class_docstr: The next version of our playlist generator that adds a 'hint' parameter to
  choose a bonus movie closest to the 'hint'.
  
  The flow performs the following steps:
  
  1) Load the genre-specific statistics from the MovieStatsFlow.
  2) In parallel branches:
     - A) Build a playlist from the top films in the requested genre.
     - B) Choose a bonus movie that has the closest string edit distance to
       the user supplied hint.
  3) Join the two to create a movie playlist and display it.
docstr: Use the Metaflow client to retrieve the latest successful run from our
  MovieStatsFlow and assign them as data artifacts in this flow.
code: def start(self):
      """
      Use the Metaflow client to retrieve the latest successful run from our
      MovieStatsFlow and assign them as data artifacts in this flow.
  
      """
      # Load the analysis from the MovieStatsFlow.
      from metaflow import Flow, get_metadata
  
      # Print metadata provider
      print("Using metadata provider: %s" % get_metadata())
  
      # Load the analysis from the MovieStatsFlow.
      run = Flow("MovieStatsFlow").latest_successful_run
      print("Using analysis from '%s'" % str(run))
  
      # Get the dataframe from the start step before we sliced into into
      # genre-specific dataframes.
      self.dataframe = run["start"].task.data.dataframe
  
      # Also grab the summary statistics.
      self.genre_stats = run.data.genre_stats
  
      # Compute our two recommendation types in parallel.
      self.next(self.bonus_movie, self.genre_movies)
  
file_path: metaflow/metaflow/tutorials/04-playlist-plus/playlist.py
imports: from metaflow import FlowSpec, step, Parameter, conda, conda_base
label_desc: Use image node_id 1 for calling the PlayListFlow obj's underlying member method code with example usage: obj.start() without return types