code: def test_qconv2dbn(randX_100_8_8_1, backend, io_type):
      """
      Test proper handling of QConv2DBatchnorm.
      """
      X = randX_100_8_8_1
      X = (
          np.round(X * 2**10) * 2**-10
      )  # make it an exact ap_fixed<16,6>
      model = Sequential()
      model.add(
          QConv2DBatchnorm(
              4,
              kernel_size=(3, 3),
              input_shape=(8, 8, 1),
              kernel_quantizer="quantized_bits(8, 0, alpha=1)",
              kernel_initializer="ones",
              bias_quantizer="quantized_bits(8, 0, alpha=1)",
              bias_initializer="zeros",
              activation="quantized_relu(8, 0)",
          )
      )
      model.compile()
  
      config = hls4ml.utils.config_from_keras_model(
          model, granularity="name", default_precision="fixed<24,8>"
      )
      output_dir = str(
          test_root_path
          / f"hls4mlprj_qkeras_qconv2dbn_{backend}_{io_type}"
      )
      hls_model = hls4ml.converters.convert_from_keras_model(
          model,
          hls_config=config,
          output_dir=output_dir,
          backend=backend,
          io_type=io_type,
      )
      hls_model.compile()
  
      y_qkeras = model.predict(X)
      y_hls4ml = hls_model.predict(X)
  
      np.testing.assert_array_equal(
          y_qkeras, y_hls4ml.reshape(y_qkeras.shape)
      )
  
imports: import warnings
  from pathlib import Path
  import numpy
  import pytest
  from qkeras.qconv2d_batchnorm import QConv2DBatchnorm
  from qkeras.qconvolutional import QDepthwiseConv2D, QSeparableConv1D, QSeparableConv2D
  from qkeras.qlayers import QActivation, QDense
  from qkeras.quantizers import binary, quantized_bits, quantized_po2, quantized_relu, quantized_sigmoid, quantized_tanh, ternary
  from qkeras.utils import _add_supported_quantized_objects
  from sklearn.datasets import fetch_openml
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import LabelEncoder, StandardScaler
  from tensorflow.keras.layers import BatchNormalization, Input
  from tensorflow.keras.models import Model, Sequential, model_from_json
  from tensorflow.keras.utils import to_categorical
  import hls4ml
label_desc: Use image node_id 16 for calling a global function with example usage: test_qconv2dbn(randX_100_8_8_1, backend, io_type) without return types