class_docstr: Distributor using a dask cluster, meaning that the calculation is spread over a cluster
class_docstr_tok: ["Distributor","using","a","dask","cluster",",","meaning","that","the","calculation","is","spread","over","a","cluster"]
docstr: Sets up a distributor that connects to a Dask Scheduler to distribute the calculation of the features
  
  :param address: the ip address and port number of the Dask Scheduler
  :type address: str
docstr_tok: ["Sets","up","a","distributor","that","connects","to","a","Dask","Scheduler","to","distribute","the","calculation","of","the","features",":","param","address",":","the","ip","address","and","port","number","of","the","Dask","Scheduler",":","type","address",":","str"]
code: def __init__(self, address):
      """
      Sets up a distributor that connects to a Dask Scheduler to distribute the calculation of the features
  
      :param address: the ip address and port number of the Dask Scheduler
      :type address: str
      """
  
      from distributed import Client
  
      self.client = Client(address=address)
  
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 1 to create a new ClusterDaskDistributor object from inherited base classes: IterableDistributorBaseClass with example: obj = ClusterDaskDistributor(address)