cf.String("fuzztime", "", "")
	cf.String("fuzzminimizetime", "", "")
	cf.StringVar(&testTrace, "trace", "", "")
	cf.Var(&testV, "v", "")
	cf.Var(&testShuffle, "shuffle", "")

	for name, ok := range passFlagToTest {
		if ok {
			cf.Var(cf.Lookup(name).Value, "test."+name, "")
		}
	}
}

// outputdirFlag implements the -outputdir flag.

	ADDQ $64, SI
	CMPQ SI, 256(SP)
	JB   loop

end:
	RET

// shuffle byte order from LE to BE
DATA flip_mask<>+0x00(SB)/8, $0x0405060700010203
DATA flip_mask<>+0x08(SB)/8, $0x0c0d0e0f08090a0b
DATA flip_mask<>+0x10(SB)/8, $0x0405060700010203
DATA flip_mask<>+0x18(SB)/8, $0x0c0d0e0f08090a0b
GLOBL flip_mask<>(SB), 8, $32

// shuffle xBxA -> 00BA
DATA shuff_00BA<>+0x00(SB)/8, $0x0b0a090803020100

		return nil
	}
	metrics.ObserveQueueLength(ctx, qs.qCfg.Name, fsName, queue.requestsWaiting.Length())
	return req
}

// shuffleShardLocked uses shuffle sharding to select a queue index
// using the given hashValue and the shuffle sharding parameters of the queueSet.
func (qs *queueSet) shuffleShardLocked(hashValue uint64, descr1, descr2 interface{}) int {
	var backHand [8]int

            }
            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

	MIX(a, b, c, d, e, 0x5A827999)

#define ROUND1x(a, b, c, d, e, index) \
	SHUFFLE(index); \
	FUNC1(a, b, c, d, e); \
	MIX(a, b, c, d, e, 0x5A827999)

#define ROUND2(a, b, c, d, e, index) \
	SHUFFLE(index); \
	FUNC2(a, b, c, d, e); \
	MIX(a, b, c, d, e, 0x6ED9EBA1)

#define ROUND3(a, b, c, d, e, index) \
	SHUFFLE(index); \
	FUNC3(a, b, c, d, e); \
	MIX(a, b, c, d, e, 0x8F1BBCDC)

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.