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',

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

                """
            ),
        ] = 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):

  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)

    if the overridden default value was truthy.
    """

    def __init__(self, value: Any):
        self.value = value

    def __bool__(self) -> bool:
        return bool(self.value)

    def __eq__(self, o: object) -> bool:
        return isinstance(o, DefaultPlaceholder) and o.value == self.value


DefaultType = TypeVar("DefaultType")

        val self = this
        return KaRendererKeywordFilter filter@{ modifier, kaAnnotated ->
            val analysisSession = this@filter
            self.filter(analysisSession, modifier, kaAnnotated) && other.filter(analysisSession, modifier, kaAnnotated)
        }
    }

    public infix fun or(other: KaRendererKeywordFilter): KaRendererKeywordFilter {
        val self = this

    statistics.min_max_statistics.global_max = 5.0

    min_value, max_value = calibration_algorithm.get_min_max_value(
        statistics, calib_opts
    )
    self.assertAllEqual((min_value, max_value), (1.0, 5.0))

  def test_average_min_max(self):
    calib_opts = stablehlo_quant_config_pb2.CalibrationOptions(
        calibration_method=_CalibrationMethod.CALIBRATION_METHOD_AVERAGE_MIN_MAX
    )

    public infix fun and(other: KaRendererOtherModifiersProvider): KaRendererOtherModifiersProvider {
        val self = this
        return object : KaRendererOtherModifiersProvider {
            override fun getOtherModifiers(analysisSession: KaSession, symbol: KaDeclarationSymbol): List<KtModifierKeywordToken> {
                return self.getOtherModifiers(analysisSession, symbol) + other.getOtherModifiers(analysisSession, symbol)
            }
        }