code: def get_stanford_annotations(
      text, core_nlp, port=9000, annotators="tokenize,ssplit,pos,lemma"
  ):
      output = core_nlp.annotate(
          text,
          properties={
              "timeout": "10000",
              "ssplit.newlineIsSentenceBreak": "two",
              "annotators": annotators,
              "outputFormat": "json",
          },
      )
      if type(output) is str:
          output = json.loads(output, strict=False)
      return output
  
file_path: NeuroNER/neuroner/brat_to_conll.py
imports: import codecs
  import glob
  import json
  import os
  from pycorenlp import StanfordCoreNLP
  import spacy
  from neuroner import utils_nlp
label_desc: Use image node_id 3 for calling a global function with example usage: get_stanford_annotations(text, core_nlp, port, annotators) and returns: output