*       interrupt).
   *   <li>Any {@link CancellationException} is propagated untouched, as is any other {@link
   *       RuntimeException} (though {@code get} implementations are discouraged from throwing such
   *       exceptions).
   * </ul>
   *
   * <p>The overall principle is to continue to treat every checked exception as a checked

    }

    @GwtIncompatible @J2ktIncompatible private static final long serialVersionUID = 0;
  }

  /**
   * Returns a supplier that always supplies {@code instance}.
   *
   * <p><b>Discouraged:</b> Prefer using {@code () -> instance}, but note that lambdas do not have
   * human-readable {@link #toString()} representations and are not serializable. If you need a

    }

    @GwtIncompatible @J2ktIncompatible private static final long serialVersionUID = 0;
  }

  /**
   * Returns a supplier that always supplies {@code instance}.
   *
   * <p><b>Discouraged:</b> Prefer using {@code () -> instance}, but note that lambdas do not have
   * human-readable {@link #toString()} representations and are not serializable. If you need a

 * limitations under the License.
 */
```

### Submitting Your Change

After you submit your pull request, a Gradle developer will review it. It is normal for this to take several iterations, so don't get discouraged by change requests. They ensure the high quality that we all enjoy.

If you need to check on [CI](http://builds.gradle.org/) status as an external contributor, you can click "Log in as guest".

## Useful tips

import java.util.Iterator;
import java.util.List;
import java.util.SortedSet;
import java.util.stream.Stream;
import org.jspecify.annotations.NonNull;
import org.jspecify.annotations.Nullable;

/**
 * A discouraged (but not deprecated) precursor to Java's superior {@link Stream} library.
 *
 * <p>The following types of methods are provided:
 *
 * <ul>

            on CPU.
    *   Add nominal support for unsigned 16-bit integer tensor types. Note that
        very few TFLite kernels support this type natively, so its use in mobile
        ML authoring is generally discouraged.
    *   Add support for unsigned 16-bit integer tensor types in cast op.
    *   Experimental support for lowering `list_ops.tensor_list_set_item` with
        `DynamicUpdateSlice`.

* A PR should have a specific use case that it is solving.

* If the PR is for a feature, it should have docs.
    * Unless it's a feature we want to discourage, like support for a corner case that we don't want users to use.
* The docs should include a source example file, not write Python directly in Markdown.