class_docstr: Base class for pruning algorithms.
  
  End users should use ``compress`` and callback APIs (WIP) to prune their models.
  
  The underlying model is instrumented upon initialization of pruner object.
  So if you want to pre-train the model, train it before creating pruner object.
  
  The compressed model can only execute in eager mode.
  
  Algorithm developers should override ``calc_masks`` method to specify pruning strategy.
  
  Parameters
  ----------
  model : tf.keras.Model
      The user model to prune.
  config_list : list of JSON object
      User configuration. The format is detailed in tutorial.
class_docstr_tok: ["Base","class","for","pruning","algorithms",".","End","users","should","use","``","compress","``","and","callback","APIs","(","WIP",")","to","prune","their","models",".","The","underlying","model","is","instrumented","upon","initialization","of","pruner","object",".","So","if","you","want","to","pre-train","the","model",",","train","it","before","creating","pruner","object",".","The","compressed","model","can","only","execute","in","eager","mode",".","Algorithm","developers","should","override","``","calc_masks","``","method","to","specify","pruning","strategy",".","Parameters","--","--","--","--","--","model",":","tf.keras.Model","The","user","model","to","prune",".","config_list",":","list","of","JSON","object","User","configuration",".","The","format","is","detailed","in","tutorial","."]
code: def _update_mask(self):
      for wrapper_idx, wrapper in enumerate(self.wrappers):
          masks = self.calc_masks(wrapper, wrapper_idx=wrapper_idx)
          if masks is not None:
              wrapper.masks = masks
  
file_path: auptimizer/src/aup/compression/tensorflow/compressor.py
imports: import logging
  from .None import default_layers
label_desc: Use image node_id 4 for calling the Pruner obj's underlying member method code with example usage: obj._update_mask() without return types