  def build_debug_model(state2vec, width, depth, nr_class):
      with Model.define_operators({'>>': chain, '**': clone}):
          model = (
              state2vec
              >> Maxout(nr_class)
          )
      return model
  
 
</s> remove         hiddens = [get_col(i) >> Maxout(width) for i in range(nr_context_tokens)]
        model = get_token_vectors >> add(*hiddens)
 </s> add         #hiddens = [get_col(i) >> Maxout(width) for i in range(nr_context_tokens)]
        features = [get_col(i) for i in range(nr_context_tokens)]
        model = get_token_vectors >> concatenate(*features) >> ReLu(width) </s> remove from thinc.neural import Model, Maxout, Softmax, Affine
 </s> add from thinc.neural import Model, ReLu, Maxout, Softmax, Affine
            #>> Maxout(width)
  def build_debug_model(state2vec, width, depth, nr_class):
      with Model.define_operators({'>>': chain, '**': clone}):
          model = (
              state2vec
            #>> Maxout(width)
              >> Maxout(nr_class)
          )
      return model
  
keep keep keep add keep keep keep keep
 Switch to single matmul for state layer 
spacy/_ml.py