[Misc] small update (vllm-project#20462)

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

Author: reidliu41

examples/offline_inference/profiling_tpu/README.md

@@ -57,7 +57,10 @@ Once you have collected your profiles with this script, you can visualize them u
 Here are most likely the dependencies you need to install:
 
 ```bash
-pip install tensorflow-cpu tensorboard-plugin-profile etils importlib_resources
+pip install tensorflow-cpu \
+    tensorboard-plugin-profile \
+    etils \
+    importlib_resources
 ```
 
 Then you just need to point TensorBoard to the directory where you saved the profiles and visit `http://localhost:6006/` in your browser:

examples/online_serving/structured_outputs/README.md

@@ -13,13 +13,15 @@ vllm serve Qwen/Qwen2.5-3B-Instruct
 To serve a reasoning model, you can use the following command:
 
 ```bash
-vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-7B --reasoning-parser deepseek_r1
+vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-7B \
+    --reasoning-parser deepseek_r1
 ```
 
 If you want to run this script standalone with `uv`, you can use the following:
 
 ```bash
-uvx --from git+https://github.com/vllm-project/vllm#subdirectory=examples/online_serving/structured_outputs structured-output
+uvx --from git+https://github.com/vllm-project/vllm#subdirectory=examples/online_serving/structured_outputs \
+    structured-output
 ```
 
 See [feature docs](https://docs.vllm.ai/en/latest/features/structured_outputs.html) for more information.
@@ -44,7 +46,9 @@ uv run structured_outputs.py --stream
 Run certain constraints, for example `structural_tag` and `regex`, streaming:
 
 ```bash
-uv run structured_outputs.py --constraint structural_tag regex --stream
+uv run structured_outputs.py \
+    --constraint structural_tag regex \
+    --stream
 ```
 
 Run all constraints, with reasoning models and streaming:

examples/others/tensorize_vllm_model.py

@@ -202,7 +202,7 @@ def parse_args():
 
 
 
-def deserialize():
+def deserialize(args, tensorizer_config):
     if args.lora_path:
         tensorizer_config.lora_dir = tensorizer_config.tensorizer_dir
         llm = LLM(model=args.model,
@@ -242,7 +242,7 @@ def deserialize():
     return llm
 
 
-if __name__ == '__main__':
+def main():
     args = parse_args()
 
     s3_access_key_id = (getattr(args, 's3_access_key_id', None)
@@ -260,8 +260,6 @@ def deserialize():
 
     model_ref = args.model
 
-    model_name = model_ref.split("/")[1]
-
     if args.command == "serialize" or args.command == "deserialize":
         keyfile = args.keyfile
     else:
@@ -309,6 +307,10 @@ def deserialize():
                 encryption_keyfile = keyfile,
                 **credentials
             )
-        deserialize()
+        deserialize(args, tensorizer_config)
     else:
         raise ValueError("Either serialize or deserialize must be specified.")
+
+
+if __name__ == "__main__":
+    main()