class_docstr: The UNet++ implementation based on PaddlePaddle.
  
  The original article refers to
  Zongwei Zhou, et, al. "UNet++: A Nested U-Net Architecture for Medical Image Segmentation"
  (https://arxiv.org/abs/1807.10165).
  
  Args:
      num_classes (int): The unique number of target classes.
      in_channels (int, optional): The channel number of input image. Default: 3.
      use_deconv (bool, optional): A bool value indicates whether using deconvolution in upsampling.
          If False, use resize_bilinear. Default: False.
      align_corners (bool): An argument of F.interpolate. It should be set to False when the output size of feature
          is even, e.g. 1024x512, otherwise it is True, e.g. 769x769.  Default: False.
      pretrained (str, optional): The path or url of pretrained model for fine tuning. Default: None.
      is_ds (bool): use deep supervision or not. Default: True
      
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code: def init_weight(self):
      if self.pretrained is not None:
          load_entire_model(self, self.pretrained)
      else:
          for sublayer in self.sublayers():
              if isinstance(sublayer, nn.Conv2D):
                  kaiming_normal_init(sublayer.weight)
              elif isinstance(
                  sublayer, (nn.BatchNorm, nn.SyncBatchNorm)
              ):
                  kaiming_normal_init(sublayer.weight)
  
file_path: PaddleSeg/paddleseg/models/unet_plusplus.py
imports: import paddle
  import paddle.nn
  from paddleseg.cvlibs import manager
  from paddleseg.utils import load_entire_model
  from paddleseg.cvlibs.param_init import kaiming_normal_init
  from paddleseg.models.layers.layer_libs import SyncBatchNorm
label_desc: Use image node_id 2 for calling the UNetPlusPlus obj's underlying member method code with example usage: obj.init_weight() without return types