                      nlp.initialize(lambda: [Example.from_dict(x, {}) for x in X])
                  msg.info("Initialized the model with dummy data.")
              except Exception:
                  msg.fail(
                      "Could not initialize the model: you'll have to provide a valid train_corpus argument in the config file.",
                      exits=1,
                  )
  
      if print_settings.get("print_after_init"):
          msg.divider(f"STEP 1 - after initialization")
 
</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) </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     prediction = model.predict(X)
 </s> add     prediction = model.predict([ex.predicted for ex in examples])
                    "Could not initialize the model: you'll have to provide a valid 'train_corpus' argument in the config file.",
                      nlp.initialize(lambda: [Example.from_dict(x, {}) for x in X])
                  msg.info("Initialized the model with dummy data.")
              except Exception:
                  msg.fail(
                    "Could not initialize the model: you'll have to provide a valid 'train_corpus' argument in the config file.",
                      exits=1,
                  )
  
      if print_settings.get("print_after_init"):
          msg.divider(f"STEP 1 - after initialization")
keep keep keep keep 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