              raise ValueError("Currently history width is hard-coded to 0")
          tok2vec = Tok2Vec(token_vector_width, embed_size,
                            pretrained_dims=cfg.get('pretrained_dims', 0))
          tok2vec = chain(tok2vec, flatten)
          lower = PrecomputableAffine(hidden_width * parser_maxout_pieces,
                      nF=cls.nr_feature, nI=token_vector_width)
          lower.nP = parser_maxout_pieces
  
          with Model.use_device('cpu'):
              upper = chain(
                  clone(LayerNorm(Maxout(hidden_width, hidden_width)), depth-1),
 
</s> remove             state_vector = state_vector.reshape(
                (state_vector.shape[0], self.nO, self.nP))
 </s> add  </s> remove         cdef np.ndarray state_vector = numpy.zeros((token_ids.shape[0], self.nO*self.nP), dtype='f')
 </s> add         cdef np.ndarray state_vector = numpy.zeros(
            (token_ids.shape[0], self.nO, self.nP), dtype='f') </s> remove                 d_vector = self.ops.backprop_maxout(d_best, mask, self.nP)
                return d_vector.reshape((d_vector.shape[0], self.nO*self.nP))
 </s> add                 return self.ops.backprop_maxout(d_best, mask, self.nP)
        lower = PrecomputableAffine(hidden_width,
                    nF=cls.nr_feature, nI=token_vector_width,
                    nP=parser_maxout_pieces)
              raise ValueError("Currently history width is hard-coded to 0")
          tok2vec = Tok2Vec(token_vector_width, embed_size,
                            pretrained_dims=cfg.get('pretrained_dims', 0))
          tok2vec = chain(tok2vec, flatten)
        lower = PrecomputableAffine(hidden_width,
                    nF=cls.nr_feature, nI=token_vector_width,
                    nP=parser_maxout_pieces)
        lower = PrecomputableAffine(hidden_width,
                    nF=cls.nr_feature, nI=token_vector_width,
                    nP=parser_maxout_pieces)
          lower.nP = parser_maxout_pieces
  
          with Model.use_device('cpu'):
              upper = chain(
                  clone(LayerNorm(Maxout(hidden_width, hidden_width)), depth-1),
keep keep keep keep replace replace keep keep keep keep keep
 Make parser consistent if maxout==1 
spacy/syntax/nn_parser.pyx