fix: missing import numpy (#5533)

* missing

* fix typo

---------

Co-authored-by: mochen.bmc <mochen.bmc@antgroup.com>

Author: wbmc

docs/spmd.md

@@ -31,7 +31,7 @@ Also, this version of the SPMD is currently only tested.optimized on Google Clou
 ## PyTorch/XLA SPMD Design Overview
 
 
-### Simple Eexample & Sharding Aannotation API
+### Simple Example & Sharding Aannotation API
 
 Users can annotate native PyTorch tensors using the `mark_sharding` API ([src](https://github.com/pytorch/xla/blob/9a5fdf3920c18275cf7dba785193636f1b39ced9/torch_xla/experimental/xla_sharding.py#L388)). This takes `torch.Tensor` as input and returns a `XLAShardedTensor` as output.
 
@@ -42,6 +42,7 @@ def mark_sharding(t: Union[torch.Tensor, XLAShardedTensor], mesh: Mesh, partitio
 Invoking `mark_sharding` API takes a user defined logical [mesh](#mesh) and [partition\_spec](#partition-spec) and generates a sharding annotation for the XLA compiler. The sharding spec is attached to the XLATensor. Here is a simple usage example from the [[RFC](https://github.com/pytorch/xla/issues/3871), to illustrate how the sharding annotation API works:
 
 ```python
+import numpy as np
 import torch
 import torch_xla.core.xla_model as xm
 import torch_xla.runtime as xr