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.

)

cc_library(
    name = "tf_buffer_hdrs",
    hdrs = [
        "tf_buffer.h",
    ],
    visibility = ["//visibility:public"],
    deps = [
        ":c_api_macros_hdrs",
    ],
)

cc_library(
    name = "tf_buffer",
    srcs = [
        "tf_buffer.cc",
    ],
    hdrs = [
        "tf_buffer.h",
    ],
    copts = tf_copts(),
    visibility = ["//visibility:public"],

                                      const char* value) {
  cell->cell.Set({value});
}

const void TFE_MonitoringStringGaugeCellValue(
    TFE_MonitoringStringGaugeCell* cell, TF_Buffer* buf) {
  tensorflow::string value = cell->cell.value();
  void* data = tensorflow::port::Malloc(value.length());
  value.copy(static_cast<char*>(data), value.length(), 0);
  buf->data = data;

  // functions, and their send/recvs corountines).
  config.set_inter_op_parallelism_threads(1);

  TF_Buffer* ret = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(config, ret));
  return ret;
}

TF_Buffer* TF_CreateRunOptions(unsigned char enable_full_trace) {
  tensorflow::RunOptions options;
  if (enable_full_trace) {

TF_CAPI_EXPORT extern TF_Buffer* TF_CreateConfig(
    unsigned char enable_xla_compilation, unsigned char gpu_memory_allow_growth,
    unsigned int num_cpu_devices);

// Create a serialized tensorflow.RunOptions proto, where RunOptions.trace_level
// is set to FULL_TRACE if `enable_full_trace` is non-zero, and NO_TRACE
// otherwise.
TF_CAPI_EXPORT extern TF_Buffer* TF_CreateRunOptions(

         node_def.input_size() == 1 && node_def.input(0) == input;
}

bool GetGraphDef(TF_Graph* graph, tensorflow::GraphDef* graph_def) {
  TF_Status* s = TF_NewStatus();
  TF_Buffer* buffer = TF_NewBuffer();
  TF_GraphToGraphDef(graph, buffer, s);
  bool ret = TF_GetCode(s) == TF_OK;
  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

    }
    tasks {
      key: 1
      value: "localhost:1"
    }
  }
}
job_name: "worker"
task_index: 1
protocol: "grpc"
)");

  TF_Status* status = TF_NewStatus();
  TF_Buffer* result = TFE_GetServerDef(expected_text_proto.c_str(), status);
  EXPECT_EQ(TF_GetCode(status), TF_OK);

  ServerDef actual;
  ASSERT_TRUE(actual.ParseFromArray(result->data, result->length));

    func->record = new tensorflow::FunctionRecord(lib.function(i), {}, false);
    funcs[i] = func;
  }
  status->status = absl::OkStatus();
  return len;
}

void TF_FunctionToFunctionDef(TF_Function* func, TF_Buffer* output_func_def,
                              TF_Status* status) {
  status->status = MessageToBuffer(func->record->fdef(), output_func_def);
}

  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  auto* cell2 = TFE_MonitoringGetCellStringGauge2(gauge2, "foo", "bar");
  TFE_MonitoringStringGaugeCellSet(cell2, "str");
  auto* buf = new TF_Buffer;
  TFE_MonitoringStringGaugeCellValue(cell2, buf);
  string data(static_cast<const char*>(buf->data), buf->length);
  TF_DeleteBuffer(buf);
  EXPECT_EQ(data, "str");
  TFE_MonitoringDeleteStringGauge2(gauge2);

    if (methods_.summarize == nullptr) {
      return tensorflow::CustomDeviceTensorHandle::SummarizeValue(summary);
    }
    TF_Status c_status;
    std::unique_ptr<TF_Buffer, decltype(&TF_DeleteBuffer)> summary_buffer(
        methods_.summarize(data_, &c_status), TF_DeleteBuffer);
    if (!c_status.status.ok()) {
      return c_status.status;
    }