Fix Codacy Warnings (#477)

---------

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

tests/test_data/test_tensor_dataset.py

@@ -9,80 +9,78 @@ input_tensor_2 = torch.rand((50, 10))
 output_tensor_2 = torch.rand((50, 2))
 
 conditions_dict_single = {
-    'data': {
-        'input': input_tensor,
-        'target': output_tensor,
+    "data": {
+        "input": input_tensor,
+        "target": output_tensor,
     }
 }
 
 conditions_dict_single_multi = {
-    'data_1': {
-        'input': input_tensor,
-        'target': output_tensor,
+    "data_1": {
+        "input": input_tensor,
+        "target": output_tensor,
+    },
+    "data_2": {
+        "input": input_tensor_2,
+        "target": output_tensor_2,
     },
-    'data_2': {
-        'input': input_tensor_2,
-        'target': output_tensor_2,
-    }
 }
 
-max_conditions_lengths_single = {
-    'data': 100
-}
+max_conditions_lengths_single = {"data": 100}
 
-max_conditions_lengths_multi = {
-    'data_1': 100,
-    'data_2': 50
-}
+max_conditions_lengths_multi = {"data_1": 100, "data_2": 50}
 
 
 @pytest.mark.parametrize(
     "conditions_dict, max_conditions_lengths",
     [
         (conditions_dict_single, max_conditions_lengths_single),
-        (conditions_dict_single_multi, max_conditions_lengths_multi)
-    ]
+        (conditions_dict_single_multi, max_conditions_lengths_multi),
+    ],
 )
 def test_constructor_tensor(conditions_dict, max_conditions_lengths):
-    dataset = PinaDatasetFactory(conditions_dict,
-                                 max_conditions_lengths=max_conditions_lengths,
-                                 automatic_batching=True)
+    dataset = PinaDatasetFactory(
+        conditions_dict,
+        max_conditions_lengths=max_conditions_lengths,
+        automatic_batching=True,
+    )
     assert isinstance(dataset, PinaTensorDataset)
 
 
 def test_getitem_single():
-    dataset = PinaDatasetFactory(conditions_dict_single,
-                                 max_conditions_lengths=max_conditions_lengths_single,
-                                 automatic_batching=False)
+    dataset = PinaDatasetFactory(
+        conditions_dict_single,
+        max_conditions_lengths=max_conditions_lengths_single,
+        automatic_batching=False,
+    )
 
     tensors = dataset.fetch_from_idx_list([i for i in range(70)])
     assert isinstance(tensors, dict)
-    assert list(tensors.keys()) == ['data']
-    assert sorted(list(tensors['data'].keys())) == [
-        'input', 'target']
-    assert isinstance(tensors['data']['input'], torch.Tensor)
-    assert tensors['data']['input'].shape == torch.Size((70, 10))
-    assert isinstance(tensors['data']['target'], torch.Tensor)
-    assert tensors['data']['target'].shape == torch.Size((70, 2))
+    assert list(tensors.keys()) == ["data"]
+    assert sorted(list(tensors["data"].keys())) == ["input", "target"]
+    assert isinstance(tensors["data"]["input"], torch.Tensor)
+    assert tensors["data"]["input"].shape == torch.Size((70, 10))
+    assert isinstance(tensors["data"]["target"], torch.Tensor)
+    assert tensors["data"]["target"].shape == torch.Size((70, 2))
 
 
 def test_getitem_multi():
-    dataset = PinaDatasetFactory(conditions_dict_single_multi,
-                                 max_conditions_lengths=max_conditions_lengths_multi,
-                                 automatic_batching=False)
+    dataset = PinaDatasetFactory(
+        conditions_dict_single_multi,
+        max_conditions_lengths=max_conditions_lengths_multi,
+        automatic_batching=False,
+    )
     tensors = dataset.fetch_from_idx_list([i for i in range(70)])
     assert isinstance(tensors, dict)
-    assert list(tensors.keys()) == ['data_1', 'data_2']
-    assert sorted(list(tensors['data_1'].keys())) == [
-        'input', 'target']
-    assert isinstance(tensors['data_1']['input'], torch.Tensor)
-    assert tensors['data_1']['input'].shape == torch.Size((70, 10))
-    assert isinstance(tensors['data_1']['target'], torch.Tensor)
-    assert tensors['data_1']['target'].shape == torch.Size((70, 2))
+    assert list(tensors.keys()) == ["data_1", "data_2"]
+    assert sorted(list(tensors["data_1"].keys())) == ["input", "target"]
+    assert isinstance(tensors["data_1"]["input"], torch.Tensor)
+    assert tensors["data_1"]["input"].shape == torch.Size((70, 10))
+    assert isinstance(tensors["data_1"]["target"], torch.Tensor)
+    assert tensors["data_1"]["target"].shape == torch.Size((70, 2))
 
-    assert sorted(list(tensors['data_2'].keys())) == [
-        'input', 'target']
-    assert isinstance(tensors['data_2']['input'], torch.Tensor)
-    assert tensors['data_2']['input'].shape == torch.Size((50, 10))
-    assert isinstance(tensors['data_2']['target'], torch.Tensor)
-    assert tensors['data_2']['target'].shape == torch.Size((50, 2))
+    assert sorted(list(tensors["data_2"].keys())) == ["input", "target"]
+    assert isinstance(tensors["data_2"]["input"], torch.Tensor)
+    assert tensors["data_2"]["input"].shape == torch.Size((50, 10))
+    assert isinstance(tensors["data_2"]["target"], torch.Tensor)
+    assert tensors["data_2"]["target"].shape == torch.Size((50, 2))