Trainer Tutorial (#271)

* adding tutorial trainer
* implementing deepcopy for AbstractProblem and LabelTensor to match Lightning Callbacks

---------

Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local>

pina/label_tensor.py

@@ -1,6 +1,6 @@
 """ Module for LabelTensor """
 
-from typing import Any
+from copy import deepcopy
 import torch
 from torch import Tensor
 
@@ -79,6 +79,21 @@ class LabelTensor(torch.Tensor):
             )
         self._labels = labels
 
+    def __deepcopy__(self, __):
+        """
+        Implements deepcopy for label tensor. By default it stores the
+        current labels and use the :meth:`~torch._tensor.Tensor.__deepcopy__`
+        method for creating a new :class:`pina.label_tensor.LabelTensor`.
+
+        :param __: Placeholder parameter.
+        :type __: None
+        :return: The deep copy of the :class:`pina.label_tensor.LabelTensor`.
+        :rtype: LabelTensor
+        """
+        labels = self.labels
+        copy_tensor = deepcopy(self.tensor)
+        return LabelTensor(copy_tensor, labels)
+    
     @property
     def labels(self):
         """Property decorator for labels

pina/problem/abstract_problem.py

@@ -2,6 +2,7 @@
 
 from abc import ABCMeta, abstractmethod
 from ..utils import merge_tensors, check_consistency
+from copy import deepcopy
 import torch
 
 
@@ -29,6 +30,23 @@ class AbstractProblem(metaclass=ABCMeta):
         # put in self.input_pts all the points that we don't need to sample
         self._span_condition_points()
 
+    def __deepcopy__(self, memo):
+        """
+        Implements deepcopy for the
+        :class:`~pina.problem.abstract_problem.AbstractProblem` class.
+
+        :param dict memo: Memory dictionary, to avoid excess copy
+        :return: The deep copy of the
+            :class:`~pina.problem.abstract_problem.AbstractProblem` class
+        :rtype: AbstractProblem
+        """
+        cls = self.__class__
+        result = cls.__new__(cls)
+        memo[id(self)] = result
+        for k, v in self.__dict__.items():
+            setattr(result, k, deepcopy(v, memo))
+        return result
+
     @property
     def input_variables(self):
         """