bulkGetSet = ImmutableList.copyOf(limit(cycle(asList), 10));
  }

  @Footprint
  Object sizeOfStriped() {
    return impl.get(numStripes);
  }

  // a place to put the locks in sizeOfPopulatedStriped so they don't get GC'd before we measure
  final List<Lock> locks = new ArrayList<>(numStripes);

  @Footprint
  Object sizeOfPopulatedStriped() {
    locks.clear();

import com.google.caliper.AfterExperiment;
import com.google.caliper.BeforeExperiment;
import com.google.caliper.Benchmark;
import com.google.caliper.Param;
import com.google.caliper.api.Footprint;
import com.google.caliper.api.VmOptions;
import com.google.common.base.Preconditions;
import com.google.common.util.concurrent.AbstractFutureBenchmarks.OldAbstractFuture;

import static java.util.Arrays.asList;

import com.google.caliper.BeforeExperiment;
import com.google.caliper.Benchmark;
import com.google.caliper.Param;
import com.google.caliper.api.Footprint;
import com.google.common.collect.BenchmarkHelpers.BiMapImpl;
import com.google.common.collect.BenchmarkHelpers.MapImpl;
import com.google.common.collect.BenchmarkHelpers.MapsImplEnum;

/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

import static com.google.common.util.concurrent.MoreExecutors.directExecutor;

import com.google.caliper.BeforeExperiment;
import com.google.caliper.Param;
import com.google.caliper.api.Footprint;
import com.google.caliper.api.SkipThisScenarioException;
import com.google.common.util.concurrent.AbstractFutureBenchmarks.Facade;
import com.google.common.util.concurrent.AbstractFutureBenchmarks.Impl;

import com.google.caliper.AfterExperiment;
import com.google.caliper.BeforeExperiment;
import com.google.caliper.Benchmark;
import com.google.caliper.Param;
import com.google.caliper.api.Footprint;
import com.google.caliper.api.VmOptions;
import java.util.HashSet;
import java.util.Set;
import java.util.concurrent.Executor;
import java.util.concurrent.atomic.AtomicInteger;

 *   <li>Access to all collection-based utilities via {@link #asList} (though at the cost of
 *       allocating garbage).
 * </ul>
 *
 * <p>Disadvantages compared to {@code long[]}:
 *
 * <ul>
 *   <li>Memory footprint has a fixed overhead (about 24 bytes per instance).
 *   <li><i>Some</i> construction use cases force the data to be copied (though several construction
 *       APIs are offered that don't).

 * traditional {@code LinkedHashMap} implementation which stores keys and count values as map
 * entries, {@code ObjectCountLinkedHashMap} minimizes object allocation and reduces memory
 * footprint.
 */
@GwtCompatible
@NullMarked
final class ObjectCountLinkedHashMap<K extends @Nullable Object> extends ObjectCountHashMap<K> {
  /** Creates an empty {@code ObjectCountLinkedHashMap} instance. */

 *   <li>Access to all collection-based utilities via {@link #asList} (though at the cost of
 *       allocating garbage).
 * </ul>
 *
 * <p>Disadvantages compared to {@code double[]}:
 *
 * <ul>
 *   <li>Memory footprint has a fixed overhead (about 24 bytes per instance).
 *   <li><i>Some</i> construction use cases force the data to be copied (though several construction
 *       APIs are offered that don't).

 *   <li>Access to all collection-based utilities via {@link #asList} (though at the cost of
 *       allocating garbage).
 * </ul>
 *
 * <p>Disadvantages compared to {@code int[]}:
 *
 * <ul>
 *   <li>Memory footprint has a fixed overhead (about 24 bytes per instance).
 *   <li><i>Some</i> construction use cases force the data to be copied (though several construction
 *       APIs are offered that don't).