#include "tensorflow/core/platform/status.h"

using tensorflow::string;

namespace {

std::vector<int64_t> TensorShapeAsVector(const tensorflow::TensorHandle& handle,
                                         absl::Status* status) {
  std::vector<int64_t> shape;
  int rank = -1;
  *status = handle.NumDims(&rank);
  if (!status->ok()) {
    return shape;
  }
  shape.reserve(rank);

     * @param c the collection of elements to be placed in the vector
     * @return a new instance of {@link Vector}
     * @see Vector#Vector(Collection)
     */
    public static <E> Vector<E> newVector(final Collection<? extends E> c) {
        return new Vector<>(c);
    }

    /**
     * Creates and returns a new instance of {@link Vector}.
     *
     * @param <E> the element type of {@link Vector}

     */
    @Test
    void testConstructorUnknownField() throws IOException {
        ASN1EncodableVector vector = new ASN1EncodableVector();
        vector.add(new DERTaggedObject(99, new DERGeneralString("unknown")));
        DERSequence sequence = new DERSequence(vector);
        byte[] encoded = new DERTaggedObject(false, BERTags.APPLICATION, 1, sequence).getEncoded();

  std::vector<std::string> tensor_id_data;
};

struct TF_ImportGraphDefResults {
  std::vector<TF_Output> return_tensors;
  std::vector<TF_Operation*> return_nodes;
  std::vector<const char*> missing_unused_key_names;
  std::vector<int> missing_unused_key_indexes;

  // Backing memory for missing_unused_key_names values.
  std::vector<std::string> missing_unused_key_names_data;
};

                           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) {

  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) {

  // 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;

  virtual bool UsesTFRT() = 0;

  // List attributes of available devices
  virtual void ListDevices(std::vector<DeviceAttributes>* devices) = 0;

  // Add `devices` into context's device manager. Context's device manager
  // will take ownership and maintain devices' lifetime.
  virtual absl::Status AddDevices(
      std::vector<std::unique_ptr<Device>> devices) = 0;

  // Block until all pending nodes are finished.

	// 31.11.15: Vector Integer Merge Instructions
	VMERGEVVM	V1, V2, V0, V3			// d781205c
	VMERGEVXM	X10, V2, V0, V3			// d741255c
	VMERGEVIM	$15, V2, V0, V3			// d7b1275c

	// 31.11.16: Vector Integer Move Instructions
	VMVVV		V2, V3				// d701015e
	VMVVX		X10, V3				// d741055e
	VMVVI		$15, V3				// d7b1075e

	// 31.12.1: Vector Single-Width Saturating Add and Subtract

- *If total drives has many common divisors the algorithm chooses the minimum amounts of erasure sets possible for a erasure set size of any N*.  In the example with 1024 drives - 4, 8, 16 are GCD factors. With 16 drives we get a total of 64 possible sets, with 8 drives we get a total of 128 possible sets, with 4 drives we get a total of 256 possible sets. So algorithm automatically chooses 64 sets, which is *16* 64 = 1024* drives in total.