class_docstr: The PP_LiteSeg implementation based on PaddlePaddle.
  
  The original article refers to "Juncai Peng, Yi Liu, Shiyu Tang, Yuying Hao, Lutao Chu,
  Guowei Chen, Zewu Wu, Zeyu Chen, Zhiliang Yu, Yuning Du, Qingqing Dang,Baohua Lai,
  Qiwen Liu, Xiaoguang Hu, Dianhai Yu, Yanjun Ma. PP-LiteSeg: A Superior Real-Time Semantic
  Segmentation Model. https://arxiv.org/abs/2204.02681".
  
  Args:
      num_classes (int): The number of target classes.
      backbone(nn.Layer): Backbone network, such as stdc1net and resnet18. The backbone must
          has feat_channels, of which the length is 5.
      backbone_indices (List(int), optional): The values indicate the indices of output of backbone.
          Default: [2, 3, 4].
      arm_type (str, optional): The type of attention refinement module. Default: ARM_Add_SpAttenAdd3.
      cm_bin_sizes (List(int), optional): The bin size of context module. Default: [1,2,4].
      cm_out_ch (int, optional): The output channel of the last context module. Default: 128.
      arm_out_chs (List(int), optional): The out channels of each arm module. Default: [64, 96, 128].
      seg_head_inter_chs (List(int), optional): The intermediate channels of segmentation head.
          Default: [64, 64, 64].
      resize_mode (str, optional): The resize mode for the upsampling operation in decoder.
          Default: bilinear.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
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 PPLiteSeg obj's underlying member method code with example usage: obj.init_weight() without return types