code: def forward(self, x, mul_L):
      mul_L = mul_L.unsqueeze(1)
      x = x.unsqueeze(1)
      gfted = torch.matmul(mul_L, x)
      gconv_input = self.spe_seq_cell(gfted).unsqueeze(2)
      igfted = torch.matmul(gconv_input, self.weight)
      igfted = torch.sum(igfted, dim=1)
      forecast_source = torch.sigmoid(self.forecast(igfted).squeeze(1))
      forecast = self.forecast_result(forecast_source)
      if self.stack_cnt == 0:
          backcast_short = self.backcast_short_cut(x).squeeze(1)
          backcast_source = torch.sigmoid(
              self.backcast(igfted) - backcast_short
          )
      else:
          backcast_source = None
      return forecast, backcast_source
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from ..losses.pytorch import MAE
  from ..common._base_multivariate import BaseMultivariate
label_desc: Use image node_id 3 for calling the StockBlockLayer obj's underlying member method code with example usage: obj.forward(x, mul_L) and returns: forecast,  backcast_source