code: def summarize(
      s_contrib, var_dict, x_sorted, mask, columns_dict, features_dict
  ):
      """
      Compute the summarized contributions of features.
  
      Parameters
      ----------
      s_contrib: pd.DataFrame
          Matrix containing contributions that will be summarized
      var_dict: pd.DataFrame
          Matrix of feature names that will be summarized
      x_sorted: pd.DataFrame
          Matrix containing the value of each feature
      mask: pd.DataFrame
          Mask to apply during the summary step
      columns_dict:
          Dict of column Names, matches column num with column name
      features_dict:
          Dict of column Label, matches column name with column label
  
      Returns
      -------
      pd.DataFrame
          Result of the summarize step
      """
      contrib_sum = summarize_el(s_contrib, mask, "contribution_")
      var_dict_sum = summarize_el(var_dict, mask, "feature_").applymap(
          lambda x: features_dict[columns_dict[x]]
          if not np.isnan(x)
          else x
      )
      x_sorted_sum = summarize_el(x_sorted, mask, "value_")
  
      # Concatenate pd.DataFrame
      summary = pd.concat(
          [contrib_sum, var_dict_sum, x_sorted_sum], axis=1
      )
  
      # Ordering columns
      ordered_columns = list(
          flatten(
              zip(
                  var_dict_sum.columns,
                  x_sorted_sum.columns,
                  contrib_sum.columns,
              )
          )
      )
      summary = summary[ordered_columns]
      return summary
  
file_path: shapash/shapash/manipulation/summarize.py
imports: import warnings
  import numpy
  import pandas
  from pandas.core.common import flatten
  from sklearn.manifold import TSNE
  from shapash.utils.transform import get_features_transform_mapping
label_desc: Use image node_id 3 for calling a global function with example usage: summarize(s_contrib, var_dict, x_sorted, mask, columns_dict, features_dict) and returns: summary