docstr: Returns `self._fitted_params` if `calling_fit = False`, otherwise returns an empty
  tuple. If `calling_fit = False`, also checks that `self._fitted_params`, which is a
  sequence of values, contains exactly `n_timeseries` values; if not, a `ValueError` is thrown.
docstr_tok: ["Returns","`","self._fitted_params","`","if","`","calling_fit","=","False","`",",","otherwise","returns","an","empty","tuple",".","If","`","calling_fit","=","False","`",",","also","checks","that","`","self._fitted_params","`",",","which","is","a","sequence","of","values",",","contains","exactly","`","n_timeseries","`","values",";","if","not",",","a","`","ValueError","`","is","thrown","."]
code: def _get_fitted_params(
      self, n_timeseries: int, calling_fit: bool
  ) -> Sequence[Any]:
      """
      Returns `self._fitted_params` if `calling_fit = False`, otherwise returns an empty
      tuple. If `calling_fit = False`, also checks that `self._fitted_params`, which is a
      sequence of values, contains exactly `n_timeseries` values; if not, a `ValueError` is thrown.
      """
      if not calling_fit:
          raise_if_not(
              self._fit_called,
              (
                  "Must call `fit` before calling `transform`/`inverse_transform`."
              ),
          )
          fitted_params = self._fitted_params
      else:
          fitted_params = tuple()
      if not self._global_fit and fitted_params:
          raise_if(
              n_timeseries > len(fitted_params),
              (
                  f"{n_timeseries} TimeSeries were provided "
                  f"but only {len(fitted_params)} TimeSeries "
                  f"were specified upon training {self.name}."
              ),
          )
      return fitted_params
  
file_path: darts/darts/dataprocessing/transformers/fittable_data_transformer.py
imports: from abc import abstractmethod
  from typing import Any, Generator, List, Mapping, Optional, Sequence, Union
  import numpy
  from darts import TimeSeries
  from darts.logging import get_logger, raise_if, raise_if_not
  from darts.utils import _build_tqdm_iterator, _parallel_apply
  from .base_data_transformer import BaseDataTransformer
label_desc: Use image node_id 6 for calling the FittableDataTransformer obj's underlying member method code with example usage: obj._get_fitted_params(n_timeseries, calling_fit) and returns: fitted_params