code: def __init__(
      self,
      low_channels: int,
      high_channels: int,
      num_classes: int,
      inter_channels: int,
  ) -> None:
      super(LRASPPHead, self).__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)
  
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 1 to create a new LRASPPHead object from inherited base classes: nn with example: obj = LRASPPHead(low_channels, high_channels, num_classes, inter_channels)