parameters: (self, logits)
docstring: Returns a dict of predictions.
  
      Args:
        logits: logits `Tensor` after applying possible centered bias.
  
      Returns:
        Dict of prediction `Tensor` keyed by `PredictionKey`.
docstring_summary: Returns a dict of predictions.
docstring_tokens: Returns a dict of predictions .
function: def _logits_to_predictions(self, logits):
      """Returns a dict of predictions.
  
      Args:
        logits: logits `Tensor` after applying possible centered bias.
  
      Returns:
        Dict of prediction `Tensor` keyed by `PredictionKey`.
      """
      with ops.name_scope(None, "predictions", (logits,)):
        class_ids = math_ops.argmax(
            logits, 1, name=prediction_key.PredictionKey.CLASSES)
        if self._label_keys:
          table = lookup_lib.index_to_string_table_from_tensor(
              mapping=self._label_keys,
              name="class_string_lookup")
          classes = table.lookup(class_ids)
        else:
          classes = class_ids
        return {
            prediction_key.PredictionKey.LOGITS: logits,
            prediction_key.PredictionKey.PROBABILITIES:
                nn.softmax(
                    logits, name=prediction_key.PredictionKey.PROBABILITIES),
            prediction_key.PredictionKey.CLASSES: classes
        }
function_tokens: def _logits_to_predictions ( self , logits ) : with ops . name_scope ( None , "predictions" , ( logits , ) ) : class_ids = math_ops . argmax ( logits , 1 , name = prediction_key . PredictionKey . CLASSES ) if self . _label_keys : table = lookup_lib . index_to_string_table_from_tensor ( mapping = self . _label_keys , name = "class_string_lookup" ) classes = table . lookup ( class_ids ) else : classes = class_ids return { prediction_key . PredictionKey . LOGITS : logits , prediction_key . PredictionKey . PROBABILITIES : nn . softmax ( logits , name = prediction_key . PredictionKey . PROBABILITIES ) , prediction_key . PredictionKey . CLASSES : classes }