class_docstr: A wrapper class for StructureScore instances, which implement a decomposable score,
  that caches local scores.
  Based on the global decomposition property of Bayesian networks for decomposable scores.
  
  Parameters
  ----------
  base_scorer: StructureScore instance
       Has to be a decomposable score.
  data: pandas DataFrame instance
      DataFrame instance where each column represents one variable.
      (If some values in the data are missing the data cells should be set to `numpy.NaN`.
      Note that pandas converts each column containing `numpy.NaN`s to dtype `float`.)
  max_size: int (optional, default 10_000)
      The maximum number of elements allowed in the cache. When the limit is reached, the least recently used
      entries will be discarded.
  **kwargs
      Additional arguments that will be handed to the super constructor.
  
  Reference
  ---------
  Koller & Friedman, Probabilistic Graphical Models - Principles and Techniques, 2009
  Section 18.3
code: def local_score(self, variable, parents):
      hashable = tuple(parents)
      return self.cache(variable, hashable)
  
file_path: pgmpy/pgmpy/estimators/ScoreCache.py
imports: from pgmpy.estimators import StructureScore
label_desc: Use image node_id 2 for calling the ScoreCache obj's underlying member method code with example usage: obj.local_score(variable, parents) and returns: self