code: def fake_embeddings(tmp_path_factory):
      """
      will return a path to a fake embeddings file with the words in SENTENCES
      """
      # could set np random seed here
      words = sorted(set([x.lower() for y in SENTENCES for x in y]))
      words = words[:-1]
      embedding_dir = tmp_path_factory.mktemp("data")
      embedding_txt = embedding_dir / "embedding.txt"
      embedding_pt = embedding_dir / "embedding.pt"
      embedding = np.random.random((len(words), EMB_DIM))
  
      with open(embedding_txt, "w", encoding="utf-8") as fout:
          for word, emb in zip(words, embedding):
              fout.write(word)
              fout.write("\t")
              fout.write("\t".join(str(x) for x in emb))
              fout.write("\n")
  
      pt = pretrain.Pretrain(str(embedding_pt), str(embedding_txt))
      pt.load()
      assert os.path.exists(embedding_pt)
      return embedding_pt
  
imports: import os
  import pytest
  import numpy
  import stanza.models.classifier
  import stanza.models.classifiers.data
  from stanza.models.classifiers.trainer import Trainer
  from stanza.models.common import pretrain
  from stanza.models.common import utils
  from stanza.tests.classifiers.test_data import train_file, dev_file, test_file, DATASET, SENTENCES
label_desc: Use image node_id 1 for calling a global function with example usage: fake_embeddings(tmp_path_factory) and returns: embedding_pt