code: def forward(self, input: Dict[str, Tensor]) -> Tensor:
      low = input["low"]
      high = input["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)
  
imports: from collections import OrderedDict
  from functools import partial
  from typing import Any, Dict, Optional
  from torch import nn, Tensor
  from torch.nn import functional
  from ...transforms._presets import SemanticSegmentation
  from ...utils import _log_api_usage_once
  from .._api import register_model, Weights, WeightsEnum
  from .._meta import _VOC_CATEGORIES
  from .._utils import _ovewrite_value_param, handle_legacy_interface, IntermediateLayerGetter
  from ..mobilenetv3 import mobilenet_v3_large, MobileNet_V3_Large_Weights, MobileNetV3
label_desc: Use image node_id 2 for calling the LRASPPHead obj's underlying member method code with example usage: obj.forward(input) and returns: unknown