input: Write a python function to train a Naive Bayes classifier on email data and return the accuracy score.
  assert round(train_naive_bayes_classifier('emails.csv'), 2) == 0.98
  assert round(train_naive_bayes_classifier('another_emails.csv'), 2) == 0.95
output: def train_naive_bayes_classifier(file_path):
      import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import accuracy_score
  
      # Load the dataset
      data = pd.read_csv(file_path)
      
      # Ensure the data has the correct columns
      if 'email' not in data.columns or 'label' not in data.columns:
          raise ValueError("Dataset must contain 'email' and 'label' columns")
      
      # Split the data into features and labels
      X = data['email']
      y = data['label']
      
      # Vectorize the text data
      vectorizer = CountVectorizer()
      X_vectorized = vectorizer.fit_transform(X)
      
      # Split the data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X_vectorized, y, test_size=0.2, random_state=42)
      
      # Train the Naive Bayes classifier
      clf = MultinomialNB()
      clf.fit(X_train, y_train)
      
      # Predict on the test set
      y_pred = clf.predict(X_test)
      
      # Calculate and return the accuracy score
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy