class_docstr: PrunePreserveQuantizeRegistry responsible for built-in keras layers.
class_docstr_tok: ["PrunePreserveQuantizeRegistry","responsible","for","built-in","keras","layers","."]
docstr: Gets class name of supported quantize config for layer.
  
  Args:
    layer: instance of keras layer
  
  Returns:
    List of supported quantize config class name.
docstr_tok: ["Gets","class","name","of","supported","quantize","config","for","layer",".","Args",":","layer",":","instance","of","keras","layer","Returns",":","List","of","supported","quantize","config","class","name","."]
code: def get_suppport_quantize_config_names(cls, layer):
      """Gets class name of supported quantize config for layer.
  
      Args:
        layer: instance of keras layer
  
      Returns:
        List of supported quantize config class name.
      """
  
      # layers without trainable weights don't need quantize_config for pqat
      if cls._no_trainable_weights(layer):
          return []
  
      return cls._LAYERS_CONFIG_MAP[
          layer.__class__
      ].quantize_config_attrs
  
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 7 for calling the PrunePreserveQuantizeRegistry obj's underlying member method code with example usage: obj.get_suppport_quantize_config_names(cls, layer) and returns: cls, list