[TensorRT Docs] Extending engine embedding docs

docs/execution-providers/TensorRT-ExecutionProvider.md

@@ -491,14 +491,16 @@ Note: The example does not specify `trt_engine_cache_path` because `onnxruntime_
 $./onnxruntime_perf_test -e tensorrt -r 1 -i "trt_engine_cache_enable|true trt_dump_ep_context_model|true" /model_database/transformer_model/model.onnx
 ```
 Once the inference is complete, the embedded engine model is saved to disk. User can then run this model just like the original one, but with a significantly quicker session creation time.
-```bask
+```bash
 $./onnxruntime_perf_test -e tensorrt -r 1 /model_database/transformer_model/model_ctx.onnx
 ```
 
 ### More about Embedded engine model / EPContext model
 * One constraint is that the entire model needs to be TRT eligible
 * When running the embedded engine model, the default setting is `trt_ep_context_embed_mode=0`, where the engine cache path is embedded and TRT EP will look for the engine cache on the disk. Alternatively, users can set `trt_ep_context_embed_mode=1`, embedding the entire engine binary data as a string in the model. However, this mode increases initialization time due to ORT graph optimization hashing the long string. Therefore, we recommend using `trt_ep_context_embed_mode=0`.
 * The default name of an embedded engine model will have `_ctx.onnx` appended to the end. Users can specify `trt_ep_context_file_path=my_ep_context_model.onnx` to overwrite this default name.
+* If an embedded engine is used the library **`nvinfer_builder_resource` of TensorRT is not required**, which is by far the largest library. This enables the case of shipping a minimal set of libraries in the case that a fixed set of models is used which are packaged as precompield engine.
+* Besides everything that embedded engines enable to accelerate the load time, they also **enable packaging an externally compiled engine** using e.g. `trtexec`. A [python script](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/tensorrt/gen_trt_engine_wrapper_onnx_model.py) that is capable of packaging such a precompiled engine into an ONNX file is included in the python tools.
 
 ## Performance Tuning
 For performance tuning, please see guidance on this page: [ONNX Runtime Perf Tuning](./../performance/tune-performance/index.md)