Fix Codacy Warnings (#477)

---------

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

tests/test_loss/test_lp_loss.py

@@ -2,9 +2,9 @@ import torch
 
 from pina.loss import LpLoss
 
-input = torch.tensor([[3.], [1.], [-8.]])
-target = torch.tensor([[6.], [4.], [2.]])
-available_reductions = ['str', 'mean', 'none']
+input = torch.tensor([[3.0], [1.0], [-8.0]])
+target = torch.tensor([[6.0], [4.0], [2.0]])
+available_reductions = ["str", "mean", "none"]
 
 
 def test_LpLoss_constructor():
@@ -12,17 +12,17 @@ def test_LpLoss_constructor():
     for reduction in available_reductions:
         LpLoss(reduction=reduction)
     # test p
-    for p in [float('inf'), -float('inf'), 1, 10, -8]:
+    for p in [float("inf"), -float("inf"), 1, 10, -8]:
         LpLoss(p=p)
 
 
 def test_LpLoss_forward():
     # l2 loss
-    loss = LpLoss(p=2, reduction='mean')
+    loss = LpLoss(p=2, reduction="mean")
     l2_loss = torch.mean(torch.sqrt((input - target).pow(2)))
     assert loss(input, target) == l2_loss
     # l1 loss
-    loss = LpLoss(p=1, reduction='sum')
+    loss = LpLoss(p=1, reduction="sum")
     l1_loss = torch.sum(torch.abs(input - target))
     assert loss(input, target) == l1_loss
 
@@ -32,16 +32,16 @@ def test_LpRelativeLoss_constructor():
     for reduction in available_reductions:
         LpLoss(reduction=reduction, relative=True)
     # test p
-    for p in [float('inf'), -float('inf'), 1, 10, -8]:
+    for p in [float("inf"), -float("inf"), 1, 10, -8]:
         LpLoss(p=p, relative=True)
 
 
 def test_LpRelativeLoss_forward():
     # l2 relative loss
-    loss = LpLoss(p=2, reduction='mean', relative=True)
+    loss = LpLoss(p=2, reduction="mean", relative=True)
     l2_loss = torch.sqrt((input - target).pow(2)) / torch.sqrt(input.pow(2))
     assert loss(input, target) == torch.mean(l2_loss)
     # l1 relative loss
-    loss = LpLoss(p=1, reduction='sum', relative=True)
+    loss = LpLoss(p=1, reduction="sum", relative=True)
     l1_loss = torch.abs(input - target) / torch.abs(input)
     assert loss(input, target) == torch.sum(l1_loss)