void testAs_Enumeration_Success() throws PACDecodingException {
        // Test successful casting from enumeration
        Vector<ASN1Integer> vector = new Vector<>();
        ASN1Integer expected = new ASN1Integer(123);
        vector.add(expected);
        ASN1Integer result = ASN1Util.as(ASN1Integer.class, vector.elements());
        assertSame(expected, result);
    }

    @Test
    void testAs_Enumeration_Failure() {

    @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

        SMBUtil.writeInt8(500000, buffer, 28); // Available allocation units
        SMBUtil.writeInt4(512, buffer, 36); // Sectors per unit
        SMBUtil.writeInt4(4096, buffer, 40); // Bytes per sector

        int result = response.readBytesWireFormat(buffer, bufferIndex);

        assertNotNull(response.getInfo());

     * @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}

  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) {

     */
    @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();

  // 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;

    std::vector<TF_Output> out;
    for (auto op : ops) {
      out.push_back({op, 0});
    }
    return out;
  }

  void Define(int num_opers, const std::vector<TF_Operation*>& opers,
              const std::vector<TF_Operation*>& inputs,
              const std::vector<TF_Operation*>& outputs,
              const std::vector<string>& output_names,
              bool expect_failure = false) {

	VWSUBWX		X10, V2, V4, V3			// ERROR "invalid vector mask register"
	VWCVTXXV	V2, V1, V3			// ERROR "invalid vector mask register"
	VWCVTUXXV	V2, V1, V3			// ERROR "invalid vector mask register"
	VZEXTVF2	V2, V3, V4			// ERROR "invalid vector mask register"
	VSEXTVF2	V2, V3, V4			// ERROR "invalid vector mask register"
	VZEXTVF4	V2, V3, V4			// ERROR "invalid vector mask register"

- *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.