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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_model(self, opt_fn, emb_sz, n_hid, n_layers, **kwargs):
 """ Method returns a RNN_Learner object, that wraps an instance of the RNN_Encoder module. Args: opt_fn (Optimizer):
 the torch optimizer function to use emb_sz (int):
 embedding size n_hid (int):
 number of hidden inputs n_layers (int):
 number of hidden layers kwargs: other arguments Returns: An instance of the RNN_Learner class. """

        m = get_language_model(self.nt, emb_sz, n_hid, n_layers, self.pad_idx, **kwargs)
        model = SingleModel(to_gpu(m))
        return RNN_Learner(self, model, opt_fn=opt_fn)