Add more tests for 100% coverage of top_k function

dpctl/tests/test_usm_ndarray_top_k.py

@@ -276,3 +276,43 @@ def test_top_k_noncontig():
     assert dpt.all(
         dpt.sort(r.indices) == dpt.asarray([125, 126, 127])
     ), r.indices
+
+
+def test_top_k_axis0():
+    get_queue_or_skip()
+
+    m, n, k = 128, 8, 3
+    x = dpt.reshape(dpt.arange(m * n, dtype=dpt.int32), (m, n))
+
+    r = dpt.top_k(x, k, axis=0, mode="smallest")
+    assert r.values.shape == (k, n)
+    assert r.indices.shape == (k, n)
+    expected_inds = dpt.reshape(dpt.arange(m, dtype=r.indices.dtype), (m, 1))[
+        :k, :
+    ]
+    assert dpt.all(
+        dpt.sort(r.indices, axis=0) == dpt.sort(expected_inds, axis=0)
+    )
+    assert dpt.all(dpt.sort(r.values, axis=0) == dpt.sort(x[:k, :], axis=0))
+
+
+def test_top_k_validation():
+    get_queue_or_skip()
+    x = dpt.ones(10, dtype=dpt.int64)
+    with pytest.raises(ValueError):
+        # k must be positive
+        dpt.top_k(x, -1)
+    with pytest.raises(TypeError):
+        # argument should be usm_ndarray
+        dpt.top_k(list(), 2)
+    x2 = dpt.reshape(x, (2, 5))
+    with pytest.raises(ValueError):
+        # k must not exceed array dimension
+        # along specified axis
+        dpt.top_k(x2, 100, axis=1)
+    with pytest.raises(ValueError):
+        # for 0d arrays, k must be 1
+        dpt.top_k(x[0], 2)
+    with pytest.raises(ValueError):
+        # mode must be "largest", or "smallest"
+        dpt.top_k(x, 2, mode="invalid")