"id": 73,
      "links": [],
      "options": {
        "cellHeight": "sm",
        "footer": {
          "countRows": false,
          "fields": "",
          "reducer": [
            "sum"
          ],
          "show": false
        },
        "showHeader": true
      },
      "pluginVersion": "10.1.5",
      "repeatDirection": "v",
      "targets": [

  TF_SetAttrString(reduce_desc, "final_op", final_op.c_str(),
                   final_op.length());
  TF_SetAttrIntList(reduce_desc, "subdiv_offsets", nullptr, 0);
  TF_AddInput(reduce_desc, x);
  TF_Operation* reduce_op = TF_FinishOperation(reduce_desc, status);
  if (TF_GetCode(status) != TF_OK) return;
  TF_Operation* operations[]{placeholder_op, reduce_op};
  TF_Output y{reduce_op, 0};
  const char* output_name = "y";

)

// Standard constants for all storage class
const (
	// Reduced redundancy storage class
	RRS = "REDUCED_REDUNDANCY"
	// Standard storage class
	STANDARD = "STANDARD"
)

// Standard constats for config info storage class
const (
	ClassStandard = "standard"
	ClassRRS      = "rrs"
	Optimize      = "optimize"
	InlineBlock   = "inline_block"

	// Reduced redundancy storage class environment variable

 *
 * <p>As compared with {@link java.util.LinkedHashMap}, this structure places significantly reduced
 * load on the garbage collector by only using a constant number of internal objects.
 *
 * <p>This class should not be assumed to be universally superior to {@code
 * java.util.LinkedHashMap}. Generally speaking, this class reduces object allocation and memory

   * There is no need for a blocking lock; when the lock is not
   * available, threads try other slots (or the base).  During these
   * retries, there is increased contention and reduced locality,
   * which is still better than alternatives.
   *
   * Per-thread hash codes are initialized to random values.
   * Contention and/or table collisions are indicated by failed

  // many clusters. Larger clusters may need more attempts to find a node
  // that suits all pending resources. This may get increased in the
  // future, but not reduced.
  //
  // +listType=set
  // +optional
  repeated string potentialNodes = 2;
}

// PodSchedulingContextStatus describes where resources for the Pod can be allocated.
message PodSchedulingContextStatus {

   * There is no need for a blocking lock; when the lock is not
   * available, threads try other slots (or the base).  During these
   * retries, there is increased contention and reduced locality,
   * which is still better than alternatives.
   *
   * Per-thread hash codes are initialized to random values.
   * Contention and/or table collisions are indicated by failed

   * There is no need for a blocking lock; when the lock is not
   * available, threads try other slots (or the base).  During these
   * retries, there is increased contention and reduced locality,
   * which is still better than alternatives.
   *
   * Per-thread hash codes are initialized to random values.
   * Contention and/or table collisions are indicated by failed

      assertEquals(0, Queues.drain(q, ImmutableList.of(), 1, 10, MILLISECONDS));

      Producer producer = new Producer(q, 1);
      // producing one, will ask for two
      Future<?> producerThread = threadPool.submit(producer);
      producer.beganProducing.await();

      // make sure we time out
      Stopwatch timer = Stopwatch.createStarted();

mapreduce.fileoutputcommitter.algorithm.version=2 # Use the latest committer version
mapreduce.job.reduce.slowstart.completedmaps=0.99 # 99% map, then reduce
mapreduce.reduce.shuffle.input.buffer.percent=0.9 # Min % buffer in RAM
mapreduce.reduce.shuffle.merge.percent=0.9 # Minimum % merges in RAM
mapreduce.reduce.speculative=false # Disable speculation for reducing
mapreduce.task.io.sort.factor=999 # Threshold before writing to disk