code: def forward(self, image, mask, backbone_features=None):
      x = torch.cat((image, mask), dim=1)
      intermediates = self.encoder(x, backbone_features)
      output = self.decoder(intermediates, image, mask)
      return {"images": output}
  
file_path: carefree-learn/cflearn/api/cv/third_party/iharm/model/base/iseunet_v1.py
imports: import torch
  import torch.nn
  from ..ops import MaskedChannelAttention
  from ..modeling.unet import UNetDecoder
  from ..modeling.unet import UNetEncoder
label_desc: Use image node_id 2 for calling the ISEUNetV1 obj's underlying member method code with example usage: obj.forward(image, mask, backbone_features) and returns: dict