In class LR_Finder, what is init_lrs, is it different from layer_opt.lr?

```
class LR_Finder(LR_Updater):
'''
Helps you find an optimal learning rate for a model, as per suggetion of 2015 CLR paper.
Learning rate is increased in linear or log scale, depending on user input, and the result of the loss funciton is retained and can be plotted later.
'''
def __init__(self, layer_opt, nb, end_lr=10, linear=False, metrics = []):
self.linear, self.stop_dv = linear, True
ratio = end_lr/layer_opt.lr
self.lr_mult = (ratio/nb) if linear else ratio**(1/nb)
super().__init__(layer_opt,metrics=metrics)
def on_train_begin(self):
super().on_train_begin()
self.best=1e9
def calc_lr(self, init_lrs):
mult = self.lr_mult*self.iteration if self.linear else self.lr_mult**self.iteration
return init_lrs * mult
def on_batch_end(self, metrics):
loss = metrics[0] if isinstance(metrics,list) else metrics
if self.stop_dv and (math.isnan(loss) or loss>self.best*4):
return True
if (loss<self.best and self.iteration>10): self.best=loss
return super().on_batch_end(metrics)
```