class_docstr: The GINet implementation based on PaddlePaddle.
  The original article refers to
  Wu, Tianyi, Yu Lu, Yu Zhu, Chuang Zhang, Ming Wu, Zhanyu Ma, and Guodong Guo. "GINet: Graph interaction network for scene parsing." In European Conference on Computer Vision, pp. 34-51. Springer, Cham, 2020.
  (https://arxiv.org/pdf/2009.06160).
  Args:
      num_classes (int): The unique number of target classes.
      backbone (Paddle.nn.Layer): Backbone network.
      backbone_indices (tuple, optional): Values in the tuple indicate the indices of output of backbone.
      enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss.
          If true, auxiliary loss will be added after LearningToDownsample module. Default: False.
      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.
      jpu (bool, optional)): whether to use jpu unit in the base forward. Default:True.
      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
  from paddle.nn import functional
  from paddleseg.utils import utils
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager
label_desc: Use image node_id 4 for calling the GINet obj's underlying member method code with example usage: obj.init_weight() without return types