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

⚠ *Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.*

* 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**

Commencez par la documentation de la classe AutoMLConfig et la source AutoMLConfig.yml liée, puis suivez la manière dont les valeurs de PipelineParameter sont validées pour primary_metric et label_column_name. Le travail est terminé lorsque les deux arguments acceptent PipelineParameter lors de la soumission d’un AutoMLStep dans une Pipeline, sans les erreurs signalées de type non pris en charge.

Rédigé par le modèle d'indexation à partir du texte de l'issue.