code: def _download_pretrained_model():
      """Downloads the pre-trained BIST model if non-existent."""
      if not path.isfile(SpacyBISTParser.dir / "bist.model"):
          print("Downloading pre-trained BIST model..")
          zip_path = SpacyBISTParser.dir / "bist-pretrained"
          makedirs(SpacyBISTParser.dir, exist_ok=True)
  
          download_unzip(
              "https://d2zs9tzlek599f.cloudfront.net/models/dep_parse/",
              "bist-pretrained.zip",
              zip_path,
          )
          print("Done.")
  
file_path: nlp-architect/nlp_architect/pipelines/spacy_bist.py
imports: from os import path, makedirs
  from nlp_architect.common.core_nlp_doc import CoreNLPDoc, _spacy_pos_to_ptb
  from nlp_architect.data.conll import ConllEntry
  from nlp_architect.models.bist_parser import BISTModel
  from nlp_architect import LIBRARY_OUT
  from nlp_architect.utils.io import download_unzip
  from nlp_architect.utils.io import validate
  from nlp_architect.utils.text import SpacyInstance
label_desc: Use image node_id 1 for calling a global function with example usage: _download_pretrained_model() without return types