code: def forward(self, inputs: Dict[str, Tensor]) -> Tensor:
      low = inputs["low"]
      high = inputs["high"]
  
      x = self.cbr(high)
      s = self.scale(high)
      x = x * s
      x = F.interpolate(
          x, size=low.shape[-2:], mode="bilinear", align_corners=False
      )
  
      return self.low_classifier(low) + self.high_classifier(x)
  
file_path: deep-learning-for-image-processing/pytorch_segmentation/lraspp/src/lraspp_model.py
imports: from collections import OrderedDict
  from typing import Dict
  import torch
  from torch import nn, Tensor
  from torch.nn import functional
  from .mobilenet_backbone import mobilenet_v3_large
label_desc: Use image node_id 2 for calling the LRASPPHead obj's underlying member method code with example usage: obj.forward(inputs) and returns: unknown