Potential feature request: PipelineArgument dynamically derived from PipelineParameter

Hello,

I couldn't find a pre-existing issue or documentation on this topic. Is it possible to create pipeline parameters which are dynamically generated from the values of other PipelineParameters?

e.g. the below shows the basic scenario we're looking for.

```python

customer_project = PipelineParameter(name="customer_project")
publish_version = PipelineParameter(name="publish_version")

# following is generated from above pipeline parameters but can still be used as an argument in Pipeline Steps
input_data_parameter = PipelineArgument(name="input_data", value_spec="base_input/{param:customer_project}/{param:publish_version}/train")
input_data = (input_data_parameter, DataPathComputeBinding(mode='mount'))
...
train_step = PythonScriptStep(
...
inputs=[input_data]
)

```

Is there a better way to do what I'm trying to do here?

Currently we have some workarounds like have a "parent" PythonScriptStep which just handles submitting child runs, and generating the right paths from pipeline arguments, but it seems like an extra step and ability to have something like above would be nicer for us.

この issue では PipelineParameter、PipelineArgument、PythonScriptStep、DataPathComputeBinding が挙げられていますが、ファイルやテストは指定されていません。まず、パラメーターから引数への補間に関する Azure Machine Learning のパイプライン API とドキュメントを確認し、次に提案された動作を親の PythonScriptStep の workaround と比較してください。対応済みとするには、サポートされるアプローチを決定し、例または実装を用意する必要があります。

索引モデルが issue の本文から書いたものです。