input: Write a python function to train a RandomForestRegressor model using a dataset and return the mean squared error of the predictions.
  assert train_random_forest("example_dataset.csv") < 1000
  assert train_random_forest("another_dataset.csv") > 10
  assert isinstance(train_random_forest("yet_another_dataset.csv"), float)
output: def train_random_forest(dataset_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 dataset
      data = pd.read_csv(dataset_path)
      
      # Assume the last column is the target and the rest are features
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
      
      # 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)
      
      # Create and train the RandomForestRegressor
      model = RandomForestRegressor(n_estimators=100, random_state=42)
      model.fit(X_train, y_train)
      
      # Make predictions
      y_pred = model.predict(X_test)
      
      # Calculate mean squared error
      mse = mean_squared_error(y_test, y_pred)
      
      return mse