code: def _load_checkpoint(self, checkpoint):
      # load pretrain model from checkpoint
      state_dict = torch.load(
          checkpoint, map_location=torch.device("cpu")
      )
      state_dict = state_dict["state_dict"]
      s = {}
      for k in state_dict:
          if "model." in k:
              s[k.replace("model.", "")] = state_dict[k]
      self.model.load_state_dict(s)
  
imports: import math
  from typing import Optional, Tuple
  import torch
  import torch.nn.functional
  import torchaudio
  import torchaudio.models.wav2vec2.components
  from dataset import _get_lengths_librilightlimited, _get_lengths_librispeech, BucketizeBatchSampler, CollateFnHubert, CollateFnLibriLightLimited, DistributedBatchSampler, HuBERTDataSet
  from lightning.pytorch import LightningModule
  from loss import hubert_loss
  from torch import Tensor
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
label_desc: Use image node_id 2 for calling the HuBERTFineTuneModule obj's underlying member method code with example usage: obj._load_checkpoint(checkpoint) without return types