namespace:
    var.query.new('namespace')
    + var.query.withDatasourceFromVariable(self.datasource)
    + var.query.queryTypes.withLabelValues(
      'namespace',
      'process_cpu_seconds_total{cluster=~"$%s"}' % self.cluster.name,
    )
    + var.query.withRefresh('time'),

  job:
    var.query.new('job')
    + var.query.withDatasourceFromVariable(self.datasource)
    + var.query.queryTypes.withLabelValues(
      'job',

                """
            ),
        ] = True,
    ):
        self.model = HTTPBaseModel(scheme="basic", description=description)
        self.scheme_name = scheme_name or self.__class__.__name__
        self.realm = realm
        self.auto_error = auto_error

    async def __call__(  # type: ignore
        self, request: Request
    ) -> Optional[HTTPBasicCredentials]:

import unittest

import requests_mock

import productpage


class ApplianceTest(unittest.TestCase):

    def setUp(self):
        self.app = productpage.app.test_client()

    @requests_mock.Mocker()
    def test_header_propagation_reviews(self, m):
        """ Check that tracing headers are forwarded correctly """
        product_id = 0
        # Register expected headers with the mock. If the headers

class Depends:
    def __init__(
        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})"

                """
            ),
        ] = True,
    ):
        self.model = OpenIdConnectModel(
            openIdConnectUrl=openIdConnectUrl, description=description
        )
        self.scheme_name = scheme_name or self.__class__.__name__
        self.auto_error = auto_error

    async def __call__(self, request: Request) -> Optional[str]:
        authorization = request.headers.get("Authorization")

                """
            ),
        ] = 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]:

class Person:
    def __init__(self, name: str):
        self.name = name


class Pet:
    def __init__(self, owner: Person, name: str):
        self.owner = owner
        self.name = name


@dataclass
class Item:
    name: str
    count: int


class DictablePerson(Person):
    def __iter__(self):
        return ((k, v) for k, v in self.__dict__.items())


class DictablePet(Pet):

                auth.
                """
            ),
        ] = None,
    ):
        self.grant_type = grant_type
        self.username = username
        self.password = password
        self.scopes = scope.split()
        self.client_id = client_id
        self.client_secret = client_secret


class OAuth2PasswordRequestFormStrict(OAuth2PasswordRequestForm):
    """

        self.path_regex, self.path_format, self.param_convertors = compile_path(path)
        self.dependant = get_dependant(path=self.path_format, call=self.endpoint)
        for depends in self.dependencies[::-1]:
            self.dependant.dependencies.insert(
                0,
                get_parameterless_sub_dependant(depends=depends, path=self.path_format),
            )

  def test_saved_model_to_stablehlo(self):
    with tempfile.TemporaryDirectory() as tempdir:
      path = build_savedmodel(tempdir)
      module_bytecode = tensorflow_to_stablehlo.savedmodel_to_stablehlo(
          input_path=path, input_arg_shapes_str='4'
      )
      with ir.Context() as ctx:
        stablehlo.register_dialect(ctx)
        module = ir.Module.parse(module_bytecode)