          except ValueError:
              try:
                  _set_output_dim(nO=7, model=model)
                  with show_validation_error():
                      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,
 
</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     X = _get_docs()
    # The output vector might differ from the official type of the output layer
 </s> add 
                    examples = [Example.from_dict(x, {}) for x in _get_docs()]
                    nlp.initialize(lambda: examples)
          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)
                  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,
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