askDisks = len(disks) // use all available drives
	}

	// However many we ask, versions must exist on ~50%
	listingQuorum := (askDisks + 1) / 2

	if askDisks > 0 && len(disks) > askDisks {
		rand.Shuffle(len(disks), func(i, j int) {
			disks[i], disks[j] = disks[j], disks[i]
		})
		fallbackDisks = disks[askDisks:]
		disks = disks[:askDisks]
	}

	// How to resolve results.

      return no_op();

    // For vectors, shuffle values by sorting instead of the obvious
    // Fisher-Yates algorithm. Fisher-Yates is simple to implement and correct,
    // but not easily parallelizable. For a sufficiently parallel architecture,
    // it is faster to sort many times, than Fisher-Yates shuffle once.
    if (input_rank == 1) {
      // Shuffle values by assigning each value a random key and sorting the

 * randomly ordered data (the probability decreases faster than exponentially in N), but if you are
 * passing in unsanitized user data then a malicious user could force it. A light shuffle of the
 * data using an unpredictable seed should normally be enough to thwart this attack.
 *
 * <p>The time taken to compute multiple quantiles on the same dataset using {@link Scale#indexes

  static bool ShouldRunTest(const TestInfo* test_info) {
    return test_info->should_run();
  }

  // Shuffles the tests in this test case.
  void ShuffleTests(internal::Random* random);

  // Restores the test order to before the first shuffle.
  void UnshuffleTests();

  // Name of the test case.
  std::string name_;
  // Name of the parameter type, or NULL if this is not a typed or a

  static bool ShouldRunTest(const TestInfo* test_info) {
    return test_info->should_run();
  }

  // Shuffles the tests in this test case.
  void ShuffleTests(internal::Random* random);

  // Restores the test order to before the first shuffle.
  void UnshuffleTests();

  // Name of the test case.
  std::string name_;
  // Name of the parameter type, or NULL if this is not a typed or a

// upon return or (b) locking is irrelevant.

// StartFunction begins the process of handling a request.  If the
// request gets queued then this function uses the given hashValue as
// the source of entropy as it shuffle-shards the request into a
// queue.  The descr1 and descr2 values play no role in the logic but
// appear in log messages.  This method does not return until the
// queuing, if any, for this request is done.  If `execute` is false

	. "gorm.io/gorm/utils/tests"
)

func TestMigrate(t *testing.T) {
	allModels := []interface{}{&User{}, &Account{}, &Pet{}, &Company{}, &Toy{}, &Language{}, &Tools{}}
	rand.Seed(time.Now().UnixNano())
	rand.Shuffle(len(allModels), func(i, j int) { allModels[i], allModels[j] = allModels[j], allModels[i] })
	DB.Migrator().DropTable("user_speaks", "user_friends", "ccc")

	if err := DB.Migrator().DropTable(allModels...); err != nil {

//	    the Go tree can run a sanity check but not spend time running
//	    exhaustive tests.
//
//	-shuffle off,on,N
//	    Randomize the execution order of tests and benchmarks.
//	    It is off by default. If -shuffle is set to on, then it will seed
//	    the randomizer using the system clock. If -shuffle is set to an
//	    integer N, then N will be used as the seed value. In both cases,

	if seed != -1 {
		rand.Seed(seed)
	}
	return rand.Intn(max-min) + min
}

// Randomizes the order of bytes in the byte array
// using Knuth Fisher-Yates shuffle algorithm.
func randomizeBytes(s []byte, seed int64) []byte {
	// special value -1 means no explicit seeding.
	if seed != -1 {
		rand.Seed(seed)
	}
	n := len(s)
	var j int

    *   `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`