EXPECT_FALSE(found_placeholder);
      found_placeholder = true;
    } else if (oper == three) {
      EXPECT_FALSE(found_scalar_const);
      found_scalar_const = true;
    } else if (oper == add) {
      EXPECT_FALSE(found_add);
      found_add = true;
    } else if (oper == neg) {
      EXPECT_FALSE(found_neg);
      found_neg = true;
    } else {
      ASSERT_TRUE(GetNodeDef(oper, &node_def));

      EXPECT_EQ(orig_ptr, TF_TensorData(t));
    } else {
      // In async mode we expect forwarding to work without releasing the input
      // handle since by the time the kernel is executed we have released the
      // handle in the client code.
      EXPECT_EQ(orig_ptr, TF_TensorData(t));
    }
  } else {
    if (forward_input) {

      // /path/to/tmp/dir/tf_fs_rng_name/`
      root_dir_ = tensorflow::strings::StrCat(
          "/", tmp_dir_,
          tensorflow::strings::StrCat("tf_fs_", rng_val_, "_", test_name), "/");
    } else {
      root_dir_ = tensorflow::io::JoinPath(
          tmp_dir_,
          tensorflow::strings::StrCat("tf_fs_", rng_val_, "_", test_name));
    }
    if (!GetParam().empty()) {

        ASSERT_TRUE(v.second) << "Duplicate edge " << iter->second << " -> "
                              << arg.name() << ". fdef: " << fdef.DebugString();
      } else {
        const auto& v = a_edges.insert({arg.name(), arg.name()});
        ASSERT_TRUE(v.second) << "Duplicate edge " << arg.name() << " -> "
                              << arg.name() << ". fdef: " << fdef.DebugString();

                                  const char* path, TF_TransactionToken** token,
                                  TF_Status* status);

  /// Returns transaction token for `path` if it is part of a transaction else
  /// starts a new transaction and adds `path` to that transaction
  ///
  /// Transaction token is returned in `token`. Ownership of the token is in

    if user_input == 'y':
      print(yes_reply)
      var = True
    elif user_input == 'n':
      print(no_reply)
      var = False
    elif not user_input:
      if enabled_by_default:
        print(yes_reply)
        var = True
      else:
        print(no_reply)
        var = False
    else:
      print('Invalid selection: {}'.format(user_input_origin))
  return var

// `max_func`. In usual usage, max_func should be set to the result of
// TF_GraphNumFunctions(g). In this case, all the functions registered in
// `g` will be returned. Else, an unspecified subset.
//
// If successful, returns the number of TF_Function* successfully set in
// `funcs` and sets status to OK. The caller takes ownership of