class_docstr: The DANet implementation based on PaddlePaddle.
  
  The original article refers to
  Fu, jun, et al. "Dual Attention Network for Scene Segmentation"
  (https://arxiv.org/pdf/1809.02983.pdf)
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone (Paddle.nn.Layer): A backbone network.
      backbone_indices (tuple): The values in the tuple indicate the indices of
          output of backbone.
      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.  Default: False.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
code: def forward(self, x):
      feats = self.backbone(x)
      feats = [feats[i] for i in self.backbone_indices]
      logit_list = self.head(feats)
      if not self.training:
          logit_list = [logit_list[0]]
  
      logit_list = [
          F.interpolate(
              logit,
              paddle.shape(x)[2:],
              mode="bilinear",
              align_corners=self.align_corners,
              align_mode=1,
          )
          for logit in logit_list
      ]
      return logit_list
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 2 for calling the DANet obj's underlying member method code with example usage: obj.forward(x) and returns: logit_list