input: Write a python function to train a RandomForestRegressor on a given dataset and return the mean squared error of the predictions.
  assert train_and_evaluate_rf('winequality.csv') < 0.5
output: def train_and_evaluate_rf(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestRegressor
      from sklearn.metrics import mean_squared_error
  
      # Load the dataset
      data = pd.read_csv(file_path)
  
      # Assuming the target column is named 'quality' and is the last column
      X = data.iloc[:, :-1]  # Features
      y = data.iloc[:, -1]   # Target
  
      # Split the data into train and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Initialize the model
      rf = RandomForestRegressor(random_state=42)
  
      # Train the model
      rf.fit(X_train, y_train)
  
      # Make predictions
      y_pred = rf.predict(X_test)
  
      # Calculate mean squared error
      mse = mean_squared_error(y_test, y_pred)
  
      return mse