diff --git a/docs/spmd.md b/docs/spmd.md
index 5d6e554092d..0a245ec5473 100644
--- a/docs/spmd.md
+++ b/docs/spmd.md
@@ -470,6 +470,7 @@ Note that I used a batch size 4 times as large since I am running it on a TPU v4
 
 We provide a `shard placement visualization debug tool` for PyTorch/XLA SPMD user on TPU/GPU/CPU with single-host/multi-host: you could use `visualize_tensor_sharding` to visualize sharded tensor, or you could use `visualize_sharding` to visualize sharing string. Here are two code examples on TPU single-host(v4-8) with `visualize_tensor_sharding` or `visualize_sharding`:
 - Code snippet used `visualize_tensor_sharding` and visualization result:
+
 ```python
 import rich
 
@@ -482,7 +483,9 @@ from torch_xla.distributed.spmd.debugging import visualize_tensor_sharding
 generated_table = visualize_tensor_sharding(t, use_color=False)
 ```
 ![alt_text](assets/spmd_debug_1.png "visualize_tensor_sharding example on TPU v4-8(single-host)")
+
 - Code snippet used `visualize_sharding` and visualization result:
+
 ```python
 from torch_xla.distributed.spmd.debugging import visualize_sharding
 sharding = '{devices=[2,2]0,1,2,3}'
@@ -498,11 +501,13 @@ We are introducing a new PyTorch/XLA SPMD feature, called ``auto-sharding``, [RF
 PyTorch/XLA auto-sharding can be enabled by one of the following:
 - Setting envvar `XLA_SPMD_AUTO=1`
 - Calling the SPMD API in the beginning of your code:
+
 ```python
 import torch_xla.runtime as xr
 xr.use_spmd(auto=True)
 ```
 - Calling `pytorch.distributed._tensor.distribute_module` with `auto-policy` and `xla`:
+
 ```python
 import torch_xla.runtime as xr
 from torch.distributed._tensor import DeviceMesh, distribute_module