Status Shape(const std::vector<int64_t>** shape) const;
  TF_DataType dtype() const { return dtype_; }

  // Sets its output argument to a summary of the values of this tensor on every
  // component device.
  Status SummarizeValue(std::string& summary);

  std::vector<TensorHandlePtr> release_tensors() { return std::move(tensors_); }

  std::vector<TFE_TensorHandle*> tensors() const {

  if (!status->status.ok()) return;

  // Initialize a input_tensor vector with `nullptr` values.
  std::vector<const Tensor*> input_tensors_vector(num_inputs, nullptr);
  // A vector to keep track of newly created `tf::Tensor` objects.
  std::vector<Tensor> all_input_tensors;
  // Update the vector with information from `input_tensors` if provided.
  if (input_tensors != nullptr) {

                           const string& op_name) {
  std::vector<int64_t> input_ids(inputs.size());
  std::vector<tensorflow::DataType> input_dtypes(inputs.size());
  for (int i = 0; i < inputs.size(); i++) {
    input_ids[i] = ToId(inputs[i]);
    input_dtypes[i] = inputs[i]->DataType();
  }
  std::vector<TapeTensor> tape_tensors;
  tape_tensors.reserve(outputs.size());
  for (auto t : outputs) {

 private:
  void DeleteInputValues();
  void ResetOutputValues();

  TF_Session* session_;
  std::vector<TF_Output> inputs_;
  std::vector<TF_Tensor*> input_values_;
  std::vector<TF_Output> outputs_;
  std::vector<TF_Tensor*> output_values_;
  std::vector<TF_Operation*> targets_;
};

  device->delete_device = &DeleteParallelDevice;
  device->execute = &ParallelDeviceExecute;
  std::vector<std::string> underlying_devices_vector;
  underlying_devices_vector.reserve(num_underlying_devices);
  for (int device_index = 0; device_index < num_underlying_devices;
       ++device_index) {
    underlying_devices_vector.push_back(underlying_devices[device_index]);
  }
  std::unique_ptr<ParallelDevice> parallel_device(

  // This cache has space for 4 blocks; we'll read from two files.
  const size_t n = 3;
  tf_gcs_filesystem::RamFileBlockCache cache(8, 32, 0, fetcher);
  std::vector<char> out;
  std::vector<char> a(n, 'a');
  std::vector<char> b(n, 'b');
  std::vector<char> A(n, 'A');
  std::vector<char> B(n, 'B');
  // Fill the cache.
  TF_EXPECT_OK(ReadCache(&cache, "a", 0, n, &out));
  EXPECT_EQ(out, a);
  EXPECT_EQ(calls, 1);

            if ( mechs != null ) {
                ASN1EncodableVector vector = new ASN1EncodableVector();
                for ( int i = 0; i < mechs.length; i++ ) {
                    vector.add(mechs[ i ]);
                }
                fields.add(new DERTaggedObject(true, 0, new DERSequence(vector)));
            }
            int ctxFlags = getContextFlags();
            if ( ctxFlags != 0 ) {

            new TestStringListGenerator() {
              @Override
              protected List<String> create(String[] elements) {
                return new Vector<>(MinimalCollection.of(elements));
              }
            })
        .named("Vector")
        .withFeatures(
            ListFeature.GENERAL_PURPOSE,
            CollectionFeature.ALLOWS_NULL_VALUES,

  int num_elems = TF_TensorElementCount(theta_tensor);
  vector<float> theta_data(num_elems);
  memcpy(theta_data.data(), TF_TensorData(theta_tensor),
         TF_TensorByteSize(theta_tensor));

  // Initialize space for the numerical gradient.
  vector<float> dtheta_approx(num_elems);

  // Get theta shape and store in theta_dims.
  int num_dims = TF_NumDims(theta_tensor);
  vector<int64_t> theta_dims(num_dims);

  // Process inputs.
  std::vector<tensorflow::OutputTensor> input_tensors;
  std::unordered_map<const Node*, std::vector<int>> input_nodes;
  status->status = tensorflow::ProcessInputs(fn_body, fn_name, ninputs, inputs,
                                             &input_tensors, &input_nodes);
  if (TF_GetCode(status) != TF_OK) return nullptr;

  // Process outputs.
  std::vector<tensorflow::OutputTensor> output_tensors;