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 the summed contributions of hidden features.
  
  Parameters
  ----------
  s_contrib: pd.DataFrame
      Matrix with both positive and negative values
  mask: pd.DataFrame
      Matrix with only True or False elements. False elements are the hidden elements.
  
  Returns
  -------
  pd.series
      Sum of contributions of hidden features.
docstr_tok: ["Compute","the","summed","contributions","of","hidden","features",".","Parameters","--","--","--","--","--","s_contrib",":","pd.DataFrame","Matrix","with","both","positive","and","negative","values","mask",":","pd.DataFrame","Matrix","with","only","True","or","False","elements",".","False","elements","are","the","hidden","elements",".","Returns","--","--","--","-","pd.series","Sum","of","contributions","of","hidden","features","."]
code: def compute_masked_contributions(self, s_contrib, masks):
      """
      Compute the summed contributions of hidden features.
  
      Parameters
      ----------
      s_contrib: pd.DataFrame
          Matrix with both positive and negative values
      mask: pd.DataFrame
          Matrix with only True or False elements. False elements are the hidden elements.
  
      Returns
      -------
      pd.series
          Sum of contributions of hidden features.
      """
      return compute_masked_contributions(s_contrib, masks)
  
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 11 for calling the SmartState obj's underlying member method code with example usage: obj.compute_masked_contributions(s_contrib, masks) and returns: compute_masked_contributions