code: def trainable_weights(self):
      # Change the order to keep the weight order after applying QAT.
      return self._dedup_weights(
          self._trainable_weights + self.layer.trainable_weights
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tensorflow.python.util import tf_inspect
  from tensorflow_model_optimization.python.core.keras import metrics
  from tensorflow_model_optimization.python.core.keras import utils
  from tensorflow_model_optimization.python.core.quantization.keras import quantize_aware_activation
  from tensorflow_model_optimization.python.core.quantization.keras import utils
label_desc: Use image node_id 2 for calling the QuantizeWrapperV2 obj's underlying member method code with example usage: obj.trainable_weights() and returns: self