class OpsDefsTest(test.TestCase):
  """Test utils."""

  def _assertOpAndComposite(self, vars_, compute_op, compute_composite, kwargs,
                            op_kwargs=None):
    if op_kwargs is None:
      op_kwargs = kwargs
    if test_util.IsMklEnabled():
      self.skipTest("Not compatible with oneDNN custom ops.")

    # compute with op.
    with backprop.GradientTape() as gt:

    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")

1. [Install MinIO Server](#install-minio-server)
2. [Use an Existing Key and Certificate with MinIO](#use-an-existing-key-and-certificate-with-minio)
3. [Generate and use Self-signed Keys and Certificates with MinIO](#generate-use-self-signed-keys-certificates)
4. [Install Certificates from Third-party CAs](#install-certificates-from-third-party-cas)

## 1. Install MinIO Server

        validate that the sample has same set of input keys before saving.

    Raises:
      KeyError: If path_map and expected_input_key_map have different keys.
    """
    self.path_map: Mapping[str, os.PathLike[str]] = path_map
    self.expected_input_key_map: Mapping[str, Collection[str]] = {}
    if expected_input_key_map is not None:
      if set(path_map.keys()) != set(expected_input_key_map.keys()):
        raise KeyError(

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


def get_person_name(one_person: Person):

        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

		func(obj *admissionregistration.Rule, c fuzz.Continue) {
			c.FuzzNoCustom(obj) // fuzz self without calling this function again
			if obj.Scope == nil {
				s := admissionregistration.AllScopes
				obj.Scope = &s
			}
		},
		func(obj *admissionregistration.ValidatingWebhook, c fuzz.Continue) {
			c.FuzzNoCustom(obj) // fuzz self without calling this function again
			if obj.FailurePolicy == nil {

    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
    )

			c.FuzzNoCustom(cm) // fuzz self without calling this function again
			if c.RandBool() {
				opt := c.RandBool()
				cm.Optional = &opt
			}
		},
		func(s *core.SecretEnvSource, c fuzz.Continue) {
			c.FuzzNoCustom(s) // fuzz self without calling this function again
		},
		func(sc *core.SecurityContext, c fuzz.Continue) {
			c.FuzzNoCustom(sc) // fuzz self without calling this function again

  model.add(tf.keras.layers.Dense(128, activation='relu'))
  model.add(tf.keras.layers.Dense(10, activation='softmax'))
  return model


class TestModule(tf.Module):

  def __init__(self):
    super(TestModule, self).__init__()
    self.model = mnist_model()

  # CHECK: func {{@[a-zA-Z_0-9]+}}(%arg0: tensor<1x28x28x1xf32> {tf._user_specified_name = "x", tf_saved_model.index_path = [0]}