Fix Codacy Warnings (#477)

---------

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

pina/problem/inverse_problem.py

@@ -1,7 +1,7 @@
 """Module for the ParametricProblem class"""
 
-import torch
 from abc import abstractmethod
+import torch
 from .abstract_problem import AbstractProblem
 
 
@@ -16,40 +16,14 @@ class InverseProblem(AbstractProblem):
     derivative term.
 
     :Example:
-        >>> from pina.problem import SpatialProblem, InverseProblem
-        >>> from pina.operator import grad
-        >>> from pina.equation import ParametricEquation, FixedValue
-        >>> from pina import Condition
-        >>> from pina.geometry import CartesianDomain
-        >>> import torch
-        >>>
-        >>> class InverseODE(SpatialProblem, InverseProblem):
-        >>>
-        >>>     output_variables = ['u']
-        >>>     spatial_domain = CartesianDomain({'x': [0, 1]})
-        >>>     unknown_parameter_domain = CartesianDomain({'alpha': [1, 10]})
-        >>>
-        >>>     def ode_equation(input_, output_, params_):
-        >>>         u_x = grad(output_, input_, components=['u'], d=['x'])
-        >>>         u = output_.extract(['u'])
-        >>>         return params_.extract(['alpha']) * u_x - u
-        >>>
-        >>>     def solution_data(input_, output_):
-        >>>         x = input_.extract(['x'])
-        >>>         solution = torch.exp(x)
-        >>>         return output_ - solution
-        >>>
-        >>>     conditions = {
-        >>>         'x0': Condition(CartesianDomain({'x': 0}), FixedValue(1.0)),
-        >>>         'D': Condition(CartesianDomain({'x': [0, 1]}), ParametricEquation(ode_equation)),
-        >>>         'data': Condition(CartesianDomain({'x': [0, 1]}), Equation(solution_data))
+        TODO
     """
 
     def __init__(self):
         super().__init__()
         # storing unknown_parameters for optimization
         self.unknown_parameters = {}
-        for i, var in enumerate(self.unknown_variables):
+        for var in self.unknown_variables:
             range_var = self.unknown_parameter_domain.range_[var]
             tensor_var = (
                 torch.rand(1, requires_grad=True) * range_var[1] + range_var[0]
@@ -61,7 +35,6 @@ class InverseProblem(AbstractProblem):
         """
         The parameters' domain of the problem.
         """
-        pass
 
     @property
     def unknown_variables(self):