code: def get_importance(self, feature: int) -> Tuple[float, float]:
      # Assert that `fit` has been called.
      assert self._trees is not None
      assert self._variances is not None
  
      self._compute_variances(feature)
  
      fractions: Union[List[float], numpy.ndarray] = []
  
      for tree_index, tree in enumerate(self._trees):
          tree_variance = tree.variance
          if tree_variance > 0.0:
              fraction = (
                  self._variances[feature][tree_index] / tree_variance
              )
              fractions = numpy.append(fractions, fraction)
  
      fractions = numpy.asarray(fractions)
  
      return float(fractions.mean()), float(fractions.std())
  
file_path: optuna/optuna/importance/_fanova/_fanova.py
imports: from typing import Dict
  from typing import List
  from typing import Optional
  from typing import Tuple
  from typing import Union
  import numpy
  from optuna._imports import try_import
  from optuna.importance._fanova._tree import _FanovaTree
label_desc: Use image node_id 3 for calling the _Fanova obj's underlying member method code with example usage: obj.get_importance(feature) and returns: float,  float