code: def level_sample_data_split(level_sample_data):
      """Split sample data into train and test sets."""
      train_test_ratio = 0.80
      level, data_df = level_sample_data
      n = data_df.shape[0]
      n_train = int(n * train_test_ratio)
      n_test = n - n_train
  
      data_train_df = data_df[:n_train]
      data_test_df = data_df[-n_test:]
  
      y_train = data_train_df["z"]
      X_train = data_train_df[["x"]]
      X_test = data_test_df[["x"]]
      fh = np.arange(X_test.shape[0]) + 1
      return level, y_train, X_train, X_test, fh
  
imports: import numpy
  import pandas
  import pytest
  from pandas.testing import assert_series_equal
  from sktime.datasets import load_airline, load_longley
  from sktime.forecasting.model_evaluation import evaluate
  from sktime.forecasting.structural import UnobservedComponents
  from sktime.split import SlidingWindowSplitter
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 2 for calling a global function with example usage: level_sample_data_split(level_sample_data) and returns: level,  y_train,  X_train,  X_test,  fh