diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/ConvertType.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/ConvertType.cs
index 8349922768..610a77983a 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/ConvertType.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/ConvertType.cs
@@ -20,17 +20,22 @@ public static void Example()
             var data = mlContext.Data.LoadFromEnumerable(rawData);
 
             // Construct the pipeline.
-            var pipeline = mlContext.Transforms.Conversion.ConvertType("SurvivedInt32", "Survived", DataKind.Int32);
+            var pipeline = mlContext.Transforms.Conversion.ConvertType(
+                "SurvivedInt32", "Survived", DataKind.Int32);
 
             // Let's train our pipeline, and then apply it to the same data.
             var transformer = pipeline.Fit(data);
             var transformedData = transformer.Transform(data);
 
-            // Display original column 'Survived' (boolean) and converted column 'SurvivedInt32' (Int32)
-            var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, true);
+            // Display original column 'Survived' (boolean) and converted column 
+            // SurvivedInt32' (Int32)
+            var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(
+                transformedData, true);
+
             foreach (var item in convertedData)
             {
-                Console.WriteLine("A:{0,-10}  Aconv:{1}", item.Survived, item.SurvivedInt32);
+                Console.WriteLine("A:{0,-10}  Aconv:{1}", item.Survived,
+                    item.SurvivedInt32);
             }
 
             // Output
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/ConvertTypeMultiColumn.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/ConvertTypeMultiColumn.cs
index b7790d9f11..bae0bfea02 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/ConvertTypeMultiColumn.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/ConvertTypeMultiColumn.cs
@@ -4,22 +4,33 @@
 
 namespace Samples.Dynamic
 {
-    // This example illustrates how to convert multiple columns of different types to one type, in this case System.Single. 
-    // This is often a useful data transformation before concatenating the features together and passing them to a particular estimator.
+    // This example illustrates how to convert multiple columns of different types
+    // to one type, in this case System.Single. 
+    // This is often a useful data transformation before concatenating the features
+    // together and passing them to a particular estimator.
     public static class ConvertTypeMultiColumn
     { 
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext(seed: 1);
 
             var rawData = new[] {
-                new InputData() { Feature1 = true, Feature2 = "0.4", Feature3 = DateTime.Now, Feature4 = 0.145},
-                new InputData() { Feature1 = false, Feature2 = "0.5", Feature3 = DateTime.Today, Feature4 = 3.14},
-                new InputData() { Feature1 = false, Feature2 = "14", Feature3 = DateTime.Today, Feature4 = 0.2046},
-                new InputData() { Feature1 = false, Feature2 = "23", Feature3 = DateTime.Now, Feature4 = 0.1206},
-                new InputData() { Feature1 = true, Feature2 = "8904", Feature3 = DateTime.UtcNow, Feature4 = 8.09},
+                new InputData() { Feature1 = true, Feature2 = "0.4",
+                    Feature3 = DateTime.Now, Feature4 = 0.145},
+
+                new InputData() { Feature1 = false, Feature2 = "0.5",
+                    Feature3 = DateTime.Today, Feature4 = 3.14},
+
+                new InputData() { Feature1 = false, Feature2 = "14",
+                    Feature3 = DateTime.Today, Feature4 = 0.2046},
+
+                new InputData() { Feature1 = false, Feature2 = "23",
+                    Feature3 = DateTime.Now, Feature4 = 0.1206},
+
+                new InputData() { Feature1 = true, Feature2 = "8904",
+                    Feature3 = DateTime.UtcNow, Feature4 = 8.09},
             };
 
             // Convert the data to an IDataView.
@@ -37,17 +48,20 @@ public static void Example()
 
             // Let's fit our pipeline to the data.
             var transformer = pipeline.Fit(data);
-            // Transforming the same data. This will add the 4 columns defined in the pipeline, containing the converted
+            // Transforming the same data. This will add the 4 columns defined in
+            // the pipeline, containing the converted
             // values of the initial columns. 
             var transformedData = transformer.Transform(data);
 
             // Shape the transformed data as a strongly typed IEnumerable.
-            var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, true);
+            var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(
+                transformedData, true);
 
             // Printing the results.
             Console.WriteLine("Converted1\t Converted2\t Converted3\t Converted4");
             foreach (var item in convertedData)
-                Console.WriteLine($"\t{item.Converted1}\t {item.Converted2}\t\t  {item.Converted3}\t {item.Converted4}");
+                Console.WriteLine($"\t{item.Converted1}\t {item.Converted2}\t\t  " +
+                    $"{item.Converted3}\t {item.Converted4}");
 
             // Transformed data.
             //
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/Hash.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/Hash.cs
index daee047cff..7617840f6d 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/Hash.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/Hash.cs
@@ -9,8 +9,8 @@ public static class Hash
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext(seed: 1);
 
             // Get a small dataset as an IEnumerable.
@@ -24,30 +24,40 @@ public static void Example()
 
             var data = mlContext.Data.LoadFromEnumerable(rawData);
 
-            // Construct the pipeline that would hash the two columns and store the results in new columns.
-            // The first transform hashes the string column and the second transform hashes the integer column.
+            // Construct the pipeline that would hash the two columns and store the
+            // results in new columns. The first transform hashes the string column
+            // and the second transform hashes the integer column.
             //
-            // Hashing is not a reversible operation, so there is no way to retrive the original value from the hashed value. 
-            // Sometimes, for debugging, or model explainability, users will need to know what values in the original columns generated
-            // the values in the hashed columns, since the algorithms will mostly use the hashed values for further computations.
-            // The Hash method will preserve the mapping from the original values to the hashed values in the Annotations of the 
-            // newly created column (column populated with the hashed values). 
-            // 
-            // Setting the maximumNumberOfInverts parameters to -1 will preserve the full map. 
-            // If that parameter is left to the default 0 value, the mapping is not preserved.
-            var pipeline = mlContext.Transforms.Conversion.Hash("CategoryHashed", "Category", numberOfBits: 16, maximumNumberOfInverts: -1)
-                          .Append(mlContext.Transforms.Conversion.Hash("AgeHashed", "Age", numberOfBits: 8));
+            // Hashing is not a reversible operation, so there is no way to retrive
+            // the original value from the hashed value. Sometimes, for debugging,
+            // or model explainability, users will need to know what values in the
+            // original columns generated the values in the hashed columns, since
+            // the algorithms will mostly use the hashed values for further
+            // computations. The Hash method will preserve the mapping from the
+            // original values to the hashed values in the Annotations of the newly
+            // created column (column populated with the hashed values). 
+            //
+            // Setting the maximumNumberOfInverts parameters to -1 will preserve the
+            // full map. If that parameter is left to the default 0 value, the
+            // mapping is not preserved.
+            var pipeline = mlContext.Transforms.Conversion.Hash("CategoryHashed",
+                "Category", numberOfBits: 16, maximumNumberOfInverts: -1)
+                .Append(mlContext.Transforms.Conversion.Hash("AgeHashed", "Age",
+                numberOfBits: 8));
 
             // Let's fit our pipeline, and then apply it to the same data.
             var transformer = pipeline.Fit(data);
             var transformedData = transformer.Transform(data);
 
-            // Convert the post transformation from the IDataView format to an IEnumerable<TransformedData> for easy consumption.
-            var convertedData = mlContext.Data.CreateEnumerable<TransformedDataPoint>(transformedData, true);
+            // Convert the post transformation from the IDataView format to an
+            // IEnumerable <TransformedData> for easy consumption.
+            var convertedData = mlContext.Data.CreateEnumerable<
+                TransformedDataPoint>(transformedData, true);
 
             Console.WriteLine("Category CategoryHashed\t Age\t AgeHashed");
             foreach (var item in convertedData)
-                Console.WriteLine($"{item.Category}\t {item.CategoryHashed}\t\t  {item.Age}\t {item.AgeHashed}");
+                Console.WriteLine($"{item.Category}\t {item.CategoryHashed}\t\t  " +
+                    $"{item.Age}\t {item.AgeHashed}");
 
             // Expected data after the transformation.
             //
@@ -58,20 +68,24 @@ public static void Example()
             // MLB      36206            18      127
             // MLS      6013             14      62
 
-            // For the Category column, where we set the maximumNumberOfInverts parameter, the names of the original categories, 
-            // and their correspondance with the generated hash values is preserved in the Annotations in the format of indices and values. 
-            // the indices array will have the hashed values, and the corresponding element, position-wise, in the values array will 
-            // contain the original value. 
+            // For the Category column, where we set the maximumNumberOfInverts
+            // parameter, the names of the original categories, and their
+            // correspondance with the generated hash values is preserved in the
+            // Annotations in the format of indices and values.the indices array
+            // will have the hashed values, and the corresponding element,
+            // position -wise, in the values array will contain the original value. 
             //
             // See below for an example on how to retrieve the mapping. 
             var slotNames = new VBuffer<ReadOnlyMemory<char>>();
-            transformedData.Schema["CategoryHashed"].Annotations.GetValue("KeyValues", ref slotNames);
+            transformedData.Schema["CategoryHashed"].Annotations.GetValue(
+                "KeyValues", ref slotNames);
 
             var indices = slotNames.GetIndices();
             var categoryNames = slotNames.GetValues();
 
             for (int i = 0; i < indices.Length; i++)
-                Console.WriteLine($"The original value of the {indices[i]} category is {categoryNames[i]}");
+                Console.WriteLine($"The original value of the {indices[i]} " +
+                    $"category is {categoryNames[i]}");
 
             // Output Data
             // 
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/KeyToValueToKey.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/KeyToValueToKey.cs
index 4d967647d0..82b2dea250 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/KeyToValueToKey.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/KeyToValueToKey.cs
@@ -11,8 +11,8 @@ public class KeyToValueToKey
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable.
@@ -27,25 +27,40 @@ public static void Example()
 
             // A pipeline to convert the terms of the 'Review' column in 
             // making use of default settings.
-            var defaultPipeline = mlContext.Transforms.Text.TokenizeIntoWords("TokenizedText", nameof(DataPoint.Review))
-                .Append(mlContext.Transforms.Conversion.MapValueToKey(nameof(TransformedData.Keys), "TokenizedText"));
+            var defaultPipeline = mlContext.Transforms.Text.TokenizeIntoWords(
+                "TokenizedText", nameof(DataPoint.Review)).Append(mlContext
+                .Transforms.Conversion.MapValueToKey(nameof(TransformedData.Keys),
+                "TokenizedText"));
 
-            // Another pipeline, that customizes the advanced settings of the ValueToKeyMappingEstimator.
-            // We can change the maximumNumberOfKeys to limit how many keys will get generated out of the set of words, 
-            // and condition the order in which they get evaluated by changing keyOrdinality from the default ByOccurence (order in which they get encountered) 
-            // to value/alphabetically.
-            var customizedPipeline = mlContext.Transforms.Text.TokenizeIntoWords("TokenizedText", nameof(DataPoint.Review))
-                .Append(mlContext.Transforms.Conversion.MapValueToKey(nameof(TransformedData.Keys), "TokenizedText", maximumNumberOfKeys: 10,
-                keyOrdinality: ValueToKeyMappingEstimator.KeyOrdinality.ByValue));
+            // Another pipeline, that customizes the advanced settings of the
+            // ValueToKeyMappingEstimator. We can change the maximumNumberOfKeys to
+            // limit how many keys will get generated out of the set of words, and
+            // condition the order in which they get evaluated by changing
+            // keyOrdinality from the default ByOccurence (order in which they get
+            // encountered) to value/alphabetically.
+            var customizedPipeline = mlContext.Transforms.Text.TokenizeIntoWords(
+                "TokenizedText", nameof(DataPoint.Review)).Append(mlContext
+                .Transforms.Conversion.MapValueToKey(nameof(TransformedData.Keys),
+                "TokenizedText", maximumNumberOfKeys: 10, keyOrdinality:
+                ValueToKeyMappingEstimator.KeyOrdinality.ByValue));
 
             // The transformed data.
-            var transformedDataDefault = defaultPipeline.Fit(trainData).Transform(trainData);
-            var transformedDataCustomized = customizedPipeline.Fit(trainData).Transform(trainData);
+            var transformedDataDefault = defaultPipeline.Fit(trainData).Transform(
+                trainData);
+
+            var transformedDataCustomized = customizedPipeline.Fit(trainData)
+                .Transform(trainData);
 
             // Getting the resulting data as an IEnumerable.
             // This will contain the newly created columns.
-            IEnumerable<TransformedData> defaultData = mlContext.Data.CreateEnumerable<TransformedData>(transformedDataDefault, reuseRowObject: false);
-            IEnumerable<TransformedData> customizedData = mlContext.Data.CreateEnumerable<TransformedData>(transformedDataCustomized, reuseRowObject: false);
+            IEnumerable<TransformedData> defaultData = mlContext.Data.
+                CreateEnumerable<TransformedData>(transformedDataDefault,
+                reuseRowObject: false);
+
+            IEnumerable<TransformedData> customizedData = mlContext.Data.
+                CreateEnumerable<TransformedData>(transformedDataCustomized,
+                reuseRowObject: false);
+
             Console.WriteLine($"Keys");
             foreach (var dataRow in defaultData)
                 Console.WriteLine($"{string.Join(',', dataRow.Keys)}");
@@ -65,13 +80,17 @@ public static void Example()
             //  8,2,9,7,6,4
             //  3,10,0,0,0
             //  3,10,0,0,0,8
-            // Retrieve the original values, by appending the KeyToValue etimator to the existing pipelines
-            // to convert the keys back to the strings.
-            var pipeline = defaultPipeline.Append(mlContext.Transforms.Conversion.MapKeyToValue(nameof(TransformedData.Keys)));
+            // Retrieve the original values, by appending the KeyToValue etimator to
+            // the existing pipelines to convert the keys back to the strings.
+            var pipeline = defaultPipeline.Append(mlContext.Transforms.Conversion
+                .MapKeyToValue(nameof(TransformedData.Keys)));
+
             transformedDataDefault = pipeline.Fit(trainData).Transform(trainData);
 
             // Preview of the DefaultColumnName column obtained.
-            var originalColumnBack = transformedDataDefault.GetColumn<VBuffer<ReadOnlyMemory<char>>>(transformedDataDefault.Schema[nameof(TransformedData.Keys)]);
+            var originalColumnBack = transformedDataDefault.GetColumn<VBuffer<
+                ReadOnlyMemory<char>>>(transformedDataDefault.Schema[nameof(
+                TransformedData.Keys)]);
 
             foreach (var row in originalColumnBack)
             {
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapKeyToBinaryVector.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapKeyToBinaryVector.cs
index e3b652f0af..57ae091124 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapKeyToBinaryVector.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapKeyToBinaryVector.cs
@@ -7,14 +7,16 @@ namespace Samples.Dynamic
 {
     class MapKeyToBinaryVector
     {
-        /// This example demonstrates the use of MapKeyToVector by mapping keys to floats[] of 0 and 1, representing the number in binary format. 
-        /// Because the ML.NET KeyType maps the missing value to zero, counting starts at 1, so the uint values
-        /// converted to KeyTypes will appear skewed by one. 
+        /// This example demonstrates the use of MapKeyToVector by mapping keys to
+        /// floats[] of 0 and 1, representing the number in binary format. 
+        /// Because the ML.NET KeyType maps the missing value to zero, counting
+        /// starts at 1, so the uint values converted to KeyTypes will appear
+        /// skewed by one. 
         /// See https://github.com/dotnet/machinelearning/blob/master/docs/code/IDataViewTypeSystem.md#key-types
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable.
@@ -30,18 +32,21 @@ public static void Example()
             var data = mlContext.Data.LoadFromEnumerable(rawData);
 
             // Constructs the ML.net pipeline
-            var pipeline = mlContext.Transforms.Conversion.MapKeyToBinaryVector("TimeframeVector", "Timeframe");
+            var pipeline = mlContext.Transforms.Conversion.MapKeyToBinaryVector(
+                "TimeframeVector", "Timeframe");
 
             // Fits the pipeline to the data.
             IDataView transformedData = pipeline.Fit(data).Transform(data);
 
             // Getting the resulting data as an IEnumerable.
             // This will contain the newly created columns.
-            IEnumerable<TransformedData> features = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);
+            IEnumerable<TransformedData> features = mlContext.Data.CreateEnumerable<
+                TransformedData>(transformedData, reuseRowObject: false);
 
             Console.WriteLine($" Timeframe           TimeframeVector");
             foreach (var featureRow in features)
-                Console.WriteLine($"{featureRow.Timeframe}\t\t\t{string.Join(',', featureRow.TimeframeVector)}");
+                Console.WriteLine($"{featureRow.Timeframe}\t\t\t" +
+                    $"{string.Join(',', featureRow.TimeframeVector)}");
 
             // Timeframe             TimeframeVector
             // 10                      0,1,0,0,1 //binary representation of 9, the original value
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapKeyToValueMultiColumn.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapKeyToValueMultiColumn.cs
index 6c801c14e5..5784faaa99 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapKeyToValueMultiColumn.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapKeyToValueMultiColumn.cs
@@ -5,22 +5,25 @@
 
 namespace Samples.Dynamic
 {
-    /// This example demonstrates the use of the ValueToKeyMappingEstimator, by mapping KeyType values to the original strings. 
-    /// For more on ML.NET KeyTypes see: https://github.com/dotnet/machinelearning/blob/master/docs/code/IDataViewTypeSystem.md#key-types 
+    /// This example demonstrates the use of the ValueToKeyMappingEstimator, by
+    /// mapping KeyType values to the original strings. For more on ML.NET KeyTypes
+    /// see: https://github.com/dotnet/machinelearning/blob/master/docs/code/IDataViewTypeSystem.md#key-types 
     public class MapKeyToValueMultiColumn
     {
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
-            // as a catalog of available operations and as the source of randomness.
-            // Setting the seed to a fixed number in this example to make outputs deterministic.
+            // Create a new context for ML.NET operations. It can be used for
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
             // Get a small dataset as an IEnumerable.
 
             // Create a list of data examples.
             var examples = GenerateRandomDataPoints(1000, 10);
 
-            // Convert the examples list to an IDataView object, which is consumable by ML.NET API.
+            // Convert the examples list to an IDataView object, which is consumable
+            // by ML.NET API.
             var dataView = mlContext.Data.LoadFromEnumerable(examples);
 
             // Create a pipeline. 
@@ -28,31 +31,41 @@ public static void Example()
                     // Convert the string labels into key types.
                     mlContext.Transforms.Conversion.MapValueToKey("Label")
                     // Apply StochasticDualCoordinateAscent multiclass trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy());
+                    .Append(mlContext.MulticlassClassification.Trainers.
+                    SdcaMaximumEntropy());
 
             // Train the model and do predictions on same data set. 
             // Typically predictions would be in a different, validation set. 
             var dataWithPredictions = pipeline.Fit(dataView).Transform(dataView);
 
-            // At this point, the Label colum is tranformed from strings, to DataViewKeyType and
-            // the transformation has added the PredictedLabel column, with same DataViewKeyType as
-            // transformed Label column.
-            // MapKeyToValue would take columns with DataViewKeyType and convert them back to thier original values.
+            // At this point, the Label colum is tranformed from strings, to
+            // DataViewKeyType and the transformation has added the PredictedLabel
+            // column, with same DataViewKeyType as transformed Label column.
+            // MapKeyToValue would take columns with DataViewKeyType and convert
+            // them back to thier original values.
             var newPipeline = mlContext.Transforms.Conversion.MapKeyToValue(new[]
             {
                 new InputOutputColumnPair("LabelOriginalValue","Label"),
-                new InputOutputColumnPair("PredictedLabelOriginalValue","PredictedLabel")
+                new InputOutputColumnPair("PredictedLabelOriginalValue",
+                "PredictedLabel")
+
             });
 
-            var transformedData = newPipeline.Fit(dataWithPredictions).Transform(dataWithPredictions);
+            var transformedData = newPipeline.Fit(dataWithPredictions).Transform(
+                dataWithPredictions);
+
             // Let's iterate over first 5 items.
             transformedData = mlContext.Data.TakeRows(transformedData, 5);
-            var values = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);
+            var values = mlContext.Data.CreateEnumerable<TransformedData>(
+                transformedData, reuseRowObject: false);
 
             // Printing the column names of the transformed data.
-            Console.WriteLine($"Label   LabelOriginalValue   PredictedLabel   PredictedLabelOriginalValue");
+            Console.WriteLine($"Label   LabelOriginalValue   PredictedLabel   " +
+                $"PredictedLabelOriginalValue");
+
             foreach (var row in values)
-                Console.WriteLine($"{row.Label}\t\t{row.LabelOriginalValue}\t\t\t{row.PredictedLabel}\t\t\t{row.PredictedLabelOriginalValue}");
+                Console.WriteLine($"{row.Label}\t\t{row.LabelOriginalValue}\t\t\t" +
+                    $"{row.PredictedLabel}\t\t\t{row.PredictedLabelOriginalValue}");
 
             // Expected output:
             //  Label   LabelOriginalValue   PredictedLabel   PredictedLabelOriginalValue
@@ -71,7 +84,8 @@ private class DataPoint
             public float[] Features { get; set; }
         }
 
-        private static List<DataPoint> GenerateRandomDataPoints(int count, int featureVectorLenght)
+        private static List<DataPoint> GenerateRandomDataPoints(int count,
+            int featureVectorLenght)
         {
             var examples = new List<DataPoint>();
             var rnd = new Random(0);
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapKeyToVector.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapKeyToVector.cs
index 12f5adc7fb..221ff8d2d4 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapKeyToVector.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapKeyToVector.cs
@@ -8,14 +8,14 @@ namespace Samples.Dynamic
 {
     class MapKeyToVector
     {
-        /// This example demonstrates the use of MapKeyToVector by mapping keys to floats[]. 
-        /// Because the ML.NET KeyType maps the missing value to zero, counting starts at 1, so the uint values
-        /// converted to KeyTypes will appear skewed by one. 
-        /// See https://github.com/dotnet/machinelearning/blob/master/docs/code/IDataViewTypeSystem.md#key-types
+        /// This example demonstrates the use of MapKeyToVector by mapping keys to
+        /// floats[]. Because the ML.NET KeyType maps the missing value to zero,
+        /// counting starts at 1, so the uint values converted to KeyTypes will
+        /// appear skewed by one. See https://github.com/dotnet/machinelearning/blob/master/docs/code/IDataViewTypeSystem.md#key-types
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable.
@@ -28,31 +28,43 @@ public static void Example()
 
             var data = mlContext.Data.LoadFromEnumerable(rawData);
 
-            // First transform just maps key type to indicator vector. i.e. it's produces vector filled with
-            // zeros with size of key cardinality and set 1 to corresponding key's value index in that array.
-            // After that we concatenate two columns with single int values into vector of ints.
-            // Third transform will create vector of keys, where key type is shared across whole vector.
-            // Forth transfrom output data as count vector and that vector would have size equal to shared key type
-            // cardinality and put key counts to corresponding indexes in array.
-            // Fifth transform output indicator vector for each key and concatenate them together.
-            // Result vector would be size of key cardinality multiplied by size of original vector.
-            var pipeline = mlContext.Transforms.Conversion.MapKeyToVector("TimeframeVector", "Timeframe")
-                           .Append(mlContext.Transforms.Concatenate("Parts", "PartA", "PartB"))
-                           .Append(mlContext.Transforms.Conversion.MapValueToKey("Parts"))
-                           .Append(mlContext.Transforms.Conversion.MapKeyToVector("PartsCount", "Parts", outputCountVector: true))
-                           .Append(mlContext.Transforms.Conversion.MapKeyToVector("PartsNoCount", "Parts"));
+            // First transform just maps key type to indicator vector. i.e. it's
+            // produces vector filled with zeros with size of key cardinality and
+            // set 1 to corresponding key's value index in that array. After that we
+            // concatenate two columns with single int values into vector of ints.
+            // Third transform will create vector of keys, where key type is shared
+            // across whole vector. Forth transfrom output data as count vector and
+            // that vector would have size equal to shared key type cardinality and
+            // put key counts to corresponding indexes in array. Fifth transform
+            // output indicator vector for each key and concatenate them together.
+            // Result vector would be size of key cardinality multiplied by size of
+            // original vector.
+            var pipeline = mlContext.Transforms.Conversion.MapKeyToVector(
+                "TimeframeVector", "Timeframe")
+                .Append(mlContext.Transforms.Concatenate("Parts", "PartA", "PartB"))
+                .Append(mlContext.Transforms.Conversion.MapValueToKey("Parts"))
+                .Append(mlContext.Transforms.Conversion.MapKeyToVector(
+                    "PartsCount", "Parts", outputCountVector:true))
+                .Append(mlContext.Transforms.Conversion.MapKeyToVector(
+                    "PartsNoCount", "Parts"));
 
             // Fits the pipeline to the data.
             IDataView transformedData = pipeline.Fit(data).Transform(data);
             
             // Getting the resulting data as an IEnumerable.
             // This will contain the newly created columns.
-            IEnumerable<TransformedData> features = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);
+            IEnumerable<TransformedData> features = mlContext.Data.CreateEnumerable<
+                TransformedData>(transformedData, reuseRowObject: false);
+
+            Console.WriteLine("Timeframe  TimeframeVector    PartsCount  " +
+                "PartsNoCount");
 
-            Console.WriteLine($"Timeframe  TimeframeVector    PartsCount  PartsNoCount");
             foreach (var featureRow in features)
-                Console.WriteLine($"{featureRow.Timeframe}          {string.Join(',', featureRow.TimeframeVector.Select(x=>x))}  " +
-                    $"{string.Join(',', featureRow.PartsCount.Select(x => x))}       {string.Join(',', featureRow.PartsNoCount.Select(x => x))}");
+                Console.WriteLine(featureRow.Timeframe + "          " +
+                    string.Join(',', featureRow.TimeframeVector.Select(x=>x)) + "  "
+                    + string.Join(',', featureRow.PartsCount.Select(x => x)) + 
+                    "       " + string.Join(',', featureRow.PartsNoCount.Select(
+                    x => x)));
 
             // Expected output:
             //  Timeframe  TimeframeVector    PartsCount  PartsNoCount
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapKeyToVectorMultiColumn.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapKeyToVectorMultiColumn.cs
index 7407ee67cf..606c371497 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapKeyToVectorMultiColumn.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapKeyToVectorMultiColumn.cs
@@ -7,14 +7,15 @@ namespace Samples.Dynamic
 {
     public class MapKeyToVectorMultiColumn
     {
-        /// This example demonstrates the use of MapKeyToVector by mapping keys to floats[] for multiple columns at once. 
-        /// Because the ML.NET KeyType maps the missing value to zero, counting starts at 1, so the uint values
+        /// This example demonstrates the use of MapKeyToVector by mapping keys to
+        /// floats[] for multiple columns at once. Because the ML.NET KeyType maps
+        /// the missing value to zero, counting starts at 1, so the uint values
         /// converted to KeyTypes will appear skewed by one. 
         /// See https://github.com/dotnet/machinelearning/blob/master/docs/code/IDataViewTypeSystem.md#key-types
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable.
@@ -40,11 +41,17 @@ public static void Example()
 
             // Getting the resulting data as an IEnumerable.
             // This will contain the newly created columns.
-            IEnumerable<TransformedData> features = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);
+            IEnumerable<TransformedData> features = mlContext.Data.CreateEnumerable<
+                TransformedData>(transformedData, reuseRowObject: false);
+
+            Console.WriteLine($" Timeframe           TimeframeVector         " +
+                $"Category    CategoryVector");
 
-            Console.WriteLine($" Timeframe           TimeframeVector         Category    CategoryVector");
             foreach (var featureRow in features)
-                Console.WriteLine($"{featureRow.Timeframe}     {string.Join(',', featureRow.TimeframeVector)}   {featureRow.Category}      {string.Join(',', featureRow.CategoryVector)}");
+                Console.WriteLine(featureRow.Timeframe + "     " +
+                    string.Join(',', featureRow.TimeframeVector) + "   " +
+                    featureRow.Category + "      " +
+                    string.Join(',', featureRow.CategoryVector));
 
             // TransformedData obtained post-transformation.
             //
@@ -59,8 +66,8 @@ public static void Example()
 
         private class DataPoint
         {
-            // The maximal value used is 9; but since 0 is reserved for missing value,
-            // we set the count to 10.
+            // The maximal value used is 9; but since 0 is reserved for missing
+            // value, we set the count to 10.
             [KeyType(10)]
             public uint Timeframe { get; set; }
 
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapValue.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapValue.cs
index 0b7b5ab4dd..60bea2b951 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapValue.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapValue.cs
@@ -6,12 +6,13 @@ namespace Samples.Dynamic
 {
     public static class MapValue
     {
-        /// This example demonstrates the use of the ValueMappingEstimator by mapping strings to other string values, or floats to strings. 
-        /// This is useful to map types to a category. 
+        /// This example demonstrates the use of the ValueMappingEstimator by 
+        /// mapping strings to other string values, or floats to strings. This is
+        /// useful to map types to a category. 
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable.
@@ -34,7 +35,8 @@ public static void Example()
             timeframeMap["12-25yrs"] = "Long";
             timeframeMap["25+yrs"] = "Long";
 
-            // Construct the mapping of strings to keys(uints) for the Timeframe column. 
+            // Construct the mapping of strings to keys(uints) for the Timeframe
+            // column. 
             var timeframeKeyMap = new Dictionary<string, uint>();
             timeframeKeyMap["0-4yrs"] = 1;
             timeframeKeyMap["0-5yrs"] = 1;
@@ -51,22 +53,31 @@ public static void Example()
             scoreMap[5] = "High";
 
             // Constructs the ML.net pipeline
-            var pipeline = mlContext.Transforms.Conversion.MapValue("TimeframeCategory", timeframeMap, "Timeframe")
-                           .Append(mlContext.Transforms.Conversion.MapValue("ScoreCategory", scoreMap, "Score"))
-                           // on the MapValue below, the treatValuesAsKeyType is set to true. The type of the Label column will be a KeyDataViewType type, 
-                           // and it can be used as input for trainers performing multiclass classification.
-                           .Append(mlContext.Transforms.Conversion.MapValue("Label", timeframeKeyMap, "Timeframe", treatValuesAsKeyType: true));
+            var pipeline = mlContext.Transforms.Conversion.MapValue(
+                "TimeframeCategory", timeframeMap, "Timeframe").Append(mlContext.
+                Transforms.Conversion.MapValue("ScoreCategory", scoreMap, "Score"))
+                // on the MapValue below, the treatValuesAsKeyType is set to true.
+                // The type of the Label column will be a KeyDataViewType type, 
+                // and it can be used as input for trainers performing multiclass
+                // classification.
+                .Append(mlContext.Transforms.Conversion.MapValue("Label",
+                timeframeKeyMap, "Timeframe", treatValuesAsKeyType: true));
 
             // Fits the pipeline to the data.
             IDataView transformedData = pipeline.Fit(data).Transform(data);
 
             // Getting the resulting data as an IEnumerable.
             // This will contain the newly created columns.
-            IEnumerable<TransformedData> features = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);
+            IEnumerable<TransformedData> features = mlContext.Data.CreateEnumerable<
+                TransformedData>(transformedData, reuseRowObject: false);
+
+            Console.WriteLine(" Timeframe   TimeframeCategory   Label    Score   " +
+                "ScoreCategory");
 
-            Console.WriteLine($" Timeframe   TimeframeCategory   Label    Score   ScoreCategory");
             foreach (var featureRow in features)
-                Console.WriteLine($"{featureRow.Timeframe}\t\t{featureRow.TimeframeCategory}\t\t\t{featureRow.Label}\t\t{featureRow.Score}\t{featureRow.ScoreCategory}");
+                Console.WriteLine($"{featureRow.Timeframe}\t\t" +
+                    $"{featureRow.TimeframeCategory}\t\t\t{featureRow.Label}\t\t" +
+                    $"{featureRow.Score}\t{featureRow.ScoreCategory}");
 
             // TransformedData obtained post-transformation.
             //
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapValueIdvLookup.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapValueIdvLookup.cs
index 9e1cbf2a99..5c57b4078a 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapValueIdvLookup.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapValueIdvLookup.cs
@@ -6,12 +6,13 @@ namespace Samples.Dynamic
 {
     public static class MapValueIdvLookup
     {
-        /// This example demonstrates the use of MapValue by mapping floats to strings, looking up the mapping in an IDataView. 
-        /// This is useful to map types to a grouping. 
+        /// This example demonstrates the use of MapValue by mapping floats to
+        /// strings, looking up the mapping in an IDataView. This is useful to map
+        /// types to a grouping. 
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable.
@@ -41,17 +42,22 @@ public static void Example()
             var lookupIdvMap = mlContext.Data.LoadFromEnumerable(lookupData);
 
             // Constructs the ValueMappingEstimator making the ML.NET pipeline
-            var pipeline = mlContext.Transforms.Conversion.MapValue("PriceCategory", lookupIdvMap, lookupIdvMap.Schema["Value"], lookupIdvMap.Schema["Category"], "Price");
+            var pipeline = mlContext.Transforms.Conversion.MapValue("PriceCategory",
+                lookupIdvMap, lookupIdvMap.Schema["Value"], lookupIdvMap.Schema[
+                    "Category"], "Price");
 
-            // Fits the ValueMappingEstimator and transforms the data converting the Price to PriceCategory.
+            // Fits the ValueMappingEstimator and transforms the data converting the
+            // Price to PriceCategory.
             IDataView transformedData = pipeline.Fit(data).Transform(data);
 
             // Getting the resulting data as an IEnumerable.
-            IEnumerable<TransformedData> features = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);
+            IEnumerable<TransformedData> features = mlContext.Data.CreateEnumerable<
+                TransformedData>(transformedData, reuseRowObject: false);
 
             Console.WriteLine($" Price   PriceCategory");
             foreach (var featureRow in features)
-                Console.WriteLine($"{featureRow.Price}\t\t{featureRow.PriceCategory}");
+                Console.WriteLine($"{featureRow.Price}\t\t" +
+                $"{featureRow.PriceCategory}");
 
             // TransformedData obtained post-transformation.
             //
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapValueToArray.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapValueToArray.cs
index 18362ec9a0..57bff5a3b8 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapValueToArray.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapValueToArray.cs
@@ -6,13 +6,16 @@ namespace Samples.Dynamic
 {
     public static class MapValueToArray
     { 
-        /// This example demonstrates the use of MapValue by mapping strings to array values, which allows for mapping data to numeric arrays. 
-        /// This functionality is useful when the generated column will serve as the Features column for a trainer. Most of the trainers take a numeric vector, as the Features column. 
-        /// In this example, we are mapping the Timeframe data to arbitrary integer arrays.
+        /// This example demonstrates the use of MapValue by mapping strings to
+        /// array values, which allows for mapping data to numeric arrays. This
+        /// functionality is useful when the generated column will serve as the
+        /// Features column for a trainer. Most of the trainers take a numeric
+        /// vector, as the Features column. In this example, we are mapping the
+        /// Timeframe data to arbitrary integer arrays.
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable.
@@ -27,8 +30,8 @@ public static void Example()
 
             var data = mlContext.Data.LoadFromEnumerable(rawData);
 
-            // Creating a list of key-value pairs to indicate the mapping between the 
-            // DataPoint values, and the arrays they should map to. 
+            // Creating a list of key-value pairs to indicate the mapping between
+            // the DataPoint values, and the arrays they should map to. 
             var timeframeMap = new Dictionary<string, int[]>();
             timeframeMap["0-4yrs"] = new int[] { 0, 5, 300 };
             timeframeMap["0-5yrs"] = new int[] { 0, 5, 300 };
@@ -37,17 +40,22 @@ public static void Example()
             timeframeMap["25+yrs"] = new int[] { 12, 50, 300 };
 
             // Constructs the ValueMappingEstimator making the ML.NET pipeline.
-            var pipeline = mlContext.Transforms.Conversion.MapValue("Features", timeframeMap, "Timeframe");
+            var pipeline = mlContext.Transforms.Conversion.MapValue("Features",
+                timeframeMap, "Timeframe");
 
-            // Fits the ValueMappingEstimator and transforms the data adding the Features column.
+            // Fits the ValueMappingEstimator and transforms the data adding the
+            // Features column.
             IDataView transformedData = pipeline.Fit(data).Transform(data);
 
             // Getting the resulting data as an IEnumerable.
-            IEnumerable<TransformedData> featuresColumn = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);
+            IEnumerable<TransformedData> featuresColumn = mlContext.Data
+                .CreateEnumerable<TransformedData>(transformedData, reuseRowObject:
+                false);
 
             Console.WriteLine($"Timeframe     Features");
             foreach (var featureRow in featuresColumn)
-                Console.WriteLine($"{featureRow.Timeframe}\t\t {string.Join(",", featureRow.Features)}");
+                Console.WriteLine($"{featureRow.Timeframe}\t\t " +
+                $"{string.Join(",", featureRow.Features)}");
 
             // Timeframe      Features
             // 0-4yrs       0, 5, 300
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapValueToKeyMultiColumn.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapValueToKeyMultiColumn.cs
index ccd79f4fae..e54578cb60 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapValueToKeyMultiColumn.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/MapValueToKeyMultiColumn.cs
@@ -6,13 +6,14 @@ namespace Samples.Dynamic
 {
     public static class MapValueToKeyMultiColumn
     {
-        /// This example demonstrates the use of the ValueToKeyMappingEstimator, by mapping strings to KeyType values. 
-        /// For more on ML.NET KeyTypes see: https://github.com/dotnet/machinelearning/blob/master/docs/code/IDataViewTypeSystem.md#key-types 
+        /// This example demonstrates the use of the ValueToKeyMappingEstimator, by
+        /// mapping strings to KeyType values. For more on ML.NET KeyTypes see:
+        /// https://github.com/dotnet/machinelearning/blob/master/docs/code/IDataViewTypeSystem.md#key-types 
         /// It is possible to have multiple values map to the same category.
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable.
@@ -30,19 +31,24 @@ public static void Example()
                 new  InputOutputColumnPair("StudyTimeCategory", "StudyTime"),
                 new  InputOutputColumnPair("CourseCategory", "Course")
                 },
-                keyOrdinality: Microsoft.ML.Transforms.ValueToKeyMappingEstimator.KeyOrdinality.ByValue,
-                addKeyValueAnnotationsAsText: true);
+                keyOrdinality: Microsoft.ML.Transforms.ValueToKeyMappingEstimator
+                    .KeyOrdinality.ByValue, addKeyValueAnnotationsAsText: true);
 
             // Fits the pipeline to the data.
             IDataView transformedData = pipeline.Fit(data).Transform(data);
 
             // Getting the resulting data as an IEnumerable.
             // This will contain the newly created columns.
-            IEnumerable<TransformedData> features = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);
+            IEnumerable<TransformedData> features = mlContext.Data.CreateEnumerable<
+                TransformedData>(transformedData, reuseRowObject: false);
+
+            Console.WriteLine($" StudyTime   StudyTimeCategory   Course    " +
+                $"CourseCategory");
 
-            Console.WriteLine($" StudyTime   StudyTimeCategory   Course    CourseCategory");
             foreach (var featureRow in features)
-                Console.WriteLine($"{featureRow.StudyTime}\t\t{featureRow.StudyTimeCategory}\t\t\t{featureRow.Course}\t\t{featureRow.CourseCategory}");
+                Console.WriteLine($"{featureRow.StudyTime}\t\t" +
+                    $"{featureRow.StudyTimeCategory}\t\t\t{featureRow.Course}\t\t" +
+                    $"{featureRow.CourseCategory}");
 
             // TransformedData obtained post-transformation.
             //
@@ -52,11 +58,13 @@ public static void Example()
             // 12-25yrs        3                   LA          3
             // 0-5yrs          2                   DS          2
 
-            // If we wanted to provide the mapping, rather than letting the transform create it, 
-            // we could do so by creating an IDataView one column containing the values to map to. 
-            // If the values in the dataset are not found in the lookup IDataView they will get mapped to the mising value, 0.
-            // The keyData are shared among the columns, therefore the keys are not contiguous for the column. 
-            // Create the lookup map data IEnumerable.  
+            // If we wanted to provide the mapping, rather than letting the
+            // transform create it, we could do so by creating an IDataView one
+            // column containing the values to map to. If the values in the dataset
+            // are not found in the lookup IDataView they will get mapped to the
+            // mising value, 0. The keyData are shared among the columns, therefore
+            // the keys are not contiguous for the column. Create the lookup map
+            // data IEnumerable.  
             var lookupData = new[] {
                 new LookupMap { Key = "0-4yrs" },
                 new LookupMap { Key = "6-11yrs" },
@@ -70,22 +78,28 @@ public static void Example()
             var lookupIdvMap = mlContext.Data.LoadFromEnumerable(lookupData);
 
             // Constructs the ML.net pipeline
-            var pipelineWithLookupMap = mlContext.Transforms.Conversion.MapValueToKey(new[] {
-                new  InputOutputColumnPair("StudyTimeCategory", "StudyTime"),
-                new  InputOutputColumnPair("CourseCategory", "Course")
-                },
-                keyData: lookupIdvMap);
+            var pipelineWithLookupMap = mlContext.Transforms.Conversion
+                .MapValueToKey(new[] {
+                    new  InputOutputColumnPair("StudyTimeCategory", "StudyTime"),
+                    new  InputOutputColumnPair("CourseCategory", "Course")
+                    },
+                    keyData: lookupIdvMap);
 
             // Fits the pipeline to the data.
             transformedData = pipelineWithLookupMap.Fit(data).Transform(data);
 
             // Getting the resulting data as an IEnumerable.
             // This will contain the newly created columns.
-            features = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);
+            features = mlContext.Data.CreateEnumerable<TransformedData>(
+                transformedData, reuseRowObject: false);
+
+            Console.WriteLine($" StudyTime   StudyTimeCategory  " +
+                $"Course CourseCategory");
 
-            Console.WriteLine($" StudyTime   StudyTimeCategory  Course CourseCategory");
             foreach (var featureRow in features)
-                Console.WriteLine($"{featureRow.StudyTime}\t\t{featureRow.StudyTimeCategory}\t\t\t{featureRow.Course}\t\t{featureRow.CourseCategory}");
+                Console.WriteLine($"{featureRow.StudyTime}\t\t" +
+                    $"{featureRow.StudyTimeCategory}\t\t\t{featureRow.Course}\t\t" +
+                    $"{featureRow.CourseCategory}");
 
             // StudyTime    StudyTimeCategory  Course     CourseCategory
             // 0 - 4yrs          1              CS              4
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/FeatureSelection/SelectFeaturesBasedOnCount.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/FeatureSelection/SelectFeaturesBasedOnCount.cs
index b4d313644a..2b5ec15af4 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/FeatureSelection/SelectFeaturesBasedOnCount.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/FeatureSelection/SelectFeaturesBasedOnCount.cs
@@ -9,8 +9,8 @@ public static class SelectFeaturesBasedOnCount
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable and convert it to an IDataView.
@@ -19,7 +19,8 @@ public static void Example()
             // Printing the columns of the input data. 
             Console.WriteLine($"NumericVector             StringVector");
             foreach (var item in rawData)
-                Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item.NumericVector), string.Join(",", item.StringVector));
+                Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item
+                    .NumericVector), string.Join(",", item.StringVector));
             
             // NumericVector             StringVector
             // 4,NaN,6                   A,WA,Male
@@ -29,19 +30,27 @@ public static void Example()
 
             var data = mlContext.Data.LoadFromEnumerable(rawData);
 
-            // We will use the SelectFeaturesBasedOnCount to retain only those slots which have at least 'count' non-default and non-missing values per slot.
+            // We will use the SelectFeaturesBasedOnCount to retain only those slots
+            // which have at least 'count' non-default and non-missing values per
+            // slot.
             var pipeline = 
-                mlContext.Transforms.FeatureSelection.SelectFeaturesBasedOnCount(outputColumnName: "NumericVector", count: 3) // Usage on numeric column.
-                .Append(mlContext.Transforms.FeatureSelection.SelectFeaturesBasedOnCount(outputColumnName: "StringVector", count: 3)); // Usage on text column.
+                mlContext.Transforms.FeatureSelection.SelectFeaturesBasedOnCount(
+                    outputColumnName: "NumericVector", count: 3) // Usage on numeric 
+                    // column.
+                .Append(mlContext.Transforms.FeatureSelection
+                .SelectFeaturesBasedOnCount(outputColumnName: "StringVector",
+                count: 3)); // Usage on text column.
 
             var transformedData = pipeline.Fit(data).Transform(data);
 
-            var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, true);
+            var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(
+                transformedData, true);
 
             // Printing the columns of the transformed data. 
             Console.WriteLine($"NumericVector             StringVector");
             foreach (var item in convertedData)
-                Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item.NumericVector), string.Join(",", item.StringVector));
+                Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item.
+                    NumericVector), string.Join(",", item.StringVector));
 
             // NumericVector             StringVector
             // 4,6                       A,Male
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/FeatureSelection/SelectFeaturesBasedOnCountMultiColumn.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/FeatureSelection/SelectFeaturesBasedOnCountMultiColumn.cs
index ae45fa375e..ff195da136 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/FeatureSelection/SelectFeaturesBasedOnCountMultiColumn.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/FeatureSelection/SelectFeaturesBasedOnCountMultiColumn.cs
@@ -9,8 +9,8 @@ public static class SelectFeaturesBasedOnCountMultiColumn
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable and convert it to an IDataView.
@@ -19,7 +19,8 @@ public static void Example()
             // Printing the columns of the input data. 
             Console.WriteLine($"NumericVector             StringVector");
             foreach (var item in rawData)
-                Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item.NumericVector), string.Join(",", item.StringVector));
+                Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item.
+                    NumericVector), string.Join(",", item.StringVector));
 
             // NumericVector             StringVector
             // 4,NaN,6                   A,WA,Male
@@ -29,22 +30,27 @@ public static void Example()
 
             var data = mlContext.Data.LoadFromEnumerable(rawData);
 
-            // We will use the SelectFeaturesBasedOnCount transform estimator, to retain only those slots which have 
-            // at least 'count' non-default values per slot.
+            // We will use the SelectFeaturesBasedOnCount transform estimator, to
+            // retain only those slots which have at least 'count' non-default
+            // values per slot.
 
-            // Multi column example. This pipeline transform two columns using the provided parameters.
-            var pipeline = mlContext.Transforms.FeatureSelection.SelectFeaturesBasedOnCount(
-                new InputOutputColumnPair[] { new InputOutputColumnPair("NumericVector"), new InputOutputColumnPair("StringVector") },
-                count: 3);
+            // Multi column example. This pipeline transform two columns using the
+            // provided parameters.
+            var pipeline = mlContext.Transforms.FeatureSelection
+                .SelectFeaturesBasedOnCount(new InputOutputColumnPair[] { new
+                InputOutputColumnPair("NumericVector"), new InputOutputColumnPair(
+                "StringVector") }, count: 3);
 
             var transformedData = pipeline.Fit(data).Transform(data);
 
-            var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, true);
+            var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(
+                transformedData, true);
 
             // Printing the columns of the transformed data. 
             Console.WriteLine($"NumericVector             StringVector");
             foreach (var item in convertedData)
-                Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item.NumericVector), string.Join(",", item.StringVector));
+                Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item
+                    .NumericVector), string.Join(",", item.StringVector));
 
             // NumericVector             StringVector
             // 4,6                       A,Male
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/FeatureSelection/SelectFeaturesBasedOnMutualInformation.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/FeatureSelection/SelectFeaturesBasedOnMutualInformation.cs
index d7aa805ceb..79d1a06021 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/FeatureSelection/SelectFeaturesBasedOnMutualInformation.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/FeatureSelection/SelectFeaturesBasedOnMutualInformation.cs
@@ -9,8 +9,8 @@ public static class SelectFeaturesBasedOnMutualInformation
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable and convert it to an IDataView.
@@ -19,7 +19,8 @@ public static void Example()
             // Printing the columns of the input data. 
             Console.WriteLine($"Label             NumericVector");
             foreach (var item in rawData)
-                Console.WriteLine("{0,-25} {1,-25}", item.Label, string.Join(",", item.NumericVector));
+                Console.WriteLine("{0,-25} {1,-25}", item.Label, string.Join(",",
+                    item.NumericVector));
 
             // Label                       NumericVector
             // True                        4,0,6
@@ -29,16 +30,19 @@ public static void Example()
 
             var data = mlContext.Data.LoadFromEnumerable(rawData);
 
-            // We define a MutualInformationFeatureSelectingEstimator that selects the top k slots in a feature 
-            // vector based on highest mutual information between that slot and a specified label. 
-            var pipeline = mlContext.Transforms.FeatureSelection.SelectFeaturesBasedOnMutualInformation(
-                outputColumnName: "NumericVector", labelColumnName: "Label",
-                slotsInOutput:2);
+            // We define a MutualInformationFeatureSelectingEstimator that selects
+            // the top k slots in a feature vector based on highest mutual
+            // information between that slot and a specified label. 
+            var pipeline = mlContext.Transforms.FeatureSelection
+                .SelectFeaturesBasedOnMutualInformation(outputColumnName:
+                "NumericVector", labelColumnName: "Label", slotsInOutput:2);
 
-            // The pipeline can then be trained, using .Fit(), and the resulting transformer can be used to transform data. 
+            // The pipeline can then be trained, using .Fit(), and the resulting
+            // transformer can be used to transform data. 
             var transformedData = pipeline.Fit(data).Transform(data);
 
-            var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, true);
+            var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(
+                transformedData, true);
 
             // Printing the columns of the transformed data. 
             Console.WriteLine($"NumericVector");
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/FeatureSelection/SelectFeaturesBasedOnMutualInformationMultiColumn.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/FeatureSelection/SelectFeaturesBasedOnMutualInformationMultiColumn.cs
index d9543cee7e..138abcbd60 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/FeatureSelection/SelectFeaturesBasedOnMutualInformationMultiColumn.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/FeatureSelection/SelectFeaturesBasedOnMutualInformationMultiColumn.cs
@@ -9,8 +9,8 @@ public static class SelectFeaturesBasedOnMutualInformationMultiColumn
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Get a small dataset as an IEnumerable and convert it to an IDataView.
@@ -19,7 +19,8 @@ public static void Example()
             // Printing the columns of the input data. 
             Console.WriteLine($"NumericVectorA            NumericVectorB");
             foreach (var item in rawData)
-                Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item.NumericVectorA), string.Join(",", item.NumericVectorB));
+                Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item
+                    .NumericVectorA), string.Join(",", item.NumericVectorB));
 
             // NumericVectorA              NumericVectorB
             // 4,0,6                       7,8,9
@@ -29,23 +30,28 @@ public static void Example()
 
             var data = mlContext.Data.LoadFromEnumerable(rawData);
 
-            // We define a MutualInformationFeatureSelectingEstimator that selects the top k slots in a feature 
-            // vector based on highest mutual information between that slot and a specified label. 
+            // We define a MutualInformationFeatureSelectingEstimator that selects
+            // the top k slots in a feature vector based on highest mutual
+            // information between that slot and a specified label. 
 
-            // Multi column example : This pipeline transform two columns using the provided parameters.
-            var pipeline = mlContext.Transforms.FeatureSelection.SelectFeaturesBasedOnMutualInformation(
-                new InputOutputColumnPair[] { new InputOutputColumnPair("NumericVectorA"), new InputOutputColumnPair("NumericVectorB") },
-                labelColumnName: "Label",
+            // Multi column example : This pipeline transform two columns using the
+            // provided parameters.
+            var pipeline = mlContext.Transforms.FeatureSelection
+                .SelectFeaturesBasedOnMutualInformation(new InputOutputColumnPair[] 
+                { new InputOutputColumnPair("NumericVectorA"), new
+                InputOutputColumnPair("NumericVectorB") }, labelColumnName: "Label",
                 slotsInOutput: 4);
 
             var transformedData = pipeline.Fit(data).Transform(data);
 
-            var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, true);
+            var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(
+                transformedData, true);
 
             // Printing the columns of the transformed data. 
             Console.WriteLine($"NumericVectorA            NumericVectorB");
             foreach (var item in convertedData)
-                Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item.NumericVectorA), string.Join(",", item.NumericVectorB));
+                Console.WriteLine("{0,-25} {1,-25}", string.Join(",", item
+                    .NumericVectorA), string.Join(",", item.NumericVectorB));
 
             // NumericVectorA              NumericVectorB
             // 4,0,6                       9
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ConvertToGrayScale.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ConvertToGrayScale.cs
index 02f8fce98b..052f949eb4 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ConvertToGrayScale.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ConvertToGrayScale.cs
@@ -8,17 +8,21 @@ namespace Samples.Dynamic
 {
     public static class ConvertToGrayscale
     {
-        // Sample that loads images from the file system, and converts them to grayscale. 
+        // Sample that loads images from the file system, and converts them to
+        // grayscale. 
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
-            // Downloading a few images, and an images.tsv file, which contains a list of the files from the dotnet/machinelearning/test/data/images/.
-            // If you inspect the fileSystem, after running this line, an "images" folder will be created, containing 4 images, and a .tsv file
+            // Downloading a few images, and an images.tsv file, which contains a
+            // list of the files from the dotnet/machinelearning/test/data/images/.
+            // If you inspect the fileSystem, after running this line, an "images"
+            // folder will be created, containing 4 images, and a .tsv file
             // enumerating the images. 
-            var imagesDataFile = Microsoft.ML.SamplesUtils.DatasetUtils.DownloadImages();
+            var imagesDataFile = Microsoft.ML.SamplesUtils.DatasetUtils
+                .DownloadImages();
 
             // Preview of the content of the images.tsv file
             //
@@ -39,8 +43,10 @@ public static void Example()
 
             var imagesFolder = Path.GetDirectoryName(imagesDataFile);
             // Image loading pipeline. 
-            var pipeline = mlContext.Transforms.LoadImages("ImageObject", imagesFolder, "ImagePath")
-                           .Append(mlContext.Transforms.ConvertToGrayscale("Grayscale", "ImageObject"));
+            var pipeline = mlContext.Transforms.LoadImages("ImageObject",
+                imagesFolder, "ImagePath")
+                .Append(mlContext.Transforms.ConvertToGrayscale("Grayscale",
+                "ImageObject"));
 
             var transformedData = pipeline.Fit(data).Transform(data);
 
@@ -54,20 +60,32 @@ public static void Example()
 
         private static void PrintColumns(IDataView transformedData)
         {
-            Console.WriteLine("{0, -25} {1, -25} {2, -25} {3, -25}", "ImagePath", "Name", "ImageObject", "Grayscale");
-            using (var cursor = transformedData.GetRowCursor(transformedData.Schema))
+            Console.WriteLine("{0, -25} {1, -25} {2, -25} {3, -25}", "ImagePath",
+                "Name", "ImageObject", "Grayscale");
+
+            using (var cursor = transformedData.GetRowCursor(transformedData
+                .Schema))
             {
-                // Note that it is best to get the getters and values *before* iteration, so as to faciliate buffer
-                // sharing (if applicable), and column-type validation once, rather than many times.
+                // Note that it is best to get the getters and values *before*
+                // iteration, so as to faciliate buffer sharing (if applicable), and
+                // column -type validation once, rather than many times.
                 ReadOnlyMemory<char> imagePath = default;
                 ReadOnlyMemory<char> name = default;
                 Bitmap imageObject = null;
                 Bitmap grayscaleImageObject = null;
 
-                var imagePathGetter = cursor.GetGetter<ReadOnlyMemory<char>>(cursor.Schema["ImagePath"]);
-                var nameGetter = cursor.GetGetter<ReadOnlyMemory<char>>(cursor.Schema["Name"]);
-                var imageObjectGetter = cursor.GetGetter<Bitmap>(cursor.Schema["ImageObject"]);
-                var grayscaleGetter = cursor.GetGetter<Bitmap>(cursor.Schema["Grayscale"]);
+                var imagePathGetter = cursor.GetGetter<ReadOnlyMemory<char>>(cursor
+                    .Schema["ImagePath"]);
+
+                var nameGetter = cursor.GetGetter<ReadOnlyMemory<char>>(cursor
+                    .Schema["Name"]);
+
+                var imageObjectGetter = cursor.GetGetter<Bitmap>(cursor.Schema[
+                    "ImageObject"]);
+
+                var grayscaleGetter = cursor.GetGetter<Bitmap>(cursor.Schema[
+                    "Grayscale"]);
+
                 while (cursor.MoveNext())
                 {
                     imagePathGetter(ref imagePath);
@@ -75,7 +93,9 @@ private static void PrintColumns(IDataView transformedData)
                     imageObjectGetter(ref imageObject);
                     grayscaleGetter(ref grayscaleImageObject);
 
-                    Console.WriteLine("{0, -25} {1, -25} {2, -25} {3, -25}", imagePath, name, imageObject.PhysicalDimension, grayscaleImageObject.PhysicalDimension);
+                    Console.WriteLine("{0, -25} {1, -25} {2, -25} {3, -25}",
+                        imagePath, name, imageObject.PhysicalDimension,
+                        grayscaleImageObject.PhysicalDimension);
                 }
 
                 // Dispose the image.
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ConvertToGrayScaleInMemory.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ConvertToGrayScaleInMemory.cs
index 883dfa5dc1..3bb0586cc0 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ConvertToGrayScaleInMemory.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ConvertToGrayScaleInMemory.cs
@@ -11,13 +11,16 @@ static public void Example()
         {
             var mlContext = new MLContext();
             // Create an image list.
-            var images = new[] { new ImageDataPoint(2, 3, Color.Blue), new ImageDataPoint(2, 3, Color.Red) };
+            var images = new[] { new ImageDataPoint(2, 3, Color.Blue), new
+                ImageDataPoint(2, 3, Color.Red) };
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var data = mlContext.Data.LoadFromEnumerable(images);
 
             // Convert image to gray scale.
-            var pipeline = mlContext.Transforms.ConvertToGrayscale("GrayImage", "Image");
+            var pipeline = mlContext.Transforms.ConvertToGrayscale("GrayImage",
+                "Image");
 
             // Fit the model.
             var model = pipeline.Fit(data);
@@ -26,7 +29,8 @@ static public void Example()
             var transformedData = model.Transform(data);
 
             // Load images in DataView back to Enumerable.
-            var transformedDataPoints = mlContext.Data.CreateEnumerable<ImageDataPoint>(transformedData, false);
+            var transformedDataPoints = mlContext.Data.CreateEnumerable<
+                ImageDataPoint>(transformedData, false);
 
             // Print out input and output pixels.
             foreach (var dataPoint in transformedDataPoints)
@@ -39,7 +43,8 @@ static public void Example()
                     {
                         var pixel = image.GetPixel(x, y);
                         var grayPixel = grayImage.GetPixel(x, y);
-                        Console.WriteLine($"The original pixel is {pixel} and its pixel in gray is {grayPixel}");
+                        Console.WriteLine($"The original pixel is {pixel} and its" +
+                            $"pixel in gray is {grayPixel}");
                     }
                 }
             }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ConvertToImage.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ConvertToImage.cs
index 04ccb71093..f0f58c7e45 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ConvertToImage.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ConvertToImage.cs
@@ -14,22 +14,25 @@ public static class ConvertToImage
         private const int numberOfChannels = 3;
         private const int inputSize = imageHeight * imageWidth * numberOfChannels;
 
-        // Sample that shows how an input array (of doubles) can be used to interop with image related estimators in ML.NET.
+        // Sample that shows how an input array (of doubles) can be used to interop
+        // with image related estimators in ML.NET.
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(4);
 
-            // Convert the list of data points to an IDataView object, which is consumable by ML.NET API.
+            // Convert the list of data points to an IDataView object, which is
+            // consumable by ML.NET API.
             var data = mlContext.Data.LoadFromEnumerable(dataPoints);
 
             // Image loading pipeline. 
-            var pipeline = mlContext.Transforms.ConvertToImage(imageHeight, imageWidth, "Image", "Features")
-                          .Append(mlContext.Transforms.ExtractPixels("Pixels", "Image"));
+            var pipeline = mlContext.Transforms.ConvertToImage(imageHeight,
+                imageWidth, "Image", "Features")
+                .Append(mlContext.Transforms.ExtractPixels("Pixels", "Image"));
 
             var transformedData = pipeline.Fit(data).Transform(data);
 
@@ -45,18 +48,25 @@ public static void Example()
 
         private static void PrintColumns(IDataView transformedData)
         {
-            Console.WriteLine("{0, -25} {1, -25} {2, -25}", "Features", "Image", "Pixels");
+            Console.WriteLine("{0, -25} {1, -25} {2, -25}", "Features", "Image",
+                "Pixels");
 
-            using (var cursor = transformedData.GetRowCursor(transformedData.Schema))
+            using (var cursor = transformedData.GetRowCursor(transformedData
+                .Schema))
             {
-                // Note that it is best to get the getters and values *before* iteration, so as to faciliate buffer
-                // sharing (if applicable), and column-type validation once, rather than many times.
+                // Note that it is best to get the getters and values *before*
+                // iteration, so as to faciliate buffer sharing (if applicable), and
+                // column -type validation once, rather than many times.
                 VBuffer<float> features = default;
                 VBuffer<float> pixels = default;
                 Bitmap imageObject = null;
 
-                var featuresGetter = cursor.GetGetter<VBuffer<float>>(cursor.Schema["Features"]);
-                var pixelsGetter = cursor.GetGetter<VBuffer<float>>(cursor.Schema["Pixels"]);
+                var featuresGetter = cursor.GetGetter<VBuffer<float>>(cursor.Schema[
+                    "Features"]);
+
+                var pixelsGetter = cursor.GetGetter<VBuffer<float>>(cursor.Schema[
+                    "Pixels"]);
+
                 var imageGetter = cursor.GetGetter<Bitmap>(cursor.Schema["Image"]);
                 while (cursor.MoveNext())
                 {
@@ -65,8 +75,10 @@ private static void PrintColumns(IDataView transformedData)
                     pixelsGetter(ref pixels);
                     imageGetter(ref imageObject);
 
-                    Console.WriteLine("{0, -25} {1, -25} {2, -25}", string.Join(",", features.DenseValues().Take(5)) + "...",
-                        imageObject.PhysicalDimension, string.Join(",", pixels.DenseValues().Take(5)) + "...");
+                    Console.WriteLine("{0, -25} {1, -25} {2, -25}", string.Join(",",
+                        features.DenseValues().Take(5)) + "...", imageObject
+                        .PhysicalDimension, string.Join(",", pixels.DenseValues()
+                        .Take(5)) + "...");
                 }
 
                 // Dispose the image.
@@ -80,12 +92,14 @@ private class DataPoint
             public float[] Features { get; set; }
         }
 
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed = 0)
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed = 0)
         {
             var random = new Random(seed);
 
             for (int i = 0; i < count; i++)
-                yield return new DataPoint { Features = Enumerable.Repeat(0, inputSize).Select(x => (float)random.Next(0, 256)).ToArray() };
+                yield return new DataPoint { Features = Enumerable.Repeat(0,
+                    inputSize).Select(x => (float)random.Next(0, 256)).ToArray() };
         }
     }
 }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/DnnFeaturizeImage.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/DnnFeaturizeImage.cs
index 97929a38cc..99d00b900c 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/DnnFeaturizeImage.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/DnnFeaturizeImage.cs
@@ -9,16 +9,20 @@ public static class DnnFeaturizeImage
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
-            // Downloading a few images, and an images.tsv file, which contains a list of the files from the dotnet/machinelearning/test/data/images/.
-            // If you inspect the fileSystem, after running this line, an "images" folder will be created, containing 4 images, and a .tsv file
+            // Downloading a few images, and an images.tsv file, which contains a
+            // list of the files from the dotnet/machinelearning/test/data/images/.
+            // If you inspect the fileSystem, after running this line, an "images"
+            // folder will be created, containing 4 images, and a .tsv file
             // enumerating the images. 
-            var imagesDataFile = Microsoft.ML.SamplesUtils.DatasetUtils.DownloadImages();
+            var imagesDataFile = Microsoft.ML.SamplesUtils.DatasetUtils
+                .DownloadImages();
 
-            // Preview of the content of the images.tsv file, which lists the images to operate on
+            // Preview of the content of the images.tsv file, which lists the images
+            // to operate on
             //
             // imagePath    imageType
             // tomato.bmp   tomato
@@ -40,16 +44,22 @@ public static void Example()
             // Installing the Microsoft.ML.DNNImageFeaturizer packages copies the models in the
             // `DnnImageModels` folder. 
             // Image loading pipeline. 
-            var pipeline = mlContext.Transforms.LoadImages("ImageObject", imagesFolder, "ImagePath")
-                          .Append(mlContext.Transforms.ResizeImages("ImageObject", imageWidth: 224, imageHeight: 224))
-                          .Append(mlContext.Transforms.ExtractPixels("Pixels", "ImageObject"))
-                          .Append(mlContext.Transforms.DnnFeaturizeImage("FeaturizedImage", m => m.ModelSelector.ResNet18(mlContext, m.OutputColumn, m.InputColumn), "Pixels"));
+            var pipeline = mlContext.Transforms.LoadImages("ImageObject",
+                imagesFolder, "ImagePath")
+                .Append(mlContext.Transforms.ResizeImages("ImageObject", imageWidth:
+                    224, imageHeight: 224))
+                .Append(mlContext.Transforms.ExtractPixels("Pixels", "ImageObject"))
+                .Append(mlContext.Transforms.DnnFeaturizeImage("FeaturizedImage",
+                    m => m.ModelSelector.ResNet18(mlContext, m.OutputColumn, m
+                    .InputColumn), "Pixels"));
 
             var transformedData = pipeline.Fit(data).Transform(data);
 
-            var FeaturizedImageColumnsPerRow = transformedData.GetColumn<float[]>("FeaturizedImage").ToArray();
+            var FeaturizedImageColumnsPerRow = transformedData.GetColumn<float[]>(
+                "FeaturizedImage").ToArray();
 
-            // Preview of FeaturizedImageColumnsPerRow for the first row, FeaturizedImageColumnsPerRow[0]
+            // Preview of FeaturizedImageColumnsPerRow for the first row,
+            // FeaturizedImageColumnsPerRow[0]
             //
             // 0.696136236
             // 0.2661711
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ExtractPixels.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ExtractPixels.cs
index 4f8793fd7a..e608e484df 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ExtractPixels.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ExtractPixels.cs
@@ -9,18 +9,22 @@ namespace Samples.Dynamic
 {
     public static class ExtractPixels
     {
-        // Sample that loads the images from the file system, resizes them (ExtractPixels requires a resizing operation), and extracts the 
-        // values of the pixels as a vector. 
+        // Sample that loads the images from the file system, resizes them (
+        // ExtractPixels requires a resizing operation), and extracts the values of
+        // the pixels as a vector. 
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
-            // Downloading a few images, and an images.tsv file, which contains a list of the files from the dotnet/machinelearning/test/data/images/.
-            // If you inspect the fileSystem, after running this line, an "images" folder will be created, containing 4 images, and a .tsv file
+            // Downloading a few images, and an images.tsv file, which contains a
+            // list of the files from the dotnet/machinelearning/test/data/images/.
+            // If you inspect the fileSystem, after running this line, an "images"
+            // folder will be created, containing 4 images, and a .tsv file
             // enumerating the images. 
-            var imagesDataFile = Microsoft.ML.SamplesUtils.DatasetUtils.DownloadImages();
+            var imagesDataFile = Microsoft.ML.SamplesUtils.DatasetUtils
+                .DownloadImages();
 
             // Preview of the content of the images.tsv file
             //
@@ -41,9 +45,13 @@ public static void Example()
 
             var imagesFolder = Path.GetDirectoryName(imagesDataFile);
             // Image loading pipeline. 
-            var pipeline = mlContext.Transforms.LoadImages("ImageObject", imagesFolder, "ImagePath")
-                          .Append(mlContext.Transforms.ResizeImages("ImageObjectResized", inputColumnName: "ImageObject", imageWidth: 100, imageHeight: 100))
-                          .Append(mlContext.Transforms.ExtractPixels("Pixels", "ImageObjectResized"));
+            var pipeline = mlContext.Transforms.LoadImages("ImageObject",
+                imagesFolder, "ImagePath")
+                .Append(mlContext.Transforms.ResizeImages("ImageObjectResized",
+                    inputColumnName: "ImageObject", imageWidth: 100, imageHeight:
+                    100))
+                .Append(mlContext.Transforms.ExtractPixels("Pixels",
+                    "ImageObjectResized"));
 
             var transformedData = pipeline.Fit(data).Transform(data);
 
@@ -59,12 +67,15 @@ public static void Example()
 
         private static void PrintColumns(IDataView transformedData)
         {
-            Console.WriteLine("{0, -25} {1, -25} {2, -25} {3, -25} {4, -25}", "ImagePath", "Name", "ImageObject", "ImageObjectResized", "Pixels");
+            Console.WriteLine("{0, -25} {1, -25} {2, -25} {3, -25} {4, -25}",
+                "ImagePath", "Name", "ImageObject", "ImageObjectResized", "Pixels");
            
-            using (var cursor = transformedData.GetRowCursor(transformedData.Schema))
+            using (var cursor = transformedData.GetRowCursor(transformedData
+                .Schema))
             {
-                // Note that it is best to get the getters and values *before* iteration, so as to faciliate buffer
-                // sharing (if applicable), and column-type validation once, rather than many times.
+                // Note that it is best to get the getters and values *before*
+                // iteration, so as to faciliate buffer sharing (if applicable), and
+                // column -type validation once, rather than many times.
 
                 ReadOnlyMemory<char> imagePath = default;
                 ReadOnlyMemory<char> name = default;
@@ -72,11 +83,21 @@ private static void PrintColumns(IDataView transformedData)
                 Bitmap resizedImageObject = null;
                 VBuffer<float> pixels = default;
 
-                var imagePathGetter = cursor.GetGetter<ReadOnlyMemory<char>>(cursor.Schema["ImagePath"]);
-                var nameGetter = cursor.GetGetter<ReadOnlyMemory<char>>(cursor.Schema["Name"]);
-                var imageObjectGetter = cursor.GetGetter<Bitmap>(cursor.Schema["ImageObject"]);
-                var resizedImageGetter = cursor.GetGetter<Bitmap>(cursor.Schema["ImageObjectResized"]);
-                var pixelsGetter = cursor.GetGetter<VBuffer<float>>(cursor.Schema["Pixels"]);
+                var imagePathGetter = cursor.GetGetter<ReadOnlyMemory<char>>(cursor
+                    .Schema["ImagePath"]);
+
+                var nameGetter = cursor.GetGetter<ReadOnlyMemory<char>>(cursor
+                    .Schema["Name"]);
+
+                var imageObjectGetter = cursor.GetGetter<Bitmap>(cursor.Schema[
+                    "ImageObject"]);
+
+                var resizedImageGetter = cursor.GetGetter<Bitmap>(cursor.Schema[
+                    "ImageObjectResized"]);
+
+                var pixelsGetter = cursor.GetGetter<VBuffer<float>>(cursor.Schema[
+                    "Pixels"]);
+
                 while (cursor.MoveNext())
                 {
                     
@@ -86,8 +107,10 @@ private static void PrintColumns(IDataView transformedData)
                     resizedImageGetter(ref resizedImageObject);
                     pixelsGetter(ref pixels);
 
-                    Console.WriteLine("{0, -25} {1, -25} {2, -25} {3, -25} {4, -25}", imagePath, name,
-                        imageObject.PhysicalDimension, resizedImageObject.PhysicalDimension, string.Join(",", pixels.DenseValues().Take(5)) + "...");
+                    Console.WriteLine("{0, -25} {1, -25} {2, -25} {3, -25} " +
+                        "{4, -25}", imagePath, name, imageObject.PhysicalDimension,
+                        resizedImageObject.PhysicalDimension, string.Join(",",
+                        pixels.DenseValues().Take(5)) + "...");
                 }
 
                 // Dispose the image.
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/LoadImages.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/LoadImages.cs
index 6dba6729e2..6d38dc6e7c 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/LoadImages.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/LoadImages.cs
@@ -11,14 +11,17 @@ public static class LoadImages
         // Loads the images of the imagesFolder into an IDataView. 
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
-            // Downloading a few images, and an images.tsv file, which contains a list of the files from the dotnet/machinelearning/test/data/images/.
-            // If you inspect the fileSystem, after running this line, an "images" folder will be created, containing 4 images, and a .tsv file
+            // Downloading a few images, and an images.tsv file, which contains a
+            // list of the files from the dotnet/machinelearning/test/data/images/.
+            // If you inspect the fileSystem, after running this line, an "images"
+            // folder will be created, containing 4 images, and a .tsv file
             // enumerating the images. 
-            var imagesDataFile = Microsoft.ML.SamplesUtils.DatasetUtils.DownloadImages();
+            var imagesDataFile = Microsoft.ML.SamplesUtils.DatasetUtils
+                .DownloadImages();
 
             // Preview of the content of the images.tsv file
             //
@@ -39,7 +42,8 @@ public static void Example()
 
             var imagesFolder = Path.GetDirectoryName(imagesDataFile);
             // Image loading pipeline. 
-            var pipeline = mlContext.Transforms.LoadImages("ImageObject", imagesFolder, "ImagePath");
+            var pipeline = mlContext.Transforms.LoadImages("ImageObject",
+                imagesFolder, "ImagePath");
 
             var transformedData = pipeline.Fit(data).Transform(data);
 
@@ -55,18 +59,28 @@ public static void Example()
         private static void PrintColumns(IDataView transformedData)
         {
             // The transformedData IDataView contains the loaded images now.
-            Console.WriteLine("{0, -25} {1, -25} {2, -25}", "ImagePath", "Name", "ImageObject");
-            using (var cursor = transformedData.GetRowCursor(transformedData.Schema))
+            Console.WriteLine("{0, -25} {1, -25} {2, -25}", "ImagePath", "Name",
+                "ImageObject");
+
+            using (var cursor = transformedData.GetRowCursor(transformedData
+                .Schema))
             {
-                // Note that it is best to get the getters and values *before* iteration, so as to faciliate buffer
-                // sharing (if applicable), and column-type validation once, rather than many times.
+                // Note that it is best to get the getters and values *before*
+                // iteration, so as to faciliate buffer sharing (if applicable),
+                // and column-type validation once, rather than many times.
                 ReadOnlyMemory<char> imagePath = default;
                 ReadOnlyMemory<char> name = default;
                 Bitmap imageObject = null;
 
-                var imagePathGetter = cursor.GetGetter<ReadOnlyMemory<char>>(cursor.Schema["ImagePath"]);
-                var nameGetter = cursor.GetGetter<ReadOnlyMemory<char>>(cursor.Schema["Name"]);
-                var imageObjectGetter = cursor.GetGetter<Bitmap>(cursor.Schema["ImageObject"]);
+                var imagePathGetter = cursor.GetGetter<ReadOnlyMemory<char>>(cursor
+                    .Schema["ImagePath"]);
+
+                var nameGetter = cursor.GetGetter<ReadOnlyMemory<char>>(cursor
+                    .Schema["Name"]);
+
+                var imageObjectGetter = cursor.GetGetter<Bitmap>(cursor.Schema[
+                    "ImageObject"]);
+
                 while (cursor.MoveNext())
                 {
                     
@@ -74,7 +88,8 @@ private static void PrintColumns(IDataView transformedData)
                     nameGetter(ref name);
                     imageObjectGetter(ref imageObject);
 
-                    Console.WriteLine("{0, -25} {1, -25} {2, -25}", imagePath, name, imageObject.PhysicalDimension);
+                    Console.WriteLine("{0, -25} {1, -25} {2, -25}", imagePath, name,
+                        imageObject.PhysicalDimension);
                 }
 
                 // Dispose the image.
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ResizeImages.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ResizeImages.cs
index 11b8905c90..696895993f 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ResizeImages.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ResizeImages.cs
@@ -11,14 +11,17 @@ public static class ResizeImages
         // Example on how to load the images from the file system, and resize them. 
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
-            // Downloading a few images, and an images.tsv file, which contains a list of the files from the dotnet/machinelearning/test/data/images/.
-            // If you inspect the fileSystem, after running this line, an "images" folder will be created, containing 4 images, and a .tsv file
+            // Downloading a few images, and an images.tsv file, which contains a
+            // list of the files from the dotnet/machinelearning/test/data/images/.
+            // If you inspect the fileSystem, after running this line, an "images"
+            // folder will be created, containing 4 images, and a .tsv file
             // enumerating the images. 
-            var imagesDataFile = Microsoft.ML.SamplesUtils.DatasetUtils.DownloadImages();
+            var imagesDataFile = Microsoft.ML.SamplesUtils.DatasetUtils
+                .DownloadImages();
 
             // Preview of the content of the images.tsv file
             //
@@ -39,8 +42,10 @@ public static void Example()
 
             var imagesFolder = Path.GetDirectoryName(imagesDataFile);
             // Image loading pipeline. 
-            var pipeline = mlContext.Transforms.LoadImages("ImageObject", imagesFolder, "ImagePath")
-                        .Append(mlContext.Transforms.ResizeImages("ImageObjectResized", inputColumnName: "ImageObject", imageWidth: 100, imageHeight: 100));
+            var pipeline = mlContext.Transforms.LoadImages("ImageObject",
+                imagesFolder, "ImagePath")
+                .Append(mlContext.Transforms.ResizeImages("ImageObjectResized",
+                inputColumnName: "ImageObject", imageWidth: 100, imageHeight: 100));
 
             var transformedData = pipeline.Fit(data).Transform(data);
             // The transformedData IDataView contains the resized images now.
@@ -57,20 +62,32 @@ public static void Example()
 
         private static void PrintColumns(IDataView transformedData)
         {
-            Console.WriteLine("{0, -25} {1, -25} {2, -25} {3, -25}", "ImagePath", "Name", "ImageObject", "ImageObjectResized");
-            using (var cursor = transformedData.GetRowCursor(transformedData.Schema))
+            Console.WriteLine("{0, -25} {1, -25} {2, -25} {3, -25}", "ImagePath",
+                "Name", "ImageObject", "ImageObjectResized");
+
+            using (var cursor = transformedData.GetRowCursor(transformedData
+                .Schema))
             {
-                // Note that it is best to get the getters and values *before* iteration, so as to faciliate buffer
-                // sharing (if applicable), and column-type validation once, rather than many times.
+                // Note that it is best to get the getters and values *before*
+                // iteration, so as to faciliate buffer sharing (if applicable), and
+                // column -type validation once, rather than many times.
                 ReadOnlyMemory<char> imagePath = default;
                 ReadOnlyMemory<char> name = default;
                 Bitmap imageObject = null;
                 Bitmap resizedImageObject = null;
 
-                var imagePathGetter = cursor.GetGetter<ReadOnlyMemory<char>>(cursor.Schema["ImagePath"]);
-                var nameGetter = cursor.GetGetter<ReadOnlyMemory<char>>(cursor.Schema["Name"]);
-                var imageObjectGetter = cursor.GetGetter<Bitmap>(cursor.Schema["ImageObject"]);
-                var resizedImageGetter = cursor.GetGetter<Bitmap>(cursor.Schema["ImageObjectResized"]);
+                var imagePathGetter = cursor.GetGetter<ReadOnlyMemory<char>>(cursor
+                    .Schema["ImagePath"]);
+
+                var nameGetter = cursor.GetGetter<ReadOnlyMemory<char>>(cursor
+                    .Schema["Name"]);
+
+                var imageObjectGetter = cursor.GetGetter<Bitmap>(cursor.Schema[
+                    "ImageObject"]);
+
+                var resizedImageGetter = cursor.GetGetter<Bitmap>(cursor.Schema[
+                    "ImageObjectResized"]);
+
                 while (cursor.MoveNext())
                 {
                     imagePathGetter(ref imagePath);
@@ -78,8 +95,9 @@ private static void PrintColumns(IDataView transformedData)
                     imageObjectGetter(ref imageObject);
                     resizedImageGetter(ref resizedImageObject);
 
-                    Console.WriteLine("{0, -25} {1, -25} {2, -25} {3, -25}", imagePath, name,
-                        imageObject.PhysicalDimension, resizedImageObject.PhysicalDimension);
+                    Console.WriteLine("{0, -25} {1, -25} {2, -25} {3, -25}",
+                        imagePath, name, imageObject.PhysicalDimension,
+                        resizedImageObject.PhysicalDimension);
                 }
 
                 // Dispose the image.