input: Write a function to load data from a CSV file and split it into training and test sets.
  assert len(split_data('data.csv')[0]) == int(len(pd.read_csv('data.csv')) * 0.8)
  assert len(split_data('data.csv')[1]) == int(len(pd.read_csv('data.csv')) * 0.2)
  assert len(split_data('data.csv')[2]) == int(len(pd.read_csv('data.csv')['winner']) * 0.8)
  assert len(split_data('data.csv')[3]) == int(len(pd.read_csv('data.csv')['winner']) * 0.2)
output: import pandas as pd
  from sklearn.model_selection import train_test_split
  
  def split_data(file_path):
      data = pd.read_csv(file_path)
      train, test = train_test_split(data, test_size=0.2, random_state=42)
      train_winner = train['winner']
      test_winner = test['winner']
      return train, test, train_winner, test_winner