Apply pre-commit formatting fixes

Co-authored-by: thinkall <3197038+thinkall@users.noreply.github.com>

Author: Copilot

flaml/automl/automl.py

@@ -156,9 +156,9 @@ def custom_metric(
                 "pred_time": pred_time,
             }
         ```
-                **Note:** When passing a custom metric function, pass the function itself 
-                (e.g., `metric=custom_metric`), not the result of calling it 
-                (e.g., `metric=custom_metric(...)`). FLAML will call your function 
+                **Note:** When passing a custom metric function, pass the function itself
+                (e.g., `metric=custom_metric`), not the result of calling it
+                (e.g., `metric=custom_metric(...)`). FLAML will call your function
                 internally during the training process.
             task: A string of the task type, e.g.,
                 'classification', 'regression', 'ts_forecast', 'rank',
@@ -471,11 +471,11 @@ def __setstate__(self, state):
     @staticmethod
     def _validate_metric_parameter(metric, allow_auto=True):
         """Validate that the metric parameter is either a string or a callable function.
-        
+
         Args:
             metric: The metric parameter to validate.
             allow_auto: Whether to allow "auto" as a valid string value.
-            
+
         Raises:
             ValueError: If metric is not a string or callable function.
         """
@@ -1838,9 +1838,9 @@ def custom_metric(
                 "pred_time": pred_time,
             }
         ```
-                **Note:** When passing a custom metric function, pass the function itself 
-                (e.g., `metric=custom_metric`), not the result of calling it 
-                (e.g., `metric=custom_metric(...)`). FLAML will call your function 
+                **Note:** When passing a custom metric function, pass the function itself
+                (e.g., `metric=custom_metric`), not the result of calling it
+                (e.g., `metric=custom_metric(...)`). FLAML will call your function
                 internally during the training process.
             task: A string of the task type, e.g.,
                 'classification', 'regression', 'ts_forecast_regression',

test/automl/test_multiclass.py

@@ -281,26 +281,28 @@ def test_custom_metric(self):
     def test_invalid_custom_metric(self):
         """Test that proper error is raised when custom_metric is called instead of passed."""
         from sklearn.datasets import load_iris
-        
+
         X_train, y_train = load_iris(return_X_y=True)
-        
+
         # Test with non-callable metric in __init__
         with self.assertRaises(ValueError) as context:
             automl = AutoML(metric=123)  # passing an int instead of function
         self.assertIn("must be either a string or a callable function", str(context.exception))
         self.assertIn("but got int", str(context.exception))
-        
+
         # Test with non-callable metric in fit
         automl = AutoML()
         with self.assertRaises(ValueError) as context:
             automl.fit(X_train=X_train, y_train=y_train, metric=[], task="classification", time_budget=1)
         self.assertIn("must be either a string or a callable function", str(context.exception))
         self.assertIn("but got list", str(context.exception))
-        
+
         # Test with tuple (simulating result of calling a function that returns tuple)
         with self.assertRaises(ValueError) as context:
             automl = AutoML()
-            automl.fit(X_train=X_train, y_train=y_train, metric=(0.5, {"loss": 0.5}), task="classification", time_budget=1)
+            automl.fit(
+                X_train=X_train, y_train=y_train, metric=(0.5, {"loss": 0.5}), task="classification", time_budget=1
+            )
         self.assertIn("must be either a string or a callable function", str(context.exception))
         self.assertIn("but got tuple", str(context.exception))