code: def parse_qbatchnorm_layer(
      keras_layer, input_names, input_shapes, data_reader
  ):
      layer, output_shape = parse_batchnorm_layer(
          keras_layer, input_names, input_shapes, data_reader
      )
  
      layer["mean_quantizer"] = get_quantizer_from_config(
          keras_layer, "mean"
      )
      layer["variance_quantizer"] = get_quantizer_from_config(
          keras_layer, "variance"
      )
      layer["beta_quantizer"] = get_quantizer_from_config(
          keras_layer, "beta"
      )
      layer["gamma_quantizer"] = get_quantizer_from_config(
          keras_layer, "gamma"
      )
  
      return layer, output_shape
  
file_path: hls4ml/hls4ml/converters/keras/qkeras.py
imports: from qkeras.quantizers import get_quantizer
  from hls4ml.converters.keras.convolution import parse_conv1d_layer, parse_conv2d_layer
  from hls4ml.converters.keras.core import parse_batchnorm_layer, parse_dense_layer
  from hls4ml.converters.keras_to_hls import keras_handler, parse_default_keras_layer
  from hls4ml.model.types import FixedPrecisionType, QKerasBinaryQuantizer, QKerasPO2Quantizer, QKerasQuantizer
label_desc: Use image node_id 7 for calling a global function with example usage: parse_qbatchnorm_layer(keras_layer, input_names, input_shapes, data_reader) and returns: layer,  output_shape