class_docstr: Pipeline for applying ``SeriesProcessor`` objects sequentially.
  
  Parameters
  ----------
  processors : List[Union[SeriesProcessor, SeriesPipeline]], optional
      List of ``SeriesProcessor`` or ``SeriesPipeline`` instances that will be applied
      sequentially to the internal series dict, by default None.
      **The processing steps will be executed in the same order as passed in this
      list.**
class_docstr_tok: ["Pipeline","for","applying","``","SeriesProcessor","``","objects","sequentially",".","Parameters","--","--","--","--","--","processors",":","List","[","Union","[","SeriesProcessor",",","SeriesPipeline","]","]",",","optional","List","of","``","SeriesProcessor","``","or","``","SeriesPipeline","``","instances","that","will","be","applied","sequentially","to","the","internal","series","dict",",","by","default","None",".","*","*","The","processing","steps","will","be","executed","in","the","same","order","as","passed","in","this","list",".","*","*"]
docstr: Return informal representation of object.
docstr_tok: ["Return","informal","representation","of","object","."]
code: def __str__(self):
      """Return informal representation of object."""
      return self.__repr__()
  
file_path: tsflex/tsflex/processing/series_pipeline.py
imports: from __future__ import annotations
  from pathlib import Path
  from typing import Dict, List, Optional, Union
  import dill
  import pandas
  from ..utils.data import flatten, series_dict_to_df, to_series_list
  from ..utils.logging import add_logging_handler, delete_logging_handlers
  from .logger import logger
  from .series_processor import SeriesProcessor
label_desc: Use image node_id 8 for calling the SeriesPipeline obj's underlying member method code with example usage: obj.__str__() and returns: self