run.log_table stores 1-item lists differently

The following code:
```
run.log_table('test',value={'x':['asd','sdf'],'y':[1,2]})
run.log_table('test1',value={'x':['asd'],'y':[1]})
run.get_metrics()
```
will yield:
`{'test': {'x': ['asd', 'sdf'], 'y': [1, 2]}, 'test1': {'x': 'asd', 'y': 1}}`

It shows that single-item lists are stored differently than lists with multiple items.

This makes the JSON produced from these inconsistent types hard to parse down the line (e.g. outside of AML as in PowerBI for example).

If we pass in a list, we would expect that it is stored as a list even if there is only one item in the list.

---
#### Document Details

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* ID: 2e24ac95-7027-6a3c-81e6-f20cc4415219
* Version Independent ID: 9498cdc2-0d04-39a3-08ce-ef9ea8f2d153
* Content: [azureml.core.run.Run class - Azure Machine Learning Python](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run.run?view=azure-ml-py)
* Content Source: [AzureML-Docset/stable/docs-ref-autogen/azureml-core/azureml.core.run.Run.yml](https://github.com/MicrosoftDocs/MachineLearning-Python-pr/blob/live/AzureML-Docset/stable/docs-ref-autogen/azureml-core/azureml.core.run.Run.yml)
* Service: **machine-learning**
* Sub-service: **core**
* GitHub Login: @debfro
* Microsoft Alias: **debfro**

首先，在 Azure Machine Learning Python SDK 情境中重現提供的 Run.log_table 和 Run.get_metrics 呼叫。追蹤單一項目清單在傳回的指標和 JSON 中的表示方式，然後新增涵蓋範圍，顯示明確傳入的單一項目清單仍維持為清單。

由索引模型根據 Issue 內容生成。