* this class separates _allocations_ from _streams_. An allocation is created by a call, used for
 * one or more streams, and then released. An allocated connection won't be stolen by other calls
 * while a redirect or authorization challenge is being handled.
 *
 * When the maximum concurrent streams limit is reduced, some allocations will be rescinded.

    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'),

   * `allocations.size() < allocationLimit` then new streams can be created on this connection.
   */
  internal var allocationLimit = 1
    private set

  /** Current calls carried by this connection. */
  val calls = mutableListOf<Reference<RealCall>>()

  /** Timestamp when `allocations.size()` reached zero. Also assigned upon initial connection. */

// should not result in any allocations.
// This can be used to reset the underlying []byte of an existing Buffer.
func TestNewBufferShallow(t *testing.T) {
	testenv.SkipIfOptimizationOff(t)
	n := testing.AllocsPerRun(1000, func() {
		buf = *NewBuffer(testBytes)
	})
	if n > 0 {
		t.Errorf("allocations occurred while shallow copying")
	}
	check(t, "NewBuffer", &buf, testString)

         "type": "timeseries"
      },
      {
         "datasource": {
            "type": "datasource",
            "uid": "-- Mixed --"
         },
         "description": "Details about memory allocations",
         "fieldConfig": {
            "defaults": {
               "custom": {
                  "fillOpacity": 10,
                  "gradientMode": "hue",
                  "showPoints": "never"

option go_package = "k8s.io/api/networking/v1alpha1";

// ClusterCIDR represents a single configuration for per-Node Pod CIDR
// allocations when the MultiCIDRRangeAllocator is enabled (see the config for
// kube-controller-manager).  A cluster may have any number of ClusterCIDR
// resources, all of which will be considered when allocating a CIDR for a
// Node.  A ClusterCIDR is eligible to be used for a given Node when the node

     *
     * https://github.com/google/guava/issues/2254
     *
     * Other kinds of Errors are possible:
     *
     * - OutOfMemoryError from allocations in setFuture(): The calculus here is similar to
     * StackOverflowError: We can't reliably call setException(error).
     *
     * - Any kind of Error from a listener. Even if we could distinguish that case (by exposing some

      + timeSeries.standardOptions.withUnit('reqps'),

    statPercent(title, targets, desc=''):
      self.stat(title, targets, desc)
      + timeSeries.standardOptions.withUnit('percentunit'),

    allocations(title, targets, desc=''):
      self.base(title, targets, desc)
      + timeSeries.standardOptions.withUnit('Bps')
      + timeSeries.standardOptions.withOverrides([
        fieldOverride.byQuery.new('B')

      eventListener.callFailed(this, result!!)
    } else {
      eventListener.callEnd(this)
    }
    return result
  }

  /**
   * Remove this call from the connection's list of allocations. Returns a socket that the caller
   * should close.
   */
  internal fun releaseConnectionNoEvents(): Socket? {
    val connection = this.connection!!
    connection.lock.assertHeld()

  //   Outputs:
  std::vector<TensorHandlePtr> op_outputs_ TF_GUARDED_BY(execution_mutex_);
  // TF_Status is an incomplete type and so can't be stack allocated. To avoid
  // unnecessary allocations each Execute call, we keep one heap-allocated
  // version for the thread.
  StatusPtr status_ TF_GUARDED_BY(execution_mutex_);

  const std::string device_;
  ExecutorPtr executor_ TF_GUARDED_BY(execution_mutex_);