🎨 Format Python code with psf/black (#297)

Co-authored-by: dario-coscia <dario-coscia@users.noreply.github.com>

pina/solvers/pinns/causalpinn.py

@@ -97,25 +97,27 @@ class CausalPINN(PINN):
         :param dict scheduler_kwargs: LR scheduler constructor keyword args.
         :param int | float eps: The exponential decay parameter. Note that this
             value is kept fixed during the training, but can be changed by means
-            of a callback, e.g. for annealing. 
+            of a callback, e.g. for annealing.
         """
         super().__init__(
-                        problem=problem,
-                        model=model,
-                        extra_features=extra_features,
-                        loss=loss,
-                        optimizer=optimizer,
-                        optimizer_kwargs=optimizer_kwargs,
-                        scheduler=scheduler,
-                        scheduler_kwargs=scheduler_kwargs,
+            problem=problem,
+            model=model,
+            extra_features=extra_features,
+            loss=loss,
+            optimizer=optimizer,
+            optimizer_kwargs=optimizer_kwargs,
+            scheduler=scheduler,
+            scheduler_kwargs=scheduler_kwargs,
         )
 
         # checking consistency
-        check_consistency(eps, (int,float))
+        check_consistency(eps, (int, float))
         self._eps = eps
         if not isinstance(self.problem, TimeDependentProblem):
-            raise ValueError('Casual PINN works only for problems'
-                             'inheritig from TimeDependentProblem.')
+            raise ValueError(
+                "Casual PINN works only for problems"
+                "inheritig from TimeDependentProblem."
+            )
 
     def loss_phys(self, samples, equation):
         """
@@ -144,14 +146,14 @@ class CausalPINN(PINN):
             )
             time_loss.append(loss_val)
         # store results
-        self.store_log(loss_value=float(sum(time_loss)/len(time_loss)))
+        self.store_log(loss_value=float(sum(time_loss) / len(time_loss)))
         # concatenate residuals
         time_loss = torch.stack(time_loss)
         # compute weights (without the gradient storing)
         with torch.no_grad():
             weights = self._compute_weights(time_loss)
         return (weights * time_loss).mean()
-    
+
     @property
     def eps(self):
         """
@@ -205,8 +207,8 @@ class CausalPINN(PINN):
         _, idx_split = time_tensor.unique(return_counts=True)
         # splitting
         chunks = torch.split(tensor, tuple(idx_split))
-        return chunks, labels # return chunks
-    
+        return chunks, labels  # return chunks
+
     def _compute_weights(self, loss):
         """
         Computes the weights for the physics loss based on the cumulative loss.