      if model.has_ref("tok2vec") and model.get_ref("tok2vec").name == "tok2vec-listener":
          tok2vec = nlp.get_pipe("tok2vec")
      goldY = None
      for e in range(3):
          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)
      if print_settings.get("print_after_training"):
          msg.divider(f"STEP 2 - after training")
          _print_model(model, print_settings)
  
      # STEP 3: the final prediction
 
</s> remove     # ugly hack to deal with Tok2Vec listeners
    tok2vec = None
    if model.has_ref("tok2vec") and model.get_ref("tok2vec").name == "tok2vec-listener":
        tok2vec = nlp.get_pipe("tok2vec")
 </s> add     # ugly hack to deal with Tok2Vec/Transformer listeners
    upstream_component = None
    if model.has_ref("tok2vec") and "tok2vec-listener" in model.get_ref("tok2vec").name:
        upstream_component = nlp.get_pipe("tok2vec")
    if model.has_ref("tok2vec") and "transformer-listener" in model.get_ref("tok2vec").name:
        upstream_component = nlp.get_pipe("transformer") </s> remove     prediction = model.predict(X)
 </s> add     prediction = model.predict([ex.predicted for ex in examples]) </s> remove     X = _get_docs()
    # The output vector might differ from the official type of the output layer
 </s> add  </s> remove 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


 </s> add  </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)}")
        if upstream_component:
            upstream_component.update(examples)
        pipe.update(examples)
      if model.has_ref("tok2vec") and model.get_ref("tok2vec").name == "tok2vec-listener":
          tok2vec = nlp.get_pipe("tok2vec")
      goldY = None
      for e in range(3):
        if upstream_component:
            upstream_component.update(examples)
        pipe.update(examples)
        if upstream_component:
            upstream_component.update(examples)
        pipe.update(examples)
        if upstream_component:
            upstream_component.update(examples)
        pipe.update(examples)
        if upstream_component:
            upstream_component.update(examples)
        pipe.update(examples)
        if upstream_component:
            upstream_component.update(examples)
        pipe.update(examples)
        if upstream_component:
            upstream_component.update(examples)
        pipe.update(examples)
        if upstream_component:
            upstream_component.update(examples)
        pipe.update(examples)
        if upstream_component:
            upstream_component.update(examples)
        pipe.update(examples)
      if print_settings.get("print_after_training"):
          msg.divider(f"STEP 2 - after training")
          _print_model(model, print_settings)
  
      # STEP 3: the final prediction
keep keep keep keep 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