code: def mase_loss(
      self,
      forecast: torch.Tensor,
      future_target: torch.Tensor,
      past_target: torch.Tensor,
      periodicity: int,
  ) -> torch.Tensor:
      factor = 1 / (
          self.context_length + self.prediction_length - periodicity
      )
  
      whole_target = torch.cat((past_target, future_target), dim=1)
      seasonal_error = factor * torch.mean(
          torch.abs(
              whole_target[:, periodicity:, ...]
              - whole_target[:, :-periodicity:, ...]
          ),
          dim=1,
      )
      flag = seasonal_error == 0
  
      return (
          torch.mean(torch.abs(future_target - forecast), dim=1)
          * torch.logical_not(flag)
      ) / (seasonal_error + flag)
  
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 5 for calling the NBEATSNetwork obj's underlying member method code with example usage: obj.mase_loss(forecast, future_target, past_target, periodicity) and returns: unknown