code: def forward(self, x):
      gp = self.gp_branch(x)
      gp = gp.expand_as(x)
  
      conv_branches = [conv(x) for conv in self.conv_branches]
      return torch.cat(conv_branches + [gp], dim=1)
  
imports: from typing import List
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
  from ..dsconv import DepthwiseSeparableConv2d
  from ..interfaces import AbstractDecoder, FeatureMapsSpecification
label_desc: Use image node_id 2 for calling the CFM obj's underlying member method code with example usage: obj.forward(x) and returns: torch