class_docstr: The UPerNet  with CAE as backbone implementation with PaddlePaddle.
  
  The original paper refers to Unified Perceptual Parsing for Scene Understanding.
  (https://arxiv.org/abs/1807.10221)
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone (Paddle.nn.Layer): Backbone network, currently support Resnet50/101.
      backbone_indices (tuple): Four values in the tuple indicate the indices of output of backbone.
      channels(int): Hidden layer channels of upernet head.
      fpn_channels(int): The fpn_channels of upernet head.
      head_channels(int): The inplane of upernet head.
      channels_fpn(int): The channels_fpn of upernet head.
      enable_auxiliary_loss (bool, optional): A bool value indicates whether adding 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_ratio(float): The dropout ratio of upernet head.
      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.utils import utils
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.models.backbones.transformer_utils import Identity
label_desc: Use image node_id 4 for calling the UPerNetCAE obj's underlying member method code with example usage: obj.init_weight() without return types