class_docstr: Implements a Lite R-ASPP Network for semantic segmentation from
  `"Searching for MobileNetV3"
  <https://arxiv.org/abs/1905.02244>`_.
  
  Args:
      backbone (nn.Module): the network used to compute the features for the model.
          The backbone should return an OrderedDict[Tensor], with the key being
          "high" for the high level feature map and "low" for the low level feature map.
      low_channels (int): the number of channels of the low level features.
      high_channels (int): the number of channels of the high level features.
      num_classes (int, optional): number of output classes of the model (including the background).
      inter_channels (int, optional): the number of channels for intermediate computations.
code: def forward(self, input: Tensor) -> Dict[str, Tensor]:
      features = self.backbone(input)
      out = self.classifier(features)
      out = F.interpolate(
          out,
          size=input.shape[-2:],
          mode="bilinear",
          align_corners=False,
      )
  
      result = OrderedDict()
      result["out"] = out
  
      return result
  
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 LRASPP obj's underlying member method code with example usage: obj.forward(input) and returns: result