input: Write a python function to load a CSV file and split the data into training and testing sets.
  assert load_and_split_data('customer_data.csv')[0].shape[0] == int(load_and_split_data('customer_data.csv')[0].shape[0] / 0.8 * 0.8)
  assert load_and_split_data('customer_data.csv')[1].shape[0] == int(load_and_split_data('customer_data.csv')[0].shape[0] / 0.8 * 0.2)
  assert load_and_split_data('customer_data.csv')[2].shape[0] == load_and_split_data('customer_data.csv')[0].shape[0]
  assert load_and_split_data('customer_data.csv')[3].shape[0] == load_and_split_data('customer_data.csv')[1].shape[0]
output: import pandas as pd
  from sklearn.model_selection import train_test_split
  
  def load_and_split_data(filepath):
      data = pd.read_csv(filepath)
      train, test = train_test_split(data, test_size=0.2, random_state=42)
      return train, test, data, test