code: def get_marginal_variance(self, features: numpy.ndarray) -> float:
      assert features.size > 0
  
      # For each midpoint along the given dimensions, traverse this tree to compute the
      # marginal predictions.
      selected_midpoints = [self._split_midpoints[f] for f in features]
      selected_sizes = [self._split_sizes[f] for f in features]
  
      product_midpoints = itertools.product(*selected_midpoints)
      product_sizes = itertools.product(*selected_sizes)
  
      sample = numpy.full(
          self._n_features, fill_value=numpy.nan, dtype=numpy.float64
      )
  
      values: Union[List[float], numpy.ndarray] = []
      weights: Union[List[float], numpy.ndarray] = []
  
      for midpoints, sizes in zip(product_midpoints, product_sizes):
          sample[features] = numpy.array(midpoints)
  
          value, weight = self._get_marginalized_statistics(sample)
          weight *= float(numpy.prod(sizes))
  
          values = numpy.append(values, value)
          weights = numpy.append(weights, weight)
  
      weights = numpy.asarray(weights)
      values = numpy.asarray(values)
      average_values = numpy.average(values, weights=weights)
      variance = numpy.average(
          (values - average_values) ** 2, weights=weights
      )
  
      assert variance >= 0.0
      return variance
  
imports: from __future__ import annotations
  from functools import lru_cache
  import itertools
  from typing import List
  from typing import Set
  from typing import Tuple
  from typing import TYPE_CHECKING
  from typing import Union
  import numpy
label_desc: Use image node_id 3 for calling the _FanovaTree obj's underlying member method code with example usage: obj.get_marginal_variance(features) and returns: variance