def create_pretraining_model(nlp, tok2vec):
output_size = nlp.vocab.vectors.data.shape[<NUM_LIT:1>]<EOL>output_layer = chain(<EOL>LN(Maxout(<NUM_LIT>, pieces=<NUM_LIT:3>)), Affine(output_size, drop_factor=<NUM_LIT:0.0>)<EOL>)<EOL>tok2vec = chain(tok2vec, flatten)<EOL>model = chain(tok2vec, output_layer)<EOL>model = masked_language_model(nlp.vocab, model)<EOL>model.tok2vec = tok2vec<EOL>model.output_layer = output_layer<EOL>model.begin_training([nlp.make_doc("<STR_LIT>")])<EOL>return model<EOL>
Define a network for the pretraining. We simply add an output layer onto
    the tok2vec input model. The tok2vec input model needs to be a model that
    takes a batch of Doc objects (as a list), and returns a list of arrays.
    Each array in the output needs to have one row per token in the doc.