Fix Codacy Warnings (#477)

---------

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

tests/test_adaptive_function.py

@@ -1,62 +1,85 @@
 import torch
 import pytest
 
-from pina.adaptive_function import (AdaptiveReLU, AdaptiveSigmoid, AdaptiveTanh,
-                            AdaptiveSiLU, AdaptiveMish, AdaptiveELU,
-                            AdaptiveCELU, AdaptiveGELU, AdaptiveSoftmin,
-                            AdaptiveSoftmax, AdaptiveSIREN, AdaptiveExp)
+from pina.adaptive_function import (
+    AdaptiveReLU,
+    AdaptiveSigmoid,
+    AdaptiveTanh,
+    AdaptiveSiLU,
+    AdaptiveMish,
+    AdaptiveELU,
+    AdaptiveCELU,
+    AdaptiveGELU,
+    AdaptiveSoftmin,
+    AdaptiveSoftmax,
+    AdaptiveSIREN,
+    AdaptiveExp,
+)
 
 
-adaptive_function = (AdaptiveReLU, AdaptiveSigmoid, AdaptiveTanh,
-                      AdaptiveSiLU, AdaptiveMish, AdaptiveELU,
-                      AdaptiveCELU, AdaptiveGELU, AdaptiveSoftmin,
-                      AdaptiveSoftmax, AdaptiveSIREN, AdaptiveExp)
+adaptive_function = (
+    AdaptiveReLU,
+    AdaptiveSigmoid,
+    AdaptiveTanh,
+    AdaptiveSiLU,
+    AdaptiveMish,
+    AdaptiveELU,
+    AdaptiveCELU,
+    AdaptiveGELU,
+    AdaptiveSoftmin,
+    AdaptiveSoftmax,
+    AdaptiveSIREN,
+    AdaptiveExp,
+)
 x = torch.rand(10, requires_grad=True)
 
+
 @pytest.mark.parametrize("Func", adaptive_function)
 def test_constructor(Func):
-    if Func.__name__ == 'AdaptiveExp':
+    if Func.__name__ == "AdaptiveExp":
         # simple
         Func()
         # setting values
-        af = Func(alpha=1., beta=2.)
+        af = Func(alpha=1.0, beta=2.0)
         assert af.alpha.requires_grad
         assert af.beta.requires_grad
-        assert af.alpha == 1.
-        assert af.beta == 2.
+        assert af.alpha == 1.0
+        assert af.beta == 2.0
     else:
         # simple
         Func()
         # setting values
-        af = Func(alpha=1., beta=2., gamma=3.)
+        af = Func(alpha=1.0, beta=2.0, gamma=3.0)
         assert af.alpha.requires_grad
         assert af.beta.requires_grad
         assert af.gamma.requires_grad
-        assert af.alpha == 1.
-        assert af.beta == 2.
-        assert af.gamma == 3.
+        assert af.alpha == 1.0
+        assert af.beta == 2.0
+        assert af.gamma == 3.0
 
     # fixed variables
-    af = Func(alpha=1., beta=2., fixed=['alpha'])
+    af = Func(alpha=1.0, beta=2.0, fixed=["alpha"])
     assert af.alpha.requires_grad is False
     assert af.beta.requires_grad
-    assert af.alpha == 1.
-    assert af.beta == 2.
+    assert af.alpha == 1.0
+    assert af.beta == 2.0
 
     with pytest.raises(TypeError):
-        Func(alpha=1., beta=2., fixed=['delta'])
+        Func(alpha=1.0, beta=2.0, fixed=["delta"])
 
     with pytest.raises(ValueError):
-        Func(alpha='s')
+        Func(alpha="s")
         Func(alpha=1)
 
-@pytest.mark.parametrize("Func", adaptive_function)      
+
+@pytest.mark.parametrize("Func", adaptive_function)
 def test_forward(Func):
     af = Func()
     af(x)
 
+
 @pytest.mark.parametrize("Func", adaptive_function)
 def test_backward(Func):
     af = Func()
     y = af(x)
-    y.mean().backward()
+    y.mean().backward()