echo "Error: Unable to autodetect the YJP library location. Please set YJPLIB variable" >&2
          exit 1
        fi
        MAVEN_OPTS="-agentpath:$YJPLIB=onexit=snapshot,onexit=memory,tracing,onlylocal $MAVEN_OPTS"
        ;;
      --enc)
        MAVEN_MAIN_CLASS="org.apache.maven.cling.MavenEncCling"
        ;;
      *)
        ;;
    esac
    shift
  done
}

## 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 <a href="https://docs.celeryq.dev" class="external-link" target="_blank">Celery</a>.

limitations under the License.
==============================================================================*/

// A simple logging device to test custom device registration.
#include <memory>

#include "tensorflow/c/c_api.h"
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/eager/c_api_test_util.h"
#include "tensorflow/c/tf_status.h"

  // We could make this a Map<E, EndpointPair<N>>. It would make incidentNodes(edge) slightly
  // faster, but also make Networks consume 5 to 20+% (increasing with average degree) more memory.
  final MapIteratorCache<E, N> edgeToReferenceNode; // referenceNode == source if directed

  /** Constructs a graph with the properties specified in {@code builder}. */

  // We could make this a Map<E, EndpointPair<N>>. It would make incidentNodes(edge) slightly
  // faster, but also make Networks consume 5 to 20+% (increasing with average degree) more memory.
  final MapIteratorCache<E, N> edgeToReferenceNode; // referenceNode == source if directed

  /** Constructs a graph with the properties specified in {@code builder}. */

	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)

 * concurrently.
 */
class Dispatcher() {
  internal val lock: ReentrantLock = ReentrantLock()

  /**
   * The maximum number of requests to execute concurrently. Above this requests queue in memory,
   * waiting for the running calls to complete.
   *
   * If more than [maxRequests] requests are in flight when this is invoked, those requests will
   * remain in flight.
   */
  var maxRequests = 64

#ifndef TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_CONTEXT_H_
#define TENSORFLOW_C_EAGER_IMMEDIATE_EXECUTION_CONTEXT_H_

#include <functional>
#include <map>
#include <memory>
#include <vector>

#include "absl/types/optional.h"
#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/eager/immediate_execution_distributed_manager.h"

fsGroupChangePolicy: "OnRootMismatch" # Additational pod annotations podAnnotations: {} # Additional pod labels podLabels: {} ## Configure resource requests and limits ## ref: http://kubernetes.io/docs/user-guide/compute-resources/ ## resources: requests: memory: 16Gi ## List of policies to be created after minio install ## ## In addition to default policies [readonly|readwrite|writeonly|consoleAdmin|diagnostics] ## you can define additional policies with custom supported actions and resources policies: []...

return f.fireWith(this,arguments),this},fired:function(){return!!o}};return f},ce.extend({Deferred:function(e){var o=[["notify","progress",ce.Callbacks("memory"),ce.Callbacks("memory"),2],["resolve","done",ce.Callbacks("once memory"),ce.Callbacks("once memory"),0,"resolved"],["reject","fail",ce.Callbacks("once memory"),ce.Callbacks("once memory"),1,"rejected"]],i="pending",a={state:function(){return i},always:function(){return s.done(arguments).fail(arguments),this},"catch":function(e){return a....