code: def sentiment(X):
      global classifier
      model_name = "SA_BANK_V131"
      model_path = ModelFetcher.get_model_path(model_name)
  
      if not classifier:
          if not os.path.exists(model_path):
              ModelFetcher.download(model_name)
          classifier = TextClassifier.load(model_path)
  
      sentence = Sentence(X)
      classifier.predict(sentence)
      labels = sentence.labels
      if not labels:
          return None
      labels = [label.value for label in labels]
      return labels
  
file_path: underthesea/underthesea/pipeline/sentiment/bank/__init__.py
imports: import logging
  import os
  import sys
  from os.path import dirname
  from underthesea.corpus.data import Sentence
  from underthesea.models.text_classifier import TextClassifier
  from underthesea.model_fetcher import ModelFetcher, UTSModel
label_desc: Use image node_id 1 for calling a global function with example usage: sentiment(X) and returns: labels, None