code: def smooth_l1_loss(
      input, target, beta: float = 1.0 / 9, size_average: bool = True
  ):
      """
      very similar to the smooth_l1_loss from pytorch, but with
      the extra beta parameter
      """
      n = torch.abs(input - target)
      # cond = n < beta
      cond = torch.lt(n, beta)
      loss = torch.where(cond, 0.5 * n**2 / beta, n - 0.5 * beta)
      if size_average:
          return loss.mean()
      return loss.sum()
  
imports: import torch
  import math
  from typing import List, Tuple
  from torch import Tensor
label_desc: Use image node_id 2 for calling a global function with example usage: smooth_l1_loss(input, target, beta, size_average) and returns: loss, loss