input: 
  def sentiment_analysis_program() -> str:
      """ The task is to create a Python program that performs sentiment analysis on product reviews using natural language processing techniques such as tokenization, removal of stopwords, normalization, and n-grams. The program should use a classification model like Multinomial Naive Bayes to assign sentiments to the reviews. Additionally, it should print a classification report based on the test data predictions.
      Example:
      sentiment_analysis_program() == '              precision    recall  f1-score   support\n\n           0       0.85      0.90      0.87       100\n           1       0.92      0.87      0.89       120\n\n    accuracy                           0.88       220\n   macro avg       0.88      0.88      0.88       220\nweighted avg       0.88      0.88      0.88       220\n'
      """
  
output:     import nltk
      from nltk.corpus import stopwords
      from nltk.tokenize import word_tokenize
      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
  
      nltk.download('punkt')
      nltk.download('stopwords')
  
      reviews = [
          "This product is amazing!",
          "I hate this product so much.",
          "The product is okay, not great.",
          "I love this product!",
          "This product is terrible."
      ]
      sentiments = [1, 0, 0, 1, 0]
  
      def preprocess(text):
          tokens = word_tokenize(text.lower())
          stop_words = set(stopwords.words('english'))
          filtered_tokens = [word for word in tokens if word.isalnum() and word not in stop_words]
          return ' '.join(filtered_tokens)
  
      preprocessed_reviews = [preprocess(review) for review in reviews]
  
      vectorizer = CountVectorizer(ngram_range=(1, 2))
      X = vectorizer.fit_transform(preprocessed_reviews)
  
      X_train, X_test, y_train, y_test = train_test_split(X, sentiments, test_size=0.2, random_state=42)
  
      model = MultinomialNB()
      model.fit(X_train, y_train)
  
      y_pred = model.predict(X_test)
  
      report = classification_report(y_test, y_pred, output_dict=True)
      report_str = classification_report(y_test, y_pred)
  
      return report_str