code: def forward(self, backcast):
      backcast = backcast[..., 0]  # can only use the target
      forecast = 0
      for stack_id in range(len(self.stacks)):
          for block_id in range(len(self.stacks[stack_id])):
              b, f = self.stacks[stack_id][block_id](backcast)
              backcast = backcast - b
              forecast = forecast + f
      return torch.unsqueeze(forecast, 2)
  
imports: import pickle
  import random
  from time import time
  from typing import Union
  import numpy
  import torch
  from torch import nn, optim
  from torch.nn import functional
  from torch.nn.functional import mse_loss, l1_loss, binary_cross_entropy, cross_entropy
  from torch.optim import Optimizer
  from .utils import PYTORCH_REGRESSION_LOSS_MAP
  from pytorch_lightning import seed_everything
label_desc: Use image node_id 4 for calling the NBeatsNet obj's underlying member method code with example usage: obj.forward(backcast) and returns: torch