private AutoCloseable mocks;

    @BeforeEach
    public void setUp() throws Exception {
        mocks = MockitoAnnotations.openMocks(this);

        // Set up mock provider to return TCP memory regions
        when(mockProvider.registerMemory(any(ByteBuffer.class), any())).thenAnswer(invocation -> {
            ByteBuffer buffer = invocation.getArgument(0);
            EnumSet<RdmaAccess> access = invocation.getArgument(1);

limitations under the License.
==============================================================================*/
#include "tensorflow/c/eager/gradients.h"

#include <memory>

#include "absl/container/flat_hash_set.h"
#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"

 * <p>This class should not be assumed to be universally superior to {@code java.util.HashMap}.
 * Generally speaking, this class reduces object allocation and memory consumption at the price of
 * moderately increased constant factors of CPU. Only use this class when there is a specific reason
 * to prioritize memory over CPU.
 *
 * @author Louis Wasserman
 * @author Jon Noack
 */
@GwtIncompatible // not worth using in GWT for now

     * <ul>
     *   <li>Changes to the POM file during the build are detected</li>
     *   <li>Cache entries don't persist beyond the current build request</li>
     *   <li>Memory is freed once the build request completes</li>
     * </ul>
     */
    static class BuildPathSource extends PathSource implements ModelSource, CacheMetadata {

        /**

     * <p>This method returns all duplicate host configurations in the system,
     * ordered by sort order, regular name, and duplicate hostname. The results
     * are limited by the configured maximum fetch size to prevent memory issues.</p>
     *
     * @return a list of all DuplicateHost entities
     */
    public List<DuplicateHost> getDuplicateHostList() {

        return duplicateHostBhv.selectList(cb -> {

* Introduce node memory pressure condition to scheduler ([#25531](https://github.com/kubernetes/kubernetes/pull/25531), [@ingvagabund](https://github.com/ingvagabund))
* Fix detection of docker cgroup on RHEL ([#25907](https://github.com/kubernetes/kubernetes/pull/25907), [@ncdc](https://github.com/ncdc))

limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_C_EAGER_ABSTRACT_OPERATION_H_
#define TENSORFLOW_C_EAGER_ABSTRACT_OPERATION_H_

#include <memory>

#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/tensor_interface.h"
#include "tensorflow/core/framework/tensor_shape.h"

					}
				} else {
					stopFn(result, nil)
				}
				ri.trackCurrentBucketObject(r.Bucket, result, success, attempts)
				globalBatchJobsMetrics.save(job.ID, ri)
				// persist in-memory state to disk after every 10secs.
				batchLogIf(ctx, ri.updateAfter(ctx, api, 10*time.Second, job))
				if success {
					break
				}
				if delay > 0 {

   * interned, and so does not prevent these instances from being garbage-collected. This most
   * likely does not perform as well as {@link #newStrongInterner}, but is the best alternative when
   * the memory usage of that implementation is unacceptable.
   */
  @GwtIncompatible // java.lang.ref.WeakReference
  public static <E> Interner<E> newWeakInterner() {
    return newBuilder().weak().build();
  }

## Caveat { #caveat }

If you need to perform heavy background computation and you don't necessarily need it to be run by the same process (for example, you don't need to share memory, variables, etc), you might benefit from using other bigger tools like [Celery](https://docs.celeryq.dev).