class_docstr: The semantic segmentation models for mobile devices.
  
  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].
      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_type (str, optional): The type of attention refinement module. Default: ARM_Add_SpAttenAdd3.
      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.
      use_last_fuse (bool, optional): Whether use fusion in the last. Default: False.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
class_docstr_tok: ["The","semantic","segmentation","models","for","mobile","devices",".","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","]",".","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_type","(","str",",","optional",")",":","The","type","of","attention","refinement","module",".","Default",":","ARM_Add_SpAttenAdd3",".","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",".","use_last_fuse","(","bool",",","optional",")",":","Whether","use","fusion","in","the","last",".","Default",":","False",".","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
label_desc: Use image node_id 3 for calling the MobileSeg obj's underlying member method code with example usage: obj.init_weight() without return types