class_docstr: PrunePreserveQuantizeRegistry responsible for built-in keras layers.
class_docstr_tok: ["PrunePreserveQuantizeRegistry","responsible","for","built-in","keras","layers","."]
docstr: Gets sparsity preservable weights from keras layer.
  
  Args:
    layer: instance of keras layer
  
  Returns:
    List of sparsity preservable weights
docstr_tok: ["Gets","sparsity","preservable","weights","from","keras","layer",".","Args",":","layer",":","instance","of","keras","layer","Returns",":","List","of","sparsity","preservable","weights"]
code: def get_sparsity_preservable_weights(cls, layer):
      """Gets sparsity preservable weights from keras layer.
  
      Args:
        layer: instance of keras layer
  
      Returns:
        List of sparsity preservable weights
      """
      return [
          getattr(layer, weight) for weight in cls._weight_names(layer)
      ]
  
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 6 for calling the PrunePreserveQuantizeRegistry obj's underlying member method code with example usage: obj.get_sparsity_preservable_weights(cls, layer) and returns: unknown