class_docstr: Generates trials to be passed to the TrialRunner.
  
  Uses the provided ``searcher`` object to generate trials. This class
  transparently handles repeating trials with score aggregation
  without embedding logic into the Searcher.
  
  Args:
      searcher: Search object that subclasses the Searcher base class. This
          is then used for generating new hyperparameter samples.
docstr: Provides one Trial object to be queued into the TrialRunner.
  
  Returns:
      Trial: Returns a single trial.
docstr_tok: ["Provides","one","Trial","object","to","be","queued","into","the","TrialRunner",".","Returns",":","Trial",":","Returns","a","single","trial","."]
code: def next_trial(self):
      """Provides one Trial object to be queued into the TrialRunner.
  
      Returns:
          Trial: Returns a single trial.
      """
      if not self.is_finished():
          return self.create_trial_if_possible(self._experiment.spec)
      return None
  
imports: import copy
  import logging
  from typing import Dict, List, Optional, Union
  from ray.tune.error import TuneError
  from ray.tune.experiment import Experiment, _convert_to_experiment_list
  from ray.tune.experiment.config_parser import _make_parser, _create_trial_from_spec
  from ray.tune.search.search_algorithm import SearchAlgorithm
  from ray.tune.search import Searcher
  from ray.tune.search.util import _set_search_properties_backwards_compatible
  from ray.tune.search.variant_generator import format_vars, _resolve_nested_dict
  from ray.tune.experiment import Trial
  from ray.tune.utils.util import flatten_dict, merge_dicts, _atomic_save, _load_newest_checkpoint
  from ray.util.annotations import DeveloperAPI
label_desc: Use image node_id 6 for calling the SearchGenerator obj's underlying member method code with example usage: obj.next_trial() and returns: None, self