register_pandas_dataframe crashes kernel when receiving dataframe with multiple columns with same name

My Synapse kernel crashed when I tried to register a dataset where the dataframe had multiple columns with the same name. I am not completely sure if this is the correct place to post this, but this is the best place I could find.

Minimum reproducible example:
```python
dataset = Dataset.Tabular.register_pandas_dataframe(
dataframe = pd.DataFrame([[1,2],[1,2]], columns=['a', 'a']),
target = datastore,
name = "bad_dataframe",
show_progress=True,
)
```
What I would expect:
To get some feedback about why it fails, and the kernel should not crash.

What I get:
```
Validating arguments.
Arguments validated.
Successfully obtained datastore reference and path.
Uploading file to managed-dataset/13e4b3c6-8730-4951-bde5-2d2bc6456e2d/
InternalError: Ipython kernel exits with code -11. Please restart your session.
```

---
#### Document Details

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* ID: 9b1aee32-5a1f-2ef1-ee76-30b17999db87
* Version Independent ID: f4785272-a81d-e9cc-c6a5-9d41c8dcd90b
* Content: [azureml.data.dataset_factory.TabularDatasetFactory class - Azure Machine Learning Python](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py)
* Content Source: [AzureML-Docset/stable/docs-ref-autogen/azureml-core/azureml.data.dataset_factory.TabularDatasetFactory.yml](https://github.com/MicrosoftDocs/MachineLearning-Python-pr/blob/live/AzureML-Docset/stable/docs-ref-autogen/azureml-core/azureml.data.dataset_factory.TabularDatasetFactory.yml)
* Service: **machine-learning**
* Sub-service: **core**
* GitHub Login: @DebFro
* Microsoft Alias: **debfro**

Commencez par exécuter l’exemple pandas DataFrame de l’issue avec des noms de colonnes en double et observez l’arrêt du kernel. Le payload identifie la source de documentation de TabularDatasetFactory à l’emplacement AzureML-Docset/stable/docs-ref-autogen/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory.yml, mais aucun fichier d’implémentation ni test n’est nommé. La tâche est considérée comme terminée lorsque l’échec renvoie des informations utiles sans faire planter l’IPython kernel.

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