Merge pull request #93081 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to master to sync with https://github.com/Microsoft/azure-docs (branch master)

Author: huypub

articles/active-directory/manage-apps/plan-an-application-integration.md

@@ -74,7 +74,7 @@ Each of your applications may have different authentication requirements. With A
 With Microsoft Azure AD Application Proxy, you can provide access to applications located inside your private network securely, from anywhere and on any device. After you have installed an application proxy connector within your environment, it can be easily configured with Azure AD.
 
 ### Integrating custom applications
-If you are writing a new application and want to assist developers in leveraging the power Azure AD, see [Guiding developers](../active-directory-applications-guiding-developers-for-lob-applications.md).
+If you are writing a new application and want to assist developers in leveraging the power of Azure AD, see [Guiding developers](../active-directory-applications-guiding-developers-for-lob-applications.md).
 
 If you want to add your custom application to the Azure Application Gallery, see [“Bring your own app” with Azure AD Self-Service SAML configuration](https://cloudblogs.microsoft.com/enterprisemobility/2015/06/17/bring-your-own-app-with-azure-ad-self-service-saml-configuration-now-in-preview/).
 

articles/aks/operator-best-practices-advanced-scheduler.md

@@ -175,7 +175,7 @@ A good example is a web application that also uses an Azure Cache for Redis. You
 | webapp-1   | webapp-2   | webapp-3   |
 | cache-1    | cache-2    | cache-3    |
 
-This example is a more complex deployment than the use of node selectors or node affinity. The deployment gives you control over how Kubernetes schedules pods on nodes and can logically isolate resources. For a complete example of this web application with Azure Cache for Redis example, see [Colocate pods on the same node][k8s-pod-affinity].
+This example is a more complex deployment than the use of node selectors or node affinity. The deployment gives you control over how Kubernetes schedules pods on nodes and can logically isolate resources. For a complete example of this web application with Azure Cache for Redis example, see [Co-locate pods on the same node][k8s-pod-affinity].
 
 ## Next steps
 

articles/cognitive-services/Custom-Vision-Service/export-model-python.md

@@ -44,15 +44,15 @@ The downloaded zip file contains a model.pb and a labels.txt. These files repres
 import tensorflow as tf
 import os
 
-graph_def = tf.GraphDef()
+graph_def = tf.compat.v1.GraphDef()
 labels = []
 
 # These are set to the default names from exported models, update as needed.
 filename = "model.pb"
 labels_filename = "labels.txt"
 
 # Import the TF graph
-with tf.gfile.GFile(filename, 'rb') as f:
+with tf.io.gfile.GFile(filename, 'rb') as f:
     graph_def.ParseFromString(f.read())
     tf.import_graph_def(graph_def, name='')
 
@@ -112,7 +112,7 @@ augmented_image = resize_to_256_square(max_square_image)
 
 ```Python
 # Get the input size of the model
-with tf.Session() as sess:
+with tf.compat.v1.Session() as sess:
     input_tensor_shape = sess.graph.get_tensor_by_name('Placeholder:0').shape.as_list()
 network_input_size = input_tensor_shape[1]
 
@@ -176,7 +176,7 @@ Once the image is prepared as a tensor, we can send it through the model for a p
 output_layer = 'loss:0'
 input_node = 'Placeholder:0'
 
-with tf.Session() as sess:
+with tf.compat.v1.Session() as sess:
     try:
         prob_tensor = sess.graph.get_tensor_by_name(output_layer)
         predictions, = sess.run(prob_tensor, {input_node: [augmented_image] })