input: Write a python function to load a CSV file, split the data into features and labels, and train a RandomForestClassifier. The function should return the accuracy of the classifier.
  assert train_random_forest('data.csv') > 0.5
output: def train_random_forest(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      # Load the CSV file
      data = pd.read_csv(file_path)
      
      # Assuming the last column is the label and the 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 RandomForestClassifier
      clf = RandomForestClassifier(random_state=42)
      clf.fit(X_train, y_train)
      
      # Make predictions on the test set
      y_pred = clf.predict(X_test)
      
      # Calculate the accuracy
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy