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.
code: def create_trial_if_possible(
      self, experiment_spec: Dict
  ) -> Optional[Trial]:
      logger.debug("creating trial")
      trial_id = Trial.generate_id()
      suggested_config = self.searcher.suggest(trial_id)
      if suggested_config == Searcher.FINISHED:
          self._finished = True
          logger.debug("Searcher has finished.")
          return
  
      if suggested_config is None:
          return
      spec = copy.deepcopy(experiment_spec)
      spec["config"] = merge_dicts(
          spec["config"], copy.deepcopy(suggested_config)
      )
  
      # Create a new trial_id if duplicate trial is created
      flattened_config = _resolve_nested_dict(spec["config"])
      self._counter += 1
      tag = "{0}_{1}".format(
          str(self._counter), format_vars(flattened_config)
      )
      trial = _create_trial_from_spec(
          spec,
          self._parser,
          evaluated_params=flatten_dict(suggested_config),
          experiment_tag=tag,
          trial_id=trial_id,
      )
      return trial
  
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 7 for calling the SearchGenerator obj's underlying member method code with example usage: obj.create_trial_if_possible(experiment_spec) and returns: trial