  
  def build_state2vec(nr_context_tokens, width, nr_vector=1000):
      ops = Model.ops
      with Model.define_operators({'|': concatenate, '+': add, '>>': chain}):
          hiddens = [get_col(i) >> Maxout(width) for i in range(nr_context_tokens)]
          model = get_token_vectors >> add(*hiddens)
      return model
  
  
  def print_shape(prefix):
      def forward(X, drop=0.):
 
</s> remove from thinc.neural import Model, Maxout, Softmax, Affine
 </s> add from thinc.neural import Model, ReLu, Maxout, Softmax, Affine
        #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)
  
  def build_state2vec(nr_context_tokens, width, nr_vector=1000):
      ops = Model.ops
      with Model.define_operators({'|': concatenate, '+': add, '>>': chain}):
        #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)
        #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)
      return model
  
  
  def print_shape(prefix):
      def forward(X, drop=0.):
keep keep keep keep replace replace keep keep keep keep keep
 Switch to single matmul for state layer 
spacy/_ml.py