code: def prepare_data():
      test_file_path = str(os.path.dirname(__file__))
      df = np.asarray(
          pd.read_csv(
              os.path.join(
                  test_file_path, "../../data/simple_sea_level.csv"
              )
          )
      )
      df = np.delete(df, 0, 1)
      df = df[0:100, :]
      t_arr = range(len(df))
  
      percent_train = 0.7
      n_train = round(percent_train * len(t_arr))
  
      hist = df
  
      forecast_length = len(t_arr) - n_train
      ds = {}
  
      task = Task(
          TaskTypesEnum.ts_forecasting,
          task_params=TsForecastingParams(
              forecast_length=forecast_length
          ),
      )
  
      idx = np.asarray(t_arr)
  
      for i in range(df.shape[1]):
          ds[f"data_source_ts/hist_{i}"] = InputData(
              idx=idx,
              features=hist[:, i],
              target=hist[:, 0],
              data_type=DataTypesEnum.ts,
              task=task,
          )
  
          ds[f"data_source_ts/exog_{i}"] = InputData(
              idx=idx,
              features=deepcopy(df[:, i]),
              target=deepcopy(hist),
              data_type=DataTypesEnum.ts,
              task=task,
          )
  
      input_data_train, input_data_test = train_test_data_setup(
          MultiModalData(ds)
      )
  
      return input_data_train, input_data_test
  
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 5 for calling a global function with example usage: prepare_data() and returns: input_data_train,  input_data_test