ParallelRunStep with CSV input introduces duplicate rows

Using AzureML, ParallelRunStep with a CSV file input, I run Shapley explanations (SHAP package) in parallel on a cluster. I see the actual mini batches run are greater than expected, leading to extra rows in the output file.

The cluster is a 10 node Standard_D64_v3 created 2/28/2022 (I have destroyed and recreated the cluster and seen the same issue).

The metric Total Mini Batches is present by default. I also log the batch size in my script, and in this way I get one metric per mini batch.
Here are my observations:

* The input CSV has no duplicate row IDs
* The file produced by ParallelRunStep has duplicate IDs
* The run completes successfully
* The number of the batch size entries is greater than total mini batches expected. The difference between these 2 is usually the exact number of extra rows.

I work around this by forcing uniqueness in the output file; however this is not ideal for large datasets.

首先，使用 CSV 輸入、記錄的批次大小指標以及 10 節點的 Standard_D64_v3 叢集，重現所描述的 AzureML ParallelRunStep 執行。將預期的迷你批次數量與輸出列 ID 進行比較，並判斷何時發生重複處理；當執行產生預期數量的批次且沒有重複 ID 時，即表示完成。

由索引模型根據 Issue 內容生成。