@Override
  public int hashCode() {
    return Sets.hashCodeImpl(this);
  }

  // This declaration is needed to make Set.iterator() and
  // ImmutableCollection.iterator() appear consistent to javac's type inference.
  @Override
  public abstract UnmodifiableIterator<E> iterator();

  abstract static class CachingAsList<E> extends ImmutableSet<E> {
    @LazyInit private transient ImmutableList<E> asList;

    @Override

      assertBasicInvariants(striped);
    }
  }

  private static void assertBasicInvariants(Striped<?> striped) {
    Set<Object> observed = Sets.newIdentityHashSet(); // for the sake of weakly referenced locks.
    // this gets the stripes with #getAt(index)
    for (int i = 0; i < striped.size(); i++) {
      Object object = striped.getAt(i);
      assertNotNull(object);

      assertBasicInvariants(striped);
    }
  }

  private static void assertBasicInvariants(Striped<?> striped) {
    Set<Object> observed = Sets.newIdentityHashSet(); // for the sake of weakly referenced locks.
    // this gets the stripes with #getAt(index)
    for (int i = 0; i < striped.size(); i++) {
      Object object = striped.getAt(i);
      assertNotNull(object);

  }

  override fun flush() {
  }

  override fun close() {
  }
}

@SuppressSignatureCheck
object AndroidLog {
  private const val MAX_LOG_LENGTH = 4000

  // Keep references to loggers to prevent their configuration from being GC'd.
  private val configuredLoggers = CopyOnWriteArraySet<Logger>()

  private val knownLoggers =
    LinkedHashMap<String, String>()
      .apply {

“parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and...

[guava-snapshot-api-diffs]: https://guava.dev/releases/snapshot-jre/api/diffs/
[Guava Explained]: https://github.com/google/guava/wiki/Home
[Guava Beta Checker]: https://github.com/google/guava-beta-checker

<!-- References -->

[using Guava in your build]: https://github.com/google/guava/wiki/UseGuavaInYourBuild

Element) bool { x1, y1, v1 := x, y, Element{} // Calculate a reference f(x, y) without aliasing. if out := f(&v, &x, &y); out != &v && isInBounds(out) { return false } // Test aliasing the first argument and the receiver. v1 = x if out := f(&v1, &v1, &y); out != &v1 || v1 != v { return false } // Test aliasing the second argument and the receiver. v1 = y if out := f(&v1, &x, &v1); out != &v1 || v1 != v { return false } // Calculate a reference f(x, x) without aliasing. if out := f(&v, &x, &x); out !=...

      status->status = TF_TensorToTensor(input_tensors[i], &input_tensor);
      if (!status->status.ok()) return;
      input_tensors_vector[i] = &input_tensor;
    }
  }

  // Create an inference context with dummy values, which will be updated later.
  InferenceContext c(TF_GRAPH_DEF_VERSION, node_def, op_reg_data->op_def,
                     std::vector<ShapeHandle>(num_inputs), input_tensors_vector,

- `GenerateKey`: Takes a key ID and generates a new data key from a master key referenced by the key ID. It returns the new data key in two different forms: The plain data key and the data key encrypted using the master key.

- `DecryptKey`: Takes a key ID and an encrypted data key and returns the plain data key - the decryption of the encrypted data key using the master key referenced by the key ID - on success or an error otherwise.

    /** The request header service for managing request header settings. */
    @Resource
    private RequestHeaderService reqHeaderService;
    /** The web config service for validating web configuration references. */
    @Resource
    private WebConfigService webConfigService;

    // ===================================================================================