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.

該 issue 提到了 PipelineParameter、PipelineArgument、PythonScriptStep 和 DataPathComputeBinding，但沒有指定檔案或測試。首先檢查 Azure Machine Learning pipeline API 和文件中關於將參數插值為引數的內容，然後將提議的行為與父級 PythonScriptStep 的 workaround 進行比較；完成的標準是確定一種受支援的方法，並提供範例或實作。

由索引模型根據 Issue 內容生成。