Rename _i to _

Author: ValerianRey

pyproject.toml

@@ -67,7 +67,6 @@ Changelog = "https://github.com/TorchJD/torchjd/blob/main/CHANGELOG.md"
 [dependency-groups]
 check = [
     "ruff>=0.14.14",
-    "mypy>=1.16.0",
     "ty>=0.0.14",
     "pre-commit>=2.9.2",  # isort doesn't work before 2.9.2
 ]

src/torchjd/aggregation/_mgda.py

@@ -53,7 +53,7 @@ def forward(self, gramian: PSDMatrix, /) -> Tensor:
         dtype = gramian.dtype
 
         alpha = torch.ones(gramian.shape[0], device=device, dtype=dtype) / gramian.shape[0]
-        for _i in range(self.max_iters):
+        for _ in range(self.max_iters):
             t = torch.argmin(gramian @ alpha)
             e_t = torch.zeros(gramian.shape[0], device=device, dtype=dtype)
             e_t[t] = 1.0

tests/unit/autogram/test_engine.py

@@ -341,7 +341,7 @@ def test_iwrm_steps_with_autogram(factory: ModuleFactory, batch_size: int, batch
     engine = Engine(model, batch_dim=batch_dim)
     optimizer = SGD(model.parameters(), lr=1e-7)
 
-    for _i in range(n_iter):
+    for _ in range(n_iter):
         inputs, targets = make_inputs_and_targets(model, batch_size)
         loss_fn = make_mse_loss_fn(targets)
         autogram_forward_backward(model, inputs, loss_fn, engine, weighting)

tests/unit/autojac/_transform/test_accumulate.py

@@ -38,7 +38,7 @@ def test_multiple_grad_accumulations(iterations: int):
     values = [ones_(shape) for shape in shapes]
     accumulate = AccumulateGrad()
 
-    for _i in range(iterations):
+    for _ in range(iterations):
         # Clone values to ensure that we accumulate values that are not ever used afterwards
         input = {key: value.clone() for key, value in zip(keys, values, strict=False)}
         accumulate(input)
@@ -122,7 +122,7 @@ def test_multiple_jac_accumulations(iterations: int):
 
     accumulate = AccumulateJac()
 
-    for _i in range(iterations):
+    for _ in range(iterations):
         # Clone values to ensure that we accumulate values that are not ever used afterwards
         input = {key: value.clone() for key, value in zip(keys, values, strict=False)}
         accumulate(input)

tests/unit/autojac/test_utils.py

@@ -152,7 +152,7 @@ def test_get_leaf_tensors_deep(depth: int):
 
     one = tensor_(1.0, requires_grad=True)
     sum_ = tensor_(0.0, requires_grad=False)
-    for _i in range(depth):
+    for _ in range(depth):
         sum_ = sum_ + one
 
     leaves = get_leaf_tensors(tensors=[sum_], excluded=set())