diff --git a/_posts/2021-3-4-pytorch-1.8-released.md b/_posts/2021-3-4-pytorch-1.8-released.md
index c2eaea405218..84d17c37d4bc 100644
--- a/_posts/2021-3-4-pytorch-1.8-released.md
+++ b/_posts/2021-3-4-pytorch-1.8-released.md
@@ -31,29 +31,6 @@ This kind of functionality is applicable in many scenarios. For example, the FX-
 
 Because FX transforms consume and produce nn.Module instances, they can be used within many existing PyTorch workflows. This includes workflows that, for example, train in Python then deploy via TorchScript.
 
-Below is an FX transform example:
-
-```python
-import torch
-import torch.fx
-
-def transform(m: nn.Module,
-             tracer_class : type = torch.fx.Tracer) -> torch.nn.Module:
-   # Step 1: Acquire a Graph representing the code in `m`
-  
-   # NOTE: torch.fx.symbolic_trace is a wrapper around a call to
-   # fx.Tracer.trace and constructing a GraphModule. We'll
-   # split that out in our transform to allow the caller to
-   # customize tracing behavior.
-   graph : torch.fx.Graph = tracer_class().trace(m)
-  
-   # Step 2: Modify this Graph or create a new one
-   graph = ...
-  
-   # Step 3: Construct a Module to return
-   return torch.fx.GraphModule(m, graph)
-
-```
 You can read more about FX in the official [documentation](https://pytorch.org/docs/master/fx.html). You can also find several examples of program transformations implemented using ```torch.fx``` [here](https://github.com/pytorch/examples/tree/master/fx). We are constantly improving FX and invite you to share any feedback you have about the toolkit on the [forums](https://discuss.pytorch.org/) or [issue tracker](https://github.com/pytorch/pytorch/issues).
 
 # Distributed Training