one more change

Author: rakshith91

sdk/formrecognizer/azure-ai-formrecognizer/README.md

@@ -263,6 +263,7 @@ If training files are within a subfolder in the container, use the [prefix][pref
 More details on setting up a container and required file structure can be found in the [service quickstart documentation][quickstart_training].
 
 To train a model, you will need Azure Blob storage container's shared access signature (SAS) URL. See [here][train-a-model-using-labeled-data] for more detailed descriptions on how to get it.
+Alternatively, a container uri can be used instead if the container is public.
 
 ```python
 from azure.ai.formrecognizer import FormTrainingClient

sdk/formrecognizer/azure-ai-formrecognizer/azure/ai/formrecognizer/_form_training_client.py

@@ -93,11 +93,13 @@ def __init__(self, endpoint, credential, **kwargs):
     def begin_training(self, training_files_url, use_training_labels, **kwargs):
         # type: (str, bool, Any) -> LROPoller[CustomFormModel]
         """Create and train a custom model. The request must include a `training_files_url` parameter that is an
-        externally accessible Azure storage blob container Uri (preferably a Shared Access Signature Uri).
+        externally accessible Azure storage blob container Uri (preferably a Shared Access Signature Uri). Note that
+        a container uri is accepted only when the container is public.
         Models are trained using documents that are of the following content type - 'application/pdf',
         'image/jpeg', 'image/png', 'image/tiff'. Other type of content in the container is ignored.
 
-        :param str training_files_url: An Azure Storage blob container's SAS URI.
+        :param str training_files_url: An Azure Storage blob container's SAS URI. A container uri can be used if the
+            container is public.
         :param bool use_training_labels: Whether to train with labels or not. Corresponding labeled files must
             exist in the blob container.
         :keyword str prefix: A case-sensitive prefix string to filter documents in the source path for

sdk/formrecognizer/azure-ai-formrecognizer/azure/ai/formrecognizer/aio/_form_training_client_async.py

@@ -102,11 +102,13 @@ async def begin_training(
             **kwargs: Any
     ) -> AsyncLROPoller[CustomFormModel]:
         """Create and train a custom model. The request must include a `training_files_url` parameter that is an
-        externally accessible Azure storage blob container Uri (preferably a Shared Access Signature Uri).
+        externally accessible Azure storage blob container Uri (preferably a Shared Access Signature Uri). Note that
+        a container uri is accepted only when the container is public.
         Models are trained using documents that are of the following content type - 'application/pdf',
         'image/jpeg', 'image/png', 'image/tiff'. Other type of content in the container is ignored.
 
-        :param str training_files_url: An Azure Storage blob container's SAS URI.
+        :param str training_files_url: An Azure Storage blob container's SAS URI. A container uri can be used if the
+            container is public.
         :param bool use_training_labels: Whether to train with labels or not. Corresponding labeled files must
             exist in the blob container.
         :keyword str prefix: A case-sensitive prefix string to filter documents in the source path for