Missing json error when trying to compile a semantic segmentation model (builtin algorithm) with Neo

**Describe the bug**
Not sure if this is a bug or an unsupported feature. We've trained a semantic segmentation model, using the built in sagemaker semantic segmentation algorithm, (FCN with resenet 50) and were able to successfully deploy it. But, we wanted to compile it with Neo in order to improve inference performance, and to be able to deploy it to an inf1 instance.
When I try to compile the model (based on examples in sample notebooks), I receive the following error:
`ClientError: InputConfiguration: No valid Mxnet model file -symbol.json found`
The model.tar.gz for semantic segmentation models contains hyperparams.json, model_algo-1, model_best.params. According to the docs, model_algo-1 is the serialized mxnet model. Aren't gluon models supported by Neo?
If not, can I manulay use gluon\mxnet to save the required symbols json from the serialized model in order to use Neo?
Thanks!

**To reproduce**
Train a Semantic Segmentation model using sagemaker builtin algorithm, with FCN and resnet 50, and try to call the estimators compile_model.

**Expected behavior**
Neo should successfully compile the model.

**Screenshots or logs**
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**System information**
A description of your system. Please provide:
- **SageMaker Python SDK version**:
- **Framework name (eg. PyTorch) or algorithm (eg. KMeans)**: Semantic segmentation (builtin)
- **Framework version**:
- **Python version**: 3.6
- **CPU or GPU**: Running in a sagemaker jupyter notebook, hosted on a a CPU instance
- **Custom Docker image (Y/N)**:

**Additional context**
Add any other context about the problem here.