              >> list2ragged()
              >> reduce_mean()
              >> Softmax(nr_class, width)
          )
      model.tok2vec = tok2vec
      return model
  
  
  def block_gradients(model):
      from thinc.api import wrap  # TODO FIX
 
</s> remove     from thinc.model import Model
    from thinc.layers import Softmax, chain, reduce_mean
    from thinc.layers import list2ragged
 </s> add     from thinc.api import Model, Softmax, chain, reduce_mean, list2ragged </s> remove from thinc.api import chain, Linear, Maxout, Softmax, LayerNorm, list2array
from thinc.api import zero_init, CosineDistance, to_categorical, get_array_module
 </s> add from thinc.api import CosineDistance, to_categorical, get_array_module </s> remove from thinc.model import Model
from thinc.api import chain
from thinc.loss import CosineDistance
from thinc.layers import Linear
 </s> add from thinc.api import Model, chain, CosineDistance, Linear </s> remove     tok2vec = Tok2Vec(width, embed_size)
 </s> add     tok2vec = build_Tok2Vec_model(
        width,
        embed_size,
        pretrained_vectors=None,
        conv_depth=4,
        bilstm_depth=0,
        window_size=1,
        maxout_pieces=3,
        subword_features=True,
        char_embed=False,
        nM=64,
        nC=8,
    ) </s> remove     tok2vec = Tok2Vec(width, embed_size)
 </s> add     # TODO: fix tok2vec arguments
    tok2vec = build_Tok2Vec_model(width, embed_size)
    model.set_ref("tok2vec", tok2vec)
              >> list2ragged()
              >> reduce_mean()
              >> Softmax(nr_class, width)
          )
    model.set_ref("tok2vec", tok2vec)
      return model
  
  
  def block_gradients(model):
      from thinc.api import wrap  # TODO FIX
keep keep keep keep replace keep keep keep keep keep
 Default settings to configurations (#4995)

* fix grad_clip naming

* cleaning up pretrained_vectors out of cfg

* further refactoring Model init's

* move Model building out of pipes

* further refactor to require a model config when creating a pipe

* small fixes

* making cfg in nn_parser more consistent

* fixing nr_class for parser

* fixing nn_parser's nO

* fix printing of loss

* architectures in own file per type, consistent naming

* convenience methods default_tagger_config and default_tok2vec_config

* let create_pipe access default config if available for that component

* default_parser_config

* move defaults to separate folder

* allow reading nlp from package or dir with argument 'name'

* architecture spacy.VocabVectors.v1 to read static vectors from file

* cleanup

* default configs for nel, textcat, morphologizer, tensorizer

* fix imports

* fixing unit tests

* fixes and clean up

* fixing defaults, nO, fix unit tests

* restore parser IO

* fix IO

* 'fix' serialization test

* add *.cfg to manifest

* fix example configs with additional arguments

* replace Morpohologizer with Tagger

* add IO bit when testing overfitting of tagger (currently failing)

* fix IO - don't initialize when reading from disk

* expand overfitting tests to also check IO goes OK

* remove dropout from HashEmbed to fix Tagger performance

* add defaults for sentrec

* update thinc

* always pass a Model instance to a Pipe

* fix piped_added statement

* remove obsolete W029

* remove obsolete errors

* restore byte checking tests (work again)

* clean up test

* further test cleanup

* convert from config to Model in create_pipe

* bring back error when component is not initialized

* cleanup

* remove calls for nlp2.begin_training

* use thinc.api in imports

* allow setting charembed's nM and nC

* fix for hardcoded nM/nC + unit test

* formatting fixes

* trigger build 
examples/training/pretrain_textcat.py