code: def write_vectors_model(tmp_dir):
      import numpy
  
      vocab = Vocab()
      vector_data = {
          "dog": numpy.random.uniform(-1, 1, (300,)),
          "cat": numpy.random.uniform(-1, 1, (300,)),
          "orange": numpy.random.uniform(-1, 1, (300,)),
      }
      for word, vector in vector_data.items():
          vocab.set_vector(word, vector)
      nlp_path = tmp_dir / "vectors_model"
      nlp = English(vocab)
      nlp.to_disk(nlp_path)
      return str(nlp_path)
  
imports: from pathlib import Path
  import numpy
  import pytest
  import srsly
  from thinc.api import Config, get_current_ops
  from spacy import util
  from spacy.lang.en import English
  from spacy.language import DEFAULT_CONFIG_PATH, DEFAULT_CONFIG_PRETRAIN_PATH
  from spacy.ml.models.multi_task import create_pretrain_vectors
  from spacy.tokens import Doc, DocBin
  from spacy.training.initialize import init_nlp
  from spacy.training.loop import train
  from spacy.training.pretrain import pretrain
  from spacy.vectors import Vectors
  from spacy.vocab import Vocab
  from ..util import make_tempdir
label_desc: Use image node_id 10 for calling a global function with example usage: write_vectors_model(tmp_dir) and returns: str