code: def test_grid_exploration_sampler_can_try_everything():
      hp_space = HyperparameterSpace(
          {
              "a": RandInt(1, 3),
              "b": RandInt(1, 3),
              "c": RandInt(1, 3),
          }
      )
      max_trials = 3 * 3 * 3
      ges = GridExplorationSampler(max_trials)
      _round: Round = Round.from_context(
          AutoMLContext.from_context(loc=ScopedLocation.default(0))
      )
      _round.with_optimizer(ges, hp_space)
  
      for _ in range(max_trials):
          with _round.new_rvs_trial():
              pass
  
      trials_hps: List[FlatDict] = _round.report.get_all_hyperparams(
          as_flat=True
      )
      unique_trials = set([tuple(r.items()) for r in trials_hps])
      assert len(unique_trials) == max_trials
  
file_path: Neuraxle/testing_neuraxle/metaopt/test_automl_sequence_validation_splitter.py
imports: from typing import List
  import numpy
  from neuraxle.base import ExecutionContext
  from neuraxle.data_container import DataContainer
  from neuraxle.hyperparams.distributions import RandInt
  from neuraxle.hyperparams.space import FlatDict, HyperparameterSpace
  from neuraxle.metaopt.auto_ml import AutoML
  from neuraxle.metaopt.callbacks import MetricCallback
  from neuraxle.metaopt.context import AutoMLContext
  from neuraxle.metaopt.data.aggregates import Round
  from neuraxle.metaopt.data.vanilla import ScopedLocation
  from neuraxle.metaopt.optimizer import GridExplorationSampler, RandomSearchSampler
  from neuraxle.metaopt.validation import KFoldCrossValidationSplitter, ValidationSplitter
  from neuraxle.pipeline import Pipeline
  from neuraxle.steps.numpy import MultiplyByN
  from sklearn.metrics import mean_squared_error
label_desc: Use image node_id 3 for calling a global function with example usage: test_grid_exploration_sampler_can_try_everything() without return types