code: def summarize_el(dataframe, mask, prefix):
      """
      Compute a summarized Matrix.
  
      Parameters
      ----------
      dataframe: pd.DataFrame
          Matrix containing contributions, label or feature names
          that will be summarized
      mask: pd.DataFrame
          Mask to apply during the summary step
      prefix: str
          prefix used for columns name
  
      Returns
      -------
      pd.DataFrame
          Result of the summarize step
      """
      matrix = dataframe.where(mask.to_numpy()).values.tolist()
      summarized_matrix = [
          [x for x in l if str(x) != "nan"] for l in matrix
      ]
      # Padding to create pd.DataFrame
      max_length = max(len(l) for l in summarized_matrix)
      for elem in summarized_matrix:
          elem.extend([np.nan] * (max_length - len(elem)))
      # Create DataFrame
      col_list = [prefix + str(x + 1) for x in list(range(max_length))]
      df_summarized_matrix = pd.DataFrame(
          summarized_matrix,
          index=list(dataframe.index),
          columns=col_list,
          dtype=object,
      )
  
      return df_summarized_matrix
  
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 1 for calling a global function with example usage: summarize_el(dataframe, mask, prefix) and returns: df_summarized_matrix