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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 smooth_l1_loss(input, target, beta=1. / 9, size_average=True):
 """ very similar to the smooth_l1_loss from pytorch, but with the extra beta parameter """

    n = torch.abs(input - target)
    cond = n < beta
    loss = torch.where(cond, 0.5 * n ** 2 / beta, n - 0.5 * beta)
    if size_average:
        return loss.mean()
    return loss.sum()