input: Write a python function to train a Multinomial Naive Bayes classifier on movie reviews and print the accuracy.
  assert train_and_evaluate_nb_classifier('sample_movie_reviews.csv') > 0.8
output: def train_and_evaluate_nb_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)
      
      # Assuming the dataset has columns 'review' for text and 'sentiment' for labels
      X = data['review']
      y = data['sentiment']
      
      # Convert text data into numerical data using CountVectorizer
      vectorizer = CountVectorizer()
      X_vectorized = vectorizer.fit_transform(X)
      
      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X_vectorized, y, test_size=0.2, random_state=42)
      
      # Create and train the Multinomial Naive Bayes classifier
      nb_classifier = MultinomialNB()
      nb_classifier.fit(X_train, y_train)
      
      # Make predictions on the test set
      y_pred = nb_classifier.predict(X_test)
      
      # Calculate and print the accuracy
      accuracy = accuracy_score(y_test, y_pred)
      print(f"Accuracy: {accuracy}")
      
      return accuracy