class_docstr: The HRNet implementation based on PaddlePaddle.
  
  The original article refers to
  Jingdong Wang, et, al. "HRNet：Deep High-Resolution Representation Learning for Visual Recognition"
  (https://arxiv.org/pdf/1908.07919.pdf).
  
  Args:
      pretrained (str, optional): The path of pretrained model.
      stage1_num_modules (int, optional): Number of modules for stage1. Default 1.
      stage1_num_blocks (list, optional): Number of blocks per module for stage1. Default (4).
      stage1_num_channels (list, optional): Number of channels per branch for stage1. Default (64).
      stage2_num_modules (int, optional): Number of modules for stage2. Default 1.
      stage2_num_blocks (list, optional): Number of blocks per module for stage2. Default (4, 4).
      stage2_num_channels (list, optional): Number of channels per branch for stage2. Default (18, 36).
      stage3_num_modules (int, optional): Number of modules for stage3. Default 4.
      stage3_num_blocks (list, optional): Number of blocks per module for stage3. Default (4, 4, 4).
      stage3_num_channels (list, optional): Number of channels per branch for stage3. Default [18, 36, 72).
      stage4_num_modules (int, optional): Number of modules for stage4. Default 3.
      stage4_num_blocks (list, optional): Number of blocks per module for stage4. Default (4, 4, 4, 4).
      stage4_num_channels (list, optional): Number of channels per branch for stage4. Default (18, 36, 72. 144).
      has_se (bool, optional): Whether to use Squeeze-and-Excitation module. 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.
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code: def forward(self, x: paddle.Tensor) -> paddle.Tensor:
      feat_list = []
      conv1 = self.conv_layer1_1(x)
      feat_list.append(conv1)
      conv2 = self.conv_layer1_2(conv1)
  
      la1 = self.la1(conv2)
  
      tr1 = self.tr1([la1])
      st2 = self.st2(tr1)
  
      tr2 = self.tr2(st2)
      st3 = self.st3(tr2)
  
      tr3 = self.tr3(st3)
      st4 = self.st4(tr3)
  
      feat_list = feat_list + st4
  
      return feat_list
  
imports: import math
  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 HRNet obj's underlying member method code with example usage: obj.forward(x) and returns: feat_list