Convert formatting to use ruff instead of yapf + isort (#26247)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

tests/entrypoints/pooling/llm/test_embedding.py

@@ -19,12 +19,14 @@ prompts = ["The chef prepared a delicious meal."]
 def llm():
     # pytest caches the fixture so we use weakref.proxy to
     # enable garbage collection
-    llm = LLM(model=MODEL_NAME,
-              max_num_batched_tokens=32768,
-              tensor_parallel_size=1,
-              gpu_memory_utilization=0.75,
-              enforce_eager=True,
-              seed=0)
+    llm = LLM(
+        model=MODEL_NAME,
+        max_num_batched_tokens=32768,
+        tensor_parallel_size=1,
+        gpu_memory_utilization=0.75,
+        enforce_eager=True,
+        seed=0,
+    )
 
     yield weakref.proxy(llm)
 
@@ -35,21 +37,20 @@ def llm():
 
 @pytest.mark.skip_global_cleanup
 def test_pooling_params(llm: LLM):
-
     def get_outputs(normalize):
-        outputs = llm.embed(prompts,
-                            pooling_params=PoolingParams(normalize=normalize),
-                            use_tqdm=False)
+        outputs = llm.embed(
+            prompts, pooling_params=PoolingParams(normalize=normalize), use_tqdm=False
+        )
         return torch.tensor([x.outputs.embedding for x in outputs])
 
     default = get_outputs(normalize=None)
     w_normal = get_outputs(normalize=True)
     wo_normal = get_outputs(normalize=False)
 
-    assert torch.allclose(default, w_normal,
-                          atol=1e-2), "Default should use normal."
-    assert not torch.allclose(w_normal, wo_normal,
-                              atol=1e-2), "wo_normal should not use normal."
-    assert torch.allclose(
-        w_normal, F.normalize(wo_normal, p=2, dim=-1),
-        atol=1e-2), "w_normal should be close to normal(wo_normal)."
+    assert torch.allclose(default, w_normal, atol=1e-2), "Default should use normal."
+    assert not torch.allclose(w_normal, wo_normal, atol=1e-2), (
+        "wo_normal should not use normal."
+    )
+    assert torch.allclose(w_normal, F.normalize(wo_normal, p=2, dim=-1), atol=1e-2), (
+        "w_normal should be close to normal(wo_normal)."
+    )