code: def forward(self, past_target: torch.Tensor):
      if len(self.net_blocks) == 1:
          _, forecast = self.net_blocks[0](past_target)
          return forecast
      else:
          backcast, forecast = self.net_blocks[0](past_target)
          backcast = past_target - backcast
          for i in range(1, len(self.net_blocks) - 1):
              b, f = self.net_blocks[i](backcast)
              backcast = backcast - b
              forecast = forecast + f
          _, last_forecast = self.net_blocks[-1](backcast)
          return forecast + last_forecast
  
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 2 for calling the NBEATSNetwork obj's underlying member method code with example usage: obj.forward(past_target) and returns: forecast, unknown