diff --git a/test/data/lightning/test_datamodule.py b/test/data/lightning/test_datamodule.py
index cb86d810dfa8..6093f5f4e53c 100644
--- a/test/data/lightning/test_datamodule.py
+++ b/test/data/lightning/test_datamodule.py
@@ -23,6 +23,7 @@
     onlyNeighborSampler,
     onlyOnline,
     withPackage,
+    has_package,
 )
 
 try:
@@ -114,12 +115,22 @@ def expect_rank_zero_user_warning(match: str):
                                       num_workers=3, shuffle=True)
         assert 'shuffle' not in datamodule.kwargs
     old_x = train_dataset._data.x.clone()
-    assert str(datamodule) == ('LightningDataset(train_dataset=MUTAG(50), '
-                               'val_dataset=MUTAG(30), '
-                               'test_dataset=MUTAG(10), '
-                               'pred_dataset=MUTAG(98), batch_size=5, '
-                               'num_workers=3, pin_memory=True, '
-                               'persistent_workers=True)')
+    new_datamodule_repr = has_package('pytorch_lightning>=2.5.0')
+    datamodule_repr = (
+        '{Train dataloader: size=50}\n'
+        '{Validation dataloader: size=30}\n'
+        '{Test dataloader: size=10}\n'
+        '{Predict dataloader: size=98}'
+        if new_datamodule_repr else
+        'LightningDataset(train_dataset=MUTAG(50), '
+        'val_dataset=MUTAG(30), '
+        'test_dataset=MUTAG(10), '
+        'pred_dataset=MUTAG(98), batch_size=5, '
+        'num_workers=3, pin_memory=True, '
+        'persistent_workers=True)'
+    )
+    assert str(datamodule) == datamodule_repr
+
     trainer.fit(model, datamodule)
     trainer.test(model, datamodule)
     new_x = train_dataset._data.x
@@ -133,10 +144,18 @@ def expect_rank_zero_user_warning(match: str):
                              log_every_n_steps=1)
 
         datamodule = LightningDataset(train_dataset, batch_size=5)
-        assert str(datamodule) == ('LightningDataset(train_dataset=MUTAG(50), '
-                                   'batch_size=5, num_workers=0, '
-                                   'pin_memory=True, '
-                                   'persistent_workers=False)')
+        datamodule_repr = (
+            '{Train dataloader: size=50}\n'
+            '{Validation dataloader: None}\n'
+            '{Test dataloader: None}\n{'
+            'Predict dataloader: None}'
+            if new_datamodule_repr else
+            'LightningDataset(train_dataset=MUTAG(50), '
+            'batch_size=5, num_workers=0, '
+            'pin_memory=True, '
+            'persistent_workers=False)'
+        )
+        assert str(datamodule) == datamodule_repr
 
         with expect_rank_zero_user_warning("defined a `validation_step`"):
             trainer.fit(model, datamodule)
@@ -231,11 +250,22 @@ def test_lightning_node_data(get_dataset, strategy_type, loader):
                                    num_workers=num_workers, **kwargs)
 
     old_x = data.x.clone().cpu()
-    assert str(datamodule) == (f'LightningNodeData(data={data_repr}, '
-                               f'loader={loader}, batch_size={batch_size}, '
-                               f'num_workers={num_workers}, {kwargs_repr}'
-                               f'pin_memory={loader != "full"}, '
-                               f'persistent_workers={loader != "full"})')
+    new_datamodule_repr = has_package('pytorch_lightning>=2.5.0')
+    flag = loader != 'full'
+    datamodule_repr = (
+        '{Train dataloader: ' + f'size={140 if flag else 1}' + '}\n'
+        '{Validation dataloader: ' + f'size={500 if flag else 1}' + '}\n'
+        '{Test dataloader: ' + f'size={1000 if flag else 1}' + '}\n'
+        '{Predict dataloader: ' + f'size={2708 if flag else 1}' + '}'
+        if new_datamodule_repr else
+        f'LightningNodeData(data={data_repr}, '
+        f'loader={loader}, batch_size={batch_size}, '
+        f'num_workers={num_workers}, {kwargs_repr}'
+        f'pin_memory={flag}, '
+        f'persistent_workers={flag})'
+    )
+    assert str(datamodule) == datamodule_repr
+
     trainer.fit(model, datamodule)
     trainer.test(model, datamodule)
     new_x = data.x.cpu()