class_docstr: Annotates layers which quantization should be applied to.
  
  QuantizeAnnotate does not actually apply quantization to the underlying
  layers but acts as a way to specify which layers quantization should be
  applied to.
  
  The wrapper functions as a NoOp or pass-through wrapper by simply delegating
  calls to the underlying layer. The presence of this wrapper indicates to code
  which actually applies quantization to determine which layers should be
  modified.
class_docstr_tok: ["Annotates","layers","which","quantization","should","be","applied","to",".","QuantizeAnnotate","does","not","actually","apply","quantization","to","the","underlying","layers","but","acts","as","a","way","to","specify","which","layers","quantization","should","be","applied","to",".","The","wrapper","functions","as","a","NoOp","or","pass-through","wrapper","by","simply","delegating","calls","to","the","underlying","layer",".","The","presence","of","this","wrapper","indicates","to","code","which","actually","applies","quantization","to","determine","which","layers","should","be","modified","."]
code: def trainable(self, value):
      self.layer.trainable = value
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import inspect
  import tensorflow
  from tensorflow_model_optimization.python.core.quantization.keras import utils
label_desc: Use image node_id 7 for calling the QuantizeAnnotate obj's underlying member method code with example usage: obj.trainable(value) without return types