"clean performance:%testProject%PerformanceAdHocTest --tests \"%scenario%\"",
                            "%performance.baselines%",
                            """--warmups %warmups% --runs %runs% --checks %checks% --profiler %profiler% %additional.gradle.parameters%""",
                            os,
                            arch,
                            "%testJavaVersion%",

                listOf(
                    "clean",
                    "performance:${testProject}PerformanceAdHocTest",
                    tests.map { """--tests "$it"""" }.joinToString(" "),
                    """--warmups 2 --runs 2 --checks none""",
                    "-PtestJavaVersion=${os.perfTestJavaVersion.major}",
                    "-PtestJavaVendor=${os.perfTestJavaVendor.name.lowercase()}",

  }

  private List<Entry<Object, Object>> warmUp(LoadingCache<Object, Object> cache) {
    return warmUp(cache, WARMUP_MIN, WARMUP_MAX);
  }

  /**
   * Returns the entries that were added to the map, so they won't fall out of a map with weak or
   * soft references until the caller drops the reference to the returned entries.
   */
  private List<Entry<Object, Object>> warmUp(

  }

  private List<Entry<Object, Object>> warmUp(LoadingCache<Object, Object> cache) {
    return warmUp(cache, WARMUP_MIN, WARMUP_MAX);
  }

  /**
   * Returns the entries that were added to the map, so they won't fall out of a map with weak or
   * soft references until the caller drops the reference to the returned entries.
   */
  private List<Entry<Object, Object>> warmUp(

    IdentityLoader<Integer> loader = identityLoader();
    LoadingCache<Integer, Integer> cache =
        CacheBuilder.newBuilder().concurrencyLevel(1).maximumSize(10).build(loader);
    CacheTesting.warmUp(cache, 0, 10);
    Set<Integer> keySet = cache.asMap().keySet();
    assertThat(keySet).containsExactly(0, 1, 2, 3, 4, 5, 6, 7, 8, 9);

    // re-order
    getAll(cache, asList(0, 1, 2));

    for (int i = 0; i < 8; i++) {
      limiter.acquire(); // // #1
    }
    stopwatch.sleepMillis(4500); // #2: back to cold state (warmup period + repay last acquire)
    for (int i = 0; i < 3; i++) { // only three steps, we're somewhere in the warmup period
      limiter.acquire(); // #3
    }

    limiter.setRate(4.0); // double the rate!

    for (int i = 0; i < 8; i++) {
      limiter.acquire(); // // #1
    }
    stopwatch.sleepMillis(4500); // #2: back to cold state (warmup period + repay last acquire)
    for (int i = 0; i < 3; i++) { // only three steps, we're somewhere in the warmup period
      limiter.acquire(); // #3
    }

    limiter.setRate(4.0); // double the rate!

          UnsupportedOperationException.class, () -> cache.asMap().keySet().addAll(asList(1, 2)));
    }
  }


  public void testKeySet_clear() {
    for (LoadingCache<Object, Object> cache : caches()) {
      warmUp(cache, 0, 100);

      Set<Object> keys = cache.asMap().keySet();
      keys.clear();
      checkEmpty(keys);
      checkEmpty(cache);
    }
  }

  public void testKeySet_empty_remove() {

    stage: Stage,
) : OsAwareBaseGradleBuildType(os = Os.LINUX, stage = stage, init = {
        id(buildTypeId(model))
        name = "Compile All"
        description = "Compiles all production/test source code and warms up the build cache"

        features {
            publishBuildStatusToGithub(model)
        }

        applyDefaults(
            model,
            this,

*   Added warmup capabilities to `tf.keras.optimizers.schedules.CosineDecay` learning rate scheduler. You can now specify an initial and target learning rate, and our scheduler will perform a linear interpolation between the two after which it will begin a decay phase....