}
            project(':lib3') {
                apply plugin: 'cpp-library'
            }
"""
        app.deck.writeToProject(file("lib1"))
        app.card.writeToProject(file("lib2"))
        app.shuffle.writeToProject(file("lib3"))

        expect:
        succeeds tasks(':lib1').debug.assemble

  os.environ[
      'TF_MLIR_TFR_LIB_DIR'] = 'tensorflow/compiler/mlir/tfr/examples/mnist'

  ds_train = tfds.load('mnist', split='train', shuffle_files=True)
  ds_train = ds_train.shuffle(1024).batch(batch_size).prefetch(64)
  ds_train = strategy.experimental_distribute_dataset(ds_train)

  with strategy.scope():
    # Create an mnist float model with the specified float state.
    model = FloatModel()

	JMP	countbody<>(SB)

// input:
//   SI: data
//   BX: data len
//   AL: byte sought
//   R8: address to put result
// This function requires the POPCNT instruction.
TEXT countbody<>(SB),NOSPLIT,$0
	// Shuffle X0 around so that each byte contains
	// the character we're looking for.
	MOVD AX, X0
	PUNPCKLBW X0, X0
	PUNPCKLBW X0, X0
	PSHUFL $0, X0, X0

	CMPQ BX, $16
	JLT small

	MOVQ $0, R12 // Accumulator

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
			}
		}

	Target   string
	Port     uint16
	Priority uint16
	Weight   uint16
}

// byPriorityWeight sorts SRV records by ascending priority and weight.
type byPriorityWeight []*SRV

// shuffleByWeight shuffles SRV records by weight using the algorithm
// described in RFC 2782.
func (addrs byPriorityWeight) shuffleByWeight() {
	sum := 0
	for _, addr := range addrs {
		sum += int(addr.Weight)
	}

```
  }];

  let arguments = (ins
    Arg<TF_Tensor, [{The tensor to be shuffled.}]>:$value,

    DefaultValuedOptionalAttr<I64Attr, "0">:$seed,
    DefaultValuedOptionalAttr<I64Attr, "0">:$seed2
  );

  let results = (outs
    Res<TF_Tensor, [{A tensor of same shape and type as `value`, shuffled along its first
dimension.}]>:$output
  );

    ]

    # 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