}
    }

    public static class NetrDfsEnumEx extends DcerpcMessage {

        @Override
        public int getOpnum () {
            return 0x15;
        }

        public int retval;
        public String dfs_name;
        public int level;
        public int prefmaxlen;
        public DfsEnumStruct info;
        public NdrLong totalentries;

                e.decode(_src);

            }
        }
    }
    public static class NetrDfsEnumEx extends DcerpcMessage {

        public int getOpnum() { return 0x15; }

        public int retval;
        public String dfs_name;
        public int level;
        public int prefmaxlen;
        public DfsEnumStruct info;
        public NdrLong totalentries;

        public NetrDfsEnumEx(String dfs_name,

        List<Map<String, Object>> docs = JsonPath.from(response).getList("data");
        int prevVal = 0;
        for (Map<String, Object> doc : docs) {
            int sortValue = Integer.parseInt(doc.get(sortField).toString());
            assertTrue(sortValue >= prevVal);
            prevVal = sortValue;
        }
    }

    @Test
    public void searchTestWithWildcard() throws Exception {

  @SuppressWarnings("unchecked")
  @Benchmark
  boolean getIfPresent(int repetitions) {
    boolean retVal = false;
    for (int i = 0; i < repetitions; ++i) {
      retVal &= Enums.getIfPresent(enumType, sampleData[i & 255]).isPresent();
    }
    return retVal;
  }

  private enum SmallEnum {
    X0,
    X1,
    X2
  }

  private enum MediumEnum {
    X0,
    X1,

  @SuppressWarnings("unchecked")
  @Benchmark
  boolean getIfPresent(int repetitions) {
    boolean retVal = false;
    for (int i = 0; i < repetitions; ++i) {
      retVal &= Enums.getIfPresent(enumType, sampleData[i & 255]).isPresent();
    }
    return retVal;
  }

  private enum SmallEnum {
    X0,
    X1,
    X2
  }

  private enum MediumEnum {
    X0,
    X1,

  auto new_retvals = llvm::to_vector<8>(old_body_ret->getOperands());
  for (int64_t i = 0; i < while_op.getNumResults(); ++i) {
    if (!ta_arg_buffer_type(i)) continue;
    auto retval = old_body_ret->getOperand(i);
    auto arg = retval.dyn_cast<BlockArgument>();
    if (!arg) {
      return while_op.emitOpError(
          "output tensor array does not alias input in a while loop");
    }

// Note: this list must be updated if ever kubelet wants to allow mutations to other fields.
func pickFieldsToHash(container *v1.Container) map[string]string {
	retval := map[string]string{
		"name":  container.Name,
		"image": container.Image,
	}
	return retval
}

// envVarsToMap constructs a map of environment name to value from a slice
// of env vars.
func envVarsToMap(envs []EnvVar) map[string]string {

                             int* num_retvals) override {
    std::vector<TFE_TensorHandle*> outputs(*num_retvals);
    TF_Status status;
    device_.execute(tensorflow::wrap(op), num_retvals, outputs.data(), &status,
                    info_);
    if (status.status.ok()) {
      for (int i = 0; i < *num_retvals; ++i) {
        retvals[i] = tensorflow::unwrap(outputs[i]);

      }
      tf_outputs[i] = t->output_;
    }
    TF_AddInputList(op_.get(), tf_outputs.data(), tf_outputs.size());
    return absl::OkStatus();
  }
  Status Execute(absl::Span<AbstractTensorHandle*> retvals,
                 int* num_retvals) override {
    auto* tf_opdesc = op_.release();
    if (tf_opdesc == nullptr) {
      return errors::InvalidArgument("AbstractOp is incomplete.");
    }

    func.func @main(%arg0: tensor<3x3x10xbf16>, %arg1: tensor<3xi32>) -> tensor<1x?x4xbf16> attributes {allow_soft_placement = false, tf.entry_function = {control_outputs = "", inputs = "_arg0,_arg1,_arg2", outputs = "_retval0"}} {
      %cst = "tf.Const"() {value = dense<[1, -1, 4]> : tensor<3xi32>} : () -> tensor<3xi32>