input: Write a python function to tokenize a given text using NLTK and generate predictions using a pre-trained model.
  assert generate_predictions('This is a test text', 'model.pkl') == ['prediction1', 'prediction2', 'prediction3', 'prediction4', 'prediction5']
  assert generate_predictions('Another example text', 'model.pkl') == ['prediction1', 'prediction2', 'prediction3']
  assert generate_predictions('Sample text for prediction', 'model.pkl') == ['prediction1', 'prediction2', 'prediction3', 'prediction4']
output: import nltk
  import pickle
  
  def generate_predictions(text, model_path):
      # Tokenize the text using NLTK
      tokens = nltk.word_tokenize(text)
  
      # Load the pre-trained model
      with open(model_path, 'rb') as model_file:
          model = pickle.load(model_file)
  
      # Generate predictions using the model
      predictions = model.predict(tokens)
      
      return predictions