var (
	memTotalMD     = NewGaugeMD(memTotal, "Total memory on the node")
	memUsedMD      = NewGaugeMD(memUsed, "Used memory on the node")
	memUsedPercMD  = NewGaugeMD(memUsedPerc, "Used memory percentage on the node")
	memFreeMD      = NewGaugeMD(memFree, "Free memory on the node")
	memBuffersMD   = NewGaugeMD(memBuffers, "Buffers memory on the node")
	memCacheMD     = NewGaugeMD(memCache, "Cache memory on the node")

This functionality in this package is mostly captured in the Arena type.
Arenas allocate large chunks of memory for Go values, so they're likely to
be inefficient for allocating only small amounts of small Go values. They're
best used in bulk, on the order of MiB of memory allocated on each use.

Note that by allowing for this limited form of manual memory allocation
that use-after-free bugs are possible with regular Go values. This package

| `minio_node_mem_buffers_avg`   | Buffers memory on the node (avg).         |
| `minio_node_mem_buffers_max`   | Buffers memory on the node (max).         |
| `minio_node_mem_cache`         | Cache memory on the node.                 |
| `minio_node_mem_cache_avg`     | Cache memory on the node (avg).           |
| `minio_node_mem_cache_max`     | Cache memory on the node (max).           |

    ]),
  ], panelHeight=5)
  + grid.makeGrid([
    row.new('Resource Usage')
    + row.withPanels([
      panels.timeSeries.bytes('Memory Usage', queries.goMemoryUsage, 'Memory usage of each running instance'),
      panels.timeSeries.allocations('Memory Allocations', queries.goAllocations, 'Details about memory allocations'),
      panels.timeSeries.base('CPU Usage', queries.cpuUsage, 'CPU usage of each running instance'),

		case <-r.qTimer.C:
			r.qCache.update()
		}
	}
}

// Delete deletes in-memory replication statistics for a bucket.
func (r *ReplicationStats) Delete(bucket string) {
	if r == nil {
		return
	}

	r.Lock()
	defer r.Unlock()
	delete(r.Cache, bucket)
}

// UpdateReplicaStat updates in-memory replica statistics with new values.
func (r *ReplicationStats) UpdateReplicaStat(bucket string, n int64) {

<p>
Go approaches its memory model in much the same way as the rest of the language,
aiming to keep the semantics simple, understandable, and useful.
This section gives a general overview of the approach and should suffice for most programmers.
The memory model is specified more formally in the next section.
</p>

<p>
A data race is defined as

	rootAccess                  bool
	syncEvents                  bool
	objectMaxVersions           int64
}

const (
	cgroupV1MemLimitFile = "/sys/fs/cgroup/memory/memory.limit_in_bytes"
	cgroupV2MemLimitFile = "/sys/fs/cgroup/memory.max"
)

func cgroupMemLimit() (limit uint64) {
	buf, err := os.ReadFile(cgroupV2MemLimitFile)
	if err != nil {
		buf, err = os.ReadFile(cgroupV1MemLimitFile)
	}

   * value slightly too high than slightly too low. If the value is not exact, the {@link
   * ImmutableLongArray} that is built will very likely occupy more memory than strictly necessary;
   * to trim memory usage, build using {@code builder.build().trimmed()}.
   */
  public static Builder builder(int initialCapacity) {
    checkArgument(initialCapacity >= 0, "Invalid initialCapacity: %s", initialCapacity);

   * value slightly too high than slightly too low. If the value is not exact, the {@link
   * ImmutableIntArray} that is built will very likely occupy more memory than strictly necessary;
   * to trim memory usage, build using {@code builder.build().trimmed()}.
   */
  public static Builder builder(int initialCapacity) {
    checkArgument(initialCapacity >= 0, "Invalid initialCapacity: %s", initialCapacity);

    TFE_TensorHandle* h, TF_Status* status);

// This function will block till the operation that produces `h` has
// completed. The memory returned might alias the internal memory used by
// TensorFlow. Hence, callers should not mutate this memory (for example by
// modifying the memory region pointed to by TF_TensorData() on the returned
// TF_Tensor).
TF_CAPI_EXPORT extern TF_Tensor* TFE_TensorHandleResolve(TFE_TensorHandle* h,