[Lint]Style: Convert vllm-ascend/ to ruff format(Batch #12) (#6177)

### What this PR does / why we need it?
**Scope of Changes**:
| File Path |
| :--- |
| `vllm_ascend/ops/triton/activation/swiglu_quant.py` |
| `vllm_ascend/ops/triton/batch_invariant/matmul.py` |
| `vllm_ascend/ops/triton/batch_invariant/mean.py` |
| `vllm_ascend/ops/triton/batch_invariant/rmsnorm.py` |
| `vllm_ascend/ops/triton/fla/chunk.py` |
| `vllm_ascend/ops/triton/fla/chunk_delta_h.py` |
| `vllm_ascend/ops/triton/fla/chunk_o.py` |
| `vllm_ascend/ops/triton/fla/chunk_scaled_dot_kkt.py` |
| `vllm_ascend/ops/triton/fla/cumsum.py` |
| `vllm_ascend/ops/triton/fla/fused_qkvzba_split_reshape.py` |
| `vllm_ascend/ops/triton/fla/l2norm.py` |
| `vllm_ascend/ops/triton/fla/layernorm_guard.py` |
| `vllm_ascend/ops/triton/fla/sigmoid_gating.py` |
| `vllm_ascend/ops/triton/fla/solve_tril.py` |
| `vllm_ascend/ops/triton/fla/utils.py` |
| `vllm_ascend/ops/triton/fla/wy_fast.py` |
| `vllm_ascend/ops/triton/fused_gdn_gating.py` |
| `vllm_ascend/ops/triton/layernorm_gated.py` |
| `vllm_ascend/ops/triton/linearnorm/split_qkv_rmsnorm_rope.py` |
| `vllm_ascend/ops/triton/mamba/causal_conv1d.py` |
| `vllm_ascend/ops/triton/reject_sample.py` |
| `vllm_ascend/ops/triton/rope.py` |
| `vllm_ascend/ops/triton/spec_decode/utils.py` |
| `vllm_ascend/ops/triton/triton_utils.py` |

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

### How was this patch tested?

- vLLM version: v0.14.0
- vLLM main:
d68209402d

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

vllm_ascend/ops/triton/layernorm_gated.py

@@ -9,6 +9,7 @@
 import torch
 from vllm.triton_utils import tl, triton
 
+
 @triton.heuristics({"HAS_BIAS": lambda args: args["B"] is not None})
 @triton.heuristics({"HAS_Z": lambda args: args["Z"] is not None})
 @triton.jit
@@ -131,11 +132,7 @@ def layer_norm_fwd_npu(
     else:
         out = torch.empty_like(x)
     assert out.stride(-1) == 1
-    mean = (
-        torch.empty((ngroups * M,), dtype=torch.float32, device=x.device)
-        if not is_rms_norm
-        else None
-    )
+    mean = torch.empty((ngroups * M,), dtype=torch.float32, device=x.device) if not is_rms_norm else None
     rstd = torch.empty((ngroups * M,), dtype=torch.float32, device=x.device)
 
     MAX_FUSED_SIZE = 65536 // x.element_size()
@@ -168,4 +165,4 @@ def layer_norm_fwd_npu(
         IS_RMS_NORM=is_rms_norm,
         # Remove multibuffer if not needed
     )
-    return out, mean, rstd
+    return out, mean, rstd