EXPECT_EQ(TF_FLOAT, TFE_TensorHandleDataType(h));

  TF_Tensor* t = TFE_TensorHandleResolve(h, status.get());
  ASSERT_EQ(16, TF_TensorByteSize(t));
  float data[4] = {0};
  memcpy(&data[0], TF_TensorData(t), TF_TensorByteSize(t));
  EXPECT_EQ(1.0, data[0]);
  EXPECT_EQ(2.0, data[1]);
  EXPECT_EQ(3.0, data[2]);
  EXPECT_EQ(4.0, data[3]);
  TF_DeleteTensor(t);
  TFE_DeleteTensorHandle(h);

  }

  TF_Tensor* FloatTensor2x2(const float* values) {
    const int64_t dims[2] = {2, 2};
    TF_Tensor* t = TF_AllocateTensor(TF_FLOAT, dims, 2, sizeof(float) * 4);
    memcpy(TF_TensorData(t), values, sizeof(float) * 4);
    return t;
  }

  TF_Operation* FloatConst2x2(TF_Graph* graph, TF_Status* s,
                              const float* values, const char* name) {

	}

	cache := store.lock()
	defer store.unlock()

	// We should only update the in-memory cache if there were no changes
	// to the in-memory cache since the disk loading began. If there
	// were changes to the in-memory cache we should wait for the next
	// cycle until we can safely update the in-memory cache.
	//
	// An in-memory cache must be replaced only if we know for sure that the

//
// Returns a TF_Buffer. The memory pointed to by the result is owned by
// lib_handle. The data in the buffer will be the serialized OpList proto for
// ops defined in the library.
TF_CAPI_EXPORT extern TF_Buffer TF_GetOpList(TF_Library* lib_handle);

// Frees the memory associated with the library handle.
// Does NOT unload the library.

   * 2^n}, its memory usage is only {@code O(n)}. When the power set is constructed, the input set
   * is merely copied. Only as the power set is iterated are the individual subsets created, and
   * these subsets themselves occupy only a small constant amount of memory.
   *
   * @param set the set of elements to construct a power set from

	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)

					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)
				}
			}()
		}

* 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))

   * 2^n}, its memory usage is only {@code O(n)}. When the power set is constructed, the input set
   * is merely copied. Only as the power set is iterated are the individual subsets created, and
   * these subsets themselves occupy only a small constant amount of memory.
   *
   * @param set the set of elements to construct a power set from

						SRDeleteOp: getSRBucketDeleteOp(globalSiteReplicationSys.isEnabled()),
					})
					writeErrorResponse(ctx, w, toAPIError(ctx, err), r.URL)
					return
				}

				// Load updated bucket metadata into memory.
				globalNotificationSys.LoadBucketMetadata(GlobalContext, bucket)

				// Make sure to add Location information here only for bucket
				w.Header().Set(xhttp.Location, pathJoin(SlashSeparator, bucket))