class_docstr: The KNet implementation based on PaddlePaddle.
  
  The original article refers to
  Wenwei Zhang, et, al. "K-Net: Towards Unified Image Segmentation"
  (https://arxiv.org/abs/2106.14855).
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone (paddle.nn.Layer): Backbone network.
      backbone_indices (tuple): Four values in the tuple indicate the indices of output of backbone.
      kernel_update_head_params (dict): The params to build KernelUpdateHead.
      kernel_generate_head_params (dict): The params to build KernelGenerateHead.
      num_stages (int, optional): The num of KernelUpdateHead. Default: 3
      channels (int, optional): The channels of intermediate layers. Default: 512.
      enable_auxiliary_loss (bool, optional): A bool value that indicates whether or not to add auxiliary loss. Default: False.
      align_corners (bool, optional): An argument of "F.interpolate". It should be set to False when the feature size is even,
          e.g. 1024x512, otherwise it is True, e.g. 769x769. Default: False.
      dropout_prob (float, optional): Dropout ratio for KNet model. Default: 0.1.
      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.cvlibs import manager
  from paddleseg.models import layers
  from .upernet import UPerNetHead
label_desc: Use image node_id 3 for calling the KNet obj's underlying member method code with example usage: obj.init_weight() without return types