟 🤔 👈 👉 🎓 👷 ⏮️ `threading.local`.

, 👥 💪 ➕ 🎱 ⚒ ⚫️ 👷 🚥 ⚫️ ⚙️ `threading.local`. `__init__`, `__setattr__`, & `__getattr__` 🛠️ 🌐 ✔ 🎱 👉 ⚙️ 🏒 🍵 🤔 👈 ⚫️ 🔜 🔗 ⏮️ FastAPI.

!!! tip
    👉 🔜 ⚒ 🏒 🎭 ☑ 🕐❔ ⚙️ ⏮️ FastAPI. 🚫 🎲 📂 ⚖️ 📪 🔗 👈 ➖ ⚙️, 🏗 ❌, ♒️.

        self, dependency: Optional[Callable[..., Any]] = None, *, use_cache: bool = True
    ):
        self.dependency = dependency
        self.use_cache = use_cache

    def __repr__(self) -> str:
        attr = getattr(self.dependency, "__name__", type(self.dependency).__name__)
        cache = "" if self.use_cache else ", use_cache=False"
        return f"{self.__class__.__name__}({attr}{cache})"


class Security(Depends):