code: def get_input_data():
      test_file_path = str(os.path.dirname(__file__))
      df = pd.read_csv(
          os.path.join(
              test_file_path, "../../data/simple_sea_level.csv"
          )
      )
      time_series = np.array(df["Level"])
      len_forecast = 50
      train_input, predict_input = train_test_data_setup(
          InputData(
              idx=range(len(time_series)),
              features=time_series,
              target=time_series,
              task=Task(
                  TaskTypesEnum.ts_forecasting,
                  TsForecastingParams(forecast_length=len_forecast),
              ),
              data_type=DataTypesEnum.ts,
          )
      )
      return train_input, predict_input
  
imports: import logging
  import os
  import shutil
  from copy import deepcopy
  from typing import Any
  import numpy
  import pandas
  from examples.advanced.time_series_forecasting.custom_model_tuning import get_fitting_custom_pipeline
  from examples.simple.pipeline_import_export import create_correct_path
  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.data.multi_modal import MultiModalData
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
label_desc: Use image node_id 4 for calling a global function with example usage: get_input_data() and returns: train_input,  predict_input