from tensorflow.python.ops import math_ops

        problem_type=ProblemType.CLASSIFICATION)

  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:
      return get_default_binary_metrics_for_eval(thresholds=[.5])
    return {}

  def get_eval_ops(self, features, logits, labels, metrics=None):
    loss = self.loss(logits, labels, features)
