class_docstr: ClusterPreserveQuantizeRegistry is 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.
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 consider 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 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 4 for calling the ClusterPreserveQuantizeRegistry obj's underlying member method code with example usage: obj.supports(cls, layer) and returns: False, True, True