code: def get_layer_opt(self, lrs, wds):
  
          
          return LayerOptimizer(self.opt_fn, self.get_layer_groups(), lrs, wds)
docs: Method returns an instance of the LayerOptimizer class, which
          allows for setting differential learning rates for different
          parts of the model.
  
          An example of how a model maybe differentiated into different parts
          for application of differential learning rates and weight decays is
          seen in ../.../courses/dl1/fastai/conv_learner.py, using the dict
          'model_meta'. Currently, this seems supported only for convolutional
          networks such as VGG-19, ResNet-XX etc.
  
          Args:
              lrs (float or list(float)): learning rate(s) for the model
  
              wds (float or list(float)): weight decay parameter(s).
  
          Returns:
              An instance of a LayerOptimizer