class_docstr: The STDCNet 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:
      base(int, optional): base channels. Default: 64.
      layers(list, optional): layers numbers list. It determines STDC block numbers of STDCNet's stage3. Defualt: [4, 5, 3].
      block_num(int,optional): block_num of features block. Default: 4.
      type(str,optional): feature fusion method "cat"/"add". Default: "cat".
      pretrained(str, optional): the path of pretrained model.
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code: def init_weight(self):
      for layer in self.sublayers():
          if isinstance(layer, nn.Conv2D):
              param_init.normal_init(layer.weight, std=0.001)
          elif isinstance(layer, (nn.BatchNorm, nn.SyncBatchNorm)):
              param_init.constant_init(layer.weight, value=1.0)
              param_init.constant_init(layer.bias, value=0.0)
      if self.pretrained is not None:
          utils.load_pretrained_model(self, self.pretrained)
  
imports: import math
  import paddle
  import paddle.nn
  from paddleseg.utils import utils
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models.layers.layer_libs import SyncBatchNorm
label_desc: Use image node_id 4 for calling the STDCNet obj's underlying member method code with example usage: obj.init_weight() without return types