              with show_validation_error():
                  (train_corpus,) = resolve_dot_names(config, dot_names)
                  nlp.initialize(lambda: train_corpus(nlp))
              msg.info("Initialized the model with the training corpus.")
          except ValueError:
              try:
                  _set_output_dim(nO=7, model=model)
                  with show_validation_error():
                      examples = [Example.from_dict(x, {}) for x in _get_docs()]
                      nlp.initialize(lambda: 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) </s> remove     X = _get_docs()
    # The output vector might differ from the official type of the output layer
 </s> add  </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     # 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         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)
            examples = list(itertools.islice(train_corpus(nlp), 5))
              with show_validation_error():
                  (train_corpus,) = resolve_dot_names(config, dot_names)
                  nlp.initialize(lambda: train_corpus(nlp))
              msg.info("Initialized the model with the training corpus.")
            examples = list(itertools.islice(train_corpus(nlp), 5))
          except ValueError:
              try:
                  _set_output_dim(nO=7, model=model)
                  with show_validation_error():
                      examples = [Example.from_dict(x, {}) for x in _get_docs()]
                      nlp.initialize(lambda: examples)
keep 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