EXPECT_TRUE(IsNeg(node_def, "add"));

  // Serialize to GraphDef.
  GraphDef graph_def2;
  ASSERT_TRUE(GetGraphDef(graph, &graph_def2));

  // Compare with first GraphDef + added NodeDef.
  NodeDef* added_node = graph_def.add_node();
  *added_node = node_def;
  EXPECT_EQ(graph_def.DebugString(), graph_def2.DebugString());

  // Look up some nodes by name.

TF_ImportGraphDefResults* TF_GraphImportGraphDefWithResults(
    TF_Graph* graph, const TF_Buffer* graph_def,
    const TF_ImportGraphDefOptions* options, TF_Status* status) {
  GraphDef def;
  if (!tensorflow::ParseProtoUnlimited(&def, graph_def->data,
                                       graph_def->length)) {
    status->status = InvalidArgument("Invalid GraphDef");
    return nullptr;
  }
  auto results = new TF_ImportGraphDefResults();

    const char* text_proto,
    std::function<void(FunctionDef*)>* mutate_proto_func, TF_Status* status) {
  tensorflow::GraphDef gdef;
  if (!tensorflow::protobuf::TextFormat::ParseFromString(text_proto, &gdef)) {
    status->status = tensorflow::errors::Internal(
        "Invalid text proto for GraphDef: ", text_proto);
    return {};
  }
  const auto& fdef_lib = gdef.library();
  if (fdef_lib.gradient_size() > 0) {

should be used with caution when working with untrusted models.

### Saved graphs and checkpoints

When loading untrusted serialized computation graphs (in form of a `GraphDef`,
`SavedModel`, or equivalent on-disk format), the set of computation primitives
available to TensorFlow is powerful enough that you should assume that the
TensorFlow process effectively executes arbitrary code.

*   The Python API will now properly set the `list` member of `AttrValue` in
    constructed `GraphDef` messages for empty lists. The serialization of some
    graphs will change, but the change is both forwards and backwards
    compatible. It will break tests that compare a generated `GraphDef` to a
    golden serialized `GraphDef` (which is discouraged).

## Thanks to our Contributors