: public std::function<Status(const std::vector<Gradient*>&,
                                  std::vector<Gradient*>*, bool)> {
 public:
  template <typename lambda_type>
  explicit ForwardFunction(lambda_type lambda)
      : std::function<Status(const std::vector<Gradient*>&,
                             std::vector<Gradient*>*, bool)>(lambda) {}
};

// Computes Jacobian-vector products using forward-mode automatic

    // Input tensors
    const std::vector<std::pair<string, Tensor>>& input_pairs,
    // Output tensors
    const std::vector<string>& output_tensor_names, TF_Tensor** c_outputs,
    // Target nodes
    const std::vector<string>& target_oper_names, TF_Buffer* run_metadata,
    TF_Status* status) {
  const int noutputs = output_tensor_names.size();
  std::vector<Tensor> outputs(noutputs);
  Status result;

  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) {

    @SuppressWarnings("unused")
    @Test
    public void testIterable() throws Exception {
        final Vector<String> vector = new Vector<String>();
        vector.add("a");
        vector.add("b");
        int count = 0;
        for (final String s : iterable(vector.elements())) {
            ++count;
        }
        assertThat(count, is(2));
    }

    /**
     * Test method for

  // 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;

        absl::InlinedVector<const void*, 4> values_vector;
        values_vector.reserve(s_size);
        absl::InlinedVector<size_t, 4> lengths_vector;
        lengths_vector.reserve(s_size);
        for (int i = 0; i < s_size; ++i) {
          const string& v = default_value.list().s(i);
          values_vector.push_back(v.data());
          lengths_vector.push_back(v.size());
        }

    }

    /**
     * {@link Vector}の新しいインスタンスを作成して返します。
     *
     * @param <E>
     *            {@link Vector}の要素型
     * @return {@link Vector}の新しいインスタンス
     * @see Vector#Vector()
     */
    public static <E> Vector<E> newVector() {
        return new Vector<>();
    }

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

limitations under the License.
==============================================================================*/

#include <memory>
#include <utility>
#include <vector>

#include "absl/strings/str_cat.h"
#include "tensorflow/c/c_api.h"
#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/eager/c_api_internal.h"

java.io.File basedir; protected String[] includes; protected String[] excludes; protected java.util.Vector filesIncluded; protected java.util.Vector filesNotIncluded; protected java.util.Vector filesExcluded; protected java.util.Vector dirsIncluded; protected java.util.Vector dirsNotIncluded; protected java.util.Vector dirsExcluded; protected java.util.Vector filesDeselected; protected java.util.Vector dirsDeselected; protected boolean haveSlowResults; protected boolean isCaseSensitive; private boolean...

                                                           double growth_factor,
                                                           int bucket_count) {
  return new TFE_MonitoringBuckets([scale, growth_factor, bucket_count]() {
    return tensorflow::monitoring::Buckets::Exponential(scale, growth_factor,
                                                        bucket_count);
  });
}