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.
class_docstr_tok: ["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","."]
docstr: Runs one batch of training data through the model.
  
  Uses a queue runner to pull one batch of data from the training files
  and run it through the model.
  
  Returns:
    A batch of softmax probabilities and model state vectors.
docstr_tok: ["Runs","one","batch","of","training","data","through","the","model",".","Uses","a","queue","runner","to","pull","one","batch","of","data","from","the","training","files","and","run","it","through","the","model",".","Returns",":","A","batch","of","softmax","probabilities","and","model","state","vectors","."]
code: def run_training_batch(self):
      """Runs one batch of training data through the model.
  
      Uses a queue runner to pull one batch of data from the training files
      and run it through the model.
  
      Returns:
        A batch of softmax probabilities and model state vectors.
      """
      if self.training_file_list is None:
          tf.logging.warn(
              "No training file path was provided, cannot run training"
              "batch"
          )
          return
  
      coord = tf.train.Coordinator()
      tf.train.start_queue_runners(sess=self.session, coord=coord)
  
      softmax, state, lengths = self.session.run(
          [self.train_softmax, self.train_state, self.train_lengths]
      )
  
      coord.request_stop()
  
      return softmax, state, lengths
  
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 13 for calling the NoteRNNLoader obj's underlying member method code with example usage: obj.run_training_batch() and returns: softmax,  state,  lengths