parameters: (examples, unused_features, predictions)
return_statement: return default_signature, {}
docstring: Creates generic signature from given examples and predictions.
  
    This is needed for backward compatibility with default behavior of
    export_estimator.
  
    Args:
      examples: `Tensor`.
      unused_features: `dict` of `Tensor`s.
      predictions: `Tensor` or `dict` of `Tensor`s.
  
    Returns:
      Tuple of default signature and empty named signatures.
  
    Raises:
      ValueError: If examples is `None`.
docstring_summary: Creates generic signature from given examples and predictions.
docstring_tokens: Creates generic signature from given examples and predictions .
function: def generic_signature_fn(examples, unused_features, predictions):
    """Creates generic signature from given examples and predictions.
  
    This is needed for backward compatibility with default behavior of
    export_estimator.
  
    Args:
      examples: `Tensor`.
      unused_features: `dict` of `Tensor`s.
      predictions: `Tensor` or `dict` of `Tensor`s.
  
    Returns:
      Tuple of default signature and empty named signatures.
  
    Raises:
      ValueError: If examples is `None`.
    """
    if examples is None:
      raise ValueError('examples cannot be None when using this signature fn.')
  
    tensors = {'inputs': examples}
    if not isinstance(predictions, dict):
      predictions = {'outputs': predictions}
    tensors.update(predictions)
    default_signature = exporter.generic_signature(tensors)
    return default_signature, {}
function_tokens: def generic_signature_fn ( examples , unused_features , predictions ) : if examples is None : raise ValueError ( 'examples cannot be None when using this signature fn.' ) tensors = { 'inputs' : examples } if not isinstance ( predictions , dict ) : predictions = { 'outputs' : predictions } tensors . update ( predictions ) default_signature = exporter . generic_signature ( tensors ) return default_signature , { }