code: def _load_state_dict_into_model(self, state_dict):
      # Convert old format to new format if needed from a PyTorch state_dict
      # copy state_dict so _load_from_state_dict can modify it
      state_dict = state_dict.copy()
      error_msgs = []
  
      # PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants
      # so we need to apply the function recursively.
      def load(module: torch.nn.Module, prefix=""):
          args = (state_dict, prefix, {}, True, [], [], error_msgs)
          module._load_from_state_dict(*args)
  
          for name, child in module._modules.items():
              if child is not None:
                  load(child, prefix + name + ".")
  
      load(self)
  
      return error_msgs
  
imports: from contextlib import contextmanager
  import pytorch_lightning
  import torch
  import torch.nn.functional
  from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.model import Decoder, Encoder
  from nemo.collections.multimodal.modules.stable_diffusion.distributions.distributions import DiagonalGaussianDistribution
  from nemo.collections.multimodal.parts.stable_diffusion.utils import instantiate_from_config
label_desc: Use image node_id 4 for calling the AutoencoderKL obj's underlying member method code with example usage: obj._load_state_dict_into_model(state_dict) and returns: error_msgs