class_docstr: ClusterPreserveQuantizeRegistry is for built-in keras layers.
code: def _weight_names(cls, layer):
      if cls._no_trainable_weights(layer):
          return []
  
      return cls._LAYERS_CONFIG_MAP[layer.__class__].weight_attrs
  
imports: import logging
  import warnings
  import tensorflow
  from tensorflow_model_optimization.python.core.clustering.keras import cluster_config
  from tensorflow_model_optimization.python.core.clustering.keras import clustering_registry
  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 5 for calling the ClusterPreserveQuantizeRegistry obj's underlying member method code with example usage: obj._weight_names(cls, layer) and returns: cls, list