input: 
  from typing import List, Any
  
  def sentiment_analysis(reviews: List[str], sentiments: List[str]) -> str:
      """
      Perform sentiment analysis on product reviews 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 prints
      a classification report based on the test set predictions.
      
      >>> sentiment_analysis(['This product is amazing!', 'I hate this product'], ['positive', 'negative'])
      '              precision    recall  f1-score   support\n\n    negative       1.00      1.00      1.00         1\n    positive       1.00      1.00      1.00         1\n\navg / total       1.00      1.00      1.00         2\n'
      """
      import numpy as np
      import pandas as pd
      from nltk.tokenize import word_tokenize
      from nltk.corpus import stopwords
      from nltk.stem import WordNetLemmatizer
      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
  
      data = pd.DataFrame({'review': reviews, 'sentiment': sentiments})
  
      data['tokenised'] = data['review'].apply(word_tokenize)
  
      stop_words = set(stopwords.words('english'))
      data['filtered'] = data['tokenised'].apply(lambda x: [val for val in x if val not in stop_words])
  
      wordnet_lemmatizer = WordNetLemmatizer()
      data['normalised'] = data['filtered'].apply(lambda x: [wordnet_lemmatizer.lemmatize(val) for val in x])
  
      cv = CountVectorizer()
      data['features'] = cv.fit_transform(data['normalised'].astype('str')).toarray()
  
      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)
  
      model = MultinomialNB(alpha=0.1)
      model.fit(X_train, y_train)
  
      predictions = model.predict(X_test)
  
      report = classification_report(y_test, predictions)
      return report
  
output:     
      return report
  