Address one more comment

Author: wschin

src/Microsoft.ML.StaticPipe/TextStaticExtensions.cs

@@ -485,16 +485,16 @@ public override IEstimator<ITransformer> Reconcile(IHostEnvironment env,
         /// Produces a bag of counts of ngrams (sequences of consecutive words ) in a given tokenized text.
         /// It does so by building a dictionary of ngrams and using the id in the dictionary as the index in the bag.
         ///
-        /// /// <see cref="ToNgrams"/> is different from <see cref="WordBagEstimatorStaticExtensions.ToBagofWords"/>
-        /// in a way that <see cref="ToNgrams"/> takes tokenized text as input while <see cref="WordBagEstimatorStaticExtensions.ToBagofWords"/> tokenizes text internally.
+        /// /// <see cref="ProduceNgrams"/> is different from <see cref="WordBagEstimatorStaticExtensions.ToBagofWords"/>
+        /// in a way that <see cref="ProduceNgrams"/> takes tokenized text as input while <see cref="WordBagEstimatorStaticExtensions.ToBagofWords"/> tokenizes text internally.
         /// </summary>
         /// <param name="input">The column to apply to.</param>
         /// <param name="ngramLength">Ngram length.</param>
         /// <param name="skipLength">Maximum number of tokens to skip when constructing an ngram.</param>
         /// <param name="allLengths">Whether to include all ngram lengths up to <paramref name="ngramLength"/> or only <paramref name="ngramLength"/>.</param>
         /// <param name="maximumNgramsCount">Maximum number of n-grams to store in the dictionary.</param>
         /// <param name="weighting">Statistical measure used to evaluate how important a word is to a document in a corpus.</param>
-        public static Vector<float> ToNgrams<TKey>(this VarVector<Key<TKey, string>> input,
+        public static Vector<float> ProduceNgrams<TKey>(this VarVector<Key<TKey, string>> input,
             int ngramLength = 1,
             int skipLength = 0,
             bool allLengths = true,

test/Microsoft.ML.StaticPipelineTesting/StaticPipeTests.cs

@@ -604,7 +604,7 @@ public void Ngrams()
             var est = data.MakeNewEstimator()
                 .Append(r => (
                     r.label,
-                    ngrams: r.text.TokenizeText().ToKey().ToNgrams(),
+                    ngrams: r.text.TokenizeText().ToKey().ProduceNgrams(),
                     ngramshash: r.text.TokenizeText().ToKey().ApplyNgramHashing()));
 
             var tdata = est.Fit(data).Transform(data);

test/Microsoft.ML.Tests/Scenarios/Api/CookbookSamples/CookbookSamples.cs

@@ -467,7 +467,7 @@ private void TextFeaturizationOn(string dataPath)
                     BagOfBigrams: r.Message.NormalizeText().ToBagofHashedWords(ngramLength: 2, allLengths: false),
 
                     // NLP pipeline 3: bag of tri-character sequences with TF-IDF weighting.
-                    BagOfTrichar: r.Message.TokenizeIntoCharacters().ToNgrams(ngramLength: 3, weighting: NgramExtractingEstimator.WeightingCriteria.TfIdf),
+                    BagOfTrichar: r.Message.TokenizeIntoCharacters().ProduceNgrams(ngramLength: 3, weighting: NgramExtractingEstimator.WeightingCriteria.TfIdf),
 
                     // NLP pipeline 4: word embeddings.
                     // PretrainedModelKind.Sswe is used here for performance of the test. In a real