class_docstr: The GloRe implementation based on PaddlePaddle.
  
  The original article refers to:
     Chen, Yunpeng, et al. "Graph-Based Global Reasoning Networks"
     (https://arxiv.org/pdf/1811.12814.pdf)
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone (Paddle.nn.Layer): Backbone network, currently support Resnet50/101.
      backbone_indices (tuple, optional): Two values in the tuple indicate the indices of output of backbone.
      gru_channels (int, optional): The number of input channels in GloRe Unit. Default: 512.
      gru_num_state (int, optional): The number of states in GloRe Unit. Default: 128.
      gru_num_node (tuple, optional): The number of nodes in GloRe Unit. Default: Default: 128.
      enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: True.
      align_corners (bool, optional): An argument of F.interpolate. It should be set to False when the feature size 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.
code: def forward(self, x):
      feat_list = self.backbone(x)
      logit_list = self.head(feat_list)
      return [
          F.interpolate(
              logit,
              paddle.shape(x)[2:],
              mode="bilinear",
              align_corners=self.align_corners,
          )
          for logit in logit_list
      ]
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 2 for calling the GloRe obj's underlying member method code with example usage: obj.forward(x) and returns: unknown