docstr: Overrides `_get_params` of `BaseDataTransformer`. Creates generator of dictionaries containing
  both the fixed parameter values (i.e. attributes defined in the child-most class), as well as
  the fitted parameter values (only if `calling_fit = False`). Those fixed parameters
  specified by `parallel_params` are given different values over each of the parallel jobs;
  every fitted parameter is given a different value for each parallel job (since `self.fit`
  returns a `Sequence` containing one fitted parameter value of each parallel job). Called by
  `transform` and `inverse_transform`.
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code: def _get_params(
      self, n_timeseries: int, calling_fit: bool = False
  ) -> Generator[Mapping[str, Any], None, None]:
      """
      Overrides `_get_params` of `BaseDataTransformer`. Creates generator of dictionaries containing
      both the fixed parameter values (i.e. attributes defined in the child-most class), as well as
      the fitted parameter values (only if `calling_fit = False`). Those fixed parameters
      specified by `parallel_params` are given different values over each of the parallel jobs;
      every fitted parameter is given a different value for each parallel job (since `self.fit`
      returns a `Sequence` containing one fitted parameter value of each parallel job). Called by
      `transform` and `inverse_transform`.
      """
      # Call `_check_fixed_params` of `BaseDataTransformer`:
      self._check_fixed_params(n_timeseries)
      fitted_params = self._get_fitted_params(n_timeseries, calling_fit)
  
      def params_generator(
          n_jobs,
          fixed_params,
          fitted_params,
          parallel_params,
          global_fit,
      ):
          fixed_params_copy = fixed_params.copy()
          for i in range(n_jobs):
              for key in parallel_params:
                  fixed_params_copy[key] = fixed_params[key][i]
              params = {}
              if fixed_params_copy:
                  params["fixed"] = fixed_params_copy
              if fitted_params:
                  params["fitted"] = (
                      fitted_params[0]
                      if global_fit
                      else fitted_params[i]
                  )
              if not params:
                  params = None
              yield params
  
      n_jobs = (
          n_timeseries if not (calling_fit and self._global_fit) else 1
      )
  
      return params_generator(
          n_jobs,
          self._fixed_params,
          fitted_params,
          self._parallel_params,
          self._global_fit,
      )
  
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 5 for calling the FittableDataTransformer obj's underlying member method code with example usage: obj._get_params(n_timeseries, calling_fit) and returns: params_generator