  
      msg.good(f"Succesfully ended analysis - model looks good.")
  
  
  def get_gradient(goldY, Y, ops):
      return ops.asarray(Y) - ops.asarray(goldY)
  
  
  def _simulate_gold(element, counter=1):
      if isinstance(element, Iterable):
          for i in range(len(element)):
              element[i] = _simulate_gold(element[i], counter + i)
          return element
      else:
          return 1 / counter
  
  
  def _sentences():
      return [
          "Apple is looking at buying U.K. startup for $1 billion",
          "Autonomous cars shift insurance liability toward manufacturers",
          "San Francisco considers banning sidewalk delivery robots",
 
</s> remove     prediction = model.predict(X)
 </s> add     prediction = model.predict([ex.predicted for ex in examples]) </s> remove         if tok2vec:
            tok2vec.update([Example.from_dict(x, {}) for x in X])
        Y, get_dX = model.begin_update(X)
        if goldY is None:
            goldY = _simulate_gold(Y)
        dY = get_gradient(goldY, Y, model.ops)
        get_dX(dY)
        model.finish_update(optimizer)
 </s> add         if upstream_component:
            upstream_component.update(examples)
        pipe.update(examples) </s> remove     if not hasattr(pipe, "model"):
        msg.fail(
            f"The component '{component}' does not specify an object that holds a Model.",
            exits=1,
        )
    model = pipe.model
    debug_model(config, T, nlp, model, print_settings=print_settings)
 </s> add     debug_model(config, T, nlp, pipe, print_settings=print_settings) </s> remove                     if node.has_dim(name):
                        msg.info(f" - dim {name}: {node.get_dim(name)}")
                    else:
                        msg.info(f" - dim {name}: {node.has_dim(name)}")

 </s> add                     msg.info(f" - dim {name}: {node.maybe_get_dim(name)}") </s> remove     model: Model,
 </s> add     pipe,
  
      msg.good(f"Succesfully ended analysis - model looks good.")
  
  













  def _sentences():
      return [
          "Apple is looking at buying U.K. startup for $1 billion",
          "Autonomous cars shift insurance liability toward manufacturers",
          "San Francisco considers banning sidewalk delivery robots",
keep keep keep keep replace replace replace replace replace replace replace replace replace replace replace replace replace keep keep keep keep keep
 Fix 'debug model' for transformers + generalize (#7973)

* add overrides to docs

* fix debug model with transformer

* assume training data is set in config 
spacy/cli/debug_model.py