input: Write a python function to load a CSV file, split the data into features and labels, and then into training and testing sets.
  assert X_train.shape[0] > 0 and X_test.shape[0] > 0
  assert y_train.shape[0] > 0 and y_test.shape[0] > 0
output: def load_and_split_csv(file_path, label_column, test_size=0.2, random_state=42):
      import pandas as pd
      from sklearn.model_selection import train_test_split
  
      # Load the CSV file
      data = pd.read_csv(file_path)
  
      # Split into features and labels
      X = data.drop(columns=[label_column])
      y = data[label_column]
  
      # Split into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)
  
      return X_train, X_test, y_train, y_test