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

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

pina/solvers/pinns/gpinn.py

@@ -90,22 +90,23 @@ class GPINN(PINN):
         :param dict scheduler_kwargs: LR scheduler constructor keyword args.
         """
         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,
         )
         if not isinstance(self.problem, SpatialProblem):
-            raise ValueError('Gradient PINN computes the gradient of the '
-                             'PINN loss with respect to the spatial '
-                             'coordinates, thus the PINA problem must be '
-                             'a SpatialProblem.')
+            raise ValueError(
+                "Gradient PINN computes the gradient of the "
+                "PINN loss with respect to the spatial "
+                "coordinates, thus the PINA problem must be "
+                "a SpatialProblem."
+            )
 
-    
     def loss_phys(self, samples, equation):
         """
         Computes the physics loss for the GPINN solver based on given
@@ -126,9 +127,9 @@ class GPINN(PINN):
         self.store_log(loss_value=float(loss_value))
         # gradient PINN loss
         loss_value = loss_value.reshape(-1, 1)
-        loss_value.labels = ['__LOSS']
+        loss_value.labels = ["__LOSS"]
         loss_grad = grad(loss_value, samples, d=self.problem.spatial_variables)
         g_loss_phys = self.loss(
             torch.zeros_like(loss_grad, requires_grad=True), loss_grad
         )
-        return loss_value + g_loss_phys
+        return loss_value + g_loss_phys