code: def convert_nner22(paths, short_name, include_space_char=True):
      assert short_name == "th_nner22"
      SHARDS = ("train", "dev", "test")
      BASE_INPUT_PATH = os.path.join(
          paths["NERBASE"],
          "thai",
          "Thai-NNER",
          "data",
          "scb-nner-th-2022",
          "postproc",
      )
  
      if not include_space_char:
          short_name = short_name + "_no_ws"
  
      for shard in SHARDS:
          input_path = os.path.join(
              BASE_INPUT_PATH, "%s.json" % (shard)
          )
          output_path = os.path.join(
              paths["NER_DATA_DIR"], "%s.%s.json" % (short_name, shard)
          )
  
          logging.info(
              "Output path for %s split at %s" % (shard, output_path)
          )
  
          data = json.load(open(input_path))
  
          documents = []
  
          for i in range(len(data)):
              token, entities = data[i]["tokens"], data[i]["entities"]
  
              token_length, sofar = len(token), 0
              document, ner_dict = [], {}
  
              for entity in entities:
                  start, stop = entity["span"]
  
                  if stop > sofar:
                      ner = entity["entity_type"].upper()
                      sofar = stop
  
                      for j in range(start, stop):
                          if j == start:
                              ner_tag = "B-" + ner
                          elif j == stop - 1:
                              ner_tag = "E-" + ner
                          else:
                              ner_tag = "I-" + ner
  
                          ner_dict[j] = (ner_tag, token[j])
  
              for k in range(token_length):
                  dict_add = {}
  
                  if k not in ner_dict:
                      dict_add["ner"], dict_add["text"] = "O", token[k]
                  else:
                      dict_add["ner"], dict_add["text"] = ner_dict[k]
  
                  document.append(dict_add)
  
              documents.append(document)
  
          with open(output_path, "w") as outfile:
              json.dump(documents, outfile, indent=1)
  
          logging.info(
              "%s.%s.json file successfully created"
              % (short_name, shard)
          )
  
imports: import os
  import logging
  import json
label_desc: Use image node_id 1 for calling a global function with example usage: convert_nner22(paths, short_name, include_space_char) without return types