Add TensorFlow documentation to the main class, and include examples on it. (#867)

* wiring the example and the text to the CSharpApi, so they appear in the Microsoft.ML.Transforms namespace, and get picked up by the documentation

* Restructuring the doc so it includes the examples.
Removing the reference from the CsharpApi, since the transform is already created in the right namespace: Microsoft.ML.Transforms.

Changing the visibility of the Summary, UserName and ShortName to internal.

* Addign the documentation to the generated class, to be consistent, since it will appear in the Microsoft.ML.Transforms namespace.

* modifying the file post merge.

* including the example inside the <member> node.
adding the <include> to the class post-merge.

* after restructuring the xml doc, the example is part of it.

* Propagating the doc structure change.

Author: sfilipi

src/Microsoft.ML.TensorFlow/TensorflowTransform.cs

@@ -33,6 +33,7 @@
 
 namespace Microsoft.ML.Transforms
 {
+    /// <include file='doc.xml' path='doc/members/member[@name="TensorflowTransform"]/*' />
     public sealed class TensorFlowTransform : ITransformer, ICanSaveModel
     {
         public sealed class Arguments : TransformInputBase
@@ -62,10 +63,10 @@ public sealed class Arguments : TransformInputBase
         public readonly string[] Outputs;
 
         public static int BatchSize = 1;
-        public const string Summary = "Transforms the data using the TensorFlow model.";
-        public const string UserName = "TensorFlowTransform";
-        public const string ShortName = "TFTransform";
-        public const string LoaderSignature = "TensorFlowTransform";
+        internal const string Summary = "Transforms the data using the TensorFlow model.";
+        internal const string UserName = "TensorFlowTransform";
+        internal const string ShortName = "TFTransform";
+        internal const string LoaderSignature = "TensorFlowTransform";
 
         private static VersionInfo GetVersionInfo()
         {
@@ -548,7 +549,11 @@ public TFTensor GetTensor()
             }
         }
 
-        [TlcModule.EntryPoint(Name = "Transforms.TensorFlowScorer", Desc = Summary, UserName = UserName, ShortName = ShortName)]
+        [TlcModule.EntryPoint(Name = "Transforms.TensorFlowScorer",
+            Desc = Summary,
+            UserName = UserName,
+            ShortName = ShortName,
+            XmlInclude = new[] { @"<include file='../Microsoft.ML.TensorFlow/doc.xml' path='doc/members/member[@name=""TensorflowTransform""]/*' />" })]
         public static CommonOutputs.TransformOutput TensorFlowScorer(IHostEnvironment env, Arguments input)
         {
             Contracts.CheckValue(env, nameof(env));

src/Microsoft.ML.TensorFlow/doc.xml

@@ -4,7 +4,7 @@
 
     <member name="TensorflowTransform">
       <summary>
-        Extracts hidden layers' values from a pre-trained Tensorflow model.
+        Extracts hidden layers&apos; values from a pre-trained Tensorflow model.
       </summary>
       <remarks>
         <para>
@@ -44,51 +44,49 @@
         </a>.
 
       </remarks>
-    </member>
-    <example name="TensorflowTransform">
       <example>
         <code language="csharp">
           pipeline.Add(new TextLoader(dataFile).CreateFrom&lt;MNISTData&gt;(useHeader: false));
           pipeline.Add(new ColumnCopier(("NumericImageVec", "Input");
           pipeline.Add(new TensorFlowScorer()
           {
-          ModelFile = model_location;
-          InputColumns = new []{ &quot;Input&quot; };
-          OutputColumns = &quot;Output&quot;
+            ModelFile = model_location;
+            InputColumns = new []{ "Input" };
+            OutputColumns = "Output"
           }
         </code>
       </example>
       <example>
         <code language="csharp">
           var pipeline = new LearningPipeline(seed: 1);
           pipeline.Add(new TextLoader(dataFile).CreateFrom&lt;CifarData&gt;(useHeader: false));
-          pipeline.Add(new ImageLoader((&quot;ImagePath&quot;, &quot;ImageReal&quot;))
+          pipeline.Add(new ImageLoader(("ImagePath", "ImageReal"))
           {
-          ImageFolder = imageFolder
+            ImageFolder = imageFolder
           });
 
-          pipeline.Add(new ImageResizer((&quot;ImageReal&quot;, &quot;ImageCropped&quot;))
+          pipeline.Add(new ImageResizer(("ImageReal", "ImageCropped"))
           {
-          ImageHeight = imageHeight,
-          ImageWidth = imageWidth,
-          Resizing = ImageResizerTransformResizingKind.IsoCrop
+            ImageHeight = imageHeight,
+            ImageWidth = imageWidth,
+            Resizing = ImageResizerTransformResizingKind.IsoCrop
           });
 
-          pipeline.Add(new ImagePixelExtractor((&quot;ImageCropped&quot;, &quot;Input&quot;))
+          pipeline.Add(new ImagePixelExtractor(("ImageCropped", "Input"))
           {
-          UseAlpha = false,
-          InterleaveArgb = true
+            UseAlpha = false,
+            InterleaveArgb = true
           });
 
           pipeline.Add(new TensorFlowScorer()
           {
-          ModelFile = model_location,
-          InputColumns = new[] { &quot;Input&quot; },
-          OutputColumns = &quot;Output&quot;
+            ModelFile = model_location,
+            InputColumns = new[] { "Input" },
+            OutputColumns = "Output"
           });
         </code>
-      </example>
     </example>
+    </member>
 
   </members>
 </doc>

src/Microsoft.ML/CSharpApi.cs

@@ -15777,9 +15777,7 @@ public SentimentAnalyzerPipelineStep(Output output)
     namespace Transforms
     {
 
-        /// <summary>
-        /// Transforms the data using the TensorFlow model.
-        /// </summary>
+        /// <include file='../Microsoft.ML.TensorFlow/doc.xml' path='doc/members/member[@name="TensorflowTransform"]/*' />
         public sealed partial class TensorFlowScorer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem
         {