code: def put(
      self,
      training_set,
      test_set,
      validation_set,
      training_set_metadata,
  ):
      logger.info(
          f"Writing preprocessed training set cache to {self.cache_map[TRAINING]}"
      )
      training_set = self.dataset_manager.save(
          self.cache_map[TRAINING],
          training_set,
          self.config,
          training_set_metadata,
          TRAINING,
      )
  
      if validation_set is not None:
          logger.info(
              f"Writing preprocessed validation set cache to {self.cache_map[VALIDATION]}"
          )
          validation_set = self.dataset_manager.save(
              self.cache_map[VALIDATION],
              validation_set,
              self.config,
              training_set_metadata,
              VALIDATION,
          )
  
      if test_set is not None:
          logger.info(
              f"Writing preprocessed test set cache to {self.cache_map[TEST]}"
          )
          test_set = self.dataset_manager.save(
              self.cache_map[TEST],
              test_set,
              self.config,
              training_set_metadata,
              TEST,
          )
  
      logger.info(
          f"Writing train set metadata to {self.cache_map[META]}"
      )
      data_utils.save_json(self.cache_map[META], training_set_metadata)
  
      return (
          training_set,
          test_set,
          validation_set,
          training_set_metadata,
      )
  
imports: import logging
  import os
  from typing import Optional
  from ludwig.constants import CHECKSUM, META, TEST, TRAINING, VALIDATION
  from ludwig.data.cache.types import alphanum, CacheableDataset
  from ludwig.data.cache.util import calculate_checksum
  from ludwig.data.dataset.base import DatasetManager
  from ludwig.utils import data_utils
  from ludwig.utils.fs_utils import delete, path_exists
label_desc: Use image node_id 3 for calling the DatasetCache obj's underlying member method code with example usage: obj.put(training_set, test_set, validation_set, training_set_metadata) and returns: training_set,  test_set,  validation_set,  training_set_metadata