class_docstr: BOHB suggestion component.
  
  
  Requires HpBandSter and ConfigSpace to be installed. You can install
  HpBandSter and ConfigSpace with: ``pip install hpbandster ConfigSpace``.
  
  This should be used in conjunction with HyperBandForBOHB.
  
  Args:
      space: Continuous ConfigSpace search space.
          Parameters will be sampled from this space which will be used
          to run trials.
      bohb_config: configuration for HpBandSter BOHB algorithm
      metric: The training result objective value attribute. If None
          but a mode was passed, the anonymous metric `_metric` will be used
          per default.
      mode: One of {min, max}. Determines whether objective is
          minimizing or maximizing the metric attribute.
      points_to_evaluate: Initial parameter suggestions to be run
          first. This is for when you already have some good parameters
          you want to run first to help the algorithm make better suggestions
          for future parameters. Needs to be a list of dicts containing the
          configurations.
      seed: Optional random seed to initialize the random number
          generator. Setting this should lead to identical initial
          configurations at each run.
      max_concurrent: Number of maximum concurrent trials.
          If this Searcher is used in a ``ConcurrencyLimiter``, the
          ``max_concurrent`` value passed to it will override the
          value passed here. Set to <= 0 for no limit on concurrency.
  
  Tune automatically converts search spaces to TuneBOHB's format:
  
  .. code-block:: python
  
      config = {
          "width": tune.uniform(0, 20),
          "height": tune.uniform(-100, 100),
          "activation": tune.choice(["relu", "tanh"])
      }
  
      algo = TuneBOHB(metric="mean_loss", mode="min")
      bohb = HyperBandForBOHB(
          time_attr="training_iteration",
          metric="mean_loss",
          mode="min",
          max_t=100)
      run(my_trainable, config=config, scheduler=bohb, search_alg=algo)
  
  If you would like to pass the search space manually, the code would
  look like this:
  
  .. code-block:: python
  
      import ConfigSpace as CS
  
      config_space = CS.ConfigurationSpace()
      config_space.add_hyperparameter(
          CS.UniformFloatHyperparameter("width", lower=0, upper=20))
      config_space.add_hyperparameter(
          CS.UniformFloatHyperparameter("height", lower=-100, upper=100))
      config_space.add_hyperparameter(
          CS.CategoricalHyperparameter(
              name="activation", choices=["relu", "tanh"]))
  
      algo = TuneBOHB(
          config_space, metric="mean_loss", mode="min")
      bohb = HyperBandForBOHB(
          time_attr="training_iteration",
          metric="mean_loss",
          mode="min",
          max_t=100)
      run(my_trainable, scheduler=bohb, search_alg=algo)
class_docstr_tok: ["BOHB","suggestion","component",".","Requires","HpBandSter","and","ConfigSpace","to","be","installed",".","You","can","install","HpBandSter","and","ConfigSpace","with",":","``","pip","install","hpbandster","ConfigSpace","``",".","This","should","be","used","in","conjunction","with","HyperBandForBOHB",".","Args",":","space",":","Continuous","ConfigSpace","search","space",".","Parameters","will","be","sampled","from","this","space","which","will","be","used","to","run","trials",".","bohb_config",":","configuration","for","HpBandSter","BOHB","algorithm","metric",":","The","training","result","objective","value","attribute",".","If","None","but","a","mode","was","passed",",","the","anonymous","metric","`","_metric","`","will","be","used","per","default",".","mode",":","One","of","{","min",",","max","}",".","Determines","whether","objective","is","minimizing","or","maximizing","the","metric","attribute",".","points_to_evaluate",":","Initial","parameter","suggestions","to","be","run","first",".","This","is","for","when","you","already","have","some","good","parameters","you","want","to","run","first","to","help","the","algorithm","make","better","suggestions","for","future","parameters",".","Needs","to","be","a","list","of","dicts","containing","the","configurations",".","seed",":","Optional","random","seed","to","initialize","the","random","number","generator",".","Setting","this","should","lead","to","identical","initial","configurations","at","each","run",".","max_concurrent",":","Number","of","maximum","concurrent","trials",".","If","this","Searcher","is","used","in","a","``","ConcurrencyLimiter","``",",","the","``","max_concurrent","``","value","passed","to","it","will","override","the","value","passed","here",".","Set","to","<","=","0","for","no","limit","on","concurrency",".","Tune","automatically","converts","search","spaces","to","TuneBOHB","'s","format",":","..","code-block",":",":","python","config","=","{","``","width","''",":","tune.uniform","(","0",",","20",")",",","``","height","''",":","tune.uniform","(","-100",",","100",")",",","``","activation","''",":","tune.choice","(","[","``","relu","''",",","``","tanh","''","]",")","}","algo","=","TuneBOHB","(","metric=","''","mean_loss","''",",","mode=","''","min","''",")","bohb","=","HyperBandForBOHB","(","time_attr=","''","training_iteration","''",",","metric=","''","mean_loss","''",",","mode=","''","min","''",",","max_t=100",")","run","(","my_trainable",",","config=config",",","scheduler=bohb",",","search_alg=algo",")","If","you","would","like","to","pass","the","search","space","manually",",","the","code","would","look","like","this",":","..","code-block",":",":","python","import","ConfigSpace","as","CS","config_space","=","CS.ConfigurationSpace","(",")","config_space.add_hyperparameter","(","CS.UniformFloatHyperparameter","(","``","width","''",",","lower=0",",","upper=20",")",")","config_space.add_hyperparameter","(","CS.UniformFloatHyperparameter","(","``","height","''",",","lower=-100",",","upper=100",")",")","config_space.add_hyperparameter","(","CS.CategoricalHyperparameter","(","name=","''","activation","''",",","choices=","[","``","relu","''",",","``","tanh","''","]",")",")","algo","=","TuneBOHB","(","config_space",",","metric=","''","mean_loss","''",",","mode=","''","min","''",")","bohb","=","HyperBandForBOHB","(","time_attr=","''","training_iteration","''",",","metric=","''","mean_loss","''",",","mode=","''","min","''",",","max_t=100",")","run","(","my_trainable",",","scheduler=bohb",",","search_alg=algo",")"]
code: def on_unpause(self, trial_id: str):
      self.paused.discard(trial_id)
      self.running.add(trial_id)
  
imports: import copy
  import logging
  import math
  from ray import cloudpickle
  from typing import Dict, List, Optional, Union
  from ray.tune.result import DEFAULT_METRIC
  from ray.tune.search.sample import Categorical, Domain, Float, Integer, LogUniform, Normal, Quantized, Uniform
  from ray.tune.search import UNRESOLVED_SEARCH_SPACE, UNDEFINED_METRIC_MODE, UNDEFINED_SEARCH_SPACE, Searcher
  from ray.tune.search.variant_generator import parse_spec_vars
  from ray.tune.utils.util import flatten_dict, unflatten_list_dict
label_desc: Use image node_id 10 for calling the TuneBOHB obj's underlying member method code with example usage: obj.on_unpause(trial_id) without return types