class_docstr: @inproceedings{Yulitehrnet21,
  title={Lite-HRNet: A Lightweight High-Resolution Network},
      author={Yu, Changqian and Xiao, Bin and Gao, Changxin and Yuan, Lu and Zhang, Lei and Sang, Nong and Wang, Jingdong},
      booktitle={CVPR},year={2021}
  }
  
  Args:
      network_type (str): the network_type should be one of ["lite_18", "lite_30", "naive", "wider_naive"],
          "naive": Simply combining the shuffle block in ShuffleNet and the highresolution design pattern in HRNet.
          "wider_naive": Naive network with wider channels in each block.
          "lite_18": Lite-HRNet-18, which replaces the pointwise convolution in a shuffle block by conditional channel weighting.
          "lite_30": Lite-HRNet-30, with more blocks compared with Lite-HRNet-18.
      in_channels (int, optional): The channels of input image. Default: 3.
      freeze_at (int): the stage to freeze
      freeze_norm (bool): whether to freeze norm in HRNet
      norm_decay (float): weight decay for normalization layer weights
      return_idx (List): the stage to return
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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
  from numbers import Integral
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from paddle.nn.initializer import Normal, Constant
  from paddleseg.cvlibs import manager
  from paddleseg import utils
label_desc: Use image node_id 2 for calling the LiteHRNet obj's underlying member method code with example usage: obj.init_weight() without return types