feat: Support placeholders for TuningStep

Description

Add support to define Hyperparameters Tuning parameters dynamically using placeholders

Fixes #85

Why is the change necessary?

Currently, it is not possible to use placeholders for Sagemaker Hyperparameters Tuning properties. The properties cannot be defined dynamically, as they need to be defined in the HyperparameterTuner which does not accept placeholders. This change makes it possible to use placeholders for HyperparameterTuner properties by using the parameters field that are passed down from the TuningStep.

Solution

Use the parameters field that is compatible with placeholders to define TuningStep properties. Merge the parameters that were generated from the HyperparameterTuner with the input parameters:

The input parameters will overwrite the parameters generated from the HyperparameterTuner if the properties were defined in both
All TuningStep properties will be placeholder compatible except for data - which requires RecordSets as data depending on the estimator used to define the tuner.
The input parameters should follow the schema described in CreateHyperParameterTuningJob API doc
Same solution was adopted for feat: Support placeholders for processing step

Testing

Added unit and integration tests

Pull Request Checklist

Please check all boxes (including N/A items)

Testing

[X] Unit tests added
[X] Integration test added
[X] Manual testing - why was it necessary? could it be automated? - N/A

Documentation

[X] docs: All relevant docs updated
[X] docstrings: All public APIs documented

Title and description

[X] Change type: Title is prefixed with change type: and follows conventional commits
[X] References: Indicate issues fixed via: Fixes #xxx

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache-2.0 license.

aws / aws-step-functions-data-science-sdk-python