  from thinc.api import add, layerize, chain, clone, concatenate, with_flatten
  from thinc.neural import Model, Maxout, Softmax, Affine
  from thinc.neural._classes.hash_embed import HashEmbed
  
  from thinc.neural._classes.convolution import ExtractWindow
  from thinc.neural._classes.static_vectors import StaticVectors
  from thinc.neural._classes.batchnorm import BatchNorm
 
</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)
from thinc.neural import Model, ReLu, Maxout, Softmax, Affine
  from thinc.api import add, layerize, chain, clone, concatenate, with_flatten
from thinc.neural import Model, ReLu, Maxout, Softmax, Affine
  from thinc.neural._classes.hash_embed import HashEmbed
  
  from thinc.neural._classes.convolution import ExtractWindow
  from thinc.neural._classes.static_vectors import StaticVectors
  from thinc.neural._classes.batchnorm import BatchNorm
keep replace keep keep keep keep keep
 Switch to single matmul for state layer 
spacy/_ml.py