input: Write a python function to classify a given text as 'Technology' using Multinomial Naive Bayes and CountVectorizer.
  assert classify_text("The company announced a range of new innovative technologies to help combat climate change.")==['Technology']
  assert classify_text("New advancements in AI are revolutionizing industries.")==['Technology']
  assert classify_text("Recent breakthroughs in renewable energy sources are promising for the future.")==['Technology']
output: def classify_text(text):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
  
      # Example dataset
      texts = [
          "The company announced a range of new innovative technologies to help combat climate change.",
          "New advancements in AI are revolutionizing industries.",
          "Recent breakthroughs in renewable energy sources are promising for the future.",
          "The government passed a new policy on healthcare.",
          "The economic growth has shown significant improvement.",
          "A new novel was released by the famous author."
      ]
      labels = ["Technology", "Technology", "Technology", "Politics", "Economy", "Literature"]
  
      # Vectorize the texts
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(texts)
  
      # Train Multinomial Naive Bayes classifier
      clf = MultinomialNB()
      clf.fit(X, labels)
  
      # Transform the input text
      text_vectorized = vectorizer.transform([text])
  
      # Predict the category
      prediction = clf.predict(text_vectorized)
      return prediction.tolist()