Fix wrong input shapes in doc-string of models
Author: kkew3
Pull request: yes

# What does this PR do?

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Fixes #37702 

This PR aims to fix wrong input shapes in the doc-string of several models. For example, the GPT2Model's API [doc](https://huggingface.co/docs/transformers/v4.51.3/model_doc/gpt2#transformers.GPT2Model.forward.position_ids) said

> position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional)

but apparently the length of `position_ids` should be identical to the `input_ids`'s length, i.e. `input_ids_length`.

By searching for keywords like `position_ids`, a (potentially non-exhaustive) list of models containing incorrect input shape doc-strings is found:

- [x] src/transformers/models/clvp/modeling_clvp.py
- [x] src/transformers/models/ctrl/modeling_ctrl.py
- [x] src/transformers/models/ctrl/modeling_tf_ctrl.py
- [ ] src/transformers/models/falcon/modeling_falcon.py
- [x] src/transformers/models/gemma/modeling_flax_gemma.py
- [x] src/transformers/models/gpt2/modeling_flax_gpt2.py
- [x] src/transformers/models/gpt2/modeling_gpt2.py
- [x] src/transformers/models/gpt2/modeling_tf_gpt2.py
- [x] src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py
- [x] src/transformers/models/gpt_neo/modeling_flax_gpt_neo.py
- [x] src/transformers/models/gpt_neo/modeling_gpt_neo.py
- [x] src/transformers/models/gptj/modeling_flax_gptj.py
- [x] src/transformers/models/gptj/modeling_tf_gptj.py
- [x] src/transformers/models/imagegpt/modeling_imagegpt.py
- [x] src/transformers/models/llama/modeling_flax_llama.py
- [x] src/transformers/models/mistral/modeling_flax_mistral.py
- [ ] src/transformers/models/moshi/modeling_moshi.py

where the **unchecked boxes** mean that: there *may* be mistakes, but I either lack the knowledge to verify them, or can't find direct evidence that these are incorrect. From my side, they are not ongoing works; so I didn't tag "[WIP]" to the PR title.

Note on commits: I split the changes into many small commits because I feel it's easier to claim how each change is supported by the code this way. The commits with "possibly" in the title indicate that the evidence that there's mistake is not as strong as the others.

## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?

It seems to me that the docs under `docs/` folder are overarching description of the models rather than API details. Therefore I didn't touch them.

## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

@Rocketknight1, who advised me to open this PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier

Library:

- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @zach-huggingface and @SunMarc
- chat templates: @Rocketknight1

Integrations:

- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber

Documentation: @stevhliu

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

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