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;
}

TF_Function* TF_GraphToFunction(const TF_Graph* fn_body, const char* fn_name,

#include "tensorflow/c/kernels.h"

// A create function. This will never actually get called in this test, it's
// just nice to know that it compiles.
void* create(TF_OpKernelConstruction* ctx) {
  TF_DataType type;
  TF_Status* s = TF_NewStatus();
  TF_OpKernelConstruction_GetAttrType(ctx, "foobar", &type, s);
  TF_DeleteStatus(s);
  return NULL;
}

// A compute function. This will never actually get called in this test, it's

      parent(nullptr),
      parent_inputs(nullptr) {
  // Tell the shape refiner to also run shape inference on functions.
  refiner.set_function_library_for_shape_inference(&graph.flib_def());
}

TF_Graph* TF_NewGraph() { return new TF_Graph; }

void TF_DeleteGraph(TF_Graph* g) {
  if (g == nullptr) return;
  g->mu.lock();
  g->delete_requested = true;
  const bool del = g->sessions.empty();

  TF_Graph* graph = TF_NewGraph();

  // Make a placeholder operation.
  TF_Operation* feed = Placeholder(graph, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Test TF_Operation*() query functions.
  EXPECT_EQ(string("feed"), string(TF_OperationName(feed)));
  EXPECT_EQ(string("Placeholder"), string(TF_OperationOpType(feed)));
  EXPECT_EQ(string(""), string(TF_OperationDevice(feed)));

}

char* TF_FunctionDebugString(TF_Function* func, size_t* len) {
  const auto& debug_str = DebugString(func->record->fdef());
  *len = debug_str.size();
  char* ret = static_cast<char*>(malloc(*len + 1));
  memcpy(ret, debug_str.c_str(), *len + 1);
  return ret;
}

// On success, returns a set of TF_Function instances from `text_proto` of
// GraphDef type. These functions must be deleted by calling TF_DeleteFunction.
//

                               TF_Buffer* buf, TF_Status* status) {
  auto* function_def = tensorflow::unwrap(ctx)->FindFunctionDef(function_name);
  if (function_def == nullptr) {
    status->status = tensorflow::errors::NotFound(
        "Unable to find FunctionDef with name: ", function_name);
    return;
  }
  string str = function_def->SerializeAsString();

        "@com_google_absl//absl/strings",
    ],
)

cc_library(
    name = "graph_function",
    srcs = ["graph_function.cc"],
    hdrs = [
        "graph_function.h",
    ],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [
        ":abstract_function",
        "//tensorflow/core:framework",
        "//tensorflow/core/platform:refcount",

    steps:
    - name: Checkout code
      uses: actions/checkout@755da8c3cf115ac066823e79a1e1788f8940201b # v3.2.0
    - name: Update the RBE Configs
      run: |
        function map() {
          # The "digest" that allows us to pull an image is not the digest as
          # returned by the API, but a sha256sum of the entire chunk of image

        closed over by local functions. Previously, such variables were
        sometimes incorrectly ignored.
    *   functions returned by the `get_concrete_function` method of
        `tf.function` objects can now be called with arguments consistent with
        the original arguments or type specs passed to `get_concrete_function`.
        This calling convention is now the preferred way to use concrete

    var_name: (String) string for name of environment variable, e.g. "TF_MYVAR".
    var_default: (String) default value string.
    ask_for_var: (String) string for how to ask for user input.
    check_success: (Function) function that takes one argument and returns a
      boolean. Should return True if the value provided is considered valid. May
      contain a complex error message if error_msg does not provide enough