      for word in words:
          _ = vocab[word]  # noqa: F841
          vocab.set_vector(word, vector_data[0])
      vocab.vectors.name = "dummy_vectors"
      link_vectors_to_models(vocab)
      assert vocab["dog"].rank == 0
      assert vocab["cat"].rank == 1
      assert vocab["SUFFIX"].rank == 2
      assert vocab.vectors.find(key="dog") == 0
      assert vocab.vectors.find(key="cat") == 1
 
</s> remove def link_vectors_to_models(
    vocab: "Vocab",
    models: List[Model] = [],
    *,
    vectors_name_attr="vectors_name",
    vectors_attr="vectors",
    key2row_attr="key2row",
    default_vectors_name="spacy_pretrained_vectors",
) -> None:
    """Supply vectors data to models."""
    vectors = vocab.vectors
    if vectors.name is None:
        vectors.name = default_vectors_name
        if vectors.data.size != 0:
            warnings.warn(Warnings.W020.format(shape=vectors.data.shape))

    for model in models:
        for node in model.walk():
            if node.attrs.get(vectors_name_attr) == vectors.name:
                node.attrs[vectors_attr] = Unserializable(vectors.data)
                node.attrs[key2row_attr] = Unserializable(vectors.key2row)


 </s> add  </s> remove         if self.vectors.name is not None:
            link_vectors_to_models(self)
 </s> add  </s> remove         link_vectors_to_models(self)
 </s> add  </s> remove         util.link_vectors_to_models(self.vocab)
 </s> add  </s> remove         util.link_vectors_to_models(self.vocab)
 </s> add 
      for word in words:
          _ = vocab[word]  # noqa: F841
          vocab.set_vector(word, vector_data[0])
      vocab.vectors.name = "dummy_vectors"

      assert vocab["dog"].rank == 0
      assert vocab["cat"].rank == 1
      assert vocab["SUFFIX"].rank == 2
      assert vocab.vectors.find(key="dog") == 0
      assert vocab.vectors.find(key="cat") == 1
keep keep keep keep replace keep keep keep keep keep
 Clean up link_vectors_to_models unused stuff 
spacy/tests/regression/test_issue2501-3000.py