class_docstr: Decorator pattern. It simply iterates the method of its member as many times as needed.
  It thus extends any class to apply its methods to a list of arguments.
class_docstr_tok: ["Decorator","pattern",".","It","simply","iterates","the","method","of","its","member","as","many","times","as","needed",".","It","thus","extends","any","class","to","apply","its","methods","to","a","list","of","arguments","."]
docstr: Regroup contributions according to features_groups parameter.
  
  Parameters
  ----------
  contributions : list
      List of contributions of each unique feature.
  features_groups : dict
      Python dict that inform which features to regroup.
  
  Returns
  -------
  pd.DataFrame
docstr_tok: ["Regroup","contributions","according","to","features_groups","parameter",".","Parameters","--","--","--","--","--","contributions",":","list","List","of","contributions","of","each","unique","feature",".","features_groups",":","dict","Python","dict","that","inform","which","features","to","regroup",".","Returns","--","--","--","-","pd.DataFrame"]
code: def compute_grouped_contributions(
      self, contributions, features_groups
  ):
      """
      Regroup contributions according to features_groups parameter.
  
      Parameters
      ----------
      contributions : list
          List of contributions of each unique feature.
      features_groups : dict
          Python dict that inform which features to regroup.
  
      Returns
      -------
      pd.DataFrame
      """
      return self.delegate(
          "compute_grouped_contributions",
          contributions,
          features_groups,
      )
  
file_path: shapash/shapash/explainer/multi_decorator.py
imports: from shapash.explainer.smart_state import SmartState
label_desc: Use image node_id 13 for calling the MultiDecorator obj's underlying member method code with example usage: obj.compute_grouped_contributions(contributions, features_groups) and returns: self