class_docstr: The STDCSeg implementation based on PaddlePaddle.
  
  The original article refers to Meituan
  Fan, Mingyuan, et al. "Rethinking BiSeNet For Real-time Semantic Segmentation."
  (https://arxiv.org/abs/2104.13188)
  
  Args:
      num_classes(int,optional): The unique number of target classes.
      use_boundary_8(bool,non-optional): Whether to use detail loss. it should be True accroding to paper for best metric. Default: True.
      Actually,if you want to use _boundary_2/_boundary_4/_boundary_16,you should append loss function number of DetailAggregateLoss.It should work properly.
      use_conv_last(bool,optional): Determine ContextPath 's inplanes variable according to whether to use bockbone's last conv. Default: False.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
code: def transform(
      self, img: Union[np.ndarray, str]
  ) -> Union[np.ndarray, str]:
      return self.transforms(img)
  
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 stdc1_seg_cityscapes.stdcnet import STDC1
  import stdc1_seg_cityscapes.layers
label_desc: Use image node_id 2 for calling the STDCSeg obj's underlying member method code with example usage: obj.transform(img) and returns: self