CI Update

 Build.Reason:Schedule
 Build.Url:https://apidrop.visualstudio.com/Content%20CI/_build/results?buildId=336350&view=results

Author: VSC-Service-Account

docs-ref-autogen/azure-datalake-store/azure.datalake.store.lib.DatalakeRESTInterface.yml

@@ -125,18 +125,18 @@ attributes:
   name: session
 - uid: azure.datalake.store.lib.DatalakeRESTInterface.ends
   name: ends
-  signature: 'ends = {''APPEND'': (''post'', set(), {''filesessionid'', ''offset'',
-    ''leaseid'', ''append'', ''syncFlag''}), ''CHECKACCESS'': (''get'', set(), {''fsaction''}),
+  signature: 'ends = {''APPEND'': (''post'', set(), {''offset'', ''syncFlag'', ''filesessionid'',
+    ''append'', ''leaseid''}), ''CHECKACCESS'': (''get'', set(), {''fsaction''}),
     ''CONCAT'': (''post'', {''sources''}, {''sources''}), ''CREATE'': (''put'', set(),
-    {''write'', ''overwrite'', ''filesessionid'', ''leaseid'', ''syncFlag''}), ''DELETE'':
+    {''syncFlag'', ''filesessionid'', ''overwrite'', ''write'', ''leaseid''}), ''DELETE'':
     (''delete'', set(), {''recursive''}), ''GETCONTENTSUMMARY'': (''get'', set(),
     set()), ''GETFILESTATUS'': (''get'', set(), set()), ''LISTSTATUS'': (''get'',
     set(), {''listAfter'', ''listSize''}), ''MKDIRS'': (''put'', set(), set()), ''MODIFYACLENTRIES'':
     (''put'', {''aclSpec''}, {''aclSpec''}), ''MSCONCAT'': (''post'', set(), {''deleteSourceDirectory''}),
-    ''MSGETACLSTATUS'': (''get'', set(), set()), ''OPEN'': (''get'', set(), {''offset'',
-    ''read'', ''filesessionid'', ''length''}), ''REMOVEACL'': (''put'', set(), set()),
+    ''MSGETACLSTATUS'': (''get'', set(), set()), ''OPEN'': (''get'', set(), {''read'',
+    ''filesessionid'', ''length'', ''offset''}), ''REMOVEACL'': (''put'', set(), set()),
     ''REMOVEACLENTRIES'': (''put'', {''aclSpec''}, {''aclSpec''}), ''REMOVEDEFAULTACL'':
     (''put'', set(), set()), ''RENAME'': (''put'', {''destination''}, {''destination''}),
     ''SETACL'': (''put'', {''aclSpec''}, {''aclSpec''}), ''SETEXPIRY'': (''put'',
-    {''expiryOption''}, {''expiryOption'', ''expireTime''}), ''SETOWNER'': (''put'',
-    set(), {''group'', ''owner''}), ''SETPERMISSION'': (''put'', set(), {''permission''})}'
+    {''expiryOption''}, {''expireTime'', ''expiryOption''}), ''SETOWNER'': (''put'',
+    set(), {''owner'', ''group''}), ''SETPERMISSION'': (''put'', set(), {''permission''})}'

preview/docs-ref-autogen/azure-ai-anomalydetector/azure.ai.anomalydetector.AnomalyDetectorClient.yml

@@ -4,48 +4,104 @@ name: AnomalyDetectorClient
 fullName: azure.ai.anomalydetector.AnomalyDetectorClient
 module: azure.ai.anomalydetector
 inheritances:
-- azure.ai.anomalydetector.operations._anomaly_detector_client_operations.AnomalyDetectorClientOperationsMixin
-summary: The Anomaly Detector API detects anomalies automatically in time series data.
-  It supports two kinds of mode, one is for stateless using, another is for stateful
-  using. In stateless mode, there are three functionalities. Entire Detect is for
-  detecting the whole series with model trained by the time series, Last Detect is
-  detecting last point with model trained by points before. ChangePoint Detect is
-  for detecting trend changes in time series. In stateful mode, user can store time
-  series, the stored time series will be used for detection anomalies. Under this
-  mode, user can still use the above three functionalities by only giving a time range
-  without preparing time series in client side. Besides the above three functionalities,
-  stateful model also provide group based detection and labeling service. By leveraging
-  labeling service user can provide labels for each detection result, these labels
-  will be used for retuning or regenerating detection models. Inconsistency detection
-  is a kind of group based detection, this detection will find inconsistency ones
-  in a set of time series. By using anomaly detector service, business customers can
-  discover incidents and establish a logic flow for root cause analysis.
+- azure.ai.anomalydetector._operations._operations.AnomalyDetectorClientOperationsMixin
+summary: 'The Anomaly Detector API detects anomalies automatically in time series
+  data.
+
+  It supports two kinds of mode, one is for stateless using, another is for
+
+  stateful using. In stateless mode, there are three functionalities. Entire
+
+  Detect is for detecting the whole series with model trained by the time series,
+
+  Last Detect is detecting last point with model trained by points before.
+
+  ChangePoint Detect is for detecting trend changes in time series. In stateful
+
+  mode, user can store time series, the stored time series will be used for
+
+  detection anomalies. Under this mode, user can still use the above three
+
+  functionalities by only giving a time range without preparing time series in
+
+  client side. Besides the above three functionalities, stateful model also
+
+  provide group based detection and labeling service. By leveraging labeling
+
+  service user can provide labels for each detection result, these labels will be
+
+  used for retuning or regenerating detection models. Inconsistency detection is
+
+  a kind of group based detection, this detection will find inconsistency ones in
+
+  a set of time series. By using anomaly detector service, business customers can
+
+  discover incidents and establish a logic flow for root cause analysis.'
 constructor:
-  syntax: 'AnomalyDetectorClient(credential: AzureKeyCredential, endpoint: str, **kwargs:
-    Any)'
+  syntax: 'AnomalyDetectorClient(endpoint: str, credential: azure.core.credentials.AzureKeyCredential,
+    **kwargs: Any)'
   parameters:
-  - name: credential
-    description: Credential needed for the client to connect to Azure.
-    isRequired: true
-    types:
-    - <xref:azure.core.credentials.AzureKeyCredential>
   - name: endpoint
     description: 'Supported Cognitive Services endpoints (protocol and hostname, for
       example:
 
-      [https://westus2.api.cognitive.microsoft.com](https://westus2.api.cognitive.microsoft.com)).'
+      [https://westus2.api.cognitive.microsoft.com](https://westus2.api.cognitive.microsoft.com)).
+      Required.'
     isRequired: true
     types:
     - <xref:str>
+  - name: credential
+    description: Credential needed for the client to connect to Azure. Required.
+    isRequired: true
+    types:
+    - <xref:azure.core.credentials.AzureKeyCredential>
   - name: api_version
-    description: 'Anomaly Detector API version (for example, v1.0). The default value
-      is
+    description: 'Api Version. Default value is "v1.1". Note that overriding this
+      default
 
-      "v1.1-preview.1". Note that overriding this default value may result in unsupported
-      behavior.'
+      value may result in unsupported behavior.'
     types:
     - <xref:str>
 methods:
 - uid: azure.ai.anomalydetector.AnomalyDetectorClient.close
   name: close
   signature: close() -> None
+- uid: azure.ai.anomalydetector.AnomalyDetectorClient.send_request
+  name: send_request
+  summary: 'Runs the network request through the client''s chained policies.
+
+
+    ```
+
+
+    >>> from azure.core.rest import HttpRequest
+
+    >>> request = HttpRequest("GET", "https://www.example.org/")
+
+    <HttpRequest [GET], url: ''https://www.example.org/''>
+
+    >>> response = client.send_request(request)
+
+    <HttpResponse: 200 OK>
+
+    ```
+
+
+    For more information on this code flow, see [https://aka.ms/azsdk/dpcodegen/python/send_request](https://aka.ms/azsdk/dpcodegen/python/send_request)'
+  signature: 'send_request(request: azure.core.rest._rest_py3.HttpRequest, **kwargs:
+    Any) -> azure.core.rest._rest_py3.HttpResponse'
+  parameters:
+  - name: request
+    description: The network request you want to make. Required.
+    isRequired: true
+    types:
+    - <xref:azure.core.rest.HttpRequest>
+  - name: stream
+    description: Whether the response payload will be streamed. Defaults to False.
+    types:
+    - <xref:bool>
+  return:
+    description: The response of your network call. Does not do error handling on
+      your response.
+    types:
+    - <xref:azure.core.rest.HttpResponse>

preview/docs-ref-autogen/azure-ai-anomalydetector/azure.ai.anomalydetector.aio.AnomalyDetectorClient.yml

@@ -4,48 +4,104 @@ name: AnomalyDetectorClient
 fullName: azure.ai.anomalydetector.aio.AnomalyDetectorClient
 module: azure.ai.anomalydetector.aio
 inheritances:
-- azure.ai.anomalydetector.aio.operations._anomaly_detector_client_operations.AnomalyDetectorClientOperationsMixin
-summary: The Anomaly Detector API detects anomalies automatically in time series data.
-  It supports two kinds of mode, one is for stateless using, another is for stateful
-  using. In stateless mode, there are three functionalities. Entire Detect is for
-  detecting the whole series with model trained by the time series, Last Detect is
-  detecting last point with model trained by points before. ChangePoint Detect is
-  for detecting trend changes in time series. In stateful mode, user can store time
-  series, the stored time series will be used for detection anomalies. Under this
-  mode, user can still use the above three functionalities by only giving a time range
-  without preparing time series in client side. Besides the above three functionalities,
-  stateful model also provide group based detection and labeling service. By leveraging
-  labeling service user can provide labels for each detection result, these labels
-  will be used for retuning or regenerating detection models. Inconsistency detection
-  is a kind of group based detection, this detection will find inconsistency ones
-  in a set of time series. By using anomaly detector service, business customers can
-  discover incidents and establish a logic flow for root cause analysis.
+- azure.ai.anomalydetector.aio._operations._operations.AnomalyDetectorClientOperationsMixin
+summary: 'The Anomaly Detector API detects anomalies automatically in time series
+  data.
+
+  It supports two kinds of mode, one is for stateless using, another is for
+
+  stateful using. In stateless mode, there are three functionalities. Entire
+
+  Detect is for detecting the whole series with model trained by the time series,
+
+  Last Detect is detecting last point with model trained by points before.
+
+  ChangePoint Detect is for detecting trend changes in time series. In stateful
+
+  mode, user can store time series, the stored time series will be used for
+
+  detection anomalies. Under this mode, user can still use the above three
+
+  functionalities by only giving a time range without preparing time series in
+
+  client side. Besides the above three functionalities, stateful model also
+
+  provide group based detection and labeling service. By leveraging labeling
+
+  service user can provide labels for each detection result, these labels will be
+
+  used for retuning or regenerating detection models. Inconsistency detection is
+
+  a kind of group based detection, this detection will find inconsistency ones in
+
+  a set of time series. By using anomaly detector service, business customers can
+
+  discover incidents and establish a logic flow for root cause analysis.'
 constructor:
-  syntax: 'AnomalyDetectorClient(credential: azure.core.credentials.AzureKeyCredential,
-    endpoint: str, **kwargs: Any)'
+  syntax: 'AnomalyDetectorClient(endpoint: str, credential: azure.core.credentials.AzureKeyCredential,
+    **kwargs: Any)'
   parameters:
-  - name: credential
-    description: Credential needed for the client to connect to Azure.
-    isRequired: true
-    types:
-    - <xref:azure.core.credentials.AzureKeyCredential>
   - name: endpoint
     description: 'Supported Cognitive Services endpoints (protocol and hostname, for
       example:
 
-      [https://westus2.api.cognitive.microsoft.com](https://westus2.api.cognitive.microsoft.com)).'
+      [https://westus2.api.cognitive.microsoft.com](https://westus2.api.cognitive.microsoft.com)).
+      Required.'
     isRequired: true
     types:
     - <xref:str>
+  - name: credential
+    description: Credential needed for the client to connect to Azure. Required.
+    isRequired: true
+    types:
+    - <xref:azure.core.credentials.AzureKeyCredential>
   - name: api_version
-    description: 'Anomaly Detector API version (for example, v1.0). The default value
-      is
+    description: 'Api Version. Default value is "v1.1". Note that overriding this
+      default
 
-      "v1.1-preview.1". Note that overriding this default value may result in unsupported
-      behavior.'
+      value may result in unsupported behavior.'
     types:
     - <xref:str>
 methods:
 - uid: azure.ai.anomalydetector.aio.AnomalyDetectorClient.close
   name: close
   signature: async close() -> None
+- uid: azure.ai.anomalydetector.aio.AnomalyDetectorClient.send_request
+  name: send_request
+  summary: 'Runs the network request through the client''s chained policies.
+
+
+    ```
+
+
+    >>> from azure.core.rest import HttpRequest
+
+    >>> request = HttpRequest("GET", "https://www.example.org/")
+
+    <HttpRequest [GET], url: ''https://www.example.org/''>
+
+    >>> response = await client.send_request(request)
+
+    <AsyncHttpResponse: 200 OK>
+
+    ```
+
+
+    For more information on this code flow, see [https://aka.ms/azsdk/dpcodegen/python/send_request](https://aka.ms/azsdk/dpcodegen/python/send_request)'
+  signature: 'send_request(request: azure.core.rest._rest_py3.HttpRequest, **kwargs:
+    Any) -> Awaitable[azure.core.rest._rest_py3.AsyncHttpResponse]'
+  parameters:
+  - name: request
+    description: The network request you want to make. Required.
+    isRequired: true
+    types:
+    - <xref:azure.core.rest.HttpRequest>
+  - name: stream
+    description: Whether the response payload will be streamed. Defaults to False.
+    types:
+    - <xref:bool>
+  return:
+    description: The response of your network call. Does not do error handling on
+      your response.
+    types:
+    - <xref:azure.core.rest.AsyncHttpResponse>