Remove example; move comments to test

python/tvm/relay/op/transform.py

@@ -51,31 +51,6 @@ def windows(data, axis, window_shape, strides):
     -------
     result : relay.Expr
         The resulting tensor.
-
-    Examples
-    --------
-    .. code-block:: python
-        # Slide a window of shape (3, 4, 5) over the x tensor, beginning with
-        # dimension 1, which slides the window over the two subtensors of
-        # shape (3, 32, 32).
-        x = relay.var("x", relay.TensorType((2, 3, 32, 32), "float32"))
-        y = relay.windows(x, 1, [3, 4, 5], [1, 2, 3])
-
-        data = np.random.rand(2, 3, 32, 32).astype("float32")
-        result = create_executor().evaluate(y, {x: relay.const(data)}).numpy()
-
-        # The resulting shape still has batch size 2. Each dimension in
-        # (1, 15, 10) represents the locations where we were able to
-        # form a window; that is, we were able to place the window
-        # in one place along the dimension of length 3, 15 places along
-        # the dimension of length 32 (when striding by 2), and 10 places
-        # along the second dimension of length 32 (when striding by 3).
-        # The remaining dimension (3, 4, 5) represent the formed windows.
-        assert result.shape == (2, 1, 15, 10, 3, 4, 5)
-
-        assert np.array_equal(result[0, 0, 0, 0, :, :, :], data[0, :, 0:4, 0:5])
-        assert np.array_equal(result[1, 0, 7, 3, :, :, :], data[1, :, 14:18, 9:14])
-        assert np.array_equal(result[1, 0, 14, 9, :, :, :], data[1, :, 28:32, 27:32])
     """
     from .. import _ffi_api as _relay_make
 

tests/python/relay/test_op_level3.py

@@ -92,8 +92,18 @@ def test_cast():
 
 
 def test_windows():
+    # Slide a window of shape (3, 4, 5) over the x tensor, beginning with
+    # dimension 1, which slides the window over the two subtensors of shape (3,
+    # 32, 32).
     x = relay.var("x", relay.TensorType((2, 3, 32, 32), "float32"))
     y = relay.windows(x, 1, [3, 4, 5], [1, 2, 3])
+
+    # The resulting shape still has batch size 2. Each dimension in (1, 15, 10)
+    # represents the locations where we were able to form a window; that is, we
+    # were able to place the window in one place along the dimension of length
+    # 3, 15 places along the dimension of length 32 (when striding by 2), and 10
+    # places along the second dimension of length 32 (when striding by 3). The
+    # remaining dimensions (3, 4, 5) represent the formed windows.
     yy = run_infer_type(y)
     assert yy.checked_type == relay.TensorType((2, 1, 15, 10, 3, 4, 5), "float32")