  ) -> Model[List[Doc], List[Floats2d]]:
      """Construct a tok2vec model out of embedding and encoding subnetworks.
      See https://explosion.ai/blog/deep-learning-formula-nlp
  
      embed (Model[List[Doc], List[Floats2d]]): Embed tokens into context-indepdent
          word vector representations.
      encode (Model[List[Floats2d], List[Floats2d]]): Encode context into the
          embeddings, using an architecture such as a CNN, BiLSTM or transformer.
      """
      receptive_field = encode.attrs.get("receptive_field", 0)
 
</s> remove         ued as action scores directly.
 </s> add         used as action scores directly. </s> remove     A IOB tag sequence encodes a sequence of non-overlapping labelled spans
 </s> add     An IOB tag sequence encodes a sequence of non-overlapping labelled spans </s> remove @registry.architectures.register("spacy.BiluoTagger.v1")
 </s> add @registry.architectures.register("spacy.BILUOTagger.v1") </s> remove dropout = null
 </s> add  </s> remove from ..tokens import Doc
 </s> add from ...tokens import Doc
    embed (Model[List[Doc], List[Floats2d]]): Embed tokens into context-independent
  ) -> Model[List[Doc], List[Floats2d]]:
      """Construct a tok2vec model out of embedding and encoding subnetworks.
      See https://explosion.ai/blog/deep-learning-formula-nlp
  
    embed (Model[List[Doc], List[Floats2d]]): Embed tokens into context-independent
          word vector representations.
      encode (Model[List[Floats2d], List[Floats2d]]): Encode context into the
          embeddings, using an architecture such as a CNN, BiLSTM or transformer.
      """
      receptive_field = encode.attrs.get("receptive_field", 0)
keep keep keep keep replace keep keep keep keep keep
 Fix imports, types and default configs 
spacy/ml/models/tok2vec.py