[MNT] Add missing load_model test for deep clusterers (Fixes #3080) (#3111)

* Add tests for loading/saving deep clusterers

* Automatic `pre-commit` fixes

* [MNT] Add comprehensive load/save tests for all deep clusterers

* Improve deep clusterer load/save tests

---------

Co-authored-by: satwiksps <[email protected]>

Author: satwiksps

aeon/clustering/deep_learning/tests/test_deep_clusterer_io.py

@@ -0,0 +1,52 @@
+"""Tests for loading Keras models in deep autoencoder clusterers."""
+
+import tempfile
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+from aeon.clustering.deep_learning._ae_abgru import AEAttentionBiGRUClusterer
+from aeon.clustering.deep_learning._ae_bgru import AEBiGRUClusterer
+from aeon.clustering.deep_learning._ae_dcnn import AEDCNNClusterer
+from aeon.clustering.deep_learning._ae_drnn import AEDRNNClusterer
+from aeon.clustering.deep_learning._ae_fcn import AEFCNClusterer
+from aeon.clustering.deep_learning._ae_resnet import AEResNetClusterer
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+ALL_DEEP_CLUSTERERS = [
+    AEAttentionBiGRUClusterer,
+    AEBiGRUClusterer,
+    AEDCNNClusterer,
+    AEDRNNClusterer,
+    AEFCNClusterer,
+    AEResNetClusterer,
+]
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies("tensorflow", severity="none"),
+    reason="TensorFlow not installed.",
+)
+@pytest.mark.parametrize("cls", ALL_DEEP_CLUSTERERS)
+def test_deep_clusterer_load_model(cls):
+    """Test that all deep autoencoder clusterers load saved Keras models correctly."""
+    X = np.random.randn(4, 10, 1).astype(np.float32)
+    params = cls._get_test_params()[0]
+    params["n_epochs"] = 1
+    params["save_best_model"] = True
+
+    with tempfile.TemporaryDirectory() as tmp:
+        params["file_path"] = tmp + "/"
+        model = cls(**params)
+        model.fit(X)
+        trained_estimator = model._estimator
+        saved = list(Path(tmp).glob("*.keras"))
+        assert saved, f"No .keras file saved for {cls.__name__}"
+        model_path = str(saved[0])
+        loaded = cls(**params)
+        loaded.load_model(model_path, trained_estimator)
+        assert loaded.model_ is not None, f"Loaded model_ is None for {cls.__name__}"
+        assert hasattr(loaded.model_, "predict"), f"Invalid model_ for {cls.__name__}"
+        preds = loaded.predict(X)
+        assert preds.shape[0] == X.shape[0], f"Predict failed for {cls.__name__}"