      maxout_pieces: StrictInt,
      nO=None,
  ):
      token_vector_width = tok2vec.get_dim("nO")
      tok2vec = chain(tok2vec, list2array())
      tok2vec.set_dim("nO", token_vector_width)
  
      lower = PrecomputableAffine(
          nO=hidden_width,
          nF=nr_feature_tokens,
          nI=tok2vec.get_dim("nO"),
 
</s> remove         nO=hidden_width,
 </s> add         nO=hidden_width if use_upper else nO, </s> remove         nP=maxout_pieces,
 </s> add         nP=maxout_pieces </s> remove         upper = model.upper
 </s> add         upper = model.get_ref("upper") </s> remove     lower.set_dim("nP", maxout_pieces)
    with use_ops("numpy"):
        # Initialize weights at zero, as it's a classification layer.
        upper = Linear(nO=nO, init_W=zero_init)
    model = ParserModel(tok2vec, lower, upper)
    return model
 </s> add     if use_upper:
        with use_ops("numpy"):
            # Initialize weights at zero, as it's a classification layer.
            upper = Linear(nO=nO, init_W=zero_init)
    else:
        upper = None
    return TransitionModel(tok2vec, lower, upper)
    tok2vec = chain(
        tok2vec,
        with_array(Linear(hidden_width, token_vector_width)),
        list2array(),
    )
    tok2vec.set_dim("nO", hidden_width)
      maxout_pieces: StrictInt,
      nO=None,
  ):
      token_vector_width = tok2vec.get_dim("nO")
    tok2vec = chain(
        tok2vec,
        with_array(Linear(hidden_width, token_vector_width)),
        list2array(),
    )
    tok2vec.set_dim("nO", hidden_width)
    tok2vec = chain(
        tok2vec,
        with_array(Linear(hidden_width, token_vector_width)),
        list2array(),
    )
    tok2vec.set_dim("nO", hidden_width)
  
      lower = PrecomputableAffine(
          nO=hidden_width,
          nF=nr_feature_tokens,
          nI=tok2vec.get_dim("nO"),
keep keep keep keep replace replace keep keep keep keep keep
 Adapt parser and NER for transformers (#5449)

* Draft layer for BILUO actions

* Fixes to biluo layer

* WIP on BILUO layer

* Add tests for BILUO layer

* Format

* Fix transitions

* Update test

* Link in the simple_ner

* Update BILUO tagger

* Update __init__

* Import simple_ner

* Update test

* Import

* Add files

* Add config

* Fix label passing for BILUO and tagger

* Fix label handling for simple_ner component

* Update simple NER test

* Update config

* Hack train script

* Update BILUO layer

* Fix SimpleNER component

* Update train_from_config

* Add biluo_to_iob helper

* Add IOB layer

* Add IOBTagger model

* Update biluo layer

* Update SimpleNER tagger

* Update BILUO

* Read random seed in train-from-config

* Update use of normal_init

* Fix normalization of gradient in SimpleNER

* Update IOBTagger

* Remove print

* Tweak masking in BILUO

* Add dropout in SimpleNER

* Update thinc

* Tidy up simple_ner

* Fix biluo model

* Unhack train-from-config

* Update setup.cfg and requirements

* Add tb_framework.py for parser model

* Try to avoid memory leak in BILUO

* Move ParserModel into spacy.ml, avoid need for subclass.

* Use updated parser model

* Remove incorrect call to model.initializre in PrecomputableAffine

* Update parser model

* Avoid divide by zero in tagger

* Add extra dropout layer in tagger

* Refine minibatch_by_words function to avoid oom

* Fix parser model after refactor

* Try to avoid div-by-zero in SimpleNER

* Fix infinite loop in minibatch_by_words

* Use SequenceCategoricalCrossentropy in Tagger

* Fix parser model when hidden layer

* Remove extra dropout from tagger

* Add extra nan check in tagger

* Fix thinc version

* Update tests and imports

* Fix test

* Update test

* Update tests

* Fix tests

* Fix test

Co-authored-by: Ines Montani <ines@ines.io> 
spacy/ml/models/parser.py