code: def forward(self, input1, input2):
      output2 = self.up(input2)
      offset = output2.size()[2] - input1.size()[2]
      padding = [offset // 2] * 4
      output1 = F.pad(input1, padding)
      output = torch.cat([output1, output2], 1)
      return self.conv(output), output2
  
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 UnetUpECRE obj's underlying member method code with example usage: obj.forward(input1, input2) and returns: self,  output2