class_docstr: Base class for various iteration possibilities. This decides what configuration should be run on what budget
  next. Typical choices are e.g. successive halving. Results from runs are processed and (depending on the
  implementations) determine the further development.
docstr: Parameters
  ----------
  
  HPB_iter: int
          The current HPBandSter iteration index.
  num_configs: list of ints
          the number of configurations in each stage of SH
  budgets: list of floats
          the budget associated with each stage
  config_sample: callable
          a function that returns a valid configuration. Its only
          argument should be the budget that this config is first
          scheduled for. This might be used to pick configurations
          that perform best after this particular budget is exhausted
          to build a better autoML system.
  logger: a logger
  result_logger: hpbandster.api.results.util.json_result_logger object
          a result logger that writes live results to disk
docstr_tok: ["Parameters","--","--","--","--","--","HPB_iter",":","int","The","current","HPBandSter","iteration","index",".","num_configs",":","list","of","ints","the","number","of","configurations","in","each","stage","of","SH","budgets",":","list","of","floats","the","budget","associated","with","each","stage","config_sample",":","callable","a","function","that","returns","a","valid","configuration",".","Its","only","argument","should","be","the","budget","that","this","config","is","first","scheduled","for",".","This","might","be","used","to","pick","configurations","that","perform","best","after","this","particular","budget","is","exhausted","to","build","a","better","autoML","system",".","logger",":","a","logger","result_logger",":","hpbandster.api.results.util.json_result_logger","object","a","result","logger","that","writes","live","results","to","disk"]
code: def __init__(
      self,
      HPB_iter,
      num_configs,
      budgets,
      config_sampler,
      logger=None,
      result_logger=None,
  ):
      """
      Parameters
      ----------
  
      HPB_iter: int
              The current HPBandSter iteration index.
      num_configs: list of ints
              the number of configurations in each stage of SH
      budgets: list of floats
              the budget associated with each stage
      config_sample: callable
              a function that returns a valid configuration. Its only
              argument should be the budget that this config is first
              scheduled for. This might be used to pick configurations
              that perform best after this particular budget is exhausted
              to build a better autoML system.
      logger: a logger
      result_logger: hpbandster.api.results.util.json_result_logger object
              a result logger that writes live results to disk
      """
  
      self.data = (
          {}
      )  # this holds all the configs and results of this iteration
      self.is_finished = False
      self.HPB_iter = HPB_iter
      self.stage = (
          0  # internal iteration, but different name for clarity
      )
      self.budgets = budgets
      self.num_configs = num_configs
      self.actual_num_configs = [0] * len(num_configs)
      self.config_sampler = config_sampler
      self.num_running = 0
      self.logger = (
          logger
          if not logger is None
          else logging.getLogger("hpbandster")
      )
      self.result_logger = result_logger
  
imports: import sys
  import logging
  import numpy
  import pdb
  from hpbandster.core.dispatcher import Job
label_desc: Use image node_id 1 to create a new BaseIteration object from inherited base classes: object with example: obj = BaseIteration(HPB_iter, num_configs, budgets, config_sampler, logger, result_logger)