class_docstr: Logical object for tracking Hyperband bracket progress. Keeps track
  of proper parameters as designated by HyperBand.
  
  Also keeps track of progress to ensure good scheduling.
class_docstr_tok: ["Logical","object","for","tracking","Hyperband","bracket","progress",".","Keeps","track","of","proper","parameters","as","designated","by","HyperBand",".","Also","keeps","track","of","progress","to","ensure","good","scheduling","."]
docstr: Clean up statistics tracking for terminated trials (either by force
  or otherwise).
  
  This may cause bad trials to continue for a long time, in the case
  where all the good trials finish early and there are only bad trials
  left in a bracket with a large max-iteration.
docstr_tok: ["Clean","up","statistics","tracking","for","terminated","trials","(","either","by","force","or","otherwise",")",".","This","may","cause","bad","trials","to","continue","for","a","long","time",",","in","the","case","where","all","the","good","trials","finish","early","and","there","are","only","bad","trials","left","in","a","bracket","with","a","large","max-iteration","."]
code: def cleanup_trial(self, trial: Trial):
      """Clean up statistics tracking for terminated trials (either by force
      or otherwise).
  
      This may cause bad trials to continue for a long time, in the case
      where all the good trials finish early and there are only bad trials
      left in a bracket with a large max-iteration."""
      self._live_trials.pop(trial, None)
  
imports: import collections
  from typing import Dict, List, Optional, Tuple, TYPE_CHECKING
  import numpy
  import logging
  from ray.util.annotations import PublicAPI
  from ray.tune.result import DEFAULT_METRIC
  from ray.tune.schedulers.trial_scheduler import FIFOScheduler, TrialScheduler
  from ray.tune.experiment import Trial
  from ray.tune.error import TuneError
label_desc: Use image node_id 10 for calling the _Bracket obj's underlying member method code with example usage: obj.cleanup_trial(trial) without return types