code: def forward(self, x):
      bs = x.size(0)
      prev = cur = self.stem(x)
      aux_logits = None
  
      for layer in self.layers:
          if isinstance(layer, AuxiliaryHead):
              if self.training:
                  aux_logits = layer(cur)
          else:
              prev, cur = layer(prev, cur)
  
      cur = self.gap(F.relu(cur)).view(bs, -1)
      cur = self.dropout(cur)
      logits = self.dense(cur)
  
      if aux_logits is not None:
          return logits, aux_logits
      return logits
  
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 MicroNetwork obj's underlying member method code with example usage: obj.forward(x) and returns: logits, logits,  aux_logits