From e0eb11ae6729cb6f6b82098c25f533db29fa56c9 Mon Sep 17 00:00:00 2001
From: Gerhardsa0 <andreasgerhards@t-online.de>
Date: Mon, 15 Jul 2024 17:01:19 +0200
Subject: [PATCH] linter changes

---
 tests/safeds/ml/nn/test_model.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/tests/safeds/ml/nn/test_model.py b/tests/safeds/ml/nn/test_model.py
index ca3afd123..2f28db782 100644
--- a/tests/safeds/ml/nn/test_model.py
+++ b/tests/safeds/ml/nn/test_model.py
@@ -53,7 +53,7 @@ def test_should_return_input_size(self, device: Device) -> None:
             ).fit(
                 Table.from_dict({"a": [1], "b": [2]}).to_tabular_dataset("a"),
             )
-            device.type
+            device.type #noqa
             assert model.input_size == 1
 
         def test_should_raise_if_epoch_size_out_of_bounds(self, device: Device) -> None:
@@ -264,7 +264,7 @@ def test_should_return_input_size(self, device: Device) -> None:
                 Table.from_dict({"a": [1, 2, 3, 4], "b": [0, 1, 0, 1]}).to_tabular_dataset("b"),
                 "accuracy",
             )
-            device.type
+            device.type # noqa
             assert model.input_size == 1
 
         def test_should_raise_if_epoch_size_out_of_bounds_when_fitting_by_exhaustive_search(
@@ -334,6 +334,7 @@ def test_should_assert_that_is_fitted_is_set_correctly_and_check_return_type(
             self,
             metric: Literal["accuracy", "precision", "recall", "f1_score"],
             positive_class: Any,
+            device: Device
         ) -> None:
             model = NeuralNetworkClassifier(InputConversionTable(), [ForwardLayer(Choice(2, 4)), ForwardLayer(1)])
             assert not model.is_fitted
@@ -342,6 +343,7 @@ def test_should_assert_that_is_fitted_is_set_correctly_and_check_return_type(
                 optimization_metric=metric,
                 positive_class=positive_class,
             )
+            device.type #noqa
             assert fitted_model.is_fitted
             assert isinstance(fitted_model, NeuralNetworkClassifier)
 
@@ -617,7 +619,7 @@ def test_should_return_input_size(self, device: Device) -> None:
             ).fit(
                 Table.from_dict({"a": [1], "b": [2]}).to_tabular_dataset("a"),
             )
-            device.type
+            device.type # noqa
             assert model.input_size == 1
 
         def test_should_raise_if_epoch_size_out_of_bounds(self, device: Device) -> None:
@@ -809,7 +811,7 @@ def test_should_return_input_size(self, device: Device) -> None:
                 Table.from_dict({"a": [1, 2, 3, 4], "b": [1.0, 2.0, 3.0, 4.0]}).to_tabular_dataset("b"),
                 "mean_squared_error",
             )
-            device.type
+            device.type # noqa
             assert model.input_size == 1
 
         def test_should_raise_if_epoch_size_out_of_bounds_when_fitting_by_exhaustive_search(
@@ -876,6 +878,7 @@ def test_should_assert_that_is_fitted_is_set_correctly_and_check_return_type(
                 "median_absolute_deviation",
                 "coefficient_of_determination",
             ],
+            device: Device
         ) -> None:
             model = NeuralNetworkRegressor(InputConversionTable(), [ForwardLayer(Choice(2, 4)), ForwardLayer(1)])
             assert not model.is_fitted
@@ -883,6 +886,7 @@ def test_should_assert_that_is_fitted_is_set_correctly_and_check_return_type(
                 Table.from_dict({"a": [1, 2, 3, 4], "b": [1.0, 2.0, 3.0, 4.0]}).to_tabular_dataset("b"),
                 optimization_metric=metric,
             )
+            device.type #noqa
             assert fitted_model.is_fitted
             assert isinstance(fitted_model, NeuralNetworkRegressor)