code: def main(model=None, output_dir=None, n_iter=15):
      
      if model is not None:
          nlp = spacy.load(model)  # load existing spaCy model
          print("Loaded model '%s'" % model)
      else:
          nlp = spacy.blank("en")  # create blank Language class
          print("Created blank 'en' model")
  
      # We'll use the built-in dependency parser class, but we want to create a
      # fresh instance – just in case.
      if "parser" in nlp.pipe_names:
          nlp.remove_pipe("parser")
      parser = nlp.create_pipe("parser")
      nlp.add_pipe(parser, first=True)
  
      for text, annotations in TRAIN_DATA:
          for dep in annotations.get("deps", []):
              parser.add_label(dep)
  
      other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "parser"]
      with nlp.disable_pipes(*other_pipes):  # only train parser
          optimizer = nlp.begin_training()
          for itn in range(n_iter):
              random.shuffle(TRAIN_DATA)
              losses = {}
              # batch up the examples using spaCy's minibatch
              batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
              for batch in batches:
                  texts, annotations = zip(*batch)
                  nlp.update(texts, annotations, sgd=optimizer, losses=losses)
              print("Losses", losses)
  
      # test the trained model
      test_model(nlp)
  
      # save model to output directory
      if output_dir is not None:
          output_dir = Path(output_dir)
          if not output_dir.exists():
              output_dir.mkdir()
          nlp.to_disk(output_dir)
          print("Saved model to", output_dir)
  
          # test the saved model
          print("Loading from", output_dir)
          nlp2 = spacy.load(output_dir)
          test_model(nlp2)
docs: Load the model, set up the pipeline and train the parser.