class_docstr: The GINetResNet101 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_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 base_forward(self, x: paddle.Tensor) -> List[paddle.Tensor]:
      feat_list = self.backbone(x)
      c1, c2, c3, c4 = [feat_list[i] for i in self.backbone_indices]
  
      if self.jpu:
          return self.jpu(c1, c2, c3, c4)
      else:
          return c1, c2, c3, c4
  
imports: import os
  from typing import Union, List, Tuple
  import paddle
  from paddle import nn
  import paddle.nn.functional
  import numpy
  from paddlehub.module.module import moduleinfo
  import paddlehub.vision.segmentation_transforms
  from paddlehub.module.cv_module import ImageSegmentationModule
  from paddleseg.utils import utils
  from paddleseg.models import layers
  from ginet_resnet101vd_ade20k.resnet import ResNet101_vd
label_desc: Use image node_id 3 for calling the GINetResNet101 obj's underlying member method code with example usage: obj.base_forward(x) and returns: self, c1,  c2,  c3,  c4