class_docstr: Implements a recurrent DQN designed to produce melody sequences.
class_docstr_tok: ["Implements","a","recurrent","DQN","designed","to","produce","melody","sequences","."]
docstr: Primes both internal models based on self.priming_mode.
  
  Returns:
    A one-hot encoding of the note output by the q_network to be used as
    the initial observation.
docstr_tok: ["Primes","both","internal","models","based","on","self.priming_mode",".","Returns",":","A","one-hot","encoding","of","the","note","output","by","the","q_network","to","be","used","as","the","initial","observation","."]
code: def prime_internal_models(self):
      """Primes both internal models based on self.priming_mode.
  
      Returns:
        A one-hot encoding of the note output by the q_network to be used as
        the initial observation.
      """
      self.prime_internal_model(self.target_q_network)
      self.prime_internal_model(self.reward_rnn)
      next_obs = self.prime_internal_model(self.q_network)
      return next_obs
  
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 46 for calling the RLTuner obj's underlying member method code with example usage: obj.prime_internal_models() and returns: next_obs