Make `AutoMLConfig` configurable with `PipelineParameter`

Please make it possible to configure `AutoMLConfig` with `PipelineParameter`. It is very disappointing that you can not configure this when submitting a `Pipeline` with an `AutoMLStep` in it. I tried to set the arguments `primary_metric` and `label_column_name`. Both failed:

`primary_metric`:

```json
{
"error": {
"code": "UserError",
"message": "Invalid argument(s) 'primary_metric' specified. Supported value(s): 'accuracy, norm_macro_recall, precision_score_weighted, average_precision_score_weighted, AUC_weighted'.",
"details_uri": "https://aka.ms/AutoMLConfig",
"target": "primary_metric",
"inner_error": {
"code": "BadArgument",
"inner_error": {
"code": "ArgumentInvalid"
}
}
}
}
```

`label_column_name`:

```json
{
"error": {
"code": "UserError",
"message": "Argument [label_column_name] is of unsupported type: []. Supported type(s): [int, str]",
"details_uri": "https://aka.ms/AutoMLConfig",
"target": "label_column_name",
"inner_error": {
"code": "BadArgument",
"inner_error": {
"code": "ArgumentInvalid"
}
},
"reference_code": "061ed905-e59e-42b9-ad95-cd18f40b4358"
}
}
```

---
#### Document Details

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* ID: 0bc2b21e-6b1a-cb94-2857-147177a29d7c
* Version Independent ID: d14620a6-a2f6-49f1-632e-73903d41de8c
* Content: [azureml.train.automl.automlconfig.AutoMLConfig class - Azure Machine Learning Python](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py)
* Content Source: [AzureML-Docset/stable/docs-ref-autogen/azureml-train-automl-client/azureml.train.automl.automlconfig.AutoMLConfig.yml](https://github.com/MicrosoftDocs/MachineLearning-Python-pr/blob/live/AzureML-Docset/stable/docs-ref-autogen/azureml-train-automl-client/azureml.train.automl.automlconfig.AutoMLConfig.yml)
* Service: **machine-learning**
* Sub-service: **core**
* GitHub Login: @DebFro
* Microsoft Alias: **debfro**

Beginne mit der Dokumentation der Klasse AutoMLConfig und der verlinkten AutoMLConfig.yml-Quelle und verfolge anschließend, wie PipelineParameter-Werte für primary_metric und label_column_name validiert werden. Erledigt ist die Aufgabe, wenn beide Argumente PipelineParameter akzeptieren, sobald ein AutoMLStep in einer Pipeline übergeben wird, ohne die gemeldeten Fehler zu nicht unterstützten Typen.

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