code: def forward(self, pprev, prev):
      pprev_, prev_ = self.preproc0(pprev), self.preproc1(prev)
  
      prev_nodes_out = [pprev_, prev_]
      nodes_used_mask = torch.zeros(
          self.num_nodes + 2, dtype=torch.bool, device=prev.device
      )
      for i in range(self.num_nodes):
          node_out, mask = self.nodes[i](prev_nodes_out)
          nodes_used_mask[: mask.size(0)] |= mask.to(node_out.device)
          prev_nodes_out.append(node_out)
  
      unused_nodes = torch.cat(
          [
              out
              for used, out in zip(nodes_used_mask, prev_nodes_out)
              if not used
          ],
          1,
      )
      unused_nodes = F.relu(unused_nodes)
      conv_weight = self.final_conv_w[:, ~nodes_used_mask, :, :, :]
      conv_weight = conv_weight.view(conv_weight.size(0), -1, 1, 1)
      out = F.conv2d(unused_nodes, conv_weight)
      return prev, self.bn(out)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from nni.nas.pytorch import mutables
  from ops import FactorizedReduce, StdConv, SepConvBN, Pool
label_desc: Use image node_id 3 for calling the ENASLayer obj's underlying member method code with example usage: obj.forward(pprev, prev) and returns: prev,  self