code: def test_split_data():
      dataframe = pd.DataFrame(
          data=[
              [1, 2, 3],
              [4, 5, 6],
              [7, 8, 9],
              [10, 11, 12],
              [13, 14, 15],
          ]
      )
      train, test = split_data(dataframe)
  
      assert len(train) == 4
      assert len(test) == 1
  
imports: from functools import partial
  from typing import Callable
  import numpy
  import pandas
  import pytest
  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.optimisers.objective.data_source_splitter import DataSourceSplitter
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
  from fedot.core.utils import split_data
  from fedot.core.data.cv_folds import cv_generator
  from test.unit.pipelines.test_decompose_pipelines import get_classification_data
  from test.unit.tasks.test_forecasting import get_ts_data
label_desc: Use image node_id 10 for calling a global function with example usage: test_split_data() without return types