code: def forward(self, x):
      x = self.squeeze_activation(self.squeeze(x))
      return torch.cat(
          [
              self.expand1x1_activation(self.expand1x1(x)),
              self.expand3x3_activation(self.expand3x3(x)),
          ],
          1,
      )
  
imports: from collections import OrderedDict
  import math
  import re
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils import model_zoo
  from torchvision.models.densenet import DenseNet
  from torchvision.models.squeezenet import squeezenet1_1
label_desc: Use image node_id 2 for calling the Fire obj's underlying member method code with example usage: obj.forward(x) and returns: torch