code: def parse_qconv2dbatchnorm_layer(
      keras_layer, input_names, input_shapes, data_reader
  ):
      intermediate_shape = list()
      conv_layer, shape_qconv = parse_qconv_layer(
          keras_layer, input_names, input_shapes, data_reader
      )
      intermediate_shape.append(shape_qconv)
      temp_shape = intermediate_shape
      batch_layer, out_shape = parse_batchnorm_layer(
          keras_layer, input_names, temp_shape, data_reader
      )
      return {**conv_layer, **batch_layer}, out_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 8 for calling a global function with example usage: parse_qconv2dbatchnorm_layer(keras_layer, input_names, input_shapes, data_reader) and returns: dict,  out_shape