parameters: (self, loss=None, predictions=None, metrics=None)
docstring: Constructor for SupervisedOutput (ie, Train or Eval output).
  
      Args:
        loss: dict of Tensors or single Tensor representing calculated loss.
        predictions: dict of Tensors or single Tensor representing model
          predictions.
        metrics: Dict of metric results keyed by name.
          The values of the dict can be one of the following:
          (1) instance of `Metric` class.
          (2) (metric_value, update_op) tuples, or a single tuple.
          metric_value must be a Tensor, and update_op must be a Tensor or Op.
  
      Raises:
        ValueError: if any of the outputs' dict keys are not strings or tuples of
          strings or the values are not Tensors (or Operations in the case of
          update_op).
docstring_summary: Constructor for SupervisedOutput (ie, Train or Eval output).
docstring_tokens: Constructor for SupervisedOutput ( ie Train or Eval output ) .
function: def __init__(self, loss=None, predictions=None, metrics=None):
      """Constructor for SupervisedOutput (ie, Train or Eval output).
  
      Args:
        loss: dict of Tensors or single Tensor representing calculated loss.
        predictions: dict of Tensors or single Tensor representing model
          predictions.
        metrics: Dict of metric results keyed by name.
          The values of the dict can be one of the following:
          (1) instance of `Metric` class.
          (2) (metric_value, update_op) tuples, or a single tuple.
          metric_value must be a Tensor, and update_op must be a Tensor or Op.
  
      Raises:
        ValueError: if any of the outputs' dict keys are not strings or tuples of
          strings or the values are not Tensors (or Operations in the case of
          update_op).
      """
  
      if loss is not None:
        loss_dict = self._wrap_and_check_outputs(loss, self.LOSS_NAME)
        self._loss = self._prefix_output_keys(loss_dict, self.LOSS_NAME)
      if predictions is not None:
        pred_dict = self._wrap_and_check_outputs(
            predictions, self.PREDICTIONS_NAME)
        self._predictions = self._prefix_output_keys(
            pred_dict, self.PREDICTIONS_NAME)
      if metrics is not None:
        self._metrics = self._wrap_and_check_metrics(metrics)
function_tokens: def __init__ ( self , loss = None , predictions = None , metrics = None ) : if loss is not None : loss_dict = self . _wrap_and_check_outputs ( loss , self . LOSS_NAME ) self . _loss = self . _prefix_output_keys ( loss_dict , self . LOSS_NAME ) if predictions is not None : pred_dict = self . _wrap_and_check_outputs ( predictions , self . PREDICTIONS_NAME ) self . _predictions = self . _prefix_output_keys ( pred_dict , self . PREDICTIONS_NAME ) if metrics is not None : self . _metrics = self . _wrap_and_check_metrics ( metrics )