class_docstr: The SegFormer implementation based on PaddlePaddle.
  
  The original article refers to
  Xie, Enze, et al. "SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers." arXiv preprint arXiv:2105.15203 (2021).
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone (Paddle.nn.Layer): A backbone network.
      embedding_dim (int): The MLP decoder channel dimension.
      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. Default: None.
code: def init_weight(self):
      if self.pretrained is not None:
          utils.load_entire_model(self, self.pretrained)
  
file_path: PaddleSeg/paddleseg/models/segformer.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  import numpy
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 2 for calling the SegFormer obj's underlying member method code with example usage: obj.init_weight() without return types