Python models Dataproc Serverless setup with packages (#5920)

Add description on how to setup python models with Dataproc Serverless
using a custom image in order to use third-party packages.

## What are you changing in this pull request and why?
In the context of running Python models in Spark using Dataproc, the
documentation
([python-models.md](https://github.com/dbt-labs/docs.getdbt.com/blob/current/website/docs/docs/build/python-models.md))
says:
> Installing packages: If you are using a Dataproc Cluster (as opposed
to Dataproc Serverless), you can add third-party packages while creating
the cluster.

I dug and found it is possible to run python models using third-party
packages in dataproc serverless. It requires to use a custom docker
image. This is very well documented on GCP's end. We currently run this
in prod without any issue. I added this in the documentation. Let me
know if you need more details on how to set this up.

## Checklist
- [x] Review the [Content style
guide](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/content-style-guide.md)
so my content adheres to these guidelines.
- [x] For [docs
versioning](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#about-versioning),
review how to [version a whole
page](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#adding-a-new-version)
and [version a block of
content](https://github.com/dbt-labs/docs.getdbt.com/blob/current/contributing/single-sourcing-content.md#versioning-blocks-of-content).
- [x] Add a checklist item for anything that needs to happen before this
PR is merged, such as "needs technical review" or "change base branch."

Adding or removing pages (delete if not applicable):
N/A

---------

Co-authored-by: Matt Shaver <[email protected]>
Co-authored-by: Leona B. Campbell <[email protected]>
Co-authored-by: Mirna Wong <[email protected]>

website/docs/docs/build/python-models.md

@@ -815,13 +815,40 @@ storage.objects.create
 storage.objects.delete
 ```
 
-**Installing packages:** If you are using a Dataproc Cluster (as opposed to Dataproc Serverless), you can add third-party packages while creating the cluster.
+**Installing packages:** 
 
-Google recommends installing Python packages on Dataproc clusters via initialization actions:
-- [How initialization actions are used](https://github.com/GoogleCloudDataproc/initialization-actions/blob/master/README.md#how-initialization-actions-are-used)
-- [Actions for installing via `pip` or `conda`](https://github.com/GoogleCloudDataproc/initialization-actions/tree/master/python)
+Installation of third-party packages on Dataproc varies depending on whether it's a [cluster](https://cloud.google.com/dataproc/docs/guides/create-cluster) or [serverless](https://cloud.google.com/dataproc-serverless/docs).  
 
-You can also install packages at cluster creation time by [defining cluster properties](https://cloud.google.com/dataproc/docs/tutorials/python-configuration#image_version_20): `dataproc:pip.packages` or `dataproc:conda.packages`.
+- **Dataproc Cluster** — Google recommends installing Python packages while creating the cluster via initialization actions:  
+    - [How initialization actions are used](https://github.com/GoogleCloudDataproc/initialization-actions/blob/master/README.md#how-initialization-actions-are-used)  
+    - [Actions for installing via `pip` or `conda`](https://github.com/GoogleCloudDataproc/initialization-actions/tree/master/python)
+
+    You can also install packages at cluster creation time by [defining cluster properties](https://cloud.google.com/dataproc/docs/tutorials/python-configuration#image_version_20): `dataproc:pip.packages` or `dataproc:conda.packages`.  
+
+- **Dataproc Serverless** — Google recommends using a [custom docker image](https://cloud.google.com/dataproc-serverless/docs/guides/custom-containers) to install thrid-party packages. The image needs to be hosted in [Google Artifact Registry](https://cloud.google.com/artifact-registry/docs). It can then be used by providing the image path in dbt profiles:
+
+    <File name='profiles.yml'>
+    ```yml
+    my-profile:
+        target: dev
+        outputs:
+            dev:
+            type: bigquery
+            method: oauth
+            project: abc-123
+            dataset: my_dataset
+            
+            # for dbt Python models to be run on Dataproc Serverless
+            gcs_bucket: dbt-python
+            dataproc_region: us-central1
+            submission_method: serverless
+            dataproc_batch:
+                runtime_config:
+                    container_image: {HOSTNAME}/{PROJECT_ID}/{IMAGE}:{TAG}
+    ```
+
+
+    </File>
 
 <Lightbox src="/img/docs/building-a-dbt-project/building-models/python-models/dataproc-pip-packages.png" title="Adding packages to install via pip at cluster startup"/>