TF_DeleteBuffer(graph_def);
  graph_def = TF_NewBuffer();
  TF_GraphToGraphDef(graph, graph_def, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Import again, with remapped control dependency, into the same graph
  TF_DeleteImportGraphDefOptions(opts);
  opts = TF_NewImportGraphDefOptions();
  TF_ImportGraphDefOptionsSetPrefix(opts, "imported4");

                  {}, false);
}

TEST_F(CApiFunctionTest, ControlDependency) {
  /*
   *                  |  |    scalar
   *                  |  |    .
   *                  v  v   . <---- control dependency
   *                  add < -
   *                   |
   *                   v
   */
  // Define
  TF_Operation* feed1 = Placeholder(func_graph_, s_, "feed1");

# build is static and TensorFlow symbols (in Python only) are loaded into the
# global symbol table in order to support op registration. This means that
# projects building with Bazel and importing TensorFlow as a dependency will not
# depend on libtensorflow_framework.so unless they opt in.
tf_cc_shared_library(
    name = "tensorflow_framework",
    additional_linker_inputs = [
        "//tensorflow:tf_framework_version_script.lds",

# Config to use a mostly-static build and disable modular op registration
# support (this will revert to loading TensorFlow with RTLD_GLOBAL in Python).
# By default, TensorFlow will build with a dependence on
# //tensorflow:libtensorflow_framework.so.
build:monolithic --define framework_shared_object=false
build:monolithic --define tsl_protobuf_header_only=false

TF_CAPI_EXPORT extern void TF_ImportGraphDefOptionsRemapControlDependency(
    TF_ImportGraphDefOptions* opts, const char* src_name, TF_Operation* dst);

// Cause the imported graph to have a control dependency on `oper`. `oper`
// should exist in the graph being imported into.
TF_CAPI_EXPORT extern void TF_ImportGraphDefOptionsAddControlDependency(
    TF_ImportGraphDefOptions* opts, TF_Operation* oper);