code: def predict(self, text, k=1, threshold=0.0, on_unicode_error='strict'):
          
  
          def check(entry):
              if entry.find('\n') != -1:
                  raise ValueError(
                      "predict processes one line at a time (remove \'\\n\')"
                  )
              entry += "\n"
              return entry
  
          if type(text) == list:
              text = [check(entry) for entry in text]
              predictions = self.f.multilinePredict(text, k, threshold, on_unicode_error)
              dt = np.dtype([('probability', 'float64'), ('label', 'object')])
              result_as_pair = np.array(predictions, dtype=dt)
  
              return result_as_pair['label'].tolist(), result_as_pair['probability']
          else:
              text = check(text)
              predictions = self.f.predict(text, k, threshold, on_unicode_error)
              probs, labels = zip(*predictions)
  
              return labels, np.array(probs, copy=False)
docs: Given a string, get a list of labels and a list of
          corresponding probabilities. k controls the number
          of returned labels. A choice of 5, will return the 5
          most probable labels. By default this returns only
          the most likely label and probability. threshold filters
          the returned labels by a threshold on probability. A
          choice of 0.5 will return labels with at least 0.5
          probability. k and threshold will be applied together to
          determine the returned labels.
  
          This function assumes to be given
          a single line of text. We split words on whitespace (space,
          newline, tab, vertical tab) and the control characters carriage
          return, formfeed and the null character.
  
          If the model is not supervised, this function will throw a ValueError.
  
          If given a list of strings, it will return a list of results as usually
          received for a single line of text.