parameters: (self, x)
return_statement: return outputs
docstring: Returns predictions for a single batch of samples.
  
      Arguments:
          x: Input samples, as a Numpy array.
  
      Returns:
          Numpy array(s) of predictions.
docstring_summary: Returns predictions for a single batch of samples.
docstring_tokens: Returns predictions for a single batch of samples .
function: def predict_on_batch(self, x):
      """Returns predictions for a single batch of samples.
  
      Arguments:
          x: Input samples, as a Numpy array.
  
      Returns:
          Numpy array(s) of predictions.
      """
      x = _standardize_input_data(x, self._feed_input_names,
                                  self._feed_input_shapes)
      if self.uses_learning_phase and not isinstance(K.learning_phase(), int):
        ins = x + [0.]
      else:
        ins = x
      self._make_predict_function()
      outputs = self.predict_function(ins)
      if len(outputs) == 1:
        return outputs[0]
      return outputs
function_tokens: def predict_on_batch ( self , x ) : x = _standardize_input_data ( x , self . _feed_input_names , self . _feed_input_shapes ) if self . uses_learning_phase and not isinstance ( K . learning_phase ( ) , int ) : ins = x + [ 0. ] else : ins = x self . _make_predict_function ( ) outputs = self . predict_function ( ins ) if len ( outputs ) == 1 : return outputs [ 0 ] return outputs