parameters: (
      examples, unused_features, predictions)
return_statement: return default_signature, {}
docstring: Classification signature from given examples and predicted probabilities.
  
    Args:
      examples: `Tensor`.
      unused_features: `dict` of `Tensor`s.
      predictions: `Tensor` of predicted probabilities or dict that contains the
        probabilities tensor as in {'probabilities', `Tensor`}.
  
    Returns:
      Tuple of default classification signature and empty named signatures.
  
    Raises:
      ValueError: If examples is `None`.
docstring_summary: Classification signature from given examples and predicted probabilities.
docstring_tokens: Classification signature from given examples and predicted probabilities .
function: def classification_signature_fn_with_prob(
      examples, unused_features, predictions):
    """Classification signature from given examples and predicted probabilities.
  
    Args:
      examples: `Tensor`.
      unused_features: `dict` of `Tensor`s.
      predictions: `Tensor` of predicted probabilities or dict that contains the
        probabilities tensor as in {'probabilities', `Tensor`}.
  
    Returns:
      Tuple of default classification 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.')
  
    if isinstance(predictions, dict):
      default_signature = exporter.classification_signature(
          examples, scores_tensor=predictions['probabilities'])
    else:
      default_signature = exporter.classification_signature(
          examples, scores_tensor=predictions)
    return default_signature, {}
function_tokens: def classification_signature_fn_with_prob ( examples , unused_features , predictions ) : if examples is None : raise ValueError ( 'examples cannot be None when using this signature fn.' ) if isinstance ( predictions , dict ) : default_signature = exporter . classification_signature ( examples , scores_tensor = predictions [ 'probabilities' ] ) else : default_signature = exporter . classification_signature ( examples , scores_tensor = predictions ) return default_signature , { }