                      doc_sample = [component(doc) for doc in doc_sample]
          self.model.initialize(X=doc_sample)
          # Get batch of example docs, example outputs to call begin_training().
          # This lets the model infer shapes.
          util.link_vectors_to_models(self.vocab)
          if sgd is None:
              sgd = self.create_optimizer()
          return sgd
  
      def add_label(self, label, values=None):
 
</s> remove         util.link_vectors_to_models(self.vocab)
 </s> add  </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         util.link_vectors_to_models(self.vocab)
 </s> add  </s> remove         link_vectors_to_models(self.vocab)
 </s> add  </s> remove         link_vectors_to_models(self)
 </s> add 
                      doc_sample = [component(doc) for doc in doc_sample]
          self.model.initialize(X=doc_sample)
          # Get batch of example docs, example outputs to call begin_training().
          # This lets the model infer shapes.

          if sgd is None:
              sgd = self.create_optimizer()
          return sgd
  
      def add_label(self, label, values=None):
keep keep keep keep replace keep keep keep keep keep
 Clean up link_vectors_to_models unused stuff 
spacy/pipeline/tagger.pyx