class_docstr: Disentangled Non-Local Neural Networks.
  
  The original article refers to
  Minghao Yin, et al. "Disentangled Non-Local Neural Networks"
  (https://arxiv.org/abs/2006.06668)
  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.
      reduction (int): Reduction factor of projection transform. Default: 2.
      use_scale (bool): Whether to scale pairwise_weight by
          sqrt(1/inter_channels). Default: False.
      mode (str): The nonlocal mode. Options are 'embedded_gaussian',
          'dot_product'. Default: 'embedded_gaussian'.
      temperature (float): Temperature to adjust attention. Default: 0.05.
      concat_input (bool): Whether concat the input and output of convs before classification layer. Default: True
      enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: True.
      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.
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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.models import layers
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils
label_desc: Use image node_id 3 for calling the DNLNet obj's underlying member method code with example usage: obj.init_weight() without return types