Apply ruff format to existing source files

One-time formatting pass to bring all files in line with ruff's
formatter, which is now enforced in CI and via pre-commit.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: danielkiv

src/rs_embed/pipelines/inference.py

@@ -392,7 +392,11 @@ def _get_input(i: int, _ctx=ctx, _mc=mc) -> np.ndarray:
             def _single(i: int, _ctx=ctx, _mc=mc) -> Embedding:
                 inp = _get_input(i) if _ctx.needs_provider_input else None
                 fmeta = None
-                if _ctx.needs_provider_input and _ctx.skey is not None and prefetch_meta is not None:
+                if (
+                    _ctx.needs_provider_input
+                    and _ctx.skey is not None
+                    and prefetch_meta is not None
+                ):
                     fmeta = prefetch_meta.get((i, _ctx.skey)) or None
                 return self.infer_single(
                     embedder=_ctx.embedder,

src/rs_embed/providers/prefetch_plan.py

@@ -40,9 +40,7 @@ def select_prefetched_channels(x_chw: np.ndarray, idx: tuple[int, ...]) -> np.nd
         if len(idx) == x.shape[1] and all(i == j for j, i in enumerate(idx)):
             return x
         return x[:, list(idx), :, :]
-    raise ValueError(
-        f"Prefetched input must be CHW or TCHW, got shape={getattr(x, 'shape', None)}"
-    )
+    raise ValueError(f"Prefetched input must be CHW or TCHW, got shape={getattr(x, 'shape', None)}")
 
 
 def build_prefetch_plan(

src/rs_embed/tools/export_requests.py

@@ -56,7 +56,6 @@ def normalize_export_target(
     raise ModelError(f"Unsupported ExportTarget layout: {target.layout!r}.")
 
 
-
 def resolve_export_model_configs(
     *,
     models: list[str | ExportModelRequest],

src/rs_embed/tools/model_defaults.py

@@ -72,6 +72,7 @@ def apply_fetch_to_sensor(sensor: SensorSpec, fetch: FetchSpec | None) -> Sensor
         return sensor
     return replace(sensor, **updates)
 
+
 def _fetch_override_sensor_for_model(model_id: str) -> SensorSpec | None:
     model_key = str(model_id).strip().lower()
     if model_key == "gse":

src/rs_embed/tools/normalization.py

@@ -57,9 +57,7 @@ def normalize_input_array(
             f"{name} must be CHW or TCHW with ndim=3/4, got shape={getattr(x, 'shape', None)}"
         )
     if expected_channels is not None and c != int(expected_channels):
-        raise ModelError(
-            f"{name} channel mismatch: got C={c}, expected C={int(expected_channels)}"
-        )
+        raise ModelError(f"{name} channel mismatch: got C={c}, expected C={int(expected_channels)}")
     return x