parameters: (
        self, x=None, input_fn=None, batch_size=None, as_iterable=False)
return_statement: return super(DNNLinearCombinedClassifier, self).predict(
          x=x, input_fn=input_fn, batch_size=batch_size, as_iterable=as_iterable)
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).
      """
      return super(DNNLinearCombinedClassifier, self).predict(
          x=x, input_fn=input_fn, batch_size=batch_size, as_iterable=as_iterable)
function_tokens: def predict_proba ( self , x = None , input_fn = None , batch_size = None , as_iterable = False ) : return super ( DNNLinearCombinedClassifier , self ) . predict ( x = x , input_fn = input_fn , batch_size = batch_size , as_iterable = as_iterable )