code: def test_batchnorm(model, data, backend, io_type):
      default_precision = (
          "ac_fixed<32, 1, true>"
          if backend == "Quartus"
          else "ac_fixed<32, 1>"
      )
  
      center = model.layers[0].center
      scale = model.layers[0].scale
      config = hls4ml.utils.config_from_keras_model(
          model, default_precision=default_precision, granularity="name"
      )
      output_dir = str(
          test_root_path
          / f"hls4mlprj_batchnorm_{backend}_{io_type}_center{center}_scale{scale}"
      )
      hls_model = hls4ml.converters.convert_from_keras_model(
          model,
          backend=backend,
          hls_config=config,
          io_type=io_type,
          output_dir=output_dir,
      )
      hls_model.compile()
  
      # Predict
      y_keras = np.squeeze(model.predict(data))
      y_hls = hls_model.predict(data)
      np.testing.assert_allclose(
          y_keras, y_hls, rtol=0, atol=atol, verbose=True
      )
  
file_path: hls4ml/test/pytest/test_batchnorm.py
imports: from pathlib import Path
  import numpy
  import pytest
  from tensorflow.keras.layers import BatchNormalization
  from tensorflow.keras.models import Sequential
  import hls4ml
label_desc: Use image node_id 3 for calling a global function with example usage: test_batchnorm(model, data, backend, io_type) without return types