code: def get_split_data():
      task_type = "regression"
      train_full, test = get_split_data_paths()
      train_file = pd.read_csv(train_full)
      x, y = (
          train_file.loc[
              :, ~train_file.columns.isin(["target"])
          ].values,
          train_file["target"].values,
      )
      x_train, x_test, y_train, y_test = train_test_split(
          x, y, test_size=0.15, random_state=24
      )
      return task_type, x_train, x_test, y_train, y_test
  
imports: import os
  from typing import Optional
  import numpy
  import pandas
  from sklearn.model_selection import train_test_split
  from cases.metocean_forecasting_problem import prepare_input_data
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.supplementary_data import SupplementaryData
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum
  from fedot.core.utils import fedot_project_root
  from test.integration.models.test_split_train_test import get_synthetic_input_data
  from test.unit.tasks.test_classification import get_iris_data, get_synthetic_classification_data
  from test.unit.tasks.test_forecasting import get_ts_data
  from test.unit.tasks.test_regression import get_synthetic_regression_data
label_desc: Use image node_id 2 for calling a global function with example usage: get_split_data() and returns: task_type,  x_train,  x_test,  y_train,  y_test