code: def get_analysis_cache_entry_keys(
      preprocessing_fn, specs, dataset_keys, force_tf_compat_v1
  ):
      """Computes the cache entry keys that would be useful for analysis.
  
      Args:
        preprocessing_fn: A tf.transform preprocessing_fn.
        specs: A dict of feature name to tf.TypeSpecs. If `force_tf_compat_v1` is
          True, this can also be feature specifications.
        dataset_keys: A set of strings which are dataset keys, they uniquely
          identify these datasets across analysis runs.
        force_tf_compat_v1: If `True`, use Tensorflow in compat.v1 mode.
  
      Returns:
        A set of cache entry keys which would be useful for analysis.
      """
      _, cache_dict = _build_analysis_graph_for_inspection(
          preprocessing_fn, specs, dataset_keys, {}, force_tf_compat_v1
      )
      result = set()
      if cache_dict:
          for dataset_cache in cache_dict.values():
              result.update(dataset_cache.keys())
      return result
  
imports: import collections
  import dataclasses
  import hashlib
  from typing import Collection, Dict, Mapping, Optional, Tuple
  from typing import OrderedDict
  import tensorflow
  from tensorflow_transform import analyzer_nodes
  from tensorflow_transform import common_types
  from tensorflow_transform import graph_tools
  from tensorflow_transform import impl_helper
  from tensorflow_transform import nodes
  from tensorflow_transform import tf2_utils
  from tensorflow_transform import tf_utils
  from tensorflow_transform.beam import analyzer_cache
  from tensorflow_transform.beam import beam_nodes
  from tensorflow_transform.beam import combiner_packing_util
label_desc: Use image node_id 5 for calling a global function with example usage: get_analysis_cache_entry_keys(preprocessing_fn, specs, dataset_keys, force_tf_compat_v1) and returns: result