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
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.
  
tensorflow_transform/beam/analysis_graph_builder.py