class_docstr: Implements a recurrent DQN designed to produce melody sequences.
class_docstr_tok: ["Implements","a","recurrent","DQN","designed","to","produce","melody","sequences","."]
code: def reward_interval_diversity(self):
      # TODO(natashajaques): music theory book also suggests having a mix of steps
      # that are both incremental and larger. Want to write a function that
      # rewards this. Could have some kind of interval_stats stored by
      # reward_preferred_intervals function.
      pass
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import os
  import random
  import urllib
  from magenta.models.rl_tuner import note_rnn_loader
  from magenta.models.rl_tuner import rl_tuner_eval_metrics
  from magenta.models.rl_tuner import rl_tuner_ops
  import matplotlib.pyplot
  from note_seq import melodies_lib
  from note_seq import midi_io
  import numpy
  import scipy.special
  from six.moves import range
  from six.moves import reload_module
  from six.moves import urllib
  import tensorflow.compat.v1
label_desc: Use image node_id 37 for calling the RLTuner obj's underlying member method code with example usage: obj.reward_interval_diversity() without return types