Fix Codacy Warnings (#477)

---------

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

tests/test_blocks/test_low_rank_block.py

@@ -5,54 +5,66 @@ from pina.model.block import LowRankBlock
 from pina import LabelTensor
 
 
-input_dimensions=2
-embedding_dimenion=1
-rank=4
-inner_size=20
-n_layers=2
-func=torch.nn.Tanh
-bias=True
+input_dimensions = 2
+embedding_dimenion = 1
+rank = 4
+inner_size = 20
+n_layers = 2
+func = torch.nn.Tanh
+bias = True
+
 
 def test_constructor():
-    LowRankBlock(input_dimensions=input_dimensions,
-                 embedding_dimenion=embedding_dimenion,
-                 rank=rank,
-                 inner_size=inner_size,
-                 n_layers=n_layers,
-                 func=func,
-                 bias=bias)
-    
+    LowRankBlock(
+        input_dimensions=input_dimensions,
+        embedding_dimenion=embedding_dimenion,
+        rank=rank,
+        inner_size=inner_size,
+        n_layers=n_layers,
+        func=func,
+        bias=bias,
+    )
+
+
 def test_constructor_wrong():
     with pytest.raises(ValueError):
-        LowRankBlock(input_dimensions=input_dimensions,
-                 embedding_dimenion=embedding_dimenion,
-                 rank=0.5,
-                 inner_size=inner_size,
-                 n_layers=n_layers,
-                 func=func,
-                 bias=bias)
-           
+        LowRankBlock(
+            input_dimensions=input_dimensions,
+            embedding_dimenion=embedding_dimenion,
+            rank=0.5,
+            inner_size=inner_size,
+            n_layers=n_layers,
+            func=func,
+            bias=bias,
+        )
+
+
 def test_forward():
-    block = LowRankBlock(input_dimensions=input_dimensions,
-                 embedding_dimenion=embedding_dimenion,
-                 rank=rank,
-                 inner_size=inner_size,
-                 n_layers=n_layers,
-                 func=func,
-                 bias=bias)
-    data = LabelTensor(torch.rand(10, 30, 3), labels=['x', 'y', 'u'])
-    block(data.extract('u'), data.extract(['x', 'y']))
+    block = LowRankBlock(
+        input_dimensions=input_dimensions,
+        embedding_dimenion=embedding_dimenion,
+        rank=rank,
+        inner_size=inner_size,
+        n_layers=n_layers,
+        func=func,
+        bias=bias,
+    )
+    data = LabelTensor(torch.rand(10, 30, 3), labels=["x", "y", "u"])
+    block(data.extract("u"), data.extract(["x", "y"]))
+
 
 def test_backward():
-    block = LowRankBlock(input_dimensions=input_dimensions,
-                 embedding_dimenion=embedding_dimenion,
-                 rank=rank,
-                 inner_size=inner_size,
-                 n_layers=n_layers,
-                 func=func,
-                 bias=bias)
-    data = LabelTensor(torch.rand(10, 30, 3), labels=['x', 'y', 'u'])
+    block = LowRankBlock(
+        input_dimensions=input_dimensions,
+        embedding_dimenion=embedding_dimenion,
+        rank=rank,
+        inner_size=inner_size,
+        n_layers=n_layers,
+        func=func,
+        bias=bias,
+    )
+    data = LabelTensor(torch.rand(10, 30, 3), labels=["x", "y", "u"])
     data.requires_grad_(True)
-    out = block(data.extract('u'), data.extract(['x', 'y']))
+    out = block(data.extract("u"), data.extract(["x", "y"]))
     loss = out.mean()
-    loss.backward()
+    loss.backward()