...

           "invariants. In the case of this pass, that means manually propagating "
           "controls to lifted parallel execute regions to the graph fetch to "
           "ensure the ops execute, as well as determining whether or not the "
           "islands created by this pass should be split after the replicated "
           "ops have been lifted.">
  ];
}

	// for cache sync ~2 times in every wait.PollImmediateUntil() loop.
	//
	// The 1s sleep in the test allows GetDeletableResources and
	// gc.resyncMonitors to run ~5 times to ensure the changes to the
	// fakeDiscoveryClient are picked up.
	go gc.Sync(tCtx, fakeDiscoveryClient, 200*time.Millisecond)

	// Wait until the sync discovers the initial resources
	time.Sleep(1 * time.Second)

probe values. # # If the probe is enabled, it is recommended to have delay=0s,period=15s,failureThreshold=4. # This ensures the pod is marked ready immediately after the startup probe passes (which has a 1s poll interval), # and doesn't spam the readiness endpoint too much # # If the probe is disabled, it is recommended to have delay=1s,period=2s,failureThreshold=30. # This ensures the startup is reasonable fast (polling every 2s). 1s delay is used since the startup is not often ready instantly....

  """Test cases regarding the use of QuantizationOptions proto.

  Run all tests cases in both the graph mode (default in TF1) and the eager mode
  (default in TF2) to ensure support for when TF2 is disabled.
  """

  class SimpleModel(module.Module):

    def __init__(self):
      self.filters = np.random.uniform(low=-1.0, high=1.0, size=(4, 3)).astype(
          'f4'
      )

          feature_group_count;
      auto new_output_type =
          RankedTensorType::get(new_output_shape, output_type.getElementType());

      // Create a Smaller Convolution (Ensure compatibility)
      auto conv_result = rewriter.create<mhlo::ConvolutionOp>(
          conv_op.getLoc(), new_output_type, sliced_input, sliced_kernel,
          conv_op.getWindowStridesAttr(), conv_op.getPaddingAttr(),

      "properties": {
        "fsType": {
          "description": "Filesystem type of the volume that you want to mount. Tip: Ensure that the filesystem type is supported by the host operating system. Examples: \"ext4\", \"xfs\", \"ntfs\". Implicitly inferred to be \"ext4\" if unspecified. More info: https://kubernetes.io/docs/concepts/storage/volumes#awselasticblockstore",

			)
			if err != nil {
				cancel()
				t.Fatalf("Failed to create framework for testing: %v", err)
			}

			tt.action(f)

			// Stop the goroutine which records metrics and ensure it's stopped.
			cancel()
			<-recorder.IsStoppedCh
			// Try to clean up the metrics buffer again in case it's not empty.
			recorder.FlushMetrics()

				if !ok || !s.IsTerminationRequested() || !s.IsTerminationStarted() || s.IsFinished() || s.IsWorking() || !s.IsDeleted() {
					t.Fatalf("unexpected requested pod termination: %#v", s)
				}

				// ensure that we recorded the appropriate state for replays
				expectedRunningPod := runtimePod(simplePod())
				if actual, expected := s.activeUpdate, (&UpdatePodOptions{
					RunningPod:     expectedRunningPod,

}

// Estimate the max size needed to hold any new trampolines created for this function. This
// is used to determine when the section can be split if it becomes too large, to ensure that
// the trampolines are in the same section as the function that uses them.
func maxSizeTrampolines(ctxt *Link, ldr *loader.Loader, s loader.Sym, isTramp bool) uint64 {