code: def load_data_and_model(model_file):
      r"""Load filtered dataset, split dataloaders and saved model.
  
      Args:
          model_file (str): The path of saved model file.
  
      Returns:
          tuple:
              - config (Config): An instance object of Config, which record parameter information in :attr:`model_file`.
              - model (AbstractRecommender): The model load from :attr:`model_file`.
              - dataset (Dataset): The filtered dataset.
              - train_data (AbstractDataLoader): The dataloader for training.
              - valid_data (AbstractDataLoader): The dataloader for validation.
              - test_data (AbstractDataLoader): The dataloader for testing.
      """
      import torch
  
      checkpoint = torch.load(model_file)
      config = checkpoint["config"]
      init_seed(config["seed"], config["reproducibility"])
      init_logger(config)
      logger = getLogger()
      logger.info(config)
  
      dataset = create_dataset(config)
      logger.info(dataset)
      train_data, valid_data, test_data = data_preparation(
          config, dataset
      )
  
      init_seed(config["seed"], config["reproducibility"])
      model = get_model(config["model"])(
          config, train_data._dataset
      ).to(config["device"])
      model.load_state_dict(checkpoint["state_dict"])
      model.load_other_parameter(checkpoint.get("other_parameter"))
  
      return config, model, dataset, train_data, valid_data, test_data
  
file_path: RecBole/recbole/quick_start/quick_start.py
imports: import logging
  import sys
  import torch.distributed
  from collections.abc import MutableMapping
  from logging import getLogger
  from ray import tune
  from recbole.config import Config
  from recbole.data import create_dataset, data_preparation
  from recbole.data.transform import construct_transform
  from recbole.utils import init_logger, get_model, get_trainer, init_seed, set_color, get_flops, get_environment
label_desc: Use image node_id 5 for calling a global function with example usage: load_data_and_model(model_file) and returns: config,  model,  dataset,  train_data,  valid_data,  test_data