return ret;
}

TF_CAPI_EXPORT extern int TFE_OpGetOutputLength(TFE_Op* op,
                                                const char* output_name,
                                                TF_Status* status) {
  int ret = -1;
  status->status = tensorflow::unwrap(op)->OutputLength(output_name, &ret);
  return ret;
}

void TFE_Execute(TFE_Op* op, TFE_TensorHandle** retvals, int* num_retvals,

  // Nothing should be compiled.
  EXPECT_EQ(0, clusters.size());
}

TEST(XlaCompilationTest, DeterministicClusterNames) {
  auto create_graph =
      [](absl::string_view output_name) -> std::unique_ptr<Graph> {
    std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global()));
    GraphDefBuilder builder(GraphDefBuilder::kFailImmediately);
    Tensor t(DT_FLOAT, TensorShape());

  const auto signature_def = signature_def_map.at("regress_x_to_y");

  const string input_name =
      signature_def.inputs().at(tensorflow::kRegressInputs).name();
  const string output_name =
      signature_def.outputs().at(tensorflow::kRegressOutputs).name();

  // Write {0, 1, 2, 3} as tensorflow::Example inputs.
  Tensor input(tensorflow::DT_STRING, TensorShape({4}));

    *   Added an `output_mode` argument to the `Discretization` and `Hashing`
        layers with the same semantics as other preprocessing layers. All
        categorical preprocessing layers now support `output_mode`.
    *   All preprocessing layer output will follow the compute dtype of a
        `tf.keras.mixed_precision.Policy`, unless constructed with