class_docstr: The Token Pyramid Transformer(TopFormer) implementation based on PaddlePaddle.
  
  The original article refers to
  Zhang, Wenqiang, Zilong Huang, Guozhong Luo, Tao Chen, Xinggang Wang, Wenyu Liu, Gang Yu,
  and Chunhua Shen. "TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation." 
  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,
  pp. 12083-12093. 2022.
  
  This model refers to https://github.com/hustvl/TopFormer.
  
  Args:
      num_classes(int,optional): The unique number of target classes.
      backbone(nn.Layer): Backbone network.
      head_use_dw (bool, optional): Whether the head use depthwise convolutions. Default: False.
      align_corners (bool, optional): Set the align_corners in resizing. Default: False.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
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code: def forward(self, x):
      x_hw = paddle.shape(x)[2:]
      x = self.backbone(x)  # len=3, 1/8,1/16,1/32
      x = self.decode_head(x)
      x = F.interpolate(
          x, x_hw, mode="bilinear", align_corners=self.align_corners
      )
  
      return [x]
  
file_path: PaddleSeg/paddleseg/models/topformer.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.top_transformer import ConvBNAct
label_desc: Use image node_id 3 for calling the TopFormer obj's underlying member method code with example usage: obj.forward(x) and returns: list