code: def ner(sentence, format=None, deep=False):
      """
      Location and classify named entities in text
  
      Parameters
      ==========
  
      sentence: {unicode, str}
          raw sentence
  
      Returns
      =======
      tokens: list of tuple with word, pos tag, chunking tag, ner tag tagged sentence
  
      Examples
      --------
  
      >>> # -*- coding: utf-8 -*-
      >>> from underthesea import ner
      >>> sentence = "Ông Putin ca ngợi những thành tựu vĩ đại của Liên Xô"
      >>> ner(sentence)
      [('Ông', 'Nc', 'B-NP', 'O'),
      ('Putin', 'Np', 'B-NP', 'B-PER'),
      ('ca ngợi', 'V', 'B-VP', 'O'),
      ('những', 'L', 'B-NP', 'O'),
      ('thành tựu', 'N', 'B-NP', 'O'),
      ('vĩ đại', 'A', 'B-AP', 'O'),
      ('của', 'E', 'B-PP', 'O'),
      ('Liên Xô', 'Np', 'B-NP', 'B-LOC')]
      """
      if not deep:
          sentence = chunk(sentence)
          crf_model = CRFNERPredictor.Instance()
          result = crf_model.predict(sentence, format)
          return result
      else:
          from .model_transformers import nlp
  
          output = nlp(sentence)
          if len(output) == 0:
              return []
          entities = [output[0]]
          for item in output[1:]:
              if item["word"].startswith("##"):
                  entities[-1]["word"] = (
                      entities[-1]["word"] + item["word"][2:]
                  )
                  entities[-1]["end"] = item["end"]
              else:
                  entities.append(item)
          return entities
  
label_desc: Use image node_id 1 for calling a global function with example usage: ner(sentence, format, deep) and returns: result, entities, list