# Gradle runtimes

Gradle is made up of the following processes that work together to "run the build":

- Gradle daemon. This is the process that actually runs the build. It hosts build logic and coordinates the lifecycle of the build. It is a long-running daemon process.
- CLI client. This is the `gradle` or `gradlew` command, and is responsible for locating, starting and interacting with the Gradle daemon, potentially downloading the Gradle distribution. 

    // Run a task that expects that it is not interrupted while it is running.
    e.execute(() -> 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

 * 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}

 * single step. This should be a more efficient implementation of multiple standard {@link ScopeArtifactFilter}
 * instances ORed together.
 *
 */
public class CumulativeScopeArtifactFilter extends AbstractScopeArtifactFilter {

    private Set<String> scopes;

    /**

 */
package org.codelibs.fess.suggest.normalizer;

import java.util.ArrayList;
import java.util.List;

/**
 * The NormalizerChain class implements the Normalizer interface and allows chaining multiple normalizers together.
 * It applies each normalizer in the order they were added to the chain.
 *
 * <p>Usage example:</p>
 * <pre>
 * NormalizerChain chain = new NormalizerChain();
 * chain.add(new SomeNormalizer());

 * governing permissions and limitations under the License.
 */
package org.codelibs.fess.suggest.index.operations;

/**
 * Encapsulates the context parameters commonly passed to content indexing operations.
 * Groups together index name, field configuration, and bad words to simplify method signatures.
 */
public class ContentIndexingContext {

    private final String index;
    private final String[] supportedFields;

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

ADRs can be written by any team.
Like code, they should be reviewed by any other relevant teams.
The goal is not to *own* a decision, but to *share* it with other teams, and improve the build tool together.

### Format

The format for ADR should follow this template:

```markdown
# ADR-000X - Title

## Status

- REPLACED by ADR-000X on 20YY-MM-DD
- DEPRECATED on 20YY-MM-DD

cmdtable = {}
command = registrar.command(cmdtable)
@command(b'goreposum', [], _('url'), norepo=True)
def goreposum(ui, url):
  """
  goreposum computes a checksum of all the named state in the remote repo.
  It hashes together all the branch names and hashes
  and then all the bookmark names and hashes.
  Tags are stored in .hgtags files in any of the branches,
  so the branch metadata includes the tags as well.
  """
  h = hashlib.sha256()

 * href="https://docs.spring.io/spring-framework/docs/current/reference/html/core.html#beans-introduction">Spring</a>.
 * Frameworks typically offer a way to register multiple listeners independently and then request
 * them together as a set (<a href="https://dagger.dev/dev-guide/multibindings">Dagger</a>, <a
 * href="https://github.com/google/guice/wiki/Multibindings">Guice</a>, <a