class_docstr: Registry 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.
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.
  
      """
      # Automatically enable layers with zero trainable weights.
      # Example: Reshape, AveragePooling2D, Maximum/Minimum, etc.
      if not layer.trainable_weights and not isinstance(
          layer, layers.RNN
      ):
          return True
  
      if layer.__class__ in cls._LAYERS_WEIGHTS_MAP:
          return True
  
      if layer.__class__ in cls._SUPPORTED_RNN_CELLS:
          return True
  
      if layer.__class__ in cls._SUPPORTED_RNN_LAYERS:
          for cell in cls._get_rnn_cells(layer):
              if (
                  cell.__class__ not in cls._SUPPORTED_RNN_CELLS
                  or isinstance(
                      cell, clusterable_layer.ClusterableLayer
                  )
              ):
                  return False
          return True
  
      if layer.__class__ in cls._SUPPORTED_MHA_LAYERS:
          return True
  
      return False
  
file_path: model-optimization/tensorflow_model_optimization/python/core/clustering/keras/clustering_registry.py
imports: import tensorflow
  from tensorflow_model_optimization.python.core.clustering.keras import clusterable_layer
  from tensorflow_model_optimization.python.core.clustering.keras import clustering_algorithm
label_desc: Use image node_id 1 for calling the ClusteringRegistry obj's underlying member method code with example usage: obj.supports(cls, layer) and returns: False, True, True, True, True, True, False