TFTransfoXLLMHeadModel doesn't accept lm_labels parameter
## 🐛 Bug

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Model I am using (Bert, XLNet....): TFTransfoXLLMHeadModel

Language I am using the model on (English, Chinese....): Other

The problem arise when using:
* [ ] the official example scripts: (give details)
* [ X ] my own modified scripts: I have a script that trains a new TransformerXL

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [ X ] my own task or dataset: The dataset is a language modeling dataset of novel symbolic data.

## To Reproduce

Steps to reproduce the behavior:

Call the TFTransfoXLLMHeadModel as such:
mems = transformer(data,
                                            lm_labels = lm_labels,
                                            mems = mems,
                                            training=True)

  File "/home/tom/.local/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py", line 891, in __call__
    outputs = self.call(cast_inputs, *args, **kwargs)
TypeError: call() got an unexpected keyword argument 'lm_labels'

If I instead include lm_labels in a dict, it is simply ignored.

## Expected behavior

The model documentation says that including lm_labels is recommended for training because it allows the adaptive softmax to be calculated more efficiently

## Environment

* OS: Ubuntu 19
* PyTorch Transformers version (or branch): 2.0.0
* Using GPU Yes

I see now that I missed something. The documentation uses the parameter 'lm_labels' but the correct parameter is just 'labels'. The documentation says that when this parameter is present, prediction logits will not be output, but this is incorrect. They are output regardless of the presence of 'labels'.<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
