return nullptr;
  }

  // Dump the op creation stacktraces for debugging purpose.
  DEBUG_DATA_DUMPER()->DumpOpCreationStackTraces(
      fn_name, kDebugGroupOpStacktrace, "initial", &fn_body->graph);

  tensorflow::StackTracesMap stack_traces;
  for (const Node* n : fn_body->graph.nodes()) {
    stack_traces[n->name()] = n->GetStackTrace();
  }

  TF_Function* tf_function = new TF_Function();

  // Same as `AddFunctionDef`, but additionally saves the `stack_traces` under
  // the key of the function definition name (to be retrieved during function
  // instantiation).
  virtual absl::Status AddFunctionDefWithStackTraces(
      const FunctionDef& fdef, const StackTracesMap& stack_traces) = 0;

  // Find and return a added function by its name.

        "Unable to find function with name: ", function_name);
    return;
  }

  tensorflow::GraphDebugInfo debug_info =
      tensorflow::StackTracesMapToGraphDebugInfo(
          function_record->stack_traces());

  string str = debug_info.SerializeAsString();
  void* data = tensorflow::port::Malloc(str.length());
  str.copy(static_cast<char*>(data), str.length(), 0);
  buf->data = data;
  buf->length = str.length();

    return;
  }

  AnnotateEagerRuntimeConstructionContext(*fdef_or.value());
  status->status = tensorflow::unwrap(ctx)->AddFunctionDefWithStackTraces(
      *fdef_or.value(), function->record->stack_traces());
}

TF_Function* TFE_ContextGetFunction(TFE_Context* ctx, const char* name,
                                    TF_Status* status) {
  tensorflow::core::RefCountPtr<tensorflow::FunctionRecord> record =

     * @param content the message to log
     */
    void debug(CharSequence content);

    /**
     * Sends a message (and accompanying exception) to the user in the <b>debug</b> error level.
     * The error's stacktrace will be output when this error level is enabled.
     *
     * @param content the message to log
     * @param error the error that caused this log
     */
    void debug(CharSequence content, Throwable error);

        PrintWriter pw = new PrintWriter(sw);
        e.printStackTrace(pw);
        String stackTrace = sw.toString();

        String fullMessage = message + "\n" + "Exception: "
                + e.getClass().getName() + "\n" + "Message: "
                + e.getMessage() + "\n" + "Stack trace:\n"
                + stackTrace;

        processingEnv.getMessager().printMessage(Diagnostic.Kind.ERROR, fullMessage);
    }

     *
     * @param content
     */
    void debug(CharSequence content);

    /**
     * Send a message (and accompanying exception) to the user in the <b>debug</b> error level.<br>
     * The error's stacktrace will be output when this error level is enabled.
     *
     * @param content
     * @param error
     */
    void debug(CharSequence content, Throwable error);

    /**

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

  // Set stack trace to be used for potential async error reporting.
  virtual void SetStackTrace(ManagedStackTrace stack_trace) = 0;

  virtual const tensorflow::AbstractOpAttrs* GetOpAttrs() const = 0;
  virtual void AddAttrs(const AbstractOpAttrs* op_attrs) = 0;

  virtual void SetCancellationManager(

NOTE: Due to the project size, the very first import can take a while and IntelliJ might become unresponsive for several seconds during this period.

        R invokerRequest = context.invokerRequest;
        Logger logger = context.logger;
        if (invokerRequest.options().showErrors().orElse(false)) {
            logger.info("Error stacktraces are turned on.");
        }
        if (context.invokerRequest.options().verbose().orElse(false)) {
            logger.debug("Message scheme: " + (MessageUtils.isColorEnabled() ? "color" : "plain"));