misses.add("INVALID");
    }

    List<String> sampleDataList = new ArrayList<>();
    sampleDataList.addAll(hits);
    sampleDataList.addAll(misses);
    Collections.shuffle(sampleDataList);
    sampleData = sampleDataList.toArray(new String[sampleDataList.size()]);
  }

  // Since we can't pass a concrete SomeEnum.class directly, we need to use a raw type.
  @SuppressWarnings("unchecked")

      misses.add("INVALID");
    }

    List<String> sampleDataList = new ArrayList<>();
    sampleDataList.addAll(hits);
    sampleDataList.addAll(misses);
    Collections.shuffle(sampleDataList);
    sampleData = sampleDataList.toArray(new String[sampleDataList.size()]);
  }

  // Since we can't pass a concrete SomeEnum.class directly, we need to use a raw type.
  @SuppressWarnings("unchecked")

		// Run for a number of times, shuffling the input to ensure that output is consistent.
		for i := 0; i < 10; i++ {
			t.Run(fmt.Sprintf("test-%d-%s-run-%d", testID, tt.name, i), func(t *testing.T) {
				if i > 0 {
					rng.Shuffle(len(tt.m), func(i, j int) {
						tt.m[i], tt.m[j] = tt.m[j], tt.m[i]
					})
				}
				gotSelected, gotOk := tt.m.resolve(&tt.r)
				if gotOk != tt.wantOk {

 * 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

		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.

			if r != needle {
				rs = append(rs, rune(r))
			}
		}
	}
	// Shuffle the runes so that they are not in descending order.
	// The sort is deterministic since this is used for benchmarks,
	// which need to be repeatable.
	rr := rand.New(rand.NewSource(1))
	rr.Shuffle(len(rs), func(i, j int) {
		rs[i], rs[j] = rs[j], rs[i]
	})
	uchars := string(rs)

    *   `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`

	. "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 {

	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

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

				var fallbackDisks []StorageAPI
				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]
				}

				requestedVersions := 0