Correct codacy warnings

pina/solvers/supervised.py

@@ -40,15 +40,13 @@ class SupervisedSolver(SolverInterface):
     accepted_condition_types = ['supervised']
     __name__ = 'SupervisedSolver'
 
-    def __init__(
-            self,
-            problem,
-            model,
-            loss=None,
-            optimizer=None,
-            scheduler=None,
-            extra_features=None
-    ):
+    def __init__(self,
+                 problem,
+                 model,
+                 loss=None,
+                 optimizer=None,
+                 scheduler=None,
+                 extra_features=None):
         """
         :param AbstractProblem problem: The formualation of the problem.
         :param torch.nn.Module model: The neural network model to use.
@@ -68,16 +66,13 @@ class SupervisedSolver(SolverInterface):
             optimizer = TorchOptimizer(torch.optim.Adam, lr=0.001)
 
         if scheduler is None:
-            scheduler = TorchScheduler(
-                torch.optim.lr_scheduler.ConstantLR)
+            scheduler = TorchScheduler(torch.optim.lr_scheduler.ConstantLR)
 
-        super().__init__(
-            models=model,
-            problem=problem,
-            optimizers=optimizer,
-            schedulers=scheduler,
-            extra_features=extra_features
-        )
+        super().__init__(models=model,
+                         problem=problem,
+                         optimizers=optimizer,
+                         schedulers=scheduler,
+                         extra_features=extra_features)
 
         # check consistency
         check_consistency(loss, (LossInterface, _Loss), subclass=False)
@@ -107,10 +102,8 @@ class SupervisedSolver(SolverInterface):
         """
         self._optimizer.hook(self._model.parameters())
         self._scheduler.hook(self._optimizer)
-        return (
-            [self._optimizer.optimizer_instance],
-            [self._scheduler.scheduler_instance]
-        )
+        return ([self._optimizer.optimizer_instance],
+                [self._scheduler.scheduler_instance])
 
     def training_step(self, batch, batch_idx):
         """Solver training step.
@@ -136,8 +129,7 @@ class SupervisedSolver(SolverInterface):
             # for data driven mode
             if not hasattr(condition, "output_points"):
                 raise NotImplementedError(
-                    f"{type(self).__name__} works only in data-driven mode."
-                )
+                    f"{type(self).__name__} works only in data-driven mode.")
 
             output_pts = out[condition_idx == condition_id]
             input_pts = pts[condition_idx == condition_id]
@@ -145,9 +137,7 @@ class SupervisedSolver(SolverInterface):
             input_pts.labels = pts.labels
             output_pts.labels = out.labels
 
-            loss = (
-                self.loss_data(input_pts=input_pts, output_pts=output_pts)
-            )
+            loss = (self.loss_data(input_pts=input_pts, output_pts=output_pts))
             loss = loss.as_subclass(torch.Tensor)
 
         self.log("mean_loss", float(loss), prog_bar=True, logger=True)