}

  @Override
  public boolean containsAll(Collection<?> targets) {
    return delegateList().containsAll(targets);
  }

  @Override
  public int size() {
    return delegateList().size();
  }

  @Override
  public boolean isEmpty() {
    return delegateList().isEmpty();
  }

  @Override
  public <T extends @Nullable Object> T[] toArray(T[] other) {

            int index = Math.abs((int) (affinityKey % list.size()));
            return list.get(index);
        }

        // No affinity, use weighted random
        return selectWeightedRandom(channels);
    }

    private ChannelInfo selectAdaptive(CommonServerMessageBlock message, Collection<ChannelInfo> channels) {
        // Adaptive strategy based on message type and size

        if (isLargeTransfer(message)) {

* **Deep Learning**: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.

            throws CyclicDependencyException, OverConstrainedVersionException {
        if (artifacts != null && !artifacts.isEmpty()) {
            children = new ArrayList<>(artifacts.size());

            for (Artifact a : artifacts) {
                if (parents.contains(a.getDependencyConflictId())) {
                    a.setDependencyTrail(getDependencyTrail());

            + "duplicate element in the list",
        firstIndex == getList().indexOf(duplicate));
    assertEquals(
        "remove(present) should decrease the size of a list by one.",
        initialSize - 1,
        getList().size());
  }

        Set set = project1.getArtifacts();
        assertNotNull(set, "No artifacts");
        assertFalse(set.isEmpty(), "No Artifacts");
        Iterator iter = set.iterator();
        assertTrue(set.size() == 4, "Set size should be 4, is " + set.size());

        while (iter.hasNext()) {
            Artifact artifact = (Artifact) iter.next();
            System.out.println("Artifact: " + artifact.getDependencyConflictId() + " " + artifact.getVersion()

    extra_copts = ["-std=c11"],
    deps = [
        ":c_api",
        ":c_api_experimental",
        ":env",
        ":kernels",
    ],
)

tf_cuda_cc_test(
    name = "c_api_test",
    size = "medium",
    srcs = ["c_api_test.cc"],
    data = [
        ":test_op1.so",
        "//tensorflow/cc/saved_model:saved_model_half_plus_two",
    ],
    extra_copts = if_pywrap(["-DTENSORFLOW_NO_SHARED_OBJECTS"]),

import com.google.common.util.concurrent.ListenableFuture;
import java.util.concurrent.CountDownLatch;
import java.util.concurrent.ExecutionException;
import junit.framework.Assert;

/**
 * A simple mock implementation of {@code Runnable} that can be used for testing ListenableFutures.
 *
 * @author Nishant Thakkar
 * @since 10.0
 */
@GwtIncompatible
public class MockFutureListener implements Runnable {

				err = msgp.WrapError(err)
				return
			}
		}
	}
	return
}

// EncodeMsg implements msgp.Encodable
func (z *BucketReplicationStat) EncodeMsg(en *msgp.Writer) (err error) {
	// map header, size 14
	// write "ReplicatedSize"
	err = en.Append(0x8e, 0xae, 0x52, 0x65, 0x70, 0x6c, 0x69, 0x63, 0x61, 0x74, 0x65, 0x64, 0x53, 0x69, 0x7a, 0x65)
	if err != nil {
		return
	}
	err = en.WriteInt64(z.ReplicatedSize)

<img src="/img/deployment/https/https05.drawio.svg">

### Response HTTP

La aplicación procesaría el request y enviaría un **response HTTP simple (sin encriptar)** al TLS Termination Proxy.