docstr: Creates generator of dictionaries containing fixed parameter values
  (i.e. attributes defined in the child-most class). Those fixed parameters
  specified by `parallel_params` are given different values over each of the
  parallel jobs. Called by `transform` and `inverse_transform`,
  if `Transformer` does *not* inherit from `FittableTransformer`.
docstr_tok: ["Creates","generator","of","dictionaries","containing","fixed","parameter","values","(","i.e",".","attributes","defined","in","the","child-most","class",")",".","Those","fixed","parameters","specified","by","`","parallel_params","`","are","given","different","values","over","each","of","the","parallel","jobs",".","Called","by","`","transform","`","and","`","inverse_transform","`",",","if","`","Transformer","`","does","*","not","*","inherit","from","`","FittableTransformer","`","."]
code: def _get_params(
      self, n_timeseries: int
  ) -> Generator[Mapping[str, Any], None, None]:
      """
      Creates generator of dictionaries containing fixed parameter values
      (i.e. attributes defined in the child-most class). Those fixed parameters
      specified by `parallel_params` are given different values over each of the
      parallel jobs. Called by `transform` and `inverse_transform`,
      if `Transformer` does *not* inherit from `FittableTransformer`.
      """
      self._check_fixed_params(n_timeseries)
  
      def params_generator(n_timeseries, fixed_params, parallel_params):
          fixed_params_copy = fixed_params.copy()
          for i in range(n_timeseries):
              for key in parallel_params:
                  fixed_params_copy[key] = fixed_params[key][i]
              if fixed_params_copy:
                  params = {"fixed": fixed_params_copy}
              else:
                  params = None
              yield params
          return None
  
      return params_generator(
          n_timeseries, self._fixed_params, self._parallel_params
      )
  
imports: from abc import ABC, abstractmethod
  from typing import Any, Generator, List, Mapping, Optional, Sequence, Union
  import numpy
  import xarray
  from darts import TimeSeries
  from darts.logging import get_logger, raise_if, raise_if_not
  from darts.utils import _build_tqdm_iterator, _parallel_apply
label_desc: Use image node_id 6 for calling the BaseDataTransformer obj's underlying member method code with example usage: obj._get_params(n_timeseries) and returns: params_generator, None