code: def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:
      input_shape = x.shape[-2:]
      backbone_out = self.backbone(x)
      x = self.up1(backbone_out["stage4"], backbone_out["stage3"])
      x = self.up2(x, backbone_out["stage2"])
      x = self.up3(x, backbone_out["stage1"])
      x = self.up4(x, backbone_out["stage0"])
      x = self.conv(x)
      x = F.interpolate(
          x, size=input_shape, mode="bilinear", align_corners=False
      )
  
      return {"out": x}
  
file_path: deep-learning-for-image-processing/pytorch_segmentation/unet/src/mobilenet_unet.py
imports: from collections import OrderedDict
  from typing import Dict
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from torchvision.models import mobilenet_v3_large
  from .unet import Up, OutConv
label_desc: Use image node_id 2 for calling the MobileV3Unet obj's underlying member method code with example usage: obj.forward(x) and returns: dict