class_docstr: Implements a recurrent DQN designed to produce melody sequences.
class_docstr_tok: ["Implements","a","recurrent","DQN","designed","to","produce","melody","sequences","."]
docstr: Samle a note uniformly at random.
  
  Returns:
    random note
docstr_tok: ["Samle","a","note","uniformly","at","random",".","Returns",":","random","note"]
code: def get_random_note(self):
      """Samle a note uniformly at random.
  
      Returns:
        random note
      """
      note_idx = np.random.randint(0, self.num_actions - 1)
      return np.array(
          rl_tuner_ops.make_onehot([note_idx], self.num_actions)
      ).flatten()
  
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 5 for calling the RLTuner obj's underlying member method code with example usage: obj.get_random_note() and returns: np