code: def truncated_laplace(mean, T, truncate_by_zero=False):
      """Generating a sample from a Laplace distribution, possible left-truncated at zero.
      A bit of explanation here https://stats.stackexchange.com/a/357598 .
      """
      assert isinstance(mean, torch.Tensor)
  
      if not truncate_by_zero:
          percentile = 0.0
      else:
          if not (mean >= 0.0).all():
              warnings.warn(
                  f"means are supposed to be non-negative, but got {mean}"
              )
              mean = torch.clamp_min(mean, 0.0)
  
          lower_bound = mean.new_tensor([0.0])
          percentile = 0.5 + 0.5 * torch.sign(lower_bound - mean) * (
              1.0 - torch.exp(-1.0 / T * torch.abs(mean - lower_bound))
          )
  
      p = (
          torch.empty_like(mean).uniform_() * (1.0 - percentile)
          + percentile
      )
      return mean - T * torch.sign(p - 0.5) * torch.log(
          1 - 2 * torch.abs(p - 0.5)
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: truncated_laplace(mean, T, truncate_by_zero) and returns: unknown