Add detector's module API argument descriptions

Add the argument descriptions of anomaly detector's module API for inference. The arguments have not been explicitly described in the github or MS doc. The added argument information is referred from the page https://westus2.dev.cognitive.microsoft.com/docs/services/AnomalyDetector/operations/post-timeseries-entire-detect, where response 400 provides the information of arguments.

Author: ChenCheng368

articles/cognitive-services/Anomaly-Detector/includes/quickstarts/anomaly-detector-client-library-python.md

@@ -113,6 +113,14 @@ Create a `DetectRequest` object with your time series, and the `TimeGranularity`
 request = DetectRequest(series=series, granularity=TimeGranularity.daily)
 ```
 
+Input argument descriptions:
+'series': required in request. must be array/list type, and have more than 12 points and no more than 8640 points. must sorted by timestamp in ascending order and cannot have duplicated timestamp. 
+'granularity': required in request. can only be one of the following: ['daily', 'minutely', 'hourly', 'weekly', 'monthly', 'yearly', 'secondly'].
+'customInterval': must be an integer > 0.
+'period': must be an integer >= 0.
+'maxAnomalyRatio': must be less than 50% of the series points (0 < maxAnomalyRatio < 0.5).
+'sensitivity': must be an integer between 0 and 99.
+
 ## Detect anomalies in the entire data set
 
 Call the API to detect anomalies through the entire time series data using the client's `detect_entire_series` method. Store the returned [EntireDetectResponse](/python/api/azure-cognitiveservices-anomalydetector/azure.cognitiveservices.anomalydetector.models.entiredetectresponse) object. Iterate through the response's `is_anomaly` list, and print the index of any `true` values. These values correspond to the index of anomalous data points, if any were found.