+ g.util.grid.makeGrid([
    row.new('Push Information')
    + row.withPanels([
      panels.timeSeries.xdsPushes(
        'XDS Pushes', queries.xdsPushes, |||
          Rate of XDS push operations, by type. This is incremented on a per-proxy basis.
        |||
      ),
      panels.timeSeries.base(
        'Events', queries.pilotEvents, |||
          Size of each xDS push.
        |||
      ),

          Count of XDS connection terminations.
          This will typically spike every 30min for each instance.
        |||
      ),
      panels.timeSeries.xdsPushes(
        'XDS Pushes', queries.ztunnelXdsMessages, |||
          Count of XDS messages, by type.
        |||
      ),
      panels.timeSeries.base('Workload Manager', queries.workloadManager, |||

               },
               "expr": "sum by (url) (irate(istio_xds_message_total{pod=~\"ztunnel-.*\"}[$__rate_interval]))",
               "legendFormat": "{{url}}"
            }
         ],
         "title": "XDS Pushes",
         "type": "timeseries"
      },
      {
         "datasource": {
            "type": "datasource",
            "uid": "-- Mixed --"
         },

               "legendFormat": "{{type}}"
            }
         ],
         "title": "XDS Pushes",
         "type": "timeseries"
      },
      {
         "datasource": {
            "type": "datasource",
            "uid": "-- Mixed --"
         },
         "description": "Size of each xDS push.\n",
         "fieldConfig": {
            "defaults": {
               "custom": {

	}
	return args, tokens
}

// invokeMacro pushes onto the input Stack a Slice that holds the macro definition with the actual
// parameters substituted for the formals.
// Invoking a macro does not touch the PC/line history.
func (in *Input) invokeMacro(macro *Macro) {
	// If the macro has no arguments, just substitute the text.
	if macro.args == nil {
		in.Push(NewSlice(in.Base(), in.Line(), macro.tokens))
		return
	}

# to build TensorFlow. Look at ci/official/envs to find which types of jobs
# push to the cache.  For macOS, use --config=tf_public_macos_cache
build:tf_public_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/january2024" --remote_upload_local_results=false
# Cache pushes are limited to TF's CI system.

  public void testSplitterIterableIsLazy_pattern() {
    if (!CommonPattern.isPcreLike()) {
      return;
    }
    assertSplitterIterableIsLazy(Splitter.onPattern(","));
  }

  /**
   * This test really pushes the boundaries of what we support. In general the splitter's behaviour
   * is not well defined if the char sequence it's splitting is mutated during iteration.
   */

		return false
	}
	return !h.endTime.IsZero()
}

// stops the heal sequence - safe to call multiple times.
func (h *healSequence) stop() {
	h.cancelCtx()
}

// pushHealResultItem - pushes a heal result item for consumption in
// the heal-status API. It blocks if there are
// maxUnconsumedHealResultItems. When it blocks, the heal sequence
// routine is effectively paused - this happens when the server has

	w.imported[name] = pkg
	return pkg, nil
}

// pushScope enters a new scope (walking a package, type, node, etc)
// and returns a function that will leave the scope (with sanity checking
// for mismatched pushes & pops)
func (w *Walker) pushScope(name string) (popFunc func()) {
	w.scope = append(w.scope, name)
	return func() {
		if len(w.scope) == 0 {
			log.Fatalf("attempt to leave scope %q with empty scope list", name)

   * uses the {@linkplain Comparable natural ordering} of its elements.
   *
   * @return the comparator used to order the elements in this queue, or {@code null} if this queue
   *     uses the natural ordering of its elements
   */
  @CanIgnoreReturnValue // pushed down from class to method
  public Comparator<? super E> comparator() {