jobPresentInCJActiveStatus: true,
		},

		// Controller should fail to schedule these, as there are too many missed starting times
		// and either no deadline or a too long deadline.
		"prev ran but done, long overdue, not past deadline, A": {
			concurrencyPolicy:          "Allow",
			schedule:                   onTheHour,
			deadline:                   longDead,
			ranPreviously:              true,

  private static final class UnsafeAtomicHelper extends AtomicHelper {
    static final sun.misc.Unsafe UNSAFE;
    static final long LISTENERS_OFFSET;
    static final long WAITERS_OFFSET;
    static final long VALUE_OFFSET;
    static final long WAITER_THREAD_OFFSET;
    static final long WAITER_NEXT_OFFSET;

    static {
      sun.misc.Unsafe unsafe = null;
      try {

  private static final class UnsafeAtomicHelper extends AtomicHelper {
    static final sun.misc.Unsafe UNSAFE;
    static final long LISTENERS_OFFSET;
    static final long WAITERS_OFFSET;
    static final long VALUE_OFFSET;
    static final long WAITER_THREAD_OFFSET;
    static final long WAITER_NEXT_OFFSET;

    static {
      sun.misc.Unsafe unsafe = null;
      try {

            final long startRecordNumber = data.getCurrentStartRecordNumber();
            final long endRecordNumber = data.getCurrentEndRecordNumber();
            final List<String> pageNumbers = data.getPageNumberList();
            final boolean partial = data.isPartialResults();
            final String searchQuery = data.getSearchQuery();
            final long requestedTime = data.getRequestedTime();

        final long startTime = systemHelper.getCurrentTimeAsLong();

        GetResponse response = null;
        final GetRequestBuilder requestBuilder = client.prepareGet(index, id);
        if (condition.build(requestBuilder)) {
            response = requestBuilder.execute().actionGet(ComponentUtil.getFessConfig().getIndexSearchTimeout());
        }

// by SumGrad and MeanGrad.
Output SumGradHelper(const Scope& scope, const Operation& op,
                     const std::vector<Output>& grad_inputs) {
  // The partial derivative for any input along a "reduced" dimension
  // is just 1, so we only need replicate the output gradient on such a
  // dimension to its "expanded" shape.
  // Running example:
  // input is
  // [[a, b, c],
  //  [d, e, f]]

};

// Lowers ResizeNearestNeighbor to an indices computations with a gather along
// the combined spatial dimensions. Generating the indices along the
// width/height index could be used to gather along each of W and H dimension
// of the input image array. To reduce to a single gather, these indices are
// combined, so a single gather can be performed along the combined spatial
// dimensions.
//

    return success();
  }
};

// Converts `TensorListConcatV2` into Unpack and Concat. First we unpack
// the input tensorlist along the first dimension, which results in N (where N
// is the first dim's size) tensors (each with shape [element_shape]). Then
// we concatenate all those tensors along the first dimension.
// The pattern will be rejected if either `element_shape` is not constant, or
// the first dimension of `input` is not known.

    private List<DependencyState> cachedFilteredDependencyStates;

    // exclusions optimizations
    private ExcludeSpec cachedNodeExclusions;
    private int previousIncomingEdgeCount;
    private long previousIncomingHash;
    private long incomingHash;
    private ExcludeSpec cachedModuleResolutionFilter;

    private StrictVersionConstraints ancestorsStrictVersionConstraints;

  /**
   * Minimum outbound web socket message size (in bytes) that will be compressed.
   * The default is 1024 bytes.
   */
  @get:JvmName("minWebSocketMessageToCompress")
  val minWebSocketMessageToCompress: Long = builder.minWebSocketMessageToCompress

  internal val routeDatabase: RouteDatabase = builder.routeDatabase ?: RouteDatabase()
  internal val taskRunner: TaskRunner = builder.taskRunner ?: TaskRunner.INSTANCE