code: def test_sub_nested_model(randX_20_15, backend, io_type):
      n_in = 15
      input_shape = (n_in,)
      keras_model = make_sub_nested_model(input_shape)
      keras_model.compile(optimizer="adam", loss="mae")
  
      config = hls4ml.utils.config_from_keras_model(
          keras_model, default_precision="fixed<24,12>"
      )
      prj_name = f"hls4mlprj_sub_nested_model_{backend}_{io_type}"
      output_dir = str(test_root_path / prj_name)
      hls_model = hls4ml.converters.convert_from_keras_model(
          keras_model,
          hls_config=config,
          output_dir=output_dir,
          io_type=io_type,
          backend=backend,
      )
      hls_model.compile()
  
      X = randX_20_15
      y_keras = keras_model.predict(X)
      y_hls4ml = hls_model.predict(X)
  
      np.testing.assert_allclose(
          y_keras.ravel(), y_hls4ml.ravel(), rtol=1e-2, atol=0.02
      )
  
file_path: hls4ml/test/pytest/test_keras_nested_model.py
imports: from pathlib import Path
  import numpy
  import pytest
  from tensorflow.keras.layers import Dense, Input
  from tensorflow.keras.models import Model, Sequential
  import hls4ml
label_desc: Use image node_id 6 for calling a global function with example usage: test_sub_nested_model(randX_20_15, backend, io_type) without return types