Update keras.py black formatter

Formatted using black...

optuna_integration/keras/keras.py

@@ -3,10 +3,8 @@
 import warnings
 
 import optuna
-
 from optuna_integration._imports import try_import
 
-
 with try_import() as _imports:
     from keras.callbacks import Callback
 
@@ -29,58 +27,35 @@ class KerasPruningCallback(Callback):
             An evaluation metric for pruning, e.g., ``val_loss`` and
             ``val_accuracy``. Please refer to `keras.Callback reference
             <https://keras.io/callbacks/#callback>`_ for further details.
-        fold:
-            Current fold number in k-fold cross validation. Used to ensure steps continue
-            from the last epoch of the previous fold.
         interval:
             Check if trial should be pruned every n-th epoch. By default ``interval=1`` and
             pruning is performed after every epoch. Increase ``interval`` to run several
             epochs faster before applying pruning.
     """
 
-    # Class variable to track the last step across folds
-    _last_step = -1  # Start at -1 so first epoch of first fold starts at 0
-
-    def __init__(
-        self, 
-        trial: optuna.trial.Trial, 
-        monitor: str,
-        fold: int = 0,
-        interval: int = 1
-    ) -> None:
+    def __init__(self, trial: optuna.trial.Trial, monitor: str, interval: int = 1) -> None:
         super().__init__()
 
         _imports.check()
 
         self._trial = trial
         self._monitor = monitor
         self._interval = interval
-        self._fold = fold
-        # For this fold, start steps after the last step used
-        self._step_offset = self.__class__._last_step + 1
 
     def on_epoch_end(self, epoch: int, logs: dict[str, float] | None = None) -> None:
         if (epoch + 1) % self._interval != 0:
             return
 
         logs = logs or {}
         current_score = logs.get(self._monitor)
-
         if current_score is None:
             message = (
                 "The metric '{}' is not in the evaluation logs for pruning. "
                 "Please make sure you set the correct metric name.".format(self._monitor)
             )
             warnings.warn(message)
             return
-
-        # Calculate current step by adding epoch to the offset
-        current_step = self._step_offset + epoch
-        # Update the class's last step tracker
-        self.__class__._last_step = current_step
-
-        self._trial.report(float(current_score), step=current_step)
-
+        self._trial.report(float(current_score), step=epoch)
         if self._trial.should_prune():
-            message = "Trial was pruned at fold {}, epoch {}.".format(self._fold, epoch)
+            message = "Trial was pruned at epoch {}.".format(epoch)
             raise optuna.TrialPruned(message)