code: def load_checkpoint(self, ckpt: str):
      state = torch.load(str(ckpt))
      for i, model in enumerate(self.model_list):
          state_dict = state[i]
          if self.use_multi_gpu:
              newer_state_dict = OrderedDict()
              for key, val in state_dict.items():
                  assert not key.startswith("module.")
                  new_key = "module." + key
                  newer_state_dict[new_key] = val
              model.load_state_dict(newer_state_dict)
          else:
              model.load_state_dict(state_dict)
          i += 1
  
imports: from collections import OrderedDict
  import os
  import numpy
  import torch
  from torch import nn
  from gluoncv.torch.utils.coot_utils import truncated_normal_fill
label_desc: Use image node_id 9 for calling the MultiModalTransformer obj's underlying member method code with example usage: obj.load_checkpoint(ckpt) without return types