code: def get_cholesterol_dataset():
      data_path = f"{fedot_project_root()}/cases/data/cholesterol/cholesterol.csv"
      data = InputData.from_csv(
          data_path, task=Task(TaskTypesEnum.regression)
      )
      train, test = train_test_data_setup(data)
      return train, 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 3 for calling a global function with example usage: get_cholesterol_dataset() and returns: train,  test