class_docstr: AutoML Hyperparameter Optimizer that randomly samples the space of random variables.
  Please refer to :class:`AutoML` for a usage example.
  
  .. seealso::
      :class:`Trainer`,
      :class:`HyperparamsRepository`,
class_docstr_tok: ["AutoML","Hyperparameter","Optimizer","that","randomly","samples","the","space","of","random","variables",".","Please","refer","to",":","class",":","`","AutoML","`","for","a","usage","example",".","..","seealso",":",":",":","class",":","`","Trainer","`",",",":","class",":","`","HyperparamsRepository","`",","]
docstr: Randomly sample the next hyperparams to try.
  
  :param round: round report
  :return: next random hyperparams
docstr_tok: ["Randomly","sample","the","next","hyperparams","to","try",".",":","param","round",":","round","report",":","return",":","next","random","hyperparams"]
code: def find_next_best_hyperparams(
      self, _round: RoundReport, hp_space: HyperparameterSpace
  ) -> HyperparameterSamples:
      """
      Randomly sample the next hyperparams to try.
  
      :param round: round report
      :return: next random hyperparams
      """
      return hp_space.rvs()
  
imports: import copy
  import itertools
  import math
  import operator
  import random
  import warnings
  from abc import ABC, abstractmethod
  from collections import OrderedDict
  from functools import reduce
  from typing import Any, List, Set, Tuple
  from neuraxle.hyperparams.distributions import ContinuousHyperparameterDistribution, DiscreteHyperparameterDistribution
  from neuraxle.hyperparams.space import FlatDict, HyperparameterSamples, HyperparameterSpace
  from neuraxle.metaopt.data.reporting import RoundReport
label_desc: Use image node_id 2 for calling the RandomSearchSampler obj's underlying member method code with example usage: obj.find_next_best_hyperparams(_round, hp_space) and returns: hp_space