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 __init__(
      self,
      backbone: nn.Module,
      low_channels: int,
      high_channels: int,
      num_classes: int,
      inter_channels: int = 128,
  ) -> None:
      super().__init__()
      _log_api_usage_once(self)
      self.backbone = backbone
      self.classifier = LRASPPHead(
          low_channels, high_channels, num_classes, inter_channels
      )
  
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 1 to create a new LRASPP object from inherited base classes: nn with example: obj = LRASPP(backbone, low_channels, high_channels, num_classes, inter_channels)