code: def variance_of_deviations(
      randomized_predictions: np.ndarray, baseline_values: np.ndarray
  ) -> np.ndarray:
      # Using the negative value here seeing that the Shapley estimation evaluates v(S u {i}) - v(S) for a subset S. In
      # case of variance, we have v(S u {i}) <= v(S), which would result in a negative contribution of players to the
      # target quantity (here, variance).
      return -np.var(
          (randomized_predictions - baseline_values).squeeze()
      )
  
imports: import random
  from typing import Dict, Optional, Union
  import numpy
  from scipy.optimize import minimize
  from sklearn.preprocessing import OneHotEncoder
  from dowhy.gcm.util.catboost_encoder import CatBoostEncoder
label_desc: Use image node_id 14 for calling a global function with example usage: variance_of_deviations(randomized_predictions, baseline_values) and returns: unknown