diff --git a/python/tvm/te/tag.py b/python/tvm/te/tag.py
index 1022875ce3dd..487e69687225 100644
--- a/python/tvm/te/tag.py
+++ b/python/tvm/te/tag.py
@@ -86,6 +86,6 @@ def tag_scope(tag):
         # or use tag_scope as decorator
         @tvm.te.tag_scope(tag="conv")
         def compute_relu(data):
-            return te.compute(data.shape, lambda *i: tvm.select(data(*i) < 0, 0.0, data(*i)))
+            return te.compute(data.shape, lambda *i: tvm.tir.Select(data(*i) < 0, 0.0, data(*i)))
     """
     return TagScope(tag)
diff --git a/python/tvm/tir/op.py b/python/tvm/tir/op.py
index 4a52787262bb..a8aef8f85495 100644
--- a/python/tvm/tir/op.py
+++ b/python/tvm/tir/op.py
@@ -950,6 +950,7 @@ def reducer(expr, axis, where=None, *args):
 
                 # there are two way to use this {0} reducer:
                 # mode 1, accept (expr, axis, where) to produce an Reduce Expr
+                # tvm.{0} represents tvm.te.{0} or tvm.tir.{0}.
                 B = te.compute((m,), lambda i: tvm.{0}(A[i, k], axis=k), name="B")
 
                 # mode 2, simply use it with multiple Exprs: