docstr: "unfreeze" module, i.e. make the module parameters(weight/bias, if exists)
  to be trained(updated) in training process.
  If 'names' is a non-empty list, unfreeze layers that match given names
  
  :param names: list of module names to be unFreezed. Default is None.
  :return: current graph model
docstr_tok: ["``","unfreeze","''","module",",","i.e",".","make","the","module","parameters","(","weight\/bias",",","if","exists",")","to","be","trained","(","updated",")","in","training","process",".","If","'names","'","is","a","non-empty","list",",","unfreeze","layers","that","match","given","names",":","param","names",":","list","of","module","names","to","be","unFreezed",".","Default","is","None",".",":","return",":","current","graph","model"]
code: def unfreeze(self, names=None):
      """
      "unfreeze" module, i.e. make the module parameters(weight/bias, if exists)
      to be trained(updated) in training process.
      If 'names' is a non-empty list, unfreeze layers that match given names
  
      :param names: list of module names to be unFreezed. Default is None.
      :return: current graph model
      """
      callZooFunc(self.bigdl_type, "unFreeze", self.value, names)
  
file_path: BigDL/python/dllib/src/bigdl/dllib/net/graph_net.py
imports: import sys
  from bigdl.dllib.nn.layer import Model
  from bigdl.dllib.feature.image import ImageSet
  from bigdl.dllib.feature.text import TextSet
  from bigdl.dllib.keras.base import ZooKerasLayer
  from bigdl.dllib.keras.utils import *
  from bigdl.dllib.nn.layer import Layer
  from bigdl.dllib.utils.file_utils import callZooFunc
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 8 for calling the GraphNet obj's underlying member method code with example usage: obj.unfreeze(names) without return types