Add `deploy_instance_count` and `deploy_instance_type` to `TrainingPipeline`

Currently, [TrainingPipeline](https://github.com/aws/aws-step-functions-data-science-sdk-python/blob/b45b282592041d3c355f7cef492798bc3bf5415a/src/stepfunctions/template/pipeline/train.py#L95) uses the same instance type and count for both train and deploy.

Different instance types and counts are desirable to address the different profiles for each workload.

Start with src/stepfunctions/template/pipeline/train.py, especially the TrainingPipeline definition linked in the issue. Trace how the current instance type and count are applied to training and deployment, then determine the configuration points for separate deploy values. Done means TrainingPipeline accepts deploy_instance_count and deploy_instance_type and uses them independently from the training settings.

Written by the indexing model from the issue text.