class_docstr: The PP_MobileSeg implementation based on PaddlePaddle.
  
  The original article refers to "Shiyu Tang, Ting Sun, Juncai Peng, Guowei Chen, Yuying Hao, 
  Manhui Lin, Zhihong Xiao, Jiangbin You, Yi Liu. PP-MobileSeg: Explore the Fast and Accurate 
  Semantic Segmentation Model on Mobile Devices. https://arxiv.org/abs/2304.05152"
  
  
  Args:
      num_classes(int): The unique number of target classes.
      backbone(nn.Layer): Backbone network.
      head_use_dw (bool, optional): Whether the head use depthwise convolutions. Default: True.
      align_corners (bool, optional): Set the align_corners in resizing. Default: False.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
      upsample (str, optional): The type of upsample module, valid for VIM is recommend to be used during inference. Default: intepolate.
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code: def init_weight(self):
      if self.pretrained is not None:
          utils.load_entire_model(self, self.pretrained)
  
file_path: PaddleSeg/paddleseg/models/pp_mobileseg.py
imports: import warnings
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
  from paddleseg.models.backbones.strideformer import ConvBNAct
label_desc: Use image node_id 2 for calling the PPMobileSeg obj's underlying member method code with example usage: obj.init_weight() without return types