def make_rnn_cell(rnn_layer_sizes, state_is_tuple=False):
    'Makes a default LSTM cell for use in the NoteRNNLoader graph.\n\n  This model is only to be used for loading the checkpoint from the research\n  paper. In general, events_rnn_graph.make_rnn_cell should be used instead.\n\n  Args:\n    rnn_layer_sizes: A list of integer sizes (in units) for each layer of the\n        RNN.\n    state_is_tuple: A boolean specifying whether to use tuple of hidden matrix\n        and cell matrix as a state instead of a concatenated matrix.\n\n  Returns:\n      A tf.contrib.rnn.MultiRNNCell based on the given hyperparameters.\n  '
    cells = []
    for num_units in rnn_layer_sizes:
        cell = tf.contrib.rnn.LSTMCell(num_units, state_is_tuple=state_is_tuple)
        cells.append(cell)
    cell = tf.contrib.rnn.MultiRNNCell(cells, state_is_tuple=state_is_tuple)
    return cell
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.contrib.rnn.MultiRNNCell based on the given hyperparameters.
def make_rnn_cell(rnn_layer_sizes, state_is_tuple=False):
    'Makes a default LSTM cell for use in the NoteRNNLoader graph.\n\n  This model is only to be used for loading the checkpoint from the research\n  paper. In general, events_rnn_graph.make_rnn_cell should be used instead.\n\n  Args:\n    rnn_layer_sizes: A list of integer sizes (in units) for each layer of the\n        RNN.\n    state_is_tuple: A boolean specifying whether to use tuple of hidden matrix\n        and cell matrix as a state instead of a concatenated matrix.\n\n  Returns:\n      A tf.contrib.rnn.MultiRNNCell based on the given hyperparameters.\n  '
    cells = []
    for num_units in rnn_layer_sizes:
        cell = tf.contrib.rnn.LSTMCell(num_units, state_is_tuple=state_is_tuple)
        cells.append(cell)
    cell = tf.contrib.rnn.MultiRNNCell(cells, state_is_tuple=state_is_tuple)
    return cell