code: def smape_loss(
      self, forecast: torch.Tensor, future_target: torch.Tensor
  ) -> torch.Tensor:
      denominator = (
          torch.abs(future_target) + torch.abs(forecast)
      ).detach()
      flag = denominator == 0
  
      return (200 / self.prediction_length) * torch.mean(
          (
              torch.abs(future_target - forecast)
              * torch.logical_not(flag)
          )
          / (denominator + flag),
          dim=1,
      )
  
imports: from typing import List, Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from gluonts.time_feature import get_seasonality
label_desc: Use image node_id 3 for calling the NBEATSNetwork obj's underlying member method code with example usage: obj.smape_loss(forecast, future_target) and returns: unknown