CGO docs experimental update (#4277)

docs/en_US/NAS/ExecutionEngines.rst

@@ -53,12 +53,14 @@ Three steps are need to use graph-based execution engine.
 
 For exporting top models, graph-based execution engine supports exporting source code for top models by running ``exp.export_top_models(formatter='code')``.
 
-CGO Execution Engine
---------------------
+CGO Execution Engine (experimental)
+-----------------------------------
 
 CGO（Cross-Graph Optimization) execution engine does cross-model optimizations based on the graph-based execution engine. In CGO execution engine, multiple models could be merged and trained together in one trial.
 Currently, it only supports ``DedupInputOptimizer`` that can merge graphs sharing the same dataset to only loading and pre-processing each batch of data once, which can avoid bottleneck on data loading. 
 
+.. note :: To use CGO engine, PyTorch-lightning above version 1.4.2 is required.
+
 To enable CGO execution engine, you need to follow these steps:
 
 1. Create RetiariiExeConfig with remote training service. CGO execution engine currently only supports remote training service.