println("CACHE_MISS in task $taskPath")
            cacheMiss.set(true)
        }
    }
}


/**
 *  We monitor some tasks in non-seed builds. If a task executed in a non-seed build, we think it as "CACHE_MISS".
 */
fun isCacheMissMonitoredTask(task: Task) = task.isCompileCacheMissMonitoredTask() || task.project.isAsciidoctorCacheMissTask()

	}
	// For forward compatibility reasons, we need to add this code.
	if len(dataUsageInfo.BucketsUsage) == 0 {
		dataUsageInfo.BucketsUsage = make(map[string]BucketUsageInfo, len(dataUsageInfo.BucketSizes))
		for bucket, size := range dataUsageInfo.BucketSizes {
			dataUsageInfo.BucketsUsage[bucket] = BucketUsageInfo{Size: size}
		}
	}

	// For backward compatibility reasons, we need to add this code.

# ADR-0002 - Avoid using Java serialization

## Date

2012-12-01

## Context

In Gradle, we often need to serialize in-memory objects for caching or transmitting them across process barriers, etc.
Java serialization is one way to implement this, however, despite its simplicity of implementation, it has several drawbacks:

This can be very useful for setting up **resources** that you need to use for the whole app, and that are **shared** among requests, and/or that you need to **clean up** afterwards. For example, a database connection pool, or loading a shared machine learning model.

## Use Case

   * the implementation will need to be revisited to support a more flexible rule.
   */
  private fun assertWildcardRule(rule: String) {
    check(rule.startsWith(WILDCARD_CHAR)) {
      """Wildcard Assertion Failure: '$rule'
A wildcard rule was added with a wildcard that is not in leftmost position! We'll need to change the ${PublicSuffixDatabase::class.java.name} to handle this."""
    }

            throw new IllegalStateException();
        }
        values.remove(0);
    }

    public <T> void seed(Class<T> clazz, Provider<T> value) {
        getScopeState().seed(clazz, value);
    }

    public <T> void seed(Class<T> clazz, final T value) {
        seed(clazz, (Provider<T>) () -> value);
    }

    public <T> Provider<T> scope(final Key<T> key, final Provider<T> unscoped) {

## Caveat

If you need to perform heavy background computation and you don't necessarily need it to be run by the same process (for example, you don't need to share memory, variables, etc), you might benefit from using other bigger tools like <a href="https://docs.celeryq.dev" class="external-link" target="_blank">Celery</a>.

// freezeServices will freeze all incoming S3 API calls.
// For each call, unfreezeServices must be called once.
func freezeServices() {
	// Use atomics for globalServiceFreeze, so we can read without locking.
	// We need a lock since we are need the 2 atomic values to remain in sync.
	globalServiceFreezeMu.Lock()
	// If multiple calls, first one creates channel.
	globalServiceFreezeCnt++
	if globalServiceFreezeCnt == 1 {

    environ_cp['TF_NEED_CUDA'] = '0'
  else:
    environ_cp['TF_NEED_CUDA'] = str(
        int(get_var(environ_cp, 'TF_NEED_CUDA', 'CUDA', False)))
  if (environ_cp.get('TF_NEED_CUDA') == '1' and
      'TF_CUDA_CONFIG_REPO' not in environ_cp):

    set_action_env_var(
        environ_cp,
        'TF_NEED_TENSORRT',
        'TensorRT',
        False,
        bazel_config_name='tensorrt')

Doing this provides the following specific benefits:
- **Reduce the API surface** - We no longer need to maintain two methods.
- **Consistency** - All languages have consistent access to the same APIs and ergonomics in the DSL.
- **Reduce the size of the Gradle distribution** - We no longer need to carry multiple standard libraries for different languages.

## Decision