diff --git a/integration/Gradient-Train-Job.py b/integration/Gradient-Train-Job.py
index f9aedf9..8aa02c2 100644
--- a/integration/Gradient-Train-Job.py
+++ b/integration/Gradient-Train-Job.py
@@ -18,6 +18,8 @@
 # MAGIC  * The Gradient Webhook has been configured
 # MAGIC  * The Databricks Job has been Gradient enabled
 # MAGIC
+# MAGIC  When bypassing the Gradient Webhook with AWS, the cluster attached to this notebook must have an instance_arn with describe_instances and describe_volumes permissions. When bypassing the Gradient Webhook with Azure, the following environment variables must be set with the correct values: "AZURE_TENANT_ID", "AZURE_SUBSCRIPTION_ID", "AZURE_CLIENT_SECRET", "AZURE_CLIENT_ID"
+# MAGIC
 # MAGIC This job will configure all runs to execute using ON DEMAND nodes only.  The orginal settings will be restored after training is complete.
 # MAGIC
 
@@ -41,7 +43,6 @@
 os.environ["SYNC_API_KEY_SECRET"] = dbutils.widgets.get("Sync API Key Secret")
 os.environ["DATABRICKS_HOST"] = dbutils.widgets.get("Databricks Host").rstrip('\/')
 
-
 print(f"DATABRICKS_JOB_ID: {DATABRICKS_JOB_ID}")
 print(f"TRAINING_RUNS: {TRAINING_RUNS}")
 print(f"BYPASS_WEBHOOK: {BYPASS_WEBHOOK}")
@@ -73,18 +74,19 @@
 else:
     raise ValueError(f"Unsupported platform: {platform}")
 
+if BYPASS_WEBHOOK:
+    access_report = sync_databricks.get_access_report()
 
-access_report = sync_databricks.get_access_report()
+    for line in access_report:
+        logger.info(line)
 
-for line in access_report:
-    logger.info(line)
-
-assert not any(line.status is AccessStatusCode.RED for line in access_report), "Required access is missing"
+    assert not any(line.status is AccessStatusCode.RED for line in access_report), "Required access is missing"
 
 # COMMAND ----------
 
+from typing import Optional
 
-def get_cluster_for_job(job: dict | None) -> dict:
+def get_cluster_for_job(job: Optional[dict]) -> dict:
 
     if job is None:
         job = sync_databricks_client.get_job(DATABRICKS_JOB_ID)
@@ -110,11 +112,10 @@ def get_cluster_for_job(job: dict | None) -> dict:
     else:
         raise ValueError("Could not identify a cluster for this job")
 
-def get_tag_for_job(job: dict, tag_key: str) -> str | None:
+def get_tag_for_job(job: dict, tag_key: str) -> Optional[str]:
     cluster = get_cluster_for_job(job)
     return cluster["custom_tags"].get(tag_key)
     
-
 def validate_job():  
     logger.info("Validating Databricks Job")
     job = sync_databricks_client.get_job(DATABRICKS_JOB_ID)
@@ -204,7 +205,7 @@ def validate_job():
 # COMMAND ----------
 
 class RecommendationError(Exception):
-    "Raised something goes wrong with the generation of a GradientML Recommendation"
+    "Raised when something goes wrong with the generation of a GradientML Recommendation"
 
     def __init__(self, error):
         super().__init__("recommendation Error: " + str(error))
@@ -227,7 +228,7 @@ def run_job(run_job_id: str):
             break
     return run
 
-def wait_for_recommendation(starting_recommendation_id: str | None) -> None:
+def wait_for_recommendation(starting_recommendation_id: Optional[str]) -> None:
     logger.info(f"waiting for log submission and rec generation and application")
     logger.info(f"starting recommendation id: {starting_recommendation_id}")