Hang using thunder.jit with tokenizer in NeMo Stable Diffusion

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### 🐛 Bug

NeMo's Stable Diffusion uses [CLIPTokenizer](https://github.com/NVIDIA/NeMo/blob/v1.23.0/nemo/collections/multimodal/modules/stable_diffusion/encoders/modules.py#L264) from HuggingFace. Adding thunder.jit to the tokenizer is causing a hang.

### To Reproduce

[tokenizer.patch](https://github.com/Lightning-AI/lightning-thunder/files/15445697/tokenizer.patch)

Steps to reproduce the behavior:

1. Apply the attached git diff to NeMo
2. Run NeMo using the command below:
```python
python examples/multimodal/text_to_image/stable_diffusion/sd_train.py trainer.precision=16 trainer.num_nodes=1 trainer.devices=1 ++exp_manager.max_time_per_run=00:00:03:00 trainer.max_steps=20 model.micro_batch_size=1 model.global_batch_size=1 model.data.synthetic_data=True exp_manager.exp_dir=/workspace/TestData/multimodal/stable_diffusion_train model.inductor=False model.cond_stage_config._target_=nemo.collections.multimodal.modules.stable_diffusion.encoders.modules.FrozenCLIPEmbedder ++model.cond_stage_config.version=openai/clip-vit-large-patch14 ++model.cond_stage_config.max_length=77 ~model.cond_stage_config.restore_from_path ~model.cond_stage_config.freeze ~model.cond_stage_config.layer model.unet_config.from_pretrained=null model.first_stage_config.from_pretrained=null model.unet_config.use_flash_attention=False model.unet_config.attention_resolutions=\[1\] model.unet_config.channel_mult=\[1\]
```
CC: @tfogal

cc @tfogal

Apply the attached tokenizer.patch to NeMo and run the provided sd_train.py command to reproduce the hang around CLIPTokenizer with thunder.jit. Start from the linked NeMo modules.py tokenizer entry point and trace the execution until the hang; done means the same Stable Diffusion training command completes without hanging.

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