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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def get_layer_opt(self, lrs, wds):
 """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 '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 """

        return LayerOptimizer(self.opt_fn, self.get_layer_groups(), lrs, wds)