code: def test_fitted_values_correct():
      simple_length, multi_length = 5, 5
      ts_input, _ = prepare_ts_for_in_sample(
          simple_length, multi_length
      )
  
      for fitted_func in [fitted_values, in_sample_fitted_values]:
          pipeline = get_simple_short_lagged_pipeline()
          train_predicted = pipeline.fit(ts_input)
  
          fitted_ts_values = fitted_func(ts_input, train_predicted)
  
          assert (
              len(fitted_ts_values.predict) == len(ts_input.target) - 4
          )
  
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 7 for calling a global function with example usage: test_fitted_values_correct() without return types