feat: add seed‐variation, idempotent and large‐input tests (#355)

Author: Fengzdadi

nebula3/pyg/__init__.py

@@ -0,0 +1,9 @@
+
+from torch_geometric.data.feature_store import FeatureStore, TensorAttr
+class NebulaFeatureStore(FeatureStore):
+
+    def __init__(self, connection, num_threads:int | None = None):
+        super().__init__()
+        self.
+
+

nebula3/pyg/pyg_feature_store.py

@@ -11,12 +11,13 @@
 TEST_VECTORS = [
     (b"", 6142509188972423790),
     (b"a", 4993892634952068459),
-    (b"abcdefgh", 8664279048047335611),  # length-8 cases
+    (b"abcdefgh", 8664279048047335611),  # length-8 bytes cases
     (b"abcdefghi", -5409788147785758033),
     ("to_be_hashed", -1098333533029391540),
     ("中文", -8591787916246384322),
 ]
 
+
 @pytest.mark.parametrize("data, expected", TEST_VECTORS)
 def test_known_vectors(data, expected):
     assert murmur_hash(data) == expected
@@ -36,3 +37,24 @@ def test_type_error():
     """
     with pytest.raises(TypeError):
         murmur_hash(12345)
+
+
+def test_seed_variation():
+    """Different seed values should produce different hashes."""
+    data = b"seed_test"
+    hash1 = murmur_hash(data, seed=0)
+    hash2 = murmur_hash(data, seed=1)
+    assert hash1 != hash2
+
+
+def test_idempotent():
+    """Repeated calls with same input must yield the same result."""
+    data = b"consistent"
+    assert murmur_hash(data) == murmur_hash(data)
+
+
+def test_large_input_performance():
+    """Large inputs should be processed without error and return an int."""
+    data = b"x" * 10_000
+    result = murmur_hash(data)
+    assert isinstance(result, int)