class_docstr: The implementation of segmenter with linear head based on PaddlePaddle.
  
  The original article refers to Strudel, Robin, et al. "Segmenter: Transformer
  for Semantic Segmentation." arXiv preprint arXiv:2105.05633 (2021).
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone (nn.Layer): The backbone transformer network.
      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
  import numpy
  from paddleseg.utils import utils
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models.backbones import vision_transformer, transformer_utils
label_desc: Use image node_id 2 for calling the LinearSegmenter obj's underlying member method code with example usage: obj.init_weight() without return types