code: def forward(self, fmaps: list[Tensor]) -> list[Tensor]:
      projected_maps = [
          proj(fmap) for proj, fmap in zip(self.input_proj, fmaps)
      ]
      projected_maps[-1] = self.aifi(projected_maps[-1])
      new_fmaps = self.ccfm(projected_maps)
      return new_fmaps
  
imports: from __future__ import annotations
  from typing import Optional
  import torch
  import torch.nn.functional
  from torch import nn
  from torch.nn.utils.fusion import fuse_conv_bn_weights
  from kornia.contrib.models.common import ConvNormAct
  from kornia.core import Module, Tensor, concatenate, pad
label_desc: Use image node_id 2 for calling the HybridEncoder obj's underlying member method code with example usage: obj.forward(fmaps) and returns: new_fmaps