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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 lr_find(self, start_lr=1e-5, end_lr=10, wds=None, linear=False, **kwargs):
 """Helps you find an optimal learning rate for a model. It uses the technique developed in the 2015 paper `Cyclical Learning Rates for Training Neural Networks`, where we simply keep increasing the learning rate from a very small value, until the loss starts decreasing. Args: start_lr (float/numpy array) : Passing in a numpy array allows you to specify learning rates for a learner's layer_groups end_lr (float) : The maximum learning rate to try. wds (iterable/float) Examples: As training moves us closer to the optimal weights for a model, the optimal learning rate will be smaller. We can take advantage of that knowledge and provide lr_find() with a starting learning rate 1000x smaller than the model's current learning rate as such: >> learn.lr_find(lr/1000) >> lrs = np.array([ 1e-4, 1e-3, 1e-2 ]) >> learn.lr_find(lrs / 1000) Notes: lr_find() may finish before going through each batch of examples if the loss decreases enough. .. _Cyclical Learning Rates for Training Neural Networks: http://arxiv.org/abs/1506.01186 """

        self.save('tmp')
        layer_opt = self.get_layer_opt(start_lr, wds)
        self.sched = LR_Finder(layer_opt, len(self.data.trn_dl), end_lr, linear=linear)
        self.fit_gen(self.model, self.data, layer_opt, 1, **kwargs)
        self.load('tmp')