input: Write a python function to load a CSV file, separate features and labels, and split the data into training and testing sets.
  assert isinstance(split_data('house_prices_data.csv'), tuple)
  assert len(split_data('house_prices_data.csv')) == 4
  assert split_data('house_prices_data.csv')[0].shape[1] == 3
output: import pandas as pd
  from sklearn.model_selection import train_test_split
  
  def split_data(file_path):
      # Load the CSV file
      data = pd.read_csv(file_path)
      
      # Assuming the last column is the label
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
      
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      return X_train, X_test, y_train, y_test