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".
      num_classes(int, optional): class number for image classification. Default: 1000.
      dropout(float,optional): dropout ratio. if >0,use dropout ratio.  Default: 0.20.
      use_conv_last(bool,optional): whether to use the last ConvBNReLU layer . Default: False.
      pretrained(str, optional): the path of pretrained model.
code: def _make_layers(self, base, layers, block_num, block):
      features = []
      features += [ConvBNRelu(3, base // 2, 3, 2)]
      features += [ConvBNRelu(base // 2, base, 3, 2)]
  
      for i, layer in enumerate(layers):
          for j in range(layer):
              if i == 0 and j == 0:
                  features.append(block(base, base * 4, block_num, 2))
              elif j == 0:
                  features.append(
                      block(
                          base * int(math.pow(2, i + 1)),
                          base * int(math.pow(2, i + 2)),
                          block_num,
                          2,
                      )
                  )
              else:
                  features.append(
                      block(
                          base * int(math.pow(2, i + 2)),
                          base * int(math.pow(2, i + 2)),
                          block_num,
                          1,
                      )
                  )
  
      return nn.Sequential(*features)
  
imports: import math
  import paddle
  import paddle.nn
  import stdc1_seg_voc.layers
label_desc: Use image node_id 3 for calling the STDCNet obj's underlying member method code with example usage: obj._make_layers(base, layers, block_num, block) and returns: nn