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_fpn(
      self, embed_dim=768, patch_size=16, out_with_norm=False
  ):
      if patch_size == 16:
          self.fpn1 = nn.Sequential(
              nn.Conv2DTranspose(
                  embed_dim, embed_dim, kernel_size=2, stride=2
              ),
              nn.SyncBatchNorm(embed_dim, momentum=0.1),
              nn.GELU(),
              nn.Conv2DTranspose(
                  embed_dim, embed_dim, kernel_size=2, stride=2
              ),
          )
  
          self.fpn2 = nn.Sequential(
              nn.Conv2DTranspose(
                  embed_dim, embed_dim, kernel_size=2, stride=2
              ),
          )
  
          self.fpn3 = Identity()
  
          self.fpn4 = nn.MaxPool2D(kernel_size=2, stride=2)
      elif patch_size == 8:
          self.fpn1 = nn.Sequential(
              nn.Conv2DTranspose(
                  embed_dim, embed_dim, kernel_size=2, stride=2
              ),
          )
  
          self.fpn2 = Identity()
  
          self.fpn3 = nn.Sequential(
              nn.MaxPool2D(kernel_size=2, stride=2),
          )
  
          self.fpn4 = nn.Sequential(
              nn.MaxPool2D(kernel_size=4, stride=4),
          )
  
      if not out_with_norm:
          self.norm = Identity()
      else:
          self.norm = nn.LayerNorm(embed_dim, epsilon=1e-6)
  
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 2 for calling the UPerNetCAE obj's underlying member method code with example usage: obj._init_fpn(embed_dim, patch_size, out_with_norm) without return types