code: def combine_masks(masks_list):
      """
      The function combine_masks computes a combined mask from a list of existing masks
      It outputs True everywhere the value is True for each mask in the list
  
      Parameters
      ----------
      masks_list : list of pandas dataframes
          List of masks used for filtering features et rows
          the shape of each mask must be equal to the initial dataset shape
  
      Returns
      -------
      pd.Dataframe of boolean
          combination of all masks.
      """
  
      if len(set(map(lambda x: x.shape, masks_list))) != 1:
          raise ValueError("Masks must have same dimensions.")
  
      masks_cube = np.dstack(masks_list)
      mask_final = np.min(masks_cube, axis=2)
  
      return pd.DataFrame(
          mask_final,
          columns=[
              "contrib_{}".format(i + 1)
              for i in range(mask_final.shape[1])
          ],
          index=masks_list[0].index,
      )
  
file_path: shapash/shapash/manipulation/filters.py
imports: import numpy
  import pandas
label_desc: Use image node_id 6 for calling a global function with example usage: combine_masks(masks_list) and returns: pd