from tensorflow.python.ops import nn


  def logits_to_predictions(self, logits, proba=False):
    if self.num_label_columns == 1:
      logits = array_ops.concat([array_ops.zeros_like(logits), logits], 1)

    if proba:
      return nn.softmax(logits)
    else:
      return math_ops.argmax(logits, 1)

  def _default_eval_metrics(self):
    if self._num_label_columns == 1:
