input: Write a python function to load a CSV file, split the data into features and labels, and train a RandomForestRegressor model. 
  assert len(train_random_forest('cars.csv')[0]) > 0
  assert len(train_random_forest('cars.csv')[1]) > 0
  assert len(train_random_forest('cars.csv')[2]) > 0
output: def train_random_forest(csv_file):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestRegressor
      
      # Load the CSV file into a DataFrame
      data = pd.read_csv(csv_file)
      
      # Assuming the last column is the label and rest are features
      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)
      
      # Train the RandomForestRegressor model
      model = RandomForestRegressor(random_state=42)
      model.fit(X_train, y_train)
      
      return X_train, y_train, model