XValidations: apiextensions.ValidationRules{
						{Rule: "self >= oldSelf.value()", OptionalOldSelf: ptr.To(true)},
						{Rule: "self >= oldSelf.orValue(1)", OptionalOldSelf: ptr.To(true)},
						{Rule: "oldSelf.hasValue() ? self >= oldSelf.value() : true", OptionalOldSelf: ptr.To(true)},
						{Rule: "self >= oldSelf", OptionalOldSelf: ptr.To(true)},
						{Rule: "self >= oldSelf.orValue('')", OptionalOldSelf: ptr.To(true)},
					},
				},

          |||
        ),
        self.query(
          'Internal Errors',
          |||
            pilot_total_xds_internal_errors{}
          |||
        ),
        self.query(
          'Push Context Errors',
          |||
            pilot_xds_push_context_errors{}
          |||
        ),
      ],

      xdsConnections: [
        self.query(
          'Connections (client reported)',

      def has_reshape(self) -> bool:
        return self.has_bias() and self.bias_size != self.filters.shape[-1]

      @def_function.function
      def matmul(self, input_tensor: core.Tensor) -> Mapping[str, core.Tensor]:
        """Performs a matrix multiplication.

        Depending on self.has_bias and self.activation_fn, it may add a bias
        term or
        go through the activaction function.

        Args:

  def test_multiple_conversion_jobs_with_calibration(self):
    # Ensure that multiple conversion jobs with calibration won't encounter any
    # concurrency issue.
    with self.pool:
      jobs = []
      for _ in range(10):
        jobs.append(self.pool.submit(self._convert_with_calibration))

      for job in jobs:
        self.assertIsNotNone(job.result())


if __name__ == '__main__':

    simple(title, targets, desc=''):
      self.base(title, targets, desc)
      + options.legend.withCalcs([])
      + options.legend.withDisplayMode('list'),

    short(title, targets, desc=''):
      self.base(title, targets, desc)
      + timeSeries.standardOptions.withUnit('short')
      + timeSeries.standardOptions.withDecimals(0),

    seconds(title, targets, desc=''):
      self.base(title, targets, desc)

    """
    for sample_1, sample_2 in zip(repr_ds_1, repr_ds_2):
      self.assertCountEqual(sample_1.keys(), sample_2.keys())

      for input_key in sample_1:
        self._assert_tensorlike_all_close(
            sess, sample_1[input_key], sample_2[input_key]
        )

  def test_not_implemented_saver(self):
    with self.assertRaisesRegex(
        NotImplementedError, '"save" is not implemented.'
    ):

    ) {
        self.intercepted = "setTestString(String)";
        self.setTestString(newValue);
    }

    @InterceptGroovyCalls
    @CallableKind.GroovyPropertyGetter
    public static boolean intercept_testFlag(
        @ParameterKind.Receiver InterceptorTestReceiver self,
        @ParameterKind.CallerClassName String consumer
    ) {
        self.intercepted = "isTestFlag()";
        return self.isTestFlag();
    }

    }

    @InterceptGroovyCalls
    @InstanceMethod
    @WithExtensionReferences(toClass = ResourceGroovyMethods.class)
    public static void intercept_eachByte(
        @Receiver File self,
        int bufferLen,
        Closure<?> closure,
        @CallerClassName String consumer
    ) throws IOException {
        Instrumented.fileOpened(self, consumer);
        ResourceGroovyMethods.eachByte(self, bufferLen, closure);

      }

  def test_static_range_quantization_by_default(self):
    model = self.SimpleModel()

    saved_model_save.save(model, self._input_saved_model_path)

    # Use default QuantizationOptions.
    converted_model = quantize_model.quantize(
        self._input_saved_model_path,
        representative_dataset=self._simple_model_data_gen(),
    )

    self.assertIsNotNone(converted_model)
    self.assertCountEqual(

      tensor_spec.TensorSpec((), dtypes.float32)
  ])
  def compute(self, a, b):
    return (a + self.x) * (b + self.y) / (self.child.z) + self.child.c


class ReferencesParent(module.Module):

  def __init__(self, parent):
    super(ReferencesParent, self).__init__()
    self.parent = parent
    self.my_variable = variables.Variable(3., name="MyVariable")


# Creates a cyclic object graph.