code: def test_out_of_sample_ts_forecast_correct():
      simple_length = 2
      multi_length = 10
      train_input, predict_input = prepare_ts_for_in_sample(
          simple_length, multi_length
      )
  
      pipeline = get_simple_short_lagged_pipeline()
      pipeline.fit(train_input)
  
      # Make simple prediction
      simple_predict = pipeline.predict(predict_input)
      simple_predicted = np.ravel(np.array(simple_predict.predict))
  
      # Make multi-step forecast for 10 elements (2 * 5 steps)
      multi_predicted = out_of_sample_ts_forecast(
          pipeline=pipeline,
          input_data=predict_input,
          horizon=multi_length,
      )
  
      assert len(simple_predicted) == simple_length
      assert len(multi_predicted) == multi_length
  
imports: import os
  import numpy
  import pandas
  from sklearn.metrics import mean_absolute_percentage_error
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.pipeline_builder import PipelineBuilder
  from fedot.core.pipelines.ts_wrappers import fitted_values
  from fedot.core.pipelines.ts_wrappers import in_sample_ts_forecast, out_of_sample_ts_forecast, in_sample_fitted_values
  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
label_desc: Use image node_id 4 for calling a global function with example usage: test_out_of_sample_ts_forecast_correct() without return types