diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 384ef2229..95dd5e8c7 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0  # Use the ref you want to point at
+    rev: v5.0.0  # Use the ref you want to point at
     hooks:
     - id: trailing-whitespace
     - id: check-ast
@@ -16,7 +16,7 @@ repos:
     - id: check-toml
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: 'v0.6.5'
+    rev: 'v0.9.2'
     hooks:
     - id: ruff
       args: [--fix, --exit-non-zero-on-fix]
@@ -25,7 +25,7 @@ repos:
       types_or: [python, jupyter]
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.11.2
+    rev: v1.14.1
     hooks:
     - id: mypy
       entry: python3 -m mypy --config-file pyproject.toml
@@ -41,7 +41,7 @@ repos:
       entry: python3 -m nbstripout
 
   - repo: https://github.com/nbQA-dev/nbQA
-    rev: 1.8.7
+    rev: 1.9.1
     hooks:
     - id: nbqa-black
     - id: nbqa-ruff
diff --git a/cyclops/data/df/feature.py b/cyclops/data/df/feature.py
index db8c6e5a5..6ac30200e 100644
--- a/cyclops/data/df/feature.py
+++ b/cyclops/data/df/feature.py
@@ -60,7 +60,7 @@ def __init__(self, **kwargs: Any) -> None:
         if kwargs[FEATURE_TYPE_ATTR] not in FEATURE_TYPES:
             raise ValueError(
                 f"""Feature type '{kwargs[FEATURE_TYPE_ATTR]}'
-                not in {', '.join(FEATURE_TYPES)}.""",
+                not in {", ".join(FEATURE_TYPES)}.""",
             )
 
         # Set attributes
diff --git a/cyclops/data/features/medical_image.py b/cyclops/data/features/medical_image.py
index 7b7d9cae9..3dcfd9583 100644
--- a/cyclops/data/features/medical_image.py
+++ b/cyclops/data/features/medical_image.py
@@ -209,11 +209,14 @@ def decode_example(
                     use_auth_token = token_per_repo_id.get(repo_id)
                 except ValueError:
                     use_auth_token = None
-                with xopen(
-                    path,
-                    "rb",
-                    use_auth_token=use_auth_token,
-                ) as file_obj, BytesIO(file_obj.read()) as buffer:
+                with (
+                    xopen(
+                        path,
+                        "rb",
+                        use_auth_token=use_auth_token,
+                    ) as file_obj,
+                    BytesIO(file_obj.read()) as buffer,
+                ):
                     image, metadata = self._read_file_from_bytes(buffer)
                     metadata["filename_or_obj"] = path
 
diff --git a/cyclops/data/impute.py b/cyclops/data/impute.py
index 4595e92be..6ab284faa 100644
--- a/cyclops/data/impute.py
+++ b/cyclops/data/impute.py
@@ -304,7 +304,7 @@ def _process_imputefunc(
             if imputefunc not in IMPUTEFUNCS:
                 raise ValueError(
                     f"""Imputefunc string {imputefunc} not supported.
-                    Supporting: {','.join(IMPUTEFUNCS)}""",
+                    Supporting: {",".join(IMPUTEFUNCS)}""",
                 )
             func = IMPUTEFUNCS[imputefunc]
         elif callable(imputefunc):
diff --git a/cyclops/data/slicer.py b/cyclops/data/slicer.py
index ad06a0728..92e74563a 100644
--- a/cyclops/data/slicer.py
+++ b/cyclops/data/slicer.py
@@ -825,8 +825,7 @@ def filter_string_contains(
     example_values = pa.array(examples[column_name])
     if not pa.types.is_string(example_values.type):
         raise ValueError(
-            "Expected string feature, but got feature of type "
-            f"{example_values.type}.",
+            f"Expected string feature, but got feature of type {example_values.type}.",
         )
 
     # get all the values that contain the given substring
diff --git a/cyclops/evaluate/evaluator.py b/cyclops/evaluate/evaluator.py
index cf4d01611..f7b455b3b 100644
--- a/cyclops/evaluate/evaluator.py
+++ b/cyclops/evaluate/evaluator.py
@@ -170,8 +170,7 @@ def _load_data(
         if split is None:
             split = choose_split(dataset, **load_dataset_kwargs)
             LOGGER.warning(
-                "Got `split=None` but `dataset` is a string. "
-                "Using `split=%s` instead.",
+                "Got `split=None` but `dataset` is a string. Using `split=%s` instead.",
                 split,
             )
 
diff --git a/cyclops/evaluate/fairness/evaluator.py b/cyclops/evaluate/fairness/evaluator.py
index 22b39d175..7f78bbea6 100644
--- a/cyclops/evaluate/fairness/evaluator.py
+++ b/cyclops/evaluate/fairness/evaluator.py
@@ -150,7 +150,7 @@ def evaluate_fairness(  # noqa: PLR0912
     # input validation and formatting
     if not isinstance(dataset, Dataset):
         raise TypeError(
-            "Expected `dataset` to be of type `Dataset`, but got " f"{type(dataset)}.",
+            f"Expected `dataset` to be of type `Dataset`, but got {type(dataset)}.",
         )
     if array_lib not in _SUPPORTED_ARRAY_LIBS:
         raise NotImplementedError(f"The array library `{array_lib}` is not supported.")
@@ -520,8 +520,7 @@ def _validate_group_bins(
     for group, bins in group_bins.items():
         if not isinstance(bins, (list, int)):
             raise TypeError(
-                f"The bins for {group} must be a list or an integer. "
-                f"Got {type(bins)}.",
+                f"The bins for {group} must be a list or an integer. Got {type(bins)}.",
             )
 
         if isinstance(bins, int) and not 2 <= bins < len(unique_values[group]):
diff --git a/cyclops/evaluate/metrics/accuracy.py b/cyclops/evaluate/metrics/accuracy.py
index 07fdc0b43..228b0ffe3 100644
--- a/cyclops/evaluate/metrics/accuracy.py
+++ b/cyclops/evaluate/metrics/accuracy.py
@@ -339,9 +339,9 @@ def __new__(  # type: ignore # mypy expects a subclass of Accuracy
                 zero_division=zero_division,
             )
         if task == "multiclass":
-            assert (
-                isinstance(num_classes, int) and num_classes > 0
-            ), "Number of classes must be specified for multiclass classification."
+            assert isinstance(num_classes, int) and num_classes > 0, (
+                "Number of classes must be specified for multiclass classification."
+            )
             return MulticlassAccuracy(
                 num_classes=num_classes,
                 top_k=top_k,
@@ -349,9 +349,9 @@ def __new__(  # type: ignore # mypy expects a subclass of Accuracy
                 zero_division=zero_division,
             )
         if task == "multilabel":
-            assert (
-                isinstance(num_labels, int) and num_labels > 0
-            ), "Number of labels must be specified for multilabel classification."
+            assert isinstance(num_labels, int) and num_labels > 0, (
+                "Number of labels must be specified for multilabel classification."
+            )
             return MultilabelAccuracy(
                 num_labels=num_labels,
                 threshold=threshold,
diff --git a/cyclops/evaluate/metrics/auroc.py b/cyclops/evaluate/metrics/auroc.py
index 903886cee..950d4f421 100644
--- a/cyclops/evaluate/metrics/auroc.py
+++ b/cyclops/evaluate/metrics/auroc.py
@@ -336,18 +336,18 @@ def __new__(  # type: ignore # mypy expects a subclass of AUROC
         if task == "binary":
             return BinaryAUROC(max_fpr=max_fpr, thresholds=thresholds)
         if task == "multiclass":
-            assert (
-                isinstance(num_classes, int) and num_classes > 0
-            ), "Number of classes must be a positive integer."
+            assert isinstance(num_classes, int) and num_classes > 0, (
+                "Number of classes must be a positive integer."
+            )
             return MulticlassAUROC(
                 num_classes=num_classes,
                 thresholds=thresholds,
                 average=average,  # type: ignore
             )
         if task == "multilabel":
-            assert (
-                isinstance(num_labels, int) and num_labels > 0
-            ), "Number of labels must be a positive integer."
+            assert isinstance(num_labels, int) and num_labels > 0, (
+                "Number of labels must be a positive integer."
+            )
             return MultilabelAUROC(
                 num_labels=num_labels,
                 thresholds=thresholds,
diff --git a/cyclops/evaluate/metrics/f_beta.py b/cyclops/evaluate/metrics/f_beta.py
index 9c0ffda5d..053dd56e8 100644
--- a/cyclops/evaluate/metrics/f_beta.py
+++ b/cyclops/evaluate/metrics/f_beta.py
@@ -363,9 +363,9 @@ def __new__(  # type: ignore # mypy expects a subclass of FbetaScore
                 zero_division=zero_division,
             )
         if task == "multiclass":
-            assert (
-                isinstance(num_classes, int) and num_classes > 0
-            ), "Number of classes must be specified for multiclass classification."
+            assert isinstance(num_classes, int) and num_classes > 0, (
+                "Number of classes must be specified for multiclass classification."
+            )
             return MulticlassFbetaScore(
                 beta=beta,
                 num_classes=num_classes,
@@ -374,9 +374,9 @@ def __new__(  # type: ignore # mypy expects a subclass of FbetaScore
                 zero_division=zero_division,
             )
         if task == "multilabel":
-            assert (
-                isinstance(num_labels, int) and num_labels > 0
-            ), "Number of labels must be specified for multilabel classification."
+            assert isinstance(num_labels, int) and num_labels > 0, (
+                "Number of labels must be specified for multilabel classification."
+            )
             return MultilabelFbetaScore(
                 beta=beta,
                 num_labels=num_labels,
@@ -682,9 +682,9 @@ def __new__(  # type: ignore # mypy expects a subclass of F1Score
                 zero_division=zero_division,
             )
         if task == "multiclass":
-            assert (
-                isinstance(num_classes, int) and num_classes > 0
-            ), "Number of classes must be specified for multiclass classification."
+            assert isinstance(num_classes, int) and num_classes > 0, (
+                "Number of classes must be specified for multiclass classification."
+            )
             return MulticlassF1Score(
                 num_classes=num_classes,
                 top_k=top_k,
@@ -692,9 +692,9 @@ def __new__(  # type: ignore # mypy expects a subclass of F1Score
                 zero_division=zero_division,
             )
         if task == "multilabel":
-            assert (
-                isinstance(num_labels, int) and num_labels > 0
-            ), "Number of labels must be specified for multilabel classification."
+            assert isinstance(num_labels, int) and num_labels > 0, (
+                "Number of labels must be specified for multilabel classification."
+            )
             return MultilabelF1Score(
                 num_labels=num_labels,
                 threshold=threshold,
diff --git a/cyclops/evaluate/metrics/functional/accuracy.py b/cyclops/evaluate/metrics/functional/accuracy.py
index 3fbaaa9cf..66eeb87c9 100644
--- a/cyclops/evaluate/metrics/functional/accuracy.py
+++ b/cyclops/evaluate/metrics/functional/accuracy.py
@@ -446,9 +446,9 @@ def accuracy(
             zero_division=zero_division,
         )
     elif task == "multiclass":
-        assert (
-            isinstance(num_classes, int) and num_classes > 0
-        ), "Number of classes must be specified for multiclass classification."
+        assert isinstance(num_classes, int) and num_classes > 0, (
+            "Number of classes must be specified for multiclass classification."
+        )
         accuracy_score = multiclass_accuracy(
             target,
             preds,
@@ -458,9 +458,9 @@ def accuracy(
             zero_division=zero_division,
         )
     elif task == "multilabel":
-        assert (
-            isinstance(num_labels, int) and num_labels > 0
-        ), "Number of labels must be specified for multilabel classification."
+        assert isinstance(num_labels, int) and num_labels > 0, (
+            "Number of labels must be specified for multilabel classification."
+        )
         accuracy_score = multilabel_accuracy(
             target,
             preds,
diff --git a/cyclops/evaluate/metrics/functional/auroc.py b/cyclops/evaluate/metrics/functional/auroc.py
index 316d8d638..9f194de3d 100644
--- a/cyclops/evaluate/metrics/functional/auroc.py
+++ b/cyclops/evaluate/metrics/functional/auroc.py
@@ -574,9 +574,9 @@ def auroc(
     if task == "binary":
         return binary_auroc(target, preds, max_fpr=max_fpr, thresholds=thresholds)
     if task == "multiclass":
-        assert (
-            isinstance(num_classes, int) and num_classes > 0
-        ), "Number of classes must be a positive integer."
+        assert isinstance(num_classes, int) and num_classes > 0, (
+            "Number of classes must be a positive integer."
+        )
         return multiclass_auroc(
             target,
             preds,
@@ -585,9 +585,9 @@ def auroc(
             average=average,  # type: ignore[arg-type]
         )
     if task == "multilabel":
-        assert (
-            isinstance(num_labels, int) and num_labels > 0
-        ), "Number of labels must be a positive integer."
+        assert isinstance(num_labels, int) and num_labels > 0, (
+            "Number of labels must be a positive integer."
+        )
         return multilabel_auroc(
             target,
             preds,
diff --git a/cyclops/evaluate/metrics/functional/f_beta.py b/cyclops/evaluate/metrics/functional/f_beta.py
index d3ce7def0..c531eaefe 100644
--- a/cyclops/evaluate/metrics/functional/f_beta.py
+++ b/cyclops/evaluate/metrics/functional/f_beta.py
@@ -468,9 +468,9 @@ def fbeta_score(
             zero_division=zero_division,
         )
     if task == "multiclass":
-        assert (
-            isinstance(num_classes, int) and num_classes > 0
-        ), "Number of classes must be specified for multiclass classification."
+        assert isinstance(num_classes, int) and num_classes > 0, (
+            "Number of classes must be specified for multiclass classification."
+        )
         return multiclass_fbeta_score(
             target,
             preds,
@@ -481,9 +481,9 @@ def fbeta_score(
             zero_division=zero_division,
         )
     if task == "multilabel":
-        assert (
-            isinstance(num_labels, int) and num_labels > 0
-        ), "Number of labels must be specified for multilabel classification."
+        assert isinstance(num_labels, int) and num_labels > 0, (
+            "Number of labels must be specified for multilabel classification."
+        )
         return multilabel_fbeta_score(
             target,
             preds,
diff --git a/cyclops/evaluate/metrics/functional/precision_recall.py b/cyclops/evaluate/metrics/functional/precision_recall.py
index 318e416c8..a355a12b9 100644
--- a/cyclops/evaluate/metrics/functional/precision_recall.py
+++ b/cyclops/evaluate/metrics/functional/precision_recall.py
@@ -427,9 +427,9 @@ def precision(
             zero_division=zero_division,
         )
     if task == "multiclass":
-        assert (
-            isinstance(num_classes, int) and num_classes > 0
-        ), "Number of classes must be specified for multiclass classification."
+        assert isinstance(num_classes, int) and num_classes > 0, (
+            "Number of classes must be specified for multiclass classification."
+        )
         return multiclass_precision(
             target,
             preds,
@@ -439,9 +439,9 @@ def precision(
             zero_division=zero_division,
         )
     if task == "multilabel":
-        assert (
-            isinstance(num_labels, int) and num_labels > 0
-        ), "Number of labels must be specified for multilabel classification."
+        assert isinstance(num_labels, int) and num_labels > 0, (
+            "Number of labels must be specified for multilabel classification."
+        )
         return multilabel_precision(
             target,
             preds,
@@ -786,9 +786,9 @@ def recall(
             zero_division=zero_division,
         )
     if task == "multiclass":
-        assert (
-            isinstance(num_classes, int) and num_classes > 0
-        ), "Number of classes must be specified for multiclass classification."
+        assert isinstance(num_classes, int) and num_classes > 0, (
+            "Number of classes must be specified for multiclass classification."
+        )
         return multiclass_recall(
             target,
             preds,
@@ -798,9 +798,9 @@ def recall(
             zero_division=zero_division,
         )
     if task == "multilabel":
-        assert (
-            isinstance(num_labels, int) and num_labels > 0
-        ), "Number of labels must be specified for multilabel classification."
+        assert isinstance(num_labels, int) and num_labels > 0, (
+            "Number of labels must be specified for multilabel classification."
+        )
         return multilabel_recall(
             target,
             preds,
diff --git a/cyclops/evaluate/metrics/functional/precision_recall_curve.py b/cyclops/evaluate/metrics/functional/precision_recall_curve.py
index a0f9b69e3..c982a5423 100644
--- a/cyclops/evaluate/metrics/functional/precision_recall_curve.py
+++ b/cyclops/evaluate/metrics/functional/precision_recall_curve.py
@@ -1056,9 +1056,9 @@ def precision_recall_curve(
             pos_label=pos_label,
         )
     if task == "multiclass":
-        assert (
-            isinstance(num_classes, int) and num_classes > 0
-        ), "Number of classes must be a positive integer."
+        assert isinstance(num_classes, int) and num_classes > 0, (
+            "Number of classes must be a positive integer."
+        )
 
         return multiclass_precision_recall_curve(
             target,
@@ -1067,9 +1067,9 @@ def precision_recall_curve(
             thresholds=thresholds,
         )
     if task == "multilabel":
-        assert (
-            isinstance(num_labels, int) and num_labels > 0
-        ), "Number of labels must be a positive integer."
+        assert isinstance(num_labels, int) and num_labels > 0, (
+            "Number of labels must be a positive integer."
+        )
 
         return multilabel_precision_recall_curve(
             target,
diff --git a/cyclops/evaluate/metrics/functional/specificity.py b/cyclops/evaluate/metrics/functional/specificity.py
index b1f1822c7..4f77ba350 100644
--- a/cyclops/evaluate/metrics/functional/specificity.py
+++ b/cyclops/evaluate/metrics/functional/specificity.py
@@ -434,9 +434,9 @@ def specificity(
             zero_division=zero_division,
         )
     if task == "multiclass":
-        assert (
-            isinstance(num_classes, int) and num_classes > 0
-        ), "Number of classes must be specified for multiclass classification."
+        assert isinstance(num_classes, int) and num_classes > 0, (
+            "Number of classes must be specified for multiclass classification."
+        )
         return multiclass_specificity(
             target,
             preds,
@@ -446,9 +446,9 @@ def specificity(
             zero_division=zero_division,
         )
     if task == "multilabel":
-        assert (
-            isinstance(num_labels, int) and num_labels > 0
-        ), "Number of labels must be specified for multilabel classification."
+        assert isinstance(num_labels, int) and num_labels > 0, (
+            "Number of labels must be specified for multilabel classification."
+        )
         return multilabel_specificity(
             target,
             preds,
diff --git a/cyclops/evaluate/metrics/functional/stat_scores.py b/cyclops/evaluate/metrics/functional/stat_scores.py
index 34a881689..fc7db1ffb 100644
--- a/cyclops/evaluate/metrics/functional/stat_scores.py
+++ b/cyclops/evaluate/metrics/functional/stat_scores.py
@@ -215,7 +215,7 @@ def _binary_stat_scores_format(
     if check:
         raise ValueError(
             f"Detected the following values in `target`: {unique_values} but"
-            f" expected only the following values {[0,1]}.",
+            f" expected only the following values {[0, 1]}.",
         )
 
     # If preds is label array, also check that it only contains [0,1] values
@@ -823,9 +823,9 @@ def stat_scores(
             threshold=threshold,
         )
     elif task == "multiclass":
-        assert (
-            isinstance(num_classes, int) and num_classes > 0
-        ), "Number of classes must be a positive integer."
+        assert isinstance(num_classes, int) and num_classes > 0, (
+            "Number of classes must be a positive integer."
+        )
         scores = multiclass_stat_scores(
             target,
             preds,
@@ -834,9 +834,9 @@ def stat_scores(
             top_k=top_k,
         )
     elif task == "multilabel":
-        assert (
-            isinstance(num_labels, int) and num_labels > 0
-        ), "Number of labels must be a positive integer."
+        assert isinstance(num_labels, int) and num_labels > 0, (
+            "Number of labels must be a positive integer."
+        )
         scores = multilabel_stat_scores(
             target,
             preds,
diff --git a/cyclops/evaluate/metrics/precision_recall.py b/cyclops/evaluate/metrics/precision_recall.py
index 6de0f33c4..2aabf4be9 100644
--- a/cyclops/evaluate/metrics/precision_recall.py
+++ b/cyclops/evaluate/metrics/precision_recall.py
@@ -345,9 +345,9 @@ def __new__(  # type: ignore # mypy expects a subclass of Precision
                 zero_division=zero_division,
             )
         if task == "multiclass":
-            assert (
-                isinstance(num_classes, int) and num_classes > 0
-            ), "Number of classes must be specified for multiclass classification."
+            assert isinstance(num_classes, int) and num_classes > 0, (
+                "Number of classes must be specified for multiclass classification."
+            )
             return MulticlassPrecision(
                 num_classes=num_classes,
                 top_k=top_k,
@@ -355,9 +355,9 @@ def __new__(  # type: ignore # mypy expects a subclass of Precision
                 zero_division=zero_division,
             )
         if task == "multilabel":
-            assert (
-                isinstance(num_labels, int) and num_labels > 0
-            ), "Number of labels must be specified for multilabel classification."
+            assert isinstance(num_labels, int) and num_labels > 0, (
+                "Number of labels must be specified for multilabel classification."
+            )
             return MultilabelPrecision(
                 num_labels=num_labels,
                 threshold=threshold,
@@ -694,9 +694,9 @@ def __new__(  # type: ignore # mypy expects a subclass of Recall
                 zero_division=zero_division,
             )
         if task == "multiclass":
-            assert (
-                isinstance(num_classes, int) and num_classes > 0
-            ), "Number of classes must be specified for multiclass classification."
+            assert isinstance(num_classes, int) and num_classes > 0, (
+                "Number of classes must be specified for multiclass classification."
+            )
             return MulticlassRecall(
                 num_classes=num_classes,
                 top_k=top_k,
@@ -704,9 +704,9 @@ def __new__(  # type: ignore # mypy expects a subclass of Recall
                 zero_division=zero_division,
             )
         if task == "multilabel":
-            assert (
-                isinstance(num_labels, int) and num_labels > 0
-            ), "Number of labels must be specified for multilabel classification."
+            assert isinstance(num_labels, int) and num_labels > 0, (
+                "Number of labels must be specified for multilabel classification."
+            )
             return MultilabelRecall(
                 num_labels=num_labels,
                 threshold=threshold,
diff --git a/cyclops/evaluate/metrics/precision_recall_curve.py b/cyclops/evaluate/metrics/precision_recall_curve.py
index 9a5ce76b5..e220c7d05 100644
--- a/cyclops/evaluate/metrics/precision_recall_curve.py
+++ b/cyclops/evaluate/metrics/precision_recall_curve.py
@@ -566,17 +566,17 @@ def __new__(  # type: ignore # mypy expects a subclass of PrecisionRecallCurve
                 pos_label=pos_label,
             )
         if task == "multiclass":
-            assert (
-                isinstance(num_classes, int) and num_classes > 0
-            ), "Number of classes must be a positive integer."
+            assert isinstance(num_classes, int) and num_classes > 0, (
+                "Number of classes must be a positive integer."
+            )
             return MulticlassPrecisionRecallCurve(
                 num_classes=num_classes,
                 thresholds=thresholds,
             )
         if task == "multilabel":
-            assert (
-                isinstance(num_labels, int) and num_labels > 0
-            ), "Number of labels must be a positive integer."
+            assert isinstance(num_labels, int) and num_labels > 0, (
+                "Number of labels must be a positive integer."
+            )
             return MultilabelPrecisionRecallCurve(
                 num_labels=num_labels,
                 thresholds=thresholds,
diff --git a/cyclops/evaluate/metrics/sensitivity.py b/cyclops/evaluate/metrics/sensitivity.py
index 5ea9ab5df..29284c630 100644
--- a/cyclops/evaluate/metrics/sensitivity.py
+++ b/cyclops/evaluate/metrics/sensitivity.py
@@ -302,9 +302,9 @@ def __new__(  # type: ignore # mypy expects a subclass of Sensitivity
                 zero_division=zero_division,
             )
         if task == "multiclass":
-            assert (
-                isinstance(num_classes, int) and num_classes > 0
-            ), "Number of classes must be specified for multiclass classification."
+            assert isinstance(num_classes, int) and num_classes > 0, (
+                "Number of classes must be specified for multiclass classification."
+            )
             return MulticlassSensitivity(
                 num_classes=num_classes,
                 top_k=top_k,
@@ -312,9 +312,9 @@ def __new__(  # type: ignore # mypy expects a subclass of Sensitivity
                 zero_division=zero_division,
             )
         if task == "multilabel":
-            assert (
-                isinstance(num_labels, int) and num_labels > 0
-            ), "Number of labels must be specified for multilabel classification."
+            assert isinstance(num_labels, int) and num_labels > 0, (
+                "Number of labels must be specified for multilabel classification."
+            )
             return MultilabelSensitivity(
                 num_labels=num_labels,
                 threshold=threshold,
diff --git a/cyclops/evaluate/metrics/specificity.py b/cyclops/evaluate/metrics/specificity.py
index e8efabca5..efe4574b0 100644
--- a/cyclops/evaluate/metrics/specificity.py
+++ b/cyclops/evaluate/metrics/specificity.py
@@ -375,9 +375,9 @@ def __new__(  # type: ignore # mypy expects a subclass of Specificity
                 zero_division=zero_division,
             )
         if task == "multiclass":
-            assert (
-                isinstance(num_classes, int) and num_classes > 0
-            ), "Number of classes must be specified for multiclass classification."
+            assert isinstance(num_classes, int) and num_classes > 0, (
+                "Number of classes must be specified for multiclass classification."
+            )
             return MulticlassSpecificity(
                 num_classes=num_classes,
                 top_k=top_k,
@@ -385,9 +385,9 @@ def __new__(  # type: ignore # mypy expects a subclass of Specificity
                 zero_division=zero_division,
             )
         if task == "multilabel":
-            assert (
-                isinstance(num_labels, int) and num_labels > 0
-            ), "Number of labels must be specified for multilabel classification."
+            assert isinstance(num_labels, int) and num_labels > 0, (
+                "Number of labels must be specified for multilabel classification."
+            )
             return MultilabelSpecificity(
                 num_labels=num_labels,
                 threshold=threshold,
diff --git a/cyclops/evaluate/metrics/stat_scores.py b/cyclops/evaluate/metrics/stat_scores.py
index 1b3fee8e9..13ac23fce 100644
--- a/cyclops/evaluate/metrics/stat_scores.py
+++ b/cyclops/evaluate/metrics/stat_scores.py
@@ -487,18 +487,18 @@ def __new__(  # type: ignore # mypy expects a subclass of StatScores
         if task == "binary":
             return BinaryStatScores(threshold=threshold, pos_label=pos_label)
         if task == "multiclass":
-            assert (
-                isinstance(num_classes, int) and num_classes > 0
-            ), "Number of classes must be a positive integer."
+            assert isinstance(num_classes, int) and num_classes > 0, (
+                "Number of classes must be a positive integer."
+            )
             return MulticlassStatScores(
                 num_classes=num_classes,
                 top_k=top_k,
                 classwise=classwise,
             )
         if task == "multilabel":
-            assert (
-                isinstance(num_labels, int) and num_labels > 0
-            ), "Number of labels must be a positive integer."
+            assert isinstance(num_labels, int) and num_labels > 0, (
+                "Number of labels must be a positive integer."
+            )
             return MultilabelStatScores(
                 num_labels=num_labels,
                 threshold=threshold,
diff --git a/cyclops/evaluate/utils.py b/cyclops/evaluate/utils.py
index 835de6263..1bbe99d1e 100644
--- a/cyclops/evaluate/utils.py
+++ b/cyclops/evaluate/utils.py
@@ -159,9 +159,11 @@ def get_columns_as_array(
     if isinstance(columns, str):
         columns = [columns]
 
-    with dataset.formatted_as("arrow", columns=columns, output_all_columns=True) if (
-        isinstance(dataset, Dataset) and dataset.format != "arrow"
-    ) else nullcontext():
+    with (
+        dataset.formatted_as("arrow", columns=columns, output_all_columns=True)
+        if (isinstance(dataset, Dataset) and dataset.format != "arrow")
+        else nullcontext()
+    ):
         out_arr = squeeze_all(
             xp.stack(
                 [xp.asarray(dataset[col].to_pylist()) for col in columns], axis=-1
diff --git a/cyclops/models/catalog.py b/cyclops/models/catalog.py
index a2571f919..082ea9cfa 100644
--- a/cyclops/models/catalog.py
+++ b/cyclops/models/catalog.py
@@ -39,13 +39,11 @@
 LOGGER = logging.getLogger(__name__)
 setup_logging(print_level="WARN", logger=LOGGER)
 _xgboost_unavailable_message = (
-    "The XGBoost library is required to use the `XGBClassifier` model. "
-    "Please install it as an extra using `python3 -m pip install 'pycyclops[xgboost]'`\
+    "The XGBoost library is required to use the `XGBClassifier` model. Please install it as an extra using `python3 -m pip install 'pycyclops[xgboost]'`\
         or using `python3 -m pip install xgboost`."
 )
 _torchxrayvision_unavailable_message = (
-    "The torchxrayvision library is required to use the `densenet` or `resnet` model. "
-    "Please install it as an extra using `python3 -m pip install 'pycyclops[torchxrayvision]'`\
+    "The torchxrayvision library is required to use the `densenet` or `resnet` model. Please install it as an extra using `python3 -m pip install 'pycyclops[torchxrayvision]'`\
         or using `python3 -m pip install torchxrayvision`."
 )
 _torch_unavailable_message = (
diff --git a/cyclops/models/wrappers/pt_model.py b/cyclops/models/wrappers/pt_model.py
index 50b3a4302..37499dec2 100644
--- a/cyclops/models/wrappers/pt_model.py
+++ b/cyclops/models/wrappers/pt_model.py
@@ -968,14 +968,17 @@ def fit(
                 splits_mapping["validation"] = val_split
 
                 format_kwargs = {} if transforms is None else {"transform": transforms}
-                with X[train_split].formatted_as(
-                    "custom" if transforms is not None else "torch",
-                    columns=feature_columns + target_columns,
-                    **format_kwargs,
-                ), X[val_split].formatted_as(
-                    "custom" if transforms is not None else "torch",
-                    columns=feature_columns + target_columns,
-                    **format_kwargs,
+                with (
+                    X[train_split].formatted_as(
+                        "custom" if transforms is not None else "torch",
+                        columns=feature_columns + target_columns,
+                        **format_kwargs,
+                    ),
+                    X[val_split].formatted_as(
+                        "custom" if transforms is not None else "torch",
+                        columns=feature_columns + target_columns,
+                        **format_kwargs,
+                    ),
                 ):
                     self.partial_fit(
                         X,
@@ -1309,7 +1312,7 @@ def save_model(self, filepath: str, overwrite: bool = True, **kwargs):
         if include_lr_scheduler:
             state_dict["lr_scheduler"] = self.lr_scheduler_.state_dict()  # type: ignore[attr-defined]
 
-        epoch = kwargs.get("epoch", None)
+        epoch = kwargs.get("epoch")
         if epoch is not None:
             filename, extension = os.path.basename(filepath).split(".")
             filepath = join(
diff --git a/cyclops/models/wrappers/sk_model.py b/cyclops/models/wrappers/sk_model.py
index b0d534f1e..07d92304e 100644
--- a/cyclops/models/wrappers/sk_model.py
+++ b/cyclops/models/wrappers/sk_model.py
@@ -400,7 +400,7 @@ def partial_fit(
             splits_mapping = {"train": "train"}
         if not hasattr(self.model_, "partial_fit"):
             raise AttributeError(
-                f"Model {self.model_name}" "does not have a `partial_fit` method.",
+                f"Model {self.model_name}does not have a `partial_fit` method.",
             )
         # Train data is a Hugging Face Dataset Dictionary.
         if isinstance(X, DatasetDict):
@@ -687,7 +687,7 @@ def predict_proba(
             splits_mapping = {"test": "test"}
         if not hasattr(self.model_, "predict_proba"):
             raise AttributeError(
-                f"Model {self.model_name}" "does not have a `predict_proba` method.",
+                f"Model {self.model_name}does not have a `predict_proba` method.",
             )
         # Data is a Hugging Face Dataset Dictionary.
         if isinstance(X, DatasetDict):
diff --git a/cyclops/monitor/tester.py b/cyclops/monitor/tester.py
index 9a37d9d0f..ac4b1d7c5 100644
--- a/cyclops/monitor/tester.py
+++ b/cyclops/monitor/tester.py
@@ -1057,9 +1057,9 @@ def get_record(self, record_type):
 
     def counts(self, record_type, max_ensemble_size=None) -> np.ndarray:
         """Get counts."""
-        assert (
-            max_ensemble_size is None or max_ensemble_size > 0
-        ), "max_ensemble_size must be positive or None"
+        assert max_ensemble_size is None or max_ensemble_size > 0, (
+            "max_ensemble_size must be positive or None"
+        )
         rec = self.get_record(record_type)
         counts = []
         for i in rec.seed.unique():
diff --git a/cyclops/report/plot/classification.py b/cyclops/report/plot/classification.py
index 60da0b884..7a5e062e1 100644
--- a/cyclops/report/plot/classification.py
+++ b/cyclops/report/plot/classification.py
@@ -86,13 +86,13 @@ def _set_class_names(self, class_names: List[str]) -> None:
 
         """
         if class_names is not None:
-            assert (
-                len(class_names) == self.class_num
-            ), "class_names must be equal to class_num"
+            assert len(class_names) == self.class_num, (
+                "class_names must be equal to class_num"
+            )
         elif self.task_type == "multilabel":
-            class_names = [f"Label_{i+1}" for i in range(self.class_num)]
+            class_names = [f"Label_{i + 1}" for i in range(self.class_num)]
         else:
-            class_names = [f"Class_{i+1}" for i in range(self.class_num)]
+            class_names = [f"Class_{i + 1}" for i in range(self.class_num)]
         self.class_names = class_names
 
     def calibration(
@@ -257,7 +257,9 @@ def threshperf(
             == len(roc_curve.thresholds)
             == len(ppv)
             == len(npv)
-        ), "Length mismatch between ROC curve, PPV, NPV. All curves need to be computed using the same thresholds"
+        ), (
+            "Length mismatch between ROC curve, PPV, NPV. All curves need to be computed using the same thresholds"
+        )
         # Define hover template to show three decimal places
         hover_template = "Threshold: %{x:.3f}<br>Metric Value: %{y:.3f}<extra></extra>"
         # Create a subplot for each metric
@@ -397,16 +399,18 @@ def roc_curve(
                 ),
             )
         else:
-            assert (
-                len(fprs) == len(tprs) == self.class_num
-            ), "fprs and tprs must be of length class_num for \
+            assert len(fprs) == len(tprs) == self.class_num, (
+                "fprs and tprs must be of length class_num for \
                 multiclass/multilabel tasks"
+            )
             for i in range(self.class_num):
                 if auroc is not None:
                     assert (
                         len(auroc) == self.class_num  # type: ignore[arg-type]
-                    ), "AUROCs must be of length class_num for \
+                    ), (
+                        "AUROCs must be of length class_num for \
                         multiclass/multilabel tasks"
+                    )
                     name = f"{self.class_names[i]} (AUC = {auroc[i]:.2f})"  # type: ignore[index] # noqa: E501
                 else:
                     name = self.class_names[i]
@@ -496,16 +500,18 @@ def roc_curve_comparison(
                 )
         else:
             for slice_name, slice_curve in roc_curves.items():
-                assert (
-                    len(slice_curve[0]) == len(slice_curve[1]) == self.class_num
-                ), f"FPRs and TPRs must be of length class_num for \
+                assert len(slice_curve[0]) == len(slice_curve[1]) == self.class_num, (
+                    f"FPRs and TPRs must be of length class_num for \
                     multiclass/multilabel tasks in slice {slice_name}"
+                )
                 for i in range(self.class_num):
                     if aurocs and slice_name in aurocs:
                         assert (
                             len(aurocs[slice_name]) == self.class_num  # type: ignore[arg-type] # noqa: E501
-                        ), "AUROCs must be of length class_num for \
+                        ), (
+                            "AUROCs must be of length class_num for \
                             multiclass/multilabel tasks"
+                        )
                         name = f"{slice_name}, {self.class_names[i]} \
                             (AUC = {aurocs[i]:.2f})"  # type: ignore[index]
                     else:
@@ -583,10 +589,10 @@ def precision_recall_curve(
             )
         else:
             trace = []
-            assert (
-                len(recalls) == len(precisions) == self.class_num
-            ), "Recalls and precisions must be of length class_num for \
+            assert len(recalls) == len(precisions) == self.class_num, (
+                "Recalls and precisions must be of length class_num for \
                 multiclass/multilabel tasks"
+            )
             for i in range(self.class_num):
                 trace.append(
                     line_plot(
@@ -668,14 +674,18 @@ def precision_recall_curve_comparison(
                     len(slice_curve.precision)
                     == len(slice_curve.recall)
                     == self.class_num
-                ), f"Recalls and precisions must be of length class_num for \
+                ), (
+                    f"Recalls and precisions must be of length class_num for \
                     multiclass/multilabel tasks in slice {slice_name}"
+                )
                 for i in range(self.class_num):
                     if auprcs and slice_name in auprcs:
                         assert (
                             len(auprcs[slice_name]) == self.class_num  # type: ignore[arg-type] # noqa: E501
-                        ), "AUPRCs must be of length class_num for \
+                        ), (
+                            "AUPRCs must be of length class_num for \
                             multiclass/multilabel tasks"
+                        )
                         name = f"{slice_name}, {self.class_names[i]} \
                             (AUC = {auprcs[i]:.2f})"
                     else:
@@ -747,8 +757,10 @@ def metrics_value(
             assert all(
                 len(value) == self.class_num  # type: ignore[arg-type]
                 for value in metrics.values()
-            ), "Every metric must be of length class_num for \
+            ), (
+                "Every metric must be of length class_num for \
                 multiclass/multilabel tasks"
+            )
             for i in range(self.class_num):
                 trace.append(
                     bar_plot(
@@ -981,10 +993,10 @@ def metrics_comparison_radar(
                     if isinstance(metric_values, list) or (
                         isinstance(metric_values, np.ndarray) and metric_values.ndim > 0
                     ):
-                        assert (
-                            len(metric_values) == self.class_num
-                        ), "Metric values must be of length class_num for \
+                        assert len(metric_values) == self.class_num, (
+                            "Metric values must be of length class_num for \
                             multiclass/multilabel tasks"
+                        )
                         radial_data.extend(metric_values)
                         theta = [
                             f"{metric_name}: {self.class_names[i]}"
diff --git a/cyclops/report/report.py b/cyclops/report/report.py
index 786dae53c..19f916581 100644
--- a/cyclops/report/report.py
+++ b/cyclops/report/report.py
@@ -668,9 +668,9 @@ def log_dataset(
         """
         # sensitive features must be in features
         if features is not None and sensitive_features is not None:
-            assert all(
-                feature in features for feature in sensitive_features
-            ), "All sensitive features must be in the features list."
+            assert all(feature in features for feature in sensitive_features), (
+                "All sensitive features must be in the features list."
+            )
 
         # TODO: plot dataset distribution
         data = {
diff --git a/cyclops/report/utils.py b/cyclops/report/utils.py
index 94c8a556b..a4f9a3a04 100644
--- a/cyclops/report/utils.py
+++ b/cyclops/report/utils.py
@@ -304,9 +304,9 @@ def get_metrics_trends(
         slice_names=slice_names,
         metric_names=metric_names,
     )
-    assert (
-        len(performance_history) > 0
-    ), "No performance history found. Check slice and metric names."
+    assert len(performance_history) > 0, (
+        "No performance history found. Check slice and metric names."
+    )
     performance_recent = []
     for metric_name, metric_value in flat_results.items():
         name_split = metric_name.split("/")
@@ -323,9 +323,9 @@ def get_metrics_trends(
         slice_names=slice_names,
         metric_names=metric_names,
     )
-    assert (
-        len(performance_recent) > 0
-    ), "No performance metrics found. Check slice and metric names."
+    assert len(performance_recent) > 0, (
+        "No performance metrics found. Check slice and metric names."
+    )
     today = dt_date.today().strftime("%Y-%m-%d")
     now = dt_datetime.now().strftime("%H-%M-%S")
     if keep_timestamps:
diff --git a/cyclops/tasks/utils.py b/cyclops/tasks/utils.py
index cb442e0bb..734227b97 100644
--- a/cyclops/tasks/utils.py
+++ b/cyclops/tasks/utils.py
@@ -108,8 +108,10 @@ def prepare_models(
         models_dict = {model_name: models}
     # models contains one model name
     elif isinstance(models, str):
-        assert models in list_models(), f"Model name is not registered! \
+        assert models in list_models(), (
+            f"Model name is not registered! \
                     Available models are: {list_models()}"
+        )
         models_dict = {models: create_model(models)}
     # models contains a list or tuple of model names or wrapped models
     elif isinstance(models, (list, tuple)):
@@ -118,8 +120,10 @@ def prepare_models(
                 model_name = _model_names_mapping.get(model.model.__name__)
                 models_dict[model_name] = model
             elif isinstance(model, str):
-                assert model in list_models(), f"Model name is not registered! \
+                assert model in list_models(), (
+                    f"Model name is not registered! \
                     Available models are: {list_models()}"
+                )
                 models_dict[model] = create_model(model)
             else:
                 raise TypeError(
diff --git a/cyclops/utils/optional.py b/cyclops/utils/optional.py
index e7795f344..07d06c240 100644
--- a/cyclops/utils/optional.py
+++ b/cyclops/utils/optional.py
@@ -64,8 +64,7 @@ def import_optional_module(
         return module
     except ModuleNotFoundError as exc:
         msg = (
-            f"Missing optional dependency '{name}'. "
-            f"Use pip or conda to install {name}."
+            f"Missing optional dependency '{name}'. Use pip or conda to install {name}."
         )
         if error == "raise":
             raise type(exc)(msg) from None
diff --git a/tests/cyclops/evaluate/metrics/experimental/test_metric.py b/tests/cyclops/evaluate/metrics/experimental/test_metric.py
index ceea71694..a925112d2 100644
--- a/tests/cyclops/evaluate/metrics/experimental/test_metric.py
+++ b/tests/cyclops/evaluate/metrics/experimental/test_metric.py
@@ -46,9 +46,9 @@ def test_add_state_factory():
     metric.add_state_default_factory("a", lambda xp: xp.asarray(0), None)  # type: ignore
     reduce_fn = metric._reductions["a"]
     assert reduce_fn is None, "Saved reduction function is not None."
-    assert (
-        metric._default_factories.get("a") is not None
-    ), "Default factory was not correctly created."
+    assert metric._default_factories.get("a") is not None, (
+        "Default factory was not correctly created."
+    )
 
     # default_factory is 'list'
     metric.add_state_default_factory("b", list)  # type: ignore
@@ -61,8 +61,7 @@ def test_add_state_factory():
     reduce_fn = metric._reductions["c"]
     assert callable(reduce_fn), "Saved reduction function is not callable."
     assert reduce_fn is dim_zero_sum, (
-        "Saved reduction function is not the same as the one used to "
-        "create the state."
+        "Saved reduction function is not the same as the one used to create the state."
     )
     assert reduce_fn(anp.asarray([1, 1])) == anp.asarray(
         2,
@@ -73,8 +72,7 @@ def test_add_state_factory():
     reduce_fn = metric._reductions["d"]
     assert callable(reduce_fn), "Saved reduction function is not callable."
     assert reduce_fn is dim_zero_mean, (
-        "Saved reduction function is not the same as the one used to "
-        "create the state."
+        "Saved reduction function is not the same as the one used to create the state."
     )
     assert np.allclose(
         reduce_fn(anp.asarray([1.0, 2.0])),
@@ -86,8 +84,7 @@ def test_add_state_factory():
     reduce_fn = metric._reductions["e"]
     assert callable(reduce_fn), "Saved reduction function is not callable."
     assert reduce_fn is dim_zero_cat, (
-        "Saved reduction function is not the same as the one used to "
-        "create the state."
+        "Saved reduction function is not the same as the one used to create the state."
     )
     np.testing.assert_array_equal(
         reduce_fn([anp.asarray([1]), anp.asarray([1])]),
@@ -100,8 +97,7 @@ def test_add_state_factory():
     reduce_fn = metric._reductions["f"]
     assert callable(reduce_fn), "Saved reduction function is not callable."
     assert reduce_fn is dim_zero_max, (
-        "Saved reduction function is not the same as the one used to "
-        "create the state."
+        "Saved reduction function is not the same as the one used to create the state."
     )
     np.testing.assert_array_equal(
         reduce_fn(anp.asarray([1, 2])),
@@ -115,8 +111,7 @@ def test_add_state_factory():
     reduce_fn = metric._reductions["g"]
     assert callable(reduce_fn), "Saved reduction function is not callable."
     assert reduce_fn is dim_zero_min, (
-        "Saved reduction function is not the same as the one used to "
-        "create the state."
+        "Saved reduction function is not the same as the one used to create the state."
     )
     np.testing.assert_array_equal(
         reduce_fn(anp.asarray([1, 2])),
diff --git a/tests/cyclops/evaluate/metrics/experimental/test_metric_dict.py b/tests/cyclops/evaluate/metrics/experimental/test_metric_dict.py
index 401726060..508ce2d6a 100644
--- a/tests/cyclops/evaluate/metrics/experimental/test_metric_dict.py
+++ b/tests/cyclops/evaluate/metrics/experimental/test_metric_dict.py
@@ -94,16 +94,16 @@ def test_metric_dict_adfix(prefix, postfix):
     # test __call__
     output = metrics(anp.asarray(1, dtype=anp.float32))
     for name in names:
-        assert (
-            name in output
-        ), f"`MetricDict` output does not contain metric {name} when called."
+        assert name in output, (
+            f"`MetricDict` output does not contain metric {name} when called."
+        )
 
     # test `compute`
     output = metrics.compute()
     for name in names:
-        assert (
-            name in output
-        ), f"`MetricDict` output does not contain metric {name} using the `compute` method."
+        assert name in output, (
+            f"`MetricDict` output does not contain metric {name} using the `compute` method."
+        )
 
     # test `clone`
     new_metrics = metrics.clone(prefix="new_")
@@ -112,9 +112,9 @@ def test_metric_dict_adfix(prefix, postfix):
         n[len(prefix) :] if prefix is not None else n for n in names
     ]
     for name in names:
-        assert (
-            f"new_{name}" in output
-        ), f"`MetricDict` output does not contain metric new_{name} when cloned."
+        assert f"new_{name}" in output, (
+            f"`MetricDict` output does not contain metric new_{name} when cloned."
+        )
 
     for k in new_metrics:
         assert "new_" in k
diff --git a/tests/cyclops/evaluate/metrics/experimental/testers.py b/tests/cyclops/evaluate/metrics/experimental/testers.py
index 4d58305d3..a0d887d93 100644
--- a/tests/cyclops/evaluate/metrics/experimental/testers.py
+++ b/tests/cyclops/evaluate/metrics/experimental/testers.py
@@ -85,9 +85,9 @@ def _class_impl_test(  # noqa: PLR0912
 
     t_size = target.shape[0]
     p_size = preds.shape[0]
-    assert (
-        p_size == t_size
-    ), f"`preds` and `target` have different number of samples: {p_size} and {t_size}."
+    assert p_size == t_size, (
+        f"`preds` and `target` have different number of samples: {p_size} and {t_size}."
+    )
     num_batches = p_size
 
     # instantiate metric
@@ -176,9 +176,9 @@ def _function_impl_test(
 
     t_size = target.shape[0]
     p_size = preds.shape[0]
-    assert (
-        p_size == t_size
-    ), f"`preds` and `target` have different number of samples: {p_size} and {t_size}."
+    assert p_size == t_size, (
+        f"`preds` and `target` have different number of samples: {p_size} and {t_size}."
+    )
 
     metric_args = metric_args or {}
     metric = partial(metric_function, **metric_args)
diff --git a/tests/cyclops/evaluate/metrics/helpers.py b/tests/cyclops/evaluate/metrics/helpers.py
index 724ab89a5..bc030e43b 100644
--- a/tests/cyclops/evaluate/metrics/helpers.py
+++ b/tests/cyclops/evaluate/metrics/helpers.py
@@ -146,5 +146,5 @@ def _assert_allclose(data_a: Any, data_b: Any, atol: float = 1e-8):
             _assert_allclose(data_a[key], data_b[key], atol=atol)
     else:
         raise ValueError(
-            f"Unknown format for comparison: {type(data_a)} and" f" {type(data_b)}",
+            f"Unknown format for comparison: {type(data_a)} and {type(data_b)}",
         )