input: Write a function to load a dataset from a CSV file and split it into training and testing sets.
  assert load_and_split_dataset("spam_classification.csv") == (X_train, X_test, y_train, y_test)
  assert len(X_train) + len(X_test) == len(pd.read_csv("spam_classification.csv")["Message"])
  assert len(y_train) + len(y_test) == len(pd.read_csv("spam_classification.csv")["Label"])
output: import pandas as pd
  from sklearn.model_selection import train_test_split
  
  def load_and_split_dataset(file_path):
      # Load the dataset from the CSV file
      dataset = pd.read_csv(file_path)
      
      # Split the dataset into features and target variable
      X = dataset["Message"]
      y = dataset["Label"]
      
      # Split the dataset 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