class_docstr: The SegNet implementation based on PaddlePaddle.
  The original article refers to
  Badrinarayanan, Vijay, et al. "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation"
  (https://arxiv.org/pdf/1511.00561.pdf).
  Args:
      num_classes (int): The unique number of target classes.
class_docstr_tok: ["The","SegNet","implementation","based","on","PaddlePaddle",".","The","original","article","refers","to","Badrinarayanan",",","Vijay",",","et","al",".","``","SegNet",":","A","Deep","Convolutional","Encoder-Decoder","Architecture","for","Image","Segmentation","''","(","https",":","\/\/arxiv.org\/pdf\/1511.00561.pdf",")",".","Args",":","num_classes","(","int",")",":","The","unique","number","of","target","classes","."]
code: def init_weight(self):
      if self.pretrained is not None:
          utils.load_entire_model(self, self.pretrained)
  
file_path: PaddleSeg/paddleseg/models/segnet.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 2 for calling the SegNet obj's underlying member method code with example usage: obj.init_weight() without return types