parameters: (self, number_of_series, series_length,
                 model_parameters=None, seed=None)
docstring: Sample synthetic data from model parameters, with optional substitutions.
  
      Returns `number_of_series` possible sequences of future values, sampled from
      the generative model with each conditioned on the previous. Samples are
      based on trained parameters, except for those parameters explicitly
      overridden in `model_parameters`.
  
      For distributions over future observations, see predict().
  
      Args:
        number_of_series: Number of time series to create.
        series_length: Length of each time series.
        model_parameters: A dictionary mapping model parameters to values, which
            replace trained parameters when generating data.
        seed: If specified, return deterministic time series according to this
            value.
      Returns:
        A dictionary with keys TrainEvalFeatures.TIMES (mapping to an array with
        shape [number_of_series, series_length]) and TrainEvalFeatures.VALUES
        (mapping to an array with shape [number_of_series, series_length,
        num_features]).
docstring_summary: Sample synthetic data from model parameters, with optional substitutions.
docstring_tokens: Sample synthetic data from model parameters with optional substitutions .
function: def generate(self, number_of_series, series_length,
                 model_parameters=None, seed=None):
      """Sample synthetic data from model parameters, with optional substitutions.
  
      Returns `number_of_series` possible sequences of future values, sampled from
      the generative model with each conditioned on the previous. Samples are
      based on trained parameters, except for those parameters explicitly
      overridden in `model_parameters`.
  
      For distributions over future observations, see predict().
  
      Args:
        number_of_series: Number of time series to create.
        series_length: Length of each time series.
        model_parameters: A dictionary mapping model parameters to values, which
            replace trained parameters when generating data.
        seed: If specified, return deterministic time series according to this
            value.
      Returns:
        A dictionary with keys TrainEvalFeatures.TIMES (mapping to an array with
        shape [number_of_series, series_length]) and TrainEvalFeatures.VALUES
        (mapping to an array with shape [number_of_series, series_length,
        num_features]).
      """
      raise NotImplementedError("This model does not support generation.")
function_tokens: def generate ( self , number_of_series , series_length , model_parameters = None , seed = None ) : raise NotImplementedError ( "This model does not support generation." )