parameters: (self, x=None, input_fn=None, batch_size=None,
                        as_iterable=True)
return_statement: return preds[key].reshape(-1)
docstring: Returns predicted classes for given features.
  
      Args:
        x: features.
        input_fn: Input function. If set, x 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 classes with shape [batch_size] (or an iterable
        of predicted classes if as_iterable is True). Each predicted class is
        represented by its class index (i.e. integer from 0 to n_classes-1).
docstring_summary: Returns predicted classes for given features.
docstring_tokens: Returns predicted classes for given features .
function: def predict_classes(self, x=None, input_fn=None, batch_size=None,
                        as_iterable=True):
      """Returns predicted classes for given features.
  
      Args:
        x: features.
        input_fn: Input function. If set, x 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 classes with shape [batch_size] (or an iterable
        of predicted classes if as_iterable is True). Each predicted class is
        represented by its class index (i.e. integer from 0 to n_classes-1).
      """
      key = prediction_key.PredictionKey.CLASSES
      preds = super(DNNClassifier, self).predict(
          x=x,
          input_fn=input_fn,
          batch_size=batch_size,
          outputs=[key],
          as_iterable=as_iterable)
      if as_iterable:
        return (pred[key] for pred in preds)
      return preds[key].reshape(-1)
function_tokens: def predict_classes ( self , x = None , input_fn = None , batch_size = None , as_iterable = True ) : key = prediction_key . PredictionKey . CLASSES preds = super ( DNNClassifier , self ) . predict ( x = x , input_fn = input_fn , batch_size = batch_size , outputs = [ key ] , as_iterable = as_iterable ) if as_iterable : return ( pred [ key ] for pred in preds ) return preds [ key ] . reshape ( - 1 )