def __init__(self):
      self.uninitialized_variable = resource_variable_ops.UninitializedVariable(
          name="uninitialized_variable", dtype=dtypes.int64)

  class Module(module.Module):
    """A module with an UninitializedVariable."""

    def __init__(self):
      super(Module, self).__init__()
      self.sub_module = SubModule()
      self.initialized_variable = variables.Variable(

                """
            ),
        ] = True,
    ):
        self.model: APIKey = APIKey(
            **{"in": APIKeyIn.query},  # type: ignore[arg-type]
            name=name,
            description=description,
        )
        self.scheme_name = scheme_name or self.__class__.__name__
        self.auto_error = auto_error

    async def __call__(self, request: Request) -> Optional[str]:

from typing import Annotated

from fastapi import Depends, FastAPI

app = FastAPI()


class FixedContentQueryChecker:
    def __init__(self, fixed_content: str):
        self.fixed_content = fixed_content

    def __call__(self, q: str = ""):
        if q:
            return self.fixed_content in q
        return False


checker = FixedContentQueryChecker("bar")


@app.get("/query-checker/")

  def __init__(self, parent):
    super(ReferencesParent, self).__init__()
    self.parent = parent
    # CHECK: tf_saved_model.global_tensor
    # CHECK-SAME: tf_saved_model.exported_names = ["child.my_variable"]
    self.my_variable = tf.Variable(3.)


# Creates a cyclic object graph.
class TestModule(tf.Module):

  def __init__(self):
    super(TestModule, self).__init__()

"""


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):

  # CHECK-NOT:    tf_saved_model.exported_names

  @tf.function(input_signature=[tf.TensorSpec([], tf.float32)])
  def callee(self, x):
    return x, self.v

  @tf.function(input_signature=[tf.TensorSpec([], tf.float32)])
  def caller(self, x):
    return self.callee(x)


if __name__ == '__main__':

app = FastAPI()


fake_items_db = [{"item_name": "Foo"}, {"item_name": "Bar"}, {"item_name": "Baz"}]


class CommonQueryParams:
    def __init__(self, q: Union[str, None] = None, skip: int = 0, limit: int = 100):
        self.q = q
        self.skip = skip
        self.limit = limit


@app.get("/items/")
async def read_items(commons: CommonQueryParams = Depends()):
    response = {}
    if commons.q:

        'dilation_h': 1,
        'padding': 'SAME',
        'act': 'RELU6'
    }

    self._assertOpAndComposite([input_, filter_, bias],
                               tf.function(gen_mnist_ops.new_conv2d),
                               ops_defs._composite_conv_add_relu, kwargs)

  def test_new_conv2d_tanh(self):
    self.skipTest('Fix tanh gradients')
    input_ = tf.random.uniform([1, 4, 4, 1])

app = FastAPI()


fake_items_db = [{"item_name": "Foo"}, {"item_name": "Bar"}, {"item_name": "Baz"}]


class CommonQueryParams:
    def __init__(self, q: Union[str, None] = None, skip: int = 0, limit: int = 100):
        self.q = q
        self.skip = skip
        self.limit = limit


@app.get("/items/")
async def read_items(commons: CommonQueryParams = Depends(CommonQueryParams)):
    response = {}
    if commons.q:

app = FastAPI()


fake_items_db = [{"item_name": "Foo"}, {"item_name": "Bar"}, {"item_name": "Baz"}]


class CommonQueryParams:
    def __init__(self, q: Union[str, None] = None, skip: int = 0, limit: int = 100):
        self.q = q
        self.skip = skip
        self.limit = limit


@app.get("/items/")
async def read_items(commons: Annotated[CommonQueryParams, Depends()]):
    response = {}
    if commons.q: