[Lint]Style: Convert test/ to ruff format(Batch #5) (#6747)

### What this PR does / why we need it?
| File Path |
| :--- |
| `tests/e2e/singlecard/compile/backend.py` |
| `tests/e2e/singlecard/compile/test_graphex_norm_quant_fusion.py` |
| `tests/e2e/singlecard/compile/test_graphex_qknorm_rope_fusion.py` |
| `tests/e2e/singlecard/compile/test_norm_quant_fusion.py` |
| `tests/e2e/singlecard/model_runner_v2/test_basic.py` |
| `tests/e2e/singlecard/test_aclgraph_accuracy.py` |
| `tests/e2e/singlecard/test_aclgraph_batch_invariant.py` |
| `tests/e2e/singlecard/test_aclgraph_mem.py` |
| `tests/e2e/singlecard/test_async_scheduling.py` |
| `tests/e2e/singlecard/test_auto_fit_max_mode_len.py` |
| `tests/e2e/singlecard/test_batch_invariant.py` |
| `tests/e2e/singlecard/test_camem.py` |
| `tests/e2e/singlecard/test_completion_with_prompt_embeds.py` |
| `tests/e2e/singlecard/test_cpu_offloading.py` |
| `tests/e2e/singlecard/test_guided_decoding.py` |
| `tests/e2e/singlecard/test_ilama_lora.py` |
| `tests/e2e/singlecard/test_llama32_lora.py` |
| `tests/e2e/singlecard/test_models.py` |
| `tests/e2e/singlecard/test_multistream_overlap_shared_expert.py` |
| `tests/e2e/singlecard/test_quantization.py` |
| `tests/e2e/singlecard/test_qwen3_multi_loras.py` |
| `tests/e2e/singlecard/test_sampler.py` |
| `tests/e2e/singlecard/test_vlm.py` |
| `tests/e2e/singlecard/test_xlite.py` |
| `tests/e2e/singlecard/utils.py` |

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.15.0
- vLLM main:
9562912cea

---------

Signed-off-by: MrZ20 <2609716663@qq.com>

tests/e2e/singlecard/test_completion_with_prompt_embeds.py

@@ -30,9 +30,7 @@ MODELS = ["Qwen/Qwen3-0.6B"]
 
 def get_prompt_embeds(chat, tokenizer, embedding_layer):
     """Convert chat messages to prompt embeddings."""
-    token_ids = tokenizer.apply_chat_template(chat,
-                                              add_generation_prompt=True,
-                                              return_tensors='pt')
+    token_ids = tokenizer.apply_chat_template(chat, add_generation_prompt=True, return_tensors="pt")
     prompt_embeds = embedding_layer(token_ids).squeeze(0)
     return prompt_embeds
 
@@ -53,15 +51,16 @@ def test_mixed_prompt_embeds_and_text(model_name):
 
     # Run inference with mixed inputs
     with VllmRunner(
-            model_name,
-            enable_prompt_embeds=True,
-            cudagraph_capture_sizes=[1, 2, 4, 8],
+        model_name,
+        enable_prompt_embeds=True,
+        cudagraph_capture_sizes=[1, 2, 4, 8],
     ) as vllm_runner:
         # Test prompt embeddings
-        embeds_output = vllm_runner.model.generate({
-            "prompt_embeds":
-            prompt_embeds,
-        })
+        embeds_output = vllm_runner.model.generate(
+            {
+                "prompt_embeds": prompt_embeds,
+            }
+        )
 
         # Test text prompt
         text_output = vllm_runner.model.generate(text_prompt)