// Create a 1D I32 tensor for representing the dimension permutation.
      auto permuation_tensor_type =
          RankedTensorType::get({input_rank}, rewriter.getIntegerType(32));
      llvm::SmallVector<Attribute, 4> permute;
      permute.reserve(input_rank);
      // First create an identity permutation tensor.
      for (int i = 0; i < input_rank; i++) {

	show("Intn(10)", r.Intn(10), r.Intn(10), r.Intn(10))
	show("Int31n(10)", r.Int31n(10), r.Int31n(10), r.Int31n(10))
	show("Int63n(10)", r.Int63n(10), r.Int63n(10), r.Int63n(10))

	// Perm generates a random permutation of the numbers [0, n).
	show("Perm", r.Perm(5), r.Perm(5), r.Perm(5))
	// Output:
	// Float32     0.2635776           0.6358173           0.6718283
	// Float64     0.628605430454327   0.4504798828572669  0.9562755949377957

	for rev := range revisions {
		for _, base := range getObjectLabels() {
			base[label.IoIstioRev.Name] = rev
			objectLabels = append(objectLabels, base)
		}
	}

	// For each permutation, we check which webhooks it matches. It must match exactly 0 or 1!
	for _, nl := range namespaceLabels {
		for _, ol := range objectLabels {
			matches := sets.New[string]()
			for name, whs := range webhooks {

// Seed seeds the rng with the provided seed.
func Seed(seed int64) {
	rng.Lock()
	defer rng.Unlock()

	rng.rand = rand.New(rand.NewSource(seed))
}

// Perm returns, as a slice of n ints, a pseudo-random permutation of the integers [0,n)
// from the default Source.
func Perm(n int) []int {
	rng.Lock()
	defer rng.Unlock()
	return rng.rand.Perm(n)
}

const (

import java.util.Set;

/**
 * Provides common assertions for querying task order.
 *
 * An 'any' rule asserts that all of the specified tasks occur in any order.
 *
 * any(':a', ':b', ':c') would match on any permutation of ':a', ':b', ':c'.
 *
 * An 'exact' rule asserts that all of the specified tasks occur in the order
 * provided.  Note that other tasks may appear - it only verifies that the
 * given tasks occur in order.
 *

	show("IntN(10)", r.IntN(10), r.IntN(10), r.IntN(10))
	show("Int32N(10)", r.Int32N(10), r.Int32N(10), r.Int32N(10))
	show("Int64N(10)", r.Int64N(10), r.Int64N(10), r.Int64N(10))

	// Perm generates a random permutation of the numbers [0, n).
	show("Perm", r.Perm(5), r.Perm(5), r.Perm(5))
	// Output:
	// Float32     0.95955694          0.8076733            0.8135684
	// Float64     0.4297927436037299  0.797802349388613    0.3883664855410056

// 'input_permutation' is a mapping from old argument numbers to new argument
// numbers, whereas 'output_permutation' is the same for outputs. Both
// 'input_permutation' and 'output_permutation' are initialized to the identity
// permutation. 'nodedef' is the NodeDef for the call to the function under
// construction, provided to allow additional attributes to be set.
// The rewrite may also change the NodeDef's operator name, and that

to do the transform. There are three parts of automatically space to depth
transformation:

1.  Transform input on the host.

    Space-to-depth performs the following permutation, which is equivalent to
    `tf.nn.space_to_depth`.

    ```python
    images = tf.reshape(images, [batch, h // block_size, block_size,
                               w // block_size, block_size, c])

				t.Errorf("Write() = (%d, %v); want (%d, nil)", n, err, len(input))
			}
		}

		// Since each read is independent, the only guarantee about the output
		// is that it is a permutation of the input in readSized groups.
		got := make([]byte, 0, count*len(input))
		for i := 0; i < cap(c); i++ {
			got = append(got, (<-c)...)
		}
		got = sortBytesInGroups(got, readSize)

		fallingFactorial := ff(test.deckSize, test.handSize)
		permutations := ff(test.handSize, test.handSize)
		allCoordinateCount := fallingFactorial / permutations
		nff := float64(test.hashMax) / float64(fallingFactorial)
		minCount := permutations * int(math.Floor(nff))
		maxCount := permutations * int(math.Ceil(nff))
		aHand := make([]int, test.handSize)
		for i := 0; i < test.hashMax; i++ {