code: def __init__(
      self,
      low_channels: int,
      high_channels: int,
      num_classes: int,
      inter_channels: int,
  ) -> None:
      super().__init__()
      self.cbr = nn.Sequential(
          nn.Conv2d(high_channels, inter_channels, 1, bias=False),
          nn.BatchNorm2d(inter_channels),
          nn.ReLU(inplace=True),
      )
      self.scale = nn.Sequential(
          nn.AdaptiveAvgPool2d(1),
          nn.Conv2d(high_channels, inter_channels, 1, bias=False),
          nn.Sigmoid(),
      )
      self.low_classifier = nn.Conv2d(low_channels, num_classes, 1)
      self.high_classifier = nn.Conv2d(inter_channels, num_classes, 1)
  
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 LRASPPHead object from inherited base classes: nn with example: obj = LRASPPHead(low_channels, high_channels, num_classes, inter_channels)