code: def output_predictions(
      output_file,
      trainer,
      data_generator,
      vocab,
      mwt_dict,
      max_seqlen=1000,
      orig_text=None,
      no_ssplit=False,
      use_regex_tokens=True,
      num_workers=0,
      postprocessor=None,
  ):
      batch_size = trainer.args["batch_size"]
      max_seqlen = max(1000, max_seqlen)
  
      all_preds, all_raw = predict(
          trainer,
          data_generator,
          batch_size,
          max_seqlen,
          use_regex_tokens,
          num_workers,
      )
  
      use_la_ittb_shorthand = trainer.args["shorthand"] == "la_ittb"
      skip_newline = trainer.args["skip_newline"]
      oov_count, offset, doc = decode_predictions(
          vocab,
          mwt_dict,
          orig_text,
          all_raw,
          all_preds,
          no_ssplit,
          skip_newline,
          use_la_ittb_shorthand,
      )
  
      # If we are provided a postprocessor, we prepare a list of pre-tokenized words and mwt flags and
      # call the postprocessor for analysis.
      if postprocessor:
          doc = postprocess_doc(doc, postprocessor, orig_text)
  
      if output_file:
          CoNLL.dict2conll(doc, output_file)
      return oov_count, offset, all_preds, doc
  
imports: from collections import Counter
  from copy import copy
  import json
  import numpy
  import re
  import logging
  import os
  from torch.utils.data import DataLoader
  import stanza.utils.default_paths
  from stanza.models.common.utils import ud_scores, harmonic_mean
  from stanza.models.common.doc import Document
  from stanza.utils.conll import CoNLL
  from stanza.models.common.doc import *
  from stanza.models.tokenization.data import SortedDataset
label_desc: Use image node_id 9 for calling a global function with example usage: output_predictions(output_file, trainer, data_generator, vocab, mwt_dict, max_seqlen, orig_text, no_ssplit, use_regex_tokens, num_workers, postprocessor) and returns: oov_count,  offset,  all_preds,  doc