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"

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"

    - [Quality of Service for memory resources](#quality-of-service-for-memory-resources)
    - [API changes and improvements for ephemeral containers](#api-changes-and-improvements-for-ephemeral-containers)
  - [Known Issues](#known-issues)

	info := madmin.GetProcInfo(context.Background(), globalLocalNodeName)
	return madminProcInfo.NewJSONWith(&info), nil
}

// GetMemInfoHandler - returns memory information.
func (s *peerRESTServer) GetMemInfoHandler(_ *grid.MSS) (*grid.JSON[madmin.MemInfo], *grid.RemoteErr) {
	info := madmin.GetMemInfo(context.Background(), globalLocalNodeName)

 * <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

#include "tensorflow/c/eager/c_api.h"

#include <algorithm>
#include <cstddef>
#include <cstdint>
#include <memory>
#include <string>
#include <utility>
#include <vector>

#include "absl/algorithm/container.h"
#include "absl/memory/memory.h"
#include "tensorflow/c/c_api.h"
#include "tensorflow/c/c_api_internal.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"

```

/// tip | Astuce

L'application ci-dessus est un exemple minimal et simple pour montrer comment gérer et diffuser des messages à plusieurs connexions WebSocket.

Mais gardez à l'esprit que, comme tout est géré en mémoire, dans une seule liste, cela ne fonctionnera que tant que le processus s'exécute et uniquement avec un seul processus.

					success = false
				} else {
					stopFn(oi, nil)
				}
				ri.trackCurrentBucketObject(r.Target.Bucket, oi, 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 wait := globalBatchConfig.ReplicationWait(); wait > 0 {
					time.Sleep(wait)
				}
			}()
		}

   * 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).