class_docstr: State pattern attached to SmartExplainer. This is the base class that only handles matrices
  of local contributions. The multi-class case is tackled in SmartMultiState.
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docstr: Compute local contributions in the original feature space, despite category encoding.
  
  Parameters
  ----------
  contributions : pandas.DataFrame
      Local contributions of a model on a prediction set.
  preprocessing : object
      Single step of preprocessing, typically a category encoder.
  agg_columns : str (default: 'sum')
      Type of aggregation performed. For Shap we want so sum contributions of one hot encoded variables.
  
  Returns
  -------
  pandas.DataFrame
      Local contributions on the original feature space (no encoding).
docstr_tok: ["Compute","local","contributions","in","the","original","feature","space",",","despite","category","encoding",".","Parameters","--","--","--","--","--","contributions",":","pandas.DataFrame","Local","contributions","of","a","model","on","a","prediction","set",".","preprocessing",":","object","Single","step","of","preprocessing",",","typically","a","category","encoder",".","agg_columns",":","str","(","default",":","'sum","'",")","Type","of","aggregation","performed",".","For","Shap","we","want","so","sum","contributions","of","one","hot","encoded","variables",".","Returns","--","--","--","-","pandas.DataFrame","Local","contributions","on","the","original","feature","space","(","no","encoding",")","."]
code: def inverse_transform_contributions(
      self, contributions, preprocessing, agg_columns="sum"
  ):
      """
      Compute local contributions in the original feature space, despite category encoding.
  
      Parameters
      ----------
      contributions : pandas.DataFrame
          Local contributions of a model on a prediction set.
      preprocessing : object
          Single step of preprocessing, typically a category encoder.
      agg_columns : str (default: 'sum')
          Type of aggregation performed. For Shap we want so sum contributions of one hot encoded variables.
  
      Returns
      -------
      pandas.DataFrame
          Local contributions on the original feature space (no encoding).
      """
      return inverse_transform_contributions(
          contributions, preprocessing, agg_columns
      )
  
imports: import numpy
  import pandas
  from shapash.decomposition.contributions import inverse_transform_contributions
  from shapash.decomposition.contributions import rank_contributions, assign_contributions
  from shapash.manipulation.filters import hide_contributions
  from shapash.manipulation.filters import cap_contributions
  from shapash.manipulation.filters import sign_contributions
  from shapash.manipulation.filters import cutoff_contributions
  from shapash.manipulation.filters import combine_masks
  from shapash.manipulation.mask import compute_masked_contributions
  from shapash.manipulation.mask import init_mask
  from shapash.manipulation.summarize import summarize, compute_features_import, group_contributions
label_desc: Use image node_id 2 for calling the SmartState obj's underlying member method code with example usage: obj.inverse_transform_contributions(contributions, preprocessing, agg_columns) and returns: inverse_transform_contributions