route.body_field, ModelField
                ), "A request body must be a Pydantic Field"
                body_fields_from_routes.append(route.body_field)
            if route.response_field:
                responses_from_routes.append(route.response_field)
            if route.response_fields:
                responses_from_routes.extend(route.response_fields.values())
            if route.callbacks:

class ConnectionManager:
    def __init__(self):
        self.active_connections: list[WebSocket] = []

    async def connect(self, websocket: WebSocket):
        await websocket.accept()
        self.active_connections.append(websocket)

    def disconnect(self, websocket: WebSocket):
        self.active_connections.remove(websocket)

    async def send_personal_message(self, message: str, websocket: WebSocket):

    path.write_bytes(b"<file content>")
    app = FastAPI()

    testing_file_store: List[UploadFile] = []

    @app.post("/uploadfile/")
    def create_upload_file(file: UploadFile):
        testing_file_store.append(file)
        return {"filename": file.filename}

    client = TestClient(app)
    with path.open("rb") as file:
        response = client.post("/uploadfile/", files={"file": file})

            # new_section = Section(title=new_title, children=new_children)
            item.title = new_title
            item.children = new_children
            new_items.append(item)
        else:
            new_items.append(item)
    return new_items


def on_nav(
    nav: Navigation, *, config: MkDocsConfig, files: Files, **kwargs: Any
) -> Navigation:

_current_module = _sys.modules[__name__]
_tf_api_dir = _os.path.dirname(_os.path.dirname(_API_MODULE.__file__))
if not hasattr(_current_module, "__path__"):
  __path__ = [_tf_api_dir]
elif _tf_api_dir not in __path__:
  __path__.append(_tf_api_dir)

# Hook external TensorFlow modules.
# Import compat before trying to import summary from tensorboard, so that
# reexport_tf_summary can get compat from sys.modules. Only needed if using
# lazy loading.

            return encoder(obj)

    try:
        data = dict(obj)
    except Exception as e:
        errors: List[Exception] = []
        errors.append(e)
        try:
            data = vars(obj)
        except Exception as e:
            errors.append(e)
            raise ValueError(errors) from e
    return jsonable_encoder(
        data,
        include=include,
        exclude=exclude,

    except AsyncDependencyError:
        errors.append("/async_raise")
        raise
    finally:
        state["/async_raise"] = "asyncgen raise finalized"


def generator_state_try(state: Dict[str, str] = Depends(get_state)):
    state["/sync_raise"] = "generator raise started"
    try:
        yield state["/sync_raise"]
    except SyncDependencyError:
        errors.append("/sync_raise")
        raise
    finally:

        try:
          __import__(short_package_name)
        except ImportError:
          logging.exception("error importing %s", short_package_name)
          failed_packages.append(package_name)

    if failed_packages:
      self.fail(
          "Failed to import"
          f" {len(failed_packages)}/{len(self.api_packages_v2)} packages"
          f" {version}:\n{failed_packages}"
      )

  if is_linux():
    if int(environ_cp.get('TF_NEED_TENSORRT', False)):
      cuda_libraries.append('tensorrt')
    if environ_cp.get('TF_NCCL_VERSION', None):
      cuda_libraries.append('nccl')
  if is_windows():
    if int(environ_cp.get('TF_NEED_TENSORRT', False)):
      cuda_libraries.append('tensorrt')
      print('WARNING: TensorRT support on Windows is experimental\n')

                ).errors()
                use_errors.extend(new_errors)
            elif isinstance(error, list):
                use_errors.extend(_normalize_errors(error))
            else:
                use_errors.append(error)
        return use_errors

    def _model_rebuild(model: Type[BaseModel]) -> None:
        model.update_forward_refs()

    def _model_dump(