code: def test_ts_single_pipeline_model_without_multioutput_support():
      time_series = generate_synthetic_data(20)
      len_forecast = 2
      train_part = time_series[:-len_forecast]
      test_part = time_series[-len_forecast:]
  
      task = Task(
          TaskTypesEnum.ts_forecasting,
          TsForecastingParams(forecast_length=len_forecast),
      )
  
      train_data = InputData(
          idx=np.arange(0, len(train_part)),
          features=train_part,
          target=train_part,
          task=task,
          data_type=DataTypesEnum.ts,
      )
  
      start_forecast = len(train_part)
      end_forecast = start_forecast + len_forecast
      idx_for_predict = np.arange(start_forecast, end_forecast)
  
      # Data for making prediction for a specific length
      test_data = InputData(
          idx=idx_for_predict,
          features=train_part,
          target=test_part,
          task=task,
          data_type=DataTypesEnum.ts,
      )
  
      for model_id in ["rfr", "gbr", "adareg", "svr", "sgdr"]:
          pipeline = get_simple_ts_pipeline(
              model_root=model_id, window_size=2
          )
  
          # making predictions for the missing part in the time series
          pipeline.fit_from_scratch(train_data)
          predicted_values = pipeline.predict(test_data)
          pipeline_forecast = np.ravel(
              np.array(predicted_values.predict)
          )
  
          test_part = np.ravel(np.array(test_part))
          mae = mean_absolute_error(test_part, pipeline_forecast)
          assert mae < 50
  
imports: from copy import deepcopy
  from typing import Optional
  import numpy
  import pandas
  import pytest
  from scipy import stats
  from sklearn.metrics import mean_absolute_error, mean_squared_error
  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.arima import ARIMAImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.statsmodels import AutoRegImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.ts_wrappers import in_sample_ts_forecast, out_of_sample_ts_forecast
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.core.utils import fedot_project_root
  from fedot.utilities.synth_dataset_generator import generate_synthetic_data
label_desc: Use image node_id 13 for calling a global function with example usage: test_ts_single_pipeline_model_without_multioutput_support() without return types