Fix to padding

        self.updates = tf.no_op(name='updates')

  def fit(self, dataset, shuffle=False, max_checkpoints_to_keep=5,

          log_every_N_batches=50, pad_batches=False):

          log_every_N_batches=50):

    """Fit the model.

    Args:

    step_per_epoch = np.ceil(float(num_datapoints)/batch_size)

    nb_epoch = self.model_params["nb_epoch"]

    log("Training for %d epochs" % nb_epoch, self.verbosity)

    if "pad_batches" in self.model_params:

      pad_batches = self.model_params["pad_batches"]

    else:

      pad_batches = False

    with self.graph.as_default():

      self.require_attributes(['loss', 'updates'])

      train_op = self.get_training_op()

    self.num_tasks = len(self.task_types)

    self.fit_transformers = None

  def fit(self, dataset, shuffle=False, pad_batches=False):

  def fit(self, dataset, shuffle=False):

    """

    Fits TensorflowGraph to data.

    """

    self.train_model.fit(dataset, shuffle=shuffle, pad_batches=pad_batches)

    self.train_model.fit(dataset, shuffle=shuffle)

  def predict_on_batch(self, X):

    """