input: 
  def perform_sentiment_analysis(data: pd.DataFrame) -> str:
      """Perform sentiment analysis on product reviews using natural language processing techniques.
      Tokenize the reviews, remove stopwords, normalize the words, and generate features using CountVectorizer.
      Finally, train a Naive Bayes classifier and return the classification report.
      >>> data = pd.DataFrame({
      ...     'review': ["This product is great!", "I hate this product.", "This product is fantastic."],
      ...     'sentiment': ['positive', 'negative', 'positive']
      ... })
      >>> perform_sentiment_analysis(data)
      '              precision    recall  f1-score   support\n\n    negative       1.00      1.00      1.00         1\n    positive       1.00      1.00      1.00         2\n\n    accuracy                           1.00         3\n   macro avg       1.00      1.00      1.00         3\nmicro avg       1.00      1.00      1.00         3\n'
      >>> data = pd.DataFrame({
      ...     'review': ["This is okay.", "I don't like it.", "Not bad."],
      ...     'sentiment': ['neutral', 'negative', 'neutral']
      ... })
      >>> perform_sentiment_analysis(data)
      '              precision    recall  f1-score   support\n\n    negative       1.00      1.00      1.00         1\n    neutral        1.00      1.00      1.00         2\n\n    accuracy                           1.00         3\n   macro avg       1.00      1.00      1.00         3\nmicro avg       1.00      1.00      1.00         3\n'
      """
  
      # Tokenise the texts
      data['tokenised'] = data['review'].apply(word_tokenize)
  
      # Remove stopwords
      stop_words = set(stopwords.words('english'))
      data['filtered'] = data['tokenised'].apply(lambda x: [val for val in x if val not in stop_words])
  
      # Normalize words
      wordnet_lemmatizer = WordNetLemmatizer()
      data['normalised'] = data['filtered'].apply(lambda x: [wordnet_lemmatizer.lemmatize(val) for val in x])
  
      # Generate features using CountVectorizer
      cv = CountVectorizer()
      data['features'] = cv.fit_transform(data['normalised'].astype('str')).toarray()
  
      # Split into training and test sets
      X = data['features']
      y = data['sentiment']
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Train model
      model = MultinomialNB(alpha=0.1)
      model.fit(X_train, y_train)
  
      # Predict using the test set
      predictions = model.predict(X_test)
  
      # Generate a classification report
      report = classification_report(y_test, predictions)
      return report
  
output:     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 classification_report
      from nltk.tokenize import word_tokenize
      from nltk.corpus import stopwords
      from nltk.stem import WordNetLemmatizer
      import pandas as pd