input: def sentiment_analysis_program(data_path: str) -> str:
      """Performs sentiment analysis on product reviews using natural language processing techniques like tokenization and n-grams. The program reads review data from a CSV file, performs tokenization, removes stopwords, normalizes words, and uses a CountVectorizer to generate features. It then trains a Multinomial Naive Bayes model and evaluates its performance using a classification report.
  
      >>> sentiment_analysis_program('reviews.csv')
      '              precision    recall  f1-score   support\n\n           0       0.85      0.89      0.87       100\n           1       0.90      0.86      0.88       120\n\navg / total       0.88      0.88      0.88       220\n'
      """
output:     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 classification_report
      from nltk.corpus import stopwords
      from nltk.tokenize import word_tokenize
      import string
  
      # Load the data
      df = pd.read_csv(data_path)
  
      # Preprocess the text data
      def preprocess_text(text):
          tokens = word_tokenize(text.lower())
          tokens = [word for word in tokens if word.isalpha()]
          tokens = [word for word in tokens if word not in stopwords.words('english')]
          return ' '.join(tokens)
  
      df['review'] = df['review'].apply(preprocess_text)
  
      # Vectorize the text data
      vectorizer = CountVectorizer(ngram_range=(1, 2))
      X = vectorizer.fit_transform(df['review'])
      y = df['sentiment']
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Train the model
      model = MultinomialNB()
      model.fit(X_train, y_train)
  
      # Evaluate the model
      y_pred = model.predict(X_test)
      report = classification_report(y_test, y_pred)
  
      return report