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;
}

          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));

  CancellationManager* cancellation_manager_ TF_GUARDED_BY(execution_mutex_);
  //   Outputs:
  std::vector<TensorHandlePtr> op_outputs_ TF_GUARDED_BY(execution_mutex_);
  // TF_Status is an incomplete type and so can't be stack allocated. To avoid
  // unnecessary allocations each Execute call, we keep one heap-allocated
  // version for the thread.
  StatusPtr status_ TF_GUARDED_BY(execution_mutex_);

  const std::string device_;

  std::vector<int64_t> tensor_stack;
  tensor_stack.reserve(target.size());
  for (auto t : target) {
    tensor_stack.push_back(t);
  }
  BackpropInitialState<BackwardFunction, TapeTensor> result;
  while (!tensor_stack.empty()) {
    int64_t tensor_id = tensor_stack.back();
    tensor_stack.pop_back();
    auto op_id_it = tensor_tape.find(tensor_id);

        "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;

                                                     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.

*   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).

    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 =

        "//tensorflow/c:tensor_interface",
        "//tensorflow/core:framework",
        "//tensorflow/core:lib",
        "//tensorflow/core:protos_all_cc",
        "//tensorflow/core/util:managed_stack_trace",
        "@com_google_absl//absl/types:optional",
        "@com_google_absl//absl/types:span",
    ],
)

cc_library(
    name = "abstract_context",
    hdrs = ["abstract_context.h"],

        "//tensorflow/core/profiler/lib:traceme",
        "//tensorflow/core/profiler/rpc/client:profiler_client_impl",
        "//tensorflow/core/profiler/rpc:profiler_server_impl",
        "//tensorflow/core/util:managed_stack_trace",
        "//tensorflow/core:all_kernels",
        "//tensorflow/core:core_cpu",
        "//tensorflow/core:direct_session",
        "//tensorflow/core:framework_internal",
        "//tensorflow/core:graph",