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 forward(self, x):
      _, _, h, w = paddle.shape(x)
      _, _, c3, c4 = self.base_forward(x)
  
      logit_list = []
      x, _ = self.head(c4)
      logit_list.append(x)
  
      if self.aux:
          auxout = self.auxlayer(c3)
  
          logit_list.append(auxout)
  
      return [
          F.interpolate(
              logit,
              [h, w],
              mode="bilinear",
              align_corners=self.align_corners,
          )
          for logit in logit_list
      ]
  
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 3 for calling the GINet obj's underlying member method code with example usage: obj.forward(x) and returns: unknown