std::vector<string> input_names(ninputs);
  std::vector<string> output_names(noutputs);
  std::vector<string> target_oper_names(ntargets);
  for (int i = 0; i < ninputs; ++i) {
    input_names[i] = c_input_names[i];
  }
  for (int i = 0; i < noutputs; ++i) {
    output_names[i] = c_output_names[i];
  }
  for (int i = 0; i < ntargets; ++i) {
    target_oper_names[i] = c_target_oper_names[i];
  }

  // Check unused input mappings
  int num_unused_input_mappings;
  const char** src_names;
  int* src_indexes;
  TF_ImportGraphDefResultsMissingUnusedInputMappings(
      results, &num_unused_input_mappings, &src_names, &src_indexes);
  ASSERT_EQ(1, num_unused_input_mappings);
  EXPECT_EQ(string("fake"), string(src_names[0]));
  EXPECT_EQ(0, src_indexes[0]);

  TF_DeleteImportGraphDefResults(results);

  // Process output names.
  std::vector<string> output_names_vec;
  if (output_names) {
    output_names_vec.reserve(noutputs);
    for (int i = 0; i < noutputs; ++i) {
      output_names_vec.push_back(string(output_names[i]));
    }
  }

  // Process control output names.
  std::vector<string> control_output_names_vec;
  if (control_output_names) {

            underlying_devices_[component_index], underlying_devices_[0])) {
      // Device names are from different address spaces, or we can't figure out
      // whether they are, so we'll fully-qualify everything.
      return underlying_devices_;
    }
  }
  std::vector<std::string> local_names;
  local_names.reserve(underlying_devices_.size());
  for (const DeviceNameUtils::ParsedName& parsed_component :

                         std::unique_ptr<tensorflow::Graph>* graph,
                         tensorflow::FunctionLibraryDefinition* flib_def,
                         std::vector<std::string>* control_ret_node_names,
                         bool* control_rets_updated) override {
    // Inject failure to function instantiation if finding a node that contains
    // the given node name (error_node_) and requested device (error_device_).

        *   Using `functools.wraps` on a function with different signature
        *   Using `functools.partial` with an invalid `tf.function` input
    *   `tf.function` now enforces input parameter names to be valid Python identifiers. Incompatible names are automatically sanitized similarly to existing SavedModel signature behavior.

  std::set<std::string> attr_names;
};

TF_AttrBuilder* TF_NewAttrBuilder(const char* op_name) {
  return new TF_AttrBuilder(op_name);
}

void TF_DeleteAttrBuilder(TF_AttrBuilder* builder) { delete builder; }

void TF_AttrBuilderSetType(TF_AttrBuilder* builder, const char* attr_name,
                           TF_DataType value) {
  auto iter = builder->attr_names.insert(attr_name).first;

        }
        # See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/toolchains/remote_config/configs.bzl
        # This is a mapping of name_container_map keys under sigbuild_tf_configs
        # to tag names on gcr.io/tensorflow-sigs/build.
        # TF 2.9
        map sigbuild-r2.9 2.9-python3.9
        map sigbuild-r2.9-python3.8 2.9-python3.8
        map sigbuild-r2.9-python3.9 2.9-python3.9

        False,
        bazel_config_name='tensorrt')

    environ_save = dict(environ_cp)
    for _ in range(_DEFAULT_PROMPT_ASK_ATTEMPTS):

      if validate_cuda_config(environ_cp):
        cuda_env_names = [
            'TF_CUDA_VERSION',
            'TF_CUBLAS_VERSION',
            'TF_CUDNN_VERSION',
            'TF_TENSORRT_VERSION',
            'TF_NCCL_VERSION',
            'TF_CUDA_PATHS',

    data = [
        ":test_op1.so",
        "//tensorflow/cc/saved_model:saved_model_half_plus_two",
    ],
    linkopts = select({
        "//tensorflow:macos": ["-headerpad_max_install_names"],
        "//conditions:default": [],
    }),
    tags = [
        "no_cuda_asan",  # TODO(b/181771536)
        "no_windows",  # TODO(b/155444728)
    ],