      backprop(gradients, sgd=optimizer)
      # Don't want to return a cupy object here
      # The gradients are modified in-place by the BERT MLM,
      # so we get an accurate loss
      loss = float((gradients ** 2).sum())
      return loss
  
  
  def make_docs(nlp, batch, min_length=1, max_length=500):
      docs = []
      for record in batch:
 
</s> remove     gradients = get_vectors_loss(model.ops, docs, predictions, objective)
 </s> add     loss, gradients = get_vectors_loss(model.ops, docs, predictions, objective) </s> remove     tracker = ProgressTracker()
 </s> add     tracker = ProgressTracker(frequency=10000) </s> remove             subword_features=True,
 </s> add             subword_features=True,  # Set to False for Chinese etc </s> remove def make_update(model, docs, optimizer, drop=0.0, objective='cosine'):
 </s> add def make_update(model, docs, optimizer, drop=0.0, objective='L2'): </s> remove     )  # Set to False for character models, e.g. Chinese
 </s> add     )
    return float(loss)
      backprop(gradients, sgd=optimizer)
      # Don't want to return a cupy object here
      # The gradients are modified in-place by the BERT MLM,
      # so we get an accurate loss
    return float(loss)
    return float(loss)
  
  
  def make_docs(nlp, batch, min_length=1, max_length=500):
      docs = []
      for record in batch:
keep keep keep keep replace replace keep keep keep keep keep
 Remove unused code from spacy pretrain 
spacy/cli/pretrain.py