class_docstr: Implements a recurrent DQN designed to produce melody sequences.
class_docstr_tok: ["Implements","a","recurrent","DQN","designed","to","produce","melody","sequences","."]
docstr: Checks a composition to see if the highest note within it is repeated.
  
  Args:
    composition: A list of integers representing the notes in the piece.
  Returns:
    True if the lowest note was unique, False otherwise.
docstr_tok: ["Checks","a","composition","to","see","if","the","highest","note","within","it","is","repeated",".","Args",":","composition",":","A","list","of","integers","representing","the","notes","in","the","piece",".","Returns",":","True","if","the","lowest","note","was","unique",",","False","otherwise","."]
code: def detect_high_unique(self, composition):
      """Checks a composition to see if the highest note within it is repeated.
  
      Args:
        composition: A list of integers representing the notes in the piece.
      Returns:
        True if the lowest note was unique, False otherwise.
      """
      max_note = max(composition)
      return list(composition).count(max_note) == 1
  
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 32 for calling the RLTuner obj's underlying member method code with example usage: obj.detect_high_unique(composition) and returns: unknown