              msg.info(f"Layer {i}: model ID {node.id}: '{node.name}'")
  
              if dimensions:
                  for name in node.dim_names:
                      if node.has_dim(name):
                          msg.info(f" - dim {name}: {node.get_dim(name)}")
                      else:
                          msg.info(f" - dim {name}: {node.has_dim(name)}")
  
              if parameters:
                  for name in node.param_names:
                      if node.has_param(name):
                          print_value = _print_matrix(node.get_param(name))
                          msg.info(f" - param {name}: {print_value}")
 
</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     # 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 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     prediction = model.predict(X)
 </s> add     prediction = model.predict([ex.predicted for ex in examples]) </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.",
                    msg.info(f" - dim {name}: {node.maybe_get_dim(name)}")
              msg.info(f"Layer {i}: model ID {node.id}: '{node.name}'")
  
              if dimensions:
                  for name in node.dim_names:
                    msg.info(f" - dim {name}: {node.maybe_get_dim(name)}")
                    msg.info(f" - dim {name}: {node.maybe_get_dim(name)}")
                    msg.info(f" - dim {name}: {node.maybe_get_dim(name)}")
                    msg.info(f" - dim {name}: {node.maybe_get_dim(name)}")
                    msg.info(f" - dim {name}: {node.maybe_get_dim(name)}")
              if parameters:
                  for name in node.param_names:
                      if node.has_param(name):
                          print_value = _print_matrix(node.get_param(name))
                          msg.info(f" - param {name}: {print_value}")
keep keep keep keep 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