docstr: Returns a list tuples of the form: (word, weight) for given topic_id.
  The weight can be interpreted as the number of times word was assigned to topic with given topic_id.
  REFERENCE: https://stackoverflow.com/a/48890889/13550699
  Args:
      topic_id(int): topic ID
      n_words=int): number of top words
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code: def get_word_weights(self, topic_id, n_words=100):
      """
      Returns a list tuples of the form: (word, weight) for given topic_id.
      The weight can be interpreted as the number of times word was assigned to topic with given topic_id.
      REFERENCE: https://stackoverflow.com/a/48890889/13550699
      Args:
          topic_id(int): topic ID
          n_words=int): number of top words
      """
      self._check_model()
      if topic_id + 1 > len(self.model.components_):
          raise ValueError(
              "topic_id must be less than %s"
              % (len(self.model.components_))
          )
      feature_names = self.vectorizer.get_feature_names_out()
      word_probs = self.model.components_[topic_id]
      word_ids = [i for i in word_probs.argsort()[: -n_words - 1 : -1]]
      words = [feature_names[i] for i in word_ids]
      probs = [word_probs[i] for i in word_ids]
      return list(zip(words, probs))
  
imports: import time
  from ..None import utils
  from ..imports import *
  from .None import preprocessor
  from .None import textutils
label_desc: Use image node_id 6 for calling the TopicModel obj's underlying member method code with example usage: obj.get_word_weights(topic_id, n_words) and returns: list