///

### `startup` and `shutdown` together { #startup-and-shutdown-together }

There's a high chance that the logic for your *startup* and *shutdown* is connected, you might want to start something and then finish it, acquire a resource and then release it, etc.

    /**
     * Shortcut for {@code getService(ArtifactResolver.class).resolve(...)}.
     *
     * @param coordinates coordinates of the artifact to resolve
     * @return requested artifact together with the path to its file
     * @throws org.apache.maven.api.services.ArtifactResolverException if the artifact resolution failed
     *
     * @see org.apache.maven.api.services.ArtifactResolver#resolve(Session, Collection)

 * ListenableFuture}</a>.
 *
 * <p>This class is GWT-compatible.
 *
 * <h3>Purpose</h3>
 *
 * <p>The main purpose of {@code ListenableFuture} is to help you chain together a graph of
 * asynchronous operations. You can chain them together manually with calls to methods like {@link
 * Futures#transform(ListenableFuture, com.google.common.base.Function, Executor) Futures.transform}

This is not a limitation of **FastAPI**, it's part of the HTTP protocol.

///

## Recap { #recap }

   * underlying hash functions together. This can be useful if you need to generate hash codes of a
   * specific length.
   *
   * <p>For example, if you need 1024-bit hash codes, you could join two {@link Hashing#sha512} hash
   * functions together: {@code Hashing.concatenating(Hashing.sha512(), Hashing.sha512())}.
   *
   * @since 19.0
   */

    e.execute(
        new Runnable() {
          @Override
          public void run() {
            assertThat(Thread.currentThread().isInterrupted()).isFalse();
          }
        });

    // Run these together.
    fakePool.runAll();

    // Check that this thread has been marked as interrupted again now that the thread has been
    // returned by SequentialExecutor. Clear the bit while checking so that the test doesn't hose

During bazel build all TensorFlow's Python dependencies are pinned to their
specific versions. This is necessary to ensure reproducibility of the build.
The pinned versions of the full transitive closure of TensorFlow's dependencies
together with their corresponding hashes are specified in
`requirements_lock_<python version>.txt` files (e.g.
`requirements_lock_3_12.txt` for `Python 3.12`).

To update the lock files, make sure

 * ListenableFuture}</a>.
 *
 * <p>This class is GWT-compatible.
 *
 * <h3>Purpose</h3>
 *
 * <p>The main purpose of {@code ListenableFuture} is to help you chain together a graph of
 * asynchronous operations. You can chain them together manually with calls to methods like {@link
 * Futures#transform(ListenableFuture, com.google.common.base.Function, Executor) Futures.transform}

 * ListenableFuture}</a>.
 *
 * <p>This class is GWT-compatible.
 *
 * <h3>Purpose</h3>
 *
 * <p>The main purpose of {@code ListenableFuture} is to help you chain together a graph of
 * asynchronous operations. You can chain them together manually with calls to methods like {@link
 * Futures#transform(ListenableFuture, com.google.common.base.Function, Executor) Futures.transform}

   * underlying hash functions together. This can be useful if you need to generate hash codes of a
   * specific length.
   *
   * <p>For example, if you need 1024-bit hash codes, you could join two {@link Hashing#sha512} hash
   * functions together: {@code Hashing.concatenating(Hashing.sha512(), Hashing.sha512())}.
   *
   * @since 19.0
   */