      *,
      print_settings: Optional[Dict[str, Any]] = None,
  ):
      if not isinstance(model, Model):
          msg.fail(
              f"Requires a Thinc Model to be analysed, but found {type(model)} instead.",
              exits=1,
          )
      if print_settings is None:
 
</s> remove     model: Model,
 </s> add     pipe, </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                     "Could not initialize the model: you'll have to provide a valid train_corpus argument in the config file.",
 </s> add                     "Could not initialize the model: you'll have to provide a valid 'train_corpus' argument in the config file.", </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                     nlp.initialize(lambda: [Example.from_dict(x, {}) for x in X])
 </s> add                     examples = [Example.from_dict(x, {}) for x in _get_docs()]
                    nlp.initialize(lambda: examples)
    if not hasattr(pipe, "model"):
        msg.fail(
            f"The component '{pipe}' does not specify an object that holds a Model.",
            exits=1,
        )
    model = pipe.model
      *,
      print_settings: Optional[Dict[str, Any]] = None,
  ):
    if not hasattr(pipe, "model"):
        msg.fail(
            f"The component '{pipe}' does not specify an object that holds a Model.",
            exits=1,
        )
    model = pipe.model
      if not isinstance(model, Model):
          msg.fail(
              f"Requires a Thinc Model to be analysed, but found {type(model)} instead.",
              exits=1,
          )
      if print_settings is None:
keep keep add keep 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