// If <handle> has rank <rank>, or its rank is unknown, return OK and return the
// shape with asserted rank in <*result>. Otherwise an error is placed into
// `status`.
TF_CAPI_EXPORT extern void TF_ShapeInferenceContextWithRank(
    TF_ShapeInferenceContext* ctx, TF_ShapeHandle* handle, int64_t rank,
    TF_ShapeHandle* result, TF_Status* status);

// If <handle> has rank at least <rank>, or its rank is unknown, return OK and

typedef struct TF_Shape TF_Shape;

// Return a new, unknown rank shape object. The caller is responsible for
// calling TF_DeleteShape to deallocate and destroy the returned shape.
TF_CAPI_EXPORT extern TF_Shape* TF_NewShape();

// Returns the rank of `shape`. If `shape` has unknown rank, returns -1.
TF_CAPI_EXPORT extern int TF_ShapeDims(const TF_Shape* shape);

    this.rankMap = rankMap;
  }

  @Override
  public int compare(T left, T right) {
    return rank(left) - rank(right); // safe because both are nonnegative
  }

  private int rank(T value) {
    Integer rank = rankMap.get(value);
    if (rank == null) {
      throw new IncomparableValueException(value);
    }
    return rank;
  }

  @Override
  public boolean equals(@CheckForNull Object object) {

    this.rankMap = rankMap;
  }

  @Override
  public int compare(T left, T right) {
    return rank(left) - rank(right); // safe because both are nonnegative
  }

  private int rank(T value) {
    Integer rank = rankMap.get(value);
    if (rank == null) {
      throw new IncomparableValueException(value);
    }
    return rank;
  }

  @Override
  public boolean equals(@CheckForNull Object object) {

                                         tensorflow::Status* status) {
  std::vector<int64_t> shape;
  int rank = -1;
  *status = handle.NumDims(&rank);
  if (!status->ok()) {
    return shape;
  }
  shape.reserve(rank);
  for (int i = 0; i < rank; ++i) {
    int64_t dim;
    *status = handle.Dim(i, &dim);
    if (!status->ok()) {
      return shape;
    }
    shape.push_back(dim);

// disabled (the default)
type lockRankStruct struct {
}

func lockInit(l *mutex, rank lockRank) {
}

func getLockRank(l *mutex) lockRank {
	return 0
}

func lockWithRank(l *mutex, rank lockRank) {
	lock2(l)
}

// This function may be called in nosplit context and thus must be nosplit.
//
//go:nosplit
func acquireLockRankAndM(rank lockRank) {
	acquirem()
}

func unlockWithRank(l *mutex) {
	unlock2(l)

package sync

import "unsafe"

// Approximation of notifyList in runtime/sema.go. Size and alignment must
// agree.
type notifyList struct {
	wait   uint32
	notify uint32
	rank   int     // rank field of the mutex
	pad    int     // pad field of the mutex
	lock   uintptr // key field of the mutex

	head unsafe.Pointer
	tail unsafe.Pointer

	"http://dbflute.org/meta/lastadi10.dtd">
<components>
	<component name="rankFusionProcessor"
		class="org.codelibs.fess.rank.fusion.RankFusionProcessor">
		<postConstruct name="setSeacher">
			<arg>
				<component
					class="org.codelibs.fess.rank.fusion.DefaultSearcher">
				</component>
			</arg>
		</postConstruct>
	</component>

      TF_ShapeHandle* result, TF_Status* status) {                         \
    auto* cc_ctx = reinterpret_cast<InferenceContext*>(ctx);               \
    auto* cc_handle = reinterpret_cast<ShapeHandle*>(handle);              \
    auto* cc_result = reinterpret_cast<ShapeHandle*>(result);              \
    Status s = cc_ctx->func_name(*cc_handle, rank, cc_result);             \

      input = InsertDilateOp(op, rewriter);
    }

    TensorType operand_type = input.getType().cast<TensorType>();
    const int64_t rank = operand_type.getRank();
    // Shape of padding should be [rank, 2].
    SmallVector<int64_t> shape{rank, 2};
    TensorType padding_type =
        operand_type.cloneWith(shape, rewriter.getI32Type());

    ArrayRef<int64_t> padding_low = op.getEdgePaddingLow();