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

		m.exitCode = 0
		return
	}

	if *shuffle != "off" {
		var n int64
		var err error
		if *shuffle == "on" {
			n = time.Now().UnixNano()
		} else {
			n, err = strconv.ParseInt(*shuffle, 10, 64)
			if err != nil {
				fmt.Fprintln(os.Stderr, `testing: -shuffle should be "off", "on", or a valid integer:`, err)
				m.exitCode = 2
				return
			}
		}

    ]

    # Extend the representative dataset with np.ndarrays.
    repr_ds.extend([
        {'tensor_key': np.random.uniform(low=-1.0, high=1.0, size=(3, 3))}
        for _ in range(4)
    ])

    random.shuffle(repr_ds)

    with self.session() as sess:
      new_repr_ds = repr_dataset.replace_tensors_by_numpy_ndarrays(
          repr_ds, sess
      )

      # The resulting dataset should not contain any tf.Tensors.

			}
			trustAnchorSet = trustAnchorSet.Insert(string(b.Bytes))
		}
	}

	// Give the list a stable ordering that changes each time Kubelet restarts.
	trustAnchorList := sets.List(trustAnchorSet)
	rand.Shuffle(len(trustAnchorList), func(i, j int) {
		trustAnchorList[i], trustAnchorList[j] = trustAnchorList[j], trustAnchorList[i]
	})

	pemTrustAnchors := []byte{}
	for _, ta := range trustAnchorList {
		b := &pem.Block{

            spec.resolveStrategy = Closure.DELEGATE_FIRST
            spec()
            children.add(childBuilder)
        }

        ProjectDescriptor build() {
            children.shuffle()
            def childDescriptors = children*.build() as Set<TestProjectDescriptor>
            def thisProject = new TestProjectDescriptor()
            thisProject.name = name

      indexes.add(index);
      expectedBuilder.put(index, expectedLargeDatasetPercentile(index));
    }
    Random random = new Random(770683168895677741L);
    Collections.shuffle(indexes, random);
    assertThat(percentiles().indexes(Ints.toArray(indexes)).compute(PSEUDORANDOM_DATASET))
        .comparingValuesUsing(QUANTILE_CORRESPONDENCE)
        .containsExactlyEntriesIn(expectedBuilder.buildOrThrow());

              return null;
            }
          });
    }
    assertEquals(allTasks.size() + 1, barrier.getParties());
    for (int i = 0; i < 1000; i++) {
      Collections.shuffle(allTasks);
      final AbstractFuture<String> future = new AbstractFuture<String>() {};
      currentFuture.set(future);
      for (Callable<?> task : allTasks) {

  // +optional
  optional int32 queues = 1;

  // `handSize` is a small positive number that configures the
  // shuffle sharding of requests into queues.  When enqueuing a request
  // at this priority level the request's flow identifier (a string
  // pair) is hashed and the hash value is used to shuffle the list
  // of queues and deal a hand of the size specified here.  The

  // +optional
  optional int32 queues = 1;

  // `handSize` is a small positive number that configures the
  // shuffle sharding of requests into queues.  When enqueuing a request
  // at this priority level the request's flow identifier (a string
  // pair) is hashed and the hash value is used to shuffle the list
  // of queues and deal a hand of the size specified here.  The

  // +optional
  optional int32 queues = 1;

  // `handSize` is a small positive number that configures the
  // shuffle sharding of requests into queues.  When enqueuing a request
  // at this priority level the request's flow identifier (a string
  // pair) is hashed and the hash value is used to shuffle the list
  // of queues and deal a hand of the size specified here.  The