()
return contrib_training.HParams(
      batch_size=128, rnn_layer_sizes=[512, 512], one_hot_length=NUM_CLASSES)
Generates the hparams used to train a basic_rnn.

  These are the hparams used in the .mag file found at
  https://github.com/magenta/magenta/tree/main/magenta/models/
  melody_rnn#pre-trained

  Returns:
    Hyperparameters of the downloadable basic_rnn pre-trained model.
Generates the hparams used to train a basic_rnn.
Generates the hparams used to train a basic_rnn .
def basic_rnn_hparams():
  """Generates the hparams used to train a basic_rnn.

  These are the hparams used in the .mag file found at
  https://github.com/magenta/magenta/tree/main/magenta/models/
  melody_rnn#pre-trained

  Returns:
    Hyperparameters of the downloadable basic_rnn pre-trained model.
  """
  # TODO(natashajaques): ability to restore basic_rnn from any .mag file.
  return contrib_training.HParams(
      batch_size=128, rnn_layer_sizes=[512, 512], one_hot_length=NUM_CLASSES)
def basic_rnn_hparams ( ) : # TODO(natashajaques): ability to restore basic_rnn from any .mag file. return contrib_training . HParams ( batch_size = 128 , rnn_layer_sizes = [ 512 , 512 ] , one_hot_length = NUM_CLASSES )