TF_Message(first_bad_status.get()));
    return result;
  }
  // For each output of the original operation, pack the per-device
  // TensorHandles we've computed into a single parallel TensorHandle.
  std::vector<std::unique_ptr<ParallelTensor>> per_device_outputs;
  per_device_outputs.reserve(first_op_output_count);
  for (int i = 0; i < first_op_output_count; ++i) {

    int approxSize = IntMath.divide(n * IntMath.log2(n, CEILING), Long.SIZE, CEILING);
    ArrayList<BigInteger> bignums = new ArrayList<>(approxSize);

    // Start from the pre-computed maximum long factorial.
    int startingNumber = LongMath.factorials.length;
    long product = LongMath.factorials[startingNumber - 1];
    // Strip off 2s from this value.

  }

  /**
   * Splits {@code sequence} into string components and makes them available through an {@link
   * Iterator}, which may be lazily evaluated. If you want an eagerly computed {@link List}, use
   * {@link #splitToList(CharSequence)}.
   *
   * @param sequence the sequence of characters to split
   * @return an iteration over the segments split from the parameter
   */

 *
 * <h3>Examples</h3>
 *
 * <p>To compute the median:
 *
 * <pre>{@code
 * double myMedian = median().compute(myDataset);
 * }</pre>
 *
 * where {@link #median()} has been statically imported.
 *
 * <p>To compute the 99th percentile:
 *
 * <pre>{@code
 * double myPercentile99 = percentiles().index(99).compute(myDataset);
 * }</pre>
 *

import java.nio.ByteOrder;
import java.util.Iterator;
import javax.annotation.CheckForNull;

/**
 * A bundle of statistical summary values -- sum, count, mean/average, min and max, and several
 * forms of variance -- that were computed from a single set of zero or more floating-point values.
 *
 * <p>There are two ways to obtain a {@code Stats} instance:
 *
 * <ul>

  }

  public void testHashCodeForBooleanOperations() {
    Predicate<@Nullable Integer> p1 = Predicates.isNull();
    Predicate<@Nullable Integer> p2 = isOdd();

    // Make sure that hash codes are not computed per-instance.
    assertEqualHashCode(Predicates.not(p1), Predicates.not(p1));

    assertEqualHashCode(Predicates.and(p1, p2), Predicates.and(p1, p2));

// will block till the operation that produces `h` has completed.
TF_CAPI_EXPORT extern const char* TFE_TensorHandleDeviceName(
    TFE_TensorHandle* h, TF_Status* status);

// Returns the name of the device in whose memory `h` resides.
//
// This function will block till the operation that produces `h` has completed.
TF_CAPI_EXPORT extern const char* TFE_TensorHandleBackingDeviceName(

	StartTime time.Time   `msg:"startTs"` // Time at which rebalance-start was issued
	EndTime   time.Time   `msg:"stopTs"`  // Time at which rebalance operation completed or rebalance-stop was called
	Status    rebalStatus `msg:"status"`  // Current state of rebalance operation. One of Started|Stopped|Completed|Failed.
}

// rebalanceMeta contains information pertaining to an ongoing rebalance operation.
type rebalanceMeta struct {

  }

  public void testHashCodeForBooleanOperations() {
    Predicate<@Nullable Integer> p1 = Predicates.isNull();
    Predicate<@Nullable Integer> p2 = isOdd();

    // Make sure that hash codes are not computed per-instance.
    assertEqualHashCode(Predicates.not(p1), Predicates.not(p1));

    assertEqualHashCode(Predicates.and(p1, p2), Predicates.and(p1, p2));

      /** Iterate a copy of the entries to defend against concurrent modification errors. */
      private val delegate = ArrayList(lruEntries.values).iterator()

      /** The snapshot to return from [next]. Null if we haven't computed that yet. */
      private var nextSnapshot: Snapshot? = null

      /** The snapshot to remove with [remove]. Null if removal is illegal. */
      private var removeSnapshot: Snapshot? = null