}
    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));
  string actual_text_proto;

         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);

    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);

struct TF_SessionOptions {
  tensorflow::SessionOptions options;
};

struct TF_DeprecatedSession {
  tensorflow::Session* session;
};

struct TF_Library {
  void* lib_handle;
  TF_Buffer op_list;
};

struct TF_Graph {
  TF_Graph();

  mutable tensorflow::mutex mu;
  tensorflow::Graph graph TF_GUARDED_BY(mu);

  // Runs shape inference.
  tensorflow::ShapeRefiner refiner TF_GUARDED_BY(mu);

#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/eager/parallel_device/parallel_device_lib.h"
#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
#include "tensorflow/c/tf_buffer.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/c/tf_status_helper.h"

namespace tensorflow {
namespace parallel_device {
namespace {

class OpDeleter {
 public:

    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;
    }

    func_ = TF_FunctionImportFunctionDef(buf.data(), buf.size(), s_);
    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
  }

  void GetAttr(const char* attr_name, AttrValue* out_attr) {
    TF_Buffer* attr_buf = TF_NewBuffer();
    TF_FunctionGetAttrValueProto(func_, attr_name, attr_buf, s_);
    ASSERT_TRUE(out_attr->ParseFromArray(attr_buf->data, attr_buf->length));
    TF_DeleteBuffer(attr_buf);
  }

// If async mode is enabled, this call blocks till all currently pending ops are
// done.
TF_CAPI_EXPORT extern void TFE_ContextExportRunMetadata(TFE_Context* ctx,
                                                        TF_Buffer* buf,
                                                        TF_Status* status);

// Some TF ops need a step container to be set to limit the lifetime of some

  int num_retvals = 1;
  TFE_Execute(matmul, &retvals[0], &num_retvals, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteOp(matmul);
  TFE_DeleteTensorHandle(m);
  TF_Buffer* b = TF_NewBuffer();
  TFE_ContextExportRunMetadata(ctx, b, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  tensorflow::RunMetadata rm;
  EXPECT_TRUE(