class_docstr: Builds graph for a Note RNN and instantiates weights from a checkpoint.
  
  Loads weights from a previously saved checkpoint file corresponding to a pre-
  trained basic_rnn model. Has functions that allow it to be primed with a MIDI
  melody, and allow it to be called to produce its predictions for the next
  note in a sequence.
  
  Used as part of the RLTuner class.
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docstr: Given a note, uses the model to predict the most probable next note.
  
  Args:
    note: A one-hot encoding of the note.
  Returns:
    Next note in the same format.
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code: def get_next_note_from_note(self, note):
      """Given a note, uses the model to predict the most probable next note.
  
      Args:
        note: A one-hot encoding of the note.
      Returns:
        Next note in the same format.
      """
      with self.graph.as_default():
          with tf.variable_scope(self.scope, reuse=True):
              singleton_lengths = np.full(self.batch_size, 1, dtype=int)
  
              input_batch = np.reshape(
                  note, (self.batch_size, 1, rl_tuner_ops.NUM_CLASSES)
              )
  
              softmax, self.state_value = self.session.run(
                  [self.softmax, self.state_tensor],
                  {
                      self.melody_sequence: input_batch,
                      self.initial_state: self.state_value,
                      self.lengths: singleton_lengths,
                  },
              )
  
              return self.get_note_from_softmax(softmax)
  
imports: import os
  from magenta.common import sequence_example_lib
  from magenta.models.rl_tuner import rl_tuner_ops
  from magenta.models.shared import events_rnn_graph
  from magenta.pipelines import melody_pipelines
  import note_seq
  from note_seq import midi_io
  from note_seq import sequences_lib
  import numpy
  import tensorflow.compat.v1
  import tf_slim
label_desc: Use image node_id 14 for calling the NoteRNNLoader obj's underlying member method code with example usage: obj.get_next_note_from_note(note) and returns: self