[Bug Report] PyTorch MME example fails with container v1.8.1

**Link to the notebook**

https://github.com/aws/amazon-sagemaker-examples/tree/master/advanced_functionality/multi_model_pytorch

**Describe the bug**

Since the release of PyTorch DLC v1.8.1, the PyTorch MME example fails to properly deploy the MME endpoint (since we deliberately just tagged `"1.8"` in the sample, to consume any bug fixes without expecting breaking changes). This was also because the sample is known to work on more recent patches of older versions e.g. `1.7.1` and `1.6.1` but not `1.7.0` and `1.6.0`, so specifying minor version only helped encourage users to hit the right versions if they tried to downgrade.

I've done a bit of investigation on this, but have been unable to find the exact cause or a solution that works. v1.8.1 upgrades TorchServe from 0.3 to 0.4, so it's likely something changed in TorchServe to stop it from recognising the model bundle & starting correctly.

**To reproduce**

Run through the `multi_model_pytorch` example notebook.

**Logs**

Some logs from the failed endpoint:

```
['torchserve', '--start', '--model-store', '/', '--ts-config', '/etc/sagemaker-ts.properties', '--log-config', '/opt/conda/lib/python3.6/site-packages/sagemaker_pytorch_serving_container/etc/log4j.properties', '--models', 'model.mar']
...
2021-09-06 07:39:28,480 [INFO ] main org.pytorch.serve.ModelServer - Loading initial models: model.mar
2021-09-06 07:39:28,484 [WARN ] main org.pytorch.serve.ModelServer - Failed to load model: model.mar
org.pytorch.serve.archive.ModelNotFoundException: Model not found at: model.mar
#011at org.pytorch.serve.archive.ModelArchive.downloadModel(ModelArchive.java:86)
#011at org.pytorch.serve.wlm.ModelManager.createModelArchive(ModelManager.java:135)
#011at org.pytorch.serve.wlm.ModelManager.registerModel(ModelManager.java:112)
#011at org.pytorch.serve.ModelServer.initModelStore(ModelServer.java:227)
#011at org.pytorch.serve.ModelServer.startRESTserver(ModelServer.java:327)
#011at org.pytorch.serve.ModelServer.startAndWait(ModelServer.java:114)
#011at org.pytorch.serve.ModelServer.main(ModelServer.java:95)
2021-09-06 07:39:28,494 [INFO ] main org.pytorch.serve.ModelServer - Initialize Inference server with: EpollServerSocketChannel.2021-09-06 07:39:28,570 [INFO ] main org.pytorch.serve.ModelServer - Inference API bind to: http://0.0.0.0:8080
2021-09-06 07:39:28,570 [INFO ] main org.pytorch.serve.ModelServer - Initialize Metrics server with: EpollServerSocketChannel.
2021-09-06 07:39:28,572 [INFO ] main org.pytorch.serve.ModelServer - Metrics API bind to: http://127.0.0.1:8082
Model server started.
```

...But **NOTE** that:

1. These warnings about `model.mar` were actually present on previous versions too which still worked. Modifying the sample to save a `model.mar` in the root of `model.tar.gz` does not fix the failure.
2. Although the server does report that it starts, no successful ping checks get passed and SageMaker eventually fails it.

Start by running the advanced_functionality/multi_model_pytorch notebook with PyTorch DLC v1.8.1 and inspect the SageMaker endpoint logs around TorchServe loading model.mar. Compare the TorchServe 0.3 and 0.4 behavior described in the report, without assuming the existing model.mar warning is the cause. Done means the endpoint deploys successfully and passes its ping checks.

Written by the indexing model from the issue text.