class_docstr: Represents the output of a supervised training or eval process.
class_docstr_tok: ["Represents","the","output","of","a","supervised","training","or","eval","process","."]
docstr: 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).
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code: 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)
  
imports: import abc
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.keras.saving.utils_v1 import signature_def_utils
  from tensorflow.python.saved_model import signature_def_utils
label_desc: Use image node_id 1 to create a new _SupervisedOutput object from inherited base classes: ExportOutput with example: obj = _SupervisedOutput(loss, predictions, metrics)