code: def forward(self, inputs):
      conv1 = self.conv1(inputs)
      gcn1 = self.gcn1(conv1)
      maxpool1 = self.maxpool1(conv1)
  
      conv2 = self.conv2(maxpool1)
      gcn2 = self.gcn2(conv2)
      maxpool2 = self.maxpool2(conv2)
  
      conv3 = self.conv3(maxpool2)
      gcn3 = self.gcn3(conv3)
      maxpool3 = self.maxpool3(conv3)
  
      conv4 = self.conv4(maxpool3)
      gcn4 = self.gcn4(conv4)
      maxpool4 = self.maxpool4(conv4)
  
      center = self.center(maxpool4)
  
      up4 = self.up_concat4(gcn4, center)
      up3 = self.up_concat3(gcn3, up4)
      up2 = self.up_concat2(gcn2, up3)
      up1 = self.up_concat1(gcn1, up2)
  
      final = self.final(up1)
      return final
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from .unet_layer import UnetConv2D, UnetUpConv2D, weights_init_kaiming, ConvBNReLU
  from .ExFuseLayer import SEB, GCN, ECRE, DAP, UnetExFuseLevel
label_desc: Use image node_id 2 for calling the UnetGCNECRE_v2 obj's underlying member method code with example usage: obj.forward(inputs) and returns: final