class_docstr: ClusterPreserveQuantizeRegistry is for built-in keras layers.
docstr: Returns whether this layer has trainable weights.
  
  Args:
    layer: The layer to check for trainable weights.
  Returns:
    True/False whether the layer has trainable weights.
code: def _no_trainable_weights(cls, layer):
      """Returns whether this layer has trainable weights.
  
      Args:
        layer: The layer to check for trainable weights.
      Returns:
        True/False whether the layer has trainable weights.
      """
      return not layer.trainable_weights
  
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 2 for calling the ClusterPreserveQuantizeRegistry obj's underlying member method code with example usage: obj._no_trainable_weights(cls, layer) and returns: unknown