[Misc][V0 Deprecation] Add __main__ guard to all offline examples (#1837)

### What this PR does / why we need it?
Add `__main__` guard to all offline examples.

- vLLM version: v0.9.2
- vLLM main:
76b494444f

---------

Signed-off-by: shen-shanshan <467638484@qq.com>

examples/offline_embed.py

@@ -19,35 +19,40 @@
 
 import os
 
-os.environ["VLLM_USE_MODELSCOPE"] = "True"
-
 import torch
 from vllm import LLM
 
+os.environ["VLLM_USE_MODELSCOPE"] = "True"
+os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
 
 def get_detailed_instruct(task_description: str, query: str) -> str:
     return f'Instruct: {task_description}\nQuery:{query}'
 
 
-# Each query must come with a one-sentence instruction that describes the task
-task = 'Given a web search query, retrieve relevant passages that answer the query'
+def main():
+    # Each query must come with a one-sentence instruction that describes the task
+    task = 'Given a web search query, retrieve relevant passages that answer the query'
 
-queries = [
-    get_detailed_instruct(task, 'What is the capital of China?'),
-    get_detailed_instruct(task, 'Explain gravity')
-]
-# No need to add instruction for retrieval documents
-documents = [
-    "The capital of China is Beijing.",
-    "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."
-]
-input_texts = queries + documents
+    queries = [
+        get_detailed_instruct(task, 'What is the capital of China?'),
+        get_detailed_instruct(task, 'Explain gravity')
+    ]
+    # No need to add instruction for retrieval documents
+    documents = [
+        "The capital of China is Beijing.",
+        "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."
+    ]
+    input_texts = queries + documents
 
-model = LLM(model="Qwen/Qwen3-Embedding-0.6B", task="embed")
+    model = LLM(model="Qwen/Qwen3-Embedding-0.6B", task="embed")
 
-outputs = model.embed(input_texts)
-embeddings = torch.tensor([o.outputs.embedding for o in outputs])
-# Calculate the similarity scores between the first two queries and the last two documents
-scores = (embeddings[:2] @ embeddings[2:].T)
-print(scores.tolist())
-# [[0.7620252966880798, 0.14078938961029053], [0.1358368694782257, 0.6013815999031067]]
+    outputs = model.embed(input_texts)
+    embeddings = torch.tensor([o.outputs.embedding for o in outputs])
+    # Calculate the similarity scores between the first two queries and the last two documents
+    scores = (embeddings[:2] @ embeddings[2:].T)
+    print(scores.tolist())
+    # [[0.7620252966880798, 0.14078938961029053], [0.1358368694782257, 0.6013815999031067]]
+
+
+if __name__ == "__main__":
+    main()