code: def forward(self, *inputs):
      feats = [
          self.conv5(inputs[-1]),
          self.conv4(inputs[-2]),
          self.conv3(inputs[-3]),
      ]
      size = feats[-1].size()[2:]
      feats[-2] = F.interpolate(
          feats[-2], size, mode="bilinear", align_corners=True
      )
      feats[-3] = F.interpolate(
          feats[-3], size, mode="bilinear", align_corners=True
      )
      feat = torch.cat(feats, dim=1)
      feat = torch.cat(
          [
              self.dilation1(feat),
              self.dilation2(feat),
              self.dilation3(feat),
              self.dilation4(feat),
          ],
          dim=1,
      )
  
      return inputs[0], inputs[1], inputs[2], feat
  
file_path: pywick/pywick/models/segmentation/da_basenets/jpu.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the JPU obj's underlying member method code with example usage: obj.forward() and returns: inputs,  inputs,  inputs,  feat