class_docstr: The DecoupledSegNetHead implementation based on PaddlePaddle.
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone_indices (tuple): Two values in the tuple indicate the indices of output of backbone.
          the first index will be taken as a low-level feature in Edge presevation component;
          the second one will be taken as input of ASPP component.
      backbone_channels (tuple): The channels of output of backbone.
      aspp_ratios (tuple): The dilation rates using in ASSP module.
      aspp_out_channels (int): The output channels of ASPP module.
      align_corners (bool): An argument of F.interpolate. It should be set to False when the output size of feature
          is even, e.g. 1024x512, otherwise it is True, e.g. 769x769.
code: def __init__(
      self,
      num_classes,
      backbone_indices,
      backbone_channels,
      aspp_ratios,
      aspp_out_channels,
      align_corners,
  ):
      super().__init__()
      self.backbone_indices = backbone_indices
      self.align_corners = align_corners
      self.aspp = layers.ASPPModule(
          aspp_ratios=aspp_ratios,
          in_channels=backbone_channels[backbone_indices[1]],
          out_channels=aspp_out_channels,
          align_corners=align_corners,
          image_pooling=True,
      )
  
      self.bot_fine = nn.Conv2D(
          backbone_channels[backbone_indices[0]], 48, 1, bias_attr=False
      )
      # decoupled
      self.squeeze_body_edge = SqueezeBodyEdge(
          256, align_corners=self.align_corners
      )
      self.edge_fusion = nn.Conv2D(256 + 48, 256, 1, bias_attr=False)
      self.sigmoid_edge = nn.Sigmoid()
      self.edge_out = nn.Sequential(
          layers.ConvBNReLU(
              in_channels=256,
              out_channels=48,
              kernel_size=3,
              bias_attr=False,
          ),
          nn.Conv2D(48, 1, 1, bias_attr=False),
      )
      self.dsn_seg_body = nn.Sequential(
          layers.ConvBNReLU(
              in_channels=256,
              out_channels=256,
              kernel_size=3,
              bias_attr=False,
          ),
          nn.Conv2D(256, num_classes, 1, bias_attr=False),
      )
  
      self.final_seg = nn.Sequential(
          layers.ConvBNReLU(
              in_channels=512,
              out_channels=256,
              kernel_size=3,
              bias_attr=False,
          ),
          layers.ConvBNReLU(
              in_channels=256,
              out_channels=256,
              kernel_size=3,
              bias_attr=False,
          ),
          nn.Conv2D(256, num_classes, kernel_size=1, bias_attr=False),
      )
  
imports: import cv2
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.models.backbones import resnet_vd
  from paddleseg.models import deeplab
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new DecoupledSegNetHead object from inherited base classes: nn with example: obj = DecoupledSegNetHead(num_classes, backbone_indices, backbone_channels, aspp_ratios, aspp_out_channels, align_corners)