code: def _compute_variances(self, feature: int) -> None:
      assert self._trees is not None
      assert self._variances is not None
      assert self._column_to_encoded_columns is not None
  
      if feature in self._variances:
          return
  
      raw_features = self._column_to_encoded_columns[feature]
      variances = numpy.empty(len(self._trees), dtype=numpy.float64)
  
      for tree_index, tree in enumerate(self._trees):
          marginal_variance = tree.get_marginal_variance(raw_features)
          variances[tree_index] = numpy.clip(
              marginal_variance, 0.0, None
          )
      self._variances[feature] = variances
  
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 4 for calling the _Fanova obj's underlying member method code with example usage: obj._compute_variances(feature) without return types