code: def make_rnn_cell(rnn_layer_sizes, state_is_tuple=False):
      """Makes a default LSTM cell for use in the NoteRNNLoader graph.
  
      This model is only to be used for loading the checkpoint from the research
      paper. In general, events_rnn_graph.make_rnn_cell should be used instead.
  
      Args:
        rnn_layer_sizes: A list of integer sizes (in units) for each layer of the
            RNN.
        state_is_tuple: A boolean specifying whether to use tuple of hidden matrix
            and cell matrix as a state instead of a concatenated matrix.
  
      Returns:
          A tf.rnn.rnn_cell.MultiRNNCell based on the given hyperparameters.
      """
      cells = []
      for num_units in rnn_layer_sizes:
          cell = tf.nn.rnn_cell.LSTMCell(
              num_units, state_is_tuple=state_is_tuple
          )
          cells.append(cell)
  
      cell = tf.nn.rnn_cell.MultiRNNCell(
          cells, state_is_tuple=state_is_tuple
      )
  
      return cell
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import random
  from magenta.contrib import training
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 14 for calling a global function with example usage: make_rnn_cell(rnn_layer_sizes, state_is_tuple) and returns: cell