class_docstr: PrunePreserveQuantizeRegistry responsible for built-in keras layers.
class_docstr_tok: ["PrunePreserveQuantizeRegistry","responsible","for","built-in","keras","layers","."]
docstr: Returns whether the registry supports this layer type.
  
  Args:
    layer: The layer to check for support.
  
  Returns:
    True/False whether the layer type is supported.
docstr_tok: ["Returns","whether","the","registry","supports","this","layer","type",".","Args",":","layer",":","The","layer","to","check","for","support",".","Returns",":","True\/False","whether","the","layer","type","is","supported","."]
code: def supports(cls, layer):
      """Returns whether the registry supports this layer type.
  
      Args:
        layer: The layer to check for support.
  
      Returns:
        True/False whether the layer type is supported.
      """
  
      # layers without trainable weights are considered supported,
      # e.g., ReLU, Softmax, and AveragePooling2D.
      if cls._no_trainable_weights(layer):
          return True
  
      if layer.__class__ in cls._LAYERS_CONFIG_MAP:
          return True
  
      return False
  
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 4 for calling the PrunePreserveQuantizeRegistry obj's underlying member method code with example usage: obj.supports(cls, layer) and returns: False, True, True