code: def test_tuner_builder_with_default_params():
      data = get_classification_data()
      pipeline = pipeline_first_test()
      tuner = TunerBuilder(data.task).build(data)
      objective_evaluate = get_objective_evaluate(
          ClassificationMetricsEnum.ROCAUC_penalty, data
      )
      assert isinstance(tuner, HyperoptTuner)
      assert np.isclose(
          tuner.objective_evaluate(pipeline).value,
          objective_evaluate.evaluate(pipeline).value,
      )
      assert isinstance(tuner.search_space, PipelineSearchSpace)
      assert tuner.iterations == DEFAULT_TUNING_ITERATIONS_NUMBER
      assert tuner.algo == tpe.suggest
      assert tuner.max_seconds == 300
  
imports: from datetime import timedelta
  from typing import Optional
  import numpy
  import pytest
  from golem.core.tuning.hyperopt_tuner import HyperoptTuner
  from golem.core.tuning.iopt_tuner import IOptTuner
  from golem.core.tuning.sequential import SequentialTuner
  from golem.core.tuning.simultaneous import SimultaneousTuner
  from hyperopt import tpe
  from fedot.core.constants import DEFAULT_TUNING_ITERATIONS_NUMBER
  from fedot.core.data.data import InputData
  from fedot.core.optimisers.objective import PipelineObjectiveEvaluate
  from fedot.core.optimisers.objective.data_source_splitter import DataSourceSplitter
  from fedot.core.optimisers.objective.metrics_objective import MetricsObjective
  from fedot.core.pipelines.tuning.search_space import PipelineSearchSpace
  from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
  from fedot.core.repository.metrics_repository import ClassificationMetricsEnum, MetricIDType
  from test.integration.pipelines.tuning.test_pipeline_tuning import get_not_default_search_space
  from test.unit.optimizer.test_pipeline_objective_eval import pipeline_first_test
  from test.unit.validation.test_table_cv import get_classification_data
label_desc: Use image node_id 2 for calling a global function with example usage: test_tuner_builder_with_default_params() without return types