[doc] fold long code block (#20795)

Signed-off-by: reidliu41 <reid201711@gmail.com>

docs/features/lora.md

@@ -279,17 +279,17 @@ Some models, e.g., [Granite Speech](https://huggingface.co/ibm-granite/granite-s
 
 To this end, we allow registration of default multimodal LoRAs to handle this automatically, where users can map each modality to a LoRA adapter to automatically apply it when the corresponding inputs are present. Note that currently, we only allow one LoRA per prompt; if several modalities are provided, each of which are registered to a given modality, none of them will be applied.
 
-Example usage for offline inference:
+??? code "Example usage for offline inference"
 
-```python
-from transformers import AutoTokenizer
-from vllm import LLM, SamplingParams
-from vllm.assets.audio import AudioAsset
+    ```python
+    from transformers import AutoTokenizer
+    from vllm import LLM, SamplingParams
+    from vllm.assets.audio import AudioAsset
 
-model_id = "ibm-granite/granite-speech-3.3-2b"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
+    model_id = "ibm-granite/granite-speech-3.3-2b"
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
 
-def get_prompt(question: str, has_audio: bool):
+    def get_prompt(question: str, has_audio: bool):
         """Build the input prompt to send to vLLM."""
         if has_audio:
             question = f"<|audio|>{question}"
@@ -302,7 +302,7 @@ def get_prompt(question: str, has_audio: bool):
         return tokenizer.apply_chat_template(chat, tokenize=False)
 
 
-model = LLM(
+    model = LLM(
         model=model_id,
         enable_lora=True,
         max_lora_rank=64,
@@ -312,31 +312,31 @@ model = LLM(
         # whenever audio is contained in the request data.
         default_mm_loras = {"audio": model_id},
         enforce_eager=True,
-)
+    )
 
-question = "can you transcribe the speech into a written format?"
-prompt_with_audio = get_prompt(
+    question = "can you transcribe the speech into a written format?"
+    prompt_with_audio = get_prompt(
         question=question,
         has_audio=True,
-)
-audio = AudioAsset("mary_had_lamb").audio_and_sample_rate
+    )
+    audio = AudioAsset("mary_had_lamb").audio_and_sample_rate
 
-inputs = {
+    inputs = {
         "prompt": prompt_with_audio,
         "multi_modal_data": {
             "audio": audio,
         }
-}
+    }
 
 
-outputs = model.generate(
+    outputs = model.generate(
         inputs,
         sampling_params=SamplingParams(
             temperature=0.2,
             max_tokens=64,
         ),
-)
-```
+    )
+    ```
 
 You can also pass a json dictionary of `--default-mm-loras` mapping modalities to LoRA model IDs. For example, when starting the server: