🎨 Format Python code with psf/black

pina/trainer.py

@@ -5,6 +5,7 @@ from .utils import check_consistency
 from .dataset import SamplePointDataset, SamplePointLoader, DataPointDataset
 from .solvers.solver import SolverInterface
 
+
 class Trainer(pytorch_lightning.Trainer):
 
     def __init__(self, solver, batch_size=None, **kwargs):
@@ -29,18 +30,20 @@ class Trainer(pytorch_lightning.Trainer):
         check_consistency(solver, SolverInterface)
         if batch_size is not None:
             check_consistency(batch_size, int)
-        
+
         self._model = solver
         self.batch_size = batch_size
 
         # create dataloader
         if solver.problem.have_sampled_points is False:
-            raise RuntimeError(f'Input points in {solver.problem.not_sampled_points} '
-                               'training are None. Please '
-                               'sample points in your problem by calling '
-                               'discretise_domain function before train '
-                               'in the provided locations.')
-        
+            raise RuntimeError(
+                f"Input points in {solver.problem.not_sampled_points} "
+                "training are None. Please "
+                "sample points in your problem by calling "
+                "discretise_domain function before train "
+                "in the provided locations."
+            )
+
         self._create_or_update_loader()
 
     def _create_or_update_loader(self):
@@ -52,21 +55,23 @@ class Trainer(pytorch_lightning.Trainer):
         devices = self._accelerator_connector._parallel_devices
 
         if len(devices) > 1:
-            raise RuntimeError('Parallel training is not supported yet.')
+            raise RuntimeError("Parallel training is not supported yet.")
 
         device = devices[0]
         dataset_phys = SamplePointDataset(self._model.problem, device)
         dataset_data = DataPointDataset(self._model.problem, device)
         self._loader = SamplePointLoader(
-            dataset_phys, dataset_data, batch_size=self.batch_size,
-            shuffle=True)
+            dataset_phys, dataset_data, batch_size=self.batch_size, shuffle=True
+        )
 
     def train(self, **kwargs):
         """
         Train the solver method.
         """
-        return super().fit(self._model, train_dataloaders=self._loader, **kwargs)
-    
+        return super().fit(
+            self._model, train_dataloaders=self._loader, **kwargs
+        )
+
     @property
     def solver(self):
         """