}
}

var generateKeyTests = []struct {
	ExtKey     [32]byte
	Random     io.Reader
	ShouldPass bool
}{
	{ExtKey: [32]byte{}, Random: nil, ShouldPass: true},              // 0
	{ExtKey: [32]byte{}, Random: rand.Reader, ShouldPass: true},      // 1
	{ExtKey: [32]byte{}, Random: shortRandom(32), ShouldPass: true},  // 2
	{ExtKey: [32]byte{}, Random: shortRandom(31), ShouldPass: false}, // 3
}

func TestGenerateKey(t *testing.T) {

    private static final Long SEED = Long.getLong("org.gradle.internal.test.excludes.seed", 58745094L)
    private static final int MAX_DEPTH = 3
    private static final int MAX_CACHED = 10_000

    private final Random random = new Random(SEED)
    private final Set<ExcludeSpec> cached = new HashSet<>()
    private final List<ExcludeSpec> cachedLinear = new ArrayList<>(MAX_CACHED)
    private int depth

    /**

        when:
        render dummyValidationProblem('Foo', 'Bar', 'with some description', 'some reason')

        then:
        outputEquals """
Type 'Foo' property 'Bar' with some description.

Reason: Some reason.
"""
    }

    def "displays plugin id when available"() {
        when:
        render dummyValidationProblem {
            inPlugin 'com.foo.bar'

load_library.load_op_library(os.path.join(_lib_dir, _lib_name))


class MnistOpsDefsTest(test_utils.OpsDefsTest):

  def test_new_conv2d_relu(self):
    input_ = tf.random.uniform([1, 4, 4, 1])
    filter_ = tf.random.uniform([2, 2, 1, 8])
    bias = tf.zeros([8])
    kwargs = {
        'input_': input_,
        'filter_': filter_,
        'bias': bias,
        'stride_w': 2,
        'stride_h': 2,

  private Queue<Integer> queue;

  private final Random random = new Random();

  @BeforeExperiment
  void setUp() {
    queue = heap.create(comparator.get());
    for (int i = 0; i < size; i++) {
      queue.add(random.nextInt());
    }
  }

  @Benchmark
  void pollAndAdd(int reps) {
    for (int i = 0; i < reps; i++) {
      // TODO(kevinb): precompute random #s?
      queue.add(queue.poll() ^ random.nextInt());

import java.math.BigInteger;
import java.util.Random;

/**
 * Utilities for benchmarks.
 *
 * <p>In many cases, we wish to vary the order of magnitude of the input as much as we want to vary
 * the input itself, so most methods which generate values use an exponential distribution varying
 * the order of magnitude of the generated values uniformly at random.
 *
 * @author Louis Wasserman
 */

  // tweak this to control hit rate
  @Param("2.5")
  double concentration;

  Random random = new Random();

  LoadingCache<Integer, Integer> cache;

  int max;

  static AtomicLong requests = new AtomicLong(0);
  static AtomicLong misses = new AtomicLong(0);

  @BeforeExperiment
  void setUp() {
    // random integers will be generated in this range, then raised to the

import org.jetbrains.kotlin.analysis.api.types.KaStarTypeProjection
import org.jetbrains.kotlin.analysis.api.types.KaTypeArgumentWithVariance
import org.jetbrains.kotlin.renderer.render

internal object KaAnnotationValueRenderer {
    fun render(value: KaAnnotationValue): String = buildString {
        renderConstantValue(value)
    }

    private fun StringBuilder.renderConstantValue(value: KaAnnotationValue) {

import java.math.BigInteger;
import java.util.Random;

/**
 * Utilities for benchmarks.
 *
 * <p>In many cases, we wish to vary the order of magnitude of the input as much as we want to vary
 * the input itself, so most methods which generate values use an exponential distribution varying
 * the order of magnitude of the generated values uniformly at random.
 *
 * @author Louis Wasserman
 */

            append(")")
            append(": ${returnType.render()}")
        }
        is KaValueParameterSymbol -> "${if (isVararg) "vararg " else ""}$name: ${returnType.render()}"
        is KaTypeParameterSymbol -> this.nameOrAnonymous.asString()
        is KaVariableSymbol -> "${if (isVal) "val" else "var"} $name: ${returnType.render()}"
        is KaClassLikeSymbol -> classId?.toString() ?: nameOrAnonymous.asString()