input: def sentiment_analysis_tool(data_path: str) -> str:
      """Perform sentiment analysis on product reviews using natural language processing 
      techniques. The program should tokenize the reviews, remove stopwords, normalize 
      the words, generate features using n-grams, and then use a classification model 
      to assign sentiments to the reviews. Finally, it should return a classification 
      report.
      >>> sentiment_analysis_tool('reviews.csv')
      '              precision    recall  f1-score   support\n\n           1       0.89      0.92      0.91       100\n           0       0.92      0.89      0.90       100\n\n    accuracy                           0.90       200\n   macro avg       0.90      0.90      0.90       200\nweighted avg       0.90      0.90      0.90       200\n'
      """
output:     import pandas as pd
      from sklearn.feature_extraction.text import TfidfVectorizer
      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
      from nltk.stem import WordNetLemmatizer
      import string
  
      # Load the data
      df = pd.read_csv(data_path)
      reviews = df['review'].tolist()
      sentiments = df['sentiment'].tolist()
  
      # Preprocessing
      stop_words = set(stopwords.words('english'))
      lemmatizer = WordNetLemmatizer()
  
      def preprocess(text):
          tokens = word_tokenize(text.lower())
          tokens = [lemmatizer.lemmatize(word) for word in tokens if word.isalpha() and word not in stop_words]
          return ' '.join(tokens)
  
      reviews = [preprocess(review) for review in reviews]
  
      # Feature extraction
      vectorizer = TfidfVectorizer(ngram_range=(1, 2))
      X = vectorizer.fit_transform(reviews)
  
      # Split the data
      X_train, X_test, y_train, y_test = train_test_split(X, sentiments, 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