chore: Bump ruff-pre-commit from v0.16.0 to 0.16.5

.pre-commit-config.yaml

@@ -10,7 +10,7 @@ repos:
     -   id: check-merge-conflict  # Check for files that contain merge conflict strings.
 
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.16.0
+    rev: v0.16.5
     hooks:
       - id: ruff-check
         args: [ --fix, --ignore, FIX ]  # Allow committing with TODOs. Only CI checks should prevent merging with TODOs.

CHANGELOG.md

@@ -117,16 +117,18 @@ changelog does not include internal changes that do not affect the user.
   ```python
   # Before
   from torchjd.aggregation import Flattening, UPGradWeighting
+
   weighting = Flattening(UPGradWeighting())
   gramian = engine.compute_gramian(losses)  # shape: [m1, m2, m2, m1]
-  weights = weighting(gramian)              # shape: [m1, m2]
+  weights = weighting(gramian)  # shape: [m1, m2]
   losses.backward(weights)
 
   # After
   from torchjd.aggregation import UPGradWeighting
+
   weighting = UPGradWeighting()
-  gramian = engine.compute_gramian(losses)           # shape: [m1 * m2, m1 * m2]
-  weights = weighting(gramian).reshape(losses.shape) # shape: [m1, m2]
+  gramian = engine.compute_gramian(losses)  # shape: [m1 * m2, m1 * m2]
+  weights = weighting(gramian).reshape(losses.shape)  # shape: [m1, m2]
   losses.backward(weights)
   ```
 
@@ -140,11 +142,13 @@ changelog does not include internal changes that do not affect the user.
   ```python
   # Before
   from torchjd.aggregation import UPGrad
+
   aggregator = UPGrad(norm_eps=1e-6, reg_eps=1e-6, solver="quadprog")
 
   # After
   from torchjd.aggregation import UPGrad
   from torchjd.linalg import QuadprogProjector
+
   aggregator = UPGrad(projector=QuadprogProjector(norm_eps=1e-6, reg_eps=1e-6))
   ```
   If you used the default `norm_eps`, `reg_eps` and `solver`, you don't have to change anything and

CONTRIBUTING.md

@@ -174,11 +174,13 @@ device and dtype, you have to use the partial functions defined in `tests/utils/
 instantiate tensors. For instance, instead of
 ```python
 import torch
+
 a = torch.ones(3, 4)
 ```
 use
 ```python
 from utils.tensors import ones_
+
 a = ones_(3, 4)
 ```
 

skills/implement-method/references/aggregators.md

@@ -119,12 +119,11 @@ allows safe mutation after construction and gives immediate, clear error message
 def alpha(self) -> float:
     return self._alpha
 
+
 @alpha.setter
 def alpha(self, value: float) -> None:
     if not (0.0 <= value <= 1.0):
-        raise ValueError(
-            f"Attribute `alpha` must be in [0, 1]. Found alpha={value!r}."
-        )
+        raise ValueError(f"Attribute `alpha` must be in [0, 1]. Found alpha={value!r}.")
     self._alpha = value
 ```
 
@@ -164,11 +163,7 @@ already defines `__str__` to return just the class name, and `Weighting` inherit
 
 ```python
 def __repr__(self) -> str:
-    return (
-        f"{self.__class__.__name__}("
-        f"alpha={self.alpha!r}, "
-        f"rho={self.rho!r})"
-    )
+    return f"{self.__class__.__name__}(alpha={self.alpha!r}, rho={self.rho!r})"
 ```
 
 ---