class_docstr: Common base class for all compressors.
  
  This class is designed for other base classes.
  Algorithms should inherit ``Pruner`` or ``Quantizer`` instead.
  
  Attributes
  ----------
  compressed_model : tf.keras.Model
      Compressed user model.
  wrappers : list of tf.keras.Model
      A wrapper is an instrumented TF ``Layer``, in ``Model`` format.
  
  Parameters
  ----------
  model : tf.keras.Model
      The user model to be compressed.
  config_list : list of JSON object
      User configuration. The format is detailed in tutorial.
  LayerWrapperClass : a class derive from Model
      The class used to instrument layers.
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code: def _instrument_sequential(self, seq):
      layers = list(seq.layers)  # seq.layers is read-only property
      need_rebuild = False
      for i, layer in enumerate(layers):
          new_layer = self._instrument(layer)
          if new_layer is not layer:
              layers[i] = new_layer
              need_rebuild = True
      return tf.keras.Sequential(layers) if need_rebuild else seq
  
file_path: auptimizer/src/aup/compression/tensorflow/compressor.py
imports: import logging
  from .None import default_layers
label_desc: Use image node_id 5 for calling the Compressor obj's underlying member method code with example usage: obj._instrument_sequential(seq) and returns: unknown