code: def get_data(data_length=500, test_length=100):
      garmonics = [
          (0.1, 0.9),
          (0.1, 1),
          (0.1, 1.1),
          (0.05, 2),
          (0.05, 5),
          (1, 0.02),
      ]
      time = np.linspace(0, 100, data_length)
      data = time * 0
      for g in garmonics:
          data += g[0] * np.sin(g[1] * 2 * np.pi / time[-1] * 25 * time)
  
      data = InputData(
          idx=np.arange(0, data.shape[0]),
          features=data,
          target=data,
          task=Task(
              TaskTypesEnum.ts_forecasting,
              TsForecastingParams(forecast_length=test_length),
          ),
          data_type=DataTypesEnum.ts,
      )
      return train_test_data_setup(data, validation_blocks=1)
  
file_path: FEDOT/test/integration/real_applications/test_model_result_reproducing.py
imports: from typing import List
  import numpy
  from fedot import Fedot
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
label_desc: Use image node_id 1 for calling a global function with example usage: get_data(data_length, test_length) and returns: train_test_data_setup