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docs/user_guide/neuroevolution.md

@@ -293,8 +293,7 @@ class CustomNE(NEProblem):
     def _network_constants(self):
         return {"d": self._d}  # Pass self._d as 'd' to networks at instantiation
 
-    def _evaluate_network(self, network: torch.nn.Module):
-        ...
+    def _evaluate_network(self, network: torch.nn.Module): ...
 ```
 
 which then allows, for example, the use of the variable `d` in the string representation of the network

src/evotorch/algorithms/functional/__init__.py

@@ -256,8 +256,7 @@ def f(x: torch.Tensor) -> torch.Tensor:
 
 
 class MyProblem(Problem):
-    def __init__(self):
-        ...
+    def __init__(self): ...
 
     def _evaluate_batch(self, batch: SolutionBatch):
         # Stateful batch evaluation code goes here

src/evotorch/core.py

@@ -610,8 +610,9 @@ def __init__(self):
                 # for initializing solutions
             )
 
-        def _evaluate_batch(self, solutions: SolutionBatch):
-            ...  # code to compute and fill the fitnesses goes here
+        def _evaluate_batch(
+            self, solutions: SolutionBatch
+        ): ...  # code to compute and fill the fitnesses goes here
 
         def _fill(self, values: torch.Tensor):
             # `values` is an empty tensor of shape (n, m) where n is the number
@@ -749,8 +750,7 @@ def _evaluate_batch(self, solutions: SolutionBatch):
     from evotorch.logging import StdOutLogger
 
 
-    def f(x: torch.Tensor) -> torch.Tensor:
-        ...
+    def f(x: torch.Tensor) -> torch.Tensor: ...
 
 
     prob = Problem("min", f, solution_length=..., dtype=torch.float32)

src/evotorch/decorators.py

@@ -254,8 +254,7 @@ def f(x: torch.Tensor) -> torch.Tensor:
 
 
     @on_device("cuda")
-    def f(x: torch.Tensor) -> torch.Tensor:
-        ...
+    def f(x: torch.Tensor) -> torch.Tensor: ...
 
 
     problem = Problem(
@@ -306,8 +305,7 @@ def _evaluate_batch(self, solutions: SolutionBatch):
 
     ```python
     @on_device("cpu")
-    def f(x: torch.Tensor) -> torch.Tensor:
-        ...
+    def f(x: torch.Tensor) -> torch.Tensor: ...
 
 
     print(f.device)  # Prints: torch.device("cpu")
@@ -499,8 +497,7 @@ def f(x: torch.Tensor) -> torch.Tensor:
 
 
     @on_aux_device
-    def f(x: torch.Tensor) -> torch.Tensor:
-        ...
+    def f(x: torch.Tensor) -> torch.Tensor: ...
 
 
     problem = Problem(
@@ -632,8 +629,7 @@ def expects_ndim(  # noqa: C901
 
 
     @expects_ndim(2, 1)
-    def f(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
-        ...
+    def f(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: ...
     ```
 
     Once decorated like this, the function `f` will gain the following
@@ -663,8 +659,7 @@ def f(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
 
     ```python
     @expects_ndim(2, 1, randomness="error")
-    def f(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
-        ...
+    def f(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: ...
     ```
 
     If `randomness` is set as "error", then, when there is batching, any
@@ -941,8 +936,7 @@ def fitness(decision_values: torch.Tensor) -> torch.Tensor:
 
     ```python
     @rowwise(randomness="error")
-    def f(x: torch.Tensor) -> torch.Tensor:
-        ...
+    def f(x: torch.Tensor) -> torch.Tensor: ...
     ```
 
     If `randomness` is set as "error", then, when there is batching, any

src/evotorch/distributions.py

@@ -1395,8 +1395,7 @@ def make_functional_grad_estimator(
     )
 
 
-    def f(x: torch.Tensor) -> torch.Tensor:
-        ...
+    def f(x: torch.Tensor) -> torch.Tensor: ...
 
 
     fgrad = make_functional_grad_estimator(