code: def prepare_data(forecast_length, is_multi_ts):
      """
      Function to form InputData from file with time-series
      """
      columns_to_use = ["61_91", "56_86", "61_86", "66_86"]
      target_column = "61_91"
      task = Task(
          TaskTypesEnum.ts_forecasting,
          TsForecastingParams(forecast_length=forecast_length),
      )
      file_path = os.path.join(
          str(fedot_project_root()),
          "cases/data/arctic/topaz_multi_ts.csv",
      )
      if is_multi_ts:
          data = InputData.from_csv_multi_time_series(
              file_path=file_path,
              task=task,
              columns_to_use=columns_to_use,
          )
      else:
          data = InputData.from_csv_time_series(
              file_path=file_path,
              task=task,
              target_column=target_column,
          )
      train_data, test_data = train_test_data_setup(data)
      return train_data, test_data, task
  
file_path: FEDOT/cases/multi_ts_level_forecasting.py
imports: import os
  import numpy
  from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error
  from examples.simple.time_series_forecasting.ts_pipelines import ts_complex_ridge_smoothing_pipeline
  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.tasks import TsForecastingParams, Task, TaskTypesEnum
  from fedot.core.utils import fedot_project_root
label_desc: Use image node_id 1 for calling a global function with example usage: prepare_data(forecast_length, is_multi_ts) and returns: train_data,  test_data,  task