*/

  ANY(ZERO, ONE, SEVERAL);

  private final Set<Feature<? super Collection>> implied;
  private final @Nullable Integer numElements;

  CollectionSize(int numElements) {
    this.implied = Collections.emptySet();
    this.numElements = numElements;
  }

  CollectionSize(Feature<? super Collection>... implied) {
    // Keep the order here, so that PerCollectionSizeTestSuiteBuilder

   */

  ANY(ZERO, ONE, SEVERAL);

  private final Set<Feature<? super Collection>> implied;
  private final @Nullable Integer numElements;

  CollectionSize(int numElements) {
    this.implied = Collections.emptySet();
    this.numElements = numElements;
  }

  CollectionSize(Feature<? super Collection>... implied) {
    // Keep the order here, so that PerCollectionSizeTestSuiteBuilder

    int added = 0;
    while (added < numElements) {
      // we could rely solely on #poll, but #drainTo might be more efficient when there are multiple
      // elements already available (e.g. LinkedBlockingQueue#drainTo locks only once)
      added += q.drainTo(buffer, numElements - added);
      if (added < numElements) { // not enough elements immediately available; will have to poll

 public:
  // Returns number of dimensions.
  virtual Status NumDims(int* num_dims) const = 0;
  // Returns number of elements across all dimensions.
  virtual Status NumElements(int64_t* num_elements) const = 0;
  // Returns size of specified dimension
  //
  // -1 indicates an unknown axis length; this is unreachable for most standard

 public:
  explicit TapeVSpace(AbstractContext* ctx) : ctx_(ctx) {}
  ~TapeVSpace() override {}

  // Returns the number of elements in the gradient tensor.
  int64_t NumElements(AbstractTensorHandle* tensor) const override;

  // Consumes references to the tensors in the gradient_tensors list and returns
  // a tensor with the result.
  AbstractTensorHandle* AggregateGradients(

template <typename Gradient, typename BackwardFunction, typename TapeTensor>
class VSpace {
 public:
  virtual ~VSpace() {}

  // Returns the number of elements in the gradient tensor.
  virtual int64_t NumElements(Gradient* tensor) const = 0;

  // Consumes references to the tensors in the gradient_tensors list and returns
  // a tensor with the result.
  virtual Gradient* AggregateGradients(

    Tensor output;
    ASSERT_EQ(absl::OkStatus(), TF_TensorToTensor(dst, &output)) << line;
    ASSERT_EQ(src.NumElements(), output.NumElements()) << line;
    for (int64_t i = 0; i < src.NumElements(); ++i) {
      ASSERT_EQ(data[i], output.flat<tstring>()(i)) << line;
    }

    TF_DeleteTensor(dst);
  }
}

TEST(CAPI, TensorEncodeDecodeStrings) {
  TestEncodeDecode(__LINE__, {});

     *
     * @param <E>
     *            {@link ArrayDeque}の要素型
     * @param numElements
     *            両端キューの初期容量の範囲の下限
     * @return {@link ArrayDeque}の新しいインスタンス
     * @see ArrayDeque#ArrayDeque(int)
     */
    public static <E> ArrayDeque<E> newArrayDeque(final int numElements) {
        return new ArrayDeque<>(numElements);
    }

    /**
     * {@link ArrayList}の新しいインスタンスを作成して返します。
     *

  if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return -1;
  }

  int64_t num_elements = -1;
  status->status = tensorflow::unwrap(h)->NumElements(&num_elements);
  return num_elements;
}

int64_t TFE_TensorHandleDim(TFE_TensorHandle* h, int dim_index,
                            TF_Status* status) {
  if (h == nullptr) {

    status->status = result;
    return;
  }

  // Store results in c_outputs[]
  for (int i = 0; i < noutputs; ++i) {
    const Tensor& src = outputs[i];
    if (!src.IsInitialized() || src.NumElements() == 0) {
      c_outputs[i] =
          EmptyTensor(static_cast<TF_DataType>(src.dtype()), src.shape());
      continue;
    }
    c_outputs[i] = TF_TensorFromTensor(src, &status->status);