// Return true if the inputs contain custom device tensor handle. It means
  // that the argument need to be handled by a custom device.
  virtual bool HasCustomDeviceInput() const = 0;

  virtual const tensorflow::OpDef* OpDef() const = 0;

  virtual absl::Status InputLength(const char* input_name, int* length) = 0;
  virtual absl::Status OutputLength(const char* output_name, int* length) = 0;

  // Test we can retrieve function OpDef from graph
  TF_Buffer* buffer = TF_NewBuffer();
  TF_GraphGetOpDef(host_graph_, func_name_, buffer, s_);
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

  // Sanity check returned OpDef
  string data(static_cast<const char*>(buffer->data), buffer->length);
  OpDef op_def;
  op_def.ParseFromString(data);
  EXPECT_EQ(op_def.name(), func_name_);

                                           TF_Status* status) {
  const tensorflow::OpDef* op_def = nullptr;
  status->status =
      tensorflow::OpRegistry::Global()->LookUpOpDef(op_name, &op_def);
  if (!status->status.ok()) return nullptr;

  if (input_index >= op_def->input_arg_size() || input_index < 0) {
    status->status = tensorflow::errors::InvalidArgument(

// Modify the request to handle the target URL
func (f *Forwarder) modifyRequest(outReq *http.Request, target *url.URL) {
	outReq.URL = copyURL(outReq.URL)
	outReq.URL.Scheme = target.Scheme
	outReq.URL.Host = target.Host

	u := f.getURLFromRequest(outReq)

	outReq.URL.Path = u.Path
	outReq.URL.RawPath = u.RawPath
	outReq.URL.RawQuery = u.RawQuery
	outReq.RequestURI = "" // Outgoing request should not have RequestURI

                      TF_Buffer* output_op_def, TF_Status* status) {
  const OpDef* op_def;
  {
    mutex_lock l(graph->mu);
    status->status = graph->graph.op_registry()->LookUpOpDef(op_name, &op_def);
    if (!status->status.ok()) return;
  }
  status->status = MessageToBuffer(*op_def, output_op_def);
}

void TF_GraphVersions(TF_Graph* graph, TF_Buffer* output_version_def,

        final String pref = ComponentUtil.getFessConfig().getQueryJsonDefaultPreference();
        if (PREFERENCE_QUERY.equals(pref)) {
            return Integer.toString(query.hashCode());
        }
        if (StringUtil.isNotBlank(pref)) {
            return pref;
        }
        return null;
    }

	cluster := &api.Cluster{
		Server: fmt.Sprintf("%s://%s", protocol, net.JoinHostPort(cfg.K8sServiceHost, cfg.K8sServicePort)),
	}

	if cfg.SkipTLSVerify {
		// User explicitly opted into insecure.
		cluster.InsecureSkipTLSVerify = true
	} else {
		caFile := model.GetOrDefault(cfg.KubeCAFile, cfg.K8sServiceAccountPath+"/ca.crt")
		caContents, err := os.ReadFile(caFile)
		if err != nil {

// Creates a new TF_ApiDefMap instance.
//
// Params:
//  op_list_buffer - TF_Buffer instance containing serialized OpList
//    protocol buffer. (See
//    https://www.tensorflow.org/code/tensorflow/core/framework/op_def.proto
//    for the OpList proto definition).
//  status - Set to OK on success and an appropriate error on failure.
TF_CAPI_EXPORT extern TF_ApiDefMap* TF_NewApiDefMap(TF_Buffer* op_list_buffer,

Op hetzelfde moment probeer je de automatische aanvulling te activeren met `Ctrl+Spatie` en je ziet:

<img src="/img/python-types/image02.png">

Nu kun je de opties bekijken en er doorheen scrollen totdat je de optie vindt die “een belletje doet rinkelen”:

<img src="/img/python-types/image03.png">

### Meer motivatie

Bekijk deze functie, deze heeft al type hints:

```Python hl_lines="1"

    ASSERT_GE(op_list.op_size(), 1);
    typedef tensorflow::protobuf::RepeatedPtrField<tensorflow::OpDef> OpDefs;
    const OpDefs& ops = op_list.op();
    bool found = std::find_if(ops.begin(), ops.end(),
                              [](const tensorflow::OpDef& op_def) {
                                return op_def.name() == "TestCApi";
                              }) != ops.end();
    EXPECT_TRUE(found);