if len(disks) < er.setDriveCount/2 {
			return fmt.Errorf("not enough drives (found=%d, healing=%d, total=%d) are available to heal `%s`", len(disks), healing, er.setDriveCount, tracker.disk.String())
		}

		rand.Shuffle(len(disks), func(i, j int) {
			disks[i], disks[j] = disks[j], disks[i]
		})

		expectedDisks := len(disks)/2 + 1
		fallbackDisks := disks[expectedDisks:]
		disks = disks[:expectedDisks]

	Queues int32 `json:"queues" protobuf:"varint,1,opt,name=queues"`

	// `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
	// request is put into one of the shortest queues in that hand.

    // a reasonable amount apart
    T cur = 0;
    for (size_t i = 0; i < tensor->NumElements(); i++) {
      tensor_flat(i) = cur;
      cur += 5e-2;
    }
    // Fischer-Yates shuffle the array
    for (size_t i = tensor->NumElements() - 1; i >= 1; i--) {
      // j <- random integer 0 <= j <= i
      size_t j = random::New64() % (i + 1);
      // swap values at i, j
      T tmp = tensor_flat(i);

	Queues int32 `json:"queues" protobuf:"varint,1,opt,name=queues"`

	// `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
	// request is put into one of the shortest queues in that hand.

	Queues int32 `json:"queues" protobuf:"varint,1,opt,name=queues"`

	// `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
	// request is put into one of the shortest queues in that hand.

	Queues int32 `json:"queues" protobuf:"varint,1,opt,name=queues"`

	// `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
	// request is put into one of the shortest queues in that hand.

	VMOVDQU		state1, (1*16)(digestPtr)

done:
	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

			NextCycle: oldCache.Info.NextCycle,
		},
		Cache: make(map[string]dataUsageEntry, len(oldCache.Cache)),
	}

	// Put all buckets into channel.
	bucketCh := make(chan BucketInfo, len(buckets))

	// Shuffle buckets to ensure total randomness of buckets, being scanned.
	// Otherwise same set of buckets get scanned across erasure sets always.
	// at any given point in time. This allows different buckets to be scanned

const (
	j0 uint32 = 0x61707865 // expa
	j1 uint32 = 0x3320646e // nd 3
	j2 uint32 = 0x79622d32 // 2-by
	j3 uint32 = 0x6b206574 // te k
)

const blockSize = 64

// quarterRound is the core of ChaCha20. It shuffles the bits of 4 state words.
// It's executed 4 times for each of the 20 ChaCha20 rounds, operating on all 16
// words each round, in columnar or diagonal groups of 4 at a time.

    *   `tf.data.Dataset.zip` now supports Python-style zipping, i.e. `Dataset.zip(a, b, c)`.
    * `tf.data.Dataset.shuffle` now supports `tf.data.UNKNOWN_CARDINALITY` When doing a "full shuffle" using  `dataset = dataset.shuffle(dataset.cardinality())`. But remember, a "full shuffle" will load the full dataset into memory so that it can be shuffled, so make sure to only use this with small datasets or datasets of small objects (like filenames).

*   `tf.math`