class_docstr: PrunePreserveQuantizeRegistry responsible for built-in keras layers.
class_docstr_tok: ["PrunePreserveQuantizeRegistry","responsible","for","built-in","keras","layers","."]
code: def __init__(self):
      self._config_quantizer_map = {
          "Default8BitQuantizeConfig": PrunePreserveDefault8BitWeightsQuantizer(),
          "Default8BitConvQuantizeConfig": PrunePreserveDefault8BitConvWeightsQuantizer(),
      }
  
imports: import tensorflow
  from tensorflow_model_optimization.python.core.quantization.keras import quant_ops
  from tensorflow_model_optimization.python.core.quantization.keras import quantizers
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantize_registry
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantizers
label_desc: Use image node_id 1 to create a new PrunePreserveQuantizeRegistry object  with example: obj = PrunePreserveQuantizeRegistry()