: 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

	f.ops = f.ops[1:]
	return f.pos, nil
}

func equalSparseEntries(x, y []sparseEntry) bool {
	return (len(x) == 0 && len(y) == 0) || reflect.DeepEqual(x, y)
}

func TestSparseEntries(t *testing.T) {
	vectors := []struct {
		in   []sparseEntry
		size int64

		wantValid    bool          // Result of validateSparseEntries
		wantAligned  []sparseEntry // Result of alignSparseEntries

  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) {

  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 {

  }
}

absl::optional<std::vector<std::unique_ptr<ParallelTensor>>>
ParallelDevice::Join(
    const std::vector<PartialTensorShape>& expected_output_shapes,
    TF_Status* status) const {
  absl::optional<std::vector<std::unique_ptr<ParallelTensor>>> result;
  // Compute per-device per-output tensors
  std::vector<std::vector<TensorHandlePtr>> per_device_output_tensors;

  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(

	"bytes"
	"compress/bzip2"
	"crypto/md5"
	"errors"
	"fmt"
	"io"
	"math"
	"os"
	"path"
	"reflect"
	"strconv"
	"strings"
	"testing"
	"time"
)

func TestReader(t *testing.T) {
	vectors := []struct {
		file    string    // Test input file
		headers []*Header // Expected output headers
		chksums []string  // MD5 checksum of files, leave as nil if not checked
		err     error     // Expected error to occur
	}{{

      separated.withSeparator("$", 4);
      fail("Expected UnsupportedOperationException");
    } catch (UnsupportedOperationException expected) {
    }
  }

  public void testBase64() {
    // The following test vectors are specified in RFC 4648 itself
    testEncodingWithSeparators(base64(), "", "");
    testEncodingWithSeparators(base64(), "f", "Zg==");
    testEncodingWithSeparators(base64(), "fo", "Zm8=");

  // 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;