Azure Python SDK for DataFactory to point to a specific GIT branch

I'm trying to update the Datasets programatically using azure-mgmt-datafactory SDK. I'm able to connect to the DataFactory, list and update datasets to the adf_publish/live mode. However, I'm interested to connect to my GIT branch first and update. Below is the sample code

from azure.identity import ClientSecretCredential
from azure.mgmt.datafactory import DataFactoryManagementClient
from azure.mgmt.datafactory.models import *
import time

subscription_id = 'xxxxx'

credentials = ClientSecretCredential(
    tenant_id='xxxx',
    client_id='xxxx',
    client_secret='xxxx'
)

adf_client = DataFactoryManagementClient(credentials, subscription_id)

# How to connect to my branch?
response = adf_client.datasets.get('xxxxxx',
                        'adf-xxx',
                        'DS_DEV_OP_XXX')

# This is giving the dataset not from my branch
print(response.as_dict())

Could you please let me know as to how to connect to my GIT branch?

Thanks in advance.

Option1:

There is no direct method to connect to a specific Git branch in the Azure-mgmt-datafactory SDK. However, you can obtain the dataset from the Data Factory using the get method of the datasets class. The resource group name, the Data Factory name, and the dataset name are the three parameters required by the get method.
You are retrieving the dataset DS DEV OP XXX from the Data Factory adf-xxx using the get method in your code. The dataset's most recent version, which might not be from your Git branch, is obtained using the get method. You must first check out the branch in your local repository in order to retrieve the dataset from a specific Git branch, and then you must access the Data Factory.

Option:2

We're going to employ Azure DevOps. The procedures will be the same if you use GitHub.

Go to Azure Data factory Studio->Manage->Git configuration->configure

Select Repository type: Azure DevOps Git, Azure Active Directory: “AAD Name”

Give all the required details like Project name, Repo name, Branch etc.

Now we can check all the data like Dataset, dataflow, linkedService etc.
Go to Azure Data Factory Studio. Select 'Author'.

1. Now we see 'dev' branch is selected by default.
2. If we do new developments in a feature branch. Click "New branch". We can save our incomplete development in this way. It is crucial to keep in mind that in order to return the code to the "dev" branch after testing the pipeline, we must "Create pull request."
3. Our modifications can all be "Published." With this, the new modifications will become "live" and the pipelines in the "dev" branch will be pushed into the "adf publish dev" branch.