input: 
  def sentiment_analysis(data_path: str) -> str:
      """ Perform sentiment analysis on product reviews using natural language processing techniques like tokenization,
      n-grams, and classification model. The function reads reviews from a CSV file, tokenizes the texts, removes stopwords,
      normalizes words, generates features using CountVectorizer, splits the data into training and test sets, trains a 
      Multinomial Naive Bayes model, and returns a classification report.
  
      >>> sentiment_analysis('reviews.csv')
      '              precision    recall  f1-score   support\\n\\n...\\n\\naccuracy                            \\t...\\nmacro avg\\t...\\n\\nweighted avg\\t...\\n\\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
      data = pd.read_csv(data_path)
      reviews = data['review'].tolist()
      labels = data['sentiment'].tolist()
  
      # Preprocess the text
      stop_words = set(stopwords.words('english'))
      def preprocess(text):
          tokens = word_tokenize(text.lower())
          tokens = [word for word in tokens if word.isalpha() and word not in stop_words]
          return ' '.join(tokens)
  
      preprocessed_reviews = [preprocess(review) for review in reviews]
  
      # Vectorize the text
      vectorizer = CountVectorizer(ngram_range=(1, 2))
      X = vectorizer.fit_transform(preprocessed_reviews)
  
      # Split the data
      X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2, random_state=42)
  
      # Train the model
      model = MultinomialNB()
      model.fit(X_train, y_train)
  
      # Predict and evaluate
      y_pred = model.predict(X_test)
      report = classification_report(y_test, y_pred)
  
      return report
  