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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(learn:Learner, start_lr:Floats=1e-7, end_lr:Floats=10, num_it:int=100, stop_div:bool=True, wd:float=None):
    "Explore lr from `start_lr` to `end_lr` over `num_it` iterations in `learn`. If `stop_div`, stops when loss diverges."
    start_lr = learn.lr_range(start_lr)
    start_lr = np.array(start_lr) if is_listy(start_lr) else start_lr
    end_lr = learn.lr_range(end_lr)
    end_lr = np.array(end_lr) if is_listy(end_lr) else end_lr
    cb = LRFinder(learn, start_lr, end_lr, num_it, stop_div)
    epochs = int(np.ceil(num_it/len(learn.data.train_dl)))
    learn.fit(epochs, start_lr, callbacks=[cb], wd=wd)