// it.
      status->status = graph::ValidateGraphHasNoCycle(session->graph->graph);
      if (!status->status.ok()) {
        session->graph->mu.unlock();
        return false;
      }

      GraphDef graph_def;
      *graph_def.mutable_versions() = graph.versions();
      // Fill graph_def with nodes with ids in the range

// =============================================================================
// Public C API entry points
//
// These are only the generic entry points for the C API. This file does not
// have any visibility into the graph/eager implementation and is only providing
// C bindings to the abstract classes defined in the
// c_api_unified_experimental_internal.h header.
//
// =============================================================================

#include "tensorflow/c/eager/graph_function.h"

#include <utility>

#include "tensorflow/c/eager/abstract_function.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {
namespace tracing {
namespace graph {
GraphFunction::GraphFunction(FunctionDef fdef)
    : AbstractFunction(kGraph),

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

const char* TF_GraphDebugString(TF_Graph* graph, size_t* len) {
  tensorflow::mutex_lock c(graph->mu);
  const auto& debug_str = graph->graph.ToGraphDefDebug().DebugString();
  *len = debug_str.size();
  char* ret = static_cast<char*>(malloc(*len + 1));
  memcpy(ret, debug_str.c_str(), *len + 1);
  return ret;

#include "tensorflow/core/common_runtime/function_optimization_registry.h"
#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/platform/casts.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/platform/test.h"

  EXPECT_EQ(1, TF_ImportGraphDefOptionsNumReturnOperations(opts));
  tensorflow::GraphDef graph_def_proto;
  ASSERT_TRUE(tensorflow::ParseProtoUnlimited(&graph_def_proto, graph_def->data,
                                              graph_def->length));
  TF_Buffer graph_def_buffer;
  graph_def_buffer.data = reinterpret_cast<const void*>(&graph_def_proto);
  graph_def_buffer.length = sizeof(tensorflow::GraphDef*);
  TF_ImportGraphDefResults* results =

  // Clean up operation and inputs.
  TF_DeleteAbstractOp(matmul_op);

  TF_AbstractFunction* func =
      TF_FinalizeFunction(graph_ctx, mm_outputs, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  /* Now that the graph is built, test graph implementation on matmul example:
    [[1,1] ,   *   [[1,1] ,   =  [[2,2],
     [1,1]]         [1,1]]        [2,2]]
  */

    TFE_DeleteContext(ctx);
  }
  TF_DeleteStatus(status);
}

TEST(CAPI, Function_ident_CPU) {
  // First create a simple identity function.
  TF_Graph* function_graph = TF_NewGraph();
  TF_OperationDescription* arg_descr =
      TF_NewOperation(function_graph, "Placeholder", "arg");
  TF_SetAttrType(arg_descr, "dtype", TF_INT32);
  TF_Status* status = TF_NewStatus();
  TF_Operation* arg = TF_FinishOperation(arg_descr, status);

  // Add a config_proto attr, to trigger grappler graph rewrites in the current
  // eager runtime.
  if (enable_grappler) {
    tensorflow::ConfigProto config;
    // Do not skip grappler optimization even for small graphs.
    config.mutable_graph_options()
        ->mutable_rewrite_options()
        ->set_min_graph_nodes(-1);
    string serialized_config;

}

TF_Operation* Mul(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
                  TF_Status* s, const char* name) {
  TF_Operation* op;
  BinaryOpHelper("Mul", l, r, graph, s, name, &op, "", true);
  return op;
}

TF_Operation* Add(TF_Output l, TF_Output r, TF_Graph* graph, TF_Status* s,
                  const char* name) {