parameters: (
        self, x=None, input_fn=None, batch_size=None, as_iterable=False)
return_statement: return preds[_PROBABILITIES]
docstring: Returns prediction probabilities for given features.
  
      Args:
        x: features.
        input_fn: Input function. If set, x and y must be None.
        batch_size: Override default batch size.
        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:
        Numpy array of predicted probabilities (or an iterable of predicted
        probabilities if as_iterable is True).
docstring_summary: Returns prediction probabilities for given features.
docstring_tokens: Returns prediction probabilities for given features .
function: def predict_proba(
        self, x=None, input_fn=None, batch_size=None, as_iterable=False):
      """Returns prediction probabilities for given features.
  
      Args:
        x: features.
        input_fn: Input function. If set, x and y must be None.
        batch_size: Override default batch size.
        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:
        Numpy array of predicted probabilities (or an iterable of predicted
        probabilities if as_iterable is True).
      """
      preds = self._estimator.predict(x=x, input_fn=input_fn,
                                      batch_size=batch_size,
                                      outputs=[_PROBABILITIES],
                                      as_iterable=as_iterable)
      if as_iterable:
        return _as_iterable(preds, output=_PROBABILITIES)
      return preds[_PROBABILITIES]
function_tokens: def predict_proba ( self , x = None , input_fn = None , batch_size = None , as_iterable = False ) : preds = self . _estimator . predict ( x = x , input_fn = input_fn , batch_size = batch_size , outputs = [ _PROBABILITIES ] , as_iterable = as_iterable ) if as_iterable : return _as_iterable ( preds , output = _PROBABILITIES ) return preds [ _PROBABILITIES ]