}

  private static final Random RANDOM = new Random(42);

  public void testLength() {
    assertThat(ImmutableDoubleArray.of().length()).isEqualTo(0);
    assertThat(ImmutableDoubleArray.of(0).length()).isEqualTo(1);
    assertThat(ImmutableDoubleArray.of(0, 1, 3).length()).isEqualTo(3);
    assertThat(ImmutableDoubleArray.of(0, 1, 3).subArray(1, 1).length()).isEqualTo(0);

        super(name, properties);
    }

    private static final long SEED = ( new Random() ).nextLong();


    @Override
    @Before
    public void setUp () throws Exception {
        super.setUp();
    }


    static Random getRandom () {
        return new Random(SEED);
    }


    @Parameters ( name = "{0}" )
    public static Collection<Object> configs () {

  }

  private static final Random RANDOM = new Random(42);

  public void testLength() {
    assertThat(ImmutableIntArray.of().length()).isEqualTo(0);
    assertThat(ImmutableIntArray.of(0).length()).isEqualTo(1);
    assertThat(ImmutableIntArray.of(0, 1, 3).length()).isEqualTo(3);
    assertThat(ImmutableIntArray.of(0, 1, 3).subArray(1, 1).length()).isEqualTo(0);

}

// CHECK-LABEL: func.func private @"random/while_body/TfMlrtAsyncWhileBody"(%arg0: tensor<i32>, %arg1: tensor<i32>) -> tensor<i32> {
func.func private @"random/while_body/TfMlrtAsyncWhileBody"(%arg0: tensor<i32>, %arg1: tensor<i32>) -> tensor<i32> {
  %0 = "tf.AddV2"(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
  return %0: tensor<i32>
}

// CHECK-LABEL: func.func private @"random/while_cond"

  private static ImmutableList<Double> generatePseudorandomDataset() {
    Random random = new Random(2211275185798966364L);
    ImmutableList.Builder<Double> largeDatasetBuilder = ImmutableList.builder();
    for (int i = 0; i < PSEUDORANDOM_DATASET_SIZE; i++) {
      largeDatasetBuilder.add(random.nextGaussian());
    }
    return largeDatasetBuilder.build();
  }

      connection: Connection,
    ) {
      if (random != null) {
        val sslSocket = connection.socket() as SSLSocket
        val session = sslSocket.session

        val masterSecretHex =
          session.masterSecret?.encoded?.toByteString()
            ?.hex()

        if (masterSecretHex != null) {
          val keyLog = "CLIENT_RANDOM $random $masterSecretHex"

          if (verbose) {

        """Initializes a MatmulModel.

        Args:
          weight_shape: Shape of the weight tensor.
        """
        self.filters = np.random.uniform(low=-1.0, high=1.0, size=weight_shape)

        if bias_fn is not None:
          self.bias = np.random.uniform(
              low=-1.0, high=1.0, size=weight_shape[-1]
          )

      def has_reshape(self) -> bool:

// A Generator can generate random values of its own type.
type Generator interface {
	// Generate returns a random instance of the type on which it is a
	// method using the size as a size hint.
	Generate(rand *rand.Rand, size int) reflect.Value
}

// randFloat32 generates a random float taking the full range of a float32.
func randFloat32(rand *rand.Rand) float32 {

   * [acquire(5), acquire(1)] takes exactly the same time as [acquire(2), acquire(3), acquire(1)].
   */
  public void testTimeToWarmUpIsHonouredEvenWithWeights() {
    Random random = new Random();
    int warmupPermits = 10;
    double[] coldFactorsToTest = {2.0, 3.0, 10.0};
    double[] qpsToTest = {4.0, 2.0, 1.0, 0.5, 0.1};
    for (int trial = 0; trial < 100; trial++) {

   * [acquire(5), acquire(1)] takes exactly the same time as [acquire(2), acquire(3), acquire(1)].
   */
  public void testTimeToWarmUpIsHonouredEvenWithWeights() {
    Random random = new Random();
    int warmupPermits = 10;
    double[] coldFactorsToTest = {2.0, 3.0, 10.0};
    double[] qpsToTest = {4.0, 2.0, 1.0, 0.5, 0.1};
    for (int trial = 0; trial < 100; trial++) {