Removing environment on an ensemble model causes inference errors

In the example autopilot pipeline notebook where the environment is popped here: https://github.com/aws/amazon-sagemaker-examples/blob/758676b77069c51944523516282732dd029a2a20/autopilot/sagemaker-autopilot-pipelines/register_autopilot_model.py#L33

When you try to deploy the model and query the endpoint you get inference errors regarding a lack of a transform on a NoneType object. Is there a reason why we pop the environment when registering the model? This also potentially removes customization on the inference returns (such as adding probability in addition to predicted label) without adding them back.

Start with register_autopilot_model.py at the linked line where the environment is popped, then deploy the model and query the endpoint to reproduce the NoneType transform error. Compare inference behavior with and without that environment removal, including whether customized returns such as probabilities remain available. Done means the example deploys successfully and inference returns the expected customized output.

Written by the indexing model from the issue text.