code: def initialize_stat_dict():
      """Initializes a dictionary which will hold statistics about compositions.
  
      Returns:
        A dictionary containing the appropriate fields initialized to 0 or an
        empty list.
      """
      stat_dict = dict()
  
      for lag in [1, 2, 3]:
          stat_dict["autocorrelation" + str(lag)] = []
  
      stat_dict["notes_not_in_key"] = 0
      stat_dict["notes_in_motif"] = 0
      stat_dict["notes_in_repeated_motif"] = 0
      stat_dict["num_starting_tonic"] = 0
      stat_dict["num_repeated_notes"] = 0
      stat_dict["num_octave_jumps"] = 0
      stat_dict["num_fifths"] = 0
      stat_dict["num_thirds"] = 0
      stat_dict["num_sixths"] = 0
      stat_dict["num_seconds"] = 0
      stat_dict["num_fourths"] = 0
      stat_dict["num_sevenths"] = 0
      stat_dict["num_rest_intervals"] = 0
      stat_dict["num_special_rest_intervals"] = 0
      stat_dict["num_in_key_preferred_intervals"] = 0
      stat_dict["num_resolved_leaps"] = 0
      stat_dict["num_leap_twice"] = 0
      stat_dict["num_high_unique"] = 0
      stat_dict["num_low_unique"] = 0
  
      return stat_dict
  
file_path: magenta/magenta/models/rl_tuner/rl_tuner_eval_metrics.py
imports: from magenta.models.rl_tuner import rl_tuner_ops
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 4 for calling a global function with example usage: initialize_stat_dict() and returns: stat_dict