class_docstr: The ANN implementation based on PaddlePaddle.
  
  The original article refers to
  Zhen, Zhu, et al. "Asymmetric Non-local Neural Networks for Semantic Segmentation"
  (https://arxiv.org/pdf/1908.07678.pdf).
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone_indices (tuple, optional): Two values in the tuple indicate the indices of output of backbone.
      key_value_channels (int, optional): The key and value channels of self-attention map in both AFNB and APNB modules.
          Default: 256.
      inter_channels (int, optional): Both input and output channels of APNB modules. Default: 512.
      psp_size (tuple, optional): The out size of pooled feature maps. Default: (1, 3, 6, 8).
      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: paddle.Tensor) -> List[paddle.Tensor]:
      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 os
  from typing import Union, List, Tuple
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddlehub.module.module import moduleinfo
  import paddlehub.vision.segmentation_transforms
  from paddlehub.module.cv_module import ImageSegmentationModule
  from ann_resnet50_voc.resnet import ResNet50_vd
  import ann_resnet50_voc.layers
label_desc: Use image node_id 3 for calling the ANN obj's underlying member method code with example usage: obj.forward(x) and returns: unknown