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.
      backbone(nn.Layer): Backbone network, STDCNet1446/STDCNet813. STDCNet1446->STDC2,STDCNet813->STDC813.
      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.
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code: def init_weight(self):
      if self.pretrained is not None:
          utils.load_entire_model(self, self.pretrained)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg import utils
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils
label_desc: Use image node_id 3 for calling the STDCSeg obj's underlying member method code with example usage: obj.init_weight() without return types