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 init_weight(self):
      if self.pretrained is not None:
          utils.load_entire_model(self, self.pretrained)
  
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 3 for calling the DANet obj's underlying member method code with example usage: obj.init_weight() without return types