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.

          distribution: 'zulu'
          java-version: 17

      - name: Setup Gradle
        uses: gradle/actions/setup-gradle@v4

      - name: Upload Artifacts
        run: ./gradlew clean publish --stacktrace
        env:
          ORG_GRADLE_PROJECT_mavenCentralUsername: ${{ secrets.SONATYPE_NEXUS_USERNAME }}
          ORG_GRADLE_PROJECT_mavenCentralPassword: ${{ secrets.SONATYPE_NEXUS_PASSWORD }}

    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();

}

TEST_F(CApiFunctionTest, TFGraphToFunctionWithStackTraces) {
  DefineFunction(func_name_, &func_);
  auto stack_traces = func_->record->stack_traces();

  EXPECT_EQ(stack_traces.size(), 4);
  EXPECT_EQ(stack_traces["neg"]->ToString({}), kNegStackToString);
  EXPECT_EQ(stack_traces["feed"]->ToString({}), kFeedStackToString);
}

TEST_F(CApiFunctionTest, TFGraphCopyFunctionWithStackTraces) {

  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(

  // 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.

active loggers:
  admin: warning
  aws: warning
  assert: warning
  backtrace: warning
  client: warning
  config: warning
  connection: warning
  dubbo: warning
  file: warning
  filter: warning
  forward_proxy: warning
  grpc: warning
  hc: warning
  health_checker: warning
  http: warning
  http2: warning
  hystrix: warning
  init: warning
  io: warning
  jwt: warning
  kafka: warning
  lua: warning

  - type: textarea
    id: actual-behavior
    attributes:
      label: "What did you see happen?"
      description: Command invocations and their associated output, functions with their arguments and return results, full stacktraces for panics (upload a file if it is very long), etc. Prefer copying text output over using screenshots.
    validations:
      required: true

  - type: textarea
    id: expected-behavior
    attributes:

        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"));

        "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();