[MNT] Fixed wrong type annotations for aeon classes

aeon/classification/sklearn/_continuous_interval_tree.py

@@ -483,7 +483,7 @@ def tree_node_splits_and_gain(self) -> tuple[list[int], list[float]]:
 
         return splits, gains
 
-    def _find_splits_gain(self, node: type[_TreeNode], splits: list, gains: list):
+    def _find_splits_gain(self, node: _TreeNode, splits: list, gains: list):
         """Recursively find the split and information gain for each tree node."""
         splits.append(node.best_split)
         gains.append(node.best_gain)

aeon/classification/sklearn/_rotation_forest_classifier.py

@@ -453,7 +453,7 @@ def _fit_estimator(
 
         return tree, pcas, groups, X_t if save_transformed_data else None
 
-    def _predict_proba_for_estimator(self, X, clf: int, pcas: type[PCA], groups):
+    def _predict_proba_for_estimator(self, X, clf: int, pcas: PCA, groups):
         X_t = np.concatenate(
             [pcas[i].transform(X[:, group]) for i, group in enumerate(groups)], axis=1
         )

aeon/regression/sklearn/_rotation_forest_regressor.py

@@ -352,7 +352,7 @@ def _fit_estimator(
 
         return tree, pcas, groups, X_t if save_transformed_data else None
 
-    def _predict_for_estimator(self, X, clf: int, pcas: type[PCA], groups):
+    def _predict_for_estimator(self, X, clf: int, pcas: PCA, groups):
         X_t = np.concatenate(
             [pcas[i].transform(X[:, group]) for i, group in enumerate(groups)], axis=1
         )