class_docstr: PrunePreserveQuantizeRegistry responsible for built-in keras layers.
class_docstr_tok: ["PrunePreserveQuantizeRegistry","responsible","for","built-in","keras","layers","."]
docstr: Returns whether disable this layer for prune preserve.
  
  Args:
    layer: The layer to check for disable.
  
  Returns:
    True/False whether disable this layer for prune preserve.
docstr_tok: ["Returns","whether","disable","this","layer","for","prune","preserve",".","Args",":","layer",":","The","layer","to","check","for","disable",".","Returns",":","True\/False","whether","disable","this","layer","for","prune","preserve","."]
code: def _disable_prune_preserve(cls, layer):
      """Returns whether disable this layer for prune preserve.
  
      Args:
        layer: The layer to check for disable.
  
      Returns:
        True/False whether disable this layer for prune preserve.
      """
  
      return layer.__class__ in cls._DISABLE_PRUNE_PRESERVE
  
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 3 for calling the PrunePreserveQuantizeRegistry obj's underlying member method code with example usage: obj._disable_prune_preserve(cls, layer) and returns: unknown