Advice on creating/wrapping `PreTrainedModel` to be compatible with the codebase?
## Environment info
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- `transformers` version: NA
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- Python version: NA
- PyTorch version (GPU?): NA
- Tensorflow version (GPU?): NA
- Using GPU in script?: NA
- Using distributed or parallel set-up in script?: NA

### Who can help
@patrickvonplaten

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Models:

- albert, bert, xlm: @LysandreJik
- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
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- gpt2: @patrickvonplaten, @LysandreJik
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Library:

- benchmarks: @patrickvonplaten
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- trainer: @sgugger
- pipelines: @LysandreJik

Documentation: @sgugger

HF projects:

- nlp datasets: [different repo](https://github.com/huggingface/nlp)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Examples:

- maintained examples (not research project or legacy): @sgugger, @patil-suraj
- research_projects/bert-loses-patience: @JetRunner
- research_projects/distillation: @VictorSanh

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## Information

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

The problem arises when using:
* [ ] the official example scripts: (give details below)
* [X] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [ ] my own task or dataset: (give details below)

## To reproduce
Thanks for the amazing library!

I'm curious if there are instructions on creating a `PreTrainedModel` subclass or creating an `nn.Module` that behaves like a `PreTrainedModel`? Suppose I want to wrap the existing model with some simple additional capabilities inside an `nn.Module`, what are some of the methods that I need to implement/override -- so that they can work well with existing examples?

I'm aware of some tutorials on creating a new model, but that seems pretty complicated and involved -- whereas I'm interested in just adding a couple of simple features.

For example, in the Seq2Seq example, I have noticed that the function signature of `model.forward` determines what data will (not) be passed to the model (as in [`trainer._remove_unused_columns`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py#L458)), and the existence of `model.prepare_decoder_input_ids_from_labels` also influences the input data (as in [`DataCollatorForSeq2Seq .__call__`](https://github.com/huggingface/transformers/blob/master/src/transformers/data/data_collator.py#L292)).

It'd be great if someone could point me to some guidance on tweaking the model to be compatible with the rest of the codebase. Thanks in advance for your time!

Steps to reproduce the behavior:

1.
2.
3.

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## Expected behavior

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Hey @HanGuo97,

We try to keep the GitHub issues for bug reports. Do you mind asking your question on the forum instead? Also there might already be similar questions on the forum, such as https://discuss.huggingface.co/t/create-a-custom-model-that-works-with-any-pretrained-transformer-body/4186. Thanks!<|||||>Got it, thanks for letting me know!