}
    }

    @Nested
    @DisplayName("Combine Tests")
    class CombineTests {

        @Test
        @DisplayName("Should combine two referral data objects")
        void testCombine() {
            // Setup first referral
            DfsReferralDataImpl first = new DfsReferralDataImpl();
            when(mockReferral.getTtl()).thenReturn(300);

     * @param tdom
     * @param thost
     * @return
     */
    protected boolean matches(CIFSContext tf, String thost, String tdom) {
        return Objects.equals(this.getCredentials(), tf.getCredentials()) && Objects.equals(this.targetHost, thost)
                && Objects.equals(this.targetDomain, tdom);
    }

conversion">serialization</abbr>" which is taking data from the code (Python) and converting it into something that can be sent through the network. For example, converting an object containing data from a database into a JSON object. Converting `datetime` objects into strings, etc.

Another big feature needed by APIs is data validation, making sure that the data is valid, given certain parameters. For example, that some field is an `int`, and not some random string. This is especially useful for...

   * case the call may even time out before the target method is ever invoked.
   *
   * @param executor the ExecutorService that will execute the method calls on the target objects;
   *     for example, a {@link Executors#newCachedThreadPool()}.
   * @since 22.0
   */
  public static SimpleTimeLimiter create(ExecutorService executor) {
    return new SimpleTimeLimiter(executor);
  }

import static com.google.common.util.concurrent.AbstractFuture.notInstanceOfDelegatingToFuture;
import static java.lang.Boolean.parseBoolean;
import static java.security.AccessController.doPrivileged;
import static java.util.Objects.requireNonNull;
import static java.util.concurrent.TimeUnit.NANOSECONDS;
import static java.util.concurrent.atomic.AtomicReferenceFieldUpdater.newUpdater;
import static java.util.logging.Level.SEVERE;

import java.io.Serializable;
import java.lang.reflect.Array;
import java.util.Arrays;
import java.util.Collection;
import java.util.Iterator;
import java.util.Map;
import java.util.Objects;
import java.util.Set;
import java.util.Spliterator;
import org.jspecify.annotations.Nullable;

/**
 * Fixed-size {@link Table} implementation backed by a two-dimensional array.
 *

You can return a `dict`, `list`, singular values as `str`, `int`, etc.

You can also return Pydantic models (you'll see more about that later).

There are many other objects and models that will be automatically converted to JSON (including ORMs, etc). Try using your favorite ones, it's highly probable that they are already supported.

## Recap { #recap }

* Import `FastAPI`.

        SmbTree tree = new SmbTree(session, "testShare", "A:");
        tree.connectionState = 2; // Connected state
        tree.tid = 123;

        // Create real request and response objects since they're not interfaces
        ServerMessageBlock request = new SmbComOpenAndX("testfile.txt", 0x01, 0, null);
        ServerMessageBlock response = new SmbComOpenAndXResponse();

        // Execute send

E também teremos anotações/documentação em conformidade.

## Modelos aninhados

Cada atributo de um modelo Pydantic tem um tipo.

Mas esse tipo pode ser outro modelo Pydantic.

Portanto, você pode declarar "objects" JSON profundamente aninhados com nomes, tipos e validações de atributos específicos.

Tudo isso, aninhado arbitrariamente.

### Defina um sub-modelo

Por exemplo, nós podemos definir um modelo `Image`:

 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.google.common.collect;

import static java.util.Objects.requireNonNull;

import com.google.common.annotations.GwtIncompatible;
import com.google.errorprone.annotations.CanIgnoreReturnValue;
import java.util.Arrays;
import java.util.Collection;
import java.util.Collections;