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__(
      self,
      num_classes,
      backbone,
      backbone_indices=[0, 1, 2, 3],
      enable_auxiliary_loss=True,
      align_corners=True,
      jpu=True,
      pretrained=None,
  ):
      super().__init__()
      self.nclass = num_classes
      self.aux = enable_auxiliary_loss
      self.jpu = jpu
  
      self.backbone = backbone
      self.backbone_indices = backbone_indices
      self.align_corners = align_corners
  
      self.jpu = (
          layers.JPU([512, 1024, 2048], width=512) if jpu else None
      )
      self.head = GIHead(in_channels=2048, nclass=num_classes)
  
      if self.aux:
          self.auxlayer = layers.AuxLayer(
              1024, 1024 // 4, num_classes, bias_attr=False
          )
  
      self.pretrained = pretrained
      self.init_weight()
  
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 1 to create a new GINet object from inherited base classes: nn with example: obj = GINet(num_classes, backbone, backbone_indices, enable_auxiliary_loss, align_corners, jpu, pretrained)