code: def test_ts_pipeline_with_stats_model(n_steps, tuner):
      """Tests tuners for time series forecasting task with AR model"""
      train_data, test_data = get_ts_data(
          n_steps=n_steps, forecast_length=5
      )
  
      ar_pipeline = Pipeline(PipelineNode("ar"))
  
      for search_space in [
          PipelineSearchSpace(),
          get_not_default_search_space(),
      ]:
          # Tune AR model
          tuner_ar = (
              TunerBuilder(train_data.task)
              .with_tuner(tuner)
              .with_metric(RegressionMetricsEnum.MSE)
              .with_iterations(3)
              .with_search_space(search_space)
              .build(train_data)
          )
          tuned_pipeline = tuner_ar.tune(ar_pipeline)
          assert tuned_pipeline is not None
          assert tuner_ar.obtained_metric is not None
  
imports: import os
  from time import time
  import pytest
  from fedot.core.repository.dataset_types import DataTypesEnum
  from golem.core.tuning.hyperopt_tuner import get_node_parameters_for_hyperopt
  from golem.core.tuning.iopt_tuner import IOptTuner
  from golem.core.tuning.optuna_tuner import OptunaTuner
  from golem.core.tuning.sequential import SequentialTuner
  from golem.core.tuning.simultaneous import SimultaneousTuner
  from golem.utilities.data_structures import ensure_wrapped_in_sequence
  from hyperopt import hp
  from hyperopt.pyll.stochastic import sample
  from examples.simple.time_series_forecasting.ts_pipelines import ts_complex_ridge_smoothing_pipeline, ts_glm_pipeline
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.statsmodels import GLMImplementation
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  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 RegressionMetricsEnum, ClassificationMetricsEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from fedot.core.utils import fedot_project_root, NESTED_PARAMS_LABEL
  from test.unit.multimodal.data_generators import get_single_task_multimodal_tabular_data, get_multimodal_pipeline
  from test.unit.tasks.test_forecasting import get_ts_data
label_desc: Use image node_id 30 for calling a global function with example usage: test_ts_pipeline_with_stats_model(n_steps, tuner) without return types