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 a mask indicating where the input matrix
  has values above a given threshold in absolute value.
  
  Parameters
  ----------
  s_contrib : pandas.DataFrame
      Local contributions, positive and negative values.
  threshold: float, optional (default: 0.1)
      User defined threshold above which local contributions are hidden.
  
  Returns
  -------
  pandas.DataFrame
      Mask with only True of False elements.
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code: def cap_contributions(self, s_contrib, threshold=0.1):
      """
      Compute a mask indicating where the input matrix
      has values above a given threshold in absolute value.
  
      Parameters
      ----------
      s_contrib : pandas.DataFrame
          Local contributions, positive and negative values.
      threshold: float, optional (default: 0.1)
          User defined threshold above which local contributions are hidden.
  
      Returns
      -------
      pandas.DataFrame
          Mask with only True of False elements.
      """
      return cap_contributions(s_contrib, threshold=threshold)
  
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 7 for calling the SmartState obj's underlying member method code with example usage: obj.cap_contributions(s_contrib, threshold) and returns: cap_contributions