code: def prepare_input_data(
      train_file_path, test_file_path, history_size: int = 1000
  ):
      """Function for preparing InputData for train and test algorithm
  
      :param train_file_path: path to the csv file for training
      :param test_file_path: path to the csv file for validation
      :param history_size: length of time series
  
      :return dataset_to_train: InputData for train
      :return dataset_to_validate: InputData for validation
      """
      # Load train and test dataframes
      full_path_train = os.path.join(
          str(fedot_project_root()), train_file_path
      )
      full_path_test = os.path.join(
          str(fedot_project_root()), test_file_path
      )
      df_train = pd.read_csv(full_path_train)
      df_test = pd.read_csv(full_path_test)
  
      ws_history = np.ravel(np.array(df_train["wind_speed"]))[
          :history_size
      ]
      ssh_history = np.ravel(np.array(df_train["sea_height"]))[
          :history_size
      ]
      ssh_obs = np.ravel(np.array(df_test["sea_height"]))[:history_size]
  
      return ssh_history, ws_history, ssh_obs
  
file_path: FEDOT/cases/metocean_forecasting_problem.py
imports: import logging
  import os
  import numpy
  import pandas
  from fedot import Fedot
  from fedot.core.repository.tasks import TsForecastingParams
  from fedot.core.utils import fedot_project_root
label_desc: Use image node_id 1 for calling a global function with example usage: prepare_input_data(train_file_path, test_file_path, history_size) and returns: ssh_history,  ws_history,  ssh_obs