code: def forward(self, x1: torch.Tensor, x2: torch.Tensor) -> torch.Tensor:
      x1 = self.up(x1)
      # [N, C, H, W]
      diff_y = x2.size()[2] - x1.size()[2]
      diff_x = x2.size()[3] - x1.size()[3]
  
      # padding_left, padding_right, padding_top, padding_bottom
      x1 = F.pad(
          x1,
          [
              diff_x // 2,
              diff_x - diff_x // 2,
              diff_y // 2,
              diff_y - diff_y // 2,
          ],
      )
  
      x = torch.cat([x2, x1], dim=1)
      x = self.conv(x)
      return x
  
imports: from typing import Dict
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the Up obj's underlying member method code with example usage: obj.forward(x1, x2) and returns: x