fixed indented code

Author: s-polly

articles/machine-learning/how-to-use-serverless-compute.md

@@ -141,18 +141,18 @@ When you [view your usage and quota in the Azure portal](how-to-manage-quotas.md
     ```python
     from azure.ai.ml import MLClient
     from azure.identity import DefaultAzureCredential
-
+    
     subscription_id = "<your-subscription-id>"
     resource_group = "<your-resource-group>"
     workspace = "<your-workspace-name>"
-
+    
     ml_client = MLClient(
         DefaultAzureCredential(),
         subscription_id,
         resource_group,
         workspace
     )
-
+    
     # Get workspace details
     ws = ml_client.workspaces.get(name=workspace)
     print(ws)
@@ -161,10 +161,10 @@ When you [view your usage and quota in the Azure portal](how-to-manage-quotas.md
 
 #  [Azure CLI](#tab/cli)
 
-  ```bash
-  
+    ```bash
+    
         az ml workspace show --name <wsname>  --resource-group <rgname>
-  ```
+    ```
   ---
     Look for the user assigned identities in the output. If it's missing, create a new workspace with a user assigned managed identity by following the instructions here: Set up service authentication - Azure Machine Learning | Microsoft Learn
 
@@ -173,41 +173,41 @@ When you [view your usage and quota in the Azure portal](how-to-manage-quotas.md
 
     # [Python SDK](#tab/python)
 
-        ```python
-    from azure.ai.ml import command
-    from azure.ai.ml import MLClient     # Handle to the workspace
-    from azure.identity import DefaultAzureCredential    # Authentication package
-    from azure.ai.ml.entities import ResourceConfiguration
-    from azure.ai.ml.entities import ManagedIdentityConfiguration
-    
-    credential = DefaultAzureCredential()
-    # Get a handle to the workspace. You can find the info on the workspace tab on ml.azure.com
-    ml_client = MLClient(
-        credential=credential,
-        subscription_id="<Azure subscription id>", 
-        resource_group_name="<Azure resource group>",
-        workspace_name="<Azure Machine Learning Workspace>",
-    )
-    job = command(
-        command="echo 'hello world'",
-        environment="azureml://registries/azureml/environments/sklearn-1.5/labels/latest",
-            identity= ManagedIdentityConfiguration(client_id="<workspace-uami-client-id>"),
-    )
-    # submit the command job
-    ml_client.create_or_update(job)
-    
-        ```
+    ```python
+        from azure.ai.ml import command
+        from azure.ai.ml import MLClient     # Handle to the workspace
+        from azure.identity import DefaultAzureCredential    # Authentication package
+        from azure.ai.ml.entities import ResourceConfiguration
+        from azure.ai.ml.entities import ManagedIdentityConfiguration
+        
+        credential = DefaultAzureCredential()
+        # Get a handle to the workspace. You can find the info on the workspace tab on ml.azure.com
+        ml_client = MLClient(
+            credential=credential,
+            subscription_id="<Azure subscription id>", 
+            resource_group_name="<Azure resource group>",
+            workspace_name="<Azure Machine Learning Workspace>",
+        )
+        job = command(
+            command="echo 'hello world'",
+            environment="azureml://registries/azureml/environments/sklearn-1.5/labels/latest",
+                identity= ManagedIdentityConfiguration(client_id="<workspace-uami-client-id>"),
+        )
+        # submit the command job
+        ml_client.create_or_update(job)
+
+    ```
 
     # [Azure CLI](#tab/cli)
 
-    ```yaml
+    ```bash
     $schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
     command: echo "hello world"
     environment:
       image: library/python:latest
     identity:
       type: managed
-    ````
+    ```
 
   ---