parameters: (
        self, x=None, input_fn=None, batch_size=None, as_iterable=False)
docstring: Returns predicted probabilty distributions for given features.
  
      Args:
        x: Matrix of shape [n_samples, n_features...]. Can be iterator that
           returns arrays of features. The training input samples for fitting the
           model. If set, `input_fn` must be `None`.
        input_fn: Input function. If set, `x` and 'batch_size' must be `None`.
        batch_size: Override default batch size. If set, 'input_fn' must be
          'None'.
        as_iterable: If True, return an iterable which keeps yielding predictions
          for each example until inputs are exhausted. Note: The inputs must
          terminate if you want the iterable to terminate (e.g. be sure to pass
          num_epochs=1 if you are using something like read_batch_features).
  
      Returns:
        A numpy array of predicted probability distributions (or an iterable of
        predicted probability distributions if as_iterable is True).
  
      Raises:
        ValueError: If x and input_fn are both provided or both `None`.
docstring_summary: Returns predicted probabilty distributions for given features.
docstring_tokens: Returns predicted probabilty distributions for given features .
function: def predict_proba(
        self, x=None, input_fn=None, batch_size=None, as_iterable=False):
      """Returns predicted probabilty distributions for given features.
  
      Args:
        x: Matrix of shape [n_samples, n_features...]. Can be iterator that
           returns arrays of features. The training input samples for fitting the
           model. If set, `input_fn` must be `None`.
        input_fn: Input function. If set, `x` and 'batch_size' must be `None`.
        batch_size: Override default batch size. If set, 'input_fn' must be
          'None'.
        as_iterable: If True, return an iterable which keeps yielding predictions
          for each example until inputs are exhausted. Note: The inputs must
          terminate if you want the iterable to terminate (e.g. be sure to pass
          num_epochs=1 if you are using something like read_batch_features).
  
      Returns:
        A numpy array of predicted probability distributions (or an iterable of
        predicted probability distributions if as_iterable is True).
  
      Raises:
        ValueError: If x and input_fn are both provided or both `None`.
      """
      predictions = super(Classifier, self).predict(
          x=x, input_fn=input_fn, batch_size=batch_size, as_iterable=as_iterable,
          outputs=[Classifier.PROBABILITY_OUTPUT])
      if as_iterable:
        return (p[Classifier.PROBABILITY_OUTPUT] for p in predictions)
      else:
        return predictions[Classifier.PROBABILITY_OUTPUT]
function_tokens: def predict_proba ( self , x = None , input_fn = None , batch_size = None , as_iterable = False ) : predictions = super ( Classifier , self ) . predict ( x = x , input_fn = input_fn , batch_size = batch_size , as_iterable = as_iterable , outputs = [ Classifier . PROBABILITY_OUTPUT ] ) if as_iterable : return ( p [ Classifier . PROBABILITY_OUTPUT ] for p in predictions ) else : return predictions [ Classifier . PROBABILITY_OUTPUT ]