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): number of output classes of the model (including the background).
      inter_channels (int, optional): the number of channels for intermediate computations.
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code: def forward(self, x: Tensor) -> Dict[str, Tensor]:
      input_shape = x.shape[-2:]
      features = self.backbone(x)
      out = self.classifier(features)
      out = F.interpolate(
          out, size=input_shape, mode="bilinear", align_corners=False
      )
  
      result = OrderedDict()
      result["out"] = out
  
      return result
  
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 LRASPP obj's underlying member method code with example usage: obj.forward(x) and returns: result