def train(self, loss, predictions_dict, labels):
    'Updates the accumalator stats and grows the ensemble.\n\n    Args:\n      loss: A scalar tensor representing average loss of examples.\n      predictions_dict: Dictionary of Rank 2 `Tensor` representing information\n          about predictions per example.\n      labels: Rank 2 `Tensor` representing labels per example. Has no effect\n          on the training and is only kept for backward compatibility.\n\n    Returns:\n      An op that adds a new tree to the ensemble.\n\n    Raises:\n      ValueError: if inputs are not valid.\n    '
    del labels
    (update_op, _, training_state) = self.update_stats(loss, predictions_dict)
    with ops.control_dependencies(update_op):
        return self.increment_step_counter_and_maybe_update_ensemble(predictions_dict, training_state)
Updates the accumalator stats and grows the ensemble.

Args:
  loss: A scalar tensor representing average loss of examples.
  predictions_dict: Dictionary of Rank 2 `Tensor` representing information
      about predictions per example.
  labels: Rank 2 `Tensor` representing labels per example. Has no effect
      on the training and is only kept for backward compatibility.

Returns:
  An op that adds a new tree to the ensemble.

Raises:
  ValueError: if inputs are not valid.
def train(self, loss, predictions_dict, labels):
    'Updates the accumalator stats and grows the ensemble.\n\n    Args:\n      loss: A scalar tensor representing average loss of examples.\n      predictions_dict: Dictionary of Rank 2 `Tensor` representing information\n          about predictions per example.\n      labels: Rank 2 `Tensor` representing labels per example. Has no effect\n          on the training and is only kept for backward compatibility.\n\n    Returns:\n      An op that adds a new tree to the ensemble.\n\n    Raises:\n      ValueError: if inputs are not valid.\n    '
    del labels
    (update_op, _, training_state) = self.update_stats(loss, predictions_dict)
    with ops.control_dependencies(update_op):
        return self.increment_step_counter_and_maybe_update_ensemble(predictions_dict, training_state)