[azureml python sdk v2] access files in URI_FOLDER output after job has finished?

I have a training job that persists some files in an URI_FOLDER output.
_How can I access those through the v2 SDK API after the job has finished?_

### 1. job setup
The output is set up like this in the `command`:
```
job = command(
# ...
outputs=dict(
outputs=Output(type=AssetTypes.URI_FOLDER, mode='rw_mount'),
),
command="python training_script.py " +
"--outputs_dir ${{outputs.outputs}} " +
# ...other arguments...
)
```
This seems to work fine, the corresponding folder is mounted correctly and accessible in the training script.

### 2. training script
In the training script, I persist a dataframe like this:
```
parser.add_argument("--outputs_dir", dest="outputs_dir", default=DEFAULT_MODEL_DIR)
# ...
some_dataframe.to_csv(os.path.join(args.outputs_dir, 'some_dataframe.csv'), index=True)
```
This works fine.

### 3. resulting dataset
After the job has finished, the outputs are available as a dataset.
This is what is shown in Azure ML Studio in the "Overview" tab for job `ivory_octopus_yd6by49kxf`:

The dataset is successfully stored in the `workspaceblobstore` datastore. I checked it in the Azure ML Studio and it looks fine.

### 4. accessing the persisted data
After the job has finished, I access the run using a `MlflowClient()`
```
MLFLOW_TRACKING_URI = ml_client.workspaces.get(name=ml_client.workspace_name).mlflow_tracking_uri
mlflow.set_tracking_uri(MLFLOW_TRACKING_URI)
mlflow_client = MlflowClient()

mlflow_run = mlflow_client.get_run("ivory_octopus_yd6by49kxf")
```
or
```
run = ml_client.jobs.get('ivory_octopus_yd6by49kxf')
# returns NodeOutput class
```

**_How can I programmatically list / get / download the outputs connected to the job?_**

Thanks!

Start with the Azure ML v2 SDK job and NodeOutput APIs shown in the issue, then compare them with the MlflowClient access path. Done means documenting or demonstrating a programmatic way to list, retrieve, and download the URI_FOLDER files after job ivory_octopus_yd6by49kxf finishes.

Written by the indexing model from the issue text.