code: def get_quantizer_from_config(keras_layer, quantizer_var):
      quantizer_config = keras_layer["config"][
          f"{quantizer_var}_quantizer"
      ]
      if keras_layer["class_name"] == "QBatchNormalization":
          return QKerasQuantizer(quantizer_config)
      elif "binary" in quantizer_config["class_name"]:
          return QKerasBinaryQuantizer(
              quantizer_config, xnor=(quantizer_var == "kernel")
          )
      elif quantizer_config["class_name"] == "quantized_po2":
          return QKerasPO2Quantizer(quantizer_config)
      else:
          return QKerasQuantizer(quantizer_config)
  
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 1 for calling a global function with example usage: get_quantizer_from_config(keras_layer, quantizer_var) and returns: QKerasQuantizer, QKerasBinaryQuantizer, QKerasPO2Quantizer, QKerasQuantizer