Fix ruff formatting issues

Signed-off-by: ajrasane <arasane@nvidia.com>

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>

Author: ajrasane

examples/torch_onnx/torch_quant_to_onnx.py

@@ -166,7 +166,11 @@ def _calibrate_uncalibrated_quantizers(model, data_loader):
             if not hasattr(module, attr_name):
                 continue
             quantizer = getattr(module, attr_name)
-            if quantizer.is_enabled and not quantizer.block_sizes and not hasattr(quantizer, "_amax"):
+            if (
+                quantizer.is_enabled
+                and not quantizer.block_sizes
+                and not hasattr(quantizer, "_amax")
+            ):
                 quantizer.enable_calib()
                 uncalibrated.append(quantizer)
 

modelopt/torch/_deploy/utils/torch_onnx.py

@@ -587,9 +587,7 @@ def get_onnx_bytes_and_metadata(
     # Disable FP8 Conv weight quantizers: TorchScript ONNX exporter requires static
     # kernel shapes, but FP8 DequantizeLinear produces dynamic shapes.
     conv_wq_context = (
-        _disable_fp8_conv_weight_quantizers(model)
-        if is_fp8_quantized(model)
-        else nullcontext()
+        _disable_fp8_conv_weight_quantizers(model) if is_fp8_quantized(model) else nullcontext()
     )
     with torch.inference_mode(), autocast, quantizer_context, conv_wq_context:
         additional_kwargs = {}