code: def test_multi_clone(model, data, backend: str, io_type: str):
      output_dir = str(
          test_root_path
          / f"hls4mlprj_multiout_network_{backend}_{io_type}"
      )
      hls_config = {
          "Model": {"Precision": "fixed<32,5>", "ReuseFactor": 1}
      }
      layer_config = {
          "dense1": {"Precision": {"result": "fixed<35,5>"}},
          "dense2": {"Precision": {"result": "fixed<40,5>"}},
          "dense1_linear": {"Precision": {"result": "fixed<35,5>"}},
          "dense2_linear": {"Precision": {"result": "fixed<40,5>"}},
      }
      hls_config["LayerName"] = layer_config
      model_hls = convert_from_keras_model(
          model,
          backend=backend,
          output_dir=output_dir,
          hls_config=hls_config,
          io_type=io_type,
      )
  
      assert (
          model_hls.graph["dense1"].attributes["result_t"]
          != model_hls.graph["dense2"].attributes["result_t"]
      )
  
      model_hls.compile()
      r_hls = model_hls.predict(data)
      r_keras = [x.numpy() for x in model(data)]
  
      assert np.allclose(r_hls[0], r_keras[0], atol=1e-5, rtol=0)
      assert np.allclose(r_hls[1], r_keras[1], atol=1e-5, rtol=0)
  
file_path: hls4ml/test/pytest/test_multiout_network.py
imports: from pathlib import Path
  import numpy
  import pytest
  from keras.layers import Dense
  from tensorflow import keras
  from hls4ml.converters import convert_from_keras_model
label_desc: Use image node_id 3 for calling a global function with example usage: test_multi_clone(model, data, backend, io_type) without return types