diff --git a/doc/source/tune/user-guide.rst b/doc/source/tune/user-guide.rst
index 5aaba58a2..5d9e6540c 100644
--- a/doc/source/tune/user-guide.rst
+++ b/doc/source/tune/user-guide.rst
@@ -222,29 +222,29 @@ To use Tune's checkpointing features, you must expose a ``checkpoint_dir`` argum
 
 .. code-block:: python
 
-        import os
-        import time
-        from ray import tune
+    import os
+    import time
+    from ray import tune
 
-        def train_func(config, checkpoint_dir=None):
-            start = 0
-            if checkpoint_dir:
-                with open(os.path.join(checkpoint_dir, "checkpoint")) as f:
-                    state = json.loads(f.read())
-                    start = state["step"] + 1
+    def train_func(config, checkpoint_dir=None):
+        start = 0
+        if checkpoint_dir:
+            with open(os.path.join(checkpoint_dir, "checkpoint")) as f:
+                state = json.loads(f.read())
+                start = state["step"] + 1
 
-            for step in range(start, 100):
-                time.sleep(1)
+        for step in range(start, 100):
+            time.sleep(1)
 
-                # Obtain a checkpoint directory
-                with tune.checkpoint_dir(step=step) as checkpoint_dir:
-                    path = os.path.join(checkpoint_dir, "checkpoint")
-                    with open(path, "w") as f:
-                        f.write(json.dumps({"step": start}))
+            # Obtain a checkpoint directory
+            with tune.checkpoint_dir(step=step) as checkpoint_dir:
+                path = os.path.join(checkpoint_dir, "checkpoint")
+                with open(path, "w") as f:
+                    f.write(json.dumps({"step": step}))
 
-                tune.report(hello="world", ray="tune")
+            tune.report(hello="world", ray="tune")
 
-        tune.run(train_func)
+    tune.run(train_func)
 
 In this example, checkpoints will be saved by training iteration to ``local_dir/exp_name/trial_name/checkpoint_<step>``.