code: def load_checkpoint(cls, model_path):
      return torch.load(model_path)
  
imports: import argparse
  import os
  import time
  import numpy
  import torch
  import torch.backends.cudnn
  import torch.nn
  import torch.optim
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  from torch.utils.data import DataLoader
  from graph4nlp.pytorch.datasets.cnn import CNNDataset
  from graph4nlp.pytorch.models.graph2seq import Graph2Seq
  from graph4nlp.pytorch.models.graph2seq_loss import Graph2SeqLoss
  from graph4nlp.pytorch.modules.config import get_basic_args
  from graph4nlp.pytorch.modules.evaluation.rouge import ROUGE
  from graph4nlp.pytorch.modules.graph_embedding_initialization.embedding_construction import WordEmbedding
  from graph4nlp.pytorch.modules.utils import constants
  from graph4nlp.pytorch.modules.utils.config_utils import get_yaml_config, update_values
  from graph4nlp.pytorch.modules.utils.copy_utils import prepare_ext_vocab
  from graph4nlp.pytorch.modules.utils.generic_utils import EarlyStopping, to_cuda
  from graph4nlp.pytorch.modules.utils.logger import Logger
  from graph4nlp.pytorch.modules.utils.summarization_utils import wordid2str
label_desc: Use image node_id 5 for calling the SumModel obj's underlying member method code with example usage: obj.load_checkpoint(cls, model_path) and returns: torch