parameters: (self, features, mode, state)
docstring: Return predictions, losses, and end state for a time series.
  
      Args:
        features: A dictionary with times, values, and (optionally) exogenous
            regressors. See `define_loss`.
        mode: The tf.estimator.ModeKeys mode to use (TRAIN, EVAL, INFER).
        state: Model-dependent state, each with size [batch size x ...]. The
            number and type will typically be fixed by the model (for example a
            mean and variance).
      Returns:
        A ModelOutputs object.
docstring_summary: Return predictions, losses, and end state for a time series.
docstring_tokens: Return predictions losses and end state for a time series .
function: def get_batch_loss(self, features, mode, state):
      """Return predictions, losses, and end state for a time series.
  
      Args:
        features: A dictionary with times, values, and (optionally) exogenous
            regressors. See `define_loss`.
        mode: The tf.estimator.ModeKeys mode to use (TRAIN, EVAL, INFER).
        state: Model-dependent state, each with size [batch size x ...]. The
            number and type will typically be fixed by the model (for example a
            mean and variance).
      Returns:
        A ModelOutputs object.
      """
      pass
function_tokens: def get_batch_loss ( self , features , mode , state ) : pass