* <p>This class should not be assumed to be universally superior to {@code java.util.HashSet}.
 * Generally speaking, this class reduces object allocation and memory consumption at the price of
 * moderately increased constant factors of CPU. Only use this class when there is a specific reason
 * to prioritize memory over CPU.
 *
 * @author Dimitris Andreou
 * @author Jon Noack
 */
@GwtIncompatible // not worth using in GWT for now

SuccessExitStatus=143

# Specifies the maximum file descriptor number that can be opened by this process
LimitNOFILE=${packaging.os.max.open.files}

# Specifies the maximum number of bytes of memory that may be locked into RAM
# Set to "infinity" if you use the 'bootstrap.mlockall: true' option
# in fess.yml and 'MAX_LOCKED_MEMORY=unlimited' in ${packaging.env.file}
#LimitMEMLOCK=infinity

    Fire<V> fire = new Fire<>(result);
    result.timer = scheduledExecutor.schedule(fire, time, unit);
    delegate.addListener(fire, directExecutor());
    return result;
  }

  /*
   * Memory visibility of these fields. There are two cases to consider.
   *
   * 1. visibility of the writes to these fields to Fire.run:
   *
   * The initial write to delegateRef is made definitely visible via the semantics of

  // ExtendSessionGraphHelper manually.
  std::atomic<bool> extend_before_run;
};

struct TF_ImportGraphDefOptions {
  tensorflow::ImportGraphDefOptions opts;

  // Backing memory for TensorId fields in opts.
  // TODO(skyewm): it'd be better if ImportGraphDefOptions owned this.
  std::vector<tensorflow::string> tensor_id_data;
};

struct TF_ImportGraphDefResults {

 */
final class Platform {
  static CharMatcher precomputeCharMatcher(CharMatcher matcher) {
    // CharMatcher.precomputed() produces CharMatchers that are maybe a little
    // faster (and that's debatable), but definitely more memory-hungry. We're
    // choosing to turn .precomputed() into a no-op in GWT, because it doesn't
    // seem to be a worthwhile tradeoff in a browser.
    return matcher;
  }

   * response body. Call `body.source().request(1024 * 1024)` to block until either that's done, or
   * 1 MiB of response data is loaded into memory. (You could use any size here, though large values
   * risk exhausting memory.)
   *
   * This returns an empty value if the trailers are available, but have no data.
   *

```
./bin/spark-submit --class org.apache.spark.examples.SparkPi \
    --master yarn-client \
    --num-executors 1 \
    --driver-memory 512m \
    --executor-memory 512m \
    --executor-cores 1 \
    examples/jars/spark-examples*.jar 10
```

The job should produce an output as shown below. Note the value of pi in the output.

```

/**
 * {@linkplain CacheBuilder Discouraged} (in favor of <a
 * href="https://github.com/ben-manes/caffeine/wiki">Caffeine</a>) caching utilities.
 *
 * <p>The core interface used to represent caches is {@link Cache}. In-memory caches can be
 * configured and created using {@link CacheBuilder}, with cache entries being loaded by {@link
 * CacheLoader}. Statistics about cache performance are exposed using {@link CacheStats}.
 *

import com.google.common.annotations.GwtIncompatible;
import com.google.common.annotations.J2ktIncompatible;
import java.io.DataOutput;
import java.io.IOException;

/**
 * An extension of {@code DataOutput} for writing to in-memory byte arrays; its methods offer
 * identical functionality but do not throw {@link IOException}.
 *
 * @author Jayaprabhakar Kadarkarai
 * @since 1.0
 */
@J2ktIncompatible
@GwtIncompatible

	Items []madmin.HealResultItem `json:"Items"`
}

// structure to hold state of all heal sequences in server memory
type allHealState struct {
	sync.RWMutex

	// map of heal path to heal sequence
	healSeqMap map[string]*healSequence // Indexed by endpoint
	// keep track of the healing status of disks in the memory
	//   false: the disk needs to be healed but no healing routine is started
	//    true: the disk is currently healing