// functions.
  virtual void MarkAsResult(Gradient* gradient) const = 0;

  // Deletes the input tensor.
  virtual void DeleteGradient(Gradient* gradient) const = 0;
};

// Traces the execution of operations, doing eager garbage collection, and
// exporting a full trace so other code can do backpropagation. Not thread-safe.
template <typename Gradient, typename BackwardFunction, typename TapeTensor>

  EXPECT_EQ(func_stack_traces->size(), 4);
  EXPECT_EQ(func_stack_traces->at("neg")->ToString({}), kNegStackToString);
  EXPECT_EQ(func_stack_traces->at("feed")->ToString({}), kFeedStackToString);

  // Verify that stack traces of grad_func is copied to graph function library.
  ASSERT_NE(grad_stack_traces, nullptr);
  EXPECT_EQ(grad_stack_traces->size(), 4);

                                                     TF_Buffer* buf,
                                                     TF_Status* status);

// Get GraphDebugInfo containing stack traces mapping to node names
TF_CAPI_EXPORT extern void TFE_ContextGetGraphDebugInfo(
    TFE_Context* ctx, const char* function_name, TF_Buffer* buf,
    TF_Status* status);

// Extracts a TF_Function from the context.

  // Same as `AddFunctionDef`, but additionally saves the `stack_traces` under
  // the key of the function definition name (to be retrieved during function
  // instantiation).
  virtual 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 =

*   Improvements to the TensorFlow debugging experience:

    *   Previously, TensorFlow error stack traces involved many internal frames,
        which could be challenging to read through, while not being actionable
        for end users. As of TF 2.7, TensorFlow filters internal frames in most
        errors that it raises, to keep stack traces short, readable, and focused
        on what's actionable for end users (their own code).

  /// Deletes the directory specified by `path` and all its contents.
  ///
  /// This is accomplished by traversing directory tree rooted at `path` and
  /// deleting entries as they are encountered, from leaves to root. Each plugin
  /// is free to choose a different approach which obtains similar results.
  ///
  /// On successful deletion, `status` must be `TF_OK` and `*undeleted_files`

          NodeDef* const node_def = graph_def.add_node();
          *node_def = node->def();
        }
      }
      *graph_def.mutable_library() = graph.flib_def().ToProto();
      if (flags::Global().more_stack_traces.value()) {
        *graph_def.mutable_debug_info() = graph.BuildDebugInfo();
      }
      session->graph->mu.unlock();
      status->status = session->session->Extend(std::move(graph_def));

  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();
  tf_function->record = new tensorflow::FunctionRecord(
      std::move(fdef), std::move(stack_traces), false);

  return tf_function;
}