code: def load_pretrained_weights(self):
      pretrained = ResNetBackbone(
          backbone=self.backbone_name,
          pretrained_base=True,
          norm_layer=self.backbone_norm_layer,
          **self._kwargs,
      )
      backbone_state_dict = self.backbone.state_dict()
      pretrained_state_dict = pretrained.state_dict()
  
      backbone_state_dict.update(pretrained_state_dict)
      self.backbone.load_state_dict(backbone_state_dict)
  
      if self.inference_mode:
          for param in self.backbone.parameters():
              param.requires_grad = False
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from contextlib import ExitStack
  from .resnet import ResNetBackbone
  from .basic_blocks import select_activation_function
  from .basic_blocks import SeparableConv2d
label_desc: Use image node_id 2 for calling the DeepLabV3Plus obj's underlying member method code with example usage: obj.load_pretrained_weights() without return types