code: def test_narrow(data, model, narrowset, capfd):
      """
      Check that the implementation does not have leftover data.
      """
      io_type = narrowset[0]
      strategy = narrowset[1]
      conv = narrowset[2]
      X = data
  
      output_dir = str(
          test_root_path
          / f"hls4mlprj_conv1d_narrow_{io_type}_{strategy}_{conv}"
      )
  
      config = hls4ml.utils.config_from_keras_model(model)
      config["Model"]["Strategy"] = strategy
      config["Model"]["ConvImplementation"] = conv
  
      hls_model = hls4ml.converters.convert_from_keras_model(
          model,
          hls_config=config,
          io_type=io_type,
          output_dir=output_dir,
      )
      hls_model.compile()
  
      # model under test predictions and accuracy
      y_keras = model.predict(X)
      y_hls4ml = hls_model.predict(X)
  
      out, _ = capfd.readouterr()
      assert "leftover data" not in out
      np.testing.assert_allclose(
          y_keras.ravel(), y_hls4ml.ravel(), atol=0.05
      )
  
file_path: hls4ml/test/pytest/test_conv1d_narrow.py
imports: from pathlib import Path
  import numpy
  import pytest
  from tensorflow.keras.layers import Conv1D
  from tensorflow.keras.models import Sequential
  import hls4ml
label_desc: Use image node_id 3 for calling a global function with example usage: test_narrow(data, model, narrowset, capfd) without return types