Enhance UX for training

Following up on the [inference UX proposal](https://github.com/aws/sagemaker-inference-toolkit/issues/64), SageMaker requires many things to be changed and adjusted so that a normal working app / package becomes usable by SageMaker.

## Pain-points

Essentially, a normal working Python package should be made to work with SageMaker without requiring things like [`entry_point` to be in the root of the `source_dir`](https://sagemaker.readthedocs.io/en/stable/frameworks/pytorch/sagemaker.pytorch.html?sagemaker.pytorch.estimator.PyTorch). We shouldn't require users to change the structure of their exising code base. Their package structure must be preserved. This makes adoption much easier.

For example,

```
package/
notebook/
train_deploy_sagemaker.ipynb
src/
foo
tests/
setup.cfg
setup.py ---> might ignore notebook
requirements.txt
```

`train_deploy_sagemaker.ipynb` to work requires change in the structure while `pip install package` followed by the notebook execution works in normal dev scenarios. This needs to be addressed.

## Proposal

For better UX, try to minimize the module wrapping idea which makes debugging very hard. My proposal is decorators like

```
import sagemaker

@sagemaker.pytorch.entry_point(session, role, instance_count, instance_type, ...)
def foo(*args, **kwargs):
...
```

so that existing code base can become usable with minimal change. Hyperparameters can be passed to `foo` via cli.