* add pipeline tuner component and dependencies.
* clean code.
* do not need force rerun.
* replace the resources.
* update metrics retrieving.
* Update test/pipeline_tuning_example/requirements.txt
* Update test/pipeline_tuning_example/train/env.yaml
* Update test/pipeline_tuning_example/tuner/env.yaml
* Update test/pipeline_tuning_example/tuner/tuner_func.py
* Update test/pipeline_tuning_example/data_prep/env.yaml
* fix issues found by lint with flake8.
* add documentation
* add data.
* do not need AML resource for local run.
* AML -> AzureML
* clean code.
* Update website/docs/Examples/Tune-AzureML pipeline.md
* rename and add pip install.
* update figure name.
* align docs with code.
* remove extra line.