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","."]
code: def __init__(
      self,
      time_attr: str,
      max_trials: int,
      init_t_attr: int,
      max_t_attr: int,
      eta: float,
      s: int,
      stop_last_trials: bool = True,
  ):
      self._live_trials = {}  # maps trial -> current result
      self._all_trials = []
      self._time_attr = time_attr  # attribute to
  
      self._n = self._n0 = max_trials
      self._r = self._r0 = init_t_attr
      self._max_t_attr = max_t_attr
      self._cumul_r = self._r0
  
      self._eta = eta
      self._halves = s
  
      self._total_work = self._calculate_total_work(
          self._n0, self._r0, s
      )
      self._completed_progress = 0
      self.stop_last_trials = stop_last_trials
      self.is_being_processed = False
  
      self.trials_to_unpause = set()
  
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 1 to create a new _Bracket object  with example: obj = _Bracket(time_attr, max_trials, init_t_attr, max_t_attr, eta, s, stop_last_trials)