code: def unit_change_nonlinear(
      background_mechanism: PredictionModel,
      background_df: pd.DataFrame,
      foreground_mechanism: PredictionModel,
      foreground_df: pd.DataFrame,
      input_column_names: List[str],
      shapley_config: Optional[ShapleyConfig] = None,
  ) -> pd.DataFrame:
      """
      Calculates the contributions of mechanism and each input to the change in the output values of a non-linear deterministic mechanism.
      The technical method is described in the following research paper:
      Kailash Budhathoki, George Michailidis, Dominik Janzing. *Explaining the root causes of unit-level changes*. arXiv, 2022.
  
      :param background_mechanism: The background mechanism.
      :param background_df: The background data.
      :param foreground_mechanism: The foreground mechanism.
      :param foreground_df: The foreground data.
      :param input_column_names: The names of the input (features) columns in both dataframes.
      :param shapley_config: The configuration for calculating Shapley values.
      :return: A pandas dataframe with attributions to each cause for the change in each output row of provided dataframes.
      """
      _check_if_input_columns_exist(
          background_df, foreground_df, input_column_names
      )
  
      def payoff(player_indicator: List[int]) -> np.ndarray:
          """The last cell in the binary vector represents the player 'mechanism'."""
          input_arrays = []
          for i, is_player_active in enumerate(player_indicator[:-1]):
              selected_df = (
                  foreground_df if is_player_active else background_df
              )
              input_arrays.append(
                  selected_df[input_column_names[i]].to_numpy()
              )
          mechanism = (
              foreground_mechanism
              if player_indicator[-1]
              else background_mechanism
          )
          return mechanism.predict(
              np.column_stack(input_arrays)
          ).flatten()
  
      contributions = estimate_shapley_values(
          payoff, len(input_column_names) + 1, shapley_config
      )
      root_causes = input_column_names + ["f"]
      return pd.DataFrame(contributions, columns=root_causes)
  
imports: from abc import abstractmethod
  from typing import List, Optional
  import numpy
  import pandas
  from sklearn.linear_model._base import LinearModel
  from sklearn.utils.validation import check_is_fitted
  from dowhy.gcm.ml.prediction_model import PredictionModel
  from dowhy.gcm.ml.regression import SklearnRegressionModel
  from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values
label_desc: Use image node_id 2 for calling a global function with example usage: unit_change_nonlinear(background_mechanism, background_df, foreground_mechanism, foreground_df, input_column_names, shapley_config) and returns: pd, mechanism