Inference Failure for MXNet MME Deployment in SageMaker

**Describe the bug**
When deploying a multi model endpoint using the SageMaker MXNet container (which uses this library) I receive the following error when I try to invoke the endpoint: "AttributeError: 'NoneType' object has no attribute 'transform'.

**To reproduce**
Train a simple MXNet model in SageMaker using the [example here](https://github.com/aws/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/mxnet_mnist/mxnet_mnist.ipynb).

At the end of the notebook, you verify that you can receive real time predictions from a SageMaker Endpoint using the model/code.

Next, add the following code to the notebook to deploy the same model as a multi model endpoint:

```
mme = MultiDataModel(name='mxnet-mme-final', model_data_prefix='',
model=model, sagemaker_session=sagemaker_session)

mme.deploy(initial_instance_count=1, instance_type='ml.t2.medium')
```

Copy the model artifact to the model_data_prefix so it's accessible by MME.

Once it finishes deploying, try to invoke it using sagemaker_runtime in boto3, you will get the same error.

**Expected behavior**
I'd expect to be able to deploy the same model to MME as I can as a single endpoint using the SageMaker MXNet container. The [SageMaker documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/multi-model-endpoints.html) says that MXNet container supports MME.

**CloudWatch Endpoint Logs at time of failure**

2021-02-11 19:25:28,443 [INFO ] pool-1-thread-1 ACCESS_LOG - /10.32.0.2:49282 "GET /ping HTTP/1.1" 200 1
2021-02-11 19:25:29,687 [WARN ] W-9000-29028cdaefc80ac4662997191 com.amazonaws.ml.mms.wlm.WorkerLifeCycle - attachIOStreams() threadName=W-9000-29028cdaefc80ac4662997191
2021-02-11 19:25:29,807 [INFO ] W-9000-29028cdaefc80ac4662997191-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - model_service_worker started with args: --sock-type unix --sock-name /home/model-server/tmp/.mms.sock.9000 --handler sagemaker_mxnet_serving_container.handler_service --model-path /opt/ml/models/29028cdaefc80ac4662997191ec53602/model --model-name 29028cdaefc80ac4662997191ec53602 --preload-model false --tmp-dir /home/model-server/tmp
2021-02-11 19:25:29,809 [INFO ] W-9000-29028cdaefc80ac4662997191-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Listening on port: /home/model-server/tmp/.mms.sock.9000
2021-02-11 19:25:29,809 [INFO ] W-9000-29028cdaefc80ac4662997191-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - [PID] 69
2021-02-11 19:25:29,809 [INFO ] W-9000-29028cdaefc80ac4662997191-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - MMS worker started.
2021-02-11 19:25:29,809 [INFO ] W-9000-29028cdaefc80ac4662997191-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Python runtime: 3.6.10
2021-02-11 19:25:29,809 [INFO ] epollEventLoopGroup-3-1 com.amazonaws.ml.mms.wlm.ModelManager - Model 29028cdaefc80ac4662997191ec53602 loaded.
2021-02-11 19:25:29,818 [INFO ] W-9000-29028cdaefc80ac4662997191 com.amazonaws.ml.mms.wlm.WorkerThread - Connecting to: /home/model-server/tmp/.mms.sock.9000
2021-02-11 19:25:29,833 [INFO ] W-9000-29028cdaefc80ac4662997191-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Connection accepted: /home/model-server/tmp/.mms.sock.9000.
2021-02-11 19:25:31,055 [INFO ] W-9000-29028cdaefc80ac4662997191-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Model 29028cdaefc80ac4662997191ec53602 loaded io_fd=86fe85fffe5a7fee-00000026-00000002-7cbbb181288e375a-efae222a
2021-02-11 19:25:31,057 [INFO ] W-9000-29028cdaefc80ac4662997191 com.amazonaws.ml.mms.wlm.WorkerThread - Backend response time: 1195
2021-02-11 19:25:31,058 [INFO ] W-9000-29028cdaefc80ac4662997191 ACCESS_LOG - /10.32.0.2:49282 "POST /models HTTP/1.1" 200 1400
2021-02-11 19:25:31,061 [WARN ] W-9000-29028cdaefc80ac4662997191 com.amazonaws.ml.mms.wlm.WorkerLifeCycle - attachIOStreams() threadName=W-29028cdaefc80ac4662997191-1
2021-02-11 19:25:31,106 [INFO ] W-29028cdaefc80ac4662997191-1-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Invoking custom service failed.
2021-02-11 19:25:31,106 [INFO ] W-9000-29028cdaefc80ac4662997191 com.amazonaws.ml.mms.wlm.WorkerThread - Backend response time: 2
2021-02-11 19:25:31,106 [INFO ] W-29028cdaefc80ac4662997191-1-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Traceback (most recent call last):
2021-02-11 19:25:31,106 [INFO ] W-29028cdaefc80ac4662997191-1-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - File "/usr/local/lib/python3.6/site-packages/mms/service.py", line 108, in predict
2021-02-11 19:25:31,106 [INFO ] W-29028cdaefc80ac4662997191-1-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - ret = self._entry_point(input_batch, self.context)
2021-02-11 19:25:31,106 [INFO ] W-29028cdaefc80ac4662997191-1-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - File "/usr/local/lib/python3.6/site-packages/sagemaker_inference/default_handler_service.py", line 50, in handle
2021-02-11 19:25:31,106 [INFO ] W-29028cdaefc80ac4662997191-1-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - return self._service.transform(data, context)
2021-02-11 19:25:31,107 [INFO ] W-29028cdaefc80ac4662997191-1-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - AttributeError: 'NoneType' object has no attribute 'transform'
2021-02-11 19:25:31,109 [INFO ] W-9000-29028cdaefc80ac4662997191 ACCESS_LOG - /10.32.0.2:49282 "POST /models/29028cdaefc80ac4662997191ec53602/invoke HTTP/1.1" 503 14

**System information**
A description of your system. Please provide:
- **Toolkit version**: Latest? The version that is installed in the SageMaker container.
- **Framework version**: 1.6.0
- **Python version**: 3.6
- **CPU or GPU**: CPU
- **Custom Docker image (Y/N)**: N

**Additional context**
I think I may know the cause of the bug.

Looking in to the handler_service.py code, the initialize() method retrieves the appropriate Transformer object and sets it to self._service. It then calls the parent class constructor, but it doesn't pass in the transformer object. The parent constructor accepts a transformer as a parameter (see [here](https://github.com/aws/sagemaker-inference-toolkit/blob/master/src/sagemaker_inference/default_handler_service.py)), but the parent class defaults the value of self._service to None, so when handle() is called by MMS, it uses the self._service set by the parent class, resulting in the error shown above.

If you look at SageMaker containers that successfully deploy MME, [they pass the Transformer() in to the default handler constructor](https://github.com/aws/sagemaker-scikit-learn-container/blob/master/src/sagemaker_sklearn_container/handler_service.py).

https://github.com/aws/sagemaker-mxnet-inference-toolkit/blob/d180ab69c809498cfcc8bc11e06c2e55a650999e/src/sagemaker_mxnet_serving_container/handler_service.py#L64-L79

Start with src/sagemaker_mxnet_serving_container/handler_service.py at the linked initialize() lines, then compare its parent call with src/sagemaker_inference/default_handler_service.py and the linked SageMaker Scikit-learn handler. Reproduce the MXNet MNIST deployment as a multi-model endpoint and invoke it; done means the request no longer fails with the NoneType transform error.

Written by the indexing model from the issue text.