code: def forward(self, data):
      if self.use_forward:
          x, adjs = data.x, data.adj
          x = F.dropout(x, p=self.dropout, training=self.training)
  
          weights = []
          for j in range(self.steps):
              weights.append(F.softmax(self.alphas_normal[j], dim=-1))
  
          x = self.cell(x, adjs, weights)
          x = F.log_softmax(x, dim=1)
          self.current_pred = x.detach()
          return x
      else:
          # for i in self.parameters():
          #    print(i)
          x = self.prediction + self.dead_tensor * 0
          return x
  
imports: import typing
  from .None import register_nas_space
  from .base import apply_fixed_architecture
  from .base import BaseSpace
  from ...model import BaseAutoModel
  from ....utils import get_logger
  from ..backend import *
  from ..utils import count_parameters, measure_latency
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn import Module
  from .gasso_space import *
  from torch.autograd import Variable
  from collections import namedtuple
label_desc: Use image node_id 3 for calling the GassoSpace obj's underlying member method code with example usage: obj.forward(data) and returns: x, x