Fix Codacy Warnings (#477)

---------

Co-authored-by: Dario Coscia <dariocos99@gmail.com>

pina/solver/solver.py

@@ -1,16 +1,15 @@
 """Solver module."""
 
+from abc import ABCMeta, abstractmethod
 import lightning
 import torch
-import sys
 
-from abc import ABCMeta, abstractmethod
+from torch._dynamo.eval_frame import OptimizedModule
 from ..problem import AbstractProblem
 from ..optim import Optimizer, Scheduler, TorchOptimizer, TorchScheduler
 from ..loss import WeightingInterface
 from ..loss.scalar_weighting import _NoWeighting
 from ..utils import check_consistency, labelize_forward
-from torch._dynamo.eval_frame import OptimizedModule
 
 
 class SolverInterface(lightning.pytorch.LightningModule, metaclass=ABCMeta):
@@ -119,6 +118,10 @@ class SolverInterface(lightning.pytorch.LightningModule, metaclass=ABCMeta):
         self.store_log("test_loss", loss, self.get_batch_size(batch))
 
     def store_log(self, name, value, batch_size):
+        """
+        TODO
+        """
+
         self.log(
             name=name,
             value=value,
@@ -128,7 +131,9 @@ class SolverInterface(lightning.pytorch.LightningModule, metaclass=ABCMeta):
 
     @abstractmethod
     def forward(self, *args, **kwargs):
-        pass
+        """
+        TODO
+        """
 
     @abstractmethod
     def optimization_cycle(self, batch):
@@ -144,7 +149,6 @@ class SolverInterface(lightning.pytorch.LightningModule, metaclass=ABCMeta):
             containing the condition name and the associated scalar loss.
         :rtype: dict(torch.Tensor)
         """
-        pass
 
     @property
     def problem(self):
@@ -169,7 +173,10 @@ class SolverInterface(lightning.pytorch.LightningModule, metaclass=ABCMeta):
 
     @staticmethod
     def get_batch_size(batch):
-        # assuming batch is a custom Batch object
+        """
+        TODO
+        """
+
         batch_size = 0
         for data in batch:
             batch_size += len(data[1]["input"])
@@ -177,10 +184,18 @@ class SolverInterface(lightning.pytorch.LightningModule, metaclass=ABCMeta):
 
     @staticmethod
     def default_torch_optimizer():
+        """
+        TODO
+        """
+
         return TorchOptimizer(torch.optim.Adam, lr=0.001)
 
     @staticmethod
     def default_torch_scheduler():
+        """
+        TODO
+        """
+
         return TorchScheduler(torch.optim.lr_scheduler.ConstantLR)
 
     def on_train_start(self):
@@ -202,6 +217,10 @@ class SolverInterface(lightning.pytorch.LightningModule, metaclass=ABCMeta):
             self._compile_model()
 
     def _check_already_compiled(self):
+        """
+        TODO
+        """
+
         models = self._pina_models
         if len(models) == 1 and isinstance(
             self._pina_models[0], torch.nn.ModuleDict
@@ -214,6 +233,10 @@ class SolverInterface(lightning.pytorch.LightningModule, metaclass=ABCMeta):
 
     @staticmethod
     def _perform_compilation(model):
+        """
+        TODO
+        """
+
         model_device = next(model.parameters()).device
         try:
             if model_device == torch.device("mps:0"):
@@ -225,7 +248,9 @@ class SolverInterface(lightning.pytorch.LightningModule, metaclass=ABCMeta):
         return model
 
 
-class SingleSolverInterface(SolverInterface):
+class SingleSolverInterface(SolverInterface, metaclass=ABCMeta):
+    """TODO"""
+
     def __init__(
         self,
         problem,
@@ -322,7 +347,7 @@ class SingleSolverInterface(SolverInterface):
         return self._pina_optimizers[0]
 
 
-class MultiSolverInterface(SolverInterface):
+class MultiSolverInterface(SolverInterface, metaclass=ABCMeta):
     """
     Multiple Solver base class. This class inherits is a wrapper of
     SolverInterface class