*     and {@link Character#MAX_RADIX}.
   */
  public static String toString(int x, int radix) {
    long asLong = x & INT_MASK;
    return Long.toString(asLong, radix);
  }

2. **Disclose significant AI involvement.** If AI tools played a major role in producing your contribution, say so in the description. We are not interested in incidental use, such as asking a chatbot for advice or accepting an autocomplete suggestion—but rather in cases where a tool generated substantial portions of the work. Disclosure will not reduce the likelihood of your PR or issue being accepted; we ask because information about...

If you have multiple changes to accept (and you're sure they ought to be accepted instead of corrected), you can use the `Accept Changes for all Errors` button to speed the process.
This button will cause a Javascript alert dialog to appear asking you to type a reason for accepting the changes, e.g. "Added new API for Gradle 8.x".

Clicking okay on the dialog will cause a copy of the `accepted-public-api-changes.json` containing your (properly sorted) addition to be downloaded.

        assertTrue(extraFieldNames.contains("aaa_field"));
        assertTrue(extraFieldNames.contains("mmm_field"));
        assertTrue(extraFieldNames.contains("zzz_field"));
        // Verify sorted order among the custom fields
        final int aIdx = extraFieldNames.indexOf("aaa_field");
        final int mIdx = extraFieldNames.indexOf("mmm_field");
        final int zIdx = extraFieldNames.indexOf("zzz_field");

 * Thread2: acquire(LockB) --X acquire(LockA)
 * </pre>
 *
 * <p>Neither thread will progress because each is waiting for the other. In more complex
 * applications, cycles can arise from interactions among more than 2 locks:
 *
 * <pre>
 * Thread1: acquire(LockA) --X acquire(LockB)
 * Thread2: acquire(LockB) --X acquire(LockC)
 * ...
 * ThreadN: acquire(LockN) --X acquire(LockA)
 * </pre>
 *

        S extends MapMakerInternalMap.Segment<K, V, E, S>>
    extends AbstractMap<K, V> implements ConcurrentMap<K, V>, Serializable {

  /*
   * The basic strategy is to subdivide the table among Segments, each of which itself is a
   * concurrently readable hash table. The map supports non-blocking reads and concurrent writes
   * across different segments.
   *

## Highlights

*   TF 2.0 delivers Keras as the central high level API used to build and train
    models. Keras provides several model-building APIs such as Sequential,
    Functional, and Subclassing along with eager execution, for immediate
    iteration and intuitive debugging, and `tf.data`, for building scalable
    input pipelines. Checkout
    [guide](https://www.tensorflow.org/beta/guide/keras/overview) for additional

        S extends MapMakerInternalMap.Segment<K, V, E, S>>
    extends AbstractMap<K, V> implements ConcurrentMap<K, V>, Serializable {

  /*
   * The basic strategy is to subdivide the table among Segments, each of which itself is a
   * concurrently readable hash table. The map supports non-blocking reads and concurrent writes
   * across different segments.
   *

})
@GwtCompatible
@NullUnmarked // TODO(cpovirk): Annotate for nullness.
final class LocalCache<K, V> extends AbstractMap<K, V> implements ConcurrentMap<K, V> {

  /*
   * The basic strategy is to subdivide the table among Segments, each of which itself is a
   * concurrently readable hash table. The map supports non-blocking reads and concurrent writes
   * across different segments.
   *

                """
            ),
        ],
        *,
        prompt: Annotated[
            str,
            Doc(
                """
                The prompt to send to the LLM provider along with the payload.

                This tells the LLM what to do with the request body.
                """
            ),
        ] = "",
    ) -> Callable[[DecoratedCallable], DecoratedCallable]: