class_docstr: The PSPNet implementation based on PaddlePaddle.
  
  The original article refers to
  Zhao, Hengshuang, et al. "Pyramid scene parsing network"
  (https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhao_Pyramid_Scene_Parsing_CVPR_2017_paper.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.
      pp_out_channels (int, optional): The output channels after Pyramid Pooling Module. Default: 1024.
      bin_sizes (tuple, optional): The out size of pooled feature maps. Default: (1,2,3,6).
      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.
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code: def transform(
      self, img: Union[np.ndarray, str]
  ) -> Union[np.ndarray, str]:
      return self.transforms(img)
  
file_path: PaddleHub/modules/image/semantic_segmentation/pspnet_resnet50_voc/module.py
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 pspnet_resnet50_voc.resnet import ResNet50_vd
  import pspnet_resnet50_voc.layers
label_desc: Use image node_id 2 for calling the PSPNet obj's underlying member method code with example usage: obj.transform(img) and returns: self