code: def get(self):
      training_set_metadata_fp = self.cache_map[META]
      if not path_exists(training_set_metadata_fp):
          return None
  
      try:
          cached_training_set_metadata = data_utils.load_json(
              training_set_metadata_fp
          )
      except Exception:
          logger.exception(
              f"Failed to load cached training set metadata at {training_set_metadata_fp}"
          )
          return None
  
      cached_training_set = (
          self.cache_map[TRAINING]
          if path_exists(self.cache_map[TRAINING])
          else None
      )
      if not cached_training_set:
          logger.warning(
              f"Failed to load cached training set at {self.cache_map[TRAINING]}"
          )
  
      cached_validation_set = (
          self.cache_map[VALIDATION]
          if path_exists(self.cache_map[VALIDATION])
          else None
      )
      if not cached_validation_set:
          logger.warning(
              f"Failed to load cached validation set at {self.cache_map[VALIDATION]}"
          )
  
      cached_test_set = (
          self.cache_map[TEST]
          if path_exists(self.cache_map[TEST])
          else None
      )
      if not cached_test_set:
          logger.warning(
              f"Failed to load cached test set at {self.cache_map[TEST]}"
          )
  
      valid = (
          self.checksum == cached_training_set_metadata.get(CHECKSUM)
          and cached_training_set is not None
      )
  
      return (
          valid,
          cached_training_set_metadata,
          cached_training_set,
          cached_test_set,
          cached_validation_set,
      )
  
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 2 for calling the DatasetCache obj's underlying member method code with example usage: obj.get() and returns: valid,  cached_training_set_metadata,  cached_training_set,  cached_test_set,  cached_validation_set, None, None