code: def forward(self, encoder, decoder):
      decoder = F.interpolate(
          decoder,
          size=encoder.size()[2:],
          mode="bilinear",
          align_corners=False,
      )
      x = torch.cat([encoder, decoder], dim=1)
      x = self.conv_bn_relu(x)
      x = F.adaptive_avg_pool2d(x, 1) * x
      return encoder + x
  
imports: from typing import List
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
  from ..dsconv import DepthwiseSeparableConv2d
  from ..interfaces import AbstractDecoder, FeatureMapsSpecification
label_desc: Use image node_id 2 for calling the AMM obj's underlying member method code with example usage: obj.forward(encoder, decoder) and returns: unknown