class_docstr: Distributor using the python build-in map, which calculates each job sequentially one after the other.
class_docstr_tok: ["Distributor","using","the","python","build-in","map",",","which","calculates","each","job","sequentially","one","after","the","other","."]
docstr: For the map command, which calculates the features sequentially, a the chunk_size of 1 will be used.
  
  :param data_length: A length which defines how many calculations there need to be.
  :type data_length: int
docstr_tok: ["For","the","map","command",",","which","calculates","the","features","sequentially",",","a","the","chunk_size","of","1","will","be","used",".",":","param","data_length",":","A","length","which","defines","how","many","calculations","there","need","to","be",".",":","type","data_length",":","int"]
code: def calculate_best_chunk_size(self, data_length):
      """
      For the map command, which calculates the features sequentially, a the chunk_size of 1 will be used.
  
      :param data_length: A length which defines how many calculations there need to be.
      :type data_length: int
      """
      return 1
  
imports: import itertools
  import math
  import warnings
  from collections.abc import Generator, Iterable
  from functools import partial
  from itertools import islice, repeat, takewhile
  from multiprocessing import Pool
  from tqdm import tqdm
  from tsfresh.feature_extraction.data import TsData
label_desc: Use image node_id 3 for calling the MapDistributor obj's underlying member method code with example usage: obj.calculate_best_chunk_size(data_length) and returns: int