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"

  // 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;

    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() {

    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) {

    // 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;

  }

  // Checks the expected result of shape inference for the given `op`.
  void CheckOutputShapes(
      TFE_Op* op,
      const std::vector<absl::optional<std::vector<int64_t>>>& input_shapes_vec,
      const std::vector<TF_Tensor*>& input_tensors,
      const absl::optional<std::vector<int64_t>>& expected_shape) {
    // Create input_shapes.
    TF_ShapeAndTypeList* input_shapes =

                Map.of("_default", List.of("QUERY1")), //
                Set.of("QUERY1"), //

        sink.writeUtf8("-------\n");
        for (int i = 2; i <= 997; i++) {
          sink.writeUtf8(String.format(" * %s = %s\n", i, factor(i)));
        }
      }

      private String factor(int n) {
        for (int i = 2; i < n; i++) {
          int x = n / i;
          if (x * i == n) return factor(x) + " × " + i;
        }
        return Integer.toString(n);
      }
    };

    Request request = new Request.Builder()

    private static final long serialVersionUID = 1L;

    /**
     * Default initial capacity.
     */
    protected static final int DEFAULT_INITIAL_CAPACITY = 16;

    /**
     * Default load factor.
     */
    protected static final float DEFAULT_LOAD_FACTOR = 0.75f;

    /**
     * Upper limit on the number of entries.
     */
    protected final int limitSize;

    /**

          sink.writeUtf8("-------\n")
          for (i in 2..997) {
            sink.writeUtf8(String.format(" * $i = ${factor(i)}\n"))
          }
        }

        private fun factor(n: Int): String {
          for (i in 2 until n) {
            val x = n / i
            if (x * i == n) return "${factor(x)} × $i"
          }
          return n.toString()
        }
      }

    val request =
      Request(