input: from typing import List
  
  
  def perform_sentiment_analysis(reviews: List[str]) -> str:
      """ Given a list of product reviews, perform sentiment analysis using natural language processing techniques.
      The function tokenizes the reviews, removes stopwords, normalizes words, and generates features using CountVectorizer.
      It then trains a Multinomial Naive Bayes classifier and predicts sentiments on the test set. Finally, it returns
      a detailed classification report.
      >>> perform_sentiment_analysis(['I love this product!', 'The worst experience ever.'])
      '              precision    recall  f1-score   support\\n\\n          neg       0.00      0.00      0.00         1\\n          pos       0.50      1.00      0.67       \
      1\\n\\n    accuracy                           0.50         2\\n   macro avg       0.25      0.50      0.33         2\\nweighted avg       0.25      0.50      0.33          \
      2\\n'
      """
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
      from sklearn.metrics import classification_report
      from sklearn.model_selection import train_test_split
      from nltk.corpus import stopwords
      from nltk.tokenize import word_tokenize
      from nltk.stem import WordNetLemmatizer
      import string
  
      # Tokenize and preprocess the reviews
      def preprocess(review):
          tokens = word_tokenize(review.lower())
          tokens = [word for word in tokens if word.isalnum()]
          tokens = [word for word in tokens if word not in stopwords.words('english')]
          lemmatizer = WordNetLemmatizer()
          tokens = [lemmatizer.lemmatize(word) for word in tokens]
          return ' '.join(tokens)
  
      preprocessed_reviews = [preprocess(review) for review in reviews]
  
      # Create labels based on the sentiment of the reviews
      labels = ['pos' if 'love' in review or 'great' in review else 'neg' for review in preprocessed_reviews]
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(preprocessed_reviews, labels, test_size=0.5, random_state=42)
  
      # Create a pipeline with CountVectorizer and Multinomial Naive Bayes
      model = make_pipeline(CountVectorizer(), MultinomialNB())
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Predict the sentiments on the test set
      y_pred = model.predict(X_test)
  
      # Generate the classification report
      report = classification_report(y_test, y_pred, output_dict=False)
  
      return report