WIP ML Build Docker container for ML repositories (Tensorflow, JAX and XLA).

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

		Pods(pod.Namespace).
		Patch(
			context.Background(),
			pod.Name,
			types.MergePatchType,
			annotationPatch,
			metav1.PatchOptions{},
			// Both "pods" and "pods/status" can mutate the metadata. However, pods/status is lower privilege, so we use that instead.
			"status",
		)
	return err
}

func AnnotateUnenrollPod(client kubernetes.Interface, pod *metav1.ObjectMeta) error {

SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE

   * <p>This is guaranteed to return zero if the dataset contains only exactly one finite value. It
   * is not guaranteed to return zero when the dataset consists of the same value multiple times,
   * due to numerical errors. However, it is guaranteed never to return a negative result.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link

All the **logic** about how to register the URLs for webhooks and the code to actually send those requests is up to you. You write it however you want to in **your own code**.

## Documenting webhooks with **FastAPI** and OpenAPI

   * addListener/SES.schedule. The later racy write in cancel() is not guaranteed to be observed,
   * however that is fine since the correctness is based on the atomic state in our base class. The
   * initial write to timer is never definitely visible to Fire.run since it is assigned after
   * SES.schedule is called. Therefore Fire.run has to check for null. However, it should be visible

SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE

   * <p>This is guaranteed to return zero if the dataset contains only exactly one finite value. It
   * is not guaranteed to return zero when the dataset consists of the same value multiple times,
   * due to numerical errors. However, it is guaranteed never to return a negative result.
   *
   * <h3>Non-finite values</h3>
   *
   * <p>If the dataset contains any non-finite values ({@link Double#POSITIVE_INFINITY}, {@link

// metadata and synchronization.
//
// This allows us to hide concrete implementations of TensorHandle from header
// files. The interface lists the common functionality that must be provided by
// any concrete implementation. However, in cases where the true concrete class
// is needed a static_cast can be applied.
class ImmediateExecutionTensorHandle : public AbstractTensorHandle {
 public:
  // Returns number of dimensions.

 * a little different from those of other Collection-related features such as {@link
 * CollectionFeature} or {@link SetFeature}.
 *
 * <p>However, when {@link CollectionSize.Require} is used to annotate a test it behaves normally
 * (i.e. it requires the collection instance under test to be a certain size for the test to run).